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Nanopore-based molecular sensing and measurement, specifically DNA sequencing, is advancing at a fast pace. Some embodiments have matured from coarse particle counters to enabling full human genome assembly. This evolution has been powered not only by improvements in the sensors themselves, but also in the assisting microelectronic CMOS readout circuitry closely interfaced to them. In this light, this paper aims to review established and emerging nanopore-based sensing modalities considered for DNA sequencing and CMOS microelectronic methods currently being used.

Readout and amplifier circuits, which are potentially appropriate for conditioning and conversion of nanopore signals for downstream processing, are studied. Furthermore, arrayed CMOS readout implementations are focused on and the relevant status of the nanopore sensor technology is reviewed as well.

Ion channel nanopore devices have unique properties compared with other electrochemical cells. Currently biological nanopores are the only variants reported which can be used for actual DNA sequencing. The translocation rate of DNA through such pores, the current range at which these cells operate on and the cell capacitance effect, all impose the necessity of using low-noise circuits in the process of signal detection. The requirement of using in-pixel low-noise circuits in turn tends to impose challenges in the implementation of large size arrays.

The study presents an overview on the readout circuits used for signal acquisition in electrochemical cell arrays and investigates the specific requirements necessary for implementation of nanopore-type electrochemical cell amplifiers and their associated readout electronics.

This study aims to examine the relationship between the perception of consumers about corporate social responsibility (CSR) and consumers’ purchasing behaviour in the retail sector. Specifically, this study investigates the impact of perceived CSR on consumer attitude and behaviour and the influence of attitude on the relationship between perceived CSR and purchase behaviour.

In this study for collection of the data, an online questionnaire was distributed among the Indian retail consumers. From the collected primary data set, 249 data points were found fit for analysis. Further, the direct, indirect and moderating effects were evaluated using the structural equation modelling technique.

It is identified that while perceived CSR has a significant influence on consumer purchase behaviour, consumer attitude is having an insignificant impact on the relationship between perceived CSR and purchase behaviour. The findings of this study also show that consumer demographics do not have any moderating impact on the relationship between perceived CSR and purchase behaviour.

The findings of this study are useful to retail managers interested in enhancing CSR. The results of this study suggest that retailers should focus on strengthening consumers’ perceptions about retailers’ CSR initiatives and enhancing co-creation activities. As an extension to this research, further study can include more potential mediators like consumer effectiveness and timing of CSR initiatives.

This study applies stakeholder theory as well as extends the classic theory of planned behaviour model and proposes the establishment of links among consumers’ perceptions about CSR, consumer attitude and behaviour around the retail sector. In addition, this study considers not only overall consumer behaviour but also specific dimensions of consumer behaviour, namely, loyalty, intention and satisfaction.

The paper investigates the suitability of the Rasch model for establishing data equivalence. The results based on a real data set are contrasted with findings from standard procedures based on CFA methods.

Sinkovics et al.'s data on technophobia was used and re‐evaluated using both classical test theory (CTT) (multiple‐group structural equations modelling) and Rasch measurement theory.

Data equivalence in particular and measurement in general cannot be addressed without reference to theory. While both procedures can be considered best practice approaches within their respective theoretical foundation of measurement, the Rasch model provides some theoretical virtues. Measurement derived from data that fit the Rasch model seems to be approximated by classical procedures reasonably well. However, the reverse is not necessarily true.

The more widespread application of Rasch models would lead to a stronger justification of measurement, in particular, in cross‐cultural studies but also whenever measures of individual respondents are of interest.

Measurement models outside the framework of CTT are still scarce exceptions in marketing research.

The purpose of this study is to propose a framework for expedited antenna optimization with numerical derivatives involving gradient variation monitoring throughout the optimization run and demonstrate it using a benchmark set of real-world wideband antennas. A comprehensive analysis of the algorithm performance involving multiple starting points is provided. The optimization results are compared with a conventional trust-region (TR) procedure, as well as the state-of-the-art accelerated TR algorithms.

The proposed algorithm is a modification of the TR gradient-based algorithm with numerical derivatives in which a monitoring of changes of the system response gradients is performed throughout the algorithm run. The gradient variations between consecutive iterations are quantified by an appropriately developed metric. Upon detecting stable patterns for particular parameter sensitivities, the costly finite differentiation (FD)-based gradient updates are suppressed; hence, the overall number of full-wave electromagnetic (EM) simulations is significantly reduced. This leads to considerable computational savings without compromising the design quality.

Monitoring of the antenna response sensitivity variations during the optimization process enables to detect the parameters for which updating the gradient information is not necessary at every iteration. When incorporated into the TR gradient-search procedures, the approach permits reduction of the computational cost of the optimization process. The proposed technique is dedicated to expedite direct optimization of antenna structures, but it can also be applied to speed up surrogate-assisted tasks, especially solving sub-problems that involve performing numerous evaluations of coarse-discretization models.

The introduced methodology opens up new possibilities for future developments of accelerated antenna optimization procedures. In particular, the presented routine can be combined with the previously reported techniques that involve replacing FD with the Broyden formula for directions that are satisfactorily well aligned with the most recent design relocation and/or performing FD in a sparse manner based on relative design relocation (with respect to the current search region) in consecutive algorithm iterations.

Benchmarking against a conventional TR procedure, as well as previously reported methods, confirms improved efficiency and reliability of the proposed approach. The applications of the framework include direct EM-driven design closure, along with surrogate-based optimization within variable-fidelity surrogate-assisted procedures. To the best of the authors’ knowledge, no comparable approach to antenna optimization has been reported elsewhere. Particularly, it surmounts established methodology by carrying out constant supervision of the antenna response gradient throughout successive algorithm iterations and using gathered observations to properly guide the optimization routine.

The diffusion and adoption (D&A) of innovation propels today's technological landscape. Crisis situations, real or perceived, motivate communities of people to take action to adopt and diffuse innovation. The D&A of innovation is an inherently human activity; yet, artificially intelligent techniques can assist humans in six different ways, especially when operating in fifth generation ecosystems that are emergent, complex, and adaptive in nature.

Humans can use artificial intelligence (AI) to match solutions to problems, design for diffusion, identify key roles in social networks, reveal unintended consequences, recommend pathways for scaling that include the effects of policy, and identify trends for fast-follower strategies. The stability of the data that artificially intelligent systems rely upon will challenge performance; nevertheless, the research in this area has positioned several promising techniques where classically narrow AI systems can assist humans. As a result, human and machine interaction can accelerate the D&A of technological innovation to respond to crisis situations.

In the first part of this paper a new method of applying the Maximum Entropy Principle (MEP) is presented, which makes use of a “frequency related” entropy, and which is valid for all stationary processes. The method is believed valid only in the case of discrete spectra. In the second part of the paper, a method of estimating continuous spectra in the presence of noise is presented, which makes use of the Mutual Information Principle (MIP). Although the method proceeds smoothly in mathematical terms, there appear to be some difficulties in interpreting the physical meaning of some of the expressions. Examples in the use of both methods are presented, for the usual practical problem of estimating a power spectrum for a process whose autocorrelation function is partially known a priori.

To date, the literature on quantile regression and least absolute deviation regression has assumed either explicitly or implicitly that the conditional quantile regression model is correctly specified. When the model is misspecified, confidence intervals and hypothesis tests based on the conventional covariance matrix are invalid. Although misspecification is a generic phenomenon and correct specification is rare in reality, there has to date been no theory proposed for inference when a conditional quantile model may be misspecified. In this paper, we allow for possible misspecification of a linear conditional quantile regression model. We obtain consistency of the quantile estimator for certain “pseudo-true” parameter values and asymptotic normality of the quantile estimator when the model is misspecified. In this case, the asymptotic covariance matrix has a novel form, not seen in earlier work, and we provide a consistent estimator of the asymptotic covariance matrix. We also propose a quick and simple test for conditional quantile misspecification based on the quantile residuals.

Large/stochastic spatiotemporal demand data sets can prove intractable for location optimization problems, motivating the need for aggregation. However, demand aggregation induces errors. Significant theoretical research has been performed related to the modifiable areal unit problem and the zone definition problem. Minimal research has been accomplished related to the specific issues inherent to spatiotemporal demand data, such as search and rescue (SAR) data. This study provides a quantitative comparison of various aggregation methodologies and their relation to distance and volume based aggregation errors.

This paper introduces and applies a framework for comparing both deterministic and stochastic aggregation methods using distance- and volume-based aggregation error metrics. This paper additionally applies weighted versions of these metrics to account for the reality that demand events are nonhomogeneous. These metrics are applied to a large, highly variable, spatiotemporal demand data set of SAR events in the Pacific Ocean. Comparisons using these metrics are conducted between six quadrat aggregations of varying scales and two zonal distribution models using hierarchical clustering.

As quadrat fidelity increases the distance-based aggregation error decreases, while the two deliberate zonal approaches further reduce this error while using fewer zones. However, the higher fidelity aggregations detrimentally affect volume error. Additionally, by splitting the SAR data set into training and test sets this paper shows the stochastic zonal distribution aggregation method is effective at simulating actual future demands.

This study indicates no singular best aggregation method exists, by quantifying trade-offs in aggregation-induced errors practitioners can utilize the method that minimizes errors most relevant to their study. Study also quantifies the ability of a stochastic zonal distribution method to effectively simulate future demand data.

Develops an original 12‐step management of technology protocol and applies it to 51 applications which range from Du Pont’s failure in Nylon to the Single Online Trade Exchange for Auto Parts procurement by GM, Ford, Daimler‐Chrysler and Renault‐Nissan. Provides many case studies with regards to the adoption of technology and describes seven chief technology officer characteristics. Discusses common errors when companies invest in technology and considers the probabilities of success. Provides 175 questions and answers to reinforce the concepts introduced. States that this substantial journal is aimed primarily at the present and potential chief technology officer to assist their survival and success in national and international markets.