Showing papers by "Wichita State University published in 2021"

Prospects for beyond the Standard Model physics searches at the Deep Underground Neutrino Experiment: DUNE Collaboration.

Abstract: The Deep Underground Neutrino Experiment (DUNE) will be a powerful tool for a variety of physics topics. The high-intensity proton beams provide a large neutrino flux, sampled by a near detector system consisting of a combination of capable precision detectors, and by the massive far detector system located deep underground. This configuration sets up DUNE as a machine for discovery, as it enables opportunities not only to perform precision neutrino measurements that may uncover deviations from the present three-flavor mixing paradigm, but also to discover new particles and unveil new interactions and symmetries beyond those predicted in the Standard Model (SM). Of the many potential beyond the Standard Model (BSM) topics DUNE will probe, this paper presents a selection of studies quantifying DUNE’s sensitivities to sterile neutrino mixing, heavy neutral leptons, non-standard interactions, CPT symmetry violation, Lorentz invariance violation, neutrino trident production, dark matter from both beam induced and cosmogenic sources, baryon number violation, and other new physics topics that complement those at high-energy colliders and significantly extend the present reach.

Industry 4.0 in Logistics and Supply Chain Management: A Systematic Literature Review

Abstract: “Industry 4.0” is a concept that focuses on automation of system and process, digitalization, and data exchange in industries. Its goal is to achieve a smart factory to reduce lead time to respond ...

Multi-Objective Computation Sharing in Energy and Delay Constrained Mobile Edge Computing Environments

Abstract: In a mobile edge computing (MEC) network, mobile devices, also called edge clients, offload their computations to multiple edge servers that provide additional computing resources. Since the edge servers are placed at the network edge, e.g., cell-phone towers, transmission delays between edge servers and edge clients are shorter compared to those of cloud computing. In addition, edge clients can offload their tasks to other nearby edge clients with available computing resources by exploiting the Fog Computing (FC) paradigm. A major challenge in MEC and FC networks is to assign the tasks from edge clients to edge servers, as well as to other edge clients, in such a way that their tasks are completed with minimum energy consumption and minimum processing delay. In this paper, we model task offloading in MEC as a constrained multi-objective optimization problem (CMOP) that minimizes both the energy consumption and task processing delay of the mobile devices. To solve the CMOP, we design an evolutionary algorithm that can efficiently find a representative sample of the best trade-offs between energy consumption and task processing delay, i.e., the Pareto-optimal front. Compared to existing approaches for task offloading in MEC, we see that our approach finds offloading decisions with lower energy consumption and task processing delay.

Supernova neutrino burst detection with the Deep Underground Neutrino Experiment

Abstract: The Deep Underground Neutrino Experiment (DUNE), a 40-kton underground liquid argon time projection chamber experiment, will be sensitive to the electron-neutrino flavor component of the burst of neutrinos expected from the next Galactic core-collapse supernova. Such an observation will bring unique insight into the astrophysics of core collapse as well as into the properties of neutrinos. The general capabilities of DUNE for neutrino detection in the relevant few- to few-tens-of-MeV neutrino energy range will be described. As an example, DUNE's ability to constrain the $ u_e$ spectral parameters of the neutrino burst will be considered.

Postinfection treatment with a protease inhibitor increases survival of mice with a fatal SARS-CoV-2 infection.

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection continues to be a serious global public health threat. The 3C-like protease (3CLpro) is a virus protease encoded by SARS-CoV-2, which is essential for virus replication. We have previously reported a series of small-molecule 3CLpro inhibitors effective for inhibiting replication of human coronaviruses including SARS-CoV-2 in cell culture and in animal models. Here we generated a series of deuterated variants of a 3CLpro inhibitor, GC376, and evaluated the antiviral effect against SARS-CoV-2. The deuterated GC376 displayed potent inhibitory activity against SARS-CoV-2 in the enzyme- and the cell-based assays. The K18-hACE2 mice develop mild to lethal infection commensurate with SARS-CoV-2 challenge doses and were proposed as a model for efficacy testing of antiviral agents. We treated lethally infected mice with a deuterated derivative of GC376. Treatment of K18-hACE2 mice at 24 h postinfection with a derivative (compound 2) resulted in increased survival of mice compared to vehicle-treated mice. Lung virus titers were decreased, and histopathological changes were ameliorated in compound 2-treated mice compared to vehicle-treated mice. Structural investigation using high-resolution crystallography illuminated binding interactions of 3CLpro of SARS-CoV-2 and SARS-CoV with deuterated variants of GC376. Taken together, deuterated GC376 variants have excellent potential as antiviral agents against SARS-CoV-2.

Applying machine learning approach in recycling

Abstract: Waste generation has been increasing drastically based on the world’s population and economic growth. This has significantly affected human health, natural life, and ecology. The utilization of limited natural resources, and the harming of the earth in the process of mineral extraction, and waste management have far exceeded limits. The recycling rate are continuously increasing; however, assessments show that humans will be creating more waste than ever before. Some difficulties during recycling include the significant expense involved during the separation of recyclable waste from non-disposable waste. Machine learning is the utilization of artificial intelligence (AI) that provides a framework to take as a structural improvement of the fact without being programmed. Machine learning concentrates on the advancement of programs that can obtain the information and use it to learn to make future decisions. The classification and separation of materials in a mixed recycling application in machine learning is a division of AI that is playing an important role for better separation of complex waste. The primary purpose of this study is to analyze AI by focusing on machine learning algorithms used in recycling systems. This study is a compilation of the most recent developments in machine learning used in recycling industries.

A fundamental review on composite materials and some of their applications in biomedical engineering

Abstract: Composites or composite materials are engineered materials that consist of two or more constituent materials with wide discrepancies in their physical, chemical, and mechanical properties. The characteristic properties of these composite are as a result of the individual properties of their constituent parts and their respective volume fractions and arrangements in the material system. Depending on the intended application, composites can be designed to satisfy specific geometrical, structural, mechanical, chemical, and sometimes aesthetic requirements. Areas of application of these synthetic materials includes construction such as in buildings and bridges, automotive industry such as in car bodies, aeronautic, naval (e.g., ships and boats), and in the biomedical fields. Although metallic, polymeric and ceramic biomaterials have been in use for medical treatments such as tissue repairs and replacements for decades, composites are just coming to light. Therefore, the main purpose of this paper is to introduce composite materials and discuss their current and potential use in the biomedical field.

Chronologically dating the early assembly of the Milky Way

Abstract: The standard cosmological model predicts that galaxies are built through hierarchical assembly on cosmological timescales1,2. The Milky Way, like other disk galaxies, underwent violent mergers and accretion of small satellite galaxies in its early history. Owing to Gaia Data Release 23 and spectroscopic surveys4, the stellar remnants of such mergers have been identified5–7. The chronological dating of such events is crucial to uncover the formation and evolution of the Galaxy at high redshift, but it has so far been challenging due to difficulties in obtaining precise ages for these oldest stars. Here we combine asteroseismology—the study of stellar oscillations—with kinematics and chemical abundances to estimate precise stellar ages (~11%) for a sample of stars observed by the Kepler space mission8. Crucially, this sample includes not only some of the oldest stars that were formed inside the Galaxy but also stars formed externally and subsequently accreted onto the Milky Way. Leveraging this resolution in age, we provide compelling evidence in favour of models in which the Galaxy had already formed a substantial population of its stars (which now reside mainly in its thick disk) before the infall of the satellite galaxy Gaia-Enceladus/Sausage5,6 around 10 billion years ago. Leveraging asteroseismology, stellar abundances and kinematics to derive precise ages for a sample of 95 stars, Montalban et al. determine that the Milky Way was already host to a substantial population of stars when it was just 3.8 billion years old, at the time of the Gaia-Enceladus accretion event.

A two-stage metaheuristic algorithm for the dynamic vehicle routing problem in Industry 4.0 approach

Abstract: Industry 4.0 is a concept that assists companies in developing a modern supply chain (MSC) system when they are faced with a dynamic process. Because Industry 4.0 focuses on mobility and real-time ...

Deep Learning-Based Advances in Protein Structure Prediction

Abstract: Obtaining an accurate description of protein structure is a fundamental step toward understanding the underpinning of biology. Although recent advances in experimental approaches have greatly enhanced our capabilities to experimentally determine protein structures, the gap between the number of protein sequences and known protein structures is ever increasing. Computational protein structure prediction is one of the ways to fill this gap. Recently, the protein structure prediction field has witnessed a lot of advances due to Deep Learning (DL)-based approaches as evidenced by the success of AlphaFold2 in the most recent Critical Assessment of protein Structure Prediction (CASP14). In this article, we highlight important milestones and progresses in the field of protein structure prediction due to DL-based methods as observed in CASP experiments. We describe advances in various steps of protein structure prediction pipeline viz. protein contact map prediction, protein distogram prediction, protein real-valued distance prediction, and Quality Assessment/refinement. We also highlight some end-to-end DL-based approaches for protein structure prediction approaches. Additionally, as there have been some recent DL-based advances in protein structure determination using Cryo-Electron (Cryo-EM) microscopy based, we also highlight some of the important progress in the field. Finally, we provide an outlook and possible future research directions for DL-based approaches in the protein structure prediction arena.

The study and application of biomolecules in deep eutectic solvents.

Abstract: Biomolecules have been thoroughly investigated in a multitude of solvents historically in order to accentuate or modulate their superlative properties in an array of applications. Ionic liquids have been extensively explored over the last two decades as potential replacements for traditional organic solvents, however, they are sometimes associated with a number of limitations primarily related to cost, convenience, accessibility, and/or sustainability. One potential solvent which is gaining considerable traction in recent years is the so-called deep eutectic solvent which holds a number of striking advantages, including biodegradability, inherently low toxicity, and a facile, low-cost, and solventless preparation from widely available natural feedstocks. In this review, we highlight recent progress and insights into biomolecular behavior within deep eutectic solvent-containing systems, including discussions of their demonstrated utility and prospects for the biostabilization of proteins and nucleic acids, free enzyme and whole-cell biocatalysis, various extraction processes (e.g., aqueous biphasic systems, nanosupported separations), drug solubilization, lignocellulose biomass treatment, and targeted therapeutic drug delivery. All indications point to the likelihood that these emerging solvents have the capacity to satisfy the requirements of environmental responsibility while unlocking biomolecular proficiency in established biomedical and biotechnological pursuits as well as a number of academic and industrial ventures not yet explored.

Improvement of mechanical and wear behavior by the development of a new tool for the friction stir processing of Mg/B4C composite

Abstract: Large and irregular particle sizes of B4C in the Mg matrix are performance-impeding challenges of the as-cast Mg-B4C composites. In an attempt to overcome this, the secondary solid-state friction stir processing of the as-cast Mg-10%B4C composite with a flow-enhancing double-pin tool was carried out and the ensuing result was compared with that of a single-pin tool. The microstructure, hardness, tensile strength, wear, and the fractured surface of the processed composites were investigated and compared. The extra pin-shearing effect and the complex pin-induced interactive material flow of the double-pin tool induce better refinement of the B4C particles in the Mg-10%B4C composite. The use of a double-pin tool increases the stirred and recrystallized vortex/swirl width, kernel average misorientation (KAM) fraction, dislocation density, hardness value at the stirred center (117 HV), and tensile strength (194 MPa) of the Mg-10%B4C composite as compared to the single-pin tool. The double-pin tool changes the fracture path of the composite away from the stirred center owing to the improved material flow and properties of the stirred center. The tribological properties (weight loss, wear rate, and coefficient of friction) of the processed composites are equally improved by the double-pin tool. A double-pin tool is thus recommended for the improvement of material flow, particle-disintegration, mechanical and tribological properties of Mg-based metal matrix composite.

A Right to Return to Earth? Emigration Policy for the Lunar State

Abstract: I argue that citizens of future lunar states should enjoy the right to emigrate. The lethality of the space environment may result in lunar settlements pursuing oppressive and illiberal norms, policies, and laws in order to resolve societal problems. A right to emigrate, which for the foreseeable future is tantamount to a right to return to Earth, has individual and societal value. For individuals, the right establishes a legal path for lunar citizens wishing to flee from averse social or political circumstances. For the lunar state, protecting the right to emigrate disincentivizes the pursuit of policies which give rise to desires to flee. After presenting the case for the right to emigrate, I respond to several objections, including an objection derived from the “brain drain” debate over terrestrial migration, as well as the objection that protecting the right to emigrate would be financially ruinous for the lunar state.

Structure-Guided Design of Conformationally Constrained Cyclohexane Inhibitors of Severe Acute Respiratory Syndrome Coronavirus-2 3CL Protease.

Abstract: A series of nondeuterated and deuterated dipeptidyl aldehyde and masked aldehyde inhibitors that incorporate in their structure a conformationally constrained cyclohexane moiety was synthesized and found to potently inhibit severe acute respiratory syndrome coronavirus-2 3CL protease in biochemical and cell-based assays. Several of the inhibitors were also found to be nanomolar inhibitors of Middle East respiratory syndrome coronavirus 3CL protease. The corresponding latent aldehyde bisulfite adducts were found to be equipotent to the precursor aldehydes. High-resolution cocrystal structures confirmed the mechanism of action and illuminated the structural determinants involved in binding. The spatial disposition of the compounds disclosed herein provides an effective means of accessing new chemical space and optimizing pharmacological activity. The cellular permeability of the identified inhibitors and lack of cytotoxicity warrant their advancement as potential therapeutics for COVID-19.

Effects of FSW Tool Plunge Depth on Properties of an Al-Mg-Si Alloy T-Joint: Thermomechanical Modeling and Experimental Evaluation

Abstract: One of the main challenging issues in friction stir welding (FSW) of stiffened structures is maximizing skin and flange mixing. Among the various parameters in FSW that can affect the quality of mixing between skin and flange is tool plunge depth (TPD). In this research, the effects of TPD during FSW of an Al-Mg-Si alloy T-joint are investigated. The computational fluid dynamics (CFD) method can help understand TPD effects on FSW of the T-joint structure. For this reason, the CFD method is employed in the simulation of heat generation, heat distribution, material flow, and defect formation during welding processes at various TPD. CFD is a powerful method that can simulate phenomena during the mixing of flange and skin that are hard to assess experimentally. For the evaluation of FSW joints, macrostructure visualization is carried out. Simulation results showed that at higher TPD, more frictional heat is generated and causes the formation of a bigger stir zone. The temperature distribution is antisymmetric to the welding line, and the concentration of heat on the advancing side (AS) is more than the retreating side (RS). Simulation results from viscosity changes and material velocity study on the stir zone indicated that the possibility of the formation of a tunnel defect on the skin–flange interface at the RS is very high. Material flow and defect formation are very sensitive to TPD. Low TPD creates internal defects with incomplete mixing of skin and flange, and high TPD forms surface flash. Higher TPD increases frictional heat and axial force that diminish the mixing of skin and flange in this joint. The optimum TPD was selected due to the best materials flow and final mechanical properties of joints.

Quantum circuit representation of Bayesian networks

Abstract: Probabilistic graphical models such as Bayesian networks are widely used to model stochastic systems to perform various types of analysis such as probabilistic prediction, risk analysis, and system health monitoring, which can become computationally expensive in large-scale systems. While demonstrations of true quantum supremacy remain rare, quantum computing applications managing to exploit the advantages of amplitude amplification have shown significant computational benefits when compared against their classical counterparts. We develop a systematic method for designing a quantum circuit to represent a generic discrete Bayesian network with nodes that may have two or more states, where nodes with more than two states are mapped to multiple qubits. The marginal probabilities associated with root nodes (nodes without any parent nodes) are represented using rotation gates, and the conditional probability tables associated with non-root nodes are represented using controlled rotation gates. The controlled rotation gates with more than one control qubit are represented using ancilla qubits. The proposed approach is demonstrated for three examples: a 4-node oil company stock prediction, a 10-node network for liquidity risk assessment, and a 9-node naive Bayes classifier for bankruptcy prediction. The circuits were designed and simulated using Qiskit, a quantum computing platform that enables simulations and also has the capability to run on real quantum hardware. The results were validated against those obtained from classical Bayesian network implementations.

Analysis of friction stir welding tool offset on the bonding and properties of al–mg–si alloy t-joints

Abstract: Research on T-configuration aluminum constructions effectively decreases fuel consumption, increases strength, and develops aerial structures. In this research, the effects of friction stir welding (FSW) tool offset (TO) on Al–Mg–Si alloy mixing and bonding in T-configurations is studied. The process is simulated by the computational fluid dynamic (CFD) technique to better understand the material mixing flow and the bonding between the skin and flange during FSW. According to the results, the best material flow can be only achieved at an appropriate TO. The appropriate TO generates enough material to fill the joint line and results in formation of the highest participation of the flange in the stir zone (SZ) area. The results show that, in the T-configuration, FSW joints provide raw materials from the retreating side (RS) of the flange that play a primary role in producing a sound mixing flow. The selected parameters were related to the geometric limitations of the raw sheets considered in this study. The failure point of all tensile samples was located on the flange. Surface tunneling is the primary defect in these joints, which is produced at high TOs. Among the analyzed cases, the most robust joint was made at +0.2 mm TO on the advancing side (AS), resulting in more than 60% strength of the base aluminum alloy being retained.

SecFact: Secure Large-scale QR and LU Factorizations

Abstract: We are now in the big data era. Due to the emerging various systems and applications, such as the Internet of Things, cyber-physical systems, smart cities, smart healthcare, we are able to collect more data than ever before. On the other hand, it makes it very difficult to analyze such massive data in order to advance our science and engineering fields. We note that QR and LU factorizations are two of the most fundamental mathematical tools for data analysis. However, conducting QR or LU factorization of an $m\times n$ m × n matrix requires computational complexity of $\mathcal{O}(m^2n)$ O ( m 2 n ) . This incurs a formidable challenge in efficiently analyzing large-scale data sets by normal users or small companies on traditional resource-limited computers. To overcome this limitation, industry and academia propose to employ cloud computing that can offer abundant computing resources. This, however, obviously raises security concerns and hence a lot of users are reluctant to reveal their data to the cloud. To this end, we propose two secure outsourcing algorithms for efficiently performing large-scale QR and LU factorizations, respectively. We implement the proposed algorithms on the Amazon Elastic Compute Cloud (EC2) platform and a laptop. The experiment results show significant time saving for the user.

All-in-one, wireless, fully flexible sodium sensor system with integrated Au/CNT/Au nanocomposites

Abstract: Advancements in functional nanomaterials and wearable electronics have demonstrated the use of solid-state ion-selective electrodes (SS-ISEs) for human health applications. Existing devices, however, still rely on separate and multiple components of flexible sensors, interconnectors, and rigid data acquisition units, which limits the wearability of the entire system on the skin for continuous analyte monitoring. Here, this paper introduces an all-in-one, wireless, fully flexible, sodium detection system that integrates gold-carbon nanotube-gold (Au/CNT/Au) sensors and flexible thin-film circuits, together on a soft elastomeric membrane. The nanocomposite sensor includes electrochemically deposited Au nanoparticles on a CNT transducer to improve the conductivity, capacitance, and interfacial contact between materials. A set of experimental electrochemical analysis confirms the high stability of the SS-ISE based on Au/CNT/Au nanocomposites. At the same time, the thin-film system shows mechanical robustness and reliability, even with repetitive bending up to 500 cycles. The fully flexible, sensor-circuit integrated system demonstrates stable sodium measurements when mounted on the skin with minimized motion artifacts. The measured sensitivity of the sodium sensor is 55.5 ± 0.3 mV/decade, along with less than 3% change when the entire device is mounted on the skin with continuous movements. Overall, the presented comprehensive study, including nanomaterials, nano-microfabrication, electrochemistry, and electronics, shows the enormous potential of the wireless all-in-one sensor system for seamless integration with various types of skin-mounted wearable health monitors.

Current progress of 4D-printing technology

Abstract: The combination of smart materials to print a three-dimensional (3D) product has primarily driven the development of innovative technology, or four-dimensional (4D) printing. 3D-printing technology seems to have provided extensive enhancement with materials, printers, and processes in the past decade. The additive manufacturing (AM) industry is discovering the latest applications, materials, and 3D printers. AM can be defined as a method of formulating 3D parts through compiling the material layer by layer, which is conventionally made of plastics, metals, or ceramics; nevertheless, “smart” materials are also being used these days. These smart materials can be adjusted with printable characteristics or structures when additional stimulants are implemented. These 3D-printed materials modify their shape or properties with time, which is the fourth dimension and can merge with conventional 3D printing. 4D printing is the system whereby a 3D-printed object changes itself into a different structure as the result of the impact of environmental stimuli such as temperature, light, or other factors. 4D printing will open new possibilities that are convenient in significant applications, will work in extreme surroundings, and will help create a transformable structure. The objective of this review is to examine and assess the reputation and development of 4D-printing technology, including the 4D-printing process, materials, and potential applications. This review determines that 4D-printing technology has potential applications in various fields, but more research work will be essential for prospective accomplishments of this technology.

Predation landscapes influence migratory prey ecology and evolution.

Abstract: Migratory prey experience spatially variable predation across their life cycle. They face unique challenges in navigating this predation landscape, which affects their perception of risk, antipredator responses, and resulting mortality. Variable and unfamiliar predator cues during migration can limit accurate perception of risk and migrants often rely on social information and learning to compensate. The energetic demands of migration constrain antipredator responses, often through context-dependent patterns. While migration can increase mortality, migrants employ diverse strategies to balance risks and rewards, including life history and antipredator responses. Humans interact frequently with migratory prey across space and alter both mortality risk and antipredator responses, which can scale up to affect migratory populations and should be considered in conservation and management.

Applications of carbon nanomaterials in perovskite solar cells for solar energy conversion

Abstract: The energy issues and environmental concern have led to intense research activities in renewable energy conversion, such as photovoltaic (PV) to convert solar energy into electricity. Perovskite solar cells (PSCs) based on metal halides are rapidly emerging as the most promising and competing PV technology due to its high record power conversion efficiencies and potentially low production costs. Conductive carbon materials, which are abundantly available and low-cost, are introduced into the PSCs. This article provides a mini review of applications of carbon materials for perovskite solar cells. Firstly, a brief introduction of the development of perovskite solar cell is provided. Secondly, applications of carbon nanomaterials in perovskite solar cells are presented and discussed. Finally, an outlook and perspective on the future research directions of carbon nanomaterial for perovskite solar cells is provided.