bluuebunny/crossref_metadata_2025 huggingface dataset:
New Insights on Environmentally Friendly Materials
Authors: Wei Ying , Chen Ziwei
Published: September 2023

In recent years, the world has been confronting a growing planet-wide crisis due to uncontrolled population growth and rapid industrialization [...]
Chinese Named Entity Recognition in Football Based on ALBERT-BiLSTM Model
Authors: An Qi , Pan Bingyu , Liu Zhitong , Du Shutong , Cui Yixiong
Published: September 2023

Football is one of the most popular sports in the world, arousing a wide range of research topics related to its off- and on-the-pitch performance. The extraction of football entities from football news helps to construct sports frameworks, integrate sports resources, and timely capture the dynamics of the sports through visual text mining results, including the connections among football players, football clubs, and football competitions, and it is of great convenience to observe and analyze the developmental tendencies of football. Therefore, in this paper, we constructed a 1000,000-word Chinese corpus in the field of football and proposed a BiLSTM-based model for named entity recognition. The ALBERT-BiLSTM combination model of deep learning is used for entity extraction of football textual data. Based on the BiLSTM model, we introduced ALBERT as a pre-training model to extract character and enhance the generalization ability of word embedding vectors. We then compared the results of two different annotation schemes, BIO and BIOE, and two deep learning models, ALBERT-BiLSTM-CRF and ALBERT BiLSTM. It was verified that the BIOE tagging was superior than BIO, and the ALBERT-BiLSTM model was more suitable for football datasets. The precision, recall, and F-Score of the model were 85.4%, 83.47%, and 84.37%, correspondingly.
FPGA Implementation of a Higher SFDR Upper DDFS Based on Non-Uniform Piecewise Linear Approximation
Authors: Liao Xuan , Zhang Longlong , Hu Xiang , Peng Yuanxi , Zhou Tong
Published: September 2023

We propose a direct digital frequency synthesizer (DDFS) by using an error-controlled piecewise linear (PWL) approximation method. For a given function and a preset max absolute error (MAE), this method iterates continuously from right to left within the input interval, dividing the entire interval into multiple segments. Within each segment, the least squares method is used to approximate the objective function, ensuring that each segment meets the error requirements. Based on this method, We first implemented a set of DDFS under different MAE to study the relationship between SFDR and MAE, and then evaluated its hardware overhead. In order to increase the frequency of the output signal, we implement a multi-core DDFS using time interleaving scheme. The experimental results show that our DDFS has significant advantages in SFDR, using fewer hardware resources to achieve high SFDR. Specifically, the SFDR of proposed DDFS can reach 114 dB using 399 LUTs, 66 flip flops and 3 DSPs. More importantly, we demonstrate through experiments that proposed DDFS breaks the SFDR theoretical upper bound of DDFS based on piecewise linear approximation methods.
Novel Technologies Used in the Assessment of Patellofemoral Pain: A Scoping Review
Authors: Arin-Bal Gamze , Bayrakci-Tunay Volga , Benedetti Maria Grazia , Leardini Alberto , Vismara Federico , Belvedere Claudio
Published: September 2023

This scoping review aims to present existing evidence on new technologies reported recently to assess patients with patellofemoral pain (PFP). The literature search was conducted in September 2023, and search engines were Medline (via Pubmed), Scopus, and Cochrane Central. The preferred search term was “patellofemoral pain”, as the 2016 PFP consensus statement recommended, and several subgroups were arranged to find any possible technology-related assessment. The total number of articles found was 7927. After eliminating duplicates, 2058 articles remained for the title and abstract screening. Methods sections of the articles were investigated for data charting. Among the 652 full-text articles, 8 met our inclusion criteria on gait analysis, 34 on imaging, and 95 on EMG. However, only 5 included innovative technology, 2 used cone-beam CT, 1 used a device in medical imaging to apply stress to the patella in anatomical directions, and 2 used a novel EMG electrode system based on a high-density linear array. The results of this review demonstrate the large use of innovative technologies in PFP, particularly using medical imaging and state-of-the-art gait analysis, sometimes used together for thorough biomechanical studies. Because modern technology can provide precise and detailed information, exploiting these to design more effective prevention campaigns and patient-specific rehabilitation programs is fundamental. Investigations are becoming increasingly translational and multidisciplinary as a fusion of technological and clinical perspectives brings significant insights to PFP.
Can Vitamin C Improve Proliferation and Viability of Smokers’ Gingival Fibroblasts on Collagen Membranes? An In Vitro Study
Authors: Alshehri Fahad Ali
Published: September 2023

Periodontal regeneration using a barrier membrane can be affected by several factors, including patient-related factors (such as smoking habits), surgical techniques, and type of barrier membrane. Smoking exposure has a negative impact on the periodontium due to its direct inhibition of gingival fibroblast function. Vitamin C is widely recognized as an antioxidant that can be used to mitigate the detrimental impact of smoking products on periodontal cells. This study aimed to investigate whether vitamin C could improve the proliferation and viability of gingival fibroblasts extracted from smoking and non-smoking donors and then cultured on non-crosslinked (CopiOs Pericardium) and crosslinked (BioMend) collagen membranes. To address this aim, human gingival fibroblasts were extracted from healthy periodontium of smoker patients (Group 1) and non-smoker patients (Group 2). The cells were cultivated and subsequently subcultured in a growth medium supplemented with the required nutrients. Subsequently, the medium at passage six was supplemented with vitamin C, i.e., at the start of the experiment. An evaluation of cell proliferation and viability was carried out using cell migration assays and AlamarBlue® assays for cells grown on BioMend and CopiOs Pericardium collagen membranes. Assessment of the morphology and attachment of gingival fibroblasts to the experimental collagen membranes was conducted using scanning electron microscopy (SEM). The viability and proliferation assessments of hGFs from the migration assay were evaluated using AlamarBlue®. The results exhibited significant fluorescent intensity of gingival fibroblasts on both membrane groups (BioMend and CopiOs Pericardium) in the smoker group compared to the non-smoker group (p < 0.05), which was interpreted to be the result of hGF metabolic activity and the exclusion of any cytotoxic effects, particularly from vitamin C addition. Vitamin C positively affected cells from the smoker group with statistically significant results in the BioMend group (Wilcoxon signed-rank test of p value < 0.05; p = 0.028). SEM images revealed the crosslinking pattern of the BioMend membrane and the non-crosslinked natural tissue structure of the CopiOs Pericardium membrane, which did not change regardless of whether the cultured smoker or non-smoker hGFs were treated with vitamin C. Small numbers of attached hGFs in membrane matrices in all samples, mainly in the peripheries, were observed. It can be concluded that the addition of vitamin C to collagen membranes in vitro seems to combat the adverse effects of smoking products on gingival fibroblasts.
Node2Node: Self-Supervised Cardiac Diffusion Tensor Image Denoising Method
Authors: Du Hongbo , Yuan Nannan , Wang Lihui
Published: September 2023

Although the existing cardiac diffusion tensor imaging (DTI) denoising methods have achieved promising results, most of them are dependent on the number of diffusion gradient directions, noise distributions, and noise levels. To address these issues, we propose a novel self-supervised cardiac DTI denoising network, Node2Node, which firstly expresses the diffusion-weighted (DW) image volumes along different directions as a graph, then the graph framelet transform (GFT) is implemented to map the DW signals into the GFT coefficients at different spectral bands, allowing us to accurately match the DW image pairs. After that, using the matched image pairs as input and target, a ResNet-like network is used to denoise in a self-supervised manner. In addition, a novel edge-aware loss based on pooling operation is proposed to retain the edge. Through comparison with several state-of-the-art methods on synthetic, ex vivo porcine, and in vivo human cardiac DTI datasets, we showed that the root mean square error (RMSE) of DW images and the average angular error (AAE) of fiber orientations obtained using Node2Node are the smallest, improved by 47.5% and 23.7%, respectively, on the synthetic dataset, demonstrating that Node2Node is not sensitive to the properties of the dataset.
3D Position Estimation of Objects for Inventory Management Automation Using Drones
Authors: Yoon Bohan , Kim Hyeonha , Youn Geonsik , Rhee Jongtae
Published: September 2023

With the recent development of drone technology, drones are being used in various fields. Drones have the advantage of being equipped with various devices to move freely and perform various tasks. In the field of inventory management, many studies have been conducted into management automation based on the drone. Drones scan a marker, such as a quick response code (QR code), attached to the shelves to obtain location information of the shelves on which the inventory is loaded. At the same time, drones perform inventory management by scanning the marker attached to the inventory to obtain inventory information. However, unlike indoor warehouses, where grids or shelves are well-defined, a storage yard is not fixed in the location where the inventory is stored. It is difficult to recognize the loading position from the marker for a storage yard without shelves and grids. Furthermore, the loading position of the inventory is not fixed. For the automation of inventory management of warehouses where shelves and grids are undefined, this paper proposes a framework that estimates the inventory 3D position in the video frame based on a deep learning model. The proposed framework uses the image segmentation model to detect and decode the marker in the video frame to estimate the 3D position of a drone and inventory. In addition, the estimated inventory 3D position is corrected using the continuity of the video frame. Experiment results on the video dataset verified that the proposed framework improved the 3D position estimation performance of the inventory. Consequently, efficient inventory management based on drones can be performed through the proposed framework for the 3D position estimation of inventory in all types of warehouses.
Clutch Pressure Plate Temperature Prediction Based on Bi-LSTM and Migration Learning
Authors: Chen Dong , Xie Bubu , Lan Chenxi , Gong Yubing
Published: September 2023

Clutch pressure plate temperature prediction is crucial for the structural design and performance evaluation of the clutch. However, due to the complexity of the clutch structure and the non-linear characteristics of temperature changes, accurate temperature prediction of the pressure plate has always been a difficult task, especially when considering cost factors. Aiming at this problem, this paper proposes a pressure plate temperature prediction method based on Bi-directional Long Short-Term Memory (Bi-LSTM) and transfer learning. First, the actual temperature data of the pressure plate under different experimental conditions is collected to establish a Bi-LSTM neural network temperature model, and then, a migration learning method is introduced to migrate the temperature experimental data to obtain a migration model. The migration model is finally applied to predict the pressure plate temperature using three samples and validated by the test. The results show that for the temperature prediction of the same type of pressure plate under different experimental conditions, the MSE (Mean Squared Error) of the approach is 7.08 °C, the R2 (R-squared) is 0.90, the maximum error is 8.47 °C, and the maximum relative error is 3.14%. For the temperature prediction of different types of pressure plates, the MSE of the approach is 3.64 °C, the R2 is 0.97, the maximum error is 5.94 °C, and the maximum relative error is 1.78%. It shows that the proposed approach achieves high-precision prediction of the clutch pressure plate temperature in the case of small samples, which is difficult to achieve with previous methods. The proposed approach can be used for the temperature prediction of the other clutches of models and working conditions and has broad application prospects.
Detection of Unknown Polymorphic Patterns Using Feature-Extracting Part of a Convolutional Autoencoder
Authors: Kucharski Przemysław , Ślot Krzysztof
Published: September 2023

Background: The present paper proposes a novel approach for detecting the presence of unknown polymorphic patterns in random symbol sequences that also comprise already known polymorphic patterns. Methods: We propose to represent rules that define the considered patterns as regular expressions and show how these expressions can be modeled using filter cascades of neural convolutional layers. We adopted a convolutional autoencoder (CAE) as a pattern detection framework. To detect unknown patterns, we first incorporated knowledge of known rules into the CAE’s convolutional feature extractor by fixing weights in some of its filter cascades. Then, we executed the learning procedure, where the weights of the remaining filters were driven by two different objectives. The first was to ensure correct sequence reconstruction, whereas the second was to prevent weights from learning the already known patterns. Results: The proposed methodology was tested on sample sequences derived from the human genome. The analysis of the experimental results provided statistically significant information on the presence or absence of polymorphic patterns that were not known in advance. Conclusions: The proposed method was able to detect the existence of unknown polymorphic patterns.
T-BT Inverse and T-GC Partial Order via the T-Product
Authors: Wang Hongxing , Wen Wei
Published: September 2023

In this paper, we extend the BT inverse to the set of third-order tensors, and we call it the T-BT inverse. We give characterizations and properties of the inverse by applying tensor decomposition. Based on the inverse, we introduce a new binary relation: T-BT order. Furthermore, by applying the T-BT order, we introduce a generalized core partial order (called T-GC partial order).
Some New Families of Finite Orthogonal Polynomials in Two Variables
Authors: Güldoğan Lekesiz Esra , Area Iván
Published: September 2023

In this paper, we generalize the study of finite sequences of orthogonal polynomials from one to two variables. In doing so, twenty three new classes of bivariate finite orthogonal polynomials are presented, obtained from the product of a finite and an infinite family of univariate orthogonal polynomials. For these new classes of bivariate finite orthogonal polynomials, we present a bivariate weight function, the domain of orthogonality, the orthogonality relation, the recurrence relations, the second-order partial differential equations, the generating functions, as well as the parameter derivatives. The limit relations among these families are also presented in Labelle’s flavor.
High-Performance Full Sodium Cells Based on MgO-Treated P2-Type Na0.67(Mn0.5Fe0.5)1−xCoxO2 Cathodes
Authors: Taskiran Nermin , Altundag Sebahat , Koleva Violeta , Altin Emine , Arshad Muhammad , Avci Sevda , Ates Mehmet Nurullah , Altin Serdar , Stoyanova Radostina
Published: September 2023

Herein, we design a cathode material based on layered Na2/3(Mn1/2Fe1/2)O2 for practical application by combining the Co substitution and MgO treatment strategies. The oxides are prepared via solid-state reactions at 900 °C. The structure, morphology, and oxidation state of transition metal ions for Co-substituted and MgO-treated oxides are carefully examined via X-ray diffraction, IR and Raman spectroscopies, FESEM with EDX, specific surface area measurement, and XPS spectroscopy. The ability of oxides to store sodium reversibly is analyzed within a temperature range of 10 to 50 °C via CV experiments, galvanostatic measurements, and EIS, using half and full sodium ion cells. The changes in the local structure and oxidation state of transition metal ions during Na+ intercalation are monitored via operando XAS experiments. It is found that the Co substituents have a positive impact on the rate capability of layered oxides, while Mg additives lead to a strong increase in the capacity and an enhancement of the cycling stability. Thus, the highest capacity is obtained for 2 at.%-MgO-treated Na2/3(Mn1/2Fe1/2)0.9Co0.1O2 (175 mAh/g, with a capacity fade of 28% after 100 cycles). In comparison with Co substituents, the Mg treatment has a crucial role in the improvement of the lattice stability during the cycling process. The best electrode materials, with a chemical formula of 2 at.%-MgO treated Na2/3(Mn1/2Fe1/2)0.9Co0.1O2, were also used for the full cells design, with hard carbon as an anode. In the voltage window of 2–4 V, the capacity of the cells was obtained as 78 mAh/g and 51 mAh/g for applied current densities of 12 mA/g and 60 mA/g, respectively.
Robust Porcine GFR Measurement with Radiotracers and Only Late Blood Samples
Authors: Jødal Lars , Brignone Juan Ignacio , Ladefoged Pui-Ki Chan , Lund Lars , Andersen Trine Borup
Published: September 2023

(1) Pigs are physiologically very relevant as animal models of human physiology. Radiotracer methods for porcine GFR (glomerular filtration rate) determination exist but require full-curve blood sampling or the application of correction formulas, which vary among studies. (2) We used porcine GFR data (40 datapoints from 20 juvenile pigs) for which the GFR was measured as the plasma clearance of [99mTc]Tc-DTPA. The reference clearance (Cl, GFR; range 41–85 mL/min) was measured from the full curve under the data. For simpler determination, an approximate clearance, Cl1, was based on the last five blood samples (acquired 120–240 min post injection). (3) The following formula for the GFR was developed: Cl = 1.27 · (Cl1)0.92. The spread (SD) was within 4% of the reference GFR. A comparison with the literature data showed that our correction formula was robust in pigs of various breeds, sizes up to approximately 200 kg, and GFRs up to approximately 400 mL/min, with a spread of up to 8%. The formula was also applicable for iohexol as the tracer. (4) A formula was developed that allows porcine GFR to be measured using only 4–5 late blood samples. This new formula can be applied across a wide range of swine breeds, animal sizes, and GFR ranges, allowing for robust determination of the GFR in pigs without full-curve blood sampling and without urine collection.
Variations in O-Glycosylation Patterns Influence Viral Pathogenicity, Infectivity, and Transmissibility in SARS-CoV-2 Variants
Authors: Onigbinde Sherifdeen , Reyes Cristian D. Gutierrez , Fowowe Mojibola , Daramola Oluwatosin , Atashi Mojgan , Bennett Andrew I. , Mechref Yehia
Published: September 2023

The highly glycosylated S protein plays a vital role in host cell invasion, making it the principal target for vaccine development. Differences in mutations observed on the spike (S) protein of SARS-CoV-2 variants may result in distinct glycosylation patterns, thus influencing immunological evasion, infectivity, and transmissibility. The glycans can mask key epitopes on the S1 protein and alter its structural conformation, allowing the virus to escape the immune system. Therefore, we comprehensively characterize O-glycosylation in eleven variants of SARS-CoV-2 S1 subunits to understand the differences observed in the biology of the variants. In-depth characterization was performed with a double digestion strategy and an efficient LC-MS/MS approach. We observed that O-glycosylation is highly conserved across all variants in the region between the NTD and RBD, whereas other domains and regions exhibit variation in O-glycosylation. Notably, omicron has the highest number of O-glycosylation sites on the S1 subunit. Also, omicron has the highest level of sialylation in the RBD and RBM functional motifs. Our findings may shed light on how differences in O-glycosylation impact viral pathogenicity in variants of SARS-CoV-2 and facilitate the development of a robust vaccine with high protective efficacy against the variants of concern.
Untreated Early Childhood Caries and Possible Links with Brain Development
Authors: Foláyan Morẹ́nikẹ́ Oluwátóyìn , Femi-Akinlosotu Omowumi Moromoke , Adeoti Bolu , Olorunmoteni Oluwatosin Eunice
Published: September 2023

Untreated early childhood caries (ECC) is a global public health concern. In the short term, untreated ECC can lead to pain, infection, and disrupted sleep, among other issues. In the long term, it is associated with poor oral health in later life, increased risk of caries in permanent teeth, and adverse effects on physical and psychological development. There may be a link between untreated ECC and adverse cognitive and neurodevelopmental outcomes in young children, although the exact pathways are not fully understood. One possible pathway is through the relationship between mastication and brain stimulation. Impaired masticatory function due to ECC can affect the hippocampus, a key region responsible for memory and learning. Furthermore, untreated ECC can cause chronic inflammation, leading to the release of pro-inflammatory cytokines that may damage the brain. Sleep disturbances resulting from ECC-related pain and discomfort can also impact brain development and cognitive functioning. Additionally, frequent use of antibiotics and analgesics to address ECC-related infections can disrupt the gut microbiome, potentially affecting the brain through the gut–brain axis. Untreated ECC can cause nutritional deficiencies and elevated nutritional risk, and can further hinder brain development. Addressing ECC comprehensively with early childhood health initiatives can help mitigate potential long-term consequences and promote optimal brain development in young children.
Spatial Green Space Accessibility in Hongkou District of Shanghai Based on Gaussian Two-Step Floating Catchment Area Method
Authors: Sun Yao , Tian Dongwei , Zhang Man , Hou Yue
Published: September 2023

Green space in urban areas is one of the most critical infrastructures for the well-being of residents, and its spatial accessibility configuration is a key indicator of urban planning layout and ecological civilization construction. Using the Gaussian two-step floating catchment area (G2SFCA) method, K-means cluster analysis, and Kriging spatial interpolation, this study examines the spatial characteristics of green space accessibility in the Hongkou District, focusing on the relationship between “green space-community” supply. The findings indicate the following: (1) green space accessibility in Hongkou District decreases as the level of green spaces decreases. Higher levels of accessibility are associated with smaller variations in spatial distribution. (2) The green space accessibility in Hongkou District is affected by the surrounding large green space. Compared to other areas, the community green spaces near Lu Xun Park and Peace Park have higher accessibility. (3) The green space accessibility in Hongkou District is impacted by the mismatch between population density and green spaces. An overlay analysis of accessibility and population density reveals that high accessibility areas have average or low-average population density. Based on the results of the green space accessibility analysis, recommendations are proposed to optimize the green space layout in Hongkou District from the perspective of spatial justice. These suggestions are aimed at promoting the equalization of green space services in Hongkou District, improving the overall level of urban green space services, with a view to improving the quality of urban living environment and creating a green and livable urban area.
Evaluation of the Effectiveness of Treatments to Remove Per- and Polyfluoroalkyl Substances from Water—Are We Using the Right Approach? Proposal of a Paradigm Shift from “Chemical Only” towards an Integrated Bio-Chemical Assessment
Authors: Carnevale Miino Marco , Halešová Taťána , Macsek Tomáš , Raček Jakub , Hlavínek Petr
Published: September 2023

Per- and polyfluoroalkyl substances (PFASs) have been under intense investigation by the scientific community due to their persistence in the environment and potentially hazardous effects on living organisms. In order to tackle the presence of these compounds in water, to date, the research has been strongly focused on the evaluation of the effectiveness of different types of technologies. Considering the extreme complexity of the matter of PFASs and our relatively low knowledge in this topic, the following question arises: is the “chemical only” approach that is followed for evaluating the effectiveness of technologies for PFAS removal from water reliable enough? In this work, some limitations of the present approach are discussed, highlighting the reasons why it cannot be considered a reliable tool to correctly estimate the effectiveness of technology when referring to emerging compounds such as PFASs. Bioassays can play a key role in moving towards an integrated bio-chemical evaluation (chemical analysis and ecotoxicological evaluation), which is strongly encouraged. This represents the only way to completely characterize a water matrix and fully evaluate the impact of technologies when dealing with micropollutants in water, such as PFASs. Future research should focus on defining an optimal battery of bioassays that specifically fit to best represent changes in water quality in terms of short- and long-term impacts on living organisms.
Gold Nanoparticles as Monoanion Sensors through Modified Electrophilicity
Authors: Martí Almudena , Arroyo Pau , Gaviña Pablo , Gil Salvador , Parra Margarita , Sáez José A.
Published: September 2023

Derived from malachite green, new triaryl-carbonium-ion-functionalized gold nanoparticles have been synthesized for detecting anions. The detection process, and concomitant colour change, is based on charge compensation on the surface of nanoparticles, which triggers their aggregation, resulting in a bathochromic shift of the plasmon resonance band. The difference in electrophilicity of the malachite green triaryl ions in solution or on gold nanoparticles makes it possible to distinguish different anions related to their nucleophilic character.
An Improved Dandelion Optimizer Algorithm for Spam Detection: Next-Generation Email Filtering System
Authors: Tubishat Mohammad , Al-Obeidat Feras , Sadiq Ali Safaa , Mirjalili Seyedali
Published: September 2023

Spam emails have become a pervasive issue in recent years, as internet users receive increasing amounts of unwanted or fake emails. To combat this issue, automatic spam detection methods have been proposed, which aim to classify emails into spam and non-spam categories. Machine learning techniques have been utilized for this task with considerable success. In this paper, we introduce a novel approach to spam email detection by presenting significant advancements to the Dandelion Optimizer (DO) algorithm. The DO is a relatively new nature-inspired optimization algorithm inspired by the flight of dandelion seeds. While the DO shows promise, it faces challenges, especially in high-dimensional problems such as feature selection for spam detection. Our primary contributions focus on enhancing the DO algorithm. Firstly, we introduce a new local search algorithm based on flipping (LSAF), designed to improve the DO’s ability to find the best solutions. Secondly, we propose a reduction equation that streamlines the population size during algorithm execution, reducing computational complexity. To showcase the effectiveness of our modified DO algorithm, which we refer to as the Improved DO (IDO), we conduct a comprehensive evaluation using the Spam base dataset from the UCI repository. However, we emphasize that our primary objective is to advance the DO algorithm, with spam email detection serving as a case study application. Comparative analysis against several popular algorithms, including Particle Swarm Optimization (PSO), the Genetic Algorithm (GA), Generalized Normal Distribution Optimization (GNDO), the Chimp Optimization Algorithm (ChOA), the Grasshopper Optimization Algorithm (GOA), Ant Lion Optimizer (ALO), and the Dragonfly Algorithm (DA), demonstrates the superior performance of our proposed IDO algorithm. It excels in accuracy, fitness, and the number of selected features, among other metrics. Our results clearly indicate that the IDO overcomes the local optima problem commonly associated with the standard DO algorithm, owing to the incorporation of LSAF and the reduction in equation methods. In summary, our paper underscores the significant advancement made in the form of the IDO algorithm, which represents a promising approach for solving high-dimensional optimization problems, with a keen focus on practical applications in real-world systems. While we employ spam email detection as a case study, our primary contribution lies in the improved DO algorithm, which is efficient, accurate, and outperforms several state-of-the-art algorithms in various metrics. This work opens avenues for enhancing optimization techniques and their applications in machine learning.
Fresh and Hardened Properties of Brick Aggregate Concrete with Maximum Aggregate Sizes of 10 mm to 75 mm
Authors: Mohammed Tarek Uddin , Mahmood Aziz Hasan , Sakib Adnan , Khan Fahim Ahmed , Sopnil Shafakat Islam
Published: September 2023

The fresh and mechanical properties of concrete made with brick aggregates of eight different maximum aggregate sizes (MAS), i.e., 10 mm, 12.5 mm, 19 mm, 25 mm, 37.5 mm, 50 mm, 63 mm, and 75 mm, were investigated. The other parameters studied were sand-to-aggregate volume ratio (s/a) (0.40 and 0.45), W/C (0.45, 0.50, and 0.55), and cement content (375 kg/m3 and 400 kg/m3). In total, 80 different concrete mixes were studied; the perimeter of the interfacial transition zone (ITZ) along the brick aggregates was quantified with an image-analysis software and the microstructure along the ITZ was investigated using a scanning-electron microscope (SEM) to corroborate the hardened properties of the concrete. Although larger MAS leads to greater slump in concrete, its effect on hardened properties is linked to other design parameters. For a cement content of 375 kg/m3 and W/C of 0.45 and 0.50, the compressive strength of concrete increases (by up to 5%–15%) with increases in MAS of up to 37.5 mm irrespective of s/a (0.40 and 0.45) and then reduces gradually. For all other cases, the compressive strength of concrete is reduced with increases in MAS. The SEM imaging confirmed the presence of weak and porous ITZ and the deposition of ettringite in the voids left by entrapped bleed water under large aggregates. The compressive strength also increased with increases in s/a from 0.40 to 0.45, predominantly for smaller MAS. Correlations between mechanical properties of concrete and stress–strain curves are proposed for different MAS.
Software System for Automatic Grading of Paper Tests
Authors: Jocovic Vladimir , Nikolic Bosko , Bacanin Nebojsa
Published: September 2023

The advent of digital technology has revolutionized numerous aspects of modern life, including the field of assessment and testing. However, paper tests, despite their seemingly archaic nature, continue to hold a prominent position in various assessment domains. The accessibility, familiarity, security, cost-effectiveness, and versatility of paper tests collectively contribute to their continued prominence. Hence, numerous educational institutions responsible for conducting examinations involving a substantial number of candidates continue to rely on paper tests. Consequently, there arises a demand for the possibility of automated assessment of these tests, aiming to alleviate the burden on teaching staff, enhance objectivity in evaluation, and expedite the delivery of test results. Therefore, diverse software systems have been developed, showcasing the capability to automatically score specific question types. Thus, it becomes imperative to categorize related question types systematically, thereby facilitating a preliminary classification based on the content and format of the questions. This classification serves the purpose of enabling effective comparison among existing software solutions. In this research paper, we present the implementation of such a software system using artificial intelligence techniques, progressively expanding its capabilities to evaluate increasingly complex question types, with the ultimate objective of achieving a comprehensive evaluation of all question types encountered in paper-based tests. The system detailed above demonstrated a recognition success rate of 99.89% on a curated dataset consisting of 734,825 multiple-choice answers. For the matching type, it achieved a recognition success rate of 99.91% on 86,450 answers. In the case of short answer type, the system achieved a recognition success rate of 95.40% on 129,675 answers.
Identifying the Producer and Grade of Matcha Tea through Three-Dimensional Fluorescence Spectroscopy Analysis and Distance Discrimination
Authors: Xu Yue , Zhou Xiangyang , Lei Wenjuan
Published: September 2023

The three-dimensional fluorescence spectroscopy features the advantage of obtaining emission spectra at different excitation wavelengths and providing more detailed information. This study established a simple method to discriminate both the producer and grade of matcha tea by coupling three-dimensional fluorescence spectroscopy analysis and distance discrimination. The matcha tea was extracted three times and three-dimensional fluorescence spectroscopies of these tea infusions were scanned; then, the dimension of three-dimensional fluorescence spectroscopies was reduced by the integration at three specific areas showing local peaks of fluorescence intensity, and a series of vectors were constructed based on a combination of integrated vectors of the three tea infusions; finally, four distances were used to discriminate the producer and grade of matcha tea, and two discriminative patterns were compared. The results indicated that proper vector construction, appropriate discriminative distance, and correct steps are three key factors to ensure the high accuracy of the discrimination. The vector based on the three-dimensional fluorescence spectroscopy of all three tea infusions resulted in a higher accuracy than those only based on spectroscopy of one or two tea infusions, and the first tea infusion was more sensitive than the other tea infusion. The Mahalanobis distance had a higher accuracy that was up to 100% when the vector is appropriate, while the other three distances were about 60–90%. The two-step discriminative pattern, identifying the producer first and the grade second, showed a higher accuracy and a smaller uncertainty than the one-step pattern of identifying both directly. These key conclusions above help discriminate the producer and grade of matcha in a quick, accurate, and green method through three-dimensional fluorescence spectroscopy, as well as in quality inspections and identifying the critical parameters of the producing process.
An Intelligent Method for Predicting Pacific Oyster (Crassostrea gigas) Freshness Using Deep Learning Fused with Malondialdehyde and Total Sulfhydryl Groups Information
Authors: Lu Tao , Yu Fanqianhui , Han Baokun , Guo Jingying , Liu Kunhua , He Shuai
Published: September 2023

To achieve a non-destructive and rapid detection of oyster freshness, an intelligent method using deep learning fused with malondialdehyde (MDA) and total sulfhydryl groups (SH) information was proposed. In this study, an “MDA-SH-storage days” polynomial fitting model and oyster meat image dataset were first built. AleNet-MDA and AlxNet-SH classification models were then constructed to automatically identify and classify four levels of oyster meat images with overall accuracies of 92.72% and 94.06%, respectively. Next, the outputs of the two models were used as the inputs to “MDA-SH-storage days” model, which ultimately succeeded in predicting the corresponding MDA content, SH content and storage day for an oyster image within 0.03 ms. Furthermore, the interpretability of the two models for oyster meat image were also investigated by feature visualization and strongest activations techniques. Thus, this study brings new thoughts on oyster freshness prediction from the perspective of computer vision and artificial intelligence.
Carthamus tinctorius L. Seed and Taraxacum coreanum Attenuate Oxidative Stress Induced by Hydrogen Peroxide in SH-SY5Y Cells
Authors: He Mei Tong , Park Chan Hum , Shin Yu Su , Kim Ji Hyun , Cho Eun Ju
Published: September 2023

Oxidative stress is closely associated with the pathology of neurodegenerative diseases. The seeds of Carthamus tinctorius L. (CTS) and Taraxacum coreanum (TC) are reported as herbal medicines for neuroprotection. This study investigated the protective effect of CTS, TC, and their combination against oxidative stress induced by H2O2 in SH-SY5Y cells. The CTS and TC combination dose-dependently increased DPPH and ·OH radical scavenging activities compared with non-combination. The combination showed a higher increased cell survival rate in H2O2-stimulated SH-SY5Y cells than CTS or TC. Moreover, CTS, TC, and their combination-treated cells reduced LDH release and apoptotic cells. CTS, TC, and their combination also inhibited NO and ROS generation. Further, the combination of up-regulated antioxidant enzymes (superoxide dismutase and glutathione peroxidase) and Bcl-2 protein expressions and down-regulated Bax expression. These findings suggest that the combination of CTS and TC may be beneficial to prevent and treat oxidative stress-mediated neurodegenerative diseases.
How Worried Are You about Food Fraud? A Preliminary Multi-Country Study among Consumers in Selected Sub-Saharan African Countries
Authors: Soon-Sinclair Jan Mei , Imathiu Samuel , Obadina Adewale Olusegun , Dongho Dongmo Fabrice Fabien , Kamgain Alex Dimitri Tchuenchieu , Moholisa Ennet , Saba Courage Kosi Setsoafia , Walekhwa Abel Wilson , Hunga Henry , Kussaga Jamal
Published: September 2023

Food fraud is an old, recurring, and global threat to public health. It poses a serious threat to food security in sub-Saharan Africa (SSA). Despite the prevalence of food fraud in SSA, little is known about how food fraud is viewed by consumers. This study aims to provide an overview of consumers’ concerns about food fraud in SSA. A multi-country survey was conducted in October 2022–31 January 2023, and 838 valid responses were returned. To reduce the large and correlated dataset, Principal Component Analysis (PCA) was used. Five components were derived from PCA: (i) Staple foods; (ii) Premium food and drink products; (iii) Trust in reliable sources; (iv) Trust in less reliable sources; and (v) Trust in food vendors. The findings revealed Ghanaian (mean rank = 509.47) and Nigerian (mean rank = 454.82) consumers tended to score higher on the measure of food fraud concern suggesting that they were less confident in the safety and quality of the food they consume. Demographic characteristics including age, number of children, personal and family experience of food fraud and PCA components such as ‘Staple foods’, ‘Trust in reliable sources’, and ‘Trust in food vendors’ significantly predicted the model. This is the first preliminary study to provide empirical findings on consumers’ concerns about food fraud in SSA. Practical and policy recommendations for the region are suggested. This includes (i) modelling the AfriFoodinTegrity in West Africa across other major regions such as Central, East, and Southern Africa; (ii) establish a regional sub-Saharan Africa Rapid Alert System for Food and Feed (SSA-RASFF) platform; and (iii) food safety and food fraud reports could be incorporated into SSA-RASFF portal for information sharing.
Numerical Solutions of the Multi-Space Fractional-Order Coupled Korteweg–De Vries Equation with Several Different Kernels
Authors: Saad Khaled Mohammed , Srivastava Hari Mohan
Published: September 2023

In this article, the authors propose to investigate the numerical solutions of several fractional-order models of the multi-space coupled Korteweg–De Vries equation involving many different kernels. In order to transform these models into a set or system of differential equations, various properties of the first-kind Chebyshev polynomial are used in this study. The main objective of the present study is to apply the spectral collocation approach for the multi-space fractional-order coupled Korteweg–De Vries equation with different kernels. We use finite differences to numerically solve these differential equations by reducing them to algebraic equations. The Newton (or, more precisely, the Newton–Raphson) method is then used to solve these resulting algebraic equations. By calculating the error involved in our approach, the precision of the numerical solution is verified. The use of spectral methods, which provide excellent accuracy and exponential convergence for issues with smooth solutions, is shown to be a benefit of the current study.
Malignant Care: Affects and Labor in Anita Nair’s Ladies Coupé (2001)
Authors: Mitra Pujarinee
Published: September 2023

Anita Nair’s Ladies Coupé (2001) is about six women who meet in an express train’s compartment in southern India. One of these women, Akhila, is the narrator of the novel, while we hear the voices of the other women only when they narrate their stories in first person to Akhila. The way the women tell these stories one by one is in the spirit of empowering Akhila, who is portrayed as a woman bound within heteronormative ideas of coupledom and gender-based expectations of care labor within patriarchal families. The women also encourage her, by example, to question the accepted ethical model of feminist practice within an already unethical patriarchal structure of society. This encouragement happens, I argue, as they recount instances of the self-acknowledged unethical care practices through which they have affectively resisted different forms of violence within the upper caste, patriarchal, heteronormative family structure. These forms of violence are intersectional as they are based on overlapping identities of caste, age, and gender.
A Vector Field Visualization Method for Trajectory Big Data
Authors: Li Aidi , Xu Zhijie , Zhang Jianqin , Li Taizeng , Cheng Xinyue , Hu Chaonan
Published: September 2023

With the rapid growth of trajectory big data, there is a need for more efficient methods to extract, analyze, and visualize these data. However, existing research on trajectory big data visualization mainly focuses on displaying trajectories for a specific period or showing spatial distribution characteristics of trajectory points in a single time slice using clustering, filtering, and other techniques. Therefore, this paper proposes a vector field visualization model for trajectory big data, aiming to effectively represent the inherent movement trends in the data and provide a more intuitive visualization of urban traffic congestion trends. The model utilizes the motion information of vehicles to create a travel vector grid and employs WebGL technology for vector field visualization rendering. The vector field effects are effectively displayed by generating many particles and simulating their movements. Furthermore, this research also designs and implements congestion trend point identification and hotspot congestion analysis, thus validating the practicality and effectiveness of trajectory big data vector field visualization. The results indicate that compared to traditional visualization methods, the vector field visualization method can demonstrate the direction and density changes in traffic flow and predict future traffic congestion. This work provides valuable data references and decision support for urban traffic management and planning.
Clinical Reproducibility of the Stone Volume Measurement: A “Kidney Stone Calculator” Study
Authors: Peyrottes Arthur , Chicaud Marie , Fourniol Cyril , Doizi Steeve , Timsit Marc-Olivier , Méjean Arnaud , Yonneau Laurent , Lebret Thierry , Audenet François , Traxer Olivier , Panthier Frederic
Published: September 2023

Background: An accurate estimation of the stone burden is the key factor for predicting retrograde intra-renal surgical outcomes. Volumetric calculations better stratify stone burden than linear measurements. We developed a free software to assess the stone volume and estimate the lithotrity duration according to 3D-segmented stone volumes, namely the Kidney Stone Calculator (KSC). The present study aimed to validate the KSC’s reproducibility in clinical cases evaluating its inter-observer and intra-observer correlations. Methods: Fifty patients that harbored renal stones were retrospectively selected from a prospective cohort. For each patient, three urologists with different experience levels in stone management made five measurements of the stone volume on non-contrast-enhanced computed tomography (NCCT) images using the KSC. Results: the overall inter-observer correlation (Kendall’s concordance coefficient) was 0.99 (p < 0.0001). All three paired analyses of the inter-observer reproducibility were superior to 0.8. The intra-observer variation coefficients varied from 4% to 6%, and Kendall’s intra-observer concordance coefficient was found to be superior to 0.98 (p < 0.0001) for each participant. Subgroup analyses showed that the segmentation of complex stones seems to be less reproductible. Conclusions: The Kidney Stone Calculator is a reliable tool for the stone burden estimation. Its extension for calculating the lithotrity duration is of major interest and could help the practitioner in surgical planning.