Showing papers by "University of Nebraska Omaha published in 2022"

Tuning the optoelectronic properties of indacenodithiophene based derivatives for efficient photovoltaic applications: A DFT approach

Abstract: In this study, five novel push-pull acceptor molecules with A-B-D-B-A arrangement have been formulated in the quest to boost the organic solar cells (OSCs), with respect to their electrical, optical, and chemical characteristics. Substitution of end-capped acceptor moieties in non-fullerene materials is an effective approach of molecular modeling, which finely tunes the optoelectronic attributes of OSCs. The recently altered molecules (Y1-Y5) were flanked with different electron withdrawing units carrying indacenodithiophene (IDT) as the central electron donating core. The density functional theory (DFT) and time-dependent density functional theory (TD-DFT) analysis were executed at B3LYP functional with 6-31G (d,p) basis set to investigate the geometrical as well as optical parameters such as quantum mechanical descriptors, light harvesting efficiency, ionization potential energy, absorption properties, electron affinity, dipole moment, molecular electrostatic potential, transition density matrix, the density of states, and reorganization energies. All of these studied molecules revealed greater electronic transitions, superior optical properties, fast charge mobilities, and better solubility in the polar solvent when compared to the reference molecule. Amongst all these derived molecules, Y1 emerged as a distinctive candidate, exhibiting the highest maximum absorption wavelength (884 nm) in chloroform along with the smallest energy gap (1.72 eV) as well as the lowest optical gap (1.40 eV). Moreover, it has the highest electron affinity and ionization potential energy, least interaction coefficient, exciton binding energy, and reorganization energy (λe = 0.00340 eV), which can be ascribed to its potent electron withdrawing moieties, which intensifies the transfer of charge between the donor and acceptor units within a molecule. We expect these modifications in the terminal groups around the central core to provide strong theoretical strategies to construct and amplify the photovoltaic parameters of OSCs in the future.

Relative positions of countries in the core-periphery structure of the European automotive industry:

Abstract: This article investigates the core-semiperiphery-periphery structure of the European automotive industry between 2003 and 2017 by drawing on the global value chains and global production networks p...

Exploring the inhibitory potential of novel bioactive compounds from mangrove actinomycetes against nsp10 the major activator of SARS-CoV-2 replication

Abstract: The current study reveals the inhibitory potential of novel bioactive compounds of mangrove actinomycetes against nsp10 of SARS-CoV-2. A total of fifty (50) novel bioactive (antibacterial, antitumor, antiviral, antioxidant, and anti-inflammatory) compounds of mangrove actinomycetes from different chemical classes such as alkaloids, dilactones, sesquiterpenes, macrolides, and benzene derivatives are used for interaction analysis against nsp10 of SARS-CoV-2. The six antiviral agents sespenine, xiamycin c, xiamycin d, xiamycin e, xiamycin methyl ester, and xiamycin A (obeyed RO5 rule) are selected based on higher binding energy, low inhibition constant values, and better-docked positions. The effective hydrogen and hydrophobic (alkyl, $$\pi$$ –sigma, $$\pi$$ – $$\pi$$ T shaped and $$\pi$$ -alkyl) interaction analysis reveals the four antivirals sespenine, xiamycin C, xiamycin methyl ester, and xiamycin A are supposed to be the most auspicious inhibitors against nsp10 of SARS-CoV-2. Quantum chemistry methods such as frontier molecular orbitals and molecular electrostatic potential are used to explain the thermal stability and chemical reactivity of ligands. The toxicity profile shows that selected ligands are safe by absorption, distribution, metabolism, excretion, and toxicity profiling and also effective for inhibition of nsp10 protein of SARS-CoV-2. The molecular dynamic simulation investigation of apo and halo forms of nsp10 done by RMSD of C $$\alpha$$ atoms of nsp10, all amino acid residues RMSF, count total number of hydrogen bonds and radius of gyration (Rg). MD simulations reveal the complexes are stable and increase the structural compactness of nsp10 in the binding pocket. The lead antiviral compounds sespenine, xiamycin C, xiamycin methyl ester, and xiamycin A are recommended as the most promising inhibitors against nsp10 of SARS-CoV-2 pathogenicity.

Quantum teleportation with one classical bit

Abstract: Quantum teleportation allows one to transmit an arbitrary qubit from point A to point B using a pair of (pre-shared) entangled qubits and classical bits of information. The conventional protocol for teleportation uses two bits of classical information and assumes that the sender has access to only one copy of the arbitrary qubit to be sent. Here, we ask whether we can do better than two bits of classical information if the sender has access to multiple copies of the qubit to be teleported. We place no restrictions on the qubit states. Consequently, we propose a modified quantum teleportation protocol that allows Alice to reset the state of the entangled pair to its initial state using only local operations. As a result, the proposed teleportation protocol requires the transmission of only one classical bit with a probability greater than one-half. This has implications for efficient quantum communications and the security of quantum cryptographic protocols based on quantum entanglement.

IoT Devices, User Authentication, and Data Management in a Secure, Validated Manner through the Blockchain System

Abstract: Advancement in technology has led to innovation in equipment, and the number of devices is increasing every day. Industries are introducing new devices every day and predicting 50 billion connected devices by 2022. These devices are deployed through the Internet, called the Internet of Things (IoT). Applications of IoT devices are weather prediction, monitoring surgery in hospitals, identification of animals using biochips, providing tracking connectivity in automobiles, smart home appliances, etc. IoT devices have limitations related to security at both the software and hardware ends. Secure user interfaces can overcome software-level limitations like front-end-user interfaces are accessed easily through public and private networks. The front-end interfaces are connected to the localized storage to contain data produced by the IoT devices. Localized storage deployed in a closed environment connected to IoT devices is more efficient than online servers from a security perspective. Blockchain has emerged as a technology or technique with capabilities to achieve secure administrational authentication and accessibility to IoT devices and their computationally produced data in a decentralized way with high reliability, interrogation, and resilience. In this paper, we propose device, end-user, and transactional authentication techniques using blockchain-embedded algorithms. The localized server interacts with the user interface to authenticate IoT devices, end-users, and their access to IoT devices. The localized server provides efficiency by reducing the load on the IoT devices by carrying out end-user heavy computational data, including end-user, IoT device authentication, and communicational transactions. Authentication data are placed on the public ledger in block form, distributed over the system nodes through blockchain algorithms.

Self-tuning portfolio-based Bayesian optimization

Abstract: Portfolio strategies for Bayesian optimization (BO) aim to mitigate the issue of choosing an acquisition function when performing black-box optimization with Gaussian processes (GP) surrogate models. In that sense, the GP-Hedge is a straightforward portfolio framework commonly used in practice. Our work proposes to overcome existing limitations on the GP-Hedge and related methods, such as reducing the influence of far past evaluations and promoting better exploration. Moreover, we aim to achieve such improvements without sacrificing the practicality of simpler portfolio strategies. More specifically, we propose a new BO method equipped with the aforementioned enhancements enabled by additional self-tuned hyperparameters, which are sampled during the optimization via Thompson sampling. We are able to update the posteriors analytically at each iteration by carefully choosing meaningful conjugate priors. The new approach, named Self-Tuning Portfolio-based BO (SETUP-BO), improves standard portfolio strategies without the need for manually tuning hyperparameters, which preserves easiness of use. We evaluate our method and its competitors in the task of hyperparameter optimization (HPO), a critical step towards automated machine learning (AutoML), following a thorough meta-surrogate benchmarking approach. We also consider a real-world scenario related to the task of fault detection in energy plants. Our methodology achieves promising results, which indicates the viability of the proposed SETUP-BO.

Analyzing an Appropriate Blend of Face-to-Face, Offline and Online Learning Approaches for the In-Service Vocational Teacher’s Training Program

Abstract: Blended learning approaches are considered as the most viable for the delivery of training to remote areas and accessing learners at a mass level. Blended learning is a combination of different learning approaches to facilitate the learners' needs. The National Vocational and Technical Training Commission (NAVTTC) conducted an in-service vocational teachers' training program through blended learning approaches in Pakistan. This study aimed to find the appropriate blend of face-to-face, online, and offline learning approaches for the training of in-service teachers in Pakistan. A mixed methods research approach was adopted. A survey collected data from 781 in-service vocational teachers who participated in training programs through blended learning approaches. The ANOVA test was applied to find the difference of the training participants' attitude toward different modes of learning. It was found that trainees had more positive attitude toward a face-to-face learning approach than online and the online learning approach than offline learning. Semi-structured interviews were also conducted with training participants, admission and placement officers, and principals. They also endorsed that face-to-face learning approaches must be given more weight than online, and the online approach should have more weight than the offline approach. This study has practical implications for technical education and vocational training (TVET) institutes in developing countries such as Pakistan to design blended learning approaches for the training of in-service vocational teachers. Future research may be conducted on the effectiveness of in-service vocational education teachers through blended learning.

MuLSi-Co: Multilayer Sinks and Cooperation-Based Data Routing Techniques for Underwater Acoustic Wireless Sensor Networks (UA-WSNs)

Abstract: Designing an efficient, reliable, and stable algorithm for underwater acoustic wireless sensor networks (UA-WSNs) needs immense attention. It is due to their notable and distinctive challenges. To address the difficulties and challenges, the article introduces two algorithms: the multilayer sink (MuLSi) algorithm and its reliable version MuLSi-Co using the cooperation technique. The first algorithm proposes a multilayered network structure instead of a solid single structure and sinks placement at the optimal position, which reduces multiple hops communication. Moreover, the best forwarder selection amongst the nodes based on nodes’ closeness to the sink is a good choice. As a result, it makes the network perform better. Unlike the traditional algorithms, the proposed scheme does not need location information about nodes. However, the MuLSi algorithm does not fulfill the requirement of reliable operation due to a single link. Therefore, the MuLSi-Co algorithm utilizes nodes’collaborative behavior for reliable information. In cooperation, the receiver has multiple copies of the same data. Then, it combines these packets for the purpose of correct data reception. The data forwarding by the relay without any latency eliminates the synchronization problem. Moreover, the overhearing of the data gets rid of duplicate transmissions. The proposed schemes are superior in energy cost and reliable exchanging of data and have more alive and less dead nodes.

The Status of Women in STEM in Higher Education in the United States during the COVID-19 Pandemic: A Literature Review 2020-2022

Abstract: This study seeks to answer the question: What effects did COVID-19 have on the status of women in science, technology, engineering, and math (STEM) in higher education in the United States? It presents a thematic review of the current literature regarding the social and professional impacts of the pandemic on this group from 2020 to 2022. The research briefly examines the challenges women in STEM faced pre-pandemic and then explores the repercussions of the pandemic to date. It reviews the literature published from the beginning of the COVID-19 pandemic to present day. Recommendations for STEM librarians serving this population are discussed.

Novel Negative Pressure Helmet Reduces Aerosolized Particles in a Simulated Prehospital Setting

Abstract: The coronavirus disease 2019 (COVID-19) pandemic has created challenges in maintaining the safety of prehospital providers caring for patients. Reports have shown increased rates of Emergency Medical Services (EMS) provider infection with COVID-19 after patient care exposure, especially while utilizing aerosol-generating procedures (AGPs). Given the increased risk and rising call volumes for AGP-necessitating complaints, development of novel devices for the protection of EMS clinicians is of great importance.Drawn from the concept of the powered air purifying respirator (PAPR), the AerosolVE helmet creates a personal negative pressure space to contain aerosolized infectious particles produced by patients, making the cabin of an EMS vehicle safer for providers. The helmet was developed initially for use in hospitals and could be of significant use in the prehospital setting. The objective of this study was to determine the efficacy and safety of the helmet in mitigating simulated infectious particle spread in varied EMS transport platforms during AGP utilization.Fifteen healthy volunteers were enrolled and distributed amongst three EMS vehicles: a ground ambulance, a medical helicopter, and a medical jet. Sodium chloride particles were used to simulate infectious particles, and particle counts were obtained in numerous locations close to the helmet and around the patient compartment. Counts near the helmet were compared to ambient air with and without use of AGPs (non-rebreather mask [NRB], continuous positive airway pressure mask [CPAP], and high-flow nasal cannula [HFNC]).Without the helmet fan on, the particle generator alone and with all AGPs produced particle counts inside the helmet significantly higher than ambient particle counts. With the fan on, there was no significant difference in particle counts around the helmet compared to baseline ambient particle counts. Particle counts at the filter exit averaged less than one despite markedly higher particle counts inside the helmet.Given the risk to EMS providers by communicable respiratory diseases, development of devices to improve safety while still enabling use of respiratory therapies is of paramount importance. The AerosolVE helmet demonstrated efficacy in creating a negative pressure environment and provided significant filtration of simulated respiratory droplets, thus making the confined space of transport vehicles potentially safer for EMS personnel.

Network Cost-Aware Geo-Distributed Data Analytics System

Abstract: Many geo-distributed data analytics (GDA) systems have focused on the network performance-bottleneck: inter-data center network bandwidth to improve performance. Unfortunately, these systems may encounter a cost-bottleneck ( ${\$}$ $ ) because they have not considered data transfer cost ( ${\$}$ $ ), one of the most expensive and heterogeneous resources in a multi-cloud environment. In this article, we present Kimchi , a network cost-aware GDA system to meet the cost-performance tradeoff by exploiting data transfer cost heterogeneity to avoid the cost-bottleneck. Kimchi determines cost-aware task placement decisions for scheduling tasks given inputs including data transfer cost, network bandwidth, input data size and locations, and desired cost-performance tradeoff preference. In addition, Kimchi is also mindful of data transfer cost in the presence of dynamics. Kimchi has been applied to two common GDA MapReduce models: synchronous barrier and asynchronous push-based shuffle. A Kimchi prototype has been implemented on Spark, and experiments show that it reduces cost by 5% $\scriptstyle \sim$ ∼ 24% without impacting performance and reduces query execution time by 45% $\scriptstyle \sim$ ∼ 70% without impacting cost compared to other baseline approaches centralized, vanilla Spark, and bandwidth-aware (e.g., Iridium). More importantly, Kimchi allows applications to explore a much richer cost-performance tradeoff space in a multi-cloud environment.