jimnoneill/paper-to-field-training huggingface dataset:
An Argument for a Reasons-Based Doxastic Voluntarism

Field: Arts and Humanities (Social Sciences)

Intentional action is usually taken to be something that is paradigmatically under the direct control of our will. Belief-formation, on the other hand, is usually taken to be involuntary and not under our control. I, however, wish to argue that we have the same kind of control over what we beliefs we form as we do under what actions we intend, or more specifically, what intentions we form. In order to argue for this, I give what is sometimes called an “analogy argument” for doxastic voluntarism. I do this by first assuming and spelling out an account of intention-formation according to which action is a reasons-based activity, an activity we engage in by responding to reasons. According to this model of action, the will is the capacity to respond to the reasons we believe we have. I then assume that belief-formation is also a reasons-based activity and thus that it allows for the will to play the same role as it does in intention-formation. I end by spelling out how the way I construct the analogy escapes some of the usual objections analogy arguments and doxastic voluntarism faces.
Testing Theories of Transfer Using Error Rate Learning Curves

Field: Psychology (Social Sciences)

Abstract We analyze naturally occurring datasets from student use of educational technologies to explore a long‐standing question of the scope of transfer of learning. We contrast a faculty theory of broad transfer with a component theory of more constrained transfer. To test these theories, we develop statistical models of them. These models use latent variables to represent mental functions that are changed while learning to cause a reduction in error rates for new tasks. Strong versions of these models provide a common explanation for the variance in task difficulty and transfer. Weak versions decouple difficulty and transfer explanations by describing task difficulty with parameters for each unique task. We evaluate these models in terms of both their prediction accuracy on held‐out data and their power in explaining task difficulty and learning transfer. In comparisons across eight datasets, we find that the component models provide both better predictions and better explanations than the faculty models. Weak model variations tend to improve generalization across students, but hurt generalization across items and make a sacrifice to explanatory power. More generally, the approach could be used to identify malleable components of cognitive functions, such as spatial reasoning or executive functions.
New R Coronae Borealis stars discovered in OGLE-III Galactic Bulge fields from their mid- and near- infrared properties

Field: Physics and Astronomy (Physical Sciences)

An R Coronae Borealis (RCB) star is a rare type of supergiant star that is increasingly thought to be the evolved merger product of two white dwarfs. Recently, many of them have been found distributed in a thin disk structure embedded inside the Galactic Bulge. This unexpected high density can give us more insight into the nature and age of RCB stars. We applied and tested successfully a new technique to find RCB stars based on the particular infrared emission. We demonstrated that RCB stars can now be found without the need of a light curve analysis, and therefore outside optically monitored fields. The selection of RCB candidates was based on their near-infrared excess and on particular mid-infrared emission of RCB shells, using photometric data from the 2MASS and Spitzer/GLIMPSE surveys. The OGLE light curves of all RCB candidates were then inspected visually and the ones presenting large and fast declines were followed-up spectroscopically . We discovered two new R Coronae Borealis stars, but also propose four new candidates. We stress that all of the 7 known RCB stars located in both Spitzer/GLIMPSE and OGLE-III fields were re-discovered, which indicates the high efficiency of our analysis. The proposed new technique to find RCB stars has been successful. It can now be extented to larger area, specially where the instellar extinction is too high to have been monitored by microlensing surveys, i.e the inner part of the Galactic Bulge.
An Analysis of the Mechanisms Involved in Glacial Lake Outburst Flooding in Nyalam, Southern Tibet, in 2018 Based on Multi-Source Data

Field: Earth and Planetary Sciences (Physical Sciences)

Glacial Lake Outburst Flood (GLOF) events, particularly prevalent in Asia’s High Mountain regions, pose a significant threat to downstream regions. However, limited understanding of triggering mechanisms and inadequate observations pose significant barriers for early warnings of impending GLOFs. The 2018 Nyalam GLOF event in southern Tibet offers a valuable opportunity for retrospective analysis. By combining optical and radar remote sensing images, meteorological data, and seismicity catalogs, we examined the spatiotemporal evolution, triggering factors, and the outburst mechanism of this event. Our analysis reveals a progressive retreat of 400–800 m for the parent glaciers between 1991 and 2018, increasing the runoff areas at glacier termini by 167% from 2015 to 2018 and contributing abundant meltwater to the glacial lake. In contrast, the lake size shrunk, potentially due to a weakening moraine dam confirmed by SAR interferometry, which detected continuous subsidence with a maximum line-of-sight (LOS) rate of ~120 mm/a over the preceding ~2.5 years. Additionally, temperature and precipitation in 2018 exceeded the prior decade’s average. Notably, no major earthquakes preceded the event. Based on these observations, we propose a likely joint mechanism involving high temperatures, heavy precipitation, and dam instability. An elevated temperature and precipitation accelerated glacial melt, increasing lake water volume and seepage through the moraine dam. This ultimately compromised dam stability and led to its failure between 3 August 2018 and 6 August 2018. Our findings demonstrate the existence of precursory signs for impending GLOFs. By monitoring the spatiotemporal evolution of environmental factors and deformation, it is possible to evaluate glacial lake risk levels. This work contributes to a more comprehensive understanding of GLOF mechanisms and is of significant importance for future glacial lake risk assessments.
Food Producers in The Peri-Urban Area of Mexico City. A Study on the Linkages between Social Capital and Food Sustainability

Field: Social Sciences (Social Sciences)

Small producers in peri-urban areas have been identified as key actors in building more sustainable urban food systems, but they often have limited capacities to develop and consolidate their initiatives. This article describes the conditions of peri-urban farmers in Mexico City who work with agroecological schemes, analyzing the role of social and cultural capital in their ability to consolidate their economic-productive projects and establish links with consumers in the city. The research was developed from an anthropological perspective based on field visits and interviews conducted in 60 production units located in the peri-urban area of Mexico City. The article discusses the literature on peri-urban agriculture and the contextual particularities of the case study and then describes a typology constructed based on the analysis of the documented cases, considering the objectives of the initiatives and the different types of social and cultural capital on which their activities and marketing strategies are based are considered. The discussion argues that the social and cultural capital of the production units are key elements in determining the viability of the agroecological transition, reaffirming the importance of social articulation and other sociocultural aspects for the promotion of sustainable food projects.
An adaptation of dual‐frame sampling for estimating bat population trends in multi‐hibernacula systems

Field: Agricultural and Biological Sciences (Life Sciences)

Abstract Reliable estimates of population trends are important for prioritizing and implementing wildlife management actions. Abundance estimates are the most informative metric of population status but are difficult and costly to achieve for rare or elusive species. For bat species that are highly mobile and nocturnal, abundance estimates are usually only possible at their roosts. Although they are relatively easy to census in individual roosts, it is often unclear whether observed trends are representative of broader populations trends. Dual‐frame sampling is useful for monitoring wildlife populations that exhibit site fidelity and are highly visible but where the proportion of the population in known sites is unknown. We adapted a dual‐frame sampling approach to estimate abundance of Townsend's big‐eared bats ( Corynorhinus townsendii ) hibernating in a system of >800 caves at Lava Beds National Monument (LBNM) in northern California, USA, in 2013–2022. Beginning with a list of opportunistically discovered hibernacula, we expanded inference to the entire system by randomly selecting sites and stratifying survey effort according to number of bats observed during previous counts. Following 4 years of surveys, we began substituting survey‐informed estimates for some site types to improve efficiency of the monitoring effort. We estimated that a mean of 2,216 ± 112 (SE) bats hibernated at the study site annually and observed fluctuations of up to 22% around the mean population estimate and as much as 21% from the previous year. Despite annual fluctuations, we did not detect a trend in the number of hibernating bats at LBNM over the study period, underlining the challenges of detecting population trends in long‐lived species not exposed to pronounced stressors. Because our analytical approach generates reliable estimates of trend while accommodating annual variability in survey effort among years, it should be attractive to wildlife managers faced with shifting priorities and resources.
Tumor growth modelling by cellular automata

Field: Computer Science (Physical Sciences)

Tumor growth is a complex process that requires mathematical modeling approaches for studying real-life cancer behavior. The use of cellular automata (CA) to represent tumor growth in its avascular stage is explained in this work, and a stochastic CA describing tumor growth is obtained, based on a differential equations system in the range of continuum mechanics. The novelty of this research is the deduction of the neighborhood structure and rules for a probabilistic CA from these differential equations that describe the evolution of the tumor growth. In addition, the influence of the stresses on tumor growth is captured by the CA.
Langmuir Wave energy upconversion in presence of Lower Hybrid Wave turbulence in Polar Ionospheric Plasma

Field: Physics and Astronomy (Physical Sciences)

The International Scientific Research Organization for Science, Engineering and Technology (ISROSET) is a Non-Profit Organization; The ISROSET is dedicated to improvement in academic sectors of Science (Chemistry, Bio-chemistry, Zoology, Botany, Biotechnology, Pharmaceutical Science, Bioscience, Bioinformatics, Biometrics, Biostatistics, Microbiology, Environmental Management, Medical Science, Forensic Science, Home Science, Library Science, Material Science Military Science, Physical Science, Physical Education Science, Educational Science, Fisheries, seed technology, Agriculture, Forestry Science, Mathematics, Physics, Statistics and Geology/Earth Science), Computer Science, Engineering and Information Technology, Commerce, Management, Economics Sociology and Social Science.
Public Perception and Trust: The Critical Foundation for AI Adoption in Healthcare

Field: Health Professions (Health Sciences)

This scholarly composition examines the critical role of public trust in easing the successful integration of artificial intelligence in healthcare settings. Despite rapid-fire technological advancement and promising performance criteria in controlled settings, AI healthcare operations frequently encounter significant perpetration challenges stemming from doubt among both providers and patients. The composition explores the incongruity between AI's specialized capabilities and its practical relinquishment, relating crucial barriers including inadequate translucency, cerebral resistance factors, and media misrepresentation. Through analysis of failed and successful perpetration cases, the composition develops a comprehensive frame for erecting secure healthcare AI centered on enhanced translucency mechanisms, targeted patient education strategies, meaningful stakeholder involvement, ethical governance structures, and balanced nonsupervisory approaches. The frame demonstrates how addressing mortal factors alongside specialized considerations creates the foundation necessary for realizing AI's implicit benefits in healthcare while respecting different stakeholder enterprises.
Variable angle total internal reflection fluorescence microscopy in s-polarization: a new approach to quantify cell-substrate distances in contacts

Field: Computer Science (Physical Sciences)

Total internal reflection fluorescence microscopy is an evanescent based fluorescence microscope providing a selective visualization of cell-substrate contacts without interference from other, deeper cellular regions. Total internal reflection fluorescence microscope is used extensively to visualize cell-substrate contacts. However, quantifying these contacts - in particular the measurement of cell-substrate distances - has not been performed often. In order to quantify the cellsubstrate distances we have developed a new theoretical method which is based on a change in the penetration depth of the evanescent field by tuning the angle of incidence slightly above the angle of total internal reflection for s-polarized light. This is simpler and much more accurate in comparison to the few existing approaches.
Indoor fingerprinting algorithm for room level accuracy with dynamic database

Field: Computer Science (Physical Sciences)

Received Signal Strength (RSS) fingerprinting based WiFi localization systems has been widely studied in recent years for indoor scenarios, whose accuracy is closely related to the reliability of the database. To attain satisfactory performance, the database needs to be updated timely to compensate for changes in the environment. In this work, we propose a rank fingerprinting based localization system, in which the radio map only takes account of the most consistent access points (APs) with most meaningful information, and uses the rank method instead of the signal strength with clustering the most correlated reference point to estimate the position. In order to keep a reasonable size of the radio map, a dimension control strategy has been proposed. The proposed algorithm deals with manifold devices to achieve high room-level accuracy in a shopping mall. It has been compared with two present algorithms, the rank based fingerprinting (RBF) algorithm and the support vector machine (SVM) based algorithm, to show its better performance.
Effect of in-situ cooling during pressure and frictional deformation on mechanical and metallurgical properties of Ni-based functionally graded material developed by wire arc additive manufacturing

Field: Materials Science (Physical Sciences)

In this article, Ni–Cr–Mo-based functionally graded material with a designed composition is successfully fabricated using the wire arc additive manufacturing process. The effect of the gradual variation of Ni, Cr and Mo concentration and the effect of high-temperature high-pressure deformation (HTHPD) in the different cooling environments on the morphology, microstructure, and mechanical properties along the gradient direction of the manufactured functionally graded materials (FGMs) have been investigated. With increasing Cr and Mo content from the bottom to the top, the FGM exhibits a gradual increase in tensile strength and microhardness. The hardness varies from 114 to 230, 130 to 205 and 164 to 256 HV for as-deposited, HTHPD with air-cooled (HTHPD-A) and HTHPD with water-cooled (HTHPD-W) samples, respectively, from bottom to top. Similarly, the average tensile strength is achieved at 793, 715 and 875 MPa in as-deposited, HTHPD-A and HTHPD-W samples, respectively. The microstructural examination of fabricated samples shows the columnar grain produced in as-deposited samples, deformed during the HTHPD process and recrystallized grains with orientation along the deformation direction has been formed.
Outpatient Management of Home Oxygen for Bronchiolitis

Field: Health Professions (Health Sciences)

Objective. Home O 2 has been shown to reduce hospitalizations for bronchiolitis but data on outpatient management of home O 2 are lacking. We aim to describe outpatient management and challenges to home O 2 for bronchiolitis. Methods. We surveyed Colorado and Utah (where home O 2 use is prevalent) chapter members of the American Academy of Pediatrics regarding bronchiolitis home O 2 management. Results. A total of 1030 providers were surveyed. The response rate was 21% (n = 214). Ninety percent of practicing primary care providers reported experience with home O 2 . Of those, 46% see patients on postdischarge day 1. Most providers see patients 1 to 3 times before stopping O 2 . Eighty percent continue O 2 for 3 to 7 days. Weaning procedures vary and 56% practice more than 1 method. Most (41%) do not use continuous pulse oximetry. Challenges include parental noncompliance (51%) and difficulty knowing when to stop the O 2 (57%). Conclusions. Management of home O 2 in patients with bronchiolitis is a common in UT and CO. Weaning practices vary. Further research is needed.
Periodic Variations of Solar Corona Index during 1939–2020

Field: Physics and Astronomy (Physical Sciences)

Periodic behaviors of solar magnetic indicators might provide a clue for the understanding of solar dynamic processes. Combining with a Lomb–Scargle periodogram, the concentration of frequency and time via a multitapered synchrosqueezed transform is applied to investigate the periodic variations of modified coronal index for the time interval from 1 January 1939 to 31 August 2020. The main results are as follows: (1) During solar cycles 19 to 23, the Schwabe cycle of the modified coronal index is operating with its length variating between 10.5 and 11-yr, and the average value of length is 10.67-yr with standard deviation of 0.14-yr. (2) The Rieger-type periods are mainly distributed in a range from 120 to 200 days. In addition, the periods vary somewhat intermittently during cycles 18 to 24, which are operating with the highest power in cycles 21 and 22 while the power is much lower in cycles 23 and 24. (3) For rotation periods, the temporal variation exhibits a highly intermittent pattern as an asymmetrical distribution with its 25th, 50th, and 75th quantile of 26, 27.8, and 31-day, respectively. (4) Other mid-range periods are also detected with an average period length of 8.07, 5.44, 3.42, 2.3, and 1.01-yr.
Difference of Suaeda japonica Populations from two Different Habitats in Sorae, Incheon, Korea

Field: Agricultural and Biological Sciences (Life Sciences)

【The morphology and flowering time of two Suaeda japonica populations from different habitats, the creek and the bank of the mudflat in Sorae, were surveyed. And standard transplantation and reciprocal transplantation were carried out to determine whether their characteristics were genetically fixed or not. Also soil sample of these habitats were analyzed. The amounts of loss on ignition, maximum field capacity, total nitrogen, exchangeable calcium and potassium, and soluble phosphorus were found to be significantly different between two habitats. Leaf thickness, leaf width, leaf length, stem height number of branches, and number of seeds were significantly different between plants of two populations. And flowering times of two populations did not overlap. According to the results of transplantation, leaf width, leaf thickness, and flowering times were the same as those at their original habitat. But the stem lenght, leaf lenght, number of branches and unmber of seeds were not. Therefore, the differences in the leaf length, leaf thickness, and flowering time seemed to bo genetically fixed. It were suggested that the creek bank populations of Suaeda japoinica were to be considered as different ecotypes.】
Identification of Emotion Parameters in Music to Modulate Human Affective States

Field: Computer Science (Physical Sciences)

Emotions are a complex aspect of human being that is being widely explored to improve human-machine interaction. This complexity makes emotions the object of study in several works, particularly the ones that aim to further understand human emotional behavior or to use affective states to control interfaces and/or create personalized content. In this context, emotion recognition is one of the key topics. The effectiveness of this recognition depends on the way the different emotions are being represented. There are two main ways to categorize emotions: the discrete model, in which emotions are directly labeled; and the two-dimensional model, which associates emotions to secondary parameters such as valence and arousal. The dimensional approach has particularly drawn attention since it can provide a more fine-grained emotion assessment. In this approach, each emotion may be represented as function of valence and arousal continuous values. However, the automatic identification of valence and arousal degrees from a particular content is a difficult task, which may be benefited by the use of artificial intelligence algorithms. Therefore, this chapter proposes an approach to the automatic identification of emotions induced by musics of different genres based on the prediction ofntheir valence and arousal parameters. To achieve this goal we extracted explicit numerical features from the songs and used this set of features to train regression models based on Linear Regression, SVM, ELM, Random Forest, and MLP. Random Forest model outperformed the others with better correlation coefficients and lower errors for predicting both arousal and valence values from the music signals. Arousal prediction with Random Forest resulted in a Pearson's correlation coefficient of 0.85 ± 0.01. While valence was predicted with a correlation of up to 0.76 ± 0.02. Furthermore, a Particle Swarm Optimization (PSO) feature selection study was also conducted in an attempt to better assess the correlation between the extracted features and the emotional parameters associated with the songs. The use of PSO significantly reduced the dimensionality of the vectors used to represent both valence and arousal information.
Answering a question on equally covered groups

Field: Mathematics (Physical Sciences)

Foguel, Moghaddamfar, Schmidt, and Velasquez-Berroteran asked in [Int. Electron. J. Algebra, 37(2025), 352-365] whether there exists a positive integer $n$ with the property that, for every finite group $G$, the Cartesian power $G^n$ can be expressed as the union of a family of proper subgroups of the same order. We prove that the answer is negative.
Situational ME-LOWA Aggregation Model for Evaluating the Best Main Battle Tank

Field: Decision Sciences (Social Sciences)

OWA (ordered weighted averaging) operators have been extensively adopted to handle MCDM problems. However, previous operators are usually independent of their situations and cannot reflect change in situations. Besides, the evaluated data with linguistic preferences should be aggregated with feasible operators. To resolve above problems, a linguistic MCDM aggregation model with situational ME-LOWA operators is proposed in this study. The proposed model is applied to evaluate the best main battle tank, and experimental results indicate that the proposed model can deal the situational group MCDM problems with linguistic preferences.