bluuebunny/crossref_metadata_2025_split huggingface dataset:
Visualization of distribution in the vitreous cavity via eye drops using ultra-heavily T2-weighted sequences in MRI: a preliminary study with enucleated pig eyes
Authors: Kato Yutaka , Yuki Kenya , Nishiguchi Koji , Naganawa Shinji 
Published: July 2024

AbstractWe investigated whether magnetic resonance imaging can visualize the distribution in the vitreous cavity via eye drops of ophthalmic solutions, gadolinium-based contrast agent, and 17O-water, and to clarify the usefulness of ultra-heavily T2-weighted sequences in the research of intraocular distribution. Five different solutions (V-ROHTO, TRAVATANZ, gadobutrol, H217O, and saline) were administered to excised pig eye specimens. The samples were scanned using T1 mapping, T2 mapping, 3D T2-weighted (echo times (TE): 500, 3200, and 4500 ms), a half-Fourier single-shot turbo-spin echo sequence (HASTE; TE: 440 and 3000 ms), and 3D-real inversion-recovery before eye drops administration. Subsequently, we used a plastic dropper to drop a 0.5 mL solution each, and images were obtained up to 26 h later. Temporal changes in the T1 and T2 values of the anterior chamber and vitreous cavity were compared. The other sequences were evaluated by determining temporal signal changes as signal intensity ratio (SIR) compared to “No drop.” The T1 and T2 values of samples treated with gadobutrol and H217O decreased over time. The SIR of samples treated with gadobutrol and H217O showed remarkable changes in the 3D T2-weighted images, whereas no remarkable temporal changes were observed in the other solutions. Longer TEs resulted in remarkable changes. We demonstrated that visualization of distribution in the vitreous cavity via eye drops could be achieved with excised pig eyes using gadobutrol and H217O, but not with ophthalmic solutions. Ultra-heavily T2-weighted sequences may be promising for the early and highly sensitive visualization of the intraocular distribution of eye drops.
Application of CAR-T cell therapy targeting mesothelin in solid tumor treatment
Authors: Chen Qiuhong , Sun Yang , Li Hua 
Published: July 2024

AbstractChimeric antigen receptor (CAR)-T-cell therapy is one of the most effective immunotherapies. CAR-T-cell therapy has achieved great success in the treatment of hematological malignancies. However, due to the characteristics of solid malignant tumors, such as on-target effects, off-tumor toxicity, an immunosuppressive tumor microenvironment (TME), and insufficient trafficking, CAR-T-cell therapy for solid tumors is still in the exploration stage. Mesothelin (MSLN) is a molecule expressed on the surface of various solid malignant tumor cells that is suitable as a target of tumor cells with high MSLN expression for CAR-T-cell therapy. This paper briefly described the development of CAR-T cell therapy and the structural features of MSLN, and especially summarized the strategies of structure optimization of MSLN-targeting CAR-T-cells and the enhancement methods of MSLN-targeting CAR-T cell anti-tumor efficacy by summarizing some preclinical experiment and clinical trials. When considering MSLN-targeting CAR-T-cell therapy as an example, this paper summarizes the efforts made by researchers in CAR-T-cell therapy for solid tumors and summarizes feasible treatment plans by integrating the existing research results.
A new approach to soft relations and soft functions
Authors: Yolcu Adem , Ozturk Taha Yasin , Bayramov Sadi 
Published: July 2024

AbstractIn this paper, a different approach is used to define a cartesian product on soft sets. This method processes both alternatives and parameters. The notion of the cartesian product is then used to define the idea of a soft relation. The concepts of reflexion, symmetry and transition are defined on the soft relation. Some properties are investigated. Also, the soft function notion is introduced. Various instances are provided as the key characteristics of the structures that are being presented are analyzed. Finally, an application is presented by building a decision making algorithm on the soft relation.
Identification of switched gated recurrent unit neural networks with a generalized Gaussian distribution
Authors: Bai Wentao , Guo Fan , Gu Suhang , Yan Chao , Jiang Chunli , Zhang Haoyu 
Published: July 2024

AbstractDue to the limitations of the model itself, the performance of switched autoregressive exogenous (SARX) models will face potential threats when modeling nonlinear hybrid dynamic systems. To address this problem, a robust identification approach of the switched gated recurrent unit (SGRU) model is developed in this paper. Firstly, all submodels of the SARX model are replaced by gated recurrent unit neural networks. The obtained SGRU model has stronger nonlinear fitting ability than the SARX model. Secondly, this paper departs from the conventional Gaussian distribution assumption for noise, opting instead for a generalized Gaussian distribution. This enables the proposed model to achieve stable prediction performance under the influence of different noises. Notably, no prior assumptions are imposed on the knowledge of operating modes in the proposed switched model. Therefore, the EM algorithm is used to solve the problem of parameter estimation with hidden variables in this paper. Finally, two simulation experiments are performed. By comparing the nonlinear fitting ability of the SGRU model with the SARX model and the prediction performance of the SGRU model under different noise distributions, the effectiveness of the proposed approach is verified.
Students' Self-work During Lectures in Calculus Courses – Cognitive and Affective Effects of a Small Intervention
Authors: Leitner Arielle , Gabel Mika 
Published: July 2024

AbstractThis study describes the effects of a small pedagogical intervention in first semester calculus at an engineering college; it is a collaboration between two lecturers: a pure mathematician and a mathematics education researcher, who wished to learn about the effects of self-work (i.e., students solving problems on their own during class) on students' exam achievements, self-efficacy, and students' written communication. Students were given mastery experiences of self-work and feedback in three out of five classes. In all five classes, students were given in-class quizzes with peer instruction. Data was collected in multiple forms: quiz results, questionnaires, exam questions, and reflections; both quantitative and qualitative analysis methods were used. The findings show self-work increases students’ engagement and self-efficacy and slightly improved students’ achievement in class and on the final exam grade. Moreover, it positively influences students' learning experience. There were nonconclusive findings for improvement in the quality of students' written communication in the final exam. Self-work can be easily incorporated, even in coordinated courses with a common syllabus and a large lecture, without requiring instructors to make big changes to their lecture style. Effects of self-work should be further studied.
Defect Engineering and Carbon Supporting to Achieve Ni-Doped CoP3 with High Catalytic Activities for Overall Water Splitting
Authors: Zha Daowei , Wang Ruoxing , Tian Shijun , Jiang Zhong-Jie , Xu Zejun , Qin Chu , Tian Xiaoning , Jiang Zhongqing 
Published: July 2024

AbstractThis work reports the use of defect engineering and carbon supporting to achieve metal-doped phosphides with high activities and stabilities for the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) in alkaline media. Specifically, the nitrogen-doped carbon nanofiber-supported Ni-doped CoP3 with rich P defects (Pv·) on the carbon cloth (p-NiCoP/NCFs@CC) is synthesized through a plasma-assisted phosphorization method. The p-NiCoP/NCFs@CC is an efficient and stable catalyst for the HER and the OER. It only needs overpotentials of 107 and 306 mV to drive 100 mA cm−2 for the HER and the OER, respectively. Its catalytic activities are higher than those of other catalysts reported recently. The high activities of the p-NiCoP/NCFs@CC mainly arise from its peculiar structural features. The density functional theory calculation indicates that the Pv· richness, the Ni doping, and the carbon supporting can optimize the adsorption of the H atoms at the catalyst surface and promote the strong electronic couplings between the carbon nanofiber-supported p-NiCoP with the surface oxide layer formed during the OER process. This gives the p-NiCoP/NCFs@CC with the high activities for the HER and the OER. When used in alkaline water electrolyzers, the p-NiCoP/NCFs@CC shows the superior activity and excellent stability for overall water splitting.
Graphical abstract
Monetary-fiscal policies design and financial shocks in currency unions
Authors: Capasso Salvatore , Foresti Pasquale 
Published: July 2024

AbstractThis paper analyzes the design of monetary and fiscal policies in a currency union by focusing on the capacity to react to symmetric and asymmetric financial shocks. The model is constructed in order to mimic the institutional design adopted for the policy making in the EMU. The paper shows how a currency union set-up like the one adopted by the EMU can easily cope with symmetric financial shocks. However, it shows how in the face of asymmetric shocks more space for fiscal interventions is crucial, especially in more peripheral member countries.
Optimizing High-Throughput Inference on Graph Neural Networks at Shared Computing Facilities with the NVIDIA Triton Inference Server
Authors: Savard Claire , Manganelli Nicholas , Holzman Burt , Gray Lindsey , Perloff Alexx , Pedro Kevin , Stenson Kevin , Ulmer Keith 
Published: July 2024

AbstractWith machine learning applications now spanning a variety of computational tasks, multi-user shared computing facilities are devoting a rapidly increasing proportion of their resources to such algorithms. Graph neural networks (GNNs), for example, have provided astounding improvements in extracting complex signatures from data and are now widely used in a variety of applications, such as particle jet classification in high energy physics (HEP). However, GNNs also come with an enormous computational penalty that requires the use of GPUs to maintain reasonable throughput. At shared computing facilities, such as those used by physicists at Fermi National Accelerator Laboratory (Fermilab), methodical resource allocation and high throughput at the many-user scale are key to ensuring that resources are being used as efficiently as possible. These facilities, however, primarily provide CPU-only nodes, which proves detrimental to time-to-insight and computational throughput for workflows that include machine learning inference. In this work, we describe how a shared computing facility can use the NVIDIA Triton Inference Server to optimize its resource allocation and computing structure, recovering high throughput while scaling out to multiple users by massively parallelizing their machine learning inference. To demonstrate the effectiveness of this system in a realistic multi-user environment, we use the Fermilab Elastic Analysis Facility augmented with the Triton Inference Server to provide scalable and high-throughput access to a HEP-specific GNN and report on the outcome.
Strategies for Sustainable Innovative Affordable Housing (SIAH) for low income families in Africa: A rapid review study
Authors: Bhanye Johannes , Lehobo Monaphathi Timothy , Mocwagae Kgosi , Shayamunda Ruvimbo 
Published: July 2024

AbstractHousing is a fundamental human need, yet access remains a significant challenge in Africa, particularly for low-income families. Rapid urbanization exacerbates this issue, highlighting the need for innovative and sustainable housing strategies that can support urban growth and improve living conditions for these families. This study employs a rapid review methodology to identify and analyze sustainable innovative affordable housing (SIAH) strategies suitable for low-income families in African cities. Through a comprehensive literature search and thematic analysis, the review explores housing strategies such as mixed-use developments, incremental housing, low rental housing, inclusionary housing, 3D printing, upcycling, micro-housing, community-led housing, co-housing, and green building practices. These approaches/strategies are not mutually exclusive and can often overlap or be integrated to address housing challenges comprehensively. The findings highlight that these strategies can significantly enhance spatial justice and promote sustainable urban development. However, challenges such as material scarcity, regulatory complexities, and societal resistance must be addressed. The study emphasizes the need for policy support, public–private partnerships, community engagement, financial incentives, and capacity building to ensure successful implementation of these strategies. The paper contributes to the existing body of knowledge by providing actionable recommendations for policymakers, urban planners, and housing advocates, aiming to foster inclusive, equitable, and sustainable cities in Africa. Future research could delineate the strategies into distinct categories based on their nature and purpose (strategy, method/tool, practice etc.), providing a clearer framework for understanding and implementation.
Global Trends in Housing Research over the Past three Decades
Authors: Ye Xinyue , Zhao Grace , Peng Qiong , Dawkins Casey , Kim Jinyhup , Wu Ling 
Published: July 2024

AbstractThis paper reviews a large number of scholarly articles in the housing field spanning the last thirty years, from 1993 to 2022 by implementing bibliometric analysis method. We examine scientific outputs, identify influential articles, journals, international collaboration and evolution of research trends. Keywords such as “Housing price,” “Housing policy,” “Affordable housing,” “Homeownership,” “Housing market,” “Urban planning,” and “Neighborhood” have been identified as the most prevalently cited terms during this period. Furthermore, the prominence of terms such as “China,” “Gentrification,” “Public housing,” “Social housing,” “Homelessness,” “Migration,” “Urbanization,” “Energy,” “Inequality,” “Land use,” “Gender,” and “Foreclosure” have grown in importance, pointing to future research trends. The analysis also reveals that articles pertaining to the COVID-19 pandemic predominantly address the comprehensive effects of the virus on aspects of mental and physical health, consumer behavior, and economic and societal challenges.
Asymptotic Behavior and Numerical Simulations of a Conservative Phase-Field Model with Two Temperatures
Authors: Doumbe Bangola Brice Landry , Ipopa Mohamed Ali , Andami Ovono Armel 
Published: July 2024

Abstract
One of the types of problem that has attracted the attention of mathematicians in recent years is the phase field system. The field of application includes materials science, where phenomena such as phase separation in alloys, crystal formation and thermal welding are legion. Among these phase transition systems, the family of conservative systems is very popular with industry. Indeed, minimising losses in production systems is a major issue for their profitability. In this paper, we study the well-posedness of the formulation and the asymptotic behaviour of the solutions, by proving the existence of a finite-dimensional global attractor for a conservative variant of the two-temperature phase field system with homogeneous Neumann boundary conditions. The inclusion of two temperatures in the definition of the enthalpy of the system is a necessity in the case of a non-simple material. In a simple material, once the phase-change temperature has been reached, the temperature of the system remains constant until the material has completely changed state. This is not true in the case of a non-simple material, where an increase in the temperature of the system is observed even after the phase-change temperature has been reached. To conclude the work, we present a method for numerically approximating the solution and carry out some numerical tests.
Algorithmic modeling of functionally graded metamaterials in 3D printed building envelopes
Authors: Goidea Ana , Popescu Mariana , Johansson Anton Tetov , Andréen David 
Published: July 2024

AbstractRecent development of powder-bed additive manufacturing promises to enable the production of architectural structures that combine high resolution and articulation with economies of scale. These capabilities can potentially be used for functionally graded metamaterials as part of the building envelope and structure, paving the way for new functionalities and performances. However, designing such multifunctional structures requires new design and modelling strategies to control, understand, and generate complex geometries and their transcalar interdependencies. The work presented here demonstrates a modeling framework that can unite multiple generative and organizational algorithms to create a unified, 3D printable building element that integrates a range of functional requirements. Our methods are based on an understanding of stigmergic principles for self-organization and developed to allow for a wide range of application scenarios and design intents. The framework is structured around a composite modeling environment based on a combination of volumetric modeling and particle-spring systems, and is developed to negotiate the large scalar range necessary for such applications. We present here a prototype demonstrator designed using this framework: Meristem Wall, a functionally integrated building envelope fabricated through a combination of powder bed 3D printing and CNC knitting.
Identification of differentially expressed genes controlling the expression of flowering in Bambara groundnut (Vigna subterranea [L.] Verdc.)
Authors: Osundare Opeyemi T. , Akinyele Benjamin O. , Odiyi Alexander C. 
Published: July 2024

AbstractBambara groundnut flowering is a crucial developmental stage in the vegetative to reproductive period. The earliness to lateness of flowering is regulated by various interconnected genetic pathways encoded by genes. Deoxyribonucleic acid (DNA) of the selected accessions was extracted through leaf samples at 3 weeks old, using Dellaporta Miniprep for Plant DNA Isolation procedure. The high-quality DNA was sequenced using Diversity Arrays Technology (DArT) markers and single nucleotide polymorphisms (SNP’s) associated with flowering was identified. There is need to investigate the genetic make-up of the cleistogamous flower of Bambara through associated genes for improvement. This research work identified four markers associated with the flowering of Vigna subterranea and the role of variant identified genes in flowering. The identified markers from the sequence and the selected amino acid sequence were used as a query to search the legume protein database in Vigna radiata. The four markers with adequate information associated with flowering in the sequence were 24385352|F|0–28:T > C-28:T > C; 27641816|F|0–17:C > T-17:C > T; 24384204|F|0–24:C > T-24:C > T and 24346601|F|0–67:T > C-67:T > C and significant at P < 1.68 × 10−4 at chromosomes 7, 11, 4, and 5. The identified genes including histones, Polyketide, cyclase/dehydrase, Transcription factor MYC/MYB N-terminal, Rhamnogalacturonate lyase, DHHC-type zinc finger protein, Putative S-adenosyl-L-methionine-dependent methyltransferase, Ribosomal protein L2, D-galactoside/L-rhamnose binding SUEL lectin domain, Lipase GDSL, Histone deacetylase superfamily, Basic-leucine zipper domain, TUP1-like enhancer of split, Zinc finger ZZ-type, Homeodomain-like, Phosphatidylethanolamine-binding protein PEBP, Leucine-rich repeat which are tools in controlling flowering in Bambara groundnut. This study revealed that Bambara groundnut flowering is controlled by the interplay of genes.
Multi‐Model Machine Learning Approach Accurately Predicts Lake Dissolved Oxygen With Multiple Environmental Inputs
Authors: Lin Shuqi , Pierson Donald C. , Ladwig Robert , Kraemer Benjamin M. , Hu Fenjuan R. S. 
Published: July 2024

AbstractAs a key water quality parameter, dissolved oxygen (DO) concentration, and particularly changes in bottom water DO is fundamental for understanding the biogeochemical processes in lake ecosystems. Based on two machine learning (ML) models, Gradient Boost Regressor (GBR) and long‐short‐term‐memory (LSTM) network, this study developed three ML model approaches: direct GBR; direct LSTM; and a 2‐step mixed ML model workflow combining both GBR and LSTM. They were used to simulate multi‐year surface and bottom DO concentrations in five lakes. All approaches were trained with readily available environmental data as predictors. Indices of lake thermal structure and mixing provided by a one‐dimensional (1‐D) hydrodynamic model were also included as predictors in the ML models. The advantages of each ML approach were not consistent for all the tested lakes, but the best one of them was defined that can estimate DO concentration with coefficient of determination (R2) up to 0.6–0.7 in each lake. All three approaches have normalized mean absolute error (NMAE) under 0.15. In a polymictic lake, the 2‐step mixed model workflow showed better representation of bottom DO concentrations, with a highest true positive rate (TPR) of hypolimnetic hypoxia detection of over 90%, while the other workflows resulted in, TPRs are around 50%. In most of the tested lakes, the predicted surface DO concentrations and variables indicating stratified conditions (i.e., Wedderburn number and the temperature difference between surface and bottom water) are essential for simulating bottom DO. The ML approaches showed promising results and could be used to support short‐ and long‐term water management plans.
The Effects of Summer Snowfall on Arctic Sea Ice Radiative Forcing
Authors: Chapman‐Dutton H. R. , Webster M. A. 
Published: July 2024

AbstractSnow is the most reflective natural surface on Earth. Since fresh snow on bare sea ice increases the surface albedo, the impact of summer snow accumulation can have a negative radiative forcing effect, which would inhibit sea ice surface melt and potentially slow sea‐ice loss. However, it is not well known how often, where, and when summer snowfall events occur on Arctic sea ice. In this study, we used in situ and model snow depth data paired with surface albedo and atmospheric conditions from satellite retrievals to characterize summer snow accumulation on Arctic sea ice from 2003 to 2017. We found that, across the Arctic, ∼2 snow accumulation events occurred on initially snow‐free conditions each year. The average snow depth and albedo increases were ∼2 cm and 0.08, respectively. 16.5% of the snow accumulation events were optically thick (>3 cm deep) and lasted 2.9 days longer than the average snow accumulation event (3.4 days). Based on a simple, multiple scattering radiative transfer model, we estimated a −0.086 ± 0.020 W m−2 change in the annual average top‐of‐the‐atmosphere radiative forcing for summer snowfall events in 2003–2017. The following work provides new information on the frequency, distribution, and duration of observed snow accumulation events over Arctic sea ice in summer. Such results may be particularly useful in understanding the impacts of ephemeral summer weather on surface albedo and their propagating effects on the radiative forcing over Arctic sea ice, as well as assessing climate model simulations of summer atmosphere‐ice processes.