Artificial Intelligence Optimization Research Articles

Experimental and artificial intelligence optimization of paint wastewater (PWW) coagulation using novel Phaseolus vulgaris seed extract (PVSE)

This study investigated the application of artificial intelligence algorithms (AIA) in the coagulation treatment of paint wastewater anchored by novel Phaseolus vulgaris seed extract (PVSE). Untreated wastewater discharge harms the ecosystem, and therefore harmful industrial effluent, such as paint wastewater, must be brought to safe discharge levels before being released into the environment. In addition to AIA, comprehensive characterization tests, coagulation kinetics, and process optimization were also executed. Characterization results revealed that total solid in the PWW was above allowable standard, justifying the need for effective particle decontamination. The XRD and FTIR characterization indicated that PVSE structure is amorphous with abundant amine groups. Results of analysis of variance (ANOVA) obtained from process modeling indicated that the coagulation-flocculation process was a nonlinear quadratic system (F-value = 45.51) which was mostly influenced by PVSE coagulant dosage (F-value = 222.48; standardized effect = 14.85). Artificial intelligence indicated that neural network training effectively captured the nonlinear nature of the system in ANN (RMSE = 0.00040194; R = 0.98497), and ANFIS (RMSE = 0.003961) algorithms. Regression coefficient obtained from process modeling highlighted the suitability of RSM (0.9662), ANN (0.9739), and ANFIS (0.9718) in forecasting the coagulation-flocculation process, while comparative statistical appraisal authenticated the superiority of ANN model over RSM and ANFIS models. The coagulation kinetics experiment, which used a coagulation kinetic model, revealed a constant flocculation constant (Kf-value) for all jar test batches and a strong association between the Menkonu coagulation-flocculation constant (Km) and Kf values. Best removal efficiency of 97.01 % was obtained using ANN coupled genetic algorithm optimization (ANN-GA) at PVSE dosage of 4 g/L, coagulation time of 29 min and temperature of 25.1oC.

Relationship between Artificial Intelligence and Business Process Optimization: Insights from Selected Banks in Anambra State

This study explored the relationship between artificial intelligence and business process optimization in selected banks in Anambra State. The population consisted of 745 employees from commercial banks in Anambra State, Nigeria. Using purposeful sampling, three banks from each senatorial district in the state were chosen, and 170 questionnaires were distributed to staff members of these selected banks. Out of the 170 distributed questionnaires, 125 were completed and returned. A Pearson correlation critical value table was used to test the assumptions, and the Pearson product- moment correlation coefficient was the statistical instrument for data analysis. The hypothesis results indicated a significant correlation between business process optimization in banks and artificial intelligence, specifically in enhancing customer service relationships and boosting cyber-security in the selected banks in Anambra State. The study recommends that the banking industry should continue to implement artificial intelligence cautiously to maintain a balance between innovative developments and the responsible and ethical use of AI. This approach will ensure improved cyber- security and customer service in banks.

A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models

With the rapid development of artificial intelligence technology, data-driven advanced models have provided new ideas and means for airfoil aerodynamic optimization. As the advanced models update and iterate, many useful explorations and attempts have been made by researchers on the integrated application of artificial intelligence and airfoil aerodynamic optimization. In this paper, many critical aerodynamic optimization steps where data-driven advanced models are employed are reviewed. These steps include geometric parameterization, aerodynamic solving and performance evaluation, and model optimization. In this way, the improvements in the airfoil aerodynamic optimization area led by data-driven advanced models are introduced. These improvements involve more accurate global description of airfoil, faster prediction of aerodynamic performance, and more intelligent optimization modeling. Finally, the challenges and prospect of applying data-driven advanced models to aerodynamic optimization are discussed.

Maximizing CSR impact: Leveraging artificial intelligence and process optimization for sustainability performance management

AbstractThis study, which is anchored to Resource‐Based View (RBV) theory, explores the relationship between artificial intelligence (AI), process optimization, organizational flexibility, and sustainability performance in organizational settings. Leveraging the RBV framework's focus on internal resources as sources of competitive advantage, this research seeks to clarify how AI adoption, process optimization, and organizational flexibility foster sustainable growth. SPSS PROCESS Macro was used to analyze the data from 288 organizations. The findings derived from the empirical data, this study verify the positive relationships between AI and process optimization as well as AI and sustainability performance, revealing the strategic nature of AI as a resource to improve operational effectiveness and environmental responsibility. Further, our results indicate the mediating role of process optimization and the moderating effect of organizational flexibility in determining the link between AI and sustainability outcomes. Moreover, results confirmed the indirect effects of AI on sustainability performance via process optimization under the boundary conditions of the organizational flexibility. These findings add to the developing literature of sustainable operational practices and provide practical suggestions that might be useful for the practitioners aiming at using AI driven by organizational capabilities to deliver sustainability performance.

Pulmonary Arterial Anatomical Patterns: a Classification Scheme Based on Lobectomy and 3D-CTBA.

Preoperative evaluation of pulmonary vascular and tracheal routes and variations is of great importance to the surgeon. Three-dimensional computed tomography bronchography and angiography (3D-CTBA) has evolved in recent years with the optimization of 3D reconstruction techniques and artificial intelligence. We aim to apply CT angiography and Exoview 3D reconstruction technology to assess patients' pulmonary arterial tree and its anatomical variants and to try to summarize a set of anatomical typing of the pulmonary arterial tree that is relatively easy and conducive to promoting teaching based on surgical habits of lobectomy. A total of 358 patients hospitalized in the Department of Thoracic Surgery of the First Affiliated Hospital of Soochow University between July 2020 and August 2021 were included in this study. We carefully analyzed the site of emanation, alignment, and number of branches of the pulmonary artery according to a uniform classification method in conjunction with the two-dimensional CT images and transformed them into 3D reconstruction models. Different types of pulmonary artery were observed in 358 cases. We evaluated the complete pulmonary artery tree and counted the number and frequency of major arteries of the pulmonary based on the surgical habits of anatomical lobectomy. The 3D-CTBA technique enables us to adequately assess the anatomy of the pulmonary arteries. Moreover, we provide a practical classification scheme of pulmonary arterial anatomical patterns based on lobectomy and 3D-CTBA. Our data can be used by clinicians in the teaching of pulmonary artery anatomy and the preoperative preparation for anatomical lobectomy.

Superior decomposition of xenobiotic RB5 dye using three-dimensional electrochemical treatment: Response surface methodology modelling, artificial intelligence, and machine learning-based optimisation approaches

The highly efficient electrochemical treatment technology for dye-polluted wastewater is one of hot research topics in industrial wastewater treatment. This study reported a three-dimensional electrochemical treatment process integrating graphite intercalation compound (GIC) adsorption, direct anodic oxidation, and ·OH oxidation for decolourising Reactive Black 5 (RB5) from aqueous solutions. The electrochemical process was optimised using the novel progressive central composite design–response surface methodology (CCD–NPRSM), hybrid artificial neural network–extreme gradient boosting (hybrid ANN–XGBoost), and classification and regression trees (CART). CCD–NPRSM and hybrid ANN–XGBoost were employed to minimise errors in evaluating the electrochemical process involving three manipulated operational parameters: current density, electrolysis (treatment) time, and initial dye concentration. The optimised decolourisation efficiencies were 99.30%, 96.63%, and 99.14% for CCD–NPRSM, hybrid ANN–XGBoost, and CART, respectively, compared to the 98.46% RB5 removal rate observed experimentally under optimum conditions: approximately 20 mA/cm2 of current density, 20 min of electrolysis time, and 65 mg/L of RB5. The optimised mineralisation efficiencies ranged between 89% and 92% for different models based on total organic carbon (TOC). Experimental studies confirmed that the predictive efficiency of optimised models ranked in the descending order of hybrid ANN–XGBoost, CCD–NPRSM, and CART. Model validation using analysis of variance (ANOVA) revealed that hybrid ANN–XGBoost had a mean squared error (MSE) and a coefficient of determination (R2) of approximately 0.014 and 0.998, respectively, for the RB5 removal efficiency, outperforming CCD–NPRSM with MSE and R2 of 0.518 and 0.998, respectively. Overall, the hybrid ANN–XGBoost approach is the most feasible technique for assessing the electrochemical treatment efficiency in RB5 dye wastewater decolourisation.

Current Status, Sizing Methodologies, Optimization Techniques, and Energy Management and Control Strategies for Co-Located Utility-Scale Wind–Solar-Based Hybrid Power Plants: A Review

The integration of renewable energy sources, such as wind and solar, into co-located hybrid power plants (HPPs) has gained significant attention as an innovative solution to address the intermittency and variability inherent in renewable systems among plant developers because of advancements in technology, economies of scale, and government policies. However, it is essential to examine different challenges and aspects during the development of a major work on large-scale hybrid plants. This includes the need for optimization, sizing, energy management, and a control strategy. Hence, this research offers a thorough examination of the present state of co-located utility-scale wind–solar-based HPPs, with a specific emphasis on the problems related to their sizing, optimization, and energy management and control strategies. The authors developed a review approach that includes compiling a database of articles, formulating inclusion and exclusion criteria, and conducting comprehensive analyses. This review highlights the limited number of peer-reviewed studies on utility-scale HPPs, indicating the need for further research, particularly in comparative studies. The integration of machine learning, artificial intelligence, and advanced optimization algorithms for real-time decision-making is highlighted as a potential avenue for addressing complex energy management challenges. The insights provided in this manuscript will be valuable for researchers aiming to further explore HPPs, contributing to the development of a cleaner, economically viable, efficient, and reliable power system.

An artificial intelligence optimization method of back analysis of unsteady-steady seepage field for the dam site under complex geological condition

An artificial intelligence optimization method of back analysis of unsteady-steady seepage field for the dam site under complex geological condition

Artificial Intelligence prediction and optimization of the mechanical strength of modified Natural Fibre/MWCNT polymer nanocomposite

Artificial Intelligence (AI), techniques like artificial neural networks (ANN), and machine learning have been used to solve a variety of engineering problems. In this study, Multiwall Carbon Nanotube (MWCNT) and Natural Fibres (NF) from plantain (Musa Paradisiaca) fiber (PF), were utilized to prepare a reinforced hybrid polymer nanocomposite for advanced composite applications. A chemical solution containing potassium permanganate (KMnO4) in acetone (C3H6O) was applied to modify the fibers to alter their surface and improve adhesion and interaction between the PF/polymer matrix. To predict and optimize the tensile strength (TS) of the prepared PF/MWCNT hybrid nanocomposite (PFMNC), the ANN model with hyper-parameter optimization in a single-layer-perceptron architecture of 3-5-1 was used with 5 neurons in the hidden layer, and Box-Behnken Design (BBD) was utilized. Scanning electron microscope (SEM) micrographs demonstrate that KMnO4 modification has impacted the TS of the hybridized nanocomposite. Mechanical Test results show that these variables impacted the TS of the PFMNC as shown by analysis of variance (ANOVA) with R2 = 0.9986. The expected findings were nearly identical to the experimental results. The model predicted an optimal tensile strength of 46.1563 Mpa. To substantiate the reliability of the empirical experimental investigation, TS analysis was performed at predicted optimal settings. TS results showed an average strength of 45.4401 Mpa. About 98.45 % of the projected tensile strength is accounted for by the model. The present study has demonstrated the effectiveness of the ANN-BBD modeling technique in achieving the appropriate mechanical property values quickly, reducing production costs, and preserving resources.

Artificial intelligence and PI gain optimisation for sensorless indirect vector control of induction motor-based electric vehicle drives

This paper aims to develop indirect vector-controlled induction motor (IM) drives for EV applications. In this paper, controllers are used in two places. The first is the speed controller, used to reduce the error between reference speed and actual EV speed. And the second one is the speed estimator used to reduce the error between the adaptive model and reference model during sensorless operation. The performance of these controllers is compared by using ANFIS, ANN and PI controllers. The real-time performance of the IM-based EV drive system is dependent on the PI gain value. Hence in this paper, seven human and nature-inspired metaheuristic optimisation techniques such as JFO, GWO, ABC, PSO, GA, TLBO and DE are used to obtain the optimised value of the PI controller.

A new MPPT controller based on a modified multiswarm PSO algorithm using an adaptive factor selection strategy for partially shaded PV systems

Maximum power point tracking (MPPT) controller is the main element in photovoltaic (PV) systems, which is used to ensure maximum power extraction under different meteorological conditions. A MPPT controller can guarantee good performance criteria even in the presence of climatic changes. To achieve this goal, several techniques have been proposed in the literature to improve robustness of the PV system control, such as artificial intelligence and multiswarm particle swarm optimization (MSPSO) algorithm. Previous research on classical MSPSO has shown that the algorithm search behavior cannot find the optimal solution for certain problems. In this context, we investigate the design of a new MPPT controller based on a modified version of heterogeneous multiswarm particle swarm optimization algorithm using an adaptive factor selection strategy (FMSPSO) for PV systems. The proposed FMSPSO can improve the tracking capability with high accuracy, less oscillations, and high robustness. To validate the proposed solution, a simulation and experimental benchmarking of a PV system are presented and analyzed. The obtained results show the effectiveness of the proposed solution compared with the classical MSPSO, fuzzy logic, and perturb and observe (P&O) control presented in other recent works.

Improved Naked Mole-Rat Algorithm Based on Variable Neighborhood Search for the N-Queens Problem

Solving problems via artificial intelligence techniques has widely prevailed in different aspects. Implementing artificial intelligence optimization algorithms for NP-hard problems is still challenging. In this manuscript, we work on implementing the Naked Mole-Rat Algorithm (NMRA) to solve the n-queens problems and overcome the challenge of applying NMRA to a discrete space set. An improvement of NMRA is applied using the aspect of local search in the Variable Neighborhood Search algorithm (VNS) with 2-opt and 3-opt. Introducing the Naked Mole Rat algorithm based on variable neighborhood search (NMRAVNS) to solve N-queens problems with different sizes. Finding the best solution or set of solutions within a plausible amount of time is the main goal of the NMRAVNS algorithm. The improvement of the proposed algorithm boosts the exploitation capability of the basic NMRA and gives a greater possibility, with the emerging search strategies, to find the global best solution. This algorithm proved successful and outperformed other algorithms and studies with a remarkable target. A detailed comparison is performed, and the data results are presented with the relevant numbers and values. NMRA and NMRAVNS comparisons are implemented and recorded. Later on, a comparison between the Meerkat Clan Algorithm, Genetic Algorithm, Particle Swarm Optimization, and NMRAVNS is tested and presented. Finally, NMRAVNS is evaluated against the examined genetic-based algorithm and listed to prove the success of the proposed algorithm. NMRAVNS outperformed previous findings and scored competitive results with a high number of queen sizes, where an average time reduction reached about 87% of other previous findings.

Who are the publics engaging in AI?

Given the importance of public engagement in governments' adoption of artificial intelligence systems, artificial intelligence researchers and practitioners spend little time reflecting on who those publics are. Classifying publics affects assumptions and affordances attributed to the publics' ability to contribute to policy or knowledge production. Further complicating definitions are the publics' role in artificial intelligence production and optimization. Our structured analysis of the corpus used a mixed method, where algorithmic generation of search terms allowed us to examine approximately 2500 articles and provided the foundation to conduct an extensive systematic literature review of approximately 100 documents. Results show the multiplicity of ways publics are framed, by examining and revealing the different semantic nuances, affordances, political and expertise lenses, and, finally, a lack of definitions. We conclude that categorizing publics represents an act of power, politics, and truth-seeking in artificial intelligence.

Development Trends and Prospects of Semiconductor Devices and Technology

Semiconductor devices and technology have undergone remarkable development over the years, driving innovations across various industries. This paper explores the historical background and evolution of semiconductor devices, examining key breakthroughs and trends. It also delves into the current state of semiconductor technology, highlighting artificial intelligence optimization, nanotechnology, liquid biopsy technology, deep learning, and quantum computers. These advancements are reshaping the landscape of semiconductor devices, promising enhanced performance and applications in diverse fields. Moreover, the impact of semiconductor technology is expanding into areas such as healthcare, energy, and beyond, offering new avenues to address global challenges. This paper underscores the significance of semiconductor technology as a cornerstone of modern technology and innovation. From its humble beginnings with the transistor to its current pivotal role in AI, nanotechnology, liquid biopsy, deep learning, and quantum computing, semiconductors continue to be the driving force behind a technological revolution that transcends boundaries and holds immense promise for our collective future.

Optimising Al2O3–water nanofluid

BackgroundThis study explores how nanofluids can be optimised to improve heat transfer in various applications. A genetic algorithm that finds the optimal parameter configuration to achieve the best performance is studied and applied. The research focuses on the critical factors of heat transfer coefficient and pressure drop, which determine the efficiency of nanofluid-based systems.The main body of the abstractThe methodology involves artificial intelligence and multi-objective optimisation techniques. Results show that pressure drop and heat transfer coefficient have an inverse relationship. The study provides a range of optimal values for nanofluid temperature, particle size, and volume fraction.ResultsThe results show that the temperature, particle size, and volume fraction should be high. Another variation will be small particle size and small volume fractions with fluid temperature around 80 °C. The analysis yielded the following configuration with the optimal PEC. Temperature (oC), particle size (nm), volume fraction (%), heat transfer coefficient (kW/m2K), pressure drop (Pas), and PEC were 82.6 °C, 175.26 nm, 4.75%, 792.49 kW/m2K, 29.94 Pas, and 5.01.ConclusionsThe research highlights the potential of Al2O3–water nanofluids to maintain pressure drop and enhance heat transfer. It contributes to understanding nanofluid optimisation and provides practical insights for designing and selecting nanofluid systems that enhance heat transfer.

Adaptive DBSCAN Clustering and GASA Optimization for Underdetermined Mixing Matrix Estimation in Fault Diagnosis of Reciprocating Compressors.

Underdetermined blind source separation (UBSS) has garnered significant attention in recent years due to its ability to separate source signals without prior knowledge, even when sensors are limited. To accurately estimate the mixed matrix, various clustering algorithms are typically employed to enhance the sparsity of the mixed matrix. Traditional clustering methods require prior knowledge of the number of direct signal sources, while modern artificial intelligence optimization algorithms are sensitive to outliers, which can affect accuracy. To address these challenges, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with Adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering as initialization, named the CYYM method. This approach incorporates two key components: an Adaptive DBSCAN to discard noise points and identify the number of source signals and GASA optimization for automatic cluster center determination. GASA combines the global spatial search capabilities of a genetic algorithm (GA) with the local search abilities of a simulated annealing algorithm (SA). Signal simulations and experimental analysis of compressor fault signals demonstrate that the CYYM method can accurately calculate the mixing matrix, facilitating successful source signal recovery. Subsequently, we analyze the recovered signals using the Refined Composite Multiscale Fuzzy Entropy (RCMFE), which, in turn, enables effective compressor connecting rod fault diagnosis. This research provides a promising approach for underdetermined source separation and offers practical applications in fault diagnosis and other fields.

Collaborative Optimization of Supply Chain Intelligent Management and Industrial Artificial Intelligence

It is urgent for the manufacturing industry to transform its development mode and achieve intelligent transformation. With the increasingly fierce global market competition, relying solely on first-class product quality can no longer guarantee a long-term competitive advantage for enterprises. Therefore, this article conducts research on the collaborative optimization of supply chain intelligent management and industrial AI(Artificial Intelligence). By timely displaying the quality status of each link, intelligent management of goods is achieved, strengthening control and tracking of product quality, greatly improving the efficiency of the quality management system, and ensuring that enterprises can provide high-quality products as much as possible. Incorporate supplier production flexibility, continuous research and development capabilities, and information technology into the criteria for selecting suppliers, seek higher quality suppliers, and establish strategic partnerships with suppliers. The research in this article is beneficial for improving the production and manufacturing efficiency of enterprises, and is an important theoretical exploration in the development process of intelligent manufacturing.

Preface

Preface It is our great pleasure to have you at the 2023 3rd International Conference on Smart Grid and Energy Internet (SGEI 2023), which took place in Chengdu, China from September 8th to 10th, 2023 (virtual conference). SGEI 2023 was successfully held and received unanimous praise from various experts and scholars all over the world.SGEI 2023 existed as an international forum for academic communications between experts and scholars in the fields of Smart Grid and Energy Internet. It promoted the research and developmental activities in related fields and scientific information interchange among all the participants, establishing research connections for them to find global partners for potential collaboration.After months of well preparation and hard work, the Proceedings of SGEI 2023 covering a bunch of papers, having been checked through rigorous review and processes to meet the requirements of publication, are smoothly published. These papers feature but are not limited to the following topics: Smart Power Distribution and Microgrid, Energy Strategy and Energy Internet, Energy and Distribution Management Systems, Power System Communication and Control, etc.We had about 80 participations both as speakers and participants, including researchers, industrial experts, teachers, students, and other practitioners. During the keynote speech part, each keynote speaker was given 30–40 minutes for keynote speech. Among them, Professor Ismail Musirin from Universiti Teknologi MARA, Malaysia performed a keynote speech. Most of his researches dealt with the algorithms and techniques development in solving problems in Power System involving Voltage Stability, Economic Power Dispatch, Transient Stability, Distributed Generation, Renewable Energy Optimization, Power System Optimization and Application of Artificial Intelligence in Power System. He has got 22 years of research experience in the field of Power System Engineering and has received numerous research grants from the Ministry of Higher Education, Malaysia (MOHE) and Ministry of Science, Technology and Innovation, Malaysia (MOSTI). The insightful speeches of each keynote speakers triggered interesting discussions and a good number of informal talks between all the participants.We would like to express our gratitude to the organizers, keynote speakers and all the participants of SGEI 2023, for their fruitful work and contribution to the success of the Conference. We are thankful to all the reviewers for providing constructive criticisms, stimulated comments and suggestions to the authors. Particularly, our special thanks go to Journal of Physics: Conference Series, for the endeavor of all its colleagues in publishing this paper volume.May this event be a spark that ignites many more new inventions in the fields related to smart grid and energy internet!The Committee of SGEI 2023List of Committee Member is available in this Pdf.

Simplified Methods for Generative Design That Combine Evaluation Techniques for Automated Conceptual Building Design

Smart design and construction have emerged as pivotal forces in the construction industry. Numerous studies have been conducted in the realm of design optimization, using artificial intelligence data-driven approaches and optimization theories. This increase in research has sparked interest in generative design, a process that automatically generates algorithm-based design alternatives, thereby reducing human effort and time by a significant margin. The objective of this study was to explore the potential of generative design to boost productivity within architectural practices and reduce redundant and unnecessary tasks for an aging construction workforce. Specifically, it illustrates the process of selecting superior alternatives by generating various three-dimensional layouts, using a generative design methodology. This occurs during the creation of a building layout concept with subsequent partial evaluations. The methodology of this study was mainly divided into four stages: objective setting, design algorithm development, the establishment of evaluation methodology, and the comparison of the results’ values. The findings of this study confirmed that the design algorithm and evaluation methodology form a single loop, generating a multitude of design alternatives that satisfy the algorithm designer’s evaluation criteria.

Artificial Intelligence and Street Space Optimization in Green Cities: New Evidence from China

In the context of the green economy and sustainable urban development, the rapid expansion of urban construction has given rise to pressing public health concerns, notably environmental pollution and the increased prevalence of chronic illnesses linked to swift urbanization. These urban health issues are escalating, prompting significant attention to the concept of creating “healthy cities”. Meanwhile, the planning and design of urban street space have a far-reaching impact on urban residents’ quality of life and health. Urban planners are facing challenges and need to follow the principle of a green economy while meeting the needs of residents for public activities and adapting to motor vehicle traffic. This study explores the optimization of urban street space to promote the harmonious coexistence between people and cars. This study actively explores the relationship between health, urban environment, and social background, focusing on promoting the harmonious coexistence between people and vehicles, especially the optimization goal of sharing urban streets. The study’s main goal is to design a road that can meet the needs of citizens’ public activities and accommodate motor vehicles, which conforms to the principle of a green economy. To achieve this, geographic information system (GIS) technology and a genetic algorithm (GA) are employed to optimize shared urban street spaces. Among them, GIS tools are used for spatial simulation to evaluate the effect of different shared street space configurations. The urban shared street space is gradually optimized through GA’s selection, crossover, and mutation operations. Simulation experiments are conducted to determine the relationship between street space utilization and the elements of a healthy city, ultimately striving to identify the optimal design parameters for shared street spaces. The research results reveal that the urban street space is optimized from the three aspects of shared allocation of facilities resources, replacement of land use functions, and mixed layout of facilities, and the utilization rate of urban streets is finally ensured to reach 53.43%, fully assuming the essential functions of urban streets. This innovative approach bridges the gap between urban development and public health, offering valuable insights for sustainable urban space planning and enhanced living environments within the framework of the green economy.