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Background/Objectives: Besides a multitude of consequences patients on chronic renal replacement therapy have, anemia is one of the most prominent factors making a significant number of patients dependent on erythropoiesis-stimulating agent (ESA) therapy. The aim of this study was to examine the relationship between the levels of a broad spectrum of thrombo-inflammatory and oxidative stress-related biomarkers and the presence and level of ESA hyporesponsiveness in patients undergoing regular chronic hemodialysis. Methods: This cross-sectional study included 96 patients treated with chronic hemodialysis. Levels of several thrombo-inflammatory and oxidative stress-related biomarkers, as well as demographic, clinical, and laboratory analyses, were collected and analyzed based on the calculated value of the ESA-hyporesponsiveness index (EHRI). Results: In the analyzed sample, 58 patients received ESAs. Of all the investigated parameters, only body mass index (BMI), level of plasminogen activator inhibitor-1, and level of L-type fatty acid binding protein (L-FABP) were observed as significant predictors of EHRI. A significant diagnostic potential for ESA resistance has been observed in BMI and L-FABP between ESA-resistant and ESA-non-resistant groups of patients (p = 0.004, area under the curve 0.763 and p = 0.014, area under the curve 0.712, respectively) with the cut-off values of 25.46 kg/m2 and 5355.24 ng/mL, respectively. Having a BMI of 25.46 kg/m2 or less and an L-FABP level higher than 5355.24 ng/mL were observed as significant predictors of ESA resistance (odds ratio 9.857 and 6.125, respectively). Conclusions: EHRI was positively predicted by low BMI and high levels of plasminogen activator inhibitor-1 and L-FABP. High levels of L-FABP and low BMI have been observed as strong predictors of ESA resistance. [ABSTRACT FROM AUTHOR]

In the production of refined sunflower oil, waxes are removed during the winterization stage, and wax crystals are separated through filtration assisted by filtration aids. Commonly used filtration aids in oil refining include perlite and diatomaceous earth. After winterization, a significant amount of filter cake remains as a by-product and is treated as waste. Today, natural cellulose fibers are being promoted as filtration aids. Their advantages are numerous, both in the production process and from an environmental perspective. However, their only disadvantage is their higher cost. Therefore, in this study, 57 filtration cycles during the industrial sunflower oil winterization step using cellulose-based filtration aids were monitored. Different process parameters, including the pressure differential on the filter, the flow rate of filtered oil, constant pressure period, the quantity of filtered oil, filtration time, the quantity of pre-coating and dosing filtration aids, the volume of filtered oil, the concentration of dosing filtration aid, as well as the mass of separated waxes, were observed. Additionally, artificial neural networks were applied to predict process parameters, optimize the process, and, above all, determine the dosage of filtration aids, which will make the process more economical. The optimal filtration process is performed at a pressure differential of 3.3 bar, lasting a total of 39 h, with 32 h at constant pressure, resulting in 322,503 kg of filtered oil and 90.41 kg of waxes. The optimal quantity of cellulose-based filtration aids employed for pre-coat was 80 kg, and for dosing, 375 kg, with an optimal concentration of 0.12% w/w. [ABSTRACT FROM AUTHOR]

Stress is defined as a reaction of the body to any given stressor, external or internal. These stressors are common among participants in sports. Therefore, this study aimed to determine the dynamics of two stress biomarkers during an official cliff diving competition. The sample of participants included six professional cliff divers (three females and three males). Their salivary cortisol (C) and alpha-amylase (AA) samples were collected during a 3-day competition (four samples on day 1 and six samples on days 2 and 3). The analysis of the results showed a non-significant increase in AA from day one to day three. On the other hand, C has an increase in the first two days (0.58 ± 0.16 µg/mL–0.61 ± 0.23 µg/mL) and then a decrease in the last competition day (0.53 ± 0.23 µg/mL). Analysis of samples collected during each day demonstrates a fluctuation of the biomarkers. For AA, the sample after the training dive on the 2nd day has the highest value (326.34 ± 280.73 U/mL), whereas on day 3, the samples after training and before the competition dive are the highest (364.50 ± 287.13 U/mL; 466.49 ± 218.39 U/mL). Regarding C levels, the sample after the competition dive tends to show the highest values (0.66 ± 0.17 µg/mL; 0.89 ± 0.29 µg/mL; 0.76 ± 0.32 µg/mL, respectively). Furthermore, the correlation between the results of competition and biomarkers is not significant. These results demonstrate how cliff diving competition tends to have a high impact on the sympathetic nervous system, as observed mainly in AA dynamics. One might contend that even though there is no significant physical strain, cliff divers demonstrate heightened stress biomarker levels that could affect their performance and focus while diving. [ABSTRACT FROM AUTHOR]

Rowing races require extreme physical and psychological effort from every athlete. This study aimed to determine the dynamics of the salivary cortisol and alpha-amylase, as well as blood lactate throughout the specific load represented by a 6 km rowing race, conducted on a rowing ergometer. The sample consisted of 11 junior and senior rowers from HVK Gusar in Split (n = 11) who actively competed at club and international levels. Variables consisted of three repeated oral samples of the hormone cortisol and the enzyme alpha-amylase determined in saliva and three repeated blood lactate samples. Potential differences in the levels of the studied variables at different time points were determined using a repeated-measures ANOVA test. The results showed different dynamics of hormonal (cortisol) and metabolic (alpha-amylase and lactates) variables. All variables experienced a significant post-race increase, while other changes were not significant. The results highlighted that high-intensity rowing causes an increase in the body's cortisol, alpha-amylase, and lactate levels. This should be implemented in rowing training to find the right balance between high and low-intensity rowing, enabling athletes' progression while reducing the risk of overtraining. [ABSTRACT FROM AUTHOR]

Ethyl pyruvate (EP) is a redox-active compound that has been previously shown to be effective in restraining immune hyperactivity in animal models of various autoimmune and chronic inflammatory diseases. Importantly, EP has also been proven to have a potent tolerogenic effect on dendritic cells (DCs). Here, the influence of EP on the signaling pathways in DCs relevant for their tolerogenicity, including anti-inflammatory NRF2 and pro-inflammatory NF-κB, was explored. Specifically, the effects of EP on DCs obtained by GM-CSF-directed differentiation of murine bone marrow precursor cells and matured under the influence of lipopolysaccharide (LPS) were examined via immunocytochemistry and RT-PCR. EP counteracted LPS-imposed morphological changes and down-regulated the LPS-induced expression of pro-inflammatory mediators in DCs. While it reduced the activation of NF-κB, EP potentiated NRF2 and downstream antioxidative molecules, thus implying the regulation of NRF2 signaling pathways as the major reason for the tolerizing effects of EP on DCs. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a chronic, age-related, progressive multisystem disease associated with neuroinflammation and immune dysfunction. This review discusses the methodological approaches used to study the changes in central and peripheral immunity in PD, the advantages and limitations of the techniques, and their applicability to humans. Although a single animal model cannot replicate all pathological features of the human disease, neuroinflammation is present in most animal models of PD and plays a critical role in understanding the involvement of the immune system (IS) in the pathogenesis of PD. The IS and its interactions with different cell types in the central nervous system (CNS) play an important role in the pathogenesis of PD. Even though culture models do not fully reflect the complexity of disease progression, they are limited in their ability to mimic long-term effects and need validation through in vivo studies. They are an indispensable tool for understanding the interplay between the IS and the pathogenesis of this disease. Understanding the immune-mediated mechanisms may lead to potential therapeutic targets for the treatment of PD. We believe that the development of methodological guidelines for experiments with animal models and PD patients is crucial to ensure the validity and consistency of the results. [ABSTRACT FROM AUTHOR]

Background and Objectives: This study analyzed the frequency of factors influencing the course and outcomes of out-of-hospital cardiac arrest (OHCA) in Serbia and the prediction of pre-hospital outcomes and survival. Materials and Methods: Data were collected during the period from 1 October 2014, to 31 September 2023, according to the protocol of the EuReCa_One study (clinical trial ID number NCT02236819). Results: Overall 9303 OHCA events were registered with a median age of 71 (IQR 61–81) years and 59.7% of them being males. The annual OHCA incidence was 85.60 ± 20.73/100,000. Within all bystander-witnessed cases, bystander-initiated cardiopulmonary resuscitation in 15.3%. Within the resuscitation-initiated group, return-of-spontaneous circulation (ROSC) on scene (any ROSC) was present in 1037/4053 cases (25.6%) and ROSC on admission to the nearest hospital in 792/4053 cases (19.5%), while 201/4053 patients survived to hospital discharge (5.0%). Predictive potential on pre-hospital outcomes was shown by several factors. Also, of all patients having any ROSC, 89.2% were admitted to the hospital alive. The probability of any ROSC dropped below 50% after 17 min passed after the emergency call and 10 min after the EMS scene arrival. These time intervals were significantly associated with survival to hospital discharge (p < 0.001). Five-minute time intervals between both emergency calls and any ROSC and EMS scene arrival and any ROSC also had a significant predictive potential for survival to hospital discharge (p < 0.001, HR 1.573, 95% CI 1.303–1.899 and p = 0.017, HR 1.184, 95% CI 1.030–1.361, respectively). Conclusions: A 10-min time on scene to any ROSC is a crucial time-related factor for achieving any ROSC, and indirectly admission ROSC and survival to hospital discharge, and represents a golden time interval spent on scene in the management of OHCA patients. A similar effect has a time interval of 17 min from an emergency call. Further investigations should be focused on factors influencing these time intervals, especially time spent on scene. [ABSTRACT FROM AUTHOR]

In the current energy usage scenario, the demands on energy load and the tariffs on the usage of electricity are two main areas that require a lot of attention. Energy forecasting is an ideal solution that would help us to better understand future needs and formulate solutions accordingly. Some important factors to investigate are the quantity and quality of smart grids as they are significantly influenced by the transportation, storage, and load management of energy. This research work is a review of various machine learning algorithms for energy grid applications like energy consumption, production, energy management, design, vehicle-to-grid transfers, and demand response. Ranking is performed with the help of key parameters and is evaluated using the Rapid Miner tool. The proposed manuscript uses various machine learning techniques for the evaluation of power quality performance to validate an efficient algorithm ranking in a grid-connected system for energy management applications. The use of renewable energy resources in grid-connected systems is more common in modern power systems. Universally, the energy usage sector (commercial and non-commercial) is undergoing an increase in demand for energy utilization that has substantial economic and ecological consequences. To overcome these issues, an integrated, ecofriendly, and smart system that meets the high energy demands is implemented in various buildings and other grid-connected applications. Among various machine learning techniques, an evaluation of seven algorithms—Naïve Bayes, artificial neural networks, linear regression, support vector machine, Q-learning, Gaussian mixture model, and principle component analysis—was conducted to determine which algorithm is the most effective in predicting energy balance. Among these algorithms, the decision tree, linear regression, and neural networks had more accurate results than the other algorithms used. As a result of this research, a proposal for energy forecast, energy balance, and management was compiled. A comparative statement of various algorithms concludes with results which suit energy management applications with high accuracy and low error rates. [ABSTRACT FROM AUTHOR]

This article presents a brushless DC motor drive using a solar photovoltaic (PV) array and grid. Solar PV array-fed drive systems typically need a DC–DC converter stage in order to optimize the solar PV array-generated power utilizing a maximum power point (MPP) tracking technique. In this work, a boost DC–DC converter is used for MPP tracking purposes. This work utilizes an incremental conductance (INC) MPP-tracking algorithm. A bridgeless asymmetrical converter without a bridge rectifier is used at the grid side to improve power quality at supply mains. The presented asymmetrical converter is an amalgamation of a second order (buck boost) with a fourth-order (Cuk) converter, which lowers the net system's order. The input inductor of the Cuk converter manages the input current profile and, thus, eradicates the need for the filter at the grid mains. The bridgeless asymmetrical converter comes with several advantages, such as rectifier removal, component reduction, and input filter elimination. The performance of the brushless DC motor is examined in this article in all three scenarios: first, when grid and solar energy are both present; second, when solar energy is the only source of energy; and third, when grid energy is the only source of energy. The dual-source-based brushless DC motor drive system has been developed on matrix-laboratory/Simulink. The results are deployed and discussed to verify the drive-system performance. The article also presents a detailed stability analysis and mathematical modeling of the presented power-quality converter and MPP tracking converter to verify different converters' stability using a bode diagram and a pole-zero plot. [ABSTRACT FROM AUTHOR]

Waxes, phospholipids, free fatty acids, peroxides, aldehydes, soap, trace metals and moisture present in crude sunflower oil have a negative effect on the oil quality and are, therefore, removed during the refining process. Waxes crystallizing at low temperatures are removed during winterization by cooling and filtration. Waxes have poor filtration characteristics and an industrial filtration process must be enhanced by the use of filtration aids, which improve filter cake structure and properties, and consequently prolong the filtration cycle. Today, traditional filtration aids (diatomite, perlite, etc.) being used in the industry are frequently replaced by cellulose-based aids. The aim of this study is to examine the effect of oil filtration assisted by two cellulose-based filtration aids on the chemical parameters (wax, moisture, phospholipids, soaps, and fatty acids), oil transparency, carotenoids, and Fe and Cu content of sunflower oil obtained in an industrial horizontal pressure leaf filter. In order to investigate the mentioned parameters, the following techniques were used: gravimetric (waxes and moisture content), spectrophotometric (phospholipids and carotenoid content and oil transparency), volumetric (soaps and free fatty acids content) as well as inductively coupled plasma mass spectrometry (ICP-MS) for Fe and Cu content. An artificial neural network model (ANN) was employed for the prediction of removal efficiency based on the chemical quality, oil transparency, Fe and Cu content in oils before filtration, as well as filtration aid quantity and filtration time. Cellulose-based filtration aids had multiple beneficial effects; on average, 99.20% of waxes, 74.88% of phospholipids, 100% of soap, 7.99% of carotenoids, 16.39% of Fe and 18.33% of Cu were removed. [ABSTRACT FROM AUTHOR]

This article presents a brushless DC motor (BLDCM) drive for a maritime electric vehicle (MEV) application. The presented BLDCM drive uses a bridgeless Cuk-buckboost (BL-Cuk-BB) converter for input-side power factor (PF) improvement. The BL-Cuk-BB converter uses the buckboost converter for the negative half-cycles of the input AC voltages and the Cuk converter for the positive half-cycles. In the case of MEVs, the drive systems are generally fed by diesel engine generators (DEGs). The asymmetric BL-Cuk-BB converter is operated in a discontinuous inductor current mode (DICM) in the present work to attain better power quality. The usage of a second-order buckboost converter with a fourth-order Cuk converter results in a decrement in the net order of the system. Additionally, the input inductor of the Cuk converter also participates as the filter component along with capacitor C2 during buckboost converter operation to enhance the power quality. The total component count reduction in the BL-Cuk-BB converter is also achieved by eliminating the usage of extra/external back-feeding diodes, which are generally used in bridgeless schemes. The present scheme uses the inbuilt anti-parallel diodes for the same purpose. The lesser components requirement in the BL-Cuk-BB-converter-based BLDCM drive implies lesser cost and volume, along with greater reliability, lower conduction losses, and lower weight of the BLDCM drive, which adds to the merits of the model. The paper includes a detailed mathematical model and stability analysis using pole-zero maps and bode plots of the BL-Cuk-BB converter for each half-supply AC voltage cycle. The BL-Cuk-BB-converter-based BLDCM drive for an EV application has been developed on the MATLAB/Simulink platform for a DICM operation, and the MATLAB simulation results have been presented for validation of the BL-Cuk-BB-converter-based BLDCM drive. [ABSTRACT FROM AUTHOR]

Stress was previously defined as a response to any demand for change. These demands are noticeable during sport events, not only in athletes but also in coaches. Therefore, this study aimed to determine the influence of slow-paced breathing (SPB) on acute stress to handball coaches during official matches. One professional handball coach, aged 37 (height, 180 cm; weight, 80 kg), took part in the study. His salivary cortisol (C) concentration and alpha-amylase (AA) activity were measured, and his heart rate (HR) was monitored during six official matches. In the first three matches the SPB training protocol was not followed. From match four to match six, the coach performed SPB training every day and directly before the match. The decrease observed in AA was statistically significant with a large effect size (1.80). The trend of change in HR is visible and similar for matches with and without SPB. However, for SPB matches, there is a lower starting point compared to matches without SPB (89.81 ± 6.26 and 96.62 ± 8.10, respectively). Moreover, values of AA on SPB matches were smaller before the match (93.92 ± 15.89) compared to the same time point in non-SPB matches (115.30 ± 26.00). For AA, there is a significant effect size in the half-time (2.00) and after the match (−2.14). SPB matches showed a lower increase in AA. SPB might be used as one of the possible tools that could help coaches in achieving a desirable mental state during the match. [ABSTRACT FROM AUTHOR]

A variety of loads are placed upon an athlete in team sports (e.g., training, match, or competitions). However, the volume of the training load plays an important role in match success. Therefore, the aim of this study was to compare the differences in biomarker dynamics during the match and during training, and to evaluate if such training represents a good stimulus for an athlete to adapt to match stress. Ten male handball players (average age of 24.1 ± 3.17 years, body height of 1.88 ± 0.64 m, and body mass of 94.6 ± 9.6 kg) took part in this study. Their saliva cortisol, testosterone, and alpha-amylase were sampled during the match and training (sessions of 90 min duration, respectively). The results showed that cortisol had higher values after the match (0.65 µg/dL) than after training (0.32 µg/dL) (p = 0.05; ES = 0.39). Testosterone concentrations had a steeper increase during a match (65%) than after training (37%). Alpha-amylase levels did not differ significantly between the match and training (p = 0.77; ES = −0.06). Overall, the results showed that the environment of a match was more stressful for the athletes; therefore, a match provoked a stronger endocrine response in the studied markers. Therefore, we concluded that a match seemed to be a stronger trigger for all of the measured biomarker responses. [ABSTRACT FROM AUTHOR]

In micro-grids (MGs), renewable energy resources (RESs) supply a major portion of the consumer demand. The intermittent nature of these RESs and the stochastic characteristics of the loads cause a frequency stabilization issue in MGs. Owing to this, in the present manuscript, the authors try to uncover the frequency stabilization/regulation issue (FRI) in a two-area MG system comprising wind turbines (WTs), an aqua-electrolyzer, a fuel cell, a bio-gas plant, a bio-diesel plant, diesel generation (DG), ship DG, electric vehicles and their energy storage devices, flywheels, and batteries in each control area. With these sources, the assessment of the FRI is carried out using different classical controllers, namely, the integral (I), proportional plus I (PI), and PI plus derivative (PID) controllers. The gain values of these I, PI, and PID controllers are tuned using the recently proposed smell agent optimization (SAO) algorithm. The simulation studies reveal the outstanding performance of the later controller compared with the former ones in view of the minimum settling period and peak amplitude deviations (overshoots and undershoots). The SAO algorithm shows superior convergence behavior when tested against particle swarm optimization and the firefly algorithm. The SAO-PID controller effectively performs in continuously changing and increased demand situations. The SAO-PID controller designed in nominal conditions was found to be insensitive to wide deviations in load demands and WT time constants. [ABSTRACT FROM AUTHOR]

The article elaborates on the accuracy of current transformers (CT) in interaction with temperature and frequency using Pearson's correlation. The first part of the analysis compares the accuracy of the mathematical model of the current transformer and the result of the measurement on the real CT using the Pearson correlation calculation. The mathematical model of CT is determined by deriving the formula of the functional error with the display of the accuracy of the measured value. The accuracy of the mathematical model is affected by the accuracy of current transformer model parameters and the calibration characteristic of the ammeter used to measure the CT current. Variables that cause deviation in the accuracy of CT are temperature and frequency. The calculation shows the effects on accuracy in both cases. The second part of the analysis refers to the calculation of the partial correlation of three quantities: (1) CT accuracy, (2) temperature, and (3) frequency on a set of 160 measurements. First, the influence of temperature on the correlation of CT accuracy and frequency is proven, following the proof of the influence of frequency on the correlation of CT accuracy and temperature. In the end, the analysis is combined by comparing the measured results of the first and second part of the analysis. [ABSTRACT FROM AUTHOR]

This paper proposes a step-up 3-Ф switched-capacitor multilevel inverter topology with minimal switch count and voltage stresses. The proposed topology is designed to provide five distinct output voltage levels from a single isolated dc source, making it suitable for medium and low-voltage applications. Each leg of the proposed topology contains four switches, one power diode, and a capacitor. The switching signals are also generated using a staircase universal modulation method. As a result, the proposed topology will operate at both low and high switching frequencies. To highlight the proposed topology's advantages, a comparison of three-phase topologies wasperformed in terms of the switching components, voltage stress, component count per level factor, and cost function withthe recent literature. The topology achieved an efficiency of about 96.7% with dynamic loading, and 75% of the switches experienced half of the peak output voltage (VDC), whereas the remaining switches experienced peak output voltage (2VDC) as voltage stress. The MATLAB/Simulink environment was used to simulate the proposed topology, and a laboratory prototype was also built to verify the inverter's theoretical justifications and real-time performance. [ABSTRACT FROM AUTHOR]

We have analyzed 127 publications for this review paper, which discuss applications of Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural language processing (NLP), internet of things security, recommendation systems, finance, and energy management. The optimization of energy use is critical in today's environment. We mainly focus on the RL application for energy management. Traditional rule-based systems have a set of predefined rules. As a result, they may become rigid and unable to adjust to changing situations or unforeseen events. RL can overcome these drawbacks. RL learns by exploring the environment randomly and based on experience, it continues to expand its knowledge. Many researchers are working on RL-based energy management systems (EMS). RL is utilized in energy applications such as optimizing energy use in smart buildings, hybrid automobiles, smart grids, and managing renewable energy resources. RL-based energy management in renewable energy contributes to achieving net zero carbon emissions and a sustainable environment. In the context of energy management technology, RL can be utilized to optimize the regulation of energy systems, such as building heating, ventilation, and air conditioning (HVAC) systems, to reduce energy consumption while maintaining a comfortable atmosphere. EMS can be accomplished by teaching an RL agent to make judgments based on sensor data, such as temperature and occupancy, to modify the HVAC system settings. RL has proven beneficial in lowering energy usage in buildings and is an active research area in smart buildings. RL can be used to optimize energy management in hybrid electric vehicles (HEVs) by learning an optimal control policy to maximize battery life and fuel efficiency. RL has acquired a remarkable position in robotics, automated cars, and gaming applications. The majority of security-related applications operate in a simulated environment. The RL-based recommender systems provide good suggestions accuracy and diversity. This article assists the novice in comprehending the foundations of reinforcement learning and its applications. [ABSTRACT FROM AUTHOR]

Frequency, tie-line power, and the terminal voltages of synchronized generators must all be kept within prescribed limits to ensure the stability of an interconnected power grid through combined automatic generation control (AGC) and automatic voltage regulator (AVR) loops. Thermal power plants, electric vehicles, and renewable energy sources—including solar and wind, geothermal, and solar thermal power plants—form the two-area integrated power system in present research. A new cascade controller named the cascaded proportional integral derivative (PID) and fractional-order PID (CPID-FOPID) controller is proposed for the first time, whose performance is compared with the PID and FOPID controller. The results show that the proposed cascade controller outperforms PID and FOPID in delivering superior dynamic characteristics, including short settling times and low oscillation amplitudes. A new metaheuristic algorithm named the coot algorithm was applied to optimize the parameters of these controllers. The suggested controller outperforms FOPID in the combined AGC and AVR problem under uncertain conditions (random load disturbance, variable input of solar irradiation, and wind power). Robustness of the controller is tested with significant variation in the turbine time constant of the thermal and geothermal power plant. In this study, authors also investigated the best possible coordination between the superconducting magnetic energy storage (SMES) and gate-controlled series capacitor (GCSC) devices to control both voltage and frequency simultaneously. The effect of communication time to the power system is analyzed in this study. Additionally, the obtained results are satisfactorily validated using OPAL-RT real-time digital simulator. [ABSTRACT FROM AUTHOR]

Background: Neoadjuvant chemoradiotherapy prior to surgery is the standard treatment for locally advanced rectal cancer. This consists in the patient's complete pathological response being achieved with no residual tumor presence in the resected specimen, which results in survival improvement. Methods: This retrospective study aimed to examine the rate of complete pathological response in patients with advanced rectal cancer treated with neoadjuvant long-course chemoradiotherapy and to examine the survival differences between the different tumor regression grade (TRG) scores. Results: A total of 154 patients were operated prior to long-course chemoradiotherapy with a total of 50 Gy plus FOLFOX protocol. Complete pathologic response was achieved in 29 (18.8%) patients. There was no statistical difference for the different pathologic responses according to gender, type of surgery, and number of harvested lymph nodes. Mean survival for all the groups was 37.2 months. Survival within a different TRG score exhibited statistical significance (p = 0.006). Overall, the survival rate during the follow-up period was of 81.8%. Conclusions: The complete pathological response rate in this study was of 18.8%. High tumor regression grade scores (TRG0 and TRG1) had a survival rate of over 90% during follow-up. Multivariate analysis identified perineural invasion and tumor regression grade as independent factors that affect survival. [ABSTRACT FROM AUTHOR]

As information technology advances quickly, so does the 5G–6G network management system, which is moving toward greater integration, decentralization, diversity, and intelligence. As flexibility is a crucial criterion for 5G–6G network architecture, we use the Software Defined Network (SDN) paradigm to make the programmability more flexible. Due to their ability to replace the current TCP/IP architecture with one that separates the control plane and data plane, software-defined networks have gained much popularity. However, they are susceptible to routing attacks. Therefore, this work proposes Lightweight Security Framework that combines blockchain technology with Software-Defined Networking (LSSDNF) to address this problem. The proposed framework adds the routing data that the controller withheld to the multichain blockchain. Here, a mininet network simulator is used to model the proposed framework. The data transfer rate or network throughput, bandwidth variation, and jitter have all been used to assess the performance of single-controller-SDN networks and multi-controller-SDN networks. The results demonstrate that the proposed framework performs better than the conventional single-controller-SDN architecture in terms of throughput, bandwidth fluctuation, and jitter. [ABSTRACT FROM AUTHOR]

An interconnected power system requires specific restrictions to be maintained for frequency, tie-line power, and the terminal voltage of synchronized generators to avoid instability. Therefore, frequency stability and voltage regulation issues are covered individually and jointly in the current research work. Initially in test system 1, automatic generation control (AGC) investigations are done on two interconnected systems with thermal plants and electric vehicles in one area and distributed generation and electric vehicles in other area. The automatic voltage regulator (AVR) problem alone is chosen for investigation in test system 2. The third test system addresses the combined AGC and AVR issues. The performance of the fractional-order tilt-integral-derivative (TID) controller is compared with that of a widely used proportional integral derivative (PID) controller in all three test systems studies. The findings demonstrate better performance of the TID controller than PID in terms of providing superior dynamic metrics, such as low peak overshoots, undershoots, and settling time, as well as decreased oscillations amplitudes. Additionally, TID performs better than PID despite randomized load disturbance, system non-linearities, and time delays in AGC and the combined AGC and AVR problem. The PSO-tuned TID controller is insensitive to variation in load damping factor and time constants of the AVR system. Finally, the results are validated by an OPAL-RT 4510 real-time digital simulator. [ABSTRACT FROM AUTHOR]

In a smart city environment, with increased demand for energy efficiency, information exchange and communication through wireless sensor networks (WSNs) plays an important role. In WSNs, the sensors are usually operating in clusters, and they are allowed to restructure for effective communication over a large area and for a long time. In this scenario, load-balanced clustering is the cost-effective means of improving the system performance. Although clustering is a discrete problem, the computational intelligence techniques are more suitable for load balancing and minimizing energy consumption with different operating constraints. The literature reveals that the swarm intelligence-inspired computational approaches give excellent results among population-based meta-heuristic approaches because of their more remarkable exploration ability. Conversely, in this work, load-balanced clustering for sustainable WSNs is presented using improved gray wolf optimization (IGWO). In a smart city environment, the significant parameters of energy-efficient load-balanced clustering involve the network lifetime, dead cluster heads, dead gateways, dead sensor nodes, and energy consumption while ensuring information exchange and communication among the sensors and cluster heads. Therefore, based on the above parameters, the proposed IGWO is compared with the existing GWO and several other techniques. Moreover, the convergence characteristics of the proposed algorithm are demonstrated for an extensive network in a smart city environment, which consists of 500 sensors and 50 cluster heads deployed in an area of 500 × 500 m2, and it was found to be significantly improved. [ABSTRACT FROM AUTHOR]

In this paper, we report on our benchmark results of the LiDAR sensors Livox Horizon, Robosense M1, Blickfeld Cube, Blickfeld Cube Range, Velodyne Velarray H800, and Innoviz Pro. The idea was to test the sensors in different typical scenarios that were defined with real-world use cases in mind, in order to find a sensor that meet the requirements of self-driving vehicles. For this, we defined static and dynamic benchmark scenarios. In the static scenarios, both LiDAR and the detection target do not move during the measurement. In dynamic scenarios, the LiDAR sensor was mounted on the vehicle which was driving toward the detection target. We tested all mentioned LiDAR sensors in both scenarios, show the results regarding the detection accuracy of the targets, and discuss their usefulness for deployment in self-driving cars. [ABSTRACT FROM AUTHOR]

The use of electric vehicles (EVs) has recently increased in a smart city environment. With this, the optimal location of the charging station is a great challenge and, hence, the energy efficiency performance (EEP) of an electrical system is important. Ideally, the EEP is realized through passive energy boosters (PEBs) and active energy boosters (AEBs). PEBs require no external resources, and EEP is achieved through altering the network topology and loading patterns, whereas, in AEBs, integrating external energy resources is a must. The EEP has also become dynamic with the integration of an energy storage system (ESS) in a deregulated environment. Customer energy requirement varies daily, weekly, and seasonally. In this scenario, the frequent change in network topology requires modifying the size and location of AEBs. It alters the customers' voltage profile, loadability margin, and supply reliability when the EV works differently as a load or source. Therefore, a comprehensive EEP analysis with different probabilistic loading patterns, including ESS, must be performed at the planning stage. This work uses a harmony search algorithm to evaluate EEP for AEBs and PEBs, in coordination, when ESS works as a load or source, at four locations, for customers' and utilities' benefits. [ABSTRACT FROM AUTHOR]

In this paper, battery electric vehicle (BEV) controllers are smartly tuned with particle swarm optimization (PSO) and genetic algorithm (GA) to ensure good speed regulation. Intelligent tuning is ensured with a proposed and well-defined cost function that aims to satisfy the design requirements in terms of minimum overshoot, fast response, and tolerable steady state input. Two proposed cost functions are formulated for both simple speed input and for driving cycles. The BEV is controlled with the field oriented control technique (FOC), and it is driven by a permanent magnet synchronous motor (PMSM). An efficient control scheme based on FOC is built using a simplified closed loop control system including BEV components such as regulators, inverter, traction machine, and sensors. Simulation results show that the optimum controller gains obtained by intelligent tuning have resulted in satisfactory BEV performance that sustains the harsh environmental conditions. Robustness tests against BEV parameter changes and environmental parameter variations confirmed the effectiveness of intelligent tuning methods. [ABSTRACT FROM AUTHOR]

Steady increase in electricity consumption, fossil fuel depletion, higher erection times of conventional plants, etc., are encouraging the use of more and more onsite renewable energy. However, due to the dynamic changes in environmental factors as well as the customer load, renewable energy generation is facing issues with reliability and quality of the supply. As a solution to all these factors, renewable energy integrated cluster microgrids are being formed globally in urban communities. However, their effectiveness in generating quality power depends on the power electronic converters that are used as an integral part of the microgrids. Thus, this paper proposes the "Fuzzy Hysteresis Current Controller (FHCC)-based Inverter" for improving the power quality in renewable energy integrated cluster microgrids that are operated either in grid-connected or autonomous mode. Here, the inverter is controlled through a fuzzy logic-based hysteresis current control loop, thereby achieving superior performance. System modelling and simulations are done using MATLAB/Simulink®. The performance analysis of the proposed and conventional inverter configurations is done by computing various power quality indices, namely voltage characteristics (swell, sag, and imbalance), frequency characteristics (deviations), and total harmonic distortion. The results reveal that the proposed FHCC-based inverter achieves a better quality of power than the traditional ST-PWM-based multilevel inverter in terms of IEEE/IEC/EN global standards for renewable energy integrated cluster microgrids application. [ABSTRACT FROM AUTHOR]

The paper presents a new model of the thermal rotor protection 49R on synchronous generators with self-excitation with the influence of generator negative sequence protection 46I2. The purpose of the analysis is to solve the problem of simultaneous occurrence of rotor overload due to excitation current and rotor overload due to the inverse component of the stator current. The numerical protections are designed to operate independently of each other, and therefore the residual thermal capacity of the copper windings is not defined with higher precision. A mathematical model that integrates these two protections is given and described. [ABSTRACT FROM AUTHOR]

This study presents the implementation of Artificial Bee Colony (ABC) optimization in an island hybrid power system model using fuzzy logic-based load frequency control. The Island Hybrid Power System considered in this study consisted of various generation units and an energy storage system. The optimized control parameters of PID using ABC were used in an intelligent fuzzy logic controller. The profiles (power & Frequency) of isolated hybrid power system were improved using a Super Conducting Magnetic Energy Storage (SMES) System. Individual controllers were used for wind turbine and diesel generators to control the power output for balancing the demand (frequency change control). Comparative analysis of power and frequency with the help of various classical and intelligent control configurations is presented. The outcome of the study shows that a minimum deviation in frequency and power is obtained through the proposed Intelligent Fuzzy Control approach for the considered isolated power system model. [ABSTRACT FROM AUTHOR]

Voltage sag is one of the most significant power quality problems in the industry and has a significant impact on induction motor safety and stability. This paper analyzes the characteristics of voltage dips in power systems and induction motors with a special emphasis on balanced dips with the help of virtual grids (regenerative grid simulator), as per IEC 61000-4-11. Three phase induction motors with 3.3 kW, 16 A coupled to a DC generator with 3.7 kW, and 7.8 A rated are considered for the test analysis. This paper aids in the development of an induction motor to achieve improved precision by taking different voltage sags into account. The experimental results benefit the design modifications of induction motors at industrial and other commercial levels of consumers regarding major power quality issues and the behavior of the induction motors. A proposed modification employing ANSYS is provided to further examine the precise performance of induction motors during sag events. [ABSTRACT FROM AUTHOR]

In order to reflect possible issues in future sole e-waste processing, an electrolyte of complex chemical composition reflecting system of sole e-waste processing was obtained by following a specially designed pyro-electrometallurgical method. The obtained non-standard electrolyte was further used for the purpose of comprehensive metal interference evaluation on the copper solvent extraction (SX) process. Optimization of the process included a variation of several process parameters, allowing determination of the effect of the most abundant and potentially the most influential impurities (Ni, Sn, Fe, and Zn) and 14 other trace elements. Moreover, comparing three commercial extractants of different active chelating groups, it was determined that branched aldoxime reagent is favorable for Cu extraction from the chemically complex system, as can be expected in future e-waste recycling. The results of this study showed that, under optimal conditions of 20 vol.% extractant concentration, feed pH 1.5, O/A ratio 3, and 10-min phase contact time, 88.1% of one stage Cu extraction was achieved. Co-extraction of the Fe, Zn, Ni, and Sn was under 8%, while Pb and trace elements were negligible. Optimal conditions (H2SO4 180 g/L, O/A = 2, and contact time 5 min) enabled 95.3% Cu stripping and under 6% of the most influential impurities. In addition, an impurity monitoring and distribution methodology enabled a better understanding and design of the process for the more efficient valorization of metals from e-waste. [ABSTRACT FROM AUTHOR]

Direct current microgrids are attaining attractiveness due to their simpler configuration and high-energy efficiency. Power transmission losses are also reduced since distributed energy resources (DERs) are located near the load. DERs such as solar panels and fuel cells produce the DC supply; hence, the system is more stable and reliable. DC microgrid has a higher power efficiency than AC microgrid. Energy storage systems that are easier to integrate may provide additional benefits. In this paper, the DC micro-grid consists of solar photovoltaic and fuel cell for power generation, proposes a hybrid energy storage system that includes a supercapacitor and lithium–ion battery for the better improvement of power capability in the energy storage system. The main objective of this research work has been done for the enhanced settling point and voltage stability with the help of different maximum power point tracking (MPPT) methods. Different control techniques such as fuzzy logic controller, neural network, and particle swarm optimization are used to evaluate PV and FC through DC–DC boost converters for this enhanced settling point. When the test results are perceived, it is evidently attained that the fuzzy MPPT method provides an increase in the tracking capability of maximum power point and at the same time reduces steady-state oscillations. In addition, the time to capture the maximum power point is 0.035 s. It is about nearly two times faster than neural network controllers and eighteen times faster than for PSO, and it has also been discovered that the preferred approach is faster compared to other control methods. [ABSTRACT FROM AUTHOR]

The role of renewable energy sources in the power grid is increasing tremendously. However, power electronic converters are used to incorporate RES into the grid without inertia. This article recommends an improved emulated inertia control approach focused on the frequency deviation and rate of change of frequency to enhance the inertia of a power system. The required inertial power calculated from emulated inertia control is delivered through hybrid energy storage systems equipped with a proper hybrid energy storage system control. The fast-varying power calculated from emulated inertia control is linked to super-capacitor. Simultaneously, the battery handles the slow varying power by regulating the DC bus voltage proportionate to the frequency variations. Further, the stability of the emulated inertia control and hybrid energy storage system controller is validated by Bode plots. The simulation results verified the correctness of the proposed emulated inertia control and hybrid energy storage system control. The real-time simulation results with the help of OPAL-RT are presented to validate the proposed method's feasibility. [ABSTRACT FROM AUTHOR]

This article focuses on addressing the data aggregation faults caused by the Phasor Measuring Unit (PMU) by installing Wireless Sensor Networks (WSN) in the grid. All data that is monitored by PMU should be sent to the base station for further action. But the data that is sent from PMU does not reach the main server properly in many situations. To avoid this situation, a sensor-based technology has been introduced in the proposed method for sensing the values that are monitored by PMU. Also, the basic parameters that are necessary for determining optimal solutions like energy consumption, distance and cost have been calculated for wireless sensors, whereas, for PMU optimal placements with cost analysis have been restrained. For analyzing and improving the accuracy of the proposed method, an effective Binary Logistic Regression (BLR) algorithm has been integrated with an objective function. The sensor will report all measured PMU values to an Online Monitoring System (OMS). To examine the effectiveness of the proposed method, the examined values are visualized in MATLAB and results prove that the proposed method using BLR is more effective than existing methods in terms of all parametric values and the much improved results have been obtained at a rate of 81.2%. [ABSTRACT FROM AUTHOR]

A main focus in microgrids is the power quality issue. The used renewable sources fluctuate and this fluctuation has to be suppressed by designing a control variable to nullify the circulating current caused by voltage fluctuations and deviations. The switching losses across power electronic switches, harmonics, and circulating current are the issues that we discuss in this article. The proposed intelligent controller is an interface between a voltage-sourced converter and a utility grid that affords default switching patterns with less switching loss, less current harmonic content, and overcurrent protection, and is capable of handling the nonlinearities and uncertainties in the grid system. The interfaced controller needs to be synchronized to a utility grid to ensure that the grid–lattice network can be fine-tuned in order to inject/absorb the prominent complex reactive energy to/from the utility grid so as to maintain the variable power factor at unity, which, in turn, will improve the system's overall efficiency for all connected nonlinear loads. The intelligent controller for stabilizing a smart grid is developed by implementing a fuzzy-built advance control configuration to achieve a faster dynamic response and a more suitable direct current link performance. The innovation in this study is the design of fuzzy-based space vector pulse width modulation controller that exploits the hysteresis current control and current compensation in a grid-connected voltage source converter. By using the proposed scheme, a current compensation strategy is proposed along with an advanced modulation controller to utilize the DC link voltage of a voltage source converter. To demonstrate the effectiveness of the proposed control scheme, offline digital time-domain simulations were carried out in MATLAB/Simulink, and the simulated results were verified using the experimental setup to prove the effectiveness, authenticity, and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

With the ever-growing power demand, the energy efficiency in commercial and residential buildings is a matter of great concern. Also, strategic energy auditing (SEA) and demand-side management (DSM) are cost-effective means to identify the requirements of power components and their operation in the energy management system. In a commercial or residential building, the major components are light sources and heating, ventilation, and air conditioning. The number of these components to be installed depends upon the technical and environmental standards. In this scenario, energy auditing (EA) allows identifying the methods, scope, and time for energy management, and it helps the costumers to manage their energy consumption wisely to reduce electricity bills. In the literature, most of the traditional strategies employed specific system techniques and algorithms, whereas, in recent years, load shifting-based DSM techniques were used under different operating scenarios. Considering these facts, the energy data in a year were collected under three different seasonal changes, i.e., severe cold, moderate, and severe heat for the variation in load demand under different environmental conditions. In this work, the energy data under three conditions were averaged, and the DSM schemes were developed for the operation of power components before energy auditing and after energy auditing. Moreover, the performance of the proposed DSM techniques was compared with the practical results in both scenarios, and, from the results, it was observed that the energy consumption reduced significantly in the proposed DSM approach. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent electronic device (IED) utilized for automatic meter readings (AMR) scheme using "Open-Source" software. This IED is utilized to measure a low-voltage intelligent electronic device) system with a boundless number of sensors, and it is accessible on the Internet of Things (IoT). The utilized equipment for this task is Arduino UNO R3 motherboard and fringe sensors, which are used for measurement of the referenced information. The Arduino motherboard is used not only for sole tranquility of equipment but also for serving as wireless fidelity (Wi-Fi) switch for the sensors. The personal computer is utilized to gather information and perform client-side calculations. The server works based on an open-source program written in Java programming language. The underlying objective of the proposed scheme is to make the meter based on the "Do It Yourself" methodology which requires considerably fewer funds. Also, it is conceivable by keeping easy to understand interface, information legitimacy, precision of measured information and convenience for the conclusive client. The information is measured in just about 1 ms which is superb for custom-designed IED. Furthermore, the measured qualities are calculated based on their RMS values to be used for analyzing and further presentation of data. [ABSTRACT FROM AUTHOR]

Voltage and frequency regulation is one of the greatest challenges for proper operation subsequent to the isolated microgrid. To validate the satisfactory electric power quality supply to customers, the proposed manuscript tries to enhance the quality of energy provided by DG (Distributed generation) units connected to the subsequent isolated grid. Microgrid and simulation-based control structure including voltage and current control feedback loops is proposed for microgrid inverters to recover voltage and frequency of the system subsequently for any fluctuations in load change. The proportional-integral (PI) controller connected to the voltage controller is an end goal to obtain smooth response in most of the consistent frameworks. The present controller creates the space vector pulse width modulation signals which are given to the three-leg inverter. The objective elements of the multiobjective optimization issue are voltage overshoot and undershoot, rise time, settling time, and integral time absolute error (ITAE). The hybrid Multiobjective Symbiotic Organism Search (MOSOS) calculation is associated for self-tuning of control parameters keeping in mind the end goal to deal with the voltage and frequency. The proposed PI controller, along with the hybrid Multiobjective Symbiotic Organism Search algorithm, provides the solution for the greatest challenge of voltage and frequency regulation in an isolated-microgrid operation. [ABSTRACT FROM AUTHOR]

Stochastic production from wind power plants imposes additional uncertainty in power system operation. It can cause problems in load and generation balancing in the power system and can also cause congestion in the transmission network. This paper deals with the problems of congestion in the transmission network, which are caused by the production of wind power plants. An optimization model for corrective congestion management is developed. Congestions are relieved by re-dispatching several cascaded hydropower plants. Optimization methodology covers the optimization period of one day divided into the 24 segments for each hour. The developed optimization methodology consists of two optimization stages. The objective of the first optimization stage is to obtain an optimal day-ahead dispatch plan of the hydropower plants that maximizes profit from selling energy to the day-ahead electricity market. If such a dispatch plan, together with the wind power plant production, causes congestion in the transmission network, the second optimization stage is started. The objective of the second optimization stage is the minimization of the re-dispatching of cascaded hydropower plants in order to avoid possible congestion. The concept of chance-constrained programming is used in order to consider uncertain wind power production. The first optimization stage is defined as a mixed-integer linear programming problem and the second optimization stage is defined as a quadratic programming (QP) problem, in combination with chance-constrained programming. The developed optimization model is tested and verified using the model of a real-life power system. [ABSTRACT FROM AUTHOR]

One of the most crucial and economically-beneficial tasks for energy customers is peak load curtailment. On account of the fast response of renewable energy resources (RERs) such as photovoltaic (PV) units and battery energy storage system (BESS), this task is closer to be efficiently implemented. Depends on the customer peak load demand and energy characteristics, the feasibility of this strategy may vary. When adaptive neuro-fuzzy inference system (ANFIS) is exploited for forecasting, it can provide many benefits to address the above-mentioned issues and facilitate its easy implementation, with short calculating time and re-trainability. This paper introduces a data-driven forecasting method based on fuzzy logic (FL) for optimized peak load reduction. First, the amount of energy generated by PV is forecasted using ANFIS which conducts output trend, and then, the BESS capacity is calculated according to the forecasted results. The trend of the load power is then decomposed in Cartesian plane into two parts, namely left and right from load peak, for the sake of searching for equal BESS capacity. Network switching sequence over consumption is provided by a fuzzy logic controller (FLC) considering BESS capacity and PV energy output. Finally, to prove the effectiveness of the proposed ANFIS-based peak power shaving/curtailment method, offline digital time-domain simulations have been performed on a test microgrid system using the data gathered from a real-life practical test microgrid system in the MATLAB/Simulink environment and the results have been experimentally verified by testing on a practical microgrid system with real-life data obtained from smart meters and also, compared with several previously-reported methods. [ABSTRACT FROM AUTHOR]