Miracle-dz/UniFAR-FTUs huggingface dataset:
Association Between Social Isolation and Eating Alone on Foods Consumed by Chinese Adolescents

Obesity is increasingly affecting Chinese adolescents due to trends in unhealthy eating, including lower fruit and vegetable consumption and increased consumption of processed foods. A cross-sectional study of adolescents was conducted in Wuhan, China, in October 2019 that included measurements of perceived social isolation, eating when anxious or depressed, diet composition, body weight, and height. Social isolation, eating when sad or anxious, and eating alone were significant predictors of processed food and sugar-sweetened beverage consumption, although only eating when sad or anxious was a significant predictor of fruit and vegetable consumption. Eating when anxious or depressed did not mediate these relationships. Social isolation was associated with consumption of processed foods and sugar-sweetened beverages by boys, but not by girls, and only eating home-cooked dinner was associated with fruit and vegetable consumption by female adolescents. Eating when sad or anxious, eating alone, and eating home-cooked meals were all associated with fruit and vegetable consumption among male adolescents. The effects of social isolation, anxiety, and depression could act to increase unhealthy eating through several mechanisms potentially mediated by chronic stress, while eating alone could also act to increase unhealthy eating due to effects of self-efficacy and food availability. The interplay between diet and outside factors, including environment, social factors, and personal psychology specific to Chinese adolescents needs to be better understood to develop comprehensive interventions for this population.
Parallel algorithms of implicit coupling in fluid-structure interaction problems ⋆

The partitioned approach in fluid-structure interaction problems makes it possible to perform computations in fluid and structure independently of each other with implicit coupling at the interface boundary. Implicit coupling between physical problems involves solving a nonlinear interface system of equations. The article deals with quasi-Newtonian algorithms for solving nonlinear systems of equations. Comparison of efficiency is shown for solving fluid-structure interaction problems with different degrees of nonlinearity.
Behavioral Consistency in the Digital Age

Efforts to infer personality from digital footprints have focused on behavioral stability at the trait level without considering situational dependency. We repeated a classic study of intraindividual consistency with secondary data (five data sets) containing 28,692 days of smartphone usage from 780 people. Using per-app measures of pickup frequency and usage duration, we found that profiles of daily smartphone usage were significantly more consistent when taken from the same user than from different users (d > 1.46). Random-forest models trained on 6 days of behavior identified each of the 780 users in test data with 35.8% accuracy for pickup frequency and 38.5% accuracy for duration frequency. This increased to 73.5% and 75.3%, respectively, when success was taken as the user appearing in the top 10 predictions (i.e., top 1%). Thus, situation-dependent stability in behavior is present in our digital lives, and its uniqueness provides both opportunities and risks to privacy.
Bivariate Modelling of the Influence of Trade on Aggregate and Disaggregate Energy Use in Ghana

The current research has explored the trade-energy nexus for Ghana using annual data within 1970-2011, using the Autoregressive distributed lag model (ARDL) and the Granger predictive causality test in bivariate modelling. The research findings provide evidence of a cointegration association between trade liberalisation (proxied by trade openness) and energy resources (proxied by aggregate energy use, electricity energy use, and fossil fuel energy use). However, there is an insignificant positive long-run effect of trade on energy use for all the energy resources under investigation. In the short-term, there is a positive significant effect of trade on electricity energy use and fossil fuel energy use, but an insignificant positive effect of trade on aggregate energy use. Concerning aggregate energy use, the research findings show that for aggregate energy use, there is causality from trade to aggregate energy use without feedback. However, in the case of electricity use and fossil fuel energy use, trade predicts energy use with feedback. For policy, energy conservation (fossil fuel energy use, and electricity energy use) may not have a deleterious effect on trade in Ghana. Future research may focus on how structural breaks and panel analysis improves the current study, controlling for the effect of other variables that influence energy use.
Synthesis of functionalized 4H-Chromenes catalyzed by lipase immobilized on magnetic nanoparticles

ABSTRACT In the work, mucor miehei lipase (MML) was covalently immobilized on the 2,4,6-trichloro-1,3,5-triazine (TCT)-modified magnetite nanoparticles. Then, the immobilized MML was utilized in the synthesis of functionalized 4H-Chromenes via a multicomponent reaction firstly. Under the optimized reaction conditions, immobilized MML displayed high catalytic performance (Yield: 81–96%) and excellent reusability, indicating a high potential for practical operation. GRAPHICAL ABSTRACT
A Generalized Multiple Criteria Data-Fitting Model With Sparsity and Entropy With Application to Growth Forecasting

In this article, we present an extended data-fitting model which involves different and conflicting criteria, and we propose an algorithm based on a scalarization technique to solve it. Our model integrates in a unique framework three different criteria, namely, a data-fitting term, and the entropy and the sparsity of the set of unknown parameters. This model can be analyzed by means of multiple criteria decision-making techniques. We then validate the proposed modified algorithm using two computational experiments: We analyze the problem of handwritten digit recognition using a logistic regression model and a deep neural network model, respectively. In the final part of the article, we employ this methodology to forecasting instead. Given the importance of forecasting techniques to predict the future, which in turn can lead to positive impacts on firm performance, we propose two numerical experiments focusing on the forecast of the US GDP. In the first one, we proceed by means of a modified iterated function system with grayscale maps-type fractal operator, and, in the second one, we implement a modified neural network-based model.
Characterization of a Non-Flowering Cabbage Mutant Discovered 42 Years Ago

A non-flowering natural cabbage mutant among the open-pollinated line ‘T15’ was found 42 years ago. The mutant was named ‘nfc’ (non-flowering cabbage) and has been propagated vegetatively by cuttings. ‘nfc’ hardly flowers during the spring season even after plenty of low-temperature periods. This study characterized the non-flowering trait of ‘nfc’ and assumed the mechanism. In the first experiment, we investigated the flowering characteristics of ‘T15’ and ‘nfc’ over three years. Throughout the 3-year cultivation period, all ‘T15’ plants flowered, while the flowering ratios of ‘nfc’ propagated by cuttings at the 1st, 2nd, and 3rd year were 0%, 32%, and 4%, respectively. In the last two years, other traits were also investigated in detail. The flowering dates of ‘nfc’ flowering plants were later than those of ‘T15’, and the average numbers of flowering shoots per flowering plant of ‘nfc’ were lower than those of ‘T15’. Moreover, the terminal bud of ‘nfc’ flowering plants continued to grow vegetatively, even when their lateral shoots flowered. In the second experiment, to verify the hypothesis that ‘nfc’ is a chimeric plant, we investigated protoplast-regenerated plants’ flowering characteristics from mesophyll protoplasts of ‘nfc’. We obtained colonies derived from different protoplasts of ‘nfc’ and regenerated plants from each colony (nfcPP lines). Suppose the ‘nfc’ is a chimeric plant, in that case, protoplast-regenerated plants’ flowering characteristics should be the same for each colony they derived. However, both flowering and non-flowering plants appeared in the same nfcPP lines derived from a single colony. From this, we concluded that ‘nfc’ is not a chimeric plant on flowering characteristics. These results indicate that ‘nfc’ is a mutant that maintains its flowering ability, but its flowering is strongly suppressed by some factors other than its structural characteristics.
The Pandemic as a Portal: Reimagining Psychological Science as Truly Open and Inclusive

Psychological science is at an inflection point: The COVID-19 pandemic has exacerbated inequalities that stem from our historically closed and exclusive culture. Meanwhile, reform efforts to change the future of our science are too narrow in focus to fully succeed. In this article, we call on psychological scientists—focusing specifically on those who use quantitative methods in the United States as one context for such conversations—to begin reimagining our discipline as fundamentally open and inclusive. First, we discuss whom our discipline was designed to serve and how this history produced the inequitable reward and support systems we see today. Second, we highlight how current institutional responses to address worsening inequalities are inadequate, as well as how our disciplinary perspective may both help and hinder our ability to craft effective solutions. Third, we take a hard look in the mirror at the disconnect between what we ostensibly value as a field and what we actually practice. Fourth and finally, we lead readers through a roadmap for reimagining psychological science in whatever roles and spaces they occupy, from an informal discussion group in a department to a formal strategic planning retreat at a scientific society.
Antioxidant Capacity of Oils of Avocado Varieties from Cameroon and Hawaii

10.21276/sajb.2018. 6.4. 6 Abstract: Avocados are medicinal foods rich in monounsaturated fatty acids which can exert many cardiovascular effects. The phytochemical content of each variety is influenced by its natural habitat. The present study compared the antioxidant potential of the oil of avocado varieties grown in Cameroon and Hawaii. Ferric reducing antioxidant power (FRAP), Total phenolic content (TPC), DPPH and ABTS radical scavenging activities characterized the antioxidant potential. The oil obtained from Serpa (Hawaiian) had the highest TPC followed by Pollock (Cameroonian) oil while those with the least TPC were Nishikawa (Hawaiian) and Fuer Florida (Cameroonian). Serpa equally topped the FRAP followed by Peterson (Cameroonian). Similarly Serpa and Peterson oil samples topped the list for DPPH and ABTS radical scavenging activities with lowest IC50 values. Overall, the Hawaiian avocado was richer in antioxidant capacity compared to the Cameroonian species.
Factors influencing catalytic behavior of titanium complexes bearing bisphenolate ligands toward ring-opening polymerization of L-lactide and ε-caprolactone

A series of titanium complexes bearing substituted diphenolate ligands ( R CH(phenolate) 2 , where R = H, CH 3 , o -OTs-phenyl, o -F-phenyl, o -OMe-phenyl, 2,4-OMe-phenyl) was synthesized and studied as catalysts for the ring opening polymerization of L -lactide and ε-caprolactone. Ligands were designed to probe the role of chelate effect and steric effect in the catalytic performance. From the structure of triphenolate (with one extra coordination site than diphenolate ligand) Ti complex, TriOTiO i Pr 2 , we found no additional chelation to influence the catalytic activity of Ti complexes. It was found that bulky aryl groups in the diphenolate ligands decreased the rate of polymerization most. We conclude that steric effect is the most controlling factor in these polymerization reactions by using Ti complexes bearing diphenolate ligands as catalysts since it is responsible for the exclusion of needed space for incoming monomer by the bulky substituents on the catalyst.
Financial reporting quality following the corporate governance reforms: A conditional conservatism perspective

How to cite this paper: Ismail, W. A. W. , Harymawan, I. , Agustia, D. , & Kamarudin, K. A. (2021). Financial reporting quality following the corporate governance reforms: A conditional conservatism perspective [Special issue]. Journal of Governance & Regulation, 10(2), 216–225. https://doi.org/10.22495/jgrv10i2siart3 Copyright © 2021 The Authors This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). https://creativecommons.org/licenses/by/
Comparison of the Operative Time and Post-Operative Pain with Peek Cage versus Autologous Iliac Crest Bone Graft in Anterior Cervical Discectomy and Fusion

Objective: To compare the operative time and post-operative pain with PEEK cage method versus autologous iliac crest bone graft in anterior cervical discectomy and fusion in the cervical spine. Material & Methods: This randomized control trial study (RCT). 90 patients with cervical disc disease, cervical stenosis with or without myelopathy and cervical trauma (fractures or facet jumps with disrupted disc) were included in the study from the Department of Neurosurgery, LGH, PINS, Lahore. Patients were evaluated with plain X-rays and MRI scans of the cervical spine. All patients underwent anterior cervical decompression and fusion. In 45 patients (group A), cervical fusion was achieved with PEEK cage method and in other 45 patients (group B), autologous Iliac Crest Bone graft was used. Results: Mean age in group A was 57.1 years and in group B, it was 54.7 years. In group A, 31.1% patients were of cervical disc disease, 28.8% of cervical degenerative stenosis and 40% were of cervical trauma. In group B, 15 patients 33.3% were of cervical disc disease, 24.4% of cervical degenerative stenosis and 42.2% were of cervical trauma. The mean Postoperative Visual Analogue Scale (VAS) score for pain was 2.8 in group A and 5.4 in group B patients. Conclusion: The patients of group A (PEEK cage method) could be easily mobilized within bed and out of bed depending upon their neurological status. Decreased operative time and less post-operative pain makes PEEK cage method superior to autologous iliac crest bone graft in anterior cervical decompression and fusion.
Identification of the fractures of carbonate reservoirs and determination of their dips from FMI image logs using Hough transform algorithm

Carbonate reservoirs are of great importance due to having many fractures and the effectiveness of these fractures in oil production. The most effective tools for studying fractures are image logs that capture high resolution images from the well. An example of these images is the FMI tool, which provide important information on the orientation, depth, and type of fracture. Today, the detection of fractures on these logs is done manually, which in the absence of sufficient experience, will encounter errors. The purpose of this study is to identify the reservoir fractures and the dips of the fractures using Canny edge detection algorithm and Hough transform algorithm and image processing operators, so that in the first stage, fractures are identified in Geolog Software and in the second stage, using MATLAB Software, fractures and their dip are interpreted.
Performance Improvement on k²-Raster Compact Data Structure for Hyperspectral Scenes

This letter proposes methods to improve data size and access time for <inline-formula> <tex-math notation="LaTeX">$k^{2}$ </tex-math></inline-formula>-raster, a losslessly compressed data structure that provides efficient storage and real-time processing. Hyperspectral scenes from real missions are used as our testing data. In previous studies, with <inline-formula> <tex-math notation="LaTeX">$k^{2}$ </tex-math></inline-formula>-raster, the size of the hyperspectral data was reduced by up to 52% compared with the uncompressed data. In this letter, we continue to explore novel ways of further reducing the data size and access time. First, we examine the possibility of using the raster matrix of hyperspectral data without any padding (unpadded matrix) while still being able to compress the structure and access the data. Second, we examine some integer encoders, more specifically the Simple family. We discuss their ability to provide random element access and compare them with directly addressable codes (DACs), the integer encoder used in the original description for <inline-formula> <tex-math notation="LaTeX">$k^{2}$ </tex-math></inline-formula>-raster. Experiments show that the use of unpadded matrices has improved the storage size up to 6% while the use of a different integer encoder reduces the storage size up to 6% and element access time up to 20%.
3D Shape Estimation of an Active Needle Inside Tissue Using 2D Ultrasound Images

This work presents a method to estimate 3D shape of an active needle inside tissue using 2D transverse ultrasound images. The shape of the needle provides a valuable feedback information for precise control and guidance of the needle inside tissue toward target. We used a series of image processing techniques to identify the needle’s cross section in the ultrasound images. Using this method, we estimated the 3D shape of a tendon-driven active needle, when bent inside a transparent phantom tissue using a robotic needle insertion system. The estimated shape of the needle was then compared with true shape of the needle captured by two cameras. At least three ultrasound images were required to estimate the needle shape with a second order polynomial function. We found an average error of 0.54mm and a maximum error of 1.00mm in shape estimation compared to the true shape of the needle captured by the cameras for an insertion depth of 70mm.
En route to a unified model for photo-electrochemical reactor optimisation. I - Photocurrent and H2 yield predictions

A semi-empirical model was developed for the prediction of photocurrent densities and implemented to predict the performance of a photo-electrochemical reactor for water splitting in alkaline solutions, using SnIV-doped α-Fe2O3 photo-anodes produced by spray pyrolysis. Photo-anodes annealed at different temperatures were characterised using photo-electrochemical impedance spectroscopy, cyclic voltammetry in the presence and absence of a hole scavenger and also the open circuit potential under high intensity illumination. Mott–Schottky analysis was used cautiously to estimate the charge carrier concentration and the flat band potential. In addition to overpotential/current distribution and ohmic potential losses, the model also accounts for absorbed photon flux, surface and bulk electron–hole recombination rates, gas desorption, bubble formation and (H2–O2) cross-over losses. This allows the model to estimate the total yield of hydrogen, charge and gas collection efficiencies. A methodology is presented here in order to evaluate the parameters required to assess the performance of a photo-electrochemical reactor in 1D and 2D geometries. The importance of taking into account bubble generation and gas desorption is discussed, together with the difficulties of measuring charge carrier concentration and electron–hole recombination in the bulk of the semiconductor, which are of major importance in the prediction of photocurrent densities.
Media Reports of the COVID-19 Pandemic: A Computational Text Analysis of English Reports in China, the UK, and the US

This study explored how news outlets, China Daily (CD), Cable News Network (CNN), and Daily Mail (DM) have reported the COVID-19 pandemic. Mainstream media is a credible communication path to guide public attention on COVID-19. Computational text analysis contributes to understanding media activities about the pandemic and promotes health information communication. The word frequency statistics and lexical diversity highlighted how pandemic reports changed in the early outbreak. A cluster analysis illustrated the frequency and semantic relationship between the highly frequent words from CD, CNN, and DM reports. Sentiment analysis was based on natural language processing when analyzing the sentiment of all headlines and the sentiment of the different words in the headlines. This study also discussed similarities and differences in the coverage by the three different media outlets at various stages of the outbreak. Three media reported comprehensive coverage of the pandemic. Since they are based in different countries, their focus and the numbers of reports are different at different stages. The richness of the vocabulary and the degree of emotion are related to their media attributes. These results can help health departments exchange information, guide accurate public awareness, and eliminate public fears regarding misconceptions about the pandemic.
Dual second-order generalized integrator based synchrophasor measurement and power-angle calculation under complicated grid conditions

As a power system dynamic monitoring device, phasor measurement units (PMUs) can collect system information and estimate the power angle of the generator. The commonly-used processing algorithm for PMU is the discrete Fourier transformation (DFT). However, due to the picket fence effect, the DFT algorithm has a poor accuracy of synchronous sampling under frequency deviation. In order to cope with the frequency deviation and the three-phase unbalance in measured signals, the dual second-order generalized integrator based Frequency-Locked Loop (DSOGI-FLL) is adopted in this paper to process the system signals. The DSOGI-FLL can improve the accuracy of the synchronous sampling by real-time frequency measurement. Meanwhile, the positive-sequence fundamental components of the system signal can be extracted by producing the orthogonal signals and filtering the harmonics. With the signals processed by the DSOGI-FLL, the power angle can be further estimated. Finally, the feasibility and effectiveness of the proposed method is validated by comparing amplitude, phase and the power angle estimated by DSOGI-FLL with those estimated by DFT.
Modality Translation in Remote Sensing Time Series

Modality translation, which aims to translate images from a source modality to a target one, has attracted a growing interest in the field of remote sensing recently. Compared to translation problems in multimedia applications, modality translation in remote sensing often suffers from inherent ambiguities, i.e., a single input image could correspond to multiple possible outputs, and the results may not be valid in the following image interpretation tasks, such as classification and change detection. To address these issues, we make the attempt to utilizing time-series data to resolve the ambiguities. We propose a novel multimodality image translation framework, which exploits temporal information from two aspects: 1) by introducing a guidance image from given temporally neighboring images in the target modality, we employ a feature mask module and transfer semantic information from temporal images to the output without requiring the use of any semantic labels and 2) while incorporating multiple pairs of images in time series, a temporal constraint is formulated during the learning process in order to guarantee the uniqueness of the prediction result. We also build a multimodal and multitemporal dataset that contains synthetic aperture radar (SAR), visible, and short-wave length infrared band (SWIR) image time series of the same scene to encourage and promote research on modality translation in remote sensing. Experiments are conducted on the dataset for two cross-modality translation tasks (SAR to visible and visible to SWIR). Both qualitative and quantitative results demonstrate the effectiveness and superiority of the proposed model.