bluuebunny/crossref_metadata_2025 huggingface dataset:
Ni(II) and Pb(II) Removal Using Bacterial Cellulose Membranes
Authors: Ojembarrena Francisco de Borja , García Sergio , Merayo Noemi , Blanco Angeles , Negro Carlos
Published: September 2023

Bacterial cellulose (BC) is a highly crystalline nanosized material with a high number of active groups. This study focuses on the synthesis of BC membranes through fermentation, their characterization and application to remove Ni(II) and Pb(II) from wastewater by adsorption under different conditions. Four-day-grown BC membranes form three-dimensional nanofibril networks with a pH of 6.3 and a high cationic demand (52.5 μeq·g−1). The pseudo-second-order kinetic model and the Sips isotherm model best describe the adsorption of both metals. The intraparticle diffusion model of Ni(II) revealed a three-step mechanism of adsorption-plateau-adsorption, while Pb(II) adsorption followed a typical reducing-slope trend up to saturation. The highest removal of Ni(II) and Pb(II) was obtained at pH 4 with a BC dosage of 400 mg·L−1. The maximum adsorption capacities were 28.18 mg·g−1 and 8.49 mg·g−1 for Ni(II) and Pb(II), respectively, involving the total coverage of the material active sites. Thermodynamically, Ni(II) adsorption was exothermic, and Pb(II) was endothermic. The obtained values of sorption heat, activation and Gibbs’ energy depicted a physisorption process. Ni(II) removal mechanism was ruled by crystallization on the metals adsorbed on the BC active groups, while Pb(II) was driven by the adsorption process, as shown by TEM images of the spent material.
Modeling Coil–Globule–Helix Transition in Polymers by Self-Interacting Random Walks
Authors: Huang Eddie , Tan Zhi-Jie
Published: September 2023

Random walks (RWs) have been important in statistical physics and can describe the statistical properties of various processes in physical, chemical, and biological systems. In this study, we have proposed a self-interacting random walk model in a continuous three-dimensional space, where the walker and its previous visits interact according to a realistic Lennard-Jones (LJ) potential uLJr=εr0/r12−2r0/r6. It is revealed that the model shows a novel globule-to-helix transition in addition to the well-known coil-to-globule collapse in its trajectory when the temperature decreases. The dependence of the structural transitions on the equilibrium distance r0 of the LJ potential and the temperature T were extensively investigated. The system showed many different structural properties, including globule–coil, helix–globule–coil, and line–coil transitions depending on the equilibrium distance r0 when the temperature T increases from low to high. We also obtained a correlation form of kBTc = λε for the relationship between the transition temperature Tc and the well depth ε, which is consistent with our numerical simulations. The implications of the random walk model on protein folding are also discussed. The present model provides a new way towards understanding the mechanism of helix formation in polymers like proteins.
Engineering Polymer-Based Porous Membrane for Sustainable Lithium-Ion Battery Separators
Authors: Li Lei , Duan Yutian
Published: September 2023

Due to the growing demand for eco-friendly products, lithium-ion batteries (LIBs) have gained widespread attention as an energy storage solution. With the global demand for clean and sustainable energy, the social, economic, and environmental significance of LIBs is becoming more widely recognized. LIBs are composed of cathode and anode electrodes, electrolytes, and separators. Notably, the separator, a pivotal and indispensable component in LIBs that primarily consists of a porous membrane material, warrants significant research attention. Researchers have thus endeavored to develop innovative systems that enhance separator performance, fortify security measures, and address prevailing limitations. Herein, this review aims to furnish researchers with comprehensive content on battery separator membranes, encompassing performance requirements, functional parameters, manufacturing protocols, scientific progress, and overall performance evaluations. Specifically, it investigates the latest breakthroughs in porous membrane design, fabrication, modification, and optimization that employ various commonly used or emerging polymeric materials. Furthermore, the article offers insights into the future trajectory of polymer-based composite membranes for LIB applications and prospective challenges awaiting scientific exploration. The robust and durable membranes developed have shown superior efficacy across diverse applications. Consequently, these proposed concepts pave the way for a circular economy that curtails waste materials, lowers process costs, and mitigates the environmental footprint.
The Hydration Mechanisms of Co-Stabilization Saline Soils by Using Multiple Solid Wastes
Authors: Lei Bolan , Fu Pingfeng , Wang Xiaoli , Ni Wen , Zhang Siqi , Wang Xiancong , Shi Jinjin , Xu Miao
Published: September 2023

In this paper, an approach was employed to fabricate a curing agent using multiple solid wastes. To determine the optimal mixing ratio, orthogonal and compaction tests were initially conducted, followed by a comparative analysis of the excitation effects elicited by sodium silicate and NaOH. Remarkably, sodium silicate demonstrated superior suitability as an activator. The final composition was established as follows: 4% sodium silicate, 26% carbide slag, 25% granulated blast furnace (GBF) slag, 35% coal fly ash, and 10% flue gas desulphurization (FGD) gypsum. Under controlled conditions of 20% of curing agent content, the unconfined compressive strength of the solidified soil at 7 d attained 1.54 MPa, thereby satisfying the rigorous construction requirements for highways across all levels. XRD and SEM-EDS analyses revealed that the principal hydration products in the system consisted of ettringite, Friedel’s salt, and C-S-H gel. These products enveloped the soil particles, with ettringite and Friedel’s salt intermingling and occupying the intricate framework formed by the gel. The synergistic interplay among the components ultimately fostered the development of a coherent soil structure and enhanced overall densification.
Conscientious Objection in an Uncertain Time: New Challenges in Ireland
Authors: Ryan Desmond , Lasek-Markey Marta , Mulligan Andrea , Hogan Linda , Tunder Bryana
Published: September 2023

This paper explores the legal framework concerning the conscientious objection rights of employees in Ireland and critically considers how the various sources within that legal framework may overlap and intersect. It specifically considers the uncertainties created by section 22 of the Health (Regulation of Termination of Pregnancy) Act 2018 and its interaction with the Constitution of Ireland, the common law, other statutory regimes in employment law, EU law and the European Convention on Human Rights. In conducting this analysis, the paper attempts to map out likely future directions of travel within the law of conscientious objection in the context of termination of pregnancy in Ireland.
Two-Level Feature-Fusion Ship Recognition Strategy Combining HOG Features with Dual-Polarized Data in SAR Images
Authors: Xie Hongtu , He Jinfeng , Lu Zheng , Hu Jun
Published: September 2023

Due to the inherent characteristics of synthetic aperture radar (SAR) imaging, SAR ship features are not obvious and the category distribution is unbalanced, which makes the task of ship recognition in SAR images quite challenging. To address the above problems, a two-level feature-fusion ship recognition strategy combining the histogram of oriented gradients (HOG) features with the dual-polarized data in the SAR images is proposed. The proposed strategy comprehensively utilizes the features extracted by the HOG operator and the shallow and deep features extracted by the Siamese network in the dual-polarized SAR ship images, which can increase the amount of information for the model learning. First, the Siamese network is used to extract the shallow and deep features from the dual-polarized SAR images, and then the HOG feature of the dual-polarized SAR images is also extracted. Furthermore, the bilinear transformation layer is used for fusing the HOG features from dual-polarized SAR images, and the grouping bilinear pooling process is used for fusing the dual-polarized shallow feature and deep feature extracted by the Siamese network, respectively. Finally, the catenation operation is used for fusing the dual-polarized HOG features and dual-polarized shallow feature and deep feature, respectively, which are used for the recognition of the SAR ship targets. Experimental results tested on the OpenSARShip2.0 dataset demonstrate the correctness and effectiveness of the proposed strategy, which can effectively improve the recognition performance of the ship targets by fusing the different level features of the dual-polarized SAR images.
Unveiling the Potential of Drone-Borne Optical Imagery in Forest Ecology: A Study on the Recognition and Mapping of Two Evergreen Coniferous Species
Authors: Korznikov Kirill , Kislov Dmitriy , Petrenko Tatyana , Dzizyurova Violetta , Doležal Jiří , Krestov Pavel , Altman Jan
Published: September 2023

The use of drone-borne imagery for tree recognition holds high potential in forestry and ecological studies. Accurate species identification and crown delineation are essential for tasks such as species mapping and ecological assessments. In this study, we compared the results of tree crown recognition across three neural networks using high-resolution optical imagery captured by an affordable drone with an RGB camera. The tasks included the detection of two evergreen coniferous tree species using the YOLOv8 neural network, the semantic segmentation of tree crowns using the U-Net neural network, and the instance segmentation of individual tree crowns using the Mask R-CNN neural network. The evaluation highlighted the strengths and limitations of each method. YOLOv8 demonstrated effective multiple-object detection (F1-score—0.990, overall accuracy (OA)—0.981), enabling detailed analysis of species distribution. U-Net achieved less accurate pixel-level segmentation for both species (F1-score—0.981, OA—0.963). Mask R-CNN provided precise instance-level segmentation, but with lower accuracy (F1-score—0.902, OA—0.822). The choice of a tree crown recognition method should align with the specific research goals. Although YOLOv8 and U-Net are suitable for mapping and species distribution assessments, Mask R-CNN offers more detailed information regarding individual tree crowns. Researchers should carefully consider their objectives and the required level of accuracy when selecting a recognition method. Solving practical problems related to tree recognition requires a multi-step process involving collaboration among experts with diverse skills and experiences, adopting a biology- and landscape-oriented approach when applying remote sensing methods to enhance recognition results. We recommend capturing images in cloudy weather to increase species recognition accuracy. Additionally, it is advisable to consider phenological features when selecting optimal seasons, such as early spring or late autumn, for distinguishing evergreen conifers in boreal or temperate zones.
A Color Matching Method for Mosaic HY-1 Satellite Images in Antarctica
Authors: Zeng Tao , Shi Lijian , Huang Lei , Zhang Ying , Zhu Haitian , Yang Xiaotong
Published: September 2023

Antarctic mapping with satellite images is an important basic task for polar environmental monitoring. Since the first Chinese marine satellite was launched in 2002, China has formed three series of more than 10 marine satellites in orbit. As global operational monitoring satellites of ocean color series, HY-1C and HY-1D have good coverage characteristics and imaging performance in polar regions, and they provide an effective tool for Antarctic monitoring and mapping. In this paper, Antarctic images acquired by the HY-1 satellite Coastal Zone Imager (CZI) sensor were used to study color matching in the mosaic process. According to the CZI characteristics for Antarctic imaging, experiments were carried out on the illuminance nonuniformity of a single image and color registration of multiple images. A gray-level segmentation color-matching method is proposed to solve the problem of image overstretching in the Antarctic image color-matching process. The results and statistical analysis show that the proposed method can effectively eliminate the color deviation between HY-1 Antarctic images, and the mosaic results have a good effect.
Estimating and Mapping Soil Salinity in Multiple Vegetation Cover Periods by Using Unmanned Aerial Vehicle Remote Sensing
Authors: Cui Xin , Han Wenting , Dong Yuxin , Zhai Xuedong , Ma Weitong , Zhang Liyuan , Huang Shenjin
Published: September 2023

Soil salinization is a severe soil degradation issue in arid and semiarid regions. The distribution of soil salinization can prove useful in mitigating soil degradation. Remote sensing monitoring technology is available for obtaining the distribution of soil salinization rapidly and nondestructively. In this study, experimental data were collected from seven study areas of the Hetao Irrigation District from July to August in 2021 and 2022. The soil salt content (SSC) was considered at various soil depths, and the crop type and time series were considered as environmental factors. We analyzed the effects of various environmental factors on the sensitivity response of unmanned aerial vehicle (UAV)-derived spectral index variables to the SSC and assessed the accuracy of SSC estimations. The five indices with the highest correlation with the SSC under various environmental factors were the input parameters used in modeling based on three machine learning algorithms. The best model was subsequently used to derive prediction distribution maps of the SSC. The results revealed that the crop type and time series did not affect the relationship strength between the SSC and spectral indices, and that the classification of the crop type and time series can considerably enhance the accuracy of SSC estimation. The mask treatment of the soil pixels can improve the correlation between some spectral indices and the SSC. The accuracies of the ANN and RFR models were higher than SVR accuracy (optimal R2 = 0.52–0.79), and the generalization ability of ANN was superior to that of RFR. In this study, considering environmental factors, a UAV remote sensing estimation and mapping method was proposed. The results of this study provide a reference for the high-precision prediction of soil salinization during the vegetation cover period.
Improved Amplitude-Phase Calibration Method of Nonlinear Array for Wide-Beam High-Frequency Surface Wave Radar
Authors: Fu Wei , Liu Han , Chen Zhihui , Yang Shu , Hu Yuandong , Wen Fangqing
Published: September 2023

The amplitude and phase errors in array elements lead to uncertainties in the estimation of arrival angles (DOA), which, in turn, affect the accuracy of current measurements for high-frequency surface wave radar (HFSWR). To address this issue, this paper proposes a passive amplitude-phase self-calibration method for a wide-beam HFSWR system with a nonlinear array. This method utilizes the aggregation of the amplitude ratio among array elements to screen reception matrices of single DOA sources. Based on the differences in reception matrices, the amplitude error is calibrated. Moreover, the cost function is calculated using the multiple signal classification (MUSIC) algorithm, and the initial phase error is first obtained after triangular array dimensionality reduction. Then, the phase error is further calibrated through quadratic-form iterative optimization. This method has been validated through simulations and real measurements. An approximately 4-day dataset obtained via HFSWR is reanalyzed in this paper. After data calibration, the radar-estimated currents were in good agreement with the buoy-measured results, with a root mean square error (RMSE) as low as 0.06 m/s and a correlation coefficient (CC) of up to 0.88. The results indicate that this method is suitable for the amplitude and phase error calibration of nonlinear arrays in wide-beam HFSWR systems.
TCNet: A Transformer–CNN Hybrid Network for Marine Aquaculture Mapping from VHSR Images
Authors: Fu Yongyong , Zhang Wenjia , Bi Xu , Wang Ping , Gao Feng
Published: September 2023

Precise delineation of marine aquaculture areas is vital for the monitoring and protection of marine resources. However, due to the coexistence of diverse marine aquaculture areas and complex marine environments, it is still difficult to accurately delineate mariculture areas from very high spatial resolution (VHSR) images. To solve such a problem, we built a novel Transformer–CNN hybrid Network, named TCNet, which combined the advantages of CNN for modeling local features and Transformer for capturing long-range dependencies. Specifically, the proposed TCNet first employed a CNN-based encoder to extract high-dimensional feature maps from input images. Then, a hierarchical lightweight Transformer module was proposed to extract the global semantic information. Finally, it employed a coarser-to-finer strategy to progressively recover and refine the classification results. The results demonstrate the effectiveness of TCNet in accurately delineating different types of mariculture areas, with an IoU value of 90.9%. Compared with other state-of-the-art CNN or Transformer-based methods, TCNet showed significant improvement both visually and quantitatively. Our methods make a significant contribution to the development of precision agricultural in coastal regions.
Synthetic Forest Stands and Point Clouds for Model Selection and Feature Space Comparison
Authors: Bester Michelle S. , Maxwell Aaron E. , Nealey Isaac , Gallagher Michael R. , Skowronski Nicholas S. , McNeil Brenden E.
Published: September 2023

The challenges inherent in field validation data, and real-world light detection and ranging (lidar) collections make it difficult to assess the best algorithms for using lidar to characterize forest stand volume. Here, we demonstrate the use of synthetic forest stands and simulated terrestrial laser scanning (TLS) for the purpose of evaluating which machine learning algorithms, scanning configurations, and feature spaces can best characterize forest stand volume. The random forest (RF) and support vector machine (SVM) algorithms generally outperformed k-nearest neighbor (kNN) for estimating plot-level vegetation volume regardless of the input feature space or number of scans. Also, the measures designed to characterize occlusion using spherical voxels generally provided higher predictive performance than measures that characterized the vertical distribution of returns using summary statistics by height bins. Given the difficulty of collecting a large number of scans to train models, and of collecting accurate and consistent field validation data, we argue that synthetic data offer an important means to parameterize models and determine appropriate sampling strategies.
Cool Skin Effect as Seen from a New Generation Geostationary Satellite Himawari-8
Authors: Zhang Yueqi , Chen Zhaohui
Published: September 2023

The cool skin effect refers to the phenomenon where the surface skin temperature of the ocean is always slightly cooler than the temperature of the water directly underneath due to the ubiquitous cooling processes at the ocean surface, especially in the absence of solar radiation. The cool skin effect plays a critical role in the estimation of heat, momentum, and gas exchange between the air and the sea. However, the scarcity of observational data greatly hinders the accurate assessment of the cool skin effect. Here, the matchup data from the new generation geostationary satellite Himawari-8 and in situ sea surface temperature (SST) observations are used to evaluate the performance and dependence on the cool skin effect in the low/mid-latitude oceans. Results show that the intensity of the cool skin effect as revealed by Himawari-8 (−0.16 K) is found to be relatively weaker than previously published cool skin models based on in situ concurrent observations. A considerable amount of warm skin signals has been detected in the high-latitude oceans (e.g., Southern Ocean) under the circumstances of positive air–sea temperature difference and high wind, which may be the main cause of discrepancies with previous thoughts on the cool skin effect.
Finding Misclassified Natura 2000 Habitats by Applying Outlier Detection to Sentinel-1 and Sentinel-2 Data
Authors: Moravec David , Barták Vojtěch , Šímová Petra
Published: September 2023

The monitoring of Natura 2000 habitats (Habitat Directive 92/43/EEC) is a key activity ensuring the sufficient protection of European biodiversity. Reporting on the status of Natura 2000 habitats is required every 6 years. Although field mapping is still an indispensable source of data on the status of Natura 2000 habitats, and very good field-based data exist in some countries, keeping the field-based habitat maps up to date can be an issue. Remote sensing techniques represent an excellent alternative. Here, we present a new method for detecting habitats that were likely misclassified during the field mapping or that have changed since then. The method identifies the possible habitat mapping errors as the so-called “attribute outliers”, i.e., outlying observations in the feature space of all relevant (spectral and other) characteristics of an individual habitat patch. We used the Czech Natura 2000 Habitat Layer as field-based habitat data. To prepare the feature space of habitat characteristics, we used a fusion of Sentinel-1 and Sentinel-2 satellite data along with a Digital Elevation Model. We compared outlier ratings using the robust Mahalanobis distance and Local Outlier Factor using three different thresholds (Tukey rule, histogram-based Scott’s rule, and 95% quantiles in χ2 distribution). The Mahalanobis distance thresholded by the 95% χ2 quantile achieved the best results, and, because of its high specificity, appeared as a promising tool for identifying erroneously mapped or changed habitats. The presented method can, therefore, be used as a guide to target field updates of Natura 2000 habitat maps or for other habitat/land cover mapping activities where the detection of misclassifications or changes is needed.
First Galileo Single-Frequency Occultation Process and Precision Analysis of FengYun3E
Authors: Yang Ming , Meng Xiangguang , Tian Haoran , Sun Yueqiang , Du Qifei , Bai Weihua , Wang Bowen , Wang Xianyi , Hu Peng , Tan Guangyuan
Published: September 2023

This article proposes a single-frequency occultation method whose core is the reconstruction of a second frequency measurement. We process the actual received Galileo E1 single-frequency occultation observation data of FengYun3E to meet the urgent need for single-frequency Galileo occultation inversion of FengYun3 E/F/G/H satellites. Galileo single-frequency occultation events are globally distributed evenly and have stable quantities. The refractive index products and dry temperature products inverted from the single-frequency occultation data are reliable at altitudes of 5–30 km. The Galileo E1 single-frequency occultation process can effectively improve the output of FengYun3E/GNOS occultation products. These results validate the feasibility and correctness of using FengYun3/GNOS for the actual Galileo single-frequency occultation process. The atmospheric occultation products of Galileo single-frequency occultation increase the quantity of global occultation products and serve as a beneficial supplement to global numerical weather prediction data sources.
Lightweight CFARNets for Landmine Detection in Ultrawideband SAR
Authors: Zhang Yansong , Song Yongping , Jin Tian
Published: September 2023

The high-resolution image obtained by ultrawideband synthetic aperture radar (UWB SAR) includes rich features such as shape and scattering features, which can be utilized for landmine discrimination and detection. Due to the high performance and automatic feature learning ability, deep network-based detection methods have been widely employed in SAR target detection. However, existing deep networks do not consider the target characteristics in SAR images, and their structures are too complicated. Therefore, lightweight deep networks with efficient and interpretable blocks are essential. This work investigates how to utilize the SAR characteristics to design a lightweight deep network. The widely employed constant false alarm rates (CFAR) detector is used as a prototype and transformed into trainable multiple-feature network filters. Based on CFAR filters, we propose a new class of networks called CFARNets which can serve as an alternative to convolutional neural networks (CNNs). Furthermore, a two-stage detection method based on CFARNets is proposed. Compared to prevailing CNNs, the complexity and number of parameters of CFARNets are significantly reduced. The features extracted by CFARNets are interpretable as CFAR filters have definite physical significance. Experimental results show that the proposed CFARNets have comparable detection performance compared to other real-time state-of-the-art detectors but with faster inference speed.
Review of Desert Mobility Assessment and Desertification Monitoring Based on Remote Sensing
Authors: Wang Zhaobin , Shi Yue , Zhang Yaonan
Published: September 2023

Desertification seriously hinders economic development and ecological security, which has led to increased research on desertification monitoring and control. Remote sensing technology is widely used in desert research due to its large detection range and ability to obtain target feature information without touching objects. In order to better monitor and control desertification, the research methods on desert mobility and dune morphology in mobile deserts were reviewed. Among them, an important index to distinguish mobile and nonmobile deserts is desert vegetation coverage. The research progress of desert vegetation coverage based on visual interpretation, the nonlinear spectral model, normalized vegetation index (NDVI) fitting and plant community classification was reviewed. The loss of vegetation in the transitional zone of the desert is a contributing factor to desertification. The new technologies and applications of desert area monitoring, the remote sensing ecological index, and desert feature information extraction were introduced and analyzed. To combat desertification more accurately and effectively, the classification methods of moving dunes based on deep learning were also reviewed. It can be concluded that desertification monitoring methods are gradually becoming more accurate and adaptive, but they remain insufficient and less mature. Therefore, exploring how to apply desertification control technology more scientifically and rationally is an extremely valuable area for research.
CNN-Bi-LSTM: A Complex Environment-Oriented Cattle Behavior Classification Network Based on the Fusion of CNN and Bi-LSTM
Authors: Gao Guohong , Wang Chengchao , Wang Jianping , Lv Yingying , Li Qian , Ma Yuxin , Zhang Xueyan , Li Zhiyu , Chen Guanglan
Published: September 2023

Cattle behavior classification technology holds a crucial position within the realm of smart cattle farming. Addressing the requisites of cattle behavior classification in the agricultural sector, this paper presents a novel cattle behavior classification network tailored for intricate environments. This network amalgamates the capabilities of CNN and Bi-LSTM. Initially, a data collection method is devised within an authentic farm setting, followed by the delineation of eight fundamental cattle behaviors. The foundational step involves utilizing VGG16 as the cornerstone of the CNN network, thereby extracting spatial feature vectors from each video data sequence. Subsequently, these features are channeled into a Bi-LSTM classification model, adept at unearthing semantic insights from temporal data in both directions. This process ensures precise recognition and categorization of cattle behaviors. To validate the model’s efficacy, ablation experiments, generalization effect assessments, and comparative analyses under consistent experimental conditions are performed. These investigations, involving module replacements within the classification model and comprehensive analysis of ablation experiments, affirm the model’s effectiveness. The self-constructed dataset about cattle is subjected to evaluation using cross-entropy loss, assessing the model’s generalization efficacy across diverse subjects and viewing perspectives. Classification performance accuracy is quantified through the application of a confusion matrix. Furthermore, a set of comparison experiments is conducted, involving three pertinent deep learning models: MASK-RCNN, CNN-LSTM, and EfficientNet-LSTM. The outcomes of these experiments unequivocally substantiate the superiority of the proposed model. Empirical results underscore the CNN-Bi-LSTM model’s commendable performance metrics: achieving 94.3% accuracy, 94.2% precision, and 93.4% recall while navigating challenges such as varying light conditions, occlusions, and environmental influences. The objective of this study is to employ a fusion of CNN and Bi-LSTM to autonomously extract features from multimodal data, thereby addressing the challenge of classifying cattle behaviors within intricate scenes. By surpassing the constraints imposed by conventional methodologies and the analysis of single-sensor data, this approach seeks to enhance the precision and generalizability of cattle behavior classification. The consequential practical, economic, and societal implications for the agricultural sector are of considerable significance.
A High-Performance Circularly Polarized and Harmonic Rejection Rectenna for Electromagnetic Energy Harvesting
Authors: Abdulwali Zaed S. A. , Alqahtani Ali H. , Aladadi Yosef T. , Alkanhal Majeed A. S. , Al-Moliki Yahya M. , Aljaloud Khaled , Alresheedi Mohammed Thamer
Published: September 2023

This paper presents a novel circularly polarized rectenna designed for efficient electromagnetic energy harvesting at the 2.45 GHz ISM band. A compact antenna structure is designed to achieve high performance in terms of radiation efficiency, axial ratio, directivity, effective area, and harmonic rejection over the entire bandwidth of the ISM frequency band. The optimized rectifier circuit enhances the RF harvested energy efficiency, with an AC-to-DC conversion efficiency ranging from 36% to 70% for low-level input power ranging from −10 dBm to 0 dBm. The stable output of DC power confirms the suitability of this design for various practical applications, including wireless sensor networks, energy harvesting power supplies, medical implants, and environmental monitoring systems. Experimental validation, which includes both the reflection coefficient and radiation patterns of the designed antenna, confirms the accuracy of the simulation. The study found that the proposed energy harvesting system has a high total efficiency ranging from 53% to 63% and is well-suited for low-power energy harvesting (0 dBm) from ambient electromagnetic radiation. The proposed circularly polarized rectenna is a competitive option for efficient electromagnetic energy harvesting, both as a standalone unit and in an array, due to its high performance, feasibility, and versatility in meeting various energy harvesting requirements. This makes it a promising and cost-effective solution for various wireless communication applications, offering great potential for efficient energy harvesting from ambient electromagnetic radiation.
Monitoring Distribution of the Therapeutic Agent Dimethyl Sulfoxide via Solvatochromic Shift of Albumin-Bound Indocyanine Green
Authors: Cho Jaedu , Nouizi Farouk , Kim Chang-Seok , Gulsen Gultekin
Published: September 2023

We recently developed a novel hyperspectral excitation-resolved near-infrared fluorescence imaging system (HER-NIRF) based on a continuous-wave wavelength-swept laser. In this study, this technique is applied to measure the distribution of the therapeutic agent dimethyl sulfoxide (DMSO) by utilizing solvatochromic shift in the spectral profile of albumin-bound Indocyanine green (ICG). Using wide-field imaging in turbid media, complex dynamics of albumin-bound ICG are measured in mixtures of dimethyl sulfoxide (DMSO) and water. Phantom experiments are conducted to evaluate the performance of the HER-NIRF system. The results show that the distribution of DMSO can be visualized in the wide-field reflection geometry. One of the main purposes of the DMSO is to act as a carrier for other drugs, enhancing their effects by facilitating skin penetration. Understanding the solubility and permeability of drugs in vivo is very important in drug discovery and development. Hence, this HER-NIRF technique has great potential to advance the utilization of the therapeutic agent DMSO by mapping its distribution via the solvatochromic shift of ICG. By customizing the operational wavelength range, this system can be applied to any other fluorophores in the near-infrared region and utilized for a wide variety of drug delivery studies.
The Lifespan of Human Activity Recognition Systems for Smart Homes
Authors: Hiremath Shruthi K. , Plötz Thomas
Published: September 2023

With the growing interest in smart home environments and in providing seamless interactions with various smart devices, robust and reliable human activity recognition (HAR) systems are becoming essential. Such systems provide automated assistance to residents or to longitudinally monitor their daily activities for health and well-being assessments, as well as for tracking (long-term) behavior changes. These systems thus contribute towards an understanding of the health and continued well-being of residents. Smart homes are personalized settings where residents engage in everyday activities in their very own idiosyncratic ways. In order to provide a fully functional HAR system that requires minimal supervision, we provide a systematic analysis and a technical definition of the lifespan of activity recognition systems for smart homes. Such a designed lifespan provides for the different phases of building the HAR system, where these different phases are motivated by an application scenario that is typically observed in the home setting. Through the aforementioned phases, we detail the technical solutions that are required to be developed for each phase such that it becomes possible to derive and continuously improve the HAR system through data-driven procedures. The detailed lifespan can be used as a framework for the design of state-of-the-art procedures corresponding to the different phases.
An Identification Method for Irregular Components Related to Terminal Blocks in Equipment Cabinet of Power Substation
Authors: Cao Weiguo , Chen Zhong , Deng Xuhui , Wu Congying , Li Tiecheng
Published: September 2023

Despite the continuous advancement of intelligent power substations, the terminal block components within equipment cabinet inspection work still often require loads of personnel. The repetitive documentary works not only lack efficiency but are also susceptible to inaccuracies introduced by substation personnel. To resolve the problem of lengthy, time-consuming inspections, a terminal block component detection and identification method is presented in this paper. The identification method is a multi-stage system that incorporates a streamlined version of You Only Look Once version 7 (YOLOv7), a fusion of YOLOv7 and differential binarization (DB), and the utilization of PaddleOCR. Firstly, the YOLOv7 Area-Oriented (YOLOv7-AO) model is developed to precisely locate the complete region of terminal blocks within substation scene images. The compact area extraction model rapidly cuts out the valid proportion of the input image. Furthermore, the DB segmentation head is integrated into the YOLOv7 model to effectively handle the densely arranged, irregularly shaped block components. To detect all the components within a target electrical cabinet of substation equipment, the YOLOv7 model with a differential binarization attention head (YOLOv7-DBAH) is proposed, integrating spatial and channel attention mechanisms. Finally, a general OCR algorithm is applied to the cropped-out instances after image distortion to match and record the component’s identity information. The experimental results show that the YOLOv7-AO model reaches high detection accuracy with good portability, gaining 4.45 times faster running speed. Moreover, the terminal block component detection results show that the YOLOv7-DBAH model achieves the highest evaluation metrics, increasing the F1-score from 0.83 to 0.89 and boosting the precision to over 0.91. The proposed method achieves the goal of terminal block component identification and can be applied in practical situations.
Increasing Interoperability between Digital Twin Standards and Specifications: Transformation of DTDL to AAS
Authors: Schmidt Carlos , Volz Friedrich , Stojanovic Ljiljana , Sutschet Gerhard
Published: September 2023

Although standards and specifications for digital twins aim to create interoperability in Industry 4.0, each standard has its own goals, focuses and representations for digital twins. This paper examines an approach to increasing interoperability between established digital twin specifications by transformation. Accordingly, several specifications are presented and requirements for transformation are examined. Following the feasibility analysis, a mapping between the Digital Twin Definition Language (DTDL) and Asset Administration Shell (AAS) was created. To examine the feasibility of this approach, the transformation was implemented and tested for a physical asset. This paper demonstrates that a generic mapping between DTDL and AAS can be applied for transformation in use cases where DTDL models are provided while AAS is required.
Advances in Hydrocarbon Speciation for Soil Gas Analysis
Authors: De Gregorio Maria Antonietta , Clerici Laura , Marinone Emma , Frattini Valeria , Dellavedova Pierluisa
Published: September 2023

In the field of contaminated site remediation, risk analyses can be carried out by the use of appropriate mathematical models, based on simplified hypotheses of real sites. The possibility to have shared and accurate analytical methods is an important challenge for both routine laboratories and supervisory institutions. Although official methods (e.g., APH2009) are already available, they are lacking in some aspects, such as applicability to all the different sampling and analysis media and ease of use in routine analysis. Here, we report our recent advances in the implementation of an empirical method for hydrocarbon speciation in an air matrix, previously developed by the authors and published in 2015. The validation results led to a coefficient of variation (CV%) between 10–15% for the hydrocarbon fractions investigated (C5-C8 aliphatic, C9-C12 aliphatic, C9-C10 aromatic and C11-C12 aromatic) and recovery of ≤20%, in agreement with the required characteristics for the analytical methods. Several real samples were analyzed by the proposed method and the results were compared to those obtained by the official approach. The comparison led to an average bias (that is, the difference between the results from the developed method and those from the conventional one) of about 10%, confirming that the innovative approach is robust, accurate and can be applied to all the analytical media used for air sampling, such as soil gas.
Immobilization of UiO-66-NH2 into Bacterial Cellulose Aerogels for Efficient Particulate Matter Filtration
Authors: Dai Qihang , Liu Ju , Zheng Junjie , Fu Bo
Published: September 2023

Metal–organic frameworks (MOFs) hold great potential for efficient removal of particulate matter (PM) due to their high porosity and tunable surface groups. In this work, sustainable bacterial cellulose (BC) was employed as a substrate for Zr-based MOF (i.e., UiO-66-NH2) deposition. Specifically, the UiO-66-NH2@BC aerogel exhibits a multi-level pore structure with a specific surface area of 103 m2/g. Pristine BC aerogels indicated a removal efficiency of 50.6% for PM2.5, with a pressure drop of 22.8 Pa. While UiO-66-NH2@BC aerogels showed a PM2.5 removal efficiency of 96.9%. The filtration mechanism can be assigned to the following reasons: (i) the interception/impaction of PM with the fibrous and porous aerogel network; and (ii) the presence of polar amine groups that boost electrostatic interactions between PM and MOFs. Moreover, as-prepared filters can be regenerated in a facile way with good reusability and long-term stability.
A Review of Artificial Intelligence-Based Optimization Applications in Traditional Active Maritime Collision Avoidance
Authors: Zhang Yi , Zhang Dapeng , Jiang Haoyu
Published: September 2023

The probability of collisions at sea has increased in recent years. Furthermore, passive collision avoidance has some disadvantages, such as low economic efficiency, while active collision avoidance techniques have some limitations. As a result of the advancement of computer technology, active collision avoidance techniques have also been optimized by using artificial intelligence-based methods. The purpose of this paper is to further the development of the field. After reviewing some passive collision avoidance schemes, the paper discusses the potential of active obstacle avoidance techniques. A time-tracing approach is used to review the evolution of active obstacle avoidance techniques, followed by a review of the main traditional active obstacle avoidance techniques. In this paper, different artificial intelligence algorithms are reviewed and analyzed. As a result of the analysis and discussion in this paper, some limitations in this field are identified. In addition, there are some suggestions and outlooks for addressing those limitations. In a way, the paper can serve as a guide for the development of the field.
Multicriteria Quantification of the Compatibility of the Targets from Romania’s Relevant Strategies with the European Green Deal
Authors: Felea Adrian Ioan , Felea Ioan , Hoble Calin Radu
Published: September 2023

This study deepens and further concretizes an analysis conducted in a prior study highlighting Romania’s goals and objectives in comparison with the sustainable development principles established through the adoption of the European Green Deal (EGD) at the European Union (EU) level. The second section of this paper presents this study’s methodology, aiming to highlight the quantification of the compatibility of the targets of Romania’s pertinent strategies with the EGD after evoking the principles of sustainable development (SD) and—in this context—the importance of achieving the objectives set by the EGD. The third section of this paper presents our findings, and the final section offers conclusions drawn from our analysis.
Effect of Regulated Deficit Irrigation (RDI) on the Growth and Development of Pear Fruit (Pyrus communis L.), var. Triunfo de Viena
Authors: Vélez-Sánchez Javier , Casierra-Posada Fánor , Fischer Gerhard
Published: September 2023

The irrigation of crops represents 70% of the world’s water use. For crops grown in high-altitude tropical climates, due to the decrease in rainfall with increasing altitude, along with the effects of global warming, it is necessary to propose alternatives for sustainable fruit production with reduced water consumption. This study was carried out in Sesquilé, Colombia (South America), at an altitude of 2595 m a.s.l. for two successive years with pear trees, var. Triunfo de Viena. The objective of this work was to determine the effect of regulated deficit irrigation (RDI) on the absolute and relative growth rates of the equatorial diameter of the fruits, the fresh and dry weights, the number of fruits, the yield per tree and the water potential of the fruits. In 2014, during the rapid growth phase of the fruit, one group of plants was irrigated at 100% of the crop evapotranspiration (ETc) (control), another at 74% and a third group at 60%. In 2015, the three groups were irrigated at 100%, 48% and 27% of the ETc. The use of RDI did not significantly affect the growth of the fruits. This study showed that the ‘Triunfo de Viena’ pear tree not only has sufficient adaptive reserves, but also has good ecological plasticity under water stress conditions in high-altitude tropical climates. In cases where water is a limiting factor for pear tree production, RDI can obtain production rates similar to those of a regularly irrigated crop, as long as this technique is used and implemented in phenological states of low sensitivity to water stress without exceeding the tolerance limits of the plants to the stressor.
Experimental and Numerical Analyses on the Frost Heave Deformation of Reclaimed Gravel from a Tunnel Excavation as a Structural Fill in Cold Mountainous Regions
Authors: Wu Libo , Niu Fujun , Lin Zhanju , Shang Yunhu , Nimbalkar Sanjay , Sheng Daichao
Published: September 2023

In cold mountainous regions of China, the construction of highways is challenging, owing to frost damage of weak subgrade soils and the difficulties posed from dealing with waste materials derived from tunnel excavation. In order to address these issues, Wu et al. proposed a new approach of using coarse gravel reclaimed from tunnel excavation as an antifrost structural fill replacing the top layer of frost-susceptible subgrade soils. This approach was validated against the results of field investigations on the highway between Tanchang county and Diebu county (the TDH) in south Gansu Province, northwest China, but only studied the results of the first year. As an environmentally friendly and sustainable ground-treatment method, this strategy merits extensive research and widespread implementation. In this study, the frost-heave deformation of a two-year monitoring period is investigated through a field trial, and a frost-heave model is applied to explore the growth of the ice lens and accomplish the quantitative prediction of frost heave based on experimental measurements. The fine particles of reclaimed gravel sediments from the Lazikou tunnel are found to be resistant to frost. The measured values of the maximum frost heave are significantly lower than the permissible limit of 50 mm specified in the Chinese standards. The reclaimed gravel could significantly reduce frost heave. With a 2 m thick gravel fill, frost heave could be reduced by more than 70% when the groundwater table is located at a depth greater than 3 m. An empirical relationship to predict the frost heave in terms of the gravel fill thickness is proposed. This study presents a safe and sustainable approach focusing on the construction of highways in cold mountainous regions.