Miracle-dz/UniFAR-FTUs huggingface dataset:
The local market makers: Swedish municipalities as preschool quasi-market organisers

ABSTRACT National policies aiming at marketisation and privatisation in welfare sectors such as Early Childhood Education and Care (ECEC) require governance and organisation to be realised. In Sweden, the municipalities are key but largely under-researched organisers for preschool quasi-market infrastructures. This study explores the different ways in which Swedish municipalities act as quasi-market organisers in the preschool setting. Following organisational theory, we analyse their market shaping activities in translating national regulations in efforts to influence, support and control their local preschool quasi-market. Documents, websites, and interviews with public officials from 30 municipalities characterised as having either a large (N = 10), medium (N = 10), or small (N = 10) private ECEC sector are analysed. The analysis highlights large variations on how municipalities act as market makers, which is further discussed in the form of three ideal types: the Frontier, the Keeper, and the Endorser. We conclude that municipalities’ varying and hybridised market shaping activities and local characteristics are important to understand the implications that emerge in terms of different rules of the game, stakeholder interdependencies and relationships, composition of market actors etc. Attentiveness to the sub-national/local actors are essential in understanding different welfare quasi-markets within national policy frameworks of marketisation and privatisation.
Top-Down Effect of Arthropod Predator Chinese Mitten Crab on Freshwater Nutrient Cycling

Simple Summary Crabs are advanced predators in aquatic ecosystems, and they can feed directly on plant litter. They can also indirectly regulate the decomposition of organic matter in the aquatic environment by influencing snail populations and the structure of the substrate microbial community through top-down effects. When the invasive organism channeled apple snail (Pomacea canaliculata) meets the native species Chinese mitten crab (Eriocheir sinensis), new interspecific relationships and food web structures are created. This is of great concern in the context of climate change. Abstract Aquatic litter decomposition is highly dependent on contributions and interactions at different trophic levels. The invasion of alien aquatic organisms like the channeled apple snail (Pomacea canaliculata) might lead to changes in the decomposition process through new species interactions in the invaded wetland. However, it is not clear how aquatic macroinvertebrate predators like the Chinese mitten crab (Eriocheir sinensis) will affect the nutrient cycle in freshwater ecosystems in the face of new benthic invasion. We used the litter bag method to explore the top-down effect of crabs on the freshwater nutrient cycle with the help of soil zymography (a technology previously used in terrestrial ecosystems). The results showed significant feeding effects of crabs and snails on lotus leaf litter and cotton strips. Crabs significantly inhibited the intake of lotus litter and cotton strips and the ability to transform the environment of snails by predation. Crabs promoted the decomposition of various litter substrates by affecting the microbial community structure in the sediment. These results suggest that arthropod predators increase the complexity of detrital food webs through direct and indirect interactions, and consequently have an important impact on the material cycle and stability of freshwater ecosystems. This top-down effect makes macrobenthos play a key role in the biological control and engineering construction of freshwater ecosystems.
From a star cluster ensemble to its formation history

Abstract The present-day sample of ultra-compact dwarf galaxies (UCDs) and globular clusters (GCs) around NGC 1399 is interpreted to be composed of individual star cluster (SC) populations. It is assumed that such an SC population forms at a constant star-formation rate (SFR), and its mass distribution is described by the embedded cluster mass function (ECMF) up to the upper limit M max. The GCs and UCDs probably formed in interactions of the progenitor galaxies during the assembly of the central Fornax galaxy cluster which is why we use them as tracers of those events. After some corrections, the overall GC/UCD mass function is decomposed into separate SC populations, each described by an ECMF. M max of each ECMF is converted to an SFR according to the SFR-M max relation, revealing the SFRs reached during the assembly of galaxies in the central Fornax galaxy cluster.
Beam Loading in Plasma Accelerators

Acceleration of particles in high-phase-velocity plasma waves is attractive because of the extremely high accelerating gradients that can be obtained. 3 However, a high gradient is only one important benchmark for an accelerator. It is also important to know how many particles can be accelerated (i.e., luminosity), what will be the beam quality (i.e., emittance), and what will be the overall efficiency of the device. In this paper, we address these issues for plasma-wave accelerators. The results apply to both the beat-wave ,4 and wake-field,s,6 accelerator schemes. Previous work by R. Ruth et al. 5 has addressed emittance matching in plasma wake fields driven by unshaped electron bunches. S. Van der Meer? has considered specialized shaping of an accelerated beam to reduce its energy spread. Here we consider simultaneously the constraints on emittance and energy spread. These lead to quantitative estimates of the maximum number of particles that can be accelerated in plasma waves and the resulting efficiency. We augment the analysis with self-consistent particle-in-cell computer simulations. In order to find the maximum number of particles that can be accelerated by a plasma wave, we first review the wake field generated by a relativistic chargedparticle bunch of arbitrary shape (Section 2). The beam-loading problem can then be analyzed by applying linear superposition of the bunch wake field and the accelerating plasma-wave field. This is done for a one-dimensional approximation in Section 3 and for three dimensions in Section 4.
Mediating Role of Emotional Intelligence in the Relationship Between Resilience and Academic Engagement in Adolescents: Differences Between Men and Women

Introduction Resilience and academic engagement have become some of the most important elements in the academic context, due to their relationships with school adjustment, the protective role against risky behaviors and the well-being of adolescents. Purpose Therefore, the objective of this study was to analyze the relationships established between the variables of resilience and academic engagement, the differences according to sex, as well as to determine the mediating role of emotional intelligence in the relationship between resilience and academic engagement in adolescence. Participants and Methods A cross-sectional descriptive study was performed. The sample consisted of 802 secondary school students, with an average age of 13.65 years (SD = 1.24) (where 50.6% were women and 49.4% men) who filled out the Connor-Davidson Resilience Scale (CD-RISC 10), the General Academic Engagement Scale for Spanish Adolescents (CAADE) and the Spanish version of the Wong Law Emotional Intelligence Scale (WLEIS-S). Results The results showed the existence of positive relationships between resilience and factors of academic engagement. Furthermore, the mediation models showed the direct effect of emotional intelligence on this relationship. On the other hand, with respect to sex, men showed significantly higher averages in resilience and emotional intelligence, with no significant differences in the variable of academic engagement. Conclusion Concluding, design of emotional intelligence intervention programs in secondary is recommended as an effective measure for promoting resilience and a positive academic trajectory.
How to better estimate bunch number at vineyard level?

Despite the extensive use of sampling to estimate the average number of grape bunches per vine, there is no clearly established sampling protocol that can be used as a reference when performing these estimations. Each practitioner therefore has their own sampling protocol. This study characterised the effect of differences between sampling protocols in terms of estimation errors. The goal was to identify the most efficient practices that will improve the early estimation of an important yield component: average bunch number. First, the appropriateness of including non-productive vines (i.e., dead and missing vines) in the sampling protocol was tested; the objective was to determine whether it is relevant to estimate two yield components simultaneously. Second, sampling protocols with sampling sites of varying size were compared to determine how the spatial distribution of observations and potential spatial autocorrelation affect estimation error. Third, a new confidence interval for estimation error was determined to express expected error as a percentage. It aimed at designing a new tool for finding the best sample size in an operational context. Tests were performed on two vineyards in the South of France, in which the number of bunches per vine had been exhaustively determined on all the plants before flowering. The results show that the simultaneous estimation of number of bunches and proportion of dead and missing vines increased the estimation errors by a factor of 2. Despite the low spatial autocorrelation of bunch number, the results show that the observation must be spread across at least 2 or 3 sampling sites to reduce estimation errors. Finally, the confidence intervals expressed as a percentage were validated and used to define an adequate sample size based on a compromise between the expected precision and the variability observed in the first measurements.
Optimasi Prediksi Cryptocurrency Menggunakan Pendekatan Deep Learning

Cryptocurrency adalah mata uang digital terdesentralisasi yang diatur oleh pemerintah pusat. Karena cryptocurrency sangat fluktuatif, analisis diperlukan sebelum menggunakan cryptocurrency untuk meminimalkan kerugian. Penelitian ini melakukan perbandingan antara model Long Short Term Memory (LSTM) dan algoritma optimasi seperti Adam dan Root Mean Square Propagation (RMSProp) untuk melakukan prediksi terhadap nilai cryptocurrency. Metode LSTM dioptimasi menggunakan Adam Optimizer dan dievaluasi berdasarkan Root Mean Square Error (RMSE). Dengan demikian diperoleh prediksi nilai RMSE sebesar 0.08217562639465784 yang merupakan nilai error yang kecil sehingga mendekati nilai aktual. Sedangkan nilai RMSE 0.10699215580552895 menggunakan RMSProp mendapatkan nilai yang lebih besar yang berdampak terhadap akurasi hasil prediksi. Dengan demikian kombinasi antara algoritma LSTM dan Adam dapat melakukan prediksi dan mengoptimasi data dengan akurat.
Electricity theft detection algorithm based on contrast learning and cluster combination discrimination

Power system is susceptible to kinds of abnormal behavior of electricity consumption. Detection of electricity theft can improve the stability of power system. Aiming at the problems of dependence on label data and low accuracy of existing electricity theft detection methods, this paper proposes a theft detection algorithm based on contrastive learning and clustering combination discrimination. The algorithm is composed of representation extraction module and clustering combination discrimination module. The representation extraction module firstly uses BiGRU to extract the context information of energy consumption data, and then obtains the representation of power data based on dilated convolutional network, and uses hierarchical contrastive learning to improve the learning effect of the network. The cluster combination discrimination module is based on K-means and adaptive DBSCAN algorithm, and uses a combination discrimination mechanism to classify the representations and determine theft. In this paper, the effectiveness of the proposed method is verified by the public dataset SGCC, and the experimental results show that the proposed method is superior to other unsupervised anomaly detection methods.
Maximum quantum entropy based optimal threshold selecting criterion for thresholding image segmentation

A novel criterion of selecting optimal threshold for thresholding image segmentation is proposed in this paper. The proposed criterion employs quantum entropy to measure the amount of information contained in digital images. Quantum entropy is able to take correlations among quantum bases into the calculation of entropy, while Shannon entropy is powerless on this, namely, quantum entropy is more accurate than Shannon entropy in quantum information measurement. Therefore, the quasi-threshold which leads to maximum quantum entropy should be adopted as the optimal threshold, because the maximum amount of information is to be obtained under this circumstance. Performance of the proposed criterion is investigated, experimental results show that the proposed criterion is of better accuracy and robustness than classical criterion which is based on Shannon entropy. © 2014 Binary Information Press.
APPRAISAL OF URBANIZATION AND ITS IMPACTS ON THE ALBEDO AND CLIMATE CHANGE OF THE PUNJAB-PAKISTAN: A SPATIO-TEMPORAL ANALYSIS

The research is aimed at evaluating the process of urbanization and it’s spatio-temporal impacts on albedo and the changing climate of the Punjab. Climate change in urban areas has become a public focal point, as urban development in our present-day environment is one of the mostcritical aspects of human activities. The shift in the reflective properties of the surface is one of theparticular climatic consequences of urbanization which most researchers have overlooked (Albedo). Changes in urbanized land surface properties resulted in a global, regional, and local changes inbiochemical cycles and environment. This analysis aims to identify the urban land use of the 1990-2018 study area (decadal) and evaluate Albedo and Climatic Variables' trends over the selected region.This study will provide a complete code of classification in Google Earth Engine for researchers. InGoogle Earth Engine, the classification technique was utilized to detect urban areas and barren landcover types by applying coding over four images of 1990, 2000, 2010, and 2018 of Punjab Province.The Albedo maps were created in the same way for the years 2000 and 2018. Climatic maps werecreated from 1990 to 2018 by applying the Inverse Distance Weighted method in a GIS system. Therehas been an apparent increase in the urban area of Punjab Province from 1990 to 2018. The urban areahas increased from 1183.5342 km2 to 8396.4618 km2 during the study period. Results from climaticmaps showed hotspots in the areas that have undergone urbanization. Overall, the accuracy of theclassification images was 81-88 percent, which was foremost to achieve the objectives of the research.
PPI Edge Infused Spatial–Spectral Adaptive Residual Network for Multispectral Filter Array Image Demosaicing

Multispectral filter array (MSFA) sensors provide a cost-effective and one-shot acquisition solution to obtain well-aligned multiband images, which are helpful for various optical and remote-sensing applications. However, the sparse spatial sampling rate and strong spectral cross correlation make MSFA image demosaicing a challenging problem. Therefore, it is essential to develop effective MSFA demosaicing solutions to reconstruct full-resolution and high-fidelity multispectral images from the raw mosaic image. In this article, we present a pseudo-panchromatic image (PPI) edge-infused spatial–spectral adaptive residual network (PPIE-SSARN) for MSFA image demosaicing. The proposed two-branch model deploys a residual subbranch to adaptively compensate for the spatial and spectral differences of reconstructed multispectral images and a PPI edge infusion subbranch to enrich the edge-related information. Moreover, we design an effective mosaic initial feature extraction module with a spatial- and spectral-adaptive weight-sharing strategy whose kernel weights can change adaptively with spatial locations and spectral bands to avoid artifacts and aliasing problems. Experimental results demonstrate the superiority of our proposed method, outperforming the state-of-the-art MSFA demosaicing approaches and achieving satisfying demosaicing results in terms of spatial accuracy and spectral fidelity. Our models and code will be publicly available.
CFD modeling of droplet generation process for medical applications using the electrostatic impulse method

The electrostatic impulse method is an established method for producing microbeads or capsules. Such particles have found application in biomedical engineering and biotechnology. The geometric properties of the droplets – constituting precursors of microbeads and capsules – can be precisely controlled by adjusting the geometry of the nozzle system, the physical properties and the ﬂow rate of the ﬂuids involved, as well as the parameters of the electrostatic impulse. In this work, a method of mathematical modeling of the droplet generation process using the electrostatic impulse method in a single nozzle system is presented. The developed mathematical model is an extension of the standard Volume of Fluid (VOF) model by addition of the eﬀect of the electric ﬁeld on the ﬂuid ﬂow. The model was implemented into the OpenFOAM toolkit for computational ﬂuid dynamics (CFD). The performed CFD simulation results showed good agreement with experimental data. As a result, the inﬂuence of all process parameters on the droplet generation process was studied. The most signiﬁcant change in droplet generation was caused by changing the electrostatic impulse strength. The presented modeling method can be used for optimization of process design and for studying the mechanisms of droplet generation. It can be extended to describe multi nozzle systems used for one-step microcapsule production.
An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks

Wireless sensor networks are usually applied in hostile areas where nodes can easily be monitored and captured by an adversary. Designing a key distribution scheme with high security and reliability, low hardware requirements, and moderate communication load is crucial for wireless sensor networks. To address the above objectives, we propose a new key distribution scheme based on an ECC asymmetric encryption algorithm. The two-way authentication mechanism in the proposed scheme not only prevents illegal nodes from accessing the network, but also prevents fake base stations from communicating with the nodes. The complete key distribution and key update methods ensure the security of session keys in both static and dynamic environments. The new key distribution scheme provides a significant performance improvement compared to the classical key distribution schemes for wireless sensor networks without sacrificing reliability. Simulation results show that the proposed new scheme reduces the communication load and key storage capacity, has significant advantages in terms of secure connectivity and attack resistance, and is fully applicable to wireless sensor networks.
End-to-end learning for autonomous vehicles: a narrow approach

Adauton Heringer. End-to-end learning for autonomous vehicles: a narrow approach. Thesis (Master’s). Institute of Mathematics and Statistics, University of São Paulo, São Paulo, 2023. Autonomous vehicles are long promised to revolutionize our civilization. Nevertheless, it has consistently failed to meet expectations in the past two decades. Based on the fundamental difference between narrow and general artificial intelligence and equipped with the theoretical approach of sociotechnical imaginaries, we criticize general autonomy: the study of autonomous vehicles as envisaged by its artificially fabricated sociotechnical imaginary utopia. By contrast, we conceptualize narrow autonomy as the study of contextlimited autonomous vehicles. Accordingly, we propose a narrow approach: instead of training a vehicle in a context-free environment, we set clear boundaries for the path the vehicle is supposed to drive. Using the latest advancements in end-to-end deep learning, we trained a convolutional neural network to map images and high-level commands straight to vehicle control, such as steering angle, throttle, and brake, in a simulated environment. Although this is a multidisciplinary conceptual work, our results indicate that by delimiting its path we can significantly improve performance and contribute to the advancements of autonomous technology.
Sex differences in alpha-synucleinopathies: a systematic review

Background Past research indicates a higher prevalence, incidence, and severe clinical manifestations of alpha-synucleinopathies in men, leading to a suggestion of neuroprotective properties of female sex hormones (especially estrogen). The potential pathomechanisms of any such effect on alpha-synucleinopathies, however, are far from understood. With that aim, we undertook to systematically review, and to critically assess, contemporary evidence on sex and gender differences in alpha-synucleinopathies using a bench-to-bedside approach. Methods In this systematic review, studies investigating sex and gender differences in alpha-synucleinopathies (Rapid Eye Movement (REM) Behavior Disorder (RBD), Parkinson’s Disease (PD), Dementia with Lewy Bodies (DLB), Multiple System Atrophy (MSA)) from 2012 to 2022 were identified using electronic database searches of PubMed, Embase and Ovid. Results One hundred sixty-two studies were included; 5 RBD, 6 MSA, 20 DLB and 131 PD studies. Overall, there is conclusive evidence to suggest sex-and gender-specific manifestation in demographics, biomarkers, genetics, clinical features, interventions, and quality of life in alpha-synucleinopathies. Only limited data exists on the effects of distinct sex hormones, with majority of studies concentrating on estrogen and its speculated neuroprotective effects. Conclusion Future studies disentangling the underlying sex-specific mechanisms of alpha-synucleinopathies are urgently needed in order to enable novel sex-specific therapeutics.
Language Anxiety Among English Foreign Language Learners

The purpose of this study was to find out how much language anxiety EFL university students have, what causes it, and how they deal with it. This study aims to fill a gap in the literature regarding the causes of language anxiety, the causes, and how people cope with it, as most researchers have not yet combined those three factors into a single study. The current study used a mixed method,a questionnaire and a semi-structured interview were used. FLCAS was used as a questionnaire to collect quantitative data. At the same time, questionnaires and semi-structured interviews were used to select the qualitative method. This study included 40 male and female EFL university students from the Faculty of Business and Social Sciences at one of Jakarta’s private universities. Their ages range from 18 to 25 years. In this study, the questionnaire items revealed two levels of anxiety: 1) low anxiety (62%) and 2) moderate anxiety (38%). Three causes of language anxiety were identified using a combination of questionnaire items and semi-structured interviews: 1) Inadequate confidence in speaking English in class; 2) Fear of making mistakes when speaking English; and 3) Lack of vocabulary/grammar knowledge. Keywords: foreign language anxiety, speaking anxiety, FLCAS, level of anxiety, causes of anxiety