Optimization Applications Research Articles

Kinematic Analysis of Human Gait in Healthy Young Adults Using IMU Sensors: Exploring Relevant Machine Learning Features for Clinical Applications.

Gait is the manner or style of walking, involving motor control and coordination to adapt to the surrounding environment. Knowing the kinesthetic markers of normal gait is essential for the diagnosis of certain pathologies or the generation of intelligent ortho-prostheses for the treatment or prevention of gait disorders. The aim of the present study was to identify the key features of normal human gait using inertial unit (IMU) recordings in a walking test. Gait analysis was conducted on 32 healthy participants (age range 19-29 years) at speeds of 2 km/h and 4 km/h using a treadmill. Dynamic data were obtained using a microcontroller (Arduino Nano 33 BLE Sense Rev2) with IMU sensors (BMI270). The collected data were processed and analyzed using a custom script (MATLAB 2022b), including the labeling of the four relevant gait phases and events (Stance, Toe-Off, Swing, and Heel Strike), computation of statistical features (64 features), and application of machine learning techniques for classification (8 classifiers). Spider plot analysis revealed significant differences in the four events created by the most relevant statistical features. Among the different classifiers tested, the Support Vector Machine (SVM) model using a Cubic kernel achieved an accuracy rate of 92.4% when differentiating between gait events using the computed statistical features. This study identifies the optimal features of acceleration and gyroscope data during normal gait. The findings suggest potential applications for injury prevention and performance optimization in individuals engaged in activities involving normal gait. The creation of spider plots is proposed to obtain a personalised fingerprint of each patient's gait fingerprint that could be used as a diagnostic tool. A deviation from a normal gait pattern can be used to identify human gait disorders. Moving forward, this information has potential for use in clinical applications in the diagnosis of gait-related disorders and developing novel orthoses and prosthetics to prevent falls and ankle sprains.

Special Issue “Optimisation Models and Applications”

Optimisation models have transcended their origins to become indispensable tools across many fields, including engineering, economics, the environment, health, systems of systems, businesses, and beyond [...]

Machine learning assisted rational design of antimicrobial peptides based on human endogenous proteins and their applications for cosmetic preservative system optimization

Preservatives are essential components in cosmetic products, but their safety issues have attracted widespread attention. There is an urgent need for safe and effective alternatives. Antimicrobial peptides (AMPs) are part of the innate immune system and have potent antimicrobial properties. Using machine learning-assisted rational design, we obtained a novel antibacterial peptide, IK-16-1, with significant antibacterial activity and maintaining safety based on β-defensins. IK-16-1 has broad-spectrum antimicrobial properties against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans, and has no haemolytic activity. The use of IK-16-1 holds promise in the cosmetics industry, since it can serve as a preservative synergist to reduce the amount of other preservatives in cosmetics. This study verified the feasibility of combining computational design with artificial intelligence prediction to design AMPs, achieving rapid screening and reducing development costs.

Multistability Mechanisms for Improving the Performance of a Piezoelectric Energy Harvester with Geometric Nonlinearities

This study presents multistability mechanisms that can enhance the energy harvesting performance of a piezoelectric energy harvester (PEH) with geometrical nonlinearities. To configure triple potential wells, static bifurcation diagrams in the structural parameter plane are depicted. On this basis, the key structural parameter is considered, of which three reasonable values are then chosen for comparing and evaluating the performances of the triple-well PEH under them. Then, intra-well responses and the corresponding voltages of the system are investigated qualitatively. A preliminary analysis of the suitable energy-harvesting conditions is carried out, which is then validated by numerical simulations of the evolution of coexisting attractors and their basins of attraction with variations in the excitation level and frequency. It follows that, under a low-level ambient excitation, the intra-well responses around the trivial equilibrium dominate the energy-harvesting performance. When the level of the environmental excitation is very low, which one of the three values of the key structural parameter is the best for improving the performance of the PEH system depends on the range of the excitation frequency; when the excitation level increases sufficiently to induce inter-well responses, the maximum one is the best for improving the performance of the PEH. The findings provide valuable insights for researchers working in the structure optimization and practical applications of geometrically nonlinear PEHs.

Quantum computing for chemistry and physics applications from a Monte Carlo perspective.

This Perspective focuses on the several overlaps between quantum algorithms and Monte Carlo methods in the domains of physics and chemistry. We will analyze the challenges and possibilities of integrating established quantum Monte Carlo solutions into quantum algorithms. These include refined energy estimators, parameter optimization, real and imaginary-time dynamics, and variational circuits. Conversely, we will review new ideas for utilizing quantum hardware to accelerate the sampling in statistical classical models, with applications in physics, chemistry, optimization, and machine learning. This review aims to be accessible to both communities and intends to foster further algorithmic developments at the intersection of quantum computing and Monte Carlo methods. Most of the works discussed in this Perspective have emerged within the last two years, indicating a rapidly growing interest in this promising area of research.

Liposome biodistribution mapping with in vivo X-ray fluorescence imaging.

Lipid-based nanoparticles are organic nanostructures constituted of phospholipids and cholesterol, displaying high in vivo biocompatibility. They have been demonstrated as effective nanocarriers for drug delivery and targeting. Mapping liposome distribution is crucial as it enables a precise understanding of delivery kinetics, tissue targeting efficiency, and potential off-target effects. Recently, ruthenium-encapsulated liposomes have shown potential for targeted drug delivery, photodynamic therapy, and optical fluorescence imaging. In the present work, we design Ru(bpy)3-encapsulated liposomes (Ru-Lipo) empowering optical and X-ray fluorescence (XRF) properties for dual mode imaging and demonstrate the passivation role of liposomes over the free Ru(bpy)3 compound. We employ whole-body XRF imaging to map the in vivo biodistribution of Ru-Lipo in mice, enabling tumor detection and longitudinal studies with elemental specificity and resolution down to the sub-millimeter scale. Quantitative XRF computed tomography on extracted organs permits targeting efficiency evaluations. These findings highlight the promising role of XRF imaging in pharmacokinetic studies and theranostic applications for the rapid optimization of drug delivery and assessment of targeting efficiency.

Intelligent Infrastructures Using Deep Learning Based Applications for Energy Optimization

Intelligent Infrastructures Using Deep Learning Based Applications for Energy Optimization

AI-driven solutions in renewable energy: A review of data science applications in solar and wind energy optimization

This comprehensive review explores the transformative role of artificial intelligence (AI) and data science in the renewable energy sector, with a particular focus on solar and wind energy optimization. The study systematically examines the intersection of AI and renewable energy, highlighting the emergence of AI-driven solutions and their impact on enhancing the efficiency, reliability, and sustainability of renewable energy systems. The review begins with an overview of renewable energy and its growing importance in the global energy mix, emphasizing the critical role of AI in this sector. It then delves into the methodological approach, outlining the research strategy and criteria for selecting relevant AI and data science studies in renewable energy. This includes a detailed analysis of data collection and synthesis techniques used to identify key AI innovations and trends in solar and wind energy optimization. The core of the review comprises an extensive literature survey on AI applications in solar and wind energy systems. It covers fundamental principles of AI in renewable energy, state-of-the-art data science techniques, and emerging trends such as novel AI algorithms and their integration into renewable energy grids. The study evaluates the technological, economic, and environmental impacts of AI in renewable energy, addressing challenges and proposing solutions. Furthermore, the review discusses the role of standards and regulatory frameworks in AI-driven renewable energy and the implications for stakeholders. It concludes with a summary of AI's role in enhancing renewable energy, future prospects, and recommendations for industry leaders and policymakers. This review provides a thorough understanding of the current state and future potential of AI in renewable energy, offering valuable insights for researchers, industry professionals, and policymakers engaged in the field of sustainable energy.

Singular value decomposition and applications in data processing and artificial intelligence

Computing matrices is a crucial and prevalent topic in the field of data processing and artificial intelligence. One of the significant and effective methods for matrix manipulation is Singular Value Decomposition (SVD). Based on matrix computations using SVD, we can perform various complex operations such as dimensionality reduction, hidden information detection, optimization, and many other applications. Singular Value Decomposition is a valuable method in data science, allowing us to decompose a matrix into its fundamental components. Similar to Principal Component Analysis (PCA), SVD helps reduce the dimensionality of data while preserving the most important information. However, SVD can be applied to non-square, non-invertible matrices, and its ability to separate fundamental components enables us to analyze more complex data. SVD has a wide range of applications in practical scenarios.

New advances for quantum‐inspired optimization

AbstractAdvances in quantum computing with applications in combinatorial optimization have evolved at an increasing rate in recent years. The quadratic unconstrained binary optimization (QUBO) model is at the center of these developments and has become recognized as an effective alternative method for representing a wide variety of combinatorial optimization problems. Additional momentum has resulted from the arrival of quantum computers and their ability to solve the Ising spin glass problem, another form of the QUBO model. This paper highlights advances in solving QUBO models and extensions to more general polynomial unconstrained binary optimization (PUBO) models as important alternatives to traditional approaches. Computational experience is provided that compares the performance of unique quantum‐inspired metaheuristic solvers—the Next Generation Quantum (NGQ) solver for QUBO models and the NGQ‐PUBO solver for PUBO models—with the performance of CPLEX and the Dwave quantum advantage solver. Extensive results, including experiments with a set of large set partitioning problems representing the largest QUBO models reported in the literature to date, along with maximum diversity and max cut problem sets, disclose that our solvers outperform both CPLEX and Dwave by a wide margin in terms of both computational time and solution quality.

Red bioactive pigment from Himalayan Janthinobacterium sp. ERMR3:09: optimization, characterization, and potential applications.

Prodigiosin is a red pigment commonly produced as a secondary metabolite by Serratia marcescens. It exhibits inherent bioactivities, including antimicrobial and anticancer, with low to no toxic effects on normal cells. The present study investigates a bioactive prodigiosin production from an atypical, red-pigmented, potentially novel Janthinobacterium sp. ERMR3:09 isolated from a glacial moraine. Statistically optimized culture parameters, i.e., w/v 1.0% glucose and 0.08% peptone as carbon and nitrogen sources, temperature 20°C, and media pH 7, resulted in a four-fold increase in the pigment yield. The upscaled production in an 8 L volume resulted in higher pigment production within a shorter period of 48h. The ultra-performance liquid chromatography (UPLC) analysis validated the identity of the purified pigment as prodigiosin that showed thermostability at 75°C for 3h. Evaluation of antimicrobial activity showed potent inhibitory effects (> 50%) against the opportunistic pathogenic fungal and Gram-positive bacterial strains. The pigment showed significant cytotoxicity (p < 0.05) towards A549 and HeLa cell lines with IC50 values of 42.2μM and 36.11μM, respectively. The study demonstrated that microbial communities from extreme niches can be ideal sources of bioactive pigments with immense pharmaceutical potential vital for the development of non-synthetic therapeutic agents.

A Novel Circuit-Based Model of Electrodialysis Avoiding the Use of Empirical Parameters

Electrodialysis (ED) is an emerging membrane-based electroseparation technology for desalination, water treatment, and ionic pollutant removal. Compared to conventional separation methods such as reverse osmosis, ED offers several advantages including lower energy consumption, higher selectivity, and lower fouling propensity. Further, coupling ED with renewable electricity sources enables it to be far more sustainable than competing technologies (reverse osmosis and evaporation) across a wide range of scales. However, the design and operation of ED systems are still challenging due to the complex interactions between the membrane, the electrolytes, and the applied electric field. To address this issue, advanced process models are required to provide accurate predictions of ED performance and to guide system optimisation. Existing ED models tend to be highly dependent on empirical parameters and thus are only applicable to a narrow range of process conditions and require several data sets to validate. Therefore, in this work we aimed to develop a glabal model of ED without reliance on experimental training data.Herein, we present a novel circuit-based model for ED. The model considers the membrane stack as a series of resistors, where the membranes and electrolytes are represented as separate resistive elements. Crucially, the membrane resistance is calculated directly from the electrostatic interaction between ions and fixed charge groups, rather than from manufacturer data or empirical sub-model. This allows for consideration of the ion identity and electrolyte concentration when determining the membrane resistance. Ohm's law is used to relate the applied voltage and stack electrical resistance to a current density, which is then converted to a flux using Faraday's law and current efficiency model. Historically, current efficiencies are calculated from transport numbers provided from membrane manufactures and assumed constant. A novel model for the current efficiency has been developed by us in which the current efficiency is a function of the trans-membrane concentration difference. A space-wise material balance is employed to determine the concentration profile along the stack, while a time-wise material balance tracks the changes in reservoir concentration for recirculating batch experiments.To validate the model, desalination experiments were conducted on a PC BED 1-4 recirculating batch system and compared the model predictions with experimental data. The model demonstrated excellent matching on a range of variables across a range of conditions, including current density, current efficiency, and ion concentration.The proposed model has various applications in ED process modelling, optimisation, and economic analysis. It can be used to evaluate the impact of different design parameters such as membrane thickness, membrane charge density, and flow rate on system performance as well as to optimise these.In summary, the circuit-based model presented in this work offers a robust and versatile tool for ED process simulation and optimisation, which can be used for effective and efficiency desalination and water treatment. Two major advancements presented include models for the membrane electrical resistance and current efficiency, which have historically been considered constant. Figure 1

Inverse Optimization: Theory and Applications

A Review of Inverse Optimization In recent years, there has been an explosion of interest in the mathematics and applications of inverse optimization. Unlike traditional optimization, which seeks to compute optimal decisions given an objective and constraints, inverse optimization takes decisions as input and determines an objective and/or constraints that render these decisions approximately or exactly optimal. In “Inverse Optimization: Theory and Applications,” Chan, Rafid, and Zhu provide a comprehensive review of both the methodological and application-oriented literature. Specifically, the authors consolidate various model properties, reformulation techniques, and computational methods of different classes of inverse optimization problems. The authors also review a wide range of application areas that include, but are not limited to, transportation, logistics, healthcare, and energy systems. The paper concludes with several major directions for future research.

Survey of Optimization Techniques in Interconnected Two Area Power system for the Application of AGC

The increasing need for energy has placed optimization at the center of power engineering research and development. Presenting the theoretical underpinnings of optimization from a practical power system perspective, this review article on Electric Power System Applications of Optimization explores state-of-the-art techniques, novel approaches, and ongoing application difficulties. This work addresses several algorithmic issues that arise from the planning and management of power systems using diverse approaches, as well as the analytical formulation of optimization. This article introduces new features related to market programs, pricing, reliability, and developments in intelligent systems with working algorithms and examples. This paper discusses the latest developments in the fields of adaptive feedback design and approximate dynamic programming. In order to describe new concepts and variations of Adaptive Dynamic Programming, this work begins with fundamental theories and theorems from functional optimization, optimum control, and dynamic programming. Because of its innovative integration of basic optimization principles with application, this review article encourages power engineers to make new discoveries in the provision of energy at the optimal rate.

Effect of Asymmetry on the Contact Time of Droplet Impact on Superhydrophobic Cylindrical Surfaces.

Reducing the contact time during the droplet impact on the surface is crucial for anti-icing, self-cleaning, and heat transfer optimization applications. This study aims to minimize the contact time by modifying the surface curvature to create an asymmetric impact process. Our experiments showed that the increase in Weber numbers (We) and the decrease in the ratio of surface diameter to droplet diameter (D*) intensify the asymmetry of the impact process, yielding four distinct rebound modes. Low asymmetry observes the liquid retract toward the central point (Rebound Modes 1 and 2), whereas high asymmetry yields a wing-like rebound (Rebound Modes 3 and 4). In Rebound Mode 1, increased asymmetry would lead to more extended contact due to the prolonged waiting period. Conversely, the reduction in contact time in Rebound Mode 2 occurs due to increased asymmetry with no waiting period. For Rebound Modes 3 and 4, the retraction time could be divided into three stages, generated by two liquid detachment modes from the surface. Analysis reveals that an increased asymmetry would reduce the retraction time during the first stage but prolong it during the third stage, with no significant effects on the second. Four correlations, each pertaining to a distinct impact mode, are proposed based on these analyses to describe the contact time concerning We and D* for droplets impacting a superhydrophobic cylindrical surface.

Liposomal encapsulation of amoxicillin via microfluidics with subsequent investigation of the significance of PEGylated therapeutics

With an increasing concern of global antimicrobial resistance, the efforts to improve the formulation of a narrowing library of therapeutic antibiotics must be confronted. The liposomal encapsulation of antibiotics using a novel and sustainable microfluidic method has been employed in this study to address this pressing issue, via a targeted, lower-dose medical approach. The study focusses upon microfluidic parameter optimisation, formulation stability, cytotoxicity, and future applications. Particle sizes of circa. 130 nm, with viable short-term (28-day) physical stability were obtained, using two different non-cytotoxic liposomal formulations, both of which displayed suitable antibacterial efficacy. The microfluidic method allowed for high encapsulation efficiencies (≈77 %) and the subsequent in vitro release profile suggested high limits of antibiotic dissociation from the nanovessels, achieving 90% release within 72 h. In addition to the experimental data, the growing use of poly(ethylene) glycol (PEG) within lipid-based formulations is discussed in relation to anti-PEG antibodies, highlighting the key pharmacological differences between PEGylated and non-PEGylated formulations and their respective advantages and drawbacks. It’s surmised that in the case of the formulations used in this study, the addition of PEG upon the liposomal membrane would still be a beneficial feature to possess owing to beneficial features such as stability, antibiotic efficacy and the capacity to further modify the liposomal membrane.

A new human-based metaheuristic algorithm for solving optimization problems based on preschool education

In this paper, with motivation from the No Free Lunch theorem, a new human-based metaheuristic algorithm named Preschool Education Optimization Algorithm (PEOA) is introduced for solving optimization problems. Human activities in the preschool education process are the fundamental inspiration in the design of PEOA. Hence, PEOA is mathematically modeled in three phases: (i) the gradual growth of the preschool teacher's educational influence, (ii) individual knowledge development guided by the teacher, and (iii) individual increase of knowledge and self-awareness. The PEOA's performance in optimization is evaluated using fifty-two standard benchmark functions encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, as well as the CEC 2017 test suite. The optimization results show that PEOA has a high ability in exploration–exploitation and can balance them during the search process. To provide a comprehensive analysis, the performance of PEOA is compared against ten well-known metaheuristic algorithms. The simulation results show that the proposed PEOA approach performs better than competing algorithms by providing effective solutions for the benchmark functions and overall ranking as the first-best optimizer. Presenting a statistical analysis of the Wilcoxon signed-rank test shows that PEOA has significant statistical superiority in competition with compared algorithms. Furthermore, the implementation of PEOA in solving twenty-two optimization problems from the CEC 2011 test suite and four engineering design problems illustrates its efficacy in real-world optimization applications.

A Comprehensive Review of Artificial Intelligence Algorithms Applications for Distributed Generation Sizing and Location Optimization to Enhance Efficiency and Reliability Indices in Radial Distribution Systems

The research aimed to understand radial distribution systems by defining them and exploring their strategies, and to explore methods for applying artificial intelligence algorithms in radial distribution systems, It also aimed to clarify types of indicators in radial distribution systems, highlighting the importance of providing a scientific and applied framework for integrating artificial intelligence in the field of energy distribution. The significance of the research lies in presenting a comprehensive and up-to-date approach based on the applications of artificial intelligence algorithms to improve the design and organization of distributed production systems in radial distribution systems. By employing a descriptive and analytical approach, the researcher was able to reach several conclusions, with the most prominent being that the application of artificial intelligence algorithms in radial distribution systems contributes to improving energy efficiency by directing it more accurately and effectively, the ability of artificial intelligence to provide precise data-dependent analyses enhances decision-making processes in radial distribution systems, The integration of artificial intelligence techniques contributes to improving overall efficiency in radial distribution systems, reducing energy losses, and enhancing energy distribution effectively. The research recommends enhancing integration between artificial intelligence techniques and radial distribution systems to ensure effective and smooth interaction, thereby increasing efficiency, Priority should be given to the safety and protection of data, and effective cybersecurity measures should be implemented to address the growing challenges in this context.

Applications of the Order Reduction Optimization of the H-Infinity Controller in Smart Structures

In this paper, our strategy is to look for locally optimum answers to a non-smooth optimization problem that has been constructed to include minimization goals and restrictions for smart structures’ vibration suppression. In both theoretical analysis and practical implementation, it is widely recognized that designing multi-objective control systems poses a considerable challenge. In this study, we assess the effectiveness of this method by employing the open-source Matlab toolbox Hifoo 2.0 and juxtapose our findings with established industry standards. We start by framing the control problem as a mathematical optimization issue and proceed to identify the controller that effectively addresses this optimization. This approach introduces the potential application of intelligent structures in tackling the challenge of vibration suppression. This study makes use of the most recent version of the freely available application Hifoo which tries to study vibration suppression with the limits outlined above in the context of multi-objective controller design. A controller directive is initially set, allowing for a lower order.

A Survey on Recent Applications of Artificial Intelligence and Optimization for Smart Grids in Smart Manufacturing

To enable highly automated manufacturing and net-zero carbon emissions, manufacturers have invested heavily in smart manufacturing. Sustainable and smart manufacturing involves improving the efficiency and environmental sustainability of various manufacturing operations such as resource allocation, data collecting and monitoring, and process control. Recently, a lot of artificial intelligence and optimization applications based on smart grid systems have improved the energy usage efficiency in various manufacturing operations. Therefore, this survey collects recent works on applications of artificial intelligence and optimization for smart grids in smart manufacturing and analyzes their features, requirements, and challenges. In addition, potential trends and further challenges for the integration of smart grids with renewable energies for smart manufacturing, applications of 5G and B5G (beyond 5G) technologies in the SG system, and next-generation smart manufacturing systems are discussed to provide references for further research.