bluuebunny/crossref_metadata_2025_split huggingface dataset:
Green or grain? Impact of green space expansion on grain production in Chinese cities and its implications for national urban greening schemes
Authors: Dong Yulin , Kuang Wenhui , Ren Zhibin , Dou Yinyin , Deng Xiangzheng 
Published: June 2024

Abstract
Context
Expansion of urban green space (UGS) enhances greenery and its benefits in cities. However, the impacts of such expansions on regional landscape sustainability and their spatiotemporal traits across the national scale remain unclear.

Objectives
This study uncovers a trade-off between agricultural ecosystem services and urban human well-being—the loss of grain production due to UGS expansion consuming cropland—in China from 2000 to 2020. It proposes a safety boundary for UGS expansion to minimize the costs of grain production while addressing the escalating demand for UGS exposure.

Methods
UGS expansion and cropland loss are quantified using land cover products, with statistical data contributing to the established relationship between grain loss and UGS coverage. UGS expansion scenarios for 2040 are simulated through bottom-up modeling.

Results
Quadrupled UGS area growth contributed approximately one-third to urban expansion during 2000–2020 in China. Despite improvements in per capita UGS area implying enhanced UGS exposure during rapid urbanization, UGS expansion resulted in a national grain production loss of 4.2 ± 1.4 million t from 2000 to 2020, accelerating post-2010. This loss is equivalent to the annual food intake of 9.3 million Chinese people. We propose a 30.87% UGS coverage target in China by 2040 to preserve UGS access while minimizing grain loss under future urbanization.

Conclusions
Acknowledging the critical role of UGS expansion in the issue of cropland loss to urbanization is imperative. Our results offer insights into ensuring human well-being and ecosystem services through landscape and urban planning.
Simulation and design of isostatic thick origami structures
Authors: Micheletti Andrea , Tiero Alessandro , Tomassetti Giuseppe 
Published: June 2024

AbstractThick origami structures are considered here as assemblies of polygonal panels hinged to each other along their edges according to a corresponding origami crease pattern. The determination of the internal actions in equilibrium with the external loads in such structures is not an easy task, owing to their high degree of static indeterminacy, and the likelihood of unwanted self-balanced internal actions induced by manufacturing imperfections. Here, we present a method for reducing the degree of static indeterminacy which can be applied to several thick origami structures to make them isostatic. The method utilizes sliding hinges, which allow relative translation along the hinge axis, to replace conventional hinges. After giving the analytical description of both types of hinges and describing a rigid folding simulation procedure based on the integration of the exponential map, we present the static analysis of a series of noteworthy examples based on the Miura-ori pattern, the Yoshimura pattern, and the Kresling pattern. Our method, based on kinematic-static duality, provides a novel design paradigm that can be applied for the design and realization of thick origami structures with adequate strength to resist external actions.
A Random Focusing Method with Jensen–Shannon Divergence for Improving Deep Neural Network Performance Ensuring Architecture Consistency
Authors: Kim Wonjik 
Published: June 2024

AbstractMultiple hidden layers in deep neural networks perform non-linear transformations, enabling the extraction of meaningful features and the identification of relationships between input and output data. However, the gap between the training and real-world data can result in network overfitting, prompting the exploration of various preventive methods. The regularization technique called ’dropout’ is widely used for deep learning models to improve the training of robust and generalized features. During the training phase with dropout, neurons in a particular layer are randomly selected to be ignored for each input. This random exclusion of neurons encourages the network to depend on different subsets of neurons at different times, fostering robustness and reducing sensitivity to specific neurons. This study introduces a novel approach called random focusing, departing from complete neuron exclusion in dropout. The proposed random focusing selectively highlights random neurons during training, aiming for a smoother transition between training and inference phases while keeping network architecture consistent. This study also incorporates Jensen–Shannon Divergence to enhance the stability and efficacy of the random focusing method. Experimental validation across tasks like image classification and semantic segmentation demonstrates the adaptability of the proposed methods across different network architectures, including convolutional neural networks and transformers.
Assessing the Effects of Workplace Contextual Factors on Turnover Intention: Evidence from U.S. Federal Employees
Authors: Hur Yongbeom 
Published: June 2024

AbstractThis study delves into the crucial topic of turnover intention among U.S. Federal Employees, shedding light on the workplace factors that play a pivotal role in mitigating this issue. Through the utilization of Ordinary Least Squares (OLS) regression analyses, the study meticulously examines various workplace contextual factors and their impact on turnover intention. The standout finding of the study underscores the paramount importance of opportunities for growth and development (Factor 4) in reducing turnover intention. This emphasizes the significance of investing in employees’ professional advancement and creating avenues for them to expand their skills and progress in their careers. Moreover, the study brings to the forefront additional factors that contribute to decreased turnover intention, such as longer tenure, higher salary, gender (with females exhibiting lower turnover intention), and age. These insights not only provide a nuanced understanding of the dynamics at play but also offer actionable strategies for organizations to address turnover concerns effectively. By identifying these key factors, the study equips HR professionals and decision-makers with valuable guidance for implementing tailored HRM practices aimed at curbing turnover and fostering a more stable and satisfied workforce within the U.S. Federal sector. Ultimately, these findings have the potential to drive tangible improvements in employee retention and organizational performance.
Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups Using a Single Model Across Cages
Authors: Camilleri Michael P. J. , Bains Rasneer S. , Williams Christopher K. I. 
Published: June 2024

AbstractBehavioural experiments often happen in specialised arenas, but this may confound the analysis. To address this issue, we provide tools to study mice in the home-cage environment, equipping biologists with the possibility to capture the temporal aspect of the individual’s behaviour and model the interaction and interdependence between cage-mates with minimal human intervention. Our main contribution is the novel Global Behaviour Model (GBM) which summarises the joint behaviour of groups of mice across cages, using a permutation matrix to match the mouse identities in each cage to the model. In support of the above, we also (a) developed the Activity Labelling Module (ALM) to automatically classify mouse behaviour from video, and (b) released two datasets, ABODe for training behaviour classifiers and IMADGE for modelling behaviour.
Nutrient Accumulation in Silver Birch (Betula pendula Roth) Biomass in a Lignite Mining Area
Authors: Rustowska Beata , Jonczak Jerzy , Pędziwiatr Artur 
Published: June 2024

AbstractThe influence of lignite mining on vegetation constitutes a key issue due to the role of plants in restoring and maintaining the ecological balance of ecosystems. In this context, the identification of its impact on the functioning of silver birch (Betula pendula Roth) as a species often colonizing disturbed habitats takes on particular importance. Therefore, we aimed to determine the changes in nutrient content in silver birch overgrowing a spoil heap and in the vicinity of a fly ash settling pond and power plant. For this purpose, plant tissues (fine and coarse roots, stemwood, bark, coarse and fine branches, leaves) and soil samples (0–10, 10–20, 20–40, 20–40 cm deep) were examined. The basic soil characteristics were determined, along with the N, P, K, Ca, Mg, S, Fe, Mn, Cu, and Zn contents of the soil and plant samples. The soils varied in terms of soil pH and were poor in total organic carbon and other elements. The plant nutrient content varied strongly across the analyzed tissues, with the leaves usually containing the most and the stemwood the least nutrients. Statistical analysis indicated significant differences between the control vs spoil heap (particularly in Mn, S, and Mg) and the stand close to the settling pond (particularly in Ca, Mn, P, K, and S). We found that the chemical properties of the spoil heap and fly ash originating from the lignite mining operations are likely factors influencing nutrient accumulation in silver birch trees.
A meta-analysis on the effects of marker coverage, status number, and size of training set on predictive accuracy and heritability estimates from genomic selection in tree breeding
Authors: Beaulieu Jean , Lenz Patrick R.N. , Laverdière Jean-Philippe , Nadeau Simon , Bousquet Jean 
Published: June 2024

AbstractGenomic selection (GS) is increasingly used in tree breeding because of the possibility to hasten breeding cycles, increase selection intensity or facilitate multi-trait selection, and to obtain less biased estimates of quantitative genetic parameters such as heritability. However, tree breeders are aiming to obtain accurate estimates of such parameters and breeding values while optimizing sampling and genotyping costs. We conducted a metadata analysis of results from 28 GS studies totalling 115 study-traits. We found that heritability estimates obtained using DNA marker-based information for a variety of traits and species were not significantly related to variation in the total number of markers ranging from about 1500 to 116 000, nor by the marker density, ranging from about 1 to 60 markers/centimorgan, nor by the status number of the breeding populations ranging from about 10 to 620, nor by the size of the training set ranging from 236 to 2458. However, the predictive accuracy of breeding values was generally higher when the status number of the breeding population was smaller, which was expected given the higher level of relatedness in small breeding populations, and the increased ability of a given number of markers to trace the long-range linkage disequilibrium in such conditions. According to expectations, the predictive accuracy also increased with the size of the training set used to build marker-based models. Genotyping arrays with a few to many thousand markers exist for several tree species and with the actual costs, GS could thus be efficiently implemented in many more tree breeding programs, delivering less biased genetic parameters and more accurate estimates of breeding values.
Association of sudden sensorineural hearing loss with meteorological factors: a time series study in Hefei, China, and a literature review
Authors: Li Xiao-Bo , Han Yan-Xun , Fu Zi-Yue , Zhang Yu-Chen , Fan Min , Sang Shu-Jia , Chen Xi-Xi , Liang Bing-Yu , Liu Yu-Chen , Lu Peng-Cheng , Li Hua-Wei , Pan Hai-Feng , Yang Jian-Ming 
Published: June 2024

AbstractAir pollution can cause disease and has become a major global environmental problem. It is currently believed that air pollution may be related to the progression of SSNHL. As a rapidly developing city in recent years, Hefei has serious air pollution. In order to explore the correlation between meteorological variables and SSNHL admissions, we conducted this study. This study investigated the short-term associations between SSNHL patients admitted to the hospital and Hefei climatic variables. The daily data on SSNHL-related hospital admissions and meteorological variables containing mean temperature (T-mean; °C), diurnal temperature range (DTR; °C), atmospheric pressure (AP; Hp), and relative humidity (RH; %), from 2014 to 2021 (2558 days), were collected. A time-series analysis integrating distributed lag non-linear models and generalized linear models was used. PubMed, Embase, Cochrane Library, and Web of Science databases were searched. Literature published up to August 2023 was reviewed to explore the potential impact mechanisms of meteorological factors on SSNHL. The mechanisms were determined in detail, focusing on wind speed, air pressure, temperature, humidity, and air pollutants. Using a median of 50.00% as a baseline, the effect of exceedingly low T-mean in the single-day hysteresis effect model began at a lag of 8 days (RR = 1.032, 95% CI: 1.001 ~ 1.064). High DTR affected the admission rate for SSNHL on lag 0 day. The significance of the effect was the greatest on that day (RR = 1.054, 95% CI: 1.007 ~ 1.104) and then gradually decreased. High and exceedingly high RH affected the admission rate SSNHL on lag 0 day, and these effects lasted for 8 and 7 days, respectively. There were significant associations between all grades of AP and SSNHL. This is the first study to assess the effect of meteorological variables on SSNHL-related admissions in China using a time-series approach. Long-term exposures to high DTR, RH values, low T-mean values, and all AP grades enhance the incidence of SSNHL in residents. Limiting exposure to extremes of ambient temperature and humidity may reduce the number of SSNHL-related hospital visits in the region. It is advisable to maintain a suitable living environment temperature and avoid extreme temperature fluctuations and high humidity. During periods of high air pollution, it is recommended to stay indoors and refrain from outdoor exercise.
A Typology of Tonal Exponence
Authors: Kaldhol Nina Hagen 
Published: June 2024

AbstractThis paper presents a typology of tonal exponence. Couched within an Abstractive Word-and-Paradigm approach to morphology, the present study builds on previous studies on exponence typology and morphological organization by extending it to the study of tone. About half the languages of the world have tone systems, and tone is an important dimension in the morphologies of numerous languages. Tone is therefore a necessary part of a comprehensive typology of exponence. This paper shows that like segmental exponents, tonal exponents may be involved in a diversity of form-function mappings, but they also pose unique challenges due to their autosegmental nature. This study aims to advance our understanding of the role of tone in the organization of morphological systems by addressing deviations from form-function isomorphism, polyfunctionality, morphomic distributions, paradigmatic layers, and inflectional class organization. It is argued that the attested diversity of form-function mappings constitutes an empirical argument for a paradigm-based view of morphology, where the attested diversity is taken at face value and the range of encoding strategies are treated as equivalent, as opposed to choosing form-function isomorphism as the theoretical ‘ideal’.
Autonomous navigation of catheters and guidewires in mechanical thrombectomy using inverse reinforcement learning
Authors: Robertshaw Harry , Karstensen Lennart , Jackson Benjamin , Granados Alejandro , Booth Thomas C. 
Published: June 2024

Abstract
Purpose
Autonomous navigation of catheters and guidewires can enhance endovascular surgery safety and efficacy, reducing procedure times and operator radiation exposure. Integrating tele-operated robotics could widen access to time-sensitive emergency procedures like mechanical thrombectomy (MT). Reinforcement learning (RL) shows potential in endovascular navigation, yet its application encounters challenges without a reward signal. This study explores the viability of autonomous guidewire navigation in MT vasculature using inverse reinforcement learning (IRL) to leverage expert demonstrations.

Methods
Employing the Simulation Open Framework Architecture (SOFA), this study established a simulation-based training and evaluation environment for MT navigation. We used IRL to infer reward functions from expert behaviour when navigating a guidewire and catheter. We utilized the soft actor-critic algorithm to train models with various reward functions and compared their performance in silico.

Results
We demonstrated feasibility of navigation using IRL. When evaluating single- versus dual-device (i.e. guidewire versus catheter and guidewire) tracking, both methods achieved high success rates of 95% and 96%, respectively. Dual tracking, however, utilized both devices mimicking an expert. A success rate of 100% and procedure time of 22.6 s were obtained when training with a reward function obtained through ‘reward shaping’. This outperformed a dense reward function (96%, 24.9 s) and an IRL-derived reward function (48%, 59.2 s).

Conclusions
We have contributed to the advancement of autonomous endovascular intervention navigation, particularly MT, by effectively employing IRL based on demonstrator expertise. The results underscore the potential of using reward shaping to efficiently train models, offering a promising avenue for enhancing the accessibility and precision of MT procedures. We envisage that future research can extend our methodology to diverse anatomical structures to enhance generalizability.
Model-based individual life-spanning documentation in visceral surgery: a proof of concept
Authors: Berlet Maximilian , Jell Alissa , Wagner Lars , Bernhard Lukas , Fuchtmann Jonas , Wegener Luca , Feussner Hubertus , Friess Helmut , Wilhelm Dirk 
Published: June 2024

Abstract
Introduction
Surgical documentation has many implications. However, its primary function is to transfer information about surgical procedures to other medical professionals. Thereby, written reports describing procedures in detail are the current standard, impeding comprehensive understanding of patient-individual life-spanning surgical course, especially if surgeries are performed at a timely distance and in diverse facilities. Therefore, we developed a novel model-based approach for documentation of visceral surgeries, denoted as 'Surgical Documentation Markup-Modeling' (SDM-M).

Material and methods
For scientific evaluation, we developed a web-based prototype software allowing for creating hierarchical anatomical models that can be modified by individual surgery-related markup information. Thus, a patient's cumulated 'surgical load' can be displayed on a timeline deploying interactive anatomical 3D models. To evaluate the possible impact on daily clinical routine, we performed an evaluation study with 24 surgeons and advanced medical students, elaborating on simulated complex surgical cases, once with classic written reports and once with our prototypical SDM-M software.

Results
Leveraging SDM-M in an experimental environment reduced the time needed for elaborating simulated complex surgical cases from 354 ± 85 s with the classic approach to 277 ± 128 s. (p = 0.00109) The perceived task load measured by the Raw NASA-TLX was reduced significantly (p = 0.00003) with decreased mental (p = 0.00004) and physical (p = 0.01403) demand. Also, time demand (p = 0.00041), performance (p = 0.00161), effort (p = 0.00024), and frustration (p = 0.00031) were improved significantly.

Discussion
Model-based approaches for life-spanning surgical documentation could improve the daily clinical elaboration and understanding of complex cases in visceral surgery. Besides reduced workload and time sparing, even a more structured assessment of individual surgical cases could foster improved planning of further surgeries, information transfer, and even scientific evaluation, considering the cumulative 'surgical load.'

Conclusion
Life-spanning model-based documentation of visceral surgical cases could significantly improve surgery and workload.
Progressive Induction Hardening: Measurement and Alteration of Residual Stresses
Authors: Holmberg Jonas , Wendel Johan , Stormvinter Albin 
Published: June 2024

AbstractProgressive induction hardening is an in-line steel heat treatment method commonly used to surface harden powertrain components. It produces a martensitic case layer with a sharp transition zone to the base material. This rapid process will induce large residual stresses, where a compressive state in the case layer will shift to a tensile state in the transition zone. For fatigue performance, it is important to quantify the magnitude and distribution of these stresses, and moreover how they depend on material and processing parameters. In this work, x-ray diffraction in combination with a layer removal method is used for efficient and robust quantification of the subsurface stress state, which combines electropolishing with either turning or milling. Characterization is done on C45E steel samples that were progressively induction hardened using either a fast or slow (27.5 or 5 mm/s, respectively) scanning speed. The results show that although the hardening procedures will meet arbitrary requirements on surface hardness, case depth and microstructure, the subsurface tensile stress peak magnitude is doubled when using a fast scanning speed. However, the near-surface compressive residual stresses are comparable. In addition, the subsurface tensile residual stress peak is compared with the on-surface tensile stresses in the fade-out zone.
Leaf temperatures of an Austrian oak are below photosynthetic temperature thresholds during a heatwave in Central Europe
Authors: Kunert Norbert 
Published: June 2024

AbstractThe summer of 2022 has been the so far hottest summer on record in Central Europe. High temperatures negatively affect the physiology of plants and cause considerable thermal stress in particular on the leaf level. The assessment of the temperature-dependent decline of the quantum use efficiency of the photosystem II (Fv/Fm) has gained much popularity to quantify the leaf-level sensitivity to thermal stress. An open question is whether leaves heat to those in vitro estimated threshold temperatures on hot days or if plants can avoid heat stress through transpirational cooling. Therefore, leaf temperatures were monitored on a non-native Austrian oak (Quercus cerris) during a heatwave in July 2022 and compared to observed air temperature and leaf thermal traits assessed with a chlorophyll fluorometer. The highest air temperature recorded during the heatwave was 42.5 °C and surpassed the breaking point temperature (temperature at 5% decline of Fv/Fm; T5) by 0.3 °C, but was 6.1 °C lower than T50 (temperature at 50% decline of Fv/Fm). However, during the hottest day, the maximum leaf temperature was significantly below the air temperature. Even the directly illuminated leaf facing south reached a maximum temperature of only 38.7 °C but reached 39.8 °C on the second hottest day when the air temperature went up to 39.6 °C. All leaves showed a certain degree of homeothermy as the slope between leaf temperature and air temperature was 0.83 (P < 0.05). In conclusion, Austrian oak can buffer thermal stress during heatwaves to a certain degree, however, leaf temperatures are only marginally below critical threshold temperatures.