Ocean crustal veins record dynamic interplay between plate-cooling-induced cracking and ocean chemistry

As ocean crust traverses away from spreading ridges, low-temperature hydrothermal minerals fill cracks to form veins, transforming the physical and chemical properties of ocean crust whilst also modifying the composition of seawater. Vein width and frequency observations compiled from the International Ocean Discovery Program (IODP) South Atlantic Transect (∼31°S) and previous scientific ocean drilling holes show that vein width distributions progressively broaden and observed strain increases with crustal age, whereas vein densities remain approximately constant. Elemental mapping and textural observations illuminate multiple precipitation and fracturing episodes that continue as the ocean crust ages. This challenges the existing notion that ocean crustal veins are passively filled; rather, they are dynamic features of ocean crust aging. These data, combined with thermal strain modelling, indicate a positive feedback mechanism where cooling of the ocean plate induces cracking and the reactivation of pre-existing veins, ultimately resulting in further cooling. Waning of this feedback provides a mechanism for the termination of the global average heat flow anomaly. Sites with total vein dilation greater than expected for their age correspond with crustal formation during periods of high atmospheric CO2. The amount of vein material thus reflects the changing balance between ocean plate cooling, ocean chemistry, and the age of the ocean crust. Our results demonstrate that ocean crust endures as an active geochemical reservoir for tens of millions of years after formation

Optimized punching shear design in steel fiber-reinforced slabs: Machine learning vs. evolutionary prediction models

This research paper focuses on utilizing Artificial Neural Networks (ANN), Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR), and Gene Expression Programming (GEP) to predict the punching shear strength of Steel Fibre-Reinforced Concrete (SFRC) slabs.In order to formulate predictions, research and analysis were carried out making use of a dataset, this dataset included several parameters that impact on punching shear strength, including SFRC slabs longitudinally and transversely, using ANN, GEP, and MOGA-EPR methods. The developed models exhibited very good performance, as the soft computing techniques (GEP and MOGA-EPR) achieved R² values of 0.91 to 0.93, while the ANN technique was higher at 0.95. Furthermore, two case studies were incorporated to carry out cost analyses of the models in real-world applications. It was shown that the efficiency of the Machine Learning (ML) models in reducing the costs of materials is relatively high, as they were capable of better predictions than the standard methods employed by the codes

Exploring purchase intention in metaverse retailing: Insights from an automotive platform

As an integration of cutting-edge digital technologies, the metaverse is set to revolutionize online retailing. This study employed a well-established metaverse automotive retailing platform in China to explore the paths influencing consumers' purchase intention when shopping in the metaverse. We adopted structural equation modeling to analyze the data obtained from 348 respondents who were planning to shop for a new car in the metaverse in China. The findings showed that the perceived social presence of others positively influences consumers’ purchase intention, as mediated by their metaverse identification. Moreover, consumer stickiness and the accompaniment of friends were found to positively moderate the effect of perceived social presence on metaverse identification in metaverse retailing. Likewise, product type positively moderated the effect of metaverse identification on purchase intention. Specifically, when consumers intended to purchase environmentally-friendly (vs. unfriendly) vehicles, a stronger positive impact of metaverse identification on purchase intention was observed. The results provide valuable insight for metaverse retailers

Using rapid reviews to support software engineering practice: a systematic review and a replication study

ContextA few years ago, rapid reviews (RR) were introduced in software engineering (SE) to address the problem that standard systematic reviews take too long and too much effort to be of value to practitioners. Prior to our study, few practice-driven RRs had been reported, and none involved collaboration with practitioners lacking SE research experience.ObjectiveTo investigate practitioners’ perspectives on the use of RRs in supporting SE practices, we aimed to validate and build upon the findings of the seminal RR in SE study, specifically considering practitioners without explicit SE research experience.MethodFirst, we studied previously conducted RRs in SE through a systematic review. Second, we carried out an external replication of the first study that proposed the use of RRs in SE. Specifically, we conducted an RR for an agile software development team looking to improve its knowledge management practices.ResultsMost of the software development team’s perceptions about RR results were positive and strongly consistent with previous research. In particular, RR results were considered more reliable than other sources of information and adequate to address the problems detected. Some months later they confirmed using some of the recommendations.ConclusionsThe results show that practitioners without explicit SE research experience appreciate the value of evidence and can make use of the results of RRs. However, SE research may need to be translated from broad recommendations to specific process change options. Our research also reveals that SE RRs reporting needs to be substantially improved

Processing of GASKAP-Hi pilot survey data using a commercial supercomputer

Modern radio telescopes generate large amounts of data, with the next generation Very Large Array (ngVLA) and the Square Kilometre Array (SKA) expected to feed up to 292 GB of visibilities per second to the science data processor (SDP). However, the continued exponential growth in the power of the world’s largest supercomputers suggests that for the foreseeable future there will be sufficient capacity available to provide for astronomers’ needs in processing ‘science ready’ products from the new generation of telescopes, with commercial platforms becoming an option for overflow capacity. The purpose of the current work is to trial the use of commercial high performance computing (HPC) for a large scale processing task in astronomy, in this case processing data from the GASKAP-Hi pilot surveys. We delineate a four-step process which can be followed by other researchers wishing to port an existing workflow from a public facility to a commercial provider. We used the process to provide reference images for an ongoing upgrade to ASKAPSoft (the ASKAP SDP software), and to provide science images for the GASKAP collaboration, using the joint deconvolution capability of WSClean. We document the approach to optimising the pipeline to minimise cost and elapsed time at the commercial provider, and give a resource estimate for processing future full survey data. Finally we document advantages, disadvantages, and lessons learned from the project, which will aid other researchers aiming to use commercial supercomputing for radio astronomy imaging. We found the key advantage to be immediate access and high availability, and the main disadvantage to be the need for improved HPC knowledge to take best advantage of the facility

Interpretative Attention Networks for Structural Component Recognition

Bridges are essential for enabling movement during environmental disasters and serve as crucial links for rescue and aid delivery. Effective bridge inspection and maintenance are more critical than ever due to increasing severity and frequency of environmental disasters. Although current state-of-the-art deep learning models have achieved good performance many challenges still exist, such as their performance on challenging datasets and their opaque-box nature makes it difficult to understand their decision-making process and identify potential biases. This research work proposes a novel architecture that incorporates innovative parallel twin attention module, synchronous amplification module, aggregated multi-feature attention module and squeeze and excitation blocks, that helps to focus on specific regions of the image plane automatically resulting in improved structural component recognition accuracy. Its parallelism helps to capture long-range dependencies enabling the model to use contextual information encompassing spatial and channel information when segmenting bridge components. Experimental results and ablation studies show that our proposed architecture outperforms the current state-of-the-art methodologies in the challenging bridge component classification dataset. We also examine our models through XAI methods to provide insights into its decision-making process and making it more trustable by highlighting the importance of different features for various similar recognition/segmentation tasks

Green credit’s impact on pollution and economic development: A study from Vietnam

Vietnam is actively pursuing sustainable economic development while transitioning to a greener economy, facing challenges in balancing economic growth with environmental sustainability. The evaluation of current sustainable development efforts and the identification of effective green financial tools are critical for Vietnam's progress. This study introduces the Sustainable Economic Development (SED) Index, a comprehensive measure of sustainable economic development quality across Vietnam's 63 provinces from 2015 to 2022. Using a range of analytical techniques, we explore the relationships between green credit, environmental pollution, and sustainable economic development during this period. Our findings indicate rapid growth in sustainable economic development until 2019, followed by a deceleration due to the COVID-19 pandemic and the Ukraine war. Green credit emerges as a pivotal factor in supporting sustainable economic growth, managing climate risks, and mitigating environmental pollution, particularly during times of uncertainty. Additionally, we observe spatial spillover effects, where the benefits of green credit and the challenges of environmental pollution transcend provincial boundaries. The study recommends promoting green credit policies, fostering regional cooperation, and enhancing local competitiveness, digital transformation, and social equality to advance Vietnam's journey toward sustainable economic development

Green’s function analysis of nonlinear thermoacoustic effects under the influence of noise in a combustion chamber

This paper presents an analytical study to predict the effect of noise in a thermoacoustic system. The starting point of our analysis is the acoustic analogy equation with two source terms: one represents the fluctuating heat release rate, and the other one represents the noise. The heat release rate is nonlinear and modelled by a generalised-law with amplitude-dependent time-lag and coupling coefficients. A Green’s function approach is used to convert the acoustic analogy equation (a PDE) into an integral equation. An essential element in this approach is the tailored Green’s function of the combustion chamber. We calculate this analytically for a 1-D combustion chamber with general end conditions and a non-uniform mean temperature. The integral equation is then used for predictions in the time-domain and frequency-domain. We focus on the following three phenomena: transient oscillations, noise-induced triggering, and hysteresis. Our predictions are in line with experimental observations reported in earlier studies: (1) Noise speeds up the time it takes a transient oscillation with growing amplitude to reach its limit cycle. (2) Noise can launch a linearly stable system into an unstable state. (3) Noise reduces the width of a hysteresis zone. Both white noise and pink noise are considered; pink noise is found to be more effective

Do you believe in the metaverse NFTs? Understanding the value proposition of NFTs in the metaverse

This study investigates consumer intention to purchase NFTs in the metaverse using the underpinnings of the theory of consumption values (TCV). This study uses a sequential mixed-methods approach (qualitative + quantitative) to explore and examine the influence of consumption values on NFT purchase intentions. The findings of the qualitative study revealed five crucial consumption values (functional, emotional, experiential, altruistic, and symbolic) driving the NFT purchase intentions in the metaverse. However, the findings of the quantitative study revealed that only four consumption values i.e., emotional value, experiential value, altruistic value, and symbolic value are significantly and positively associated with NFTs purchase intention in the metaverse. Further, consumer inspiration towards NFTs significantly meditates the association between emotional value, symbolic value, and NFTs purchase intention in the metaverse. Also, trend affinity moderates the mediating effect of consumer inspiration on the relationship between emotional value, symbolic value, and NFTs purchase intention in the metaverse. This research makes a substantial contribution to the growing body of literature concerning the metaverse and NFTs. Additionally, it enhances our comprehension of consumer behaviour within the metaverse context. Also, this study provides several implications for marketers and organizations

Hypervalent iodine mediated cyclization of bishomoallylamides to prolinols

A change in mechanism was observed in the hypervalent iodine mediated cyclization of N-alkenylamides when the carbon chain between the alkene and the amide increased from two to three atoms. In the latter case, cyclization at the amide nitrogen to form the pyrrolidine ring was favored over cyclization at the amide oxygen. A DFT study was undertaken to rationalize the change in mechanism of this cyclization process. In addition, reaction conditions were developed, and the scope of this cyclization studied