Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and performance of an ecosystem‐scale forest soil warming experiment with infrared heater arrays

How forest ecosystems respond to climate warming will determine forest trajectories over the next 100 years. However, the potential effects of elevated temperature on forests remain unclear, primarily because of the absence of long‐term and large‐size field warming experiments in forests, especially in Asia. Here, we present the design and performance of an ecosystem‐scale warming experiment using infrared (IR) heater arrays in a 60‐year‐old temperate mixed forest at Qingyuan Forest CERN in northeastern China. In paired 108 m2 plots (n = 3), the surface soils were constantly elevated 2°C above control plots with a feedback control system over 4 years (2018–2021). Subsoils down to 60 cm depth were warmed 1.2–2°C. Soil warming did not significantly affect soil moisture either in surface soils or in subsoils. Turn‐off time due to weather extremes (heavy rains, snow) and power outages only accounted for 2.5% of the total warming period. In conclusion, we provide a proof‐of‐principle setup that allows long‐term analysis of forest response to warming temperatures in large‐size field plots. Importantly, our warming experiment demonstrated the feasibility of IR heater arrays for soil warming in tall‐statured forest ecosystems.
Investigating critical factors influencing the acceptance of e-learning during COVID-19

Background: In order to prevent the spread of the Covid-19 virus, a temporary interruption of teaching and educational activities in classrooms occurred. Most schools and faculties were forced to switch from traditional to online teaching. Purpose: This research aims to examine the key factors influencing students' intention to use e-learning, as well as predictors of student satisfaction with online teaching during the Covid-19 pandemic. Study design/methodology/approach: The analysis includes 312 students on the territory of the Republic of Serbia who use e-learning. Reliability analysis, confirmatory factor analysis and structural equations modeling are applied in the paper. Findings/conclusions: It is found that course design significantly affects perceived usefulness, perceived ease of use and quality of e-learning, and perceived usefulness and quality of e-learning are the main drivers of student satisfaction. Then, perceived usefulness, perceived ease of use and satisfaction with online teaching are important predictors of the attitude towards the use of e-learning, and attitude is an important driver of the intention to use e-learning. The results of the research and the implications derived from them can be helpful to educational institutions in creating, introducing and implementing e-learning, as well as increasing student satisfaction with online teaching during the pandemic. Limitations/future research: The limitation of the research stems from the selection of the sample (students). In addition, the research was conducted on the territory of Serbia, so the results cannot be generalized. Third, the possible bias of the respondents in giving answers can lead to wrong conclusions. The recommendation for future research is to examine the attitudes of professors who use e-learning, in addition to students. Another recommendation is to do a segment analysis (by gender, year of study) in order to develop specific strategies for each segment. Another suggestion is to compare students' opinions on e-learning and traditional ways of learning.
The potential of CircRNA1002 as a biomarker in hepatitis B virus-related hepatocellular carcinoma

Background Although hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, there is a lack of effective diagnostic measures. Circular RNAs (circRNAs) can be used as biomarkers for monitoring the occurrence and development of HCC. However, a convenient and reliable serum circRNA biomarker is not currently available. Materials & Methods CircRNA expression profiles were explored using high-throughput sequencing technology, and targeted circRNAs and mRNAs were validated by quantitative reverse transcription PCR (RT-qPCR). The biological functions of circRNAs were investigated using Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Downstream miRNAs and mRNAs of dysregulated circRNAs were predicted using TargetScan, miRanda, and miRDB; then circRNA-miRNA-mRNA interaction networks were constructed based on sequencing data and the Cancer Genome Atlas (TCGA). Results A total of 50,327 circRNAs were identified, with 1,187 circRNAs significantly differentially expressed between hepatitis B virus (HBV)-related HCC and HBV asymptomatic carriers. Among these circRNAs, four (circRNA1002, circRNA7941, circRNA 39338, and circRNA44142) were validated by RT-qPCR as being statistically different either in HCC tissue or serum samples. circRNA1002 was significantly down-regulated in both HCC serum and tissue, indicating its reliability. Bioinformatics analysis showed that circRNA1002-associated genes were enriched in GO terms relating to hormone pathway and cell-cell interaction processes, which are involved in the progression of HCC. Conclusion Our circRNA analysis of HCC patients and HBV asymptomatic carriers showed that circRNA1002 may be a reliable serum biomarker for HCC. These results could provide an improved assay for the early detection of HCC.
Pembuatan dan Pendampingan Pengelolaan Website Sekolah KB-TK Hj. Isriati Baiturrahman 1 Semarang

Website sebagai media publikasi online bagi sekolah saat ini sudah menjadi kebutuhan yang sangat mendesak. Semua informasi dapat dengan mudah tersampaikan secara lengkap dan lugas dengan media website. Kondisi masyarakat yang terdampak pandemi covid-19 menjadikan kebutuhan website menjadi sebuah keniscayaan bagi sebuah sekolah selain menggunakan media sosial. KB-TK Hj. Isriati Baiturrahman 1 Semarang sampai tahun 2020 senantiasa menyampaikan informasi seputar kegiatan sekolah dan pendaftaran peserta didik baru dengan menggunakan media sosial. Hasil observasi awal, sekolah teridentifikasi belum memiliki website mandiri, sehingga dengan diskusi terkait dengan kebutuhan sekolah dilakukan pembuatan dan pengembangan website dengan domain sekolah (.sch). Website yang dibuat menggunakan platform wordpress yang mudah dalam pengelolaannya, sehingga diharapkan admin web dapat melakukan pengelolaan website sekolah secara mandiri. Kegiatan pembuatan dan pendampingan menggunakan metode adalah 1) tahap observasi, 2) tahap pembuatan website 3) tahap pendampingan pengelolaan website 4) tahap monitoring dan evaluasi. Hasil dan manfaat dari kegiatan pengabdian masyarakat salah satunya adalah sekolah mempunyai website sekolah, memberikan pengetahuan tentang pengelolaan website sekolah bagi admin dan cara membuat artikel maupun konten untuk guru.Website as an online publication media for schools has now become a very urgent need. All information can be easily conveyed in a complete and straightforward manner with the website media. The condition of the community affected by the COVID-19 pandemic has made the need for a website a necessity for a school apart from using social media. KB-TK Hj. Isriati Baiturrahman 1 Semarang until 2020 always conveys information about school activities and registration of new students using social media. The results of initial observations, identified schools do not yet have an independent website, so with discussions related to school needs, the website is made and developed with the school domain (.sch). Websites created using the WordPress platform are easy to manage, so it is hoped that web admins can manage school websites independently. The activities of making and assisting using the method are 1) the observation stage, 2) the website creation stage, 3) the mentoring stage for website management 4) the monitoring and evaluation stage. One of the results and benefits of community service activities is that schools have a school website, provide knowledge about managing school websites for admins and how to create articles and content for teachers.
Using a consumption function to explain the Lucas Critique to undergraduate students

PurposeThe purpose of this paper is to contribute to the teaching of undergraduate macroeconomics.Design/methodology/approachTo suggest a roadmap, based on a consumption function, to be used by instructors willing to teach the Lucas Critique subject.FindingsTherefore, this paper proposes a lesson, which consists of three parts, to help undergraduates better understand the subject: (1) a grading exercise to bring the topic closer to students’ lives; (2) a Keynesian and an optimal consumption function, followed by an example based on an unemployment insurance policy; and (3) two optional topics consisting of extensions of the optimal consumption function and some empirical results related to the Lucas Critique.Originality/valueThe Lucas Critique influenced the evolution of research in macroeconomics, but it is not easily grasped in a classroom.
NF-kB (p50/p65)-Mediated Pro-Inflammatory microRNA (miRNA) Signaling in Alzheimer's Disease (AD)

A small family of NF-kB (p50/p65)-regulated microRNAs (miRNAs) has been identified and partially characterized in Alzheimer’s disease (AD) brain in the hippocampal CA1 and temporal lobe neocortical regions, and in reactive oxygen species (ROS)-, cytokine interleukin 1-beta (IL1β)-, amyloid beta 42 (Aβ42) peptideand/or lipopolysaccharide (LPS)-stressed human neuronalglial (HNG) cells in primary co-culture. This microRNA family currently includes miRNA-9, miRNA-30b, miRNA-34a, miRNA-125b, miRNA-146a and miRNA-155. Other brain-enriched miRNA species may be involved. Experimental evidence suggests that in neurological diseases, the upregulation of this small pro-inflammatory miRNA family orchestrates a pathogenic gene expression program that can explain many aspects of AD onset, and propagation and severity of the disease including failure of microglial-mediated clearance of amyloid-beta (Aβ) and other endstage peptides from brain cells, amyloidogenesis, astrogliosis, deficits in neurotropism, neuronal cell atrophy, and downregulation in the production of essential cytoskeletal components and synaptic signaling elements. This opinion article will contribute to the interpretation of the most recent findings in the current research area involving NF-kB-regulated miRNA-mediated signaling pathways, and how this information may aid in the advancement of therapeutic strategies for more effective clinical management of AD and other progressive age-related and lethal neurodegenerative disorders.
Neural network technologies for analysis and risk assessment in forecasting the market of industrial financial instruments

The issues of risk analysis and assessment in forecasting the dynamics of prices on the stock exchange and the OTC market have always been a difficult task for many researchers and analysts. Successful investment is largely determined by knowledge of the future situation in the financial market. The article deals with the issues of search and development of technical tools for risk analysis and assessment in forecasting the market of derivative financial instruments under conditions of uncertainty. A recurrent network with a long-term short-term memory cell LSTM, which is one of the most powerful prediction models, was chosen as the architecture of neural networks. The development of technical tools for solving this problem was carried out on the basis of the C# programming language. An additional TA-Lib library served as a mathematical base for technical analysis, from which 42 technical analysis indicators were used during the development process, and for working with the LSTM recurrent network, the library Keras.NET. The ability to efficiently link memory and input remote data using these network memory cells provides a dynamic understanding of the data structure over time. The estimates of the accuracy of the forecast for the cost indicators of a derivative financial instrument obtained as a result of computer experiments showed the high efficiency of the constructed neural network model.
Clinical Condition, Nutritional Status and Food Consumption of Patients with Maple Syrup Urine Disease

Maple syrup urine disease (MSUD) is an inborn error of metabolism caused by an enzyme deficiency and leads to the accumulation of branched-chain amino acids (BCAAs), which are toxic to the body in high concentrations. Part of the treatment is based on dietary control and a reduced intake of BCAAs. The objective of this study was to describe the clinical condition, nutritional status and food consumption of patients with MSUD. Two patients diagnosed with MSUD were selected. Interviews and nutritional, body composition, and food consumption assessments were carried out. Energy, macronutrients, micronutrients, BCCAs, and fiber contents were estimate. All clinical data were obtained from medical records. Both patients showed manifestations of neuropsychomotor development delay with intellectual disability. Regarding nutritional status, one patient was overweight, and the other was underweight, with low functional capacity in both of them. A monotonous dietary pattern and a low intake of energy, proteins, vitamins, and minerals were observed, but there was a high consumption of BCAAs by one of the patients. Developmental and intellectual disabilities were observed besides the changes in nutritional status (underweight and overweight), low functional capacity, dietary inadequacies in terms of energy, macronutrients, and micronutrients, besides a monotonous food consumption pattern.
Molecular Profiling of Kenyan Acute Myeloid Leukemia Patients

Acute myeloid leukemia (AML) is an infrequent disease, and it is associated with high morbidity and mortality. It harbors a unique configuration of cytogenetic abnormalities and molecular mutations that can be detected using microscopic and molecular methods respectively. These genetic tests are core elements of diagnosis and prognostication in high-income countries. They are routinely incorporated in clinical decision making, allowing for the individualization of therapy. However, these tests are largely inaccessible to most patients in Kenya and therefore no data has been reported on this group of patients. The main purpose of this study is to describe the cytogenetic and molecular abnormalities of acute myeloid leukemia patients seen at the hemato-oncology unit of Kenyatta National Hospital. A cross-sectional descriptive study was carried out over a 3-month period on ten patients with a diagnosis of AML. Social demographics and clinical data were collected through a study proforma. A peripheral blood sample was collected for conventional metaphase G-banding technique and next generation sequencing. Particularly, targeted DNA sequencing (Illumina myeloid panel) and whole exome sequencing (WES) were performed. Cytogenetic analysis failed in 10/10 cases. Targeted sequencing was successfully obtained in 8 cases, whereas WES in 7. Cytogenetic studies yielded no results. There were 20 mutations detected across 10 commonly mutated genes. All patients had at least one clinically relevant mutation. Based on ELN criteria, NGS identified three patients with high-risk mutations, affecting TP53 (n = 2) and RUNX1 (n = 1). One patient was classified as favorable (PML-RARA) while 4 were standard risk. However, WT1 mutations associated with unfavorable prognosis were recorded in additional 2 cases. WES showed concordant results with targeted sequencing while unveiling more mutations that warrant further attention. In conclusion, we provide the first molecular profiling study of AML patients in Kenya including application of advanced next generation sequencing technologies, highlighting current limitations of AML diagnostics and treatment while confirming the relevance of NGS in AML characterization.
Analysis of LoRaWAN Transactions for TEG-Powered Environment-Monitoring Devices

Long-Range (LoRa) transmission technology is potentially a suitable solution in abundant applications such as smart cities, smart industries, smart health, and others, although it is challenging and complex to implement. LoRa is a non-cellular modulation technology for Long-Range Wide-Area Networks (LoRaWAN) and is suitable for Internet of Things (IoT) solutions through its long-range and low-power consumption characteristics. The present paper provides a comprehensive analysis of LoRa wireless transactions through several measurements, which differ in LoRa parameter configuration. The results showed dependency of the power consumed by the transaction on the selected Effective Isotropic Radiated Power (EIRP). The quantity of energy consumed by the transaction also significantly depends on the selected data rate (combination of the spread factor and bandwidth) and payload.
Adaptive Control of Local Updating and Model Compression for Efficient Federated Learning

Data generated at the network edge can be processed locally by leveraging the paradigm of Edge Computing (EC). Aided by EC, Federated Learning (FL) has been becoming a practical and popular approach for distributed machine learning over locally distributed data. However, FL faces three critical challenges, i.e., resource constraint, system heterogeneity and context dynamics in EC. To address these challenges, we present a training-efficient FL method, termed FedLamp, by optimizing both the Local updating frequency and model compression ratio in the resource-constrained EC systems. We theoretically analyze the model convergence rate and obtain a convergence upper bound related to the local updating frequency and model compression ratio. Upon the convergence bound, we propose a control algorithm, that adaptively determines diverse and appropriate local updating frequencies and model compression ratios for different edge nodes, so as to reduce the waiting time and enhance the training efficiency. We evaluate the performance of FedLamp through extensive simulation and testbed experiments. Evaluation results show that FedLamp can reduce the traffic consumption by 63% and the completion time by about 52% for achieving the similar test accuracy, compared to the baselines.
DeepStep: Direct Detection of Walking Pedestrian From Motion by a Vehicle Camera

Pedestrian detection has wide applications in intelligent transportation. It is essential to understand pedestrian’s position and action instantaneously for autonomous driving. Most algorithms divide these tasks into sequential procedures where pedestrians are detected from shape-based features in video frames, and their behaviors are analyzed with frame tracking. Different from those, this work introduces a deep learning-based pedestrian detection method that only uses motion cues. The pedestrian motion, which is much different from that of static background and dynamic vehicles, is investigated in the spatial-temporal domain. The pedestrian leg movement forms a chain-type trace in the motion profile images even if the ego-vehicle moves. Instead of modeling walking actions based on kinematics, the chain structure is directly learned from a large pedestrian dataset in driving videos. This method works for the more challenging scenes observed on moving vehicles than those scenes from static cameras. The aim is to detect not only pedestrians promptly but also predict their walking direction in the driving space. Since a video is reduced to temporal images, real-time performance is achieved with a high mean average precision and a low false-positive rate on a publicly available dataset.
Vision-Based Object Recognition and Precise Localization for Space Body Control

The space motion control is an important issue on space robot, rendezvous and docking, small satellite formation, and some on-orbit services. The motion control needs robust object detection and high-precision object localization. Among many sensing systems such as laser radar, inertia sensors, and GPS navigation, vision-based navigation is more adaptive to noncontact applications in the close distance and in high-dynamic environment. In this work, a vision-based system serving for a free-floating robot inside the spacecraft is introduced, and the method to measure space body 6-DOF position-attitude is presented. At first, the deep-learning method is applied for robust object detection in the complex background, and after the object is navigated at the close distance, the reference marker is used for more precise matching and edge detection. After the accurate coordinates are gotten in the image sequence, the object space position and attitude are calculated by the geometry method and used for fine control. The experimental results show that the recognition method based on deep-learning at a distance and marker matching in close range effectively eliminates the false target recognition and improves the precision of positioning at the same time. The testing result shows the recognition accuracy rate is 99.8% and the localization precision is far less than 1% in 1.5 meters. The high-speed camera and embedded electronic platform driven by GPU are applied for accelerating the image processing speed so that the system works at best by 70 frames per second. The contribution of this work is to introduce the deep-learning method for precision motion control and in the meanwhile ensure both the robustness and real time of the system. It aims at making such vision-based system more practicable in the real-space applications.
Soil CO2 Emission Largely Dominates the Total Ecosystem CO2 Emission at Canadian Boreal Forest

The natural carbon dioxide (CO2) emission from the ecosystem, also termed as the ecosystem respiration (Reco), is the primary natural source of atmospheric CO2. The contemporary models rely on empirical functions to represent decomposition of litter with multiple soil carbon pools decaying at different rates in estimating Reco variations and its partitioning into autotrophic (Ra) (originating from plants) and heterotrophic (originating mostly from microorganisms) respiration (Rh) in relation to variation in temperature and soil water content. Microbially-mediated litter decomposition scheme representation are not very popular yet. However, microbial enzymatic processes play integral role in litter as well as soil organic matter (SOM) decomposition. Here we developed a mechanistic model comprising of multiple hydro-biogeochemical modules in the soil and water assessment tool (SWAT) code to explicitly incorporate microbial-enzymatic litter decomposition and decomposition of SOM for separately estimating regional-scale Ra, Rh and Reco. Modeled annual mean Reco values are found varying from 1,600 to 8,200 kg C ha−1 yr−1 in 2000–2013 within the boreal forest covered sub-basins of the Athabasca River Basin (ARB), Canada. While, for the 2000–2013 period, the annual mean Ra, Rh and soil CO2 emission (Rs) are varying within 800–6,000 kg C ha−1 yr−1, 700–4,200 kg C ha−1 yr−1 and 1,200–5,000 kg C ha−1 yr−1, respectively. Rs generally dominates Reco with nearly 60–90% contribution in most of the sub-basins in ARB. The model estimates corroborate well with the site-scale and satellite-based estimates reported at similar land use and climatic regions. Mechanistic modeling of Reco and its components are critical to understanding future climate change feedbacks and to help reduce uncertainties particularly in the boreal and subarctic regions that has huge soil carbon store.