bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis and Characterization of Boronate Affinity Three-Dimensionally Ordered Macroporous Materials
Authors: Li Zhipeng , Zhang Luxia , Han Xiangyu , An Qinchen , Chen Mengying , Song Zichang , Dong Linyi , Wang Xianhua , Yu Yang
Published: May 2024

Sample pretreatment is a key step for qualitative and quantitative analysis of trace substances in complex samples. Cis-dihydroxyl (cis-diol) group-containing substances exist widely in biological samples and can be selectively bound by boronate affinity adsorbents. Based on this, in this article, we proposed a simple method for the preparation of novel spherical three-dimensionally ordered macropore (3DOM) materials based on a combination of the boronate affinity technique and colloidal crystal template method. The prepared 3DOM materials were characterized using Fourier transform–infrared spectroscopy, scanning electron microscopy, X-ray photoelectron spectroscopy, and thermo-gravimetric analysis, and results showed that they possessed the characteristics of a high specific surface area, high porosity, and more boronic acid recognition sites. The adsorption performance evaluation results showed that the maximum adsorption capacity of the boron affinity 3DOMs on ovalbumin (OVA) could reach to 438.79 mg/g. Kinetic and isothermal adsorption experiments indicated that the boronate affinity 3DOM material exhibited a high affinity and selectivity towards OVA and adenosine. Sodium dodecyl sulfate–polyacrylamide gel electrophoresis analysis of the proteins in egg whites was conducted and proved that the glycoprotein in the egg whites could be separated and enriched with a good performance. Therefore, a novel boronate affinity 3DOM material a with highly ordered and interconnected pore structure was prepared and could be applied in the separation and enrichment of molecules with cis-diol groups from complex samples with a good selectivity, efficiency, and high throughput.
Optimizing Epoxy Molding Compound Processing: A Multi-Sensor Approach to Enhance Material Characterization and Process Reliability
Authors: Vogelwaid Julian , Bayer Martin , Walz Michael , Hampel Felix , Kutuzova Larysa , Lorenz Günter , Kandelbauer Andreas , Jacob Timo
Published: May 2024

The in-line control of curing during the molding process significantly improves product quality and ensures the reliability of packaging materials with the required thermo-mechanical and adhesion properties. The choice of the morphological and thermo-mechanical properties of the molded material, and the accuracy of their determination through carefully selected thermo-analytical methods, play a crucial role in the qualitative prediction of trends in packaging product properties as process parameters are varied. This work aimed to verify the quality of the models and their validation using a highly filled molding resin with an identical chemical composition but 10 wt% difference in silica particles (SPs). Morphological and mechanical material properties were determined by dielectric analysis (DEA), differential scanning calorimetry (DSC), warpage analysis and dynamic mechanical analysis (DMA). The effects of temperature and injection speed on the morphological properties were analyzed through the design of experiments (DoE) and illustrated by response surface plots. A comprehensive approach to monitor the evolution of ionic viscosity (IV), residual enthalpy (dHrest), glass transition temperature (Tg), and storage modulus (E) as a function of the transfer-mold process parameters and post-mold-cure (PMC) conditions of the material was established. The reliability of Tg estimation was tested using two methods: warpage analysis and DMA. The noticeable deterioration in the quality of the analytical signal for highly filled materials at high cure rates is discussed. Controlling the temperature by increasing the injection speed leads to the formation of a polymer network with a lower Tg and an increased storage modulus, indicating a lower density and a more heterogeneous structure due to the high heating rate and shear heating effect.
A Review of Plasma-Synthesized and Plasma Surface-Modified Piezoelectric Polymer Films for Nanogenerators and Sensors
Authors: Jung Eun-Young , Suleiman Habeeb Olaitan , Tae Heung-Sik , Park Choon-Sang
Published: May 2024

In this review, we introduce recently developed plasma-based approaches for depositing and treating piezoelectric nanoparticles (NPs) and piezoelectric polymer films for nanogenerator (NG) and sensor applications. We also present the properties and an overview of recently synthesized or modified piezoelectric materials on piezoelectric polymers to highlight the existing challenges and future directions of plasma methods under vacuum, low pressure, and ambient air conditions. The various plasma processes involved in piezoelectric NGs and sensors, including plasma-based vapor deposition, dielectric barrier discharge, and surface modification, are introduced and summarized for controlling various surface properties (etching, roughening, crosslinking, functionalization, and crystallinity).
Preparation of PANI/CuPc/PDMS Composite Elastomer with High Dielectric Constant and Low Modulus Assisted by Electric Fields
Authors: Hu Jinjin , Chu Beizhi , Liu Xueqing , Wei Huaixiao , Wang Jianwen , Kan Xue , Xia Yumin , Huang Shuohan , Chen Yuwei
Published: May 2024

Dielectric elastomer is a kind of electronic electroactive polymer, which plays an important role in the application of soft robots and flexible electronics. In this study, an all-organic polyaniline/copper phthalocyanine/silicone rubber (PANI/CuPc/PDMS) dielectric composite with superior comprehensive properties was prepared by manipulating the arrangement of filler in a polymer matrix assisted by electric fields. Both CuPc particles and PANI particles can form network structures in the PDMS matrix by self-assembly under electric fields, which can enhance the dielectric properties of the composites at low filler content. The dielectric constant of the assembled PANI/CuPc/PDMS composites can reach up to 140 at 100 Hz when the content of CuPc and PANI particles is 4 wt% and 2.5 wt%, respectively. Moreover, the elastic modulus of the composites remains below 2 MPa, which is important for electro-deforming. The strain of assembled PANI/CuPc/PDMS three-phase composites at low electric field strength (2 kV/mm) can increase up to five times the composites with randomly dispersed particles, which makes this composite have potential application in the field of soft robots and flexible electronics.
Effect of Technological Factors on the Extraction of Polymeric Condensed Tannins from Acacia Species
Authors: Osman Zeinab , Pizzi Antonio , Elbadawi Mohammed Elamin , Mehats Jérémy , Mohammed Wadah , Charrier Bertrand
Published: May 2024

The aim of this research work was to investigate the influence of parameters such as particle size, mass/solvent ratio, temperature and spray drying on the tannin extraction process in order to develop cost-effective methods with better environmental and structural performance. The pods of Acacia nilotica ssp. tomentosa (ANT) were fractionated into three fractions, coarse fraction (C) (>2 mm), medium fraction (M) (1–2 mm), and fine fraction (F) < 1 mµ), and extracted with different water-to-pod ratios (2:1, 4:1 and 6:1) at different temperatures (30, 50 and 70 °C). The best results were scaled up using the three fractions of ANT, its bark and the bark of Acacia seyal var. seyal (ASS). Part of their extract was spray dried. The tannin content and total polyphenolic materials were evaluated using standard methods. Their adhesives were tested for their tensile strength. Tannins of ASS were characterized by 13C NMR and MALDI-TOF. The results revealed that the fine fraction (F) gave the highest percentage of tannins in both small and scaled-up experiments. The results of the tensile strength conformed to the European standard. The 13C NMR spectra of ANT and ASS showed that the bark contained condensed tannins mainly consisting of procyanidins/prodelphinidin of 70%/30% and 60%/40%, respectively. MALDI–TOF spectra confirmed the results obtained by 13C NMR and detailed the presence of flavonoid monomers and oligomers, some of which were linked to short carbohydrate monomers or dimers.
Three-Dimensional Heterogeneous Salt Cavern Underground Gas Storage Water Solution Cavity Model Study
Authors: Cen Xueqi , Meng Xinggang , Ren Zongxiao , Cao Jiajun
Published: May 2024

In recent years, with the rapid development of salt cavern gas storage reservoir construction in China, the characteristics of salt rock reservoirs with strong non-homogeneity and many interlayers have brought challenges to the dynamic prediction of water solution cavity construction. Aiming to solve this problem, this paper constructs a three-dimensional non-homogeneous salt cavern reservoir water-soluble cavity building prediction model, which takes into full consideration the non-homogeneity of salt rock reservoirs, interlayers, reservoir temperatures, and water injection process parameters, among other factors. By comparing the calculation results of the software compiled by the model with those of other numerical simulation software, we show that the model can accurately reflect the influence of geological parameters on the cavity morphology under the condition of non-uniform geological parameters, with higher simulation accuracy, and ultimately analyze individual examples. It can provide important theoretical support and practical guidance for the construction of a salt cavern gas storage reservoir.
Experimental Analysis of the Mechanical Properties and Failure Behavior of Deep Coalbed Methane Reservoir Rocks
Authors: Wang Haiyang , Yang Shugang , Zhang Linpeng , Xiao Yunfeng , Su Xu , Yu Wenqiang , Zhou Desheng
Published: May 2024

A comprehensive understanding of the mechanical characteristics of deep coalbed methane reservoir rocks (DCMRR) is crucial for the safe and efficient development of deep coalbed gas resources. In this study, the microstructural and mechanical features of the coal seam roof, floor, and the coal seam itself were analyzed through laboratory experiments. The impact mechanisms of drilling fluid and fracturing fluid hydration on the mechanical properties and failure behavior of coal seam rocks were investigated. The experimental results indicate that the main minerals in coal seams are clay and amorphous substances, with kaolinite being the predominant clay mineral component in coal seam rocks. The rock of the coal seam roof and floor exhibits strong elasticity and high compressive strength, while the rock in the coal seam section shows a lower compressive capacity with pronounced plastic deformation characteristics. The content of kaolinite shows a good correlation with the mechanical properties of DCMRR. As the kaolinite content increases, the strength of DCMRR gradually decreases, and deformability enhances. After immersion in drilling fluid and slickwater, the strength of coal seam rocks significantly decreases, leading to shear fracture zones and localized strong damage features after rock compression failure. The analysis of the mechanical properties of DCMRR suggests that the horizontal well trajectory should be close to the coal seam roof, and strong sealing agents should be added to drilling fluid to reduce the risk of wellbore collapse. Enhancing the hydration of slickwater is beneficial for the formation of a more complex fracture network in deep coalbed methane reservoir.
Green Supply Chain Optimization Based on Two-Stage Heuristic Algorithm
Authors: Lei Chunrui , Zhang Heng , Yan Xingyou , Miao Qiang
Published: May 2024

Green supply chain management is critical for driving sustainable development and addressing escalating environmental challenges faced by companies. However, due to the multidimensionality of cost–benefit analysis and the intricacies of supply chain operations, strategic decision-making regarding green supply chains is inherently complex. This paper proposes a green supply chain optimization framework based on a two-stage heuristic algorithm. First, anchored in the interests of intermediary core enterprises, this work integrates upstream procurement and transportation of products with downstream logistics and distribution. In this aspect, a three-tier green complex supply chain model incorporating economic and environmental factors is developed to consider carbon emissions, product non-conformance rates, delay rates, and transportation costs. The overarching goal is to comprehensively optimize the trade-off between supply chain costs and carbon emissions. Subsequently, a two-stage heuristic algorithm is devised to solve the model by combining the cuckoo search algorithm with the brainstorming optimization algorithm. Specifically, an adaptive crossover–mutation operator is introduced to enhance the search performance of the brainstorming optimization algorithm, which caters to both global and local search perspectives. Experimental results and comparison studies demonstrate that the proposed method performs well within the modeling and optimization of the green supply chain. The proposed method facilitates the efficient determination of ordering strategies and transportation plans within tight deadlines, thereby offering valuable support to decision-makers in central enterprises for supply chain management, ultimately maximizing their benefits.
Mus4mCPred: Accurate Identification of DNA N4-Methylcytosine Sites in Mouse Genome Using Multi-View Feature Learning and Deep Hybrid Network
Authors: Wang Xiao , Du Qian , Wang Rong
Published: May 2024

N4-methylcytosine (4mC) is a critical epigenetic modification that plays a pivotal role in the regulation of a multitude of biological processes, including gene expression, DNA replication, and cellular differentiation. Traditional experimental methods for detecting DNA N4-methylcytosine sites are time-consuming, labor-intensive, and costly, making them unsuitable for large-scale or high-throughput research. Computational methods for identifying DNA N4-methylcytosine sites enable the rapid and cost-effective analysis of DNA 4mC sites across entire genomes. In this study, we focus on the identification of DNA 4mC sites in the mouse genome. Although there are already some computational methods that can predict DNA 4mC sites in the mouse genome, there is still significant room for improvement in accurately predicting them due to their inability to fully capture the multifaceted characteristics of DNA sequences. To address this issue, we propose a new deep learning predictor called Mus4mCPred, which utilizes multi-view feature learning and deep hybrid networks for accurately predicting DNA 4mC sites in the mouse genome. The predictor Mus4mCPred firstly employed different encoding methods to extract the feature vectors of DNA sequences, then input these features generated by different encoding methods into various hybrid deep learning models for the learning and extraction of more sophisticated representations of these features, and finally fused the extracted multi-view features to serve as the final features for DNA 4mC site prediction in the mouse genome. Multi-view features enabled the more comprehensive capture of data characteristics, enhancing the feature representation of DNA sequences. The independent test results showed that the sensitivity (Sn), specificity (Sp), accuracy (Acc), and Matthews’ correlation coefficient (MCC) were 0.7688, 0.9375, 0.8531, and 0.7165, respectively. The predictor Mus4mCPred outperformed other state-of-the-art methods, achieving the accurate identification of 4mC sites in the mouse genome.
Block-Circulant Approximation of the Precision Matrix for Assimilating SWOT Altimetry Data
Authors: Yaremchuk Max , Beattie Christopher , Panteleev Gleb , D’Addezio Joseph
Published: May 2024

The recently deployed Surface Water and Ocean Topography (SWOT) mission for the first time has observed the ocean surface at a spatial resolution of 1 km, thus giving an opportunity to directly monitor submesoscale sea surface height (SSH) variations that have a typical magnitude of a few centimeters. This progress comes at the expense of the necessity to take into account numerous uncertainties in calibration of the quality-controlled altimeter data. Of particular importance is the proper filtering of spatially correlated errors caused by the uncertainties in geometry and orientation of the on-board interferometer. These “systematic” errors dominate the SWOT error budget and are likely to have a notable signature in the SSH products available to the oceanographic community. In this study, we explore the utility of the block-circulant (BC) approximation of the SWOT precision matrix developed by the Jet Propulsion Laboratory for assessment of a mission’s accuracy, including the possible impact of the systematic errors on the assimilation of the wide-swath altimeter data into numerical models. It is found that BC approximation of the precision matrix has sufficient (90–99%) accuracy for a wide range of significant wave heights of the ocean surface, and, therefore, could potentially serve as an efficient preconditioner for data assimilation problems involving altimetry observations by space-borne interferometers. An extensive set of variational data assimilation (DA) experiments demonstrates that BC approximation provides more accurate SSH retrievals compared to approximations, assuming a spatially uncorrelated observation error field as is currently adopted in operational DA systems.
Geophysical Survey as Part of Rescue Archaeological Excavation on Large Construction Projects—Case Study: Road I/16 Slaný–Velvary (Czech Republic)
Authors: Tencer Tomáš , Malyková Drahomíra , Milo Peter
Published: May 2024

Regions densely populated with archaeological monuments pose significant challenges for construction investors and archaeologists during the planning stages of major construction projects. Recognising the archaeological potential of these areas is crucial for planning effective rescue excavations, which have become a standard procedure in construction. This study explores the utility of non-invasive prospection techniques, including artefact field surveys, multispectral imaging, and magnetic surveys, in assessing the area chosen for the I/16 Slaný–Velvary road construction. We specifically focused on the contributions these methods make towards understanding the archaeological context of the proposed construction site. The findings from the magnetic survey were compared with the results of actual archaeological excavations. Through manual visual analysis and statistical spatial correlation, we assessed the effectiveness of the magnetic survey. The ability of magnetic survey to locate different types of archaeological objects proved to be dependent on various factors. The variability of the environmental setting, particularly pedological and geological conditions, is essential. However, the modern anthropogenic impact and the very nature of individual archaeological objects, especially their dimensions, also play an essential role.
A Comprehensive Assessment of Climate Change and Anthropogenic Effects on Surface Water Resources in the Lake Urmia Basin, Iran
Authors: Kazemi Garajeh Mohammad , Akbari Rojin , Aghaei Chaleshtori Sepide , Shenavaei Abbasi Mohammad , Tramutoli Valerio , Lim Samsung , Sadeqi Amin
Published: May 2024

In recent decades, the depletion of surface water resources within the Lake Urmia Basin (LUB), Iran, has emerged as a significant environmental concern. Both anthropogenic activities and climate change have influenced the availability and distribution of surface water resources in this area. This research endeavors to provide a comprehensive evaluation of the impacts of climate change and anthropogenic activities on surface water resources across the LUB. Various critical climatic and anthropogenic factors affecting surface water bodies, such as air temperature (AT), cropland (CL), potential evapotranspiration (PET), snow cover, precipitation, built-up areas, and groundwater salinity, were analyzed from 2000 to 2021 using the Google Earth Engine (GEE) cloud platform. The JRC-Global surface water mapping layers V1.4, with a spatial resolution of 30 m, were employed to monitor surface water patterns. Additionally, the Mann–Kendall (MK) non-parametric trend test was utilized to identify statistically significant trends in the time series data. The results reveal negative correlations of −0.56, −0.89, −0.09, −0.99, and −0.79 between AT, CL, snow cover, built-up areas, and groundwater salinity with surface water resources, respectively. Conversely, positive correlations of 0.07 and 0.12 were observed between precipitation and PET and surface water resources, respectively. Notably, the findings indicate that approximately 40% of the surface water bodies in the LUB have remained permanent over the past four decades. However, there has been a loss of around 30% of permanent water resources, transitioning into seasonal water bodies, which now account for nearly 13% of the total. The results of our research also indicate that December and January are the months with the most water presence over the LUB from 1984 to 2021. This is because these months align with winter in the LUB, during which there is no water consumption for the agriculture sector. The driest months in the study area are August, September, and October, with the presence of water almost at zero percent. These months coincide with the summer and autumn seasons in the study area. In summary, the results underscore the significant impact of human activities on surface water resources compared to climatic variables.
Self-Paced Multi-Scale Joint Feature Mapper for Multi-Objective Change Detection in Heterogeneous Images
Authors: Wang Ying , Dang Kelin , Yang Rennong , Song Qi , Li Hao , Gong Maoguo
Published: May 2024

Heterogeneous image change detection is a very practical and challenging task because the data in the original image have a large distribution difference and the labeled samples of the remote sensing image are usually very few. In this study, we focus on solving the issue of comparing heterogeneous images without supervision. This paper first designs a self-paced multi-scale joint feature mapper (SMJFM) for the mapping of heterogeneous data to similar feature spaces for comparison and incorporates a self-paced learning strategy to weaken the mapper’s capture of non-consistent information. Then, the difference information in the output of the mapper is evaluated from two perspectives, namely noise robustness and detail preservation effectiveness; then, the change detection problem is modeled as a multi-objective optimization problem. We decompose this multi-objective optimization problem into several scalar optimization subproblems with different weights, and use particle swarm optimization to optimize these subproblems. Finally, the robust evaluation strategy is used to fuse the multi-scale change information to obtain a high-precision binary change map. Compared with previous methods, the proposed SMJFM framework has the following three main advantages: First, the unsupervised design alleviates the dilemma of few labels in remote sensing images. Secondly, the introduction of self-paced learning enhances SMJFM’s capture of the unchanged region mapping relationship between heterogeneous images. Finally, the multi-scale change information fusion strategy enhances the robustness of the framework to outliers in the original data.
A Two-Stage SAR Image Generation Algorithm Based on GAN with Reinforced Constraint Filtering and Compensation Techniques
Authors: Liu Ming , Wang Hongchen , Chen Shichao , Tao Mingliang , Wei Jingbiao
Published: May 2024

Generative adversarial network (GAN) can generate diverse and high-resolution images for data augmentation. However, when GAN is applied to the synthetic aperture radar (SAR) dataset, the generated categories are not of the same quality. The unrealistic category will affect the performance of the subsequent automatic target recognition (ATR). To overcome the problem, we propose a reinforced constraint filtering with compensation afterwards GAN (RCFCA-GAN) algorithm to generate SAR images. The proposed algorithm includes two stages. We focus on improving the quality of easily generated categories in Stage 1. Then, we record the categories that are hard to generate and compensate by using traditional augmentation methods in Stage 2. Thus, the overall quality of the generated images is improved. We conduct experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset. Recognition accuracy and Fréchet inception distance (FID) acquired by the proposed algorithm indicate its effectiveness.
Convolutional Neural Networks Applied to Antimony Quantification via Soil Laboratory Reflectance Spectroscopy in Northern Portugal: Opportunities and Challenges
Authors: Carvalho Morgana , Cardoso-Fernandes Joana , Lima Alexandre , Teodoro Ana C.
Published: May 2024

Antimony (Sb) has gained significance as a critical raw material (CRM) within the European Union (EU) due to its strategic importance in various industrial sectors, particularly in the textile industry for flame retardants and as a component of Sb-based semiconductor materials. Moreover, Sb is emerging as a potential alternative for anodes used in lithium-ion batteries, a key element in the energy transition. This study explored the feasibility of identifying and quantifying Sb mineralisations through the spectral signature of soils using laboratory reflectance spectroscopy, a non-invasive remote sensing technique, and by employing convolutional neural networks (CNNs). Standard signal pre-processing techniques were applied to the spectral data, and the soils were analysed by inductively coupled plasma mass spectrometry (ICP-MS). Despite achieving high R-squared (0.7) values and an RMSE of 173 ppm for Sb, the study faces a significant challenge of generalisation of the model to new data. Despite the limitations, this study provides valuable insights into potential strategies for future research in this field.
An Editorial for the Special Issue “Processing and Application of Weather Radar Data”
Authors: Qi Youcun , Zhang Zhe , Zhao Zhanfeng , Seo Bong-Chul , Li Huiqi
Published: May 2024

In 2019, the World Meteorological Organization (WMO) pointed out the following based on the statistics from 2007 to 2019: in natural disasters, 90% of losses are related to meteorology, of which heavy storms and floods account for more than 70% [...]
Correction: Vanneschi et al. Hazard Assessment of Rocky Slopes: An Integrated Photogrammetry–GIS Approach Including Fracture Density and Probability of Failure Data. Remote Sens. 2022, 14, 1438
Authors: Vanneschi Claudio , Rindinella Andrea , Salvini Riccardo
Published: May 2024

In the published publication [...]
Identifying Heterogeneity in SAR Data with New Test Statistics
Authors: Frery Alejandro C. , Alpala Janeth , Nascimento Abraão D. C.
Published: May 2024

This paper presents a statistical approach to identify the underlying roughness characteristics in synthetic aperture radar (SAR) intensity data. The physical modeling of this kind of data allows the use of the Gamma distribution in the presence of fully developed speckle, i.e., when there are infinitely many independent backscatterers per resolution cell, and none dominates the return. Such areas are often called “homogeneous” or “textureless” regions. The GI0 distribution is also a widely accepted law for heterogeneous and extremely heterogeneous regions, i.e., areas where the fully developed speckle hypotheses do not hold. We propose three test statistics to distinguish between homogeneous and inhomogeneous regions, i.e., between gamma and GI0 distributed data, both with a known number of looks. The first test statistic uses a bootstrapped non-parametric estimator of Shannon entropy, providing a robust assessment in uncertain distributional assumptions. The second test uses the classical coefficient of variation (CV). The third test uses an alternative form of estimating the CV based on the ratio of the mean absolute deviation from the median to the median. We apply our test statistic to create maps of p-values for the homogeneity hypothesis. Finally, we show that our proposal, the entropy-based test, outperforms existing methods, such as the classical CV and its alternative variant, in identifying heterogeneity when applied to both simulated and actual data.
Efficient Data Management in Agricultural IoT: Compression, Security, and MQTT Protocol Analysis
Authors: Has Mislav , Kreković Dora , Kušek Mario , Podnar Žarko Ivana
Published: May 2024

The integration of Internet of Things (IoT) technology into agriculture has revolutionized farming practices by using connected devices and sensors to optimize processes and facilitate sustainable execution. Because most IoT devices have limited resources, the vital requirement to efficiently manage data traffic while ensuring data security in agricultural IoT solutions creates several challenges. Therefore, it is important to study the data amount that IoT protocols generate for resource-constrained devices, as it has a direct impact on the device performance and overall usability of the IoT solution. In this paper, we present a comprehensive study that focuses on optimizing data transmission in agricultural IoT solutions with the use of compression algorithms and secure technologies. Through experimentation and analysis, we evaluate different approaches to minimize data traffic while protecting sensitive agricultural data. Our results highlight the effectiveness of compression algorithms, especially Huffman coding, in reducing data size and optimizing resource usage. In addition, the integration of encryption techniques, such as AES, provides the security of the transmitted data without incurring significant overhead. By assessing different communication scenarios, we identify the most efficient approach, a combination of Huffman encoding and AES encryption, to strike a balance between data security and transmission efficiency.
Deep Transfer Learning Method Using Self-Pixel and Global Channel Attentive Regularization
Authors: Kang Changhee , Kang Sang-ug
Published: May 2024

The purpose of this paper is to propose a novel transfer learning regularization method based on knowledge distillation. Recently, transfer learning methods have been used in various fields. However, problems such as knowledge loss still occur during the process of transfer learning to a new target dataset. To solve these problems, there are various regularization methods based on knowledge distillation techniques. In this paper, we propose a transfer learning regularization method based on feature map alignment used in the field of knowledge distillation. The proposed method is composed of two attention-based submodules: self-pixel attention (SPA) and global channel attention (GCA). The self-pixel attention submodule utilizes both the feature maps of the source and target models, so that it provides an opportunity to jointly consider the features of the target and the knowledge of the source. The global channel attention submodule determines the importance of channels through all layers, unlike the existing methods that calculate these only within a single layer. Accordingly, transfer learning regularization is performed by considering both the interior of each single layer and the depth of the entire layer. Consequently, the proposed method using both of these submodules showed overall improved classification accuracy than the existing methods in classification experiments on commonly used datasets.
Designing High Performance Carbon/ZnSn(OH)6-Based Humidity Sensors
Authors: Zhang Min , Jia Hongguang , Wang Shuying , Zhang Zhenya
Published: May 2024

In this work, pure phase and carbon/ZnSn(OH)6 samples were synthesized by a hydrothermal method. The composite sample’s structure, morphology, and functional groups were investigated by X-ray diffraction, field-emission scanning electron microscopy, transmission electron microscopy, and Fourier transform infrared spectroscopy. Subsequently, ZnSn(OH)6 samples were modified with different carbon contents, and their humidity-sensing properties were investigated. The introduction of carbon increased the specific surface area of pure ZnSn(OH)6 samples, thus significantly improving the sensors’ humidity sensing response. The C10-ZnSn(OH)6 sensor exhibited a high response, up to three orders of magnitude, a humidity hysteresisof 13.5%, a fast response time of 3.2 s, and a recovery time of 24.4 s. The humidity sensor’s possible humidity sensing mechanism was also analyzed using the AC complex impedance puissance method with a simulated equivalent circuit. These results revealed that ZnSn(OH)6 can effectively detect ambient humidity and that the introduction of carbon significantly improves its humidity-sensing performance. The study provides an effective strategy for understanding and designing ZnSn(OH)6-based humidity sensors.
Hybrid Vibration Sensor for Equipment Monitoring and Diagnostics
Authors: Bryakin Ivan V. , Bochkarev Igor V. , Khramshin Vadim R. , Gasiyarov Vadim R.
Published: May 2024

Vibration diagnostics based on vibroacoustic signal data belong to the most common ways to monitor the technical condition of equipment and technical structures. The paper considers the general issues of vibration-based diagnostics and shows that in general, it is required to monitor both axial and torsional oscillations, as well as the inclination angle, occurring during the operation of various technical objects. To comprehensively monitor these parameters, a hybrid vibration sensor is proposed, simultaneously implementing three operating modes: recording linear displacements of the vibrating object; recording the rotation angle of the object at its torsional oscillations; recording the object angular deviation from the vertical component of the natural local geomagnetic field, i.e., the inclinometer mode. The proposed hybrid sensor design is described, and a theoretical analysis of the sensor’s operation in each of the aforementioned operating modes is performed. The authors show that in the inclinometer mode the sensor actually operates as a fluxgate meter. Generalizing the results of the sensor’s operation simultaneously in all three operating modes, an equation for the total output data signal has been obtained, which allows for obtaining the required information on the current values of linear displacements and rotation and inclination angles by selectively filtering it with respective three filters tuned to specific frequencies. The experimental studies of the proposed hybrid vibration sensor confirmed its ability to record various vibrational disturbances and changes in the inclination angle of the monitored object.
Effects of Therapies Involving Plyometric-Jump Training on Physical Fitness of Youth with Cerebral Palsy: A Systematic Review with Meta-Analysis
Authors: Garcia-Carrillo Exal , Ramirez-Campillo Rodrigo , Izquierdo Mikel , Elnaggar Ragab , Afonso José , Peñailillo Luis , Araneda Rodrigo , Ebner-Karestinos Daniela , Granacher Urs
Published: May 2024

The aim of this systematic review was to assess the effects of plyometric-jump training (PJT) on the physical fitness of youth with cerebral palsy (CP) compared with controls (i.e., standard therapy). The PRISMA 2020 guidelines were followed. Eligibility was assessed using the PICOS approach. Literature searches were conducted using the PubMed, Web of Science, and SCOPUS databases. Methodological study quality was assessed using the PEDro scale. Data were meta-analyzed by applying a random-effects model to calculate Hedges’ g effect sizes (ES), along with 95% confidence intervals (95% CI). The impact of heterogeneity was assessed (I2 statistic), and the certainty of evidence was determined using the GRADE approach. Eight randomized-controlled studies with low-to-moderate methodological quality were included, involving male (n = 225) and female (n = 138) youth aged 9.5 to 14.6 years. PJT interventions lasted between 8 and 12 weeks with 2–4 weekly sessions. Compared with controls, PJT improved the muscle strength (ES = 0.66 [moderate], 95% CI = 0.36–0.96, p < 0.001, I2 = 5.4%), static (ES = 0.69 [moderate], 95% CI= 0.33–1.04, p < 0.001, I2 = 0.0%) and dynamic balance (ES = 0.85 [moderate], 95% CI = 0.12–1.58, p = 0.023, I2 = 81.6%) of youth with CP. Therefore, PJT improves muscle strength and static and dynamic balance in youth with CP compared with controls. However, more high-quality randomized-controlled trials with larger sample sizes are needed to provide a more definitive recommendation regarding the use and safety of PJT to improve measures of physical fitness.
Analysing the Barriers of Strategic Entrepreneurship: A DEMATEL Approach
Authors: Khan Shahbaz , Fatma Nosheen , Ali Sadia Samar , Dhamija Aruna , Naqvi Doa
Published: May 2024

Entrepreneurship research has developed in the last twenty years and now the focus is on Strategic Entrepreneurship (SE). SE can provide the sustainable growth of an organisation and increase its competitiveness globally. Despite these advantages, developing countries cannot reap the benefits of SE due to various barriers. Therefore, this study aims to identify and model the barriers of SE to the development of organisational management. Initially, the barriers of SE are identified through a literature review and further validated with a domain expert. The causal relationship among the barriers is modelled using the decision-making trial and evaluation laboratory (DEMATEL) method. The result suggests that low awareness of SE, risk aversion, and low financial support are the major barriers in the development of SE that need to be mitigated. Further, this analysis also categorises these barriers into a cause-and-effect group. Six barriers belong to the cause group and the remaining four are part of the effect group. Knowledge of the barriers is helpful for policymakers to design development strategies and helps business development managers in the successive planning of the organisation. The understanding of the interrelationship among the barriers will help the organisation to remove these barriers in an optimal manner. The findings of the study will be helpful for top management and strategic planners to advance design thinking and strategic planning. The contribution of this research lies in the identification of barriers to SE and their causal relationships, which have been scarcely examined in the existing literature.
Hydrodynamic Cavitation and Advanced Oxidation for Enhanced Degradation of Persistent Organic Pollutants: A Review
Authors: Yeneneh Anteneh Mesfin , Al Balushi Khadija , Jafary Tahereh , Al Marshudi Amjad Said
Published: May 2024

Water pollution has become a major environmental menace due to municipal and industrial effluents discharged into water bodies. Several processes have been devised for the treatment and disposal of wastewater and sludge. Yet, most of the conventional technologies do not meet the requirements of sustainability as they impose a higher load on the environment in terms of resource depletion and toxic waste generation. Recently, sustainable innovative technologies, like hydrodynamic cavitation (HC), have emerged as energy-efficient methods, which can enhance the conventional wastewater treatment processes. HC is a very effective technique for the intensification of processes, like aeration, activated sludge treatment, and anaerobic digestion processes in conventional wastewater treatment plants, particularly for the enhanced degradation of persistent pollutants. On the other hand, advanced oxidation is a proven enhancement method for wastewater treatment. This review provides a comprehensive overview of recently published literature on the application of HC for the treatment of persistent organic pollutants. The potential synergistic impact of HC coupled with advanced oxidation and alternative pre-treatment methods was also reviewed in this study. Moreover, an overview of the present state of model-based research work for HC reactors and a feasibility analysis of various advanced oxidation process is also covered. Options for the pilot-to-large scale implementation of HC and advanced oxidation technologies to ensure the better sustainability of wastewater treatment plants are recommended.
Greenhouse Gas Reduction Potential and Economics of Green Hydrogen via Water Electrolysis: A Systematic Review of Value-Chain-Wide Decarbonization
Authors: Du Lifeng , Yang Yanmei , Zhou Luli , Liu Min
Published: May 2024

Green hydrogen generated via water electrolysis has become an essential energy carrier for achieving carbon neutrality globally because of its versatility in renewable energy consumption and decarbonization applications in hard-to-abate sectors; however, there is a lack of systematic analyses of its abatement potential and economics as an alternative to traditional technological decarbonization pathways. Based on bibliometric analysis and systematic evaluation methods, this study characterizes and analyzes the literature on the Web of Science from 1996 to 2023, identifying research hotspots, methodological models, and research trends in green hydrogen for mitigating climate change across total value chain systems. Our review shows that this research theme has entered a rapid development phase since 2016, with developed countries possessing more scientific results and closer partnerships. Difficult-to-abate sectoral applications and cleaner production are the most famous value chain links, and research hotspots focus on three major influencing factors: the environment; techno-economics; and energy. Green hydrogen applications, which include carbon avoidance and embedding to realize carbon recycling, have considerable carbon reduction potential; however, uncertainty limits the influence of carbon reduction cost assessment indicators based on financial analysis methods for policy guidance. The abatement costs in the decarbonization sector vary widely across value chains, electricity sources, baseline scenarios, technology mixes, and time scenarios. This review shows that thematic research trends are focused on improving and optimizing solutions to uncertainties, as well as studying multisectoral synergies and the application of abatement assessment metrics.
Geological Disaster Susceptibility Evaluation Using Machine Learning: A Case Study of the Atal Tunnel in Tibetan Plateau
Authors: Bian Yu , Chen Hao , Liu Zujian , Chen Ling , Guo Ya , Yang Yongpeng
Published: May 2024

Tunnels serve as vital arteries in the realm of transportation and infrastructure, facilitating the seamless flow of movement across challenging terrains. With the increasing demand experienced by the traffic network on the Tibetan Plateau, deep-buried, lengthy tunnels have become one of the extremely important types of roads for local residents to pass through. Geological disaster susceptibility mapping by hybrid models has been proven to be an effective means to reduce the losses caused by disasters in a large area. However, there has been relatively little research conducted in tunnel areas with significant human activity. To explore the feasibility of conducting geological disaster susceptibility assessment in tunnel areas, we chose the Atal Tunnel as a study project; as a strategic passageway, this exemplifies the significant geological hurdles encountered on the Tibetan Plateau. Employing multi-source remote sensing data, we meticulously mapped the distribution of geological disasters and identified nine environmental and geological variables pivotal for susceptibility evaluation. We harnessed interpretable ensemble learning models to assess this susceptibility, comparing the efficacy of four distinct models: the weight of evidence method (WoE), the frequency ratio (FR), logistic regression (LR) and the support vector machine (SVM). The precision of our findings was rigorously tested using metrics such as the percentage of disaster area encompassed within each risk level, the Area Under the Curve (AUC) value, and the Receiver Operating Characteristic (ROC) curve, and all results were highly accurate. Notably, the WoE-LR model achieved superior performance, boasting an impressive accuracy rate of 90.7%. Through model interpretation, we discerned that the alignment of the road line is the most critical determinant in the evaluation of tunnel geological disaster susceptibility, underscoring the high precision of our model. The extension and successful application of this research in plateau areas hold profound implications for sustainable development. Moreover, the practical application of these research findings will provide a practical reference for the design and construction of projects in similar plateau areas, with positive outcomes that extend well beyond the immediate geographical area of the projects.
Impacts of China’s Main Grain-Producing Areas on Agricultural Carbon Emissions: A Sustainable Development Perspective
Authors: Wang Shuokai , Jin Mingming , Liu Lei , Cao Fangping
Published: May 2024

A path of green, low-carbon development in agriculture is to be explored in the face of global warming, which promotes sustainable development. Taking the policy of the main grain-producing area (MGPA) as a special background, this study shows the impact of the MGPA on reducing agricultural carbon emissions (ACEs). In the empirical analysis, a difference-in-differences (DID) model was constructed using panel data from 1999 to 2020 across 31 provinces in China to examine the impact of the MGPA on agricultural carbon emission reduction (ACER). The findings indicate the following: (1) The ACER is significantly promoted after the implementation of the MGPA policy. The results passed robustness and endogeneity tests. (2) The mechanism test reveals that the policy can achieve said reduction by advancing the level of planting specialization. (3) An analysis of the heterogeneity reveals that the implementation of this policy leads to a substantial decrease in ACE in regions characterized by lower economic development and imbalances in the structure of factors involved in agricultural production. Furthermore, in areas where small-scale farming prevails, the policy demonstrates enhanced effectiveness in ACER. The outcomes of this study not only elucidate the link between the MGPA and ACE but also serve as a valuable roadmap for achieving ACER and sustainable development. Additionally, they contribute to expanding the notion of environmentally friendly, low-carbon agriculture in the context of small-scale farming.
Utility of Water-Based Databases for Underground Water Management: Legal and System Perspective
Authors: Klimach Anna , Zębek Elżbieta
Published: May 2024

Groundwater is a strategic environmental resource due to its use to human consumption, and therefore requires special protection and monitoring in many databases. In Poland, groundwater data are included in different typical water-related databases such as Hydroportal, Portal of the State Hydrogeological Service and Portal of the Central Geological Database, which is linked to an integrated real estate information system (IREIS). This article aims to demonstrate how IREIS is used to manage groundwater in Poland. The analysis indicates that shortcomings and gaps are noticeable, e.g., duplication of data and significant lack of data necessary for the implementation of the legal instruments. It is therefore a priority to establish a harmonised permitting and sustainable management of resources by public authorities, supported by an appropriate information and resource system for the EU. There is a need for an increase in the amount of information in databases and a reduction in the number of databases with groundwater information. The results of the analysis of these information systems can provide guidance to other EU countries for more effective groundwater protection and management.