bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nonconvex Quadratically-Constrained Feasibility Problems: An Inside-Ellipsoids Outside-Sphere Model
Authors: Abolpour Roozbeh , Hesamzadeh Mohammad Reza , Dehghani Maryam 
Published: January 2025

Abstract
This paper proposes a new approach for solving Quadratically Constrained Feasibility Problems (QCFPs). We introduce an isomorphic mapping (one-to-one and onto correspondence), which equivalently converts the QCFP to an optimization problem called the Inside-Ellipsoids Outside-Sphere Problem (IEOSP). This mapping preserves the convexity of convex constraints, but it converts all non-convex constraints to convex ones. The QCFP is a feasibility problem with non-convex constraints, while the IEOSP is an optimization problem with a convex feasible region and a non-convex objective function. It is shown that the global optimal solution of IEOSP is a feasible solution of the QCFP. Comparing the structures of QCFP and the proposed IEOSP, the second model only has one extra variable compared to the original QCFP because it employs one slack variable for the mapping. Thus, the problem dimension approximately remains unchanged. Due to the convexity of all constraints in IEOSP, it has a well-defined feasible region. Therefore, it can be solved much easier than the original QCFP. This paper proposes a solution algorithm for IEOSP that iteratively solves a convex optimization problem. The algorithm is mathematically shown to reach either a feasible solution of the QCFP or a local solution of the IEOSP. To illustrate our theoretical developments, a comprehensive numerical experiment is performed, and 500 different QCFPs are studied. All these numerical experiments confirm the promising performance and applicability of our theoretical developments in the current paper.
Krasnoselskii–Mann Iterations: Inertia, Perturbations and Approximation
Authors: Cortild Daniel , Peypouquet Juan 
Published: January 2025

Abstract
This paper is concerned with the study of a family of fixed point iterations combining relaxation with different inertial (acceleration) principles. We provide a systematic, unified and insightful analysis of the hypotheses that ensure their weak, strong and linear convergence, either matching or improving previous results obtained by analysing particular cases separately. We also show that these methods are robust with respect to different kinds of perturbations–which may come from computational errors, intentional deviations, as well as regularisation or approximation schemes–under surprisingly weak assumptions. Although we mostly focus on theoretical aspects, numerical illustrations in image inpainting and electricity production markets reveal possible trends in the behaviour of these types of methods.
Estimation of leaf area index in sagebrush steppe with low cost unoccupied aerial systems
Authors: Woodruff Craig D. , Clark Patrick E. , Olsoy Peter J. , Enterkine Josh 
Published: January 2025

Abstract

Context
Leaf area index (LAI) strongly influences the carbon and water cycle in drylands, but accurate estimation of LAI relies on field methods that are expensive and time intensive. Very high-resolution imagery from unoccupied aerial systems (UAS) offers a potential solution for monitoring LAI, but estimation methods derived from cost effective red, green, and blue (RGB) sensors are untested in these semi-arid ecosystems.

Objectives
The objective of our study was to test whether LAI could be estimated with very high resolution UAS collected RGB and canopy height data. Additionally, we sought to validate the model accuracy at the plot (1 m2) scale, test the accuracy at the macroplot (1 ha) scale, and assess the within plot impact of shadows.

Methods
We used a Random Forest machine learning model to estimate LAI in a Wyoming big sagebrush community in the Reynolds Creek Experimental Watershed using high resolution (< 1 cm2) UAS imagery collected in 2021 as predictors and plot scale point intercept (quadrat design) field data as the LAI reference.

Results
Random Forest modeled estimates of LAI were accurate at the plot (r2 = 0.69, MAE = 0.08, RMSE = 0.10), and the macroplot scales (error of 0.065), and mean within plot shadow error was 0.06.

Conclusions
This research demonstrates high resolution UAS data can rapidly and accurately estimate LAI, with a limited number of field measurements, potentially allowing land managers to survey seasonally and spatially heterogeneous LAI 1 hectare at a time over the vast rangelands in the Great Basin and similar ecosystems worldwide.
Joint plant-spraypoint detector with ConvNeXt modules and HistMatch normalization
Authors: Ford Jonathan , Sadgrove Edmund , Paul David 
Published: January 2025

Abstract

Context
Serrated tussock (Nassella trichotoma) is a weed of national significance in Australia which offers little to no nutritional value to livestock, and has the potential to reduce carrying capacity and agricultural return of infested pastures.

Aims
The aim of this study was to adapt existing Convolutional Neural Networks (CNNs) for plant segmentation and spraypoint detection in the challenging environments of pastures.

Methods
CNNs that were designed for joint plant and stem segmentation in crop fields were repurposed for dual-task applications in pastures. Given the poor performance of these models in complex pasture environments, a new model drawing inspiration from the recently proposed ConvNeXt was developed, tested for its effectiveness on unseen field data, and enhanced with a novel normalization technique, called HistMatch.

Key results
Experimentation demonstrated that unlike pre-existing models, which were designed for the simpler environments encountered in early-stage crop fields, our model was able to generalize well to growing conditions not seen during training, achieving 0.807 mIoU and 0.796 F1-score for the plant and spraypoint tasks respectively. This is in comparison to pre-existing models, which achieved 0.270 - 0.454 mIoU and 0.073 - 0.496 F1-score for the same tasks. These results were further improved to 0.854 mIoU and 0.806 F1-score using HistMatch normalization. In spite of greater model complexity, our model had a inference time of 15.7 ms which was comparable to pre-existing models, and suitable for real-time applications.

Conclusion
Models with greater complexity are required for the relatively complex environments encountered in pastures, but this greater complexity need not come at the expense of real time capability. HistMatch normalization can improve model accuracy, and is particularly effective in cases where models are struggling to generalize well to testing conditions that vary significantly from those seen during training.

Implications and impacts
The successful adaptation and improvement of CNNs for weed management in pastures could significantly reduce the reliance on blanket herbicide application. HistMatch normalization could also be considered for other agricultural applications, including weed management and disease detection in crop fields and orchards.
Dynamics of particle segregation and its impact on mechanical properties
Authors: Dai Shaoheng , Gao Feng , Niu Gang , He Xuzhen , Zhang Sheng , Sheng Daichao 
Published: January 2025

Abstract
Particle segregation is a widespread phenomenon in nature. Vertical vibration systems have been a focal point in studying particle segregation, providing valuable insights into the mechanisms and patterns that influence this process. However, despite extensive research on the mechanisms and patterns of particle separation, the consequences, particularly the mechanical properties of samples resulting from particle segregation, remain less understood. This study aims to investigate the segregation process of a binary mixture under vertical vibration and examine the consequences through monotonic and cyclic triaxial drained tests. The results reveal that large and small particles segregate nearly simultaneously, with more thorough separation observed for large particles. The segregation index, D

s
, effectively describes this evolution process, offering a quantitative metric for both mixing and segregation. Granular temperature analysis unveils three distinct states during segregation: solid-like, fluid-like, and solid–liquid transitional phase, corresponding to varying activity levels of particle segregation. Drained triaxial shear tests demonstrate the sensitivity of stress–strain relationships to the degree of segregation. Interestingly, ultimate strength is found to be essentially unrelated to the degree of segregation. When the segregation index approaches zero, signifying particles approaching a uniform distribution, the granular system reaches a harmonic state. This state exhibits optimal mechanical performance characterised by maximum peak stress, friction angle, and the highest elastic modulus. These findings underscore the potential impact of segregation on the mechanical response of granular mixtures and emphasise the necessity of a comprehensive understanding of particle segregation in soil mechanics.
Echocardiographic Updates in the Assessment of Cardiomyopathy
Authors: Liu Baoqiong , Suthar Kandarp , Gerula Christine M. 
Published: January 2025

Abstract

Purpose of Review
This review aims to provide an updated overview of the role of echocardiography in the assessment of cardiomyopathies, highlighting recent findings and technological advancements.

Recent Findings
Over the past few years, significant advancements in echocardiographic techniques have improved diagnostic accuracy and provided important prognostic value in the assessment of cardiomyopathies.

Summary
Cardiomyopathy is a group of diseases affecting the heart muscle. Echocardiography, a non-invasive imaging modality provides crucial information on cardiac structure, function, and hemodynamics. Recent advancements, including strain imaging, speckle-tracking, and 3D echocardiography enhance the precision of structural and functional assessments, while artificial intelligence integration improves diagnostic accuracy and workflow efficiency. These advancements not only refine diagnostic capabilities but also provide prognostic insights and facilitate better patient outcomes.
Witchcraft, Envy, and Norm Enforcement in Mauritius
Authors: Willard Aiyana K. , Rosun Nachita , Lesage Kirsten , Horský Jan , Xygalatas Dimitris 
Published: January 2025

Abstract
Recent research has shown that an array of religious beliefs can be used to enforce socially normative behaviour, but the application of these theories to other supernatural beliefs, including witchcraft, is still nascent. Across two pre-registered studies in Mauritius, we examine how witchcraft is believed to be caused by envy and how this belief can create and enforce social norms around not causing envy. Data was collected in-person in Mauritius. In study 1 (N = 445), we found that both practicing witchcraft and being motivated by envy or self-interest increase perceptions of harm. These motivations also increase the rate with which people suggest a person was doing witchcraft, with envy having the stronger effect. Belief that someone was doing witchcraft increases the negativity with which one views that person and damages their reputation. In study 2 (N = 292), we found that when a person breaks a norm around causing envy, participants believe that a subsequent misfortune is cause by witchcraft, but not by God. When someone acts selfishly towards others a subsequent misfortune is believed to be caused by God but not witchcraft. This suggests that witchcraft beliefs, but not religious ones, are enforcing norms around preventing envy. Together, these studies suggest that witchcraft beliefs can support locally specific social norms, and that these norms might be different than those supported by religion.
Sex dimorphic associations of Prader–Willi imprinted gene expressions in umbilical cord with prenatal and postnatal growth in healthy infants
Authors: Mas-Parés Berta , Carreras-Badosa Gemma , Gómez-Vilarrubla Ariadna , De Arriba-Muñoz Antonio , Lafalla-Bernard Olivia , Prats-Puig Anna , De Zegher Francis , Ibañez Lourdes , Haqq Andrea M. , Bassols Judit , Lopez-Bermejo Abel 
Published: January 2025

Abstract

Background
The impact of Prader–Willi syndrome (PWS) domain gene expression on the growth of healthy children is not well understood. This study investigated associations between PWS domain gene expression in umbilical cord tissue and prenatal and postnatal growth, considering potential sex differences.

Methods
Relative gene expression of paternally expressed MAGEL2, NDN, and SNURF-SNRPN, and the small nucleolar RNAs SNORD116 and SNORD115 were determined by real-time quantitative polymerase chain reaction in umbilical cord tissue from 122 healthy newborns (59 girls and 63 boys). Gene expression levels were correlated with auxological measures at birth, infancy, and childhood (ages 2, 4, and 6 years).

Results

MAGEL2, NDN, SNORD116, and SNORD115 expression in the umbilical cord was negatively associated with birth weight, length, and placental weight (P < 0.001). Postnatally, these genes were positively associated with weight and length at 3 months (P < 0.001) and weight gain from birth to ages 1, 2, and 4 years (P < 0.01). Negative associations at birth were stronger in girls (P < 0.001), while positive associations during infancy and childhood were stronger in boys (P < 0.001). MAGEL2, SNORD116, and SNORD115 expression predicted early-postnatal growth, explaining the higher growth rate in boys compared to girls and accounting for sex differences up to 1.5 kg in weight and 3 cm in height during infancy.

Conclusions
Paternally expressed PWS domain gene expression in the umbilical cord was negatively associated with prenatal growth and positively with early-postnatal growth in healthy infants. This gene expression may predict early human postnatal growth and promote the well-known sex dimorphism in growth. These results can also help in understanding the etiology of PWS, which remains unclear.

Graphical abstract
Significance of Avicennia Marina in the Arabian Gulf Environment: A Review
Authors: Haseeba Kaiprath Puthiyapurayil , Aboobacker Valliyil Mohammed , Vethamony Ponnumony , Al-Khayat Jassim Abdulla 
Published: January 2025

Abstract
Mangroves are ecologically important and highly productive intertidal plants that thrive in saline to brackish environments in tropical and subtropical shallow-water habitats. Avicennia marina is the only mangrove species found in the Arabian Gulf (hereafter the ‘Gulf’) coastal region, marking the northernmost boundary of mangroves in the Indo-Pacific region. This species predominantly inhabits sandy substrate with low organic content and is recognized for its high tolerance to extreme temperature and salinity variations. It has also proved its resilience to the impact of the Gulf War oil spill. Despite these challenges, mangrove ecosystems in this region continue to provide critical ecosystem services, sustaining biodiversity, livelihood support, climate change mitigation, and recreational opportunities. However, in the past two decades Gulf mangroves have been facing significant threats, including extensive coastal development, shoreline modification by establishing marinas and ports, and climate change-associated impacts such as sea-level rise and altered precipitation patterns, which pose significant risks to this fragile ecosystem. These challenges endanger mangrove ecosystems’ structure and productivity and present immediate and long-term risks to associated species and ecosystem services. However, recent years have witnessed successful mangrove restoration projects, leading to expansion and notable ecological advantages, including increased biodiversity and improved shoreline protection. This review addresses the current knowledge gap by providing an overview of Avicennia marina distribution in the Gulf, exploring the significance of its ecosystem services, analyzing the impacts of stressors, and examining conservation and restoration efforts, with attention to nature-based and biotechnological approaches for promoting mangrove growth and conservation in the Gulf.
Sequence-variable attention temporal convolutional network for volcanic lithology identification based on well logs
Authors: Feng Hanlin , Zhang Zitong , Zhang Chunlei , Zhong Chengcheng , Ma Qiaoyu 
Published: January 2025

Abstract
A Sequence-Variable Attention Temporal Convolutional Network (SVA-TCN) is proposed for lithology classification based on well log data. This study aims to address the issue that native TCN pays insufficient attention to crucial logging variables and sequence structural features in well log tasks. A novel Sequence-Variable Attention Mechanism module is designed to effectively extract features by adding the bidirectional attention mechanism, which comprises the sequence attention mechanism and the variable attention mechanism. The sequence attention mechanism, acting along the depth dimension, focuses on modeling the multiscale sequence features. The variable attention mechanism, working along the logging variable dimension, contributes to learning the importance of different logging variables and exploring the internal correlations among them. To verify the validity of the model, experiments are conducted in the volcanic reservoir of the Yingcheng Formation in the Xujiaweizi Depression of the Songliao Basin. Seven logging variables sensitive to lithological changes are selected, including compressional wave slowness, density, and compensated neutron logging, etc., to construct a lithology identification model using SVA-TCN. Compared with machine learning and deep learning methods, the SVA-TCN demonstrates a remarkable accuracy of 99.00%, surpassing the accuracy of the comparison methods by 0.37–17.69%. The findings of this study indicate that the SVA-TCN model is well-suited for lithology identification in volcanic reservoirs. It provides more reliable prediction results and exhibits good stability and generalization, offering a new avenue to volcanic lithology identification.
A variable transformation to box constraints for solving variational inequalities
Authors: Banz Lothar , Schröder Andreas 
Published: January 2025

Abstract
For a class of linear variational inequalities resulting, e.g., from Signorini contact problems, a simple variable transformation (or synonymously a basis transformation) can be used to convert the linear inequality constraints into simple box constraints. This enables the use of efficient, stable and simple to implement iterative solvers. This manuscript shows how this variable transformation can be implicitly incorporated within an accelerated projected SOR scheme (APSOR) as well as the primal-dual active set method. In particular, some possible acceleration steps are proposed and the global convergence of the APSOR scheme is proven. Moreover, some implementational aspects are discussed and it is demonstrated that the accelerated projected symmetric SOR scheme is competitive to the locally quadratic converging primal-dual active set method.
The role of green networks for environmental innovation in European regions
Authors: Fabrizi Andrea , Fiorelli Cristiana , Meliciani Valentina 
Published: January 2025

Abstract
This paper investigates the role of green research networks in green innovation capabilities (proxied by green patents) in European regions. Our hypothesis is based on the idea that cross-border collaboration facilitates the diffusion of knowledge, thereby favouring the green innovation of the regions belonging to the network. We exploit information contained in the European Framework Programmes by looking at the role of intra and extra-regional collaborations and at the diversity of institutional partners. We find that both intra and extra regional collaborations matter for green innovation, although external knowledge appears to be more relevant. We also find a positive effect of firms in the network, of projects involving both firms and universities and a non-linear effect of network heterogeneity. We discuss the implications of the results for green innovation policies.
App Assisted Depression Self-Management in Integrated Primary Care: A Pilot Study Exploring Patient and Provider Experiences
Authors: Johnson David J. , Emerson Margaret R. , Dinkel Danae , Thomas Robbin , Watanabe-Galloway Shinobu , Mickles Maria S. , Culjat Carli 
Published: January 2025

Abstract
The integrated primary care (IPC) setting is currently the de facto institution for accessing mental healthcare among underserved populations. However, many barriers exist for these populations when seeking regular and adequate mental healthcare, including provider availability and transportation challenges. This study piloted the incorporation of mindLAMP, a mHealth app, into IPC treatment to offer a low-resource intervention designed to improve and expand access to mental healthcare for underserved patients between face-to-face visits. This study gathered data from a sample of both behavioral health providers and patients (N = 6) in IPC settings to pilot the use of the mindLAMP app among traditionally underserved patients with depression. The Patient Activation Measure and Patient Health Questionnaire were used to assess patient outcomes in addition to conducting a directed content analysis on qualitative data obtained with interviews of both patients and providers. Results from the mixed methods study design indicate that implementing this digital intervention in the IPC setting is feasible and positively experienced by both patients and providers. Perceived positive outcomes were reported by both patients and providers after incorporating the mHealth app into their mental healthcare. However, both patients and providers discussed barriers to mHealth app use that need to be addressed in order to accommodate more widespread use. Further, constraints and barriers at the provider, patient, and healthcare system levels were discussed and need to be examined further to increase the implementation of this multilevel digital intervention.
Bivalve shell beds in the Triassic of the Sierra De Gádor (Alpujárride complex, Betic Cordillera, SE Spain)
Authors: Ros-Franch Sonia , Sola Fernando , Braga Juan C. , Márquez-Aliaga Ana 
Published: January 2025

Abstract
The Peñón the Bernal section in the Sierra de Gádor comprises a succession of limestones, marly limestones and marls in the upper part of the Gádor unit, which belongs to the Alpujárride Complex in the Internal Zones of the Betic Cordillera. Several shell beds in this section and the locality of La Zarba in the same unit include relatively well-preserved macroinvertebrate assemblages. These beds reflect shell concentrations caused by storms and later colonised by epifaunal communities of cemented, epibyssate and endobyssate bivalves. Sedimentary features, fossil traces, and composition of bivalve assemblages indicate that the succession accumulated in a peritidal environment in a restricted carbonate platform with cyclic changes in water depth. The identified bivalve and ammonoid taxa suggest a Ladinian (Middle Triassic) age for the limestones, marly limestones and marls culminating the Gádor unit, so far considered as Upper Triassic in age. Like in other domains in the Betic Cordillera, the macroinvertebrate fauna has strong affinities with the Sephardic bioprovince.