bluuebunny/crossref_metadata_2025_split huggingface dataset:
Seasonal forecasts have sufficient skill to inform some agricultural decisions
Authors: Kondal Ashish , Hegewisch Katherine , Liu Mingliang , Abatzoglou John T , Adam Jennifer C , Nijssen Bart , Rajagopalan Kirti 
Published: October 2024

Abstract
Seasonal forecasts, which look several months into the future, are currently underutilized in active decision-making, particularly for agricultural and natural resource management. This underutilization can be attributed to the absence of forecasts for decision-relevant variables at the required spatiotemporal resolution and at the time when the decisions are made and a perception of poor skill by decision-makers. Addressing these constraints, we quantified the skill of seasonal forecasts in informing two agricultural decisions with differing decision timeframes and influencer variables: (a) whether to apply fertilizer in fall or wait until spring based on expected winter temperatures, and (b) drought response, such as whether to lease water based on expectations of drought. We also looked into how early the forecast can be provided without significant degradation in skill. Currently, drought response decisions are typically formulated in April, utilizing drought forecasts issued in the same month, while fall fertilization decisions are generally made between August and September. There is growing interest among stakeholders in the availability of earlier forecasts to inform these critical choices. We utilized the North American multi-model ensemble (NMME) hindcasts for the time period 1982–2020 over the Pacific Northwest US (PNW) to obtain meteorological variables. Runoff was estimated via simulations of the coupled crop-hydrology VIC-CropSyst model. The skill assessment with the Heidke Skill Score (HSS) yielded promising outcomes in both decisions for the entire PNW region. Notably, NMME’s positive skill (median HSS of 30%) in predicting warmer winters identifies years when fertilizer application should be avoided to prevent fertilizer loss through mineralization (and associated costs). Similarly, there is skill in forecasting drought conditions in most irrigated watersheds for up to two months in advance of April, the current decision time. In conclusion, our findings affirm that contrary to the perception of low skill and resulting underutilization, current seasonal forecasts hold the potential to inform at least some key agricultural decisions.
The convolutional neural network for Pacific decadal oscillation forecast
Authors: Skanupong Nutta , Xu Yongsheng , Yu Lejiang , Wan Zhang , Wang Shuo 
Published: October 2024

Abstract
The Pacific decadal oscillation (PDO) is often described as a long-lived El Niño-like pattern of Pacific climate variability, and it has widespread climate and ecosystem impacts. PDO forecasts can provide useful information for policymakers on how to handle PDO impacts. Nevertheless, due to the long duration of the PDO cycles and their complex formation mechanisms, it remains a challenge to predict long lead time PDO. In this paper, we propose a transfer-learning-enhanced convolutional neural network (CNN) to tackle complex ocean dynamic forecasting and predict PDO events with up to a one-year lead time. Our method first trains the CNN on historical simulations from Coupled Model Intercomparison Project 6 (CMIP6), covering the period from 1850 to 1972. This prior knowledge is then refined by further training the model with observational data from 1854 to 1972, ensuring robust performance on unseen data. Additionally, k-fold cross-validation is also employed to evaluate the model’s performance across diverse subsets of data, enhancing its reliability. Throughout the testing phase from 1983 to 2022, the CNN model consistently outperforms existing dynamical forecast systems, exhibiting superior correlation skills in predicting annual mean PDO indices and PDO phases, including displaying resilience to seasonal variations. The transferred CNN is thus a powerful method to predict PDO events and is potentially valuable for a wide range of applications. This work directly supports the objectives of the World Climate Research Programme Grand Challenge on Climate Prediction.
Mechanical, tribological and modal characteristics of eco-friendly coir/sugarcane fillers reinforced polymer composites
Authors: Shankar A N , George Manuel , Krishna S M Murali , Kakaravada Ismail , S Singholi Ajay K , Lakshmi V V K , Chacko Mathews , Kumar K Arun , Jindal Prakhar , Selvaraj Rajeshkumar 
Published: October 2024

Abstract
Polymer-based composites have been drawing the attention of the research community for many decades, not only in academia but also in industry. However, continuously increasing environmental concerns have led the researchers to focus on natural composite materials. This is a challenge for researchers to develop a natural composite without compromising the composites’ excellent mechanical properties and tribological performance. In this research, coir and sugarcane are selected as the natural fillers, and epoxy resin has been chosen for matrix material. To look into the crystallinity of composites, XRD analysis was done. In addition, a mechanical study was done to look at the manufactured composites’ tensile and flexural characteristics. The tribological performance (i.e., wear rate and friction coefficient) of the composite samples is investigated by using a pin-on-disc setup. The parameters such as filler loading and normal load affecting the tribological performance of epoxy-based natural composites are studied. The results show that the wear and friction characteristics of the composite reinforced with sugarcane and coir were 10.78% and 57.80% lower than those of the neat composite, respectively.
Effects of weed management on soil metagenomic composition in cultivated chickpea fields
Authors: Imran Hammami , Nouha Ferchichi , Wael Toukebri , M’sehli Wissal , Thouraya Ben Hammouda , Arwa Ajengui , Cyrine Guiga , Darine Trabelsi 
Published: October 2024

Abstract
Non-chemical methods, including mechanical and manual weed management are important for improving crop yields and preserving soil microbial diversity. In Tunisia, chickpea cultivation uses a combination of these methods to suppress weeds. This study aimed to evaluate the impact of weeding practices on chickpea (Cicer arietinum L. cultivar REBHA) yield and soil microbial diversity. Field experiments were conducted at the Technical Center for Biological Agriculture in Essaida, Tunisia, using six plots with manual and no weeding treatments. Chickpea yield was measured, and soil samples were collected for metagenomic analysis. Floristic surveys identified 13 weed species, with Chenopodium album L., Rumex acetosa L., and Urtica dioica L. being the most dominant. Seed yield ranged from 23.2 to 26.2 qls/ha in non-weeded plots and from 25.1 to 30.1 qls/ha in weeded plots, showing an increase in yield (11.75%) with manual weeding. Soil metagenomics revealed changes in bacterial community composition between the two treatments. The dominant phylum was Pseudomonadota, whose relative abundance increased from 24.88% in non-weeded plots to 34.76% in weeded plots. Alpha diversity indices showed lower species richness and diversity in weeded soils, with 62000 OTUs in weeded plots compared to 43202 OTUs in non-weeded plots. Betaproteobacteria and Alphaproteobacteria exhibited higher OTU counts in weeded soils. The Simpson diversity index was lower in non-weeded soils (0.025) than in weeded soils (0.075), indicating a more irregular microbial distribution in non-weeded plots. Thus, manual weeding improved chickpea yield and altered the soil bacterial community, increasing diversity in key microbial taxa. This study highlights the complex interaction between weed management practices and soil microbial ecosystems, which may influence crop productivity.
Evaluating the efficacy of lead-free piezoelectric materials in microcantilever based vibration energy harvesters
Authors: Manvi Mahammadrafeeq , K B Mruthyunjaya Swamy 
Published: October 2024

Abstract
The piezoelectric materials have been extensively utilized in various applications, such as sensors, actuators, and energy harvesters. This study evaluates the performance of six lead-free piezoelectric materials- aluminium nitride (AlN), barium titanate (BaTiO3), lithium niobate (LiNbO3), lithium tantalate (LiTaO3), polyvinylidene fluoride (PVDF), and zinc oxide (ZnO) in MEMS-based piezoelectric vibration energy harvesters (PVEHs) using cantilever configurations. Finite element analysis via COMSOL Multiphysics was employed to assess the deflection, voltage, and power outputs of these materials at their resonance frequencies, both with and without proof masses. The results indicate that BaTiO3 and PVDF cantilevers exhibited the highest voltage outputs, reaching 207.14 mV and 202.07 mV, respectively, with AlN also showing comparable performance at 184.72 mV. ZnO-based cantilevers demonstrated the highest power output of 1.35 nW without proof masses and 190.5 nW with proof masses, indicating its potential for high-power applications. The addition of proof masses generally reduced resonant frequencies but enhanced power outputs, like for ZnO. This comprehensive analysis underscores the critical impact of material selection and structural modifications on the efficiency of PVEHs, with BaTiO3, PVDF, and ZnO emerging as the most promising candidates for optimizing energy harvesting devices. This research lays a foundation for further advancements in piezoelectric MEMS technology, aiming for more efficient energy harvesting solutions.
A deep learning method for pointer meter reading recognition in inspection robots at refrigeration stations
Authors: Wang Kai , Yu Junqi , Feng Chunyong , Guo Jvgang , Chen Yisheng , Dong Zhenping , Liu Zongyi 
Published: October 2024

Abstract
To improve the intelligent operation and maintenance of large public building refrigeration stations, various automatic reading methods for pointer meters used in inspection robots have been developed. However, most methods exhibit low detection accuracy for pointer meters in refrigeration stations and poor segmentation of pointers and scale lines, ultimately leading to inaccurate readings. To address the challenges posed by the complex environments of refrigeration stations, this study proposes a deep learning-based image recognition method for precise pointer meter readings. Firstly, the pointer meters in the environment are detected and located by the YOLOV8-CBAM-Wise-IoU model. Secondly, an improved Fast Semantic Segmentation Network (Fast-SCNN) is used for image segmentation of the pointers and scale lines of the pointer meters, and a perspective transformation method is employed to correct images of tilted meters. Then, the image is transformed into a rectangular image using polar coordinate transformation. Finally, the accurate reading is calculated using the distance method, based on the relative distances between the pointers and scale lines. This research conducted experimental tests using an inspection robot in the actual environment of a large public building’s refrigeration station. The results demonstrate that the proposed method for pointer meter reading recognition achieves a fiducial error within 0.41%, which can realize the inspection task of the pointer meter in refrigeration stations.
Experimental investigation on optimizing wire-cut EDM process parameters of hybrid MMC(AA7475/ZrO2/Gr) using artificial neural network
Authors: Singuru Madhavarao , V V S Kesava Rao , Chekuri Rama Bhadri Raju 
Published: October 2024

Abstract
Wire cut Electrical discharge machining (WCEDM) is a widely used method for machining complex shapes in advanced materials like metal matrix composites (MMCs) and hybrid metal matrix composites (HMMCs). To address these challenges, this study focusses on the wire-cut EDM (WCEDM) process of a workpiece made from zirconium dioxide and graphite-reinforced aluminium alloy 7475 with a molybdenum electrode. The effects of input process variables such as peak current (IP), pulse-on-time (TON), and flushing pressure (PF) on the output response features are investigated. These output responses include material removal rate (MRR), surface roughness (SR), and wire wear ratio (WWR). To optimise the process parameters, the Taguchi design technique is used. An artificial neural network (ANN) with a feed-forward back propagation architecture is utilised to find the best fit for the optimisation challenges. ANN predicted the results with an accuracy of 97.81% for MRR, 97.95% for SR and 95.865% for WWR. The results reveal that the WCEDM of AA7475/ZrO2/Gr with a molybdenum electrode achieved minimal WWR and SR, while maximizing the MRR.
BayeSMART: Bayesian clustering of multi-sample spatially resolved transcriptomics data
Authors: Guo Yanghong , Zhu Bencong , Tang Chen , Rong Ruichen , Ma Ying , Xiao Guanghua , Xu Lin , Li Qiwei 
Published: October 2024

Abstract
The field of spatially resolved transcriptomics (SRT) has greatly advanced our understanding of cellular microenvironments by integrating spatial information with molecular data collected from multiple tissue sections or individuals. However, methods for multi-sample spatial clustering are lacking, and existing methods primarily rely on molecular information alone. This paper introduces BayeSMART, a Bayesian statistical method designed to identify spatial domains across multiple samples. BayeSMART leverages artificial intelligence (AI)-reconstructed single-cell level information from the paired histology images of multi-sample SRT datasets while simultaneously considering the spatial context of gene expression. The AI integration enables BayeSMART to effectively interpret the spatial domains. We conducted case studies using four datasets from various tissue types and SRT platforms, and compared BayeSMART with alternative multi-sample spatial clustering approaches and a number of state-of-the-art methods for single-sample SRT analysis, demonstrating that it surpasses existing methods in terms of clustering accuracy, interpretability, and computational efficiency. BayeSMART offers new insights into the spatial organization of cells in multi-sample SRT data.
Social-semantic knowledge in frontotemporal dementia and after anterior temporal lobe resection
Authors: Rouse Matthew A , Halai Ajay D , Ramanan Siddharth , Rogers Timothy T , Garrard Peter , Patterson Karalyn , Rowe James B , Lambon Ralph Matthew A 
Published: October 2024

Abstract
Degraded semantic memory is a prominent feature of frontotemporal dementia (FTD). It is classically associated with semantic dementia and anterior temporal lobe (ATL) atrophy, but semantic knowledge can also be compromised in behavioural variant FTD. Motivated by understanding behavioural change in FTD, recent research has focused selectively on social-semantic knowledge, with proposals that the right ATL is specialized for social concepts. Previous studies have assessed very different types of social concepts and have not compared performance with that of matched non-social concepts. Consequently, it remains unclear to what extent various social concepts are (i) concurrently impaired in FTD, (ii) distinct from general semantic memory and (iii) differentially supported by the left and right ATL. This study assessed multiple aspects of social-semantic knowledge and general conceptual knowledge across cohorts with ATL damage arising from either neurodegeneration or resection. We assembled a test battery measuring knowledge of multiple types of social concept. Performance was compared with non-social general conceptual knowledge, measured using the Cambridge Semantic Memory Test Battery and other matched non–social-semantic tests. Our trans-diagnostic approach included behavioural variant FTD, semantic dementia and ‘mixed’ intermediate cases to capture the FTD clinical spectrum, as well as age-matched healthy controls. People with unilateral left or right ATL resection for temporal lobe epilepsy were also recruited to assess how selective damage to the left or right ATL impacts social- and non–social-semantic knowledge. Social- and non–social-semantic deficits were severe and highly correlated in FTD. Much milder impairments were found after unilateral ATL resection, with no left versus right differences in social-semantic knowledge or general semantic processing and with only naming showing a greater deficit following left versus right damage. A principal component analysis of all behavioural measures in the FTD cohort extracted three components, interpreted as capturing (i) FTD severity, (ii) semantic memory and (iii) executive function. Social and non-social measures both loaded heavily on the same semantic memory component, and scores on this factor were uniquely associated with bilateral ATL grey matter volume but not with the degree of ATL asymmetry. Together, these findings demonstrate that both social- and non–social-semantic knowledge degrade in FTD (semantic dementia and behavioural variant FTD) following bilateral ATL atrophy. We propose that social-semantic knowledge is part of a broader conceptual system underpinned by a bilaterally implemented, functionally unitary semantic hub in the ATLs. Our results also highlight the value of a trans-diagnostic approach for investigating the neuroanatomical underpinnings of cognitive deficits in FTD.
Comparative Analysis of Deep Learning Techniques for Global Horizontal Irradiance Forecasting in U.S. Cities
Authors: Alkhatib Fathy Yassin , Alsadi Juman , Ramadan Mariam , Nasser Ruba , Awdallah Abeer , Chrysikopoulos Constantinos V , Maalouf Maher 
Published: October 2024

Abstract
Accurate solar radiation estimation is crucial for the optimal design of solar energy systems used in numerous applications. Thus, this research aims to investigate the forecasting of hourly global horizontal irradiance using both univariate and multivariate methods. Deep learning techniques, including long-short-term memory, convolutional neural networks, and a hybrid of convolutional neural networks/long-short-term memory are employed. The effects of fixed and varying learning rates are explored under the condition of a fixed window size of 48 hours. Data collected from three major cities in the US are employed to cover a broad range of annually received solar radiation. The data are divided into three subsets: 60% are used for training, 20% for cross-validation, and 20% for testing. The results revealed that the convolutional neural networks and long-short-term memory models outperform the hybrid convolutional neural networks /long-short-term memory model based on the lower values of the root mean square error (RMSE), mean absolute error (MAE), and higher coefficient of determination (R2). For instance, the multivariate long-short-term memory with fixed learning rate (RMSE=0.345, MAE=0.387, R2=0.994) is the best-performing model for Rochester, the multivariate convolutional neural networks with fixed learning rate (RMSE=32.89, MAE=15.35, R2=0.928) is the best-performing model for Seattle, and the univariate convolutional neural networks with variable learning rate (RMSE=048.2, MAE=23.66, R2=0.959) is the best-performing model for Tucson. Different learning rates were shown to not significantly influence the prediction of sunlight. Furthermore, it was concluded that changing the window size does not necessarily improve performance. This study demonstrates the efficacy of variable learning rates and hybrid models in improving global horizontal irradiance forecast accuracy.
Reconciliation of weak pairwise spike–train correlations and highly coherent local field potentials across space
Authors: Senk Johanna , Hagen Espen , van Albada Sacha J , Diesmann Markus 
Published: October 2024

Abstract
Multi-electrode arrays covering several square millimeters of neural tissue provide simultaneous access to population signals such as extracellular potentials and spiking activity of one hundred or more individual neurons. The interpretation of the recorded data calls for multiscale computational models with corresponding spatial dimensions and signal predictions. Multi-layer spiking neuron network models of local cortical circuits covering about $1\,{\text{mm}^{2}}$ have been developed, integrating experimentally obtained neuron-type-specific connectivity data and reproducing features of observed in-vivo spiking statistics. Local field potentials can be computed from the simulated spiking activity. We here extend a local network and local field potential model to an area of $4\times 4\,{\text{mm}^{2}}$, preserving the neuron density and introducing distance-dependent connection probabilities and conduction delays. We find that the upscaling procedure preserves the overall spiking statistics of the original model and reproduces asynchronous irregular spiking across populations and weak pairwise spike–train correlations in agreement with experimental recordings from sensory cortex. Also compatible with experimental observations, the correlation of local field potential signals is strong and decays over a distance of several hundred micrometers. Enhanced spatial coherence in the low-gamma band around $50\,\text{Hz}$ may explain the recent report of an apparent band-pass filter effect in the spatial reach of the local field potential.
Impact of sex-specific thresholds for low flow in assessment of prognosis in concordantly and discordantly graded aortic valve stenosis
Authors: Bahlmann Edda , Gerdts Eva , Einarsen Eigir , Midtbø Helga , Pedersen Eva R , Rossebø Anne , Willems Stephan , Cramariuc Dana 
Published: October 2024

Abstract

Aims
Sex-specific low flow was recently defined as stroke volume index (SVi) ≤ 40 mL/m² in men and ≤32 mL/m² in women. We tested the prognostic association of these cut-offs in patients with aortic stenosis (AS) with concordantly and discordantly graded AS [concordantly graded AS by energy loss (CGASEL) and discordantly graded AS by energy loss (DGASEL)] based on pressure recovery adjusted aortic valve area [energy loss (EL)].

Methods and results
Data from 1351 patients with asymptomatic AS, peak jet velocity &lt;4 m/s, and preserved left ventricular ejection fraction enrolled in the Simvastatin and Ezetimibe in Aortic Stenosis study were used. DGASEL was defined as EL &lt;1.0 cm² with mean aortic gradient &lt;40 mmHg and CGASEL as EL ≥1.0 cm² with mean aortic gradient &lt;40 mmHg. Patients were further grouped into normal and low flow. The outcome was combined all-cause death and hospitalization for heart failure (HF). CGASEL with normal/low flow was present in 915/253 patients, and DGASEL with normal/low flow was present in 57/126 patients. During a median follow-up of 4.3 years, event-free survival was lower in patients with DGASEL irrespective of flow compared to CGASEL with normal flow (P &lt; 0.05). In Cox regression analysis, DGASEL with normal or low flow were both associated with increased risk of all-cause death and hospitalization for HF after adjustment for age, sex, heart rate, randomized study treatment, hypertension, aortic valve replacement, and aortic valve calcification (P &lt; 0.05). No survival difference was found between patients with normal vs. low flow within groups of DGASEL or CGASEL.

Conclusion
Identification of low flow by the proposed sex-specific thresholds of SVi needs more prognostic validation before application in clinical practice.

ClinicalTrials.gov identifier
NCT00092677.