Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of climatic factors on the dynamics of radial increments of Norway spruce, European beech and sessile oak.

Correlations of increment indexes with average monthly temperatures and total monthly precipitation were studied on annual ring series of 455 trees of Norway spruce (Picea abies [L.] Karst.), sessile oak (Quercus petrea Liebl.) and European beech (Fagus sylvatica L.). Data on precipitation from the period 1901–2005 and on temperatures from the period 1931–2005 were used. Statistically significant dependences with correlation coefficients in the range of 0.2–0.5 were confirmed. All tree species react positively to precipitation mainly in June and July. An increase in precipitation by 1 mm when compared with the long-term average results in an increase in increment index of spruce almost by 0.13%. This index in oak and beech increases only by a half value of the value for spruce. Precipitation from the second half of the vegetation period of the previous year is also important. Higher temperatures during the vegetation period affect increment changes mostly negatively. With temperature increase by 1°C, when compared with the long-term average, the increment index of trees decreases by about 1–2%.
ASSESSMENT OF CONSERVATION RESERVE PROGRAM FIELDS WITHIN THE CURRENT DISTRIBUTION OF LESSER PRAIRIE-CHICKEN

Populations of lesser prairie-chicken (Tympanuchus pallidicinctus) have declined by more than 90%, due primarily to the conversion of sand-sage and mixed-grass prairie to agriculture, overgrazing by domestic livestock, juniper encroachment, and fossil-fuel development. Degradation of native habitats has made restored cropland through the Conservation Reserve Program (CRP) potentially one of the best management options for lesser prairie-chicken. An estimated 1.4 million hectares of CRP exist within the lesser prairie-chicken range. We assessed 1,019 CRP fields representing more than 51,000 hectares within the current distribution of the lesser prairie-chicken. We sampled various grassland plantings including Farm Service Agency conservation practices 1,2,4,4D, 10, and 25. In the context of lesser prairie-chicken habitat requirements for nesting and brood-rearing, our data suggest the following conservation practices (CP) have the highest potential for lesser prairie-chicken management: in Colorado and New Mexico, CPl0 and CP2; in Oklahoma, CP2, followed by CPs 25 and 10; in northeast Texas, CP2, and in northwest Texas CPs 1, 10, and 2. Kansas CRP fields consistently displayed a high forb component and tall average grass height, habitat attributes that are consistent with the incidence of range expansion and population stability of the lesser prairie-chicken within that state. These field assessments are the first step in a process to target fields for CRP re-enrollment and to guide management to benefit lesser prairie-chicken.
Exploring the Structural Relationship between Sexual Dysfunctional Beliefs, Marital Satisfaction, and Sexual Well-Being in Iranian Women: The Role of Sexual Cognitive Schemas

Abstract This study examines the connection between sexually dysfunctional beliefs, sexual well-being, and marital satisfaction among Iranian married women (N = 355, ages 18-65). Structural equation modeling (SEM) and mediation analyses were employed to explore these relationships. Results reveal that sexual dysfunctional beliefs negatively impact both sexual well-being and marital satisfaction. Furthermore, sexual cognitive schemas mediate this relationship, underscoring the importance of addressing negative beliefs and promoting healthy cognitive schemas in interventions. This research highlights the influence of cultural and societal factors on Iranian women’s sexual attitudes and behaviors, emphasizing the need for tailored interventions to enhance sexual and relational outcomes.
A Hierarchical Theory for the Tensile Stiffness of Non-Buckling Fractal-Inspired Interconnects

The design of non-buckling interconnects with thick sections has gained important applications in stretchable inorganic electronics due to their simultaneous achievement of high stretchability, low resistance, and low heat generation. However, at the same time, such a design sharply increased the tensile stiffness, which is detrimental to the conformal fit and skin comfort. Introducing the fractal design into the non-buckling interconnects is a promising approach to greatly reduce the tensile stiffness while maintaining other excellent performances. Here, a hierarchical theory is proposed for the tensile stiffness of the non-buckling fractal-inspired interconnects with an arbitrary shape at each order, which is verified by the finite element analysis. The results show that the tensile stiffness of the non-buckling fractal-inspired interconnects decreases with the increase in either the height/span ratio or the number of fractal orders but is not highly correlated with the ratio of the two adjacent dimensions. When the ratio of the two adjacent dimensions and height/span ratio are fixed, the tensile stiffness of the serpentine fractal-inspired interconnect is smaller than that of sinusoidal and zigzag fractal-inspired interconnects. These findings are of great significance for the design of non-buckling fractal-inspired interconnects of stretchable inorganic electronics.
High angular diffusion tensor imaging estimation from minimal evenly distributed diffusion gradient directions

Diffusion-weighted Imaging (DWI) is a non-invasive imaging technique based on Magnetic Resonance Imaging (MRI) principles to measure water diffusivity and reveal details of the underlying brain micro-structure. By fitting a tensor model to quantify the directionality of water diffusion a Diffusion Tensor Image (DTI) can be derived and scalar measures, such as fractional anisotropy (FA), can then be estimated from the DTI to summarise quantitative microstructural information for clinical studies. In particular, FA has been shown to be a useful research metric to identify tissue abnormalities in neurological disease (e.g. decreased anisotropy as a proxy for tissue damage). However, time constraints in clinical practice lead to low angular resolution diffusion imaging (LARDI) acquisitions that can cause inaccurate FA value estimates when compared to those generated from high angular resolution diffusion imaging (HARDI) acquisitions. In this work, we propose High Angular DTI Estimation Network (HADTI-Net) to estimate an enhanced DTI model from LARDI with a set of minimal and evenly distributed diffusion gradient directions. Extensive experiments have been conducted to show the reliability and generalisation of HADTI-Net to generate high angular DTI estimation from any minimal evenly distributed diffusion gradient directions and to explore the feasibility of applying a data-driven method for this task. The code repository of this work and other related works can be found at https://mri-synthesis.github. io/.
Understanding the Factors Influencing Higher Education Students’ Intention to Adopt Artificial Intelligence-Based Robots

Although there has been some progress, the integration of artificial intelligence into higher education remains far from sufficient. The demand for teachers will persist for some time; however, with the introduction of AI-based robots into classrooms, the role of teachers has been reduced to a minimum. The purpose of the current study was to evaluate Chinese higher education students’ intentions to adopt AI-based robots for educational purposes. Based on the Technology Acceptance Model (TAM) 3 model, the current study proposes 14 hypotheses to evaluate students’ intention to adopt AI-based robots in education. The students’ data were collected and analyzed using PLS-SEM. The study findings revealed that 12 hypotheses were accepted and two were rejected. The results indicate that students are willing to accept AI-based robots in their education. However, the findings revealed an insignificant influence of job relevance and robot anxiety on perceived usefulness and ease of use, respectively. The findings of this study will provide insight into university administrations regarding the significance of AI-based robots in education. Moreover, the findings will help robot developers, policymakers, and university administrators design and implement AI-based robots that fulfill contemporary education needs.
Unequal Tweets: Black Disadvantage is (Re)tweeted More but Discussed Less Than White Privilege

ABSTRACT Disadvantage and privilege work together to uphold systems of inequality. Nevertheless, racial inequality is often described as Black disadvantage, while White privilege remains less visible. This one-sided framing in public discourse may result in equally one-sided understandings of and policies aimed at reducing inequality. In the present research, we examined the use of and the reactions to Black disadvantage and White privilege frames in tweets. Twitter stands out as a public sphere inspiring both online and offline political discussions and protests around racial inequality (e.g. #BlackLivesMatter). We analyzed the framing of tweets using a combination of a rule-based and a machine-learning approach, resulting in two corpora of 11,292 (Study 1) and 31,984 tweets (Study 2, a direct replication of Study 1) using comparative frames of racial inequality. Users overall more often framed inequality as Black disadvantage than as White privilege. Moreover, tweets with a disadvantage frame were more often retweeted, but less often quoted and replied to than tweets with a privilege frame. These results show that racial inequality is often one-sidedly framed in real online conversations and that this pattern may be reinforced by other users because they preferably pass on disadvantage frames. However, focusing on White privilege may provoke more discussion about racial inequality. Although effect sizes were small, these effects can impact content and perspectives in mainstream media, public opinion, and political agendas by guiding attention to certain aspects of racial inequality, but not others.
Fucoidan from Ericaria crinita Alleviates Inflammation in Rat Paw Edema, Downregulates Pro-Inflammatory Cytokine Levels, and Shows Antioxidant Activity

Fucoidans are sulfated polysaccharides detected mainly in the cell walls of brown seaweeds. Here, we examined the effects of single doses of fucoidan derived from Ericaria crinita (formerly Cystoseira crinita) on carrageenan-induced paw inflammation in rats. The serum levels of TNF-α, IL-1β, IL-6, and IL-10 of rats with LPS-induced systemic inflammation after 14 days of treatment were also evaluated. Subchronic treatment with fucoidan from E. crinita attenuated the inflammation during the late phase of the degraded carrageenan-induced paw edema (3rd to 5th hour after carrageenan injection) with peak activity at the 3rd hour after the application. Both doses of fucoidan from E. crinita (25 and 50 mg/kg bw) significantly decreased the levels of all tested pro-inflammatory cytokines (IL-1β, TNF-α, and IL-6) in the serum of rats with a model of system inflammation but had no effect on the anti-inflammatory cytokine IL-10. The results showed that the repeated application of fucoidan has a more prominent effect on the levels of some pro-inflammatory cytokines in serum in comparison to a single dose of the sulfated polysaccharide. This reveals the potential of E. crinita fucoidan as an anti-inflammatory agent. Furthermore, E. crinita fucoidan exhibited in vitro antioxidant capacity, determined by 2,2-diphenyl-1-picryl-hydrazyl radical scavenging and ferric reducing antioxidant power assays as follows: IC50 = 412 µg/mL and 118.72 μM Trolox equivalent/g, respectively.
Differences in Male-Killing Rickettsia Bacteria between Lineages of the Invasive Gall-Causing Pest Leptocybe invasa

Simple Summary This study compared the differences in symbiotic bacteria between the A and B lineages of the Leptocybe invasa, so as to provide a reference for the scientific control of the pest. Abstract (1) Background: Leptocybe invasa (Hymenoptera: Eulophidae) is a global invasive pest that seriously damages eucalyptus plants and has caused serious harm to forestry production in many countries. Two genotypically distinct lineages of L. invasa have been detected outside of Australia, namely, lineage A and lineage B. However, the composition and abundance of endosymbiotic bacteria in L. invasa are still unclear between lineages. Therefore, the purpose of this study was to compare the bacterial communities in female adults of L. invasa of different lineages distributed in the same domain; (2) Methods: The PacBio Sequel II platform was used to compare bacterial community composition between lineages of L. invasa by sequencing the V1–V9 region of the 16S rRNA gene, and fluorescence quantitative PCR was used to compare the relative expression of Rickettsia between lineages of L. invasa; (3) Results: A total of 437 operational taxonomic units (OTUs) were obtained. These OTUs were subdivided into 20 phyla, 32 classes, 77 orders, 129 families, and 217 genera. At the genus level, the dominant bacteria in lineage A and lineage B were Rickettsia and Bacteroides, respectively. There were differences in the bacterial community of L. invasa between lineages, and the abundance and relative expression of Rickettsia in lineage A were significantly higher than those in lineage B; (4) Conclusions: There were differences in the bacterial community of L. invasa between lineages, and the abundance and relative expression of Rickettsia in lineage A were significantly higher than those in lineage B.
INBOUND MARKETING IN THE HOSPITALITY INDUSTRY: A SYSTEMATIC REVIEW IN THE LAST 12 YEARS

This paper aims to analyze the evolution in the research of digital marketing tools and strategies used by inbound marketing in the main journals related to this term and related topics to the hospitality industry. The results obtained have led to detect a progressive increase in the number of publications analyzing inbound marketing tools in recent years (2010-2021), which point to the construction of a Marketing 5.0 paradigm. A high rotation of topics has also been detected, describing a dynamic and fluctuating research field. It is recommended that more research be carried out on inbound marketing (as a strategic model of structured marketing) to allow for an integrative vision of technologies, which until now have been analyzed without a defined backbone linked to the hotel business.
Simulateur de canal de propagation basé sur une approche physico-statistique et adapté à la modélisation des multitrajets pour les systèmes de navigation par satellite

Ce travail de these porte sur la modelisation des phenomenes de propagation affectant les signaux de navigation par satellite en environnement urbain dense avec une focalisation particuliere sur les multitrajets et l'aspect large bande du canal de propagation espace/Terre. Le simulateur de canal pseudo temps-reel developpe, SCHUN (Simplified CHannel for Urban Navigation), repose sur une approche hybride physico-statistique. La composante statistique de la modelisation permet essentiellement de generer une ville virtuelle a partir de distributions de bâtiments connues. Le reste de la modelisation s'appuie sur une approche physique simplifiee ou les interactions ondes electromagnetiques/ville virtuelle reposent d'une part sur un modele de macro-diffusion a l'echelle des facades, (3CM (Three Component Model)), et d'autre part sur un modele physique de masquage du trajet direct par les bâtiments. Les principales methodes numeriques sous-jacentes sont l'optique physique et la theorie uniforme de la diffraction. Le simulateur de canal SCHUN ouvre aujourd'hui des perspectives interessantes pour la modelisation large bande du canal de propagation espace/Terre. Optimise pour des temps de calcul raisonnables, alliant une composante statistique a une composante physique simplifiee, ce simulateur a ete concu et valide par des mesures experimentales pour repondre a des besoins de simulation des systemes a diversite de satellite, diversite de reception, diversite de polarisation ou encore diversite de frequence pour des applications de navigation par satellite.
Design of music intelligent recommendation based on user interest preference model

With the rapid development of the Internet, the number of digital music has increased dramatically. It is very difficult for users to dig out the songs they are interested in from the massive digital music information, and the problem of "information overload" has emerged. A search engine is a tool for retrieving information. When a user submits a query to the search engine, the relevant content is returned to the user after retrieval. The search results of different search engines are different. Through the analysis of users' behaviors, we can understand users' search habits and realize a search engine that is more in line with users' needs. In order to accurately recommend interesting music to users and improve the market share of application objects, this paper designs a music intelligent recommendation system based on user interest preference, which is composed of data acquisition module, offline data processing module and online recommendation module. The data acquisition module collects user registration information, online scoring data of target music, and conducts online survey to collect relevant data. Data analysis and experiments show that the model is superior to the comparison model in terms of training efficiency and accuracy of recommendation, thus verifying the effectiveness of integrating user's long-term and short-term preferences and user influence for recommendation.
Association between weather conditions, snow-lie and snowbed vegetation

Snowbed vegetation contains both vascular plants and bryophytes. The latest snowbeds cover areas that are of predominantly, if not exclusively, bryophyte flora while the vascular plants are generally confined to the periphery of such late snowbeds. It is hypothesised that the exclusion of vascular flora from the snowbed core is the result of the shortened growing season generated by late-lying snow, which the bryophyte flora is better able to tolerate. The snowbed bryophytes cannot, however, tolerate the competition offered by the vascular flora in the peripheral areas from which they are absent. Data indicate that some of the bryophyte snowbed species are inhabiting optimal conditions in the snowbed core, rather than tolerating sub-optimal conditions. Adaptation and acclimation responses observed in peripheral vascular species indicate that these are inhabiting sub-optimal conditions in the snowbed periphery. The relationship between snow-lie and climate is examined, with to the construction and examination of a second hypothesis that snowbed loyalty in the Scottish Highlands is high, while duration of snow cover is variable. Snow-lie loyalty is the product of prevailing wind conditions, which are persistent and consistent in Scotland leading to consistency in late snowbed location, while the occurrence of mid-winter thaws at all altitudes makes duration of snow cover through accumulated snow depth much more
Baicalin: A potential therapeutic agent for diabetes and renal protection

Background: Diabetes is a complex metabolic disease manifested by raised glucose levels in the blood and impaired insulin function leading to various organ complications, including diabetic nephropathy. Baicalin, a flavonoid derived from Scutellaria baicalensis Georgi, has garnered substantial attention for its diverse beneficial effects, including anti-inflammatory, anti-allergic, anti- apoptotic properties, etc. Intriguingly, in vivo studies in rats have further unveiled baicalin’s potential to directly modulate pancreatic beta cells, suggesting a promising role as an anti-diabetic agent.Objective: The purpose of this study is to comprehensively explore the anti-diabetic effect of baicalin, focusing on key parameters such as plasma insulin levels, glucose levels, hemoglobin, and glycated hemoglobin levels in streptozotocin-induced diabetic rats. Additionally, we sought to explore Baicalin’s ability to provide renal protection by evaluating serum renal markers. Methodology: This study involved a total of 30 Wistar albino male rats. Diabetes was created in rats by a single intraperitoneal streptozotocin injection (40 mg/kg). After 72 hours, the rats with diabetes were segregated into four treatment groups (Group II to Group V) comprising 6 animals each. Group I consists of six normal control rats (without diabetes). The groups received different treatment protocols, including normal saline, DMSO, Baicalin (50 mg/kg/day), and glibenclamide (6 mg/kg/day) for 45 days. Throughout the study, meticulous observations were made regarding the animals’ general appearance, body weight, behavior, and their fasting glucose levels in venous blood.Results: Oral dosing with Baicalin at the rate of 50 mg/kg body weight revealed notable enhancements in insulin secretion and hemoglobin levels, alongside notable reductions in blood levels of glucose and glycated hemoglobin compared to the glibenclamide-treated type 2 diabetic rats. Additionally, Baicalin displayed a protective action on renal tissue, as shown by reduced serum creatinine, uric acid, and urea levels.Conclusion: Our investigation unveils Baicalin’s potential as a promising anti-diabetic agent with the added benefit of renal tissue protection. The observed improvements in various physiological parameters warrant further exploration of Baicalin’s therapeutic mechanisms and clinical applications, presenting it as a compelling candidate for diabetes management and diabetic nephropathy prevention. IAEC Approval No: AVMC/IAEC/2019/07/25/08 Keywords: Baicalin, Blood glucose, Diabetes, Male wistar rats, Streptozotocin
Automated Vehicle Identification Based on Car-Following Data With Machine Learning

Vehicles with adaptive cruise control, i.e., SAE Levels 1 and 2 automated vehicles (AVs), have been operating on roads with a significant and rapidly growing penetration rate. Identifying these AVs is critical to understanding near-future mixed traffic characteristics and managing highway mobility and safety. This study identifies adaptive cruise control-equipped vehicles from human-driven vehicles (HVs) by constructing a set of learning-based models using car-following trajectories in a short time window. It is extendible to Level 3 and + AV identification when data is available. To compare model performance and draw physical insights, two physics-based models are proposed based on the premise that, in general, the car-following behavior of an AV is less volatile than an HV. Four car-following datasets, including AV makes from different manufacturers, are mixed to build a comprehensive identification model. Results show that physics-based approaches identify more than 80% AVs and 70% HVs. The identification accuracy of learning-based models is even higher. For example, the cluster-aware long short-term memory network identifies 98.79% of AVs and 95.45% of HVs. Learning-based identification models developed by this study can be integrated with the existing infrastructure (e.g., surveillance cameras), which have been used to extract car-following trajectories, to detect AVs in mixed traffic streams. This opens unparalleled data-driven opportunities to analyze and control mixed traffic to enhance safety (e.g., notifying surrounding traffic of the presence of AVs) and mobility (e.g., opening AV dedicated lanes when the percentage is great enough).