Miracle-dz/UniFAR-FTUs huggingface dataset:
Sounds like meritocracy to my ears: exploring the link between inequality in popular music and personal culture

ABSTRACT Extant research documents the impact of meritocratic narratives in news media that justify economic inequality. This paper inductively explores whether popular music is a source of cultural frames about inequality. We construct an original dataset combining user data from Spotify with lyrics from Genius and employ unsupervised computational text analysis to classify the content of the 3,660 most popular songs across 23 European countries. Drawing on Lizardo’s enculturation framework, we analyze lyrics through the lens of public culture and explore their link with individual beliefs as a reflection of personal culture. We find that, in more unequal societies, songs that frame inequalities as a structural issue (lyrics about ‘Struggle’ or omnipresent ‘Risks’) are more popular than those adopting a meritocratic frame (songs we describe as ‘Bragging Rights’ or those telling a ‘Rags to Riches’ tale). Moreover, we find that the presence in public culture of a certain frame is associated with the expression of frame-consistent individual beliefs about inequality. We conclude by reflecting on the promise of automatic text classification for the study of lyrics, the theorized role of popular music in the study of culture, and by proposing venues for future research.
Patch-U-Net: tree species classification method based on U-Net with class-balanced jigsaw resampling

ABSTRACT Automatic tree species classification based on remote-sensing images can significantly improve the efficiency of tree species investigation and save considerable cost of human labor. A fully convolutional network (FCN) can automatically extract tree species-related features to achieve higher classification performance. However, this kind of method needs a large quantity of training data. Due to few samples and unbalanced sample distribution of tree species remote-sensing images, directly applying FCN to tree species image classification task could not achieve good results. We proposed Patch-U-Net to tackle the above problem. Our method adopts the class-balanced jigsaw resampling strategy to explicitly balance inter-class distribution and augment data in patch-wise. Besides, it extracts multi-scale information of each patch by combining the encoder–decoder and skip connection structure. We compared Patch-U-Net with six existing methods, and Patch-U-Net achieved the best performance. Specifically, Pixel Accuracy (PA), Mean Intersection over Union (MIoU), and Frequency Weighted Intersection over Union (FWIoU) of Patch-U-Net are 80.33%, 57.46%, and 67.37%, which are 14.3%, 33.16%, and 17.81% higher than those of the baseline model U-Net, respectively. The results show that Patch-U-Net can improve the performance of remote-sensing tree species classification by solving the problem of unbalanced samples, which is more suitable for the remote-sensing image classification of tree species with imbalance species.
Experimental verification of predicted capability of a wind turbine drivetrain test bench to replicate dynamic loads onto multi-megawatt nacelles

A total of 27 test profiles from the IEC 61400-1 design load cases were tested using a 7.5-MW wind turbine drivetrain test bench and two multi-megawatt wind turbine drivetrains. Each test profile consisted of simultaneous vertical, lateral, and longitudinal forces, yawing and nodding bending moment, and rotational speed. These test-bench inputs were compared with the forces, bending moments, and speed that were applied to the wind turbine drivetrains to quantify the test-bench tracking error. This tracking error was quantified for a range of ramp-rate limits of the yawing and nodding bending moments. The experimental results were compared with predictions from an evaluation method for the capability of wind turbine drivetrain test benches to replicate dynamic loads. The method’s predictive capability was found to be sufficient for the goal of early screening and its formulation is applicable to any wind turbine drivetrain test bench and drivetrain design.
The Influence of Price, Service Quality, and Word Of Mouth on the Purchase Decision of PT Daniel Samudra Abadi's Loading and Unloading Services

PT Daniel Samudra Abadi is a company engaged in stevedoring services. The services provided are specialized in products, such as steel (cold rolled steel sheet, steel sheet in coil, steel plate, prime concast steel slab, etc.), pulp, and general cargo. The purpose of this research is to determine the influence of price, service quality and word of mouth on the purchase decision of stevedoring service. The population in this research are consumers of PT Daniel Samudra Abadi who have used service from 2018-2020 period with 31 companies as a sample. Determination of the sample in this research using saturated samples. This research uses multiple linear regression analysis. The results showed that all independent variables, namely price, service quality and word of mouth have a positive and significant effect on the purchase decision of PT Daniel Samudra Abadi stevedoring service.
Research on Multi-objective Optimal PMU Placement Based on Error Analysis Theory and Improved GASA

Aiming at enhancing rationality and efficiency of optimal PMU placement, the paper proposed a new error analysis method and use improved Genetic-simulated Annealing Algorithm to optimize the PMU placement in several IEEE standard test systems. The new error analysis method takes both measurement redundancy and state estimation accuracy into account without calculating Jacobian matrix, and can also obtain the network observability. The research results prove that the method and algorithm used here can not only find all solutions of PMU placements satisfying full network observability, but also make a further progress in whole system measurement accuracy, and thus, its effectiveness and advantages can be varified.
Feature extraction of dance movement based on deep learning and deformable part model

In complex scenes, the accuracy of dance movement recognition is not high. Therefore, this paper proposes a deep learning and deformable part model (DPM) for dance movement feature extraction. Firstly, the number of filters in DPM is increased, and the branch and bound algorithm is combined to improve the accuracy. Secondly, deep neural network model is used to sample points of interest according to human dance movements. The features extracted from the DPM and deep neural network are fused. It achieves a large reduction in the number of model parameters and avoids the network being too deep. Finally, dance movement recognition is performed on the input data through the full connection layer. Experimental results show that the proposed method in this paper can get the recognition result more quickly and accurately on the dance movement data set.
More than a feeling: Cognitive beliefs and positive—but not negative—affect predict overall attitudes toward predators

Attitudes, which can be thought of as the sum of individuals' thoughts, feelings, and beliefs concerning an attitude object, inform how people interact with the world around them. An understanding of attitudes may play an important role in promoting desirable human behavior, and attitudes studies should be incorporated into any behavior‐change intervention. One framework for understanding attitudes is the ABC, or “tripartite” model, which says that affect (“A,” i.e., emotional response); previous behavior (“B”); and cognition (“C,” i.e., beliefs) are the basis for an individual's attitude. Although this framework is widely used in social psychology, few conservation studies break down the “attitude” monolith into these more usable components. In this study, we sought apply the ABC framework to understand how affect and cognitive beliefs relate to overall attitude toward predators across southern Kenya and north‐west Zimbabwe. We used a factor analysis approach to identify latent affective (n = 3) and cognitive (n = 3) factors relating to human–predator interactions. These factors were then used to construct a regression model, which examined the power of the psychological factors to explain overall attitudes toward predators. We found that the model explained 27% of variation in attitudes, with four independently significant factors: location; perceived harms of living with predators; perceived benefits of killing predators; and positive affect. Although effect sizes were relatively small, these results suggest that cognitive beliefs may substantially influence attitudes, and therefore that interventions which seek to (a) remove the factual basis for negative beliefs and (b) improve perceptions or dispel unfounded beliefs may play a significant role in changing overall attitudes toward predators.
Two-Stage Factorized Power Architecture DC–DC Converter for Spacecraft Secondary Power Supply System

In this article, we propose a two-stage factorized power architecture (FPA) dc–dc converter, which caters to applications that require high efficiency, high power density, low ripple, and high dynamic response of space-borne low-voltage and high-current power supply. The first-stage 48 V intermediate bus converter (IBC) adopts the six-phase interleaving magnetic integration buck converter (IMIBC). A new array high symmetry multi-phase coupled inductor (CI) is constructed, and a general zero-voltage switching (ZVS) control strategy is proposed. The designed IBC provides 2.4 kW power with a density of up to 1400 W/in3 and efficiency up to 97.15%. The second-stage point of load converter (POLC) adopts the four-phase interleaving magnetic integration LLC dc/dc transformer (DCX). A new method of current sharing, which is based on the inverse coupling resonant inductor, is proposed. This method yields good current sharing under a 15% mismatch of resonance parameters. A highly symmetrical four-phase resonant CI and a four-phase magnetic integration transformer are developed; the design of magnetic components is optimized by constructing a high-precision magnetic circuit mode. The designed POLC has a power density of up to 700 W/in3 and an efficiency of up to 97.1%. Finally, the experimental prototype is fully tested against a similar type of Vicor’s products. According to the results, the power supply designed in this article has several benefits with regard to efficiency, power density, transient response speed, and ripple.
The good ol’ days: White identity, racial nostalgia, and the perpetuation of racial extremism

A prevailing theme in White nationalist rhetoric is nostalgia for a time when Whites dominated American culture and had unchallenged status. The present research examines a form of collective nostalgia called racial nostalgia and its association with negative intergroup attitudes and extreme ideologies (White nationalism). In Studies 1 and 2, racial nostalgia was associated with higher racial identity, anti-immigrant attitudes, and White nationalism. Study 2 revealed that racial nostalgia was related to extreme ideologies, in part, through perceptions that immigrants and racial minorities posed realistic/symbolic threats. Study 3 manipulated nostalgia using a writing prompt (“America’s racial past” vs. “games of America’s past”) and an identity prime (prime vs. no prime). Racial nostalgia was higher in the racial prompt versus the games prompt condition, regardless of identity prime. Furthermore, there were significant indirect effects of the nostalgia manipulation on support for anti-immigrant policies and endorsement of White nationalism through increased racial nostalgia and its association with perceived threats. These findings show that racial nostalgia can be a maladaptive form of collective nostalgia linked to a sense of loss and threat, and can make people sympathetic to extreme racial ideologies.
Applied Research in the Deformation Monitoring Based on Elman Neural Network Method

As a rapid development of nonlinear science in dealing with some background unclear and extremely complex information,neural network will show its unique superiority.This article applies Elman neural network to deformation monitoring of landslide to set up the forecasting model and Matlab neural network toolbox of MATLAB program design is applied to concrete examples.Through the model of prediction accuracy,the Elman neural network model to landslide monitoring and forecasting of feasibility is verified.
Mechanism and Structural Insights Into a Novel Esterase, E53, Isolated From Erythrobacter longus

Esterases are a class of enzymes that split esters into an acid and an alcohol in a chemical reaction with water, having high potential in pharmaceutical, food and biofuel industrial applications. To advance the understanding of esterases, we have identified and characterized E53, an alkalophilic esterase from a marine bacterium Erythrobacter longus. The crystal structures of wild type E53 and three variants were solved successfully using the X-ray diffraction method. Phylogenetic analysis classified E53 as a member of the family IV esterase. The enzyme showed highest activity against p-nitrophenyl butyrate substrate at pH 8.5–9.5 and 40°C. Based on the structural feature, the catalytic pocket was defined as R1 (catalytic center), R2 (pocket entrance), and R3 (end area of pocket) regions. Nine variants were generated spanning R1–R3 and thorough functional studies were performed. Detailed structural analysis and the results obtained from the mutagenesis study revealed that mutations in the R1 region could regulate the catalytic reaction in both positive and negative directions; expanding the bottleneck in R2 region has improved the enzymatic activity; and R3 region was associated with the determination of the pH pattern of E53. N166A in R3 region showed reduced activity only under alkaline conditions, and structural analysis indicated the role of N166 in stabilizing the loop by forming a hydrogen bond with L193 and G233. In summary, the systematic studies on E53 performed in this work provide structural and functional insights into alkaliphilic esterases and further our knowledge of these enzymes.
New Prognostic Frontiers for Lung Neuroendocrine Tumors

Purpose Well-differentiated lung neuroendocrine tumors (Lu-NET) are classified as typical (TC) and atypical (AC) carcinoids, based on mitotic counts and necrosis. However, prognostic factors, other than tumor node metastasis (TNM) stage and the histopathological diagnosis, are still lacking. The current study is aimed to identify potential prognostic factors to better stratify lung NET, thus, improving patients’ treatment strategy and follow-up. Methods A multicentric retrospective study, including 300 Lung NET, all surgically removed, from Italian and Spanish Institutions. Results Median age 61 years (13-86), 37.7% were males, 25.0% were AC, 42.0% were located in the lung left parenchyma, 80.3% presented a TNM stage I-II. Mitotic count was ≥2 per 10 high power field (HPF) in 24.7%, necrosis in 13.0%. Median overall survival (OS) was 46.1 months (0.6-323), median progression free survival (PFS) was 36.0 months (0.3-323). Female sex correlated with a more indolent disease (T1; N0; lower Ki67; lower mitotic count and the absence of necrosis). Left-sided primary tumors were associated with higher mitotic count and necrosis. At Cox multivariate regression model, age, left-sided tumors and nodal (N) positive status resulted independent negative prognostic factors for OS and PFS. Conclusions This study confirms the prognostic relevance of TNM stage and diagnosis to stratify LuNET. However, the current analysis suggests a wider spectrum of clinical and pathological prognostic factors, including age and primary tumor’s location. These parameters could help clinicians to personalize the management of Lu-NET.
Geometry and Topology Optimization of Switched Reluctance Machines: A Review

Switched reluctance machines (SRMs) have recently attracted more interest in many applications due to the volatile prices of rare-earth permanent magnets (PMs) used in permanent magnet synchronous machines (PMSMs). They also have rugged construction and can operate at high speeds and high temperatures. However, acoustic noise and high torque ripples, in addition to the relatively low torque density, present significant challenges. Geometry and topology optimization are applied to overcome these challenges and enable SRMs to compete with PMSMs. Key geometric design parameters are optimized to minimize various objective functions within geometry optimization. On the other hand, the material distribution in a particular design space within the machine domain may be optimized using topology optimization. We discuss how these techniques are applied to optimize the geometries and topologies of SRMs to enhance machine performance. As optimizing the machine geometry and material distribution at the design phase is of substantial significance, this work offers a comprehensive literature review on the current state of the art and the possible trends in the optimization techniques of SRMs. The paper also reviews different configurations of SRMs and stochastic and deterministic optimization techniques utilized in optimizing different configurations of the machine.
Applications of the Decision Tree in Business Field

With the advent of the information age and the development of the Internet, the data show explosive growth, machine learning is becoming a more and more popular field. This paper lists some different applications of decision trees in the business fields which are related to personal credit and the stock market, The data of this paper are all collected from the official website. With two examples, two different algorithms are used to establish the models which are the C4.5 algorithm and the C5.0 algorithm respectively. Therefore, two algorithms would be discussed and compared in this paper to illustrate both their advantages and disadvantages. C5.0 is an advanced algorithm based on the invention of C4.5. For some variables with a relatively small number of indicators and simple logical relationships, the prediction accuracy of the decision tree algorithm is high, which makes this algorithm suitable for analyzing the data.
Business Analysis in Modeling of Financial Risk

The bank's main source of profit is loans, the money lent out by charging interest to make a profit, but with great risk of not being able to recover. In economic globalization, especially in the context of financial internationalization, loan risk control is always an important research topic for banks. In this paper, for the data set of bank loan risk control, we use four statistical analysis models to predict loan defaults: Logistic Regression, Random Forest, Neural Network, and XGB, respectively. Draw ROC curves of the four models and cluster the users. Meanwhile, the AUC values of the four models were calculated. Through simple comparison and analysis, we select the optimal method and probe into the effect and importance of the coefficients.
Classroom teachers' online teaching experiences during the COVID-19 pandemic: The perspective of technological pedagogical content knowledge

This qualitative study aims at evaluating the online teaching experiences of classroom teachers during the New Coronavirus Disease (COVID-19) period in terms of the Technological Pedagogical Content Knowledge (TPACK). The interviews were conducted with 17 classroom teachers within the scope of the current research. The data were collected via open-ended semi-structured interview questions. As a result of the analysis of the data, the classroom teachers considered themselves competent in the components of Technological Knowledge (TK), Content Knowledge (CK), Pedagogical Knowledge (PK) and Pedagogical Content Knowledge (PCK), they did not consider themselves competent in the Technological Content Knowledge (TCK) and Technological Pedagogical Knowledge (TPK) components, which were the result of the integration of these components with technology. When the participants were addressed in terms of the online teaching experiences, technology uses, competencies and professional training needs as a whole during the COVID-19 period, it was found that they did not use digital tools primarily for pedagogical purposes. Therefore, the teachers needed extensive pedagogical support when designing digital teaching. All of these results suggest that 21st century teachers need to develop their knowledge and skills in terms of CK, PK, TCK, and TPK that emerge with the transfer of such knowledge to technological processes and TPACK that forms the intersection of all information.
Sustainable Improvement of Residual Lateritic Soils Using Alum

Cement stabilization has gained popularity in the tropics despite its known side effects. In Nigeria, a developing nation for instance, high cost and negative environmental impacts accompanying the use of cement constitute major drawbacks raising concerns. These shortcomings often make more sustainable alternatives necessary in a depressed economy. The current study explores the effects of alum on swelling potentials and strength of fine-grained Nigerian lateritic soils with a view to ascertaining the viability of the additive as cost-effective, eco-friendly and sustainable substitute for improving engineering properties of the soils as highway embankment or pavement materials. Particle-size analysis and Atterberg limits tests conducted according to British Standards (BS 1377) and ASTM-D4318 respectively, aided in AASHTO classification of the un-treated soils as A-6 (clayey soil), A-4 (silty soil) and A-7-5 (clayey soil). These are poor to fair subgrade materials requiring stabilization. Similarly, high amounts of fines in the natural soils suggest susceptibility to recurring shrink and swell during alternating dry and wet seasons typical of tropical regions. However, alum-treated soils show reduction in plasticity with strong negative correlations. This implies reduction in swelling potentials and improvement in strength with alum treatment. Also, California bearing ratio (CBR) and shear strength, which followed the British Standards indicate strong positive correlations with alum content for the granite- and charnockite-derived soils. This indicates improved strength. Generally, the results suggest that alum has the potential to improve plastic fine-grained lateritic soils in line with the Federal Ministry of Works and Housing general specification for roads and bridges, with combined engineering, economic and environmental benefits. engineering tests were conducted, viz: consistency limits, CBR and Triaxial unconsolidated-undrained tests. The consistency limits tests followed the ASTM-D4318 specifications while the CBR and triaxial tests were conducted in accordance with the British standard, BS 1377 (1990) specifications. For the triaxial unconsolidated-undrained test, confining pressures of 100, 200 and 300 kPa were applied.