Miracle-dz/UniFAR-FTUs huggingface dataset:
Including Surface Kinetic Effects in Simple Models of Ice Vapor Diffusion

AbstractA model for kinetically limited vapor growth and aspect ratio evolution of atmospheric single ice crystals is presented. The method is based on the adaptive habit model of J. Chen and D. Lamb but is modified to include the deposition coefficients through a theory that accounts for axis-dependent growth. Deposition coefficients are predicted for each axis direction based on laboratory-determined critical supersaturations and therefore extends the adaptive habit approach and the capacitance model to low ice supersaturations. The new model is used to simulate changes in single-crystal primary habit in comparison to a hexagonal growth model. Results show that the new model captures the first-order features of axis-dependent, kinetically limited growth. The model reproduces not only the strong reductions in growth as supersaturations decrease but is also able to reproduce the near cessation of minor axis growth as saturations decline. While the new model reproduces the qualitative features of kinetical.
Distribution of Endothelial Nitric Oxide Synthase Gene 27 bp-VNTR Polymorphism in Turabah Region, Saudi Arabia

Different population distribution of 27 bp-VNTR in intron4 of eNOS gene polymorphism and its association with predisposition of certain diseases may be of geographical and ethnical important. In this study we investigated distribution of 27 bp-VNTR in intron4 of eNOS gene polymorphism in Turabah population. Genotyping of eNOS VNTR was done by PCR in 225 unrelated individuals from population living in Turabah province. The allele/and genotype frequencies of the polymorphism were tested for Hardy-Weinberg equilibrium (WBE) and compared to two Arabic populations (Egyptian and Tunisians) using Chi Square Goodness-of-Fittest. The study revealed that; the alleles and genotypes distribution of eNOS VNTR 27-bp polymorphism showed a significant difference from these expected frequencies indicating that the population is not in Hardy-Weinberg equilibrium. comparing alleles and genotypes frequencies in study population with those of Tunisian and Egyptian populations had demonstrated significant differences of genotypes frequencies between study and both Tunisian and Egyptian populations .
Feshbach resonances in ultracold gases

In this chapter, we describe scattering resonance phenomena in general, and focus on the mechanism of Feshbach resonances, for which a multi-channel treatment is required. We derive the dependence of the scattering phase shift on magnetic field and collision energy. From this, the scattering length and effective range coefficient can be extracted, expressions which are particularly useful for ultracold gases.
VANETs and Internet of Things (IoT): A Discussion

Recent advancement of wireless technology and Internet of Things (IoT) have brought a significant development in Vehicular Ad hoc Networks (VANETs). VANET and IoT are the key elements in the current Intelligent Transport System (ITS). Various research on VANET and IoT shows that both have substantial effects in smart transportation system. This paper aims to discuss and illustrate the main challenges and drawbacks; the routing protocols, security and privacy experienced by VANETs respectively. This paper also would like to address the importance of IoT based on VANET in traffic control management system to cope up with the new wireless technology era. Section I of this paper provides a brief explanation on VANETs and IoT, section II discusses the main challenges suffered by VANETs and IoT, section III covers on the existing applications of VANETs and IoT and section IV conclude the views.
Onboard Stereo Vision for Drone Pursuit or Sense and Avoid

We describe a new, on-board, short range perception system that enables micro aerial vehicles (MAVs) to detect, track, and follow or avoid nearby drones (within 2-20 meters) in GPS-denied environments. Each vehicle is able to sense its neighborhood and adapt its motion accordingly without use of centralized reasoning or inter-vehicle communication. To enable a lightweight, low power solution, on-board stereo cameras are used for detection and tracking with depth images, while a downward-looking camera and an inertial measurement unit are used to estimate the position of the observer without use of GPS. We illustrate the robustness and accuracy of this approach through real-time, outdoor leader-follower experiments with three quadrotors. Our experiments show that state-of-art trackers are far less robust in detection against cluttered background. This demonstrates that stereo vision is a highly effective approach to perception for safe navigation of multiple MAVs in close proximity.
Atomic Force Microscopy-Based Molecular Recognition : A Promising Alternative to Environmental Contaminants Detection

Nowadays, there are major concerns about global environmental problems, resulting from high population growth and consequent increase in industrial production and contaminants generation. The high global demand for food, for example, is causing the excessive use of pesticides and fertilizers, used to increase and ensure the quality of production. However, most of these chemicals accumulate, causing soil and groundwater contamination. Within this context, the development of new methods, with greater sensitivity and selectivity, in order to detect these compounds even at extremely low concentrations, is essential for the prospection of contaminated areas. Regarding the sensitivity, the employment of nanosensors has proved to be extremely efficient for pesticide detection. When combined with biomolecules, those nanosystems become selective being able to detect only one type or class of pesticides. This chapter will present potential methods for agrochemicals detection, based on a combination of nanobiosensors with different operating modes of Atomic Force Microscopy, in particular, Chemical Force Microscopy.
Occupancy Grid Maps for Localization and Mapping

In order to perform motion planning it is usually necessary to define some representation of the environment and have some method of determining the robot’s location in that environment. Mapping is the problem of determining the representation, while localization is the problem of finding the robot’s position. Many probabilistic techniques for localization depend on the map being defined as a finite sized set of landmarks which the robot’s sensors observe, giving their relative displacement from the robot. However, physical sensors do not usually detect landmarks unambiguously. Instead, they report the distance to the nearest obstacle, or return an image of the environment. In order to use a landmark based algorithm, the sensor readings must be pre-processed in a separate step to convert the raw sensor data into a set of detected landmarks, such as in [Leonard and Durrant-Whyte 1991]. The additional step introduces more error into any algorithm, as well as discarding much of the sensor information which does not detect any landmark. One of the primary drawbacks of landmark based maps is the data association problem. Because raw sensor data is not labelled with the correct landmark detected, the sensor processing must somehow determine exactly which landmark was observed. If mistakes are made the localization and mapping algorithms which depend on the sensor data will fail. In order to compensate for the data association problem, many localization and SLAM algorithms include a method for determining the associations between the sensor data and the landmarks, however these techniques add significantly to the complexity of the solutions. Also, they do not solve the problem of actually finding landmarks in the raw sensor readings. Some examples of these algorithms include GraphSLAM [Folkesson and Christensen 2004] and Sparse Extended Information Filters (SEIF) [Thrun et al. 2004], both of which can be implemented to handle data associations in a probabilistic way as described in [Thrun et al. 2005]. Even with these integrated solutions, the data association problem adds a significant amount of error.
A Review on Human Activity Recognition using HOG Feature

Human activity recognition has received a great attention in the recent years due to its applications, especially in medical, security, industry fields. This paper presents a new way for detecting the human activity from the single image captured from a single camera. Different methods are there for the detection of human activity. One of them is HOG which is mostly used for human detection. In our study we found that the results of HOG feature is very fast and accurate as compared to other methods.
The powerful jet of an off-nuclear intermediate-mass black hole in the spiral galaxy NGC 2276

Jet ejection by accreting black holes is a mass invariant mechanism unifying stellar and supermassive black holes (SMBHs) that should also apply for intermediate-mass black holes (IMBHs),whicharethoughttobetheseedsfromwhichSMBHsform.Wepresentthedetection of an off-nuclear IMBH of ∼5 × 10 4 Mlocated in an unusual spiral arm of the galaxy NGC 2276 based on quasi-simultaneous Chandra X-ray observations and European VLBI Network (EVN) radio observations. The IMBH, NGC2276-3c, possesses a 1.8 pc radio jet that is oriented in the same direction as large-scale (∼650 pc) radio lobes and whose emission is consistent with flat to optically thin synchrotron emission between 1.6 and 5 GHz. Its jet kinetic power (4 × 10 40 erg s −1 ) is comparable to its radiative output and its jet efficiency (≥46percent) is as large as that of SMBHs. A region of ∼300 pc along the jet devoid of young stars could provide observational evidence of jet feedback from an IMBH. The discovery confirms that the accretion physics is mass invariant and that seed IMBHs in the early Universe possibly had powerful jets that were an important source of feedback.
Fast, Precise Myelin Water Quantification using DESS MRI and Kernel Learning

Purpose: To investigate the feasibility of myelin water content quantification using fast dual-echo steady-state (DESS) scans and machine learning with kernels. Methods: We optimized combinations of steady-state (SS) scans for precisely estimating the fast-relaxing signal fraction ff of a two-compartment signal model, subject to a scan time constraint. We estimated ff from the optimized DESS acquisition using a recently developed method for rapid parameter estimation via regression with kernels (PERK). We compared DESS PERK ff estimates to conventional myelin water fraction (MWF) estimates from a longer multi-echo spin-echo (MESE) acquisition in simulation, in vivo, and ex vivo studies. Results: Simulations demonstrate that DESS PERK ff estimators and MESE MWF estimators achieve comparable error levels. In vivo and ex vivo experiments demonstrate that MESE MWF and DESS PERK ff estimates are quantitatively comparable measures of WM myelin water content. To our knowledge, these experiments are the first to demonstrate myelin water images from a SS acquisition that are quantitatively similar to conventional MESE MWF images. Conclusion: Combinations of fast DESS scans can be designed to enable precise ff estimation. PERK is well-suited for ff estimation. DESS PERK ff and MESE MWF estimates are quantitatively similar measures of WM myelin water content.
Heterogeneous Expectations, Boom-Bust Housing Cycles, and Supply Conditions: A Nonlinear Dynamics Approach

We combine a standard stock-flow housing market model, incorporating explicit relationships between house prices, the housing stock, and the rent level, with a parsimonious expectation formation scheme of housing market investors, reflecting an evolving mix of extrapolative and regressive expectation rules. The model results in a two-dimensional discrete-time nonlinear dynamical system. Based on realistic parameters, the model is able to generate endogenous boom-bust housing market dynamics with lasting periods of overvaluation and overbuilding. We thus exploit our model to investigate how real forces, in particular supply conditions, interact with expectations-driven housing market fluctuations.
Even perturbations of the self-similar Vaidya space-time

We study even parity metric and matter perturbations of all angular modes in self-similar Vaidya space-time. We focus on the case where the background contains a naked singularity. Initial conditions are imposed, describing a finite perturbation emerging from the portion of flat space-time preceding the matter-filled region of space-time. The most general perturbation satisfying the initial conditions is allowed to impinge upon the Cauchy horizon (CH), where the perturbation remains finite: There is no “blue-sheet” instability. However, when the perturbation evolves through the CH and onto the second future similarity horizon of the naked singularity, divergence necessarily occurs: This surface is found to be unstable. The analysis is based on the study of individual modes following a Mellin transform of the perturbation. We present an argument that the full perturbation remains finite after resummation of the (possibly infinite number of) modes.
Crosstalk the Microdialysis in Scientific Research: from Principle to its Applications

The microdialysis technique is an invasive, expensive and a new real-time continuous in vivo sampling technique. The microdialysis technique with high sensitivity, good selectivity of modern analytical techniques combined with truly online real-time in vivo determination of the endogenous substances and drugs, which can effectively shorten the sampling time and increase the sample stability, can save time and cost savings and improve time resolution to accurately observe the rapid changes that are a major breakthrough in vivo drug analysis. When considering the need for on-line coupled with analysis of microdialysis, the probe recovery, perfusion rate, relative molecular weight analytes, such as the impact on the recovery of the probe, as well as analysis requires sample volume, analytes concentration range, the methods of the sample and related factors limit of quantification, analysis speed and others. In the recent years, the microdialysis technique is applied in all the fields such as the brain, blood, skin and others. But however, this technology itself also has some limitations such as the determination of oil-water partition coefficient greater drug determination, low levels content of drugs determination, the probes implanted reproducibility and other problems. In this review, we would like to focus on the microdialysis technique principles, its advantages and limitations, the each type of the microdialysis probes, the microdialysis technique quantitative factors, the application on the brain, blood, eye, pharmacokinetic, distribution study and others, and the microdialysis technique on-line coupled to the modern analysis technology.
A Hybrid Model for Recognizing Handwritten Bangla Characters using Support Vector Machine

Considering the real time scenario, hand written bangla recognition getting a drastic part to the research community. Though various studies have been performed for Bengali handwritten recognition, but a robust model for Bangla Handwritten classification is still in practice. Therefore a hybrid model is presented in this paper, which intent to classify Bangla handwritten characters. The Proposed model combines Zernike moments, raw binary pixels and histogram of oriented gradients features for recognizing Bangla hand written characters which is feed to the Support Vector Machine classifier. It is observed that, the proposed model outsails existing models with smaller epochs. Proposed model is trained and test with “Bangla Lekha Isolated” dataset which consists of 30000 characters where 24,000 for training dataset and 6,000 for testing . This system shows 46.98% for Zernike Moments, 66.60% for Raw Binary Pixels and 87.62% for Histogram of Oriented Gradients where overall combined features achieve an accuracy of 94.88% in recognizing characters which achieves the best accuracy rate reported till date for this dataset.
A Survey on Different Duplicate Detection Methods

: Duplicate records availability is a common phenomenon in real world entities. These duplicate items are available in database because of multiple entries for the same data, incomplete data entries and errors during transactions. In today’s world the d ata sets are very complex and removing the duplicates is a difficult task. Duplicate detection method helps to find out such cases where there are multiple entries for the same entity in real world. In most of the cases duplicate entries cause transactional errors and hence resulting into Operational and Strategic Decision making in an Organization and hence resulting into losses on monetary terms and Brand Image of the Organization. A given example may be multiple Aadhar Cards (Government Identification Cards in India) created for the same person through different locations and the data is used in different systems for identification purposes across industries and locations. The focus in this paper is to compare traditional duplicate detection methods Incremental Sorted Neighborhood Method (ISNM), Duplicate Count Strategy (DCS++) method, Progressive Sorted Neighborhood Method (PSNM) method and PPSNM (Parallel Progressive sorted neighborhood Method).
Lack of genetic differentiation between anadromous and resident sympatric brown trout (Salmo trutta) in a Normandy population

The level of genetic differentiation between anadromous and non-andromous trout (Salmo trutta L.) coex- isting in a Norman river (Oir river) was examined at 15 microsatellites markers. Despite a large amount of variation, no genetic differentiation was found between forms. Four domesticated stocks were also genotyped with the same loci. Highly significant differences were found with natural samples allowing to exclude a recent stocking effect on the Oir population. We conclude that the resident and anadromous trout form a single naturally reproducing unit in this river system. However, this conclusion could reflect particular ecological or demographic conditions in the Oir basin and needs to be validated in other hydrographical and ecological contexts.
Isolation, screening and characterization of bacteria from Rhizospheric soils for different plant growth promotion (PGP) activities: an in vitro study

Plant growth promoting rhizobacteria (PGPR) are a group of bacteria that can be found in the rhizosphere, in association with roots which can enhance the growth of plant directly or indirectly. A large number of bacteria including species of Pseudomonas, Azospirillum, Azotobacter, Klebsiella, Enterobacter, Alcaligenes, Arthrobacter, Burkholderia, Bacillus, Rhizobium and Serratia have reported to enhance plant growth. In the present study, six French bean rhizospheric soil samples were collected from different location of Shimla and Solan in H.P (India). A total of thirty bacteria were isolated and in vitro screening was done for different plant growth promotion activities i.e. phosphate solublization, IAA production, ammonia production, ACC deaminase activity, HCN production and catalase. In the present work twelve bacterial isolates were positive for phosphate solublization. IAA production was shown by almost all the bacterial isolates. Three isolates were positive for ammonia production. ACC deaminase activity was shown by nine isolates. Two isolates were positive for HCN production and all the isolates were found to be catalase positive. Seven isolates were showing maximum plant growth promotion activities and further identified on the basis of colony morphology, Gram staining and biochemical tests. These isolates were identified as Acinetobacter sp. , Bacillus sp. , Enterobacter sp. , Micrococcus sp. , and Pseudomonas sp. As PGPR are environmental friendly and offer sustainable approach to increase production of crops and health. Therefore, these isolates can be utilized for biofertilizer formulation under local agro-climatic conditions of Himachal Pradesh.
A Study of Rotary-Brake Design for Utilizing in Motorized Upper Limb Prosthesis

A number of motorized upper limb prosthesis has been investigated and developed in recent years with several new technologies: reach and grasp object at the same time with water-resistant of electric terminal device in Utah arm, audible and vibratory feedback in Boston digital arm, thoughtcontrolled for patients who have undergone TMR surgery in Otto Bock’s dynamic arm, for instance. However, an important problem found in elbow parts of those prostheses, is power constraint. The reason is occurred when amputee wants to move their prosthesis arm to a desired position or to lock an elbow or holding an object; the power is consumed for their motion control. Such the prosthesis require more energy, therefore, the power storage, weight and cost are needed to be increased. To solve these problems, a new brake/clutch system has been studied and designed for consuming less energy but provides suitable torque, and two-directional rotation which works similar to other electric motors. Therefore, the purpose of this study is to demonstrate a new design of our brake/clutch system that can work in two directions as a normal-hinge joint as seen in general prosthesis products. KeywordsNon-backdrivable mechanism, Sprag clutch, Motorized prosthesis, Elbow.
Improving Human-Machine Interfaces for Construction Equipment Operations with Mixed and Augmented Reality

Construction equipment operation and management is a process that operates the machine in different working conditions, monitors the operation condition of equipment, maintains and repairs equipment components by referring to technical specifications, and generates more information for future access (Wang & Dunston 2006a). These construction operations are being carried out on different platforms. This makes it difficult to visualize and process critical information at any time and place. Many evolving information technology (IT) systems bring solutions into the construction field data management and sharing (Yang et al. 2000). Based on the data/information managed and stored in such IT tools, innovative and efficient human-machine interfaces that could visually merge these data into the current workspace of crew can be developed to increase the operators’ situational awareness and achieve less information access cost of operators and maintenance crew. Mixed and Augmented Reality can fulfill this vision by creating an augmented workspace through inserting digital contents into the physical space where operators work. Such augmented workspace is realized by integrating the power and flexibility of computing environments with the comfort and familiarity of the traditional workspace (Wang & Dunston 2006a). Augmented Reality (AR) opens a promising gate for integrating designs into their to-be-built real-world context. For example, AR could assist in quality assurance by comparing as-built facility with corresponding design as well as constructability review prior to the actual construction. Outdoor AR systems could assist in demonstrating architects/designs/constructors/owners what a new structure/building would look like at its final setting for evaluating functions and esthetics of a design. For example, an urban designer could show clients/politicians what a new building would look like as they walk around the neighborhood, to better understand how a tall building might affect surrounding residents. Outdoor and mobile AR systems have just begun to become feasible due to advances in tracking and computing. Traditional tracking systems used for AR registration are intended for use in controlled indoor spaces and are unsuitable for unprepared outdoor environments such as those found on typical construction sites (Behzadan Kamat 2006). Except wide applications in tabletop and indoor AR systems, fiducial markers could also be an easy way for outdoor AR. For example, Klinker et al. (2001) accurately erected a virtual