Miracle-dz/UniFAR-FTUs huggingface dataset:
User adoption of a hybrid social tagging approach in an online knowledge community

Purpose Online knowledge communities make great contributions to global knowledge sharing and innovation. Resource tagging approaches have been widely adopted in such communities to describe, annotate and organize knowledge resources mainly through users’ participation. However, it is unclear what causes the adoption of a particular resource tagging approach. The purpose of this paper is to identify factors that drive users to use a hybrid social tagging approach. Design/methodology/approach Technology acceptance model and social cognitive theory are adopted to support an integrated model proposed in this paper. Zhihu, one of the most popular online knowledge communities in China, is taken as the survey context. A survey was conducted with a questionnaire and collected data were analyzed through structural equation model. Findings A new hybrid social resource tagging approach was refined and described. The empirical results revealed that self-efficacy, perceived usefulness (PU) and perceived ease of use exert positive effect on users’ attitude. Moreover, social influence, PU and attitude impact significantly on users’ intention to use a hybrid social resource tagging approach. Originality/value Theoretically, this study enriches the type of resource tagging approaches and recognizes factors influencing user adoption to use it. Regarding the practical parts, the results provide online information system providers and designers with referential strategies to improve the performance of the current tagging approaches and promote them.
Coding Improvement in the DAC Algorithm

Digital images are a particular type of data. They have a lot of applications. Huge image datasets have been collected. Their processing, storing, analyzing, and transferring via networks require great expenses. For this reason, a development of low resource intensive image processing algorithms is of particular importance. Discrete atomic compression (DAC) is an image compression algorithm based on atomic functions. It provides a combination of data compression and protection features with machine learning oriented data representation. Also, DAC has low time and spatial complexity, which makes its application promising. In this paper, we improve a compression performance of this algorithm. It is shown that, by applying the proposed coding scheme a compression ratio can be increased and, hence, greater memory savings can be obtained.
A3C: A novel attention-based 3D-CNN model for accurately identifying clinical lung lesions

In this paper, a novel attention-based 3D-CNN (A3C) model is proposed to realize the accurate diagnosis of the radiation pneumonitis (RP), checkpoint inhibitor-related pneumonitis (CIP) and pulmonary lymphangitic carcinomatosis (PLC) from computed tomography (CT) images. In particular, the 3D convolution operators are employed due to the intrinsic ability of capturing spatial-temporal dependencies within volumetric medical images, and the channel attention mechanism is adopted to effectively address the issues of feature inter-dependencies and heterogeneity inherent in multi-class pulmonary pathology classification tasks. Moreover, by introducing multiple data augmentation techniques, the model ability of learning invariant and discriminative features from the limited annotated data is effectively enhanced. In pulmonary lesions diagnosis task, the proposed A3C outperforms the baseline models by yielding the highest accuracy of 1.0, showing the great competitiveness in providing reliable diagnostic references.
Low Power Low Noise CMOS RF Amplifiers for Wireless Applications: A Review

The desire for low power, low noise, broadband CMOS amplifiers using various topologies for wireless applications that has appeared over the last few years is presented in this review article. A significant component of contemporaneous wireless communication applications is the circuit design of very high-performance CMOS amplifiers. A variety of topologies, including active inductor topology, resistive feedback topology, common source cascaded topology, noise canceling technique, and current reused topology, can be used to construct low power, low noise GHz bandwidth CMOS amplifier systems. In this survey paper, the circuit design has presented the applications, problems, and current developments of broadband CMOS amplifiers, which have been updated to achieve a high-performance amplifier parameters. From this survey, the feedback, common gate amplifier, and inductive source degeneration are the commonly used topologies to perform the desired performance. The parameters considered are the noise, power, supply voltage, and the gain, by drawing comparisons between the various topologies. Inductive degeneration is one of the most effective strategies because it supplies a resistive component in the absence of a resistor during analysis. Thermal noise and power dissipation are decreased, based on the noise performance and input matching.
Dynamic State Estimation with Multi-level Data Recognition Considering New Energy Sources

The integration of new energy sources in modern power systems has resulted in increased volatility and challenges associated with measurement data. This paper proposes a novel dynamic state estimation method for power system analysis that incorporates multi-level data recognition considering new energy sources. The proposed approach is based on the robust Cubature Kalman Filter algorithm, which leverages the innovation vector to achieve Level 1 data recognition. Additionally, the proposed approach utilizes classification and recognition based on measurement data characteristics to further refine data recognition. The proposed method applies dynamically adjusting the noise scale factor based on recognition results to correct adversarial data and improve the accuracy of the estimation. Numerical simulation analysis carried out on the improved IEEE-57 node system demonstrated the feasibility and effectiveness of the proposed methodology.
Design and Implementation of an Autonomous System for Crack Detection

In recent years, visual Simultaneous Localization and Mapping (SLAM) systems, particularly the Oriented FAST and Rotated BRIEF SLAM (ORB-SLAM2), have shown significant promise in robotic navigation in indoor environments. However, previous implementations often struggled with real-time processing and environmental dynamicity, limiting their practical deployment. This study evaluates the performance of ORB-SLAM2 using different sequences of the Tum RGBD dataset to construct 3D maps in real-time with an RGB-D camera (Kinectv2). The study findings indicate that ORB-SLAM2 facilitates effective real-time autonomous navigation using Turtlebot2, although previous related studies have demonstrated limitations in real-time processing capabilities and adaptive object recognition. To address these challenges, the deployment of a real-time object detection system using YOLO, a state-of-the-art one stage object detector, and a convolution neural network that surpasses other real-time object detectors in speed and accuracy. In this process, we first built our own dataset by labeling images, and then trained a YOLOv4 model to detect cracks. We tested the model in several ways, including real-time crack detection using an RGB-D camera (Kinectv2). The results were efficient, demonstrating the model's success in accurately identifying the object and predicting its location.
Using qflux to constrain modeled Congo Basin rainfall in the CMIP5 ensemble

Coupled models are the tools by which we diagnose and project future climate, yet in certain regions they are critically underevaluated. The Congo Basin is one such region which has received limited scientific attention, due to the severe scarcity of observational data. There is a large difference in the climatology of rainfall in global coupled climate models over the basin. This study attempts to address this research gap by evaluating modeled rainfall magnitude and distribution amongst global coupled models in the Coupled Model Intercomparison Project 5 (CMIP5) ensemble. Mean monthly rainfall between models varies by up to a factor of 5 in some months, and models disagree on the location of maximum rainfall. The ensemble mean, which is usually considered a “best estimate” of coupled model output, does not agree with any single model, and as such is unlikely to present a possible rainfall state. Moisture flux (qflux) convergence (which is assumed to be better constrained than parameterized rainfall) is found to have a strong relationship with rainfall; strongest correlations occur at 700 hPa in March–May (r = 0.70) and 850 hPa in June–August, September–November, and December–February (r = 0.66, r = 0.71, and r = 0.81). In the absence of observations, this relationship could be used to constrain the wide spectrum of modeled rainfall and give a better understanding of Congo rainfall climatology. Analysis of moisture transport pathways indicates that modeled rainfall is sensitive to the amount of moisture entering the basin. A targeted observation campaign at key Congo Basin boundaries could therefore help to constrain model rainfall.
Assessing the Structural Validity of the Knee Injury and Osteoarthritis Outcome Score Scale

Background: The Knee Injury and Osteoarthritis Outcome Score (KOOS) scale is used to assess patient perspectives on knee health. However, the structural validity of the KOOS has not been sufficiently tested; therefore, our objective was to assess the KOOS in a large, multi-site database of patient responses who were receiving care for knee pathology. Methods: A cross-sectional study was conducted using the Surgical Outcome System (SOS) database. A confirmatory factor analysis (CFA) was conducted to assess the proposed five-factor KOOS using a priori cut-off values. Because model fit indices were not met, a subsequent exploratory factor analysis (EFA) was conducted to identify a parsimonious model. The resulting four-factor structure (i.e., KOOS SF-12) was then assessed using CFA and subjected to multigroup invariance testing. Results: The original KOOS model did not meet rigorous CFA fit recommendations. The KOOS SF-12 did meet model fit recommendations and passed all invariance testing between intervention procedure, sex, and age groups. Conclusion: The KOOS failed to meet model fit recommendations. The KOOS SF-12 met model fit recommendations, maintained a multi-factorial structure, and was invariant across all tested groups. The KOOS did not demonstrate sound structural validity. A refined KOOS SF-12 model that met recommended model fit indices and invariance testing criteria was identified. Our findings provide initial support for a multidimensional KOOS structure (i.e., KOOS SF-12) that is a more psychometrically sound instrument for measuring patient-reported knee health.
Approaches for Producing Fungal Cellulases Through Submerged Fermentation.

Fungal cellulases are the most sought-after biological molecules produced from microbial sources in the last four decades. Owing to their emerging applications in the bioenergy industry for hydrolyzing cellulose, for which they are the most abundant source on this planet, research trends are shifting heavily toward adapting to submerged fermentation. However, filamentous fungal species, which are efficient cellulase producers, are well-adapted to low-moisture solid support as the substrate, such as in nature. Therefore, various fermentation strategies are currently being investigated to adapt them to submerged fermentation for large and high-quality production of cellulases. Emerging research trends, such as the use of inexpensive feedstocks, nutrient and/or culture optimization, innovative bioreactor designs, microparticle-assisted fungal growth, and innovative genetic engineering approaches, are some of the recent efforts by researchers to exploit the full potential of these biological molecules. This review discusses some of these strategies and their success rates in various research conditions. In addition, specific focus was provided to both increasing the market value of cellulases and the innovative strategies required to enhance their production on an industrial scale.
Contact Resistance Change Due to Air Gap Generation Between Turns of NI REBCO Pancake Coils

Rare-earth barium copper oxide (REBCO) magnets with excellent current characteristics under high temperature and high magnetic field has been developed for various applications worldwide. No-insulation (NI) REBCO coils allow currents to avoid flowing into normal-state transition regions due to turn-to-turn direct contacts, improving the thermal stability on the NI REBCO coils. The turn-to-turn contact resistances on NI REBCO coils are very important for thermal stability; but they change according to the turn-to-turn contact surface conditions. The contact condition is severely deteriorated when REBCO tape surfaces are not in contact. Our previous research pointed out that deformations on small-bore NI insert coils derived from strong stresses make some air gaps under high external magnetic field and high operating current, resulting in increase in the turn-to-turn contact resistances. In this paper, to investigate the air gap behaviors, an electromagnetic and stress simulation considering individual turn movements is conducted. A contact area ratio and a whole contact resistance of an NI REBCO pancake coil is computed. As the result, under the operating current of 50 A, the turn-to-turn contact conditions deteriorate by air gap generation between turns with the increase of the background fields. However, under 10 T, the turn-to-turn contact conditions improve as the operating current increases beyond 50 A. The air gap related with the distribution pattern of BJR stress, but not the strength of BJR stress.
An Accurate Predictive Method of Crosstalk Peaks Considering Dynamic Transfer Characteristics and Miller Ramp for SiC MOSFETs

Silicon carbide-based metal-oxide-semiconductor field effect transistors (SiC mosfets) have been widely applied due to their lower on-resistance and parasitic parameters compared with silicon devices. However, the unexpected crosstalk phenomenon occurs and could lead to false turn-on or gate-oxide damage of the devices. In this article, an accurate predictive method of crosstalk peaks is proposed to provide theoretical guidance for crosstalk evaluation and suppression, which can improve the reliability of the systems. First, the generation of crosstalk and conventional predictive method are illustrated. Second, an improved crosstalk model considering dynamic transfer characteristics and Miller ramp is proposed. Furthermore, the dynamic transfer characteristics and Miller charge of SiC mosfets are obtained experimentally to realize the necessary supplement of the data provided by manufacturers. Finally, the crosstalk peaks predicted by the proposed method are compared experimentally with the actual measured values. The results have verified the proposed method can achieve accurate prediction of crosstalk peaks, and the relative errors of the predicted crosstalk peaks are below 10%.
Soft Robotic Glove Modulating Joint Torque Through Novel Passive Extensor Mechanisms

Soft robotic gloves have been widely researched to enhance the quality of life of people who cannot use their hands freely. Despite significant advancements in this field, most existing solutions have not adequately addressed adaptability to various individuals and tasks. Our robot overcomes these challenges through a novel passive extension mechanism, which enables adjustment of assistance torque for each finger joint adequately to various situations. To enable diverse assistance torque profiles without the need for extra actuators, two passive extensors with different moment arms for each finger joint have been implemented. A variable stiffness passive extensor adjuster (VSPE adjuster) is designed to change the stiffness and tension of each passive extensor easily. We've thoroughly verified the performance of each component through simulation and experiments. In the final demonstration of its feasibility, the robot was tested on three healthy participants and one participant with spinal cord injury, exhibiting its adaptability and promising potential for future real-world applications.
Altered N6-Methyladenosine Modification Patterns and Transcript Profiles Contributes to Cognitive Dysfunction in High-Fat Induced Diabetic Mice

N6-methyladenosine (m6A) constitutes the paramount post-transcriptional modification within eukaryotic mRNA. This modification is subjected to stimulus-dependent regulation within the central nervous system of mammals, being influenced by sensory experiences, learning processes, and injuries. The patterns of m6A methylation within the hippocampal region of diabetes cognitive impairment (DCI) has not been investigated. A DCI model was established by feeding a high-fat diet to C57BL/6J mice. m6A and RNA sequencing was conducted to profile the m6A-tagged transcripts in the hippocampus. Methylated RNA immunoprecipitation with next-generation sequencing and RNA sequencing analyses yielded differentially m6A-modified and expressed genes in the hippocampus of DCI mice, which were enriched in pathways involving synaptic transmission and axonal guidance. Mechanistic analyses revealed a remarkable change in m6A modification levels through alteration of the mRNA expression of m6A methyltransferases (METTL3 and METTL14) and demethylase (FTO) in the hippocampus of DCI mice. We identified a co-mediated specific RNA regulatory strategy that broadens the epigenetic regulatory mechanism of RNA-induced neurodegenerative disorders associated with metabolic and endocrine diseases.
Early Reactivation of Emotion in ERPs to Neutral Retrieval Cues

Memory retrieval is thought to involve the reactivation of encoding processes. Previous fMRI work has indicated that reactivation processes are modulated by the residual effects of the prior emotional encoding context; different spatial patterns emerge during retrieval of memories previously associated with negative compared to positive or neutral context. Other research suggests that ERP indicators of memory retrieval processes, like the left parietal old/new effect, can also be modulated by emotion, but the spatial distribution and temporal dynamics of these effects is unclear. In the current study, we examined when emotion affects memory reactivation and whether that timing reflects processes that come before and may guide successful retrieval or post-recollection recovery of emotional episodic detail. While recording EEG, participants (N = 25) viewed neutral words paired with negative, positive or neutral pictures during encoding, followed by a recognition test for the words. Analyses focused on ERPs during the recognition test. In line with prior ERP studies, we found an early positive-going parietally distributed effect starting around 200 ms post retrieval-cue onset. This effect emerged for words that had been encoded in an emotional compared to neutral context (no valence differences), before the general old/new effect. This emotion-dependent effect occurred in an early time window, suggesting emotion-related reactivation is a precursor to successful recognition.
Transdermal patches: Design and current approaches to painless drug delivery

Abstract Use of transdermal patches can evade many issues associated with oral drug delivery, such as first-pass hepatic metabolism, enzymatic digestion attack, drug hydrolysis and degradation in acidic media, drug fluctuations, and gastrointestinal irritation. This article reviews various transdermal patches available in the market, types, structural components, polymer role, and the required assessment tools. Although transdermal patches have medical applications for smoking cessation, pain relief, osteoporosis, contraception, motion sickness, angina pectoris, and cardiac disorders, advances in formulation development are ongoing to make transdermal patches capable of delivering more challenging drugs. Transdermal patches can be tailored and developed according to the physicochemical properties of active and inactive components, and applicability for long-term use. Therefore, a number of chemical approaches and physical techniques for transdermal patch development are under investigation.
Ensuring Quality in Biometric Systems

Biometric system is using for personal recognition of people in many social and economical activities now a days. A good biometric trait should be measurable, distinctive and stable over time. Real-world deployment of biometric systems often has to contend with degraded signal quality and erratic behavior of the biometric data. For last few years biometric data quality measure become an important concern after poor pathological sample and other many causes. The user, sensor and environmental facts are causes to quality degradation of biometric system. This study approaches that have been used to extract additional information about the biometric data that can then be used to improve performance in degraded conditions and also discuss about the sensor and environmental facts . This study will also discuss how this problem can be overcome to maintain the quality in biometric system with a special emphasis on face, fingerprint, iris modalities with different organizational standards.
A Display Source-Driver IC Featuring Multistage-Cascaded 10-Bit DAC and True-DC-Interpolative Super-OTA Buffer

This article presents an area-efficient 10-bit source-driver IC (SD-IC) for mobile displays. Addressing the challenge of exponential die area growth associated with higher digital-to-analog converter (DAC) resolution dominating color depth, we propose a three-stage-cascading interpolation design solution. In the first stage, a two-output voltage selector selects adjacent voltages from the global resistor string. The proposed overlap-switch merging (OSM) technique reduces its size by two times compared to conventional two-output selectors. The second-stage interpolation employs a successive-approximation-register (SAR)-interpolating switched-capacitor (SI-SC) DAC, enhanced with bit-adaptive switch-size (BASS) modulation to minimize the switching errors. The proposed SI-SC DAC also features an intrinsic two-output design for further interpolation in the subsequent stage. In the last (third) stage, a super-class-AB operational transconductance amplifier (OTA)-based buffer amplifier performs accurate 1-bit true-dc interpolation with no overhead while driving the final output with a fast slew rate. The prototype chip was fabricated in a 180-nm 1.8/5-V CMOS process. The maximum differential nonlinearity (DNL) and integral nonlinearity (INL) were measured to be −0.37 and 1.17 LSB, respectively, in a 10-bit resolution. The maximum deviation of voltage outputs (DVOs) achieved was 15.5 mV. The proposed chip exhibits a source channel size of 2211 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}^{2}$ </tex-math></inline-formula>, which is the smallest reported to date, along with a competitive slew rate of 44 V/<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> measured at <inline-formula> <tex-math notation="LaTeX">$C_{\text {Load}}\,\,=$ </tex-math></inline-formula> 100 pF.