bluuebunny/crossref_metadata_2025_split huggingface dataset:
Indian Agriculture Under Climate Change: The Competing Effect of Temperature and Rainfall Anomalies
Authors: Gallé Johannes , Katzenberger Anja 
Published: July 2024

Abstract
The latest generation of global climate models robustly projects that monsoon rainfall anomalies in India will significantly increase in the 21st century due to global warming. This raises the question of the impact of these changes on the agricultural yield. Based on annual district data for the years 1966-2014, we estimate the relationship between weather indices (amount of seasonal rainfall, number of wet days, average temperature) and the most widely grown kharif crops, including rice, in a flexible non-parametric way. We use the empirical relationship in order to predict district-specific crop yield based on the climate projections of eight evaluated state-of-the-art climate models under two global warming scenarios for the years 2021-2100. We find that the loss in rice yield by the end of the 21st century lies on average between 3 - 22% depending on the underlying emission scenario. For the sustainable scenario impacts range from an increase of 3.2% to a decrease of 12.1% for individual districts. In the worst-case scenario, all districts are negatively affected, with a predicted decrease in rice yield ranging from 34% to a decrease of 11.5% in the long run. Potential gains due to increasing rainfall are more than offset by the negative impacts of increasing temperature. Adaptation efforts in the worst-case global warming scenario would need to cut the negative impacts of temperature by 50% in order to reach the outcome of the sustainable scenario.
Feeding Assistance Skill Score: development and verification of reliability and validity
Authors: Nagano Ayano , Maeda Keisuke , Matsumoto Tomohiro , Murotani Kenta , Wakabayashi Hidetaka , Koyama Tamami , Nagai Takako , Mori Naoharu 
Published: July 2024

Abstract
Purpose
Feeding assistance should be safe and improve the assisted individual’s ability to feed, yet objective tools for evaluating these skills are lacking. This study focuses on developing a tool for assessing feeding assistance skills.

Methods
A group consisting of 25 experts employed the Delphi method to achieve a consensus on the essential items necessary for assessing feeding assistance skills. Subsequently, a draft score using a three-point scale was developed. To test the reliability of the draft scores, a group of 20 patients and 20 nurses was matched to record a meal assistance scene, which were independently evaluated by three raters. We computed the AC1 statistic to assess both intra- and inter-rater reliability, and further examined correlations between the Feeding Assistance Skill Score (FASS) scores and outcome items to verify validity.

Results
Initially, an 18-item draft score was generated using the Delphi method. Subsequently, seven items were omitted from the intra- or inter-rater reliability analysis. Furthermore, after discussion, researchers removed one item that assessed food cognition, because the agreement score between the two items was 91%. Finally, the 10-item FASS was developed, showing a correlation with dietary intake upon validation.

Conclusions
We successfully developed a scoring system for peer evaluation of feeding assistance skills. Future studies should aim to validate the FASS. The implementation of the FASS aims to assess the effectiveness of feeding assistance training and care quality, potentially improving safety and satisfaction for those receiving feeding assistance.
Predicting irrigation water quality indices in a typical mining dominated area in the Upper West region of Ghana using multiple machine learning techniques
Authors: Kazapoe Raymond Webrah , Sagoe Samuel Dzidefo , Abu Mahamuda 
Published: July 2024

AbstractThe quality of groundwater resources in artisanal mining districts in Ghana is under threat due to pollution; rendering the resource unsafe for drinking and irrigation purposes. This makes the assessment of the quality of groundwater resources a relevant aspect of groundwater studies as it informs decision making and monitoring. This study adopts 3 Machine Learning (ML) models, Support Vector Regression (SVR), Gradient Boost Regression (GBR), and Artificial Neural Network (ANN), to evaluate a variety of irrigation water quality metrics such as Sodium Percentage (Na%), Soluble Sodium Percentage (SSP), Sodium Adsorption Ratio (SAR), Residual Sodium Carbonate (RSC), Permeability Index (PI), Pollution Index of Groundwater (PIG), Kelly’s Ratio (KR), and Magnesium Hazard (MH). 105 samples were collected from a mining area in Northern Ghana and analysed through traditional methods. The Irrigation Water Quality Indices (IWQIs) demonstrate that all water samples are suitable for use as irrigable water with the exception of MH, Na%, PI, and PIG which revealed that 69.52%, 8.57%, 29.52%, and 3.81% are inappropriate for irrigation. SVR, GBR and ANN were used to establish important factors that may influence IWQIs in the area. The measured data was used as independent variables, and the derived IWQIs, the dependent variables. The results revealed that ANN, GBR, and SVR are all viable options for the prediction of IWQIs, but GBR exhibited variable performance in some indices making it lack consistency and thus falls a bit short compared to ANN and SVR. SVR models overall performed best with SVR-RSC having the highest accuracy.
CommentClass: A Robust Ensemble Machine Learning Model for Comment Classification
Authors: Rahman Md. Mostafizer , Shiplu Ariful Islam , Watanobe Yutaka 
Published: July 2024

AbstractEnormous amounts of data are generated in the form of feedback or comments from online platforms such as social media, e-commerce, education, and programming. This feedback and comments hold significant value for making important strategic decisions; therefore, effectively analyzing them poses a major challenge. This research addresses the imperative need for an efficient comment classification model. To fill this research gap, we propose a robust ensemble machine learning (ML) model called CommentClass (RF+AdaBoost+SVM+Soft-Voting), specifically designed for the comment classification task. First, we developed eight (08) pipelines using various combinations of ML algorithms. Next, the fundamental ensemble techniques such as stacking, blending, hard-voting, soft-voting, and averaging are incorporated into these pipelines to improve comment classification performance. These ensemble models are able to discern the latent characteristics of diverse text comments, classifying them to achieve superior accuracy. The proposed CommentClass ensemble model achieved an impressive accuracy and F1-score of approximately 98% for comment classification on the YouTube dataset. This result represents an improvement in accuracy by approximately +3% compared to prior research on the same dataset. Moreover, the proposed CommentClass model obtained higher F1-scores of 90.26%, 87.04%, and 75.74%, on the Spambase, IMDB, and Twitter datasets, respectively, compared to other sophisticated models. Furthermore, the proposed CommentClass model exhibited significant accuracy on the SMS dataset and two distinct synthetic datasets.
Entrepreneurship education in East, West and North Africa Higher Education Institutions (HEIs): mapping agripreneurship curriculum and lecturers’ competencies
Authors: Okello Dickson , Mshenga Patience , Saidi Mwanarusi , Juma Fahad , Maria Sassi , Egeru Antony , Gogo Elisha 
Published: July 2024

AbstractThis study focused on mapping agripreneurship curriculum and lecturers’ competencies in 8 African Higher Education Institutions (HEIs), participating in the Strengthening Agri-Entrepreneurship and Community Engagement Training in East, West and North Africa (AgriENGAGE) joint Project. A sample of 194 lecturers from East, West, and North Africa participated in the study. The study employed a mixed method approach with concurrent nested research design methodology to collect information. Among the main results found in this study, the key aspects that were implemented in curricula were; business plan development (µ = 3.57), business opportunities identification (µ = 3.56), and communicating with value chain actors (= 3.51). Concerning the forms of entrepreneurship taught, most institutions focused on subsistence agripreneurship (µ = 3.23) with growth-oriented agripreneurship (µ = 2.50) and innovative-oriented agripreneurship (µ = 2.50) having the lowest rating. Regarding teaching methods within the provision of entrepreneurship education, most of the institutions were using traditional teaching methodologies with the common ones being formal lectures, field trips, in-class group discussions, and group project-based learning. Most lecturers had low teaching competencies concerning instructional quality and a moderate/average level of entrepreneurial competencies. The study recommends that universities should implement more professional development and pedagogical training over and above the subject competency to improve lecturers’ entrepreneurial competency.
Exploring the challenges of RNAi-based strategies for crop protection
Authors: Zhao Jian-Hua , Liu Qing-Yan , Xie Zong-Ming , Guo Hui-Shan 
Published: July 2024

AbstractRNA silencing (or RNA interference, RNAi) initiated by double-stranded RNAs is a conserved mechanism for regulating gene expression in eukaryotes. RNAi-based crop protection strategies, including host-induced gene silencing (HIGS), spray-induced gene silencing (SIGS) and microbe-induced gene silencing (MIGS), have been successfully used against various pests and pathogens. Here, we highlight the challenges surrounding dsRNA design, large-scale production of dsRNA and dsRNA delivery systems. Addressing these questions will accelerate the lab-to-field transition of RNAi-based strategies. Moreover, based on studies of exogenous dsRNA-induced RNAi inheritance in Caenorhabditis elegans, we speculate that RNAi-based strategies would confer longer-lasting protection for crops against pests or fungal pathogens.
A Rare Simultaneous Detection of a Mid‐Latitude Plasma Depleted Structure in O(1D) 630.0 and O(1S) 557.7 nm All‐Sky Airglow Images on a Geomagnetically Quiet Night
Authors: Patgiri D. , Rathi R. , Yadav V. , Chakrabarty D. , Sunil Krishna M. V. , Kannaujiya S. , Pavan Chaitanya P. , Patra A. K. , Liu Jann‐Yenq , Sarkhel S. 
Published: July 2024

AbstractThis letter reports first simultaneous detection of F‐region plasma‐depleted structure in O(1D) 630.0 and O(1S) 557.7 nm airglow images on a geomagnetically quiet‐night (Ap = 3) of 26 June 2021 from mid‐latitude station (Hanle, India) due to enhanced thermospheric 557.7 nm emission. Since nighttime thermospheric 557.7 nm emission over mid‐latitudes is predominantly masked by significantly larger mesospheric component, F‐region plasma structures are rarely observed in 557.7 nm images. Interestingly, thermospheric 557.7 nm emission was not significant on the following geomagnetically quiet‐night as bands of medium‐scale traveling ionospheric disturbance were only observed in 630.0 nm images. Poleward wind generated by Equatorial Temperature and Wind Anomaly transported plasma from the boundary of equatorial ionization anomaly, causing significant electron density enhancement around 250 km and descent of F‐layer peak over Hanle on 26 June 2021. This amplified the dissociative recombination enabling the simultaneous detection of plasma‐depleted structure in 557.7 and 630.0 nm images.
Observing and Modeling Short‐Term Changes in Basal Friction During Rain‐Induced Speed‐Ups on an Alpine Glacier
Authors: Togaibekov Anuar , Gimbert Florent , Gilbert Adrien , Walpersdorf Andrea 
Published: July 2024

AbstractBasal shear stress on hard‐bedded glaciers results from normal stress against bed roughness, which depends on basal water pressure and cavity size. These quantities are related in a steady state but are expected to behave differently under rapid changes in water input, which may lead to a transient frictional response not captured by existing friction laws. Here, we investigate transient friction using Global Positioning System vertical displacement and horizontal velocity observations, basal water pressure measurements, and cavitation model predictions during rain‐induced speed‐up events at Glacier d'Argentière, French Alps. We observe up to a threefold increase in horizontal surface velocity, spatially migrating at rates consistent with subglacial flow drainage, and associated with surface uplift and increased water pressure. We show that frictional changes are mainly driven by changes in water pressure at nearly constant cavity size. We propose a generalized friction law capable of capturing observations in both the transient and steady‐state regimes.
Time Sequence of Magnetospheric Responses to a Southward IMF Turning
Authors: Samsonov Andrey , Milan Stephen , Buzulukova Natalia , Sibeck David , Forsyth Colin , Branduardi‐Raymont Graziella , Dai Lei 
Published: July 2024

AbstractWe use MHD simulations to study the time sequence of magnetospheric responses to a synthetic event with a southward interplanetary magnetic field (IMF) turning. The onset of dayside magnetopause reconnection launches a weak rarefaction wave and sunward flow in the equatorial magnetosphere simultaneously with a tailward flow through the polar cap. This convection results in the accumulation of magnetic flux in the tail lobes and thinning of the tail current layer which provides favorable conditions for the onset of nightside reconnection. The onset of nightside reconnection about 40 min later closes the Dungey convection cycle, resulting in a second increase in the sunward flow in the equatorial plane. Variations of the magnetopause standoff distance as well as the size of the polar cap (PC) may indicate the onsets of the dayside and nightside reconnections. We compare the results of two MHD models and discuss their differences.
Downscaled‐GRACE Data Reveal Anthropogenic and Climate‐Induced Water Storage Decline Across the Indus Basin
Authors: Arshad Arfan , Mirchi Ali , Taghvaeian Saleh , AghaKouchak Amir 
Published: July 2024

AbstractGRACE (Gravity Recovery and Climate Experiment) has been widely used to evaluate terrestrial water storage (TWS) and groundwater storage (GWS). However, the coarse‐resolution of GRACE data has limited the ability to identify local vulnerabilities in water storage changes associated with climatic and anthropogenic stressors. This study employs high‐resolution (1 km2) GRACE data generated through machine learning (ML) based statistical downscaling to illuminate TWS and GWS dynamics across twenty sub‐regions in the Indus Basin. Monthly TWS and GWS anomalies obtained from a geographically weighted random forest (RFgw) model maintained good consistency with original GRACE data at the 25 km2 grid scale. The downscaled data at 1 km2 resolution illustrate the spatial heterogeneity of TWS and GWS depletion within each sub‐region. Comparison with in‐situ GWS from 2,200 monitoring wells shows that downscaling of GRACE data significantly improves agreement with in‐situ data, evidenced by higher Kling‐Gupta Efficiency (0.50–0.85) and correlation coefficients (0.60–0.95). Hotspots with the highest TWS and GWS decline rate between 2002 and 2023 were Dehli Doab (−442, −585 mm/year), BIST Doab (−367, −556 mm/year), Rajasthan (−242, −381 mm/year), and BARI (−188, −333 mm/year). Based on a general additive model, 47%–83% of the TWS decline was associated with anthropogenic stressors mainly due to increasing trends of crop sown area, water consumption, and human settlements. The decline rate of TWS and GWS anomalies was lower (i.e., −25 to −75 mm/year) in upstream sub‐regions (e.g., Yogo, Gilgit, Khurmong, Kabul) where climatic factors (downward shortwave radiations, air temperature, and sea surface temperature) explained 72%–91% of TWS/GWS changes. The relative influences of climatic and anthropogenic stressors varied across sub‐regions, underscoring the complex interplay of natural‐human activities in the basin. These findings inform place‐based water resource management in the Indus Basin by advancing the understanding of local vulnerabilities.
Snow Depth Extraction From Time‐Lapse Imagery Using a Keypoint Deep Learning Model
Authors: Breen C. M. , Currier W. R. , Vuyovich C. , Miao Z. , Prugh L. R. 
Published: July 2024

AbstractSnow pole time‐lapse photography, in which a snow pole of a known height is installed in front of a camera and photographed repeatedly over the course of a snow season, allows a large network of sites to be established relatively quickly and affordably. However, current approaches for extracting snow depth from snow poles typically relies on time intensive manual photo processing. By integrating computer vision algorithms with snow pole photography, we present a method that uses a keypoint detection model to automatically observe snow height across a network of sites. At 20 snow pole locations from Grand Mesa, CO (n = 9,722 images), our model successfully predicts the top and bottom of the pole with a mean absolute error (MAE) of 1.30 cm. To assess model generalizability, we tested the model on 12 sites in Washington State (n = 1,770 images). When the Colorado trained model was fine‐tuned using a subset of Washington images, the model predicted snow depth with a MAE of 4.0 cm. Best performance was achieved when both data sets were included during training, with a MAE of 2.05 cm for Colorado images and a MAE of 1.14 cm for Washington images. We demonstrate that, especially when trained using a subset of site‐specific data, a keypoint detection model can accelerate snow pole automation. This algorithm brings the hydrology community one step closer to a generalized snow pole detection model, and we call for a future model that integrates across time‐lapse images from additional locations.
Replaying the Tape of Academia: Fourteen Alternative Practices for the Physical Sciences
Authors: Kemeny Preston Cosslett , Phillips Alexandra Atlee , Johnson Daniel Lee 
Published: July 2024

AbstractThe evolution of modern academic practices, analogous to the evolution of biological systems, reflects the influence of both contingency and determinism. From a theoretical perspective, how then could academic practices differ from those that were inherited? Would any alternative outcomes be more just, equitable, diverse, or inclusive? Here we present 14 alternative academic practices that might be attained upon replaying the tape of academia and evaluate their benefits and drawbacks. Oriented primarily around the physical sciences within the United States, these alternative practices reconsider common activities within the broad categories of the graduate student experience, faculty careers, evaluation methods, peer review and publication, and conference norms. Consideration of these alternative practices can guide within‐system change and large‐scale restructuring of academia to address the myriad challenges facing researchers and students. Conversely, alternative practices may introduce new issues or exacerbate existing problems. These alternative practices are meant to be imaginative, not prescriptive, and we hope their underlying ideas spur reflection and conversation on the existing practices embedded within academic culture. Readers are encouraged to complete a brief survey regarding their impressions of the alternative practices, available at the following link: rebrand.ly/AlternativePractices2024.
Cloud Radiative Feedback to the Large‐Scale Atmospheric Circulation Greatly Reduces Monsoon‐Season Wet Bias Over the Tibetan Plateau in Climate Modeling
Authors: Liu Jiarui , Yang Kun , Zhao Dingchi , Wu Peili , Wang Jiamin , Zhou Xu , Lin Yanluan , Lu Hui , Jiang Yaozhi , Shi Jiancheng 
Published: July 2024

AbstractOver‐estimation of summer precipitation over the Tibetan Plateau (TP) is a well‐known and persistent problem in most climate models. This study demonstrates the impact of a Gaussian Probability Density Function cloud fraction scheme on rainfall simulations using the Weather Research and Forecasting model. It is found that this scheme in both 0.1° and 0.05° resolutions significantly reduces the wet bias through both local feedbacks and large‐scale dynamic process. Specifically, increased cloud water/ice content with this scheme reduces surface shortwave radiation, and consequently surface heat fluxes and evapotranspiration. This, in turn, dampens the large‐scale thermal effect of the TP and weakens the exaggerated monsoon circulation and low‐level moisture convergence. It is this large‐scale dynamic process that contributes the most (∼70%) to the wet bias reduction. Although this paper presents a modeling study, it highlights the cloud radiative feedback to the large‐scale dynamics and precipitation over the TP.
Widening Multi‐Beam Scan Angle of Conventional Waveguide Lens Antennas by Increasing Focal Points for Multi‐Feed Excitations
Authors: Panigrahi S. , Kawdungta S. , Torrungrueng D. , Chou H.‐T. 
Published: July 2024

AbstractThis paper presents an alternative approach to improve the achievable beam scan angle of a traditional multi‐beam waveguide lens antenna. Due to the focusing mechanism by manipulating the geometrical curvatures of the waveguide lens, the angular scan range is limited in the conventional waveguide lens design using dual‐focal points of excitations because the geometrical curvatures of the waveguide lens only provide two design freedoms. To overcome this limitation, a solution of treating the waveguide lens as a transmit array consisting of non‐identical elements is proposed so that each element of the antenna array can be well calibrated to improve the maximum scan angular range, where a third focus point of excitation can be created by adding another design freedom from the differentiation between non‐identical elements. Each element of this new transmitting array can be well calibrated with the help of a mathematical expression to improve the maximum angular scan range. Numerical simulations show that the proposed antenna architecture exhibits better radiation characteristics than the traditional waveguide lens antenna. Radiation characteristics are studied and compared for both types of lens antennas to validate the design concept. The proposed triple‐focal point provides a higher gain than the traditional lens antenna with fewer antenna elements. The gains of the beams at ±10°, ±20°, ±30°, and ±40° are found to be 27.78, 26.94, 26.19, and 24.04 dBi, respectively. From the comparison, it is seen that the variation in gain by the proposed triple‐focal array design is more stable than the conventional dual‐focal point design.