Worcester Polytechnic Institute

We present a comprehensive, up-to-date compilation of band parameters for the technologically important III–V zinc blende and wurtzite compound semiconductors: GaAs, GaSb, GaP, GaN, AlAs, AlSb, AlP, AlN, InAs, InSb, InP, and InN, along with their ternary and quaternary alloys. Based on a review of the existing literature, complete and consistent parameter sets are given for all materials. Emphasizing the quantities required for band structure calculations, we tabulate the direct and indirect energy gaps, spin-orbit, and crystal-field splittings, alloy bowing parameters, effective masses for electrons, heavy, light, and split-off holes, Luttinger parameters, interband momentum matrix elements, and deformation potentials, including temperature and alloy-composition dependences where available. Heterostructure band offsets are also given, on an absolute scale that allows any material to be aligned relative to any other.

A significant revision of a best-selling text for the introductory digital signal processing course. This book presents the fundamentals of discrete-time signals, systems, and modern digital processing and applications for students in electrical engineering, computer engineering, and computer science.The book is suitable for either a one-semester or a two-semester undergraduate level course in discrete systems and digital signal processing. It is also intended for use in a one-semester first-year graduate-level course in digital signal processing.

Globalisation of supply chains makes their management and control more difficult. Blockchain technology, as a distributed digital ledger technology which ensures transparency, traceability, and security, is showing promise for easing some global supply chain management problems. In this paper, blockchain technology and smart contracts are critically examined with potential application to supply chain management. Local and global government, community, and consumer pressures to meet sustainability goals prompt us to further investigate how blockchain can address and aid supply chain sustainability. Part of this critical examination is how blockchains, a potentially disruptive technology that is early in its evolution, can overcome many potential barriers. Four blockchain technology adoption barriers categories are introduced; inter-organisational, intra-organisational, technical, and external barriers. True blockchain-led transformation of business and supply chain is still in progress and in its early stages; we propose future research propositions and directions that can provide insights into overcoming barriers and adoption of blockchain technology for supply chain management.

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts.

Azobenzene undergoes trans→cis isomerization when irradiated with light tuned to an appropriate wavelength. The reverse cis→trans isomerization can be driven by light or occurs thermally in the dark. Azobenzene's photochromatic properties make it an ideal component of numerous molecular devices and functional materials. Despite the abundance of application-driven research, azobenzene photochemistry and the isomerization mechanism remain topics of investigation. Additional substituents on the azobenzene ring system change the spectroscopic properties and isomerization mechanism. This critical review details the studies completed to date on the 3 main classes of azobenzene derivatives. Understanding the differences in photochemistry, which originate from substitution, is imperative in exploiting azobenzene in the desired applications.

Abstract This study examines how product innovation contributes to the renewal of the firm through its dynamic and reciprocal relation with the firm's competences. Field research in five high‐tech firms of varying age, size, and level of diversification is combined with analysis of existing theory to develop the findings of the study. Based on the notion that new products are created by linking competences relating to technologies and customers, a typology is derived that classifies new product projects based on whether a new product can draw on existing competences, or whether it requires competences the firm does not yet have. Following organizational learning theory, these options are conceptualized as exploitation and exploration. These organizational learning concepts are used to gain a dynamic and path‐dependent view of product innovation and firm development, and to reveal the unique nature and challenges of different types of product innovation. Copyright © 2002 John Wiley & Sons, Ltd.

Cloud computing is an emerging computing paradigm in which resources of the computing infrastructure are provided as services over the Internet. As promising as it is, this paradigm also brings forth many new challenges for data security and access control when users outsource sensitive data for sharing on cloud servers, which are not within the same trusted domain as data owners. To keep sensitive user data confidential against untrusted servers, existing solutions usually apply cryptographic methods by disclosing data decryption keys only to authorized users. However, in doing so, these solutions inevitably introduce a heavy computation overhead on the data owner for key distribution and data management when fine-grained data access control is desired, and thus do not scale well. The problem of simultaneously achieving fine-grainedness, scalability, and data confidentiality of access control actually still remains unresolved. This paper addresses this challenging open issue by, on one hand, defining and enforcing access policies based on data attributes, and, on the other hand, allowing the data owner to delegate most of the computation tasks involved in fine-grained data access control to untrusted cloud servers without disclosing the underlying data contents. We achieve this goal by exploiting and uniquely combining techniques of attribute-based encryption (ABE), proxy re-encryption, and lazy re-encryption. Our proposed scheme also has salient properties of user access privilege confidentiality and user secret key accountability. Extensive analysis shows that our proposed scheme is highly efficient and provably secure under existing security models.

Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. It moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. This unique paradigm brings about many new security challenges, which have not been well understood. This work studies the problem of ensuring the integrity of data storage in Cloud Computing. In particular, we consider the task of allowing a third party auditor (TPA), on behalf of the cloud client, to verify the integrity of the dynamic data stored in the cloud. The introduction of TPA eliminates the involvement of the client through the auditing of whether his data stored in the cloud are indeed intact, which can be important in achieving economies of scale for Cloud Computing. The support for data dynamics via the most general forms of data operation, such as block modification, insertion, and deletion, is also a significant step toward practicality, since services in Cloud Computing are not limited to archive or backup data only. While prior works on ensuring remote data integrity often lacks the support of either public auditability or dynamic data operations, this paper achieves both. We first identify the difficulties and potential security problems of direct extensions with fully dynamic data updates from prior works and then show how to construct an elegant verification scheme for the seamless integration of these two salient features in our protocol design. In particular, to achieve efficient data dynamics, we improve the existing proof of storage models by manipulating the classic Merkle Hash Tree construction for block tag authentication. To support efficient handling of multiple auditing tasks, we further explore the technique of bilinear aggregate signature to extend our main result into a multiuser setting, where TPA can perform multiple auditing tasks simultaneously. Extensive security and performance analysis show that the proposed schemes are highly efficient and provably secure.

Deep autoencoders, and other deep neural networks, have demonstrated their effectiveness in discovering non-linear features across many problem domains. However, in many real-world problems, large outliers and pervasive noise are commonplace, and one may not have access to clean training data as required by standard deep denoising autoencoders. Herein, we demonstrate novel extensions to deep autoencoders which not only maintain a deep autoencoders' ability to discover high quality, non-linear features but can also eliminate outliers and noise without access to any clean training data. Our model is inspired by Robust Principal Component Analysis, and we split the input data X into two parts, $X = L_{D} + S$, where $L_{D}$ can be effectively reconstructed by a deep autoencoder and $S$ contains the outliers and noise in the original data X. Since such splitting increases the robustness of standard deep autoencoders, we name our model a "Robust Deep Autoencoder (RDA)". Further, we present generalizations of our results to grouped sparsity norms which allow one to distinguish random anomalies from other types of structured corruptions, such as a collection of features being corrupted across many instances or a collection of instances having more corruptions than their fellows. Such "Group Robust Deep Autoencoders (GRDA)" give rise to novel anomaly detection approaches whose superior performance we demonstrate on a selection of benchmark problems.

Cloud Computing is the long dreamed vision of computing as a utility, where users can remotely store their data into the cloud so as to enjoy the on-demand high quality applications and services from a shared pool of configurable computing resources. By data outsourcing, users can be relieved from the burden of local data storage and maintenance. However, the fact that users no longer have physical possession of the possibly large size of outsourced data makes the data integrity protection in Cloud Computing a very challenging and potentially formidable task, especially for users with constrained computing resources and capabilities. Thus, enabling public auditability for cloud data storage security is of critical importance so that users can resort to an external audit party to check the integrity of outsourced data when needed. To securely introduce an effective third party auditor (TPA), the following two fundamental requirements have to be met: 1) TPA should be able to efficiently audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user; 2) The third party auditing process should bring in no new vulnerabilities towards user data privacy. In this paper, we utilize and uniquely combine the public key based homomorphic authenticator with random masking to achieve the privacy-preserving public cloud data auditing system, which meets all above requirements. To support efficient handling of multiple auditing tasks, we further explore the technique of bilinear aggregate signature to extend our main result into a multi-user setting, where TPA can perform multiple auditing tasks simultaneously. Extensive security and performance analysis shows the proposed schemes are provably secure and highly efficient.

The fourth industrial revolution, also labelled Industry 4.0, was beget with emergent and disruptive intelligence and information technologies. These new technologies are enabling ever-higher levels of production efficiencies. They also have the potential to dramatically influence social and environmental sustainable development. Organizations need to consider Industry 4.0 technologies contribution to sustainability. Sufficient guidance, in this respect, is lacking in the scholarly or practitioner literature. In this study, we further examine Industry 4.0 technologies in terms of application and sustainability implications. We introduce a measures framework for sustainability based on the United Nations Sustainable Development Goals; incorporating various economic, environmental and social attributes. We also develop a hybrid multi-situation decision method integrating hesitant fuzzy set, cumulative prospect theory and VIKOR. This method can effectively evaluate Industry 4.0 technologies based on their sustainable performance and application. We apply the method using secondary case information from a report of the World Economic Forum. The results show that mobile technology has the greatest impact on sustainability in all industries, and nanotechnology, mobile technology, simulation and drones have the highest impact on sustainability in the automotive, electronics, food and beverage, and textile, apparel and footwear industries, respectively. Our recommendation is to take advantage of Industry 4.0 technology adoption to improve sustainability impact but each technology needs to be carefully evaluated as specific technology will variably influence industry and sustainability dimensions. Investment in such technologies should consider appropriate priority investment and championing.

The electromagnetic theory of the torque exerted by a beam of polarized light on a doubly refracting plate which alters its state of polarization is summarized. The same quantitative result is obtained by assigning an angular momentum of $\ensuremath{\hbar} (\ensuremath{-}\ensuremath{\hbar})$ to each quantum of left (right) circularly polarized light in a vacuum, and assuming the conservation of angular momentum holds at the face of the plate. The apparatus used to detect and measure this effect was designed to enhance the moment of force to be measured by an appropriate arrangement of quartz wave plates, and to reduce interferences. The results of about 120 determinations by two observers working independently show the magnitude and sign of the effect to be correct, and show that it varies as predicted by the theory with each of three experimental variables which could be independently adjusted.

We review the history and current trends in the field of Educational Data Mining (EDM). We consider the methodological profile of research in the early years of EDM, compared to in 2008 and 2009, and discuss trends and shifts in the research conducted by this community. In particular, we discuss the increased emphasis on prediction, the emergence of work using existing models to make scientific discoveries ("discovery with models"), and the reduction in the frequency of relationship mining within the EDM community. We discuss two ways that researchers have attempted to categorize the diversity of research in educational data mining research, and review the types of research problems that these methods have been used to address. The most cited papers in EDM between 1995 and 2005 are listed, and their influence on the EDM community (and beyond the EDM community) is discussed.

In Context ATA-QUALITY (DQ) PROBLEMS ARE INCREASINGLY EVIdent, particularly in organizational databases. Indeed, 50% to 80% of computerized criminal records in the U.S. were found to be inaccurate, incomplete, or ambiguous. The social and economic impact of poor-quality data costs billions of dollars. [5-7, 10]. Organizational databases, however, reside in the larger context of information systems (IS). Within this larger context, data is collected from multiple data sources and stored in databases. From this stored data, useful information 1 is generated for organizational decision-making. A new study reveals businesses are defining data quality with the consumer in mind.

Although replication is a central tenet of science, direct replications are rare in psychology. This research tested variation in the replicability of 13 classic and contemporary effects across 36 independent samples totaling 6,344 participants. In the aggregate, 10 effects replicated consistently. One effect – imagined contact reducing prejudice – showed weak support for replicability. And two effects – flag priming influencing conservatism and currency priming influencing system justification – did not replicate. We compared whether the conditions such as lab versus online or US versus international sample predicted effect magnitudes. By and large they did not. The results of this small sample of effects suggest that replicability is more dependent on the effect itself than on the sample and setting used to investigate the effect.

Abstract This article considers how to estimate Bayesian credible and highest probability density (HPD) intervals for parameters of interest and provides a simple Monte Carlo approach to approximate these Bayesian intervals when a sample of the relevant parameters can be generated from their respective marginal posterior distribution using a Markov chain Monte Carlo (MCMC) sampling algorithm. We also develop a Monte Carlo method to compute HPD intervals for the parameters of interest from the desired posterior distribution using a sample from an importance sampling distribution. We apply our methodology to a Bayesian hierarchical model that has a posterior density containing analytically intractable integrals that depend on the (hyper) parameters. We further show that our methods are useful not only for calculating the HPD intervals for the parameters of interest but also for computing the HPD intervals for functions of the parameters. Necessary theory is developed and illustrative examples—including a simulation study—are given.

A seeded growth method for the fabrication of high-permeance, high-separation-factor zeolite (siliceous ZSM-5, [Si96O192]-MFI) membranes is reported. The method consists of growing the crystals of an oriented seed layer to a well-intergrown film by avoiding events that lead to a loss of preferred orientation, such as twin overgrowths and random nucleation. Organic polycations are used as zeolite crystal shape modifiers to enhance relative growth rates along the desirable out-of-plane direction. The polycrystalline films are thin (approximately 1 micrometer) with single grains extending along the film thickness and with large in-plane grain size (approximately 1 micrometer). The preferred orientation is such that straight channels with an open diameter of approximately 5.5 angstroms run down the membrane thickness. Comparison with previously reported membranes shows that these microstructurally optimized films have superior performance for the separation of organic mixtures with components that have small differences in size and shape, such as xylene isomers.

An alternative formulation of the "perfectly matched layer" mesh truncation scheme is introduced. The present scheme is based on using a layer of diagonally anisotropic material to absorb outgoing waves from the computation domain. The material properties can be chosen such that the interface between the absorbing material and free space is reflection-less for all frequencies, polarizations, and angles of incidence. This approach does not involve a modification of Maxwell's equations and is easy to implement in codes that allow the use of anisotropic material properties.

The emerging field of soft robotics makes use of many classes of materials including metals, low glass transition temperature (Tg) plastics, and high Tg elastomers. Dependent on the specific design, all of these materials may result in extrinsically soft robots. Organic elastomers, however, have elastic moduli ranging from tens of megapascals down to kilopascals; robots composed of such materials are intrinsically soft − they are always compliant independent of their shape. This class of soft machines has been used to reduce control complexity and manufacturing cost of robots, while enabling sophisticated and novel functionalities often in direct contact with humans. This review focuses on a particular type of intrinsically soft, elastomeric robot − those powered via fluidic pressurization.

We conducted preregistered replications of 28 classic and contemporary published findings, with protocols that were peer reviewed in advance, to examine variation in effect magnitudes across samples and settings. Each protocol was administered to approximately half of 125 samples that comprised 15,305 participants from 36 countries and territories. Using the conventional criterion of statistical significance ( p < .05), we found that 15 (54%) of the replications provided evidence of a statistically significant effect in the same direction as the original finding. With a strict significance criterion ( p < .0001), 14 (50%) of the replications still provided such evidence, a reflection of the extremely high-powered design. Seven (25%) of the replications yielded effect sizes larger than the original ones, and 21 (75%) yielded effect sizes smaller than the original ones. The median comparable Cohen’s ds were 0.60 for the original findings and 0.15 for the replications. The effect sizes were small (< 0.20) in 16 of the replications (57%), and 9 effects (32%) were in the direction opposite the direction of the original effect. Across settings, the Q statistic indicated significant heterogeneity in 11 (39%) of the replication effects, and most of those were among the findings with the largest overall effect sizes; only 1 effect that was near zero in the aggregate showed significant heterogeneity according to this measure. Only 1 effect had a tau value greater than .20, an indication of moderate heterogeneity. Eight others had tau values near or slightly above .10, an indication of slight heterogeneity. Moderation tests indicated that very little heterogeneity was attributable to the order in which the tasks were performed or whether the tasks were administered in lab versus online. Exploratory comparisons revealed little heterogeneity between Western, educated, industrialized, rich, and democratic (WEIRD) cultures and less WEIRD cultures (i.e., cultures with relatively high and low WEIRDness scores, respectively). Cumulatively, variability in the observed effect sizes was attributable more to the effect being studied than to the sample or setting in which it was studied.