The purpose of coatings is to protect or enhance the functionality of the substrate material, irrespective of the field in which the material was designed. The use of coatings in medicine is rapidly expanding with the objective of enhancing the osseointegration ability of metallic materials such as titanium. The aim of this study was to obtain biomimetic hydroxyapatite (HAp)-based coatings on titanium by using the pulsed galvanostatic method. The morphology of the HAp-based coatings revealed the presence of very thin and wide plate-like crystals, grown perpendicular to the Ti substrate, while the chemical composition highlighted a Ca/P ratio of 1.66, which is close to that of stoichiometric HAp (1.67). The main phases and chemical bonds identified confirmed the presence of the HAp phase in the developed coatings. A roughness of 228 nm and a contact angle of approx. 17° were obtained for the HAp coatings, highlighting a hydrophilic character. In terms of biomineralization and electrochemical behavior, it was shown that the HAp coatings have significantly enhanced the titanium properties. Finally, the in vitro cell tests carried out with human mesenchymal stem cells showed that the Ti samples coated with HAp have increased cell viability, extracellular matrix, and Ca intracellular deposition when compared with the uncoated Ti, indicating the beneficial effect.

Three-dimensional (3D) printing is a highly effective scaffold manufacturing technique that may revolutionize tissue engineering and regenerative medicine. The use of scaffolds, along with growth factors and cells, remains among the most promising approaches to organ regeneration. However, the applications of hard 3D-printed scaffolds may be limited by their poor surface properties, which play a crucial role in cell recruitment and infiltration, tissue-scaffold integration, and anti-inflammatory properties. However, various prerequisites must be met before 3D-printed scaffolds can be applied clinically to the human body. Consequently, various attempts have been made to modify the surfaces, porosities, and mechanical properties of these scaffolds. Techniques that involve the chemical and material modification of surfaces can also be applied to enhance scaffold efficacy. This review summarizes the characteristics and discusses the developmental directions of the latest 3D-printing technologies according to its intended application in unmet clinical needs.

The widespread application of exoskeletons driven by soft actuators in motion assistance and medical rehabilitation has proven effective for patients who struggle with precise object grasping and suffer from insufficient hand strength due to strokes or other conditions. Repetitive passive flexion/extension exercises and active grasp training are known to aid in the restoration of motor nerve function. However, conventional pneumatic artificial muscles (PAMs) used for hand rehabilitation typically allow for bending in only one direction, thereby limiting multi-degree-of-freedom movements. Moreover, establishing precise models for PAMs is challenging, making accurate control difficult to achieve. To address these challenges, we explored the design and fabrication of a bidirectionally bending PAM. The design parameters were optimized based on actual rehabilitation needs and a finite element analysis. Additionally, a dynamic model for the PAM was established using elastic strain energy and the Lagrange equation. Building on this, an adaptive position control method employing a radial basis function neural network, optimized for parameters and hidden layer nodes, was developed to enhance the accuracy of these soft PAMs in assisting patients with hand grasping. Finally, a wearable soft hand rehabilitation exoskeleton was designed, offering two modes, passive training and active grasp, aimed at helping patients regain their grasp ability.

Alpha-synuclein (ASyn) is a protein that is known to play a critical role in Parkinson's disease (PD) due to its propensity for misfolding and aggregation. Furthermore, this process leads to oxidative stress and the formation of free radicals that cause neuronal damage. In this study, we have utilized a biomimetic approach to design new peptides derived from marine natural resources. The peptides were designed using a peptide scrambling approach where antioxidant moieties were combined with fibrillary inhibition motifs in order to design peptides that would have a dual targeting effect on ASyn misfolding. Of the 20 designed peptides, 12 were selected for examining binding interactions through molecular docking and molecular dynamics approaches, which revealed that the peptides were binding to the pre-NAC and NAC (non-amyloid component) domain residues such as Tyr39, Asn65, Gly86, and Ala85, among others. Because ASyn filaments derived from Lewy body dementia (LBD) have a different secondary structure compared to pathogenic ASyn fibrils, both forms were tested computationally. Five of those peptides were utilized for laboratory validation based on those results. The binding interactions with fibrils were confirmed using surface plasmon resonance studies, where EQALMPWIWYWKDPNGS, PYYYWKDPNGS, and PYYYWKELAQM showed higher binding. Secondary structural analyses revealed their ability to induce conformational changes in ASyn fibrils. Additionally, PYYYWKDPNGS and PYYYWKELAQM also demonstrated antioxidant properties. This study provides insight into the binding interactions of varying forms of ASyn implicated in PD. The peptides may be further investigated for mitigating fibrillation at the cellular level and may have the potential to target ASyn.

Photopolymerization-based three-dimensional (3D) printing techniques such as stereolithography (SLA) attract considerable attention owing to their superior resolution, low cost, and relatively high printing speed. However, the lack of studies on improving the mechanical properties of 3D materials highlights the importance of delving deeper into additive manufacturing research. These materials possess considerable potential in the medical field, particularly for applications such as anatomical models, medical devices, and implants. In this study, we investigated the enhancement of mechanical strength in 3D-printed photopolymers through the incorporation of potassium titanate powder (KTiO), with a particular focus on potential applications in medical devices. The mechanical strength of the photopolymer containing potassium titanate was analyzed by measuring its flexural strength, hardness, and tensile strength. Additionally, poly(ethylene glycol) (PEG) was used as a stabilizer to optimize the dispersion of potassium titanate in the photopolymer. The flexural strengths of the printed specimens were in the range of 15-39 MPa (Megapascals), while the measured surface hardness and tensile strength were in the range of 41-80 HDD (Hardness shore D) and 2.3-15 MPa, respectively. Furthermore, the output resolution was investigated by testing it with a line-patterned structure. The 3D-printing photopolymer without PEG stabilizers produced line patterns with a thickness of 0.3 mm, whereas the 3D-printed resin containing a PEG stabilizer produced line patterns with a thickness of 0.2 mm. These findings demonstrate that the composite materials not only exhibit improved mechanical performance but also allow for high-resolution printing. Furthermore, this composite material was successfully utilized to print implants for pre-surgical inspection. This process ensures the precision and quality of medical device production, emphasizing the material's practical value in advanced medical applications.

The use of biopolymers in pharmaceuticals is well established, particularly for encapsulating biologically active compounds due to their beneficial properties. Alginate, widely recognized for its excellent encapsulation abilities, is the most commonly used biopolymer, while starch, typically known as insoluble dietary fiber, also serves as an effective agent for trapping and protecting compounds during processing, storage, and gastrointestinal transit. Sodium alginate-starch capsules with varying compositions were analyzed to develop metformin hydrochloride (MET) containing capsules with adequate physicochemical properties. In vitro testing with simulated gastrointestinal fluids showed that after 1 h, capsules with equal amounts of alginate and starch had a higher swelling ratio and better drug release behavior, despite lower MET entrapment efficiency compared to other formulations. Microstructural analysis revealed stability in simulated gastric fluids and solubility in simulated intestinal fluids, key factors in drug development. The results suggest that these biopolymeric compositions are highly resistant to gastric fluids and minimally soluble in the intestines, making them suitable for extended drug release. This research evaluates key technological parameters of a cost-effective encapsulation method for the controlled release of active substances, providing a versatile solution for pharmaceutical and biomedical applications.

Snake robots require autonomous localization and mapping capabilities for field applications. However, the characteristics of their motion, such as large turning angles and fast rotation speeds, can lead to issues like drift or even failure in positioning and map building. In response to this situation, this paper starts from the gait motion characteristics of the snake robot itself, proposing an improved gait motion method and a tightly coupled method based on IMU and visual information to solve the problem of poor algorithm convergence caused by head-shaking in snake robot SLAM. Firstly, the adaptability of several typical gaits of the snake robot to SLAM methods was evaluated. Secondly, the serpentine gait was selected as the object of gait improvement, and a head stability control method for the snake robot was proposed, thereby reducing the interference of the snake robot's motion on the sensors. Thirdly, a visual-inertial tightly coupled SLAM method for the snake robot's serpentine gait and Arc-Rolling gait was proposed, and the method was verified to enhance the robustness of the visual SLAM algorithm and improve the positioning and mapping accuracy of the snake robot. Finally, experiments proved that the methods proposed in this paper can effectively improve the accuracy of positioning and map building for snake robots.

The success of image segmentation is mainly dependent on the optimal choice of thresholds. Compared to bi-level thresholding, multi-level thresholding is a more time-consuming process, so this paper utilizes the gray wolf optimizer (GWO) algorithm to address this issue and enhance accuracy. To acquire the optimal thresholds at different levels, we modify the GWO (MGWO) in terms of leader selection, position update, and mutation. We also use the Otsu method and Kapur entropy as objective functions. The performance of MGWO is compared with other color image segmentation algorithms on ten images from the BSD500 dataset in terms of objective values, variance, signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM). Experimental and non-parametric statistical analyses demonstrate that MGWO performs excellently in the multi-level thresholding segmentation of color images.

Humans typically make decisions based on past experiences and observations, while in the field of robotic manipulation, the robot's action prediction often relies solely on current observations, which tends to make robots overlook environmental changes or become ineffective when current observations are suboptimal. To address this pivotal challenge in robotics, inspired by human cognitive processes, we propose our method which integrates historical learning and multi-view attention to improve the performance of robotic manipulation. Based on a spatio-temporal attention mechanism, our method not only combines observations from current and past steps but also integrates historical actions to better perceive changes in robots' behaviours and their impacts on the environment. We also employ a mutual information-based multi-view attention module to automatically focus on valuable perspectives, thereby incorporating more effective information for decision-making. Furthermore, inspired by human visual system which processes both global context and local texture details, we have devised a method that merges semantic and texture features, aiding robots in understanding the task and enhancing their capability to handle fine-grained tasks. Extensive experiments in RLBench and real-world scenarios demonstrate that our method effectively handles various tasks and exhibits notable robustness and adaptability.

Despite the implementation of numerous interventions to enhance urban traffic safety, the estimation of the risk of traffic crashes resulting in life-threatening and economic costs remains a significant challenge. In light of the above, an online inference method for traffic crash risk based on the self-developed TAR-DETR and WOA-SA-SVM methods is proposed. The method's robust data inference capabilities can be applied to autonomous mobile robots and vehicle systems, enabling real-time road condition prediction, continuous risk monitoring, and timely roadside assistance. First, a self-developed dataset for urban traffic object detection, named TAR-1, is created by extracting traffic information from major roads around Hainan University in China and incorporating Russian car crash news. Secondly, we develop an innovative Context-Guided Reconstruction Feature Network-based Urban Traffic Objects Detection Model (TAR-DETR). The model demonstrates a detection accuracy of 76.8% for urban traffic objects, which exceeds the performance of other state-of-the-art object detection models. The TAR-DETR model is employed in TAR-1 to extract urban traffic risk features, and the resulting feature dataset was designated as TAR-2. TAR-2 comprises six risk features and three categories. A new inference algorithm based on WOA-SA-SVM is proposed to optimize the parameters (C, g) of the SVM, thereby enhancing the accuracy and robustness of urban traffic crash risk inference. The algorithm is developed by combining the Whale Optimization Algorithm (WOA) and Simulated Annealing (SA), resulting in a Hybrid Bionic Intelligent Optimization Algorithm. The TAR-2 dataset is inputted into a Support Vector Machine (SVM) optimized using a hybrid algorithm and used to infer the risk of urban traffic crashes. The proposed WOA-SA-SVM method achieves an average accuracy of 80% in urban traffic crash risk inference.

In response to the rising prevalence of hypertension due to lifestyle changes, this study introduces a novel approach for non-contact blood pressure (BP) monitoring. Recognizing the "silent killer" nature of hypertension, this research focuses on developing accessible, non-invasive BP measurement methods. This study compares two distinct non-contact BP measurement approaches: one combining the Nelder-Mead simplex method with particle swarm optimization (NM-PSO) and the other using machine learning regression analysis. In the NM-PSO method, a standard webcam captures continuous images of the palm, extracting physiological data through light wave reflection and employing independent component analysis (ICA) to remove noise artifacts. The NM-PSO achieves a verified root mean square error (RMSE) of 2.71 mmHg for systolic blood pressure (SBP) and 3.42 mmHg for diastolic blood pressure (DBP). Alternatively, the regression method derives BP values through machine learning-based regression formulas, resulting in an RMSE of 2.88 mmHg for SBP and 2.60 mmHg for DBP. Both methods enable fast, accurate, and convenient BP measurement within 10 s, suitable for home use. This study demonstrates a cost-effective solution for non-contact BP monitoring and highlights each method's advantages. The NM-PSO approach emphasizes optimization in noise handling, while the regression method leverages formulaic efficiency in BP estimation. These results offer a biomimetic approach that could replace traditional contact-based BP measurement devices, contributing to enhanced accessibility in hypertension management.

The 3D printing of a biomimetic scaffold with a high hydroxyapatite (HA) content (>80%) and excellent mechanical property is a serious challenge because of the difficulty of forming and printing, insufficient cohesion, and low mechanical property of the scaffold. In this study, hydroxyapatite whiskers (HAWs), with their superior mechanical property, biodegradability, and biocompatibility, were used to reinforce spherical HA scaffolds by 3D printing. The compressive strength and energy absorption capacity of HAW-reinforced spherical HA (HAW/HA) scaffolds increased when the HAW/HA ratio increased from 0:10 to 4:6 and then dropped with any further increases in the HAW/HA ratio. Bioceramic content (HAWs and spherical HA) in the scaffolds reached 83%, and the scaffold with a HAW/HA ratio of 4:6 (4-HAW/HA) exhibited an optimum compressive strength and energy absorption capacity. The scaffold using polyvinyl alcohol (PVA) as an additive possessed a good bonding between HA and PVA as well as a higher strength, which allowed the scaffold to bear a higher stress at the same strain. The compressive strength and toughness of the 4-HAW/HA-PVA scaffold were 1.96 and 1.63 times that of the 4-HAW/HA scaffold with hydroxypropyl methyl cellulose (HPMC), respectively. The mechanical property and inorganic components of the biomimetic HAW/HA scaffold were similar to those of human bone, which would make it ideal for repairing bone defects.

Cerebral activities were measured during 21 essions in NeurOptimal (NO)-trained patients with bruxism. Salivary cortisol levels were quantified for each six training sessions (session 1, 6, 12, 18, 21) in 12 patients with bruxism after performing their pre- and post-NeurOptimal sessions. Their cortisol levels were compared with controls (without stress). We evaluated whether NO overtraining could reduce stress in bruxism after 21 repeated sessions with/without inhalation by using nasal impregned filters with this essential oil ( = 12). This study enrolled 89 participants (590 salivary samples for cortisol assessment by ELISA ng/mL). Salivary samples were collected at several NO learning sessions (session 1, 6, 12, 18, and 21). In the present study, we assessed whether essential oil exposure during 21 NO training sessions can promote anxiolytic effects by reducing cortisol levels in Bruxismitle patients or modulate their brain activities. The experimental design also included control subjects without NO training ( = 30) and unstressed participants without bruxism, as well as trained NeurOptimal ( = 5) participants during the 21 sessions, also including control subjects without stress. In our study, NeurOptimal post-training decreased cortisol levels in Bruxismitle patients, reducing stress scores on the Hamilton II scale after 21 NO sessions; finally, essential oil exposure during NO training could enhance anxiolytic effects of repeated NO in Bruxismitle patients. The parameter divergence as an index of cerebral activity evaluates the reached difference between cerebral activity at pre-learning (PRE) minus post-training (POST) values in Bruxismitle participants with/without odor exposure during each NO training sessions. As a consequence of NO overtraining, these cerebral activities fluctuate reaching a calm state while anxious states are associated with high divergences. The reduction in divergences when they are close to zero by habituation means a final calm state is reached by NO overtraining, while higher divergences mean anxiogenic states. Collectively, essential oil inhalation during NO training could decrease salivary cortisol levels after 21 NO training sessions in Bruxismitle.

Anthocyanin is an important flavonoid with antioxidant, anticancer, and anti-inflammatory properties. This research investigates the anthocyanin content of Cavendish banana inflorescence, a by-product often discarded as agricultural waste. The study employs two drying methods, namely oven-drying and freeze-drying, followed by accelerated solvent extraction using acidified water and methanol. Liquid chromatography-mass spectroscopy (LC-MS) results confirm banana inflorescence as a rich source of anthocyanins. According to LC-MS analysis, freeze-dried banana inflorescence extracted in methanol at 80 °C exhibits the highest anthocyanin content (130.01 mg/100 g). This sample also demonstrates superior characteristics, including a chroma value of 40.02 ± 0.01, a redness value of 38.09 ± 0.16, 18.46 ± 0.02 °Brix, a total phenolic content of 42.5 ± 1.00 mg/g, expressed as gallic acid equivalents, and a total antioxidant activity of 71.33 ± 0.08% when assessed with the DPPH method. Furthermore, the study identifies the predominant anthocyanin as cyanidin, along with the presence of other anthocyanins such as delphinidin (Dp), malvidin (Mv), petunidin (Pt), pelargonidin (Pg), and peonidin (Pn). Interestingly, the extracted anthocyanins demonstrate pH sensitivity, changing from red to brown as pH increases. These findings highlight the potential of utilizing Cavendish banana inflorescence for anthocyanin extraction, offering sustainable waste valorization methods with promising applications in biomimetics and bioinspiration fields.

Drug administration is commonly used to treat chronic wounds but faces challenges such as poor bioavailability, instability, and uncontrollable release. Existing drug delivery platforms are limited by chemical instability, poor functionality, complex synthesis, and toxic by-products. Presently, research efforts are focused on developing novel drug carriers to enhance drug efficacy. Guanidinium Covalent Organic Nanosheets (gCONs) offer promising alternatives due to their high porosity, surface area, loading capacity, and ability to provide controlled, sustained, and target-specific drug delivery. Herein, we successfully synthesized self-exfoliated gCONs using a Schiff base condensation reaction and embedded curcumin (CUR), a polyphenolic pleiotropic drug with antioxidant and anti-inflammatory properties, via the wet impregnation method. The BET porosimeter exhibited the filling of gCON pores with CUR. Morphological investigations revealed the formation of sheet-like structures in gCON. Culturing human dermal fibroblasts (hDFs) on gCON demonstrated cytocompatibility even at a concentration as high as 1000 µg/mL. Drug release studies demonstrated a controlled and sustained release of CUR over an extended period of 5 days, facilitated by the high loading capacity of gCON. Furthermore, the inherent antioxidant and anti-inflammatory properties of CUR were preserved after loading into the gCON, underscoring the potential of CUR-loaded gCON formulation for effective therapeutic applications. Conclusively, this study provides fundamental information relevant to the performance of gCONs as a drug delivery system and the synergistic effect of CUR and CONs addressing issues like drug bioavailability and instability.

Feature selection (FS) constitutes a critical stage within the realms of machine learning and data mining, with the objective of eliminating irrelevant features while guaranteeing model accuracy. Nevertheless, in datasets featuring a multitude of features, choosing the optimal feature poses a significant challenge. This study presents an enhanced Sand Cat Swarm Optimization algorithm (MSCSO) to improve the feature selection process, augmenting the algorithm's global search capacity and convergence rate via multiple innovative strategies. Specifically, this study devised logistic chaotic mapping and lens imaging reverse learning approaches for population initialization to enhance population diversity; balanced global exploration and local development capabilities through nonlinear parameter processing; and introduced a Weibull flight strategy and triangular parade strategy to optimize individual position updates. Additionally, the Gaussian-Cauchy mutation strategy was employed to improve the algorithm's ability to overcome local optima. The experimental results demonstrate that MSCSO performs well on 65.2% of the test functions in the CEC2005 benchmark test; on the 15 datasets of UCI, MSCSO achieved the best average fitness in 93.3% of the datasets and achieved the fewest feature selections in 86.7% of the datasets while attaining the best average accuracy across 100% of the datasets, significantly outperforming other comparative algorithms.

Recent advancements in natural language processing (NLP) have been significantly driven by the development of large language models (LLMs). Despite their impressive performance across various language tasks, these models still encounter challenges when processing tabular data. This study investigates the optimization of fine-tuning strategies for LLMs specifically in the context of tabular data processing. The focus is on the effects of decimal truncation, multi-dataset mixing, and the ordering of JSON key-value pairs on model performance. Experimental results indicate that decimal truncation reduces data noise, thereby enhancing the model's learning efficiency. Additionally, multi-dataset mixing improves the model's generalization and stability, while the random shuffling of key-value pair orders increases the model's adaptability to changes in data structure. These findings underscore the significant impact of these strategies on model performance and robustness. The research provides novel insights into improving the practical effectiveness of LLMs and offers effective data processing methods for researchers in related fields. By thoroughly analyzing these strategies, this study aims to establish theoretical foundations and practical guidance for the future optimization of LLMs across a broader range of application scenarios.

Nowadays, metaheuristic algorithms have been applied to optimize last lower-surface models. Also, the last lower-surface model has been adjusted through the computational algorithms to perform custom shoe lasts. Therefore, it is necessary to implement nature-inspired metaheuristic algorithms to perform the adjustment of last lower-surface model to the footprint topography. In this study, a metaheuristic genetic algorithm is implemented to adjust the last lower surface model to the footprint topography. The genetic algorithm is constructed through an objective function, which is defined through the last lower Bezier model and footprint topography, where a mean error function moves the last lower surface toward the footprint topography through the initial population. Also, the search space is deduced from the last lower surface and footprint topography. In this way, the genetic algorithm performs explorations and exploitations to optimize a Bezier surface model, which generates the adjusted last lower surface, where the surface is recovered via laser line scanning. Thus, the metaheuristic algorithm enhances the last lower-surface adjustment to improve the custom last manufacture. This contribution is elucidated by a discussion based on the proposed metaheuristic algorithm for surface model adjustment and the optimization methods implemented in recent years.

The research suggests a production method of insulating composites created from lignocellulosic agricultural biomass with fungal mycelium as a binder agent and offers a deeper investigation of their thermophysical properties. Particularly, the samples were meticulously evaluated for density and thermal conductivity. The function was built on the suggestion by the authors regarding the thermal conductivity-weight ratio indicator. The metric was initially introduced to assess the correlation between these parameters and was also applied to qualitatively evaluate the biocomposite among other commonly used natural insulations. An applied polynomial trend analysis indicated that the most effective densities for the wheat, hemp, and flax, which were 60, 85, and 105 kg·m respectively. It was determined that the optimal density for wheat and hemp composites corresponded to values of 0.28 and 0.20 W·kg·m·K of the coefficient, respectively. These values were superior to those revealed in other common natural insulating materials, such as cork, cotton stalks, hempcrete, timber, etc. As a result, the proposed insulating material may offer numerous opportunities for application in industrial settings of civil engineering.

Improving the three-dimensional reconstruction of underwater scenes is a challenging and hot topic in the field of underwater robot vision system research. High dynamic interference underwater has always been one of the key issues affecting the 3D reconstruction of underwater scenes. However, due to the complex underwater environment and insufficient light, existing target detection algorithms cannot meet the requirements. This paper uses the YOLOv8 network as the basis of the algorithm and proposes an underwater dynamic target detection algorithm based on improved YOLOv8. This algorithm first improves the feature extraction layer of the YOLOv8 network, improves the convolutional network structure of Bottleneck, reduces the amount of calculation and improves detection accuracy. Secondly, it adds an improved SE attention mechanism to make the network have a better feature extraction effect; in addition, the confidence box loss function of the network is improved, and the CIoU loss function is replaced by the MPDIoU loss function, which effectively improves the model convergence speed. Experimental results show that the mAP value of the improved YOLOv8 underwater dynamic target detection algorithm proposed in this article can reach 95.1%, and it can detect underwater dynamic targets more accurately, especially small dynamic targets in complex underwater scenes.

The combustion optimization problem of the circulation fluidized bed boiler is regarded as a difficult multi-objective optimization problem that requires simultaneously improving the boiler thermal efficiency and reducing the NOx emissions concentration. In order to solve the above-mentioned problem, a new multi-objective optimization framework that incorporates an interpretable CatBoost model and modified slime mould algorithm is proposed. Firstly, the interpretable CatBoost model combined with TreeSHAP is applied to model the boiler thermal efficiency and NOx emissions concentration. Simultaneously, data correlation analysis is conducted based on the established models. Finally, a kind of modified slime mould algorithm is proposed and used to optimize the adjustable operation parameters of one 330 MW circulation fluidized bed boiler. The experimental results show that the proposed framework can effectively improve the boiler thermal efficiency and reduce the NOx emissions concentration, where the average optimization ratio for thermal efficiency reaches +0.68%, the average optimization ratio for NOx emission concentration reaches -37.55%, and the average optimization time is 6.40 s. In addition, the superiority of the proposed method is demonstrated by ten benchmark testing functions and two constrained optimization problems. Therefore, the proposed framework is an effective artificial intelligence approach for the modeling and optimization of complex systems.