Empirical Analysis of Privacy Preservation Models for Cyber Physical Deployments from a Pragmatic Perspective

The difficulty of privacy protection in cyber-physical installations encompasses several sectors and calls for methods like encryption, hashing, secure routing, obfuscation, and data exchange, among others. To create a privacy preservation model for cyber physical deployments, it is advised that data privacy, location privacy, temporal privacy, node privacy, route privacy, and other types of privacy be taken into account. Consideration must also be given to other types of privacy, such as temporal privacy. The computationally challenging process of incorporating these models into any wireless network also affects quality of service (QoS) variables including end-to-end latency, throughput, energy use, and packet delivery ratio. The best privacy models must be used by network designers and should have the least negative influence on these quality-of-service characteristics. The designers used common privacy models for the goal of protecting cyber-physical infrastructure in order to achieve this. The limitations of these installations' interconnection and interface-ability are not taken into account in this. As a result, even while network security has increased, the network's overall quality of service has dropped. The many state-of-the-art methods for preserving privacy in cyber-physical deployments without compromising their performance in terms of quality of service are examined and analyzed in this research. Lowering the likelihood that such circumstances might arise is the aim of this investigation and review. These models are rated according to how much privacy they provide, how long it takes from start to finish to transfer data, how much energy they use, and how fast their networks are. In order to maximize privacy while maintaining a high degree of service performance, the comparison will assist network designers and researchers in selecting the optimal models for their particular deployments. Additionally, the author of this book offers a variety of tactics that, when used together, might improve each reader's performance. This study also provides a range of tried-and-true machine learning approaches that networks may take into account and examine in order to enhance their privacy performance

Physics-informed Neural Networks approach to solve the Blasius function

Deep learning techniques with neural networks have been used effectively in computational fluid dynamics (CFD) to obtain solutions to nonlinear differential equations. This paper presents a physics-informed neural network (PINN) approach to solve the Blasius function. This method eliminates the process of changing the non-linear differential equation to an initial value problem. Also, it tackles the convergence issue arising in the conventional series solution. It is seen that this method produces results that are at par with the numerical and conventional methods. The solution is extended to the negative axis to show that PINNs capture the singularity of the function at $\eta=-5.69

An Enhanced K-Medoid Clustering Algorithm

Data mining is a technique of mining information from the raw data. It is a non trivial process of identifying valid and useful patterns in data. Some of the major Data Mining techniques used for analysis are Association, Classification and Clustering etc. Clustering is used to group homogenous kind of data, but it is different approach from classification process. In the classification process data is grouped on the predefined domains or subjects. A basic clustering technique represents a list of topics for each data and calculates the distance for how accurately a data fit into a group. The Cluster is helpful to get fascinating patterns and structures from an outsized set of knowledge. There are a lots of clustering algorithms that have been proposed and they can be divided as: partitional, grid, density, model and hierarchical based. This paper propose the new enhanced algorithm for k-medoid clustering algorithm which eliminates the deficiency of existing k-medoid algorithm. It first calculates the initial medoids ‘k’ as per needs of users and then gives relatively better cluster. It follows an organized way to generate initial medoid and applies an effective approach for allocation of data points into the clusters. It reduces the mean square error without sacrificing the execution time and memory use as compared to the existing k-medoid algorithm

Maternal and fetal factors associated with stillbirth in singleton pregnancies in 13 hospitals across six states in India: a prospective cohort study

Methods: We conducted a secondary data analysis of a hospital-based cohort from the Maternal and Perinatal Health Research collaboration, India (MaatHRI), including pregnant women who gave birth between October 2018–September 2023. Data from 9823 singleton pregnancies recruited from 13 hospitals across six Indian states were included. Univariable and multivariable Poisson regression analysis were performed to examine the relationship between stillbirth and potential risk factors. Model prediction was assessed using the area under the receiver-operating characteristic (AUROC) curve. Results: There were 216 stillbirths (48 antepartum and 168 intrapartum) in the study population, representing an overall stillbirth rate of 22.0 per 1000 total births (95% confidence interval [CI]: 19.2–25.1). Modifiable risk factors for stillbirth were: receiving less than four antenatal check-ups (adjusted relative risk [aRR]: 1.75, 95% CI: 1.25–2.47), not taking any iron and folic acid supplementation during pregnancy (aRR: 7.23, 95% CI: 2.12–45.33) and having severe anemia in the third trimester (aRR: 3.37, 95% CI: 1.97–6.11). Having pregnancy/fetal complications such as hypertensive disorders of pregnancy (aRR: 1.59, 95% CI: 1.03–2.36), preterm birth (aRR: 4.41, 95% CI: 3.21–6.08) and birth weight below the 10th percentile for gestational age (aRR: 1.35, 95% CI: 1.02–1.79) were also associated with an increased risk of stillbirth. Identified risk factors explained 78.2% (95% CI: 75.0%–81.4%) of the risk of stillbirth in the population. Conclusion: Addressing potentially modifiable antenatal factors could reduce the risk of stillbirths in India

Captive breeding of a near threatened fish, pengba Osteobrama belangeri (Valenciennes, 1844) using three different inducing agents

Farm reared pengba, Osteobrama belangeri were induced to spawn in captivity during August, 2012 by injecting three different synthetic hormones, Ovaprim, Ovatide and Gonopro-FH. Single dose (1 ml kg-1 body weight) of each hormone was administered and results were recorded. Spawning was observed within 8 h after injection. Hatching of eggs were observed after 22±2 h of incubation at 27±1OC. The mean fertilization rate was 84.05±0.36% for Ovaprim, 79.17±3.95% for Ovatide and 84.85±0.89% for Gonopro-FH treated fish. The mean hatching rate was 84.69±1.73% with Ovaprim, 75.01±1.92% with Ovatide and 86.52±0.88% with Gonopro-FH. Gonopro-FH and Ovaprim gave 5.67 and 4.88% higher fertilization rate as well as 11.5 and 9.69% more hatching rate of eggs respectively as compared to Ovatide. Ovaprim and Gonopro-FH were found to be more effective in induced breeding of O. belangeri

Search for new physics in multijet events with at least one photon and large missing transverse momentum in proton-proton collisions at 13 TeV

A search for new physics in final states consisting of at least one photon, multiple jets, and large missing transverse momentum is presented, using proton-proton collision events at a center-of-mass energy of 13 TeV. The data correspond to an integrated luminosity of 137 fb−1, recorded by the CMS experiment at the CERN LHC from 2016 to 2018. The events are divided into mutually exclusive bins characterized by the missing transverse momentum, the number of jets, the number of b-tagged jets, and jets consistent with the presence of hadronically decaying W, Z, or Higgs bosons. The observed data are found to be consistent with the prediction from standard model processes. The results are interpreted in the context of simplified models of pair production of supersymmetric particles via strong and electroweak interactions. Depending on the details of the signal models, gluinos and squarks of masses up to 2.35 and 1.43 TeV, respectively, and electroweakinos of masses up to 1.23 TeV are excluded at 95% confidence level

Observation of the Rare Decay of the η Meson to Four Muons

A search for the rare η→μ+μ−μ+μ− double-Dalitz decay is performed using a sample of proton-proton collisions, collected by the CMS experiment at the CERN LHC with high-rate muon triggers during 2017 and 2018 and corresponding to an integrated luminosity of 101  fb−1. A signal having a statistical significance well in excess of 5 standard deviations is observed. Using the η→μ+μ− decay as normalization, the branching fraction B(η→μ+μ−μ+μ−)=[5.0±0.8(stat)±0.7(syst)±0.7(B2μ)]×10−9 is measured, where the last term is the uncertainty in the normalization channel branching fraction. This work achieves an improved precision of over 5 orders of magnitude compared to previous results, leading to the first measurement of this branching fraction, which is found to agree with theoretical predictions

Measurements of inclusive and differential cross sections for the Higgs boson production and decay to four-leptons in proton-proton collisions at s \sqrt{s} = 13 TeV

Measurements of the inclusive and differential fiducial cross sections for the Higgs boson production in the H → ZZ → 4ℓ (ℓ = e, μ) decay channel are presented. The results are obtained from the analysis of proton-proton collision data recorded by the CMS experiment at the CERN LHC at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb−1. The measured inclusive fiducial cross section is 2.73 ± 0.26 fb, in agreement with the standard model expectation of 2.86 ± 0.1 fb. Differential cross sections are measured as a function of several kinematic observables sensitive to the Higgs boson production and decay to four leptons. A set of double-differential measurements is also performed, yielding a comprehensive characterization of the four leptons final state. Constraints on the Higgs boson trilinear coupling and on the bottom and charm quark coupling modifiers are derived from its transverse momentum distribution. All results are consistent with theoretical predictions from the standard model