bluuebunny/crossref_metadata_2025 huggingface dataset:
Unravelling Glucoraphanin and Glucoerucin Metabolism across Broccoli Sprout Development: Insights from Metabolite and Transcriptome Analysis
Authors: Wang Jiansheng , Shen Yusen , Sheng Xiaoguang , Yu Huifang , Song Mengfei , Wang Qiaomei , Gu Honghui
Published: March 2024

Variations in the concentration of glucoraphanin (GRA) and glucoerucin (GER), as well as the corresponding breakdown products, isothiocyanates (ITCs) and nitriles, were investigated during the growth of broccoli sprouts. The concentrations of GRA and GER decreased sharply from 33.66 µmol/g to 11.48 µmol/g and 12.98 µmol/g to 8.23 µmol/g, respectively, after seed germination. From the third to the seventh day, both GRA and GER were maintained as relatively stable. The highest concentrations of sulforaphane (17.16 µmol/g) and erucin (12.26 µmol/g) were observed on the first day. Hereafter, the concentrations of nitrile hydrolyzed from GRA or GER were higher than those of the corresponding ITCs. Moreover, the ratio of sulforaphane to sulforaphane nitrile decreased from 1.35 to 0.164 from 1 d to 5 d, with a similar trend exhibited for erucin/erucin nitrile after 2 d. RNA-seq analysis showed that BolMYB28 and BolCYP83A1, involved in aliphatic glucosinolate (GSL) biosynthesis, remained largely unexpressed until the third day. In contrast, the genes operating within the GSL-myrosinase hydrolysis pathway were highly expressed right from the beginning, with their expression levels increasing significantly after the third day. Additionally, we identified two BolESPs and six BolNSPs that might play important roles in promoting the production of nitriles during the development of broccoli sprouts.
The Multiple Promoting Effects of Suaeda glauca Root Exudates on the Growth of Alfalfa under NaCl Stress
Authors: Dong Linling , Hua Yi , Gao Zhiqiang , Wu Hanfu , Hou Yu , Chu Yingying , Zhang Jinwei , Cui Guowen
Published: March 2024

Under abiotic stress, plant root exudates can improve plant growth performance. However, studies on the effect of root exudates on the stress resistance of another plant are insufficient. In this study, root exudates (REs) were extracted from Suaeda glauca to explore their effect on alfalfa seedlings under salt stress. The results showed that the plant height and fresh weight of alfalfa significantly increased by 47.72% and 53.39% after 7 days of RE treatment at a 0.4% NaCl concentration. Under 1.2% salt stress, REs reduced the Malondialdehyde content in alfalfa by 30.14% and increased the activity of its antioxidant enzymes (peroxidase and catalase) and the content of its osmotic regulators (soluble sugar and proline) by 60.68%, 52%, 45.67%, and 38.67%, respectively. Soil enzyme activity and the abundance of soil-beneficial bacteria were increased by REs. Spearman analysis showed that urease and neutral phosphatase were related to the richness of beneficial bacteria. Redundancy analysis confirmed that urease affected the composition of the soil bacterial community. The partial least squares structural equation model (PLS-SEM) revealed that REs had a direct positive effect on alfalfa growth under salt stress by regulating the plant’s injury and antioxidant systems, and the soil bacterial community had an indirect positive effect on alfalfa growth through soil enzyme activity.
Eucalypt Extracts Prepared by a No-Waste Method and Their 3D-Printed Dosage Forms Show Antimicrobial and Anti-Inflammatory Activity
Authors: Koshovyi Oleh , Komisarenko Mykola , Osolodchenko Tatyana , Komissarenko Andrey , Mändar Reet , Kõljalg Siiri , Heinämäki Jyrki , Raal Ain
Published: March 2024

The pharmaceutical industry usually utilizes either hydrophobic or hydrophilic substances extracted from raw plant materials to prepare a final product. However, the waste products from the plant material still contain biologically active components with the opposite solubility. The aim of this study was to enhance the comprehensive usability of plant materials by developing a new no-waste extraction method for eucalypt leaves and by investigating the phytochemical and pharmacological properties of eucalypt extracts and their 3D-printed dosage forms. The present extraction method enabled us to prepare both hydrophobic soft extracts and hydrophilic (aqueous) dry extracts. We identified a total of 28 terpenes in the hydrophobic soft extract. In the hydrophilic dry extract, a total of 57 substances were identified, and 26 of them were successfully isolated. The eucalypt extracts studied showed significant antimicrobial activity against Staphylococcus aureus, Pseudomonas aeruginosa, Bacillus subtilis, Candida albicans, Corynebacterium diphtheriae gravis, and Corynebacterium diphtheriae mitis. The anti-inflammatory activity of the dry extract was studied using a formalin-induced-edema model in mice. The maximum anti-exudative effect of the dry extract was 61.5% at a dose of 20 mg/kg. Composite gels of polyethylene oxide (PEO) and eucalypt extract were developed, and the key process parameters for semi-solid extrusion (SSE) 3D printing of such gels were verified. The SSE 3D-printed preparations of novel synergistically acting eucalypt extracts could have uses in antimicrobial and anti-inflammatory medicinal applications.
Generation Potential and Characteristics of Kerogen Cracking Gas of Over-Mature Shale
Authors: Zhang Lin , Du Zhili , Jin Xiao , Li Jian , Lu Bin
Published: March 2024

To investigate the characteristics and generation potential of gas generated from over-mature shale, hydrous and anhydrous pyrolysis experiments were carried out on the Longmaxi Formation in the Anwen 1 well of the Sichuan Basin of China at temperatures of 400–598 °C and pressures of 50 Mpa, with (hydrous) and without (anhydrous) the addition of liquid water. The results show that in the presence of water, the total yield of carbon-containing gases (i.e., the sum of methane, ethane, and carbon dioxide) was increased by up to 1.8 times when compared to the total yield from the anhydrous pyrolysis experiments. The increased yield of carbon dioxide and methane accounted for 89% and 10.5% of the total increased yield of carbon-containing gases. This indicated that the participation of water could have promoted the release of carbon from over-mature shale, like we used in this study. The methane generated in the hydrous pyrolysis experiments was heavier, with a δ13C value of −21.27‰ (544 °C) compared to that generated in the anhydrous pyrolysis experiments, which showed a lighter δ13C of −33.70‰ (544 °C). It is noteworthy that the δ13C values of the methane from hydrous pyrolysis at >500 °C were even heavier than that of the kerogen from the over-mature shale, although the δ13C values of the methane show an overall increasing trend with increasing temperature both in hydrous and anhydrous pyrolysis. The carbon dioxide from hydrous pyrolysis was less enriched in 13C relative to that from anhydrous pyrolysis. Specifically, the δ 13C values of the carbon dioxide increased with the increasing temperature in anhydrous pyrolysis, whereas they remained nearly constant with increasing temperature in hydrous pyrolysis. The overall lighter δ13C values of the carbon dioxide generated in the hydrous pyrolysis experiments likely indicate that water tends to prompt the release of lighter carbon and/or suppress the release of heavier carbon from over-mature shale in the form of carbon dioxide, especially at higher temperatures, for example, of >510 °C.
A Method for Image-Based Interpretation of the Pulverized Coal Cloud in the Blast Furnace Tuyeres
Authors: Zhou Guanwei , Saxén Henrik , Mattila Olli , Yu Yaowei
Published: March 2024

The conditions in the combustion zones, i.e., the raceways, are crucial for the operation of the blast furnace. In recent years, advancements in tuyere cameras and image processing and interpretation techniques have provided a better means by which to obtain information from this region of the furnace. In this study, a comprehensive approach is proposed to visually monitor the status of the pulverized coal cloud at the tuyeres based on a carefully designed processing strategy. Firstly, tuyere images are preprocessed to remove noise and enhance image quality, applying the adaptive Otsu algorithm to detect the edges of the coal cloud, enabling precise delineation of the pulverized coal region. Next, a Swin–Unet model, which combines the strengths of Swin Transformer and U-Net architecture, is employed for accurate segmentation of the coal cloud area. The extracted pulverized coal cloud features are analyzed using RGB super-pixel weighting, which takes into account the variations in color within the cloud region. It is demonstrated that the pulverized coal injection rate shows a correlation with the state of the cloud detected based on the images. The effectiveness of this visual monitoring method is validated using real-world data obtained from a blast furnace of SSAB Europe. The experimental results align with earlier research findings and practical operational experience.
Control-Volume-Based Exergy Method of Truncated Busemann Inlets in Off-Design Conditions
Authors: Zhu Meijun , Zhou Shuai , Liu Yang , Li Zhehong , Chen Ziyun
Published: March 2024

A scramjet engine consisting of several components is a highly coupled system that urgently needs a universal performance metric. Exergy is considered as a potential universal currency to assess the performance of scramjet engines. In this paper, a control-volume-based exergy method for the Reynolds-averaged Navier–Stokes solution of truncated and corrected Busemann inlets was proposed. An exergy postprocessing code was developed to achieve this method. Qualitative and quantitative analyses of exergies in the Busemann inlets were performed. A complete understanding of the evolution process of anergy and the location where anergy occurs in the inlet at various operation conditions was also obtained. The results show that the exergy destroyed in the Busemann inlet can be decomposed into shock wave anergy, viscous anergy and thermal anergy. Shock wave anergy accounts for less than 4% of the total exergy destroyed while thermal anergy and viscous anergy, in a roughly equivalent magnitude, contribute to almost all the remaining. The vast majority of inflow exergy is converted into boundary pressure work and thermal exergy. Some of the thermal exergy excluded by the computation of the total pressure recovery coefficient belongs to the available energy, as this partial energy will be further converted into useful work in combustion chambers.
A New Vegetation Observable Derived from Spaceborne GNSS-R and Its Application to Vegetation Water Content Retrieval
Authors: Chen Fade , Liu Lilong , Guo Fei , Huang Liangke
Published: March 2024

In this study, a new vegetation observable derived from spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) was developed. Firstly, a linear relationship between the Cyclone Global Navigation Satellite System (CYGNSS) reflectivity and soil moisture was derived based on the tau-omega (τ−w) model. The intercept and slope of this linear function were associated with the vegetation properties. Moreover, the intercept is not affected by soil moisture and depends only on vegetation properties. Secondly, to validate the new observable, the intercept demonstrated a significant correlation with vegetation water content (VWC), with the highest correlation coefficient of 0.742. Based on the intercept and slope, a linear model and an artificial neural network (ANN) model were established to retrieve VWC by combining geographical location and land cover information. The correlation coefficient and root-mean-square error (RMSE) of VWC retrieval based on the linear model were 0.795 and 2.155 kg/m2, respectively. The correlation coefficient and RMSE for the ANN model were 0.940 and 1.392 kg/m2, respectively. Compared with the linear model, the ANN model greatly improves the global VWC retrieval in accuracy, especially in areas with poor linear model retrieval results. Therefore, compared with conventional remote sensing techniques, the spaceborne GNSS-R can provide a new and effective approach to global VWC monitoring.
Correction of 2π Phase Jumps for Silicon Photonic Sensors Based on Mach Zehnder Interferometers with Application in Gas and Biosensing
Authors: Laplatine Loic , Messaoudene Sonia , Gaignebet Nicolas , Herrier Cyril , Livache Thierry
Published: March 2024

Silicon photonic sensors based on Mach Zehnder Interferometers (MZIs) have applications spanning from biological and olfactory sensors to temperature and ultrasound sensors. Although a coherent detection scheme can solve the issues of sensitivity fading and ambiguity in phase direction, the measured phase remains 2π periodic. This implies that the acquisition frequency should ensure a phase shift lower than π between each measurement point to prevent 2π phase jumps. Here, we describe and experimentally characterize two methods based on reference MZIs with lower sensitivities to alleviate this drawback. These solutions improve the measurement robustness and allow the lowering of the acquisition frequency. The first method is based on the phase derivative sign comparison. When a discrepancy is detected, the reference MZI is used to choose whether 2π should be added or removed from the nominal MZI. It can correct 2π phase jumps regardless of the sensitivity ratio, so that a single reference MZI can be used to correct multiple nominal MZIs. This first method relaxes the acquisition frequency requirement by a factor of almost two. However, it cannot correct phase jumps of 4π, 6π or higher between two measurement points. The second method is based on the comparison between the measured phase from the nominal MZI and the phase expected from the reference MZI. It can correct multiple 2π phase jumps but requires at least one reference MZI per biofunctionalization. It will also constrain the corrected phase to lie in a limited interval of [−π, +π] around the expected value, and might fail to correct phase shifts above a few tens of radians depending on the disparity of the nominal sensors responses. Nonetheless, for phase shift lower than typically 20 radians, this method allows the lowering of the acquisition frequency almost arbitrarily.
Insight into Anomaly Detection and Prediction and Mobile Network Security Enhancement Leveraging K-Means Clustering on Call Detail Records
Authors: Aziz Zagroz , Bestak Robert
Published: March 2024

The dynamic and evolving nature of mobile networks necessitates a proactive approach to security, one that goes beyond traditional methods and embraces innovative strategies such as anomaly detection and prediction. This study delves into the realm of mobile network security and reliability enhancement through the lens of anomaly detection and prediction, leveraging K-means clustering on call detail records (CDRs). By analyzing CDRs, which encapsulate comprehensive information about call activities, messaging, and data usage, this research aimed to unveil hidden patterns indicative of anomalous behavior within mobile networks and security breaches. We utilized 14 million one-year CDR records. The mobile network used had deployed the latest network generation, 5G, with various sources of network elements. Through a systematic analysis of historical CDR data, this study offers insights into the underlying trends and anomalies prevalent in mobile network traffic. Furthermore, by harnessing the predictive capabilities of the K-means algorithm, the proposed framework facilitates the anticipation of future anomalies based on learned patterns, thereby enhancing proactive security measures. The findings of this research can contribute to the advancement of mobile network security by providing a deeper understanding of anomalous behavior and effective prediction mechanisms. The utilization of K-means clustering on CDR data offers a scalable and efficient approach to anomaly detection, with 96% accuracy, making it well suited for network reliability and security applications in large-scale mobile networks for 5G networks and beyond.
A Handheld Laser-Scanning-Based Methodology for Monitoring Tree Growth in Chestnut Orchards
Authors: Pereira-Obaya Dimas , Cabo Carlos , Ordóñez Celestino , Rodríguez-Pérez José Ramón
Published: March 2024

Chestnut and chestnut byproducts are of worldwide interest, so there is a constant need to develop faster and more accurate monitoring techniques. Recent advances in simultaneous localization and mapping (SLAM) algorithms and user accessibility have led to increased use of handheld mobile laser scanning (HHLS) in precision agriculture. We propose a tree growth monitoring methodology, based on HHLS point cloud processing, that calculates the length of branches through spatial discretization of the point cloud for each tree. The methodology was tested by comparing two point clouds collected almost simultaneously for each of a set of sweet chestnut trees. The results obtained indicated that our HHLS method was reliable and accurate in efficiently monitoring sweet chestnut tree growth. The same methodology was used to calculate the growth of the same set of trees over 37 weeks (from spring to winter). Differences in week 0 and week 37 scans showed an approximate mean growth of 0.22 m, with a standard deviation of around 0.16 m reflecting heterogeneous tree growth.
Point Cloud Denoising and Feature Preservation: An Adaptive Kernel Approach Based on Local Density and Global Statistics
Authors: Wang Lianchao , Chen Yijin , Song Wenhui , Xu Hanghang
Published: March 2024

Noise removal is a critical stage in the preprocessing of point clouds, exerting a significant impact on subsequent processes such as point cloud classification, segmentation, feature extraction, and 3D reconstruction. The exploration of methods capable of adapting to and effectively handling the noise in point clouds from real-world outdoor scenes remains an open and practically significant issue. Addressing this issue, this study proposes an adaptive kernel approach based on local density and global statistics (AKA-LDGS). This method constructs the overall framework for point cloud denoising using Bayesian estimation theory. It dynamically sets the prior probabilities of real and noise points according to the spatial function relationship, which varies with the distance from the points to the center of the LiDAR. The probability density function (PDF) for real points is constructed using a multivariate Gaussian distribution, while the PDF for noise points is established using a data-driven, non-parametric adaptive kernel density estimation (KDE) approach. Experimental results demonstrate that this method can effectively remove noise from point clouds in real-world outdoor scenes while maintaining the overall structural features of the point cloud.
A New Approach to Vascular Screening: Identification of Impaired Vascular Function Using the FMSF Technique
Authors: Marcinek Andrzej , Katarzynska Joanna , Gebicki Jerzy
Published: March 2024

Arterial blood pressure monitoring plays an important role in preventive medicine, allowing, in selected cases, the identification of vascular dysfunction. In this review, we propose a new non-invasive approach to assessment of the circulatory system, based on its reaction to hypoxia induced by post-occlusive reactive hyperemia (PORH). Three key parameters can be used for vascular screening: the Reactive Hyperemia Response (RHR), which represents the overall reaction of the macro- and microcirculation to transient hypoxia; Hypoxia Sensitivity (HS), which reflects hypoxia-induced activation of myogenic oscillations of the microcirculation; and Normoxia Oscillatory Index (NOI), which characterizes microcirculatory oscillations under normoxia conditions. A method for assessing these parameters, analogous in simplicity to arterial blood pressure measurement, is provided by the Flow Mediated Skin Fluorescence (FMSF) technique. Reference values are proposed based on numerous test measurements.
Improving Classification Performance in Dendritic Neuron Models through Practical Initialization Strategies
Authors: Wen Xiaohao , Zhou Mengchu , Albeshri Aiiad , Huang Lukui , Luo Xudong , Ning Dan
Published: March 2024

A dendritic neuron model (DNM) is a deep neural network model with a unique dendritic tree structure and activation function. Effective initialization of its model parameters is crucial for its learning performance. This work proposes a novel initialization method specifically designed to improve the performance of DNM in classifying high-dimensional data, notable for its simplicity, speed, and straightforward implementation. Extensive experiments on benchmark datasets show that the proposed method outperforms traditional and recent initialization methods, particularly in datasets consisting of high-dimensional data. In addition, valuable insights into the behavior of DNM during training and the impact of initialization on its learning performance are provided. This research contributes to the understanding of the initialization problem in deep learning and provides insights into the development of more effective initialization methods for other types of neural network models. The proposed initialization method can serve as a reference for future research on initialization techniques in deep learning.
A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias
Authors: Mousavi Seyedeh Somayyeh , Reyna Matthew A. , Clifford Gari D. , Sameni Reza
Published: March 2024

Regular blood pressure (BP) monitoring in clinical and ambulatory settings plays a crucial role in the prevention, diagnosis, treatment, and management of cardiovascular diseases. Recently, the widespread adoption of ambulatory BP measurement devices has been predominantly driven by the increased prevalence of hypertension and its associated risks and clinical conditions. Recent guidelines advocate for regular BP monitoring as part of regular clinical visits or even at home. This increased utilization of BP measurement technologies has raised significant concerns regarding the accuracy of reported BP values across settings. In this survey, which focuses mainly on cuff-based BP monitoring technologies, we highlight how BP measurements can demonstrate substantial biases and variances due to factors such as measurement and device errors, demographics, and body habitus. With these inherent biases, the development of a new generation of cuff-based BP devices that use artificial intelligence (AI) has significant potential. We present future avenues where AI-assisted technologies can leverage the extensive clinical literature on BP-related studies together with the large collections of BP records available in electronic health records. These resources can be combined with machine learning approaches, including deep learning and Bayesian inference, to remove BP measurement biases and provide individualized BP-related cardiovascular risk indexes.
Three-Dimensional Image Visualization under Photon-Starved Conditions Using N Observations and Statistical Estimation
Authors: Kim Hyun-Woo , Lee Min-Chul , Cho Myungjin
Published: March 2024

In this paper, we propose a method for the three-dimensional (3D) image visualization of objects under photon-starved conditions using multiple observations and statistical estimation. To visualize 3D objects under these conditions, photon counting integral imaging was used, which can extract photons from 3D objects using the Poisson random process. However, this process may not reconstruct 3D images under severely photon-starved conditions due to a lack of photons. Therefore, to solve this problem, in this paper, we propose N-observation photon-counting integral imaging with statistical estimation. Since photons are extracted randomly using the Poisson distribution, increasing the samples of photons can improve the accuracy of photon extraction. In addition, by using a statistical estimation method, such as maximum likelihood estimation, 3D images can be reconstructed. To prove our proposed method, we implemented the optical experiment and calculated its performance metrics, which included the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), peak-to-correlation energy (PCE), and the peak sidelobe ratio (PSR).
Predictions of Aeroengines’ Infrared Radiation Characteristics Based on HKELM Optimized by the Improved Dung Beetle Optimizer
Authors: Qiao Lei , Chen Lihai , Li Yiwen , Hua Weizhuo , Wang Ping , Cui You
Published: March 2024

To solve the problems of high computational cost and the long time required by the simulation and calculation of aeroengines’ exhaust systems, a method of predicting the characteristics of infrared radiation based on the hybrid kernel extreme learning machine (HKELM) optimized by the improved dung beetle optimizer (IDBO) was proposed. Firstly, the Levy flight strategy and variable spiral strategy were introduced to improve the optimization performance of the dung beetle optimizer (DBO) algorithm. Secondly, the superiority of IDBO algorithm was verified by using 23 benchmark functions. In addition, the Wilcoxon signed-rank test was applied to evaluate the experimental results, which proved the superiority of the IDBO algorithm over other current prominent metaheuristic algorithms. Finally, the hyperparameters of HKELM were optimized by the IDBO algorithm, and the IDBO-HKELM model was applied to the prediction of characteristics of infrared radiation of a typical axisymmetric nozzle. The results showed that the RMSE and MAE of the IDBO-HKELM model were 20.64 and 8.83, respectively, which verified the high accuracy and feasibility of the proposed method for predictions of aeroengines’ infrared radiation characteristics.
Performance of Self-Sensing Cement-Stabilized Sand under Various Loading Conditions
Authors: Roshan Mohammad Jawed , Abedi Mohammadmahdi , Gomes Correia António , Fangueiro Raul
Published: March 2024

Numerous elements, such as the composition and characteristics of carbon nanomaterials, the composition and characteristics of the matrix material, moisture levels, temperature, and loading circumstances, influence the piezoresistive behavior of self-sensing cementitious composites. While some past research has explored the impact of some of these factors on the performance of self-sensing cementitious composites, additional investigations need to be conducted to delve into how loading conditions affect the sensitivity of self-sensing cement-stabilized composites. Therefore, this study explores the influences of various loading conditions (i.e., location of loading regarding the location of recording electrodes, and loading level) on the electromechanical performance of self-sensing cement-stabilized sand. To this end, firstly, the evaluation of the percolation threshold based on 10% cement-stabilized sand specimens containing various multiwall carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) was performed. Then, 10% cement-stabilized sand containing 4% MWCNTs/GNPs was tested under various cyclic compressive stresses. The results suggested that the distance between the loading area and the electrode location used for recording the electrical resistance significantly impacted the sensitivity of cement-stabilized sand. Optimal sensitivity was achieved when the electrodes were positioned directly beneath the loading area. Moreover, the study showed that the stress sensitivity of self-sensing cement-stabilized sand increased proportionally with the stress level. An examination through scanning electron microscopy (SEM) demonstrated that the loading condition influences the bridging characteristics of carbon nanomaterials in cement-stabilized sand, leading to diverse electromechanical behaviors emerging based on the loading condition. This study underscores the importance of considering specific parameters when designing self-sensing cement-stabilized sand for application in practical field use.
Practical RGB-to-XYZ Color Transformation Matrix Estimation under Different Lighting Conditions for Graffiti Documentation
Authors: Molada-Tebar Adolfo , Verhoeven Geert J. , Hernández-López David , González-Aguilera Diego
Published: March 2024

Color data are often required for cultural heritage documentation. These data are typically acquired via standard digital cameras since they facilitate a quick and cost-effective way to extract RGB values from photos. However, cameras’ absolute sensor responses are device-dependent and thus not colorimetric. One way to still achieve relatively accurate color data is via camera characterization, a procedure which computes a bespoke RGB-to-XYZ matrix to transform camera-dependent RGB values into the device-independent CIE XYZ color space. This article applies and assesses camera characterization techniques in heritage documentation, particularly graffiti photographed in the academic project INDIGO. To this end, this paper presents COOLPI (COlor Operations Library for Processing Images), a novel Python-based toolbox for colorimetric and spectral work, including white-point-preserving camera characterization from photos captured under diverse, real-world lighting conditions. The results highlight the colorimetric accuracy achievable through COOLPI’s color-processing pipelines, affirming their suitability for heritage documentation.
Real-Time Speed Estimation for an Induction Motor: An Automated Tuning of an Extended Kalman Filter Using Voltage–Current Sensors
Authors: Miloud Ines , Cauet Sebastien , Etien Erik , Salameh Jack P. , Ungerer Alexandre
Published: March 2024

This paper aims at achieving real-time optimal speed estimation for an induction motor using the Extended Kalman filter (EKF). Speed estimation is essential for fault diagnosis in Motor Current Signature Analysis (MCSA). The estimation accuracy is obtained by exploring the noise covariance matrices estimation of the EKF algorithm. The noise covariance matrices are determined using a modified subspace model identification approach. In order to reach this goal, this method compares an estimated model of a deterministic system, derived from available input–output datasets (using voltage–current sensors), with the discrete-time state-space representation used in the Kalman filter equations. This comparison leads to the determination of model uncertainties, which are subsequently represented as noise covariance matrices. Based on the fifth-order nonlinear model of the induction motor, the rotor speed is estimated with the optimized EKF algorithm, and the algorithm is tested experimentally.
When Women Ask, Does Curiosity Help?
Authors: Mislin Alexandra , Tuncel Ece , Prewitt Lucie
Published: March 2024

This research examines the potential social benefits of displaying curiosity during a negotiation. Past research has found women who ask directly in distributive agentic settings can suffer negative social consequences and obtain worse objective outcomes compared to men. In three experiments (N = 600) using different negotiation contexts, we found men and women who approach negotiations with curiosity reap the same economic benefits of asking directly but without incurring a social cost. We also found that perceived warmth partially accounts for the positive effects of curiosity (vs. asking directly) on negotiators’ social outcomes. Finally, our results reveal women feel more comfortable conveying curiosity compared to using a direct approach in their negotiations. We discuss the implications of these findings in enhancing negotiation effectiveness for both women and men.
Move to Flow: The Benefits and Barriers of a Physical Activity Nature-Based Pilot Programme
Authors: Marcen Celia , Cardona-Linares Antonio José , Pradas Francisco , Ortega-Zayas Miguel Ángel
Published: March 2024

This study aims to assess the potential benefits and barriers of Move to Flow (MtF), a nature-based physical activity (PA) programme structured in two levels that include movements related to the animal, vegetal, and inert nature. A questionnaire was applied to 133 participants from Spain, Latvia, and Serbia. The instrument was structured in the following sections: socio-economic variables; sports participation; and health and physical condition perception. In addition, the Exercise Benefits and Barriers Scale (EBBS), which assesses these aspects on the physical, psychological, and social levels, was included. Results showed that men aged 36–50 obtained the highest total and benefit scores, while women and the youngest scored higher in barriers. Data analysis shows associations between weekly engagement in physical activity and intensity (p < 0.001) and the perceived benefits of MtF. In the case of intensity, it is the same with barriers (p < 0.001). Similarly, there is an association between benefits and barriers and perceived health status (p < 0.001) and physical condition (p < 0.001). Furthermore, positive correlations were found in MtF between PA frequency, intensity, and health and physical condition (r = 0.755). In conclusion, this study has demonstrated MtF’s value as a cost-effective tool that empowers people to take an active role in improving their overall health and well-being.
MMKG-PAR: Multi-Modal Knowledge Graphs-Based Personalized Attraction Recommendation
Authors: Zhang Gengyue , Li Hao , Li Shuangling , Wang Beibei , Ding Zhixing
Published: March 2024

As the tourism industry rapidly develops, providing personalized attraction recommendations has become a hot research area. Knowledge graphs, with their rich semantic information and entity relationships, not only enhance the accuracy and personalization of recommendation systems but also energize the sustainable development of the tourism industry. Current research mainly focuses on single-modal knowledge modeling, limiting the in-depth understanding of complex entity characteristics and relationships. To address this challenge, this paper proposes a multi-modal knowledge graphs-based personalized attraction recommendation (MMKG-PAR) model. We utilized data from the “Travel Yunnan” app, along with users’ historical interaction data, to construct a collaborative multi-modal knowledge graph for Yunnan tourist attractions, which includes various forms such as images and text. Then, we employed advanced feature extraction methods to extract useful features from multi-modal data (images and text), and these were used as entity attributes to enhance the representation of entity nodes. To more effectively process graph-structured data and capture the complex relationships between nodes, our model incorporated graph neural networks and introduced an attention mechanism for mining and inferring higher-order information about entities. Additionally, MMKG-PAR introduced a dynamic time-weighted strategy for representing users, effectively capturing and precisely describing the dynamics of user behavior. Experimental results demonstrate that MMKG-PAR surpasses existing methods in personalized recommendations, providing significant support for the continuous development and innovation in the tourism industry.
Perceptions from Member-Consumers of a University Community for Sustainable and Healthy Eating: Evidence from Greece
Authors: Mastora Athina , Chatzitheodoridis Fotios , Skalkos Dimitris
Published: March 2024

Healthy eating is a vital component of a sustainable daily life, especially after the COVID-19 pandemic. In this study, we investigated the perceptions and attitudes of the members of a Greek university community, especially the young members, on: (i) health itself, (ii) healthy eating, and (iii) healthy eating campaigns today. The research was conducted electronically in the last three months of 2023 via a questionnaire survey and 1046 member-consumers participated. Statistical analysis, including descriptive and cluster analysis to group respondents into homogenous segments, was performed by employing a one-way ANOVA. The highlights of the results indicate that consumers perceive physical and mental health and energy efficiency as health preconditions, while they perceive a balanced and sustainable diet as the main parameter of healthy eating. They are influenced mostly by doctors and health providers and use information mostly based on common sense and their upbringing for their healthy eating choices. The cluster analysis revealed two distinctive groups of consumers categorized by this study as the “approachables” (54.5%), affected by today’s sustainable knowledge and concepts on healthy eating, and the “conservatives” (45.5%), who are reluctant to accepted these as such.
State of the Art of Research towards Sustainable Power Electronics
Authors: Salomez Florentin , Helbling Hugo , Almanza Morgan , Soupremanien Ulrich , Viné Guillaume , Voldoire Adrien , Allard Bruno , Ben-Ahmed Hamid , Chatroux Daniel , Cizeron Antoine , Delhommais Mylène , Fayolle-Lecocq Murielle , Grennerat Vincent , Jeannin Pierre-Oliver , Laudebat Lionel , Rahmani Boubakr , Vidal Paul-Étienne , Villa Luiz , Dupont Laurent , Crébier Jean-Christophe
Published: March 2024

Sustainability in power electronics is a recent research topic. It takes place among current actions to grasp design choices that enable eco-design and circular economy in the domain. This paper shows the results and analysis of a literature review at the intersection of power electronics and sustainability without considering the reliability study of the power electronics systems. The first part explains the scope of the study. The second part shows a bibliometric analysis of the collected publications that underlines a pioneering position at the European level. The third part details the state-of-the-art and its analysis over four investigation topics which are: tools and methods, indicators, circularity and materials. This paper and the work behind are the results of collaboration at the French national level, as part of the workgroup CEPPS (Convertisseurs Electronique de Puissance Plus Soutenables—More Sustainable Power Electronics Converters) supported by the CNRS (Centre National de la Recherche Scientifique—French National Centre for Scientific Research) research group SEEDS (Systèmes d’énergie électrique dans leurs dimensions sociétales—Societal dimensions of electrical energy systems).
Social Empowerment and Its Effect on Poverty Alleviation for Sustainable Development among Women Entrepreneurs in the Nigerian Agricultural Sector
Authors: Ogbari Mercy Ejovwokeoghene , Folorunso Flourish , Simon-Ilogho Busola , Adebayo Olufunke , Olanrewaju Kofoworola , Efegbudu Joy , Omoregbe Michael
Published: March 2024

Despite the fact that the issue of female poverty is widespread, continuous attempts are made to combat it and advance women’s empowerment. To achieve the goals of sustainable development, such as eradicating poverty, attaining gender equality, promoting good jobs and economic growth, and eliminating inequities, creative strategies must be developed. This study focused on the Nigerian Youth and Women Farmers Association in order to examine the effect of social empowerment on alleviating poverty in Nigeria’s agricultural sector. Out of the 353 issued questionnaires, 335 were returned and used in this study’s investigation. The structural equation model with partial least squares (SEM-PLS) was used to analyse the acquired data. The results showed an important correlation between social empowerment and reductions in poverty among women working in the agricultural sector. This highlights how crucial it is to work together, provide resources, and extend support in order to empower women socially and foster an atmosphere that supports their social and economic advancement. This research recommends that lawmakers, organisations, and governmental entities actively support the empowerment of women in the agricultural sector in order to encourage sustainable farming practices among female entrepreneurs.
Evaluation Research on Energy-Saving Retrofitting of Roofs of Traditional Wood-Structured Dwellings Based on the Continuation of Historical Features: A Case Study of Guangdu Village No. 280 Dwelling in Zhejiang
Authors: Mao Zhuoxun , Rao Xiaoxiao , Qi Feng
Published: March 2024

China has numerous traditional wooden dwellings located in regions with hot summers and cold winters. The historic dwellings lack proper thermal insulation and have excessive energy consumption in the building structure, failing to suit the needs of modern people. Hence, it is crucial to enhance their energy efficiency through essential actions. Roofs, being the fifth side of the building envelope, are frequently neglected when it comes to enhancing their insulating capabilities. The study aimed to implement energy-efficient roof alterations while preserving the historical features of traditional structures. This study focuses on enhancing the energy efficiency of a traditional wood-structured dwelling in Guangdu Village, Zhejiang Province, China, by installing composite thermal insulation panels on the interior of the roof using an easy restoration approach. The actual risk of condensation and the impact of retrofit processes on historical features determine the choice and limitations of energy-saving retrofit materials and methods. This study evaluates the transformation using two indicators: energy efficiency and economic feasibility. The numerical simulation results indicate that traditional dwellings have an annual energy savings rate of 16.66% and an investment payback period of 7.25 years. This study demonstrates the feasibility of energy-saving roof renovation measures for these traditional wood-structured dwellings. The measures improve energy efficiency and are affordable for residents. This study can offer technical suggestions for enhancing the energy efficiency of traditional wood-structured dwellings.
The Government Subsidy Policies for Organic Agriculture Based on Evolutionary Game Theory
Authors: Yang Xingyi , Dai Xiaopei , Zhang Yijing
Published: March 2024

Organic agriculture is a sustainable form of farming that can protect the environment. However, the high production costs of organic agriculture deter farmers from switching to organic farming. To support the development of organic agriculture, many governments offer subsidies to farmers or retailers. We develop an evolutionary game model to investigate the effect of government subsidies on organic agriculture and the conditions under which the government should subsidize farmers or retailers. We find that subsidizing farmers or retailers can promote agricultural development. Government subsidies lower the requirement for social responsibility and the premium that retailers offer. Furthermore, if the consumer’s social responsibility is sufficiently low, the government’s best choice is to subsidize retailers. If the social responsibility premium is medium, the government’s best choice is to subsidize farmers. Finally, higher subsidies motivate more farmers to produce organic products when the government subsidizes retailers. Conversely, if the government subsidizes farmers, it is more conducive to reducing the burden on retailers to purchase organic products.
Are New Campus Mobility Trends Causing Health Concerns?
Authors: Kotval-K Zeenat , Khandelwal Shruti , Kassens-Noor Eva , Qu Tongbin Teresa , Wilson Mark
Published: March 2024

An influx of new mobility trends such as fare-free bus transportation, ride hail, and e-scooter services to improve access and affordability of transportation on campus may be shifting the travel behavior of campus patrons such that it affects their long-term health outcomes. The main research questions explored in this study are as follows: (1) why university patrons choose new modes of travel?; (2) what existing mode did the new modes of travel replace for the riders?; and (3) is the average body mass index (BMI) of users primarily using non-motorized transit options lower than those using motorized or both (referred to as hybrid) for on-campus travel needs? An online survey was administered to a campus community (n = 3309) including students (48%), faculty (15%), and staff (37%) in fall of 2018 when fare-free bus transportation and e-scooters became available on campus, and a gradual increase in ridership of ride-hail services was simultaneously observed. This study found that campus patrons were more inclined to replace active modes of travel with affordable and accessible modes of transportation, thereby substituting their walking or biking routine with app-based transportation services. The mean BMI among travelers who chose motorized transportation modes was more than active travelers, and the BMI was statistically significantly associated with age, gender, race, class standing (undergraduate/graduate), and residence on/off campus. This study concludes with suggestions to prevent substitution of active with non-active transport choices and provides policy guidelines to increase awareness on achieving physical activity levels through active modes of travel for university patrons.
Symmetry-Enhanced, Improved Pathfinder Algorithm-Based Multi-Strategy Fusion for Engineering Optimization Problems
Authors: Mao Xuedi , Wang Bing , Ye Wenjian , Chai Yuxin
Published: March 2024

The pathfinder algorithm (PFA) starts with a random search for the initial population, which is then partitioned into only a pathfinder phase and a follower phase. This approach often results in issues like poor solution accuracy, slow convergence, and susceptibility to local optima in the PFA. To address these challenges, a multi-strategy fusion approach is proposed in the symmetry-enhanced, improved pathfinder algorithm-based multi-strategy fusion for engineering optimization problems (IPFA) for function optimization problems. First, the elite opposition-based learning mechanism is incorporated to improve the population diversity and population quality, to enhance the solution accuracy of the algorithm; second, to enhance the convergence speed of the algorithm, the escape energy factor is embedded into the prey-hunting phase of the GWO and replaces the follower phase in the PFA, which increases the diversity of the algorithm and improves the search efficiency of the algorithm; lastly, to solve the problem of easily falling into the local optimum, the optimal individual position is perturbed using the dimension-by-dimension mutation method of t-distribution, which helps the individual to jump out of the local optimum rapidly and advance toward other regions. The IPFA is used for testing on 16 classical benchmark test functions and 29 complex CEC2017 function sets. The final optimization results of PFA and IPFA in pressure vessels are 5984.8222 and 5948.3597, respectively. The final optimization results in tension springs are 0.012719 and 0.012699, respectively, which are comparable with the original algorithm and other algorithms. A comparison between the original algorithm and other algorithms shows that the IPFA algorithm is significantly enhanced in terms of solution accuracy, and the lower engineering cost further verifies the robustness of the IPFA algorithm.
On the Sums over Inverse Powers of Zeros of the Hurwitz Zeta Function and Some Related Properties of These Zeros
Authors: Sekatskii Sergey
Published: March 2024

Recently, we have applied the generalized Littlewood theorem concerning contour integrals of the logarithm of the analytical function to find the sums over inverse powers of zeros for the incomplete gamma and Riemann zeta functions, polygamma functions, and elliptical functions. Here, the same theorem is applied to study such sums for the zeros of the Hurwitz zeta function ζ(s,z), including the sum over the inverse first power of its appropriately defined non-trivial zeros. We also study some related properties of the Hurwitz zeta function zeros. In particular, we show that, for any natural N and small real ε, when z tends to n = 0, −1, −2… we can find at least N zeros of ζ(s,z) in the ε neighborhood of 0 for sufficiently small |z+n|, as well as one simple zero tending to 1, etc.