bluuebunny/crossref_metadata_2025 huggingface dataset:
A Cloud Water Path-Based Model for Cloudy-Sky Downward Longwave Radiation Estimation from FY-4A Data
Authors: Yu Shanshan , Xin Xiaozhou , Zhang Hailong , Li Li , Zhu Lin , Liu Qinhuo
Published: November 2023

Clouds are a critical factor in regulating the climate system, and estimating cloudy-sky Surface Downward Longwave Radiation (SDLR) from satellite data is significant for global climate change research. The models based on cloud water path (CWP) are less affected by cloud parameter uncertainties and have superior accuracy in SDLR satellite estimation when compared to those empirical and parameterized models relying mainly on cloud fraction or cloud-base temperature. However, existing CWP-based models tend to overestimate the low SDLR values and underestimate the larger SDLR. This study found that this phenomenon was caused by the fact that the models do not account for the varying relationships between cloud radiative effects and key parameters under different Liquid Water Path (LWP) and Precipitable Water Vapor (PWV) ranges. Based upon this observation, this study utilized Fengyun-4A (FY-4A) cloud parameters and ERA5 data as data sources to develop a new CWP-based model where the model coefficients depend on the cloud phase and cloud water path range. The accuracy of the new model’s estimated SDLR is 20.8 W/m2 for cloudy pixels, with accuracies of 19.4 W/m2 and 23.5 W/m2 for overcast and partly cloudy conditions, respectively. In contrast, the accuracy of the old CWP-based model was 22.4, 21.2, and 24.8 W/m2, respectively. The underestimation and overestimation present in the old CWP-based model are effectively corrected by the new model. The new model exhibited higher accuracy under various station locations, cloud cover scenarios, and cloud phase conditions compared to the old one. Comparatively, the new model showcased its most remarkable improvements in situations involving overcast conditions, water clouds with low PWV and low LWP values, ice clouds with large PWV, and conditions with PWV ≥ 5 cm. Over a temporal scale, the new model effectively captured the seasonal variations in SDLR.
CCRANet: A Two-Stage Local Attention Network for Single-Frame Low-Resolution Infrared Small Target Detection
Authors: Wang Wenjing , Xiao Chengwang , Dou Haofeng , Liang Ruixiang , Yuan Huaibin , Zhao Guanghui , Chen Zhiwei , Huang Yuhang
Published: November 2023

Infrared small target detection technology is widely used in infrared search and tracking, infrared precision guidance, low and slow small aircraft detection, and other projects. Its detection ability is very important in terms of finding unknown targets as early as possible, warning in time, and allowing for enough response time for the security system. This paper combines the target characteristics of low-resolution infrared small target images and studies the infrared small target detection method under a complex background based on the attention mechanism. The main contents of this paper are as follows: (1) by sorting through and expanding the existing datasets, we construct a single-frame low-resolution infrared small target (SLR-IRST) dataset and evaluate the existing datasets on three aspects—target number, target category, and target size; (2) to improve the pixel-level metrics of low-resolution infrared small target detection, we propose a small target detection network with two stages and a corresponding method. Regarding the SLR-IRST dataset, the proposed method is superior to the existing methods in terms of pixel-level metrics and target-level metrics and has certain advantages in model processing speed.
Research on Prediction of Surface Deformation in Mining Areas Based on TPE-Optimized Integrated Models and Multi-Temporal InSAR
Authors: Long Sichun , Liu Maoqi , Xiong Chaohui , Li Tao , Wu Wenhao , Ding Hongjun , Zhang Liya , Zhu Chuanguang , Lu Shide
Published: November 2023

The prevailing research on forecasting surface deformations within mining territories predominantly hinges on parameter-centric numerical models, which manifest constraints concerning applicability and parameter reliability. Although Multi-Temporal InSAR (MT-InSAR) technology furnishes an abundance of data, the underlying information within these data has yet to be fully unearthed. Consequently, this paper advocates a novel methodology for prognosticating mining area surface deformation by integrating ensemble learning with MT-InSAR technology. Initially predicated upon the MT-InSAR monitoring outcomes, the target variables for the ensemble learning dataset were procured by melding distance-based features with spatial autocorrelation theory. In the ensuing phase, spatial stratified sampling alongside mutual information methodologies were deployed to select the features of the dataset. Utilizing the MT-InSAR monitoring data from the Zixing coal mine in Hunan, China, the relationship between fault slippage and coal extraction in the study area was rigorously analyzed using Granger causality tests and Johansen cointegration assays, thereby acquiring the dataset requisite for training the Bagging model. Subsequently, leveraging the Bagging technique, ensemble models were constructed employing Decision Trees, Support Vector Regression, and Multi-layer Perceptron as foundational estimators. Furthermore, the Tree-structured Parzen Estimator (TPE) optimization algorithm was applied to the Bagging model, resulting in an optimal model for predicting fault slip in mining areas. In comparison with the baseline model, the performance increased by 25.88%, confirming the effectiveness of the data preprocessing method outlined in this study. This result also demonstrates the innovation and feasibility of combining ensemble learning with MT-InSAR technology for predicting mining area surface deformation. This investigation is the first to integrate TPE-optimized ensemble models with MT-InSAR technology, offering a new perspective for predicting surface deformation in mining territories and providing valuable insights for further uncovering the hidden information in MT-InSAR monitoring data.
A Flexible PDMS-Based Optical Biosensor for Stretch Monitoring in Cardiac Tissue Samples
Authors: Sannino Andrea , Velarte Antonio , Otín Aránzazu , Artigas José Ignacio , Oliván-Viguera Aida
Published: November 2023

Cardiotoxicity, characterized by adverse impacts on normal heart function due to drug exposure, is a significant concern due to the potentially serious side effects associated with various pharmaceuticals. It is essential to detect the cardiotoxicity of a drug as early as possible in the testing phase of a medical composite. Therefore, there is a pressing need for more reliable in vitro models that accurately mimic the in vivo conditions of cardiac biopsies. In a functional beating heart, cardiac muscle cells are under the effect of static and cyclic stretches. It has been demonstrated that cultured cardiac biopsies can benefit from external mechanical loads that resemble the in vivo condition, increasing the probability of cardiotoxicity detection in the early testing stages. In this work, a biosensor is designed and fabricated to allow for stretch monitoring in biopsies and tissue cultures using an innovative sensing mechanism. The detection setup is based on a biocompatible, thin, flexible membrane—where the samples are attached—which is used as an optical waveguide to detect pressure-caused shape changes and stretches. Various prototypes have been fabricated with a cost-effective process, and different measurements have been carried out to experimentally validate the proposed measurement technique. From these evaluations, stretches of up to 1.5% have been measured, but the performed simulations point towards the possibility of expanding the considered technique up to 10–30% stretches.
New Approach Based on Pix2Pix–YOLOv7 mmWave Radar for Target Detection and Classification
Authors: Lamane Mohamed , Tabaa Mohamed , Klilou Abdessamad
Published: November 2023

Frequency modulated continuous wave (FMCW) radar is increasingly used for various detection and classification applications in different fields, such as autonomous vehicles and mining fields. Our objective is to increase the classification accuracy of objects detected using millimeter-wave radar. We have developed an approach based on millimeter-wave radar. The proposed solution combines the use of an FMCW radar, a YOLOv7 model, and the Pix2Pix architecture. The latter architecture was used to reduce noise in the heatmaps. We create a dataset of 4125 heatmaps annotated with five different object classes. To evaluate the proposed approach, 14 different models were trained using the annotated heatmap dataset. In the initial experiment, we compared the models using metrics such as mean average precision (mAP), precision, and recall. The results showed that the proposed model of YOLOv7 (YOLOv7-PM) was the most efficient in terms of mAP_0.5, which reached 90.1%, and achieved a mAP_0.5:0.95 of 49.51%. In the second experiment, we compared the models with a cleaned dataset generated using the Pix2Pix architecture. As a result, we observed improved performances, with the Pix2Pix + YOLOv7-PM model achieving the best mAP_0.5, reaching 91.82%, and a mAP_0.5:0.95 of 52.59%.
A Multi-Scale Recursive Attention Feature Fusion Network for Image Super-Resolution Reconstruction Algorithm
Authors: Han Xiaowei , Wang Lei , Wang Xiaopeng , Zhang Pengchao , Xu Haoran
Published: November 2023

In recent years, deep convolutional neural networks (CNNs) have made significant progress in single-image super-resolution (SISR) tasks. Despite their good performance, the single-image super-resolution task remains a challenging one due to problems with underutilization of feature information and loss of feature details. In this paper, a multi-scale recursive attention feature fusion network (MSRAFFN) is proposed for this purpose. The network consists of three parts: a shallow feature extraction module, a multi-scale recursive attention feature fusion module, and a reconstruction module. The shallow features of the image are first extracted by the shallow feature extraction module. Then, the feature information at different scales is extracted by the multi-scale recursive attention feature fusion network block (MSRAFFB) to enhance the channel features of the network through the attention mechanism and fully fuse the feature information at different scales in order to improve the network’s performance. In addition, the image features at different levels are integrated through cross-layer connections using residual connections. Finally, in the reconstruction module, the upsampling capability of the deconvolution module is used to enlarge the image while extracting its high-frequency information in order to obtain a sharper high-resolution image and achieve a better visual effect. Through extensive experiments on a benchmark dataset, the proposed network model is shown to have better performance than other models in terms of both subjective visual effects and objective evaluation metrics.
Robust Time-of-Arrival Location Estimation Algorithms for Wildlife Tracking
Authors: Arnon Eitam , Cain Shlomo , Uzan Assaf , Nathan Ran , Spiegel Orr , Toledo Sivan
Published: November 2023

Time-of-arrival transmitter localization systems, which use measurements from an array of sensors to estimate the location of a radio or acoustic emitter, are now widely used for tracking wildlife. Outlier measurements can severely corrupt estimated locations. This article describes a new suite of location estimation algorithms for such systems. The new algorithms detect and discard outlier time-of-arrival observations, which can be caused by non-line-of-sight propagation, radio interference, clock glitches, or an overestimation of the signal-to-noise ratio. The new algorithms also detect cases in which two locations are equally consistent with measurements and can usually select the correct one. The new algorithms can also infer approximate altitude information from a digital elevation map to improve location estimates close to one of the sensors. Finally, the new algorithms approximate the covariance matrix of location estimates in a simpler and more reliable way than the baseline algorithm. Extensive testing on real-world data involving mobile transmitters attached to wild animals demonstrates the efficacy of the new algorithms. Performance testing also shows that the new algorithms are fast and that they can easily cope with high-throughput real-time loads.
Deep Learning-Based Automatic Modulation Classification Using Robust CNN Architecture for Cognitive Radio Networks
Authors: Abd-Elaziz Ola Fekry , Abdalla Mahmoud , Elsayed Rania A.
Published: November 2023

Automatic modulation classification (AMC) is an essential technique in intelligent receivers of non-cooperative communication systems such as cognitive radio networks and military applications. This article proposes a robust automatic modulation classification model based on a new architecture of a convolutional neural network (CNN). The basic building convolutional blocks of the proposed model include asymmetric kernels organized in parallel combinations to extract more meaningful and powerful features from the raw I/Q sequences of the received signals. These blocks are connected via skip connection to avoid vanishing gradient problems. The experimental results reveal that the proposed model performs well in classifying nine different modulation schemes simulated with different real wireless channel impairments, including AWGN, Rician multipath fading, and clock offset. The performance of the proposed system systems shows that it outperforms its best rivals from the literature in recognizing the modulation type. The proposed CNN architecture remarkably improves classification accuracy at low SNRs, which is appropriate in realistic scenarios. It achieves 86.1% accuracy at −2 dB SNR. Furthermore, it reaches an accuracy of 96.5% at 0 dB SNR and 99.8% at 10 dB SNR. The proposed architecture has strong feature extraction abilities that can effectively recognize 16QAM and 64QAM signals, the challenging modulation schemes of the same modulation family, with an overall average accuracy of 81.02%.
Research on Positioning Accuracy of Mobile Robot in Indoor Environment Based on Improved RTABMAP Algorithm
Authors: Zhou Shijie , Li Zelun , Lv Zhongliang , Zhou Chuande , Wu Pengcheng , Zhu Changshuang , Liu Wei
Published: November 2023

Visual simultaneous localization and mapping is a widely used technology for mobile robots to carry out precise positioning in the environment of GNSS technology failure. However, as the robot moves around indoors, its position accuracy will gradually decrease over time due to common and unavoidable environmental factors. In this paper, we propose an improved method called RTABMAP-VIWO, which is based on RTABMAP. The basic idea is to use an Extended Kalman Filter (EKF) framework for fusion attitude estimates from the wheel odometry and IMU, and provide new prediction values. This helps to reduce the local cumulative error of RTABMAP and make it more accurate. We compare and evaluate three kinds of SLAM methods using both public datasets and real indoor scenes. In the dataset experiments, our proposed method reduces the Root-Mean-Square Error (RMSE) coefficient by 48.1% compared to the RTABMAP, and the coefficient is also reduced by at least 29.4% in the real environment experiments. The results demonstrate that the improved method is feasible. By incorporating the IMU into the RTABMAP method, the trajectory and posture errors of the mobile robot are significantly improved.
A Telechelic Fluorescent Indicator Based on Polymer Conformational Change for Free Copper(II) Ions
Authors: Chen Yuan , Si Bo , Cote Noah , Planalp Roy P. , Seitz Rudi
Published: November 2023

A novel copper(II) ion indicator based on polymer conformational change is designed and its chemo-response to the target analyte is tested in this paper. The word ‘telechelic’ in the title means that a polymer has two different fluorophores on either end. If one of them is a fluorescent donor and the other is a fluorescent acceptor, then the extent of Foerster resonance energy transfer (FRET) will depend on polymer conformation. The sensitivity of these sensors is tunable based on the chain length and the amount of the receptor on the polymer. This is revealed by the fluorescence response of 30mer, 50mer, and 100mer of poly(N-isopropyl)acrylamide with different amounts of metal chelation monomers. We also address the change in fluorescence over time due to the untangling of poly(N-isopropylacrylamide) in water. The fluorescent signal can maintain stability after metal binding. The photoluminescence results agree with the length calculation of polyelectrolytes. A fluorescent standard curve is created for the measurement of different concentrations of copper ions. The sensing limit can reach 10−10 M analytes, which is suitable for the measurement of chemicals in trace amounts in the environment.
The Challenges and Advantages of Distributed Fiber Optic Strain Monitoring in and on the Cementitious Matrix of Concrete Beams
Authors: Weisbrich Martin , Messerer Dennis , Holschemacher Klaus
Published: November 2023

Distributed fiber optic strain measurement techniques have become increasingly important in recent years, especially in the field of structural health monitoring of reinforced concrete structures. Numerous publications show the various monitoring possibilities from bridges to special heavy structures. The present study is intended to demonstrate the possibilities, but also the challenges, of distributed fiber optic strain measurement in reinforced concrete structures. For this purpose, concrete beams for 3-point bending tests were equipped with optical fibers on the reinforcement and concrete surface as well as in the concrete matrix in order to record the strains in the compression and tension zone. In parallel, an analytical approach based on the maximum strains in the uncracked and cracked states was performed using the Eurocode 2 interpolation coefficient. In principle, the structural design correlates with the measured values, but the strains are underestimated, especially in the cracked zone. During load increase, structural distortions in the compression zone affected the strain signal, making reliable evaluation in this zone difficult. The information content of distributed fiber optic strain measurement in reinforced concrete structures can offer tremendous opportunities. Future research should consider all aspects of the bond, sensor selection and positioning. In addition, there is a lack of information on the long-term stability of the joint and the fiber coating, as well as the effects of dynamic loading.
Influence of Accelerometer Sensor Position for Measurement of Lateral Acceleration of Delivery Van for Cargo Securement
Authors: Jagelcak Juraj , Kubanova Jaroslava
Published: November 2023

The use of sensors in monitoring lateral accelerations in delivery van transport focuses on measuring lateral accelerations on routes with roundabouts and curves to increase road safety. Using microelectromechanical system (MEMS) sensors, it measures the lateral accelerations acting on the vehicle and the load being transported during the test drives to study vehicle dynamics of delivery van for cargo securing, which is essential to the decision of where accelerometer sensors should be placed when monitoring accelerations or performing cargo securing tests. Using an accelerometer and position tracking, accelerations can be detected when traversing curves and roundabouts at selected locations on the vehicle and load. Manual labeling of acceleration events has been used to identify different lateral acceleration events and regression analysis to determine the relationship between lateral accelerations at different sensor positions. The level of acceleration on the roof of the vehicle was found to be like that occurring on a lashed load with limited movements. If we compare the mean values of the lateral accelerations of the individual events between the sensors, the sensor on the side of the vehicle body at the height of the sensor on the load had approximately 5% lower mean values than the sensor on the roof. The sensor on the load measured approximately 5% higher mean values than the sensor on the roof. Hence, the mean lateral accelerations of the individual events for the sensor on the load are 10% higher than for the sensor at the same height on the vehicle body. The values of the mean lateral accelerations of the delivery van from the sensor on the roof of the vehicle are closer to the values of the accelerations of the sensor on the load than to the values of the sensor on the body of the vehicle at the same height.
Dynamic and Real-Time Object Detection Based on Deep Learning for Home Service Robots
Authors: Ye Yangqing , Ma Xiaolon , Zhou Xuanyi , Bao Guanjun , Wan Weiwei , Cai Shibo
Published: November 2023

Home service robots operating indoors, such as inside houses and offices, require the real-time and accurate identification and location of target objects to perform service tasks efficiently. However, images captured by visual sensors while in motion states usually contain varying degrees of blurriness, presenting a significant challenge for object detection. In particular, daily life scenes contain small objects like fruits and tableware, which are often occluded, further complicating object recognition and positioning. A dynamic and real-time object detection algorithm is proposed for home service robots. This is composed of an image deblurring algorithm and an object detection algorithm. To improve the clarity of motion-blurred images, the DA-Multi-DCGAN algorithm is proposed. It comprises an embedded dynamic adjustment mechanism and a multimodal multiscale fusion structure based on robot motion and surrounding environmental information, enabling the deblurring processing of images that are captured under different motion states. Compared with DeblurGAN, DA-Multi-DCGAN had a 5.07 improvement in Peak Signal-to-Noise Ratio (PSNR) and a 0.022 improvement in Structural Similarity (SSIM). An AT-LI-YOLO method is proposed for small and occluded object detection. Based on depthwise separable convolution, this method highlights key areas and integrates salient features by embedding the attention module in the AT-Resblock to improve the sensitivity and detection precision of small objects and partially occluded objects. It also employs a lightweight network unit Lightblock to reduce the network’s parameters and computational complexity, which improves its computational efficiency. Compared with YOLOv3, the mean average precision (mAP) of AT-LI-YOLO increased by 3.19%, and the detection precision of small objects, such as apples and oranges and partially occluded objects, increased by 19.12% and 29.52%, respectively. Moreover, the model inference efficiency had a 7 ms reduction in processing time. Based on the typical home activities of older people and children, the dataset Grasp-17 was established for the training and testing of the proposed method. Using the TensorRT neural network inference engine of the developed service robot prototype, the proposed dynamic and real-time object detection algorithm required 29 ms, which meets the real-time requirement of smooth vision.
WiCHORD+: A Scalable, Sustainable, and P2P Chord-Based Ecosystem for Smart Agriculture Applications
Authors: Balatsouras Christos-Panagiotis , Karras Aristeidis , Karras Christos , Karydis Ioannis , Sioutas Spyros
Published: November 2023

In the evolving landscape of Industry 4.0, the convergence of peer-to-peer (P2P) systems, LoRa-enabled wireless sensor networks (WSNs), and distributed hash tables (DHTs) represents a major advancement that enhances sustainability in the modern agriculture framework and its applications. In this study, we propose a P2P Chord-based ecosystem for sustainable and smart agriculture applications, inspired by the inner workings of the Chord protocol. The node-centric approach of WiCHORD+ is a standout feature, streamlining operations in WSNs and leading to more energy-efficient and straightforward system interactions. Instead of traditional key-centric methods, WiCHORD+ is a node-centric protocol that is compatible with the inherent characteristics of WSNs. This unique design integrates seamlessly with distributed hash tables (DHTs), providing an efficient mechanism to locate nodes and ensure robust data retrieval while reducing energy consumption. Additionally, by utilizing the MAC address of each node in data routing, WiCHORD+ offers a more direct and efficient data lookup mechanism, essential for the timely and energy-efficient operation of WSNs. While the increasing dependence of smart agriculture on cloud computing environments for data storage and machine learning techniques for real-time prediction and analytics continues, frameworks like the proposed WiCHORD+ appear promising for future IoT applications due to their compatibility with modern devices and peripherals. Ultimately, the proposed approach aims to effectively incorporate LoRa, WSNs, DHTs, cloud computing, and machine learning, by providing practical solutions to the ongoing challenges in the current smart agriculture landscape and IoT applications.
Cooperating and Competing Digital Twins for Industrie 4.0 in Urban Planning Contexts
Authors: Herzog Otthein , Jarke Matthias , Wu Siegfried Zhiqiang
Published: November 2023

Digital twins are emerging as a prime analysis, prediction, and control concepts for enabling the Industrie 4.0 vision of cyber-physical production systems (CPPSs). Today’s growing complexity and volatility cannot be handled by monolithic digital twins but require a fundamentally decentralized paradigm of cooperating digital twins. Moreover, societal trends such as worldwide urbanization and growing emphasis on sustainability highlight competing goals that must be reflected not just in cooperating but also competing digital twins, often even interacting in “coopetition”. This paper argues for multi-agent systems (MASs) to address this challenge, using the example of embedding industrial digital twins into an urban planning context. We provide a technical discussion of suitable MAS frameworks and interaction protocols; data architecture options for efficient data supply from heterogeneous sensor streams and sovereignty in data sharing; and strategic analysis for scoping a digital twin systems design among domain experts and decision makers. To illustrate the way still in front of research and practice, the paper reviews some success stories of MASs in Industrie/Logistics 4.0 settings and sketches a comprehensive vision for digital twin-based holistic urban planning.
Improving the Efficiency of Cement Mortar to Immobilize Sulfate in Industrial Wastewater Using Different Nanoparticles
Authors: Zheng Yuxia , Guo Mingke , Zhang Xin , Xia Zehua , Zhao Juan , Shi Siyu
Published: November 2023

The disposal of industrial wastewater (IWW) discharged from factories is a significant topic in the environment field, and the use of cement-based materials is a useful way to treat materials with unexpected ions. In this work, IWW with abundant SO42− collected from a factory was utilized to prepare cement mortar (IWWCM), and three kinds of nanomaterials (NMs), including nano-SiO2 (NS), nano-CaCO3 (NC), and nano-metakaolin (NMK), were used to improve the performance of IWWCM. The compressive strengths, hydration degree, hydration products, and micropore structure of the specimens were investigated. The test results showed that IWW reduced the strength of the specimens, and the use of NMs could compensate for this strength reduction. To be specific, the 28-day strength of the freshwater (FW) mixed specimen was 44.6 MPa, and the use of IWW decreased this value to 41.8 MPa. However, the strengths of the specimens with NMs were all higher than 50 MPa, indicating the advantage of NMs for the strengths of the IWWCMs. Moreover, the IWWCM showed a lower hydration degree with a poor pore structure, whereas the use of NMs in IWWCMs refined these properties, explaining the strength increase in the specimens. The results of the SO42− content measurements also showed that the use of NMs could improve the SO42− binding ratio, which is conducive to relieving the pressure of IWW disposal for industrial factories.
EEG-Based Seizure Detection Using Variable-Frequency Complex Demodulation and Convolutional Neural Networks
Authors: Veeranki Yedukondala Rao , McNaboe Riley , Posada-Quintero Hugo F.
Published: November 2023

Epilepsy is a complex neurological disorder characterized by recurrent and unpredictable seizures that affect millions of people around the world. Early and accurate epilepsy detection is critical for timely medical intervention and improved patient outcomes. Several methods and classifiers for automated epilepsy detection have been developed in previous research. However, the existing research landscape requires innovative approaches that can further improve the accuracy of diagnosing and managing patients. This study investigates the application of variable-frequency complex demodulation (VFCDM) and convolutional neural networks (CNN) to discriminate between healthy, interictal, and ictal states using electroencephalogram (EEG) data. For testing this approach, the EEG signals were collected from the publicly available Bonn dataset. A high-resolution time–frequency spectrum (TFS) of each EEG signal was obtained using the VFCDM. The TFS images were fed to the CNN classifier for the classification of the signals. The performance of CNN was evaluated using leave-one-subject-out cross-validation (LOSO CV). The TFS shows variations in its frequency for different states that correspond to variation in the neural activity. The LOSO CV approach yields a consistently high performance, ranging from 90% to 99% between different combinations of healthy and epilepsy states (interictal and ictal). The extensive LOSO CV validation approach ensures the reliability and robustness of the proposed method. As a result, the research contributes to advancing the field of epilepsy detection and brings us one step closer to developing practical, reliable, and efficient diagnostic tools for clinical applications.
Identity and Multiplicity of Belonging in a Europe in Search of Democracy
Authors: Chistolini Sandra , Porcarelli Andrea , Lastrucci Emilio
Published: November 2023

The research we present concerns the narratives of political and social identity of young adolescents sampled in Northern, Central and Southern Italy. Our qualitative analysis is based on constructivism and aims to enhance narratives by privileging the communication of meanings elaborated by the young people. Consistent with this perspective is our choice of the paradigm of pedagogical personalism, which places the human being as a ‘person’ at the center of reflection. The diverse universe of youth manifests multifaceted aspects that it is possible to bring out using interaction with deliberative, open, and non-pre-conceptual discussion. The link between semantic cores such as democracy, freedom, rights, interculturalism, and citizenship runs through the analyses of the entire research (based on the study by Ross’s work in 2019, Finding Political Identities: Young People in a Changing Europe), examined here specifically in the Italian context. The article presents some contrasting aspects of the way young people living in Europe. They harbor uncertainties and discontinuities from the universe of values inherited from previous generations. However, from the tunnel of doubt young people show that they know how to emerge by outlining the forms of a promise of social commitment containing a hope for change, defined as the design of a future in which alternatives to the current situation can be found. In the background, the theme of democracy shows as an aspiration for something still struggling to be born.
A Study of Population Aging and Urban–Rural Residents’ Consumption Habits from a Spatial Spillover Perspective: Evidence from China
Authors: Shao Xiao , Yang Yuanshuai
Published: November 2023

The issue of population aging and imbalanced urban–rural development are two major economic realities and difficulties in China. They constitute important factors affecting the sustainable development of urban and rural areas. The main objective of this study is to analyze the impact of population aging on the consumption levels of urban and rural residents, including overall and subdivided. The secondary objective is to analyze whether this impact has spatial spillover effects so that the bias caused by ignoring the mutual influence between regions could be eliminated. We used global and local Moran’s I, the spatial SLM model, and the SEM model to econometrically analyze the provincial panel data of urban and rural areas obtained from 2010 to 2021. This study found that, first, the distributions of the total consumption and population aging levels of Chinese urban and rural areas are not random and all have obvious spatial dependence characteristics. More specifically, all distributions present significant H-H or L-L clustering phenomena in local space. In other words, high-level areas are surrounded by high-level areas, while low-level areas are surrounded by low-level areas. Second, population aging has a significant negative impact on the overall consumption levels of urban residents and positively increases the consumption level of rural residents, which indicates that population aging will eliminate the urban–rural consumption gap and balance regional economic development to a certain extent. Third, from the perspective of consumption structure, the negative impact of population aging on different consumption expenditure items of urban residents varies, and the effect on various consumption expenditure items of rural residents also shows heterogeneity. Fourth, in terms of the spatial spillover effect, the influence of population aging on the consumption level of urban residents has a significant negative spatial spillover effect; however, in the rural group, the spatial spillover effect caused by the population aging level on the consumption level is not significant. This study provides a new spatial perspective for studying aging and urban–rural consumption inequality behavior, and serves as a reference for the government to further promote the sustainable development of urban–rural consumption in the future.
Changes in Hydraulics–Water Quality–Bloom–Aquatic Habitat Using an Integrated Chain Modeling and Artificial Intelligence Technique in the Yeongsan River
Authors: Choi Byungwoong , Park Jonghwan , Kang Tae-Woo , Ha Don-Woo , Hwang Seong-Yun , Lee Won-Seok , Na Eunhye , Choi Jiyeon
Published: November 2023

This study utilized the Delft3D and HABITAT models to investigate the impact of environmental changes resulting from various weir operation scenarios on aquatic habitats and ecosystem health. The weirs were configured to operate with their sluice gates either fully or partially open. The Delft3D model effectively predicted the dominance of diatoms and green algae due to physicochemical changes in weir operation, replicating adaptive processes such as algal growth, competition, and succession. The model indicated a transition to diatom dominance when weirs were fully open and green algae became abundant. The analysis of aquatic ecosystem health in this study, focusing on habitat changes using the HABITAT model, revealed an improvement in aquatic ecosystem health by one level, even with a single weir sluice gate fully open. Furthermore, the utilization of all input variables in the prediction of algae, through the application of artificial intelligence technology, considerably improved prediction accuracy when compared with selectively employing variables with high correlations to changes in chlorophyll-a concentration. These findings underscore the significance of considering various weir operation scenarios and employing advanced modeling techniques to effectively manage and maintain the health of aquatic ecosystems in the face of environmental changes.
The Impact of the Local Green Economy of Ghana: A General Equilibrium Analysis
Authors: Yeboah Osei-Agyeman , Amoah Nicholas Mensah , Fuseini Shaibu , Sugri Issah
Published: November 2023

The Ghanaian forestry sector has experienced a paradigm shift toward resilient and sustainable forest management practices through climate change mitigation and adaptation policies. Ghana has acknowledged the urgent need to address these issues via comprehensive policy measures as a nation that is vulnerable to the adverse effects of climate change. To assess economy-wide impacts, this paper employs a computable general equilibrium with a static specific factor model that can be readily simulated with various vectors of price changes. The model uses a competitive production structure that assumes constant returns, full employment, competitive pricing, and perfect labor mobility across sectors. Factor shares, industry shares, and substitution matrices from 2022 Labor Force Report’s data were constructed for four skilled groups (managers, professionals, service, and production) across industry, service, the rest of agriculture, and forestry sectors. Predictions of price increases from climate change mitigation policies compliance were simulated for the long run and short run. Forestry, agricultural, and industrial sectors will enjoy higher prices under a green economy, but these would not translate into higher wages. It is rather capital owners in agriculture and forestry that would benefit. Wage adjustments to the green economy are smaller than what might be expected in a partial equilibrium model.
Analysis of Microbial Community in Circulating Cooling Water System of Coal Power Plant during Reagent Conversion
Authors: Wang Yichao , Ye Jiangyu , Xu Mingzhi , Li Yunyi , Dou Jianjun
Published: November 2023

The use of phosphorus-containing chemical corrosion and scale inhibitors has been found to result in excessive phosphorus discharge and an inability to reduce the high concentration of CODcr in the circulating cooling water, thereby making it challenging to comply with increasingly stringent sewage discharge standards. This study aims to assess the practicality of utilizing biological corrosion and scale inhibitors in coal power plants’ operation, as well as investigating the correlation between water quality indicators and microbial communities during the conversion period. The data illustrates that, in comparison to the chemical method, there is a decrease in turbidity of the circulating water from 19.44 NTU to 9.60 NTU, a reduction in CODcr from 71.55 mg/L to 45.47 mg/L, and a drop in TP from 2.35 mg/L to 0.38 mg/L. Microbial community analysis during the transition period reveals that microorganisms rapidly establish a new equilibrium in the circulating water, sediment, and fiber ball, resulting in significantly different microbial community structures. The relative abundance of corrosive microorganisms such as Flavobacterium, Pedomicrobium, and Hydrogenophaga is significantly diminished in the circulating water, whilst the abundance of anaerobic microorganisms like Anaerolineaceae and Rhodopseudomonas in the sediment also declines. Conversely, there is an increased presence of microorganisms associated with contaminant degradation, such as CL500-3 and SM1A02. These findings suggest a decrease in the risk of system corrosion and an enhancement in contaminant degradation capability. This study provides evidence supporting the replacement of chemical agents with biological agents in circulating cooling water systems, contributing to more environmentally friendly and sustainable practices.
Investigation into the Enhancement Characteristics of Fly Ash and Polypropylene Fibers on Calcium Carbide-Residue-Stabilized Soil
Authors: Luo Xi , Li Peilong , Ma Ziqiang , Pei Yi , Ding Zhan , Chen Ruxin , Fan Wenxuan
Published: November 2023

The recycling and reuse of waste materials is an important part of promoting sustainable development. Encouraged by cleaner production and a circular economy, the introduction of calcium carbide residue (CCR) for the stabilization of soil foundations has become a hot topic in the road engineering industry. Aiming at the efficient application of CCR-stabilized soils, the optimization of the material composition was focused on in this work. Fly ash and polypropylene fibers were introduced into the preparation of CCR-stabilized soils, and their effects on the mechanical properties and water stability were tested. The findings highlight that the strength of fly-ash–carbide-residue-stabilized soil was higher than that of carbide-residue-stabilized soil at the same curing age. Furthermore, the unconfined compressive strength, splitting strength, and water stability of CCR–fly-ash-composite-stabilized soil initially increased and then decreased with a rise in polypropylene fiber content. The peak values of confining compressive and splitting strength were observed when the polypropylene fiber content was 1.2‰, while the water stability coefficient A reached its peak value at 0.8‰. From the standpoint of the comprehensive performance improvement and economy of composite-stabilized soil, it is advised that the dosage of polypropylene fibers falls within the range of 0.8–1.2‰. The engineering technical indexes of polypropylene-fiber–CCR-composite-stabilized soil fulfilled the requirements of the specification and had a satisfactory effect on delaying the cracking of the specimen. It is expected that this investigation will provide support for the resource utilization of CCR and the sustainable development of road construction.
Special Issue Editorial: Biodiversity and Ecosystem Services in Forest Ecosystems
Authors: Bohnett Eve , An Li
Published: November 2023

With the degradation and destruction of ecosystem structures and functions, humans are losing essential goods and services from forest ecosystems [...]
Removal of Inorganic Pollutants from Wastewater: Innovative Technologies and Toxicity Assessment
Authors: Kumar Vinay , Sharma Munish , Sondhi Sonica , Kaur Komalpreet , Sharma Deepak , Sharma Shivali , Utreja Divya
Published: November 2023

In the past decade, a sudden increase in the number and kind of emerging water contaminants has been observed. The emerging contaminants can be categorized as organic or inorganic. Organic contaminants have been known for years, and techniques for their detection and remediation have been developed. However, inorganic pollutants are much more common. This is because they are detected in very low or negligible concentrations and are equally toxic as organic pollutants at higher concentrations. To boost the research on inorganic pollutant contamination, advancements in detection and quantification techniques are required. The presented paper discusses major inorganic pollutants such as metals and their salts, inorganic fertilizers, sulfides, acids and bases, and ammonia and oxides of nitrogen. In addition, it discusses the inorganic toxicants’ toxicity to organisms and the environment, upgraded quantification methods, and advancements in inorganic toxicant mitigation. Moreover, the major bottlenecks in the quantification and removal of inorganic pollutants are discussed at the end.
Preservation of the Posterior Interspinous Ligamentary Complex in Posterior and Transforaminal Lumbar Interbody Fusion
Authors: Gondar Renato , Jesse Christopher Marvin , Schär Ralph T. , Fichtner Jens , Fung Christian , Raabe Andreas , Ulrich Christian T.
Published: November 2023

Posterior and transforaminal lumbar interbody fusion (PLIF and TLIF) allow some variation between surgeons, particularly regarding the extent of resection of the posterior interspinous ligamentary complex (PILC) with uncertain implications for outcome. The aim of this study was to assess the importance of preserving the PILC when performing PLIF or TLIF. Systematic review of clinical outcomes (adjacent segment degeneration (ASDG), fusion rate, reoperation rate, and visual analog scale (VAS) scores for back and leg pain) after PLIF/TLIF matched for integrity of PILC, Oswestry Disability Index (ODI) score, and radiological parameters. A total of 191 patients from 2 studies (1 prospective randomized control trial (RCT) and 1 retrospective observational cohort study) were identified. 102 (53.4%) had fusion (PLIF/TLIF) with preserved PILC. All 120 patients in the RCT underwent a L4–L5 single-level fusion, while the 71 patients in the retrospective cohort underwent surgery between T11 and S1. In the retrospective cohort study, significant differences between groups in mean number of fixed levels (4.8 ± 1.0 vs. 4.2 ± 0.5), decompressed levels (2.4 ± 0.7 vs. 3.0 ± 0.7), and interbody fusions (1.2 ± 0.9 vs. 2.0 ± 1.0) were reported. In each of the studies, all groups reported an improved ODI score at 3 months after surgery and at last follow-up. In each of the studies, the incidence of radiographic ASDG was significantly higher for the PILC resection group in both studies (9.0% vs. 43.0%, p < 0.01 and 23.0% vs. 49.0%, p = 0.042). Lumbar lordosis (which decreased after PILC resection in the RCT, p < 0.05) also differed between groups. Taken as a whole, these results suggest that preservation of the PILC during PLIF/TLIF surgery prevents future ASDG and loss of lumbar lordosis as well as the potential clinical consequences of these changes. Further prospective studies are needed.
Investigation of Japanese Onomatopoeia Tsuru-Tsuru as a Suitable Sensory Attribute for Perception of Surface Properties of Textiles by Non-Trained Individuals
Authors: Romao Santos Raphael , Katayama Nozomi , Sukigara Sachiko
Published: November 2023

In recent years, using non-trained individuals in sensory science for analytical tasks has gained traction. Identifying attributes enabling non-experts to express perceptions is crucial for accurate fabric assessments and effective communication of product properties to consumers. This study explored the potential utility of the Japanese onomatopoeia tsuru-tsuru as a sensory attribute for evaluating fabric surface properties. Two experiments were conducted. The first ranked 16 samples to identify those eliciting the tsuru-tsuru sensation, exploring its relationship with attributes like smoothness and coolness. The second involved a detailed descriptive sensory evaluation of selected samples, examining the relationship between tsuru-tsuru, coolness, slipperiness, and hardness. Fabric preferences assessment and cluster analysis were also employed to understand individual differences in perception. Sensory assessments revealed tsuru-tsuru’s comparable performance with attributes like smoothness and slipperiness, facilitating effective sample distinction. Participants showed high agreement, indicating shared perceptions. Preferences favored fabrics with tsuru-tsuru qualities. The clustering analysis revealed participants had slightly different interpretations of tsuru-tsuru. Overall, results indicated the positive potential value of tsuru-tsuru in describing textile surfaces for non-trained individuals.