bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of fatty acid composition in intact and minced fat of European autochthonous pigs breeds by Near infrared spectroscopy
Authors: Parrini Silvia , Sirtori Francesco , Čandek-Potokar Marjeta , Charneca Rui , Crovetti Alessandro , Kušec Ivona Djurkin , Sanchez Elena González , Cebrian Mercedes Maria Izquierdo , Garcia Ana Haro , Karolyi Danijel , Lebret Benedicte , Ortiz Alberto , Panella-Riera Nuria , Petig Matthias , Pires Preciosa Jesus da Costa , Tejerina David , Razmaite Violeta , Aquilani Chiara , Bozzi Riccardo 
Published: March 2023

Abstract
The fatty acids profile has been playing a decisive role in recent years, thanks to technological, sensory and health demands from producers and consumers. The application of NIRS technique on fat tissues, could lead to more efficient, practical, and economical in the quality control. The study aim was to assess the accuracy of Fourier Transformed Near Infrared Spectroscopy technique to determine fatty acids composition in fat of 12 European local pig breeds. A total of 439 spectra of backfat were collected both in intact and minced tissue and then were analyzed using gas chromatographic analysis. Predictive equations were developed using the 80% of samples for the calibration, followed by full cross validation, and the remaining 20% for the external validation test. NIRS analysis of minced samples allowed a better response for fatty acid families, n3 PUFA, n6 PUFA and for the screening (high, medium, low value) of the major fatty acids. Intact fat prediction, although with a lower predictive ability, seems suitable for PUFA and n6 PUFA while for other families allows only a discrimination between high and low values.
Adaptations to the Welsh National Exercise Referral Scheme during the COVID-19 pandemic: A qualitative study exploring the experiences of service users and providers and supplementary out-of-pocket costs analysis
Authors: Newby Katie , Howlett Neil , Wagner Adam P , Lloyd Nigel , Freethy Imogen , Bontoft Charis , Fakoya Olujoke , Jackson Shelley , Jackson Carla , Wills Wendy , McKibben Mary-Ann , Petherick Annie , Brown Katherine 
Published: March 2023

Abstract
Background: Despite the proliferation of exercise referral schemes in the UK, evidence on their efficacy is equivocal. The Welsh National Exercise Referral Scheme (NERS) is heavily used but inequalities in uptake have been reported. As a result of the COVID-19 pandemic, NERS was initially suspended and then transitioned from standard face-to-face delivery to alternative remote methods, including virtual delivery. The aim of this study was to explore the barriers and facilitators to uptake and engagement of NERS when delivered in face-to-face and virtual formats, and to examine the cost to service users of engaging with the scheme in these different ways.
Methods: This was a qualitative study with supplementary costs analysis. Maximum variation sampling was used to recruit participants. Interviews with service users (n=21) and one person who declined the service, and three focus groups with service providers (n=19), were conducted. Framework analysis was used to analyse the qualitative data. Quantitative data obtained through the interviews on service users’ out-of-pocket costs of attending face-to-face or virtual classes were summarised using descriptive statistics.
Results: Five key themes were identified from the qualitative analysis which summarised barriers and facilitators to uptake and engagement as perceived and experienced by the different stakeholders. Themes included: opaqueness and uncertainty around referral; Exercise Referral Professionals (ERPs) allaying concerns and providing reassurance at scheme entry; the mixed appeal and accessibility of virtual delivery; factors that support ongoing engagement; and personal and financial circumstances restricting uptake and engagement.
Conclusions: This study indicates that offering a virtual version of NERS could make the scheme more accessible to those who are typically underserved, provided strategies to address digital inclusion are addressed. The findings also provide wider evidence to inform adaptations that could be made to ensure that other exercise referral schemes can optimise virtual delivery.
Richness of Arbuscular Mycorrhizal Fungi Under Different Agroforestry Practices in the Drylands of Southern Ethiopia
Authors: Masebo Nebiyou , Birhane Emiru , Takele Serekebrehane , Belay Zerihun , Lucena Juan J. , Anjulo Agena 
Published: March 2023

Abstract
The conversion of an agroforestry based agricultural system to monocropping farming system influences the distribution and composition of arbuscular mycorrhizal fungi (AMF). The objective of this paper was to analyze AMF species diversity, spore density and root colonization across different agroforestry practices (AFP) in southern Ethiopia. Soil and root samples were collected from homegarden, cropland, woodlot, and trees on soil and water conservation based AFP. AMF spores were extracted from the soil and species diversity evaluated using morphological analysis and root colonization from root samples. The spore density, root colonization and the composition of AMF were significantly different among the AFP (P < 0.05). In this study, a total of 43 AMF morphotypes belonging to 11 genera were identified, dominated by Acaulospora (32.56%) followed by Claroideoglomus (18.60%). Homegardens had the highest spore density (7641.5 spore100 g− 1 dry soil) and the lowest was recorded in croplands (683.6 spore100 g− 1 dry soil). Woodlot had the highest root colonization (54.75%) followed by homegarden (48.25%). The highest isolation frequency (63.63%) was recorded for Acaulospora scrobiculata. Distribution of AMF species and diversity were significantly related to soil total nitrogen and organic carbon. The homegarden and woodlot AFP were suitable for soil AMF reserves and conservation.
Experimental Investigation of High Filler Loading of SiO 2 on the Mechanical and Dynamic Mechanical Analysis of Natural PALF fibre-Based Hybrid Composite
Authors: G Velmurugan , V Siva Shankar , M Nagaraj 
Published: March 2023

Abstract
This work aims to investigate the effect of SiO2 nanoparticles on the mechanical properties of pineapple leaf fiber (PALF) epoxy hybrid composites. The compression molding process was used to create the composite. To achieve the aforementioned goals, the blends were made using 25% PALF and varied weight proportions (3wt%, 6wt%, and 9wt%) of SiO2 nanoparticles. Tensile, bending, impact, interlaminar shear, shoreline D hardness, and dynamic mechanical analysis were all evaluated. SEM was used to examine the morphology of the materials, and an FTIR spectrometer was used to look for the presence of organic chemicals in fiber-reinforced composite materials. The findings show that adding 25% PALF fiber and 6% SiO2 nanoparticles (D-type) to the epoxy polymer improved the thermal and mechanical properties of the composites. It can be attributed to the improved interaction and homogeneous dispersion of the fillers and epoxy polymers. Moreover, the water uptake parameters of all samples were studied. The findings showed that the inclusion of reinforcements boosts the water uptake of the composite significantly. The initial deterioration rate of the SiO2-incorporated hybrids is almost the same, at about 400°C, which is considerably greater than that of the beginning breakdown temperatures of PALF (300°C), according to the thermography study. This might imply that the fiber and polymers form a stronger bond, reducing polymer movement and increasing the thermostability of the combination.
MOF-derived Fe2O3/MoSe2 Heterostructure for Promoted Electrocatalytic Nitrogen Fixation
Authors: Huang Liming , Tao Leiming , Pang Kui , He Shuying , Zhu Guanhua , Duan Linhai , Wen Chenglin , Yu Changlin , Ji Hongbing 
Published: March 2023

Abstract
Due to the low activity and poor selectivity of current artificial nitrogen fixation catalysts, there is an urgent need to develop efficient and environmentally friendly Electrochemical ammonia synthesis (EAS) electrocatalysts. Electrochemical ammonia synthesis is considered an environmentally friendly and sustainable method for artificial nitrogen fixation. Herein, Fe2O3 nanoparticles assembled on MoSe2 (Fe2O3/MoSe2) were first developed and regarded as an efficient electrocatalytic nitrogen fixation catalyst with high electroactive. The Fe2O3/MoSe2 composites exhibited excellent NRR activity with an NH3 yield of 55.52 µg∙h− 1∙mg− 1 at -0.5 V and Faradaic efficiency of 9.6% at -0.6 V vs. RHE. Notably, the Fe2O3/MoSe2 composites exhibited excellent stability and durability in recycling tests. Density functional theory (DFT) calculations revealed that the interfacial charge transport from Fe2O3 to MoSe2 could significantly enhance the Electrochemical nitrogen reduction reaction (NRR) activity of Fe2O3/MoSe2 by promoting the conductivity of Fe2O3/MoSe2 and reducing the free energy barrier for the rate-determining of *N2 to *N2H formation step. This work provides a promising avenue for the green synthesis of NH3.
BRLMSESCT: Design of a Bioinspired Reinforcement Learning Model for Side-Effect Analysis of Spinal Cord Tumours
Authors: Mohanty Rajanikanta , V Ramana T , rout Narendra kumar 
Published: March 2023

Abstract
Due to improper postures, and unhealthy lifestyle of millennials, there has been an exponential increase in spinal cord related diseases. These include Slip Discs, Spine Injuries, Tumours, etc. each of which has multiple side-effects on the human body. To analyze these conditions, a wide variety of image processing models are developed by researchers. But most of these models do not analyze side-effects of spinal cord tumours on other body parts, due to which their applicability is limited when used for clinical trials. The main novelty of this work is to analyze side effects resulting due to spinal cord tumours, and to perform this task a novel Bioinspired Reinforcement Learning Model for Side-Effect Analysis of Spinal Cord Tumours is discussed in this text. The proposed model initially uses a Recurrent Neural Network (RNN) based on combination of Long-Short-Term Memory (LSTM) & Gated Recurrent Unit (GRU) for extraction of highly dense image features. These features allow the model to estimate tumour positions in Computer Tomography (CT) scans. The extracted features are classified via the RNN Model, which assists in high accuracy classification & localization of spinal cord tumours. These classification & localization results are linked with blood reports to estimate side-effects on kidney, lungs, heart activity and vitamin levels. To perform this correlation, a Grey Wolf Optimization (GWO) Model is used, which assists in linking tumour type, and size with blood report parameters. The GWO Model evaluates a fitness function, that fuses tumour levels with its side-effects on individual body parts. This fusion is done via analysis of temporal blood reports, which evaluates effects of different tumour types-and-sizes on individual body parameters. Due to a combination of GWO with LSTM & GRU based RNN, the model is capable of showcasing high accuracy of tumour classification, with better precision of correlation with side effects when compared with state-of-the-art models. It was observed that the proposed model was able to achieve 98.5% accuracy for tumour classification, 96.4% correlation precision with kidney diseases, 95.8% correlation precision with lung diseases, 96.2% correlation precision with heart diseases, and 91.5% correlation precision with vitamin deficiencies. Due to such a high performance, the model is capable of deployment for real-time clinical applications.
Geoinformatics of soil loss using GEE in Tigray regional state of Ethiopia.
Authors: Islam Zubairul , Belay Fikre , Hadgu Tadesse Brhane , Teshale Haftom , Debebe Yalembrhan 
Published: March 2023

Abstract
Soil loss and its geostatistical analysis was studied at the kebele level in Tigray. The method applied to estimate soil loss was the revised universal soil loss equation. Earth Engine's public data archive was used for data collection. The R-factor was developed from the SM2RAIN-ASCAT (2007–2021) global daily satellite rainfall data, the K-factor was developed from USDA-3A1A1A_M/v02 soil data, the C-factor was derived from MODIS/006/MOD13A2, and LS factor was derived from WWF Hydro SHEDS Hydrologically Conditioned DEM. By integrating all factor, the soil loss was obtained by the RUSLE model. Spatial Autocorrelation (Morans I) statistic was used to identify the pattern of soil loss and Ordinary Least Squares (OLS) linear regression was used to model a soil loss in terms of its relationships to R, K, LS, C, and P factors. The grouping analysis tool was used to Group kebele based on soil loss. The results indicate that the estimated average soil erosion is 82760 t ha− 1 y− 1. The pattern of soil loss at the kebele level was found highly clustered with a z-score of 23.39. The groping analysis tool divides the kebele into five categories to identify the cause of spatial variation of the soil loss in Tigray. Groups 1, 4 & 5 were found as in the outlier positions due to the high LS factor. The results deliver valuable information for decision-makers and planners to take suitable land administration measures to minimize the soil loss. It, therefore, indicates google earth engine is a significant platform to analyze the RUSLE model for evaluating and mapping soil erosion quantitatively and spatially.
Validation of a clinicopathological prognostic score for elderly patients undergoing endoscopic endonasal surgery for pituitary adenoma: a single center 10 years’ experience and review of the literature
Authors: PERNA Giuseppe DI , DE MARCO Raffaele , PESARESI Alessandro , PORTONERO Irene , BALDASSARRE Bianca Maria , PERANO Francesco , PRENCIPE Nunzia , GROTTOLI Silvia , BERTERO Luca , CASSONI Paola , GARBOSSA Diego , LANOTTE Michele Maria , ZENGA Francesco 
Published: March 2023

Abstract
Objective
Evaluate the effectiveness of surgery for pituitary adenomas in the elderly and the reliability of the Trouillas score in patients with ≥ 65 years old.
Methods
A single center retrospective observational study was conducted for the period 2012–2020 recruiting all the patients over 65 years old underwent to primary endoscopic endonasal removal of both functioning and non-functioning pituitary adenomas. Radiological and histopathological features were examined for classification. Time-to-relapse was estimated using Kaplan-Meier product limit.
Results
103 patients were recruited. Mean age at surgery was 72.2 years (range 65–83). The majority of tumors were non-functioning macroadenomas (83%). A gross total resection (GTR) was achieved in 46/73 (63%) of young elderly (age 65–74) and 14/30 (47%) of old elderly (age ≥ 75). According to Trouillas' classification criteria, 57 tumors resulted to be grade 1a, 8 grade 1b, 33 grade 2a, and 5 grade 2b. The mean TTR was 32.3 months ± 18.5. Mitotic index (p = 0.024) and a Trouillas grade (p = 0.048) remained statistically significant at multivariate analysis. Patients with Trouillas grade 1a/2a showed better rates of TTR than patients with Trouillas grade 1b/2b (mean DFS: 82 vs 49 months, p = 0.006) although the number of patients representing the second group (13) demands a critical interpretation of the result.
Conclusions
The possibility to predict tumor’s regrowth can define different timing for follow-up and could justify a more aggressive surgical treatment even among elderly patients, aiming to achieve GTR and significantly reduce the need for redo surgery.
Green synthesis of ZnO dopped Piper chaudocanum leaf extract by co-precipitation method: characterization, antibacterial and anticancer activity
Authors: Thao Truong Thi , Tam Khieu Thi 
Published: March 2023

Abstract
In this study, zinc oxide nanoparticles (ZnO NPs) were synthesized from Piper chaudocanum leaf extract by co-precipitation method with different processes and extract volumes. Biosynthesized ZnO NPs were characterized by various techniques including XRD, FTIR, FESEM, PL test, UV-Vis DRS, and compared to chemically synthesized ZnO NPs. Most of the ZnO NPs samples were pure and had a hexagonal wurtzite phase. The particle sizes were about 22 to 40 nm and had a characteristic part of the Piper chaudocanum extract on the surface of ZnO NPs. The particle sizes, surface features and optical properties of samples depend on the order of the reaction steps and the extract volumes. The ZnO NPs synthesized by adding the extract to zinc acetate solution before adjusting pH were potent antimicrobial agents against pathogenic bacteria. The highest antibacterial activity was observed against P. aeruginosa (42 nm) and S. aureus (39 nm). Moreover, these ZnO NPs were moderately effective on KB cells with the IC50 value of 43.53 ± 2.98 µg/mL.
Consequences of adopting national consumption-based climate targets alongside the territorial commitments under the Paris Agreement
Authors: Morfeldt Johannes , Larsson Jörgen , Andersson David , Johansson Daniel , Rootzén Johan , Hult Cecilia , Karlsson Ida 
Published: March 2023

Abstract
National emission reduction targets under the Paris Agreement have a territorial focus, incentivizing mitigation actions domestically. Here we scrutinize the theoretical basis for adopting complementary consumption-based net-zero emission targets and assess the consequences of adopting such proposed targets for Sweden. We apply scenario analyses based on a prospective lifecycle assessment framework. The framework is a hybrid between bottom-up simulations – for passenger travel, construction and housing, and food – and top-down analyses covering remaining consumption. In this work, we show how consumption-based climate targets accentuate the need for new demand-side climate policies that contribute to reducing emissions along value chains of products and services. Combining advanced mitigation technologies with behavioral changes could achieve emission reductions from 9.8 tons of carbon dioxide equivalents per capita in 2019 to between 2.7 and 4.8 tons by 2045 for Swedish residents, depending on global decarbonization pathways.
Imbalance multiclass Problem: A robust Feature Enhancement-based Framework for Liver Lesion Classification
Authors: Hu Rui , Song Yuqing , Liu Yi , Zhu Yan , Feng Nuo , Qiu Chengjian , Han Kai , Teng Qiaoying , Ul Haq Imran , Liu Zhe 
Published: March 2023

Abstract
The classification of liver lesions in CT images is essential for the diagnosis and treatment of liver diseases. Since the characteristics of different classes of lesions are similar and the degree of differentiation is not obvious, it is difficult to accurately classify different classes of liver lesions, especially imbalanced multiclass distribution. To this end, we propose a novel feature enhancement-based framework for imbalanced liver lesion classification. Specifically, a liver lesion processing method is introduced to expand data based on Augmentor. Besides, dual feature enhancement based on feature refinement extraction and feature global correlation is designed to enhance feature extraction ability. To further alleviate the problem of class imbalance, the improved loss function based on standard Cross-Entropy (CE) is also adopted to make the network pay more attention to the class with fewer samples. Experiments on 551 liver lesions in 120 patients showed that: 1) Our proposed framework improved the classification performance of liver tumors under imbalanced multiclass data distribution; 2) The dual feature enhancement was lightweight and efficient which enhanced the semantic expression of overall features without introducing additional parameters.
Multi-Objective linear fractional solid transportation problem with uncertain variables
Authors: Joshi Vishwas Deep , Saini Rachana , Singh Jagdev 
Published: March 2023

Abstract
In this paper, the multi-objective linear fractional solid transportation problem (MOLFSTP) under uncertainty is studied, where all parameters are treated as zigzag uncertain variables. For the uncertain MOLFSTP model, the expected value model (EVM) and the optimistic value model (OVM) were established using two different ranking parameters of the uncertainty principle. Using the principal basis of uncertainty, both models are converted to correlated deterministic form and then to a single-objective model using a two-resolution method. Fuzzy programming method using distance minimization and linear membership functions. Next, we use Lingo 17.0 optimization tools to solve a two-model single-objective problem and arrive at a Pareto optimal solution. Lastly, numerical outcomes demonstrate the application of the model and the algorithm.
Corneal Reverberation Artifacts on Ocular Ultrasound
Authors: BANERJEE MOUSUMI , Azad Shorya Vardhan , Mandal Sohini , Nayak Shreya , Venkatesh Pradeep , Chawla Rohan 
Published: March 2023

Abstract
Objective: To describe a novel reverberation artifact named corneal reverberation artifact in ocular ultrasound.
Design: Prospective observational study.
Subjects: 30 eyes with decompensated corneas were grouped into two groups- group A (with positive corneal reverberation on USG) and group B (without reverberation artifact).
Methods: 30 eyes with decompensated corneas were studied to compare the characteristic anterior segment optical coherence tomography (AS-OCT) findings in eyes with reverberations observed on ultrasound (similar to IOL reverberations) and eyes with no reverberations.
Main outcome measures: Characteristic reproducible findings on ASOCT in eyes with positive reverberation.
Results: ASOCT images in group A eyes revealed a distinct transition from hyperreflective part of the cornea to iso-reflective cornea. A sharp demarcation line could be appreciated between anterior and posterior part of the cornea. ASOCT images in group B eyes did not have any drastic change in reflectivity from hyper to iso-reflective portion of the cornea. A uniform blending of reflectivity between anterior and posterior part of the cornea was noted in group B.
Conclusion: Sharp difference in reflectivity between anterior and posterior part of the cornea might contribute to the characteristic artifact noted in our patients.