Optimization Techniques Research Articles

Harnessing the potential of microalgae for the production of monoclonal antibodies and other recombinant proteins.

Monoclonal antibodies (mAbs) have become indispensable tools in various fields, from research to therapeutics, diagnostics, and industries. However, their production, primarily in mammalian cell culture systems, is cost-intensive and resource-demanding. Microalgae, diverse photosynthetic microorganisms, are gaining attention as a favorable option for manufacturing mAbs and various other recombinant proteins. This review explores the potential of microalgae as a robust expression system for biomanufacturing high-value proteins. It also highlights the diversity of microalgae species suitable for recombinant protein. Nuclear and chloroplast genomes of some microalgae have been engineered to express mAbs and other valuable proteins. Codon optimization, vector construction, and other genetic engineering techniques have significantly improved recombinant protein expression in microalgae. These accomplishments demonstrate the potential of microalgae for biopharmaceutical manufacturing. Microalgal biotechnology holds promise for revolutionizing the production of mAbs and other therapeutic proteins, offering a sustainable and cost-effective solution to address critical healthcare needs.

Hybrid deep learning-based traffic congestion control in IoT environment using enhanced arithmetic optimization technique

The Internet of Things (IoT) is essential in several Internet application areas and remains a key technology for communication technologies. Shorter delays in transmission between Roadside Units (RSUs) and vehicles, road safety, and smooth traffic flow are the major difficulties of Intelligent Transportation System (ITS). Machine Learning (ML) was an advanced technique to find hidden insights into ITSs. This article introduces an Improved Arithmetic Optimization with Deep Learning Driven Traffic Congestion Control (IAOADL-TCC) for ITS in Smart Cities. The presented IAOADL-TCC model enables traffic data collection and route traffic on existing routes for avoiding traffic congestion in smart cities. The IAOADL-TCC algorithm exploits a hybrid convolution neural network attention long short-term memory (HCNN-ALSTM) method for traffic congestion control. In addition, an IAOA-based hyperparameter tuning strategy is derived to optimally modify the parameters of the HCNN-ALSTM model. The presented IAOADL-TCC model effectively enhances the flow of traffic and reduces congestion. The experimental validation was performed using the road traffic dataset from the Kaggle repository. The proposed model obtained an average accuracy of 98.03 % with an error rate of 1.97 %. The experimental analysis stated the superior performance of the IAOADL-TCC approach over other DL methods.

Optimizing capacitor size and placement in radial distribution networks for maximum efficiency

As distribution systems continue to expand, they face challenges such as increased system losses and inadequate voltage regulation. To address these issues, shunt capacitors are being deployed in distribution networks. These capacitors offer reactive power compensation, enhance power factor, improve voltage profiles, promote system stability, and significantly reduce losses. However, determining the appropriate capacitor sizes and their optimal placements requires careful consideration of both technical and economic factors. The nonlinear nature of optimal capacitor placement and sizing, leveraging optimization techniques becomes crucial in identifying the best locations and values for capacitors. This paper demonstrates the effective utilization of Particle Swarm Optimization (PSO) and Real Coded Genetic Algorithm (RCGA) optimization techniques for capacitor placement and selection. The optimization techniques are applied to a 33-bus IEEE standard radial distribution system, to reduce the real power loss and to improve the voltage profile considering both constant and variable loads. Both PSO and RCGA algorithms identify suitable locations for the placement of capacitors for reactive power compensation within the distribution system. By optimizing the objective function associated with capacitor placement costs and maximizing annual cost savings, the PSO and RCGA techniques yield promising results. After implementing the optimal capacitor placements at the identified candidate nodes, a significant reduction in losses within the radial distribution system is observed. Moreover, the cost savings achieved through optimal placement and sizing are substantial.

An enhanced jellyfish search optimizer for stochastic energy management of multi-microgrids with wind turbines, biomass and PV generation systems considering uncertainty

The energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, the energy management (EM) of the MMGs became a complex and strenuous task with high penetration of renewable energy resources due to the stochastic nature of these resources along with the load fluctuations. In this regard, this paper aims to solve the EM problem of the MMGs with the optimal inclusion of photovoltaic (PV) systems, wind turbines (WTs), and biomass systems. In this regard, this paper proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving the EM of MMGs for the 85-bus MMGS system to minimize the total cost, and the system performance improvement concurrently. The proposed algorithm is based on the Weibull Flight Motion (WFM) and the Fitness Distance Balance (FDB) mechanisms to tackle the stagnation problem of the conventional JSO technique. The performance of the EJSO is tested on standard and CEC 2019 benchmark functions and the obtained results are compared to optimization techniques. As per the obtained results, EJSO is a powerful method for solving the EM compared to other optimization method like Sand Cat Swarm Optimization (SCSO), Dandelion Optimizer (DO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the standard Jellyfish Search Optimizer (JSO). The obtained results reveal that the EM solution by the suggested EJSO can reduce the cost by 44.75% while the system voltage profile and stability are enhanced by 40.8% and 10.56%, respectively.

A comprehensive study on modern optimization techniques for engineering applications

Rapid industrialization has fueled the need for effective optimization solutions, which has led to the widespread use of meta-heuristic algorithms. Among the repertoire of over 600, over 300 new methodologies have been developed in the last ten years. This increase highlights the need for a sophisticated grasp of these novel methods. The use of biological and natural phenomena to inform meta-heuristic optimization strategies has seen a paradigm shift in recent years. The observed trend indicates an increasing acknowledgement of the effectiveness of bio-inspired methodologies in tackling intricate engineering problems, providing solutions that exhibit rapid convergence rates and unmatched fitness scores. This study thoroughly examines the latest advancements in bio-inspired optimisation techniques. This work investigates each method’s unique characteristics, optimization properties, and operational paradigms to determine how revolutionary these approaches could be for problem-solving paradigms. Additionally, extensive comparative analyses against conventional benchmarks, such as metrics such as search history, trajectory plots, and fitness functions, are conducted to elucidate the superiority of these new approaches. Our findings demonstrate the revolutionary potential of bio-inspired optimizers and provide new directions for future research to refine and expand upon these intriguing methodologies. Our survey could be a lighthouse, guiding scientists towards innovative solutions rooted in various natural mechanisms.

Hybrid GRU-CNN bilinear parameters initialization for quantum approximate optimization algorithm

The Quantum Approximate Optimization Algorithm (QAOA), a pivotal paradigm in the realm of variational quantum algorithms (VQAs), offers promising computational advantages for tackling combinatorial optimization problems. Well-defined initial circuit parameters, responsible for preparing a parameterized quantum state encoding the solution, play a key role in optimizing QAOA. However, classical optimization techniques encounter challenges in discerning optimal parameters that align with the optimal solution. In this work, we propose a hybrid optimization approach that integrates Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN), and a bilinear strategy as an innovative alternative to conventional optimizers for predicting optimal parameters of QAOA circuits. GRU serves to stochastically initialize favorable parameters for depth-1 circuits, while CNN predicts initial parameters for depth-2 circuits based on the optimized parameters of depth-1 circuits. To assess the efficacy of our approach, we conducted a comparative analysis with traditional initialization methods using QAOA on Erdős-Rényi graph instances, revealing superior optimal approximation ratios. We employ the bilinear strategy to initialize QAOA circuit parameters at greater depths, with reference parameters obtained from GRU-CNN optimization. This approach allows us to forecast parameters for a depth-12 QAOA circuit, yielding a remarkable approximation ratio of 0.998 across 10 qubits, which surpasses that of the random initialization strategy and the PPN2 method at a depth of 10. The proposed hybrid GRU-CNN bilinear optimization method significantly improves the effectiveness and accuracy of parameters initialization, offering a promising iterative framework for QAOA that elevates its performance.

A novel approach using copper oxide nanocomposites-based biosensor for lactate detection in athletes

Accurate monitoring tools are necessary for lactate, a critical biomarker linked to anaerobic metabolism and muscular performance. Lactate dehydrogenase (LDH) immobilized on MXene (Ti3C2Tx)-supported CuO nanocomposites (MCNs) modified glassy carbon electrode (LDH/MCN/GCE) is the innovative electrochemical biosensor for lactate detection presented in this work. CuO nanostructure integration onto MXene nanosheets, MCN production, and LDH immobilization were all successfully accomplished, according to structural characterization performed using XRD, SEM, XPS, and FTIR. The best lactate oxidation current was shown by LDH/MCN/GCE, according to electrochemical study. With a low detection limit of 0.25 µM and a high sensitivity of 5.92107 µA/mM, the biosensor exhibited a linear response range of 0.1–121 mM. The remarkable selectivity, stability, and sensitivity were attributed to enhanced LDH immobilization made possible by MCNs and MXene. The promise of LDH/MCN/GCE for bioanalytical applications is highlighted by promising results from practical testing in human blood samples, opening the door for improvements in lactate monitoring and performance optimization techniques.

P-176 A novel electrophoretic sperm isolation system achieves equivalent ICSI outcomes to the combined density gradient centrifugation and swim-up method in a significantly shorter processing time

Abstract Study question Does an electrophoretically sperm isolation method, the Felix™ system, have clinical or operational advantages over the combined density gradient centrifugation and swim-up (DGCS) method? Summary answer The Felix™ system produces results that are comparable to the DGCS method, however, Felix™ has the ability to isolate spermatozoa more efficiently. What is known already The isolation of healthy spermatozoa to improve ART outcomes has received enormous attention in the literature recently. Although swim-up and density gradient centrifugation are the most common sperm separation methods, the optimal technique is still unclear. Recently, good sperm selection approaches using various non-centrifugal devices have emerged. Previous reports have shown that a quick and simple sperm separation device, the Felix™ system, can isolate high-quality spermatozoa and improve the results of ART. The Felix™ system combines electrophoresis and separation membranes to collect negatively charged mature normal sperm. Study design, size, duration A prospective oocyte sibling study was conducted between March 2023 and December 2023. The study involved infertile couples (n = 45) with a semen volume of 2.3 mL or more, a motile sperm concentration of 2 million/mL or more, maternal age under 43 years, and at least four mature oocytes at the time of oocyte retrieval. Ejaculated semen was prepared separately using DGCS and Felix™ on the day of oocyte retrieval and subsequent ICSI results were analyzed. Participants/materials, setting, methods In the DGCS group, semen was layered on a 45/90% of sperm grad and centrifuged for 15 minutes, followed by a 30 min swim-up. Subsequently, 0.3 mL of supernatant was collected. In the Felix™ group, 0.3 mL of sperm was collected following six minutes of membrane hydration and electrophoresis. Sperm concentration, total motility, progressive motility and DNA fragmentation (SDFR) were measured. Fertilization, blastocyst development, good-quality blastocyst development, and embryo utilisation rates were also assessed. Main results and the role of chance The average female age at the time of oocyte retrieval was 36.5±4.0 years, and the average male age was 38.1±5.9 years. Of 401 oocytes, 200 and 201 oocytes underwent ICSI using sperm prepared by DGCS and Felix™, respectively. The average processing time of Felix™ was significantly shorter than that of DGCS (54.0±4.3 vs. 7.4±0.2 minutes, p < 0.001, respectively). No significant difference was observed between the DGCS and Felix™ groups for sperm concentration (p = 0.49), motility (p = 0.05), progressive motility (p = 0.34), and SDFR (p = 0.09). Furthermore, there were no significant differences between the two groups with regards to fertilization rate (81.0% vs. 80.6%, p = 0.92), blastocyst development rate (52.9% vs. 58.4%, p = 0.60), good-quality blastocyst development rate (26.1% vs. 35.7%, p = 0.06), and embryo utilisation rate (54.3% vs 58.0%, p = 0.50). Limitations, reasons for caution Although the DGCS method was used as a comparison group, it is necessary to compare the Felix™ system with DGC alone, swim-up alone, or other sperm isolation methods. Larger, more powered studies are required to determine the impact on pregnancy and live birth outcomes. Wider implications of the findings The Felix™ system is a viable option for sperm isolation. While DGCS requires complicated procedures and time-consuming process including centrifugation and incubation; Felix™ requires six minutes only. The Felix™ system reduces processing times significantly and therefore can improve laboratory operations and workflow. Trial registration number Not Applicable

Open Reduction and Internal Fixation versus Minimally Invasive Plate Osteosynthesis in Management of Distal Tibia Extra-Articular Fracture (Mini Thesis)

Abstract Background Extra-articular distal tibia fracture is one of the difficult fractures to treat and the optimal treatment remains controversial, however plates seem to be the obvious choice for treating this type of fractures. The mechanism on how to achieve the balance between anatomic reduction and indirect reduction as well as direct and indirect healing remains unclear. Thus, the key point is whether to use ORIF or MIPO in treating fractures. Aim of the Work to provide a complete overview of the optimal plating technique for distal tibia fractures by evaluating and comparing the clinical and functional outcomes of minimally invasive plate osteosynthesis (MIPO) vs Open reduction and internal fixation (ORIF) for treating extra- articular distal tibial fractures. Outcomes of interest include operation time, time to union, non-union, mal-union, infection, secondary operations, and functional outcomes (AOFAS score). Patients and Methods From August 2020 to August 2021, a prospective randomized study was conducted at the department of orthopedic surgery of Ain-shams University Hospital including 36 patients to compare MIPO vs. ORIF in fixation of extra-articular distal tibial fractures. The patients were allotted to the MIPO or ORIF group with the flip of a coin. Results Regarding complications rate, no cases developed infection in MIPO technique. On the other hand, the ORIF technique had an infection incidence of 16.6% overall, with two cases (11.11 %) developing superficial infection and one case (5.56 %) developing deep infection. There was no significant difference in the incidence of mal-union between both techniques with no cases of non-union in both groups. The MIPO group had only one case of sagittal plane malunion (5.56%), compared with no cases in the ORIF group. There was no significant difference between both groups in secondary operations rate, with one case in each group. Conclusion Both MIPO and ORIF have similar therapeutic efficacy regarding the functional outcome and can be used safely in distal tibial extra-articular fractures. Although MIPO is advantageous over ORIF with earlier weight-bearing, earlier union, and clinical lower incidence of infection, it has delayed operation time.

Analysis of the multiple drivers of vegetation cover evolution in the Taihangshan-Yanshan region

The Taihangshan-Yanshan region (TYR) is an important ecological barrier area for Beijing-Tianjin-Hebei, and the effectiveness of its ecological restoration and protection is of great significance to the ecological security pattern of North China. Based on the FVC data from 2000 to 2021, residual analysis, parametric optimal geodetector technique (OPGD) and multi-scale geographically weighted regression analysis (MGWR) were used to clarify the the multivariate driving mechanism of the evolution of FVC in the TYR. Results show that: (1) FVC changes in the TYR show a slowly fluctuating upward trend, with an average growth rate of 0.02/10a, and a spatial pattern of "high in the northwest and low in the southeast"; more than half of the FVC increased during the 22-year period. (2) The results of residual analysis showed that the effects of temperature and precipitation on FVC were very limited, and a considerable proportion (80.80% and 76.78%) of the improved and degraded areas were influenced by other factors. (3) The results of OPGD showed that the main influencing factors of the spatial differentiation of FVC included evapotranspiration, surface temperature, land use type, nighttime light intensity, soil type, and vegetation type (q > 0.2); The explanatory rates of the two-factor interactions were greater than those of the single factor, which showed either nonlinear enhancement or bifactorial enhancement, among which, the interaction of evapotranspiration with mean air and surface temperature has the strongest effect on the spatial and temporal evolution of FVC (q = 0.75). Surface temperature between 4.98 and 10.4 °C, evapotranspiration between 638 and 762 mm/a, and nighttime light between 1.96 and 7.78 lm/m2 favoured an increase in vegetation cover, and vegetation developed on lysimetric soils was more inclined to be of high cover. (4) The correlation between each variable and FVC showed different performance, GDP, elevation, slope and FVC showed significant positive correlation in most regions, while population size, urban population proportion, GDP proportion of primary and secondary industries, and nighttime light intensity all showed negative correlation with FVC to different degrees. The results can provide data for formulating regional environmental protection and restoration policies.

P-738 Reproductive surgery for fertility preservation: Robotic single-port myomectomy (da Vinci® SP) versus robotic single-site myomectomy (da Vinci® Xi or Si)

Abstract Study question To compare clinical and surgical outcomes of robotic single-port myomectomy using da Vinci® SP and robotic single-site myomectomy with Xi or Si for fertility preservation. Summary answer The robotic single-port myomectomy (da Vinci® SP) might be feasible, even if the myoma is deep-seated, compared to robotic single-site myomectomy (Xi or Si system). What is known already Myomas are clinically apparent in about 25% of women and become symptomatic in their reproductive age. Myomectomy is the choice of treatment for women desiring uterine preservation, especially in deep-seated fibroid that can affect pregnancy. Although young women prefer minimal invasive surgical technique with less scar, single-port laparoscopic myomectomy has some limitations. Adoption of the da Vinci® system made it possible to overcome the weaknesses of laparoscopy. Owing to the ability of single-port laparoscopy to reduce pain and improve patient satisfaction, robotic single-site surgery was developed in order of the da Vinci® Si, Xi and the latest new SP system. Study design, size, duration We retrospectively reviewed medical records of 214 patients who underwent robotic single-port myomectomy (RSPM, n = 111) using the da Vinci® SP surgical system or robotic single-site myomectomy (RSSM, n = 103) with the da Vinci® Xi or Si system between October 2015 and September 2023 at Ewha Womans University Mokdong Hospital. Baseline characteristics and operative outcomes were compared and analyzed between the RSPM and RSSM groups. Participants/materials, setting, methods We assessed FIGO classification and maximum diameter of fibroid. The incision time, docking time, console time, wound closure time, total operation time, estimated blood loss (EBL), chopping time for specimen removal, the number and weight of removed myoma were measured. Main results and the role of chance Mean age (38.5±6.6 vs 37.3±6.6 years) and anthropometric index were not significantly different between RSPM and RSSM groups. Although the diameter (7.1±1.4 vs 7.2±2.0 cm), number (3.1±2.7 vs 2.5±2.2), and weight (149.1±107.1 vs 146.4±130.6 g) of myoma were not significantly different between the two groups, the more deep-seated myoma diagnosed by FIGO classification (2.9±1.5 vs 4.6±2.0, p<0.0001) could be resected in RSPM group. The incision (3.6±1.9 vs 7.3±4.4 min, p <0.0001) and docking time (2.7±1.4 vs 4.6±1.6 min. p<0.0001) were less in the group of RSPM compared to RSSM that was earlier implementation. Depending on the differences in da Vinci® SP and Xi or Si systems, the wound was slightly larger, requiring shorter chopping time (6.4±5.0 vs 8.4±7.2 min, p=0.022) and longer closure time (12.8±4.4 vs 10.9±3.6 min, p=0.001) in the RSPM group. Additionally, more complex surgeries with larger blood loss (264.1±140.2 vs 176.8±132.0 ml, p<0.0001), longer console time (60.5±33.2 vs 49.7±22.6 min, p=0.016) and hospitalization (4.6±0.9 vs 4.3±0.6 days, p=0.005) were performed in the RSPM group, but the difference in hemoglobin (2.5±1.0 vs 2.6±0.9 g/dL) before and after surgery was not significant. Limitations, reasons for caution All surgeries underwent using the da Vinci systems by one gynecologic surgeon who had experience performing almost 2,000 gynecologic robotic surgeries and the follow-up period was not enough to conclude the reproductive outcome. Wider implications of the findings The RSPM using the da Vinci® SP surgical system could be recommended for patient who planned future pregnancy. However, the optimal surgical technique should be selected for successful fertility preservation of reproductive surgery. Trial registration number 2023-03-038

Topology Optimization of Auxetic Hyperelastic Biomaterials for Enhanced Tailored Properties and Ultra‐High Expansion

AbstractTopology optimization using finite element analysis offers a promising approach for designing new biomaterials with mechanical properties similar to human skin and superior auxetic properties compared to conventional materials. This innovative technique addresses the challenges associated with trial‐and‐error‐based material design and experimental iterations. In this study, finite element‐based topology optimization is employed to achieve optimized material structural patterns that maximize the lateral expansion of previously established auxetic designs and replicate specific directional properties observed in human skin. The topology‐optimized models, including slit, I‐shape, anisotropic I‐shape, triangular pattern, and re‐entrant shapes, demonstrate material structures with lower maximum stress, reducing the likelihood of material rupture failure and enhancing lateral expansion by more than 50% compared to the initial patterns. These novel designs and their unique behaviors are verified using the theoretical model of hyperelastic auxetic materials. This study represents the first analysis of topology optimization applied to soft hyperelastic biomaterials, focusing on both maximizing auxetic properties and replicating auxetic properties of human skin. The auxetic designs and topology optimization technique developed in this research hold potential for integration into biomedical and personal protective applications.

Mix design determination procedure for geopolymer concrete based on target strength method

This study presents the development and validation of a mix design determination procedure for geopolymer concrete to achieve the desired compressive strength. The procedure integrates artificial neural network (ANN) model developed based on a comprehensive data base from literature, data clustering, and parameter optimization techniques to enhance accuracy and reliability. Experimental validation is undertaken to demonstrate the mix design determination procedure’s capability to accurately predict mix designs for geopolymer concrete based on the target compressive strength, validating its efficacy for mix proportion determination. The integration of chemical oxide content in fly ash, curing time, curing temperature, and activator properties results in a 15.9% improvement in prediction accuracy for the training dataset and a 68.3% enhancement for the testing dataset, compared to the base ANN model that includes only the weight of fly ash and activator properties. Employing data clustering techniques enables the identification of prior estimates for the mix design parameters related to specific fly ash types and target compressive strength, streamlining the mix design process by analyzing pertinent data subsets. Parameter optimization ensures refined mix proportions, achieving the desired target strength economically while minimizing material waste and cost. The development of a user interface facilitates easy manipulation of mix designs, catering to users of varying expertise levels. Additional options for deeper insights into geopolymer concrete characteristics can be integrated into the mix design determination procedure. To assess the mix design determination procedure's ability to generalize effectively, a variety of fly ash samples with distinct chemical compositions were utilized, differing from those already present in the database. This approach allows for a thorough evaluation of the mix design determination procedure's performance when presented with fly ash compositions it has not encountered before. By doing so, this provides insights into the adaptability of the mix design determination procedure beyond the limitations of the training and testing datasets.

Optimal demand management of smart energy hybrid system based on multi-objective optimization problem

This paper presents a multi-objective problem for the energy planning of an energy hybrid system. The problem considers three main objectives: minimizing emission pollution and operation cost on the generation side, addressing consumers' dissatisfaction with the electrical demand, and reducing deviation from optimal levels in the 24 hours ahead to flatten demand profiles. To achieve this, a demand flexibility strategy is implemented, involving the optimal shifting of electrical demand using deferrable loads. The proposed approach utilizes the augmented epsilon-constraint method to identify Pareto solutions for the objectives. Additionally, the TOPSIS decision-making technique is employed to select the optimal solution from the set of Pareto solutions. The effectiveness and robustness of the proposed approach are validated through two case studies. Overall, the paper highlights the importance of considering multiple objectives in the energy scheduling of hybrid systems and demonstrates the effectiveness of the proposed approach in achieving a balance between environmental, economic, and consumer satisfaction goals. The use of demand flexibility strategies and multi-objective optimization techniques can significantly improve the operation energy systems, paving the way for more efficient energy management practices. The implementation of demand side management has resulted in a significant reduction of 2.8% and 64.9% in the first and second objectives, respectively, compared to the absence of such management.

Generalization of Bernoulli polynomials to find optimal solution of fractional hematopoietic stem cells model

Abstract The study introduces a fractional mathematical model in the Caputo sense for hematopoietic stem cell-based therapy, utilizing generalized Bernoulli polynomials (GBPs) and operational matrices to solve a system of nonlinear equations. The significance of the study lies in the potential therapeutic applications of hematopoietic stem cells (HSCs), particularly in the context of HIV infection treatment, and the innovative use of GBPs and Lagrange multipliers in solving the fractional hematopoietic stem cells model (FHSCM). The aim of the study is to introduce an optimization algorithm for approximating the solution of the FHSCM using GBPs and Lagrange multipliers, and to provide a comprehensive exploration of the mathematical techniques employed in this context. The research methodology involves formulating operational matrices for fractional derivatives of GBPs, conducting a convergence analysis of the proposed method, and demonstrating the accuracy of the method through numerical simulations. The major conclusion is the successful introduction of GBPs in the context of the FHSCM, featuring innovative control parameters and a novel optimization technique. The study also highlights the significance of the proposed method in providing accurate solutions for the FHSCM, thus contributing to the field of mathematical modeling in biological and medical research.

Detecting review fraud using metaheuristic graph neural networks

AbstractAlthough online reviews play a critical role in shaping consumer behavior and establishing institutional trust, this reliance on online reviews has given rise to a proliferation of fake reviews. Consequently, there is an urgent need for the development of robust mechanisms to identify and mitigate these inauthentic reviews. In this study, we introduce an innovative approach for the detection of fake reviews, leveraging a metaheuristic graph neural network model. Our methodology begins by extracting distinctive features from review text, comprising of term frequency—inverse document frequency (TF-IDF) vectors and bidirectional encoder representation transformer (BERT) embedding vectors. We feed these features into a graph convolutional network (GCN) that uses message passing to label each review. Furthermore, we address the optimization of our graph neural network models using Harris Hawk and Cat Swarm optimization techniques. Our experiments reveal that optimization of the GNN with Harris Hawk Optimization yields a significantly enhanced model performance, further validating the efficacy of our detection framework. This research contributes to the ongoing efforts in the realm of online review authenticity and showcases the potential of advanced techniques to combat the proliferation of fraudulent reviews.

Virtual synchronous generator control with parameters optimization based on new WaOA algorithm for grid-feeding inverters

The widespread integration of renewable energy sources (RESs) within traditional power systems causes a considerable impact, such as a decrease in total inertia, damping properties, and large frequency deviation. To tackle this issue, numerous research have been proposed to introduce the virtual synchronous generator (VSG) control strategy as an effective solution applied to power electronics inverters operating in grid-forming or grid-feeding control modes. However, the proper tunning of the inertia and damping parameters is considered as a significant challenge to ensure the system stability and reliability. In this paper, an efficient meta-heuristic Walrus Optimization Algorithm (WaOA) -based VSG control intended for grid-feeding inverters within a weak grid is proposed. The objective of this work is to offer a proper tuning of the VSG parameters, thereby improving the dynamic characteristics of the microgrid (MG) and guaranteeing a stable operation. In addition, it highlights the adopted optimization technique which is introduced on VSG control while ensuring minimum objective function value. Through offline simulations in MATLAB/Simulink, the superiority of the suggested WaOA method is first confirmed, in which it offers the lowest Integral Square Error (ISE) fitness function value of 0.026, compared to the Mountain Gazelle Optimizer (MGO) and Tunicate Swarm Algorithm (TSA) with 0.027 9 and 0.0281, respectively. Second, the performance of the WaOA-based VSG control is compared to the ones based on MGO and TSA techniques under active and reactive power variations. The obtained results demonstrate that the proposed VSG control ensures a better transient performance, especially in terms of under and overshoots.

Predicting the Optimal Treatment for Diseases Using the Genetic Method by Develop (PSO) Optimization Technique

Recently, most researchers have been interested in finding appropriate solutions to the spread of diseases, and the data on diseases considered big data, so dealing with big data is difficult in terms of obtaining highly accurate results because of the missing values. To enhance the deals with this type of data, this paper will use artificial intelligence techniques to design a system to predict the optimal treatment for diseases, regardless of disease type with normalization and processing missing data for dataset of diseases . The Particle Swarm Optimization (PSO) algorithm used to find the best solutions since genetic algorithms have characteristics that distinguish them from other algorithms such as mutation and crossover. The 1X method of crossover has been used to develop the PSO algorithm to give the best prediction for the appropriate treatment for these diseases by using the mathematical equations that are explained in 1.3.1. To prove the enhancement of the PSO algorithm after development, the results obtained from the PSO algorithm were compared before and after development by measuring the Fest Fitness Value (accuracy), Error rate, and MSE measurement, the results displayed improvement after development of the algorithm by increased values of accuracy reached to 94% and decreased of Error rate and MSE

A comparative study of machine learning and response surface methodologies for optimizing wear parameters of ECAP-processed ZX30 alloy

Magnesium, valued for its lightweight, recyclability, and biocompatibility, faces challenges like its poor wear behavior and mechanical properties that limit its adaptation for a multitude of applications. In this study, various statistical analyses, and machine learning (ML) techniques were employed to optimize equal channel angular pressing (ECAP) process parameters for improving the wear behavior of Mg-3wt.% Zn-0.7 wt% Ca alloy. ECAP was conducted up to four passes via route Bc at 250 °C. Wear testing of both as-annealed (AA) and ECAP-processed alloys was performed using the dry ball-on-flat wear method under varying loads, speeds, and time. One pass (1P) and 4Bc-ECAP resulted in a notable uniform grain refinement of 86 % and 91 %, respectively, compared to the AA. X-ray diffraction (XRD) analysis confirmed a refined structure attributed to extensive dynamic recrystallization. Mechanical wear testing revealed a significant reduction in volume loss (VL), up to 56 % and 28.5 % after 1P and 4Bc samples, respectively, compared to the AA sample, supported by the observed texture intensity. The coefficient of friction (COF) stabilizes at 0.30–0.45, indicating low friction characteristics. Next, by adjusting wear load and speed through design of experiments (DOE), the wear output responses, VL and COF, were experimentally investigated. The output responses were predicted in the second step using ML, 3D response surface plots, and statistical analysis of variance (ANOVA). According to the regression model, the minimal VL was attained at a 5 N applied load. Also, the wear speed and VL at different passes are inversely proportional. On the other hand, the optimal COF was obtained at applied load about 2–3 N and 250 mm/s at different passes. The wear process variables were then optimized using different optimization techniques namely, genetic algorithm (GA), hybrid DOE–GA, and multi-objective genetic algorithm (MOGA) approaches.

EXACT: How to train your accuracy

Classification tasks are typically evaluated based on accuracy. However, due to the discontinuous nature of accuracy, it cannot be directly optimized using gradient-based methods. The conventional approach involves minimizing surrogate losses such as cross-entropy or hinge loss, which may result in suboptimal performance. In this paper, we introduce a novel optimization technique that incorporates stochasticity into the model’s output and focuses on optimizing the expected accuracy, defined as the accuracy of the stochastic model. Comprehensive experimental evaluations demonstrate that our proposed optimization method significantly enhances performance across various classification tasks, including SVHN, CIFAR-10, CIFAR-100, and ImageNet.