Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuous-time marginal pricing of electricity

The current practice of discrete-time electricity pricing starts to fall short in providing an accurate economic signal reflecting the continuous-time variations of load and generation schedule in power systems. This paper introduces the fundamental mathematical theory of continuous-time marginal electricity pricing. We first formulate the continuous-time unit commitment (UC) problem as a constrained variational problem, and subsequently define the continuous-time economic dispatch (ED) problem where the binary commitment variables are fixed to their optimal values. We then prove that the continuous-time marginal electricity price equals to the Lagrange multiplier of the variational power balance constraint in the continuous-time ED problem. The proposed continuous-time marginal price is not only dependent to the incremental generation cost rate, but also to the incremental ramping cost rate of the units, thus embedding the ramping costs in calculation of the marginal electricity price. The numerical results demonstrate that the continuous-time marginal price manifests the behavior of the constantly varying load and generation schedule in power systems.
Review of Characteristic Components, Traditional and Pharmacological Properties of Punica granatum

Punica granatum belonging to genus Punica family Punicaceae is considered as a wonder fruit for its ample pharmacological activities. It is generally well-known as pomegranate (Anar) inherent to central regions of Asia from Turkmenistan to Northern India and Iran. Various components of pomegranate are medicinally significant like fruit, peel, leaves, seeds, flower, root and bark. A pronounced variety of nutritive compounds like organic acid, fatty acids, sterols, minerals and vitamins have been found in pomegranate. However, phytochemical researches have shown the bioactive constituents including alkaloids, anthocyanin, ellagic acid, tannins, flavonoids and other phenolic compounds depending on the cultivars. Numerous studies revealed promising health benefits of P. granatum particularly anti-inflammatory, anti-bacterial, antioxidant, antidiabetic, antimicrobial, anti-hypertensive and anti-proliferative activities which are receiving great interest from researchers and pharmaceutical concerns. The current review embraces some recent findings underlining the pharmacological properties of Punica granatum that can be useful for motivating further research and for promoting its consumption as significant and safe medicinal food for human health.
Probing the glass transition in reversible cross-linked polymer composites

Understanding the nature of glass transition is still a great challenge. Glass transition is widely observed in many glassy materials; however, it has never been unambiguously observed in reversible cross-linked polymer, which is an ideal model of the percolation process. Herein, we report the synthesis of a reversible cross-linked polymer incorporated with four-armed Diels–Alder (DA) dynamic covalent bonds, and the robust experimental observation of percolation-induced glass transition in this reversible four-armed cross-linked polymer (DAMF1). Temperature-modulated differential scanning calorimetry (TMDSC) experiment results clearly revealed the presence of a glass transition along with an endothermic or exothermic peak associated with DA/retro-DA (RDA) reaction related to the reconstitution/disassociation of the DAMF1's four-armed cross-linked network. In situ13C variable-temperature solid-state NMR experiments further confirmed the DA/RDA reaction during glass transition at a molecular level. The above experimental results provide a direct experimental evidence for the recently developed percolation model of glass transition, which provides new insights into the nature of glass transition.
Managing Laggards: The Importance of a Deep Sales Bench

Sales leaders often use threats of punishment to manage poor performers (i.e., laggards), but little research has examined the effect of these threats. The current research addresses this gap by investigating an intervention termed the “bench program” with a field-based quasi experiment and a randomized lab experiment. In the field, the company under study told salespeople in treatment districts that a trainee would replace them at the end of the year if they failed to hit their quota and placed last in their district. Difference-in-differences analyses of matched treatment and control groups show that the bench program had an immediate and sustained impact on performance. Moreover, laggards improved their performance more than higher performers, and salespeople with larger advice networks improved their performance more than salespeople with smaller advice networks. A lab experiment compares the bench program with a program that had the same threat of firing but did not have replacements in sight. Performance in the bench program exceeded that in the firing condition, indicating that the vividness of a threat can increase its deterrent value.
Providing privacy preserving in Cloud computing

People can only enjoy the full benefits of Cloud computing if we can address the very real privacy and security concerns that come along with storing sensitive personal information in databases and software scattered around the Internet. There are many service provider in the internet, we can call each service as a cloud, each cloud service will exchange data with other cloud, so when the data is exchanged between the clouds, there exist the problem of disclosure of privacy. So the privacy disclosure problem about individual or company is inevitably exposed when releasing or sharing data in the cloud service. Privacy is an important issue for cloud computing, both in terms of legal compliance and user trust, and needs to be considered at every phase of design. Our paper provides some privacy preserving technologies used in cloud computing services.
Tomato root transformation followed by inoculation with Ralstonia solanacearum for straightforward genetic analysis of bacterial wilt disease

Ralstonia solanacearum is a devastating soil borne vascular pathogen that is able to infect a large range of plant species, causing an important threat to agriculture. However, the Ralstonia model is considerably under-explored in comparison to other models involving bacterial plant pathogens, such as Pseudomonas syringae in Arabidopsis. Research targeted to understanding the interaction between Ralstonia and crop plants is essential to develop sustainable solutions to fight against bacterial wilt disease, but is currently hindered by the lack of straightforward experimental assays to characterize the different components of the interaction in native host plants. In this scenario, we have developed an easy method to perform genetic analysis of Ralstonia infection of tomato, a natural host of Ralstonia. This method is based on Agrobacterium rhizogenes-mediated transformation of tomato roots, followed by Ralstonia soil-drenching inoculation of the resulting plants, containing transformed roots expressing the construct of interest. The versatility of the root transformation assay allows performing either gene overexpression or gene silencing mediated by RNAi. As a proof of concept, we used this method to show that RNAi-mediated silencing of SlCESA6 of tomato roots conferred resistance to Ralstonia. Here, we describe this method in detail, enabling genetic approaches to understand bacterial wilt disease in a relative short time and with small requirements of equipment and plant growth space. SUMMARY A versatile method for tomato root transformation followed by inoculation with Ralstonia solanacearum to perform straightforward genetic analysis for the study of bacterial wilt disease.
The STACEE ground-based gamma-ray detector

We describe the design and performance of the solar tower atmospheric Cherenkov effect experiment (STACEE) in its complete configuration. STACEE uses the heliostats of a solar energy research facility to collect and focus the Cherenkov photons produced in gamma-ray induced air showers. The light is concentrated onto an array of photomultiplier tubes located near the top of a tower. The large Cherenkov photon collection area of STACEE results in a gamma-ray energy threshold below that of previous ground-based detectors. STACEE is being used to observe pulsars, supernova remnants, active galactic nuclei, and gamma-ray bursts.
A new degree distribution for LT codes for broadcasting in ad-hoc network using network coding

In this paper we consider a network in which one of the nodes wants to send a packet to all the other nodes in the network by broadcasting the packet. At each instant one of the nodes acts as a source and transmits a packet which has to be received by all the other nodes in the network We propose a new distribution for LT codes to perform network coding in a distributed manner. Using LT codes which are fountain codes for Network coding at source independent backbone with our particular distribution decreases the number of transmission required to broadcast the packet. Also this distribution allows a better control on the average degree of encoded packets. We carry out simulations to corroborate our idea.
Stochastic Decoding of LDPC Codes over GF(q)

Nonbinary LDPC codes have been shown to outperform currently used codes for magnetic recording and several other channels. Currently proposed nonbinary decoder architectures have very high complexity for high-throughput implementations and sacrifice error-correction performance to maintain realizable complexity. In this paper, we present an alternative decoding algorithm based on stochastic computation that has a very simple implementation and minimal performance loss when compared to the sum-product algorithm. We demonstrate the performance of the algorithm when applied to a GF(16) code and provide details of the hardware resources required for an implementation.
HAIL RAIN AND ITS GEOGRAPHICAL REPERCUSSIONS IN THE COUNTRYSIDE OF SÃO PAULO (SP) STATE

This article aims to perform an analysis of episodic characteristics of the spatial repercussions of the hail storm that occurred in the autumn of 2016, in the urban area of Rio Claro (SP). Through the radar images were examined the genesis, the evolution and the spatial temporal dissipation of the phenomenon. In this context, it was sought to highlight the atmospheric systems operating in the week of hail precipitation episode, occurred on the night of June 1th 2016. To analyze the socioeconomic impacts generated by hail rain, was used information provided by the Civil Defense. The use of statistical techniques, it was possible to verify the correlation between the occurrences recorded by Civil Defense, the social vulnerability aspect in the most affected areas and the type of cover used in the roofs of the dwellings.
Modeling, Design, and Verification for the Analog Front-End of a MEMS-Based Parallel Scanning-Probe Storage Device

We present an integrated analog front-end (AFE) for the read-channel of a parallel scanning-probe storage device. The read/write element is based on an array of microfabricated silicon cantilevers equipped with heating elements to form nanometer-sized indentations in a polymer surface using integral atomic-force microscope (AFM) tips. An accurate cantilever model based on the combination of a thermal/electrical lumped-element model and a behavioral model of the electrostatic/mechanical part are introduced. The behavioral model of the electrostatic/mechanical part is automatically generated from a full finite-element model (FEM). The model is completely implemented in Verilog-A and was used to co-develop the integrated analog front-end circuitry together with the read/write cantilever. The cantilever model and the analog front-end were simulated together and the results were experimentally verified. The approach chosen is well suited for system-level simulation and verification/extraction in a design environment based on standard EDA tools.
An improved mean shift algorithm for moving object tracking

Moving object tracking is one of the key technologies in video surveillance. Mean shift algorithm fails to track the moving object in complicated environment. In this paper, a new strategy is proposed to improve the tracking ability of mean shift algorithm, in which the contrast between object and background along with similarity evaluation are applied for generating and updating object model. To eliminate the interference of the most similar features between tracking object and background, the coefficient ratio of the object to surrounding environment is first imported to generate the object model. To make sure the accuracy of updating object model, the effective way that combines similarity evaluation and Kalman filtering prediction is then applied for judge whether the tracking object is sheltered by other objects or background. The experimental results have shown that the proposed method can tack the moving object stably.
Study of the further reduction of shaft voltage of brushless DC motor with insulated rotor driven by PWM inverter

Authors succeeded in reduction of the shaft voltage of a motor with an insulated rotor driven by PWM inverter previously. This paper proposes a new method of further reduction of the shaft voltage of the motor with the insulated rotor. A non-grounding common-mode equivalent circuit of the motor is examined, and the effect of the further reduction by a new method is verified by calculation of the shaft voltage from the equivalent circuit and measurement of the shaft voltage of the motor.
Maximum likelihood neural networks for adaptive classification

Summary form only given, as follows. A maximum likelihood neural network has been designed for problems which require an adaptive estimation of metrics in classification spaces. Examples of such problems are an XOR problem and most classification problems with multiple classes having complicated classifier boundaries. The metric estimation has the capability of achieving flexible classifier boundary shapes using a simple architecture without hidden layers. This neural network learns much more efficiently than other neural networks or classification algorithms, and it approaches the theoretical bounds on adaptive efficiency according to the Cramer-Rao theorem. It also provides for optimal fusing of all the available information, such as a priori and real-time information coming from a variety of sensors of the same or different types, and utilizes fuzzy classification variables to provide for the efficient utilization of incomplete erroneous data, including numeric and symbolic data. <<ETX>>
Detecting colorectal polyps with use of artificial intelligence

Colorectal cancer (CRC) is a major cause of cancer-related mortality in most countries. Colonoscopy during which all neoplastic and pre-malignant polyps (e.g., adenomas) are eradicated is considered beneficial in decreasing the incidence of CRCs and their associated mortality (1,2). This concept has been supported by several large-scale prospective studies (3). The quality of the colonoscopy procedure, however, varies according to the expertise of the endoscopist.
Patterns and rates of ecological diversification among Neotropical cichlid fishes

ii Acknowledgments. iv List of Tables . viii List of Figures . ix General Introduction . 1 References . 3 Chapter 1. Ecological diversification associated with the pharyngeal jaw diversity of Neotropical cichlid fishes . 6 Abstract . 6 Introduction . 7 Methods . 9 Results . 13 Discussion . 16 References . 23 Chapter 2. Pharyngeal jaw evolution and trophic niche filling after the colonization of the Americas by cichlid fishes . 44 Abstract . 44 Introduction . 45 Methods . 48
Influence of driving current on photometric performances of a white light OLED

In this article, we demonstrate how driving current affects photometric performances of a white light OLED (WOLED) for general lighting purpose. Experiments reveal that ripple current with 40% amplitude, 50Hz-100Hz frequency, and sinusoidal and triangle waveform has little influence on the test OLED. In PWM mode, both duty cycle and frequency of pulse current affect the OLED's performance such as efficacy and CCT. Such influences become worse at high frequency and low duty cycle. The reason for this influence is considerable difference between electron and hole mobility, and the movement of recombination zone therefrom.
Resonant substrate-integrated near-field sensors with improved sensitivity

The sensitivity of near-field sensors with substrate-integrated waveguide (SIW) resonator is studied. The structure of the sensors is based on a folded cylindrical cavity. Its shape and size are varied by exploiting the field symmetry of the excited mode. The resulting mode transformation is discussed. Several sensors of different geometries with resonant frequency close to 8 GHz are analyzed with respect to their sensitivity to ethanol and water. By adequately choosing the shape the sensitivity of the sensor, i.e., the shift of its resonant frequency caused by a dielectric sample, can be increased significantly.
A novel interference cancellation technique for optical CDMA system

Multiple Access Interference (MAI) is a serious source of noise in Optical Code Division Multiple Access (OCDMA) system. In this perspective, a new technique has been proposed in this paper to reduce the effect of MAI in OCDMA receiver. The effect of interference arising from other user code to the intended user has been reduced by subtracting the interference (multiplied by a factor) from the received signal. It has been observed that the distance between different codes influences the value of MAI on the intended user. Hence a new technique has been proposed to optimize the hardware requirement and complexity of the receiver for reducing MAI. The structure has been implemented using Advanced Binary Optical Logic Gates (ABLOGs) leading to reduction of optical-electrical conversion time. Simulations have been carried out using MATLAB to obtain the variation of error probability with number of users for different iterations. Thus, the effectiveness of the proposed technique has been established in terms of the improvement in the Bit Error Rate (BER). It has also been established that this new method gives better result in first iteration leading to faster simulation time.
Models and Algorithms for Detection and Tracking of Coordinated Groups

In this paper, we describe models and algorithms for detection and tracking of group and individual targets. We develop two novel group dynamical models, within a continuous time setting, that aim to mimic behavioural properties of groups. We also describe two possible ways of modeling interactions between closely spaced targets using Markov Random Field (MRF) and repulsive forces. These can be combined together with a group structure transition model to create realistic evolving group models. We use a Markov Chain Monte Carlo (MCMC)-Particles Algorithm to perform sequential inference. Computer simulations demonstrate the ability of the algorithm to detect and track targets within groups, as well as infer the correct group structure over time.
Models and Methods for Network Function Virtualization (NFV) Architectures

Due to the exponential growth of service demands, telecommunication networks are populated with a large and increasing variety of proprietary hardware appliances, and this leads to an increase in the cost and the complexity of the network management. To overcome this issue, the NFV paradigm is proposed, which allows dynamically allocating the Virtual Network Functions (VNFs) and therefore obtaining flexible network services provision, thus reducing the capital and operating costs. In this thesis, we focus on the VNF Placement and Routing (VNF-PR) problem, which aims to find the location of the VNFs to allocate optimally resources to serve the demands. From an optimization point of view, the problem can be modeled as the combination of a facility location problem (for the VNF location and server dimensioning) and a network design problem (for the demands routing). Both problems are widely studied in the literature, but their combination represents, to the best of our knowledge, a new challenge. We start working on a realistic VNF-PR problem to understand the impact of different policies on the overall network management cost and performance. To this end, we extend the work in [1] by considering more realistic features and constraints of NFV infrastructures and we propose a linear programming model and a math-heuristic to solve it. In order to better understand the problem structure and its properties, in the second part of our work, we focus on the theoretical study of the problem by extracting a simplified, yet significant variant. We provide results on the computational complexity under different graph topology and capacity cases. Then, we propose two mathematical programming formulations and we test them on a common testbed with more than 100 different test instances under different capacity settings. Finally, we address the scalability issue by proposing ILP-based constructive methods and heuristics to efficiently deal with large size instances (with up to 60 nodes and 1800 demands). We show that our proposed heuristics can efficiently solve medium size instances (with up to 30 nodes and 1000 demands) of challenging capacity cases and provide feasible solutions for large size instances of the most difficult capacity cases, for which the models cannot find any solution even with a significant computational time.
Combination of non-invasive approaches for general assessment of induction motors

There exists no single quantity able to diagnose all possible failures taking place in induction motors. Currents and vibrations monitoring are rather common in the industry, but each of these quantities alone can only detect some specific failures. Moreover, even for the specific faults that a quantity is supposed to detect, many problems may rise. As a consequence, a reliable and general diagnosis system cannot rely on a single quantity. On the other hand, it would be desirable to rely on quantities that can be measured in a non-invasive way which is a crucial requirement in many industrial applications. This paper proposes a twofold method to detect electromechanical failures in induction motors. The method relies on analysis of currents (steady-state + transient) combined with analysis of infrared data captured by using appropriate cameras. Each of these non-invasive techniques may provide complementary information that may be very useful to diagnose an enough wide range of failures. In the present paper, the detection of three illustrative faults is analyzed: broken rotor bars, fan damages and bearing failures. The results show the potential of the methodology that may be especially suitable for large, expensive motors where the prevention of eventual failures justifies the costs of such system, due to the catastrophic implications that these unexpected faults may have.