bluuebunny/crossref_metadata_2025_split huggingface dataset:
Restarts Subject to Approximate Sharpness: A Parameter-Free and Optimal Scheme For First-Order Methods
Authors: Adcock Ben , Colbrook Matthew J. , Neyra-Nesterenko Maksym 
Published: February 2025

Abstract
Sharpness is an almost generic assumption in continuous optimization that bounds the distance from minima by objective function suboptimality. It facilitates the acceleration of first-order methods through restarts. However, sharpness involves problem-specific constants that are typically unknown, and restart schemes typically reduce convergence rates. Moreover, these schemes are challenging to apply in the presence of noise or with approximate model classes (e.g., in compressive imaging or learning problems), and they generally assume that the first-order method used produces feasible iterates. We consider the assumption of approximate sharpness, a generalization of sharpness that incorporates an unknown constant perturbation to the objective function error. This constant offers greater robustness (e.g., with respect to noise or relaxation of model classes) for finding approximate minimizers. By employing a new type of search over the unknown constants, we design a restart scheme that applies to general first-order methods and does not require the first-order method to produce feasible iterates. Our scheme maintains the same convergence rate as when the constants are known. The convergence rates we achieve for various first-order methods match the optimal rates or improve on previously established rates for a wide range of problems. We showcase our restart scheme in several examples and highlight potential future applications and developments of our framework and theory.
Association patterns between farmland abandonment and the non-native Common Waxbill (Estrilda astrild) in the eastern Iberian Peninsula
Authors: Lucio-Puig Pau , Muñoz-Mas Rafael , Garófano-Gómez Virginia , Merle Hugo , Gómez Jaime , Monrós Juan S. 
Published: February 2025

Abstract
Pet trading has led to a growing number of bird introductions worldwide, which has negatively affected recipient ecosystems and native species. Studying the seasonal and spatial distribution patterns of native and non-native bird species, such as the Common Waxbill (Estrilda astrild), is fundamental for understanding the ecological interactions between these two groups of species. For this purpose, we ran a year-round survey of passerine communities in two Mediterranean wetlands of the eastern Iberian Peninsula and investigated Common Waxbill’s feeding preferences. We studied the Common Waxbill association with native birds and their habitat use. The cluster analysis indicated that the Common Waxbill was the most abundant species of a distinct community, with peak presence in autumn. The random forests analysis revealed that it preferred human-modified land uses. Furthermore, farmland abandonment and the presence of two invasive plants, Pampas Grass (Cortaderia selloana) and Giant Reed (Arundo donax), positively influenced Common Waxbill presence. However, when eaten plants were divided into native, non-native and invasive, the Common Waxbill did not exhibit any preference. This corroborates that the Common Waxbill has significant ecological plasticity, which allows it to exploit a wide range of feeding resources in human-modified habitats. This study enhances knowledge of the interactions between native birds and invasive plants due to farmland abandonment, and of one of the most globally widespread non-native bird species. It highlights the need to monitor both groups of species and their long-term interactions.
Increased heterozygosity and body condition result from admixed translocation of the threatened Mogollon Narrow-headed Gartersnake (Thamnophis rufipunctatus)
Authors: Wood Dustin A. , Christman Bruce L. , Jennings Randy D. , Rose Jonathan , Nowak Erika , Schofer Justin , Vandergast Amy G. 
Published: February 2025

Abstract
Enhancing gene flow through translocations can be a useful tool in recovering small and isolated populations. However, it is not devoid of genetic risks, such as outbreeding depression in future generations, that can have negative consequences in terms of the establishment and mean fitness of the population. Studies that monitor the long-term effects of genetic rescue on populations in the wild are few, especially for snakes. We used long-term genetic monitoring and body condition indices to investigate the consequences of conservation translocation and genetic admixture in a Mogollon Narrow-headed Gartersnake (Thamnophis rufipunctatus) wild population. We compared genetic diversity and fitness metrics among the source and recipient populations to evaluate individual- and population-level fitness responses related to the conservation translocation. Our study found persistent captures with continued monitoring for over a decade post-release, and the recipient population showed lower inbreeding values and an increase in heterozygosity that was 19% higher than the two source populations. Snakes sampled in the recipient population had higher individual heterozygosity and body condition than those in the extant source and reference populations. Further, Bayesian regression models supported a significant positive relationship between heterozygosity and body condition after accounting for among-site differences, suggesting that efforts to increase heterozygosity can improve mean fitness in these populations. Our study highlights the potential benefits of conservation translocation from multiple source populations to restore the distribution and increase heterozygosity and population fitness of this threatened gartersnake. Alongside ecological restoration, translocation programs could be used to ensure both the persistence and resilience of populations throughout the species’ range.
Monitoring the Multiple Stages of Climate Tipping Systems from Space: Do the GCOS Essential Climate Variables Meet the Needs?
Authors: Loriani S. , Bartsch A. , Calamita E. , Donges J. F. , Hebden S. , Hirota M. , Landolfi A. , Nagler T. , Sakschewski B. , Staal A. , Verbesselt J. , Winkelmann R. , Wood R. , Wunderling N. 
Published: February 2025

Abstract
Many components of the Earth system feature self-reinforcing feedback processes that can potentially scale up a small initial change to a fundamental state change of the underlying system in a sometimes abrupt or irreversible manner beyond a critical threshold. Such tipping points can be found across a wide range of spatial and temporal scales and are expressed in very different observable variables. For example, early-warning signals of approaching critical transitions may manifest in localised spatial pattern formation of vegetation within years as observed for the Amazon rainforest. In contrast, the susceptibility of ice sheets to tipping dynamics can unfold at basin to sub-continental scales, over centuries to even millennia. Accordingly, to improve the understanding of the underlying processes, to capture present-day system states and to monitor early-warning signals, tipping point science relies on diverse data products. To that end, Earth observation has proven indispensable as it provides a broad range of data products with varying spatio-temporal scales and resolutions. Here we review the observable characteristics of selected potential climate tipping systems associated with the multiple stages of a tipping process: This includes i) gaining system and process understanding, ii) detecting early-warning signals for resilience loss when approaching potential tipping points and iii) monitoring progressing tipping dynamics across scales in space and time. By assessing how well the observational requirements are met by the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS), we identify gaps in the portfolio and what is needed to better characterise potential candidate tipping elements. Gaps have been identified for the Amazon forest system (vegetation water content), permafrost (ground subsidence), Atlantic Meridional Overturning Circulation, AMOC (section mass, heat and fresh water transports and freshwater input from ice sheet edges) and ice sheets (e.g. surface melt). For many of the ECVs, issues in specifications have been identified. Of main concern are spatial resolution and missing variables, calling for an update of the ECVS or a separate, dedicated catalogue of tipping variables.
Within-person Relations Between Social Skills and Symptoms of Oppositional Defiant Disorder and Conduct Disorder from Preschool to Adolescence – A Birth Cohort Study
Authors: Husby Silje Merethe , Ascaco Lourdes Ezpeleta , Wichstrøm Lars 
Published: February 2025

Abstract
Effective treatment of oppositional defiant disorder (ODD) and conduct disorder (CD) is dependent on etiological knowledge about what factors contribute to their development and maintenance. Previous research points to a role for social skills in this process, but findings are mixed, possibly due to methodological factors. A sample drawn from two birth cohorts (n = 1,079, 50.1% girls) in the city of Trondheim, Norway, was assessed biennially from age 4 to 16 years. Social skills were rated by teachers and parents and measures of symptoms of ODD and CD were obtained through diagnostic interviews of parents and children (from age 8). Bidirectional relations between within-person changes in social skills and symptoms of ODD and CD, respectively, were assessed by a random intercept cross-lagged model. Increased parent-reported social skills predicted reduced symptoms of both ODD and CD from preschool to adolescence, while teacher-rated social skills predicted reduced symptoms of ODD from 4 to 14 years but predicted reduced symptoms of CD from 6 to 8 years only. Symptoms of ODD and CD predicted reduced teacher-rated social skills on two occasions (6 to 8 years for CD and 8 to 10 years for ODD), but there were no reciprocal paths involving parent-rated social skills. Findings provide support for including social skills training in the prevention and treatment of symptoms of ODD and CD.
Artificial Intelligence (AI) – Powered Documentation Systems in Healthcare: A Systematic Review
Authors: Bracken Aisling , Reilly Clodagh , Feeley Aoife , Sheehan Eoin , Merghani Khalid , Feeley Iain 
Published: February 2025

Abstract
Artificial Intelligence (AI) driven documentation systems are positioned to enhance documentation efficiency and reduce documentation burden in the healthcare setting. The administrative burden associated with clinical documentation has been identified as a major contributor to health care professional (HCP) burnout. The current systematic review aims to evaluate the efficiency, quality, and stakeholder opinion regarding the use of AI-driven documentation systems. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines a comprehensive search was conducted across PubMed, Embase and Cochrane library. Two independent reviewers applied inclusion and exclusion criteria to identify eligible studies. Details of AI technology, document type, document quality and stakeholder experience were extracted. The review included 11 studies. All included studies utilised Chat generated pretrained transformer (Chat GPT, Open AI, CA, USA) or an ambient AI technology. Both forms of AI demonstrated significant potential to improve documentation efficiency. Despite efficiency gains, the quality of AI-generated documentation varied across studies. The heterogeneity of methods utilised to assess document quality influenced interpretation of results. HCP opinion was generally positive, users highlighted ease of use and reduced task load as primary benefits. However, HCPs also expressed concerns about the reliability and validity of AI-generated documentation. Chat GPT and ambient AI show promise in enhancing the efficiency and quality of clinical documentation. While the efficiency benefits are clear, the challenges associated with accuracy and consistency need to be addressed. HCP experiences indicate a cautious optimism towards AI integration, however reliability will depend on continued refinement and validation of the technology.
Random Descent Steps in a Probability Maximization Scheme
Authors: Csizmás Edit , Drenyovszki Rajmund , Szántai Tamás , Fábián Csaba I. 
Published: February 2025

Abstract
Gradient computation of multivariate distribution functions calls for considerable effort. Hence coordinate descent and derivative-free approaches are attractive. This paper deals with constrained convex problems. We perform random descent steps in an approximation scheme that is an inexact cutting-plane method from a dual viewpoint. We prove that the scheme converges and present a computational study comparing different descent methods applied in the approximation scheme.
Enhanced Electrocatalytic Performance of Eco-friendly Nanomaterial-Based Sensors for the Analysis of Pharmaceutically Important Compounds
Authors: Kucuk Ipek , Sadak Selenay , Silah Hulya , Dindar Cigdem Kanbes , Bozal-Palabiyik Burcin , Uslu Bengi 
Published: February 2025

Abstract
Green synthesis is a sustainable alternative to traditional chemical methods for nanomaterial-based sensors because it is more affordable, scalable, and does not involve any harmful contaminants when using green materials’ extracts as stabilizing and reducing agents for nanoparticle synthesis. The green-synthesized nanoparticles are extremely attractive for various pharmaceutical applications. This review article examines the most preferred eco-friendly nanomaterials, their synthesis and characterization, and their pharmaceutical applications based on the selected studies conducted in the last five years. It concludes that the green synthesis methods allow the transformation of metals into nanoparticles or green materials that act as precursors to carbon-based nanomaterials. The nanoscale materials obtained through green synthesis methods contribute to low toxic, environmentally benign, easy, and low-cost sensing and enhanced electrocatalytic performance.
Roles of researchers in inter- and transdisciplinary sustainability research: a reflection tool
Authors: Hofmann Benjamin , Salomon Hanna , Hoffmann Sabine 
Published: February 2025

Abstract
Inter- and transdisciplinary (ITD) research is increasingly called for and supported to promote sustainable transformation through knowledge co-production, knowledge integration, and solution development. The paper explores what is needed to support researchers in reflecting on their new roles in ITD research. We introduce a reflection tool that makes the growing literature on researchers’ roles in sustainability science applicable to ITD projects. Its design is based on the arguments that each researcher can have several roles within one research project and that focusing on a few key roles increases clarity compared to differentiating many specialized roles. The tool consists of (1) a researcher survey that operationalizes six prominent roles (traditional scientist, self-reflexive scientist, knowledge integrator, knowledge broker, process facilitator, and change agent), (2) a visualization of role profiles from the survey, and (3) a set of reflection questions on related opportunities, challenges, and coping strategies on individual and project level. We empirically applied the tool in two ITD research projects focused on sustainable food production and water and forest ecosystem management. Comparative application of the tool yielded diverse role profiles of researchers in both projects, with different patterns for senior and junior researchers as well as natural and social scientists. The reflection produced a collection of opportunities, challenges, and coping strategies that corroborates and extends insights from ITD literature. We discuss how the tool triggers multi-dimensional reflection on roles (individual and project level, role combinations, self-perception and external perception) and outline opportunities for further strengthening such reflection in ITD research.
Gate-to-Gate Life Cycle Assessment of Printed AlSi10Mg Laser Powder Bed Fusion Jobs in Additive Manufacturing Factories
Authors: Cozzolino Ersilia , Papa Ilaria , Astarita Antonello 
Published: February 2025

Abstract
Among the different advantages, powder bed fusion (PBF) processes allow obtaining the parts having complex geometries, avoiding additional steps to assemble the parts. As a result, time and costs are strongly reduced. Nevertheless, energy and resource consumption in PBF processes is not negligible. Given the lack of real primary data on additive manufacturing (AM) processes in open-source software life cycle assessment (LCA) databases, an in-depth analysis of real industrial situations on the of manufactured products is essential for global knowledge of the real green aspect of the technology used. What is more, studies existing in the literature are sometimes contradictory on the consumption of resources to perform the PBF processes. This is due to the high variability among the different systems used, on the one hand, and also to the proper and efficient use of the machine in AM environments. Also, since a lot of studies are based on the estimation of resource consumption, they can overestimate or underestimate, effectively, the real usage. Especially, on argon consumption, literature seems to be very contradictory. This work aims to fill this gap of knowledge existing. In particular, a “gate-to-gate” LCA of AlSi10Mg components produced by the laser powder bed fusion (LPBF) process is presented in this work. Four scenarios of different production mixes have been performed, monitored and evaluated from a sustainable perspective. Based on the results obtained by the LCA carried out using experimentally measured data of typically printed LPBF jobs, guidelines for sustainable decision-making in AM factories are provided.
On a classification of planar functions in characteristic three
Authors: Andreoli Samuele , Budaghyan Lilya , Coulter Robert , Haukenes Alise , Kaleyski Nikolay , Piccione Enrico 
Published: February 2025

Abstract
Planar functions are functions over a finite field that have optimal combinatorial properties and they have applications in several branches of mathematics, including algebra, projective geometry and cryptography. There are two relevant equivalence relations for planar functions, that are isotopic equivalence and CCZ-equivalence. Classification of planar functions is performed via CCZ-equivalence which arises from cryptographic applications. In the case of quadratic planar functions, isotopic equivalence, coming from connections to commutative semifields, is more general than CCZ-equivalence and isotopic transformations can be considered as a construction method providing up to two CCZ-inequivalent mappings. In this paper, we first survey known infinite classes and sporadic cases of planar functions up to CCZ-equivalence, aiming to exclude equivalent cases and to identify those with the potential to provide additional functions via isotopic equivalence. In particular, for fields of order

$$\varvec{3^n}$$

3
n

with

$$\varvec{n\le 11}$$

n
≤
11

, we completely resolve if and when isotopic equivalence provides different CCZ-classes for all currently known planar functions. Further, we perform an extensive computational investigation on some of these fields and find seven new sporadic planar functions over

$$\varvec{\mathbb {F}_{3^6}}$$

F

3
6

and two over

$$\varvec{\mathbb {F}_{3^9}}$$

F

3
9

. Finally, we give new simple quadrinomial representatives for the Dickson family of planar functions.
Success of deaf adults: a 30-year scoping review
Authors: Ferreiro-Lago Emilio , Pardo-Guijarro María Jesús , Gutierrez-Sigut Eva 
Published: February 2025

Abstract
This scoping review examines how academic, career, and life success of deaf and hard-of-hearing (DHH) individuals has been investigated, as well as which measures have been used to assess success and its associated factors. A search of studies published in English, Spanish and Portuguese between 1992 and 2022 in nine databases was conducted, complemented by a manual search. Of the 18,716 potential records retrieved (18,655 from databases and 61 from the manual search), 67 were included in this review, encompassing a total of 1,233,472 individuals. The results showed a predominant focus on academic and career success and to a lesser extent on life success, an educational and professional gap with the hearing population, gender inequalities among DHH people, and a preference for objective measures of success. A variety of factors influence success trajectories, including social capital, family support, and educational services for academic success; level of education for career success; and positive interpersonal relationships and membership in the deaf community for life satisfaction. Limitations regarding language and quality assessment are acknowledged.
Early Detection of Sugar Beet Cercospora Leaf Spot Disease Using Machine Learning-Assisted Thermal Image Processing Method
Authors: Tuğrul Koç Mehmet 
Published: February 2025

Abstract
Early diagnosis of diseases in agriculture is an important factor in reducing the negative environmental impacts by effectively and economically managing the losses caused by these diseases and reducing the use of chemicals. There are different options within the scope of remote sensing for the early detection of diseases. Among these, choosing a method that can detect diseases accurately without harming the plant and the environment is important. Today, positive developments have been made toward non-invasive and effective detection of diseases with thermal camera-based image processing techniques. In this context, there is potential for disease detection with data collection, image processing, and the determination of the characteristics of disease agents through thermal imaging. The research was based on Cercospora leaf spot (Cercospora beticola Sacc.) diseases which have significant economic loss potential in sugar beet. The effectiveness of the proposed method was evaluated in experiments involving Cercospora beticola, utilizing a climate station early warning system and UAV-based thermal images across three subjects and six replicate field trial plots. Analyses were made for the early detection of diseases by comparing thermal images taken from the field with multispectral images taken simultaneously. It was investigated whether it was possible to diagnose the disease early before physical symptoms were seen using image processing and machine learning methods. The variability of leaves was analyzed using field images, thermal images, and machine learning algorithms. Thermal imaging enables the rapid detection of potential disease development by measuring increases in leaf temperature in infrared wavelengths. However, a significant limitation of this method in practice is its sensitivity to climate factors such as air temperature and humidity, which can cause rapid fluctuations in the index. This study compared five machine learning algorithms based on four key metrics. MS imaging achieved about 25% higher accuracy in predicting early disease than TE imaging. This study indicates that thermal imaging provides valuable information but is not as effective as multispectral imaging in detecting early-stage stress factors related to diseases.