bluuebunny/crossref_metadata_2025_split huggingface dataset:
Overriding in Teams: The Role of Beliefs, Social Image, and Gender
Authors: Guo Joyce , Recalde María P. 
Published: June 2022

To shed light on the factors that affect who speaks up in teams in the workplace, we study willingness to speak up after someone has raised an opinion. We call voicing disagreement overriding and study this behavior in a laboratory experiment where participants answer multiple choice questions in pairs. In a control treatment, participants interact anonymously. In a photo treatment, both participants see the photo of the person they are matched with at the beginning of the group task. Using a series of incentivized tasks, we elicit beliefs about the likelihood that each possible answer option to a question is correct. This allows us to measure disagreement and to tease apart the role of disagreement versus preferences in the decision to override ideas in teams. Results show that anonymity increases overriding. This treatment effect is driven by social image costs. Analysis of heterogeneity in behavior by gender reveals no differences between the likelihood that men and women override. However, we find some evidence that men and women are treated differently; when participants disagree with their partner, they are more likely to override a woman than a man. Preferences seem to in part explain the differential treatment of men and women. Studying group performance, we find that overriding helps groups on average, while the gender composition of teams does not affect team performance. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: This work was supported by the University of Melbourne and the Australian Research Council [Grant DE190100585]. J. Guo received a Kinsman Studentship from the University of Melbourne to conduct this research. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.4434 .
Subsurface Drainage for Minimizing the Risk of Subsoil Compaction in Seasonally-Frozen Soils
Authors: Mante Afua Adobea , Ndulue Emeka Leonard , Sri Ranjan Ramanathan , Zvomuya Francis , Kaja Krishna 
Published: June 2022

HighlightsSubsoil is highly susceptible to compaction based on texture and packing density.Imperfect natural drainage increases the risk of subsoil vulnerability to compaction.Irrespective of drain spacing, the risk for subsoil compaction was high.The upper soil layer protects the subsoil from compaction at drain spacing = 12 m in this soil. Abstract. Subsoil compaction negatively impacts soil hydrological processes and promotes anaerobic conditions, reducing soil productivity and enhancing greenhouse gas emissions from the soil. Additionally, it is challenging and expensive to alleviate subsoil compaction once it occurs. The objective of this study was to assess the effectiveness of subsurface drainage in minimizing the risk of subsoil compaction under different weather patterns in Southern Manitoba. The assessment of the risk of subsoil compaction was done in two stages. That is, 1) determination of the subsoil’s intrinsic susceptibility to compaction based on soil texture and packing density and 2) determination of the wetness condition of the subsoil and ability of the strength of the upper layer of the soil to protect the subsoil. A long-term simulation of soil water content data (i.e., 2000 to 2015) under different drainage spacings (i.e., 8, 10, 12, 15, 25, and 30 m) maintained at 0.9-m depth was obtained to determine the soil wetness condition using a validated HYDRUS 2D/3D model. The study showed that the subsoil’s intrinsic susceptibility level to compaction at the study site was high, implying that the subsoil had a very weak natural potential to resist compaction. Throughout the 16 years considered, the subsoil wetness condition was either “moist” or “wet” irrespective of drain spacing, making the subsoil very vulnerable to compaction. However, for drain spacing = 12 m, the subsoil was found to be protected for most of the spring operation period with minimum impact on the spring operation days based on the criterion that the soil water content in the upper layer should be equal to or less than 90% of the lower plastic limit. In contrast, drain spacing wider than 12 m resulted in a lack of protection of the subsoil for 21 to 50 d. The intrinsic susceptibility of the subsoil to compaction, the “imperfect” internal natural drainage, and excess soil water during the early growing season suggest it is critical to consider the benefits of installing subsurface drains at narrower spacing (= 12 m) because of the ability to improve the soil wetness condition for field operations and prevent short and long-term impacts due to subsoil compaction. Keywords: Bulk density, HYDRUS (2D/3D), Lower plastic limit, Sandy loam, Soil water content.
Precise Segmentation of Remote Sensing Cage Images Based on SegNet and Voting Mechanism
Authors: Liu Yunpeng , Xia Xin , Hu Zhuhua , Fu Shengpeng 
Published: June 2022

HighlightsA Remote Sensing Cage Segmentation (RSCS) dataset is constructed.The SegNet network is introduced to achieve precise segmentation.Random cropping, data augmentation, and three-channel separation operations are used to construct the training sets.The proposed sliding window overlap cropping method and two rounds of voting are used to improve the segmentation accuracy.Abstract. In mariculture, improper cage layout and excessive density of mariculture will lead to deterioration of water quality and the growth of harmful bacteria. However, relying solely on manual measurement will consume a considerable amount of manpower and material resources. Therefore, we propose a precise segmentation scheme for remote sensing cage images based on SegNet and voting mechanism. First, a Remote Sensing Cage Segmentation (RSCS) dataset is constructed. Second, the number of collected samples is too small and the sample sizes are too large. Random cropping, data augmentation, and three-channel separation operations are used to construct the training sets. Nine training sets consisting of three image sizes and three single channels are generated. Finally, the proposed sliding window overlap cropping method and two rounds of voting are used on the test samples to improve the segmentation accuracy. The experimental results show that using sliding window overlap cropping, three-channel voting, and three-size voting can improve mIoU (mean Intersection over Union) by up to 0.9%, 1.9%, and 0.6%, respectively. By using the proposed final scheme, the mIoU of test samples can reach 0.89. Keywords: Mariculture, Remote image segmentation, SegNet, Sliding window overlap cropping, Voting mechanism.
Investigating the Influence of Grain Drying with Ambient Air Versus Heated Air within an Eighth-Scale Grain Drying Bin
Authors: Lewis Micah A. , Trabelsi Samir 
Published: June 2022

HighlightsDrying of corn and wheat was monitored and controlled in real-time via the eighth-scale grain drying systemHeat and mass transfer during drying were simulated for corn and wheat when dried with heated and ambient airSimulated results provided prediction of preheating, constant-rate, and falling-rate periods at different heights within the drying grainMoisture content determined from simulation compared well with empirical data; root mean square error (RMSE) for corn and wheat (heated and ambient) were 0.61%, 0.45%, 0.61%, and 0.50% moisture content w.b., respectivelyAbstract. The choice of whether to use heated or ambient air during grain and oilseed drying is often complex, and it has a great effect on the overall quality of the product. A significant problem when drying with heated air is overdrying the bottom layer in attempting to dry the top layer. This is possible because grains or oilseeds within a storage bin can be more than 15.2 m deep. However, while the bottom layer is less likely to be overdried when using ambient air, there is a risk that the upper layers could be subject to mold and degradation if moisture is not removed quickly enough. An eighth-scale grain drying bin was equipped with temperature, moisture, and relative humidity sensors at different heights to observe temperature and moisture migration throughout the 60-cm deep bed as corn and wheat were dried. For both commodities, trials were run where they were dried with heated air and ambient air. Likewise, thin-layer drying models were applied to the deep bed, and the drying process was simulated using the finite difference method for both commodities using ambient and heated air. The absolute humidity, air temperature, grain temperature, and moisture content were modeled throughout the drying bed with respect to location and time. Data obtained from the simulations compared well with the experimental data. When comparing the moisture content determined from simulation with the same measured with the microwave moisture sensor within the drying system, root mean square error (RMSE) values for corn and wheat were 0.61 (heated air), and 0.45% and 0.50% (ambient air) moisture content wet basis (w.b.), respectively. A 30% increase in drying time was observed when ambient air was used to dry wheat. A 2-% increase in ending moisture content was observed when drying with ambient air for both commodities. The empirical results obtained from the eighth-scale drying system confirmed the results obtained from simulation for each trial. While results obtained may be unique to the current study, the effectiveness of the models was demonstrated. Keywords: Drying modeling, Grain drying, Heat transfer, Mass transfer, Microwave sensing.
Optimization and Experimental Study of Structural Parameters of Compliant End-Effector for Robotic Cherry Tomato Harvesting
Authors: Xie Huaibei 
Published: June 2022

HighlightsA compliant end-effector with nonlinear structure was proposed for robotic cherry tomato harvesting.The key parameters of compliant end-effector were optimized by response surface methodology(RSM), and the optimal parameter combination was obtained.Experiments verified the harvesting performance of the optimized compliant end-effector. For fruits of different sizes, the picking success rate reached 84.2%.The damage percentage of cherry tomato could be controlled below 4.22%, and the extrusion deformation of cherry tomato could be controlled below 3.98mm.Abstract.The harvesting performance of end-effector is very important for robotic cherry tomato harvesting. In this article, in order to obtain good harvesting performance, a compliant end-effector with nonlinear structure was proposed and its key structural parameters were optimized by response surface methodology(RSM). According to the expected results, the angle (ESA) formed by the extension segments of two compliant grippers 10° and the difference of the shape variable (SVD) 13 mm, at optimal settings, which was obtained by the addition of the length of the offset section (OSL) 1.3 mm, the angle of the inclined section (ISA) 17°, the thickness of the extension section (EST) 4.6 mm and the length of the extension section (ESL) 20 mm. Besides, experiments verified the harvesting performance of the optimized compliant end-effector. For fruits of different sizes, the picking success rate reached 84.2%. The damage percentage of cherry tomato could be controlled below 4.22%, and the extrusion deformation of cherry tomato could be controlled below 3.98 mm. Keywords: Cherry tomato, Compliant mechanism, End-effector, Response surface, Robotic harvesting.
Fractionation and Upgrade of Endocarp Lignin to Carbon- Silicon Nanocomposites as an Anode Material In Lithium-Ion Batteries
Authors: Vin-Nnajiofor Makua C. , Lino Wenqi , Debolt Seth , Cheng Yang-Tse , Shi Jian 
Published: June 2022

HighlightsDeep eutectic solvent (DES) pretreatment of endocarp biomass led to higher overall glucose yield and lignin extraction.High performance anode materials were synthesized using Si nanoparticles and DES or alkaline extracted lignin.The core-shell structure formed during pyrolysis contributes to the enhanced electrochemical performance.Abstract.A holistic use of lignocellulosic biomass by converting lignin to high-value products and cellulose and hemicelluloses to biofuel will greatly enhance the economic viability of a biorefinery. In this study, a three-dimensional, interconnected carbon/silicon composite was synthesized from silicon nanoparticles (Si NPs) with either deep eutectic solvent (DES) or alkaline pretreatment-extracted lignin from endocarp biomass. The endocarp lignin derived composite electrodes exhibited superior electrochemical performance in a half-coin cell lithium-ion battery setup. DES lignin derived C/Si NPs composite reached a discharging capacity of 1563 mAh g-1; while alkaline lignin derived C/Si NPs composite reaching a discharging capacity of 1605 mAh g-1 at a current density of 0.72 A g-1. Despite the comparable electrochemical performance, DES pretreatment led to a higher overall sugar yield from the liquid streams after pretreatment and enzymatic hydrolysis. This study demonstrates the utilization of endocarp lignin in electrochemical energy storage applications. Keywords: Anode, Lignin, Lithium-ion battery, Silicon nanoparticles.
Improvement of the focusability of petawatt laser pulses after nonlinear
post-compression
Authors: Martyanov Mikhail , Mironov Sergey , Starodubtsev Mikhail , Soloviev Alexander , Kochetkov Anton , Ginzburg Vladislav , Shaikin Andrey , Khazanov Efim 
Published: June 2022

The nonlinear nature of laser pulse post-compression inevitably leads to wavefront distortions. These distortions are nonstationary, i.e., they vary during the pulse, which significantly complicates the use of adaptive optics. We propose four approaches to compensation of wavefront distortions by means of standard adaptive mirrors, three of which can be implemented experimentally. Numerical simulations show that these approaches allow achieving a focal spot intensity close to the case of a perfectly flat wavefront.
Discrete modeling for integration and analysis of large-scale signaling networks
Authors: Vignet Pierre , Coquet Jean , Auber Sébastien , Boudet Matéo , Siegel Anne , Théret Nathalie 
Published: June 2022

Most biological processes are orchestrated by large-scale molecular networks which are described in large-scale model repositories and whose dynamics are extremely complex. An observed phenotype is a state of this system that results from control mechanisms whose identification is key to its understanding. The Biological Pathway Exchange (BioPAX) format is widely used to standardize the biological information relative to regulatory processes. However, few modeling approaches developed so far enable for computing the events that control a phenotype in large-scale networks.
Here we developed an integrated approach to build large-scale dynamic networks from BioPAX knowledge databases in order to analyse trajectories and to identify sets of biological entities that control a phenotype. The Cadbiom approach relies on the guarded transitions formalism, a discrete modeling approach which models a system dynamics by taking into account competition and cooperation events in chains of reactions. The method can be applied to every BioPAX (large-scale) model thanks to a specific package which automatically generates Cadbiom models from BioPAX files.
The Cadbiom framework was applied to the BioPAX version of two resources (PID, KEGG) of the Pathway Commons database and to the Atlas of Cancer Signalling Network (ACSN). As a case-study, it was used to characterize sets of biological entities implicated in the epithelial-mesenchymal transition. Our results highlight the similarities between the PID and ACSN resources in terms of biological content, and underline the heterogeneity of usage of the BioPAX semantics limiting the fusion of models that require curation. Causality analyses demonstrate the smart complementarity of the databases in terms of combinatorics of controllers that explain a phenotype. From a biological perspective, our results show the specificity of controllers for epithelial and mesenchymal phenotypes that are consistent with the literature and identify a novel signature for intermediate states.
Estimating the global demand curve for a leishmaniasis vaccine: A generalisable approach based on global burden of disease estimates
Authors: Mohan Sakshi , Revill Paul , Malvolti Stefano , Malhame Melissa , Sculpher Mark , Kaye Paul M. 
Published: June 2022

BackgroundA pressing need exists to develop vaccines for neglected diseases, including leishmaniasis. However, the development of new vaccines is dependent on their value to two key players–vaccine developers and manufacturers who need to have confidence in the global demand in order to commit to research and production; and governments (or other international funders) who need to signal demand based on the potential public health benefits of the vaccine in their local context, as well as its affordability. A detailed global epidemiological analysis is rarely available before a vaccine enters a market due to lack of resources as well as insufficient global data necessary for such an analysis. Our study seeks to bridge this information gap by providing a generalisable approach to estimating the commercial and public health value of a vaccine in development relying primarily on publicly available Global Burden of Disease (GBD) data. This simplified approach is easily replicable and can be used to guide discussions and investments into vaccines and other health technologies where evidence constraints exist. The approach is demonstrated through the estimation of the demand curve for a future leishmaniasis vaccine.Methodology/Principal findingsWe project the ability to pay over the period 2030–2040 for a vaccine preventing cutaneous and visceral leishmaniasis (CL / VL), using an illustrative set of countries which account for most of the global disease burden. First, based on previous work on vaccine demand projections in these countries and CL / VL GBD-reported incidence rates, we project the potential long-term impact of the vaccine on disability-adjusted life years (DALYs) averted as a result of reduced incidence. Then, we apply an economic framework to our estimates to determine vaccine affordability based on the abilities to pay of governments and global funders, leading to estimates of the demand and market size. Based on our estimates, the maximum ability-to-pay of a leishmaniasis vaccine (per course, including delivery costs), given the current estimates of incidence and population at risk, is higher than $5 for 25–30% of the countries considered, with the average value-based maximum price, weighted by quantity demanded, being $5.7–6 [$0.3 - $34.5], and total demand of over 560 million courses.Conclusion/SignificanceOur results demonstrate that both the quantity of vaccines estimated to be required by the countries considered as well as their ability-to-pay could make a vaccine for leishmaniasis commercially attractive to potential manufacturers. The methodology used can be equally applied to other technology developments targeting health in developing countries.
Deep learning models for forecasting dengue fever based on climate data in Vietnam
Authors: Nguyen Van-Hau , Tuyet-Hanh Tran Thi , Mulhall James , Minh Hoang Van , Duong Trung Q. , Chien Nguyen Van , Nhung Nguyen Thi Trang , Lan Vu Hoang , Minh Hoang Ba , Cuong Do , Bich Nguyen Ngoc , Quyen Nguyen Huu , Linh Tran Nu Quy , Tho Nguyen Thi , Nghia Ngu Duy , Anh Le Van Quoc , Phan Diep T. M. , Hung Nguyen Quoc Viet , Son Mai Thai 
Published: June 2022

Background
Dengue fever (DF) represents a significant health burden in Vietnam, which is forecast to worsen under climate change. The development of an early-warning system for DF has been selected as a prioritised health adaptation measure to climate change in Vietnam.

Objective
This study aimed to develop an accurate DF prediction model in Vietnam using a wide range of meteorological factors as inputs to inform public health responses for outbreak prevention in the context of future climate change.

Methods
Convolutional neural network (CNN), Transformer, long short-term memory (LSTM), and attention-enhanced LSTM (LSTM-ATT) models were compared with traditional machine learning models on weather-based DF forecasting. Models were developed using lagged DF incidence and meteorological variables (measures of temperature, humidity, rainfall, evaporation, and sunshine hours) as inputs for 20 provinces throughout Vietnam. Data from 1997–2013 were used to train models, which were then evaluated using data from 2014–2016 by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).

Results and discussion
LSTM-ATT displayed the highest performance, scoring average places of 1.60 for RMSE-based ranking and 1.95 for MAE-based ranking. Notably, it was able to forecast DF incidence better than LSTM in 13 or 14 out of 20 provinces for MAE or RMSE, respectively. Moreover, LSTM-ATT was able to accurately predict DF incidence and outbreak months up to 3 months ahead, though performance dropped slightly compared to short-term forecasts. To the best of our knowledge, this is the first time deep learning methods have been employed for the prediction of both long- and short-term DF incidence and outbreaks in Vietnam using unique, rich meteorological features.

Conclusion
This study demonstrates the usefulness of deep learning models for meteorological factor-based DF forecasting. LSTM-ATT should be further explored for mitigation strategies against DF and other climate-sensitive diseases in the coming years.