bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pancreaticoduodenectomy in patients < 75 years versus ≥ 75 years old: a comparative study
Authors: Ergenç Muhammer , Uprak Tevfik Kıvılcım , Özocak Ayşegül Bahar , Karpuz Şakir , Coşkun Mümin , Yeğen Cumhur , Atıcı Ali Emre 
Published: July 2024

Abstract
Objective
This study aimed to compare the postoperative outcomes of < 75-year-old patients and ≥ 75-year-old patients who underwent pancreaticoduodenectomy (PD) for pancreatic head and periampullary region tumors.

Methods
Patients who underwent PD in our hospital between February 2019 and December 2023 were evaluated. Demographics, Eastern Cooperative Oncology Group Performance Status (ECOG-PS) scores, American Society of Anesthesiologists (ASA) scores, comorbidities, hospital stays, complications, and clinicopathological features were analyzed. Patients were divided into < 75 years (Group A) and ≥ 75 years (Group B) groups and compared.

Results
The median age of the entire cohort (n = 155) was 66 years (IQR = 16). There was a significant difference between Group A (n = 128) and Group B (n = 27) regarding the ECOG-PS and ASA scores. There was no significant difference between the groups regarding postoperative complications. The 30-day mortality rate was greater in Group B (p = 0.017). Group B had a cumulative median survival of 10 months, whereas Group A had a median survival of 28 months, with a statistically significant difference (p < 0.001). When age groups were stratified according to ECOG-PS, for ECOG-PS 2–3 Group A, survival was 15 months; for ECOG-PS 2–3 Group B, survival was eight months, and the difference was not statistically significant (p = 0.628).

Conclusions
With the increasing aging population, patient selection for PD should not be based solely on age. This study demonstrated that PD is safe for patients older than 75 years. In older patients, performance status and the optimization of comorbidities should be considered when deciding on a candidate’s suitability for surgery.
Controlled Twill Surface Structure Endowing Nanofiber Composite Membrane Excellent Electromagnetic Interference Shielding
Authors: Tao Dechang , Wen Xin , Yang Chenguang , Yan Kun , Li Zhiyao , Wang Wenwen , Wang Dong 
Published: July 2024

AbstractInspired by the Chinese Knotting weave structure, an electromagnetic interference (EMI) nanofiber composite membrane with a twill surface was prepared. Poly(vinyl alcohol-co-ethylene) (Pva-co-PE) nanofibers and twill nylon fabric were used as the matrix and filter templates, respectively. A Pva-co-PE-MXene/silver nanowire (Pva-co-PE-MXene/AgNW, PMxAg) membrane was successfully prepared using a template method. When the MXene/AgNW content was only 7.4 wt% (PM7.4Ag), the EMI shielding efficiency (SE) of the composite membrane with the oblique twill structure on the surface was 103.9 dB and the surface twill structure improved the EMI by 38.5%. This result was attributed to the pre-interference of the oblique twill structure in the direction of the incident EM wave, which enhanced the probability of the electromagnetic waves randomly colliding with the MXene nanosheets. Simultaneously, the internal reflection and ohmic and resonance losses were enhanced. The PM7.4Ag membrane with the twill structure exhibited both an outstanding tensile strength of 22.8 MPa and EMI SE/t of 3925.2 dB cm−1. Moreover, the PMxAg nanocomposite membranes demonstrated an excellent thermal management performance, hydrophobicity, non-flammability, and performance stability, which was demonstrated by an EMI SE of 97.3% in a high-temperature environment of 140 °C. The successful preparation of surface-twill composite membranes makes it difficult to achieve both a low filler content and a high EMI SE in electromagnetic shielding materials. This strategy provides a new approach for preparing thin membranes with excellent EMI properties.
Control of Online-Appointment Systems When the Booking Status Signals Quality of Service
Authors: Kaluza Isabel , Voigt Guido , Haase Knut , Dietze Antonia 
Published: July 2024

AbstractWe revisit a service provider’s problem to match supply and demand via an online appointment system such as a doctor in the health care sector. We identify in a survey that an extensive set of available appointments leads to significantly less demand because customers infer a lower quality of the service, as part of an observational learning process. We capture the quality inference effect in a multinomial logit framework and present a Markov decision process for solving the problem of releasing available slots of the appointment system to optimality aiming at maximizing the expected profits. We further evaluate several simple decision rules and provide management insights on which rule to apply under different generic scenarios. Different from current literature, offering all available appointments may lead to suboptimal results when accounting for the quality inference effect. The profit-maximizing strategy then is to offer a subset of the available appointments.
Research on the Optimization Method of Project-Based Learning Design for Chinese Teaching Based on Interference-Tolerant Fast Convergence Zeroing Neural Network
Authors: Bai Weihua , Geng Guoli , Fu Xuan 
Published: July 2024

AbstractThe system known as project-based learning, which is applied to specific courses without compromising the coverage of the necessary technical material, uses projects to drive knowledge. The plan and implementation of project-based learning in Chinese teaching as a major project, which embraces undergraduate creativity and places an emphasis on real-world, open-ended projects are discussed in this paper. In this paper, research on optimization method of project-based learning design for Chinese teaching based on optimized interference-tolerant fast convergence zeroing neural network (PBLD-ITFCZNN-BRO). It consists of three stages, import phase, main stage and evaluation stage. In the initial phase, the teacher separated the students to groups before the lecture to make sure that every group poses various traits, with some strong leadership skills and hands-on skills. The second phase of the PBL procedure helped transform what is primarily a passive learning environment (taking notes, listening, and sitting) into a more dynamic, student-centered, and interactive one. Students presented data, articulated their concepts, and then optimized their approaches to problem-solving during the evaluation step. The teachers concluded by summarizing. The performance of the proposed PBLD-ITFCZNN-BRO approach contains 15.26%, 20.42% and 21.27% greater accuracy, and 15.61%, 17.50% and 20.24% greater precision rate, compared with Investigation of Computer Network Technology on New Media Problem-Basis Learning Teaching Mode (CNT-PBLTM), PBL Model Basis application on Deep Learning in Physical Education Classroom Integrating Production with Education (PBL-DL-PEC), Interdisciplinary project-based learning: experiences with reflections from teaching electronic engineering at china (PBL-EEC) techniques, respectively.
Spatiotemporally Explicit Epidemic Model for West Nile Virus Outbreak in Germany: An Inversely Calibrated Approach
Authors: Mbaoma Oliver Chinonso , Thomas Stephanie Margarete , Beierkuhnlein Carl 
Published: July 2024

AbstractSince the first autochthonous transmission of West Nile Virus was detected in Germany (WNV) in 2018, it has become endemic in several parts of the country and is continuing to spread due to the attainment of a suitable environment for vector occurrence and pathogen transmission. Increasing temperature associated with a changing climate has been identified as a potential driver of mosquito-borne disease in temperate regions. This scenario justifies the need for the development of a spatially and temporarily explicit model that describes the dynamics of WNV transmission in Germany. In this study, we developed a process-based mechanistic epidemic model driven by environmental and epidemiological data. Functional traits of mosquitoes and birds of interest were used to parameterize our compartmental model appropriately. Air temperature, precipitation, and relative humidity were the key climatic forcings used to replicate the fundamental niche responsible for supporting mosquito population and infection transmission risks in the study area. An inverse calibration method was used to optimize our parameter selection. Our model was able to generate spatially and temporally explicit basic reproductive number (R0) maps showing dynamics of the WNV occurrences across Germany, which was strongly associated with the deviation from daily means of climatic forcings, signaling the impact of a changing climate in vector-borne disease dynamics. Epidemiological data for human infections sourced from Robert Koch Institute and animal cases collected from the Animal Diseases Information System (TSIS) of the Friedrich-Loeffler-Institute were used to validate model-simulated transmission rates. From our results, it was evident that West Nile Virus is likely to spread towards the western parts of Germany with the rapid attainment of environmental suitability for vector mosquitoes and amplifying host birds, especially short-distance migratory birds. Locations with high risk of WNV outbreak (Baden-Württemberg, Bavaria, Berlin, Brandenburg, Hamburg, North Rhine-Westphalia, Rhineland-Palatinate, Saarland, Saxony-Anhalt and Saxony) were shown on R0 maps. This study presents a path for developing an early warning system for vector-borne diseases driven by climate change.
Text-to-building: experiments with AI-generated 3D geometry for building design and structure generation
Authors: Bono Giuseppe 
Published: July 2024

AbstractThe paper seeks to investigate novel potentials for building design and structure generation that arise at the intersection of computational design and AI-generated 3D geometries. Although the use of AI technologies is exponentially increasing inside the architectural discipline, the design of spatial building configurations using AI-generated 3D geometries is still limited in its applications and represents an ongoing field of investigation in advanced architectural research. In this regard, several questions still need to be answered: how can we design new building typologies from AI-generated 3D geometries? And how can we use these typologies to shape both the real and the virtual world?The paper proposes a new approach to architectural design where artificial intelligence is used as the starting point for design exploration, while computational design procedures are employed to convert AI-generated 3D geometries into building elements – such as columns, beams, horizontal and vertical surfaces. The paper starts with a general overview of the current use of artificial intelligence inside the architectural discipline, and then it moves towards the explanation of specific AI generative models for 3D geometry reconstruction and representation. Subsequently, the proposed working pipeline is analysed in more detail – from the creation of 3D geometries using generative AI models to the conversion of such geometries into building elements that can be further designed and optimised using computational design tools and methods. The results shown in the paper are achieved using Shap-E as the main AI model, though the proposed pipeline can be implemented with multiple AI models. The paper ends by showing some of the generated results, finally adding some considerations to the relationship between human and artificial creativity inside the architectural discipline.The work presented in the paper suggests that the use of computational design tools and methods combined with the tectonics of the latent space opens new opportunities for topological and typological explorations. In a time where traditional architectural typologies are moving towards stagnation due to their inability to satisfy new human needs and ways of living, exploring AI-based working pipelines related to architectural design allows the definition of new design solutions for the generation of new architectural spaces. In doing so, the serendipitous aspect of AI biases is used as an auxiliary force to inform design decisions, promoting the discovery of a new inbuilt dynamism between human and artificial creativity. In a time where AI is everywhere, understanding the measure of such dynamism represents a key aspect for the future of the architectural discipline.
Effect of Uncertainty in Water Vapor Continuum Absorption on CO2 Forcing, Longwave Feedback, and Climate Sensitivity
Authors: Roemer Florian E. , Buehler Stefan A. , Kluft Lukas , Pincus Robert 
Published: July 2024

AbstractWe investigate the effect of uncertainty in water vapor continuum absorption at terrestrial wavenumbers on CO2 forcing , longwave feedback λ, and climate sensitivity at surface temperatures Ts between 270 and 330 K. We calculate this uncertainty using a line‐by‐line radiative‐transfer model and a single‐column atmospheric model, assuming a moist‐adiabatic temperature lapse‐rate and 80% relative humidity in the troposphere, an isothermal stratosphere, and clear skies. Due to the lack of a comprehensive model of continuum uncertainty, we represent continuum uncertainty in two different idealized approaches: In the first, we assume that the total continuum absorption is constrained at reference conditions; in the second, we assume that the total continuum absorption is constrained for all atmospheres in our model. In both approaches, we decrease the self continuum by 10% and adjust the foreign continuum accordingly. We find that continuum uncertainty mainly affects through its effect on λ. In the first approach, continuum uncertainty mainly affects λ through a decrease in the total continuum absorption with Ts; in the second approach, continuum uncertainty affects λ through a vertical redistribution of continuum absorption. In both experiments, the effect of continuum uncertainty on is modest at Ts = 288 K (≈0.02 K) but substantial at Ts ≥ 300 K (up to 0.2 K), because at high Ts, the effects of decreasing the self continuum and increasing the foreign continuum have the same sign. These results highlight the importance of a correct partitioning between self and foreign continuum to accurately determine the temperature dependence of Earth's climate sensitivity.
Cool Roofs Could Be Most Effective at Reducing Outdoor Urban Temperatures in London (United Kingdom) Compared With Other Roof Top and Vegetation Interventions: A Mesoscale Urban Climate Modeling Study
Authors: Brousse O. , Simpson C. , Zonato A. , Martilli A. , Taylor J. , Davies M. , Heaviside C. 
Published: July 2024

AbstractComprehensive studies comparing impacts of building and street levels interventions on air temperature at metropolitan scales are still lacking despite increased urban heat‐related mortality and morbidity. We therefore model the impact of 9 interventions on air temperatures at 2 m during 2 hot days from the summer 2018 in the Greater London Authority area using the WRF BEP‐BEM climate model. We find that on average cool roofs most effectively reduce temperatures (∼−1.2°C), outperforming green roofs (∼0°C), solar panels (∼−0.5°C) and street level vegetation (∼−0.3°C). Application of air conditioning across London (United Kingdom) increases air temperatures by ∼+0.15°C. A practicable deployment of solar panels could cover its related energetic consumption. Current practicable deployments of green roofs and solar panels are ineffective at large scale reduction of temperatures. We provide a detailed decomposition of the surface energy balance to explain changes in air temperature and guide future decision‐making.
Autotrophic Respiration Is More Sensitive to Nitrogen and Phosphorus Supply Than Heterotrophic Respiration in Semiarid Grassland
Authors: Zhou Junjie , Chen Zhifei , Jian Chunxia , Luo Yang , Niu Furong , Palta Jairo A. , Xu Bingcheng 
Published: July 2024

AbstractQuantification of autotrophic (Ra) and heterotrophic (Rh) components of soil respiration (Rs) could greatly improve our understanding of terrestrial carbon cycling. Here, we investigated the effect of nitrogen (N) and phosphorus (P) supply on soil respiration rates and its components, and seasonal cumulative soil CO2 efflux in a semiarid grassland. A two‐factor experiment of N (0, 50 and 100 kg N ha−1 yr−1) and P (0, 18 and 36 kg P ha−1 yr−1) supply was conducted on the Loess Plateau in 2018–2021. Our results indicated that the promoting effect of combination of N and P supply on soil respiration and its components was greater than that of N/P supply alone. N and/or P supply increased the cumulative soil CO2 efflux in both non‐growing and growing seasons, and the non‐growing season CO2 efflux accounted for ∼25% of the growing season CO2 efflux. The increase of cumulative CO2 efflux from Ra in response to N and/or P supply was significantly greater than that of Rh in 2018 and 2019, while it shifted to the opposite, that is, a greater increase in Rh than in Ra in 2020 and 2021. Our results suggest that 100 kg N ha−1 yr−1 supply alone is an optimum fertilization scheme to trade‐off grassland productivity and soil CO2 emissions. Our results highlighted that the promoting effects of N and P fertilization on different soil CO2 efflux components could be reversed in a relatively short period (4 years), and this should be considered when nutrient addition is adapted to restore degraded grasslands.
Advances in research on immunocyte iron metabolism, ferroptosis, and their regulatory roles in autoimmune and autoinflammatory diseases
Authors: Zeng Liuting , Yang Kailin , Yu Ganpeng , Hao Wensa , Zhu Xiaofei , Ge Anqi , Chen Junpeng , Sun Lingyun 
Published: July 2024

AbstractAutoimmune diseases commonly affect various systems, but their etiology and pathogenesis remain unclear. Currently, increasing research has highlighted the role of ferroptosis in immune regulation, with immune cells being a crucial component of the body’s immune system. This review provides an overview and discusses the relationship between ferroptosis, programmed cell death in immune cells, and autoimmune diseases. Additionally, it summarizes the role of various key targets of ferroptosis, such as GPX4 and TFR, in immune cell immune responses. Furthermore, the release of multiple molecules, including damage-associated molecular patterns (DAMPs), following cell death by ferroptosis, is examined, as these molecules further influence the differentiation and function of immune cells, thereby affecting the occurrence and progression of autoimmune diseases. Moreover, immune cells secrete immune factors or their metabolites, which also impact the occurrence of ferroptosis in target organs and tissues involved in autoimmune diseases. Iron chelators, chloroquine and its derivatives, antioxidants, chloroquine derivatives, and calreticulin have been demonstrated to be effective in animal studies for certain autoimmune diseases, exerting anti-inflammatory and immunomodulatory effects. Finally, a brief summary and future perspectives on the research of autoimmune diseases are provided, aiming to guide disease treatment strategies.
A pH-sensitive closed-loop nanomachine to control hyperexcitability at the single neuron level
Authors: Merolla Assunta , Michetti Caterina , Moschetta Matteo , Vacca Francesca , Ciano Lorenzo , Emionite Laura , Astigiano Simonetta , Romei Alessandra , Horenkamp Simone , Berglund Ken , Gross Robert E. , Cesca Fabrizia , Colombo Elisabetta , Benfenati Fabio 
Published: July 2024

AbstractEpilepsy affects 1% of the general population and 30% of patients are resistant to antiepileptic drugs. Although optogenetics is an efficient antiepileptic strategy, the difficulty of illuminating deep brain areas poses translational challenges. Thus, the search of alternative light sources is strongly needed. Here, we develop pH-sensitive inhibitory luminopsin (pHIL), a closed-loop chemo-optogenetic nanomachine composed of a luciferase-based light generator, a fluorescent sensor of intracellular pH (E2GFP), and an optogenetic actuator (halorhodopsin) for silencing neuronal activity. Stimulated by coelenterazine, pHIL experiences bioluminescence resonance energy transfer between luciferase and E2GFP which, under conditions of acidic pH, activates halorhodopsin. In primary neurons, pHIL senses the intracellular pH drop associated with hyperactivity and optogenetically aborts paroxysmal activity elicited by the administration of convulsants. The expression of pHIL in hippocampal pyramidal neurons is effective in decreasing duration and increasing latency of pilocarpine-induced tonic-clonic seizures upon in vivo coelenterazine administration, without affecting higher brain functions. The same treatment is effective in markedly decreasing seizure manifestations in a murine model of genetic epilepsy. The results indicate that pHIL represents a potentially promising closed-loop chemo-optogenetic strategy to treat drug-refractory epilepsy.
Steady motion of 80-nm-size skyrmions in a 100-nm-wide track
Authors: Song Dongsheng , Wang Weiwei , Zhang Shuisen , Liu Yizhou , Wang Ning , Zheng Fengshan , Tian Mingliang , Dunin-Borkowski Rafal E. , Zang Jiadong , Du Haifeng 
Published: July 2024

AbstractThe current-driven movement of magnetic skyrmions along a nanostripe is essential for the advancement and functionality of a new category of spintronic devices resembling racetracks. Despite extensive research into skyrmion dynamics, experimental verification of current-induced motion of ultra-small skyrmions within an ultrathin nanostripe is still pending. Here, we unveil the motion of individual 80 nm-size skyrmions in an FeGe track with an ultrathin width of 100 nm. The skyrmions can move steadily along the track over a broad range of current densities by using controlled pulse durations of as low as 2 ns. The potential landscape, arising from the magnetic edge twists in such a geometrically confined system, introduces skyrmion inertia and ensures efficient motion with a vanishing skyrmion Hall angle. Our results showcase the steady motion of skyrmions in an ultrathin track, offering a practical pathway for implementing skyrmion-based spintronic devices.
Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study
Authors: Omee Sadman Sadeed , Fu Nihang , Dong Rongzhi , Hu Ming , Hu Jianjun 
Published: July 2024

AbstractIn real-world materials research, machine learning (ML) models are usually expected to predict and discover novel exceptional materials that deviate from the known materials. It is thus a pressing question to provide an objective evaluation of ML model performances in property prediction of out-of-distribution (OOD) materials that are different from the training set. Traditional performance evaluation of materials property prediction models through the random splitting of the dataset frequently results in artificially high-performance assessments due to the inherent redundancy of typical material datasets. Here we present a comprehensive benchmark study of structure-based graph neural networks (GNNs) for extrapolative OOD materials property prediction. We formulate five different categories of OOD ML problems for three benchmark datasets from the MatBench study. Our extensive experiments show that current state-of-the-art GNN algorithms significantly underperform for the OOD property prediction tasks on average compared to their baselines in the MatBench study, demonstrating a crucial generalization gap in realistic material prediction tasks. We further examine the latent physical spaces of these GNN models and identify the sources of CGCNN, ALIGNN, and DeeperGATGNN’s significantly more robust OOD performance than those of the current best models in the MatBench study (coGN and coNGN) as a case study for the perovskites dataset, and provide insights to improve their performance.