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Currently, tissue product producers try to meet consumers’ requirements to retain their loyalty. In perforated products, such as toilet paper, these requirements involve the paper being portioned along the perforation line and not outside of it. Thus, it becomes necessary to enhance the behavior of the perforation line in perforated tissue papers. The current study aimed to verify if the perforation line for 0° (the solution found in commercial perforated products) is the best solution to maximize the perforation efficiency. A finite element (FE) simulation was used to validate the experimental data, where the deviations from the experiments were 5.2% for the case with a 4 mm perforation length and 8.8% for a perforation of 2 mm, and optimize the perforation efficiency using the genetic algorithm while considering two different cases. In the first case, the blank distance and the perforation line angle were varied, with the best configuration being achieved with a blank distance of 0.1 mm and an inclination angle of 0.56°. For the second case, the blank distance was fixed to 1.0 mm and the only variable to be optimized was the inclination angle of the perforation line. It was found that the best angle inclination was 0.67°. In both cases, it was verified that a slight inclination in the perforation line will favor partitioning and therefore the perforation efficiency.

Banana stem is a known alternative to wood for the production of pulp and paper. During the production of paper, it is extremely important to save costs and time while at the same time enhancing quality of produced paper. In the present study, paper was developed from banana stem fiber by optimizing its density. Response surface methodology (RSM) using Box-Behnken design (BBD) was used to determine optimum blending parameters of the developed papers. The influence of banana stem fiber amount (250-1000 g), water amount (1-2liters) and blending time (5-10 min) on paper density was analyzed using software Stat-Ease 360, Version 13. The optimum conditions of banana stem fiber amount, water amount and blending time to achieve a density of 675.75 g/m3 were determined as 461.83 g, 1.31liters, and 8.53 min, respectively. ANOVA results revealed that the quadratic model best fits the density response, with blending time and banana stem fiber amount as the most significant model terms. Linear effects were more predominant than quadratic and interaction effects. The developed density model was in close agreement with experimental findings with an R2 value of 0.9748. Developed paper had cellulosic contents, with volatiles as high as 82.11%wb and 2.11%wb ash composition. Future works on thermal properties of paper are encouraged.

This study carried out the anaerobic digestion of recycled paper mill wastewater (RPMW) in a high-rate novel anaerobic baffled reactor. The parametric interaction between influent chemical oxygen demand (CODin) and hydraulic retention time (HRT) was modeled, and process responses were optimized by the response surface methodology (RSM) using a three-level factorial design. The results showed that the optimal condition was determined at CODin of 4,000 mg/L and HRT of 2 days and predicted values for COD removal, biochemical oxygen demand (BOD) removal, lignin removal, CH4 content, and CH4 production were found to be 94%, 98%, 68%, 85%, and 20.8 L CH4/d, respectively. According to the statistical analysis of the RSM, all models were significant with very low probability values (from 0.0045 to

Demand for green environments has stimulated research on the migration from conventional to renewable energy sources. In the case of combustible energy for domestic applications, briquettes produced from biomass have proven to be a good replacement for wood fuel. In this study, investigation on the performance analysis and optimization of mix ratio of briquettes produced from sugarcane bagasse using composite binders (waste paper and clay) was carried out. Box-Behnken Design feature of the Design Expert software was employed for the optimization of the briquettes mix ratio. Briquettes were produced from sugarcane bagasse based on the optimum conditions of 9.12% clay, 12.57% waste paper, 78.31% proportion of sugarcane bagasse and 5 KN compaction pressure. The produced briquette was found to have a compressive strength of 6.4715 MPa and 0.857 g/min burning rate. The results of the study shows that the experimental values were close to the predicted values with a percentage error of 3.11% for compressive strength and1.79% for burning rate which confirms the validity and adequacy of the predicted models.

Abstract By way of broadening the use of diverse sustainable bioethanol feedstocks, the potentials of Paper mulberry fruit juice (PMFJ), as a non-food, sugar-based substrate, were evaluated for fuel ethanol production. The suitability of PMFJ was proven, as maximum ethanol concentration (56.4 g/L) and yield (0.39 g/g) were achieved within half a day of the start of fermentation, corresponding to very high ethanol productivity of 4.7 g/L/hr. The established potentials were further optimally maximized through the response surface methodology (RSM). At the optimal temperature of 30 °C, yeast concentration of 0.55 g/L, and pH of 5, ethanol concentration, productivity, and yield obtained were 73.69 g/L, 4.61 g/L/hr, and 0.48 g/g, respectively. Under these ideal conditions, diverse metal salts were afterward screened for their effects on PMFJ fermentation. Based on a two-level fractional factorial design, nutrient addition had no positive impact on ethanol production. Thus, under the optimal process conditions, and without any external nutrient supplementation, bioethanol from PMFJ compared favorably with typical sugar-based energy crops, highlighting its resourcefulness as a high-value biomass resource for fuel ethanol production. Graphical Abstract

In paper production, a jumbo reel is cut into multiple intermediate rolls, and each intermediate roll is then sheeted as finished goods. This problem is called a cutting stock problem and is proven to be NP-hard. The objective is to minimize material waste or trim loss from all the cuttings. In the case that any intermediate roll is not entirely used for its associated order, the intermediate roll itself could turn to be a dead stock. We use the concept of universal sizes of intermediate rolls to eliminate the dead stock. A pre-defined number of universal sizes of intermediate rolls is to be used to serve all the orders. The problem is solved using Reinforcement Artificial Bee Colony algorithm with Integer Linear Programming subroutine. This proposed approach is then tested with a set of 1,055 orders and 127 different sizes of sheet papers from a paper manufacturer. The results reveal that our method outperforms other algorithms. Our method offers the total trim loss of 3.51%, compared to the trim loss reported by the industry of at least 5%. This approach not only reduces the number of partially cut rolls, but also decreases the number of the jumbo reels needed to serve all the orders. Therefore, both the inventory cost and material cost can be saved.

In a society where advances and innovations occur on a daily basis, outreach and meaningful engagement with the general public become more challenging. The amount of information produced and repackaged surpasses existing systems in place to ensure truthful and factual engagement with the public, especially with complex matters regarding health science. This perspective paper discusses the value of contextualization and optimization for creating transparent and engaging content. We reflect on the innovative Transformative Storytelling Technique as a new category creating hybrid content to guide the experience of audiences, starting with the case of informal caregivers helping individuals living with neurological conditions. Moreover, we share our perspective on the important considerations for current and future development of highly targeted content using this technique. We include reflexions around the risks and ethical principles needed in the utilization and dissemination of “guided” content for the general public.

A multi-modal search space can be defined as having multiple attraction basins – each basin has a single local optimum which is reached from all points in that basin when greedy local search is used. Optimization in multi-modal search spaces can then be viewed as a two-phase process. The first phase is exploration in which the most promising attraction basin is identified. The second phase is exploitation in which the best solution (i.e. the local optimum) within the previously identified attraction basin is attained. The goal of thresheld convergence is to improve the performance of search techniques during the first phase of exploration. The effectiveness of thresheld convergence has been demonstrated through applications to existing metaheuristics such as particle swarm optimization and differential evolution, and through the development of novel metaheuristics such as minimum population search and leaders and followers.

Lignin extracted from black liquor in chemical pulp mills can potentially replace fossil carbon feedstocks in fuels and materials, thereby increasing the economic and environmental added values of woody biomass. However, since lignin extraction reduces the electricity generation of the mill, the added value depends on the characteristics of the electricity market in which the mill operates. In this study, a model mill is exposed to two different electricity price profiles: the low and steady prices of south-central Sweden in Year 2019; and the high and volatile prices of the same region in Year 2022. For the model mill, investments in lignin extraction designed to increase pulp production are economically viable and have low levels of sensitivity to electricity price levels and price volatility. The viability of lignin extraction without increased pulp production depends on the relationship between the electricity and lignin prices. With stable electricity prices, or steady mill operation, a rule-of-thumb holds that for lignin extraction to be viable, the lignin price (€/t) must be 1.8-times the average electricity price (€/MWh) plus 40 €/t for the supply of chemicals. With volatile electricity prices and flexible operation of the recovery boiler, the mill can shift the loss in electricity sales to low-price hours, thereby saving 15–70 % of the operational costs of lignin extraction, as compared to steady operation. This effect can be further enhanced by increasing the capacity of the lignin extraction process or extending the size of the black liquor storage tank. The proposed flexibility measures allow the market-integrated pulp mill to export lignin to replace fossil carbon supplies in other sectors, while supporting the electricity system during hours with high demand and low supply.

A pilot-scale aerated lagoon was used for the aerobic treatment of pulp and paper mill effluent from September 1 to December 1, 2014. The aerated lagoon was installed at the chemistry laboratory in the Chemical Engineering Department at the Durango Institute of Technology and was fed with real pulp and paper mill effluent. The experimental work was run under various operating conditions. The operating parameters (total and volatile suspended solids (VSS) and dissolved oxygen concentration (DO)) and environmental variables (temperature, pH, COD, and BOD5 of influent water) were monitored daily. In all the experiments conducted, the aerated lagoon generated an effluent of optimal quality complying with the requirements of SEMARNAT (2003) and CONAGUA (2003). A model that explains the behavior of the system under realistic operating conditions was obtained. The model indicated an optimal DO of approximately 4 mg/L for concentrations up to 1000 mg/L, showing variations in concentrations above this value. This data indicate that the flexibility of the bacterial formulation and its ability to adapt to environmental changes play an important role in the stability of an aerated lagoon.

Aiming at the problem of the high labor intensity,low eyeleting precision and low work efficiency of conventional paper flowerpot eyeletter with manual operation,an automatic eyeletter is proposed. And then,through building the mathematic model,the analysis and optimization of this mechanical structure is carried out by using mathematic analytic software. At last,two cam contour curves meeting the requirement of cam mechanism motion law well is designed. As a result,a strong theoretical basis for further study and object production in the future is provided.

Optimization of the ultrafiltration (UF) process to remove colloidal substances from a paper mill’s treated effluent was investigated in this study. The effects of four operating parameters in a UF system (transmembrane pressure (TMP), cross-flow velocity (CFV), temperature and molecular weight cut-off (MWCO)) on the average permeate flux (Jv), organic matter chemical oxygen demand (COD) rejection rate and the cumulative flux decline (SFD), was investigated by robust experimental design using the Taguchi method. Analysis of variance (ANOVA) for an L9 orthogonal array were used to determine the significance of the individual factors, that is to say, to determine which factor has more and which less influence over the UF response variables. Analysis of the percentage contribution (P%) indicated that the TMP and MWCO have the greatest contribution to the average permeate flux and SFD. In the case of the COD rejection rate, the results showed that MWCO has the highest contribution followed by CFV. The Taguchi method and the utility concept were employed to optimize the multiple response variables. The optimal conditions were found to be 2.0 bar of transmembrane pressure, 1.041 m/s of the cross-flow velocity, 15 °C of the temperature, and 100 kDa MWCO. The validation experiments under the optimal conditions achieved Jv, COD rejection rate and SFD results of 81.15 L·m−2·h−1, 43.90% and 6.01, respectively. Additionally, SST and turbidity decreased by about 99% and 99.5%, respectively, and reduction in particle size from around 458–1281 nm to 12.71–24.36 nm was achieved. The field-emission scanning electron microscopy images under optimal conditions showed that membrane fouling takes place at the highest rate in the first 30 min of UF. The results demonstrate the validity of the approach of using the Taguchi method and utility concept to obtain the optimal membrane conditions for the wastewater treatment using a reduced number of experiments.

Wavelet neural networks (WNNs) were used to investigate the influence of operational variables in the soda-ethanol pulping of oil palm fronds (viz. NaOH concentration (10-30%), ethanol concentration (15-75%), cooking temperature (150-190 ºC), and time (60-180 min)) on the resulting pulp and paper properties (viz. screened yield, kappa number, tensile index, and tear index). Performance assessments demonstrated the predictive capability of WNNs, in that the experimental results of the dependent variables with error less than 6% were reproduced, while satisfactory R-squared values were obtained. It thus corroborated the good fit of the WNNs model for simulating the soda-ethanol pulping process for oil palm fronds.

A multi-modal search space can be defined as having multiple attraction basins – each basin has a single local optimum which is reached from all points in that basin when greedy local search is used. Optimization in multi-modal search spaces can then be viewed as a two-phase process. The first phase is exploration in which the most promising attraction basin is identified. The second phase is exploitation in which the best solution (i.e. the local optimum) within the previously identified attraction basin is attained. The goal of thresheld convergence is to improve the performance of search techniques during the first phase of exploration. The effectiveness of thresheld convergence has been demonstrated through applications to existing metaheuristics such as particle swarm optimization and differential evolution, and through the development of novel metaheuristics such as minimum population search and leaders and followers.

A multi-modal search space can be defined as having multiple attraction basins – each basin has a single local optimum which is reached from all points in that basin when greedy local search is used. Optimization in multi-modal search spaces can then be viewed as a two-phase process. The first phase is exploration in which the most promising attraction basin is identified. The second phase is exploitation in which the best solution (i.e. the local optimum) within the previously identified attraction basin is attained. The goal of thresheld convergence is to improve the performance of search techniques during the first phase of exploration. The effectiveness of thresheld convergence has been demonstrated through applications to existing metaheuristics such as particle swarm optimization and differential evolution, and through the development of novel metaheuristics such as minimum population search and leaders and followers.

A multi-modal search space can be defined as having multiple attraction basins – each basin has a single local optimum which is reached from all points in that basin when greedy local search is used. Optimization in multi-modal search spaces can then be viewed as a two-phase process. The first phase is exploration in which the most promising attraction basin is identified. The second phase is exploitation in which the best solution (i.e. the local optimum) within the previously identified attraction basin is attained. The goal of thresheld convergence is to improve the performance of search techniques during the first phase of exploration. The effectiveness of thresheld convergence has been demonstrated through applications to existing metaheuristics such as particle swarm optimization and differential evolution, and through the development of novel metaheuristics such as minimum population search and leaders and followers.

At the preliminary stage of aircraft design, it is usually required to solve the problem of insufficient initial data for applying traditional algoristic-type mathematical programming models. In many cases, numerous input parameters cannot be accurately specified at the time of making design decisions. If the inaccuracy of parameters is not taken into account, the actual values of target functions may differ significantly from the calculated ones when solving optimization problems. In this regard, the issue of current interest is the development of algorithms to improve the reliability of design decisions under conditions of epistemological uncertainty when experts engage in the formation of initial data. The paper considers the problem of selecting aerodynamic characteristics and engine parameters of maneuverable aircraft under conditions of uncertainty associated with inaccuracy of expert data. The applied problem under consideration is further complicated by the necessity to include “black box” models in the algorithms being developed. The paper proposes algorithms that apply the uncertainty theory together with “black box” models that implement optimization calculation techniques derived from previous engineering practice of aircraft design. Using these algorithms, experts are able to set uncertain parameters whereby the lack of data is factored in using uncertainty distribution functions. In cases of monotonicity of uncertain parameters in target functions, application of the uncertainty theory provides a significant reduction in computational costs compared to the method of statistical modeling in optimization calculations. The paper presents the results of computational studies of the developed algorithms. A genetic algorithm (mathematical optimization solver) is used to optimize the search for design solutions. Pareto frontiers are obtained for different confidence levels enabling to make design decisions including values of aerodynamic characteristics and engine parameters of maneuverable aircraft.

As optimization problems grow increasingly complex, characterized by their intricate difficulty, larger-scale, and diverse constraints, swarm intelligence optimization algorithms have emerged as an effective solution for addressing these multifaceted challenges. Among these, the marine predators algorithm, a recent innovation in intelligent optimization algorithms, has demonstrated remarkable efficacy in solving optimization issues. However, its application to complex CEC test function sets and engineering constraint problems reveals several limitations, including limited adaptive ability, low optimization accuracy, and high local shackle probability. This paper proposes an enhanced version of the marine predators algorithm designed to overcome its inherent shortcomings. The enhancement begins with the integration of a learning automata guided teaching–learning search mechanism during the marine memory stage. This adjustment aims to strike a better balance between exploration and exploitation across different iteration periods. Subsequently, the introduction of a logarithmic spiral exploration mechanism phase strengthens the algorithm’s ability to conduct nuanced searchers around the optimal solution, thereby improving convergence accuracy. Finally, an improved adaptive relative reflection strategy is added at the end of each iteration to enhance the algorithm’s capability to escape local optima and reduce the risk of local shackling. The optimization performance of this refined algorithm is evaluated through parameter sensitivity analysis, determining the optimal parameter values. To validate its effectiveness, the improved algorithm undergoes testing against six benchmark algorithms, including the basic marine predators algorithm and its variants, as well as other improved algorithms and those recognized with awards in the CEC2017 test suite across 100 dimensions. The evaluation focuses on optimization accuracy, the Wilcoxon rank sum test, and boxplot analysis. The test results indicate that the improved algorithm proposed in this paper outperforms the other six benchmark algorithms in optimization precision, convergence rate, and solution stability, particularly when solving complex functions in high-dimensional (100 dimensions) spaces. Furthermore, the applicability and superior performance of the improved algorithm are demonstrated through comparative analysis with four established algorithms on challenging engineering design optimization problems. These include welded beam design, process synthesis, heat exchanger network design, and design optimization of industrial refrigeration systems. The findings unequivocally showcase the enhanced algorithm’s exceptional ability to solve various engineering constraint problems effectively.

A novel mathematical modeling approach for the Triple-Diode Model (TDM) and its parameter estimation is introduced in this paper. For the first time in the literature, this paper presents two analytical solutions for calculating the current in TDM solar cells using the Lambert W function. Additionally, a new metaheuristic algorithm, the Adaptive Evaporation Rate Water Cycle Algorithm (Adaptive ERWCA), is proposed for estimating TDM solar cell parameters. The proposed algorithm is tested using both the new analytical solutions and a well-known iterative approach for the current-voltage characteristics of the TDM solar cells model. Extensive testing is conducted on three well-known solar cells/modules and a panel under various weather conditions. The results demonstrate that the proposed analytical expressions for the current-voltage characteristics are accurate and highly effective for representing TDM solar cell current. The accuracy of the parameter estimates obtained with the application of the proposed algorithm, considering both analytical solutions and the iterative approach, is compared with existing literature results. It is shown that the proposed algorithm outperforms existing algorithms in solar cell parameter estimation across both the analytical and iterative approaches for current-voltage characteristics. Remarkably, for the RTC France solar cell, the SOLAREX MSX 60 solar module, and the PHOTOWATT-PWP 201 solar module, the obtained Root Mean Square Errors (RMSE) are 0.00075141, 0.01157153, and 0.0010578, respectively. These results are currently the best reported in the literature.

This paper discusses the application of nonlinear regression to forecast and optimize the operation of catalytic cracking units under conditions of fuzzy information. Catalytic cracking is a crucial process in oil refining that produces high-quality gasoline and other light hydrocarbon products. However, the complexity of the process and the uncertainty of initial data complicate the modeling and optimization of plant operations. To address this issue, a nonlinear regression method is proposed that accommodates the fuzziness of input and output parameters described by linguistic variables. The methodology includes the collection and formalization of expert knowledge, the construction of fuzzy models, and their integration into the process control system. Forecasting is performed by creating regression models that describe the relationships between operational parameters and product quality characteristics. The paper presents a procedure for developing and applying nonlinear regression models, describes algorithms for synthesizing linguistic models, and provides examples of their use to optimize the operation of catalytic cracking units. The modeling results demonstrate the high adequacy and accuracy of the proposed method, as well as its advantages over traditional approaches in conditions of uncertainty and data scarcity. The scientific novelty of the research lies in the development and testing of advanced nonlinear regression models adapted for analyzing and optimizing catalytic cracking processes based on fuzzy data. These methods take into account the specificity and uncertainty of process data, improving the accuracy and reliability of forecasts, which facilitates more effective management of production processes in the petrochemical industry. The main reason for conducting this study is the need to improve the control of oil refining processes, particularly catalytic cracking, which plays an important role in producing high-quality gasoline. The complexity of this process and the presence of fuzzy information caused by fuzzy initial data require the development of new modeling and optimization methods. Existing traditional models based on deterministic methods are often insufficient under uncertainty. This leads to a decrease in the accuracy of process control, which can negatively affect the quality of the final product and production efficiency. The use of nonlinear regression in combination with fuzzy logic is a more flexible and adaptive approach that allows you to take into account the fuzziness and uncertainty of data and use expert knowledge to build models that match the actual operating conditions of the units. Thus, this study aims to solve the key problems associated with data uncertainty and the complexity of the catalytic cracking process, which will improve the accuracy of forecasting and optimization of the units. The main contribution is creating a model that uses nonlinear regression methods in combination with fuzzy logic. This allows uncertainty in input data (such as reactor temperature or pressure) to be effectively considered and processed to improve gasoline and other product yield forecasts. It is shown that using nonlinear regression combined with fuzzy logic significantly improves the management of technological processes, increases the output and quality of products, and reduces production costs. The conclusion of the paper discusses the prospects for further development of the methodology and its application to solve similar tasks in other areas of chemical technology.

Abstract In the realm of green communications, the focus is on achieving high spectrum efficiency and low energy consumption. This paper addresses the crucial goal of reducing energy usage in green cognitive radio networks (CRNs) during communication between secondary users (SUs) and primary users (PUs). This paper proposed an energy consumption optimization model (ECOM) for green CRN utilizing collaborative spectrum sensing thereby minimizing the environmental impact and prolonging the operational lifetime of devices. The collaborative spectrum sensing proved its role in optimizing the energy consumption in the green CRN. An energy-efficient scheduling algorithm is implemented in ECOM, in which the SUs can be scheduled to perform their sensing process in a time-division manner to reduce energy consumption. Applied collaborative spectrum sensing serves as a valuable resource for researchers, network operators, and policymakers seeking to balance the increasing demand for wireless communication services with the imperative of sustainability. The simulation results and mathematical proof emphasize that ECOM demonstrates reduced energy usage and increased average effective throughput when compared to other recent models.

In the dynamic field of Customer Retention Prediction (CRP), strategic marketing and promotion efforts targeting specific customers are crucial. Understanding customer behavior and identifying churn indicators are vital for devising effective retention strategies. However, identifying customers likely to terminate services presents a challenge, leading to data imbalance issues. Existing CRP studies using Machine Learning (ML) techniques and data imbalance methods face problems such as overfitting and computational complexity. Similarly, recent CRP studies employing Deep Learning (DL) approaches rely on data sampling techniques, which can result in overfitting and a lack of cost sensitivity. Additionally, DL approaches struggle with slow convergence and get stuck in local minima. This paper introduces an effective hybrid of Deep Learning (DL) classifiers focusing on cost-metric integration to address data imbalance issues and period-shift Cosine Annealing Learning Rate (ps-CALR) to accelerate model training, ultimately enhancing performance. Three Telecom datasets, namely IBM, Iranian, and Orange, were used to assess the model performance. Empirical findings show that the hybrid DL classifiers significantly improved CRP over conventional ML. This paper contributes methodological advancements and practical insights for effective customer retention in the telecom industry.

Abstract The aim of the present paper is to optimize the values of different parameters related to the image denoising algorithm involving Caputo Fabrizio fractional integral operator of non-singular type with the Mittag-Leffler function in generalized form. The algorithm aims to find the coefficients of a kernel to remove the noise from images. The optimization of kernel coefficients are done on the basis of different numerical parameters like Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structure Similarity Index measure (SSIM) and Image Enhancement Factor (IEF). The performance of the proposed algorithm is investigated through above-mentioned numeric parameters and visual perception with the other prevailed algorithms. Experimental results demonstrate that the proposed optimized kernel based on generalized fractional operator performs favorably compared to state of the art methods. The uniqueness of the paper is to highlight the optimized values of performance parameters for different values of fractional order. The novelty of the presented work lies in the development of a kernel utilizing coefficients from a fractional integral operator, specifically involving the Mittag-Leffler function in a more generalized form.

The article discusses an approach to analyzing processes in industrial reactors using advanced systems with feedback. The authors describe the system preparation and image reconstruction algorithms of ultrasonic tomographs (USTs) that have applications in industry. The research presented in this paper focuses on developing effective strategies for controlling and monitoring chemical reaction processes in reactors. Using advanced data processing techniques, the authors propose systems with feedback that enable accurate analysis and optimization of reactor operating conditions. A key aspect is ultrasonic tomographic imaging, which provides precise data on the state of the process in the reactor. In summary, the paper presents a state-of-the-art approach to analyzing chemical reaction processes in industrial reactors using advanced feedback systems and ultrasonic imaging technologies. The proposed solutions are essential for improving efficiency and process control in the chemical industry.

High theoretical energy density endows lithium–sulfur batteries to be a promising candidate of the secondary batteries. Numerous studies have been implemented relying on exploring efficient host materials or separator modifying layers to solve the problematic shuttling and insufficient conversion of soluble polysulfides, whereas few studies have focused on the modification of the cathode collector. Herein, a high-performance sulfur cathode is manufactured with carbon paper/MnO2 as the cathode collector and liquid lithium polysulfides as the electrode material. The interface of carbon paper/MnO2 is proposed to afford fast electronic transport, strong chemical adsorption, and effective electrocatalysis to confine the diffusion of lithium polysulfides and facilitate their conversion during the charge/discharge process. More importantly, with no conductive additives and binders assisting, the gravimetric energy density of the sulfur cathode could be largely improved. Specifically, lithium–sulfur batteries using carbon paper/MnO2 as a cathode collector could stably circulate for 200 cycles at 0.2 C with a capacity of 664 mAh g−1, which is higher than that of carbon paper as a cathode collector (486 mAh g−1). This work may provide a new perspective to enhance the electrochemical performance of lithium–sulfur batteries by optimizing the cathode collector.

Embedded devices are used in many domains, including healthcare, industries, and home automation, all of which entail significant workloads. As a direct consequence, the embedded devices require retrieval and processing of data of large volume, which occupy large memory space in the embedded devices. Compression along with workload characterization is an effective technique for minimizing memory usage and to the improvement of endurance of memory in such devices. This paper investigates the embedded workload characterization using Extreme Learning Machine (ELM) that is particularly suitable for large-scale datasets and real-time applications. Though ELM is single-layer feedforward network, its input weight randomization has a considerable effect on the accuracy of the classification. In this paper, the authors have proposed a hybrid algorithm for the optimization of the randomization of ELM. The Particle Swarm Optimizer (PSO), the Genetic Algorithm (GA), the Ant Colony Optimizer (ACO), and the Whale Optimization Algorithm (WOA) were used in the optimization of the classification process in ELM to increase the accuracy of the results. The input data must be categorized based on the energy consumed by each workload to proceed with further processing according to the system requirements. This paper explores and analyses the performance of hybridized ELM-Genetic algorithm (ELM-GA), ELM-Particle Swarm Optimization (PSO), ELM-Ant Colony Optimizer (ELM-ACO), and ELM- Whale Optimization Algorithm (ELM-WOA) optimization algorithms. The embedded benchmarks Internet of Medical Things (IoMT), Mi Benchmark (MiBench), and Embedded Microprocessor Benchmark Consortium (EEMBC) have been used as a dataset for classification in this paper. The extensive experimental study shows that the hybridized ELM-WOA provides a 98.5 % classification accuracy, 98.1 % specificity, and 98 % sensitivity compared to the other optimization methods discussed in this paper.

Globally, effective and efficient healthcare is critical to the wellbeing and standard of living of any society. Unfortunately, several distant communities far from the national grid do not have access to reliable power supply, owing to economic, environmental, and technical challenges. Furthermore, unreliable, unavailable, and uneconomical power supply to these communities contributes significantly to the delivery of substandard or absence of qualitative healthcare services, resulting in higher mortality rates and associated difficulty in attracting qualified healthcare workers to the affected communities. Given these circumstances, this paper aims to conduct a comprehensive review of the status of renewable energy available to rural healthcare clinics around the globe, emphasizing its potential, analysis, procedures, modeling techniques, and case studies. In this light, several renewable energy modeling techniques were reviewed to examine the optimum power supply to the referenced healthcare centers in remote communities. To this end, analytical techniques and standard indices for reliable power supply to the isolated healthcare centers are suggested. Specifically, different battery storage systems that are suitable for rural healthcare systems are examined, and the most economical and realistic procedure for the maintenance of microgrid power systems for sustainable healthcare delivery is defined. Finally, this paper will serve as a valuable resource for policymakers, researchers, and experts in rural power supply to remote healthcare centers globally.

Purpose – The purpose of this paper is to eliminate the fluctuations in train arrival and departure times caused by skewed distributions in interval operation times. These fluctuations arise from random origin and process factors during interval operations and can accumulate over multiple intervals. The aim is to enhance the robustness of high-speed rail station arrival and departure track utilization schemes. Design/methodology/approach – To achieve this objective, the paper simulates actual train operations, incorporating the fluctuations in interval operation times into the utilization of arrival and departure tracks at the station. The Monte Carlo simulation method is adopted to solve this problem. This approach transforms a nonlinear model, which includes constraints from probability distribution functions and is difficult to solve directly, into a linear programming model that is easier to handle. The method then linearly weights two objectives to optimize the solution. Findings – Through the application of Monte Carlo simulation, the study successfully converts the complex nonlinear model with probability distribution function constraints into a manageable linear programming model. By continuously adjusting the weighting coefficients of the linear objectives, the method is able to optimize the Pareto solution. Notably, this approach does not require extensive scene data to obtain a satisfactory Pareto solution set. Originality/value – The paper contributes to the field by introducing a novel method for optimizing high-speed rail station arrival and departure track utilization in the presence of fluctuations in interval operation times. The use of Monte Carlo simulation to transform the problem into a tractable linear programming model represents a significant advancement. Furthermore, the method’s ability to produce satisfactory Pareto solutions without relying on extensive data sets adds to its practical value and applicability in real-world scenarios.

Abstract Cameroon is currently grappling with a significant energy crisis, which is adversely affecting its economy due to cost, reliability, and availability constraints within the power infrastructure. While electrochemical storage presents a potential remedy, its implementation faces hurdles like high costs and technical limitations. Conversely, generator-based systems, although a viable alternative, bring their own set of issues such as noise pollution and demanding maintenance requirements. This paper meticulously assesses a novel hybrid energy system specifically engineered to meet the diverse energy needs of Douala, Cameroon. By employing advanced simulation techniques, especially the Hybrid Optimization Model for Electric Renewable (HOMER) Pro program, the study carefully examines the intricacies of load demands across distinct consumer categories while accommodating varied pricing models. The paper offers a detailed analysis of the proposed grid-connected PV/Diesel/Generator system, aiming to gauge its performance, economic feasibility, and reliability in ensuring uninterrupted energy supply. Notably, the study unveils significant potential for cost reduction per kilowatt-hour, indicating promising updated rates of $0.07/kW, $0.08/kW, and $0.06/kW for low, medium, and high usage groups, respectively. Furthermore, the research underscores the importance of overcoming operational challenges and constraints such as temperature fluctuations, equipment costs, and regulatory compliance. It also acknowledges the impact of operational nuances like maintenance and grid integration on system efficiency. As the world progresses towards renewable energy adoption and hybrid systems, this investigation lays a strong foundation for future advancements in renewable energy integration and energy management strategies. It strives to create a sustainable energy ecosystem in Cameroon and beyond, where hybrid energy systems play a pivotal role in mitigating power deficiencies and supporting sustainable development.

Abstract The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Despite the progress, reinforcement learning still faces hurdles when it comes to spatial resource allocation. There remains a gap in its ability to fully grasp the diversity and intricacy of real-world resources. The environmental models used in reinforcement learning may not always capture the spatial dynamics accurately. Moreover, in situations laden with strict and numerous constraints, reinforcement learning can sometimes fall short in offering feasible strategies. Consequently, this paper is dedicated to summarizing and reviewing current theoretical approaches and practical research that utilize reinforcement learning to address issues pertaining to spatial resource allocation. In addition, the paper accentuates several unresolved challenges that urgently necessitate future focus and exploration within this realm and proposes viable approaches for these challenges. This research furnishes valuable insights that may assist scholars in gaining a more nuanced understanding of the problems, opportunities, and potential directions concerning the application of reinforcement learning in spatial resource allocation.

Abstract A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy.

Abstract As the proportion of renewable energy penetrated in power systems further increases, the optimal operation faces a large obstacle to satisfying various conflict interests simultaneously. This paper first formulates a many‐objective optimal operation problem of the new power system considering a comprehensive set of economic, environmental, and reliable objectives. To figure out this problem efficiently, this paper proposes a probability confidence correlation analysis method (PCCA) utilizing a t‐distributed stochastic neighbor embedding (T‐SNE) standard to greatly preserve the distribution information and separability of the objectives. Consequently, the objectives can be aggregated and the objective dimension is reduced based on the important sorting and correlation of the objectives. Based on the outcome of the T‐SNE, the adopted optimal operation problem with low dimensional objectives is solved by a multiple producer group search optimizer (GSOMP) with less computation burden, to obtain the Pareto‐optimal solution set. Simulation studies are conducted on IEEE 30‐bus, 39‐bus and 57‐bus systems to investigate the performance of the proposed PCCA in terms of efficiency and accuracy, making comparisons of the existing many‐objective optimization algorithms.

BackgroundThe target of this paper is to improve the performance of the microbial electrolysis cell (MEC). The performance of MEC including bio-hydrogen production and energy recovery is depending on the values of three controlling parameters including buffer concentration, dilution factor, and applied voltage.ProblemTherefore, defining the optimal values of three controlling parameters is the challenge of the work.MethodologyIn this paper the artificial gorilla troops optimization has been combined with and ANFIS modelling to increase the bio-hydrogen production from MEC. At first, using measured data, a model is created to simulate the MEC in terms of three controlling parameters. Then, for first time, an artificial gorilla troops optimization (AGTO) has been used to determine the optimal values of buffer concentration, dilution factor, and applied voltage to boost simultaneously bio-hydrogen production and energy recovery of MEC. To demonstrate the superiority of integration between ANFIS modelling and AGTO, the obtained results are compared with RSM methodology, and artificial neural network integrated with particle swarm optimization.FindingsFor hydrogen yield model, the RMSE lowered from 67.5 using RSM to 5.562 using ANFIS (decreased by 91.7%) as compared to RSM. The R-square for prediction rises from 0.94 (using RSM) to 0.99 (using ANFIS) by about 5.32%. For the ANFIS model of energy recovery, the RMSE decreased from 31.7 to 2.83 utilising ANFIS, a decrease of 91%. The R-square for prediction rises from 0.95 (using RSM) to 0.986 (using ANFIS) by about 3.8%. Compared with measured data, the integration between ANFIS and AGTO succeed to increase the hydrogen yield from 576.3 mL/g-VS to 843.32 mL/g-VS. in sum, the total performance of the MEC has been increased by 34.74%, 29.9% and 24.38% respectively compared to measured data, RSM and ANN-PSO.

Forecasting typhoon waves during typhoons is crucial. In this paper, the numerical wave model SWAN was enhanced through integration with two machine learning methods: the Back Propagation Neural Network and Random Forest. This integration facilitated the development of two distinct models, namely SWAN-BP and SWAN-Tree. Through correlation analysis, key input features were identified for the machine learning models. The forecasts from the SWAN model were subsequently utilized as inputs to enhance further wave prediction. These hybrid models were validated using data from Typhoon Doksuri (2023) and Typhoon Nesat (2017). The results indicated significant improvements in predicting typhoon-induced wave heights with both the SWAN-BP and SWAN-Tree models compared to the original SWAN model. Specifically, the SWAN-BP model demonstrated a 33% improvement in accuracy for the Typhoon Doksuri, whereas the SWAN-Tree model exhibited a 24% improvement. For Typhoon Nesat, the accuracy improvements were 23% for the SWAN-BP model and 21% for the SWAN-Tree model. These findings demonstrate that integrating wave numerical models with machine learning techniques can significantly enhance the predictive accuracy of numerical models. This approach offers a cost-effective means to improve the existing wave forecasting database. Traditionally, the direct use of meteorological and oceanographic data for typhoon wave prediction might be compromised by biases inherent in the numerical wave models. However, the SWAN-BP and SWAN-Tree models effectively reduce these biases, thereby providing more accurate and robust predictions. In conclusion, this paper enhances the predictive accuracy of the SWAN model and establishes a crucial foundation for more precise typhoon wave forecasting through the application of machine learning techniques.

This paper discusses the growing influence of renewable energy and distributed generation, emphasizing the need for smart control systems to maximize benefits and optimize network performance. However, the absence of a standardized evaluation framework makes it challenging to compare different control systems effectively, especially in large-scale hybrid networks with both AC and DC components. While hybrid energy systems show promise for greener and more reliable power networks, they introduce complexity to control methods. Researchers are exploring innovative approaches, including linear and nonlinear techniques, to leverage renewable energy sources effectively in hybrid grids. The paper provides an overview of heuristic evolutionary optimization methods for microgrids (AC, DC, and hybrid AC-DC), highlighting promising techniques such as Crow Search Algorithm, Modified Crow Search Algorithm, Particle Swarm Optimization, and Genetic Algorithms. Comparative analysis suggests that the Modified Crow Search Algorithm performs best across various evaluation criteria, indicating its potential for optimizing microgrids effectively.

The rapid development of the Internet of Things (IoT) has extensively promoted the development of Wireless Sensor Networks (WSNs), an essential technology for series displaying perception and data collected from the physical world. In densely distributed areas, sensor nodes are unevenly distributed, which leads to the network coverage build-up and the consequent efficiency and effectiveness of WSNs. To address this issue, this paper proposes a new method for WSN coverage optimization based on the Reptile Search Algorithm (RSA). In the past, the Reptile Search algorithm has been used to solve optimization problems, which means it can improve different processes. However, the RSA needs to track the trajectory of optimal individuals in each iteration, which will ignore non-optimal individuals' bioeconomic characteristics. Therefore, the paper introduces a distribution estimation strategy into the RSA framework, which can fully mine all the positional information hidden in the entire population. We selected several functions as optimization test benchmark functions to evaluate the feasibility of the proposed method. This paper compares the proposed improved RSA with the standard RSA and some traditional optimization algorithms. The result has been calculated through a series of experiments on network coverage optimization, and the change of parameters also determines the effect of the RSA in the optimization of network coverage. The simulated results of the three similar network coverage optimization experiments show that the improved RSA can be used efficiently within different scenarios.

The paper describes possibilities of increasing the energy efficiency and reducing the takeoff weight of unmanned medium-heavy vertical takeoff and landing aerial vehicles of the airplane type. The authors propose a new hybrid type of unmanned aerial vehicle with a hybrid propulsion system, its aerodynamic design, method of realization of vertical takeoff/landing and cruising mode of flight which make it possible to reduce the takeoff weight of the aircraft, the weight of the basic propulsion system and the mass growth factor in comparison with the existing unmanned aerial vehicle of similar class, made according to the previously known technical solutions. The authors propose a methodology for optimizing the parameters of the configuration of the unmanned aerial vehicle considering the peculiarities of the implementation of vertical takeoff. The paper presents calculations of characteristics of vertical takeoff and landing unmanned aerial vehicles of existing types and the new hybrid type. The authors give quantitative estimates of improving unmanned aerial vehicle characteristics due to the new proposed technical solutions.

Due to the rapid population and economic growth, the demand for water has increased. In addition, the natural resources are limited and degrade because of several factors such as the climate change. These challenges lead to reduce the ability of providing water at the required quantity and quality. One of solutions to maintain the sustainability of water supply from different sources is reuse of wastewater. For this aim, it is crucial to optimize wastewater systems. This research paper aims to describe different modelling possibilities and optimization methods for various components of integrated urban wastewater systems. The main conclusion of this research paper is the lack of study of optimum design and operation of urban wastewater systems in a holistic method. Moreover, most of previous studies on integrated wastewater management have been conducted on combined sewer systems.

This paper presents the results of studies on the transformation of geraniol (GA) in the presence of the natural mineral bentonite. The paper determines the influence of temperature, catalyst content, and reaction time on the course of the process. In order to determine the most favorable process conditions, the catalytic tests were carried out without solvent and under atmospheric pressure. Three functions were chosen to determine the most favorable process conditions: GA conversion and the selectivities of the main products: linalool – LO and beta-pinene – BP. In addition, the paper optimize GA transformation process based on response surface methodology (RSM). The impact of the most relevant process indicators was presented. For all factors of the method, their effects on all primary parameters were determined in the form of second-degree polynomials, and such process conditions were determined to achieve their maximum.

The paper considers the problems of macroparameter control in a linear dynamic system. In the authors’ previous paper, such problems in controlling nonlinear systems were investigated. Variations of the considered functionals and necessary optimality conditions were obtained. This paper focuses on the trajectory beam control in the linear case. In this case, the problem of beam control can be reduced to the problem of the individual trajectory control. The developed approach may be of interest in the study of charged particle dynamics in accelerating and focusing structures, as well as in the construction of the velocity field in automatic image processing.

This paper addresses the often overlooked, yet critical, aspect of designing and optimizing the surface pipeline network for the transportation of geothermal fluids from the wellheads to the delivery point, such as greenhouses, food drying plants, or fish farming units. While research on the geothermal industry predominately focuses on the reservoir and well engineering aspects of exploitation, insufficient attention has been given to the design of the pipeline network, leading to improper design and significant, yet avoidable, energy losses. Thus, this paper presents a comprehensive methodology for modeling and simulating geothermal fluid flow within the pipeline network by fully considering all hydraulic (friction, viscous flow, and gravity effects) and thermal (open air and underground pipeline heat loss) phenomena. These two aspects are handled simultaneously by setting up and solving the coupled set of the governing (differential) equations. We also demonstrate the difficulties that arise when attempting the solution of the mathematical problem, such as potential instability or lack of convergence. Finally, a fully detailed study of the real-world geothermal production system is presented utilizing the developed methodology to optimize the design and operation conditions of the system. By integrating debottlenecking strategies into the analysis, this approach not only maximizes power output, but also identifies and mitigates constraints within the system, ensuring efficient operation and performance increase.

RNA-binding proteins (RBPs) play an important role in regulating biological processes, such as gene regulation. Understanding their behaviors, for example, their binding site, can be helpful in understanding RBP-related diseases. Studies have focused on predicting RNA binding by means of machine learning algorithms including deep convolutional neural network models. One of the integral parts of modeling deep learning is achieving optimal hyperparameter tuning and minimizing a loss function using optimization algorithms. In this paper, we investigate the role of optimization in the RBP classification problem using the CLIP-Seq 21 dataset. Three optimization methods are employed on the RNA–protein binding CNN prediction model; namely, grid search, random search, and Bayesian optimizer. The empirical results show an AUC of 94.42%, 93.78%, 93.23% and 92.68% on the ELAVL1C, ELAVL1B, ELAVL1A, and HNRNPC datasets, respectively, and a mean AUC of 85.30 on 24 datasets. This paper’s findings provide evidence on the role of optimizers in improving the performance of RNA–protein binding prediction.

The removal efficiency of Ni (II) ions from aqueous solution using regenerated cellulose was studied. The effects of solution pH, time, initial metal concentration and adsorbent dosage on metal adsorption efficiency were investigated. Response surface methodology (RSM) was applied to predict the behavior of the system. Based on the developed model, pH was found to be the main factor which had the highest influence on Ni (II) removal efficiency. It was observed that an increase in the pH from 3.75 to 7.25 resulted in a 51.6% increase in Ni (II) removal efficiency. Additionally, the time and adsorbent dosage were found to have positive influence on metal removal efficiency while Ni (II) removal efficiency reduced with initial metal concentration. The optimization of the integral main factors was performed. The suggested optimum values for pH, time, initial metal concentration, adsorbent dosage and Ni (II) removal efficiency were 6.4, 175.27 min, 32.5 ppm, 0.4 g and 98%, respectively.

ABSTRACTDue to the numerous infrastructure development projects taking place in developing nations, large amounts of construction and demolition waste (C&DW) are unavoidable. Most C&DW management strategies, however, involve ‘isolated’ operations with high overhead. Waste data transfers are difficult to watch over. Important information needed for decision-making is, therefore, challenging to obtain. This paper’s goal is to discuss C&DW management from an integrative standpoint. Using GIS functions and a fuzzy analytical hierarchy approach, the best sites for waste management facilities can be found. A mixed integer optimization model is created to identify optimal location and allocation of waste management facilities. The objective is to reduce waste collection costs under different operational scenarios. By incorporating ‘constructs elements’ and ‘connected elements’ that are linked, isolated C&DW management ventures are transformed into successful C&DW management ventures. The effectiveness and efficiency of waste management strategies are evaluated using GIS analysis. A dashboard for tracking waste data flows is created to show how stakeholder decision making can be made easier. The proposed integrative approach differs from other approaches since it re-purposes location-allocation models and GIS data to optimize ‘isolated’ C&DW management practices. In addition, the notion of ‘construct elements’ that can be time-shared is outlined. Moreover, the notion of ‘connected elements’ that are dynamic, time-limited, and yet transient enough to provide the necessary interconnectivity is developed to support and complement the seamless function of a network of C&DW management facilities. The usefulness of the integrative approach is illustrated by a case study. Analysis of case studies demonstrates that waste management strategies in developing countries can be made more effective and waste collection efficiency can be increased. According to comparative studies, updating current waste management facilities can cut operating costs by 26%, while implementing an integrative strategy can cut costs by 52%. The discussions in this paper can help towns reevaluate C&DW management and put useful, unified C&DW management techniques into practice.

The aim of the paper work is to optimize the sex ratio in a selection plan, according to model developed by King (1961), which will be proposed to be applied for prolificacy improvement in Prolific Line Palas. The method used in this paper work is modeling, which exist in the most animal breeding scientifically papers. After the simulations, we observed that the most convenient variant was that which prefigure use of 13 rams on reproduction activity. This variant offer a genetic gain per generation by 0.47497 additive standard deviations.

The aim of the paper work is to optimize the population structure in a selection plan, according to model developed by King (1961), which will be proposed to be applied for prolificacy improvement in Prolific Line Palas. The method used in this paper work is modeling, which exist in the most animal breeding scientifically papers. After the simulations, we observed that the most convenient variant was that which prefigure a weight of two groups by 70% (A) and 30% (B). This variant offer a genetic gain per generation by 0.452 additive standard deviations.

Abstract Tricubic interpolation, originally introduced by Lekien and Marsden (Int J Numer Methods Eng. 2005; 63(3): 455–471), has been a cornerstone in the field of interpolation, providing C1 continuous interpolations within three‐dimensional spaces. However, real‐world applications often demand higher levels of smoothness within M‐dimensional spaces. This paper introduces LocalPoly interpolation, a novel generalization of tricubic interpolation that extends to Cn continuity and M dimensions. A key property is the use of solely local data for interpolation, allowing for on‐demand computation of interpolation polynomials, which is particularly advantageous in scenarios where a minor subset of the space is of interest. We rigorously prove the Cn continuity achieved by the LocalPoly interpolation method; the proof features a numerically exact method for computing polynomial coefficients. The enhanced continuity is of great relevance in optimization algorithms, where efficient convergence often relies on the availability of C2 information. The paper explores the use of LocalPoly interpolation applied to a squared distance field in ℝ3, offering insights into computational efficiency and practical implications. It also discusses future research directions to address the method's limitations in terms of dimensionality, making it a valuable addition to the toolbox of interpolation methods for various scientific and engineering applications.

The optimal power flow (OPF) problem remains a popular and challenging work in optimizing power systems. Although researchers have suggested many optimization algorithms to solve this problem in the literature, their comparison studies lack fairness and transparency. As these studies increase in number, they deviate from a standard test system, considering a common security and technical constraints., there is a growing trend away from a standard test system. Different studies used different search ranges for the same decision and constraint parameters, different than the standard ranges suggested by IEEE systems. This caused many unfair comparisons in literature. Furthermore, these studies are generally not transparent enough so that their results cannot be verified. This has resulted in numerous infeasible solutions in the literature, violating the limits of constraint parameters. The recent incorporating of renewable energy sources in OPF studies has made this situation more complicated. Sorting through the literature and identifying those OPF applications having exactly the same test conditions is a challenging process. The main contribution is this paper adapts the modified effective butterfly algorithm (MEBO) to solve OPF problem under the common parameter constraints and sufficient transparency. The focus is on a transparent comparison with works in the literature with the same constraint values. This paper compares the performance of the proposed algorithm with other state-of-the-art algorithms in the literature, focusing on the wind energy and without wind energy IEEE 30-bus and IEEE 57-bus systems and the most commonly used constraints. The results demonstrate the efficiency and superiority of the proposed algorithm. For instance, in the 30-bus test system, compared to the initial case, fuel cost has been reduced by 11.42 %, emission by 14.33 %, L-index by 45.10 %, active power losses by 51.60 %, and voltage deviation by 92.70 %.

Educational institutions have had the pending task of knowing what really happens in their classrooms, which professors make a real difference, which students would need help to improve their performance, and even more importantly, how to manage the enormous amount of information gathered each year. To address this problem, in this paper we propose the use of retrospective data from 210 undergraduate courses in Spain, which equals 1,320 European credits, 10,500 students, and four complete academic years (18/19, 19/20, 20/21, 21/22) to generate a monitoring tool or automatic dashboard with which to try to optimize the operation of such institutions and help them to make (even more) optimal decisions. To do this, all courses were grouped into ten typologies or categories: (1) if they were 'soft skills'/'hard skills' courses, (2) if they were taught in the 1st, 2nd, 3rd or 4th academic year, (3) if the professor was male or female, (4) whether the course was taught in English or not, (5) whether the professor was internal and permanent staff of the university or an associate professor with a temporary contractual relationship with the institution, (6) whether his/her main professional activity was teaching, therefore being more academic-oriented or this was rather a hobby and a complementary professional activity, therefore being more practical-oriented, (7) their age, (8) if several professors shared the course or only one taught it, (9) if it was a semester or annual course, (10) and if it was taught during the Covid-19 period or outside of it. Our results show that, despite the existence of numerous types of methodologies to achieve the desired student motivation, there would only be one category that would generate significant differences in the grades of the students: if they were ‘soft skills’/’hard skills’ courses. However, on the contrary, there would be much more types of courses (and professors) which would affect the preferences and valuation of the student to the teaching received (and effectively paid by him/her). That is, despite reaching the same academic results, students do value one type of professors better than others. Likewise, through this work it is intended to demonstrate the non-existence of the "false belief" that better evaluations received by professors are since they give higher grades to their students, regardless of its dispersion. In this way, it is intended to prevent possible atypical behaviors by professors. Thus, this paper gives light to (re)consider which variables should actually matter in educational institutions, analyzing its teaching-learning processes, management and even the retention of its key players.

This paper proposes a GSCND that combines environmental, energy, and economic sustainability aspects of the crisis. Global SCs have been disrupted by the epidemic, underlining the need for a GSCND. Environmental and energy sustainability issues are closely linked to SC and economic systems. By examining available energy and total costs, integrated SC models aim to ensure the profitability of investments and the effective use of energy in economic systems. An example quantitatively illustrates the ability of the proposed model to reduce total costs and public energy consumption. The model enables several improvements in reducing the total cost and energy variables during a crisis. The weighted Tchebycheff method is used to optimize mathematical modeling. This network allows maximizing energy consumption while reducing the total economic cost. The special thing about this research paper is that it analyzes the COVID-19 epidemic simultaneously from an energy and financial perspective.