Miracle-dz/UniFAR-FTUs huggingface dataset:
Trends and variability in snowmelt in China under climate change

Abstract. Snowmelt is a major fresh water resource, and quantifying snowmelt and its variability under climate change is necessary for planning and management of water resources. Spatiotemporal changes in snow properties in China have drawn wide attention in recent decades; however, country-wide assessments of snowmelt are lacking. Using precipitation and temperature data with a high spatial resolution (0.5 seconds, approximately 1 km), this study calculated the monthly snowmelt in China for the 1951–2017 period using a simple temperature index model, and the model outputs were validated using snowfall, snow depth, snow cover extent and snow water equivalent. Precipitation and temperature scenarios developed from five CMIP5 models were used to predict future snowmelt in China under three different representative concentration pathways (RCP) scenarios (RCP2.6, RCP4.5 and RCP8.5). The results showed that the mean annual snowmelt in China from 1951 to 2017 was 2.41 × 1011 m3. The mean annual snowmelts in Northern Xinjiang, Northeast China, and the Tibetan Plateau – China’s three main stable snow cover regions – were 0.18 × 1011 m3, 0.42 × 1011 m3 and 1.15 × 1011 m3, respectively. From 1951 to 2017, the snowmelt increased significantly in the Tibetan Plateau and decreased significantly in North, Central and Southeast China. In the whole of China, there was a decreasing trend in snowmelt, but this was not statistically significant. The mean annual snowmelt runoff ratios were generally more than 10 % in almost all third-level basins in West China, more than 5 % in third-level basins in North and Northeast China, and less than 2 % in third-level basins in South China. From 1951 to 2017, the annual snowmelt runoff ratios decreased in most third-level basins in China. Under RCP2.6, RCP4.5 and RCP8.5, the projected snowmelt in China in 2030s (2050s, 2090s) may decrease by 13.4 % (16.3 %, 13.8 %), 19.1 % (19.8 %, 22.5 %), 17.1 % (24.7 %, 42.8 %) compared with the historical period (1951–2017), respectively. Most of the projected mean annual snowmelt runoff ratios in third-level basins in different decades (2030s, 2050s and 2090s) were lower than those in the historical period. Low temperature regions can tolerate more warming, and the snowmelt change in these regions is mainly influenced by precipitation; however, the snowmelt change in warm regions is more sensitive to temperature increases. The spatial variability of snowmelt changes may lead to regional differences in the impact of snowmelt on water supply.
International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383 – Vol. 16, No. 18, 2021

The increasing development in smart and mobile technologies transforms learning environments into smart learning environments. Students process information and learn in different ways, and this can affect the teaching and learning process. To provide a system capable of adapting learning contents based on students' learning behavior in a learning environment, the automated classification of the learners' learning patterns offers a concrete means for teachers to personalize students' learning. Previously, this research proposed a model of a self-regulated smart learning environment called the metacognitive smart learning environment model (MSLEM). The model identified five metacognitive skills-goal settings (GS), help-seeking (HS), task strategies (TS), time-management (TM), and self-evaluation (SE) that are critical for online learning success. Based on these skills, this paper develops a learning agent to classify students' learning styles using artificial neural networks (ANN), which mapped to FelderSilverman Learning Style Model (FSLSM) as the expected outputs. The receiver operating characteristic (ROC) curve was used to determine the consistency of classification data, and positive results were obtained with an average accuracy of 93%. The data from the students were grouped into six training and testing, each with a different splitting ratio and different training accuracy values for the various percentages of Felder-Silverman Learning Style dimensions. Keywords—Self-regulated learning, smart learning environment, personalized learning, learning styles, artificial neural network
Identifying Central Nodes in Directed and Weighted Networks

An issue of critical interest in complex network analysis is the identification of key players or important nodes. Centrality measures quantify the notion of importance and hence provide a mechanism to rank nodes within a network. Several centrality measures have been proposed for un-weighted, undirected networks but applying or modifying them for networks in which edges are weighted and directed is challenging. Existing centrality measures for weighted, directed networks are by and large domain-specific. Depending upon the application, these measures prefer either the incoming or the outgoing links of a node to measure its importance. In this paper, we introduce a new centrality measure, Affinity Centrality, that leverages both weighted in-degrees as well as out-degrees of a node’s local neighborhood. A tuning parameter permits the user to give preference to a node’s neighbors in either incoming or outgoing direction. To evaluate the effectiveness of the proposed measure, we use three types of real-world networks migration, trade, and animal social networks. Experimental results on these weighted, directed networks demonstrate that our centrality measure can rank nodes in consonance to the ground truth much better than the other established measures. Keywords—Centrality; weighted network; directed network; migration network; world input output trade network; community structure
Short Pulse Laser Synthesis of Transition-Metal Dichalcogenide Nanostructures under Ambient Conditions

The study of inorganic nanometer-scale materials with hollow closed-cage structures, such as inorganic fullerene-like (IF) nanostructures and inorganic nanotubes (INTs), is a rapidly growing field. Numerous kinds of IF nanostructures and INTs were synthesized for a variety of applications, particularly for lubrication, functional coatings, and reinforcement of polymer matrices. To date, such nanostructures have been synthesized mostly by heating a transition metal or oxide thereof in the presence of precursor gases, which are however toxic and hazardous. In this context, one frontier of research in this field is the development of new avenues for the green synthesis of IF structures and INTs, directly from the bulk of layered compounds. In the present work, we demonstrate a simple room-temperature and environmentally friendly approach for the synthesis of IF nanostructures and INTs via ultrashort-pulse laser ablation of a mixture of transition-metal dichalcogenides in bulk form mixed with Pb/PbO, in ambient air. The method can be considered as a synergy of photothermally and photochemically induced chemical transformations. The ultrafast-laser-induced excitation of the material, complemented with the formation of extended hot annealing regions in the presence of the metal catalyst, facilitates the formation of different nanostructures. Being fast, easy, and material-independent, our method offers new opportunities for the synthesis of IF nanostructures and INTs from different bulk metal chalcogenide compounds. On the basis of the capabilities of laser technology as well, this method could advantageously be further developed into a versatile tool for the simultaneous growth and patterning of such nanostructures in preselected positions for a variety of applications.
COPD_A_252896 1391..1401

Ruirui Duan 1–4 Hongtao Niu 2–4 Tao Yu Han Cui 2–4,6 Ting Yang Ke Hao Chen Wang 1–4,6 1Peking University China-Japan Friendship School of Clinical Medicine, Beijing, People’s Republic of China; 2Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing, People’s Republic of China; 3National Clinical Research Center for Respiratory Diseases, Beijing, People’s Republic of China; 4Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Beijing, People’s Republic of China; 5Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, People’s Republic of China; 6Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People’s Republic of China; 7Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA Purpose: Circular RNAs (circRNAs) regulate other RNA transcripts by competing for shared microRNAs, which play roles in the pathogenesis of many diseases, including chronic obstructive pulmonary disease (COPD). However, the role of circRNAs in COPD remains unknown. This study aimed to investigate the expression profile and the role of circRNAs in COPD. Patients and Methods: Twenty-one COPD patients and twenty-one normal controls were recruited. Total RNAs were collected from peripheral blood mononuclear cells (PBMCs) of each participant. CircRNAs and protein-coding mRNAs were profiled by microarray and systematically compared between patients with COPD and control subjects. The top differentially expressed circRNAs and mRNAs were validated by quantitative real-time PCR (RTqPCR). Functional analysis identified pathways relevant to the pathogenesis of COPD. Next, the circRNA target pathway network, the circRNA-miRNA-mRNA network (ceRNA network) and functional ceRNA regulatory modules were constructed. Results: In total, 2132 circRNAs and 2734 protein-coding mRNAs were differentially expressed (|fold change| >1.5 and P-value <0.05) in COPD patients. Six out of nine selected RNAs were confirmed by RT-qPCR validation. Our functional analysis suggested that immune imbalances and inflammatory responses play roles in the pathogenesis of COPD. The ceRNA network highlighted the differentially expressed circRNAs and their related miRNAs and mRNAs in COPD. In the circRNA target pathway network and functional ceRNA regulatory modules, hsa_circRNA_0008672 appeared in the top three KEGG pathways (NOD-like receptor signaling pathway, natural killer cell mediated cytotoxicity and Th17 cell differentiation) and may act as the miRNA sponge regulating the hsa_circRNA_0008672/miR-1265/MAPK1 axis. Conclusion: Our findings demonstrate critical roles of the circRNAs in COPD molecular etiology. The data support a plausible mechanism that circRNAs may be involved in the development of COPD by affecting the immune balance. Moreover, the hsa_circRNA_0008672/miR1265/MAPK1 axis may contribute to the pathogenesis of COPD, warranting further investigation.
Prediction for Compound Activity in Large Drug Datasets Using Efficient Machine Learning Approaches

Modern drug design requires activity prediction within a large number of chemical compounds using their descriptors that are often generated with high-noise in high-dimensional space. Both computational performance and classification quality face great challenges if machine learning algorithms are to be applied successfully. For computational efficiency, we implement the proximal support vector machine (PSVM) since it only depends on linear operations and can be trained faster than support vector machines (SVM) using quadratic optimization. For even larger datasets, we use parallel computing to make the training and classification time acceptable. To improve the classification quality, we implement and compare the SVM, k-nearest neighbor, decision tree and the naive Bayes classifiers. We measure the classification qualities by using the cross-validation accuracies, generalization accuracies, and the false positive and false negative ratios in ROC (receiver operating characteristics) curves. We also conduct feature selection in order to find the most important features and gain insights into the nature of the descriptors of the compounds. Features are easy to select using linear SVMs but the selection may be biased. We use a nonlinear kernel SVM in the feature selection process to achieve a higher ranking quality. To fully understand the properties of the noisy features in the dataset, we experiment with different number of features using the SVM classifier to obtain an optimal number of features.
Changes in chemical composition, fatty acids and sensory quality of fried catfish fillets (Clariars gariepinus)

Modification is proximate composition fatty acids chemical quality as well as changes in organoleptic traits were study in raw Cat fish fillets (Clarias gariepinus), pan-fried fillets in sun flower oil for 4 minutes and reheated fried cat fish fillets (in conventional oven for 15 min. at 80℃) after storage for one week at 5±1℃ and for one month at 20±2℃. after frying of cat fish fillets the moisture, Saturated Fatty Acids (SFA),Monounsaturated Fatty Acids (MUFA),Total Volatile Basis Nitrogen (TVBN) and Trymethylamin Nitrogen (TMAN) were reduced while, protein, fat, ash, polyunsaturated Fatty Acids (PUFA) Thiobarbeturic Acid (TBA) and Peroxide Value (PV) were increased due to chilling and frozen storage of fried catfish fillets, the gross chemical composition showed no significant changes ( p˃ 0.05), whereas slow increase in SFA and slow decrease in (MUFA) and ( PUFA) was found all chemical quality (TVBN,TMAN,TBA, and PV) of Cat fish fillet fried and stored at 5±1 ℃ and -20±2℃ were higher ( p˃0.05), as compared with the samples after frying directly. Organoleptic evolution scores of colour, taste, flavor, juiciness, and overall acceptability were highest for samples after fried directly while it showed significant decrease (p˃0.05) after storage at 5±1℃ and at -20±2℃ for one month respectively.
Understanding EFL Teachers’ Identity Construction in a Private Language School: A Positioning Analysis

Conceptualizing language teacher identity as a reciprocal, contextual and discursive process developed by teachers’ positionality in multiple social, cultural, linguistic, professional and educational discourses, this study explored identity positionings reflected in the lived experiences of two female Iranian English language teachers who learned and taught English in their home country. Drawing from Bamberg’s (1997) three-level positioning approach to Positioning Theory as the theoretical and analytical framework, this study reports on the analysis of narrative interviews and written narrative journals collected throughout two restorying cycles. The findings revealed that each teacher took different approaches in positioning the various characters in their storylines. They also denote that labeling games (e.g., native vs. non-native) as well as contradictory institutional policies impacted the teachers’ identity negotiation process while influencing their positioning in the given discourse. The study concludes with implications of the findings in ELT community and teacher education.
Optimal Precoder Design for MIMO-OFDM-based Joint Automotive Radar-Communication Networks

Large-scale deployment of connected vehicles with cooperative awareness technologies increases the demand for vehicle-to-everything (V2X) communication spectrum in 5.9 GHz that is mainly allocated for the exchange of safety messages. To supplement V2X communication and support the high data rates needed by broadband applications, the millimeter-wave (mmWave) automotive radar spectrum at 76-81 GHz can be utilized. For this purpose, joint radar-communication systems have been proposed in the literature to perform both functions using the same waveform and hardware. While multiple-input and multiple-output (MIMO) communication with multiple users enables independent data streaming for high throughput, MIMO radar processing provides high-resolution imaging that is crucial for safety-critical systems. However, employing conventional precoding methods designed for communication generates directional beams that impair MIMO radar imaging and target tracking capabilities during data streaming. In this paper, we propose a MIMO joint automotive radar-communication (JARC) framework based on orthogonal frequency division multiplexing (OFDM) waveform. First, we show that the MIMO-OFDM preamble can be exploited for both MIMO radar processing and estimation of the communication channel. Then, we propose an optimal precoder design method that enables high accuracy target tracking while transmitting independent data streams to multiple receivers. The proposed methods provide high-resolution radar imaging and high throughput capabilities for MIMO JARC networks. Finally, we evaluate the efficacy of the proposed methods through numerical simulations.
Analysis of Financial Decision Making with Loss Aversion∗

In this paper we consider a utility function that has a kink at the reference point and exhibits loss aversion. First we obtain an approximation of the expected utility of a loss averse decision maker. Then, in the spirit of Arrow and Pratt, we derive the expression for a risk premium. Finally we find an approximate solution to the optimal capital allocation problem and derive the expression for a portfolio performance measure. Our analysis generalizes the mean-variance utility of Tobin and Markowitz, the Arrow-Pratt measure of risk, and the Sharpe ratio. We show that a loss averse decision maker distinguishes between three sources of risk. Consequently, the characterization of the risk attitude of a loss averse decision maker involves three types of aversions, namely, aversion to loss, aversion to uncertainty in gains, and aversion to uncertainty in losses. We illustrate that if the decision maker exhibits a risk-seeking behavior in the domain for loss as in prospect theory, then neither the standard deviation nor the downside deviation can be used as a proper risk measure.
Reassessment of the radiocesium resuspension flux from contaminated ground surfaces in East Japan

Abstract. Resuspension of 137Cs from the contaminated ground surface to the atmosphere is essential for understanding the environmental behaviors of 137Cs and estimating external and inhalation exposure of residents. Kajino et al. (2016) assessed the 137Cs resuspension flux from bare soil and forest ecosystems in East Japan in 2013 using a numerical simulation constrained by surface air concentration measurements. However, the simulation was found to underestimate the observed deposition amounts by two orders of magnitude. The reason for this underestimation is that the simulation assumed that resuspended 137Cs is carried by submicron aerosols, which have low deposition rates. Based on the observational indications that soil dust and bioaerosols are the major carriers of resuspended 137Cs, a new simulation is performed with higher deposition rates constrained by both surface concentrations and deposition amounts. In the new estimation, the areal total annual resuspension of 137Cs in 2013 is 25.7 TBq, which is equivalent to 0.96 % of the initial deposition (2.68 PBq). Due to the rapid deposition rates, the annual redeposition amount is also large at 10.6 TBq, approximately 40 % of the resuspended 137Cs. The resuspension rate through the atmosphere (0.96 % y−1) seems slow, but it (2.6 × 10−5 d−1) may not be negligibly small compared to the actual decreasing trend of the ambient gamma dose rate obtained in Fukushima Prefecture after the radioactive decay of 137Cs plus 134Cs in 2013 is subtracted (1.0–7.9 × 10−4 d−1): Resuspension can account for 1–10 % of the decreasing rate due to decontamination and natural decay through land surface processes. The current simulation underestimated the 137Cs deposition in Fukushima city in winter by more than an order of magnitude, indicating the presence of additional resuspension sources. The site of Fukushima city is surrounded by major roads. Heavy traffic on wet and muddy roads after snow removal operations could generate superlarge (approximately 100 µm in diameter) road dust or road salt particles, which is not included in the model but might contribute to the observed 137Cs at the site.
Deep Audio-Visual Speech Separation Based on Facial Motion

We present a deep neural network that relies on facial motion and time-domain audio for isolating speech signals from a mixture of speeches and background noises. Recent studies in deep learning-based audio-visual speech separation and speech enhancement have proven that leveraging visual information in addition to audio can yield substantial improvement to the prediction quality and robustness. We propose to use facial motion, inferred from optical ﬂow techniques, as a visual feature input for our model. Combined with state-of-the-art audio-only speech separation approaches, we demonstrate that facial motion signiﬁcantly improves the speech quality as well as the versatility of the model. Our proposed method offers a signal-to-distortion improvement of up to 4.2 dB on two-speaker mixtures when compared to other audio-visual approaches.
DMSO_A_235335 81..88

The Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, and TheDepartment of Cardiology, Qilu Hospital of Shandong University, Jinan, Shandong, People’s Republic of China; The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Department of Critical Care Medicine, Jinan, Shandong, People’s Republic of China; Department ofGeneralMedicine,QiluHospital of Shandong University, Jinan, Shandong, People’s Republic of China; Department of Geriatric Medicine, Qilu Hospital of Shandong University, and The Key Laboratory of Cardiovascular Proteomics of Shandong Province, Jinan, Shandong, People’s Republic of China Purpose: To investigate the impacts of skeletal muscle mass on carotid atherosclerosis in
Erlang SGX Protecting Confidential Erlang Workloads with Intel SGX

Secure enclaves, such as Intel sgx, provide a trusted execution environment which offers integrity and confidentiality guarantees to supported applications. In this thesis, we show how Erlang can be protected to harden telecommunication workloads by utilizing hardware-based Intel sgx. First, we demonstrate how an untrusted Erlang runtime can execute trusted c code inside of sgx via Erlang C Nodes and nifs. A possible use case is the protection of cryptographic functionality which is demonstrated with OpenSSL inside of sgx, callable from Erlang. Second, to protect the Erlang runtime itself, a number of alternatives are explored as to enable execution of Erlang code inside of the enclave. However, Erlang ships with beam, an advanced virtual machine, which performs frequent syscalls and io activity that drastically increases the complexity of porting it to sgx enclaves. Therefore, two prominent third-party frameworks are tested which aim to support generic applications inside of sgx: the Library os Graphene and the shim-layer solution scone. Third, alternatives to implement a custom solution are discussed which could yield performance and security benefits over the generic frameworks while protecting an Erlang runtime. The complete source code for this thesis is available under a permissive bsd 3 license.1
EFFECTIVE ASYMPTOTICS ANALYSIS FOR FINANCE

It is known that an adaptation of Newton’s method allows for the computation of functional inverses of formal power series. We show that it is possible to successfully use a similar algorithm in a fairly general analytical framework. This is well suited for functions that are highly tangent to identity and that can be expanded with respect to asymptotic scales of “exp-log functions”. We next apply our algorithm to various well-known functions coming from the world of quantitative finance. In particular, we deduce asymptotic expansions for the inverses of the Gaussian and the Black–Scholes pricing functions.