Riverine Microplastic Quantification: A Novel Approach Integrating Satellite Images, Neural Network, and Suspended Sediment Data as a Proxy

Rivers transport terrestrial microplastics (MP) to the marine system, demanding cost-effective and frequent monitoring, which is attainable through remote sensing. This study aims to develop and test microplastic concentration (MPC) models directly by satellite images and indirectly through suspended sediment concentration (SSC) as a proxy employing a neural network algorithm. These models relied upon high spatial (26 sites) and temporal (198 samples) SSC and MPC data in the Tisza River, along with optical and active sensor reflectance/backscattering. A feedforward MLP neural network was used to calibrate and validate the direct models employing k-fold cross-validation (five data folds) and the Optuna library for hyperparameter optimization. The spatiotemporal generalization capability of the developed models was assessed under various hydrological scenarios. The findings revealed that hydrology fundamentally influences the SSC and MPC. The indirect estimation method of MPC using SSC as a proxy demonstrated higher accuracy (R2 = 0.17–0.88) than the direct method (R2 = 0–0.2), due to the limitations of satellite sensors to directly estimate the very low MPCs in rivers. However, the estimation accuracy of the indirect method varied with lower accuracy (R2 = 0.17, RMSE = 12.9 item/m3 and MAE = 9.4 item/m3) during low stages and very high (R2 = 0.88, RMSE = 7.8 item/m3 and MAE = 10.8 item/m3) during floods. The worst estimates were achieved based on Sentinel-1. Although the accuracy of the MPC models is moderate, it still has practical applicability, especially during floods and employing proxy models. This study is one of the very initial attempts towards MPC quantification, thus more studies incorporating denser spatiotemporal data, additional water quality parameters, and surface roughness data are warranted to improve the estimation accuracy

Distributed Massive MIMO for LEO Satellite Networks

The ultra-dense deployment of interconnected satellites will characterize future low Earth orbit (LEO) mega-constellations. Exploiting this towards a more efficient satellite network (SatNet), this paper proposes a novel LEO SatNet architecture based on distributed massive multiple-input multiple-output (DM-MIMO) technology allowing ground user terminals to be connected to a cluster of satellites. To this end, we investigate various aspects of DM-MIMO-based satellite network design, the benefits of using this architecture, the associated challenges, and the potential solutions. In addition, we propose a distributed joint power allocation and handover management (D-JPAHM) technique that jointly optimizes the power allocation and handover management processes in a cross-layer manner. This framework aims to maximize the network throughput and minimize the handover rate while considering the quality-of-service (QoS) demands of user terminals and the power capabilities of the satellites. Moreover, we devise an artificial intelligence (AI)-based solution to efficiently implement the proposed D-JPAHM framework in a manner suitable for real-time operation and the dynamic SatNet environment. To the best of our knowledge, this is the first work to introduce and study DM-MIMO technology in LEO SatNets. Extensive simulation results reveal the superiority of the proposed architecture and solutions compared to conventional approaches in the literature.Comment: arXiv admin note: text overlap with arXiv:2106.0983

A rare case of normotensive HELLP syndrome complicated with massive ascites: Spontaneous resolution

AbstractHELLP develops in approximately 0.1–0.8% of pregnancies overall and as many as 15–20% of patients with HELLP syndrome do not have antecedent hypertension or proteinuria. The risk factor for development of ascites is extensive structural damage of the microvasculature in patients complicated by HELLP. The aim of this study is to report a case with HELLP syndrome complicated with massive ascites after vaginal delivery that eventually resolved spontaneously

Future Ultra-Dense LEO Satellite Networks: A Cell-Free Massive MIMO Approach

Low Earth orbit (LEO) satellite networks (SatNets) are envisioned to play a crucial role in providing global and ubiquitous connectivity efficiently. Accordingly, in the coming years, thousands of LEO satellites will be launched to create ultradense LEO mega-constellations, and the Third Generation Partnership Project (3GPP) is working on evolving fifth-generation (5G) systems to support such non-terrestrial networks (NTN). However, many challenges are associated with the deployment of LEOs from communications and networking perspectives. In this paper, we propose a novel cell-free massive multiple-input multiple-output (CF-mMIMO) based architecture for future ultra-dense LEO SatNets. We discuss various aspects of network design, such as duplexing mode, pilot assignment, beamforming, and handover management. In addition, we propose a joint optimization framework for the power allocation and handover management processes to maximize the network throughput and minimize the handover rate while ensuring quality-of-service (QoS) satisfaction for users. To the best of our knowledge, this is the first work to introduce and study CF-mMIMO-based LEO SatNets. Extensive simulation results demonstrate the superior performance of the proposed architecture and solutions compared to those of conventional single-satellite connectivity and handover techniques from the literature.Comment: 6 pages, 3 figure

The effects of social and political dislocation on Persianate children's literature : change and continuity

This thesis seeks to investigate the various forces that have shaped modern Persianate children‘s literature - history, revolution, political climate, government, institutions, writers, education, and so on. The historical origins of tales popular in modern times, and of themes recurrent in stories from past times to present are analyzed, along with other factors which have shaped Persianate children‘s literature. The thesis begins with a historical and theoretical overview relating to change and continuity in Persianate children‘s literature. It examines the influence of ancient texts on modern Persianate children‘s stories. The cultural development reflected in the organizational infrastructure of institutions is also examined, as well as other contemporary influences, both social and political, in order to assess how these have affected modern Persianate children‘s literature. The contents of children‘s books are analyzed from different aspects, including their representation of social values. Concerns of children themselves are shown in examples of their own work; in addition, works of illustrators of children‘s books, and examples from the extended body of Persianate children‘s literature in Tajikistan are analyzed. Modern children‘s literature is the product of a number of influences and while differences can be perceived between historical periods, underlying similarities can also be seen which show a continuity of socio-political purpose, either supporting the status quo or challenging it. The thesis is concerned with this interplay between the recurring uses of children‘s literature; moralistic, didactic, the political agenda of its authors, criticism of the status quo, etc. and the surface changes which attract attention and which create an appearance of change in its underlying purpose. Fashions and styles may change, but children still read, firstly in order to learn to read, and then for information and amusement. The author contends that, in reality a limited number of changes are possible in the purpose of children‘s literature, and the age-old arguments likewise continue about what those are: entertainment or preparation for the harsh realities of life, retreat into fantasy and acceptance of one‘s place or incitement to rebel and change the world.Information ScienceD.Litt. et Phil

Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery

Despite the substantial impact of rivers on the global marine litter problem, riverine litter has been accorded inadequate consideration. Therefore, our objective was to detect riverine litter by utilizing middle-scale multispectral satellite images and machine learning (ML), with the Tisza River (Hungary) as a study area. The Very High Resolution (VHR) images obtained from the Google Earth database were employed to recognize some riverine litter spots (a blend of anthropogenic and natural substances). These litter spots served as the basis for training and validating five supervised machine-learning algorithms based on Sentinel-2 images [Artificial Neural Network (ANN), Support Vector Classifier (SVC), Random Forest (RF), Naïve Bays (NB) and Decision Tree (DT)]. To evaluate the generalization capability of the developed models, they were tested on larger unseen data under varying hydrological conditions and with different litter sizes. Besides the best-performing model was used to investigate the spatio-temporal variations of riverine litter in the Middel Tisza. According to the results, almost all the developed models showed favorable metrics based on the validation dataset (e.g., F1-score; SVC: 0.94, ANN: 0.93, RF: 0.91, DT: 0.90, and NB: 0.83); however, during the testing process, they showed medium (e.g., F1-score; RF:0.69, SVC: 0.62; ANN: 0.62) to poor performance (e.g., F1-score; NB: 0.48; DT: 0.45). The capability of all models to detect litter was bounded to the pixel size of the Sentinel-2 images. Based on the spatio-temporal investigation, hydraulic structures (e.g., Kisköre Dam) are the greatest litter accumulation spots. Although the highest transport rate of litter occurs during floods, the largest litter spot area upstream of the Kisköre Dam was observed at low stages in summer. This study represents a preliminary step in the automatic detection of riverine litter; therefore, additional research incorporating a larger dataset with more representative small litter spots, as well as finer spatial resolution images is necessary

Optimized resource allocation techniques for critical machine-type communications in mixed LTE networks

To implement the revolutionary Internet of Things (IoT) paradigm, the evolution of the communication networks to incorporate machine-type communications (MTC), in addition to conventional human-type communications (HTC) has become inevitable. Critical MTC, in contrast to massive MTC, represents that type of communications that requires high network availability, ultra-high reliability, very low latency, and high security, to enable what is known as mission-critical IoT. Due to the fact that cellular networks are considered one of the most promising wireless technologies to serve critical MTC, the International Telecommunication Union (ITU) targets critical MTC as a major use case, along with the enhanced mobile broadband (eMBB) and massive MTC, in the design of the upcoming generation of cellular networks. Therefore, the Third Generation Partnership Project (3GPP) is evolving the current Long-Term Evolution (LTE) standard to efficiently serve critical MTC to fulfill the fifth-generation (5G) requirements using the evolved LTE (eLTE) in addition to the new radio (NR). In this regard, 3GPP has introduced several enhancements in the latest releases to support critical MTC in LTE, which is designed mainly for HTC. However, guaranteeing stringent quality-of-service (QoS) for critical MTC while not sacrificing that of conventional HTC is a challenging task from the radio resource management perspective. In this dissertation, we optimize the resource allocation and scheduling process for critical MTC in mixed LTE networks in different operational and implementation cases. We target maximizing the overall system utility while providing accurate guarantees for the QoS requirements of critical MTC, through a cross-layer design, and that of HTC as well. For this purpose, we utilize advanced techniques from the queueing theory and mathematical optimization. In addition, we adopt heuristic approaches and matching-based techniques to design computationally-efficient resource allocation schemes to be used in practice. In this regard, we analyze the proposed methods from a practical perspective. Furthermore, we run extensive simulations to evaluate the performance of the proposed techniques, validate the theoretical analysis, and compare the performance with other schemes. The simulation results reveal a close-to-optimal performance for the proposed algorithms while outperforming other techniques from the literature