A Practical Regular LDPC Coded Scheme for Physical-Layer Information Security

In this paper,we aim to design a practical low complexity low-density parity-check(LDPC)coded scheme to build a secure open channel and protect information from eavesdropping.To this end,we first propose a punctured LDPC coded scheme,where the information bits in a codeword are punctured and only the parity check bits are transmitted to the receiver.We further propose a notion of check node type distribution and derive multi-edge type extrinsic information transfer functions to estimate the security performance,instead of the well-known weak metric bit error rate.We optimize the check node type distribution in terms of the signal-to-noise ratio(SNR)gap and modify the progressive edge growth algorithm to design finite-length codes.Numerical results show that our proposed scheme can achieve a lower computational complexity and a smaller security gap,compared to the existing scrambling and puncturing schemes.

ESR-PINNs:Physics-informed neural networks with expansion-shrinkage resampling selection strategies

Neural network methods have been widely used in many fields of scientific research with the rapid increase of computing power.The physics-informed neural networks(PINNs)have received much attention as a major breakthrough in solving partial differential equations using neural networks.In this paper,a resampling technique based on the expansion-shrinkage point(ESP)selection strategy is developed to dynamically modify the distribution of training points in accordance with the performance of the neural networks.In this new approach both training sites with slight changes in residual values and training points with large residuals are taken into account.In order to make the distribution of training points more uniform,the concept of continuity is further introduced and incorporated.This method successfully addresses the issue that the neural network becomes ill or even crashes due to the extensive alteration of training point distribution.The effectiveness of the improved physics-informed neural networks with expansion-shrinkage resampling is demonstrated through a series of numerical experiments.

Information Field and Physical Noise Generator Entanglement

In experiments with physical noise generators based on electronic or photonic quantum events, significant deviations from random distributions have been repeatedly observed. To explain these effects, intention-based interactions between consciousness and mind with physical random processes have been suggested, either being caused by individual minds or by a proposed global mind. As these explanations involve physically undefined objects such as “mind” and “consciousness”, an explanatory model based on the concept of an information field is given herein, based on the concept of generalized quantum entanglement, including the entanglement of physical noise processes with information fields and an analogy to quantum teleportation. In addition, the non-random hypothesis of using such a physical noise generator in capturing qualitative characteristics of individuals is tested in a randomized controlled study with 100 participants.

Unleashing the Power of Information Theory: Enhancing Accuracy in Modeling Physical Phenomena

When building a model of a physical phenomenon or process, scientists face an inevitable compromise between the simplicity of the model (qualitative-quantitative set of variables) and its accuracy. For hundreds of years, the visual simplicity of a law testified to the genius and depth of the physical thinking of the scientist who proposed it. Currently, the desire for a deeper physical understanding of the surrounding world and newly discovered physical phenomena motivates researchers to increase the number of variables considered in a model. This direction leads to an increased probability of choosing an inaccurate or even erroneous model. This study describes a method for estimating the limit of measurement accuracy, taking into account the stage of model building in terms of storage, transmission, processing and use of information by the observer. This limit, due to the finite amount of information stored in the model, allows you to select the optimal number of variables for the best reproduction of the observed object and calculate the exact values of the threshold discrepancy between the model and the phenomenon under study in measurement theory. We consider two examples: measurement of the speed of sound and measurement of physical constants.

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

Wireless Physical Layer Security with Imperfect Channel State Information: A Survey

Physical layer security is an emerging technique for improving wireless communication security, which is widely regarded as a complement to cryptographic technologies. To design physical layer security techniques for practical scenarios, uncertainty and imperfections in the channel knowledge need to be taken into account. This paper is a survey of recent research on physical layer security that considers imperfect channel state information （CSI） at communication nodes. We first give an overview of the main information-theoretic measures of secrecy performance with imperfect CSI. Then, we describe several signal processing enhancements in secure transmission designs. These enhancements include secure on-off transmission, beamforming with artificial noise, and secure communication assisted by relay nodes or in cognitive radio systems. Recent studies of physical layer security in large-scale decentralized wireless networks are also summarized. Finally, open problems for on-going and future research are discussed.

Establishing minimum clinically important difference values for the Patient-Reported Outcomes Measurement Information System Physical Function, hip disability and osteoarthritis outcome score for joint reconstruction, and knee injury and osteoarthritis out

AIM To establish minimum clinically important difference(MCID) for measurements in an orthopaedic patient population with joint disorders.METHODS Adult patients aged 18 years and older seeking care for joint conditions at an orthopaedic clinic took the Patient-Reported Outcomes Measurement Information System Physical Function(PROMIS~? PF) computerized adaptive test(CAT), hip disability and osteoarthritis outcome score for joint reconstruction(HOOS JR), and the knee injury and osteoarthritis outcome score for joint reconstruction(KOOS JR) from February 2014 to April 2017. MCIDs were calculated using anchorbased and distribution-based methods. Patient reports of meaningful change in function since their first clinic encounter were used as an anchor.RESULTS There were 2226 patients who participated with a mean age of 61.16(SD = 12.84) years, 41.6% male, and 89.7% Caucasian. Mean change ranged from 7.29 to 8.41 for the PROMIS~? PF CAT, from 14.81 to 19.68 for the HOOS JR, and from 14.51 to 18.85 for the KOOS JR. ROC cut-offs ranged from 1.97-8.18 for the PF CAT, 6.33-43.36 for the HOOS JR, and 2.21-8.16 for the KOOS JR. Distribution-based methods estimated MCID values ranging from 2.45 to 21.55 for the PROMIS~? PF CAT; from 3.90 to 43.61 for the HOOS JR, and from 3.98 to 40.67 for the KOOS JR. The median MCID value in the range was similar to the mean change score for each measure and was 7.9 for the PF CAT, 18.0 for the HOOS JR, and 15.1 for the KOOS JR.CONCLUSION This is the first comprehensive study providing a wide range of MCIDs for the PROMIS? PF, HOOS JR, and KOOS JR in orthopaedic patients with joint ailments.

Measurement and Assessment of Physical Activity by Information and Communication Technology

This study provides explorative insights into the information and communication technology （ICT） for promoting the physical activity level. ICT has provided innovative ideas and perspectives for PA measurement, assessment, evaluation and health intervention. ICT that aims to increase exercise for the entire population should be of a well-oriented and easy-to-use design with the options of tailored and personalized feedback, coaching, and ranking and supporting; it should be capable of setting goals and working with a schedule and be accompanied by a website to provide overviews of the users＇ exercise results and progress.

Mass-Oscillators as Information Memories of Action

In the theory of physical information, the physical phenomena of electromagnetism, quantum mechanics and gravity can be described by means of the action as information enclosed in four dimensional structures with oscillator properties, under the conditions of the Hamilton principle. The present report shows that it is also possible to simulate the behaviour of the mass under these conditions. As a result, among other things, the statements are obtained that the mass is stored virtual action;the rest frame of elementary objects and the inertia of matter are caused by the action stored in the mass oscillators.

The Information Protection in Automatic Reconstruction of Not Continuous Geophysical Data Series

We show a quantitative technique characterized by low numerical mediation for the reconstruction of temporal sequences of geophysical data of length L interrupted for a time ΔT where . The aim is to protect the information acquired before and after the interruption by means of a numerical protocol with the lowest possible calculation weight. The signal reconstruction process is based on the synthesis of the low frequency signal extracted for subsampling (subsampling &#8711Dirac = ΔT in phase with ΔT) with the high frequency signal recorded before the crash. The SYRec (SYnthetic REConstruction) method for simplicity and speed of calculation and for spectral response stability is particularly effective in the studies of high speed transient phenomena that develop in very perturbed fields. This operative condition is found a mental when almost immediate informational responses are required to the observation system. In this example we are dealing with geomagnetic data coming from an uw counter intrusion magnetic system. The system produces (on time) information about the transit of local magnetic singularities (magnetic perturbations with low spatial extension), originated by quasi-point form and kinematic sources (divers), in harbors magnetic underwater fields. The performances of stability of the SYRec system make it usable also in long and medium period of observation (activity of geomagnetic observatories).

Physics informed machine learning: Seismic wave equation

Similar to many fields of sciences,recent deep learning advances have been applied extensively in geosciences for both small-and large-scale problems.However,the necessity of using large training data and the’black box’nature of learning have limited them in practice and difficult to interpret.Furthermore,including the governing equations and physical facts in such methods is also another challenge,which entails either ignoring the physics or simplifying them using unrealistic data.To address such issues,physics informed machine learning methods have been developed which can integrate the governing physics law into the learning process.In this work,a 1-dimensional(1 D)time-dependent seismic wave equation is considered and solved using two methods,namely Gaussian process(GP)and physics informed neural networks.We show that these meshless methods are trained by smaller amount of data and can predict the solution of the equation with even high accuracy.They are also capable of inverting any parameter involved in the governing equation such as wave velocity in our case.Results show that the GP can predict the solution of the seismic wave equation with a lower level of error,while our developed neural network is more accurate for velocity(P-and S-wave)and density inversion.

Information visualization,physicality and intuitive use for tangible user interfaces

In the last two decades, tangible user interfaces （TUIs） have emerged as a new interface type that interlinks the digital and physical worlds. TUIs show a potential to enhance the way in which people interact with digital information. First, this paper exam- ines the existing body of work on tangible user interfaces and discusses their application domains, especially information visualiza- tion. Then it provides a definition of intuitive use and reviews formerly separated ideas on physicality. As interaction has an impact on the overall product experience, we also discuss whether intuitive use influences the users＇ aesthetic judgements of such products.

Discovering Phase Field Models from Image Data with the Pseudo-Spectral Physics Informed Neural Networks

In this paper,we introduce a new deep learning framework for discovering the phase-field models from existing image data.The new framework embraces the approximation power of physics informed neural networks(PINNs)and the computational efficiency of the pseudo-spectral methods,which we named pseudo-spectral PINN or SPINN.Unlike the baseline PINN,the pseudo-spectral PINN has several advantages.First of all,it requires less training data.A minimum of two temporal snapshots with uniform spatial resolution would be adequate.Secondly,it is computationally efficient,as the pseudo-spectral method is used for spatial discretization.Thirdly,it requires less trainable parameters compared with the baseline PINN,which significantly simplifies the training process and potentially assures fewer local minima or saddle points.We illustrate the effectiveness of pseudo-spectral PINN through several numerical examples.The newly proposed pseudo-spectral PINN is rather general,and it can be readily applied to discover other FDE-based models from image data.

Can Information Theory Help to Formulate an Optimal Model of a Physical Phenomenon?

This article deals with the problem of calculating the comparative uncertainty of the main variable in the model of the studied physical phenomenon, which depends on a qualitative and quantitative set of variables. The choice of variables is determined by preliminary information available to the observer and dependent on his knowledge, experience and intuition. The finite value of the amount of information available to the researcher leads to the inevitable aberration of the observed object. This causes the existence of an unremovable and intractable processing by any statistical methods, a comparative (respectively, relative) uncertainty of the model. The goal is to present a theoretical justification for the existence of this uncertainty and proposes a procedure for its calculation. The practical application of the informational method for choosing the preferred model for the Einstein formula and for calculating the speed of sound is demonstrated.

Information behaviors of Chinese K-12physical education teachers:A survey study

Purpose: Given the unique characteristics of physical education(PE) teaching in K-12education,PE teachers' information behaviors deserve special attention.This article reports a survey study of PE teachers' information behaviors,covering information literacy skills and behaviors of information seeking and information use.Design/methodology/approach:A questionnaire survey was conducted of K-12 PE teachers in the Tianjin municipal region of China,with a response rate of 61.9%.Findings:PE teachers lack skills with information retrieval systems in general.The Internet continues to be their primary information source,and they rely more on personal collection and colleagues than the school library for teaching materials.They rarely develop a searching strategy,employ querying tactics,or use advanced search functions,and they tend to be content with finding a few relevant articles.Research limitations:The survey is limited to the Tianjin municipal region in scope.Though attempting to reach 210 participants from 40 schools,it yielded only 130 valid responses.A larger survey covering more regions and with greater responses may be useful.Practical implications:Insights from this study inform the educational and on-job training of K-12 PE teachers to improve their information literacy skills.Originality/value:Little research exists on PE teachers' behaviors of information seeking.This study bridges the gap and enriches our understanding of K-12 teachers' information behaviors.

A new method to solve the Reynolds equation including mass-conserving cavitation by physics informed neural networks(PINNs)with both soft and hard constraints

In this work,a new method to solve the Reynolds equation including mass-conserving cavitation by using the physics informed neural networks(PINNs)is proposed.The complementarity relationship between the pressure and the void fraction is used.There are several difficulties in problem solving,and the solutions are provided.Firstly,the difficulty for considering the pressure inequality constraint by PINNs is solved by transferring it into one equality constraint without introducing error.While the void fraction inequality constraint is considered by using the hard constraint with the max-min function.Secondly,to avoid the fluctuation of the boundary value problems,the hard constraint method is also utilized to apply the boundary pressure values and the corresponding functions are provided.Lastly,for avoiding the trivial solution the limitation for the mean value of the void fraction is applied.The results are validated against existing data,and both the incompressible and compressible lubricant are considered.Good agreement can be found for both the domain and domain boundaries.

Secure Transmission Scheme Based on Dual-Thresholds for Simultaneous Wireless Information and Power Transfer

This paper studies a simultaneous wireless information and power transfer system with multiple external eavesdroppers and internal curious users.We model the random network by Poisson cluster process in consideration of the case where eavesdroppers hide around certain targets.Focusing on the users that work in harvesting-transmitting mode with time switching receivers,we establish communication model via time division multiple access.On this basis,we propose a lightweight secure transmission scheme based on dual-thresholds for physical-layer security enhancement,which consists of two protocols applied to the downlink(DL) and uplink(UL) transmission respectively.In the DL,we design a dynamic information-power switching transmission protocol based on signal-to-noise ratio threshold,which provides an opportunistic approach to reform the fixed period allocation of information and power transfer;in the UL,an energy threshold is proposed for users to control the transmission,which is called a user-led on-off transmission protocol.Furthermore,we give a comprehensive performance analysis for the proposed scheme in terms of delay,reliability,security and secrecy throughput.Based on the analysis results,we optimize the two thresholds and the DL-UL allocationcoefficient to maximize the secrecy throughput.Simulation results show the proposed scheme can bring about a substantial secrecy gain.

Physics-constrained indirect supervised learning

This study proposes a supervised learning method that does not rely on labels.We use variables associated with the label as indirect labels,and construct an indirect physics-constrained loss based on the physical mechanism to train the model.In the training process,the model prediction is mapped to the space of value that conforms to the physical mechanism through the projection matrix,and then the model is trained based on the indirect labels.The final prediction result of the model conforms to the physical mechanism between indirect label and label,and also meets the constraints of the indirect label.The present study also develops projection matrix normalization and prediction covariance analysis to ensure that the model can be fully trained.Finally,the effect of the physics-constrained indirect supervised learning is verified based on a well log generation problem.