bluuebunny/crossref_metadata_2025 huggingface dataset:
Regulation of Tomato Fruit Autophagic Flux and Promotion of Fruit Ripening by the Autophagy-Related Gene SlATG8f
Authors: Wen Cen , Luo Taimin , He Zhuo , Li Yunzhou , Yan Jianmin , Xu Wen
Published: September 2023

Autophagy is a highly conserved self-degradation process that involves the degradation and recycling of cellular components and organelles. Although the involvement of autophagy in metabolic changes during fruit ripening has been preliminarily demonstrated, the variations in autophagic flux and specific functional roles in tomato fruit ripening remain to be elucidated. In this study, we analyzed the variations in autophagic flux during tomato fruit ripening. The results revealed differential expression of the SlATG8 family members during tomato fruit ripening. Transmission electron microscopy observations and dansylcadaverine (MDC) staining confirmed the presence of autophagy at the cellular level in tomato fruits. Furthermore, the overexpression of SlATG8f induced the formation of autophagosomes, increased autophagic flux within tomato fruits, and effectively enhanced the expression of ATG8 proteins during the color-transition phase of fruit ripening, thus promoting tomato fruit maturation. SlATG8f overexpression also led to the accumulation of vitamin C (VC) and soluble solids while reducing acidity in the fruit. Collectively, our findings highlight the pivotal role of SlATG8f in enhancing tomato fruit ripening, providing insights into the mechanistic involvement of autophagy in this process. This research contributes to a better understanding of the key factors that regulate tomato fruit quality and offers a theoretical basis for tomato variety improvement.
A SUPERMAN-like Gene Controls the Locule Number of Tomato Fruit
Authors: Zhang Mi , Zhou Enbai , Li Meng , Tian Shenglan , Xiao Han
Published: September 2023

Tomato (Solanum lycopersicum) fruits are derived from fertilized ovaries formed during flower development. Thus, fruit morphology is tightly linked to carpel number and identity. The SUPERMAN (SUP) gene is a key transcription repressor to define the stamen–carpel boundary and to control floral meristem determinacy. Despite SUP functions having been characterized in a few plant species, its functions have not yet been explored in tomato. In this study, we identified and characterized a fascinated and multi-locule fruit (fmf) mutant in Solanum pimpinellifolium background harboring a nonsense mutation in the coding sequence of a zinc finger gene orthologous to SUP. The fmf mutant produces supersex flowers containing increased numbers of stamens and carpels and sets malformed seedless fruits with complete flowers frequently formed on the distal end. fmf alleles in cultivated tomato background created by CRISPR-Cas9 showed similar floral and fruit phenotypes. Our results provide insight into the functional conservation and diversification of SUP members in different species. We also speculate the FMF gene may be a potential target for yield improvement in tomato by genetic engineering.
Resolving the Puzzle of Iris maackii (Iridaceae): A Morphological Insight into Its Taxonomy
Authors: Boltenkov Eugeny V.
Published: September 2023

Since the early 20th century, Iris maackii (Iridaceae) has been considered a synonym of I. laevigata, a synonym of I. pseudacorus, or an accepted species. The current concept of I. maackii in the literature and databases is often applied to yellow-flowered plants with prominently veined rosette leaves, which are diagnostic features of I. pseudacorus growing in Northeast Asia. Therefore, the objective was to clarify the taxonomic identity of I. maackii. This study is based on a critical examination of the literature, on the observed morphological characters in the holotype of I. maackii, and on a morphological comparison of I. maackii with living plants of I. laevigata and I. pseudacorus. Additionally, a morphometric comparison of the seed characters was carried out to clarify the morphological distinction among I. maackii, I. laevigata, and I. pseudacorus. A careful study demonstrated that the rosette leaf texture and the morphology of the flowering stem, fruit, and seeds of I. maackii are identical to or within the variation range of I. laevigata. Thus, I. maackii is morphologically non-distinct from I. laevigata and should be recognized as a taxonomic synonym of the latter. An image of the holotype of I. maackii is provided along with detailed illustrations of I. laevigata and I. pseudacorus.
Influence of Catalyst Content and Epoxy/Carboxylate Ratio on Isothermal Creep of Epoxy Vitrimers
Authors: Palmieri Barbara , Cilento Fabrizia , Amendola Eugenio , Valente Teodoro , Dello Iacono Stefania , Giordano Michele , Martone Alfonso
Published: September 2023

In the present work, a commercial epoxy based on epoxy anhydride and tertiary amine was modified by a metallic catalyst (Zn2+) to induce vitrimeric behavior by promoting the transesterification reaction. The effect of two different epoxy/acid ratios (1 and 0.6) at two different zinc acetate amounts (Zn(Ac)2) on the thermomechanical and viscoelastic performances of the epoxy vitrimers were investigated. Creep experiments showed an increase in molecular mobility above the critical “Vitrimeric” temperature (Tv) of 170 °C proportionally to the amount of Zn(Ac)2. A procedure based on Burger’s model was set up to investigate the effect of catalyst content on the vitrimer ability to flow as the effect of the dynamic exchange reaction. The analysis showed that in the case of a balanced epoxy/acid formulation, the amount of catalyst needed for promoting molecular mobility is 5%. This system showed a value of elastic modulus and dynamic viscosity at 170 °C of 9.50 MPa and 2.23 GPas, respectively. The material was easily thermoformed in compression molding, paving the way for the recyclability and weldability of the thermoset system.
Design of a New Phthalocyanine-Based Ion-Imprinted Polymer for Selective Lithium Recovery from Desalination Plant Reverse Osmosis Waste
Authors: Jamoussi Bassem , Chakroun Radhouane , Al-Mur Bandar A. , Halawani Riyadh F. , Aloufi Fahed A. , Chaabani Anis , Aljohani Naif S.
Published: September 2023

In this study, a novel technique is introduced that involves the combination of an ion-imprinted polymer and solid-phase extraction to selectively adsorb lithium ions from reverse osmosis brine. In the process of synthesizing ion-imprinted polymers, phthalocyanine acrylate acted as the functional monomer responsible for lithium chelation. The structural and morphological characteristics of the molecularly imprinted polymers and non-imprinted polymers were assessed using Fourier transform infrared spectroscopy and scanning electron microscopy. The adsorption data for Li on an ion-imprinted polymer showed an excellent fit to the Langmuir isotherm, with a maximum adsorption capacity (Qm) of 3.2 mg·g−1. Comprehensive chemical analyses revealed a significant Li concentration with a higher value of 45.36 mg/L. Through the implementation of a central composite design approach, the adsorption and desorption procedures were systematically optimized by varying the pH, temperature, sorbent mass, and elution volume. This systematic approach allowed the identification of the most efficient operating conditions for extracting lithium from seawater reverse osmosis brine using ion-imprinted polymer–solid-phase extraction. The optimum operating conditions for the highest efficiency of adsorbing Li+ were determined to be a pH of 8.49 and a temperature of 45.5 °C. The efficiency of ion-imprinted polymer regeneration was evaluated through a cycle of the adsorption–desorption process, which resulted in Li recoveries of up to 80%. The recovery of Li from the spiked brine sample obtained from the desalination plant reverse osmosis waste through the ion-imprinted polymer ranged from 62.8% to 71.53%.
Application of a Metal Cobalt Based on 4,6-Bis(imidazol-1-yl)isophthalicacid Metal-Organic -Framework Materials in Photocatalytic CO2 Reduction, Antibacterial, and Dye Adsorption
Authors: Han Yue , Zhao Lun , Jing Hongwei , Song Guanying , Wang Ziyun , Li Jiayu , Yang Yi
Published: September 2023

In this paper, the reported MOF ([Co(bimip)(H2O)0.5]·0.5H2O) was employed in photocatalytic CO2 reduction, antibacterial, and dye adsorption experiments. The photocatalytic activity of the MOF for CO2 reduction was systematically investigated. The high average CO generation rate of 3421.59 μmol·g−1·h−1 after 12 h confirms the efficient photocatalytic CO2 reduction ability of the MOF. At the same time, the MOF can completely inhibit the growth of S. aureus and C. albicans within 24 h when its concentration reaches 400 μg/mL and 500 μg/mL, respectively. The MOF has an adsorption capacity for CR. The adsorption rate was 83.42% at 60 min, and the adsorption capacity of the MOF for CR reached 500.00 mg·g−1.
The Analysis and Rapid Non-Destructive Evaluation of Yongchuan Xiuya Quality Based on NIRS Combined with Machine Learning Methods
Authors: Zang Ying , Wang Jie , Wu Xiuhong , Chang Rui , Wang Yi , Luo Hongyu , Zhong Yingfu , Wu Quan , Chen Zhengming , Deng Min
Published: September 2023

This paper attempts to analyze and assess Yongchuan Xiuya tea quality quickly, accurately, and digitally. The sensory evaluation method was first used to assess Yongchuan Xiuya tea quality, and then near infrared spectroscopy (NIRS) was obtained, and standard methods were applied to the testing of the chemical components. Next, principal component analysis (PCA) and the correlation coefficient method were used to comprehensively screen out the representative components. Finally, NIRS combined with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN) methods were applied to build quality evaluation models for Yongchuan Xiuya tea, respectively, and external samples were employed to examine the practical application results of the best model. The cumulative variance contribution rate of the first three principal components of the ingredients in tea was 97.73%. Seven components closely related to tea quality were screened out, namely, amino acids, total catechin, epigallocatechin gallate (EGCG), tea polyphenols, water extracts, epicatechin gallate (ECG), and epigallocatechin (EGC) (p < 0.01). Between the two models established to predict the tea quality, the model built by the PLS method had the better results, whose coefficient of determination of prediction (Rp2) and root mean square error of prediction (RMSEP) were 0.7955 and 1.2263, respectively, and the best results were obtained by the nonlinear BP-ANN model, whose Rp2 and RMSEP were 0.9315 and 0.6787, respectively. The 10 external Yongchuan Xiuya samples were employed to test the best BP-ANN model, and the results of R2 and RMSEP were 0.9579 and 0.6086, respectively, meaning that the model has good robustness. Therefore, the model established by NIRS combined with the BP-ANN method can be used to assess Yongchuan Xiuya tea quality rapidly, accurately, and digitally, and it can also provide new ideas and methods for evaluating the quality of other teas.
What Confucian Eco-Ethics Can Teach Us about Solving the Dilemma of Interpreting the Concept of Sustainability
Authors: Li Xian , Wei Fuming
Published: September 2023

Sustainability is at the heart of the concept of the common home. By prioritizing sustainability, we can create a better common home and ensure the well-being of present and future generations. However, there is a dilemma in the interpretation of sustainability, which is mainly characterized by the irreconcilability between “weak sustainability” and “strong sustainability”. The dilemma is partly rooted in some Western philosophical traditions such as the Western separatist mindset, anthropocentrism, and technological solutionism, which have contributed to human subjugation. This paper proposes Confucian eco-ethics to resolve this dilemma. First, Confucian eco-ethics embraces the holistic worldview of “anthropocosmic” that establishes an ontological understanding of the interconnectedness and interdependence between humans and nature, which transcends the Western dichotomy of subject and object and resolves the dualism between human beings and nature. Second, Confucian eco-ethics advocates “pushing oneself to all things” and considers human beings and nature as an ethical community, which emphasizes the ethical responsibility of human beings to protect nature, thus remedying the dilemma that anthropocentrism and ecocentrism have too little or too much responsibility for nature. Third, Confucianism endorses benevolence as a core value for managing technology to achieve sustainable development, and it favors a comprehensive approach that combines technological innovation, values reform, and institutional reform to solve ecological problems. To do this, we analyze the Dujiangyan Water Hydro-Project Hydraulic Project as a case study to illustrate the practical feasibility of Confucian eco-ethics in achieving sustainable development. The conclusion suggests that Confucian eco-ethics can enrich and expand sustainability theory, offering an alternative pathway for a better common home.
Assessment of Community Dependence and Perceptions of Wetlands in the Upper Chindwin Basin, Myanmar
Authors: Saluja Ridhi , Prasad Satish , Lwin Than Htway , Soe Hein Htet , Pottinger-Glass Chloe , Piman Thanapon
Published: September 2023

Understanding the relationship between communities and wetland ecosystems is imperative to facilitate the development of wetland management and conservation strategies that can effectively safeguard wetland health and sustain the flow of ecosystem services. To understand the knowledge, attitude, and perception of communities on wetland ecosystem services, a survey was conducted involving 133 households from 4 villages dependent on 5 wetlands within the Upper Chindwin Basin (UCB), northwestern Myanmar. Most of the respondents extracted wetland resources for subsistence and income. A total of 84% of the households depended on wetland fish for their primary protein consumption, while 70% (n = 94) collected fuelwood from wetlands for subsistence. The survey participants unanimously recognized the benefits of wetland ecosystem services (i.e., provisioning benefits), particularly for fish, food, fiber, fuel, natural medicines, ornamental resources, and minerals. A total of 97% of the participants lacked knowledge of any existing law or regulation that ensures wetland protection in Myanmar. Furthermore, 87% of the respondents concurred that the government has not adequately endeavored to promote awareness of wetland conservation in this remote area due to lack of capacity and resources. This study establishes a baseline for the region and recommends designing and implementing a community-centric wetland action plan. This action plan provides a self-sustaining and cost-effective approach to conserve wetlands and is crucial in enhancing the capacity of dependent communities to participate and eventually lead wetland management of UCB.
Extracting and Evaluating Urban Entities in China from 2000 to 2020 Based on SNPP-VIIRS-like Data
Authors: Withanage Neel Chaminda , Shi Kaifang , Shen Jingwei
Published: September 2023

It is crucial to evaluate the expansion of urban entities to implement sustainable urban planning strategies in China. Thus, this study attempted to extract and evaluate the growth of urban entities 270 prefecture cities in mainland China (2000–2020) using a novel approach based on consistent night light images. After the urban entities were extracted, a rationality assessment was carried out to compare the derived urban entities with the LandScan population product, Landsat, and road network results. Additionally, the results were compared with other physical extent products, such as the Moderate Resolution Imaging Spectrometer (MODIS) and urban built-up area products (HE) products. According to the findings, the urban entities were basically consistent with the LandScan, road network, and HE and MODIS products. However, the urban entities more accurately reflected the concentration of human activities than did the impervious extents of the MODIS and HE products. At the prefecture levels, the area of urban entities increased from 8082 km2 to 74,417 km2 between 2000 and 2020, showing an average growth rate of 10.8% over those twenty years. As a reliable supplementary resource and guide for urban mapping, this research will inform new research on the K-means algorithm and on variations in NTL data brightness threshold dynamics at regional and global scales.
Ecological Water Requirement of Vegetation and Water Stress Assessment in the Middle Reaches of the Keriya River Basin
Authors: Wang Ranran , Zayit Abudoukeremujiang , He Xuemin , Han Dongyang , Yang Guang , Lv Guanghui
Published: September 2023

Desert oases are vital for maintaining the ecological balance in arid regions’ inland river basins. However, fine-grained assessments of water stress in desert oasis ecosystems are limited. In our study, we aimed to evaluate the water stress in desert oasis ecosystems in the middle reaches of the Keriya River Basin, with a specific focus on their ecological functions and optimizing water resource management. We hypothesized that evapotranspiration has significant effects on ecological water consumption. First, we estimated the actual evapotranspiration (ET) and potential evapotranspiration (PET) based on the SEBS (surface energy balance system) model and remote sensing downscaling model. Then, the ecological water requirement (EWR) and ecological water stress (EWS) index were constructed to evaluate the ecological water resource utilization. Finally, we explored the influencing factors and proposed coping strategies. It was found that regions with higher ET values were mainly concentrated along the Keriya River and its adjacent farmland areas, while the lower values were observed in bare land or grassland areas. The total EWR exhibited the sequence of grassland > cropland > forest, while the EWR per unit area followed the opposite order. The grassland’s EWS showed a distinct seasonal response, with severe, moderate, and mild water shortages and water plenitude corresponding to spring, summer, autumn, and winter, respectively. In contrast, the land use types with the lowest EWS were water areas that remained in a state of water plentitude grade (0.08–0.20) throughout the year. Temperature and vegetation index were identified as the primary influencing factors. Overall, this study provides a reliable method for evaluating the EWR and EWS values of basin scale vegetation, which can serve as a scientific basis for formulating water resource management and regulation policies in the region.
Remote Sensing and Deep Learning to Understand Noisy OpenStreetMap
Authors: Usmani Munazza , Bovolo Francesca , Napolitano Maurizio
Published: September 2023

The OpenStreetMap (OSM) project is an open-source, community-based, user-generated street map/data service. It is the most popular project within the state of the art for crowdsourcing. Although geometrical features and tags of annotations in OSM are usually precise (particularly in metropolitan areas), there are instances where volunteer mapping is inaccurate. Despite the appeal of using OSM semantic information with remote sensing images, to train deep learning models, the crowdsourced data quality is inconsistent. High-resolution remote sensing image segmentation is a mature application in many fields, such as urban planning, updated mapping, city sensing, and others. Typically, supervised methods trained with annotated data may learn to anticipate the object location, but misclassification may occur due to noise in training data. This article combines Very High Resolution (VHR) remote sensing data with computer vision methods to deal with noisy OSM. This work deals with OSM misalignment ambiguity (positional inaccuracy) concerning satellite imagery and uses a Convolutional Neural Network (CNN) approach to detect missing buildings in OSM. We propose a translating method to align the OSM vector data with the satellite data. This strategy increases the correlation between the imagery and the building vector data to reduce the noise in OSM data. A series of experiments demonstrate that our approach plays a significant role in (1) resolving the misalignment issue, (2) instance-semantic segmentation of buildings with missing building information in OSM (never labeled or constructed in between image acquisitions), and (3) change detection mapping. The good results of precision (0.96) and recall (0.96) demonstrate the viability of high-resolution satellite imagery and OSM for building detection/change detection using a deep learning approach.
A Discriminative Model for Early Detection of Anthracnose in Strawberry Plants Based on Hyperspectral Imaging Technology
Authors: Liu Chao , Cao Yifei , Wu Ejiao , Yang Risheng , Xu Huanliang , Qiao Yushan
Published: September 2023

Strawberry anthracnose, caused by Colletotrichum spp., is a major disease that causes tremendous damage to cultivated strawberry plants (Fragaria × ananassa Duch.). Examining and distinguishing plants potentially carrying the pathogen is one of the most effective ways to prevent and control strawberry anthracnose disease. Herein, we used this method on Colletotrichum gloeosporioides at the crown site on indoor strawberry plants and established a classification and distinguishing model based on measurement of the spectral and textural characteristics of the disease-free zone near the disease center. The results, based on the successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), and interval random frog (IRF), extracted 5, 14, and 11 characteristic wavelengths, respectively. The SPA extracted fewer effective characteristic wavelengths, while IRF covered more information. A total of 12 dimensional texture features (TFs) were extracted from the first three minimum noise fraction (MNF) images using a grayscale co-occurrence matrix (GLCM). The combined dataset modeling of spectral and TFs performed better than single-feature modeling. The accuracy rates of the IRF + TF + BP model test set for healthy, asymptomatic, and symptomatic samples were 99.1%, 93.5%, and 94.5%, the recall rates were 100%, 94%, and 93%, and the F1 scores were 0.9955, 0.9375, and 0.9374, respectively. The total modeling time was 10.9 s, meaning that this model demonstrated the best comprehensive performance of all the constructed models. The model lays a technical foundation for the early, non-destructive detection of strawberry anthracnose.
Analysis of Infrared Spectral Radiance of O2 1.27 μm Band Based on Space-Based Limb Detection
Authors: Bai Jingyu , Bai Lu , Li Jinlu , Huang Chao , Guo Lixin
Published: September 2023

The infrared spectral radiance of O2 is of great significance for space-based infrared detection. In this work, based on the demand for near-infrared spectral radiance of O2 limb detection, a method of spectral radiance calculation coupled with an atmospheric remote sensing model of limb detection is proposed. According to the selection criteria of fine spectral lines, the most suitable spectral lines of the O2 1.27 μm band for detection are given. Specifically, the limb infrared radiances of the O2 1.27 μm band were simulated by using the spectral line data from the spectral database, and the effects of molecular self-absorption were also considered. Furthermore, the infrared spectral radiance distribution of the O2 1.27 μm band was simulated under the influence of altitude, and finally, the detectability of the 1.27 μm band of O2 molecules was analyzed using the criteria of spectral line selection, radiance intensity, spectral separation range and temperature sensitivity. The calculation results show that the spectral radiance of the 1.27 μm band of O2 molecules first increases and then decreases with the decrease of the limb height, and the radiance reaches the peak value in the range of 40–50 km. In terms of the selection of spectral lines, the two groups of spectral lines R7R7, R7Q8 and R11R11, R11Q12 are most suitable for the limb detection and measurement of the O2 1.27 μm band.
Enhancing Feature Detection and Matching in Low-Pixel-Resolution Hyperspectral Images Using 3D Convolution-Based Siamese Networks
Authors: Perera Chamika Janith , Premachandra Chinthaka , Kawanaka Hiroharu
Published: September 2023

Today, hyperspectral imaging plays an integral part in the remote sensing and precision agriculture field. Identifying the matching key points between hyperspectral images is an important step in tasks such as image registration, localization, object recognition, and object tracking. Low-pixel resolution hyperspectral imaging is a recent introduction to the field, bringing benefits such as lower cost and form factor compared to traditional systems. However, the use of limited pixel resolution challenges even state-of-the-art feature detection and matching methods, leading to difficulties in generating robust feature matches for images with repeated textures, low textures, low sharpness, and low contrast. Moreover, the use of narrower optics in these cameras adds to the challenges during the feature-matching stage, particularly for images captured during low-altitude flight missions. In order to enhance the robustness of feature detection and matching in low pixel resolution images, in this study we propose a novel approach utilizing 3D Convolution-based Siamese networks. Compared to state-of-the-art methods, this approach takes advantage of all the spectral information available in hyperspectral imaging in order to filter out incorrect matches and produce a robust set of matches. The proposed method initially generates feature matches through a combination of Phase Stretch Transformation-based edge detection and SIFT features. Subsequently, a 3D Convolution-based Siamese network is utilized to filter out inaccurate matches, producing a highly accurate set of feature matches. Evaluation of the proposed method demonstrates its superiority over state-of-the-art approaches in cases where they fail to produce feature matches. Additionally, it competes effectively with the other evaluated methods when generating feature matches in low-pixel resolution hyperspectral images. This research contributes to the advancement of low pixel resolution hyperspectral imaging techniques, and we believe it can specifically aid in mosaic generation of low pixel resolution hyperspectral images.
A Novel Efficient Dynamic Throttling Strategy for Blockchain-Based Intrusion Detection Systems in 6G-Enabled VSNs
Authors: Alevizos Lampis , Ta Vinh Thong , Eiza Max Hashem
Published: September 2023

Vehicular Social Networks (VSNs) have emerged as a new social interaction paradigm, where vehicles can form social networks on the roads to improve the convenience/safety of passengers. VSNs are part of Vehicle to Everything (V2X) services, which is one of the industrial verticals in the coming sixth generation (6G) networks. The lower latency, higher connection density, and near-100% coverage envisaged in 6G will enable more efficient implementation of VSNs applications. The purpose of this study is to address the problem of lateral movements of attackers who could compromise one device in a VSN, given the large number of connected devices and services in VSNs and attack other devices and vehicles. This challenge is addressed via our proposed Blockchain-based Collaborative Distributed Intrusion Detection (BCDID) system with a novel Dynamic Throttling Strategy (DTS) to detect and prevent attackers’ lateral movements in VSNs. Our experiments showed how the proposed DTS improve the effectiveness of the BCDID system in terms of detection capabilities and handling queries three times faster than the default strategy with 350k queries tested. We concluded that our DTS strategy can increase transaction processing capacity in the BCDID system and improve its performance while maintaining the integrity of data on-chain.
Remote Sensing Image Scene Classification in Hybrid Classical–Quantum Transferring CNN with Small Samples
Authors: Zhang Zhouwei , Mi Xiaofei , Yang Jian , Wei Xiangqin , Liu Yan , Yan Jian , Liu Peizhuo , Gu Xingfa , Yu Tao
Published: September 2023

The scope of this research lies in the combination of pre-trained Convolutional Neural Networks (CNNs) and Quantum Convolutional Neural Networks (QCNN) in application to Remote Sensing Image Scene Classification(RSISC). Deep learning (RL) is improving by leaps and bounds pretrained CNNs in Remote Sensing Image (RSI) analysis, and pre-trained CNNs have shown remarkable performance in remote sensing image scene classification (RSISC). Nonetheless, CNNs training require massive, annotated data as samples. When labeled samples are not sufficient, the most common solution is using pre-trained CNNs with a great deal of natural image datasets (e.g., ImageNet). However, these pre-trained CNNs require a large quantity of labelled data for training, which is often not feasible in RSISC, especially when the target RSIs have different imaging mechanisms from RGB natural images. In this paper, we proposed an improved hybrid classical–quantum transfer learning CNNs composed of classical and quantum elements to classify open-source RSI dataset. The classical part of the model is made up of a ResNet network which extracts useful features from RSI datasets. To further refine the network performance, a tensor quantum circuit is subsequently employed by tuning parameters on near-term quantum processors. We tested our models on the open-source RSI dataset. In our comparative study, we have concluded that the hybrid classical–quantum transferring CNN has achieved better performance than other pre-trained CNNs based RSISC methods with small training samples. Moreover, it has been proven that the proposed algorithm improves the classification accuracy while greatly decreasing the amount of model parameters and the sum of training data.
Identification, Taxonomy and Performance Assessment of Type 1 and Type 2 Spin Bowling Deliveries with a Smart Cricket Ball
Authors: Ferdinands René , Doljin Batdelger , Fuss Franz
Published: September 2023

Spin bowling deliveries in cricket, finger spin and wrist spin, are usually (Type 1, T1) performed with forearm supination and pronation, respectively, but can also be executed with opposite movements (Type 2, T2), specifically forearm pronation and supination, respectively. The aim of this study is to identify the differences between T1 and T2 using an advanced smart cricket ball, as well as to assess the dynamics of T1 and T2. With the hand aligned to the ball’s coordinate system, the angular velocity vector, specifically the x-, y- and z-components of its unit vector and its yaw and pitch angles, were used to identify time windows where T1 and T2 deliveries were clearly separated. Such a window was found 0.44 s before the peak torque, and maximum separation was achieved when plotting the y-component against the z-component of the unit vector, or the yaw angle against the pitch angle. In terms of physical performance, T1 deliveries are easier to bowl than T2; in terms of skill performance, wrist spin deliveries are easier to bowl than finger spin. Because the smart ball allows differentiation between T1 and T2 deliveries, it is an ideal tool for talent identification and improving performance through more efficient training.
Generative Adversarial Network (GAN)-Based Autonomous Penetration Testing for Web Applications
Authors: Chowdhary Ankur , Jha Kritshekhar , Zhao Ming
Published: September 2023

The web application market has shown rapid growth in recent years. The expansion of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has created new web-based communication and sensing frameworks. Current security research utilizes source code analysis and manual exploitation of web applications, to identify security vulnerabilities, such as Cross-Site Scripting (XSS) and SQL Injection, in these emerging fields. The attack samples generated as part of web application penetration testing on sensor networks can be easily blocked, using Web Application Firewalls (WAFs). In this research work, we propose an autonomous penetration testing framework that utilizes Generative Adversarial Networks (GANs). We overcome the limitations of vanilla GANs by using conditional sequence generation. This technique helps in identifying key features for XSS attacks. We trained a generative model based on attack labels and attack features. The attack features were identified using semantic tokenization, and the attack payloads were generated using conditional sequence GAN. The generated attack samples can be used to target web applications protected by WAFs in an automated manner. This model scales well on a large-scale web application platform, and it saves the significant effort invested in manual penetration testing.
Multi-Source Soil Moisture Data Fusion Based on Spherical Cap Harmonic Analysis and Helmert Variance Component Estimation in the Western U.S.
Authors: Chen Hao , Chen Peng , Wang Rong , Qiu Liangcai , Tang Fucai , Xiong Mingzhu
Published: September 2023

Soil moisture (SM) is a vital climate variable in the interaction process between the Earth’s atmosphere and land. However, global soil moisture products from various satellite missions and land surface models are affected by inherently discontinuous observations and coarse spatial resolution, which limits their application at fine spatial scales. To address this problem, this paper integrates three diverse types of datasets from in situ, satellites, and models through Spherical cap harmonic analysis (SCHA) and Helmert variance component estimation (HVCE) to produce 1 km of spatio-temporally continuous SM products with high accuracy. First, this paper eliminates the bias between different datasets and in situ sites and resamples the datasets before data fusion. Then, multi-source SM data fusion is performed based on the SCHA and HVCE methods. Finally, this paper evaluates the fused products from three aspects, including the performance of representative sites under different climate types, the overall performance of validation sites, and the comparison with other products. The results show that the fused products have better performance than other SM products. In the representative sites, the minimal correlation coefficient (R) of the fused products is above 0.85, and the largest root mean square error (RMSE) is below 0.040 m3 m−3. For all validation sites, the R and RMSE of the fused products are 0.889 and 0.036 m3 m−3, respectively, while the R for other products is below 0.75 and the RMSE is above 0.06 m3 m−3. In comparison to other SM products, the fused products exhibit superior performance, generally align more closely with in situ measurements, and possess the ability to accurately and finely capture the spatial and temporal variability of surface SM.
Construction of Asbestos Slate Deep-Learning Training-Data Model Based on Drone Images
Authors: Baek Seung-Chan , Lee Kwang-Hyun , Kim In-Ho , Seo Dong-Min , Park Kiyong
Published: September 2023

The detection of asbestos roof slate by drone is necessary to avoid the safety risks and costs associated with visual inspection. Moreover, the use of deep-learning models increases the speed as well as reduces the cost of analyzing the images provided by the drone. In this study, we developed a comprehensive learning model using supervised and unsupervised classification techniques for the accurate classification of roof slate. We ensured the accuracy of our model using a low altitude of 100 m, which led to a ground sampling distance of 3 cm/pixel. Furthermore, we ensured that the model was comprehensive by including images captured under a variety of light and meteorological conditions and from a variety of angles. After applying the two classification methods to develop the learning dataset and employing the as-developed model for classification, 12 images were misclassified out of 475. Visual inspection and an adjustment of the classification system were performed, and the model was updated to precisely classify all 475 images. These results show that supervised and unsupervised classification can be used together to improve the accuracy of a deep-learning model for the detection of asbestos roof slate.
Stress Monitoring of Segment Structure during the Construction of the Small-Diameter Shield Tunnel
Authors: Ding Liang , Sun Yi-Jie , Zhang Wen-Zhi , Bi Gang , Xu Hong-Zhong
Published: September 2023

Segmental stress during the construction process plays a pivotal role in assessing the safety and quality of shield tunnels. Fiber Bragg grating (FBG) sensing technology has been proposed for tunnel segment stress monitoring. A laboratory test was conducted to validate the reliable strain measurement of FBG sensors. The field in situ monitoring of a sewerage shield tunnel was carried out to examine the longitudinal and circumferential stresses experienced by the segments throughout the construction phase. The cyclic fluctuations in stress were found to be synchronized with the variations in shield thrust. A comparison was made between the longitudinal and circumferential stress variations observed during the shield-driving and segment-assembly processes. Additionally, the time required for the grouting to reach its full curing strength was estimated, revealing its impact on the stress levels and range of the pipe segment. The findings of this study offer an enhanced understanding of the stress state and health condition of small-diameter shield tunnels, which can help in optimizing the design and construction process of tunnel segments, as well.
Mobile Manipulators in Industry 4.0: A Review of Developments for Industrial Applications
Authors: Ghodsian Nooshin , Benfriha Khaled , Olabi Adel , Gopinath Varun , Arnou Aurélien
Published: September 2023

In the realm of Industry 4.0, diverse technologies such as AI, Cyber-Physical Systems, IoT, and advanced sensors converge to shape smarter future factories. Mobile manipulators (MMs) are pivotal, fostering flexibility, adaptability, and collaboration in industrial processes. On one hand, MMs offer a remarkable level of flexibility, adaptability, and collaboration in industrial processes, facilitating swift production line changes and efficiency enhancements. On the other hand, their integration into real manufacturing environments requires meticulous considerations, such as safety, human–robot interaction, and cybersecurity. This article delves into MMs’ essential role in achieving Industry 4.0’s automation and adaptability by integrating mobility with manipulation capabilities. The study reviews MMs’ industrial applications and integration into manufacturing systems. The most observed applications are logistics (49%) and manufacturing (33%). As Industry 4.0 advances, the paper emphasizes updating and aligning MMs with the smart factory concept by networks of sensors and the real-time analysis of them, especially for an enhanced human–robot interaction. Another objective is categorizing considerations for MMs’ utilization in Industry 4.0-aligned manufacturing. This review methodically covers a wide range of considerations and evaluates existing solutions. It shows a more comprehensive approach to understanding MMs in Industry 4.0 than previous works. Key focus areas encompass perception, data analysis, connectivity, human–robot interaction, safety, virtualization, and cybersecurity. By bringing together different aspects, this research emphasizes a more integrated view of the role and challenges of MMs in the Industry 4.0 paradigm and provides insights into aspects often overlooked. A detailed and synthetic analysis of existing knowledge was performed, and insights into their future path in Industry 4.0 environments were provided as part of the contributions of this paper. The article also appraises initiatives in these domains, along with a succinct technology readiness analysis. To sum up, this study highlights MMs’ pivotal role in Industry 4.0, encompassing their influence on adaptability, automation, and efficiency.
Gendered Perspectives on Intimate Partner Violence: A Comparative Study of General Population, Students and Professionals’ Beliefs
Authors: Almeida Iris , Ramalho Ana , Morgado Rafaela , Baúto Ricardo Ventura
Published: September 2023

Domestic violence is a worldwide crime recognized as a severe violation of Human Rights, which includes Intimate Partner Violence (IPV). The studies remark that the asymmetries in the social relations between men and women result in domination dynamics. Thus, this study analyzed the relationship between gender and IPV beliefs in the general population, university students, and healthcare/safety/justice professionals by comparing IPV legitimization between men and women and with age. The sample was composed by 3413 Portuguese participants, 1551 men (45.4%) and 1826 women (54.6%), aged 18 to 100 (M = 37.97; SD = 18.09), 1936 participants from the general population (56.7%), 866 university students [e.g., healthcare students] (25.4%) and 611 healthcare/safety/justice professionals [e.g., doctors, psychologists, police officers, lawyers] (17.9%). The sample filled out the Scale of Beliefs about Marital Violence (ECVC), a self-report scale on beliefs about IPV. Results confirmed our hypothesis that men have significantly higher levels of IPV legitimization than women. In accordance with our second hypothesis, significant positive correlations were found between age and IPV beliefs. As age increases, older people tend to be more tolerant of IPV, and young people tend to be less endorsing such IPV beliefs. Finally, we found the hypothesis that university students and healthcare/safety/justice professionals have lower levels of beliefs compared with other participants in the general population. Findings show that we need to work hard with the social evolution in men’s and women’s beliefs on IPV, reinforcing the importance of targeting IPV prevention by gender and age in the general population but also in students and professionals.
Sustainability Material Topics and Materiality Analysis in the Chemical Industry
Authors: Papafloratos Triantafyllos , Markidis Ioannis , Kotzaivazoglou Iordanis , Fragidis Garyfallos
Published: September 2023

Chemicals are an essential ingredient for many industries. The chemical industry has a direct environmental and social impact and massive potential to contribute towards a more sustainable world. Environmental and social awareness and pressure from society are increasing, customers expect from companies to show increased corporate social responsibility (CSR), and there are increasing legal requirements for companies to disclose information on their social, economic, and environmental performance through CSR reporting. This paper explores the most material sustainability topics in the chemical industry. We reviewed 25 sustainability reports of some of the biggest companies in the chemical industry—in terms of annual sales. We studied the reports, created a database, and then analyzed the findings. We identified the topics that were most commonly reported as being material by the chemical industry. This enabled us to identify trends, differences, or similarities within the data. The industry’s main environmental impacts are pollution and energy consumption, which is directly related to carbon emissions. Other material issues include health and safety, and water and waste management. Overall, it became apparent that the chemical industry recognizes the importance that the materiality assessment plays, but the content and detail of the sustainability reports can be improved.
Serial Mediation Model Linking Returnee Entrepreneurship Education and Green Returnee Entrepreneurial Behavior: An Analysis of Environmental Improvement
Authors: Yin Qing , Wang Delu , Wang Yadong
Published: September 2023

With the increasingly negative impact of environmental pollution on human production and life caused by the non-green operation of enterprises, a new generation of returnee entrepreneurs is called upon to take on regional environmental protection and global ecological improvement. This study examined the impact of returnee entrepreneurship education (REE). A conceptual model is constructed based on REE as the core factor of environmental sustainability and uses the occurrence of green entrepreneurial behavior among new university graduates returning to their hometowns as an evaluation basis. Convenience sampling was applied, and the relevant data were collected from 358 new university graduates in Jiangsu Province, China who received REE during their university years. Empirical analysis based on partial least squares structural equation modeling shows that REE evokes a commitment to the environment (CE) and has an indirect significant impact on green returnee entrepreneurial behavior (GREB) through institutional support and intention. However, a CE did not have a significant direct effect. The findings of this study have significant reference value for decision-makers in government departments in developing countries, universities, and many social groups that are actively responding to the United Nations Sustainable Development Initiative.
Detection and Analysis of Dubas-Infested Date Palm Trees Using Deep Learning, Remote Sensing, and GIS Techniques in Wadi Bani Kharus
Authors: Al-Mulla Yaseen , Ali Ahsan , Parimi Krishna
Published: September 2023

Many insects attack date palm trees but date palm trees in the Sultanate are particularly under threat due to the spread of pests and the Dubas bug (Db). Date palm productivity in Oman has been reduced by 28% due to Db infestation. The manual field detection of these pests requires huge efforts and costs, making field surveys time consuming and difficult. In this context, remote sensing integrated with deep learning techniques can help in the early detection of Db infestation. A total of 240 date palms with corrected geospatial locations and coordinates and their health status were systematically recorded throughout the 66-square-kilometer study area. We used advanced remote sensing tools and deep learning techniques to detect individual palm trees and their health levels in terms of Db infestation. Very-high-resolution (50 cm) satellite images rendered in visible and NIR bands were used as datasets to delineate and identify individual tree positions and determine their health condition. Our proposed method resulted in an overall accuracy of 87% for the detection of date palm trees and 85% for the detection of health levels of the plants. The overall detection accuracy of high and low infestation levels was observed with high precision at 95% and 93%, respectively. Hence, we can conclude with confidence that our technique performed well by accurately detecting individual date palm trees and determining their level of Db infestation. The approach used in this study can also provide farmers with useful knowledge regarding the Db risk and damage control for better management of Db. Moreover, the model used in this study may also lay the foundations for other models to detect infested plants and trees other than date palms.