bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization of In Vitro Germination, Viability Tests and Storage of Paeonia ostii Pollen
Authors: Li Mengchen , Jiang Fengfei , Huang Linbo , Wang Hui , Song Wenqing , Zhang Xiaoxiao , Zhang Yanlong , Niu Lixin
Published: June 2023

Paeonia ostii is an important woody oil crop mainly cross-pollinated. However, the low yield has become an important factor restricting the industrial development of P. ostii. Cross-pollination has become one of the important measures to increase the seed yield. Therefore, conservation of pollen with high vitality is crucial to ensure successful pollination of P. ostii. In this study, we found an effective methodological system to assess the viability, ability to germinate, and optimal storage conditions of P. ostii pollen grains. The optimal medium in vitro was 50 g/L sucrose, 100 mg/L boric acid, 50 g/L PEG6000, 100 mg/L potassium nitrate, 300 mg/L calcium nitrate, and 200 mg/L magnesium sulfate at pH 5.4. Optimal germination condition in vitro was achieved at 25 °C for 120 min, allowing easy observation of the germination percentage and length of the pollen tubes. In addition, the viability of pollen grains was assessed by comparing nine staining methods. Among them, MTT, TTC, benzidine-H2O2, and FDA were effective to distinguish between viable and non-viable pollen, and the results of the FDA staining method were similar to the pollen germination percentage in vitro. After evaluation of pollen storage, thawing and rehydration experiments showed that thawing at 4 °C for 30 min and rehydration at 25 °C for 30 min increased the germination percentage of pollen grains stored at low temperatures. The low-temperature storage experiments showed that 4 °C was suitable for short-term storage of P. ostii pollen grains, while −80 °C was suitable for long-term storage. This is the first report on the in vitro germination, viability tests, and storage of P. ostii pollen grains, which will provide useful information for P. ostii germplasm conservation and artificial pollination.
Effect of Fiber Misalignment and Environmental Temperature on the Compressive Behavior of Fiber Composites
Authors: Drummer Jonas , Tafesh Felwa , Fiedler Bodo
Published: June 2023

This experimental study investigated how defects, in particular fiber misalignment, affect the mechanical behavior of glass fiber composites (GFRP) under compressive loading. GFRP cross-plies with three different types of fiber misalignment, namely a fold, a wave, and an in-plane undulation, were fabricated using the resin transfer molding process. The compressive tests were performed at four different temperatures, in order to investigate the role of a change in the matrix properties on the strength of the composite. The experiments showed that the defects, especially at lower temperatures, had a significant impact on the mechanical properties of the composite, exceeding the proportion of the defects inside the composite. With increasing temperature, the damage mechanism changed from fiber-dominated to matrix-dominated and, in doing so, decreased the significance of fiber misalignment for the mechanical behavior.
Thermoelasticity of Injection-Molded Parts
Authors: Turk Janez , Svenšek Daniel
Published: June 2023

In the realm of injection-molded parts, small length scale deformation defects such as sink marks often pose a major challenge to the aesthetics or functionality of the parts. To address this problem, we present a comprehensive thermoelastomechanical approach that calculates the deformation of injection molded plastic by solving the elastic problem at each time step. In our methodology, two treatments of the molten core are considered: one as a liquid and the other as a rubbery state. Our results suggest that the rubbery state treatment provides higher accuracy in predicting the deformation results, as it maintains the displacement of the localized thermal shrinkage in its vicinity. The validity of our method is supported by empirical measurements on produced parts from the existing literature as well as on samples that we molded independently.
Influence of Waste Material Additives on the Performance of a Novel Hybrid Sol-Gel Coating on Mild Steel in 3.5% NaCl Medium
Authors: Suleiman Rami K. , Adesina Akeem Y. , Olalekan Ogunlakin Nasirudeen , Kumar Arumugam Madhan , Al-Badour Fadi A. , Subbaiah Sowrirajan
Published: June 2023

This article presents the synthesis of a novel hybrid sol-gel coating and its functionalization with various waste material additives. The unmodified and modified hybrid coatings were deposited on mild steel (MS) substrates, and their anticorrosion performance in a 3.5 wt.% sodium chloride corrosive environment was assessed using potentiodynamic polarization and impedance electrochemical techniques. The Fourier Transformed Infrared Spectrometry (FTIR) spectral, thermal, surface-roughness, scratch-resistance, and contact-angle characterizations were also conducted on the fabricated coatings. Electrochemical techniques proved that the coating sample loaded with the limestone additive showed the best anticorrosion behavior in the saline environment after 4 weeks of exposure. Moreover, the obtained morphological analysis data indicated better surface integrity and cross-link density for this sample compared to other waste-modified coatings. Conversely, the tire rubber and activated carbon additives showed a severe negative impact on the thermal, mechanical, and barrier properties of the parent coating, which can be attributed to the high porosity and less integrated natures of these modified coating formulations proved by their morphological images. Still, all loaded waste additives to the hybrid coating have enhanced its adhesion to the steel surface, as indicated by scratch resistance testing. Overall, the results of the present study show the need for maintaining a balance between the economic value of the modification methodology of hybrid coatings and the type of the loaded waste material additive.
Advances in Polysaccharide Production Based on the Co-Culture of Microbes
Authors: Peng Wanrong , Guo Xueying , Xu Xinyi , Zou Dan , Zou Hang , Yang Xingyong
Published: June 2023

Microbial polysaccharides are natural carbohydrates that can confer adhesion capacity to cells and protect them from harsh environments. Due to their various physiological activities, these macromolecules are widely used in food, medicine, environmental, cosmetic, and textile applications. Microbial co-culture is an important strategy that is used to increase the production of microbial polysaccharides or produce new polysaccharides (structural alterations). This is achieved by exploiting the symbiotic/antagonistic/chemo-sensitive interactions between microbes and stimulating the expression of relevant silent genes. In this article, we review the performance of polysaccharides produced using microbial co-culture in terms of yield, antioxidant activity, and antibacterial, antitumor, and anti-inflammatory properties, in addition to the advantages and application prospects of co-culture. Moreover, the potential for microbial polysaccharides to be used in various applications is discussed.
Dimension Reduction and Classifier-Based Feature Selection for Oversampled Gene Expression Data and Cancer Classification
Authors: Petinrin Olutomilayo Olayemi , Saeed Faisal , Salim Naomie , Toseef Muhammad , Liu Zhe , Muyide Ibukun Omotayo
Published: June 2023

Gene expression data are usually known for having a large number of features. Usually, some of these features are irrelevant and redundant. However, in some cases, all features, despite being numerous, show high importance and contribute to the data analysis. In a similar fashion, gene expression data sometimes have limited instances with a high rate of imbalance among the classes. This can limit the exposure of a classification model to instances of different categories, thereby influencing the performance of the model. In this study, we proposed a cancer detection approach that utilized data preprocessing techniques such as oversampling, feature selection, and classification models. The study used SVMSMOTE for the oversampling of the six examined datasets. Further, we examined different techniques for feature selection using dimension reduction methods and classifier-based feature ranking and selection. We trained six machine learning algorithms, using repeated 5-fold cross-validation on different microarray datasets. The performance of the algorithms differed based on the data and feature reduction technique used.
Quantitative Characteristics of Micro Bedding Fractures in the Wufeng–Longmaxi Formation Based on High-Resolution Map Imaging Technology
Authors: Zhao Conghui , Wu Dong , Hu Fengbo , Sun Meng , Li Tao , Wang Hu
Published: June 2023

The study of microfractures in shale is mainly based on qualitative description. Conversely, quantitative description of the parameters of shale microfractures can provide a quantitative basis for shale fracture characterization and shale physical properties. Nine shale reservoir samples of the Wufeng–Longmaxi Formation in the Jiaoshiba area were studied, using the backscattered two-dimensional multiscale resolution imaging technology, combined with high-resolution map imaging technology (MAPS), and thousands of images were obtained using scanning electron microscopy. Gray image analysis was used to extract microfracture information from images (2 × 2 cm multiresolution). The “maximum circle method” was used to calculate the length and aperture characteristics of the fractures. Parameters such as the area of the bedding fractures, the surface rate of the fractures, and the linear density of the fractures were obtained by the integration of apertures. The fracture length was between 2~7 mm, the aperture was between 1~6 μm, the linear density was between 1~6/m and the surface rate was 1%. The bedding fractures do not contribute much to the porosity of the shale reservoir; however, shale reservoirs with high porosity have a high development of bedding fractures and good permeability. The development of a bedding fracture is controlled by the lithology within shale reservoirs. Different types of lithology contain different bedding fractures, but they have a certain regularity. Moreover, the content of organic matter and TOC (total organic content) in the shale reservoir control the development of a bedding fracture, where a high organic and TOC content are accompanied by a high number of fractures.
Minimising Leachate Wastewater Generated from NaOH-Catalysed Biodiesel Synthesis from Methanol
Authors: Pérez-Méndez Mario Alberto , Fraga-Cruz Guadalupe Selene , Jiménez-García Gladys , Maya-Yescas Rafael , Nápoles-Rivera Fabricio
Published: June 2023

Currently, energy generation based on fossil fuels is producing negative environmental impacts; two of the main symptoms of these impacts are water pollution and climate change. Consequently, the search for new technologies to satisfy the energy demand must have the goal to minimise possible impacts to the environment. There are alternatives with biofuels and, among them, biodiesel. The cheapest reaction pathway for biodiesel production is the transesterification of triglycerides by methanol in the presence of sodium hydroxide; however, this option can contaminate large volumes of water used in the final leach of the biodiesel product. Therefore, a feasible way of producing this biofuel while simultaneously minimising leaching water will be environmentally friendly and will improve economical savings. The present study developed an experimental design in order to minimise the addition of NaOH during biodiesel production by the basic homogeneous pathway. The best operating conditions were 46 °C, methanol in situ 7.5% v/v and NaOH 0.035 M. These conditions allowed to reduce the leaching water amount by 25% compared to techniques reported in the literature; however, the yield to biodiesel decreased from 98 wt.% to 87 wt.% when a model waste oil was used instead of virgin oil.
Fault Location of Distribution Network Based on Back Propagation Neural Network Optimization Algorithm
Authors: Zhou Chuan , Gui Suying , Liu Yan , Ma Junpeng , Wang Hao
Published: June 2023

Research on fault diagnosis and positioning of the distribution network (DN) has always been an important research direction related to power supply safety performance. The back propagation neural network (BPNN) is a commonly used intelligent algorithm for fault location research in the DN. To improve the accuracy of dual fault diagnosis in the DN, this study optimizes BPNN by combining the genetic algorithm (GA) and cloud theory. The two types of BPNN before and after optimization are used for single fault and dual fault diagnosis of the DN, respectively. The experimental results show that the optimized BPNN has certain effectiveness and stability. The optimized BPNN requires 25.65 ms of runtime and 365 simulation steps. And in diagnosis and positioning of dual faults, the optimized BPNN exhibits a higher fault diagnosis rate, with an accuracy of 89%. In comparison to ROC curves, the optimized BPNN has a larger area under the curve and its curve is smoother. The results confirm that the optimized BPNN has high efficiency and accuracy.
Human (Relational) Dignity: Perspectives of Followers of Indigenous Religions of Indonesia
Authors: Maarif Samsul
Published: June 2023

Religion and dignity are a two-sided coin for followers of Indigenous Religions in Indonesia. Dignity is the relationally inherent worth in both human and non-human beings. To give and receive dignity, one ought to be religious, which is to engage in interpersonal relationships with all beings, human and non-human. This article draws on data from two decades of engagement with followers of Indigenous Religions through extensive fieldwork, activism, and community service. It explores the distinctive worldviews and practices of Indonesian Indigenous Peoples, which many have maintained in the face of external incursions by governments, corporations, and missionaries, and internal encroachments from within their communities. Their worldviews spring from interrelational cosmology, which posits that relational dignity is a religious norm. This cosmology is institutionalized with adat (customary) systems that enact and reproduce relational dignity. The article concludes with a call to better understand and recognize Indigenous Religions by expanding the definition of religion to include the notion of relational dignity when considering how scholars and policymakers conceptualize and implement policies on freedom of religion or belief.
Prediction Aerospace Software to Detect Solar Activity and the Fast Tracking of Solar Activity Using Remote Sensing Instruments in Orbit
Authors: Shao Yingqiu , Yang Xiaohu , Li Zhanfeng , Huang Yu , Li Bo , Lin Guanyu , Guo Xu , Li Jifeng
Published: June 2023

At present, solar remote sensing instruments face problems regarding the short detection time for solar activity and the need to preheat the electronics before detection. The accurate orbit prediction function ensures that the maximum error of the payload electronics system preheating time is less than 8 s, enabling the electronics to obtain stable and reliable solar remote sensing data, and allowing the prediction function to adapt to the changes in preheating time and the limit position of turntable rotation. To achieve the complete detection of solar activity by a payload in orbit, a turntable with two directions of rotation was accurately controlled to achieve rapid capture and track the Sun’s activity, and the delay time for tracking the Sun successfully was less than 8 s to ensure that the payload made full use of the solar detection time for full detection. The turntable’s tracking error was less than 0.002°, allowing the payload to obtain high-precision solar remote sensing data in orbit. The above methods enable the payload to realize the complete, stable, and high-precision detection of solar activity in orbit for wavelengths ranging approximately from 165 nm to 2400 nm.
A Knowledge Graph-Driven CNN for Radar Emitter Identification
Authors: Chen Yingchao , Li Peng , Yan Erxing , Jing Zehuan , Liu Gaogao , Wang Zhao
Published: June 2023

In recent years, the rapid development of deep learning technology has brought new opportunities for specific emitter identification and has greatly improved the performance of radar emitter identification. The most specific emitter identification methods, based on deep learning, have focused more on studying network structures and data preprocessing. However, the data selection and utilization have a significant impact on the emitter recognition efficiency, and the method to adaptively determine the two parameters by a specific recognition model has yet to be studied. This paper proposes a knowledge graph-driven convolutional neural network (KG-1D-CNN) to solve this problem. The relationship network between radar data is modeled via the knowledge graph and uses 1D-CNN as the metric kernel to measure these relationships in the knowledge graph construction process. In the recognition process, a precise dataset is constructed based on the knowledge graph according to the task requirement. The network is designed to recognize target emitter individuals from easy to difficult by the precise dataset. In the experiments, most algorithms achieved good recognition results in the high SNR case (10–15 dB), while only the proposed method could achieve more than a 90% recognition rate in the low SNR case (0–5 dB). The experimental results demonstrate the efficacy of the proposed method.
Multi-Level Convolutional Network for Ground-Based Star Image Enhancement
Authors: Liu Lei , Niu Zhaodong , Li Yabo , Sun Quan
Published: June 2023

The monitoring of space debris is important for spacecraft such as satellites operating in orbit, but the background in star images taken by ground-based telescopes is relatively complex, including stray light caused by diffuse reflections from celestial bodies such as the Earth or Moon, interference from clouds in the atmosphere, etc. This has a serious impact on the monitoring of dim and small space debris targets. In order to solve the interference problem posed by a complex background, and improve the signal-to-noise ratio between the target and the background, in this paper, we propose a novel star image enhancement algorithm, MBS-Net, based on background suppression. Specifically, the network contains three parts, namely the background information estimation stage, multi-level U-Net cascade module, and recursive feature fusion stage. In addition, we propose a new multi-scale convolutional block, which can laterally fuse multi-scale perceptual field information, which has fewer parameters and fitting capability compared to ordinary convolution. For training, we combine simulation and real data, and use parameters obtained on the simulation data as pre-training parameters by way of parameter migration. Experiments show that the algorithm proposed in this paper achieves competitive performance in all evaluation metrics on multiple real ground-based datasets.
A Technique for Generating Preliminary Satellite Data to Evaluate SUHI Using Cloud Computing: A Case Study in Moscow, Russia
Authors: Le Minh Tuan , Bakaeva Natalia
Published: June 2023

The expansion of construction zones, transportation, and utilities for industry and high-tech areas due to human activities has caused the deterioration of the natural ecological environment. As cities face problems related to the surface urban heat island (SUHI) effect and environmental pollution, there is an urgent need to develop new methods for the ecological–microclimatic assessment and structural–functional planning of urban areas. The main goal of this study was to demonstrate the evolution of the surface urban heat island (SUHI) effect in Moscow over a long period and to determine the interaction between SUHIs and urban pollution islands (UPIs) using a geospatial analysis platform while optimizing vegetation classification with machine learning. Additionally, we are creating a digital database for modeling the sustainability of cities on the GEE platform using cloud computing. This study used cloud computing and remote sensing image analysis platforms for a 17-year temporal-series ecological–microclimatic assessment, which provided a sequence of values describing the ongoing process of changes in the ecological conditions of Moscow over time. Combining machine learning with the random forest algorithm (RF) improved vegetation classification accuracy while reducing computation time. The study findings demonstrated how the SUHI affected Moscow’s territory and showed the urban areas significantly impacted by this phenomenon. The locations of surface urban heat islands in Moscow and areas affected by SUHI and UPI were identified using numerical modeling of the urban thermal field variance index (UTFVI). From the findings, we identified the need to develop a new method for obtaining geospatial data for assessing the interaction between UPIs and SUHIs using cloud computing and mathematical data models.
Learning a Deep Attention Dilated Residual Convolutional Neural Network for Landslide Susceptibility Mapping in Hanzhong City, Shaanxi Province, China
Authors: Ma Yu , Xu Shenghua , Jiang Tao , Wang Zhuolu , Wang Yong , Liu Mengmeng , Li Xiaoyan , Ma Xinrui
Published: June 2023

The analysis and evaluation of landslide susceptibility are of great significance in preventing and managing geological hazards. Aiming at the problems of insufficient information caused by the limited number of landslide datasets, complex information of landslide evaluation factors, and low prediction accuracy of landslide susceptibility, a landslide susceptibility evaluation method based on the deep attention dilated residual convolutional neural network (DADRCNN) is proposed. First, the dilated convolution unit (DCU) is used to increase the network receptive field, aggregate multi-scale information, and enhance the model ability to capture the characteristics of landslide evaluation factors. Second, the deep residual module (DRM) is used to solve the issue of gradient disappearance and better extract data features by overlaying the residual function mapping layer and increasing the network depth. Finally, the channel attention residual module (CARM) is introduced to learn the varying importance of different landslide evaluation factors, and assign different weights to improve the susceptibility prediction accuracy. The experimental results show that the DADRCNN method can extract features around the sample points, expand the receptive field, and deeply mine the information. It mitigates the lack of sample information in training, focuses on important feature information, and significantly improves the prediction accuracy.
Early Detection of Wheat Yellow Rust Disease and Its Impact on Terminal Yield with Multi-Spectral UAV-Imagery
Authors: Nguyen Canh , Sagan Vasit , Skobalski Juan , Severo Juan Ignacio
Published: June 2023

The food production system is vulnerable to diseases more than ever, and the threat is increasing in an era of climate change that creates more favorable conditions for emerging diseases. Fortunately, scientists and engineers are making great strides to introduce farming innovations to tackle the challenge. Unmanned aerial vehicle (UAV) remote sensing is among the innovations and thus is widely applied for crop health monitoring and phenotyping. This study demonstrated the versatility of aerial remote sensing in diagnosing yellow rust infection in spring wheats in a timely manner and determining an intervenable period to prevent yield loss. A small UAV equipped with an aerial multispectral sensor periodically flew over, and collected remotely sensed images of, an experimental field in Chacabuco (−34.64; −60.46), Argentina during the 2021 growing season. Post-collection images at the plot level were engaged in a thorough feature-engineering process by handcrafting disease-centric vegetation indices (VIs) from the spectral dimension, and grey-level co-occurrence matrix (GLCM) texture features from the spatial dimension. A machine learning pipeline entailing a support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) was constructed to identify locations of healthy, mild infection, and severe infection plots in the field. A custom 3-dimensional convolutional neural network (3D-CNN) relying on the feature learning mechanism was an alternative prediction method. The study found red-edge (690–740 nm) and near infrared (NIR) (740–1000 nm) as vital spectral bands for distinguishing healthy and severely infected wheats. The carotenoid reflectance index 2 (CRI2), soil-adjusted vegetation index 2 (SAVI2), and GLCM contrast texture at an optimal distance d = 5 and angular direction θ = 135° were the most correlated features. The 3D-CNN-based wheat disease monitoring performed at 60% detection accuracy as early as 40 days after sowing (DAS), when crops were tillering, increasing to 71% and 77% at the later booting and flowering stages (100–120 DAS), and reaching a peak accuracy of 79% for the spectral-spatio-temporal fused data model. The success of early disease diagnosis from low-cost multispectral UAVs not only shed new light on crop breeding and pathology but also aided crop growers by informing them of a prevention period that could potentially preserve 3–7% of the yield at the confidence level of 95%.
Application of a Modified BPS Neural Network Based on Three-Way Decision Theory in an Effectiveness Evaluation for a Remote Sensing Satellite Cluster
Authors: Lei Ming , Dong Yunfeng , Li Zhi , Zhang Chao
Published: June 2023

The remote sensing satellite cluster system, as an important component of the next generation of space architecture in the United States, has important application prospects in the military field. In order to improve the effects of time, with regard to the effectiveness evaluation of the remote sensing satellite cluster system, neural network methods are generally used to satisfy the requirements of real-time decision-making assistance in the military field. However, there are two problems that emerge when applying the existing neural network methods to an effectiveness evaluation of the remote sensing satellite cluster. On the one hand, the neural network model architecture needs to be designed specifically for the remote sensing satellite cluster system. On the other hand, there is still a lack of hyperparameter optimization methods that consume less time and have good optimization effects for the established neural network model. In this regard, two main modifications were made to the back-propagation neural network, to which an effectiveness evaluation was applied. The first comprised a new architecture named BPS, which was designed for the back-propagation neural network so as to improve its prediction accuracy. In BP architecture, one back-propagation neural network is established for each indicator involved in the effectiveness evaluation indicator system of the remote sensing satellite cluster; the output of each back-propagation neural network model is modified to the residual value between the corresponding indicator value and the value that is predicted through a multiple linear regression analysis of the corresponding indicator. The second modification involved the multi-round traversal method, which is based on the three-way decision theory, and it was proposed in order to significantly improve the model’s training time, which is a new type of hyperparameter optimization method. The results show that compared with the traditional simulation model, the modified back-propagation neural network model based on three-way decision theory can quickly and effectively provide stable and accurate evaluation results; this can assist with and meet the requirements for real-time decision-making in the military field.
Vibration Detection and Degraded Image Restoration of Space Camera Based on Correlation Imaging of Rolling-Shutter CMOS
Authors: Liu Hailong , Lv Hengyi , Han Chengshan , Zhao Yuchen
Published: June 2023

To mitigate the influence of satellite platform vibrations on space camera imaging quality, a novel approach is proposed to detect vibration parameters based on correlation imaging of rolling-shutter CMOS. In the meantime, a restoration method to address the image degradation of rolling-shutter CMOS caused by such vibrations is proposed. The vibration parameter detection method utilizes the time-sharing and row-by-row imaging principle of rolling-shutter CMOS to obtain relative offset by comparing two frames of correlation images from continuous imaging. Then, the space camera’s vibration parameters are derived from the fitting curve parameters of the relative offset. According to the detected vibration parameters, the discrete point spread function is obtained, and the rolling-shutter CMOS image degradation caused by vibration is restored row by row. The verification experiments demonstrate that the proposed detection method for two-dimensional vibration achieves a relative accuracy of less than 1% in period detection and less than 2% in amplitude detection. Additionally, the proposed restoration method can enhance the MTF index by over 20%. The experimental results demonstrate that the detection method is capable of detecting high-frequency vibrations through low-frame-frequency image sequences, and it exhibits excellent applicability in both push-scan cameras and staring cameras. The restoration method effectively enhances the evaluation parameters of image quality and yields a remarkable restorative effect on degraded images.
Automatic Detection of Abnormal EEG Signals Using WaveNet and LSTM
Authors: Albaqami Hezam , Hassan Ghulam Mubashar , Datta Amitava
Published: June 2023

Neurological disorders have an extreme impact on global health, affecting an estimated one billion individuals worldwide. According to the World Health Organization (WHO), these neurological disorders contribute to approximately six million deaths annually, representing a significant burden. Early and accurate identification of brain pathological features in electroencephalogram (EEG) recordings is crucial for the diagnosis and management of these disorders. However, manual evaluation of EEG recordings is not only time-consuming but also requires specialized skills. This problem is exacerbated by the scarcity of trained neurologists in the healthcare sector, especially in low- and middle-income countries. These factors emphasize the necessity for automated diagnostic processes. With the advancement of machine learning algorithms, there is a great interest in automating the process of early diagnoses using EEGs. Therefore, this paper presents a novel deep learning model consisting of two distinct paths, WaveNet–Long Short-Term Memory (LSTM) and LSTM, for the automatic detection of abnormal raw EEG data. Through multiple ablation experiments, we demonstrated the effectiveness and importance of all parts of our proposed model. The performance of our proposed model was evaluated using TUH abnormal EEG Corpus V.2.0.0. (TUAB) and achieved a high classification accuracy of 88.76%, which is higher than in the existing state-of-the-art research studies. Moreover, we demonstrated the generalization of our proposed model by evaluating it on another independent dataset, TUEP, without any hyperparameter tuning or adjustment. The obtained accuracy was 97.45% for the classification between normal and abnormal EEG recordings, confirming the robustness of our proposed model.
Hybrid Shell-Beam Inverse Finite Element Method for the Shape Sensing of Stiffened Thin-Walled Structures: Formulation and Experimental Validation on a Composite Wing-Shaped Panel
Authors: Esposito Marco , Roy Rinto , Surace Cecilia , Gherlone Marco
Published: June 2023

This work presents a novel methodology for the accurate and efficient elastic deformation reconstruction of thin-walled and stiffened structures from discrete strains. It builds on the inverse finite element method (iFEM), a variationally-based shape-sensing approach that reconstructs structural displacements by matching a set of analytical and experimental strains in a least-squares sense. As iFEM employs the finite element framework to discretize the structural domain and as the displacements and strains are approximated using element shape functions, the kind of element used influences the accuracy and efficiency of the iFEM analysis. This problem is addressed in the present work through a novel discretization scheme that combines beam and shell inverse elements to develop an iFEM model of the structure. Such a hybrid discretization paradigm paves the way for more accurate shape-sensing of geometrically complex structures using fewer sensor measurements and lower computational effort than traditional approaches. The hybrid iFEM is experimentally demonstrated in this work for the shape sensing of bending and torsional deformations of a composite stiffened wing panel instrumented with strain rosettes and fiber-optic sensors. The experimental results are accurate, robust, and computationally efficient, demonstrating the potential of this hybrid scheme for developing an efficient digital twin for online structural monitoring and control.
Ratio Indexes Based on Spectral Electroencephalographic Brainwaves for Assessment of Mental Involvement: A Systematic Review
Authors: Marcantoni Ilaria , Assogna Raffaella , Del Borrello Giulia , Di Stefano Marina , Morano Martina , Romagnoli Sofia , Leoni Chiara , Bruschi Giulia , Sbrollini Agnese , Morettini Micaela , Burattini Laura
Published: June 2023

Background: This review systematically examined the scientific literature about electroencephalogram-derived ratio indexes used to assess human mental involvement, in order to deduce what they are, how they are defined and used, and what their best fields of application are. (2) Methods: The review was carried out according to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. (3) Results: From the search query, 82 documents resulted. The majority (82%) were classified as related to mental strain, while 12% were classified as related to sensory and emotion aspects, and 6% to movement. The electroencephalographic electrode montage used was low-density in 13%, high-density in 6% and very-low-density in 81% of documents. The most used electrode positions for computation of involvement indexes were in the frontal and prefrontal cortex. Overall, 37 different formulations of involvement indexes were found. None of them could be directly related to a specific field of application. (4) Conclusions: Standardization in the definition of these indexes is missing, both in the considered frequency bands and in the exploited electrodes. Future research may focus on the development of indexes with a unique definition to monitor and characterize mental involvement.
MineSDS: A Unified Framework for Small Object Detection and Drivable Area Segmentation for Open-Pit Mining Scenario
Authors: Liu Yong , Li Cheng , Huang Jiade , Gao Ming
Published: June 2023

To tackle the challenges posed by dense small objects and fuzzy boundaries on unstructured roads in the mining scenario, we proposed an end-to-end small object detection and drivable area segmentation framework for open-pit mining. We employed a convolutional network backbone as a feature extractor for both two tasks, as multi-task learning yielded promising results in autonomous driving perception. To address small object detection, we introduced a lightweight attention module that allowed our network to focus more on the spatial and channel dimensions of small objects without impeding inference time. We also used a convolutional block attention module in the drivable area segmentation subnetwork, which assigned more weight to road boundaries to improve feature mapping capabilities. Furthermore, to improve our network perception accuracy of both tasks, we used weighted summation when designing the loss function. We validated the effectiveness of our approach by testing it on pre-collected mining data which were called Minescape. Our detection results on the Minescape dataset showed 87.8% mAP index, which was 9.3% higher than state-of-the-art algorithms. Our segmentation results surpassed the comparison algorithm by 1 percent in MIoU index. Our experimental results demonstrated that our approach achieves competitive performance.
Task-Incremental Learning for Drone Pilot Identification Scheme
Authors: Han Liyao , Zhong Xiangping , Zhang Yanning
Published: June 2023

With the maturity of Unmanned Aerial Vehicle (UAV) technology and the development of Industrial Internet of Things, drones have become an indispensable part of intelligent transportation systems. Due to the absence of an effective identification scheme, most commercial drones suffer from impersonation attacks during their flight procedure. Some pioneering works have already attempted to validate the pilot’s legal status at the beginning and during the flight time. However, the off-the-shelf pilot identification scheme can not adapt to the dynamic pilot membership management due to a lack of extensibility. To address this challenge, we propose an incremental learning-based drone pilot identification scheme to protect drones from impersonation attacks. By utilizing the pilot temporal operational behavioral traits, the proposed identification scheme could validate pilot legal status and dynamically adapt newly registered pilots into a well-constructed identification scheme for dynamic pilot membership management. After systemic experiments, the proposed scheme was capable of achieving the best average identification accuracy with 95.71% on P450 and 94.23% on S500. With the number of registered pilots being increased, the proposed scheme still maintains high identification performance for the newly added and the previously registered pilots. Owing to the minimal system overhead, this identification scheme demonstrates high potential to protect drones from impersonation attacks.
Context-Aware Statistical Dead Reckoning for Localization in IoT Scenarios
Authors: Munoz-Rodriguez David , Villalpando-Hernandez Rafaela , Vargas-Rosales Cesar
Published: June 2023

The current trends in 5G and 6G systems anticipate vast communication capabilities and the deployment of massive heterogeneous connectivity with more than a million internet of things (IoT) and other devices per square kilometer and up to ten million gadgets in 6G scenarios. In addition, the new generation of smart industries and the energy of things (EoT) context demand novel, reliable, energy-efficient network protocols involving massive sensor cooperation. Such scenarios impose new demands and opportunities to cope with the ever-growing cooperative dense ad hoc environments. Position location information (PLI) plays a crucial role as an enabler of several location-aware network protocols and applications. In this paper, we have proposed a novel context-aware statistical dead reckoning localization technique suitable for high dense cooperative sensor networks, where direct angle and distance estimations between peers are not required along the route, as in other dead reckoning-based localization approaches, but they are obtainable from the node’s context information. Validation of the proposed technique was assessed in several scenarios through simulations, achieving localization errors as low as 0.072 m for the worst case analyzed.
Impact of Compressional Force, Croscarmellose Sodium, and Microcrystalline Cellulose on Black Pepper Extract Tablet Properties Based on Design of Experiments Approach
Authors: Monton Chaowalit , Wunnakup Thaniya , Suksaeree Jirapornchai , Charoenchai Laksana , Chankana Natawat
Published: June 2023

This study aimed to prepare tablets of black pepper extract using the Design of Experiments (DOE) approach. The levels of three factors—compressional force, croscarmellose sodium (CCS), and microcrystalline cellulose (MCC)—were screened using the one-factor-at-a-time technique, followed by the DOE utilizing the Box–Behnken design. The respective variations for each factor were as follows: compressional force (1500–2500 psi), CCS (1–3%), and MCC (32–42%). The results indicated that compressional force significantly decreased tablet thickness and friability, while increasing hardness and prolonging disintegration time. CCS significantly shortened disintegration time but did not affect tablet thickness, hardness, and friability. MCC, on the other hand, significantly increased tablet thickness and hardness, while significantly decreasing friability. Furthermore, the study observed interactions among factors and quadratic effects of each factor, which significantly influenced tablet properties. The optimal tablet formulation consisted of 2.2% CCS, 37% MCC, and a compressional force of 2000 psi. These tablets had a weight of 198.39 ± 0.49 mg, a diameter of 9.67 ± 0.01 mm, a thickness of 1.98 ± 0.02 mm, a hardness of 7.36 ± 0.24 kP, a friability of 0.11 ± 0.02%, and a disintegration time of 5.59 ± 0.39 min. The actual values obtained using the optimal conditions closely matched the predicted values, with a low percent error (less than 5%). In conclusion, the application of the DOE approach successfully developed tablets of black pepper extract, which can be utilized as food supplement products.
Exploring Migrant Students’ Attitudes towards Their Multilingual Identities through Language Portraits
Authors: Stavrakaki Antonia , Manoli Peggy
Published: June 2023

The increasing linguistic and cultural diversity in contemporary societies inevitably affects the field of education by challenging teachers to cope with the coexistence of different languages in the classroom. The present research was intended to investigate migrant children’s attitudes towards languages through language portraits in order to help educators obtain insights into student multilingual repertoires and experiences. To this end, by adopting a qualitative approach, the study used linguistic portraits and semi-structured interviews to collect the data. The participants of the study were 10 primary school children whose ages ranged from 8 to 12 with a migrant background who have been living in Greece, particularly on the island of Crete. Using the method of content analysis, the findings of the study indicated that migrant children made specific color choices based on flags, emotions, and world experience, and they put colors on parts of the body according to their functions, which signified students’ multilingual identities and experiences. Moreover, the findings highlighted multilingual students’ need to negotiate their multiple linguistic repertoires, make choices between the languages, prioritize them, rank them, or give priority to the second language, Greek, without, however, abandoning their first languages. The present study aspires to contribute to the relevant research and draws implications for the implementation of multilingual education and culturally sustaining pedagogies.
Functional Ego States, Behavior Patterns, and Social Interaction of Bulgarian Adolescents and Their Parents
Authors: Bekir Sezgin , Tair Ergyul
Published: June 2023

Adolescence is a dynamic period of transition, and interaction with parents is important for successfully passing through it. This article presents results from a study of three transactional analysis theory personality constructs of adolescents and their parents—functional ego states, life positions, and stroke economy. The sample included 215 students from 12 to 16 years old and 215 parents from 30 to 55 years old. The results show significant differences between the ego states Adult, Adapted Child, Nurturing Parent and Normative Parent of the adolescents and their parents, which are more expressed in the parents. It was found that the life position, “I am OK—you are OK” is more strongly expressed in parents, while the life position, “I am not OK—you are not OK” is leading in adolescents. In their interactions with others, adolescents express modesty, self-doubt, and underestimation of their own success, while parents are more assertive, confident, and resilient in their behavior. The obtained results can be used in different programs to improve social interaction and increase the effectiveness of adolescent-parent relationships.
Effect of Whey Protein Concentrate on Rheological Properties of Gluten-Free Doughs and Their Performance in Cookie Applications
Authors: Zhu Lijia , Snider Luke , Vu Thanh Hien , Desam Gnana Prasuna , Herald Tomas J. , Dogan Hulya , Khaled Alfadhl Y. , Adedeji Akinbode A. , Alavi Sajid
Published: June 2023

Gluten-free foods continue to be a hot topic and trend in the food market because more people are being diagnosed with gluten intolerance. Whey is a by- or co-product of the dairy industry and is considered a waste stream. In this study, whey protein concentrate (WPC), one of the whey products, was added at 8, 9, 10, 11 and 12% levels to sorghum and corn flours to make gluten-free products in the form of cookies. Mixograph and subjective evaluation showed that optimal water absorption (corn: 50–55%; sorghum: 55–60%) increased with increasing WPC level in both sorghum and corn flour dough systems. Increasing WPC from 8 to 12% resulted in a decrease in storage modulus (G’) and loss modulus (G’’) for both sorghum and corn doughs. Corn dough rheological properties were less affected by WPC addition as compared to sorghum. The diameter of gluten-free sorghum and corn cookies significantly increased with the fortification of WPC. The color of sorghum and corn cookies became darker as the WPC level increased. Cookies prepared with 10% WPC addition showed the most hardness and brittleness, probably due to the gelling property of WPC. This study contributes to the sustainable utilization of whey product and helps understand the performance of WPC during the processing of gluten-free products and its potential for making food snacks such as cookies in food manufacturing.
“Future Compass”, a Tool That Allows Us to See the Right Horizon—Integration of Topic Modeling and Multiple-Factor Analysis
Authors: Sugino Hiroaki , Sekiguchi Tatsuya , Terada Yuuki , Hayashi Naoki
Published: June 2023

Coastal social–ecological systems (SES), particularly in large bays, are critical for fisheries, transportation, and disaster prevention in island and coastal countries. To achieve the sustainability of such bays, public involvement is recently considered inevitable for planning and management, but the increasing complexity of variables and future visions to be considered is one difficulty when trying to include many stakeholders and public opinions. To address this challenge, a free-associative description questionnaire survey was used in this study to extract holistic coastal residents’ future visions for Tokyo Bay, including both positive and negative outcomes. By integrating biterm topic modeling (BTM) and multiple-factor analysis (MFA), this study succeeded to aggregate and visualize the various future visions of Tokyo Bay with enhanced comprehensibility. As one outcome, the linkages and differences between the major topics in the positive and negative future visions were visualized as vectors in a correlation circle. Also, the study found that these two kinds of future vectors are not always polar opposites, but, rather, some of them are interlinked, pointing in the same direction. This highlights the importance of measuring the balance between two kinds of future vectors in consensus-building in order to search for the optimal future direction. Finally, the study discusses the potential of this method as a “Future Compass”, for implementing future-oriented consensus-building toward the sustainability of SES.