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To achieve digital transformation, organizations have continued to rely on integrating the capabilities of information technology (IT) to facilitate decision-making and developing their reconfiguration capability to enhance agile operations. The pressure imposed by digital transformation necessitates investigations on leveraging different IT capabilities to attain substantial organizational agility in an optimal configuration. This study aims to provide a new perspective on balancing IT structural capabilities and proposes a framework for evaluating their coalignment and complementary returns based on resource orchestration theory.

A multi-method approach is used to evaluate the research model. This study tests hypotheses and explores the potential coalignment and complementary returns of balance in structural models and response surface analysis. Then, it analyzes the qualitative data and provides complementary findings to corroborate and confirm complex relationships.

Balanced structural IT capabilities facilitate organizational agility but cooperate differently with internal (e.g. IT proactive stance) and external (e.g. environmental volatility) environmental factors. Balance between IT integration and reconfiguration must be maintained from several approaches during search/selection and configuration/deployment.

This study theorizes and empirically investigates the interactive mechanisms of two IT capabilities in influencing organizational agility under different boundary conditions. It enriches the understanding of balancing capabilities for organizational agility in digital transformation.

There have been limited investigations on the mechanical characteristics of tunnels supported by corrugated plate structures during fault dislocation. The authors obtained circumferential and axial deformations of the spiral corrugated pipe at various fault displacements. Lastly, the authors examined the impact of reinforced spiral stiffness and soil constraints on the support performance of corrugated plate tunnels under fault displacement.

By employing the theory of similarity ratios, the authors conducted model tests on spiral corrugated plate support using loose sand and PVC (polyvinyl chloride) spiral corrugated PE pipes for cross-fault tunnels. Subsequently, the soil spring coefficient for tunnel–soil interaction was determined in accordance with ASCE (American Society of Civil Engineers) specifications. Numerical simulations were performed on spiral corrugated pipes with fault dislocation, and the results were compared with the experimental data, enabling the determination of the variation pattern of the soil spring coefficient.

The findings indicate that the maximum axial tensile and compressive strains occur on both sides of the fault. As the reinforced spiral stiffness reaches a certain threshold, the deformation of the corrugated plate tunnel and the maximum fault displacement stabilize. Furthermore, a stronger soil constraint leads to a lower maximum fault displacement that the tunnel can withstand.

In this study, the calculation formula for density similarity ratio cannot be taken into account due to the limitations of the helical corrugated tube process and the focus on the deformation pattern of helical corrugated tubes under fault action.

This study provides a basis for the mechanical properties of helical corrugated tube tunnels under fault misalignment and offers optimization solutions.

This study investigates the convergence-of-interest and entrenchment effects on the relationship between managerial ownership and financial distress using evidence from the Chinese stock market. It also analyzes whether the relationship is mediated by research and development (R&D) investment.

Using a dataset consisting of 19,059 firm-year observations of Chinese listed companies in the Shanghai and Shenzhen Stock Exchanges between 2010 and 2020, this study employs both piecewise and curvilinear models.

The results indicate that managerial ownership has a negative association with firm financial distress in both the low (below 12%) and high (above 18%) convergence-of-interest regions of managerial ownership, suggesting that managerial ownership in this region may contribute to improve firm financial status. Meanwhile, managerial ownership has a positive association with firm financial distress in the entrenchment region (12–18%), implying that managerial ownership in the entrenchment region may contribute to impair firm financial status. Furthermore, the results show that R&D investment mediates the association between managerial ownership and financial distress.

This study is the first to provide evidence of a nonlinear relationship between managerial ownership and financial distress, and identify the entrenchment region in the Chinese setting.

The authors target the interrelationships between non-fungible tokens (NFTs), decentralized finance (DeFi) and carbon allowances (CA) markets during 2021–2023. The recent shift of crypto and DeFi miners from China (the People's Republic of China, PRC) green hydro energy to dirty fuel energies elsewhere induces investments in carbon offsetting instruments; this is a backdrop to the authors’ investigation.

The quantile vector autoregression (VAR) approach is employed to examine extreme-quantile-connectedness and spillovers among the NFT Index (NFTI), DeFi Pulse Index (DPI), KraneShares Global Carbon Strategy ETF price (KRBN) and the Solactive Carbon Emission Allowances Rolling Futures Total Return Index (SOLCARBT).

At bull markets, DPI is the only consistent net shock transmitter as NFTI transmits innovations only at the most extreme quantile. At bear markets, KRBN and SOLCARBT are net shock transmitters, while NFTI is the only consistent net shock receiver. The receiver-transmitter roles change as a function of the market conditions. The increases in the relative tail dependence correspond to the stress events, which make systemic connectedness augment, turning market-specific idiosyncratic considerations less relevant.

The shift of digital asset miners from the PRC has resulted in excessive fuel energy consumption and aggravated environmental consequences regarding NFTs and DeFi mining. Although there exist numerous studies dedicated to CA trading and its role in carbon print reduction, the direct nexus between NFT, DeFi and CA has never been addressed in the literature. The originality of the authors’ research consists in bridging this void. Results are valuable for portfolio managers in bull and bear markets, as the authors show that connectedness is more intense under such conditions.

The aim of this study is to present an overview of sustainable practices in the development of waterproof breathable fabrics for garments. It aims to provide insights into the current state of academic research in this domain and identify and analyze major sustainable trends in the field.

This study conducts a thorough examination of research publications sourced from the Scopus database spanning the years 2013–2023 by employing a systematic approach. The research utilizes both descriptive analysis and content analysis to identify trends, notable journals and leading countries in sustainable waterproof breathable fabric development.

The study reveals a notable increase in studies focusing on sustainable approaches in the development of waterproof breathable fabrics for garments. Descriptive analysis highlights the most prominent journal and leading country in terms of research volume. Content analysis identifies four key trends: minimizing chemical usage, developing easily degradable materials, creating fabrics promoting health and well-being and initiatives to reduce energy consumption.

The main limitation of this research lies in its exclusive reliance on the Scopus database.

The insights derived from this study offer practical guidance for prospective researchers interested in investigating sustainable approaches to developing waterproof breathable fabric for garments. The identified trends provide a foundation for aligning research endeavors with contemporary global perspectives, facilitating the integration of sustainable methodologies into the garment industry.

This systematic literature review contributes original insights by synthesizing current research trends and outlining evolving sustainable practices in the development of waterproof breathable fabrics. The identification of key focus areas adds a novel perspective to existing knowledge.

This paper aims to study the influences of eccentricity on the fastener load and bearing strength of the eccentric connection in the aircraft structure.

The special experiment is designed for the researches. The fastener loads of the eccentric connection are gained by using the derived formulas and numerical analysis, and the fastener load rules is verified by the experiment. The bearing strength of the eccentric connection is investigated by the experiments under different eccentricities compared with that gained from the experiment.

The study results are summarized as follows. Magnitude of the fastener load in the eccentric connection is greatly affected by distance from the fastener to the centroid of the fastener cluster and that from the fastener to the concentrated load. With the increase of eccentricity of the homolateral concentrated load, the fastener load increases, and difference of the fastener loads becomes larger, forming the short plate effect of the bucket. It means that fastener with the maximum load (the shortest plate of the bucket) leads to decrease of the bearing strength of the eccentric connection (the capacity of the bucket).

The investigation on the influence of eccentricity on the bearing strength of eccentric connection is firstly presented. The vector expression of the fastener load in eccentric connection is firstly derived. And the influencing mechanism of the fastener load on the bearing strengths of the different eccentric connections is demonstrated. The study results can provide guidance for the structure design of the eccentric connection.

This study addresses the reliability of a composite fiber (carbon fibers/epoxy matrix) at microscopic level, with a specific focus on its behavior under compressive stresses. The primary goal is to investigate the factors that influence the reliability of the composite, specifically considering the effects of initial fiber deformation and fiber volume fraction.

The analysis involves a multi-step approach. Initially, micromechanics theory is employed to derive limit state equations that define the stress levels at which the fiber remains within an acceptable range of deformation. To assess the composite's structural reliability, a dedicated code is developed using the Monte Carlo method, incorporating random variables.

Results highlight the significance of initial fiber deformation and volume fraction on the composite's reliability. They indicate that the level of initial deformation of the fibers plays a crucial role in determining the composite reliability. A fiber with 0.5% initial deformation exhibits the ability to endure up to 28% additional stress compared to a fiber with 1% initial deformation. Conversely, a higher fiber volume fraction contributes positively to the composite's reliability. A composite with 60% fiber content and 0.5% initial deformation can support up to 40% additional stress compared to a composite containing 40% fibers with the same deformation.

The study's originality lies in its comprehensive exploration of the factors affecting the reliability of carbon fiber-epoxy matrix composites under compressive stresses. The integration of micromechanics theory and the Monte Carlo method for structural reliability analysis contributes to a thorough understanding of the composite's behavior. The findings shed light on the critical roles played by initial fiber deformation and fiber volume fraction in determining the overall reliability of the composite. Additionally, the study underscores the importance of careful fiber placement during the manufacturing process and emphasizes the role of volume fraction in ensuring the final product's reliability.

Urbanization is driving the growth of China’s carbon footprint. It’s important to investigate what factors, how and to what extent, affect carbon footprints embedded in various categories of rural and urban households’ consumption.

We employ an environmental extended input-output model to assess and compare the rural-urban household carbon footprints and perform a multivariant regression analysis to identify the varying relationships of the determinants on rural and urban household carbon footprints based on the panel data of Chinese households from 2012 to 2018.

The results show evidence of urbanity density effect on direct carbon footprints and countervailing effect on indirect carbon footprints. The old dependency ratio has no significant effect on rural family emissions but has a significantly negative effect on urban direct and indirect carbon footprints. A higher child dependency ratio is associated with less rural household carbon emissions while the opposite is true for urban households. Taking advantage of recycled fuel saves direct carbon emissions and this green lifestyle benefits urban households more by saving more carbon emissions. There is a positive relationship between consumption structure ratio and direct carbon footprints while a negative relationship with indirect carbon footprints and this impact is less significant for urban households. The higher the price level of water, electricity and fuel, the lower the rural household’s direct carbon footprints. Private car ownership consistently augments household carbon footprints across rural and urban areas.

This paper provides comprehensive findings to understand the relationships between an array of determinants and China’s rural-urban carbon emissions, empowering China’s contribution to the global effort on climate mitigation.

Bitcoin has indeed been universally acknowledged as an investment asset in recent decades, after the boom-and-bust of cryptocurrency values. Because of its extreme volatility, it requires accurate forecasts to build economic decisions. Although prior research has utilized machine learning to improve Bitcoin price prediction accuracy, few have looked into the plausibility of using multiple modeling approaches on datasets containing varying data types and volumetric attributes. Thus, this paper aims to propose a bitcoin price prediction model.

In this research work, a bitcoin price prediction model is introduced by following three major phases: Data collection, feature extraction and price prediction. Initially, the collected Bitcoin time-series data will be preprocessed and the original features will be extracted. To make this work good-fit with a high level of accuracy, we have been extracting the second order technical indicator based features like average true range (ATR), modified-exponential moving average (M-EMA), relative strength index and rate of change and proposed decomposed inter-day difference. Subsequently, these extracted features along with the original features will be subjected to prediction phase, where the prediction of bitcoin price value is attained precisely from the constructed two-level ensemble classifier. The two-level ensemble classifier will be the amalgamation of two fabulous classifiers: optimized convolutional neural network (CNN) and bidirectional long/short-term memory (BiLSTM). To cope up with the volatility characteristics of bitcoin prices, it is planned to fine-tune the weight parameter of CNN by a new hybrid optimization model. The proposed hybrid optimization model referred as black widow updated rain optimization (BWURO) model will be conceptual blended of rain optimization algorithm and black widow optimization algorithm.

The proposed work is compared over the existing models in terms of convergence, MAE, MAPE, MARE, MSE, MSPE, MRSE, Root Mean Square Error (RMSE), RMSPE and RMSRE, respectively. These evaluations have been conducted for both algorithmic performance as well as classifier performance. At LP = 50, the MAE of the proposed work is 0.023372, which is 59.8%, 72.2%, 62.14% and 64.08% better than BWURO + Bi-LSTM, CNN + BWURO, NN + BWURO and SVM + BWURO, respectively.

In this research work, a new modified EMA feature is extracted, which makes the bitcoin price prediction more efficient. In this research work, a two-level ensemble classifier is constructed in the price prediction phase by blending the Bi-LSTM and optimized CNN, respectively. To deal with the volatility of bitcoin values, a novel hybrid optimization model is used to fine-tune the weight parameter of CNN.

This research aims to create a methodology that integrates optimization techniques into preliminary cost estimates and predicts the impacts of design alternatives of steel pedestrian bridges (SPBs). The cost estimation process uses two main parameters, but the main goal is to create a cost estimation model.

This study explores a flexible model design that uses computing capabilities for decision-making. Using cost optimization techniques, the model can select an optimal pedestrian bridge system based on multiple criteria that may change independently. This research focuses on four types of SPB systems prevalent in Egypt and worldwide. The study also suggests developing a computerized cost and weight optimization model that enables decision-makers to select the optimal system for SPBs in keeping up with the criteria established for that system.

In this paper, the authors developed an optimization model for cost estimates of SPBs. The model considers two main parameters: weight and cost. The main contribution of this study based on a parametric study is to propose an approach that enables structural engineers and designers to select the optimum system for SPBs.

The implications of this research from a practical perspective are that the study outlines a feasible approach to develop a computerized model that utilizes the capabilities of computing for quick cost optimization that enables decision-makers to select the optimal system for four common SPBs based on multiple criteria that may change independently and in concert with cost optimization during the preliminary design stage.

The model can choose an optimal system for SPBs based on multiple criteria that may change independently and in concert with cost optimization. The resulting optimization model can forecast the optimum cost of the SPBs for different structural spans and road spans based on local unit costs of materials cost of steel structures, fabrication, erection and painting works.

The authors developed a computerized model that uses spreadsheet software's capabilities for cost optimization, enabling decision-makers to select the optimal system for SPBs meeting the criteria established for such a system. Based on structural characteristics and material unit costs, this study shows that using the optimization model for estimating the total direct cost of SPB systems, the project cost can be accurately predicted based on the conceptual design status, and positive prediction outcomes are achieved.