Semantic TRIZ feasibility in technology development, innovation, and production: A systematic review

The study unfolds with an acknowledgment of the extensive exploration of TRIZ components, spanning a solid philosophy, quantitative and inductive methods, and practical tools, over the years. While the adoption of Semantic TRIZ (S-TRIZ) in high-tech industries for system development, innovation, and production has increased, the application of AI technologies to specific TRIZ components remains unexplored. This systematic literature review is conducted to delve into the detailed integration of AI with TRIZ, particularly S-TRIZ. The results elucidate the current state of AI applications within TRIZ, identifying focal TRIZ components and areas requiring further study. Additionally, the study highlights the trending AI technologies in this context. This exploration serves as a foundational resource for researchers, developers, and inventors, providing valuable insights into the integration of AI technologies with TRIZ concepts. The study not only paves the way for the development and automation of S-TRIZ but also outlines limitations for future research, guiding the trajectory of advancements in this interdisciplinary field.

Systematic Literature Review on Visual Analytics of Predictive Maintenance in the Manufacturing Industry

The widespread adoption of cyber-physical systems and other cutting-edge digital technology in manufacturing industry production facilities may motivate stakeholders to embrace the idea of Industry 4.0. Some industrial companies already have different sensors installed on their machines; however, without proper analysis, the data collected is not useful. This systematic review's main goal is to synthesize the existing evidence on the application of predictive maintenance (PdM) with visual aids and to identify the key knowledge gaps in areas including utilities, power generation, industry, and energy consumption. After a thorough search and evaluation for relevancy, 37 documents were identified. Moreover, we identified the visual analytics of PdM, including anomaly detection, planning/scheduling, exploratory data analysis (EDA), and explainable artificial intelligence (XAI). The findings revealed that anomaly detection was a major domain in PdM-related works. We conclude that most of the literature lacks depth in terms of an overall framework that combines data-driven and knowledge-driven techniques of PdM in the manufacturing industry. Some works that utilized both techniques indicated promising results, but there is insufficient research on involving maintenance personnel's feedback in the latter stage of PdM architecture. Thus, there are still pertinent issues that need to be investigated, and limitations that need to be overcome before PdM is deployed with minimal human involvement.

A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos

Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods.

Structural basis of electron transfer modulation in the purple CuA center

The X-ray structure of an engineered purple CuA center in azurin from Pseudomonas aeruginosa has been determined and refined at 1.65 A resolution. Two independent purple CuA azurin molecules are in the asymmetric unit of a new P21 crystal, and they have nearly identical conformations (rmsd of 0.27 A for backbone atoms). The purple CuA azurin was produced by the loop-engineering strategy, and the resulting overall structure is unperturbed. The insertion of a slightly larger Cu-binding loop into azurin causes the two structural domains of azurin to move away from each other. The high-resolution structure reveals the detailed environment of the delocalized mixed-valence [Cu(1.5).Cu(1.5)] binuclear purple CuA center, which serves as a useful reference model for other native proteins, and provides a firm basis for understanding results from spectroscopic and functional studies of this class of copper center in biology. The two independent Cu-Cu distances of 2.42 and 2.35 A (with respective concomitant adjustments of ligand-Cu distances) are consistent with that (2.39 A) obtained from X-ray absorption spectroscopy with the same molecule, and are among the shortest Cu-Cu bonds observed to date in proteins or inorganic complexes. A comparison of the purple CuA azurin structure with those of other CuA centers reveals an important relationship between the angular position of the two His imidazole rings with respect to the Cu2S2(Cys) core plane and the distance between the Cu and the axial ligand. This relationship strongly suggests that the fine structural variation of different CuA centers can be correlated with the angular positions of the two histidine rings because, from these positions, one can predict the relative axial ligand interactions, which are responsible for modulating the Cu-Cu distance and the electron transfer properties of the CuA centers.

Enhanced rate of intramolecular electron transfer in an engineered purple CuA azurin

The recent expression of an azurin mutant where the blue type 1 copper site is replaced by the purple CuA site of Paracoccus denitrificans cytochrome c oxidase has yielded an optimal system for examining the unique electron mediation properties of the binuclear CuA center, because both type 1 and CuA centers are placed in the same location in the protein while all other structural elements remain the same. Long-range electron transfer is induced between the disulfide radical anion, produced pulse radiolytically, and the oxidized binuclear CuA center in the purple azurin mutant. The rate constant of this intramolecular process, kET = 650 +/- 60 s-1 at 298 K and pH 5.1, is almost 3-fold faster than for the same process in the wild-type single blue copper azurin from Pseudomonas aeruginosa (250 +/- 20 s-1), in spite of a smaller driving force (0.69 eV for purple CuA azurin vs. 0.76 eV for blue copper azurin). The reorganization energy of the CuA center is calculated to be 0.4 eV, which is only 50% of that found for the wild-type azurin. These results represent a direct comparison of electron transfer properties of the blue and purple CuA sites in the same protein framework and provide support for the notion that the binuclear purple CuA center is a more efficient electron transfer agent than the blue single copper center because reactivity of the former involves a lower reorganization energy.