Showing papers by "University of North Texas published in 2020"

Celebrating 20 Years of Genetic Discoveries in Legume Nodulation and Symbiotic Nitrogen Fixation

Abstract: Since 1999, various forward- and reverse-genetic approaches have uncovered nearly 200 genes required for symbiotic nitrogen fixation (SNF) in legumes. These discoveries advanced our understanding of the evolution of SNF in plants and its relationship to other beneficial endosymbioses, signaling between plants and microbes, the control of microbial infection of plant cells, the control of plant cell division leading to nodule development, autoregulation of nodulation, intracellular accommodation of bacteria, nodule oxygen homeostasis, the control of bacteroid differentiation, metabolism and transport supporting symbiosis, and the control of nodule senescence. This review catalogs and contextualizes all of the plant genes currently known to be required for SNF in two model legume species, Medicago truncatula and Lotus japonicus, and two crop species, Glycine max (soybean) and Phaseolus vulgaris (common bean). We also briefly consider the future of SNF genetics in the era of pan-genomics and genome editing.

Massive MIMO Systems for 5G and Beyond Networks-Overview, Recent Trends, Challenges, and Future Research Direction.

Abstract: The global bandwidth shortage in the wireless communication sector has motivated the study and exploration of wireless access technology known as massive Multiple-Input Multiple-Output (MIMO). Massive MIMO is one of the key enabling technology for next-generation networks, which groups together antennas at both transmitter and the receiver to provide high spectral and energy efficiency using relatively simple processing. Obtaining a better understating of the massive MIMO system to overcome the fundamental issues of this technology is vital for the successful deployment of 5G—and beyond—networks to realize various applications of the intelligent sensing system. In this paper, we present a comprehensive overview of the key enabling technologies required for 5G and 6G networks, highlighting the massive MIMO systems. We discuss all the fundamental challenges related to pilot contamination, channel estimation, precoding, user scheduling, energy efficiency, and signal detection in a massive MIMO system and discuss some state-of-the-art mitigation techniques. We outline recent trends such as terahertz communication, ultra massive MIMO (UM-MIMO), visible light communication (VLC), machine learning, and deep learning for massive MIMO systems. Additionally, we discuss crucial open research issues that direct future research in massive MIMO systems for 5G and beyond networks.

Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers

Abstract: The present guide provides a practical guide to conducting latent profile analysis (LPA) in the Mplus software system. This guide is intended for researchers familiar with some latent variable mode...

Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea.

Abstract: Background: SARS-CoV-2 (severe acute respiratory coronavirus 2) was spreading rapidly in South Korea at the end of February 2020 following its initial outbreak in China, making Korea the new center of global attention. The role of social media amid the current coronavirus disease (COVID-19) pandemic has often been criticized, but little systematic research has been conducted on this issue. Social media functions as a convenient source of information in pandemic situations. Objective: Few infodemiology studies have applied network analysis in conjunction with content analysis. This study investigates information transmission networks and news-sharing behaviors regarding COVID-19 on Twitter in Korea. The real time aggregation of social media data can serve as a starting point for designing strategic messages for health campaigns and establishing an effective communication system during this outbreak. Methods: Korean COVID-19-related Twitter data were collected on February 29, 2020. Our final sample comprised of 43,832 users and 78,233 relationships on Twitter. We generated four networks in terms of key issues regarding COVID-19 in Korea. This study comparatively investigates how COVID-19-related issues have circulated on Twitter through network analysis. Next, we classified top news channels shared via tweets. Lastly, we conducted a content analysis of news frames used in the top-shared sources. Results: The network analysis suggests that the spread of information was faster in the Coronavirus network than in the other networks (Corona19, Shincheon, and Daegu). People who used the word “Coronavirus” communicated more frequently with each other. The spread of information was faster, and the diameter value was lower than for those who used other terms. Many of the news items highlighted the positive roles being played by individuals and groups, directing readers’ attention to the crisis. Ethical issues such as deviant behavior among the population and an entertainment frame highlighting celebrity donations also emerged often. There was a significant difference in the use of nonportal (n=14) and portal news (n=26) sites between the four network types. The news frames used in the top sources were similar across the networks (P=.89, 95% CI 0.004-0.006). Tweets containing medically framed news articles (mean 7.571, SD 1.988) were found to be more popular than tweets that included news articles adopting nonmedical frames (mean 5.060, SD 2.904; N=40, P=.03, 95% CI 0.169-4.852). Conclusions: Most of the popular news on Twitter had nonmedical frames. Nevertheless, the spillover effect of the news articles that delivered medical information about COVID-19 was greater than that of news with nonmedical frames. Social media network analytics cannot replace the work of public health officials; however, monitoring public conversations and media news that propagates rapidly can assist public health professionals in their complex and fast-paced decision-making processes.

Systemic signaling during abiotic stress combination in plants.

Abstract: Extreme environmental conditions, such as heat, salinity, and decreased water availability, can have a devastating impact on plant growth and productivity, potentially resulting in the collapse of entire ecosystems. Stress-induced systemic signaling and systemic acquired acclimation play canonical roles in plant survival during episodes of environmental stress. Recent studies revealed that in response to a single abiotic stress, applied to a single leaf, plants mount a comprehensive stress-specific systemic response that includes the accumulation of many different stress-specific transcripts and metabolites, as well as a coordinated stress-specific whole-plant stomatal response. However, in nature plants are routinely subjected to a combination of two or more different abiotic stresses, each potentially triggering its own stress-specific systemic response, highlighting a new fundamental question in plant biology: are plants capable of integrating two different systemic signals simultaneously generated during conditions of stress combination? Here we show that plants can integrate two different systemic signals simultaneously generated during stress combination, and that the manner in which plants sense the different stresses that trigger these signals (i.e., at the same or different parts of the plant) makes a significant difference in how fast and efficient they induce systemic reactive oxygen species (ROS) signals; transcriptomic, hormonal, and stomatal responses; as well as plant acclimation. Our results shed light on how plants acclimate to their environment and survive a combination of different abiotic stresses. In addition, they highlight a key role for systemic ROS signals in coordinating the response of different leaves to stress.

Metal-Organic Frameworks for Enzyme Immobilization: Beyond Host Matrix Materials

Abstract: Enzyme immobilization in metal-organic frameworks (MOFs) as a promising strategy is attracting the interest of scientists from different disciplines with the expansion of MOFs' development. Different from other traditional host materials, their unique strengths of high surface areas, large yet adjustable pore sizes, functionalizable pore walls, and diverse architectures make MOFs an ideal platform to investigate hosted enzymes, which is critical to the industrial and commercial process. In addition to the protective function of MOFs, the extensive roles of MOFs in the enzyme immobilization are being well-explored by making full use of their remarkable properties like well-defined structure, high porosity, and tunable functionality. Such development shifts the focus from the exploration of immobilization strategies toward functionalization. Meanwhile, this would undoubtedly contribute to a better understanding of enzymes in regards to the structural transformation after being hosted in a confinement environment, particularly to the orientation and conformation change as well as the interplay between enzyme and matrix MOFs. In this Outlook, we target a comprehensive review of the role diversities of the host matrix MOF based on the current enzyme immobilization research, along with proposing an outlook toward the future development of this field, including the representatives of potential techniques and methodologies being capable of studying the hosted enzymes.

Toward a 7-P framework for international marketing

Abstract: The purpose of this paper is to develop a generalized 7 P-theoretical framework for strategic planning as part of international marketing (Potential, Path, Process, Pace, Pattern, Problems and Perf...

Early Signs Indicate That COVID-19 Is Exacerbating Gender Inequality in the Labor Force

Abstract: In this data visualization, the authors examine how the coronavirus disease 2019 (COVID-19) crisis in the United States has affected labor force participation, unemployment, and work hours across gender and parental status. Using data from the Current Population Survey, the authors compare estimates between February and April 2020 to examine the period of time before the COVID-19 outbreak in the United States to the height of the first wave, when stay-at-home orders were issued across the country. The findings illustrate that women, particularly mothers, have employment disproportionately affected by COVID-19. Mothers are more likely than fathers to exit the labor force and become unemployed. Among heterosexual married couples of which both partners work in telecommuting-capable occupations, mothers have scaled back their work hours to a far greater extent than fathers. These patterns suggest that the COVID-19 crisis is already worsening existing gender inequality, with long-term implications for women's employment.

An Experiment-Based Review of Low-Light Image Enhancement Methods

Abstract: Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested.

PoBT: A Lightweight Consensus Algorithm for Scalable IoT Business Blockchain

Abstract: Efficient and smart business processes are heavily dependent on the Internet of Things (IoT) networks, where end-to-end optimization is critical to the success of the whole ecosystem. These systems, including industrial, healthcare, and others, are large scale complex networks of heterogeneous devices. This introduces many security and access control challenges. Blockchain has emerged as an effective solution for addressing several such challenges. However, the basic algorithms used in the business blockchain are not feasible for large scale IoT systems. To make them scalable for IoT, the complex consensus-based security has to be downgraded. In this article, we propose a novel lightweight proof of block and trade (PoBT) consensus algorithm for IoT blockchain and its integration framework. This solution allows the validation of trades as well as blocks with reduced computation time. Also, we present a ledger distribution mechanism to decrease the memory requirements of IoT nodes. The analysis and evaluation of security aspects, computation time, memory, and bandwidth requirements show significant improvement in the performance of the overall system.

High-Performance, Long-Life, Rechargeable Li-CO2 Batteries based on a 3D Holey Graphene Cathode Implanted with Single Iron Atoms.

Abstract: A highly efficient cathode catalyst for rechargeable Li-CO2 batteries is successfully synthesized by implanting single iron atoms into 3D porous carbon architectures, consisting of interconnected N,S-codoped holey graphene (HG) sheets. The unique porous 3D hierarchical architecture of the catalyst with a large surface area and sufficient space within the interconnected HG framework can not only facilitate electron transport and CO2 /Li+ diffusion, but also allow for a high uptake of Li2 CO3 to ensure a high capacity. Consequently, the resultant rechargeable Li-CO2 batteries exhibit a low potential gap of ≈1.17 V at 100 mA g-1 and can be repeatedly charged and discharged for over 200 cycles with a cut-off capacity of 1000 mAh g-1 at a high current density of 1 A g-1 . Density functional theory calculations are performed and the observed appealing catalytic performance is correlated with the hierarchical structure of the carbon catalyst. This work provides an effective approach to the development of highly efficient cathode catalysts for metal-CO2 batteries and beyond.

Energy consumption analysis of Virtual Machine migration in cloud using hybrid swarm optimization (ABC–BA)

Abstract: A cloud data center consumes more energy for computation and switching servers between modes. Virtual Machine (VM) migration enhances the execution of cloud server farm in terms of energy proficiency, adaptation to internal failure, and accessibility. Cloud suppliers, be that as it may, ought to likewise enhance for amounts like energy consumption and administrations costs and in this manner, trying to have all the Virtual Machines with the least measures of physical equipment machines conceivable. The part of virtualization is critical and its execution is subjected to VM migration and machine allotment. A greater amount of the energy is caught up in the cloud; consequently, the use of various calculations is required for sparing energy and productivity upgradation in the proposed work. In the proposed work, the Naive Bayes classifier with hybrid optimization using Artificial Bee Colony–Bat Algorithm (ABC–BA) was implemented to reduce the energy consumption in VM migration. The proposed method was evaluated in CloudSim and the performances were compared using performance index such as success &failure rate, and energy consumption. It is observed from the implementation results that the proposed method reduces energy consumption compared to other existing methods. From the implementation outcomes of the proposed work, it was understood that the model was able to achieve the minimum energy consumption and failure rate i.e., 1000–1200 kWh, 0.2 with maximum success rate and accuracy of 1 and 97.77%.

EasyBand: A Wearable for Safety-Aware Mobility During Pandemic Outbreak

Abstract: COVID-19 (Corona Virus Disease 2019) is a pandemic, which has been spreading exponentially around the globe. Many countries adopted stay-at-home or lockdown policies to control its spreading. However, prolonged stay-at-home may cause worse effects like economical crises, unemployment, food scarcity, and mental health problems of individuals. This article presents a smart consumer electronics solution to facilitate safe and gradual opening after stay-at-home restrictions are lifted. An Internet of Medical Things enabled wearable called EasyBand is introduced to limit the growth of new positive cases by autocontact tracing and by encouraging essential social distancing.

Stable and High-Energy-Density Zn-Ion Rechargeable Batteries Based on a MoS2-Coated Zn Anode

Abstract: Recently, aqueous Zn-ion rechargeable batteries have drawn increasing research attention as an alternative energy storage system relative to the current Li-ion batteries due to their intrinsic properties of high safety, low cost, and high theoretical volumetric capacity. Nevertheless, unwanted dendrite growth on the Zn anode and unstable cathode materials restrict their practical application. In this study, a unique 2D MoS2 coating on a Zn anode using an electrochemical deposition method has been developed for preventing dendrite growth and intricate side reactions. The coated MoS2 layer is a vertically oriented structure that makes the flow of Zn ions easy with a uniform electric field distribution on the anode, resulting in a uniform stripping and plating of Zn2+. In addition, the MoS2 coating enhances anodic diffusion of Zn ions and reduces the series resistance as confirmed by EIS analysis and therefore improves the overall battery performance. The full cell assembled with the MoS2-Zn anode and MnO2 cathode exhibits an excellent reversible specific capacity of 638 mAh/g at 0.1 A/g and stable cycle performance over 2000 cycles with no dendrite formation at the Zn electrode. The presented MoS2 coating on Zn is a facile, scalable, and promising technology for practical Zn-ion batteries with a long life cycle and high safety.

Origins of Boosted Charge Storage on Heteroatom-Doped Carbons.

Abstract: Although tremendous efforts have been devoted to understanding the origin of boosted charge storage on heteroatom-doped carbons, none of the present studies has shown a whole landscape. Herein, by both experimental evidence and theoretical simulation, it is demonstrated that heteroatom doping not only results in a broadened operating voltage, but also successfully promotes the specific capacitance in aqueous supercapacitors. In particular, the electrolyte cations adsorbed on heteroatom-doped carbon can effectively inhibit hydrogen evolution reaction, a key step of water decomposition during the charging process, which broadens the voltage window of aqueous electrolytes even beyond the thermodynamic limit of water (1.23 V). Furthermore, the reduced adsorption energy of heteroatom-doped carbon consequently leads to more stored cations on the heteroatom-doped carbon surface, thus yielding a boosted charge storage performance.

Metabolomics as an Emerging Tool for the Study of Plant-Pathogen Interactions.

Abstract: Plants defend themselves from most microbial attacks via mechanisms including cell wall fortification, production of antimicrobial compounds, and generation of reactive oxygen species. Successful pathogens overcome these host defenses, as well as obtain nutrients from the host. Perturbations of plant metabolism play a central role in determining the outcome of attempted infections. Metabolomic analyses, for example between healthy, newly infected and diseased or resistant plants, have the potential to reveal perturbations to signaling or output pathways with key roles in determining the outcome of a plant-microbe interaction. However, application of this -omic and its tools in plant pathology studies is lagging relative to genomic and transcriptomic methods. Thus, it is imperative to bring the power of metabolomics to bear on the study of plant resistance/susceptibility. This review discusses metabolomics studies that link changes in primary or specialized metabolism to the defense responses of plants against bacterial, fungal, nematode, and viral pathogens. Also examined are cases where metabolomics unveils virulence mechanisms used by pathogens. Finally, how integrating metabolomics with other -omics can advance plant pathology research is discussed.

Edge-Assisted Privacy-Preserving Raw Data Sharing Framework for Connected Autonomous Vehicles

Abstract: Data sharing among connected and autonomous vehicles without any protection will cause private information leakage. Simply encrypting data introduces a heavy overhead; most importantly, when encrypted data (ciphertext) is decrypted on a vehicle, the receiver will be fully aware of the sender's data, implying potential data leakage. To tackle these issues, we propose an edge-assisted privacy-preserving raw data sharing framework. First, we leverage the additive secret sharing technique to encrypt raw data into two ciphertexts and construct two classes of secure functions. The functions are then used to implement a privacy-preserving convolutional neural network (P-CNN). Finally, two edge servers are deployed to cooperatively execute P-CNN to extract features from two ciphertexts to obtain the same object detection results as the original CNN. We adopt the VGG16 model as a case study to illustrate how to construct P-CNN and employ the KITTI dataset to verify our solution. Experiment results demonstrate that P-CNN offers exactly the same classification results as the VGG16 model with negligible error, and the communication overhead and computational cost on the edge servers are less than existing solutions without leaking private information.

Preserving Data Privacy via Federated Learning: Challenges and Solutions

Abstract: Data have always been a major priority for businesses of all sizes. Businesses tend to enhance their ability in contextualizing data and draw new insights from it as the data itself proliferates with the advancement of technologies. Federated learning acts as a special form of privacy-preserving machine learning technique and can contextualize the data. It is a decentralized training approach for privately collecting and training the data provided by mobile devices, which are located at different geographical locations. Furthermore, users can benefit from obtaining a well-trained machine learning model without sending their privacy-sensitive personal data to the cloud. This article focuses on the most significant challenges associated with the preservation of data privacy via federated learning. Valuable attack mechanisms are discussed, and associated solutions are highlighted to the corresponding attack. Several research aspects along with promising future directions and applications via federated learning are additionally discussed.

Smart farming: Agriculture's shift from a labor intensive to technology native industry

Abstract: Since human beings transitioned to an agrarian lifestyle, technological advancements have enabled evolutions in agriculture, resulting in greater varieties and yields of crops. However, as society faces the effects of climate change and the resulting social challenges, agriculture is at a unique point in its history. Recent advancements in a number of key technologies have placed agriculture at the precipice of another evolution that could not only affect the variety and yield of crops, but also climatological and social outcomes as well. Specifically, advancements with the Internet of Things, artificial intelligence, and robotics among others have enabled data-driven and automated agriculture. This paper intends to provide a review of current and emerging agriculture technology applications as well as research efforts by examining the advancement of these key technologies in the context of smart farming. We also present future directions for these agriculture technology applications.