bluuebunny/crossref_metadata_2025_split huggingface dataset:
Feasibility of CO2 Pipeline Construction to Enable Gigaton-Scale Carbon Dioxide Removals: Evidence from historical precedent
Authors: Roberts Cameron , Nemet Gregory F 
Published: September 2024

Abstract

We assess the feasibility of a rapid CO2 pipeline buildout using historical evidence from oil and gas pipelines. We answer four questions: 1) What length of pipeline network will be required to achieve the benchmarks of 1 GT or 100 Mt of CO2 in 2050? 2) What have been the fastest national oil and gas pipeline buildouts achieved in a 25-year period ? 3) Are the pipeline requirements for gigaton-scale CO2 removals feasible given these historical precedents, and 4) Under what political, economic, and social circumstances have rapid pipeline buildouts occurred? Modelling studies projecting 100 Mt of CO2 transportation and sequestration capacity by 2050 suggest rates of pipeline construction that are precedented in 18 national 25 year build-outs during the twentieth and twenty-fist centuries. For 1 Gt, only two 25-year national pipeline build-outs (both in the USA) achieve the rate of pipeline construction that the modelling studies suggest would be required, only three 25-year periods of global pipeline construction meet the benchmark. Rapid construction of fossil fuel pipelines has benefited from strong economic and institutional drivers, which may not apply to CO2 pipelines in the same way. Our findings are reason for caution about the likelihood of CO2 pipeline buildouts keeping pace with CO2 removal targets.
Biological invasions and their potential economic costs in Morocco
Authors: Jamaai Jazila El , Taheri Ahmed , Ballesteros-Mejia Liliana , Ahmed Danish , Bang Alok , Diagne Christophe , Courchamp Franck , Angulo Elena 
Published: September 2024

Abstract

Biological invasions pose substantial economic threats globally, yet detailed cost assessments for many Global South nations, especially in Africa, remain scarce. This study presents the first comprehensive breakdown of the potential costs of biological invasions in Morocco. We identified 551 invasive alien species, comprising approximately 1.76% of Morocco's biodiversity. Using the InvaCost database, we gathered cost data for the 12 most frequent invasive species with available data. Calculating the mean annual cost for each species and extrapolating based on their prevalence in Morocco, we estimated the potential annual economic impact to be US$ 1.61 billion. Urban species management and damage, especially pigeons, accounted for a substantial portion of this impact. Invasive plant species also emerged as prevalent and costly. Annual management costs exceeded damage costs (US$ 1.50 billion vs. 856.80 million), driven mainly by pigeon management. Costs by authorities and stakeholders outweighed agricultural costs (US$ 1.49 billion vs. 859.10 million). Despite challenges in extrapolating cost data from other regions, this study underscores the urgent need for targeted management and policy interventions to minimize the spread of invasive species and reduce their economic toll. Morocco can implement proactive management measures and foster international collaborations to tackle this socio-ecological crisis, ensuring long-term sustainability and prosperity.
Non-convex fractional-order TV model for image inpainting
Authors: Lian Wenhui , Liu Xinwu 
Published: September 2024

Abstract

This paper aims to address the challenge of effectively processing missing or corrupted image areas using the known image information. In this study, we consider a novel non-convex and non-smooth variational model tailored for image inpainting. Our scheme introduces the non-convex potential function into the fractional order total variation regularization. This innovative technique effectively mitigates blurred contours and alleviates the undesirable staircase effect, significantly improving restoration quality. Numerically, to tackle the generated optimization problem, we design a practical primal-dual algorithm that integrates with the iteratively reweighted 1 algorithm. Extensive simulation experiments, comparing our novel model with other approaches, conclusively demonstrate its capability to achieve the visually realistic inpainting while yielding the highest quantitative results.
Elucidating the enzyme network driving Amaryllidaceae Alkaloids Biosynthesis
Authors: Desgagne-Penix Isabel , Lamichhane Basanta , Gélinas Sarah-Eve , Merindol Natacha , Koirala Manoj , Santos Karen Cristine Gonçalves dos , Germain Hugo 
Published: September 2024

Abstract

Amaryllidaceae alkaloids (AAs) are diverse bioactive metabolites with significant pharmaceutical potential, derived from 4´-O-methylnorbelladine (4´OM). The biosynthesis of these compounds involves the condensation of tyramine and 3,4-dihydroxybenzaldehyde by norbelladine synthase (NBS) and/or noroxomaritidine/norcraugsodine reductase (NR), followed by O-methylation. Cytochrome P450 enzymes, particularly the CYP96T family, introduce further structural diversity through C-C couplings, resulting in lycorine, galanthamine, and crinine cores. Despite their importance, the exact biosynthetic pathways remain poorly defined. In this study, we characterized key enzymes from Leucojum aestivum (La), providing crucial insight into AA biosynthesis. Transient expression in Nicotiana benthamiana demonstrated that LaNBS and LaNRII catalyze the conversion of tyramine and 3,4-dihydroxybenzaldehyde to norbelladine, which is subsequently O-methylated by a norbelladine-4´-O-methyltransferase (LaN4´OMT). Co-agroinfiltration of LaNBS, LaNRII, LaN4´OMT, and LaCYP96T1 resulted in the production of various phenol-coupled products, with lycorine as the predominant compound, alongside with haemanthamine, crinine/vittatine, and norgalanthamine. This study identifies LaCYP96T1 and LaCYP96T2 as the first monocot enzymes capable of catalyzing all three regioselective C-C phenol couplings, highlighting the substrate promiscuity of LaNRII. The findings not only elucidate crital steps in AA biosynthesis but also open new avenues for biotechnological application in producing valuable alkaloids, offering potential for novel drug development.
Pre-Therapeutic Prognostic Factors Influencing Outcomes of Brain Hypothermia Therapy for Hypoxic-Ischemic Encephalopathy
Authors: Kurimoto Tomonori , Tokuhisa Takuya , Hayasaka Itaru , Yamamoto Tsuyoshi , Ibara Satoshi 
Published: September 2024

Abstract

Perinatal hypoxic-ischemic brain injury can cause long-term neurodevelopmental impairment (NDI), including motor, cognitive, and behavioral deficits. Hypoxic-ischemic encephalopathy (HIE) affects approximately 1.3–1.7/1,000 live births. Although brain hypothermia therapy (BHT) can significantly reduce mortality and NDI in HIE, half of infants with moderate to severe HIE still experience death or NDI. Herein, we enrolled 144 infants with HIE treated with BHT at Kagoshima City Hospital, Japan between January 2000 and October 2022, of whom 100 were evaluated. Clinical data, amplitude-integrated electroencephalograms (aEEG), Thompson scores, and resuscitation data were analyzed to identify predictive factors for developmental outcomes at 18 months. Significant predictors of death cerebral palsy (CP); a developmental quotient (DQ) < 70 including low Apgar scores at 1/5/10 min; low umbilical artery pH; flat trace or continuous low voltage on aEEG; a high Thompson score; chest compressions during resuscitation; and adrenaline during resuscitation. Flat trace or continuous low voltage on aEEG, Thompson score ≥ 12, and chest compressions remained significant predictors on multivariate regression analysis. When combined, these criteria had a high sensitivity (0.704) and specificity (0.0096), with an AUC of 0.86. These factors can guide early prognostic predictions before the initiation of BHT, thereby providing critical prognostic information for treatment.
Analysis of Spatial Distribution of Precipitation Using Hydrological Modeling of Watersheds
Authors: Gorjizade Ali , Shahbazi Ali 
Published: September 2024

Abstract

One of the significant challenges in hydrological modeling is the spatial variability of rainfall; even a slight change in rainfall patterns can lead to fundamental differences in runoff responses, affecting the accuracy of predictions. Moreover, the complexity of rainfall patterns, influenced by topography, increasingly leads to mispredictions. Therefore, this study investigates and analyzes the spatial distribution of rainfall and the role of the dispersion of raindrop data and its impact on rainfall-runoff modeling using raindrop data from two gridded precipitation products, PERSIANN-CDR and ERA5, at daily and monthly scales with the semi-distributed SWAT model. For this purpose, raindrop data from four stations—Talezang, Tang-e Panj Bakhtiari, Sepid Dasht Sezar, and the Dez Dam Rain gauge stations and Tele-Zang hydrometry station —from 2008 to 2019 were utilized.
The results of the simulations compared with the observed data indicate that due to the lack of a suitable raingage network and spatial distribution of rainfall, the SWAT rainfall-runoff simulation model was unable to establish a correspondence between the rainfall amounts and their corresponding discharge values. Consequently, an appropriate modeling with the observed data could not be achieved, as evidenced by the NSE coefficient values of 0.52 and -0.01 for daily calibration and validation periods, respectively, and 0.76 and 0.82 for monthly periods.
Additionally, it was observed that in the daily hydrological evaluation, the performance of the PERSIANN-CDR dataset was better, with NSE values of 0.29 and 0.59 for the calibration and validation periods, respectively. In contrast, the ERA5 dataset displayed superior capabilities in monthly hydrological evaluation for rainfall-runoff modeling, with calibration and validation coefficients of 0.77 and 0.82, respectively.
Pollen-derived fatty acids and amino acids mediate variance in pollinator visitation
Authors: Murray Anne F. , Leonhardt Sara D. , Stout Jane C. , Ruedenauer Fabian A. , Vanderplanck Maryse , Russo Laura 
Published: September 2024

Abstract

Pollinators help maintain functional landscapes and are sensitive to floral nutritional quality. Both proteins and lipids influence pollinator foraging, but the role of individual biochemical components in pollen remains unclear. We conducted an experiment comprising common garden plots of six plant species (Asteraceae, Rosaceae, Onagraceae, Boraginaceae, and Plantaginaceae). These plots were treated with low concentrations of agrochemicals, including fertilizer, herbicide, and a combination of both to induce intra-specific variation in floral chemistry. We recorded insect visitation to inflorescences over two years and eight sites in Dublin, Ireland. We analyzed the pollen amino acid and fatty acid content, quantifying the concentrations of 51 fatty acids and 17 amino acids of the six focal plant species across the four agrochemical treatments. We tested relationships between the pollen composition and the insect visitation matrix as well as an insect trait matrix including sociality, body size, nesting behavior, and whether the insect was a bee or hoverfly. We found: i) the agrochemical treatments did not affect the biochemical composition of the pollen; ii) there were many strong associations between fatty acids, amino acids, insect traits, and visitation; and iii) specific compounds with strong associations (montanic acid, cysteine, and proline) explained more of the variance in insect abundance (honeybees, bumble bees, and hoverflies) than the total amino acid or fatty acid concentrations in the pollen. Our results suggest it is important to evaluate the contribution of individual biochemical compounds in pollen to insect visitation, and also that different insect species respond to different pollen compounds.
Resettlement and the Agricultural Productivity of Origin Locations
Authors: Gebresilasse Mesay 
Published: September 2024

Abstract

How do large out-migration shocks affect agricultural productivity in origin locations? Planned resettlements, forced migration, and climate change have resulted in various relocation schemes across the globe. In this paper, I examine the effects of large-scale out-migration on origin locations considering a government-mandated resettlement in Ethiopia that relocated close to 600,000 people from densely-populated and drought-prone areas. Using a difference-indifference strategy that exploits variation in out-resettlement across districts with similar environmental stressors, I find that agricultural productivity increases relatively in districts with a greater share of out-resettled population. These effects persist for several years after the end of the resettlement program. The results suggest that improvements in land distribution facilitated by the resettlement helped to lessen land degradation and increase agricultural productivity.
JEL Codes: O12, O13, O15, O21, Q12, Q15, Q18, Q24, Q32
A Causal Intercomparison framework unravels precipitation drivers in Global Storm-Resolving Models
Authors: Ricard Lucile , Beucler Tom , Stephan Claudia , Nenes Athanasios 
Published: September 2024

Abstract

Correctly representing convective precipitation remains a long-standing problem in climate models, due to its highly parameterized nature and unclear role of drivers interacting over a wide range of spatial scales. We analyze and compare simulations of Global Storm-Resolving Models, using a methodology based on dimensionality reduction and causal inference, to unravel the contribution of large-scale variables and storm-scale dynamics on precipitation distribution. We derive regions of Column Relative Humidity (CRH), which exclude sharp humidity gradients and help define coherent thermodynamic environments, which are subsequently found to control precipitation throughout half of the tropics. The control of CRH on precipitation is notably amplified by considering explicitly the intermediate role of the convective area. Moreover, the effect values are consistent across models and quantiles, which could be further employed to constrain GCMs. Our results show that the most extreme intensities (99.9th percentile) cannot be adequately represented without high-resolution data on vertical velocity. However, their effect on precipitation varies considerably across models and precipitation quantiles, making it more difficult to develop a constraint on storm-scale control.
Real-Time Infrared Pedestrian Recognition Model on Edge Devices
Authors: Liu Li , Huang Kaiye , Li Yujian , Zhang Chuxia , Zhang Shuo , Hu Zhengyu 
Published: September 2024

Abstract

Aiming at the lightweight and real-time detection requirements of infrared pedestrians on edge devices, this paper proposes an improved infrared pedestrian recognition model based on YOLOv5n. The model effectively reduces the number of parameters by introducing the GhostConv and SlimNeck modules in the lightweight design. To enhance real-time performance, the model reduces the number of parameters and improves operation speed through the PAGCP pruning strategy. After experimental analysis, a balance between operation speed and accuracy is achieved. The results show that compared to the original model, the proposed model decreases the number of parameters by 1.52M, increases running speed by 21%, and decreases mAP.50 by only 0.4%. The running speed on Jetson Nano reaches 0.12s per image. The proposed model effectively ensures high detection accuracy under lightweight and real-time requirements, providing technical support for the deployment of edge devices in scenarios such as autonomous driving.
Microfibers degradation processes in water under simulated solar irradiation: a small- and wide-angle X-ray scattering study
Authors: Piccinini Astra , Lucia Giulia , Colarossi Daniele , Principi Paolo , Amenitsch Heinz , Pittura Lucia , Regoli Francesco , Gorbi Stefania , Spinozzi Francesco 
Published: September 2024

Abstract

Microfibers are pollutants of significant concern, reaching aquatic environments and posing threats to living organisms. In these environments, microfibers undergo various degradation processes, reducing their size and enabling interactions with biological systems. Photodegradation is a primary driver, leading to fiber fragmentation and shrinkage. This study investigates the impact of UV irradiation on five different microfibers (cotton, cellulose acetate, polyamide, polyester, linen) in seawater and freshwater, simulating one year of sunlight exposure using a solar simulator. Structural changes at the nanoscopic level are analyzed through synchrotron small- and wide-angle X-ray scattering experiments, employing a novel model that describes macrofibrils as bundles of microfibrils. This model enables the determination of macrofibril diameter, flexibility, and length, as well as microfibril radius, spacing, and packing order. Results show cotton and polyester in seawater experience significant macrofibril enlargement, likely due to increased microfibril spacing. Cellulose acetate remains stable in seawater but shows increased microfibril dimensions in freshwater. Polyamide exhibits minimal structural changes, indicating low sensitivity to UV irradiation. Linen, uniquely, shows a reduction in macrofibril size in both water types, suggesting a potential for fragmentation. These findings highlight the diverse and complex responses of microfibers to UV irradiation, emphasizing the importance of considering material-specific behaviors in assessing environmental persistence and potential ecological impact.
Effective cataract surgical and refractive error coverages in the State of Qatar
Authors: Ashwal Shadi M Al , Rabiu Muhammad Mansur , Mccormick Ian , AlQahtani Omar , Algadi Halla Salih , Kokku Suresh Babu , Al-Thani Mohamed Hamad 
Published: September 2024

Abstract

Purpose
To evaluate the progress in Qatar's eye care since 2009, focusing on effective cataract surgical and refractive error coverages, leading to enhance eye health strategies and action plans.
Methods
A modified Rapid Assessment of Avoidable Blindness (RAAB) survey was employed using multi-stage sampling in all persons 50 years and older in Qatar. The study focused on uncorrected refractive errors, cataract surgery coverage and effectiveness, and visual acuity assessment, with secure data gathering.
Results
There were 339 individuals out of 3,206 examined participants who underwent cataract surgery out of which 66.1% of the operated eyes obtained ‘Good’ post-operative outcomes (Vision ≥ 6/12). The Effective Cataract Surgical Coverage (eCSC) was 61.2%, which is on par with developed countries. WHO set a global target for countries to achieve a 30 percentage-point increase in eCSC by 2030, accordingly Qatar's target should be 91.2% by 2030. Additionally, the study found that the Refractive Error Coverage and the effective Refractive Coverage across were 77.8% and 74.3%, respectively. Notably, these rates were lowest among Qatari women.
Conclusion
Qatar's eye care services have shown an improvement since the 2009 RAAB, with disparities in outcomes based on gender and nationality. Continued efforts are needed to meet WHO standards and reduce quality gaps, particularly among Qataris.
Assessing Landslide Susceptibility in Chefchaouen, North-West Morocco Using Remote Sensing and GIS: A Weighted Overlay Analysis Approach
Authors: Dahmani Lahcen , Laaribya Said , NAIM Hafida , DINDAROGLU Turgay 
Published: September 2024

Abstract

This study maps landslide susceptibility in the Chefchaouen region of northwestern Morocco using remote sensing and Geographic Information Systems (GIS) with a weighted overlay analysis approach. Field data collected from five strategically selected sites were used to validate the model's results. Key environmental factors considered include slope, aspect, elevation, vegetation cover, and soil characteristics. The results show a Kappa coefficient of 0.72, indicating substantial agreement between observed and expected accuracies. The user's and producer's accuracy for low susceptibility areas is 75%, for medium susceptibility areas is 85%, and for high susceptibility areas is 80%. These findings indicate a good match between the model's predictions and actual field conditions. Validation of the Landslide Susceptibility Zoning (LSZ) map revealed that most high-risk areas are located on steep slopes in the northern and western regions of Chefchaouen. Nine major landslide-prone zones were identified, providing crucial information for stakeholders to formulate effective mitigation measures. Despite several limitations, such as the temporal scope of data and the resolution of satellite imagery, this research offers significant insights into landslide risk management in the Chefchaouen region. Future perspectives include integrating long-term climate change scenarios and improving methodology to better capture local geological complexity. This study emphasizes the importance of sustainable land and vegetation management practices to reduce landslide risks.
Real-Time Evaluation of the Improved Eagle Strategy Model in the Internet of Things
Authors: Rajendran Venushini , Ramasamy R Kanesaraj 
Published: September 2024

Abstract

With the rapid expansion of cloud computing and the pervasive growth of IoT across industries and educational sectors, the need for efficient remote data management and service orchestration has become paramount. Web services, facilitated by APIs, offer a modular approach to integrating and streamlining complex business processes. However, real-time monitoring and optimal service selection within large-scale, cloud-based repositories remain significant challenges. This study introduces the Improved Eagle Strategy (IES) hybrid model, designed to drastically reduce computation time while ensuring accurate service selection tailored to specific user requirements. Through comprehensive NetLogo simulations, the IES model demonstrates superior efficiency in service selection compared to existing methodologies. Additionally, the IES model's application through a web dashboard system highlights its capability to manage both functional and non-functional service attributes effectively. When deployed on real-time IoT devices, the IES model not only enhances computation speed but also ensures a more responsive and user-centric service environment. This research underscores the transformative potential of the IES model in optimizing cloud computing processes, particularly within the IoT ecosystem.
Comparing sparse Llama 3 and Llama 2 models for on-device AI assistants
Authors: Liu Bo 
Published: September 2024

Abstract

Sparse connectivity is ubiquitous in biological neural networks. In the machine learning field, it has a long history to train dense neural networks then prune them for sparse connectivity. Recently, this research direction was extended to pruning and sparsifying large language models (LLMs), for which many numerical methods have been invented. Previously, we adopted one of such methods to prune the Llama-2 7B model, in order to develop small-sized language models for on-device AI assistants. We figured out that its sparsity limit is 60%, at which the pruned Llama-2 7B model could generate fluent and helpful answers to daily queries despite occasional factual inaccuracies. In this article, we extended previous pruning and evaluation methods to the newly-released Llama-3 8B model. We found that this Llama 3 model has a sparsity limit of 53%, at which it produced fewer factual inaccuracies than Llama 2 at the sparsity limits but required more parameters. We hypothesized that the accuracy improvement is due to the longer context window and larger training dataset used in Llama 3, while the increased parameter requirement is due to its versatility in generating both text and code output. Based on these results and insights, we proposed future directions to boost the model performance after pruning, paving the way for lightweight LLMs as on-device AI assistants.