Simplex Method Research Articles

A simple and effective non-invasive method for the analysis of volatile organic compounds in exhaled air

Breath analysis is a cutting-edge technique that collects significant medical information by analyzing the substances found in exhaled breath. Fluctuations in the levels of chemicals such as acetone can serve as indicators of medical ailments or contact with hazardous substances such as occupational exposure. In order to detect acetone in exhaled air, techniques such as needle trap coupled with gas chromatography have been devised. This procedure involves the use of a nanocomposite called Mil101(Fe)@Fe3O4@SiO2.The nanocomposite is synthesized using a chemical hydrothermal method. In order to examine the properties of the Mil101(Fe)@Fe3O4@SiO2 nanocomposite, researchers utilized a range of analytical techniques including scanning electron microscopy (SEM), thermal analysis (TGA), x-ray analysis (EDX), Brunauer-Emmett-Teller (BET) method, and Fourier Transform Infrared Spectroscopy (FT-IR). The efficiency of the NTD-GC-FID method depends on several parameters, such as temperature, desorption time, fracture passage volume, sampling time, sample temperature, and sampling flow rate. These parameters have been studied using the simplex method to optimize the process. The optimal conditions yielded a limit of detection (LODs) of 0.4 µg/L and a limit of quantification (LOQs) of 1.1 µg/L. The calibration curve for the NTD-GC-FID method ranged from 0.011 to 10 mgl−1 with an acceptable error range. Moreover, the method demonstrated good repeatability and reproducibility, with calculated values of 3.9 and 4.6 %, respectively. The proposed method was successfully employed in this study to identify and measure acetone in the exhaled air of individuals with diabetes, yielding satisfactory results. The needle trap tool was used over 100 times without any decline in its efficiency for adsorbing acetone. In summary, this research presents a new analytical approach, namely the NTD-GC-FID method, to detect and measure acetone in exhaled air. Compared to the other methods for analyzing acetone, the application of Mil101(Fe)@Fe3O4@SiO2 nanocomposite demonstrated promising results in detection of diabetes.

Design and optimization of flexible DGS-based microstrip antenna for wearable devices in the Sub-6 GHz range using the nelder-mead simplex algorithm

This study introduces a Nelder-Mead Simplex Algorithm based low-profile, flexible, and wearable defected ground structure (DGS)-loaded deformed microstrip antenna for Sub-6 GHz 5 G applications. It is fabricated on a low-loss 20 mil Rogers RT/Duroid 5880 substrate and the antenna demonstrates robust performance with reflection coefficient magnitude (|S11|) of –22.34 dB with peak gain of about 5.57 dBi at resonant frequency (fr) of 2.185 GHz. Comprehensive key performance metrics such as |S11|, far-field gain patterns, and specific absorption rate (SAR) of the proposed antenna is demonstrated. The SAR for 1 g and 10 g tissues at various separation distances are analyzed and presented by utilizing human multilayer phantom model with antenna. Additionally, the lowest SAR is measured to be 0.0959 W/kg for 1 g of tissue and 0.0755 W/kg for 10 g of tissue when the antenna is placed 0.291λ0 away from the model. The study also explores the proposed antenna performance under various bending conditions for the conformability analysis and in different on-body scenarios, with placement on the hand, leg, and chest. The measured |S11| values, particularly strong on the chest, underscore the antenna effectiveness for wearable technology.

Examining the brightness variability, accretion disk, and evolutionary stage of the binary OGLE-LMC-ECL-14413

Several intermediate-mass close binary systems exhibit photometric cycles longer than their orbital periods, potentially due to changes in their accretion disks. Past studies indicate that analyzing historical light curves can provide valuable insights into disk evolution and track variations in mass transfer rates within these systems. Our study aims to elucidate both short-term and long-term variations in the light curve of the eclipsing system with a particular focus on the unusual reversals in eclipse depth. We aim to clarify the role of the accretion disk in these fluctuations, especially in long-cycle changes spanning hundreds of days. Additionally, we seek to determine the evolutionary stage of the system and gain insights into the internal structure of its stellar components. We analyzed photometric time series from the Optical Gravitational Lensing Experiment (OGLE) project in the $I$ and $V$ bands, and from the MAssive Compact Halo Objects (MACHO) project in the $B_ M $ and $R_ M $ bands, covering a period of 30.85 years. Using light curve data from 27 epochs, we constructed models of the accretion disk. An optimized simplex algorithm was employed to solve the inverse problem, deriving the best-fit parameters for the stars, orbit, and disk. We also utilized the Modules for Experiments in Stellar Astrophysics ( MESA ) software to assess the evolutionary stage of the binary system, investigating the progenitors and potential future developments. We found an orbital period of 38.15917 pm 0.00054 days and a long-term cycle of approximately 780 days. Temperature, mass, radius, and surface gravity values were determined for both stars. The photometric orbital cycle and the long-term cycle are consistent with a disk containing variable physical properties, including two shock regions. The disk encircles the more massive star and the system brightness variations align with the long-term cycle at orbital phase 0.25. Our mass transfer rate calculations correspond to these brightness changes. MESA simulations indicate weak magnetic fields in the donor star's subsurface, which are insufficient to influence mass transfer rates significantly.

A new simplex algorithm for interval-valued Fermatean fuzzy Linear programming problems

A new simplex algorithm for interval-valued Fermatean fuzzy Linear programming problems

OPTIMALISASI JUMLAH PRODUKSI ROTI ADIL BAKERY DI DESA MEUSEE KECAMATAN KUTA BLANG KABUPATEN BIREUEN

The Adil Bakery bread agroindustry is one of the industries that produces bread with contemporary variants, namely; Tiramissu bread, blueberry bread, strawberry bread, chocolate bread and srikaya bread. In the implementation of production and marketing activities, this bread agroindustry does not always run smoothly, there are several obstacles that occur, such as erratic bread sales every day, such as bread not being sold out within a day. Therefore, Adil Bakery requires optimal production quantity planning to determine the number of products that will be produced for each type of bread variant in a day. This research was conducted to analyze the optimal combination of bread production quantities and to determine the maximum amount of profit obtained by the Adil Bakery agroindustry. The data analysis method used is quantitative with a linear programming model using the simplex method with the help of QM For Windows software. The results of the research show that in one production the optimal bread production combination for the Adil Bakery bread agroindustry to produce maximum profits is 460 pcs of tiramissu bread, 750 pcs of blueberry bread, 750 pcs of strawberry bread, 1,080 pcs of chocolate bread pcs and 750 pcs of srikaya bread. Meanwhile, the maximum profit that will be obtained by the Adil Bakery bread agroindustry in one production is IDR 6,541.12 per five variants. If production is under optimal conditions, the profit contribution that Adil Bakery will receive will increase by IDR 3,105,236 per five variants from the profit contribution under actual conditions. Keywords: Agroindustry, Bread, Optimization, Profit

Comparative study of deterministic and stochastic optimization algorithms applied to the absorption of CO2 by alkanolamine solution

Abstract A model based on simulated annealing approach is used with e-UNIQUAC model as well as two other algorithms to study the absorption of carbon dioxide by monoethanolamine solutions. In this work, we propose to compare the performance (through the knowledge of RMSD values) of two stochastic methods, namely the GA (genetic algorithm) and SA (simulated annealing) methods and a deterministic method that is the simplex method. These methods were applied to the absorption of carbon dioxide by an alkanolamine solution using a chemical equilibrium model and a thermodynamic equilibrium model. The latter is based on the use of the modified-UNIQUAC (UNIQUAC-electrolyte) model instead of e-NRTL model for the liquid phase and a fugacity model for the vapor phase. The chemical equilibrium in this work represents the absorption of CO2 by monoethanolamine solution at different temperatures. Solving the coupled system of material and charge balances gives us the carbon dioxide pressure while taking into account the non-ideality of the system. The three-optimization methods show good agreement with the experimental data with a better performance of the simulated annealing method..

ANALISIS NILAI TAMBAH DAN OPTIMASI KEUNTUNGAN PRODUKSI OLAHAN KERANG KAMPAK (STUDI KASUS PADA UMKM BUNDA SURABAYA)

This research was conducted at UMKM Bunda Surabaya, Surabaya City, with a focus on value-added analysis and profit optimization of processed shellfish production. The research method used was a case study with non-probability sampling, and data collection through interviews, questionnaires, observation, and secondary data. Data analysis was carried out descriptively and quantitatively, including the calculation of production costs, revenues, and profits. The Hayami method was used to calculate added value, while the simplex method was used for production profit optimization. The results showed that Bunda Surabaya MSMEs earned profits from the production of shredded clams, clam sticks, and clam chili sauce, with a fairly high added value. The highest profit was obtained from the clam sauce product amounting to Rp. 775,201 per one production process. The added value of shredded clams is Rp. 94,726 per kg of raw material with a value-added ratio of 63.15%. This research is expected to provide benefits for the shellfish processing industry and improve the welfare of the fishing community.

Ensembled snake optimiser to solve multiobjective mixed energy generation scheduling

This paper presents Ensembled Snake Optimiser (ESO), to optimise the scheduling of mixed energy generation from coordinated thermal, hydro, pumped-storage hydro, and solar units. It tackles operational constraints while minimising non-convex and non-linear objectives. To reduce the pollutants emitted and operating cost of thermal and solar units, the mixed energy generation scheduling problem uses committed hydro, thermal, and solar units to generate power, pumped-storage hydro units to maintain water levels, and hydro units to maximise available water volume. Utilising the water volume and actively exceeding the power-generating limit are the primary obstacles to satisfy the load requirement. The heuristics are utilised to satisfy the load demand and water volume constraints. The binary-optimistic approach commits solar units. The snake optimisation algorithm tends to get trapped in the local minima while solving complex engineering optimisation problems, which leads to sluggish convergence behaviour. A local search, simplex search, and extended opposition-based learning are investigated to improve its exploitation aspect, convergence behaviour, and procure good solutions. Three electric power systems are undertaken for the simulation studies. ESO gives better results. The significant cost savings for integrated energy scheduling are ranging from 10–15%. The rapid convergence behaviour and whisker box plots justify ESO’s robustness.

Mercury Spectrophotometric Modeling Using MESSENGER Data

Mercury is covered by a regolith that affects how light is scattered from the planet’s surface. To deduce the physical properties of Mercury’s regolith, we use spectrophotometry from the Mercury Dual Imaging System instrument of NASA’s MErcury Surface, Space ENvironment, GEochemistry and Ranging mission. The data come in eight colors in wavelengths of 433.2–996.2 nm, with phase angles of 20°–125°. A theoretical particulate-medium model is used to interpret the observed reflectance. The model includes a shadowing correction that depends on three geometry parameters of the regolith, allowing for the retrieval of the physical regolith structure from the spectrophotometry. The most important parameter is the packing density v, while the other two parameters describe the regolith’s roughness as a fractional Brownian motion surface: the Hurst exponent H in the horizontal and the amplitude σ in the vertical direction. The numerical implementation of the model includes a set of discrete parameter values, which we extend by using trilinear interpolation: 0.15–0.55 for v, 0.20–0.80 for H, and 0.00–0.10 for σ. We optimize the model parameters in the least-squares sense using the Nelder–Mead simplex method followed by Markov Chain Monte Carlo (MCMC) sampling. Our results indicate that Mercury’s regolith is densely packed (v = 0.547 ± 0.004) with moderate horizontal variations (H = 0.606 ± 0.009) and large height variations (σ = 0.0998 ± 0.0003). The MCMC solution allows us to predict the spectrophotometry for differing viewing geometries. Future work includes improving the implementation of the model by increasing the packing density values.

Optimal design of antenna arrays for microwave power transmission with multiple receiving targets in the radiative near-field

This study proposes a method for maximizing the beam collection efficiency (BCE) for a microwave power transmission system with multiple receiving targets in the radiative near-field region. The electric and magnetic fields of the transmitting array are calculated via the superposition principle. Through theoretical derivation, the BCE maximization problem is simplified into finding the maximum ratio of two real quadratic forms. Based on the theory of matrices, the optimal BCE and its corresponding excitations of the transmitting array can be determined by finding the largest characteristic value and its associated characteristic vector. In practice, the required power for multiple receiving targets may be different. To meet this requirement, a BCE optimization model is established, considering the constraints of the problem of allocable power for each receiving target. A hybrid grey wolf optimizer and Nelder–Mead simplex method is adopted to address the optimization problem. To verify the effectiveness of the proposed method, numerical experiments on focusing the power radiated on two parallel receiving targets are conducted first. Then, two rotating receiving targets are employed to show its universality. Finally, three and four receiving targets are adopted to further evaluate the validity of the proposed method.

Моделирование и визуализация поверхностей сложной формы с помощью интерполяционных кривых

This article presents an approach to modeling and visualizing surfaces of complex shapes using interpolation curves with predetermined geometric properties. A modified Bezier curve of n-order was used as interpolation curves. Modification of a Bezier arc into an interpolation curve is possible both with and without preserving tangents. When preserving tangents, the Bezier arc retains its properties as a contour arc and acquires the ability to pass through preset points. The considered modification is possible in several variations: universal, based on the uniform distribution of the parameter during the modification process, and adaptive, when the parameter values are adapted to the initial data. The use of interpolation curves makes it possible to implement a special case of the moving simplex method, an analogue of which in geometric modeling and computer-aided design systems is the section operation (or lofting). The difference is that a continuous curve is used as a generating surface instead of a piecewise one. To ensure the functionality of such a connection, we give examples of models of the surface of an onion dome and a vase using various guides. An analysis of the obtained results was carried out. The introduction of research results into CAD/CAM will significantly expand their tools in terms of shape formation and visualization of surfaces and bodies that have predetermined geometric requirements.

Analyzing the Characteristic of Simplex Algorithm

Simplex algorithm is a kind of iterated algorithm which is used to solve linear programming problems. It is raised by George Dantzig in 1974 and is still a valid tool especially when the scale of problem is relatively small or secondary. Simplex algorithm starts at an initializing feasible solution, and constantly approach to the optimal solution until the optimal solution is found or unbounded. Serving as a classical algorithm solving linear programming problem, it plays an important role in both theoretical. With the continuous development of computing technology, the simplex method and its varieties will continue in optimization. This research aims to help people make better decisions in optimization problems by summarizing the formula, characteristics and problem solutions of simplex algorithm. The summary is come from existing literature and including the condition of using simplex algorithm, all kind of different solutions, an example of simplex tableau method and the future of simplex algorithm.

New Robust Estimation in Compound Exponential Weibull-Poisson Distribution for both contaminated and non-contaminated Data

تناول البحث مقارنة طريقتين لتقدير معلمات توزيع بواسون–ويبل الاسي المركب ذو الاربع معلمات هي طريقة الامكان الاعظم الاعتيادية وخوارزمية Downhill Simplex وذلك بالاعتماد على حالتين للبيانات الاولى تفترض حالة البيانات الطبيعية (غير الملوثة) والثانية تفترض حالة تلوث البيانات. ومن خلال تنفيذ تجربة المحاكاة لحجوم عينات مختلفة وقيم اولية للمعلمات وتحت مستويات مختلفة للتلويث تبين ان خوارزمية Downhill Simplexكانت هي الافضل في تقدير المعلمات والدالة الاحتمالية ودالة المعولية للتوزيع المركب في حالتي البيانات الطبيعية والملوثة.

Optimizing Transportation Logistics for Cost Efficiency in a Garment Factory: A Linear Programming Approach Using LINDO

The garment industry in Bangladesh plays a pivotal role in the nation's economy, yet it faces significant challenges in optimizing transportation logistics to minimize costs and enhance efficiency. This paper uses linear programming techniques to negotiate the optimization of transportation logistics in a garment factory located in Gazipur, Bangladesh. The primary objective is to develop an optimized transportation plan that reduces costs while satisfying supply and demand constraints across multiple suppliers, production facilities, and distribution centers. The study formulates the transportation problem as a linear programming model and employs the simplex method to determine the optimal solution. LINDO software is utilized for model implementation and optimization. The model incorporates real data from the garment factory, including supply capacities, demand requirements, and transportation costs. Results from the LINDO optimization reveal a detailed transportation plan that minimizes total transportation costs, demonstrating significant cost savings compared to existing practices. The optimized plan improves operational efficiency and provides a robust decision-support tool for logistics managers. Sensitivity analysis is conducted to ensure the model's robustness under varying conditions. The methodologies and findings are scalable and adaptable to other industrial settings, offering valuable insights for enhancing logistical efficiency in the garment industry. The study also highlights potential areas for future research, including integrating environmental considerations and advanced optimization techniques. Through this research, the garment factory in Gazipur can achieve substantial cost reductions and operational improvements, enhancing its competitiveness in the global market.

Nelder-Mead Optimized Weighted Voting Ensemble Learning for Network Intrusion Detection

The rise in internet usage and data transfer rates has led to numerous anomalies. Hence, anomaly-based intrusion detection systems (IDS) are essential in cybersecurity because of their ability to identify unknown cyber-attacks, especially zero-day attacks that signature-based IDS cannot detect. This study proposes an ensemble classification for intrusion detection using a weighted soft voting system with KNN, XGBoost, and Random Forest base models. The base model weights are optimized using the Nelder-Mead simplex method to improve the overall ensemble performance. We propose a robust intrusion detection framework that uses soft-voting classifier-level weights optimized using the Nelder-Mead algorithm and feature selection. We evaluated the system's performance using the KDD99 and UNSW-NB15 datasets, which demonstrated that the proposed approach exceeded other existing methods in respect of accuracy and provided comparable results with fewer features. The proposed system and its hyperparameter optimization technique were compared with other cyber threat detection and mitigation systems to determine their relative effectiveness and efficiency.

Pareto-critical equilibrium condition for non-linear multiobjective optimization problems with applications

The new (necessary) Pareto-critical equilibrium condition (PCEC) discussed in this study is based on the Pareto-critical condition for non-linear optimization problems. The proposed condition is evaluated by defining a multiobjective optimization problems (MOOPs) subject to a set of constraints. Then this MOOPs is converted to a single objective optimization problem by using the available optimization techniques, for instance, the weighted sum method, and solved by the non-linear Nelder-Mead simplex method. The obtained solution points satisfy the proposed PCEC of an unconstrained multiobjective optimization problem (MOOPs) under the convexity assumption. This PCEC is evaluated in a search that either results in a point inside the feasible region or a point for which the PCEC holds. The resulting Pareto-critical equilibrium condition is globally valid for convex problems. Numerical experiments are carried out in order to clarify the proposed condition's validity. Moreover, the proposed PCEC is used to place a downlink transmit beamforming system base station in an ideal position as a first application and is also employed in situations where there are multiple agents working towards a common goal as a second application.

Centralised Control and Energy Management of Multiple Interconnected Standalone AC Microgrids

When microgrids operate autonomously, they must curtail the surplus of renewable energy sources (RES) while minimising reliance on gas. However, when interconnected, microgrids can collaboratively minimise RES curtailment and gas consumption due to the ability of exchanging power. This paper presents a centralised controller and energy management of multiple standalone AC microgrids interconnected to a common AC bus using back-to-back converters. Each microgrid consists of RES, a battery, a gas-powered auxiliary unit, and a load. The battery’s state of charge (SOC) is controlled and is used in the AC bus frequency to indicate whether the microgrid has a surplus or shortage of power. High-level global droop control exchanges power between the microgrids. The optimisation problem for this interconnected system is modelled cooperatively to determine the optimal dispatch solution that minimises the energy cost from the auxiliary unit. The optimal dispatch is solved in three cases using the Nelder–Mead simplex algorithm under different settings: one-variable optimisation, three-variable optimisation with the standard droop equation, and three-variable optimisation with a modified droop equation. The optimised performance results are compared with those of the non-optimised benchmark to determine the percentage of optimal performance. The simulation results show that the total energy cost from the auxiliary unit is minimised by 8.98%.

Bi-Functional Materials for Sulfur Cathode and Lithium Metal Anode of Lithium-Sulfur Batteries: Status and Challenges.

Over the past decade, the most fundamental challenges faced by the development of lithium-sulfur batteries (LSBs) and their effective solutions have been extensively studied. To further transfer LSBs from the research phase into the industrial phase, strategies to improve the performance of LSBs under practical conditions are comprehensively investigated. These strategies can simultaneously optimize the sulfur cathode and Li-metal anode to account for their interactions under practical conditions, without involving complex preparation or costly processes. Therefore, "two-in-one" strategies, which meet the above requirements because they can simultaneously improve the performance of both electrodes, are widely investigated. However, their development faces several challenges, such as confused design ideas for bi-functional sites and simplex evaluation methods (i. e. evaluating strategies based on their bi-functionality only). To date, as few reviews have focused on these challenges, the modification direction of these strategies is indistinct, hindering further developments in the field. In this review, the advances achieved in "two-in-one" strategies and categorizing them based on their design ideas are summarized. These strategies are then comprehensively evaluated in terms of bi-functionality, large-scale preparation, impact on energy density, and economy. Finally, the challenges still faced by these strategies and some research prospects are discussed.

Optimization and modelling of bioethanol production by the fermentation of CCN‐51 cocoa mucilage using the sequential simplex method and the modified Gompertz model

AbstractThis work deals with the optimization of bioethanol production through a fermentation process of CCN‐51 cocoa mucilage, based on increased concentrations of the Saccharomyces cerevisiae yeast. Cocoa mucilage, considered biomass waste, was selected for its high productivity and the large volumes generated in the cocoa industrial chain in Ecuador. The optimization of the fermentation process was performed using the sequential simplex method with two variables, and the results were experimentally confirmed by quantifying bioethanol through the microdiffusion method. The best operational conditions corresponded to a temperature of 35°C and a pH of 4. Regarding the concentration of yeast, it was found that the optimal value was 8 g/L, since lower concentrations led to low productivities, while higher concentrations resulted in inadequate functioning of the bioreactor. The best results reached a productivity of 1.35 ± 0.04 g/L · h and a maximum bioethanol concentration of 28.3 ± 0.8 g/L for a processing time of 21 h. The production of bioethanol was modelled using the modified Gompertz equation and simulated in MATLAB®, yielding a bioethanol production rate of 2.42 g/L · h with a correlation coefficient (R2) of 0.95. These results contribute to the knowledge of bioethanol production using cocoa mucilage and seek to add a positive value to this residue, whose management and final disposition have both undesirable environmental and economic effects.

Linear fractional transportation problem in bipolar fuzzy environment

This article proposes a solution methodology for the Linear Fractional Transportation Problem (LFTP) by incorporating bipolar fuzzy sets (BFSs) to accommodate both positive and negative judgmental perspectives. The approach explores Zimmermann's extension within bipolar fuzzy environment to compare outcomes. In this context, the cost function and constraint coefficients are depicted using interval-valued trapezoidal bipolar fuzzy numbers (IVTrBFNs) and defuzzified by (s, t)-cut. The initial approach employs the simplex method and fuzzy optimization method, renowned for its effectiveness in obtaining optimal solutions. In the alternative method, Bipolar fuzzy programming approach (BFPA) is utilized for better outcome. In this method the LFTP is altered to a Multi-Objective Transportation Problem (MOTP), and BFPA extends Zimmermann's technique under suitable positive and negative membership functions, converting MOTP to a one-objective transportation problem (TP) and solved using LINGO software. Supporting this proposed method some theorems are formulated to demonstrate that the most effective solution of the single-objective TP is a Pareto optimal solution for the corresponding MOTP. A quantitative example is provided for better understanding of the proposed BFPA method alongside two other approaches.