Miracle-dz/UniFAR-FTUs huggingface dataset:
A dynamic spatial-temporal attention ensemble model for traffic flow prediction

With the advent of the era of big data, a large number of sensors are scattered on traffic roads, generating traffic flow data, posing huge challenges to traffic flow prediction. Therefore, capturing the spatial features of traffic flow data is greatly significant to improve the accuracy of traffic flow predicted models. Analysis of spatial and temporal characteristics of traffic flow data, this paper proposes a Spatial-Temporal Attention (MA-STLGAT) model to predict the traffic speed in the traffic network. The Graph Attention Network (GAT) assigns different weights to neighbor nodes to capture the dynamic topology structure. Learning the temporal correlation of traffic flow data through Long ShortTerm Memory network (LSTM), and Multi-Head Attention is introduced to learn the contribution of nodes, improve the convergence speed and save computing costs. The experimental results on real data set PeMSD7 show that the predict performance of MA-STLGAT is better than other baseline models.
Sum Rate and Max-Min Rate for Cellular-Enabled UAV Swarm Networks

This paper investigates the fundamental rate performances in the highly promising cellular-enabled unmanned aerial vehicle (UAV) swarm networks, which can provide ubiquitous wireless connectivity for supporting various Internet of things (IoT) applications. We first provide the formulations for the sum rate maximization and max-min rate, which are two nonlinear optimization problems subject to the constraints of UAV transmit power and antenna parameters at base station (BS). For the sum rate maximization problem, we propose an iterative algorithm to solve it utilizing the Karush–Kuhn–Tucker (KKT) condition. For the max-min rate problem, we transform it to an equivalent conditional eigenvalue problem based on the nonlinear Perron-Frobenius theory, and thus design an iterative algorithm to obtain the solution of such problem. Finally, numerical results are presented to indicate the effect of some key parameters on the rate performances in such networks.
Research on stability control of multi-axle special vehicle based on active steering

In order to improve the maneuverability and stability of a certain type of multi-axle special vehicle under low adhesion coefficient and high-speed conditions, an active steering controller is designed with this type of multi-axle special vehicle as the research object. Firstly, the dynamic model of this type of special vehicle is built based on Trucksim, and the accuracy of the model is verified by real vehicle experiments, which lays a foundation for the research on active steering control. Then, an active steering controller is designed based on the Ackerman steering principle with the zero centroid slip angle as the control objective. Finally, the model of the controller is built through simulink, and the cosimulation with the vehicle dynamics model of Trucksim is carried out to verify the designed active steering controller. The simulation results show that the designed active steering controller has an ideal control effect on the roll stability and handling stability of the vehicle under high speed conditions and low adhesion road coefficient.
A systematic review and meta-analysis on blood levels of cytokines/chemokines in COVID-19 cases

We assessed the blood levels of the most important factors such as cytokines/chemokines in Coronavirus disease-2019 (COVID-19). PubMed/Medline and Scopus as two important databases were searched up to March 26, 2020. To analyze the data, we used Review Manager 5.3 software. Out of forty-two records retrieved from two databases, 10 studies were involved in the analysis. Thirty-three cytokines/chemokines were checked. The levels of 27 cytokines/chemokines in COVID-19 patients were higher than the healthy controls, and among 20 cytokines/chemokines;the levels of 10 cytokines/chemokines in severe COVID-19 patients were higher than non-severe COVID-19 patients. Also, out of three cytokines, one had a higher level in the intensive care unit (ICU) patients compared to the non-ICU patients. The findings showed the cytokine storm syndrome in COVID-19 patients, especially in patients with severe disease.
Why elasticities from demand models using different dependent variables should not be compared

ABSTRACT Tourism demand modelling is one of the most recurrent topics in tourism economics literature. However, difficulties in defining tourism as a product have led to different ways of capturing the concept of tourism demand. In this paper, the main measures used in relevant empirical literature are considered, relating them from a theoretical point of view and analysing the empirical consequences and comparability of each one's use on the elasticity values of the determinants. Special attention is paid to the price elasticity when tourism demand models, based on tourism expenditures or receipts, are compared with those that use the number of tourists. The results show that different demand measures can lead to different estimated elasticity values that are not comparable. Furthermore, in the specific case of the price elasticity, the divergence in the estimated elasticity value of the different models can be quantified.
Operational Probabilistic Power Flow Analysis for Hybrid AC-DC Interconnected Power Systems With High Penetration of Offshore Wind Energy

With increasing penetration of intermittent renewable energy, power systems are facing great pressures in real-time operation and control. Minutes ahead short-term static risk assessment using the probabilistic power flow (PPF) can help system operators detect potential over-limit risks in advance and take preventive measures in time. However, current probability models of renewable power outputs lack enough adaption and efficiency in operational stage, and the PPF formulation also neglects the effects of control system and tie line power exchange of large power systems. This paper establishes a systematic operational PPF scheme for hybrid AC–DC interconnected power systems with offshore wind energy integration. An adaptive probabilistic modeling and fast update method is proposed to model multiple time-varying offshore wind power outputs. Considering different responses of control systems and AC/DC tie line power exchanges, a novel hybrid PPF is proposed to reflect power flow uncertainty more robustly and accurately. The above techniques are validated on a modified IEEE 30 bus system and the whole scheme is applied to the short-term static risk assessment of a real system in Southern China. The results show that our proposed scheme can well meet online calculation requirements, and can also achieve a more realistic characterization of operational PPF.
Modified Fourier expansions: theory, construction and applications

Modified Fourier expansions present an alternative to more standard algorithms for the approximation of nonperiodic functions in bounded domains. This thesis addresses the theory of such expansions, their effective construction and computation, and their application to the numerical solution of partial differential equations. As the name indicates, modified Fourier expansions are closely related to classical Fourier series. The latter are naturally defined in the d-variate cube, and, in an analogous fashion, we primarily study modified Fourier expansions in this domain. However, whilst Fourier coefficients are commonly computed with the Fast Fourier Transform (FFT), we use modern numerical quadratures instead. In contrast to the FFT, such schemes are adaptive, leading to great potential savings in computational cost. Standard algorithms for the approximation of nonperiodic functions in d-variate cubes exhibit complexities that grow exponentially with dimension. The aforementioned quadratures permit the design of approximations based on modified Fourier expansions that do not possess this feature. Consequently, such schemes are increasingly effective in higher dimensions. When applied to the numerical solution of boundary value problems, such savings in computational cost impart benefits over more commonly used polynomial-based methods. Moreover, regardless of the dimensionality of the problem, modified Fourier methods lead to well-conditioned matrices and corresponding linear systems that can be solved cheaply with standard iterative techniques. The theoretical component of this thesis furnishes modified Fourier expansions with a convergence analysis in arbitrary dimensions. In particular, we prove uniform convergence of modified Fourier expansions under rather general conditions. Furthermore, it is known that the notion of modified Fourier expansions can be effectively generalised, resulting in a family of approximation bases sharing many of the features of the modified Fourier case. The purpose of such a generalisation is to obtain both faster rates and higher degrees of convergence. Having detailed the approximation-theoretic properties of modified Fourier expansions, we extend this analysis to the general case and thereby verify this improvement. A central drawback of these expansions is that their convergence rate is both fixed and typically slow. This makes the construction of effective convergence acceleration techniques imperative. In the final part of this thesis, we design and analyse a robust method, applicable in arbitrary numbers of dimensions, for accelerating convergence of modified Fourier expansions. When employed in the approximation of multivariate functions, this culminates in efficient, high-order approximants comprising relatively small numbers of terms.
Stability of Dynamical Systems: On the Role of Monotonic and Non-Monotonic Lyapunov Functions

Introduction. - Dynamical Systems. - Fundamental Theory: The Principal Stability and Boundedness Results on Metric Spaces. -Fundamental Theory: Specialized Stability and Boundedness Results on Metric Spaces. - Applications to a Class of Discrete-Event Systems. - Finite-Dimensional Dynamical Systems. - Finite-Dimensional Dynamical Systems: Specialized Results. - Applications to Finite-Dimensional Dynamical Systems. - Infinite-Dimensional Dynamical Systems.
An Assessment of Mentions of Adverse Drug Events on Social Media With Natural Language Processing: Model Development and Analysis

Background Adverse reactions to drugs attract significant concern in both clinical practice and public health monitoring. Multiple measures have been put into place to increase postmarketing surveillance of the adverse effects of drugs and to improve drug safety. These measures include implementing spontaneous reporting systems and developing automated natural language processing systems based on data from electronic health records and social media to collect evidence of adverse drug events that can be further investigated as possible adverse reactions. Objective While using social media for collecting evidence of adverse drug events has potential, it is not clear whether social media are a reliable source for this information. Our work aims to (1) develop natural language processing approaches to identify adverse drug events on social media and (2) assess the reliability of social media data to identify adverse drug events. Methods We propose a collocated long short-term memory network model with attentive pooling and aggregated, contextual representation generated by a pretrained model. We applied this model on large-scale Twitter data to identify adverse drug event–related tweets. We conducted a qualitative content analysis of these tweets to validate the reliability of social media data as a means to collect such information. Results The model outperformed a variant without contextual representation during both the validation and evaluation phases. Through the content analysis of adverse drug event tweets, we observed that adverse drug event–related discussions had 7 themes. Mental health–related, sleep-related, and pain-related adverse drug event discussions were most frequent. We also contrast known adverse drug reactions to those mentioned in tweets. Conclusions We observed a distinct improvement in the model when it used contextual information. However, our results reveal weak generalizability of the current systems to unseen data. Additional research is needed to fully utilize social media data and improve the robustness and reliability of natural language processing systems. The content analysis, on the other hand, showed that Twitter covered a sufficiently wide range of adverse drug events, as well as known adverse reactions, for the drugs mentioned in tweets. Our work demonstrates that social media can be a reliable data source for collecting adverse drug event mentions.
Effects of continuous and rotational cropping practices on soil fungal communities in pineapple cultivation

Background Rotational cropping practices can change the fungal structure and diversity of cropping soil, and these changes can promote crop development. However, only a few studies have explored the effects of rotational cropping of pineapple on soil fungal diversity. Methods In this study, we investigated fungal diversity in continuous and rotational cropping soil of pineapple in Xuwen and Leizhou of China in summer and winter through high throughput sequencing of the fungal internal transcribed spacer region. Results The diversity and richness of the fungal community were observed to be significantly increased after rotational cropping in Xuwen and Leizhou in summer, whereas no changes were observed in winter. Furthermore, Ascomycota, Basidiomycota, Zygomcota, and Chytridiomycota were the dominant phyla, and Chaetomium, Penicillium, Fusarium, Trichoderma, and Cryptococcus were the dominant genera in the continuous and rotational cropping soil of pineapple, respectively, in both summer and winter. Chytridiomycota at phylum level and Gibberella at genus level were observed in rotational cropping soil; however, Ascomycota at the phylum level and Chaetomium at the genus level were the most abundant fungi, and their abundance dramatically decreased in continuous cropping soil. Redundancy analysis revealed that rotational cropping reduced the correlation between environmental parameters and the fungal community in winter. In addition, several fungal biomarkers were found in Xuwen in both continuous and rotational cropping soil samples, including Sporobolomyces, Aspergillus, Corynascus sp JHG 2007, and Corynascus at the genus level, Penicillium and fungal sp p1s11 at the species level in rotational cropping soil, and ales family Incertae sedis and Sordariomycetes at the class level in continuous cropping soil. These results revealed the changes in the structure and diversity of fungal community in continuous and rotational cropping practices for pineapple cultivation, which may be associated with crop yield and quality.
Resolution in species distribution models shapes spatial patterns of plant multifaceted diversity

Species distribution models (SDMs) are statistical tools that relate species observations to environmental conditions to retrieve ecological niches and predict species’ potential geographic distributions. The quality and robustness of SDMs clearly depend on good modelling practices including ascertaining the ecological relevance of predictors for the studied species and choosing an appropriate spatial resolution (or ‘grain size’). While past studies showed improved model performance with increasing resolution for sessile organisms, there is still no consensus regarding how inappropriate resolution of predictors can impede understanding and mapping of multiple facets of diversity. Here, we modelled the distribution of 1180 plant species across the European Alps for two sets of predictors (climate and soil) at resolutions ranging from 100-m to 40-km. We assessed predictors’ importance for each resolution, calculated taxonomic (TD), relative phylogenetic (rPD) and functional diversity (rFD) accordingly, and compared the resulting diversities across space. In accordance with previous studies, we found the predictive performance to generally decrease with decreasing predictor resolution. Overall, multifaceted diversity was found to be strongly affected by resolution, particularly rPD, as exhibited by weak to average linear relationships between 100-m and 1-km resolutions (0.13 ≤ R 2 ≤ 0.57). Our results demonstrate the necessity of using highly resolved predictors to explain and predict sessile species distributions, especially in mountain environments. Using coarser resolution predictors might cause multifaceted diversity to be strongly mispredicted, with important consequences for biodiversity management and conservation.
Oxidative Stress, Antioxidant Enzymes and Risk of Hypertension in Obese Type 2 Diabetic Subjects in Osogbo, Osun State, Nigeria

Obesity is a chronic disease often associated with type 2 diabetes mellitus (T2DM) and metabolic syndrome. Coexistence of diabetes and hypertension have adverse clinical outcomes with micro and macrovascular complications. This study investigates the relationship of oxidative stress markers and antioxidants with occurrence of hypertension in obese type 2 diabetic subjects in Osun State, Nigeria. Four hundred and forty-five participants made up of 138 non-obese diabetics, 107 obese diabetic subjects attending two tertiary Hospitals in Osogbo in addition to 100 obese non-diabetes (positive controls) and 100 non-obese non-diabetes (negative controls) were enrolled in this study. FBS (9.55±0.13 mmol/l) and HbA1c (9.51±0.15 %) showed highest significant increase in obese diabetic subjects compared to other groups. Mean serum MDA was highest among obese diabetics (p<0.05) while least values of superoxide dismutase (121.09±3.10 µ/ml) and catalase (24.97±0.66 pg/ml) were recorded in non-obese diabetics (p<0.05). 21.0% of non-obese diabetics (n=29), 54.2% of obese diabetics (n=58), 45.0% of obese non-diabetic (n=45) and 21% of non-obese non-diabetics (n=21) were hypertensive. Age, sex, marital status, religion and occupation (χ2=9.856, 8.405, 22.701, 12.066 and 14.468) respectively were all significantly associated with occurrence of hypertension among obese diabetics. Receiver operating characteristics (ROC) and area under the curve (AUC) showed MDA having the highest cut off point among obese diabetes subject: 0.529 (0.42-0.64 95% CI) with a steep increase in LDL: 0.505(0.40-0.62 95% CI). This study revealed that increased oxidative stress and reduced antioxidant defense enzymes are strongly associated with dyslipidemia among obese diabetic subject.
Social protests, neoliberalism and democratic institutions in Chile

ABSTRACT With the case of contemporary Chile at hand, the article examines the institutional contradiction between neoliberalism and democracy as a source of social protests and popular rebellions. Chile transitioned in 1990 to a representative democracy, presumably encouraging political equality and participation. However, given the orientation of governments toward fostering capitalist accumulation, Chile did not develop mechanisms for fully incorporating into the political arena the emerging and increasingly resourceful civil society. After decades of incubation, this contradiction produced collective grievances that activated social movements and popular revolts. This coalesced in 2019 when a national-scale social uprising opened a process of constitutional change and democratic innovation. I illustrate this argument by examining contemporary student, indigenous, women and labor mobilizations. Democratic governments responded differently to the demands of these four movements depending on the extent they threatened capital accumulation and state sovereignty. I also pay special attention to the 2019 social uprising and the ongoing constitutional change process (until March 2022), which brings exciting innovations to deliberation and democracy.
Saliency-constrained semantic learning for airport target recognition of aerial images

Abstract. The airport target recognition method for remote sensing images is generally based on image matching, which is significantly affected by the variations of illumination, viewpoints, scale, and so on. As a well-known semantic model for target recognition, bag-of-features (BoF) performs k-means clustering on enormous local feature descriptors and thus generates the visual words to represent the images. We propose a fast automatic recognition framework for an airport target of a low-resolution remote sensing image under a complicated environment. It can be viewed as a two-phase procedure: detection and then classification. Concretely, it first utilizes a visual attention model for locating the salient region, and then detects possible candidate targets and extracts saliency-constrained scale invariant feature transform descriptors to build a high-level semantics model. Consequently, BoF is applied to mine the high-level semantics of targets. Different from k-means in a traditional BoF, we employ locality preserving indexing (LPI) to obtain the visual words. Because LPI can consider the intrinsic local structure of descriptors and further enhance the ability of words to describe the image content, it can accurately classify the detected candidate targets. Experiments on the dataset of 10 kinds of airport aerial images demonstrate the feasibility and effectiveness of the proposed method.
Numerical Modeling Efforts in Support of 3-D Environmental Variability and Acoustic Vector Field Studies

Abstract : The goals of this research included the study of the effects of three-dimensional (3-D) environmental variability on the flow of energy in the complex intensity field, and two-dimensional (2-D) scattering from evolving rough surfaces. In the former case, the fully 3-D forward problem was modeled in order to gain a deeper understanding of the impact of shallow water features, such as solitons, on the propagation of acoustic energy. In the latter, broadband rough surface scattering was computed by incorporating sophisticated models of an evolving one-dimensional (1-D) rough surface into the propagation model. In addition to characterizing the statistics of the scattered field, the evolving surface will allow direct analysis of the associated Doppler from rough surface scattering. OBJECTIVES The overall objective of this work was to study the three-dimensional response of the acoustic vector field in the presence of environmental variability, and the two-dimensional scattering response of the acoustic field due to an evolving rough surface. APPROACH For the 3-D propagation portion of this work, a previously developed technique for computing the acoustic vector field from a PE model,[1,2,3,5,6] was expanded to generate solutions in 3-D environments using a Cartesian coordinate system. The model was then employed at the Univ of Rhode Island to produce vector field data in a 3-D shallow water environment which incorporated perturbations due to shallow water non-linear internal wave structures.
An Ethnic Security Dilemma in Bosnia-Herzegovina: Civic Pride and Civics Education

Abstract This article attempts to explore the link between education and democracy. Education is supposed to serve as a unifying factor and socialization agent among citizens of a state; teaching them who they are and what their country expects of them. The role of the educational system is important for the state in building a civic identity and patriotism among students. In Bosnia-Herzegovina (BiH), students complete a “Democracy and Human Rights” civics education course in primary and secondary schools; however, the current pedagogical implementation of instilling a civic identity is low, with little attention paid to civic identity promotion. This article examines the notions of civic pride and education among high school seniors in BiH via statistical analysis of original field data (n=5,749 surveys; 78 high schools in 53 towns). Identity politics and ethnic saliency are explored, with concluding views on the lack of (perceived) rights among the Croat student population. Cross-cutting cleavages and interpersonal trust are low, with the ethnic promoted over the civic.