Showing papers by "University of Central Florida published in 2015"

Recent Advances in Two-Dimensional Materials beyond Graphene

Abstract: The isolation of graphene in 2004 from graphite was a defining moment for the “birth” of a field: two-dimensional (2D) materials In recent years, there has been a rapidly increasing number of papers focusing on non-graphene layered materials, including transition-metal dichalcogenides (TMDs), because of the new properties and applications that emerge upon 2D confinement Here, we review significant recent advances and important new developments in 2D materials “beyond graphene” We provide insight into the theoretical modeling and understanding of the van der Waals (vdW) forces that hold together the 2D layers in bulk solids, as well as their excitonic properties and growth morphologies Additionally, we highlight recent breakthroughs in TMD synthesis and characterization and discuss the newest families of 2D materials, including monoelement 2D materials (ie, silicene, phosphorene, etc) and transition metal carbide- and carbon nitride-based MXenes We then discuss the doping and functionalization of 2

Supercapacitor electrode materials: nanostructures from 0 to 3 dimensions

Abstract: Supercapacitors have drawn considerable attention in recent years due to their high specific power, long cycle life, and ability to bridge the power/energy gap between conventional capacitors and batteries/fuel cells. Nanostructured electrode materials have demonstrated superior electrochemical properties in producing high-performance supercapacitors. In this review article, we describe the recent progress and advances in designing nanostructured supercapacitor electrode materials based on various dimensions ranging from zero to three. We highlight the effect of nanostructures on the properties of supercapacitors including specific capacitance, rate capability and cycle stability, which may serve as a guideline for the next generation of supercapacitor electrode design.

TEER Measurement Techniques for In Vitro Barrier Model Systems

Abstract: Transepithelial/transendothelial electrical resistance (TEER) is a widely accepted quantitative technique to measure the integrity of tight junction dynamics in cell culture models of endothelial and epithelial monolayers. TEER values are strong indicators of the integrity of the cellular barriers before they are evaluated for transport of drugs or chemicals. TEER measurements can be performed in real time without cell damage and generally are based on measuring ohmic resistance or measuring impedance across a wide spectrum of frequencies. The measurements for various cell types have been reported with commercially available measurement systems and also with custom-built microfluidic implementations. Some of the barrier models that have been widely characterized using TEER include the blood–brain barrier (BBB), gastrointestinal (GI) tract, and pulmonary models. Variations in these values can arise due to factors such as temperature, medium formulation, and passage number of cells. The aim of this article ...

Sparse Convolutional Neural Networks

Abstract: Deep neural networks have achieved remarkable performance in both image classification and object detection problems, at the cost of a large number of parameters and computational complexity. In this work, we show how to reduce the redundancy in these parameters using a sparse decomposition. Maximum sparsity is obtained by exploiting both inter-channel and intra-channel redundancy, with a fine-tuning step that minimize the recognition loss caused by maximizing sparsity. This procedure zeros out more than 90% of parameters, with a drop of accuracy that is less than 1% on the ILSVRC2012 dataset. We also propose an efficient sparse matrix multiplication algorithm on CPU for Sparse Convolutional Neural Networks (SCNN) models. Our CPU implementation demonstrates much higher efficiency than the off-the-shelf sparse matrix libraries, with a significant speedup realized over the original dense network. In addition, we apply the SCNN model to the object detection problem, in conjunction with a cascade model and sparse fully connected layers, to achieve significant speedups.

Heterogeneous Network Embedding via Deep Architectures

Abstract: Data embedding is used in many machine learning applications to create low-dimensional feature representations, which preserves the structure of data points in their original space. In this paper, we examine the scenario of a heterogeneous network with nodes and content of various types. Such networks are notoriously difficult to mine because of the bewildering combination of heterogeneous contents and structures. The creation of a multidimensional embedding of such data opens the door to the use of a wide variety of off-the-shelf mining techniques for multidimensional data. Despite the importance of this problem, limited efforts have been made on embedding a network of scalable, dynamic and heterogeneous data. In such cases, both the content and linkage structure provide important cues for creating a unified feature representation of the underlying network. In this paper, we design a deep embedding algorithm for networked data. A highly nonlinear multi-layered embedding function is used to capture the complex interactions between the heterogeneous data in a network. Our goal is to create a multi-resolution deep embedding function, that reflects both the local and global network structures, and makes the resulting embedding useful for a variety of data mining tasks. In particular, we demonstrate that the rich content and linkage information in a heterogeneous network can be captured by such an approach, so that similarities among cross-modal data can be measured directly in a common embedding space. Once this goal has been achieved, a wide variety of data mining problems can be solved by applying off-the-shelf algorithms designed for handling vector representations. Our experiments on real-world network datasets show the effectiveness and scalability of the proposed algorithm as compared to the state-of-the-art embedding methods.

State of science: mental workload in ergonomics

Abstract: Mental workload (MWL) is one of the most widely used concepts in ergonomics and human factors and represents a topic of increasing importance. Since modern technology in many working environments imposes ever more cognitive demands upon operators while physical demands diminish, understanding how MWL impinges on performance is increasingly critical. Yet, MWL is also one of the most nebulous concepts, with numerous definitions and dimensions associated with it. Moreover, MWL research has had a tendency to focus on complex, often safety-critical systems (e.g. transport, process control). Here we provide a general overview of the current state of affairs regarding the understanding, measurement and application of MWL in the design of complex systems over the last three decades. We conclude by discussing contemporary challenges for applied research, such as the interaction between cognitive workload and physical workload, and the quantification of workload ‘redlines’ which specify when operators are approachi...

Organized hypocrisy, organizational façades, and sustainability reporting

Abstract: Sustainability discourse is becoming ubiquitous. Still, a significant gap persists between corporate sustainability talk and practice. Prior research on corporate sustainability reporting has relied primarily on two competing theoretical framings, signaling theory and legitimacy theory, which often produce contradictory results regarding the significance and effects of such disclosures. Thus, despite this substantial body of research, the role that sustainability disclosures can play in any transition toward a less unsustainable society remains unclear. In an effort to advance our collective understanding of voluntary corporate sustainability reporting, we propose a richer and more nuanced theoretical lens by drawing on prior work in organized hypocrisy (Brunsson, 1989) and organizational facades (Abrahamson & Baumard, 2008; Nystrom & Strabuck, 1984). We argue that contradictory societal and institutional pressures, in essence, require organizations to engage in hypocrisy and develop facades, thereby severely limiting the prospects that sustainability reports will ever evolve into substantive disclosures. To illustrate the use of these theoretical concepts, we employ them to examine the talk, decisions, and actions of two highly visible U.S.-based multinational oil and gas corporations during the time period of significant national debate over oil exploration in the Alaskan National Wildlife Refuge. We conclude that the concepts of organizational facade and organized hypocrisy are beneficial to the sustainability disclosure literature because they provide theoretical space to more formally acknowledge and incorporate how the prevailing economic system and conflicting stakeholder demands constrain the action choices of individual corporations.

Stochastic Optimization for Unit Commitment—A Review

Abstract: Optimization models have been widely used in the power industry to aid the decision-making process of scheduling and dispatching electric power generation resources, a process known as unit commitment (UC). Since UC’s birth, there have been two major waves of revolution on UC research and real life practice. The first wave has made mixed integer programming stand out from the early solution and modeling approaches for deterministic UC, such as priority list, dynamic programming, and Lagrangian relaxation. With the high penetration of renewable energy, increasing deregulation of the electricity industry, and growing demands on system reliability, the next wave is focused on transitioning from traditional deterministic approaches to stochastic optimization for unit commitment. Since the literature has grown rapidly in the past several years, this paper is to review the works that have contributed to the modeling and computational aspects of stochastic optimization (SO) based UC. Relevant lines of future research are also discussed to help transform research advances into real-world applications.

Differential Recurrent Neural Networks for Action Recognition

Abstract: The long short-term memory (LSTM) neural network is capable of processing complex sequential information since it utilizes special gating schemes for learning representations from long input sequences. It has the potential to model any time-series or sequential data, where the current hidden state has to be considered in the context of the past hidden states. This property makes LSTM an ideal choice to learn the complex dynamics of various actions. Unfortunately, the conventional LSTMs do not consider the impact of spatio-temporal dynamics corresponding to the given salient motion patterns, when they gate the information that ought to be memorized through time. To address this problem, we propose a differential gating scheme for the LSTM neural network, which emphasizes on the change in information gain caused by the salient motions between the successive frames. This change in information gain is quantified by Derivative of States (DoS), and thus the proposed LSTM model is termed as differential Recurrent Neural Network (dRNN). We demonstrate the effectiveness of the proposed model by automatically recognizing actions from the real-world 2D and 3D human action datasets. Our study is one of the first works towards demonstrating the potential of learning complex time-series representations via high-order derivatives of states.

Optical dielectric function of silver

Abstract: Using broadband spectroscopic ellipsometry, the authors determine the complex valued dielectric function of silver films from 0.05 eV (\ensuremath{\lambda}=25 \ensuremath{\mu}) to 4.14 eV (\ensuremath{\lambda} = 300 nm) with a statistical uncertainty of less than 1%. While several previous similar measurements exist, they span considerably shorter energy ranges and report partially inconsistent results. In view of the wide-ranging applications of silver in nanophotonics, plasmonics and optical metamaterials, we anticipate this paper to become a standard reference for many scientists and engineers.

Multicolor CRISPR labeling of chromosomal loci in human cells

Abstract: The intranuclear location of genomic loci and the dynamics of these loci are important parameters for understanding the spatial and temporal regulation of gene expression. Recently it has proven possible to visualize endogenous genomic loci in live cells by the use of transcription activator-like effectors (TALEs), as well as modified versions of the bacterial immunity clustered regularly interspersed short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) system. Here we report the design of multicolor versions of CRISPR using catalytically inactive Cas9 endonuclease (dCas9) from three bacterial orthologs. Each pair of dCas9-fluorescent proteins and cognate single-guide RNAs (sgRNAs) efficiently labeled several target loci in live human cells. Using pairs of differently colored dCas9-sgRNAs, it was possible to determine the intranuclear distance between loci on different chromosomes. In addition, the fluorescence spatial resolution between two loci on the same chromosome could be determined and related to the linear distance between them on the chromosome's physical map, thereby permitting assessment of the DNA compaction of such regions in a live cell.

How global biodiversity hotspots may go unrecognized: Lessons from the North American Coastal Plain

Abstract: Biodiversity hotspots are conservation priorities. We identify the North American Coastal Plain (NACP) as a global hotspot based on the classic definition, a region with > 1500 endemic plant species and > 70% habitat loss. This region has been bypassed in prior designations due to misconceptions and myths about its ecology and history. These fallacies include: (1) young age of the NACP, climatic instability over time and submergence during high sea-level stands; (2) climatic and environmental homogeneity; (3) closed forest as the climax vegetation; and (4) fire regimes that are mostly anthropogenic. We show that the NACP is older and more climatically stable than usually assumed, spatially heterogeneous and extremely rich in species and endemics for its range of latitude, especially within pine savannas and other mostly herbaceous and fire-dependent communities. We suspect systematic biases and misconceptions, in addition to missing information, obscure the existence of similarly biologically significant regions world-wide. Potential solutions to this problem include (1) increased field biological surveys and taxonomic determinations, especially within grassy biomes and regions with low soil fertility, which tend to have much overlooked biodiversity; (2) more research on the climatic refugium role of hotspots, given that regions of high endemism often coincide with regions with low velocity of climate change; (3) in low-lying coastal regions, consideration of the heterogeneity in land area generated by historically fluctuating sea levels, which likely enhanced opportunities for evolution of endemic species; and (4) immediate actions to establish new protected areas and implement science-based management to restore evolutionary environmental conditions in newly recognized hotspots.

Guidelines for the Echocardiographic Assessment of Atrial Septal Defect and Patent Foramen Ovale: From the American Society of Echocardiography and Society for Cardiac Angiography and Interventions

Abstract: Target Audience 911 Objectives 91

Understanding and improving teamwork in organizations: a scientifically based practical guide

Abstract: Teams are pervasive in today's world, and rightfully so as we need them. Drawing upon the existing extensive body of research surrounding the topic of teamwork, we delineate nine “critical considerations” that serve as a practical heuristic by which HR leaders can determine what is needed when they face situations involving teamwork. Our heuristic is not intended to be the definitive set of all considerations for teamwork, but instead consolidates key findings from a vast literature to provide an integrated understanding of the underpinnings of teamwork—specifically, what should be considered when selecting, developing, and maintaining teams. This heuristic is designed to help those in practice diagnose team-based problems by providing a clear focus on relevant aspects of teamwork. To this end, we first define teamwork and its related elements. Second, we offer a high-level conceptualization of and justification for the nine selected considerations underlying the heuristic, which is followed by a more in-depth synthesis of related literature as well as empirically-driven practical guidance. Third, we conclude with a discussion regarding how this heuristic may best be used from a practical standpoint, as well as offer areas for future research regarding both teamwork and its critical considerations

GMMCP tracker: Globally optimal Generalized Maximum Multi Clique problem for multiple object tracking

Abstract: Data association is the backbone to many multiple object tracking (MOT) methods. In this paper we formulate data association as a Generalized Maximum Multi Clique problem (GMMCP). We show that this is the ideal case of modeling tracking in real world scenario where all the pairwise relationships between targets in a batch of frames are taken into account. Previous works assume simplified version of our tracker either in problem formulation or problem optimization. However, we propose a solution using GMMCP where no simplification is assumed in either steps. We show that the NP hard problem of GMMCP can be formulated through Binary-Integer Program where for small and medium size MOT problems the solution can be found efficiently. We further propose a speed-up method, employing Aggregated Dummy Nodes for modeling occlusion and miss-detection, which reduces the size of the input graph without using any heuristics. We show that, using the speedup method, our tracker lends itself to real-time implementation which is plausible in many applications. We evaluated our tracker on six challenging sequences of Town Center, TUD-Crossing, TUD-Stadtmitte, Parking-lot 1, Parking-lot 2 and Parking-lot pizza and show favorable improvement against state of art.

Controllable spatiotemporal nonlinear effects in multimode fibres

Abstract: Highly nonlinear effects are observed in graded-index multimode optical fibres. Multimode fibres are of interest for next-generation telecommunications systems and the construction of high-energy fibre lasers. However, relatively little work has explored nonlinear pulse propagation in multimode fibres. Here, we consider highly nonlinear ultrashort pulse propagation in the anomalous-dispersion regime of a graded-index multimode fibre. Low modal dispersion and strong nonlinear coupling between the fibre's many spatial modes result in interesting behaviour. We observe spatiotemporal effects reminiscent of nonlinear optics in bulk media—self-focusing and multiple filamentation1,2—at a fraction of the usual power. By adjusting the spatial initial conditions, we generate on-demand, megawatt, ultrashort pulses tunable between 1,550 and 2,200 nm; dispersive waves over one octave; intense combs of visible light; and a multi-octave-spanning supercontinuum. Our results indicate that multimode fibres present unique opportunities for observing new spatiotemporal dynamics and phenomena. They also enable the realization of a new type of tunable, broadband fibre source that could be useful for many applications.

Big Data applications in real-time traffic operation and safety monitoring and improvement on urban expressways

Abstract: The advent of Big Data era has transformed the outlook of numerous fields in science and engineering. The transportation arena also has great expectations of taking the advantage of Big Data enabled by the popularization of Intelligent Transportation Systems (ITS). In this study, the viability of a proactive real-time traffic monitoring strategy evaluating operation and safety simultaneously was explored. The objective is to improve the system performance of urban expressways by reducing congestion and crash risk. In particular, Microwave Vehicle Detection System (MVDS) deployed on an expressway network in Orlando was utilized to achieve the objectives. The system consisting of 275 detectors covers 75 miles of the expressway network, with average spacing less than 1 mile. Comprehensive traffic flow parameters per lane are continuously archived on one-minute interval basis. The scale of the network, dense deployment of detection system, richness of information and continuous collection turn MVDS as the ideal source of Big Data. It was found that congestion on urban expressways was highly localized and time-specific. As expected, the morning and evening peak hours were the most congested time periods. The results of congestion evaluation encouraged real-time safety analysis to unveil the effects of traffic dynamics on crash occurrence. Data mining (random forest) and Bayesian inference techniques were implemented in real-time crash prediction models. The identified effects, both indirect (peak hour, higher volume and lower speed upstream of crash locations) and direct (higher congestion index downstream to crash locations) congestion indicators confirmed the significant impact of congestion on rear-end crash likelihood. As a response, reliability analysis was introduced to determine the appropriate time to trigger safety warnings according to the congestion intensity. Findings of this paper demonstrate the importance to jointly monitor and improve traffic operation and safety. The Big Data generated by the ITS systems is worth further exploration to bring all their full potential for more proactive traffic management.

Why does self-reported emotional intelligence predict job performance? A meta-analytic investigation of mixed EI.

Abstract: Recent empirical reviews have claimed a surprisingly strong relationship between job performance and self-reported emotional intelligence (also commonly called trait EI or mixed EI), suggesting self-reported/mixed EI is one of the best known predictors of job performance (e.g., ρ = .47; Joseph & Newman, 2010b). Results further suggest mixed EI can robustly predict job performance beyond cognitive ability and Big Five personality traits (Joseph & Newman, 2010b; O'Boyle, Humphrey, Pollack, Hawver, & Story, 2011). These criterion-related validity results are problematic, given the paucity of evidence and the questionable construct validity of mixed EI measures themselves. In the current research, we update and reevaluate existing evidence for mixed EI, in light of prior work regarding the content of mixed EI measures. Results of the current meta-analysis demonstrate that (a) the content of mixed EI measures strongly overlaps with a set of well-known psychological constructs (i.e., ability EI, self-efficacy, and self-rated performance, in addition to Conscientiousness, Emotional Stability, Extraversion, and general mental ability; multiple R = .79), (b) an updated estimate of the meta-analytic correlation between mixed EI and supervisor-rated job performance is ρ = .29, and (c) the mixed EI-job performance relationship becomes nil (β = -.02) after controlling for the set of covariates listed above. Findings help to establish the construct validity of mixed EI measures and further support an intuitive theoretical explanation for the uncommonly high association between mixed EI and job performance--mixed EI instruments assess a combination of ability EI and self-perceptions, in addition to personality and cognitive ability.

College of American pathologists' laboratory standards for next-generation sequencing clinical tests

Abstract: Context.— The higher throughput and lower per-base cost of next-generation sequencing (NGS) as compared to Sanger sequencing has led to its rapid adoption in clinical testing. The number of laborat...

Catalytic Properties and Biomedical Applications of Cerium Oxide Nanoparticles

Abstract: Cerium oxide nanoparticles (nanoceria) have shown promise as catalytic antioxidants in the test tube, cell culture models and animal models of disease However given the reactivity that is well established at the surface of these nanoparticles, the biological utilization of nanoceria as a therapeutic still poses many challenges Moreover the form that these particles take in a biological environment, such as the changes that can occur due to a protein corona, are not well established This review aims to summarize the existing literature on biological use of nanoceria, and to raise questions about what further study is needed to apply this interesting catalytic material to biomedical applications These questions include: 1) How does preparation, exposure dose, route and experimental model influence the reported effects of nanoceria in animal studies? 2) What are the considerations to develop nanoceria as a therapeutic agent in regards to these parameters? 3) What biological targets of reactive oxygen species (ROS) and reactive nitrogen species (RNS) are relevant to this targeting, and how do these properties also influence the safety of these nanomaterials?

Evaluation of candidate vaccine approaches for MERS-CoV

Abstract: The emergence of Middle East respiratory syndrome coronavirus (MERS-CoV) as a cause of severe respiratory disease highlights the need for effective approaches to CoV vaccine development. Efforts focused solely on the receptor-binding domain (RBD) of the viral Spike (S) glycoprotein may not optimize neutralizing antibody (NAb) responses. Here we show that immunogens based on full-length S DNA and S1 subunit protein elicit robust serum-neutralizing activity against several MERS-CoV strains in mice and non-human primates. Serological analysis and isolation of murine monoclonal antibodies revealed that immunization elicits NAbs to RBD and, non-RBD portions of S1 and S2 subunit. Multiple neutralization mechanisms were demonstrated by solving the atomic structure of a NAb-RBD complex, through sequencing of neutralization escape viruses and by constructing MERS-CoV S variants for serological assays. Immunization of rhesus macaques confers protection against MERS-CoV-induced radiographic pneumonia, as assessed using computerized tomography, supporting this strategy as a promising approach for MERS-CoV vaccine development.

Polarization-independent actively tunable colour generation on imprinted plasmonic surfaces.

Abstract: Structural colour arising from nanostructured metallic surfaces offers many benefits compared to conventional pigmentation based display technologies, such as increased resolution and scalability of their optical response with structure dimensions. However, once these structures are fabricated their optical characteristics remain static, limiting their potential application. Here, by using a specially designed nanostructured plasmonic surface in conjunction with high birefringence liquid crystals, we demonstrate a tunable polarizationindependent reflective surface where the colour of the surface is changed as a function of applied voltage. A large range of colour tunability is achieved over previous reports by utilizing an engineered surface which allows full liquid crystal reorientation while maximizing the overlap between plasmonic fields and liquid crystal. In combination with imprinted structures of varying periods, a full range of colours spanning the entire visible spectrum is achieved, paving the way towards dynamic pixels for reflective displays.

The platform trial: an efficient strategy for evaluating multiple treatments.

Abstract: The drug development enterprise is struggling. The development of new therapies is limited by high costs, slow progress, and a high failure rate, even in the late stages of development. Clinical trials are most commonly based on a “one population, one drug, one disease” strategy, in which the clinical trial infrastructure is created to test a single treatment in a homogeneous population. This approach has been largely unsuccessful for multiple diseases, including sepsis, dementia, and stroke. Despite promising preclinical and early human trials, there have been numerous negative phase 3 trials of treatments for Alzheimer disease1 and more than 40 negative phase 3 trials of neuroprotectants for stroke.2 Effective treatments for such diseases will likely require combining treatments to affect multiple targets in complex cellular pathways and, perhaps, tailoring treatments to subgroups defined by genetic, proteomic, metabolomic, or other markers.3 There has been increasing interest in efficient trial strategies designed to evaluate multiple treatments and combinations of treatments, in heterogeneous patient populations, with the capability to add new treatments in the future and eliminate investigational treatments lacking efficacy. The term “platform trial” is sometimes used to describe trials designed with these goals in mind, signifying the intent to build an experimental platform that will exist after the evaluation of any particular treatment.4 Currently, platform trials are enrolling patients or are under development in oncology, infectious diseases, neurology, and intensive care. Platform trials are an extension of adaptive trial design. An adaptive trial allows prespecified changes in key trial characteristics during the conduct of the trial in response to information accumulating during the trial; however, most adaptive trials focus on evaluating a single treatment in a single population. Examples of adaptive trials include traditional group-sequential trials, as well as trials incorporating reestimation of sample size or using variable randomization proportions (responseadaptive randomization).5 A platform trial is a type of adaptive trial designed to evaluate multiple treatments efficiently.

Observation of optical solitons in PT-symmetric lattices

Abstract: Controlling light transport in nonlinear active environments is a topic of considerable interest in the field of optics. In such complex arrangements, of particular importance is to devise strategies to subdue chaotic behaviour even in the presence of gain/loss and nonlinearity, which often assume adversarial roles. Quite recently, notions of parity-time (PT) symmetry have been suggested in photonic settings as a means to enforce stable energy flow in platforms that simultaneously employ both amplification and attenuation. Here we report the experimental observation of optical solitons in PT-symmetric lattices. Unlike other non-conservative nonlinear arrangements where self-trapped states appear as fixed points in the parameter space of the governing equations, discrete PT solitons form a continuous parametric family of solutions. The possibility of synthesizing PT-symmetric saturable absorbers, where a nonlinear wave finds a lossless path through an otherwise absorptive system is also demonstrated.

Privacy and Security in Internet of Things and Wearable Devices

Abstract: Enter the nascent era of Internet of Things (IoT) and wearable devices, where small embedded devices loaded with sensors collect information from its surroundings, process it, and relay it to remote locations for further analysis. Albeit looking harmless, these nascent technologies raise security and privacy concerns. We pose the question of the possibility and effects of compromising such devices. Concentrating on the design flow of IoT and wearable devices, we discuss some common design practices and their implications on security and privacy. Two representatives from each category, the Google Nest Thermostat and the Nike+ Fuelband, are selected as examples on how current industry practices of security as an afterthought or an add-on affect the resulting device and the potential consequences to the user's security and privacy. We then discuss design flow enhancements, through which security mechanisms can efficiently be added into a device, vastly differing from traditional practices.

Integrating the organizational change literature: a model for successful change

Abstract: Purpose – The purpose of this paper is to contribute a roadmap to the change management literature, and provide definitions for describing change types, change enablers and change methods. This paper also proposes aligning the change type with the change method to find the effect on the change outcomes. New researchers can use this paper to get an overview of the change management discipline along with the main concepts that help in understanding the different dimensions of and relationships between the change types and methods in the literature. Managers can use this paper to describe and classify their organizational change situation and select an implementation method for systematic change and for change management. Design/methodology/approach – This framework is designed based on literature review and experts judgment. Findings – The results of the research propose a hypothesis that describes the relationships between the change types and methods and how this relationship can affect the change outcome...