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Hybrid deep learning and isogeometric analysis for bearing capacity assessment of sand over clay
No full textIn this paper, Isogeometric analysis (IGA) is effectively integrated with machine learning (ML) to investigate the bearing capacity of strip footings in layered soil profiles, with a focus on a sand-over-clay configuration. The study begins with the generation of a comprehensive dataset of 10,000 samples from IGA upper bound (UB) limit analyses, facilitating an in-depth examination of various material and geometric conditions. A hybrid deep neural network, specifically the Whale Optimization Algorithm-Deep Neural Network (WOA-DNN), is then employed to utilize these 10,000 outputs for precise bearing capacity predictions. Notably, the WOA-DNN model outperforms conventional ML techniques, offering a robust and accurate prediction tool. This innovative approach explores a broad range of design parameters, including sand layer depth, load-to-soil unit weight ratio, internal friction angle, cohesion, and footing roughness. A detailed analysis of the dataset reveals the significant influence of these parameters on bearing capacity, providing valuable insights for practical foundation design. This research demonstrates the usefulness of data-driven techniques in optimizing the design of shallow foundations within layered soil profiles, marking a significant stride in geotechnical engineering advancements
Non‐selective Defect Minimization towards Highly Efficient Metal‐Organic Framework Membranes for Gas Separation
No full textThe persistence of defects in polycrystalline membranes poses a substantial obstacle to reaching the theoretical molecular sieving separation and scaling up production. The low membrane selectivity in most reported literature is largely due to the unavoidable non-selective defects during synthesis, leading to a mismatch between the well-defined pore structure of polycrystalline molecular sieve materials. This paper presents a novel approach for minimizing non-selective defects in metal–organic framework (MOF) membranes by a constricted crystal growth strategy in a confined environment. The in situ ZIF formation using the densely packed seeding array between the substrate and the pre-grown top ZIF layer yields a confined membrane interlayer, which is highly uniform with a tightly packed crystalline structure. Unlike uncontrolled crystal growth, we purposely regulate the interlayer membrane growth in the direction parallel to the substrate. A notable 99 % decrease in defects in the confined interlayer was achieved compared to the random-grown top layer, leading to a ~353 % increment in H2/N2 selectivity over the non-confined reference MOF membrane. The performance of this new membrane sits in the optimal range above the Robeson upper bound. The membrane boasts a balanced high H2 permeability (>5000 Barrer) and selectivity (>50), significantly surpassing peer ZIF membranes
A numerical study of deep excavations adjacent to existing tunnels: Integrating CPTU and SDMT to calibrate soil constitutive model
No full textAccurate determination of parameters for an advanced soil constitutive model highly relies on laboratory testing, even though it is notoriously difficult to obtain undisturbed samples for soft soils. This study explores the potential use of in-situ tests, such as piezocone penetration tests (CPTU) and seismic dilatometer tests (SDMT), to estimate the constitutive model parameters. Based on a case history of a deep excavation adjacent to existing tunnels in silt/sand-dominated sediments, a calibration approach of a set of the HSSmall (Hardening Soil Model with Small Strain Stiffness) model parameters is presented, and the derived parameters are used to numerically compute the interactive responses of tunnels and deep excavations. Several comparisons against field monitoring data indicate that the numerical model with the CPTU/SDMT-interpreted HSSmall model parameters adequately reproduces observed deformation responses of deep excavations adjacent to tunnels. However, the use of laboratory tests with disturbed samples to estimate the stiffness parameters of the HSSmall model may lead to an overconservative solution. This finding supports the use of CPTU/SDMT to provide representative parameters for a range of soil layers, leading to the conclusion that tunnel linings may be beneficial to mitigate ground movements and wall deflections due to a barrier effect
Respiratory rate estimation from photoplethysmogram baseline wandering by harmonic analysis and sequential fusion
No full textPhotoplethysmogram (PPG) is typically employed to monitor heart rate and estimate respiratory rate (RR). The majority of PPG-based respiratory rate estimation methods necessitate the identification of individual heartbeat peaks or the decomposition of PPG signals, which is laborious and exhibits restricted performance. This paper presents a novel method for estimating respiratory rate, which employs frequency-domain analysis and sequential fusion over time. The proposed method exploits the harmonics of the PPG baseline wandering induced by respiration, seeking the respiratory frequency that maximizes the signal harmonic power. A sequential fusion between signal windows is designed to regularize the results and suppress possible significant errors while evaluating the quality of each fused output. The algorithm is verified using PPG signals from the CapnoBase and BIDMC datasets and yielded mean absolute errors (MAE) of 0.1 and 2.0 breaths per minute for each dataset, respectively, when providing respiratory rate estimates for all windows. Furthermore, the accuracy of the method can be improved by the exclusion of low-quality windows based on the proposed sequential assessment of quality. The application of harmonic analysis and sequential fusion techniques represents a novel approach for estimating respiratory rate from PPG with enhanced performance. The MATLAB code for the processing of the two datasets is accessible via the following link: https://github.com/Chi1988723/PPG-respiratory-rate-estimation.git
Impact of Corporate Climate Change Performance on Information Asymmetry: International Evidence
Full text linkIn this study, we examine the association between climate change performance and information asymmetry using 6,367 firm-year observations from 2011 to 2020 across 26 countries. We find that climate change performance is negatively associated with information asymmetry, suggesting that firms with higher climate change performance tend to have lower information asymmetry. We also find that the negative association between climate change performance and information asymmetry is stronger for firms with a higher level of institutional ownership and better corporate governance. Further analyses show a more pronounced negative association between climate change performance and information asymmetry for firms domiciled in countries with stakeholder-oriented business culture, a national emissions trading scheme, and a higher level of climate change performance. Our study’s findings have significant implications for capital market participants, managers, policymakers, researchers, and practitioners worldwide in understanding the role of corporate climate change performance in the capital market
Exploring the nexus of digital inclusion and environmental sustainability: insights from Cambodia
No full textThis research examines the confluence of digital inclusion and environmental sustainability in Cambodia, utilizing quantitative survey data from 380 participants. The study probes the effects of digital access, literacy, technology use, and sustainability education on sustainability awareness, adoption of sustainable practices, and environmental impact. The findings reveal the positive role of digital transformation in bolstering environmental sustainability. Digital literacy initiatives, access to digital technologies, and sustainability education integrated into digital inclusion efforts significantly enhance the adoption of sustainable practices and reduce environmental impact. The research offers valuable insights for formulating inclusive and environmentally sound policies and strategies, reinforcing the significance of integrating digital inclusion and environmental sustainability in a developing context
Continuous Purchase Intention of Organic Personal Care Products: Evidence from India
Full text linkThis study was undertaken to understand the antecedents of continuous purchase intention (CPI) of organic personal care products (OPCPs). It draws on the theory of planned behavior and the stimulus–organism–response theory to build an integrative conceptual framework. Most past studies have been conducted in developed countries, where the organic products market is more evolved. Partial least squares path modeling was used to examine various relationships among a sample of 1,378 consumers in India who buy only OPCPs. Product knowledge (PK) is the strongest influencer of attitude which has a high impact on satisfaction which in turn affects CPI positively. PK has greater significance in developing countries which have a higher share of counterfeit and unbranded products. While many studies have been conducted on CPI of organic food, there are only a few on OPCP. Among these, studies on the CPI of OPCP in developing countries are scarce
The forgotten contexts of evaluation
Full text linkContext is a well-discussed but still elusive concept in evaluation. The authors of this article acknowledge there is considerable literature on context in an evaluation setting, with multiple frameworks, theories and methodologies guided by context. However, the authors believe there is little practical and pragmatic guidance for evaluators regarding how to locate and determine what is contextually relevant. To address this gap, the authors have mapped tools to Rog’s ‘bringing the background to the foreground’ framework. An additional contribution to the literature is the addition of the ‘action context’ to Rog’s framework. This describes the period following evaluation that results in problem redefinition and intervention redesign. This article provides guidance to evaluators on how to locate context through the application of well-established tools, primarily borrowed from the business disciplines
Exploring female broadway performers' experiences of singing in mix
Full text linkThis study explored the lived experiences of 36 top-tier female musical theatre performers, each with at least one Broadway credit, who selfidentify as highly skilled in singing with mix. The aim was to gain a nuanced understanding of their vocal mix in the context of their broader artistic practices. Using reflexive thematic analysis of survey data, five critical areas of focus were identified: the role of the mix in career success and longevity, affective experiences during performance, enduring adversity in professional practice, performers’ conceptualizations of mixing, and their methods for achieving a balanced vocal mix. The data suggests that most participants possessed natural skill in mixing prior to formal voice training,introducing the factor of innate talent into the discussion. Their mastery of mix appears to be largely experiential and intuitive, potentially shaped more by inherent ability than by explicit technical knowledge or formal instruction. These themes raise important questions about singing pedagogy and the integration of voice science, highlighting the need for further research into the interplay of innate talent, experiential learning, and formal training in developing top-tier musical theatre performers
Assessing Pan-Canada wildfire susceptibility by integrating satellite data with novel hybrid deep learning and black widow optimizer algorithms
Full text linkIn light of the rising frequency of severe wildfires and their widespread socio-ecological impacts, it is essential to develop cost-effective and reliable methods for accurately predicting and mapping wildfire occurrences. This study aimed to develop several novel deep-learning models to determine the probability of wildfire occurrence on a national scale in Canada by integrating remote sensing data, deep learning, and metaheuristic algorithms. In the present study, novel standalone long short-term memory (LSTM), recurrent neural network (RNN), bidirectional LSTM (BiLSTM), and bidirectional RNN (BiRNN) models were developed, and these were hybridized with a black widow optimizer (BWO). To train and test the models, 4240 historical (2014–2023) large wildfire locations were collected across Canada. Fourteen wildfire-related predictors were used to map wildfire susceptibility, with the Gini coefficient determining each predictor's importance in wildfire occurrence. Finally, the developed models were evaluated and tested using the area under the receiver operating characteristic curve (AUC), and other statistical error metrics. During the testing stage, the hybrid BiLSTM-BWO model outperformed the other models (AUC = 0.9686), followed by RNN-BWO (AUC = 0.9683), LSTM-BWO (AUC = 0.9672), BiRNN-BWO (AUC = 0.9643), BiLSTM (AUC = 0.9420), LSTM (AUC = 0.9367), BiRNN (AUC = 0.9247) and RNN (AUC = 0.8737). Based on the BiLSTM-BWO model, 19.7 %, 42.6 %, 13.4 %, 14.5 %, and 9.8 % of Canada was classified as having very low, low, moderate, high, and very high susceptibility to future wildfires, respectively. Saskatchewan, Manitoba, British Columbia and Alberta were among the provinces with large areas of very high susceptibility to wildfires, while Prince Edward Island and Newfoundland and Labrador from Atlantic Canada had the lowest probability of wildfire occurrence. According to the Gini coefficient, windspeed, land use and land cover, precipitation, specific humidity and maximum temperature had the strongest impact on wildfire susceptibility across Canada. This study highlights the effectiveness of the developed hybrid models in wildfire prediction and their potential to improve land management, wildfire prevention, and mitigation strategies in Canada's future