Miracle-dz/UniFAR-FTUs huggingface dataset:
Product Unit Gene Model and Its Application in Vehicle Leaf-Spring Configuration Design

One of the most important problems that must be solved in Product Configuration Design is how to effectively deal with the confliction between variability of individual customer requirement and high efficiency, accuracy and intelligence. Considering the feature of self organization, self adaptability, hereditariness and variability of product gene, product unit gene model is built by analogue of the structure of human gene, and the method of self-organizing configuration solving is presented based on product unit gene synthesis. Meantime, some rules of gene synthesis are designed and the process of self-organizing configuration solving is described. At the end of the paper, the feasibility of the method is testified by an example of vehicle leaf-spring. Accordingly, some suggestions of improvement and further research are given in the result.
Brain regions supporting intentional and incidental memory: a PET study

REGIONAL brain activity associated with intentional and incidental memory retrieval was studied with PET. Previously studied and new words were presented in either an intentional or an incidental memory task. Type of task was crossed with an encoding manipulation (‘deep’ vs ‘shallow’) which varied the probability that studied items would be remembered. In both tasks, deeply encoded items were associated with greater activation in the left hippocampus than were items that had received shallow encoding, suggesting that the involvement of the hippocampus in memory retrieval is independent of whether remembering is intentional or incidental. Right prefrontal and bilateral parietal cortex were more activated during the intentional task than during the incidental task, irrespective of encoding condition. Thus, these regions play a more extensive role in memory retrieval when remembering is intentional.
Optimal switching control of a step-down DC-DC converter

This paper applies a general computational technique for optimal control of switched-mode hybrid systems, recently developed by the authors, to voltage-regulation problems in a step-down DC-DC converter. Unlike existing techniques that are based on model-predictive control and a specific algebraic structure of the problem, the algorithms presented here are based on gradient descent with Armijo step size, and consequently can incorporate time-dependent state constraints in a natural way. The approach proposed in this paper is complementary to the extant MPC-based techniques, and it appears to compare favorably with some of the established works. Two problems are being addressed: one concerns pulse-width modulation and computes the optimal duty ratio, and the other computes an optimal switching schedule without a fixed cycle time.
Desired Compensation Adaptive Robust Control ∗

A desired compensation adaptive robust control (DCARC) framework is presented for nonlinear systems having both parametric uncertainties and uncertain nonlinearities. The paper first considers a class of higher order nonlinear systems transformable to a normal form with matched model uncertainties. For this class of uncertain systems, the desired values of all states for tracking a known desired trajectory can be predetermined and the usual desired compensation concept can be used to synthesize DCARC laws. The paper then focuses on systems with unmatched model uncertainties, in which the desired values of the intermediate state variables for perfect output tracking of a known desired trajectory cannot be predetermined. A novel way of formulating desired compensation concept is proposed and a DCARC backstepping design is developed to overcome the design difficulties associated with unmatched model uncertainties. The proposed DCARC framework has the unique feature that the adaptive model compensation and the regressor depend on the reference output trajectory and on-line parameter estimates only. Such a structure has several implementation advantages. First, the adaptive model compensation is always bounded when projection type adaption law is used, and thus does not affect the closed-loop system stability. As a result, the interaction between the parameter adaptation and the robust control law is reduced, which may facilitate the controller gain tuning process considerably. Second, the effect of measurement noise on the adaptive model compensation and on the parameter adaptation law is minimized. Consequently, a faster adaptation rate can be chosen in implementation to speed up the transient response and to improve overall tracking performance. These claims have been verified in the comparative experimental studies of several applications. DOI: 10.1115/1.3211087
State-of-the-art comparative cost modeling of heavily-loaded wireless heterogeneous networks

The motivation of this paper comes from the ongoing need of the telecommunications industry to respond to the revenue traffic decoupling trend. Besides innovation of data services in order to increase the average revenue per user, the ultimate goal of the mobile operators is to increase their cost and operational efficiency. For that purpose we developed “state-of-the-art” comparative analysis of the heterogeneous wireless networks in order to determine the most cost effective radio network deployment strategies as a function of excessive demand levels of up to 100 GB per user and month. We perform the modeling by consideration of the unit cost drivers relevant for the various base station classes which provide different coverage and high capacity performance, coming with the LTE-Advanced radio access technology or IEEE 802.11ac Wi-Fi standard. The outcome of our cost model is the proper assessment of the total investment to cover and adequately serve the particular area of interest. The key finding is that the small cell solutions like pico, femto cells and Wi-Fi access points are more cost efficient when new macro base station sites need to be deployed or when very high demand levels need to be satisfied. In all other evaluated cases the importance of the spectrum size comes to the highest level together with the introduction of the LTE-Advanced carrier aggregation functionality.
Challenges That Make/Break the Athlete’s Quest to Become an Entrepreneur: A Qualitative Study About Fans’ Perceptions

Abstract Athletes are a new type of celebrity in the world. Following other celebrities, there are several examples of athletes who have used their persona of fame to go a step further with their so-called personal brands and actually launch products carrying their names. As athletes are considered brands in themselves, these sorts of activities (i.e., introducing a product brand) are considered as an extension of the athlete’s brand. Given the nature of the research, this study employed a qualitative design. A purposeful convenience sampling technique was employed to select study participants according to a set of predetermined criteria. The final sample comprised nine fans. The fans identified five major factors that could be important in the context of an athlete’s decision to start his/her own brand. The five identified factors that could decide the fate of an athlete’s brand extensions were the athlete’s origin, extension fit, gender, performance, and impression. According to fans, these factors play an important role in the athlete’s venture as an entrepreneur. The study suggests that although fans understand that athletes are brands in themselves, Pakistani fans are not fond of the idea of athletes becoming entrepreneurs due to the fans’ emotional attachment to the athletes. As this is the first study expressing fans’ perspectives in the context of athlete brand extension, this study is an addition to the academic literature and theories of athlete branding and athletes as entrepreneurs.
A Framework for Incorporating "ilities" in Tradespace Studies

Non-traditional design criteria such as flexibility, robustness, survivability and others (collectively referred to as the “ilities”) are increasingly recognized as critical system properties for the success of aerospace programs. While most decision makers would agree that the ilities are important system properties, they are neither well-defined nor easily evaluated in isolation. While some evaluation methodologies do exist (e.g., real options for flexibility), there is a need for a holistic framework for describing systems with these properties. This paper will explore the use of these “ilities” properties as selection criteria in tradespace studies. A framework is described that can be used to incorporate ilities into conceptual design and tradespace studies in a systematic way. The framework is based on describing ilities in terms of changes in three dimensions: changes in the context, changes in the needs, and changes in the systems itself. The ilities are then interpreted as methods of navigating changes in this space. Quantitative analysis can be accomplished through Epoch/Era Analysis linking multiple discrete tradespace studies into a coherent timeline. An extended example is presented to demonstrate an analysis and visualization of the survivability of a space tug system to LEO orbital debris over a ten year lifetime. The results indicate that passive shielding for satellites in LEO provides only marginal benefit, at best, given the debris distribution. The smaller satellite designs in the tradespace display an unfavorable tradeoff of cost versus survivability. The example case study is a simple application of the framework, and points out the potential challenges of displaying multidimensional, non-aggregate data in “ilities” dynamic tradespace studies. The framework and case study suggest that using Epoch/Era Analysis, “ilities” will emerge as properties describing how systems change across epochs, and can be prescribed as visual and quantitative strategies that can be traded in terms of benefit and cost over time.
Neural Plasticity and Proliferation in the Generation of Antidepressant Effects: Hippocampal Implication

It is widely accepted that changes underlying depression and antidepressant-like effects involve not only alterations in the levels of neurotransmitters as monoamines and their receptors in the brain, but also structural and functional changes far beyond. During the last two decades, emerging theories are providing new explanations about the neurobiology of depression and the mechanism of action of antidepressant strategies based on cellular changes at the CNS level. The neurotrophic/plasticity hypothesis of depression, proposed more than a decade ago, is now supported by multiple basic and clinical studies focused on the role of intracellular-signalling cascades that govern neural proliferation and plasticity. Herein, we review the state-of-the-art of the changes in these signalling pathways which appear to underlie both depressive disorders and antidepressant actions. We will especially focus on the hippocampal cellularity and plasticity modulation by serotonin, trophic factors as brain-derived neurotrophic factor (BDNF), and vascular endothelial growth factor (VEGF) through intracellular signalling pathways—cAMP, Wnt/β-catenin, and mTOR. Connecting the classic monoaminergic hypothesis with proliferation/neuroplasticity-related evidence is an appealing and comprehensive attempt for improving our knowledge about the neurobiological events leading to depression and associated to antidepressant therapies.
Super-Resolution Sparse MIMO-OFDM Channel Estimation Based on Spatial and Temporal Correlations

This letter proposes a parametric sparse multiple input multiple output (MIMO)-OFDM channel estimation scheme based on the finite rate of innovation (FRI) theory, whereby super-resolution estimates of path delays with arbitrary values can be achieved. Meanwhile, both the spatial and temporal correlations of wireless MIMO channels are exploited to improve the accuracy of the channel estimation. For outdoor communication scenarios, where wireless channels are sparse in nature, path delays of different transmit-receive antenna pairs share a common sparse pattern due to the spatial correlation of MIMO channels. Meanwhile, the channel sparse pattern is nearly unchanged during several adjacent OFDM symbols due to the temporal correlation of MIMO channels. By simultaneously exploiting those MIMO channel characteristics, the proposed scheme performs better than existing state-of-the-art schemes. Furthermore, by joint processing of signals associated with different antennas, the pilot overhead can be reduced under the framework of the FRI theory.
Birational models and flips

This survey treats two chapters in the theory of log minimal models, namely, the chapter on different notions of models in this theory and the chapter on birational flips, that is, log flips, mainly in dimension 3. Our treatment is based on ideas and results of the second author: his paper on log flips (and also on material from the University of Utah workshop) for the first chapter, and his paper on prelimiting flips (together with surveys of these results by Corti and Iskovskikh) for the second chapter, where a complete proof of the existence of log flips in dimension 3 is given. At present, this proof is the simplest one, and the authors hope that it can be understood by a broad circle of mathematicians.
Relationship of Abusive Supervision and Creativity of Academic Staff in Public Sector Universities: Mediating role of Knowledge Sharing

The importance of knowledge management enhances the competitive advantage of an organization in today's knowledge-based economy is agreed upon. In this era of globalized economy, knowledge is the main driver for growth. Knowledge promotes creativity is proved in the extant literature which is effected by abusive supervision. In this present study, we examine the process through which abusive supervision affects the creativity of employees. In particular, with the likelihood that abusive supervision is related to creativity of the employees and mediated by knowledge sharing behavior. Thus, the aim of present study is to investigate the effect of abusive supervision on the creativity of academic staff in the public sector universities with the mediating role of knowledge sharing. In order to analyze the relationship among the variables involved in the study, utilized the quantitative research methodology, deductive approach and cross-sectional time horizon . The questionnaire survey technique was adopted to collect the data. A five-point Likert scales with significant measures was used. The targeted population of the present study was the academic staff of three universities (Lahore College for Women University, The University of the Punjab, and Government College University, Lahore). Total 330 employees were selected as a sample through utilizing the proportionate stratified random sampling technique and among them 298 was fit for the analysis. The statistical tool, SPSS and AMOS was utilized to analyze the collected data to obtain the results. The results of the study show that abusive supervision has a negative effect on employee creativity and knowledge sharing of employees whereas; knowledge sharing has significant positive effect on employee creativity. Though, knowledge sharing is partially mediating the relationship between abusive supervision and knowledge sharing.
Spatially augmented reality

To create an effective illusion of virtual objects coexisting with the real world, see-through HMD-based Augmented Reality techniques supplement the user's view with images of virtual objects. We introduce here a new paradigm, Spatially Augmented Reality (SAR), where virtual objects are rendered directly within or on the user's physical space. A key benefit of SAR is that the user does not need to wear a head-mounted display. Instead, with the use of spatial displays, wide field of view and possibly high-resolution images of virtual objects can be integrated directly into the environment. For example, the virtual objects can be realized by using digital light projectors to "paint" 2D/3D imagery onto real surfaces, or by using built-in flat panel displays. In this paper we present the rendering method used in our implementation and discuss the fundamentally different visible artifacts that arise as a result of errors in tracker measurements. Finally, we speculate about how SAR techniques might be combined with see-through AR to provide an even more compelling AR experience.
The Cool Supergiant Population of the Massive Young Star Cluster RSGC1

We present new high-resolution near-IR spectroscopy and OH maser observations to investigate the population of cool luminous stars of the young massive Galactic cluster RSGC1. Using the 2.293 μm CO band-head feature, we make high-precision radial velocity measurements of 16 of the 17 candidate red supergiants (RSGs) identified by Figer et al. We show that F16 and F17 are foreground stars, while we confirm that the rest are indeed physically associated RSGs. We determine that star F15, also associated with the cluster, is a yellow hypergiant based on its luminosity and spectroscopic similarity to ρ Cas. Using the cluster’s radial velocity, we have derived the kinematic distance to the cluster and revisited the stars’ temperatures and luminosities. We find a larger spread of luminosities than in the discovery paper, consistent with a cluster age 30% older than previously thought (12 ± 2 Myr), and a total initial mass of (3 ± 1) × 104 M☉. The spatial coincidence of the OH maser with F13, combined with similar radial velocities, is compelling evidence that the two are related. Combining our results with recent SiO and H2O maser observations, we find that those stars with maser emission are the most luminous in the cluster. From this we suggest that the maser active phase is associated with the end of the RSG stage, when the luminosity-mass ratios are at their highest.
A Case of a C-Stem Fracture at the Head-Neck Junction and a Review of the Literature

We report the first case of a fracture of the standard C-stem in combination with a large metal-on-metal articulation. This occurred at the head-neck junction. Analysis of the fractured stem showed evidence of fatigue failure with possible corrosion. The use of large femoral heads with neck adaptors and narrow tapers should be used with caution, especially in heavy, active patients.
A hybrid sequential feature selection approach for the diagnosis of Alzheimer's Disease

The timely and accurate diagnosis of Alzheimer's Disease (AD) is important for preventing the progress of the irreversible disease. Recently, various types of imaging techniques, e.g. Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), have been widely used for the diagnosis of AD. Despite their usefulness, the image data generated are high-dimensional and noisy which make it difficult to give accurate diagnosis. In this paper, we propose a novel feature selection approach to detect the informative features from MRI data, which improves both the diagnosis accuracy and reduces the computational cost. Benchmarking results on the AD challenge datasets show that our proposed approach outperforms other popular feature selection methods. Furthermore, compared with the top winners of the challenge, our approach performs best in diagnosis of AD and has comparable performance with others in the detection of mild cognitive impairments (MCIs). The good performance on the real data demonstrate that our proposed approach is effective in prediction of ADs.
Discrimination of Dynamic Tactile Contact by Temporally Precise Event Sensing in Spiking Neuromorphic Networks

This paper presents a neuromorphic tactile encoding methodology that utilizes a temporally precise event-based representation of sensory signals. We introduce a novel concept where touch signals are characterized as patterns of millisecond precise binary events to denote pressure changes. This approach is amenable to a sparse signal representation and enables the extraction of relevant features from thousands of sensing elements with sub-millisecond temporal precision. We also proposed measures adopted from computational neuroscience to study the information content within the spiking representations of artificial tactile signals. Implemented on a state-of-the-art 4096 element tactile sensor array with 5.2 kHz sampling frequency, we demonstrate the classification of transient impact events while utilizing 20 times less communication bandwidth compared to frame based representations. Spiking sensor responses to a large library of contact conditions were also synthesized using finite element simulations, illustrating an 8-fold improvement in information content and a 4-fold reduction in classification latency when millisecond-precise temporal structures are available. Our research represents a significant advance, demonstrating that a neuromorphic spatiotemporal representation of touch is well suited to rapid identification of critical contact events, making it suitable for dynamic tactile sensing in robotic and prosthetic applications.
Hierarchical Bayesian Data Analysis in Radiometric SAR System Calibration: A Case Study on Transponder Calibration with RADARSAT-2 Data

A synthetic aperture radar (SAR) system requires external absolute calibration so that radiometric measurements can be exploited in numerous scientific and commercial applications. Besides estimating a calibration factor, metrological standards also demand the derivation of a respective calibration uncertainty. This uncertainty is currently not systematically determined. Here for the first time it is proposed to use hierarchical modeling and Bayesian statistics as a consistent method for handling and analyzing the hierarchical data typically acquired during external calibration campaigns. Through the use of Markov chain Monte Carlo simulations, a joint posterior probability can be conveniently derived from measurement data despite the necessary grouping of data samples. The applicability of the method is demonstrated through a case study: The radar reflectivity of DLR’s new C-band Kalibri transponder is derived through a series of RADARSAT-2 acquisitions and a comparison with reference point targets (corner reflectors). The systematic derivation of calibration uncertainties is seen as an important step toward traceable radiometric calibration of synthetic aperture radars.
Extraction and characterization of natural cellulose fibers from common milkweed stems

Natural cellulose fibers with cellulose content, strength, and elongation higher than that of milkweed floss and between that of cotton and linen have been obtained from the stems of common milkweed plants. Although milkweed floss is a unique natural cellulose fiber with low density, the short length and low elongation make milkweed floss unsuitable as a textile fiber. The possibility of using the stems of milkweed plant as a source for natural cellulose fibers was explored in this research. Natural cellulose fibers extracted from milkweed stems have been characterized for their composition, structure, and properties. Fibers obtained from milkweed stems have about 75% cellulose, higher than the cellulose in milkweed floss but lower than that in cotton and linen. Milkweed stem fibers have low % crystallinity when compared with cotton and linen but the strength of the fibers is similar to cotton and elongation is higher than that of linen fibers. POLYM. ENG. SCI., 2009. © 2009 Society of Plastics Engineers