bluuebunny/crossref_metadata_2025_split huggingface dataset:
Wildfire effects on ecosystem services in two disparate California watersheds: A Case Study
Authors: Busari Ibrahim , Sloggy Matthew R. , Rad Mani Rouhi , Sahoo Debabrata , Drury Stacy A , Escobedo Francisco J. 
Published: April 2024

Abstract
Ecosystem services are important for human well-being and maintaining environmental quality objectives. The growing concern over extreme wildfire events in various watersheds necessitates understanding their impacts particularly on regulating ecosystems services. In this study, we used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to examine how two wildfires that occurred in California, USA in 2017 impacted water provisioning, soil loss and sediment delivery, carbon sequestration services, and nutrient delivery in the waterways. We also related the distributional impacts of wildfire to ecosystem service supply based on various sociodemographic factors across the affected communities to assess their vulnerabilities. We find that a year following the fires, the amount of biomass in forestland, woodland, and chaparral declined, as expected, in both studied watersheds, while the amount of grassland increased. This change in vegetation resulted in the loss of about 200,000 tons of carbon from the Mark West subwatershed and about 160,000 tons of carbon from the Southern California watersheds. Furthermore, the fires increased the expected mean annual water yield significantly for both watersheds by 5% and 42%, respectively. Our analysis shows an increase in the expected post-fire phosphorus and nitrogen export. Using regression analyses to determine the effect of wildfire on the distributional impacts to ecosystem services across communities in the watersheds, we did find evidence of differences between communities with respect to the pre-fire distribution of ecosystem services. However, we did not find that post-fire condition either exacerbated or alleviated these distributional impacts and inequities.
Educational Policy as Predictor of Computational Thinking: A Supervised Machine Learning Approach
Authors: Ezeamuzie Ndudi O. 
Published: April 2024

Abstract

Background: Computational thinking is derived from arguments that the underlying practices in computer science augment problem-solving. Most studies investigated computational thinking development as a function of learners’ factors, instructional strategies and learning environment. However, the influence of the wider community such as educational policies on computational thinking remains unclear.
Objectives: This study examines the impact of basic and technology-related educational policies on the development of computational thinking.
Methods: Using supervised machine learning, computational thinking achievements of 31,823 eighth graders across nine countries were analysed. Seven rule-based and tree-based classification models were generated and triangulated to determine how educational policies predicted students’ computational thinking.
Results and Conclusions: Predictions show that students have higher propensity to develop computational thinking skills when schools exercise full autonomy in governance and explicitly embed computational thinking in their curriculum. Plans to support students, teachers and schools with technology or introduce 1:1 computing have no discernible predicted influence on students’ computational thinking achievement.
Implications: Although predictions deduced from these attributes are not generalizable, traces of how educational policies affect computational thinking exist to articulate more fronts for future research on the influence of educational policies on computational thinking.
Dynamic Response Analysis of Simply Supported Bridge through Experimental and Numerical Modelling Approaches
Authors: Singh Om Narayan , Mandal Krishnendu , Dey Kaushik 
Published: April 2024

Abstract

In earlier days, bridges were designed with the maximum possible static load on them. In the failure of some bridge structures, it is established that dynamic load often plays a vital role in the failure of bridges. Considering this, it is established that the responses of the bridges under dynamic loading should be evaluated for prior understanding of the bridge condition. This paper reports one such case study carried out on a newly constructed bridge to understand the response of the bridge under dynamic loading. The physical experiments are carried out to monitor the response of the bridge under the dynamic loading from the movement of a 35.5 Metric Tonne (MT) vehicle weight at varying speeds. Numerical simulation of the bridge structure is also carried out using modal analysis and transient analysis methods. The response of the bridge is simulated for varying dynamic load conditions generated through vehicle speed ranging from 30 km/h to 87 km/h and the vehicle weight ranging from 35.5 MT to 106.5 MT. A generic equation for a simply supported bridge for varying speed and vehicle weight is proposed. This study, however, has the limitation of ignoring the influence of uncontrolled environmental factors such as humidity, temperature, and wind effects on bridge dynamic behaviours.
Exploring postpartum women's experiences, perspectives, and expectations in maternal health care at a Chinese maternity care center: A qualitative study
Authors: Zhang Xiaoqian , Liu Yang , Li Tang , Buntinx Frank , Vermandere Mieke 
Published: April 2024

Abstract

Background
The postpartum period presents critical challenges for new mothers, involving significant physical and emotional adjustments. Traditionally, Chinese culture has addressed this phase through "doing the month," emphasizing rest and specific cultural practices. With the shift towards modernization, urban China has seen a rise in professional maternal care centers, yet research on the experiences and expectations of women utilizing these services remains scarce. This study focuses on exploring the experiences, perspectives, and expectations of postpartum women in a Chinese maternal care center, assessing the integration of traditional practices with modern healthcare standards.
Methods
In this study, 16 participants who had utilized the hospital's maternal care center within the previous year were chosen via purposive sampling in Qingdao, China. Data were collected through semi-structured in-depth interviews and analyzed employing The Qualitative Analysis Guide of Leuven (QUAGOL).
Results
Analysis revealed three main themes: co-created health management, shared knowledge and growth, and interpersonal connections and support. The study highlighted the benefits of a collaborative approach to healthcare, where personalized care and maternal autonomy were prioritized. It also pointed out the educational aspect of motherhood, emphasizing the role of community support and shared experiences in knowledge acquisition. Furthermore, the study highlighted the positive impact of social dynamics, such as family, peer, and professional support, emphasizing their crucial contribution to the postpartum recovery process.
Conclusion
The research shed light on the intricate balance between traditional Chinese postpartum practices and modern medical care, underlining the necessity for a holistic approach that caters to the physical, emotional, and social well-being of new mothers. The findings advocate for co-created health management, emphasizing the role of shared knowledge and interpersonal support in enhancing postpartum care. Additionally, the impact of cultural and societal elements on postpartum experiences calls for culturally sensitive healthcare practices. This study contributes valuable insights into improving postpartum care in China, suggesting an integrated approach that incorporates both traditional and contemporary healthcare practices to support the holistic well-being of new mothers. Future studies are encouraged to expand the research scope to encompass a broader array of experiences and to utilize quantitative methods for further validation.
Anti Thyroglobulin Interferences in TSH Measurement, A Case Report
Authors: Bakhtiari Sajjad 
Published: April 2024

Abstract

Thyroglobulin (Tg) is the most expressed protein in the thyroid gland that stores iodine and produces thyroid hormones. Tg production is specific to malignant thyroid cells, making it an ideal indicator after removing thyroid tissue. As a result, it is used as a tumor marker post-surgery to assess any disease recurrence. The presence of Thyroglobulin antibody (TgAb) which is produced in autoimmune diseases can interfere with Tg value. The evaluation of plasma TSH levels is a highly sensitive diagnostic method for primary thyroid-related issues, although it can be affected by various interferences. in the present case, the high titer of TgAb interferes with TSH measurement as well as leads to unreliable Tg results.
Cultural and Creative Employment Across Italian Regions
Authors: Leogrande Angelo 
Published: April 2024

Abstract
in the following article I analyze the trend of cultural and creative employment in the Italian regions between 2004 and 2022 through the use of ISTAT-BES data. After presenting a static analysis, I also present the results of the clustering analysis aimed at identifying groupings between Italian regions. Subsequently, an econometric model is proposed for estimating the value of cultural and creative employment in the Italian regions. Finally, I compare various machine learning models for predicting the value of cultural and creative employment. The results are critically discussed through an economic policy analysis.
JEL CODE: O3, O31, O32, O33, O34
From Microscope to AI: Developing an Integrated Diagnostic System for Endometrial Cytology
Authors: Terasaki Mika , Tanaka Shun , Shimokawa Ichito , Toda Etsuko , Takakuma Shoichiro , Tabata Ryo , Sakae Kensuke , Kajimoto Yusuke , Kunugi Shinobu , Shimizu Akira , Terasaki Yasuhiro 
Published: April 2024

Abstract

Objective
To explore the integration of artificial intelligence (AI)-assisted diagnostics into a cytology workflow, focusing on real-time detection of abnormal cell clusters in endometrial cytology without relying on whole-slide imaging (WSI), utilizing a YOLOv5x-based model.
Methods
We employed the YOLOv5x object detection model pretrained on the COCO dataset because of its high-speed and accurate detection capabilities. This study involved real-time direct detection of abnormal cell clusters using a CCD camera attached to a microscope, with the aim of enhancing diagnostic efficiency and accuracy in endometrial cytology. The model was further refined through transfer learning using actual cytology case images, emphasizing the need for a delicate balance between technological advancement and clinical integration.
Results
The integration of our AI model into the diagnostic workflow significantly reduced the time required for diagnosis compared to traditional methods, as demonstrated by the performance metrics that matched or exceeded those of pathologists. This breakthrough underscores the potential of AI to improve diagnostic workflows, particularly in settings where resources or pathology services are limited.
Conclusion
This study presents the first instance of an AI-assisted system for endometrial cytology that operates in real time under a microscope, negating the need for WSI. Our findings highlight the feasibility of embedding AI directly into existing clinical practices, offering significant time savings and potentially matching the diagnostic accuracy of specialists. The successful integration of this technology is a critical step forward in the application of AI in the medical field, paving the way for broader adoption and further research into user-friendly AI applications in pathology diagnostics.
An overview of lattice results for parton distribution functions
Authors: Del Debbio Luigi 
Published: April 2024

Following a ground-breaking proposal by Ji [Phys. Rev. Lett. 110, 262002 (2013)], numerical simulations of Quantum Chromo Dynamics (QCD) on a Euclidean lattice have provided new, valuable information on the structure of hadrons. In this talk, we briefly review the lattice approach to the reconstruction of parton densities, highlighting the connection between lattice observables and parton densities, with a focus on theoretical issues. Since parton distributions are extracted from lattice data by solving an inverse problem, we discuss some of the difficulties that affect these determinations and how they can be formulated in a Bayesian framework.
Climate change-induced spatiotemporal variations of land use land cover by using multitemporal satellite imagery analysis
Authors: Ahmad Izhar , Waseem Muhammad , Hussain Sadaquat , Leta Megersa Kebede 
Published: April 2024

ABSTRACT
This study examines Islamabad's landscape changes over four decades, attributing land degradation to shifts in land use and cover. Using Landsat imagery from 1980 to 2023, it analyzes urban growth in five categories. By employing the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI), it notes built-up areas expanding to 61% by 2023, agricultural land contraction, and fluctuating forest cover. Water bodies and bare land decrease significantly. With high accuracy values, NDVI fluctuates from +0.4523 in 1980 to +0.1596 in 2010, rebounding to +0.4422. Fluctuations in barren soil, vegetation, and built-up areas potentially contribute to temperature and rainfall changes. The study explores LULC and land surface temperature correlation. Surveyed respondents (755) express concerns about environmental changes, anticipating reduced rainfall and increased drought. Valuable for sustainable development goals, the study informs policy formulation for effective urban planning and land use control.
Why have SGLT2 Inhibitors Failed to Achieve the Desired Success in
COVID-19?
Authors: Cumhur Cure Medine , Cure Erkan 
Published: April 2024

Abstract::
The SARS-CoV-2 virus emerged towards the end of 2019 and caused a major worldwide pandemic
lasting at least 2 years, causing a disease called COVID-19. SARS-CoV-2 caused a severe infection with direct
cellular toxicity, stimulation of cytokine release, increased oxidative stress, disruption of endothelial structure,
and thromboinflammation, as well as angiotensin-converting enzyme 2 (ACE2) down-regulation-mediated
renin-angiotensin system (RAS) activation. In addition to glucosuria and natriuresis, sodium-glucose transport
protein 2 (SGLT2) inhibitors (SGLT2i) cause weight loss, a decrease in glucose levels with an insulin-independent
mechanism, an increase in erythropoietin levels and erythropoiesis, an increase in autophagy and lysosomal
degradation, Na+/H+-changer inhibition, prevention of ischemia/reperfusion injury, oxidative stress
and they have many positive effects such as reducing inflammation and improving vascular function. There
was great anticipation for SGLT2i in treating patients with diabetes with COVID-19, but current data suggest
they are not very effective. Moreover, there has been great confusion in the literature about the effects of
SGLT2i on COVID-19 patients with diabetes . Various factors, including increased SGLT1 activity, lack of angiotensin
receptor blocker co-administration, the potential for ketoacidosis, kidney injury, and disruptions in
fluid and electrolyte levels, may have hindered SGLT2i's effectiveness against COVID-19. In addition, the duration
of use of SGLT2i and their impact on erythropoiesis, blood viscosity, cholesterol levels, and vitamin D
levels may also have played a role in their failure to treat the virus. This article aims to uncover the reasons for
the confusion in the literature and to unravel why SGLT2i failed to succeed in COVID-19 based on some solid
evidence as well as speculative and personal perspectives.
Skin Microbial Composition and Genetic Mutation Analysis in Precision
Medicine for Epidermolysis Bullosa
Authors: Syafarina Inna , Mazaya Maulida , Indrawati Ariani , Akbar Sharfina Zahra , Sukowati Caecilia , Sadikin Rifki 
Published: April 2024

Abstract:
Epidermolysis bullosa (EB) is an inherited skin disease representing a spectrum of rare
genetic disorders. These conditions share the common trait that causes fragile skin, resulting in the
development of blisters and erosions. The inheritance follows an autosomal pattern, and the array
of clinical presentations leads to significant physical suffering, considerable morbidity, and mortality.
Despite EB having no cure, effectively managing EB remains an exceptional challenge due to
its rarity and complexity, occasionally casting a profound impact on the lives of affected individuals.
Considering that EB management requires a multidisciplinary approach, this sometimes
worsens the condition of patients with EB due to inappropriate handling. Thus, more appropriate
and precise treatment management of EB is essentially needed. Advanced technology in medicine
and health comes into the bioinformatics era. Including treatment for skin diseases, omics-based
approaches aim to evaluate and handle better disease management and treatment. In this work, we
review several approaches regarding the implementation of omics-based technology, including genetics,
pathogenic mutation, skin microbiomics, and metagenomics analysis for EB. In addition,
we highlight recent updates on the potential of metagenomics analysis in precision medicine for
EB.
An Efficient Approach for Diabetes Classification Using Feature Selection and Hyperparameter Tuning
Authors: Lohani Bhanu Prakash , Dagur. Arvind , Shukla Dhirendra 
Published: April 2024

Background:
Diabetes mellitus, stemming from insulin deficiency or resistance, poses
acute and chronic health issues driven by factors like age, obesity, genetics, and lifestyle. It significantly
impacts health, leading to conditions like heart disease, vision problems, and kidney dysfunction,
with a notable mortality rate reported by the WHO in 2019. The modern diet has escalated
diabetes risk. Machine learning techniques play a pivotal role in disease prediction, aiding
timely interventions.

Objective:
The primary aim of this research work is to explore and contrast the effectiveness of
various existing machine-learning models for diabetes disease classification. The goal is to identify
the optimal solution that yields the highest accuracy.

Methods:
In the initial phase, we implemented data pre-processing, followed by the application of
a diverse range of machine learning methods to classify diabetes mellitus. Subsequently, a comprehensive
analysis was conducted on machine learning algorithms, considering both the complete
dataset features and those selected through Particle Swarm Optimization (PSO). The assessment
covered various metrics such as accuracy score, precision, F1 score, and log loss for Support Vector
Classifier (SVC), K-Nearest Neighbours (KNN), Random Forest (RF), ADA Boost, XG Boost,
Extra Tree, and Decision Tree. Ultimately, the introduction of hyperparameter tuning was aimed
at enhancing performance and attaining the highest level of accuracy.

Results:
The proposed model HSVC combines the Particle Swarm Optimization (PSO) feature
selection strategy with optimized hyperparameters, showcasing outstanding performance and
achieving an accuracy of 98.66%.

Conclusion:
The models developed in this study can potentially be applied or recommended for
the classification of other health conditions in different domains, such as Parkinson’s disease,
heart disease, and many more.

conclusion:
The use of machine learning (ML) techniques is highly regarded for its effectiveness in disease diagnosis. By detecting diseases early, patients can receive prompt medical attention, leading to improved health outcomes. This study examines several ML classification models for predicting diabetes in patients, with a focus on their accuracy. After examining several ML classification models, we have concluded that SVM is providing the best result on the chosen dataset. Then we have applied the concept of hyper tuning parameter with SVM and the improved accuracy was 98.66% for diabetes classification. The models developed in this study can potentially
The effect of knee replacement on renal function in patients with hypertension and type 2 diabetes mellitus
Authors: Eliseyeva L. N. , Tikhomirova N. Yu. , Zhdamarova O. I. , Kartashova S. V. , Malkhasyan I. G. 
Published: April 2024

Objective: to evaluate the effect of knee replacement on kidney function in patients with hypertension and type 2 diabetes mellitus. Materials and methods: at stage 1, 308 people suffering from hypertension (GB), stage III osteoarthritis of the knee joint (OA CS), type 2 diabetes mellitus (DM) were examined. GB occurred in 308 people, DM — in 66 people, and OA CS — in 216 people. Depending on the presence of OA CS, observation and comparison groups were identified. Within each group, subgroups are distinguished by the presence of SD: subgroup 1 (n=170) — OA CS +GB, subgroup 2 (n=46) — OA CS+GB+SD, subgroup 3 (n=72) — GB, subgroup 4 (n=20) — GB+SD. At stage 2, the dynamics of GFR after total knee arthroplasty (TEP CS) in subgroups 1 and 2 and against the background of AGT therapy in subgroups 3 and 4 were evaluated. Results: at the 1st stage, it was found that the proportion of people with GFR 60 ml/min/1.73 m2 was significantly higher in subgroup 1 (OA CS+GB) compared with subgroup 3 of patients with GB without joint pathology (40.6% vs. 12.5%, p0.01). The presence of DM in patients with GB significantly affected renal filtration function, which is reflected in an increase in serum creatinine levels in subgroup 4 (GB+DM) by 20.02% and a decrease in GFR by 15.35%.Against the background of the performed TEP in the group with GB, 64.06% had a stable GFR value or its increase, and in the subgroup of GB+ DM after TEP, 72.41% of patients had an increase or stable state of renal filtration capacity. At the same time, during the same follow-up period in the comparison group (in patients with GB without comorbid conditions), only 39.13% of cases had stable GFR values, and a decrease in GFR was recorded in 60.87%. At the same time, the dynamics of GFR values manifested itself in an increase of 11.19% in patients of subgroup 5 (TEP CS + GB) and by 12.47% in the group of the described knee joint intervention with a combination of GB and DM. In patients with controlled hypertension, GFR decreased by 6.6%. Conclusion: knee replacement in patients with hypertension and type 2 diabetes mellitus can slow down the rate of GFR reduction.