Miracle-dz/UniFAR-FTUs huggingface dataset:
Conjugate Mass Transfer Modeling for VOC Source and Sink Behavior of Porous Building Materials: When to Apply It?

Volatile organic compounds (VOC) are major indoor air pollutants. Physical models have been developed to predict VOC source (emission) and sink behavior (sorption) of building materials. They frequently adopt the conventional convection approach using a third-kind boundary condition. This conventional convection approach in conjunction with the commonly used Sherwood number correlation is based on the assumptions of constant wall concentration at the material-air interface and quasi-steady convective mass transfer in the fluid (air). In this study, the validity of these assumptions is theoretically investigated. An analytical model using the conventional convection approach and a numerical conjugate mass transfer model are developed. The conjugate mass transfer models consider unsteady two-dimensional laminar forced convection over a flat plate coupled with unsteady one-dimensional diffusion and sorption within the porous solid through the concentration and the flux continuities at the material-air interface. The simulation results indicate that the assumptions can lead to a significant overestimation of the wall concentration especially in the early transfer phase. When the effect on the VOC source/sink behavior is quantified by the total transfer time, which is the time required to emit/absorb 99% of the maximum transferable VOC mass, the analytical model results in less than 5% error in the predicted value when VOC transfer is controlled by internal diffusion, i.e., Biot number larger than 9 for (ε + K) 100.
Finite Element Analysis of Spur Gears on Modal and Stress

Being established and assembled in UG, the model file of spur gears was imported to ANSYS workbench, where the contact between gears was built, meshing completed later. Then support and loads was imposed on the finite element model to obtain characteristics of modal and stress distribution on the teeth. After that they each were compared with working frequency and theoretically maximum contact stress and we found the fact was they fit well. By doing this, it is sound to say finite element analysis of spur gears on modal and stress is effective, feasible and reliable.
Improved automatic adjustment of density and contrast in FCR system using neural network

FCR system has an automatic adjustment of image density and contrast by analyzing the histogram of image data in the radiation field. Advanced image recognition methods proposed in this paper can improve the automatic adjustment performance, in which neural network technology is used. There are two methods. Both methods are basically used 3-layer neural network with back propagation. The image data are directly input to the input-layer in one method and the histogram data is input in the other method. The former is effective to the imaging menu such as shoulder joint in which the position of interest region occupied on the histogram changes by difference of positioning and the latter is effective to the imaging menu such as chest-pediatrics in which the histogram shape changes by difference of positioning. We experimentally confirm the validity of these methods (about the automatic adjustment performance) as compared with the conventional histogram analysis methods.
Intra- and Extracellular Effector Vesicles From Human T And NK Cells: Same-Same, but Different?

Cytotoxic T lymphocytes (CTL) and Natural Killer (NK) cells utilize an overlapping effector arsenal for the elimination of target cells. It was initially proposed that all cytotoxic effector proteins are stored in lysosome-related effector vesicles (LREV) termed “secretory lysosomes” as a common storage compartment and are only released into the immunological synapse formed between the effector and target cell. The analysis of enriched LREV, however, revealed an uneven distribution of individual effectors in morphologically distinct vesicular entities. Two major populations of LREV were distinguished based on their protein content and signal requirements for degranulation. Light vesicles carrying FasL and 15 kDa granulysin are released in a PKC-dependent and Ca2+-independent manner, whereas dense granules containing perforin, granzymes and 9 kDa granulysin require Ca2+-signaling as a hallmark of classical degranulation. Notably, both types of LREV do not only contain the mentioned cytolytic effectors, but also store and transport diverse other immunomodulatory proteins including MHC class I and II, costimulatory and adhesion molecules, enzymes (i.e. CD26/DPP4) or cytokines. Interestingly, the recent analyses of CTL- or NK cell-derived extracellular vesicles (EV) revealed the presence of a related mixture of proteins in microvesicles or exosomes that in fact resemble fingerprints of the cells of origin. This overlapping protein profile indicates a direct relation of intra- and extracellular vesicles. Since EV potentially also interact with cells at distant sites (apart from the IS), they might act as additional effector vesicles or intercellular communicators in a more systemic fashion.
Sequential Coupling Shows Minor Effects of Fluid Dynamics on Myocardial Deformation in a Realistic Whole-Heart Model

Background: The human heart is a masterpiece of the highest complexity coordinating multi-physics aspects on a multi-scale range. Thus, modeling the cardiac function in silico to reproduce physiological characteristics and diseases remains challenging. Especially the complex simulation of the blood's hemodynamics and its interaction with the myocardial tissue requires a high accuracy of the underlying computational models and solvers. These demanding aspects make whole-heart fully-coupled simulations computationally highly expensive and call for simpler but still accurate models. While the mechanical deformation during the heart cycle drives the blood flow, less is known about the feedback of the blood flow onto the myocardial tissue. Methods and Results: To solve the fluid-structure interaction problem, we suggest a cycle-to-cycle coupling of the structural deformation and the fluid dynamics. In a first step, the displacement of the endocardial wall in the mechanical simulation serves as a unidirectional boundary condition for the fluid simulation. After a complete heart cycle of fluid simulation, a spatially resolved pressure factor (PF) is extracted and returned to the next iteration of the solid mechanical simulation, closing the loop of the iterative coupling procedure. All simulations were performed on an individualized whole heart geometry. The effect of the sequential coupling was assessed by global measures such as the change in deformation and—as an example of diagnostically relevant information—the particle residence time. The mechanical displacement was up to 2 mm after the first iteration. In the second iteration, the deviation was in the sub-millimeter range, implying that already one iteration of the proposed cycle-to-cycle coupling is sufficient to converge to a coupled limit cycle. Conclusion: Cycle-to-cycle coupling between cardiac mechanics and fluid dynamics can be a promising approach to account for fluid-structure interaction with low computational effort. In an individualized healthy whole-heart model, one iteration sufficed to obtain converged and physiologically plausible results.
High-performance current-mode instrumentation amplifier circuit

A high-performance current-mode instrumentation amplifier circuit is described in this paper. It has a high common mode rejection ratio, high gain, high accuracy, wide bandwidth and bandwidth gain-independence. It utilizes commercially available integrated circuits and is easily implemented. Experimental results show that at low frequencies a common mode rejection ratio of 120 dB is attainable.
Genipin Cross-Linked Decellularized Nucleus Pulposus Hydrogel-Like Cell Delivery System Induces Differentiation of ADSCs and Retards Intervertebral Disc Degeneration

Intervertebral disc degeneration (IDD) is the pathological basis of disc degenerative diseases (DDD). Reduction in the number of cells and degeneration of the extracellular matrix (ECM) in the nucleus pulposus (NP) are characteristics of IDD. Bio-hydrogel combined with stem cell transplantation is a promising treatment. Injectable ECM hydrogels have good biological activity and in-situ gelatinization. However, its biomechanics and stability are insufficient to provide adequate mechanical support for intervertebral discs and to maintain the long-term differential stimulus for seeded stem cells. In our study, we developed genipin cross-linked decellularized nucleus pulposus hydrogel (GDH) as delivery system. We evaluated the mechanical properties, stability, biocompatibility, and differentiation induction of GDH cross-linked with different concentrations of genipin in vitro. The GDH-loaded adipose-derived mesenchymal stem cells (ADSCs) (GDHA) were injected into the rat degenerated coccygeal intervertebral disc. The effect of intervertebral disc regeneration in vivo was evaluated. The results showed that GDH with 0.02% of genipin had similar elastic modulus to human nucleus pulposus, good biocompatibility, and inducibility of expressing NP-related genes. In vivo studies showed that GDHA improved the survival of ADSCs and improved the intervertebral height, MRI index, and histological grading score. In conclusion, GDH, as an outstanding bio-hydrogel cell delivery system, has the therapeutic potential for retarding IDD.
Investigation of the band gap widening effect in thin silicon double gate MOSFETs

The topic of this project is the analysis of the subthreshold current in thin silicon Double Gate (DG) MOSFETs, with a particular focus on observing the so-called effective band gap widening effect using electrical measurements. DG MOSFETs with a very thin silicon layer are expected to be promising candidates for the next generation CMOS technology: moving from single to double gate gives better control over the channel region, which improves the short channel performance and gives rise to an ideal subthreshold slope. As for the drain current, the charge carriers have to traverse an energy barrier when owing from source to drain. For low gate bias, the height of the barrier is mainly determined by the band gap, making particularly the subtreshold current suitable for observing changes in the effective band gap. As the silicon layer thickness (i.e. the channel region) is shrunk to values below about 15 nm, quantum mechanical effects are expected to show up in the device characteristics. One of those effects is the widening of the effective band gap, which originates from splitting of both the conduction and valence band into subbands, due to confinement of the charge carriers in the very thin silicon layer between the Si=SiO2 interfaces. The effective band gap widening effect is investigated using temperature dependent IV and CV measurements. Essentially, both measurements rely on the strong temperature dependence of diffusion, which constitutes the dominant part of carrier transport in the subthreshold regime. Generally speaking, by comparing the characteristics of a device with a 'thick' silicon layer to the characteristics of a thin device (i.e. in which carrier confinement is important), the effective band gap widening effect should become apparent. The results obtained from the IV measurements indicate that an increase in effective band gap occurs for decreasing silicon layer thickness. For a detailed quantitative analysis however, the measurements should be repeated on a larger number of devices, with thinner layers, fabricated in a more accurate process. The CV measurements show that the device behavior in depletion and weak inversion is dominated by the implanted p+ and n+ regions adjacent to the intrinsic channel, being more pronounced for shorter devices. This should be investigated further with additional simulations, particularly concerning the temperature dependence.
Towards a Standard Qualification Plan (SQP) for Composite Materials - Initial Draft

This report details a draft Standard Qualification Plan (SQP) for the initial qualification of preimpregnated (prepreg) fibre-reinforced thermoset and thermoplastic materials systems, in order to allow quality control, material selection and preliminary design to be undertaken. It is aimed at reducing the substantial costs involved in qualifying materials. The SQP dose not constitute a full qualification programme, rather, ideally, a standardized building block forming the initial stage of a qualification or certification programme. A survey of existing qualification initiatives was carried out together with close industrial consultation in order to determine the appropriate content and format of the SQP. The procedure comprises a Standard Qualification Plan (SQP) which contains the minimum common test requirements for initial materials qualification, and an Extended Qualification Plan (EQP) encompassing further test options to meet specific requirements. The SQP/EQP prescribes test matrices, a specimen sampling scheme, a data presentation format and data analysis methods. Where possible ISO standards are chosen as the preferred test method. Part 1 of this report details the objectives and formulation of the SQP, whilst Part 2 details the proposed draft procedure.
Alterations in Emotional Diversity Correspond With Increased Severity of Attenuated Positive and Negative Symptoms in the Clinical High-Risk Syndrome

Background: Alterations in emotional functioning are a key feature of psychosis and are present in individuals with a clinical high-risk (CHR) syndrome. However, little is known about alterations in emotional diversity (i.e., the variety and relative abundance of emotions that humans experience) and clinical correlates in this population. Methods: Individuals meeting criteria for a CHR syndrome (N = 47) and matched healthy controls (HC) (N = 58) completed the modified Differential Emotions Scale (used to derive scores of total, positive, and negative emotional diversity) and clinical interviews (i.e., Structured Interview for Psychosis-Risk Syndromes). Results: Findings showed that the CHR group experienced lower levels of positive emotional diversity compared to HCs. Among the CHR individuals, lower levels of positive and higher levels of negative emotional diversity were associated with more severe attenuated positive and negative symptoms. Analyses controlled for mean levels of emotion and current antipsychotic medication use. Discussion: Results demonstrate that altered emotional diversity (in particular lower levels of positive and higher levels of negative emotional diversity) is a clinically relevant marker in CHR individuals, above and beyond alterations in mean levels of emotional experiences. Future studies may probe sources, downstream consequences, and potential modifiability of decreased emotional diversity in individuals at CHR.
Host Factor Interaction Networks Identified by Integrative Bioinformatics Analysis Reveals Therapeutic Implications in COPD Patients With COVID-19

Background: The COVID-19 pandemic poses an imminent threat to humanity, especially for those who have comorbidities. Evidence of COVID-19 and COPD comorbidities is accumulating. However, data revealing the molecular mechanism of COVID-19 and COPD comorbid diseases is limited. Methods: We got COVID-19/COPD -related genes from different databases by restricted screening conditions (top500), respectively, and then supplemented with COVID-19/COPD-associated genes (FDR<0.05, |LogFC|≥1) from clinical sample data sets. By taking the intersection, 42 co-morbid host factors for COVID-19 and COPD were finally obtained. On the basis of shared host factors, we conducted a series of bioinformatics analysis, including protein-protein interaction analysis, gene ontology and pathway enrichment analysis, transcription factor-gene interaction network analysis, gene-microRNA co-regulatory network analysis, tissue-specific enrichment analysis and candidate drug prediction. Results: We revealed the comorbidity mechanism of COVID-19 and COPD from the perspective of host factor interaction, obtained the top ten gene and 3 modules with different biological functions. Furthermore, we have obtained the signaling pathways and concluded that dexamethasone, estradiol, progesterone, and nitric oxide shows effective interventions. Conclusion: This study revealed host factor interaction networks for COVID-19 and COPD, which could confirm the potential drugs for treating the comorbidity, ultimately, enhancing the management of the respiratory disease.
Research on MRI classification method of Alzheimer's Disease brain based on convolutional neural network

Alzheimer's Disease (AD) is a common which has a very high incidence of disease and incidence in the elderly population and Mild Cognitive Impairment (MCI) is the prodromal stage of AD, and its symptoms are between normal and dementia. As time progresses, MCI patients are at great risk of continuing to deteriorate into complete dementia, so the distinction between the transformed state and the stable state of MCI is also particularly important. In recent years, the rapid development of deep learning provides a new direction for the classification of AD which is also used to achieve the classification of AD in this paper.
Well Recovered and More Creative? A Longitudinal Study on the Relationship Between Vacation and Creativity

The aim of this study was to investigate employees’ self-reported creativity before and after vacation and to examine the impact of recovery experiences (detachment, relaxation, mastery, meaning, autonomy, affiliation) on changes in creativity. The DRAMMA model of Newman et al. provides the theoretical background of our approach. Longitudinal data was assessed with four repeated measurements. The study encompassed data from 274 white-collar workers. Analyses showed that employees subjectively perceive their creativity to benefit not immediately after their vacation but 2 weeks later. Detachment was significantly related to lower creativity within persons, while mastery experiences explained differences in creativity between persons. This study provides a detailed picture of changes in creativity around vacations.
Paradise Regained? Localised and Limited Nature Connexions in the UK's Covid-19 Lockdown

Research has suggested that connexions between humans and the natural world lead to increased well-being and generate pro-environmental attitudes, which in turn benefit nature. This article asks whether users of outdoor public spaces in the UK during the Covid-19 pandemic in 2020 experienced greater connectedness with nature, consistent with the five “pathways to nature connectedness” identified in previous research. Semi-structured interviews were conducted with a purposive sample of 42 individuals on their use of green and public spaces during the UK's lockdown, while a further 29 participants responded to an online survey. While the research revealed the importance of nature connectedness, only three of the five pathways were well-evidenced, and these connexions were frequently mediated by social activities. The article advances the study of nature connectedness by identifying challenges in applying the pathways framework and suggesting areas for further research to understand how the pathways operate in real-world conditions.
A Survey on Aspect-Based Sentiment Classification

With the constantly growing number of reviews and other sentiment-bearing texts on the Web, the demand for automatic sentiment analysis algorithms continues to expand. Aspect-based sentiment classification (ABSC) allows for the automatic extraction of highly fine-grained sentiment information from text documents or sentences. In this survey, the rapidly evolving state of the research on ABSC is reviewed. A novel taxonomy is proposed that categorizes the ABSC models into three major categories: knowledge-based, machine learning, and hybrid models. This taxonomy is accompanied with summarizing overviews of the reported model performances, and both technical and intuitive explanations of the various ABSC models. State-of-the-art ABSC models are discussed, such as models based on the transformer model, and hybrid deep learning models that incorporate knowledge bases. Additionally, various techniques for representing the model inputs and evaluating the model outputs are reviewed. Furthermore, trends in the research on ABSC are identified and a discussion is provided on the ways in which the field of ABSC can be advanced in the future.
Remote Evaluation of Parkinson's Disease Using a Conventional Webcam and Artificial Intelligence

Objective: This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease (PD) remotely using an accessible standard webcam. Methods: We consecutively enrolled a cohort of 42 patients with PD and healthy subjects (HSs). The participants were recorded performing MDS-UPDRS III bradykinesia upper limb tasks with a computer webcam. The video frames were processed using the artificial intelligence algorithms tracking the movements of the hands. The video extracted features were correlated with clinical rating using the Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale and inertial measurement units (IMUs). The developed classifiers were validated on an independent dataset. Results: We found significant differences in the motor performance of the patients with PD and HSs in all the bradykinesia upper limb motor tasks. The best performing classifiers were unilateral finger tapping and hand movement speed. The model correlated both with the IMUs for quantitative assessment of motor function and the clinical scales, hence demonstrating concurrent validity with the existing methods. Conclusions: We present here the proof-of-concept of a novel webcam-based technology to remotely detect the parkinsonian features using artificial intelligence. This method has preliminarily achieved a very high diagnostic accuracy and could be easily expanded to other disease manifestations to support PD management.
OPTIMUM GEOMETRY FOR TORQUE RIPPLE MINIMIZATION OF SWITCHED RELUCTANCE MOTORS

This paper briefly describes an approach to determine the optimum magnetic circuit parameters to minimize low speed torque ripple for switched reluctance (SR) motors. For prediction of the torque ripple, normalized data obtained from field solution and a neural network approach is used. Comparison of experimental results with computations illustrates the accuracy of the method. The optimization method is briefly described and some results are presented.