Search Results (2,655)

Effective project selection is a critical determinant of success for Design for Six Sigma (DFSS), particularly in automotive environments defined by high technical complexity and constrained resources. Because these selection tasks involve competing priorities, they are fundamentally multi-criteria decision-making (MCDA) problems that directly impact a company’s economic performance. This paper proposes a hybrid decision-support framework that integrates the Analytic Hierarchy Process (AHP) with a normalized scoring model. In this approach, classical AHP pairwise comparisons are used to derive consistent criteria weights, while project alternatives are evaluated on a 1–10 normalized scale to ensure the model remains scalable and practical for an industrial setting. The framework was empirically validated through a case study in an automotive company evaluating twelve DFSS project concepts. The results reveal that experts prioritize Product Quality (33%) and Cost/Functionality (33%) above all other factors, with these two criteria accounting for 66% of the total decision weight. Furthermore, the study established classification rules where projects scoring above 7.2 showed high implementation potential, while those below 5.2 were frequently discontinued. This structured approach enables a transparent and justifiable prioritization process that supports economic and operational sustainability by significantly reducing wasted engineering hours and prototype costs. Full article

Accurate carbon price forecasting is critical for trading decisions, risk management and policy formulation in carbon markets. However, mainstream decomposition-ensemble models suffer from two key drawbacks: point-wise modeling fails to capture long-term temporal dependencies, while independent modeling of decomposed trend and seasonal components leads to serious information loss. To address these limitations, this paper proposes a novel Dual-Path Interactive Attention Network (DPIANet) for carbon price time series forecasting, whose dual-parallel architecture consists of a Dual Interaction Attention (DIA) Block and a Decomposition–Subsequence Interaction Attention (DSIA) Block. First, DPIANet employs a patch-wise partitioning strategy to extract local temporal semantic information inaccessible to traditional point-wise segmentation. The DIA Block jointly captures temporal dependencies between different patches within the same sequence and inter-feature dependencies within the same time step. In parallel, the DSIA Block extracts interactive features between decomposed trend and seasonal subsequences, fusing these features with original subsequences to enhance representation and mitigate decomposition-induced information loss. A dual-layer feature selection method (PMI and XGBoost-SHAP) is adopted to identify key driving factors. Experiments on four representative Chinese regional carbon trading markets covering 2014-2020 show that DPIANet achieves superior prediction performance over state-of-the-art models in terms of MSE and MAE, with competitive robustness across different market characteristics, providing practical decision support for carbon market stakeholders. Full article

Wastewater membrane bioreactors (MBRs) have become an important advanced treatment technology due to their ability to produce high-quality effluent suitable for discharge and water reuse. However, their broader and more sustainable application remains constrained by membrane fouling, elevated energy demand, and the operational complexity of coupled biological and membrane separation processes. This comprehensive review critically evaluates the growing application of machine learning (ML), explainable artificial intelligence (XAI), and digital twin (DT) technologies in MBR systems. Published studies on fouling prediction, energy optimization, effluent quality estimation, and intelligent operational support are critically evaluated, with explicit attention to model performance, dataset limitations, and generalizability. The reviewed literature shows that ML models, particularly ensemble methods, support vector machines, and deep learning approaches, have demonstrated strong potential for predicting major MBR performance indicators, including transmembrane pressure, permeate flux, fouling resistance, and selected effluent-quality variables. In parallel, XAI methods such as SHAP, LIME, and Anchors are increasingly being used to enhance model transparency and to reveal the dominant factors controlling process performance. Digital twin frameworks further extend this potential by enabling the integration of mechanistic understanding, online sensor data, data-driven prediction, and interpretable decision support within real-time operational platforms. Nevertheless, several barriers continue to hinder practical implementation, including the limited number of full-scale studies, the scarcity of openly accessible and standardized datasets, insufficient consideration of uncertainty and model drift, and the early-stage maturity of DT deployment in operational plants. The evidence reviewed suggests that integrating ML, XAI, and DT can substantially improve the reliability, interpretability, and operational efficiency of MBR systems. Future research should therefore focus on full-scale validation, the development of benchmark datasets, uncertainty-aware modeling, and practical deployment strategies for interpretable intelligent MBR management. Full article

Co-channel interference (CCI) remains a critical factor affecting link reliability in narrowband wireless systems, especially in scenarios with intensive frequency reuse, overlapping coverage, and dense terminal access. Existing interference detection methods are either computationally simple but insufficiently sensitive to short-term spectral variations, or highly accurate but dependent on labeled data and nontrivial inference resources. To address this issue, this paper proposes a lightweight CCI detection method in the FFT domain based on spectrum-jump analysis. The proposed method does not rely on absolute power growth as the primary interference indicator. Instead, it tracks the temporal inconsistency of dominant spectral-bin indices across consecutive FFT frames and converts recurrent peak-bin migration into an interference decision through a short-window counting mechanism. The method is computationally efficient, interpretable, and suitable for real-time deployment without offline model training. SDR-based measurements are combined with controlled repeated experiments to assess detector performance under varying signal-to-noise ratio (SNR), interference-to-signal ratio (ISR), carrier-frequency offset (CFO), multi-peak ambiguity, and two-path Rayleigh fading conditions. On the measured SDR record, the proposed method captures all interference-positive windows after the marked onset, while the controlled SNR/ISR experiments yield an overall detection probability of 96.0% over 250 CCI trials with no false alarms over 250 normal trials. ROC and precision–recall analyses further show that the selected threshold lies within a broad validation plateau. The results also reveal clear applicability boundaries: when the CFO approaches zero, when the interference is very weak, or when multiple stationary peaks have nearly equal power, dominant-bin migration may be weak or ambiguous. Therefore, the proposed approach is a low-complexity online detector for CCI cases that induce observable FFT-bin instability, and it can also serve as a front-end trigger for more advanced interference analysis modules. Full article

Sustainable (ESG) investments have gained significant interest, prompting renewed attention to retail investors’ decision-making processes. ESG investing is motivated by both financial concerns and psychological factors. However, despite growing interest, the motivational underpinnings of sustainable asset allocation remain underexplored. This study bridges economic psychology and sustainable finance to examine drivers of ESG investment intentions and choices in the Italian market. Drawing on the Theory of Planned Behavior, it explores how attitudes, subjective norms, perceived behavioral control, and trust shape ESG investing intentions and choices. Results show that each factor significantly influences investing intentions when considered independently. In particular, the affective dimension of attitudes emerges as especially relevant. These findings challenge traditional views of financial rationality in ESG contexts, suggesting that the motivations of sustainability-oriented investors may differ meaningfully from those of traditional investors. Practical implications are that ESG communication should appeal to emotional and ethical dimensions of decisions, while educational initiatives should enhance investors’ ability to critically assess ESG-related information. Full article

Environmental, Social, and Governance (ESG) metrics have become a cornerstone to sustainable finance, yet their measurement and predictability remain constrained by data heterogeneity, methodological divergence, and disclosure bias. This study develops a comprehensive ESG prediction framework grounded in the Design Science Research paradigm, integrating advanced machine learning techniques with rigorous data preprocessing, feature selection, and temporal validation. Using firm-level data from Refinitiv and Bloomberg, the analysis distinguishes between ESG composite performance and disclosure-based robustness, addressing a critical gap in the literature. Ensemble learning models, including Random Forest and XGBoost, are evaluated alongside deep learning architectures using multiple sampling strategies and rolling-window validation. The results demonstrate that ESG performance is moderately forecastable, with ensemble methods consistently outperforming neural networks in structured datasets. In contrast, disclosure robustness exhibits lower predictability, reflecting its dependence on discretionary strategic reporting and institutional factors. The findings highlight the importance of data quality, model selection, and validation design in ESG analytics, while emphasizing the limitations of deep learning in tabular financial contexts. The integration of explainable artificial intelligence further enhances interpretability by identifying key predictors of ESG outcomes. Overall, the study contributes to the literature by providing a robust, interpretable, and methodologically rigorous framework for ESG prediction, with implications for investors, regulators, and corporate decision-making. Full article

Operational shutdowns following hazardous liquid pipeline incidents are critical but poorly understood events that impact the U.S. energy supply. Although prior research has investigated the causes and outcomes of pipeline failures, limited work has explained what drives both the likelihood of a shutdown and the duration once it begins. The goal of this study is to address this gap by developing a hurdle regression model to examine the two-stage shutdown mechanism in pipeline incidents, using the Pipeline and Hazardous Materials Safety Administration (PHMSA) incident dataset from 2010 to 2025. The hurdle model consists of a logistic regression restricted to pre-decision predictors to model the probability of shutdown, and a lognormal regression to model the duration of those leading to shutdown. The results revealed that distinct factors are associated with each outcome. Shutdown probability is associated with pre-decision operational and contextual indicators, including operating pressure at the time of incident, accident type, location, monitoring presence, and response delay. In contrast, shutdown duration is associated with logistical complexity and post-incident severity, including incidents at pipeline crossings, pressures exceeding 110% of the maximum operating pressure, and reported property damage. These findings, while exploratory in nature given the use of public incident data, offer practical reference points for operators and regulators who aim to shorten recovery time and strengthen the resilience of energy infrastructure. Full article

This paper addresses the important issue of the proper management and protection of subterranean monuments. It concerns the analysis and decoding of the microclimate that is created in heritage structures, which are structures located beneath the soil or carved into rock. The aim of this study is to understand the hygrothermal processes occurring in the mass of underground structural elements, such as evaporation, condensation, water content, and heat fluxes, based on the principles of building physics. The methodology used is the following: a systematic literature review on the topic, an overview of the factors affecting the microclimate, the assessment methodology, and the simulation tools used to decode and evaluate microclimate in subterranean heritage structures; a discussion of the current gaps; and finally, a proposal for future directions for research. A review of the literature reveals that researchers worldwide have employed similar methodologies to approach this complex issue. Recordings and analyses of the microclimate inside underground monuments lead to decision-making and the formulation of actions for optimal preservation. Due to the large number of parameters involved in microclimate analysis, computer software for numerical simulation has been used in many cases. Following the review of the relevant literature in the field of study, a critical discussion concludes by proposing directions for future research on this important topic. Basic results of this research identify current gaps, problems, and limitations. These include technical and practical issues or gaps concerning lack of data for material properties and weather conditions. Another significant limitation arises from the complexity of physical interactions, as well as from the human factor, which involves the proper use of the simulation program and the correct interpretation of the calculation results. This study demonstrates that the microclimate of subterranean heritage structures is the result of complex interactions between climate, geology, architectural design, material properties, and human use. Across different geographical and cultural contexts, subterranean monuments exhibit distinct microclimatic behaviors. The comparative analysis of case studies highlights that while subterranean environments generally benefit from thermal stability, they remain highly vulnerable to moisture dynamics, ventilation changes, and external climatic coupling. Hence, there is a necessity for context-specific approaches rather than generalized conservation solutions. Decoding subterranean microclimates requires a multidisciplinary framework that combines environmental monitoring, material indicators, architectural analysis, and numerical modeling. Full article

Unsafe behaviors among construction workers constitute a major contributing factor to construction accidents, making it critically important to explore their underlying mechanisms and temporal dynamics from a cognitive perspective. This study employed an exploratory sequential mixed-methods approach. Initially, grounded theory was used to conduct three-level coding of in-depth interview data from 35 construction workers, resulting in the development of a cognitive theory model of unsafe behavior among construction workers comprising two main categories: ‘ perceptual recognition’ and ‘cognitive response’. Subsequently, a questionnaire was designed based on this model, and a 10-day longitudinal survey was conducted among 300 workers. Multi-group structural equation modelling was employed to analyze the temporal variation in the relationship between cognitive ability and unsafe behavior. The results indicate that: workers’ cognitive abilities can be decomposed into four dimensions—perceiving danger, identifying hazards, perceptual response, and decision-making response—and further summarized into two higher-order factors: perceptual recognition and cognitive response; (2) cognitive abilities are significantly negatively correlated with unsafe behavior; (3) this relationship exhibits significant temporal variations, with the inhibitory effect on Day 5 (path coefficient −0.95) being stronger than that on Day 1 (−0.88) and Day 10 (−0.50); furthermore, the ‘cognitive response → decision-making response’ path also shows significant differences between Day 5 and Day 10. The study reveals the pattern of fluctuations over time in the inhibitory effects of workers’ cognitive ability on unsafe behavior, providing a theoretical basis for construction companies to implement dynamic and targeted safety interventions. Full article

The long-term operational reliability of underground gas storage (UGS) facilities in depleted reservoirs is significantly challenged by reservoir damage during multi-cycle injection and production (I&P). While the impact of cycle numbers has been extensively studied, the influence of variable pressure depletion rates remains insufficiently quantified. This study investigates the reservoir damage mechanisms of sandstone cores from the Sichuan Basin under different depletion rates (0.5 and 2.5 MPa/min) over 20 I&P cycles. Experimental results indicate that the pressure depletion rate is a decisive factor in permeability impairment. For the sample subjected to a fast depletion rate (2.5 MPa/min), the total permeability loss reached 18.2%, which is 2.16 times higher than that of the slow-rate sample (8.4% at 0.5 MPa/min). Notably, the high-rate sample sustained nearly 60% of its total damage within the initial three cycles, highlighting a critical window of vulnerability during early UGS operations. Theoretical hydrodynamic analysis suggests that at 2.5 MPa/min, the instantaneous shear force (6.42 nN) exceeds the representative adhesion force of clay minerals (~5.0 nN), which may increase the likelihood of clay mobilization under the present experimental conditions. Combined with the XRD-identified clay content and the observed permeability evolution, the damage is interpreted as being likely associated with fines migration and pore-throat plugging. Based on these findings, a “Slow-Start” operational protocol—maintaining depletion rates below 1.0 MPa/min during the initial cycles—is preliminarily recommended under the present experimental conditions to help preserve reservoir conductivity and extend facility longevity. Full article

Background/objectives: With the rapid expansion of Internet-based health information, individuals increasingly rely on digital sources to obtain medical knowledge and manage their health. Health literacy plays a critical role in determining how effectively individuals access, understand, and utilize such information. This study aimed to examine the association between subjective health perception and health information literacy-related indicators among Korean adults. Methods: This cross-sectional study utilized secondary data from the 2019 “Health Information Literacy Improvement Study” conducted by the Korea Institute for Health and Social Affairs. A total of 1000 adults aged 19–69 years were included in the analysis. Participants were categorized into three groups according to subjective health perception (good, normal, and poor). Descriptive statistics and chi-square tests were conducted to examine differences. In addition, multinomial logistic regression analysis was performed to identify factors associated with subjective health perception. Results: Participants with better subjective health perception reported fewer chronic diseases (p < 0.001), healthier dietary behaviors (p < 0.001), and more frequent health information seeking (p = 0.023). They also reported greater ease in finding and understanding health information (p < 0.001). Multinomial logistic regression analysis revealed that health information literacy-related factors, including information-seeking behavior and the ability to evaluate information reliability, were significantly associated with subjective health perception. Individuals with fewer chronic diseases and healthier behaviors were less likely to report poor subjective health. Conclusions: Subjective health perception was significantly associated with multiple health information literacy-related indicators and health-seeking behaviors. These findings highlight the importance of improving health information literacy-related competencies to support informed health decision-making and promote positive health perceptions. Full article

Bench slopes in open-pit mines are highly susceptible to progressive deformation and instability due to the coupled effects of excavation disturbance, rock mass weathering, and extreme rainfall, posing significant challenges to rapid risk assessment and engineering decision-making. To address the limitations of conventional methods in efficiency and adaptability under complex multi-factor conditions, this study proposes a hybrid simulation–artificial intelligence framework for rapid slope stability assessment and bench face angle optimization. Multi-scenario numerical simulations were conducted by integrating geological investigation data, laboratory and in situ mechanical parameters, and extreme rainfall conditions to characterize slope deformation and failure mechanisms and generate a dataset for machine learning model training. Machine learning models were trained using slope height, bench face angle, unit weight, cohesion, and friction angle as inputs, and safety factors under natural and extreme rainfall conditions as outputs, with hyperparameters optimized by Bayesian optimization. The results indicate that highly weathered rock masses dominate shallow deformation and act as critical weak zones, while extreme rainfall significantly accelerates instability evolution and reduces slope safety factors. Among the RF, SVR, and ELM models, the Bayesian-optimized support vector regression (BO-SVR) exhibits the best predictive performance (R² > 0.98). SHapley Additive exPlanations (SHAP) analysis reveals that slope height and shear strength parameters are the dominant controlling factors, whereas unit weight has a relatively limited influence. Validation using real landslide cases shows good agreement with numerical simulations, confirming the reliability of the proposed framework. The developed approach enables rapid risk evaluation and supports bench face angle optimization, providing an effective tool for intelligent slope management in open-pit mining. Full article

Evaluations of medical artificial intelligence (AI) explanations often rely on self-reported trust, perceived usefulness, acceptance, or final decision outcomes, while less directly characterizing whether users check evidence around AI outputs during decision making. In AI-assisted chest radiograph interpretation, a critical process-level question is whether users return from the AI output to the original image evidence when further scrutiny is needed. To address this question, we examined whether verifiable explanations—explanations designed to make AI recommendations checkable against the original image evidence—are associated with process markers of visual evidence checking in AI-assisted chest radiograph interpretation using eye-tracking and human-factors process measures. A 2 × 2 between-subjects experiment manipulated verifiable explanations (present vs. absent) and risk context (high vs. low), with AI recommendation correctness embedded at the trial level. Fifty-six clinically trained participants each completed 24 interpretation trials. Analyses focused primarily on gaze transitions between the AI output and the original image and dwell time on the original image, with response time and exploratory verification-related behavioral states used as auxiliary process measures. Verifiable explanations did not simply increase acceptance of AI recommendations. Instead, when AI recommendations were incorrect, they were most clearly associated with more frequent AI–image transitions and longer absolute dwell time on the original image evidence. Exploratory state-based analyses further suggested a lower tendency toward no-verify adopt under incorrect AI recommendations, but these findings were treated as complementary rather than primary evidence. Overall, the value of verifiable explanations lies not only in final decisions but in whether they make AI recommendations more inspectable against the original evidence. These findings provide eye-tracking evidence consistent with visual evidence checking in AI-assisted diagnostic interfaces and underscore the value of process-sensitive human-factors measures in medical AI evaluation. Full article

To address the challenges of strong volatility, pronounced non-stationarity, and the inability of single models to simultaneously capture local dynamics and global dependencies in photovoltaic (PV) power series under complex weather conditions, this study proposes a short-term PV power forecasting framework that integrates weather-based clustering, signal decomposition, parameter optimization, and hybrid neural networks. First, a density-based clustering algorithm, namely Density-Based Spatial Clustering of Applications with Noise (DBSCAN), is employed to partition historical samples into distinct weather regimes, thereby mitigating the impact of heterogeneous meteorological conditions on model stability. Second, to handle the strong non-stationarity of PV power series, Variational Mode Decomposition (VMD) is introduced to decompose the original signal into multiple intrinsic components. The Crested Porcupine Optimizer (CPO) is further utilized to adaptively optimize key VMD parameters, including the number of modes and the penalty factor, thereby improving decomposition quality. Finally, a hybrid LSTM–Transformer forecasting model is constructed to jointly capture local temporal dynamics and long-range dependencies. The Newton–Raphson-Based Optimizer (NRBO) is employed to optimize critical hyperparameters, including the learning rate, regularization coefficient, and the number of hidden units, thereby enhancing model performance. The proposed method is validated using real-world data from a PV power station in Alice Springs, Australia. Experimental results demonstrate that, compared with the LSTM–Transformer baseline, the proposed model achieves reductions in RMSE of 0.086, 0.082, and 0.097 kW, and reductions in MAE of 0.062, 0.082, and 0.081 kW under clear-sky, cloudy, and rainy/snowy conditions, respectively. The corresponding R² values reach 0.993, 0.968, and 0.958. These results indicate that the proposed framework exhibits strong predictive performance across different weather scenarios and provides a reliable reference for short-term PV power forecasting and grid dispatching decisions. Full article

Port and shipping enterprises face significant uncertainty in making effective sustainable investment decisions to meet pressing carbon reduction targets. This study addresses this challenge by developing a fuzzy multi-method framework to identify and prioritize pivotal factors that guide sustainable investments. An evolutionary game model simulates the influencing factors, while the triangular fuzzy number (TFN) and evidential reasoning (ER) algorithm assess their importance and operability. The decision-making trial and evaluation laboratory (DEMATEL) method further refines these assessments. Finally, the Bayesian probability method corrects the posteriori probability, providing a comprehensive ranking. The results reveal that low-carbon technology is the most critical driver of sustainable investment, whereas environmental factors consistently rank the lowest in importance. This methodology aids ports and shipping enterprises in making sustainable investment decisions to reduce carbon emissions. Full article