Miracle-dz/UniFAR-FTUs huggingface dataset:
Architectural Languages for the Microservices Architecture: A systematic mapping study

The interest in the research on the architectural languages of microservices architecture has been increasing since 2015. This gives rise to new architectural language proposals to address different microservice-specific challenges. This diversity of architectural languages means that there is still no predominant architectural language. This mapping study aims to identify the architectural languages used in a microservices architecture by following a systematic methodology that covers the years from 2015 to 2021, and a research protocol was created to allow the study's reproducibility. We identified and analyzed studies that answered the research questions and applied data extraction guidelines and thematic synthesis to provide an overview of the microservices architectural languages. We found a total of 69 primary studies which propose architectural languages that were grouped in challenges of microservices architecture they address and architectural and design characteristics, which used base languages such as UML, BPMN, graphs, and others. Lastly, our findings show that there are several options to model microservice systems according to the challenges of this architectural style that they address.
a study of the generalized tukey lambda family

The Tukey lambda family of distributions together with its extensions have played an important role in statistical practice. In this paper a con¬tinuously defined two-parameter generalization of this family, which holds promise of a variety of additional applications, is variously studied. The coefficients of skewness and kurtosis and the density shapes of its members are examined and the family is related to the classical Pearsonian system of distributions.
Analysis of Broadband Electromagnetic Scattering based on RFG-FFT with Cube Polynomial Inter/Extrapolation Method

To analyze the broadband scattering problem, an efficient RFG-FFT with cube polynomial inter/extrapolation method is presented. Due to its great performance for accelerating the matrix vector product, RFG-FFT is applied to solve electrically large problems. To overcome bottleneck of repeatedly constructing the impedance matrix at each frequency, the combined cube polynomial inter/extrapolation method is incorporated into the filling process of the impedance matrix. The high efficiency and accuracy of the proposed method are verified by a comparison with the numerical method.
NMR-Based Metabolomics Demonstrates a Metabolic Change during Early Developmental Stages from Healthy Infants to Young Children

The present study aims to identify the salivary metabolic profile of healthy infants and young children, and to correlate this with age, salivary gland maturation, and dentition. Forty-eight children were selected after clinical evaluation in which all intraoral structures were examined. Total unstimulated saliva was collected, and salivary metabolites were analyzed by 1H Nuclear Magnetic Resonance (NMR) at 25 °C. Partial least squares discriminant analysis (PLS-DA), orthogonal PLS-DA (O-PLS-DA), and univariate analysis were used, adopting a 95% confidence interval. The study showed a distinct salivary metabolomic profile related to age and developmental phase. The saliva of children in the pre-eruption teeth period showed a different metabolite profile than that of children after the eruption. However, more evident changes were observed in the saliva profile of children older than 30 months. Alanine, choline, ethanol, lactate, and sugar region were found in higher levels in the saliva of patients before 30 months old. Acetate, N-acetyl sugar, butyrate, caproate, creatinine, leucine, phenylalanine, propionate, valine, succinate, and valerate were found to be more abundant in the saliva of children after 30 months old. The saliva profile is a result of changes in age and dental eruption, and these findings can be useful for monitoring the physiological changes that occur in infancy.
Circular RNA CircCDKN2B−AS_006 Promotes the Tumor-like Growth and Metastasis of Rheumatoid Arthritis Synovial Fibroblasts by Targeting the miR−1258/RUNX1 Axis

Rheumatoid arthritis (RA) is an autoimmune polyarthritis in which synovial fibroblasts (SFs) play a major role in cartilage and bone destruction through tumor−like proliferation, migration, and invasion. Circular RNAs (circRNAs) have emerged as vital regulators for tumor progression. However, the regulatory role, clinical significance, and underlying mechanisms of circRNAs in RASF tumor−like growth and metastasis remain largely unknown. Differentially expressed circRNAs in synovium samples from patients with RA and patients with joint trauma were identified via RNA sequencing. Subsequently, in vitro and in vivo experiments were performed to investigate the functional roles of circCDKN2B−AS_006 in RASF proliferation, migration, and invasion. CircCDKN2B−AS_006 was upregulated in synovium samples from patients with RA and promoted the tumor-like proliferation, migration, and invasion of RASFs. Mechanistically, circCDKN2B−AS_006 was shown to regulate the expression of runt−related transcription factor 1 (RUNX1) by sponging miR-1258, influencing the Wnt/β−catenin signaling pathway, and promoting the epithelial−to−mesenchymal transition (EMT) in RASFs. Moreover, in the collagen−induced arthritis (CIA) mouse model, intra−articular injection of lentivirus−shcircCDKN2B−AS_006 was capable of alleviating the severity of arthritis and inhibiting the aggressive behaviors of SFs. Furthermore, the correlation analysis results revealed that the circCDKN2B−AS_006/miR−1258/RUNX1 axis in the synovium was correlated with the clinical indicators of RA patients. CircCDKN2B−AS_006 promoted the proliferation, migration, and invasion of RASFs by modulating the miR−1258/RUNX1 axis.
Research on Network Traffic Anomaly Detection Method Based on Temporal Convolutional Network

In recent years, with the exponential growth of network traffic and the increase of network attacks, anomaly detection of network traffic has always been a hot research issue. Many network traffic anomaly detection studies focus on the temporal characteristics of network traffic. However, most of these studies start from statistical characteristics and pursue the accuracy of network traffic anomaly detection, which lead to the lag of network traffic anomaly detection. In this paper, a network traffic anomaly detection method combining temporal convolution network and dynamic time warping algorithm is proposed to solve the lag problem of network traffic anomaly detection. The predictor trained by temporal convolution network can predict what the user's next benign behavior series should be like from the user's current behavior series, and then the dynamic time warping algorithm can judge the abnormal user by calculating the similarity between the user's actual behavior series and the predicted behavior series. We carried out our verification experiment on the original pcap file of CICIDS2017 dataset, and finally achieved an average negative sample recall rate of 92% and a false alarm rate of less than 40%. This result is ideal on the premise of ensuring timeliness. It can be used as the first level screening system of the intrusion detection system to screen possible criminal attackers in time, so that network security managers can make timely security measures. We will continue to improve our research to reduce false positives.
Personalized Differentially Private Online Minimum Bipartite Matching in Spatial Crowdsourcing

Mobile devices and mobile applications benefit people's mobility. In some applications such as Uber and Didi, drivers and passengers (i.e., users) submit locations to the server to match each other. However, the server may be untrusted and location privacy becomes a huge concern. Users expect to enjoy matching while protecting their locations and different users have different levels of location privacy. In this paper, we consider the personalized privacy-preserving online minimum bipartite matching problem. To solve this problem, we design a Personalized Quadtree-Based privacy-preserving matching Framework (PQBF). In this framework, we propose a privacy method using the truncated Geometric Mechanism on the quadTree (GMT). GMT allows users to intuitively select generalization regions with different sizes, which is user-friendly. We then formulate the distance on the quadtree using earth mover's distance (EMD) to select the closest worker-task pair. Considering the huge time overhead of distance calculation, we provide a way to accelerate. We show that our method satisfies $\epsilon$ - geo-indistinguishability on the quadtree and give the time complexity of PQBF. Experiments on synthetic data and real data show that our mechanism is more effective than previous mechanisms in terms of total distance. PQBF saves up to 35.9% total distance than a state-of-the-art algorithm.
A Mixed Cross-coupling Microstrip Filter with Multiple Transmission Zeros

A novel three-pole microstrip filter with four transmission zeros is proposed. The coupling between input and output hairpin-comb resonators of the trisection filters, regarded as the cross-coupling, provides a close-to-band transmission zero. This cross-coupling component is also a mixed EM coupling component, which introduces another adjustable transmission zero beyond the passband. Due to the combination of the cross-coupling technique and the mixed electric and magnetic (EM) coupling approach, multiple transmission zeros can be achieved, which means a high rejection level. Therefore, fewer resonators are needed to obtain the same number of transmission zeros than inline filters. The measured result is in good agreement with the simulated S-parameters.
Implementation Method for Broadband Metasurfaces Based on a Distributed Transmission-Line Model

We propose an implementation method for broadband metasurfaces based on a one-dimensional distributed transmission-line model. Five types of one-dimensional meander microstrip-line structures are proposed and a metasurface refracting a normal incident plane wave with a 20-degree angle is implemented with those. Line length of each structure is determined from the theoretical formula of the model and the error from the theoretical value is modified by eigenfrequency analysis. Full-wave simulations are carried out, and the broadband refracting operations and the validity of the proposed method are confirmed.
Study of Non-Orthogonal Multiple Access Technology for Satellite Communications

In this paper, the availability of the non-orthogonal multiple access (NOMA) to the satellite communication system is verified firstly by analyzing the distribution of the absolute value of any two satellite users' signal-to-noise ratio (SNR) gap. Then, the uplink and downlink NOMA schemes which are suitable for the satellite communications are studied. Finally, the link-level simulation is carried out under the satellite channel. The simulation results show that compared with the orthogonal multiple access (OMA), NOMA performs better in the satellite communication systems.
Discrete least-norm approximation by nonnegative (trigonometric) polynomials and rational functions

Polynomials, trigonometric polynomials, and rational functions are widely used for the discrete approximation of functions or simulation models. Often, it is known beforehand that the underlying unknown function has certain properties, e.g., nonnegative or increasing on a certain region. However, the approximation may not inherit these properties automatically. We present some methodology (using semidefinite programming and results from real algebraic geometry) for least-norm approximation by polynomials, trigonometric polynomials, and rational functions that preserve nonnegativity.
Computer-Aided System for COVID-19 Using Semi-supervised-based Ensemble Learning and Reinforcement Learning

Coronavirus Disease 2019(COVID-19) has shocked the world with its rapid spread and enormous threat to life and has continued up to the present. In this paper, a computer-aided system is proposed to detect infections and predict the disease progression of COVID-19. A high-quality CT scan database labeled with time-stamps and clinicopathologic variables is constructed to provide data support. To our knowledge, it is the only database with time relevance in the community. An object detection model is then trained to annotate infected regions. Using those regions, we detect the infections using a model with semi-supervised-based ensemble learning and predict the disease progression depending on reinforcement learning. We achieve an mAP of 0.92 for object detection. The accuracy for detecting infections is 98.46%, with a sensitivity of 97.68%, a specificity of 99.24%, and an AUC of 0.987. Significantly, the accuracy of predicting disease progression is 90.32% according to the timeline. It is a state-of-the-art result and can be used for clinical usage.
Causes of Chinese Mainland's Major Chemicals-Related Accidents from 2015 to 2020: A Review Based on Complex Network Theory

The chemical industry is one of the most typical long process industries, with complex processes, correlation and coupling, and harsh operating conditions. These complex system features made chemical accidents prone to occur frequently, and most accidents were directly or indirectly caused by human actions. Human factors ran through the accident occurrence process and played a decisive role in accident occurrence, evolution, and emergency handling. This study made loss-prevention recommendations for the chemical industry after conducting a review of accident reports and creating a complex network model.
Evaluation of characteristics of fast-response pressure-sensitive paint under low-pressure conditions

In this study, fast-response polymer-ceramic pressure-sensitive paints (PC-PSP) were developed and evaluated for pressure measurement under low-pressure conditions. The PC-PSP using poly[1-trimethylsilyl)-1-propyne] (poly(TMSP)), which has high oxygen permeability under low-pressure conditions, was developed in this study. The static and dynamic characteristics of the developed poly(TMSP)-based PC-PSP were evaluated in comparison with those of conventional poly(isobutyl methacrylate) (poly(IBM)) binder and ruthenium-complex-based PC-PSPs, which have been used for pressure measurements under atmospheric pressure conditions. The particle mass content of titanium dioxide of PC-PSPs with poly(TMSP) was changed from 90 wt% to 98 wt% to increase the frequency response. The critical pigment volume concentration, so called CPVC, of the PC-PSP with poly(TMSP) and hydrophobic particles and hydrophilic particles were 95–98 wt% and 90–95 wt%, respectively. The PC-PSP using hydrophilic particles with poly(TMSP) and a particle mass content of 98 wt% could provide a cut-off frequency of approximately 4.5 kHz and a high local Stern–Volmer coefficient of 0.5 at low pressure of 2 kPa.
Euclidean Schwarzschild negative mode

The Euclidean (imaginary time) Schwarzschild solution of general relativity is known to possess a spin-2 metric perturbation which decreases its Euclidean action. This ''negative mode'' contributes an imaginary part to the effective action, and renders hot flat space unstable against the nucleation of black holes. In this paper, we enclose the black hole in a spherical ''box'' by imposing boundary conditions on the perturbations. Two conditions, which correspond to a fixed temperature (isothermal wall) and fixed energy (reflecting wall) are examined. The isothermal boundary condition eliminates the negative mode if the box is small enough, and stabilizes hot flat space.
Investigation of Histamine Removal by Electrodialysis from the Fermented Fish Sauce and Its Effects on the Flavor

Histamine is one of the most concerned safety indicators in fish sauce. Considering its charge property, electrodialysis (ED) was used to control the histamine in fish sauce, and studies were focused on three operating parameters: input current, pH, and flow velocity. A Box–Behnken design and response surface methodology was adopted to derive a statistical model, which indicated that 5.1 A input current, pH 3.8, and 40 L∙h−1 flow velocity were optimal operation conditions. Under this condition, the histamine removal rate reached 53.41% and the histamine content met the allowable histamine limit of below 400 mg·kg−1 in fish sauce, while the amino nitrogen (ANN) loss rate was only 15.46%. In addition, amino acids and volatile compounds changed differently during ED. As a result, with decreased histamine, the fish sauce after ED was also less salty and less fishy. The study first explored utilizing ED to remove histamine from fish sauce, which has positive implications for promoting the safety of aquatic products.
A trait-based framework for seagrass ecology: Trends and prospects

In the last three decades, quantitative approaches that rely on organism traits instead of taxonomy have advanced different fields of ecological research through establishing the mechanistic links between environmental drivers, functional traits, and ecosystem functions. A research subfield where trait-based approaches have been frequently used but poorly synthesized is the ecology of seagrasses; marine angiosperms that colonized the ocean 100M YA and today make up productive yet threatened coastal ecosystems globally. Here, we compiled a comprehensive trait-based response-effect framework (TBF) which builds on previous concepts and ideas, including the use of traits for the study of community assembly processes, from dispersal and response to abiotic and biotic factors, to ecosystem function and service provision. We then apply this framework to the global seagrass literature, using a systematic review to identify the strengths, gaps, and opportunities of the field. Seagrass trait research has mostly focused on the effect of environmental drivers on traits, i.e., “environmental filtering” (72%), whereas links between traits and functions are less common (26.9%). Despite the richness of trait-based data available, concepts related to TBFs are rare in the seagrass literature (15% of studies), including the relative importance of neutral and niche assembly processes, or the influence of trait dominance or complementarity in ecosystem function provision. These knowledge gaps indicate ample potential for further research, highlighting the need to understand the links between the unique traits of seagrasses and the ecosystem services they provide.
FETI‐DP for Anisotropic Problems

Our work on FETI-DP [2, 3, 6, 4] for anisotropic problems is motivated from simulations of arterial walls in the context of ﬁnite elasticity, see also [1]. Arterial walls can be viewed as ﬁber reinforced materials with two sets of collagene ﬁbers which are crosswire helically wound around the artery. It is well known that large anisotropies have a negative effect on the convergence of iterative solvers since the anisotropies are reﬂected in the spectrum of the operator. In domain decomposition methods, in the case of scalar problems, anisotropies are related to a anisotropic decomposition of the computational domain. In the FETI-DP domain decomposition method we may consider three remedies to anisotropies: Adaption of the coarse problem, modiﬁcation of the preconditioner and adaption of the domain decomposition. Experiments for a linear problem in 2D and for a single direction of the anisotropy show that an enlargement of the coarse problem (primal constraints) can improve convergence. In principle, when using a very large coarse space, independence of the anisotropy can be achieved. Unfortunately, these coarse spaces are too large to be efﬁcient for 3D problems within the context of a standard FETI-DP algorithm [4]. An inexact FETI-DP [5] variant could be considered instead but then a good preconditioner for the (anisotropic) coarse problem has to be constructed. For standard FETI-DP, we therefore investigate the adaption of the domain decomposition to the anisotropic problem. In geometric multigrid methods semi-coarsening and line relaxation have classically
Oxylipins as Biomarkers for Aromatase Inhibitor-Induced Arthralgia (AIA) in Breast Cancer Patients

Aromatase inhibitor-induced arthralgia (AIA) presents a major problem for patients with breast cancer but is poorly understood. This prospective study explored the inflammatory metabolomic changes in the development of AIA. This single-arm, prospective clinical trial enrolled 28 postmenopausal women with early-stage (0–3) ER+ breast cancer starting adjuvant anastrozole. Patients completed the Breast Cancer Prevention Trial (BCPT) Symptom Checklist and the Western Ontario and McMaster Universities Arthritis Index (WOMAC) at 0, 3, and 6 months. The plasma levels of four polyunsaturated fatty acids (PUFAs) and 48 oxylipins were quantified at each timepoint. The subscores for WOMAC-pain and stiffness as well as BCPT-total, hot flash, and musculoskeletal pain significantly increased from baseline to 6 months (all p < 0.05). PUFA and oxylipin levels were stable over time. The baseline levels of 8-HETE were positively associated with worsening BCPT-total, BCPT-hot flash, BCPT-musculoskeletal pain, WOMAC-pain, and WOMAC- stiffness at 6 months (all p < 0.05). Both 9-HOTrE and 13(S)-HOTrE were related to worsening hot flash, and 5-HETE was related to worsening stiffness (all p < 0.05). This is the first study to prospectively characterize oxylipin and PUFA levels in patients with breast cancer starting adjuvant anastrozole. The oxylipin 8-HETE should be investigated further as a potential biomarker for AIA.
Effects of Sesuvium verrucosum Raf. Compost and vermicompost on the growth and production parameters of the Solanum lycopersicum L. crop

The objectives of this work were to use Sesuvium verrucosum to produce stable and mature compost and vermicompost and to evaluate their influence on the growth and production parameters of the Solanum lycopersicum L. crop. For the preparation of vermicompost, the earthworm Eisenia fetida (Savigny) was used. The proposed treatments were: T1 (negative control) [no organic fertilizer + urea], T2 (positive control) [bovine manure (100%) + urea], T3 (compost) [bovine manure (100%) + urea], T4 (compost) [S. verrucosum (25%) + bovine manure (75%) + urea], T5 (compost) [S. verrucosum (50%) + bovine manure (50%) + urea], T6 (compost) [S. verrucosum (75%) + bovine manure (25%) + urea], T7 (vermicompost) [bovine manure (100%) + urea], and T8 (vermicompost) [S. verrucosum (25%) + bovine manure (75%) + urea]. The results showed that all the treatments were stable, mature and rich in nutrients after 140 days. In general, treatments made from S. verrucosum showed a high pH (7.97–8.45) and electrical conductivity (3.83–8.85 dS m-1). Nonetheless, excluding T7, the treatments made from a proportion of 25% halophyte and 75% bovine manure (T8 and T4) achieved the highest morphological parameters in the different variables that were evaluated, as well as the highest yields compared to controls T1 and T2, followed by T3, T5 and T6, respectively. Therefore, it was concluded that S. verrucosum can serve as a stable and mature organic amendment, rich in nutrients, and that can be used to improve the growth and development of the S. lycopersicum crop.