bluuebunny/crossref_metadata_2025_split huggingface dataset:
Broadband low-frequency bandgap in the elastic metamaterial under load conditions
Authors: Zhou Xuewen , Zeng Yi , Zhang Kun , Yang Zhen , Zhao Xiangwei , Xie Zhenzhen , He Chuanchuan 
Published: January 2025

Abstract
In the past two decades, metamaterials have captivated scientists worldwide due to their unique bandgap characteristics, which have been extensively validated through numerous numerical simulations and experiments. This study presents a novel elastic metamaterial designed to function under load conditions. The band structure was computed using finite element method (FEM) software, revealing a bandgap frequency range from 440 Hz to 2880 Hz, with a relative bandwidth of approximately 1.47. Experimental results from shaking table tests, alongside numerical simulations, have demonstrated the attenuation capabilities of the elastic metamaterial when subjected to a 40 kg load. This research not only offers a promising approach to controlling elastic waves but also introduces innovative design concepts for vibration isolation under load.
Propagation of envelope solitons in the presence of generalized (r; q) distribution of electrons
Authors: Akhtar N , Hussain S 
Published: January 2025

Abstract
The study has been made for modulational instability of ion acoustic waves in an electron-ion unmagnetized plasma where ions are dynamic and warm while electrons follows the generalized (r, q) distribution. The Krylov Bogoliubov Mitropolsky (KBM) method is employed to derive the nonlinear Schrödinger equation (NLSE). The dispersive and nonlinear coefficients P and Q are retrieved which depend onσ

i
(Temperature ratio) and spectral indices

r
,
q

.

The bright and dark solitons are excited for the case PQ > 0 and PQ < 0 respectively. Amplitude of bright solitons is characterized by a localized intensity peak and dark solitons by a localized intensity hole on a homogeneous background. The growth rates and modulationally stable/unstable zones are analyzed numerically. It is established that spectral indices (r, q) of electron distribution function and temperature ratio of ion to electron play a major role in the creation of bright/dark envelope solitons in electron-ion plasmas in the presence of nonthermal distribution.
Characterization of the crosstalk properties of image intensifier spot energy
Authors: Xu Shihao , Bai Yonglin , Li Ran , Cao Weiwei , Shi Dalian , Lv Linwei , Liang Xiaozhen , Gao Jiarui 
Published: January 2025

Abstract
In the field of high-energy particle detection, Intensified scientific CMOS (IsCMOS) cameras, which utilize image intensifier coupling, can enhance the input spot, generate a grey-value image of the improved spot, and subsequently reconstruct the crystal deposition energy based on the grey value. The image intensifier, playing a critical role in the crosstalk energy calibration of the spot, is a crucial component of the detector enhancement camera. Reducing the crosstalk of the spot output by the image intensifier contributes to the subsequent accurate calibration of high-energy cosmic ray energy. We have designed a test bed to quantify the spot energy crosstalk from Wavelength Shifting Fibers (WLSF) and conducted an in-depth analysis of the spot energy crosstalk in image intensifiers with varying MCP structures. The results indicate that the spot energy crosstalk is higher for weak signals and that increasing the MIP value of the input signal cannot effectively reduce the spot energy crosstalk when the spot is saturated. Nevertheless, when the spot is not saturated, the spot energy crosstalk can be effectively reduced by increasing the operating voltage of the MCP. Furthermore, reducing the aspect ratio of the MCP and increasing the opening area ratio can also mitigate the spot energy crosstalk in the image intensifier.&#xD;
Foundational guidelines for enhancing neurotechnology research and development through end-user involvement
Authors: Güemes Amparo , Costa Tiago da Silva , Makin Tamar R 
Published: January 2025

Abstract
Neurotechnologies are increasingly becoming integrated with our everyday lives, our bodies and our mental states. As the popularity and impact of neurotechnology grows, so does our responsibility to ensure we understand its particular implications on its end users, as well as broader ethical and societal implications. There are many different terms and frameworks to articulate the concept of involving end users in the technology development lifecycle, for example: ‘Public and Patient Involvement and Engagement’ (PPIE), ‘lived experience’, ‘co-design’ or ‘co-production’. The objective of this tutorial is to utilise the PPIE framework to develop clear guidelines for implementing a robust involvement process of current and future end-users in neurotechnology, with emphasis on patient involvement.
After an introduction that coveys the tangible and conceptual benefits of user involvement, we first guide the reader to develop a general strategy towards setting up their own PPIE process. We then help the reader map out their relevant stakeholders and provide advice on how to consider user diversity and representation. We also provide advice and tools on how to quantify the outcomes of the engagement.
We consolidate advice from various online sources to orient individual teams (and their funders) to carve up their own approach to meaningful involvement. Key outputs include a stakeholder mapping tool, methods to measure the impact of engagement, and a structured checklist for transparent reporting.
Enabling end-users and other stakeholders to participate in the development of neurotechnology, even at its earliest stages of conception, will help us better navigate our design around ethical, social, and usability considerations, and deliver more impactful technologies. The overall aim is the establishment of gold-standard methodologies for ensuring that patient and public insights are at the forefront of our scientific inquiry and product development.
Tropical montane forest loss dominated by increased 1–10 hectare-sized patches
Authors: He Xinyue , Spracklen Dominick V , Holden Joseph , Zeng Zhenzhong 
Published: January 2025

Abstract
Tropical forest loss continues across mountain regions at alarming rates, threatening biodiversity, carbon storage and ecosystem sustainability. To improve our understanding of the dynamics of tropical mountain forest loss, this study focuses on the trends in patch sizes of forest loss during the 21st century. The annual area of tropical mountain forest loss surged from 0.7 million hectares in 2001–2003 to >2.5 million hectares in 2019–2021. There was an increase across all categories in terms of the size of forest loss patches, but strikingly, more than half of this increase was attributed to the proliferation of intermediate-sized forest loss patches spanning 1–10 ha. Concurrently, there was a diminishing proportion of small-scale montane forest loss patches (<1 ha) across all tropical continents over time. Despite their reduced overall proportion, the annual area of small forest loss patches increased, primarily influenced by trends in the Asia-Pacific region. Our study provides up-to-date and spatially explicit information on the scale of tropical mountain forest loss, and temporal trends associated with these patterns, which is crucial for assessing the sustainability of mountain forest ecosystems, highlighting the need for targeted, region-specific strategies to slow or reverse forest loss.
Assessing the robustness and implications of econometric estimates of climate sensitivity
Authors: Storelvmo Trude , Yuan Menghan , Leirvik Thomas , Alterskjær Kari , Phillips Peter C B , Smith Chris 
Published: January 2025

Abstract
Earth’s transient climate response (TCR) quantifies the global mean surface air temperature change due to a doubling of atmospheric

CO
2

concentration after 70 years of a compounding 1% per year increase. TCR is highly correlated with near-term climate projections, and thus of relevance for climate policy, but remains poorly constrained in part due to uncertainties in the representation of key physical processes in Earth System Models (ESMs). Within state-of-the-art ESMs participating in the Coupled Model Intercomparison Project (CMIP6), the TCR range (1.1 ∘C–2.9 ∘C) is too wide to offer useful guidance to policymakers. Similarly, the sixth report of the Intergovernmental Panel on Climate Change, while not solely reliant on ESMs for its TCR assessment, produced a very likely range of 1.2 ∘C–2.4 ∘C. To complement earlier, ESM-based, estimates, we here present a new TCR estimate of 2.17 (1.72–2.77) ∘C (95% confidence interval), derived based on a statistical relationship between surface air temperature and observational proxies for its main drivers, i.e. changes in atmospheric greenhouse gases and aerosols. We show that, within uncertainty, this method correctly diagnoses TCR from 20 CMIP6 ESMs if the same input variables are taken from the ESMs that are available from observations. This increases confidence in the new observation-based central estimate and range, which is respectively higher and narrower than the mean and spread of the estimates from the entire ensemble of CMIP6. Many ESM-based estimates tend to produce TCRs lower than the observational range reported here. Our findings suggest that a misrepresentation of the aerosol cooling effect could be the cause of this discrepancy. Further, the revised TCR estimate suggests a downward revision of the remaining carbon budgets aligned with the overarching goal of the Paris agreement.
Hybrid data augmentation strategies for robust deep learning classification of corneal topographic maptopographic map
Authors: Chaari Abir , Kallel Imen Fourati , Kammoun Sonda , Frikha Mondher 
Published: January 2025

Abstract
Deep learning has emerged as a powerful tool in medical imaging, particularly for corneal topographic map classification. However, the scarcity of labeled data poses a significant challenge to achieving robust performance. This study investigates the impact of various data augmentation strategies on enhancing the performance of a customized convolutional neural network model for corneal topographic map classification. We propose a hybrid data augmentation approach that combines traditional transformations, generative adversarial networks, and specific generative models. Experimental results demonstrate that the hybrid data augmentation method, achieves the highest accuracy of 99.54%, significantly outperforming individual data augmentation techniques. This hybrid approach not only improves model accuracy but also mitigates overfitting issues, making it a promising solution for medical image classification tasks with limited data availability.
Research on the Driving Paths of Bilateral Renewable Energy Trade between Neighboring Countries and China from the Perspective of Behavioral Attribution
Authors: Yang Zijie 
Published: January 2025

Abstract
The bilateral trade in renewable energy between China and its neighbors represents a global cooperative endeavor influenced by various factors. However, few studies have conducted a comprehensive examination of the behavioral attribution-based reasons behind the bilateral cooperation in renewable energy between China and its neighbors. This study breaks down the aspects of cooperation willingness, cooperation capability and cooperation environment, along with their respective components, based on international behavioral attribution theory. To determine the configurational pathways of each constituent element's role in the bilateral renewable energy trade cooperation between each case country and China, this paper employs a mixed-method approach of Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fs-QCA), selecting 20 'country-stage' cases based on data availability. The study finds that for neighboring nations to engage in substantial renewable energy trade with China, extensive use of market-incentive policy instruments is essential. Additionally, favorable bilateral political ties, a lower level of nationalism and increased energy security within surrounding nations are critical factors in fostering extensive renewable energy trade with China. Meanwhile, the economic vulnerability and reliance of different nations on China have varying impacts on their final trade in renewable energy with China. The study identifies five configurational pathways that drive large-scale renewable energy trade cooperation between neighboring countries and China, as well as four configurational pathways that constrain such cooperation.
Deal or no deal: U.S. homeowners’ willingness to pay for residential building retrofits
Authors: Berzolla Zachary , Meng Ting , Reinhart Christoph 
Published: January 2025

Abstract
With buildings accounting for nearly 40% of global carbon emissions, deep energy retrofits to reduce emissions in the majority of existing buildings are necessary to meet global climate goals. Yet a deep retrofit can easily cost $50 000 or more, limiting its affordability and thus likelihood of adoption for many households. This paper presents a model of homeowners willingness to pay for different retrofits. The model is meant to quantify the willingness of homeowners to make the economic decision to upgrade their buildings. It can be used to inform the required subsidies to catalyze adoption across the socio-economic spectrum and thus achieve emissions reduction goals. The model is based on a survey of 1200 homeowners in the Northeastern U.S. A logistic regression is carried out on whether or not a respondent is willing to pay for a retrofit. The predictor variables are based on the socioeconomic factors asked in the survey. Key factors influencing homeowners’ willingness to pay include the upfront cost, their income, and their concern about emissions from their home. Notably, increasing concern about emissions for all respondents from the median of ‘slightly concerned’ to ‘extremely concerned’ led to a 50% savings in retrofit subsidy cost. The model can help jurisdictions throughout the U.S. better allocate limited resources to catalyze higher adoption rates of building retrofits.
Using satellite-based sun-induced chlorophyll fluorescence and spectral reflectance for improving terrestrial CO2 flux estimates of India
Authors: Ravi Aparnna , Pillai Dhanyalekshmi , Gerbig Christoph , Sitch Stephen , Zaehle Sonke , Thilakan Vishnu , Jha Chandra Shekhar , Mathew Thara Anna 
Published: January 2025

Abstract
Significant uncertainties in terrestrial carbon fluxes exist in regions with limited ground-based observations, impacting our understanding of ecosystem carbon dynamics and emission reduction needs. This is particularly true for areas with sparse measurement networks, like India. To address this, we explore the potential of satellite measurements from various missions such as Sentinel-5 Precursor (S5P) and the Orbiting Carbon Observatory-2 (OCO-2) to improve terrestrial biosphere CO2 fluxes of India. We follow a data-driven approach, which simulates spatial and temporal distributions of Gross Primary Productivity (GPP), Net Ecosystem Exchange (NEE), and ecosystem Respiration (Reco). We improve these model predictions by additionally using satellite-based Solar Induced chlorophyll Fluorescence (SIF), Soil Temperature (ST), and Soil Moisture (SM) specific to the vegetation classes of the domain. Different model refinements were performed to present the improved hourly distributions of terrestrial biospheric CO2 fluxes on a 0.1◦×0.1◦ grid from 2012 to 2020. Among them, the best-performing model simulations show reasonable agreement with eddy covariance observations for 2012 - 2018. For example, our best NEE and GPP predictions are highly correlated with observations with squared correlation coefficient (R2) values of 0.68 (NEE) and 0.74 (GPP) at the monthly scale for 2018. Based on our improved estimations, the annual NEE and GPP show values within the range from -0.38 Pg C yr-1 to -0.53 Pg C yr-1 (land C sink) and 3.39 Pg C yr-1 to 3.88 Pg C yr-1, respectively over India for 2012 - 2020. Our novel approach and findings highlight the potential of satellite-based SIF measurements to detail the ecosystem-scale vegetation responses across various biomes in India. The use of satellite observations, as demonstrated in this study, offers a scalable solution for regions lacking sufficient ground-based observations to estimate biospheric carbon fluxes reliably.
Near-infrared photoimmunotherapy: mechanisms, applications, and future perspectives in cancer research
Authors: Allen Derek , Szoo Madeline JoAnna , van Bergen Tessa D , Seppelin Ani , Oh Jeonghyun , Saad Mohammad A 
Published: January 2025

Abstract
Photoimmunotherapy (PIT) involves the targeted delivery of a photosensitizer through antibody conjugation, which, upon binding to its cellular target and activation by external irradiation, induces localized toxicity. This approach addresses several limitations of conventional cancer therapies, such as chemo- and radiotherapies, which result in off-target effects that significantly reduce patient quality of life. Furthermore, PIT improves on the challenges encountered with photodynamic therapy (PDT), such as nonspecific localization of the photosensitizer, which often results in unintended toxicities. Although PIT was first proposed in the early 1980s, its clinical applications have been constrained by limitations in antibody engineering, conjugation chemistries, and optical technologies. However, recent advances in antibody–drug conjugate (ADC) research and the emergence of sophisticated laser technologies have greatly benefited the broader applicability of PIT. Notably, the first near-infrared photoimmunotherapy (NIR-PIT) treatment for head and neck cancer has been approved in Japan and is currently in phase III clinical trials in the USA. A significant advantage of PIT over traditional ADCs in cancer management is the agnostic nature of PDT, making it more adaptable to different tumor types. Specifically, PIT can act on cancer stem cells and cancer cells displaying treatment resistance and aggressive phenotypes—a capability beyond the scope of ADCs alone. This review provides an overview of the mechanism of action of NIR-PIT, highlighting its adaptability and application in cancer therapeutics, and concludes by exploring the potential of PIT in advancing cancer treatments.
A new phylogenetic framework for the genus Kalanchoe (Crassulaceae) and implications for infrageneric classification
Authors: Rodewald Seraina E , Klein David-Paul , Shtein Ronen , Smith Gideon F , Joyce Elizabeth , Morales Briones Diego F , Bernhard Sven , Letsara Rokiman , Mertes Hannah , Hühn Philipp , Kadereit Gudrun 
Published: January 2025

Abstract

Background and Aims
Kalanchoe is a diverse genus in the Crassulaceae, with centres of diversity in Madagascar and sub-Saharan Africa. The genus is known for its popularity in horticulture, its use as a model system for research on CAM photosynthesis and vegetative reproduction, its high invasive potential, and its use in traditional medicine. The genus-rank circumscription and infrageneric classification of Kalanchoe have been the subject of debate for centuries, especially regarding the status and rank of what is now treated as K. subg. Bryophyllum and K. subg. Kitchingia. We aim to generate a densely sampled phylogeny of Kalanchoe s.l. and evaluate the current infrageneric classification system.

Methods
We inferred a phylogenetic tree for Kalanchoe using a ddRAD sequencing approach, covering 70% of taxa and four out of five subgenera currently recognised in the genus.

Key Results
We recovered four well-supported clades, partially corresponding to the current subgeneric classification. Kalanchoe subg. Calophygia resolves as sister to the rest of the genus. The relationships among the three remaining clades, however, receive less support. The predominantly mainland African K. subg. Kalanchoe forms a strongly supported clade that resolves as sister to K. subg. Bryophyllum. These two clades are together sister to a clade containing mainly species from K. subg. Kitchingia and K. sect. Pubescentes.

Conclusions
The current subgeneric classification of Kalanchoe is partially backed up by our phylogenetic tree but requires further refinement. The tree topology suggests a Malagasy origin of the genus and one dispersal event to the African mainland, with subsequent dispersal from continental Africa to the Arabian Peninsula and Southeast Asia. The formation of bulbils on the leaf margin is restricted to a larger clade within K. subg. Bryophyllum and thus only evolved once. Our tree provides a framework for further taxonomic, evolutionary, and physiological research on the genus.
Mapping Longitudinal Psychiatric Signatures in Huntington’s Disease
Authors: De Paepe Audrey E , Giannoula Alexia , Garcia-Gorro Clara , Rodriguez-Dechicha Nadia , Vaquer Irene , Calopa Matilde , Sanz Ferran , Furlong Laura I , de Diego-Balaguer Ruth , Camara Estela 
Published: January 2025

Abstract

Objective
Although Huntington’s disease is characterized by motor onset, psychiatric disturbances may present years prior and affect functioning. However, there is inter-individual variability in psychiatric expression and progression. This study therefore strives to stratify longitudinal psychiatric signatures that may inform Huntington’s disease prognosis, with potential clinical applications.

Methods
Forty-six Huntington’s disease gene carriers (21 premanifest, 25 manifest; 31 female; age range 25–69) underwent short-Problem Behavior Assessment for depression, irritability, apathy, and dysexecutive behaviors for up to six longitudinal visits. The Disease Trajectories software, a machine-learning approach, was employed to perform unsupervised clustering of psychiatric trajectories. Linear fits were calculated for each cluster. Lastly, the main clusters of shared trajectories were assessed for group differences in demographic and clinical characteristics.

Results
The Disease Trajectories analysis software identified two main psychiatric patterns comprising premanifest and manifest patients that explained 54% of the sample. These two clusters evinced a dissociation in the development of depression and irritability; the first cluster was defined by increasing irritability with no depression and the second by a rise-and-fall in depression with no irritability. Both clusters showed a longitudinal increase in clinically relevant apathy and dysexecutive behaviors.

Conclusions
Ultimately, through the detection of individual-level psychiatric trajectories with machine-learning, this exploratory study reveals that a dissociation of depression and irritability is apparent even in premanifest stages. These findings underscore individual differences in the severity of longitudinal multivariate clinical characteristics for real-world patient stratification, with implications for precision medicine.