bluuebunny/crossref_metadata_2025 huggingface dataset:
Damage Identification in Steel Girder Based on Vibration Responses of Different Sinusoidal Excitations and Wavelet Packet Permutation Entropy
Authors: Zhou Yutao , Zhuang Yizhou , Sinha Jyoti K.
Published: September 2024

Damage identification, both in terms of size and location, in bridges is important for timely maintenance and to avoid any catastrophic failure. An earlier experimental study showed that damage in a steel box girder orthotropic plate can be successfully detected using the measured vibration acceleration data. In this study, the wavelet packet decomposition (WPD) method is used to analyze the measured vibration acceleration responses and then the estimation of the permutation entropy (PE) on the re-constructed signals. A damage index is then defined based on the permutation entropy difference (PED) between the damaged and the healthy conditions to detect the location and size of the damage. The method is further validated through the finite element (FE) model of a steel box girder and the computed vibration acceleration responses when subjected to the sinusoidal excitations at different frequencies. In addition, the robustness of the methodology under different white noise interference conditions is also verified. The results show that the proposed methodology can effectively identify the location of human-made damage and accurately estimate the degree of damage under different frequencies of sinusoidal excitation. The method has shown a strong anti-noise property.
Application of a Novel High-Order WENO Scheme in LES Simulations
Authors: Zhang Shuo , Zhong Dongdong , Wang Hao , Wu Xingshuang , Ge Ning
Published: September 2024

To achieve high-fidelity large eddy simulation (LES) predictions of complex flows while keeping computational costs manageable, this study integrates a high-order WENO-ZQ scheme into the LES framework. The WENO-ZQ scheme has been extensively studied for its accuracy, robustness, and computational cost in inviscid flow applications. This study extended the WENO-ZQ scheme to viscous flows by integrating it into a three-dimensional structured grid LES CFD solver. High-fidelity simulations of turbulent boundary layer flow and supersonic compression ramp flows were conducted, with the scheme being applied for the first time to study laminar boundary layer transition and separation flows in the high-load, low-pressure turbine PakB cascade. Classic numerical case validations for viscous conditions demonstrate that the WENO-ZQ scheme, compared to the same-order WENO-JS scheme, exhibits lower dispersion and dissipation errors, faster convergence, and better high-frequency wave resolution. It maintains high-resolution accuracy with fewer grid points. In application cases, the WENO-ZQ scheme accurately captures the three-dimensional flow characteristics of shockwave–boundary layer interactions in supersonic compression ramps and shows high accuracy and resolution in predicting separation and separation-induced transition in low-pressure turbines.
Rearrangement of Single Atoms by Solving Assignment Problems via Convolutional Neural Network
Authors: Rattanamongkhonkun Kanya , Pongvuthithum Radom , Likasiri Chulin
Published: September 2024

This paper aims to present an approach to address the atom rearrangement problem by developing Convolutional Neural Network (CNN) models. These models predict the coordinates for atom movements while ensuring collision-free transitions and filling all vacancies in the target array. The process begins with designing a cost function for the assignment problem that incorporates constraints to prevent collisions and guarantee vacancy filling. We then build and train CNN models using datasets for three different grid sizes: 10×10, 13×13, and 21×21. Our models achieve high accuracy in predicting atom positions, with individual position accuracies of 99.63%, 98.93%, and 97.24%, respectively, for the aforementioned grid sizes. Despite limitations in training larger models due to hardware constraints, our approach demonstrates significant improvements in speed and accuracy. The final section of the paper presents detailed accuracy results and calculation times for each model, highlighting the potential of CNN-based methods in optimizing atom rearrangement processes.
Modeling and Multi-Objective Optimization Design of High-Speed on/off Valve System
Authors: Ma Yexin , Wang Dongjie , Shen Yang
Published: September 2024

The design of the high-speed on/off valve is challenging due to the interrelated structural parameters of its driving actuator. Hence, this study proposes a multi-objective optimization approach that integrates a backpropagation neural network and artificial fish swarm algorithm optimization techniques to accurately model the electromagnetic solenoid structure. The backpropagation neural network is fitted and trained using simulation data to obtain a reduced-order model of the system, enabling the precise prediction of the system’s output based on the input structural parameters. By employing the artificial fish swarm algorithms, with optimization objectives focusing on the valve’s opening and closing times, a Pareto optimal solution set comprising 30 solutions is generated. Utilizing the optimized structural parameters, a prototype is manufactured and an experimental setup is constructed to verify the dynamic characteristics and flow pressure drop. The high-speed on/off valve achieves an approximate opening and closing time of 3 ms. Notably, the system output predicted using the backpropagation neural network (BPNN) exhibits consistency with the experimental findings, providing a reliable alternative to mathematical modeling.
Investigating the Turbulent Vertical Dispersion in a Strong Shear Dominated Neutral Atmospheric Boundary Layer
Authors: Degrazia Gervásio Annes , Costa Felipe Denardin , Martins Luís Gustavo Nogueira , Camponogara Luis Fernando , Stefanello Michel , da Rosa Cinara Ewerling , Tirabassi Tiziano
Published: September 2024

The primary focus of this article is to derive a solution to obtain the asymptotic turbulent dispersion parameter provided by the spectral Taylor statistical diffusion model. Unlike previous articles, which employed the Dirac delta function to solve the eddy diffusivity formula, in this study, we used the Dirac delta function properties to obtain directly the asymptotic turbulent dispersion parameter from the particles’ spatial dispersion variance described in terms of the Eulerian turbulence spectrum and of the scale factor defined formally as the ratio between Lagrangian and Eulerian timescales. From the Kolmogorov 1941 theory, a detailed derivation for this scale factor is presented. Furthermore, using high mean wind speed data generated by local topographic features, a magnitude for the Kolmogorov constant for the neutral atmospheric boundary layer is evaluated. Thus, this magnitude when added to other values obtained from the selected studies found in the literature provides an average value for the Kolmogorov constant that agrees with large eddy simulation data results. Therefore, this average value allows to obtain a more reliable description of this scale factor. Finally, employing analytical formulations for the observed neutral turbulent spectra and for the velocity variances as well as turbulent statistical quantities measured in a surface neutral atmospheric boundary layer, a vertical dispersion parameter is derived. This vertical dispersion parameter when utilized in a simple Gaussian diffusion model is able to reproduce well contaminant observed concentrations.The Gaussian simulated concentrations also compare well with those simulated by a Lagrangian stochastic particle dispersion model that uses observed vertical spectral peak frequency values at distinct levels of the neutral surface boundary layer. Therefore, the present study shows that the observational determination of a single vertical spectral peak frequency is sufficient to obtain a realistic vertical dispersion parameter characterizing the dispersive effect in the turbulent environment of the surface neutral atmospheric boundary layer.
Fourier Series Related to p-Trigonometric Functions
Authors: Alibrahim Ali Hamzah , Das Saptarshi
Published: September 2024

In this paper, we introduce the concept of generalized Fourier series, generated by the p-trigonometric functions, namely cosp and sinp, recently introduced related to the generalized complex numbers systems. The aim of this study is to represent a periodic signal as a sum of p-sine and p-cosine functions. In order to achieve this, we first present the integrals of the product of the same or different family of p-trigonometric functions over the full period of these functions to understand the orthogonality properties. Next, we use these integrals to derive the coefficients of the generalized p-Fourier series along with a few examples. The generalized Fourier series can be used to expand an arbitrary forcing function in the solution of a non-homogeneous linear ordinary differential equation (ODE) with constant coefficients.
Fermatean Probabilistic Hesitant Fuzzy Power Bonferroni Aggregation Operators with Dual Probabilistic Information and Their Application in Green Supplier Selection
Authors: Ruan Chuanyang , Yan Lin
Published: September 2024

In the realm of management decision-making, the selection of green suppliers has long been a complex issue. Companies must take a holistic approach, evaluating potential suppliers based on their capabilities, economic viability, and environmental impact. The decision-making process, fraught with intricacies and uncertainties, urgently demands the development of a scientifically sound and efficient method for guidance. Since the concept of Fermatean fuzzy sets (FFSs) was proposed, it has been proved to be an effective tool for solving multi-attribute decision-making (MADM) problems in complicated realistic situations. And the Power Bonferroni mean (PBM) operator, combining the strengths of the power average (PA) and Bonferroni mean (BM), excels in considering attribute interactions for a thorough evaluation. To ensure a comprehensive and sufficient evaluation framework for supplier selection, this paper introduces innovative aggregation operators that extend the PBM and integrate probabilistic information into Fermatean hesitant fuzzy sets (FHFSs) and Fermatean probabilistic hesitant fuzzy sets (FPHFSs). It successively proposes the Fermatean hesitant fuzzy power Bonferroni mean (FHFPBM), Fermatean hesitant fuzzy weighted power Bonferroni mean (FHFWPBM), and Fermatean hesitant fuzzy probabilistic weighted power Bonferroni mean (FHFPWPBM) operators, examining their key properties like idempotency, boundedness, and permutation invariance. By further integrating PBM with probabilistic information into FPHFSs, three new Fermatean probabilistic hesitant fuzzy power Bonferroni aggregation operators are developed: the Fermatean probabilistic hesitant fuzzy power Bonferroni mean (FPHFPBM), Fermatean probabilistic hesitant fuzzy weighted power Bonferroni mean (FPHFWPBM), and Fermatean probabilistic hesitant fuzzy probabilistic weighted power Bonferroni mean (FPHFPWPBM). Subsequently, a MADM method based on these operators is constructed. Finally, a numerical example concerning the selection of green suppliers is presented to demonstrate the applicability and effectiveness of this method using the FPHFPWPBM operator.
Physics-Based Equivalent Circuit Model Motivated by the Doyle–Fuller–Newman Model
Authors: Bihn Stephan , Rinner Jonas , Witzenhausen Heiko , Krause Florian , Ringbeck Florian , Sauer Dirk Uwe
Published: September 2024

This work introduces a sophisticated impedance-based equivalent circuit model of the electrochemical processes inside a lithium-ion battery cell. The influence on the electrical voltage response is derived and merged into a mathematical calculation framework describing all fundamental phenomena inside a battery. The parameters, whose sole influences on the electric behaviour cannot be separated at the cell level, are summarised to derive a model with purely electrical quantities. We significantly reduce the model order compared to a physicochemical model while ensuring a minimal approximation error. Utilising the findings from the model derivation, we develop a parameterisation procedure to separate the individual processes occurring in the battery and to support a hypothesis of the assignment to positive and negative electrodes based on several indicia. For this purpose, electrochemical impedance spectroscopy and correlation analysis are used to calculate the distribution of the time constants. The final parameterised model has physics-based parameter variations, which ensures that the simulation over broad ranges of temperatures and states of charge results in a reasonable voltage response. The model’s physical basis enables extrapolation beyond the measured operation area, and the model verification shows less than a 10 mV root mean square error over a wide range of operations.
Supervised Density-Based Metric Learning Based on Bhattacharya Distance for Imbalanced Data Classification Problems
Authors: Jalali Mojahed Atena , Moattar Mohammad Hossein , Ghaffari Hamidreza
Published: September 2024

Learning distance metrics and distinguishing between samples from different classes are among the most important topics in machine learning. This article proposes a new distance metric learning approach tailored for highly imbalanced datasets. Imbalanced datasets suffer from a lack of data in the minority class, and the differences in class density strongly affect the efficiency of the classification algorithms. Therefore, the density of the classes is considered the main basis of learning the new distance metric. It is possible that the data of one class are composed of several densities, that is, the class is a combination of several normal distributions with different means and variances. In this paper, considering that classes may be multimodal, the distribution of each class is assumed in the form of a mixture of multivariate Gaussian densities. A density-based clustering algorithm is used for determining the number of components followed by the estimation of the parameters of the Gaussian components using maximum a posteriori density estimation. Then, the Bhattacharya distance between the Gaussian mixtures of the classes is maximized using an iterative scheme. To reach a large between-class margin, the distance between the external components is increased while decreasing the distance between the internal components. The proposed method is evaluated on 15 imbalanced datasets using the k-nearest neighbor (KNN) classifier. The results of the experiments show that using the proposed method significantly improves the efficiency of the classifier in imbalance classification problems. Also, when the imbalance ratio is very high and it is not possible to correctly identify minority class samples, the proposed method still provides acceptable performance.
AMPK Activation Serves as a Common Pro-Survival Pathway in Esophageal Adenocarcinoma Cells
Authors: McNamee Niamh , Rajagopalan Pavithra , Tal-Mason Aya , Roytburd Samuel , Sachdeva Uma M.
Published: September 2024

Esophageal adenocarcinoma (EAC) is a subtype of esophageal cancer that is difficult to treat, with overall poor survival and frequent recurrence despite curative-intent treatment strategies. There is limited understanding of EAC resistance mechanisms to chemotherapy or radiation. We have found that the AMP-activated protein kinase (AMPK) can serve a pro-survival function in EAC cells in response to cytotoxic treatments. Treatment with the IL-6 inhibitor tocilizumab, which previously has been shown to inhibit EAC organoid growth, resulted in the activation of AMPK in the OE33 EAC cell line, which was accompanied by a decrease in MTORC1 signaling and an increase in oxidative mitochondrial metabolism, both known downstream effects of AMPK activation to promote cell survival under conditions of metabolic stress. This increase in oxidative metabolism was abrogated in cells with a genetic knockdown of AMPK expression. Furthermore, we found that AMPK was activated in OE33 cells following treatment with cisplatin or ionizing radiation. Treatment with the AMPK inhibitor Compound C or genetic knockdown of AMPK expression enhanced cell death in a synergistic manner with chemotherapeutics or ionizing radiation. These findings were recapitulated in human patient-derived EAC organoids, suggesting that AMPK may be a common pro-survival mechanism to confer treatment resistance in EAC and may serve as a novel target to enhance the efficacy of current and future treatment strategies.
Vascular Cytokines and Atherosclerosis: Differential Serum Levels of TRAIL, IL-18, and OPG in Obstructive Coronary Artery Disease
Authors: Bate Katharine A. , Genetzakis Elijah , Vescovi Joshua , Gray Michael P. , Celermajer David S. , McGuire Helen M. , Grieve Stuart M. , Vernon Stephen T. , Cartland Siân P. , Yang Jean Y. , Kavurma Mary M. , Figtree Gemma A.
Published: September 2024

The risk-factor-based prediction of atherosclerotic coronary artery disease (CAD) remains suboptimal, particularly in the absence of any of the standard modifiable cardiovascular risk factors (SMuRFs), making the discovery of biomarkers that correlate with atherosclerosis burden critically important. We hypothesized that cytokines and receptors associated with inflammation in CAD—tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), interleukin-18 (IL-18), and osteoprotegerin (OPG)—would be independently associated with CAD. To determine this, we measured the serum biomarker levels of 993 participants from the BioHEART study who had CT coronary angiograms that were scored for severity of stenosis and plaque composition. We found that the quartiles of TRAIL, OPG, and IL-18 were significantly associated with disease scores, and that the IL-18/TRAIL and OPG/TRAIL ratios demonstrated significant differences between no CAD vs. STEMI whereas only the OPG/TRAIL ratio showed differences between no CAD and obstructive CAD (stenosis > 50%). However, these associations did not persist after adjustment for age, sex, SMuRFs, and a family history of CAD. In conclusion, TRAIL, IL-18, and OPG and the derived ratios of IL-18/TRAIL and OPG/TRAIL demonstrate significant associations with raw disease scores and risk factors, but these markers are not discriminatory biomarkers for the prediction of CAD when incorporated into multi-variable risk models.
Are Melanocortin Receptors Present in Extant Protochordates?
Authors: Ji Ren-Lei , Jiang Shan-Shan , Kleinau Gunnar , Scheerer Patrick , Tao Ya-Xiong
Published: September 2024

Functional melanocortin receptor (MCR) genes have been identified in the genomes of early chordates, e.g., the cyclostomata. Whether they appear in the most ancient chordates such as cephalochordate and urochordata, however, remains unclear due to missing genetic data. Herein, we studied five putative (from NCBI database), sequence-based predicted MCR-like receptors from urochordata and cephalochordate, including Styela clava, Ciona intestinalis, Branchiostoma floridae, and Branchiostoma belcheri. The BLAST and phylogenetic analyses suggested a relationship between these specific receptors and vertebrate MCRs. However, several essential residues for MCR functions in vertebrates were missing in these putative chordata MCRs. To test receptor functionality, several experimental studies were conducted. Binding assays and functional analyses showed no specific binding and no ligand-induced cAMP or ERK1/2 signaling (with either endogenous α-MSH or synthetic ligands for MC4R), despite successfully expressing four receptors in HEK 293T cells. These four receptors showed high basal cAMP signaling, likely mediated by ligand-independent Gs coupling. In summary, our results suggest that the five predicted MCR-like receptors are, indeed, class A G protein-coupled receptors (GPCRs), which in four cases show high constitutive activity in the Gs-cAMP signaling pathway but are not MCR-like receptors in terms of ligand recognition of known MCR ligands. These receptors might be ancient G protein-coupled receptors with so far unidentified ligands.
Recent Advances in Characterization and Valorization of Lignin and Its Value-Added Products: Challenges and Future Perspectives
Authors: Ali Shehbaz , Rani Abida , Dar Mudasir , Qaisrani Muther , Noman Muhammad , Yoganathan Kamaraj , Asad Muhammad , Berhanu Ashenafi , Barwant Mukul , Zhu Daochen
Published: September 2024

Lignin, the earth’s second-most abundant biopolymer after cellulose, has long been relegated to low-value byproducts in the pulp and paper industry. However, recent advancements in valorization are transforming lignin into a sustainable and versatile feedstock for producing high-value biofuels, bioplastics, and specialty chemicals. This review explores the conversion of lignin’s complex structure, composed of syringyl (S), guaiacyl (G), and p-hydroxyphenyl (H) units, into value-added products. We critically assess various biochemical and analytical techniques employed for comprehensive lignin characterization. Additionally, we explore strategies for lignin upgrading and functionalization to enhance its suitability for advanced biomaterials. The review emphasizes key areas of lignin valorization, including catalytic depolymerization methods, along with the associated challenges and advancements. We discuss its potential as a feedstock for diverse products such as biofuels, bioplastics, carbon fibers, adhesives, and phenolic compounds. Furthermore, the review briefly explores lignin’s inherent properties as a UV protectant and antioxidant, alongside its potential for incorporation into polymer blends and composites. By presenting recent advancements and case studies from the literature, this review highlights the significant economic and environmental benefits of lignin valorization, including waste reduction, lower greenhouse gas emissions, and decreased reliance on non-renewable resources. Finally, we address future perspectives and challenges associated with achieving large-scale, techno-economically feasible, and environmentally sustainable lignin valorization.
The Potential Diagnostic Utility of SMAD4 and ACCS in the Context of Inflammation in Type 2 Diabetes Mellitus Patients
Authors: Abdalsada Habiba Khdair , Abdulsaheb Yusra Sebri , Zolghadri Samaneh , Al-Hakeim Hussein Kadhem , Stanek Agata
Published: September 2024

The search for new parameters for the prediction of type 2 diabetes mellitus (T2DM) or its harmful consequences remains an important field of study. Depending on the low-grade inflammatory nature of diabetes, we investigated three proteins in T2DM patients: 1-aminocyclopropane-1-carboxylate synthase (ACCS), granulocyte–colony-stimulating factor (G-CSF), and Sma Mothers Against Decapentaplegic homolog-4 (SMAD4). In brief, sixty T2DM and thirty healthy controls had their serum levels of ACCS, G-CSF, SMAD4, and insulin tested using the ELISA method. The insulin resistance (IR) parameter (HOMA2IR), beta-cell function percentage (HOMA2%B), and insulin sensitivity (HOMA2%S) were all determined by the Homeostasis Model Assessment-2 (HOMA2) calculator. The predictability of these protein levels was investigated by neural network (NN) analysis and was associated with measures of IR. Based on the results, ACCS, G-CSF, and SMAD4 increased significantly in the T2DM group compared with the controls. Their levels depend on IR status and inflammation. The multivariate GLM indicated the independence of the levels of these proteins on the covariates or drugs taken. The receiver operating characteristic area under the curve (AUC) for the prediction of T2DM using NN analysis is 0.902, with a sensitivity of 71.4% and a specificity of 93.8%. The network predicts T2DM well with predicted pseudoprobabilities over 0.5. The model’s predictive capability (normalized importance) revealed that ACCS is the best model (100%) for the prediction of T2DM, followed by G-CSF (75.5%) and SMAD4 (69.6%). It can be concluded that ACCS, G-CSF, and SMAD4 are important proteins in T2DM prediction, and their increase is associated with the presence of inflammation.
Effects of Prostaglandin E1 and Balloon Atrial Septostomy on Cerebral Blood Flow and Oxygenation in Newborns Diagnosed with Transposition of the Great Arteries
Authors: Cucerea Manuela , Ognean Maria-Livia , Pinzariu Alin-Constantin , Simon Marta , Suciu Laura Mihaela , Ghiga Dana-Valentina , Moldovan Elena , Moscalu Mihaela
Published: September 2024

Dextro-transposition of the great arteries (D-TGA) is a critical congenital heart defect that can impact neurodevelopment due to cerebral perfusion and oxygenation disorders followed by alterations in synaptogenesis, gyrification, sulcation, and the microstructure. Brain injuries can occur both pre-operatively and postoperatively, especially white matter injuries, neuronal loss, and stroke. Materials and Methods: In a retrospective study conducted at a tertiary center between 2016 and 2023, we investigated the early effects of Prostaglandin E1 (PGE1) administration and balloon atrial septostomy (BAS) on cerebral blood flow and oxygenation in inborn neonates with D-TGA. Cerebral Doppler Ultrasound in the anterior cerebral artery (ACA) was performed to assess the resistive index (RI), Peak Systolic Velocity (PSV), and End-Diastolic Velocity (EVD) before PGE1, before the BAS procedure, and 24 h after birth. Cerebral regional saturations of oxygen (crSO2) and cerebral fractional tissue oxygen extraction (cFTOE) were evaluated. D-TGA patients were divided into the PGE1 group and the PGE1 + BAS group. Age-matched healthy controls were used for comparison. Results: All 83 D-TGA newborns received PGE1 within two hours after delivery, of whom 46 (55.42%) underwent BAS. In addition, 77 newborns composed the control group. PGE1 administration increased crSO2 from 47% to 50% in the PGE1 group, but lower than in controls at 24 h of life, while cFTOE remained elevated. The RI increased 24 h after delivery (0.718 vs. 0.769; p = 0.000002) due to decreased EDV (10.71 vs. 8.74; p < 0.0001) following PGE1 treatment. The BAS procedure resulted in a significant increase in crSO2 from 42% to 51% at 24 h of life in the PGE1 + BAS group. Doppler parameters exhibited a similar trend as observed in the PGE1 group. Conclusions: PGE1 treatment and BAS are lifesaving interventions that may improve cerebral perfusion and oxygenation in newborns with D-TGA during the transition period, as reflected by increasing SpO2 and crSO2.
Epinephrine, Pregabalin, and Crizotinib as Three Medicines with Polish Implications over Three Last Centuries and in View of Three Different Drug Discovery Approaches
Authors: Kawczak Piotr , Feszak Igor , Bączek Tomasz
Published: September 2024

The discovery of epinephrine (adrenaline) and its subsequent implications in medicine owes significant contributions to Cybulski across different centuries, who, in 1894, was pivotal in identifying the adrenal medulla’s role in blood pressure regulation and naming the active substance “nadnerczyna”, known today as adrenaline. His work demonstrated the adrenal glands’ critical function in the body’s regulatory mechanisms beyond the nervous system. Cybulski’s groundbreaking research laid foundational knowledge for future endocrinological studies and pharmaceutical advancements. In the late 20th century, Andruszkiewicz collaborated with Silverman at Northwestern University to develop pregabalin, the active ingredient in Lyrica. Their innovative synthesis of gamma-aminobutyric acid derivatives led to a significant advancement in treating epilepsy, neuropathic pain, and fibromyalgia. Andruszkiewicz’s expertise in organic chemistry and enzymology was crucial in this collaborative effort, resulting in the successful development and commercialization of Lyrica. Additionally, Mroczkowski’s leadership at Pfizer contributed to the development of crizotinib, a notable anaplastic lymphoma kinase and proto-oncogene 1 tyrosine-protein kinase inhibitor used to treat specific types of non-small cell lung cancer. Her work exemplifies the continuing influence of Polish researchers in pioneering drug discovery and advancing therapeutic treatments over the past three centuries. These contributions highlight Poland’s significant role in global pharmaceutical innovations and medical research.
miRNA in the Diagnosis and Treatment of Critical Limb Ischemia
Authors: Popescu Alexandra Ioana , Rață Andreea Luciana , Vlad Daliborca , Vlad Cristian , Popescu Roxana , Onofrei Ramona Roxana , Morelli Marialuisa , Pantea Stelian , Barac Sorin
Published: September 2024

Chronic threatening limb ischemia of the inferior limbs (CLTI) is the final stage of peripheral arterial disease (PAD) and is one of the most feared atherosclerotic manifestations because if left untreated, in time, it can lead to amputation. Although there are currently numerous treatment techniques, both open and endovascular, it is a pathology that has no underlying treatment. Therefore, current studies are very much focused on new therapeutic possibilities that can be applied in the early stages of the atherosclerotic process. In numerous studies in the literature, miRNAs have been identified as important markers of atherosclerosis. The present study aims to identify the expression of three miRNAs—miR-199a, miR-20a, and miR-30c—in patients with chronic limb-threatening ischemia in the pre- and post-revascularization periods. The aim of the study is to identify whether these three markers play a role in critical ischemia and whether they have the potential for future use in new treatments of this pathology.
Roles of Polymer Concentration and Ionic Strength in the Deposition of Chitosan of Fungal Origin onto Negatively Charged Surfaces
Authors: Ormeño-Martínez María , Guzmán Eduardo , Fernández-Peña Laura , Greaves Andrew J. , Bureau Lionel , Ortega Francisco , Rubio Ramón G. , Luengo Gustavo S.
Published: September 2024

This study examines the potential of fungal chitosan derived from Aspergillus niger as a sustainable alternative to traditional petrochemical-based ingredients in cosmetic products. Specifically, the research examines the solubility of fungal chitosan in aqueous solutions of varying ionic strength and its adsorption onto negatively charged surfaces that mimic human hair keratin. The adsorption behavior, water content, and frictional properties of chitosan films were evaluated using a quartz crystal microbalance with dissipation monitoring and a surface force apparatus (SFA). The findings indicated that fungal chitosan exhibits good solubility at a pH of 4.5. Conversely, the adsorption of chitosan is subject to the influence of both polymer concentration and ionic strength. At the lowest ionic strengths, a screening-enhanced adsorption process occurs as a consequence of the reduction in chitosan solubility in the presence of salt. This results in the depletion of polymer chains from the solution and their subsequent deposition. An increase in ionic strength above 15–20 mM results in a worsening of the chitosan–surface interaction, due to the simultaneous screening of both the chitosan and the surface charges. This results in a hindrance to the adsorption process. The deposited films are highly hydrated, and this hydration increases with both polymer concentration and ionic strength. Furthermore, the films exhibit a predominantly elastic behavior, and the response of the films under shear deformations shows a strong dependence on the polymer concentration. These findings contribute to the development of environmentally friendly cosmetic formulations that meet consumer demands for sustainability.
Online and Offline Aggressive Behaviors in Adolescence: The Role of Self-Regulatory Self-Efficacy Beliefs
Authors: Favini Ainzara , Lunetti Carolina , Virzì Alessia Teresa , Cannito Loreta , Culcasi Flavia , Quarto Tiziana , Palladino Paola
Published: September 2024

Self-regulatory self-efficacy belief (i.e., SRSE) represents a fundamental factor for adjustment in adolescence, as a vehicle to promote positive behaviors and protect youths from transgressions and maladjustment. Research attested that, during adolescence, boys are more vulnerable to externalizing behaviors than girls, especially when they perceive themselves as scarcely capable of managing and orienting their behaviors and when they possess impairments in impulsivity. Previous studies firmly supported the crucial role of SRSE, especially in the offline context in adolescence. Still, very few studies investigated its impact in the online context, although nowadays, the Internet represents one of the most significant environments for youths’ daily lives. Thus, we aimed to examine the protective moderating role of SRSE in online and offline aggressive behaviors beyond youths’ temperamental vulnerabilities, such as high impulsivity. A sample of 318 Italian adolescents (Mage = 15.21; SD = 0.51; 57% boys; 40% girls; 3% third gender) were asked to complete the Impulsivity Scale at Wave 1, online and offline aggressive behaviors scales at Wave 2, and SRSE at Wave 2. The multiple-group-by-gender moderation model evidenced that, for what concerns online aggression, beyond the direct effects of impulsivity in boys and girls, SRSE directly affected online aggression and mediated the effect of impulsivity in girls. In contrast, impulsivity significantly affected offline aggressive behaviors only indirectly through the impact of SRSE, and SRSE directly influenced these behaviors in both genders. These results hold for the effects of youth’s age, sexual orientation, socioeconomic status, and years of education completed. This work preliminary evidenced that, contrary to previous studies which focused mainly on the vulnerability of boys to aggressive conduct, impulsivity had direct effects on online aggressive behaviors in girls, and SRSE can protect adolescents of both genders in the online context by predicting low online aggression and represents a protective factor from the indirect effects that impulsivity impairments can also have in the offline context.
The Noise: A Silent Threat to the Recovery of Patients in Neonatal Intensive Care Units
Authors: Rodríguez-Montaño Víctor M. , Puyana-Romero Virginia , Hernández-Molina Ricardo , Beira-Jiménez Juan L.
Published: September 2024

The architectural configuration of the space plays a crucial role in the acoustics of neonatal intensive care units (NICUs). The design of the environment, the materials used in construction, and the organisation of noise sources within the room significantly influence the noise levels present in these critical areas. For this research, the noise levels found in two hospitals, with different architectural configurations and sizes but similar construction materials, have been analysed. Data were recorded at 1 s intervals over a period of more than 24 h. The data collected in these hospitals confirm the magnitude of the noise problem in NICUs, highlighting the urgent need to address it effectively to ensure an appropriate and safe environment for the recovery of neonates. Good architectural planning can help mitigate noise, while poor configuration can exacerbate acoustic problems, negatively affecting both patients and healthcare staff. It is crucial to involve architects, acoustic engineers, and healthcare staff in the design and renovation of NICUs to create spaces that not only meet medical needs but also consider the auditory well-being of neonates.
The Fluid Behavior of a Non-Orifice TLCD under Harmonic Excitation: From Experiments to Analytical Solution
Authors: Serbes Sefer Arda , Engin Tahsin , Kemerli Muaz , Kayrakoğlu Egemen , Aydın Ahmet
Published: September 2024

Tuned liquid column damper (TLCD) is a well-known liquid damper designed to absorb the vibration of structures used in many applications, such as high-story buildings, wind turbines, and offshore platforms, requiring an accurate mathematical determination of the liquid level to model the TLCD structure system motion. The mathematical model of a TLCD is a nonlinear ordinary differential equation, unlike the structure, due to the term containing a viscous damping coefficient, and cannot be solved analytically. In this study, the fluid behavior of a TLCD without an orifice, directly connected to a shaking table under harmonic excitation, was investigated experimentally and a new linearization coefficient was proposed to be used in the mathematical model. First, the nonlinear mathematical model was transformed to a nondimensional form to better analyze the parameter relations, focusing on the steady-state amplitude of the liquid level during the harmonic excitation. The experimental data were then processed using the fourth-order Runge–Kutta method, and a correlation to calculate the viscous damping coefficient was proposed in the dimensionless form. Accordingly, a novel empirical model was proposed for the dimensionless steady-state amplitude of the liquid level using this correlation. Finally, with the help of the proposed correlation and the empirical model, an original linearization coefficient was introduced which does not need experimental data. The nonlinear mathematical model was linearized by using the developed linearization coefficient and solved analytically using the Laplace transform method. The study presents a generalized method for the analytical determination of the liquid level in a non-orifice TLCD under harmonic excitation, using a correlation and an empirical model proposed for the first time in this study, providing a novel and simple solution to be used in the examination of various TLCD structure systems.
Mapping the Digital Transformation Maturity of the Building Construction Industry Using Structural Equation Modeling
Authors: Naji Khalid K. , Gunduz Murat , Al-Hababi Hamed
Published: September 2024

Construction projects play a vital role in the global economy. However, the construction industry still lags in the adoption of digital technologies which have the potential to alleviate inefficiencies. There is also a lack of construction professionals with skills to implement digital technologies, and the industry is plagued by inadequate research and development (R&D) and low productivity. This paper applies the Digital Transformation Readiness Level Index in Building Construction (DTRLIIBC) to investigate digital transformation in the construction industry, and specifically in the construction phase, by identifying technologies, alternatives, policy incentives to ensure best practices, and infrastructure needed to smoothly implement digital technologies. A survey was distributed to executive managers, department managers, project managers, senior engineers, and supervisors in the construction industry. Interviews were also conducted with 13 experts with more than 20 years of experience, using the Delphi method to ensure the validity and reliability of the factors identified as significant based on their experience and perspectives. This study thus assessed potential factors related to digital transformation, along with identifying factors enabling the use of the DTRLIIBC itself. Structural Equation Modeling (SEM) was employed to identify causal relationships among the variables while minimizing measurement errors, as SEM was considered a multiple regression analysis to improve the efficiency of the model. To ascertain the model’s robustness and stability, a comprehensive evaluation was conducted that included tests for multivariate normality conformance, validity and reliability assessments, and accuracy evaluations to improve overall performance in the construction industry. The benefit of implementing SEM is its ability to provide a clear decision-making alternative as well as a potential vision for construction industry firms to improve efficiency and productivity in construction projects.
Orally Ingested Micro- and Nano-Plastics: A Hidden Driver of Inflammatory Bowel Disease and Colorectal Cancer
Authors: Bruno Annalisa , Dovizio Melania , Milillo Cristina , Aruffo Eleonora , Pesce Mirko , Gatta Marco , Chiacchiaretta Piero , Di Carlo Piero , Ballerini Patrizia
Published: September 2024

Micro- and nano-plastics (MNPLs) can move along the food chain to higher-level organisms including humans. Three significant routes for MNPLs have been reported: ingestion, inhalation, and dermal contact. Accumulating evidence supports the intestinal toxicity of ingested MNPLs and their role as drivers for increased incidence of colorectal cancer (CRC) in high-risk populations such as inflammatory bowel disease (IBD) patients. However, the mechanisms are largely unknown. In this review, by using the leading scientific publication databases (Web of Science, Google Scholar, Scopus, PubMed, and ScienceDirect), we explored the possible effects and related mechanisms of MNPL exposure on the gut epithelium in healthy conditions and IBD patients. The summarized evidence supports the idea that oral MNPL exposure may contribute to intestinal epithelial damage, thus promoting and sustaining the chronic development of intestinal inflammation, mainly in high-risk populations such as IBD patients. Colonic mucus layer disruption may further facilitate MNPL passage into the bloodstream, thus contributing to the toxic effects of MNPLs on different organ systems and platelet activation, which may, in turn, contribute to the chronic development of inflammation and CRC development. Further exploration of this threat to human health is warranted to reduce potential adverse effects and CRC risk.
Green Catalysts and/or Green Solvents for Sustainable Multi-Component Reactions
Authors: Messire Gatien , Caillet Emma , Berteina-Raboin Sabine
Published: September 2024

Here, we describe some well-known multicomponent reactions and the progress made over the past decade to make these processes even more environmentally friendly. We focus on the Mannich, Hantzsch, Biginelli, Ugi, Passerini, Petasis, and Groebke–Blackburn–Bienaymé reactions. After describing the origin of the reactions and their mechanisms, we summarize some advances in terms of the eco-compatibility of these different MCRs. These are followed by examples of some reactions, considered as variants, which are less well documented but which are promising in terms of structures generated or synthetic routes.
Advanced XPS-Based Techniques in the Characterization of Catalytic Materials: A Mini-Review
Authors: Cui Yuanyuan , Liao Yifan , Sun Youbao , Wang Wenchang , Wu Jinqi , Dai Weilin , Huang Taohong
Published: September 2024

X-ray photoelectron spectroscopy (XPS) technology is extensively applied in the field of catalysts, offering deep insights into their electronic structures and chemical composition. The development of advanced techniques based on XPS instrumentation allows for a deeper and more holistic exploration of the characteristics of catalytic materials. This mini-review introduces and summarizes the primary applications of XPS-based analysis methods, including ion scattering spectroscopy (ISS) for analyzing single atomic layers, angle-resolved XPS, high energy X-ray sources and argon ion sputtering, each providing different depths of information about a sample. It also summarizes the use of inert atmosphere transfer devices and high-temperature reactors for quasi in situ monitoring as well as the integration of in situ techniques, including light irradiation XPS, to study catalysts’ behavior under realistic conditions.
The Role of circHIPK3 in Tumorigenesis and Its Potential as a Biomarker in Lung Cancer
Authors: Siedlecki Eryk , Remiszewski Piotr , Stec Rafał
Published: September 2024

Lung cancer treatment and detection can be improved by the identification of new biomarkers. Novel approaches in investigating circular RNAs (circRNAs) as biomarkers have yielded promising results. A circRNA molecule circHIPK3 was found to be widely expressed in non-small-cell lung cancer (NSCLC) cells, where it plays a crucial role in lung cancer tumorigenesis. CircHIPK3 promotes lung cancer progression by sponging oncosuppressive miRNAs such as miR-124, miR-381-3p, miR-149, and miR-107, which results in increased cell proliferation, migration, and resistance to therapies. Inhibiting circHIPK3 has been demonstrated to suppress tumour growth and induce apoptosis, which suggests its potential use in the development of new lung cancer treatment strategies targeting circHIPK3-related pathways. As a biomarker, circHIPK3 shows promise for early detection and monitoring of lung cancer. CircHIPK3 increased expression levels in lung cancer cells, and its potential link to metastasis risk highlights its clinical relevance. Given the promising preliminary findings, more clinical trials are needed to validate circHIPK3 efficacy as a biomarker. Moreover, future research should determine if the mechanisms discovered in NSCLC apply to small cell lung cancer (SCLC) to investigate circHIPK3-targeted therapies for SCLC.
Investigating Grape Seed Extract as a Natural Antibacterial Agent for Water Disinfection in Saudi Arabia: A Pilot Chemical, Phytochemical, Heavy-Metal, Mineral, and CB-Dock Study Employing Water and Urine Samples
Authors: Felemban Shifa , Hamouda Asmaa Fathi
Published: September 2024

Microorganisms remain in water from various sources after desalination and other treatments, posing health risks. We explored alternative natural disinfection agents, focusing on grape seed extract (GSE). We collected local grape seeds in Saudi Arabia and analyzed their chemical components. Using gas chromatography–mass spectrometry and inductively coupled plasma mass spectrometry, we identified essential phytochemicals in the GSE, including polyphenols, flavonoids, and alkaloids. Notably, the GSE was free from bacteria and heavy-metal contamination and rich in beneficial nutrient metals. We conducted qualitative analyses on local water and urine samples to detect bacterial infections, heavy metals, and minerals. To assess GSE’s antibacterial potential, we performed molecular docking analysis. Our results reveal a strong binding energy between GSE and bacterial protein receptors, parallel to that of standard antibiotics. Additionally, the results of the laboratory pilot investigations align with those of computational analyses, confirming GSE’s efficacy. Agar well diffusion tests demonstrated significantly greater zones of inhibition for the crude oil extract compared with both diluted GSE and the positive control against the bacteria detected in the water and urine samples. Furthermore, we identified contamination by four bacterial strains and heavy metals in water samples and female urine samples, highlighting the need for effective water disinfectants. GSE shows promise as a safe and potent natural water disinfectant.
Association of Night Snacking and Screen Time on Sleep Behavior in Japanese Toddlers: A Cross-Sectional Study
Authors: Furutani Akiko , Nitta Lyie , Mochida Seiko , Makino Naomichi , Nozawa Yuki , Tahara Yu , Shibata Shigenobu
Published: September 2024

Background/Objectives: Irregular lifestyles, such as sleep deprivation and disordered eating, disrupt the circadian clock and are linked to health issues. This study investigates the relationship among chronotypes, social jet lag, night-snacking habits, and screen time in toddlers. Methods: A survey of 6177 mothers of children aged 3–8 years was conducted in June 2022. Means comparison and multiple regression analyses were used to analyze the data. Results: Toddlers who consumed night snacks were more likely to be evening types and experienced longer social jet lag. Longer screen time during night snacking correlated with evening-type tendencies. Juice and ice cream were identified as evening-type snacks. Conclusions: We suggest that stopping snacks after dinner is crucial to prevent evening-type eating. Especially, high-sugar and high-fat night snacks like juice and ice cream may contribute to nocturnal habits and disrupt circadian rhythms in children.