Effect of corrosion on the bond behavior of steel-reinforced, alkali-activated slag concrete

Alkali-activated slag concrete (ASC) is regarded as one of the most promising sustainable construction materials for replacing ordinary Portland cement concrete (OPC) due to its comparable strength and outstanding durability in challenging environments. In this study, the corrosion of steel bars embedded in ASC and OPC was studied by means of an electrically accelerated corrosion test of steel bars in concrete. Meanwhile, the bond performance of the corroded steel bars embedded in ASC was tested and compared with corresponding OPC groups. The results showed that ASC and OPC behaved differently in terms of bond deterioration. The high chemical resistance of ASC decreased the corrosion of steel bars and, thus, increased the residue bond strength and the bond stiffness. © 2023 by the authors

Best Practices in Disaster Public Communications: Evacuation Alerting and Social Media

This research project examines the current state of the practice for disaster public communication, the distrust of government, the training available to public information officers, and the literature available to guide the design of effective public outreach messaging, especially for rapid on-set events. Growing distrust in government had led to lack of public confidence in public agency messaging during emergencies, yet public agency public information officers are using multiple pathways, including both traditional and social media resources, to try to reach impacted communities effectively. The introduction explains the development of wildfire events in the West and their context. A literature review displays the sociological and political research that guides the development of public outreach, warning and evacuation. The findings display the SCU Complex Fire and CZU Complex Fire of 2020 as case studies of outreach efforts during rapid onset wildfire events and explains techniques of data scraping that could enhance public messaging. The analysis categorizes a variety of best practices in disaster communications. The project concludes with a white paper outlining a pathway toward creating a cell phone app that would provide event, time and location specific information about a disaster event, using official sources and social media

The NVIDIA AI City Challenge

Web image analysis has witnessed an AI renaissance. The ILSVRC benchmark has been instrumental in providing a corpus and standardized evaluation. The NVIDIA AI City Challenge is envisioned to provide similar impetus to the analysis of image and video data that helps make cities smarter and safer. In its first year, this Challenge has focused on traffic video data. While millions of traffic video cameras around the world capture data, albeit low-quality, very little automated analysis and value creation results. Lack of labeled data, and trained models that can be deployed at the edge of the city fabric, ensure that most traffic video data goes through little or no automated analysis. Real-time and batch analysis of this data can provide vital breakthroughs in real-time traffic management as well as pedestrian safety. The NVIDIA AI City Challenge brought together 29 teams from universities in 4 continents to collaboratively annotate a 125 hour data set and then compete on detection, localization and classification tasks as well as traffic and safety application analytics tasks. The result is the largest high quality annotated data set, a set of models trained using NVIDIA AI City Edge to Cloud platform and ready to be deployed at the edge solving traffic and safety problems for cities worldwide

Experimental and Statistical Study on Mechanical Characteristics of Geopolymer Concrete

This paper studies the statistical correlation in mechanical characteristics of class F fly ash based geopolymer concrete (CFGPC). Experimentally measured values of the compressive strength, elastic modulus and indirect tensile strength of CFGPC specimens made from class F fly ash (CFA) were presented and analyzed. The results were compared with those of corresponding ordinary Portland cement concrete (OPCC) using statistical hypothesis tests. Results illustrated that when possessing similar compressive and tensile strength, the elastic modulus for CFGPC is significantly lower than that of OPCC. The corresponding expressions recommended by standards for the case of OPCC is proved to be inaccurate when applied in the case of CFGPC. Statistical regression was used to identify tendencies and correlations within the mechanical characteristics of CFGPC, as well as the empirical equations for predicting tensile strength and elastic modulus of CFGPC from its compressive strength values. In conclusion, CFGPC and OPCC has significant differences in terms of the correlations between mechanical properties. The empirical equations obtained in this study could provide relatively accurate predictions on the mechanical behavior of CFGPC

Asparagine-rich protein (NRP) mediates stress response by regulating biosynthesis of plant secondary metabolites in Arabidopsis

The plant-specific stress response protein NRP (asparagine-rich protein) is characterized by an asparagine-rich domain at its N-terminus and a conserved development and cell death (DCD) domain at its C-terminus. Previous transcriptional studies and phenotypic analyses have demonstrated the involvement of NRP in response to severe stress conditions, such as high salt and ER Endoplasmic reticulum-stress. We have recently identified distinct roles for NRP in biotic- and abiotic-stress signaling pathways, in which NRP interacts with different signaling proteins to change their subcellular localizations and stability. Here, to further explore the function of NRP, a transcriptome analysis was carried out on nrp1nrp2 knock-out lines at different life stages or under different growing conditions. The most significant changes in the transcriptome at both stages and conditions turned out to be the induction of the synthesis of secondary metabolites (SMs). Such an observation implicates that NRP is a general stress-responsive protein involved in various challenges faced by plants during their life cycle, which might involve a broad alteration in the distribution of SMs

Effects of Integrated Rice-Frog Farming on Paddy Field Greenhouse Gas Emissions

Integrated rice-frog farming (IRFF), as a mode of ecological farming, is fundamental in realizing sustainable development in agriculture. Yet its production of greenhouse gas (GHG) emissions remains unclear. Here, a randomized plot field experiment was performed to study the GHG emissions for various farming systems during the rice growing season. The farming systems included: conventional farming (CF), green integrated rice-frog farming (GIRF), and organic integrated rice-frog farming (OIRF). Results indicate that the cumulative methane (CH4) emissions from the whole growth period were divergent for the three farming systems, with OIRF having the highest value and CF having the lowest. For nitrous oxide (N2O) emissions, the order is reversed. IRFF significantly increased the dissolved oxygen (DO), soil redox potential (Eh), total organic carbon (TOC) content, and soil C:N ratio, which is closely related to GHG emissions in rice fields. Additionally, the average emissions of carbon dioxide (CO2) from soils during rice growing seasons ranged from 2312.27 to 2589.62 kg ha−1 and showed no significant difference in the three treatments. Rice yield in the GIRF and OIRF were lower (2.0% and 16.7%) than the control. The CH4 emissions contributed to 83.0–96.8% of global warming potential (GWP). Compared to CF, the treatment of GIRF and OIRF increased the GWP by 41.3% and 98.2% during the whole growing period of rice, respectively. IRFF significantly increased greenhouse gas intensity (GHGI, 0.79 kg CO2-eq ha−1 grain yield), by 91.1% over the control. Compared to the OIRF, GIRF decreased the GHGI by approximately 39.4% (0.59 kg CO2-eq ha−1 grain yield), which was 44.2% higher than that of the control. The results of structural equation model showed that the contribution of fertilization to CH4 emissions in paddy fields was much greater than that of frog activity. Moreover, frog activity could decrease GWP by reducing CH4 emissions from rice fields. And while GIRF showed a slight increase in GHG emissions, it could still be considered as a good strategy for providing an environmentally-friendly option in maintaining crop yield in paddy fields

13 Conducto en T biomimético para reducir la resistencia local de un sistema de ventilación y aire acondicionado

El ahorro de energía en los edificios es una de las medidas importantes que se utilizan para reducir la escasez de energía y fomentar el desarrollo sostenible1- 3. El consumo de energía de los sistemas de conductos de ventilación y aire acondicionado (como, por ejemplo, los tipos de sistemas de transporte y distribución de aire) ha recibido considerable atención. El consumo de energía del ventilador causado por la resistencia de los sistemas de ventilación y aire acondicionado representa aproximadamente del 20% al 40% del consumo de energía de los edificios públicos; para algunos edificios, los niveles son incluso superiores a los de los sistemas de refrigeración acondicionados4, 5. Por lo tanto, determinar cómo optimizar el rendimiento de los sistemas de conductos de ventilación y aire acondicionado, reduciendo así la resistencia y disminuyendo el consumo de energía del ventilador, se ha convertido en un tema clave

Nutrient Characterization in Soil Aggregate Fractions with Different Fertilizer Treatments in Greenhouse Vegetable Cultivation

Fertilization affects the formation and stability of soil aggregate, as well as the nutrient status of soil aggregate. However, the potential effect of compost on soil aggregate and its nutrient characteristics is still unclear. In view of this, we conducted a greenhouse vegetable cultivation experiment to evaluate soil water-stable aggregate (WSA) and its stability indices and aggregate nutrient stoichiometry characteristics at 0 to 20 cm soil depth with four treatments: (1) no fertilizer (CK), (2) chemical fertilizer (CF), (3) organic fertilizer (OF), and (4) chemical fertilizer plus organic fertilizer (CO). The results showed that the proportion of the 2 to 0.25 mm fraction was the greatest, followed by 0.25 to 0.053 mm, which accounted for 41.83 to 49.53% and 28.60 to 31.88% by weight, respectively. The mean weight diameter (MWD) value and the proportion of the >0.25 mm fraction in the CF, OF, and CO treatments were significantly higher than in the CK treatment. Within the fertilization treatments, the MWD and the proportion of the >0.25 mm fraction in the CO were significantly higher than those in CF and OF. Among all the aggregates, the soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) contents were the highest in the fraction of 0.25 to 0.053 mm. The CF, OF, and CO treatments significantly increased the SOC, TN, and TP contents compared with the CK treatment. The SOC content of fractions >2 mm and 0.25 to 0.053 mm in the CO treatment was significantly higher than that of the CF and OF treatments, and the TN and TP contents in all the aggregates (except < 0.053 mm) were the highest in the CO treatment. The SOC, TN, and TP contents in the 2 to 0.25 mm and 0.25 to 0.053 mm components contributed greatly to the soil SOC, TN, and TP reserves. There was no noticeable difference in the nutrient stoichiometry of the soil aggregate between the different treatments. Redundancy analysis (RDA) revealed that the soil physicochemical factors, including SOC, TN, TP, and pH, significantly explained the stability of the soil aggregate. To summarize, chemical fertilizer combined with organic fertilizer positively affected the stability and nutrient accumulation of soil aggregates in greenhouse dryland

Optimising digital signal processor‐based defect detection in smart manufacturing with lightweight convolutional neural networks

Abstract Industrial defect detection is an important part of intelligent manufacturing, and Internet of things (IoT)‐based defect detection is receiving more and more attention. Although deep learning (DL) can help defect detection reduce the cost and improve the accuracy of traditional manual quality inspection, DL requires huge computational resources and is difficult to be simply deployed on IoT devices with limited computational power and memory resources. Digital signal processor (DSP) is an important IoT device with small size, high performance and low energy consumption, which has been widely used in intelligent manufacturing. In order to perform accurate defect detection on DSP, the authors proposed various optimisation strategies and then used a parallel scheme to scale the model to execute on multiple cores. The authors’ method evaluated it on Northeastern University Surface Defect Dataset, Magnetic Tile Defect Dataset, Rail Surface Defect Dataset and Silk Cylinder Defect Dataset, and the experimental results showed that the authors’ method obtains faster speeds without accuracy loss compared to running the same Convolutional Neural Networks model on a mainstream desktop CPU. This means that the authors’ method can realise efficient and accurate defect detection on IoT devices with limited computational power and memory resources, which opens up new possibilities for future development in the field of smart manufacturing