bluuebunny/crossref_metadata_2025_split huggingface dataset:
Outcomes of the intraoperative relaxed muscle positioning technique in strabismus surgery for thyroid eye disease
Authors: Eisenberg Marina A. , Traboulsi Elias I. 
Published: February 2025

ABSTRACTThyroid eye disease (TED) can cause incomitant strabismus with muscle restriction, resulting in diplopia and patient discomfort and discontent. Surgical correction of TED‐associated restrictive strabismus has been attempted, with fixed and adjustable suture success rates varying from 38% to 82%. This paper reviews five articles that explored the utility of the intraoperative relaxed muscle positioning (IRMP) technique in patients with restrictive strabismus due to TED. While IRMP is a useful technique for addressing TED‐related strabismus and is effective in patients with incomitant strabismus, disease reactivation, staged and unplanned surgeries, combined horizontal and vertical deviations, and previous orbital decompression surgeries, it is probable that this approach can be utilized in children with restrictive types of strabismus. Therefore, further studies are required.
Health or Economics: Unpacking Concern Type and Severity in the COVID‐19 Pandemic
Authors: Penta Samantha , Koyratty Nadia , Clay Lauren , Silver Amber 
Published: February 2025

ABSTRACTSARS‐CoV‐2 represents a direct threat to the population, with concerns for public health being cited as justification for a range of protective action recommendations and governmental policies. At the same time, the economic impacts associated with both experiencing COVID‐19 and/or its associated protective measures have featured prominently in debates of how the pandemic should be managed. While the widespread presence of COVID‐19 has produced large‐scale exposure to the pandemic, the wide variation in direct exposures to the virus, variations in individual and household vulnerability, as well as governmental approaches to managing the pandemic have resulted in wide variation in individual pandemic experiences. This study builds on existing research on threat perception by looking at two types of threat perceptions for COVID‐19: health concerns and economic concerns. Using survey data of adults in New York, Louisiana, and Washington states, this study explores people's level of concern for health and economic impacts to themselves and their associations with several pandemic‐related stressors. The results of this study demonstrate that reduced income during the pandemic was the only stressor that appeared to influence participants' COVID‐19 concerns across multiple analyses.
Extending GEO Satellite Operations: Timing and Challenges of Inclined Orbit Transitions
Authors: Yilmaz Umit Cezmi 
Published: February 2025

ABSTRACTMaximizing the operational lifespan of Geostationary Earth Orbit (GEO) satellites is a critical objective for satellite operators. Throughout their service life, satellites are maintained within a designated control box through North–South (NS) and East–West (EW) station‐keeping maneuvers. NS maneuvers, which adjust the satellite's inclination relative to the equatorial plane, require significantly more propellant than EW maneuvers. As a result, operators often choose to discontinue NS maneuvers, allowing the satellite to transition into an “inclined orbit” while relying on EW maneuvers to manage orbit drift and maintain longitude positioning. This approach is typically adopted when the satellite nears the end of its operational life or when fuel conservation becomes a priority. A critical challenge in this process is determining the optimal timing for initiating inclined orbit, which involves carefully balancing factors such as fuel availability, satellite health, mission requirements, and operational constraints. This study provides a comprehensive analysis of these factors, with a detailed exploration of timing optimization, and concludes with a practical case study to illustrate the findings.
Lipidome Alterations of Prefrontal Cortex and Hippocampus of Mice‐Induced Sleep Deprivation
Authors: Guleken Zozan , Çalis Hakan , Ceylan Zeynep , Depciuch Joanna , Cebulski Jozef , Saribal Devrim 
Published: February 2025

AbstractChronic sleep deprivation (SD) impairscognitive function and promotes neurodegenerative changes, particularly in thehippocampus and prefrontal cortex. Study investigates the biochemical andimmunological effects of SD and evaluates the neuroprotective properties ofmelatonin using Fourier Transform Raman (FT‐Raman) spectroscopy. Hippocampaland cortical tissues from four groups of micecontrol, melatonin‐treated, sleep‐deprived, and sleep‐deprived with melatoninwere analyzed. FT‐Ramanspectra revealed significant changes in C─H bending, C─C vibrations, amides, and lipids between control and SD groups. SD led to increased lipidperoxidation, especially in the hippocampus, while melatonin treatment reducedlipid peroxidation and amide levels. In the cortex, melatonin mitigatedbiochemical alterations associated with SD, such as elevated amides and lipids.Immunological assessments showed SD‐induced changes in T helper (Th) cellsubpopulations, including an increase in effector Th cells and a decrease innaive Th cells. Melatonin treatment normalized these changes. PrincipalComponent Analysis (PCA) indicated clear separation between control and SDsamples, with PC1 and PC2 explaining 95.58% of the variance. Classificationusing the GS‐kNN algorithm achieved prefrontal cortex (accuracies: 79.167%) andin the hippocampus (accuracies: 70.833%). These findings highlight SD‐inducedbiochemical and immunological alterations and suggest melatonin'sneuroprotective role in reducing oxidative stress and modulating immunedysfunction.
Transition Metal‐Based Perovskite Derivatives for Selective CO2 Photoreduction: Role of Orbital Occupancy
Authors: Choudhary Shivani , Tailor Naveen Kumar , Venkanna Guguloth , Singh Nikhil , Nayak Pabitra Kumar , Kaur Jagmeet , Deka Diku Raj , Peter Sebastian C. , Ghosh Dibyajyoti , Pant Kamal Kishore , Tripathi Komal , Satapathi Soumitra 
Published: February 2025

AbstractTransition metals are renowned for their effective catalytic properties. Incorporating transition metals into halide perovskite derivatives is a key strategy for tuning the properties of perovskites to enhance their photocatalytic performance. Understanding the d‐orbital occupancy and spin activity of these transition metals in the CO2 photoreduction process is essential for fully realizing the photocatalytic potential of these materials. In this study, layered perovskite derivatives are synthesized using cobalt (Co) and copper (Cu) as transition metal components. We observed that Cu and Co exhibit complementary absorption properties attributed to their d‐orbital configuration. Additionally, (DMAP)2CuCl4 (DMAP = 4‐Dimethylaminopyridine) exhibited the highest performance in CO2 photoreduction with remarkable selectivity for CH4 formation (≈97%). Pressure‐dependent experiments showed that higher pressures enhance catalytic activity by improving CO2 saturation and adsorption, accelerating the reaction rate and boosting product yield. The ferromagnetism, hysteresis, and strong spin species detection of (DMAP)2CuCl4 enhance carrier separation and charge availability, boosting CO2 conversion efficiency. Further, the first‐principles‐based atomistic computations reveal that a more delocalized conduction band edge makes mobile electrons available for CO2 reduction in (DMAP)2CuX4. These findings guide the design of selective CO2 reduction photocatalysts and highlight layered perovskite derivatives for sustainable energy solutions.
A Deep Dive Into the Study of Nitrogen‐Doped Carbons as Electrocatalysts for the Oxygen Reduction Reaction via Design of Experiments
Authors: Smith Lettie A. , Burrow James N. , Eichler J. Ehren , Tang Franklin , Lauro Samantha N. , Zhan Xun , Warner Jamie H. , Mullins C. Buddie 
Published: February 2025

AbstractA design of experiments (DoE) approach is applied to the study of nitrogen (N)‐doped carbons prepared via a molten salt templating method using the eutectic salt lithium chloride/potassium chloride (LiCl/KCl) and the precursors sucrose and melamine (N precursor). This approach is used to deconvolute effects from surface composition and porosity on the electrocatalytic performance of N‐doped carbons as oxygen reduction reaction (ORR) electrocatalysts. Additionally, DoE is implemented to reveal the synthesis‐structure‐function relationship for the prepared materials over an entire design space. From this work, it is evident that the N precursor content has the greatest impact on the tunability of material properties (e.g., N‐content, pyridinic N content, surface area, pore size distribution, etc.) followed by pyrolysis temperature and salt mass. Additionally, without adequate porosity (surface area ≥ 500 m2 g−1, micropore volume > 0.15 cc g−1, etc.) and electrochemically active surface area, activity and selectivity for the ORR via N‐functionalization is significantly reduced. Optimization of the studied design space indicates that an N precursor content of 35 wt.%–38 wt.%, pyrolysis temperature ≤ 900 °C, and a salt mass < 15 g would garner the necessary N‐content (∼7–8 at%) and porosity to achieve the most active and selective N‐doped carbon ORR electrocatalysts.
Solubility Challenges and Strategies for Organic Sodium‐Ion Batteries: Status and Perspectives
Authors: Qi Ying , Zhao Huaping , Chen Lizhuang , Lei Yong 
Published: February 2025

AbstractOrganic sodium‐ion batteries (OSIBs) possessing excellent characteristics of low price, abundant sources, and eco‐compatibility have gained numerous attentions in the recent decade. However, solubility is one of the main severe limitations of the application of OSIBs, especially for small organic molecules. The dissolution of molecules into electrolytes can cause the loss of active materials, the pulverization of electrodes, and even short circuits in batteries, as active materials may shuttle through separators, thus leading to poor cycling stability for sodium‐ion batteries. Thus, there is an urgent need to develop insoluble organic molecules for OSIBs. The advanced development of OSIBs over the past decades is overviewed, and the primary challenges faced by long‐cycling OSIBs in terms of solubility are systematically analyzed. Focusing on the three core components of the battery system electrodes, separators, and electrolytes, targeted optimization strategies are proposed to mitigate solubility issues and enhance battery performance. In addition, the prospects of OSIBs toward long‐cycle stability and practical application are explored.
Silver Nanocube Epitaxy via Nanogap‐Induced Electrostatics
Authors: Fajri Muhammad L. , Capitaine Anna , Santinacci Lionel , Sciacca Beniamino 
Published: February 2025

AbstractSilver nanostructures are highly valued in nanophotonic devices due to their appealing plasmonic properties and affordability relative to gold. Yet, fabricating high‐quality, monocrystalline silver nanostructures, with full control over the shape, is challenging. A mild, liquid‐phase method for the epitaxial welding of adjacent monocrystalline silver nanocubes in reductant‐free conditions is introduced to prevent the formation of detrimental nuclei on the surface that can degrade the nanostructures' optical qualities. The mechanism is thoroughly investigated and it is found that the nanocubes themselves can act as reducing agents, promoting growth preferentially into the gap as a result of electrostatic interactions. By controlling experimental parameters such as temperature, pH, and the introduction of capping agents, a balance between nanocube epitaxy and shape retention is achieved. Finally, by applying this procedure to nanoparticle assembled in predefined meta‐atoms, the feasibility of creating intricate silver nanostructures, that are monocrystalline as verified by transmission electron microscopy (TEM), is demonstrated. This advancement paves the way for bottom‐up fabrication of optical metasurfaces that can be swiftly integrated in devices.
Person‐centered care at population scale: The Swedish registry for behavioral and psychological symptoms of dementia
Authors: Jönsson Linus , Wibom Moa , Londos Elisabet , Nägga Katarina 
Published: February 2025

AbstractINTRODUCTIONBehavioral and psychological symptoms of dementia (BPSD) are a common driver of suffering and high care needs. We describe the Swedish BPSD registry, founded in 2010 to develop an evidence base for quality improvement in the care of patients with BPSD. Further, we illustrate the potential of the registry by evaluating how individual BPSD affects mortality.METHODSThe registry provides a framework for documenting the occurrence of BPSD, formulating individual care plans, and following up outcomes. Symptoms are recorded by the nursing home version of the neuropsychiatric inventory (NPI), and data are entered by trained staff, mainly at institutional care facilities.RESULTSEnrollment in the registry totaled 114,869 patients with dementia and a mean age of 84 years. Patients were followed until death (median overall survival 2.2 years) or loss to follow‐up (median time under observation 4.2 years in patients remaining alive). Common symptoms included agitation/aggression, aberrant motor behavior, and irritability. Mortality increased with NPI severity and use of neuroleptics but decreased in patients receiving cholinesterase inhibitors or memantine.DISCUSSIONThe scale, completeness, and duration of the registry, together with the possibility of linking to other data sources, offer great potential for data‐driven research.Highlights
The Swedish BPSD Registry, founded in 2010, has followed over 114,000 patients collecting data on symptoms, care plans, interventions and outcomes.
The registry provides a framework for providing and evaluating person‐centered care for patients with BPSD, and represents an unparalleled data source for research into BPSD and its management.
Mortality increased in patients with more severe BPSD symptoms and for those treated with neuroleptics, but decreased in patients receiving cholinesterase inhibitors or mematine.
Barriers and Motivators for Women in Surgical Training: A Qualitative Study From Malaysia
Authors: Roslani April Camilla , Uzoigwe Anthonia Ginika , Rajandram Retnagowri , Low Wah Yun 
Published: February 2025

ABSTRACTBackgroundWomen are significantly underrepresented in surgical specialties, particularly in Southeast Asia. The factors influencing this gender disparity remain poorly understood. This study aims to explore the motivations, challenges, and barriers faced by female medical doctors in pursuing surgical careers in Malaysia.MethodsAn exploratory qualitative study was conducted between May 2016 and November 2017, using semi‐structured in‐depth interviews (IDIs) and focus group discussions (FGDs) with male and female senior surgeons, medical officers, and surgical trainees. A coding tree was constructed and refined after initial rounds of data analysis. Thematic content analysis was performed using the NVivo 11 Pro (Windows) qualitative data analysis software.ResultsTen IDIs and five FGDs (N = 34 participants) revealed three key themes: (A) influences on trainees' career choices, (B) challenges encountered during surgical training, and (C) trainee empowerment. Participants highlighted the demanding nature of surgical training, the lack of flexibility in work schedules, and the male‐dominated work environment as significant barriers for women. Despite these challenges, supportive family structures and strong mentorship were found to be critical for the retention of women in surgery.ConclusionInstitutional support, mentorship opportunities, and culture change are essential for improving the representation and retention of female surgeons in Malaysia. Tailored policies that acknowledge the unique challenges faced by women in surgery could foster a more inclusive and supportive environment.
Celebrity as Catalyst: Connecting Leadership Development to pop Culture
Authors: Devies Brittany , Pacheco Derrick Raphael , Haynes Lauren A. , Drummond Madison B. , Estrella‐Padilla Derek 
Published: February 2025

ABSTRACTPop culture has served as a catalyst for leadership development, both creating and manufacturing culture while also creating the parameters for us to understand culture in a public context. While pop culture has influenced how people engage with leadership development across contexts, celebrities have been the manifestation of pop culture throughout history. Through the influences of socialization and the evolution of pop culture, the field of leadership development has been impacted in a myriad of ways. This article will explore pedagogical techniques that leadership educators can utilize in curricular and co‐curricular learning environments and unique recommendations, implications, and applications across various contexts.
Creating Change: Art Activism and Leadership Development
Authors: Cruz Jessica A. , Vermont Leighton E. , Watkins Sally R. 
Published: February 2025

ABSTRACTThis article advocates the inclusion of the arts, and specifically, art activism, as a beneficial pedagogical approach to leadership development. Focus is given to using examples of art activism to introduce the student leader activist identity continuum (SLAIC), the exploration of leadership identity, and situating activism as transformative leadership. This article situates art activism as a means of transformative leadership and advocates using activist art to introduce and support movement along the SLAIC.
Graph Neural Network Knowledge Graph Recommendation Model Integrating Deep Domain Information and Important Domain Information
Authors: Zhang Xuelian 
Published: February 2025

Abstract
The importance between source and target nodes is often overlooked in higher-order information, which can introduce excessive noise in complex network scenarios, thereby affecting recommendation performance. To address this issue, we propose a graph neural network-based knowledge graph recommendation model that integrates deep domain information and important domain information, aiming to enhance recommendation accuracy and diversity. Initially, we pre-train the knowledge graph using graph embedding techniques to obtain structural information. Subsequently, we employ a graph convolutional network to delve deeper into the semantic information of the knowledge graph, capturing rich structural and semantic details from both depth and importance perspectives. Finally, we compute the interaction probability between users and items using the inner product of the enhanced vectors and user vectors, facilitating recommendations. Experiments on the Last-FM, Book-Crossing, and MovieLens-20M datasets yield AUC and F1 scores of 83.2% and 75.4%, 74.9% and 66.8%, 97.9% and 93.1%, respectively. Additionally, Recall@50 scores are 34.5%, 11.2%, and 35.0%. Our model outperforms RippleNet, KGCN, LKGR, and other models, indicating that integrating knowledge graph recommendation models with meta-graph neighborhoods effectively improves recommendation performance.
An Approach to Microenvironment-Based Particle Swarm Optimization Algorithm
Authors: Chen Tingting , Zhang Zhenya , Wang Ping , Cheng Hongmei 
Published: February 2025

Abstract
The Insect Intelligent Building (I2B) platform is a distributed information processing system based on a microenvironment network. To solve optimization problems in microenvironments using the Particle Swarm Optimization (PSO) algorithm, this paper designs the information exchange strategy of the PSO algorithm in microenvironments. It optimizes the particle velocity and position updating mechanism to propose the Microenvironment-based Particle Swarm Optimization (MPSO) algorithm. In the microenvironment network, the information exchange strategy is used for collaborative computation among microenvironments, which can simultaneously solve a given optimization problem, thus generating multiple candidate optimal solutions and finally determining the optimal solution. The experimental results indicate that the MPSO algorithm proposed in this paper can solve the optimization problem more efficiently.
Design of Location and Assistive Service Software Based on Android System
Authors: Wang Wenbin , He Kezhen , Ma Yuchun 
Published: February 2025

Abstract
In the process of using smartphones, elderly individuals often unintentionally set their phones to silent mode or turn off Wi-Fi, resulting in disruptions to answering calls and receiving WeChat video messages. This article implements a whitelist feature that automatically restores the phone to normal mode, monitors Wi-Fi status and reminds users to enable it. Additionally, it can discreetly check the geographical location and send low balance notifications to whitelisted phones, assisting the elderly in using smartphones without obstacles.
Research on Federated Radial Basis Function Neural Network Based on Genetic Algorithm
Authors: Ma Yandong , Tan Gaifang , Tang Song , Ge Suli , Wang Zhiqiang 
Published: February 2025

Abstract
The radial basis function neural network model is optimized for application scenarios such as uneven data distribution, privacy sensitivity, or lack of direct access to the original data. Firstly, federated learning is applied to the radial basis function neural network, facilitating cross-multicenter distributed collaborative training. Then, genetic algorithms are used to optimize the hyperparameters of the federated radial basis function neural network model. Comparative experiments were conducted using the publicly available National health and nutrition examination survey 2013–2014 (NHANES) age prediction subset dataset and the proprietary HEART dataset. Compared with traditional centralized learning methods, the proposed model demonstrates superior performance, providing new ideas and methods for solving distributed learning and data privacy problems.
Improving Water Hyacinth Extraction from UAV Images Using Enhanced U-Net
Authors: Niu Zhiru , Cai Wei , Xu Daowen , Jin Shaofei 
Published: February 2025

Abstract
This paper addresses the issues of accuracy and efficiency in extracting the distribution areas of water hyacinth. A small-scale water hyacinth dataset was established based on drone-collected photos of water hyacinth in river channels, which was utilized for semantic segmentation tasks. A method based on deep learning was proposed to extract water hyacinth distribution areas from high-resolution drone remote sensing images. An efficient, accurate, and automated convolutional neural network called AttUNet was designed for this purpose, which eliminates the need for manually designed rules and can automatically learn remote sensing features of water hyacinth and extract distribution areas from images, thereby improving the accuracy and efficiency of acquiring relevant data. The research demonstrates that the proposed method can automatically extract features from massive high-resolution drone images, fully exploring complex nonlinear features, spectral features, and texture features in high-resolution drone images. The overall accuracy of extracting water hyacinth distribution areas in the study area reached 98.78%, with MIOU coefficient and mRecall coefficient of 95.86% and 98.01% respectively, both of which surpass the accuracy indicators of Deeplabv3+ and U-Net. The deep learning method for water hyacinth classification can fully exploit spectral, texture, and latent feature information in the data, and make it more suitable for extracting water hyacinth distribution information than traditional remote sensing classification methods.