Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion Trajectory Control System for Production Line Robots Based on Variable Domain Fuzzy Control

Mechatronics production line is widely used in the production and operation of all walks of life, which greatly reduces the amount of labor of employees, improves the production level of enterprises, and creates great social and economic benefits for enterprises. The traditional manipulator system has modeling uncertainty and is vulnerable to external interference. In this paper, a compound control scheme is designed by combining the torque controller and fuzzy compensator, which effectively improves the system's response speed and greatly reduces the steady-state tracking error of the system. At the same time, the tracking process of the scheme is stable and flexible, and the chattering is minimal, which effectively improves the trajectory tracking performance of the system. Experiments show that the algorithm effectively solves the contradiction between computational complexity and control accuracy, improves the system's response speed, and reduces the system's steady-state tracking error.
A Behavior Analysis of the Impact of Semantic Relationships on Topic Discovery

Information Technologies have generated large amounts of documents available for analysis and use. Information systems can provide the user with the necessary data for a speciﬁc purpose without human intervention, saving time in providing the response expected by the user. Some traditional models of topic discovery provide essential information in the literature, but it is still necessary to incorporate the knowledge that a person can use when reading a document. In this work, an analysis of the behavior of the techniques of Latent Dirichlet Analysis, Latent Semantic Analysis, and Probabilistic Latent Semantic Analysis is carried out incorporating the semantic relationships of the type hypernym, hyponym, synonymy, holonymy, and meronymy extracted from an external source of knowledge as WordNet. In order to improve the results obtained by applying the three mentioned techniques in a set of documents without adding external knowledge. Compared to the initial results, our experimental results improved when incorporating semantic relationships, such as hypernyms and synonyms. The best result was obtained when using the Lesk algorithm for word sense disambiguation and subsequently applying Latent Dirichlet Analysis.
Image splicing forgery detection by combining synthetic adversarial networks and hybrid dense U‐net based on multiple spaces

With the popularity of image editing tools, the originality and information security of images are facing serious threats. The most common threat is splicing forgery that copies a part of the area from one donor image to the acceptor one. Some research works were proposed to protect the image originality, whereas they are still difficult to apply in practice. There are two main reasons: (a) very limited data for learning models; (b) huge attribute differences between the donor and acceptor images. We propose two novel tasks to conquer the above challenges: Synthetic Adversarial Networks (SANs) and Hybrid Dense U‐Net (HDU‐Net). SAN finds the most secluded position for inserting tampered areas in an image by learning the association between scenes and objects, and can enlarge the original small data set by more than 40 times. We call the data set SF‐Data generated by SAN. We combine the dense U‐Net that detects the differences of the essential attributes of image with four spaces containing more available feature information to propose HDU‐Net. Then, the synthetic data set SF‐Data are used to train HDU‐Net. We perform various attack experiments on several public data sets to demonstrate the effectiveness and robustness of our method.
Do childhood adversity and sensory processing sensitivity interact to predict meaningful activity engagement in adulthood?

Introduction A core tenet of occupational therapy is that engagement in meaningful activities is integral for a person’s health and wellbeing. Childhood adversity can have lifelong impacts on physical and mental health; however, the effects of childhood experiences are dependent on the presence of susceptibility factors, including sensory processing sensitivity. To date, the effects of childhood adversity and sensory processing sensitivity have not been investigated for meaningful activity engagement. This study seeks to determine whether childhood adversity is related to engagement in meaningful activities in adulthood and whether this relationship is moderated by sensory processing sensitivity. Method Using convenience snowball sampling, a cross-sectional online survey was conducted with 328 adults. Participants completed a battery of questionnaires, including the Risky Families Questionnaire, Highly Sensitive Person Scale and Engagement in Meaningful Activities Scale. Results Correlational analyses found that childhood adversity was related to significantly less meaningful activity engagement in adulthood, but this relationship was not moderated by sensory processing sensitivity. Conclusion This study showed, for the first time, that a history of childhood adversity is linked to less engagement in meaningful activities in adulthood. While further research is needed, supporting meaningful activity engagement may be necessary for adults who experienced childhood adversity.
“For me, it is an intellectual freedom issue”: Drag storytimes, neutrality, and ALA core values

Drag storytimes are increasingly popular programing events in which drag performers lead storytime in public libraries or other settings; they have been both popular and contentious. In this study, we utilized data from a national survey of 458 library staff and 26 subsequent interviews to investigate connections between drag storytime, intellectual freedom, neutrality, and other core librarianship values. The data was analyzed inductively and several key themes emerged: hosting drag storytimes is an intellectual freedom stance; various perspectives on the American Library Association stance in support of drag storytimes; connections to other core values; emphasizing diversity to serve one’s community; and contesting the neutrality of libraries. We found difficult-to-reconcile stances of “presenting all sides” and “taking a side,” but we suggest a way forward by focusing on the outcome of collective self-governance (based on core values of intellectual freedom and democracy).
Public sector management

Funkcionalna i institucionalna složenost javnog sektora implicira i vrlo složenim sustavom upravljanja. Nemogucnost primjene iskljucivo ekonomskih kriterija u određivanju ciljeva djelovanja kao i mjerenja rezultata, uz naglasenu politicku komponentu rezultira nezadovoljavajucim stanjem u ekonomiji javnog sektora. Neefikasan javni sektor, umjesto podrske razvoju, postao je njegov ogranicavajuci cimbenik kako na lokalnoj tako i na nacionalnoj razini. U poduzetnickom sektoru prisutan je zamah korporativnog upravljanja. Upravljanje u tom kontekstu strukturirano je procesima vezanim za poslovno odlucivanje, odgovornost prema korisnicima, kontroli i ponasanju menadžmenta. Razvoj i reforme javnog sektora streme prema istom cilju, a to je bolje upravljanje javnim resursima uz istodobno smanjenje javnih rashoda sto istodobno treba rezultirati boljem zadovoljenju javnih potreba. U Hrvatskoj su posljednjih godina, ponajvise pod zakonskom prisilom i zahtjevima za pristupanje EU, intenzivirani brojni reformski procesi te stvorene pretpostake suradnji javnog i privatnog sektora. Sve navedeno iziskuje intenziviranje znanstvenih i obrazovnih aktivnosti na usvajanju suvremenih tehnika i metoda javnog menadžmenta i „dobrog upravljanja“. U tom kontekstu Ekonomski fakultet Sveucilista u Rijeci razvio je poslijediplomski specijalisticki studij „Menadžment u javnom sektoru“ koji polaznike osposobljava za uspjesno obavljanje javno menadžerskih zadaca. U radu se sažeto predstavljaju nake nacelne postavke javnog menadžmenta u hrvatskim prilikama te istice specijalisticko obrazovanje kao jedno od pretpostavki efikasnijeg upravljanja.
Expression Profile of Genes Related to the Th17 Pathway in Macrophages Infected by Leishmania major and Leishmania amazonensis: The Use of Gene Regulatory Networks in Modeling This Pathway

Leishmania amazonensis and Leishmania major are the causative agents of cutaneous and mucocutaneous diseases. The infections‘ outcome depends on host–parasite interactions and Th1/Th2 response, and in cutaneous form, regulation of Th17 cytokines has been reported to maintain inflammation in lesions. Despite that, the Th17 regulatory scenario remains unclear. With the aim to gain a better understanding of the transcription factors (TFs) and genes involved in Th17 induction, in this study, the role of inducing factors of the Th17 pathway in Leishmania–macrophage infection was addressed through computational modeling of gene regulatory networks (GRNs). The Th17 GRN modeling integrated experimentally validated data available in the literature and gene expression data from a time-series RNA-seq experiment (4, 24, 48, and 72 h post-infection). The generated model comprises a total of 10 TFs, 22 coding genes, and 16 cytokines related to the Th17 immune modulation. Addressing the Th17 induction in infected and uninfected macrophages, an increase of 2- to 3-fold in 4–24 h was observed in the former. However, there was a decrease in basal levels at 48–72 h for both groups. In order to evaluate the possible outcomes triggered by GRN component modulation in the Th17 pathway. The generated GRN models promoted an integrative and dynamic view of Leishmania–macrophage interaction over time that extends beyond the analysis of single-gene expression.
Feature-Selection High-Resolution Network With Hypersphere Embedding for Semantic Segmentation of VHR Remote Sensing Images

Very-high-resolution (VHR) remote sensing images contain various multiscale objects, such as large-scale buildings and small-scale cars. However, these multiscale objects cannot be considered simultaneously in the widely used backbones with a large downsampling factor (e.g., VGG-like and ResNet-like), resulting in the appearance of various context aggregation approaches, such as fusing low-level features and attention-based modules. To alleviate this problem caused by backbones with a large downsampling factor, we propose a feature-selection high-resolution network (FSHRNet) based on an observation: if the features maintain high resolution throughout the network, a high precision segmentation result can be obtained by only using a $1\times $ 1 convolution layer with no need for complex context aggregation modules. Specifically, the backbone of FSHRNet is a multibranch structure similar to HRNet where the high-resolution branch is the principal line. Then, a lightweight dynamic weight module, named the feature-selection convolution (FSConv) layer, is presented to fuse multiresolution features, allowing adaptively feature selection based on the characteristic of objects. Finally, a specially designed $1\times $ 1 convolution layer derived from hypersphere embedding is used to produce the segmentation result. Experiments with other widely used methods show that the proposed FSHRNet obtains competitive performance on the ISPRS Vaihingen dataset, the ISPRS Potsdam dataset, and the iSAID dataset.
Mathematical Model of 72 W Cosmic Module with MATLAB/SIMULINK

The Earth life is dependable upon infinite source of green energy. There is an increasing trends for the use of solar cells in industry and domestic appliances because cosmic energy is expected to play significant role in future grids as non-conventional resources. The Large- scaled integration of non-conventional sources into the grid is possible by the computer simulations so there is extreme growing demand for computer simulation of non- conventional resources. The Electrical energy gained by the photon energy of the sun technically named as Photovoltaic (PV). The proposed model is based upon the maximised electric power extraction from the solar cell radiation and electrical driver interface for implementing electrical characteristic of power limited systems in power simulations. This paper gives us details steps of one-by-one procedures for the simulation of photovoltaic (PV) modules with Matlab/Simulink. Single diode equivalent circuit is processed in order to examine I-V and P-V properties are obtained for the typical 72W PV module. The suggested model is designed using a user-friendly idol and a communication box like simulink section libraries. The model is dynamic in computational multiplicity and it is easy to build for representing wide-range of photovoltaic (PV) installations.
Recent results on e +e − annihilation to hadrons in the SND experiment

Recent results of the SND experiment at the VEPP-2000 collider on e +e − annihilation to hadrons below 2 GeV are presented. In particular, we discuss measurements of the e +e − → π +π − and e +e − → nn¯ cross sections. The processes e +e − → π +π −π 0 , K+K−π 0 , ηπ0 γ and 2ηγ were under investigation as well. The preliminary results on the e +e − → ωπ0 → π +π −2π 0 cross section measurement are also presented.
Carbamazepine Overdose after Psychiatric Conditions: A Case Study for Postmortem Analysis in Human Bone

Carbamazepine is the main option used as a preventive medication to treat bipolar disorder when there is no response to lithium. Carbamazepine toxicity is defined as serum levels greater than 12 μg/mL, with severe toxicity occurring over 40 μg/mL, reduced to 30 μg/mL when combined with pharmacological treatment, i.e., benzodiazepines or antidepressants. For these reasons, it is necessary to find a validated tool to determine carbamazepine levels in an autopsy to rule out suicide or to know if the death was a consequence of an adverse drug reaction (ADR), especially when only bones can be accessed. We have validated a tool to detect and quantify drug concentration in bone. Our results showed a peak for carbamazepine at minute 12 and a mass fragment of 193 m/z. This case study is the first time in the literature that carbamazepine has been detected and quantified in bone. These results demonstrate that carbamazepine can be detected in bone tissue from forensic cases, but almost more importantly, that the method proposed is valid, reliable, and trustworthy.
Dual‐band dual inverted‐F/loop antennas as a compact decoupled building block for forming eight 3.5/5.8‐GHz MIMO antennas in the future smartphone

With a large number of multi‐input multi‐output (MIMO) antennas embedded in the smartphone, much higher data throughput can be obtained for the smartphone in the MIMO operation. Furthermore, in order to be operational in heterogeneous networks, such as the mobile network and the wireless wide area network (WLAN), dual‐band operation of the MIMO antennas is desirable. However, owing to very limited space available in the smartphone, it has been a great challenge to dispose more MIMO antennas therein, not to mention the dual‐band MIMO antennas. To address this challenge, we present a compact decoupled 3.5/5.8 GHz (3400–3600/5725–5875 MHz) dual‐band building block formed by dual inverted‐F/loop antennas and its application for building eight dual‐band MIMO antennas for the mobile and WLAN operations. Details of the dual‐band building block and the fabricated eight MIMO antennas are described. The channel capacity of the eight MIMO antennas in an 8 × 8 MIMO system is also calculated and discussed. The obtained MIMO channel capacities are respectively about 37 and 42 bps/Hz in the 3.5 and 5.8 GHz bands, much higher than the present fourth‐generation (4G) 2 × 2 MIMO capacity (about 10 bps/Hz or less). The proposed eight MIMO antennas will hence be promising for future smartphone applications, such as in the fifth‐generation (5G) communications.
Ownership Characteristics and Financial Performance: Evidence from Chinese Split-Share Structure Reform

This paper investigates the relationship between two characteristics of corporate governance (concentrated and state ownership) and firm financial performance by bringing new and extensive evidence from an emerging market. Further, this study examines the impact of the recent stock split reform in China on the corporate ownership characteristics–firm performance relationship. The final sample of this study is comprised of 234 firms with 2340 annual observation values. The study hypotheses are examined using regression analysis of panel data. We found that concentrated ownership is positively and significantly related to firm performance. However, state ownership has a significant negative impact on firm performance. Further, we observed that the stock split reform has a substantial and positive effect on the ownership–corporate financial performance relationship. In particular, the positive relationship between ownership concentration and firm performance has increased following the split-share structure reform. The negative relationship between state ownership and corporate financial performance has been mitigated following the split-share structure reform. We contribute to the existing literature on corporate governance by investigating the ownership–corporate financial performance relationship in a unique research setting based on the impact of an exogenous regulatory change, namely, the split-share structure reform in China. The study presents implications for regulators, investors, and researchers interested in examining developing markets such as China. Our results imply that the institutional reform of the Chinese stock market has benefitted investors through enhancing corporate financial performance. The findings suggest that the reform of the Chinese stock market has significantly shaped the impact of ownership structure on corporate financial performance in a valuable way for effective capital allocation. Thus, collectively, the split-share structure reform enhances the quality of corporate governance, which is pivotal to the growth of the country’s economy. This, in turn, has policy implications for other emerging economies.
A Novel System and Experimental Study for 3D Finger Multibiometrics

Fingers contain various discriminative biometric traits, such as fingerprint, finger vein, finger knuckle, finger shape, and so on, which are complementary in identity information. However, only one or a few traits are used in most current research and practical applications, while others are ignored, resulting in degraded recognition performance and vulnerability to forgery. In this paper, we make the first attempt to collect and study all biometric traits on the finger. Firstly, a novel multi-view, multi-spectral 3D finger imaging system is proposed. To the best of our knowledge, it is the first biometric imaging system capable of capturing almost all finger-based traits. We scanned numerous fingers with this 3D finger imaging system, obtaining external skin images and internal vein images from 6 different views. The proposed 3D finger reconstruction and texture mapping algorithms are then used to generate 3D finger models with skin and vein textures. Second, we establish a benchmark dataset, namely the Large-scale Finger Multi-Biometric database and benchmark for 3D Finger Biometrics (LFMB-3DFB). The LFMB-3DFB contains 695 fingers, and each finger is acquired 10 times, yielding 6 finger skin images and 6 finger vein images for a total of 83,400 images and 6,950 3D finger models. Finally, we design a more scientific and comprehensive evaluation protocol to conduct extensive experimental research and analysis on this database for both subject-independent verification and subject-independent close-set identification tasks. Comprehensive and rigorous experiments for 2D finger traits recognition, multi-view finger traits recognition, 3D finger traits recognition, and score-level fusion on LFMB-3DFB are carried out, and excellent results are achieved. The LFMB-3DFB database will be released at https://github.com/SCUT-BIP-Lab/LFMB-3DFB to promote 3D finger multi-biometric research using cutting-edge imaging techniques.
Fault Diagnosis for Power Transformers through Semi-Supervised Transfer Learning

The fault diagnosis of power transformers is a challenging problem. The massive multisource fault is heterogeneous, the type of fault is undetermined sometimes, and one device has only met a few kinds of faults in the past. We propose a fault diagnosis method based on deep neural networks and a semi-supervised transfer learning framework called adaptive reinforcement (AR) to solve the above limitations. The innovation of this framework consists of its enhancement of the consistency regularization algorithm. The experiments were conducted on real-world 110 kV power transformers’ three-phase fault grounding currents of the iron cores from various devices with four types of faults: Phases A, B, C and ABC to ground. We trained the model on the source domain and then transferred the model to the target domain, which included the unbalanced and undefined fault datasets. The results show that our proposed model reaches over 95% accuracy in classifying the type of fault and outperforms other popular networks. Our AR framework fits target devices’ fault data with fewer dozen epochs than other novel semi-supervised techniques. Combining the deep neural network and the AR framework helps diagnose the power transformers, which lack diagnosis knowledge, with much less training time and reliable accuracy.
A Maize CBM Domain Containing the Protein ZmCBM48-1 Positively Regulates Starch Synthesis in the Rice Endosperm

Starch directly determines the grain yield and quality. The key enzymes participating in the process of starch synthesis have been cloned and characterized. Nevertheless, the regulatory mechanisms of starch synthesis remain unclear. In this study, we identified a novel starch regulatory gene, ZmCBM48-1, which contained a carbohydrate-binding module 48 (CBM48) domain. ZmCBM48-1 was highly expressed in the maize endosperm and was localized in the plastids. Compared with the wild type lines, the overexpression of ZmCBM48-1 in rice altered the grain size and 1000-grain weight, increased the starch content, and decreased the soluble sugar content. Additionally, the transgenic rice seeds exhibited an alterant endosperm cell shape and starch structure. Meanwhile, the physicochemical characteristics (gelatinization properties) of starch were influenced in the transgenic lines of the endosperm compared with the wild type seeds. Furthermore, ZmCBM48-1 played a positive regulatory role in the starch synthesis pathway by up-regulating several starch synthesis-related genes. Collectively, the results presented here suggest that ZmCBM48-1 acts as a key regulatory factor in starch synthesis, and could be helpful for devising strategies for modulating starch production for a high yield and good quality in maize endosperm.