bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stochastic homogenisation for functionals defined on asymptotically piecewise rigid functions
Authors: Donnarumma Antonio Flavio , Friedrich Manuel 
Published: January 2025

Abstract
We study stochastic homogenisation of free-discontinuity surface functionals defined on piecewise rigid functions which arise in the study of fracture in brittle materials. In particular, under standard assumptions on the density, we show that there exists a

$$\Gamma $$

Γ

-limit almost surely and that it can be represented by a surface integral. In addition, the effective density can be characterised via a suitable asymptotic cell formula and is deterministic under an ergodicity assumption. We also show via

$$\Gamma $$

Γ

-convergence that the homogenised functional defined on piecewise rigid functions can be recovered from a Griffith-type model by passing to the limit of vanishing elastic deformations.
The Random RFPA Method for Modelling Rock Failure
Authors: Gong Bin , Zhao Tao , Thusyanthan Indrasenan , Tang Chun’an , Zhou Gordon G. D. 
Published: January 2025

Abstract
The random rock failure process analysis (RRFPA) method was developed in this research to characterize the material spatial variability and uncertainty in rock failure modelling. The random field theory (RFT) was integrated with the traditional rock failure process analysis (RFPA) to model rock heterogeneity. In this approach, the variation of rock properties is represented as a function of relative distance, such that the influence of material intrinsic correlation on its fracturing behaviour can be appropriately captured. To validate the theory, 300 RRFPA simulations were conducted to investigate the failure characteristics of rock samples under compressive loading. The results showed that by incorporating a spectrum of material properties, the numerical outcomes exhibited distinct upper and lower bounds of stress across all testing scenarios, closely aligning with the experimental relationships. The histograms for uniaxial compressive strength and elastic modulus showed that both properties followed normal distributions, with the average values of 10.099 MPa and 1.818 GPa, respectively. The corresponding coefficients of variation were 0.450 and 0.038. The localized failure tended to result in a more rapid release of acoustic emission energy, but generated smaller cumulative energy compared to the overall failure pattern. In general, the maximum relative error of the RRFPA model was only 0.66% for uniaxial compressive strength, elastic modulus, and critical axial strain.
Breathing in virtual reality for promoting mental health: a scoping review
Authors: Pancini Elisa , Di Natale Anna Flavia , Villani Daniela 
Published: January 2025

Abstract
Breathing techniques have long been recognized as effective strategies for promoting mental health-related outcomes such as relaxation and emotional regulation. Virtual reality (VR), with its immersion and interactivity, offers the possibility to amplify the effectiveness of breathing techniques in promoting mental health. Recently, there has been an increasing use of VR to support breathing, although a formal summary of this integration is lacking. Following the PRISMA-ScR reporting guidelines, this scoping review aims to investigate the integration of breathing techniques and VR experiences and examine their potential effects on mental health-related outcomes. The synthesis of the reviewed studies (n = 18) reveals that different VR experiences can be used to strengthen breathing techniques. VR can, for example, reduce distractions and enhance focus during breathing exercises, provide direct, real-time feedback on the practice, and be used to create personalized environments, allowing users to train their breathing techniques in various contexts, from calming virtual nature settings to safe environments for practicing breathing skills that can be applied to real-life situations. Furthermore, this scoping review reveals the positive effects of VR-based breathing interventions on reducing clinical symptoms and increasing well-being and physical health outcomes. Overall, while acknowledging some methodological heterogeneity in the existing studies, the findings demonstrate the unique and promising potential of integrating breathing techniques with VR experiences. Future research is recommended to understand long-term effects, user experience, and the potential customization of interventions for diverse clinical and well-being contexts.
Production, Environment, and Population Growth
Authors: Dzhumashev Ratbek , Kazakevitch Gennadi 
Published: January 2025

Abstract
This paper analyses the joint evolution of production, environment, and population in the long run. The paper demonstrates that the link between output, resources, and population creates obstacles to sustained growth. This is because the law of conservation of mass implies that long-run growth of output would depend on increasing resource use. The amount of resources used in production directly increases pollution and causes environmental deterioration. Income growth and a fall in the quality of the environment raise the relative cost of living and affect fertility negatively. This leads to a trade-off between income growth and population growth. If resource use damages the environment at an increasing rate, then the sustainability of the environment requires that the total resource use should be capped. In this case, any per capita growth comes at the cost of a static or even falling population. The only way to ameliorate such a strict trade-off is to promote ’green’ technologies and policies that allow for growth with lesser pollution.
Necessary and Sufficient Elastic Stability Conditions for Single Crystals
Authors: Knowles Kevin M. 
Published: January 2025

Abstract
Necessary and sufficient elastic stability conditions for single crystals for the seven crystal systems are specified for both stiffness and compliance tensors. For Laue classes of four of these crystal systems, conditions for the positive-definite forms of suitably chosen 4 × 4 real symmetric matrices are shown to be both useful and relevant. Other supposedly equivalent and often simpler conditions proposed in the literature for tetragonal, orthorhombic and monoclinic crystal systems are analysed; all are shown to be incorrect.
Assessing microclimatic influences in Colombo metropolitan area (CMA) amidst global climate change: a comprehensive study from 1980 to 2022
Authors: Fonseka Panchali U. , Zhang Hongsheng , Premasiri Ranjith , Samarasuriya Chaminda , Rathnayake Upaka 
Published: January 2025

Abstract
Climate change has become an emerging topic, leading to widespread damage. However, when considering climate, attention is drawn to various scales, and urban microclimate has emerged as a trending subject due to its direct relevance to human living environments. Among the microclimatic factors, temperature and precipitation are utilized in order to identify trends. The identification of changes in precipitation and temperature from ground stations poses difficulties due to the lack of well-distributed stations; thus, satellite-based products are gaining popularity. The satellite products were validated against ground data, following which time-series and spatial analyses were conducted. The rainfall anomaly index, seasonality index, heat wave magnitude index, and mean temperature differ in the Colombo Metropolitan Area compared to the entire country. Each index is calculated decadal-wise to identify trends. By utilizing four climate indices, the analysis endeavors to investigate the microclimate identification in Colombo Metropolitan Area compared to its surrounding areas such as the Western Province and the entire country. This study aids local authorities in mitigating climate change by enhancing city resilience. These findings underscore the importance of understanding and addressing the impacts of climate change on temperature extremes to mitigate potential adverse effects on human activities and the environment. Understanding the specific reasons for spatial changes in rainfall anomalies often necessitates extensive climate modeling and data analysis.
Genetic diversity and population structure of Turkish European chestnut (Castanea sativa) genotypes assessed using start codon targeted polymorphism (SCoT) markers
Authors: Orman Erdal , Çakar Deniz , Alkan Mehtap , Özer Göksel , Güler Emrah , Gündoğdu Muttalip 
Published: January 2025

Abstract
The European chestnut (Castanea sativa) is an important nut crop that grows naturally in the Black Sea and Aegean regions of Türkiye. This study examined the genetic diversity and population structure of chestnut genotypes from prominent regions in Türkiye using Start Codon Targeted Polymorphism (SCoT) markers. A total of 44 Turkish chestnut genotypes from the Aegean, Marmara, and Black Sea regions, along with a control group of French variety, were analyzed. The SCoT primers underwent tests to select the most suitable ones, producing 8 selected amplified fragments, 65.34% of which were found to be polymorphic. The unweighted pair group method with arithmetic mean (UPGMA) and principal coordinate analysis (PCoA) analyses showed clear discrimination between two populations based on their origins, which was supported by the population structure analysis. The analysis of molecular variance (AMOVA) analysis revealed that 3% of the genetic variation was within populations and 97% was among individuals. The out-group (French variety) showed the furthest genetic similarity, and genetic similarity values decreased with increasing geographic distance. The SCoT primers successfully fingerprinted chestnut genotypes and could be used in future research to analyze the phylogeny of chestnuts using genomic DNA.
Interactive sequential generative models for team sports
Authors: Fassmeyer Dennis , Cordes Moritz , Brefeld Ulf 
Published: January 2025

Abstract
Understanding spatiotemporal coordination of players in team sports is key to movement models, pattern detection, and computational tactics. Existing generative models propose to capture all stochasticity by a single latent variable and may suffer from entangled representations, or aim to uncover interaction structures of players but then do not focus on their generative ability. As a remedy, we propose a hierarchical latent variable model for predicting trajectories of multiple players. In the generative model, both, discrete role assignments and a latent interaction graph are sampled to allow for different models in subsequent node updates and message passing operations between nodes, where standard Gaussian latent variables are employed per agent and timestep. We cast our approach as a variational autoencoder that provides a disentangled latent space to capture variability in team sport movements and propose a neural architecture for its optimization. We empirically evaluate our approach on tracking data from basketball and soccer and observe that our contribution outperforms the state-of-art in all experiments.
Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE): Science and Mission Overview
Authors: Wang Chi , Branduardi-Raymont Graziella , Escoubet C. Philippe , Forsyth Colin 
Published: January 2025

Abstract
The Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) was proposed to the Chinese Academy of Science (CAS) and the European Space Agency (ESA) following a joint call for science missions issued in January 2015. SMILE was proposed by a team of European and Chinese scientists, led by two mission Co-PIs, one from China and one from Europe. SMILE was selected in June 2015, and its budget adopted by the Chinese Academy of Sciences in November 2016 and the ESA Science Programme Committee in March 2019, respectively. SMILE will investigate the connection between the Sun and the Earth using a new technique that will image the magnetopause and polar cusps: the key regions where the solar wind impinges on Earth’s magnetic field. Simultaneously, SMILE will image the auroras borealis in an ultraviolet waveband, providing long-duration continuous observations of the northern polar regions. In addition, the ion and magnetic field characteristics of the magnetospheric lobes, magnetosheath and solar wind will be measured by the in-situ instrument package. Here, we present the science goals, instruments and planned orbit. In addition the Working Groups that are supporting the preparation of the mission and the coordination with other magnetospheric missions are described.
Zwischen Räumen: Wie sich Nähe und Distanz in Städten durch die Sharing Economy verändern
Authors: Oberthür Jörg , Henning Christoph , Janzing Malte , Katzer Henrike , Bönnighausen Helen 
Published: January 2025

Zusammenfassung
Der Beitrag untersucht, ob und ggf. wie sich durch die Sharing Economy als spezifische, nichtexklusive Praxis des Besitzens und Nutzens von Eigentumsobjekten stadträumliche und soziale Beziehungsverhältnisse reproduzieren, aber auch wechselseitig irritieren und neu entwickeln können. Hierzu wird ein methodologischer Analyserahmen gewählt, der ausgehend von Georg Simmel und in begrifflicher Anlehnung an Hartmut Rosa Phänomene und Praktiken des Teilens im Hinblick auf die dabei eingegangenen Beziehungen zu Dingen, zu anderen Personen sowie zu sich selbst untersucht. Unter Anknüpfung an Simmel wird zwischen Privatheit und Öffentlichkeit als Sphären unterschieden, in denen Nähe und Distanz gleichzeitig unter räumlichen wie sozialen Gesichtspunkten praktisch ausgehandelt und institutionell verankert werden. Eigentum und dessen Nutzung – allein oder gemeinsam mit anderen – spielen dabei eine zentrale Rolle, weil sich abhängig von spezifischen Eigentumsobjekten und ihrem jeweiligen Gebrauch räumliche Strukturen bilden und erhalten, aber auch verändern können. Anhand eigener empirischer Befunde zur geteilten Nutzung von Wohnraum (Homesharing) und Fahrzeugen (Carsharing) wird der Frage nachgegangen, welchen Einfluss Sharing-Praktiken auf Nähe- und Distanz-Verhältnisse und damit auf die (Re‑)Strukturierung von Räumen innerhalb von Städten haben.
Quick Self-Compassion: Italian Validation, Network Analysis, Item Response Theory Analysis, and Correlates of the Self-Compassion Scale Short Form
Authors: Fuochi Giulia , Voci Alberto , Moè Angelica 
Published: January 2025

Abstract

Objectives
This study aimed to test the factorial structure and gender invariance of the Self-Compassion Scale Short Form (SCS-SF) in the Italian context (Aim 1), underlying relationships among SCS-SF items (Aim 2), questionnaire performance of SCS-SF (Aim 3), and correlations of SCS-SF (and its subscales) with cognitive, affective, and well-being variables (Aim 4).

Method
Six questionnaires including the SCS-SF and different correlates were respectively administered to six Italian convenience samples (total n = 2068). We performed confirmatory (ULS estimator) factor analysis (Aim 1), exploratory graph analysis and network analysis (Aim 2), multidimensional item response theory (IRT) analyses (Aim 3), and correlational analyses (Aim 4). We tested Aims 1–3 on the global sample and Aim 4 on the six samples separately.

Results
We found a two-factor (hierarchical) solution — which also had strict gender invariance — identifying Compassionate and Uncompassionate Self-Responding (CSR and USR), respectively composed of positive and negative items, while three- and six-factor structures did not hold (Aim 1). The SCS-SF network was structured into the CSR and USR clusters (Aim 2). CSR and — especially — USR had a satisfactory performance in terms of IRT discrimination and information (Aim 3). Correlational analyses supported the convergent and discriminant validity of the scales, showing the cognitive and emotion-regulation strategies associated with self-compassion and confirming that CSR and USR have slightly different correlates (Aim 4).

Conclusions
The Italian SCS-SF and its CSR and USR subscales are valid instruments. Future research and interventions can employ CSR and USR to assess different nuances of self-kindness and self-criticism.
Novel palm peat lignocellulosic adsorbent derived from agricultural residues for efficient methylene blue dye removal from textile wastewater
Authors: Gaber Mohamed Mohamed , Shokry Hassan , Hassanin Ahmed H. , Awad Said , Samy Mahmoud , Elkady Marwa 
Published: January 2025

Abstract
Palm Peat (PP), the world’s first rich lignocellulosic medium derived from date palm agricultural residues, has not been previously explored for environmental purification. This study evaluates PP's performance in adsorbing methylene blue (MB) dye. PP is characterized by a spongy, porous structure with a surface area of 16 m2/g. It possesses a significant carbon and oxygen composition and features active surface functional groups. Under conditions of 30 mg/L initial MB concentration, 1 g/L PP dose, T = 30 °C, pH 7, and 900 rpm stirring speed, PP achieved a 68.26% MB removal efficiency within 90 min. Although higher temperatures enhanced MB removal efficiencies, room temperature (30 °C) was chosen for subsequent experiments to assess adsorption performance under ambient conditions and minimize energy consumption. Stirring speeds exceeding 900 rpm reduce MB removal efficiency, likely due to shear forces disrupting the interaction between MB molecules and PP or causing desorption of previously adsorbed dye molecules. Response surface methodology combined with a central composite design was employed to optimize the initial MB concentration, PP dosage, and solution pH. Under the optimum conditions, PP achieved 97.89% MB removal. PP exhibited strong stability over five adsorption cycles. Adsorption occurs via π-π stacking, hydrogen bonding, hydrophobic-hydrophobic interactions, and electrostatic interaction with the process being endothermic and following the Langmuir isotherm model and pseudo-second-order kinetic model. The adsorption efficiency studies across different water matrices revealed the lowest degradation rate in the drain water matrix. PP achieved 71.5% MB removal and 48.16% TOC removal from real textile wastewater.
A period prevalence study of palliative care need and provision in adult patients attending hospital-based dialysis units
Authors: Cooper Alannah L. , Panizza Natalie , Bartlett Rebecca , Martin-Robins Dipna , Brown Janie A. 
Published: January 2025

Abstract

Background
Advanced chronic kidney disease is a life-limiting disease that is known to benefit from palliative care. Unmet palliative care need in patients with kidney failure is commonly reported but the level of need among patients receiving haemodialysis is unknown.

Methods
A period prevalence study of adult patients attending two hospital-based dialysis units was conducted. Patient medical records were reviewed using the Gold Standards Framework Proactive Indication Guidance to assess for potential palliative care need.

Results
A total of 128 patient medical records were reviewed, 45% (n = 58) of patients could have potentially benefitted from palliative care. Of the patients with indicators for palliative care, 72% (n = 42) had no evidence of receiving or awaiting any form of palliative care. High levels of palliative care need were found in patients who identified as Aboriginal or Torres Strait Islander and non-Indigenous patients.

Conclusions
This study found high levels of palliative care need among adult patients attending hospital-based dialysis units. The majority of patients with indicators were not receiving any form of palliative care.

Graphical abstract
Effect of Different Heat Treatment Procedures on the Corrosion Behavior of High-Manganese Austenitic Steels
Authors: Gürol Uğur , Kocaman Engin 
Published: January 2025

Abstract
This study investigates the effects of different heat treatment procedures on the corrosion behavior of high-manganese austenitic steel containing molybdenum. Five samples were prepared, including as-cast and heat-treated specimens, with varying processes such as tempering, single and double solution annealing, and aging. The study focuses on microstructural changes, carbide dissolution, and the formation of protective molybdenum-rich oxides. Microstructural analysis using scanning electron microscopy and X-ray diffraction was conducted to understand phase distribution. At the same time, corrosion resistance was evaluated through potentiodynamic polarization and electrochemical impedance spectroscopy. The results reveal that double solution annealing leads to the most homogeneous microstructure and significantly enhances corrosion resistance by forming stable molybdenum oxide layers, underscoring the crucial role of molybdenum oxides in surface protection. Among the samples, the corrosion resistance ranked from best to worst is as follows: double solution-annealed (Ht-5), solution-annealed (Ht-3), aged after solution annealing (Ht-4), tempered (Ht-2), and as-cast (Ht-1). This highlights the crucial role of molybdenum oxides in surface protection. The findings demonstrate the effectiveness of advanced heat treatments in improving the corrosion resistance of high-manganese austenitic steels for industrial applications.
Condition assessment of bolted connections in steel structures using deep learning
Authors: Jafari Faezeh , Dorafshan Sattar 
Published: January 2025

Abstract
Steel structure connections are prone to bolt loosening and subsequent loss of strength or stability if not inspected periodically. Use of noncontact sensing and artificial intelligence can substantially increase the safety and efficiency of these inspections; however, the existing defect detection models do not account for variability of defects in both missing and loosened bolt connections. Furthermore, the performance of available deep learning models can be substantially diminished due to the presence of background noise in images of real structures. Finding these defects could be even more difficult in highway steel structures due to the complex and continuously changing nature of background. Additionally, no datasets are available to properly use these models for defect detection. In this paper, Mask RCNN (MRCN), You Only Look Once (YOLO), and Faster RCNN (FRCNN) models were adapted for investigation. These models were trained and tuned on a unique dataset-developed by the Authors comprised of 1100 raw images with connection defects of laboratory-made and in-service structures. A series of image enhancement and augmentation techniques were used to superimpose bolt and nut defects on intact images of in-service ancillary structure and to increase the size in training dataset to 1500. After models achieved satisfactory performance in laboratory images, they were tested on field and lab data (including 200 images with defective bolt). Thermal images were used to develop an efficient way to mask image background in visual images. The result indicated that removing background decreased the false positive rate (FRP) around 65% between the investigated deep learning models on UAS image. Overall accuracy of these models estimated between where 90% and 93%when FRCNN and Mask RCN.