Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiparticle Entanglement of Hot Trapped Ions

We propose an efficient method to produce multiparticle entangled states of ions in an ion trap for which a wide range of interesting effects and applications have been suggested. Our preparation scheme exploits the collective vibrational motion of the ions, but it works in such a way that this motion need not be fully controlled in the experiment. The ions may, e.g., be in thermal motion and exchange mechanical energy with a surrounding heat bath without detrimental effects on the internal state preparation. Our scheme does not require access to the individual ions in the trap.
Centralized mechanism for device encapsulation prevention in internet of things

The future of internet-of-thing had established a wide network and given aspiration to the prominent application. in this context, implicate of exploiting the vulnerability can causes real security breaches which can be fatal in some application like defence, health care and many more. The Impersonation and encapsulation of nodes can reveal information regarding network. In this paper, our focus is to prevent such transition of information in adversary by opting self-destructive mechanism. We are classifying the threats on the basis of status and condition and acting according to its severity. Moreover the estimation based on received signal strength (RSS) is being used to locate the adversary insurgency. The simulation result is clearly shown the discrimination between the adversary commodity and genuine one.
A dynamic adaptive algorithm for statistical up-scaling based short-term wind power prediction of regions

Wind Power Prediction (WPP) of regions is beneficial for the department of power grid dispatching to evaluate the wind power output characteristics from a global perspective and to formulate a scientific and reasonable scheduling scheme for power grid. The statistical up-scaling based WPP of regions is one of the most popular methods, which has been widely applied by the industrial companies. The selection of reference wind farms is one of the key factors of the statistical up-scaling method based WPP of regions. However, most of the methods for the selection of reference wind farms are not flexible, which restrict the improvement of the WPP accuracy, as the weather of wind farm sub-regions are always changeable. To overcome the challenge, a dynamic adaptive algorithm based WPP of regions is presented in the paper, in terms of the dynamic selection of reference wind farms, the dynamic selection of the WPP methods based on the classification of the weather types of the wind farms and the dynamic selection of the up-scaling method. The results show that the proposed method is with higher accuracy than the traditional statistical up-scaling based WPP of regions and is applicable for industrial WPP of regional wind farms.
Transcriptome analysis of hepatic gene expression and DNA methylation in methionine- and betaine-supplemented geese (Anser cygnoides domesticus)

Dietary methionine (Met) restriction produces a coordinated series of transcriptional responses in the liver that limits growth performance and amino acid metabolism. Methyl donor supplementation with betaine (Bet) may protect against this disturbance and affect the molecular basis of gene regulation. However, a lack of genetic information remains an obstacle to understand the mechanisms underlying the relationship between Met and Bet supplementation and its effects on genetic mechanisms. The goal of this study was to identify the effects of dietary supplementation of Met and Bet on growth performance, transcriptomic gene expression, and epigenetic mechanisms in geese on a Met-deficient diet. One hundred and fifty 21-day-old healthy male Yangzhou geese of similar body weight were randomly distributed into 3 groups with 5 replicates per treatment and 10 geese per replicate: Met-deficient diet (Control), Control+1.2 g/kg of Met (Met), and Control+0.6 g/kg of Bet (Bet). All geese had free access to the diet and water throughout rearing. Our results indicated that supplementation of 1.2 g/kg of Met in Met-deficient feed increased growth performance and plasma homocysteine (HCY) levels, indicating increased transsulfuration flux in the liver. Supplementation of 0.6 g/kg Bet had no apparent sparing effect on Met needs for growth performance in growing geese. The expression of many genes critical for Met metabolism is increased in Met supplementation group. In the Bet-supplemented group, genes involved in energy production and conversion were up-regulated. Dietary supplementation with Bet and Met also altered DNA methylation. We observed changes in the methylation of the LOC106032502 promoter and corresponding changes in mRNA expression. In conclusion, Met and Bet supplementation in geese affects the transcriptional regulatory network and alters the hepatic DNA methylation of LOC106032502.
Analysis and design of solar power system interface utility using ZVS converter

In the present work, the development of a grid-connected ZVS inverter with a reduced number of power conversion stages and fewer passive components for single-phase photovoltaic applications, suitable for conversion from low-voltage (40–60V) DC to high voltage AC (230VAC;RMS) at average power levels of 175 W and below, (the design specifications given inTable I) is presented. The circuit topology is based on a full-bridge ZVS inverter with an auxiliary circuit, a high-frequency transformer, and a novel half-wave cyclo-converter. The ZVS converter employs an asymmetric auxiliary circuit to provide the reactive current to the full-bridge semiconductor switches, which guarantees ZVS at the switching instants of the MOSFETs. The main operational characteristics of this topology are identified and analyzed, and a multidimensional control technique is applied, firstly to determine the optimum value of the reactive current injected by the auxiliary circuit to guarantee ZVS and secondly for an improved realization of the proposed architecture that enables reduced device losses compared to other realizations along with flexible control, enabling very high efficiencies to be achieved.
Not Me, But Reflects Me: Validating a Simple Implicit Measure of Psychological Capital

ABSTRACT We propose the recently introduced implicit measure of psychological capital (PsyCap), the Implicit Psychological Capital Questionnaire (I-PCQ; Harms & Luthans, 2012), can provide a needed valid alternative to the self-report Psychological Capital Questionnaire (PCQ). We explain the development of the I-PCQ items, assess the structural validity of the instrument, test its vulnerability to response distortion, and assess its capacity to predicting work attitudes and behaviors vis-à-vis the PCQ and Big Five personality facets. We found that the I-PCQ demonstrated acceptable structural validity that was consistent with and added to prior theorizing, was resistant to response distortion, and predicted work outcomes above and beyond the widely used self-report PCQ and Big Five personality traits. We conclude that the I-PCQ represents a valid, easily administered measure of psychological capital that minimizes problems associated with self-reports and is appropriate for use in work settings.
A constraint programming-based resource allocation and scheduling of map reduce jobs with service level agreement

As Hadoop is progressively becoming widespread technology in large-scale data analysis, there is a growing need for providing inevitable services to users who have strict requirements on job completion times, which are typically capture in service level agreements (i.e., SLA comprising an initial start time, a required execution time, priority and an end-to-end deadline). While earliest deadline first scheduling (EDF) like algorithms are trendy in guarantee job deadlines in real-time systems, they are not successful in a dynamic Hadoop environment. The problem of resource allocation and scheduling is modeled using constraint programming. We develop a distinctive Map Reduce constraint programming based matchmaking and scheduling algorithm (CP-RM) that can handle MapReduce jobs with deadlines and achieve high system performance. The MRCP-RM algorithm is integrated into Hadoop, which is a widely used open source implementation of the MapReduce programming model, as a new scheduler called the CP-RM Scheduler. We analyze the CP-RM Scheduler's performance as a comparison with an earliest deadline first (EDF) Hadoop scheduler, which is implemented by extending Hadoop's default FIFO scheduler. We analyze the CP-Scheduler's performance as a comparison with an earliest deadline first (EDF) Hadoop scheduler, which is implemented by extending Hadoop's default FIFO scheduler. The results of the performance evaluation demonstrate the effectiveness of CP-RM in generating a schedule that leads to a low proportion of jobs missing their deadlines and also provide insights into system behavior and performance. In the experiments performed on a Hadoop cluster deployed on local system, it is observed that CP-RM achieved on average a 56% lower p compared to an EDF-Scheduler for a different of workload.
Systematic review of big data analytics in governance

With advent of technology, data is increasing abruptly day by day. Traditional database systems are not capable of processing and handling such a voluminous data. Big data analytics has the capability of processing, handling and analyzing the large datasets or stream of data. Big data analytics play important role in fields such as healthcare, agriculture, smart grid and policy making. Big data analytics with governance improves planning and decision making phases for government projects. It helps to improve the quality of government services. This paper focuses on the different application areas of governance in which big data analytics play a role and tools used to handle big data management problem. The existing work is classified into different categories and is presented using visualizations. This paper also deals with challenges related to governance field.
PATCH NEAR-FIELD ACOUSTIC HOLOGRAPHY BASED ON PRESSURE ACOUSTIC RADIATION MODES *

To improve the reconstruction accuracy and resolution in Near-field acoustic holography with small aperture, a patch NAH based on pressure acoustic radiation modes is put forward. Firstly, the filter property of acoustic radiation mode is utilized to extrapolate hologram with small number of measurement points. Then, information is reconstructed with the extrapolated data. The numerical simulation results indicate that reconstruction accuracy and resolution can be improved effectively by using the presented method.
Novel TE-TM mode splitter on lithium niobate using nickel indiffusion and proton exchange techniques

A new TE-TM mode splitter with an asymmetric Y-junction structure operated by directly focusing randomly polarised light on to a titanium indiffused waveguide which is then directed to an ordinary-polarised nickel indiffusion waveguide and an extraordinary-polarised proton exchange waveguide in lithium niobate is demonstrated. The measured extinction ratio is greater than 20 dB for both TE and TM modes.
Design and development of portable potentiostat for advanced research in electrochemical biosensing

Embedded system based, portable potentiostat for Linear Sweep Voltammetry(LSV) is successfully developed. The uniqueness of the system is that it is developed by using off the shelf components for high performance, high resolution and low cost three electrode electrochemical biosensor. It is interfaced with PC by USB. Most important parameters that need to set in electrochemical analysis are scan rate, start potential, end potential and step size of linear sweep voltage. In this system, external 16-bit DAC and 24-bit ADC are interfaced which gives the LSV resolution of 763nV and can capture minimum sensor current of 125nA. This kind of signal resolution is needed for detection of low analyte concentration. Most importantly the system does not need any external memory to store the captured sensor data. An employment of cool term software aids the real-time data capture from biosensor and save it in. txt file format for further parameter estimation.
Non-Contact Human Body Parameter Measurement Based on Kinect Sensor

: In this work we implemented a system to obtain human body parameter measurement without physically contacting the user. This implementation is contained the methods of obtaining 3D measurements using Kinect v2 depth sensor . The developed system at the initial stage is capable of detecting and obtaining personalized body parameters such as height, shoulder length, neck to hip length, hip to leg length and arm length by incorporating the necessary skeleton joints and front perimeter at chest, stomach and waist by incorporating the necessary 3D pixels. According to the results, the measurement on height and arm length of the person are relatively in good agreement with the actual values since the error is less than 5% and measurement has been taken in centimeters. Maximum 12% of an error incorporated of calculating front perimeter at chest. Experimental results obtained from the developed system are in acceptable range for dressing purpose and ultimately helpful for designing a real time 3D virtual dressing room.
Pgc-1α repression and high-fat diet induce age-related macular degeneration-like phenotypes in mice

ABSTRACT Age-related macular degeneration (AMD) is the major cause of blindness in the elderly in developed countries and its prevalence is increasing with the aging population. AMD initially affects the retinal pigment epithelium (RPE) and gradually leads to secondary photoreceptor degeneration. Recent studies have associated mitochondrial damage with AMD, and we have observed mitochondrial and autophagic dysfunction and repressed peroxisome proliferator-activated receptor-γ coactivator 1α (PGC-1α; also known as Ppargc1a) in native RPE from AMD donor eyes and their respective induced pluripotent stem cell-derived RPE. To further investigate the effect of PGC-1α repression, we have established a mouse model by feeding Pgc-1α+/− mice with a high-fat diet (HFD) and investigated RPE and retinal health. We show that when mice expressing lower levels of Pgc-1α are exposed to HFD, they present AMD-like abnormalities in RPE and retinal morphology and function. These abnormalities include basal laminar deposits, thickening of Bruch's membrane with drusen marker-containing deposits, RPE and photoreceptor degeneration, decreased mitochondrial activity, increased levels of reactive oxygen species, decreased autophagy dynamics/flux, and increased inflammatory response in the RPE and retina. Our study shows that Pgc-1α is important in outer retina biology and that Pgc-1α+/− mice fed with HFD provide a promising model to study AMD, opening doors for novel treatment strategies. Summary: A new mouse model has been established that exhibits characteristics of human age-related macular degeneration; the model will facilitate further studies of AMD disease mechanisms.
CLASSIFICATION IN MACHINE LEARNING CONTEXT

Due to the vast amount of data, searching and obtaining relevant information on the web is a challenging task. Despite that a broad range of classification techniques have been proposed to improve the information retrieval methods, many difficulties are still present because of the continuous increase in the amount of web contents, as well as its diversity. In this paper, we propose a method that automatically identifies and classifies user queries by using a domain specific syntax approach – this approach is based on the syntactical pattern of each type of search query. A framework is developed to test the performance of the proposed method. Experimental results show that our approach leads to accurate identification of different query types.
A novel threshold-based clustering method to solve K-means weaknesses

Nowadays, using data mining techniques to discover hidden patterns in various data sets is very common. Among all data mining techniques, clustering algorithms are particularly important and K-means algorithm is one of the most popular clustering methods. Simplicity, flexibility and performance in large data sets are the most important advantages of the K-means algorithm. On the other hand, some factors such as determining the number of clusters by user, outlier data and sensitivity to initial centers of the clusters and thus possibility of reaching a local minimum can reduce the efficiency of the K-means algorithm. In the present study, the idea of the K-means clustering algorithm and its advantages and disadvantages is introduced and then a new method is proposed for data clustering which overcomes the limitations of the K-means algorithm. Therefore, accuracy and efficiency of the clustering method can strongly be increased. In the proposed algorithm, to avoid identifying the number of clusters by users, similarity and threshold measures are used for clustering. Also in this algorithm the outlier data are identified and hence their negative impact on the clustering can be avoided. The experimental results show that the proposed method can overcome K-means disadvantages and improve its efficiency.
A power electronic design approach to a power supply unit for A1kW high-frequency transmitter in naval applications

This paper is aimed at design, analysis and implementation of 1kW power supply unit for high-frequency transmitter in naval applications. Most of the equipments used by Army/Navy are based on 42V DC supply. Hence developing a PSU, that produces 42V DC from AC mains play a vital role. The design prototype consists of a boost power factor correction (PFC) circuit, half-bridge inverter followed by a rectifier. Power conversion process consists of two stages. In this work AC to DC conversion topology is used, however it is necessary to keep additional control circuitry to control power factor. In order to maintain good power factor, a boost Power Factor Correction (PFC) circuit is employed. The proposed AC/DC converter is designed to operate at 100 kHz. Further the proposed AC/DC converter illustrates achievement of line, load regulation and good power factor as per design requirements. Also ripples obtained are within the safe limits and design is validated using experimental results.
Efficient ID-based Message Authentication with Enhanced Privacy in Wireless Ad-hoc Networks

Message authentication is one of the most effective ways to thwart unauthorized messages from being transmitted in wireless ad-hoc networks such as wireless sensor networks (WSNs) and vehicular ad-hoc networks (VANETs). For this reason, many message authentication schemes have been developed, based on either symmetric-key cryptosystems or public-key cryptosystems. Public-key cryptosystems have simpler key management and are easier to scale, thus are more suitable for message authentication. Among all kinds of public-key cryptosystems, identity based cryptosystem is the most attractive since the public keys can be derived locally. In this paper, we propose an efficient message authentication with enhanced privacy (IMAEP) scheme based on ring signature and identity based signature. We prove that besides unconditional privacy, the proposed scheme can achieve enhanced privacy which can defend against full key exposure attack. We also prove that the proposed scheme can achieve unforgeability against adaptive chosen-message-and-identity attack. Through comparing the proposed scheme with the scheme proposed in [1] with the same level of enhanced privacy, we demonstrate that our scheme has the following advantages: 1) our scheme has much lower computational overhead, 2) our scheme can retain unconditional privacy.
Optimal deployment density for maximum coverage of drone small cells

With the proliferation of small and mini drones, Drone Small Cells (DSCs) can cooperative multiple drones to provide communication service for ground users as emergency means or supplementary ones of traditional terrestrial cellular networks. In this paper, we study the fundamental problem of optimizing the deployment density of DSCs to achieve the maximum coverage performance. Most related works do not consider cumulative inter-cell interference when studying the coverage performance of DSCs. First, we derive an approximate and closed-form expression of the cumulative inter-cell interference which comes from both probabilistic Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) links. Then, we analyze the coverage performance of DSCs and derive the transcendental function of optimal deployment density to obtain the maximum coverage. Last, we propose an algorithm to get the optimal deployment density with low complexity. We conduct both field experiments and Matlab simulations to verify the correctness of theoretical analysis. In addition, we show the impact of some factors on the relation between the deployment density and coverage performance through extensive numerical simulations.
A survey on QOS routing techniques used in network on chip

Recent trends in VLSI research fields archives the many cores systems are integrated into single chip silicon processor in terms of submicron unit. Also these multi core systems are performing huge parallel computation process efficiently using Network on Chip architecture. As router the important duty that in tends to eliminate the overall delay as well as Power dissipation. In this survey paper we are detailing common router techniques and problems with their operating techniques. And suggestion for improving their overall performance.
Performance Analysis and Evaluation of Cloud Radio Access Networks

The cloud radio access network (C-RAN) has been recognized as a promising paradigm for the emerging fifthgeneration wireless communication network. Unlike traditional coordinated multi-point (CoMP) technique aiming at cell edge users in long term evolution advanced (LTE-A) networks, CRANs support large-scale coordination (LSC) for the whole users owing to the pooling of baseband processing resources. However, the performance gap between C-RANs and CoMP-enabled LTEA networks has not been evaluated yet. In this paper, we discuss the LSC in C-RANs and traditional CoMP in LTE-A networks, considering the effects of the network density and imperfect channel state information. Then system level performances of C-RANs and CoMP-enabled LTE-A small cell networks are evaluated for a dense hotspot scenario. Results demonstrate that compared with the network without coordination, the CRAN improves the performance significantly with the increasing network density while the gain of the CoMP-enabled LTE-A network keeps nearly constant. Moreover, the C-RAN is more sensitive to the channel accuracy, which should be carefully considered for the implementation of C-RANs.
On Fair Rule Caching in Software Defined Radio Access Networks

In software defined radio access networks, base stations (BSs) operate according to commands received from a centralized controller. To avoid asking the controller frequently, BSs store these commands as rules in flow tables with limited size. This limitation necessitates fetching rules of some users from the controller causing longer processing delays. In this letter, we formulate the fair allocation of flow table spaces to the users within a cell as a mixed integer linear program which is an NP-hard problem. Nonetheless, we propose an optimal low-complexity solution. Finally, we show superior performance of this strategy versus other possible schemes.
FPSMM: Fuzzy probabilistic based semi morkov model among the sensor nodes for realtime applications

Emerging technology in terms of hardware and software over wireless sensor network result in various applications The main limitation of sensor nodes are less life span, more energy consumption. Many papers has explained about the stochastic model but nothing came out well. This leads to the motivation of our research work by proposing fuzzy based semi morkov model. This proposed model is very apt for more power management in evebt driven condition. The performance analysis part shows that our proposed work contribute better life time performance.