How does organisational absorptive capacity matter in the assimilation of enterprise information systems?

Extant literature offers two mostly distinct perspectives on enterprise systems assimilation – driven either by internal expertise and learning capability or by external institutional pressures. This study combines the two perspectives and subscribes to the view that organisations’ learning capability moderates their acquiescence to institutional pressures. The study then anchors organisational learning capability to the concept of absorptive capacity and proposes that its two dimensions – potential absorptive capacity (PACAP) and realised absorptive capacity (RACAP) – affect enterprise systems assimilation through different pathways. Our survey‐based empirical study of Enterprise Resource Planning (ERP) systems in the post‐implementation stage reveals that while both PACAP and RACAP have a positive direct impact on assimilation, PACAP positively moderates the impact of mimetic (institutional) pressures, but not normative (institutional) pressures, on assimilation; whereas RACAP positively moderates the impact of normative pressures, but not mimetic pressures, on assimilation. Thus, our theoretical contribution lies in understanding the distinct ways in which PACAP and RACAP moderate the influence of external institutional pressures on enterprise systems assimilation.     

Adaptive relay co-ordination using a busbar splitting approach for a system integrity protection scheme

Power system faults can often result in excessively high currents. If sustained for a long time, such high currents can damage system equipment. Thus, it is desirable to operate the relays in the minimum possible time. In this paper, a busbar splitting approach is used for adaptive relay setting and co-ordination purposes for a system integrity protection scheme (SIPS). Whenever a fault occurs, the busbar splitting scheme splits a bus to convert a loop into a radial structure. The splitting schemes are chosen such that the net fault current is also reduced. Busbar splitting eliminates the dependency upon minimum breakpoints set (MBPS) and reduces the relay operating time, thus making it adaptive. The proposed methodology is incorporated into the IEEE 14-bus and IEEE 30-bus systems with single and multiple fault conditions. The modeling and simulation carried out in ETAP, and the results of the proposed busbar splitting-based relay co-ordination are compared with the MBPS splitting-based relay co-ordination.     

Analyzing the efficacy of using digital ink devices in a learning environment

There has been increased interest on the impact of mobile devices such as PDAs and Tablet PCs in introducing new pedagogical approaches and active learning experiences. We propose an intelligent system that efficiently addresses the inherent subjectivity in student perception of note taking and information retrieval. We employ the idea of cross indexing the digital ink notes with matching electronic documents in the repository. Latent Semantic Indexing is used to perform document and page level indexing. Thus for each retrieved document, the user can go over to the relevant pages that match the query. Techniques to handle problems such as polysemy (multiple meanings of a word) in large databases, document folding and no match for query are discussed. We tested our system for its performance, usability and effectiveness in the learning process. The results from the exploratory studies reveal that the proposed system provides a highly enhanced student learning experience, thereby facilitating high test scores.

Artificial neural network-driven federated learning for heart stroke prediction in healthcare 4.0 underlying 5G

In recent years, smart healthcare, artificial intelligence (AI)-aided diagnostics, and automated surgical robots are just a few of the innovations that have emerged and gained popularity with the advent of Healthcare 4.0. Such technologies are powered by machine learning (ML) and deep learning (DL), which are preferable for disease diagnosis, identifying patterns, prescribing treatments, and forecasting diseases like stroke prediction, cancer prediction and so forth. Nevertheless, much data is needed for AI, ML, and DL-based systems to train effectively and provide the desired outcomes. Further, it raises concerns about data privacy, security, communication overhead, regulatory compliance and so forth. Federated learning (FL) is a technology that protects data security and privacy by limiting data sharing and utilizing model information of distributed systems to enhance performance. However, existing approaches are traditionally verified on pre-established datasets that fail to capture real-life applicability. Therefore, this study proposes an AI-enabled stroke prediction architecture consisting of FL based on the artificial neural network (ANN) model using data from actual stroke cases. This architecture can be implemented on healthcare-based wearable devices (WD) for real-time use as it is effective, precise, and computationally affordable. In order to continuously enhance the performance of the global model, the proposed FL-based architecture aggregates the optimizer weights of many clients using a fifth-generation (5G) communication channel. Then, the performance of the proposed FL-based architecture is studied based on multiple parameters such as accuracy, precision, recall, bit error rate, and spectral noise. It outperforms the traditional approaches regarding accuracy, which is 5% to 10% higher.     

Texture and Microstructural Evolution in Pearlitic Steel During Triaxial Compression

This article presents the deformation behavior of high-strength pearlitic steel deformed by triaxial compression to achieve ultra-fine ferrite grain size with fragmented cementite. The consequent evolution of microstructure and texture has been studied using scanning electron microscopy, electron back-scatter diffraction, and X-ray diffraction. The synergistic effect of diffusion and deformation leads to the uniform dissolution of cementite at higher temperature. At lower temperature, significant grain refinement of ferrite phase occurs by deformation and exhibits a characteristic deformation texture. In contrast, the high-temperature deformed sample shows a weaker texture with cube component for the ferrite phase, indicating the occurrence of recrystallization. The different mechanisms responsible for the refinement of ferrite as well as the fragmentation of cementite and their interaction with each other have been analyzed. Viscoplastic self-consistent simulation was employed to understand deformation texture in the ferrite phase during triaxial compression.     

Enhancement of electroconductivity of polyaniline/graphene oxide nanocomposites through in situ emulsion polymerization

The present study introduces a systematic approach to disperse graphene oxide (GO) during emulsion polymerization (EP) of Polyaniline (PANI) to form nanocomposites with improved electrical conductivities. PANI/GO samples were fabricated by loading different weight percents (wt%) of GO through modified in situ EP of the aniline monomer. The polymerization process was carried out in the presence of a functionalized protonic acid such as dodecyl benzene sulfonic acid, which acts both as an emulsifier and protonating agent. The microstructure of the PANI/GO nanocomposites was studied by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, UV–Vis spectrometry, Fourier transform infrared, differential thermal, and thermogravimetric analyses. The formed nanocomposites exhibited superior morphology and thermal stability. Meanwhile, the electrical conductivities of the nanocomposite pellets pressed at different applied pressures were determined using the four-probe analyzer. It was observed that the addition of GO was an essential component to improving the thermal stability and electrical conductivities of the PANI/GO nanocomposites. The electrical conductivities of the nanocomposites were considerably enhanced as compared to those of the individual PANI samples pressed at the same pressures. An enhanced conductivity of 474 S/m was observed at 5 wt% GO loading and an applied pressure of 6 t. Therefore, PANI/GO composites with desirable properties for various semiconductor applications can be obtained by in situ addition of GO during the polymerization process.     

Green composites based on high‐density polyethylene and Saccharum spontaneum: Effect of filler content on morphology,thermal, and mechanical properties

Here we report the preparation and characterization of a green composite based on high‐density polyethylene and Kaans grass (Saccharum spontaneum). The composites were prepared by conventional melt‐mixing method, using maximum loading of Kaans grass in powder form (KG‐filler) to achieve acceptable range of required properties. Maleic anhydride grafted polyethylene was used as compatibilizer to achieve effective interaction for improved surface adhesion which was confirmed by FT‐IR spectroscopy. Morphological studies revealed good interaction between the base polymer matrices and the KG‐fillers that improved the mechanical and thermal properties of the composites up to certain (10 phr) KG‐filler loading. Study on water absorption property revealed moderate increase in weight at higher KG‐filler loadings. Thermogravimetric analysis (TGA) and melt flow index (MFI) studies indicated retention of thermal stability and flow property of the HDPE/KG‐filler composite at lower filler loadings. POLYM. COMPOS., 36:2157–2166, 2015. © 2014 Society of Plastics Engineers     

Determination of material removal rate in wire electro-discharge machining of metal matrix composites using dimensional analysis

Wire electro-discharge machining (WEDM) is a vital process in manufacturing intricate shapes. The present work proposes a semi-empirical model for material removal rate in WEDM based on thermo-physical properties of the work piece and machining parameters such as pulse on-time and average gap voltage. The model is developed by using dimensional analysis and non-linear estimation technique such as quasi-Newton and simplex. Predictability of the proposed model is more than 99% for all work materials studied. The work materials were silicon carbide particulate reinforced aluminium matrix composites. The experiments and model prediction show significant role of coefficient of thermal expansion in WEDM of these materials. In addition, an empirical model, based on response surface method, has also been developed. The comparison of these models shows significant agreement in the predictions.     

LOCA frequency evaluation using expert elicitation

The double-ended-guillotine break (DEGB) criterion of the largest primary piping system in the plant, which generally provides the limiting condition for the emergency core cooling system requirements, is widely recognized as an extremely unlikely event. As a result, US Nuclear Regulatory Commission (NRC) staff are currently considering a risk-informed revision of the design-basis break size requirements for commercial nuclear power plants. In support of this effort, loss-of-coolant accident (LOCA) frequency estimates have been developed using an expert elicitation process by consolidating service history data and insights from probabilistic fracture mechanics (PFM) studies with knowledge of plant design, operation, and material performance. Baseline LOCA frequency estimates for the 5th percentile, median, mean and 95th percentile were determined from each panelist's elicitation responses. Group estimates were determined by aggregating the individual estimates using the geometric mean of the individual estimates for each frequency parameter. Group variability was estimated by calculating 95% confidence bounds for each of the group frequency parameters (i.e., median, mean, and 5th and 95th percentiles). A number of sensitivity analyses were conducted to examine the effects on the quantitative results from varying the assumptions, structure and techniques of the baseline analysis procedure.     

Nonlinear analysis techniques of block masonry walls in nuclear power plants

Concrete masonry walls have been used extensively in nuclear power plants as non-load bearing partitions serving as pipe supports, fire walls, radiation shielding barriers, and similar heavy construction separations. When subjected to earthquake loads, these walls should maintain their structural integrity. However, some of the walls do not meet design requirements based on working stress allowables. Consequently, utilities have used non-linear analysis techniques, such as the arching theory and the energy balance technique, to qualify such walls. This paper presents a critical review of the applicability of non-linear analysis techniques for both unreinforced and reinforced block masonry walls under seismic loading. These techniques are critically assessed in light of the performance of walls from limited available test data. It is concluded that additional test data are needed to justify the use of nonlinear analysis techniques to qualify block walls in nuclear power plants.