Cornering the universal shape of fluctuations

Understanding the fluctuations of observables is one of the main goals in science, be it theoretical or experimental, quantum or classical. We investigate such fluctuations in a subregion of the full system, focusing on geometries with sharp corners. We report that the angle dependence is super-universal: up to a numerical prefactor, this function does not depend on anything, provided the system under study is uniform, isotropic, and correlations do not decay too slowly. The prefactor contains important physical information: we show in particular that it gives access to the long-wavelength limit of the structure factor. We exemplify our findings with fractional quantum Hall states, topological insulators, scale invariant quantum critical theories, and metals. We suggest experimental tests, and anticipate that our findings can be generalized to other spatial dimensions or geometries. In addition, we highlight the similarities of the fluctuation shape dependence with findings relating to quantum entanglement measures.

Applications of Artificial Intelligence (AI) in Libraries

Artificial intelligence (AI) has brought about new prospects for expanding research in all areas. The presence of artificial intelligence technologies in all spheres of work has made the future promising. The application of AI has contributed immensely to the provision and use of library information resources and has helped to achieve the goals and objectives of the library. Librarians need to be innovative in their thinking to stay relevant in their jobs because AI has found numerous applications in libraries ranging from book filing to book delivery. Its application brought about several new possibilities in the library such as connecting physical library information resources and electronic resources, and also associating video help with physical information materials and objects. The chapter discussed some components of AI, library services it can be applied to, the benefits of its application, as well as the challenges libraries face in the application of artificial intelligence in the library.

A Semiparametric Model-Based Friction Compensation Method for Multi-Joint Industrial Robot

Industrial robots have received enormous attention due to their widespread uses in modern manufacturing. However, due to the frictional discontinuous and other unknown dynamics in robotic system, existing researches are limited to simulation and single- or double-joint robot. In this paper, we introduce a semiparametric controller combined by a radial basis function neural network (RBFNN) and complete physical model considering joint friction. First, to extend the NN controller to real-world problems, the continuously differentiable friction (CDF) model is adopted to bring physical information into the learning process. Then, RBFNN is employed to approximate the model error and other unmolded dynamics, and the parameters of CDF model are updated online according to its learning ability. The stability of the robot system can be guaranteed by the Lyapunov theory. The primary parameters of CDF model are determined by the identification experiment and subsequently iteratively updated by the NN. Real-time tracking tasks are performed on a six degree of freedom (DoF) manipulator to follow the desired trajectory. Experimental results demonstrate the effectiveness and superiority of the proposed controller, especially at low speed.

Kepastian Hukum Pemegang Hak atas Tanah dalam Sistem Hukum Pertanahan di Indonesia

Land rights certificates still face the possibility of lawsuits from other parties who feel they have rights to the land, so that if it can be legally proven that he is the real owner, the land rights certificate can be canceled. The purpose of this study is to determine the legal certainty of land rights holders in the land law system in Indonesia and the government's efforts to provide legal certainty to land rights holders. The type of research used in this research is normative legal research. Sources of data used are primary and secondary data sources. The technique of collecting legal materials is done by recording, studying and reading legal doctrines related to the legal issues under study. The legal material analysis technique used to process the legal material obtained is by using analytical descriptive data analysis. The results of the study indicate that the evidentiary strength of a certificate of land rights owned by the right holder is basically guaranteed by law because it is written clearly about the types of rights, physical information regarding the land, the burden on the land and legal events that are interconnected with the land. then the data is considered correct

Differential Entropy: An Appropriate Analysis to Interpret the Shape Complexity of Self-Similar Organic Islands

Differential entropy, along with fractal dimension, is herein employed to describe and interpret the shape complexity of self-similar organic islands. The islands are imaged with in situ Atomic Force Microscopy, following, step-by-step, the evolution of their shape while deposition proceeds. The fractal dimension shows a linear correlation with the film thickness, whereas the differential entropy presents an exponential plateau. Plotting differential entropy versus fractal dimension, a linear correlation can be found. This analysis enables one to discern the 6T growth on different surfaces, i.e., native SiOx or 6T layer, and suggests a more comprehensive interpretation of the shape evolution. Changes in fractal dimension reflect rougher variations of the island contour, whereas changes in differential entropy correlates with finer contour details. The computation of differential entropy therefore helps to obtain more physical information on the island shape dependence on the substrate, beyond the standard description obtained with the fractal dimension.

Autopoiesis and Its Efficacy—A Metacybernetic View

This paper seeks to explain the nature of autopoiesis and its capacity to be efficacious, and to do this, it uses agency theory as embedded in metacybernetics. Agency, as a generalised intelligent adaptive living system, can anticipate the future once it has internalised a representation of an active contextual situation through autopoiesis. The role of observation and the nature of internalisation will be discussed, explaining that the latter has two states that determine agency properties of cognition. These are assimilation and accommodation. Assimilation is an information process and results in implicit cognition and recognition, whereas accommodation uses assimilated information delivering explicit cognition, recognition, and conscious awareness with rationality. Similarly, anticipation, a required property of the living, has two states, weak and strong, and these correspond to the two states of internalisation. Autopoiesis has various properties identifiable through the lenses of three autonomous but configurable schemas: General Collective Intelligence (GCI), Eigenform, and Extreme Physical Information (EPI). GCI is a pragmatic evolutionary approach concerned with a contextually connected purposeful and relatable set of task processes, each undertaken by a team of subagencies seeking collective fitness. Eigenform is a symbolic approach that is concerned with how observations can be suitably internalised and thus be used as a token to determine future behaviour, and how that which has been internalised can be adopted to anticipate the future. Extreme Physical Information (EPI) is an empirical approach concerned with acquiring information through observation of an unknown parameter through sampling regimes. The paper represents the conceptualisations of each schema in terms of autopoietic efficacy, and explores their configurative possibilities. It will adopt the ideas delivered to enhance explanations of the nature of autopoiesis and its efficacy within metacybernetics, providing a shift in thinking about autopoiesis and self-organisation.

Estimation of Health-Related Physical Fitness (HRPF) Levels of the General Public Using Artificial Neural Network with the National Fitness Award (NFA) Datasets

Estimation of health-related physical fitness (HRPF) levels of individuals is indispensable for providing personalized training programs in smart fitness services. In this study, we propose an artificial neural network (ANN)-based estimation model to predict HRPF levels of the general public using simple affordable physical information. The model is designed to use seven inputs of personal physical information, including age, gender, height, weight, percent body fat, waist circumference, and body mass index (BMI), to estimate levels of muscle strength, flexibility, maximum rate of oxygen consumption (VO2max), and muscular endurance. HRPF data (197,719 sets) gathered from the National Fitness Award dataset are used for training (70%) and validation (30%) of the model. In-depth analysis of the model’s estimation accuracy is conducted to derive optimal estimation accuracy. This included input/output correlation, hidden layer structures, data standardization, and outlier removals. The performance of the model is evaluated by comparing the estimation accuracy with that of a multiple linear regression (MLR) model. The results demonstrate that the proposed model achieved up to 10.06% and 30.53% improvement in terms of R2 and SEE, respectively, compared to the MLR model and provides reliable estimation of HRPF levels acceptable to smart fitness applications.

Mobile Edge Computing for the Internet of Vehicles

The advancement of cyber physical information has led to the pervasive use of smart vehicles while enabling various types of powerful mobile applications, which usually require high-intensity processing under strict delay constraints. Given their limited on-board computing capabilities, smart vehicles can offload these processing tasks to edge servers for execution. However, a highly dynamic topology, a complex vehicular communication environment, and edge node heterogeneity pose significant challenges in vehicular edge computing management. To address these challenges, in this chapter we investigate the characteristics of edge computing from both the application and service perspectives and introduce a hierarchical edge computing framework. Moreover, we leverage artificial intelligence technology to propose efficient task offloading and resource scheduling schemes.

Influencing Factors of e-Commerce Enterprise Development Based on Mobile Computing Big Data Analysis

With the development of increasingly advanced information technology and electronic technology, especially with regard to physical information systems, cloud computing systems, and social services, big data will be widely visible, creating benefits for people and at the same time facing huge challenges. In addition, with the advent of the era of big data, the scale of data sets is getting larger and larger. Traditional data analysis methods can no longer solve the problem of large-scale data sets, and the hidden information behind big data is digging out, especially in the field of e-commerce. We have become a key factor in competition among enterprises. We use a support vector machine method based on parallel computing to analyze the data. First, the training samples are divided into several working subsets through the SOM self-organizing neural network classification method. Compared with the ever-increasing progress of information technology and electronic equipment, especially the related physical information system finally merges the training results of each working set, so as to quickly deal with the problem of massive data prediction and analysis. This paper proposes that big data has the flexibility of expansion and quality assessment system, so it is meaningful to replace the double-sidedness of quality assessment with big data. Finally, considering the excellent performance of parallel support vector machines in data mining and analysis, we apply this method to the big data analysis of e-commerce. The research results show that parallel support vector machines can solve the problem of processing large-scale data sets. The emergence of data dirty problems has increased the effective rate by at least 70%.