Computational notebooks (e.g., Jupyter, Google Colab) are widely used by datascientists. A key feature of notebooks is the interactive computing model ofiteratively executing cells (i.e., a set of statements) and observing theresult (e.g., model or plot). Unfortunately, existing notebook systems do notoffer time-traveling to past states: when the user executes a cell, thenotebook session state consisting of user-defined variables can be irreversiblymodified - e.g., the user cannot 'un-drop' a dataframe column. This is because,unlike DBMS, existing notebook systems do not keep track of the session state.Existing techniques for checkpointing and restoring session states, such asOS-level memory snapshot or application-level session dump, are insufficient:checkpointing can incur prohibitive storage costs and may fail, whilerestoration can only be inefficiently performed from scratch by fully loadingcheckpoint files. In this paper, we introduce a new notebook system, Kishu, that offerstime-traveling to and from arbitrary notebook states using an efficient andfault-tolerant incremental checkpoint and checkout mechanism. Kishu createsincremental checkpoints that are small and correctly preserve complexinter-variable dependencies at a novel Co-variable granularity. Then, to returnto a previous state, Kishu accurately identifies the state difference betweenthe current and target states to perform incremental checkout at sub-secondlatency with minimal data loading. Kishu is compatible with 146 object classesfrom popular data science libraries (e.g., Ray, Spark, PyTorch), and reducescheckpoint size and checkout time by up to 4.55x and 9.02x, respectively, on avariety of notebooks.
Metallic tubular structures are significant for impact-resistant applications, with aluminium tubes offering a balance of strength and ductility. However, their crashworthiness characteristics can be further enhanced by incorporating thermoplastic polymer composite fillers. In this study, an experimental investigation was conducted on the transverse crushing performance of 3D-printed polymer composite filled aluminium tubes to evaluate their crushing behavior and energy absorption characteristics. Commercially available aluminium cylindrical tubes were used, while the polymer composite fillers were fabricated using the fused deposition modeling (FDM) technique. To analyze their transverse crushing force-deformation characteristics, the experimental setup involved quasi-static transverse compression tests on tubes with different diameters. Obtained outcomes revealed that the proposed hybrid tubes exhibited superior energy absorption due to the synergistic interaction between the aluminum tube and polymeric core. The maximum value of energy absorbing ability is 998.53 kJ/g for T-Al-PCF-60 tube which is greater than that of all the tube configurations tested. The findings highlight the potential of the proposed 3D-printed polymer composite-filled aluminum hybrid tubes as effective crashworthy structures for impact mitigation in automotive, aerospace, and protective applications.
Four-wave mixing (FWM) is the most dominating feature for 440 × 14 Gbps super dense wavelength division multiplexing system, which almost spoil the performance of super dense communication signals. So, the effect of FWM has mitigated with the implementation of optical phase conjugation with combination of dispersion modified fiber and fiber grating compensator. Further, power level of the transmitting signals has also enhanced with the arrangement of RAMAN-SOA hybrid optical amplifier. Furthermore, final analysis has recommended that proposed techniques are the acceptable approach for the proposed super dense communication system.