bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis and Characterization of Statistical and Block Copolymers of n-Hexyl Isocyanate and 3-(Triethoxysilyl) Propyl Isocyanate via Coordination Polymerization
Authors: Panteli Maria , Mantzara Dimitra , Katara Aikaterini , Choinopoulos Ioannis , Pitsikalis Marinos
Published: October 2023

Well-defined statistical copolymers of n-hexyl isocyanate, HIC, and 3-(triethoxysilyl)propyl isocyanate, TESPI, were synthesized via coordination polymerization mechanism, employing a chiral half-titanocene complex as initiator. The monomer reactivity ratios of the statistical copolymers were calculated using linear graphical methods and the computer program COPOINT in the frame of the terminal model. The molecular and structural characteristics of the copolymers were also calculated. The kinetics of the thermal decomposition of the statistical copolymers was studied by Thermogravimetric Analysis, TGA, and Differential Thermogravimetry, DTG, and the activation energy of this process was calculated employing several theoretical models. In addition, block copolymers constituted from PHIC and PTESPI blocks were synthesized by sequential coordination polymerization. All samples were characterized by nuclear magnetic resonance, NMR, spectroscopy and size exclusion chromatography, SEC. The thermal stability of the blocks was also studied by TGA and DTG and compared to the corresponding statistical copolymers.
Effect of Different Compatibilizers on the Mechanical, Flame Retardant, and Rheological Properties of Highly Filled Linear Low-Density Polyethylene/Magnesium Hydroxide Composites
Authors: Sun Beibei , Dang Li , Bi Qiuyan , Li Rujie , Gong Qiuhui , Wan Zhihao , Xu Shiai
Published: October 2023

Maleic anhydride-modified homopolymerized polypropylene (PP-g-MAH) and maleic anhydride-modified polyolefin elastomer (POE-g-MAH) were used as bulking agents to improve the poor processing and mechanical properties of highly filled composites due to high filler content. In this study, a series of linear low-density polyethylene (LLDPE)/magnesium hydroxide (MH) composites were prepared by the melt blending method, and the effects of the compatibilizer on the mechanical properties, flame retardancy, and rheological behavior of the composites were investigated. The addition of the compatibilizer decreased the limiting oxygen index (LOI) values of the composites, but they were all greater than 30.00%, which belonged to the flame retardant grade. Mechanical property tests showed that the addition of the compatibilizer significantly increased the tensile and impact strengths of the LLDPE/60MH (MH addition of 60 wt%) composites. Specifically, the addition of 5 wt% POE-g-MAH increased 154.07% and 415.47% compared to the LLDPE/60MH composites, respectively. The rotational rheology test showed that the addition of the compatibilizer could effectively improve the processing flow properties of the composites. However, due to the hydrocarbon structure of the compatibilizer, its flame retardant properties were adversely affected. This study provides a strategy that can improve the processing and mechanical properties of highly filled composites.
Polysaccharide/Silica Microcapsules Prepared via Ionic Gelation Combined with Spray Drying: Application in the Release of Hydrophilic Substances and Catalysis
Authors: Elzayat Asmaa M. , Adam-Cervera Inés , Albus Marie , Cháfer Amparo , Badia José D. , Pérez-Pla Francisco F. , Muñoz-Espí Rafael
Published: October 2023

Polysaccharide/silica hybrid microcapsules were prepared using ionic gelation followed by spray-drying. Chitosan and alginate were used as biopolymer matrices, and in situ prepared silica was used as a structuring additive. The prepared microparticles were used in two very different applications: the encapsulation of hydrophilic molecules, and as a support for palladium nanoparticles used as catalysts for a model organic reaction, namely the reduction of p-nitrophenol by sodium borhydride. In the first application, erioglaucine disodium salt, taken as a model hydrophilic substance, was encapsulated in situ during the preparation of the microparticles. The results indicate that the presence of silica nanostructures, integrated within the polymer matrix, affect the morphology and the stability of the particles, retarding the release of the encapsulated substance. In the second application, chloropalladate was complexed on the surface of chitosan microparticles, and palladium(II) was subsequently reduced to palladium(0) to obtain heterogeneous catalysts with an excellent performance.
Bond Parameter Calibration and Crushing Process Analysis of Brown Rice Kernels
Authors: Shi Ziang , Liu Xiaopeng , Zhang Yonglin , Zhou Jin , Li Hui , Duan Feng
Published: October 2023

Aiming to resolve the practical problem of brown rice kernels being easily broken due to overprocessing during processing (milling and polishing), brown rice kernels of Japonica rice, after hulling, were used as the research object in this study. Firstly, through a texture meter test, the discrete element bonding parameters (Kn is normal stiffness per unit area, Kτ is tangential stiffness per unit area, Cn is critical normal stiffness, Cτ is critical shear stiffness) were obtained. Secondly, a brown rice kernels’ bonding particle model was established by EDEM, and then a second orthogonal rotational combination test was carried out to calibrate the discrete bonding parameters, Kn = 4.43 × 1012 N/m3, Kτ = 6.13 × 1011 N/m3, Cn = 2.55 × 107 Pa, and Cτ = 7.92 × 107 Pa. The error of parameter calibration was within 5%, and the results were able to reflect the actual situation more realistically. Finally, analysis of the crushing process of brown rice kernels showed that their ability to withstand shear damage was not as great as their pressure-bearing capacity. The design of the relevant mechanism and the setting of parameters should be based on the critical shear stiffness of brown rice kernels, and the actual shear force Fτ* set during the processing should be smaller than the theoretical critical shear force Fτ (Fτ* < Fτ = 9.11 N). This study can provide a theoretical basis for optimizing the key structure and operating parameters of rice milling machines and polishing machines to effectively solve the practical problem of increased crushing of brown rice kernels due to overprocessing.
Rapid Prediction of the In Situ Pyrolysis Performance of Tar-Rich Coal Using the POD Method
Authors: Wang Zhendong , Ye Qianhao , Li Mingjie , Cheng Xiangqiang , Wei Jinjia , Yang Fu , Duan Zhonghui
Published: October 2023

In this paper, a POD reduced-order interpolation model for solving the in situ pyrolysis process of tar-rich coal is employed to predict the flow and heat transfer performance in the porous media region so as to save computational resources and realize fast calculations. Numerical simulation using the finite volume method (FVM) is firstly used to obtain sample data, based on the samples through the primary function and spectral coefficients of the solutions. The physical field information and parameter distribution under different conditions of inlet temperature, inlet velocity and permeability are predicted. The results are compared with those of FVM to verify the accuracy of the calculated results. The relative mean deviation (RME) of the results of the POD prediction of each parameter for each working condition was synthesized to be no more than 5%. The performance of in situ pyrolysis of tar-rich coal is then investigated, and the oil and gas production are predicted. As the inlet velocity increases from 0.3 m/s to 0.9 m/s, the fraction of high-quality oil and gas production reaches 0.47 and then decreases to 0.38. Increasing the inlet temperature and permeability has a negative effect on the fraction of high-quality hydrocarbon production, after which the quality fraction of high-quality oil and gas dropped sharply to about 0.22. Porosity has a positive impact on the oil and gas production. When the porosity reaches 0.3, the quality fraction of high-quality oil and gas can reach 0.27.
Communicating the Crusading Activity of the Kings of Navarre in the 14th and 15th Centuries
Authors: Pavón Benito Julia
Published: October 2023

The mediaeval historiographical memory of the Crusades in the Kingdom of Navarre is unique precisely because two of its monarchs, the Counts of Champagne—Theobald I and II—actively participated in the Crusader campaigns during 1239–1241 and in 1270, respectively. Despite the importance of the Crusades which, starting from the early twelfth century, also encompassed the connection of this kingdom with Jerusalem’s paradigms of the warrior and religious pilgrimage, it can be asserted that there are scarcely any traces of narrative communication in Navarre about the Crusades, either politically or ideologically. This paper analyses the question of documentation and communication about the Crusades from the study of the chronicles of the Kingdom of Navarre in the Late Middle Ages. The purpose is to identify the characteristics and keys of the texts, dissimilar to the welcoming impact of the Crusades in Hispanic and European political, cultural and religious spheres.
Hybrid Cross-Feature Interaction Attention Module for Object Detection in Intelligent Mobile Scenes
Authors: Tian Di , Han Yi , Liu Yongtao , Li Jiabo , Zhang Ping , Liu Ming
Published: October 2023

Object detection is one of the fundamental tasks in computer vision, holding immense significance in the realm of intelligent mobile scenes. This paper proposes a hybrid cross-feature interaction (HCFI) attention module for object detection in intelligent mobile scenes. Firstly, the paper introduces multiple kernel (MK) spatial pyramid pooling (SPP) based on SPP and improves the channel attention using its structure. This results in a hybrid cross-channel interaction (HCCI) attention module with better cross-channel interaction performance. Additionally, we bolster spatial attention by incorporating dilated convolutions, leading to the creation of the cross-spatial interaction (CSI) attention module with superior cross-spatial interaction performance. By seamlessly combining the above two modules, we achieve an improved HCFI attention module without resorting to computationally expensive operations. Through a series of experiments involving various detectors and datasets, our proposed method consistently demonstrates superior performance. This results in a performance improvement of 1.53% for YOLOX on COCO and a performance boost of 2.05% for YOLOv5 on BDD100K. Furthermore, we propose a solution that combines HCCI and HCFI to address the challenge of extremely small output feature layers in detectors, such as SSD. The experimental results indicate that the proposed method significantly improves the attention capability of object detection in intelligent mobile scenes.
Diabetic Plantar Foot Segmentation in Active Thermography Using a Two-Stage Adaptive Gamma Transform and a Deep Neural Network
Authors: Cao Zhenjie , Zeng Zhi , Xie Jinfang , Zhai Hao , Yin Ying , Ma Yue , Tian Yibin
Published: October 2023

Pathological conditions in diabetic feet cause surface temperature variations, which can be captured quantitatively using infrared thermography. Thermal images captured during recovery of diabetic feet after active cooling may reveal richer information than those from passive thermography, but diseased foot regions may exhibit very small temperature differences compared with the surrounding area, complicating plantar foot segmentation in such cold-stressed active thermography. In this study, we investigate new plantar foot segmentation methods for thermal images obtained via cold-stressed active thermography without the complementary information from color or depth channels. To better deal with the temporal variations in thermal image contrast when planar feet are recovering from cold immersion, we propose an image pre-processing method using a two-stage adaptive gamma transform to alleviate the impact of such contrast variations. To improve upon existing deep neural networks for segmenting planar feet from cold-stressed infrared thermograms, a new deep neural network, the Plantar Foot Segmentation Network (PFSNet), is proposed to better extract foot contours. It combines the fundamental U-shaped network structure, a multi-scale feature extraction module, and a convolutional block attention module with a feature fusion network. The PFSNet, in combination with the two-stage adaptive gamma transform, outperforms multiple existing deep neural networks in plantar foot segmentation for single-channel infrared images from cold-stressed infrared thermography, achieving an accuracy of 97.3% and 95.4% as measured by Intersection over Union (IOU) and Dice Similarity Coefficient (DSC) respectively.
Real-Time Recognition Method for Key Signals of Rock Fracture Acoustic Emissions Based on Deep Learning
Authors: Sun Lin , Lin Lisen , Yao Xulong , Zhang Yanbo , Tao Zhigang , Ling Peng
Published: October 2023

The characteristics of acoustic emission signals generated in the process of rock deformation and fission contain rich information on internal rock damage. The use of acoustic emissions monitoring technology can analyze and identify the precursor information of rock failure. At present, in the field of acoustic emissions monitoring and the early warning of rock fracture disasters, there is no real-time identification method for a disaster precursor characteristic signal. It is easy to lose information by analyzing the characteristic parameters of traditional acoustic emissions to find signals that serve as precursors to disasters, and analysis has mostly been based on post-analysis, which leads to poor real-time recognition of disaster precursor characteristics and low application levels in the engineering field. Based on this, this paper regards the acoustic emissions signal of rock fracture as a kind of speech signal generated by rock fracture uses this idea of speech recognition for reference alongside spectral analysis (STFT) and Mel frequency analysis to realize the feature extraction of acoustic emissions from rock fracture. In deep learning, based on the VGG16 convolutional neural network and AlexNet convolutional neural network, six intelligent real-time recognition models of rock fracture and key acoustic emission signals were constructed, and the network structure and loss function of traditional VGG16 were optimized. The experimental results show that these six deep-learning models can achieve the real-time intelligent recognition of key signals, and Mel, combined with the improved VGG16, achieved the best performance with 87.68% accuracy and 81.05% recall. Then, by comparing multiple groups of signal recognition models, Mel+VGG-FL proposed in this paper was verified as having a high recognition accuracy and certain recognition efficiency, performing the intelligent real-time recognition of key acoustic emission signals in the process of rock fracture more accurately, which can provide new ideas and methods for related research and the real-time intelligent recognition of rock fracture precursor characteristics.
Editorial for the Special Issue Recognition Robotics
Authors: Martínez-Otzeta José
Published: October 2023

Perception of the environment is an essential skill for robotic applications that interact with their surroundings [...]
Regularized Maximum Correntropy Criterion Kalman Filter for Uncalibrated Visual Servoing in the Presence of Non-Gaussian Feature Tracking Noise
Authors: Leite Glauber Rodrigues , Araújo Ícaro Bezerra Queiroz de , Martins Allan de Medeiros
Published: October 2023

Some advantages of using cameras as sensor devices on feedback systems are the flexibility of the data it represents, the possibility to extract real-time information, and the fact that it does not require contact to operate. However, in unstructured scenarios, Image-Based Visual Servoing (IBVS) robot tasks are challenging. Camera calibration and robot kinematics can approximate a jacobian that maps the image features space to the robot actuation space, but they can become error-prone or require online changes. Uncalibrated visual servoing (UVS) aims at executing visual servoing tasks without previous camera calibration or through camera model uncertainties. One way to accomplish that is through jacobian identification using environment information in an estimator, such as the Kalman filter. The Kalman filter is optimal with Gaussian noise, but unstructured environments may present target occlusion, reflection, and other characteristics that confuse feature extraction algorithms, generating outliers. This work proposes RMCKF, a correntropy-induced estimator based on the Kalman Filter and the Maximum Correntropy Criterion that can handle non-Gaussian feature extraction noise. Unlike other approaches, we designed RMCKF for particularities in UVS, to deal with independent features, the IBVS control action, and simulated annealing. We designed Monte Carlo experiments to test RMCKF with non-Gaussian Kalman Filter-based techniques. The results showed that the proposed technique could outperform its relatives, especially in impulsive noise scenarios and various starting configurations.
Using Distributed Temperature Sensing for Long-Term Monitoring of Pockmark Activity in the Gulf of Patras (Greece): Data Processing Hints and Preliminary Findings
Authors: Fakiris Elias , Papatheodorou George , Christodoulou Dimitris , Roumelioti Zafeiria , Sokos Efthimios , Geraga Maria , Giannakopoulos Vasileios , Dimas Xenophon , Ferentinos George
Published: October 2023

Patras Gulf pockmark field (Western Greece) is a tectonically controlled field that has been activated at least twice by strong earthquakes (M5.4, 14 July 1993 and M6.4, 8 June 2008), and episodic gas seepages have been recorded in the past using geophysical means. A distributed temperature sensor (DTS) system was deployed inside a shallow pockmark and along an active fault at the northern end of the field. This ongoing experiment represents the first long-term monitoring ever conducted on gas-bearing pockmarks and active faults by the DTS system. For now, we have acquired and analyzed data regarding about 1.56 years. One of the primary objectives of this study is to establish methodological queues for data processing and analysis, including spectral analysis and incomplete data treatment techniques, to be standardized for use in further stages of the experiment. Spectral analysis was proven capable of separating the temperature footprint of background environmental components, such as sea-atmosphere heat flux, tides, and winds/waves, from high-frequency temperature residuals. Those residuals represent unusual events that might be correlated to seismicity. Monitoring the causal relationship between seismic activity and seabed water temperature changes in the field was thus attempted. No significant local earthquakes occurred during the monitoring period. Although the relation between seismicity and irregular seabed water temperature events was not systematic, we postulate that four thermal events have a causative link with the local seismicity. The DTS system constitutes a low-cost monitoring system, and the promising preliminary results of this experiment suggest that it is worth testing for a longer period.
Comparison of Imaging Radar Configurations for Roadway Inspection and Characterization
Authors: Wu Mengda , Ferro-Famil Laurent , Boutet Frederic , Wang Yide
Published: October 2023

This paper investigates the performance of a wide variety of radar imaging modes, such as nadir-looking B-scan, or side-looking synthetic aperture radar tomographic acquisitions, performed in both back- and forward-scattering geometries, for the inspection and characterization of roadways. Nadir-looking B-scan corresponds to a low-complexity mode exploiting the direct return from the response, whereas side-looking configurations allow the utilization of angular and polarimetric diversity in order to analyze advanced features. The main objective of this paper is to evaluate the ability of each configuration, independently of aspects related to operational implementation, to discriminate and localize shallow underground defects in the wearing course of roadways, and to estimate key geophysical parameters, such as roughness and dielectric permittivity. Campaign measurements are conducted using short-range radar stepped-frequency continuous-waveform (SFCW) devices operated in the C and X bands, at the pavement fatigue carousel of Université Gustave Eiffel, over debonded areas with artificial defects. The results indicate the great potential of the newly proposed forward-scattering tomographic configuration for detecting slight defects and characterizing roadways. Case studies, performed in the presence of narrow horizontal heterogeneities which cannot be detected using classical B-scan, show that both the coherent integration along an aperture using the back-projection algorithm, and the exploitation of scattering mechanisms specific to the forward-looking bistatic geometry, allows anomalous echoes to be detected and further characterized, confirming the efficacy of radar imaging techniques in such applications.
Identity Work in Athletes: A Systematic Review of the Literature
Authors: Chun Yoonki , Wendling Elodie , Sagas Michael
Published: October 2023

The identity work process allows athletes to achieve a continuous development, revision, and maintenance of themselves. It provides insight into their self-perceptions and particularly intensifies during critical life events. While this process has been widely acknowledged, scant attention has been given to explicitly identifying the specific activities (i.e., identity work modes) involved in athletic identity work and integrating an overarching framework to inform coherent and continuous identities. Thus, we conducted a systematic review of the athletic identity literature to assess how this perspective is represented. Following the PRISMA guidelines, we reviewed 54 articles and analyzed the overall characteristics, bibliographical networks, and accumulated empirical findings. Through this process, we were able to identify the impact of having a strong athletic identity on key variables within and outside of sport. Based on the findings, we examined how identity work modes are depicted and discussed in the literature. Further discussion on how athletic identity literature can contribute to the broader body of knowledge is outlined.
Analysis of Morphological, Physiological, and Biochemical Traits of Salt Stress Tolerance in Asian Rice Cultivars at Seedling and Early Vegetative Stages
Authors: Alkahtani Jawaher , Dwiningsih Yheni
Published: October 2023

Rice (Oryza sativa L.) is a primary energy food for the Asian population. One of the greatest constraints in rice production is soil salinity because rice is very susceptible to salt. Meanwhile, many agricultural lands in Asia are in saline areas. It is important to identify and develop salt-tolerant rice varieties that highly adapt to Asian climates. By combining morphological, physiological, and biochemical assessments for screening the salt tolerance of 116 Asian rice cultivars, we were able to classify them into tolerant, moderate, and sensitive rice cultivars under salinity stress conditions and also understand salt tolerance mechanisms. The rice cultivars that are salt-tolerant include Pokkali from India, TCCP 266 and IR 45427 from the Philippines, and Namyang 7 from Republic of Korea. However, salt-sensitive rice varieties like IR29 and IR58 are from the Philippines, and Daegudo and Guweoldo are from Korea. The salt-tolerant varieties showed signs of tolerance, including a lower percent reduction in germination percentage, root length, root fresh weight, shoot length, plant biomass, and chlorophyll content. In order to maintain the cellular osmotic balance under saline conditions, the salt-tolerant varieties exhibited less membrane damage, a lower Na/K ratio, high proline and sugar accumulation, and lower levels of malondialdehyde (MDA) and hydrogen peroxide (H2O2). Pokkali from India, TCCP 266 and IR 45427 from the Philippines, and Namyang 7 from Republic of Korea are recommended as valuable germplasm resources for Asian rice breeding programs in saline agricultural areas.
Trace Metals in PM10 and Associated Health Risk in Two Urban Sites Located in Campeche
Authors: Cerón Bretón Julia Griselda , Cerón Bretón Rosa María , Espinosa Guzmán Alberto Antonio , Rangel Marrón Marcela , Guarnaccia Claudio , Rossi Domenico , Vargas Canto María de Guadalupe , Aguilar Ucán Claudia Alejandra , Lara Severino Reyna del Carmen , Ruíz Marín Alejandro , Canedo López Yunuen , Romero Carlos Montalvo , Carranco Lozada Simón Eduardo , Ramírez Lara Evangelina , Solano Moreno Maricela Sallonara
Published: October 2023

This study reports the trace metal concentrations in PM10 collected in two urban sites of Campeche, Mexico (the INAH site, located in the downtown, and the TNL site, located in the Tecnologico Nacional de Mexico campus Lerma). Trace metals (Cd, Co, Cu, Fe, Mn, and Zn) were determined by flame atomic absorption spectroscopy, with Fe and Mn being the most dominant species. Cd and Co showed high values of enrichment factors, indicating that they were highly enriched by anthropogenic sources. A health risk assessment was carried out according to the IRIS-EPA methodology considering both carcinogenic and non-carcinogenic effects and different age and gender population groups. The TNL site showed cancer risk coefficients that exceed the maximum limits established by the EPA and the WHO (1 × 10−6 and 1 × 10−5, respectively), being higher for Cd and Co. The non-carcinogenic risk exceeded the limits recommended by the EPA, being higher in the INAH site for cobalt (HQ = 28.92). There is a latent risk that the exposed population may develop cardiovascular and respiratory diseases due to inhalation of the metals measured in PM10. It is necessary to carry out more continuous monitoring campaigns of trace metals linked to PM10 in this area to enable a better understanding of the effects of these contaminants on the health of the exposed population. The results of the present work constitute the first efforts to evaluate the trace metals concentrations in PM10 in urban areas of the city of Campeche, Campeche and can be used to develop programs focused on improving air quality.
Environmental Analysis of the Valorization of Woody Biomass Residues: A Comparative Study with Vine Pruning Leftovers in Portugal
Authors: Simões Carla L. , Simoes Ricardo , Gonçalves Ana Sofia , Nunes Leonel J. R.
Published: October 2023

Evaluating Global Warming Potential (GWP) in waste management scenarios is crucial, especially in light of the escalating global concern for climate change and the pivotal role that waste management plays in mitigating this crisis. This research examines the GWP of three distinct waste management scenarios, each with a unique approach: (1) open burning, a method involving direct combustion with a GWP of 1600.1 kg·CO2eq, chiefly attributed to direct emissions without any mitigation tactics; (2) energy recovery, which capitalizes on converting waste into energy, yielding a GWP of 1255.4 kg·CO2eq, the reduction resulting primarily from avoided heat production; and (3) pyrolysis, an advanced thermal decomposition process that remarkably registers a negative GWP of −1595.1 kg·CO2eq, mainly credited to the carbon sequestration capacity of biochar production and optimal energy conversion efficiency. These outcomes emphasize the ecological merits of waste management approaches that produce lower, or even better, negative GWP values. In particular, pyrolysis emerges as a powerful way of transforming waste management into a potential carbon sink, proving crucial for climate change counteraction. Nevertheless, for effective real-world deployment, the study highlights the importance of addressing technical, economic, and societal challenges, underscoring the need for holistic, interdisciplinary research.
Scenario-Based Optimization towards Sustainable Power Generation in Sudan
Authors: Ali Babkir , Gamil Ahmed
Published: October 2023

Sudan faces an electricity supply shortage despite its abundant natural resources. This paper aims to manage these resources for sustainable power generation to meet Sudan’s electricity demand. The sustainability assessment integrates quantitative analysis of power generation’s impacts on water, land, and greenhouse gas (GHG) emissions, in addition to the levelized cost of electricity (LCOE). Cost-effective, resource- and GHG emission-effective, and GHG-stringent scenarios are executed in this study to investigate the impact of different constraints on the sustainability of power generation in Sudan. The average LCOEAV for these three scenarios is 43.64–100.00 USD/MWh, with the lowest in the cost-effective scenario and the highest in the resource- and GHG emission-effective scenario. The LCOEAV for the stringent scenario is 32% higher than the cost-effective scenario. The two governmental and lowest-cost plans, which serve as the business-as-usual cases in this study, are optimized and comparatively evaluated. The sensitivity analysis is conducted by reducing each clean energy pathway to a minimum LCOE of 42.89 USD/MWh. Solar–photovoltaic (PV), wind, and hydroelectricity pathways are the most sensitive to the LCOE and can significantly contribute to Sudan’s total power generation if their costs are minimal. A rational scenario for power generation in Sudan is developed to improve sustainability performance and avoid the unreliability of the studied scenarios and cases. The rational average generation mix comprises 44% clean energy, 46% fossil fuels, and 10% imported electricity pathways.
Quantifying Road Transport Resilience to Emergencies: Evidence from China
Authors: Zhang Xue , Lu Yi , Wang Jie , Yuan Donghui , Huang Xianwen
Published: October 2023

Facing the shock of emergencies, how resilient is the road transport system? In this process, how are the system’s absorption capacity, adaptability, and recovery capacity? These are very important for the development of sustainable road transportation. Taking China’s road transport during the COVID-19 pandemic as the research object, this paper introduces an integrated resilience quantification method, draws a road transport resilience curve, and comprehensively and dynamically analyzes and compares the resilience of China’s road transport system at different stages among different regions and under different epidemic waves. The results show that the resilience of road passenger and freight transport differs in the face of external disturbance. Freight transport resilience is better than that of passenger transport. Compared to passenger transport, freight transport is more robust; the impacted speed is slower, the recovery speed is faster, the recovery capacity is stronger, and the affected period is shorter. There is regional heterogeneity in road transport resilience. This heterogeneity is reflected in the whole change process of system performance with external disturbance, including absorption capacity, adaptation capacity, and recovery capacity. The resilience of road transport under different waves of the epidemic is different. Compared to the first wave of the epidemic, the resilience of road transport indicators at all stages has been dramatically improved in the later rebound wave of the epidemic. This can help in the development of evidence-based road transport sustainability strategies.
Forecasting Accuracy of Traditional Regression, Machine Learning, and Deep Learning: A Study of Environmental Emissions in Saudi Arabia
Authors: Sarwar Suleman , Aziz Ghazala , Balsalobre-Lorente Daniel
Published: October 2023

Currently, the world is facing the problem of climate change and other environmental issues due to higher emissions of greenhouse gases. Saudi Arabia is not an exception due to the dependence of the Saudi economy on fossil fuels, which adds to the problem. However, due to the nonlinear pattern of pollution-creating gases, including nitrogen and sulfur dioxide, it is not effortless to rely on forecasting accuracy. Nevertheless, it is essential to denoise the data to extract the reliable outcomes used by different econometric approaches. Hence, the current paper introduces a hybrid model combining compressed sensor denoising (CSD) with traditional regression, machine learning, and deep learning techniques. Comparing different hybrid models and various denoising techniques revealed that CSD-GAN is the best model for accurately predicting NO2 and SO2, as compared with ARIMA, RLS, and SVR. Also, when the comparison is made between predicted and actual NO2 and SO2 levels, these are aligned, proving that CSD-GAN is superior in its level and direction of prediction. It can be concluded that the GAN model is the best hybrid model for predicting NO2 and SO2 emissions in Saudi Arabia. Hence, this model is recommended to policymakers for predicting environmental externalities and framing policies accordingly.
Enhancing Microseismic Signal Classification in Metal Mines Using Transformer-Based Deep Learning
Authors: Peng Pingan , Lei Ru , Wang Jinmiao
Published: October 2023

As microseismic monitoring technology gains widespread application in mine risk pre-warning, the demand for automatic data processing has become increasingly evident. One crucial requirement that has emerged is the automatic classification of signals. To address this, we propose a Transformer-based method for signal classification, leveraging the global feature extraction capability of the Transformer model. Firstly, the original waveform data were framed, windowed, and feature-extracted to obtain a 16 × 16 feature matrix, serving as the primary input for the subsequent microseismic signal classification models. Then, we verified the classification performance of the Transformer model compared with five microseismic signal classification models, including VGG16, ResNet18, ResNet34, SVM, and KNN. The experimental results demonstrate the effectiveness of the Transformer model, which outperforms previous methods in terms of accuracy, precision, recall, and F1 score. In addition, a comprehensive analysis was performed to investigate the impact of the Transformer model’s parameters and feature importance on outcomes, which provides a valuable reference for further enhancing microseismic signal classification performance.
Coupling of Forest Carbon Densities with Landscape Patterns and Climate Change in the Lesser Khingan Mountains, Northeast China
Authors: Wang Xinghui , Sun Yuman , Jia Weiwei , Wang Hezhi , Zhu Wancai
Published: October 2023

This research investigated the effects of the forest landscape composition and spatial distribution and local climate change’s lag effects on the carbon density of stands and provides a reference for optimizing the stand structure and sustainable management of forest resources in Xinqing District, Yichun City, Heilongjiang Province, China. Using second-class forest resource survey data of the Xinqing Forestry Bureau for 2007 and 2017, the forest carbon density, landscape pattern index and local forest climate were quantified by ArcGIS10.7, Fragstats4.2 and SPSS25, and a coupling coordination degree model was constructed to reflect their correlations. The overall broadleaved mixed forest area was larger in the new green area, and the overall forest productivity had improved in the past ten years. Forest management gradually improved from nonforest to forest land, resulting in a high degree of fragmentation in the surrounding landscape. The coupling research on the forest carbon density and the forest landscape pattern index and local climate index showed that, overall, the landscape pattern and the impact of climate change on the forest carbon density had a positive interaction; adjustments and improvements can be made to the forest carbon density in the poor-condition area by combining specific situations of the landscape pattern and climate change.
Dynamic Simulation Analysis of Influencing Factors of Energy Consumption of Public Building Envelope in Central China
Authors: Yue Jianwei , Lu Jing , Yan Chao , Liu Jiahui , Pan Yuqin
Published: October 2023

It difficult for buildings in cold regions to reasonably meet ultra-low energy consumption requirements. To explore the influencing factors of building energy consumption in this type of climatic region, this paper takes a typical public building in Zhengzhou, China as a case study to dynamically simulate an entire building. The influence of the window glass, window-to-wall ratio (WWR), thermal insulation material thickness and other factors on building energy consumption is considered. The sensitivity of envelope design factors to energy consumption is analyzed by the orthogonal test. The results indicate that the effect of changing the technical parameters of the external envelope structure on building energy efficiency is significant. Moreover, range analysis is used to determine the primary and secondary factors with regard to their influence on the energy consumption of the building. The WWR, the type of external window glass and the thickness of the external wall insulation layer should be considered first in energy-saving design; the energy-saving potential of the roof insulation thickness is not as significant as these factors. The results of the study can provide a reference for the energy-saving design of public buildings in Central China.
Mechanism and Empirical Evidence of Green Taxation Influencing Carbon Emissions in China’s Yangtze River Economic Belt
Authors: Fang Xingcun , Wei Mengting , Huang Wei-Chiao
Published: October 2023

Based on the panel data of 100 cities in China’s Yangtze River Economic Belt from 2010 to 2020 and the extended STIRPAT model, this paper uses SYS-GMM to empirically study the impact of green taxation on carbon emissions in the Yangtze River Economic Belt. Then, it explores the effect path of green taxation on regional carbon emissions using the intermediary effect model and analyzes the threshold characteristics of the influence of urban greening level on the regional carbon emissions of green taxation using the threshold effect model. The results show that, (1) from 2010 to 2020, the carbon emissions in China’s Yangtze River Economic Belt showed a slow rising trend, and carbon emissions in the lower reaches were significantly higher than those in the middle and upper reaches. (2) Green taxation can significantly suppress carbon emissions in the Yangtze River Economic Belt. However, green taxation has the weakest inhibitory effect on carbon emissions in the upstream region and is slightly stronger in the middle reaches, with the strongest inhibitory effect on carbon emission in the downstream region. (3) From the perspective of the action path, the level of green technology innovation has a significant partial mediating effect. Green taxation mainly realizes carbon emission reductions by improving the level of urban green technology innovation, and its intermediary effect accounts for 17.6% of the total effect of green taxation on regional carbon emissions and 15.6% of the total effect of green taxation on per capita carbon emission intensity. (4) Further research shows that the emission reduction effect of green taxation is also influenced by the level of urban greening, showing a single threshold effect. Before reaching the threshold value, the inhibition effect of green taxation on carbon emission levels is relatively strong, and after crossing the threshold value, the inhibition effect is weakened.
Advancements in Doping Strategies for Enhanced Photocatalysts and Adsorbents in Environmental Remediation
Authors: Sen Pramita , Bhattacharya Praneel , Mukherjee Gargi , Ganguly Jumasri , Marik Berochan , Thapliyal Devyani , Verma Sarojini , Verros George D. , Chauhan Manvendra Singh , Arya Raj Kumar
Published: October 2023

Environmental pollution poses a pressing global challenge, demanding innovative solutions for effective pollutant removal. Photocatalysts, particularly titanium dioxide (TiO2), are renowned for their catalytic prowess; however, they often require ultraviolet light for activation. Researchers had turned to doping with metals and non-metals to extend their utility into the visible spectrum. While this approach shows promise, it also presents challenges such as material stability and dopant leaching. Co-doping, involving both metals and non-metals, has emerged as a viable strategy to mitigate these limitations. Inthe fieldof adsorbents, carbon-based materials doped with nitrogen are gaining attention for their improved adsorption capabilities and CO2/N2 selectivity. Nitrogen doping enhances surface area and fosters interactions between acidic CO2 molecules and basic nitrogen functionalities. The optimal combination of an ultramicroporous surface area and specific nitrogen functional groups is key to achievehigh CO2 uptake values and selectivity. The integration of photocatalysis and adsorption processes in doped materials has shown synergistic pollutant removal efficiency. Various synthesis methods, including sol–gel, co-precipitation, and hydrothermal approaches had been employed to create hybrid units of doped photocatalysts and adsorbents. While progress has been made in enhancing the performance of doped materials at the laboratory scale, challenges persist in transitioning these technologies to large-scale industrial applications. Rigorous studies are needed to investigate the impact of doping on material structure and stability, optimize process parameters, and assess performance in real-world industrial reactors. These advancements are promising foraddressing environmental pollution challenges, promoting sustainability, and paving the way for a cleaner and healthier future. This manuscript provides a comprehensive overview of recent developments in doping strategies for photocatalysts and adsorbents, offering insights into the potential of these materials to revolutionize environmental remediation technologies.
Current and Future Trends in Textiles for Concrete Construction Applications
Authors: Scheurer Martin , Friese Danny , Penzel Paul , Dittel Gözdem , Bhat Shantanu , Overhage Vanessa , Hahn Lars , Heins Kira , Cherif Chokri , Gries Thomas
Published: October 2023

Textile-reinforced concrete (TRC) is a composite material consisting of a concrete matrix with a high-performance reinforcement made of technical textiles. TRC offers unique mechanical properties for the construction industry, enabling the construction of lightweight, material-minimized structures with high load-bearing potential. In addition, compared with traditional concrete design, TRC offers unique possibilities to realize free-form, double-curved structures. After more than 20 years of research, TRC is increasingly entering the market, with several demonstrator elements and buildings completed and initial commercialization successfully finished. Nevertheless, research into this highly topical area is still ongoing. In this paper, the authors give an overview of the current and future trends in the research and application of textiles in concrete construction applications. These trends include topics such as maximizing the textile utilization rate by improving the mechanical load-bearing performance (e.g., by adapting bond behavior), increasing design freedom by utilizing novel manufacturing methods (e.g., based on robotics), adding further value to textile reinforcements by the integration of additional functions in smart textile solutions (e.g., in textile sensors), and research into increasing the sustainability of TRC (e.g., using recycled fibers).
Diversity and Complexity of Internally Deleted Viral Genomes in Influenza A Virus Subpopulations with Enhanced Interferon-Inducing Phenotypes
Authors: Ghorbani Amir , Ngunjiri John M. , Rendon Gloria , Brooke Christopher B. , Kenney Scott P. , Lee Chang-Won
Published: October 2023

Influenza A virus (IAV) populations harbor large subpopulations of defective-interfering particles characterized by internally deleted viral genomes. These internally deleted genomes have demonstrated the ability to suppress infectivity and boost innate immunity, rendering them promising for therapeutic and immunogenic applications. In this study, we aimed to investigate the diversity and complexity of the internally deleted IAV genomes within a panel of plaque-purified avian influenza viruses selected for their enhanced interferon-inducing phenotypes. Our findings unveiled that the abundance and diversity of internally deleted viral genomes were contingent upon the viral subculture and plaque purification processes. We observed a heightened occurrence of internally deleted genomes with distinct junctions in viral clones exhibiting enhanced interferon-inducing phenotypes, accompanied by additional truncation in the nonstructural 1 protein linker region (NS1Δ76-86). Computational analyses suggest the internally deleted IAV genomes can encode a broad range of carboxy-terminally truncated and intrinsically disordered proteins with variable lengths and amino acid composition. Further research is imperative to unravel the underlying mechanisms driving the increased diversity of internal deletions within the genomes of viral clones exhibiting enhanced interferon-inducing capacities and to explore their potential for modulating cellular processes and immunity.
Effectiveness of Cell-Based Quadrivalent Seasonal Influenza Vaccine: A Systematic Review and Meta-Analysis
Authors: Coleman Brenda L. , Gutmanis Iris , McGovern Ian , Haag Mendel
Published: October 2023

Cell-based seasonal influenza vaccine viruses may more closely match recommended vaccine strains than egg-based options. We sought to evaluate the effectiveness of seasonal cell-based quadrivalent influenza vaccine (QIVc), as reported in the published literature. A systematic literature review was conducted (PROSPERO CRD42020160851) to identify publications reporting on the effectiveness of QIVc in persons aged ≥6 months relative to no vaccination or to standard-dose, egg-based quadrivalent or trivalent influenza vaccines (QIVe/TIVe). Publications from between 1 January 2016 and 25 February 2022 were considered. The review identified 18 relevant publications spanning three influenza seasons from the 2017–2020 period, with an overall pooled relative vaccine effectiveness (rVE) of 8.4% (95% CI, 6.5–10.2%) for QIVc vs. QIVe/TIVe. Among persons aged 4–64 years, the pooled rVE was 16.2% (95% CI, 7.6–24.8%) for 2017–2018, 6.1% (4.9–7.3%) for 2018–2019, and 10.1% (6.3–14.0%) for 2019–2020. For adults aged ≥65 years, the pooled rVE was 9.9% (95% CI, 6.9–12.9%) in the egg-adapted 2017–2018 season, whereas there was no significant difference in 2018–2019. For persons aged 4–64 years, QIVc was consistently more effective than QIVe/TIVe over the three influenza seasons. For persons aged ≥65 years, protection with QIVc was greater than QIVe or TIVe during the 2017–2018 season and comparable in 2018–2019.