Miracle-dz/UniFAR-FTUs huggingface dataset:
Bapang Mask's Symbolic Power in the Attempt to Break Muri's Record in Malang District

Bapang mask is one of the figures in Malang’s puppet show, which is unique compared to other masks. It has a long nose and a sharp, red-colored face with bulging eyes. It possesses a manly character and enjoys flattery, and is a symbol of the legitimacy of the Malang district government’s power. The symbol of the Bapang mask in MURI’s record-breaking attempt is a practice of power that legitimizes culture in dominating the domain of a discourse struggle (between politics and preserving culture). This study describes Bapang mask’s symbolic power in breaking the MURI’s record in Malang district. This qualitative study used interviews, document analysis, and observations, which were analyzed using a poststructuralist approach through Pierre Bourdieu’s theory of practice. The results of the study revealed that the symbolic system through the discourse of preserving Bapang masks in MURI’s record-breaking attempt is a representation of a symbolic system capable of producing power on political and cultural domination. This symbolic power forms the legitimacy of truth in the attempt to preserve traditional arts using Bapang mask. Keywords: symbolic power, discourse, preserving
Contractions of subcurves of families of log curves

Abstract Let C be a nodal curve, and let E be a union of semistable subcurves of C. We consider the problem of contracting the connected components of E to singularities in a way that preserves the genus of C and makes sense in families. In order to do this, we introduce the notion of mesa curve, a nodal curve C with a logarithmic structure and a piecewise linear function on the tropicalization of C. We then construct a contraction of the support of inside of C for families of mesa curves.
Gut microbial community response to herbicide exposure in a ground beetle

Gut microbiota plays a key role in physiological processes of insects, including nutritional metabolism, development, immunity and detoxification. Environmental stressors such as herbicides, used to optimize and improve crop yields, may interfere with the mutualistic relationships causing negative consequences for the host health. Dinitroaniline herbicides, for example pendimethalin, are used worldwide in pre‐emergence application to control grass and some broadleaf weeds. They target microtubules to arrest cell division and inhibit the development of roots and shoots. Effects of a pendimethalin‐based herbicide were assessed on the gut microbial community of Pterostichus melas italicus Dejean, 1828 (Coleoptera, Carabidae). The exposure effect was tested in vivo by using a recommended field rate (4 L per ha, 330 gL−1 of active ingredient) and evaluating the variability of responses in 21 days, corresponding to the half‐life of pendimethalin. The 16S rRNA sequencing data showed that the gut lumen was dominated by Proteobacteria, Firmicutes, Fusobacteria, Tenericutes and Bacteroidetes. The exposure interfered with the bacterial community richness and diversity associated with the gut from 2 days after the treatment. The differential abundance analyses highlighted a shift involving Lactobacillaceae, Streptomycetaceae, Neisseriaceae, Ruminococcaceae and Enterobacteriaceae. An increase in species such as Enterobacter sp. , Pseudomonas sp. , Pantoea sp and Paracoccus sp. involved in the herbicide degradation was also recorded after 21 days of exposure. Phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) analysis indicated that the exposure has effects on the most predicted functional categories of gut microbiota related to metabolic function including carbohydrate, amino acid and lipid metabolism. These results demonstrate that pendimethalin can impact microbial communities associated with generalist predators inhabiting croplands leading to severe implications for the species’ ecological role. Understanding the effects of herbicides such as pendimethalin on ground beetles may help to protect beneficial soil insects that have a crucial role in the ecosystem services.
Does online investor sentiment impact stock returns? Evidence from the Chinese stock market

ABSTRACT The authors use crawler software to capture the data of investor post content and reading volume from the stock bar of Eastmoney and then utilize textual analysis method to construct positive and negative investor sentiment indicators. The authors use VAR model to examine the Granger causality between investor sentiment and stock returns for the Chinese stock market. The results indicate that negative sentiment indicators have more explanatory power in determining stock returns than positive sentiment indicators. Evidence from impulse responses also shows that investor sentiment reacts to stock returns in short-term.
Vehicle Re-identication in Multi-branches Network

Vehicle re-identification (Re-ID) aims to solve the problem of matching and identifying the same vehicles under the scene of cross multiple surveillance cameras. Finding the target vehicle quickly and accurately in the massive vehicle database is extremely important for public security, traffic surveillance and applications on smart city. However, it is very challenging due to the orientation variations, illumination changes, occlusion, low resolution, rapid vehicle movement, and amounts of similar vehicle models. In order to overcome these problems and improve the accuracy of vehicle re-identification, a multi-branches network is proposed, which is integrated by global-local feature fusion, channel attention mechanism, and weighted local feature. First, the fusion of global and local features is to obtain more complete information of the vehicle and enhance the learning ability of the model; second, the purpose of embedding the channel attention module in the feature extraction branch is to extract the personalized feature of the vehicle; finally, the influence of sky area and noise information on feature extraction is weakened by weighted local feature. The comprehensive experiments implemented on the mainstream evaluation datasets including VeRi-776, VRIC, and VehicleID indicate that our method can effectively improve the accuracy of vehicle re-identification and is superior to the state-of-the-art methods.
Dynamic Scheduling Algorithm in Cyber Mimic Defense Architecture of Volunteer Computing

Volunteer computing uses computers volunteered by the general public to do distributed scientific computing. Volunteer computing is being used in high-energy physics, molecular biology, medicine, astrophysics, climate study, and other areas. These projects have attained unprecedented computing power. However, with the development of information technology, the traditional defense system cannot deal with the unknown security problems of volunteer computing . At the same time, Cyber Mimic Defense (CMD) can defend the unknown attack behavior through its three characteristics: dynamic, heterogeneous, and redundant. As an important part of the CMD, the dynamic scheduling algorithm realizes the dynamic change of the service centralized executor, which can enusre the security and reliability of CMD of volunteer computing . Aiming at the problems of passive scheduling and large scheduling granularity existing in the existing scheduling algorithms, this article first proposes a scheduling algorithm based on time threshold and task threshold and realizes the dynamic randomness of mimic defense from two different dimensions; finally, combining time threshold and random threshold, a dynamic scheduling algorithm based on multi-level queue is proposed. The experiment shows that the dynamic scheduling algorithm based on multi-level queue can take both security and reliability into account, has better dynamic heterogeneous redundancy characteristics, and can effectively prevent the transformation rule of heterogeneous executors from being mastered by attackers.
Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

Knowing the expected crop yield in the current growing season provides valuable information for farmers, policy makers, and food processing plants. One of the main benefits of using reliable forecasting tools is generating more income from grown crops. Information on the amount of crop yielding before harvesting helps to guide the adoption of an appropriate strategy for managing agricultural products. The difficulty in creating forecasting models is related to the appropriate selection of independent variables. Their proper selection requires a perfect knowledge of the research object. The following article presents and discusses the most commonly used independent variables in agricultural crop yield prediction modeling based on artificial neural networks (ANNs). Particular attention is paid to environmental variables, such as climatic data, air temperature, total precipitation, insolation, and soil parameters. The possibility of using plant productivity indices and vegetation indices, which are valuable predictors obtained due to the application of remote sensing techniques, are analyzed in detail. The paper emphasizes that the increasingly common use of remote sensing and photogrammetric tools enables the development of precision agriculture. In addition, some limitations in the application of certain input variables are specified, as well as further possibilities for the development of non-linear modeling, using artificial neural networks as a tool supporting the practical use of and improvement in precision farming techniques.
Invertebrates found on floating Sargassum horneri (Fucales, Sargassaceae) under the Kuroshio Current in the Pacific Ocean

ABSTRACT The ecology and biogeography of the invertebrate epifauna related to floating Sargassum in the East China Sea were studied. Floating algal rafts composed of only Sargassum horneri (Turner) C. Agardh were collected in the East China Sea in March 2012 when the rafts were distributed not only on the continental shelf but also, unusually, south and east of the Kuroshio Current in the Pacific Ocean. In total, 71 samples collected at 11 stations were studied. Of the 11 invertebrate taxa recognized, harpacticoid copepods were most abundant, followed by malacostracans, and cirripedes. The densities of these three taxa were higher than those of other invertebrates. The number of invertebrate taxa was not correlated with the algal wet weight. There were more invertebrate taxa in the coastal area around Amami-Oshima Island than in other areas. The trajectories of drifting buoys indicated that the algal rafts collected in this area floated longer than those in other areas, allowing the colonization of invertebrates from the surrounding water.
Influence of cloud microphysical parameterization on the characteristics of monsoon depressions over the Indian region

Indian summer monsoon (ISM) low‐pressure systems are considered as the lifeline for seasonal monsoon rainfall over the Indian region. However, the current models have limitations in predicting their characteristics and rainfall. This study assesses the influence of five cloud microphysical parameterization Schemes (MP) on simulations of 14 monsoon deep depressions (DD) using the weather research and forecasting (WRF) model. The simulations are carried out with a lead time up to 96 hr in a nested domain resolution of 27, 9, and 3 km. The five cloud microphysical parameterizations, that is, WRF Double moment (WDM6), WRF single moment (WSM6), Milbrandt (MIL), Thompson (THOM), and Aerosol Aware Thompson (AAT), are considered in this study, leading to a total of 70 simulations. The results are validated through composite analysis at a radial distance of 300 km from the respective storm centres. The choice of MP significantly impacts the key characteristics of the monsoon DDs such as rainfall, wind, temperature, moisture, humidity, hydrometeors, and associated convective processes. In terms of rainfall, it is found that WDM6 (AAT) has the best (worst) performance with a lead time up to day‐4. Besides, WDM6 has simulated the best result in terms of temperature and specific humidity. Examining the hydrometeors distribution around the storm intense convective region (i.e., 300 km), it is noted that frozen hydrometeors (i.e., ice, snow, and graupel) are mainly modulating the rainfall. There is a general tendency to overestimate snow and graupel among MP except WDM6. Further, ice hydrometeors are well represented in WDM6 compared with others leading to better rainfall forecast. The moisture flux convergence and absolute vorticity are two major mechanisms determining the convection within the storm's core zone, and WDM6 stands out the best among these schemes. The findings of this study are relevant and have direct consequences to the operational applications.
“It’s about Building a Network of Support”: Australian Service Provider Experiences Supporting Refugee Survivors of Sexual and Gender-Based Violence

Abstract Refugees face a heightened risk of sexual and gender-based violence during conflict, transit and in countries of refuge. Despite long-term impacts on survivors, scholarship regarding effective responses remains limited. This paper presents a qualitative thematic analysis from in-depth interviews with 22 Australian service providers. Using a socio-ecological intersectional framework it explores the challenges faced in supporting survivors while highlighting features of effective practice. Findings emphasize the need for a holistic and integrated approach, responsive to socio-economic and immigration conditions, the impact of stigma and fear of authorities, service-system funding models, and the need for an appropriately supported culturally diverse workforce.
Bacterial Community and Its Response
to Environmental Factors in River Sediments
from Downtown of Tianjin, China

Bacterial community in urban river sediments are important participants in the biogeochemical cycles of nutrients. Although the bacterial community in sediments and its response to environmental factors have been studied in individual river or branch, knowledge of that in urban river network is limited. The purpose of this study was to characterize the bacterial community in sediments from urban river network from downtown of Tianjin, China. Bacterial community was investigated using highthroughput sequencing technology and the response of bacterial community to environmental factors was revealed by redundancy analysis (RDA). The predominant bacteria phyla in urban river network were Proteobacteria (39.1%), Bacteroidetes (13.3%) and Chloroflexi (10.4%). The diversity and richness indexes of bacterial community in sediments from low urbanized area were higher than that from high urbanized area. The bacterial community in sediments from different area exhibited significant spatial differences, which was also related to urbanization. Particle size and ion extractable nitrogen (IEF-N) were identified as the main factors influencing the bacterial community in the urban river network. The results indicated that anthropogenic activity played important role in bacterial community and diversity in the urban river network.
Improved RSSD-Based Source Localization With Unknown Sensor Position Errors

In this letter, a received signal strength difference (RSSD) approach is presented to localize a source with unknown transmit power in the presence of sensor position errors. The performance of conventional least squares (LS) algorithms is degraded because they consider measurement errors rather than estimation errors. An algorithm is presented here to overcome this problem. First, a robust minimax mean squared error (MSE) estimator is developed based on the estimation error for bounded location estimation and sensor position errors to minimize the worst case sum of the variance and squared norm of the bias. This nonlinear problem is solved by transforming the nonconvex objective function into a convex optimization problem using the S-procedure, relaxation, and semidefinite programming. This problem is extended to the unknown path loss exponent case. Necessary and sufficient conditions are given for convergence of the proposed RSSD convex relaxation of MSE semidefinite programming (RCRM-SDP) approach. Simulation results are presented which confirm the robustness of this method for sufficiently large signal to noise ratios (SNRs).
Thermodynamic Properties of Actinide-Zirconium Dioxide Solid-Solutions Relevant for Advanced Nuclear Fuels

Currently, spent nuclear fuel (SNF) from commercial reactors is composed of 95-99% UO{sub 2} and 1-5% fission products and transuranium elements. Thus, the primary waste form is the UO{sub 2} matrix, which over time will corrode to a variety of U(VI)-secondary phases. Alternative nuclear fuels, such as inert-matrix fuels and mixed-oxide fuels, have been studied for their in-reactor performance; however little research has been conducted to understand the behavior of these fuels as a wasteform. We use density functional theory and Monte-Carlo methods to understand the solid solution behavior of (Ac, Zr)O{sub 2} (Ac = Th, U, Np, Pu) phases. The end members of interest include ZrO{sub 2}, ThO{sub 2}, UO{sub 2}, NpO{sub 2}, and PuO{sub 2}, and all share the cubic-fluorite structure. The excess enthalpy of mixing ({delta}H{sub excess}), excess Gibbs free energy of mixing ({delta}G{sub excess}), and excess configurational entropy ({delta}S{sub excess}) are calculated for the above solid solution series, and from {delta}G{sub excess}, miscibility gaps are identified. In conclusion: Density functional theory and Monte Carlo simulations were used in this study to determine the thermodynamic properties of binary oxide solid solutions that are useful for advanced nuclear fuel applications. The DFT results for each solid solution seriesmore » is of the single cubic unit cell; therefore, only one interaction parameter was calculated. Based on the Monte-Carlo results, on the order of 25% Zr can be incorporated into the AcO{sub 2} structures (Ac = Th, U, Np, or Pu). However, only trace amounts of the actinides can be incorporated into ZrO{sub 2}. Considering larger systems in the DFT calculations will improve results, because more cation-cation interaction parameters can be calculated. Configurational ordering can also be considered with larger systems. Molecular dynamic simulations will be used to determine the temperature dependence of the different solid solutions that is more specific to their respective lattice dynamics. (authors)« less
A New Method for Temperature of the Kindling Point based on Thermal Balance

In order to confirm the actual temperature of the kindling point simply and truly,the new method for confirm the temperature of the kindling point is puts forward.Utilizing the combustion and pyrogenation thermal-gravity experiment data of Zhanjiang Power plant,it is analyzed that the new method,the constant weightlessness rate method,TG(thermal-gravity) dividing point method,DTG(differential thermal-gravity) dividing point method and TG-DTG dividing point method confirmed the temperature of the kindling point.It is obtained that the results of the new method accord with those of the constant weightlessness rate method for high-volatilization coals and are more close to the actual temperature of the kindling point for low-volatilization coals;and the results of the new method accord with the TG-DTG dividing point method for any coals and the new method is more simple.
A methodology for design and simulation of soft grippers

Soft grippers are a growing type of end-effectors that are developed to balance out the simplicity and versatility of the grippers. Today, however, literature lacks a promising method for designing accurate, durable, and high-payload soft grippers. This paper therefore identifies the essential steps in order to design such grippers, and proposes a framework that links the essential steps in order to optimize the design of soft grippers according to the requirements. Finally, functionality and performance of the design grippers are experimentally verified and analyzed via a case study of a real industrial application.
An ensemble‐based predictive mutation testing approach that considers impact of unreached mutants

Predictive mutation testing (PMT) is a technique to predict whether a mutant is killed, using machine learning approaches. Researchers have proposed various methods for PMT over the years. However, the impact of unreached mutants on PMT is not fully addressed. A mutant is unreached if the statement on which the mutant is generated is not executed by any test cases. We aim at showing that unreached mutants can inflate PMT results. Moreover, we propose an alternative approach to PMT, suggesting a different interpretation for PMT. To this end, we replicated the previous PMT research. We empirically evaluated the suggested approach on 654 Java projects provided by prior literature. Our results indicate that the performance of PMT drastically decreases in terms of area under a receiver operating characteristic curve (AUC) from 0.833 to 0.517. Furthermore, PMT performs worse than random guesses on 27% of the projects. The proposed approach improves the PMT results, achieving the average AUC value of 0.613. As a result, we recommend researchers to remove unreached mutants when reporting the results.
Brain Tumor Detection based on Ensemble Learning

In this paper, we propose methods for brain tumor detection in MRI images based on ensemble learning. We build upon prior research on ensemble methods by testing the concatenation of pre-trained models: features extracted via transfer learning are merged and segmented by classification algorithms or a stacked ensemble of those algorithms. The proposed approach achieved accuracy scores of 0.98 , outperforming a benchmark VGG-16 model. Considerations to granular computing are given in the paper as well.
The millennial customer journey: a Phygital mapping of emotional, behavioural, and social experiences

The wider possibility of connectivity offers additional opportunities for customers to experience value propositions. The online world is only one side of the customer experience. The integration of digital technologies, social presence and physical elements increases the complexity of customer journey. This paper aims to map the phygital customer journey by focusing on millennials. ,The study adopted a qualitative methodology to investigate 50 millennials from Italy. Millennials had to describe, in two phases, a journey they had recently made. First, they used sticky notes with no restrictions on expressing their feelings and structuring their CJ. Second, customers transferred the sticky notes’ contents, consider the information provided and map the journey with additional details using the Uxpressia software. ,This paper frames the Millennials customer journey as a cycle of four moments: connect, explore, buy and use. Each moment enacts the customer experience as a mixture of emotional, behavioural and social responses. Online and offline interactions blur the boundaries between the physical and digital world (i.e. phygital): millennials move back-and-forth or jump from one action to another according to the evolving path of emotions and interactions. ,The phygital customer journey provides an alternative understanding of customer journey occurring as a fuzzy process or loop. A phygital map develops as a circular path of moments seen as phenomenological microworlds of events, interactions, relationships and emotions.