This study develops an integrated decision-support framework to advance green supply chain management (GSCM) by systematically linking Environmental, Social, and Governance (ESG) practices, environmental product innovation, corporate performance, and strategic alternatives. Employing the Analytic Network Process (ANP), the proposed model captures complex interdependencies and feedback relationships across life-cycle value chain stages, enabling a holistic evaluation of sustainability-oriented strategies. A Delphi panel comprising 15 experts from academia, industry, and government is used to validate the evaluation criteria and network structure. The empirical results indicate that eco-friendly design, energy and resource efficiency, and carbon-climate management are the most influential drivers shaping green supply chain performance. Moreover, operational and sustainability performance are found to exert greater strategic importance than short-term financial performance, highlighting GSCM as a long-term capability-building approach rather than a cost-centered initiative. To enhance analytical adaptability, this study proposes a conceptual extension integrating neural feature extraction (NFE) signals with ANP-based expert weights. The NFE module is not empirically trained or validated; rather, it illustrates a theoretically consistent mechanism for incorporating data-driven feature signals into structured multi-criteria decision frameworks. Empirical validation of the NFE component is proposed as a future research direction.
This study examines the effect of increased stock liquidity on the speed of corporate leverage adjustment toward the optimal leverage. We find that overleveraged firms with high liquidity reduce their leverages at a lower speed than that of their low-liquidity counterparts. In contrast, we find that underleveraged firms with high liquidity adjust leverage at a higher speed than that of their low-liquidity counterparts. These empirical results are attributed to the fact that both overleveraged and underleveraged firms with high liquidity face a lower cost of debt when managers can make more informed investment decisions from enhanced liquidity. Our empirical findings shed new light on the importance of stock liquidity in firms’ dynamic capital structure adjustments.
This work aims to investigate the rehabilitation effectiveness on the loading resistance of a locally-corroded reinforced concrete (RC) slab using the scaled experiment. It includes seven scaled RC slabs, including one uncorroded slab and six corroded slabs retrofitted with carbon fibre-reinforced polymers (CFRP) strip plates (CFRP-p), CFRP sheets (CFRP-s), and spot-welded wire mesh (WWM). Besides the retrofit work in the rehabilitation, this work adopts non-shrinkage cement mortar and lightweight epoxy mortar to do the patch repair of the corroded regions. The slab specimens are corroded using the electrical-chemical method. After the patch repair and retrofit, the monotonic loading tests are conducted to investigate their mechanical behaviour, especially focusing on the application of CFRP-p, which is more convenient in the site construction. Test results show CFRP-p improves loading resistance more than CFRP-s and WWM, reducing member deformation and maximum crack width. It also effectively enhances the stiffness of corrosion-damaged RC slabs under serviceability conditions. The moment strength of CFRP-p retrofitted specimens is calculated using the ACI 440.2 R code and shows good agreement with test results. This method effectively determines the optimal CFRP strip plate area to compensate for corrosion-induced strength loss. Finally, the work suggests construction guidelines for CFRP-p use in locally-corroded RC slab.
This study reveals that nine critical minerals underpinning the global energy transition form a hierarchically integrated commodity network, one that is simultaneously highly interconnected and persistently asymmetric in its shock transmission structure. Using annual price data spanning 1962–2020, and employing Diebold–Yilmaz connectedness indices, rolling-window analysis, saliency mapping, ablation studies, and Diebold–Mariano predictive tests, we find that nearly half of all forecast error variance is attributable to cross-mineral shocks, with connectedness surging above 70
This study evaluated the performance of artificial neural network (ANN) in determining pre-bored precast concrete (PC) pile capacities, utilizing a database of 39 drained pile load tests under axial compression. The ANN model developed in this study is limited in applicability to pre-bored PC piles installed under drained conditions in Southern Taiwan. Measured capacities, obtained through various interpretation methods, were compared with the predicted capacities. Predicted capacities were determined in two approaches: (1) via conventional methods in determining pile capacity, namely beta method for side resistance and the general bearing capacity equation for tip resistance, and (2), using ANN to predict the load-displacement curves. Based on the analysis, the L1 method yielded the lowest interpreted capacities and displacements, while the Fuller and Hoy method and Chin method resulted in significantly higher capacities and displacements. The measured versus ANN-predicted loads resulted in r2 values ranging from 0.93 to 0.97, indicating a good fit. The ULS model factors, which are the ratio of the measured to predicted values, exhibited low dispersion, with most values being unconservative. The SLS model factors a and b for serviceability limit states are also provided for reliability-based design using the hyperbolic curve fitting method. The ANN-predicted capacities closely matched the L2 measured capacities and predicted capacities computed via conventional methods. Additionally, the distribution range of the data points of the hyperbolic model factors a and b does not differ significantly between the conventional method and ANN, when the load is normalized by the predicted capacity. This demonstrates the potential of using ANN in combination with a hyperbolic approach in predicting the load-displacement curve. This study emphasizes the importance of continuous improvement in predictive modeling techniques for pile foundation designs.
