bluuebunny/crossref_metadata_2025 huggingface dataset:
Vehicle–Bridge Coupling Vibration Analysis of Long-Span Self-Anchored Suspension Bridges Based on Cross-Section Mass–Shear Center Heterocentricity
Authors: Li Zheng , Li Aoli , Chen Daihai , Xu Shizhan , Yang Chaoxu
Published: November 2024

In order to study the influence of the difference between the center of mass and shear center position of the main girder cross-section on the coupled vibration response of a vehicle–bridge, and in accordance with the theory of finite element analysis, we derive the stiffness matrix of the spatial girder unit with the main girder cross-section mass–shear center heterocentricity, use finite element software to establish a bridge model, select a three-axle heavy vehicle, and solve the coupled vibration equation of the vehicle–bridge by the separation method. A large-span self-anchored suspension bridge is taken as the research object, and a self-programming program is used to calculate and analyze the influence of the main girder cross-section mass–shear center heterocentricity, driving lanes, and speed on the coupled vibration response of the vehicle–bridge. The results show the following: the main girder cross-section mass–shear center heterocentricity has a significant effect on the transverse dynamic response of the bridge, and the peak values of transverse displacement and acceleration in the main span are increased by about 87% and 136%; the outward shift of lanes has a greater effect on the transverse dynamic response of the bridge; and the vibration response of the bridge while considering mass–shear center heterocentricity is more affected under different vehicle speeds.
Thermal Control Strategy for the Sustainable Use of Large Classrooms Responding to User Demands in a School Building
Authors: Ahn Jonghoon
Published: November 2024

In order to respond to the needs of education, the importance of various learning activities other than subject courses is gradually increasing in schools. Therefore, classrooms in schools are organized in a variable form depending on the educational situations and demands, and it is necessary to improve their energy efficiency in the operation without compromising indoor thermal quality. This study examines the efficiency of thermal control models that can perform cooling and heating energy supply when using one large classroom composed of two architectural modules. Through an adaptive process, a proposed model determines the efficient heating and cooling air supply according to the room conditions derived from the occupant schedules. The model optimizes the condition of supply air in order to mitigate users’ thermal comfort. Then, the results from this adaptive process are trained by iterative learning using a neural network, and a newly improved model derived from the learning process is tested to achieve both energy use and thermal comfort improvement. As a result, it is confirmed that the proposed learning model shows about 2.78% improvement in energy efficiency and about 72.73% improvement in thermal comfort consistency as compared to the thermostat control. This model can help to efficiently operate school buildings by increasing the usability of various classrooms.
Seismic Response of Prestressed Self-Centering Moment-Resisting Frames
Authors: Yan Xueyuan , Shi Shen , Mao Huimin , Lin Zhongnan
Published: November 2024

This paper aims to examine the seismic response of prestressed self-centering moment-resisting frames (PSC-MRFs) based on concrete-filled double steel tubular (CFDST) columns and RC beams. The beam of this novel connection is divided into two parts, connected by bolts and tendons, and the beam includes a gap opening feature, which could be regarded as a normal single beam in the field. Cyclic loading analysis was performed on one-story frames with different initial parameters arranged in adjacent bays. Nonlinear dynamic analysis was conducted on a six-story frame under two seismic hazard levels. The cyclic loading analysis showed favorable self-centering performance of the frame even when the hysteretic energy dissipation ratio reached 0.808. Seismic analysis results showed that compared with the in situ reinforced concrete frame, PSC-MRFs generally had similar maximum inter-story drifts under fortification earthquakes, but the residual inter-story drifts were reduced by 33%; under rare earthquakes, the maximum inter-story drifts and residual inter-story drifts of PSC-MRFs were reduced by 22% and more than 90%, respectively. In the adjacent bays on the same story of PSC-MRFs, connections with smaller imminent moments of gap opening opened earlier under earthquake, and the maximum opening angle was larger. The general seismic performance and self-centering of PSC-MRFs was significantly more advantageous than that of in situ reinforced concrete frames.
Small-Scale and Large-Scale Modeling of Fiber-Reinforced Concrete Girders
Authors: Landović Aleksandar , Čeh Arpad , Starčev-Ćurčin Anka , Šešlija Miloš
Published: November 2024

Experimental and computational research on the behavior of small-scale and large-scale fiber-reinforced concrete (FRC) beams is presented in this paper. The experimental part included the small-scale bending tests, which were conducted on three 1.3 m long by 0.1 m wide by 0.15 m high rectangular simply supported beams, and the large-scale test that was conducted on 12.8 m long by 0.2 m wide by 1.3 m two-chords girder. The concrete mixture in the large-scale test was designed with environmentally more justifiable supplementary materials (binder and fibers), striving for sustainable excellence. To accurately predict the mechanical behavior of tested models, a numerical model incorporating the real nonlinear materials laws is used. A numerical model based on finite element analysis (FEA) is developed. The FEA model is created using a smeared crack approach with a constitutive law for the tensile behavior of FRC derived from an inverse analysis based on prism bending tests. The numerical model is validated against experimental results and the accuracy of numerical predictions based on finite element modeling showed a good correlation with the test data. The FEA-based model makes it easier to predict how FRC structures fail under transversal loading and can serve as a foundation for creating new design processes. Additionally, the presented research is aimed at the feasibility of recycled steel FRC field application in building structures. The usage of recycled steel fibers could achieve environmental benefits through the adoption of sustainable materials. The present study showcased the possibility of modeling reinforced concrete structural building parts made with recycled steel fibers using available software.
New High Efficiency and Strength Bending Strain Sensor Based on Piezoelectric Stacks
Authors: Sidorov Vladimir , Rogacheva Nelly , Zheglova Yulia
Published: November 2024

This article is devoted to a mathematical model of a new piezoelectric sensor used for measuring bending strains. The first simple model of a piezoelectric sensor of bending deformations (we will call it a classical sensor) was presented in our previous paper. The classical sensor is a one-dimensional three-layer structure, in which the two outer layers are made of piezoelectric ceramic with preliminary polarization across the thickness of the sensor, and one elastic middle layer is located between these piezoelectric layers. In the present modified model of the new sensor, piezoelectric stacks are used instead of simple piezoelectric elements. As shown in the paper, this kind of piezoelectric composite sensor with stacks allows us to significantly increase the value and stability of the measured electrical signal and increase the accuracy of strains measurement. Piezoelectric ceramic is a brittle material. The use of stacks significantly reduces brittleness by enclosing thin layers of piezoelectric ceramic in a metal matrix. Piezoelectric laminated stacks have a periodic structure, and we will use the mathematical homogenization method to correctly determine their effective moduli (physical constants). Increasing the reliability of the proposed sensors, as well as the accuracy and stability of their deformation measurements, is aimed at enhancement of the mechanical safety of building structures, increasing the efficiency of their monitoring. The most important characteristic of any sensor is its efficiency. Our first classical bending strain sensor has a simple structure and an efficiency approaching the value of the coupling coefficient k31 (k31 is a constant describing a known physical property of a piezoelectric material). Our classic piezoelectric flexural strain sensor has an efficiency of the order of the coupling coefficient k31. For piezoelectric materials with a strong piezoelectric effect, the k31 value is approximately 0.30–0.35. The efficiency of our classical sensor is hundreds of times greater than the efficiency of the most popular tangential (longitudinal) strain sensor, developed by Lord Kelvin. The efficiency of the flexural strain sensor using stacks is of the order of the coupling coefficient k33. For the sensor with piezoelectric stacks, the value of efficiency is approximately 0.60–0.70. Note that the efficiency of the improved sensor is twice as high as the efficiency of our classic flexural strain sensor.
The Santa María Micaela Residential Complex in Valencia (Spain) Study of the Original Design to Assess Its Bioclimatic Potentials for Energy Upgrading
Authors: Angileri Giuseppe , Bernardo Graziella , Currò Giuseppina , Fiandaca Ornella , Minutoli Fabio , Palmero Iglesias Luis Manuel , Russo Giovanni Francesco
Published: November 2024

The existing built heritage is excessively energy intensive compared to the standards required by European policies that promote zero- or near-zero-energy buildings. Hence the need to promote a radical energy requalification of the existing stock through ad hoc solutions. In the modelling of buildings undergoing redevelopment, the boundary conditions considered by the designer are often underestimated, resulting in a digital model that does not perfectly adhere to reality, due to a lack of historical and documentary knowledge. The present work—which concerns the Santa Maria Micaela residential complex built in Valencia by architect Santiago Artal Ríos, a representative work of Spanish Modernism—aims to overcome this vulnus with modelling that also takes into account historical and archive information. The housing complex was studied using a multidisciplinary approach with historical–archival analyses and site surveys that allowed BIM modelling and localisation in a WEB-GIS platform. The modelling took into account the peculiarities of the original design (exposure, windiness, and shading) and data from historical research (stratigraphy of building elements, dimensions, types of materials). The energy simulation, on the other hand, referred to a representative dwelling unit of the complex, and using SolidWorks software the ventilation flows were evaluated, which made it possible to create a model that was more in keeping with reality and to more correctly identify the performance upgrading proposal. The energy improvement was then evaluated according to the hypothesised interventions using two different analysis methodologies, TerMus and CE3X, for direct comparison. The transposition into WebGIS then made it possible to assess the potential of a digital platform to enhance information sharing.
Murine Xenograft Models as Preclinical Tools in Endometrial Cancer Research
Authors: Yildiz Merve , Romano Andrea , Xanthoulea Sofia
Published: November 2024

Murine xenograft models are valuable and increasingly used preclinical tools in cancer research to understand disease pathogenesis and guide treatment options. The aim of this narrative review is to summarize the studies that employed mouse xenograft models, using cell lines, patient-derived tumors, or organoids, in endometrial cancer (EC) research, detailing their methodology and main findings. We identified 27 articles reporting on heterotopic EC xenografts, including subcutaneous, subrenal capsule, intraperitoneal, and retro-orbital models, and 18 articles using orthotopic xenografts. Subcutaneous xenografts generated using either cell lines or patient tumors have been widely used; however, their low engraftment rates and the inability to recapitulate main clinical features such as metastases limit their translational value. Subrenal capsule models showed improved engraftment rates compared to subcutaneous models, but tumors exhibited slower and constrained tumor growth. Orthotopic models are technically more challenging to generate and monitor, but tumor growth occurs in a relevant microenvironment and EC ortho-xenografts exhibit high engraftment rates and metastases to clinically relevant sites. Cell line-based xenograft (CDX) models are attractive tools because they are convenient, easy to use, and amenable to genetic modifications, making them suitable for proof-of-concept approaches and large-scale studies. EC xenografts developed from patient tumors (PDTXs) are more labor/cost-intensive for their establishment but can capture the genetic and molecular heterogeneity within and across histologic subtypes and can inform personalized patient treatment. EC organoid-based xenograft (PDOX) models combine the advantages of both CDXs and PDTXs since they are more time- and cost-effective, faithfully maintain tumor characteristics and therapeutic responses, and can be genetically modified. Despite substantial progress in EC management, there are still several unmet needs. Efficient targeted treatments are currently indicated only for a small subgroup of patients, while women with recurrent or advanced-stage EC have very few therapeutic options and their prognosis remains unfavorable. Novel (targeted) drugs, combinational regimens and tools to predict the real drug response in patients are urgently needed. Xenograft models are expected to inform about disease mechanisms and to help identify novel therapeutic options and suitable target patients.
Mass Spectrometry-Based Workflow for the Identification and Quantification of Alternative and Canonical Proteins in Pancreatic Cancer Cells
Authors: Guillon Clémence , Pichereaux Carole , Lazar Ikrame , Chaoui Karima , Mouton-Barbosa Emmanuelle , Liauzun Mehdi , Gourbeyre Edith , Altiner Pinar , Bouyssié David , Stella Alexandre , Burlet-Schiltz Odile , Plaza Serge , Martineau Yvan , Fabre Bertrand
Published: November 2024

The identification of small proteins and proteins produced from unannotated open reading frames (called alternative proteins or AltProts) has changed our vision of the proteome and has attracted more and more attention from the scientific community. Despite several studies investigating particular AltProts in diseases and demonstrating their importance in such context, we are still missing data on their expression and functions in many pathologies. Among these, pancreatic ductal adenocarcinoma (PDAC) is a particularly relevant case to study alternative proteins. Indeed, late detection of this disease, notably due to the lack of reliable biomarkers of early-stage PDAC, and the fact that tumors rapidly develop resistance to most of the treatments used in the clinics warrant the exploration of new repertoires of molecules. In the present article, we aim to investigate the alternative proteome of pancreatic cancer cell lines as a first attempt to decipher the expression of AltProts in PDAC. Thanks to a combined data-dependent and data-independent acquisition mass spectrometry workflow, we were able to identify tryptic peptides matching 113 AltProts in a panel of 6 cell lines. In addition, we identified AltProts differentially expressed between pancreatic cancer cell lines and other cells (HeLa and HEK293T). Finally, mining the TCGA and Gtex databases showed that the corresponding transcripts encoding several AltProts we identified are differentially expressed between PDAC tumors and normal tissues and are correlated with the patient’s survival.
Evaluation of the Peripheral and Central Auditory Systems in Children and Adolescents Before and After COVID-19 Infection
Authors: Siqueira Julia , Sanfins Milaine Dominici , Skarzynski Piotr Henryk , Skarzynska Magdalena Beata , Colella-Santos Maria Francisca
Published: November 2024

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. During and after COVID-19, audiovestibular symptoms and impairments have been reported. Objectives: This study aimed to investigate the impacts of COVID-19 on the peripheral and central auditory systems of children and adolescents following the acute COVID-19 phase based on behavioral, electroacoustic, and electrophysiological audiological assessments. Methods: This is a primary, prospective, observational, and cross-sectional study of 23 children aged 8 to 15 years who acquired confirmed COVID-19 and who, before infection, had not had any auditory complaints or school complications. The results were compared with pre-pandemic data collected from a similar group of 23 children who had normal peripheral and central hearing and good school performance. Each participant answered a questionnaire about child development, school, and health history and underwent tests including pure-tone audiometry and high-frequency audiometry, imitanciometry, transient evoked otoacoustic emissions, and distortion product otoacoustic emissions. They also received tests of Brainstem Auditory Evoked Potentials, Long Latency Auditory Evoked Potentials, Dichotic Digits Test, Sentence Identification Test, Dichotic Consonant–Vowel Test, Frequency Pattern Test, and Gaps-In-Noise Test. Results: Significant differences were observed between the groups, with the study group showing worse thresholds compared to the control group at both standard audiometric frequencies and at higher frequencies, although both groups were still within normal limits (p ≤ 0.05). In addition, the study group had a higher prevalence of absent responses, as identified by otoacoustic emissions and acoustic reflexes. In terms of central auditory performance, the study group showed ABRs with significantly longer latencies of waves I, III, and V compared to the control group. The study group also performed less well on the Dichotic Digits and Pediatric Speech Identification tests. Conclusions: COVID-19 appears to alter the auditory system, both peripherally at the level of the outer hair cells and more centrally.
Comparing Images from Near-Infrared Light Reflection and Bitewing Radiography to Detect Proximal Caries in Primary Teeth
Authors: Shmueli Aviv , Fux-Noy Avia , Davidovich Esti , Ram Diana , Moskovitz Moti
Published: November 2024

Objectives: The present prospective study aimed to compare near-infrared light reflection (NIRI) and bitewing radiographs (BWR) images to detect proximal caries in primary teeth. Methods: 71 children underwent routine BWR, and scans were performed using an intra-oral scanner (iTero Element 5D, Align Technology, Tempe, AZ, USA), including a near-infrared light source (850 nm) and sensor. Five specialist pediatric dentists examined the NIRI and BWR images. Results: The average participant age was 7.8 years. A total of 1004 proximal surfaces of primary molars and canines were examined, 209 carious lesions were detected on BWR, and 227 on NIRI. Comparison between all carious lesions detected on BWR and NIRI: Sensitivity (53.6%); Specificity (85.5%); Positive Predictive Value (PPV) (49.3%); Negative Predictive Value (NPV) (87.5%). Comparison between carious lesions involving the DEJ detected on BWR and at any level in NIRI: Sensitivity (61%); Specificity (83.4%), PPV (36.6%); NPV (93.2%). Comparison between enamel-only carious lesions detected on BWR and all lesions detected using NIRI: Sensitivity (44.8%); Specificity (85.5%); PPV (20.7%); NPV (94.8%). Conclusions: No additional diagnostic information can be gleaned from BWR if initial caries lesions in the enamel are not detected by clinical examination or in images from a NIRI scanner, making BWR unnecessary.
The Use of Acetazolamide to Prevent Acute Kidney Injury in Patients with Cancer on High-Dose Methotrexate Treatment: A Retrospective Pilot Analysis
Authors: Lin Lindon , Batt Tracey , Peterson Gregory M.
Published: November 2024

Background: High-dose methotrexate (HDMTX) chemotherapy is associated with a significant risk of acute kidney injury (AKI). Acetazolamide is thought to increase methotrexate solubility via urinary alkalinisation, potentially reducing the risk of crystalline nephropathy. A tertiary hospital has included acetazolamide in its HDMTX protocols, although data on the risks and benefits are limited. This study evaluated the role of acetazolamide in managing patients receiving HDMTX and identified risk factors for AKI. Methods: The retrospective cohort pilot study included consecutive hospital patients who received HDMTX (≥500 mg/m2). Data collected from digital medical records included demographics, comorbidities, methotrexate dosages and serum concentrations, and pathology results. The development of AKI was defined by the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Relationships between variables and AKI were initially assessed using Mann–Whitney U-tests and chi-square tests, and significant variables were further analysed using logistic regression to identify independent predictors of AKI. Results: Among 66 HDMTX treatment cycles in 31 patients, AKI occurred in 0/7 cycles with acetazolamide versus 14/59 cycles without (p = 0.33). Increasing age, the presence of hypertension, and concurrent use of beta-lactam antibiotics were associated with the development of AKI. Age was identified as the strongest independent risk factor for AKI (odds ratio 1.12, p = 0.034). Conclusions: Optimising management protocols, especially for older patients, is essential to reduce AKI risk during HDMTX therapy. While acetazolamide did not appear to reduce the risk of AKI, this pilot study was limited by a small sample size. Large randomised controlled trials are needed to assess efficacy and patient outcomes.
Mangrove Area Trends in Mexico Due to Anthropogenic Activities: A Synthesis of Five Decades (1970–2020)
Authors: Antúnez Pablo
Published: November 2024

This paper presents a meta-analysis of mangrove area in Mexico, using linear mixed models to assess trends from 1970 to 2020. The objective is to highlight the changes in the extent of these vital ecosystems over the past five decades. The analysis reveals a concerning decline of approximately 163.33 hectares per year from 1970 to 2005. Although a rebound was observed starting in 2016—likely due to effective conservation efforts—these ecosystems continue to decline overall. The states that have shown a consistent decline in mangrove area include Campeche, Sinaloa, Nayarit, Chiapas, Veracruz, Oaxaca, Guerrero, Colima, and Jalisco. Threats to mangroves vary significantly by region. In the North Pacific, the expansion of aquaculture farms has contributed to over 60% of mangrove loss. In contrast, the Yucatán Peninsula faces challenges from urban development, oil exploitation, and road expansion. Additionally, tourism activities have severely impacted the states of Colima, Jalisco, Guerrero, and Quintana Roo. In the Gulf of Mexico, the primary threats include aquaculture, transportation routes, and hydraulic infrastructure. Based on these findings, seven action strategies for the ecological restoration of mangroves are proposed. These strategies, drawn from successful case studies and existing literature, include: comprehensive restoration initiatives, expansion of research and data sources, updates to current regulations, regulation of anthropogenic activities, inter-institutional coordination, education and awareness-raising efforts, and continuous monitoring and evaluation.
ZnOnp/CaCO3 Core–Shell Nanoparticle Coatings on Kraft Paper: A Comparative Study of Antimicrobial Efficacy, Tensile Strength, and Hydrophobicity
Authors: Sengsuk Theerarat , Yotthanon Nicha , Songtipya Ponusa , Nakaramontri Yeampon , Sridach Waranyou , Songtipya Ladawan
Published: November 2024

This study introduces a novel paper coating approach using modified zinc oxide (ZnO), providing a comparison with conventional materials used in the paper industry. The research focused on determining the concentration for effective microbial growth inhibition and evaluates the impact of different ZnO types on coated-paper properties, including antimicrobial activity, surface morphology, tensile strength, and water absorption. Specifically, ZnO microparticles (ZnOws), ZnO nanoparticles (ZnOnp), and modified ZnOnp (ZnOnp-CaCO3, with a core–shell structure composed of calcium carbonate [CaCO3] and nano-zinc oxide) were incorporated into coating formulations at varying concentrations (0 × MIC, 1 × MIC, 2 × MIC, and 3 × MIC, based on minimum inhibitory concentrations [MICs]). The results demonstrated that among all tested microorganisms, ZnOnp-CaCO3 showed the lowest MIC values. ZnOnp-CaCO3-coated paper exhibited superior antimicrobial activity against both Gram-positive and Gram-negative bacteria, as well as fungi, outperforming ZnOws and ZnOnp. At 1 × MIC, %inhibition for E. coli, S. aureus, and A. niger were 98.3%, 99.1%, and 90.8%, respectively. Additionally, ZnOnp-CaCO3 coatings caused minimal color change in the paper compared to the other ZnO variants. The coating did not negatively impact the mechanical properties of the paper across all ZnO types and concentrations. Water absorption tests showed increased hydrophobicity with higher ZnO content, with ZnOnp and ZnOnp-CaCO3 exhibiting greater reductions in water absorption than ZnOws. Overall, ZnOnp-CaCO3 showed strong potential as an antimicrobial agent for paper surfaces, making it ideal for packaging and hygiene products. By partially replacing ZnOnp with inexpensive CaCO3 core particles, ZnOnp-CaCO3 delivers enhanced performance, reduced costs, and greater sustainability for large-scale applications.
Research on Mechanized Plasma Gouging of Weldable Construction Steels
Authors: Olah Arthur , Machedon-Pisu Mihai , Machedon-Pisu Teodor
Published: November 2024

Grooving or gouging is a process of thermal processing of metals or welded semi-finished products. The gouging process is generally used to process the root of butt welds to remove possible welding defects. The possibility to obtain and characterize the surfaces after manual and mechanized plasma cutting in the field of bridges and viaducts from highways to construction sites in order to increase the quality of butt-welded joints is analyzed in this article. In order to mechanize the process, a linear universal displacement device for welding, cutting, or slitting that ensures the uniform movement of the torch was designed and executed herein. As a result, with the help of this device and the tests performed on a construction steel S355J2 after mechanized plasma cutting, the cut samples obtained facilitated the analysis of the macrostructure and microstructure of the thermally influenced zone and of the intermediate zone between the thermally influenced zone and the base material as well as their hardness. By mechanizing the cutting process, the productivity and quality of the surfaces obtained by this process has increased. The plasma gouging process is an ecological process and by mechanizing it, the manufacturing cost can be reduced.
Asymptotic and Probabilistic Perturbation Analysis of Controllable Subspaces
Authors: Angelova Vera , Konstantinov Mihail , Petkov Petko
Published: November 2024

In this paper, we consider the sensitivity of the controllable subspaces of single-input linear control systems to small perturbations of the system matrices. The analysis is based on the strict component-wise asymptotic bounds for the matrix of the orthogonal transformation to canonical form derived by the method of the splitting operators. The asymptotic bounds are used to obtain probabilistic bounds on the angles between perturbed and unperturbed controllable subspaces implementing the Markoff inequality. It is demonstrated that the probability bounds allow us to obtain sensitivity estimates, which are much tighter than the usual deterministic bounds. The analysis is illustrated by a high-order example.
Unified Ecosystem for Data Sharing and AI-Driven Predictive Maintenance in Aviation
Authors: Kabashkin Igor , Susanin Vitaly
Published: November 2024

The aviation industry faces considerable challenges in maintenance management due to the complexities of data standardization, data sharing, and predictive maintenance capabilities. This paper introduces a unified ecosystem for data sharing and AI-driven predictive maintenance designed to address these challenges by integrating real-time and historical data from diverse sources, including aircraft sensors, maintenance logs, and operational records. The proposed ecosystem enables predictive analytics and anomaly detection, enhancing decision-making processes for airlines, maintenance, repair, and overhaul providers, and regulatory bodies. Key elements of the ecosystem include a modular design with feedback loops, scalable AI models for predictive maintenance, and robust data-sharing frameworks. This paper outlines the architecture of a unified aviation maintenance ecosystem built around multiple data sources, including aircraft sensors, maintenance logs, flight data, weather data, and manufacturer specifications. By integrating various components and stakeholders, the system achieves its full potential through several key use cases: monitoring aircraft component health, predicting component failures, receiving maintenance alerts, performing preventive maintenance, and generating compliance reports. Each use case is described in detail and supported by illustrative dataflow diagrams. The findings underscore the transformative impact of such an ecosystem on aviation maintenance practices, marking a significant step toward safer, more efficient, and sustainable aviation operations.
Seborrheic Dermatitis: From Microbiome and Skin Barrier Involvement to Emerging Approaches in Dermocosmetic Treatment
Authors: Galizia Giulia , Belloni Fortina Anna , Semenzato Alessandra
Published: November 2024

Seborrheic dermatitis (SD) is a chronic inflammatory skin disease that primarily affects sebaceous-rich areas such as the scalp, face, and upper trunk. While the precise etiology remains multifactorial, the role of the skin microbiome, particularly the proliferation of Malassezia species, and alterations in the skin barrier function are critical in its pathogenesis. Disruption of the skin barrier, characterized by increased transepidermal water loss (TEWL) and reduced production of epidermal lipids, creates a favorable environment for microbial overgrowth and inflammation. Recent insights highlight the interplay between the impaired barrier function, immune responses, and the skin microbiome in perpetuating the disease. Additionally, novel dermocosmetic approaches are emerging that target these underlying mechanisms, offering promising therapeutic avenues. This review provides a comprehensive overview of the involvement of skin microbiome and barrier dysfunction in seborrheic dermatitis and discusses the potential of advanced dermocosmetic treatments aimed at restoring skin homeostasis and preventing disease recurrence.
Aberrances of the Wound Healing Process: A Review
Authors: Fernandez-Guarino Montserrat , Naharro-Rodriguez Jorge , Bacci Stefano
Published: November 2024

Wound healing is a complex biological process that can lead to chronic wounds, keloids, and hypertrophic scars when disrupted. Chronic wounds result from a prolonged inflammatory phase and impaired re-epithelialization. Keloids are characterized by excessive collagen deposition beyond the original wound boundaries, driven by persistent inflammation and fibroblast hyperactivity. Hypertrophic scars, on the other hand, are confined to the wound edges and are caused by an imbalance in collagen synthesis and degradation, typically resolving over time. The therapeutic approach to wound healing impairment involves a range of strategies, including non-invasive (which focus on supporting the natural healing process), minimally invasive, and aggressive interventions (such as surgical approach, often reserved for severe or refractory cases). Emerging therapies, including stem cell treatments and botulinum toxin injections, offer new hope for improving outcomes in patients with wound healing impairments. This review highlights the distinct mechanisms underlying chronic wounds, keloids, and hypertrophic scars and discusses their respective therapeutic approaches, focusing on both established and emerging therapies. Understanding these mechanisms is crucial for optimizing treatment strategies and improving patient outcomes.
Preparation and Characterization of Al2O3/h-BN Composite Coatings by Atmospheric Plasma Spraying (APS)
Authors: Guo Yifeng , Lan Lingxiao , Qin Boying , Liang Xinghua , Wang Yujiang , Wang Yu , Chen Zhikun
Published: November 2024

To improve the adhesion strength of polymer functional films, corona treatment is required. Corona rollers are key components for corona treatment, which are used in high-voltage electric fields for a long time. In this work, in order to improve electrical insulation, arc resistance, wear resistance, and chemical stability, a coating is usually sprayed on the surface of the corona roller. Al2O3/h-BN composite coatings are prepared on the surface substrate of a corona roller (20 steel) by atmospheric plasma spraying (APS) technology. Scanning Electron Microscope (SEM) and X-ray diffraction (XRD) analysis showed that the Al2O3/h-BN composite coating had a layered structure and compactness. Two kinds of Al2O3/h-BN composite coatings are prepared under different APS process parameters; the porosities of A coating and B coating are 6.04% and 4.75%, the microhardnesses are 781 ± 0.5 Hv and 840.5 ± 0.5 Hv, and the adhesion strengths are 22.0 MPa and 22.3 MPa, respectively. The A and B volume resistivity of the coatings are 9.29 × 1010 Ω·cm and 3.55 × 1010 Ω·cm, respectively. The volume resistivity and porosity of the coatings are negatively correlated, and they decrease with the increase in spraying current. But for both coatings, volume resistivity is greater than 1 × 1010 Ω cm. These results indicate that the Al2O3/h-BN composite coatings, as a new type of electrode roller coating, satisfy the use requirement. Al2O3/h-BN composite coatings can become the potential for ceramic coatings that have good mechanics and insulation performance.
Circulating MicroRNAs as Biomarkers for the Early Diagnosis of Lung Cancer and Its Differentiation from Tuberculosis
Authors: Ashirbekov Yeldar , Khamitova Nazgul , Satken Kantemir , Abaildayev Arman , Pinskiy Ilya , Yeleussizov Askar , Yegenova Laura , Kairanbayeva Anargul , Kadirshe Danara , Utegenova Gulzhakhan , Jainakbayev Nurlan , Sharipov Kamalidin
Published: November 2024

Background: The differential diagnosis of tuberculosis (TB) and lung cancer (LC) is often challenging due to similar clinicopathological presentations when bacterial shedding is negative, which can lead to delays in treatment. In this study, we tested the potential of plasma-circulating microRNAs (miRNAs) for the early and differential diagnosis of TB and LC. Methods: We conducted a two-phase study: profiling 188 miRNAs in pooled plasma samples and validating 14 selected miRNAs in individual plasma samples from 68 LC patients, 38 pulmonary TB patients, and 41 healthy controls. Results: Twelve miRNAs were significantly elevated in LC patients compared to controls and TB patients, while two miRNAs were significantly elevated in TB patients compared to controls. ROC analysis demonstrated that miR-130b-3p, miR-1-3p, miR-423-5p, and miR-200a-3p had good discriminatory ability to distinguish LC patients (including those with stage I tumours) from healthy individuals and miR-130b-3p, miR-423-5p, miR-15b-5p, and miR-18b-5p effectively distinguished LC patients (including those with stage I tumours) from TB patients. Additionally, miR-18b-5p showed good discriminatory ability between SCLC and NSCLC patients. Conclusions: Circulating miRNAs hold strong potential for the early detection of LC and for distinguishing LC from TB.
AI-Based Noise-Reduction Filter for Whole-Body Planar Bone Scintigraphy Reliably Improves Low-Count Images
Authors: Csikos Csaba , Barna Sándor , Kovács Ákos , Czina Péter , Budai Ádám , Szoliková Melinda , Nagy Iván Gábor , Husztik Borbála , Kiszler Gábor , Garai Ildikó
Published: November 2024

Background/Objectives: Artificial intelligence (AI) is a promising tool for the enhancement of physician workflow and serves to further improve the efficiency of their diagnostic evaluations. This study aimed to assess the performance of an AI-based bone scan noise-reduction filter on noisy, low-count images in a routine clinical environment. Methods: The performance of the AI bone-scan filter (BS-AI filter) in question was retrospectively evaluated on 47 different patients’ 99mTc-MDP bone scintigraphy image pairs (anterior- and posterior-view images), which were obtained in such a manner as to represent the diverse characteristics of the general patient population. The BS-AI filter was tested on artificially degraded noisy images—75, 50, and 25% of total counts—which were generated by binominal sampling. The AI-filtered and unfiltered images were concurrently appraised for image quality and contrast by three nuclear medicine physicians. It was also determined whether there was any difference between the lesions seen on the unfiltered and filtered images. For quantitative analysis, an automatic lesion detector (BS-AI annotator) was utilized as a segmentation algorithm. The total number of lesions and their locations as detected by the BS-AI annotator in the BS-AI-filtered low-count images was compared to the total-count filtered images. The total number of pixels labeled as lesions in the filtered low-count images in relation to the number of pixels in the total-count filtered images was also compared to ensure the filtering process did not change lesion sizes significantly. The comparison of pixel numbers was performed using the reduced-count filtered images that contained only those lesions that were detected in the total-count images. Results: Based on visual assessment, observers agreed that image contrast and quality were better in the BS-AI-filtered images, increasing their diagnostic confidence. Similarities in lesion numbers and sites detected by the BS-AI annotator compared to filtered total-count images were 89%, 83%, and 75% for images degraded to counts of 75%, 50%, and 25%, respectively. No significant difference was found in the number of annotated pixels between filtered images with different counts (p > 0.05). Conclusions: Our findings indicate that the BS-AI noise-reduction filter enhances image quality and contrast without loss of vital information. The implementation of this filter in routine diagnostic procedures reliably improves diagnostic confidence in low-count images and elicits a reduction in the administered dose or acquisition time by a minimum of 50% relative to the original dose or acquisition time.
Comparative Analysis of qSOFA, PRIEST, PAINT, and ISARIC4C Scores in Predicting Severe COVID-19 Outcomes Among Patients Aged over 75 Years
Authors: Rosca Daniela , Krishna Vamsi , Chetarajupalli Chandramouli , Jianu Adelina Maria , Deak Ilona Emoke , Virzob Claudia Raluca Balasa , Laitin Sorina Maria Denisa , Boruga Madalina , Lighezan Rodica
Published: November 2024

Background: Elderly patients, particularly those over 75 years old, have been disproportionately affected by COVID-19, exhibiting higher rates of severe outcomes, such as ICU admissions and mortality. This study aimed to evaluate and compare the effectiveness of various clinical scoring systems—qSOFA, PRIEST, PAINT, and ISARIC4C—in predicting ICU admission, the need for mechanical ventilation, and mortality among elderly COVID-19 patients. Methods: In this retrospective cohort study conducted at two tertiary care hospitals, 131 elderly patients (aged ≥ 75) and 226 younger controls (aged < 65) with confirmed COVID-19 were included. Clinical scores were computed at admission and five days after symptom onset. Kaplan–Meier survival analysis and Receiver Operating Characteristic (ROC) curve analysis were performed to assess the predictive performance of the scores regarding severe outcomes. Results: Kaplan–Meier analysis indicated significantly lower survival probabilities for elderly patients with high scores at admission. Those with an ISARIC4C score above 11.8 had a survival probability of 25% compared to 74% for those below this threshold (p < 0.001). Similarly, elderly patients with a qSOFA score above 2.1 had a survival probability of 36% compared to 72% for those with lower scores (p < 0.001). The PRIEST and PAINT scores also demonstrated predictive validity; patients with a PRIEST score above 6.3 and a PAINT score above 6.5 at admission showed comparable decreases in survival probabilities. ROC analysis at five days post-symptom onset revealed that the ISARIC4C score had the highest area under the curve (AUC) of 0.772, suggesting excellent predictive validity for severe outcomes, including mortality. The optimal cutoffs identified were 11.2 for ISARIC4C, 6.3 for PRIEST, and 6.5 for PAINT, each displaying high sensitivity and specificity. Conclusions: The ISARIC4C, qSOFA, PRIEST, and PAINT scores are robust predictors of severe outcomes in elderly COVID-19 patients over 75 years old, as confirmed by Kaplan–Meier and ROC analyses. These tools can be crucial for early identification of patients at high risk of adverse outcomes, guiding clinical decision making, and optimizing resource allocation. The use of these scoring systems should be encouraged in clinical settings to enhance the management of elderly COVID-19 patients. Further research is necessary to validate these findings across different populations and settings.
Creative Self-Efficacy and Social Skills in a Portuguese Sample of University Students: Links with Self-Esteem, Academic Achievement and Life Satisfaction
Authors: González Moreno Alba , Simões Celeste , Santos Anabela Caetano , Molero Jurado María del Mar
Published: November 2024

Creative self-efficacy and social skills are two elements that can significantly enhance personal and professional development. The main objective of this research is to analyze the relations established between creative self-efficacy and social skills with other variables such as self-esteem, academic performance, and life satisfaction. The participants included in the methodology of this study are a total of 238 Portuguese university students. The instruments used were the Creative Self-Efficacy Scale, the Social Skills Questionnaire (CHASO), the Rosenberg Self-Esteem Scale (RSES), the Satisfaction with Life Scale (SWLS), and a series of ad hoc questions to assess academic performance. The results obtained indicate the existence of significant relations between creative self-efficacy and social skills, with the latter also being positively associated with self-esteem and life satisfaction. Analyses indicate that there are significant differences according to gender, academic performance, and the level of self-esteem of the participants. In addition, variables such as self-esteem, academic performance, and fluency act as predictors of life satisfaction. The importance of further exploring and understanding the complex relationship between creative self-efficacy, social skills, and individual well-being in the university context is discussed.
Hierarchical Security Authentication with Attention-Enhanced Convolutional Network for Internet of Things
Authors: Qiu Xiaoying , Zhao Guangxu , Yu Jinwei , Jiang Wenbao , Guo Zhaozhong , Xu Maozhi
Published: November 2024

As security authentication issues continue to arise in future wireless communication networks, researchers are working hard to further improve authentication techniques. Recently, physical layer authentication (PLA) has received widespread attention for its lightweight nature compared to traditional encryption methods based on keys and blockchain. However, the existing PLA mechanisms based on a fixed decision threshold have low reliability in dynamic environments. Moreover, PLA solutions are typically based on binary authentication, and these binary-type schemes cannot provide different levels of access control. To address these challenges, this article introduces the concept of hierarchical security authentication, aiming to achieve multi-level secure authorization access. In order to further improve the accuracy of identity verification, we design an Attention-Enhanced Convolutional Network (AECN) model that integrates the attention mechanism. Specifically, by introducing a confidence score branch, the proposed AECN-based PLA scheme completes authentication without a threshold, thus avoiding the issues stemming from inappropriate threshold settings in conventional PLA schemes. The simulation results show that our proposed AECN framework outperforms existing algorithms at different levels of security authentication.
Deep Semantics-Enhanced Neural Code Search
Authors: Yin Ying , Ma Longfei , Gong Yuqi , Shi Yucen , Wahab Fazal , Zhao Yuhai
Published: November 2024

Code search uses natural language queries to retrieve code snippets from a vast database, identifying those that are semantically similar to the query. This enables developers to reuse code and enhance software development efficiency. Most existing code search algorithms focus on capturing semantic and structural features by learning from both text and code graph structures. However, these algorithms often struggle to capture deeper semantic and structural features within these sources, leading to lower accuracy in code search results. To address this issue, this paper proposes a novel semantics-enhanced neural code search algorithm called SENCS, which employs graph serialization and a two-stage attention mechanism. First, the code program dependency graph is transformed into a unique serialized encoding, and a bidirectional long short-term memory (LSTM) model is used to learn the structural information of the code in the graph sequence to generate code vectors rich in structural features. Second, a two-stage attention mechanism enhances the embedded vectors by assigning different weight information to various code features during the code feature fusion phase, capturing significant feature information from different code feature sequences, resulting in code vectors rich in semantic and structural information. To validate the performance of the proposed code search algorithm, extensive experiments were conducted on two widely used code search datasets, CodeSearchNet and JavaNet. The experimental results show that the proposed SENCS algorithm improves the average code search accuracy metrics by 8.30 % (MRR) and 17.85% (DCG) and compared to the best baseline code search model in the literature, with an average improvement of 14.86% in the SR@1 metric. Experiments with two open-source datasets demonstrate SENCS achieves a better search effect than state of-the-art models.