Image Super-Resolution using Efficient Striped Window Transformer

Transformers have achieved remarkable results in single-image super-resolution (SR). However, the challenge of balancing model performance and complexity has hindered their application in lightweight SR (LSR). To tackle this challenge, we propose an efficient striped window transformer (ESWT). We revisit the normalization layer in the transformer and design a concise and efficient transformer structure to build the ESWT. Furthermore, we introduce a striped window mechanism to model long-term dependencies more efficiently. To fully exploit the potential of the ESWT, we propose a novel flexible window training strategy that can improve the performance of the ESWT without additional cost. Extensive experiments show that ESWT outperforms state-of-the-art LSR transformers, and achieves a better trade-off between model performance and complexity. The ESWT requires fewer parameters, incurs faster inference, smaller FLOPs, and less memory consumption, making it a promising solution for LSR.Comment: SOTA lightweight super-resolution transformer. 8 pages, 9 figures and 6 tables. The Code is available at https://github.com/Fried-Rice-Lab/FriedRiceLa

Long lead-time radar rainfall nowcasting method incorporating atmospheric conditions using long short-term memory networks

High-resolution radar rainfall data have great potential for rainfall predictions up to 6 h ahead (nowcasting); however, conventional extrapolation approaches based on in-built physical assumptions yield poor performance at longer lead times (3–6 h), which limits their operational utility. Moreover, atmospheric factors in radar estimate errors are often ignored. This study proposed a radar rainfall nowcasting method that attempts to achieve accurate nowcasting of 6 h using long short-term memory (LSTM) networks. Atmospheric conditions were considered to reduce radar estimate errors. To build radar nowcasting models based on LSTM networks (LSTM-RN), approximately 11 years of radar, gauge rainfall, and atmospheric data from the UK were obtained. Compared with the models built on optical flow (OF-RN) and random forest (RF-RN), LSTM-RN had the lowest root-mean-square errors (RMSE), highest correlation coefficients (COR), and mean bias errors closest to 0. Furthermore, LSTM-RN showed a growing advantage at longer lead times, with the RMSE decreasing by 17.99% and 7.17% compared with that of OF-RN and RF-RN, respectively. The results also revealed a strong relationship between LSTM-RN performance and weather conditions. This study provides an effective solution for nowcasting radar rainfall at long lead times, which enhances the forecast value and supports practical utility

Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models

This paper presents a comprehensive survey of ChatGPT and GPT-4, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains. Indeed, key innovations such as large-scale pre-training that captures knowledge across the entire world wide web, instruction fine-tuning and Reinforcement Learning from Human Feedback (RLHF) have played significant roles in enhancing LLMs' adaptability and performance. We performed an in-depth analysis of 194 relevant papers on arXiv, encompassing trend analysis, word cloud representation, and distribution analysis across various application domains. The findings reveal a significant and increasing interest in ChatGPT/GPT-4 research, predominantly centered on direct natural language processing applications, while also demonstrating considerable potential in areas ranging from education and history to mathematics, medicine, and physics. This study endeavors to furnish insights into ChatGPT's capabilities, potential implications, ethical concerns, and offer direction for future advancements in this field.Comment: 35 pages, 3 figure

Catalytic oxidation of ethyl acetate over silver catalysts supported on CeO2 with different morphologies

In this study, the Ag/CeO2 catalysts with three different morphologies of ceria nanocubes (CeO2-c), nanorods (CeO2-r) and nano-octahedra (CeO2-o) as supports were prepared by wet impregnation. Various characterizations such as XRD, BET, TEM/HRTEM, HAADF-STEM, XPS and H-2-TPR were conducted, and a four step catalytic transformation was accordingly proposed. It was found that Ag/CeO2-c showed much more metallic Ag, active oxygen species and higher reducibility than those of Ag/CeO2-r and Ag/CeO2-o mainly due to the morphology effect. Ag/CeO2 and CeO2 catalysts were evaluated in the oxidation of ethyl acetate, and their catalytic activities were in the sequence of Ag/CeO2-c > Ag/CeO2-r > CeO2-r > CeO2-c > Ag/CeO2-o > CeO2-o. Besides, in-situ FTIR results revealed that the organic species on the catalyst surface were mainly acetates and adsorbed ethyl acetate for Ag/CeO2-c and CeO2-c, respectively, suggesting that addition of Ag promoted the complete oxidation of ethyl acetate

Toxic Effects of TiO₂ NPs on Zebrafish

Titanium dioxide nanoparticles (TiO2 NPs) have become a widely used nanomaterial due to the photocatalytic activity and absorption of ultraviolet light of specific wavelengths. This study investigated the toxic effects of rutile TiO2 NPs on zebrafish by examining its embryos and adults. In the embryo acute toxicity test, exposure to 100 mg/L TiO2 NPs didn’t affect the hatching rate of zebrafish embryos, and there was no sign of deformity. In the adult toxicity test, the effects of TiO2 NPs on oxidative damage in liver, intestine and gill tissue were studied. Enzyme linked immunosorbent assay (ELISA) and fluorescence-based quantitative real-time reverse transcription PCR (qRT-PCR) were used to detect the three antioxidant enzymes: superoxide dismutase (SOD), catalase (CAT) and glutathione S transferase (GSTs) in the above mentioned zebrafish organs at protein and gene levels. The results showed that long-term exposure to TiO2 NPs can cause oxidative damage to organisms; and compared with the control group, the activity of the three kinds of enzyme declined somewhat at the protein level. In addition, long-term exposure to TiO2 NPs could cause high expression of CAT, SOD and GSTs in three organs of adult zebrafish in order to counter the adverse reaction. The effects of long-term exposure to TiO2 NPs to adult zebrafish were more obvious in the liver and gill

Effect of CaO on NOx Reduction by Selective Non-Catalytic Reduction under Variable Gas Compositions in a Simulated Cement Precalciner Atmosphere

High-concentration CaO particles and gas compositions have a significant influence on NOx reduction by selective non-catalytic reduction (SNCR) in cement precalciners. The effect of gas composition on NOx reduction by SNCR with NH3 was studied in a cement precalciner atmosphere with and without CaO at 700–1100 °C. It was found that CaO significantly lowers NOx reduction efficiency between 750 °C and 1000 °C, which is attributed to the catalytic oxidation of NH3 to NO. Although increasing NH3 concentration was advantageous to NOx reduction, the existence of CaO led to the opposite result at 750–900 °C. Adding H2O can suppress the negative effect of CaO on NOx reduction. Decreasing O2 content from 10% to 1% shifts the temperature range in which CaO has a significant effect from 750–1000 °C to 800–1050 °C. CO has a variety of influences on the CaO effect under different experimental conditions. The influences of NH3, H2O, O2, and CO on the effect of CaO can be attributed to the impacts of the gas compositions on gas-phase NH3 conversion, gas-solid catalytic NH3 oxidation, or both processes. A proposed pathway for the effect of gas compositions on NOx reduction in CaO-containing SNCR process was developed that well predicted the CaO-containing SNCR process

Impact of Indoor Air Pollution in Pakistan—Causes and Management

This state-of-the-art review is designed to provide a factual analysis of indoor air pollution in Pakistan. Primarily, the main sources of indoor air pollution and related air pollutants were analyzed. Key sources of indoor air pollution include household energy sources (biomass, wood, coal, tobacco, and low temperatures) producing particulate matter (PM), dust particles, smoke, COx, noxious gases, bioaerosols, airborne microflora, and flame retardants. According to the literature, rural regions of Pakistan using biomass indoor fuels have a high indoor PM concentration in the range of 4000–9000 μg/m3. In rural/urban regions, indoor smoking also leads to high PM2.5 levels of ~1800 μg/m3, which can cause pulmonary infections. In hospitals, PM concentrations were detected up to 1000 μg/m3, causing repeated infections in patients. Indoor ingestion of dust containing polychlorinated biphenyl concentrations was observed at high levels (~8.79–34.39 ng/g) in cities; this can cause serious health effects such as cancer risks and a loss of working productivity. Moreover, indoor microflora and bacteria (~10,000–15,000 cfu m−3) in urban/rural regions cause respiratory/cancer risks. In this context, indoor air quality (IAQ) monitoring and management strategies have been somewhat developed; however, their implementation in Pakistan’s rural/urban indoor environments is still needed. Various challenges were identified for monitoring/regulating IAQ. There is a firm need for industry–academia–research cooperation and for the involvement of government/agencies to support indoor air pollution control/management and for intervention strategies