Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Cloud Type on Earth's Energy Balance

The Effect of Cloud Type on Earth’s Energy Budget by Yun Hang Clouds have long been recognized as one of the largest uncertainties to Earth's changing energy budget. Previous research indicates that improved assessment of cloud impacts on climate requires a better understanding of individual cloud types and their radiative effects. This work documents the effects of nine distinct cloud types on atmospheric radiative balance and heating using 2007-2010 data from CloudSat's multi-sensor radiative fluxes and heating rates product (FLXHR-LIDAR). This dataset leverages high-resolution vertical cloud and aerosol information from CloudSat and CALIPSO to provide the most accurate estimates of vertically-resolved radiative fluxes available to date. The effects of three common cloud classes will be highlighted in detail: cirrus, stratocumulus, and deep convection, to contrast their dramatically different effects on climate. The findings support the qualitative conclusion that cirrus clouds warm the planet and stratocumulus clouds cool the planet, while the longwave and shortwave cloud radiative effect of deep convective cloud cancel each other in the tropics. In addition, cloud types will be regrouped in order to compare the results with classical cloud classifications based on passive sensors including those from the International Satellite Cloud Climatology Project (ISCCP) and Earth Radiation Budget Experiment (ERBE) archives. The new CloudSat/CALIPSO estimate of annual average shortwave forcing is -53 W/m in good agreement with previous estimates
Alginate hydrogel-guided rAAV-mediated FGF-2 and TGF-β delivery and overexpression stimulates the biological activities of human meniscal fibrochondrocytes for meniscus repair

Meniscus lesions are unsolved problems that impede the functions of stability, load bearing, and shock absorption in the knee joint and for which there is no satisfying therapeutic option to date. Gene therapy using clinically adapted recombinant adeno-associated virus (rAAV) vectors is a powerful tool to enhance meniscus repair, especially when applied in combination with tissue engineering strategies that may allow to counteract a possible neutralization and/or dissemination of the rAAV particles in the recipient. Here, we examined the ability of an alginate (AlgPH155) hydrogel to formulate and deliver rAAV vectors carrying the genes for the reparative fibroblast growth factor ( FGF-2 ) and transforming growth factor beta ( TGF-β ) as a means to trigger the biological activities in human meniscal fibrochondrocytes. The results show that effective rAAV-mediated FGF-2 and TGF-β overexpression via alginate (AlgPH155) hydrogel-guided vector administration equally led to enhanced levels of cell proliferation and of specific matrix deposition (proteoglycans, type-I/-III collagen) in the cells over an extended period of time (21 days, the latest time point evaluated) relative to the control treatments (hydrogel without vector or with a vector carrying the reporter lacZ gene). These effects were associated with an increase in the expression of the contractile alpha smooth muscle actin ( α -SMA) marker, a determinant of the meniscus response injury, and with decreased levels of pro-inflammatory markers (interleukin 1 beta—IL1 β , tumor necrosis factor alpha—TNF-α ). These findings show the potential of alginate hydrogel-guided rAAV-mediated gene therapy as a new, off-the-shelf therapeutic system for meniscus repair.
Assessing the environmental and health impacts of plastic production and recycling

Plastic production and recycling have become integral processes in modern society, but their environmental and health impacts have garnered significant attention in recent years. This review outlines key findings from a comprehensive assessment of these impacts, drawing from a range of scientific literature and empirical studies. The environmental footprint of plastic production encompasses various stages, from extraction of raw materials to manufacturing and distribution. These processes contribute to greenhouse gas emissions, energy consumption, and pollution of air, water, and soil. Additionally, plastic waste, particularly single-use items, poses a significant threat to ecosystems and wildlife, with marine environments being particularly vulnerable. While recycling is often promoted as a solution to mitigate the environmental impact of plastics, its effectiveness is limited by various factors. Challenges such as contamination, inadequate infrastructure, and low rates of collection and recycling hinder the potential benefits. Moreover, the recycling process itself can generate pollutants and emissions, albeit to a lesser extent than primary production. Beyond environmental concerns, the health implications of plastic use are increasingly recognized. Plastics contain additives such as phthalates and bisphenols, which have been linked to endocrine disruption, reproductive issues, and other health problems in humans and wildlife. Furthermore, the accumulation of microplastics in the environment raises concerns about potential bioaccumulation and transfer through the food chain, with implications for human health. Addressing the environmental and health impacts of plastic production and recycling requires a multifaceted approach, including reduction of plastic consumption, improvement of recycling infrastructure and technologies, development of alternative materials, and policy interventions to promote sustainable practices. This assessment highlights the complex interplay between plastic usage, environmental degradation, and public health, underscoring the need for concerted efforts to mitigate these challenges.
Integrating 21st Century Skills in Curriculum and Material Development Course

The Curriculum and Material Development (CMD) course content is contextualized in accordance with technological advancements in the era of the Fourth Industrial Revolution and the community's need for educators who possess 21st-century skills. This revision involves integrating 21st-century skill values into curriculum development, syllabi, teaching materials, media, methods, and learning evaluation. Therefore, this study focuses on integrating 21st-century skills and 21st-century digital skills, including critical thinking, creative thinking, collaboration, communication, information literacy, media literacy, technology literacy, flexibility, initiative, social skills, productivity, and leadership, into the CMD course. This study employs the Research and Development method, which involves identifying the problem, describing existing issues, creating a design, validating the design with experts, revising the design of the English learning model, conducting trials, and making revisions. The study's outcomes include three areas of integrating 21st-century skills into the CMD course. The first area involves integrating 21st-century skills into the preliminary activity of designing lesson plans. Secondly, in the process, 21st-century skills should be integrated into the design of learning materials and tasks. Finally, after the activity, 21st-century skills should be integrated into the evaluation and reflection process. The CMD course aims to integrate 21st-century skills throughout the language curriculum. It covers Communicative Syllabus Design, Text-Based Syllabus Design, Product-Oriented Syllabus Design, Process-Oriented Syllabus Design, Content-Based Curriculum, Integrated Curriculum, EFL Curriculum, and EFL Syllabuses and Lesson Plans for Indonesian Students.
Downscaling Solar-Induced Chlorophyll Fluorescence to a 0.05° Monthly Product Using AVHRR Data in East Asia (1995–2003)

Satellite-based solar-induced chlorophyll fluorescence (SIF) has emerged as a valuable tool for monitoring the photosynthetic activity of vegetation at both regional and global scales. Downscaling techniques offer the opportunity to utilize coarse-spatial-resolution SIF products for investigating carbon cycles and ecological processes at finer resolutions. However, the lack of pre-2000 downscaled products and the limited utilization of residual information in existing models pose challenges in fully harnessing the potential of SIF downscaling. In this study, we generated a new monthly SIF product, DSIFRFK*_EA0.05, at a resolution of 0.05° in East Asia from July 1995 to June 2003. Random forest kriging (RFK) was employed, incorporating GOME SIF, AVHRR data, ERA5 climate data, and using the optimal explanatory variables. Compared with other downscaled results, the produced DSIFRFK*_EA0.05 has higher accuracy and more accurate details of SIF distribution with the highest mean R2 value of 0.83 and smallest root mean squared error value of 0.08 mW/m2/nm/sr. The DSIFRFK*_EA0.05 estimates showed a strong correlation with gross primary productivity data from eight flux sites (R2 = 0.73), as well as high correlation coefficient values of 0.73, 0.88, and 0.89 with three other fine-resolution products. This study addresses the gap in statistical downscaling of SIF before 2000 and demonstrates the feasibility of utilizing AVHRR data for fine-resolution SIF prediction. Our developed product offers improved detail compared to the original GOME SIF, thereby enhancing the application of satellite SIF for understanding early carbon cycles and ecological processes at a finer resolution.
The fog computing for internet of things: review, characteristics and challenges, and open issues

The internet of things (IoT) research envisions a world in which common place objects are linked to the internet and trade, store, process, and gather data from their surroundings. Due to their inherent resource limitations, IoT devices are typically unable to directly host application services, despite their increasing importance for facilitating the supply of data to enable electronic services. Since it can survive and work in tandem with centralized cloud systems and extends the latter toward the network edge, fog computing (FC) may be an appropriate paradigm to get around these restrictions. This paper reviews the overview of the IoT in terms of application and design parameters and FC. Meanwhile, this paper presents the architecture of fog computing for IoT (FC-IoT) in terms of communication, security, data quality, sensing and actuation management, codification, analysis, and decision-making. Additionally, this review provides several characteristics and challenges of FC-IoT. Finally, open issues for this paper have been discussed.
Bayesian Model for Academic Performance Prediction in Learning Analytics

The learning analytics dashboard (LAD) enables the prediction, tracking, and early recommendation of actions based on academic performance, student conduct, cognitive abilities, and personality traits. The creation of a questionnaire, which involved expert validation and a pilot test, came before the data gathering. Students independent traits, such as behaviour, cognitive skill, and personality, and academic data are the contingent variables. The study flow comprises knowledge acquisition, data collection and analysis, implementation that executes experiments, and evaluation. An exploratory data analysis (EDA) has been performed to investigate the patterns and trends within the data collected on students' characteristics. Simultaneously, a Bayesian prediction model has been created and trained using the gathered data. The forecasts yielded an accuracy of 81%. Additionally, the F1 scores of 0.74 indicate a moderate level of ability, while scores of 0.90 suggest a high level of performance, and scores of 0.98 indicate an excellent level of performance. The interpretation of the F1 score is conducted within the specific realm of the issue and compared to manual calculations. A satisfactory level of performance is considered acceptable. Within the scope of this investigation, a sensitivity score of 0.8 is deemed favourable due to its ability to accurately identify the related risk of misclassification.
Identification long non-coding RNAs, miRNAs and their targets in witches’ broom disease tolerance in cocoa (Theobroma cacao L.)

A large part of transcripts is non-coding, which is transcribed from junk DNA; long non-coding RNAs and micro-RNAs regulate theexpression levels of mRNAs. For the first time, we identified lncRNAs and miRNAs with their regulatory role in the disease toleranceof Theobroma cacao. In this study, about 2616 lncRNAs and 153 miRNAs were identified from 10 RNA-seq data representing healthyand witches’ broom diseased tissues of cocoa. Around 604 lncRNAs are differentially expressed among healthy and diseased tissues.LncRNAs targeted 9692 mRNAs; 8827 are cis-acting, and 765 are trans-acting. Among targeted mRNAs, 281 are disease resistance-related transcripts, and 211 transcription factors (TFs) belong to more than 50 TF families, which were found to be involved in the regulation of the disease tolerance process. The identified 153 miRNAs belong to 27 miR families, and around 5337 mRNAs are targeted by the miRNAs, among them 114 codes for TFs and 170 codes for disease resistance protein. Ethylene responsive factor, bHLH, WRKY, MYB, bZIP, GTE, GATA, and heat stress transcription factors are the dominant TFs targeted by lncRNAs, and miRNAs play vital roles in disease progression and tolerance. A total of 55 lncRNAs-miRNAs interacting pair is identified, which were working on endogenous target mimics (e-TMs) mechanism and influenced the expression of 955 mRNAs. The ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) biological pathway analysis reveal that ncRNAs and their targets mRNAs code for transcription factors and genes that are involved in the disease tolerance processes, including synthesis of disease resistance proteins, amino acids, antibiotics, intracellular proteins that directly or indirectly recognize pathogen effectors are essential for plant biotic stress condition. The present study provides lncRNA and miRNA-based regulatory insight into the genes governing disease progression and tolerance in cocoa.
Research on the Temperature Distribution in Electrically Heated Offshore Heavy Oil Wellbores

The electric heating process for lifting heavy oil has been widely applied. However, research on its temperature field laws mostly focuses on onshore heavy oil wells, while research offshore is limited. Therefore, based on the energy conservation equation and heat transfer theory, a transient one-dimensional wellbore temperature model coupled with the temperature and viscosity of heavy oil and considering the effect of time was developed. In order to verify the accuracy of the model, the results of the previous model were used for comparison with the present model, and the results showed that the model has good accuracy. The results show that a reasonable selection of the process parameters of electric heating can increase the production of heavy oil while saving development costs and improving the economic benefits of the oilfield. The conclusions and recommendations of this paper can provide a theoretical basis and guiding suggestions for the optimal design of process parameters for lifting heavy oil using an offshore electric heating process.
Effect of Seedling Density on Growth Characteristics of Cabbage (Brassica oleracea var. capitata) cv. kf-65 in Nursery

Crop yield is largely influenced by the seedling quality and establishment. Seedling density is an important factor that plays a vital role in producing quality seedlings. Seedling growth characteristics of cabbage cv. kf-65 was evaluated at four inter row and intra row spacing: 0.5cm×1.0cm, 1.0cm×1.0cm, 1.5cm×1.5cm, and 2.0cm×2.0cm in the field of Institute of Agriculture and Animal Science, Lamjung Campus during September-October, 2018. Seed germination was 96% under a partially controlled germinator in the lab whereas mean germination in the field was found to be 62%. Treatments were laid out in Randomized Complete Block Design with five replications. Data was collected after 23 days using the destructive sampling method. Plant height, leaf number, leaf area, fresh shoot and root weight and % dry matter was recorded from five samples from each replication of every treatment. Plant population had a significant effect on plant leaf area, fresh shoot and root weight and % dry weight. The 2.0cm×2.0cm spacing had significantly higher leaf area (25.3cm2), fresh shoot (1.33g) and root weight (0.06g). Dry weight % (23%) was significantly higher for 1.5cm×1.5cm spacing. The result indicates that wider spacing (≥ 2cm) was found to be favourable to obtaining robust and quality seedlings.
Efficient Task Scheduling Approach in Edge-Cloud Continuum based on Flower Pollination and Improved Shuffled Frog Leaping Algorithm

The rise of edge-cloud continuum computing is a result of the growing significance of edge computing, which has become a complementary or substitute option for traditional cloud services. The convergence of networking and computers presents a notable challenge due to their distinct historical development. Task scheduling is a major challenge in the context of edge-cloud continuum computing. The selection of the execution location of tasks, is crucial in meeting the quality-of-service (QoS) requirements of applications. An efficient scheduling strategy for distributing workloads among virtual machines in the edge-cloud continuum data center is mandatory to ensure the fulfilment of QoS requirements for both customer and service provider. Existing research used metaheuristic algorithm to solve tak scheduling problem, however, must of the existing metaheuristics used suffers from falling into local mina due to their inefficiency to avoid unfeasible region in the solution search space. Therefore, there is a dire need for an efficient metaheuristic algorithm for task scheduling. This study proposed an FPA-ISFLA task scheduling model using hybrid flower pollination and improved shuffled frog leaping algorithms. The simulation results indicate that the FPA-ISFLA algorithm is superior to the PSO algorithm in terms of makespan time, resource utilization, and execution cost reduction, especially with an increasing number of tasks.
Off‐fault deformations and shallow slip deficit from dynamic rupture simulations with fault zone plasticity

Kinematic source inversions of major (M≥7) strike‐slip earthquakes show that the slip at depth exceeds surface displacements measured in the field, and it has been suggested that this shallow slip deficit (SSD) is caused by distributed plastic deformation near the surface. We perform dynamic rupture simulations of M 7.2–7.4 earthquakes in elastoplastic media and analyze the sensitivity of SSD and off‐fault deformation (OFD) to rock quality parameters. While linear simulations clearly underpredict observed SSD and OFDs, nonlinear simulations for a moderately fractured fault damage zone predict a SSD of 44–53% and OFDs of 39–48%, consistent with the 30–60% SSD and 46 ± 10% (1σ) OFD reported for the 1992 M 7.3 Landers earthquake. Both SSD and OFDs are sensitive to the quality of the fractured rock mass inside the fault damage zone, and surface rupture is almost entirely suppressed in poor quality material.
Opportunities and Challenges in Data-Centric AI

Artificial intelligence (AI) systems are trained to solve complex problems and learn to perform specific tasks by using large volumes of data, such as prediction, classification, recognition, decision-making, etc. In the past three decades, AI research has focused mostly on the model-centric approach compared to the data-centric approach. In the model-centric approach, the focus is to improve the code or model architecture to enhance performance, whereas in data-centric AI, the focus is to improve the dataset to enhance performance. Data is food for AI. As a result, there has been a recent push in the AI community toward data-centric AI from model-centric AI. This paper provides a comprehensive and critical analysis of the current state of research in data-centric AI, presenting insights into the latest developments in this rapidly evolving field. By emphasizing the importance of data in AI, the paper identifies the key challenges and opportunities that must be addressed to improve the effectiveness of AI systems. Finally, this paper gives some recommendations for research opportunities in data-centric AI.
Evaluating the Effect of the Opioid Epidemic on Pregnant Women and Newborns in America

The opioid epidemic in America has emerged as a critical public health challenge, affecting various parts of the population. Among the vulnerable groups impacted, pregnant women and newborns bear a significant burden of the crisis. This literature review aims to evaluate the effects of the opioid epidemic on pregnant women and newborns in America, by investigating the prevalence of opioid use during pregnancy, existing healthcare interventions, and socioeconomic and psychological factors that play a role in this crisis. Adverse maternal outcomes, such as mortality and morbidity, and neonatal challenges, including Neonatal Abstinence Syndrome, are also examined. Based on the findings, the best strategies to combat these issues are improving access to treatment and support services, integrating mental health care with prenatal services, and enhancing medical education and training for healthcare providers involved in the care of pregnant opioid users. This study seeks to highlight the urgent need for collaborative efforts from policymakers, healthcare providers, community organizations, and the public to address the impact of the opioid epidemic on pregnant women and newborns effectively.
Wideband Multi-polarization Reconfigurable Antenna based on Non-uniform Polarization Convert AMC Reflector

A novel design of wideband multi-polarization reconfigurable antenna is proposed, based on a non-uniform polarization convert artificial magnetic conductor (AMC) reflector. The proposed antenna consists of a radiator element and an AMC reflector. Firstly, a modified polarization convert AMC reflector is designed. The non-uniform AMC reflector causes an enhancement of 3 dB axial ratio (AR) performance. Secondly, a wideband linearly polarized monopole antenna is presented as the main radiator, utilizing the broadband characteristic of a C-shaped monopole. The polarization reconfigurability of the proposed antenna can be achieved by properly rotating the AMC reflector, which can be switched between linear polarization (LP), left-hand circular polarization (LHCP), and right-hand circular polarization (RHCP). A prototype of the proposed antenna is fabricated and experimented with to validate the theoretical performance. The measured results show a -10 dB impedance bandwidth of 42.7% and 44.4% for LP and CP modes, respectively, and a 3 dB AR bandwidth of 20% for CP modes. In addition, the measured peak gain reaches 8 dBi/dBic. A good agreement is shown between the simulation and measurement, pointing to the good performance of the proposed antenna.