bluuebunny/crossref_metadata_2025 huggingface dataset:
In Vitro Radioenhancement Using Ultrasound-Stimulated Microbubbles: A Comparison of Suspension and Adherent Cell States
Authors: McCorkell Giulia , Nakayama Masao , Feltis Bryce , Piva Terrence J. , Geso Moshi
Published: August 2023

Background: Ultrasound-stimulated microbubbles (USMB) have shown potential for enhancing radiation treatment via cavitation and sonoporation mechanisms. However, in vitro studies have produced inconsistent results, with adherent cells demonstrating no radioenhancement. This study aims to investigate the effect of cell adherence on in vitro radioenhancement using USMB and radiation. Method: Lung metastases of follicular thyroid carcinoma cells (FTC-238) and non-small cell lung carcinoma cells (NCI-H727) were treated, both when adhered and in suspension, using 1.6% (v/v) Definity™ microbubbles, ~90 s of 2 MHz ultrasound with mechanical index 0.9, and either 3 Gy or 6 Gy of megavoltage (MV) X-rays. The cell viability was measured using an MTS assay 72 h post-treatment, and statistical analysis was conducted using a three-way analysis of variance. Results: Statistically significant differences were observed for cells treated when adherent compared to suspended. An additive effect was detected in NCI-H727 cells treated in suspension, but not while adherent, while no enhancement was observed for FTC-238 cells in either culture state. Conclusions: To the best of our knowledge, this is the first study to directly compare the effect of cell adherence on the radioenhancement potential of USMB in vitro, and the first to do so using a metastatic cell line.
Can Women and Religion (Catholic) Save Modern Leadership?
Authors: Brzezińska Monika Maria
Published: August 2023

This article examines the theoretical potential of the gender leadership model of women motivated by the Catholic religion in the context of its ability to socially and politically activate and its effectiveness in terms of assumed goals. Numerous studies on gender indicate a certain effectiveness of gender politics (including gender equality and neutrality) in reducing the deficit of women in politics, but it turns out that in the long term it does not effectively solve the problem of the asymmetry of gender representation in the world of politics. The analysis of the decision-making processes of a selected group of Saints, Blessed and Venerable Servants of God, recognised by the Catholic Church as a model to follow, indicates that women with a strong internal religious motivation were more determined to act and enter the sphere of public activity (including politics) than those motivated only externally. They preferred a relational style of political leadership, but also practiced its goal-oriented form. These leaders mixed strategies, adjusting them to their own capabilities and the needs of their surroundings, making them more effective and efficient in achieving their goals despite the limitations resulting from external systemic conditionalities.
“Being Rooted in Love”: The Trinitarian Ontological Perspective of Simone Weil’s Notion of Rootedness
Authors: Echaurren Alejandra Novoa , Sanches Noemi
Published: August 2023

This paper aims to explore the concept of rootedness in the final political reflections of Simone Weil. According to the French philosopher, rootedness represents “the most important and least recognized need of the human soul”. Weil goes beyond territorial aspects and considers the deep and authentic rootedness in one’s cultural heritage as an indispensable condition for the full development of human beings, both on individual and communal levels. Each culture originates from a unique and irreplaceable source of truth, shaping human environments in distinct ways. True rootedness fosters a genuine love for one’s place of origin and extends that sentiment to others, promoting a fraternal and solidarity-based relationship among diverse human communities worldwide. This political vision is grounded in an ontological reflection of being as a relation, which, from a Trinitarian perspective, can provide genuine meaning to human relationships.
Orientalism’s Hinduism, Orientalism’s Islam, and the Twilight of the Subcontinental Imagination
Authors: Basu Anustup
Published: August 2023

Using the figure of the ethnic Pathan/Pashtun as a trope in South Asian culture, this essay provides a genealogical account of the modern emergence of Hindu–Muslim “religious” conflicts played along the lines of nation-thinking in the Indian subcontinent. This modern phenomenon begins in the late 18th century, with the orientalist transcriptions of a vast conglomerate of diverse Indic faiths into a Brahminical–Sanskritic Hinduism and a similar telescoping of complex Islamic intellectual traditions into what we can call a “Mohammedanism” overdetermined by Islamic law. As such, both these transcriptions had to fulfill certain Christological expectations of western anthropology in order to emerge as “religions” and “world religions”, that is, when, as Talal Asad has shown, “religion” was constructed as an anthropological category within the parameters of European secular introspection and the modern expansion of empire. Both Hinduism and Islam therefore had to have a book, a prophetic figure, a doctrinal core, and a singular compendium of laws. Upper caste Sanskritic traditions therefore dominated Hinduism, and a legal supremacist position dominated the modern reckoning of Islam at the expense of philosophy, metaphysics, poesis, and varieties of artistic self-making. Together, the two phenomena also created the historical illusion (now industrialized) that Brahminism always defined Hindu societies and the Sharia was always a total fact of Islam.
Understanding Key Drivers of Participant Cash Flows for Individually Managed Stable Value Funds
Authors: Alimoradian Behzad , Jakubiak Jeffrey , Loisel Stephane , Salhi Yahia
Published: August 2023

In this paper, we investigate the behavioral and statistical characteristics of cash flows for stable value funds provided by numerous U.S. employee benefit plans. We analyze participant-initiated aggregated cash flow data, representing approximately 80% of the market for large employer plans with stand-alone stable value wraps within a 401(k) offering. By leveraging this unique dataset and contextualizing the 401(k) ecosystem, we examine numerous behavioral lapse hypotheses. Our findings highlight key behavioral lapse hypotheses for modeling lapses and generating risk scenarios. We demonstrate that cash flows exhibit medium- to long-term non-monotonic trends. Factors within the plan sponsor’s ecosystem, such as employment growth, default 401(k) plan options, and the introduction of new investment options, significantly impact participant cash flow behavior indirectly. Moreover, we find that flight-to-safety behavior plays a dominant role during global market crises. Although the risk of mass lapses due to reputational issues is observed, their probability of occurrence is low. Other behavioral hypotheses discussed in the literature, such as the moneyness hypothesis, are found to be less prevalent in this context.
Edge Real-Time Object Detection and DPU-Based Hardware Implementation for Optical Remote Sensing Images
Authors: Li Chao , Xu Rui , Lv Yong , Zhao Yonghui , Jing Weipeng
Published: August 2023

The accuracy of current deep learning algorithms has certainly increased. However, deploying deep learning networks on edge devices with limited resources is challenging due to their inherent depth and high parameter count. Here, we proposed an improved YOLO model based on an attention mechanism and receptive field (RFA-YOLO) model, applying the MobileNeXt network as the backbone to reduce parameters and complexity, adopting the Receptive Field Block (RFB) and Efficient Channel Attention (ECA) modules to improve the detection accuracy of multi-scale and small objects. Meanwhile, an FPGA-based model deployment solution was proposed to implement parallel acceleration and low-power deployment of the detection algorithm model, which achieved real-time object detection for optical remote sensing images. We implement the proposed DPU and Vitis AI-based object detection algorithms with FPGA deployment to achieve low power consumption and real-time performance requirements. Experimental results on DIOR dataset demonstrate the effectiveness and superiority of our RFA-YOLO model for object detection algorithms. Moreover, to evaluate the performance of the proposed hardware implementation, it was implemented on a Xilinx ZCU104 board. Results of the experiments for hardware and software simulation show that our DPU-based hardware implementation are more power efficient than central processing units (CPUs) and graphics processing units (GPUs), and have the potential to be applied to onboard processing systems with limited resources and power consumption.
Spatio-Temporal Characteristics and Differences in Snow Density between the Tibet Plateau and the Arctic
Authors: Zhao Wenyu , Mu Cuicui , Wu Xiaodong , Zhong Xinyue , Peng Xiaoqing , Liu Yijing , Sun Yanhua , Liang Benben , Zhang Tingjun
Published: August 2023

The Tibet Plateau (TP) and the Arctic are typically cold regions with abundant snow cover, which plays a key role in land surface processes. Knowledge of variations in snow density is essential for understanding hydrology, ecology, and snow cover feedback. Here, we utilized extensive measurements recorded by 697 ground-based snow sites during 1950–2019 to identify the spatio-temporal characteristics of snow density in these two regions. We examined the spatial heterogeneity of snow density for different snow classes, which are from a global seasonal snow cover classification system, with each class determined from air temperature, precipitation, and wind speed climatologies. We also investigated possible mechanisms driving observed snow density differences. The long-term mean snow density in the Arctic was 1.6 times that of the TP. Slight differences were noted in the monthly TP snow densities, with values ranging from 122 ± 29 to 158 ± 52 kg/m3. In the Arctic, however, a clear increasing trend was shown from October to June, particularly with a rate of 30.3 kg/m3 per month from March to June. For the same snow class, the average snow density in the Arctic was higher than that in the TP. The Arctic was characterized mainly by a longer snowfall duration and deeper snow cover, with some areas showing perennial snow cover. In contrast, the TP was dominated by seasonal snow cover that was shallower and warmer, with less (more) snowfall in winter (spring). The results will be helpful for future simulations of snow cover changes and land interactions at high latitudes and altitudes.
Fusion of Identification Information from ESM Sensors and Radars Using Dezert–Smarandache Theory Rules
Authors: Pietkiewicz Tadeusz
Published: August 2023

This paper presents a method of fusion of identification (attribute) information provided by two types of sensors: combined primary and secondary (IFF) surveillance radars and ESMs (electronic support measures). In the first section, the basic taxonomy of attribute identification is adopted in accordance with the standards of STANAG 1241 ed. 5 and STANAG 1241 ed. 6 (draft). These standards provide the following basic values of the attribute identifications: FRIEND; HOSTILE; NEUTRAL; UNKNOWN; and additional values, namely ASSUMED FRIEND and SUSPECT. The basis of theoretical considerations is Dezert–Smarandache theory (DSmT) of inference. This paper presents and uses in practice six information-fusion rules proposed by DSmT, i.e., the proportional conflict redistribution rules (PCR1, PCR2, PCR3, PCR4, PCR5, and PCR6), for combining identification information from different ESM sensors and radars. This paper demonstrates the rules of determining attribute information by an ESM sensor equipped with the database of radar emitters. It is proposed that each signal vector sent by the ESM sensor contains an extension specifying a randomized identification declaration (hypothesis)—a basic belief assignment (BBA). This paper also presents a model for determining the basic belief assignment for a combined primary and secondary radar. Results of the PCR rules of sensor information combining for different scenarios of a radio electronic situation (deterministic and Monte Carlo) are presented in the final part of this paper. They confirm the legitimacy of the use of Dezert–Smarandache theory in information fusion for primary radars, secondary radars, and ESM sensors.
Clutter and Interference Cancellation in River Surface Velocity Measurement with a Coherent S-Band Radar
Authors: Zeng Yichen , Chen Zezong , Zhao Chen , Wei Yunyu , He Jiangheng
Published: August 2023

Using a Doppler radar to measure river surface velocity is a safe and effective technique. However, the measurement would be severely affected by undesired targets that enter the illuminated area of radar. The issue is worsened when measuring the surface velocities of wide rivers because undesired targets such as boats and ships are more likely to be present. The buoy boats fixed on the river surface and cargo ships sailing on the river would generate ground clutter and moving target interference, respectively. The clutter and interference can mask the signal produced by the Bragg scattering and seriously bias the extraction result of river surface velocity. This paper proposes two effective methods to remove ground clutter and moving target interference, respectively. One is an improved phase-based method that eliminates ground clutter after obtaining its boundaries through the phase in the frequency domain, and another is an improved constant false alarm rate (CFAR) detector that combines smallest-of selection logic and a multi-step deletion scheme to detect and remove interference in the time-Doppler spectrum. The experimental data measuring the surface velocity of the Yangtze River with a coherent S-band radar in July 2022 are used to verify the proposed methods. The results show that the proposed methods can effectively remove ground clutter and moving target interference, respectively. After clutter and interference cancellation, a more reasonable result of river surface velocity distribution can be extracted. Therefore, the methods proposed in this paper can be used to remove clutter and interference when extracting the surface velocity of rivers with numerous undesired targets.
Enabling High-Resolution Micro-Vibration Detection Using Ground-Based Synthetic Aperture Radar: A Case Study for Pipeline Monitoring
Authors: Hosseiny Benyamin , Amini Jalal , Aghababaei Hossein , Ferraioli Giampaolo
Published: August 2023

The wellbeing of pipelines is influenced by a range of factors, such as internal and external pressures, as well as deterioration over time due to issues like erosion and corrosion. It is thus essential to establish a reliable monitoring system that can precisely examine pipeline behavior over time in order to prevent potential damages. To this end, pipelines are inspected based on internal and external approaches. Radar, as a non-contact sensing system, can be a suitable choice for external pipeline inspection. Radar is capable of the transmission and receiving of thousands of signals in a second, which reconstructs the displacement signal and is used for a vibration analysis. Synthetic aperture radar (SAR) imaging adds cross-range resolution to radar signals. However, a data acquisition rate of longer than several seconds makes it unsuitable for sub-second vibration monitoring. This study aims to address this limitation by presenting a method for high-resolution vibration monitoring using ground-based SAR (GBSAR) signals. To this end, a signal processing method by modifying the radar’s signal model is presented, which allows for estimating scattering targets’ vibration parameters and angle of arrival with high resolution. The proposed method is validated with numerical simulation and a real case study comprising water pipelines. Moreover, various analyses are presented for the in-depth evaluation of the method’s performance in different situations. The results indicate that the proposed method can be effective in detecting pipeline vibration frequencies with micro-scale amplitudes while providing high spatial resolution for generating accurate vibration maps of pipelines. Also, the comparison with the radar observations shows a high degree of agreement between the frequency responses with the maximum error of 0.25 Hz in some rare instances.
Simulation and Driving Factor Analysis of Satellite-Observed Terrestrial Water Storage Anomaly in the Pearl River Basin Using Deep Learning
Authors: Huang Haijun , Feng Guanbin , Cao Yeer , Feng Guanning , Dai Zhikai , Tian Peizhi , Wei Juncheng , Cai Xitian
Published: August 2023

Accurate estimation of terrestrial water storage (TWS) and understanding its driving factors are crucial for effective hydrological assessment and water resource management. The launches of the Gravity Recovery and Climate Experiment (GRACE) satellites and their successor, GRACE Follow-On (GRACE-FO), combined with deep learning algorithms, have opened new avenues for such investigations. In this study, we employed a long short-term memory (LSTM) neural network model to simulate TWS anomaly (TWSA) in the Pearl River Basin (PRB) from 2003 to 2020, using precipitation, temperature, runoff, evapotranspiration, and leaf area index (LAI) data. The performance of the LSTM model was rigorously evaluated, achieving a high average correlation coefficient (r) of 0.967 and an average Nash–Sutcliffe efficiency (NSE) coefficient of 0.912 on the testing set. To unravel the relative importance of each driving factor and assess the impact of different lead times, we employed the SHapley Additive exPlanations (SHAP) method. Our results revealed that precipitation exerted the most significant influence on TWSA in the PRB, with a one-month lead time exhibiting the greatest impact. Evapotranspiration, runoff, temperature, and LAI also played important roles, with interactive effects among these factors. Moreover, we observed an accumulation effect of precipitation and evapotranspiration on TWSA, particularly with shorter lead times. Overall, the SHAP method provides an alternative approach for the quantitative analysis of natural driving factors at the basin scale, shedding light on the natural dominant influences on TWSA in the PRB. The combination of satellite observations and deep learning techniques holds promise for advancing our understanding of TWS dynamics and enhancing water resource management strategies.
Prediction of Unpaved Road Conditions Using High-Resolution Optical Satellite Imagery and Machine Learning
Authors: Workman Robin , Wong Patrick , Wright Alex , Wang Zhao
Published: August 2023

Rural roads play a crucial role in fostering economic and social development in Africa. Local Road Authorities (LRAs) struggle to collect road condition data using conventional means due to logistical and resource issues. Poor road conditions and restricted mobility have severe economic consequences for the transport of goods and services. Lack of maintenance can increase costs three-fold. In this work, a novel framework is proposed in which earth observations using high-resolution optical satellite imagery are applied to measure the condition of unpaved roads, providing a vital input to maintenance planning and prioritisation. A trial was conducted using this method on 83 roads in Tanzania totalling 131.7 km. The experimental results demonstrate that, by analysing variations in pixel intensity of the road surface, the condition can be estimated with an accuracy of 71.9% when compared to ground truth information. Machine Learning techniques are applied to the same network to test the performance of the system in predicting road conditions. A blended classifier approach achieves an accuracy of 88%. The proposed framework enables LRAs to define the information they receive based on their specific priorities, offering a rapid, objective, consistent and potentially cost-effective system that overcomes the current challenges faced by LRAs.
Glacier Change and Its Influencing Factors in the Northern Part of the Kunlun Mountains
Authors: Niu Shuting , Sun Meiping , Wang Guoyu , Wang Weisheng , Yao Xiaojun , Zhang Cong
Published: August 2023

The glaciers in northwest China are mainly distributed in the northern part of the Himalayas, the Kunlun Mountains, and the Tianshan Mountains. Glaciers are an important freshwater resource in the northern part of the Kunlun Mountains, and the melting of glaciers and snow provides an assured source of water for rivers on the southern edge of the Tarim Basin. Based on the first glacier inventory dataset on China (1968), the second glacier inventory dataset on China (2009), and the glacier inventory dataset on Western China in 2018, this study used DEM data, Landsat remote sensing images, and ERA5 atmospheric reanalysis data to investigate glacier change and its influencing factors with respect to the northern part of the Kunlun Mountains. The results showed that there were 9273 glaciers in the northern part of the Kunlun Mountains in 2018, with an area of about 11,762.72 km2, an ice inventory of about 1168.53 km3, and an average length per glacier of about 1.68 km. The glaciers were mainly distributed at altitudes of 5300–6100 m (7574.66 km2). From 1968 to 2018, the number of glaciers in the northern stretch of the Kunlun Mountains increased by 343, while the glacier area decreased by 2452.80 km2 (−0.14%/a). From 2009 to 2018, the glacier area at the altitude of 4900 m to 6100 m decreased in the northern section of the Kunlun Mountains, and the glacier area at the remaining altitude increased slightly (10.67 km2). From 1968 to 2018, the glacier area and glacier length in all river basins decreased. The relative rate of glacier area change in the Qarqan River basin from 2009 to 2018 was five times that of 1968–2009, and this needs significant attention. From 1968 to 2018, both temperature and precipitation increased to varying degrees, and the increase in precipitation was beneficial to the accumulation of glaciers. Therefore, the increase in temperature was the main cause of glacier change in the northern section of the Kunlun Mountains.
A Parametric Study of MPSO-ANN Techniques in Gas-Bearing Distribution Prediction Using Multicomponent Seismic Data
Authors: Yang Jiuqiang , Lin Niantian , Zhang Kai , Jia Lingyun , Zhang Dong , Li Guihua , Zhang Jinwei
Published: August 2023

Predicting the oil–gas-bearing distribution of unconventional reservoirs is challenging because of the complex seismic response relationship of these reservoirs. Artificial neural network (ANN) technology has been popular in seismic reservoir prediction because of its self-learning and nonlinear expression abilities. However, problems in the training process of ANNs, such as slow convergence speed and local minima, affect the prediction accuracy. Therefore, this study proposes a hybrid prediction method that combines mutation particle swarm optimization (MPSO) and ANN (MPSO-ANN). It uses the powerful search ability of MPSO to address local optimization problems during training and improve the performance of ANN models in gas-bearing distribution prediction. Furthermore, because the predictions of ANN models require good data sources, multicomponent seismic data that can provide rich gas reservoir information are used as input for MPSO-ANN learning. First, the hyperparameters of the ANN model were analyzed, and ANNs with different structures were constructed. The initial ANN model before optimization exhibited good predictive performance. Then, the parameter settings of MPSO were analyzed, and the MPSO-ANN model was obtained by using MPSO to optimize the weights and biases of the developed ANN model. Finally, the gas-bearing distribution was predicted using multicomponent seismic data. The results indicate that the developed MPSO-ANN model (MSE = 0.0058, RMSE = 0.0762, R2 = 0.9761) has better predictive performance than the PSO-ANN (MSE = 0.0062, RMSE = 0.0786, R2 = 0.9713) and unoptimized ANN models (MSE = 0.0069, RMSE = 0.0833, R2 = 0.9625) on the test dataset. Additionally, the gas-bearing distribution prediction results were consistent overall with the actual drilling results, further verifying the feasibility of this method. The research results may contribute to the application of PSO and ANN in reservoir prediction and other fields.
Geospatial Assessment of Managed Aquifer Recharge Potential Sites in Punjab, Pakistan
Authors: Afzal Muhammad , Liu Tie , Butt Asim Qayyum , Nadeem Adeel Ahmed , Ali Sikandar , Pan Xiaohui
Published: August 2023

Groundwater is a precious natural resource that is vital to various aspects of life. Punjab is experiencing groundwater stress due to urbanization and population growth, leading to overuse and reduced aquifer recharge. Sustainable groundwater supplies can only be created through better management and artificial recharge techniques. This study uses multi-influencing factor, literature-based, and combined techniques to identify and characterize groundwater-managed aquifer recharge potential sites (GWMARPSs) in Punjab. There are limitations to the previous work in this field, and these factors have not been used to estimate GWRPSs in the study area. The study uses GIS and RS techniques to overlay twelve geo-informative layers, with rainfall being the most significant factor. High-quality data and observations from the field are incorporated into the model. The study classifies the GWMARPSs into five categories, with Punjab having 0.34%, 13.29%, 60.68%, 25.26%, and 0.43% of the least, poorly, moderately, well-, and highly suitable sites. Punjab’s southern regions are least suitable for recharge, while some areas in eastern and northern Punjab are well-suited for recharge. Alluvial plains, valleys, low-lying areas, and areas with volcanic landforms are classified as least to poorly suitable zones. Model predictions are validated using piezometric level data and ROC and exhibit good performance (AUC, 0.74). This study could serve as a baseline for future groundwater research.
Guided Local Feature Matching with Transformer
Authors: Du Siliang , Xiao Yilin , Huang Jingwei , Sun Mingwei , Liu Mingzhong
Published: August 2023

GLFNet is proposed to be utilized for the detection and matching of local features among remote-sensing images, with existing sparse feature points being leveraged as guided points. Local feature matching is a crucial step in remote-sensing applications and 3D reconstruction. However, existing methods that detect feature points in image pairs and match them separately may fail to establish correct matches among images with significant differences in lighting or perspectives. To address this issue, the problem is reformulated as the extraction of corresponding features in the target image, given guided points from the source image as explicit guidance. The approach is designed to encourage the sharing of landmarks by searching for regions in the target image with features similar to the guided points in the source image. For this purpose, GLFNet is developed as a feature extraction and search network. The main challenge lies in efficiently searching for accurate matches, considering the massive number of guided points. To tackle this problem, the search network is divided into a coarse-level match network-based guided point transformer that narrows the search space and a fine-level regression network that produces accurate matches. The experimental results on challenging datasets demonstrate that the proposed method provides robust matching and benefits various applications, including remote-sensing image registration, optical flow estimation, visual localization, and reconstruction registration. Overall, a promising solution is offered by this approach to the problem of local feature matching in remote-sensing applications.
Exploring the Potential of Multi-Temporal Crop Canopy Models and Vegetation Indices from Pleiades Imagery for Yield Estimation
Authors: Dimov Dimo , Noack Patrick
Published: August 2023

In this paper, we demonstrate the capabilities of Pleiades-1a imagery for very high resolution (VHR) crop yield estimation by utilizing the predictor variables from the horizontal-spectral information, through Normalized Difference Vegetation Indices (NDVI), and the vertical-volumetric crop characteristics, through the derivation of Crop Canopy Models (CCMs), from the stereo imaging capacity of the satellite. CCMs captured by Unmanned Aerial Vehicles are widely used in precision farming applications, but they are not suitable for the mapping of large or inaccessible areas. We further explore the spatiotemporal relationship of the CCMs and the NDVI for five observation dates during the growing season for eight selected crop fields in Germany with harvester-measured ground truth crop yield. Moreover, we explore different CCM normalization methods, as well as linear and non-linear regression algorithms, for the crop yield estimation. Overall, using the Extremely Randomized Trees regression, the combination of CCMs and NDVI achieves an R2 coefficient of determination of 0.92.
Reliability of GPM IMERG Satellite Precipitation Data for Modelling Flash Flood Events in Selected Watersheds in the UAE
Authors: Hamouda Mohamed A. , Hinge Gilbert , Yemane Henok S. , Al Mosteka Hasan , Makki Mohammed , Mohamed Mohamed M.
Published: August 2023

Arid regions are prone to unprecedented extreme rainfall events that often result in severe flash floods. Using near-real-time precipitation data in hydrological modelling can aid in flood preparedness. This study analyzed rainfall data obtained from Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG V. 06) since 2001 to highlight recent trends of extreme rainfall indices for three selected watersheds in the UAE. Additionally, to validate the trends, the present study incorporated CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) into the analysis. Furthermore, for the first time, this study assessed the performance of the three products of IMERG in modelling flash flood events in the selected watersheds of UAE. A physical-based, fully distributed model was used to simulate the heaviest storm event. Also, a sensitivity analysis of the model’s output to variations in the input parameters was conducted using the one-factor-at-a-time method. The result of the trend analysis indicated that IMERG and CHIRPS show similar trends in both datasets, indicating agreement and reliability in their observations. However, there are a few instances where IMERG and CHIRPS show slight discrepancies in the nature of the trend. In general, the trend analysis results indicated an increasing trend of total precipitation (mm) and consecutive wet days, which suggests a rise in the risk of flash floods. The simulation of the flash flood event showed that the IMERG final product outperformed the other two products, closely matching the model output of the gauge rainfall data with mean absolute error (MAE) of 1.5, 2.37, and 0.5 for Wadi Ham, Wadi Taween, and Wadi Maidaq, respectively. The model’s performance was positively correlated with the size of the watershed. The sensitivity analysis results demonstrated that the model’s output was most sensitive to infiltration parameters. The study’s outcomes provide a good opportunity to improve near-real-time impact evaluation of flash flood events in the watersheds of the UAE.
Spreading of Localized Information across an Entire 3D Electrical Resistivity Volume via Constrained EMI Inversion Based on a Realistic Prior Distribution
Authors: Zaru Nicola , Rossi Matteo , Vacca Giuseppina , Vignoli Giulio
Published: August 2023

Frequency-domain electromagnetic induction (EMI) methods are commonly used to map vast areas quickly and with minimum logistical efforts. Unfortunately, they are often characterized by a very limited number of frequencies and severe ill-posedness. On the other hand, electrical resistivity tomography (ERT) approaches are usually considered more reliable; for example, they do not require specific calibration procedures and can be easily inverted in 2D/3D. However, ERT surveys are, by far, more demanding and time consuming, allowing for the deployment of a few acquisition lines per day. Ideally, the optimal would be to have the advantages of both approaches: ease of acquisition while keeping robustness and reliability. The present work raises from the necessity to cope with this issue and from the importance of enforcing realistic constraints to the data inversion without being limited to (over)simplistic spatial constraints (for example, characterizing the smooth and/or sharp regularization). Accordingly, the present research demonstrates, by means of synthetic and field data, how the EMI inversion—based on realistic prior models—can be further enhanced by incorporating additional pre-existing pieces of information. While the proposed scheme is quite general, in the specific examples discussed here, these additional pieces of information are, respectively, a reference model along a line across the survey area, and an ERT section. The field EMI results were verified against extensive ground penetrating radar (GPR) measurements and boreholes.
Multi-Scale Response Analysis and Displacement Prediction of Landslides Using Deep Learning with JTFA: A Case Study in the Three Gorges Reservoir, China
Authors: Jiang Yanan , Liao Lu , Luo Huiyuan , Zhu Xing , Lu Zhong
Published: August 2023

Reservoir water and rainfall, leading to fluctuations groundwater levels, are the main triggering factors that induce landslides in the Three Gorges Reservoir area. This study investigates the response mechanism of landslide deformation under reservoir water and rainfall variations through long-time on-site observations. To address the non-stationary characteristics of the time-series records, joint time-frequency analysis (JTFA) is first introduced into our landslide prediction model. This model employs optimal variational mode decomposition (VMD) to obtain specific signal components with clear physical meaning, such as trend component and periodic components. Then, multi-scale response analysis between the displacement and external factors three wavelet methods was conducted. The analysis results show a 1 year primary cycle of the time series associated with the landslide evolution. The reservoir water level and rainfall show anti-phase fluctuations. The periodic displacement correlates significantly with rainfall, lagging by about two months. The reservoir water is anti-phase with the landslide displacement, preceding it by approximately three months (−51 ± 8° phase difference). For landslide displacement prediction, the gated recurrent units (GRU) neural network model is integrated into the deep learning forecasting architecture. The model takes into account the correlation and hysteresis effect of input variables. Through six experiments, we investigate the effect of data volume on model predictions to determine the optimal model. The results demonstrate that our proposed model ensures high performance in landslide prediction. Moreover, a comparison with six other intelligent algorithms shows the advantages of our model in terms of time-effectiveness and long-sequence forecasting.
A Comparative Study of Ground-Gridded and Satellite-Derived Formaldehyde during Ozone Episodes in the Chinese Greater Bay Area
Authors: Zhao Yiming , Mo Xujun , Wang Hao , Li Jiangyong , Gong Daocheng , Wang Dakang , Li Qinqin , Liu Yunfeng , Liu Xiaoting , Wang Jinnian , Wang Boguang
Published: August 2023

Formaldehyde (HCHO) plays an important role in atmospheric photochemical reactions. Comparative studies between ground-based and satellite observations are necessary to assess and promote the potential use of column HCHO as a proxy for surface HCHO and volatile organic compound (VOC) oxidation. Previous studies have only validated temporal and vertical profile variations at one point, with limited studies comparing horizontal spatial variations due to sparse monitoring sites. The photochemistry-active Chinese Greater Bay Area (GBA) is a typical megacity cluster as well as a large hotspot of HCHO globally, which recorded a high incidence of ozone (O3) pollution. Here, we conducted the first comparative study of ground-gridded (HCHOgg) and satellite-derived (HCHOsd) HCHO during typical O3 episodes in the GBA. Our results revealed a good correlation between HCHOgg and HCHOsd, with a correlation coefficient higher than 0.5. Cloud coverage and ground pixel sizes were found to be the dominant factors affecting the quality of HCHOsd and contributing to the varying satellite pixel density. Daily averages of HCHOsd effectively improved the HCHOsd accuracy, except in areas with low satellite pixel density. Furthermore, a new quality control procedure was established to improve HCHOsd from Level 2 to Level 3, which demonstrated good application performance in O3 sensitivity analysis. Our findings indicate that the correlation between satellite observations and surface air quality can be optimized by spatiotemporal averaging of hourly HCHOsd, given the advent of geostationary satellites. Considering the representative range of sampling sites in this comparative study, we recommend establishing VOC monitoring stations within a 50 km radius in the GBA to further analyze and control photochemical pollution.
Model-Based Predictive Control and Reinforcement Learning for Planning Vehicle-Parking Trajectories for Vertical Parking Spaces
Authors: Shi Junren , Li Kexin , Piao Changhao , Gao Jun , Chen Lizhi
Published: August 2023

This paper proposes a vehicle-parking trajectory planning method that addresses the issues of a long trajectory planning time and difficult training convergence during automatic parking. The process involves two stages: finding a parking space and parking planning. The first stage uses model predictive control (MPC) for trajectory tracking from the initial position of the vehicle to the starting point of the parking operation. The second stage employs the proximal policy optimization (PPO) algorithm to transform the parking behavior into a reinforcement learning process. A four-dimensional reward function is set to evaluate the strategy based on a formal reward, guiding the adjustment of neural network parameters and reducing the exploration of invalid actions. Finally, a simulation environment is built for the parking scene, and a network framework is designed. The proposed method is compared with the deep deterministic policy gradient and double-delay deep deterministic policy gradient algorithms in the same scene. Results confirm that the MPC controller accurately performs trajectory-tracking control with minimal steering wheel angle changes and smooth, continuous movement. The PPO-based reinforcement learning method achieves shorter learning times, totaling only 30% and 37.5% of the deep deterministic policy gradient (DDPG) and twin-delayed deep deterministic policy gradient (TD3), and the number of iterations to reach convergence for the PPO algorithm with the introduction of the four-dimensional evaluation metrics is 75% and 68% shorter compared to the DDPG and TD3 algorithms, respectively. This study demonstrates the effectiveness of the proposed method in addressing a slow convergence and long training times in parking trajectory planning, improving parking timeliness.
Renovation or Redevelopment: The Case of Smart Decision-Support in Aging Buildings
Authors: Wu Bin , Maalek Reza
Published: August 2023

In Germany, as in many developed countries, over 60% of buildings were constructed before 1978, where most are in critical condition, requiring either demolition with plans for redevelopment or renovation and rehabilitation. Given the urgency of climate action and relevant sustainable development goals set by the United Nations, more attention must be shifted toward the various sustainability aspects when deciding on a strategy for the renovation or redevelopment of existing buildings. To this end, this study focused on developing a smart decision support framework for aging buildings based on lifecycle sustainability considerations. The framework integrated digital technological advancements, such as building information modeling (BIM), point clouds processing with field information modeling (FIM)®, and structural optimization, together with lifecycle assessment to evaluate and rate the environmental impact of different solutions. Three sustainability aspects, namely, cost, energy consumption, and carbon emissions, were quantitatively evaluated and compared in two scenarios, namely, renovation, and demolition or deconstruction combined with redevelopment. A real building constructed in 1961 was the subject of the experiments to validate the framework. The result outlined the limitations and advantages of each method in terms of economics and sustainability. It was further observed that optimizing the building design with the goal of reducing embodied energy and carbon in compliance with modern energy standards was crucial to improving overall energy performance. This work demonstrated that the BIM-based framework developed to assess the environmental impact of rehabilitation work in aging buildings can provide effective ratings to guide decision-making in real-world projects.
A Spatio-Temporal Task Allocation Model in Mobile Crowdsensing Based on Knowledge Graph
Authors: Zhao Bingxu , Dong Hongbin , Yang Dongmei
Published: August 2023

With the increasing popularity of wireless networks and the development of smart cities, the Mobile Crowdsourcing System (MCS) has emerged as a framework for automatically assigning spatiotemporal tasks to workers. The study of mobile crowdsourcing makes a valuable research contribution to community service and urban route planning. However, previous algorithms have faced challenges in effectively addressing task allocation issues with massive spatial data. In this paper, we propose a novel solution to the spatiotemporal task allocation problem using a knowledge graph. Firstly, we construct a robust spatiotemporal knowledge graph (STKG) and employ a knowledge graph embedding algorithm to learn the representations of nodes and edges. Next, we utilize these representations to build a task transition graph, which is a weighted and learning-based graph that highlights important neighbors for each task. We then apply a simplified Graph Convolutional Network (GCN) and an RNN-based model to enhance task representations and capture sequential transition patterns on the task transition graph. Furthermore, we design a similarity function to facilitate personalized task allocation. Through experimental results, we demonstrate that our solution achieves higher accuracy compared to existing approaches when tested on three real datasets. These research findings are significant as they contribute to an 18.01% improvement in spatiotemporal task allocation accuracy.
Assessing the Progress of Smart Cities in Saudi Arabia
Authors: Aldegheishem Abdulaziz
Published: August 2023

Information and communication technology is changing the manner in which urban policies are designed. Saudi Arabia bases its smart initiative on the use of information and communication technologies in six dimensions, including economy, people, environment, living, mobility, and governance to improve quality of life and sustainable environment. This study draws on four Saudi Arabian cities including Riyadh, Makkah, Jeddah, and Medina, and aims to analyze their progress in the transformation into smart cities. The six identified areas were assessed using 57 indicators based on national and international information and literature. The results show that the four cities are progressing successfully into smart cities, with the highest progress evident for smart economy and the lowest progress for smart mobility in all investigated cities. Study findings show that Riyadh has made the most progress in the six smart city dimensions, concluding that Riyadh has been efficiently executing the smart city initiative with an aim to be a unique model in the world.
Offshoring Refugees: Colonial Echoes of the UK-Rwanda Migration and Economic Development Partnership
Authors: Collyer Michael , Shahani Uttara
Published: August 2023

British proposals to forcibly deport asylum seekers to Rwanda have raised fierce opposition from across the political spectrum in the UK and internationally. These proposals differ from official practices of deportation as they have developed in liberal democracies since the 1970s. There are certainly some international parallels, such as Australia’s ‘Pacific Solution’ of ‘offshoring’ asylum, which is often cited as an inspiration. Yet a much clearer precedent involving the forcible movement of people to countries where they have no personal or legal connection existed for many years in the British Empire. Colonial policies of forcible removal, relocation, displacement, and dispersal around the Empire are well established. We draw attention to these longer histories before investigating more recent cases of the dispersal of refugees within the British Empire in the twentieth century. In many cases, such forced dispersal concerned those who had been recognised as refugees who were interned and subsequently moved elsewhere in the Empire. Such policies were designed to prevent the arrival of refugees in the UK. These policies have provided inspiration for asylum practices in some postcolonial states—Israel is reported to have reached an agreement with Uganda and Rwanda to deport asylum seekers from Sudan and Eritrea, although these are not public. In this paper, we highlight how these colonial practices of forcible displacement of individuals inform the current agreement between the UK and Rwanda.
Assessing Combining Abilities of Popcorn Inbred Lines for Agronomic and Root Traits under Contrasting Water Conditions: Towards Developing Drought-Tolerant Genotypes
Authors: Bispo Rosimeire Barboza , Junior Antônio Teixeira do Amaral , Kamphorst Samuel Henrique , Lima Valter Jário de , Pena Guilherme Ferreira , Santos Talles de Oliveira , Leite Jhean Torres , Viana Flávia Nicácio , Júnior Divino Rosa dos Santos , Lamêgo Danielle Leal , Oliveira Uéliton Alves de , Ribeiro Rodrigo Moreira , Pereira Telma Nair Santana , Khan Shahid
Published: August 2023

Developing popcorn genotypes that are adapted to water-deficit conditions is crucial due to the significant impact of this abiotic stress on grain yield. This study aimed to assess the combining abilities of preselected popcorn lines for agronomic and root traits under well-watered and water-stressed conditions. A circulating diallel analysis was conducted to investigate the effects of the general and specific combining abilities of 10 lines and their respective hybrids for various traits, including productivity, popping expansion, expanded popcorn volume, 100-grain weight, ear length, ear width, support root angle, crown root angle, number of support roots, number of crown roots, density of support roots, and density of crown roots. Non-additive action genes predominantly influenced the agronomic traits under both water conditions. In contrast, the root traits displayed a greater proportion of additive action gene expression under both conditions, despite being significantly affected by the environment. Lines L61, L71, and L76 demonstrated positive and high effects for general combining ability. The hybrids L65/L76, L71/L76, and L61/L75, in addition to exhibiting satisfactory effects of their specific combining ability, also displayed the most favorable phenotypic responses for most agronomic and root traits, making them ideal candidates for inclusion in popcorn breeding programs aiming to develop drought-tolerant genotypes.