bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of a Multispectral UAS to Assess the Cover and Biomass of the Invasive Dune Species Carpobrotus edulis
Authors: Meyer Manuel de Figueiredo , Gonçalves José Alberto , Cunha Jacinto Fernando Ribeiro , Ramos Sandra Cristina da Costa e Silva , Bio Ana Maria Ferreira
Published: May 2023

Remote sensing can support dune ecosystem conservation. Unoccupied Aircraft Systems (UAS) equipped with multispectral cameras can provide information for identifying different vegetation species, including Carpobrotus edulis—one of the most prominent alien species in Portuguese dune ecosystems. This work investigates the use of multispectral UAS for C. edulis identification and biomass estimation. A UAS with a five-band multispectral camera was used to capture images from the sand dunes of the Cávado River spit. Simultaneously, field samples of C. edulis were collected for laboratorial quantification of biomass through Dry Weight (DW). Five supervised classification algorithms were tested to estimate the total area of C. edulis, with the Random Forest algorithm achieving the best results (C. edulis Producer Accuracy (PA) = 0.91, C. edulis User Accuracy (UA) = 0.80, kappa = 0.87, Overall Accuracy (OA) = 0.89). Sixteen vegetation indices (VIs) were assessed to estimate the Above-Ground Biomass (AGB) of C. edulis, using three regression models to correlate the sample areas VI and DW. An exponential regression model of the Renormalized Difference Vegetation Index (RDVI) presented the best fit for C. edulis DW (R2 = 0.86; p-value < 0.05; normalised root mean square error (NRMSE) = 0.09). This result was later used to estimate the total AGB in the area, which can be used for monitoring and management plans—namely, removal campaigns.
Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique
Authors: He Yan , Jia Kebin , Wei Zhihao
Published: May 2023

Forests are critical to mitigating global climate change and regulating climate through their role in the global carbon and water cycles. Accurate monitoring of forest cover is, therefore, essential. Image segmentation networks based on convolutional neural networks have shown significant advantages in remote sensing image analysis with the development of deep learning. However, deep learning networks typically require a large amount of manual ground truth labels for training, and existing widely used image segmentation networks struggle to extract details from large-scale high resolution satellite imagery. Improving the accuracy of forest image segmentation remains a challenge. To reduce the cost of manual labelling, this paper proposed a data augmentation method that expands the training data by modifying the spatial distribution of forest remote sensing images. In addition, to improve the ability of the network to extract multi-scale detailed features and the feature information from the NIR band of satellite images, we proposed a high-resolution forest remote sensing image segmentation network by fusing multi-scale features based on double input. The experimental results using the Sanjiangyuan plateau forest dataset show that our method achieves an IoU of 90.19%, which outperforms prevalent image segmentation networks. These results demonstrate that the proposed approaches can extract forests from remote sensing images more effectively and accurately.
Mapping Phenology of Complicated Wetland Landscapes through Harmonizing Landsat and Sentinel-2 Imagery
Authors: Fan Chang , Yang Jilin , Zhao Guosong , Dai Junhu , Zhu Mengyao , Dong Jinwei , Liu Ruoqi , Zhang Geli
Published: May 2023

Wetlands are important CO2 sinks and methane sources, and their seasonality and phenological cycle play an essential role in understanding the carbon budget. However, given the spatial heterogeneity of wetland landscapes and the coarser spatial resolution of satellites, the phenological retrievals of wetlands are challenging. Here we examined the phenology of wetlands from 30 m harmonized Landsat/Sentinel-2 (LandSent30) and 500 m MODIS satellite observations using the ground phenology network PhenoCam as a benchmark. This study used all 11 available wetland PhenoCam sites (about 30 site years), covering diverse wetland types from different climate zones. We found that the LandSent30-based phenology results were in overall higher consistency with the PhenoCam results compared to MODIS, which could be related to the better explanation capacity of LandSent30 data in the heterogeneous landscapes of wetlands. This also means that the LandSent30 has an advantage over the 500 m MODIS regarding wetland vegetation phenological retrievals. It should be noted that the LandSent30 did not show a greatly improved performance, which could be related to the specificity and complexity of the wetlands landscape. We also illustrated the potential effects of the location and observation direction of PhenoCam cameras, the selection of Region of Interest (ROI), as well as the landscape composition of the site. Overall, this study highlights the complexity of wetland phenology from both ground and remote sensing observations at different scales, which paves the road for understanding the role of wetlands in global climate change and provides a basis for understanding the real phenological changes of wetland surfaces.
Land Subsidence in the Singapore Coastal Area with Long Time Series of TerraSAR-X SAR Data
Authors: Bai Zechao , Wang Yanping , Li Mengwei , Sun Ying , Zhang Xuedong , Wu Yewei , Li Yang , Li Dan
Published: May 2023

Global sea level rise is a major environmental concern for many countries and cities, particularly for low-lying coastal areas where urban development is threatened by the combined effects of sea level rise and land subsidence. This study employed an improved two-layer network Persistent Scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) technology to obtain high-precision land subsidence in Singapore from 2015 to 2019. Landsat images from 1973 to 2020 were also utilized to extract changes in Singapore’s coastline. Geological, topographical, and global sea level rise data were integrated to investigate the causes and impacts of land subsidence in Singapore. The results indicate that the areas with severe subsidence coincide with land reclamation areas, where subsidence is mainly due to soil consolidation. Based on WorldDEM, land subsidence, and sea level rise data, the maximum inundation depth in Singapore by 2050 is estimated to be 1.24 m, with the Marina Bay area in Singapore’s central business district being the most vulnerable to sea level rise. This study provides data support and a scientific basis for understanding the impact of land subsidence on Singapore’s coastal areas under the influence of multiple factors using advanced InSAR technology.
Multi-Constrained Seismic Multi-Parameter Full Waveform Inversion Based on Projected Quasi-Newton Algorithm
Authors: Feng Deshan , Li Bingchao , Cao Cen , Wang Xun , Li Dianbo , Chen Cheng
Published: May 2023

The multi-parameter full waveform inversion (FWI) that integrates velocity and density can make full use of the kinematic and dynamic information of the measured data to reconstruct the underground model. However, it faces problems of crosstalk between multiple parameters and strong nonlinearity. This research proposes a multi-constrained, multi-parameter FWI framework based on the projected quasi-Newton algorithm. This framework can introduce multiple types of prior geological information, which can effectively improve the problem of multi-parameter inversion. Additionally, the quasi-Newton method can eliminate the crosstalk phenomenon to further improve the inversion convergence speed. Taking the 1994BP model as an example, the results show that the projected quasi-Newton method has a faster convergence speed than the spectral projected gradient method, and reduces the crosstalk between parameters; multiple constraint sets are uniquely projected onto the intersection to ensure that the estimated values of model parameters meet multiple constraints. We also experiment with the overthrust model, which shows that the framework we proposed can improve the inversion accuracy and has good adaptability. The proposed multi-parameter inversion framework can be compatible with more prior information to obtain an inversion model that conforms to geological understanding and shows great potential in seismic exploration.
Multivariate Calibration of the SWAT Model Using Remotely Sensed Datasets
Authors: Dangol Sijal , Zhang Xuesong , Liang Xin-Zhong , Anderson Martha , Crow Wade , Lee Sangchul , Moglen Glenn E. , McCarty Gregory W.
Published: May 2023

Remotely sensed hydrologic variables, in conjunction with streamflow data, have been increasingly used to conduct multivariable calibration of hydrologic model parameters. Here, we calibrated the Soil and Water Assessment Tool (SWAT) model using different combinations of streamflow and remotely sensed hydrologic variables, including Atmosphere–Land Exchange Inverse (ALEXI) Evapotranspiration (ET), Moderate Resolution Imaging Spectroradiometer (MODIS) ET, and Soil MERGE (SMERGE) soil moisture. The results show that adding remotely sensed ET and soil moisture to the traditionally used streamflow for model calibration can impact the number and values of parameters sensitive to hydrologic modeling, but it does not necessarily improve the model performance. However, using remotely sensed ET or soil moisture data alone led to deterioration in model performance as compared with using streamflow only. In addition, we observed large discrepancies between ALEXI or MODIS ET data and the choice between these two datasets for model calibration can have significant implications for the performance of the SWAT model. The use of different combinations of streamflow, ET, and soil moisture data also resulted in noticeable differences in simulated hydrologic processes, such as runoff, percolation, and groundwater discharge. Finally, we compared the performance of SWAT and the SWAT-Carbon (SWAT-C) model under different multivariate calibration setups, and these two models exhibited pronounced differences in their performance in the validation period. Based on these results, we recommend (1) the assessment of various remotely sensed data (when multiple options available) for model calibration before choosing them for complementing the traditionally used streamflow data and (2) that different model structures be considered in the model calibration process to support robust hydrologic modeling.
Spatiotemporal Variation Characteristics of Groundwater Storage and Its Driving Factors and Ecological Effects in Tibetan Plateau
Authors: Ren Wenhao , Gao Yanyan , Qian Hui , Ma Yaoming , Su Zhongbo , Ma Weiqiang , Liu Yu , Xu Panpan
Published: May 2023

Known as the “Asian Water Tower”, the Tibetan Plateau (TP) is a rich water resource and serves an important ecological function. Climate change may cause changes to the water cycle, and these changes may affect the alpine vegetation growth. However, the variation characteristics of groundwater storage (GWS) and its driving factors and associated ecological effects in the TP are poorly understood. In this study, terrestrial water storage changes retrieved by GRACE (Gravity Recovery and Climate Experiment) were combined with GLDAS (Global Land Data Assimilation System) to estimate the GWS changes in the TP. The temporal and spatial variation characteristics of GWS were identified using linear regression and the modified Mann–Kendall (MMK) test, respectively. The analyses showed that the GWS of the TP decreased at an average rate of −0.89 mm/a from January 2003 to December 2021, but since January 2016, it gradually recovered at a rate of 1.47 mm/a. This shows that the GWS in the eastern and northern parts of the TP is decreasing, while the GWS in the western and southern parts is increasing. The influence of climate change on GWS in time and space was determined using the correlation analysis method. Decreased precipitation and permafrost degradation caused by increasing temperatures will lead to a decrease in GWS. On the other hand, rising temperatures may result in an increase in GWS in regions where glaciers are distributed. In this study, the ecological effects were represented by the relationship between GWS and vegetation change. A decline in GWS means that the vegetation will not receive enough water, leading to a decrease in the NDVI and the eventual degradation of grassland to sand, desert, or other kinds of unused land on the TP. On the other hand, an increase in GWS would promote vegetation restoration. The results of this study offer a new opportunity to reveal the groundwater changes in a cryosphere region and to assess the impact of changes in hydrological conditions on ecology.
Chlorophyll-Specific Absorption Coefficient of Phytoplankton in World Oceans: Seasonal and Regional Variability
Authors: Wei Jianwei , Wang Menghua , Mikelsons Karlis , Jiang Lide
Published: May 2023

This study investigates the seasonal and regional variability in the chlorophyll-specific absorption coefficient of phytoplankton at 443 nm (aph*(443); unit: m2 mg−1) in surface oceans. It is focused on the time series data derived from the satellite products of chlorophyll-a (Chl-a) concentration and the phytoplankton absorption coefficient. Global estimates of aph*(443) reveal a decreasing gradient from the open ocean toward the coastal environment, with considerable spatial variance. Seasonal variations are prominent over most oceans, resulting in substantial deviations from the climatological means. A sinusoidal model was fitted to the monthly time series data to characterize the annual and semiannual features. The amplitudes and the phases of the monthly data were latitudinally dependent. The occurrence times of the maximum aph*(443) values were six months out of phase between the northern and southern hemispheres. Satellite observations present a global mean relationship between aph*(443) and Chl-a comparable with those obtained via in situ measurements. However, the seasonal/regional aph*(443) and Chl-a relationships can significantly depart from the global mean relationship. We propose a hypothesis that aph*(443) can be predicted as a function of geolocation and time. Preliminary validations with in situ matchup data confirm that the proposed model is a promising alternative to the traditional approaches requiring Chl-a as the input. The present exploration helps understand the phytoplankton biogeography and facilitates future efforts to improve bio-optical modeling, including estimating the primary production.
Land Subsidence in a Coastal City Based on SBAS-InSAR Monitoring: A Case Study of Zhuhai, China
Authors: Sun Huimin , Peng Hongxia , Zeng Min , Wang Simiao , Pan Yujie , Pi Pengcheng , Xue Zixuan , Zhao Xinwen , Zhang Ao , Liu Fengmei
Published: May 2023

The superimposed effects of sea level rise caused by global warming and land subsidence seriously threaten the sustainable development of coastal cities. In recent years, an important coastal city in China, Zhuhai, has been suffering from severe and widespread land subsidence; however, the characteristics, triggers, and vulnerability assessment of ground subsidence in Zhuhai are still unclear. Therefore, we used the SBAS-InSAR technique to process 51 Sentinel-1A images to monitor the land subsidence in Zhuhai during the period from August 2016 to June 2019. The results showed that there was extensive land subsidence in the study area, with a maximum rate of −109.75 mm/yr. The surface had sequentially undergone a process of minor uplift and decline fluctuation, sharp settlement, and stable subsidence. The distribution and evolution of land subsidence were controlled by tectonic fractures and triggered by the thickness of soft soil, the intensity of groundwater development, and the seasonal changes of atmospheric precipitation. The comprehensive index method and the analytic hierarchy process were applied to derive extremely high subsidence vulnerability in several village communities and some traffic arteries in Zhuhai. Our research provides a theoretical basis for urban disaster prevention in Zhuhai and the construction planning of coastal cities around the world.
Cross-Viewpoint Template Matching Based on Heterogeneous Feature Alignment and Pixel-Wise Consensus for Air- and Space-Based Platforms
Authors: Hui Tian , Xu Yuelei , Zhou Qing , Yuan Chaofeng , Rasol Jarhinbek
Published: May 2023

Template matching is the fundamental task in remote sensing image processing of air- and space-based platforms. Due to the heterogeneous image sources, different scales and different viewpoints, the realization of a general end-to-end matching model is still a challenging task. Considering the abovementioned problems, we propose a cross-view remote sensing image matching method. Firstly, a spatial attention map was proposed to solve the problem of the domain gap. It is produced by two-dimensional Gaussian distribution and eliminates the distance between the distributed heterogeneous features. Secondly, in order to perform matching at different flight altitudes, a multi-scale matching method was proposed to perform matching on three down-sampling scales in turn and confirm the optimal result. Thirdly, to improve the adaptability of the viewpoint changes, a pixel-wise consensus method based on a correlation layer was applied. Finally, we trained the proposed model based on weakly supervised learning, which does not require extensive annotation but only labels one pair of feature points of the template image and search image. The robustness and effectiveness of the proposed methods were demonstrated by evaluation on various datasets. Our method accommodates three types of template matching with different viewpoints, including SAR to RGB, infrared to RGB, and RGB to RGB.
Analyzing Long-Term High-Rise Building Areas Changes Using Deep Learning and Multisource Satellite Images
Authors: Yao Shun , Li Liwei , Cheng Gang , Zhang Bing
Published: May 2023

High-rise building areas (HRBs) provide significant social and environmental services and play a crucial role in modern urbanization. The large-scale and long-term spatial distribution of HRBs is of great interest to many fields, such as urban planning and local climate analysis. While previous studies have confirmed the value of Sentinel-2 images in extracting HRBs and their changes, current work is limited to relatively local areas and short-term analysis. One reason is due to the fact that the earliest Sentinel-2 image can only date back to 2015. To address this research gap, this paper proposes an efficient procedure to intelligently extract HRBs and their changes from multitemporal Landsat-7 and Sentinel-2 images, using a specifically designed fully convolutional network. To validate the proposed method, we selected four typical cities in China, namely, Beijing, Shanghai, Guangzhou, and Zhengzhou, as study areas. We utilized Landsat-7 images acquired in 2000 and 2010, along with Sentinel-2 images acquired in 2020, as experimental data. We extracted and analyzed three periods of HRBs and their changes in the four cities, along with urban rail terminal data and gross domestic product (GDP) data in the same period. The results show that the proposed method can efficiently extract HRBs and their changes in the four cities over the past 20 years, with an overall accuracy of more than 90%. HRBs changes are primarily driven by urban planning policies and geographical factors. There is a strong positive correlation between the increase in HRBs and the increase in rail terminals, both in terms of quantity and spatial distribution. Additionally, there is a positive correlation between HRBs increase and GDP increase in terms of quantity, but the trend varies in different cities due to their diverse developing modes. Overall, the results indicate that the proposed method can be a potential operational tool to extract large-scale and long-term HRBs and their changes in China.
Road Feature Detection for Advance Driver Assistance System Using Deep Learning
Authors: Nadeem Hamza , Javed Kashif , Nadeem Zain , Khan Muhammad Jawad , Rubab Saddaf , Yon Dong Keon , Naqvi Rizwan Ali
Published: May 2023

Hundreds of people are injured or killed in road accidents. These accidents are caused by several intrinsic and extrinsic factors, including the attentiveness of the driver towards the road and its associated features. These features include approaching vehicles, pedestrians, and static fixtures, such as road lanes and traffic signs. If a driver is made aware of these features in a timely manner, a huge chunk of these accidents can be avoided. This study proposes a computer vision-based solution for detecting and recognizing traffic types and signs to help drivers pave the door for self-driving cars. A real-world roadside dataset was collected under varying lighting and road conditions, and individual frames were annotated. Two deep learning models, YOLOv7 and Faster RCNN, were trained on this custom-collected dataset to detect the aforementioned road features. The models produced mean Average Precision (mAP) scores of 87.20% and 75.64%, respectively, along with class accuracies of over 98.80%; all of these were state-of-the-art. The proposed model provides an excellent benchmark to build on to help improve traffic situations and enable future technological advances, such as Advance Driver Assistance System (ADAS) and self-driving cars.
DNS for IoT: A Survey
Authors: Ayoub Ibrahim , Balakrichenan Sandoche , Khawam Kinda , Ampeau Benoît
Published: May 2023

The Internet of Things (IoT) is paving the way to becoming necessary in numerous aspects of people’s lives. IoT is becoming integrated in many domains, such as medical, industrial, and personal. Recent years have witnessed the creation of many IoT technologies that differ not only in their applications and use cases but also in standards. The absence of universally accepted standards and the variety of technologies are only some challenges the IoT market faces. Other challenges include the constrained nature of most IoT devices, the diverse identification schemes, the inadequate security mechanisms, and the lack of interoperability between different technologies. The Domain Name System (DNS) persisted throughout the years as the Internet’s naming service and accumulated more trust from users with the introduction of its security extensions. DNS could be utilized to address some of the challenges the IoT market faces. However, using DNS for IoT applications might jeopardize DNS infrastructure. In this survey, we study the coexistence of DNS and IoT. We define IoT, present its architecture and discuss its main challenges. We then introduce DNS and its function; we discuss its security and privacy drawbacks and the extensions standardized to address them. We further discuss the uses of DNS in IoT environments to address some of IoT’s challenges and the impact these uses might have on DNS.
A GaN-HEMT Active Drain-Pumped Mixer for S-Band FMCW Radar Front-End Applications
Authors: Pagnini Lorenzo , Collodi Giovanni , Cidronali Alessandro
Published: May 2023

This paper reports for the first time a drain-pumped (DP) mixer using Gallium Nitride (GaN) HEMT technology. Specifically, it describes a method aimed to predict the optimum bias conditions for active DP-mixers, leading to high conversion gain (CG) and linearity, along with the efficient use of the local oscillator drive level. A mixer prototype was designed and fabricated according to the discussed design principles; it exhibited a CG and an input third-order intercept point (IIP3) of +10dB and +11dBm, respectively, with a local oscillator power level of 20 dBm at about 3.7 GHz. In terms of gain and linearity, both figures exceed the documented limitations for the class of mixers considered in this work. To the authors’ best knowledge, this is the first DP mixer operating in the S-band. The prototype was also tested in a radar-like setup operating in the S-band frequency-modulated continuous-wave (FMCW) mode. Measurements carried out in the radar setup resulted in +39.7dB and +34.7dB of IF signal-to-noise-ratio (SNR) for the DP and the resistive mixers, respectively. For comparison purposes, a resistive mixer was designed and fabricated using the same GaN HEMT technology; a detailed comparison between the two topologies is discussed in the paper, thus further highlighting the capability of the DP-mixer for system applications.
Low-Power IoT for Monitoring Unconnected Remote Areas
Authors: Andreadis Alessandro , Giambene Giovanni , Zambon Riccardo
Published: May 2023

This paper deals with IoT devices deployed in remote areas without terrestrial Internet connectivity. We consider connecting IoT devices on the ground to the Internet through an aerial system based on an Unmanned Aerial Vehicle (UAV) for smart agriculture and environmental monitoring. The UAV flying over the remote area receives data from distributed IoT devices. The transmissions between the ground sensors and the UAV are carried out via LoRa. We have proposed a synchronization protocol for the opportunistic communication of LoRa IoT devices with a gateway onboard the UAV to save node battery life. Class A LoRa nodes on the ground transmit only when the UAV is expected to pass close to them; otherwise, they stay in the sleeping state most of the time. This paper provides a detailed description of the formulation of the synchronization protocol. The UAV’s flying dynamics have been considered for characterizing its speed and the time of visibility of each IoT sensor. Our model has allowed an analytical approach that can help to determine the best settings for LoRa transmissions. Finally, experiments have been carried out to assess the path loss attenuation, and a laboratory setup of the synchronization protocol has been implemented for the preliminary validation of our scheme.
Lightweight Scene Text Recognition Based on Transformer
Authors: Luan Xin , Zhang Jinwei , Xu Miaomiao , Silamu Wushouer , Li Yanbing
Published: May 2023

Scene text recognition (STR) has been a hot research field in computer vision, aiming to recognize text in natural scenes using computers. Currently, attention-based encoder–decoder frameworks struggle to precisely align feature regions with the target object when dealing with complex and low-quality images, a phenomenon known as attention drift. Additionally, with the rise of Transformer, the increasing size of parameters results in higher computational costs. In order to solve the above problems, based on the latest research results of Vision Transformer (ViT), we utilize an additional position-enhancement branch to alleviate attention drift and dynamically fused position information with visual information to achieve better recognition accuracy. The experimental results demonstrate that our model achieves a 3% higher average recognition accuracy on the test set compared to the baseline. Meanwhile, our model maintains the advantage of a small number of parameters and fast inference speed, achieving a good balance between accuracy, speed, and computational load.
Feature Papers in Vehicular Sensing
Authors: Jiménez Felipe
Published: May 2023

This Special Issue compiles papers submitted by the Editorial Board Members of the Vehicular Sensing Section and outstanding scholars in this field [...]
An Automated Skill Assessment Framework Based on Visual Motion Signals and a Deep Neural Network in Robot-Assisted Minimally Invasive Surgery
Authors: Pan Mingzhang , Wang Shuo , Li Jingao , Li Jing , Yang Xiuze , Liang Ke
Published: May 2023

Surgical skill assessment can quantify the quality of the surgical operation via the motion state of the surgical instrument tip (SIT), which is considered one of the effective primary means by which to improve the accuracy of surgical operation. Traditional methods have displayed promising results in skill assessment. However, this success is predicated on the SIT sensors, making these approaches impractical when employing the minimally invasive surgical robot with such a tiny end size. To address the assessment issue regarding the operation quality of robot-assisted minimally invasive surgery (RAMIS), this paper proposes a new automatic framework for assessing surgical skills based on visual motion tracking and deep learning. The new method innovatively combines vision and kinematics. The kernel correlation filter (KCF) is introduced in order to obtain the key motion signals of the SIT and classify them by using the residual neural network (ResNet), realizing automated skill assessment in RAMIS. To verify its effectiveness and accuracy, the proposed method is applied to the public minimally invasive surgical robot dataset, the JIGSAWS. The results show that the method based on visual motion tracking technology and a deep neural network model can effectively and accurately assess the skill of robot-assisted surgery in near real-time. In a fairly short computational processing time of 3 to 5 s, the average accuracy of the assessment method is 92.04% and 84.80% in distinguishing two and three skill levels. This study makes an important contribution to the safe and high-quality development of RAMIS.
Investigating the Structural and Functional Changes in the Optic Nerve in Patients with Early Glaucoma Using the Optical Coherence Tomography (OCT) and RETeval System
Authors: Bekollari Marsida , Dettoraki Maria , Stavrou Valentina , Skouroliakou Aikaterini , Liaparinos Panagiotis
Published: May 2023

The present manuscript introduces an investigation of the structural and functional changes in the optic nerve in patients undergoing glaucoma treatment by comparing optical coherence tomography (OCT) measurements and RETeval system parameters. For such a purpose, 140 eyes were examined at the Ophthalmology Clinic of the “Elpis” General Hospital of Athens between October 2022 and April 2023. A total of 59 out of 140 eyes were from patients with early glaucoma under treatment (case group), 63 were healthy eyes (control group) and 18 were excluded. The experimental measurements were statistically analyzed using the SPSS software package. The main outcomes are summarized below: (i) there was no statistical difference between the right and left eye for both groups, (ii) statistical differences were found between age interval subgroups (30–54 and 55–80 years old) for the control group, mainly for the time response part of the RETeval parameters. Such difference was not indicated by the OCT system, and (iii) a statistical difference occurred between the control and case group for both OCT (through the retinal nerve fiber layer–RNFL thickness) and the RETeval parameters (through the photopic negative response–PhNR). RNFL was found to be correlated to b-wave (ms) and W-ratio parameters. In conclusion, the PhNR obtained by the RETeval system could be a valuable supplementary tool for the objective examination of patients with early glaucoma.
FA-Net: A Fused Feature for Multi-Head Attention Recoding Network for Pear Leaf Nutritional Deficiency Diagnosis with Visual RGB-Image Depth and Shallow Features
Authors: Song Yi , Liu Li , Rao Yuan , Zhang Xiaodan , Jin Xiu
Published: May 2023

Accurate diagnosis of pear tree nutrient deficiency symptoms is vital for the timely adoption of fertilization and treatment. This study proposes a novel method on the fused feature multi-head attention recording network with image depth and shallow feature fusion for diagnosing nutrient deficiency symptoms in pear leaves. First, the shallow features of nutrient-deficient pear leaf images are extracted using manual feature extraction methods, and the depth features are extracted by the deep network model. Second, the shallow features are fused with the depth features using serial fusion. In addition, the fused features are trained using three classification algorithms, F-Net, FC-Net, and FA-Net, proposed in this paper. Finally, we compare the performance of single feature-based and fusion feature-based identification algorithms in the nutrient-deficient pear leaf diagnostic task. The best classification performance is achieved by fusing the depth features output from the ConvNeXt-Base deep network model with shallow features using the proposed FA-Net network, which improved the average accuracy by 15.34 and 10.19 percentage points, respectively, compared with the original ConvNeXt-Base model and the shallow feature-based recognition model. The result can accurately recognize pear leaf deficiency images by providing a theoretical foundation for identifying plant nutrient-deficient leaves.
HA-FPN: Hierarchical Attention Feature Pyramid Network for Object Detection
Authors: Dang Jin , Tang Xiaofen , Li Shuai
Published: May 2023

The goals of object detection are to accurately detect and locate objects of various sizes in digital images. Multi-scale processing technology can improve the detection accuracy of the detector. Feature pyramid networks (FPNs) have been proven to be effective in extracting multi-scaled features. However, most existing object detection methods recognize objects in isolation, without considering contextual information between objects. Moreover, for the sake of computational efficiency, a significant reduction in the channel dimension may lead to the loss of semantic information. This study explores the utilization of attention mechanisms to augment the representational power and efficiency of features, ultimately improving the accuracy and efficiency of object detection. The study proposed a novel hierarchical attention feature pyramid network (HA-FPN), which comprises two key components: transformer feature pyramid networks (TFPNs) and channel attention modules (CAMs). In TFPNs, multi-scaled convolutional features are embedded as tokens and self-attention is applied to across both the intra- and inter-scales to capture contextual information between the tokens. CAMs are employed to select the channels with rich channel information to alleviate massive channel information losses. By introducing contextual information and attention mechanisms, the HA-FPN significantly improves the accuracy of bounding box detection, leading to more precise identification and localization of target objects. Extensive experiments conducted on the challenging MS COCO dataset demonstrate that the proposed HA-FPN outperforms existing multi-object detection models, while incurring minimal computational overhead.
Exploring School Bullying: Designing the Research Question with Young Co-Researchers
Authors: O’Brien Niamh , Doyle Audrey
Published: May 2023

Participatory Action Research (PAR) empowers young people to work alongside adult researchers to determine the purpose/scope of research projects. By naming the purpose of the research, young people have the potential to transform it. Beginning with a broad question, we worked in collaboration with secondary school students (co-researchers) and staff to decide on the focal research question. Prior to recruiting the co-researchers, we conducted an ‘exploration study’ using a qualitative online questionnaire distributed to the wider school population, to ascertain the core bullying issues. Although the questionnaire highlighted complexities in recognising bullying, misogynistic behaviour was stressed as a particular concern. In-depth discussions with the co-researchers and reflections on the questionnaire findings over several months gave us further insight into this issue, as well as the complexities of determining bullying/banter. Our research question was determined as: “Does gender bullying happen at this school?” Two distinct methodological and process-related insights arose from this work: power dynamics and the construct of time as duration and a non-linear process. This paper contributes to the literature on hearing stakeholder views as well as on actively including students in designing and developing research foundations, that is the research question, an under-explored topic in the wider literature.
Cairenes’ Storytelling: Pedestrian Scenarios as a Normative Factor When Enforcing Street Changes in Residential Areas
Authors: Abusaada Hisham , Elshater Abeer
Published: May 2023

Pedestrian scenarios refer to all types of transit, including unidirectional, bidirectional, and crossing actions. This study argues that pedestrian scenarios are critical normative factors that must be considered when implementing street changes in existent residential areas. It focuses on pedestrian safety and reliable access. Making improvements to urban streets without adhering to the assessment criteria for street design results in the presence of more cars on the road, which makes crossing streets unsafe. The aim here is to provide assessment criteria for street development projects. This study used three qualitative methods, starting with a scoping review to define the urban street improvement assessment criteria. A spatial analysis was conducted using geographical maps and site visits to determine how specific residential areas have changed. Then, a storytelling analysis method, based on episodic narrative interviews with an anonymous sample of 21 residents, workers, and visitors, was imposed. The results yielded pedestrians’ stories about how street improvements affected pedestrian scenarios on two streets in the Ard el Golf residential area in Cairo, Egypt. The results showed that unplanned changes in urban streets’ socio-spatial configurations affected residents’ preferences for pedestrian safety and their reliable access to services on either side of the street. Our results reveal that practitioners can develop these assessment criteria for pedestrian preferences through storytelling techniques. The concluding remarks outline a set of criteria for assessing improvement projects of urban streets. The added value here is that practitioners can learn from users’ storytelling, and thus avoid street risks when undertaking improvement projects on other urban streets and cities.
Post-Pandemic Mental Health: Psychological Distress and Burnout Syndrome in Regular Basic Education Teachers
Authors: Estrada-Araoz Edwin Gustavo , Bautista Quispe Judith Annie , Velazco Reyes Benjamin , Mamani Coaquira Humberto , Ascona Garcia Papa Pio , Arias Palomino Yessenia Luz
Published: May 2023

The COVID-19 pandemic has affected the mental health of regular basic education teachers. Despite the fact that in recent months the number of infections has decreased significantly, the return to face-to-face classes is of great concern to teachers due to the adverse educational context they must face. In this sense, the objective of this study was to determine whether or not psychological distress is significantly related to burnout syndrome in regular basic education teachers upon their return to face-to-face classes. This research employed a quantitative approach, the design was non-experimental, and the type of study was descriptive–correlational and cross-sectional. In total, 184 teachers participated and responded using the Psychological Distress Scale and the Maslach Burnout Inventory, which are instruments with adequate psychometric properties. The results indicated that 40.7% of the teachers had a moderate level of psychological distress, and 45.1% also had a moderate level of burnout syndrome. Likewise, it was found that the Pearson’s r correlation coefficient between the variables psychological distress and burnout syndrome was 0.752, and the p-value was below the level of significance (p < 0.05). It was concluded that there is a direct and significant relationship between psychological distress and burnout syndrome in regular basic education teachers upon their return to face-to-face classes. For this reason, it is suggested that the Ministry of Education should design policies that allow a reassessment of the work that teachers have been carrying out and that promote the implementation of preventive and corrective programs to improve their mental health.
Finest Magic Cloth or a Naked Emperor? The SKQuest Data Set on Software Metrics for Improving Transparency and Quality
Authors: Prause Christian R. , Gerlich Ralf
Published: May 2023

Software development has a problem with transparency/visibility. As an intangible product, software and its intermediate development results are hard to see or touch. Customers of custom software have difficulties checking progress, and risk coming out with costly but low-quality software. In the space domain with its often expensive and one-of-a-kind devices, which are developed in complex multitier supply chains, the risk is even greater. This paper presents the SKQuest data set. It contains the completed responses with 190 variables from an empirical study with over 100 software experts. The data set covers distinct aspects of measuring metrics and transparency in software projects. To show what information lies in the data set, the paper investigates, and affirms, from different perspectives, the following questions: Is transparency a problem in software development projects? Is there a desire for more transparency in projects? Can metrics contribute to improving the situation? Moreover, it attempts to replicate the results of an earlier study. The main contribution of this paper is, however, the SKQuest data set that is published with this paper in CSV formatas. It is a tool that enables systematic investigations of software metrics and allows research on how they can improve the efficiency of the software lifecycle, not limited to, but particularly with respect to transparency. Consequently, the paper may serve as a starting point for future research avenues in academia and industry and help to improve existing and future standards in software development.
Evaluating the Local Language Dimensions for Effective Teaching and Learning Sustainability in the Secondary Education System in Southeast Nigeria: Results from a Small-Scale Study
Authors: Ezepue Evelyn I. , Nwankwor Paulinus P. , Chukwuemeka-Nworu Ijeoma Joyce , Ozioko Agatha Nkechi , Egbe Chris Onyedikachi , Ujah Jacinta , Nduka Chinyere , Edikpa Edith C.
Published: May 2023

The paper reports on a pilot study conducted with private and secondary schools in the southeast region of Nigeria to establish the impact of integrating the Igbo language into the secondary education system to ensure performance and language sustainability in the region. The aim is to measure the impact of using Igbo as the language of instruction combined with English language on academic performances and the perceived benefits, challenges and policy implications. The study, involving teachers and students drawn from two private and public secondary schools, employed a combination of quasi-experimental and survey methods for quantitative data collection. A performance assessment test of the students on Civic Education, a core subject in the Nigerian secondary education curriculum, revealed improved academic performances after adjusting for the effect of type of school and sex of students. Students taught with Igbo as the language of instruction supported with English performed better compared to those taught with English. The perceived benefits border on effective cognitive development, reducing learning poverty and strengthening of social relationships. However, the perceived challenges of suitably qualified teachers, infrastructure and suitable instructional materials will hinder effective implementation and transition. Careful planning and strategies will enhance the effective integration process.
A Systematic Literature Review of GHRM: Organizational Sustainable Performance Reimagined Using a New Holistic Framework
Authors: Chowdhury Shah Ridwan , Mendy John , Rahman Mahfuzur
Published: May 2023

Despite the plethora of explications of the direct and indirect impacts of green people management practices on different dimensions of individual and organizational performance and sustainability, a holistic model demonstrating the constituent aspects and impacts of such sustainability on organizational, individual, and team performance is missing. The objective of this study is to address this gap/void through a review of 127 papers on green human resource management (GHRM) following a systematic literature review approach. Based on the systematic review, this study used a thematic analysis, which identified twenty-four disparate people and organizational aspects and grouped the most used ones into five theoretical lenses, including AMO = ability–motivation–opportunity, RBV = resource-based view, SHT = stakeholder theory, SET =social exchange theory, and SIT = social identity theory. These five sets of results were used to develop the first-of-its-kind holistic framework showing how GHRM works in a cyclical process to fill the missing gap in how to sustainably improve individual, group, and organizational performance for multiple organizational stakeholders. Second, this article contributes theoretically to the social engagement and social identity theories, thereby extending Deci and Ryan’s organismic integration and self-determination theories to show how GHRM practices can be implemented for sustainable organizational performance. Third, this study also proposed a new and more sustainable bottom line for business organizations seeking to improve their performance, and this contribution is referred to as sustainable GHRM-organizational performance (SGHRM-OrgP). Finally, this study proposes a research agenda highlighting where more research areas are needed. Despite the potential that such a model offers for organizational sustainability, the authors recognize the next research step of applying its constituent parts in practically optimizing performance.
Sustainable Design Orientation in Furniture-Manufacturing SMEs in Zimbabwe
Authors: Chipambwa Walter , Moalosi Richie , Rapitsenyane Yaone , Molwane Olefile Bethuel
Published: May 2023

Small and Medium Enterprises (SMEs) have become the backbone of many nations as they contribute immensely to the growth of their economies. In Zimbabwe, they have filled in the gap left by the big companies after the economic crisis of the early 2000s. The impact of SMEs is small when we look at their environmental, economic, and social impact, but when grouped, it becomes a considerable contribution. Evidence from the literature shows that SMEs have a short lifespan. Therefore, sustainability in manufacturing SMEs in emerging economies and their survival strategy have been discussed. This study aimed to examine sustainability in the context of design in furniture-manufacturing SMEs in Zimbabwe. The qualitative study used a purposively selected sample of ten SMEs where the owner and/or manager and designer were the main participants. Observations were also conducted on-site in all ten cases where evidence was noted for analysis. The study reveals that SMEs in emerging economies face many challenges, such as a lack of finance, skilled human resources, and management commitment that hamper their capacity to adopt sustainable design in their practices. Sustainability adoption in these SMEs is more of adherent to the regulatory framework, especially considering the environmental aspect. The study concludes by recommending that policy makers in government should create tax rebate incentives that should be awarded to those SMEs that score highly in all three pillars of sustainability.