Search Results (193)

Government support has long been viewed as a key driver of sustainable transformation and green technological progress. However, the underlying mechanisms (“how”) through which preferential policies influence green innovation, as well as the contextual conditions (“when”) that shape their effectiveness, remain insufficiently understood. Drawing on resource dependence theory, this study develops a dual-mediation framework to investigate how preferential tax policies promote both the quantity and quality of green innovation—by enhancing R&D investment as an internal mechanism and alleviating financing constraints as an external mechanism. These effects are especially salient among non-state-owned enterprises, firms in resource-constrained industries, and those situated in environmentally challenged regions—contexts that entail higher dependence on external support for sustainable development. Leveraging China’s 2017 R&D tax reduction policy as a quasi-natural experiment, this study uses a sample of high-tech small- and medium-sized enterprises (SMEs) to test the hypotheses. The findings provide robust evidence on how preferential policies contribute to corporate sustainability through green innovation and identify the conditions under which policy tools are most effective. This research offers important implications for designing targeted, sustainability-oriented innovation policies that support SMEs in transitioning toward more sustainable practices. Full article

Considering the complexity of the metro pit construction environment, the existing risk early-warning methods cannot ensure high-precision early warning. A high-accuracy metro pit collapse risk fusion early-warning method is proposed in present study. The main contributions include (1) presenting a new input to the fusion model by optimizing the machine learning model through a multi-step rolling method, and then using the basic probability assignment values obtained from the cloud model as input to the fusion model and (2) developing an improved methodology to address the paradoxical results of the fusion of traditional Dempster–Shafer evidence theory when there is a high level of conflict in multi-source risk prediction data. The proposed method is successfully applied to the Guangzhou Metro station project. By analyzing the early-warning results of 240 moments in 6 monitoring points, compared with the single information source method and the traditional D-S method, the early-warning accuracy of this method is increased by 15.8% and 10.8% respectively, the false alarm rate is reduced by 6.3% and 5.5%, respectively, and the missed alarm rate is reduced by 9.5% and 5.3%, respectively. The high-accuracy fusion early-warning method proposed in this paper has good universality and effectiveness in the early warning of subway foundation pit collapse risk. Full article

This study is titled “Success Factors in Transport Interventions: A Mixed-Method Systematic Review (1990–2022)”. The purpose of the systematic review is to (1) identify effective interventions for transitioning individuals from private car reliance to sustainable transport, (2) summarise psychosocial theories shaping transportation choices and identify enablers and barriers influencing sustainable mode adoption, and (3) determine the success factors for interventions promoting sustainable transport choices. The last search was conducted on 18 November 2022. Five databases (Scopus, Web of Science, MEDLINE, APA PsycInfo, and ProQuest) were searched using customised Boolean search strings. The identified papers were included or excluded based on the following criteria: (a) reported a modal shift from car users or cars to less CO₂-emitting modes of transport, (b) covered the adoption of low-carbon transport alternatives, (c) comprised interventions to promote sustainable transport, (d) assessed or measured the effectiveness of interventions, or (e) proposed behavioural models related to mode choice and/or psychosocial barriers or drivers for car/no-car use. The identified papers eligible for inclusion were critically appraised using Sirriyeh’s Quality Assessment Tool for Studies with Diverse Designs. Inter-rater reliability was assessed using Cohen’s Kappa to evaluate the risk of bias throughout the review process, and low-quality studies identified by the quality assessment were excluded to prevent sample bias. Qualitative data were extracted in a contextually relevant manner, preserving context and meaning to avoid the author’s bias of misinterpretation. Data were extracted using a form derived from the Joanna Briggs Institute. Data transformation and synthesis followed the recommendations of the Joanna Briggs Institution for mixed-method systematic reviews using a convergent integrated approach. Of the 7999 studies, 4 qualitative, 2 mixed-method, and 30 quantitative studies successfully passed all three screening cycles and were included in the review. Many of these studies focused on modelling individuals’ mode choice decisions from a psychological perspective. In contrast, case studies explored various transport interventions to enhance sustainability in densely populated areas. Nevertheless, the current systematic reviews do not show how individuals’ inner dispositions, such as acceptance, intention, or attitude, have evolved from before to after the implementation of schemes. Of the 11 integrated findings, 9 concerned enablers and barriers to an individual’s sustainable mode choice behaviour. In addition, two integrated findings emerged based on the effectiveness of the interventions. Although numerous interventions target public acceptance of sustainable transport, this systematic review reveals a critical knowledge gap regarding their longitudinal impact on individuals and effectiveness in influencing behavioural change. However, the study may be affected by language bias as it only included peer-reviewed articles published in English. Due to methodological heterogeneity across the studies, a meta-analysis was not feasible. Further high-quality research is needed to strengthen the evidence. This systematic review is self-funded and has been registered on the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY; Registration Number INPLASY202420011). Full article

Lithium-ion batteries (LIBs) are widely used in consumer electronics, electric vehicles, and renewable energy systems, but their performance decays with their lifespan, which poses safety risks. Therefore, it is crucial to develop remaining useful life (RUL) prediction technology. This paper proposes a RUL prediction method for lithium-ion batteries based on an improved Dempster–Shafer (D-S) evidence theory framework, which aims to improve the accuracy and robustness of prediction by integrating the advantages of a wavelet packet decomposition convolutional neural network (WPD-CNN) and an extended Kalman filter (EKF). The results show that the improved D-S theory overcomes the limitations of the classical D-S theory, improves the accuracy and robustness of diagnosis and prediction, and can effectively integrate multi-source information. Experimental verification shows that the fused model is significantly better than a single model in terms of prediction accuracy and robustness, providing an efficient and reliable solution for fault diagnosis and health management of lithium-ion batteries. Full article

The intrinsic hazards associated with high-pressure hydrogen, combined with electromechanical interactions in hybrid architectures, pose significant challenges in predicting potential system risks during the conceptual design phase. In this paper, a risk analysis methodology integrating systems theoretic process analysis (STPA), D-S evidence theory, and Bayesian networks (BN) is established. The approach employs STPA to identify unsafe control actions and analyze their loss scenarios. Subsequently, D-S evidence theory quantifies the likelihood of risk factors, while the BN model’s nodal uncertainties to construct a risk network identifying critical risk-inducing events. This methodology provides a comprehensive risk analysis process that identifies systemic risk elements, quantifies risk probabilities, and incorporates uncertainties for quantitative risk assessment. These insights inform risk-averse design decisions for hydrogen–electric hybrid powered aircraft. A case study demonstrates the framework’s effectiveness. The approach bridges theoretical risk analysis with early-stage engineering practice, delivering actionable guidance for advancing zero-emission aviation. Full article

The agricultural production process contributes to the global issue of pesticide pollution. Based on the static toxicity test of diuron (DCMU) on Daphnia magna (D. magna) for EC₅₀-48 h, a concentration range of 0.2 to 1 mg/L was set as sublethal concentrations, while lethal concentrations were set at 2 mg/L and 4 mg/L. This study analyzes the toxic response patterns of the swimming behavior indicators of D. magna exposed to different concentrations of DCMU. The results showed that the average speed (V) of D. magna decreased step by step with exposure time, regardless of exposure to sublethal concentration or lethal concentration. However, during the same short-term exposure period, the V of D. magna at lethal concentration was higher than that at sublethal concentration, which indicates that the swimming behavior of D. magna exposed to DCMU may be stimulated and accelerated. Compared to the control group, there is a statistically significant difference in the V of D. magna after short-term exposure, especially showing an extremely significant difference after 5 min of exposure. Evidently, compared to the traditional 48 h static toxicity testing method, the swimming behavior indicators of D. magna show a more sensitive response to DCMU after 5 min of exposure, making it more suitable for rapid toxicity detection. By expanding the range of exposure concentrations, it was found that the V indicator of D. magna responded significantly to a DCMU concentration of 0.05 mg/L after only 5 min of exposure, and a high degree of correlation was observed between the indicator and the exposure concentration. Through nonlinear fitting, the relationship between V and the dose–effect of DCMU toxicity presents an S-shaped curve, with R² > 0.9. Consequently, it becomes possible to study the dose–effect relationship between the changes in the swimming behavior indicators of D. magna and the stress concentration based on this theory. This further establishes a foundation for the development of comprehensive aquatic toxicity rapid detection technology based on the toxic response of swimming behavior indicators. Full article

This paper proposes a pipeline leakage detection technology that integrates machine learning algorithms with Dempster–Shafer (DS) evidence theory. By implementing five machine learning algorithms, this study constructs pipeline pressure and flow signal characteristics through wavelet decomposition. The data were normalized and processed using principal component analysis to prepare the algorithm for training. A new method for constructing basic probability functions using a confusion matrix and a simple support function is proposed and compared with the traditional triangular fuzzy number method. The basic probability function of the identification sample is refined by calculating a comprehensive discount factor. Finally, the results from multiple algorithms are fused using DS evidence theory. Experimental results demonstrate that after combining multiple algorithms, the average accuracy improves by 0.1565%, and the precision of the triangular fuzzy number method is enhanced by 0.091%. Full article

Aero engines are complex coupled systems in which faults in one subsystem can propagate and affect the performance of others. Relying on single-source performance parameters is often insufficient for accurately assessing component degradation. Although multi-source fusion diagnosis methods, such as those based on Bayesian networks, have been widely applied, their diagnostic performance remains limited when prior knowledge is scarce. To address this challenge, this paper proposes a multi-source information fusion diagnosis method for aero engine fault detection based on Dempster–Shafer (D-S) evidence theory. Data from gas path and vibration subsystems are separately processed to extract fault features, and a decision-level fusion strategy is employed to achieve comprehensive diagnoses. A case study based on real operational data from a two-shaft aero engine demonstrates that the proposed method significantly improves diagnostic performance. Specifically, the Bayesian-network-based fusion method achieves a diagnostic confidence of 87.2% without prior knowledge and 91.2% with prior knowledge incorporated, whereas D-S evidence theory attains a higher fault confidence of 99.6% without requiring any prior information. Full article

As global warming and environmental issues become increasingly prominent, steel enterprises, as a carbon-intensive industry, face urgent challenges in energy saving and emission reduction (ESER). This study develops a novel evaluation model integrating the WSR methodology, the cloud matter-element model, and an improved D-S evidence theory to address the fuzziness, randomness, and uncertainty in ESER assessments. A case study demonstrates that this approach can address the correlation between ESER indicators; quantify the evaluation process; and optimize issues related to fuzziness, randomness, and uncertainty. This finding provides a systematic evaluation framework for ESER in steel enterprises operating under the long-process production model (the blast furnace-converter model), offering valuable insights for formulating comprehensive ESER strategies throughout the entire production process. Full article

Landscape spatial structure serves as the foundational framework for vegetation arrangement and spatial organization, playing a crucial role in assessing landscape morphology. Traditional 2D graph theory methods have provided insights into planar structural characteristics but fail to capture the complexity of three-dimensional spatial attributes and organizational processes inherent in landscape systems. To address these limitations, this study proposes a novel multidimensional framework for the quantitative analysis and evaluation of landscape spatial structure by integrating 3D point cloud technology with spatial network analysis. The methodology consists of three key components: (1) the formulation of multidimensional spatial organization theory, (2) spatial unit extraction and structure analysis through ArcGIS 10.5 and Cytoscape v3.6.1, and (3) the development of an indicator system for evaluating spatial structure organization. The framework was validated through the analysis of 30 urban parks, where the regularity and range of indicators are generalized to establish evaluation criteria and determine weights. The findings indicate that spatial structure indicators are moderation indicators with optimal value ranges. The evaluation system was subsequently applied across the 30 parks for comprehensive evaluation. A total of 6 of 30 parks have comprehensive scores over 0.95. In practical application, the design score of Shuyang Park improved from 0.692 to 0.826 after evaluation and optimization, demonstrating the method’s effectiveness. This study underscores the potential of digital methodologies in advancing landscape spatial structure modeling, enhancing the understanding of spatial organization, and transitioning subjective assessments toward evidence-based objective evaluations. The proposed methodology and findings offer valuable insights for diagnosing, assessing, optimizing, and managing urban green spaces. Full article

Influential node identification is an important and hot topic in the field of complex network science. Classical algorithms for identifying influential nodes are typically based on a single attribute of nodes or the simple fusion of a few attributes. However, these methods perform poorly in real networks with high complexity and diversity. To address this issue, a new method based on the Dempster–Shafer (DS) evidence theory is proposed in this paper, which improves the efficiency of identifying influential nodes through the following three aspects. Firstly, Dempster–Shafer evidence theory quantifies uncertainty through its basic belief assignment function and combines evidence from different information sources, enabling it to effectively handle uncertainty. Secondly, Dempster–Shafer evidence theory processes conflicting evidence using Dempster’s rule of combination, enhancing the reliability of decision-making. Lastly, in complex networks, information may come from multiple dimensions, and the Dempster–Shafer theory can effectively integrate this multidimensional information. To verify the effectiveness of the proposed method, extensive experiments are conducted on real-world complex networks. The results show that, compared to the other algorithms, attacking the influential nodes identified by the DS method is more likely to lead to the disintegration of the network, which indicates that the DS method is more effective for identifying the key nodes in the network. To further validate the reliability of the proposed algorithm, we use the visibility graph algorithm to convert the GBP futures time series into a complex network and then rank the nodes in the network using the DS method. The results show that the top-ranked nodes correspond to the peaks and troughs of the time series, which represents the key turning points in price changes. By conducting an in-depth analysis, investors can uncover major events that influence price trends, once again confirming the effectiveness of the algorithm. Full article

Geothermal resources are crucial for sustainable energy development, yet accurately detecting geothermal anomalies in complex terrains remains a significant challenge. This study develops a multi-source thermal infrared approach to enhance geothermal anomaly detection using Landsat 8 and ASTER land surface temperature (LST) data. The Yangbajing–Yangyi Basin in Tibet, characterized by high altitude and rugged topography, serves as the study area. Landsat 8 winter time-series data from 2013 to 2023 were processed on the Google Earth Engine (GEE) platform to generate multi-year average LST images. After water body removal and altitude correction, a local block thresholding method was applied to extract daytime geothermal anomalies. For nighttime data, ASTER LST products were analyzed using global, local block, elevation zoning, and fault buffer strategies to extract anomalies, which were then fused using Dempster–Shafer (D–S) evidence theory. A joint daytime–nighttime analysis identified stable geothermal anomaly regions, with results closely aligning with known geothermal fields and borehole distributions while predicting new potential anomaly zones. Additionally, a 21-year time-series analysis of MODIS nighttime LST data identified four significant thermal anomaly areas, interpreted as potential magma chambers, whose spatial distributions align with the identified anomalies. This multi-source approach highlights the potential of integrating thermal infrared data for geothermal anomaly detection, providing valuable insights for exploration in geologically complex regions. Full article

This paper addresses the critical challenges of inadequate localization and low quantification precision in structural damage identification by introducing a novel approach that integrates Dempster–Shafer (D-S) evidence theory with the Self-Adaptive Differential Evolution (SDE) algorithm. First, modal parameters are extracted from a simply supported beam using the finite element (FE) method, and the corresponding index values are computed based on the formulated damage identification index equations. Next, these indices are applied to analyze damage localization in both single-position and multi-position scenarios within the simply supported beam. The SDE algorithm is then employed to dynamically optimize the initial weights and thresholds of various algorithms, ensuring the assignment of optimal values. Finally, the resulting data are input into the model for training, yielding a prediction model with enhanced accuracy that can precisely estimate the damage severity of the simply supported beam. The findings demonstrate that the three proposed damage identification indices—DI_1,i,j, DI_2,i,j, and DSDI_i,j—not only achieve high accuracy in damage localization but also significantly improve the precision of algorithms optimized by the SDE. These methods exhibit strong accuracy and robustness, providing a valuable reference for damage identification in small-to-medium-span simply supported beam bridges. Full article

Miscarriages of justice led to concerns that forensic science reports were prosecution-biassed and led to elementary errors of probability. The European Network of Forensic Science Institutes (EFNSI) and other institutes developed standards requiring reporting of the probability of evidence under all hypotheses (usually prosecution and defence hypotheses) with the likelihood ratio (LR). $LR={\displaystyle \frac{p\left(E|H_p\right)}{p\left(E|H_d\right)}},$ values > 1, being probative for a prosecution hypothesis. In elementary two-variable conditional probability theory (Baye’s theorem), the LR is also an updating factor which multiplies the odds of guilt for each item of evidence considered. Although this is not true for multiple-variable probability theory, the value of the LR as a valid measure of evidential probity remains. Forensic scientists are experts in evidence and should not stray into the role of the Court to consider the probability of the hypotheses given the totality of the evidence: $p\left(H_p{,H}_d|,E_1,E_2\dots E_n\right).$ Medical experts may be required to assist the court with diagnoses (the hypothesis), but this privilege is balanced by vigilance that experts do not stray beyond their expertise. A narrow interpretation of expertise hinders the evaluation of the evidence under hypotheses adjacent to the area of expertise. This paradox may be overcome by experts declaring competence in areas adjacent to their main area of expertise. Regulatory bodies do not currently require medical experts to adhere to EFNSI guidelines in evaluative reporting. Legal opinion is divided on whether probability theory can be applied to cases requiring medical expertise. Medical experts should, in their reports, clearly separate evaluating the probability of the evidence (where evaluative reporting should apply) and evaluating the probability of hypotheses where methodology should be prioritised over opinion. The reckless misapplication of elementary probability theory, typically transposing conditional probabilities or neglecting prior odds, may lead to the jury being misled into believing posterior odds of guilt are many orders of magnitude greater than reality. Medical experts should declare training in elementary probability theory. Inaccurate probabilities are a joint enterprise between all who inform or advise the jury, so all must be trained in elementary probability theory. Full article

Short-term traffic flow prediction plays a crucial role in transportation systems by describing the time evolution of traffic flow over short periods, such as seconds, minutes, or hours. It helps people make informed decisions about their routes to avoid congested areas and enables traffic management departments to quickly adjust road capacities and implement effective traffic management strategies. In recent years, numerous studies have been conducted in this area. However, there is a significant gap in research regarding the uncertainty of short-term traffic flow, which negatively impacts the accuracy and robustness of traffic flow prediction models. In this paper, we propose a novel comprehensive entropy-cloud model that includes two algorithms: the Fused Cloud Model Inference based on DS Evidence Theory (FCMI-DS) and the Cloud Model Inference and Prediction based on Compensation Mechanism (CMICM). These algorithms are designed to address the short-term traffic flow prediction problem. By utilizing the cloud model of historical flow data to guide future short-term predictions, our approach improves prediction accuracy and stability. Additionally, we provide relevant mathematical proofs to support our methodology. Full article