Flexible Capacity Research Articles

Improved Breakdown Strength and Restrained Leakage Current of Sandwich Structure Ferroelectric Polymers Utilizing Ultra-Thin Al2O3 Nanosheets.

Flexible capacity applications demand a large energy storage density and high breakdown electric field strength of flexible films. Here, P(VDF-HFP) with ultra-thin Al2O3 nanosheet composite films were designed and fabricated through an electrospinning process followed by hot-pressing into a sandwich structure. The results show that the insulating ultra-thin Al2O3 nanosheets and the sandwich structure can enhance the composites' breakdown strength (by 24.8%) and energy density (by 30.6%) compared to the P(VDF-HFP) polymer matrix. An energy storage density of 23.5 J/cm3 at the ultrahigh breakdown strength of 740 kV/mm can be therefore realized. The insulating test and phase-field simulation results reveal that ultra-thin nanosheets insulating buffer layers can reduce the leakage current in composites; thus, it affects the electric field spatial distribution to enhance breakdown strength. Our research provides a feasible method to increase the breakdown strength of ferroelectric polymers, which is comparable to those of non-ferroelectric polymers.

Rational Simplification and Rigidity in Human Planning.

Planning underpins the impressive flexibility of goal-directed behavior. However, even when planning, people can display surprising rigidity in how they think about problems (e.g., "functional fixedness") that lead them astray. How can our capacity for behavioral flexibility be reconciled with our susceptibility to conceptual inflexibility? We propose that these tendencies reflect avoidance of two cognitive costs: the cost of representing task details and the cost of switching between representations. To test this hypothesis, we developed a novel paradigm that affords participants opportunities to choose different families of simplified representations to plan. In two preregistered, online studies (Ns = 377 and 294 adults), we found that participants' optimal behavior, suboptimal behavior, and reaction time were explained by a computational model that formalized people's avoidance of representational complexity and switching. These results demonstrate how the selection of simplified, rigid representations leads to the otherwise puzzling combination of flexibility and inflexibility observed in problem solving.

Effect of the Degree of Filling on Mechanical Properties of Polymeric Specimens from Polyethylene Terephthalate Glycol and Polylactic Acid Produced by 3D Printing

Based 3D printing has become very popular in recent years due to the emergence of projects for low-cost machines, making the technology very accessible. In view of this, some polymers, in general, in thermoplastic filaments, are placed on the market for application in this type of printing technique, making it increasingly necessary to develop research for the characterization of materials to provide information on physical, thermal and mechanical properties. For the development of this work, the polymer poly(ethylene glycol terephthalate) (PETG) was used for a comparative study in relation to poly(lactic acid) (PLA). PETG is obtained by adding modified glycol to the material composition during the polymerization process. It consists of a polymer with a glass transition temperature close to 80°C, with mechanical properties similar to those of PET, with the advantages of notable tenacity, flexibility, and high processing capacity, and PLA is a polymer synthesized from corn sugar, potatoes, and sugar cane, through bioconversion and polymerization. PLA presents biocompatibility, biodegradability, and biological absorption, presenting good mechanical properties, processability, thermal stability and low environmental impact. Mechanical tests of compressive strength and flexural strength were carried out. In the compressive strength test, the specimen with 100% filling presented a deformation 76% greater than the specimen with 50% filling. This can be attributed to the mechanical property of the PETG polymer, as it is very ductile, thus facilitating the processability of this artifact. The mechanical flexural strength tests carried out with the PLA polymer with the highest filling percentages (100% and 50%) showed less deformation until failure, characterizing them as more ductile materials. On the other hand, specimens with 30% filling showed ~215% greater deformation than specimens with 100% filling. With this, it can be seen that PLA has greater flexibility and tenacity for fillings of low percentages, due to the internal spacing absorbing the impact of loads. The PLA polymer showed better mechanical properties, such as Young's modulus, ductility and more satisfactory resistance when compared to the PETG polymer. As well as the synthesis of PLA, it characterizes the process in a more sustainable way, as it is a biopolymer, in addition to its excellent processability.

Electrochemically exfoliated reduced graphene oxide-based freestanding separator-integrated electrode with embedded C-coated SiOx as the current collector-free anode in flexible batteries

We developed a facile, direct approach for the preparation of freestanding separator-integrated anodes with high flexibility and good capacity by mixing electrochemically exfoliated reduced graphene oxide (rGO) and C-coated SiOx (CSO) via vacuum filtration on a glass fiber separator. The type of current density applied during exfoliation significantly affected the electrochemical properties of graphene, and high electrical conductivity was obtained under the optimized conditions. The mixture of rGO and CSO exerted positive synergistic effects, including alleviated volume expansion, high specific capacity, and enhanced electrical conductivity of rGO, on battery performance, which may enable the development of current collector-free flexible Lithium-ion batteries. The optimized separator-integrated flexible anode exhibited a high reversible capacity at the fifth cycle, as well as satisfactory capacity retention over 200 cycles, which illustrates the superior rate capability and structural durability of this device.

Closed-Loop Supply Chain Network Design with Flexible Capacity under Uncertain Environment

This paper incorporates flexible facility capacity and government subsidy factors into the consideration of the design of a closed-loop supply chain(CLSC) network based on an uncertain environment. Considering the minimization of economic cost and carbon emission, a multi-objective multi-period multi-product mixed integer linear programming model with fixed and flexible facility capacity is constructed respectively. The robust optimization method is applied to deal with the uncertain environment of demand, recycled product quality, and recycling rate faced by the CLSC, and the robust models under six uncertain sets are constructed respectively. For model solving, the designed algorithm uses the augmented ϵ-constraint method to handle multi-objective problems and introduces a three-stage method on top of the Benders decomposition algorithm to accelerate the efficiency of solving the main problem. Finally, through numerical cases, a CSLC with a flexible supply strategy can manage economic and environmental costs to cope with the negative impacts of an uncertain environment, while this paper verifies the effectiveness of the government subsidy strategy under different conditions and analyzes the potential limitations.

The Problem of Machine Part Operations Optimal Scheduling in the Production Industry Based on a Customer’s Order

This research focuses on small- and medium-sized businesses that provide machining or other process services but do not produce their own products. Their daily manufacturing schedule varies according to client needs. Small- and medium-sized businesses strive to operate in these circumstances by extending their customer base and creating adequate production planning targets. Their resources are limited, including the technical and technological components of their equipment, tools, people resources, time, and capacities. As a result, planning operations with the present resources of small- and medium-sized businesses in the midst of the global economic crisis is a widespread issue that must be addressed. This study seeks to offer a novel mathematical optimization model based on a genetic algorithm to address job shop scheduling and capacity planning difficulties in small- and medium-sized businesses, therefore improving performance management and production planning procedures. On the basis of the created optimization model, an appropriate software solution, and quantitative data concerning the job shop scheduling and capacity planning challenges of manufacturing operations in small- and medium-sized businesses, the study findings will be obtained. The practical implications include the establishment and development of a decision support system based on the genetic algorithm optimization method, which may improve the effectiveness of the flexible job shop scheduling problem and capacity planning in the production planning process. The given model and the application of the differential precedence preservative crossover operator within genetic algorithms are what constitute the novelty of this study.

Modeling and Adaptive Control for Tendon Sheath Artificial Muscle Actuated Bending-Tip Systems With Unknown Parameters and Input Hysteresis: An Experimental Research

Due to the flexible structure and extension capacity, the tendon-sheath artificial muscle (TSAM) actuated bending-tip (TAB) system has attracted increasing efforts in the field of rehabilitation and surgical robots. However, the TAB system usually suffers from unknown input hysteresis, unidentifiable parameters, and unexpected external disturbance, which bring great challenges for precise dynamic modeling and robust controller design. So far, there is still no available strategy to deal with these complicated nonlinearities for the TAB system. In this paper, the Euler-Lagrange dynamics is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> established for the TAB system based on rigorous energy analysis. Then, a novel adaptive controller is developed based on the original dynamics <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without</i> any approximation, which can suppress the unavoidable effects of unknown parameters and input hysteresis, and achieve high-precision tracking performance, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">simultaneously</i> . Specially, the proposed adaptive method can also weaken the widely-used constant curvature assumption. By utilizing the Lyapunov-based technique, the stability of the closed-loop system is proven. Finally, to further validate the effectiveness and the robustness of the proposed controller, a series of hardware experiments are carried out on a self-built flexible ureteroscopy robot.

Vaccine supply chain network design by considering viability, robustness and risk

This study aims to develop a solution for the Viable Vaccine Supply Chain Network Design (VVSCND) problem, which concurrently addresses multiple factors such as sustainability, resiliency, agility, risk, and robustness. To achieve this, the researchers propose a Mixed-Integer Linear Programming (MILP) model based on a Robust Stochastic Programming (RSP) approach, which minimizes the weighted expected and max cost function. Furthermore, the research considers factors such as limiting CO2 emissions, introducing flexible capacity, and ensuring the reliability and redundancy of facilities to establish a more agile and environmentally sustainable supply chain. The key decisions in the proposed methodology involve determining facility location and product flow within an efficient healthcare system. According to the findings, the cost function of the VVSCND problem was only marginally higher, by 0.04%, than its counterpart in a VSCND that did not consider any risks or worst-case scenarios. However, increasing the conservatism coefficient or agility coefficient by 50% and 10%, respectively, leads to similar increases in the cost function. Similarly, the resiliency coefficient has a direct relationship with the cost function. Overall, the study demonstrates that an optimal VSCND can be achieved by considering multiple factors through RSP-based MILP modeling.

Enhancing Skeletal Muscle Rehabilitation: The Effects of Diclofenac Phonophoresis and Shock Wave Therapy on Serum Creatine Kinase in Athletes With Delayed-Onset Muscle Soreness.

Background Delayed-onset muscle soreness (DOMS) is a common condition in athletes characterized by muscle pain and stiffness after intense or unfamiliar exercise. It significantly impairs an athlete's performance by reducing muscle strength, flexibility, and overall physical capacity, often leading to suboptimal training or competition outcomes. Managing and mitigating DOMS is crucial for athletes to maintain peak performance and prevent potential injuries. The evaluation of effective treatment techniques is essential for expediting DOMS recovery by identifying biomarkers of skeletal muscle damage. This approach not only aids in optimizing recovery strategies but also contributes to the rehabilitation process, enabling athletes to return to peak performance quickly and safely. This study aims to evaluate the effects of shock wave therapy and diclofenac phonophoresis on serum creatine kinase levels in novice athletes suffering from DOMS in comparison to a control group. This research aims to assess the potential benefits of these therapeutic interventions in reducing skeletal muscle damage and enhancing recovery for individuals new to athletic training. Methodology A total of 48 novice athletes were recruited using simple random sampling and the block randomization approach to participate in this single-blind, multi-group, repeated-measures design. Male novice athletes in the age group of 18-25 years were included, and athletes with elevated serum creatine kinase variables at baseline measurements were excluded from the study. Before obtaining a written informed consent form, athletes were made aware of the procedure and associated risks. Group A received focused shock wave therapy, Group B received diclofenac phonophoresis, while Group C received no treatment. The Epley formula was used to compute the one-repetition maximum for each subject. Blood samples were taken at the baseline, 24, 48, 72, and 96 hours. Blood samples were taken before initiation of the study, as well as 24 hours, 48 hours, 72 hours, and 96 hours after DOMS was induced. Creatine kinase skeletal muscle biomarker was used as a dependent variable. Results In the evaluation of serum creatine kinase levels using analysis of variance (ANOVA), no statistically significant differences were observed between the three groups at baseline and 24-hour measurements (p > 0.05). However, statistically significant differences were found between the three groups at 48 hours, 72 hours, and 96 hours (p < 0.05). The repeated-measures ANOVA revealed significant overall changes in creatine kinase levels within the three groups, indicating time-dependent effects (p < 0.05). Specifically, during the 48 to 96-hour interval, the shock wave therapy group showed a lower mean value compared to the diclofenac phonophoresis group, followed by the control group. Conclusions The study demonstrates that a single administration of focused shock wave therapy effectively mitigated the elevation of creatine kinase levels in novice athletes with DOMS, surpassing the outcomes of diclofenac phonophoresis and the control group. These findings suggest the potential benefits of shock wave therapy in accelerating recovery from DOMS in the athletic population.

Обзор солнечной энергетики Германии и Казахстана: политика, текущее состояние и перспектива

Every year, the development of the solar industry is accelerating due to lower technology prices and industry decarbonizing issues. The article discusses the experience of deploying the solar industry in Germany and Kazakhstan. The article based on an analytical study of the current state and policy in the field of solar energy. Data for analysis taken from official reports of the International Energy Agency (IEA), the German Federal Ministry for Economics and Climate Protection (BMWK), the Ministry of Energy of the Republic of Kazakhstan and peer-reviewed literature. According to the analysis, the potential of solar energy around the world is huge. Due to the constant development of technology and cheaper prices, in the future, solar energy may become the primary source of electricity for many countries. For Germany, solar energy, as well as whole RES, are not only a source of electricity, but also a mechanism that ensures the country's energy security. For Kazakhstan, solar energy is a tool for the decarbonizing of energy sector. In order to reduce carbon emissions and introduce renewable sources to the level of Germany, Kazakhstan now needs to take transformative measures: modernize electrical networks and equipment, develop legislative regulation and incentives that allow the deployment of all types of solar technologies, ensure flexible capacities and create its own production of solar panels.

A single neuron in C. elegans orchestrates multiple motor outputs through parallel modes of transmission

Animals generate a wide range of highly coordinated motor outputs, which allows them to execute purposeful behaviors. Individual neurons in the circuits that generate behaviors have a remarkable capacity for flexibility as they exhibit multiple axonal projections, transmitter systems, and modes of neural activity. How these multi-functional properties of neurons enable the generation of adaptive behaviors remains unknown. Here, we show that the HSN neuron in C.elegans evokes multiple motor programs over different timescales to enable a suite of behavioral changes during egg laying. Using HSN activity perturbations and invivo calcium imaging, we show that HSN acutely increases egg laying and locomotion while also biasing the animals toward low-speed dwelling behavior over minutes. The acute effects of HSN on egg laying and high-speed locomotion are mediated by separate sets of HSN transmitters and different HSN axonal compartments. The long-lasting effects on dwelling are mediated in part by HSN release of serotonin, which is taken up and re-released by NSM, another serotonergic neuron class that directly evokes dwelling. Our results show how the multi-functional properties of a single neuron allow it to induce a coordinated suite of behaviors and also reveal that neurons can borrow serotonin from one another to control behavior.

Soft magnetic wood composites with chain-aligned Fe3O4 nanoparticles for magnetically driven actuators

Intelligently responsive wood-derived materials have long been sought due to their sustainability, earth abundance, and low carbon footprint, with promising applications in sensors, flexible electronics, and soft robotics. However, insensitive responsiveness and unsatisfactory mechanical properties have hindered the development of intelligent drive systems. This work reports on soft magnetic wood composite (SMWC) with a sandwich structure consisting of densified wood and polydimethylsiloxane (PDMS) mixed with Fe3O4. We treat natural wood chemically to improve flexibility, especially the lignin partial removal while keeping the wood structure intact. Benefiting from densification and magnetic-assisted techniques, multilayer SMWC with chain-arranged Fe3O4 can be prepared, which exhibits 183.5 MPa mechanical strength along the longitudinal direction, flexible bending capacity, a water contact angle of 100.9° and a saturation magnetization strength of 9.81 emu g−1 for 40 wt% Fe3O4. Importantly, the chain arrangement of Fe3O4 facilitates the sensitive control of SMWC compared to non-chain arranged structures. We have designed a star-shaped actuator that demonstrates controlled magnetic response, which can perform transporting cargo through linear and rotational movements on the water. SMWC with chain-aligned Fe3O4 exhibits good flexibility, excellent mechanical properties, and superior magnetic responsiveness, which is essential for exploring the responsiveness of wireless sensors and soft robots.

Dynamic Pricing and Capacity Optimization in Railways

Problem definition: Revenue management in railways distinguishes itself from that in traditional sectors, such as airline, hotel, and fashion retail, in several important ways. (i) Capacity is substantially more flexible in the sense that changes to the capacity of a train can often be made throughout the sales horizon. Consequently, the joint optimization of prices and capacity assumes genuine importance. (ii) Capacity can only be added in discrete “chunks” (i.e., coaches). (iii) Passengers with unreserved tickets can travel in any of the multiple trains available during the day. Further, passengers in unreserved coaches are allowed to travel by standing, thus giving rise to the need to manage congestion. Motivated by our work with a major railway company in Japan, we analyze the problem of jointly optimizing pricing and capacity; this problem is more-general version of the canonical multiproduct dynamic-pricing problem. Methodology/results: Our analysis yields four asymptotically optimal policies. From the viewpoint of the pricing decisions, our policies can be classified into two types—static and dynamic. With respect to the timing of the capacity decisions, our policies are again of two types—fixed capacity and flexible capacity. We establish the convergence rates of these policies; when demand and supply are scaled by a factor [Formula: see text], the optimality gaps of the static policies scale proportional to [Formula: see text], and those of the dynamic policies scale proportional to [Formula: see text]. We illustrate the attractive performance of our policies on a test suite of instances based on real-world operations of the high-speed “Shinkansen” trains in Japan and develop associated insights. Managerial implications: Our work provides railway administrators with simple and effective policies for pricing, capacity, and congestion management. Our policies cater to different contingencies that decision makers may face in practice: the need for static or dynamic prices and for fixed or flexible capacity. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0246 .

Waste cotton fabric-derived multimodal heating textile for comfortable and reliable personal thermal management

Wearable textiles with multiple desirable thermal functions still face significant challenges due to the sophisticated manufacturing techniques and limited sources. This work aimed to innovatively propose a convenient means to up-recycle waste cotton fabrics into an all-in-one heating textile integrating electric heating, outdoor solar heating, and indoor radiative heating for efficacious personal thermal management. The multimodal heating textile is realized by the in-situ growth of vertically-aligned polyaniline nanofibers similar to moth compound eye structure on the surface of cotton fibers and assembling silver nanowires on the backside. Methods for instrument analysis and simulating human skin were employed to evaluate the heating performance of the prepared heating fabric. The heating cotton fabric exhibits superb visible light absorptivity (∼98.5%), allowing the fabric 36.7 °C higher than that of the normal cotton under one solar irradiance. Meanwhile, the high mid-infrared reflectivity (∼61.0%) endows the fabric with exceptional radiative heating performance for human body. The high conductivity ensures the terrific electric heating ability (∼74 °C at 0.7 V) of the fabric. Fortunately, the fabric also possesses excellent electromagnetic interference shielding (∼60 dB), breathability, flexibility, self-cleaning performance, and antibacterial capacity. The findings in this work open a promising direction on how to convert waste fabrics into high value-added products and develop efficacious advanced heating textile, which is benefit for realizing the ideal personal thermal management and mitigating global climate change.

Antifragile, sustainable, and agile supply chain network design by considering resiliency, robustness, risk, and environmental requirements.

This research suggests an Antifragile, Sustainable and Agile Supply Chain Network Design (ASASCND) as a new network design that integrates these concepts considering resiliency, robustness, risk, and environmental requirements. The cost function combines a novel method with robust stochastic optimization and Entropic Value atRisk (EVaR). This model combines expected value, maximum and EVaR of cost as an objective function. This research adds antifragility by the effect of learning on variable parameters, sustainability by considering the environmental and social issues, resiliency and agility by flexible capacity, and multi-resource and demand satisfaction constraints to the model. The case study is in the automotive industry. This model compares the main problem by considering antifragility without thinking about antifragility. The ASASCND cost is - 0.3% less than without considering antifragility. In addition, when the conservatism coefficient grows, the cost function increase. In addition, the antifragility coefficient and the confidence level affect positively, and the agility coefficient negatively affects the cost function. Expanding the model scale changes the cost function and time computation because the antifragility coefficient changes variable cost. Finally, managerial insights and practical implications are explained.

A three-stage mechanism for flexibility-oriented energy management of renewable-based community microgrids with high penetration of smart homes and electric vehicles

A multi-stage mechanism for flexibility-oriented energy management (FOEM) of the distribution system is developed in this article, which main novelty is providing the flexibility requirements of the main grid by resources within renewable-based microgrids (RBMGs), including distributed generations (DGs), storage systems (SDs), internet-of-things enabled smart homes (IESHs), and plug-in electric vehicles (PEVs). In the first stage, IESHs are programmed in a decentralized space subject to satisfying the residents' satisfaction index (SI). In the second stage, RBMGs are programmed according to the announced plan of IESHs. Ultimately, in the third stage, the main grid operator calculates the required flexibility capacities of the system according to the announced plans of RBMGs, where a risk-averse strategy is adopted to manage the risk of programming. Next, the main grid operator announces the required flexibility capacities to RBMGs and IESHs so that they can re-plan with the possibility of participating in flexibility markets. The numerical results clarify that the participation of RBMGs, IESHs, and plug-in electric vehicles (PEVs) in providing flexibility capacities not only reduces the dependence of the distribution system on the upstream grid (UG), but also reduces the total daily costs by 30.7%.

Rapid and flexible battery capacity estimation using random short-time charging segments based on residual convolutional networks

Monitoring the capacity of lithium-ion battery is a crucial task to ensure its safety and reliability during long-term use. However, conventional capacity estimation methods heavily rely on the specially designed operating conditions or durations of charge/discharge cycles, limiting their applications in real-world operations. To address such challenges, in this paper, a fast and flexible method is proposed to accurately estimate battery capacity based on a residual convolutional neural network using only small pieces of raw measurement data. And Bayesian optimization as well as network slimming are introduced to optimize and prune the network structure. Then, the proposed model is validated in two public battery degradation data sets, containing two types of batteries and six types of charging strategies in total. It's shown that the model is flexible enough to cope with the application scenarios of different charging strategies, different sampling frequencies and different voltage ranges starting at arbitrary initial SOCs, while still achieving fast and accurate capacity estimation. The mean absolute errors on 38 LFP batteries and 8 LCO batteries with a four-fold cross validation approach are all below 0.5% throughout the whole life of the batteries. Comprehensive case studies are also carried out to investigate the influence of capacity increment size, compression ratio of input data and the depth of network on the trade-offs between the model accuracy and its practicability for wide applications.

Zero leverage and dividend policy

This study investigates zero leverage firms and their choice to pay dividends among New Zealand (NZ) listed firms for the period 2007 – 2021. Overall our findings indicate that after allowing for financial constraints limiting firm debt capacity and firm preference for financial flexibility, firm priority to pay dividends affects firm debt levels. Additional analysis shows that dividend paying firms with growth opportunities will increase short-term debt to maintain dividend payments.

Análisis de las capacidades físicas de los estudiantes deportistas de tercer año de bachillertao de la unidad educativa atahualpa

Introducción Physical capacities are defined as the specific characteristics that each individual possesses, they are based on mechanical actions, energetic, metabolic and physiological processes of the human being; it is the set of capacities that the human body possesses to perform physical activity, that is, it constitutes the expression of several corporal abilities that help students to perform different physical activities. Physical capacities are observable, since they are characterized because they are measurable, depending on the function and anatomical aspect, these capacities can be developed through training and organized and systematic sports practice. Objetivos To analyze the physical capacities of third year high school student athletes of the Atahualpa Educational Unit Método The study presents a quantitative approach, since different physical tests were applied to analyze the physical capacities of the student athletes of the Atahualpa Educational Unit, therefore, the following were evaluated: endurance, speed, strength and mobility. These results were statistically evaluated with the application of the statistical software SPSS version 24. The study was applied to a sample of 46 athletes, 22 men and 24 women. Principales resultados Regarding the Course-Navette test used to measure resistance, the results show that male athletes presented significant differences in comparison to the female gender; the result of the application of the 50m speed test, the male athletes as well as the female group registered similar values whose average was 1.82 s; as for the results of the application of the abdominal test in 30s, the male gender presented an average of 17. Finally, to evaluate flexibility, the Wells test was used to obtain significant differences in the women’s group with an average of 14.17 compared to the men’s group, which obtained 12.43. Conclusiones After the application of the physical tests in the study sample, it can be concluded that the male athletes obtained a greater difference in the endurance and strength test, while the female group observed significant differences in the flexibility capacity.

Intelligent Power Control of Inverter Air Conditioners in Power Systems: A Brain Emotional Learning-Based Approach

Inverter air-conditioning (IAC) units have been proved to be effective in frequency regulation by providing flexible capacities. This paper proposes a brain emotional learning (BEL)-based controller to provide the IACs with control signals to be efficiently involved in the frequency regulation in power systems. The BEL-based controller can learn quick-auto, making it appropriate in systems facing uncertainty. To assess the BEL controller performance in realistic conditions, the uncertainties as a consequence of variations in system parameters and load level are considered. The goal is to use the BEL controller to increase the IAC units' ability to track regulation signals accurately in uncertain circumstances. The controller is compared to a fuzzy-PI control, a proportional control scheme, a model predictive control and a linear quadratic regulator control. A delay-dependent stability criterion is used to calculate the highest time delay in the IACs response under which the system maintains stability. In addition, this paper presents an BEL-based coordinator to coordinate the IACs and traditional generation units for compensating considerable frequency variations caused by the time delays. Case studies are accomplished on a multi-area power system in MATLAB/Simulink environment. Eventually, real-time verifications by OPAL-RT real-time digital simulator on the simulated power system are executed to assess the control method.