bluuebunny/crossref_metadata_2025_split huggingface dataset:
Aggregation formulation for on‐site multidepot vehicle scheduling scenario
Authors: Gao Yi , Tang Yuanjie , Liu Rengkui 
Published: April 2024

AbstractThe multidepot vehicle scheduling problem (MDVSP) is a fundamental public transport challenge. To address the large‐scale model and inherent solution symmetry associated with the traditional trip‐to‐trip connection‐based approach for MDVSP, a new trip‐to‐route (T2R) connection‐based approach is proposed. Considering real‐world problem characteristics with numerous trips sharing common origin–destination stations and travel times on one route, this approach aggregates same vehicle possible trip sequences into a T2R connection. Two time‐space network aggregation (TSNA) flow formulation versions, route pair‐based TSNA and station pair‐based TSNA, were constructed. Furthermore, TSNA equivalence under any given decomposition strategy, including first‐in‐first‐out, with the multicommodity network flow (MCNF) model was demonstrated. Given the favorable separable TSNA structure, an alternating direction method of multipliers (ADMM)‐based procedure is proposed to decompose the MDVSP into multiple subproblems that can be linearized and readily solved using commercial solvers. The quality of the solutions was assessed using lower bounds obtained from the Lagrangian relaxation problem. The effectiveness and superiority of the proposed MDVSP models and algorithms were subsequently confirmed using random data sets and real‐world instances.
Rapid pedestrian‐level wind field prediction for early‐stage design using Pareto‐optimized convolutional neural networks
Authors: Clemente Alfredo Vicente , Giljarhus Knut Erik Teigen , Oggiano Luca , Ruocco Massimiliano 
Published: April 2024

AbstractTraditional computational fluid dynamics (CFD) methods used for wind field prediction can be time‐consuming, limiting architectural creativity in the early‐stage design process. Deep learning models have the potential to significantly speed up wind field prediction. This work introduces a convolutional neural network (CNN) approach based on the U‐Net architecture, to rapidly predict wind in simplified urban environments, representative of early‐stage design. The process of generating a wind field prediction at pedestrian level is reformulated from a 3D CFD simulation into a 2D image‐to‐image translation task, using the projected building heights as input. Testing on standard consumer hardware shows that our model can efficiently predict wind velocities in urban settings in less than 1 ms. Further tests on different configurations of the model, combined with a Pareto front analysis, helped identify the trade‐off between accuracy and computational efficiency. The fastest configuration is close to seven times faster, while having a relative loss, which is 1.8 times higher than the most accurate configuration. This CNN‐based approach provides a fast and efficient method for pedestrian wind comfort (PWC) analysis, potentially aiding in more efficient urban design processes.
Development of myasthenia gravis: 4 years after durvalumab‐induced myositis
Authors: Nosaki Yasunobu , Odate Mitsuru , Takenaka Hiroyuki , Yokoi Takamasa , Iwai Katsushige 
Published: April 2024

AbstractWe report a 74‐year‐old man diagnosed with myasthenia gravis (MG) accompanied by anti‐acetylcholine receptor antibody seroconversion and positive for anti‐titin antibody, with onset delayed by 4 years after the final durvalumab administration, which preceded myositis and organizing pneumonia (OP). Durvalumab, an anti‐programmed cell death ligand 1 antibody, is effective in patients with cancer and causes immune‐related adverse events (irAEs) retrospectively. Patients treated with immune checkpoint inhibitors require vigilant monitoring as irAEs can arise at any phase, regardless of the time after immune checkpoint inhibitor discontinuation.
Pushing the envelope: do narrowly and widely distributed Eucalyptus species differ in response to climate warming?
Authors: Drake John E. , Vårhammar Angelica , Aspinwall Michael J. , Pfautsch Sebastian , Ghannoum Oula , Tissue David T. , Tjoelker Mark G. 
Published: April 2024

Summary

Contemporary climate change will push many tree species into conditions that are outside their current climate envelopes. Using the Eucalyptus genus as a model, we addressed whether species with narrower geographical distributions show constrained ability to cope with warming relative to species with wider distributions, and whether this ability differs among species from tropical and temperate climates.

We grew seedlings of widely and narrowly distributed Eucalyptus species from temperate and tropical Australia in a glasshouse under two temperature regimes: the summer temperature at seed origin and +3.5°C. We measured physical traits and leaf‐level gas exchange to assess warming influences on growth rates, allocation patterns, and physiological acclimation capacity.

Warming generally stimulated growth, such that higher relative growth rates early in development placed seedlings on a trajectory of greater mass accumulation. The growth enhancement under warming was larger among widely than narrowly distributed species and among temperate rather than tropical provenances. The differential growth enhancement was primarily attributable to leaf area production and adjustments of specific leaf area.

Our results suggest that tree species, including those with climate envelopes that will be exceeded by contemporary climate warming, possess capacity to physiologically acclimate but may have varying ability to adjust morphology.
The deixis of literature: On the conditions for recognizing computers as authors
Authors: Bajohr Hannes 
Published: April 2024

AbstractTaking the deictic judgment that is the modernist gesture of declaring something to be art as a starting point, this essay suggests an analogous deixis as a necessary condition for literature. This deixis also can serve as the basis for discussing the expectations of computer‐generated texts. Against the idea that computers or AI systems need only produce sufficiently good output in order to be considered authors, the essay proposes an approach that takes the social recognition of the deictic act within a community of judgment as a precondition for authorship. As an alternative to the Turing test, which is based on the paradigm of deception (people are tricked into considering computer‐written text to be written by humans), the essay favors a version of Susan Leigh Star's “Durkheim test,” which is based on the paradigm of co‐sociality (people directly recognize computers as social actors). Only if the gesture of a machine declaring something to be art is recognized as a deictic judgment in the full sense can one plausibly speak of computer authorship.
Conserved cold tolerance of Rhagoletis species from different host fruits and elevations in Colorado, USA
Authors: Lemay Katelyn , Moore Mackenzie , Brown Paige , Gadey Lahari , Ragland Gregory J. , Toxopeus Jantina 
Published: April 2024

AbstractUnderstanding and characterizing how insects tolerate low temperatures is important for predicting their overwintering survival and subsequent geographic spread. This study characterized the cold tolerance of two members of the Rhagoletis genus (Diptera: Tephritidae) in Colorado, USA. Pupae were collected from the infested fruits in late summer and early fall. For the first time, we show that the rosehip fly Rhagoletis basiola Osten Sacken is freeze‐avoidant; overwintering pupae could supercool to temperatures as low as −26°C and survive. Interestingly, the temperature at which ice forms (supercooling point; SCP) did not vary between R. basiola at high (c. 2900 m above sea level [m a.s.l.]) and lower (c. 1650 m a.s.l.) elevations. We also report the apple maggot R. pomonella Walsh infesting an unusual host fruit, the Dolgo crabapple, in close proximity to infested hawthorn trees. R. pomonella infesting hawthorn fruits and crabapples had similar SCPs and survived temperatures as low as −21°C. Pupae from both host fruits also survived prolonged exposure (2 weeks or more) to mild low temperatures (0 to −5°C). Further study into the mechanisms underlying the impressive and conserved cold tolerance of R. pomonella and R. basiola is an interesting avenue for future research.
Occurrence and field evaluation of promising sugarcane genotypes for resistance to midrib red rot disease in China
Authors: Jiang Chunxiu , Xu Yuzhi , Wei Yibin , Khan Muhammad Tahir , Wu Yuling , Li Huojian , Chen Jun , Zhang Chi , Zeng Lifang , Zhang Muqing 
Published: April 2024

AbstractSugarcane midrib red rot caused by Colletotrichum falcatum is widespread in China. Standardizing the resistance evaluation methods and screening resistant varieties against this disease is crucial. C. falcatum was determined as the causal agent of midrib red rot by isolating and identifying pathogenic fungi from diseased leaves in Guangxi, China. A field trial of 237 sugarcane genotypes was conducted for three consecutive years to investigate disease incidence and indices for three experimental sites in Guangxi. The combined analysis of variance showed that disease incidence was significantly affected by genotype, year, location and their interactions. Cluster and discriminant analysis showed that 237 sugarcane genotypes were classified into five categories, including 20 as highly resistant, 82 as resistant, 71 as moderately susceptible, 46 as susceptible and 18 as highly susceptible. This study has enhanced our understanding of sugarcane resistance to midrib red rot, selected disease‐resistant parents and laid the foundation for developing elite disease‐resistant varieties.
Silicon induces ROS scavengers, hormone signalling, antifungal metabolites, and silicon deposition against brown stripe disease in sugarcane
Authors: Chen Jiaoyun , Li Yongjia , Zeng Zhen , Zhao Xiaofeng , Zhang Yi , Li Xiaonan , Chen Jianwen , Shen Wankuan 
Published: April 2024

AbstractBipolaris setariae is known to cause brown stripe disease in sugarcane, resulting in significant yield losses. Silicon (Si) has the potential to enhance plant growth and biotic resistance. In this study, the impact of Si on brown stripe disease was investigated across susceptible and resistant sugarcane varieties, utilizing four Si concentrations (0, 15, 30, and 45 g per barrel of Na2SiO3·5H2O). Si significantly reduced the incidence of brown stripe disease (7.41–59.23%) and alleviated damage to sugarcane growth parameters, photosynthetic parameters, and photosynthetic pigments. Submicroscopic observations revealed that Si induced the accumulation of silicified cells in leaves, reduced spore accumulation, decreased stomatal size, and protected organelles from B. setariae damage. In addition, Si increased the activity of antioxidant enzymes (superoxide dismutase, peroxidase, and catalase), reduced reactive oxygen species production (malondialdehyde and hydrogen peroxide) and modulated the expression of genes associated with hormone signalling (PR1, TGA, AOS, AOC, LOX, PYL8, and SnRK2), leading to the accumulation of abscisic acid and jasmonic acid and inhibiting SA synthesis. Si also activated the activity of metabolism‐related enzymes (polyphenol oxidase and phenylalanine ammonia lyase) and the gene expression of PAL‐dependent genes (PAL, C4H, and 4CL), regulating the accumulation of metabolites, such as chlorogenic acid and lignin. The antifungal test showed that chlorogenic acid (15ug μL−1) had a significant inhibitory effect on the growth of B. setariae. This study is the first to demonstrate the inhibitory effect of Si on B. setariae in sugarcane, highlighting Si as a promising and environmentally friendly strategy for managing brown stripe disease.
Assessing relative risks of municipal wastewater disposal options for Southeast Florida
Authors: Bloetscher Frederick , Meeroff Daniel E. , Conboy Kelley , Sham Chi Ho , Fergen Robert , Gallant Richard , Hart James , Shibata T. , Tuccillo Mary Ellen , Englehardt James D. 
Published: April 2024

AbstractA comparative assessment of the risks of the three current wastewater effluent disposal options and three other potential options was conducted for Southeast Florida communities. The question was how the risk to humans from the use of potable reuse compares to the other five available wastewater disposal alternatives. The need for this type of risk assessment is due to the potential to use potable reuse as a water supply and the potential resistance from the public as a result of such a proposal. Water quality data relevant to disposal of wastewater treatment plant effluent from South Florida utilities along with water quality data on the receiving waters and drinking water standards were obtained for the project. The comparison of the public health risks associated with these disposal alternatives indicated that health risks associated with deep wells and direct potable reuse were generally lower than those of the other alternatives.
The effect of fiscal drag on income distribution and work incentives: A microsimulation analysis on selected African countries
Authors: Shahir Adnan Abdulaziz , Figari Francesco 
Published: April 2024

AbstractAlthough the effect of fiscal drag is well‐studied in the industrialised world, empirical evidence from developing economies remains limited. Against this backdrop, this study aims to explore the effect of fiscal drag on income distribution and work incentives. To this end, the study employs SOUTHMOD, the tax‐benefit microsimulation model, for six African countries: Ethiopia, South Africa, Tanzania, Uganda, Mozambique, and Zambia. Three important conclusions are drawn from our empirical investigation. First, in the absence of proper tax parameter adjustment, the distribution of fiscal drag is determined by the liability progression of personal income tax in the pre‐inflation period. Second, the impact of fiscal drag on the redistributive effects and progressivity of personal income taxes is differentiated among countries. On the one hand, it reduces the progressivity of personal income tax in Ethiopia, South Africa, Tanzania, Uganda, and Zambia; on the other hand, it improves progressivity in Mozambique. However, it decreases the redistributive effect of personal income tax only in Ethiopia, Tanzania, and Uganda. Third, fiscal drag reduces financial work incentives to increase earnings in all countries. Addressing fiscal drag becomes very crucial considering the soaring inflation due to the war between Ukraine and Russia.
Mahalanobis balancing: A multivariate perspective on approximate covariate balancing
Authors: Dai Yimin , Yan Ying 
Published: April 2024

AbstractIn the past decade, various exact balancing‐based weighting methods were introduced to the causal inference literature. It eliminates covariate imbalance by imposing balancing constraints in a certain optimization problem, which can nevertheless be infeasible when there is bad overlap between the covariate distributions in the treated and control groups or when the covariates are high dimensional. Recently, approximate balancing was proposed as an alternative balancing framework. It resolves the feasibility issue by using inequality moment constraints instead. However, it can be difficult to select the threshold parameters. Moreover, moment constraints may not fully capture the discrepancy of covariate distributions. In this paper, we propose Mahalanobis balancing to approximately balance covariate distributions from a multivariate perspective. We use a quadratic constraint to control overall imbalance with a single threshold parameter, which can be tuned by a simple selection procedure. We show that the dual problem of Mahalanobis balancing is an norm‐based regularized regression problem, and establish interesting connection to propensity score models. We derive asymptotic properties, discuss the high‐dimensional scenario, and make extensive numerical comparisons with existing balancing methods.
First comprehensive higher level phylogeny of Zygaenidae (Lepidoptera) including estimated ages of the major lineages and a review of known zygaenid fossils
Authors: Mirić Mirela , Efetov Konstantin A. , Tarmann Gerhard M. , Chiocchio Andrea , Heikkilä Maria , Wagner David L. , Rota Jadranka 
Published: April 2024

AbstractZygaenidae, also known as burnet, forester, smoky, or leaf skeletonizer moths, are a family of mainly diurnal moths well known for their aposematic colouration and the ability to release hydrogen cyanide as a defence mechanism. So far, few attempts have been made to understand the evolutionary history of the global zygaenid fauna. Here, we inferred the most comprehensive molecular phylogeny for Zygaenidae to date and estimated the lineage timing‐of‐divergence with a Bayesian approach. Building on earlier work, we significantly increased the taxon and gene sampling for the family, which here included data from 30 gene fragments, recovered from public databases or newly sequenced, for almost 30% of the species representing 92 genera (49%) and all five subfamilies. We recovered strong support for the monophyly of Zygaenidae, Chalcosiinae, and Zygaeninae. Procridinae were recovered as monophyletic with low support, whereas the monophyly of Callizygaeninae remains untested as we sampled only one of the two genera. In the core dataset, we recovered Procridinae as sister to Callizygaeninae + Chalcosiinae. This large clade is the sister lineage to Zygaeninae. The position of Inouelinae could not be resolved. The lineage leading to the extant Zygaenidae appears to have diverged in Late Cretaceous (ca. 86 Ma), while the divergence among the subfamilies occurred several million years before the Cretaceous–Paleogene mass extinction event (ca. 66 Ma). Additionally, we provide a review of known fossil Zygaenidae as Appendix S1. Our results form a strong basis for future studies of zygaenid biosystematics, including their ecology, evolution, and behaviour.
Trainee burnout: Are there modifiable factors?
Authors: Thompson Meredith , Maldonado Nicholas , Srihari Caroline , McFarlane Antionette , Patrie James , Fernandez Rosemarie , Sudhir Amita 
Published: April 2024

AbstractBackgroundBurnout is a complex phenomenon and a major concern in graduate medical education as it directly impacts trainee well‐being. Identifying modifiable lifestyle factors over which trainees have immediate control could support timely, actionable, individual and programme‐level interventions to combat it.ObjectiveThe objective of this pilot study is to describe modifiable lifestyle factors that may limit the development of burnout in medical residents and fellows.MethodsWe performed a cross‐sectional survey of residents and fellows at academic medical centre from September 2017 to October 2017. Participants completed the Maslach Burnout Inventory and a questionnaire designed to identify factors hypothesised to be protective against burnout.FindingsA total of 205/805 (25%) trainees completed the survey with a mean (SD) age of 29.7 (2.6) years. 52% (n = 107) were female. Averaging at least 7 h of sleep per night was found to have a significant association with lower scores for the emotional exhaustion (24.8 [11, p = 0.04]) and depersonalisation (11.1 [6.4, p = 0.02]) dimensions of burnout. Additionally, self‐identifying as a healthy eater was found to have a significant association with lower scores for emotional exhaustion (25 [11.5, p = 0.03]) and depersonalisation (11.5 [6.6, p = 0.04]) as well. Workouts, hobbies, identifying with organised religion, praying, meditation and mindfulness activities were not associated with a difference.ConclusionsAdequate sleep (7 or more hours per night) and healthy eating are modifiable individual‐level lifestyle factors associated with lower burnout scores in trainees. These items could be a target for trainee education and programme level support initiatives.
A high‐performance cellular automata model for urban expansion simulation based on convolution and graphic processing unit
Authors: Zeng Haoran , Wang Haijun , Zhang Bin 
Published: April 2024

AbstractCellular automata (CA) models are effective tools for simulating future urban expansion. With the widespread use of high‐resolution geospatial data for CA simulation, the computational intensity of CA models has increased. Additionally, due to the continuous development of CA modeling research, many scholars have made improvements to the models to enhance their simulation accuracy, resulting in an increasing computational complexity of the model. Consequently, the simulation task based on CA requires vast computing time and memory space. In recent years, deep learning (DL) has experienced rapid development. Many open‐source DL frameworks support graphic processing unit (GPU) parallel computing and provide efficient application programming interfaces (APIs) that can be easily called to handle tasks of interest. In this study, a high‐performance CA model was constructed based on the similarity between the neighborhood effect calculation process of the CA model and the convolutional process in a convolutional neural network (CNN). The convolution function in the DL library is used to calculate the neighborhood effect of the CA model to reduce the time and memory consumption of CA‐based simulation. The experimental results show that compared with the conventional CA model, the execution time of the GPU‐convolution‐CA model proposed in this study has been reduced by more than 98%.
Classification of the quality of canine and feline ventrodorsal and dorsoventral thoracic radiographs through machine learning
Authors: Tahghighi Peyman , Appleby Ryan B. , Norena Nicole , Ukwatta Eran , Komeili Amin 
Published: April 2024

AbstractThoracic radiographs are an essential diagnostic tool in companion animal medicine and are frequently used as a part of routine workups in patients presenting for coughing, respiratory distress, cardiovascular diseases, and for staging of neoplasia. Quality control is a critical aspect of radiology practice in preventing misdiagnosis and ensuring consistent, accurate, and reliable diagnostic imaging. Implementing an effective quality control procedure in radiology can impact patient outcomes, facilitate clinical decision‐making, and decrease healthcare costs. In this study, a machine learning‐based quality classification model is suggested for canine and feline thoracic radiographs captured in both ventrodorsal and dorsoventral positions. The problem of quality classification was divided into collimation, positioning, and exposure, and then an automatic classification method was proposed for each based on deep learning and machine learning. We utilized a dataset of 899 radiographs of dogs and cats. Evaluations using fivefold cross‐validation resulted in an F1 score and AUC score of 91.33 (95% CI: 88.37–94.29) and 91.10 (95% CI: 88.16–94.03), respectively. Results indicated that the proposed automatic quality classification has the potential to be implemented in radiology clinics to improve radiograph quality and reduce nondiagnostic images.