Miracle-dz/UniFAR-FTUs huggingface dataset:
Cipher Text Policy Attribute based Encryption (Cp-Abe) using Concealed Policy in Public Cloud

Cloud computing focus on the data storage and the management. Since the cloud is open source for the user there have to protect the privacy and the security of the data which are less maintenances. There are several approaches designed to establish a secure data. The Signature work of this scheme is to allow the user to verify the shared in the cloud using the secret key. The anonymzed access policy can be viewed by the Cloud Service Provider (CSP) and the user; hence the recipient information will not be leaked or disclosed. The novel idea of this method is to conserve the privacy of the access policy using Cipher text Policy Access Based Encryption (CP- ABE) scheme. We have proposed a novel system, “Concealed Policy using mandatory and Role based”, using time elapsed of 10 secs. Within the required time the user, should response to the cloud using their Secret key(SK) to decrypt the file, and the owners with the Public key (PK), must encrypt the file using the mandatory access and upload it to the Third Party Auditor (TPA). This will improves the security to the user in the cloud environment. The concealed policy relies on two stages, there is Mandatory policy and Role based policy using the signature verification. Which are more efficient to the user to encrypt and decrypt the file. Without verification of the signature, the legitimate user cannot access the particulars in the cloud environment. If the signature of the user is valid, there can access (decrypt) the file using the SK. whereas, invalid signature occurs the TPA (Third Party Auditor) traces the reasonable user responsible for the signature and check with the mandatory access to encrypt the file, Nether less the access den ailed.
PROJECT ECHO - SYSTEM CALCULATIONS

Abstract : The primary objective of Project Echo was the transmission of radio communications between points on the earth by reflection from the balloon satellite. This paper describes system calculations made in preparation for the experiment and their adaptation to the problem of interpreting results. The calculations include path loss computations, expected audio signal to-noise ratios, and received signal strength based on orbital parameters.
A Methodology for the Integration of Multiple Distributed Heterogeneous Databases: Application to Databases of the Tomahawk Engineering Community.

Abstract : Engineering 2000 is a project initiated by Naval Surface Warfare Center (NSWC) and designed to develop an infrastructure for the sharing of engineering data and information to support the current and future needs of the Tomahawk engineering support community. The goal of Engineering 2000 is to integrate the engineering, logistics, and management automated tools of the Tomahawk community into a single infrastructure. This thesis investigates the integration of Tomahawk distributed heterogeneous databases. It develops a six-step methodology for identifying an integrated strategy and architecture for heterogenous databases in a distributed environment and apply it to the two most significant databases used by the Tomahawk engineering community, Tomahawk Information Management Engineering System (TIMES) and Tomahawk Engineering Exchange Network (TEXN). The application of the methodology to these databases suggests a loosely coupled architecture for integrating their data. (MM)
Radioxenons: Their role in monitoring a Comprehensive Test Ban Treaty

Monitoring for xenon radionuclides which are produced in a nuclear detonation can provide a strong deterrent to the violation of a Comprehensive nuclear Test Ban Treaty (CTBT). There are 18 known radioactive xenon isotopes produced in nuclear fission with half-lives ranging from less than one second to 11.9 days. However, only four of these remain in significant amounts more than a day after a detonation. In order for radioxenon monitoring to be practical, it was necessary to develop an automated measurement system which could operate unattended for periods of months, measure the entire spectrum of radioxenons, and provide hundreds of times better sensitivities than current laboratory procedures. This capability was developed at the US Department of Energy`s Pacific Northwest National Laboratory based on rapid separation of atmospheric xenon coupled with a unique high sensitivity measurement device for the radioxenons. A fieldable prototype analyzer is scheduled for testing in August 1996 with commercial availability planned by 1998.
Does index investing work in bonds

Purpose - The purpose of this paper is to examine operating characteristics, risk and performance measures of all available vehicles for index investing in US bond funds during the 15-year period from April, 1994 to March, 2009. The results shed light on the important issue of bond index mutual funds (BIMFs) and bond exchange-traded funds (BETFs) performance compared with average of all bond mutual funds. Design/methodology/approach - Data were obtained from Morningstar Principia. Operating characteristics include expense ratios, annual turnover rates, and tax cost ratios. Performance measures include average annual returns and return percentile rank in category, risks (measured by standard deviation) and risk-adjusted returns (measured by the Sharpe ratio). Findings - BIMFs and BETFs have significantly lower expense ratios and annual turnover rates than category averages. Their returns and risk-adjusted returns are significantly higher than bond category averages. Research limitations/implications - Future studies will be able to benefit from a larger sample size, longer performance records, and the strength of bond index funds in foreign markets. Practical implications - Both BIMFs and bond exchange-traded mutual funds have significantly lower expense and annual turnover rates, making them preferred investment choices. Social implications - Efforts by active bond mutual fund managers to beat index benchmarks have largely failed. Investors should be wary of bond mutual fund managers touting their ability to beat the average or a bond index. Originality/value - The advantage of investment in BIMFs and BETFs is clear.
State Fragility: Towards a Multi-Dimensional Empirical Typology

The purpose of this paper is to demonstrate that, empirically, state fragility – conceptualised as a multi-dimensional phenomenon along the categories of authority, capacity and legitimacy – comes in several distinct configurations, yet that the number of such configurations is rather limited. We suggest that this finding has useful, previously unexplored implications for policy design vis-a-vis fragile states. We do not intend to call into question the necessity of country-specific analysis. A better grasp of “typical” forms of fragility, however, should help development agencies to better prepare for the types of situations they are most likely to be confronted with. The final section of the paper explores some of the practical implications that can be derived from our classification.
Operational modal analysis and wavelet transformation for damage identification in wind turbine blades

This study demonstrates an application of a previously proposed modal and wavelet analysis-based damage identification method to a wind turbine blade. A trailing edge debonding was introduced to an SSP 34-m blade mounted on a test rig. Operational modal analysis was conducted to obtain mode shapes for undamaged and damaged states of the blade. Subsequently, the mode shapes were analyzed with one-dimensional continuous wavelet transformations for damage identification. The basic idea of the method is that structural damage will introduce local mode shape irregularities which are captured in the continuous wavelet transformation by significantly magnified transform coefficients, thus providing combined damage detection, localization, and size assessment. It was found that due to the nature of the proposed method, the value of the identification results highly depends on the number of employed measurement points. Since a limited number of measurement points were utilized in the experiments, only certain damage-sensitive modes, in which pronounced damage-induced mode shape changes occur, are applicable for valid identification of the damage.
Measuring Meaningful Differences: Sensory Testing Based Decision Making in an Industrial Context; Applications of Signal Detection Theory and Thurstonian Modelling

markdownabstract__Abstract__ "In the Fast Moving Consumer Goods industry, results from sensory research form the basis for many important business decisions. Examples of such decisions are whether to launch new products, change existing products in order to make them more healthy or sustainable, or whether to continue with specific novel technological developments. To make good quality decisions, it is important that the sensory methods used are fast, accurate and deliver robust results. Signal detection theory and Thurstonian modelling can improve the effectiveness of sensory research, and these theories have been applied to one specific type of methods; sensory difference tests. Sensory difference tests are used to measure small differences between products, and can be used to answer important questions like: “Are these two products similar in taste? ”, “Does this new ingredient make the product different? ”, and “Will our consumers be able to notice the differences? ” Two signal detection applications have been investigated. The first application is to compare test methods and identify how to optimize them, as there are many methods available that largely differ in performance. With this knowledge, more effective methods can be selected or specifically designed. The second application is to integrate results from different studies to improve the effectiveness of sensory testing in general, for example by relating sensory differences detected by a trained panel “In Lab” to differences found by consumers “In Home”. Such knowledge can make future studies more predictive of what really matters to consumers, and improve the quality of decision making based on sensory results whilst reducing the amount of testing required. "
DepNet: An automated industrial intelligent system using deep learning for video‐based depression analysis

As a common mental disorder, depression has attracted many researchers from affective computing field to estimate the depression severity. However, existing approaches based on Deep Learning (DL) are mainly focused on single facial image without considering the sequence information for predicting the depression scale. In this paper, an integrated framework, termed DepNet, for automatic diagnosis of depression that adopts facial images sequence from videos is proposed. Specifically, several pretrained models are adopted to represent the low‐level features, and Feature Aggregation Module is proposed to capture the high‐level characteristic information for depression analysis. More importantly, the discriminative characteristic of depression on faces can be mined to assist the clinicians to diagnose the severity of the depressed subjects. Multiscale experiments carried out on AVEC2013 and AVEC2014 databases have shown the excellent performance of the intelligent approach. The root mean‐square error between the predicted values and the Beck Depression Inventory‐II scores is 9.17 and 9.01 on the two databases, respectively, which are lower than those of the state‐of‐the‐art video‐based depression recognition methods.
Model-based control of hopper dredgers

The modern trailing suction hopper dredgers are advanced ships that are equipped with many automation systems that can be controlled with integrated computer systems from the bridge. From the operators it is expected that they generate the right set-points for all these systems. The latest ships are designed such that one operator is responsible for controlling the complete ship. This can only be achieved with a high degree of automation. The determination of the set-points is not unambiguous, since vital information such as the soil type, which is necessary to make a correct decision, is not always at hand. The subject of this thesis is the development of a control system that strives to optimise the dredging process operation. This control system must take into account all the aspects that influence the performance, such as the excavation process, the pumping process and the sand sedimentation process in the hopper. Whereas the consequences of the control actions are only observable in the performance at the end of the cycle, a control strategy has been chosen which is based on model predictive control (MPC). The results are based on numerical simulations and compared with measured data and simulated scenarios. These scenarios are comparable with the work method in practice. The improvement compared with the scenarios varies between 2% for coarse sand and 22% for fine sand, calculated in cubic metre sand per second for a complete dredging cycle. An important conclusion is that the sand sedimentation process in the hopper plays in important role when determining the optimal set-points for sand with a grain size smaller than 0.3 mm. Since in practice the focus of operation is on the excavation production, an increase up to 22% can be achieved by taking the sedimentation process into account.
WinGridder - An interactive grid generator for TOUGH - A user's manual (Version 1.0)

WinGridder is a Windows-based software package for designing, generating, and visualizing at various spatial scales numerical grids used in reservoir simulations and groundwater modeling studies. Development of this software was motivated by the requirements of the TOUGH (Transport of Unsaturated Groundwater and Heat) family of codes (Pruess 1987, 1991) for simulating subsurface processes related to high-level nuclear waste isolation in partially saturated geological media. Although the TOUGH family of codes has great flexibility in handling the variety of grid information required to describe complex objects, designing and generating a suitable irregular grid can be a tedious and error-prone process, even with the help of existing grid generating programs. This is especially true when the number of cells and connections is very large. The processes of inspecting the quality of the grid or extracting sub-grids or other specific grid information are also complex. The mesh maker embedded within TOUGH2 generates only uniform numerical grids and handles only one set of uniform fracture and matrix properties throughout the model domain. This is not suitable for grid generation in complex flow and transport simulations (such as those of Yucca Mountain, which have heterogeneity in both fracture and matrix media). As a result, the software program Amesh (Haukwa 2000) was developed to generate irregular, effective-continuum (ECM) grids.
Preclinical assessment of chitosan–polyvinyl alcohol–graphene oxide nanocomposite scaffolds as a wound dressing

The present research aimed to examine the biological properties of chitosan (CS)–polyvinyl alcohol (PVA) scaffolds reinforced with graphene oxide (GO) nanosheets, as wound dressings. The scaffolds were characterized by various techniques. The scanning electron microscopy (SEM) and thermogravimetry analyses (TGAs) were used to investigate distribution of the GO within the polymer. The viscoelastic properties were evaluated by dynamic mechanical thermal analysis (DMTA) to examine the quality of a wound dressing. In vitro and in vivo studies were conducted to assess the biocompatibility of the scaffolds as wound dressing. The cell viability and proliferation results indicated that mouse fibroblast cells (L929) could adhere on the 50CS–50PVA/3 wt% GO scaffold. Herewith, the fabricated CS–PVA–GO nanocomposite scaffolds are suggested as promising biomaterials for skin tissue engineering and wound dressing.
The effect of tip clearance and tip gap geometry on the performance of a one and a half stage axial gas turbine.

In a previous work of a similar nature, the performance of a low speed axial turbine with a second stage nozzle was examined with respect to the effect of the variation of tip clearance for various tip shapes. Present findings suggest some interesting phenomena, including the effect of tip clearance on the flow within the rotor and show that poor resolution from a transducer and insufficient data points in the critical tip region, where a high velocity peak was found, were responsible for a number of incorrect conclusions in the original study. In terms of blade tip geometry, a standard flat tip shape was found to deliver only a marginally better performance when compared to a double squealer tip and the two streamlined shapes previously investigated. Although contemporary opinion suggests that a streamlined tip should increase the leakage flow and hence cause greater mixing losses, the machine efficiency was not significantly reduced. This is an exciting result since it suggests that a streamlined tip shape can be used to alleviate the problem of blade tip burnout without significantly reducing machine efficiency. When the single stage performance in the absence of a second nozzle was examined, slightly different trends were obtained. The low entropy tips produced slightly lower mixing loss, suggesting that the internal gap loss is an important parameter in determining the rate at which the leakage jet mixes downstream of the rotor. The flow behind the rotor (ie time averaged) was found to be in remarkable agreement with linear cascade data when time averaged even though the latter did not include any effects of relative motion. An increase in clearance was seen to reduce the Euler work and also to cause a deficit of mass flow across the remainder of the blade right down to the hub. The
TRAINING PROGRAM FOR IMPROVING CLINICAL TEACHING SKILLS AS A COMPETENCE OF CLINICAL INSTRUCTORS

Background: Clinical teaching is an important component of clinical education. In nursing, clinical teaching is ensured by clinical nurse educators. The quality of the student-teacher interaction in the clinical field can either facilitate or hinder the students' integration of theory to practice. The study aimed to assess the effect of training program on clinical teaching skills of the clinical instructors at faculty of nursing, Suez Canal University. Design: A quasi-experimental study design. Setting: this study was conducted in three settings as follows: Faculty of nursing SCU, Suez Canal university hospitals and Ismailia general hospital. Sample: Convenient sample were included all clinical instructors (43) who participate in students' clinical teaching during the academic years 2016/2017 and 2017/2018.Tools: Tool (1): Self-Need Assessment Sheet which included two parts. Part I: personnel data of clinical instructors, Part II: it contains question about the needs related to clinical teaching skills. Tool (2): clinical teaching skills observational checklist. Results: There was a statistically significant improvement between mean scores of clinical instructors in relation to their skills at pre, post and follow up. Mean score at pre-program was (46.6±4.62) which increased at post program (82.4±2.8) and slightly declined at follow-up to (82.1±2.93). Conclusion: the clinical teaching skills of clinical instructors improved post and follow up implementation of the program compared to pre implementation of the program. Recommendations: Ongoing professional education for clinical instructors about clinical teaching skills and a clinical teaching efficacy scale must be developed to evaluate clinical nursing instructors based on the attributes of rules and their capabilities.
Topology in non-Hermitian Chern insulator systems with skin effect

The non-Hermitian skin effect can arise in materials which have asymmetrical hoppings between atoms or resonating units that makes the bulk eigen-spectrum sensitive to boundary conditions. When skin effect emerges, even eigenstates in the bulk continuum can become localized on the edges, making the distinction between edge and bulk states challenging. We establish the bulk-boundary correspondence for the a Chern insulator model with non-Hermitian skin effect by combining two approaches (“non-Bloch” approach and “biorthogonal” approach). Both approaches aim at suppressing the skin effect but they are based on different mathematical tools. A biorthogonal inverse participation ratio is used as a measure to distinguish between bulk states and edge states, and a non-Bloch Chern number is used to characterize the topology and predict the number of topological edge bands. The resulting phase diagram has interesting features that are not found in Hermitian systems. For example, one topological transition and two non-Hermitian phase transitions can be induced by merely tuning a single parameter.
On Some Summability Methods for a q-Analogue of an Integral Type Operator Based on Multivariate q-Lagrange Polynomials

Abstract The present paper considers a q-analogue of an operator defined by Erkuş-Duman et al. (Calcolo, 45(1):53–67, 2008) involving q-Lagrange polynomials in several variables. The Korovkin type theorems in the settings of deferred weighted A-statistical convergence and the power series method are investigated.
Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations

Imputation of missing data is a task that plays a vital role in a number of engineering and science applications. Often such missing data arise in experimental observations from limitations of sensors or post-processing transformation errors. Other times they arise from numerical and algorithmic constraints in computer simulations. One such instance and the application emphasis of this paper are numerical simulations of storm surge. The simulation data corresponds to time-series surge predictions over a number of save points within the geographic domain of interest, creating a spatio-temporal imputation problem where the surge points are heavily correlated spatially and temporally, and the missing values regions are structurally distributed at random. Very recently, machine learning techniques such as neural network methods have been developed and employed for missing data imputation tasks. Generative Adversarial Nets (GANs) and GAN-based techniques have particularly attracted attention as unsupervised machine learning methods. In this study, the Generative Adversarial Imputation Nets (GAIN) performance is improved by applying convolutional neural networks instead of fully connected layers to better capture the correlation of data and promote learning from the adjacent surge points. Another adjustment to the method needed specifically for the studied data is to consider the coordinates of the points as additional features to provide the model more information through the convolutional layers. We name our proposed method as Convolutional Generative Adversarial Imputation Nets (Conv-GAIN). The proposed method's performance by considering the improvements and adaptations required for the storm surge data is assessed and compared to the original GAIN and a few other techniques. The results show that Conv-GAIN has better performance than the alternative methods on the studied data.