Values Of Epsilon Research Articles

CLUSTER ANALYSIS OF OBESITY RISK LEVELS USING K-MEANS AND DBSCAN METHODS

Obesity is defined as excessive fat accumulation and abnormal accumulation of adipose tissue in the human body that poses health risks. The causes of obesity are multifactorial and include environmental and individual factors. Several factors that cause obesity include genetic, behavioral and environmental factors. Obesity causes various problems in various fields, including health, employment, demographics, economics and family. The problem of obesity has a significant impact on public health. Therefore, understanding and predicting the level of obesity risk is important in efforts to prevent and treat obesity risk. Data on eating habits, physical activity, and other factors associated with obesity levels in certain populations can provide an important basis for understanding obesity risk. This research clusters the risk of obesity to find hidden patterns in the data. The stages in this research consist of pre-processing, clustering, and analysis. The clustering methods used are K-means and DBSCAN. In clustering using the K-means method with a parameter value of k , results are obtained with the same pattern as clustering using the DBSCAN method with a parameter value of epsilon and a minimum sample . In clustering using the K-means method with a parameter value of k , Four clusters were formed which had different patterns. The clustering results obtained in this research can be used as an effort to prevent and treat the risk of obesity.

DFT and experimental investigations on Pr substituted WO3 for electronic, thermoelectric and optical applications

The properties of Pr-doped WO3 thin films were studied to examine the optical, thermoelectric, and electrical response for optoelectronic and photovoltaic applications. The thin films were synthesized using the spin coating technique. The various properties were explored by employing the DFT approach. The X-ray diffraction analysis showed the cubic phase crystalline structure of pure and Pr-doped compositions. The morphology contained uniform rod-like uniform features of WO3 thin films which changed to the un-even and non-uniform geometry with increased width in maximum Pr-doped compositions. The projected density of states revealed the major contribution of O-p in valance and W-d in the conduction band while Pr-f orbital showed an additional significant contribution in the Pr-doped compositions. The thermoelectric properties were significantly changed with the increase in Pr doping contents. The highest values of real epsilon and refractive index were found approximately 8.8 and 3.5 for maximum Pr-doped contents. The absorption coefficient and optical conductivity displayed an increasing trend with increment of Pr-doping contents suggesting the suitability with enhanced photovoltaic and optoelectronic properties. A reduction in the band gap of WO3 was observed with Pr-doping content.

A Comparative Study for Privacy-Aware Recommendation Systems

Recommendation systems are sophisticated processes for information filtering designed to offer users tailored recommendations based on their preferences and interests. Users need help to choose between options as the amount of information on the web grows. As a result, it is critical to deliver personalized recommendations to consumers to promote user loyalty and satisfaction. Because recommender systems use sensitive user information such as ratings, comments, likes, and dislikes, this information can be leaked if no privacy measures are taken. As a result, we presented a comparison of privacy-aware recommendation systems in this paper. Two experiments are carried out. In the first experiment, we examined collaborative filtering algorithms on perturbed ratings and then compared hybrid, collaborative, and content-based algorithms on perturbed ratings in the second experiment. According to the results, the Singular Value Decomposition++ (SVDpp) algorithm presented the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values for epsilon 100, with 0.8889 and 0.6822, respectively. Furthermore, for epsilon 100, the hybrid filtering technique had the lowest RMSE and MAE rates of 0.90664 and 0.69813, respectively.

Response surface optimization and support vector regression modeling of microwave-assisted essential oil extraction from cumin seeds

The current research involved creating models using Response Surface Methodology (RSM) and Support Vector Regression (SVR) to forecast the amount of extractable essential oil that can be obtained from powdered cumin seeds. Influence of microwave power (140–280–420–560–700 W), amount of water (500–600–700–800–900 ml), duration of distillation (30–45–60–75–90 min) and soak time (15–30–45–60–75 min) on essential oil yield were investigated. Microwave Assisted Extraction (MAE) allowed higher recoveries compared to conventional Soxhlet extraction, without altering the chemical components of the extract. A five-level four FCC experimental design was developed using Minitab (15.1.20.0). A total of 31 runs were performed in microwave-assisted extraction apparatus. Experimental data obtained was then used for developing RSM and SVR models for the prediction of the yield of essential oil. The optimum conditions for maximum yield of cumin oil were given by RSM. Maximum yield of 3.4 ml (0.017 ml/g) was found at 140 W of microwave power, 500 ml of water, 90 min duration of distillation, and 15 min of soak time. In this work, epsilon SVR with RBF kernel was used. The grid search (depth-first search) methodology was applied for tuning the values of epsilon, gamma, and cost using the LIBSVM module on the MATLAB interface. The statistical parameters namely, average absolute relative error (AARE), coefficient of determination (R2), standard deviation (SD), and root mean square error (RMSE) were selected as the performance parameters. The developed SVR model was compared with the RSM model. The AARE values of 2.27% and 1.29%, R2 values of 0.86 and 0.99, SD values of 1.73 and 0.29, and RMSE values of 0.0284 and 0.0132 were obtained for RSM and SVR models respectively. It is found that SVR is more accurate and better tool for modeling of MAE process.

A single-stage optimization procedure for data envelopment analysis

In data envelopment analysis, a conventional procedure for testing efficiency of decision making units (DMUs) consists of two stages, each requiring solution of a linear program. The first stage identifies the input or output radial efficiency of a DMU, and the second stage maximizes the sum of component input and output slacks. A traditional alternative is the single-stage approximation of the two-stage procedure in which the objective functions of the first and second stages are combined, with the latter multiplied by a small positive constant epsilon. A known drawback of such approach is that very small values of epsilon may cause computational inaccuracies and large values do not allow a good approximation. In this paper, we develop a new single-stage linear programming approach that does not require the specification of a small constant epsilon and is completely equivalent to the conventional two-stage solution procedure. It produces exactly the same sets of primal and dual optimal solutions as the two separate stages of the two-stage approach. The new single-stage procedure is applicable to any convex polyhedral technology.

Customer segmentation with RFM models and demographic variable using DBSCAN algorithm

The aims of this research was to identify prospective customers by conducting customer segmentation based on recency, frequency, monetary (RFM) values and demographic variables. The step were selected the data and normalized. The normalized data were clustered using the density based spatial clustering of applications with noise (DBSCAN) algorithm. The k-dist graph was utilized with RStudio tools to identify the best values for epsilon and MinPts. The outcome of utilizing epsilon 0.06 and MinPts 3 was the identification of 5 clusters and 31 data points considered as noise, resulting in a silhouette index (SI) value of 0.4222. Based on the average RFM values, cluster 1 was categorized as prospective customers, while clusters 2, 3, 4, and 5 were designated as loyal customers. Furthermore, according to demographic analysis, the majority of customers are between the ages of 35 to 45, female, married, and housewives. Women, groceries, such as rice and cooking oil, were the most popular products. Besides, the customers were mostly lecturers and lived in Pekanbaru. This was compatible with the customer target of people from upper middle class, such as lecturers, and with the location of the mart as well, which was near a campus.

Structural and optical investigations on ZnO-PVDF-NiO advanced polymer composites for modern electronic devices

Polymer composites are recently introduced as flexible candidates for modern electronic devices. Transition metals oxides incorporated PVDF polymer composite thin films were successfully synthesized and investigated for an optical response. The effect of ZnO and NiO nanoparticles as PVDF fillers is studied in this work. Experimentally pure and doped PVDF uniform and evenly distributed thin films were synthesized by sol-gel based spin coating method. Structural studies were carried out with x-ray diffraction analysis which reveals the sharp traces of ZnO and NiO and endorses the presence of crystalline fillers in PVDF polymer composite thin films. The Field emission scanning electron microscope was used to examine the surface morphology of prepared thin films containing a smooth, uniform distribution and compact polymer traces. Refractive index, extinction, absorption coefficient, reflectivity, optical conductivity and real epsilon were extracted using spectroscopic ellipsometry. Enhanced response of optical parameters was observed in nanoparticles containing PVDF polymer composites. The absorption coefficient seems to increase with the increment of nano-filler contents, which makes these materials suitable for photovoltaic applications. The maxima of the refractive index were recorded as 1.6 for pure PVDF and 2.1 for maximum nano-filler content with an incredible shift to higher energy values. The optical conductivity was observed to increase with the incorporation of nanoparticles in PVDF. The maximum values of real epsilon were recorded as 2.3 for pure PVDF and 3.9 for the highest nano-filler containing composition. The increasing trend of real epsilon in nano-fillers containing compositions is attributed to the enhanced polarization and storage capability of these composites. The outcomes of this work are considered that ZnO-PVDF-NiO advanced polymer composites are promising candidates for enhanced modern electronic devices.

Gravity in {varvec{d=2+epsilon }} dimensions and realizations of the diffeomorphisms group

We discuss two separate realizations of the diffeomorphism group for metric gravity, which give rise to theories that are classically equivalent, but quantum mechanically distinct. We renormalize them in d=2+epsilon dimensions, developing a new procedure for dimensional continuation of metric theories and highlighting connections with the constructions that previously appeared in the literature. Our hope is to frame candidates for ultraviolet completions of quantum gravity in d>2 and give some perturbative mean to assess their existence in d=4, but also to speculate on some potential obstructions in the continuation of such candidates to finite values of epsilon . Our results suggest the presence of a conformal window in d which seems to extend to values higher than four.

An intelligent decision-making method for anti-jamming communication based on deep reinforcement learning

In order to solve the problem of intelligent anti-jamming decision-making in battlefield communication, this paper designs an intelligent decision-making method for communication anti-jamming based on deep reinforcement learning. Introducing experience replay and dynamic epsilon mechanism based on PHC under the framework of DQN algorithm, a dynamic epsilon-DQN intelligent decision-making method is proposed. The algorithm can better select the value of epsilon according to the state of the decision network and improve the convergence speed and decision success rate. During the decision-making process, the jamming signals of all communication frequencies are detected, and the results are input into the decision-making algorithm as jamming discriminant information, so that we can effectively avoid being jammed under the condition of no prior jamming information. The experimental results show that the proposed method adapts to various communication models, has a fast decision-making speed, and the average success rate of the convergent algorithm can reach more than 95%, which has a great advantage over the existing decision-making methods.

Spark plasma sintered GR-CNT/CaCu3Ti4O12 ceramic nanocomposites with tunable epsilon-negative and epsilon-near-zero property

Graphene-carbon nanotube/copper calcium titanate (GR-CNT/CCTO) ceramic nanocomposites were prepared by spark plasma sintering (SPS) technology. The correlation between GR-CNT content and epsilon-negative property was clarified in detail. Electrical percolation behavior was confirmed in nanocomposites with GR-CNT content rising, corresponding to change from hopping conductivity to metal-like conductivity. When three-dimensional (3D) GR-CNT networks were constructed in CCTO ceramics, epsilon-negative property was obtained, originating from low-frequency collective plasma oscillation of delocalized electrons in formed networks. Epsilon-near-zero (ENZ) property was respectively obtained at ~360 MHz and ~255 MHz. The negative value of epsilon ranged from ~103 to ~104, and plasma frequency of the composites increased from 12.8 GHz to 34.6 GHz with GR-CNT content ranging from 10 wt% to 12 wt%, which was analyzed by Drude model. In addition, the dielectric loss behavior and impedance response also were discussed. The inductive character of epsilon-negative materials was expounded via equivalent circuit analysis. We provide an effective paradigm of carbon/ceramic nanocomposites with epsilon-negative property by constructing 3D GR-CNT networks, which facilitates the clarification of regulation mechanism of epsilon-negative materials.

Designing Model for Truck Assignment Problem in Beef Delivery Using DBSCAN Algorithm

In beef route delivery, many logistics companies have problems in making delivery plans correctly. Most logistics companies have a limited number of trucks, but there are shipping schedules, locations and volumes of demand is not fixed, in addition to returning products with various reasons that must be collected. Therefore the purpose of this study is to assign trucks to fulfill all beef delivery activities at a minimum cost. One reason for research on truck assignments in beef delivery is the high cost of shipping because it uses cold chains. This study uses the DBSCAN method to obtain density values based on customer distance to the central warehouse and the number of delivery requests, the data used in this study is the data of product delivery to customers for 6 months. Clustering research results using DBSCAN show the maximum values for epsilon 0.7 and Minpoints 2 are 3 clusters with 6 noises. with these results divided of trucks based on clusters is 2 trucks for the area of Banten, 7 trucks for the area of Jakarta, Bogor, and Bekasi, 2 trucks for Malang, and Surabaya. And 1 truck for the Bali area and noise or non-permanent delivery. Keywords: Truck assignment, DBSCAN, algorithm, beef delivery.

Communication—Tunable and Weakly Epsilon-Negative Property Realized by Metallic Granular Metacomposites at Radio-Frequency Region

We fabricated metallic granular metacomposites by low-temperature pressureless sintering. A percolation behavior appears, corresponding to the observation of metal-like conductivity and epsilon-negative. Weakly epsilon-negative property is achieved in 70 wt% FeCrNi/CaCu3Ti4O12 metacomposites, showing small absolute value of epsilon between 0 and −150. The epsilon-negative property is tuned by adjusting metal content in metacomposites and high negative value of real permittivity (∼−104) is achieved. The superposition of Lorentz and Drude model explains the radio-frequency (RF) epsilon-negative. Epsilon-negative property correlates with inductive character as investigated by equivalent circuit analysis. Our work benefits the practical applications of epsilon-negative property in electromagnetic fields.

Bondi accretion in the spherically symmetric Johannsen\u2013Psaltis spacetime

The Johannsen–Psaltis spacetime explicitly violates the no-hair theorem. It describes rotating black holes with scalar hair in the form of parametric deviations from the Kerr metric. In principle, black hole solutions in any modified theory of gravity could be written in terms of the Johannsen–Psaltis metric. We study the accretion of gas onto a static limit of this spacetime. We utilise a recently proposed pseudo–Newtonian formulation of the dynamics around arbitrary static, spherically symmetric spacetimes. We obtain a potential that generalises the Paczyński–Wiita potential to the static Johannsen–Psaltis metric. We also perform a fully relativistic analysis of the geodesic equations in the static Johannsen–Psaltis spacetime. We find that positive (negative) values of the scalar hair parameter, epsilon _{3}, lower (raise) the accretion rate. Similarly, positive (negative) values of epsilon _{3} reduce (increase) the gravitational acceleration of radially infalling massive particles.

Towards an Improved Strategy for Solving Multi-Armed Bandit Problem

Multi-Armed Bandit (MAB) problem is one of the classical reinforcements learning problems that describe the friction between the agent’s exploration and exploitation. This study explores metaheuristics as optimization strategies to support Epsilon greedy in achieving an improved reward maximization strategy in MAB. In view of this, Annealing Epsilon greedy is adapted and PSO Epsilon greedy strategy is newly introduced. These two metaheuristics-based MAB strategies are implemented with input parameters, such as number of slot machines, number of iterations, and epsilon values, to investigate the maximized rewards under different conditions. This study found that rewards maximized increase as the number of iterations increase, except in PSO Epsilon Greedy where there is a non-linear behavior. Our Annealing-Epsilon greedy strategy performed better than Epsilon Greedy when the number of slot machines is 10, but Epsilon greedy did better when the number of slot machines is 5. At the optimal value of Epsilon, which we found at 0.06, Annealing Epsilon greedy performed better than Epsilon greedy when the number of iterations is 1000. But at number of iterations ≥ 1000, Epsilon greedy performed better than Annealing Epsilon greedy. A stable reward maximization values are observed for Epsilon greedy strategy within Epsilon values 0.02 and 0.1, and a drastic decline at epsilon > 0.1.

Differential Privacy in Practice: Expose your Epsilons!

Differential privacy is at a turning point. Implementations have been successfully leveraged in private industry, the public sector, and academia in a wide variety of applications, allowing scientists, engineers, and researchers the ability to learn about populations of interest without specifically learning about these individuals. Because differential privacy allows us to quantify cumulative privacy loss, these differentially private systems will, for the first time, allow us to measure and compare the total privacy loss due to these personal data-intensive activities. Appropriately leveraged, this could be a watershed moment for privacy.
 Like other technologies and techniques that allow for a range of instantiations, implementation details matter. When meaningfully implemented, differential privacy supports deep data-driven insights with minimal worst-case privacy loss. When not meaningfully implemented, differential privacy delivers privacy mostly in name. Using differential privacy to maximize learning while providing a meaningful degree of privacy requires judicious choices with respect to the privacy parameter epsilon, among other factors. However, there is little understanding of what is the optimal value of epsilon for a given system or classes of systems/purposes/data etc. or how to go about figuring it out.
 To understand current differential privacy implementations and how organizations make these key choices in practice, we conducted interviews with practitioners to learn from their experiences of implementing differential privacy. We found no clear consensus on how to choose epsilon, nor is there agreement on how to approach this and other key implementation decisions. Given the importance of these implementation details there is a need for shared learning amongst the differential privacy community. To serve these purposes, we propose the creation of the Epsilon Registry—a publicly available communal body of knowledge about differential privacy implementations that can be used by various stakeholders to drive the identification and adoption of judicious differentially private implementations.

Efficient Herd – Outlier Detection in Livestock Monitoring System Based on Density – Based Spatial Clustering

In today's society, increasing the quality and the productivity of dairy products are very important and need detailed data collection and analysis. Manual collection of data and its analysis for livestock monitoring is costly in terms of high man power and time consumption. In order to overcome this deficit, object detection and clustering methods are investigated in this research as it is in line with Smart Farming 4.0. Faster RCNN is used to help ranchers to detect livestock while clustering methods help to detect the herds and outliers effectively and efficiently. In clustering methods, K-means clustering technique and Density-Based Spatial Clustering of Application with Noise or DBScan clustering technique are adopted. In K-means clustering, k is an important parameter which represents the number of clusters. By changing the number of clusters, the pattern of clusters is observed. Then, the best k value is selected. In DBScan clustering, epsilon is an important parameter which represents the circle radius from a particular data point. The higher the value of epsilon, the formation of clusters becomes easier as it is easy to accept data point in a larger circle radius to form cluster. By changing the epsilon, the pattern of cluster is observed and chosen. Euclidean distance and Manhattan distance are used to compare the effects of different distance metrics on the results of clusters. Cluster pattern is compared between K-means and DBScan techniques. Obtained results show that DBScan overwhelmed K-means in term of efficient clustering in detecting the herds and outliers of livestock.

Postcollisional delamination and partial melting of enriched lithospheric mantle: Evidence from Oligocene (ca. 30 Ma) potassium-rich lavas in the Gemuchaka area of the central Qiangtang Block, Tibet

Postcollisional potassium (K)-rich magmatic rocks have important implications for unveiling the deep geodynamic processes active during the evolution of orogenic belts and the surface uplift of plateaus. However, their petrogenesis remains highly controversial. Here we report on zircon U-Pb and mineral Ar-40-Ar-39 age, zircon Hf-O isotope, mineral composition, whole-rock major and trace element composition, and Nd-Sr isotope data for the postcollisional K-rich lavas in the Gemuchaka area of the central Qiangtang Block (central Tibet). Age dating suggests that these lavas were generated in the late Early Oligocene (ca. 30 Ma). Two series of rocks were recognized: silica-undersaturated and -saturated lavas. Both series are geochemically characterized by continuous variation trends. All rocks are enriched in light rare earth elements (LREEs) relative to heavy REEs (HREEs) and depleted in Ta-Nb-Ti, with negligible Sr and Eu and obviously positive Pb anomalies. The rocks have higher Th/ La (0.2-0.8) and Nb/Ta (18.6-19.8) values than asthenospheric mantle-derived rocks but slightly variable values of epsilon(Nd) (t) (-6.0 to -4.6), (Sr-87/Sr-86)(i) (0.7076-0.7083), and zircon epsilon(HF)(t) (-7.2 to +0.8) and delta O-18 (7.1-8.5 parts per thousand). We suggest that the parental magmas of these K-rich lavas were likely derived by variable partial melting of lithospheric mantle metaso-matized by subducting continental sediment-derived melts with the addition of minor asthenospheric components. Element modeling results suggest that the basic end members were likely generated by fractionation processes dominated by olivine and minor clinopyroxene from parental magmas, and some other evolved rocks were generated by crystal (olivine, clinopyroxene, Fe-Ti oxides) fractionation from basic magmas. Combining geological and Cenozoic magmatic rock data in the Qiangtang Block, we propose that the Gemuchaka magmatism was most likely triggered by the delamination of lithospheric mantle thickened by early (Paleocene- Eocene) intracontinental subduction and subsequent asthenospheric upwelling.

An integrated data envelopment analysis and mixed integer non-linear programming model for linearizing the common set of weights

The problem of ranking efficient decision making units (DMUs) is of interest from both theoretical and practical points of view. In this paper, we propose an integrated data envelopment analysis and mixed integer non-linear programming (MINLP) model to find the most efficient DMU using a common set of weights. We linearize the MINLP model to an equivalent mixed integer linear programming (MILP) model by eliminating the non-linear constraints in which the products of variables are incorporated. The formulated MILP model is simpler and computationally more efficient. In addition, we introduce a model for finding the value of epsilon, since the improper choice of the non-Archimedean epsilon may result in infeasible conditions. We use a real-life facility layout problem to demonstrate the applicability and exhibit the efficacy of the proposed model.

The Electromagnetic Counterpart of the Binary Neutron Star Merger LIGO/Virgo GW170817. V. Rising X-Ray Emission from an Off-axis Jet

Abstract We report the discovery of rising X-ray emission from the binary neutron star merger event GW170817. This is the first detection of X-ray emission from a gravitational-wave (GW) source. Observations acquired with the Chandra X-ray Observatory (CXO) at t ≈ 2.3 days post-merger reveal no significant emission, with L x ≲ 3.2 × 10 38 erg s − 1 (isotropic-equivalent). Continued monitoring revealed the presence of an X-ray source that brightened with time, reaching L x ≈ 9 × 10 38 erg s − 1 at ≈ 15.1 days post-merger. We interpret these findings in the context of isotropic and collimated relativistic outflows (both on- and off-axis). We find that the broadband X-ray to radio observations are consistent with emission from a relativistic jet with kinetic energy E k ∼ 10 49 − 50 erg , viewed off-axis with θ obs ∼ 20 ° – 40 ° . Our models favor a circumbinary density n ∼ 10 − 4 – 10 − 2 cm − 3 , depending on the value of the microphysical parameter ϵ B = 10 − 4 – 10 − 2 . A central-engine origin of the X-ray emission is unlikely. Future X-ray observations at t ≳ 100 days, when the target will be observable again with the CXO, will provide additional constraints to solve the model degeneracies and test our predictions. Our inferences on θ obs are testable with GW information on GW170817 from advanced LIGO/Virgo on the binary inclination.

Optical transmission characteristics of epsilon negative tri-metal layer electrode for OLED

In this work, the optical characteristics of various epsilon negative tri-metal layer electrodes on organic light-emitting diodes (OLEDs) are investigated by full wave simulation and experiments to establish a transmittance database based on the kind and thickness of the metals. The established database shows that the optical transmittance of tri-metal layer electrode heavily depends on the absolute value of negative epsilon and the thickness of each metal layer. Tri-metal layer electrode structures were fabricated by sandwiching Ag, Cu, and Al between two Ni layers for red phosphorescent OLEDs. The electrical and optical characteristics of indium-tin-oxide (ITO) based and tri-metal layer electrodes-based devices are compared and the results indicate that the proposed tri-metal layer could replace ITO in optical applications.