The paper presents a new conception of quasi-distributed sensor for simultaneous measurement of several physical fields and the results of an experimental study of this sensor. A distinctive feature of the sensor defined by the sensitive RC elements connected in the original tree-like structure. The proposed is the structure of the sensor and measurement system as well as corresponding measurement algorithm. The high accuracy demonstrated by the sensor’s prototype gives possibility to effective use the proposed sensors in many technical and scientific applications.

The application of the sensors optical fiber in the areas of scientific instrumentation and industrial instrumentation is very attractive due to its numerous advantages. In the industry of civil engineering for example, quasi-distributed sensors made with optical fiber are used for reliable strain and temperature measurements. Here, a quasi-distributed sensor in the frequency domain is discussed. The sensor consists of a series of low-finesse Fabry-Perot interferometers where each Fabry-Perot interferometer acts as a local sensor. Fabry-Perot interferometers are formed by pairs of identical low reflective Bragg gratings imprinted in a single mode fiber. All interferometer sensors have different cavity length, provoking the frequency-domain multiplexing. The optical signal represents the superposition of all interference patterns which can be decomposed using the Fourier transform. The frequency spectrum is analyzed and sensor´s properties were defined. Following, a quasi-distributed sensor was numerically simulated. Our sensor simulation considers sensor properties, signal processing, noise system and instrumentation. The numerical results show the behavior of resolution vs. signal-to-noise ratio. From our results, the Fabry-Perot sensor has high resolution and low resolutions. Both resolutions are conceivable because the FDPA algorithm elaborates two evaluations of Bragg wavelength shift

Objects recognition is a necessary task in smart city environments. This recognition can be used in processes such as the reconstruction of the environment map or the intelligent navigation of vehicles. This paper proposes an architecture that integrates heterogeneous distributed information to recognize objects in intelligent environments. The architecture is based on the IoT / Industry 4.0 model to interconnect the devices, called Smart Resources. Smart Resources can process local sensor data and send information to other devices. These other devices can be located in the same operating range, the Edge, in the same intranet, the Fog, or on the Internet, the Cloud. Smart Resources must have an intelligent layer in order to be able to process the information. A system with two Smart Resources equipped with different image sensors has been implemented to validate the architecture. Experiments show that the integration of information increases the certainty in the recognition of objects between 2\% and 4\%. Consequently, in the field of intelligent environments, it seems appropriate to provide the devices with intelligence, but also capabilities to collaborate closely with other devices.

This review paper deals with recently proposed algorithms for distributed blind macro-calibration of sensor networks based on consensus (synchronization), not requiring any fusion center. The basic algorithm, performing the estimation of the local calibration parameters, is derived commencing from appropriate local criteria, and developing the corresponding gradient descent scheme. It is shown that the estimated parameters of the calibration functions asymptotically converge, in the mean-square sense and with probability one (w.p.1), to such values that ensure consensus on calibrated sensor gains and calibrated sensor offsets. For the more realistic case in which additive measurement noise, communication dropouts and additive communication noise are present, two algorithm modifications are introduced: one using a simple compensation term, and a more robust one based on an instrumental variable. By utilizing stochastic approximation arguments it is shown that the modified algorithms also achieve asymptotic agreement for calibrated sensor gains and offsets, in the mean-square sense and w.p.1. Convergence rate is analyzed in terms of an upper bound of the mean-square error. It is also shown that the communications between nodes can be completely asynchronous, which is of substantial importance for real-world applications. Suggestions for design of \textit{a priori} adjustable weights are given. Finally, it is shown that, if there is a subset of (precalibrated) reference sensors with fixed calibration parameters, the calibrated sensor gains and offsets of the rest of the sensors do not achieve consensus - they converge to different points dictated by the reference sensors and the network characteristics. Wide applicability and efficacy of these algorithms are illustrated on several simulation examples.

Since smart cities aim at becoming self-monitoring and self-response systems, their deployment relies on close resource monitoring through large-scale urban sensing. The subsequent gathering of massive amounts of data makes essential the development of event filtering mechanisms that enable the selection of what is relevant and trustworthy. Due to the rise of mobile event producers, location information has become a valuable filtering criterion as it not only offers extra information on the event described but also enhances trust on the producer. Implementing mechanisms that validate the quality of location information becomes then imperative. The lack of such strategies in cloud architectures compels the adoption of new communication schemes for IoT-based urban services. To serve the demand for location verification in urban event-based systems (DEBS), we have designed three different fog architectures that combine proximity and cloud communication. Moreover, we have successfully assessed their performance using network simulations with realistic urban traces.

Distributed fiber optic vibration sensors based on Φ-OTDR using Rayleigh backscattering of highly coherent light source pulses are very popular type in the area of sensors due to many attractive features, mainly due to its distributed manner of sensing, i.e. they can operate as thousands of local sensors simultaneously. The paper summarizes and describes several important modifications of the original sensor scheme to enhance its key performance parameters and to extend its application potential. A multi-fiber sensor form was proposed. Single- and two-fiber sensors were designed, constructed and tested both in the laboratory and in a real environment. Variants of two-fiber sensor utilizing optical switches for the sensor flexibility enhancement are proposed and their applications are described.

Real-time multi-axis distributed tactile sensing is a critical capability if robots are to perform stable gripping and dexterous manipulation, as it provides crucial information about the sensor-object interface. In this paper, we present an optical-based 6-axis tactile sensor designed in a fingertip shape for robotic dexterous manipulation. The distributed sensor can precisely estimate local XYZ force and displacement at ten distinct locations, and provide global XYZ force and torque measurements. Its compact size, comparable to that of a human thumb, and minimal thickness allow seamless integration onto existing robotic fingers, eliminating the need for complex modifications to the gripper. The proposed sensor design uses a simple, low-cost fabrication method. Moreover, the optical transduction approach uses light angle and intensity sensing to infer force and displacement from deformations of individual sensing units that form the overall sensor, providing distributed 6-axis sensing. The local force precision at each sensing unit in the X, Y, and Z axes is 20.89 mN, 19.19 mN, and 43.22 mN, respectively, over an local force range of approximately ±1.5 N in X and Y and 0 to -2 N in Z. The local displacement precision in the X, Y and Z axes is 56.70 μm, 50.18 μm and 13.83 μm, respectively, over a local displacement range of ±2 mm in the XY directions and 0 to -1.5mm in Z (i.e., compression). Additionally, the sensor can measure global torques, Tx, Ty and Tz, with a precision of of 1.90 N-mm, 1.54 N-mm and 1.26 N-mm, respectively. The fabricated design is showcased by integrating it with an OnRobot RG2 gripper and illustrating real-time measurements during in simple demonstration task which generate changing global forces and torques.

New setting is introduced to study quasi-degree and quasi-co-degree arising from co-neighborhood. quasi-degree and quasi-co-degree is about a vertex which are applied into the setting of neutrosophic graphs. . The structure of set is studied and general results are obtained. Also, some classes of neutrosophic graphs namely path-neutrosophic graphs, cycle-neutrosophic graphs, complete-neutrosophic graphs and star-neutrosophic graphs, complete-bipartite-neutrosophic graphs and complete-multipartite-neutrosophic graphs are investigated in the terms of a vertex which is called either quasi-degree or quasi-co-degree. Neutrosophic number is reused in this way. It’s applied to use the type of neutrosophic number in the way that, three values of a vertex are used and they’ve same share to construct this number to compare with other vertices. Summation of three values of vertex makes one number and applying it to a comparison. This approach facilitates identifying vertices which form quasi-degree and quasi-co-degree. Quasi-degree is a value of a vertex which is maximum amid all values of vertices which are neighbors to a fixed vertex. Quasi-co-degree is a value of an edge which is maximum amid all values of edges which are neighbors to a fixed vertex but corresponded vertex is representative for this notion. Using different values which are related to a vertex inspire us to focus on edge and vertices which are corresponded to a fixed vertex. The notion of neighborhood is used to collect either vertices are titled neighbors or edges are incident to fixed vertex. In both settings, some classes of well-known neutrosophic graphs are studied. Some clarifications for each result and each definitions are provided. Using fixed vertex has key role to have these notions in the form of vertex or edge. The value of an edge has eligibility to call quasi-co-degree but the value of a vertex has eligibility to call quasi-degree. Some results get more frameworks and perspective about these definitions. The way in that, two vertices have connection together, open the way to define neighborhood and co-neighborhood. The maximum values in neighborhood and co-neighborhood introduces quasi-degree and quasi-co-degree, respectively. New name is chosen from degree. Since amid all vertices with different degrees, one vertex is chosen. In other words, one vertex is fixed and its degree turns out quasi-degree where two degrees could be assigned to a vertex. Degree of edges and degree of vertices. The number of edges which are incident to the vertex and the number of vertices which are neighbors to the vertex. Degree and co-degree are the notions which are transformed to use in quasi-style. Two neutrosophic values introduce two neutrosophic vertices separately in each settings. These notions are applied into neutrosophic graphs as individuals but not family of them as drawbacks for these notions. Finding special neutrosophic graphs which are well-known, is an open way to purse this study. Some problems are proposed to pursue this study. Basic familiarities with graph theory and neutrosophic graph theory are proposed for this article.

New setting is introduced to study co-neighborhood, neutrosophic t-neighborhood, neutrosophic quasi-vertex set, neutrosophic quasi-order, neutrosophic neighborhood, neutrosophic co-t-neighborhood, neutrosophic quasi-edge set, neutrosophic quasi-size, Neutrosophic number, neutrosophic co-neighborhood, co-neutrosophic number, quasi-number and quasi-co-number. Some classes of neutrosophic graphs are investigated.

Integrated Model of Distributed Systems is used for modeling and verification. In formalism, the distributed system is modeled as a collection of server states and agent messages. The evolution of the system takes the form of actions that transform the global system configuration (states and messages) into a new configuration. Formalism is used in the Dedan verification environment for finding different kinds of deadlocks: communication deadlocks in the server view and resource deadlocks in the agent view. For other purposes, a conversion has been developed to equivalent models: to Petri nets for structural analysis and do Distributed Autonomous and Asynchronous Automata (DA3) for easy graphical modeling in terms of system components. In addition, it is possible to simulate a verified system on distributed components in DA3. The automata have two forms: Server-DA3 (S-DA3) for the server view and Agent-DA3 (A-DA3) for the agent view. DA3 formalism is compared to other concepts of distributed automata known from the literature.

In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, it shines the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using three-fold cross validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. This is followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, and the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time-series multivariate sensor readings.

In this work, A hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, spotting the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using 3-fold cross-validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. Followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data, in order to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, as well as the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time series multivariate sensor readings.

Continuous Distributed Constraint Optimization Problem (C-DCOPs) are a powerful framework to model problems with continuous variables in multi-agent systems. Previous works about C-DCOP algorithms mainly use pseudo-trees to guarantee the anytime property or are without anytime property guarantees. However, there is a risk of privacy leakage in the pseudo-tree due to utility passing between agents. Therefore, based on the basic constraint graph instead of the pseudo-tree, we (i) extend the Maximum Gain Message (MGM) algorithm by combining the local search strategy to solve C-DCOPs, named Continuous MGM (C-MGM), and it’s able to guarantee the monotonicity of the solution quality; (ii) propose a Parallel C-MGM (C-PMGM) algorithm to improve the solution quality through parallel random search; and (iii) introduce the differential search into C-PMGM to design a Parallel Differential Search C-MGM (C-PDSM) algorithm, which constructs a heuristic method to speed up convergence and improve solution quality. Compared to other anytime C-DCOP algorithms using pseudo-trees, the proposed three algorithms can exhibit better performance in avoiding privacy violations. We theoretically prove that the proposed algorithms are the anytime algorithms and empirically demonstrate that our algorithms outperform the state-of-the-art C-DCOP algorithms.

The technology for determining the point’s coordinates on the earth’s surface using the global navigation satellite system (GNSS) is becoming the norm along with ground-based methods. In this case, determining coordinates does not cause any particular difficulties. However, to identify normal heights using this technology with a given accuracy, special research is required. The fact is that satellite determinations of geodetic heights (h) over an ellipsoid surface differ from ground-based measurements of normal height (HN) over a quasi-geoid surface by a certain value called quasi-geoid height or height anomaly (ζ). In relation to determining heights of a certain territory, the concept of geoid height (N) is usually operated when dealing with a geoid model. In this work, geodetic and normal heights are determined for 5 control points in three different regions in Lebanon, where measurements are carried out using GNSS technology and geometric levelling. The obtained quasi-geoid heights are compared with geoid heights derived from the global Earth model EGM2008. The results obtained showed that, in the absence of gravimetric data, the combination of global Earth model data, geometric levelling for selected areas and satellite determinations allows the creation of a high accurate altitude network for mountainous areas.

Analytical solutions for radiation dominated phase of Quasi Steady State Cosmology in Friedmann-Robertson-Walkar models are obtained. We find that matter density is positive in all the cases (k = 0,-1,1). The nature of Hubble parameter (H) in [0,2] is discussed. The deceleration parameter (q) is marginally less than zero indicating accelerating universe. The scale factor (S) is graphically shown with time. The model represents oscillating universe between the above mentioned limits. Because of bounce in QSSC, the maximum density phase is still matter dominated. The models represent singularity free model. We also find that the models have event horizon i.e. no observer beyond the proper distance rH can communicate each other in FRW mdels for radiation dominated phase in the frame work of QSSC. The FRW models are special classes of Bianchi type I, V, IX space-times with zero, negative and positive curvatures respectively. Initially i.e. at  = 0, the model represents steady model. We have tried to show how a good fit can be obtained to the observations in the framework of QSSC during radiation dominated phase.

We prove a quasi-pseudometric version of the strong Ekeland principle proved by P. Georgiev and T. Suzuki.

The new barrier mode in Apache Spark allows embedding distributed deep learning training as a Spark stage to simplify the distributed training workflow. In Spark, a task in a stage doesn’t depend on any other tasks in the same stage, and hence it can be scheduled independently. However, several algorithms require more sophisticated inter-task communications, similar to the MPI paradigm. By combining distributed message passing (using asynchronous network IO), OpenJDK’s new auto-vectorization and Spark’s barrier execution mode, we can add non-map/reduce based algorithms, such as Cannon’s distributed matrix multiplication to Spark. We document an efficient distributed matrix multiplication using Cannon’s algorithm, which improves significantly on the performance of the existing MLlib implementation. Used within a barrier task, the algorithm described herein results in an up to 24% performance increase on a 10,000x10,000 square matrix with a significantly lower memory footprint. Applications of efficient matrix multiplication include, among others, accelerating the training and implementation of deep convolutional neural network based workloads, and thus such efficient algorithms can play a ground-breaking role in faster, more efficient execution of even the most complicated machine learning tasks

The world of query-response systems heavily depends on the cloud storage solutions, distributed data transfers and locality of users etc. When data stores and users are distributed geographically, it is essential to organize distributed data cache points at ideal locations to minimize data transfers. This leads to the question, what data to cache in which location. To answer this, we are developing an adaptive distributed data caching framework that can identify suitable data chunks to cache and move across a network of community cache locations. This paper details the first step of the process: the sub-query fragmentation technique to fragment data into portable objects. Evaluation suggests that sub-query fragments enable distributed learning methods to understand query patterns and association between sub-queries. The sub-query objects can be modelled easily as input dataset to implement machine learning models to assist cache maintenance.

This paper introduces an innovative Particle Swarm Optimization (PSO) Algorithm in-corporating Sobol and Halton Random number samplings. It evaluates the enhanced PSO's performance against conventional PSO employing Monte Carlo Random Number Samplings. The comparison involves assessing the algorithms across nine benchmark problems and the renowned Travelling Salesman Problem (TSP). The re-sults reveal consistent enhancements achieved by the enhanced PSO utilizing Sob-ol/Halton samplings across the benchmark problems. Particularly noteworthy are the substantial improvements demonstrated by the enhanced PSO in solving the Travel-ling Salesman Problem. These findings underscore the efficacy of employing Sobol and Halton random number generation methods in enhancing algorithm efficiency.

With the advent of submesoscale O(1km) permitting basin-scale ocean simulations, the seasonality in the mesoscale O(50km) eddies with kinetic energies peaking in summer has been commonly attributed to submesoscale eddies feeding back onto the mesoscale via an inverse energy cascade under the constraint of stratification and Earth’s rotation. In contrast, by running a 101-member, seasonally forced, three-layer quasi-geostrophic (QG) ensemble configured to represent an idealized double-gyre system of the subtropical and subpolar basin, we find that the mesoscale kinetic energy shows a seasonality consistent with the summer peak without resolving the submesoscales; by definition, a QG model only resolves small Rossby number dynamics (O(Ro)≪1) while as submesoscale dynamics are associated with O(Ro)∼1. Here, by quantifying the Lorenz cycle of the mean and eddy energy, defined as the ensemble mean and fluctuations about the mean respectively, we propose a different mechanism from the inverse energy cascade by which the stabilization and strengthening of the western-boundary current during summer due to increased stratification leads to a shedding of stronger mesoscale eddies from the separated jet. Conversely, the opposite occurs during the winter; the separated jet destablizes and results in overall lower mean and eddy kinetic energies despite the domain being more susceptible to baroclinic instability from weaker stratification.

Deadlock is a real problem that can cause expensive, time-dependent hang-ups or failures in distributed computing systems. Some studies have been conducted with deadlock detection and resolution in generalized request computing models, most of these studies are based on the diffusing computation scheme, where forward and backward propagated probe and reply messages respectively are diffused between dependent processes. The replies carry the dependency information between processes for the initiator. Using this dependency information initiator can detect and resolve the deadlock cycle. It has been proven in the literature that a deadlock cycle length distribution is skewed with a large majority of cycles (90%) having length two. Based on this fact an improved algorithm is proposed for handling deadlock detection in distributed computing systems under the distributed request model where each process has several requests at a time. The improved algorithm depends mainly on message propagation and timeout policy. Probe and reply messages are propagated between the initiator and its successors only to detect deadlock cycles between them. Global timeout policy is used to detect potential deadlock cycles with large length distribution. The proposed algorithm can handle concurrent executions. Simulation experiments are performed to see the effectiveness of the improved algorithm. It is found that the improved algorithm compares favorably with other existing algorithms and it shows better results for several performance metrics especially in reducing deadlock latency and execution time.

Blockchains such as the bitcoin blockchain depend on reaching a global consensus on the distributed ledger; therefore, they suffer from well know scalability problems. This paper proposes an algorithm that avoids double-spending in the short term with just O(√n) messages; each node receiving money off-chain performs the due diligence of consulting k√n random nodes to check if any of them is aware of double-spending. Two nodes receiving double-spent money will in this way consult at least one common node with very high probability, due to the ‘birthday paradox’, and any common honest node consulted will detect the fraud. Since the velocity of money in the real world has coins circulating through at most a few wallets per day, the size of the due diligence communication is small in the short term. This `k-root-n’ algorithm is suitable for an environment with synchronous or asynchronous (but with fairly low latency) communication and with Byzantine faults. The presented k-root-n algorithm should be practical to avoid double-spending with arbitrarily high probability, while feasibly coping with the throughput of all world commerce. It is resistant to Sybil attacks even beyond 50% of nodes. In the long term, the k-root-n algorithm is less efficient. Therefore, it should preferably be used as a complement and not a replacement to a global distributed ledger technology.

A quantum particle constrained between two high potential barriers provides a paradigmatic example of a system sustaining quasi bound (or resonance) states. When the system is prepared in one of such quasi bound states, the wave function approximately maintains its shape but decays in time in a nearly exponential manner radiating into the surrounding space, the lifetime being of the order of the reciprocal of the width of the resonance peak in the transmission spectrum. Naively, one could think that adding more lateral barriers would preferentially slow down or prevent the quantum decay since tunneling is expected to become less probable and because of quantum backflow induced by multiple scattering processes. However, this is not always the case and in the early stage of the dynamics quantum decay can be accelerated (rather than decelerated) by additional lateral barriers, even when the barrier heights are arbitrarily large. The decay acceleration originates from resonant tunneling effects and is associated to large deviations from an exponential decay law. We discuss such a counterintuitive phenomenon by considering the hopping dynamics of a quantum particle on a tight-binding lattice with on-site potential barriers.

In this study, we present more than 8 years of observations of the quasi-16-day wave (Q16DW) in the mesosphere and lower thermosphere (MLT) wind at middle latitudes observed by the Mengcheng (33.4°N, 116.5°E) meteor radar. The long-term variation in amplitudes calculated from the data between April 2014 and December 2022 shows enhanced wave activity during winter and early spring (near equinox) and suppressed wave activity during the summer. The Q16DWs are relatively weak in the meridional wind. During the winter months, the Q16DWs in the zonal component exhibit a burst below 85 km, and their amplitudes reach up to 10 m/s. In the early spring, the Q16DWs strengthen above 90 km with amplitudes in excess of 12 m/s. The phase differences between the zonal and meridional components of the Q16DW are, on average, slightly smaller than 90°, suggesting the existence of orthogonal relationships between them. During strong bursts, the periods of the Q16DW in winter range between 15 and 18 d, whereas in winter, the periods tend to be more diffuse. The wintertime Q16DW is amplified, on average, when the zonal wind shear peaks, suggesting that barotropic instability may be one source of Q16DW. Q16DW amplitudes exhibit considerable interannual variability; however, a relationship between the 11-year solar cycle and the Q16DW is not found.

The purpose of this work is to study Rossby wave parameters in total ozone over Arctic in 2000–2021. We consider the averages in the January–March period, when stratospheric trace gases (including ozone) in sudden stratospheric warming events are strongly disturbed by planetary waves. To characterize the wave parameters, we analyzed ozone data at the latitudes of 50° N (the sub-vortex area), 60° N (the polar vortex edge) and 70° N (inner region of the polar vortex). Total ozone column (TOC) measurements during 22-year time interval were used from Total Ozone Mapping Spectrometer (TOMS) / Earth Probe and Ozone Mapping Instrument (OMI) / Aura satellite observations. The total ozone zonal distribution and variations in the parameters of the Fourier spectral components with zonal wave numbers m = 1–5 are presented. Daily and interannual variations in TOC, amplitudes and phases of spectral wave components, and linear trends of the quasi-stationary wave 1 (QSW1) amplitudes are discussed. The positive TOC peaks inside the vortex in 2010 and 2018 alternate with negative ones in 2011 and 2020. The latter TOC anomalies correspond to severe depletion of stratospheric ozone over the Arctic in the strong vortex conditions due to anomalously low activity of planetary waves. Variations in TOC in sub-vortex region exhibit the statistically significant negative trend –4.8±5.4 DU decade–1 in QSW1 amplitude, while the trend is statistically insignificant at the vortex edge region due to increased TOC variability. Processes associated with polar vortex dynamics are discussed, including quasi-stationary vortex asymmetry and quasi-circumpolar migration of the wave-1 phase at the vortex edge.

The Boltzmann-Gibbs (BG) entropy has been used in a wide variety of problems for more than a century. It is well known that BG entropy is extensive, but for certain systems such as those dictated by long-range interactions, the entropy must be non-extensive. Tsallis entropy possesses non-extensive characteristics, which is parametrized by a variable q (q = 1 being the classic BG limit), but unless q is determined from microscopic dynamics, the model remains but a phenomenological tool. To this date very few examples have emerged in which q can be computed from first principles. This paper shows that the space plasma environment, which is governed by long-range collective electromagnetic interaction, represents a perfect example for which the q parameter can be computed from micro-physics. By taking the electron velocity distribution function measured in the heliospheric environment into account, and considering them to be in quasi equilibrium state with electrostatic turbulence known as the quasi-thermal noise, it is shown that the value corresponding to q = 9/13 = 0.6923 may be deduced. This prediction is verified against observation made by spacecraft, and it is shown to be in excellent agreement.

Analyzing network robustness against random failures or malicious attacks is a critical research issue in network science as it helps to enhance the robustness of beneficial networks or efficiently disintegrate harmful networks. Most studies commonly neglect the impact of the attack success rate (ASR) and assume that attacks on the network will always be successful. However, in real-world scenarios, an attack may not always succeed. This paper proposes a novel robustness measure called Robustness-ASR (RASR), which utilizes mathematical expectations to assess network robustness when considering the ASR of each node. To efficiently compute the RASR for large-scale networks, a parallel algorithm named PRQMC is presented, which leverages randomized quasi-Monte Carlo integration to approximate the RASR with a faster convergence rate. Additionally, a new attack strategy named HBnnsAGP is introduced to better assess the lower bound of network RASR. Finally, the experimental results on 6 representative real-world complex networks demonstrate the effectiveness of the proposed methods compared with the state-of-the-art baselines.

In many research fields, statistical probability models are often used to analyze real-world data. However, data from many fields, such as the environment, economics, and health care, do not necessarily fit traditional models. New empirical models need to be developed to improve their fit. In this paper, we explore a further extension of the quasi-Lindley model. Maximum likelihood, least square error, Anderson-Darling algorithm, and expectation-maximization algorithm are four techniques for estimating the parameters under study. All techniques provide accurate and reliable estimates of the parameters. However, the mean square error of the expectation maximization approach was lower. The usefulness of the proposed model was demonstrated by analyzing a dynamical systems data set, and the analysis shows that it outperforms the other models in all statistical models considered.

In this article, we analyze the thermodynamics, thermal fluctuations and quasi-normal modes of a torus-like charge AdS black hole. The parameters of a torus-like charged AdS black hole are its mass, electric charge, and cosmological constant. We used a torus-like charge AdS black hole to evaluate thermodynamics through thermal fluctuations and found that it significantly affected the corrected thermodynamic potentials. Black holes of larger radii are more stable and significant. The effect of thermal fluctuation has been investigated on the other thermodynamic potentials such as the Hemohltz free energy, Gibbs free energy, enthalpy, and specific heat, using the concept of corrected entropy. Finally, we use null geodesics to deal with quasi-normal modes. The null geodesics provide the photon sphere angular velocity and Lyapunov exponent, which are the real and imaginary components of the quasi-normal modes in the eikonal limit. With inceasing charge and photon radius, angular velocity reaches its maximum, similar to Lyapunov's exponent.

Mesoscale eddies, the weather system of the oceans, although being on the scales of O(20-100km), have a disproportionate role in shaping the mean jets such as the separated Gulf Stream in the North Atlantic Ocean, which is on the scale of O(1000km) in the along-jet direction. With the increase in computational power, we are now able to partially resolve the eddies in basin-scale and global ocean simulations, a model resolution often referred to as mesoscale permitting. It is well known, however, that due to grid-scale numerical viscosity, mesoscale permitting simulations have less energetic eddies and consequently weaker eddy feedback onto the mean flow. In this study, we run a quasi-geostrophic model at mesoscale resolving resolution in a double gyre configuration and formulate a deterministic parametrization for the eddy rectification term of potential vorticity (PV), namely, the eddy PV flux divergence. We have moderate success in reproducing the spatial patterns and magnitude of eddy kinetic and potential energy diagnosed from the model. One novel point about our approach is that we account for non-local eddy feedbacks onto the mean flow by solving the eddy PV equation prognostically in addition to the mean flow. In return, we are able to parametrize the variability in total (mean+eddy) PV at each time step instead of solely the mean PV. A closure for the total PV is beneficial as we are able to account for both the mean state and extreme events.

The influence of the grain structure on the tensile deformation strength is studied for precipitation-strengthened Cu-0.2%Zr at 673 K. Subgrains and grains are formed by ECAP and annealing. The fraction of high-angle boundaries increases with prestrain. Subgrains and grains coarsen during deformation. This leads to softening in the quasi-stationary state. The initial quasi-stationary state of severely predeformed, ultrafine-grained material exhibits relatively high rate-sensitivity at relatively high stresses. This is interpreted as result of the stress dependences of the quasi-stationary subgrain size and the volume fraction of subgrain-free grains.

This research work deals with the modeling and control of hybrid photovoltaic (PV) - Wind micro-grid using Quasi Z-Source inverter. This inverter provides better buck/boost characteristics, able to regulate the phase angle output, less harmonic content, no requirement of the filter and has high power performance characteristics over conventional inverter as major benefits. A SEPIC converter as dc-dc switched power apparatus is employed for maximum power point tracking (MPPT) functions which provides high voltage gain throughout the process. Moreover, a modified power ratio variable step (MPRVS) based perturb & observe (P&O) method has been proposed in the PV MPPT action which forces the operating point close to maximum power point (MPP). Practical responses justify the performance of hybrid PV-Wind micro-grid with Quasi Z-Source inverter structure.

Recently, there has been a growing interest in distributed edge computing resource scheduling. For example, application scenarios such as intelligent traffic systems and Internet of Things(IoT) intelligent monitoring require the scheduling and migration of distributed resources. Distributed resource scheduling needs to consider the cost of resource scheduling, with the primary goal of finding the optimal strategy among various feasible solutions. There are different definitions for optimization objectives in different application scenarios, such as cost, transmission delay, energy consumption, etc. Current research mainly considers the optimization problem of local resource scheduling but needs more consideration of global resource scheduling. This paper defines a global resource scheduling problem for distributed edge computing and proves that the problem is NP-Hard. A heuristic solution strategy based on the Ant Colony Algorithm(ACO) was proposed, and Particle Swarm Optimization(PSO) was used to optimize the parameters of the ACO. Finally, an experimental comparative analysis was conducted to demonstrate that the algorithm proposed in this paper has good accuracy and iteration cost performance.

The distributed acoustic sensing (DAS) has great potential for monitoring natural-resource reservoirs and borehole conditions. However, the large volume of data and complicated wavefield add challenges to processing and interpretation. In this study, we demonstrate that seismic interferometry based on deconvolution is a convenient tool for analyzing this complicated wavefield. We extract coherent wave from the observation of a borehole DAS system at the Brady geothermal field in Nevada. Then, we analyze the coherent reverberating waves, which are used for monitoring temporal changes of the system. These reverberations are tirelessly observed in the vertical borehole DAS data due to cable or casing ringing. The deconvolution method allows us to examine the wavefield at different boundary conditions. We interpret the deconvolved wavefields using a simple 1D string model. The velocity of this wave varies with depth, observation time, temperature, and pressure. We find the velocity is sensitive to disturbances in the borehole related to increasing operation intensity. The velocity decreases with rising temperature, which potentially suggests that the DAS cable or the casing are subjected to high temperature. This reverberation can be decomposed into distinct vibration modes in the spectrum. We find that the wave is dispersive, and the the fundamental mode propagate with a large velocity. The method can be useful for monitoring borehole conditions or reservoir property changes. For the later, we need better coupling than through only friction in the vertical borehole to obtain coherent energy from the formation.

This research emphasizes a meta-heuristic Modified Ant Lion Optimizer (MALO) optimization approach for simultaneous utilization of DSTATCOM devices and distributed Photovoltaic (PV) sources with network reconfiguration in a radial power distribution scheme. In a radial power distribution network with network reconfiguration majority of the research is based on Constant Power model analysis. However, it is noticed that load models have a substantial impact on the distributed PV sources and the D-STATCOM device's optimal size and position. The effect of the Constant Power (CP) and Polynomial (ZIP) with load growth load models for the simultaneous insertion of distributed PV sources and DSTATCOM devices with network reconfiguration examined in this research work for power system planning. The penetration levels of distributed PV sources considered for the investigation are 25%, 50%, 75%, and 100%. The principal objective of this research is to reduce network total power losses, enhance the voltage magnitude profile at all buses, and reduce the overall operating cost while adhering to equality and inequality constraints. The proposed algorithm verified on 118-node test systems. The investigation is carried out for planning network upgradation to a High Voltage Distribution System(HVDS) on 317 nodes in the rural Bangalore Electricity Supply Company Limited(BESCOM) radial distribution scheme. Simulated results obtained by this method are validated with the BAT algorithm and available techniques in the literature.

This study explores the potential to reach a circular economy for post-consumer recycled polyethylene terephthalate (rPET) packaging and bottles by using it as a distributed recycling for additive manufacturing (DRAM) feedstock. Specifically, rPET is processed using only an open source toolchain with fused particle fabrication (FPF) or fused granular fabrication (FGF) processing. In this study, first the impact of granulation, sifting and heating (and their combination) is quantified on the shape and size distribution of the rPET flakes. Then feeding studies were performed to see if they could be printed through an external feeder or needed to be direct printed with a hopper using two Gigabot X machines, one with extended part cooling and one without. Print settings were optimized based on thermal characterization and for the latter which was shown to print rPET directly from shredded water bottles mechanical testing is performed. The results showed that geometry was important for extended feeding tubes and direct printed using a hopper. Further there is a wide disparity in the physical properties of rPET from water bottles depending on source and the history of the material. Future work is needed to enable water bottles to be used as a widespread DRAM feedstock.

In this paper, we consider a class of bilevel programming problems (abbreviated as, BLPP). Exploiting the generalized approximate convexity assumptions, we investigate the relations among the solutions of approximate Minty (respectively, Stampacchia) type variational inequalities (abbreviated as, AMTVI (respectively, ASTVI)), and the local ϵ-quasi solutions of the BLPP. Moreover, by employing the generalized Knaster–Kuratowski–Mazurkiewicz (abbreviated as, KKM)-Fan’s lemma, we derive some existence results for the solutions of approximate variational inequalities (abbreviated as, AVI), namely, AMTVI and ASTVI. A non-trivial example is given to highlight the importance of the established results. To the best of our knowledge, there is no research paper available in the literature that establishes relationships between the AVI and the BLPP under the assumptions of generalized approximate convexity in terms of limiting subdifferentials.

Polymer-based composites are widely used in the automotive, security, aeronautical and space industries, to mention a few. This is because of their good mechanical properties, which are similar to those of metals but with the attraction of being lightweight. Kevlar® as a reinforcement is extensively used in the security industry owing to its good ballistic properties. This investigation presents the mechanical characterization based on in-plane quasi-static tensile testing of Kevlar® composite using a universal testing system. The methodology developed for the preparation of coupons is based on pressure, temperature and time. As a result of this work, elastic moduli (EL and ET), Poisson’s ratio (νLT), shear modulus (GLT) and strengths (XT, YT, S) were obtained. It is worth mentioning that there is scarce or no characterization of this material in the literature, and those studies that do characterize it do not present the coupon thermoforming conditions or the reasons of the specimen dimensions (width, length and thickness).

This work presents a new type of chaotic dynamical system originated from Dirichlet Eta function. Although the exposition describes only a bidimensional instance, it is certainly possible to construct higher dimensional such models. The novelty resides in the multitude of interesting characteristics concentrated in only one kind of dynamical system, mainly because of its simple abstract definition - it provokes dissipative, chaotic and itinerant behavior, and its paths typically display nonrecurring quasi-attractors and associate regions of ruin. In addition, it is discrete and nonautonomous, being additively driven by a simple real vector sequence. Also, the defining formulas have parameters which permit to activate bifurcatory effects, mainly related to setting the number of quasi-attractors and overall geometric complexity and diversity. In this fashion, it holds a certain similarity with classical itinerant chaotic systems, but it is a completely different object. One of its parameters is very influential and may be intuitively compared to a type of "potential energy", capable of changing the duration of sojourns etc. In this fashion, there is a potential opportunity to employ artificial inference techniques for designing specific structures on paths. Of course, the other parameters are also relevant, but not necessarily in the control of the number of quasi-attractors and the permanence periods near them. It is important to state that the described system was found during a previous investigation about certain features of the Riemann hypothesis, using the Dirichlet Eta function instead of the Riemann Zeta function - the components of the system under study here were part of certain series whose convergence had to be established, and that scenario produced this very positive side effect. The text also suggests an atypical, but important, potential application in philosophy: the representation of the Hindu concepts of Samsara and Moksha.

The fundamental theory of nuclear interactions, Quantum Chromodynamics (QCD), operates in terms of quarks and gluons at higher resolution. At low resolution the relevant degrees of freedom are nucleons. Two-nucleon Short-Range Correlations (SRC) help to interconnect these two descriptions. SRCs are temporary fluctuations of strongly interacting close pairs of nucleons. The distance between the two nucleons is comparable to their radii and their relative momenta are larger than the fermi sea level. According to the electron scattering experiments held in the last decade, SRCs have far-reaching impacts on many-body systems, the nucleon-nucleon interactions, and nuclear substructure. The modern experiments with ion beams and cryogenic liquid hydrogen target make it possible to study properties of the nuclear fragments after quasi-elastic knockout of a single nucleon or an SRC pair. The first SRC experiment at BM@N (2018) with a carbon beam has shown that detection of an intact 11B nucleus after interaction selects out the quasi-elastic knockout reaction with minimal contribution of initial- and final-state interactions. Also, 25 events of SRC-breakups showed agreement in SRC properties as knows from electron beam experiments. The analysis of the second measurement of SRC at BM@N held in 2022 with an improved setup is currently ongoing. The SRC project at JINR moved to a new experimental area in 2023, where the next measurement is being planned in terms of experimental setup and physics goals.

In this paper, a fuzzy logic-based energy management system and multi-port quasi-z-source converter are proposed that utilizes hybrid renewable energy sources. The system ensures that each energy source module can be used individually by employing fuzzy logic to define power modes. This approach also helps prevent switching losses resulting from extra switching of the source modules. Also, the proposed energy management does not have a mathematical model, so applicability is simple and it is suitable for different multiple input topologies. The Mamdani fuzzy inference system can be designed to capture the nonlinear behavior of the system because of the linguistic rules. Moreover, by adopting the quasi-z-source network to the end of the multi-port converter, the switching losses of the multi-port modules have been significantly reduced. The simulation results are obtained using MATLAB Simulink, and the experimental results are obtained by connecting the circuit to MATLAB Simulink via an Arduino Due.

In this work, the recently proposed cracking elements method (CEM) is used for simulating the damaging processes of structures with initial imperfections. CEM is built in the framework of conventional FEM which is formally like a special type of finite element. The disconnected piecewise cracks are used for representing crack paths. Taking the advantages of CEM that both the initiations and propagations of cracks can be naturally captured, we numerically study the uni-axial compression tests of specimens with multiple joints and fissures, where the cracks may propagate from the tips, or from some other unexpected positions. Though uni-axial compression tests are considered, mainly tensile damage criteria are used in the numerical model. On one hand, the results demonstrate the robustness and effectiveness of the CEM while on the other hand, some drawbacks of the present model are demonstrated, showing the future work.

The aim of this paper is to discuss the relations between various notions of sequential completeness and the corresponding notions of completeness by nets or by filters in the setting of quasi-metric spaces. We propose a new definition of right $K$-Cauchy net in a quasi-metric space for which the corresponding completeness is equivalent to the sequential completeness. In this way we complete some results of R.~A. Stoltenberg, Proc. London Math. Soc. \textbf{17} (1967), 226--240, and V.~Gregori and J.~Ferrer, Proc. Lond. Math. Soc., III Ser., \textbf{49} (1984), 36. A discussion on nets defined over ordered or pre-ordered directed sets is also included.

In the field of machine learning, cluster analysis has always been a very important technology for determining useful or implicit characteristics in the data. However, the current mainstream cluster analysis algorithms require comprehensive analysis of the overall data to obtain the best parameters in the algorithm. As a result, handling large-scale datasets would be difficult. This research proposes a distributed related clustering mechanism for Unsupervised Learning, which assumes that if adjacent data are similar, a group can be formed by relating to more data points. Therefore, when processing data, large-scale datasets can be distributed to multiple computers, and the correlation of any two datasets in each computer can be calculated simultaneously. Later, results are processed through aggregation and filtering before assembled into groups. This method would greatly reduce the pre-processing and execution time of the dataset; in practical application, it only needs to focus on how the relevance of the data is designed. In addition, the experimental results show the accuracy, applicability, and ease of use of this method.

This article will provide an overview on internet of things (IoT), 5G communication System and Distributed Clouds. The basic concepts and benefits will be briefly presented, along with current standardization activities. In a nutshell, but the research will focus on relating internet of things, 5G and Distributed Cloud Computing.

Blockchain technology enables a decentralized and distributed environment with no need for a central authority. Transactions are simultaneously secure and trustworthy due to the use of cryptographic principles. In recent years, blockchain technology has become very trendy and penetrated different domains, mostly due to the popularity of cryptocurrencies. One field where blockchain technology has tremendous potential is healthcare, due to the need for a more patient-centric approach to healthcare systems and to connect disparate systems and increase the accuracy of electronic healthcare records (EHRs). In this systematic review, an analysis of state-of-the-art blockchain research in the field of healthcare is conducted. The aim is to reveal the potential applications of the technology and to highlight the challenges and possible directions of blockchain research in healthcare. First, background information is discussed, followed by a description of the exact methodology used in this paper. Next, an analysis of the results is given, which includes a bibliometric overview, an analysis of gathered data and its properties, and the results of a literature quality assessment. Lastly, there is a discussion of the results from the analysis. The findings indicate that blockchain technology research in healthcare is increasing and it is mostly used for data sharing, managing health records and access control. Other scenarios are very rare. Most research is aimed at presenting novel structural designs in the form of frameworks, architectures or models. Findings also show that technical details about the used blockchain elements are not given in most of the analyzed publications and that most research does not present any prototype implementation or implementation details. Often even with a prototype implementation, no details about blockchain elements are given.

We demonstrated an ITO-free, highly transparent organic solar cell with the potential to be integrated into window panes for energy harvesting purposes. A transparent, conductive ZnO/Ag/ZnO multilayer electrode and a Ag:Ca thin film electrode were used in this transparent device as the bottom and top electrode, respectively. To further improve the transmittance of the solar cell, the thickness of the top ZnO layer was investigated by both experiment and simulation. An average visible transmittance of > 60% was reached, with a maximum transmittance of 73% at 556 nm. Both top and bottom illumination of the solar cell generated comparable power conversion efficiencies, which indicates the wide application of this solar cell structure. In addition, we fabricated distributed Bragg reflector mirrors with sputtered SiO2 and TiO2, which efficiently increased the power conversion efficiency over 20% for the solar cells on glass and PET substrates,

In this paper, two types of the general fractional derivatives of distributed order as well as a corresponding fractional integral of distributed type are defined and their basic properties are investigated. The general fractional derivatives of distributed order are constructed for a special class of the one-parametric Sonin kernels with a power law singularities at the origin. The conventional fractional derivatives of distributed order based on the Riemann-Liouville and the Caputo fractional derivatives are particular cases of the general fractional derivatives of distributed order introduced in this paper.

The objective of this study is to examine a nonparametric estimate , using the kernel approach, of the conditional distribution function of a scalar response variable that is given a random variable whose values take place in a separable real Hilbert space. The observations will be dependent on one another in a quasi-associated fashion. The pointwise practically perfect consistencies with rates of this estimator are established by us under some broad conditions. The study’s major objective is to investigate the convergence rate of the proposed estimator and its application in the convergence rate and asymptotic normality of the hazard function. The asymptotic normality of the developed estimator is established precisely. Simulation studies were conducted to investigate the behavior of the asymptotic property in the context of finite sample data.

A structural study of the 6√3 reconstruction conversion on 4H-SiC substrate surface in an Ar medium with a short sublimation annealing time into quasi-free epitaxial graphene by means of hydrogen intercalation between the upper layer of SiC and the adjacent reconstructed layer was carried out using the technique of reflection high-energy electron diffraction (RHEED) characterizing the surface of a crystalline material. A slight violation of the 6√3 reconstruction layer formation uniformity was found. The results of the study of the crystal structure of quasi-free graphene and single-layer graphene comprising a buffer layer formed on 4H-SiC in the traditional way in an Ar atmosphere without intercalation were compared.

We present set-theoretical solutions of the Yang-Baxter equation in BCK–algebras. Some solutions in BCK−algebras are not solutions in other structures (such as MV −algebras). Related to our investigations we also consider some new structures: Boolean coalgebras and a unified braid condition – quantum Yang-Baxter equation. Finally, we will see how poetry has accompanied the development / history of the Yang–Baxter equation.

Although nanotechnology is a new and rapidly growing area of science, the impact of nanomaterials on living organisms is unknown in many aspects. In this regard it is extremely important to perform toxicological tests, but complete characterization of all varying preparations is extremely laborious. The computational technique called quantitative structure-activity relationship, or QSAR, allows reducing the cost of time- and resource-consuming nanotoxicity tests. In this review, (Q)SAR cytotoxicity studies of the past decade are systematically considered. We regard here five classes of engineered nanomaterials (ENMs): metal oxides, metal-containing nanoparticles, multi-walled carbon nanotubes, fullerenes, and silica nanoparticles. Some studies reveal that QSAR models are better than classification SAR models, while other reports conclude that SAR is more precise than QSAR. The quasi-QSAR method appears to be the most promising tool, as it allows accurately taking experimental conditions into account. However, experimental artifacts are a major concern in this case

The wave packet consisting of two harmonic plane waves with the same frequencies, but with different wave vectors is considered. The dispersion relation of a packet is structurally similar to the dispersion relation of a relativistic particle with a nonzero rest mass. The possibility of controlling the group velocity of a quasi-monochromatic wave packet by varying the angle between the wave vectors of its constituent waves is discussed.

We investigate entropy generation in unsteady three-dimensional axisymmetric MHD nanofluid flow over a non-linearly stretching sheet. The flow is subject to thermal radiation and a chemical reaction. The conservation equations were solved using the spectral quasi-linearization method. The novelty of the work is in the study of entropy generation in three-dimensional axisymmetric MHD nanofluid and the choice of the spectral quasilinearization method as the solution method. The effects of Brownian motion and thermophoresis are also taken into account when the nanofluid particle volume fraction on the boundary in passively controlled. The results show that as the Hartman number increases, both the Nusselt number and the Sherwood number decrease whereas the skin friction increases. It is further shown that an increase in the thermal radiation parameter corresponds to a decrease in the Nusselt number. Moreover, entropy generation increases with the physical parameters.

The emergence of Distributed Ledger Technology (DLT) in the past decade has challenged our imagination to discover new, innovative and disruptive solutions to problems in domains ranging from finance and healthcare to supply chain and smart cities. However, the enormous energy consumption that has been observed in some of the most successful DLT applications raises the question of their long term sustainability. This article reviews the standardization efforts of the International Telecommunications Union (ITU) to provide guidelines to regulators and policy makers for making informed decisions on the applicability and sustainability of DLT architectures from the point of view of energy consumption.

Power grid fault diagnosis technology has been widely used at home and abroad. With the access of distributed power sources and diversified loads, the distribution network has changed from a passive network to an active network, and the current has changed from "one-way" to "two-way". The structure of distribution network, equipment environment and operating conditions are becoming more and more complex, and the fault diagnosis and localization of active distribution network is becoming more and more difficult. With the rapid development of artificial intelligence, power grid fault diagnosis based on intelligence is unprecedented. This paper introduces the research status of distributed photovoltaic grid-connected fault diagnosis technology, and expounds the history of fault diagnosis technology, which mainly includes expert system, artificial neural network, Bayesian network, fuzzy set theory, rough set theory, Petri net analytical model and multi-source information fusion, etc. The applicability and characteristics of these diagnoses are described, and the existing defects and the overall direction of improvement are briefly described Finally, the key technical problems and future directions in the field of power system fault diagnosis are pointed out by combining theory with practice, so as to promote the further development of this field.

With a high proportion of distributed photovoltaic and lower fossil energy integrated into the distribution network, it is very difficult to ensure the reliability of power supply. The distributed photovoltaic planning model based on big data is proposed. According to the impact stochastic photovoltaics and loads on reliability planning, the static and dynamic capacity-load ratios are proposed. The big data analysis model of distributed photovoltaic planning is established. The big data multi-scenario generation and reduction algorithm of stochastic distributed photovoltaic and load planning is studied, and a source-load big data scenario matching model is proposed. Ac-cording to the source load big data scenario, the dynamic capacity-load ratio of the distribution network is obtained. The static capacity-load ratio calculation method in distribution network planning is studied to ensure the reliability of power supply. Finally, the IEEE 33-bus system is used as an example. The results show that distributed photovoltaic planning based on big data and multi-scenario methods can improve photovoltaic utilization and power supply reliability.

Knowledge graphs (KGs) play a crucial role in many applications, such as question answering, but incompleteness is an urgent issue for their broad application. Much research in knowledge graph completion (KGC) has been performed to resolve this issue. The methods of KGC can be classified into two major categories: rule-based reasoning and embedding-based reasoning. The former has high accuracy and good interpretability, but a major challenge is to obtain effective rules on large-scale KGs. The latter has good efficiency and scalability, but it relies heavily on data richness and cannot fully use domain knowledge in the form of logical rules. We propose a novel method that injects rules and learns representations iteratively to take full advantage of rules and embeddings. Specifically, we model the conclusions of rule groundings as 0-1 variables and use a rule confidence regularizer to remove the uncertainty of the conclusions. The proposed approach has the following advantages: 1) It combines the benefits of both rules and knowledge graph embeddings (KGEs) and achieves a good balance between efficiency and scalability. 2) It uses an iterative method to continuously improve KGEs and remove incorrect rule conclusions. Evaluations on two public datasets show that our method outperforms the current state-of-the-art methods, improving performance by 2.7% and 4.3% in mean reciprocal rank (MRR).

As various services have been generating large scale graphs to represent multiple relationships between objects, studies have been conducted to obtain subgraphs with particular patterns. In this paper, we propose a distributed query processing method to efficiently search a subgraph for a large graph on Spark. To reduce unnecessary processing costs, the search order is determined by filtering scores using the probability distribution. The partitioned queries are searched in parallel in the distributed graph of each slave node according to the search order, and the local search results obtained from each slave node are combined and returned. The query is partitioned in triplets based on the determined search order. The performance of the proposed method is compared with the performance of existing methods to demonstrate its superiority.

The use of fiber optics as a tool for different kinds of geotechnical monitoring can become highly attractive and cost effective when compared to conventional instruments such as piezometers, inclinometers, among others. A single fiber optic cable may cover a larger monitoring area compared to conventional instrumentation, and the possibility of monitoring more than one physical quantity with the same fiber optic cable. Consulting the literature, it is possible to find several different examples where distributed fiber optic systems are being used. For the use of any sensor, a calibration curve is required. In the case of strain sensors, calibration is required to derive strain values from the frequency measurement quantity. However, fiber optic sensor cable manufacturers often do not provide their own calibration parameters and consult the values in specialized literature, which can result in monitoring errors. In this context, this article presents a bench adjusted for tests on single mode fiber optic cables, as well as results of tensile tests aimed at defining the function of strain variations of two different optical fiber cables, manufactured by different companies, using two different distributed interrogators. This paper also proposes a methodology for calibrating of the fiber optic cables deformation since the calibration parameters are provided by the cable manufacturers, which can result in errors depending on the application. It should be noted that there are few manufacturers of fiber optic cables aimed at application in civil engineering. Therefore, with the calibration methodology proposed in this paper, it is intended to transmit to the academic community the possibility of obtaining calibration parameters of any fiber optic cable, even those manufactured for telecommunications purposes and not only for cables manufactured with the intended use in civil engineering. Because of this fact, researchers will not be restricted to the acquisition of special cables for their applications. Based on the results, it was possible to conclude that the application of calibrated fiber optic sensors in experimental piles foundations is viable, in order to evaluate the load-displacement behavior of these elements, under different loading conditions.

The distributed long-range sensing system using the standard telecommunication single-mode optical fiber in a function of a distributed sensor for sensing of mechanical vibrations is described. Various events generating vibrations such as walking or running person, moving cars, trains and others can be detected, localized and classified. The sensor and related sensing system components were designed and constructed and the system was tested both in the laboratory and in the real situation with 88 km telecom optical link, and the results are presented.

Frauds and misconducts have been common in the history of science. Recent events connected to the COVID-19 pandemic have highlighted how the risks and consequences of this are no longer acceptable. Two papers, addressing the treatment of COVID-19, have been published in two of the most prestigious medical journals. In both, the authors declared to have analysed electronic records from a private corporation, which apparently collected data of tens of thousands of patients, coming from thousands of hospitals. Both papers have been retracted a few weeks later. When such events happen, the confidence of the population in scientific research is likely to be weakened. The objective of this paper is to highlight how the current system endangers not only the reliability of scientific research, but also the very foundations of the trust system on which modern healthcare is based. Having shed the light on the dangers of a system without appropriate monitoring, we propose to improve the research process using the promising aspects of the distributed ledger technology which, thanks to the characteristics of immutability, decentralization and transparency, appears among the best solutions to avoid the repetition of the mistakes linked to the recent and past history of research.

With the interest and attention that the Blockchain and the Distributed Ledger Technologies (DLT) have recently demanded, the technology is advancing at a very high rate. With investors and applications in a wide variety of fields, a lot of funding and efforts are being driven into bringing the technology to everyday use. The community and companies are coming up with new ways to collaborate, which makes the blockchain ecosystem evolve at full tilt. Consequently, this paper’s aim is to review the academic and grey literature and to provide readers with information about the evolution, benefits and challenges of the Public Distributed Ledger Technologies and to discuss the latest solutions, which are being developed for bringing decentralization closer to the mainstream. The paper reviews the Directed Acyclic Graph (DAG) structured distributed ledgers with focus on the Hedera Hashgraph, a novelty DLT bringing a unique consensus algorithm with new use-cases enabled by new cryptoeconomic mechanisms as well as vital services, such as Solidity smart contracts and distributed file storage. Lastly, we are going to explore second-layer network protocols, a major topic for solving the scalability issues, empower Bitcoin and DLTs and also disrupt cryptocurrency exchanges focusing on the Lightning Network.

This article presents an in-depth review of Virtual Power Plant (VPP), its organization in the energy system and its prospects in the face of the promising future of the increasing bidirectional complexity exposed in the current scenario. A new paradigm for the operations of modern power distribution and transmission systems requires greater grid flexibility that is accompanying an extensive change in the structure of electricity markets, the fruit of the development of the renewable industry and of the growing photovoltaic systems popularized, but in contrast due to the intermittent nature cause variable uncertainties in the power system. Innovative concepts like VPP are becoming a reality, establishing an efficient and effective mechanism. The objectives and components are described in a comprehensive way, and some of the most important are pointed out and presented in detail to contribute with a description of the energy systems and the implicit research needed for sustainability and resilience in the eminent energy scenario with this technology. In addition, the literature and studies of this technology already indicate a direction of this new tool as a promising solution to manage the uncertainties of the renewable energies.

The resource allocation for tasks in heterogeneous distributed system is a well known NP-hard problem. For the sake of making the makespan is minimized, it is hard to distribute the tasks to proper processors. The problem is even more complex and challenging when the processors have unavailable time and the tasks type are various. This paper investigates a resource allocation problem for hybrid tasks comprising both divisible and bag-of-tasks(BoT) in heterogeneous distributed system when the processors has unavailable time. First, the mathematical model, which minimizes the makespan of the hybrid tasks when the processors have unavailable time, is established. Second, we propose a scheduling algorithm referred to as bag-of-tasks allocate-pull and divisible task allocation (BoTAPDTA) algorithm for handling hybrid tasks on heterogeneous distributed systems. In addition, to solving the optimization model efficiently, a generic algorithm(GA) is proposed. For the sake of reducing the search space and solving the optimization model effectively, a two step scheduling algorithm(TSGA), which first allocate bag-of-tasks(BoT) using generic algorithm and then assign divisible task to processors like BoTAPDTA, is designed. Finally, numerical simulation experiments are conducted, and experimental results indicate the effectiveness of the proposed model and algorithm.

In this paper, a class of coupled competitive neural networks, which subject to disturbance and discontinuous activation functions, is concerned. To realize the fixed-time quasi-bipartite synchronization, an aperiodic intermittent controller is initially designed. Subsequently, by combining the fixed-time stability theory and nonsmooth analysis, several criteria are established to ensure the bipartite synchronization in fixed time. Moreover, synchronization error bounds and settling time estimates are provided. Finally, numerical simulations are presented to verify the main results.

The purpose of the present paper is to introduce and investigate new subclasses of analytic function class of bi univalent function defined in open unit disk connected with a linear q-convolution operator, which are associated with the quasi-subordination. We find coefficients estimate |h_2 |,|h_3 | for functions in these subclasses. Several known and new consequences of these results are also pointed out.

Currently, efficient utilization of low-grade thermal energy is a great challenge. Thermoelectricity is an extremely promising method of generating electrical energy from temperature differences. As a promising energy conversion technology, thermo-electrochemical cells (TECs) have attracted much attention in recent years for their ability to convert thermal energy directly into electricity with high thermal power. Within TECs, anions and cations gain and lose electrons, respectively at the electrodes using the potential difference between the hot and cold terminals of the electrodes by redox couples. Additionally, the anions and cations therein are constantly circulating and mobile via concentration diffusion and thermal diffusion, providing an uninterrupted supply of power to the exterior. This review article focuses mainly on the operation of TECs, recent ad-vances in redox couples, electrolytes, and electrodes. The outlook for optimization strategies re-garding TECs is also presented in this paper.

This study investigates the application of Unmanned Aerial Vehicles (UAVs) equipped with a Micasense RedEdge-MX multispectral camera for the estimation of Secchi Depth (SD) in inland water bodies. The research analyzed and compared five sun-glint correction methodologies — Hedley, Goodman, Lyzenga, Joyce, and threshold removed glint to model the SD values derived from UAV multispectral imagery, highlighting the role of reflectance accuracy and algorithmic precision in SD modeling. While Goodman's method showed a higher correlation (0.92) with in situ SD measurements, Hedley's method exhibited the smallest average deviation (0.65 m), suggesting its potential in water resource management, environmental monitoring, and ecological modeling. The study also underscored the Quasi-Analytical Algorithm (QAA) potential in estimating SD due to its flexibility to process data from various sensors without requiring in situ measurements, offering scalability for large-scale water quality surveys. The accuracy of SD measures, calculated using QAA, was related to variability in water constituents of coloured dissolved organic matter and the solar zenith angle. A practical workflow for SD acquisition using UAVs and multispectral data was proposed for monitoring inland water bodies.

In response to the fire protection design challenges faced by the integrated station-city engineering under the new model of station-city fusion, such as large building volume, diverse traffic types, functional integration, spatial complexity, and inapplicable standards, this paper selects Chongqing East Station as a typical engineering case. The fire safety engineering methods and performance-based fire protection design ideas were used. The paper summarized the fire protection difficulties, such as multifunctional combination construction, large fire protection distance, large fire protection zoning area, long evacuation distance, and design of evacuation (quasi) safety zones. Combining with the actual situation of the engineering, this paper analyzed the basic technical measures taken to address these design challenges. By adopting the method of fire safety engineering, the safety of the design scheme is verified, which can provide a solution approach for the current fire protection design challenges in the integrated station-city engineering.

Reasonable allocation of production plans and saving the overall electricity cost are crucial for large manufacturing conglomerates, which is an effective way for conglomerates to realize open-source and cost-saving. This paper develops an optimization model of off-site industrial production scheduling for enterprises in response to the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's electric power and the difficulty of promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and different electricity prices in different places to optimize the scheduling of industrial production in different places. The model is embedded with two sub-models of the electricity price prediction model and distributed photovoltaic kWh cost model to complete the model parameters, in which the electricity price prediction model utilizes the LSTM neural network. The integrated model is then solved by the particle swarm algorithm. To verify the effectiveness of the model in solving the problem of off-site production scheduling on the demand side of electricity, the research team researched two off-site pharmaceutical factories belonging to the same large pharmaceutical company. Finally, the research data were substituted into the model for solving, and it was concluded that the optimization model could significantly reduce the cost of electricity consumption of the enterprise by about 7.9 %.

In this paper, we propose and demonstrate a network analysis optical frequency domain reflectometer (NA-OFDR) for distributed temperature measurements at high spatial (down to 3 cm) and temperature resolution. The system makes use of a frequency-stepped, continuous-wave (cw) laser, whose output light is modulated using a vector network analyzer. The latter is also used to demodulate the amplitude of the beat signal obtained by coherently mixing the Rayleigh backscattered light with a local oscillator. The system is capable of high measurand resolution (50 mK at 3-cm spatial resolution), thanks to the high sensitivity of coherent Rayleigh scattering to temperature. Furthermore, compared to the conventional optical-frequency domain reflectometry (OFDR), the proposed system does not rely on the use of a tunable laser, therefore it is less prone to limitations related to the laser coherence or sweep nonlinearity. Two configurations are analyzed, both numerically and experimentally, based on either a double sideband or single sideband modulated probe light. The results confirm the validity of the proposed approach.

Remote distributed generation control and demand response is nowadays a basic need, especially for the Distributed Network Operators (DNOs). The integration of Renewable Energy Sources (RES) with stochastic production characteristics into the grid in conjunction with the newly appeared stochastic demand consumers (i.e., electric vehicles) hardens the ef-forts of the DNOs to keep the grid’s operation within safe limits and prevent cascading black-outs while staying in compliance with the SAIDI and SAIFI indices during maintenance and re-pair operations. Taking also under consideration the aging of the existing grid infrastructure, making it year by year more prone to failure, it is yet of great significance for the DNOs to have access to real-time feedback from grid’s infrastructure, fast, with low-cost upgrade interventions, easily deployed on the field and with fast response potential, in order to be able to perform real-time grid management (RTGM). In this article, we describe the architecture, development and deployment of a hybrid real-time control system of distributed generation (DG) units, which has the potential to be applied easily to DNO’s substations, RES plants and consumers (i.e. electric vehicles’ charging stations), that combines a low-cost minimum intervention into the electricity grid and its components and eliminates all communication interoperability problems between the different vendors’ installed electrical equipment.

Reinforcement learning is used for air combat problems in recent years, and the idea of curriculum learning is often used for reinforcement learning, but traditional curriculum learning suffers from the problem of plasticity loss in neural networks. Plasticity loss is the difficulty of learning new knowledge after the network has converged. To this end, we propose a motivational curriculum learning distributed proximal policy optimization (MCLDPPO) algorithm, through which agents trained can significantly outperform the predictive game tree and mainstream reinforcement learning methods. The motivational curriculum learning is to help the agent gradually improve their combat ability by observing the agent's unsatisfactory performance and providing corresponding rewards as a guide. Additionally, a complete tactical maneuver is encapsulated based on existing air combat knowledge, and through the flexible use of these maneuvers, some tactics beyond human knowledge can be realized. In addition, we designed an interruption mechanism for the agent to increase the frequency of decision-making when the agent faces an emergency. When the number of threats the agent receives changes, the current action will be interrupted to reacquire observations and make decisions again. Using the interruption mechanism can significantly improve the ability of the agent. To simulate actual air combat better, we built an air combat environment based on digital twin technology to create an air combat engine core such as missiles and radars. Based on this environment, we develop a distributed intelligent training system that employs Redis as the cache and obtains the data generated in the distributed environment through a multi-threaded TCP/IP protocol, which significantly increases the training data throughput and helps the agent accelerates convergence. The experimental results demonstrate that the agent can fully exploit the situational information to make reasonable decisions and affords air combat tactical adaption, verifying the effectiveness of the algorithmic framework proposed in this paper.

Abstract: Despite existing several techniques for distributed sensing (temperature and strain) using standard Single Mode optical Fiber (SMF), compensating or decoupling both effects is mandatory for many applications. Currently, most of the decoupling techniques require special optical fibers and are difficult to implement with high spatial resolution distributed techniques, such as ϕ-PA-OFDR. So, this work’s objective is to study the feasibility of decoupling temperature and strain out of a ϕ-PA-OFDR readouts taken over an SMF. For this purpose, the readouts will be subjected to a study using several Machine Learning algorithms, among them, Deep Neural Networks. The motivation which underlies this target is the current blockage in the widespread use of Fiber Optic Sensors in situations where both strain and temperature change, due to the coupled dependence of currently developed sensing methods. Instead of using other types of sensors or even other interrogation methods, the objective of this work is to analyze the available information in order to develop a sensing method capable of providing information about strain and temperature simultaneously.

A micro Sagnac interferometer integration is proposed for electron cloud distributed sensors. The Sagnac interferometer consists of four microring probes integrated into a Sagnac loop. Each of the microring probes is embedded with the silver bars to form the plasmonic wave oscillation. At the center microrings, electrons are trapped and oscillated by the whispering gallery modes (WGMs), where the plasmonic antennas are established and applied for wireless fidelity (WiFi) and light fidelity (LiFi) transmissions for distributed sensors. The antenna gains are 2.59dB, 0.93dB, 1.75dB, and 1.16dB respectively for the four antennas formed at the center microrings. The polarized light of 1.50µm wavelength is fed into the interferometer input, which is polarized randomly into upstream and downstream directions. The polarization components can be obtained by the space-time modulation control. By controlling the electron cloud spin orientation, the space-time projection can be applied, and the ultra-high measurement resolution can be obtained in terms of fast switching time (change in phase). In manipulation, the applied stimuli are substituted by the change in input source power. The light input power variation causes a change in electron cloud density. Similarly, when the electron cloud is excited by the microscopic medium, which can be employed as the microscopic sensors. The WGM sensors have sensitivities of 1.35µm-2, 0.90µm-2, 0.97µm-2 and, 0.81µm-2, respectively. The WGMs behave as a four-point probe for the electron cloud distributed sensors, where the electron cloud sensitivities of 2.31 prads-1mm3 (electrons)-1, 2.27prads-1mm3 (electrons)-1, 2.22 prads-1mm3(electrons)-1, 2.38prads-1mm3(electrons)-1 are respectively obtained.

A debris flows generation related to a poorly sorted mixture of soil, catchment topography and rainfall characteristic. Runoff of some depth on valley resulting from intensive rainfall can incur the sediments movement of beds or adjacent banks. The fluid flow in channel affected by rainfall parameters combinations, such as duration, intensity, cumulative rainfall, etc., is the key factor for debris movement. In this paper, the rainfall characteristics and occurrence conditions of debris flow in Xiongmao gully on July, 26th, 2016, have been explored, combined with field survey and indoor simulation experiment on the collected critical discharge parameters of debris movement. Further, debris distribution and the critical discharge characteristics have been analysed, by means of investigation on the catchment topography and occurrence cause of the debris flow, analysis of the critical discharge parameters on which the channel debris began to move, and K value clustering analysis method to characterize the rainfall pattern of the studied area, the discharge calculation of debris flow occurring in different rainfall patterns. The results have shown that, for the debris flow occurrence in Xiongmao gully, the debris initiation on the middle reaches of the gully provide the majority of solid particles for the disaster on July, 26th, 2016, and the upstream confluent provided catchment. Based on the relationship obtained from laboratory test, in which the calculated critical discharge was 43.8m3/s, less than the peak discharge (Qc =66.7m3/s), calculated by morphological method. In addition, it has been indicated that the dominated rainfall patterns of the studied area are first-quartile and second-quartile, that is, the rainfall is primarily at earlier or middle to preliminary stage of this time rainfall event. The critical discharge for the occurrence of debris flow on July, 26th was achieved 20a rainfall frequency, the larger runoff volume generated on shorten heavily rainfall. Based on individuality characteristics, such as distributed hydrological analysis, critical discharge and rainfall pattern of debris flow, the forewarning could be more efficient.

Distributed Energy Resources might have a severe influence on Power Line Communications, as they can generate interfering signals and high frequency emissions or supraharmonics that may cause loss of metering and control data. In this paper, the influence of various energy resources on Narrowband Power Line Communications is described and analyzed through several test measurements performed in a real microgrid. Accordingly, the paper describes the effects on smart metering communications through MAC layer analysis. Results show that the commutation frequency of inverters and the presence of battery chargers are remarkable sources of disturbance in low voltage distribution networks. In this sense, the results presented can contribute to efforts towards standardization and normative of emissions at higher frequencies higher, such as CENELEC EN 50160 and IEC/TS 62749.

Quantum theory is the land of indiscernible things, of things that in some situations cannot be put apart. This contrasts in much with standard mathematics and classical logic, which were elaborated to deal with \textit{individuals}, discernible things. Many thinkers, among them the mathematician Yuri Manin, have proposed that a more general theory of `sets' than standard set theories (where sets are collections of discernible elements) should be elaborated to cope with quantum physics. Quasi-set theory was proposed with such an aim. In this paper, we consider some aspects of this theory and discuss the way we can use such a theory as a framework for constructing deformable (not rigid) structures where indiscernible things can be considered but with one additional and fundamental detail: these structures cannot be extended to rigid structures as it happens in the standard frameworks. Really, the way of dealing with indiscernible (or indistinguishable) elements within a `standard' framework such as a `classical' set theory (like the ZFC system) requires the confinement of the discussion to deformable (non-rigid) structures. But in ZFC every structure can be extended to a rigid structure (encompassing only the trivial automorphism -- the identity function), so that in the extended structure (or in the whole universe) one can realize that the supposed indiscernible objects are not indiscernible at all. Quasi-set theory is such that the universe of quasi-sets is deformable and in such a theory one can construct deformable structures which cannot be extended to rigid ones. The aim of this paper is to put these things in a clear way.

Variations in the implant thread shape and occlusal load behavior may result in significant changes in the biological and mechanical properties of dental implants and surrounding bone tissue. Most previous studies consider a single implant thread design, an isotropic bone structure, and a static occlusal load. However, the effects of different thread designs, bone material properties, and loading conditions are important concerns in clinical practice. Accordingly, the present study performs Finite Element Analysis (FEA) simulations to investigate the static, quasi-static and dynamic response of the implant and implanted bone material under various thread designs and occlusal loading directions (buccal-lingual, mesiodistal and apical). The simulations focus specifically on the von Mises stress, displacement, shear stress, compressive stress and tensile stress within the implant and the surrounding bone. The results show that the thread design and occlusal loading rate have a significant effect on the stress distribution and deformation of the implant and bone structure during clinical applications. Overall, the results provide a useful insight into the design of enhanced dental implants for an improved load transfer efficiency and success rate.

In the wake of the current SARS-CoV-2 pandemic devastating the world, it is imperative to elucidate the comparative genomics of geographically-diverse strains of this novel coronavirus to gain insights into its microevolution, pathogenesis and control. Here we explore the molecular nature, genome-wide frequency, and gene-wise distribution of mutations in three distinct datasets encompassing 68 SARS-CoV-2 RNA-genomes altogether. While phylogenomic analysis revealed parallelism between the evolutionary paths charted by distinct quasispecies clusters of the virus, occurrence of mutations across genomes was found to be non-random. Whereas deletion mutations are extremely scarce and insertions totally absent, of all the instances of single nucleotide substitution detected, the overwhelming majority were transition mutations with cytidine to uridine being the most prevalent type. Propensity of this transition could be attributed to hydrolytic deamination mediated by ultra-violet irradiation or bisulfite reagent, both of which find wide usage as sterilizer/disinfectant. Transversions, albeit few and predominated by the guanosine to uridine form, were found concentrated in loci encoding the structural proteins of the virus, so might confer versatile tissue-colonization potentials. Mutation frequency of the three distinct genome-sets ranged narrowly between 0.07-1.08 × 10^-4 nucleotides positions mutated per nucleotide aligned. Gene-wise mapping of the global mutations illuminated the highly conserved nature of the genes encoding the non-structural proteins Nsp7, Nsp8 (two essential cofactors of the viral RNA-dependent RNA-polymerase) and Nsp9 (Nsp8-interacting single-strand RNA-binding protein), plus the envelope protein E (involved in SARS-CoV-2 assembly, budding and pathogenesis). These mutation-free genomic loci and/or their protein products could be potent targets for future drug designing/targeting.

The asymptotic dynamics of the classes of rational difference equations (RDEs) of third order defined over the positive real-line as $$\displaystyle{x_{n+1}=\frac{x_{n}}{ax_n+bx_{n-1}+cx_{n-2}}}, \displaystyle{x_{n+1}=\frac{x_{n-1}}{ax_n+bx_{n-1}+cx_{n-2}}}, \displaystyle{x_{n+1}=\frac{x_{n-2}}{ax_n+bx_{n-1}+cx_{n-2}}}$$ and $$\displaystyle{x_{n+1}=\frac{ax_n+bx_{n-1}+cx_{n-2}}{x_{n}}}, \displaystyle{x_{n+1}=\frac{ax_n+bx_{n-1}+cx_{n-2}}{x_{n-1}}}, \displaystyle{x_{n+1}=\frac{ax_n+bx_{n-1}+cx_{n-2}}{x_{n-2}}}$$ is investigated computationally with theoretical discussions and examples. It is noted that all the parameters $a, b, c$ and the initial values $x_{-2}, x_{-1}$ and $x_0$ are all positive real numbers such that the denominator is always positive. Several periodic solutions with high periods of the RDEs as well as their inter-intra dynamical behaviours are studied.

This paper proposed a three-layer distributed simulation network architecture: 1. Distributed virtual simulation network, 2. Perception and control sub-network, and 3. Collaborative communication service network. This simulation architecture runs on a distributed platform. The platform can provide a unique virtual scenario and several simulation services to verify the individual robot system's basic perception, control, and planning algorithms and the distributed collaborative algorithms of heterogeneous multi-robot systems. Furthermore, we designed a simulation experiment scenario for classical heterogeneous robot systems such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). At last, We gave a rough estimation model of the bandwidth.

Software-defined networking (SDN) divides a traditional network into a programmable data plane and a centralized control plane. This technology is expected to transform traditional network companies that provide flexible and intelligent network operations. The core component utilized to oversee every data plane action is the controller in the control plane. In order to achieve optimal performances, the controller’s performance and capabilities are therefore crucial. There are lots of controller research available in existing literature. Nevertheless, no qualitative comparison study of OpenFlow-enabled distributed but logically centralized controllers exists for them. This article also includes a quantitative investigation of the performance of several distributed but logically centralized SDN controllers in custom network scenarios using Mininet, as well as a thorough qualitative comparison of them. More precisely, we give a qualitative evaluation of their attributes and classify and categorize 13 distributed but logically centralized SDN controllers according to their capabilities. Additionally, we offer a comprehensive SDN emulation tool called Mininet-based SDN controller performance assessment in this study. Using six performance metrics—bandwidth, round- trip time, delay, jitter, packet loss, and throughput—this work also assesses five distributed but logically centralized controllers against two custom network scenarios (uniform and non-uniform host distribution). Our investigation shows that the OpenDayLight controller performs the best across all performance criteria, with the exception of delay, packet loss and round trip time, where the Ryu controller excels. In the entire experiment, the HyperFlow and ONOS controller performs the worst in terms of all performance metrics. Finally, we discuss detailed research findings on the performance. These experimental results provide a decision-making guideline when selecting a controller.

Consumer IoT (CIoT) manufacturers seek customer feedback to enhance their products and services, creating a smart ecosystem like a smart home. Due to security and privacy concerns, Blockchain-based federated learning (BCFL) ecosystems can let CIoT manufacturers update their Machine Learning (ML) model using end-user data. FL uses privacy-preserving ML techniques to forecast customers' needs and consumption habits, and blockchain replaces the centralised aggregator to safeguard the ecosystem. However, Blockchain technology (BCT) struggles with scalability and quick ledger expansion. In BCFL, local model generation and secure aggregation are other issues. This research contributes a novel architecture emphasising Gateway Peer (GWP) in blockchain network to resolve scalability, ledger optimisation and secure model transmission issues. In the architecture we replace the centralised aggregator by the blockchain network, while GWP restricts the number of local transactions to execute in BCN. Considering the security and privacy of FL processes, we have added differential privacy and advanced normalisation techniques to ML processes. The approaches strengthen end-users' cyber security and encourage the adoption of technological innovation standards by service providers. The proposed approach has been tested extensively using a well-respected Stanford Cars dataset. We experimentally demonstrate that the proposed architecture makes the network scalable and optimises the ledger significantly. In addition, the normalisation technique outperforms batch normalisation when features are under DP protection.

Distributed Manufacturing (DM) is becoming increasingly important in operations management due to its potential to support sustainability goals, reduce risks in global supply chains, and boost local economies. However, previous analyses of the advantages and disadvantages of DM have mainly focused on operations, overlooking additional benefits across the supply chain. For example, DM can enable local sourcing, better serve end-users, and tackle reverse supply chain challenges. While economies of scale are essential for reducing costs and improving productivity, highly centralised manufacturing can increase transportation costs, vulnerability, and supply chain disruptions, particularly during pandemics or other times of restricted transportation. To address these challenges, this study introduces a Multi-attribute Decision Support System (MADSS) and assessment process that considers impacts across the supply chain and guides stakeholders, academics, and decision-makers. The MADSS handles quantitative and qualitative information, missing data, and uncertainty, and a team of experts from academia and industry in New Zealand has developed the evaluation. The MADSS was used to analyse DM and traditional construction alternatives from economic, social, environmental, and resilience perspectives in New Zealand. The research contributes to a better understanding of the impacts of DM across the entire supply chain. Also, it proposes a flexible decision-making framework to engage with stakeholders and support decision-making in other industries and regions.

In this paper, feasibility of the initial alignment of the gyro-free inertial navigation system (GF-INS) is investigated. Initial roll and initial pitch are obtained as the conventional INS since centripetal acceleration is very small. Relation between initial heading and accelerometers’ outputs of the gyro-free inertial measurement unit (GF-IMU) is derived. It is determined from the relation whether initial heading can be obtained in 15 GF-IMUs which have been presented in research literatures. The relationship between the accelerometer errors of GF-IMU and the initial heading error is derived. When gyros are additionally attached in one axis or two axes of the body frame, it is investigated that initial heading can be obtained. The initial heading error is also calculated from the accelerometer errors and the gyroscope errors. The results show that the initial heading from only accelerometer outputs of the GF-IMU cannot be used in the GF-INS since the error is too large. It can also be seen from the results that the initial heading can be used in the GF-INS if two gyroscopes are additionally attached to the roll and pitch axes of the body frame.

Centralized control algorithm limits the hardware flexibility of a Modular Multilevel Converter (MMC). Therefore, industrial MMC applications have started adopting distributed control structures. Even though distributed controller reduces a single point of failure risk compared to the centralized controller, the failure risk of the entire control systems increases due to the number of local controllers. However, the distributed controller can be programmed in such a way as to bypass the faulty local controller and take care of the power modules with the adjacent local controllers. This paper implements a modular distributed fault-tolerant controller for a scaled laboratory MMC prototype. Experimental results show that an MMC can operate without interruption, even under two local controller failures. Besides, the experimental results are verified with the Opal-RT, a real-time simulator with the same controller in a Control Hardware in Loop (CHIL) environment.

The problem of reliable function computation is extended by imposing privacy, secrecy, and storage constraints on a remote source whose noisy measurements are observed by multiple parties. The main additions to the classic function computation problem include 1) privacy leakage to an eavesdropper is measured with respect to the remote source rather than the transmitting terminals’ observed sequences; 2) the information leakage to a fusion center with respect to the remote source is considered as a new privacy leakage metric; 3) the function computed is allowed to be a distorted version of the target function, which allows to reduce the storage rate as compared to a reliable function computation scenario in addition to reducing secrecy and privacy leakages; 4) two transmitting node observations are used to compute a function. Inner and outer bounds on the rate regions are derived for lossless and lossy single-function computation with two transmitting nodes, which recover previous results in the literature. For special cases that include invertible and partially-invertible functions, and degraded measurement channels, exact lossless and lossy rate regions are characterized, and one exact region is evaluated for an example scenario.

The energy efficient problem of parallel-connected pumps in heating, ventilation, and air-conditioning (HVAC) systems has received an increasing attention in recent years. While many pump optimization methods are proposed and show great performance, pumps are not always energy efficient and lack flexibility. In this paper, we propose a distributed control algorithm for parallel pumps in HVAC system in a peer-to-peer setting. Based on a spanning tree of the network of the intelligent nodes and a population of potential solutions randomly sampled, the algorithm makes optimal control decision for pumps to minimize energy consumption and meet the system demand. The theoretical analysis on convergence of the algorithm is established. Unlike traditional control structure, the whole system is fully distributed and each pump is controlled by an intelligent node that runs identical control code and coordinates with other nodes through direct data exchange. Simulation experiments on 6 parallel-connected pumps are provided for different working cases to demonstrate the effectiveness of the proposed algorithm and compare with other four methods. The results show that our method strictly satisfies the demand constraint and presents a good energy saving potential, the convergence guarantee, the flexibility. The maximum energy saving can be up to 29.92%. Besides, the hardware test clearly presents that our method can perform on low-cost Raspberry Pi3 and reduce system cost.

Following an updated outlook of global energy production and utilization, we show through selected examples from both developing and developed countries how distributed generation from renewable energy sources, and from solar energy in particular, is the key solution to ending energy poverty across the world. Guidelines aimed at policy makers suggest a systems view of energy that will be instrumental in guiding the transition from fossil fuels to combustion-free renewable energy for all energy end uses.

The paper deals with the modelization and optimization of an integrated multi-component energy system. On-off operation and presence-absence of components must be described by means of binary decision variables, besides equality and inequality constraints; furthermore, the synthesis and the operation of the energy system should be optimized at the same time. In this paper a hierarchical optimization strategy is used, adopting a genetic algorithm in the higher optimization level, to choose the main binary decision variables, whilst a MILP algorithm is used in the lower level, to choose the optimal operation of the system and to supply the merit function to the genetic algorithm. The method is then applied to a distributed generation system, which has to be designed for a set of users located in the center of a small town in the North-East of Italy. The results show the advantage of distributed cogeneration, when the optimal synthesis and operation of the whole system are adopted, and significant reduction in the computing time by using the proposed two-level optimization procedure.

Aiming at the problems that large amount of video monitoring image data in underground coal mines leads to difficulties in transmission and storage, compressed sensing theory is introduced to encode and decode video images, and a new distributed video coding scheme is proposed. In order to obtain more sparse representation and more general applicability, a block-based adaptive sparse base scheme is proposed. For the acquisition of side information, fixed weight is usually used to synthesize side information and the correlation between different image blocks is neglected, a block-based classification weighted side information generation scheme is proposed. Experimental results show that the block-based classification codec scheme can make full use of inter-frame correlation. Under the appropriate sampling rate, the PSNR value of video reconstruction increases, which effectively improves the quality of video frame reconstruction.

Fiber optic sensors cannot measure damage; for getting information about damage from strain measurements, additional strategies are needed, and several alternatives have been proposed. This paper discuss two independent concepts: the first one is based on detecting the new strains appearing around a damage spot; the structure does not need to be under loads; the technique is very robust, damage detectability is high, but it requires sensors to be located very close to the damage, so it is a local technique. The second approach offers a wider coverage of the structure, it is based on identifying the changes caused by the damage on the strains field in the whole structure for similar external loads. Damage location does not need to be known a priori, detectability is dependent upon the sensors network density, damage size and the external loads. Examples of application to real structures are given.

Layered halide perovskites have emerged as a promising contender in solid-state lighting thanks to their tunable bandgap, high luminescence quantum yield, and high color purity. However, the fabrication of perovskite light-emitting devices in laboratories usually experiences low de-vice-to-device reproducibility since perovskite crystallization is highly sensitive to ambient condi-tions, such as humidity and temperature. Although device processing inside gloveboxes is primarily used to reduce the influence of oxygen and moisture, several extraneous variables, including gas-eous pollution traces and thermal fluctuations in the inert atmosphere or contaminations from re-sidual solvents confined in the enclosed environment, can destabilize the crystallization process and alter the properties of the emissive layers. Here, we examine different experimental set-ups for spin-coating deposition of layered perovskites in inert atmospheres and their influences on the formation of polycrystalline thin films. Our results demonstrate that fluctuations in the glovebox properties (concentrations of residual O2 and H2O or solvent traces), even in very short timescales, can negatively impact the consistency of the perovskite film formation, while thermal variation plays a relatively minor role in this phenomenon. Furthermore, careful storage of chemical species inside the workstation is critical for reproducing high-quality perovskite layers. Consequently, when applying our most controlled environment for perovskite deposition, the photoluminescence life-time of perovskite thin films shows a standard deviation of only 3%, whereas the reference set-up yields a 15% standard deviation. Regarding complete perovskite light-emitting diodes, the uncer-tainties in statistical luminance and EQE data are significantly reduced from 230% and 140% to 38% and 42%, respectively.

We present evidence of the anchoring bias and disposition effect (DE) in investor trading decisions across different asset markets (stable and volatile assets) and market scenarios (stable and volatile markets), as well as how these biases affect investors' portfolio performance. The study employs a quasi-experimental design, allowing subjects to engage in interactive trading with four securities, two with potential negative returns and two with potential positive returns, within a simulated asset market. Our findings reveal the presence of the disposition effect and anchoring bias among individual investors in India. Second, market scenarios or volatility can influence the investors’ behavioral biases. The disposition effect was more prominent in volatile markets and the total market, while the anchoring bias was significant in stable and total markets. Additionally, investors exhibiting the disposition effect exhibit lower portfolio performance, while those demonstrating anchoring bias perform relatively better. The study's findings can help individual investors understand their behavioral biases, avoid them in future trading decisions, and improve portfolio performance. Financial institutions and regulatory agencies could identify investment products and supportive investor interactions towards better market trade decisions.

This paper proposes a parameter coordination optimization design of Power System Stabilizer (PSS) based on an improved Quasi-Affine Transformation Evolutionary (QUATRE) algorithm to suppress low-frequency oscillation and improve the dynamic stability of power system. To begin, the Simulated Annealing (SA) algorithm randomly updates the globally optimal solution of each QUATRE iteration and matches the inferior solution with a certain probability to escape the local extreme point. This new algorithm is first applied to power system. Second, Since damping ratio is one of the criteria to measure the dynamic stability of power system, this paper sets the objective function according to the principle of maximization of damping coefficient of electromechanical mode, and uses SA-QUATRE to search a group of global optimal PSS parameter combinations to improve the stability margin of the system as much as possible. Finally, the method's rationality and validity were validated by applying it to the simulation examples of IEEE 4-machine 2-area system with different operation states. The comparison with the traditional optimization algorithm shows that the proposed method has more advantages for multi-machine PSS parameter coordination optimization, and can restrain the low-frequency oscillation of power system more effectively and enhance the system stability.

Gradient based methods are popularly used in training neural networks and can be broadly categorized into first and second order methods. Second order methods have shown to have better convergence compared to first order methods, especially in solving highly nonlinear problems. The BFGS quasi-Newton method is the most commonly studied second order method for neural network training. Recent methods have shown to speed up the convergence of the BFGS method using the Nesterov’s acclerated gradient and momentum terms. The SR1 quasi-Newton method though less commonly used in training neural networks, are known to have interesting properties and provide good Hessian approximations when used with a trust-region approach. Thus, this paper aims to investigate accelerating the Symmetric Rank-1 (SR1) quasi-Newton method with the Nesterov’s gradient for training neural networks and briefly discuss its convergence. The performance of the proposed method is evaluated on a function approximation and image classification problem.

Splines and quasi-interpolation operators are important both in approximation theory and applications. In this paper, we construct a family of quasi-interpolation operators for the bivariate quintic spline spaces S₅³ (∆_mn⁽²⁾). Moreover, the properties of the proposed quasi-interpolation operators are studied, as well as its applications for solving two-dimensional Burgers’ equation and image reconstruction. Some numerical examples show that these methods, which are easy to implement, provide accurate results.

Abstract: In order to accelerate deployment of distributed recycling by providing low-cost feed stocks of granulated post-consumer waste plastic, this study analyzes an open source waste plastic granulator system. It is designed, built and tested for its ability to convert post-consumer waste, 3-D printed products and waste into polymer feedstock for recyclebots of fused particle/granule printers. The technical specifications of the device are quantified in terms of power consumption (380 to 404W for PET and PLA, respectively) and particle size distribution. The open source device can be fabricated for less than USD$2000 in materials. The experimentally-measured power use is only a minor contribution to the overall embodied energy of distributed recycling of waste plastic. The resultant plastic particle size distributions were found to be appropriate for use in both recyclebots and direct material extrusion 3-D printers. Simple retrofits are shown to reduce sound levels during operation by 4dB-5dB for the vacuum. These results indicate that the open source waste plastic granulator is an appropriate technology for community, library, makespace, fab lab or small business-based distributed recycling.