Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison among Manual and Automatic Calibration Methods in VISSIM in an Expressway (Chihuahua, Mexico)

Traffic microsimulation is an essential tool in urban transportation and road planning. Its calibration is essential to attain representative results validated with real-world conditions. VISSIM (Verkehr in Stadten—SIMulationsmodell) operates with the Wiedemann’s psycho-physical car-following model for freeway travel that considers safety distances (standstill and movement) during simulation. Calibration in this paper was achieved by using two different approaches: a) manual and b) genetic algorithm (with the GEH statistic formula) calibration techniques. Calibration and validation of this model were performed at the Periferico de la Juventud expressway in Chihuahua City, in northern Mexico. The Periferico de la Juventud (PDJ) has a N-S orientation and a length of ca. 20 km, with its northern section being its most congested portion. Its highest vehicle volume occurs at noon, with 3700 vehicles per hour, with 95% being passenger cars and the other 5% heavy goods vehicles. PDJ’s speed limit is 70 km·h-1, but the driver’s behavior has a tendency towards the aggressive performance. A total of 82 standstill and 82 look-ahead distances were obtained from unmanned aerial vehicles (UAV) images, with values ranging from 0.8 to 4.7 m and from 0.2 to 28 m, respectively. VISSIM calibrated parameter values were calculated for this expressway, being slightly above than the VISSIM default ones; and was validated with travel times and look-ahead distances. Results contribute information for the city’s future installment of public transportation systems, and should help decision makers deal with future urban planning.
Amazon EC2 Spot Price Prediction Using Regression Random Forests

Spot instances were introduced by Amazon EC2 in December 2009 to sell its spare capacity through auction based market mechanism. Despite its extremely low prices, cloud spot market has low utilization. Spot pricing being dynamic, spot instances are prone to out-of bid failure. Bidding complexity is another reason why users today still fear using spot instances. This work aims to present Regression Random Forests (RRFs) model to predict one-week-ahead and one-day-ahead spot prices. The prediction would assist cloud users to plan in advance when to acquire spot instances, estimate execution costs, and also assist them in bid decision making to minimize execution costs and out-of-bid failure probability. Simulations with 12 months real Amazon EC2 spot history traces to forecast future spot prices show the effectiveness of the proposed technique. Comparison of RRFs based spot price forecasts with existing non-parametric machine learning models reveal that RRFs based forecast accuracy outperforms other models. We measure predictive accuracy using MAPE, MCPE, OOB Error and speed. Evaluation results show that <inline-formula><tex-math notation="LaTeX">$MAPE < = 10\% \ $</tex-math><alternatives><mml:math><mml:mrow><mml:mi>M</mml:mi><mml:mi>A</mml:mi><mml:mi>P</mml:mi><mml:mi>E</mml:mi><mml:mo><</mml:mo><mml:mo>=</mml:mo><mml:mn>10</mml:mn><mml:mo>%</mml:mo><mml:mspace width="4pt"/></mml:mrow></mml:math><inline-graphic xlink:href="khandelwal-ieq1-2780159.gif"/></alternatives></inline-formula> for 66 to 92 percent and <inline-formula><tex-math notation="LaTeX">$MCPE < = 15\% \ $</tex-math><alternatives><mml:math><mml:mrow><mml:mi>M</mml:mi><mml:mi>C</mml:mi><mml:mi>P</mml:mi><mml:mi>E</mml:mi><mml:mo><</mml:mo><mml:mo>=</mml:mo><mml:mn>15</mml:mn><mml:mo>%</mml:mo><mml:mspace width="4pt"/></mml:mrow></mml:math><inline-graphic xlink:href="khandelwal-ieq2-2780159.gif"/></alternatives></inline-formula> for 35 to 81 percent of one-day-ahead predictions with prediction time less than one second. <inline-formula><tex-math notation="LaTeX">$MAPE < = 15\% \ $</tex-math><alternatives><mml:math><mml:mrow><mml:mi>M</mml:mi><mml:mi>A</mml:mi><mml:mi>P</mml:mi><mml:mi>E</mml:mi><mml:mo><</mml:mo><mml:mo>=</mml:mo><mml:mn>15</mml:mn><mml:mo>%</mml:mo><mml:mspace width="4pt"/></mml:mrow></mml:math><inline-graphic xlink:href="khandelwal-ieq3-2780159.gif"/></alternatives></inline-formula> for 71 to 96 percent of one-week-ahead predictions.
Prototyping learning and congruence in new realities

The educational system in the industrialized 20th Century, a monolithic delivery model, prepared students for a hierarchical livelihood in either blue collar or white collar worlds. Today, a different landscape is predicted for the workforce. And it is rapidly changing and advancing. Will Richardson points out that by 2020 more than half the US workforce will be “freelancers, consultants and independent workers” (Richardson, 2012). While forecasts and predictions vary, continuing studies support this workforce evolution. The continued revolution in digital technology is pervasive today with mobile devices and the Internet providing an abundance of information, knowledge and opportunity with the potential for a student customized learning experience. Anticipating this shift, Richardson recognizes the need for students to master learning instead of content as is assessed currently. This situates well with John Seely Brown describing “agency” as active participation, creating and building as a principal attribute of a student in this new educational model while inscribing “empathy” as a second requisite quality (Brown, 2013). A new strategy for learning, understanding and doing is required that encourages agency with individuals actively experiencing new technologies and realities for creating and communicating that support deeper experiences and shift perspectives in ways not possible before. This is required to imbue discovery, creativity and new craft toward the most appropriately designed solutions in a highly technological and evermore complex world. Universal Constructs, with new tools for seeing and making, become the framework to weave design thinking, STEM and 21st Century Skills together holistically to better define the potentials for learning, understanding and doing. A pilot program called the Forward Learning Experience (FLEx) was launched in 2014 with the intention to introduce the framework above to students today. As of July 2017, the FLEx has reached almost 45,000 constituents of Iowa, primarily K-12 students, and has undergone initial reviews with positive results showing its potential capacities toward a new educational and learning model. Leading students with forward looking experiences, strategies and frameworks through the FLEx or similar opportunities to augment core skills through emergent technologies for seeing
Thermodynamics and kinetics of methane hydrate formation in nanoporous media : theory and molecular simulation

Methane hydrate is a non-stoichiometric crystal in which water molecules form hydrogen-bonded cages that entrap methane molecules. Abundant methane hydrate resources can be found on Earth, especially trapped in mineral porous rocks (e.g., clay, permafrost, seafloor, etc.). For this reason, understanding the thermodynamics and formation kinetics of methane hydrate confined in porous media is receiving a great deal of attention. In this thesis, we combine computer modeling and theoretical approaches to determine the thermodynamics and formation kinetics of methane hydrate confined in porous media. First, the state-of-the-art on the thermodynamics and formation kinetics of methane hydrate is presented. Second, different molecular simulation strategies, including free energy calculations using the Einstein molecule approach, the direct coexistence method, and the hyperparallel tempering technique, are used to assess the phase stability of bulk methane hydrate at various temperatures and pressures. Third, among these strategies, the direct coexistence method is chosen to determine the shift in melting point upon confinement in pores, $Delta T_m = T_{m}^{pore} - T_{m}^{bulk}$ where $T_m^{pore}$ and $T_m^{bulk}$ are the melting temperatures of bulk and confined methane hydrate. We found that confinement decreases the melting temperature, $T_m^{pore}<T_m^{bulk}$. The shift in melting temperature using the direct coexistence method is consistent with the Gibbs-Thompson equation which predicts that the shift in melting temperature linearly depends on the reciprocal of pore width, i.e., $Delta T_m/T_m^{bulk} sim k_{GB}/D_p$. The quantitative validity of this classical thermodynamic equation to describe such confinement and surface effects is also addressed. The surface tensions of methane hydrate-substrate and liquid water-substrate interfaces are determined using molecular dynamics to quantitatively validate the Gibbs-Thompson equation. Molecular dynamics simulations are also performed to determine important thermodynamic properties of bulk and confined methane hydrate: (a) thermal conductivity $lambda$ using the Green-Kubo formalism and the autocorrelation function of the heat-flux and (b) the thermal expansion $alpha_P$ and isothermal compressibility $kappa_T$. Finally, some conclusions and perspectives for future work are given.
Fatigue Analysis for Void Repair of Cement Concrete Pavement with Under Slab by Polymer Grouting

After the appearing of voids beneath cement concrete slabs, the pavement loses a continuous and uniform lower support structure, and the stress state of the road panel is extremely unfavorable. The polymer grouting repair is timesaving, efficient and pollution-free. In order to verify the performance improvement and fatigue damage evolution of cement concrete pavement before and after grouting repair, a material damage constitutive model was established. The UMAT subprogram was introduced into the finite element software ABAQUS to analyze the structure under the action of moving cyclic loading, stress response and fatigue damage evolution process before and after regional grouting repair. The results show that the Mises stress and vertical displacement of the grouting repairing slab are very close to the normal state, which indicates that the grouting repair has a prominent influence on the bottom void of the slab. With the rise of loading time, the fatigue damage of the pavement structure is increasing, but the trend is gradually reduced, and the number of load times and the degree of fatigue damage are nonlinear. From the long-term cyclic loading and comprehensive analysis of the construction period, the polymer grouting repair is better than cementitious grout.
RELIABLE ACOUSTIC LINK USING NON-COHERENT TURBO-CODED FSK

The contents of this thesis explore the challenge of implementing a reliable, wireless, communication link in the highly variable environments of shallow underwater channels. The physical variability of the underwater acoustic channel is described with respect to signal attenuation, acoustic wave reflection and refraction, and temporal variation due to motion. These characteristics are implemented in a software model to represent a realistic ocean environment for in-lab algorithm design. The process for designing simple, non-coherent communication algorithms (32-FSK and 16-parallel BFSK) to achieve robust performances are discussed. Robustness is achieved through the implementation of turbo code. The results of the communication algorithms demonstrate successful communication for distances up to 10 km validated through sea trial, but yielded low-data rates (32 bps to 1.67 bps). Zero errors were received during the sea trial for the turbo coded algorithms. To confidently quantify the reliability of these algorithms, more symbols must be transmitted.
Mathematical Modeling of Battery and Ultra Capacitor for Photo Voltaic System

In this paper the scientific displaying of battery and ultra capacitor is performed for solar photovoltaic system. The expansion of the ultra capacitor bank displays the requirement for a philosophy to upgrade the photovoltaic system to avoid abundance battery stockpiling. This work traces the strategy used to streamline the blend of photovoltaic boards, batteries, and ultra capacitors for a given solar radiation and load profile. Lessening supplies of petroleum derivative, top oil and the natural effect of non-renewable energy sources on the earth has energized a development in manageable energies, for example, wind and solar power.
Statistical analysis of second repair welding on dissimilar material using Taguchi method

This paper presents an experimental investigation on effect of welding parameter to repair welding of dissimilar material using Gas Metal Arc Welding (GMAW) machine. Taguchi Method has been used to analyse the statistical analysis by using Minitab Software. Repair welding getting more popular nowadays due to saving cost and prolong the material service life. The effect of repair welding of dissimilar material on mechanical properties have been studied. Statistical analysis using Taguchi Method by Minitab Software with three times repetition of L9 orthogonal array. Setting up GMAW machine with current (150A-170A), voltage (17V-21V) and jig rotational speed (50rpm-100rpm) were prepared with 3.00mm of Stainless Steel 304L and 3.60mm Carbon Steel 1387.Result shows that voltage gave a huge impact to tensile testing and micro hardness of second repair welding. While current and jig rotational speed gave less impact to responses. The benefit shown have caused repair welding to be accepted and applied in the industry. Regular and total maintenance is able to contribute to the savings while producing good preventive maintenance schedule.
Particle-based perception of garment folding for robotic manipulation purposes

The research focuses on the development of a robust home service robot that is capable of doing multiple types of household chores. This work considers the problem of garment perception and folding procedure by a home service robot, focusing on the task of recognizing a piece of spread-out garment on a flat platform. The robotic platform setup is as general as possible to enable the robot to cater for multiple types of household chores. We propose a novel approach to understand the perception for a piece of garment using particle-based polygonal model and an algorithm to best-fit the model into the garment in an image directly rather than designing the template from a software. The simplest way to analyze the basic configuration of a piece of unknown garment is to spread out the garment on a flat platform. There must be contrast between the background and the garment color to enable proper recognition process. At the moment, the garment is aligned at a certain orientation for simplicity. We defined the particle-based polygonal model for three garment categories: towel, shirt, and trousers. Each category has its own model and parameters. We presume a garment consists of at least one main body and other supplementary parts, for example, collar or sleeve, if they exist. Hence, we consider towel consists of only one main body, whereas shirt and trousers consist of one main body and two supplementary parts. Centroid and contour for each part of garment are measured after the garment is discretized. By matching the particle-based model into its discretized contour and generate the combined model, the parameters for each category of garment can be estimated. Once the parameters of garment are obtained from the proposed model, the folding procedure is then determined. Practical garment folding using different types of garments were executed to evaluate the proposed approach. Based on the experimental results, the proposed particle-based polygonal models for the three types of garments have been successful, where the parameters can be estimated, which provides important decision on the folding algorithm. Using our approach, different types and sizes of garment can be robustly handled by a home service robot.
Inputs and outputs in the process specification language

Inputs and outputs are ubiquitous in flow modeling, including popular programming languages. This paper examines how inputs and outputs can be formalized in the Process Specification Language (PSL) to reduce ambiguity and increase expressiveness compared to conventional flow modeling representations. Inputs and outputs are shown to be early design stage notions independent of existing PSL concepts, preconditions and postconditions in particular. The paper defines axioms for input and output, and constraints on existing PSL concepts. Some of these relate early and late stage design, while others provide for multiple views of inputs and outputs. It also identifies which aspects of input are metatheoretic and consequently outside the scope of PSL.
Balanced Deep Supervised Hashing

: Recently, Convolutional Neural Network (CNN) based hashing method has achieved its promising performance for image retrieval task. However, tackling the discrepancy between quantization error minimization and discriminability maximization of network outputs simultaneously still remains unsolved. Motivated by the concern, we propose a novel Balanced Deep Supervised Hashing (BDSH) based on variant posterior probability to learn compact discriminability-preserving binary code for large scale image data. Distinguished from the previous works, BDSH can search an equilibrium point within the discrepancy. Towards the goal, a delicate objective function is utilized to maximize the discriminability of the output space with the variant posterior probability of the pair-wise label. A quantization regularizer is utilized as a relaxation from real-value outputs to the desired discrete values (e.g., -1/+1). Extensive experiments on the benchmark datasets show that our method can yield state-of-the-art image retrieval performance from various perspectives.
CALIBRATION OF THE HYSTERESIS LOOP OF SMA WIRES HEATED BY ELECTRIC CURRENT

The undesirable vibrations in all structures types (buildings, vehicles, machines and electronics) affect the durability, performance and the control of these structures. Damping of these vibrations is the concern of many researches using smart materials. The super-elastic behavior of shape memory alloys (SMA) may be utilized in the active damping of structures vibrations. SMA wire was obtained for this research but we had to calibrate it to get loadelongation-heat relations. In the process of SMA calibrating, an experimental device was designed to measure different loadings and the corresponding elongations at different heating conditions. To control the temperature an electric direct current was used. The obtained experimental results were similar to the results in several published researches. The relation between the electric current and the crystal transformation of SMA under different loadings is well defined. Clear stress-strain curves showing the hysteresis loop at different current values were obtained.
Optimized ultrasonic‐assisted extraction of papaya seed oil from Hainan/Eksotika variety

Abstract Hainan/Eksotika papaya is a popular cultivated plant in Hainan Island, China. Papaya seed oil (PSO) contains functional compounds with good antioxidant activity, especially monounsaturated fatty acids. In this work, the ultrasound‐assisted extraction (UAE) of PSO was optimized using response surface methodology. It was found that the optimal extraction performance was realized when the elevated time was set to 20 min, the ultrasound power was set to 250 W, and the n‐hexane‐to‐sample ratio was set to 16:1 (v/w). The highest yield of PSO (32.27%) was obtained under the optimal conditions, and PSO showed good oxidative stability. Differential scanning calorimetry analysis showed that the melting point of Hainan/Eksotika PSO was low, while its crystallization temperature was high. FTIR and NMR were used to analyze the chemical structure of PSO, which also proved that PSO possessed good stability without oxidative degradation. In addition, scanning electron micrograph was employed to investigate the change in seed microscopic structure. The results showed UAE caused serious structural damage of sample cell membranes and walls, which help oil access to the solvent with a high extraction ratio. The results indicated that UAE is an efficient environmental‐friendly, and promising technique could be applied to produce PSO or other edible oil with a better health‐beneficial value in food industry.
EFFECT OF SCREEN MESH WICK AND AL2O3 NANOFLUID CONCENTRATION ON CIRCULAR HEAT PIPE PERFORMANCE

In this paper the experimental study of the behavior of screen wick and nanofluid to improve the performance of a circular heat pipe. Pure water and Al2O3-water based nanofluid are used as working fluids. An experimental setup is designed and constructed to study the heat pipe performance under different operating conditions. The effect of filling ratio, volume fraction of nano-particle in the base fluid, screen mesh as a wick and heat input rate on the thermal resistance is investigated. Total thermal resistance of the heat pipe for pure water and Al2O3-water based nanofluid is also predicted. An experimental correlation is obtained to predict the influence of Prandtl number and dimensionless heat transfer rate, Kq on thermal resistance. Thermal resistance decreases with increasing Al2O3-water based nanofluid compared to that of pure water. The experimental data is compared to the available data from previous work. The range of operating parameter are filling ratio 40% to 100%, nanofluid concentration from 0.0% to 2.0%, and heat rate from 8 to 32 watt. The agreement is found to be fairly good.
Nano-structured Cellulose as Green Adsorbents for Water Purification: A Mini Review

Celluloses are natural polysaccharides that have garnered attentions in recent years due to their sustainability, availability and notable applications in various fields. Whilst demand of clean water sources keep increasing, modified nano-structured cellulose derived from agricultural waste showed a good prospect in adsorbing pollutants from water. To date, large number of studies have reported the performance of nanocellulose in removing wide range of pollutants from effluents. The purpose of this mini review is to present an overview of existing literatures on the utilization of nanocellulose-based materials as adsorbent for water remediation and make aware of possible development of integrating adsorption and nanotechnology for water separation and purification.
A review on metal-organic framework: Synthesis, properties and ap-plication

Metal organic framework is a class of hybrid network of supramolecular solid materials comprised of a large number of inorganic and organic linkers all bounded to metal ions in a well-organized fashion. This type of compounds possess a greater surface area with an advantage of changing pore sizes, diversified and beautiful structure which withdrew an intense interest in this field. In the present review articles, the structural aspects, classification, methods of synthesis, various factors affecting the synthesis and stability, properties and applications have been discussed. Recent advances in the field and new directions to explore the future scope and applications of MOFs have been incorporated in this article to provide current status of the field.
Ablation characteristics of aluminum alloy and stainless steel induced by picosecond laser pulses

Laser ablation of aluminum alloy and stainless steel were investigated experimentally with a picosecond laser (1053 nm, 8.6 ps, 1Hz). We analyzed and compared the damage threshold and morphology of stainless steel and aluminum alloy. The single-pulse ablation threshold of stainless steel is higher than that of aluminum alloy. The ablation morphology are quite different under multi-pulse irradiation, and the micro-scale periodic surface structures are formed on the stainless steel surface but not on the aluminum alloy sample. The influence of metal thermodynamic properties on laser ablation characteristics is analyzed.