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This paper aims to present the edge scattering dominant circuit modeling. The effect of crosstalk on gate oxide reliability (GOR), along with the mitigation using shielding technique is further studied.

An equivalent distributed Resistance Inductance Capacitance circuit of capacitively coupled interconnects of multilayer graphene nanoribbon (MLGNR) has been considered for T Simulation Program with Integrated Circuit Emphasis (TSPICE) simulations under functional and dynamic switching conditions. Complementary metal oxide semiconductor driver transistors are modeled by high performance predictive technology model that drive the distributed segment with a capacitive load of 0.001 fF, V_DD and clock frequency as 0.7 V and 0.2 GHz, respectively, at 14 nm technology node.

The results reveal that the crosstalk induced delay and noise area are dominated by the overall mean free path (MFP) (i.e. including the effect of edge roughness induced scattering), in contrary to, acoustic and optical scattering limited MFP with the temperature, width and length variations. Further, GOR, estimated in terms of average failure rate (AFR), shows that the shielding technique is an effective method to minimize the relative GOR failure rate by, 0.93e-7 and 0.7e-7, in comparison to the non-shielded case with variations in interconnect’s length and width, respectively.

Considering realistic circuit modeling for MLGNR interconnects by incorporating the edge roughness induced scattering mechanism, the outcomes exhibit more penalty in terms of crosstalk induced noise area and delay. The shielding technique is found to be an effective mitigating technique for minimizing AFR in coupled MLGNR interconnects.

This research aims to explore the factors that affect the adoption of Web 2.0 among knowledge workers. The research specifically investigated the role of factors related to both knowledge seeking and knowledge sharing, in the context of Web 2.0 use by health care professionals.

For this research, a cross-sectional survey design was adopted. The data were analyzed using the partial least square-structural equation modeling.

The results confirmed that the intention to adopt Web 2.0 depends upon both the knowledge-seeking and the knowledge-sharing attitudes. However, between the two, it is knowledge-sharing factors that are more important. Health care professionals tend to share knowledge driven by intrinsic motivators rather than by extrinsic motivators. On the other hand, knowledge-seeking attitude was determined by usefulness of knowledge and was not affected by the effort involved.

All the respondents were health care professionals from India, and convenience sampling was used to reach them. This may limit the generalizability of the findings.

This research provides useful insights on implementing Web 2.0-based knowledge management systems, specifically for health care professionals. Particularly, it emphasizes the need to focus on reinforcing intrinsic motivators like self-efficacy and the joy of sharing.

It is perhaps the first study that integrates the factors related to knowledge sharing and seeking in a single theoretical model, thereby presents and tests a more realistic model of knowledge management.

This research aims to uncover how the adoption process of a mobile payment solution unfolds in its local use. We examined micro-enterprises’ adoption of UPI-based payment system in India.

We employed an ethnographic approach to study the micro-entrepreneurs’ daily practice and adopted a relational ontology to conduct an “in-practice” enquiry of the UPI payment adoption process.

Applying the practice perspective of Technology Affordances and Constraints Theory, we found that UPI adoption was shaped through a dynamic interplay of emerging affordances and constraints. New features, such as All-in-one QR, enabled actions like cross-platform money transfer but also introduced challenges like fraud. Advancements in the technology addressed previous challenges but also created new hurdles. This cycle of evolving affordances and constraints within the contextual use of UPI payment technology shaped its adoption process.

The findings contribute to the existing body of mobile payment literature by elucidating the role of localized and ongoing enactment of affordances and constraints. Unlike existing adoption theories, using relational concepts of affordances and constraints revealed how the integration of mobile payment into practice leads to emergent use patterns and widespread adoption.

We emphasize the need for solutions that address localized challenges and contextual needs. The study advises policymakers to consider technology’s benefits and limitations in interventions.

The study offers a unique view on mobile payment adoption, highlighting the role of technological materiality and temporality. It reveals how the changing materiality of technology shapes the adoption process through localized affordances and constraints.

The purpose of this paper is to estimate the multicomponent reliability by assuming the unit-Gompertz (UG) distribution. Both stress and strength are assumed to have an UG distribution with common scale parameter.

The reliability of a multicomponent stress–strength system is obtained by the maximum likelihood (MLE) and Bayesian method of estimation. Bayes estimates of system reliability are obtained by using Lindley’s approximation and Metropolis–Hastings (M–H) algorithm methods when all the parameters are unknown. The highest posterior density credible interval is obtained by using M–H algorithm method. Besides, uniformly minimum variance unbiased estimator and exact Bayes estimates of system reliability have been obtained when the common scale parameter is known and the results are compared for both small and large samples.

Based on the simulation results, the authors observe that Bayes method provides better estimation results as compared to MLE. Proposed asymptotic and HPD intervals show satisfactory coverage probabilities. However, average length of HPD intervals tends to remain shorter than the corresponding asymptotic interval. Overall the authors have observed that better estimates of the reliability may be achieved when the common scale parameter is known.

Most of the lifetime distributions used in reliability analysis, such as exponential, Lindley, gamma, lognormal, Weibull and Chen, only exhibit constant, monotonically increasing, decreasing and bathtub-shaped hazard rates. However, in many applications in reliability and survival analysis, the most realistic hazard rates are upside-down bathtub and bathtub-shaped, which are found in the unit-Gompertz distribution. Furthermore, when reliability is measured as percentage or ratio, it is important to have models defined on the unit interval in order to have plausible results. Therefore, the authors have studied the multicomponent stress–strength reliability under the unit-Gompertz distribution by comparing the MLEs, Bayes estimators and UMVUEs.

The purpose of this article is to derive inference for multicomponent reliability where stress-strength variables follow unit generalized Rayleigh (GR) distributions with common scale parameter.

The authors derive inference for the unknown parametric function using classical and Bayesian approaches. In sequel, (weighted) least square (LS) and maximum product of spacing methods are used to estimate the reliability. Bootstrapping is also considered for this purpose. Bayesian inference is derived under gamma prior distributions. In consequence credible intervals are constructed. For the known common scale, unbiased estimator is obtained and is compared with the corresponding exact Bayes estimate.

Different point and interval estimators of the reliability are examined using Monte Carlo simulations for different sample sizes. In summary, the authors observe that Bayes estimators obtained using gamma prior distributions perform well compared to the other studied estimators. The average length (AL) of highest posterior density (HPD) interval remains shorter than other proposed intervals. Further coverage probabilities of all the intervals are reasonably satisfactory. A data analysis is also presented in support of studied estimation methods. It is noted that proposed methods work good for the considered estimation problem.

In the literature various probability distributions which are often analyzed in life test studies are mostly unbounded in nature, that is, their support of positive probabilities lie in infinite interval. This class of distributions includes generalized exponential, Burr family, gamma, lognormal and Weibull models, among others. In many situations the authors need to analyze data which lie in bounded interval like average height of individual, survival time from a disease, income per-capita etc. Thus use of probability models with support on finite intervals becomes inevitable. The authors have investigated stress-strength reliability based on unit GR distribution. Useful comments are obtained based on the numerical study.

The complexity of citizenship behavior in organizations has long been a focus of research. Traditional methodologies have been predominantly used to address this complexity. This paper aims to tackle the problem using a cutting-edge technological tool: business process mining. The objective is to enhance citizenship behaviors by leveraging primary data collected from 326 white-collar employees in the Indian service industry.

The study focuses on two main processes: training and creativity, with the ultimate goal of fostering organizational citizenship behavior (OCB), both in its overall manifestation (OCB-O) and its individual components (OCB-I). Seven different machine learning algorithms were used: artificial neural, behavior, prediction network, linear discriminant classifier, K-nearest neighbor, support vector machine, extreme gradient boosting (XGBoost), random forest and naive Bayes. The approach involved mining the most effective path for predicting the outcome and automating the entire process to enhance efficiency and sustainability.

The study successfully predicted the OCB-O construct, demonstrating the effectiveness of the approach. An optimized path for prediction was identified, highlighting the potential for automation to streamline the process and improve accuracy. These findings suggest that leveraging automation can facilitate the prediction of behavioral constructs, enabling the customization of policies for future employees.

The findings have significant implications for organizations aiming to enhance citizenship behaviors among their employees. By leveraging advanced technological tools such as business process mining and machine learning algorithms, companies can develop more effective strategies for fostering desirable behaviors. Furthermore, the automation of these processes offers the potential to streamline operations, reduce manual effort and improve predictive accuracy.

This study contributes to the existing literature by offering a novel approach to addressing the complexity of citizenship behavior in organizations. By combining business process mining with machine learning techniques, a unique perspective is provided on how technological advancements can be leveraged to enhance organizational outcomes. Moreover, the findings underscore the value of automation in refining existing processes and developing models applicable to future employees, thus improving overall organizational efficiency and effectiveness.

This paper aims to propose to study the control of tube parameters in terms of diameter, separation between adjacent tubes and length, on delay and power dissipation in single-walled carbon nanotube (SWCNT) bundle interconnect for VLSI circuits.

The paper considers a distributed-RLC model of interconnect. A CMOS-inverter driving a distributed-RLC model of interconnect with load of 1 pF. A 0.1 GHz pulse of 2 ns rise time provides input to the CMOS-inverter. For SPICE simulation, predictive technology model (PTM) is used for the CMOS-driver. The performance of this setup is studied by SPICE simulation in 22 nm technology node. The results are compared with those of currently used copper interconnect.

SPICE simulation results reveal that delay increases with increase in separation between tubes and diameter whereas the reverse is true for power dissipation. The authors also find that SWCNT bundle interconnects are of lower delay than copper interconnect at various lengths and higher power dissipation due to dominance of larger capacitance of tube bundle.

The investigations show that tube parameters can control delay and this can also be utilized to decrease power dissipation in SWCNT bundle interconnects for VLSI applications.

Introduction: Lending is an age-old concept, and Peer-to-Peer (P2P) lending is not new. The reduction in the issuing of loans by banks has made people switch from traditional to online mode. The introduction of the online P2P lending industry is in its nascent stage of growth. As this industry is relatively new, understanding user experience, sentiments, and emotions would be helpful for the industry to innovate as per customer requirements.

Purpose: To explore the patterns in the sentiments expressed by users of ‘Cashkumar’ based on Google reviews.

Methodology: Sentiments have been analysed using user experience in risk, cost, ease of use, and loan processing time. Python application was used for sentiment analysis of Google reviews.

Findings: The sentiment analysis results showed that the average sentiment score was 0.7144, which indicates that the user sentiment towards ‘Cashkumar’ is positive. The reviews reflect that the users, especially borrowers were satisfied with the platform’s services and happy with loan processing time. The other factors – ease of use, cost, and risk – were not given much importance by users. Both lenders and borrowers faced a few issues, but the results of the lender’s sentiment analysis could not be generalised due to a smaller number of posted reviews.

In this chapter, the authors argue that technology can be advantageous to children and young people’s mental health. The authors argue that social media platforms can foster social connection, social support and access to important information to support mental health. The authors also highlight the risks, particularly the research which links technology to mental ill-health. The authors argue that the digital curriculum in schools should develop young people’s knowledge of digital literacy, digital citizenship and digital resilience. Finally, the authors explore the potential role that technological applications (apps) can play in supporting children and young people’s mental health. The authors argue that although research is in its infancy, some studies have produced promising results.

The purpose of the paper is to highlight the area on how can we bridge the skills gap in post-coronavirus Britain through role-relevant qualifications.

The paper draws on the author’s personal observations and opinions learned through experience in the field.

The paper explores a number of potential solutions to the skills gap, including post-pandemic digital transformation creating a bigger rift between supply and demand; addressing the digital skills vacuum, which intensifies the war on talent; upskilling; and plugging the skills gap with industry-focused education and training.

The paper offers original insight based on the author’s unique perspective.