Miracle-dz/UniFAR-FTUs huggingface dataset:
How do aspects of selfhood relate to depression and anxiety among youth? A meta-analysis

Abstract Adolescents' sense of self has important implications for their mental health. Despite more than two decades of work, scholars have yet to amass evidence across studies to elucidate the role of selfhood in the mental health of adolescents. Underpinned by the conceptual model of selfhood, this meta-analytic review investigated the strength of associations of different facets of selfhood and their associated traits with depression and anxiety, moderating factors that attenuate or exacerbate these associations, and their causal influences. Using mixed-effects modeling, which included 558 effect sizes from 298 studies and 274 370 adolescents from 39 countries, our findings revealed that adolescents' self-esteem/self-concept [r = −0.518, p < 0.0001; (95% CI −0.49 to −0.547)] and self-compassion [r = −0.455, p < 0.0001; (95% CI −0.568 to −0.343)] demonstrating largest effect sizes in their associations with depression. Self-esteem/self-concept, self-compassion, self-awareness, self-efficacy, and self-regulation had similar moderate negative associations with anxiety. Meta-regressions revealed that adolescent age and type of informants (parents v. adolescents) were important moderators. Findings on causal influences indicated bidirectional causations, particularly low self-esteem/self-concept, self-awareness and self-efficacy drive higher depression and vice-versa. In contrast, the different self traits did not demonstrate specific causal direction with anxiety. These results pinpoint self traits that are pivotal in relating to adolescent mental health functioning. We discussed the theoretical implications of our findings in terms of how they advance theory of selfhood for adolescent mental health, and the practical implications of building selfhood as cultivating psychological skills for mental health.
Functional Characterisation of the Rare SCN5A p.E1225K Variant, Segregating in a Brugada Syndrome Familial Case, in Human Cardiomyocytes from Pluripotent Stem Cells

Brugada syndrome (BrS) is an inherited autosomal dominant cardiac channelopathy. Pathogenic rare mutations in the SCN5A gene, encoding the alpha-subunit of the voltage-dependent cardiac Na+ channel protein (Nav1.5), are identified in 20% of BrS patients, affecting the correct function of the channel. To date, even though hundreds of SCN5A variants have been associated with BrS, the underlying pathogenic mechanisms are still unclear in most cases. Therefore, the functional characterization of the SCN5A BrS rare variants still represents a major hurdle and is fundamental to confirming their pathogenic effect. Human cardiomyocytes (CMs) differentiated from pluripotent stem cells (PSCs) have been extensively demonstrated to be reliable platforms for investigating cardiac diseases, being able to recapitulate specific traits of disease, including arrhythmic events and conduction abnormalities. Based on this, in this study, we performed a functional analysis of the BrS familial rare variant NM_198056. 2:c.3673G>A (NP_932173.1:p.Glu1225Lys), which has been never functionally characterized before in a cardiac-relevant context, as the human cardiomyocyte. Using a specific lentiviral vector encoding a GFP-tagged SCN5A gene carrying the specific c.3673G>A variant and CMs differentiated from control PSCs (PSC-CMs), we demonstrated an impairment of the mutated Nav1.5, thus suggesting the pathogenicity of the rare BrS detected variant. More broadly, our work supports the application of PSC-CMs for the assessment of the pathogenicity of gene variants, the identification of which is increasing exponentially due to the advances in next-generation sequencing methods and their massive use in genetic testing.
3-D Full-Waveform Modeling and Analysis of Induced Polarization and Magnetic Viscosity Effect in Time-Domain Electromagnetic Method

With the improvement in the accuracy of time-domain electromagnetic method (TDEM) observations for geophysical exploration, abnormal diffusion phenomena have become a more salient focus of research. In particular, the induced polarization (IP) effect and magnetic viscosity (MV) effect are often observed in the exploration of polymetallic ore, with IP effect leading to a negative TDEM response, and MV effect generating a power-law decay of −0.6 to 1.4 in the late stage response. Ignoring these effects can lead to incorrect data interpretation. Therefore, to model and analyze both IP effect and MV effect accurately, a 3-D numerical modeling method for IP–MV effect is proposed. The Cole–Cole conductivity and Cole–Cole susceptibility models are approximated in the time domain using the multiple-zero-pole (MZP) method. Then, the diffusion equations for the electric and magnetization intensity fields are derived as control equations, while the 3-D modeling of the IP–MV effect with full waveform is realized based on the improved recursive convolution technique and finite-difference time-domain (FDTD) method. The effectiveness is verified by comparing with the 1-D numerical integration solutions. The response characteristics of IP effect and MV effect are analyzed with full trapezoidal waveform, and a complex model including IP–MV effect is discussed. It is demonstrated that IP–MV effect can be better observed at the ON-time stage. The proposed method can effectively model the diffusion process of IP–MV effect with the full waveform, which can be helpful for improving the inversion accuracy and detection precision for complex geological formations.
The DØ Monte Carlo

The DO/ detector at the Fermilab Tevatron began its first data taking run in May 1992. For analysis of the expected 25 pb−1 data sample, roughly half a million simulated events will be needed. The GEANT‐based Monte Carlo program used to generate these events is described, together with comparisons to test beam data. Some novel techniques used to speed up execution and simplify geometrical input are described.
Two Adaptive Image Pre-processing Chains for Face Recognition Rate Enhancement

In this paper, two adaptive image enhancement and de-noising chains are produced. Our aim is to enhance the face image quality that stored in a large database for face recognition applications. Each processing chain consists of three steps, the first chain is proposed to enhance Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) recognition rate, in the first step of this chain, the face images are de-noised with Haar wavelet de-noising filter at level ten of decomposition, in the second step, the de-noised image is adjusted to enhance the image contrast, and in the third step the high pass filter ‘Laplacian of Gaussian filter’ is used for detecting edges in face images. The second chain is proposed to enhance Linear Discriminat Analysis LDA and Kernel Fisher Analysis (KFA) recognition rate, in the first step of this chain the image contrast is adjusted and entered to histogram equalization as second step and in the third step the image is de-noised with Haar wavelet de-noising filter at level ten of decomposition. Our approaches produced good result and contributed in raising the recognition rate in PCA, KPCA, LDA and KFA up to 10%, 20%, 5% and 4% respectively when 400 face images are used.
Empirical Best Linear Unbiased Prediction for Out of Sample Areas

Models for small area estimation based on a random effects specification typically assume population units in different areas are uncorrelated. However, they can be extended to account for the correlation between areas by assuming that area random effects are spatially correlated. In this paper we suggest a simple variance-covariance structure for such a spatial correlation structure within the context of a linear model for the population characteristic of interest, and derive estimates of parameters and components of variance using maximum likelihood and restricted maximum likelihood methods. This allows empirical best linear unbiased predictions for area totals to be computed for areas in sample as well as those that are not in sample. An expression for the mean cross-product error (MCPE) matrix of these predicted small area totals is derived, as is an estimator of this matrix. The estimation approach described in the paper is then evaluated by a simulation study, which compares the new method with other methods of small area estimation for this situation.
Inferring spatial structure from feature correspondences (machine, computer vision, robotics, mobile, artificial intelligence)

Building a three-dimensional map of the local environment is a critical task for many computer vision systems. This dissertation discusses two methods for building such a map: model-based vision and image sequence analysis. A model-based vision system uses previously stored models of objects to recognize objects in images and to estimate their locations relative to the camera. In the form of image sequence analysis discussed here, a mobile robot's vision system compares two or more images formed at different locations to infer the structure of the scene and the robot's path through it. The aspect of model-based vision considered is the estimate of an object's location once the object has been recognized and correspondences between image and model features established. New algorithms are introduced for models consisting of simple geometric features: a set of coplanar points, a set of coplanar lines, a circle, and a set of general lines in space. Next a simple technique for nonlinear, sequential estimation is applied to a problem in image sequence analysis. A camera moves through an planar scene consisting of a set of points; the problem is to estimate the camera locations and the scene points' positions from the images. Processing images sequentially saves computation and provides information as the camera traverses its path. Nonlinear estimation theory enables transforming prior distributions of image noise into posterior distributions on scene points' positions and camera locations that are used to interpret subsequent images. Finally a technique for image sequence analysis to exploit knowledge of the camera path is presented. Bolles and Baker's Epipolar-Plane Image Analysis, originally applicable to linear camera paths and fixed viewing directions, is extended to unrestricted viewing directions. Next duality in the projective plane is used to extend the analysis to general paths in the plane and to curved and moving objects; this technique is applied to an image sequence collected by the SRI mobile robot to reconstruct a planar slice of the scene. Duality in projective space is used to derive the extension to general paths in space.
β-Hydroxy-β-methyl Butyrate Regulates the Lipid Metabolism, Mitochondrial Function, and Fat Browning of Adipocytes

A growing number of in vivo studies demonstrated that β-hydroxy-β-methyl butyrate (HMB) can serve as a lipid-lowering nutrient. Despite this interesting observation, the use of adipocytes as a model for research is yet to be explored. To ascertain the effects of HMB on the lipid metabolism of adipocytes and elucidate the underlying mechanisms, the 3T3-L1 cell line was employed. Firstly, serial doses of HMB were added to 3T3-L1 preadipocytes to evaluate the effects of HMB on cell proliferation. HMB (50 µM) significantly promoted the proliferation of preadipocytes. Next, we investigated whether HMB could attenuate fat accumulation in adipocytes. The results show that HMB treatment (50 µM) reduced the triglyceride (TG) content. Furthermore, HMB was found to inhibit lipid accumulation by suppressing the expression of lipogenic proteins (C/EBPα and PPARγ) and increasing the expression of lipolysis-related proteins (p-AMPK, p-Sirt1, HSL, and UCP3). We also determined the concentrations of several lipid metabolism-related enzymes and fatty acid composition in adipocytes. The HMB-treated cells showed reduced G6PD, LPL, and ATGL concentrations. Moreover, HMB improved the fatty acid composition in adipocytes, manifested by increases in the contents of n6 and n3 PUFAs. The enhancement of the mitochondrial respiratory function of 3T3-L1 adipocytes was confirmed via Seahorse metabolic assay, which showed that HMB treatment elevated basal mitochondrial respiration, ATP production, H+ leak, maximal respiration, and non-mitochondrial respiration. In addition, HMB enhanced fat browning of adipocytes, and this effect might be associated with the activation of the PRDM16/PGC-1α/UCP1 pathway. Taken together, HMB-induced changes in the lipid metabolism and mitochondrial function may contribute to preventing fat deposition and improving insulin sensitivity.
Digital Innovation and Core Competence of Manufacturing Industry: Moderating Role of Absorptive Capacity

ABSTRACT In the digital economy era, traditional manufacturing enterprises face a significant challenge due to fierce competition regarding products, channels, digital technology, management, and core competencies. As a result, many enterprises strive to leverage digital technology to gain new processes, products, services, and business models, thereby enhancing their competitive edge. This paper mainly focuses on how digital innovation based on different value-creation methods can help enterprises build their core competence. Using a sample of 254 questionnaires from manufacturing enterprises in China, this study investigates whether and how efficiency-based, convergence-based, and generativity-based digital innovation supports the development of core competence using ordinary least squares (OLS) regression. Further, this paper examines the moderating effect of absorptive capacity. The empirical results indicate that digital innovation encourages the establishment of core competence, and absorptive capacity strengthens the role of efficiency-based digital innovation but inhibits convergence-based and generativity-based digital innovation. This study contributes to a clearer understanding of the significance of digital innovation for Chinese manufacturing enterprises and provides useful insights for other countries to rethink their innovation models.
A compact and high-speed plasmonic slot waveguide coupled with photonic waveguide based 2x2 electro-optic switch

We report an electro-optic switch at 1.55 μm wavelength based on Mach-Zehnder Interferometer (MZI) in which, the MZI arms and the input-output 3-dB couplers are designed using plasmonic and photonic waveguides re- spectively. More than 90% photons are successfully converted into surface plasmons through tapering the both photonic as well as plasmonic waveguide. The plasmonic and photonic mode profiles, interaction between plas- monic and RF fields, confinement of plasmonic field in plasmonic waveguide, and trade-off between loss and confinement are thoroughly examined before the combined optical and electrical simulation of the switch. The switching voltage is less than 6 Volts, the MZI arm length is only 15 μm, the crosstalk is less than -35 dB, and the maximum switching speed is 11.55 ps. The footprint of the switch is only 290x50 μm2.
Properties of Palm Oil Ash Geopolymer Containing Alumina Powder and Field Para Rubber Latex

Most geopolymer binder is produced using raw materials comprising powder with high silica and alumina content. Additionally, fine aggregate is prepared with river sand for high bulk density. This research proposes using palm oil ash (POA) for the main binder and palm oil clinker (POC) for the fine aggregate. The chemical composition of POA has high levels of silica but low alumina, so it must undergo partial replacement with alumina powder (AP). POA and POC are waste by-products of electrical power plants. The properties to be investigated include compressive strength, bulk density, water absorption, and microstructure. The effect of mixture composition, i.e., POA and field Para rubber latex (FPRL), on those properties is of particular interest. POA was substituted by AP and FPRL at 2.5%, 5%, 7.5%, and 10%, and at 1%, 3%, 5%, and 10%, respectively. Geopolymer mortars were cured at ambient temperature for 24 hours and kept at ambient temperature until testing. The compressive strengths of the geopolymer mortars were tested at 1, 7, and 28 days. The results showed that the optimal mixture consisted of 5% AP in the case of AP only and 1% FPRL in the case of FPRL only, while the ternary optimal mixture of 1% FPRL and 7.5% AP achieved higher compressive strengths than the control (CT) sample at 28.16, 19.98, and 25.30 MPa, respectively, after 28 days of curing. Bulk density increased with the addition of AP and FPRL. The microstructures of the geopolymer samples investigated using SEM-EDX showed the presence of different elements with different mixtures and displayed a dense, compact geopolymer matrix with high compressive strength. Using large amounts of POA in combination with AP and FPRL improved the environmental aspects of landfill disposal. Doi: 10.28991/CEJ-2023-09-05-017 Full Text: PDF
Fault Diagnosis With Robustness and Lightweight Synergy Under Noisy Environment

Over the years, deep learning-based fault diagnosis approaches have made remarkable progress. However, the diagnosis accuracy has been on a steady decline because of noise interference in industrial applications. In addition, the accuracy improvement is often accompanied by an increase in computational cost, which makes it difficult to achieve engineering-embedded deployment. To find the balance between robustness and complexity, a new intelligent fault diagnosis framework named lightweight multiscale dilated causal convolutional attention network (Li-MDCCAN) is proposed. Based on dual fusion mechanisms, the proposed network realizes high robustness by adopting multiscale strategy and attention modules, which explores multiscale and multidimensional features along three parallel paths. Furthermore, lightweight convolutional blocks are introduced to reduce the network’s complexity. The performance of Li-MDCCAN is verified with six different datasets and compared with those of four advanced fault diagnosis networks. The experimental results indicate the effectiveness and superiority of Li-MDCCAN in increasing robustness and decreasing complexity. Moreover, prior sensor placement is discussed to guide applications under limited engineering conditions.
Onset of the Asian Summer Monsoon in 1979 and the Effect of the Tibetan Plateau

Abstract The time evolution of the general circulation over Asia during an 80-day period from mid-April to early July 1979 is studied using objectively analyzed FGGE Level II-b data. Through the analyses of the wind, temperature, precipitation, outgoing longwave radiation (OLR) flux, and heat and moisture budgets in the area 0°–50°N, 40°–130°E, the major changes of the circulation leading to the onset of the summer monsoon and the role of the Tibetan Plateau in these changes are examined. During the analyzed period the general circulation underwent two distinct stages of abrupt transitions resulting in the successive onsets of early summer rains over Southeast Asia and the Indian summer monsoon. The first transition occurred in May in which low-level southwesterlies began over the longitudes east of 80°E (from the Bay of Bengal to the South China Sea), resulting in the spreading of early summer rains over Assam, the Bay of Bengal coasts of Burma and the Malay Peninsula, Thailand, Indochina and the South C.
Determinants of Subscription Renewal Behavior in Sport Spectatorship Services: A CHAID Decision Tree Modeling Approach

Attempts were made to identify the prioritized determinants of season ticket holders’ (STHs) renewal behavior. Also, grounded in the lens of luxury fever, we explored how the determinants are differently weighed and processed across two types of STHs, including regular (R-STHs) vs. premium seat holders (P-STHs). Through a partnership with a Big Ten athletic department, the attendance data were obtained and analyzed. This study adapted a decision tree modeling approach to predict renewal/churning behavior by setting learning decision rules in the CHAID algorithm, a supervised learning algorithm family. Results of this study indicate that discounted tickets, affordability, and entertainment were important determinants for R-STHs. By contrast, tenure as a ticket holder, game outcomes, and business use were significantly weighed for P-STHs. Recent attendance and exclusive benefits were important branches for both decision trees. Th is research expands the understanding of the subscription-based services industry by applying a fresh data mining approach.
RAIN: Risk Assessment Framework Based on an Interdependent-Input Propagation Network for a 5G Network

It is essential for mobile network operators to provide effective protective measures for assets in 5G networks to mitigate various threats, and risk assessment plays an important role in decision-making for protective measures. Organizations refer to risk assessment to determine the priority for protective measures, and multiple studies have proposed assessments that take various aspects and methodologies into consideration. Nevertheless, these efforts are not sufficiently useful in a practical sense. Existing studies lack numerical results to make cost-efficient decisions and cannot be automatically updated when the security policy is altered. Finally, a unified assessment framework is necessary. Hence, we propose a quantitative risk assessment framework, RAIN, to solve these problems for the 5G network. A customized weighted network combined with an interdependent-input weighting method enables the framework to provide holistic and quantitative assessment results and can be used for any scenario in a 5G network. With the assessment results, organizations can prioritize the implementation of protective measures for the target assets. In addition, the framework is flexible for policy changes and suitable for different systems in 5G networks. We implement our framework on a 5G stand alone core network system with different scenarios.
“Computational Fluid Dynamics Analysis of the Infrared Emission from a Generic Hypersonic Glide Vehicle”—A Response

Abstract A recent paper by Candler and Leyva in Science & Global Security comments on our 2020 paper “Modelling the Performance of Hypersonic Boost-Glide Missiles” analyzing the capabilities of hypersonic boost-glide weapons. They provide useful new data on several previously uncertain aspects of glide vehicle aerodynamics and report results from computational fluid dynamics calculations of heating and infrared light emission from hypersonic vehicles during the glide phase. They report infrared emissions lower than those we reported but still above the minimum detection threshold of modern U.S. space sensors. We discuss how Candler and Leyva’s new data can be incorporated into our analytical model and identify significant, unresolved discrepancies between their results and those of a previously published computational fluid dynamics analysis of the same glide vehicle. Finally, we comment on the role of social processes in the construction of knowledge about hypersonic weapon performance.
Molecular assembly with a figure-of-eight nanohoop as a strategy for the collection and stabilization of cyclo[18]carbon.

The recently synthesized novel figure-of-eight nanohoop with two strained oligoparaphenylenes (OPPs) was theoretically designed to collect and stabilize new allotropic carbon cyclo[18]carbon (C18) through molecular assembly. The size adaptability and shape complementarity of C18 to OPP make it possible for them to combine into extraordinary ring-in-ring supramolecules. Thermodynamic analysis of 2C18@OPP showed that the host-guest complex should spontaneously form below 404 K. Molecular dynamics (MD) simulations demonstrated that the assembly of C18 and OPP into host-guest complexes of up to 1 : 2 can occur at ambient temperature. Various real-space function analyses revealed that the nature of the non-covalent interaction between C18 and OPP is the van der Waals (vdW) attraction characterized by π-π stacking. Photoexcitation makes the host-guest complexes less stable in their S1 state by causing the central linker to flatten.