bluuebunny/crossref_metadata_2025 huggingface dataset:
Visual Communication and Evangelizing Art in the Temple of San Francisco of La Paz (Bolivia)
Authors: Matas Musso Josefina Leonor , Matos Silva Fátima
Published: July 2023

The temple of San Francisco de La Paz is one of the best examples of the mestizo baroque style in Bolivia. The richness of the interior of the temple contributes to creating a theatrical and symbolic space, intending to evangelize and transmit the new values of the Catholic faith, through its iconographic programme. Our analysis highlights the differentiation of interior space using altarpiece programmes, thus evidencing how interior architecture is used for communication purposes. We conclude that interior space is transformed for evangelizing purposes.
Five Looks at Emmaus: Revelation, Resonance, and the Sacramental Imagination
Authors: Godzieba Anthony J.
Published: July 2023

The intersection between religious experience and aesthetic experience has become so obvious that the current “aesthetic turn” in Christian theology no longer needs to be defended. In this essay, I discuss that intersection point from the point of view of Roman Catholicism, in order to demonstrate the bold claim that the arts and the performance they evoke from us are as important as the creed for Catholicism. The essay aims to do three things: first, to examine that intersection point and emphasize the elements of intentionality and desire; second, to analyze one expression of that intersection, namely the connection among Catholic faith claims, the visual arts, and Catholicism’s incarnational-sacramental imagination (using depictions of the post-Resurrection Emmaus story); third, to use hints from Hartmut Rosa’s recent work on “resonance” to tease out how revelation and transformation occur at this intersection.
IESRGAN: Enhanced U-Net Structured Generative Adversarial Network for Remote Sensing Image Super-Resolution Reconstruction
Authors: Yue Xiaohan , Liu Danfeng , Wang Liguo , Benediktsson Jón Atli , Meng Linghong , Deng Lei
Published: July 2023

With the continuous development of modern remote sensing satellite technology, high-resolution (HR) remote sensing image data have gradually become widely used. However, due to the vastness of areas that need to be monitored and the difficulty in obtaining HR images, most monitoring projects still rely on low-resolution (LR) data for the regions being monitored. The emergence of remote sensing image super-resolution (SR) reconstruction technology effectively compensates for the lack of original HR images. This paper proposes an Improved Enhanced Super-Resolution Generative Adversarial Network (IESRGAN) based on an enhanced U-Net structure for a 4× scale detail reconstruction of LR images using NaSC-TG2 remote sensing images. In this method, in-depth research has been performed and consequent improvements have been made to the generator and discriminator within the GAN network. Specifically, before introducing Residual-in-Residual Dense Blocks (RRDB), in the proposed method, input images are subjected to reflective padding to enhance edge information. Meanwhile, a U-Net structure is adopted for the discriminator, incorporating spectral normalization to focus on semantic and structural changes between real and fake images, thereby improving generated image quality and GAN performance. To evaluate the effectiveness and generalization ability of our proposed model, experiments were conducted on multiple real-world remote sensing image datasets. Experimental results demonstrate that IESRGAN exhibits strong generalization capabilities while delivering outstanding performance in terms of PSNR, SSIM, and LPIPS image evaluation metrics.
HyperSFormer: A Transformer-Based End-to-End Hyperspectral Image Classification Method for Crop Classification
Authors: Xie Jiaxing , Hua Jiajun , Chen Shaonan , Wu Peiwen , Gao Peng , Sun Daozong , Lyu Zhendong , Lyu Shilei , Xue Xiuyun , Lu Jianqiang
Published: July 2023

Crop classification of large-scale agricultural land is crucial for crop monitoring and yield estimation. Hyperspectral image classification has proven to be an effective method for this task. Most current popular hyperspectral image classification methods are based on image classification, specifically on convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In contrast, this paper focuses on methods based on semantic segmentation and proposes a new transformer-based approach called HyperSFormer for crop hyperspectral image classification. The key enhancement of the proposed method is the replacement of the encoder in SegFormer with an improved Swin Transformer while keeping the SegFormer decoder. The entire model adopts a simple and uniform transformer architecture. Additionally, the paper introduces the hyper patch embedding (HPE) module to extract spectral and local spatial information from the hyperspectral images, which enhances the effectiveness of the features used as input for the model. To ensure detailed model processing and achieve end-to-end hyperspectral image classification, the transpose padding upsample (TPU) module is proposed for the model’s output. In order to address the problem of insufficient and imbalanced samples in hyperspectral image classification, the paper designs an adaptive min log sampling (AMLS) strategy and a loss function that incorporates dice loss and focal loss to assist model training. Experimental results using three public hyperspectral image datasets demonstrate the strong performance of HyperSFormer, particularly in the presence of imbalanced sample data, complex negative samples, and mixed sample classes. HyperSFormer outperforms state-of-the-art methods, including fast patch-free global learning (FPGA), a spectral–spatial-dependent global learning framework (SSDGL), and SegFormer, by at least 2.7% in the mean intersection over union (mIoU). It also improves the overall accuracy and average accuracy values by at least 0.9% and 0.3%, respectively, and the kappa coefficient by at least 0.011. Furthermore, ablation experiments were conducted to determine the optimal hyperparameter and loss function settings for the proposed method, validating the rationality of these settings and the fusion loss function.
Towards the Accurate Automatic Detection of Mesoscale Convective Systems in Remote Sensing Data: From Data Mining to Deep Learning Models and Their Applications
Authors: Krinitskiy Mikhail , Sprygin Alexander , Elizarov Svyatoslav , Narizhnaya Alexandra , Shikhov Andrei , Chernokulsky Alexander
Published: July 2023

Mesoscale convective systems (MCSs) and associated hazardous meteorological phenomena cause considerable economic damage and even loss of lives in the mid-latitudes. The mechanisms behind the formation and intensification of MCSs are still not well understood due to limited observational data and inaccurate climate models. Improving the prediction and understanding of MCSs is a high-priority area in hydrometeorology. One may study MCSs either employing high-resolution atmospheric modeling or through the analysis of remote sensing images which are known to reflect some of the characteristics of MCSs, including high temperature gradients of cloud-top, specific spatial shapes of temperature patterns, etc. However, research on MCSs using remote sensing data is limited by inadequate (in size) databases of satellite-identified MCSs and poorly equipped automated tools for MCS identification and tracking. In this study, we present (a) the GeoAnnotateAssisted tool for fast and convenient visual identification of MCSs in satellite imagery, which is capable of providing AI-generated suggestions of MCS labels; (b) the Dataset of Mesoscale Convective Systems over the European Territory of Russia (DaMesCoS-ETR), which we created using this tool, and (c) the Deep Convolutional Neural Network for the Identification of Mesoscale Convective Systems (MesCoSNet), constructed following the RetinaNet architecture, which is capable of identifying MCSs in Meteosat MSG/SEVIRI data. We demonstrate that our neural network, optimized in terms of its hyperparameters, provides high MCS identification quality (mAP=0.75, true positive rate TPR=0.61) and a well-specified detection uncertainty (false alarm ratio FAR=0.36). Additionally, we demonstrate potential applications of the GeoAnnotateAssisted labelling tool, the DaMesCoS-ETR dataset, and the MesCoSNet neural network in addressing MCS research challenges. Specifically, we present the climatology of axisymmetric MCSs over the European territory of Russia from 2014 to 2020 during summer seasons (May to September), obtained using MesCoSNet with Meteosat MSG/SEVIRI data. The automated identification of MCSs by the MesCoSNet artificial neural network opens up new avenues for previously unattainable MCS research topics.
The Aerosol Optical Characteristics in Different Dust Events Based on a 532 nm and 355 nm Polarization Lidar in Beijing
Authors: Chen Zhenyi , Huang Yifeng , Yao Zhiliang , Zhang Tianshu , Fan Guangqiang , Cao Xinyue , Ji Chengli
Published: July 2023

Extreme weather events are happening more frequently as a result of global climate change. Dust storms broke out in the spring of 2017 in China and drastically impacted the local air quality. In this study, a variety of data, including aerosol vertical profiles, surface particle concentration, meteorological parameters, and MODIS–derived aerosol optical depth, as well as backward trajectory analysis, were employed to analyze two dust events from April to May in Beijing. The dust plumes were mainly concentrated below 0.8 km, with peak PM10 values of 1000 μg·m−3 and 300 μg·m−3 in the two cases. The aerosols showed different vertical distribution characteristics. The pure dust in case 1 from 4 to 5 May 2017 had a longer duration (2 days) and presented a larger aerosol extinction coefficient (2.27 km−1 at 355 nm and 1.25 km−1 at 532 nm) than that of the mixed dust in case 2 on 17 April 2017 (2.01 km−1 at 355 nm and 1.33 km−1 at 532 nm). The particle depolarization ratio (PDR) remained constant (0.24 ± 0.03 in case 1) from the surface to 0.8 km in height. In contrast, the PDR profile in the mixed dust (case 2) layer was split into two regions—large values exceeding 0.15 above 0.6 km and small values of 0.11 ± 0.03 below 0.6 km. The influence of meteorological information on aerosol distribution was also investigated, and wind was predominant through the observing period. The pure dust in case 1 was mainly from Mongolia, with strong northwest winds, while the near-surface mixed pollution was caused by the combination of long-transported sand and local emission. Furthermore, lidar-derived profiles of dust mass concentrations in the two cases were presented. This study reveals the vertical characteristics of dust aerosols in the production and dissipation of localized dust events and confirms the efficacy of thorough observations with multiple approaches from the ground to space to monitor dust events in real time.
ORCNN-X: Attention-Driven Multiscale Network for Detecting Small Objects in Complex Aerial Scenes
Authors: Li Yanfen , Wang Hanxiang , Dang L. Minh , Song Hyoung-Kyu , Moon Hyeonjoon
Published: July 2023

Currently, object detection on remote sensing images has drawn significant attention due to its extensive applications, including environmental monitoring, urban planning, and disaster assessment. However, detecting objects in the aerial images captured by remote sensors presents unique challenges compared to natural images, such as low resolution, complex backgrounds, and variations in scale and angle. Prior object detection algorithms are limited in their ability to identify oriented small objects, especially in aerial images where small objects are usually obscured by background noise. To address the above limitations, a novel framework (ORCNN-X) was proposed for oriented small object detection in remote sensing images by improving the Oriented RCNN. The framework adopts a multiscale feature extraction network (ResNeSt+) with a dynamic attention module (DCSA) and an effective feature fusion mechanism (W-PAFPN) to enhance the model’s perception ability and handle variations in scale and angle. The proposed framework is evaluated based on two public benchmark datasets, DOTA and HRSC2016. The experiments demonstrate its state-of-the-art performance in aspects of detection accuracy and speed. The presented model can also represent more objective spatial location information according to the feature visualization maps. Specifically, our model outperforms the baseline model by 1.43% mAP50 and 1.37% mAP12 on DOTA and HRSC2016 datasets, respectively.
Multi-Objective Optimization Method for Signalized Intersections in Intelligent Traffic Network
Authors: Zhang Xinghui , Fan Xiumei , Yu Shunyuan , Shan Axida , Men Rui
Published: July 2023

Urban intersections are one of the most common sources of traffic congestion. Especially for multiple intersections, an appropriate control method should be able to regulate the traffic flow within the control area. The intersection signal-timing problem is crucial for ensuring efficient traffic operations, with the key issues being the determination of a traffic model and the design of an optimization algorithm. So, an optimization method for signalized intersections integrating a multi-objective model and an NSGAIII-DAE algorithm is established in this paper. Firstly, the multi-objective model is constructed including the usual signal control delay and traffic capacity indices. In addition, the conflict delay caused by right-turning vehicles crossing straight-going non-motor vehicles is considered and combined with the proposed algorithm, enabling the traffic model to better balance the traffic efficiency of intersections without adding infrastructure. Secondly, to address the challenges of diversity and convergence faced by the classic NSGA-III algorithm in solving traffic models with high-dimensional search spaces, a denoising autoencoder (DAE) is adopted to learn the compact representation of the original high-dimensional search space. Some genetic operations are performed in the compressed space and then mapped back to the original search space through the DAE. As a result, an appropriate balance between the local and global searching in an iteration can be achieved. To validate the proposed method, numerical experiments were conducted using actual traffic data from intersections in Jinzhou, China. The numerical results show that the signal control delay and conflict delay are significantly reduced compared with the existing algorithm, and the optimal reduction is 33.7% and 31.3%, respectively. The capacity value obtained by the proposed method in this paper is lower than that of the compared algorithm, but it is also 11.5% higher than that of the current scheme in this case. The comparisons and discussions demonstrate the effectiveness of the proposed method designed for improving the efficiency of signalized intersections.
Botnet Detection and Mitigation Model for IoT Networks Using Federated Learning
Authors: de Caldas Filho Francisco Lopes , Soares Samuel Carlos Meneses , Oroski Elder , de Oliveira Albuquerque Robson , da Mata Rafael Zerbini Alves , de Mendonça Fábio Lúcio Lopes , de Sousa Júnior Rafael Timóteo
Published: July 2023

The Internet of Things (IoT) introduces significant security vulnerabilities, raising concerns about cyber-attacks. Attackers exploit these vulnerabilities to launch distributed denial-of-service (DDoS) attacks, compromising availability and causing financial damage to digital infrastructure. This study focuses on mitigating DDoS attacks in corporate local networks by developing a model that operates closer to the attack source. The model utilizes Host Intrusion Detection Systems (HIDS) to identify anomalous behaviors in IoT devices and employs network-based intrusion detection approaches through a Network Intrusion Detection System (NIDS) for comprehensive attack identification. Additionally, a Host Intrusion Detection and Prevention System (HIDPS) is implemented in a fog computing infrastructure for real-time and precise attack detection. The proposed model integrates NIDS with federated learning, allowing devices to locally analyze their data and contribute to the detection of anomalous traffic. The distributed architecture enhances security by preventing volumetric attack traffic from reaching internet service providers and destination servers. This research contributes to the advancement of cybersecurity in local network environments and strengthens the protection of IoT networks against malicious traffic. This work highlights the efficiency of using a federated training and detection procedure through deep learning to minimize the impact of a single point of failure (SPOF) and reduce the workload of each device, thus achieving accuracy of 89.753% during detection and increasing privacy issues in a decentralized IoT infrastructure with a near-real-time detection and mitigation system.
The Lightweight Anchor Dynamic Assignment Algorithm for Object Detection
Authors: Han Ping , Zhuang Xujun , Zuo Huahong , Lou Ping , Chen Xiao
Published: July 2023

Smart security based on object detection is one of the important applications of edge computing in IoT. Anchors in object detection refer to points on the feature map, which can be used to generate anchor boxes and serve as training samples. Current object detection models do not consider the aspect ratio of the ground-truth boxes in anchor assignment and are not well-adapted to objects with very different shapes. Therefore, this paper proposes the Lightweight Anchor Dynamic Assignment algorithm (LADA) for object detection. LADA does not change the structure of the original detection model; first, it selects an equal proportional center region based on the aspect ratio of the ground-truth box, then calculates the combined loss of anchors, and finally divides the positive and negative samples more efficiently by dynamic loss threshold without additional models. The algorithm solves the problems of poor adaptability and difficulty in the selection of the best positive samples based on IoU assignment, and the sample assignment for eccentric objects and objects with different aspect ratios was more reasonable. Compared with existing sample assignment algorithms, the LADA algorithm outperforms the MS COCO dataset by 1.66% over the AP of the baseline FCOS, and 0.76% and 0.24% over the AP of the ATSS algorithm and the PAA algorithm, respectively, with the same model structure, which demonstrates the effectiveness of the LADA algorithm.
Frustrated Total Internal Reflection Measurement System for Pilot Inceptor Grip Pressure
Authors: Zanoni Andrea , Garbo Pierre , Masarati Pierangelo , Quaranta Giuseppe
Published: July 2023

Sensing the interaction between the pilot and the control inceptors can provide important information about the pilot’s activity during flight, potentially enabling the objective measurement of the pilot workload, the application of preventive actions against loss of situational awareness, and the identification of the insurgence of adverse couplings with the vehicle dynamics. This work presents an innovative pressure-sensing device developed to be seamlessly integrated into the grips of conventional aircraft control inceptors. The sensor, based on frustrated total internal reflection of light, is composed of low-cost elements and can be easily manufactured to be applicable to different hand pressure ranges. The characteristics of the sensor are first demonstrated in laboratory calibration tests. Subsequently, applications in flight simulator testing are presented, focusing on the objective representation of the pilot’s instantaneous workload.
Efficient Photovoltaic Unit for Power Delivering to Stand-Alone Direct Current Buildings Using Artificial Intelligence Approach Based MPP Tracker
Authors: Attia Hussain , Delama Fernando
Published: July 2023

There are many remote buildings that cannot be supplied by alternating electricity of the utility grid. Due to this, this study proposes adopting Direct Current (DC) appliances for a stand-alone remote building. Direct Current can be supplied from a suitable photovoltaic array which can harvest renewable solar energy. This proposal guarantees an efficient power system by removing the necessity of including an inverter, power filter, insulation transformer, and a complicated controller, which are usually needed for producing Alternating Current (AC) power to feed AC loads using a PV system. When the proposal is applied, the PV system will be more efficient, simple, affordable, and more compact. A detailed power requirement calculation for a typical house uses DC appliances, generalized steps to design a suitable PV array, and an Artificial Neural Network (ANN) algorithm for guaranteeing Maximum Power Point Tracking (MPPT); all of which are introduced for remote buildings. The main contribution of this paper is proposing an integrated design of a DC unit of 11 kW·h PV system for stand-alone buildings that eliminates three stages that improves the system performance compared to AC unit. The introduced study includes PV array calculation based on PV module of 220 W with an intelligent algorithm of four layers. The Mean Squared Error (MSE) of the proposed ANN equals 2.7107 × 10−5 to guarantee a fast and accurate MPP tracking for continuously harvesting maximum power from the incident sunlight. An energy storage unit of 12 batteries 12 V/150 Ah of matrix dimensions 3 × 4 is designed in the DC unit for energy saving to feed the DC appliances during night hours. MATLAB/Simulink Version R2015b is used to simulate the introduced DC power unit and collect the testing records for analyzing the unit performance.
DSRA-DETR: An Improved DETR for Multiscale Traffic Sign Detection
Authors: Xia Jiaao , Li Meijuan , Liu Weikang , Chen Xuebo
Published: July 2023

Traffic sign detection plays an important role in improving the capabilities of automated driving systems by addressing road safety challenges in sustainable urban living. In this paper, we present DSRA-DETR, a novel approach focused on improving multiscale detection performance. Our approach integrates the dilated spatial pyramid pooling model (DSPP) and the multiscale feature residual aggregation module (FRAM) to aggregate features at various scales. These modules excel at reducing feature noise and minimizing loss of low-level features during feature map extraction. Additionally, they enhance the model’s capability to detect objects at different scales, thereby improving the accuracy and robustness of traffic sign detection. We evaluate the performance of our method on two widely used datasets, the GTSDB and CCTSDB, and achieve impressive average accuracies (APs) of 76.13% and 78.24%, respectively. Compared with other well-known algorithms, our method shows a significant improvement in detection accuracy, demonstrating its superiority and generality. Our proposed method shows great potential for improving the performance of traffic sign detection for autonomous driving systems and will help in the development of safe and efficient autonomous driving technologies.
Social, Environmental, and Governance Factors on Supply-Chain Performance with Mediating Technology Adoption
Authors: Li Weijia , Liu Ziyang
Published: July 2023

The COVID-19 pandemic has brought significant risks and challenges to businesses. In order to restore the supply chain, this paper incorporates social, environmental, and governance (ESG) factors into the study of corporate supply chains. The aim is to explore the factors that influence supply-chain resilience and performance from these three perspectives, with a particular focus on the role of willingness to adopt green innovation technologies. To encompass a wider audience and multiple industry sectors, this study employs a questionnaire survey method, targeting managers of Chinese companies, and utilizes analytical tools such as SPSS 26.0 and AMOS 26.0 for data analysis and validation. The research findings indicate that supply-chain collaboration, supply-chain management capabilities, supply-chain risks, and green-product innovation have a positive impact on a firm’s willingness to adopt innovative technologies, subsequently leading to positive effects on supply-chain resilience and performance. By incorporating ESG factors into the scope of supply-chain research, this study expands the research domain and scope of ESG. Additionally, enhancing corporate social responsibility awareness and sustainable development consciousness holds great significance for the recovery of enterprise supply-chain development. This study also offers new insights for businesses to enhance their supply-chain management.
Development, Characterisation, and Consumer Acceptance of an Innovative Vegan Burger with Seaweed
Authors: Fernandes Wilson D. , Pinto Filipa R. , Barroso Sónia , Gil Maria M.
Published: July 2023

What consumers choose when purchasing food is of most importance to promote sustainability. The consumption of more sustainable foods should be stimulated, for example, by using more sustainable ingredients and by consumer education. Therefore, an innovative and highly nutritious vegan burger with seaweed (VBS) was developed using sustainable ingredients, such as pulses—grass pea (Lathyrus sativus L.) and chickpea (Cicer arietinum L.)—and the seaweed Dulse (Palmaria palmata L.) from aquaculture. VBS was analysed for its physico-chemical and nutritional characteristics, including antioxidant activity (DPPH, TPC) and fatty acid and mineral element profiles. Shelf life and consumer acceptability were determined. The VBS was shown to be a source of protein (8.01 ± 0.14% fresh weight (FW)), fibre (5.75% FW), and mineral elements, such as P, Fe, rich in Mg, Mn, and Cu, while having low sodium content. Moreover, it presents a low sugar content. Furthermore, no antioxidant activity was detected. The pasteurised and vacuum-packed product had a shelf life of 90 days and was well accepted by consumers (64.0% acceptance). It may be concluded that an innovative VBS, nutritionally rich and with a shelf life of 90 days, was developed and well accepted by consumers, which is a good addition to a rich and diverse diet.
Analyzing Attitudes to Promote Sustainability: The Adaptation of the Environmental Concern Scale (ECs) to the Italian Context
Authors: Zammitti Andrea , Santisi Giuseppe , Magnano Paola , Di Nuovo Santo
Published: July 2023

The aim of this study was to examine the psychometric properties of the Environmental Concern scale (ECs) in the Italian context. Three studies were conducted. In Study 1, we carried out an exploratory factor analysis and a 2-factor solution-biospheric concern and egoistic concern—was confirmed. In Study 2, we tested the structure of the eight-items version of the ECs, using confirmatory factor analysis. The 2-factor structure was the best factorial solution. In this study we correlated the dimensions of ECs with life satisfaction and climate change worry. As expected, biospheric concern was significantly related to climate change worry, and egoistic concern was significantly related to life satisfaction. In Study 3, we tested the gender invariance. The ECs structure was the same for men and women. These studies demonstrate that the ECs possess good construct validity, factor structure, and invariance between genders. The measure can be used in the Italian context for future research.
A Model for Yield Estimation Based on Sea Buckthorn Images
Authors: Du Yingjie , Wang Haichao , Wang Chunguang , Zhang Chunhui , Zong Zheying
Published: July 2023

Sea buckthorn is an extremely drought-tolerant, resilient and sustainable crop that can be grown in areas with harsh climates and scarce resources to provide a source of nutrition and income for the local population. The use of image-based yield estimation methods allows for better management of sea buckthorn cultivation to improve its productivity and sustainability, while the error in fruit yield information due to occlusion can be well reduced by combining and analysing the image features extracted using binocular cameras. In this paper, mature wild sea buckthorn in the mountainous areas north of Hohhot City, Inner Mongolia Autonomous Region, were used as the study target. Firstly, complete images of sea buckthorn branches were collected by binocular cameras and features were extracted. The extracted features include the colour index of sea buckthorn fruits, the number of fruits and a total of four texture parameters, ASM, CON, COR and HOM. The features with significant correlation to sea buckthorn fruit weight were selected by correlation calculation of the feature parameters, the obtained correlation features were introduced into the BP neural network model for training and then the sea buckthorn estimation model was obtained. The results showed that the best yield estimation model was achieved by combining the COR index with the colour index and the number of sea buckthorn fruits, with a coefficient of determination R2 = 0.99267 and a root mean square error RMSE = 0.5214.
Assessing Stakeholders’ Preferences for Future Rice Farming Practices in the Mekong Delta, Vietnam
Authors: Berg Håkan , Dang Simon , Tam Nguyen Thanh
Published: July 2023

Rice farming is the dominating agriculture activity in the Mekong Delta and has been of significant importance for the region’s economic development, but it has also had an impact on the environment. Recent governmental policies emphasize the need for sustainable and climate-resilient agriculture; however, policy reforms pushing for transformation towards sustainable socio-agricultural systems are compounded by a lack of coherent and shared visions. Gaps between policy making and implementation and stakeholders’ divergent visions of what is meant by sustainable agriculture and how that can be achieved hinder progress. To address this, the Q-methodology was used to elucidate and integrate different perspectives from 41 stakeholders on 35 statements related to agriculture developments in the Mekong Delta. Under the theoretical lens of sustainability, and ecosystem services, the results unveiled four major development schemes, namely production through intensification, sustainable intensification, production through nature, and people and nature in balance. The majority of the stakeholders in this study believed that future agriculture strategies in the Mekong Delta should be designed to promote more diverse and integrated farming methods, emphasizing the environmental and social dimensions of sustainable development. They acknowledged the multifunctional benefits of rice field ecosystems and emphasized the importance of supporting, regulating, and cultural ecosystem services for a long-term and healthy production of food and increased resilience of the Delta’s social-ecological systems.
Evaluating the Effectiveness of the Earthquake Early Warning Message in China: An Affordance Perspective Using Immersive Virtual Reality
Authors: He Zijian , Han Peng , Chen Zhiran , Liang Yixuan , Yang Zhihong , Li Tao
Published: July 2023

The early earthquake warning (EEW) system is essential for mitigating the effects of seismic incidents. However, in China, the design of EEW messages has not received much attention. This study employs affordance theory to examine the effectiveness of the EEW message generated by the Institute of Care-Life (ICL) in China, specifically by investigating four aspects of affordances: functional, cognitive, sensory, and emotional affordance. With 68 participants, we conducted an immersive virtual reality experiment. The results revealed that the ICL EEW message has a strong emotional affordance but inadequate functional, cognitive, and sensory affordance. These data provide recommendations for enhancing EEW messages, which could result in better interaction during earthquakes in China. This study investigated the viability of immersive virtual reality as a research tool for EEW. It increases understanding of the elements that determine the effectiveness of EEW communications, leading to better preparedness and response measures, reducing the impact of earthquakes and saving lives and property.
Historical Trends and Characteristics of Meteorological Drought Based on Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index over the Past 70 Years in China (1951–2020)
Authors: Sun Jiwei , Bi Shuoben , Bashir Bashar , Ge Zhangxi , Wu Kexin , Alsalman Abdullah , Ayugi Brian Odhiambo , Alsafadi Karam
Published: July 2023

Against the backdrop of global climate change, the frequency of drought events is increasing, leading to significant impacts on human society and development. Therefore, it is crucial to study the propagation patterns and trends of drought characteristics over a long timescale. The main objective of this study is to delineate the dynamics of drought characteristics by examining their propagation patterns in China from 1951 to 2020. In this study, precipitation data from meteorological stations across mainland China were used. A comprehensive dataset consisting of 700 stations over the past 70 years was collected and analyzed. To ensure data accuracy, the GPCC (the Global Precipitation Climatology Center) database was employed for data correction and gap-filling. Long-term drought evolution was assessed using both the SPI-12 (standardized precipitation index) and SPEI-12 (standardized precipitation evapotranspiration index) to detect drought characteristics. Two Moran indices were applied to identify propagation patterns, and the MK (the Mann–Kendall) analysis method, along with the Theil–Sen slope estimator, was utilized to track historical trends of these indices. The findings of this study reveal the following key results: (i) Based on the SPI-12, the main areas of China that are prone to drought are mostly concentrated around the Hu Huanyong Line, indicating a tendency towards drying based on the decadal change analysis. (ii) The distribution of drought-prone areas in China, as indicated by the SPEI-12, is extensive and widely distributed, with a correlation to urbanization and population density. These drought-prone areas are gradually expanding. (iii) Between 2010 and 2011, China experienced the most severe drought event in nearly 70 years, affecting nearly 50% of the country’s area with a high degree of severity. This event may be attributed to atmospheric circulation variability, exacerbated by the impact of urbanization on precipitation and drought. (iv) The frequency of drought occurrence in China gradually decreases from south to north, with the northeast and northern regions being less affected. However, areas with less frequent droughts experience longer and more severe drought durations. In conclusion, this study provides valuable insights into the characteristics and propagation patterns of drought in China, offering essential information for the development of effective strategies to mitigate the impacts of drought events.
Cu2O Heterojunction Solar Cell with Photovoltaic Properties Enhanced by a Ti Buffer Layer
Authors: Wang Binghao , Chen Zhiqiang , Zhao Feng
Published: July 2023

In this study, semiconductor oxide cuprite (Cu2O) and indium tin oxide (ITO) heterojunction solar cells with and without a 10 nm thick titanium (Ti) thin film as the buffer layer were fabricated and characterized for comparison. The Cu2O film was formed by low-cost electrodeposition, and Ti and ITO layers were deposited on a glass substrate by sputtering. The interfacial microstructures, surface topology, and electrical and photovoltaic properties of both solar cells were investigated. The test results showed that the Ti buffer layer changed the surface morphology, resistivity, and contact potential of the electrodeposited Cu2O film. With these changes, the photovoltaic performances of the Cu2O/Ti/ITO solar cell including open-circuit voltage (VOC) and short-circuit current (ISC) were all enhanced compared to the Cu2O/ITO solar cell, and the power conversion efficiency was improved from 1.78% to 2.54%. This study offers a promising method to improve the efficiency of Cu2O-based solar cells for sustainability in material resource, environment and eco-system, and energy production.
Impact of Climate Change on the “Trabocchi Coast” (Italy): The Trabocco Turchino Case Study
Authors: Mascitelli Alessandra , Prestileo Fernanda , Stella Eleonora Maria , Aruffo Eleonora , López Campos Luisa Irazú López , Federico Stefano , Torcasio Rosa Claudia , Corsi Anna , Di Carlo Piero Di , Dietrich Stefano
Published: July 2023

The increasing and extreme weather phenomena observed in the Mediterranean basin are only one aspect of the problem which has broader effects on population, structures and infrastructure.Each of these aspects is itself characterized by a wide variety of issues, which are increasingly leading studies toward a multidimensional assessment of impacts (economic, social and environmental). In this study, we focus on the impact related to the increase in extreme weather events in a specific area characterized by typical vernacular architecture: the “trabocchi” of the Italian Adriatic coast, whose identification as cultural heritage is the result of historical events and social dynamics closely linked to the collective imagination and for which inclusion as intangible cultural heritage in the UNESCO World heritage List has been requested. The weather event investigation was performed considering both long-term large-scale (using the ERA5 dataset) analysis and short-term small-scale (models and ground-based sensors) analysis. The results provide an overview of the event dynamics and enhanced understanding of the area’s vulnerability factors to extreme weather phenomena, as well as emphasized the need, in order to protect the integrity of the asset, to study environment changes and to plan concrete actions aimed at conservation, including social actions, to mitigate the problem.
The Rise and Evolution of Wind Tower Designs in Egypt and the Middle East
Authors: Nessim Marian A. , Elshabshiri Aya , Bassily Virginia , Soliman Niriman , Tarabieh Khaled , Goubran Sherif
Published: July 2023

Throughout history, vernacular architecture has sought to provide inhabitants with comfort, using local materials and techniques while drawing inspiration from the local culture. This goal has helped natural and passive environmental building techniques to emerge, evolve, and develop. Even though we are increasingly dependent on mechanical ventilation and cooling solutions, passive techniques are in favor due to global climate challenges and the drive toward sustainable construction. One of the most well-known passive cooling techniques is the windcatcher, or wind tower, as it is known in the Middle East (also known as a malqaf in Egypt). Windcatchers, which appeared in Egypt during the Pharaonic era, were also present in other vernacular Middle Eastern countries such as Iran and Iraq, and they differed in design and materials. This research aims to extract, analyze, and compare windcatchers throughout historical eras in Egypt and other Middle Eastern countries across three main eras: ancient, medieval, and modern. This study thus provides a timeline for developing these passive cooling systems, demonstrating how they were integrated into architecture over millennia. This study also investigates the design differences in these vernacular models, including their shapes, number of sides, and orientation, and correlates them to climatic and architectural conditions. The results highlight that the vernacular wind towers corresponded to the prevailing wind directions and the ventilation needs of the connected spaces. Furthermore, the findings question the effectiveness and appropriateness of some of the modern incorporations of wind towers, which borrow their design from local precedents.
Evaluation of Ca-Based Sorbents for Gaseous HCl Emissions Adsorption
Authors: Ravina Marco , Marotta Edoardo , Cerutti Alberto , Zanetti Giovanna , Ruffino Barbara , Panepinto Deborah , Zanetti Mariachiara
Published: July 2023

The problem of acid gas exhaust emissions treatment has not been fully resolved at present. Dry adsorption of acid gases with alkaline sorbents is currently being investigated, to improve solid sorbents. In this study, 5 types of hydrated lime were characterised and tested. The sorption capacities were measured by means of a system consisting of a feed line (HCl/N2), a thermostatic reactor and a water absorber. The physical characteristics of sorbent samples were also compared. Analyses conducted with scanning electronic microscopy revealed that sample C1 showed uniform particle distribution. Samples C2 and C3 showed the co-presence of fine and coarse particles. Sample C4 showed very fine particles with agglomeration phenomena. In sample C5, fibrous elements were found. Energy dispersive spectrometry (EDS) analyses showed a similar composition of the samples, with the exception of the presence of Mg in some of them. After 30 min of testing, the following differences in sorption capacities with respect to C1 (3.59 mg g−1) were found: C2, −20%; C3, −13%; C4, −17%; C5, −3%. Higher sorption capacities were associated with more uniform particle size distributions. Conversely, agglomeration of fine particles may have adversely affected the performance of sorbents.
Citizen Science and The University of Queensland Seismograph Stations (UQSS)—A Study of Seismic T Waves in S-W Pacific Ocean
Authors: Lynam Colin John , Karunaratne Asanka
Published: July 2023

Seismologists listen to Earth’s noise as it rips apart (faulting), exudes (volcanoes) and swallows (subducts) large volumes of rock. Your mobile phone is most likely detecting such noise, right now! This paper is about one such specific noise, the T wave. It summarises an early and successful piece of citizen science, performed within The University of Queensland Seismograph Stations (UQSS) observatory, in cooperation with colleagues at CSIRO. It was designed to encourage young STEM students from Brisbane high schools to engage in “real” research, back in 1995. Bear in mind, this is a time period when science is changing considerably from analog to digital media and operational recording methods. The citizen science students used a pre-prepared decadal collection (1980–1990) of T waves, derived from the Brisbane seismograph (BRS) observatory data catalogue. BRS has been operating since 1937 and is part of the global World-Wide Seismograph Station Network (WWSSN). Fortunately, seismology is a very collaborative field. There is a lot of data analysis involved in the science of recording earthquake signals, with auxiliary definitive catalogues, observers logbooks, housing of the recordings themselves (analog and digital) and the software mediums that change over time. It equally tests housekeeping proficiency, where a maze of record-keeping problems can be encountered in a longitudinal data collection study such as this. Having completed the project report, Earthquake generated T phases on BRS Seismograph (Brisbane, Q’ld) a predictor for Tasman Sea Tsunamis? their (analog) results sat in a cupboard until recently. The project was re-analysed in 2022 for a higher-degree student, discovering a timely climate change implication for the study. The original research question has now been amplified with a brief literature review. We observe that currently in Australia, university and government earth science observatories have diminished, and in their place, public seismic networks (PSN) have evolved, either in backyard sheds or school science labs. We now additionally propose here that the level of expertise required ideally fits the role of advancing citizen science, for a real science advantage. This is already a topical citizen disaster preparedness action area, and we propose that it has applications as a possible educational strategy for citizen engagement in today’s climate emergency. In addition, we are hopeful that other researchers in oceanography will read this paper and decide to explore the ocean’s temperature rise phenomenon through the eyes of seismological observers.
Teaching Licensure and Education Quality: Teachers’ Perceptions
Authors: Ghamrawi Norma , Abu-Tineh Abdullah , Shal Tarek
Published: July 2023

This study surveyed 1008 teachers from K–12 public schools in Qatar regarding a performance-based teaching licensure that replaced its portfolio-based licensure. The survey, which was validated using an exploratory factor analysis (EFA), consisted of four domains: (1) Clarity of licensure procedures; (2) The perceived educational value of the license; (3) Personal gains from the license; and (4) Teachers’ recommendations for improving the licensure. Findings revealed a complex interplay of views, wherein teachers acknowledged the clarity of the licensure protocols and recognized their potential benefits for education quality. However, there remained a disconnect when it came to perceiving the protocols’ direct impact on the teachers’ own professional growth. These insights serve as valuable resources for policymakers and stakeholders, emphasizing the importance of aligning licensure policies with teachers’ needs and aspirations. Ultimately, the study underscores the significance of continuous dialogue and reflection to shape a more effective and meaningful teaching licensure framework. It contributes to the global discourse surrounding teaching licensure, accentuating the paramount significance of fostering continuous dialogue and introspection in crafting an efficacious and resonant licensure framework.
Interference of Meteorological Variables on Night Sky Observation in Rural and Urban Zones of South-Western Spain
Authors: Martínez-Martín Alejandro , Bocho-Roas Adrián , Carmona-Fernández Diego , Calderón-Godoy Manuel , Jaramillo-Morán Miguel Ángel , González Juan Félix
Published: July 2023

Several factors affect the visibility of the night sky, most significantly artificial light at night (ALAN), meteorological conditions and Moon illumination. There are few studies that analyse the effect of meteorological conditions and Moon illumination, especially in rural areas (where the ALAN level is lower). An analysis of these variables could be relevant for sky observation, especially for astronomical observatories, usually located in rural areas with low ALAN. Knowledge of the relevant variables in the observation allows for the correct planning of the observation. The observer can consult meteorological agencies for forecasts of these variables, allowing him to know in advance whether the observation will be carried out under optimal conditions. In this study, a photometer and a weather station were installed in various municipalities located in rural areas of south-western Spain. This equipment was also installed in the city of Badajoz (urban area with high ALAN). These devices carried out simultaneous measurements of night sky darkness (NSB) and meteorological conditions for a period of approximately 6 months. A statistical analysis of the recorded data was carried out. Only the Moon illumination was slightly correlated with NSB in both cases (−0.3 in rural areas and −0.35 in urban areas). Sky temperature only proved a correlation with NSB in the urban area (−0.41). The correlations indicated are significant at the 0.01 level. The results obtained in the urban area were confirmed by a second analysis using meteorological data provided by a meteorological agency, obtaining similar results. A web application was developed that allows for real-time consultation of NSB and meteorological data for each area, which can help observe the starry sky. Globally, the results obtained in this study could help in the planning of stargazing in both urban and rural areas, allowing one to know in advance if weather conditions would affect the perception capacity.