Simple Baseline for Weather Forecasting Using Spatiotemporal Context Aggregation Network

Traditional weather forecasting relies on domain expertise and computationally intensive numerical simulation systems. Recently, with the development of a data-driven approach, weather forecasting based on deep learning has been receiving attention. Deep learning-based weather forecasting has made stunning progress, from various backbone studies using CNN, RNN, and Transformer to training strategies using weather observations datasets with auxiliary inputs. All of this progress has contributed to the field of weather forecasting; however, many elements and complex structures of deep learning models prevent us from reaching physical interpretations. This paper proposes a SImple baseline with a spatiotemporal context Aggregation Network (SIANet) that achieved state-of-the-art in 4 parts of 5 benchmarks of W4C22. This simple but efficient structure uses only satellite images and CNNs in an end-to-end fashion without using a multi-model ensemble or fine-tuning. This simplicity of SIANet can be used as a solid baseline that can be easily applied in weather forecasting using deep learning.Comment: 1st place solution for stage1 and Core Transfer in the Weather4Cast competition on NeurIPS 2

Domain Generalization Strategy to Train Classifiers Robust to Spatial-Temporal Shift

Deep learning-based weather prediction models have advanced significantly in recent years. However, data-driven models based on deep learning are difficult to apply to real-world applications because they are vulnerable to spatial-temporal shifts. A weather prediction task is especially susceptible to spatial-temporal shifts when the model is overfitted to locality and seasonality. In this paper, we propose a training strategy to make the weather prediction model robust to spatial-temporal shifts. We first analyze the effect of hyperparameters and augmentations of the existing training strategy on the spatial-temporal shift robustness of the model. Next, we propose an optimal combination of hyperparameters and augmentation based on the analysis results and a test-time augmentation. We performed all experiments on the W4C22 Transfer dataset and achieved the 1st performance.Comment: Core Transfer Track 1st place solution in Weather4Cast competition at NeuIPS2

Metodologias alternativas no ensino de física

Screening a compound library of quinolinone derivatives identified compound 11a as a new P2X7 receptor antagonist. To optimize its activity, we assessed structure-activity relationships (SAR) at three different positions, R_1, R_2 and R_3, of the quinolinone scaffold. SAR analysis suggested that a carboxylic acid ethyl ester group at the R_1 position, an adamantyl carboxamide group at R_2 and a 4-methoxy substitution at the R_3 position are the best substituents for the antagonism of P2X7R activity. However, because most of the quinolinone derivatives showed low inhibitory effects in an IL-1β ELISA assay, the core structure was further modified to a quinoline skeleton with chloride or substituted phenyl groups. The optimized antagonists with the quinoline scaffold included 2-chloro-5-adamantyl-quinoline derivative (16c) and 2-(4-hydroxymethylphenyl)-5-adamantyl-quinoline derivative (17k), with IC_(50) values of 4 and 3 nM, respectively. In contrast to the quinolinone derivatives, the antagonistic effects of the quinoline compounds (16c and 17k) were paralleled by their ability to inhibit the release of the pro-inflammatory cytokine, IL-1β, from LPS/IFN-γ/BzATP-stimulated THP-1 cells (IC_(50) of 7 and 12 nM, respectively). In addition, potent P2X7R antagonists significantly inhibited the sphere size of TS15-88 glioblastoma cells

Health to Eat: A Smart Plate with Food Recognition, Classification, and Weight Measurement for Type-2 Diabetic Mellitus Patients’ Nutrition Control

The management of type 2 diabetes mellitus (T2DM) is generally not only focused on pharmacological therapy. Medical nutrition therapy is often forgotten by patients for several reasons, such as difficulty determining the right nutritional pattern for themselves, regulating their daily nutritional patterns, or even not heeding nutritional diet recommendations given by doctors. Management of nutritional therapy is one of the important efforts that can be made by diabetic patients to prevent an increase in the complexity of the disease. Setting a diet with proper nutrition will help patients manage a healthy diet. The development of Smart Plate Health to Eat is a technological innovation that helps patients and users know the type of food, weight, and nutrients contained in certain foods. This study involved 50 types of food with a total of 30,800 foods using the YOLOv5s algorithm, where the identification, measurement of weight, and nutrition of food were investigated using a Chenbo load cell weight sensor (1 kg), an HX711 weight weighing A/D module pressure sensor, and an IMX219-160 camera module (waveshare). The results of this study showed good identification accuracy in the analysis of four types of food: rice (58%), braised quail eggs in soy sauce (60%), spicy beef soup (62%), and dried radish (31%), with accuracy for weight and nutrition (100%)

A case study of activity-based costing in allocating rebar fabrication costs to projects

How to improve cost allocation for reinforced steel bar (rebar) is an ongoing topic of debate among construction manufacturers and contractors. Traditionally, many fabrication shops have used a single overhead-cost pool accounting system. However, a new costing method, activity-based costing (ABC), may provide more advantages than the traditional system. In this case study, a single overhead-cost pool system is compared with the ABC method to demonstrate how ABC improves cost allocation and provides other benefits. The case study findings indicate that ABC provides such benefits as (1) accurate manufacturing costs; (2) cost information on processes; and (3) information on cost drivers. This paper also bridges the construction and cost accounting literature. Our study contributes to the construction management literature by offering a different cost allocation method to refine fabrication costs assigned to projects. The findings are expected to serve as a reference for industry professionals who recognize the shortcomings of a traditional single overhead-cost pool system and are in need of a more accurate costing system.Rebar fabrication, activity-based costing, overhead cost allocation,

Health to Eat: A Smart Plate with Food Recognition, Classification, and Weight Measurement for Type-2 Diabetic Mellitus Patients’ Nutrition Control

The management of type 2 diabetes mellitus (T2DM) is generally not only focused on pharmacological therapy. Medical nutrition therapy is often forgotten by patients for several reasons, such as difficulty determining the right nutritional pattern for themselves, regulating their daily nutritional patterns, or even not heeding nutritional diet recommendations given by doctors. Management of nutritional therapy is one of the important efforts that can be made by diabetic patients to prevent an increase in the complexity of the disease. Setting a diet with proper nutrition will help patients manage a healthy diet. The development of Smart Plate Health to Eat is a technological innovation that helps patients and users know the type of food, weight, and nutrients contained in certain foods. This study involved 50 types of food with a total of 30,800 foods using the YOLOv5s algorithm, where the identification, measurement of weight, and nutrition of food were investigated using a Chenbo load cell weight sensor (1 kg), an HX711 weight weighing A/D module pressure sensor, and an IMX219-160 camera module (waveshare). The results of this study showed good identification accuracy in the analysis of four types of food: rice (58%), braised quail eggs in soy sauce (60%), spicy beef soup (62%), and dried radish (31%), with accuracy for weight and nutrition (100%)

Low-Frequency Noise Characteristics in HfO₂-Based Metal-Ferroelectric-Metal Capacitors

The transport mechanism of HfO2-based metal-ferroelectric-metal (MFM) capacitors was investigated using low-frequency noise (LFN) measurements for the first time. The current–voltage measurement results revealed that the leakage behavior of the fabricated MFM capacitor was caused by the trap-related Poole–Frenkel transport mechanism, which was confirmed by the LFN measurements. The current noise power spectral densities (SI) obtained from the LFN measurements followed 1/f noise shapes and exhibited a constant electric field (E) × SI/I2 noise behavior. No polarization dependency was observed in the transport characteristics of the MFM capacitor owing to its structural symmetry

Fast Reconstruction of 3D Density Distribution around the Sun Based on the MAS by Deep Learning

This study is the first attempt to generate a three-dimensional (3D) coronal electron density distribution based on the pix2pixHD model, whose computing time is much shorter than that of the magnetohydrodynamic (MHD) simulation. For this, we consider photospheric solar magnetic fields as input, and electron density distribution simulated with the MHD Algorithm outside a Sphere (MAS) at a given solar radius is taken as output. We consider 155 pairs of Carrington rotations as inputs and outputs from 2010 June to 2022 April for training and testing. We train 152 deep-learning models for 152 solar radii, which are taken up to 30 solar radii. The artificial intelligence (AI) generated 3D electron densities from this study are quite consistent with the simulated ones from lower radii to higher radii, with an average correlation coefficient 0.97. The computing time of testing data sets up to 30 solar radii of 152 deep-learning models is about 45.2 s using the NVIDIA TITAN XP graphics-processing unit, which is much less than the typical simulation time of MAS. We find that the synthetic coronagraphic images estimated from the deep-learning models are similar to the Solar Heliospheric Observatory (SOHO)/Large Angle and Spectroscopic Coronagraph C3 coronagraph data, especially during the solar minimum period. The AI-generated coronal density distribution from this study can be used for space weather models on a near-real-time basis

Mitochondria-Targeting Peptoids

Mitochondria-specific delivery methods offer a valuable tool for studying mitochondria-related diseases and provide breakthroughs in therapeutic development. Although several small-molecule and peptide-based transporters have been developed, peptoids, proteolysis-resistant peptidomimetics, are a promising alternative to current approaches. We designed a series of amphipathic peptoids and evaluated their cellular uptake and mitochondrial localization. Two peptoids with cyclohexyl residues demonstrated highly efficient cell penetration and mitochondrial localization without significant adverse effects on the cells and mitochondria. These mitochondria-targeting peptoids could facilitate the selective and robust targeted delivery of bioactive compounds, such as drugs, antioxidants, and photosensitizers, with minimal off-target effects

Implementation of a Smart Antenna Base Station for Mobile WiMAX Based on OFDMA

We present an implementation of a mobile-WiMAX (m-WiMAX) base station (BS) that supports smart antenna (SA) functionality. To implement the m-WiMAX SA BS, we must address a number of key issues in baseband signal processing related to symbol-timing acquisition, the beamforming scheme, and accurate calibration. We propose appropriate solutions and implement an m-WiMAX SA BS accordingly. Experimental tests were performed to verify the validity of the solutions. Results showed a 3.5-time (5.5 dB) link-budget enhancement on the uplink compared to a single antenna system. In addition, the experimental results were consistent with the results of the computer simulation