Optimization Process Research Articles

Biological decolorisation of the anionic Dye Acid Blue 9 by bacterial consortium: A sustainable and ecofriendly approach for the treatment of textile wastewater

Industrialization has led to the proliferation of dye waste, posing significant environmental challenges due to the structural complexity of dyes. Various industries discharge untreated toxic waste into water bodies, posing severe environmental hazards to humans as well as aquatic ecosystem. Synthetic dyes from textiles exacerbate this issue due to their toxicity, making the management of industrial wastewater a significant environmental challenge. This study offers a potential solution by investigating the degradation of hazardous Acid Blue-9 (AB9) dye using bacterial consortium containing Pseudomonas fluorescens and Bacillus subtilis. The experiments were conducted in minimal salt media with varying concentrations of different parameters (pH, temperature, initial dye concentration, size of inoculum, carbon and nitrogen sources) used for optimization process. Using the One-Factor-At-A-Time method, the consortium in the study was able to decolorize at a noteworthy rate of decolorisation about 85–87.2 %. This rate was notably increased to around 90.02 % with the implementation of Response Surface Methodology. Additionally, the decolorisation process was further confirmed by using UV-Visible Spectroscopy and Fourier Transform InfraRed Spectroscopy. Additionally, a phytotoxicity assessment test on crops of agricultural significance supports the potential of this bacterial consortium for the bioremediation of AB9 contaminated water. The dye samples treated with bacterial consortium showed significantly higher seed germination rates (∼82.9 %), along with increased shoot (∼9.13 cm) and root lengths(∼3.01 cm), compared to the untreated samples. This promising finding suggests the potential for reusing treated water in agriculture and industry, offering a sustainable solution to water pollution.

Investigation of EDM erosion behavior for Ni-based superalloy using experimental and machine learning approach

The rapid growth of the global population and the consequent increase in manufacturing activities have heightened the need for sustainable practices to protect the environment. Non-traditional manufacturing processes, such as electric discharge machining (EDM), often use oil-based dielectric fluids like kerosene, which emit toxic fumes and aerosols, contributing to environmental pollution. To address this, the reuse of waste cooking oil (WCO) as a dielectric fluid in EDM presents a promising solution. This method not only tackles the issue of waste oil disposal but also supports the circular economy by repurposing waste materials. EDM, frequently criticized for its low cutting rates, can be enhanced by using nitrogen assisted modified Cu electrodes (cryogenically treated (CT)). This study focused on machining the difficult-to-cut nickel-based superalloy, IN718 by incorporating Sillimanite nano-powder with Span-60 (S-60). The erosion performance characteristics such as material removal rate (MRR), electrode wear rate (EWR), and accuracy index (AI) were evaluated. An artificial neural network (ANN) was employed to predict and correlate experimental and predicted values. Significant improvements were achieved using the non-dominated sorting genetic algorithm (NSGA-II) in the confirmation investigations. With a non-treated (NT) Cu electrode, the confirmatory experiments showed a 79.81 % increase in MRR, an 18.99 % decrease in EWR, and a 0.89 % improvement in AI. For the CT Cu electrode, MRR increased by 85.48 %, EWR decreased by 32.90 %, and AI improved by 0.53 % when using optimal processing parameters. This study demonstrates the potential for significant environmental and performance benefits in EDM by reusing WCO and optimizing electrode treatments.

Evaluation and Optimization of Cement Slurry Systems for Ultra-Deep Well Cementing at 220 °C

With the depletion of shallow oil and gas resources, wells are being drilled to deeper and deeper depths to find new hydrocarbon reserves. This study presents the selection and optimization process of the cement slurries to be used for the deepest well ever drilled in China, with a planned vertical depth of 11,100 m. The bottomhole circulating and static temperatures of the well were estimated to be 210 °C and 220 °C, respectively, while the bottomhole pressure was estimated to be 130 MPa. Laboratory tests simulating the bottomhole conditions were conducted to evaluate and compare the slurry formulations supplied by four different service providers. Test results indicated that the inappropriate use of a stirred fluid loss testing apparatus could lead to overdesign of the fluid loss properties of the cement slurry, which could, in turn, lead to abnormal gelation of the cement slurry during thickening time tests. The initial formulation given by different service providers could meet most of the design requirements, except for the long-term strength stability. The combined addition of crystalline silica and a reactive aluminum-bearing compound to oil well cement is critical for preventing microstructure coarsening and strength retrogression at 220 °C. Two of the finally optimized cement slurry formulations had thickening times more than 4 h, API fluid loss values less than 50 mL, sedimentation stability better than 0.02 g/cm3, and compressive strengths higher than 30 MPa during the curing period from 1 d to 30 d.

NMPC‐Based Control of Underactuated Spacecraft: A Neighboring Extremal Approach Extended to SE(3)

ABSTRACTThe present work revolves around employing the benefits of neighboring extremal approach in the nonlinear model predictive control of mechanical systems evolving on SE(3). In the NMPC process, necessary conditions of optimality are extracted based on some discrete‐time version of the equations of motion referred as LGVI and the obtained TPBVP equations are solved using simplified sensitivity derivatives by means of an indirect shooting method. Taking into consideration that the repetitive optimization process of the NMPC is time consuming, making its implementation challenging, a method of neighboring extremal is proposed here for recalculating the responses of the systems in scenarios that face some unexpected changes in their parameters or are encountering some last‐minute re‐planning schemes. Based on the existing responses of the system to the first set of initial conditions, the reaction of the system to the altered set of initial conditions can be reconstructed without the need to solve the whole optimization process from scratch by utilizing the features of the NE method. A spacecraft model evolving on SE(3) with actuation constraints confirms the efficiency of the whole process in term of accuracy versus computation burden. Forasmuch as actuator failure is a probable yet important event in aerial/spatial missions, the performance of the control system in dealing with such situations is examined. The algorithm robustness and its capability to compensate the effects of the actuator loss is checked.

Preoperative chemo-CIRT in Re/BRe pancreatic cancer: Insights from a multicenter prospective phase II clinical study (NCT03822936).

There is debate about the optimal management of borderline resectable (bRe) and resectable (Re) pancreatic ductal adenocarcinoma (PDAC). Both preclinical and clinical evidence showed that carbon ion radiotherapy (CIRT) produces superior control on radioresistant histologies compared to conventional photon beam radiotherapy (RT). However, so far there is a lack of data concerning the integration of CIRT in a multimodal approach with chemotherapy and surgery for bRe/Re. We recently presented the first analysis of a multicenter prospective phase II clinical study aimed at assessing the feasibility and effectiveness of a neoadjuvant chemotherapy + short course of CIRT followed by surgery and adjuvant chemotherapy in the management of bRe/Re PDAC. The study was terminated early due to low patient enrollment.Herein, we reported a post-hoc analysis focusing on toxicity, dosimetry and translational assessment. In our experience, CIRT can be integrated into a multimodal treatment strategy for bRe/Re PDAC, alongside chemotherapy and surgery. A case of fatal liver failure occurring three months post-surgery has been documented, likely related to the combination approach. Although the treatment plans were satisfactory according to the Local Effect Model (LEM) model, recalculations using the modified Microdosimetric Kinetic Model (mMKM) revealed suboptimal target coverage. Additionally, we observed an increased expression of PD-L1 following CIRT. This multimodal approach was well tolerated; however, clinicians should carefully monitor for vascular disorders during follow-up and further investigate surgical techniques after CIRT. The increased PD-L1 expression supports the immunogenic effects of particle therapy and lays the groundwork for future studies. To enhance the therapeutic ratio of CIRT treatments, integrating dose-averaged LETd (LETd-based objectives into the plan optimization process should be considered. ClinicalTrials.gov Identifier: NCT03822936.

DeLoSo: Detecting Logic Synthesis Optimization Faults Based on Configuration Diversity

Logic synthesis tools are the core components of digital circuit design, which convert programs written in hardware description languages into gate-level netlists, and optimize the netlists. However, the netlist optimization is complex, with numerous optimization parameters to be configured. Any minor optimization faults in logic synthesis tools may cause circuit diagrams to significantly deviate from the original design, posing risks in target systems. We propose DeLoSo, the De tector of Lo gic S ynthesis o ptimization faults, the first method specifically designed to identify potential faults in the optimization processes of logic synthesis tools. DeLoSo relies on netlist differences and parameter variations to guide the generation of diverse Logic Synthesis Optimization Configuration (LSOC) combinations to thoroughly test the optimization process. DeLoSo consists of three components: LSOC generator, which generates diverse LSOC combinations through configuration recombination and mutation; LSOC diversity evaluator, which assesses the diversity of optimization configurations; and LSOC validator, which validates the generated LSOC combinations to discover optimization faults. DeLoSo identified 19 faults in two established logic synthesis tools (i.e., Vivado and Yosys); 15 of them have been fixed by vendors. Particularly, the community maintainers of Yosys have considered incorporating DeLoSo into Yosys’ existing test suite.

Explainable Optimisation through Online and Offline Hyper-heuristics

Abstract - Research in the explainability of optimisation techniques has largely focused on metaheuristics and their movement of solutions around the search landscape. Hyper-heuristics create a different challenge for explainability as they make use of many more operators, or low-level heuristics and learning algorithms which modify their probability of selection online. This paper describes a set of methods for explaining hyper-heuristics decisions in both online and offline scenarios using selection hyper-heuristics as an example. These methods help to explain various aspects of the function of hyper-heuristics both at a particular juncture in the optimisation process and through time. Visualisations of each method acting on sequences provide an understanding of which operators are being utilised and when, and in which combinations to produce a greater understanding of the algorithm-problem nexus in hyper-heuristic search. These methods are demonstrated on a range of problems including those in operational research and water distribution network optimisation. They demonstrate the insight that can be generated from optimisation using selection hyper-heuristics, including building an understanding of heuristic usage, useful combinations of heuristics and heuristic parameterisations. Furthermore the dynamics of heuristic utility are explored throughout an optimisation run and we show that it is possible to cluster problem instances according to heuristic selection alone, providing insight into the perception of problems from a hyper-heuristic perspective.

PB-MNSGA-II algorithm and its application for multi-objective optimization design of under-vehicle equipment

Structural damage caused to under-vehicle equipment under adverse working conditions affects the safe operation of high-speed electric multiple units (EMUs). To address the low optimization efficiency and take into account vibration fatigue in traditional under-vehicle equipment design, herein, a multi-objective optimization design method (PB-MNSGA-II) that combines particle swarm optimization-back propagation (PSO-BP) neural network and modified non-dominated sorting Genetic Algorithm II (MNSGA-II) was proposed. It can help engineers choose better equipment component thickness and achieve efficient and accurate design. First, MNSGA-II, the core content of PB-MNSGA-II, was proposed, which improved four aspects of NSGA-II: Latin hypercube sampling population initialization, adaptive crossover and mutation probability, mutation interference of Levy flight strategy, and optimal elite strategy. Then, zero-ductility transition (ZDT) series functions were employed to test MNSGA-II, thereby verifying its convergence and computational efficiency. Subsequently, utilizing the frame structure of the traction transformer (TTR) as an illustrative example, the PB-MNSGA-II approach was employed to enable multi-objective optimization. During this optimization process, the focus was on random vibration fatigue damage, thus enhancing the rationality of the optimization outcomes. After structural optimization, the mass of the TTR decreased by 20.1 kg, and the maximum vibration fatigue damage value decreased from 1.09 to 0.808, verifying the effectiveness and feasibility of the proposed method. The findings of this study provide important insights for the multi-objective optimization design of under-vehicle equipment under complex operating conditions.

Artificial Neural Network to Predict Curvature Light Shelf Design Related Daylighting Optimization on Office Spaces

Energy Optimization in building design field now has been revolutionized due to AI and machine learning applications. Leveraging daylight to reduce artificial lighting consumption holds promise for significant energy savings, yet the nonlinear nature of daylight patterns poses challenges in prediction and optimization. This study proposes a novel approach to automated light shelf design using machine learning algorithms, specifically artificial neural networks (ANNs) such as recurrent neural networks (RNN) by long short term memory (LSTM), to accelerate daylighting simulation and optimization processes. The methodology employed two distinct approaches: Firstly, we employed the theoretical-analytical approach to explore methods for utilizing machine learning in natural lighting and light shelf parameters. Second, the practical and applied method involved creating a predictive model for designing the light shelf using appropriate AI and ML techniques. This model is based on an office geometry model at the El Arish weather file in Egypt, four-dimensional training room models with three internal zones-oriented south. Rhinoceros and Grasshopper, two parametric simulation tools, are used to normalize and optimize light shelf parameters like geometry cross-section, curvature surface, width, height position, depth, tilt angle, and reflectance materials. Then, the Galapagos plug-in and Colibri2 are used for dataset creation and optimization. The results demonstrate that automated light shelf operation has a significant impact on internal daylighting quality. RNNs enable rapid prediction of optimization models, reducing time consumption in the early design phase. ML facilitates decision-making by generating evaluative criteria from user-selected design options. RNNs were classified as good and bad and used LSTM to enhance prediction accuracy for efficient illuminance values metric in zones 1 and 2. Challenges include increasing the simulation procedure's efficiency. The results of model accuracy reached 99%. Hence, future research should prioritize resolving the previously identified concerns. In conclusion, this study underscores the importance of ML-driven approaches in early design phases to optimize building energy consumption and pave the way for sustainable architectural practices.

Model for Predicting Maize Crop Yield on Small Farms Using Clusterwise Linear Regression and GRASP

Planting a crop involves several key steps: resource assessment, crop selection, crop rotation, planting schedules, soil preparation, planting, care, and harvesting of crops. In this context, estimating the productivity of a crop based on available information, such as expected climatic conditions and agricultural practices, helps farmers reduce the uncertainty of their investment. In Colombia, maize is the fourth most important crop in the country. Significant efforts are required to improve productivity in traditional and technified production systems. In this sense, this research proposes and evaluates an approach called Clusterwise Linear Regression (CLR) to predict the crop maize yield in small farms, considering data on climate, soil, fertilization, and management practices, among others. To develop the CLR model, we conducted the following steps: data collection and preparation, clustering using k-means, cluster optimization with Greedy Random Adaptive Search Procedure (GRASP), and performance evaluation. The cluster optimization process allows the identification of clusters with similar characteristics and generates multiple linear regression models with mixed variables that explain the yield of the farms on each cluster. The Simulated Multiple Start Annealing (MSSA) metaheuristics were also evaluated, but the results of GRASP were the best. The results indicate that the proposed CLR approach is more effective than the linear and nonlinear algorithms mentioned in the literature, such as multiple lasso linear regression, random forests, XGBoost, and support vector machines. These algorithms achieved an accuracy of 70%. However, with the new CLR model, a significantly improved accuracy of 87% was achieved with test data. The clusters’ studies revealed key factors affecting crop yield, such as fertilization, drainage, and soil type. This transparency is a benefit over black-box models, which can be harder to interpret. This advancement can allow farmers to make better decisions about the management of their crops.

Wind Farm Power Maximisation via Wake Steering: A Gaussian Process‐Based Yaw‐Dependent Parameter Tuning Approach

ABSTRACTMaximising the power production of wind farms is vital to meet the growing demand for wind energy and reduce its cost. Wake effects, resulting from the aerodynamic interactions between turbines in a wind farm, significantly impact farm efficiency, leading to substantial annual power losses. Wake steering, an influential control strategy, involves mitigating wake effects by strategically yaw misaligning upstream turbines to deflect their wakes. Conventional wake steering approaches typically rely on physics‐based analytical wake models with their parameters often calibrated using higher fidelity data. However, these approaches determine a fixed set of parameters prior to conducting wake steering, neglecting each parameter's dependency on yaw misalignment (i.e. the optimisation variables) exhibited throughout the optimisation process, potentially affecting its accuracy. To address this limitation, this paper introduces a novel data‐driven parameter tuning approach that integrates higher fidelity power measurements using Gaussian processes to continuously adapt parameters in lower fidelity wake models based on the current farm's yaw configuration. The effectiveness of the proposed approach is demonstrated on a wind farm and a layout corresponding to the Horns Rev wind farm, where various wind directions are investigated. The results reveal that the approach can enable a lower fidelity model to capture more complex physics, thereby improving its accuracy in wake steering optimisation, while maintaining robustness and computational efficiency. This method holds promise for real‐time control applications and can be extended to other control strategies and closed‐loop frameworks.

Effect of the succinonitrile additive, electrode processing, and N/P ratios in the performance of high‐voltage lithium‐ion batteries using LiNi0.5Mn1.5O4 cathode

AbstractThe progressive improvement in the gravimetric energy density of lithium‐ion batteries (LIBs) leads to electrolyte design, positive electrode, and full‐cell optimization processes. The spinel lithium nickel manganese oxide (LiNi0.5Mn1.5O4, LNMO) is one of the potential candidates for the next generation of LIBs applied for electric vehicles due to high working potential (4.75 V vs. Li+/Li), affordable price, and environmental friendliness. Nevertheless, the degradation of cycling performance at high potential induces massive challenges for commercializing LNMO‐based batteries. This study investigated the impact of succinonitrile (SN), electrode processing, and N/P ratios to strengthen the LNMO half‐cell and graphite||LNMO full‐cell performance. According to the performance in half‐cell, the sample contained 85 wt% LNMO: 7.5 wt% C65: 7.5 wt% PVDF/NMP combining with the electrolyte 1.5 M LiPF6 in EC:EMC:DMC (2:1:7 – v/v) at 0.5 wt% SN seems to be the optimal condition for further full‐cell. In addition, the full‐cell with N/P = 1.3 displays a remarkable initial capacity of 118.75 mAh g−1, a Coulombic efficiency of 91.64%, and a superior gravimetric energy density of 537.71 mWh g−1. Moreover, it maintains a capacity retention of around 58% at the current density of 0.1C after 100 cycles. As a result, this study presents an optimal solution to augment the material's potential for commercialization in the immediate future.

Deep learning analysis of histopathological images predicts immunotherapy prognosis and reveals tumour microenvironment features in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer mortality worldwide. Immune checkpoint inhibitors (ICIs) have emerged as a crucial treatment option for patients with advanced NSCLC. However, only a subset of patients experience clinical benefit from ICIs. Therefore, identifying biomarkers that can predict response to ICIs is imperative for optimising patient selection. Hematoxylin and eosin (H&E) images of NSCLC patients were obtained from the local cohort (n = 106) and The Cancer Genome Atlas (TCGA) (n = 899). We developed an ICI-related pathological prognostic signature (ir-PPS) based on H&E stained histopathology images to predict prognosis in NSCLC patients treated with ICIs using deep learning. To accomplish this, we employed a modified ResNet model (ResNet18-PG), a widely-used deep learning architecture well-known for its effectiveness in handling complex image recognition tasks. Our modifications include a progressive growing strategy to improve the stability of model training and the use of the AdamW optimiser, which enhances the optimisation process by adjusting the learning rate based on training dynamics. The deep learning model, ResNet18-PG, achieved an area under the receiver operating characteristic curve (AUC) of 0.918 and a recall of 0.995 on the local cohort. The ir-PPS effectively risk-stratified NSCLC patients. Patients in the low-risk group (n = 40) had significantly improved progression-free survival (PFS) after ICI treatment compared to those in the high-risk group (n = 66, log-rank P = 0.004, hazard ratio (HR) = 3.65, 95%CI: 1.75-7.60). The ir-PPS demonstrated good discriminatory power for predicting 6-month PFS (AUC = 0.750), 12-month PFS (AUC = 0.677), and 18-month PFS (AUC = 0.662). The low-risk group exhibited increased expression of immune checkpoint molecules, cytotoxicity-related genes, an elevated abundance of tumour-infiltrating lymphocytes, and enhanced activity in immune stimulatory pathways. The ir-PPS signature derived from H&E images using deep learning could predict ICIs prognosis in NSCLC patients. The ir-PPS provides a novel imaging biomarker that may help select optimal candidates for ICIs therapy in NSCLC.

Calibration-Based ALE Model Order Reduction for Hyperbolic Problems with Self-Similar Travelling Discontinuities

We propose a novel Model Order Reduction framework that is able to handle solutions of hyperbolic problems characterized by multiple travelling discontinuities. By means of an optimization based approach, we introduce suitable calibration maps that allow us to transform the original solution manifold into a lower dimensional one. The novelty of the methodology is represented by the fact that the optimization process does not require the knowledge of the discontinuities location. The optimization can be carried out simply by choosing some reference control points, thus avoiding the use of some implicit shock tracking techniques, which would translate into an increased computational effort during the offline phase. In the online phase, we rely on a non-intrusive approach, where the coefficients of the projection of the reduced order solution onto the reduced space are recovered by means of an Artificial Neural Network. To validate the methodology, we present numerical results for the 1D Sod shock tube problem, for the 2D double Mach reflection problem, also in the parametric case, and for the triple point problem.

Mitigating social bias in sentiment classification via ethnicity-aware algorithmic design

Sentiment analysis tools are frequently employed to analyze large amounts of natural language data gathered from social networks and generate valuable insights on public opinion. Research has discovered that these tools tend to be biased against some demographic groups, based on social attributes such as gender, age, and ethnicity. Sentiment classification works dealt with this issue by means of data balancing and algorithmic approaches. However, one crucial limitation of existing methods is the inability to tackle social bias while maintaining satisfactory model performance. In this paper, we aim to fill this gap by proposing a sentiment classification method that entails ethnicity-aware algorithmic design. Specifically, our method involves balanced training and a custom ethnicity-aware loss function that leverages ethnicity group information to foster a fair model optimization process. The proposed loss incentivizes the model to iteratively improve accuracy for currently underperforming demographic or social groups, therefore simultaneously decreasing social bias and boosting overall performance. Our extensive qualitative and quantitative experimental evaluation involving a large corpus of user reviews demonstrated the effectiveness of the proposed method, also when compared to popular baselines for sentiment classification.

Flexible Piezoelectric Nanocolumnar Composite Films as Flat-Panel Loudspeakers: Application and Modeling.

Highly anisotropic piezoelectric composites promise to progress electroacoustic devices as a class by combining the advantages of both piezoceramics and polymers. Fundamentally, piezoelectric loudspeakers employ the converse piezoelectric effect to convert electrical to mechanical energy. Quasi-1-3 piezoceramic/polymer composites enable flat-panel loudspeakers that are tunable in elastic modulus, with opportunities for mechanical flexibility, optical transparency, and large-area coverage. Their processing route enables relatively flexible design parameters, such as the particle loading, polymer-matrix modulus, film thickness, film size, and electrode-material stiffness. Alternative processing routes of electric field (E-field) aligned-piezoelectric composites are demonstrated, including using the relaxor ferroelectric lead magnesium niobate-lead titanate (PMN-PT) to enhance the acoustic performance and photocurable resins to accelerate the materials processing. Material properties critical for dielectrophoresis are characterized, and loudspeakers were fabricated based on the optimal processing conditions. Subsequently, electroacoustic characterization explores the effect of loudspeaker size, substrate stiffness, the microphone distance, the piezoceramic material, and the matrix modulus. Finally, finite-element (FE) modeling of the electromechanical behavior validates the natural frequencies and modes shapes of the loudspeakers via the analytical solution and frequency response to electrical and mechanical excitation. Good correspondence between the predicted electroacoustic performance and experimentally validated model is observed.

Particle swarm optimization of high Q factor interdigitated E-shaped metamaterial for refractive index sensing

AbstractMetamaterials for refractive index sensing applications have garnered significant attention in recent years. However, achieving an optimal balance between high sensitivity and quality factor, which together determine the Figure of Merit of sensors, necessitates extensive optimization efforts. In this paper, we present the metaheuristic optimization of a refractive index-based plasmonic sensor operating at a wavelength of 1500 nm, which has the potential to open new avenues for applications in biomedical sensing and beyond. Particle Swarm Optimization is utilized to maximize the performance of the proposed infrared metamaterial sensor. The proposed design features two overlapping E-shaped silver patches placed on top of a grounded $$\mathrm {Al_{2}O_{3}}$$ Al 2 O 3 substrate. This configuration results in a narrow-band absorption spectrum with peak resonances caused by the confinement of the electric field within the gaps of the E-shaped metallic arms. The metaheuristic optimization process yielded sensor dimensions that achieve a high sensitivity of 834.7 nm/RIU,Q = 865 and a Figure of Merit of 481.34 $$\textrm{RIU}^{-1}$$ RIU - 1 , demonstrating outstanding performance in cancer cell detection.

Optimizing a hybrid wind-solar-biomass system with battery and hydrogen storage using generic algorithm-particle swarm optimization for performance assessment

This paper investigates the optimal design of a hybrid renewable energy system, integrating wind turbines, solar photovoltaic systems, biomass, and battery and hydrogen storage to ensure a reliable energy supply at the lowest annual cost for a residential load in Kern County, USA. The hybrid generic algorithm particle swarm optimization (GAPSO) algorithm was adopted to determine the optimal configuration of parameters and cost-effectiveness, considering technical, economic, environmental, and social performance indicators. The generic algorithm (GA) and particle swarm optimization (PSO) validate the effectiveness of the proposed technique, showcasing its efficiency in system optimization. The findings indicate that GAPSO outperforms GA and PSO due to its rapid convergence, lowest final fitness value, and stable optimization process. The hybrid GAPSO's performance, combined with the different capacities of wind turbines (4,561 kW), solar PV (8,480 kW), biomass (2,261 kW), battery banks (8,000 kWh), and fuel cells (2,392 kW), resulted in an annual cost of $6,239,193; energy cost and net present value of $0.48/kWh and $101,333,937. The system maintained a supply loss of 0.8 %, achieved an availability index of 99.2 %, a renewable energy fraction of 88.87 %, GHGs emission of 953,615 kg, land use of 3,842,875 m2, and water consumption 528,678 L respectively. GAPSO achieved a 2.17 % and 0.01 % improvement in cost-effectiveness and 11.11 % increase in reliability compared to GA and PSO.

Massive Point Cloud Processing for Efficient Construction Quality Inspection and Control

The construction of large-scale civil infrastructures requires massive spatiotemporal data to support the management and control of scheduling, quality control, and safety monitoring. Existing artificial-intelligence-based data processing algorithms rely heavily on experienced engineers to adjust the parameters of data processing, which is inefficient and time-consuming when dealing with huge datasets. Limited studies have compared the performance of different algorithms on a unified dataset. This study proposes a framework and evaluation system for comparing different data processing policies for processing huge spatiotemporal data in construction quality control. The proposed method compares the combination of multiple types of algorithms involved in the processing of massive point cloud data. The performance of data processing strategies is evaluated through this framework, and the optimal point cloud processing strategies are explored based on registration accuracy and data fidelity. Results show that a reasonable choice of combinations of point cloud sampling, filtering, and registration algorithms can significantly improve the efficiency of point cloud data processing and satisfy engineering demands for data accuracy and completeness. The proposed method can be applied to the civil engineering problem of processing a large amount of point cloud data and selecting the optimal processing method.

Analysis and Optimization of Super Duplex Stainless Steel Deposition in Wire Arc Additive Manufacturing Using Machine Learning Techniques

<div>This article presents experimental investigations and machine learning-based analysis on depositions of super duplex stainless steel (SDSS ER2594) material in wire arc additive manufacturing (WAAM) considering the process parameters namely voltage, wire feed rate, torch travel speed, and gas flow rate. Deposition efficiency and surface height values of the accumulated material were measured to build machine learning models using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The developed ANN model could predict the deposition efficiency and surface height with mean absolute deviations (MADs) of 8.9% and 16.1%, respectively. The MAD for prediction of the two responses for ANFIS model was found to be 6.1% and 14.9% as compared to the experimental data. Multi-objective optimization was also performed to obtain optimal solutions to achieve desired deposition results. Mechanical properties and microstructures of the deposited materials with optimal processing parameters were found comparable to that of the base materials.</div>