Social Media Discussion on Covid-19 Impact on Mental Health in the US, UK, and India

Discovered in December 2019, Coronavirus (Covid-19) is an infectious disease that has spread rapidly around the world. The World Health Organization (WHO) declared Covid-19 a pandemic in March 2020. The pandemic has increased the occurrence of mental health problems including depression, stress, and anxiety. This research used real-life Tweets collected related to Covid-19 from March 2020 until October 2021. The objective is to analyze Tweets from the US, UK, and India to discover what topics people are discussing about Covid-19\u27s impact on mental health. The theme for the US was related to government and politics, some dominant users in the group are news accounts and people who have occupations such as journalists, hosts, and presenters. The UK’s theme focused on friends and family relations, and it showed the caring for the public safety, resulting in doctors and medical workers as dominant users. India is focusing on mental health and education. However, some important users identified are the majority news related accounts and people related to politics

CSI-PPPNet: A One-Sided One-for-All Deep Learning Framework for Massive MIMO CSI Feedback

To reduce multiuser interference and maximize the spectrum efficiency in orthogonal frequency division duplexing massive multiple-input multiple-output (MIMO) systems, the downlink channel state information (CSI) estimated at the user equipment (UE) is required at the base station (BS). This paper presents a novel method for massive MIMO CSI feedback via a one-sided one-for-all deep learning framework. The CSI is compressed via linear projections at the UE, and is recovered at the BS using deep learning (DL) with plug-and-play priors (PPP). Instead of using handcrafted regularizers for the wireless channel responses, the proposed approach, namely CSI-PPPNet, exploits a DL based denoisor in place of the proximal operator of the prior in an alternating optimization scheme. In this way, a DL model trained once for denoising can be repurposed for CSI recovery tasks with arbitrary compression ratio. The one-sided one-for-all framework reduces model storage space, relieves the burden of joint model training and model delivery, and could be applied at UEs with limited device memories and computation power. Extensive experiments over the open indoor and urban macro scenarios show the effectiveness and advantages of the proposed method

Evaluation of Hybrid VMAT Advantages and Robustness Considering Setup Errors Using Surface Guided Dose Accumulation for Internal Lymph Mammary Nodes Irradiation of Postmastectomy Radiotherapy

ObjectivesSetup error is a key factor affecting postmastectomy radiotherapy (PMRT) and irradiation of the internal mammary lymph nodes is the most investigated aspect for PMRT patients. In this study, we evaluated the robustness, radiobiological, and dosimetric benefits of the hybrid volumetric modulated arc therapy (H-VMAT) planning technique based on the setup error in dose accumulation using a surface-guided system for radiation therapy.MethodsWe retrospectively selected 32 patients treated by a radiation oncologist and evaluated the clinical target volume (CTV), including internal lymph node irradiation (IMNIs), and considered the planning target volume (PTV) margin to be 5 mm. Three different planning techniques were evaluated: tangential-VMAT (T-VMAT), intensity-modulated radiation therapy (IMRT), and H-VMAT. The interfraction and intrafraction setup errors were analyzed in each field and the accumulated dose was evaluated as the patients underwent daily surface-guided monitoring. These parameters were included while evaluating CTV coverage, the dose required for the left anterior descending artery (LAD) and the left ventricle (LV), the normal tissue complication probability (NTCP) for the heart and lungs, and the second cancer complication probability (SCCP) for contralateral breast (CB).ResultsWhen the setup error was accounted for dose accumulation, T-VMAT (95.51%) and H-VMAT (95.48%) had a higher CTV coverage than IMRT (91.25%). In the NTCP for the heart, H-VMAT (0.04%) was higher than T-VMAT (0.01%) and lower than IMRT (0.2%). However, the SCCP (1.05%) of CB using H-VMAT was lower than that using T-VMAT (2%) as well as delivery efficiency. And T-VMAT (3.72) and IMRT (10.5).had higher plan complexity than H-VMAT (3.71).ConclusionsIn this study, based on the dose accumulation of setup error for patients with left-sided PMRT with IMNI, we found that the H-VMAT technique was superior for achieving an optimum balance between target coverage, OAR dose, complication probability, plan robustness, and complexity

A life prediction method based on MDFF and DITCN-ABiGRU mixed network model

A single network model exhibits limitations in the life prediction of rotating machinery for the various fault types and uncertain fault occurrence. Therefore, a network prediction model combining multi-domain feature fusion (MDFF) and distributed TCN-Attention-BiGRU (DITCN-ABiGRU) is proposed to enable a more accurate life prediction of rotating machinery. Firstly, the features of vibration signals collected from multiple sensors are extracted in the time, frequency, and time-frequency domains. Subsequently, dimensionality reduction optimization is conducted on these multi-domain features to eliminate useless information features. The temporal convolutional network (TCN) model is constructed to capture the critical information reflecting the fault characteristics of rotating machinery through the attention mechanism, and the dependencies of the whole training process are captured by the BiGRU network. Finally, precise prediction of the lifespan of rotating machinery is achieved by constructing a health indicator curve (HI). The proposed methods are verified through the life prediction of rolling bearings from the IEEE PHM Challenge 2012 dataset and ball screw pairs from a designed experiment. The experimental results show that the proposed MDFF and DITCN-ABiGRU model achieves a better score and lower error than the convolutional neural network (CNN) and GRU models

Challenges of Physical Layer Security in a Satellite-Terrestrial Network

Integrated satellite-terrestrial networks (ISTNs) are one of the new research directions for future 5G networks. Satellite coverage enables 5G communication throughout most of the world. However, the security concerns associated with the inherent broadcast nature of satellites are rarely considered. As an emerging security paradigm in 5G terrestrial communication, physical layer security (PLS) could have potential applications in an ISTN. This article describes the challenges of applying PLS to an ISTN, including correlated channels, co-channel interference, multiuserand multi-eavesdropper scenarios, and reliability concerns. The impacts of these challenges on the security performance of an ISTN and the corresponding solutions are analyzed for further comparison. Finally, we conclude the article by predicting future trends of PLS in an ISTN

Research on Sustainable Design of Regeneration for Traditional Settlement Based on Ecotect Software

Based on the concept of sustainable protection and regeneration, this thesis retrospected and analysed the history and culture, public space and courtyard space of Miaohou village in Hancheng to explore the historical prototype. And combined with Ecotect Analysis software to simulate microclimate environment, the public spaces was reasonable updated and predicted from both qualitative and quantitative aspects to explore the rationalized strategy of rural renaissance. It will provide reference for the development of related construction practice and theory in the future

Reconfigurable droplet networks.

Droplet networks stabilized by lipid interfacial bilayers or colloidal particles have been extensively investigated in recent years and are of great interest for compartmentalized reactions and biological functions. However, current design strategies are disadvantaged by complex preparations and limited droplet size. Here, by using the assembly and jamming of cucurbit[8]uril surfactants at the oil-water interface, we show a novel means of preparing droplet networks that are multi-responsive, reconfigurable, and internally connected over macroscopic distances. Openings between the droplets enable the exchange of matter, affording a platform for chemical reactions and material synthesis. Our work requires only a manual compression to construct complex patterns of droplet networks, underscoring the simplicity of this strategy and the range of potential applications

Deep Learning-Based Image Recognition of Agricultural Pests

Pests and diseases are an inevitable problem in agricultural production, causing substantial economic losses yearly. The application of convolutional neural networks to the intelligent recognition of crop pest images has become increasingly popular due to advances in deep learning methods and the rise of large-scale datasets. However, the diversity and complexity of pest samples, the size of sample images, and the number of examples all directly affect the performance of convolutional neural networks. Therefore, we designed a new target-detection framework based on Cascade RCNN (Regions with CNN features), aiming to solve the problems of large image size, many pest types, and small and unbalanced numbers of samples in pest sample datasets. Specifically, this study performed data enhancement on the original samples to solve the problem of a small and unbalanced number of examples in the dataset and developed a sliding window cropping method, which could increase the perceptual field to learn sample features more accurately and in more detail without changing the original image size. Secondly, combining the attention mechanism with the FPN (Feature Pyramid Networks) layer enabled the model to learn sample features that were more important for the current task from both channel and space aspects. Compared with the current popular target-detection frameworks, the average precision value of our model ([email protected]) was 84.16%, the value of ([email protected]:0.95) was 65.23%, the precision was 67.79%, and the F1 score was 82.34%. The experiments showed that our model solved the problem of convolutional neural networks being challenging to use because of the wide variety of pest types, the large size of sample images, and the difficulty of identifying tiny pests