Input Interval Research Articles

Intelligent computing framework to analyze the transmission risk of COVID-19: Meyer wavelet artificial neural networks

The optimum control methods for the epidemiology of the COVID-19 model are acknowledged using a novel advanced intelligent computing infrastructure that joins artificial neural networks with unsupervised learning-based optimizers i.e., Genetic Algorithms (GA) and sequential quadratic programming (SQP). Unsupervised learning strategy is provided which depends on the wavelet basis's sequential deconstruction of stochastic data. The weights or selection values of neural networks are utilizing cumulative algorithms of Meyer wavelet artificial neural networks (MWANNs) optimized with global search Genetic Algorithms (GAs) and Sequential Quadratic Programming (SQP), referred to as MWANNs-GA-SQP and the design technique is utilized to determine the COVID-19 model for five different scenarios employing different step sizes and input intervals. The findings of this research article examined that in order to minimize the total disease transmission at the lowest cost and complexity, safety, focused medical care, and exterior sterilization methods applicability. The provided data is validated through various graphical simulations, which surely authenticate the effectiveness and robustness of the proposed solver. The suggested solver, MWANNs-GA-SQP, is tested in a variety of circumstances to examine that how reliable, safe, and tolerant. Using the proposed MWANNs hubristic intelligent approach, an objective optimization function is created in feed forward neural networking to minimize the mean square error. An investigation of the hybrid GA-SQP is used to confirm the accuracy and dependability of the MWANNs model results. Mean absolute graphs have been constructed to assess the integrity and efficiency of the proposed methodology. The accuracy and reliability of the suggested method are demonstrated by constantly achieving maximum variables of analytical assessment criteria computed for a large appropriate variety of distinct trials.

Reliability analysis of deep tunnels in spatially varying brittle rocks using interval and random field modelling

Rock properties are estimated using objective lab/in-situ testing and subjective judgements invoking different types of uncertainties, i.e., aleatory, and epistemic, along them, often indicated by varying information levels. This study presents a unified reliability method to integrate the spatial variability of inputs modelled via alternate uncertainty models (intervals and probability-boxes (p-boxes)) with those modelled as stochastic variables via probability distributions. The methodology employs advanced Karhunen–Loève decomposition to generate interval and random fields of inputs modelled via alternate and stochastic models, respectively. Input properties are allocated to the zones of the numerical model in Fast Lagrangian Analysis of Continua-2D (FLAC-2D) based on their spatial dependency and correlation functions through a developed MATLAB-FLAC coupled code. The methodology is demonstrated for a deep tunnel in Canada to be constructed along a massive rock prone to brittle failures. Intact rock properties are modelled as stochastic variables due to objective estimation, while Geological Strength Index (GSI) and deformation modulus are modelled using alternate models (interval and p-box, respectively) due to subjective and hybrid estimation (double uncertainty propagation algorithm). The results of the methodology are compared with those of traditional deterministic and random field methods. The methodology reduces the subjectivity invoked by including unavailable additional information (e.g., assuming probability distributions of inputs based on literature) and propagates the originally available information of inputs accurately. The final outputs are the p-boxes of response parameters (i.e., displacements and damage zone) instead of their fixed values (deterministic analysis) and probability distributions (traditional reliability analysis), indicating the propagation of both impreciseness and variability of inputs by the method. For this case study, the p-boxes of outputs were bounding their values/distributions estimated via traditional analyses, verifying the accuracy of the methodology. Further, the impreciseness in the outputs, highest in the damage zone extent, was due to imprecision in the estimated GSI.

Validation of Ecology and Energy Parameters of Diesel Exhausts Using Different Fuel Mixtures, Consisting of Hydrogenated Vegetable Oil and Diesel Fuels, Presented at Real Market: Approaches Using Artificial Neural Network for Large-Scale Predictions

Machine learning models have been used to precisely forecast emissions from diesel engines, specifically examining the impact of various fuel types (HVO10, HVO 30, HVO40, HVO50) on the accuracy of emission forecasts. The research has revealed that models with different numbers of perceptrons had greater initial error rates, which subsequently reached a stable state after further training. Additionally, the research has revealed that augmenting the proportion of Hydrogenated Vegetable Oil (HVO) resulted in the enhanced precision of emission predictions. The use of visual data representations, such as histograms and scatter plots, yielded significant insights into the model’s versatility across different fuel types. The discovery of these results is vital for enhancing engine performance and fulfilling environmental regulations. This study highlights the capacity of machine learning in monitoring the environment and controlling engines and proposes further investigation into enhancing models and making real-time predictive adjustments. The novelty of the research is based on the determination of the input interface (a sufficient amount of input parameters, including chemical as well as technical), which characterizes the different regimes of the diesel engine. The novelty of the methodology is based on the selection of a suitable ANN type and architecture, which allows us to predict the required parameters for a wide range of input intervals (different types of mixtures consisting of HVO and pure diesel, different loads, different RPMs, etc.).

Adaptive finite‐time recoil control of deepwater drilling riser systems with nonlinear forces and saturation input using switched event‐triggered scheme

AbstractThis article investigates the finite‐time recoil control problem of deepwater drilling riser systems with nonlinear friction force, tension force and saturation input. For the friction force, a new approximation model based on radial basis function neural networks is presented, which not only can ensure a better approximation effect than the sine‐function type and exponential‐polynomial‐function type computational models, but also can be easily used for control design. Different from the existing linear optimal control methods, a neural‐network‐based adaptive backstepping control method is proposed to deal with the nonlinear friction and tension forces, which can achieve better recoil control responses. An auxiliary system combining with the change of coordinates is employed to compensate the saturation input effect. To prolong the average release interval of control input while preserving satisfied control performance, a new switched event‐triggered control (ETC) scheme is developed, in which the triggering conditions are switched between the fixed and relative thresholds based on the strength of control signal. With the event‐triggered controller, the practical finite‐time stability condition of recoil control system is derived and the Zeno behavior is avoided. Numerical results are given to show the advantages of the proposed methods in handling the model nonlinearities, finite‐time stability and ETC performance of riser‐tension systems.

Demonstrating a new evaluation method on ReLU based Neural Networks for classification problems

Deep neural networks, which have proven to be effective methods to solve complex problems, can even be applied in decision systems controlling critical processes. However, in such applications the outcomes of the neural network must be checked if it we have a clear understanding regarding the operation of network at given input intervals. The most straightforward, though often computationally expensive approach for this checking process evaluates the network at discrete input points and estimates the expected outputs at the given interval. The present research aims to develop a novel approach that can identify in the case of specific input intervals whether the operation process and the output of a neural network can be considered as known or unknown. During the presented case study, we investigated the ReLU (Rectified Linear Unit) and Sigmoid activation functions, using the double moon and the Banknote Authentication classification problems for demonstration.Our method can be applied to identify certain input intervals where the given neural network cannot support critical decisions. The evaluation is performed based on a nonlinear system of equations and inequalities built on arbitrary continuous activation functions. To define the critical intervals of the input variables (i.e. where the decision-making system should not be relied on), those input variable combinations are identified which result in a non-expected output value. This inverse logic is intended to identify intervals of the input variables where the response of the system and the correct decision are not identical. The presented demonstration examples supported our assumption that the number of the neurons and the dimension of the decision space have a significant impact on the complexity of the evaluation process.

A marine record of Patagonian ice sheet changes over the past 140,000 years

Terrestrial glacial records from the Patagonian Andes and New Zealand Alps document quasi-synchronous Southern Hemisphere-wide glacier advances during the late Quaternary. However, these records are inherently incomplete. Here, we provide a continuous marine record of western-central Patagonian ice sheet (PIS) extent over a complete glacial-interglacial cycle back into the penultimate glacial (~140 ka). Sediment core MR16-09 PC03, located at 46°S and ~150 km offshore Chile, received high terrestrial sediment and meltwater input when the central PIS extended westward. We use biomarkers, foraminiferal oxygen isotopes, and major elemental data to reconstruct terrestrial sediment and freshwater input related to PIS variations. Our sediment record documents three intervals of general PIS marginal fluctuations, during Marine Isotope Stage (MIS) 6 (140 to 135 ka), MIS 4 (~70 to 60 ka), and late MIS 3 to MIS 2 (~40 to 18 ka). These higher terrigenous input intervals occurred during sea-level low stands, when the western PIS covered most of the Chilean fjords, which today retain glaciofluvial sediments. During these intervals, high-amplitude phases of enhanced sediment supply occur at millennial timescales, reflecting increased ice discharge most likely due to a growing PIS. We assign the late MIS 3 to MIS 2 phases and, by inference, older advances to Antarctic cold stages. We conclude that the increased sediment/meltwater release during Southern Hemisphere millennial-scale cold phases was likely related to higher precipitation caused by enhanced westerly winds at the northwestern margin of the PIS. Our records complement terrestrial archives and provide evidence for PIS climate sensitivity.

An innovative fuzzy logic based controller for solid oxide fuel cells

The solid oxide fuel cell (SOFC) stands out as a vital component in sustainable distributed generation systems, thanks to its reliability and adaptable power generation capabilities. This adaptability allows the SOFC to efficiently balance grid power by adjusting its power output. Nevertheless, maintaining the desired fuel usage rate and achieving rapid load tracking pose considerable hardships in SOFC control. To deal with these difficulties, this study introduces a novel approach, the Cascaded Double Input Interval Type-2 Fuzzy Logic Controller (C-DIT2-FLC), designed for SOFC control. In this innovative model, the Improved Salp Swarm Algorithm (ISSA) is applied to determine the crucial parameters of Proportional Integral-Proportional Derivative (PI-PD) cascade controllers. The C-DIT2-FLC dynamically adjusts the controller gains, which are optimized using ISSA, to enhance the effectiveness of SOFC operations. The research results substantiate the superior efficacy of the C-DIT2-FLC over conventional controllers, thereby providing enhanced control and performance capabilities for SOFC systems.

Integration of government accounting system reform and university infrastructure consolidation based on type two fuzzy sets

Abstract Type II fuzzy set can innovate the accounting method of infrastructure consolidation and integration work. Based on this, this paper constructs a new and feasible accounting system for the integration of government accounting systems and university infrastructure. The specific application is that any point on the financial subordination domain and its corresponding subordination interval constitutes a type two fuzzy set, and the fuzzy set is used as the object for modeling. Using the interval type-two fuzzy set for word calculation, the result of the calculation is output by the decoder and sorted, and the value of the accounting account amount can be derived. The mutual subset measure between the input interval type II fuzzy set and the rule antecedent interval type II fuzzy set is used to check the accounting bills of the rule activation interval and to verify the performance of its use in the capital consolidation work of S universities. The experimental results show that after transferring the balance of $4,982,600 for prepaid construction and $4,956,200 for prepaid provisioning to the prepaid account on the basis of ensuring the consistency of the output KM algorithm, the balance of the account becomes $9,938,800. This result shows that the type II fuzzy set saves 42% of the arithmetic cost for this transfer of university infrastructure accounts, and contributes to the realization of the government accounting system reform and the improvement of financial management of the integration of university infrastructure accounts.

The effect of an event-triggered controller on non-strict feedback system based on the single input interval type-2 fuzzy method

Non-Strict Feedback (NSF) physical systems are broader than Strict-Feedback (SF) systems. The system state variables depend on their system functions, unlike SF systems that the system variables do not depend on their system function. Consequently, NSF physical systems are challenging to control. To address this control challenge in NSF physical systems, this research proposed an Event-Triggered (ET) adaptive backstepping control method based on Single Input Interval Type-2 Fuzzy (SIIT-2F) for NSF system with input delay. The effect of the method is examined on a third order system using transient and steady state performance as a metric. The ET mechanism prompts due to violation of a predefined condition (mismatch between the actuator and the controller), uncertainty of the system dynamics was handled by the Radial Basis Function Neural Network (RBFNN). The input delay was handled by a developed auxiliary system, and the SIIT-2F was used to minimize error between state variables and auxiliary states of the adaptive backstepping controller. The results of this work were compared with adaptive neural control for NSF method. The method has an average percentage performance improvement of23% peak overshoot for state variables; and 47% for adaptive variables, the average performance improvement for the absolute peak undershoot are 50%, and 60% respectively for the state and auxiliary variables. In addition, the settling time average performance improvement is 11%, 21%, and 10% for state, auxiliary, and adaptive variables, respectively. The results of this study underline that the ET based SIIT-2F controller improved the performance of NSF systems compared to the adaptive neural control method.

Dynamic consensus of linear multi-agent system using self-triggered distributed model predictive control

This article discusses self-triggering algorithm using distributed model predictive control (DMPC) to achieve dynamic consensus in linear multi-agent systems (MASs). The iterative computations and communications required at each time step in traditional consensus algorithms cause escalation of the energy consumption and shorten the life span of the MAS. An attempt to solve this problem is made by proposing a sequential self-triggering consensus algorithm, where each agent computes its own triggering instants. A Laguerre based DMPC design is adopted that notably reduces the computational complexity of conventional DMPC. The proposed self-triggered DMPC algorithm optimizes the control input and triggering interval while guaranteeing the dynamic consensus of the agents. By virtue of the Laguerre function based control architecture, the additional computations owing to the self-triggered algorithm do not impose stress on the controller; yet reduce the load on communication resources. The equality constraint on the terminal state of the agents is utilized along with Lyapunov criteria to establish the closed loop stability of the MAS. The proposed scheme achieves a considerable drop in controller design computations as well as data transmissions among agents, and the same is established by comparing these traits of existing schemes while achieving comparable performance. The proposed algorithm is verified through simulation of platoon configuration of vehicles, each of which is modeled as a linear multi-input multi-output (MIMO) system.

Efficient Error Estimation for High-Level Design Space Exploration of Approximate Computing Systems

This article presents an error estimation technique for a data-flow graph (DFG) representation of an approximate computing (AC) circuit. The technique, which may be used during the high-level design of digital circuits, estimates error metrics for the outputs of the approximate circuit. The proposed technique receives as an input, output error characterizations of arithmetic modules in a high-level library. The error modeling of a library module is accomplished by dividing the input (operand) ranges into intervals and then characterizing the output error for different combinations of these input intervals. Subsequently, the error for each combination is stored in a lookup table (LUT). The module error models are integrated into a design space exploration (DSE) framework to evaluate different combinations of exact and approximate realizations of various operations in the DFG. The DSE, which performs error calculation and propagation from inputs to outputs of the target DFG, may be used to explore trade-offs between the output error and other design metrics of the approximate circuit, e.g., energy efficiency. The efficacy of the proposed method is assessed for three image processing benchmarks. Results for these benchmarks demonstrate that the framework can efficiently generate the Pareto frontier (PF) in the trade-off space of accuracy versus energy efficiency for the targeted benchmarks. Compared to a purely simulation-based exploration, the proposed technique provides an average of 92 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> speed improvement.

Fuel Cell Fed Electrical Vehicle Performance Analysis with Enriched Switched Parameter Cuk Converter

Background: This study aims to evaluate the performance of a 1.26 kW Proton Exchange Membrane Fuel Cell (PEMFC) fed Electric Vehicle (EV) using an Enriched Switched Parameter Cuk (ESPC) converter and an Elman Back Propagation (EBP) maximum power point tracking algorithm (MPPT). The acceptance of fuel cell-fed EVs in modern society is critical to the development of a pollution-free environment. One of the significant contributors to excessive pollution is transportation on public roads using internal combustion engines powered by crude oil as their primary energy source. Objective: This study identifies suitable high voltage gain DC-DC converters with minimum duty cycle operation for fuel cell-fed electric vehicle systems and develops an intelligent MPPT controller for hybrid electric vehicle applications. Methods: In this study, MATLAB/Simulink environment is used to design a 1.26 kW PEMFC powered electric vehicle. To integrate PEMFC to BLDC motor, an Enriched Switched Parameter Cuk converter is built with a high static converter voltage gain. Results: The effectiveness and performance of the fuel cell-fed EV system are investigated using perturb and observe method and Elman Back Propagation MPPT approaches for various fuel cell input temperature conditions and intervals. Conclusion: This study discusses the use of low-voltage fuel cell sources with power electronic converters that are available for various high gains in the literature. The proposed ESPC is designed to reduce stress on power converter components and is intended for low-voltage FC-fed electric vehicle applications.

Quantitative Assessment of Cold Injury in Tea Plants by Terahertz Spectroscopy Method

Cold injury (CI) causes irreversible damage to tea plants, which results in decline in the quality of famous teas and huge economic loss. A new, quick, non-destructive method is provided to assess the CI of tea leaf based on terahertz (THz) spectroscopy. Absorbance of the samples was measured with THz spectroscopy in frequency bands from 0.1 to 1.6 THz under low temperature treatments of 4.0, 0, −2.5, −5.0, −7.5, and −10.0 °C. Fast Fourier transformation was explored to decompose the endpoint signal to improve the ratio of signal to air and convert the time-domain spectra to the corresponding frequency-domain spectra. To improve the separation of overlap signals caused by substantial scattering of injured cells in the leaf, two-dimensional correlation spectroscopy (2DCOS) and average intensity (AI) were introduced into the partial least squares regression (PLSR) to build 2DCOS–PLSR and AI–PLSR models. Quantitative assessments of the 2DCOS–PLSR and AI–PLSR models were conducted to evaluate the three models. The assessment results showed that the correlation coefficients of the 2DCOS–PLSR model (R2D) were 0.7873, 0.8305, and 0.9103, respectively. The root mean square errors of the 2DCOS–PLSR model (RMSE2D) were 0.6032, 0.5763, and 0.5221, respectively. For the AI–PLSR model, RAI values were 0.7477, 0.7691, and 0.8974, respectively. RMSEAI values were 0.6038, 0.5962, and 0.5797. The combination of THz spectroscopy with the 2DCOS–PLSR model provided a better benchmark for the input interval selection and improved the accuracy of cold-injury detection results.

Affine Policies and Principal Components Analysis for Self-Scheduling in CAES Facilities

This paper presents a novel methodology based on Principal Components Analysis (PCA) and Affine Policies (AP) for self-scheduling of a price-taker Compressed Air Energy Storage (CAES) facility operating under uncertainties. The proposed PCA-AP model is developed from the facility owner&#x0027;s perspective, which partakes in energy, spinning, and idle reserve markets. A methodology is proposed to select the required price uncertainty intervals from actual data based on a Box Cox technique. For a more realistic representation, the detailed thermodynamic characteristics of the CAES facility are considered, taking into account as well modern CAES facilities that may charge and discharge concurrently. To validate the proposed PCA-AP model and approach, the results obtained are compared with an existing Affine Arithmetic (AA) model, which is also based on an affine approach, and Monte Carlo Simulations (MCS), which can be considered as the benchmark for comparison purposes. The input data, forecast prices and intervals of uncertainty, are taken from the Ontario-Canada electricity market for 2015-2019. From the studies presented, it can be observed that the new PCA-AP approach provides less conservative results as compared to the AA approach, and hence can be considered an adequate methodology for day-ahead operations in systems with significant sources of uncertainty.

Influence of downstream cross-sectional area ratio on flow boiling characteristics of expanding micro pin fin heat sinks

In the present study, influence of downstream cross-sectional area ratio on flow boiling behavior of expanding micro-pin-fin heat sinks was experimentally studied. Three structured micro-pin-fin heat sinks, namely, Type1, Type2 and Type3 were used. Pure saturated conditions were taken into consideration in heat input interval of 90 W – 180 W. To show relation between mass flux and area ratio, two mass fluxes were considered (136 kg m−2 s−1 and 250 kg m−2 s−1). Outcomes showed that there was an optimum cross-sectional ratio maximizing thermal performance for expanding micro-pin-fin heat sinks. The advantage of prevention of bubble blockage was lost after a threshold expansion ratio due to dominant-compressible-vapor-based instabilities at the downstream region; and in this regard, mass flux played critical role due to liquid inertia force. For the given conditions, compared to uniformly distributed micro-pin-fin heat sink (Type1), expanding heat sinks (Type2 and Type3) led to increase in heat transfer coefficients up to, respectively, 498.9% and 216.5% at 250 kg m−2 s−1. Compared to Type1; Type2 and Type3 respectively decreased pressure drop up to 31.8% and 32.1% at 136 kg m−2 s−1. However, after heat input of 150 W, Type 3 experienced boiling crisis, and surface temperature jumped.

A Stochastic Model for an Input Control Problem in a Two-Level Supply Chain for Production-Time-Dependent Products with Random Demands

Inspired by the poultry farming process, we studied an input control problem in a two-level supply chain for production-time-dependent products with random demands. Poultry farms deliver chicks in batches, and the raising process begins at timed intervals. Chicks become broilers after a predetermined raising time. The broilers in the process are shipped to manufacturing plants to satisfy the demand. The remaining chicks grow to the next product size to satisfy the demands for larger chickens. This procedure is repeated until the chicks are fully grown. After the chicks are grown to satisfy the demand for the largest size, the remaining chicks are discarded. In this paper, a stochastic model is presented to study an input control problem in the poultry farming process. Because of the production density, feed, and temperature control, one important issue in the operation of a poultry company is the determination of the raising interval and quantity of input (chicks). While existing mathematical models can provide effective information on the production-planning problem of systems, research has not been conducted on cases of random demand. Identifying a recursive structure and Markovian property for the number of raw materials (chicks) and the unfulfilled demand for each product type in the system, we demonstrate that embedded Markov chain models can be obtained. The equilibrium probabilities of the models can be calculated using matrix analytic methods or probability generating functions. Various numerical experiments are conducted to analyze how performance measures such as amount of disposal, unsatisfied demands, and total cost (considering disposal cost and opportunity cost) change with system parameters.

Effect of droplet transfer mode on composition homogeneity of twin-wire plasma arc additively manufactured titanium aluminide

Twin-wire plasma arc additive manufacturing (TW-PAAM) is an innovative process for titanium aluminide manufacture which is low in equipment cost and high in efficiency. However, as an in situ alloying process, the composition homogeneity is typically a source of concern. The present work investigates this subject by observing the element input mode at various welding wire positions with a high-speed camera system and particle tracking method. It is found that when the welding wires are in a high position, the droplet enters the molten pool from the tip of the titanium wire, and the alloy element input interval is long, resulting in the formation of bands with high aluminium content at the edge of the deposition layer. When the welding wires are close to the workpiece surface, the droplet transfer mode switches to a mixture of dual wire-bridge transfer and Ti wire-bridge transfer. The diameter and transfer interval of the droplets decrease, as well as the heterogeneity of the deposition layer. The conclusion establishes a correlation between droplet transfer mode and composition homogeneity, which is beneficial for optimizing the TW-PAAM titanium aluminide fabrication process.

A Novel Method for Gas Disaster Prevention during the Construction Period in Coal Penetration Tunnels—A Stepwise Prediction of Gas Concentration Based on the LSTM Method

Aiming at the tunnel gas disaster can produce major safety problems such as combustion, explosion, and coal and gas outbursts. Firstly, a time series consisting of the tunnel gas concentration was used as the entry point for the article, and the gas prediction models based on multiple intelligent computational methods were established for comparison to determine the optimal network prediction model. Then, this study proposed a stepwise prediction method which is based on the optimal network prediction model for gas disaster prevention during the construction period of tunnels at the excavation workface. The length of the input step, output step, and interval step were considered by the method to investigate the effect on the predictive performance of the model. The model was extrapolated by the rolling prediction method, and the adaptive grid search method was used to determine the optimal parameter combination of stepwise prediction. Finally, a stepwise prediction of short-term gas concentration trends was achieved for each construction process at the excavation workface. As a result, the best LSTM network prediction model was preferred with an R2 value of 0.94 for the fit and MAE and RMSE values of 3.2% and 4.3%. Results based on stepwise predictions showed that single-step prediction is more accurate than multi-step predictions when a reasonable input step size was determined. Moreover, with the length of both the interval step and the output step, the model prediction accuracy showed a decreasing trend. Generally speaking, the single-step continuous and interval prediction of the gas concentration at the excavation workface can be realized by the gas stepwise prediction method, and the gas concentration value can be obtained at any time in the prediction. It can also realize the transformation from single-step point prediction to multi-step trend prediction, and obtain the accurate prediction of gas concentration change trends in the stepwise prediction range (t+1~t+5). Therefore, the important security guarantee can be provided by stepwise prediction for subsequent gas disaster safety prevention and efficient tunnel production.

Design a Robust Powering 5G Telecommunication System Based-Sliding Mode Control

Compared to the previous communication networks, 5G networks provide safe and ultra-reliable connections and enable much more reliable communication with shorter response times, higher data speeds, and a much larger number of simultaneous users. With the growth of the utilization of 5G technology in several applications, the challenges of these power networks will also increase. The power plant in the 5G systems includes several converters feeding dc loads. Most of the loads in the 5G power plant behave as a constant power load (CPL). Due to the negative impedance specifications of CPLs, the stability of such a power electronic converter is threatened when feeding the nonideal CPLs. In response to this challenge, eliminating the destructive effect of these CPLs in the 5G systems is a necessity. To stabilize the voltage of the dc–dc converter feeding CPLs in the 5G systems, this article presents a model-free nonsingular terminal sliding-mode control (MFNTSMC) with a single input interval type-2 fuzzy logic control (SIT2-FLC) to solve the instability problem of the power electronic converters feeding CPLs in the 5G systems. The hardware-in-the-loop tests are accomplished to verify the accuracy of the MFNTSMC based SIT2-FLC suggested controller against the instant changes of the CPL power.

Investigating assembly timing of Ediacaran Serra dos Órgãos batholith – Hints on prolonged magmatism at Ribeira belt, SE Brazil

The Pan-African/Brasiliano orogeny, result of the West-Gondwana assembly from the Neoproterozoic to Ordovician, had produced the Ribeira and Araçuaí belts at the edges of the São Francisco and Congo cratonic margins. In the central Ribeira belt, the docking of accretionary systems to the reworked continental margin of the São Francisco paleoplate culminated in a complex Ediacaran granite production event. Syn-to late-collisional I- and S-type granites and hybrid granitoids intruded the reworked margins and accreted continental masses, as well as the Tonian to Ediacaran juvenile magmatic arcs represented by the Serra da Prata and Rio Negro Complexes. The Serra dos Órgãos batholith is the main intrusion emplaced at the juvenile crust, forming an elongated NE-SW trending tabular body that crops out in the highland region of Rio de Janeiro State, Southeast Brazil. Geochemical and geochronological data in addition to field and petrological evidence was gathered to explore the assembly timing of the SOB granitic end-member. Zircon U–Pb SHRIMP and LA-ICP-MS geochronological data yield a prolonged emplacement timelapse for the studied granites and, along with published U–Pb ages of the SOB granodioritic end-member, enable the characterization of the Serra dos Órgãos as a composite batholith. The wide time interval of heat input inside intermediate crust is consistent with a prolonged, slow-cooling hot orogenic belt model for the Araçuaí-Ribeira Orogenic System.