bluuebunny/crossref_metadata_2025_split huggingface dataset:
CP_DeepNet: a novel automated system for COVID-19 and pneumonia detection through lung X-rays
Authors: Mehmood Muhammad Hamza , Hassan Farman , Rahman Auliya Ur , Khan Wasiat , Mostafa Samih M. , Ghadi Yazeed Yasin , Algarni Abdulmohsen , Ali Mudasser 
Published: March 2024

AbstractIn recent years, the COVID-19 outbreak has affected humanity across the globe. The frequent symptoms of COVID-19 are identical to the normal flu, such as fever and cough. COVID-19 disseminates rapidly, and it has become a prominent cause of mortality. Nowadays, the new wave of COVID-19 has created significant impacts in China. This virus can have detrimental effects on people of all ages, particularly the elderly, due to their weak immune systems. The real-time polymerase chain reaction (RT-PCR) examination is typically performed for the identification of coronavirus. RT-PCR is an expensive and time requiring method, accompanied by a significant rate of false negative detections. Therefore, it is mandatory to develop an inexpensive, fast, and reliable method to detect COVID-19. X-ray images are generally utilized to detect diverse respiratory conditions like pulmonary infections, breathlessness syndrome, lung cancer, air collection in spaces of the lungs, etc. This study has also utilized a chest X-ray dataset to identify COVID-19 and pneumonia. In this research work, we proposed a novel deep learning model CP_DeepNet, which is based on a pre-trained deep learning model such as SqueezeNet, and further added three blocks of convolutional layers to it for assessing the classification efficacy. Furthermore, we employed a data augmentation method for generating more images to overcome the problem of model overfitting. We utilized COVID-19 radiograph dataset for evaluating the performance of the proposed model. To elaborate further, we obtained significant results with accuracy of 99.32%, a precision of 100%, a recall of 99%, a specificity of 99.2%, an area under the curve of 99.78%, and an F1-score of 99.49% on CP_DeepNet for the binary classification of COVID-19 and normal class. We also employed CP_DeepNet for the multiclass classification of COVID-19, pneumonia, and normal person, in which CP_DeepNet achieved accuracy, precision, recall, specificity, area under curve, and F1-score of 99.62%, 99.79%, 99.52%, 99.69, 99.62, and 99.72%, respectively. Comparative analysis of experimental results with different preexisting techniques shows that the proposed model is more dependable as compared to RT-PCR and other prevailing modern techniques for the detection of COVID-19.
Low Cubilin/Myeloperoxidase ratio as a promising biomarker for prognosis of high-grade T1 bladder cancer
Authors: Silva Medeiros Mariana , Botelho de Carvalho Luís André , Alves Marta , Papoila Ana , Baptista Carreira dos Santos Hugo Miguel , Capelo-Martínez José-Luis , Viegas de Campos Pinheiro Luís Manuel 
Published: March 2024

Abstract
Purpose
T1 bladder cancer is known for its high progression and recurrence rates. Identifying aggressive tumours at the non-muscle-invasive stage is crucial to allow early interventions and subsequently increase patient survival. This study aimed to investigate the potential of the cubilin/myeloperoxidase (CUBN/MPO) ratio as a high-grade T1 bladder cancer biomarker.

Methods
Urine samples were collected from 30 patients who underwent transurethral resection of the tumour with high-grade T1 bladder cancer (June 2015 to December 2019) before surgery. The urinary proteome was analysed using high-resolution mass spectrometry and the CUBN/MPO ratio was calculated. The primary outcome was the recurrence during the follow-up (around 31.5 months after resection). Univariate Cox regression and Kaplan–Meier curves were used for data analysis.

Results
Patients with a low CUBN/MPO ratio exhibited upregulated MPO and/or downregulated CUBN. This group of patients had a higher incidence of disease recurrence and progression. Low CUBN/MPO ratio was significantly associated with a higher likelihood of recurrence, progression, and death. It is worth noting that this study was exploratory and conducted on a small sample size, so further research is needed to validate these findings in larger cohorts.

Conclusion
This study highlights the potential of the CUBN/MPO ratio as a prognostic biomarker for high-grade T1 bladder cancer.
The Functional Study of Response Regulator ArlR Mutants in Staphylococcus Aureus
Authors: Zhou Jinhong , Refat Moath , Guo Yucheng , Zhang Jiaxin , Jiao Min , He Wenbo , He Xiaoyu , Rabie Mai A. , Ouyang Zhenlin , Zheng Fang 
Published: March 2024

AbstractStaphylococcus aureus is a major cause of hospital-associated infections worldwide. The organism’s ability to form biofilms has led to resistance against current treatment options such as beta-lactams, glycopeptides, and daptomycin. The ArlRS two-component system is a crucial regulatory system necessary for S. aureus autolysis, biofilm formation, capsule synthesis, and virulence. This study aims to investigate the role of the arlR deletion mutant in the detection and activation of S. aureus. We created an arlR deleted mutant and complementary strains and characterized their impact on the strains using partial growth measurement. The quantitative real-time PCR was performed to determine the expression of icaA, and the microscopic images of adherent cells were captured at the optical density of 600 to determine the primary bacterial adhesion. The biofilm formation assay was utilized to investigate the number of adherent cells using crystal violet staining. Eventually, the Triton X-100 autolysis assay was used to determine the influence of arlR on the cell autolytic activities. Our findings indicate that the deletion of arlR reduced the transcriptional expression of icaA but not icaR in the ica operon, leading to decrease in polysaccharide intercellular adhesin (PIA) synthesis. Compared to the wild-type and the complementary mutants, the arlR mutant exhibited decreased in biofilm production but increased autolysis. It concluded that the S. aureus response regulatory ArlR influences biofilm formation, agglutination, and autolysis. This work has significantly expanded our knowledge of the ArlRS two-component regulatory system and could aid in the development of novel antimicrobial strategies against S. aureus.
An innovative approach for investigation of overpressure due to hydrocarbon generation: a regional study on Kazhdumi formation, South-western Zagros Basin, Iran
Authors: Sadeghtabaghi Zahra , Kadkhodaie Ali , Mehdipour Vali , Kadkhodaie Rahim 
Published: March 2024

AbstractOverpressure is always considered as a severe problem in the oil industry. Besides creating life risks through serious accidents while drilling, failure to correctly identify high-pressure intervals causes a significant increase in costs and prolongs the drilling process. Kazhdumi Formation in the Abadan Plain is considered as a high-pressure formation in several wells. Various reasons can cause overpressure problem. Hydrocarbon generation is one cause of abnormal pressure in source rocks. Understanding hydrocarbon generation potential can be a helpful approach since the Kazhdumi Formation is considered as a probable source rock in this area. In this paper, in order to better understand the problem of abnormal pressure in Kazhdumi Formation, geochemical concepts and tools have been applied. To that way, 1D petroleum system modeling of five wells was done, and the thermal maturity level of Kazhdumi Formation was determined and then, compared with drilling records. The results indicate that in wells where this formation has sufficient organic matter and has reached an early mature level, there is an abnormal pressure problem. Otherwise, this formation does not show abnormal pressure. Also, geochemical data are not available in all drilled wells, which makes impossible the assessing of hydrocarbon generation role. Therefore, petrophysical well logs (sonic (DT), neutron (NPHI), density (RHOB), spectral gamma ray (SGR), and resistivity (RES)) as well as 137 sets of geochemical data belonging to 13 wells from 7 oilfields in the Abadan Plain were used to predict geochemical indicators. Using artificial neural networks, geochemical data of a well in Abadan Plain were predicted. This selected well has a high-pressure problem in Kazhdumi Formation, but no geochemical data are available in this well. The results of predicted geochemical data show that the high-pressure phenomenon in this well may also be due to hydrocarbon generation. The precise understanding of the abnormal pressure, resulting from hydrocarbons generation, requires comprehensive studies and a full investigation of the studied area. However, the results of this paper help to predict approximately the behavior of the source rocks before drilling. Acquiring this overview will aid in reducing drilling hazards and costs.
Changes in cardiac functions in patients treated with parathyroidectomy for secondary hyperparathyroidism
Authors: Benli Sami , Yesil Emrah , Tazeoglu Deniz , Ozcan Cumhur , Ozcan Ismail Turkay , Dag Ahmet 
Published: March 2024

AbstractOur study aims to investigate the changes in cardiac functions, especially myocardial performance index (MPI), in patients who underwent parathyroidectomy for secondary hyperparathyroidism. Patients who underwent parathyroidectomy for secondary hyperparathyroidism between June 2010 and September 2021 were analyzed retrospectively. The patients were divided into two groups: those who underwent total parathyroidectomy (group 1) and those who underwent subtotal parathyroidectomy (group 2). The groups were compared according to the echocardiogram findings performed in the preoperative period and the postoperative sixth month. In addition, cardiac structure, and systolic and diastolic functions, especially myocardial performance index, were evaluated by echocardiography and Doppler imaging. Thirty-seven patients were examined; 16 (43.2%) underwent total parathyroidectomy, and 21 (56.8%) had subtotal parathyroidectomy performed. Group 1’s mean left ventricular end-systolic diameter (LVES) decreased from 2.53 ± 0.57 to 2.35 ± 0.37 cm after parathyroidectomy. In Group 1, the postoperative value of LVES and end-systolic volume decreased significantly compared to the preoperative period (p = 0.042, p = 0.008, respectively). EF increased from 59.25 ± 0.05 to 67.81 ± 4.04. In Group 1, EF and EV postoperatively increased significantly compared to the preoperative period (p = 0.023, p = 0.021, respectively). The mean MPI decreased from 0.45 ± 0.07 to 0.39 ± 0.04 after parathyroidectomy in group 1. In group 2, it decreased from 0.46 ± 0.06 to 0.40 ± 0.04 (p < 0.001). The present study provides an improvement in myocardial functions after parathyroidectomy. While LVES, EF, ejection volume, end-systolic volume, and MPI improved in both groups, the MPI improvement was more evident in the total parathyroidectomy group.
Graphical Abstract
Estimates for low Steklov eigenvalues of surfaces with several boundary components
Authors: Perrin Hélène 
Published: March 2024

R\'esum\'eIn this article, we give computable lower bounds for the first non-zero Steklov eigenvalue $$\sigma _1$$

σ
1

of a compact connected 2-dimensional Riemannian manifold M with several cylindrical boundary components. These estimates show how the geometry of M away from the boundary affects this eigenvalue. They involve geometric quantities specific to manifolds with boundary such as the extrinsic diameter of the boundary. In a second part, we give lower and upper estimates for the low Steklov eigenvalues of a hyperbolic surface with a geodesic boundary in terms of the length of some families of geodesics. This result is similar to a well known result of Schoen, Wolpert and Yau for Laplace eigenvalues on a closed hyperbolic surface.
Exploring the Intersection of Artificial Intelligence and Microgrids in Developing Economies: A Review of Practical Applications
Authors: Bodewes William , de Hoog Julian , Ratnam Elizabeth L. , Halgamuge Saman 
Published: March 2024

Abstract
Purpose of Review
This paper reviews practical challenges for microgrid electrification projects in low- and middle-income economies, proposing a Social-Technical-Economic-Political (STEP) framework. With our STEP framework, we review recent Artificial Intelligence (AI) methods capable of accelerating microgrid adoption in developing economies.

Recent Findings
Many authors have employed novel AI methods in microgrid applications including to support energy management systems, fault detection, generation sizing, and load forecasting. Despite these research initiatives, limited works have investigated the specific challenges for developing economies. That is, high-income countries often have high-quality power, reliable wireless communication infrastructure, and greater access to equipment and technical skills. Accordingly, there are numerous opportunities for the adaptation of AI methods to meet the constraints of developing economies.

Summary
In this paper, we provide a comprehensive review of the electrification challenges in developing economies alongside an assessment of novel AI approaches for microgrid applications. We also identify emerging opportunities for AI research in the context of developing economies and our proposed STEP framework.
Long-term pasireotide therapy in acromegaly: extensive real-life experience of a referral center
Authors: Pirchio R. , Auriemma R. S. , Vergura A. , Pivonello R. , Colao A. 
Published: March 2024

Abstract
Purpose
Pasireotide is a novel therapeutic option for patients with acromegaly resistant to first-generation somatostatin receptor ligands. To date, real-life data are still scant, therefore, the aim of the current study is to evaluate the impact of long-term pasireotide therapy on disease control, pituitary tumor size, gluco-insulinemic and lipid profile in a real-life setting.

Methods
Retrospective study of data prospectively collected, evaluating hormonal, tumoral, and metabolic data of 28 patients with acromegaly administered with pasireotide in a pituitary tertiary referral center.

Results
Within the first 12 months of treatment, 70.4% of patients achieved normal IGF-I levels, which was maintained at 36-month evaluation in these responders patients. Patients who started with pasireotide 60 mg monthly exhibited significantly lower IGF-I levels after 36 months (p = 0.05) as compared to patients administered first with pasireotide 20 or 40 mg monthly. The maximal tumoral diameter was significantly decreased after 12 months of pasireotide (p < 0.001) and a further reduction was registered throughout the following months, with 41.2% of patients achieving a significant reduction (> 25% of baseline measurement) after 36 months of treatment. Fasting glucose significantly increased during the first 6 months (p < 0.001) with a gradual rise in diabetes prevalence during the following months, resulting diabetes prevalence after 36 months of pasireotide significantly increased compared to baseline (p = 0.003), although with glycated hemoglobin levels within the normal range. Diabetes was managed using oral glucose-lowering drugs or glucagon-like peptide 1 agonists, with no patient requiring insulin therapy. Pasireotide improved lipid profile, mainly during the first 12 months of treatment, by increasing HDL and decreasing triglycerides levels.

Conclusion
Pasireotide is effective and safe in the long-term. Hyperglycemia is a common event and is manageable even without insulin treatment.
The Unseen Threat of the Synergistic Effects of Microplastics and Heavy Metals in Aquatic Environments: A Critical Review
Authors: Adeleye Aderemi Timothy , Bahar Md Mezbaul , Megharaj Mallavarapu , Fang Cheng , Rahman Mohammad Mahmudur 
Published: March 2024

Abstract
Purpose of Review
The synergistic effects of microplastics (MPs) and heavy metals are becoming major threats to aquatic life and human well-being. Therefore, understanding synergistic interactions between MPs and heavy metals is crucial to comprehend their environmental impacts.

Recent Findings
The mechanisms such as electrostatic attraction, surface interactions, ion exchange, hydrogen bonding, hydrophobic forces, and π–π interactions behind the synergistic effects of MPs and heavy metals were critically reviewed and justified. In addition, the roles of surface chemistry in these interactions were also emphasized. Finally, efficient remediation techniques aligning with a circular economy-based initiative to promote sustainable solutions were recommended to mitigate plastic-heavy metal pollution to achieve a cleaner environment.

Summary
This review examines the combined impact of MPs and heavy metals in aquatic ecosystems, detailing their mechanistic interactions, and consequences with proposed sustainable solutions. Additionally, this review highlights the MP-heavy metal contamination risks and emphasizes the need for further research to safeguard aquatic life and human health.
Sensitivity Improvements for Picosecond Ultrasonic Thickness Measurements in Gold and Tungsten Nanoscale Films
Authors: Dong Jiaqi , Yao Chengyuan , Zhu Yuanhao , Li Shaojie , Liu Bowen , Fan JinTao , Hu Chunguang , Song Youjian , Hu Minglie 
Published: March 2024

AbstractPicosecond ultrasonics, as a nondestructive and noncontact method, can be employed for nanoscale metallic film thickness measurements. The sensitivity of the system, which determines the measurement precision and practicability of this technique, is often limited by the weak intensity of the ultrasonic signal. To solve this problem, we investigate the distinct mechanisms involved in picosecond ultrasonic thickness measurement for two types of metals, namely tungsten (W) and gold (Au). For thickness measurement in W films, theory and simulation show that optimizing the pump and probe laser wavelengths, which determine the intensity and shape of the ultrasonic signal, is critical to improving measurement sensitivity, while for Au film measurements, where acoustic-induced beam distortion is dominant, the signal intensity can be optimized by selecting an appropriate aperture size and sample position. The above approaches are validated in experiments. A dual-wavelength pump–probe system is constructed based on a passively mode-locked ytterbium-doped fiber laser. The smoothing method and multipeak Gaussian fitting are employed for the extraction of ultrasonic time-of-flight. Subnanometer measurement precision is achieved in a series of W and Au films with thicknesses of 43–750 nm. This work can be applied to various high-precision, noncontact measurements of metal film thickness in the semiconductor industry.
What patient-reported outcome measures may be suitable for research involving older adults with frailty? A scoping review
Authors: Long S. O. , Hope S. V. 
Published: March 2024

Abstract
Introduction
The need to develop and evaluate frailty-related interventions is increasingly important, and inclusion of patient-reported outcomes is vital. Patient-reported outcomes can be defined as measures of health, quality of life or functional status reported directly by patients with no clinician interpretation. Numerous validated questionnaires can thus be considered patient-reported outcome measures (PROMs). This review aimed to identify existing PROMs currently used in quantitative research that may be suitable for older people with frailty.

Method
PubMed and Cochrane were searched up to 24/11/22. Inclusion criteria were quantitative studies, use of a PROM, and either measurement of frailty or inclusion of older adult participants. Criteria were created to distinguish PROMs from questionnaire-based clinical assessments. 197 papers were screened. PROMs were categorized according to the domain assessed, as derived from a published consensus ‘Standard Set of Health Outcome Measures for Older People’.

Results
88 studies were included. 112 unique PROMs were used 289 times, most frequently the SF-36 (n = 21), EQ-5D (n = 21) and Barthel Index (n = 14). The most frequently assessed outcome domains included Mood and Emotional Health and Activities of Daily Living, with fewer assessments of Participation in Decision-Making and Carer Burden.

Conclusions
PROM usage in frailty research is highly heterogeneous. Frequently used PROMs omit important outcomes identified by older adults. Further research should evaluate the importance of specific outcomes and identify PROMs relevant to people at different stages of frailty. Consistent and appropriate PROM use in frailty research would facilitate more effective comparisons and meaningful evaluation of frailty interventions.
Training environmental sound classification models for real-world deployment in edge devices
Authors: Goulão Manuel , Bandeira Lourenço , Martins Bruno , L. Oliveira Arlindo 
Published: March 2024

AbstractThe interest in smart city technologies has grown in recent years, and a major challenge is to develop methods that can extract useful information from data collected by sensors in the city. One possible scenario is the use of sound sensors to detect passing vehicles, sirens, and other sounds on the streets. However, classifying sounds in a street environment is a complex task due to various factors that can affect sound quality, such as weather, traffic volume, and microphone quality. This paper presents a deep learning model for multi-label sound classification that can be deployed in the real world on edge devices. We describe two key components, namely data collection and preparation, and the methodology to train the model including a pre-train using knowledge distillation. We benchmark our models on the ESC-50 dataset and show an accuracy of 85.4%, comparable to similar state-of-the-art models requiring significantly more computational resources. We also evaluated the model using data collected in the real world by early prototypes of luminaires integrating edge devices, with results showing that the approach works well for most vehicles but has significant limitations for the classes “person” and “bicycle”. Given the difference between the benchmarking and the real-world results, we claim that the quality and quantity of public and private data for this type of task is the main limitation. Finally, all results show great benefits in pretraining the model using knowledge distillation.
Reviewing the frontier: modeling and energy management strategies for sustainable 100% renewable microgrids
Authors: Elazab Rasha , Dahab Ahmed Abo , Adma Maged Abo , Hassan Hany Abdo 
Published: March 2024

AbstractThe surge in global interest in sustainable energy solutions has thrust 100% renewable energy microgrids into the spotlight. This paper thoroughly explores the technical complexities surrounding the adoption of these microgrids, providing an in-depth examination of both the opportunities and challenges embedded in this paradigm shift. The review examines pivotal aspects, including intricate modelling methodologies for renewable energy sources, real-time energy management systems, and sophisticated strategies for navigating short-term uncertainties. Innovative approaches to real-time energy management are dissected for their potential to tune operational efficiency finely. Furthermore, the study investigates methodological frameworks to address short-term uncertainty, leveraging cutting-edge techniques such as machine learning, robust optimization, and information gap decision theory. Despite the pivotal role short-term uncertainty plays, it frequently occupies a subordinate position in research, eclipsed by the presumption of minimal economic impact. This study challenges this prevalent notion, underscoring the indispensable need for exhaustive research on uncertainty. Such comprehensive exploration is essential to ensure the practicality and sustainability of 100% renewable energy grids. The paper concludes by emphasizing the importance of addressing short-term uncertainty and providing nuanced insights that can facilitate the effective implementation and ongoing development of these grids within the dynamic landscape of electrical energy systems.