Miracle-dz/UniFAR-FTUs huggingface dataset:
Advanced Electron Microscopy Techniques in Nanomaterials Characterization at NASA Glenn Research Center

In this chapter, the author will review several advanced microscopy techniques developed at the NASA Glenn Research Center in the last 5 years. Topical areas include: unconventional approach to investigate the fine nanoporous structure of aerogels by scanning electron microscopy, new limits for transmission electron microscopy investigation of dispersion and chirality of single-walled carbon nano‐ tubes within a polymer matrix, the importance of microstructure of porous tin dioxide nanostructure that lead to first time detection of methane at room temperature without doping or catalyst, in situ SEM methods to study the thermal stability of nanoparticles on Graphene/Cu based materials, electron beam irradiation effects on carbon nanotube yarns electrical properties, and nanoindentation work of multiphase thermoelectric material.
Double Fano resonance in a side-by-side gratings structure

It is shown theoretically and numerically that a metal-dielectric or an all-dielectric side-by-side gratings structure can support non-degenerate guided modes coupled to the same radation mode. This realizes an easy to fabricate, simple system supporting a nearly degenerate, efficiently coupled, double Fano resonance. The ensuing relatively narrow and high-visibility spectral line-shape can be useful for realizing ultra-thin optical filters.
Evaluation of process systems operating envelopes

This thesis addresses the problem of worst-case steady-state design of process systems under uncertainty, also known as robust design. Designing for the worst case is of great importance when considering systems for deployment in extreme and hostile environments, where operational failures cannot be risked due to extraordinarily high economic and/or environmental expense. For this unique scenario, the cost of “overdesigning” the process far outweighs the cost associated with operational failure. Hence, it must be guaranteed that the process is sufficiently robust in order to avoid operational failures. Many engineering, economic, and operations research applications are concerned with worst-case scenarios. Classically, these problems give rise to a type of leader-follower game, or Stackelberg game, commonly known as the “minimax” problem, or more precisely as a max-min or min-max optimization problem. However, since the application here is to steady-state design, the problem formulation results in a more general nonconvex equality-constrained min-max program, for which no previously available algorithm can solve effectively. Under certain assumptions, the equality constraints, which correspond to the steady-state model, can be eliminated from the problem by solving them for the state variables as implicit functions of the control variables and uncertainty parameters. This approach eliminates explicit functional dependence on the state variables, and in turn reduces the dimensionality of the original problem. However, this embeds implicit functions in the program, which have no explicit algebraic form and can only be approximated using numerical methods. By doing this, the max-min program can be reformulated as a more computationally tractable semi-infinite program, with the caveat that there are embedded implicit functions. Semi-infinite programming with embedded implicit functions is a new approach to modeling worst-case design problems. Furthermore, modeling process systems—especially those associated with chemical engineering—often results in highly nonconvex functions. The primary contribution of this thesis is a mathematical tool for solving implicit semi-infinite programs and assessing robust feasibility of process systems using a rigorous model-based approach. This tool has the ability to determine, with mathematical certainty, whether or not a physical process system based on the proposed design will fail in the worst case by taking into account uncertainty in the model parameters and uncertainty in the environment.
Possible Reasons of Bias in Estimating the Cost of Living Index by the CPI

The consumer price index (CPI) tends to overestimate the cost of living because it uses a fixed basket of goods and services and does not take into account substitution of items when their relative prices change. On the other hand, to measure “pure” price changes statistical agencies attempt to adjust for quality improvements by disregarding price changes that are caused by improvements in quality. However, the consumer may not use or want the quality improvement. When adjusting for such quality improvements they will not impact the CPI, while the actual cost of living has increased. Hence, the CPI will tend to underestimate cost of living when adjusting for quality changes that are not used by the consumers. The paper discusses the CPI bias caused by inseparable property of item characteristics and suggests using direct price comparison method instead of comparing quality adjusted prices. Estimates of the upper-level substitution bias and the quality adjustment bias for Georgia are also provided in the paper. 1 The author thanks Tengiz Tsekvava for helpful comments and Khatuna Aptsiauri for contributions in data analysis. 2 The views expressed in the paper are those of the author and do not necessarily reflect views of the National Statistics Office of Georgia.
Multiobjective sensitivity analysis and optimization of distributed hydrologic model MOBIDIC

Abstract. Calibration of distributed hydrologic models usually involves how to deal with the large number of distributed parameters and optimization problems with multiple but often conflicting objectives that arise in a natural fashion. This study presents a multiobjective sensitivity and optimization approach to handle these problems for the MOBIDIC (MOdello di Bilancio Idrologico DIstribuito e Continuo) distributed hydrologic model, which combines two sensitivity analysis techniques (the Morris method and the state-dependent parameter (SDP) method) with multiobjective optimization (MOO) approach e-NSGAII (Non-dominated Sorting Genetic Algorithm-II). This approach was implemented to calibrate MOBIDIC with its application to the Davidson watershed, North Carolina, with three objective functions, i.e., the standardized root mean square error (SRMSE) of logarithmic transformed discharge, the water balance index, and the mean absolute error of the logarithmic transformed flow duration curve, and its results were compared with those of a single objective optimization (SOO) with the traditional Nelder–Mead simplex algorithm used in MOBIDIC by taking the objective function as the Euclidean norm of these three objectives. Results show that (1) the two sensitivity analysis techniques are effective and efficient for determining the sensitive processes and insensitive parameters: surface runoff and evaporation are very sensitive processes to all three objective functions, while groundwater recession and soil hydraulic conductivity are not sensitive and were excluded in the optimization. (2) Both MOO and SOO lead to acceptable simulations; e.g., for MOO, the average Nash–Sutcliffe value is 0.75 in the calibration period and 0.70 in the validation period. (3) Evaporation and surface runoff show similar importance for watershed water balance, while the contribution of baseflow can be ignored. (4) Compared to SOO, which was dependent on the initial starting location, MOO provides more insight into parameter sensitivity and the conflicting characteristics of these objective functions. Multiobjective sensitivity analysis and optimization provide an alternative way for future MOBIDIC modeling.
A Non‐Gaussian Ornstein–Uhlenbeck Process for Electricity Spot Price Modeling and Derivatives Pricing

A mean‐reverting model is proposed for the spot price dynamics of electricity which includes seasonality of the prices and spikes. The dynamics is a sum of non‐Gaussian Ornstein–Uhlenbeck processes with jump processes giving the normal variations and spike behaviour of the prices. The amplitude and frequency of jumps may be seasonally dependent. The proposed dynamics ensures that spot prices are positive, and that the dynamics is simple enough to allow for analytical pricing of electricity forward and futures contracts. Electricity forward and futures contracts have the distinctive feature of delivery over a period rather than at a fixed point in time, which leads to quite complicated expressions when using the more traditional multiplicative models for spot price dynamics. In a simulation example it is demonstrated that the model seems to be sufficiently flexible to capture the observed dynamics of electricity spot prices. The pricing of European call and put options written on electricity forward contracts is also discussed.
On-Demand Data Gathering with a Drone-Based Mobile Sink in Wireless Sensor Networks Exploiting Wake-Up Receivers

The introduction of a drone–based mobile sink into wireless sensor networks (WSNs), which has flexible mobility to move to each sensor node and gather data with a single–hop transmission, makes cumbersome multi–hop transmissions unnecessary, thereby facilitating data gathering from widely–spread sensor nodes. However, each sensor node spends significant amount of energy during their idle state where they wait for the mobile sink to come close to their vicinity for data gathering. In order to solve this problem, in this paper, we apply a wake–up receiver to each sensor node, which consumes much smaller power than the main radio used for data transmissions. The main radio interface is woken up only when the wake–up receiver attached to each node detects a wake–up signal transmitted by the mobile sink. For this mobile and on–demand data gathering, this paper proposes a route control framework that decides the mobility route for a drone–based mobile sink, considering the interactions between wake–up control and physical layer (PHY) and medium access control (MAC) layer operations. We investigate the optimality and effectiveness of the route obtained by the proposed framework with computer simulations. Furthermore, we present experimental results obtained with our test–bed of a WSN employing a drone–based mobile sink and wake– up receivers. All these results give us the insight on the role of wake–up receiver in mobile and on–demand sensing data gathering and its interactions with protocol/system designs. key words: wireless sensor networks, wake–up receiver, mobile sink, drone, experimental prototype and implementation
Atmospheric Deposition History of Trace Metals and Metalloids for the Last 200 Years Recorded by Three Peat Cores in Great Hinggan Mountain, Northeast China

A large number of studies on trace metals and metalloids (TMs) accumulations in peatlands have been reported in Europe and North America. Comparatively little information is available on peat chronological records of atmospheric TMs flux in China. Therefore, the objective of our study was to determine the concentrations and accumulation rates (ARs) of TMs in Motianling peatland from Great Hinggan Mountain, northeast China, and to assess these in relation to establish a historical profile of atmospheric metal emissions from anthropogenic sources. To meet these aims we analyzed 14 TMs (As, Ba, Cd, Co, Cr, Cu, Mo, Ni, Pb, Sr, Sb, Tl, and Zn) and Pb isotopes (206Pb, 207Pb, 208Pb) using ICP-AES and ICP-MS, respectively, in three peat sections dated by 210Pb and 137Cs techniques (approximately spanning the last 200 years). There is a general agreement in the elemental concentration profiles which suggests that all investigated elements were conserved in the Motianling bog. Three principal components were discriminated by principal componentanalysis (PCA) based on Eigen-values >1 and explaining 85% of the total variance of element concentrations: the first component representing Ba, Co, Cr, Mo, Ni, Sr and Tl reflected the lithogenic source; the second component covering As, Cu and Sb, and Cd is associated with an anthropogenic source from ore mining and processing; the third component (Pb isotope, Pb and Zn) is affected by anthropogenic Pb pollution from industrial manufacturing and fossil-fuel combustion. The pre-industrial background of typical pollution elements was estimated as the average concentrations of TMs in peat samples prior to 1830 AD and with a 207Pb/206Pb ratio close to 1.9. ARs and enrichment factors (EFs) of TMs suggested enhanced metal concentrations near the surface of the peatland (in peat layers dated from the 1980s) linked to an increasing trend since the 2000s. This pollution pattern is also fingerprinted by the Pb isotopic composition, even after the ban of leaded gasoline use in China. Emissions from coal and leaded gasoline combustions in northern China are regarded as one of the major sources of anthropogenic Pb input in this region; meanwhile, the long-distance transportation of Pb-bearing aerosols from Mongolia should be also taken into consideration. The reconstructed history of TMs’ pollution over the past ca. 200 years is in agreement with the industrial development in China and clearly illustrates the influence of human activities on local rural environments. This study shows the utility of taking multi-cores to show the heterogeneity in peat accumulation and applying PCA, EF and Pb isotope methods in multi-proxies analyses for establishing a high resolution geochemical metal record from peatland.
Evaluation of Feature Detectors for Infrared Imaging in view of Active Debris Removal

Active Debris Removal (ADR) is a type of rendezvous missions in which the target is uncooperative. Since there are more unknowns, importance of continuous measurement updates becomes clear where infrared cameras could fill this gap at low cost. Despite the theoretical advantages, the question still remains; can they practically work in ADR scenarios? In this paper, we focused on fundamentals of object detection, recognition and tracking "features" in the way they are influenced by the characteristics of these thermal-imaging systems and how the thermodynamics of the scene could affect their traceability. We evaluated some of the point and edge based feature extraction methods for their performances in ADR scenarios and to be used in a relative navigation sensor. Various spatial and frequency based feature detection algorithms were investigated and deeply evaluated in order to cover a wider perspective.
Pharmacognostical Potential of Capparis decidua Edgew.

Capparis decidua (Family, Capparidaceae) is commonly known as ‘Kair’. It is distributed throughout the arid regions of India and other countries. Kair is a caducous plant, so in the foliage condition mainly stem and fruits are common. It is also known as Capparis aphylla. Since the plant is xerophytic, it is generally found in dessert area and is highly draught resistant plant which can survive for long. Tribal people prepare pickle from the fruits of Kair. Some of the communities use Kair fruits as vegetables and food additives. The plant is used traditionally as anti-inflammatory, laxative, anti-diabetic, anthelmintic, antibacterial, astringent, digestive, diaphoretic and anodyne. It is reported to possess beneficial effects in various ailments, like rheumatism, asthma, diabetes, liver disorders, hypercholesterolemia, hypertension and microbial infections. Studies have revealed the presence of various phytoconstituents especially spermidine alkaloids, glucosinolates and other glycosides, β-sitosterol, rutin, l-stachydrine, hydrocarbons and terpenolides. The present review is an attempt to highlight the traditional uses, pharmacognostical, phytochemical and pharmacological reports on Capparis decidua.
Nonlinear spectroscopy of controllable many-body quantum systems

We establish a novel approach to probing spatially resolved multitime correlation functions of interacting many-body systems, with scalable experimental overheads. Specifically, designing nonlinear measurement protocols for multidimensional spectra in a chain of trapped ions with single-site addressability enables us, for example, to distinguish coherent from incoherent transport processes, to quantify potential anharmonicities, and to identify decoherence-free subspaces.
DICE-E: A Framework for Conducting Darknet Identification, Collection, Evaluation with Ethics

Society’s growing dependence on computers and information technologies has been matched by an escalation of the frequency and sophistication of cyber attacks committed by criminals operating from the Darknet. As a result, security researchers have taken an interest in scrutinizing the Darknet and other underground web communities to develop a better understanding of cybercriminals and emerging threats. However, many scholars lack the capability or expertise to operationalize Darknet research and are thus unable to contribute to this increasingly impactful body of literature. This article introduces a framework for guiding such research, called Darknet Identification, Collection, Evaluation, with Ethics (DICE-E). The DICE-E framework provides a focused reference point and detailed guidelines for scholars wishing to become active in the Darknet research stream. Four steps to conducting Darknet forum research are outlined: (1) identification of Darknet data sources, (2) data collection strategies, (3) evaluation of Darknet data, and (4) ethical concerns related to Darknet research. To illustrate how DICE-E can be utilized, an example empirical study is reported. This exemplar illustrates how DICE-E can guide scholars through key decision points when attempting to incorporate the Darknet within their research.
Effects of index selection on the performance and carcass composition of sheep given foods of different protein concentrations ad libitum

Sheep of a line selected on an index to increase carcass lean content at 150 days of age (selected (S); no. = 90), and an unselected control line (control (C); no. = 90), were given ad libitum foods of three different protein concentrations (192, 141 and 120 g/kg dry matter). Growth was measured from about 21 to 114 kg live weight. The carcasses of each line were analysed for lean, fat and bone at three widely varying weights in both males and females. Level of protein did not affect the extent to which S was superior to C in the content of fat (0·86 as much) or lean (1·08 as much) in the carcass. The fat concentration of the carcass increased, and the lean concentration decreased, as dietary protein concentration was reduced ( P < 0·01). On the highest level of protein used, the S line grew 1·17 times as fast and was 1·10 times as efficient compared with C. The extent to which growth rate in S exceeded that in C was greater on the highest level of protein used (92·3 g/day) than on the two lower protein diets (26·4 g/day). The difference of 65·9 (s.e. 18·4) g/day was significant ( P < 0·01). On the diet of highest protein concentration, growth was well described by a Gompertz function. The S line had an estimated maximum growth rate 1·25 times that of the C when averaged across males and females. A Spillman function was used to describe weight in terms of cumulative intake. It worked well for all three levels of dietary protein concentration. S sheep performed better than unselected sheep on foods differing in protein concentration and over a wide range of live weights, suggesting benefits are likely within the diverse farming environments found in practice.
Oxford Handbook of the Psychology of Religion & Spirituality

The state of theory in the psychology of religion and spirituality (PRS) is a study in contrasts. William James, the founder of the field, was one of the greatest theoretical psychologists of all time. His writings not only provide a fine legacy of thinking about religious experience, but also include many profound works on the nature of knowledge and human inquiry. Given this history, one would expect the field of PRS to have a rich body of discussions about the theoretical foundations of the discipline.
Sensor-based Continuous Arabic Sign Language Recognition

A Master of Science thesis in Computer Engineering by Noor Ali Tubaiz entitled, "Sensor-based Continuous Arabic Sign Language Recognition," submitted in June 2014. Thesis advisor is Dr. Tamer Shanableh and thesis co-advisor is Dr. Khaled Assaleh. Available are both soft and hard copies of the thesis.
Development of Methodology to Maintain Primary Cultures of Normal and Malignant Human Prostatic Epithelial Cells In Vivo

Abstract : Our objective is to develop a realistic preclinical model of prostate cancer by developing methodology that supports the survival, growth and differentiation of primary cultures of prostate cells in mice. During year I, we focused on the method of implantation and the implantation site as the most critical elements in achieving this goal. In light of increasing evidence that stem cells are the only cells in cancers that have potential for sustained self-renewal, we now believe that primary cultures in fact will never be capable of forming tumors, regardless of the method of implantation, unless they contain stem cells. Analysis of the primary cultures established by our routine protocols suggested that stem cells were not present. Therefore, we devoted year two of this project to developing new methodology to establish prostate cancer stem cells in primary culture. Key steps included defining a protocol to isolate single, viable cells that retained cell surface antigens from fresh human cancer specimens, isolating distinct subpopulations of single cells expressing putative stem cell antigens, and determining the culture conditions required for single cells to attach and proliferate. These achievements now allow us to return to our original objective of defining optimal in vivo conditions, but with primary cultures that have the requisite stem cells necessary for tumor formation
Gender Classification with Jointing Multiple Models for Occlusion Images

A facilitated and effective gender recognition approach is desirable for various applications, such as for intelligent surveillance systems, human-computer interactions, and consumer behavior analysis. Since the human face conveys clear sexual dimorphism, the use of facial features seems an intuitive way to recognize gender. This paper proposes an efficient gender classification method using multiple classifiers to overcome the occlusion problem. The experiment is tested via 5-fold cross validation on the FERET and AR databases to evaluate the performance. The results show the proposed approach achieves higher accuracy than previous methods.