bluuebunny/crossref_metadata_2025 huggingface dataset:
Design and Performance Analysis of a Torsional Soft Actuator Based on Superelastic Materials
Authors: Xu Zhengyun , Liu Haiqiang , Feng Weihua , Yang Huaming , Nie Xin , Zhou Rougang
Published: May 2023

Conducting related research is crucial since there are still many problems that need to be resolved in the research on soft robots in the areas of material selection, structure design and manufacture, and drive control. Soft manipulators, a subset of soft robots, are now a popular area of study for many researchers. In comparison to typical manipulators, soft manipulators feature a high degree of gripping flexibility and a basic morphological structure. They are composed of flexible materials. It has a wide range of potential applications in healthcare, rehabilitation, bionics, and detection, and it can compensate for the drawbacks of rigid manipulators in some use scenarios. A modular soft-body torsional gripping system is developed after a torsional and gripping actuator is conceived, constructed, and its performance is examined. The torsion actuator and the grasping actuator can be combined in the system in a modular fashion. With the help of RGB-D vision algorithms, this multi-modular setup makes it possible to combine soft actuators with various twisting degrees and achieve exact gripping. Through pneumatic control, the target object is precisely grasped and rotated at various angles, enabling rotation of the target object in three dimensions.
Multi-Stream Graph-based Deep Neural Networks for Skeleton-based Sign Language Recognition
Authors: Miah Abu Saleh Musa , Hasan Md. Al Mehedi , Jang Si-Woong , Lee Hyoun-Sup , Shin Jungpil
Published: May 2023

Sign Language Recognition (SLR) aims to bridge speech-impaired and general communities by recognizing signs from given videos. Researchers still face challenges developing efficient SLR systems because of the video’s complex background, light illumination, and subject structures. Recently many researchers developed a skeleton-based sign language recognition system to overcome the subject and background variation of hand gesture signs. However, skeleton-based SLR is still under exploration due to the lack of information and annotations on hand key points. More recently, researchers included body and face information with the hand gesture for the SLR, but their performance and efficiency are unsatisfactory. We proposed a Multi-Stream Graph-based Deep Neural Network (SL-GDN) for a skeleton-based SLR system to overcome the problems. The main purpose of the proposed SL-GDN approach is to improve the efficiency and performance of the SLR system with a low computational cost based on the human body pose in the form of 2D landmark locations. In the procedure, firstly, we constructed a skeleton graph based on the selected 27 whole-body key points among 67 key points to solve the inefficiency problems. Then we proposed multi-stream SL-GDN to extract features from the whole-body skeleton graph for four streams. Finally, we concatenated the four different features and applied a classification module to refine the feature and recognize corresponding sign classes. Our data-driven and graph construction method increases the system’s flexibility and brings high generability to adapt various data samples. We used three large-scale benchmark SLR datasets to evaluate the proposed model: WLASL, AUTSL and CSL. The demonstrated performance accuracy table proved the superiority of the proposed model, and we believe this will be considered a great invention in the SLR domain.
Improving Sensitivity of Ferrous Particle Sensors with Permanent Magnet
Authors: Hong Sung-Ho
Published: May 2023

This study aimed to improve sensitivity of ferrous particle sensors used in various mechanical systems such as wind power gearboxes to detect abnormalities by measuring the amount of ferrous wear particles generated by metal-to-metal contact. Existing sensors collect ferrous particles using a permanent magnet. However, its ability to detect abnormalities is limited because it only measures amounts of ferrous particles collected on the top of the sensor. To overcome this limitation, this study evaluated sensibility of an existing sensor using a multi-physics analysis method and improved it by changing the core shape inside the sensor. The detection ability of the improved sensor was analytically shown to be better than the existing sensor. This study highlights the use of analytical methods and multi-physics analysis for developing a ferrous particle sensor and presents a method to compare sensibility analytically. Results of the improved sensor are expected to enhance the ability to detect abnormalities in the lubrication system where ferrous wear particles are generated. This study will contribute to the development of more effective ferrous particle sensors, which can potentially improve the performance and reliability of mechanical systems.
Does Climate Finance Support Institutional Adaptive Capacity in Caribbean Small Island and Developing States? An Analysis of the Green Climate Fund Readiness Grants
Authors: Ricci Liana , Mangenot Maryline
Published: May 2023

The impacts of climate change are already felt across the globe, and (SIDS) are at the forefront. Small Islands Developing States (SIDS) are extremely vulnerable to climate change and adaptation is crucial, however they often lack funding or the fiscal capacity to make the necessary invest-ments and require support from climate finance instruments. The Green Climate Fund (GCF) was designed with the objective of achieving a “paradigm shift” towards low-carbon and climate resilient country-driven development pathway. Despite the amounts invested, assessing the impacts of climate finance on adaptation and adaptive capacity, particularly at the institutional level remains a challenge. Researchers identified two key components for more efficient adapta-tion policies at the national level: the degree of adaptation mainstreaming and institutional adaptive capacity. In SIDS, institutional capacity at the national level is seen as a key component to achieve the objectives of climate change strategies, and is supported by several programmes, including the Green Climate Fund Readiness Preparatory Support Programmes. However, to date few studies have analysed the linkages between climate finance, adaptation mainstreaming and adaptive institutional capacity. Through the review of the Readiness Grants and semi-structured interviews in three Caribbean SIDS, this research assess how climate finance may promote in-stitutional change through the mainstreaming of adaptation policies at the national level and contribute to more institutional adaptive capacity. It shows that the grants had a positive impact, which can be limited to by the strength of the institutions in place. These results demonstrate that access to climate finance can create a window of opportunity for countries to accelerate institu-tional change and allow to make recommendations on how to maximise the impacts adaptation funds. More in-depth studies would be needed to examine the complementary influence of the different climate finance flows (multilateral or bilateral) and their interplay with national institu-tional mechanisms.
The Applicability of Machine Learning Methods to the Characterization of Fibrous Gas Diffusion Layers
Authors: Froning Dieter , Hoppe Eugen , Peters Ralf
Published: May 2023

Porous materials can be characterized by well-trained neural networks. In this study, fibrous paper-type gas diffusion layers were trained with artificial data created by a stochastic geometry model. The features of the data were calculated by means of transport simulations using the Lattice–Boltzmann method based on stochastic micro-structures. A convolutional neural network was developed that can predict the permeability and tortuosity of the material, through-plane and in-plane. The characteristics of real data, both uncompressed and compressed, were predicted. The data was represented by reconstructed images of different sizes and image resolutions. Image artifacts are also a source of potential errors in the prediction. The Kozeny–Carman trend was used to evaluate the prediction of permeability and tortuosity of compressed real data. Using this method, it was possible to decide if the predictions on compressed data were appropriate.
Phytoplankton Dynamics and Biogeochemistry of the Black Sea
Authors: Silkin Vladimir , Pautova Larisa , Podymov Oleg , Chasovnikov Valery , Lifanchuk Anna , Fedorov Aleksey , Kluchantseva Agnislava
Published: May 2023

Biogeochemistry of waters is an essential regulator of phytoplankton dynamics, determining the level of species bloom and the change of dominants. The paper investigated the seasonal dynamics of phytoplankton and the nutrient concentration and their ratios in the NE Black Sea in 2017-2021. Two taxonomic groups, diatoms, and coccolithophores, determine seasonal dynamics and significantly contribute to the total phytoplankton biomass. Coccolithophores annually, except in 2020, formed bloom in early June. Large diatoms in summer annually, except in 2019, dominated with biomass exceeding 1000 mg m-3. During the bloom of these taxonomic groups, their contribution to the total phytoplankton biomass exceeded 90%. Each group has characteristic biogeochemical niches in the nitrogen and phosphorus concentrations coordinates. The regulation of the biogeochemistry of water is the position of the seasonal thermocline. With a high-lying and sharp gradient thermocline (the average for five years is 6.87 m), low nitrogen concentrations and a nitrogen-to-phosphorus ratio below the Redfield ratio are created in the upper mixed layer. These conditions are optimal for the dominance of coccolithophores. When the thermocline is deepened (the average for five years is 17.96 m), the phosphorus concentration decreases significantly and the ratio of nitrogen to phosphorus is significantly higher than the Redfield ratio, and these conditions lead to the dominance of large diatoms. The results of experimental studies with nitrogen and phosphorus additives in the natural phytoplankton population confirm the above statements. The addition of phosphorus leads to an increased role of coccolithophores in the total phytoplankton biomass, the addition of nitrogen alone contributes to the growth of large diatoms, and the combined addition of phosphorus and nitrogen in a ratio close to the Redfield ratio leads to the dominance of small diatoms.
Critical Compactness Bound of a Select Class of Compact Stars
Authors: Barman Satarupa , Sharma Ranjan
Published: May 2023

Tolman VII solution \citep{ref-journal 2} is an exact analytic solution to the Einstein field equations describing the space-time of a static spherically symmetric distribution of matter. The solution has been shown to be capable of describing the interior of compact objects like neutron stars. Generalized \cite{ref-journal 13} and modified \cite{ref-journal 10} versions of the solution are also available in the literature, which have been subsequently developed to accommodate more realistic descriptions of neutron stars. The stability of the modified Tolman VII solution has recently been analyzed by Posada {\em et al} \cite{ref-journal 9 }, who evaluated a critical value of the adiabatic index above which the stellar configuration becomes unstable against radial oscillations. In this paper, making use of the generalized version of the Tolman VII solution, we prescribe an upper bound on the compactness ($M/R$) beyond which the star becomes unstable. Our investigation is based on the stability analysis of a star against radial oscillations developed by Chandrasekhar \cite{ref-journal 1}. The analysis brings out to attention the role of a particular model parameter in the generalized Tolman VII solution which can be linked to the inhomogeneity of the matter distribution vis-a-vis equation of state (EOS).
The Solar Energy Potential of Greece for Flat-Plate Solar Panels Fixed on Dual-Axis Systems
Authors: Kambezidis Harry D. , Mimidis Konstantinos , Kavadias Kosmas A.
Published: May 2023

The objective of the present work is to investigate the performance of flat-plate solar panels in Greece that continuously follow the daily motion of the Sun. To that end, the annual energy sums are estimated on such surfaces from hourly solar horizontal radiation values at 43 locations covering all of Greece. The solar horizontal radiation values are embedded in the typical meteorological years of the sites obtained from the PV-GIS tool. All calculations use a near-real surface albedo; an isotropic and an anisotropic model are used to estimate the diffuse-inclined radiation. The analysis provides regression equations for the energy sums as a function of time (month, season). The annual energy sums are found to vary between 2247 kWhm−2 and 2878 kWhm−2 under all-sky conditions with the anisotropic transposition model. Finally, maps of Greece showing the distribution of the annual and seasonal solar energy sums under all- and clear-sky conditions are derived for the first time.
COVID-19 Diagnosis by Extracting New Features from Lung CT Images Using Fractional Fourier Transform
Authors: Rashidi Saeid , Nokhostin Allie
Published: May 2023

Covid-19 is a lung disease caused by a Coronavirus family virus. Due to its extraordinary prevalence and death rates, it has spread quickly to every country in the world. Thus, achieving peaks and outlines and curing different types of relapses is extremely important. Given the worldwide prevalence of Coronavirus and the participation of physicians in all countries, Information has been gathered regarding the properties of the virus, its diverse types, and the means of analyzing it. Numerous approaches have been used to identify this evolving virus. It is generally considered the most accurate and acceptable method of examining the patient's lungs and chest through a CT scan. As part of the feature extraction process, a method known as fractional Fourier transform (FrFT) has been applied as one of the time-frequency domain transformations. The proposed method was applied to a database consisting of 2481 CT images. Following the transformation of all images into equal sizes and the removal of non-lung areas, multiple combination windows are used to reduce the number of features extracted from the images. In this paper, the results obtained for KNN and SVM classification have been obtained with accuracy values of 99.84% and 99.90%, respectively.
Treatment Outcomes and Associated Factors among Tuberculosis Patients from Selected Rural Eastern Cape Hospitals: An Ambidirectional Study
Authors: Faye Lindiwe M. , Hosu Mojisola Clara , Iruedo Joshua , Vasaikar Sandeep , Nokoyo Kholisa Amanda , Tsuro Urgent , Apalata Teke
Published: May 2023

An essential metric for determining the efficacy of tuberculosis (TB) control programs is the evaluation of TB treatment outcomes. Assessing treatment outcomes is fundamental to facilitating the End TB Strategy set target. Clinic records from 457 patients with DR-TB were examined for data collection while 101 patients were followed up prospectively. Data were analyzed using Stata version 17.0. The odds ratio and 95% confidence interval were calculated to check the association between variables. P ≤ 0.05 was con-sidered statistically significant. Of the 427 participants, 65.8% had successful treatment whilst 34.2% had unsuccessful TB treatment. A total of 61.2% and 39% of the HIV-positive and HIV-negative participants had a successful TB treatment whilst 66% and 34% of both HIV-negative and positive participants had unsuccessful TB treatment. From 101 patients that were followed up, smokers had longer treatment outcomes compared to non-smokers. In the study with HIV/TB co-infection, men predominated. HIV and tuberculosis co-infection made therapy difficult with unfavorable effects on TB management. The treatment success rate (65.8%) is lower than the WHO threshold standard with a high proportion of patients lost to follow-up. The co-infection of tuberculosis and HIV resulted in undesirable treatment outcomes. Strengthening TB surveillance and control is recommended.
Antioxidative and Anti-inflammatory Activities of Rosebud Extracts of Newly-Crossbred Roses
Authors: Wang Cuicui , Kim In-Jeong , Seong Hye-Rim , Noh Chan Ho , Park Sangryong , Kim Tae Myoung , Jeong Heon Sang , Kim Ka Young , Kim Seung Tae , Yuk Hyun-Gyun , Kwon Sang-Chul , Choi Ehn-Kyoung , Kim Yun-Bae
Published: May 2023

Oxidative stress and inflammation are basic pathogenic factors involved in tissue injury, pain, as well as acute and chronic diseases. Since long-term uses of steroids and non-steroidal anti-inflammatory drugs (NSAIDs) cause severe adverse effects, novel effective materials with minimal side effects are required. In this study, polyphenol content and antioxidative activity of rosebud extracts from 24 newly-crossbred Korean roses were analyzed. Among them, Pretty Velvet rosebud extract (PVRE) were found to contain high polyphenols and to show in vitro antioxidative and anti-inflammatory activities. In RAW 264.7 cells stimulated with lipopolysaccharide (LPS), PVRE down-regulated mRNA expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2), and thereby decreased nitric oxide (NO) and prostaglandin E2 (PGE2) production. In an air-pouch inflammation model, treatment with PVRE decreased λ-carrageenan-induced tissue exudation, infiltration of inflammatory cells including neutrophils, monocytes and lymphocytes, and tumor necrosis factor-α and interleukin-1β concentrations, as achieved with dexamethasone (a steroid). Notably, PVRE also inhibited PGE2, similar to dexamethasone and indomethacin (an NSAID). The anti-inflammatory effects of PVRE were confirmed by microscopic findings, attenuating tissue erythema, edema, and inflammatory cell infiltration. These results indicate that PVRE exhibits dual (steroid- and NSAID-like) anti-inflammatory activities by blocking both the iNOS ― NO and COX-2 ― PG pathways, and that PVRE could be a promising anti-inflammatory material for diverse issue injuries.
Using HVS Dual-Pathway and Contrast Sensitivity to Blindly Assess Image Quality
Authors: Chen Fan , Fu Hong , Yu Hengyong , Chu Ying
Published: May 2023

Blind image quality assessment (BIQA) aims to evaluate image quality in a way that closely matches human perception. To achieve this goal, the strengths of deep learning and the characteristics of human visual system (HVS) can be combined. In this paper, inspired by the ventral pathway and dorsal pathway of HVS, a dual-pathway convolutional neural network is proposed for BIQA task. The proposed method consists of two pathways: “what” pathway, which mimics the ventral pathway of HVS to extract the content features of distorted images, and the “where” pathway, which mimics the dorsal pathway of HVS to extract the global shape features of distorted images. Then, the features from the two pathways are fused and mapped to an image quality score. Additionally, the gradient images weighted by the contrast sensitivity are used as the input to the “where” pathway, allowing it to extract global shape features that are more sensitive to human perception. Moreover, a dual-pathway multi-scale feature fusion module is designed to fuse the multi-scale features of the two pathways, enabling the model to capture both global features and local details, thus improving the overall performance of the model. The experiments conducted on six databases show that the proposed method achieves the state-of-the-art performance.
Computed Tomographic Assessment of Pituitary Gland Dimensions in Domestic Short-Haired Cats
Authors: Costanza Dario , Coluccia Pierpaolo , Auletta Luigi , Castiello Erica , Navas Luigi , Greco Adelaide , Meomartino Leonardo
Published: May 2023

The detection of subtle changes in the pituitary dimensions has relevant implications since this gland is crucial for the endocrine system. In cats, few studies established the cut-offs values of the pituitary gland’s dimensions, but using small and inhomogeneous samples. The aims of this study were: to determine by computed tomography (CT) the pituitary linear dimensions and the pituitary-to-brain (P:B) ratio in a sample of domestic short-haired (DSH) cats; to assess the effects of sex, age and weight on pituitary dimensions; to evaluate the inter- and intra-observer agreement for such measurements. Exclusion criteria were: clinical, laboratory or CT alterations of pituitary gland or brain diseases, fractures of the neurocranium, or diabetes mellitus. Pituitary dimensions and brain area were assessed by two different observers using multiplanar reconstructions and automated segmentation tools. Fifty-one cats were included in the final sample. The intraclass correlation coefficient for intra- and inter-observer reliability showed good/excellent and moderate/good reliability, respectively. No differences between sexes were detected, and negligible correlations were found with age and weight. Findings from the current study provided normal reference values for pituitary height (1.88 – 4.01 mm) and P:B ratio (0.25 – 0.49), useful for assessing abnormally enlarged pituitary gland in DSH cats.
AIQS-DB: Revolutionizing Simultaneous Analysis of Organic Compounds
Authors: Bui Minh , Nguyen Minh , Le Nhan , Truong Minh , Nguyen Tung , Le Huong , Trung Nguyen Quang , Nguyen Thao
Published: May 2023

This article summarizes studies using the AIQS-DB method to analyze various types of samples for different purposes. This is a method developed to simultaneously analyze nearly 1500 compounds and is widely used worldwide. The method is highly effective for environmental samples such as water, soil, sediment, and air. Recognizing its potential, this article aims to promote the further application and development of AIQS-DB in research related to food analysis and source tracing. Furthermore, if a suitable dataset can be constructed, it could help research quickly and cost-effectively discover the new bioactive compounds from plant medicine.
Efficacy of Different Dietary Patterns in the Treatment of Functional Gastrointestinal Disorders in Children and Adolescents. A Systematic Review of Intervention Studies
Authors: Katsagoni Christina N. , Karagianni Vasiliki-Maria , Papadopoulou Alexandra
Published: May 2023

Functional gastrointestinal disorders (FGIDs) are common in children and adolescents. In recent years, interest in the role of diet in the treatment of FGIDs has increased. Currently, interest focus-es on the low-FODMAP diet (LFD), the fructose- or lactose-restricted diet (FRD or LRD), the glu-ten-free diet (GFD), and the Mediterranean diet (MD). In this review, we focus on the role of these dietary patterns in the FGIDs most commonly diagnosed in clinical practice, namely irritable bowel syndrome (IBS), functional abdominal pain (FAP), functional dyspepsia (FD), and func-tional constipation (FC). Fifteen clinical trials were systematically reviewed (both RCTs and sin-gle arm clinical trials). We demonstrated the lack of high-quality intervention trials. Based on current evidence, low-FODMAP diet, LRD, FRD, or GFD have no place in daily practice for the management of children and adolescents with FGIDs. Nevertheless, some patients with IBS or RAP may have some benefit from the use of a low-FODMAP diet or FRD/LRD. Limited data sug-gest that MD may be promising in the management of FGIDs, especially in IBS patients, but more data are required to investigate the mechanisms of its protective effects.
Sensitivity Enhancement for Separation-Based Analytical Techniques Utilizing Large Volume Injection and Analyte Derivatization for Trace Analysis in Bio-Fluids
Authors: Farouk Hanan , Ebrahim Hager , Sonbol Heba , Malak Monika , Kamal Maha , Ibrahim Noha , Shawky Ahmed , Zarad Walaa , Emad Ahmed , Emara Samy
Published: May 2023

Despite the fact that strong routine separation methodologies can give reliable specificity and validity at usual working pharmaceuticals concentrations, they may fail at very low concentration levels. This poses considerable challenges for researchers inves-tigating product purity and therapeutic drug monitoring. Sensitivity enhancement pro-cedures are thus required to maximize the performance of separation techniques. Large volume injection, solid phase extraction/solid phase enrichment (SPE/SPEn), pre-, post-, and in-column derivatization, as well as the use of sensitive detection devices are the simplest strategies for improving sensitivity of the separation-based analytical techniques. Large volume injection of samples with online SPE/SPEn coupled with separation techniques increased sensitivity and improved detection as well as quantification limits without affecting peak shape and system performance. Although the primary purpose of derivatization is to improve sensitivity and selectivity, greener derivatization is growing in popularity and should be considered in analytical chemistry. In general, two strategies are essential for accomplishing greener derivatization goals. The first is the search for and use of ecologically acceptable derivatizing reagents, solvents, and reaction conditions. The second is miniaturization and automation of analytical methods. This review discusses significant advances in separation-based analytical techniques, specifically enrichment approaches and detector signal improvement for pharmaceutical quantification in various matrices at very low concentration levels. As a result of improved analytical systems setup in drug assays, the possibility of high-throughput analyses was also highlighted.
International Students’ Perceptions Towards their Learning Experience in an International Network Seminar in Japan: During and Post the COVID-19 Pandemic
Authors: Soendergaard Mikael , Takita Fuyuko , Bartels Ingrid
Published: May 2023

The COVID-19 pandemic has struck educational experience systems around the globe. This paper investigates and evaluates the student participants’ perceptions who joined the international exchange seminar on global citizenship and peace held at a University in Hiroshima, Japan. Approximately seventy students and faculty members from nine to ten different universities from around the globe joined this summer program in August 2021 (online) and 2022 (face-to-face). This study is a mixed-method study. The first part consists of a quantitative analysis of BEVI data obtained from the students in the seminar before COVID-19 and after. The research concludes that there are no changes in the way students learn. The second part consists of qualitative data. This data shows the perceptions of students of online teaching versus hybrid teaching. It compares the differences in participants’ perceptions reported in students’ feedback on the programs during and post-COVID-19. Our results confirm prominent differences exist in the students’ perceptions of their learning experience during the pre-pandemic and post-pandemic periods. The findings of this study suggest that universities need to strive and define a meaning and purpose of international seminars, which enables students to experience a high level of intercultural social interaction face-to-face.
Classification of Dental Teeth X-Ray Images Using a Deep Learning CNN Model
Authors: Liao Dankai , Hargreaves Carol Anne
Published: May 2023

Panoramic and periapical radiograph tools help dentists diagnose the most common dental diseases. Generally, dentists identify dental caries manually by inspecting X-ray images. However, due to their heavy workload, or poor image quality, dentists may sometimes overlook some unnoticeable dental caries, which may ultimately hinder the patient treatment. The purpose of this study was to develop an algorithm that classifies the teeth X-Ray images into three categories of “Normal”, “Caries”, and “Filled”. Our study used a dataset of 116 patients and 3712 single teeth images for training, validation and testing. Images were pre-processed using a sharpening filter and an intensity color map. We used a pre-trained transfer learning model, the NASNetMobile, which served as the feature extractor and the Convolutional Neural Network (CNN) model served as the classifier. The training dataset had a “Recall” of 0.92, 0.90 and 0.91 for “Normal”, “Caries” and “Filled” respectively and the test dataset had a “Recall” of 0.86, 0.81 and 0.85 for “Normal”, “Caries” and “Filled” respectively. The classification of the teeth X-Rays was successful and can be valuable for dentist as the artificial intelligence algorithm can serve as a decision support tool to aid dentists when they need to diagnose dental treatment.
Context-Based Synthesis of Unmeasurable Signals before Attenuation in Line-Scan X-ray Transmission
Authors: Xiong Tianzhong , Ye Wenhua
Published: May 2023

Material identification based on R_value (transparency natural logarithm ratio of low-energy to high-energy) of line-scan dual-energy X-ray transmission (DE-XRT) has a good prospect for industrial application. Unfortunately, the DE-XRT signals before attenuation cannot be directly measured, whereas their precision is very important to R_value. Therefore, a context-based signal synthesis method which takes the filtered signals that remove high-frequency noises and retain low-frequency actual fluctuations as the reference value, and takes into account the vertical (forward/column) direction and horizontal (scanning/row) direction anisotropy of line-scan images was proposed. The vertical is a time series with continuity of signal trend; the horizontal is a spatial characteristic with the fluctuation synchronization within the same row signals. The special synthesis evaluations of curve synthesis difference and surface synthesis difference were also proposed. Experimental results show that the tow evaluations are both only about 0.0007, and it only takes 35 ms to complete the surface synthesis of 119×119 pixels on the CPU with 3.4 GHz main frequency. The presented method can achieve high signal synthesis precision and calculation real-time, so as to facilitate DE-XRT material identification. It can be extended to improve the precision of numerical synthesis in other line-scan uncomplete signals.
Mushroom-Legume-Based Minced Meat: Physico-chemical and Sensorial Properties
Authors: Mazumder Md. Anisur Rahman , Sukchot Shanipa , Phonphimai Piyawan , Ketnawa Sunantha , Chaijan Manat , Grossmann Lutz , Rawdkuen Saroat
Published: May 2023

A growing number of health conscious consumers are looking for animal protein alternatives with similar texture, appearance, and flavor. There has been a lot of interest in meat analogs as potential meat substitutes. However, research and development still needs to find any alternative non-meat materials. The objective of the current research was to develop fungi minced meat alternative (FMMA) from edible mushrooms. Pleurotus Sajor-caju (SC) was used as the starting material. SC mushroom was selected for starting materials based on high protein content (41.99%.) and sensory attributes. Chickpea flour was used to improve the textural properties by mixing with SC mushroom at a ratio of 0:50, 12.5:37.5, 25:25, 37.5:12.5, and 50:0 (w/w). Textural and sensory attributes suggest that SC mushroom to chickpea at a ratio of 37.5:12.5, shows higher acceptability of FMMA with the protein content up to 47%. Beetroot extract 0.2% (w/w), and 5% (w/w) canola oil showed the most acceptable color parameters and consumer acceptability. This research suggested that SC mushroom with 12.5% chickpea flour, 0.2% beetroot extract and 5% canola oil could be suitable ingredients for the mushroom-based FMMA.
Antioxidant, Anti-Inflammatory and Anti-Cancer Properties of Amygdalin Extracted from Three Cassava Varieties Cultivated in Benin
Authors: Lehmane Halfane , Kohonou Arnaud N , Tchogou Atchadé Pascal , BA Rafiatou , Dah-Nouvlessounon Durand , Didagbe Oscar , Sina Haziz , Senou Maximin , Adjanohoun Adolphe , Baba-Moussa Lamine
Published: May 2023

: Given that cancer is a disease that is rampant in the world and especially in Africa where the population has enormous difficulty in treating it, plants are a safer and less expensive alterna-tive. Cassava is one of the plant species valued in Benin because of its numerous medicinal and nutritional virtues. This study evaluated the biological activities of amygdalin from the organs of three cassava varieties most produced in Benin (BEN, RB, and MJ). HPLC analysis was used to quantify amygdalin in cassava organs and derivatives. Phytochemical screening was performed to determine secondary metabolite groups. DPPH and FRAP methods were used to assess anti-oxidant activity. Cytotoxicity of the extracts was tested on Artemia salina larvae. The an-ti-inflammatory activity was evaluated in vivo on albino mouse paw edema model induced by 5% formalin. The anticancer activity was evaluated in vivo on Wistar rats rendered cancerous by 1,2-dimethylhydrazine (DMH) using 5-fluorouracil as reference molecule. The results showed that the organs of all three-cassava varieties contained glycosides, flavonoids, saponosides, ster-oids, tannins, coumarins, and cyanogenic derivatives. Young stems and fresh leaves of cassava had the highest amygdalin concentration with 11142.99 µg 10 g-1 and 9251.14 µg 10 g-1 respec-tively. The Agbeli derivative was more concentrated in amygdalin with a content of 401.56 µg 10 g-1 than the others derivatives. The antioxidant activity results showed that the amygdalin ex-tracts were found to be DPPH radical scavengers with IC50 values ranging from 0.18 mg mL-1 to 2.35 mg mL-1. The cytotoxicity test showed no toxicity of the extracts toward shrimp larvae. Ad-ministration of amygdalin extracts from the leaves of BEN and MJ varieties prevents inflamma-tory edema. The percentages of edema inhibition varied between 21.77% and 27.89%. These val-ues are similar (p> 0.05) to that of acetylsalicylic acid (25.20%). Amygdalin extract of BEN variety significantly (p<0.0001) reduces edema. Cancer induction with DMH was inhibited by both BEN extract. In both preventive and curative treatments, rats fed with amygdalin extracts showed low anti-cancer activity under the effect of DMH and the significant difference in biochemical results. Thus, the organs of all three cassava varieties studied have secondary metabolites and good an-tioxydant activity. The leaves contain high levels of amygdalin and can be used as an-ti-inflammatory and anticancer agents.
DataSpace in the Sky: A Novel Decentralized Framework to Secure Drones Data Sharing in B5G for Industry 4.0 toward Industry 5.0
Authors: Alsamhi Saeed Haomood , Curry Edward , Hawbani Ammar , Kumar Santosh , Ul Hassan Umair , Rajput Navin Singh
Published: May 2023

Dataspaces are decentralized and open ecosystems that guarantee trustworthy and secure sharing of data among their participants. In recent years, dataspaces have gained popularity due to their design for managing and sharing heterogeneous data from various sources and domains; and their capability to incrementally solve data integration issues. Leveraging dataspace and advanced technologies plays a vital role in solving many real-world applications effectively and efficiently in real-time. Drone technology is one type of technology that can be deployed to gather data from different resources in harsh or smart environments. Beyond fifth-generation (B5G) communication networks significantly contribute to drones’ development and widespread use by providing low latency and high throughput. Therefore, data sharing among drones in B5G networks offer significant potential to enhance commercial and civilian applications. However, several security issues for collaboration and data sharing, such as data privacy leakage, because of sensitive data and the lack of trustworthy centralized monitoring. Furthermore, sharing data is one of the essential requirements for drone collaboration to achieve their tasks effectively and efficiently in real-time. This conceptual framework presents a novel dataspace in the sky, focusing on securing drone data sharing in B5G for Industry 4.0 toward Industry 5.0. We present how Federated Learning (FL) assists drones in collaboration effectively and efficiently, sharing models instead of raw data. However, because of the fragility of the central curator, the reliability of contribution recording, and the poor quality of shared local models, there are still significant security and privacy issues for drone-assisted smart environments in B5G. Therefore, we present the conceptual framework for leveraging blockchain and FL to secure and manage data sharing of collaborative drones’ dataspace in space in a decentralized fashion. The decentralisation of dataspaces would significantly expand the drive and market for the development of citizen-friendly mobility services.
Gender-Related Differences in the Citation Impact of Scientific Publications and Improving the Authors’ Productivity
Authors: Kuchanskyi Oleksandr , Andrashko Yurii , Biloshchytskyi Andrii , Omirbayev Serik , Mukhatayev Aidos , Biloshchytska Svitlana , Faizullin Adil
Published: May 2023

The article's purpose is a citation analysis of the impact of scientific publications by authors of different gender compositions. The PageRank method was chosen to calculate the citation impact of scientific publications, and the citation has also estimated the impact of scientific publications based on the number of citations. The normalized citation impact of scientific publications is calculated according to nine subsets of scientific publications that correspond to patterns of different gender compositions of authors. Also, these estimates were calculated for each country with which the authors of the publications are affiliated. The Citation database was chosen for the scientometric analysis Network Dataset ( ver . 13). The dataset includes more than 5 million scientific publications and 48 million citations. The main subject areas of scientific publications in this database are computer science, artificial intelligence, mathematics, engineering, etc. The results indicate that articles with a predominantly male composition are cited more than articles with a mixed or female composition of authors in this direction. Analysis of advantages in dynamics indicates that in the last decade for developed countries, there has been a decrease in the connection between the citation impact of scientific publications and the gender composition of their authors. However, the obtained results still confirm the presence of gender inequality in science, which may be related to socioeconomic and cultural characteristics, natural homophily, and other factors that contribute to the appearance of gender gaps. An essential consequence of overcoming these gaps, including in science, is ensuring the rights of people in all their diversity.
Driving Factors of the Hydrological Response of a Tropical Watershed: the Ankavia River Basin in Madagascar
Authors: Ramahaimandimby Zonirina , Randriamaherisoa Alain , Vanclooster Marnik , Bielders Charles L.
Published: May 2023

Understanding the hydrological behavior of watersheds and their driving factors is crucial for sustainable water resources management. However, at large scales, this task remains challenging due to the spatial heterogeneity in landscapes, topography, land use, geology, and soil properties. In this context, the aim of this study was to identify the key factors that influence the hydrological system of four watersheds: Ankavia (WS1: 55% forest cover), Ankaviabe (WS2: 77% forest cover), Sahafihitry (WS3: 41% forest cover), and Antsahovy (WS4: 48% forest cover), over a 10-month study period. These catchments are located within the SAVA region of northeastern Madagascar and have a humid-tropical climate. We investigated the relationship between selected catchment descriptors (CD) and hydrological signatures (HS) by using a Pearson coefficient-based correlation matrix. More specifically, CD extracted from topography/morphology (T), land use (LU), soil (S), and geological characteristics (G) were correlated with HS, including base flow index (BFI), runoff coefficient (rc), peak flow (Qp), runoff event time scales (ts), high flows (Q5), low flows (Q95), and mean discharge (q_mean). The analysis revealed that land use, soil properties, and geology seem to be the best predictors for BFI and Q95, while soil properties mainly govern rc, Qp, Q5, ts, and q_mean. These findings provide valuable insights into the key drivers of hydrologic behavior that can inform water resource management strategies. In particular, WS2 has better flood buffering capacity but suffers from lower base flows in the dry season potentially due to higher evapotranspiration. Conversely, WS3 and WS4 (and to a lesser extent WS1) have lower flood buffering capacity, but these watersheds experience less pronounced low flows in the dry season due to higher base flow index resulting from lower evapotranspiration. The results emphasize the importance of sustainable land use practices and conservation efforts, which are essential for the sustainable development of the region. By incorporating these practices into water management strategies, we can help ensure a more stable and reliable water supply for communities and ecosystems within the region.
Analyzing the Relationship Between Green Finance and Agricultural Industrial Upgrading Using a Panel Data Study of 31 Provinces of China
Authors: Zhu Min , Sun Mengqi , Elahi Ehsan , Li Yajie , Khalid Zainab
Published: May 2023

This study utilized panel data from 31 Chinese provinces over a period of nine years to investigate the impact of green finance on the upgrading of the agricultural industrial structure. A fixed-effect model was employed, and the findings indicate that green financing has a positive effect on the growth of China's agricultural industry. However, regional disparities exist, particularly in the uneven distribution of green financing across the eastern, central, and western regions. Moreover, it emphasizes the need to consider regional differences and tailor development strategies accordingly. To promote further development and transformation of China's agricultural industrial structure, the study recommends innovative green financial products, improved regulations and policies, and the integration of digital technologies.
The Influence of Ag+/Ti4+ Ratio on Structural, Optical and Photocatalytic Properties of MWCNT-TiO2-Ag Nanocomposites
Authors: Suciu Ramona Crina , Zagrai Mioara , Popa Adriana , Toloman Dana , Berghian-Grosan Camelia , Stefan Maria
Published: May 2023

In this paper we propose a simple procedure to obtain multi-walled carbon nanotubes (MWCNT) decorated with TiO2-Ag nanoparticles (MWCNT-TiO2-Ag). MWCNT were decorated with TiO2-Ag via combined functionalisation with -OH and –COOH groups and polymer wrapping technique using poly(allylamine hydrochloride) (PAH). TiO2 modified Ag nanoparticles were synthesized through Pechini method using mixture of acetylacetonate-modified titanium (IV) isopropoxide with silver nitrate (the Ag+/Ti4+ atomic ratio, is 0.5, 1,0, 1.5, 2.0 and 2.5 %, respectively) and the L(+)-ascorbic acid, as reducing agents. XRD analysis revealed the formation of nanocomposite containing CNT, TiO2 (anatase and rutile) and Ag. By SEM was evidenced the presence of the nanoparticles on MWCNT surface. The increases of the ratio between Ag+ and Ti4+ decrease the band gap energy of the samples. In Raman spectra the characteristic vibration of the TiO2, Ag and C atoms of graphite are identified. The photocatalytic activity of the MWCNT-TiO2-Ag nanocomposite was assessed by examining the degradation of Allura red (E129) aqueous solution under UV irradiation. The dye photodegradation process follows a pseudo – first – order kinetic with respect to the Langmuir – Hinshelwood reaction mechanism.
Short–Term Memory Capacity Across Time and Language Estimated from Ancient and Modern Literary Texts
Authors: Matricciani Emilio
Published: May 2023

We study the short−term memory capacity of ancient readers of the original New Testament written in Greek, of its translations to Latin and modern languages. To model the short–term capacity, we have considered the number of words per interpunctions, the “word interval” , because this parameter can model how the human mind memorizes “chunks” of information. Since can be calculated for any alphabetical text, we can perform experiments − otherwise impossible − with ancient readers by studying the literary works they used to read. The “experiments” compare the of texts of a language/translation to those of another language/translation by measuring the minimum average probability of finding joint readers (those who can read both texts because of their similar short–-term memory capacity) and by defining an “overlap index”. We also define a population of universal readers who can read any the New Testament written in alphabetical language. More than 50% of the readers of specific languages overlap with the universal readers with probability . Future work is vast, with many research tracks, because alphabetical literatures are very large and allow many experiments, such as comparing authors, translations or even texts written by artificial intelligence tools.
Niobium Pentachloride as a Stoichiometric Reagent in the Sequential Transesterification-Esterification Reactions for the Synthesis of Methyl Salicylate and the Study of Its Antimicrobial Activity
Authors: Barbosa Sandro Luiz , NELSON DAVID LEE , Souza Milton de Freitas , Silva Lucas Paconio , SANTOS Wallans TORRES PIO DOS , WENTZ ALEXANDRE PEREIRA , Macedo Erenilda F. de , Queiroz Rafaela C. , Tada Dayane B. , Hurtado Carolina R. , Conceição Katia , Hurtado Gabriela R. , Pessoa Fernando L. P. , Bueno Gabriel de P. , Clososki Giuliano C.
Published: May 2023

Methyl Salicylate (MS), the principal constituent of Wintergreen oil (WO) was obtained from acetyl salicylic acid (ASA) by sequential transesterification-esterification reaction promoted by NbCl5for the first time. The reagents were added simultaneously, and the reaction process involved the transesterification and esterification reactions, which were accompanied by thin layer chromatography and gas chromatography. The conversion rate via GC was 100%, and the MS yield was 94%. A cytotoxicity of 50% and 64% for cultured S. aureus and metastatic melanoma cells, respectively, was observed for a concentration of 0.6 mg/mL, whereas no cytotoxicity for non-tumor cells was observed for this concentration, and it is considered to be the optimum concentration.