Search Results (327)

Against the backdrop of the dual carbon goals, the construction industry—as the primary source of carbon emissions accounting for 50.9%—is increasingly relying on green technological innovation to drive its sustainable development transformation. However, construction enterprises currently face three core challenges: the significant incremental costs associated with adopting green technologies, insufficient green credit supply from financial institutions, especially banks, and inadequate policy coordination among government departments. Furthermore, misaligned interests among multiple stakeholders exacerbate the implementation challenges of green technological innovation, hindering the industry′s low-carbon transition. Therefore, systematically exploring the interaction patterns and functional mechanisms among construction enterprises, government agencies, and banks in green technology innovation decision-making is crucial. This study will provide theoretical and empirical support for the green transformation of the construction industry within the dual-carbon framework. This study establishes a tripartite game model involving construction companies, governments, and banks, centered around the decision-making phase of green technology innovation. By integrating evolutionary game theory with system dynamics (SD) approaches, it uncovers the evolutionary trajectories and underlying mechanisms of strategies adopted by each stakeholder. Research indicates that construction companies, governments, and banks ultimately maintain equilibrium at the (1,1,1) point. The study underscores the pivotal role of government guidance during the decision-making stage, highlighting that sustained implementation of proactive policies can foster positive interactions and a balance between construction companies’ pursuit of green technology innovation and banks’ provision of green credit. It can shorten the time required for enterprises and banks to evolve their strategies. Suppressing the probability of innovation failure moderates both parties′ strategies, and adjusting parameters such as green credit interest rates and government subsidies can optimize choices. This research not only enhances the theoretical understanding of green technology innovation in the construction sector but also offers practical insights for promoting industry-wide green innovation, improving the quality of green buildings, and regulating market order. Full article

Shimer documents that the search-and-matching model driven by productivity shocks explains only a small share of the observed volatility of unemployment and vacancies, which is known as the Shimer puzzle. We revisit this evidence by replacing the representative firm’s optimization with a deep reinforcement learning (DRL) agent that learns its vacancy-posting policy through interaction in a Diamond–Mortensen–Pissarides (DMP) model. Comparing the learning economy with a conventional log-linearized DSGE solution under the same parameters, we find that while both frameworks preserve a downward-sloping Beveridge curve, learning-based economy produces much higher volatility in key labor market variables and returns to a steady state more slowly after shocks. These results point to bounded rationality and endogenous learning as mechanisms for labor market fluctuations and suggest that reinforcement learning can serve as a useful complement to standard macroeconomic analysis. Full article

Cities worldwide are transitioning from demolition–redevelopment-driven expansion to high-quality regeneration centered on stock upgrading, cultural continuity, and long-term operations. Against the backdrop of China’s high-quality urban renewal phase guided by the “anti-massive demolition and construction” policy, this study constructs a calibrated tripartite evolutionary game among government, investors, and residents. By embedding culture–economy parameters—cultural renovation intensity (k), operational profit-sharing ratio between investors and residents (j), cultural identification coefficient (i), and cost-sharing coefficient (w)—we establish a behavioral interaction mechanism of “cultural value conversion–benefit-sharing–cultural identification–cost-sharing.” Simulations based on replicator dynamics demonstrate that sustained tripartite cooperation requires four conditions: cultural intensity surpasses the cost threshold (k ∈ [0.6, 0.7]); the profit-sharing ratio preserves market incentives (j ∈ [0.25, 0.35]); cultural identification reaches a minimum threshold (i ≥ 0.4); and residents’ cost-sharing does not exceed their benefit capacity (w ≤ 0.2). These findings reveal the core tension in China’s high-quality urban renewal stage—namely, the challenge of instituting sustainable operational mechanisms under cultural protection constraints—and globally provide a quantifiable policy toolbox for culture-led urban regeneration. Full article

The transformation of energy systems towards low emission is one of the key assumptions of the climate and energy policy of the European Union and many countries around the world. These changes include not only the power and transport sectors but also the heating of residential buildings, which consume significant amounts of energy and emit large amounts of greenhouse gases. This article presents a detailed comparative analysis of the costs of heating using an air-to-water heat pump and a condensing gas boiler. The study concerned a retrofitted single-family building from the 1990s, located in southern Poland. The calculations were made taking into account daily meteorological data for two full heating seasons: 2022/2023 and 2023/2024. This approach made it possible to more precisely reproduce real operating conditions. The study was conducted for various configurations of the central heating system: surface and radiator. The following parameters were also taken into account: (1) variable heat pump parameters, such as supply temperature LWT and coefficient of performance COP; (2) current tariffs for electricity and natural gas; and (3) forecasted tariffs for electricity and natural gas in the conditions of market liberalization and phasing out of protective mechanisms. A comparison of the two heating seasons revealed lower costs with a heat pump. In some cases, the cost of heat generated by a gas boiler was over 100% higher than with a heat pump. This applies to both heating seasons. Under the current tariffs, the calculated gas cost for the first season was PLN 6856 (EUR 1605) (1 EUR = 4.27 PLN) compared to heat pump heating costs ranging from PLN 3191 to PLN 4576 (EUR 747 to 1072). For future gas and electricity tariffs, the costs were PLN 8227 (EUR 1926) for gas and PLN 3841 to PLN 5304 (EUR 899 to 1242) for a heat pump. Similarly, for the second heating season, these values were PLN 6055 (EUR 1418) for gas heating and PLN 2741–3917 (EUR 642–917) for a heat pump under the current tariffs, and PLN 7267 (EUR 1702) and PLN 3307–4540 (EUR 774–1064) under future tariffs. This means percentage savings of between approximately 33% and 55%, depending on the heating type and tariff. Therefore, the obtained results indicate the higher profitability of using an air heat pump compared to a gas boiler. This advantage was maintained in all the discussed scenarios, and its scale depended on the type of installation, supply temperature, and the selected electricity tariff. The highest economic profitability was noted for low-temperature systems. These results can provide a basis for making rational investment and design decisions in the context of the energy transformation of single-family housing. Full article

Omni-channel retailers arise to address the deficiencies in consumers’ online shopping experiences; the resulting competition between such retailers and traditional online platforms presents substantial challenges for green product manufacturers. A three-level game model is established to examine a manufacturer’s green contract design (product pricing and greenness determination) and mode selection under the competition between an online platform and a new retailer providing omni-channel services to end customers. The manufacturer can select between two modes: supplying a green product to the online platform and new retailer (mode RR) or selling it directly through the online platform and reselling it to the new retailer (mode PR). Our findings indicate that, first, even if the relationship between the manufacturer and new retailer has changed from cooperation under mode RR to competition and cooperation under mode PR, the manufacturer still favors two-channel sales over single-channel sales and affects consumer channel choices to adjust market shares through mode selection. Second, regarding the impacts of the key parameters on the manufacturer, downstream e-commerce platform retailers and environment are intricate and nuanced. While raising the omni-channel service level enhances profitability in the new retailer across both modes, its environmental impacts differ significantly between them. Additionally, it can harm the online platform in some cases. Nevertheless, when the parameters fall within suitable ranges, the manufacturer and both downstream retailers have a consistent preference for improved omni-channel services under both modes. Finally, there is a significant divergence in mode preferences among the manufacturer and both downstream platform retailers. Due to the first-mover advantage, the manufacturer opts for mode RR over mode PR in most cases. Notably, within a specific range of parameters, they consistently prefer mode RR, which also proves beneficial for the environment, resulting in a Pareto optimal outcome. This proposes a concrete cooperation mechanism among the manufacturer, retailers, and consumers from quantitative insights, which can promote green products to achieve the objective of low-carbon environmental protection. Full article

The surface quality of industrial gaskets directly impacts sealing performance, operational reliability, and market competitiveness. Inadequate or unreliable defect detection in silicone gaskets can lead to frequent maintenance, undetected faults, and security risks in downstream systems. This paper presents VST-YOLOv8, a trustworthy and secure defect detection framework built upon an enhanced YOLOv8 architecture. To address the limitations of C2F feature extraction in the traditional YOLOv8 backbone, we integrate the lightweight Mobile Vision Transformer v2 (ViT v2) to improve global feature representation while maintaining interpretability. For real-time industrial deployment, we incorporate the Gating-Structured Convolution (GSConv) module, which adaptively adjusts convolution kernels to emphasize features of different shapes, ensuring stable detection under varying production conditions. A Slim-neck structure reduces parameter count and computational complexity without sacrificing accuracy, contributing to robustness against performance degradation. Additionally, the Triplet Attention mechanism combines channel, spatial, and fine-grained attention to enhance feature discrimination, improving reliability in challenging visual environments. Experimental results show that VST-YOLOv8 achieves higher accuracy and recall compared to the baseline YOLOv8, while maintaining low latency suitable for edge deployment. When integrated with secure industrial control systems, the proposed framework supports authenticated, tamper-resistant detection pipelines, ensuring both operational efficiency and data integrity in real-world production. These contributions strengthen trust in AI-driven quality inspection, making the system suitable for safety-critical manufacturing processes. Full article

The integration of renewable energy into the grid has led to problems such as low utilization rate of energy storage resources (“underutilization after construction”) and insufficient system stability. This paper studied the co-evolution mechanism of power market entities empowered by shared energy storage. Based on the interaction among power generation enterprises, power grid operators, and government regulatory agencies, this paper constructed a three-party evolutionary game model. The model introduced a dynamic reward and punishment mechanism as well as a random interference mechanism, which makes it more in line with the actual situation. The stability conditions of the game players were analyzed by using stochastic differential equations, and the influences of key parameters and incentive mechanisms on the stability of the game players were investigated through numerical simulation. The main research results showed the following: (1) The benefits of shared energy storage and opportunistic gains had a significant impact on the strategic choices of power generation companies and grid operators. (2) The regulatory efficiency had significantly promoted the long-term stable maintenance of the system. (3) Dynamic incentives were superior to static incentives in promoting cooperation, while the deterrent effect of static penalties is stronger than that of dynamic penalties. (4) The increase in the intensity of random disturbances led to strategy oscillation. This study suggested that the government implement gradient-based dynamic incentives, maintain strict static penalties to curb opportunism, and enhance regulatory robustness against uncertainty. This research provided theoretical and practical inspirations for optimizing energy storage incentive policies and promoting multi-subject coordination in the power market. Full article

Against the backdrop of escalating atmospheric carbon dioxide concentrations, carbon emission trading systems (ETS) have emerged as pivotal policy instruments, with China’s ETS playing a prominent role globally. The carbon price, central to ETS functionality, guides resource allocation and corporate strategies. Due to unexpected events, political conflicts, limited access to data information, and insufficient cognitive levels of market participants, there are epistemic uncertainties in the fluctuations of carbon and energy prices. Existing studies often lack effective handling of these epistemic uncertainties in energy prices and carbon prices. Therefore, the core objective of this study is to reveal the dynamic linkage patterns between energy prices and carbon prices, and to quantify the impact mechanism of epistemic uncertainties on their relationship with the help of uncertain differential equations. Methodologically, a dynamic model of carbon and energy prices was constructed, and analytical solutions were derived and their mathematical properties were analyzed to characterize the linkage between carbon and energy prices. Furthermore, based on the observation data of coal prices in Qinhuangdao Port and national carbon prices, the unknown parameters of the proposed model were estimated, and uncertain hypothesis tests were conducted to verify the rationality of the proposed model. Results showed that the mean squared error of the established model for fitting the linkage relationship between carbon and energy prices was 0.76, with the fitting error controlled within $3.72\%$. Moreover, the prediction error was 1.$88\%$. Meanwhile, the $5\%$ value at risk (VaR) of the logarithmic return rate of carbon prices was predicted to be $-0.0369$. The research indicates that this methodology provides a feasible framework for capturing the uncertain interactions in the carbon-energy market. The price linkage mechanism revealed by it helps market participants optimize their risk management strategies and provides more accurate decision-making references for policymakers. Full article

The problem of end-of-life (EoL) fibre-reinforced polymer (FRP) wind turbine blades (WTBs) poses a growing challenge due to the absence of an integrated circular value chain currently available on the market. A key barrier is the information gap between the EoL condition of WTB components and their second-life application requirements. This study addresses this question by focusing on the spar cap, which is an internal structural component with high repurposing potential. A framework has been developed to determine the as-received mechanical properties of spar caps from different EoL WTB models, targeting repurpose in the construction sector. The experimental programme encompasses fibre architecture assessment, calcination processes and mechanical tests in both longitudinal and transverse directions of three different WTB models. Results suggest that the spar caps appear to retain their strength and stiffness, with no evidence of degradation from previous service life. However, notable variation in properties is observed. To account for this, a prediction tool is proposed to estimate the as-received mechanical properties based on practically accessible parameters, thereby supporting decision-making. The results of this study contribute to enabling the repurposing of EoL spar cap beams from the wind energy sector for applications in the construction sector. Full article

The inspection of the appearance quality of tea leaves is vital for market classification and value assessment within the tea industry. Nevertheless, many existing detection approaches rely on sophisticated model architectures, which hinder their practical use on devices with limited computational resources. This study proposes a lightweight object detection network, TeaAppearanceLiteNet, tailored for tea leaf appearance analysis. A novel C3k2_PartialConv module is introduced to significantly reduce computational redundancy while maintaining effective feature extraction. The CBMA_MSCA attention mechanism is incorporated to enable the multi-scale modeling of channel attention, enhancing the perception accuracy of features at various scales. By incorporating the Detect_PinwheelShapedConv head, the spatial representation power of the network is significantly improved. In addition, the MPDIoU_ShapeIoU loss is formulated to enhance the correspondence between predicted and ground-truth bounding boxes across multiple dimensions—covering spatial location, geometric shape, and scale—which contributes to a more stable regression and higher detection accuracy. Experimental results demonstrate that, compared to baseline methods, TeaAppearanceLiteNet achieves a 12.27% improvement in accuracy, reaching a mAP@0.5 of 84.06% with an inference speed of 157.81 FPS. The parameter count is only 1.83% of traditional models. The compact and high-efficiency design of TeaAppearanceLiteNet enables its deployment on mobile and edge devices, thereby supporting the digitalization and intelligent upgrading of the tea industry under the framework of smart agriculture. Full article

To address the challenge of detecting small defects in Printed Circuit Boards (PCBs), a YOLO-HDEW model based on the enhanced YOLOv8 architecture is proposed. A high-resolution detection layer is introduced at the P2 feature level to improve sensitivity to small targets. Depthwise Separable Convolution (DSConv) is used for downsampling, reducing parameter complexity. An Edge-enhanced Multi-scale Parallel Attention mechanism (EMP-Attention) is integrated to capture multi-scale and edge features. The EMP mechanism is incorporated into the C2f module to form the C2f-EMP module, and dynamic non-monotonic Wise-IoU (W-IoU) loss is employed to enhance bounding box regression. The model is evaluated on the PKU-Market-PCB, DeepPCB, and NEU-DET datasets, with experimental results showing that YOLO-HDEW achieves 98.1% accuracy, 91.6% recall, 90.3% mAP@0.5, and 61.7% mAP@0.5:0.95, surpassing YOLOv8 by 1.5%, 2.3%, 1.2%, and 1.9%, respectively. Additionally, the model demonstrates strong generalization performance on the DeePCB and NEU-DET datasets. These results indicate that YOLO-HDEW significantly improves detection accuracy while maintaining a manageable model size, offering an effective solution for PCB defect detection. Full article

Background/Objectives: Hip-level amputees face ambulatory challenges due to the lack of a lower limb and prosthetic hip power. Some hip-level amputees restore mobility by using a prosthesis with hip, knee, and ankle joints. Powered prosthetic joints contain an actuator that provides external flexion-extension moments to assist with movement. Powered knee and powered ankle-foot units are on the market, but no viable powered hip unit is commercially available. This research details the development of a novel powered four-bar prosthetic hip joint that can be integrated into a full-leg prosthesis. Methods: The hip joint design consisted of a four-bar linkage with a harmonic drive DC motor placed in the inferior link and an additional linkage to transfer torque from the motor to the hip center of rotation. Link lengths were determined through engineering optimization. Device strength was demonstrated with force and finite element analysis and with ISO 15032:2000 A100 static compression tests. Walking tests with a wearable hip-knee-ankle-foot prosthesis simulator, containing the novel powered hip, were conducted with three able-bodied participants. Each participant walked back and forth on a level 10 m walkway. Custom hardware and software captured joint angles. Spatiotemporal parameters were determined from video clips processed in the Kinovea software (ver. 0.9.5). Results: The powered hip passed all force and finite element checks and ISO 15032:2000 A100 static compression tests. The participants, weighing 96 ± 2 kg, achieved steady gait at 0.45 ± 0.11 m/s with the powered hip. Participant kinematic gait profiles resembled those seen in transfemoral amputee gait. Some gait asymmetries occurred between the sound and prosthetic legs. No signs of mechanical failure were seen. Most design requirements were met. Areas for powered hip improvement include hip flexion range, mechanical advantage at high hip flexion, and device mass. Conclusions: The novel powered four-bar hip provides safe level-ground walking with a full-leg prosthesis simulator and is viable for future testing with hip-level amputees. Full article

Multivariate time series forecasting is crucial for numerous practical applications ranging from financial markets to climate monitoring. Traditional multivariate time series forecasting methods primarily adopt a time-centric modeling paradigm, applying attention mechanisms to the temporal dimension, which presents significant limitations when handling complex dependencies between variables. To better capture inter-variable interaction patterns, this paper proposes the Variable-Centric Transformer (VCformer), which shifts the attention paradigm from time-centric to variable-centric through sequence transposition. Building upon this foundation, we further design a dual-scale architecture that simultaneously models feature representations at both the original variable level and variable group level. Combined with an adaptive variable grouping mechanism, we construct a parameter-sharing dual-path encoder and finally select the optimal feature fusion strategy through comparative experiments. Experimental results on seven benchmark datasets demonstrate that VCformer achieves comprehensive improvements in prediction accuracy compared to traditional time-centric methods, while exhibiting stronger modeling capabilities on high-dimensional data. Ablation studies and interpretability analysis further validate the effectiveness of each component. Full article

Products with complex structures are structurally intricate and involve multiple professional fields and engineering construction elements, making it difficult for a single contractor to independently develop and manufacture such complex structural products. Therefore, during the research, development, and production of complex products, collaboration between manufacturers and suppliers is essential to ensure the smooth completion of projects. In this process, a complex supply chain network is often formed to achieve collaborative cooperation among all project participants. Within such a complex supply chain network, issues such as delayed delivery, poor product quality, or low resource utilization by any participant may trigger the bullwhip effect. This, in turn, can negatively impact the delivery cycle, product cost, and quality of the entire complex product, causing it to lose favorable competitive positions such as quality advantages and delivery advantages in fierce market competition. Therefore, this paper firstly explores the mechanism of complex product manufacturing and the supply network of complex product manufacturing, in order to grasp the inherent structure of complex product manufacturing with a focus on identifying symmetrical properties among supply chain nodes. Secondly, a complex product supply chain network model is constructed with the Graphical Evaluation and Review Technique (GERT), incorporating symmetry constraints to reflect balanced resource allocation and mutual dependencies among symmetrical nodes. Then, from the perspective of supply chain, we focus on identifying the shortcomings of supply chain suppliers and optimizing the management cost of the whole supply chain in order to improve the quality of complex products, delivery level, and cost saving level. This study constructs a Restricted Grey GERT (RG-GERT) network model with constrained outputs, integrates moment-generating functions and Mason’s Formula to derive transfer functions, and employs a hybrid algorithm (genetic algorithm combined with non-linear programming) to solve the multi-objective optimization problem (MOOP) for joint optimization of delivery time, quality, and cost. Empirical analysis is conducted using simulated data from Y Company’s aerospace equipment supply chain, covering interval parameters such as delivery time [5–30 days], cost [40,000–640,000 CNY], and quality [0.85–1.0], validated with industry-specific constraints. Empirical analysis using Y Company’s aerospace supply chain data shows that the model achieves a maximum customer satisfaction of 0.96, with resource utilization efficiency of inefficient suppliers improved by 15–20% (p < 0.05) after secondary optimization. Key contributions include (1) integrating symmetry analysis to simplify network modeling; (2) extending GERT with grey parameters for non-probabilistic uncertainty; (3) developing a two-stage optimization framework linking customer satisfaction and resource efficiency. Full article

This study investigates the liquidity dynamics in non-traditional financial markets by simulating trading environments for fractional ownership of illiquid alternative investments, grounded in empirical tick data from a Swiss FinTech platform covering December 2022 to June 2024. The research translates an operational digital secondary market into a heterogeneous agent-based simulation model within the theoretical framework of market microstructure and complex systems theory. The main objective is to assess whether a simple agent-based model (ABM) can replicate empirical liquidity patterns and to evaluate how market rules and parameter changes influence simulated liquidity distributions. The findings show that (i) the simulated liquidity closely matches empirical distributions not only in mean and variance but also in higher-order moments; (ii) the ABM reproduces key stylized facts observed in the data; and (iii) seemingly simple interventions in market rules can have unintended consequences on liquidity due to the complex interplay between agent behavior and trading mechanics. These insights have practical implications for digital platform designers, investors, and regulators, highlighting the importance of accounting for agent heterogeneity and endogenous market dynamics when shaping secondary market structures. Full article