bluuebunny/crossref_metadata_2025 huggingface dataset:
The Long-Distance Transport of Some Plant Hormones and Possible Involvement of Lipid-Binding and Transfer Proteins in Hormonal Transport
Authors: Akhiyarova Guzel , Finkina Ekaterina I. , Zhang Kewei , Veselov Dmitriy , Vafina Gulnara , Ovchinnikova Tatiana V. , Kudoyarova Guzel
Published: February 2024

Adaptation to changes in the environment depends, in part, on signaling between plant organs to integrate adaptive response at the level of the whole organism. Changes in the delivery of hormones from one organ to another through the vascular system strongly suggest that hormone transport is involved in the transmission of signals over long distances. However, there is evidence that, alternatively, systemic responses may be brought about by other kinds of signals (e.g., hydraulic or electrical) capable of inducing changes in hormone metabolism in distant organs. Long-distance transport of hormones is therefore a matter of debate. This review summarizes arguments for and against the involvement of the long-distance transport of cytokinins in signaling mineral nutrient availability from roots to the shoot. It also assesses the evidence for the role of abscisic acid (ABA) and jasmonates in long-distance signaling of water deficiency and the possibility that Lipid-Binding and Transfer Proteins (LBTPs) facilitate the long-distance transport of hormones. It is assumed that proteins of this type raise the solubility of hydrophobic substances such as ABA and jasmonates in hydrophilic spaces, thereby enabling their movement in solution throughout the plant. This review collates evidence that LBTPs bind to cytokinins, ABA, and jasmonates and that cytokinins, ABA, and LBTPs are present in xylem and phloem sap and co-localize at sites of loading into vascular tissues and at sites of unloading from the phloem. The available evidence indicates a functional interaction between LBTPs and these hormones.
Pre-Admission Oral Clonidine to Reduce Severe Pre-Operative Anxiety in Pediatric Patients with Behavioral Disorders: A Case Series
Authors: Verdecchia Nicole , Nelson Ryan , White Shante , Cladis Franklyn
Published: February 2024

Controlling preoperative anxiety is necessary in pediatric patients to avoid adverse effects such as emergence delirium, behavioral problems, post-traumatic stress disorder, anxiety prior to future procedures, and increased analgesic doses in the recovery room. Some patients, especially ones with behavioral issues, have a difficult time arriving at the hospital. Medications given at home can be helpful. We describe a case series of six patients who received pre-admission oral clonidine prior to arrival to the hospital. The patients were all able to enter the hospital without difficulty and the families reported less anxiety and more cooperation subjectively compared with previous experiences. Transient intraoperative hypotension was a side effect of oral clonidine, with no long-term sequelae.
Monitoring Access to Child Medicines: Introducing a Standardized Set of Age-Appropriate Medicines
Authors: Joosse Iris R. , Mantel-Teeuwisse Aukje K. , Suleman Fatima , van den Ham Hendrika A.
Published: February 2024

Monitoring access to pediatric medicines as part of the Sustainable Development Goal (SDG) agenda for 2030 requires surveying age-appropriate medicines. This study aimed to develop tracer sets of essential age-appropriate medicines for use in SDG indicator 3.b.3 or in conjunction with other methodologies for monitoring access to medicines. Two sets of medicines were developed, one for young children (1 month to 5 years) and one for school-aged children (5–12 years). Priority diseases were selected based on the global burden of disease and linked to active ingredients of first choice according to treatment guidelines and the World Health Organization (WHO) Model List of Essential Medicines for Children (EMLc). To ensure clinical relevance, the Delphi technique was employed to identify areas of (dis)agreement among clinical pediatric experts. During two consultation rounds, experts were invited to indicate (dis)agreement. Five experts per age group were largely in agreement with the initial selections, but various therapeutic alternatives were suggested for addition. A second consultation round with five experts did not lead to major adjustments. The final sets included 26 treatment options for both groups. Specific age-appropriate formulations were selected from the WHO EMLc 2023. These two globally representative tracer sets of medicines consider the particular needs of children and could aid countries in the critical monitoring of accessibility to pediatric medicines.
Atmospheric Patterns in Porto Velho, Rondônia, Southwestern Amazon, in a Rhythmic Context between 2017 and 2018
Authors: Tejas Graziela T. , Nunes Dorisvalder D. , Souza Reginaldo M. S. , Querino Carlos A. S. , Faria Marlon R. , Floresta Daiana C. B. , Galvani Emerson , Watanabe Michel , Gobo João P. A.
Published: February 2024

This paper aims to analyze the weather conditions in Porto Velho (Rondonia, Brazil, Western Amazon) and the influence of air masses on the climatic elements between 2017 and 2018, using rhythmic analysis. Climatic data were obtained through the official weather station, tabulated and statistically organized, and processed in R Studio programming language. The monitoring of air masses occurred through the synoptic charts of the Navy Hydrography Center. The results were analyzed by dry–rainy transition season, rainy season, wet–dry transition season, and dry season. Thus, the results point out that the Tropical Continental mass (mTc) acted up to 62.9%, responsible for the low precipitation index in October 2017. Although the mass has characteristics of warm and unstable weather, it is even lower than the action of the mEc. In January 2018, there was an 85.5% prevalence of the Continental Equatorial Mass (mEc), added to the action of the South Atlantic Convergence Zone (ZCAS), which contributed to an accumulated rainfall of 443 mm/month. In April 2018, the mEC acted with 56.7%, reaching 35.5% in August. Another highlight was the performance of the Tropical Atlantic mass (mTa) (27.4%) and mTc (19.4%), both of which had a crucial role in the dry season, followed by the Polar Atlantic mass (mPa) (17.7%), that contributed to the phenomenon of “coldness” in the region. Therefore, the mEc is extremely important in the control of the relative humidity of the air and the precipitations, while the mTc is a dissipator of winds that, at times, inhibits the performance of the mEc.
A Comprehensive Review of Cathodic Arc Evaporation Physical Vapour Deposition (CAE-PVD) Coatings for Enhanced Tribological Performance
Authors: Muhammed Musa , Javidani Mousa , Ebrahimi Sadrabadi Tahere , Heidari Majid , Levasseur Tom , Jahazi Mohammad
Published: February 2024

In the realm of industries focused on tribology, such as the machining industry, among others, the primary objective has been tribological performance enhancement, given its substantial impact on production cost. Amid the variety of tribological enhancement techniques, cathodic arc evaporation physical vapour deposition (CAE-PVD) coatings have emerged as a promising solution offering both tribological performance enhancement and cost-effectiveness. This review article aims to systematically present the subject of CAE-PVD coatings in light of the tribological performance enhancement. It commences with a comprehensive discussion on substrate preparation, emphasizing the significant effect of substrate roughness on the coating properties and the ensuing tribological performance. The literature analysis conducted revealed that optimum tribological performance could be achieved with an average roughness (Ra) of 0.1 µm. Subsequently, the article explores the CAE-PVD process and the coating’s microstructural evolution with emphasis on advances in macroparticles (MPs) formation and reduction. Further discussions are provided on the characterization of the coatings’ microstructural, mechanical, electrochemical and tribological properties. Most importantly, crucial analytical discussions highlighting the impact of deposition parameters namely: arc current, temperature and substrate bias on the coating properties are also provided. The examination of the analyzed literature revealed that the optimum tribological performance can be attained with a 70 to 100 A arc current, a substrate bias ranging from −100 to −200 V and a deposition temperature exceeding 300 °C. The article further explores advancements in coating doping, monolayer and multilayer coating architectures of CAE-PVD coatings. Finally, invaluable recommendations for future exploration by prospective researchers to further enrich the field of study are also provided.
Study of Non-Transformable t’-YSZ by Addition of Niobia for TBC Application
Authors: Almeida Daniel Soares de , Neto Francisco Piorino , Henriques Vinicius André Rodrigues , Assis João Marcos Kruszynski de , Gonςalves Polyana Alves Radi , Takahashi Renata Jesuina , Reis Danieli Aparecida Pereira
Published: February 2024

The high toughness of zirconia is paving the way for the development of new materials for application in TBC for gas turbine blades. The main aim of this work was the obtainment of tetragonal zirconia polycrystalline (TZP) with high density, from mixtures of high-purity powder of zirconia, yttria, and niobia with different compositions (14.5 to 21 mol%), through the processes of cold pressing by uniaxial pressing and by isostatic pressing, followed by air sintering processes at 1550 °C for 1 h. The samples were characterized for phase composition by X-ray diffraction, Rietveld analysis, and morphology by transmission electron microscopy and energy dispersive spectroscopy analyses. Mechanical and tribological resistance was evaluated by fracture toughness and nanoindentation tests as well as Weibull statistics. The incorporation of yttria and niobia resulted in relatively denser ceramics with stabilization of the tetragonal phase which was confirmed by detailed X-ray diffraction analysis. Modified ceramics for TBC with 17.5 mol% of yttria and niobia showed higher hardness and fracture toughness, 16.16 GPa and 173.38 GPa, respectively. Through nano hardness measurements, it was possible to verify the effect of the samples’ ferroelasticity. Thus, the addition of niobia and yttria to zirconia represents an opportunity for the development of new materials with increasing mechanical and tribological resistance for TBC application.
Selection of PEG-Matrix Combinations to Achieve High Performance Form-Stable Phase Change Materials for Building Applications
Authors: Sarcinella Antonella , Frigione Mariaenrica
Published: February 2024

The construction sector’s pursuit of sustainability, driven by growing concerns about climate change and resource depletion, requires innovative solutions to reduce the energy consumption necessary to ensure thermal comfort in buildings. The introduction of phase change materials (PCMs) in construction elements represents an effective solution to these problems. PCMs are, in fact, able to regulate internal temperature by storing and releasing thermal energy during their phase transitions. In particular, polyethylene glycol (PEG)-based organic PCMs offer high heat storage capacity, compatibility with building materials, and minimal environmental impact. They are often used in building applications incorporated in an inert matrix, using the “form-stable method”. This article critically examines various matrices proposed in the existing literature to realize PEG-based PCMs, with the aim of analyzing their influence on the final characteristics of any PCM. In addition, an attempt to correlate the quantity of PEG with the heat stored and released by the PCM is presented, using a linear regression model applied to groups of matrices of the same chemical nature. The results of these analyses would, in fact, provide useful indications for an optimal choice of the PEG/matrix system capable of responding to specific application needs, particularly in the building sector.
Post-Wear Surface Morphology Assessment of Selective Laser Melting (SLM) AlSi10Mg Specimens after Heat Exposure to Different Gas Flames
Authors: Akhtar Maaz , Muzamil Muhammad , Samiuddin Muhammad , Alsaleh Naser , Khan Rashid , Khan Mahad Ali , Djuansjah Joy , Siddiqui Ali Khursheed , Majeed Arfan
Published: February 2024

The wear surface morphology of AlSi10Mg specimens, originally manufactured using selective laser melting (SLM), has been analyzed in the context of exposure to heat from gas flames. The first stage of the experimental work included the performance of surface heat-exposure on SLM-prepared specimens through oxyacetylene gas welding. Gas welding was utilized with three different flames, namely; reducing, neutral, and oxidizing on the as-built specimens of SLM. The post-surface-treated specimens were subjected to pin-on-disk wear testing against fixed parameters. After the performance of wear testing at two different radii, the mass loss of each of the four types of specimens was calculated including the three specimens exposed to heat along with the as-built specimens. The results showed that the maximum amount of mass losses at 24 mm and 30 mm radii belongs to the neutral flame specimens and the least was for the as-built condition specimens. Upon analysis, the heat-exposure specimens through all three types of gas flames resulted in an increase in the amount of mass in contrast to the as-built specimens. Moreover, the morphologies of the developed wear tracks at surfaces were examined using the scanning electron microscope (SEM) for the understating of the mechanism.
Effect of 3D-Printed Porous Titanium Alloy Pore Structure on Bone Regeneration: A Review
Authors: He Si , Zhu Jiang , Jing Yiwan , Long Shuai , Tang Lu , Cheng Lijia , Shi Zheng
Published: February 2024

As a biomedical material, porous titanium alloy has gained widespread recognition and application within the field of orthopedics. Its remarkable biocompatibility, bioactivity, and mechanical properties establish it as a promising material for facilitating bone regeneration. A well-designed porous structure can lower the material’s modulus while retaining ample strength, rendering it more akin to natural bone tissue. The progression of additive manufacturing (AM) technology has significantly propelled the advancement of porous implants, simplifying the production of such structures. AM allows for the customization of porous implants with various shapes and sizes tailored to individual patients. Additionally, it enables the design of microscopic-scale porous structures to closely mimic natural bone, thus opening up avenues for the development of porous titanium alloy bone implants that can better stimulate bone regeneration. This article reviews the research progress on the structural design and preparation methods of porous titanium alloy bone implants, analyzes the porous structure design parameters that affect the performance of the implant, and discusses the application of porous medical titanium alloys. By comparing the effects of the parameters of different porosity, pore shape, and pore size on implant performance, it was concluded that pore diameters in the range of 500~800 μm and porosity in the range of 70%–90% have better bone-regeneration effects. At the same time, when the pore structure is a diamond, rhombohedral, or cube structure, it has better mechanical properties and bone-regeneration effects, providing a reference range for the application of clinical porous implants.
Investigating Color-Blind User-Interface Accessibility via Simulated Interfaces
Authors: Jamil Amaan , Denes Gyorgy
Published: February 2024

Over 300 million people who live with color vision deficiency (CVD) have a decreased ability to distinguish between colors, limiting their ability to interact with websites and software packages. User-interface designers have taken various approaches to tackle the issue, with most offering a high-contrast mode. The Web Content Accessibility Guidelines (WCAG) outline some best practices for maintaining accessibility that have been adopted and recommended by several governments; however, it is currently uncertain how this impacts perceived user functionality and if this could result in a reduced aesthetic look. In the absence of subjective data, we aim to investigate how a CVD observer might rate the functionality and aesthetics of existing UIs. However, the design of a comparative study of CVD vs. non-CVD populations is inherently hard; therefore, we build on the successful field of physiologically based CVD models and propose a novel simulation-based experimental protocol, where non-CVD observers rate the relative aesthetics and functionality of screenshots of 20 popular websites as seen in full color vs. with simulated CVD. Our results show that relative aesthetics and functionality correlate positively and that an operating-system-wide high-contrast mode can reduce both aesthetics and functionality. While our results are only valid in the context of simulated CVD screenshots, the approach has the benefit of being easily deployable, and can help to spot a number of common pitfalls in production. Finally, we propose a AAA–A classification of the interfaces we analyzed.
Implementing Virtualization on Single-Board Computers: A Case Study on Edge Computing
Authors: Lambropoulos Georgios , Mitropoulos Sarandis , Douligeris Christos , Maglaras Leandros
Published: February 2024

The widespread adoption of cloud computing has resulted in centralized datacenter structures; however, there is a requirement for smaller-scale distributed infrastructures to meet the demands for speed, responsiveness, and security for critical applications. Single-Board Computers (SBCs) present numerous advantages such as low power consumption, low cost, minimal heat emission, and high processing power, making them suitable for applications such as the Internet of Things (IoT), experimentation, and other advanced projects. This paper investigates the possibility of adopting virtualization technology on Single-Board Computers (SBCs) for the implementation of reliable and cost-efficient edge-computing environments.The results of this study are based on experimental implementations and testing conducted in the course of a case study performed on the edge infrastructure of a financial organization, where workload migration was achieved from a traditional to an SBC-based edge infrastructure. The performance of the two infrastructures was studied and compared during this process, providing important insights into the power efficiency gains, resource utilization, and overall suitability for the organization’s operational needs.
Cast Microstructure and Crystallographic Features of Al3Sc Dendrites in High Sc-Contained Al-Sc Alloys
Authors: He Jinjiang , Jia Qian , Ding Zhaochong , Wang Xingquan , Cao Xiaomeng , Cao Ziqi , Gu Xinfu
Published: February 2024

Al-Sc alloys containing high Sc content are employed as sputtering targets for the fabrication of high-performance piezoelectric films during magnetic sputtering. Due to the high proportion of the Al3Sc phase, their workability is quite limited, and they are often used in the as-cast state. In this study, the crystallography of Al3Sc dendrites in as-casted Al-10at.%Sc and Al-20at.%Sc samples is examined using electron backscatter diffraction (EBSD). With increasing Sc content, the fraction of Al3Sc also increases. The Al3Sc dendrites exhibit a cubic relationship with the Al matrix in both alloys. However, in Al-10%Sc alloys, the facets of the Al3Sc dendrites are parallel to {001} planes, while twinning is observed in Al-20at.%. The twinning plane is parallel to the {111} plane, and the dendrite growth direction aligns with the <110> directions. The different morphologies of the dendrite structures in these two alloys are discussed in relation to thermodynamic and kinetic considerations based on the phase diagram and nucleation rate.
The Role of Diamonds Dispersed in Ferronematic Liquid Crystals on Structural Properties
Authors: Bury Peter , Veveričík Marek , Černobila František , Tomašovičová Natália , Lacková Veronika , Kónyová Katarína , Šafařík Ivo , Petrenko Viktor , Tomchuk Oleksandr , Timko Milan , Kopčanský Peter
Published: February 2024

A study of the role of diamond nanoparticles on 5CB liquid crystal composites with Fe3O4 nanoparticles is presented. Composite ferronematic systems based on the nematic liquid crystal 5CB doped with Fe3O4 magnetic nanoparticles and additionally bound to diamond nanoparticles (DNPs), of a volume concentration of 3.2 mg/mL, 1.6 mg/mL and 0.32 mg/mL, were investigated using both magneto-optical effect and surface acoustic waves (SAWs) to study the role of diamond nanoparticles on the structural properties of ferronematic liquid crystals. The responses of light transmission and SAW attenuation to an external magnetic field were investigated experimentally under a linearly increasing and decreasing magnetic field, respectively. Investigations of the phase transition temperature shift of individual composites were also performed. The experimental results highlighted a decrease in the threshold field in the ferronematic LC composites compared to the pure 5CB as well as its further decrease after mixing Fe3O4 with diamond powder. Concerning the transition temperature, its increase with an increase in the volume fraction of both kinds of nanoparticles was registered. The role of diamond nanoparticles in the structural changes and the large residual light transition and/or attenuation (memory effect) were also observed. The presented results confirmed the potential of diamond nanoparticles in nematic composites to modify their properties which could lead to final applications.
Complication Prediction after Esophagectomy with Machine Learning
Authors: van de Beld Jorn-Jan , Crull David , Mikhal Julia , Geerdink Jeroen , Veldhuis Anouk , Poel Mannes , Kouwenhoven Ewout A.
Published: February 2024

Esophageal cancer can be treated effectively with esophagectomy; however, the postoperative complication rate is high. In this paper, we study to what extent machine learning methods can predict anastomotic leakage and pneumonia up to two days in advance. We use a dataset with 417 patients who underwent esophagectomy between 2011 and 2021. The dataset contains multimodal temporal information, specifically, laboratory results, vital signs, thorax images, and preoperative patient characteristics. The best models scored mean test set AUROCs of 0.87 and 0.82 for leakage 1 and 2 days ahead, respectively. For pneumonia, this was 0.74 and 0.61 for 1 and 2 days ahead, respectively. We conclude that machine learning models can effectively predict anastomotic leakage and pneumonia after esophagectomy.
Progress of Multiparameter Magnetic Resonance Imaging in Bladder Cancer: A Comprehensive Literature Review
Authors: He Kangwen , Meng Xiaoyan , Wang Yanchun , Feng Cui , Liu Zheng , Li Zhen , Niu Yonghua
Published: February 2024

Magnetic resonance imaging (MRI) has been proven to be an indispensable imaging method in bladder cancer, and it can accurately identify muscular invasion of bladder cancer. Multiparameter MRI is a promising tool widely used for preoperative staging evaluation of bladder cancer. Vesical Imaging-Reporting and Data System (VI-RADS) scoring has proven to be a reliable tool for local staging of bladder cancer with high accuracy in preoperative staging, but VI-RADS still faces challenges and needs further improvement. Artificial intelligence (AI) holds great promise in improving the accuracy of diagnosis and predicting the prognosis of bladder cancer. Automated machine learning techniques based on radiomics features derived from MRI have been utilized in bladder cancer diagnosis and have demonstrated promising potential for practical implementation. Future work should focus on conducting more prospective, multicenter studies to validate the additional value of quantitative studies and optimize prediction models by combining other biomarkers, such as urine and serum biomarkers. This review assesses the value of multiparameter MRI in the accurate evaluation of muscular invasion of bladder cancer, as well as the current status and progress of its application in the evaluation of efficacy and prognosis.
Clinical Predictive Modeling of Heart Failure: Domain Description, Models’ Characteristics and Literature Review
Authors: Odrobina Igor
Published: February 2024

This study attempts to identify and briefly describe the current directions in applied and theoretical clinical prediction research. Context-rich chronic heart failure syndrome (CHFS) telemedicine provides the medical foundation for this effort. In the chronic stage of heart failure, there are sudden exacerbations of syndromes with subsequent hospitalizations, which are called acute decompensation of heart failure (ADHF). These decompensations are the subject of diagnostic and prognostic predictions. The primary purpose of ADHF predictions is to clarify the current and future health status of patients and subsequently optimize therapeutic responses. We proposed a simplified discrete-state disease model as an attempt at a typical summarization of a medical subject before starting predictive modeling. The study tries also to structure the essential common characteristics of quantitative models in order to understand the issue in an application context. The last part provides an overview of prediction works in the field of CHFS. These three parts provide the reader with a comprehensive view of quantitative clinical predictive modeling in heart failure telemedicine with an emphasis on several key general aspects. The target community is medical researchers seeking to align their clinical studies with prognostic or diagnostic predictive modeling, as well as other predictive researchers. The study was written by a non-medical expert.
Umbilical Cord Wraps around a Newborn’s Legs like Ankle Shackles
Authors: Huang Kun-Long , Tsai Ching-Chang , Cheng Hsin-Hsin , Lai Yun-Ju , Lee Pei-Fang , Hsu Te-Yao
Published: February 2024

A 36-year-old woman, gravida 3, para 1 (previous cesarean section), with one medical abortion, and no history of systemic diseases presented an unremarkable medical history during prenatal visits. The final prenatal ultrasound at 38 weeks of gestation showed a vertex presentation, a weight of 2600 g, a normal amniotic fluid level, and the placenta located on the posterior wall of the uterus. Fetal cardiotocography conducted before delivery reported a reactive heart rate without decelerations. The Doppler wave analysis of the fetal umbilical artery was normal (the ratio of peak-systolic flow velocity to the end-diastolic flow velocity was 2.5) without the absence or reversal of end-diastolic velocity. The total score of the fetal biophysical profile by ultrasound was 8. The night before the scheduled cesarean section, she experienced heightened anxiety and was unable to sleep, noting “crazy” fetal movements throughout the night. During the cesarean section, it was discovered that the umbilical cord was wrapped around the newborn’s legs, resembling ankle shackles. The fetal weight was 2740 g, and Apgar scores were 9 at the first minute and 10 at the fifth minute. The motility of the neonatal legs was normal without cyanosis or neurological adverse outcomes.
A Machine Learning Predictive Model of Bloodstream Infection in Hospitalized Patients
Authors: Murri Rita , De Angelis Giulia , Antenucci Laura , Fiori Barbara , Rinaldi Riccardo , Fantoni Massimo , Damiani Andrea , Patarnello Stefano , Sanguinetti Maurizio , Valentini Vincenzo , Posteraro Brunella , Masciocchi Carlotta
Published: February 2024

The aim of the study was to build a machine learning-based predictive model to discriminate between hospitalized patients at low risk and high risk of bloodstream infection (BSI). A Data Mart including all patients hospitalized between January 2016 and December 2019 with suspected BSI was built. Multivariate logistic regression was applied to develop a clinically interpretable machine learning predictive model. The model was trained on 2016–2018 data and tested on 2019 data. A feature selection based on a univariate logistic regression first selected candidate predictors of BSI. A multivariate logistic regression with stepwise feature selection in five-fold cross-validation was applied to express the risk of BSI. A total of 5660 hospitalizations (4026 and 1634 in the training and the validation subsets, respectively) were included. Eleven predictors of BSI were identified. The performance of the model in terms of AUROC was 0.74. Based on the interquartile predicted risk score, 508 (31.1%) patients were defined as being at low risk, 776 (47.5%) at medium risk, and 350 (21.4%) at high risk of BSI. Of them, 14.2% (72/508), 30.8% (239/776), and 64% (224/350) had a BSI, respectively. The performance of the predictive model of BSI is promising. Computational infrastructure and machine learning models can help clinicians identify people at low risk for BSI, ultimately supporting an antibiotic stewardship approach.
Monocyte Chemoattractant Protein-1 (MCP-1), Activin-A and Clusterin in Children and Adolescents with Obesity or Type-1 Diabetes Mellitus
Authors: Kostopoulou Eirini , Kalavrizioti Dimitra , Davoulou Panagiota , Papachristou Evangelos , Sinopidis Xenophon , Fouzas Sotirios , Dassios Theodore , Gkentzi Despoina , Kyriakou Stavroula Ioanna , Karatza Ageliki , Dimitriou Gabriel , Goumenos Dimitrios , Spiliotis Bessie E. , Plotas Panagiotis , Papasotiriou Marios
Published: February 2024

Inflammation plays a crucial role in diabetes and obesity through macrophage activation. Macrophage chemoattractant protein-1 (MCP-1), activin-A, and clusterin are chemokines with known roles in diabetes and obesity. The aim of this study is to investigate their possible diagnostic and/or early prognostic values in children and adolescents with obesity and type-1 diabetes mellitus (T1DM). Methods: We obtained serum samples from children and adolescents with a history of T1DM or obesity, in order to measure and compare MCP-1, activin-A, and clusterin concentrations. Results: Forty-three subjects were included in each of the three groups (controls, T1DM, and obesity). MCP-1 values were positively correlated to BMI z-score. Activin-A was increased in children with obesity compared to the control group. A trend for higher values was detected in children with T1DM. MCP-1 and activin-A levels were positively correlated. Clusterin levels showed a trend towards lower values in children with T1DM or obesity compared to the control group and were negatively correlated to renal function. Conclusions: The inflammation markers MCP-1, activin-A, and clusterin are not altered in children with T1DM. Conversely, obesity in children is positively correlated to serum MCP-1 values and characterized by higher activin-A levels, which may reflect an already established systematic inflammation with obesity since childhood.
Development and Comparison of Treatment Decision Tools for Glucocorticoid-Induced Osteoporosis
Authors: Chen Jia-Feng , Yu Shan-Fu , Chiu Wen-Chan , Ko Chi-Hua , Hsu Chung-Yuan , Lai Han-Ming , Chen Ying-Chou , Su Yu-Jih , Kang Hong-Yo , Cheng Tien-Tsai
Published: February 2024

Long-term Glucocorticoid (GC) use results in compromised bone strength and fractures, and several treatment recommendations have been developed to prevent fractures, but none have been validated in a real-world setting. This study aims to create a treatment decision tool and compares this tool to the treatment suggestions from the American College of Rheumatology (ACR), International Osteoporosis Foundation and European Calcified Tissue Society (IOF-ECTS), and GC-adjusted Fracture Risk Assessment Tool (GC-FRAX), above the intervention threshold. We utilized registry data gathered at Chang Gung Memorial Hospital at Kaohsiung, Taiwan, between September 2014 and April 2021. This research is a single-center, observational, and case-controlled study. We recruited participants using prednisone for at least 2.5 mg/day or the equivalent dose for over 3 months, excluding those younger than 40, those with malignancies, or those currently undergoing anti-osteoporosis therapy. The primary endpoint was new fragility fractures within 3 years, including morphometric vertebral fractures detected at baseline and with a follow-up thoracic–lumbar spine X-ray. Participants were randomly allocated into derivation and validation sets. We developed the Steroid-Associated Fracture Evaluation (SAFE) tool in the derivation cohort by assessing the weights of exploratory variables via logistic regression. Prediction performance was compared in the validation set by the receiver operating characteristic (ROC) curve, the area under the curve (AUC), and sensitivity and specificity. A total of 424 treatment-naïve subjects were enrolled, and 83 (19.6%) experienced new fractures within 3 years. The final formula of the SAFE tool includes osteoporosis (1 point), an accumulated GC dose ≥ 750 mg within 6 months (or equivalent prednisolone of ≥4.5 mg/day for 6 months) (1 point), a BMI ≥ 23.5 (1 point), previous fractures (1 point), and elderliness of ≥70 years (2 points). In the validation set, a treatment decision based on the SAFE ≥ 2 points demonstrated an AUC of 0.65, with a sensitivity/specificity/accuracy of 75.9/54.0/58.9, with an ACR of 0.56 (100.0/11.0/31.0), IOF-ECTS 0.61 (75.9/46.0/52.7), and GC-FRAX 0.62 (82.8/42.0/51.2). Among current GIOP recommendations, the SAFE score serves as an appropriate treatment decision tool with increased accuracy and specificity.
Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model
Authors: Aygun Umran , Yagin Fatma Hilal , Yagin Burak , Yasar Seyma , Colak Cemil , Ozkan Ahmet Selim , Ardigò Luca Paolo
Published: February 2024

This study aims to develop an interpretable prediction model based on explainable artificial intelligence to predict bacterial sepsis and discover important biomarkers. A total of 1572 adult patients, 560 of whom were sepsis positive and 1012 of whom were negative, who were admitted to the emergency department with suspicion of sepsis, were examined. We investigated the performance characteristics of sepsis biomarkers alone and in combination for confirmed sepsis diagnosis using Sepsis-3 criteria. Three different tree-based algorithms—Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost)—were used for sepsis prediction, and after examining comprehensive performance metrics, descriptions of the optimal model were obtained with the SHAP method. The XGBoost model achieved accuracy of 0.898 (0.868–0.929) and area under the ROC curve (AUC) of 0.940 (0.898–0.980) with a 95% confidence interval. The five biomarkers for predicting sepsis were age, respiratory rate, oxygen saturation, procalcitonin, and positive blood culture. SHAP results revealed that older age, higher respiratory rate, procalcitonin, neutrophil–lymphocyte count ratio, C-reactive protein, plaque, leukocyte particle concentration, as well as lower oxygen saturation, systolic blood pressure, and hemoglobin levels increased the risk of sepsis. As a result, the Explainable Artificial Intelligence (XAI)-based prediction model can guide clinicians in the early diagnosis and treatment of sepsis, providing more effective sepsis management and potentially reducing mortality rates and medical costs.
Trajectory-Tracking Control for Quadrotors Using an Adaptive Integral Terminal Sliding Mode under External Disturbances
Authors: Jiao Shipeng , Wang Jun , Hua Yuchen , Zhuang Ye , Yu Xuetian
Published: February 2024

In the face of external disturbances affecting the trajectory tracking of quadrotors, a control scheme targeted at accurate position and attitude trajectory tracking was designed. Initially, a quadrotor dynamic model, essential for control design, was derived. Adaptive integral backstepping control (AIBS) was then employed within the position loop, enabling the upper boundaries of disturbances to be estimated through adaptive estimation. Subsequently, a new adaptive backstepping fast nonsingular integral terminal sliding mode control (ABFNITSM) was proposed to enable adherence to the desired Euler angles. Rapid convergence and accurate tracking were facilitated by the incorporation of the nonsingular terminal sliding mode and an integral component. The dead zone technique was deployed to curtail estimation errors, while a saturation function was used to eradicate the phenomenon of chattering. Finally, to validate the proposed control scheme, simulation experiments were conducted in the Simulink environment, and the results were contrasted with those obtained from traditional integral terminal sliding mode control (ITSM) and integral backstepping control (IBS), providing evidence of the effectiveness of the proposed method.
A Supervised Reinforcement Learning Algorithm for Controlling Drone Hovering
Authors: Wu Jiying , Yang Zhong , Zhuo Haoze , Xu Changliang , Zhang Chi , He Naifeng , Liao Luwei , Wang Zhiyong
Published: February 2024

The application of drones carrying different devices for aerial hovering operations is becoming increasingly widespread, but currently there is very little research relying on reinforcement learning methods for hovering control, and it has not been implemented on physical machines. Drone’s behavior space regarding hover control is continuous and large-scale, making it difficult for basic algorithms and value-based reinforcement learning (RL) algorithms to have good results. In response to this issue, this article applies a watcher-actor-critic (WAC) algorithm to the drone’s hover control, which can quickly lock the exploration direction and achieve high robustness of the drone’s hover control while improving learning efficiency and reducing learning costs. This article first utilizes the actor-critic algorithm based on behavioral value Q (QAC) and the deep deterministic policy gradient algorithm (DDPG) for drone hover control learning. Subsequently, an actor-critic algorithm with an added watcher is proposed, in which the watcher uses a PID controller with parameters provided by a neural network as the dynamic monitor, transforming the learning process into supervised learning. Finally, this article uses a classic reinforcement learning environment library, Gym, and a current mainstream reinforcement learning framework, PARL, for simulation, and deploys the algorithm to a practical environment. A multi-sensor fusion strategy-based autonomous localization method for unmanned aerial vehicles is used for practical exercises. The simulation and experimental results show that the training episodes of WAC are reduced by 20% compared to the DDPG and 55% compared to the QAC, and the proposed algorithm has a higher learning efficiency, faster convergence speed, and smoother hovering effect compared to the QAC and DDPG.
A Circularly Polarized Complementary Antenna with Substrate Integrated Coaxial Line Feed for X-Band Applications
Authors: Ji Zhuoqiao , Sun Guanghua , Wang Kaixu , Wong Hang , Yu Zhan , Li Zhengguo , Wei Changning , Liu Pei
Published: February 2024

This work presents a design for a complementary antenna with circular polarization that has a wide operating bandwidth, stable broadside radiation, and stable gain for X-band applications. The proposed antenna consists of an irregular loop and a parasitic electric dipole, which work together to produce equivalent radiation from the magnetic and electric dipoles. By arranging the dipole and the loop in a specific geometry, this antenna effectively generates circularly polarized wave propagation. A substrate integrated coaxial line (SICL) is applied to feed the antenna through an aperture cutting on the ground. The proposed antenna achieves a wide axial ratio (AR) and impedance bandwidths of 27.4% (from 8.5 to 11.22 GHz, for the AR ≤ 3 dB) and 39.6% (from 7.5 to 11.2 GHz, for the reflection coefficient ≤ −10 dB), respectively. Moreover, the antenna maintains a stable broadside radiation pattern across the operating bandwidth, with an average gain of 10 dBic. This proposed antenna design is competitive for X-band wireless communications.
RadiantVisions: Illuminating Low-Light Imagery with a Multi-Scale Branch Network
Authors: Zhang Yu , Jiang Shan , Tang Xiangyun
Published: February 2024

In the realms of the Internet of Things (IoT) and artificial intelligence (AI) security, ensuring the integrity and quality of visual data becomes paramount, especially under low-light conditions, where low-light image enhancement emerges as a crucial technology. However, the current methods for enhancing images under low-light conditions still face some challenging issues, including the inability to effectively handle uneven illumination distribution, suboptimal denoising performance, and insufficient correlation among a branch network. Addressing these issues, the Multi-Scale Branch Network is proposed. It utilizes multi-scale feature extraction to handle uneven illumination distribution, introduces denoising functions to mitigate noise issues arising from image enhancement, and establishes correlations between network branches to enhance information exchange. Additionally, our approach incorporates a vision transformer to enhance feature extraction and context understanding. The process begins with capturing raw RGB data, which are then optimized through sophisticated image signal processor (ISP) techniques, resulting in a refined visual output. This method significantly improves image brightness and reduces noise, achieving remarkable improvements in low-light image enhancement compared to similar methods. Using the LOL-V2-real dataset, we achieved improvements of 0.255 in PSNR and 0.23 in SSIM, with decreases of 0.003 in MAE and 0.009 in LPIPS, compared to the state-of-the-art methods. Rigorous experimentation confirmed the reliability of this approach in enhancing image quality under low-light conditions.
A Hybrid Group Multi-Criteria Approach Based on SAW, TOPSIS, VIKOR, and COPRAS Methods for Complex IoT Selection Problems
Authors: Radulescu Constanta Zoie , Radulescu Marius
Published: February 2024

The growth of Internet of Things (IoT) systems is driven by their potential to improve efficiency, enhance decision-making, and create new business opportunities across various domains. In this paper, the main selection problems in IoT-type systems, criteria used in multi-criteria evaluation, and multi-criteria methods used for solving IoT selection problems are identified. Then, a Hybrid Group Multi-Criteria Approach for solving selection problems in IoT-type systems is proposed. The approach contains the Best Worst Method (BWM) weighting method, multi-criteria Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and Complex Proportional Assessment Method (COPRAS), and a method that combines the solutions obtained using the four considered multi-criteria methods to obtain a single solution. The SAW, TOPSIS, VIKOR, and COPRAS methods were analyzed in relation to their advantages, disadvantages, inputs, outputs, measurement scale, type of normalization, aggregation method, parameters, complexity of implementation, and interactivity. An application of the Hybrid Group Multi-Criteria Approach for IoT platform selection and a comparison between the SAW, TOPSIS, VIKOR, and COPRAS solutions and the solution of the proposed approach is realized. A Spearman correlation analysis is presented.
Coalitional Distributed Model Predictive Control Strategy with Switching Topologies for Multi-Agent Systems
Authors: Maxim Anca , Pauca Ovidiu , Caruntu Constantin F.
Published: February 2024

Controlling multi-agent systems (MASs) has attracted increased interest within the control community. Since the control challenge consists of the fact that each agent has limited local capabilities, our adopted solution is tailored so that a group of such entities works together and shares resources and information to fulfill a given task. In this work, we propose a coalitional control solution using the distributed model predictive control (DMPC) framework, suitable for a multi-agent system. The methodology has a switching mechanism that selects the best communication topology for the overall system. The proposed control algorithm was validated in simulation using a homogeneous vehicle platooning application with longitudinal dynamics. The available communication topologies were specifically tailored taking into account the information flow between adjacent vehicles. The obtained results show that when the platoon’s string stability is risked, the algorithm switches between different communication topologies. The resulting coalitions between vehicles ensure an increase in the overall stability of the entire system and prove the efficacy of our proposed methodology.
Modeling the Temporal Response of Gated ZnO Nanowire Field Emitter by Considering the Charging and Self-Heating Effect for Improving the Response Speed
Authors: Chen Yicong , Wang Chengyun , Song Guichen , Deng Shaozhi , Chen Jun
Published: February 2024

ZnO nanowire is a promising candidate for large-area gated field emitter arrays. How to improve its temporal response is one of the key problems to be solved for applications. In this work, a device model for a gated ZnO nanowire field emitter with consideration of charging and self-heating effect has been established to investigate its temporal response. It is found that while the charging effect is responsible for the delay at the beginning of the pulse, the self-heating effect which induces delay due to the thermal conduction process can shorten the charging time because of its lowering of nanowire resistance. The response time can be minimized when these two effects are balanced at an optimal field which is below the critical field for thermal runaway. We further investigate the optimal response time of a nanowire with the same resistance but a different length, radius, and electrical properties. The results imply that a lower heat capacity and higher critical temperature for thermal runaway are in favor of a shorter response time, which must be taken into account in the reduction in nanowire resistance for improving response speed. All the above should be useful for the device design of a fast-response gated ZnO nanowire field emitter array.
Enhancing Small-Cell Capacity with Wireless Backhaul
Authors: Tao Ran , Liu Wuling
Published: February 2024

Recently, hyperdense small cells have been proposed to meet the challenge of the tremendous increment in cellular data service requirements. To reduce the deployment cost, as well as operated cost, these small cells are usually connected to limited backhauls, in which case the backhaul capacity may become a bottleneck in busy hours. In this paper, we propose an optimal scheme for the small cells to utilize the macrocell links as its wireless backhaul. Based on stochastic geometry, the analytical expressions of network capacity with in-band and out-band wireless backhaul are derived and validated using simulation results. The optimized results show that our proposed scheme can significantly improve the network performance in scenarios with a high traffic load.