Miracle-dz/UniFAR-FTUs huggingface dataset:
Lift and Drag Characteristics of a Blended-Wing Body Aircraft

The aerodynamic characteristics of a blended-wing body aircraft were assessed in the Air Force Institute of Technology low-speed wind tunnel. The scaled-down model (Re ∼ 10 5 and M = 0.10 to 0.20) of a strike tanker consisted of a shaped fuselage and slender, sweptback wings. The model evaluation and analysis process included force and moment measurements acquired from a wind-tunnel balance along with complementary pressure sensitive paint measurements and computational fluid dynamics solutions for a few cases. The force and moment coefficient data suggested that the aircraft is affected by both vortex lift and potential flow lift mechanisms. This was manifested in an apparent stall mechanism with a significant dependence on airspeed. One of the most intriguing aspects of the results was the striking difference in the force and moment measurements before and after the paint was applied to the surface. Although the roughness for both models was below the threshold suggested by 2-D boundary layer theory for all data acquired, the application of the paint led to a clear and repeatable effect on the force coefficient data. The lift slope was steeper and the onset of stall, when it occurred, was sudden across the entire wing for sufficiently smooth models. By contrast, the lift slope was reduced and stall was indicated only through a mild change in lift slope for the measurements corresponding to the rougher surface.
An improved formulation of an optimizing Rayleigh-Ritz technique for closed dielectric waveguide analysis

It is shown that use of the E/sub Z//H/sub Z/ formulation to describe the modes of the homogeneous rectangular waveguide leads to a relative convergence phenomenon as well as to incorrect loss calculations. The method is reformulated using an E/sub x//E/sub y//H/sub x//H/sub y/ description of the homogeneous modes. The formulation is validated for step-index waveguides through nonperturbational calculations of the propagation and attenuation constants of the round step-index dielectric waveguide. Comparison with the direct eigenvalue solution shows excellent agreement for the dominant and three higher-order modes. The formulation is validated for graded-index waveguides through calculation of the dispersion curves for three modes of a Gaussian-Gaussian graded-index channel waveguide. Comparison of the results with those of two other methods shows excellent agreement. >
Total dose effects on the shallow-trench isolation leakage current characteristics in a 0.35 /spl mu/m SiGe BiCMOS technology

The effects of gamma irradiation on the shallow-trench isolation (STI) leakage currents in a SiGe BiCMOS technology are investigated for the first time, and shown to be strongly dependent on the irradiation gate bias and operating substrate bias. A positive irradiation gate bias significantly enhances the STI leakage, suggesting a strong field assisted nature of the charge buildup process in the STI. Numerical simulations also suggest the existence of fixed positive charges deep in the bulk along the STI/Si interface. A negative substrate bias, however, effectively suppresses the STI leakage, and can be used to eliminate the leakage produced by the charges deep in the bulk under irradiation.
Intelligent Algorithms for Enhanced Diversity of Sample Nodes in Electronic Commerce System

The traditional recommendation algorithm for e-commerce has the problems of single product category and inconsistent recommendation products with user preferences, resulting in the lack of diversity and accuracy. An Intelligent Recommendation Algorithm for enhancing diversity of sample nodes in commercial system is proposed. After obtaining the user's past operation records, an attribute of the recommendation target is selected by calculating the constructed user preference matrix. Through user similarity calculation, similar users are screened and specific targets are further screened to achieve the function of recommendation algorithm. In order to verify the accuracy and diversity, a simulation comparison experiment is carried out with the traditional algorithm. Compared with the traditional algorithm, the intelligent algorithm with enhanced diversity of sample nodes in e-commerce system can improve the accuracy and diversity of the two indicators, and can be extended.
Measurement techniques and preliminary results of drop tests with full scale spent fuel transport and storage casks

Abstract The experimental testing and quantitative stress analysis of new cask designs becomes more and more important with increasing size of casks and their impact limiters, in order to assess the safety margins as well as for calculation validation reasons. Full scale drop testing of spent fuel transport and storage casks is performed by the Federal Institute for Materials Research and Testing within the type B package approval procedure and for safety demonstration reasons respectively. In drop tests for demonstrating ability of a package to withstand normal and accident conditions of transport, the instrumentation of a specimen is an important tool to evaluate its mechanical behaviour during impact. Generally, the instrumentation incorporates the measurement of strains and accelerations at the package. Test results as deceleration–time and strain–time functions constitute a main basis for the validation of assumptions in the safety analysis, for the evaluation of calculations based on finite element methods and extrapolation of scale model testing on full sized package within approval design tests. Besides, all of the above mentioned instrumentation, the flatness of the sealing area and the circularity of the cask lid flange are determined by using three-dimensional coordinate measurements to evaluate the mechanical impact behaviour of the cask flange region after the drop test sequence. Indispensable are measurements concerning leak tightness of the closure lid system. Additionally, specific photogrammetric measurements are used to characterise the conditions of the closure lid system after drop test in more detail. The preparation and interpretation of the results is in progress and indicates possible correlations between the leak tightness rate and measures of the relative movement of the lid.
Accurate Q-factor estimation of optically amplified systems in the presence of waveform distortions

We present an accurate method for the Q-factor estimation even in the presence of waveform distortions in optically amplified systems. The method stands on the assumption that the dominant noises over the bit error rate (BER) are those superimposed on the nearest rails to the decision level. It is clarified that the assumption is usually satisfied. The results also show that, in numerical system evaluation, we should evaluate not only the eye opening but also the probability of occurrence of the rail level.
Delineation of discrete conduit networks in karst aquifers via combined analysis of tracer tests and geophysical data

Abstract. Assessment of the karst network geometry based on field data is an important challenge in the accurate modeling of karst aquifers. In this study, we propose an integrated approach for the identification of effective three-dimensional (3D) discrete karst conduit networks conditioned on tracer tests and geophysical data. The procedure is threefold: i) tracer breakthrough curves (BTCs) are processed via a regularized inversion procedure to determine the minimum number of distinct tracer flow paths between injection and monitoring points, ii) available surface-based geophysical data and borehole-logging measurements are aggregated into a 3D proxy model of aquifer hydraulic properties, and iii) single or multiple tracer flow paths are identified through the application of an alternative shortest path (SP) algorithm to the 3D proxy model. The capability of the proposed approach to adequately capture the geometrical structure of actual karst conduit systems mainly depends on the sensitivity of geophysical signals to karst features, whereas the relative completeness of the identified conduit network depends on the number and spatial configuration of tracer tests. The applicability of the proposed approach is illustrated through a case study at the Hydrogeological Experimental Site (HES) in Poitiers, France.
Optimization of Landsat Chl-a Retrieval Algorithms in Freshwater Lakes through Classification of Optical Water Types

The application of remote sensing data to empirical models of inland surface water chlorophyll-a concentrations (chl-a) has been in development since the launch of the Landsat 4 satellite series in 1982. However, establishing an empirical model using a chl-a retrieval algorithm is difficult due to the spatial heterogeneity of inland lake water properties. Classification of optical water types (OWTs; i.e., differentially observed water spectra due to differences in water properties) has grown in favour in recent years over traditional non-turbid vs. turbid classifications. This study examined whether top-of-atmosphere reflectance observations in visible to near-infrared bands from Landsat 4, 5, 7, and 8 sensors can be used to identify unique OWTs using a guided unsupervised classification approach in which OWTs are defined through both remotely sensed reflectance and surface water chemistry data taken from samples in North American and Swedish lakes. Linear regressions of algorithms (Landsat reflectance bands, band ratios, products, or combinations) to lake surface water chl-a were built for each OWT. The performances of chl-a retrieval algorithms within each OWT were compared to those of global chl-a algorithms to test the effectiveness of OWT classification. Seven unique OWTs were identified and then fit into four categories with varying degrees of brightness as follows: turbid lakes with a low chl-a:turbidity ratio; turbid lakes with a mixture of high chl-a and turbidity measurements; oligotrophic or mesotrophic lakes with a mixture of low chl-a and turbidity measurements; and eutrophic lakes with a high chl-a:turbidity ratio. With one exception (r2 = 0.26, p = 0.08), the best performing algorithm in each OWT showed improvement (r2 = 0.69–0.91, p < 0.05), compared with the best performing algorithm for all lakes combined (r2 = 0.52, p < 0.05). Landsat reflectance can be used to extract OWTs in inland lakes to provide improved prediction of chl-a over large extents and long time series, giving researchers an opportunity to study the trophic states of unmonitored lakes.
Spaz! The Effects of Local Latency on Player Actions in a Desktop-Based Exergame

Technological advancement has decreased network latencies while simultaneously increasing local latencies. This may impact exergames—video games that incorporate exercise—the most since exergames tend to have complicated platforms to capture player actions. This article presents a study using a custom desktop-based exergame that controls for local latency and measures player performance and quality of experience (QoE). Analysis of the results from a 37-person user study shows that while player performance and QoE degrade with latency, exergame actions are fairly tolerant of even hundreds of milliseconds of latency. Our data point toward a crucial tipping point at latency values of approximately 400 ms.
Interlamination voltages in large turbogenerators

The magnetic flux in a generator induces a voltage between adjacent laminations of the stator core. These voltages are uniformly low (less than 50 mV) along the length of the core, and so only light interlamination insulation is required to prevent troublesome heating in the core. However, near the end of the core, additional interlamination voltages are induced by the axial magnetic fields in the core-end region and the radial leakage field at the core back. These voltages result from the segmentation of the laminations, and their magnitude depends on the way in which individual laminations are earthed to the stator building bars. These `segmentation voltages? are calculated by considering the eddy currents induced in the laminations by the axial magnetic fields, the currents flowing from the laminations to the stator building bars and the currents induced in these bars by the radial leakage fields. It is shown that, if the contact resistance between the laminations and the building bars is relatively high, and varies from contact to contact, the segmentation voltages are an order of magnitude higher than those induced by the main generator flux and hence sufficient to cause troublesome heating in damaged areas of the core.
Training Convolutional Neural Networks to Score Pneumonia in Slaughtered Pigs

Simple Summary Scoring lesions in slaughtered pigs can provide useful feedback to the swine industry, although the systematic recording of lesions is very challenging and time consuming. Artificial intelligence offers interesting opportunities to solve highly repetitive tasks, such as those performed by veterinarians at postmortem inspection in high-throughput slaughterhouses and to consistently analyze large amounts of data. The present investigation indicates that enzootic pneumonia-like lesions can be effectively detected and quantified through artificial intelligence methods under routine slaughter conditions. Abstract The slaughterhouse can act as a valid checkpoint to estimate the prevalence and the economic impact of diseases in farm animals. At present, scoring lesions is a challenging and time-consuming activity, which is carried out by veterinarians serving the slaughter chain. Over recent years, artificial intelligence(AI) has gained traction in many fields of research, including livestock production. In particular, AI-based methods appear able to solve highly repetitive tasks and to consistently analyze large amounts of data, such as those collected by veterinarians during postmortem inspection in high-throughput slaughterhouses. The present study aims to develop an AI-based method capable of recognizing and quantifying enzootic pneumonia-like lesions on digital images captured from slaughtered pigs under routine abattoir conditions. Overall, the data indicate that the AI-based method proposed herein could properly identify and score enzootic pneumonia-like lesions without interfering with the slaughter chain routine. According to European legislation, the application of such a method avoids the handling of carcasses and organs, decreasing the risk of microbial contamination, and could provide further alternatives in the field of food hygiene.
Fabrication of Antireflection Micro/Nanostructures on the Surface of Aluminum Alloy by Femtosecond Laser

Designed micro-nano structures on the surface of aluminum alloy provide excellent light trapping properties that can be used extensively in thermal photovoltaics, sensors, etc. However, the fabrication of high-performance antireflective micro-nano structures on aluminum alloy is challenging because aluminum has shallow intrinsic losses and weak absorption. A two-step strategy is proposed for fabricating broadband antireflection structures by superimposing nanostructures onto microscale structures. By optimizing the processing parameters of femtosecond laser, the average reflectances of 2.6% within the visible spectral region (400–800 nm) and 5.14% within the Vis-NIR spectral region (400–2500 nm) are obtained.
Real-Time PCR Method for the Rapid Detection and Quantification of Pathogenic Staphylococcus Species Based on Novel Molecular Target Genes

Coagulase-positive Staphylococcus aureus is a foodborne pathogen considered one of the causes of food-related disease outbreaks. Like S. aureus, Staphylococcus capitis, Staphylococcus caprae, and S. epidermidis are opportunistic pathogens causing clinical infections and food contamination. The objective of our study was to develop a rapid, accurate, and monitoring technique to detect four Staphylococcus species in food. Four novel molecular targets (GntR family transcriptional regulator for S. aureus, phosphomannomutase for S. epidermidis, FAD-dependent urate hydroxylase for S. capitis, and Gram-positive signal peptide protein for S. caprae) were mined based on pan-genome analysis. Primers targeting molecular target genes showed 100% specificity for 100 non-target reference strains. The detection limit in pure cultures and artificially contaminated food samples was 102 colony-forming unit/mL for S. aureus, S. capitis, S. caprae, and S. epidermidis. Moreover, real-time polymerase chain reaction successfully detected strains isolated from various food matrices. Thus, our method allows an accurate and rapid monitoring of Staphylococcus species and may help control staphylococcal contamination of food.
Six Sigma in the service sector: a focus on non-normal data

Six Sigma quality initiatives remain relatively rare in the service sector. While a robust assortment of statistical tools is available, the prevalence of attribute data in the service sector often obfuscates the applicability and appropriate selection of such tools. In the service sector, the normal distribution is not as commonplace as in the manufacturing industry. Accordingly, required approximations and assumptions in common statistical analysis are frequently invalid. In the service industries, where attribute data is most often encountered, one must use certain statistical quality improvement tools with particular caution. By nature, rarely does empirical data characterising service processes fit the normal distribution. Fortunately, there is an array of tools and methods for handling non-normal data that broaden the applicability and usefulness of Six Sigma. This article presents a statistical toolkit particularly useful for non-normal or more specifically, attribute or discrete data.
Evaluation of the MRSA/SA ELITe MGB Assay for the Detection of Staphylococcus aureus in Bone and Joint Infections

Bone and joint infections represent a potentially devastating complication of prosthetic orthopedic joint replacement, thus requiring both rapid and appropriate antibiotic treatment. Staphylococcus aureus is one of the most common pathogens involved in this pathology. Being able to assert its presence is the first step of efficient patient management. ABSTRACT Bone and joint infections represent a potentially devastating complication of prosthetic orthopedic joint replacement, thus requiring both rapid and appropriate antibiotic treatment. Staphylococcus aureus is one of the most common pathogens involved in this pathology. Being able to assert its presence is the first step of efficient patient management. This monocenter study evaluated the MRSA/SA ELITe MGB assay for the molecular detection of S. aureus and methicillin-resistant S. aureus (MRSA) in bone and joint biopsy specimens and synovial fluids. This test, together with conventional techniques, including standard cultures and the 16S rRNA amplification assay, was performed on 208 successive perioperative samples collected prospectively for 1 year obtained from 129 patients. Using conventional techniques, we detected a microbial pathogen in 76 samples from 58 patients, 40 of which were identified as S. aureus. The limit of detection (LOD) of the MRSA/SA ELITe MGB assay was experimentally determined for bone and joint biopsy specimens and synovial fluids using negative samples spiked with S. aureus ATCC 43300. The sensitivities of S. aureus detection with the MRSA/SA ELITe MGB assay were 82.5% (33/40 samples) and 97.5% (39/40 samples) using the manufacturer’s LOD and an experimentally determined LOD, respectively. Interestingly, using the osteoarticular specific LOD, 15 additional samples were determined to be positive for S. aureus DNA with the MRSA/SA ELITe MGB assay; in all cases, these samples were obtained from patients considered to be infected with S. aureus according to their clinical and microbiological records. The results were available within 24 h, which could help to expedite therapeutic decisions.
Optimal cutter selection for complex three-axis NC mould machining

Proper cutter selection can reduce NC machining time greatly. Most researchers select cutters based on tool path generation, which is very time consuming, and only a very limited number of cutters can be selected. A new cutter selection methodology for complex mould machining based on an efficient interference detection algorithm is introduced. With this approach, the feasible regions for the candidate cutters are first identified without tool path generation. The machining times for different cutter combinations are then estimated based on the areas of the feasible regions and the cutter feed rates. The set of cutters that can machine the workpiece with minimum time is selected as the optimal candidate cutters. Since no tool path needs to be generated before cutter selection, the cutters can be selected efficiently, and the number of the cutters that can be used in NC machining can be quite large. The system has been tested with several industrial parts and it can select optimal cutters effectively and efficiently.
Poverty and Economic Behavior: Gambling at Social Security Paydays

The goal of this research is to explore whether actual lottery revenues are sensitive to scarcity, as measured by intra-monthly variation in financial resources. Exogenous paydays of social security benefits are employed to generate the intra-monthly variation in financial resources. Using two million observations on daily lottery revenues that cover more than 2,500 lottery outlets in Israel for two years (2015-2016), I find that gambling revenue spikes at social security paydays. The estimation results imply that on Income Support payday aggregate lottery revenues are higher by 5 percent after controlling for outlet, weekday, holidays, month and year fixed effects. However, the calculated aggregate response of lottery revenues on Income Support payday is quite small and equal 0.5 percent of the total monthly payments deposited to the bank account of Income Support recipients. In addition, the other social security and salary paydays induce a trivial impact relative to total monthly payments deposited to the bank account of the respective recipients. These results survive a list of sensitivity analyses and pass a placebo test.
Professor and institution characteristics: Relations to technology use and teacher preparation in literacy methods courses

Abstract One-hundred ninety-nine literacy professors responded to an online survey. A multivariate outcome logit/probit model was used to analyze these data to determine how professor and institution characteristics were related to (a) technologies that professors use in their courses, (b) technologies that professors prepare teachers to use, (b) teaching methods that professors use to prepare teachers, and (c) strategies that professors prepare teachers to use in K-12 instruction. While some institutional characteristics predicted literacy teacher preparation, professor characteristics predominantly predicted how teachers were prepared to integrate technologies in literacy instruction, especially professors’ knowledge, comfort with using technologies, and their rank.