bluuebunny/crossref_metadata_2025 huggingface dataset:
A Convolutional Fuzzy Neural Network Active Noise Cancellation Approach without Error Sensors for Autonomous Rail Rapid Transit
Authors: Li Tao , He Yuyao , Wang Minqi , Zhao Kaihui , Wang Ning , Gui Weihua , Feng Jianghua , Yang Jun
Published: August 2023

Autonomous rail rapid transit (ART) is a new type of multiunit, articulated, rubber-wheeled urban transport system. The noise sources of ART have significant time-varying characteristics. It is unsuitable to track the error signal by installing too many error sensors, which poses a significant challenge in the active noise control of ART. Thus, this paper proposes a convolutional fuzzy neural network-based active noise cancellation approach without error sensors to solve this problem. The proposed approach utilizes convolutional neural network (CNN) to extract the noise signal characteristics of ART and trains multiple noise source signals using a CNN to estimate the virtual error signal in the target area. In addition, the proposed approach adopts fuzzy neural network (FNN) for adaptive adjustment of filter weight coefficients to achieve real-time noise tracking control with fast convergence and small error in the convergence process. The experimental results demonstrate that the proposed approach can effectively reduce ART low-frequency noise without error sensors, and the average sound pressure level in the target area is reduced more compared with conventional approaches.
Removal of Phosphorus from Domestic Sewage in Rural Areas Using Oyster Shell-Modified Agricultural Waste–Rice Husk Biochar
Authors: Xu Cancan , Liu Rui , Chen Lvjun
Published: August 2023

In order to promote the improvement of rural living environments, the treatment of rural domestic sewage has attracted much attention in China. Meanwhile, the rural regions’ sewage discharge standards are becoming increasingly stringent. However, the standard compliance rate of the total phosphorus (TP) is very low, and the TP has become the main limiting pollutant for the water pollutant discharge standards of rural domestic sewage treatment facilities. In this study, oyster shell waste was employed as a calcium source, and agricultural waste–rice husk was used as a carbon source to synthesize calcium-modified biochar adsorbent materials (Ca-BC) by a simple one-step pyrolysis method. The resultant Ca-BC adsorbent materials demonstrated efficient phosphate (P) adsorption from aqueous solutions over a wide pH range (3–11) and adsorption selectivity. Ca-BC’s adsorption capacity for P increased with the pyrolysis temperature, increasing from 700 °C to 900 °C, which was attributed to the higher specific surface area and calcium oxide content at higher pyrolysis temperatures. The Ca-BC sample, which was made from oyster shells and rice husks with a mass ratio of 2:1 and a pyrolysis temperature of 900 °C, had a maximum adsorption capacity of 196.2 mg/g. The Langmuir model and pseudo-second-order model were the best at describing the adsorption process, and the predominant sorption mechanism for P is the precipitation of calcium oxide or calcium hydroxide with phosphate to create hydroxyapatite. Ca-BC can effectively remove P from rural domestic sewage. The removal rate of the total phosphorus (TP) in rural domestic sewage is 93.9–99.4%. After the adsorption treatment, the discharge of the TP in the rural sewage met the second-grade (TP < 3 mg/L) or even the first-grade (TP < 2 mg/L) Discharge Standard of Water Pollutants for Centralized Rural Sewage Treatment Facilities (DB33/973-2021). This study provides an experimental basis for efficient P removal by Ca-BC adsorbent materials and suggests possible applications in rural domestic sewage.
Laudato Si’ and Spatial Turn: A Theological Approach
Authors: Palma Alexandre
Published: August 2023

This article analyses the encyclical letter Laudato Si’ against the backdrop of the contemporary and theological “spatial turn”. It asks if and how Laudato Si’ incorporates the main elements of this movement that pushes for a reappraisal of space and/or place in theological reasoning. This inquiry is motivated by a public and constructive understanding of theology, with the conviction that it too should bring its specific contribution to the challenges of the “Common Home”. The article starts by characterizing this theological “spatial turn”, and by putting it into the context of similar trends in other human and social sciences. Secondly, it focuses on the analysis of Laudato Si’, namely by considering the role played in it by spatiality. Thirdly, and in a brief systematic way, it presents some concluding remarks that describe how Laudato Si’ may be interpreted in the “spatial turn” framework. Following these remarks, the article concludes that there is a moderate incorporation in Laudato Si’ of the concerns that promote and sustain a theological “spatial turn”.
Robust Space-Time Adaptive Processing Method for GNSS Receivers in Coherent Signal Environments
Authors: Meng Zhen , Shen Feng
Published: August 2023

In the coherent signal environments caused by multipath propagation, the interference suppression performance of the global navigation satellite systems (GNSS) receivers decreases sharply. In this paper, a robust space-time adaptive processing (STAP) method for GNSS receivers is proposed to suppress interferences in coherent signal environments, by using the modified space-time two-dimensional iterative adaptive approach (ST2D-IAA) spectrum estimation. This method applies the IAA algorithm to the ST2D signal model of GNSS receivers, and further modifies the ST2D-IAA algorithm to accurately estimate the power spectrum and noise power simultaneously. The space-time interference-plus-noise covariance matrix (STINCM) is reconstructed by using the estimated power spectrum and noise power in the interference angle region. Based on the reconstructed STINCM, we construct the STAP beamforming optimization problem for the space-time steering vector (STSV) error vector, and further correct the STSV of GNSS signal. Finally, the weight vector of STAP beamforming is calculated by using the reconstructed STINCM and the corrected STSV of GNSS signal. Simulation results show that the proposed method can suppress interferences in coherent signal environments and outperforms the current methods.
Combining Chlorophyll Fluorescence and Vegetation Reflectance Indices to Estimate Non-Photochemical Quenching (NPQ) of Rice at the Leaf Scale
Authors: Jiang Hao , Liu Zhigang , Wang Jin , Yang Peiqi , Zhang Runfei , Zhang Xiuping , Zheng Pu
Published: August 2023

Non-photochemical quenching (NPQ) is an indicator of crop stress. Until now, only a limited number of studies have focused on how to estimate NPQ using remote sensing technology. The main challenge is the complicated regulatory mechanism of NPQ. NPQ can be divided into energy-dependent (qE) and non-energy-dependent (non-qE) quenching. The contribution of these two components varies with environmental factors, such as light intensity and stress level due to the different response mechanisms. This study aims to explore the feasibility of estimating NPQ using photosynthesis-related vegetation parameters available from remote sensing by considering the two components of NPQ. We concurrently measured passive vegetation reflectance spectra by spectrometer, as well as active fluorescence parameters by pulse-amplitude modulated (PAM) of rice (Oryza sativa) leaves. Subsequently, we explored the ability of the selected vegetation parameters (including the photochemical reflectance index (PRI), inverted red-edge chlorophyll index (IRECI), near-infrared reflectance of vegetation (NIRv), and fluorescence quantum yield (ΦF)) to estimate NPQ. Based on different combinations of these remote sensing parameters, empirical models were established to estimate NPQ using the linear regression method. Experimental analysis shows that the contribution of qE and non-qE components varied under different illumination conditions. Under high illumination, the NPQ was attributed primarily to the qE component, while under low illumination, it was equally attributed to the qE and non-qE components. Among all tested parameters, ΦF was sensitive to the qE component variation, while IRECI and NIRv were sensitive to the non-qE component variation. Under high illumination, integrating ΦF in the regression model captured NPQ variations well (R2 > 0.74). Under low illumination, ΦF, IRECI, and NIRv explained 24%, 62%, and 65% of the variation in NPQ, respectively, while coupling IRECI or NIRv with ΦF considerably improved the accuracy of NPQ estimation (R2 > 0.9). For all the samples under both low and high illumination, the combination of ΦF with at least one of the other parameters (including IRECI, NIRv and PAR) offers a more versatile and reliable approach to estimating NPQ than using any single parameter alone. The findings of this study contribute to the further development of remote sensing methods for NPQ estimation at the canopy scale in the future.
Internet of Medical Things and Healthcare 4.0: Trends, Requirements, Challenges, and Research Directions
Authors: Osama Manar , Ateya Abdelhamied A. , Sayed Mohammed S. , Hammad Mohamed , Pławiak Paweł , Abd El-Latif Ahmed A. , Elsayed Rania A.
Published: August 2023

Healthcare 4.0 is a recent e-health paradigm associated with the concept of Industry 4.0. It provides approaches to achieving precision medicine that delivers healthcare services based on the patient’s characteristics. Moreover, Healthcare 4.0 enables telemedicine, including telesurgery, early predictions, and diagnosis of diseases. This represents an important paradigm for modern societies, especially with the current situation of pandemics. The release of the fifth-generation cellular system (5G), the current advances in wearable device manufacturing, and the recent technologies, e.g., artificial intelligence (AI), edge computing, and the Internet of Things (IoT), are the main drivers of evolutions of Healthcare 4.0 systems. To this end, this work considers introducing recent advances, trends, and requirements of the Internet of Medical Things (IoMT) and Healthcare 4.0 systems. The ultimate requirements of such networks in the era of 5G and next-generation networks are discussed. Moreover, the design challenges and current research directions of these networks. The key enabling technologies of such systems, including AI and distributed edge computing, are discussed.
GRP-YOLOv5: An Improved Bearing Defect Detection Algorithm Based on YOLOv5
Authors: Zhao Yue , Chen Bolun , Liu Bushi , Yu Cuiying , Wang Ling , Wang Shanshan
Published: August 2023

Currently, most chemical transmission equipment relies on bearings to support rotating shafts and to transmit power. However, bearing defects can lead to a series of failures in the equipment, resulting in reduced production efficiency. To prevent such occurrences, this paper proposes an improved bearing defect detection algorithm based on YOLOv5. Firstly, to mitigate the influence of the similarity between bearing defects and non-defective regions on the detection performance, gamma transformation is introduced in the preprocessing stage of the model to adjust the image’s grayscale and contrast. Secondly, to better capture the details and semantic information of the defects, this approach incorporates the ResC2Net model with a residual-like structure during the feature-extraction stage, enabling more nonlinear transformations and channel interaction operations so as to enhance the model’s perception and representation capabilities of the defect targets. Additionally, PConv convolution is added in the feature fusion part to increase the network depth and better capture the detailed information of defects while maintaining time complexity. The experimental results demonstrate that the GRP-YOLOv5 model achieves a mAP@0.5 of 93.5%, a mAP@0.5:0.95 of 52.7%, and has a model size of 25 MB. Compared to other experimental models, GRP-YOLOv5 exhibits excellent performance in bearing defect detection accuracy. However, the model’s FPS (frames per second) performance is not satisfactory. Despite its small size of 25 MB, the processing speed is relatively slow, which may have some impact on real-time or high-throughput applications. This limitation should be considered in future research and in the optimization efforts to improve the overall performance of the model.
Body Boundary Measurement Using Multiple Line Lasers for a Focused Microwave Thermotherapy System: A Proof-of-Concept Study
Authors: Jeong Janghoon , Son Seong-Ho
Published: August 2023

A focused microwave thermotherapy system for non-invasively treating cancerous tumors has recently been actively developed. To accurately focus on the target location, the system needs information about the patient’s body boundary. However, a water bolus is placed between the human body and the microwave applicators to allow the microwave to penetrate the body more easily and cool the body’s skin. The structural configuration makes it difficult to measure the body boundary. In this paper, we present a body boundary measurement method using multiple line lasers and cameras for the application of a focused microwave thermotherapy system. Even with a lack of acquired boundary data, a completely closed boundary line can be reconstructed. In addition, real-time movement tracking is possible as it can be measured quickly, even in situations where the patient is moving, such as breathing and wriggling. The performance is verified with several indicators in a water-filled experimental testbed.
Non-Contact Infrared Thermometers and Thermal Scanners for Human Body Temperature Monitoring: A Systematic Review
Authors: Zhao Yuanzhe , Bergmann Jeroen H. M.
Published: August 2023

In recent years, non-contact infrared thermometers (NCITs) and infrared thermography (IRT) have gained prominence as convenient, non-invasive tools for human body temperature measurement. Despite their widespread adoption in a range of settings, there remain questions about their accuracy under varying conditions. This systematic review sought to critically evaluate the performance of NCITs and IRT in body temperature monitoring, synthesizing evidence from a total of 72 unique settings from 32 studies. The studies incorporated in our review ranged from climate-controlled room investigations to clinical applications. Our primary findings showed that NCITs and IRT can provide accurate and reliable body temperature measurements in specific settings and conditions. We revealed that while both NCITs and IRT displayed a consistent positive correlation with conventional, contact-based temperature measurement tools, NCITs demonstrated slightly superior accuracy over IRT. A total of 29 of 50 settings from NCIT studies and 4 of 22 settings from IRT studies achieved accuracy levels within a range of ±0.3 °C. Furthermore, we found that several factors influenced the performance of these devices. These included the measurement location, the type of sensor, the reference and tool, individual physiological attributes, and the surrounding environmental conditions. Our research underscores the critical need for further studies in this area to refine our understanding of these influential factors and to develop standardized guidelines for the use of NCITs and IRT.
Deep Learning-Based Subsurface Damage Localization Using Full-Field Surface Strains
Authors: Pal Ashish , Meng Wei , Nagarajaiah Satish
Published: August 2023

Structures in their service life are often damaged as a result of aging or extreme events such as earthquakes or storms. It is essential to detect damage in a timely fashion to ensure the safe operation of the structure. If left unchecked, subsurface damage (SSD) can cause significant internal damage and may result in premature structural failure. In this study, a Convolutional Neural Network (CNN) has been developed for SSD detection using surface strain measurements. The adopted network architecture is capable of pixel-level image segmentation, that is, it classifies each location of strain measurement as damaged or undamaged. The CNN which is fed full-field strain measurements as an input image of size 256 × 256 projects the SSD onto an output image of the same size. The data for network training is generated by numerical simulation of aluminum bars with different damage scenarios, including single damage and double damage cases at a random location, direction, length, and thickness. The trained network achieves an Intersection over Union (IoU) score of 0.790 for the validation set and 0.794 for the testing set. To check the applicability of the trained network on materials other than aluminum, testing is performed on a numerically generated steel dataset. The IoU score is 0.793, the same as the aluminum dataset, affirming the network’s capability to apply to materials exhibiting a similar stress–strain relationship. To check the generalization potential of the network, it is tested on triple damage cases; the IoU score is found to be 0.764, suggesting that the network works well for unseen damage patterns as well. The network was also found to provide accurate predictions for real experimental data obtained from Strain Sensing Smart Skin (S4). This proves the efficacy of the network to work in real-life scenarios utilizing the full potential of the novel full-field strain sensing methods such as S4. The performance of the proposed network affirms that it can be used as a non-destructive testing method for subsurface crack detection and localization.
A Wearable Assistant Device for the Hearing Impaired to Recognize Emergency Vehicle Sirens with Edge Computing
Authors: Chin Chiun-Li , Lin Chia-Chun , Wang Jing-Wen , Chin Wei-Cheng , Chen Yu-Hsiang , Chang Sheng-Wen , Huang Pei-Chen , Zhu Xin , Hsu Yu-Lun , Liu Shing-Hong
Published: August 2023

Wearable assistant devices play an important role in daily life for people with disabilities. Those who have hearing impairments may face dangers while walking or driving on the road. The major danger is their inability to hear warning sounds from cars or ambulances. Thus, the aim of this study is to develop a wearable assistant device with edge computing, allowing the hearing impaired to recognize the warning sounds from vehicles on the road. An EfficientNet-based, fuzzy rank-based ensemble model was proposed to classify seven audio sounds, and it was embedded in an Arduino Nano 33 BLE Sense development board. The audio files were obtained from the CREMA-D dataset and the Large-Scale Audio dataset of emergency vehicle sirens on the road, with a total number of 8756 files. The seven audio sounds included four vocalizations and three sirens. The audio signal was converted into a spectrogram by using the short-time Fourier transform for feature extraction. When one of the three sirens was detected, the wearable assistant device presented alarms by vibrating and displaying messages on the OLED panel. The performances of the EfficientNet-based, fuzzy rank-based ensemble model in offline computing achieved an accuracy of 97.1%, precision of 97.79%, sensitivity of 96.8%, and specificity of 97.04%. In edge computing, the results comprised an accuracy of 95.2%, precision of 93.2%, sensitivity of 95.3%, and specificity of 95.1%. Thus, the proposed wearable assistant device has the potential benefit of helping the hearing impaired to avoid traffic accidents.
Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach
Authors: Raza Ahmad , Ali Mohsin , Ehsan Muhammad Khurram , Sodhro Ali Hassan
Published: August 2023

The rapid technological advancements in the current modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients’ health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient’s health data by exploiting the primary user’s (PU) spectrum. In this paper, tree-based algorithms (TBAs) of machine learning (ML) are investigated to evaluate spectrum sensing in CR-based smart healthcare systems. The required data sets for TBAs are created based on the probability of detection (Pd) and probability of false alarm (Pf). These data sets are used to train and test the system by using fine tree, coarse tree, ensemble boosted tree, medium tree, ensemble bagged tree, ensemble RUSBoosted tree, and optimizable tree. Training and testing accuracies of all TBAs are calculated for both simulated and theoretical data sets. The comparison of training and testing accuracies of all classifiers is presented for the different numbers of received signal samples. Results depict that optimizable tree gives the best accuracy results to evaluate the spectrum sensing with minimum classification error (MCE).
Transitioning from Supramolecular Chemistry to Molecularly Imprinted Polymers in Chemical Sensing
Authors: Mujahid Adnan , Afzal Adeel , Dickert Franz L.
Published: August 2023

This perspective article focuses on the overwhelming significance of molecular recognition in biological processes and its emulation in synthetic molecules and polymers for chemical sensing. The historical journey, from early investigations into enzyme catalysis and antibody–antigen interactions to Nobel Prize-winning breakthroughs in supramolecular chemistry, emphasizes the development of tailored molecular recognition materials. The discovery of supramolecular chemistry and molecular imprinting, as a versatile method for mimicking biological recognition, is discussed. The ability of supramolecular structures to develop selective host–guest interactions and the flexible design of molecularly imprinted polymers (MIPs) are highlighted, discussing their applications in chemical sensing. MIPs, mimicking the selectivity of natural receptors, offer advantages like rapid synthesis and cost-effectiveness. Finally, addressing major challenges in the field, this article summarizes the advancement of molecular recognition-based systems for chemical sensing and their transformative potential.
Low-Power AlGaN/GaN Triangular Microcantilever for Air Flow Detection
Authors: Uppalapati Balaadithya , Gajula Durga , Bava Manav , Muthusamy Lavanya , Koley Goutam
Published: August 2023

This paper investigates an AlGaN/GaN triangular microcantilever with a heated apex for airflow detection utilizing a very simple two-terminal sensor configuration. Thermal microscope images were used to verify that the apex region of the microcantilever reached significantly higher temperatures than other parts under applied voltage bias. The sensor response was found to vary linearly with airflow rate when tested over a range of airflow varying from 16 to 2000 sccm. The noise-limited flow volume measurement yielded ~4 sccm resolution, while the velocity resolution was found to be 0.241 cm/s, which is one of the best reported so far for thermal sensors. The sensor was able to operate at a very low power consumption level of ~5 mW, which is one of the lowest reported for these types of sensors. The intrinsic response time of the sensor was estimated to be on the order of a few ms, limited by its thermal properties. Overall, the microcantilever sensor, with its simple geometry and measurement configurations, was found to exhibit attractive performance metrics useful for various sensing applications.
Dual-Path Information Fusion and Twin Attention-Driven Global Modeling for Solar Irradiance Prediction
Authors: Yang Yushi , Tang Zhanjun , Li Zhiyuan , He Jianfeng , Shi Xiaobing , Zhu Yuting
Published: August 2023

Accurate prediction of solar irradiance holds significant value for renewable energy usage and power grid management. However, traditional forecasting methods often overlook the time dependence of solar irradiance sequences and the varying importance of different influencing factors. To address this issue, this study proposes a dual-path information fusion and twin attention-driven solar irradiance forecasting model. The proposed framework comprises three components: a residual attention temporal convolution block (RACB), a dual-path information fusion module (DIFM), and a twin self-attention module (TSAM). These components collectively enhance the performance of multi-step solar irradiance forecasting. First, the RACB is designed to enable the network to adaptively learn important features while suppressing irrelevant ones. Second, the DIFM is implemented to reinforce the model’s robustness against input data variations and integrate multi-scale features. Lastly, the TSAM is introduced to extract long-term temporal dependencies from the sequence and facilitate multi-step prediction. In the solar irradiance forecasting experiments, the proposed model is compared with six benchmark models across four datasets. In the one-step predictions, the average performance metrics RMSE, MAE, and MAPE of the four datasets decreased within the ranges of 0.463–2.390 W/m2, 0.439–2.005 W/m2, and 1.3–9.2%, respectively. Additionally, the average R2 value across the four datasets increased by 0.008 to 0.059. The experimental results indicate that the model proposed in this study exhibits enhanced accuracy and robustness in predictive performance, making it a reliable alternative for solar irradiance forecasting.
Bandwidth Enhancement of a V-Band Klystron with Stagger-Tuned Multiple Radial Re-Entrant Cavities
Authors: Kumar M. Santosh , Maity Santigopal , Mandal Soumaya , Pal Debasish , Koley Chaitali , Bandyopadhyay Ayan Kumar
Published: August 2023

The V-band frequencies are becoming popular due to their application potential towards secure high data rate communications. This article reports bandwidth enhancement of an 11-cavity V-band Klystron amplifier employing staggered tuning. A systematic approach is presented to stagger-tune the periodically allocated multiple cavities of the Klystron operating at 60.1 GHz. Using the three-dimensional particle-in-cell (PIC) simulation, it is shown that, employing the proposed approach, the −3 dB bandwidth of the device (with peak tuned configuration) has been increased from 165 MHz to 540 MHz, demonstrating a 260% increment. The −1 dB bandwidth of the device is estimated to be 270 MHz. The proposed approach of stagger tuning may be employed for similar devices employing multiple RF cavities to meet the requirement of wide bandwidth.
Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment
Authors: Nadeem Muhammad , Dilshad Naqqash , Alghamdi Norah Saleh , Dang L. Minh , Song Hyoung-Kyu , Nam Junyoung , Moon Hyeonjoon
Published: August 2023

The recognition of fire at its early stages and stopping it from causing socioeconomic and environmental disasters remains a demanding task. Despite the availability of convincing networks, there is a need to develop a lightweight network for resource-constraint devices rather than real-time fire detection in smart city contexts. To overcome this shortcoming, we presented a novel efficient lightweight network called FlameNet for fire detection in a smart city environment. Our proposed network works via two main steps: first, it detects the fire using the FlameNet; then, an alert is initiated and directed to the fire, medical, and rescue departments. Furthermore, we incorporate the MSA module to efficiently prioritize and enhance relevant fire-related prominent features for effective fire detection. The newly developed Ignited-Flames dataset is utilized to undertake a thorough analysis of several convolutional neural network (CNN) models. Additionally, the proposed FlameNet achieves 99.40% accuracy for fire detection. The empirical findings and analysis of multiple factors such as model accuracy, size, and processing time prove that the suggested model is suitable for fire detection.
Modern Hydrogen Technologies in the Face of Climate Change—Analysis of Strategy and Development in Polish Conditions
Authors: Włodarczyk Renata , Kaleja Paulina
Published: August 2023

The energy production market based on hydrogen technologies is an innovative solution that will allow the industry to achieve climate neutrality in the future in Poland and in the world. The paper presents the idea of using hydrogen as a modern energy carrier, and devices that, in cooperation with renewable energy sources, produce the so-called green hydrogen and the applicable legal acts that allow for the implementation of the new technology were analyzed. Energy transformation is inevitable, and according to reports on good practices in European Union countries, hydrogen and the hydrogen value chain (production, transport and transmission, storage, use in transport, and energy) have wide potential. Thanks to joint projects and subsidies from the EU, initiatives supporting hydrogen technologies are created, such as hydrogen clusters and hydrogen valleys, and EU and national strategic programs set the main goals. Poland is one of the leaders in hydrogen production both in the world and in Europe. Domestic tycoons from the energy, refining, and chemical industries are involved in the projects. Eight hydrogen valleys that have recently been created in Poland successfully implement the assumptions of the “Polish Hydrogen Strategy until 2030 with a perspective until 2040” and “Energy Policy of Poland until 2040”, which are in line with the assumptions of the most important legal acts of the EU, including the European Union’s energy and climate policy, the Green Deal, and the Fit for 55 Package. The review of the analysis of the development of hydrogen technologies in Poland shows that Poland does not differ from other European countries. As part of the assumptions of the European Hydrogen Strategy and the trend related to the management of energy surpluses, electrolyzers with a capacity of at least 6 GW will be installed in Poland in 2020–2024. It is also assumed that in the next phase, planned for 2025–2030, hydrogen will be a carrier in the energy system in Poland. Poland, as a member of the EU, is the creator of documents that take into account the assumptions of the European Union Commission and systematically implement the assumed goals. The strategy of activities supporting the development of hydrogen technologies in Poland and the value chain includes very extensive activities related to, among others, obtaining hydrogen, using hydrogen in transport, energy, and industry, developing human resources for the new economy, supporting the activities of hydrogen valley stakeholders, building hydrogen refueling stations, and cooperation among Poland, Slovakia, and the Czech Republic as part of the HydrogenEagle project.
Elucidating the Potential of Biochar-Bentonite Composite and Kaolinite-Based Seed Balls for the Remediation of Coal Mining Impacted Heavy Metals Contaminated Soil
Authors: Medha Isha , Chandra Subhash , Bhattacharya Jayanta
Published: August 2023

Globally, open-pit coal mining is associated with severe land use impact and the contamination of soil and water resources with heavy metals. Thus, in growing economies like India, where coal is a significant energy source, the heavy metals contamination of soil and water become ubiquitous. This study uses a unique remediation approach by developing biochar-bentonite-based seed balls encapsulating Shorgham grass seeds at their core for application in the contaminated soil. The seed ball was developed by using the bentonite biochar composite in varying weight fractions of 0.5–5% with respect to the kaolinite, whose fractions in the seed ball also varied at one, three, and five parts. The seed balls were applied to the pots containing 3 kg of heavy-metals-contaminated soil for a pot-culture study in a polyhouse for a period of four months. Initial soil analysis results indicated that the mine soil samples showed poor nutrient and organic matter content and were contaminated with heavy metals such as Ni, Zn, Cr, and Cd. Post-pot-culture soil analysis results indicated that the application of seed balls containing five fractions of biochar composite with its combination with three and five-weight fractions of kaolinite showed substantial improvement in the pH, available nutrients, organic matter content, soil enzymes, and overall soil fertility index compared to the controlled study and other cases. The same combination of seed balls also significantly reduced the plant-available fractions of Ni, Zn, Cr, and Cd in the soil, indicating the stabilization of heavy metals within the soil matrix. Also, the application of seed balls substantially improved the plant physiology and reduced the release of stress hormones within the plant cells, indicating improvement in the plant’s biotic and abiotic stress factors. Thus, the application of seed balls in heavy metals contaminated soils, particularly over a large stretch of land, could be a low-cost and viable remediation technique.
The Influence of Animal Glue as an Additive on the Properties of Lime Architectural Grouts
Authors: Padovnik Andreja , Bokan-Bosiljkov Violeta
Published: August 2023

Organic additives from plant and animal extracts were commonly used in lime mortar in the past to improve and modify its properties. In modern times, they have been replaced by inorganic additives. The objective of this research is to investigate the influence of fish animal glue and the role of the filler particle size distribution on the fresh and hardened properties and durability of lime grouts. Wet density, water retention, fluidity, and injectability were tested in the fresh state. It was found that the particle size distribution of the selected filler, which can increase the packing density of the solid particles of the grout, and the W/B ratio have a great influence on water retention and fluidity. In the hardened state, porosity and compressive and splitting tensile strength were evaluated on 90-day- and 365-day-old specimens. The presence of animal glue improved the mechanical properties, due to a higher carbonation rate. The combination of the two fillers that resulted in a better packing of filler particles decreased the splitting tensile strength of the grout. To investigate the durability of the selected grouts, adhesion strength was measured on disc-sandwich models after non-accelerated and accelerated aging. The results show that the adhesive strength of grouts aged under laboratory conditions is lower than that of grouts subjected to accelerated aging.
Temporal and Spatial Changes of Drought Characteristics in Temperate Steppes in China from 1960 to 2020
Authors: Chen Jie , Zhang Bo , Zhou Jing , Guo Feng
Published: August 2023

To study the change in drought characteristics, it is helpful to explore the dynamics of drought, analyze the rules of drought, and prevent drought. The annual and seasonal standardized precipitation evapotranspiration index (SPEI) was calculated based on the meteorological data of the temperate steppe region of China from 1960 to 2020, and the spatio-temporal variation of drought characteristics was analyzed by combining the run-course theory to identify drought characteristics. The results show that: (1) During 1960–2020, the SPEI of the temperate steppe region fluctuated in the range of −1.5 to 1.5 and decreased significantly at a rate of −0.02·a−1 (p < 0.01). In general, there is an increasing trend of drought in the temperate steppe region. (2) There is little difference in the duration of drought in the four seasons, and the very low duration of drought is about 2 months. The cumulative drought intensity was higher in the semi-arid and semi-humid zones of the temperate zone. (3) The very low value of drought frequency occurred in summer, and the very high value of drought frequency occurred in winter. From SPEI-3 to SPEI-12, that is, from seasonal scale to interannual scale, with the increase of time scale, the distribution of the extreme value of drought frequency moved to the southwest, semi-arid, and arid areas. The frequency of the four drought levels showed light drought > middle drought > severe drought > extreme drought in all time scales and all zones.
Triaxial Shear Analysis Using Discrete Element Methods for Sandy Soil with an Improved Flexible Membrane Boundary
Authors: Yang Tingting , Zheng Weicheng , Zhang Hongguang , Yue Xiabing
Published: August 2023

Exploring strain localization has substantial potential to significantly impact the disaster resilience and mitigation capabilities of infrastructure, thus influencing project sustainability. Consequently, the field of sustainable geotechnical engineering is progressively directing attention toward studying soil strain localization. This research focuses on triaxial testing to deepen our understanding of this phenomenon by applying discrete element methods, thereby fostering the advancement of sustainable geotechnical engineering practices. While rigid-wall-based discrete element triaxial tests have been extensively studied, using flexible boundaries in these tests has received limited attention. This study introduces a three-stage method to enhance stress application in flexible membranes by applying confining pressure. A comparison of triaxial tests was conducted at both macroscopic and microscopic scales, utilizing flexible and rigid boundaries. Moreover, numerical simulations were performed on flexible membrane samples with various particle sizes to identify appropriate dimensions for flexible boundaries. Our results demonstrate that the improved flexible membrane provides more accurate representations of macroscopic and microscopic sample variations than rigid walls. Keeping the particle sizes for flexible membranes within the range of 0.2 to 0.8 times the characteristic particle size (r) is essential for striking a balance between simulation accuracy and computational efficiency. These findings enhance the accuracy of triaxial compression test simulations and offer a valuable foundation for studying strain localization in soils. Understanding these phenomena is essential for various geotechnical engineering applications, such as foundation design and slope stability analysis. Furthermore, these findings form a pivotal foundation for resource optimization and enhancing the reliability of engineered structures, thereby driving the advancement of sustainable geotechnical engineering practices.
Complexity Study on Multi-Field Coupling Systems for Underground Coal Fires
Authors: Wang Shaofeng , Guo Sida , Yang Yalan
Published: August 2023

Underground coal fires are a major disaster that needs to be urgently addressed in the coal mining industry, as they can cause waste of resources, environmental pollution, threaten the life and health of species, and cause serious damage to society and the economy. Currently, the research on the prevention and control of underground coal fires focuses more on the technical level and lacks scientific guidance at the methodological level. Based on this research gap, this paper uses CiteSpace 6.1.R6 to analyze the research hotspots and subjects in the field of underground coal fires and gives a comprehensive research overview of its microscopic chemical reaction substance and macroscopic multi-field coupling characteristics. Then, from the methodological level, it summarizes the complex system characteristics of underground coal fires and puts forward the elements and principles for the prevention and control of underground coal fire disasters under the paradigm of complex system research. This study proposes a new way of thinking for the prevention and control of underground coal fires and helps to build a whole-process prevention and control system.
Estimation of the Leaf Area Index of Winter Rapeseed Based on Hyperspectral and Machine Learning
Authors: Zhang Wei , Li Zhijun , Pu Yang , Zhang Yunteng , Tang Zijun , Fu Junyu , Xu Wenjie , Xiang Youzhen , Zhang Fucang
Published: August 2023

Leaf area index (LAI) is essential for evaluating crop growth and development. Destructive manual measurement methods mainly achieve traditional crop LAI acquisition. Due to the advantages of being fast and non-destructive, spectroscopy technology provides a feasible method for obtaining crop LAI. In order to achieve efficient acquisition of winter oilseed rape LAI, this study collected hyperspectral data and LAI data at the full-bloom stage of winter oilseed rape. It calculated the spectral indexes related to the LAI of the original spectrum and the first-order differential spectrum, respectively. The index with the highest correlation with the LAI of winter oilseed rape at the flowering stage was selected as the optimal spectral index for input. Subsequently, three machine learning methods, Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF), were used to construct the LAI model of winter oilseed rape, and the model was tested. The results show that the correlation coefficient between the spectral index calculated by the first-order differential processing of the original spectral data and the LAI of winter rapeseed is significantly improved compared with the original data. Among them, the spectral index NDVI with the best correlation coefficient with LAI can be obtained under the first-order differential: the correlation coefficient is 0.734, and the wavelength combination is 716 nm and 724 nm. At the same time, we found that when the input variables are the same, the RF model has higher estimation accuracy than the other models. The best estimation accuracy is obtained when the input variable is the first-order differential spectral index. The R2 of the model validation set is 0.810, RMSE is 0.455 cm2/cm2, MRE is 10.465%, and the model accuracy is high. The results of this study can provide a theoretical basis for crop monitoring based on spectral technology and provide a theoretical basis for crop growth.
Restorative Environmental Perception’s Influence on Post-Tour Behavior of Desert Off-Road Self-Driving Tourists: The Mediating Role of Flow Experience
Authors: Li Cai , He Jingyi
Published: August 2023

Desert tourism has always been one of the main types of adventure tourism in the world. Restorative environmental perception and flow experience are closely related concepts in adventure activities, but few studies have examined the mechanisms of their effects on tourists’ post-tour behaviors. Desert off-road self-driving tours, as a typical adventure tourism project, are suitable for studying the relationships among three factors. We chose the Kumutag Desert as our study area, as it attracts many off-road self-driving tourists every year. Based on SOR theory, data were collected through questionnaires, and partial least squares structural equation models were constructed using SmartPLS 3.0 software. It was found that (1) restorative environmental perception has a direct positive impact on visitors’ flow experiences; (2) flow experience has a direct positive influence on tourists’ revisit behaviors and recommendation behaviors; (3) novelty, escape, fascination, and compatibility in restorative environmental perception have a direct positive influence on tourists’ post-tour behaviors; (4) flow experience partially mediates the relationships among novelty, escape, fascination, compatibility, and post-tour behavior. This article deepens the understanding of the mechanism by which restorative environmental perception and flow experience affect tourists’ post-tour behaviors while expanding the scope through three studies and providing theoretical support for subsequent research on adventure tourism.
Removal Efficiency and Adsorption Kinetics of Methyl Orange from Wastewater by Commercial Activated Carbon
Authors: Serban Gabriel Valentin , Iancu Vasile Ion , Dinu Cristina , Tenea Anda , Vasilache Nicoleta , Cristea Ionut , Niculescu Marcela , Ionescu Ioana , Chiriac Florentina Laura
Published: August 2023

This research investigates commercial activated carbon (AC) potential to remove methyl orange (MO) dye removal from aqueous solution using a batch process. The AC material was characterized using FTIR spectroscopy and SEM analysis. The effect of the main operating parameters, such as the pH, adsorbent dosage, contact time, and initial dye concentration, was studied. MO removal could be accomplished within 30 min at a pH value of 3. The calculated maximum MO adsorption capacity onto activated carbon was 129.3 mg/g, while the removal efficiency was 97.8%. Adsorption results were analyzed by studying the Langmuir and Freundlich isotherm models. The MO adsorption data on activated carbon were better explained by the Langmuir isotherm than by the Freundlich isotherm. The pseudo-second-order kinetic model may have had an effect on the MO dye adsorption on AC material. This research showed that the commercial activated carbon can be used as an effective sorbent for MO removal from wastewater sample. Moreover, the AC material has good reusability and practical utilization capacities.
Acoustic-Gas Coupling Response Law in the Whole Process of Coal and Gas Outburst
Authors: Zhang Chaolin , Zeng Wei , Xu Jiang , Peng Shoujian , Yin Shan , Jiang Qiaozhen , Liu Mingliang
Published: August 2023

The intensification of the global energy crisis has led to an increasing demand for coal. China is a major coal-producing country in the world and also the country with the most severe coal and gas outburst disasters. Thus, the coal and gas outburst experiment was conducted, and the following results were obtained: the whole outburst process was divided into three stages, namely the outburst preparation stage, the outburst gestation stage, and the outburst development stage. The gas pressure and acoustic emission signals show significant changes in all three stages, while the variation patterns are different. The gas pressure changes were strongest and the acoustic emission signals were highest during the development stage. Therefore, the outburst development stage was further subdivided into four phases, and the correlation between acoustic emission and gas pressure in each phase was analyzed in detail. Furthermore, the acoustic emission signals in three stages were compared and analyzed. The peak values of acoustic emission count and energy reached 285 times·s−1 and 245 V in the preparation stage and reached 265 times·s−1 and 231 V in the gestation stage, respectively, only 1.66%~1.78% and 2.19%~2.32% of the development stage, namely 15,980 times·s−1 and 10,566 V. Moreover, it was found that the cumulative count and cumulative energy showed a parabolic relationship with the development time of the outburst. Based on the above experimental results, during the production process in coal mines, the dangerous state of outbursts can be monitored through gas pressure changes in the outburst preparation stage and gestation stage. Once in the development stage, more sensitive signals of acoustic emission and their fitting results are used for outburst hazard monitoring and early warning. Monitoring and warning of outbursts of combined gas pressure and acoustic emission signals can effectively improve the safety level of coal mine production.