bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving building extraction from high-resolution aerial images: Error correction and performance enhancement using deep learning on the Inria dataset
Authors: Ekiz Serdar , Acar Ugur 
Published: February 2025

Extracting buildings from images is crucial for urban management, urban planning, and post-disaster change detection. Over the years, various approaches have been tried, but the recent application of deep learning has greatly improved the success of such studies. In this study, the Inria dataset was used, consisting of 180 high-resolution aerial images. The study compared the performance of various architectures. DeepLabv3+ emerged as the most successful, with Accuracy, IoU, and F1 Scores of 96.77%, 89.85%, and 94.53%, respectively. Attention U-Net followed, scoring 95.31%, 85.49%, and 91.95%. U-Net, tested with different encoders, achieved average results of 97.22%, 84.78%, and 90.79%. SE-ResNeXt-50 was the best-performing encoder, followed by SE-ResNet-50, ResNeXt-50, and ResNet-50. UNet++ achieved 94.48% Accuracy, 83.09% IoU, and 90.45% F1 Score, while U2Net obtained 94.09%, 82.26%, and 89.88%, making them less successful. When examining the models under challenging conditions, SE-ResNeXt-50 was the most robust, successfully handling scenarios like occlusion by trees and complex indoor gardens. Conversely, Attention U-Net and UNet++ were more prone to errors, particularly when vehicles were parked near buildings or in the presence of shipping containers, where false positives were common. ResNet-50 struggled with concrete gardens, while U2Net showed better results in scenarios involving indoor gardens. These results, compared to other studies using the same dataset with different pixel sizes, show that eliminating erroneous data and resizing images can enhance the performance of deep learning networks. Therefore, by refining the data and adjusting the image sizes, models can make more accurate and efficient building detections.
The role of tele-herbal consultations in workplace wellness
Authors: Hajialiasgari Fatemeh , Bagheri Kian , Anastasio Albert , Ghahremani Zahra , Shariat Ardalan 
Published: February 2025

Background:  Musculoskeletal disorders are widespread among sedentary office workers, impacting both their physical and mental well-being. There is increasing interest in incorporating herbal interventions as part of a holistic approach to address these challenges, blending traditional and culturally relevant treatments with modern techniques to enhance overall health and wellness. Objective:  This commentary aims to highlight the role of utilizing electronic platforms to provide consultations regarding herbal medicine (or tele-herbal consultations) in managing musculoskeletal disorders and impacting pain relief, changes in psychological well-being, and improvements in workplace productivity among sedentary office workers. Methods:  This study discusses the influence of herbal treatments on both physical and psychological dimensions of musculoskeletal disorders, offering insights into holistic healing approaches, based on previously published papers. It also highlights the feasibility and acceptance of tele-herbal consultations. Results:  The data gathered from previous studies shows that herbal interventions can enhance both subjective physical performance and psychological well-being in office workers. Tele-herbal consultation may be a culturally sensitive solution to provide pain relief and improvements in workplace productivity. Conclusion:  Although the findings of previous studies are encouraging, further research is needed to establish standardized tele-herbal protocols that can effectively relieve pain, boost well-being, and promote a more active, health-conscious lifestyle for office workers. Ultimately, these interventions could lead to improvements in workplace productivity.
COVID-19 recognition from chest X-ray images by combining deep learning with transfer learning
Authors: Zhang Chang-Jiang , Ruan Lu-Ting , Ji Ling-Feng , Feng Li-Li , Tang Fu-Qin 
Published: February 2025

Objective Based on the current research status, this paper proposes a deep learning model named Covid-DenseNet for COVID-19 detection from CXR (computed tomography) images, aiming to build a model with smaller computational complexity, stronger generalization ability, and excellent performance on benchmark datasets and other datasets with different sample distribution features and sample sizes. Methods The proposed model first extracts and obtains features of multiple scales from the input image through transfer learning, followed by assigning internal weights to the extracted features through the attention mechanism to enhance important features and suppress irrelevant features; finally, the model fuses these features of different scales through the multi-scale fusion architecture we designed to obtain richer semantic information and improve modeling efficiency. Results We evaluated our model and compared it with advanced models on three publicly available chest radiology datasets of different types, one of which is the baseline dataset, on which we constructed the model Covid-DenseNet, and the recognition accuracy on this test set was 96.89%, respectively. With recognition accuracy of 98.02% and 96.21% on the other two publicly available datasets, our model performs better than other advanced models. In addition, the performance of the model was further evaluated on external test sets, trained on data sets with balanced sample distribution (experiment 1) and unbalanced sample distribution (experiment 2), identified on the same external test set, and compared with DenseNet121. The recognition accuracy of the model in experiment 1 and experiment 2 is 80% and 77.5% respectively, which is 3.33% and 4.17% higher than that of DenseNet121 on external test set. On this basis, we also changed the number of samples in experiment 1 and experiment 2, and compared the impact of the change in the number of training set samples on the recognition accuracy of the model on the external test set. The results showed that when the number of samples increased and the sample features became more abundant, the trained Covid-DenseNet performed better on the external test set and the model became more robust. Conclusion Compared with other advanced models, our model has achieved better results on multiple datasets, and the recognition effect on external test sets is also quite good, with good generalization performance and robustness, and with the enrichment of sample features, the robustness of the model is further improved, and it has better clinical practice ability.
Developing an Intervention Program Based on a Cohort of Sonographers’ Occupational Burnout Experience and Mitigating Associated Factors to Improve Worker Well-Being
Authors: Callpani Veneta 
Published: February 2025

Objective: The aim of this study was to explore and understand sonographers’ occupational burnout experience and develop a curriculum for an intervention program, to mitigate this type of distress. Materials and Methods: A convergent parallel mixed-method design was employed to better understand sonographers’ experience of occupational burnout and develop a curriculum for an intervention program that targets specific needs of sonographers. The quantitative data were collected via an online survey, which included demographic questions, ranking factors that contribute to burnout on a 5-point Likert scale, and prevalence and feelings around conflict at work. Simultaneously, qualitative data were gathered through semistructured interviews conducted remotely with eight sonographers. Results: A total of 104 sonographers responded to the survey and eight sonographers participated in the online interview. Most of the participants were females (83.5%), working full-time (88.5%). Among this cohort, 60.6% self-reported experiencing burnout. In both qualitative and quantitative data, the experience of conflict at work was reported as stress or anxiety provoking. Stress management and conflict resolution were the most rated topics that participants wanted to be included in the intervention program. A total of 46.1% respondents from the survey reported being interested in participating in such a program. Conclusion: This study explored sonographers’ occupational burnout experience to develop a curriculum for an intervention program, tailored to their needs. The study findings indicated that this cohort of sonographers were aware of burnout and open to participating in an intervention program. Based on the results of this study, a group therapy curriculum was developed to help sonographers deal with conflict at work, managing stress, and mitigating occupational burnout.
Transitioning between clinical and academic practice from the perspectives of clinical academic trainees, academic training programme directors and academic supervisors: a mixed methods study
Authors: Payne Ruth , Frejah Isabella , Abbey Elizabeth , Badcoe Rosemary , Delaney Brigitte , Mitchell Caroline 
Published: February 2025

Abstract

Background
The career pathway of clinical academics in the UK is challenging. To pursue academic endeavors, trainees often undertake approved time ‘Out of Programme for Research’ (OOPR), a standalone research fellow post or join an ‘Integrated Academic Training’ pathway. Time out of training may impact their clinical skills, confidence and competency. The aim of this qualitative study was to explore the challenges associated with returning to clinical training after prolonged leave for academic trainees.

Methods
Stakeholders were clinical academic trainees and supervisors within the Yorkshire and Humber region of England, and training programme academic leads from universities across England. Qualitative data-analyses of verbatim recorded data from three focus groups and 12 individual telephone interviews were conducted within an a priori framework.

Results
Returning to a high-stress environment with a perceived lack of specialty-level advocacy and support, feeling isolated from peers, struggling to balance competing demands, meet clinical and academic expectations and managing clinical deskilling in a trainee’s return to clinical training were common experiences described by stakeholders. There was a lack of recognition from academic leads, however, on the impact of such challenges on the trainees’ subsequent ability to successfully integrate their clinical and academic careers. Various solutions were identified by stakeholders to overcome such barriers, including a normalised, phased, individualised supported return to work and capacity building for supervisors.

Conclusions
There is an apparent disconnect between the clinical and academic world, with clinical academic trainees stuck between the two, being pulled by each, feeling like they are not quite meeting the expectations of either. Time away from training for trainees on OOPR is often longer than for other reasons for time out of programme (typically 3–4 years if completing a doctoral degree). Given the importance of clinical academics in bridging clinical research and practice, and the concerns recently raised about the rate of attrition of clinician scientists within the NHS, it is of the utmost importance that clinical academic trainees are supported throughout all stages of their careers.
Comparison of sectorial and financial data for ESG scoring of mutual funds with machine learning
Authors: Martin-Melero Inigo , Gomez-Martinez Raul , Medrano-Garcia Maria Luisa , Hernandez-Perlines Felipe 
Published: February 2025

Abstract
Recently, funds and corporations have adopted environmental/social/governance (ESG)-related criteria as increasingly important criteria when evaluating investments. The ESG scores of mutual funds, which pool money from many individuals, are, therefore, significant as a sign of social commitment and deliver long-term results for investors deciding in which fund to invest. However, this scoring process is not transparent, and computing ESG scores requires an extensive review and monitoring of accountancy data. In this study, Machine Learning (ML) was employed to predict six ESG scores of 3192 mutual funds by comparing the performance of the models built solely using 13 variables of ESG-controversial sectorial involvement and 37 variables of financial data. In both cases, three classification and three regression algorithms were applied to the 72 simulations. The results demonstrate an acceptable fit of the Random Forest and Gradient Boosting algorithms in regression exercises (R2 values of 60–80%) and good prediction capabilities for classification (accuracies of approximately 70–80%). The models obtained similar performance when predicting ESG scores either from financial data or ESG-controversial sectorial involvement, illustrating how financial variables are good predictors of ESG scores as data on direct involvement in non-ESG funds’ activities. Thus, a relationship between the financial performance of a fund and its ESG score exists and is predictable by ML, saving time and resources.
Association of apple powdery mildew (Podosphaera leucotricha) and anthracnose (Neofabraea malicorticis) epidemics with environmental factors and agronomic practices in Chencha highlands, southern Ethiopia
Authors: Arato Asnake Abera , Mengesha Getachew Gudero , Biress Zemenu Fentahun , Borano Biniam Boraysho 
Published: February 2025

Abstract
Apple powdery mildew and anthracnose are widely distributed and major constraints on apple production around the world. The objective of the study was to determine powdery mildew and anthracnose prevalence, intensity, and association between biophysical factors. To this effect, field assessments were conducted in Chencha highlands of southern Ethiopia during the 2021 and 2022 production years. A multistage random sampling approach was followed for data collection, and the association between disease severity and biophysical factors was analyzed using a logistic regression model. A total of 164 apple orchards were inspected in five of the top apple-producing farmer associations (FAs) in the district. Results showed that the diseases were prevalent in apple fields, although the level of importance varied among FAs. Mean powdery mildew and anthracnose severity were lower in 2021 (23.56 and 22.39%) than in 2022 (28.89 and 26.11%), respectively. Age of tree, cropping system, disease status and management, yield, and weed showed highly significant (P < 0.0001) associations with powdery mildew and anthracnose severity. Likewise, altitude, production year, survey site, and relative humidity were significantly (P < 0.0001–0.05) associated with anthracnose severity in both single and multiple reduced variable models. In the model, precipitation was highly significant (P < 0.0001) in association with powdery mildew severity than anthracnose. Aged trees, monocropping, disease, and weed management, along with favorable environmental conditions, were highly contributing factors to the high (> 26% for powdery mildew and > 24% for anthracnose) disease severity in the model. Conversely, recently established orchards (≤ 5 years), mixed crops with legumes, and pruning with appropriate sanitation applied as disease management practices contributed to low (< 26% for powdery mildew and < 24% for anthracnose) disease severity. In conclusion, the current study indicated that powdery mildew and anthracnose diseases were major constraints on apple production, and their epidemic development was significantly influenced by biophysical factors. Hence, the findings may provide a basis for developing powdery mildew and anthracnose sustainable management strategies, which would ensure apple production in the study areas and in agro-ecologies of a similar kind elsewhere. Thus, following intercropping and/or mixed cropping and proper disease and weed management, along with other good agronomic practices, is suggested to manage the two diseases in the area and other related agroecologies.
Influence of Bariatric Surgery on Endothelial and Glycocalyx Biomarkers in Obesity and Type 2 Diabetes
Authors: Andersson Maja , Ågren Anna , Henriksson Peter , Wallén Håkan , Thorell Anders 
Published: February 2025

Abstract

Context
Bariatric surgery is associated with reduced risk of cardiometabolic disease in obesity and type 2 diabetes (T2D). The mechanisms are not fully understood, but improvement in endothelial dysfunction has been implicated.

Objective
This work aimed to assess endothelial biomarkers before and after surgery.

Methods
A prospective cohort study with 2-year follow-up was conducted at a single center in Stockholm, Sweden. Participants included adults undergoing bariatric surgery, 28 with and 33 without T2D. Intervention included Roux-en-Y gastric bypass preceded by a 2-week low-calorie diet (LCD). Main outcome measures included plasma concentrations of glycocalyx biomarkers (hyaluronan [HA] and syndecan-1), E-Selectin, von Willebrand factor (VWF), and thrombomodulin (TM).

Results
At baseline, patients with diabetes had higher concentrations of E-Selectin (P = .041) whereas other biomarkers did not differ between groups. After LCD, E-Selectin, syndecan-1, and VWF were reduced. Two years after surgery, TM was unchanged whereas E-Selectin decreased, geometric mean (CV%) 41 (40) to 24 (61) ng/mL, syndecan-1 from 50 (73) to 38 (81) ng/mL, and VWF from 120 (52) to 103 (45)%, while HA increased from 25 (96) to 40 (78) ng/mL (P &lt; .001 for all). E-Selectin initially declined faster in patients with diabetes (P &lt; .003); otherwise the biomarker changes did not differ between groups. Variables with the highest predictive value for improvement in biomarkers were decrease in body weight and fat mass and increase in insulin sensitivity (HOMA-IR).

Conclusion
Bariatric surgery is associated with sustained, beneficial alterations in biomarkers of glycocalyx and endothelial function in patients with obesity, both with and without T2D. It is suggested that reduced body weight/fat mass and improved insulin sensitivity are of particular importance for these alterations.
Instructional strategies for different learning styles: a study
Authors: V Prasanthi B. 
Published: February 2025

Learning is a continuous process for two classes of people (for both teacher & student). We all learns something new every day. It is gaining knowledge by studying; being taught & experiencing, whereas teaching is defined as just a more formal approach to deliver lessons with learning as end result. Teaching & learning are permitted not only within the dimensions, but it should happen beyond that. A learning style can best be explained as the most unique way in which a student prefers to learn new concepts. Students learn quicker and more effectively when teaching style is matched to their individual preferred styles. This paper explains about existing learning styles and also suggests few teaching methodologies that are to be adopted for different learning styles, which creates inter-est in students that leads to outstanding performance in academics.Â Â
Adapted spectral-differential method for construction of differentially 4-uniform piecewise-linear permutations, orthomorphisms, and involutions of the field F2n
$\mathbb{F}_{2^{n}}$
Authors: Menyachikhin Andrey V. 
Published: February 2025

Abstract
We propose a method to construct permutations of the field

F

2

n

$\mathbb{F}_{2^{n}}$

with low value of the Δ-uniformity such that their restrictions to the cosets of the group

F

2

n

×

$\mathbb{F}^{\times}_{2^{n}}$

by some of its subgroup H are linear. Using the proposed method, we construct a large number of new CCZ-nonequivalent differentially 4-uniform permutations, orthomorphisms, and involutions over the field

F

2

n

$\mathbb{F}_{2^{n}}$

with n = 6, 8.
Nutritional value and consumer assessment of 12 avocado crosses between cvs. Hass × Pionero
Authors: Martínez-Damián María Teresa , Ojeda-Barrios Damaris Leopoldina , Lugo-Espinosa Jesús , Barrientos-Priego Alejandro Facundo , Cruz-Alvarez Oscar 
Published: February 2025

Abstract
The widespread popularity of fresh avocados is linked to perceptions of their high nutritional value and sensory appeal. The cultivar Hass accounts for 95% of world production, so we used this cultivar as a reference to evaluate the nutritional and consumer assessment of 12 elite genotypes from the cross Hass × Pionero. A completely randomised experimental design was established, with three replicates per genotype (39 trees in total). The following characteristics were assessed: shelf life, fruit weight (FW), fruit weight loss (FWL), firmness, colour, chlorophyll (Chl) a and b, total carotenoids (TCr), total phenols (TP), total flavonoids (TFl), antioxidant capacity (AC), dry matter (DM), oil, lutein, β-carotene, and α-tocopherol. Sensory and quality characteristics, external and internal, were also assessed: flavour, texture, and fibrousness. The genotypes exhibited significant differences (p ≤ 0.05) for shelf life, WL, LWF, Chl, TCr, TP, and AC. Oil content was higher in the genotypes H×P40, H×P96, and S2/405. Lutein content varied in the genotypes S2/405 (0.713 mg kg−1), β-carotene in S2/27 and S2/29 (0.189 and 0.187 mg kg−1), and α-tocopherol in H×P40 (237.73 mg kg−1). On the other hand, the multivariate analysis facilitated the discernment of five distinct groups of genotypes, each clearly distinguished by their content of bioactive compounds and sensory attributes. Overall, the analysis identified several genotypes with quality, taste, and texture characteristics similar to those of Hass and one with less fibrousness (S2/249). Results identify several genotypes having good commercial potential for fresh consumption and as sources of bioactive compounds.
Management of Renal Vascular Injuries by Angioembolization in a Low Resource Setting: A Case Series with Review of Literature
Authors: Saifullah Khalid  , Manzoor Ahmad  , Ezaz Ahmed  , Wasif Mohammad Ali  , Ali Imad 
Published: February 2025

Background: Renal vascular injuries, like renal artery pseudoaneurysms and arteriovenous fistulas, are rare but serious complications following renal procedures and trauma. Standard management requires costly microcatheters for angioembolization. This study investigates a cost-effective method using diagnostic catheters and affordable coils in a low-resource setting.Methods: We analyzed five patients from December 2023 to June 2024 who had persistent hematuria or hemoglobin drops after renal intervention or trauma. CT angiography identified their injuries, and angioembolization was performed with diagnostic catheters and coils, reducing procedural costs.Results: The use of diagnostic catheters and coils successfully managed all cases. Patients experienced resolution of hematuria and were discharged in 2–3 days. Follow-up over three months revealed no recurrence or significant renal function issues.Conclusion: Diagnostic catheters and coils offer an effective method for managing renal vascular injuries in resource-limited settings, achieving satisfactory outcomes with minimal morbidity.