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The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers. [ABSTRACT FROM AUTHOR]

Computer vision-based inspection methods show promise for automating post-earthquake building inspections. These methods survey a building with unmanned aerial vehicles and automatically detect damage in the collected images. Nevertheless, assessing the damage's impact on structural safety requires localizing damage to specific building components with known design and function. This paper proposes a BIM-based automated inspection framework to provide context for visual surveys. A deep learning-based semantic segmentation algorithm is trained to automatically identify damage in images. The BIM automatically associates any identified damage with specific building components. Then, components are classified into damage states consistent with component fragility models for integration with a structural analysis. To demonstrate the framework, methods are developed to photorealistically simulate severe structural damage in a synthetic computer graphics environment. A graphics model of a real building in Urbana, Illinois, is generated to test the framework; the model is integrated with a structural analysis to apply earthquake damage in a physically realistic manner. A simulated UAV survey is flown of the graphics model and the framework is applied. The method achieves high accuracy in assigning damage states to visible structural components. This assignment enables integration with a performance-based earthquake assessment to classify building safety. [ABSTRACT FROM AUTHOR]

The existing machine learning (ML) models for vibration-based damage assessments often use highly compressed features or are restricted to preprocessed signals with fixed durations and sampling rates. Additionally, the learning capacities of ML models and computational resources are limited, which restricts using raw signals as direct input. This paper studied Mel filter banks (MFBs) for seismic signal processing, inspired by speech recognition technology. It is argued that the same filter designs in audio engineering may not be appropriate for seismic records, and therefore, a customized filter bank formulation was developed. Hybrid deep learning models for rapid assessments (HyDRA) were introduced as multibranch neural network architectures that enable end-to-end training for different types of processed vibration data structures. Moreover, the performance-based earthquake engineering (PBEE) equation was adjusted to integrate ML model uncertainties for probabilistic assessments. The proposed concepts were validated in a case study based on a data set of 32,400 nonlinear time-history analyses of a highway bridge in California. Insights and guidelines are provided for optimum filter design based on 5,184 experiments. Several HyDRA architectures were compared with benchmark models. The optimized MFB feature type outperformed features obtained from continuous wavelet transform and a stacked vector of conventional earthquake engineering indexes. A Bayesian variant of HyDRA was investigated to showcase its integration in the modified PBEE equation. Adopting custom filter banks with the HyDRA architecture enables effective feature extraction from raw vibration records by diversifying feature space and preserving information in the time and frequency domains. [ABSTRACT FROM AUTHOR]

This paper explores disciplinary patterns of assessment and feedback, using data from the Transforming the Experience of Students through Assessment project. Its central research question concerns the effect of disciplinary assessment patterns on student learning. Audit data from 18 degree programmes at 8 UK universities showed variations in assessment patterns across three disciplinary fields: Humanities, Professional and Science courses. There were variations in assessment demands; in the quantity of feedback and in the proportion of examinations. Statistical analysis of Assessment Experience Questionnaire data (n = 762) explored whether these differences influenced students' perceptions of learning across the disciplines. Findings showed that there were no significant differences in students' perceptions of learning from examinations. Humanities students evaluated the appropriateness of their assessment lower than other discipline groups; professional students were less clear about goals and standards. The researchers propose explanations for these findings and suggest avenues for further research. [ABSTRACT FROM PUBLISHER]