bluuebunny/crossref_metadata_2025 huggingface dataset:
The Evolution of the Corrosion Mechanism of Structural Steel Exposed to the Urban Industrial Atmosphere for Seven Years
Authors: Wu Haiying , Luo Yaozhi , Zhou Guangen
Published: April 2023

The corrosion mechanism and characteristics of steel in typical atmospheric environments directly affect the rationality of corrosion protection methods. This study investigates the corrosion evolution law of Q235 steel that has been exposed to the urban industrial atmosphere for seven years. The mass loss is used for corrosion dynamics analysis. The rust layers have been characterized by SEM, EDS, and XRD. Finally, the corrosion mechanism was analyzed through a combination of electrochemical methods, corrosion kinetics, and rust layer characteristics. The mass loss results indicate that a two-stage corrosion power function law can still effectively describe the corrosion rate of a seven-year exposure that complies with the power function law. The short-term corrosion results fail to fully reflect the corrosion performance of Q235 steel. The typical morphological structures of γ-FeOOH and α-FeOOH are identified, and the rust layers change from a loose and flat form to a granular and, finally, compact into a smooth surface. The crystalline phases of the rust layers include α-FeOOH, γ-FeOOH, Fe3O4/γ-Fe2O3 and α-Fe2O3. Corrosion products in the initial period are mainly γ-FeOOH, followed by α-FeOOH, and a small amount of Fe3O4/γ-Fe2O3. With the increase in exposure time, α-FeOOH and Fe3O4/γ-Fe2O3 in the rust layer increase. SO2 and Fe3O4/γ-Fe2O3 are the primary factors accelerating steel corrosion. During the first three years of atmospheric corrosion, the primary corrosion mechanism was governed by the acid cycle reaction mechanism. However, from the fifth year of atmospheric corrosion, oxygen-absorbing corrosion began to gradually dominate, specifically oxygen-absorbing corrosion.
Technical, Environmental, and Cost Assessment of Granite Sludge Valorisation
Authors: Surra Elena , Sousa João , Correia Manuela , Carvalheiras João , Labrincha João A. , Marques José C. , Lapa Nuno , Delerue-Matos Cristina
Published: April 2023

The granite sludge (GS) produced during block sawing can be exploited as alternative raw material in ceramic and concrete industries. Based on the case study of a Portuguese granite processing plant, this work analysed, by experimental tests and Environmental and Cost Life Cycle analyses, the feasibility of GS valorisation as a substitute (i) for feldspar in a ceramic paste and (ii) fine–medium inert filler in structural concrete. The results demonstrated that both the valorisation pathways are more advantageous than GS landfilling. Due to granulometric, mineralogical composition and shrinkage, GS can substitute feldspar in sandstone tiles or tableware products, although its tinting effect can limit noble whitish ceramic applications. In structural concrete mixes, 5% w/w GS instead of fine inert filler reduces the compressive strength and increases the water:cement ratio. The GS generates lower environmental impacts as a substitute for inert filler than as a substitute for feldspar in most of the impact categories analysed, even though the latter valorisation pathway provides higher benefits in Climate Change and the Depletion of Fossil resources, Water, and Ozone. If no monetary value is recognised for GS valorisation by the market, the sustainability of GS life cycle cost decreases when compared to its landfilling.
Hash Indexing-Based Image Matching for 3D Reconstruction
Authors: Cao Mingwei , Jiang Haiyan , Zhao Haifeng
Published: April 2023

Image matching is a basic task in three-dimensional reconstruction, which, in recent years, has attracted extensive attention in academic and industrial circles. However, when dealing with large-scale image datasets, these methods have low accuracy and slow speeds. To improve the effectiveness of modern image matching methods, this paper proposes an image matching method for 3D reconstruction. The proposed method can obtain high matching accuracy through hash index in a very short amount of time. The core of hash matching includes two parts: creating the hash table and hash index. The former is used to encode local feature descriptors into hash codes, and the latter is used to search candidates for query feature points. In addition, the proposed method is extremely robust to image scaling and transformation by using various verifications. A comprehensive experiment was carried out using several challenging datasets to evaluate the performance of hash matching. Experimental results show that the HashMatch presents excellent results compared to the state-of-the-art methods in both computational efficiency and matching accuracy.
Application of Wavelet Transform for the Detection of Cetacean Acoustic Signals
Authors: He Ruilin , Dai Yang , Liu Siyi , Yang Yuhao , Wang Yingdong , Fan Wei , Zhang Shengmao
Published: April 2023

Cetaceans are an important part of the ocean ecosystem and are widely distributed in seas across the world. Cetaceans are heavily reliant on acoustic signals for communication. Some Odontoceti can perceive their environments using their sonar system, including the detection, localization, discrimination, and recognition of objects. Acoustic signals are one of the most commonly used types of data for Cetacean research, and it is necessary to develop Cetacean acoustic signal detection methods. This study compared the performance of a manual method, short-time Fourier transform (STFT), and wavelet transform (WT) in Cetacean acoustic signal detection. The results showed that WT performs better in click detection. According to this research, we propose using STFT for whistle and burst-pulse marking and WT for click marking in dataset building. This research will be helpful in facilitating research on the habits and behaviors of groups and individuals, thus providing information to develop methods for protecting species and developing biological resources.
Fixed-Time Optimization of Perturbed Multi-Agent Systems under the Resource Constraints
Authors: Wang Bing , Wang Fumian , Chen Yuquan , Peng Chen
Published: April 2023

In this paper, a novel fixed-time distributed optimization algorithm is proposed to solve the multi-agent collaborative optimization (MSCO) problem with local inequality constraints, global equation constraints and unknown disturbances. At first, a penalty function method is used to eliminate the local inequality constraints and transform the original problem into a problem without local constraints. Then, a novel three-stage control scheme is designed to achieve a robust fixed-time convergence. In the first stage, a fixed-time reaching law is given to completely eliminate the effect of unknown disturbances with the aid of the integral sliding mode control method; in the second stage, a suitable interaction strategy is provided such that the whole system could satisfy the global constraints in fixed-time; in the third stage, a fixed-time gradient optimization algorithm of the multi-agent system is presented, with which the states of all the agents will converge to the minimum value of the global objective in a fixed-time. Finally, the effectiveness of the proposed control strategy is verified in the problem of wind farm co-generation with 60 wind turbines.
Concrete Beams Reinforced with High Strength Rebar in Combination with External Steel Tape
Authors: Bobalo Taras , Blikharskyy Yaroslav , Selejdak Jacek , Kopiika Nadiia , Blikharskyy Zinoviy
Published: April 2023

The use of combined reinforcement in the form of external S275 steel tape and A1000 high-strength rebar is generally interesting for research. The use of a package of reinforcement enables a better choice of a rational cross-section area of reinforcement by varying the rebar diameter or the width of the steel tape. In addition, an interesting issue for research is the limit strain values of reinforcement of different strength classes since they can differ significantly, which affects the operation of the structure as a whole. For structures with combined reinforcement, there is still the number of issues not studied yet: for example, the stages of inclusion in the work of reinforcement and the magnitude of forces perceived by particular type of the reinforcement, the process and reasons for the destruction of experimental samples, the feasibility of such reinforcement, and the effect of high-strength rebar on the strength and deformability of reinforced concrete structures. Given that the issue of combined reinforcement is not sufficiently studied, the main task of the study was to investigate the stress–strain state of reinforced concrete beams with combined reinforcement (high-strength A1000 steel bars in combination with external S275 steel tape) in more detail.
Deep Canine Topography: Captive-Zombies or Free-Flowing Relational Bodies?
Authors: O’Brien Darren
Published: April 2023

For the last three years I have been making walks with my canine companion as part of an Arts and Humanities Research Council funded fine art practice-based PhD at Nottingham Trent University, UK, which considers walking art as a shared human–canine practice. In this paper I reflect upon the doings of deep canine topography as a practice, with particular attention to the ethical questions raised. In the short meditation on the ethics of human animal artistic collaboration that follows, I will wander through the complex web of human–canine kinship, explored through art practice. Joining us on our walk through this tricky landscape are Rosi Braidotti, Jack Halberstam, Dona Haraway, Ron Broglio, Gilles Deleuze and Felix Guattari, and a host of other actors whose concepts and theories provide a rich source of ethical discussion, which fellow artists might find helpful.
Variations in Infrared Complex Refractive Index Spectra of Surface Soils from Global Dust Entrainment Regions
Authors: Sadrian Mohammad R. , Calvin Wendy M. , Perrin Andrew E. , Engelbrecht Johann P. , Moosmüller Hans
Published: April 2023

We explored spectral diversity in infrared (IR, 4–25 μm) imaginary (k) and real (n) indices of refraction (optical constants) for 26 surface soils that originated from global dust entrainment regions. The k spectra were derived from optical transmission spectra of KBr pellets containing dispersed dust and the n spectra were estimated with the subtractive Kramers–Kronig (SKK) method. We compared k and n obtained by this study with previously published values. Our optical constants from the KBr technique are comparable in magnitude and spectral shape to those from previous studies that used dust aerosols re-suspended in a chamber. This suggests that the SKK method can offer a reliable mechanism for measuring IR spectra of both optical constants for soils. The soils used here exhibit additional spectral variability compared with past studies. We note that the appearance and the shift in positions of the strongest absorption peaks are due to combinations of silicates with overlapping absorption, rather than being related to a single mineral. We note that all dust optical constants have substantially lower magnitudes compared to those of pure minerals. Our results can improve estimates of mineral dust radiative effects and support quantification of surface mineral estimates using satellite and ground-based remote sensing.
Developments of Efficient Trigonometric Quantile Regression Models for Bounded Response Data
Authors: Nasiru Suleman , Chesneau Christophe
Published: April 2023

The choice of an appropriate regression model for econometric modeling minimizes information loss and also leads to sound inferences. In this study, we develop four quantile regression models based on trigonometric extensions of the unit generalized half-normal distributions for the modeling of a bounded response variable defined on the unit interval. The desirable shapes of these distributions, such as left-skewed, right-skewed, reversed-J, approximately symmetric, and bathtub shapes, make them competitive models for bounded responses with such traits. The maximum likelihood method is used to estimate the parameters of the regression models, and Monte Carlo simulation results confirm the efficiency of the method. We demonstrate the utility of our models by investigating the relationship between OECD countries’ educational attainment levels, labor market insecurity, and homicide rates. The diagnostics reveal that all our models provide a good fit to the data because the residuals are well behaved. A comparative analysis of the trigonometric quantile regression models with the unit generalized half-normal quantile regression model shows that the trigonometric models are the best. However, the sine unit generalized half-normal (SUGHN) quantile regression model is the best overall. It is observed that labor market insecurity and the homicide rate have significant negative effects on the educational attainment values of the OECD countries.
Reliability Estimation under Normal Operating Conditions for Progressively Type-II XLindley Censored Data
Authors: Alotaibi Refah , Nassar Mazen , Elshahhat Ahmed
Published: April 2023

This paper assumes constant-stress accelerated life tests when the lifespan of the test units follows the XLindley distribution. In addition to the maximum likelihood estimation, the Bayesian estimation of the model parameters is acquired based on progressively Type-II censored samples. The point and interval estimations of the model parameters and some reliability indices under normal operating conditions at mission time are derived using both estimation methods. Using the Markov chain Monte Carlo algorithm, the Bayes estimates are calculated using the squared error loss function. Simulating the performances of the different estimation methods is performed to illustrate the proposed methodology. As an example of how the proposed methods can be applied, we look at two real-life accelerated life test cases. According to the numerical outcomes and based on some criteria, including the root of the mean square error and interval length, we can conclude that the Bayesian estimation method based on the Markov chain Monte Carlo procedure performs better than the classical methods in evaluating the XLindley parameters and some of its reliability measures when a constant-stress accelerated life test is applied with progressively Type-II censoring.
Fractional-Order Sliding-Mode Observers for the Estimation of State-of-Charge and State-of-Health of Lithium Batteries
Authors: Zhou Minghao , Wei Kemeng , Wu Xiaogang , Weng Ling , Su Hongyu , Wang Dong , Zhang Yuanke , Li Jialin
Published: April 2023

Lithium batteries are widely used in power storage and new energy vehicles due to their high energy density and long cycle life. The accurate and real-time estimation for the state-of-charge (SoC) and the state-of-health (SoH) of lithium batteries is of great significance to improve battery life, reliability, and utilization efficiency. In this paper, three cascaded fractional-order sliding-mode observers (FOSMOs) are designed for the estimation of SoC by observing the terminal voltage, the polarization voltage, and the open-circuit voltage of a lithium cell, respectively. Furthermore, to calculate the value of the SoH, two FOSMOs are developed to estimate the capacity and internal resistance of the lithium cell. The control signals of the observers are continuous by utilizing fractional-order sliding manifolds without low-pass filters. Compared with the existing sliding-mode observers for SoC and SoH, weaker chattering, faster response, and higher estimation accuracy are obtained in the proposed method. Finally, the experiment tests demonstrate the validity and feasibility of the proposed observer design method.
Modeling and Simulation of Non-Aqueous Redox Flow Batteries: A Mini-Review
Authors: Zhou Haotian , Zhang Ruiping , Ma Qiang , Li Zhuo , Su Huaneng , Lu Ping , Yang Weiwei , Xu Qian
Published: April 2023

Redox flow batteries (RFBs) have been widely recognized in the domain of large-scale energy storage due to their simple structure, long lifetime, quick response, decoupling of capacity and power, and structural simplicity. Because of the limited open circuit voltage (OCV) by hydrogen and oxygen evolution reactions, together with the relatively low solubility of active species, RFBs with aqueous electrolytes are challenging to reach high energy densities. Researchers have been trying to develop new solvent systems without water to remove the electrochemical window limitation of water and pursue higher cell potential. However, non-aqueous solvents are also hindered by some key problems, such as high viscosity and poor safety. Meeting these challenges require a comprehensive understanding of relevant structural design parameters and multi-variable operation in the non-aqueous flow battery (NAFB) system. Modeling and simulation are not only an effective way to understand the basic mechanism of flow batteries at different scales of size and time but also an ideal tool for optimizing the reaction process, battery assembly, and the whole flow battery installation. This review paper introduces the development of the non-aqueous flow battery, the challenges it faces, and the research progress of related modeling and simulation for verification or optimization. Finally, the future development prospects of the non-aqueous flow battery model are pointed out, especially for those systems and fields that have not yet been explored.
Identification and Error Analysis of Lithium-Ion Battery Oriented to Cloud Data Application Scenario
Authors: Zhang Fang , Sun Tao , Xu Bowen , Zheng Yuejiu , Lai Xin , Zhou Long
Published: April 2023

The label-less characteristics of real vehicle data make engineering modeling and capacity identification of lithium-ion batteries face great challenges. Different from ideal laboratory data, the raw data collected from vehicle driving cycles have a great adverse impact on effective modeling and capacity identification of lithium-ion batteries due to the randomness and unpredictability of vehicle driving conditions, sampling frequency, sampling resolution, data loss, and other factors. Therefore, data cleaning and optimization is processed and the capacity of a battery pack is identified subsequently in combination with the improved two-point method. The current available capacity is obtained by a Fuzzy Kalman filter optimization capacity estimation curve, making use of the charging and discharging data segments. This algorithm is integrated into a new energy big data cloud platform. The results show that the identification algorithm of capacity is applied successfully from academic to engineering fields by charge and discharge mutual verification, and that life expectancy meets the engineering requirements.
Metagenomic Shotgun Sequencing Reveals Specific Human Gut Microbiota Associated with Insulin Resistance and Body Fat Distribution in Saudi Women
Authors: Aljuraiban Ghadeer S. , Alfhili Mohammad A. , Aldhwayan Madhawi M. , Aljazairy Esra’a A. , Al-Musharaf Sara
Published: April 2023

(1) Background: Gut microbiota dysbiosis may lead to diseases such as insulin resistance and obesity. We aimed to investigate the relationship between insulin resistance, body fat distribution, and gut microbiota composition. (2) Methods: The present study included 92 Saudi women (18–25 years) with obesity (body mass index (BMI) ≥ 30 kg/m2, n = 44) and with normal weight (BMI 18.50–24.99 kg/m2, n = 48). Body composition indices, biochemical data, and stool samples were collected. The whole-genome shotgun sequencing technique was used to analyze the gut microbiota. Participants were divided into subgroups stratified by the homeostatic model assessment for insulin resistance (HOMA-IR) and other adiposity indices. (3) Results: HOMA-IR was inversely correlated with Actinobacteria (r = −0.31, p = 0.003), fasting blood glucose was inversely correlated with Bifidobacterium kashiwanohense (r = −0.22, p = 0.03), and insulin was inversely correlated with Bifidobacterium adolescentis (r = −0.22, p = 0.04). There were significant differences in α- and β-diversities in those with high HOMA-IR and waist–hip ratio (WHR) compared to low HOMA-IR and WHR (p = 0.02, 0.03, respectively). (4) Conclusions: Our findings highlight the relationship between specific gut microbiota at different taxonomic levels and measures of glycemic control in Saudi Arabian women. Future studies are required to determine the role of the identified strains in the development of insulin resistance.
Immuno-Sensing at Ultra-Low Concentration of TG2 Protein by Organic Electrochemical Transistors
Authors: Preziosi Valentina , Barra Mario , Villella Valeria Rachela , Esposito Speranza , D’Angelo Pasquale , Marasso Simone Luigi , Cocuzza Matteo , Cassinese Antonio , Guido Stefano
Published: April 2023

Transglutaminase 2 (TG2) is a ubiquitously expressed member of the transglutaminase family with Ca2+-dependent protein crosslinking activity. Its subcellular localization is crucial in determining its function, and indeed, TG2 is found in the extracellular matrix, mitochondria, recycling endosomes, plasma membrane, cytosol, and nucleus because it is associated with cell growth, differentiation, and apoptosis. It is involved in several pathologies, such as celiac disease, cardiovascular, hepatic, renal, and fibrosis diseases, carrying out opposite functions of up and down regulation in the progression of the same pathology. Therefore, this fine regulation requires a very sensitive and specific method of identification of TG2, which is to be detected in very small quantities in a deregulated condition. Here, we demonstrate the possibility of detecting TG2 down to attomolar concentration by using organic electrochemical transistors driven by gold electrodes functionalized with anti-TG2 antibodies. In particular, a direct correlation between the TG2 concentration and the transistor transconductance values, as extracted from typical transfer curves, was found. Overall, our findings highlight the potentialities of this new biosensing approach for the detection of TG2 in the context of pathological diseases, offering a rapid and cost-effective alternative to traditional methods.
Label-Free Detection of T4 Polynucleotide Kinase Activity and Inhibition via Malachite Green Aptamer Generated from Ligation-Triggered Transcription
Authors: Si Jingyi , Zhou Wei , Fang Ying , Zhou Da , Gao Yifan , Yao Qunyan , Shen Xizhong , Zhu Changfeng
Published: April 2023

Polynucleotide kinase (PNK) is a key enzyme that is necessary for ligation-based DNA repair. The activity assay and inhibitor screening for PNK may contribute to the prediction and improvement of tumor treatment sensitivity, respectively. Herein, we developed a simple, low-background, and label-free method for both T4 PNK activity detection and inhibitor screening by combining a designed ligation-triggered T7 transcriptional amplification system and a crafty light-up malachite green aptamer. Moreover, this method successfully detected PNK activity in the complex biological matrix with satisfactory outcomes, indicating its great potential in clinical practice.
A Study on the Impact of Digital Management on Willingness to Transfer Knowledge in Whole-Process Engineering Consulting Projects
Authors: Wang Qing’e , Ding Mengmeng
Published: April 2023

Against the background of the current development of China’s engineering consulting industry, the technology acceptance model (TAM) is applied to construct a model of digital management influencing the willingness to transfer knowledge in whole-process engineering consulting projects. Digital management is used as the independent variable, perceived usefulness and perceived ease of use as the mediating variables, and the willingness to transfer knowledge as the dependent variable to investigate the influence relationship among the three. In this paper, 263 sample data are selected and structural equation modeling is used to analyze the data. The results show that digital management has a significant positive influence on the willingness to transfer knowledge; perceived usefulness and perceived ease of use play a mediating role in the relationship between digital management and the willingness to transfer knowledge. This paper enriches the research on knowledge transfer of engineering consulting. From the perspective of digital management, measures are made for improving the willingness of members to transfer knowledge and for lifting management efficiency in the whole process of the engineering consulting project.
Effects of Different Types of Fibers on Fresh and Hardened Properties of Cement and Geopolymer-Based 3D Printed Mixtures: A Review
Authors: Ramezani Amir , Modaresi Shahriar , Dashti Pooria , GivKashi Mohammad Rasul , Moodi Faramarz , Ramezanianpour Ali Akbar
Published: April 2023

Three-dimensional printed concrete (3DPC) is emerging as a new building material. Due to automation, this method dramatically decreases construction time and material wastage while increasing construction quality. Despite the mentioned benefits, this technology faces various issues. Among these issues, the inability to use steel bars for reinforcement and early age cracking because of the low water-to-binder ratio and high amount of binders can be mentioned. In this regard, due to the superior properties of fiber-reinforced concrete (FRC), such as high first crack strength, tensile strength, improvement ductility, and resistance to shrinkage cracking, one of the effective ways to reinforce the mixture of the 3DPC is to use fibers instead of steel bars. Regarding the mentioned issues, the effects of different fibers, such as steel, carbon fibers and so on, on fresh and mechanical properties and dimensional stabilities of hardened concrete have been reviewed. It is predicted that using fibers, especially hybrid fibers, not only covers the deficiencies of initial cracking of 3DPC, but also can be used instead of steel bars; therefore, this material can play a pivotal role in the construction industry’s future.
Exploring Simultaneous Upgrading and Purification of Biomass−Gasified Gases Using Plasma Catalysis
Authors: He Wenyu , Xu Bin , Lang Lin , Yang Wenshen , Liu Huacai , Zhan Hao , Xie Jianjun , Yin Xiuli , Wu Chuangzhi
Published: April 2023

Tar and substantial CH4 and CO2 are contained in gasified fuels, which pose an obstacle to direct chemical synthesis, and this is a predominant challenge for biomass gasification technology. Herein, a packed−bed dielectric barrier discharge (DBD) reactor was built for simultaneous CH4 dry reforming and tar removal with a La−Ni/γ−Al2O3 catalyst. The interaction between CH4 dry reforming and tar removal in plasma catalysis was investigated. The results indicated that plasma catalysis can achieve high−efficiency simultaneous tar removal and CH4 dry reforming, as indicated by the reactants’ conversion (14% increase for CCH4 and CCO2 at 450 °C in the presence of tar and a 37% increase for the tar removal rate at 360 °C when CH4 and CO2 were introduced), and the mechanism for mutual promotion of CH4 dry reforming and tar removal was elucidated through catalyst characterization results. In addition, a possible reaction mechanism for tar removal via plasma catalysis was proposed. These findings provide valuable insights for simultaneous upgrading and purification of gases generated by biomass gasification.
Application of Spectroscopy Techniques for Monitoring (Bio)Catalytic Processes in Continuously Operated Microreactor Systems
Authors: Jurina Tamara , Sokač Cvetnić Tea , Šalić Anita , Benković Maja , Valinger Davor , Gajdoš Kljusurić Jasenka , Zelić Bruno , Jurinjak Tušek Ana
Published: April 2023

In the last twenty years, the application of microreactors in chemical and biochemical industrial processes has increased significantly. The use of microreactor systems ensures efficient process intensification due to the excellent heat and mass transfer within the microchannels. Monitoring the concentrations in the microchannels is critical for a better understanding of the physical and chemical processes occurring in micromixers and microreactors. Therefore, there is a growing interest in performing in-line and on-line analyses of chemical and/or biochemical processes. This creates tremendous opportunities for the incorporation of spectroscopic detection techniques into production and processing lines in various industries. In this work, an overview of current applications of ultraviolet–visible, infrared, Raman spectroscopy, NMR, MALDI-TOF-MS, and ESI-MS for monitoring (bio)catalytic processes in continuously operated microreactor systems is presented. The manuscript includes a description of the advantages and disadvantages of the analytical methods listed, with particular emphasis on the chemometric methods used for spectroscopic data analysis.
Selective Oxidation of Tetrahydrofuran to Gamma-Butyrolactone over Spinel ZnFe2O4 Nanoparticle Catalyst
Authors: Abduh Naaser A. Y. , Al-Kahtani Abdullah , Algarni Tahani Saad , Al-Odayni Abdel-Basit
Published: April 2023

The selective oxidation of tetrahydrofuran (THF) to gamma-butyrolactone (GBL) on spinel ZnFe2O4 nanoparticles (ZFNPs) was investigated. The catalyst was prepared with the coprecipitation method and characterized by FTIR, XRD, TEM, SEM, EDS, TGA, XPS, and BET surface area. The characterization techniques showed that a nonuniform spherical spinal oxide with an average particle size of 26 nm was formed. The oxidation reaction was carried out using hydrogen peroxide as an oxidizing agent under solvent-free conditions. GC-MS analysis revealed that the main product was GBL. 2-hydroxytetrahydrofuran (THF-2-OH), gamma-hydroxybutyric acid (GHBA), and gamma-hydroxybutaldehyde (GHBAl) were obtained as minor products. The effects of different reaction parameters, such as temperature, H2O2/THF mole ratio, catalyst dose, reaction time, and reusability, were evaluated. A 47.3% conversion of THF with an 88.2% selectivity of GBL was achieved by conducting the reaction at 80 °C for nine hours using a 1:1 mole ratio of H2O2/THF. A slight increase in the conversion degree was attained at higher temperatures; however, an over-oxidation process was observed as the temperature exceeded 80 °C. The catalyst remained effective and stable over four reuses.
Ocular Delivery of Therapeutic Agents by Cell-Penetrating Peptides
Authors: Nhàn Nguyễn Thị Thanh , Maidana Daniel E. , Yamada Kaori H.
Published: April 2023

Cell-penetrating peptides (CPPs) are short peptides with the ability to translocate through the cell membrane to facilitate their cellular uptake. CPPs can be used as drug-delivery systems for molecules that are difficult to uptake. Ocular drug delivery is challenging due to the structural and physiological complexity of the eye. CPPs may be tailored to overcome this challenge, facilitating cellular uptake and delivery to the targeted area. Retinal diseases occur at the posterior pole of the eye; thus, intravitreal injections are needed to deliver drugs at an effective concentration in situ. However, frequent injections have risks of causing vision-threatening complications. Recent investigations have focused on developing long-acting drugs and drug delivery systems to reduce the frequency of injections. In fact, conjugation with CPP could deliver FDA-approved drugs to the back of the eye, as seen by topical application in animal models. This review summarizes recent advances in CPPs, protein/peptide-based drugs for eye diseases, and the use of CPPs for drug delivery based on systematic searches in PubMed and clinical trials. We highlight targeted therapies and explore the potential of CPPs and peptide-based drugs for eye diseases.
Microfluidic Detection of Adenylate Kinase as a Cell Damage Biomarker
Authors: Domingues Cristiana , Fontes Pedro Mendes , Condelipes Pedro G. M. , Marques Vanda , Afonso Marta B. , Chu Virginia , Rodrigues Cecília M. P. , Conde João Pedro
Published: April 2023

In vitro cell cultures are used as models for drug discovery. The detection of cell damage biomarkers such as adenylate kinase (AK) is often used in drug screening and cell biology experiments. A microfluidic platform for AK detection was developed with the capability of detecting the AK resulting from the lysis of 10–100 human colorectal adenocarcinoma HCT116 cells. For this assay, AK was captured on the surface of microbeads integrated into a microfluidic device and optically detected using a fluorescently labelled anti-AK antibody. Microfluidic technologies have in addition been used to develop two- and three-dimensional cell culture models that have the potential to accelerate drug discovery. The microfluidic platform was used to detect the AK resulting from the lysis of HCT116 cells cultivated in a microfluidic biochip, demonstrating the potential for the integration of the miniaturised biosensor with the cell chip.
A Pilot Study of a Panel of Ocular Inflammation Biomarkers in Patients with Primary Sjögren’s Syndrome
Authors: Boto de los Bueis Ana , de la Fuente Miguel , Montejano-Milner Rafael , del Hierro Zarzuelo Almudena , Vecino Elena , Acera Arantxa
Published: April 2023

Ocular diseases have a strong impact on individuals, the effects of which extend from milder visual impairment to blindness. Due to this and to their prevalence, these conditions constitute important health, social and economic challenges. Thus, improvements in their early detection and diagnosis will help dampen the impact of these conditions, both on patients and on healthcare systems alike. In this sense, identifying tear biomarkers could establish better non-invasive approaches to diagnose these diseases and to monitor responses to therapy. With this in mind, we developed a solid phase capture assay, based on antibody microarrays, to quantify S100A6, MMP-9 and CST4 in human tear samples, and we used these arrays to study tear samples from healthy controls and patients with Sjögren’s Syndrome, at times concomitant with rheumatoid arthritis. Our results point out that the detection of S100A6 in tear samples seems to be positively correlated to rheumatoid arthritis, consistent with the systemic nature of this autoinflammatory pathology. Thus, we provide evidence that antibody microarrays may potentially help diagnose certain pathologies, possibly paving the way for significant improvements in the future care of these patients.
The Case of an Endometrial Cancer Patient with Breast Cancer Who Has Achieved Long-Term Survival via Letrozole Monotherapy
Authors: Ishikawa Masako , Nakayama Kentaro , Razia Sultana , Yamashita Hitomi , Ishibashi Tomoka , Haraga Hikaru , Kanno Kosuke , Ishikawa Noriyoshi , Kyo Satoru
Published: April 2023

Herein, we present the successful treatment of a 92-year-old woman who experienced recurrent EC in the vaginal stump and para-aortic lymph nodes. The patient was first treated with paclitaxel and carboplatin for recurrent EC, which was abandoned after two cycles of chemotherapy because of G4 hematologic toxicity. Later, the patient was treated with letrozole for early-stage breast cancer, which was diagnosed simultaneously with EC recurrence. After four months of hormonal therapy, a partial response was observed not only in the lesions in the breast, but also those in the vaginal stump and para-aortic lymph nodes. She had no recurrence of breast cancer or EC, even after six years of treatment with letrozole-based hormonal therapy. Subsequent whole-exome sequencing using the genomic DNA isolated from the surgical specimen in the uterine tumor identified several genetic variants, including actionable mutations, such as CTNNB1 (p.S37F), PIK3R1 (p.M582Is_10), and TP53 c.375 + 5G>T. These data suggest that the efficacy of letrozole is mediated by blocking the mammalian target of the rapamycin pathway. The findings of this study, substantiated via genetic analysis, suggest the possibility of long-term disease-free survival, even in elderly patients with recurrent EC, which was thought to be difficult to cure completely.
The Diagnostic Value of Cerebrospinal Fluid Lactate for Detection of Sepsis in Community-Acquired Bacterial Meningitis
Authors: Nitsch Louisa , Ehrentraut Stefan Felix , Grobe-Einsler Marcus , Bode Felix J. , Banat Mohammed , Schneider Matthias , Lehmann Felix , Zimmermann Julian , Weller Johannes
Published: April 2023

Community-acquired bacterial meningitis conveys significant morbidity and mortality due to intracranial and systemic complications, and sepsis is a major contributor to the latter. While cerebrospinal fluid (CSF) analysis is essential in the diagnosis of bacterial meningitis, its predictive utility for detection of sepsis is unknown. We investigated the diagnostic performance of CSF parameters for sepsis defined by the Sepsis-3 criteria in a retrospective cohort of patients with community-acquired bacterial meningitis. Among 103 patients, 69.5% developed sepsis. CSF lactate was associated with sepsis with an odds ratio of 1.11 (p = 0.022), while CSF cell counts, glucose and protein levels were not (all p > 0.4). Employing the optimal cutoff of 8.2 mmol/L, elevated CSF lactate resulted in a sensitivity of 81.5% and specificity of 61.5% for sepsis. In exploratory analyses, CSF lactate was also associated with in-hospital mortality with an odds ratio of 1.21 (p = 0.011). Elevated CSF lactate might contribute to early diagnosis of sepsis as well as prognostication in patients with community-acquired bacterial meningitis.
Image Quality Analysis of Photon-Counting CT Compared with Dual-Source CT: A Phantom Study for Chest CT Examinations
Authors: Deleu Marine , Maurice Jean-Baptiste , Devos Laura , Remy Martine , Dubus François
Published: April 2023

A comparison was made between the image quality of a photon-counting CT (PCCT) and a dual-source CT (DSCT). The evaluation of image quality was performed using a Catphan CT phantom, and the physical metrics, such as the noise power spectrum and task transfer function, were measured for both PCCT and DSCT at three CT dose indices (1, 5 and 10 mGy). Polyenergetic and virtual monoenergetic reconstructions were used to evaluate the performance differences by simulating a Gaussian spot with a radius of 5 mm and calculating the detectability index. The highest iterative reconstruction level was able to decrease the noise by about 70% compared with the filtered back projection using a parenchyma reconstruction kernel. The PCCT task transfer functions remained constant, while those of the DSCT increased with the reconstruction strength level. At monoenergetic 70 keV, a 50% decrease in noise was observed for DSCT with image smoothing, while PCCT had the same 50% decrease in noise without any smoothing. The PCCT detectability index at a reconstruction strength level of two was equivalent to the highest level of ADMIRE 5 for DSCT. The PCCT showed its superiority over the DSCT, especially for lung nodule detection.
Discovering Learning Potential in Secondary Education Using a Dynamic Screening Instrument
Authors: van Graafeiland Nina , Veerbeek Jochanan , Janssen Barbara , Vogelaar Bart
Published: April 2023

This study aimed to investigate the effectiveness of a newly developed dynamic screening instrument, using a learning phase with standardized prompts, to assess first year secondary school students’ potential for learning. This instrument aimed to provide an alternative to the current static tests. The study sample included 55 children (mean age = 13.17 years) from different Dutch educational tracks. The dynamic screener consisted of the subtests reading, mathematics, working memory, planning, divergent thinking, and inductive reasoning. Each subtest employed a test–training–test design. Based on randomized blocking, half of the children received graduated prompt training between pre-test and post-test, while the other half did not. On some, but not all, subtests, training seemed to lead to an increase in performance. Additionally, some constructs measured through the dynamic screener were related to current school performance. This pilot study provides preliminary support for the use of such an instrument to gain more insight into children’s learning potential and instructional needs. Directions for future research are discussed.
Evaluating Explainable Artificial Intelligence Methods Based on Feature Elimination: A Functionality-Grounded Approach
Authors: Elkhawaga Ghada , Elzeki Omar , Abuelkheir Mervat , Reichert Manfred
Published: April 2023

Although predictions based on machine learning are reaching unprecedented levels of accuracy, understanding the underlying mechanisms of a machine learning model is far from trivial. Therefore, explaining machine learning outcomes is gaining more interest with an increasing need to understand, trust, justify, and improve both the predictions and the prediction process. This, in turn, necessitates providing mechanisms to evaluate explainability methods as well as to measure their ability to fulfill their designated tasks. In this paper, we introduce a technique to extract the most important features from a data perspective. We propose metrics to quantify the ability of an explainability method to convey and communicate the underlying concepts available in the data. Furthermore, we evaluate the ability of an eXplainable Artificial Intelligence (XAI) method to reason about the reliance of a Machine Learning (ML) model on the extracted features. Through experiments, we further, prove that our approach enables differentiating explainability methods independent of the underlying experimental settings. The proposed metrics can be used to functionally evaluate the extent to which an explainability method is able to extract the patterns discovered by a machine learning model. Our approach provides a means to quantitatively differentiate global explainability methods in order to deepen user trust not only in the predictions generated but also in their explanations.
Fixed (Trackside) Energy Storage System for DC Electric Railways Based on Full-SiC Isolated DC-DC Converters
Authors: Fabre Joseph , Ladoux Philippe , Caron Hervé
Published: April 2023

At present, in several European railway networks using traditional DC electrification systems, it is not possible to increase traffic nor to operate locomotives at their nominal power ratings. Trackside energy storage systems (TESSs) can be an alternative solution for the creation of new substations. A TESS limits contact line voltage drops and smooths the power absorbed during peak traffic. Thus, the efficiency of the power system can be increased while limiting costs and the environmental impact. This paper proposes a new topology of a TESS based on full-SiC isolated DC/DC converters associated with lithium-ion batteries and galvanic isolation, offering major advantages for operational safety. In the event of a fault, the input and output terminals of the converters are electrically separated, and the contact line voltage can never be directly applied to the batteries. In addition, the use of SiC MOSFETs makes it possible to obtain excellent efficiency with a high switching frequency. The first part of this paper presents the main characteristics of an elementary TESS module, while the second part proposes a sizing methodology for the typical case of a 1.5 kV DC line, which shows the limits of using TESSs to reinforce a power supply. Finally, the experimental results of an elementary module prototype are presented.
Motion-Induced Noise Detection of Electrode-Pair Towed Antennas Using Helix Coil Sensors
Authors: Xie Xu , Zuo Hao , Wei Shize , Wu Qin
Published: April 2023

Electrode-pair towed antennas are widely utilized for marine electromagnetic detection, underwater communication, and other purposes. However, the motion-induced noise created by antenna vibrations due to environmental turbulence affects extremely low frequency and super low frequency (ELF/SLF) communications. In this article, we presented a method for detecting the motion-induced noise of electrode-pair towed antennas using helix coil sensors. The equivalent resistance, inductance, capacitance, and conductance (RLCG) transmission model and parameters were derived based on the mirror method of the twisted structure with a shielding layer inside. A water-flow cycling experimental platform was constructed to evaluate two types of antenna sections. Electrode-pair, accelerators, and helix coils sensors signals were sampled and analyzed in time and frequency domain. The comparison results demonstrated that the helix coil sensors achieved a high correlation with the electrode-pair towed antenna in various vibration speeds ranging from 0.7 to 0.9.
A Novel Channel Pruning Compression Algorithm Combined with an Attention Mechanism
Authors: Zhao Ming , Luo Tie , Peng Sheng-Lung , Tan Junbo
Published: April 2023

To solve the problem of complex network models with a large number of redundant parameters, a pruning algorithm combined with an attention mechanism is proposed. Firstly, the basic training is performed once, and the network model is then re-trained with the attention mechanism for the baseline. The obtained model is pruned based on channel correlation, and finally a simplified model is obtained via continuous cyclic iteration while the accuracy rate is kept as close as possible to that of the baseline model. The algorithm was experimentally validated on ResNet based on different datasets, and the results showed that the algorithm provided strong adaptability to different datasets and different network structures. For the CIFAR-100 dataset, ResNet50 was pruned to reduce the amount of model parameters by 80.3% and the amount of computation by 69.4%, while maintaining accuracy. For the ImageNet dataset, the ResNet50 parameter volume was compressed by 2.49 times and the computational volume was compressed by 3.01 times. The ResNet101 parameter volume was reduced by 61.2%, and the computational volume was reduced by 68.5%. Compared with the traditional fixed threshold, the model achieves better results in terms of detection accuracy, compression effect, and inference speed.
Detection of Metabolic Syndrome Using Insulin Resistance Indexes: A Cross-Sectional Observational Cohort Study
Authors: Fornari Laurindo Lucas , Minniti Giulia , José Tofano Ricardo , Quesada Karina , Federighi Baisi Chagas Eduardo , Maria Barbalho Sandra
Published: April 2023

Insulin resistance (IR) is considered cardinal to the pathophysiology of metabolic syndrome (MetS). Previously, several simple indexes of IR calculated from biochemical and anthropometric variables have been proposed. However, these indexes are population-dependent; therefore, further studies on a global scale are necessary. The present study assessed the diagnostic accuracy of eight IR indicators, namely, METS-IR, TG-HDL-c, TyG, TyG-BMI, TyG-NC, TyG-NHtR, TyG-WC, and TyG-WHtR, in indicating MetS among a Brazilian population. For this, 268 patients (152 men and 116 women, 53–59 years of age) were included in the study, out of which 111 were diagnosed with MetS according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III). All indexes achieved significant accuracy, with TyG-WC (0.849 (0.800–0.889)), TyG (0.837 (0.787–0.879)), and TG-HDL-c (0.817 (0.765–0.861)) having the highest area under the curve (AUC). Further, the most heightened diagnostic sensitivities were observed for TG-HDL-c (90.99%), TyG-WC (89.19%), and TyG-NC (84.68%), whereas the highest diagnostic specificities were noted for TyG (73.89%), TyG-WHtR (72.61%), and TyG-WC (66.88%). Thus, TyG-WC, TyG, and TG-HDL-c reached the greatest AUC values in our analyses, making them useful diagnostic indicators of MetS, and crucial for patients’ clinical management.