Tennessee Technological University

IT assimilation is regarded as an important outcome in the efforts of firms to leverage the potential of information technologies in their business activities and strategies. Despite significant investments in information technology, considerable diversity exists in how well firms have been able to assimilate IT and leverage the business value of IT. This research draws upon the emerging knowledge-based and resource-based views of the firm to examine the influence of three factors on IT assimilation: (i) quality of senior leadership, (ii) sophistication of IT infrastructures, and (iii) organizational size. Drawing upon a large-scale sample survey where responses were obtained from CIOs and senior business executives who were members of the firms' top management teams, the study examines a variety of mostly normative prescriptions. The findings provide robust evidence about the impacts of CIOs' business and IT knowledge on IT assimilation. Further, we find that CIOs' membership in top management teams and their informal interactions with TMT members enhance their knowledge, particularly their business knowledge. We find that the intensity of the relationship between CIO's interactions with the top management team and their level of IT and business knowledge is much stronger in firms that articulate a transformational IT vision. The sophistication of IT infrastructures was also found to significantly impact IT assimilation. Surprisingly, the IT knowledge of senior business executives was not found to be a significant influence on IT assimilation. The implications of these findings for evolving a deeper understanding of the dynamics underlying IT assimilation are presented.

Abstract This article summarizes the changes in landscape structure because of human land management over the last several centuries, and using observed and modeled data, documents how these changes have altered biogeophysical and biogeochemical surface fluxes on the local, mesoscale, and regional scales. Remaining research issues are presented including whether these landscape changes alter large‐scale atmospheric circulation patterns far from where the land use and land cover changes occur. We conclude that existing climate assessments have not yet adequately factored in this climate forcing. For those regions that have undergone intensive human landscape change, or would undergo intensive change in the future, we conclude that the failure to factor in this forcing risks a misalignment of investment in climate mitigation and adaptation. WIREs Clim Change 2011, 2:828–850. doi: 10.1002/wcc.144 This article is categorized under: Paleoclimates and Current Trends > Climate Forcing

Abstract Energy harvesting technologies have been explored by researchers for more than two decades as an alternative to conventional power sources (e.g. batteries) for small-sized and low-power electronic devices. The limited life-time and necessity for periodic recharging or replacement of batteries has been a consistent issue in portable, remote, and implantable devices. Ambient energy can usually be found in the form of solar energy, thermal energy, and vibration energy. Amongst these energy sources, vibration energy presents a persistent presence in nature and manmade structures. Various materials and transduction mechanisms have the ability to convert vibratory energy to useful electrical energy, such as piezoelectric, electromagnetic, and electrostatic generators. Piezoelectric transducers, with their inherent electromechanical coupling and high power density compared to electromagnetic and electrostatic transducers, have been widely explored to generate power from vibration energy sources. A topical review of piezoelectric energy harvesting methods was carried out and published in this journal by the authors in 2007. Since 2007, countless researchers have introduced novel materials, transduction mechanisms, electrical circuits, and analytical models to improve various aspects of piezoelectric energy harvesting devices. Additionally, many researchers have also reported novel applications of piezoelectric energy harvesting technology in the past decade. While the body of literature in the field of piezoelectric energy harvesting has grown significantly since 2007, this paper presents an update to the authors’ previous review paper by summarizing the notable developments in the field of piezoelectric energy harvesting through the past decade.

Undoubtedly, the evolution of Generative AI (GenAI) models has been the highlight of digital transformation in the year 2022. As the different GenAI models like ChatGPT and Google Bard continue to foster their complexity and capability, it’s critical to understand its consequences from a cybersecurity perspective. Several instances recently have demonstrated the use of GenAI tools in both the defensive and offensive side of cybersecurity, and focusing on the social, ethical and privacy implications this technology possesses. This research paper highlights the limitations, challenges, potential risks, and opportunities of GenAI in the domain of cybersecurity and privacy. The work presents the vulnerabilities of ChatGPT, which can be exploited by malicious users to exfiltrate malicious information bypassing the ethical constraints on the model. This paper demonstrates successful example attacks like Jailbreaks, reverse psychology, and prompt injection attacks on the ChatGPT. The paper also investigates how cyber offenders can use the GenAI tools in developing cyber attacks, and explore the scenarios where ChatGPT can be used by adversaries to create social engineering attacks, phishing attacks, automated hacking, attack payload generation, malware creation, and polymorphic malware. This paper then examines defense techniques and uses GenAI tools to improve security measures, including cyber defense automation, reporting, threat intelligence, secure code generation and detection, attack identification, developing ethical guidelines, incidence response plans, and malware detection. We will also discuss the social, legal, and ethical implications of ChatGPT. In conclusion, the paper highlights open challenges and future directions to make this GenAI secure, safe, trustworthy, and ethical as the community understands its cybersecurity impacts.

Abstract Pearly mussels (Unionacea) are widespread, abundant, and important in freshwater ecosystems around the world. Catastrophic declines in pearly mussel populations in North America and other parts of the world have led to a flurry of research on mussel biology, ecology, and conservation. Recent research on mussel feeding, life history, spatial patterning, and declines has augmented, modified, or overturned long-held ideas about the ecology of these animals. Pearly mussel research has begun to benefit from and contribute to current ideas about suspension feeding, life-history theory, metapopulations, flow refuges, spatial patterning and its effects, and management of endangered species. At the same time, significant gaps in understanding and apparent paradoxes in pearly mussel ecology have been exposed. To conserve remaining mussel populations, scientists and managers must simultaneously and aggressively pursue both rigorous research and conservation actions.

Protein-protein bond formations, such as antibody-antigen complexation or aggregation of protein monomers into dimers and larger aggregates, occur with bimolecular rate constants on the order of 10(6) M-1.s-1, which is only 3 orders of magnitude slower than the diffusion-limited Smoluchowski rate. However, since the protein-protein bond requires rotational alignment to within a few angstroms of tolerance, purely geometric estimates would suggest that the observed rates might be 6 orders of magnitude below the Smoluchowski rate. Previous theoretical treatments have not been solved for the highly specific docking criteria of protein-protein association--the entire subunit interface must be aligned within 2 A of the correct position. Several studies have suggested that diffusion alone could not produce the rapid association kinetics and have postulated "lengthy collisions" and/or the operation of electrostatic or hydrophobic steering forces to accelerate the association. In the present study, the Brownian dynamics simulation method is used to compute the rate of association of neutral spherical model proteins with the stated docking criteria. The Brownian simulation predicts a rate of 2 x 10(6) M-1.s-1 for this generic protein-protein association, a rate that is 2000 times faster than that predicted by the simplest geometric calculation and is essentially equal to the rates observed for protein-protein association in aqueous solution. This high rate is obtained by simple diffusive processes and does not require any attractive or steering forces beyond those achieved for a partially formed bond. The rate enhancement is attributed to a diffusive entrapment effect, in which a protein pair surrounded and trapped by water undergoes multiple collisions with rotational reorientation during each encounter.

Internet of Things (IoT) and smart computing technologies have revolutionized every sphere of 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century humans. IoT technologies and the data driven services they offer were beyond imagination just a decade ago. Now, they surround us and influence a variety of domains such as automobile, smart home, healthcare, etc. In particular, the Agriculture and Farming industries have also embraced this technological intervention. Smart devices are widely used by a range of people from farmers to entrepreneurs. These technologies are used in a variety of ways, from finding real-time status of crops and soil moisture content to deploying drones to assist with tasks such as applying pesticide spray. However, the use of IoT and smart communication technologies introduce a vast exposure to cybersecurity threats and vulnerabilities in smart farming environments. Such cyber attacks have the potential to disrupt the economies of countries that are widely dependent on agriculture. In this paper, we present a holistic study on security and privacy in a smart farming ecosystem. The paper outlines a multi layered architecture relevant to the precision agriculture domain and discusses the security and privacy issues in this dynamic and distributed cyber physical environment. Further more, the paper elaborates on potential cyber attack scenarios and highlights open research challenges and future directions.

This paper presents an application of dynamically driven recurrent networks (DDRNs) in online electric vehicle (EV) battery analysis. In this paper, a nonlinear autoregressive with exogenous inputs (NARX) architecture of the DDRN is designed for both state of charge (SOC) and state of health (SOH) estimation. Unlike other techniques, this estimation strategy is subject to the global feedback theorem (GFT) which increases both computational intelligence and robustness while maintaining reasonable simplicity. The proposed technique requires no model or knowledge of battery's internal parameters, but rather uses the battery's voltage, charge/discharge currents, and ambient temperature variations to accurately estimate battery's SOC and SOH simultaneously. The presented method is evaluated experimentally using two different batteries namely lithium iron phosphate (LiFePO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> ) and lithium titanate (LTO) both subject to dynamic charge and discharge current profiles and change in ambient temperature. Results highlight the robustness of this method to battery's nonlinear dynamic nature, hysteresis, aging, dynamic current profile, and parametric uncertainties. The simplicity and robustness of this method make it suitable and effective for EVs' battery management system (BMS).

User participation has been widely touted by the MIS community as a means to improve user satisfaction within systems development. This claim, however, has not been consistently substantiated in the empirical literature. In seeking to explain such equivocal results, the effects of four contingency factors—task complexity, system complexity, user influence, and userdeveloper communication—on the relationship between user participation and user satisfaction were investigated. As suggested in the literature, this research tests hypotheses that these specific contingency factors should aid in identifying situations where user participation would have a strong relationship with satisfaction. Analysis of 151 independent systems development projects in eight different organizations indicated that user participation has a direct relationship with user satisfaction. In addition, the four contingency factors were found to play key roles on this relationship. Task complexity and system complexity proved to be pure moderators. That is, the strength of the participation-satisfaction relationship depended on the level of these factors. In projects where there was a high level of task complexity or system complexity, the relationship between user participation and user satisfaction was significantly stronger than in projects where task complexity or system complexity was low. User influence and user-developer communication were shown to be independent predictors of user satisfaction. That is, user influence, or user-developer communication, was positively related to user satisfaction regardless of the level of participation. The results help explain the relationship between user participation and user satisfaction by suggesting the nature of the relationship under different sets of conditions. The implications are relevant to systems developers and to academicians seeking to explain how, when, why, and where user participation is needed.

This paper presents a holistic model of country-of-origin (COO) influence based on a narrative review of empirical studies of country-of-origin evaluations conducted from 1995-2005 when significant structural changes were occurring in international markets. The model depicts COO evaluations as subject to a number of culturally-derived antecedents and moderated by both product-based and individual consumer factors. In addition, the model shows holistic brand constructs such as brand image to moderate the effect of COO on product quality evaluations and purchase intentions.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper discusses the need for modeling and simulation of electric and hybrid vehicles. Different modeling methods such as physics-based Resistive Companion Form technique and Bond Graph method are presented with powertrain component and system modeling examples. The modeling and simulation capabilities of existing tools such as Powertrain System Analysis Toolkit (PSAT), ADvanced VehIcle SimulatOR (ADVISOR), PSIM, and Virtual Test Bed are demonstrated through application examples. Since power electronics is indispensable in hybrid vehicles, the issue of numerical oscillations in dynamic simulations involving power electronics is briefly addressed. </para>

We present recent developments of stochastic descriptions of nuclear dynamics. We focus on the newly introduced microscopic descriptions, such as stochastic extensions of currently used kinetic equations, as well as on more phenomenological, macroscopic approaches. We show to what extent these stochastic descriptions may offer a proper picture of nuclear dynamics both in strongly out of equilibrium situations, such as the ones encountered in energetic heavy-ion collisions or in closer to equilibrium situations such as the deexcitation of hot nuclei by thermal fission. In Section 1 we present a pedestrian introduction to the stochastic description of dynamical systems. We start from the elementary Brownian motion and introduce the Langevin and Fokker-Planck descriptions of the motion on that occasion. A few words are then spent to discuss the numerical methods developed for simulating stochastic equations. Section 2 of the paper is devoted to a formal introduction and discussion of both macroscopic and microscopic stochastic descriptions of nuclear dynamics. After a brief introduction reminding general concepts of equilibrium statistical physics we focus on microscopic descriptions of the many-body problem. We introduce here the Boltzmann Langevin equation which will provide a basis for many subsequent discussions. After having discussed the obtention of this equation from various points of view (from density matrix and Green's function techniques in particular), we consider reduced versions of this equation as well as a Fokker-Planck alternative. Section 3 is devoted to an analysis of fission by means of Langevin or Fokker-Planck-like approaches. We mainly discuss phenomenological approaches and spend some time in a detailed presentation of the ingredients entering these models. We present results obtained in these dynamical calculations when a proper account of particle evaporation is included for describing the fission of hot nuclei. Critical comparisons with experimental data are also provided. In Section 4 we focus on the application of the Boltzmann Langevin Equation to various situations encountered in energetic nuclear collisions. We first remind some typical examples for which this stochastic approach is both necessary and well suited. Typical applications are nuclear multifragmentation and subthreshold particle production, such as in particular kaon production. We discuss possible simulations of this equation and present some results in realistic calculations of collisions. We particularly focus on the dynamics of collective variables such as the quadrupole moment of the momentum distribution. We finally discuss other numerical simulations developed in the field. The last section before conclusion is devoted to extensions presently developed in the field of microscopic stochastic descriptions of nuclear dynamics. We present as a first step a relativistic version of the theory, then focus on fluid dynamics reductions. We finally discuss in some detail the recently introduced Stochastic time-dependent Hartree-Fock theory, which could provide new interesting developments.

In this paper, efficient approximate solutions are developed for microscale diffusion inside porous electrodes. Approximate solutions developed for the microscale diffusion are then coupled with governing equations for the macroscale to predict the electrochemical behavior of a lithium-ion cell sandwich. Approximate solutions developed facilitate the numerical simulation of batteries by reducing the number of differential algebraic equations resulting from the discretization of governing equations.

A method is developed and tested for extracting diffusion-controlled rate constants for condensed phase bimolecular reactions from Brownian dynamics trajectory simulations. This method will be useful when highly detailed model systems are employed, such as those required to explore the complicated range of interactions between enzymes and their substrates. The method is verified by comparing with exact analytical results for simple cases of spheres with uniform reactivity subject to various centrosymmetric Coulombic and Oseen slip hydrodynamic interactions. The utility of the method is illustrated for more complicated cases involving anisotropic reactivity and rotational diffusion.

This study explicates the indirect process through which news media use influences political participation. Specifically, it investigates the role of political knowledge and efficacy as mediators between communication and online/offline political participation within the framework of an O-S-R-O-R (Orientation-Stimulus-Reasoning-Orientation-Response) model of communication effects. Results from structural equation modeling analysis support the idea that political knowledge and efficacy function as significant mediators. In addition, results expound the increasing importance of the Internet in facilitating political participation. Implications of findings, limitations of this study, and suggestions for future research are discussed.

Annie Selden, John Selden, Validations of Proofs Considered as Texts: Can Undergraduates Tell Whether an Argument Proves a Theorem?, Journal for Research in Mathematics Education, Vol. 34, No. 1 (Jan., 2003), pp. 4-36

Model-based analysis tools, built on assumptions and simplifications, are difficult to handle smart grids with data characterized by volume, velocity, variety, and veracity (i.e., 4Vs data). This paper, using random matrix theory (RMT), motivates data-driven tools to perceive the complex grids in high-dimension; meanwhile, an architecture with detailed procedures is proposed. In algorithm perspective, the architecture performs a high-dimensional analysis and compares the findings with RMT predictions to conduct anomaly detections. Mean spectral radius (MSR), as a statistical indicator, is defined to reflect the correlations of system data in different dimensions. In management mode perspective, a group-work mode is discussed for smart grids operation. This mode breaks through regional limitations for energy flows and data flows, and makes advanced big data analyses possible. For a specific large-scale zone-dividing system with multiple connected utilities, each site, operating under the group-work mode, is able to work out the regional MSR only with its own measured/simulated data. The large-scale interconnected system, in this way, is naturally decoupled from statistical parameters perspective, rather than from engineering models perspective. Furthermore, a comparative analysis of these distributed MSRs, even with imperceptible different raw data, will produce a contour line to detect the event and locate the source. It demonstrates that the architecture is compatible with the block calculation only using the regional small database; beyond that, this architecture, as a data-driven solution, is sensitive to system situation awareness, and practical for real large-scale interconnected systems. Five case studies and their visualizations validate the designed architecture in various fields of power systems. To our best knowledge, this paper is the first attempt to apply big data technology into smart grids.

Using a systems perspective, a conceptual model is developed that encompasses a broad class of systems whose fundamental purpose is the support of managerial actions and decision making. The term management support systems (MSS) is used to label this broad class. This model is based on an extensive review of the relevant literature and available research. The result provides an integrated systems model of the phenomena involved and points to gaps in the research that arise largely from the attempts to examine various classes of MSS as separate entities. The research presented here is based on the premise that there are fundamental core consistencies or similarities among various types of systems that have evolved in the past several decades to support decision making. It presents a conceptual, theoretical model drawn from findings about various types of support systems described in the literature such as decision support systems (DSS), executive information systems (EIS), knowledge management systems (KMS), and business intelligence (BI). Pragmatic insights are provided by the conceptual model and recommendations for future research are discussed.

Brownian dynamics computer simulations of the diffusional association of electron transport proteins cytochrome c (cyt c) and cytochrome c peroxidase (cyt c per) were performed. A highly detailed and realistic model of the protein structures and their electrostatic interactions was used that was based on an atomic-level spatial description. Several structural features played a role in enhancing and optimizing the electron transfer efficiency of this reaction. Favorable electrostatic interactions facilitated long-lived nonspecific encounters between the proteins that allowed the severe orientational criteria for reaction to be overcome by rotational diffusion during encounters. Thus a "reduction-in-dimensionality" effect operated. The proteins achieved plausible electron transfer orientations in a multitude of electrostatically stable encounter complexes, rather than in a single dominant complex.

Functionalization of electrospun mats with antimicrobial nanomaterials is an attractive strategy to develop polymer coating materials to prevent bacterial colonization on surfaces. In this study we demonstrated a feasible approach to produce antimicrobial electrospun mats through a postfabrication binding of graphene-based nanocomposites to the nanofibers' surface. A mixture of poly(lactide-co-glycolide) (PLGA) and chitosan was electrospun to yield cylindrical and narrow-diameter (356 nm) polymeric fibers. To achieve a robust antimicrobial property, the PLGA-chitosan mats were functionalized with graphene oxide decorated with silver nanoparticles (GO-Ag) via a chemical reaction between the carboxyl groups of graphene and the primary amine functional groups on the PLGA-chitosan fibers using 3-(dimethylamino)propyl-N'-ethylcarbodiimide hydrochloride and N-hydroxysuccinimide as cross-linking agents. The attachment of GO-Ag sheets to the surface of PLGA-chitosan fibers was successfully revealed by scanning and transmission electron images. Upon direct contact with bacterial cells, the PLGA-chitosan mats functionalized with GO-Ag nanocomposites were able to effectively inactivate both Gram-negative (Escherichia coli and Pseudomonas aeruginosa) and Gram-positive (Staphylococcus aureus) bacteria. Our results suggest that covalent binding of GO-Ag nanocomposites to the surface of PLGA-chitosan mats opens up new opportunities for the production of cost-effective, scalable, and biodegradable coating materials with the ability to hinder microbial proliferation on solid surfaces.