bluuebunny/crossref_metadata_2025 huggingface dataset:
Tackling Single-Use-Plastic in small touristic islands to reduce marine litter: co-identifying the best mix of policy interventions
Authors: Guittard Alice , Akinsete Ebun , Demian Elias , Koundouri Phoebe , Papadaki Lydia , Tombrou Xenia
Published: July 2023

Marine litter is a worldwide issue affecting local communities with increasing environmental and economic impacts, with Single-Use-Plastic (SUP) pollution being of specific concern. The tourism industry has been identified as one of the major sector contributing to marine plastic pollution therefor in need to take urgent actions. Small islands are particularly vulnerable and need locally adapted strategies to effectively tackle this issue. This study proposes the use of a participative system-based approach to co-design, with local stakeholders, a roadmap tackling Single-Use-Plastic (SUP) in the hospitality industry of small Greek touristic islands. Policy, industry, civil society and academia representatives were involved in a participative co-creation process to co-identify the best mix of policy instruments and innovations (social and technological) adapted to the local island context, capable of reducing plastic consumption and littering in the island while fostering behavioural change (from consumers and local businesses perspective). Results show the lack of knowledge and awareness, the limited financial resources and expertise, and the low efficiency of the waste management system as the main challenges. A roadmap of short and mid-term actions, based on the Circular Economy reduce, reuse, recycle principles was co-developed, including raising awareness campaigns across stakeholder groups, positive economic incentives, development of capacity building and partnerships within the quadruple helix (academia, business, public authorities and civil society representatives), forming the basis of the municipal island free SUP strategy. The active involvement of public and private actors as well as the civil society, empowering local actors and developing trust across stakeholder groups are key factors to develop a successful strategy able to tackle plastic pollution locally. Pilot experiments also proved to be a determinant for the adoption of sustainable solutions in the context of small islands.
Diurnal and seasonal variations of the occurrence of ionospheric irregularities over Nigeria from GNSS data
Authors: Ikani Ojochenemi , Okeke Francisca Nneka , Okpala Kingsley Chukwudi , Okoh Daniel , Rabiu Babatunde
Published: July 2023

We investigate the occurrence of ionospheric irregularities in Nigeria during 2014, using the Global Navigation Satellite System (GNSS) total electron content (TEC) rate of change index (ROTI). We categorized days with |Dst| < 30 nT as quiet days and days with |Dst| ≥ 50 nT as disturbed days, during both the daytime and nighttime periods. Our results reveal significant latitudinal differences in both quiet and disturbed conditions. The quiet-time observations indicate that irregularities were consistent across all the stations. The occurrence was strong and consistent at CLBR station in Calabar, Nigeria, which is located further away from the geomagnetic equator (and closer to the southern anomaly crest) than the other receivers. TEC variability is greatly influenced by the geographical location of the Earth’s magnetic field, while its spatial patterns of variability are modulated by the Earth’s diurnal rotation. Near the September equinox, the occurrence of irregularities was inhibited during disturbed periods, but an opposite pattern was observed during the March equinox across all stations. Two peaks of occurrence were observed: one in March and the other in September respectively. The strength of irregularities was generally greater in March equinox compared to September equinox, and they were more pronounced during pre-midnight hours. This pattern could be attributed to the suppressing role of the dynamo electric field, requiring further investigation.
Life Satisfaction Before and During COVID-19 Pandemic in Thailand
Authors: Phulkerd Sirinya , Thapsuwan Sasinee , Soottipong Gray Rossarin , Chamratrithirong Aphichat , Pattaravanich Umaporn , Ungchusak Chantana , Saonuam Pairoj
Published: July 2023

Objective: To investigate prevalence of life satisfaction in the Thai population before and during the COVID-19 epidemic, and factors associated with life satisfaction during the epidemic.

Methods: Multistage sampling was used to draw a sample from the Thai population. A total of 3,115 Thai participants age 15 years or older from a nationally-representative longitudinal survey in 2019 and in 2021 were included in this study. The study applied the Scale with Life Satisfaction (SWLS) instrument to measure life satisfaction among the Thai population before and during the COVID-19 epidemic. Multiple regression analysis was used to investigate the association between life satisfaction and other variables. The follow-up survey response rate for individuals was 44.8%.

Results: An average life satisfaction score during the COVID-19 epidemic (in 2021) was 22.4 which decreased from 25.5 before the COVID-19 epidemic (in 2019). More than one-third of the participants (36.5%) reported having less life satisfaction during the epidemic, which was nearly 20 percentage points higher than before the epidemic (17.7%). Controlling for life satisfaction in 2019, the analysis found statistical associations between demographic and economic characteristics and health-related behaviours, and life satisfaction during 2021. People in the older age cohorts (p ≤ 0.001), in a rural area (p ≤ 0.05), having higher education (p ≤ 0.001), still being employed (p ≤ 0.01) and becoming unemployed (p ≤ 0.01) had higher life satisfaction. The possibility of higher life satisfaction was also found in people who maintained good health (p ≤ 0.01), sufficient physical activity (p ≤ 0.001), and fruit and vegetable intake (p ≤ 0.01). People with income loss during the epidemic had lower life satisfaction (p ≤ 0.05).

Conclusion: The findings suggest that policies and systems for resilience and social protection are needed for empowering individuals—especially the poor and vulnerable—to cope with crises, and improve health and wellbeing outcomes.
An Integrated Multi-Model Fusion System for Automatically Diagnosing the Severity of Wheat Fusarium Head Blight
Authors: Wang Ya-Hong , Li Jun-Jiang , Su Wen-Hao
Published: July 2023

Fusarium has become a major impediment to stable wheat production in many regions worldwide. Infected wheat plants not only experience reduced yield and quality but their spikes generate toxins that pose a significant threat to human and animal health. Currently, there are two primary methods for effectively controlling Fusarium head blight (FHB): spraying quantitative chemical agents and breeding disease-resistant wheat varieties. The premise of both methods is to accurately diagnosis the severity of wheat FHB in real time. In this study, a deep learning-based multi-model fusion system was developed for integrated detection of FHB severity. Combination schemes of network frameworks and backbones for wheat spike and spot segmentation were investigated. The training results demonstrated that Mobilev3-Deeplabv3+ exhibits strong multi-scale feature refinement capabilities and achieved a high segmentation accuracy of 97.6% for high-throughput wheat spike images. By implementing parallel feature fusion from high- to low-resolution inputs, w48-Hrnet excelled at recognizing fine and complex FHB spots, resulting in up to 99.8% accuracy. Refinement of wheat FHB grading classification from the perspectives of epidemic control (zero to five levels) and breeding (zero to 14 levels) has been accomplished. In addition, the effectiveness of introducing HSV color feature as a weighting factor into the evaluation model for grading of wheat spikes was verified. The multi-model fusion algorithm, developed specifically for the all-in-one process, successfully accomplished the tasks of segmentation, extraction, and classification, with an overall accuracy of 92.6% for FHB severity grades. The integrated system, combining deep learning and image analysis, provides a reliable and nondestructive diagnosis of wheat FHB, enabling real-time monitoring for farmers and researchers.
Serotyping and Antimicrobial Resistance Profiling of Multidrug-Resistant Non-Typhoidal Salmonella from Farm Animals in Hunan, China
Authors: Zhang Zhuohui , Li Jiyun , Zhou Rushun , Xu Qianqian , Qu Shiyin , Lin Hongguang , Wang Yan , Li Pishun , Zheng Xiaofeng
Published: July 2023

Non-typhoidal Salmonella (NTS) is a foodborne pathogen and a prevalent causative agent for disease outbreaks globally. The Salmonella enterica serovar 4,[5],12:i:- (S.4,[5],12:i:-) belongs to the monophasic variant of Salmonella typhimurium, which is of current global concern. In this study, the epidemiology and genomic characterization of S. 4,[5],12:i:- isolates from 17 livestock farms in Hunan Province between 2019 and 2020, as well as their susceptibility to 14 antimicrobial agents, were profiled. Twelve Salmonella serotypes were identified using the White–Kauffmann–Le Minor scheme, and whole-genome sequencing analyses were conducted based on these isolates. Overall, 107 Salmonella strains were isolated, of which 73% (78/107) were multidrug resistant. Resistance to tetracycline (85.05%) was found to be the most prevalent, followed by the oqxAB and aac(6′)-Ib-cr genes. S. typhimurium (monophasic) 4,[5],12:i:- was the most common serotype, followed by S. typhimurium and S. derby. Most antimicrobial-resistant strains were isolated from pigs, indicating that they could be important reservoirs of resistant non-typhoidal Salmonella strains. The presence of similar genetic environments in S. 4,[5],12:i:- indicates both vertical and horizontal transmission of resistance plasmids, which may promote the spread of drug resistance genes. Appropriate measures should be taken to curb the prevalence of S. 4,[5],12:i:-.
Torque Vectoring Control Strategies Comparison for Hybrid Vehicles with Two Rear Electric Motors
Authors: de Carvalho Pinheiro Henrique , Carello Massimiliana , Punta Elisabetta
Published: July 2023

In today’s automotive industry, electrification is a major trend. In-wheel electric motors are among the most promising technologies yet to be fully developed. Indeed, the presence of multiple in-wheel motors acting as independent actuators allows for the implementation of innovative active systems and control strategies. This paper analyzes different design possibilities for a torque vectoring system applied to an originally compact front-wheel drive hybrid electric vehicle with one internal combustion engine for the front axle and two added electric motors integrated in the wheels of the rear axle. A 14 degrees of freedom vehicle model is present o accurately reproduce the nonlinearities of vehicle dynamic phenomena and exploited to obtain high-fidelity numerical simulation results. Different control methods are compared, a PID, an LQR, and four different sliding mode control strategies. All controllers achieve sufficiently good results in terms of lateral dynamics compared with the basic hybrid version. The various aspects and features of the different strategies are analyzed and discussed. Chattering reduction strategies are developed to improve the performance of sliding mode controllers. For a complete overview, control systems are compared using a performance factor that weighs control accuracy and effort in different driving maneuvers, i.e., ramp and step steering maneuvers performed under quite different conditions ranging up to the limits.
A Multi-Scale Deep Back-Projection Backbone for Face Super-Resolution with Diffusion Models
Authors: Gao Juhao , Tang Ni , Zhang Dongxiao
Published: July 2023

Face verification and recognition are important tasks that have made great progress in recent years. However, recognizing low-resolution faces from small images is still a difficult problem. In this paper, we advocate using diffusion models (DMs) to enhance face resolution and improve their quality for various downstream applications. Most existing DMs for super-resolution use U-Net as their backbone network, which only exploits multi-scale features along the spatial dimension. These approaches result in a slow convergence of corresponding DMs and the inability to capture complex details and fine textures. To address this issue, we propose a novel conditional generative model based on DMs called BPSR3, which replaces the U-Net in super-resolution via repeated refinement (SR3) with a multi-scale deep back-projection network structure. BPSR3 can extract richer features not only in depth but also in breadth. This helps to effectively refine the image quality at different scales. The experimental results on facial datasets show that BPSR3 significantly improved both convergence speed and reconstruction performance. BPSR3 has about 1/4 of the parameters of SR3 but achieves a 50.1% improvement in PSNR, a 19.8% improvement in SSIM, and a 15.4% reduction in FID. Our contribution lies in achieving less time and space consumption and better reconstruction results. In addition, we propose an idea of enhancing the performance of DMs by replacing the U-Net with a better network.
The Behavior of Nonlinear Tsunami Waves Running on the Shelf
Authors: Arsen’yev Sergey A. , Eppelbaum Lev V.
Published: July 2023

The problem of creating methods for calculating tsunami parameters and predicting these dangerous events is currently being solved by integrating the equations of the theory of water waves. Both numerical methods and powerful computers are used, as well as analytical solutions. The essential stage is the stage of the tsunami reaching the shelf and shallow coastal waters. The tsunami amplitude increases here, and nonlinear effects become important. Nonlinearity excludes the solution’s unicity and the superposition principle’s fulfillment. The nonlinear theory can have many solutions, depending on various external conditions; there could be nontrivial ones. In this article, we explore the properties of several nonlinear solutions. With their help, we can find the maximum possible amplitude of tsunami waves when approaching the coast and estimate the seismological parameters of an earthquake-generating tsunami.
Full-Scale Testing and Analytical Modeling of Rebar Cages Reinforced with Mechanical U-Bolt Connectors
Authors: Vahedi Masood , Ebrahimian Hamed , Itani Ahmad M.
Published: July 2023

Rebar cages are the skeletons of reinforced concrete structures. These temporary structures consist of longitudinal and transverse reinforcing bars connected by tie-wires. Given the relatively low strength of tie-wire connections, replacing tie-wires with mechanical connectors such as U-bolts can improve the stability and strength of rebar cages. This paper aims to understand the behavior of large prefabricated rebar cages reinforced with mechanical U-bolt connectors through experimental and analytical investigations. Twenty-six full-scale experimental tests are performed on five different underground pile-shaft rebar cages with tie-wire and U-bolt connectors to determine their behavior during different site handling conditions. The data obtained from the experiments are used to develop and calibrate detailed finite element models that can predict the complex response behavior of rebar cages reinforced with U-bolt connectors. The results show that U-bolt connectors can effectively ensure rebar cage integrity even under extreme loading conditions. Additionally, it is concluded that the presence of U-bolt connectors allows for the elimination of internal stiffening elements, which are common for large rebar cages, adding to the simplicity and efficiency of the construction process. The results of this study can be used as a basis for establishing analysis, design, fabrication, and handling guidelines for rebar cages.
Quality of Soybean Seeds after Microwave Drying
Authors: Faria Rute Quelvia de , Santos Amanda R. P. dos , Vasco Lainara C. P. dos Santos , Gariepy Yvan , Sartori Maria M. P. , Raghavan Vijaya
Published: July 2023

The use of electromagnetic energy for drying seeds has been shown to be a promising alternative. However, studies show that the technique still lacks careful evaluation due to the sensitivity of soybean seeds to damage caused using microwaves. Studies have shown that the use of microwaves during drying can be safe in seeds of soybeans, corn, and beans, among others. These studies recognize that drying using microwaves has a great potential for savings in the drying process, as it considerably reduces the drying time. This work aimed to evaluate the immediate damage caused in soybean seeds subjected to drying at temperatures of 40, 50, and 60 °C, with power of 0, 0.5, and 1 W/g at a frequency of 2.45 GHz. The results of the vigor and germination tests showed that the action of microwaves on seeds was not only safe and more efficient but also brought about increases in seed germination and vigor. However, the process must be judicious and obey precise indices of power and temperature to not cause damage to the seeds.
Earthquake Detection Using Stacked Normalized Recurrent Neural Network (SNRNN)
Authors: Bilal Muhammad Atif , Wang Yongzhi , Ji Yanju , Akhter Muhammad Pervez , Liu Hengxi
Published: July 2023

Earthquakes threaten people, homes, and infrastructure. Earthquake detection is a complex task because it does not show any specific pattern, unlike object detection from images. Convolutional neural networks have been widely used for earthquake detection but have problems like vanishing gradients, exploding, and parameter optimization. The ensemble learning approach combines multiple models, each of which attempts to compensate for the shortcomings of the others to enhance performance. This article proposes an ensemble learning model based on a stacked normalized recurrent neural network (SNRNN) for earthquake detection. The proposed model uses three recurrent neural network models (RNN, GRU, and LSTM) with batch normalization and layer normalization. After preprocessing the waveform data, the RNN, GRU, and LSTM extract the feature map sequentially. Batch normalization and layer normalization methods take place in mini-batches and input layers for stable and faster training of the model and improving its performance. We trained and tested the proposed model on 6574 events from 2000 to 2018 (18 years) in Turkey, a highly targeted region. The SNRNN achieves RMSE values of 3.16 and 3.24 for magnitude and depth detection. The SNRNN model outperforms the three baseline models, as seen by their low RMSE values.
Spectrum-Based Logistic Regression Modeling for the Sea Bottom Soil Categorization
Authors: Kushnir Uri , Frid Vladimir
Published: July 2023

The present analysis of state of the art portrays that actual time series or spectrum backscattered data from a point on the sea bottom are rarely used as features for machine learning models. The paper deals with the artificial intelligence techniques used to examine CHIRP-recorded data. The data were collected using a CHIRP sub-bottom profiler to study two sand bottom sites and two sandstone bottom sites in the offshore zone of Ashqelon City (Southern Israel). The first reflection time series and spectra of all the traces from the four sites generated two training and two test sets. Two logistic regression models were trained using the training sets and evaluated for accuracy using the test sets. The examination results indicate that types of sea bottom can be quantitatively characterized by applying logistic regression models to either the backscatter time series of a frequency-modulated signal or the spectrum of that backscatter. The examination accuracy reached 90% for the time series and 94% for the spectra. The application of spectral data as features for more advanced machine learning algorithms and the advantages of their combination with other types of data have great potential for future research and the enhancement of remote marine soil classification.
Snapshot-Based Multispectral Imaging for Heat Stress Detection in Southern-Type Garlic
Authors: Ryu Jinhwan , Wi Seunghwan , Lee Hoonsoo
Published: July 2023

This study aims to develop a model for detecting heat stress in southern-type garlic using a multispectral snapshot camera. Raw snapshot images were obtained from garlic cloves during the garlic bulb enlargement period, capturing the visible (Vis) and near-infrared (NIR) regions. Image preprocessing was applied to obtain a 38-wavelength spectrum by combining a 16-wavelength image in the Vis region and a 22-wavelength image in the NIR region. These spectral data were then utilized to develop models, including PLS-DA, LS-SVM, DNN, and recurrence plots-based CNN (RP-CNN). On average, the LS-SVM model demonstrated the best performance in detecting heat stress during the garlic bulb enlargement period. This is attributed to the nonlinear nature of the spectral differences between groups caused by abiotic stress in garlic. The LS-SVM model is particularly effective at capturing such nonlinear relationships. Among the model images, LS-SVM yielded the best performance, followed by RP-CNN, DNN, and PLS-DA. Therefore, this study confirms the potential of snapshot-based multispectral imaging for measuring changes in garlic crops induced by high-temperature stress.
Generalized Function Projective Synchronization of Two Different Chaotic Systems with Uncertain Parameters
Authors: Zhen Bin , Zhang Yu
Published: July 2023

This study proposes a new approach to realize generalized function projective synchronization (GFPS) between two different chaotic systems with uncertain parameters. The GFPS condition is derived by converting the differential equations describing the synchronization error systems into a series of Volterra integral equations on the basis of the Laplace transform method and convolution theorem, which are solved by applying the successive approximation method in the theory of integral equations. Compared with the results obtained by constructing Lyapunov functions or calculating the conditional Lyapunov exponents, the uncertain parameters and the scaling function factors considered in this paper have fewer restrictions, and the parameter update laws designed for the estimation of the uncertain parameters are simpler and easier to realize physically.
Deep Learning Enhances Radiologists’ Detection of Potential Spinal Malignancies in CT Scans
Authors: Gilberg Leonard , Teodorescu Bianca , Maerkisch Leander , Baumgart Andre , Ramaesh Rishi , Gomes Ataide Elmer Jeto , Koç Ali Murat
Published: July 2023

Incidental spinal bone lesions, potential indicators of malignancies, are frequently underreported in abdominal and thoracic CT imaging due to scan focus and diagnostic bias towards patient complaints. Here, we evaluate a deep-learning algorithm (DLA) designed to support radiologists’ reporting of incidental lesions during routine clinical practice. The present study is structured into two phases: unaided and AI-assisted. A total of 32 scans from multiple radiology centers were selected randomly and independently annotated by two experts. The U-Net-like architecture-based DLA used for the AI-assisted phase showed a sensitivity of 75.0% in identifying potentially malignant spinal bone lesions. Six radiologists of varying experience levels participated in this observational study. During routine reporting, the DLA helped improve the radiologists’ sensitivity by 20.8 percentage points. Notably, DLA-generated false-positive predictions did not significantly bias radiologists in their final diagnosis. These observations clearly indicate that using a suitable DLA improves the detection of otherwise missed potentially malignant spinal cases. Our results further emphasize the potential of artificial intelligence as a second reader in the clinical setting.
Simulation and Experimental Study on the Responses of Subcellular Structures in Tumor Cells Induced by 5 ns Pulsed Electric Fields
Authors: Yao Chenguo , Ma Xin , Qian Kun , Wang Yancheng , Dong Shoulong
Published: July 2023

In order to explore the bioelectric effect of 5 ns pulsed electric fields on tumor cells, a spherical single-cell multiphysics model was first established based on the finite element simulation platform. In consideration of the dielectric relaxation of the biological plasma membrane under the high-frequency electric fields, the electroporation and Maxwell stress tensors on the cell membrane and nuclear envelope were analyzed; secondly, taking MDA-MB-231 cells as the research object, combined with fluorescent probe technology, the state change and fluorescence dissipation of its subcellular structure exposed to pulse fields were studied. The results showed that 5 ns pulsed electric fields directly acted inside the cell, causing an electroporation effect and tensile stress on the nuclear envelope, destroying the integrity and order of the cytoskeleton, and damaging the functions of subcellular structures including endoplasmic reticulum, mitochondria, etc. This study provides theoretical and experimental evidence for the research and application of a high-voltage short pulse in the field of biomedical engineering.
Exploring the Feasibility of Intrapartum GBS Collection to Identify Residual GBS in a Pilot Study of an Antenatal Probiotic Intervention
Authors: Malloy Emily , Hanson Lisa , VandeVusse Leona , Robinson Karen , Singh Maharaj , Forgie Marie
Published: July 2023

(1) Background: We aimed to explore the feasibility of collecting intrapartum maternal Group B Streptococcus (GBS) colonization and immediate post-birth neonatal GBS colonization cultures for use in a larger trial and to identify cases of residual GBS, which were hypothesized to be less common in the probiotics group. (2) Methods: This sub-study added additional outcome measures to the parent study to identify intrapartum and neonatal colonization and compare between probiotic and placebo groups and to identify cases of residual GBS. Intrapartum maternal vaginal and rectal GBS cultures were collected at the time of admission to a hospital for labor and to give birth. Neonatal oral and nasopharynx GBS cultures were collected within 1–2 h of giving birth. (3) Results: Thirty intrapartum samples were collected; twenty-eight had complete data. The antepartum GBS results significantly predicted the intrapartum results (p = 0.005), with 86.7% of cultures remaining the same at both time points. There were four cases where the intrapartum GBS results were different to the 36-week antepartum cultures results. A case of residual GBS was identified in one probiotic group participant. None of the neonatal swabs were positive for GBS. No cases of EOGBSD occurred in infants born to the study participants. (4) Conclusions: Although the 36–37 week GBS results significantly predicted the intrapartum results, the utility for a larger research trial on probiotics to reduce antenatal GBS is unclear. Intrapartum GBS swab collection was feasible in a busy nurse, midwife, and physician practice. GBS was not recovered from neonatal oral and nasopharyngeal swabs. The pathways of neonatal GBS colonization require further study.
Volatile Organic Compound Identification-Based Tuberculosis Screening among TB Suspects: A Diagnostic Accuracy Study
Authors: Badola Mayank , Agrawal Anurag , Roy Debabrata , Sinha Richa , Goyal Avisham , Jeet Narayan
Published: July 2023

Tuberculosis (TB) affects a third of the global population, and a large population of infected individuals still remain undiagnosed—making the visible burden only the tip of the iceberg. The detection of tuberculosis in close-proximity patients is one of the key priorities for attaining the Sustainable Development Goals (SDG) of TB elimination by 2030. With the current battery of screening tests failing to cover this need, the authors of this paper examined a simple and inexpensive point-of-care breath analyzer (TSI-3000(I)), which is based on detecting the volatile organic compounds that are emitted from infected cells and released in exhaled breath as a screening tool for the detection of TB. A single-center pilot study for assessing the diagnostic accuracy of the point-of-care Tuberculosis Breath Analyzer was conducted, and it was compared against the WHO-recommended TrueNat assay, which is a rapid molecular test and was also treated as the reference standard in this study. Of the 334 enrolled participants with TB signs/symptoms, 42.51% were TrueNat positive for Mycobacterium tuberculosis. The sensitivity of the Tuberculosis Breath Analyzer was found to be 95.7%, with a specificity of 91.3% and a ROC area of 0.935. The test kit showed considerable/significant high sensitivity and specificity as reliability indicators. The performance of the Tuberculosis Breath Analyzer tested was found to be comparable in efficiency to that of the TrueNat assay. A large cohort-based multicentric study is feasibly required to further validate and extrapolate the results of the pilot study.
Numerical Analysis of New Hybrid Algorithms for Solving Nonlinear Equations
Authors: Vivas-Cortez Miguel , Ali Naseem Zulfiqar , Khan Awais Gul , Awan Muhammad Uzair
Published: July 2023

In this paper, we propose two new hybrid methods for solving nonlinear equations, utilizing the advantages of classical methods (bisection, trisection, and modified false position), i.e., bisection-modified false position (Bi-MFP) and trisection-modified false position (Tri-MFP). We implemented the proposed algorithms for several benchmark problems. We discuss the graphical analysis of these problems with respect to the number of iterations and the average CPU time.
MicroRNAs in Juvenile Idiopathic Arthritis: State of the Art and Future Perspectives
Authors: Pelassa Simone , Raggi Federica , Rossi Chiara , Bosco Maria Carla
Published: July 2023

Juvenile Idiopathic Arthritis (JIA) represents the most common chronic pediatric arthritis in Western countries and a leading cause of disability in children. Despite recent clinical achievements, patient management is still hindered by a lack of diagnostic/prognostic biomarkers and targeted treatment protocols. MicroRNAs (miRNAs) are short non-coding RNAs playing a key role in gene regulation, and their involvement in many pathologies has been widely reported in the literature. In recent decades, miRNA’s contribution to the regulation of the immune system and the pathogenesis of autoimmune diseases has been demonstrated. Furthermore, miRNAs isolated from patients’ biological samples are currently under investigation for their potential as novel biomarkers. This review aims to provide an overview of the state of the art on miRNA investigation in JIA. The literature addressing the expression of miRNAs in different types of biological samples isolated from JIA patients was reviewed, focusing in particular on their potential application as diagnostic/prognostic biomarkers. The role of miRNAs in the regulation of immune responses in affected joints will also be discussed along with their potential utility as markers of patients’ responses to therapeutic approaches. This information will be of value to investigators in the field of pediatric rheumatology, encouraging further research to increase our knowledge of miRNAs’ potential for future clinical applications in JIA.
An Integrated Glycosylation Signature of Rheumatoid Arthritis
Authors: Mayboroda Oleg A. , Lageveen-Kammeijer Guinevere S. M. , Wuhrer Manfred , Dolhain Radboud J. E. M.
Published: July 2023

Rheumatoid arthritis (RA) Is a highly prevalent autoimmune disease that affects the joints but also various other organs. The disease is characterized by autoantibodies that are often already observed pre-disease. Since the 1980s, it has been known that antibody glycosylation is different in RA as compared to control individuals. While the literature on glycosylation changes in RA is dominated by reports on serum or plasma immunoglobulin G (IgG), our recent studies have indicated that the glycosylation changes observed for immunoglobulin A (IgA) and total serum N-glycome (TSNG) may be similarly prominent, and useful in differentiating between the RA patients and controls, or as a proxy of the disease activity. In this study, we integrated and compared the RA glycosylation signatures of IgG, IgA and TSNG, all determined in the pregnancy-induced amelioration of rheumatoid arthritis (PARA) cohort. We assessed the association of the altered glycosylation patterns with the disease, autoantibody positivity and disease activity. Our analyses indicated a common, composite glycosylation signature of RA that was independent of the autoantibody status.
Homocysteine: Biochemistry, Molecular Biology, and Role in Disease 2021
Authors: Sitdikova Guzel , Hermann Anton
Published: July 2023

Homocysteine is increasingly recognized as an important molecule in a wide variety of cellular functions [...]
NAFLD and AATD Are Two Diseases with Unbalanced Lipid Metabolism: Similarities and Differences
Authors: Perez-Luz Sara , Matamala Nerea , Gomez-Mariano Gema , Janciauskiene Sabina , Martínez-Delgado Beatriz
Published: July 2023

Non-alcoholic fatty liver disease (NAFLD) is a type of steatosis commonly associated with obesity, dyslipidemia, hypertension, and diabetes. Other diseases such as inherited alpha-1 antitrypsin deficiency (AATD) have also been related to the development of liver steatosis. The primary reasons leading to hepatic lipid deposits can be genetic and epigenetic, and the outcomes range from benign steatosis to liver failure, as well as to extrahepatic diseases. Progressive hepatocellular damage and dysregulated systemic immune responses can affect extrahepatic organs, specifically the heart and lungs. In this review, we discuss the similarities and differences between the molecular pathways of NAFLD and AATD, and the putative value of hepatic organoids as novel models to investigate the physio pathological mechanisms of liver steatosis.
Adult IDH Wild-Type Glioblastoma Ultrastructural Investigation Suggests a Possible Correlation between Morphological Biomarkers and Ki-67 Index
Authors: Familiari Pietro , Relucenti Michela , Lapolla Pierfrancesco , Palmieri Mauro , Antonelli Manila , Cristiano Loredana , Barbaranelli Claudio , Catalano Myriam , D’Angelo Luca , Familiari Giuseppe , Santoro Antonio , Frati Alessandro , Bruzzaniti Placido
Published: July 2023

Glioblastoma is an aggressive brain tumor with an average life expectancy between 14 and 16 months after diagnosis. The Ki-67 labeling index (LI), a measure of cellular proliferation, is emerging as a prognostic marker in GBM. In this study, we investigated the ultrastructure of glioblastoma tissue from 9 patients with the same molecular profile (adult IDH wild-type glioblastoma, wild-type ATRX, and positive for TP53 expression, GFAP expression, and EGFR overexpression) to find possible ultrastructural features to be used as biomarkers and correlated with the only parameter that differs among our samples, the Ki-67 LI. Our main results were the visualization of the anatomical basis of astrocyte-endothelial cells crosstalk; the ultrastructural in situ imaging of clusters of hyperactivated microglia cells (MsEVs); the ultrastructural in situ imaging of microglia cells storing lipid vesicles (MsLVs); the ultrastructural in situ imaging of neoplastic cells mitophagy (NCsM). The statistical analysis of our data indicated that MsEVs and MsLVs correlate with the Ki-67 LI value. We can thus assume they are good candidates to be considered morphological biomarkers correlating to Ki-67 LI. The role of NCsM instead must be further evaluated. Our study findings demonstrate that by combining ultrastructural characteristics with molecular information, we can discover biomarkers that have the potential to enhance diagnostic precision, aid in treatment decision-making, identify targets for therapy, and enable personalized treatment plans tailored to each patient. However, further research with larger sample sizes is needed to validate these findings and fully utilize the potential of ultrastructural analysis in managing glioblastoma.
Bisphenol A Imprinted Electrochemical Sensor Based on Graphene Quantum Dots with Boron Functionalized g-C3N4 in Food Samples
Authors: Deveci Haci Ahmet , Mavioğlu Kaya Müge , Kaya İnan , Bankoğlu Yola Bahar , Atar Necip , Yola Mehmet Lütfi
Published: July 2023

A molecular imprinted electrochemical sensor based on boron-functionalized graphitic carbon nitride (B-g-C3N4) and graphene quantum dots (GQDs) was presented for selective determination of bisphenol A (BPA). In particular, by combining the selectivity and high stability properties, which are the most important advantages of molecular imprinted polymers, and the highly sensitive properties of GQDs/B-g-C3N4 nanocomposite, a highly selective and sensitive analytical method was developed for BPA analysis. Firstly, GQDs/B-g-C3N4 nanocomposite was characterized by using microscopic, spectroscopic, and electrochemical techniques. This novel molecular imprinted electrochemical sensor for BPA detection demonstrated a linearity of 1.0 × 10−11–1.0 × 10−9 M and a low detection limit (LOD, 3.0 × 10−12 M). BPA-imprinted polymer on GQDs/B-g-C3N4 nanocomposite also showed good stability, repeatability and selectivity in food samples.
Exploring the Students’ Perceived Effectiveness of Online Education during the COVID-19 Pandemic: Empirical Analysis Using Structural Equation Modeling (SEM)
Authors: Ali Qamar , Abbas Azhar , Raza Ali , Khan Muhammad Tariq Iqbal , Zulfiqar Hasan , Iqbal Muhammad Amjed , Nayak Roshan K. , Alotaibi Bader Alhafi
Published: July 2023

The world faced COVID-19, which was a threat to public health and disturbed the educational system and economic stability. Educational institutes were closed for a longer period, and students faced difficulty to complete their syllabus. The government adopted a policy of “suspending classes without stopping learning” to continue education activities. However, student satisfaction with online education is a growing concern. Satisfaction of students is an important indicator of academic quality. Therefore, this study attempts to investigate the influencing factors behind learning satisfaction using information from 335 students from various institutes in Pakistan. This research examined the impact of computer and internet knowledge, instructor and course material, and Learning Management Systems (LMS) on learning satisfaction. The path coefficients were obtained via Partial Least Square-Structural Equation Modeling (PLS-SEM). The LMS is a tool that facilitates the learning process with the provision of all types of educational material. The path coefficient was more in the case of LMS (0.489), which indicates its positive and significant role to attain learning satisfaction. The instructor and course material ordered second (0.261), which shows that the quality of an instructor and course material also plays a positive role to attain learning satisfaction. The computer and internet are essential ingredients of online education, showing a significant and positive path coefficient (0.123), implying that computer and internet knowledge could enhance learning satisfaction. The universities should develop their LMS to implement online education with quality course materials. It is also vital that the instructor should be up to date with modern learning techniques while ensuring internet connectivity, especially in rural areas. The government should provide an internet connection to students at discounted rates.
Promoting Psychosocial Well-Being and Empowerment of Immigrant Women: A Systematic Review of Interventions
Authors: Silva Patricia , Pereira Henrique
Published: July 2023

This systematic review (SLR), based on the PRISMA 2020 guidelines, aims to present a current overview of interventions aimed at promoting the psychosocial well-being and/or empowerment (PWE) of immigrant women in order to guide future projects. Data collection was performed in the SCOPUS and Web of Science databases, with studies published between 2012 and 20 March 2023 in English, Portuguese, and Spanish. Inclusion and exclusion criteria were based on the PICO guidelines: (P) immigrant women, (I) interventions to improve PWE, (C) comparison between the initial and final phases, and (O) evaluated results for PWE. Risk of bias was assessed, and most of the studies met more than 80% of the JBI bias criteria and had moderate quality on GRADE. Thirteen studies with 585 participants were included, mostly non-randomized, non-equivalent, and with an experimental-control group design. The main components of interventions were health education/psychoeducation, counseling, cognitive restructuring, and expressive therapies. A descriptive synthesis of qualitative and quantitative data was made to evaluate the results of the interventions in PWE. In the experimental studies, results assessed improvements mainly in mood and depression levels, and stress reduction. Empowerment components were less covered. Experimental groups performed better in almost all variables in the comparison with control groups. The strongest interventions were psychoeducation and cognitive restructuring techniques. The main limitations of the studies were the lack of quality of several studies, sample size and representativeness, language, and the possibility of response bias. Even taking this into account, this article makes an original contribution by advocating for evidence-based practice and offering significant implications for health professionals, policy makers, and researchers that work with the integrative health of immigrant women. This SLR is registered in PROPESRO Registration: CRD42023399683. PS is a research fellow of the Foundation for Science and Technology (FCT) of Portugal.
New Interaction Formulae for Plastic Resistances of Z-Sections under Combinations of Bending Moments My,Ed, Mz,Ed and Bimoment BEd
Authors: Agüero Antonio , Baláž Ivan , Koleková Yvona , Lázaro Mario
Published: July 2023

Exact interaction formulae for the plastic resistances of Z-sections under a combination of three internal forces, i.e., the bending moment about the strong axis My,Ed, bending moment about the weak axis Mz,Ed and bimoment BEd, were created and analyzed. The exact interaction curves obtained by linear programming have enabled us to create and verify the proposed approximate interaction formulae. An interaction formula that takes into account these three internal forces is missing in the Eurocodes. A large parametric study was performed for rolled Z profiles. The differences between the values of the approximate interaction formulae and exact interaction curves were analyzed and summarized. The importance of correct analyses of Z-sections in Part 3 is described and examples of incorrect calculations in many publications are collected and corrected. Several researchers have analyzed plastic resistances of I-, H-, T- and U-sections under the combination of internal forces, but nobody has studied Z-sections which are frequently used as purlins. Another motivation is to show how to calculate the properties and normal stresses in the sections without axes of symmetry, which are frequently calculated in incorrect ways, when using non-principal axes of the section.
Permeability and Its Measurements Tested in Abstract Forms and Four Chinese New Towns
Authors: Dai Xiaoling , Dong Qi , Wang Haofeng , Jin Jiafeng , Fang Xin
Published: July 2023

It is widely accepted that permeability is a key attribute for sustainable development, but two puzzles remain unsolved. First, there is no consensus on its measurement. Second, there are still many new urban developments that lack this quality; regardless, it is a well-known urban design principle. This paper reviews different permeability measurements and proposes to categorize them into four quadrants, then compares eleven of them with four sets of abstract forms and four real cases from Southeast China. The two measures rooted in space syntax theory demonstrate their robustness in the abstract forms. In contrast, the area-based measures cannot quantify the differences between places with similar content. In the actual case studies, by comparing the differences between original plans and the real built forms, we suggest that three traps impair their permeability and therefore deviate greatly from the initial design visions, resulting in automobile-oriented travel patterns. The two new measures were used to quantify the different levels of permeability in these four cases. It is concluded that the qualitative urban design principles may fail to be delivered without a quantitative and precise description of urban form. Consistent measures and more empirical cases are needed to reveal the contribution of spatial networks to sustainable development.
Building on Foundations: Venetoclax-Based Combinations in the Treatment of Acute Myeloid Leukemia
Authors: Oyogoa Emmanuella , Traer Elie , Tyner Jeffrey , Lachowiez Curtis
Published: July 2023

Frontline acute myeloid leukemia (AML) treatment is determined by a combination of patient and genetic factors. This includes patient fitness (i.e., comorbidities that increase the risk of treatment-related mortality) and genetic characteristics, including cytogenetic events and gene mutations. In older unfit patients, the standard of care treatment is typically venetoclax (VEN) combined with hypomethylating agents (HMA). Recently, several drugs have been developed targeting specific genomic subgroups of AML patients, enabling individualized therapy. This has resulted in investigations of doublet and triplet combinations incorporating VEN aimed at overcoming known resistance mechanisms and improving outcomes in older patients with AML. These combinations include isocitrate dehydrogenase-1/2 (IDH1/2) inhibitors (i.e., ivosidenib and enasidenib), fms-like tyrosine kinase 3 (FLT3) inhibitors (i.e., gilteritinib), anti-CD47 antibodies (i.e., magrolimab), mouse double minute-2 (MDM2) inhibitors, and p53 reactivators (i.e., eprenetapopt). This review summarizes ongoing trials aimed at overcoming known VEN resistance mechanisms and improving outcomes beyond that observed with HMA + VEN combinations in the treatment of AML.
Passive Solid Chemosensor as Saliva Point of Need Analysis for Ammonium Determination by Using a Smartphone
Authors: Monforte-Gómez Belén , Hakobyan Lusine , Molins-Legua Carmen , Campíns-Falcó Pilar
Published: July 2023

Point-of-need analysis is of great interest nowadays. It refers to the timely analysis or detection of a specific parameter or substance at the location or moment it is needed, often with the aim of providing rapid and on-site results for informed decision-making or immediate interventions. This approach has gained interest in various fields but has not been extensively explored in bioanalytical chemistry. In order to contribute in this way, the analysis of ammonium in saliva as a biological fluid is proposed here. For that purpose, a passive solid sensor of 1,2-naphthoquinone-4-sulfonic acid sodium salt (NQS) embedded in polydimethylsiloxane (PDMS) doped with silica nanoparticles and an ionic liquid was proposed. The assay was developed by delivering ammonia from saliva in a confined atmosphere containing the sensor for 20 to 45 min. Measurements were carried out by absorbance from a benchtop diffuse reflectance spectrophotometer and a fiber optic miniaturized portable spectrometer coupled to a smartphone for point-of-need analysis. Another option for this kind of analysis was the use of the color intensity from digitalized images obtained by a smartphone by isolating the intensity in the color planes R (red), G (green), and B (blue). Good figures of merit were obtained for all three types of instruments, bearing in mind the ammonium content in saliva. Results for 30 samples of male and female volunteers (n = 30) demonstrated the usefulness of the assay, values of mg NH4+/mL saliva between 0.02 and 0.27 were found, and no matrix effect was present. Recoveries for spiked samples were around 100% for all methodologies. Selectivity was demonstrated from spectra obtained from benchtop instruments and the fiber optic mini spectrometer. Two applications were applied for directly determining the ammonium concentration in saliva.