bluuebunny/crossref_metadata_2025 huggingface dataset:
Astaxanthin Supplementation Augments the Benefits of CrossFit Workouts on Semaphorin 3C and Other Adipokines in Males with Obesity
Authors: Supriya Rashmi , Shishvan Sevda Rahbari , Kefayati Movahed , Abednatanzi Hossein , Razi Omid , Bagheri Reza , Escobar Kurt A. , Pashaei Zhaleh , Saeidi Ayoub , Shahrbanian Shahnaz , Bagchi Sovan , Sengupta Pallav , Al Kiyumi Maisa Hamed , Heinrich Katie M. , Zouhal Hassane
Published: November 2023

Regular physical activity and the use of nutritional supplements, including antioxidants, are recognized as efficacious approaches for the prevention and mitigation of obesity-related complications. This study investigated the effects of 12 weeks of CrossFit training combined with astaxanthin (ASX) supplementation on some plasma adipokines in males with obesity. Sixty-eight males with obesity (BMI: 33.6 ± 1.4 kg·m−2) were randomly assigned into four groups: the control group (CG; n = 11), ASX supplementation group (SG; n = 11), CrossFit group (TG; n = 11), and training plus supplement group (TSG; n = 11). Participants underwent 12 weeks of supplementation with ASX or placebo (20 mg/day capsule daily), CrossFit training, or a combination of both interventions. Plasma levels of semaphorin 3C (SEMA3C), apelin, chemerin, omentin1, visfatin, resistin, adiponectin, leptin, vaspin, and RBP4 were measured 72 h before the first training session and after the last training session. The plasma levels of all measured adipokines were significantly altered in SG, TG, and TSG groups (p < 0.05). The reduction of resistin was significantly higher in TSG than in SG (p < 0.05). The plasma levels of omentin1 were significantly higher in both training groups of TG and TSG than SG (p < 0.05), although such a meaningful difference was not observed between both training groups (p > 0.05). Significant differences were found in the reductions of plasma levels of vaspin, visfatin, apelin, RBP4, chemerin, and SEMA3C between the SG and TSG groups (p < 0.05). The study found that a 12-week intervention using ASX supplementation and CrossFit exercises resulted in significant improvements in several adipokines among male individuals with obesity. Notably, the combined approach of supplementation and training had the most pronounced results. The findings presented in this study indicate that the supplementation of ASX and participation in CrossFit exercise have the potential to be effective therapies in mitigating complications associated with obesity and enhancing metabolic health.
Carbohydrate Core–Shell Electrosprayed Microcapsules for Enhanced Oxidative Stability of Vitamin A Palmitate
Authors: Fallahasghari Elnaz Z. , Højgaard Lynge Marie , Espholin Gudnason Emma , Munkerup Kristin , Mendes Ana C. , Chronakis Ioannis S.
Published: November 2023

Vitamin A is an essential micronutrient that is readily oxidized. In this study, the encapsulation of vitamin A palmitate (AP) within a core–shell carbohydrate matrix by co-axial electrospray and its oxidative stability was evaluated. The electrosprayed core–shell microcapsules consisted of a shell of octenyl succinic anhydride (OSA) modified corn starch, maltose (Hi-Cap), and a core of ethyl cellulose–AP (average diameter of about 3.7 µm). The effect of different compounds (digestion-resistant maltodextrin, soy protein hydrolysate, casein protein hydrolysate, and lecithin) added to the base core–shell matrix formulation on the oxidative stability of AP was investigated. The oxidative stability of AP was evaluated using isothermal and non-isothermal differential scanning calorimetry (DSC), and Raman and Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy methods. The core–shell carbohydrate matrix minimizes the amount of AP present at the microparticle surface, thus protecting AP from oxidation. Furthermore, the most effective oxidation protection was achieved when casein protein hydrolysate was added to the core of the microcapsule due to hydrophobic and hydrogen bond interactions with AP and by the resistant maltodextrin in the shell, which acted as a filler. The utilization of ethanol as a solvent for the dispersion of the core compounds increased the hydrophobicity of the hydrolyzed proteins and contributed to the enhancement of their antioxidant ability. Both the carbohydrate core–shell microcapsule prepared by co-axial electrospray and the addition of oxidation protection compounds enhance the oxidative stability of the encapsulated AP.
Exploring Environmental Settings to Improve the Printability of Paroxetine-Loaded Filaments by Fused Deposition Modelling
Authors: Figueiredo Sara , Fernandes Ana I. , Carvalho Fátima G. , Pinto João F.
Published: November 2023

The successful integration of hot-melt extrusion (HME) and fused deposition modelling (FDM) depends on a better understanding of the impact of environmental conditions on the printability of formulations, since they significantly affect the properties of the raw materials, whose control is crucial to enable three-dimensional printing (3DP). Hence, the objective of this work was to investigate the correlation between the environmental settings and the properties of paroxetine (PRX)-loaded filaments, previously produced by HME, which affect printability by FDM. The influence of different drying methods of the physical mixtures (PMs) and HME-filaments (FILs) on the quality and printability of these products was also assessed. The printability of FILs was evaluated in terms of the water content, and the mechanical and thermal properties of the products. Stability studies and physicochemical, thermal, and in vitro dissolution tests were carried out on the 3D-printed tablets. Stability studies demonstrated the high ductility of the PRX loaded FILs, especially under high humidity conditions. Under low humidity storage conditions (11% RH), the FILs became stiffer and were successfully used to feed the FDM printer. Water removal was slow when carried out passively in a controlled atmosphere (desiccator) or accelerated by using active drying methods (heat or microwave). Pre-drying of the PRX/excipients and/or PMs did not show any positive effect on the printability of the FIL. On the contrary, dry heat and, preferably, microwave mediated drying processes were shown to reduce the holding time required for successful FDM printing, enabling on-demand production at the point of care.
Photonic Measurement for Doppler Frequency Shift and Angle of Arrival Based on Integrated Dual-Parallel Dual-Drive Modulator
Authors: Wang Ruiqiong , Zhai Weile , Fan Yangyu , Tan Jiajun , Wang Xubo , Gao Yongsheng
Published: November 2023

A microwave photonic Doppler frequency shift (DFS) and angle of arrival (AOA) measurement method based on a dual-parallel dual-drive Mach–Zehnder modulator (DP-DDMZM) is proposed and demonstrated. A sawtooth wave signal is used to drive the DC port of the modulator to realize the optical frequency shift, and thus the direction discrimination of DFS is realized. Due to single-sideband modulation, the proposed system can avoid periodic power fading and the separation of the remote antenna unit (RAU) and central office (CO) can be achieved. In the experiment, the microwave DFS is estimated with a clear direction and a maximum measurement error of 0.25 Hz over an ultrawide operation frequency from 6 to 36 GHz. The experiment also proves that the phase error of AOA measurement is less than 1.5 degrees. Compared with the traditional electronic microwave measurement scheme, the proposed scheme has great competitive advantages in future broadband electronic applications due to the features of multifunction, large bandwidth and anti-interference.
Mimicking Transmural Helical Cardiomyofibre Orientation Using Bouligand-like Pore Structures in Ice-Templated Collagen Scaffolds
Authors: Zhang Huijie L. , Sinha Sanjay , Cameron Ruth E. , Best Serena M.
Published: November 2023

The helical arrangement of cardiac muscle fibres underpins the contractile properties of the heart chamber. Across the heart wall, the helical angle of the aligned fibres changes gradually across the range of 90–180°. It is essential to recreate this structural hierarchy in vitro for developing functional artificial tissue. Ice templating can achieve single-oriented pore alignment via unidirectional ice solidification with a flat base mould design. We hypothesise that the orientation of aligned pores can be controlled simply via base topography, and we propose a scalable base design to recapitulate the transmural fibre orientation. We have utilised finite element simulations for rapid testing of base designs, followed by experimental confirmation of the Bouligand-like orientation. X-ray microtomography of experimental samples showed a gradual shift of 106 ± 10°, with the flexibility to tailor pore size and spatial helical angle distribution for personalised medicine.
Flexible Polyurethane Foams from Bio-Based Polyols: Prepolymer Synthesis and Characterization
Authors: Losio Simona , Cifarelli Angelica , Vignali Adriano , Tomaselli Simona , Bertini Fabio
Published: November 2023

Bio-polyols (BPOs), characterized by a hydroxyl number up to around 90 mg KOH/g, narrow polydispersity index and relatively low molecular mass up to 2000 g/mol, were synthetized from partially and completely epoxidized soybean and linseed oils and caprylic acid or 3-phenyl butyric acid. These BPOs were used in the presence of toluene diisocyanate to produce polyurethane (PU) foams by using a quasi-prepolymer method involving a two-step reaction. A detailed structural investigation of the prepolymers from toluene diisocyanate and both BPOs and polypropylene glycol was conducted by SEC and solution NMR. The apparent density of the foams was in the range of 40–90 kg/m3, with higher values for foams from the aromatic acid. All the foams showed an open-cell structure with uniform and regular shape and uniform size. The specific Young’s moduli and compression deflection values suggest superior mechanical properties than the reference foams. The novel synthesized polyurethanes are excellent candidates to partially replace petroleum-based materials.
Improvement of Water Vapor Permeability in Polypropylene Composite Films by the Synergy of Carbon Nanotubes and β-Nucleating Agents
Authors: Visvini Glykeria A. , Mathioudakis Georgios N. , Soto Beobide Amaia , Piperigkou Zoi , Giannakas Aris E. , Messaritakis Stavros , Sotiriou Giannis , Voyiatzis George A.
Published: November 2023

A notable application of polymeric nanocomposites is the design of water vapor permeable (WVP) membranes. “Breathable” membranes can be created by the incorporation of micro/nanofillers, such as CaCO3, that interrupt the continuity of the polymeric phase and when subjected to additional uniaxial or biaxial stretching this process leads to the formation of micro/nanoporous structures. Among the candidate nanofillers, carbon nanotubes (CNTs) have demonstrated excellent intrinsic WVP properties. In this study, chemically modified MWCNTs with oligo olefin-type groups (MWCNT-g-PP) are incorporated by melt processes into a PP matrix; a β-nucleating agent (β-ΝA) is also added. The crystallization behavior of the nanocomposite films is evaluated by differential scanning calorimetry (DSC) and X-ray diffraction (XRD). The WVP performance of the films is assessed via the “wet” cup method. The nanohybrid systems, incorporating both MWCNT-g-PP and β-NA, exhibit enhanced WVP compared to films containing only MWCNT-g-PP or β-NA. This improvement can be attributed to the significant increase in the growth of α-type crystals taking place at the edges of the CNTs. This increased crystal growth exerts a form of stress on the metastable β-phase, thereby expanding the initial microporosity. In parallel, the coexistence of the inherently water vapor-permeable CNTs, further enhances the water vapor permeability reaching a specific water vapor transmission rate (Sp.WVTR) of 5500 μm.g/m2.day in the hybrid composite compared to 1000 μm.g/m2.day in neat PP. Notably, the functionalized MWCNT-g-PP used as nanofiller in the preparation of the “breathable” PP films demonstrated no noteworthy cytotoxicity levels within the low concentration range used, an important factor in terms of sustainability.
Aspergillus fumigatus Lytic Polysaccharide Monooxygenase AfLPMO9D: Biochemical Properties and Photoactivation of a Multi-Domain AA9 Enzyme
Authors: Hamann Pedro Ricardo Vieira , Vacilotto Milena Moreira , Segato Fernando , Polikarpov Igor
Published: November 2023

Lytic polysaccharide monooxygenases (LPMOs) are critical players in enzymatic deconstruction of cellulose. A number of LPMOs have been identified at a genomics level; however, they still need to be characterized and validated for use in industrial processes aimed at cellulose deconstruction. In the present study, we biochemically characterized a new LPMO, a member of auxiliary activities family 9 (AA9) from the filamentous fungus Aspergillus fumigatus (AfLPMO9D). This LPMO demonstrated higher efficiency against amorphous cellulose as compared to more recalcitrant forms of cellulose such as bacterial cellulose and Avicel. AfLPMO9D has a capacity to oxidize the substrate at either the C1 or C4 positions, with pH-dependent regioselectivity. Photoactivation experiments demonstrated that light-stimulated chlorophyllin triggers AfLPMO9D activation without requirements of an external electron donor. AfLPMO9D is capable of boosting phosphoric acid-swollen cellulose depolymerization via GH7 endoglucanase and cellobiohydrolase. The results of the present study might help to elucidate the role of different LPMOs in cellulosic fiber deconstruction.
Adaptive Latin Hypercube Sampling for a Surrogate-Based Optimization with Artificial Neural Network
Authors: Borisut Prapatsorn , Nuchitprasittichai Aroonsri
Published: November 2023

A significant number of sample points are often required for surrogate-based optimization when utilizing process simulations to cover the entire system space. This necessity is particularly pronounced in complex simulations or high-dimensional physical experiments, where a large number of sample points is essential. In this study, we have developed an adaptive Latin hypercube sampling (LHS) method that generates additional sample points from areas with the highest output deviations to optimize the required number of samples. The surrogate model used for the optimization problem is artificial neural networks (ANNs). The standard for measuring solution accuracy is the percent error of the optimal solution. The outcomes of the proposed algorithm were compared to those of random sampling for validation. As case studies, we chose three different chemical processes to illustrate problems of varying complexity and numbers of variables. The findings indicate that for all case studies, the proposed LHS optimization algorithm required fewer sample points than random sampling to achieve optimal solutions of similar quality. To extend the application of this methodology, we recommend further applying it to fields beyond chemical engineering and higher-dimensional problems.
A New Fracturing Method to Improve Stimulation Effect of Marl Tight Oil Reservoir in Sichuan Basin
Authors: Wang Yang , Fan Yu , Li Song , Lv Zefei , He Rui , Wang Liang
Published: November 2023

China’s argillaceous limestone reservoir has a lot of oil and gas resources, and hydraulic fracturing of the argillaceous limestone reservoir faces many difficulties. The first problem is that the heterogeneity of the argillaceous limestone reservoir is strong, and it is difficult to optimize fracturing parameters. The second problem is that there are a lot of natural fractures in the argillaceous limestone reservoir, which leads to a lot of fracturing fluid loss. The third problem is that the closure pressure of the argillaceous limestone reservoir is high, and the conductivity of fractures decreases rapidly under high closure pressure. The last problem is that the fracture shape of the argillaceous limestone reservoir is complex, and the law of proppant migration is unclear. The main research methods in this paper include reservoir numerical simulation, fluid-loss-reducer performance evaluation, flow conductivity tests and proppant migration visualization. To solve the above problems, this paper establishes the fracturing productivity prediction model of complex lithology reservoirs and defines the optimal hydraulic fracturing parameters of the argillous limestone reservoir in the Sichuan Basin. The 70/140 mesh ceramide was selected as the fluid loss additive after an evaluation of the sealing properties of different mesh ceramides. At the same time, the hydraulic fracture conductivity test is carried out in this paper, and it is confirmed that the fracture conductivity of 70/140 mesh and 40/70 mesh composite particle-size ceramics mixed according to the mass ratio of 5:5 is the highest. When the closure pressure is 40 MPa, the conductivity of a mixture of 70/140 mesh ceramic and 40/70 mesh ceramic is 35.6% higher than that of a mixture of 70/140 mesh ceramic and 30/50 mesh ceramic. The proppant migration visualization device is used to evaluate the morphology of the sand dike formed by the ceramsite, and it is clear that the shape of the sand dike is the best when the mass ratio of 70/140 mesh ceramsite and 40/70 mesh ceramsite is 6:4. The research results achieved a good stimulation effect in the SC1 well. The daily oil production of the SC1 well is 20 t, and the monitoring results of the wide-area electromagnetic method show that the fracturing fracture length of the SC1 well is up to 129 m.
Anti-Cancer Properties of Flaxseed Proteome
Authors: Merkher Yulia , Kontareva Elizaveta , Alexandrova Anastasia , Javaraiah Rajesha , Pustovalova Margarita , Leonov Sergey
Published: November 2023

Flaxseed has been recognized as a valuable source of nutrients and bioactive compounds, including proteins that possess various health benefits. In recent years, studies have shown that flaxseed proteins, including albumins, globulins, glutelin, and prolamins, possess anti-cancer properties. These properties are attributed to their ability to inhibit cancer cell proliferation, induce apoptosis, and interfere with cancer cell signaling pathways, ultimately leading to the inhibition of metastasis. Moreover, flaxseed proteins have been reported to modulate cancer cell mechanobiology, leading to changes in cell behavior and reduced cancer cell migration and invasion. This review provides an overview of the anti-cancer properties of flaxseed proteins, with a focus on their potential use in cancer treatment. Additionally, it highlights the need for further research to fully establish the potential of flaxseed proteins in cancer therapy.
Ventriloquial Acts in Sor María de Jesús de Ágreda’s Mística Ciudad de Dios
Authors: Hernández Rosilie
Published: November 2023

Sor María de Ágreda (1602–1665), a Franciscan nun and Abbess of the Conceptionist monastery at Ágreda, was a prolific writer whose theological works are yet to be extensively studied. In this article, I examine the practice of divine ventriloquism in Sor María’s mystical (auto)biography of the Virgin of the Immaculate Conception, Mística ciudad de Dios (1670). I aim to examine the complexity inherent in Sor María’s ‘unmediated’ ventriloquizing of sacred voices and the positionality and power appropriated through this act. The argument focuses on the use of the ventriloquial mechanism, its relationship to Sor María’s authorial position, and how readers may conceptualize the production and reception of the sacred voice. The textual perforance with which readers are presented in Mística ciudad de Dios provides a rich example of how women religious writers appropriated divine authority, resulting in a complex position of agency and self-fashioned individuality.
Market Reaction to Delisting Announcements in Frontier Markets: Evidence from the Vietnam Stock Market
Authors: Truong Loc Dong , Friday H. Swint , Ngo Tran My
Published: November 2023

This paper aims to measure the effects of delisting on stock returns for the Vietnam stock market. This study employs a sample of 118 stocks that were compulsorily delisted from the market between January 2011 and December 2021. Using an event study methodology, the empirical findings confirm that the delisting has negative effects on stock returns in the Vietnam stock market. Specifically, results derived from tests show that the average abnormal return of delisted stocks continuously declines during three trading days following the announcement of delisting. Moreover, it is found that the differences in cumulative abnormal returns between post-delisting and pre-delisting periods are significantly negative for all tracking periods. Apart from the negative effect of delisting on stock abnormal returns, we also find that the impact of delisting on stock returns for smaller companies is greater than for bigger companies. These results imply that investors can earn abnormal returns by using delisting information in the Vietnam stock market.
Snow Avalanche Hazard Mapping Using a GIS-Based AHP Approach: A Case of Glaciers in Northern Pakistan from 2012 to 2022
Authors: Rafique Afia , Dasti Muhammad Y. S. , Ullah Barkat , Awwad Fuad A. , Ismail Emad A. A. , Saqib Zulfiqar Ahmad
Published: November 2023

Snow avalanches are a type of serious natural disaster that commonly occur in snow-covered mountains with steep terrain characteristics. Susceptibility analysis of avalanches is a pressing issue today and helps decision makers to implement appropriate avalanche risk reduction strategies. Avalanche susceptibility maps provide a preliminary method for evaluating places that are likely to be vulnerable to avalanches to stop or reduce the risks of such disasters. The current study aims to identify areas that are vulnerable to avalanches (ranging from extremely high and low danger) by considering geo-morphological and geological variables and employing an Analytical Hierarchy Approach (AHP) in the GIS platform to identify potential snow avalanche zones in the Karakoram region in Northern Pakistan. The Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) was used to extract the elevation, slope, aspect, terrain roughness, and curvature of the study area. This study includes the risk identification variable of land cover (LC), which was obtained from the Landsat 8 Operational Land Imager (OLI) satellite. The obtained result showed that the approach established in this study provided a quick and reliable tool to map avalanches in the study area, and it might also work with other glacier sites in other parts of the world for snow avalanche susceptibility and risk assessments.
Infrared Moving Small Target Detection Based on Space–Time Combination in Complex Scenes
Authors: Wang Yao , Cao Lihua , Su Keke , Dai Deen , Li Ning , Wu Di
Published: November 2023

In the infrared small target images with complex backgrounds, there exist various interferences that share similar characteristics with the target (such as building edges). The accurate detection of small targets is crucial in applications involving infrared search and tracking. However, traditional detection methods based on small target feature detection in a single frame image may result in higher error rates due to insufficient features. Therefore, in this paper, we propose an infrared moving object detection method that integrates spatio-temporal information. To address the limitations of single-frame detection, we introduce a temporal sequence of images to suppress false alarms caused by single-frame detection through analyzing motion features within the sequence. Firstly, based on spatial feature detection, we propose a multi-scale layered contrast feature (MLCF) filtering for preliminary target extraction. Secondly, we utilize the spatio-temporal context (STC) as a feature to track the image sequence point by point, obtaining global motion features. Statistical characteristics are calculated to obtain motion vector data that correspond to abnormal motion, enabling the accurate localization of moving targets. Finally, by combining spatial and temporal features, we determine the precise positions of the targets. The effectiveness of our method is evaluated using a real infrared dataset. Through analysis of the experimental results, our approach demonstrates stronger background suppression capabilities and lower false alarm rates compared to other existing methods. Moreover, our detection rate is similar or even superior to these algorithms, providing further evidence of the efficacy of our algorithm.
Research and Implementation of Millet Ear Detection Method Based on Lightweight YOLOv5
Authors: Qiu Shujin , Li Yun , Gao Jian , Li Xiaobin , Yuan Xiangyang , Liu Zhenyu , Cui Qingliang , Wu Cuiqing
Published: November 2023

As the millet ears are dense, small in size, and serious occlusion in the complex grain field scene, the target detection model suitable for this environment requires high computing power, and it is difficult to deploy the real-time detection of millet ears on mobile devices. A lightweight real-time detection method for millet ears is based on YOLOv5. First, the YOLOv5s model is improved by replacing the YOLOv5s backbone feature extraction network with the MobilenetV3 lightweight model to reduce model size. Then, using the multi-feature fusion detection structure, the micro-scale detection layer is augmented to reduce high-level feature maps and low-level feature maps. The Merge-NMS technique is used in post-processing for target information loss to reduce the influence of boundary blur on the detection effect and increase the detection accuracy of small and obstructed targets. Finally, the models reconstructed by different improved methods are trained and tested on the self-built millet ear data set. The AP value of the improved model in this study reaches 97.78%, F1-score is 94.20%, and the model size is only 7.56 MB, which is 53.28% of the standard YoloV5s model size, and has a better detection speed. Compared with other classical target detection models, it shows strong robustness and generalization ability. The lightweight model performs better in the detection of pictures and videos in the Jetson Nano. The results show that the improved lightweight YOLOv5 millet detection model in this study can overcome the influence of complex environments, and significantly improve the detection effect of millet under dense distribution and occlusion conditions. The millet detection model is deployed on the Jetson Nano, and the millet detection system is implemented based on the PyQt5 framework. The detection accuracy and detection speed of the millet detection system can meet the actual needs of intelligent agricultural machinery equipment and has a good application prospect.
High-Range and High-Linearity 2D Angle Measurement System for a Fast Steering Mirror
Authors: Du Boshi , Lv Yong , Liu Lishuang , Liu Yang
Published: November 2023

In order to solve the problem of the insufficient range of the traditional fast mirror (FSM) angle measurement system in practical applications, a 2D large-angle FSM photoelectric angle measurement system based on the principle of diffuse reflection is proposed. A mathematical model of the angle measurement system is established by combining the physical properties of the diffuse reflecting plate, such as the rotation angle, rotation center, rotation radius, reflection coefficient and the radius of the diffuse reflecting surface. This paper proposes a method that optimizes the degree of nonlinearity based on this mathematical model. The system is designed and tested. The experimental results show that changing the diffuse reflection surface area can improve the nonlinearity of the angle measurement system effectively. When the radius of the diffuse reflection surface is 3.3 mm, the range is ±20°, the non-linearity is 0.74%, and the resolution can reach up to 2.3″. The system’s body is simple and compact. It is also capable of measuring a wider range of angles while linearity is guaranteed.
Red Tide Detection Method Based on Improved U-Net Model-Taking GOCI Data in East China Sea as an Example
Authors: Han Yanling , Ding Tianhong , Cui Pengxia , Wang Xiaotong , Zheng Bowen , Shen Xiaojing , Ma Zhenling , Zhang Yun , Pan Haiyan , Yang Shuhu
Published: November 2023

In the coastal areas of China, the eutrophication of seawater leads to the continuous occurrence of red tide, which has caused great damage to Marine fisheries and aquatic resources. Therefore, the detection and prediction of red tide have important research significance. The rapid development of optical remote sensing technology and deep-learning technology provides technical means for realizing large-scale and high-precision red tide detection. However, the difficulty of the accurate detection of red tide edges with complex boundaries limits the further improvement of red tide detection accuracy. In view of the above problems, this paper takes GOCI data in the East China Sea as an example and proposes an improved U-Net red tide detection method. In the improved U-Net method, NDVI was introduced to enhance the characteristic information of the red tide to improve the separability between the red tide and seawater. At the same time, the ECA channel attention mechanism was introduced to give different weights according to the influence of different bands on red tide detection, and the spectral characteristics of different channels were fully mined to further extract red tide characteristics. A shallow feature extraction module based on Atrous Spatial Pyramid Convolution (ASPC) was designed to improve the U-Net model. The red tide feature information in a multi-scale context was fused under multiple sampling rates to enhance the model’s ability to extract features at different scales. The problem of limited accuracy improvement in red tide edge detection with complex boundaries is solved via the fusion of deep and shallow features and multi-scale spatial features. Compared with other methods, the method proposed in this paper achieves better results and can detect red tide edges with complex boundaries, and the accuracy, precision, recall, and F1-score are 95.90%, 97.15%, 91.53%, and 0.94, respectively. In addition, the red tide detection experiments in other regions with relatively concentrated distribution also prove that the method has good applicability.
Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders
Authors: Ates Cihan , Höfchen Tobias , Witt Mario , Koch Rainer , Bauer Hans-Jörg
Published: November 2023

Predictive maintenance is considered a proactive approach that capitalizes on advanced sensing technologies and data analytics to anticipate potential equipment malfunctions, enabling cost savings and improved operational efficiency. For journal bearings, predictive maintenance assumes critical significance due to the inherent complexity and vital role of these components in mechanical systems. The primary objective of this study is to develop a data-driven methodology for indirectly determining the wear condition by leveraging experimentally collected vibration data. To accomplish this goal, a novel experimental procedure was devised to expedite wear formation on journal bearings. Seventeen bearings were tested and the collected sensor data were employed to evaluate the predictive capabilities of various sensors and mounting configurations. The effects of different downsampling methods and sampling rates on the sensor data were also explored within the framework of feature engineering. The downsampled sensor data were further processed using convolutional autoencoders (CAEs) to extract a latent state vector, which was found to exhibit a strong correlation with the wear state of the bearing. Remarkably, the CAE, trained on unlabeled measurements, demonstrated an impressive performance in wear estimation, achieving an average Pearson coefficient of 91% in four different experimental configurations. In essence, the proposed methodology facilitated an accurate estimation of the wear of the journal bearings, even when working with a limited amount of labeled data.
Deep Learning Based Feature Selection and Ensemble Learning for Sintering State Recognition
Authors: Xu Xinran , Zhou Xiaojun
Published: November 2023

Sintering is a commonly used agglomeration process to prepare iron ore fines for blast furnace. The quality of sinter significantly impacts the blast furnace ironmaking process. In the vast majority of sintering plants, the judgment of sintering quality still relies on the intuitive observation of the cross section at sintering machine tail by operators, which is susceptible to the external environment and the experience of operators. In this paper, we propose a new sintering state recognition method using deep learning based feature selection and ensemble learning. First, features from the infrared thermal images of sinter cross section at the tail of the sinterer are extracted based on ResNeXt. Then, to eliminate the irrelevant, redundant and noisy features, an efficient feature selection method based on binary state transition algorithm (BSTA) is proposed to find the truly useful features. Subsequently, an ensemble learning (EL) method based on group decision making (GDM) is proposed to recognize the sintering states. Novel combination strategies considering the varying performance of the base learners are designed to further improve recognition accuracy. Industrial experiments conducted at a steel plant verify the effectiveness and superiority of the proposed method.
Mid-Infrared Gas Sensing Based on Electromagnetically Induced Transparency in Coupled Plasmonic Resonators
Authors: Shafaay Sarah , Mohamed Sherif , Swillam Mohamed
Published: November 2023

The existence of surface plasmon polaritons in doped silicon micro-scale structures has opened up new and innovative possibilities for applications, such as sensing, imaging, and photonics. A CMOS-compatible doped Si plasmonic sensor is proposed and investigated. The plasmon resonance can be tuned by controlling the carrier density and dopant concentration. In this paper, we demonstrate that using silicon doped with phosphorus at a concentration of 5 × 1020 cm−3 can induce surface plasmon resonance in the mid-infrared region. Two ring resonators of two different radii based on metal–insulator–metal waveguide structures are studied individually. Then, the two ring resonators are integrated in the same device. When the two ring resonators are coupled and resonate at the same frequency; two distinct resonance spectral lines are generated with striking features that improve its potential use for sensing and modulation applications. The propagating plasmonic mode is studied, including its mode profile and bend loss. We evaluate the effectiveness of a microstructure gas sensor with dimensions of 15 μm × 15 μm by measuring its sensitivity and selectivity towards methane and ethane gases. Small alterations in the surrounding refractive index led to noticeable shifts in the resonance peak. The sensor achieved a sensitivity of 7539.9 nm/RIU at the mid-infrared spectral range around the 7.7 μm wavelength. Furthermore, by combining the resonators, we can achieve a smaller full width at half maximum (FWHM), which will ultimately result in greater sensitivity than using a single-ring resonator or other plasmonic resonator configurations. Once the sensitivity and selectivity of the sensor are measured, the FOM can be calculated by dividing the sensitivity by the selectivity of the sensor, resulting in an FOM of 6732.
An Optimization Method for Non-IID Federated Learning Based on Deep Reinforcement Learning
Authors: Meng Xutao , Li Yong , Lu Jianchao , Ren Xianglin
Published: November 2023

Federated learning (FL) is a distributed machine learning paradigm that enables a large number of clients to collaboratively train models without sharing data. However, when the private dataset between clients is not independent and identically distributed (non-IID), the local training objective is inconsistent with the global training objective, which possibly causes the convergence speed of FL to slow down, or even not converge. In this paper, we design a novel FL framework based on deep reinforcement learning (DRL), named FedRLCS. In FedRLCS, we primarily improved the greedy strategy and action space of the double DQN (DDQN) algorithm, enabling the server to select the optimal subset of clients from a non-IID dataset to participate in training, thereby accelerating model convergence and reaching the target accuracy in fewer communication epochs. In simulation experiments, we partition multiple datasets with different strategies to simulate non-IID on local clients. We adopt four models (LeNet-5, MobileNetV2, ResNet-18, ResNet-34) on the four datasets (CIFAR-10, CIFAR-100, NICO, Tiny ImageNet), respectively, and conduct comparative experiments with five state-of-the-art non-IID FL methods. Experimental results show that FedRLCS reduces the number of communication rounds required by 10–70% with the same target accuracy without increasing the computation and storage costs for all clients.
Heavy Metals in Wetland Ecosystem: Investigating Metal Contamination in Waterbirds via Primary Feathers and Its Effect on Population and Diversity
Authors: Pandiyan Jeganathan , Arumugam Radjassegarin , Al-Ghanim Khalid A. , Sachivkina Nadezhda , Nicoletti Marcello , Govindarajan Marimuthu
Published: November 2023

Wetlands are dynamic ecosystems that provide feeding and nesting grounds for diverse species of waterbirds. The quality of wetland habitat may have an impact on the density, diversity, and species richness of waterbirds. Toxic metal contamination is one of the most significant threats to wetland habitats. Feathers are a key indicator of heavy metal contamination in avian communities as a non-invasive method. We examined the levels of Arsenic (As), Cadmium (Cd), Cobalt (Co), Chromium (Cr), Copper (Cu), Lead (Pb), Nickel (Ni), and Zinc (Zn) using ICP-AAS and standards of digestion procedure from the primary feathers of 10 distinct species of waterbirds. The study was conducted at four wetlands, viz., Point Calimere Wildlife Sanctuary (Ramsar site); Pallikaranai Marshland (Ramsar site); Perunthottam freshwater lake (unprotected wetland), Tamil Nadu and the Pulicat Lake, Andhra Pradesh, (Ramsar site), India. The Large crested tern had higher concentrations of As, Co, Cr, and Ni. Cu was greater in the Indian pond heron, and Zn was higher in the Grey heron. The accumulation of metals differed among the waterbirds (p < 0.05), and the inter-correlation of metals found positive influences between the tested metals, i.e., Co was positively associated with As, Cr had a positive correlation with As and Co, and Ni was positively correlated with As, Co, Cr, and Cu. In contrast, Pb had a positive association with Cu and Ni. The Zn was associated with Co, Cr and Cu. The level of metals in waterbirds was Zn > Cu > Cr > Ni > Pb > Co > Cd > As. The results showed that metal levels in the primary feathers of waterbirds were greater than the other species of waterbirds examined across the world. Thus, the study emphasizes that managing wetlands and controlling pollution is crucial to saving waterbirds; otherwise, the population and diversity of waterbirds will decline and become a significant threat to waterbird communities.
Effect of a 6-Week Preseason Training Protocol on Physiological and Muscle Damage Markers in High-Level Female and Male Basketball Players
Authors: Mexis Dimitrios , Nomikos Tzortzis , Mitsopoulos Nikolaos , Kostopoulos Nikolaos
Published: November 2023

This study aimed to investigate the effects of a 6-week preseason functional and plyometric fitness training protocol, on physiological and biochemical markers of performance and exercise-induced muscle damage, and to compare the response of these markers between high-level female and male basketball players. The sample of the study consisted of 19 professional athletes (10 male; 9 female) competing in two different teams. The examined markers were body mass, BMI, fat percentage, speed, acceleration, explosiveness, vertical jumping ability, creatine kinase (CK) and lactate dehydrogenase (LDH). The preseason training period improved speed, acceleration, explosiveness and vertical jumping ability (~1–8%) and led to significant fat percentage reductions in both groups equivalently. CK and LDH increased similarly in both groups, and the percentage increases were higher for CK compared to LDH. Further investigation and a larger sample size are required in order to determine an approach that is more capable of maximizing performance without causing any possible injuries that may be related to muscle damage.
A Cluster-Then-Route Framework for Bike Rebalancing in Free-Floating Bike-Sharing Systems
Authors: Sun Jiaqing , He Yulin , Zhang Jiantong
Published: November 2023

Bike-sharing systems suffer from the problem of imbalances in bicycle inventory between areas. In this paper, we investigate the rebalancing problem as it applies to free-floating bike-sharing systems in which the bicycles can be rented and returned almost anywhere. To solve the rebalancing problem efficiently, we propose a framework that includes (1) rebalancing nodes at which requirements for the redistribution (pickup or delivery) of bicycles are determined, (2) “self-balanced” clusters of rebalancing nodes, and (3) bicycle redistribution by service vehicles within each cluster. We propose a multi-period synchronous rebalancing method in which a rebalancing period is divided into several sub-periods. Based on the anticipated redistribution demand at each node in each sub-period, the service vehicle relocates bicycles between nodes. This method improves the efficiency of the system and minimizes rebalancing costs over the entire rebalancing period, rather than for a single sub-period. The proposed framework is tested based on data from the Mobike (Meituan) free-floating bike-sharing system. The test results demonstrate the effectiveness of the proposed methodologies and show that multi-period synchronous rebalancing is superior to single-period rebalancing.
Resilience-Driven Optimal Sizing of Energy Storage Systems in Remote Microgrids
Authors: Picioroaga Irina , Luca Madalina , Tudose Andrei , Sidea Dorian , Eremia Mircea , Bulac Constantin
Published: November 2023

As climate changes intensify the frequency of severe outages, the resilience of electricity supply systems becomes a major concern. In order to simultaneously combat the climate problems and ensure electricity supply in isolated areas, renewable energy sources (RES) have been widely implemented in recent years. However, without the use of energy storage, they show low reliability due to their intermittent output. Therefore, this article proposes a methodology to achieve the optimal sizing of an energy storage system (ESS) to ensure predefined periods of safe operation for an ensemble consisting of multiple loads, renewable energy sources and controllable generators, located in a remote microgrid. In this regard, a mixed integer linear programming (MILP) model has been proposed to reduce the outages impact of critical loads by calculating the optimal ESS capacity and defining the proper resources management within the off-grid microgrid, while ensuring a cost-effective operation of its components.
Improved End-to-End Data Security Approach for Cloud Computing
Authors: Ghosh Soumalya , Verma Shiv Kumar , Ghosh Uttam , Al-Numay Mohammed
Published: November 2023

Cloud computing is one of the major cutting-edge technologies that is growing at a gigantic rate to redefine computation through service-oriented computing. It has addressed the issue of owning and managing computational infrastructure by providing service through a pay-and-use model. However, a major possible hindrance is security breaches, especially when the sender uploads or the receiver downloads the data from a remotely accessed server. It is a very generic approach to ensuring data security through different encryption techniques, but it might not be able to maintain the security standard. This paper proposes an end-to-end data security approach from the sender side to the receiver side by adding extra padding sequences, as well as randomized salting, followed by hashing and an encryption technique. The effectiveness of the proposed method was established using both a simulated system and mathematical formulations with different performance metrics. Furthermore, its performance was compared with those of contemporary algorithms, showing that the proposed algorithm creates a larger ciphertext that is almost impossible to crack due to randomization modules. However, it has significantly longer encryption and decryption times, although our primary concern is ensuring security, not reducing time.
Analyzing Core Competencies and Correlation Paths of Emerging Engineering Talent in the Construction Industry—An Integrated ISM–MICMAC Approach
Authors: Zhang Ping , Ma Shuai-Ge , Zhao Yue-Nan , Cao Xin-Ying
Published: November 2023

The structure of core competencies is the key to constructing the “Chinese Standards” of engineering education and cultivating quality emerging engineering talent. This article synthesized the research results of existing theoretical analyses and educational practices. In total, 11 core competencies of emerging engineering talent were extracted via a literature review and the Delphi method, of which the hierarchical relationships and correlation paths between the core competencies of emerging engineering talent were determined via the use of Interpretive Structural Modeling (ISM) and Matrice d’ Impacts Croisés Multiplication Appliqués à un Classement (MICMAC), where corresponding improvement strategies were eventually proposed. It was found that professional knowledge and engineering thinking, as independent factors, have the greatest impact on the core competencies of emerging engineering talent. Taking the lead in cultivating the core competencies, which are dependence factors, including entrepreneurial ability, lifelong learning ability, professional ethics, patriotism and collectivism, and intercultural competence, is more conducive to improving the quality of emerging engineering talent, enabling the cultivation of high-quality composite talent with strong engineering practical abilities, innovation abilities, and international competitiveness. The hierarchical relationships and correlation paths determined in this study provide theoretical support for developing scientific objectives for emerging engineering talent training, carrying out educational practice, and reducing the difficulty in application practices. The results of this research support the viewpoint that “professional foundation and practical ability” should be stressed. Limited by the disciplinary backgrounds of the authors, the compatibility between the core competencies’ framework of emerging engineering talent proposed in this article based on the construction industry and other industries still needs to be further explored.
Impact of White Noise on the Exact Solutions of the Stochastic Riemann Wave Equation in Quantum Mechanics
Authors: Mohammed Wael , Cesarano Clemente , Rizk Doaa , Aly Elkhateeb , El-Morshedy Mahmoud
Published: November 2023

In this article, the stochastic Riemann wave equation (SRWE) forced by white noise in the Itô sense is considered. The extended tanh function and mapping methods are applied to obtain new elliptic, rational, hyperbolic, and trigonometric stochastic solutions. Furthermore, we generalize some previous studies. The obtained solutions are important in explaining some exciting physical phenomena, since the SRWE is required for describing wave propagation. We plot numerous 3D and 2D graphical representations to explain how the multiplicative white noise influences the exact solutions of the SRWE. We can infer that the introduction of multiplicative white noise disrupts the symmetry of the solutions and serves to stabilize the solutions of the SRWE.