Miracle-dz/UniFAR-FTUs huggingface dataset:
Aftermath of the COVID-19 Pandemic: Resilience and Mental Health of Emerging Adult University Students

This study explored the relationship between COVID-19 impacts and resilience in emerging adulthood during the final two months of the pandemic. It aimed to examine whether mental health symptoms moderated this relationship. In total, 205 university students completed an online questionnaire survey. Regression analysis was used to examine the prediction of resilience by pandemic-related impacts, and moderation analysis was used to explore the potential moderating effect of mental health on the relationship between impacts and resilience. The findings failed to confirm the hypothesis that total COVID-19 impacts would predict resilience. Rather, resource-type impacts predicted resilience [B = 0.17, p < 0.5]. Significant partial correlations found among resource, financial, and psychological impacts may go some way toward clarifying connections between impacts and resilience [for resource-type impact with financial-type impact, r = 0.48, p < 0.01; for resource-type impact with psychological impact, r = 0.22, p < 0.01]. The results confirmed the hypothesis that mental health symptoms would moderate the relationship between pandemic impacts and resilience [for the overall model, R = 0.41, ΔR2 = 0.16, MSE = 0.76, F (4, 200) = 10.19, p < 0.001; for the interaction between total COVID impacts and resilience, ΔR2 = 0.017, F (1, 200) = 3.98, p < 0.05]. Thus, emerging adult students with low or moderate levels of symptomatology were more resilient, independently of the level of pandemic-related stressors they faced. Those experiencing higher levels of mental health symptoms, in tandem with high levels of pandemic-related impacts, exhibited increasingly higher resilience levels [b = 0.17, 95% CI [0.02, 0.32], t = 2.26, p = 0.025]. These youths may be better equipped to handle severe stress and adversity thanks to skills and resources they possess and are experienced in using. The implications of these findings for each group of young people are discussed for their usefulness in directing future research and interventions to foster resilience during current and future crises and health pandemics.
Generative Adversarial and Self-Supervised Dehazing Network

Owing to the fast developments of economics, a lot of devices and objects have been connected and have formed the Internet of Things (IoT). Visual sensors have been applied in vehicle navigation, traffic situational awareness, and traffic safety management. However, the particles in the air degrade the imaging quality, which affects the performance of vehicle navigation, traffic situational awareness, and traffic safety management. Deep-learning-based dehazing methods were proposed to address this issue. However, these methods are trained with simulated hazy images and cannot generalize to natural haze images well. To address the domain shift problem, some methods resort to zero-shot learning or domain adaption to boost the generalization of the model on natural haze images. However, the relevance between dehazed results and clean images is ignored by zero-shot dehazing methods. Domain-adaption-based dehazing methods ignore the relationship between the dehazed results and the hazy images. To overcome these issues, a generative adversarial and self-supervised dehazing network is introduced to boost the dehazing performance on real haze images. First, generative adversarial is employed to construct the relevance between dehazed results and haze-free images, which can boost the natural appearance of dehazed results. Second, self-supervised learning is employed to construct the relevance between the dehazed results and hazy images, which can restrict the solution space of dehazing. To show the effectiveness of the proposed model, we conduct extensive experiments on real and simulated haze images. Compared with state-of-the-art methods, the proposed model achieves state-of-the-art dehazing performance.
Impact of dust particles on the output power of photovoltaic modules in Calabar, Cross River State

During the dry season, dust particles on photovoltaic modules (PV modules) are a regular occurrence in Calabar, Cross River State. This dusty condition or environment is caused by the migration of dust particles from the Sahara region via the east-west wind. The concentration of dust on PV modules has an impact on the overall performance of solar energy systems. This research looks into the effect of dust particles on the output power of solar modules. This investigation was carried out by subjecting 80-watt monocrystalline PV modules to an outdoor experiment and analyzing the changes in current and voltage from the control and dusty PV modules with a digital multimeter. Other environmental factors such as relative humidity, ambient temperature, and solar radiation were also measured with a hygrometer, thermometer, and solar power meter respectively. Appropriate formulars were employed to calculate output power and efficiency. Dust particles on the surface of PV modules dramatically reduces the quantity of sunlight that penetrate the panel by 21.10%, resulting in a drop in the system's overall output power. The study emphasizes on the importance of frequent cleaning and maintenance of PV systems.
The materials design ontology

In the materials design domain, much of the data from materials calculations is stored in different heterogeneous databases with different data and access models. Therefore, accessing and integrating data from different sources is challenging. As ontology-based access and integration alleviates these issues, in this paper we address data access and interoperability for computational materials databases by developing the Materials Design Ontology. This ontology is inspired by and guided by the OPTIMADE effort that aims to make materials databases interoperable and includes many of the data providers in computational materials science. In this paper, first, we describe the development and the content of the Materials Design Ontology. Then, we use a topic model-based approach to propose additional candidate concepts for the ontology. Finally, we show the use of the Materials Design Ontology by a proof-of-concept implementation of a data access and integration system for materials databases based on the ontology.11 This paper is an extension of (In The Semantic Web – ISWC 2020 – 19th International Semantic Web Conference, Proceedings, Part II (2000) 212–227 Springer) with results from (In ESWC Workshop on Domain Ontologies for Research Data Management in Industry Commons of Materials and Manufacturing 2021 1–11) and currently unpublished results regarding an application using the ontology.
Retrospective T2 quantification from conventional weighted MRI of the prostate based on deep learning

Purpose To develop a deep learning-based method to retrospectively quantify T2 from conventional T1- and T2-weighted images. Methods Twenty-five subjects were imaged using a multi-echo spin-echo sequence to estimate reference prostate T2 maps. Conventional T1- and T2-weighted images were acquired as the input images. A U-Net based neural network was developed to directly estimate T2 maps from the weighted images using a four-fold cross-validation training strategy. The structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean percentage error (MPE), and Pearson correlation coefficient were calculated to evaluate the quality of network-estimated T2 maps. To explore the potential of this approach in clinical practice, a retrospective T2 quantification was performed on a high-risk prostate cancer cohort (Group 1) and a low-risk active surveillance cohort (Group 2). Tumor and non-tumor T2 values were evaluated by an experienced radiologist based on region of interest (ROI) analysis. Results The T2 maps generated by the trained network were consistent with the corresponding reference. Prostate tissue structures and contrast were well preserved, with a PSNR of 26.41 ± 1.17 dB, an SSIM of 0.85 ± 0.02, and a Pearson correlation coefficient of 0.86. Quantitative ROI analyses performed on 38 prostate cancer patients revealed estimated T2 values of 80.4 ± 14.4 ms and 106.8 ± 16.3 ms for tumor and non-tumor regions, respectively. ROI measurements showed a significant difference between tumor and non-tumor regions of the estimated T2 maps (P < 0.001). In the two-timepoints active surveillance cohort, patients defined as progressors exhibited lower estimated T2 values of the tumor ROIs at the second time point compared to the first time point. Additionally, the T2 difference between two time points for progressors was significantly greater than that for non-progressors (P = 0.010). Conclusion A deep learning method was developed to estimate prostate T2 maps retrospectively from clinically acquired T1- and T2-weighted images, which has the potential to improve prostate cancer diagnosis and characterization without requiring extra scans.
Designing a doctor evaluation index system for an online medical platform based on the information system success model in China

Objective In the context of “internet + medical health” and emphasis on evaluation mechanism for medical and health talents in China, we design an evaluation index system for doctors on online medical platforms by synthesizing two patterns of existing online medical platforms, which is the first step to enhance the capabilities of doctors on online medical platforms. Methods Based on the doctor evaluation model integrating information systems success model (ISS-DE model) and grounded theory, the evaluation indicators were obtained through expert interviews, offline medical institutions investigation, online platforms investigation, and literature research, and were assigned weights using the analytic hierarchy process (AHP) method. A working group composed of 23 experts was set up to review and determine the competency standards of doctors on the online medical platforms. Results A new indicator framework covering 3 dimensions of system quality, service quality and information quality was constructed in this study. The index system included 3 first-level indicators, 8 s-level indicators and 60 third-level indicators, and each indicator was given different weightage. Conclusion The complete index system constructed by the Delphi method in this study is suitable for China’s online medical platforms, which will help to improve the quality of platforms and the ability of doctors, thus promoting the process of internet medical integration.
Implicit Quiescent Optical Solitons for the Dispersive Concatenation Model with Nonlinear Chromatic Dispersion by Lie Symmetry

The primary purpose of this paper is to investigate and recover implicit quiescent optical solitons in the context of the dispersive concatenation model in nonlinear optics. Specifically, the study focuses on a model that incorporates nonlinear chromatic dispersion and includes Kerr and power-law self-phase modulation effects. The objective is to identify and characterize these soliton solutions within this complex optical system. To achieve this purpose, we employ the Lie symmetry analysis method. Lie symmetry analysis is a powerful mathematical technique commonly used in physics and engineering to identify symmetries and invariance properties of differential equations. In this context, it is used to uncover the underlying symmetries of the nonlinear optical model, which in turn aids in the recovery of the quiescent optical solitons. This method involves mathematical derivations and calculations to determine the solutions. The outcomes of the current paper include the successful recovery of implicit quiescent optical solitons for the dispersive concatenation model with nonlinear chromatic dispersion, Kerr, and power-law self-phase modulation. The study provides mathematical expressions and constraints on the model’s parameters that yield upper and lower bounds for these solutions. Essentially, this paper presents a set of mathematical descriptions for the optical solitons that can exist within the described optical system. The present paper contributes to the field of nonlinear optics by exploring the behavior of optical solitons in a model that combines multiple nonlinear effects. This extends our understanding of complex optical systems.
Children’s participation in documentation processes in local outdoor spaces

This article describes a study that aimed to investigate how children perform and document Land Art in local places near their kindergarten and the visualization of their experiences through their drawings back in the kindergarten. Documentation is important for a kindergarten’s planning and evaluation of pedagogical practices and activities. Studies have shown that using visual methods, such as drawings and photographs created by children themselves, to provide information related to children’s perspectives, actions and attitudes is a way for children to communicate and make meaning of their experiences. Encounters with the natural world through the performance of Land Art offer children the opportunity to directly sense, interact with and know ‘the world of materials’. Children’s interactions correspond with natural materials and the environment. Participatory observation was used, following a group of twelve children, aged four and five years in a year’s period to their trips near a shoreline. Data consists of photographs, voice recordings, video, drawings, and narratives to explore ways children interact with and make meaning of place by performing Land Art. The results show that children’s photographs and drawings are ways to ‘communicate’ with their experiences and engage with, pay attention to and visualize their perspectives. The findings might have implications for new approaches to documenting children’s voices and experiences through performing Land Art and drawings.
The Involuntary Nature of Belief: Exploring Cognitive, Social, Neurological, and Computational Perspectives

Faith, as a powerful guiding force in our daily lives, means that individuals or groups accept certain statements as true. The debate over whether faith is voluntary or involuntary continues. This article attempts to demonstrate that all faith is inherently involuntary, challenging the notion of voluntary faith. Shed light on the involuntary nature of beliefs by exploring belief formation from a cognitive and social perspective, and the role of neural and computational mechanisms in decision making, and highlight the complex interplay between cognitive processes, social influences, and neural mechanisms that shape our beliefs.
Discovery of microRNAs in Pyrus stigma exudates opens new research avenues in Horticulture

Abstract In many plant species, flower stigma secretions are important in early stages of sexual reproduction. Previous chemical analysis and proteomic characterization of these exudates provided insights into their biological function. Nevertheless, the presence of nucleic acids in the stigma exudates has not been previously reported. Here, we studied the stigma exudates of Pyrus communis, Pyrus pyrifolia, and Pyrus syriaca and showed them to harbor extracellular RNAs of various sizes. RNA sequencing revealed, for the first time, the presence of known Rosaceae mature microRNAs (miRs), also abundant in the stigma source tissue. Predicted targets of the exudate miRs in the Arabidopsis thaliana genome include genes involved in various biological processes. Several of these genes are pollen transcribed, suggesting possible involvement of exudate miRs in transcriptional regulation of the pollen. Moreover, extracellular miRs can potentially act across kingdoms and target genes of stigma interacting organisms/microorganisms, thus opening novel applicative avenues in Horticulture.
A study of the mixing state of black carbon in urban zone

[1] The knowledge of the mixing state of black carbon particle with other aerosol species is critical for adequate simulations of the direct radiative effect of black carbon particles and its effect on climate. This paper reports the investigation of the mixing state of black carbon aerosol in the urban zone. The study uses a combination of in situ and ground-based remote sensing observations conducted during the ESCOMPTE experiment, which took place in industrialized region in France in summer of 2001. The criteria we used for identifying mixing state relies on the known enhancement of absorption for aerosol composed by internal versus external mixtures of black carbon with weakly absorbing aerosol components. First, using in situ aerosol data, we performed Mie computations and reconstructed the single scattering albedo of aerosol for the two different mixing assumptions: black carbon mixed externally or internally with other aerosol species. Then, we compared the obtained values ωo,int and ωo,ext with the retrievals of ωo from independent AERONET Sun-photometric measurements. The aerosol single scattering albedo (ωo,aer.) derived from the AERONET photometer observations (with the mean value equal to 0.84 ± 0.04) was found to be close to ωo,ext reconstructed from in situ observation under assumptions of external mixture. This similarity between AERONET values and external mixture simulations was observed during all the days studied. Our conclusion on external mixture of black carbon aerosol with other particles in urban zone during ESCOMPTE (close to the pollution source) is coherent with observations made during other independent studies reported in a number of recent publications.
Application of a calibrated/validated Agricultural Policy/Environmental eXtender model to assess sediment and nutrient delivery from the Wildcat Creek Mississippi River Basin Initiative-Cooperative Conservation Partnership Initiative

Wildcat Creek, a tributary to the Wabash River, was identified by the USDA Natural Resources Conservation Service (NRCS) as a priority watershed for its high sediment and nutrient loading contributions to the Mississippi River. As part of the Mississippi River Basin Initiative (MRBI), the incorporation of conservation practices was implemented through a Cooperative Conservation Partnership Initiative (CCPI) agreement using existing USDA programs. The objective of this work was to evaluate the potential water quality benefits of conservation practices placed in the Wildcat Creek Watershed through the MRBI-CCPI. Specifically, seven output variables (surface runoff, tile flow, sediments, total phosphorus [P], soluble P, soluble nitrogen [N], and soluble N in tile) were compared for four different row crop conservation practices (residue and tillage management no-till, residue and tillage management mulch-till, cover crops, and forage and biomass planting), and two manure application scenarios. Using the Agricultural Policy/Environmental eXtender (APEX) model, calibrated and validated for a field within the St. Joseph River Watershed, a modeling assessment was developed for current conservation practices incorporated at a similar watershed, Wildcat Creek. Annual average values were estimated for each simulated scenario, and percentage reductions were reported. Cover crops consistently reduced sediment and nutrient loads in runoff by more than 50% compared to the tillage baseline scenario, and removal of the manure application in the waste utilization scenario reduced the predicted total and soluble P amounts by 75% and 92%, respectively. When extrapolating the edge-of-field values predicted by the model to the area occupied by each conservation practice throughout the duration of the contracts at the Wildcat Creek Watershed, the total reductions were estimated to be almost 7,000 Mg (7,700 tn) of sediment (68% decrease), 20,000 kg (44,092 lb) of total P (73% decrease), 4,000 kg (8,818.5 lb) of soluble N in runoff (50% decrease), and 185,000 kg (414,00 lb) of soluble N in tile flow (43% decrease). Agricultural Policy/Environmental eXtender modeling is a useful tool for predicting sediment and nutrient loading information at the field-scale, which can be used to calculate the relative impact of conservation practices at the watershed scale when monitoring data are not available. The modeling results confirm the positive impact of these conservation practices and of the MRBI-CCPI initiative at improving water quality at the edge-of-field, and potentially at the Wildcat Creek, the Greater Wabash River Basins, and ultimately the Mississippi River Basin.
Blind motion deblurring using improved DeblurGAN

To develop a fast and effective image deblurring method, the blind recovery of motion‐blurred images based on DeblurGAN(GAN, Generative Adversarial Networks) is researched.Firstly, the number of residual modules in the DeblurGAN network is changed, and an attempt is made to optimize the network structure to achieve better results in the blind recovery of motion‐blurred images. Secondly, an image deblurring method based on the improved DeblurGAN network is proposed.This paper makes corresponding adjustments to the generator and discriminator networks to change their input and output size to 512 × 512 while keeping the overall structure of the network unchanged, and the experimental results show that the quality of the restored images has been greatly improved. In addition, the images were divided into four classes of images,to achieve improved restoration of blurred images with specific content. Experimental results on the benchmark GoPro dataset validate that the improved DeblurGAN achieves more than 1.5 dB improvement than DeblurGAN in terms of peak signal‐to‐noise ratio (PSNR) as trained utilizing the same amount of data, and structural similarity assessment (SSIM) evaluation means and maximum values increased between 0.2 and 0.3. The improved DeblurGAN is more effective in terms of both blur removal and detail recovery.