bluuebunny/crossref_metadata_2025 huggingface dataset:
Visible Light-Mediated Inactivation of H1N1 Virus UsingPolymer-Based Heterojunction Photocatalyst
Authors: Porcu Stefania , Maloccu Stefania , Corona Angela , Hazra Moulika , David Tullia Carla , Chiriu Daniele , Carbonaro Carlo Maria , Tramontano Enzo , Ricci Pier Carlo
Published: June 2023

It is well known that viruses cannot replicate on their own but only inside the cells of target tissues in the organism, resulting in the destruction of the cells or, in some cases, their transformation into cancer cells. While viruses have relatively low resistance in the environment, their ability to survive longer is based on environmental conditions and the type of substrate on which they are deposited. Recently, the potential for safe and efficient viral inactivation by photocatalysis has garnered increasing attention. In this study, the Phenyl carbon nitride/TiO2 heterojunction system, a hybrid organic–inorganic photocatalyst, was utilized to investigate its effectiveness in degrading the flu virus (H1N1). The system was activated by a white-LED lamp, and the process was tested on MDCK cells infected with the flu virus. The results of the study demonstrate the hybrid photocatalyst’s ability to cause the virus to degrade, highlighting its effectiveness for safe and efficient viral inactivation in the visible light range. Additionally, the study underscores the advantages of using this hybrid photocatalyst over traditional inorganic photocatalysts, which typically only work in the ultraviolet range.
The Influence of Thermal and Mechanical Stress on the Electrical Conductivity of ITO-Coated Polycarbonate Films
Authors: Johannes Christopher , Lins Fabian , Meyer Miriam , Hartung Michael , Heim Hans-Peter
Published: June 2023

The influence of thermomechanical stress on the conductivity of indium tin oxide (ITO)-coated polycarbonate (PC) films was investigated. PC is the industry’s standard material for window panes. ITO coatings on polyethylene terephthalate (PET) films are the main commercially available option; as such, most investigations refer to this combination. The investigations in this study aim to investigate the critical crack initiation strain at different temperatures and crack initiation temperatures for two different coating thicknesses and for a commercially available PET/ITO film for validation purposes. Additionally, the cyclic load was investigated. The results show the comparatively sensitive behavior of the PC/ITO films, with a crack initiation strain at room temperature of 0.3–0.4% and critical temperatures of 58 °C and 83 °C, with high variation depending on the film’s thickness. Under thermomechanical loading, the crack initiation strain decreases with increasing temperatures.
Properties of Composites Based on Recycled Polypropylene and Silico-Aluminous Industrial Waste
Authors: Teodorescu George-Mihail , Vuluga Zina , Oancea Florin , Ionita Andreea , Paceagiu Jenica , Ghiurea Marius , Nicolae Cristian-Andi , Gabor Augusta Raluca , Raditoiu Valentin
Published: June 2023

There is an ever-growing interest in recovering and recycling waste materials due to their hazardous nature to the environment and human health. Recently, especially since the beginning of the COVID-19 pandemic, disposable medical face masks have been a major source of pollution, hence the rise in studies being conducted on how to recover and recycle this waste. At the same time, fly ash, an aluminosilicate waste, is being repurposed in various studies. The general approach to recycling these materials is to process and transform them into novel composites with potential applications in various industries. This work aims to investigate the properties of composites based on silico-aluminous industrial waste (ashes) and recycled polypropylene from disposable medical face masks and to create usefulness for these materials. Polypropylene/ash composites were prepared through melt processing methods, and samples were analyzed to get a general overview of the properties of these composites. Results showed that the polypropylene recycled from face masks used together with silico-aluminous ash can be processed through industrial melt processing methods and that the addition of only 5 wt% ash with a particle size of less than 90 µm, increases the thermal stability and the stiffness of the polypropylene matrix while maintaining its mechanical strength. Further investigations are needed to find specific applications in some industrial fields.
Understanding the Mechanical, Surface, and Color Behavior of Oral Bioactive Prosthetic Polymers under Biodegradation Processes
Authors: Neves Cristina B. , Costa Joana , Portugal Jaime , Bettencourt Ana F.
Published: June 2023

Changes in the properties of resin-based polymers exposed to the oral environment can emerge when chlorhexidine (CHX) is incorporated to develop bioactive systems for treating denture stomatitis. Three reline resins loaded with CHX were prepared: 2.5 wt% in Kooliner (K), 5 wt% in Ufi Gel Hard (UFI), and Probase Cold (PC). A total of 60 specimens were submitted to physical aging (1000 cycles of thermal fluctuations, 5–55 °C) or chemical aging (28 days of pH fluctuations in artificial saliva, 6 h at pH = 3, 18 h at pH = 7). Knoop microhardness (30 s, 98 mN), 3-point flexural strength (5 mm/min), and surface energy were tested. Color changes (ΔE) were determined using the CIELab system. Data were submitted to non-parametric tests (α = 0.05). After aging, bioactive K and UFI specimens were not different from the controls (resins without CHX) in mechanical and surface properties. Thermally aged CHX-loaded PC specimens showed decreased microhardness and flexural strength but not under adequate levels for function. The color change was observed in all CHX-loaded specimens that underwent chemical aging. The long-term use of CHX bioactive systems based on reline resins generally does not impair removable dentures’ proper mechanical and aesthetic functions.
Study on Instability Characteristics of the Directional Borehole on the Coal-Seam Roof: A Case Study of the Tingnan Coal Mine
Authors: Wang Zhie , Yang Xin , Wang Gongda , Gong Haiwen
Published: June 2023

Directional long drilling on the roof is an effective gas control measure in the goaf, but there is little research on the stability of the surrounding rock. In this study, the geological conditions of the #4 coal seam in the Tingnan Coal Mine, Shaanxi Province, China taken as the application background, and the deformation characteristics of boreholes under four typical coal and rock conditions were first analyzed based on the Universal Distinct Element Code (UDEC) numerical simulation. Secondly, the stress, strain, and plastic deformation of the rock surrounding the borehole with different diameters were carried out using the Fast Lagrangian Analysis of Continua 3D (FLAC 3D). The effect of the casing on the stability of the borehole was also simulated. The results showed that the borehole stability of coal and mudstone was lower than that of fine-grained sandstone and coarse-grained sandstone. The larger the borehole diameter, the lower the stability. The borehole tended to be unstable, especially when the diameter was 160 mm and 200 mm. Traditional pipes can provide some protection, but for large boreholes, the protection is poor. Based on the above research, uniaxial compression tests were carried out on various internal support tubes, such as ‘line-shaped’, ‘Y-shaped’, and ‘cross-shaped’. The results showed that the cross-shaped pipe had the highest compressive strength, which was 4–5 times that of the other types of protective pipe and had a good protective effect. The research results can provide reliable technical support for the protection of directional boreholes on roofs through strata and have important implications for the popularization and application of the directional long borehole technique.
Carbon Dioxide Capture and Product Characteristics Using Steel Slag in a Mineral Carbonation Plant
Authors: Lee Hyesung , Kim Tae Wook , Kim Soung Hyoun , Lin Yu-Wei , Li Chien-Tsung , Choi YongMan , Choi Changsik
Published: June 2023

Carbon capture and storage (CCS) technology can reduce CO2 emissions by 85 to 95% for power plants and kilns with high CO2 emissions. Among CCS technologies, carbon dioxide capture using steel slag is a method of carbonating minerals by combining oxidized metals in the slag, such as CaO, MgO, and SiO2, with CO2. This study assessed the amount of CO2 captured and the sequestration efficiency in operating a mineral carbonation plant with a CO2 capture capacity of 5 tons/day by treating the exhaust gas from a municipal waste incinerator and identified the characteristics of the mineral carbonation products. As a result, the average concentration of CO2 in the inflow and outflow gas during the reaction time was 10.0% and 1.1%, respectively, and the average CO2 sequestration efficiency was 89.7%. This resulted in a conversion rate of CaO of > 90%. This study manifested that mineral carbonation products are more stable than steel slag as a construction material and are effective at sequestering CO2 by forming chemically stable CaCO3.
Effect of Flexible Operation on Residual Life of High-Temperature Components of Power Plants
Authors: Heo Jun , Park Mingyu , Kim Jeong-Myun , Jang Dong-Won , Han Ji-Hoon
Published: June 2023

Electricity generation from renewable energy sources is emerging as a result of global carbon emission reduction policies. However, most renewable energy sources are non-dispatchable and cannot be adjusted to meet the fluctuating electricity demands of society. A flexible operation process has been proposed as an effective solution to compensate for the unstable nature of renewable energy sources. Thermal load fluctuations during flexible operation may cause creep–fatigue damage to the high-temperature components of thermal power plants, as they are designed with a focus on creep damage under a constant power level. This study investigated the residual life of high-temperature components, such as a superheater tube and a reheater header, to failure under flexible operation conditions using finite element analysis and empirical models. First, we determined an analytical solution for the straightened superheater tube under thermal conditions and compared it with the numerical solution to verify the numerical models. Through the verified finite element model, the creep–fatigue life of the reheater header was estimated by considering flexible operation factors and employing the Coffin–Manson and Larson–Miller models. Although fatigue damage increases with decreasing minimum load and ramp rate, we confirmed that creep damage significantly affects the residual life during flexible operation. In addition, a surrogate model was proposed to evaluate the residual life of the reheater as a function of the flexible operation factors using the machine learning methodology, based on the results of finite element methods. It can be used to predict its residual life without performing complex thermo-structural analysis and relying on empirical models for fatigue and creep life. We expect our findings to contribute to the efficient operation of thermal power plants by optimizing the flexible operation factors.
Study on the Influence of the Strain-Softening of the Surrounding Rock with Buried Depth on Gas Extraction Boreholes
Authors: Cui Junqi , Zheng Yun , Yan Xiangxiang , Hou Yunbing , Xie Shengrong , Chen Dongdong , Ren Yuxin
Published: June 2023

The buried depth of coal seams in China gradually increases as shallow resources decrease. The purpose of this article is to reveal the effect of buried depth on gas extraction boreholes. Firstly, we analyzed the shortcomings of the Mohr–Coulomb (M-C) constitutive model for simulating excavation problems and introduced the strain-softening (S-S) model and its advantages. Subsequently, we constructed the gas extraction models with different buried depths based on the S-S model and combined them with the evolution equations for permeability and the equations for gas migration. Then, we studied the difference between the M-C and S-S models using numerical simulations. We found that the influence of the S-S phenomenon in the surrounding rock of boreholes is more significant as the buried depth increases—that is, the strain-softening has a significant buried depth effect. When the buried depths were 300, 500, and 700 m, the maximum ratios of permeability to the initial permeability obtained from the Mohr–Coulomb model were 1.37, 6.88, and 97.56, respectively; the maximum ratios of permeability to the initial permeability obtained from the strain-softening model were 2.06, 291.23, and 3629.66. The differences in the increase zone of permeability from the two models also increased with the increase in the buried depth. The distribution curves of gas pressure in the surrounding rock of boreholes obtained from the S-S model were below those of the M-C constitutive model at the same buried depth, and the zones with slowly increasing gas pressure with the increase in buried depth were more obvious in the S-S model compared with the M-C constitutive model. The differences in borehole effective extraction radius also increased gradually between the S-S and M-C models as the buried depth increased. Through the analysis of the research results, we found that S-S of the surrounding rock has a lesser influence on the gas extraction boreholes, and the S-S phenomenon in the surrounding rock of boreholes can be ignored when the buried depth is relatively shallow. It is increasingly necessary to consider the S-S phenomenon in the surrounding rock of boreholes with the gradual increase in the buried depth.
Highly Sensitive Ethylene Sensors Based on Ultrafine Pd Nanoparticles-Decorated Porous ZnO Nanosheets and Their Application in Fruit Ripeness Detection
Authors: Jin Zhen , Wang De-Cai , Xie Wen-Jie , Ding Yi , Li Jie
Published: June 2023

Ethylene is the most common ripening phytohormone in fruits, and excess ethylene can overripen the fruit. However, the in-field detection of ethylene is still limited. In this work, ultrafine Pd nanoparticles-decorated porous ZnO nanosheets (UPNP ZnO nanosheets) were conveniently synthesized through a facile solvent reduction method. The UPNP ZnO nanosheets were characterized using scanning electron microscopy, transmission electron microscopy, energy dispersive spectrum, X-ray diffraction and X-ray photoelectron spectroscopy. The ZnO nanosheets were uniformly coated with Pd nanoparticles. The size of the Pd nanoparticle was very small, with a diameter of approximately 2 nm. Due to the unique structure of the porous ZnO nanosheets and the excellent catalytic properties of the ultrafine Pd nanoparticles, the as-prepared samples showed very high sensing performance in ethylene detection. The lowest detection concentration was 10 ppb, which is the lowest detection limit to our knowledge. It has been proved that the decoration of ultrafine Pd nanoparticles can largely increase the relative percentage of chemisorbed oxygen and deficient oxygen, which are benefits for ethylene oxidation, and actually accelerate the process of the sensing reaction. Furthermore, the UPNP ZnO nanosheets can even be applied in fruit maturity detection. Using mangos as an example, our experiment revealed that the response of UPNP ZnO nanosheets to mangos at different maturity stages was quite different. This result suggests that our product has broad application prospects in monitoring fruit ripening stage.
Experimental Investigation on the Influence of Temperature on Coal and Gas Outbursts
Authors: Wang Xiaoqi , Qi Xiaohan , Ma Heng , Li Shengnan
Published: June 2023

With the increasing mining depth, the dynamic disaster of coal and gas outbursts in coal mines has become increasingly prominent, and the bursting liability of coal and rock mass in deep coal seam mining is a necessary condition for the occurrence of rock burst and an important index to measure the failure of coal and rock mass. Thermal damage leads to rock instability and failure, which seriously influences the safe and efficient operation of coal mines. To investigate the effect of thermal damage on the bursting liability of deep coals, the burst tendency index of standard coal was measured after subjecting it to thermal damage at different temperatures. The effects of different thermal damage temperatures on the uniaxial compressive strength index, dynamic failure duration, stiffness ratio index, effective impact energy index, residual energy index change rate, and impact energy velocity of the coal and the influence of the post-peak failure mode of the coal were evaluated. The results revealed that the uniaxial compressive strength of the coal generally decreased with increasing thermal damage temperature. At temperatures above 200 °C, the strength significantly decreased. The comprehensive impact property index indicated that, with increasing thermal impact temperature, the burst tendency first increased up to the peak value at 200 °C and then gradually decreased. With the increase in the thermal damage temperature, the burst tendency decreased and disappeared in the temperature range of 250–300 °C, and the failure mode of the coal changed from brittle failure to brittle plastic failure, and finally ductile failure. The influence of thermal damage on coal bursting liability is studied, which provides a theoretical basis for preventing and controlling coal impact ground pressure hazards.
A Mathematical Model for Force Prediction in Single Point Incremental Sheet Forming with Validation by Experiments and Simulation
Authors: Singh Ravi Prakash , Kumar Santosh , Singh Pankaj Kumar , Meraz Md. , Srivastwa Ashutosh Kumar , Salunkhe Sachin , Hussein H. M. A. , Nasr Emad S. Abouel , Kamrani Ali
Published: June 2023

Incremental sheet forming (ISF) is an emerging technology that has shown great potential in forming customized three-dimensional (3D) parts without the use of product-specific dies. The forming force is reduced in ISF due to the localized nature of deformation and successive forming. Forming force plays an important role in modeling the process accurately, so it needs to be evaluated accurately. Some attempts have been made earlier to calculate the forming force; however, they are mostly limited to empirical formulae for evaluating the average forming force and its different components. The current work presents a mathematical model for force prediction during ISF in a 3D polar coordinate system. The model can be used to predict forces for axis-symmetric cones of different wall angles and also for incremental hole flanging. Axial force component, resultant force in the r-θ plane, and total force have been calculated using the developed mathematical model appearing at different forming depths. The cone with the same geometrical parameters and experimental conditions was modeled and simulated on ABAQUS, and finally, experiments were carried out using a six-axis industrial robot. The mathematical model can be used to calculate forces for any wall angle, but for comparison purposes, a 45° wall angle cone has been used for analytical, numerical, and experimental validation. The total force calculated from the mathematical model had a very high level of accuracy with the force measured experimentally, and the maximum error was 4.25%. The result obtained from the FEA model also had a good level of accuracy for calculating total force, and the maximum error was 4.89%.
Development of LoRa Communication System for Effective Transmission of Data from Underground Coal Mines
Authors: Kumar Paul Prasanna , Paul Partha Sarathi , Ananda Manjunath
Published: June 2023

Underground coal mining is a challenging and hazardous occupation that requires constant monitoring of environmental parameters to ensure the safety of miners. A handful of research has been carried out in developing wireless monitoring devices to monitor underground mine workings using Wi-Fi and ZigBee, which has limitations such as limited range, interference, reliability, power consumption, and security. The main objective of this study is to develop an Underground Gas Monitoring Device using LoRa communication for the effective transmission of monitored data. The testing was carried out in an underground mine model to measure the propagation of the LoRa signal with a line of sight and non-line of sight. It was observed that the system performs fairly well in both situations. During testing, it was observed that there was a drop in RSSI at 14 m for non-line of sight, and beyond 17 m no signal was received. Hence, for every 15 m, a booster is required to be placed to maintain efficient and reliable propagation of signals. In the event of an increase in gas level beyond the threshold limit in the underground mine, the developed system actuates the siren in the underground, boosters, and on the surface.
Religious Boundaries through Emotions: The Representation of Emotions and Their Group-Forming Function in Alevi Poetry
Authors: Kara Cem
Published: June 2023

Although emotions occupy an important place in Alevism, their representation in Alevi history and the present has not yet been sufficiently researched. This study addresses this desideratum and discusses the representation and codification of emotions on the basis of central representatives of Alevi poetry. The focus of this study is the conjunction of constitutive teachings with basic emotions. In the poems, religious beliefs that are considered constitutive are explicitly linked to emotions such as love, grief and anger. In this way, central beliefs become emotionally charged and correspondingly more accentuated. At the same time, the poems convey an emotional expectation to the target audience: various rhetorical stylistic devices are used to convey to the addressees how they should react emotionally to certain ideas, memories and beliefs. In this way, these emotions fulfil the function of feeling rules that must be observed in order to be part of the collective. The analysis of Alevi poetry suggests that emotions have been an important factor in the history of Alevism for social order, group formation and religio-cultural demarcation.
A Lightweight Dual-Branch Swin Transformer for Remote Sensing Scene Classification
Authors: Zheng Fujian , Lin Shuai , Zhou Wei , Huang Hong
Published: June 2023

The main challenge of scene classification is to understand the semantic context information of high-resolution remote sensing images. Although vision transformer (ViT)-based methods have been explored to boost the long-range dependencies of high-resolution remote sensing images, the connectivity between neighboring windows is still limited. Meanwhile, ViT-based methods commonly contain a large number of parameters, resulting in a huge computational consumption. In this paper, a novel lightweight dual-branch swin transformer (LDBST) method for remote sensing scene classification is proposed, and the discriminative ability of scene features is increased through combining a ViT branch and convolutional neural network (CNN) branch. First, based on the hierarchical swin transformer model, LDBST divides the input features of each stage into two parts, which are then separately fed into the two branches. For the ViT branch, a dual multilayer perceptron structure with a depthwise convolutional layer, termed Conv-MLP, is integrated into the branch to boost the connections with neighboring windows. Then, a simple-structured CNN branch with maximum pooling preserves the strong features of the scene feature map. Specifically, the CNN branch lightens the LDBST, by avoiding complex multi-head attention and multilayer perceptron computations. To obtain better feature representation, LDBST was pretrained on the large-scale remote scene classification images of the MLRSN and RSD46-WHU datasets. These two pretrained weights were fine-tuned on target scene classification datasets. The experimental results showed that the proposed LDBST method was more effective than some other advanced remote sensing scene classification methods.
Net Primary Productivity Estimation of Terrestrial Ecosystems in China with Regard to Saturation Effects and Its Spatiotemporal Evolutionary Impact Factors
Authors: Qi Shuaiyang , Zhang Huaiqing , Zhang Meng
Published: June 2023

The net primary productivity (NPP) of vegetation holds a pivotal character for the global carbon balance as a key parameter for characterizing terrestrial ecological processes. The most commonly used indices for estimating vegetation NPP, for instance, the normalized difference vegetation index (NDVI), often suffer from saturation issues that can compromise the accuracy of NPP estimation. This research utilizes a new vegetation index based on the radial basis function (RBF) to estimate vegetation NPP in Chinese terrestrial ecosystems over the past two decades (2001–2020) and investigates the spatiotemporal variation characteristics of NPP and the driving mechanisms. The results indicate that the kernel vegetation index (kNDVI) can effectively alleviate the saturation problem and significantly improve the accuracy of NPP estimation compared to NDVI. Over the past two decades, the NPP of Chinese terrestrial vegetation ranged from 64.13 to 79.72 g C/m2, with a mean value of 72.75 g C/m2, showing a fluctuating upward trend. Changes in the NPP of terrestrial ecosystems in China are mainly affected by precipitation. The dominant factors influencing NPP changes varied over time and had different impacts. For instance, in the period of 2001–2005 the climate had a positive effect on NPP changes, with the dominant factors being evaporation and precipitation. However, in the period of 2010–2015 the dominant climate factors shifted to evaporation and temperature, and their effect on NPP changes became negative. The outcomes of this research aim to serve as a foundation for carbon cycle research and ecosystem environment construction in China.
Open-Source Implementations of the Reactive Asset Administration Shell: A Survey
Authors: Jacoby Michael , Baumann Michael , Bischoff Tino , Mees Hans , Müller Jens , Stojanovic Ljiljana , Volz Friedrich
Published: June 2023

The use of open-source software is crucial for the digitalization of manufacturing, including the implementation of Digital Twins as envisioned in Industry 4.0. This research paper provides a comprehensive comparison of free and open-source implementations of the reactive Asset Administration Shell (AAS) for creating Digital Twins. A structured search on GitHub and Google Scholar was conducted, leading to the selection of four implementations for detailed analysis. Objective evaluation criteria were defined, and a testing framework was created to test support for the most common AAS model elements and API calls. The results show that all implementations support at least a minimal set of required features while none implement the specification in all details, which highlights the challenges of implementing the AAS specification and the incompatibility between different implementations. This paper is therefore the first attempt at a comprehensive comparison of AAS implementations and identifies potential areas for improvement in future implementations. It also provides valuable insights for software developers and researchers in the field of AAS-based Digital Twins.
Transfer Learning for Sentiment Classification Using Bidirectional Encoder Representations from Transformers (BERT) Model
Authors: Areshey Ali , Mathkour Hassan
Published: June 2023

Sentiment is currently one of the most emerging areas of research due to the large amount of web content coming from social networking websites. Sentiment analysis is a crucial process for recommending systems for most people. Generally, the purpose of sentiment analysis is to determine an author’s attitude toward a subject or the overall tone of a document. There is a huge collection of studies that make an effort to predict how useful online reviews will be and have produced conflicting results on the efficacy of different methodologies. Furthermore, many of the current solutions employ manual feature generation and conventional shallow learning methods, which restrict generalization. As a result, the goal of this research is to develop a general approach using transfer learning by applying the “BERT (Bidirectional Encoder Representations from Transformers)”-based model. The efficiency of BERT classification is then evaluated by comparing it with similar machine learning techniques. In the experimental evaluation, the proposed model demonstrated superior performance in terms of outstanding prediction and high accuracy compared to earlier research. Comparative tests conducted on positive and negative Yelp reviews reveal that fine-tuned BERT classification performs better than other approaches. In addition, it is observed that BERT classifiers using batch size and sequence length significantly affect classification performance.
Deep Learning-Aided Modulation Recognition for Non-Orthogonal Signals
Authors: Fan Jiaqi , Wu Linna , Zhang Jinbo , Dong Junwei , Wen Zhong , Zhang Zehui
Published: June 2023

Automatic Modulation Recognition (AMR) can obtain the modulation mode of the received signal for subsequent processing without the assistance of the transmitter. Although the existing AMR methods have been mature for the orthogonal signals, these methods face challenges when deployed in non-orthogonal transmission systems due to the superimposed signals. In this paper, we aim to develop efficient AMR methods for both downlink and uplink non-orthogonal transmission signals using deep learning-based data-driven classification methodology. Specifically, for downlink non-orthogonal signals, we propose a Bi-directional Long Short-Term Memory (BiLSTM)-based AMR method that exploits long-term data dependence to automatically learn irregular signal constellation shapes. Transfer learning is further incorporated to improve recognition accuracy and robustness under varying transmission conditions. For uplink non-orthogonal signals, the combinatorial number of classification types explodes exponentially with the number of signal layers, which becomes the major obstacle to AMR. We develop a spatio-temporal fusion network based on the attention mechanism to efficiently extract spatio-temporal features, and network details are optimized according to the superposition characteristics of non-orthogonal signals. Experiments show that the proposed deep learning-based methods outperform their conventional counterparts in both downlink and uplink non-orthogonal systems. In a typical uplink scenario with three non-orthogonal signal layers, the recognition accuracy can approach 96.6% in the Gaussian channel, which is 19% higher than the vanilla Convolution Neural Network.
A Systematic Survey of Data Augmentation of ECG Signals for AI Applications
Authors: Rahman Md Moklesur , Rivolta Massimo Walter , Badilini Fabio , Sassi Roberto
Published: June 2023

AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study’s objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring.
Laser-Based Door Localization for Autonomous Mobile Service Robots
Authors: Müller Steffen , Müller Tristan , Ahmed Aamir , Gross Horst-Michael
Published: June 2023

For autonomous mobile service robots, closed doors that are in their way are restricting obstacles. In order to open doors with on-board manipulation skills, a robot needs to be able to localize the door’s key features, such as the hinge and handle, as well as the current opening angle. While there are vision-based approaches for detecting doors and handles in images, we concentrate on analyzing 2D laser range scans. This requires less computational effort, and laser-scan sensors are available on most mobile robot platforms. Therefore, we developed three different machine learning approaches and a heuristic method based on line fitting able to extract the required position data. The algorithms are compared with respect to localization accuracy with help of a dataset containing laser range scans of doors. Our LaserDoors dataset is publicly available for academic use. Pros and cons of the individual methods are discussed; basically, the machine learning methods could outperform the heuristic method, but require special training data when applied in a real application.
Spatiotemporal Clustering of Parking Lots at the City Level for Efficiently Sharing Occupancy Forecasting Models
Authors: Mufida Miratul Khusna , Ait El Cadi Abdessamad , Delot Thierry , Trépanier Martin , Zekri Dorsaf
Published: June 2023

This study aims to address the challenge of developing accurate and efficient parking occupancy forecasting models at the city level for autonomous vehicles. Although deep learning techniques have been successfully employed to develop such models for individual parking lots, it is a resource-intensive process that requires significant amounts of time and data for each parking lot. To overcome this challenge, we propose a novel two-step clustering technique that groups parking lots based on their spatiotemporal patterns. By identifying the relevant spatial and temporal characteristics of each parking lot (parking profile) and grouping them accordingly, our approach allows for the development of accurate occupancy forecasting models for a set of parking lots, thereby reducing computational costs and improving model transferability. Our models were built and evaluated using real-time parking data. The obtained correlation rates of 86% for the spatial dimension, 96% for the temporal one, and 92% for both demonstrate the effectiveness of the proposed strategy in reducing model deployment costs while improving model applicability and transfer learning across parking lots.
A Light-Weight Network for Small Insulator and Defect Detection Using UAV Imaging Based on Improved YOLOv5
Authors: Zhang Tong , Zhang Yinan , Xin Min , Liao Jiashe , Xie Qingfeng
Published: June 2023

Insulator defect detection is of great significance to compromise the stability of the power transmission line. The state-of-the-art object detection network, YOLOv5, has been widely used in insulator and defect detection. However, the YOLOv5 network has limitations such as poor detection rate and high computational loads in detecting small insulator defects. To solve these problems, we proposed a light-weight network for insulator and defect detection. In this network, we introduced the Ghost module into the YOLOv5 backbone and neck to reduce the parameters and model size to enhance the performance of unmanned aerial vehicles (UAVs). Besides, we added small object detection anchors and layers for small defect detection. In addition, we optimized the backbone of YOLOv5 by applying convolutional block attention modules (CBAM) to focus on critical information for insulator and defect detection and suppress uncritical information. The experiment result shows the mean average precision (mAP) is set to 0.5, and the mAP is set from 0.5 to 0.95 of our model and can reach 99.4% and 91.7%; the parameters and model size were reduced to 3,807,372 and 8.79 M, which can be easily deployed to embedded devices such as UAVs. Moreover, the speed of detection can reach 10.9 ms/image, which can meet the real-time detection requirement.
Water Retention Characteristics of Superabsorbent Polymers (SAPs) Used as Soil Amendments
Authors: Takahashi Masamichi , Kosaka Izumi , Ohta Seiichi
Published: June 2023

Superabsorbent polymers (SAPs) are used as a soil amendment for retaining water, but suitable methods for the application of SAPs have not yet been developed. Here, we characterized a variety of soil–SAP mixtures prepared using four different types of SAP in terms of their water absorption and release characteristics. The teabag method was applied to characterize the soil–SAP mixtures, except for measurements of the matric potential. The results showed that the variations in water absorbency among the four SAPs in isolation became insignificant when they were mixed with sandy soils. The rates of water released from the soil–SAP mixtures under heated conditions were mitigated with decreasing water content, which prolonged the time until desiccation of the mixtures. The water absorbency of the SAPs significantly decreased in salt solutions (KCl and CaCl2), but their absorbency mostly recovered following immersion in tap water. The soil–dry SAP mixtures retained a larger amount of water than the soil–gel SAP mixtures. Swollen SAPs predominantly retained water in the range of −0.98 to −3.92 kPa, suggesting that SAP induces a transition from gravitational water to readily plant-available water by swelling itself. SAPs barely increased the amount of plant-available water in a potential range of −3.92 to −98.1 kPa, but significantly increased the soil water at <−98.1 kPa. The soil water content increased with an increasing SAP application rate, whereas the proportion of plant-available water declined. Our findings indicated that the performance of SAPs depends on the pore space and a saline environment in the soil and that low SAP application rates are suitable for maximizing the water available to plants in sandy soils.
Performance Improvement of Flux Switching Permanent Magnet Wind Generator Using Magnetic Flux Barrier Design
Authors: Torn Vanna , Seangwong Pattasad , Fernando Nuwantha , Siritaratiwat Apirat , Khunkitti Pirat
Published: June 2023

Flux-switching permanent magnet (FSPM) machines have attracted significant research attention in the field of wind power generation. In this study, the utilization of a magnetic flux barrier to improve the performance of the nine-phase FSPM generator designed for low-speed wind power applications is conducted. The proposed approach involves introducing magnetic flux barriers of different topologies to the conventional FSPM generator and analyzing their performance using 2D finite element simulations. Results suggested that |-shaped magnetic flux barriers exhibited the highest performance among other topologies, making them the appropriate choice for this generator. The geometry of the |-shaped flux barriers was further optimized using response surface methodology to maximize the generator’s performance. The proposed generator exhibits a significant decrease in cogging torque, achieving a remarkable reduction of up to 23.7%, while maintaining electromotive force. Moreover, it shows a significant decrease in permanent magnet eddy-current loss, with a noteworthy reduction of up to 51%. Additionally, significant improvements were demonstrated in terms of electromagnetic torque, torque ripple, output power, and efficiency. Details on the physical reasoning behind these improvements have been provided. Overall, the proposed FSPM generator with inserted flux barriers has the potential to meet the demands of low-speed wind power generation effectively.
Paradigms, Methods, and Tools for Multicriteria Decision Models in Sustainable Industry 4.0 Oriented Manufacturing Systems
Authors: Da Silva Lucas Borges Leal , Ferreira Evanielle Barbosa , Ferreira Rodrigo José Pires , Frej Eduarda Asfora , Roselli Lucia Reis Peixoto , De Almeida Adiel Teixeira
Published: June 2023

The rise of Industry 4.0 tools transforms the way production plants are planned, controlled, and monitored, allowing organizations to achieve greater flexibility, efficiency, and cost reduction. This way, the use of emerging technologies provides a new look at the industrial sector, particularly concerning sustainability issues in a society that suffers worsening effects from climate change. On the other hand, the degree of success when implementing Industry 4.0 practices in sustainable-oriented manufacturing systems is closely related to hard decision-making, which involves, in practice, multiple and even conflicting criteria to model multidimensional problems. For that reason, it is worth recognizing the importance of the multi-criteria decision-making/aid (MCDM/A) approach to support decision modeling and application so that this research field can be better explored to enhance Industry 4.0 implementation and innovative advances in operations management. Given this backdrop, this paper undertakes a systematic literature review (SLR) of 118 papers, thereby combining a set of predefined keywords with several exclusion criteria to detect the literature that is related to sustainable decisions in manufacturing systems that introduced Industry 4.0 practices in the Web of Science (Clarivate Analytics) database. Cross-matching important research metrics from these papers encourages this work to provide readers with two axes of discussion with the aid of five research questions: a bibliometric analysis and a content analysis. Among many findings, some guidelines for decision-making are put forward to share insights and provide decision-makers, scholars, experts, stakeholders, and other professionals with an overall managerial background when applying sustainable-oriented multicriteria models in manufacturing systems.
The Impact of Perceived Barriers to Career Advancement: A Study with a Sample of Italian University Students
Authors: Marini Mara , Livi Stefano , Prislei Laura , Parisse Chiara , Cecalupo Alessandra , Scarci Federica , Benvenuto Guido
Published: June 2023

The goals established by the 2030 Agenda for Sustainable Development, and the new needs that globalization has created, have prompted researchers to examine young people’s educational and professional experiences in the context of economic and social disadvantages, considering agency capacities and psychological factors. Following these research interests, this study aimed to evaluate Italian university students’ perceptions of socioeconomic barriers to achieving professional goals and the effects of these perceived barriers on some indicators of adaptation. The study included 401 Italian university students (M = 24.18, SD = 3.33). The results demonstrated that economic constraints can be a psychological barrier to career advancement by negatively influencing the perception of being able to actively choose one’s professional future and by diminishing the psychological resources required to adapt to complex environments. In turn, this can indirectly affect the perceived gap between a person’s career goals and their progress toward achieving them, thereby impeding their adaptation processes. The implications of the research are discussed.
Achieving Zero Stunting: A Sustainable Development Goal Interlinkage Approach at District Level
Authors: Komarulzaman Ahmad , Andoyo Robi , Anna Zuzy , Ghina Aisyah Amatul , Halim Putri Riswani , Napitupulu Herlina , Karunia Monica Ruth , Andriani Annisa
Published: June 2023

The sustainable development goals (SDGs) provide an integrated framework of targets and indicators, including the elimination of stunting, to support better development planning. Indonesia faces a significant challenge as it ranks fourth globally in terms of stunting prevalence, exacerbated by disparities across regions, gender, and socioeconomic status, further compounded by the ongoing COVID-19 pandemic. Given the interlinked nature of SDGs, this study provides empirical support for the prioritization of SDG indicators, primarily in the context of stunting elimination at the district level in Indonesia. This study employed a combination of economic complexity and network theory, utilizing data from a comprehensive set of 54 indicators spanning 28 targets within 13 SDG goals in 514 districts. The analysis is based on network metrics, including revealed comparative advantage (RCA), proximity, centrality, and density to establish the SDG interlinkage network and identify key priority indicators. The findings highlight the importance of prioritizing indicators such as civil registration, health facilities and services, access to basic facilities and housing, and access to ICT in efforts to reduce stunting, particularly among disadvantaged households. Given the unique resources and capacities of each region, our analysis offers district-specific prioritization strategies for stunting elimination.
Investigation into Occurrence Mechanism of Rock Burst Induced by Water Drainage in Deep Mines
Authors: Wang Bo , Feng Guorui , Jiang Fuxing , Ma Junpeng , Wang Chao , Li Zhu , Wu Wenda
Published: June 2023

Confined aquifers widely exist in the strata of the Ordos mining area. Water drainage before mining is an effective measure to avoid water inrush disasters caused by the connectivity between mining-induced roof fractures and aquifers. However, rock burst disasters occur frequently in the mining process of many water drainage working faces. The statistics show that the surrounding rock of nine water drainage working faces in five mines has suffered different degrees of rock burst, which seriously restricts the safe, efficient production and sustainable development of ten-million-ton modern mines in China. Based on this, taking the 22,106 longwall working face (LW22106) of the Shilawusu Mine, Ordos, as the engineering background, this paper investigates the occurrence mechanism of water drainage on rock burst in the roof water-rich area by using theoretical analysis, similar material simulation, microseismic measurement and other methods. The main conclusions are as follows: (1) After the drainage of the water-rich area, the pressure relief zone, pressurized zone and pressure stabilization zone are formed in sequence from the center to both sides. The width of the pressure relief zone is consistent with that of the water-rich area, and the width of the pressurized zone is about 35 m on one side. (2) When the mining is passing the pressurized zone, the coal rock mass is under the joint influence of gravity stress, mining disturbance stress and drainage transfer stress. The superimposed stress generally exceeds 2.5 times the uniaxial compressive strength (UCS) of the coal rock mass, and the maximum reaches 3.24 times, far exceeding the critical value of rock burst (1.5 times UCS), which is the main reason to induce rock burst. (3) The dynamic change in the rock burst risk areas before and after drainage in water-rich area no. 4 has been predicted, and the number of risk areas increases from 4 before drainage to 13 after drainage. Since the stress superposition effect differs due to different mining speeds, it is proposed that the dynamic regulation of mining speed in the front and at the back of the drainage area is an effective and efficient method for rock burst prevention and control. The research results will provide a theoretical basis and technical support for the prevention and control in the roof water-rich area of deep mines.