bluuebunny/crossref_metadata_2025 huggingface dataset:
The association between Geriatric Nutritional Risk Index and KSD disease: results from National Health and Nutrition Examination Survey 2007–2018
Authors: Tang Zhicheng , Zhong Hongzheng , Zhi Qingqing , Chen Yinqiu , Zhang JiaHao , Li Zhibiao , Lu Zechao , Liu Can , Tang Fucai , He Zhaohui , Sun Xuan
Published: November 2024

Background

Kidney stone disease (KSD) is a widespread problem in urology. But the associations between the Geriatric Nutritional Risk Index (GNRI), an important indicator for assessing the nutritional status of elderly hospitalized patients, and KSD are understudied.

Objective

Utilizing data from the National Health and Nutrition Examination Survey (NHANES) spanning 2007–2018, our study analyzed the correlation between the GNRI and KSD prevalence at cross-sectional level. The aim of the study was to explore the association between GNRI and the prevalence of KSD to identify potential risk factors and inform prevention and management strategies for KSD.

Methods

This cross-sectional study analyzed data focusing on 26,803 adults (20–80 years) after screening for complete data. It evaluated GNRI, a formula involving albumin, present, and ideal body weight, stratifying participants into quartiles. The primary outcome was the history of KSD, based on self-reports. Covariates included demographic, health, and lifestyle factors. Statistical analysis employed t-tests, ANOVA, Wilcoxon, and Kruskal-Wallis tests, with logistic regression modeling GNRI’s impact on KSD prevalence, assessing odds ratios and potential multicollinearity, and sensitivity analyses excluding individuals with low eGFR and adjusting cycle years.

Results

Significant disparities are found in GNRI distributions between individuals with and without kidney stones. Higher GNRI levels are more common in kidney stone patients, with 39.249% in the highest quartile versus 33.334% in those without stones. Notably, those in the highest GNRI quartile (Q4) show a lower prevalence of kidney stone disease (KSD) than those in the lowest (Q1), with rates of 11.988% versus 8.631%, respectively (P < 0.0001). Adjusted model results reveal that higher GNRI quartiles (Q3-Q4) correlate with reduced KSD prevalence, with odds ratios of 0.85 (95% CI [0.72, 1.00]) and 0.76 (95% CI [0.65, 0.89]). A nonlinear inverse relationship exists between GNRI levels and KSD prevalence across the population (P < 0.001), confirming that higher GNRI lowers KSD prevalence. Subgroup and sensitivity analyses support these findings.

Conclusion

The study underscores a significant, albeit nonlinear, association between elevated GNRI levels and decreased KSD prevalence. This relationship highlights the importance of nutritional assessment and management in KSD prevention strategies.
ANFIS Fuzzy convolutional neural network model for leaf disease detection
Authors: Kim Tae-hoon , Shahroz Mobeen , Alabdullah Bayan , Innab Nisreen , Baili Jamel , Umer Muhammad , Majeed Fiaz , Ashraf Imran
Published: November 2024

Leaf disease detection is critical in agriculture, as it directly impacts crop health, yield, and quality. Early and accurate detection of leaf diseases can prevent the spread of infections, reduce the need for chemical treatments, and minimize crop losses. This not only ensures food security but also supports sustainable farming practices. Effective leaf disease detection systems empower farmers with the knowledge to take timely actions, leading to healthier crops and more efficient resource management. In an era of increasing global food demand and environmental challenges, advanced leaf disease detection technologies are indispensable for modern agriculture. This study presents an innovative approach for detecting pepper bell leaf disease using an ANFIS Fuzzy convolutional neural network (CNN) integrated with local binary pattern (LBP) features. Experiments involve using the models without LBP, as well as, with LBP features. For both sets of experiments, the proposed ANFIS CNN model performs superbly. It shows an accuracy score of 0.8478 without using LBP features while its precision, recall, and F1 scores are 0.8959, 0.9045, and 0.8953, respectively. Incorporating LBP features, the proposed model achieved exceptional performance, with accuracy, precision, recall, and an F1 score of higher than 99%. Comprehensive comparisons with state-of-the-art techniques further highlight the superiority of the proposed method. Additionally, cross-validation was applied to ensure the robustness and reliability of the results. This approach demonstrates a significant advancement in agricultural disease detection, promising enhanced accuracy and efficiency in real-world applications.
Functional dynamics of human ubiquitin C-terminal hydrolases
Authors: Puri Sarita , Hsu Shang-Te Danny
Published: November 2024

Ubiquitin C-terminal hydrolases (UCHs) are crucial enzymes within the ubiquitin-proteasome system, characterized by a characteristic Gordian knotted topology. Another important structural feature of the UCH family is a hydrophobic β-sheet core containing a conserved catalytic triad of cysteine, histidine, and aspartate wrapped by several α-helices and a crossover loop. The catalytic triad cleaves the (iso) peptide bond at the C-terminus of ubiquitin via a nucleophilic attack. The highly dynamic crossover loop is involved in substrate binding and selectivity. UCHs play vital roles in various cellular processes, such as cell signaling, DNA repair, neuroprotection, and tumor suppression. Point mutations in catalytic and non-catalytic residues of UCHs are linked to various diseases, including cancers and neurodegeneration. Additionally, post-translational modifications (PTMs), such as oxidation, impact the deubiquitinase activity of UCHs and increase aggregation propensity. This review focuses on how disease-associated point mutations, PTMs, and interactions with different binding partners modulate the structural and functional dynamics of UCHs and how perturbations of these functional dynamics are characterized using a battery of biophysical techniques to gain insights into the molecular mechanisms underlying UCH dysfunction and diseases.
How components of ESG performance impact corporate risk-taking?
Authors: Sun Chuanwang , Lu Shuangji , Zhang Yaodan
Published: November 2024

Corporate risk-taking (CRT) is crucial for sustainable business development. The focus of this study is to examine the relationship between components of ESG performance (ESGP) and corporate risk-taking. We conduct an empirical analysis using CSI ESG score data of A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2009 to 2022. The conclusions are as follows: (1) ESGP exerts a facilitating effect on CRT. Specifically, environmental performance inhibit CRT, whereas social responsibility and corporate governance performance enhance CRT. (2) The facilitating effect of ESGP on CRT is more pronounced at lower levels of equity concentration. (3) ESGP enhances CRT by increasing the level of innovation and institutional investor shareholding. The contribution of this study is to help firms change CRT by adjusting the components of ESGP.
Effects of moringa polysaccharides on growth performance, immune function, rumen morphology, and microbial community structure in early-weaned goat kids
Authors: Liu Jinyang , Chen Jinyu , Fang Sicheng , Sun Baoli , Li Yaokun , Guo Yongqing , Deng Ming , Zhou Duoen , Liu Dewu , Liu Guangbin
Published: November 2024

The aim of this research was to investigate the effects of adding moringa polysaccharides (MOP) on the growth performance, immune function, rumen tissue morphology, and rumen microbial community in early-weaned goat kids. Twenty-one 7-day-old Leizhou male goat kids weighing (3.05 ± 0.63) kg, were randomly divided into a control group (CON group), a low-dose group (LOW group), and a high-dose group (HIG group). MOP was added to the goat kids’ milk replacer (MR) at 0, 0.15, and 0.3% (on dry matter basis),fed until 60 days of age, and four goat kids in each group with body weights close to the mean of each group were selected for slaughter. The results showed that, compared to the CON group, the MOP groups significantly improved final body weight, body measurements, daily weight gain, and feed intake of the early weaned goat kids; significantly reduced the content of propionic acid, butyric acid, valeric acid, and ammoniacal nitrogen; and in addition, the addition of MOP could significantly increase the height of rumen nipple, the content of immunoglobulin G (IgG) in the serum. The HIG group significantly increased rumen pH, rumen muscularis layer thickness, rumen wall thickness, and serum immunoglobulin A (IgA), and immunoglobulin M (IgM). In conclusion, the addition of MOP positively impacted the growth performance, serum immune function, and rumen tissue morphology in early-weaned goat kids.
Model-Assisted Probabilistic Neural Networks for Effective Turbofan Fault Diagnosis
Authors: Romesis Christoforos , Aretakis Nikolaos , Mathioudakis Konstantinos
Published: November 2024

A diagnostic method for gas-path faults of turbofan engines, relying on a Probabilistic Neural Network (PNN) coupled with a thermodynamic model of the engine, is presented. The novel aspect of the method is that its training information is generated dynamically by an accompanying Engine Performance Model. In the proposed approach, the PNN efficiently addresses the first step of a diagnostic process (i.e., detection of the faulty component at the current operating point), while with the aid of an adaptive engine model, the fault is then further isolated and identified. A description of the proposed method and training aspects of the PNN are presented. The method is applied to the case of a mixed-flow turbofan engine to diagnose common gas-path faults in compressors and turbines (i.e., fouling, FOD, erosion, and tip clearance). Its performance is evaluated using realistic fault data that may be acquired at various operating conditions within a flight envelope.
An Improved UNet-Based Path Recognition Method in Low-Light Environments
Authors: Zhong Wei , Yang Wanting , Zhu Junhuan , Jia Weidong , Dong Xiang , Ou Mingxiong
Published: November 2024

The fruit industry is a significant economic sector in China, with modern orchards gradually transitioning to trellis orchards. For mechanized orchard operations, automatic vehicle navigation is essential. However, in trellis orchards, the shading from trees results in low average light intensity and large variations in lighting, posing challenges for path navigation. To address this, a path navigation algorithm for trellis orchards is proposed based on the UNet-CBAM model. The network structures of UNet, FCN, and SegNet are compared to identify and select the optimal structure for further improvement. Among the three attention mechanisms of channel attention, spatial attention, and combined attention, the most effective mechanism is identified. The optimal attention mechanism is incorporated into the optimized network to enhance the model’s ability to detect path edges and improve detection performance. To validate the effectiveness and generalizability of the model, a total of 400 images were collected under varying lighting intensities. The experimental results show that this method achieves an accuracy of 97.63%, a recall of 93.94%, and an Intersection over Union (IoU) of 92.19%. These results significantly enhance path recognition accuracy in trellis orchards, particularly under low light under conditions. Compared to the FCN and SegNet algorithms, this method provides higher detection accuracy and offers a new theoretical foundation and research approach for path recognition in low-light environments.
Development of a Prediction Model for Specific Fuel Consumption in Rotary Tillage Based on Actual Operation
Authors: Kim Seung-Jun , Jang Moon-Kyeong , Hwang Seok-Joon , Lee Won Suk , Nam Ju-Seok
Published: November 2024

Tractor fuel consumption has typically been predicted using indoor test results under specific conditions. This study analyzes the factors affecting fuel consumption during rotary tillage in actual fields and develops a prediction model. The test field was divided into sections using a grid method, and rotary tillage operations were performed to measure various parameters, including soil strength, tractor’s transmission and PTO gear stages, tillage pitch, travel speed, engine and PTO shaft torque and speed, and fuel consumption. Pearson correlation identified variables affecting specific fuel consumption, and regression analysis was used to develop a prediction model. The model’s accuracy was analyzed using the coefficient of determination (R2) and root mean square error (RMSE), and it was compared with the ASABE’s fuel consumption prediction model. The test results showed that higher transmission and PTO gear stages, and tillage pitch decreased specific fuel consumption, while soil strength had no significant effect. Thus, operating at higher gear and PTO stages within suitable conditions enhances energy efficiency in rotary tillage. Statistical analysis showed that specific fuel consumption significantly correlated with tractor travel speed, PTO shaft power, and PTO shaft speed. The prediction model, including these variables, had the highest accuracy with R2 of 0.91 and RMSE of 0.011 L/kW·h. The developed prediction model showed significantly improved accuracy compared to the ASABE model, indicating that it can predict specific fuel consumption based on key operational variables in actual rotary tillage operations.
The Use of Machine Learning to Assess the Impact of the Ozonation Process on Selected Mechanical Properties of Japanese Quince Fruits
Authors: Gorzelany Józef , Kuźniar Piotr , Zardzewiały Miłosz , Pentoś Katarzyna , Murawski Tadeusz , Wojciechowski Wiesław , Kurek Jarosław
Published: November 2024

In this study, selected mechanical properties of fruits of six varieties of Japanese quince (Chaenomeles japonica) were investigated. The influence of their storage time and the applied ozone at a concentration of 10 ppm for 15 and 30 min on water content, skin and flesh puncture force, deformation to puncture and puncture energy was determined. After 60 days of storage, the fruits of the tested varieties showed a decrease in the average water content from 97.94% to 94.39%. No influence of the ozonation process on the change in water content in the fruits was noted. The tests showed a significant influence of ozonation and storage time on the increase in the punch puncture force of the skin and flesh, deformation and puncture energy of the fruits. In order to establish the relationship between storage conditions for various varieties and selected mechanical parameters, a novel machine learning method was employed. The best model accuracy was achieved for energy, with a MAPE of 10% and a coefficient of correlation (R) of 0.92 for the test data set. The best metamodels for force and deformation produced slightly higher MAPE (12% and 17%, respectively) and R of 0.72 and 0.88.
The Impact of Grafting with Different Rootstocks on Eggplant (Solanum melongena L.) Growth and Its Rhizosphere Soil Microecology
Authors: Du Gongfu , Zhu Dan , He Huang , Li Xiaoliang , Yang Yan , Qi Zhiqiang
Published: November 2024

This study investigated the effects of grafting on eggplant growth, yield, and disease resistance, with a focus on microbial dynamics in the rhizosphere. Eggplant scions were grafted onto rootstocks of wild eggplant and tomato, with self-rooted eggplants serving as controls. Greenhouse experiments were conducted over an eight-month growing period, using standard field practices such as film mulching and integrated water–fertilizer management. High-throughput sequencing was used to analyze the biological properties and microbial community of the rhizosphere soil. Results showed that plants grafted onto ‘Huimei Zhenba’ and ‘Torvum’ rootstocks yielded up to 36.89% more than self-rooted controls, achieving yields of 4619.59 kg and 4399.73 kg per 667 m², respectively. The disease incidence of bacterial wilt was reduced to as low as 3.33% in the ‘Huimei Zhenba’ treatment, compared to 55.56% in non-grafted controls. Additionally, grafted plants exhibited increased stem diameter and chlorophyll content, with the TL/HM combination reaching 54.23 ± 3.17 SPAD units. The enhanced microbial biomass of carbon, nitrogen, and phosphorus, particularly in the TL/HM treatment (377.59 mg/kg, 28.31 mg/kg, and 36.30 mg/kg, respectively), supports a more nutrient-rich rhizosphere environment. Moreover, soil enzyme activities, such as β-glucosidase and phosphatase, were significantly higher in grafted plants, enhancing nutrient cycling and potentially increasing resistance to pathogens. Overall, grafted eggplants demonstrated enhanced soil microbial biomass, enzyme activity, and a more diverse microbial community, which are critical factors contributing to the improved yield and disease resistance observed in grafted crops.
Trends in Global Agricultural Carbon Emission Research: A Bibliometric Analysis
Authors: Hu Jinhao , Dong Jianhua , Xu Dan , Yang Qiliang , Liang Jiaping , Li Na , Wang Haipeng
Published: November 2024

As climate change intensifies and countries actively pursue carbon peaking and carbon neutrality targets, agriculture has emerged as a significant source of carbon emissions. A comprehensive analysis of global agricultural carbon emission research can enhance the agricultural environment and achieve a mutually beneficial outcome for environmental protection and economic development. Despite the evolution of research domains and methodologies, the global context remains closely connected to the current state of the discipline. Drawing on the Web of Science core collection, this paper develops a knowledge network framework, examines the current status and hotspots of agricultural carbon emissions, forecasts future development trends, and analyzes the findings using CiteSpace visualization software. The findings indicate that the number of papers on agricultural carbon emissions has been increasing annually, with minor fluctuations; time series analysis and sustainable development have emerged as the current focal points, and relevant institutions are collaborating increasingly closely. However, cooperation among scholars requires further enhancement. Countries such as China, the United States, and Germany are the primary nations for paper publication. The hotspot analysis reveals a high frequency of keywords such as greenhouse gas emissions and climate change, indicating that research on agricultural carbon emissions has matured and the emphasis has shifted from accounting to management. This paper develops a domain knowledge framework to assist readers in understanding agricultural carbon emission patterns and provide resources for further research. Follow-up studies should enhance both comprehensiveness and breadth, promote interdisciplinary cooperation, provide a scientific foundation for policymakers, and outline future research directions.
Impact of Drying–Wetting Cycles on Nitrification Inhibitors (DMPP and DMPSA) in a Greenhouse Experiment with Two Contrasting Mediterranean Soils
Authors: Sánchez-Martin Laura , Bozal-Leorri Adrián , Rodrigues Janaina M. , González-Murua Carmen , Aparicio Pedro , García-Marco Sonia , Vallejo Antonio
Published: November 2024

Studies of the impact of nitrification inhibitors (NIs), specifically DMPP and DMPSA, on N2O emissions during “hot moments” have produced conflicting results regarding their effectiveness after rewetting. This study aimed to clarify the effectiveness of NIs in reducing N2O emissions by assessing residual DMP concentration and its influence on ammonia-oxidizing bacteria (AOB) in two pot experiments using calcareous (Soil C, Calcic Haploxerept) and acidic soils (Soil A, Dystric Xerochrepts). Fertilizer treatments included urea (U), DMPP, and DMPSA. The experiments were divided into Phase I (water application to dry period, 44 days) and Phase II (rewetting from days 101 to 121). In both phases for Soil C, total N2O emissions were reduced by 88% and 90% for DMPP and DMPSA, respectively, compared with U alone. While in Phase I, the efficacy of NIs was linked to the regulation of AOB populations, in Phase II this group was not affected by NIs, suggesting that nitrification may not be the predominant process after rewetting. In Soil A, higher concentrations of DMP from DMPP were maintained compared to Soil C at the end of each phase. Despite this, NIs had no significant effect due to low nitrification rates and limited amoA gene abundance, indicating unfavorable conditions for nitrifiers. The study highlights the need to optimize NIs to reduce N2O emissions and improve nitrogen efficiency, while understanding their interactions with the soil. This knowledge is necessary in order to design fertilization strategies that improve the sustainability of agriculture under climate change.
An Investigation of Virulence Genes of Staphylococcus aureus in Autologous Vaccines Against Sheep Mastitis
Authors: Sezzi Erminia , Fanelli Rita , Gobbi Deborah , Scandurra Paolo , Mannucci Valerio , Usai Isabella , Ragionieri Giovanni , Mezher Ziad , Fichi Gianluca
Published: November 2024

Staphylococcus aureus is well known to be the primary causal agent of clinical or subclinical mastitis in dairy sheep. The production of virulence factors allows S. aureus strains to cause mastitis. In the present study, 96 strains isolated from dairy sheep farms used for the production of autologous vaccines were tested for enterotoxin and hemolysin genes by PCR. In addition, 14 strains isolated from half udders of ewes with subclinical mastitis belonging to a single farm were also tested for the same genes. The phylogenetic trees were constructed, and spatial analysis was performed. Overall, 20 gene patterns were identified, but 43.64% of the tested strains showed the same profile (sec+, sel+, hla+, hld+, hlgAC+). Considering only the enterotoxin genes, four profiles were identified while the evaluation of the hemolysin genes revealed the presence of 12 gene patterns. In the farm with subclinical mastitis, six gene profiles were found. Spatial analysis of the isolated strains and their virulence genes did not show a specific pattern. The present study highlights the importance of identifying and analyzing virulence genes of S. aureus strains involved in dairy sheep mastitis, and the presence of different strains in the same farm.
The Role of the Equestrian Professional in Bridle and Bit Fit in the United Kingdom
Authors: Nankervis Kathryn , Williams Jane M. , Fisher Diana , MacKechnie-Guire Russell
Published: November 2024

To ensure the comfort of ridden horses, equipment including bridles and bits should be suitable and fit correctly. Horse owners employ a variety of equestrian professionals with varying degrees of responsibility regarding bridle and bit fit. This study aimed to compare the frequency and manner in which equestrian professionals within a multidisciplinary team (MDT) engage with clients on matters of bridle and bit fit, and to capture their perceptions of the key issues in bridle and bit fit. The participants completed an online questionnaire to obtain information regarding participant demographics, bridle fit and bit fit for the horse. Descriptive statistics and Kruskal–Wallis analyses were performed (p ≤ 0.05). Inductive content analysis identified themes from open-question responses. The results from 377 respondents were analyzed: 184 saddle fitters, 116 coaches and 77 therapists. The results showed that 65% of coaches ‘nearly always’ or ‘very often’ ask clients when they last had their bridle fitted, which was significantly more than saddle fitters (p = 0.002). More saddle fitters assess bridle fit statically and dynamically compared with coaches (p = 0.02). ‘Browband too small’ featured in the top three bridle fit issues for all professions. More professionals reported issues of bit suitability than fit. The findings demonstrate that whilst various equestrian professionals assess and act upon issues of bridle and bit fit, the extent to which this occurs is lower than for the saddle. Further work should quantify the prevalence of issues in bridle and bit fit and suitability in ridden horses.
Active Surveillance of Patients Colonized with CRE: A Single-Center Study Based on a Combined Molecular/Culture Protocol
Authors: Orena Beatrice Silvia , Liporace Maria Francesca , Teri Antonio , Girelli Daniela , Salari Federica , Mutti Michela , Giordano Gabriele , Alteri Claudia , Gentiloni Silverj Flaminia , Matinato Caterina , Callegaro Annapaola , Cariani Lisa
Published: November 2024

Background/Objectives: Carbapenem-resistant Enterobacteriaceae (CRE) are types of bacteria that need urgent attention globally. Active surveillance programs at hospitals are essential for the early identification of CRE carriers and the timely adoption of infection control measures. We aimed to analyze the epidemiology of CRE identified by multiplex RT-PCR in rectal swabs of patients upon admission to high-risk wards and to compare data obtained from both molecular and culture CRE screening. Methods: A total of 2861 rectal swabs, prospectively collected within 12–24 h of admission, underwent molecular screening for identification of K. pneumoniae carbapenemase (KPC), New Delhi metallo-β-lactamase (NDM), Verona integron-mediated metallo-β-lactamase (VIM), imipenemase (IMP), and OXA-48 (AllplexTM Entero-DR Assay). Only samples that tested positive or invalid underwent culture testing (Agar MacConkey and CHROMID® CARBA plates, bioMérieux, Craponne, France). Results: A total of 118 out of 2861 (about 4%) were positive for at least one carbapenem-resistant gene by a molecular approach (MA), with KPC, NDM, and VIM having the highest prevalence. Culture testing confirmed the presence of carbapenemase in 89 samples (75.4%), showing a disagreement rate of about 25% between the two methods, which, unfortunately, rises up to 60% for VIM. The dominant bacterial species were K. pneumoniae and E. coli (MALDI-TOF mass spectrometry). Conclusions: Our data underlined the need for the molecular screening of CRE carriers in order to implement active surveillance protocol in critical care settings and to improve infection control measures.
Antimicrobial Impact of Wood Vinegar Produced Through Co-Pyrolysis of Eucalyptus Wood and Aromatic Herbs
Authors: Gama Gil Sander Próspero , Pimenta Alexandre Santos , Feijó Francisco Marlon Carneiro , Aires Caio Augusto Martins , de Melo Rafael Rodolfo , dos Santos Caio Sérgio , de Medeiros Lúcio César Dantas , da Costa Monteiro Thays Vieira , Fasciotti Maíra , de Medeiros Priscila Lira , de Morais Maria Rita Macêdo , de Azevedo Tatiane Kelly Barbosa
Published: November 2024

Background: The search for substances that can overcome microorganisms’ resistance and enhance the antimicrobial activity of given products has attracted the attention of researchers. Eucalyptus wood vinegar (WV) is a promising product for developing alternative antimicrobials. Objectives: This study aimed to evaluate whether the production of WV in the co-pyrolysis of eucalyptus wood with aromatic herbs would incorporate compounds from them into WV and if that would enhance its antimicrobial action. Methodology: WV was produced alone and through co-pyrolysis with marjoram (Origanum majorana), Peruvian oregano (Origanum vulgare), rosemary (Salvia rosmarinus), thyme (Thymus vulgaris), and Turkish oregano (Origanum onites) at a proportion of 25% of herbs to the bone-dry wood weight. The antimicrobial effects were assessed against strains of gram-negative and -positive bacteria, and Candida glabrata. Microorganisms’ colony growth in agar had their absorbances recorded after inoculation and incubation. Chemical characterization of the new products was performed by gas chromatography and mass spectrometry (GC/MS). Results: After coproduction, there were relevant chemical changes concerning the original WV. Thymol, for instance, was incorporated into the WV through co-pyrolysis with marjoram, Peruvian and Turkish oregano, and thyme. The coproducts were more efficient than the WV produced only with wood, with thyme-incorporated products having the highest efficiency. This can be attributed to the increase and incorporation of the substances after coproduction, and particularly the role of thymol in enhancing the antimicrobial action. Conclusion: Given the results, the co-production of WV with eucalyptus wood and aromatic herbs has the potential to provide alternative antimicrobial products.
Mediterranean Diet and Olive Oil Redox Interactions on Lactate Dehydrogenase Mediated by Gut Oscillibacter in Patients with Long-COVID-19 Syndrome
Authors: Cuevas-Sierra Amanda , de la O Victor , Higuera-Gómez Andrea , Chero-Sandoval Lourdes , de Cuevillas Begoña , Martínez-Urbistondo María , Moreno-Torres Victor , Pintos-Pascual Ilduara , Castejón Raquel , Martínez J. Alfredo
Published: November 2024

Chronic viral inflammation is associated with oxidative stress and changes in gut microbiota. The Mediterranean diet (MD), with recognized anti-inflammatory and antioxidant properties, modulates gut microorganisms, specifically on the interaction between extra virgin olive oil, a key component of the MD with well-documented antioxidant effects. This study investigated the influence of adherence to MD and antioxidant-rich foods (extra virgin olive oil) on biochemical, inflammatory, and microbiota profiles in patients with chronic inflammation defined as a prolonged inflammatory response due to immune dysregulation following the acute phase of the viral infection. Participants were classified into low (n = 54) and high (n = 134) MD adherence groups (cut-off of 7 points based on previous studies utilizing the same threshold in the assessment of MD adherence). Gut microbiota was sequenced using the 16S technique, and the adherence to MD was assessed using a validated questionnaire for a Spanish population. High adherence to the MD was linked to significant improvements in inflammatory and oxidative stress markers, including reductions in LDL-cholesterol, glucose, and lactate dehydrogenase (LDH) levels, an indicative of redox balance, as well as a significant higher consumption of antioxidant foods. Moreover, gut microbiota analysis revealed distinct compositional shifts and a lower abundance of the Oscillibacter genus in the high adherence group. Notably, a significant interaction was observed between MD adherence and extra virgin olive oil consumption, with Oscillibacter abundance influencing LDH levels, suggesting that the MD antioxidant properties may modulate inflammation through gut microbiota-mediated mechanisms. These findings provide new evidence that adherence to the Mediterranean diet can reduce inflammatory markers in patients with long-COVID-19, a population that has not been extensively studied, while also highlighting the potential role of the bacterial genus Oscillibacter in modulating this effect.
LG-YOLOv8: A Lightweight Safety Helmet Detection Algorithm Combined with Feature Enhancement
Authors: Fan Zhipeng , Wu Yayun , Liu Wei , Chen Ming , Qiu Zeguo
Published: November 2024

In the realm of construction site monitoring, ensuring the proper use of safety helmets is crucial. Addressing the issues of high parameter values and sluggish detection speed in current safety helmet detection algorithms, a feature-enhanced lightweight algorithm, LG-YOLOv8, was introduced. Firstly, we introduce C2f-GhostDynamicConv as a powerful tool. This module enhances feature extraction to represent safety helmet wearing features, aiming to improve the efficiency of computing resource utilization. Secondly, the Bi-directional Feature Pyramid (BiFPN) was employed to further enrich the feature information, integrating feature maps from various levels to achieve more comprehensive semantic information. Finally, to enhance the training speed of the model and achieve a more lightweight outcome, we introduce a novel lightweight asymmetric detection head (LADH-Head) to optimize the original YOLOv8-n’s detection head. Evaluations on the SWHD dataset confirm the effectiveness of the LG-YOLOv8 algorithm. Compared to the original YOLOv8-n algorithm, our approach achieves a mean Average Precision (mAP) of 94.1%, a 59.8% reduction in parameters, a 54.3% decrease in FLOPs, a 44.2% increase in FPS, and a 2.7 MB compression of the model size. Therefore, LG-YOLOv8 has high accuracy and fast detection speed for safety helmet detection, which realizes real-time accurate detection of safety helmets and an ideal lightweight effect.
Robotized Mobile Platform for Non-Destructive Inspection of Aircraft Structures
Authors: Toman Rafał , Rogala Tomasz , Synaszko Piotr , Katunin Andrzej
Published: November 2024

The robotization of the non-destructive inspection of aircraft is essential for improving the accuracy and duration of performed inspections, being an integral part of inspection and data management systems within the currently developed NDT 4.0 concept. In this paper, the authors presented the design and testing of a universal mobile platform with interchangeable sensing systems for the non-destructive inspection of aircraft structures with various angles of inclination. As a result of the performed studies, a low-cost approach of automation of existing measurement devices used for inspection was proposed. The constructed prototype of the mobile platform was equipped with eddy current testing probe and successfully passed both laboratory and environmental tests, demonstrating its performance in various conditions. The presented approach confirms the effectiveness of the automation of the inspection process using climbing robots and defining the directions of possible development of automation in non-destructive testing in aviation.
Research on Climactic Chapter Recognition of a Chinese Long Novel Based on Plot Description
Authors: Liu Zhongbao , Wan Guangwen , Liu Yingbin , Hu Jianan
Published: November 2024

Many readers continue to pursue Chinese long novels in the past several decades because of diverse characters and fascinating plots. The climactic chapter is an important part of a Chinese long novel, where the key conflict develops to the extreme point. However, how to quickly and accurately recognize the climactic chapter remains a common problem for many readers in their reading choices. This paper conducts research on recognizing the climactic chapter of a Chinese long novel by accurately describing its plot. The proposed method consists of two parts; one is the extraction of key elements, such as viewpoint paragraphs, non-viewpoint paragraphs, chapter keywords, major characters etc. The other part is the climactic chapter recognition, which applies the Bidirectional Gate Recurrent Unit (BiGRU) model and the multi-head attention to recognize the climactic chapter, on the basis of the chapter plot description matrix. Comparative experiments on the corpus named The Condor Trilogy show that the proposed method in this paper has a better recognition performance compared with the existing models, such as Naive Bayesian (NB), Support Vector Machine (SVM), Roberta-large, and the Bidirectional Long-Short Term Memory (BiLSTM) network. Ablation experiments validated the effectiveness of primary components in the proposed method.
Evaluating Rock Brittleness Through a Novel Statistical Damage Theory-Based Index: A Case Study on Sandstones
Authors: Wu Na , Jiang Bei , Ai Ting , Liang Zhengzhao
Published: November 2024

Evaluating the brittleness of rocks or rock masses is a fundamental problem in geotechnical engineering. This study proposed a new index that expresses brittleness as the rate of damage development in rock. The brittleness index was derived from statistical damage theory. It depends on the four material parameters, i.e., the peak strain, peak strength, Poisson’s ratio and elastic modulus. The validity of the proposed brittleness index was confirmed through two case studies, including triaxial compression test results for coals subjected to varying confining pressures and for sandstones at various temperatures. Uniaxial compression experiments were then performed on rock-like materials to examine the effects of model size and joint dip angle on rock brittleness using the proposed brittleness index. Results show that the brittleness of the jointed specimens varies in a complex pattern with the model size and joint dip angle. Generally, the brittleness index initially reduces and then grows with the increasing joint dip angle, and larger specimens tend to be more brittle. Furthermore, large specimens containing horizontal or vertical joints are particularly susceptible to brittle damage. The proposed brittleness index has merits such as a clear physical meaning and simple expression, making it a valuable tool for evaluating rock brittleness.
Enhanced Magnetic Resonance Imaging-Based Brain Tumor Classification with a Hybrid Swin Transformer and ResNet50V2 Model
Authors: Al Bataineh Abeer Fayez , Nahar Khalid M. O. , Khafajeh Hayel , Samara Ghassan , Alazaidah Raed , Nasayreh Ahmad , Bashkami Ayah , Gharaibeh Hasan , Dawaghreh Waed
Published: November 2024

Brain tumors can be serious; consequently, rapid and accurate detection is crucial. Nevertheless, a variety of obstacles, such as poor imaging resolution, doubts over the accuracy of data, a lack of diverse tumor classes and stages, and the possibility of misunderstanding, present challenges to achieve an accurate and final diagnosis. Effective brain cancer detection is crucial for patients’ safety and health. Deep learning systems provide the capability to assist radiologists in quickly and accurately detecting diagnoses. This study presents an innovative deep learning approach that utilizes the Swin Transformer. The suggested method entails integrating the Swin Transformer with the pretrained deep learning model Resnet50V2, called (SwT+Resnet50V2). The objective of this modification is to decrease memory utilization, enhance classification accuracy, and reduce training complexity. The self-attention mechanism of the Swin Transformer identifies distant relationships and captures the overall context. Resnet 50V2 improves both accuracy and training speed by extracting adaptive features from the Swin Transformer’s dependencies. We evaluate the proposed framework using two publicly accessible brain magnetic resonance imaging (MRI) datasets, each including two and four distinct classes, respectively. Employing data augmentation and transfer learning techniques enhances model performance, leading to more dependable and cost-effective training. The suggested model achieves an impressive accuracy of 99.9% on the binary-labeled dataset and 96.8% on the four-labeled dataset, outperforming the VGG16, MobileNetV2, Resnet50V2, EfficientNetV2B3, ConvNeXtTiny, and convolutional neural network (CNN) algorithms used for comparison. This demonstrates that the Swin transducer, when combined with Resnet50V2, is capable of accurately diagnosing brain tumors. This method leverages the combination of SwT+Resnet50V2 to create an innovative diagnostic tool. Radiologists have the potential to accelerate and improve the detection of brain tumors, leading to improved patient outcomes and reduced risks.
Utilization of Microencapsulated Polyphenols to Enhance the Bioactive Compound Content in Whole Grain Bread: Recipe Optimization
Authors: Bińkowska Weronika , Szpicer Arkadiusz , Stelmasiak Adrian , Wojtasik-Kalinowska Iwona , Półtorak Andrzej
Published: November 2024

In today’s health-conscious society, there is an increasing consumer demand for functional foods that not only satisfy nutritional needs but also promote overall well-being. The aim of this study was to develop a bread formulation enriched with microencapsulated polyphenols, oat β-glucan concentrate, and sour fermented beetroot juice to enhance its nutritional profile and health benefits. To protect sensitive polyphenols from thermal degradation during baking, the microencapsulation technique was employed to maintain their bioactivity. The influence of these ingredients on the physio-chemical parameters of bread (dough viscosity, hardness, porosity, bioactive ingredients content, color, and volatile compounds profiles), as well as sensory acceptability, was evaluated. Using response surface methodology, the formulation was optimized to achieve a product with high polyphenol and β-glucan content. The optimized formulation included a content of 4.60% sour fermented beetroot juice, 6.29% β-glucan concentrate, and 2.77% microencapsulated polyphenols. The final bread demonstrated significant antioxidant activity and high consumer acceptability, indicating its potential as a functional alternative to traditional bread. This innovative approach addresses the demand for healthier food options and emphasizes the role of functional ingredients in improving dietary quality and promoting health benefits.
Effect of a Sensorimotor Training Program for Aerial Maneuvers in Junior Surfers
Authors: Seixas Pedro , Oliveira Raul , Carita Isabel , Davis Ian , Moreira Miguel
Published: November 2024

The purpose of this study was to examine the impact of a sensorimotor training program on maximum ankle dorsiflexion (ankle DF), coordination, dynamic balance and postural control, and lower-limb muscle power, in competitive junior surfers, and its relation to parameters of sensorimotor control required to perform aerial maneuvers. Twelve junior competitive surfers followed a 7-week sensorimotor training program, being assessed pre- and post-program with the knee-to-wall test (KW), Y-Balance test—lower quarter (YBT-LQ), and the countermovement jump test (CMJ). Post-training assessment revealed positive effects on the KW (ankle DF) distance, which increased approximately 2 cm (p < 0.001) for both ankles, and all scores for the YTB-LQ (coordination, dynamic balance, and postural control) variables increased, being significant (p < 0.005) for some reach distances (YBT-LQ—Anterior Left, YBT-LQ—Postero-medial Left, and YTB-LQ Anterior Right). YBT-LQ Anterior Reach Asymmetry also improved by decreasing 1.62 cm (p < 0.001) and the CMJ height (lower limb muscle power) increased 2.89 cm (p < 0.001). The training program proved to effectively enhance parameters of physical performance for this sample, including ankle DF, coordination, dynamic balance, postural control, and lower limb muscle power. This tailored-made task approach can help to optimize surfing performance capabilities and contribute to reducing the risk of injuries while performing aerials.
The AI-Powered Evolution of Big Data
Authors: Kumar Yulia , Marchena Jose , Awlla Ardalan H. , Li J. Jenny , Abdalla Hemn Barzan
Published: November 2024

The rapid advancement of artificial intelligence (AI), coupled with the global rollout of 4G and 5G networks, has fundamentally transformed the Big Data landscape, redefining data management and analysis methodologies. The ability to manage and analyze such vast and varied datasets has exceeded the capacity of any individual or organization. This study introduces an enhanced framework that expands upon the traditional four Vs of Big Data—volume, velocity, volatility, and veracity—by incorporating six additional dimensions: value, validity, visualization, variability, volatility, and vulnerability. This comprehensive framework offers a novel and straightforward approach to understanding and addressing the complexities of Big Data in the AI era. This article further explores the use of ‘Big D’, an AI-driven, RAG-based Big Data analytical bot powered by the ChatGPT-4o model (ChatGPT version 4.0). This article’s innovation represents a significant advance in the field, accelerating and deepening the extraction and analysis of insights from large-scale datasets. This will enable us to develop a more nuanced and comprehensive understanding of intricate data landscapes. In addition, we proposed a framework and analytical tools that contribute to the evolution of Big Data analytics, particularly in the context of AI-driven processes.
CFD and Artificial Intelligence-Based Machine Learning Synergy for the Assessment of Syngas-Utilizing Pre-Reformer in r-SOC Technology Advancement
Authors: Peksen Murphy M.
Published: November 2024

This study demonstrates the significant advantages of integrating computational fluid dynamics (CFD) with artificial intelligence (AI)-based machine learning (ML) to optimize the pre-reforming process for reversible solid oxide cell (r-SOC) technologies. It places a distinct focus on the relationship between process variables, aiming to enhance the preparation of quality r-SOC-ready fuel, which is an indispensable element for successful operation. Evaluating the intricate thermochemistry of syngas-containing reforming processes involves employing an experimentally validated CFD model. The model serves as the foundation for gathering essential data, crucial for the development and training of AI-based machine learning models. The developed model forecasts and optimizes reforming processes across diverse fuel compositions, encompassing oxygen-containing syngas blends and controlled feedstock outlet process conditions. Impressively, the model’s predictions align closely with CFD outcomes with an error margin as low as 0.34%, underscoring its accuracy and reliability. This research significantly contributes to a deeper understanding and the qualitative enhancement of preparing high-quality syngas for SOC under improved process conditions. Enabling the early availability of valuable information drives forward sustainable research and ensures the safe, consistent operation assessment of r-SOC. Additionally, this strategic approach substantially reduces the need for resource-intensive experiments.
The Effectiveness of Extracts of Spent Grape Pomaces in Improving the Oxidative Stability of Grapeseed Oil
Authors: Cisneros Miluska , Canazza Elisa , Mihaylova Dasha , Lante Anna
Published: November 2024

Spent grape pomace is a by-product of Grappa production that is usually considered waste. Therefore, in this study, in order to promote the sustainable use of by-products from the food industry, we aimed to optimize the extraction of antioxidants from spent grape pomace and their use to improve the oxidative stability of grapeseed oil. Ultrasound-assisted extraction maximized the total phenolic content and antioxidant activity, with the latter measured according to the ferric reducing antioxidant power. The best extraction conditions identified for spent grape pomace from red and white grapes were an amplitude of 40%, a duration of 22 min and a ratio of 1:37 and an amplitude of 40%, a duration of 25 min and a ratio of 1:45, respectively. Grapeseed oil, which is rich in polyunsaturated fatty acids and susceptible to rancidity, fortified with 10%, 20% and 30% of these extracts was evaluated in terms of its oxidative stability using the Rancimat method and compared with a control oil and an oil fortified with the synthetic antioxidant BHT at the highest legal level (200 ppm). For oil fortified with 30% of the extracts obtained from red and white pomace under the best conditions, increases in the induction time of 39% and 25% compared to the control and 23.01% and 10.62% compared to the BHT-fortified oil, respectively, were reported. This study highlights the potential of using grape pomace extracts as eco-friendly antioxidants to stabilize oil and contribute to the sustainability of the food industry at the same time.