University of Dayton

The large-scale practical application of fuel cells will be difficult to realize if the expensive platinum-based electrocatalysts for oxygen reduction reactions (ORRs) cannot be replaced by other efficient, low-cost, and stable electrodes. Here, we report that vertically aligned nitrogen-containing carbon nanotubes (VA-NCNTs) can act as a metal-free electrode with a much better electrocatalytic activity, long-term operation stability, and tolerance to crossover effect than platinum for oxygen reduction in alkaline fuel cells. In air-saturated 0.1 molar potassium hydroxide, we observed a steady-state output potential of -80 millivolts and a current density of 4.1 milliamps per square centimeter at -0.22 volts, compared with -85 millivolts and 1.1 milliamps per square centimeter at -0.20 volts for a platinum-carbon electrode. The incorporation of electron-accepting nitrogen atoms in the conjugated nanotube carbon plane appears to impart a relatively high positive charge density on adjacent carbon atoms. This effect, coupled with aligning the NCNTs, provides a four-electron pathway for the ORR on VA-NCNTs with a superb performance.

A new energy paradigm, consisting of greater reliance on renewable energy sources and increased concern for energy efficiency in the total energy lifecycle, has accelerated research into energy-related technologies. Due to their ubiquity, magnetic materials play an important role in improving the efficiency and performance of devices in electric power generation, conditioning, conversion, transportation, and other energy-use sectors of the economy. This review focuses on the state-of-the-art hard and soft magnets and magnetocaloric materials, with an emphasis on their optimization for energy applications. Specifically, the impact of hard magnets on electric motor and transportation technologies, of soft magnetic materials on electricity generation and conversion technologies, and of magnetocaloric materials for refrigeration technologies, are discussed. The synthesis, characterization, and property evaluation of the materials, with an emphasis on structure-property relationships, are discussed in the context of their respective markets, as well as their potential impact on energy efficiency. Finally, considering future bottlenecks in raw materials, options for the recycling of rare-earth intermetallics for hard magnets will be discussed.

The acceptance of new information technologies by their intended users persists as an important issue for researchers and practitioners of information systems. Several models have been developed in the literature to facilitate understanding of the process by which new information technologies are adopted. This paper proposes a new construct that further illuminates the relationships explicit in the technology acceptance models and describes an operational measure for this construct that possesses desirable psychometric properties. The construct, personal innovativeness in the domain of information technology, is hypothesized to exhibit moderating effects on the antecedents as well as the consequences of individual perceptions about a new information technology. The construct was developed and validated in the context of the innovation represented by the World-Wide Web. Implications for theory and practice are discussed.

An overview of the recent developments in the field of cylindrical vector beams is provided. As one class of spatially variant polarization, cylindrical vector beams are the axially symmetric beam solution to the full vector electromagnetic wave equation. These beams can be generated via different active and passive methods. Techniques for manipulating these beams while maintaining the polarization symmetry have also been developed. Their special polarization symmetry gives rise to unique high-numerical-aperture focusing properties that find important applications in nanoscale optical imaging and manipulation. The prospects for cylindrical vector beams and their applications in other fields are also briefly discussed.

ABSTRACT Persuading users to adopt new information technologies persists as an important problem confronting those responsible for implementing new information systems. In order to better understand and manage the process of new technology adoption, several theoretical models have been proposed, of which the technology acceptance model (TAM) has gained considerable support. Beliefs and attitudes represent significant constructs in TAM. A parallel research stream suggests that individual difference factors are important in information technology acceptance but does not explicate the process by which acceptance is influenced. The objective of this paper is to clarify this process by proposing a theoretical model wherein the relationship between individual differences and IT acceptance is hypothesized to be mediated by the constructs of the technology acceptance model. In essence then, these factors are viewed as influencing an individual's beliefs about an information technology innovation; this relationship is further supported by drawing upon extensive research in learning. The theoretical model was tested in an empirical study of 230 users of an information technology innovation. Results confirm the basic structure of the model, including the mediating role of beliefs. Results also identify several individual difference variables that have significant effects on TAM's beliefs. Theoretical contributions and practical implications that follow are discussed.

ABSTRACT The often paradoxical relationship between investment in information technology and gains in productivity has recently been attributed to a lack of user acceptance of information technology innovations. Diverse streams of research have attempted to explain and predict user acceptance of new information technologies. A common theme underlying these various research streams is the inclusion of the perceived characteristics of an innovation as key independent variables. Furthermore, prior research has utilized different outcomes to represent user acceptance behavior. In this paper we focus on individual's perceptions about the characteristics of the target technology as explanatory and predictive variables for acceptance behavior, and present an empirical study examining the effects of these perceptions on two frequently used outcomes in the context of the innovation represented by the World Wide Web. The two outcomes examined are initial use of an innovation and intentions to continue such use in the future, that is, to routinize technology use. Two research questions motivated and guided the study. First, are the perceptions that predict initial use the same as those that predict future use intentions? Our results confirm, as hypothesized by prior research, that innovation characteristics do explain acceptance behavior. The results further reveal that the specific characteristics that are relevant for each acceptance outcome are different. The second research question asks if perceived voluntariness plays a role in technology acceptance. Results show that external pressure has an impact on adopters' acceptance behavior. Theoretical and practical implications that follow are presented.

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others. This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began. Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

Purpose – The purpose of this paper is to systematically and critically review the literature related to process design and modeling of fused deposition modeling (FDM) and similar extrusion-based additive manufacturing (AM) or rapid prototyping processes. Design/methodology/approach – A systematic review of the literature focusing on process design and mathematical process modeling was carried out. Findings – FDM and similar processes are among the most widely used rapid prototyping processes with growing application in finished part manufacturing. Key elements of the typical processes, including the material feed mechanism, liquefier and print nozzle; the build surface and environment; and approaches to part finishing are described. Approaches to estimating the motor torque and power required to achieve a desired filament feed rate are presented. Models of required heat flux, shear on the melt and pressure drop in the liquefier are reviewed. On leaving the print nozzle, die swelling and bead cooling are considered. Approaches to modeling the spread of a deposited road of material and the bonding of polymer roads to one another are also reviewed. Originality/value – To date, no other systematic review of process design and modeling research related to melt extrusion AM has been published. Understanding and improving process models will be key to improving system process controls, as well as enabling the development of advanced engineering material feedstocks for FDM processes.

Gametogenesis is a complex biological process that is particularly sensitive to environmental insults such as chemicals. Many chemicals have a negative impact on the germline, either by directly affecting the germ cells, or indirectly through their action on the somatic nursing cells. Ultimately, these effects can inhibit fertility, and they may have negative consequences for the development of the offspring. Recently, nanomaterials such as nanotubes, nanowires, fullerene derivatives (buckyballs), and quantum dots have received enormous national attention in the creation of new types of analytical tools for biotechnology and the life sciences. Despite the wide application of nanomaterials, there is a serious lack of information concerning their impact on human health and the environment. Thus, there are limited studies available on toxicity of nanoparticles for risk assessment of nanomaterials. The purpose of this study was to assess the suitability of a mouse spermatogonial stem cell line as a model to assess nanotoxicity in the male germline in vitro. The effects of different types of nanoparticles on these cells were evaluated by light microscopy, and by cell proliferation and standard cytotoxicity assays. Our results demonstrate a concentration-dependent toxicity for all types of particles tested, whereas the corresponding soluble salts had no significant effect. Silver nanoparticles were the most toxic while molybdenum trioxide (MoO(3)) nanoparticles were the least toxic. Our results suggest that this cell line provides a valuable model with which to assess the cytotoxicity of nanoparticles in the germ line in vitro.

In 3 studies, the authors developed and began to validate a measure of the propensity to act rashly in response to positive affective states (positive urgency). In Study 1, they developed a content-valid 14-item scale, showed that the measure was unidimensional, and showed that positive urgency was distinct from impulsivity-like constructs identified in 2 models of impulsive behavior. In Study 2, they showed that positive urgency explained variance in risky behavior not explained by measures of other impulsivity-like constructs, differentially explained positive mood-based risky behavior, differentiated individuals at risk for problem gambling from those not at risk, and interacted with drinking motives and expectancies as predicted to explain problem drinking behavior. In Study 3, they confirmed the hypothesis that positive urgency differentiated alcoholics from both eating-disordered and control individuals.

Bio-medical image segmentation is one of the promising sectors where nuclei segmentation from high-resolution histopathological images enables extraction of very high-quality features for nuclear morphometrics and other analysis metrics in the field of digital pathology. The traditional methods including Otsu thresholding and watershed methods do not work properly in different challenging cases. However, Deep Learning (DL) based approaches are showing tremendous success in different modalities of bio-medical imaging including computation pathology. Recently, the Recurrent Residual U-Net (R2U-Net) has been proposed, which has shown state-of-the-art (SOTA) performance in different modalities (retinal blood vessel, skin cancer, and lung segmentation) in medical image segmentation. However, in this implementation, the R2U-Net is applied to nuclei segmentation for the first time on a publicly available dataset that was collected from the Data Science Bowl Grand Challenge in 2018. The R2U-Net shows around 92.15% segmentation accuracy in terms of the Dice Coefficient (DC) during the testing phase. In addition, the qualitative results show accurate segmentation, which clearly demonstrates the robustness of the R2U-Net model for the nuclei segmentation task.

Abstract We create a culture dictionary using one of the latest machine learning techniques—the word embedding model—and 209,480 earnings call transcripts. We score the five corporate cultural values of innovation, integrity, quality, respect, and teamwork for 62,664 firm-year observations over the period 2001–2018. We show that an innovative culture is broader than the usual measures of corporate innovation – R&D expenses and the number of patents. Moreover, we show that corporate culture correlates with business outcomes, including operational efficiency, risk-taking, earnings management, executive compensation design, firm value, and deal making, and that the culture-performance link is more pronounced in bad times. Finally, we present suggestive evidence that corporate culture is shaped by major corporate events, such as mergers and acquisitions.

Introduction to Optics is now available in a re-issued edition from Cambridge University Press. Designed to offer a comprehensive and engaging introduction to intermediate and upper level undergraduate physics and engineering students, this text also allows instructors to select specialized content to suit individual curricular needs and goals. Specific features of the text, in terms of coverage beyond traditional areas, include extensive use of matrices in dealing with ray tracing, polarization, and multiple thin-film interference; three chapters devoted to lasers; a separate chapter on the optics of the eye; and individual chapters on holography, coherence, fiber optics, interferometry, Fourier optics, nonlinear optics, and Fresnel equations.

Deep learning (DL)-based semantic segmentation methods have been providing state-of-the-art performance in the past few years. More specifically, these techniques have been successfully applied in medical image classification, segmentation, and detection tasks. One DL technique, U-Net, has become one of the most popular for these applications. We propose a recurrent U-Net model and a recurrent residual U-Net model, which are named RU-Net and R2U-Net, respectively. The proposed models utilize the power of U-Net, residual networks, and recurrent convolutional neural networks. There are several advantages to using these proposed architectures for segmentation tasks. First, a residual unit helps when training deep architectures. Second, feature accumulation with recurrent residual convolutional layers ensures better feature representation for segmentation tasks. Third, it allows us to design better U-Net architectures with the same number of network parameters with better performance for medical image segmentation. The proposed models are tested on three benchmark datasets, such as blood vessel segmentation in retinal images, skin cancer segmentation, and lung lesion segmentation. The experimental results show superior performance on segmentation tasks compared to equivalent models, including a variant of a fully connected convolutional neural network called SegNet, U-Net, and residual U-Net.

Metallic particles are generally considered difficult to trap due to strong scattering and absorption forces. In this paper, numerical studies show that optical tweezers using radial polarization can stably trap metallic particles in 3-dimension. The extremely strong axial component of a highly focused radially polarized beam provides a large gradient force. Meanwhile, this strong axial field component does not contribute to the Poynting vector along the optical axis. Consequently, it does not create axial scattering/absorption forces. Owing to the spatial separation of the gradient force and scattering/absorption forces, a stable 3-D optical trap for metallic particles can be formed.

In many imaging systems, the detector array is not sufficiently dense to adequately sample the scene with the desired field of view. This is particularly true for many infrared focal plane arrays. Thus, the resulting images may be severely aliased. This paper examines a technique for estimating a high-resolution image, with reduced aliasing, from a sequence of undersampled frames. Several approaches to this problem have been investigated previously. However, in this paper a maximum a posteriori (MAP) framework for jointly estimating image registration parameters and the high-resolution image is presented. Several previous approaches have relied on knowing the registration parameters a priori or have utilized registration techniques not specifically designed to treat severely aliased images. In the proposed method, the registration parameters are iteratively updated along with the high-resolution image in a cyclic coordinate-descent optimization procedure. Experimental results are provided to illustrate the performance of the proposed MAP algorithm using both visible and infrared images. Quantitative error analysis is provided and several images are shown for subjective evaluation.

By producing a train of absorption or dispersion signals (continuous-wave magnetic resonance) or free induction decays (pulsed magnetic resonance) it is possible to save time in spin-lattice relaxation measurements due to the fact that it is not necessary to wait for equilibrium magnetization before initiating the train. The relaxation time may be calculated from the train according to a simple rapidly converging iteration.

cubic meters of rock and glacier ice collapsed from the steep north face of Ronti Peak. The rock and ice avalanche rapidly transformed into an extraordinarily large and mobile debris flow that transported boulders greater than 20 meters in diameter and scoured the valley walls up to 220 meters above the valley floor. The intersection of the hazard cascade with downvalley infrastructure resulted in a disaster, which highlights key questions about adequate monitoring and sustainable development in the Himalaya as well as other remote, high-mountain environments.

This article uses a differential salience‐comparable salience approach to examine the effects of work demands and resources on work‐to‐family conflict and facilitation. The analysis is based on data from 1,938 employed adults living with a family member who were interviewed for the 1997 National Study of the Changing Workforce. The results support the differential salience approach by indicating that time‐ and strain‐based work demands show relatively strong positive relationships to work‐to‐family conflict, whereas enabling resources and psychological rewards show relatively strong positive relationships to work‐to‐family facilitation. The availability of time‐based family support policies and work‐family organizational support is negatively related to conflict and positively related to facilitation, thereby supporting the comparable salience approach.

Purpose – The purpose of this paper is to critically review the literature related to dimensional accuracy and surface roughness for fused deposition modeling and similar extrusion-based additive manufacturing or rapid prototyping processes. Design/methodology/approach – A systematic review of the literature was carried out by focusing on the relationship between process and product design parameters and the dimensional and surface properties of finished parts. Methods for evaluating these performance parameters are also reviewed. Findings – Fused deposition modeling® and related processes are the most widely used polymer rapid prototyping processes. For many applications, resolution, dimensional accuracy and surface roughness are among the most important properties in final parts. The influence of feedstock properties and system design on dimensional accuracy and resolution is reviewed. Thermal warping and shrinkage are often major sources of dimensional error in finished parts. This phenomenon is explored along with various approaches for evaluating dimensional accuracy. Product design parameters, in particular, slice height, strongly impact surface roughness. A geometric model for surface roughness is also reviewed. Originality/value – This represents the first review of extrusion AM processes focusing on dimensional accuracy and surface roughness. Understanding and improving relationships between materials, design parameters and the ultimate properties of finished parts will be key to improving extrusion AM processes and expanding their applications.