Effects of Thymbra spicata extract and Thymol on morphine withdrawal syndrome in mice (insights to the liver function, antioxidant, and behavioral responses).

Safe chemicals for drug withdrawal can be extracted from natural sources. This study investigates the effects of clonidine and Thymbra spicata extract (TSE) on mice suffering from morphine withdrawal syndrome. Thymol, which is the active constituent in TSE, was also tested. A total of 90 mice were divided into nine groups. Group 1 was the control group, while Group 2 was given only morphine, and Group 3 received morphine and 0.2 mg/kg of clonidine. Groups 4-6 were given morphine along with 100, 200, and 300 mg/kg of TSE, respectively. Groups 7-9 received morphine plus 30, 60, and 90 mg/kg of Thymol, respectively, for 7 days. An oral naloxone challenge of 3 mg/kg was used to induce withdrawal syndrome in all groups. Improvement of liver enzyme levels (aspartate aminotransferase, alkaline phosphatase, and alanine transaminase) (p < .01) and behavioral responses (frequencies of jumping, frequencies of two-legged standing, Straub tail reaction) (p < .01) were significantly observed in the groups receiving TSE and Thymol (Groups 4-9) compared to Group 2. Additionally, antioxidant activity in these groups was improved compared to Group 2. Nitric oxide significantly decreased in Groups 4 and 6 compared to Groups 2 and 3 (p < .01). Superoxide dismutase increased dramatically in Groups 5, 8, and 9 compared to Groups 2 and 3 (p < .01). Groups 5-9 were significantly different from Group 2 in terms of malondialdehyde levels (p < .01). Certain doses of TSE and Thymol were found to alleviate the narcotics withdrawal symptoms. This similar effect to clonidine can pave the way for their administration in humans.

Antimicrobial Efficacy of Saline, Chlorhexidine, and Zataria Multiflora and Mentha Piperita Essential Oils in Root Canal Irrigation of Primary Molars.

Objectives: This study aimed to compare the antimicrobial efficacy of saline, 0.5% and 2% Zataria multiflora (Z. multiflora) essential oil, 0.5% and 2% Mentha piperita (M. piperita) essential oil, and 0.2% chlorhexidine (CHX) as root canal irrigants for primary molar teeth. Materials and Methods: A total of 64 primary molars were used in this in vitro study. The teeth were randomly assigned to six groups (N=10). The root canals were prepared up to file #35, and all teeth were sterilized before contamination with Enterococcus faecalis (E. faecalis; ATCC 29212) suspension. After 48 hours of incubation, the root canals in each group were irrigated with the respective irrigants. Sterile paper points were then used to collect microbial samples from the root canals. A colony counter was used to count the number of colony-forming units (CFUs). Data were analyzed by SPSS version 20 (alpha=0.05). Results: The colony count was significantly different among the groups (P<0.001), and 2% M. piperita (P=0.009), 0.5% Z. multiflora (P=0.021), and 0.2% CHX (P=0.002) were significantly more effective than saline in elimination of E. faecalis. The ascending order of microbial count after irrigation was as follows: saline > 0.5% M. piperita > 0.2% CHX > 2% M. piperita > 0.5% Z. multiflora. Conclusion: The current study showed the optimal antibacterial activity of 0.5% Z. multiflora essential oil and 2% M. piperita essential oil against E. faecalis, and indicated their possible efficacy for use as an irrigant for root canal irrigation of primary molars.

Environment random interaction of rime optimization with Nelder-Mead simplex for parameter estimation of photovoltaic models.

As countries attach importance to environmental protection, clean energy has become a hot topic. Among them, solar energy, as one of the efficient and easily accessible clean energy sources, has received widespread attention. An essential component in converting solar energy into electricity are solar cells. However, a major optimization difficulty remains in precisely and effectively calculating the parameters of photovoltaic (PV) models. In this regard, this study introduces an improved rime optimization algorithm (RIME), namely ERINMRIME, which integrates the Nelder-Mead simplex (NMs) with the environment random interaction (ERI) strategy. In the later phases of ERINMRIME, the ERI strategy serves as a complementary mechanism for augmenting the solution space exploration ability of the agent. By facilitating external interactions, this method improves the algorithm's efficacy in conducting a global search by keeping it from becoming stuck in local optima. Moreover, by incorporating NMs, ERINMRIME enhances its ability to do local searches, leading to improved space exploration. To evaluate ERINMRIME's optimization performance on PV models, this study conducted experiments on four different models: the single diode model (SDM), the double diode model (DDM), the three-diode model (TDM), and the photovoltaic (PV) module model. The experimental results show that ERINMRIME reduces root mean square error for SDM, DDM, TDM, and PV module models by 46.23%, 59.32%, 61.49%, and 23.95%, respectively, compared with the original RIME. Furthermore, this study compared ERINMRIME with nine improved classical algorithms. The results show that ERINMRIME is a remarkable competitor. Ultimately, this study evaluated the performance of ERINMRIME across three distinct commercial PV models, while considering varying irradiation and temperature conditions. The performance of ERINMRIME is superior to existing similar algorithms in different irradiation and temperature conditions. Therefore, ERINMRIME is an algorithm with great potential in identifying and recognizing unknown parameters of PV models.

Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation.

Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-threshold image segmentation methods are favored for their computational simplicity and operational efficiency. Existing threshold selection techniques in multi-threshold image segmentation, such as Kapur based on exhaustive enumeration, often hamper efficiency and accuracy. The whale optimization algorithm (WOA) has shown promise in addressing this challenge, but issues persist, including poor stability, low efficiency, and accuracy in COVID-19 threshold image segmentation. To tackle these issues, we introduce a Latin hypercube sampling initialization-based multi-strategy enhanced WOA (CAGWOA). It incorporates a COS sampling initialization strategy (COSI), an adaptive global search approach (GS), and an all-dimensional neighborhood mechanism (ADN). COSI leverages probability density functions created from Latin hypercube sampling, ensuring even solution space coverage to improve the stability of the segmentation model. GS widens the exploration scope to combat stagnation during iterations and improve segmentation efficiency. ADN refines convergence accuracy around optimal individuals to improve segmentation accuracy. CAGWOA's performance is validated through experiments on various benchmark function test sets. Furthermore, we apply CAGWOA alongside similar methods in a multi-threshold image segmentation model for comparative experiments on lung X-ray images of infected patients. The results demonstrate CAGWOA's superiority, including better image detail preservation, clear segmentation boundaries, and adaptability across different threshold levels.

Enhanced differential evolution algorithm for feature selection in tuberculous pleural effusion clinical characteristics analysis.

Tuberculous pleural effusion poses a significant threat to human health due to its potential for severe disease and mortality. Without timely treatment, it may lead to fatal consequences. Therefore, early identification and prompt treatment are crucial for preventing problems such as chronic lung disease, respiratory failure, and death. This study proposes an enhanced differential evolution algorithm based on colony predation and dispersed foraging strategies. A series of experiments conducted on the IEEE CEC 2017 competition dataset validated the global optimization capability of the method. Additionally, a binary version of the algorithm is introduced to assess the algorithm's ability to address feature selection problems. Comprehensive comparisons of the effectiveness of the proposed algorithm with 8 similar algorithms were conducted using public datasets with feature sizes ranging from 10 to 10,000. Experimental results demonstrate that the proposed method is an effective feature selection approach. Furthermore, a predictive model for tuberculous pleural effusion is established by integrating the proposed algorithm with support vector machines. The performance of the proposed model is validated using clinical records collected from 140 tuberculous pleural effusion patients, totaling 10,780 instances. Experimental results indicate that the proposed model can identify key correlated indicators such as pleural effusion adenosine deaminase, temperature, white blood cell count, and pleural effusion color, aiding in the clinical feature analysis of tuberculous pleural effusion and providing early warning for its treatment and prediction.

Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data.

Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.

A Sustainable Multi-Objective Model for Capacitated-Electric-Vehicle-Routing-Problem Considering Hard and Soft Time Windows as Well as Partial Recharging.

Due to the high pollution of the transportation sector, nowadays the role of electric vehicles has been noticed more and more by governments, organizations, and environmentally friendly people. On the other hand, the problem of electric vehicle routing (EVRP) has been widely studied in recent years. This paper deals with an extended version of EVRP, in which electric vehicles (EVs) deliver goods to customers. The limited battery capacity of EVs causes their operational domains to be less than those of gasoline vehicles. For this purpose, several charging stations are considered in this study for EVs. In addition, depending on the operational domain, a full charge may not be needed, which reduces the operation time. Therefore, partial recharging is also taken into account in the present research. This problem is formulated as a multi-objective integer linear programming model, whose objective functions include economic, environmental, and social aspects. Then, the preemptive fuzzy goal programming method (PFGP) is exploited as an exact method to solve small-sized problems. Also, two hybrid meta-heuristic algorithms inspired by nature, including MOSA, MOGWO, MOPSO, and NSGAII_TLBO, are utilized to solve large-sized problems. The results obtained from solving the numerous test problems demonstrate that the hybrid meta-heuristic algorithm can provide efficient solutions in terms of quality and non-dominated solutions in all test problems. In addition, the performance of the algorithms was compared in terms of four indexes: time, MID, MOCV, and HV. Moreover, statistical analysis is performed to investigate whether there is a significant difference between the performance of the algorithms. The results indicate that the MOSA algorithm performs better in terms of the time index. On the other hand, the NSGA-II-TLBO algorithm outperforms in terms of the MID, MOCV, and HV indexes.

Parrot optimizer: Algorithm and applications to medical problems.

Stochastic optimization methods have gained significant prominence as effective techniques in contemporary research, addressing complex optimization challenges efficiently. This paper introduces the Parrot Optimizer (PO), an efficient optimization method inspired by key behaviors observed in trained Pyrrhura Molinae parrots. The study features qualitative analysis and comprehensive experiments to showcase the distinct characteristics of the Parrot Optimizer in handling various optimization problems. Performance evaluation involves benchmarking the proposed PO on 35 functions, encompassing classical cases and problems from the IEEE CEC 2022 test sets, and comparing it with eight popular algorithms. The results vividly highlight the competitive advantages of the PO in terms of its exploratory and exploitative traits. Furthermore, parameter sensitivity experiments explore the adaptability of the proposed PO under varying configurations. The developed PO demonstrates effectiveness and superiority when applied to engineering design problems. To further extend the assessment to real-world applications, we included the application of PO to disease diagnosis and medical image segmentation problems, which are highly relevant and significant in the medical field. In conclusion, the findings substantiate that the PO is a promising and competitive algorithm, surpassing some existing algorithms in the literature. The supplementary files and open source codes of the proposed Parrot Optimizer (PO) is available at https://aliasgharheidari.com/PO.html and https://github.com/junbolian/PO.

Photoactivation of LOV domains with chemiluminescence.

Optogenetics has opened new possibilities in the remote control of diverse cellular functions with high spatiotemporal precision using light. However, delivering light to optically non-transparent systems remains a challenge. Here, we describe the photoactivation of light-oxygen-voltage-sensing domains (LOV domains) with in situ generated light from a chemiluminescence reaction between luminol and H2O2. This activation is possible due to the spectral overlap between the blue chemiluminescence emission and the absorption bands of the flavin chromophore in LOV domains. All four LOV domain proteins with diverse backgrounds and structures (iLID, BcLOV4, nMagHigh/pMagHigh, and VVDHigh) were photoactivated by chemiluminescence as demonstrated using a bead aggregation assay. The photoactivation with chemiluminescence required a critical light-output below which the LOV domains reversed back to their dark state with protein characteristic kinetics. Furthermore, spatially confined chemiluminescence produced inside giant unilamellar vesicles (GUVs) was able to photoactivate proteins both on the membrane and in solution, leading to the recruitment of the corresponding proteins to the GUV membrane. Finally, we showed that reactive oxygen species produced by neutrophil like cells can be converted into sufficient chemiluminescence to recruit the photoswitchable protein BcLOV4-mCherry from solution to the cell membrane. The findings highlight the utility of chemiluminescence as an endogenous light source for optogenetic applications, offering new possibilities for studying cellular processes in optically non-transparent systems.

Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats.

Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants and manufactured substances conferring toxicity to human health. The present study investigated whether pyrene, a type of PAH, harms rats. Our research provides an effective feature selection strategy for the animal dataset from Wenzhou Medical University's Experimental Animal Center to thoroughly examine the impacts of PAH toxicity on rat features. Initially, we devised a high-performance optimization method (SCBA) and added the Sobol sequence, vertical crossover and horizontal crossover mechanisms to the bat algorithm (BA). The SCBA-KELM model, which combines SCBA with the kernel extreme learning machine model (KELM), has excellent accuracy and high stability for selecting features. Benchmark function tests are then used in this research to verify the overall optimization performance of SCBA. In this paper, the feature selection performance of SCBA-KELM is verified using various comparative experiments. According to the results, the features of the genes PXR, CAR, CYP2B1/2 and CYP1A1/2 have the most impact on rats. The SCBA-KELM model's classification performance for the gene dataset was 100%, and the model's precision value for the public dataset was around 96%, as determined by the classification index. In conclusion, the model utilized in this research is anticipated to be a reliable and valuable approach for toxicological classification and assessment.

An Enhanced RIME Optimizer with Horizontal and Vertical Crossover for Discriminating Microseismic and Blasting Signals in Deep Mines.

Real-time monitoring of rock stability during the mining process is critical. This paper first proposed a RIME algorithm (CCRIME) based on vertical and horizontal crossover search strategies to improve the quality of the solutions obtained by the RIME algorithm and further enhance its search capabilities. Then, by constructing a binary version of CCRIME, the key parameters of FKNN were optimized using a binary conversion method. Finally, a discrete CCRIME-based BCCRIME was developed, which uses an S-shaped function transformation approach to address the feature selection issue by converting the search result into a real number that can only be zero or one. The performance of CCRIME was examined in this study from various perspectives, utilizing 30 benchmark functions from IEEE CEC2017. Basic algorithm comparison tests and sophisticated variant algorithm comparison experiments were also carried out. In addition, this paper also used collected microseismic and blasting data for classification prediction to verify the ability of the BCCRIME-FKNN model to process real data. This paper provides new ideas and methods for real-time monitoring of rock mass stability during deep well mineral resource mining.

Face Image Segmentation Using Boosted Grey Wolf Optimizer.

Image segmentation methods have received widespread attention in face image recognition, which can divide each pixel in the image into different regions and effectively distinguish the face region from the background for further recognition. Threshold segmentation, a common image segmentation method, suffers from the problem that the computational complexity shows exponential growth with the increase in the segmentation threshold level. Therefore, in order to improve the segmentation quality and obtain the segmentation thresholds more efficiently, a multi-threshold image segmentation framework based on a meta-heuristic optimization technique combined with Kapur's entropy is proposed in this study. A meta-heuristic optimization method based on an improved grey wolf optimizer variant is proposed to optimize the 2D Kapur's entropy of the greyscale and nonlocal mean 2D histograms generated by image computation. In order to verify the advancement of the method, experiments compared with the state-of-the-art method on IEEE CEC2020 and face image segmentation public dataset were conducted in this paper. The proposed method has achieved better results than other methods in various tests at 18 thresholds with an average feature similarity of 0.8792, an average structural similarity of 0.8532, and an average peak signal-to-noise ratio of 24.9 dB. It can be used as an effective tool for face segmentation.

An accelerated sine mapping whale optimizer for feature selection.

An improved whale optimization algorithm (SWEWOA) is presented for global optimization issues. Firstly, the sine mapping initialization strategy (SS) is used to generate the population. Secondly, the escape energy (EE) is introduced to balance the exploration and exploitation of WOA. Finally, the wormhole search (WS) strengthens the capacity for exploitation. The hybrid design effectively reinforces the optimization capability of SWEWOA. To prove the effectiveness of the design, SWEWOA is performed in two test sets, CEC 2017 and 2022, respectively. The advantage of SWEWOA is demonstrated in 26 superior comparison algorithms. Then a new feature selection method called BSWEWOA-KELM is developed based on the binary SWEWOA and kernel extreme learning machine (KELM). To verify its performance, 8 high-performance algorithms are selected and experimentally studied in 16 public datasets of different difficulty. The test results demonstrate that SWEWOA performs excellently in selecting the most valuable features for classification problems.

Advanced slime mould algorithm incorporating differential evolution and Powell mechanism for engineering design.

The slime mould algorithm (SMA) is a population-based swarm intelligence optimization algorithm that simulates the oscillatory foraging behavior of slime moulds. To overcome its drawbacks of slow convergence speed and premature convergence, this paper proposes an improved algorithm named PSMADE, which integrates the differential evolution algorithm (DE) and the Powell mechanism. PSMADE utilizes crossover and mutation operations of DE to enhance individual diversity and improve global search capability. Additionally, it incorporates the Powell mechanism with a taboo table to strengthen local search and facilitate convergence toward better solutions. The performance of PSMADE is evaluated by comparing it with 14 metaheuristic algorithms (MA) and 15 improved MAs on the CEC 2014 benchmarks, as well as solving four constrained real-world engineering problems. Experimental results demonstrate that PSMADE effectively compensates for the limitations of SMA and exhibits outstanding performance in solving various complex problems, showing potential as an effective problem-solving tool.

An Enhanced Hunger Games Search Optimization with Application to Constrained Engineering Optimization Problems.

The Hunger Games Search (HGS) is an innovative optimizer that operates without relying on gradients and utilizes a population-based approach. It draws inspiration from the collaborative foraging activities observed in social animals in their natural habitats. However, despite its notable strengths, HGS is subject to limitations, including inadequate diversity, premature convergence, and susceptibility to local optima. To overcome these challenges, this study introduces two adjusted strategies to enhance the original HGS algorithm. The first adaptive strategy combines the Logarithmic Spiral (LS) technique with Opposition-based Learning (OBL), resulting in the LS-OBL approach. This strategy plays a pivotal role in reducing the search space and maintaining population diversity within HGS, effectively augmenting the algorithm's exploration capabilities. The second adaptive strategy, the dynamic Rosenbrock Method (RM), contributes to HGS by adjusting the search direction and step size. This adjustment enables HGS to escape from suboptimal solutions and enhances its convergence accuracy. Combined, these two strategies form the improved algorithm proposed in this study, referred to as RLHGS. To assess the efficacy of the introduced strategies, specific experiments are designed to evaluate the impact of LS-OBL and RM on enhancing HGS performance. The experimental results unequivocally demonstrate that integrating these two strategies significantly enhances the capabilities of HGS. Furthermore, RLHGS is compared against eight state-of-the-art algorithms using 23 well-established benchmark functions and the CEC2020 test suite. The experimental results consistently indicate that RLHGS outperforms the other algorithms, securing the top rank in both test suites. This compelling evidence substantiates the superior functionality and performance of RLHGS compared to its counterparts. Moreover, RLHGS is applied to address four constrained real-world engineering optimization problems. The final results underscore the effectiveness of RLHGS in tackling such problems, further supporting its value as an efficient optimization method.

Recent Advances in the Support Layer, Interlayer and Active Layer of TFC and TFN Organic Solvent Nanofiltration (OSN) Membranes: A Review.

Although separation of solutes from organic solutions is considered a challenging process, it is inevitable in various chemical, petrochemical and pharmaceutical industries. OSN membranes are the heart of OSN technology that are widely utilized to separate various solutes and contaminants from organic solvents, which is now considered an emerging field. Hence, numerous studies have been attracted to this field to manufacture novel membranes with outstanding properties. Thin-film composite (TFC) and nanocomposite (TFN) membranes are two different classes of membranes that have been recently utilized for this purpose. TFC and TFN membranes are made up of similar layers, and the difference is the use of various nanoparticles in TFN membranes, which are classified into two types of porous and nonporous ones, for enhancing the permeate flux. This study aims to review recent advances in TFC and TFN membranes fabricated for organic solvent nanofiltration (OSN) applications. Here, we will first study the materials used to fabricate the support layer, not only the membranes which are not stable in organic solvents and require to be cross-linked, but also those which are inherently stable in harsh media and do not need any cross-linking step, and all of their advantages and disadvantages. Then, we will study the effects of fabricating different interlayers on the performance of the membranes, and the mechanisms of introducing an interlayer in the regulation of the PA structure. At the final step, we will study the type of monomers utilized for the fabrication of the active layer, the effect of surfactants in reducing the tension between the monomers and the membrane surface, and the type of nanoparticles used in the active layer of TFN membranes and their effects in enhancing the membrane separation performance.

Face-to-face, online, or blended: which method is more effective in teaching electrocardiogram to medical students.

BACKGROUND: Electrocardiogram (ECG) remains an important medical diagnostic and screening tool. This study aimed to compare the effectiveness of online classes instead of traditional face-to-face or blended methods in medical students' ECG learning. METHODS: Two hundred and fifteen medical students (including 105 (48.8%) males and 110 (51.2%) females) were studied from February 2021 to February 2022. Regardless of their grade, participants were divided into three groups: online, face-to-face, and blended. Then all participants sat for an ECG interpretation exam, and their results were compared. RESULTS: Twenty-six (12.1%) participants were residents, and 189 (87.9%) were interns. Thirty-five (16.3%), 85 (39.5%), and 95 (44.2%) participants were taught ECG through face-to-face, online, and blended methods, respectively. Regarding participants' preferences on teaching methods, 118 (54.9%) preferred face-to-face learning, and the remaining 97 (45.1%) chose online learning (p < 0.001). The blended method seemed more promising in almost half of the exam questions regarding teaching method effectiveness. The mean total exam score was also significantly higher in participants who were taught blended than in the others (7.20 ± 1.89, p = 0.017). Face-to-face (5.97 ± 2.33) and online teaching methods (6.07 ± 2.07) had similar efficacy according to the mean total score (p = 0.819). CONCLUSION: While most students preferred face-to-face learning to online learning, a blended method seemed more promising regarding students' skill enhancement to interpret ECG.

From coarse to fine: Two-stage deep residual attention generative adversarial network for repair of iris textures obscured by eyelids and eyelashes.

We propose a two-stage deep residual attention generative adversarial network (TSDRA-GAN) for inpainting iris textures obscured by eyelids. This two-stage generation approach ensures that the semantic and texture information of the generated images is preserved. In the second stage of the fine network, a modified residual block (MRB) is used to further extract features and mitigate the performance degradation caused by the deepening of the network, thus following the concept of using a residual structure as a component of the encoder. In addition, for the skip connection part of this phase, we propose a dual-attention computing connection (DACC) to computationally fuse the features of the encoder and decoder in both directions to achieve more effective information fusion for iris inpainting tasks. Under completely fair and equal experimental conditions, it is shown that the method presented in this paper can effectively restore original iris images and improve recognition accuracy.

Improved Colony Predation Algorithm Optimized Convolutional Neural Networks for Electrocardiogram Signal Classification.

Recently, swarm intelligence algorithms have received much attention because of their flexibility for solving complex problems in the real world. Recently, a new algorithm called the colony predation algorithm (CPA) has been proposed, taking inspiration from the predatory habits of groups in nature. However, CPA suffers from poor exploratory ability and cannot always escape solutions known as local optima. Therefore, to improve the global search capability of CPA, an improved variant (OLCPA) incorporating an orthogonal learning strategy is proposed in this paper. Then, considering the fact that the swarm intelligence algorithm can go beyond the local optimum and find the global optimum solution, a novel OLCPA-CNN model is proposed, which uses the OLCPA algorithm to tune the parameters of the convolutional neural network. To verify the performance of OLCPA, comparison experiments are designed to compare with other traditional metaheuristics and advanced algorithms on IEEE CEC 2017 benchmark functions. The experimental results show that OLCPA ranks first in performance compared to the other algorithms. Additionally, the OLCPA-CNN model achieves high accuracy rates of 97.7% and 97.8% in classifying the MIT-BIH Arrhythmia and European ST-T datasets.

A new truck-drone routing problem for parcel delivery by considering energy consumption and altitude.

The high population density in metropolitan areas, high-rise buildings, and changes in people's lifestyles have completely changed the way postal packages are delivered. People no longer go to the ground floor to receive a postal package. In the meantime, the delivery of postal packages through the balconies and windows of the units on the upper floors of the buildings will gradually become inevitable. Hence, a new Vehicle Routing Problem with Drone mathematical model has been developed with the objective of minimizing total delivery time and with the ability to deliver postal packages in the path of drones at different heights. In addition, the drone's energy consumption is computed by taking into account wind speed, the weight of the postal parcel, the weight of the drone's body, and other factors in the drone's path. A two-phase algorithm based on the nearest neighborhood and local search is presented to solve the developed mathematical model in different instances. Several small-sized test problems are designed and solved, and the performance of the heuristic approach is evaluated compared to the outputs of the CPLEX solver. Finally, the proposed model is implemented on a real-world scale to demonstrate the efficacy and applicability of the proposed model as well as the heuristic approach. The results show that the model successfully finds the optimal planning of the delivery routes, especially when we deal with delivery points at different heights.