Miracle-dz/UniFAR-FTUs huggingface dataset:
Ambient humidity monitoring using a 1D photonic crystal sensor fabricated with glancing angle deposition

We present the design, fabrication, and characterization of a GLAD-fabricated photonic crystal sensor with a bandgap located in the visible optical spectrum. The photonic crystal is fabricated from TiO2 using electron-beam evaporation in a GLAD capable vacuum deposition system. Changes in humidity over a wide range (from 3% to 90% relative humidity) are detected by a colour change in the film due to movement of the photonic bandgap. The colour changes are quantified by measuring the transmittance of white light. Coupling the sensitivity of the film with a simple visual feedback system eliminates the need for complicated measurement techniques. This is desirable to minimize the cost and power consumption of the sensor device, making it amenable to large-scale production and deployment.
Fabrication of Transparent Lanthanum‐Doped Yttria Ceramics by Combination of Two‐Step Sintering and Vacuum Sintering

A new strategy for the sintering of transparent lanthanum-doped yttrium oxide was presented. This method is the extension of the well-accepted two-step sintering process for the densification of nanomaterials. The samples were firstly sintered at an intermediate temperature T1 (1450°C) in air. Then the temperature decreased quickly to a lower point T2 (900°-1100°C) in air and held for more than 20 h in order to facilitate densification with limited grain growth. Nanocrystalline yttria ceramic with a high relative density was then obtained, which can be further densified at higher temperature T3 (> 1700°C) in vacuum. After sintering, transparent yttria doped with 10% lanthanum ceramic was obtained with the grain size around 25 μm without annealing. The samples were densified mainly in air at low temperature, it is more easy for operation. The transmittance at 580 nm is 77%, which was equal or better than that from other sintering method for transparent yttria ceramics.
Lambdas in the liftshaft—functional programming and an embedded architecture

Embedded computer systems seem to be the antithesis of functional language systems. Embedded systems are small, stand-alone, and are often forced to accept inelegant design compromises due to hardware cost. They run continuously and are reactive, that is, their primary goal is to monitor sensors and control effectors, using observed external events to trigger state-changing control actions. Yet this paper describes how functional abstraction can tame the inelegance of embedded systems. Architectural compromises can be made in device drivers, programmed within the functional language, but a function-level interface is presented to the application programmer. Examples are given from a liftshaft case study.
Web Personalization as a Persuasion Strategy: An Elaboration Likelihood Model Perspective

With advances in tracking and database technologies, firms are increasingly able to understand their customers and translate this understanding into products and services that appeal to them. Technologies such as collaborative filtering, data mining, and click-stream analysis enable firms to customize their offerings at the individual level. While there has been a lot of hype about web personalization recently, our understanding of its effectiveness is far from conclusive. Drawing on the elaboration likelihood model (ELM) literature, this research takes the view that the interaction between a firm and its customers is one of communicating a persuasive message to the customers driven by business objectives. In particular, we examine three major elements of a web personalization strategy: level of preference matching, recommendation set size, and sorting cue. These elements can be manipulated by a firm in implementing its personalization strategy. This research also investigates a personal disposition, need for cognition, which plays a role in assessing the effectiveness of web personalization. Research hypotheses are tested using 1,000 subjects in three field experiments based on a ring-tone download website. Our findings indicate the saliency of these variables in different stages of the persuasion process. Theoretical and practical implications of the findings are discussed.
Genetic diversity of trout (genus Salmo) from its most eastern native range based on mitochondrial DNA and nuclear gene variation

Russia and western Asia harbour trout populations that have been classified as distinct species and subspecies, most often on the basis of morphological and ecological variation. In order to assess their origins and to verify whether traditional taxonomy reflects their evolutionary distinctiveness, we documented their genetic relationships on the basis of mitochondrial DNA (mtDNA) RFLP, mtDNA sequence analysis, and allozyme variation. Both mtDNA and nuclear gene variation defined two ancient phylogenetic assemblages of populations distributed among northern (Baltic, White, Barents), and southern (Black, Caspian, Aral) sea basins, between which gene flow has been possible but limited in postglacial times. These results supported the traditional taxonomic differentiation between populations of these two regions. They provided weak support for the taxonomic distinction of southern brown trout (Salmo trutta) populations based on their basin of origin. They also refuted the hypothesis that L. Sevan trout (Salmo ischchan) diverged from a primitive brown trout ancestor. Nevertheless, all trout populations from southern sea basins possessed private alleles or mtDNA genotypes and were genetically distinct Therefore, they represent unique gene pools that warrant individual recognition for conservation and management.
The Analysis of Aperture-Coupled Microstrip Antennas with Utilization the FDTD Method

The object of this article is usage of the FTDT method to analysis of planar antennas. The matter of Yee's cell as a basic element of characteristic of filed in FTDT method and well as Yee 's algorithm are presented. The question of source modeling and stimulation of antenna, boundary conditions and method of analysis of planar antennas' structure are also discussed. Some results of usage of the method have also been presented, as a confirmation of accuracy and usefulness for analysis of planar antennas. Aperture coupled microstrip antenna represents very broad bandwidth, lightweight, rigid construction. This type antenna has much better parameters like different type of microstrip antennas.
Notes on Maxwell & Delaney

The ANOVA table shows the F and p values for the main effects of group and angle , and the group:angle interaction. Examination of the table shows that the effects of group and angle are evaluated with different error terms. The aov command assumes that the between- and within-subjects variables are fixed
Instruments to Assess Secondhand Smoke Exposure in Large Cohorts of Never Smokers: The Smoke Scales

The objectives of this study were to: (i) to develop questionnaires that can identify never-smoking children and adults experiencing increased exposure to secondhand smoke (SHS+), (ii) to determine their validity against hair nicotine, and (iii) assess their reliability. A sample of 191 children (85 males; 106 females; 7–18 years) and 95 adult (23 males; 72 females; 18–62 years) never-smokers consented to hair nicotine analysis and answered a large number of questions assessing all sources of SHS. A randomly-selected 30% answered the questions again after 20–30 days. Prevalence of SHS+ in children and adults was 0.52±0.07 and 0.67±0.10, respectively (p<0.05). The Smoke Scale for Children (SS-C) and the Smoke Scale for Adults (SS-A) were developed via factor analysis and included nine questions each. Positivity criteria for SS-C and SS-A via receiver operating characteristics curve analysis were identified at >16.5 and >16, respectively. Significant Kappa agreement (p<0.05) was confirmed when comparing the SS-C and SS-A to hair nicotine concentration. Reliability analyses demonstrated that the SS-C and SS-A scores obtained on two different days are highly correlated (p<0.001) and not significantly different (p>0.05). Area under the curve and McNemar's Chi-square showed no pair-wise differences in sensitivity and specificity at the cutoff point between the two different days for SS-C and SS-A (p>0.05). We conclude that the SS-C and the SS-A represent valid, reliable, practical, and inexpensive instruments to identify children and adult never-smokers exposed to increased SHS. Future research should aim to further increase the validity of the two questionnaires.
A Back Propagation Artificial Neural Network based Model for Detecting and Predicting Fraudulent Financial Reporting

Fraudulent financial reporting has become an important issue in accounting profession, the implementation of selfassessment system appears as incentives to companies to misstate their financial reports to reduce tax obligation. Fraudulent financial reporting may cause fast losses to government income, as well as losses to the users of financial reports; several recent Studies have examined the feasibility of using various machine learning techniques in business and industrial applications. The purpose of this research is to propose a back propagation based artificial neural network model for Fraudulent Financial Reporting detection and prediction. Another main objective for this proposed model is using it in measuring the financial performance assessment by detecting the positive and negative deviations in certain important accounts balances such as net sales, and accounts receivable, which will support top managers in taking important strategic financial decisions for their companies. The proposed model was implemented using NeuronsSolution ANN software and has been applied on two large Egyptian companies managing electricity distribution in Egypt. The implementation results of this proposed model showed that the model is successful, efficient and reliable in detecting and predicting fraudulent financial reporting, and also the assessment of any company's financial performance. General Terms Machine Learning, Artificial Neural Networks
Prioritizing Audio Features Selection Using Analysis Hierarchy Process As A Mean To Extend User Control In Concatenative Sound Synthesis

User control is one of the most important heuristic principles of a system design as it gives users the freedom to choose a system’s functions and as a mean of communicating instructions to the system before performing a specific task. Existing concatenative sound synthesis systems call the need for a more flexible user control function, in particular during feature selection. This paper studies the effect of different combination of audio features, their orders and weights have on the segment match. It also proposes that a decision making methods such as the Analysis Hierarchy Process should also be incorporated into the system to enable weights to be generated automatically for each feature.
Evaluation of local detectors on non-planar scenes

This paper presents for the first time a method to evaluate the performance of local detectors under viewpoint changes on complex, realistic, and practically relevant scenes. The main contribution is a method which allows the automatic verification of detected corresponding points and regions for non-planar scenes. Using this method the performances of 10 different local detectors were evaluated in a large scale experiment. A ranking of the different detectors has been established based on this evaluation.
Enhancing Visual Perception and Motor Accuracy among School Children through a Mindfulness and Compassion Program

The present study assessed the effects of the mindfulness/compassion cultivating program: “Call to Care-Israel” on the performance in visual perception (VP) and motor accuracy, as well as on anxiety levels and self-reported mindfulness among 4th and 5th grade students. One hundred and thirty-eight children participated in the program for 24 weekly sessions, while 78 children served as controls. Repeated measures ANOVA’s yielded significant interactions between time of measurement and group for VP, motor accuracy, reported mindfulness, and anxiety. Post hoc tests revealed significant improvements in the four aforementioned measures in the experimental group only. In addition, significant correlations were obtained between the improvement in motor accuracy and the reduction in anxiety and the increase in mindfulness. Since VP and motor accuracy are basic skills associated with quantifiable academic characteristics, such as reading and mathematical abilities, the results may suggest that mindfulness practice has the ability to improve academic achievements.
KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation

We present KADABRA, a new algorithm to approximate betweenness centrality in directed and undirected graphs, which significantly outperforms all previous approaches on real-world complex networks. The efficiency of the new algorithm relies on two new theoretical contributions, of independent interest. The first contribution focuses on sampling shortest paths, a subroutine used by most algorithms that approximate betweenness centrality. We show that, on realistic random graph models, we can perform this task in time |E|1/2+o(1) with high probability, obtaining a significant speedup with respect to the Θ(|E|) worst-case performance. We experimentally show that this new technique achieves similar speedups on real-world complex networks, as well. The second contribution is a new rigorous application of the adaptive sampling technique. This approach decreases the total number of shortest paths that need to be sampled to compute all betweenness centralities with a given absolute error, and it also handles more general problems, such as computing the k most central nodes. Furthermore, our analysis is general, and it might be extended to other settings.
Inherent Dangers in Database Security

With the danger/risk of data theft looming large over the horizon of the Internet user involved in e-banking, online shopping, transaction etc. it becomes imperative to identify the dangers involved and employ security checks. Usually big stores have secure SSL [1] connections to be used by the owners of credit cards. Use of base64 encoding instead of hexadecimal and similarly using AES-128[2] cipher algorithm certainly heightens database security because these methods ensure high security where as they shorten the length of the encrypted string. Carrying forward these two above stated methods i.e. base64 encoding and AES-128 cipher algorithm, a more secure approach will be to use random number generator in which is required only to store the 8-byte random component instead of full 16 bytes. The existing methods of encryption are also not fool proof for high end professionals. The main thrust of this paper is to identify, pinpoint and find the solutions for the inherent dangers involved in the database storage.
Parallel solution of the subset‐sum problem: an empirical study

The subset‐sum problem is a well‐known NP‐complete combinatorial problem that is solvable in pseudo‐polynomial time, that is, time proportional to the number of input objects multiplied by the sum of their sizes. This product defines the size of the dynamic programming table used to solve the problem. We show how this problem can be parallelized on three contemporary architectures, that is, a 128‐processor Cray Extreme Multithreading (XMT) massively multithreaded machine, a 16‐processor IBM x3755 shared memory machine, and a 240‐core NVIDIA FX 5800 graphics processing unit (GPU). We show that it is straightforward to parallelize this algorithm on the Cray XMT primarily because of the word‐level locking that is available on this architecture. For the other two machines, we present an alternating word algorithm that can implement an efficient solution. Our results show that the GPU performs well for problems whose tables fit within the device memory. Because GPUs typically have memories in the order of 10GB, such architectures are best for small problem sizes that have tables of size approximately 1010. The IBM x3755 performs very well on medium‐sized problems that fit within its 64‐GB memory but has poor scalability as the number of processors increases and is unable to sustain performance as the problem size increases. This machine tends to saturate for problem sizes of 1011 bits. The Cray XMT shows very good scaling for large problems and demonstrates sustained performance as the problem size increases. However, this machine has poor scaling for small problem sizes; it performs best for problem sizes of 1012 bits or more. The results in this paper illustrate that the subset‐sum problem can be parallelized well on all three architectures, albeit for different ranges of problem sizes. The performance of these three machines under varying problem sizes show the strengths and weaknesses of the three architectures. Copyright © 2012 John Wiley & Sons, Ltd.
Noise-tolerance analysis for detection and reconstruction of absorbing inhomogeneities with diffuse optical tomography using single- and phase-correlated dual-source schemes

An iterative reconstruction procedure is used to invert intensity data from both single- and phase-correlated dual-source illuminations for absorption inhomogeneities. The Jacobian for the dual source is constructed by an algebraic addition of the Jacobians estimated for the two sources separately. By numerical simulations, it is shown that the dual-source scheme performs superior to the single-source system in regard to (i) noise tolerance in data and (ii) ability to reconstruct smaller and lower contrast objects. The quality of reconstructions from single-source data, as indicated by mean-square error at convergence, is markedly poorer compared to their dual-source counterpart, when noise in data was in excess of 2%. With fixed contrast and decreasing inhomogeneity diameter, our simulations showed that, for diameters below 7 mm, the dual-source scheme has a higher percentage contrast recovery compared to the single-source scheme. Similarly, the dual-source scheme reconstructs to a higher percentage contrast recovery from lower contrast inhomogeneity, in comparison to the single-source scheme.
Abducing Priorities to Derive Intended Conclusions

We introduce a framework for finding preference information to derive desired conclusions in nonmonotonic reasoning. A new abductive framework called preference abduction enables us to infer an appropriate set of priorities to explain the given observation skeptically, thereby resolving the multiple extension problem in the answer set semantics for extended logic programs. Preference abduction is also combined with a usual form of abduction in abductive logic programming, and has applications such as specification of rule preference in legal reasoning and preference view update. The issue of learning abducibles and priorities is also discussed, in which abduction to a particular cause is equivalent to abduction to preference.
Modelling and identification of a nonlinear power-amplifier with memory for nonlinear digital adaptive pre-distortion

In this paper a comparison between a memoryless nonlinear power amplifier model and a special nonlinear model with memory (Wiener-model) based on measurements on a power amplifier is presented. With moderate signal bandwidths of only 2 MHz the nonlinear model with memory outperforms the memoryless model. The amplifier model is intended to be used for pre-distortion purposes. Furthermore, an adaptive algorithm for finding the model parameters is presented.
The Phosphate Transporter Gene OsPht1;8 Is Involved in Phosphate Homeostasis in Rice1[W][OA]

Plant phosphate transporters (PTs) are active in the uptake of inorganic phosphate (Pi) from the soil and its translocation within the plant. Here, we report on the biological properties and physiological roles of OsPht1;8 (OsPT8), one of the PTs belonging to the Pht1 family in rice (Oryza sativa). Expression of a β-glucuronidase and green fluorescent protein reporter gene driven by the OsPT8 promoter showed that OsPT8 is expressed in various tissue organs from roots to seeds independent of Pi supply. OsPT8 was able to complement a yeast Pi-uptake mutant and increase Pi accumulation of Xenopus laevis oocytes when supplied with micromolar 33Pi concentrations at their external solution, indicating that it has a high affinity for Pi transport. Overexpression of OsPT8 resulted in excessive Pi in both roots and shoots and Pi toxic symptoms under the high-Pi supply condition. In contrast, knockdown of OsPT8 by RNA interference decreased Pi uptake and plant growth under both high- and low-Pi conditions. Moreover, OsPT8 suppression resulted in an increase of phosphorus content in the panicle axis and in a decrease of phosphorus content in unfilled grain hulls, accompanied by lower seed-setting rate. Altogether, our data suggest that OsPT8 is involved in Pi homeostasis in rice and is critical for plant growth and development.
Experimental and Numerical Study on the Behavior of Rubberized Concrete

In this study, the properties of rubberized concrete were examined and finite-element (FE) models were developed to investigate the accuracy of a current concrete material model in predicting the compressive behavior of rubberized concrete. The rubber particles, with a size of 1.18 mm were used at 0 %, 6 %, 12 %, and 18 % volume replacement of fine aggregate, keeping the proportions of gravel, water, and cement the same in all mixtures. Cylindrical and beam specimens were prepared and tested to evaluate the effect of rubber content on the density, compressive strength, elastic modulus, and damping ratio of the concrete. The results indicated that the damping ratio of rubberized concrete increased by 5.5 %, 27.8 %, and 64.8 % with rubber replacement of 6 %, 12 %, and 18 %, respectively. In addition to the experimental study, non-linear finite-element analysis was carried out using LS-DYNA software (Livermore Software Technology Corporation). The FE model developed in this paper was able to closely simulate the compressive behavior of the rubberized concrete specimens. The stiffness, compressive strength, volumetric response, and the dilation behavior obtained using the FE analysis agreed well with the values measured in the experimental work. The results show that the current concrete material model can be considered for rubberized concrete, provided that the compressive strength is modified to account for the reduction in strength caused by the added rubber.
Finite-Sample Analysis of Proximal Gradient TD Algorithms

In this paper, we show for the first time how gradient TD (GTD) reinforcement learning methods can be formally derived as true stochastic gradient algorithms, not with respect to their original objective functions as previously attempted, but rather using derived primal-dual saddle-point objective functions. We then conduct a saddle-point error analysis to obtain finite-sample bounds on their performance. Previous analyses of this class of algorithms use stochastic approximation techniques to prove asymptotic convergence, and no finite-sample analysis had been attempted. Two novel GTD algorithms are also proposed, namely projected GTD2 and GTD2-MP, which use proximal "mirror maps" to yield improved convergence guarantees and acceleration, respectively. The results of our theoretical analysis imply that the GTD family of algorithms are comparable and may indeed be preferred over existing least squares TD methods for off-policy learning, due to their linear complexity. We provide experimental results showing the improved performance of our accelerated gradient TD methods.