Showing papers by "Wichita State University published in 2018"

Surface modification of hexagonal boron nitride nanomaterials: a review

Abstract: Hexagonal boron nitride (h-BN) nanomaterials, such as boron nitride nanotubes, boron nitride nanofibers, and boron nitride nanosheets, are among the most promising inorganic nanomaterials in recent years. Their unique properties, including high mechanical stiffness, wide band gap, excellent thermal conductivity, and thermal stability, suggest many potential applications in various engineering fields. In particular, h-BN nanomaterials have been widely used as functional fillers to fabricate high-performance polymer nanocomposites. Like other nanomaterials, prior to their utilization in nanocomposites, surface modification of h-BNs is often necessary in order to prevent their strong tendency to aggregate, and to improve their dispersion and interfacial properties in polymer nanocomposites. However, the high chemical inertness and resistance to oxidation of h-BNs make it rather difficult to functionalize h-BNs. The methods frequently used to oxidize graphitic carbon nanomaterials are not quite successful on h-BNs. Therefore, many novel approaches have been studied to modify h-BN nanostructures. In this review, different surface modification strategies were discussed including various covalent and non-covalent surface modification strategies through wet or dry chemical routes. Meanwhile, the effects of these surface modification methods on the resulting material structures and properties were also reviewed. At last, a number of theoretical studies on the reactivity of h-BNs with different chemical agents have been conducted to explore new surface modification routes, which were also addressed in this review.

New constraints on oscillation parameters from νe appearance and νμ disappearance in the NOvA experiment

Abstract: For full abstract please refer to Official URL link”, or if there is a document attached which contains the abstract, “For full abstract please refer to attached document

BCC-Phased PdCu Alloy as a Highly Active Electrocatalyst for Hydrogen Oxidation in Alkaline Electrolytes

Abstract: Anion-exchange membrane fuel cells hold promise to greatly reduce cost by employing nonprecious metal cathode catalysts. More efficient anode catalysts are needed, however, to improve the sluggish hydrogen oxidation reaction in alkaline electrolytes. We report that BCC-phased PdCu alloy nanoparticles, synthesized via a wet-chemistry method with a critical thermal treatment, exhibit up to 20-fold HOR improvement in both mass and specific activities, compared with the FCC-phased PdCu counterparts. HOR activity of the BCC-phased PdCu is 4 times or 2 times that of Pd/C or Pt/C, respectively, in the same alkaline electrolyte. In situ HE-XRD measurements reveal that the transformation of PdCu crystalline structure favors, at low annealing temperature (<300 °C), the formation of FCC structure. At higher annealing temperatures (300–500 °C), a BCC structure dominates the PdCu NPs. Density functional theory (DFT) computations unravel a similar H binding strength and a much stronger OH binding of the PdCu BCC surfac...

Predicting graft survival among kidney transplant recipients: A Bayesian decision support model

Abstract: Predicting the graft survival for kidney transplantation is a high stakes undertaking considering the shortage of available organs and the utilization of healthcare resources. The strength of any predictive model depends on the selection of proper predictors. However, despite improvements in acute rejection management and short-term graft survival, the accurate prediction of kidney transplant outcomes remains suboptimal. Among other approaches, machine-learning techniques have the potential to offer solutions to this prediction problem in kidney transplantation. This study offers a novel methodological solution to this prediction problem by: (a) analyzing the retrospective database including > 31,000 U.S. patients; (b) introducing a comprehensive feature selection framework that accounts for medical literature, data analytics methods and elastic net (EN) regression (c) using sensitivity analyses and information fusion to evaluate and combine features from several machine learning approaches (i.e., support vector machines (SVM), artificial neural networks (ANN), and Bootstrap Forest (BF)); (d) constructing several different scenarios by merging different sets of features that are optioned through these fused data mining models and statistical models in addition to expert knowledge; and (e) using best performing sets in Bayesian belief network (BBN) algorithm to identify non-linear relationships and the interactions between explanatory factors and risk levels for kidney graft survival. The results showed that the predictor set obtained through fused data mining model and literature review outperformed the all other alternative predictors sets with the scores of 0.602, 0.684, 0.495 for F-Measure, Average Accuracy, and G-Mean, respectively. Overall, our findings provide novel insights about risk prediction that could potentially help in improving the outcome of kidney transplants. This methodology can also be applied to other similar transplant data sets.

Fault Detector and Switch Placement in Cyber-Enabled Power Distribution Network

Abstract: Cyber-enabled operation is needed for smart distribution system implementation. The interaction of cyber and power components will affect system reliability. This paper focuses on developing an analytical reliability model for fault detection, isolation, and service restoration for smart distribution feeders. The impact of end-to-end outage probability of data communication along with sending, receiving, and relaying communication node failures is incorporated into the model. Vulnerability of system to cyber attack as an emerging cause of reliability degradation is also investigated. An optimal placement of fault detectors and switching devices are determined in this work to improve reliability. The sum of customer service interruption cost and investment cost is considered as an objective function to be minimized. Bus 2 of the Roy Billinton test system and a typical 27-node distribution network are used to illustrate the role of communication infrastructure malfunction on the planning problem in a distribution feeder. Results and discussions show the necessity of incorporating communication infrastructure failure into the power distribution system planning problem.

Learning Discriminative Features for Speaker Identification and Verification.

Abstract: The success of any Text Independent Speaker Identification and/or Verification system relies upon the system’s capability to learn discriminative features. In this paper we propose a Convolutional Neural Network (CNN) Architecture based on the popular Very Deep VGG [1] CNNs, with key modifications to accommodate variable length spectrogram inputs, reduce the model disk space requirements and reduce the number of parameters, resulting in significant reduction in training times. We also propose a unified deep learning system for both Text-Independent Speaker Recognition and Speaker Verification, by training the proposed network architecture under the joint supervision of Softmax loss and Center loss [2] to obtain highly discriminative deep features that are suited for both Speaker Identification and Verification Tasks. We use the recently released VoxCeleb dataset [3], which contains hundreds of thousands of real world utterances of over 1200 celebrities belonging to various ethnicities, for benchmarking our approach. Our best CNN model achieved a Top1 accuracy of 84.6%, a 4% absolute improvement over VoxCeleb’s approach, whereas training in conjunction with Center Loss improved the Top-1 accuracy to 89.5%, a 9% absolute improvement over Voxceleb’s approach.

Thermoset composite recycling – Driving forces, development, and evolution of new opportunities:

Abstract: Thermoset composites represent a substantial challenge for recycling, even as composite products increase in market interest. The concept of putting all future thermoset composite products into lan...

Factors influencing STEM career aspirations of underrepresented high school students

Abstract: A shortage of female and minority students pursuing science, technology, engineering, and mathematics (STEM) careers has prompted researchers and policy makers to examine the current STEM supply pipeline. This study examined factors influencing STEM career aspirations of a nationally representative sample of 9th-grade students (N = 21,444). Characteristics of students who aspired to STEM careers and non-STEM careers were examined. Guided by the career aspirations model (Mau & Bikos, 2000), the authors conducted logistic regression analyses to investigate variables predicting STEM career aspirations. Results indicated that race, gender, socioeconomic status, math interest, and science self-efficacy were the most important predictors of STEM career aspirations. Counselors in school and related career services contexts are encouraged to consider these important factors in identifying high school students who are interested in STEM career choices, as well as in planning career interventions to facilitate their career paths. Future researchers could test the applicability of this model with middle school students or adults.

Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression

Abstract: As the need for engineers continues to increase, a growing focus has been placed on recruiting students into the field of engineering and retaining the students who select engineering as their field of study. As a result of this concentration on student retention, numerous studies have been conducted to identify, understand, and confirm relationships between student attributes and attrition. Methods of prediction have also been evaluated and compared. Utilizing the attributes found in previous studies to have correlation with student attrition, this study considers the results of three different prediction methods—logistic regression, a multi-layer perceptron artificial neural network, and a probabilistic neural network (PNN)—to predict engineering student retention at a case study university. The purpose of this study was to introduce the PNN to the study of engineering student retention prediction and compare the results of the PNN to other commonly used methods in this field of study. The accuracy, sensitivity, specificity and overall results for each method are reported, compared, and discussed as the major contribution of this paper.

Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network

Abstract: No-shows are becoming a major problem in primary care facilities, creating additional costs for the facility while adversely affecting the quality of patient care. Accurately predicting no-shows plays an important role in the overbooking strategy. In this study, a hybrid probabilistic prediction framework based on the elastic net (EN) variable-selection methodology integrated with probabilistic Bayesian Belief Network (BBN) is proposed. The study predicts the “no-show probability of the patient(s)” using demographics, socioeconomic status, current appointment information, and appointment attendance history of the patient and the family. The proposed framework is validated using ten years of local pediatric clinic data. It is shown that this EN-based BBN framework is a comparable prediction methodology regarding the best approaches found in the literature. More importantly, this methodology provides novel information on the interrelations of predictors and the conditional probability of predicting “no-shows.” The output of the model can be applied to the appointment scheduling system for a robust overbooking strategy.

Understanding Auditors’ Sense of Responsibility for Detecting Fraud Within Organizations

Abstract: The objective of this study is to evaluate auditors’ perceived responsibility for fraud detection. Auditors play a critical role in managing fraud risk within organizations. Although professional standards and guidance prescribe responsibility in the area, little is known about auditors’ sense of responsibility for fraud detection, the factors affecting perceived responsibility, and how responsibility affects auditor performance. We use the triangle model of responsibility as a theoretical basis for examining responsibility and the effects of accountability, fraud type, and auditor type on auditors’ perceived fraud detection responsibility. We also test how perceived responsibility affects auditor brainstorming performance given the importance of brainstorming in audits. A sample of 878 auditors (241 external auditors and 637 internal auditors) participated in an experiment with accountability pressure and fraud type manipulated randomly between subjects. As predicted, accountable auditors report higher detection responsibility than anonymous auditors. We also find a significant fraud type × auditor type interaction with external auditors perceiving the most detection responsibility for financial statement fraud, while internal auditors report similar detection responsibility for all fraud types. Analysis of the triangle model’s formative links reveals that professional obligation and personal control are significantly related to responsibility, while task clarity is not. Finally, the results indicate that perceived responsibility positively affects the number of detection procedures brainstormed and partially mediates the significant accountability–brainstorming relation.

Synthesis of electrospun polyacrylonitrile- derived carbon fibers and comparison of properties with bulk form

Abstract: This study deals with the fabrication of polyacrylonitrile (PAN) nanofibers via an electrospinning process followed by stabilizing and carbonization in order to remove all non-carboneous matter and ensure a pure carboneous material. The as-spun PAN fibers were stabilized in air at 270°C for one hour and then carbonized at 750, 850, and 950°C in an inert atmosphere (argon) for another one hour. Differential scanning calorimetry and Raman spectroscopy were employed to determine the thermal and chemical properties of PAN. Surface features and morphologies of PAN-derived carbon nanofibers were investigated by means of scanning electron microscopy (SEM). SEM micrograms showed that fiber diameters were reduced after carbonization due to evolution of toxic gases and dehydrogenation. The Raman spectra of carbonized fibers manifested D/G peaks. The Raman spectroscopy peaks of 1100 and 500 cm-1 manifested the formation of γ phase and another peak at 900 cm-1 manifested the formation of α-phase. The water contact angle measurement of carbonized PAN fibers indicated that the nanofibers were superhydrophobic (θ > 150o) due to the formation of bumpy and pitted surface after carbonization. In DSC experiment, the stabilized fibers showed a broad exothermic peak at 308°C due to cyclization process. The mechanical andThermal analysis was used to ascertain mechanical properties of carbonized PAN fibers. PAN-derived carbon nanofibers possess excellent physica and mechanical properties and therefore, they may be suitable for many industrial applications such as energy, biomedical, and aerospace.

NOMA-Based Energy-Efficient Wireless Powered Communications

Abstract: In this paper, we study the performance of non-orthogonal multiple access (NOMA) schemes in wireless powered communication networks (WPCN) focusing on the system energy efficiency (EE). We consider multiple energy harvesting user equipments (UEs) that operate based on harvest-then-transmit protocol. The uplink information transfer is carried out by using power-domain multiplexing, and the receiver decodes each UE’s data in such a way that the UE with the best channel gain is decoded without interference. In order to determine optimal resource allocation strategies, we formulate optimization problems considering two models, namely half-duplex and asynchronous transmission, based on how downlink and uplink operations are coordinated. In both cases, we have concave-linear fractional problems, and hence Dinkelbach’s method can be applied to obtain the globally optimal solutions. Thus, we first derive analytical expressions for the harvesting interval, and then we provide an algorithm to describe the complete procedure. Furthermore, we incorporate delay-limited sources and investigate the impact of statistical queuing constraints on the energy-efficient allocation of operating intervals. We formulate an optimization problem that maximizes the system effective-EE while UEs are applying NOMA scheme for uplink information transfer. Since the problem satisfies pseudo-concavity, we provide an iterative algorithm using bisection method to determine the unique solution. In the numerical results, we observe that broadcasting at higher power level is more energy efficient for WPCN with uplink NOMA. Additionally, exponential decay quality of service parameter has considerable impact on the optimal solution, and in the presence of strict constraints, more time is allocated for downlink interval under half-duplex operation with uplink time-division multiple access mode.

How Does Solvation Layer Mobility Affect Protein Structural Dynamics

Abstract: Solvation is critical for protein structural dynamics. Spectroscopic studies have indicated relationships between protein and solvent dynamics, and rates of gas binding to heme proteins in aqueous solution were previously observed to depend inversely on solution viscosity. In this work, the solvent-compatible enzyme Candida antarctica lipase B, which functions in aqueous and organic solvents, was modeled using molecular dynamics simulations. Data was obtained for the enzyme in acetonitrile, cyclohexane, n-butanol, and tert-butanol, in addition to water. Protein dynamics and solvation shell dynamics are characterized regionally: for each α-helix, β-sheet, and loop or connector region. Correlations are seen between solvent mobility and protein flexibility. So, does local viscosity explain the relationship between protein structural dynamics and solvation layer dynamics? Halle and Davidovic presented a cogent analysis of data describing the global hydrodynamics of a protein (tumbling in solution) that fits a model in which the protein’s interfacial viscosity is higher than that of bulk water’s, due to retarded water dynamics in the hydration layer (measured in NMR τ2 reorientation times). Numerous experiments have shown coupling between protein and solvation layer dynamics in site-specific measurements. Our data provides spatially-resolved characterization of solvent shell dynamics, showing correlations between regional solvation layer dynamics and protein dynamics in both aqueous and organic solvents. Correlations between protein flexibility and inverse solvent viscosity (1/η) are considered across several protein regions and for a rather disparate collection of solvents. It is seen that the correlation is consistently higher when local solvent shell dynamics are considered, rather than bulk viscosity. Protein flexibility is seen to correlate best with either the local interfacial viscosity or the ratio of the mobility of an organic solvent in a regional solvation layer relative to hydration dynamics around the same region. Results provide insight into the function of aqueous proteins, while also suggesting a framework for interpreting and predicting enzyme structural dynamics in non-aqueous solvents, based on the mobility of solvents within the solvation layer. We suggest that Kramers’ theory may be used in future work to model protein conformational transitions in different solvents by incorporating local viscosity effects.

Linear and Differential Ion Mobility Separations of Middle-Down Proteoforms

Abstract: Comprehensive characterization of proteomes comprising the same proteins with distinct post-translational modifications (PTMs) is a staggering challenge Many such proteoforms are isomers (localization variants) that require separation followed by top-down or middle-down mass spectrometric analyses, but condensed-phase separations are ineffective in those size ranges The variants for "middle-down" peptides were resolved by differential ion mobility spectrometry (FAIMS), relying on the mobility increment at high electric fields, but not previously by linear IMS on the basis of absolute mobility We now use complete histone tails with diverse PTMs on alternative sites to demonstrate that high-resolution linear IMS, here trapped IMS (TIMS), broadly resolves the variants of ∼50 residues in full or into binary mixtures quantifiable by tandem MS, largely thanks to orthogonal separations across charge states Separations using traveling-wave (TWIMS) and/or involving various time scales and electrospray ionization source conditions are similar (with lower resolution for TWIMS), showing the transferability of results across linear IMS instruments The linear IMS and FAIMS dimensions are substantially orthogonal, suggesting FAIMS/IMS/MS as a powerful platform for proteoform analyses

Non-Invasive Electromagnetic Skin Patch Sensor to Measure Intracranial Fluid–Volume Shifts

Abstract: Elevated intracranial fluid volume can drive intracranial pressure increases, which can potentially result in numerous neurological complications or death. This study’s focus was to develop a passive skin patch sensor for the head that would non-invasively measure cranial fluid volume shifts. The sensor consists of a single baseline component configured into a rectangular planar spiral with a self-resonant frequency response when impinged upon by external radio frequency sweeps. Fluid volume changes (10 mL increments) were detected through cranial bone using the sensor on a dry human skull model. Preliminary human tests utilized two sensors to determine feasibility of detecting fluid volume shifts in the complex environment of the human body. The correlation between fluid volume changes and shifts in the first resonance frequency using the dry human skull was classified as a second order polynomial with R2 = 0.97. During preliminary and secondary human tests, a ≈24 MHz and an average of ≈45.07 MHz shifts in the principal resonant frequency were measured respectively, corresponding to the induced cephalad bio-fluid shifts. This electromagnetic resonant sensor may provide a non-invasive method to monitor shifts in fluid volume and assist with medical scenarios including stroke, cerebral hemorrhage, concussion, or monitoring intracranial pressure.

NOMA-based Energy-Efficient Wireless Powered Communications

Abstract: In this paper, we study the performance of non-orthogonal multiple access (NOMA) schemes in wireless powered communication networks (WPCN) focusing on the system energy efficiency (EE). We consider multiple energy harvesting user equipments (UEs) that operate based on harvest-then-transmit protocol. The uplink information transfer is carried out by using power-domain multiplexing, and the receiver decodes each UE's data in such a way that the UE with the best channel gain is decoded without interference. In order to determine optimal resource allocation strategies, we formulate optimization problems considering two models, namely half-duplex and asynchronous transmission, based on how downlink and uplink operations are coordinated. In both cases, we have concave-linear fractional problems, and hence Dinkelbach's method can be applied to obtain the globally optimal solutions. Thus, we first derive analytical expressions for the harvesting interval, and then we provide an algorithm to describe the complete procedure. Furthermore, we incorporate delay-limited sources and investigate the impact of statistical queuing constraints on the energy-efficient allocation of operating intervals. We formulate an optimization problem that maximizes the system effective-EE while UEs are applying NOMA scheme for uplink information transfer. Since the problem satisfies pseudo-concavity, we provide an iterative algorithm using bisection method to determine the unique solution. In the numerical results, we observe that broadcasting at higher power level is more energy efficient for WPCN with uplink NOMA. Additionally, exponential decay QoS parameter has considerable impact on the optimal solution, and in the presence of strict constraints, more time is allocated for downlink interval under half-duplex operation with uplink TDMA mode.

Entropy connects water structure and dynamics in protein hydration layer.

Abstract: The enzyme Candida Antarctica lipase B (CALB) serves here as a model for understanding connections among hydration layer dynamics, solvation shell structure, and protein surface structure. The structure and dynamics of water molecules in the hydration layer were characterized for regions of the CALB surface, divided around each α-helix, β-sheet, and loop structure. Heterogeneous hydration dynamics were observed around the surface of the enzyme, in line with spectroscopic observations of other proteins. Regional differences in the structure of the biomolecular hydration layer were found to be concomitant with variations in dynamics. In particular, it was seen that regions of higher density exhibit faster water dynamics. This is analogous to the behavior of bulk water, where dynamics (diffusion coefficients) are connected to water structure (density and tetrahedrality) by excess (or pair) entropy, detailed in the Rosenfeld scaling relationship. Additionally, effects of protein surface topology and hydrophobicity on water structure and dynamics were evaluated using multiregression analysis, showing that topology has a somewhat larger effect on hydration layer structure-dynamics. Concave and hydrophobic protein surfaces favor a less dense and more tetrahedral solvation layer, akin to a more ice-like structure, with slower dynamics. Results show that pairwise entropies of local hydration layers, calculated from regional radial distribution functions, scale logarithmically with local hydration dynamics. Thus, the Rosenfeld relationship describes the heterogeneous structure-dynamics of the hydration layer around the enzyme CALB. These findings raise the question of whether this may be a general principle for understanding the structure-dynamics of biomolecular solvation.

Efficient Secure Outsourcing of Large-Scale Sparse Linear Systems of Equations

Abstract: Solving large-scale sparse linear systems of equations (SLSEs) is one of the most common and fundamental problems in big data, but it is very challenging for resource-limited users. Cloud computing has been proposed as a timely, efficient, and cost-effective way of solving such expensive computing tasks. Nevertheless, one critical concern in cloud computing is data privacy. Specifically, clients’ SLSEs usually contain private information that should remain hidden from the cloud for ethical, legal, or security reasons. Many previous works on secure outsourcing of linear systems of equations (LSEs) have high computational complexity, and do not exploit the sparsity in the LSEs. More importantly, they share a common serious problem, i.e., a huge number of memory I/O operations. This problem has been largely neglected in the past, but in fact is of particular importance and may eventually render those outsourcing schemes impractical. In this paper, we develop an efficient and practical secure outsourcing algorithm for solving large-scale SLSEs, which has low computational and memory I/O complexities and can protect clients’ privacy well. We implement our algorithm on Amazon Elastic Compute Cloud, and find that the proposed algorithm offers significant time savings for the client (up to 74 percent) compared to previous algorithms.

Determining Optimal Forming of Flexible Microgrids in the Presence of Demand Response in Smart Distribution Systems

Abstract: Implementing microgrids in power systems will improve the network reliability and reduce the impact of outages on end-users. Determining the most efficient boundaries of microgrids under contingencies is one of the main challenges for utilities from reliability and economics points of view. Currently, most research works have been focused on predefined boundary or static microgrids regardless system conditions and priority or importance of customers. In this paper, a novel concept for designing and operation of flexible microgrids in order to improve the reliability of a power distribution system is proposed. Compared to current approaches, boundaries of the proposed flexible microgrids can be extended or shrunk based on generation and demand levels, technical constraints, and customers’ comfort. Furthermore, a demand response (DR) program is performed to maintain a balance between generation and consumption in the microgrid. In this paper, genetic algorithm (GA) and mixed-integer linear programming (MILP) are simultaneously applied to a model and solve two-stage optimization considering utilities’ profits and customers’ satisfaction. In planning level, GA is utilized for sitting and sizing of distributed generations and placement of switches. In operation level, MILP is used to select target switches as boundaries of optimal microgrids, model priority of customers, and determine the contribution of each load in the DR program. The case study is also presented and final results show the superiority of the proposed method compared with the traditional fixed boundaries method in microgrids.

Side-Channel Inference Attacks on Mobile Keypads Using Smartwatches

Abstract: Smartwatches enable many novel applications and are fast gaining popularity. However, the presence of a diverse set of on-board sensors provides an additional attack surface to malicious software and services on these devices. In this paper, we investigate the feasibility of key press inference attacks on handheld numeric touchpads by using smartwatch motion sensors as a side-channel. We consider different typing scenarios, and propose multiple attack approaches to exploit the characteristics of the observed wrist movements for inferring individual key presses. Experimental evaluation using commercial off-the-shelf smartwatches and smartphones show that key press inference using smartwatch motion sensors is not only fairly accurate, but also comparable with similar attacks using smartphone motion sensors. Additionally, hand movements captured by a combination of both smartwatch and smartphone motion sensors yields better inference accuracy than either device considered individually.