Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of shearing on crystallization behavior of poly(ethylene terephthalate)

The shear-induced crystallization behavior of PET was investigated by measuring the time-dependent storage modulus (G′) and dynamic viscosity (η′) with a parallel-plate rheometer at different temperatures and shear rate. The morphology of shear-induced crystallized PET was measured by DSC, X-ray, and polarizing optical microscopy. When a constant shear rate was added to the molten polymer, the shear stress increased with the time as a result of the orientation of molecular chains. The induction time of crystallization is decreased with frequency. Moreover, the rate of isothermal crystallization of PET was notably decreased with increasing temperature. The shape of spherulites is changed to ellipsoid in the direction of shear. In addition, aggregation of spherulites is increased with increasing frequency. Particularly, the row nucleation morphology could be observed under polarized light for ω = 1. From the results of DSC, the melting point and enthalpy have a tendency to decrease slightly with increasing frequency. The crystallite size and perfectness decreased with frequency, which was confirmed with X-ray data. The unit length of the crystallographic unit cell of the PET increased and the (1 0 3) plane peak increased with increasing frequency. © 2001 John Wiley & Sons, Inc. J Appl Polym Sci 80: 2640–2646, 2001
Optimal selection of sink nodes in wireless sensor networks in adversarial environments

In this paper, we address the problem of assigning the sink role to a subset of nodes in a wireless sensor network with a given topology such that the resulting network configuration is robust against denial-of-service type attacks such as node destruction, battery exhaustion and jamming. In order to measure robustness, we introduce new metrics based on a notion defined in [1]. We argue that our metrics are more appropriate to measure the robustness of network configurations than the widely known connectivity based metrics. We formalize the problem of selecting the sink nodes as an optimization problem aiming at minimizing the deployment budget while achieving a certain level of robustness.We propose an efficient greedy heuristic algorithm that approximates the optimal solution reasonably well.
Investigation on Indoor Air Quality of Public Sites in Tainan Area

This study selected 21 representative public sites for the survey of indoor air quality in Tainan area, including hospital, school, fitness center, government office, library, theater, transport station, and supermarket. Indoor air quality was first assessed by direct detection apparatus, including CO2, CO, HCHO, TVOCs, bacteria, fungi, PM10, PM2.5, O3 and temperature. Based on the results of walk-through detection, the spatial distribution of indoor air contaminants was further measured in a 24 hour period by the EPA standard method. The detailed measurements illustrated HCHO, O3, PM10 and PM2.5 concentrations are lower than the suggested threshold levels in all public sites. CO2 concentrations at hospitals and school are all exceeding category 1 threshold of 600 ppm. Bacteria exceeding the suggested threshold of 500 CFU/m3 for category 1 and 1000 CFU/m3 for category 2 is popular at most public sites. One fitness center was found exceeding the TVOCs threshold of 3 ppm, and outdoor air can be a potential source. The high levels of CO2 and bacteria were a common indoor air quality problem, and the regulated strategy of crowd control and air conditioning management was required for a healthy indoor environment.
Semiparametric Multivariate Density Estimation for Positive Data Using Copulas

The estimation of density functions for positive multivariate data is discussed. The proposed approach is semiparametric. The estimator combines gamma kernels or local linear kernels, also called boundary kernels, for the estimation of the marginal densities with parametric copulas to model the dependence. This semiparametric approach is robust both to the well-known boundary bias problem and the curse of dimensionality problem. Mean integrated squared error properties, including the rate of convergence, the uniform strong consistency and the asymptotic normality are derived. A simulation study investigates the finite sample performance of the estimator. The proposed estimator performs very well, also for data without boundary bias problems. For bandwidths choice in practice, the univariate least squares cross validation method for the bandwidth of the marginal density estimators is investigated. Applications in the field of finance are provided.
Hybrid GA-BF based intelligent PID active queue management control design for TCP network

Active queue management (AQM) is a key congestion control scheme for reducing packet loss and improving network utilization in TCP/IP networks. This paper proposes a hybrid intelligent proportional-integral-derivative (PID) controller as an active queue manager for Internet routers. This paper deals with hybrid system (GA-BF) based on the conventional GA (Genetic Algorithm) and BF (Bacterial Foraging) which is social foraging behavior of bacteria. A hybrid (GA-BF) is employed to derive optimal or near optimal PID controller gains such that an objective function of step response characteristic's parameter is minimized, and thereby a stable queue length, low packet loss, and high link utilization for TCP networks are guaranteed. The performance of the proposed control scheme is evaluated in various network scenarios via a series of numerical simulations.
Enhancing Performance Sustainability of Fault Tolerant Routing Algorithms in NoC-Based Architectures

Reliability of embedded systems and devices is becoming a challenge with technology scaling. To deal with the reliability issues, fault tolerant solutions are needed. The design paradigm for future System-on-Chip (SoC) implementation is Network-on-Chip (NoC). Fault tolerance in NoC can be achieved at many abstraction levels. Many fault tolerant architectures and routing algorithms have already been proposed for NoC but the utilization of resources, affected indirectly by faults is yet to be addressed. In this paper, we propose a NoC architecture, which sustains the overall system performance by utilizing resources, which cannot be used by other architectures under faults. An approach towards a proper virtual-channel (VC) sharing strategy is proposed, based on communication bandwidth requirements. The technique can be applied to any NoC architecture, including 3-D NoCs. Extensive quantitative experiments with synthetic benchmarks, including uniform, transpose and negative exponential distribution (NED), demonstrate considerable improvement in terms of performance sustainability under faulty conditions compared to existing VC-based NoC architectures.
Minimal Conductance-Based Model of Auditory Coincidence Detector Neurons

Sound localization is a fundamental sensory function of a wide variety of animals. The interaural time difference (ITD), an important cue for sound localization, is computed in the auditory brainstem. In our previous modeling study, we introduced a two-compartment Hodgkin-Huxley type model to investigate how cellular and synaptic specializations may contribute to precise ITD computation of the barn owl's auditory coincidence detector neuron. Although our model successfully reproduced fundamental physiological properties observed in vivo, it was unsuitable for mathematical analyses and large scale simulations because of a number of nonlinear variables. In the present study, we reduce our former model into three types of conductance-based integrate-and-fire (IF) models. We test their electrophysiological properties using data from published in vivo and in vitro studies. Their robustness to parameter changes and computational efficiencies are also examined. Our numerical results suggest that the single-compartment active IF model is superior to other reduced models in terms of physiological reproducibility and computational performance. This model will allow future theoretical studies that use more rigorous mathematical analysis and network simulations.
Pooling Faces: Template Based Face Recognition with Pooled Face Images

We propose a novel approach to template based face recognition. Our dual goal is to both increase recognition accuracy and reduce the computational and storage costs of template matching. To do this, we leverage on an approach which was proven effective in many other domains, but, to our knowledge, never fully explored for face images: average pooling of face photos. We show how (and why! ) the space of a template's images can be partitioned and then pooled based on image quality and head pose and the effect this has on accuracy and template size. We perform extensive tests on the IJB-A and Janus CS2 template based face identification and verification benchmarks. These show that not only does our approach outperform published state of the art despite requiring far fewer cross template comparisons, but also, surprisingly, that image pooling performs on par with deep feature pooling.
Nano Fluids for Improving Efficiency in Wind Turbine Cooling System

In order to reduce the entry of moisture, salt, sand and other external contaminations into the nacelle and also to reduce the fan noise which reaches the exterior, in this work a study of an innovative cooling system for off-shore wind turbine has been carried out. The new cooling technique is based on the use of nanofluids (engineered colloidal suspensions of nanoparticles in a base fluid). Nanofluids allow to increase the thermal conductivity of fluids and so to reduce the heat exchange surface and the heat transfer fluid flow rate due to the increased heat capacity. To reduce the amount of nanofluids circulating in the cooling system, the performance of a two-stage cooling circuit has been investigated. The first circuit takes the heat out of the generator and of the accessories whereas the second circuit, coupled with the first via an heat exchanger, dissipates the heat into the ambient. For the second circuit two options have been investigated. In the first solution the waste heat is dispersed using the tower as dissipator whereas in the second option the waste heat is exchanged with a titanium heat exchanger using marine water as heat transfer fluid.Both solutions assure high efficiency of heat exchange, long technical life expectancy and limited maintenance requirements.Keywords: Wind turbine, nanofluid, cooling system.
Morphology of planetary nebulae with binary cores - The effect of gravitational focusing by the companion to the mass-losing star

We present hydrodynamical models of planetary nebulae with detached binary cores. The models are obtained according to the interacting winds scenario. Slow AGB wind models are produced by means of 3-D SPH simulations. The SPH results provide initial conditions for the evolution of a fast and rarefied wind injected into the AGB wind. In close binaries the density distribution of the slow wind is significantly modified by the gravity of the secondary, resulting in an enhanced density region close to the orbital plane of the system, and low density regions elongated perpendicularly to the orbital plane (we refer to those effects as gravitational focusing ). The fast wind propagating through such a medium naturally develops a bipolar structure. In wider binaries the effect of gravitational focusing is weaker, and elliptical nebulae are produced instead of bipolar ones. In binaries wider than ~10 AU the effect becomes unimportant, and the resulting nebulae are almost entirely spherical apart from local corrugations caused by hydrodynamical instabilities. Thus, gravitational focusing alone is capable of generating a broad range of morphologies (from nearly spherical to strongly bipolar). The results are discussed in relation to nebulae associated with symbiotic stars.
Study of creep–fatigue behavior in a 1000 MW rotor using a unified viscoplastic constitutive model with damage

A unified viscoplastic constitutive model with damage is presented to describe creep–fatigue deformation behavior. Creep and fatigue tests under different temperature and loading conditions were conducted to validate the material model. Good agreement was achieved between the simulated and measured values. The material model was further applied to a specific part of a steam turbine rotor for numeric investigation of the creep–fatigue behavior. The multiaxial mechanical behavior of the rotor was studied in detail in terms of temperature, von Mises stress, equivalent strain, accumulated plastic strain, and damage. The results illustrate that the stress is concentrated on the notch of the groove. In addition, although the accumulation of damage at the rotor locations is dominated by creep behavior, the results actually disclose that the combination of creep and fatigue deformation behavior further accelerate the damage.
Mathematical models of anisothermal phase transformations in steels, and predicted plastic behaviour

Abstract n this paper, the kinetics of anisothermal structural transformations are first discussed. Classical models are shown to raise difficulties because they do not take into account the existence of a temperature–dependent equilibrium proportion of the phases. A very simple model incorporating this factor is proposed and shown to lead to a good correlation between experimental and theoretical results it is also shown to be compatible with the thermodynamics of irreversible processes. The model can be extended to include the possibility of isothermal kinetics of the Johnson–Mehl type, as well as the influence of austenite grain size. The influence of the transformations on plastic behaviour (transformation plasticity) is next discussed. A simple model is described using first purely heuristic considerations, then a more microscopic and physical approach. Possible improvements to this model are suggested, especially the inclusion of the fact that the microscopic plasticity generated by volume differenc.
Brothers in Arms: Cooperation in Defence

In experiments, people behave more cooperatively when they are aware of an external threat, while in the field, we observe surprisingly high levels of cooperation and altruism within groups in conflict situations such as civil wars. We provide an explanation for these phenomena. We introduce a model in which different groups vary in their willingness to help each other against external attackers. Attackers infer the cooperativeness of a group from its members' behaviour under attack, and may be deterred by a group which bands together against an initial attack. Then, even self-interested individuals may behave cooperatively when threatened, so as to mimic more cooperative groups. By doing so, they drive away attackers and increase their own future security. We argue that a group's reputation is a public good with a natural weakest-link structure. We test the implications of our model in a laboratory experiment.
SOS4, A Pyridoxal Kinase Gene, Is Required for Root Hair Development in Arabidopsis1

Root hair development in plants is controlled by many genetic, hormonal, and environmental factors. A number of genes have been shown to be important for root hair formation. Arabidopsissalt overly sensitive 4 mutants were originally identified by screening for NaCl-hypersensitive growth. TheSOS4 (Salt Overly Sensitive 4) gene was recently isolated by map-based cloning and shown to encode a pyridoxal (PL) kinase involved in the production of PL-5-phosphate, which is an important cofactor for various enzymes and a ligand for certain ion transporters. The root growth of sos4 mutants is slower than that of the wild type. Microscopic observations revealed thatsos4 mutants do not have root hairs in the maturation zone. The sos4 mutations block the initiation of most root hairs, and impair the tip growth of those that are initiated. The root hairless phenotype of sos4 mutants was complemented by the wild-type SOS4 gene. SOS4promoter-β-glucuronidase analysis showed thatSOS4 is expressed in the root hair and other hair-like structures. Consistent with SOS4 function as a PL kinase, in vitro application of pyridoxine and pyridoxamine, but not PL, partially rescued the root hair defect in sos4 mutants. 1-Aminocyclopropane-1-carboxylic acid and 2,4-dichlorophenoxyacetic acid treatments promoted root hair formation in both wild-type andsos4 plants, indicating that geneticallySOS4 functions upstream of ethylene and auxin in root hair development. The possible role of SOS4 in ethylene and auxin biosynthesis is discussed.
Modellig of adrenaline counterregulatory action during hypoglycaemia in Type 1 Diabetes

The risk of hypoglycaemia is still a limiting factor for achieving near-normoglycaemia in subjects with Type 1 Diabetes. From the metabolic perspective, counterregulation has a significant influence in plasma glucose concentration during hypoglycaemia. In Type 1 Diabetes, adrenaline is the first line of counterregulatory hormone response to hypoglycaemia due to the early impairment of glucagon secretion. In a previous work, an adrenaline secretion model was developed. Here, adrenaline counterregulatory action is addressed by means of an extension of the well-known Bergman Minimal Model. The added terms were based on both adrenaline concentration after counterregulatory response begins and the influence of hypoglycaemic levels of glucose in blood. Individual parameters were estimated using data from a eu-hypoglycaemic clamp study by means of global optimization algorithms, in particular CMA-ES. Analysis of plasma glucose prediction accuracy demonstrated the importance of considering counterregulation in predictive algorithms when hypoglycaemic events occur.
Effects of duplicated objectives in many-objective optimization problems on the search behavior of hypervolume-based evolutionary algorithms

A number of evolutionary multiobjective optimization (EMO) algorithms have already been proposed and applied to various problems. Each EMO algorithm has a different search behavior on a different problem. It is important to understand characteristics of the search behavior of each EMO algorithm when we choose an appropriate EMO algorithm for a particular multiobjective problem. However, the search behavior on many-objective optimization problems has not been well studied yet. In this paper, we examine how the existence of duplicated objectives affects the search behavior of SMS-EMOA, which is a well-known hypervolume-based EMO algorithm with high search ability. First we explain that hypervolume calculation depends on the choice of a reference point and the normalization of each objective. Next we illustrate the effect of duplicated objectives on the hypervolume calculation. Then we discuss the effect of these three factors (i.e., reference point, objective value normalization, and duplicated objectives) on the search behavior of SMS-EMOA. It is shown through computational experiments that the existence of duplicated objectives biases the multiobjective search by SMS-EMOA towards a part of the Pareto front with good objective values of the duplicated objectives.
Quadratic 0 – 1 optimization using separable underestimators

Binary programs with a quadratic objective function are NP-hard in general, even if the linear optimization problem over the same feasible set is tractable. In this paper, we address such problems by computing quadratic global underestimators of the objective function that are separable but not necessarily convex. Exploiting the binarity constraint on the variables, a minimizer of the separable underestimator over the feasible set can be computed by solving an appropriate linear minimization problem over the same feasible set. Embedding the resulting lower bounds into a branch-and-bound framework, we obtain an exact algorithm for the original quadratic binary program. The main practical challenge is the fast computation of an appropriate underestimator, which in our approach reduces to solving a series of semidefinite programs. We exploit the special structure of the resulting problems and propose a tailored coordinate-descent method for their solution. Our extensive experimental results on various quadratic combinatorial optimization problems show that our approach outperforms both Cplex and the related QCR method. For the quadratic shortest path problem, we thus provide the fastest exact approach currently available.
Advanced thermal insulation for energy efficient buildings : structural performance of aerogel composite panels

Aerogels are well known as exceptional thermal insulators. Thermal conductivities of 9 to 10 mW/m.K have been achieved at atmospheric pressure, and a moderate vacuum (between 1/3 and 1/10 of an atmosphere) can lower this number even further, to around 5 mW/m.K. However aerogels for insulation purposes are not widespread on the market. One of the major shortcomings of aerogels that prevent them from being more widely used is their extreme mechanical weakness and brittleness. In this thesis, we characterize and explain these low mechanical properties. We then propose a composite structure for a rigid aerogel panel, a sandwich panel with a truss core filled with monolithic aerogel. Mechanical and thermal properties are derived and partially tested for the truss cores. Several designs are studied and mechanical properties are derived in order to compare these designs. Some criteria for an efficient designs are given. Finally, we describe a first attempt to manufacture one of these composite structures on a small scale. Thesis Supervisor: Lorna J. Gibson Title: Matoula S. Salapatas Professor of Material Sciences and Engineering
Comparative Analysis of Multidimensional, Quantitative Data

When analyzing multidimensional, quantitative data, the comparison of two or more groups of dimensions is a common task. Typical sources of such data are experiments in biology, physics or engineering, which are conducted in different configurations and use replicates to ensure statistically significant results. One common way to analyze this data is to filter it using statistical methods and then run clustering algorithms to group similar values. The clustering results can be visualized using heat maps, which show differences between groups as changes in color. However, in cases where groups of dimensions have an a priori meaning, it is not desirable to cluster all dimensions combined, since a clustering algorithm can fragment continuous blocks of records. Furthermore, identifying relevant elements in heat maps becomes more difficult as the number of dimensions increases. To aid in such situations, we have developed Matchmaker, a visualization technique that allows researchers to arbitrarily arrange and compare multiple groups of dimensions at the same time. We create separate groups of dimensions which can be clustered individually, and place them in an arrangement of heat maps reminiscent of parallel coordinates. To identify relations, we render bundled curves and ribbons between related records in different groups. We then allow interactive drill-downs using enlarged detail views of the data, which enable in-depth comparisons of clusters between groups. To reduce visual clutter, we minimize crossings between the views. This paper concludes with two case studies. The first demonstrates the value of our technique for the comparison of clustering algorithms. In the second, biologists use our system to investigate why certain strains of mice develop liver disease while others remain healthy, informally showing the efficacy of our system when analyzing multidimensional data containing distinct groups of dimensions.