Miracle-dz/UniFAR-FTUs huggingface dataset:
AI-based prediction for Ultra Reliable Low Latency service performance in industrial environments

This article investigates the usage of Artificial Intelligence (AI) techniques in the prediction of network performance for Industrial Internet of Things (IIoT). In industrial environments, 5G Ultra Reliable Low Latency Communications (URLLC) are intended for serving critical services with very stringent latency requirements, such as those involving collaborative robots. Even if the flexible 5G New Radio (NR) design is able to achieve the target IIoT performances, the necessary spectrum resources need to be available and reserved for URLLC. A Quality of Service (QoS) prediction scheme is thus needed for anticipating performance degradation and undertake necessary actions, such as network resource provisioning or application adaptation, e.g. by entering an adapted mode. We explore the design of AI algorithms for QoS prediction in industrial environments, and compare different tools for regression and classification, including Neural Networks (NN) and K Nearest Neighbors (K-NN). We explore prediction based on Signal to Interference and Noise Ratio (SINR), or simply based on the position of robots within the plant. As the latency degradation event is rare in general, we observe that the training data is highly imbalanced leading to a low prediction accuracy. We show how the prediction performance can be enhanced by importance sampling techniques and by a modified detection threshold in what we call M-KNN scheme.
Kinematic Calibration in Local Assembly Space of a Six-axis Industrial Robot for Precise Assembly

The research on the calibration of industrial robots mainly focuses on the global workspace, but it is difficult to ensure that industrial robots have good absolute positioning accuracy in the workspace. This paper proposes a kinematic calibrating method of industrial robot in local assembly space to improve the positioning accuracy. The kinematic error model of industrial robot is established based on modified Denavit-Hartenberg (MDH) model. The influence of redundant error parameters on kinematic parameter identification is analyzed. The method used in kinematic parameters identification is improved by using correlation tolerance and matrix singular value decomposition. Then, simulation and experimental test are carried to investigate the performance of the calibrating method. The experimental results indicate that the positioning accuracy inside the workspace is significantly reduced from 1.716 mm to 0.149 mm.
Conformational Behavior of Polyampholytes Grafted onto Spherical Particles

We developed a method to investigate the conformational behavior of the spherical brushes using molecular dynamics (MD) simulations. The brush consists of the spherical particle coated with polyampholyte chains. The effects of charge sequences on radial distribution function and monomer density for the polyampholyte brushes were investigated. We found that the brush was folded when the diblock polyampholyte chains grafted on spherical particles.
Hierarchical Federated Learning for the Next Generation IoT

Federated Learning is a promising decentralized machine learning approach, which has the potential to realize the vision of next-generation internet-of-things (NGIoT), by offering intelligent services and meeting the privacy and low latency requirements. By leveraging the combination of edge servers, as intermediate model aggregators, and the central cloud server, as global model aggregator, the concept of Hierarchical Federated Learning (HFL) has recently emerged. In this paper, we aim to minimize the delay of a global HFL round, under user energy requirements. We jointly optimize the computation and communication resources, as well as the user-edge assignment, in order to minimize the overall delay. The formulated non-convex combinatorial problem, is optimally solved by being decomposed into two disjoint subproblems, namely the resource allocation and user-edge assignment. Finally, the simulation results demonstrate the effectiveness of the proposed methods in terms of delay reduction, compared to selected benchmarks, while insights for the networks' behavior are provided.
Sequential Statistical Analysis-Based Method for Attacks Detection in Cognitive Radio Networks

This Cognitive radio networks are vulnerable to specific intrusions due to the unique cognitive characteristics of these networks. This DoS attacks are known as the Primary User Emulation Attack and the Spectrum Sensing Data Falsification. If the intruder behavior is not statistically identical to the behavior of the primary users, intrusion detection techniques based on observing the energy of the received signals can be used. Both machine learning-based intrusion detection and sequential statistical analysis can be effectively applied. However, in some cases, statistical sequential analysis has some advantages in dealing with such challenges. This paper discusses aspects of using statistical sequential analysis methods to detect attacks in Cognitive radio networks.
IoT for the future of sustainable supply chain management in Industry 4.0: A Systematic Literature Review

Over the last decade. , adopting sustainability has become an increasingly important feature for industries to remain competitive in the global market. The global digital revolution altered the physical events in the manufacturing industries. , such as procurement. , production/assembling, and distribution of goods. In the context of Industry 4.0, the future supply chains (SCs) on how physical objects are transported, handled, stored, supplied, realized, and used around the world can be reshaped by the Physical Internet to improve SC efficiency and sustainability, and Internet of things (IoT) allows industry-relevant items such as materials, sensors, machines, and products, or in terms of Physical Internet containers, to all be connected and communicate with one another. On the other hand, manufacturing companies are more concerned about the adoption of sustainability in the SC. Furthermore, as consumers become more concerned about purchasing environmentally friendly products, SCs are motivated to spend more on sustainability efforts and Industry 4.0 technology, resulting in a more sustainable world. Changing market conditions have compelled these organizations to examine their SC efforts to effectively penetrate sustainability through diverse strategies such as lean, green, circular, and industry 4.0, among others. This paper seeks to identify the contributions of IoT to sustainable supply chain management (SSC) through a systematic literature review of the existing literature. In the current context, one of the focus areas for researchers and practitioners in the IoT. Researchers working in the SSC and industry 4.0 will benefit from the findings of this study, while practitioners can use the prioritized solution measures to develop effective strategies to overcome sustainable supply chain adoption problems.
Relativistic Gas Drag on Dust Grains and Implications

We study the drag force on grains moving at relativistic velocities through interstellar gas and explore its application. First, we derive a new analytical formula of the drag force at high energies and find that it is significantly reduced compared to the classical model. Second, we apply the obtained drag force to calculate the terminal velocities of interstellar grains by strong radiation sources such as supernovae and active galactic nuclei (AGNs). We find that grains can be accelerated to relativistic velocities by very luminous AGNs. We then quantify the deceleration of relativistic spacecraft proposed by the Breakthrough Starshot initiative due to gas drag on a relativistic lightsail. We find that the spacecraft’s decrease in speed is negligible because of the suppression of gas drag at relativistic velocities, suggesting that the lightsail may be open for communication during its journey to α Centauri without causing a considerable delay. Finally, we show that the damage to relativistic thin lightsails by interstellar dust is a minor effect.
Backscattering in a helical liquid induced by Rashba spin-orbit coupling and electron interactions: Locality, symmetry, and cutoff aspects

The combination of the time-reversal-symmetric single-particle backscattering field (commonly known as Rashba spin-orbit coupling) and nonbackscattering electron interactions is generally expected to produce inelastic backscattering in one-dimensional helical electron liquids at the edge of two-dimensional topological insulators, as theoretically predicted in a number of works. An opposite conclusion of absent backscattering was reached in a recent work [H. -Y. Xie et al., Phys. Rev. Lett. 116, 086603 (2016)] for the ``local'' model of the backscattering field and interactions. Motivated to resolve this potential controversy, in the present work, we study backscattering effects employing fermionic perturbation theory and considering quite general forms of the backscattering field and electron interactions. We discover that backscattering effects are crucially sensitive to the locality properties of the backscattering field and electron interactions, to the symmetry of the latter, as well as to the presence or absence of the cutoff of the electron spectrum. We find that backscattering is indeed absent under the following assumptions: (i) local backscattering field; (ii.a) local or (ii.b) SU(2)-symmetric interactions; (iii) absent cutoff of the edge-state spectrum. However, violation of any of these conditions leads to backscattering. This also reconciles with the results based on the bosonization technique. We calculate the associated backscattering current, establish its low-bias scaling behavior, and predict a crossover between two different scaling regimes. The main implication of our findings is that backscattering of some magnitude is inevitable in a real system, although it could be quite suppressed for nearly local backscattering field and interactions.
Research on Fault Prognostic of Photovoltaic System Based on LSTM-SA

In the fault prognostic of photovoltaic systems, it is difficult to establish mathematical or physical models of complex components or systems. Therefore, this paper proposes a hybrid model of LSTM-SA, based on the principle of self-attention(SA) mechanism and long short-term memory (LSTM) neural network, combining the idea of self-attention and LSTM for timing problems processing capability to prognosticate faults of different equipment. Experimental verification of LSTM, LSTM-SA, BPNN and RNN models using the data of#102, #110 and #519 equipment respectively shows that the root mean square error (RMSE) of the model based on LSTM-SA is lower than that of the other three models in sunny days, indicating that the LSTM model with self-attention mechanism is optimized. Finally, the mixed model based on LSTM-SA is used to prognosticate the fault of different devices. The results are as follows: the fault of the #611 device at the 136th time point, and the fault of the #513 device at the 187th time point.
Knowledge Graph Semantic Annotation and Population with Real-Time Events Data from GDELT

The availability of large event data brings new opportunities for social analysis and informed policy-making processes. GDELT (the Global Database of Events, Language, and Tone) is a knowledge base that contains event data from news media from all around the world in different languages, and captures the main events about physical activities, entities and concepts. Semantically annotating and representing event data from GDELT for knowledge graph construction in real time is a challenging task. Current related work does not benefit from other freely available background information and Linked Open Data (LOD) sources. This paper presents ongoing work on automatic semantic annotation and representation of the GDELT 2.0 event stream in real time and discusses the benefits of representing events using semantic technologies according to an Event Description Ontology.
Text to 3D,2D scene generation systems, Frameworks and approaches: a survey

Generating 3D scene and animation plays an important role in many domain of our daily life. However, the difficulty of its creating manually and its consumption of effort and time which pushed many and many researchers to shift their interest into the field of automatic 3D scene generation and precisely from natural language description. The results obtained during the last few years revived hopes for the widespread availability of 3D scenes in our daily activities. In this survey, we will give an overview of the most popular “Text” to “3D scenes” converting systems and their architectures highlighting their strengths and weaknesses.
A Multichannel Miniaturized Dielectric Blood Coagulometer for Point-of-Care Assessment of Hemostasis

This paper presents a standalone, multichannel, miniaturized impedance analyzer (MIA) for dielectric coagulometry measurements on human whole blood in a microfluidic sensor. The system incorporates a front-end interface board for 4-channel impedance measurements at an excitation frequency of 1 MHz, an integrated resistive heater formed by a printed-circuit board (PCB) trace to keep the blood sample at a physiologic temperature of 37°C, an ADALM2000 software-defined instrument module for signal generation and data acquisition, and a Raspberry Pi-based embedded computer with 7-inch touchscreen display for signal processing and user interface. When measuring fixed test impedances, the 4-channel MIA exhibits an excellent agreement to a commercial Agilent 4294A impedance analyzer with rms errors of <0.3% over a capacitance range of 47–330 pF and <0.5% over a conductance range of 2.13–10 mS. Dielectric coagulometry measurements on human whole blood also show a very good agreement between the MIA and the Agilent impedance analyzer.
Exploiting Lens Antenna Arrays in Uplink mmWave MU-MIMO Networks: Joint Beamforming Optimization

This paper considers a lens antenna array-assisted millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) system. The base station's beam selection matrix and user terminals' phase-only beamformers are jointly designed with the aim of maximizing the uplink sum rate. In order to deal with the formulated mixed-integer optimization problem, a penalty dual decomposition (PDD)-based iterative algorithm is developed via capitalizing on the weighted minimum mean square error (WMMSE), block coordinate descent (BCD), and minorization-maximization (MM) techniques. Moreover, a low-complexity sequential optimization (SO)-based algorithm is proposed at the cost of a slight sum rate performance loss. Numerical results demonstrate that the proposed methods can achieve higher sum rates than state-of-the-art methods.
Exact black-hole formation with a conformally coupled scalar field in three dimensions

We present exact dynamical and inhomogeneous solutions in three-dimensional AdS gravity with a conformally coupled scalar field. They contain stealth configurations of the scalar field overflying the BTZ spacetime and also solutions with a non-vanishing energy-momentum tensor. The latter non-stealth class consists of the solution obtained by Xu and its analytic extension. It is shown that this proper extension represents: (i) an eternally shrinking dynamical black hole, (ii) a curious spacetime which admits an event horizon without any trapped surface, or (iii) gravitational collapse of a scalar field in an asymptotically AdS spacetime. In the last case, by attaching the solution regularly to the past massless BTZ spacetime with a vanishing scalar field, the whole spacetime represents the black-hole formation from regular initial data in an asymptotically AdS spacetime. Depending on the parameters, the formed black hole can be asymptotically static in far future.
Requirements for DataOps to foster Dynamic Capabilities in Organizations - A mixed methods approach

Digital technologies and the associated availability of ever-greater volumes of data present companies with existential challenges. The possibilities of utilizing a wide variety of data force organizations to react quickly. One way to tackle issues of organizational data utilization is implementing DataOps practices, which assist organizations in setting up the data management structure agilely. The paper starts precisely here, discussing how DataOps can foster the building of dynamic capabilities suitable to help companies act agilely in this ever-changing digital environment. To do this, the paper reports on a study to derive conceptual requirements for organizations to adopt DataOps practices based on Leavitt's Diamond Model. We approach this task through a mixed-method study design combining a quantitative multi-vocal literature review (MVLR) of the existing literature corpus and complementing these findings through a semi-structured interview study with experts from the domain.
Weak-Form Efficiency of Shariah-compliant Finance Stocks: Evidence from KLSE of Malaysia

We examine the weak-form efficient market hypothesis (EMH) for the selected Shariah-compliant finance stocks from the Kuala Lumpur Stock Exchange (KLSE) from 1/17/92 to 8/26/2022. Based on the ADF and artificial neural network (ANN) unit root tests, our results suggest that all three Shariah-compliant stocks are efficient before the COVID-19 period. Our results of ANN test suggest that BURSA and BIMB are inefficient during the COVID-19 period. These results have some notable policy implications.
Dwarf and Low Surface Brightness Galaxies

I review the evolutionary status of the field populations of starbursting dwarf galaxies and low surface brightness spirals. Particular attention is paid to the long standing problem of the progenitors for star burst galaxies, which may be related to a population of small, low surface brightness dwarfs. Larger low surface brightness spirals are known to evolve slowly, converting little of their gas mass into stars and metals in a Hubble time. Their slow evolution is controlled by their low densities, which arise naturally from formation by late collapse of low amplitude perturbations in the initial density field.
IT Business Value Measurement and Communication among German CIOs: A Conceptual Framework

This paper applies design science research methodology to iteratively develop a framework for measuring and communicating IT business value from a CIO perspective. The framework design is based on analysis and integration of literature combined with empirical findings. The framework was evaluated by CIO interviews and a practical feasibility study. The results show that IT business value can be measured and communicated using our framework by applying six consecutive process steps. Thereby, IT business value is not a single number but a set of quantitative and qualitative metrics relevant to stakeholders. Our framework represents a novel and integrated approach on how CIOs can select appropriate metrics, measure, and communicate IT business value to stakeholders. In addition, the paper provides insights for CIOs on how to be successful in IT business value management.
Next-Generation Wearable Wireless EEG Recorder: The future of accessible neural applications, from mental health monitoring to a noninvasive brain–computer interface

EEG recording has been widely used in a variety of applications, including sleep studies, mental health monitoring, brain–computer interfaces (BCIs), etc. However, mainstream clinical recording systems are still tethered with wet electrodes that require special preparation, limiting the setup to well-equipped labs and preventing the collection of longitudinal data. In this article, we summarize the special characteristics of EEG signals and review key circuit design techniques in EEG signal acquisition and data telemetry. With careful system optimization, a wireless distributed recording setup is possible for nonintrusive EEG data collection, which will enable long-term mental health monitoring and other important applications.
Testing and Reliability of Computing-In Memories: Solutions and Challenges

Various computing-in-memory designs have been proposed as a possible computing architecture for the data-centric computing applications. Existing memories such as random access memories, flash memories, and emerging memories can be modified as computing-in-memories (CIMs) to support computing operations. In CIMs, computing operations typically are more complex and have smaller sensing margin than the read operation. Furthermore, storage devices of emerging memories are more unreliable than transistors. Those make the testing and reliability of CIMs become worse. In this paper, we first review existing testing and reliability-enhancement methods for CIMs. Then, testing and reliability challenges of CIMs are discussed.
Unobstructed symplectic packing for tori and hyper-Kähler manifolds

Let M be a closed symplectic manifold of volume V. We say that the symplectic packings of M by balls are unobstructed if any collection of disjoint symplectic balls (of possibly different radii) of total volume less than V admits a symplectic embedding to M. In 1994, McDuff and Polterovich proved that symplectic packings of Kahler manifolds by balls can be characterized in terms of the Kahler cones of their blow-ups. When M is a Kahler manifold which is not a union of its proper subvarieties (such a manifold is called Campana simple), these Kahler cones can be described explicitly using the Demailly and Paun structure theorem. We prove that for any Campana simple Kahler manifold, as well as for any manifold which is a limit of Campana simple manifolds in a smooth deformation, the symplectic packings by balls are unobstructed. This is used to show that the symplectic packings by balls of all even-dimensional tori equipped with Kahler symplectic forms and of all hyper-Kahler manifolds of maximal holonomy are unobstructed. This generalizes a previous result by Latschev–McDuff–Schlenk. We also consider symplectic packings by other shapes and show, using Ratner’s orbit closure theorem, that any even-dimensional torus equipped with a Kahler form whose cohomology class is not proportional to a rational one admits a full symplectic packing by any number of equal polydisks (and, in particular, by any number of equal cubes).
A systematic review of the supply chain resilience measurement literature

The COVID 19 pandemic that emerged in late 2019 is one of the most critical supply chain crises globally and in human history, prompting managers and academics improve the resilience of supply chains. This article is written with the aim of reviewing the existing knowledge on measuring supply chain resilience to choose and target the supply chain practices adopted to apply resilience during a disruption, of a disaster or an uncertain event.
In principle demonstration of quantum secret sharing in the IBM quantum computer

Quantum secret sharing is a way to share secret messages amongst a number of clients in a group with unconditional security. For the first time, Hillery et al. [Phys. Rev. A 59, 1829 (1999)] proposed the quantum version of classical secret sharing protocol using GHZ states. Here, we implement this quantum secret sharing protocol in IBM 5-qubit quantum processor 'ibmqx4' and compare the experimentally obtained results with the theoretically predicted ones. The results are analyzed through quantum state tomography technique and the fidelity of these states were calculated for different number of executions made in the device. It is concluded that the experimental results match with the theoretical values with a high fidelity.