A Comprehensive Review of Deep Learning-based Single Image Super-resolution

Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods. This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super-resolution. The survey classifies the image SR methods into four categories, i.e., classical methods, supervised learning-based methods, unsupervised learning-based methods, and domain-specific SR methods. We also introduce the problem of SR to provide intuition about image quality metrics, available reference datasets, and SR challenges. Deep learning-based approaches of SR are evaluated using a reference dataset. Some of the reviewed state-of-the-art image SR methods include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN), multiscale residual network (MSRN), meta residual dense network (Meta-RDN), recurrent back-projection network (RBPN), second-order attention network (SAN), SR feedback network (SRFBN) and the wavelet-based residual attention network (WRAN). Finally, this survey is concluded with future directions and trends in SR and open problems in SR to be addressed by the researchers.Comment: 56 Pages, 11 Figures, 5 Table

Learning Task Skills and Goals Simultaneously from Physical Interaction

In real-world human-robot systems, it is essential for a robot to comprehend human objectives and respond accordingly while performing an extended series of motor actions. Although human objective alignment has recently emerged as a promising paradigm in the realm of physical human-robot interaction, its application is typically confined to generating simple motions due to inherent theoretical limitations. In this work, our goal is to develop a general formulation to learn manipulation functional modules and long-term task goals simultaneously from physical human-robot interaction. We show the feasibility of our framework in enabling robots to align their behaviors with the long-term task objectives inferred from human interactions.Comment: 2 pages, 1 figure. Accepted by CASE 2023 Special Session on The Next-Generation Resilient Cyber-Physical Manufacturing Network

Maintaining Rice Production while Mitigating Methane and Nitrous Oxide Emissions from Paddy Fields in China: Evaluating Tradeoffs by Using Coupled Agricultural Systems Models

China is the largest rice producing and consuming country in the world, accounting for more than 25% of global production and consumption. Rice cultivation is also one of the main sources of anthropogenic methane (CH4) and nitrous oxide (N2O) emissions. The challenge of maintaining food security while reducing greenhouse gas emissions is an important tradeoff issue for both scientists and policy makers. A systematical evaluation of tradeoffs requires attention across spatial scales and over time in order to characterize the complex interactions across agricultural systems components. We couple three well-known models that capture different key agricultural processes in order to improve the tradeoff analysis. These models are the DNDC biogeochemical model of soil denitrification-decomposition processes, the DSSAT crop growth and development model for decision support and agro-technology analysis, and the regional AEZ crop productivity assessment tool based on agro-ecological analysis. The calibration of eco-physiological parameters and model evaluation used the phenology and management records of 1981-2010 at nine agro-meteorological stations spanning the major rice producing regions of China. The eco-physiological parameters were calibrated with the GLUE optimization algorithms of DSSAT and then converted to the counterparts of DNDC. The upscaling of DNDC was carried out within each cropping zone as classified by AEZ. The emissions of CH4 and N2O associated with rice production under different management scenarios were simulated with the DNDC at each site and also each 1010 km grid-cell across each cropping zone. Our results indicate that it is feasible to maintain rice yields while reducing CH4 and N2O emissions through careful management changes. Our simulations indicated that a reduction of fertilizer applications by 5-35% and the introduction of midseason drainage across the nine study sites resulted in reduced CH4 emission by 17-40% and N2O emission by 12-60%, without negative consequences on rice yield

Classifying MCI Subtypes in Community-Dwelling Elderly Using Cross-Sectional and Longitudinal MRI-Based Biomarkers

Amnestic MCI (aMCI) and non-amnestic MCI (naMCI) are considered to differ in etiology and outcome. Accurately classifying MCI into meaningful subtypes would enable early intervention with targeted treatment. In this study, we employed structural magnetic resonance imaging (MRI) for MCI subtype classification. This was carried out in a sample of 184 community-dwelling individuals (aged 73–85 years). Cortical surface based measurements were computed from longitudinal and cross-sectional scans. By introducing a feature selection algorithm, we identified a set of discriminative features, and further investigated the temporal patterns of these features. A voting classifier was trained and evaluated via 10 iterations of cross-validation. The best classification accuracies achieved were: 77% (naMCI vs. aMCI), 81% (aMCI vs. cognitively normal (CN)) and 70% (naMCI vs. CN). The best results for differentiating aMCI from naMCI were achieved with baseline features. Hippocampus, amygdala and frontal pole were found to be most discriminative for classifying MCI subtypes. Additionally, we observed the dynamics of classification of several MRI biomarkers. Learning the dynamics of atrophy may aid in the development of better biomarkers, as it may track the progression of cognitive impairment

Ticagrelor vs Clopidogrel in CYP2C19 loss-of-function carriers with Stroke or TIA

BACKGROUNDComparisons between ticagrelor- aspirin and clopidogrel-aspirin in CYP2C19 loss-of-function carriers have not been well studied for secondary stroke prevention.METHODSWe conducted a randomized, double-blind, placebo-controlled trial of 6,412 patients with a minor ischemic stroke or TIA who carried CYP2C19 LOF alleles determined by point-of-care testing. Patients were randomly assigned within 24 hours after symptom onset, in a 1:1 ratio to receive ticagrelor (180 mg loading dose on day 1 followed by 90 mg twice daily for days 2 through 90) or clopidogrel (300 mg loading dose on day 1 followed by 75 mg per day for days 2 through 90), plus aspirin (75-300 mg loading dose followed by 75 mg daily for 21 days). The primary efficacy outcome was stroke and the primary safety outcome was severe or moderate bleeding, both within 90 days. RESULTSStroke occurred within 90 days in 191 (6.0%) versus 243 (7.6%), respectively (hazard ratio, 0.77; 95% confidence interval, 0.64 to 0.94; P=0.008). Moderate or severe bleeding occurred in 9 patients (0.3%) in the ticagrelor-aspirin group and in 11 patients (0.3%) in the clopidogrel-aspirin group; any bleeding event occurred in 170 patients (5.3%) vs 80 (2.5%), respectively. CONCLUSIONSAmong Chinese patients with minor ischemic stroke or TIA within 24 hours after symptoms onset who were carriers of CYP2C19 loss-of-function alleles, ticagrelor- aspirin was modestly better than clopidogrel-aspirin for reducing the risk of stroke but was associated with more total bleeding events at 90 days. (CHANCE-2 ClinicalTrials.gov number, NCT04078737.

Deep learning's fitness for purpose: A transformation problem frame's perspective

Abstract Combined sewer overflows represent significant risks to human health as untreated water is discharged to the environment. Municipalities, such as the Metropolitan Sewer District of Greater Cincinnati (MSDGC), recently began collecting large amounts of water‐related data and considering the adoption of deep learning (DL) solutions like recurrent neural network (RNN) for predicting overflow events. Clearly, assessing the DL's fitness for the purpose requires a systematic understanding of the problem context. In this study, we propose a requirements engineering framework that uses the problem frames to identify and structure the stakeholder concerns, analyses the physical situations in which the high‐quality data assumptions may not hold, and derives the software testing criteria in the form of metamorphic relations that incorporate both input transformations and output comparisons. Applying our framework to MSDGC's overflow prediction problem enables a principled way to evaluate different RNN solutions in meeting the requirements

The effects of Cu and Mn on the microstructure, mechanical, corrosion properties and biocompatibility of Zn–4Ag alloy

Zinc (Zn) alloys have been paid increasing attention in the field of biodegradable implantable materials due to their moderate degradation rate compared to magnesium (Mg) and iron (Fe) alloys. In this study, Zn–4Ag, Zn–4Ag–Cu and Zn–4Ag–Mn were prepared to investigate the effects of Cu and Mn elements on the microstructure and properties of Zn–4Ag alloys, and the addition of Cu and Mn improved the mechanical properties and degradation rate of Zn–4Ag alloys. The tensile strength of Zn–Ag after rolling was increased from 141.9 MPa to 168.4 MPa and 206.3 MPa after the addition of Cu and Mn. The degradation rate of Zn–4Ag increased from 0.17 mm/year to 0.22 mm/year and 0.39 mm/year in the first 5 days after the addition of Cu and Mn. The cytotoxicity testing showed good biocompatibility for human umbilical vein endothelial cells (HUVEC) in as-rolled Zn–4Ag–Mn diluted 4 times, and its cytotoxicity showed grade 0 or 1 toxicity with the cell survival rate of 83.7%. The antibacterial experiment showed the highest antibacterial rate for methicillin-resistant Staphylococcus aureus (MRSA) in as-rolled Zn–4Ag–Cu with the antibacterial rate of 90.3%

Network Modeling and Dynamic Mechanisms of Multi-Hazards—A Case Study of Typhoon Mangkhut

Coastal areas are home to billions of people and assets that are prone to natural disasters and climate change. In this study, we established a disaster network to assess the multi-hazards (gale and heavy rain) of typhoon disasters, specifically Typhoon Mangkhut of 2018 in coastal China, by applying the methodology of a bipartite network in both time dimension and spatial dimension. In this network, the edge set and adjacent matrix are based on the connection between an hour and a city with a multi-hazards impact that includes gales and heavy rain. We analyze the characteristics and structure of this disaster network and assess the multi-hazards that arose from Typhoon Mangkhut in different areas. The result shows that there are 14 cities in the core area and 21 cities in the periphery area, based on core–periphery classification in the disaster network. Although more damage area belongs to the periphery area, the percentage of the population affected by the typhoon and direct economic loss in GDP in the core area was 69.68% and 0.22% respectively, which is much higher than in the periphery area (55.58% and 0.06%, respectively) The core area suffered more from multi-hazards and had more disaster loss. This study shows that it is feasible to assess multiple hazards with a disaster network based on the bipartite network

Hierarchical and Droop Method for DC Micro-grid Bus Voltage under Isolated operation

Nowadays, distributed generation technology is of great help to the efficient utilization of new energy. If the distributed power supply is connected to the DC micro-grid, it will be more secure and reliable. Therefore, it is necessary to control the voltage stability of the DC bus and ensure the balance of the source charge power of system to ensure the power supply quality and safety of the DC micro-grid. There are two operation modes of DC micro-grid: connected operation and isolated operation, and isolated operation control is the foundation and key of system-level control of DC micro-grid. To solve the problem of bus voltage fluctuation in isolated dc micro-grid, this study proposes a voltage hierarchical-droop control strategy for DC micro-grid, which can effectively improve the stability of the DC bus voltage. Last, this study builds the simulation model of DC micro-grid in the MATLAB/Simulink platform to verify the validity and feasibility of the proposed control strategy