Global Scheme Research Articles

Load-Aware Channel Allocation for IEEE 802.11ah-Based Networks

Most wireless communication technologies addressed to the Internet of Things (IoT) applications face the bottleneck of dense and large-scale use cases. One solution to this problem is a periodic channel reservation strategy, in which only a small group of stations can compete for channel access during a given period. The IEEE 802.11ah standard, a.k.a. Wi-Fi HaLow, deploys this idea in its channel access protocol, named Restricted Access Window (RAW). A single RAW consists of one or more RAW slots during which only designated stations can contend for channel access. This paper considers an IEEE 802.11ah-based network with randomly distributed stations around the Access Point (AP), operating under a Rayleigh-fading channel with capture enabled. We develop an analytical model to evaluate the contention of a group of stations and propose a Load-Aware Channel Allocation (LACA) algorithm for the RAW slot period. The LACA algorithm ensures the delivery of all packets that designated stations carry, allowing the allocation of load-aware RAW slots, which is effective in enhancing the Age of Information (AoI). We evaluate the Packet Delivery Ratio (PDR) and channel usage within a pre-allocated RAW slot to prove the effectiveness of our proposal. We further study the impact of the spatial distribution of the stations around the AP and the capture effect under a Rayleigh channel on the performance of the proposed LACA algorithm. Extensive simulations are used to validate our analytical results. Our proposal provides a load-aware and adaptive channel allocation scheme based on the dynamic conditions of the network. Our model can be implemented in a global configuration scheme for the RAW mechanism in heterogeneous networks or for alternative communication technologies that address dense scenarios with the integration of periodic channel reservations.

Hybrid Output Regulation of Hysteretic Actuators based on Single-Crystal Shape Memory Alloy Wires

In this paper, we present a novel hybrid feedback controller for a hysteretic actuator based on smart materials. The system under investigation consists of a single-crystal Shape Memory Alloy wire coupled with a biasing mechanism. Starting from a physics-based hybrid description of the nonlinear actuator behavior, a Throw-Catch hybrid strategy is developed to achieve asymptotic regulation of the system output (i.e., position) based on a combination of global and local control schemes. The control law permits the desired regulation goal to be achieved without the need to measure or estimate the complete system state, by solely relying on the actuator output information. After being described, the controller is validated via simulations.

The Performance of Supervised Machine Learning Based Relay Selection in Cooperative NOMA

This paper aims to exploit the benefits of supervised machine learning algorithms in resolving joint relay selection and non-orthogonal multiple access (NOMA) transmission in cooperative networks. Both relay selection and NOMA are capable to increase the system throughput. The optimization of the joint mixed-integer relay selection under the NOMA transmission problem is hard to be solved for achieving the global maximum of the system throughput. To simplify this complexity, supervised and unsupervised machine learning algorithms have a strong profile in dealing with hard optimization problems. The supervised learning algorithms have shown promising results in relay selection, and one of the widely applied supervised learning algorithms is the support vector machine (SVM). Therefore, in this paper, relay selection is approached as an SVM multi-class classification problem. The main concept of the SVM classification algorithm is to optimize the parameters of the SVM classifiers by training the classifiers with a large set of system realizations. The major advantage of this method is that the training stage can be performed offline as the optimizing of the SVM classifiers parameters requires high processing power, then the trained SVM multi-class classifiers can be used directly with no more training required during the system operation. Simulation results validate that the performance of the proposed supervised learning-based scheme is close to that of the global optimum exhaustive search relay selection scheme and outperforms the other available schemes. In addition, the proposed scheme is considerably simpler than the exhaustive search scheme, primarily when the number of relays is large.

Designing Meyer wavelet neural networks for the three-species food chain model

<abstract> <p>The current research work is related to present the numerical solutions of three-species food chain model (TS-FCM) by exploiting the strength of Meyer wavelet neural networks (MWNNs) along with the global and local search competencies. The particle swarm optimization technique works as a global operator, while the sequential quadratic programming scheme is applied as a local operator for the TS-FCM. The nonlinear TS-FCM is dependent upon three categories, called consistent of prey populations, specialist predator and top predator. The optimization of an error-based fitness function is presented by using the hybrid computing efficiency of the global and local search schemes, which is designed through the differential form of the designed ordinary differential model and its initial conditions. The proposed results of the TS-FCM are calculated through the stochastic numerical techniques and further comparison is performed by the Adams method to check the exactness of the scheme. The absolute error in good ranges is performed, which shows the competency of the proposed solver. Moreover, different statistical procedures have also been used to check the reliability of the proposed stochastic procedure along with forty numbers of independent trials and 10 numbers of neurons.</p> </abstract>

COMPUTATIONAL PERFORMANCES OF MORLET WAVELET NEURAL NETWORK FOR SOLVING A NONLINEAR DYNAMIC BASED ON THE MATHEMATICAL MODEL OF THE AFFECTION OF LAYLA AND MAJNUN

The aim of this study is to design a novel stochastic solver through the Morlet wavelet neural networks (MWNNs) for solving the mathematical Layla and Majnun (LM) system. The numerical representations of the mathematical LM system have been presented by using the MWNNs along with the optimization is performed through the hybridization of the global and local search schemes. The local active-set (AS) and global genetic algorithm (GA) operators have been used to optimize an error-based merit function using the differential LM model and its initial conditions. The correctness of the MWNNs using the local and global operators is observed through the comparison of the obtained solutions and the Adams scheme, which is used as a reference solution. For the stability of the MWNNs using the global and local operators, the statistical performances with different operators have been provided using the multiple executions to solve the nonlinear LM system.

PROJECTION SYNCHRONIZATION OF FUNCTIONAL FRACTIONAL-ORDER NEURAL NETWORKS WITH VARIABLE COEFFICIENTS

In this paper, the projection synchronization problem of functional fractional-order neural networks with variable coefficients and Caputo derivatives is studied. Firstly, a simple global projection synchronization scheme is designed according to the open-loop and adaptive feedback control. Secondly, by constructing a suitable Lyapunov function and utilizing the properties of delayed fractional-order differential inequalities, some criteria for the global projective synchronization of the variable coefficient functional neural networks with Caputo derivatives are obtained. Finally, a numerical example with many numerical simulations is employed to demonstrate the correctness and validity of the proposed method in this paper.

Estimating global numbers of farmed fishes killed for food annually from 1990 to 2019.

Global farmed finfish production increased from 9 to 56 million tonnes between 1990 and 2019. Although finfishes are now widely recognised as sentient beings, production is still being quantified as biomass rather than number of individuals (in contrast to farmed mammals and birds). Here, we estimate the global number of farmed finfishes slaughtered using FAO aquaculture production tonnages (1990-2019 data) and estimates of individual weight at killing (determined from internet searches at species and country level where possible). We relate these numbers to knowledge on humane slaughter, animal welfare law, and certification schemes. Since 1990, farmed finfish numbers killed annually for food have increased nine-fold, to 124 billion (1.24 × 1011, range 78-171 billion) in 2019. This figure does not represent the total number farmed (due to mortalities during rearing and non-food production) and is expected to increase as aquaculture expands. Our estimates indicate that farmed finfishes now outnumber the 80 billion farmed birds and mammals killed globally each year for food. The majority are produced in Asia. Inhumane slaughter practices cause suffering for most farmed finfishes. Most, 70-72%, have no legal welfare protection, and less than 1% have any fish-specific legal protection, at slaughter. The main global certification schemes in 2013-2015 accounted for 2% of slaughtered farmed finfishes. Fishes for which species-specific parameters for automated humane stunning are published comprise 20-24%. As the dominant taxa of farmed vertebrates, finfishes would benefit from better welfare if species-specific humane slaughter was defined and incorporated into laws and certification schemes.

A Literature Review on Attacks Prevention and Profiling in Cloud Computing

Nowadays, cloud computing is taking more and more part of the services market. However, this technology is still subject to security attacks. In this paper, we aim to provide a literature review on the widely existing solutions for preventing and profiling security attacks in cloud computing and their limitations in detecting different (un)known attacks. Especially, we will discuss the traditional intrusion and prevention systems that have limitations on detecting unknown attacks compared to their capability of detecting and preventing known ones. Therefore, we will draw recommendations and propose by the end a global scheme of a new system that can detect both different known and unknown attacks using machine learning and additional security factors.

Global scheme of the basic interactions and their geometrical interpretations

Our space-time consists of three 3-dimensional spaces: space S, space rotations SR and time T. First are considered the basic possible 4 cases for exchange among them: 1. r ? s, 2. s ? r, 3. r ? t, and 4. s ? t, where s ? S, r ? SR, and t ? T. Analogous to the affine group of translations and rotations A, it is considered a space group Gs of 6 ? 6 matrices, which is isomorphic to the group Spin(4). The space metric observed by the particles is found. Further are considered 4 generalized exchanges 1*, 2*, 3* and 4*, induced by the cases 1, 2, 3, and 4. The case 1* leads to the electro-weak interaction, and it is a consequence of non-commutativity between one translation and one rotation in the space group Gs. The case 2* leads to the strong interaction, and it is a consequence of non-commutativity between two translations in the space group Gs. It leads also to the galactic acceleration which is observed at the periphery of each galaxy, and now we do not need dark matter in order to explain the motion of the distant stars in the galaxies. The case 3* leads to electromagnetic interaction, and it is a consequence of non-commutativity between one translation and one rotation in the affine group A. The case 4* leads to gravitational interaction and it is a consequence of non-commutativity between one translation and one ?radial translation? in the affine group A. The corresponding accelerations are deduced and for a fixed space positions they are of type a = rot(??) (gauge invariant), but the quantum and wave effects are neglected. It is also predicted a new gravity-weak interaction, which belongs to the case 2*.

Automatic 3D CT liver segmentation based on fast global minimization of probabilistic active contour.

Automatic liver segmentation from computed tomography (CT) images is an essential preprocessing step for computer-aided diagnosis of liver diseases. However, due to the large differences in liver shapes, low-contrast to adjacent tissues, and existence of tumors or other abnormalities, liver segmentation has been very challenging. This study presents an accurate and fast liver segmentation method based on a novel probabilistic active contour (PAC) model and its fast global minimization scheme (3D-FGMPAC), which is explainable as compared with deep learningmethods. The proposed method first constructs a slice-indexed-histogram to localize the volume of interest (VOI) and estimate the probability that a voxel belongs to the liver according its intensity. The probabilistic image would be used to initialize the 3D PAC model. Secondly, a new contour indicator function, which is a component of the model, is produced by combining the gradient-based edge detection and Hessian-matrix-based surface detection. Then, a fast numerical scheme derived for the 3D PAC model is performed to evolve the initial probabilistic image into the global minimizer of the model, which is a smoothed probabilistic image showing a distinctly highlighted liver. Next, a simple region-growing strategy is applied to extract the whole liver mask from the smoothed probabilistic image. Finally, a B-spline surface is constructed to fit the patch of the rib cage to prevent possible leakage into adjacent intercostaltissues. The proposed method is evaluated on two public datasets. The average Dice score, volume overlap error, volume difference, symmetric surface distance and volume processing time are 0.96, 7.35%, 0.02%, 1.17 mm and 19.8 s for the Sliver07 dataset, and 0.95, 8.89%, , 1.45 mm and 23.08 s for the 3Dircadb dataset,respectively. The proposed fully-automatic approach can effectively segment the liver from low-contrast and complex backgrounds. The quantitative and qualitative results demonstrate that the proposed segmentation method outperforms state-of-the-art traditional automatic liver segmentation algorithms and achieves very competitive performance compared with recent deep leaning-basedmethods.

Heat Integration for Phenols and Ammonia Recovery Process of Coal Gasification Wastewater Considering Optimization of Process Parameters

A heat integration optimization method that considers the changes in process parameters is proposed to find the global optimal process scheme for a coal chemical company’s phenols and ammonia recovery process. The phenols and ammonia recovery process is simulated by Aspen Plus, and a programming method for heat exchanger networks synthesis that can simultaneously optimize process parameters and heat integration is constructed by Matlab. Taking the total annual cost as the objective function, the following process parameters are optimized: the hot feed temperature and cold/hot feed ratio of sour water stripper, the temperature of three-step partial condensation system, the feed temperature and column pressure of both solvent distillation column and solvent stripper. The result shows that, compared with the heat integration process under original process parameters, the new heat integration process saves 14.3% energy consumption and reduces the total annual cost by about 15.1%. The new heat integration process provides guidance for the optimization of the phenols and ammonia recovery process. The proposed heat integration optimization method based on changing process parameters is an effective and practical tool that offers good application prospects.

Low clock skew superconductor adiabatic quantum-flux-parametron logic circuits based on grid-distributed blocks

Adiabatic quantum-flux-parametron (AQFP) is a promising superconductor logic family exhibiting extremely low switching energy. Traditional excitation of AQFP circuits depends on a pair of ac sources (i.e., four-phase clocking), whose currents are propagated throughout the chip to excite and clock each gate sequentially. This scheme, however, produces a considerably large clock skew due to the long propagation of the current pair and will heavily limit the scalability of an AQFP circuit. In this work, a global clocking scheme for low skew AQFP circuits is proposed based on microwave H-tree excitation networks and grid-distributed blocks. The H-tree network starts with a single transmission line (TL) but is exponentially split to several levels of TLs by using passive splitters, creating multiple leaves at the final level. A large-scale AQFP circuit can thus be distributed into several local blocks and clocked synchronously by the split currents from these leaves. Therefore, the accumulation of clock skew is limited to a small value only within each local block. For validation, a test circuit comprising four blocks with data interconnections between each other, and a 1-to-4 H-tree excitation network is demonstrated, where we obtain correct operation and wide excitation margins at gigahertz frequencies. The proposed clocking scheme is advantageous for the realization of very large-scale adiabatic superconductor logic circuits in the future.

FairHealth: Long-Term Proportional Fairness-Driven 5G Edge Healthcare in Internet of Medical Things

Recently, the Internet of Medical Things (IoMT) could offload healthcare services to 5G edge computing for low latency. However, some existing works assumed altruistic patients will sacrifice quality of service for the global optimum. For priority-aware and deadline-sensitive healthcare, this sufficient and simplified assumption will undermine the engagement enthusiasm, i.e., unfairness. To address this issue, we propose a long-term proportional fairness-driven 5G edge healthcare, i.e., FairHealth. First, we establish a long-term Nash bargaining game to model the service offloading, considering the stochastic demand and dynamic environment. We then design a Lyapunov-based proportional-fairness resource scheduling algorithm, which decouples the long-term fairness problem into single-slot subproblems, realizing a tradeoff between service stability and fairness. Moreover, we propose a block-coordinate descent method to iteratively solve nonconvex fair subproblems. Simulation results show that our scheme can improve 74.44% of the fairness index (i.e., Nash product), compared with the classic global time-optimal scheme.

On the Global Maximization of Network Lifetime in Wireless Rechargeable Sensor Networks

In a Wireless Rechargeable Sensor Network (WRSN), a mobile charger (MC) moves and supplies energy for sensor nodes to maintain the network operation. Hence, optimizing the charging schedule of MC is essential to maximize the network lifetime in WRSNs. The existing works only target the local optimization of network lifetime limited to MC’s subsequent charging round. The network lifetime has been normally reflected in a different metric that is not directly related to the final charging round period. To the best of our knowledge, this work is the first to address the global maximization of network lifetime in WRSNs, which optimizes not only the subsequent charging round but all charging rounds over the entire network lifetime. Another uniqueness is the joint consideration of both the charging path and charging time optimization problems. As a solution, we propose a genetic algorithm (GA)-based global optimization scheme that considers all the possible charging rounds. The GA has a novel mutation operation that mutates gene sizes for representing charging schedules with a varying number of charging rounds. The experiment results show that our algorithm can extend the network lifetime by 35.1 times on average and 38.6 times in the best case compared to existing ones.

The Role of Environmental, Social, and Governance Performance on Attracting Foreign Ownership: Evidence from Saudi Arabia

This research aims to examine whether corporate social responsibility (CSR) and corporate governance (CG) attract foreign investors as key indicators of a firm’s sustainability. By adopting both stakeholder theory and legitimacy theory, it is assumed that a firm could build trustworthiness and legitimacy with its stakeholders by enhancing its environmental, social, and governance (ESG) performance. Using a sample of 110 firms from the Saudi stock market from different industries, this study employs both OLS and System-GMM estimation to test the effect of both ESG performance and CG on foreign investment in Saudi Arabia. The findings indicate that ESG performance positively affects foreign investment. Additionally, it is found that the corporate governance score has a greater effect than social and environmental scores. These empirical findings suggest that companies in Saudi Arabia should adopt global schemes to improve ESG performance to maximize the share of foreign investment, thus boosting the country’s economy and increasing the level of competitive advantages and sustainability.

Phase Transitions and Electric Properties of PbBr2 under High Pressure: A First-Principles Study.

PbBr2 has recently attracted considerable attention as a precursor for lead halide perovskite-based devices because of its attractive properties. It is known that pressure can modify the chemical and physical properties of materials by altering the distance between atoms in the lattice. Here, a global structure-searching scheme was used to explore the high-pressure structures of PbBr2, whose structures and properties at high pressure are still far from clear. Three new phases of PbBr2 were predicted in the pressure range of 0-200 GPa, and the pressure-driven phase transition sequence of orthorhombic Pnma (0-52 GPa) → tetragonal I4/mmm (52-80 GPa) → orthorhombic Cmca (80-153.5 GPa) → orthorhombic Immm (153.5-200 GPa) is proposed. Electronic calculations indicate a semiconductor-to-metallic transition of PbBr2 in the Cmca phase at ~120 GPa. Our present results could be helpful in improving the understanding of fundamental physical properties and provide insights to modulate the structural and related photoelectric properties of PbBr2.

Accretion process, magnetic fields, and apsidal motion in the pre-main sequence binary DQ Tau

ABSTRACT Classical T Tauri stars (CTTSs) are young stellar objects that accrete materials from their accretion disc influenced by their strong magnetic field. The magnetic pressure truncates the disc at a few stellar radii and forces the material to leave the disc plane and fall onto the stellar surface by following the magnetic field lines. However, this global scheme may be disturbed by the presence of a companion interacting gravitationally with the accreting component. This work is aiming to study the accretion and the magnetic field of the tight eccentric binary DQ Tau, composed of two equal-mass (∼ 0.6 M⊙) CTTSs interacting at different orbital phases. We investigated the variability of the system using a high-resolution spectroscopic and spectropolarimetric monitoring performed with ESPaDOnS at the CFHT. We provide the first ever magnetic field analysis of this system, the Zeeman–Doppler imaging revealed a stronger magnetic field for the secondary than the primary (1.2 and 0.5 kG, respectively), but the small-scale fields analysed through Zeeman intensification yielded similar strengths (about 2.5 kG). The magnetic field topology and strengths are compatible with the accretion processes on CTTSs. Both components of this system are accreting, with a change of the main accretor during the orbital motion. In addition, the system displays a strong enhancement of the mass accretion rate at periastron and apastron. We also discovered, for the first time in this system, the apsidal motion of the orbital ellipse.

A general approach for supporting nonblocking data structures on distributed-memory systems

Nonblocking data structures are an essential part of many parallel applications in that they can help to improve fault tolerance and performance. Although there are scores of nonblocking data structures, such as stacks, queues, double-ended queues (deques), lists, widely used in practice, most of them are designed to be used on shared-memory machines only, and cannot be used in a distributed-memory setting. Several recent studies focus on the development of novel tailor-made nonblocking distributed data structures and omit the potential for adapting a great wealth of existing nonblocking shared-memory ones for the distributed-memory case. Hence, we propose a general approach for bridging the gap between most existing nonblocking data structures and distributed-memory machines in this work. Several challenges, such as safe memory reclamation and solving the ABA problem, must be overcome. To address these issues, we present a global memory management scheme. The scheme takes advantage of hazard pointers which are widely used to tackle the problems in shared-memory environments. To demonstrate our general approach, we take stacks as a typical example of nonblocking data structures. This work also provides a survey of well-known nonblocking stack algorithms along with our analysis and evaluation in distributed-memory environments. Moreover, this paper depicts how to improve performance of a stack algorithm by making use of node locality. Besides, a cost model based on worst cases is devised to help gain a better understanding into experimental results of nonblocking distributed data structures, along with our analysis of the influence of two popular lock-free programming patterns on performance.

A HEURISTIC COMPUTING APPROACH USING SEQUENTIAL QUADRATIC PROGRAMMING TO SOLVE THE FIFTH KIND OF INDUCTION MOTOR MODEL

The purpose of the current investigation is to solve the fifth kind of induction motor model using an advanced computational scheme by operating the artificial neural networks (ANNs), global scheme as genetic algorithm (GA) along with the rapid local search sequential quadratic programming technique (SQPT), i.e. ANN-GA-SQPT. ANNs are implemented to discretize the fifth kind of induction motor model to express the merit function based on the mean square error. The numerical presentation of the proposed ANN-GA-SQPT is pragmatic for three different problems based on the fifth kind of induction motor model to authenticate the efficacy, consistency and importance of the proposed ANN-GA-SQPT. Moreover, statistical representations are provided in order to check the precision, convergence and accuracy of the present ANN-GA-SQPT.

High-Gain Observer-Based Advanced Nonlinear Control of a Grid-Connected Wind Energy Conversion System with Sensorless Maximum Power Point Tracking

This paper deals with the control development of a wind energy conversion system (WECS) interfaced to a utility grid by using a doubly fed induction generator (DFIG), a back-to-back (B2B) converter and an RL filter for optimal power extraction. The aim was to design a sensorless controller to improve the system reliability and to simultaneously achieve the regulation of the generator speed, reactive power and DC-link voltage. The proposed global control scheme combines: (i) a high-gain observer employed to estimate the generator speed and the mechanical torque, usually regarded as accessible, (ii) a sensorless MPPT block developed to provide optimal generator speed reference, which is designed on the basis of the mechanical observer and a polynomial wind-speed estimator and (iii) a finite-time controller (FTC) applied to the B2B converter to meet the output reference’s tracking objectives in a short predefined finite time by using the backstepping and Lyapunov approaches. The proposed controller performance is formally analysed, and its capabilities are verified by numerical simulations using a 2 MW DFIG wind turbine (WT) under different operating conditions.