Miracle-dz/UniFAR-FTUs huggingface dataset:
The VIMOS Public Extragalactic Redshift Survey (VIPERS)

We use the unparalleled statistics of the VIPERS survey to investigate the relation between the surface mean stellar mass density Σ = ℳ/(2 πRe2) of massive passive galaxies (MPGs, ℳ ≥ 1011 M⊙) and their local environment in the redshift range 0.5 ≤ z ≤ 0.8. Passive galaxies were selected on the basis of their NUVrK colors (∼900 objects), and the environment was defined as the galaxy density contrast, δ, using the fifth nearest-neighbor approach. The analysis of Σ versus δ was carried out in two stellar mass bins. In galaxies with ℳ ≤ 2 × 1011 M⊙, no correlation between Σ and δ is observed. This implies that the accretion of satellite galaxies, which is more frequent in denser environments (groups or cluster outskirts) and efficient in reducing the galaxy Σ, is not relevant in the formation and evolution of these systems. Conversely, in galaxies with ℳ > 2 × 1011 M⊙, we find an excess of MPGs with low Σ and a deficit of high-Σ MPGs in the densest regions with respect to other environments. We interpret this result as due to the migration of some high-Σ MPGs (< 1% of the total population of MPGs) into low-Σ MPGs, probably through mergers or cannibalism of small satellites. In summary, our results imply that the accretion of satellite galaxies has a marginal role in the mass-assembly history of most MPGs. We have previously found that the number density of VIPERS massive star-forming galaxies (MSFGs) declines rapidily from z = 0.8 to z = 0.5, which mirrors the rapid increase in the number density of MPGs. This indicates that the MSFGs at z ≥ 0.8 migrate to the MPG population. Here, we investigate the Σ–δ relation of MSFGs at z ≥ 0.8 and find that it is consistent within 1σ with that of low-Σ MPGs at z ≤ 0.8. Thus, the results of this and our previous paper show that MSFGs at z ≥ 0.8 are consistent in terms of number and environment with being the progenitors of low-Σ MPGs at z < 0.8.
An oocyte-expressed ax-tubulin gene in Xenopus laevis; sequences required for the iniitiation of transcription

We have studied the expression of XaT14, a member of the ax-tubulin multigene family in Xenopus laevis. Small amounts of XaT14 RNA are detectable in a range of cell types, but much higher levels are present in ovary and tissue culture cells. In oocytes XaT14 transcripts accumulate during early vitellogenesis but their level declines in more advanced stages. Faithful and efficient initiation of transcription occurred on cloned XaT14 injected into oocytes even at low template levels. We have examined the amount of transcript produced by various deletion mutants relative to a co-injected control gene. The presence of 200bp of DNA 5' and 53bp of DNA 3' to the initiation site sufficed for high levels of promoter activity, although maximum activity required 560 bp of 5' flanking DNA. The DNA between -200 and -60 was necessary for transcription in oocytes and contains several sequence motifs implicated in transcriptional regulation including three CCAAT boxes and a sequence resembling a heat shock element. An 8 bp deletion that removed the latter element from 5kb of 5'-flanking DNA reduced promoter activity by 60%.
The Influence of Dielectric Thickness on the Power Bandwidth of Planar Transformers

This paper considers an ideal planar transformer wherein only the electromagnetic parasitics (stray capacitive and leakage inductance) arising out of the transformer geometry are taken into account, assuming lossless conditions. A suitable electrically equivalent circuit model for the planar transformer is used to analyze its frequency and power transfer characteristics; this model was validated by a three dimensional electromagnetic simulation of the planar transformer structure in FEKO electromagnetic simulation software. The effect of dielectric thickness on the bandwidth of the transformer has been analyzed based on the premise that the inherent stray capacitance and leakage inductance elements would affect the power transfer characteristics of the transformer. It has been found that the dielectric thickness of a planar transformer can be optimized so as to maximize the frequency bandwidth. It is also shown that the bandwidth is found to be sensitive to the thickness of the dielectric beyond the optimum thickness threshold topt. Convenient closed form analytic expressions for the optimum dielectric thickness and the resultant maximum bandwidth are derived and presented. It is argued that these results can be readily used to benefit the design of air-core PCB/Planar transformers.
Real-time dispersion interferometry for density feedback in fusion devices

Interferometry as one of the most common core fusion diagnostics has traditionally suffered from incomplete vibration compensation. Dispersion interferometry promises a more complete compensation of vibrations. For this reason it is being employed in an increasing number of experiments. However, thus far none of them have shown reliable real-time low-latency processing of dispersion interferometry data. Nonetheless this is a necessity for most machines when trying to do density feedback control, most notably in long discharges like the ones planned at the W7-X stellarator and ITER. In this paper we report the development of a new phase extraction method specifically developed for real-time evaluation using field programmable gate arrays (FPGA). It has been shown to operate reliably during the operation phase OP1.2a at W7-X and is now routinely being used by the W7-X density feedback system up to very high densities above 1.4×1020 m-2 without showing 2π-wraps and exhibits increased wrap stability by double-data-rate sampling. A rigorous error analysis has been conducted shedding insights into the signal composition of a dispersion interferometer have been gained. This includes the environmental effects, most notably air humidity, on the phase measurement and the correction thereof.
The Formulation of Comprehensive Information Security Culture Dimensions for Information Security Policy Compliance Study

Few studies have shown there is significant relationship between Information Security Culture (ISC) and Information Security Policy (ISP) compliance behaviour. However, these findings still could not conclude the actual effect of ISC towards employees’ ISP compliance. There are issues of consistency and comprehensiveness of dimensions in representing ISC concept in all previous studies. While these dimensions are different from one study to another, there are also some ISC key factors in the ISC literature that are not being included in those studies. A comprehensive and more cohesive ISC dimensions that could represent all the key factors of ISC concept should be formulated in order to get more conclusive findings on the relationship between ISC and ISP compliance behavior. This paper discusses the formulation process of new comprehensive ISC dimensions to represent a holistic concept of ISC. The underlying concepts used in this formulation are based on widely accepted concepts of organizational culture and ISC conceptual model to make sure that the new formulated dimensions are supportive to all levels comprised in those concepts. Seven new dimensions have been proposed. These dimensions cover most of the ISC key factors in literature. The formulated dimensions also supported by previous theoretical and empirical findings from various ISC-related studies. Finally, we have demonstrated that these new comprehensive dimensions could be used to represent a holistic concept of ISC to be examined in ISP compliance behavior study.
Control Charts for Poisson Count Data with Varying Sample Sizes

Various cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts have been recommended to monitor a process with Poisson count data when the sample size varies. We evaluate the ability of these CUSUM and EWMA methods in detecting increases in the Poisson rate by calculating the steady-state average run length (ARL) performance for the charts. Our simulation study indicates that the CUSUM chart based on the generalized likelihood-ratio method is best at monitoring Poisson count data at the out-of-control shift for which it is designed when the sample size varies randomly. We also propose a new EWMA method that has good steady-state ARL performance.
A Novel Auxiliary Resonant Snubber Inverter Using Wide Bandgap Devices

A novel auxiliary resonant snubber is introduced. The design and operation are carried out, in which this snubber circuitry enables main Gallium Nitride (GaN) switches operating under zero voltage switching (ZVS) condition, and auxiliary Silicon Carbide (SiC) diodes switching under zero current switching (ZCS) condition. Besides, the auxiliary snubber circuitry gating algorithm is also optimized which allows reducing the switching and conduction loss in auxiliary GaN switches to obtain higher system efficiency and better thermal performance. This proposed inverter can be applied to wide range of potential applications, such as string solar inverter, renewable energy combined distributed generation, dc-ac part of bi-directional electrical vehicle (EV) on-board charger, and uninterruptible power supply (UPS), etc.
A deep learning approach for power system knowledge discovery based on multitask learning

Power system security assessment is an important and challenging problem. Large variations in loads and power generation present increased risks to the secure operation of power systems. This study proposes a distributed deep network structure for power system security knowledge discovery based on multitask learning to monitor and control power grids more properly and effectively. First, a deep neural network structure based on the deep belief network (DBN) is designed to non-linearly extract deep and abstract features layer-by-layer for total transfer capability (TTC) regression tasks. Then, a distributed training algorithm for the deep structure is developed to accelerate the training process. Furthermore, multitask learning is adopted by grouping and training-related tasks together to improve the task performance. Finally, the accuracy and efficiency of the deep structure are evaluated using the Guangdong Power Grid in China. The simulation results demonstrate that the proposed model can outperform the existing shallow models in terms of accuracy and stability and can meet the requirements of online computing efficiency.
Vision‐based vibration monitoring using existing cameras installed within a building

A new methodology for the monitoring of dynamic characteristics of buildings has been proposed. It is based on digital image analysis leveraging pre‐existing cameras installed within a building, such as surveillance cameras. The method is used for real‐time measurement of interstory drifts, from which dynamic characteristics (e.g., fundamental period) can easily be extracted to detect damage to the structural system. It has applications particularly in the case of post‐earthquake damage estimation and when the installation of traditional sensors and monitoring systems is cost prohibitive. The methodology is demonstrated through both lab‐scale and full‐scale shake table tests.
Subsea Equipment Decommissioning Using Fiber Rope

A comparative study of lifting lines for subsea equipment decommissioning was conducted to evaluate the applicability of fiber ropes. Generally, conventional steel wire ropes are used for subsea equipment decommissioning operations, but there are some disadvantages in using steel wires as the lifting lines at deepwater depth. To overcome the disadvantages, fiber ropes are proposed for using as lifting lines. The comparative methods to evaluate the performance of both lifting lines include three sections of calculations, payload capacity, and horizontal offset due to current, critical length of lifting line. Moreover, dynamic analysis using Orcaflex was performed to compare the dynamic forces occurring in the lifting lines during subsea equipment decommissioning. The results showed that the fiber ropes had advantages in payload capacity, critical length of lifting line and lower dynamic forces occurred compared to the steel wire ropes at deepwater depth.
Design of a Robust Five-Phase Ferrite-Assisted Synchronous Reluctance Motor With Low Demagnetization and Mechanical Deformation

This study presents the design of a robust five-phase ferrite (Fe) permanent-magnet-assisted synchronous reluctance motor (PMaSynRM) to mitigate two major design challenges: irreversible demagnetization and mechanical deformation. Recently, rare-earth magnet-based five-phase PMaSynRMs have been proposed for vehicular applications due to their better fault tolerance, higher torque density, and low torque ripple compared to their three-phase counterparts. However, rare-earth magnets are expensive and, their supplies were disrupted multiple times in past few years. Therefore, to avoid rare-earth magnets in traction motors, this study proposes a five-phase PMaSynRM with Fe magnet. Special attention is paid to avoid irreversible demagnetization and deformation as these are two major concerns in any Fe-based machine. The rotor structure is uniquely optimized with additional geometrical parameters to reduce the risk of irreversible demagnetization. To increase the structural stability of the rotor, optimal center post and weight reduction notches are adopted. The proposed Fe-based five-phase PMaSynRM has been comparatively studied over a rare-earth magnet-based PMaSynRM. The lumped parameter model and finite element model have been utilized for design optimization and performance analysis. Finally, A 3 kW prototype of the proposed five-phase Fe PMaSynRM has been fabricated, experimentally tested, and compared with the benchmark rare-earth PMaSynRM to validate the proposed design and simulations results.
Combination of High-Resolution Optical Coherence Tomography and Raman Spectroscopy for Improved Staging and Grading in Bladder Cancer

We present a combination of optical coherence tomography (OCT) and Raman spectroscopy (RS) for improved diagnosis and discrimination of different stages and grades of bladder cancer ex vivo by linking the complementary information provided by these two techniques. Bladder samples were obtained from biopsies dissected via transurethral resection of the bladder tumor (TURBT). As OCT provides structural information rapidly, it was used as a red-flag technology to scan the bladder wall for suspicious lesions with the ability to discriminate malignant tissue from healthy urothelium. Upon identification of degenerated tissue via OCT, RS was implemented to determine the molecular characteristics via point measurements at suspicious sites. Combining the complementary information of both modalities allows not only for staging, but also for differentiation of low-grade and high-grade cancer based on a multivariate statistical analysis. OCT was able to clearly differentiate between healthy and malignant tissue by tomogram inspection and achieved an accuracy of 71% in the staging of the tumor, from pTa to pT2, through texture analysis followed by k-nearest neighbor classification. RS yielded an accuracy of 93% in discriminating low-grade from high-grade lesions via principal component analysis followed by k-nearest neighbor classification. In this study, we show the potential of a multi-modal approach with OCT for fast pre-screening and staging of cancerous lesions followed by RS for enhanced discrimination of low-grade and high-grade bladder cancer in a non-destructive, label-free and non-invasive way.
Optimal Design and CFD Analysis of Wing of a Small-Scale UAV to Obtain Maximum Efficiency

In the recent past small-scale Unmanned Aerial Vehicles (or Unmanned Aerial System as it is called presently) have evolved as an important tool in non-conventional fields like agriculture, e-Commerce, policing, medical logistics in addition to military applications. This paper presents the complete methodology (very limited article exists) applied to optimally design the wing of a small scale Unmanned Aerial Vehicle with help of widely used CFD software, ANSYS to maximize its efficiency. In this study, the application of computational methods in the iterative design process is successfully explored. The various design parameters and features of wing also have been explored with help of CFD analysis to derive the advantages. The strength and stiffness analysis of the UAV wing has also been carried out using ANSYS. The modelling of wing and associated parts were carried out on CATIA. The final design of the wing has two spars, 20 ribs a side with mid wing root strengthening, near elliptical planform and has an all composite structure. The wing is lighter in weight as compared to a similar wing made from Aluminum view weight optimization, and strong enough to meet all in-flight load conditions with safety margins. This paper demonstrates the design process/methodology to optimally design an efficient small-scale UAV.
Integrating an Innovation Concentration into the Engineering Curriculum

A recent survey of global innovation-based competitiveness ranked the US 6th overall across 40 countries in innovation-based competitiveness. In fact, the US is falling behind, ranking last in year-to-year improvement in innovation and competitiveness [1]. To improve on this, the US must produce more STEM graduates capable of driving innovation. Many mid-sized universities in rural and low-technology states lack a culture or ecosystem that fosters innovation. To grow into thriving centers of technological innovation, these states must change their culture. STEM students – particularly engineering students – need a background in innovation processes, as well as real-world connections and experiences, to help develop their innovation instincts. The College of Engineering (CoE) in collaboration with the Walton College of Business (WCOB) at the University of Arkansas is addressing these needs by developing a scalable, repeatable, applied innovation curriculum through a multi-discipline, multi-college academic innovation track. Starting as freshmen, engineering students will be partnered with business students to focus on innovation principles and processes, while incorporating classroom content with hands-on experiences and internships to focus on new product development. Students from both colleges learn why innovation matters, how to work together to create innovation, and how to solve problems that have market demand. To expand this change to an innovation climate, the University will partner with regional engineering and technology industries in a manner that can serve as a model for other institutions. The CoE will use a combination of qualitative and quantitative data to evaluate and iteratively improve the program with the overall objective of attracting, retaining, and graduating students who are capable of innovating. These students will enter the workforce better prepared to improve the economic health and competitiveness of the US [2]. This paper focuses on the strategy, design and development of this program, lessons learned, and plans for the future.
An investigation of enablers and inhibitors of crowdfunding adoption: Empirical evidence from startups in China

In recent years, crowdfunding has emerged as a new fundraising technique for startup ventures. Crowdfunding allows small businesses and startups to seek funding from a large number of private investors, the so‐called “crowdfunders. ” Crowdfunding is beneficial for startup ventures by offering a variety of financial and nonfinancial benefits such as helping to solve funding difficulties, providing value‐added involvement, facilitating access to further funding, and providing publicity. In contrast, diffusion processes of new business innovations have become increasingly complicatedly and multifaceted in recent years. Potential users today are exposed to a wide range of influences that include word‐of‐mouth communications, network externalities, and social signals. Despite the advantages associated with crowdfunding, the crowdfunding industry faces slow rates of diffusion in the Chinese market because crowdfunding ventures often fail to translate into adoption behavior. This study investigates the enablers and inhibitors of crowdfunding adoption intention from the perspective of startups by employing the two‐factor theory, status quo bias theory, and innovation diffusion theory. These theories allow crowdfunding researchers and platform managers to test the relative influence of both reasons for (enablers) and reasons against (inhibitors) crowdfunding adoption in a single framework. In conclusion, considering the phenomenon of startups in China, the results of this study will fill the paucity of empirical research in the innovation resistance area, shedding light on the motivation of entrepreneurs to avoid crowdfunding as a financing course as well as providing crowdfunding managers and policymakers with strategic recommendations.
A New Hybrid Type of Deployable Structure: Origami-scissor Hinged

Deployable structures can transform, expand and contract due to their geometric, material and mechanical properties; applications spread across multiple fields including aerospace technology and temporary, mobile and transformable architecture. There are many different types of deployable structures. For instance, the art of origami has developed concepts for paper folds that can expand and contract. Another type is that of scissor-hinged structures, made by bars joined by pivots. These two different types, origami and scissors, have so far remained separate types within the field. This geometry research unifies both types and makes an origami-scissor structure, which has a double deployment. This new technology has potential applications in architecture and engineering, such as transportable pavilions, aerospace or robotic applications.
A Comparative Experimental Study on Fault Diagnosis of Rolling Element Bearing Using Acoustic Emission and Soft Computing Techniques

In engineering processes, Health condition Monitoring (HCM) is a faultfinding task to guarantee consistency of rotating machinery. Rolling Element Bearings (REBs) are the main origin of damage in such equipment. They are the key components used in most of the rotating devices. These faults are mainly caused due to premature failure or improper installation of the machine element. Finding and analysis of defects is very vital in rotating machinery for its optimality. A test-rig was established to analyze the various line defects in REBs under different speeds and load conditions. The Acoustic Emission (AE) signatures responses are obtained and analyzed. Soft computing methods, especially Artificial Neural Network (ANN) and Support Vector Machine (SVM) used to compare with experimental results for fault diagnosis of bearings. The AE statistical features were fed as inputs in ANN and SVM. A comparative experimental study on prediction of seeded defect size is carried out using soft computing methods. This study concludes that these methods can be used for prediction of rolling element bearings seeded faults. © 2018 Published by Faculty of Engineering Corresponding author: Nerella Mary Jasmin Department of Mechanical Engineering, A.U. College of Engineering (A), Visakhapatnam, A.P, India. E-mail: jasminmary2@gmail.com