Optimization Approach Research Articles

A novel algorithm MPPT controller based on the herd horse optimization for photovoltaic systems under partial shadow conditions

In recent years, a significant scientific issue has been the creation of maximum power point tracking (MPPT) methods to increase the energy production of PV plants. Moreover, to try to cope with the unparalleled operating conditions of PV plants, many bio-inspired meta-heuristic algorithms have already been suggested in the literature, but their implementation is often complex and difficult. In this sense, we propose a novel algorithm for monitoring the (MPPT), using the newly meta-heuristic approach of herd horse optimization (HHO). A DC/DC boost converter is utilised in the suggested controllers to extract the most power possible from the PV resource. The system is programmed and modelled using the MATLAB/SIMULINK software, which also studies four shadow models and a 3S1P topography of single-junction solar arrays. Considering partial shading conditions (PSC), the success of the power values in the global maximum power point (GMPP) of the proposed method is between 99.64% and 99.07%. Besides the time to capture the GMPP by the proposed algorithm is between 0.396 s and 1.666 s, shorter than that of the CSA and FPA algorithms. Comparison with the CSA and FPA optimizers confirmed the quality of the MPPT-based HHO algorithm for GMPP extraction in different (PSC).

Universal terminal for cloud quantum computing

To bring the quantum computing capacities to the personal edge devices, the optimum approach is to have simple non-error-corrected personal devices that offload the computational tasks to scalable quantum computers via edge servers with cryogenic components and fault-tolerant schemes. Hence the network elements deploy different encoding protocols. This article proposes quantum terminals that are compatible with different encoding protocols; paving the way for realizing mobile edge-quantum computing. By accommodating the atomic lattice processor inside a cavity, the entangling mechanism is provided by the Rydberg cavity-QED technology. The auxiliary atom, responsible for photon emission, senses the logical qubit state via the long-range Rydberg interaction. In other words, the state of logical qubit determines the interaction-induced level-shift at the central atom and hence derives the system over distinguished eigenstates, featuring photon emission at the early or late times controlled by quantum interference. Applying an entanglement-swapping gate on two emitted photons would make the far-separated logical qubits entangled regardless of their encoding protocols. The proposed scheme provides a universal photonic interface for clustering the processors and connecting them with the quantum memories and quantum cloud compatible with different encoding formats.

Effect of the Combined Treatment of Taoren Chengqi Decoction with Laser Photocoagulation on Diabetic Retinopathy

This study investigates the therapeutic effect of the combined treatment of Taoren Chengqi decoction with laser photocoagulation on DR. In animal experiments, A total of 18 rats were used and randomly divided into blank groups, model groups, and treatment groups. The rats in the model group and treatment group were intraperitoneally injected with 60 mg/kg streptozotocin. Moreover, the TCT group was gavaged with Tao-ren Cheng-qi decoction, while the other groups received an equivalent volume of normal saline once daily for 8 weeks. Fasting blood glucose (FBG) and serum interleukin (IL)-6 and IL-1β were determined. In the clinical trial, a total of 80 patients with DR were divided into a laser group (treated with laser photocoagulation) and a combination group (treated with laser photocoagulation+Taoren Chengqi decoction). The central macular thickness (CMT), best-corrected visual acuity (BCVA), and serum levels of high-density lipoprotein cholesterol, low-density-lipoprotein cholesterol, total cholesterol, triglycerides, glycated hemoglobin, and hypoxia-inducible factor 1α were measured in the patients. In animal experiments, we observe that Tao-ren Cheng-qi decoction can effectively reduce the levels of FBG, IL-6, and IL-1β in rats models with DR. Clinical studies revealed that the combination of Taoren Chengqi decoction with laser photocoagulation reduced the levels of blood glucose and lipid-related indices, and improved the inflammatory state, BCVA, and CMT. Therefore, this study suggests that the combined treatment of laser surgery and Taoren Chengqi decoction can be considered an optimal therapeutic approach for patients with DR.

Multilevel Thresholding-based Medical Image Segmentation using Hybrid Particle Cuckoo Swarm Optimization

Background: The most important aspect of medical image processing and analysis is image segmentation. Fundamentally, the outcomes of segmentation have an impact on all subsequent image testing methods, including object representation and characterization, measuring of features, and even higher-level procedures. The problem with image segmentation is recognition and perceptual completion while segmenting the image. However, these issues can be resolved by multilevel optimization techniques. However, multilevel thresholding will become more computationally intensive with increasing thresholds. Optimization algorithms can resolve these issues. Therefore, hybrid optimization is used for image segmentation in this research work. Methods: The researchers propose a Multilevel Thresholding-based Segmentation using a Hybrid Optimization approach with an adaptive bilateral filter to resolve the optimization challenges in medical image segmentation. The proposed model utilizes Kapur's entropy as the objective function in the nature-inspired optimization algorithm. Results: The result is evaluated using parameters such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The researchers perform result analysis with variable thresholding levels on KAU-BCMD and mini-MIAS datasets. The highest PSNR, SSIM, and FSIM achieved were 31.9672, 0.9501, and 0.9728 respectively. The results of the hybrid model are compared with state-of-the-art models, demonstrating its efficiency. Conclusion: The research concludes that the proposed Multilevel thresholding-based segmentation using a Hybrid Optimization approach effectively solves optimization challenges in medical image segmentation. The results indicate its efficiency compared to existing models. The research work highlights the potential of the proposed hybrid model for improving image processing and analysis in the medical field.

Diagnosis and endovascular management of vasospasm after aneurysmal subarachnoid hemorrhage — survey of real-life practices

BackgroundVasospasm and delayed cerebral ischemia (DCI) are the leading causes of morbidity and mortality after intracranial aneurysmal subarachnoid hemorrhage (aSAH). Vasospasm detection, prevention and management, especially endovascular management varies from...

Performance of two laser motion modes versus conventional orthodontic ceramic brackets debonding technique on enamel surface topography.

The risk of enamel deterioration that frequently coexists with debonding of orthodontic teeth brackets elevates the mandate for finding an optimum approach for debonding them without harmful effects. This in-vitro study is intended to compare the effects of two different laser modes (scanning and circular) and a conventional method on the enamel surface after debonding orthodontic brackets. 66 extracted premolars were assigned into 3 groups. After that, light-cure composite resin was used to attach the ceramic brackets to the teeth. Amongst the test groups, Group I: specimens that were debonded using conventional debonding using pliers; Group 2: specimens that were debonded using Er, Cr: YSGG laser applications using the circular motion method; and Group 3: specimens that were debonded using Er, Cr: YSGG laser applications using the scanning motion method. Adhesive Remnant Index (ARI) assessment, intra-pulpal temperature increase, enamel surface roughness after polishing, and assessment of the microstructure of enamel were carried out with scanning electron microscopy. The gathered information was examined statistically. The conventional debonding method had a significantly higher proportion of adhesive remnant index (ARI) scores of 2 and 3 in comparison to the circular (p < .004) and scanning laser groups (p < .001). There was no significant difference in ARI scores between the circular and scanning laser groups (p > .05). Moreover, the circular and scanning laser debonding methods resulted in a significantly higher proportion of Enamel Surface Roughness (ESR) scores of 0 and a lower proportion of ESR scores of 3 compared to the conventional technique group (p < .001). However, there was no significant difference in ESR scores between the circular and scanning laser methods (p = .945). Lastly, the average intra-pulpal temperature was significantly higher in the circular laser group (1.9 ± 0.5 ) compared to the scanning laser group (0.9 ± 0.2) with p < .001. Er, Cr: YSGG laser irradiation is a tool that shows promise for debonding ceramic brackets with minimal harm to the enamel surface. The scanning laser technique is more desirable due to the lower intra-pulpal temperature increase.

Neuro-Adaptive Cooperative Control for Dual-Arm Robots With Position and Velocity Constraints: An Optimal Torque Allocation Approach

Neuro-Adaptive Cooperative Control for Dual-Arm Robots With Position and Velocity Constraints: An Optimal Torque Allocation Approach

A Low-Cost Optimization Approach for Solving Minimum Norm Linear Systems and Linear Least-Squares Problems

A Low-Cost Optimization Approach for Solving Minimum Norm Linear Systems and Linear Least-Squares Problems

A Closed Formalism for Anatomy-Independent Projection and Optimization of Magnetic Stimulation Coils on Arbitrarily Shaped Surfaces.

Transcranial magnetic stimulation (TMS) is a popular method for the noninvasive stimulation of neurons in the brain. It has become a standard instrument in experimental brain research and has been approved for a range of diagnostic and therapeutic applications. These applications require appropriately shaped coils. Various applications have been established or approved for specific coil designs with their corresponding spatial electric field distributions. However, the specific coil implementation may no longer be appropriate from the perspective of available material and manufacturing opportunities or considering the latest understanding of how to achieve induced electric fields in the head most efficiently. Furthermore, in some cases, field measurements of coils with unknown winding or a user-defined field are available and require an actual implementation. Similar applications exist for magnetic resonance imaging coils. This work aims at introducing a complete formalism free from heuristics, iterative optimization, and ad-hoc or manual steps to form practical stimulation coils with individual turns to either equivalently match an existing coil or produce a given field. The target coil can reside on practically any sufficiently large or closed surface adjacent to or around the head. The method derives an equivalent field through vector projection exploiting the well-known Huygens' and Love's equivalence principle. In contrast to other coil design or optimization approaches recently presented, the procedure is an explicit forward Hilbert-space vector projection or basis change. For demonstration, we map a commercial figure-of-eight coil as one of the most widely used devices and a more intricate coil recently approved clinically for addiction treatment (H4) onto a bent surface close to the head for highest efficiency and lowest field energy. The resulting projections are within ≤4% of the target field and reduce the necessary pulse energy by more than 40%.

Day-case minimally invasive parathyroidectomy for solitary parathyroid adenoma: An optimised approach.

Day-case minimally invasive parathyroidectomy for solitary parathyroid adenoma: An optimised approach.

Optimising the dosage of bio-rejuvenators in asphalt recycling: A rejuvenation index based approach

While significant research efforts have been devoted to the rejuvenation of aged bitumen using bio-rejuvenators, a universally recognised approach for dosage optimisation is still yet to be established. Aiming to develop standardised methods to determine the optimal dosages of six types of bio-rejuvenators, this study validated the use of a rheology-based rejuvenation index as a potential universal method. Rheological properties of rejuvenated binders, determined through frequency sweep tests, multiple stress creep and recovery tests, linear amplitude sweep tests, and bending beam rheometer (BBR) tests were carried out with the properties of virgin binder as reference targets. The results indicated that the applied rheology-based rejuvenation index is a feasible approach. This index can ensure improved low- and intermediate-temperature (fatigue) performance of aged bitumen while maintaining high-temperature performance within acceptable limits. Further characterisation of the binders by Fourier Transform Infrared Spectroscopy (FTIR) analysis suggested that the bio-rejuvenation processes will commonly only involve physical softening rather than a chemical reversal of ageing. Therefore, the use of chemistry-based indices is likely not feasible.

Comparing chemical transfection, electroporation, and lentiviral vector transduction to achieve optimal transfection conditions in the Vero cell line

BackgroundTransfection is an important analytical method for studying gene expression in the cellular environment. There are some barriers to efficient DNA transfection in host cells, including circumventing the plasma membrane, escaping endosomal compartmentalization, autophagy, immune sensing pathways, and translocating the nuclear envelope. Therefore, it would be very useful to introduce an optimum transfection approach to achieve a high transfection efficiency in the Vero cell line. The aim of this study was to compare various transfection techniques and introduce a highly efficient method for gene delivery in Vero cells.MethodsIn the current study, three transfection methods were used, including chemical transfection, electroporation, and lentiviral vector transduction, to obtain the optimum transfection conditions in the Vero cell line. Vero cells were cultured and transfected with chemical transfection reagents, electroporation, or HIV-1-based lentivectors under different experimental conditions. Transfection efficiency was assessed using flow cytometry and fluorescence microscopy to detect GFP-positive cells.ResultsAmong the tested methods, TurboFect™ chemical transfection exhibited the highest efficiency. Optimal transfection conditions were achieved using 1 µg DNA and 4 µL TurboFect™ in 6 × 104 Vero cells.ConclusionTurboFect™, a cationic polymer transfection reagent, demonstrated superior transfection efficiency in Vero cells compared with electroporation and lentivirus particles, and is the optimal choice for chemical transfection in the Vero cell line.

Human resources development: Evaluating perceptions against policy

The objective of sustaining smart, sustainable and cohesive inclusive growth, framed by the European strategy, is in line with the global commitments of the Sustainable Development Goals. Thus, an analysis of people’s attitudes towards social policy and perceptions of professional development in Poland against European development policy is crucial. The nationwide representative sample included 1000 respondents aged 18–64. The study used principal component analysis (PCA), an approach for the multi-response optimisation. The results indicate that respondents’ evaluations are in line with the direction of EU labour policy, where their decisions correspond to the planned priorities oriented towards inclusion and cohesion. The PCA analysis showed that employee competencies and priorities for individual standing can be considered strongly correlated. Competencies were also found to be important for the platform to support systemic development. However, the items included in the analysis that are beyond the scope of the ESF+ , although even correlated, are of lesser importance. The research results allow to expand knowledge on the perception of the development policy facets promoted by the EU, supplemented by themes of leadership or immigrants. The study may become a starting point for inclusive and cohesive working climate research.

Machine learning based data governance methods for demand response databases

With the increasing level of grid intelligence and the related demand response database expanding, it is important to study a compound problem data governance method for demand response, while the traditional data governance methods have problems such as not considering data temporality and ignoring the impact of noise and duplicate data on data repair. As a result, this project will develop an anomaly data extraction and repair model based on two-way long and short memory networks, and repair the anomaly data by respective noise smoothing, missing data filling, and duplicate data cleaning. The paper also provides an adaptive moment estimation approach for optimisation to raise the model’s accuracy. The outcomes demonstrated that the study model’s precision for anomalous data extraction was 100% and its recall rate was 80%, which was a significant improvement over the previous state. In terms of anomalous data repair, the research model had the root mean square error value and lowest mean absolute percentage error value when compared with related models, at 0.0049 MPa and 1.375% respectively. Both the abnormal data extraction and repair performance of the research model are greatly improved over the related models, and have important value in the abnormal data governance of demand response databases.

Energy Efficiency Optimisation of Joint Computational Task Offloading and Resource Allocation Using Particle Swarm Optimisation Approach in Vehicular Edge Networks.

With the progression of smart vehicles, i.e., connected autonomous vehicles (CAVs), and wireless technologies, there has been an increased need for substantial computational operations for tasks such as path planning, scene recognition, and vision-based object detection. Managing these intensive computational applications is concerned with significant energy consumption. Hence, for this article, a low-cost and sustainable solution using computational offloading and efficient resource allocation at edge devices within the Internet of Vehicles (IoV) framework has been utilised. To address the quality of service (QoS) among vehicles, a trade-off between energy consumption and computational time has been taken into consideration while deciding on the offloading process and resource allocation. The offloading process has been assigned at a minimum wireless resource block level to adapt to the beyond 5G (B5G) network. The novel approach of joint optimisation of computational resources and task offloading decisions uses the meta-heuristic particle swarm optimisation (PSO) algorithm and decision analysis (DA) to find the near-optimal solution. Subsequently, a comparison is made with other proposed algorithms, namely CTORA, CODO, and Heuristics, in terms of computational efficiency and latency. The performance analysis reveals that the numerical results outperform existing algorithms, demonstrating an 8% and a 5% increase in energy efficiency.

Cross validation based transfer learning for cross-sectional non-linear shrinkage: A data-driven approach in portfolio optimization

Enhanced covariance estimation approaches, such as (non-)linear shrinkage, are well established in the literature. Non-linear shrinkage estimators generally minimize a certain loss function regarding statistical assumptions about the future covariance matrix. At the same time, the problem of covariance estimation is traditionally considered from a rather restrictive view since the only available data to determine the estimation parameters is given by the return history of the actual portfolio constituents. In this study, we propose a novel and purely data-driven perspective on covariance estimation. We present a non-linear shrinkage estimator that determines the estimation parameters using cross validation to be historically optimal on a disjoint dataset of assets according to the given objective, such as minimum variance or maximum risk-adjusted return. We then transfer the historically optimal estimation parameters learned on the disjoint dataset to the actual covariance estimation problem. Thereby, the sample eigenvalues are corrected in a purely data-driven way, agnostic to theoretically derived parameters. Another benefit of focusing on disjoint data is that we address the problem of limited data availability in high-dimensional estimation problems when the number of assets exceeds the history length. Our empirical evaluation, based on a total of six stock market indices and various problem dimensions, shows that our approach outperforms existing cross-sectional estimators in minimizing variance and maximizing risk-adjusted return. While our study is limited to the cross-section, the method of parameter selection using cross validation and transfer learning can also be combined with other estimators, such as time-series methods.

Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives.

Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.

Intrusion Response Systems for the 5G Networks and Beyond: A New Joint Security-vs-QoS Optimization Approach

Intrusion Response Systems for the 5G Networks and Beyond: A New Joint Security-vs-QoS Optimization Approach

Scheduling Approaches for Joint Optimization of Age and Delay in Industrial Wireless Networks

Scheduling Approaches for Joint Optimization of Age and Delay in Industrial Wireless Networks

A Proximal ADMM-Based Distributed Optimal Energy Management Approach for Smart Grid With Stochastic Wind Power

A Proximal ADMM-Based Distributed Optimal Energy Management Approach for Smart Grid With Stochastic Wind Power