In the current study, the effects of orthographic and phonological processing in Chinese character copying were investigated using a data set extracted from a database containing handwriting data of 856 stimuli; the responses of which were collected from 100 participants. To investigate the effect of character frequency, radical frequency, and phonetic regularity, 151 phonetic compounds were selected from the database because (1) their corresponding phonetic radicals were all free-standing characters, (2) their corresponding phonetic radicals were located at either the right or the bottom positions in the characters, and (3) no more than 10% of the participants made errors when copying these target characters. The results of the linear mixed effect models revealed that after controlling for inter-stroke distance (ISD) and stroke number, the inter-stroke intervals (ISIs) at the radical and logographeme boundaries were significantly longer, indicating significant orthographic processing in the immediate copying task that radicals and logographemes were used as writing units. In addition, shorter ISIs at the logographeme boundary associated with higher radical frequency, and shorter ISIs at the radical boundary associated with higher character frequency and regular characters, were observed. These observations indicated significant orthographic and phonological effects in the immediate copying task. Finally, the significant phonetic regularity effect observed also supported the notion that phonology contributes to Chinese character writing and that the effects of central processing, including character frequency and phonetic regularity, cascade over peripheral processing during Chinese character copying. Numerous psycholinguistic studies have been conducted to investigate how people retrieve abstract orthographic codes from their mental lexicon and how they are converted into motoric execution in the process of handwriting. The architecture of the writing process can be divided into central and peripheral processing . The cenjin4; since this study was conducted in Hong Kong, where traditional Chinese characters and Cantonese are used, in this paper, phonetic transcriptions are represented in jyutping, a romanization system developed by the Linguistic Society of Hong Kong). The orthographic forms of Chinese characters are compilations of strokes organized in square constructions. One major type of Chinese characters, called phonetic compounds, contain semantic radicals that give clues meaning and phonetic radicals that give clues to phonology. For example, the character 評 (ping4) <criticize> can be decomposed into the semantic radical 言 <speech-related>, which indicates clues to its meaning, and the phonetic radical 平 (ping4) <flat>, which indicates clues to its phonology. Semantic and phonetic radicals were demonstrated in many studies to be involved in the decoding process of phonetic compounds  corresponds to one syllable and one morpheme most of the time . For exads e.g., . It has ds e.g., .The roles of radicals in writing Chinese characters can possibly be 2-fold. First, the functions of individual radicals, i.e., the semantic and phonological information associated with the radicals, may affect the semantic and phonological processing in the writing process. Second, as frequently occurring orthographic units, they may affect the orthographic processing in the writing process.via their corresponding syllables.Processing of semantic and phonological information associated with semantic and phonetic radicals is also evident in writing phonetic compound characters e.g., . For exaThe processing of phonological information associated with phonetic radicals in writing is also in line with the trend of research that concerns whether phonology is involved in the process of orthographic code retrieval e.g., . Given tIn another study by As for orthographic processing in Chinese character writing, one of the most frequently asked questions concerns the grain size of the orthographic units involved in the processing e.g., . In addiDistinguishing between logographemes and radicals, however, is not always straightforward. One reason is that some logographemes share the same orthographic forms with radicals. For example, the logographeme 木 in the character 堡 (bou2) <castle> shares the same orthographic form with the phonetic radical 木 (muk6) <wood> in the character 沐 (muk6) <bathe>. In the latter case, the radical 木 gives clues to the phonological form of the target character 沐, whereas in the former case, the logographeme 木 contributes to neither the sound nor the meaning of the target character 堡.In this study, logographemes and radicals were defined according to whether they carry functions of semantic and phonological in the character contexts or not. Using the examples in the above, 保 (bou2) <protect> and 土 (tou2) <earth> are regarded as the radicals of the character 堡 because the former gives clues to phonology while the latter gives clues to meaning. The constituents of the radical 保  are regarded as logographemes.With recent advancements in the technology of digital graphic tablets, an increasing number of studies have been conducted using handwriting measures to investigate the writing process e.g., . AlthougFor example, using a copying task involving suffixed and pseudo-suffixed words, Applying similar measurements, In the current study, similar handwriting measures were used to investigate the orthographic and phonological processing in a simple Chinese character copying task. To investigate the orthographic processing in copying Chinese characters, the participants’ use of orthographic units of different grain sizes was examined. The participants’ use of orthographic units of different grain sizes in copying Chinese characters was observed in two measures. In the first measure, ISIs located between orthographic units were compared with ISIs located within orthographic units. If orthographic units of a particular grain size are processed in copying Chinese character, the ISIs located before the orthographic units should be longer than the ISIs within the orthographic units. Therefore, to investigate if radicals are processed in copying Chinese characters, the ISIs located at the radical boundaries were compared with ISIs within radicals. Similarly, to investigate if logographemes are processed in copying Chinese characters, the ISIs located at the logographeme boundaries were compared with ISIs within logographemes. It was expected that similar boundary effect as reported in Finally, to investigate the phonological processing in Chinese character copying, the effect of phonetic regularity was observed using regular and irregular characters as stimuli. If the syllables associated with the characters and that of the phonetic radicals were involved in the processing, the mismatched syllables in the irregular condition compared with the shared syllables in the regular condition was expected to result in a competition effect. Hence, longer ISIs were expected to be associated with irregular characters relative to regular characters. Particularly, it was expected that if the syllables associated with the characters and syllables associated with the phonetic radicals are involved in Chinese character copying, longer ISIs at the radical boundaries  would beTo summarize, the aim of the current study was to investigate the orthographic and phonological processing in Chinese character copying using handwriting measures. To investigate the orthographic processing in Chinese character copying, the effects of radical boundary and logographeme boundary on ISIs, the effects of character frequency on between-radical ISIs, and the effects of radical frequency on between-logographeme ISIs were observed. To investigate the phonological processing in Chinese character copying, the effect of phonetic regularity on between-radical ISIs was observed.SD = 1.8) with normal or corrected-to-normal vision were recruited. All of the participants were native Cantonese speakers born and who received mainstream education in Hong Kong. None of the participants reported a history of cognitive, learning, or motor problems. Each participant was instructed to copy 172 characters from the total 856 stimuli in the database arranged in random order, such that for each stimulus, the handwriting data of at least 20 participants were collected.The data set reported in this study was extracted from the Database of Radicals in Written Chinese with Reliable Logographeme Boundaries . In the In the current study, 151 target characters were selected because (1) their corresponding phonetic radicals were all free-standing characters such that the regularity of the target characters could be defined, (2) their corresponding phonetic radicals were located at either the right or the bottom positions in the characters, and (3) no more than 10% of the participants made errors when copying these target characters. The following lexical and sub-lexical variables of the selected characters were also derived from the HKCCN.There are approximately 7.6 million characters in the HKCCN. The character frequency value of each of the target items refers to the counts of appearance of the character per million.Each ISI (and the corresponding ISD) obtained in the writing process was categorized into between-radical ISI, between-logographeme ISI, or within-logographeme ISI according to the position in which it occurred in the writing process. An example of the different ISIs is given in Radical frequency refers to the counts of different characters containing the corresponding radicals in the HKCCN. Radical frequencies of between-logographemes ISIs and within-logographeme ISIs refer to the frequencies of the radicals that they occurred in. Radical frequencies of between-radicals ISIs refer to the frequencies of the subsequent radicals. Using the example given in Characters that shared the identical syllables with their corresponding phonetic radicals were categorized as regular. Those that did not share the identical syllables with their corresponding phonetic radicals were categorized as irregular.Age of acquisition (AoA) of each target character was obtained from the Canto-Lexicon Project . In the In the copying task, 7-in tablets with a resolution of 1,820 × 1,200 and a refresh rate of 60 Hz were used in this study. The tablets were quad-core with 2.20 GHz processing speed and they ran the latest Android 4.1.1 version. The tablets were installed with a homebrew Android application that controlled the display of the stimuli and recorded the written responses of the participants using the open-sourced MotionEvent package.An immediate copying task was used. Each participant was instructed to use one tablet and one stylus pen in the copying task. Two pre-experimental training trials on using the stylus pen to write on the tablet were conducted to ensure that the participants knew how to manage the pen and tablet. In the randomly ordered experimental trials, a target character was displayed and the participants were required to directly copy the presented character on the tablet screen using the stylus pen. The target character remained on the screen during the entire writing process. The participants were instructed to write each stroke precisely by avoiding merging successive strokes. The elapsed time and coordinates each time the stylus pen touched or left the tablet screen were recorded accordingly. The duration of the whole experiment was about 15 min.The ISIs and the corresponding ISDs were calculated based on the coordinates, where the stylus pen left and retouched the tablet screen. Finally, the entire writing process and the final written output were also obtained.t > 2. The results of the statistical models are summarized in Linear mixed effect models with maximal model structure  were comData from the items with ISIs beyond three standard deviations from the mean  were excluded from the analysis. The results showed that the ISI increased with ISD (0.53 ± 0.01). The longer the ISD, the longer were the ISI. Besides, ISI also increased with stroke number (14.85 ± 2.77), meaning that longer ISIs were associated with characters with more strokes. Effect of AoA was not significant in predicting ISI.Results showed that ISIs located at the radical boundaries were significantly longer than ISIs within radicals . Results also showed that ISIs located at the logographeme boundaries were significantly longer than ISIs within logographemes . Results indicated a significant effect of regularity . The current study obtained handwriting performance from participants using an immediate Chinese character copying task. The results showed that, consistent with previous handwriting studies e.g., , the ISDUnlike results reported in previous studies e.g., , the effThe major aim of this study was to investigate the orthographic and phonological processing in Chinese character copying. The effects of radical boundary and logographeme boundary on ISIs, the effects of character frequency and phonetic regularity on between-radical ISIs, and the effects of radical frequency on between-logographeme ISIs were observed. The effects of these factors were discussed as follow.The significance of between-logographeme ISIs in the handwriting process supported the notion that logographemes are functional units in writing Chinese characters . PreviouMoreover, it was observed that consistent with previous findings , longer The association of longer between-radical ISIs with lower-frequency characters is also consistent with previous findings . TherefoIn addition to character frequency, the results also indicated that the between-radical ISIs were affected by the regularity of the characters. The results also indicated shorter between-radical ISIs of regular characters compared with that of irregular ones. This observation suggests that the syllables associated with the characters and that of their corresponding phonetic radicals were involved in the processing. In the irregular character condition, the syllables associated with the characters and the syllables associated with their corresponding phonetic radicals were different. The conflicts between the mismatched syllables probably resulted in the longer between-radical ISIs. On the other hand, no such conflicts existed between the matched syllables associated with the characters and their corresponding phonetic radicals in the regular condition. Hence, the between-radical ISIs were not affected in the regular condition.The results indicated that despite the immediate copying task allowed a stroke-by-stroke approach, which requires minimal orthographic processing, in writing, the participants showed the tendency to break the target characters down into frequently occurring smaller constituents as processing units. The significantly longer between-radicals ISIs and between-logographeme ISIs supported this notion. This tendency to use radicals and logographemes as writing units was consistent with previous studies e.g., . BesidesSimilarly, despite the task requirement of the immediate copying task can be fulfilled using a stroke-by-stroke approach without accessing to orthography and phonology, significant phonetic regularity effect was still observed, indicated that phonology is processed during character copying. It is important to highlight that if only the syllables associated with characters or syllables associated with the phonetic radicals were involved in the copying process, the phonetic regularity effect would not be significant. In order to explain the significant phonetic regularity effect observed, a mismatch between the syllables associated with the irregular characters and the syllables associated with the phonetic radicals have to be assumed. Therefore, the significant phonetic regularity effect observed indicated that both syllables associated with characters and syllables associated with phonetic radicals were involved in the character copying process.The contribution of phonology in writing Chinese characters has been reported in previous studies using written picture naming tasks  and semaThe writing process can be divided into central and peripheral processing . The ortThe significance of the between-logographeme ISIs in the handwriting process observed in the current study supports the finding that the graphic motor patterns of logographemes are represented in the brain for peripheral processing in Chinese character writing. That the retrieval of the grapho-motor patterns of logographemes and/or the planning for their execution was affected by the frequency of the radicals in which the logographemes occurred further added complications to the significance of the between-logographeme ISIs in the handwriting process.Two possible explanations are proposed. The first possibility is that both the graphic motor patterns of radicals and graphic motor patterns of logographemes are available in the mental representations and they can be flexibly used during the peripheral processing of Chinese characters writing. When radical frequencies are high, the graphic motor patterns of the radicals can be used directly for peripheral processing in character writing. On the other hand, when radical frequencies are low, they will be broken down into smaller grain size logographemes for peripheral processing in character writing. This explains the observed significant radical frequency effect at the logographeme boundary.Alternatively, it is also possible that the significant radical frequency effect at the logographeme boundary reflected the cascading of central processing to peripheral processing in character writing e.g., . AccordiAn increasing number of studies have provided supportive evidence to the idea of a cascaded relationship between central processing and peripheral processing in writing e.g., . PreviouIt is important to point out that in the current study, the dataset was extracted from a large database for the experiment, including stimuli that did not belong to the regular and irregular categories. Unlike the typical factorial design in which only regular and irregular characters are included as stimuli, in the experiment, there were phonetic compounds and non-phonetic compounds in the stimuli set organized in random order. There were advantages and disadvantages to using the two different designs.In a typical factorial design, the stimuli set is selected more carefully, such as matching the constituent phonetic radicals, the character frequencies, and stroke numbers in the two categories. This allows a relatively “purer” comparison between regular and irregular conditions. In the current design, although character frequency and stroke number between the two sets of stimuli were not statistically different, the group size and the set of phonetic radicals did not match. Hence, the comparison between regular and irregular conditions may not have been ideal. Future studies using stimuli matched perfectly in terms of constituent phonetic radicals, character frequencies, stroke numbers, and group size are recommended to further verify the findings of the current study.Nevertheless, there was an advantage to using the current design. Because the selected items were embedded in a large set of different characters and the stimuli were presented in random order during the experiment, the unselected items actually served as fillers in the study. The regularity effect, therefore, was analyzed in a relatively more “natural” way in this study. Hence, one may expect that the regularity effect exists in natural Chinese character copying.Finally, although there are certain limitations, e.g., the level of difficulties in the retrieval of orthographic representations is kept minimal, associated with the use of an immediate copying task to investigate the handwriting process, the potential of the application of the task in clinical settings, such as assessing individuals with developmental or acquired dysgraphia, should not be underrated. One common difficulty in studying dysgraphia concerns the high frequency of empty responses obtained in tasks involving high demands in cognitive and lexical processing, such as writing-to-dictation. As reported in the current study, using an immediate copying task, on the other hand, should allow the detections of certain effects associated with central and peripheral processing of writing among individuals with dysgraphia. Consequently, the possible difficulties underlying certain individuals’ writing problems can be hypothesized and tested accordingly.In the current study, orthographic and phonological processing in Chinese character copying were tested. Significant orthographic and phonological effects were observed in the immediate copying task, supporting the idea that word copying also relies on the lexical pathways of writing . The sigAll datasets generated for this study are included in the article/The studies involving human participants were reviewed and approved by the Human Subjects Ethics Sub-committee of the Hong Kong Polytechnic University. The patients/participants provided their written informed consent to participate in this study.DK-YL is responsible for experiment design, data analysis, and preparation of the manuscript.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The unmanned aerial vehicle (UAV) path planning problem is a type of complex multi-constraint optimization problem that requires a reasonable mathematical model and an efficient path planning algorithm. In this paper, the fitness function including fuel consumption cost, altitude cost, and threat cost is established. There are also four set constraints including maximum flight distance, minimum flight altitude, maximum turn angle, and maximum climb angle. The constrained optimization problem is transformed into an unconstrained optimization problem by using the penalty function introduced. To solve the model, a multiple population hybrid equilibrium optimizer (MHEO) is proposed. Firstly, the population is divided into three subpopulations based on fitness and different strategies are executed separately. Secondly, a Gaussian distribution estimation strategy is introduced to enhance the performance of MHEO by using the dominant information of the populations to guide the population evolution. The equilibrium pool is adjusted to enhance population diversity. Furthermore, the Lévy flight strategy and the inferior solution shift strategy are used to help the algorithm get rid of stagnation. The CEC2017 test suite was used to evaluate the performance of MHEO, and the results show that MHEO has a faster convergence speed and better convergence accuracy compared to the comparison algorithms. The path planning simulation experiments show that MHEO can steadily and efficiently plan flight paths that satisfy the constraints, proving the superiority of the MHEO algorithm while verifying the feasibility of the path planning model. With the continuous development of UAVs, the use of this technology in civilian commercial and military fields is increasing. Path planning and routing problems are common in the domain of commercial UAVs such as logistics drones ,2,3. ForA series of intelligent optimization algorithms have been used to solve the UAV path planning problem thanks to continuous research in its area. A hybrid genetic algorithm combined with a ray casting algorithm is proposed by Gustavo et al.  for solvRecently, an intelligent optimization algorithm inspired by the volume–mass balance model–the equilibrium optimizer—has been proposed. Faramarzi et al.  shows thAlthough EO has competitive advantages over other algorithms, it still suffers from slow convergence, low accuracy, and the tendency to fall into local optima in some cases. It is known from the No Free Lunch theory  that no In order to improve the performance of the basic EO, this paper employs three strategies to improve the EO. Firstly, we adopt a multiple population strategy as a way to balance the exploitation and exploration ability of the algorithm. Secondly, a Gaussian distribution estimation strategy using the dominant population information to guide the evolution of individuals is introduced to enhance the algorithm performance. In addition, the Levy flight strategy and inferior solution shift strategy are used to apply perturbations to the particles to help the algorithm get rid of stagnation. The main innovations of this paper are summarized as follows.(1) A multiple population hybrid equalization optimizer (MHEO) is proposed.(2) A path planning model with three costs and four constraints is developed.(3) The CEC2017 test set was used to evaluate the MHEO performance. Compared with other algorithms, MHEO has better performance.(4) MHEO has been used to solve the path planning problem and obtains more satisfactory results.The remainder of this paper is organized as follows. The basic EO is presented in detail in ith generation of individual particles, The equilibrium optimizer (EO) is a physics-based metaheuristic algorithm. Similar to other metaheuristic algorithms, EO generates a randomly distributed initial population in the search space with the following equation.After initialization, the four particles with the best fitness are selected to form the equilibrium pool, and the equilibrium pool particles are updated after each iteration.EO balances the exploitation and exploration of the algorithm by the variation of the exponential term The generation rate The In summary, the equation for generating candidate particles by EO can be described as follows:Compared with other metaheuristic optimization algorithms, EO solves optimization problems effectively. However, EO relies on the particles in the equilibrium pool to generate candidate particles, which still suffers from the defects of reduced population diversity and falling into the local optimum. In order to overcome the shortcomings of basic EO, this paper proposes a multiple population hybrid equilibrium optimizer (MHEO). The population is divided into three subpopulations and different position update formulas are adopted to enhance the population diversity and improve the performance of the algorithm. A stagnation perturbation strategy is introduced to help the algorithm jump out of the local optimum. The mathematical model of MHEO is described in the following detail.In this paper, the whole population is divided into three subpopulations according to their fitness: the exploitation population, the equilibrium population, and the exploration population. In the optimization process, the three populations are updated separately according to different strategies and then evaluated together to divide the new three subpopulations. Specifically, the exploitation population uses information from the dominant population to exploit and provide better solutions. The equilibrium population balances exploitation behavior and exploration behavior. Exploration populations are exploring more search areas and maintaining population diversity. For the optimization algorithm, extensive exploration is mainly performed in the early stage to ensure that it does not fall into the local optimum, while precise exploitation is mainly performed in the later stage to ensure convergence efficiency. Thus, the size of the three subpopulations is dynamically changing, and the mathematical model is described as follows:In Equation (9), the three subpopulations are sorted from smallest to largest based on fitness as follows.The basic EO mainly uses the individuals in the equilibrium pool for updating. However, when the individuals in the equilibrium pool fall into a local optimum, the quality of the whole population will be affected. To fully utilize the information of the dominant population, the Gaussian distribution estimation strategy is introduced to enhance the performance of the algorithm considering that it can use a probability model to represent the relationship between individuals. The Gaussian distribution estimation strategy computes a probability distribution model using the current dominant population and generates new candidates based on the probability distribution model sampling. In this paper, the distribution model is estimated using a weighted maximum likelihood estimation method, and the mathematical model of this strategy is described as follows:For the exploitation population, we use the dominant population information to modify the direction of particle evolution as a way to enhance the optimization ability of the algorithm. The dominant population equation is as follows.For the equilibrium population, For the exploration population, since they are far from the optimal solution, the mean points should be far from the inferior solution as a way to drive the sampling points closer to the optimal solution, while the sampling points for each inferior solution are different, which helps to enhance the exploration ability of the algorithm. The exploration population equation is described as follows.When an algorithm stalls, two types of methods are usually used to help the algorithm jump and get rid of the stagnation. One type is the restart strategy. It helps the algorithm to get out of stagnation by randomly initializing particles in the search space. However, this method does not refer to the current population of the information and is too random to be effective in general. Therefore, this paper uses another type of method to help the algorithm get rid of stagnation–particle perturbation strategy.Lévy flight is an important non-Gaussian random walk whose random steps obey a large tailed probability distribution . Due to When the algorithm stalls, the shift of the mean value is considered to expand the search range as a way to enhance the population diversity. Meanwhile, it can effectively utilize the information of inferior solutions and avoid the waste of inferior solutions. The specific mathematical model is described as follows:In the optimization process, the average fitness of the dominant population is used to determine whether the algorithm is in stagnation. If the average fitness of the dominant population does not change in two consecutive iterations, the algorithm is considered to be in stagnation and the two-particle perturbation strategies above are employed. The mathematical model is described as follows:After each iteration is finished, new populations are generated using the following selection mechanism.Algorithm 1. The procedure of MHEO1. Set the maximum iterations as 2. Set the population size as 3. Assign free parameters 4. Initialize the position using Equation (1);5. While 6. Estimate weight mean 7. Evaluate population and form Equilibrium pool using Equation (16);8. Calculate 8. Generate subpopulations using Equation (11);8. Update i = 1:NP9. For 10. if 11. 12. Else13. 14. 15. Elseif 16. 17. Else18. 19. End if;20. End if;21. End if;22. Generate new populations by Equation (25)23. End for24. 25. End while;26. Output the best individual The pseudo-code of the proposed multiple population hybrid equilibrium optimizer (MHEO) is described below (Algorithm 1).The battlefield environment construction problem is mainly to solve the establishment of 3D terrain environment and the establishment of various types of threats. In this paper, we mainly use Digital Elevation Map (DEM), and the 3D mountain terrain is described as follows.Since this paper mainly considers static path planning, the deployment position of enemy air defense systems and the corresponding weapon performance will be obtained by various detection methods before the mission is carried out. In addition, the UAV mainly relies on low altitude assault and defense to carry out strike missions, and when encountering enemy radar or anti-aircraft fire, it usually adopts a close ground turn for evasion instead of climbing altitude to get rid of it.Based on the above considerations, in order to ensure the survival rate of UAV flying to the strike target and the simplicity of the trajectory planning model, we equate the threat range of the threat source to a cylindrical obstacle model with the maximum cross-sectional area covering the threat area. In summary, the battlefield environment is constructed as shown in Solving the path planning problem requires the establishment of a suitable fitness function and the consideration of various constraints affecting the quality of the path. The static global 3D path planning model contains two main aspects. One is the cost function, which is the objective function of the intelligent optimization algorithm. We need to consider the cost of fuel consumption, the cost of flight altitude, and the cost of the integrated threat of the UAV. The second is the performance constraints of the UAV itself, such as the maximum flight path constraint, the minimum flight altitude constraint, the maximum turn angle constraint, and the maximum climb angle constraint.In actual combat missions, there is a limit to the amount of fuel a UAV can carry. The UAV cannot fly without limitation and needs to ensure that the UAV returns safely after the mission. In this paper, it is assumed that UAV performs uniform rate flight, so the size of the track length reflects the size of fuel consumption, i.e., the cost of UAV flight fuel consumption can be expressed by the track length.Flying at low altitudes is beneficial to play the role of terrain shielding and reduce the risk of being detected by radar . TherefoThe UAV will encounter enemy air defense systems in its mission, which include detection radar, anti-aircraft artillery, ground-to-air missiles and other threats. The above threats are approximated as a cylindrical area in the three-dimensional plane, and the detection range or strike range is used as its radius, stipulating that the UAV cannot pass through the cylindrical area.Assuming that the maximum range that the UAV can fly with fuel is When the UAV performs a low-altitude surprise strike mission, although flying close to the ground can reduce the probability of being detected by radar, in the actual combat space, the terrain is complex and the risk of the aircraft crashing to the ground can easily occur. Therefore, in order to ensure UAV flight safety, a minimum flight altitude needs to be set. Each flight track altitude Due to the limitation of the UAV’s own maneuverability, the maximum turn angle of the actual flight needs to be limited. At the same time, the UAV will slightly deviate from the planned trajectory during the turn. The trajectory curvature should therefore ensure that the planned path can be effectively followed by the UAV. The angle between the generated adjacent trajectory segments The maximum climb angle is the maximum angle at which the UAV can climb or descend in flight and is another important criterion for achieving UAV maneuvers in addition to the maximum turn angle. Similar to the maximum turn angle, the climb angle between trajectory segments Algorithm 2. MHEO for unmanned aerial vehicle (UAV) path planning1. Set the parameters of CASSA same as Algorithm 1;2. Set the start point, target point, the boundaries of battlefield space;3. Set the position and range of threats;4. While 5. Each particle represents a path; calculate the fitness 6. Estimate weight mean 7. Evaluate population and form Equilibrium pool using Equation (16);8. Update 9. For 10. Else, if 11. Else, if 12. Else, if 13. If the new path is better than before, update it;14. 15. End while;16. Output the best path In summary, the objective function model for the path planning used in this paper is as follows.To comprehensively verify the superior performance of the MHEO, the algorithm is tested using the IEEE CEC2017 single-objective test function. The CEC2017 test suite includes 28 test functions, among which F1 belongs to unimodal test functions, which are used to test the convergence accuracy of the algorithm; F2–F8 belong to multimodal test functions, which are mainly used to test the ability of the algorithm to jump out of the local optimum; F9–F18 and F18–F28 are hybrid and composite functions, respectively, which are composed of complex functions and can be used to test the potential of the algorithm to solve complex optimization problems in the real world. The definition of the function and the optimal value are referred to the Ref. .Three improved swarm intelligence optimization algorithms HFPSO , GEDGWO The maximum number of iterations is 600, and the population size is 500 in each experiment. The parameter settings of each algorithm refer to the original literature, as shown in As shown in To analyze the solution quality of the improved algorithms, box plots are drawn based on the results of each algorithm solving the test function 51 times independently, as shown in Convergence speed and convergence accuracy are also important indicators of algorithm performance, and In order to statistically analyze the differences between the algorithms, the Wilcoxon signed-rank test was employed to test each algorithm at a significance level of Time cost is one of the important indicators of algorithm performance. In this section, we will evaluate the ability in solving the path planning problem by MHEO through simulation experiments. The experiments are conducted on a simulation platform with AMD R7 4700U, 1.8GHz,16GB RAM, and the programming environment is MATLAB R2016b. The parameters of the path planning model are set as follows: the battlefield space is The UAV start point is  and the target point is . The two algorithms were solved independently for the path planning model 20 times, and the statistical results are recorded in From the statistical results in The UAV start point is , the target point is , and the threat source settings are shown in From the statistical results in In this paper, we proposed an improved equilibrium optimizer, a three-dimensional trajectory planning model, and the improved algorithm applied to solve the model.In the proposed MHEO, a multiple population strategy is used to combine the basic EO and Gaussian distribution estimation strategy as a way to enhance the algorithm performance. The basic EO relies on the top three optimal individuals of the equilibrium pool for updating, which can easily lead to local optimum, while the Gaussian distribution estimation strategy is able to use the dominant population information to correct the evolutionary direction, thus avoiding this shortcoming. When the algorithm stagnates, two-particle perturbation strategies are used. A Lévy flight strategy is applied to the dominant population to help the dominant individuals get rid of the local optimum. An inferior solution shift strategy is imposed on the inferior individuals to help the worse individuals search more space.CEC2017 was used to test the performance of the MHEO and the algorithm performance was evaluated by statistical analysis, stability analysis, convergence analysis, and Wilcoxon test. The experimental results show that MHEO has better performance and ranks first among the seven algorithms, outperforming HFPSO, GEDGWO, VCS, MPA, SMA and EO. In addition, MHEO is applied to solve the path planning model. Simulation results show that MHEO can steadily plan flight paths with better quality. The path planning model proposed in this paper is feasible. On the other hand, the time cost of MHEO is higher than that of EO due to the increased time cost caused by the calculation of the covariance matrix. However, for static path planning, the optimization accuracy is more important compared to the time cost. Therefore, the time cost due to the increased performance is acceptable.There is still a lot of work to be done in the future. For our fund, the establishment of a single UAV path planning model and the improvement of algorithms have been completed so far. In the future, we need to further solve the multi-UAV collaborative mission planning, including multi-UAV path planning and multi-UAV target assignment. In addition, we need to further reduce the time cost of the algorithm to enable it to solve real-time problems.


Down-SAmpling based Variation Estimation), a method to evaluate the purity of single-cell transcriptome clusters and to identify misclassified cells. The method utilizes down-sampling to eliminate differences in sampling noise and uses a log-likelihood based metric to help identify misclassified cells. In addition, DSAVE estimates the number of cells needed in a population to achieve a stable average gene expression profile within a certain gene expression range. We show that DSAVE can be used to find potentially misclassified cells that are not detectable by similar tools and reveal the cause of their divergence from the other cells, such as differing cell state or cell type. With the growing use of single-cell RNA-seq, we foresee that DSAVE will be an increasingly useful tool for comparing and purifying subpopulations in single-cell RNA-Seq datasets.Single-cell RNA sequencing has become a valuable tool for investigating cell types in complex tissues, where clustering of cells enables the identification and comparison of cell populations. Although many studies have sought to develop and compare different clustering approaches, a deeper investigation into the properties of the resulting populations is lacking. Specifically, the presence of misclassified cells can influence downstream analyses, highlighting the need to assess subpopulation purity and to detect such cells. We developed DSAVE ( All cells in the human body are unique—no two cells have exactly the same transcriptional profile. Recent advances in single-cell RNA sequencing have enabled examination of the heterogeneity among individual cells, including data from several hundreds of thousands of cells in the same experiment –3. A difThe purpose of clustering is to create cell populations based on biological traits, such as cell type or different cell states. Despite the variety of clustering algorithms available, misclassification of cells, where some cells assigned to a cluster exhibit a greater biological difference from the others, is still a large problem –8. The dIn addition to misclassifications, the data for some cells may be of poor technical quality. There are many existing cell-wise quality control metrics for single-cell RNA-Seq data (e.g. number of unique molecular identifiers (UMIs), mitochondrial gene content, etc.), and a common approach is to discard cells based on a combination of these metrics , 12. Chon, p), where n is the number of molecules sampled from a cell and p is a vector describing the probabilities of drawing molecules and is directly proportional to the mean expression of each gene in a cell population. This model has previously been used for describing sampling noise for UMI-based technologies [The high technical variation in single-cell sequencing data poses a challenge for clustering as well as downstream analyses. The often-dominating source of technical variation is the sampling noise, which arises from the limited number of mRNA molecules and reads per cell, where two perfectly identical cells will appear different due to random and incomplete sampling of their transcriptome. We define sampling noise as the variation between cells drawn from a multinomial distribution, Mult are useful for advanced computational methods such as digital cytometry  and genoDown-SAmpling based Variation Estimation), which includes methods for estimating cell population size, finding misclassified cells, and estimating the non-sampling cell-to-cell variation in cell populations. For the latter method, we introduce the concept BTM  variation, which represents all cell-to-cell variation within a cell population except sampling noise. The metric is comparable across cell types and datasets since it is independent from the often large and population-dependent sampling noise. We show that the BTM variation can be used as a relative measure for cluster purity, and demonstrate its utility in identifying datasets with high non-sampling technical variation, since the technical variation in such cases is much larger than its biological counterpart.Here, we sought to address three problems in single-cell data: 1) determining a suitable pool size for generating cell-type specific GEPs; 2) estimating the purity of single-cell clusters; and 3) identifying misclassified or low-quality cells. In addition, we investigated if the quality of clustered single-cell data can be estimated from the cell-to-cell variation by identifying technical non-sampling variation. To address these problems, we developed DSAVE  cell population is generated from the aligned cell population by sampling from the mean gene expression profile of the population. This cell population is similar to its aligned counterpart, except that the only variation present is sampling noise. The total variation, based on the coefficient of variation (CV), is then calculated as a function of gene expression for both the aligned and SNO cell populations. The variation of the SNO cell population is then subtracted from the variation of the aligned cell population to obtain the difference between total variation and sampling noise at different levels of gene expression. This difference represents the BTM variation and is used to calculate the DSAVE variation score. The following sections describe these steps in more detail.One typical use case for the BTM variation score is to calculate the score for one cell population and compare it with the scores of cell populations from other datasets. To make this practical, it is convenient to pre-calculate the scores of the other datasets and include them with the software package. The DSAVE variation score is only comparable among different cell populations when using a common set of genes, the same number of cells, and identical distributions of counts per cell, which is matched in a procedure called alignment (described below). This property of the score makes it challenging to pre-calculate comparable values for cell populations. Some genes may not be present in a new cell population that is to be investigated, it may not have the same number of cells, and the distribution of counts may be such that it cannot be downsampled to match the distribution of the populations for which the score was precalculated. To solve this issue, a template is defined, which states the genes included, the number of cells to use, and/or the distribution of counts per cell to which the populations should be downsampled. The template parameters are selected to work for most datasets while still using enough data to produce a stable score calculation.The template is created from a master cell population, a list of datasets for gene selection, the target number of cells (N), the target average counts per cell (C), and the upper and lower fractions of outliers to discard . The genes to use are chosen as the intersection of the genes available in the dataset list supplied. N cells are then randomly selected from the master cell population, and randomly downsampled to yield a count distribution per cell with an average count that equals C, thereby generating a cell count distribution to use for calculation of the DSAVE variation score. Several standard templates, which require different numbers of cells, were derived from several datasets and are provided with the DSAVE package along with precalculated scores for multiple cell populations. The vignette of the R package describes how to create a new template should the need arise.The purpose of cell population alignment is to normalize all cell populations to achieve similar sampling noise while retaining their original BTM variation. The alignment procedure uses the information stated in a template to first randomly sample a fixed number of cells (as stated in the template) from the original population to include in the aligned counterpart. The second step is to discard all genes which are not included in the template. In the final step, all cells in the population are randomly downsampled to match the distribution of total reads per cell that is stated in the template , where p is a vector of probabilities based on the cell population’s mean expression of the genes and n is the total number of counts for the cell. The probability pi for each gene in the p vector is calculated asEi is the gene expression in counts per million (CPM).The purpose of generating a SNO cell population from an aligned cell population is to obtain a cell population with similar sampling noise but no additional sources of variation, meaning that all cells in the population are identical apart from the sampling noise. To achieve this, we simulate gene counts for each cell by randomly sampling reads from a multinomial distribution, Mult, calculated gene-wise over all cells in the population. The total variation metric outliers . The lowoutliers .Dr of total variation values for all ranges is then calculated, where the total variation for each range value is calculated as the mean total variation of all genes within the range. The geometric mean expression of the genes within each range is also stored as a vector Er for later use. Geometric mean is used instead of arithmetic mean because the gene expression values are not log transformed.To make the total variation metric comparable across cell populations, it is not suitable to compare the variation gene-wise since the expression of a gene can vary between cell populations. Likewise, calculating the mean over all genes would introduce a bias if the gene expression distribution differs between cell populations, since the BTM variation is not completely constant over the gene expression range. To minimize the impact of such differences, the variation is instead calculated separately for 1,000 partially overlapping ranges of gene expression. The ranges are selected as described in To reduce the effect of outlier genes on the variation score, we also remove the most and least variable genes  is generated by linear interpolation between values in the total variation vector Dr at points along the geometric mean expression vector Er. f(e) thus represents the mean total variation at gene expression e. The variation per gene expression is then sampled from f(e) at k = 1000 logarithmically distributed points between 10 and 1000 CPM for both the aligned and SNO cell populations, where the expression at point j is denoted ej. At each point, the variation of the SNO cell population is subtracted from the variation of the aligned cell population to yield a difference between the total variation and the sampling noise as a function of gene expression. To reduce stochasticity, the values are calculated as the mean of nit = 15 iterations with different cell subpopulations sampled from the whole cell population, where the number of iterations were determined empirically (f(e) for each iteration, which are denoted fa,i(e) and fSNO,i(e) for the aligned and SNO cell populations, respectively, at iteration i. The DSAVE BTM variation score (DVS) is then calculated as the mean of the differences between fa,iand fSNO,i across all points and iterations, according to:The DSAVE irically . This yiWe note that that the DSAVE variation score is stochastic in nature and does not yield identical scores for repeated runs of the same data, although the scores are similar.S, of counts. Cells with fewer counts than S are discarded and will have an undefined log-likelihood. The probability pi that a count sampled from the mean gene expression profile of the cell population belongs to gene i is calculated asEx is the mean expression (CPM) of gene x in the cell population. The probability po to get the observed counts vector x  is then calculated as the probability density function of Mult at the value x, where Mult is the multinomial distribution, n is the counts observed for the cell, and p a vector of all pi. The log likelihood, L, is then calculated as the natural logarithm of po.To identify cell outliers in a cell population, we determine for each cell the probability of measuring the observed count distribution, assuming that we sample from the mean gene expression profile of the cell population. To avoid multiplication of many small numbers, we calculate the log-likelihood instead of probability. To make the metric comparable between cells, we first down-sample all cells to the same number, cell divergence as the negative log likelihood, yielding a positive value that increases with decreasing p0. To reduce stochasticity from down-sampling, the divergence is calculated 15 times, and the median of those values is used for each cell. The cell divergence cannot be compared between cell populations, since it depends on the gene expression profile and the number of counts to which the cells are down-sampled. The number 15 was determined empirically to yield a good correlation between runs  values correspond to a larger difference between a cell and the mean gene expression of the cell population. We define the een runs .gene-wise cell divergence. The metric represents the probability po,i of getting the observed count for gene i by sampling from the average gene expression of the population. This metric is calculated from the probability density function of Bi, where Bi is a binomial distribution for gene i, n is the number of counts for the cell, and pi is the probability that a randomly selected molecule belongs to the gene, as defined earlier. The log likelihood for each gene, Li, is calculated as the natural logarithm of po,i. The gene-wise cell divergence is then defined as the negative log likelihood, -Li.To investigate the cause of divergence for individual cells, we also introduced a log-likelihood metric per gene for each cell, the To test the statistical significance of the reduction of BTM variation from removing divergent cells, we removed an equal number of randomly selected cells and recalculated the DSAVE variation score. The procedure was repeated for 1000 iterations and we used a one-sided one-sample t-test with the null hypothesis that removing a randomly selected set of cells yields an equally large reduction in BTM variation as when the divergent cells are removed.relaimpo [lmg. We created a DSAVE template using only 1000 cells, which enabled us to gather 51 cell populations from four datasets, five tissues  and five cell types. The DSAVE BTM variation score was calculated for all samples and used as dependent variable in the analysis. We then created a design matrix of Boolean variables for the samples, where each variable represented either a dataset, a cell type or a tissue. One variable of each type was omitted and merged into an intercept ; all other factors therefore represent how they explain any differences in score from the samples matching the intercept. For the LC dataset, the intercept was represented by healthy tissue instead of blood, due to the lack of blood samples. In practice, this means that the LC dataset factor also includes any differences in BTM variation between blood and healthy lung tissue.The purpose of the relative importance analysis was to determine which factors are most important for explaining the differences in variation between different cell populations. We used the R package relaimpo   and highly (CPM > 100) expressed genes separately . Lowly eWe also investigated the gene count distribution for different cell populations to understand how differences in this distribution contribute to the total variation. When generating GEPs from single-cell data, it is important to minimize cell misclassifications and technical variation. It would therefore be valuable to measure these properties, since it would enable selection of datasets with small technical variation and detection of mixed cell populations. Although the BTM variation of a cell population is not a direct measure of these properties, it is useful for estimating the reliability of pooled gene expression profiles, since both misclassifications and technical effects will increase the BTM variation.To quantify the BTM variation, we generated SNO (Sampling Noise Only) cell populations from the observed cell populations by randomly sampling the same number of reads from the mean expression profile of the observed cell population. The cell populations were aligned to a template to obtain populations with nearly identical sampling noise. This involved down-sampling and limiting the analysis to the set of shared genes see . To makeTo facilitate comparison of BTM variation across cell populations, we represent the difference in variation between the aligned cell population and its SNO counterpart as a single value called the DSAVE BTM variation score. We performed a technical evaluation of the DSAVE BTM variation score, shown in We further investigated how the DSAVE variation score varies for a collection of cell populations from different public datasets, and found that the BTM variation differs substantially between them . For allTo evaluate the ability of the DSAVE variation score to determine the quality of the cell type classification, we created mixes of B and T cells and compared their BTM variation to that of the pure cell populations. We then performed a relative importance analysis using the DSAVE variation score as the dependent variable  to gain We also performed the relative importance analysis without the breast cancer dataset to see if technical factors still explained most of the differences in variation . In thisWe conclude that there are two ways to use the BTM variation score: 1) to compare the BTM variation of different datasets, where a large variation may indicate a technical problem with either the dataset or the clustering method applied, and 2) to compare different ways of dividing a collection of cells into populations. For the latter, the DSAVE score should be calculated without removing outlier genes, since they are likely differently expressed in misclassified cells.divergence. The metric is based on the log-likelihood of getting the observed expression profile of each cell when sampling from the mean expression profile of the population  to first identify genes that largely contributed to the divergence of individual cells, by sorting the genes by the minimum value of their gene-wise cell divergence. Among the 40 genes with the lowest values, we found three groups of genes worth noting. Next, we investigated individual divergent cells to try to explain their high divergence. We used the To determine if misclassification is a larger problem when trying to separate more similar cells into clusters, we investigated the population of follicular B cells in the LC dataset . We init−10, one-sample t-test, comparing against the null hypothesis that removing randomly selected cells would yield the same decrease in BTM variation), which is a substantially larger difference than the uncertainty of the metric (−10). These results show that the BTM variation score is useful for estimating cluster purity. The score in this case was calculated using a modified template, which used 1900 cells and discarded no outlier genes. The reason for this is that one of the cell populations had fewer than 2000 cells after outlier removal and that removal of gene outliers leads to less emphasis on misclassified cells in the score calculation, which is not desirable when evaluating cluster purity.Finally, we evaluated the ability of the DSAVE variation score to distinguish between the cell populations before and after removal of misclassified cells. We removed 25 NRBC and plasma cells from the T-cell population, resulting in a decrease in BTM variation from 0.1387 to 0.1353  to see if the same cells could be found using the other tools. While the other tools found many other potentially misclassified cells, they only found a small portion of the cells identified by DSAVE. This is likely because these tools operate on principal components, similar to the clustering, while DSAVE does not. To simplify analyses such as these, we have implemented an R function displaying an interactive map with all cells in a population, where hovering over a cell with the mouse displays the 5 most divergent genes and their divergence value .Single-cell RNA-Seq enables post-experimental formation of cell populations using clustering methods. However, the limited number of molecules in each cell combined with technical limitations of single-cell RNA-Seq and clustering methods introduce errors that may adversely affect downstream analyses. Although much effort has been spent on developing clustering methods, there is still a need for follow-up analysis and correction, since cell misclassifications are common. Minimizing such errors is essential to produce reliable output from analyses based on these generated cell populations, both indirectly to reduce the unwanted variation for further computational approaches and for the final biological interpretation, where misclassified cells can falsely introduce non-expressed genes.In this study, we developed methods to estimate the cell population size needed for the variation of its pooled gene expression profile to equal that of bulk samples, and to detect misclassified and low-quality cells. We used the coefficient of variation (CV) together with cell population alignment strategies to measure BTM variation, and used the probability of obtaining the observed counts by sampling from the mean cell population expression to define cell divergence. These metrics enabled the identification of misclassified cells and comparison of the BTM variation among different cell populations, both for the purpose of comparing the purity of different clusters and for comparing the BTM variation between datasets to detect potential technical issues. We found that our methods were successful in detecting misclassified or low-quality cells, which were not detected by the original authors of the dataset. We also showed that PCA, UMAP, Jackstraw, and scReClassify could not detect most misclassified cells detected by DSAVE. In addition, we show that our divergence metric is consistent with other known QC metrics of low-quality cells, such as low UMI count and high mitochondrial content. Methods for cell type classification other than clustering, such as scmap  and CHETAs part of this study, we developed a method to estimate the required size of a single-cell pool for the variation of its mean to be equal to that of bulk samples. Our results indicate that the required pool size is dependent on the gene expression range, where highly expressed genes require fewer cells, and vice versa. In a previous study, it was estimated that 30–100 cells were sufficient to approach the expression profile of bulk samples . AlthougIn this work, we sought to investigate the efficacy of using cell-to-cell variation in cell populations to estimate cell homogeneity. Since the sampling noise is large and of less interest for such applications, we developed a method to estimate the non-sampling cell-to-cell (BTM) variation of a cell population such that is comparable across cell types and datasets. Our results indicate that the BTM variation is indeed useful for estimating cell homogeneity in cell populations, where removal of misclassified cells reduces the BTM variation score. The metric can thus be used for evaluating which clustering algorithm performs best for the data at hand. In addition, the BTM variation can potentially be used to detect high technical variation in cell populations. Our relative importance analysis indicates that high BTM scores relative to reference datasets is associated with certain datasets, suggesting either technical issues or cell misclassifications. Previous studies support the existence of technical variation in different forms , 41, whiA limitation of DSAVE is that the DSAVE variation score requires a template, which potentially will make its use more difficult if no matching template is available. Templates can technically be used across cell populations, as long as the cell populations have enough cells, enough counts per cell, and a reasonably matching set of detected genes, but it can be questioned whether the results are reliable across different types of cell populations. We show in the relative importance analysis that the BTM variation generally does not vary much across cell types when using the same template, suggesting that a template can be used across different cell types. However, the template cannot be used across species, since the gene sets would differ; new templates must therefore be created for species where no templates are available.DSAVE is also limited in that purification of cell populations is not automated. Although DSAVE can identify which genes differ in divergent cells, it still requires manual investigation of those genes to explain why the cells are divergent. Another limitation of DSAVE is the inability to separate biological and technical contributions to the BTM variation. However, if the BTM variation is larger than what was previously observed for similar conditions, it suggests that the cause is primarily technical.A possible future extension of the DSAVE method is to combine the identification of divergent cells with automatic cell type identification, similar to what is described in scmap  and CHETIn this study, we developed the method DSAVE to improve the quality of cell populations by providing tools for a post-clustering cell population improvement step. We demonstrated that DSAVE could find misclassified cells in publicly available datasets, suggesting that DSAVE would be a useful addition to the tools currently available for analysis of single-cell data.Clustering methods are imperfect in their ability to separate cells into pure cell populations based on single-cell RNA-Seq data. DSAVE provides tools to analyze and improve the homogeneity of single-cell clusters, which supports the discovery of biological phenomena in the data. Furthermore, the tools help estimate the cell population size needed to create robust cell-type specific gene expression profiles from single-cell data. DSAVE also provides an opportunity to investigate the technical variation in datasets, which may be helpful to avoid working with publicly available datasets with high noise levels. We envision that DSAVE would make a valuable addition to the set of tools available for the analysis of single-cell RNA-Seq data.S1 FigA. Correlation between the DSAVE score run with and without log transformation of data. B. Standard deviation of the DSAVE score as a function of number of repeated iterations. The score was calculated 15 times and the standard deviation of the results were plotted against the number of iterations used in each calculation. Since the standard deviation is dependent on the template, we ran the calculation for two different templates; the standard template using 2,000 cells and the modified template used in the relative importance analysis, using 1,000 cells. Fifteen iterations was selected as a reasonable balance between computation time and stability of the metric. C. Test of reproducibility for the divergence metric for a mix of B and T cells. The Pearson correlation between the two runs is 0.998, confirming that 15 iterations is enough to produce a stable metric.(PDF)Click here for additional data file.S2 FigA. DSAVE variation score for mixed populations of T cells and monocytes as a function of the fraction of monocytes in the mix. B. Patient-to-patient variation for T cells. The figure shows the extent to which the cell-to-cell variation increases if cells from multiple patients are mixed into the cell population . The datasets were aligned with a template using 1,941 cells and an average of 570 UMIs per cell to facilitate analysis of all populations. C. Relative importance of dataset, tissue and cell type for the DSAVE BTM variation score, here without the samples and covariates from the BC dataset. D. Number of detected genes per cell vs divergence for T cells from the HCA CB dataset. E. PCA showing misclassified T cells from the LC dataset detected using DSAVE. The plot shows that PCA is not able to identify the misclassified cells, at least not from the first two components. This is likely because PCA, in contrast to the divergence, looks for trends in the whole cell population; a few outlier cells will likely not have a large impact on the PCA.(PDF)Click here for additional data file.S3 FigThe figure shows the divergence for all cells in a population of dendritic cells from the PBMC68k dataset (using cell classifications from the authors). Hovering with the mouse over a cell displays the five genes with the highest gene-wise cell divergence (i.e. the genes that diverges the most from the mean expression of the population). In this particular case we see the gene PPBP, which is a gene highly expressed in megakaryocytes, suggesting a presence of misclassified megakaryocytes in this particular cluster.(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S1 Note(PDF)Click here for additional data file.S2 Note(PDF)Click here for additional data file.S3 Note(PDF)Click here for additional data file.S4 Note(PDF)Click here for additional data file.


The emergence of biomedical sensor devices, wireless communication, and innovation in other technologies for healthcare applications result in the evolution of a new area of research that is termed as Wireless Body Area Networks (WBANs). WBAN originates from Wireless Sensor Networks (WSNs), which are used for implementing many healthcare systems integrated with networks and wireless devices to ensure remote healthcare monitoring. WBAN is a network of wearable devices implanted in or on the human body. The main aim of WBAN is to collect the human vital signs/physiological data  round-the-clock from patients that demand secure, optimal and efficient routing techniques. The efficient, secure, and reliable designing of routing protocol is a difficult task in WBAN due to its diverse characteristic and restraints, such as energy consumption and temperature-rise of implanted sensors. The two significant constraints, overheating of nodes and energy efficiency must be taken into account while designing a reliable blockchain-enabled WBAN routing protocol. The purpose of this study is to achieve stability and efficiency in the routing of WBAN through managing temperature and energy limitations. Moreover, the blockchain provides security, transparency, and lightweight solution for the interoperability of physiological data with other medical personnel in the healthcare ecosystem. In this research work, the blockchain-based Adaptive Thermal-/Energy-Aware Routing (ATEAR) protocol for WBAN is proposed. Temperature rise, energy consumption, and throughput are the evaluation metrics considered to analyze the performance of ATEAR for data transmission. In contrast, transaction throughput, latency, and resource utilization are used to investigate the outcome of the blockchain system. Hyperledger Caliper, a benchmarking tool, is used to evaluate the performance of the blockchain system in terms of CPU utilization, memory, and memory utilization. The results show that by preserving residual energy and avoiding overheated nodes as forwarders, high throughput is achieved with the ultimate increase of the network lifetime. Castalia, a simulation tool, is used to evaluate the performance of the proposed protocol, and its comparison is made with Multipath Ring Routing Protocol (MRRP), thermal-aware routing algorithm (TARA), and Shortest-Hop (SHR). Evaluation results illustrate that the proposed protocol performs significantly better in balancing of temperature (to avoid damaging heat effect on the body tissues) and energy consumption (to prevent the replacement of battery and to increase the embedded sensor node life) with efficient data transmission achieving a high throughput value. In the current era, health is the primary factor of concern for every person . A largeTelemedicine is the use of advanced information technology and telecommunication that provide several healthcare services to patients . TelemedThe current healthcare system does not meet the advanced systems requirements for an optimal operation because these systems have no reliable and consistent structure in terms of data sharing, security, and access control techniques. Hence, it is mandatory to design a secure wireless system to improve the data-access operation under the security and privacy of government regulations to assure accountability for monitoring and analyzing patient vital signs. Blockchain is a distributed ledger technology, which holds the secure log of translations . The newThe healthcare application integrated with implanted sensors for the acquisition of physiological data innovates the mechanism of traditional healthcare into WBAN . WBAN isDuring the last decade, several studies have been carried out, and many system have been developed on WBAN. The various characteristics of WBAN have raised the number of issues in a different layer of WBAN. At physical layer problems of interoperability, temperature control, changing topology, interference, fault acceptance, security, etc. The issues related to the MAC layer are dynamic channel assignment, control packets overhead, protocol overhead, throughput, synchronization, delay control, etc. The problems related to the network layer are mobility, localization, traffic control, temperature and heat control, optimum routing, etc. .To design an optimized (minimum heat-rise and maximize energy conservation to enhance network lifetime) routing protocol for WBAN by considering primary metrics, i.e., node temperature, residual energy for next-hop selection.To build a self-adaptive routing algorithm, capable of selecting alternative forwarder nodes in case of unavailability of a node due to heat and residual energy.To develop a scalable system which fulfills the requirements of practical healthcare application, integrated with several IoT healthcare based on blockchain.Lightweight: The developed system provide the cross-platform communication between the WBAN and blockchain network using RESTful API.Transparency: The proposed blockchain-enabled WBAN system maintains the integrity of the patient’s vital signs data, and also provides unauthorized access to the personal medical information.Scalability: The proposed system fulfills the requirements of implant in-body or on-body several implanted sensors to diverse constrained networks to a single blockchain.High Throughput: The design platform is based on permissioned blockchain network, where all the set of entities are authorized and authentic. Hence, consensus protocol such as crash fault tolerant (CFT), and byzantine fault-tolerant (BFT) can be used to elevate the throughput of the network.The main contribution of the paper is as follows:In this article, we propose a blockchain-enabled WBAN based self-adaptive thermal-energy aware routing protocol. Our proposed system is based on blockchain where miniature sensors are implanted in/on the patient body in such a way that these healthcare senors get the physiological data and share with the certified user of the system within the blockchain network. Furthermore, the design system also contributes with two main constraints, i.e., implanted devices in the patient body, which can damage adjacent body tissues as a continual transfer of physiological data and node replacement due to energy conservation. Therefore to establish optimal (minimum heat-rise and maximize energy conservation to enhance network lifetime) routing protocol for WBAN by considering primary metrics, i.e., node temperature, residual energy for next-hop selection. Furthermore, the self-adaptive routing algorithm is capable of selecting alternative forwarder nodes in case of the unavailability of a node due to heat and residual energy. The results show that by preserving residual energy and avoiding overheated nodes as forwarders, high throughput is achieved with the ultimate increase of the network lifetime. Moreover, the composer rest-server is used to create the RESTful API to trigger the application-specific services provided by the blockchain network. The access control rules offered by the smart contract are used to authenticate and authorized the users and information related to patients and other medical data, e.g., only the authorized medical personnel are allowed to manipulate and access the patient medical information. The traditional database is replaced by the CouchDB to overcome the issue of redundancy and support large transactions of data. Therefore, each peer in the network is equipped with CouchDB to support file storage within the entire blockchain network. Castalia , a simulThe remaining of this paper is categorized as follows: In WBAN, routing is a challenging task because of the limited available resources like limited memory, computational power, and energy source . Energy WBAN can be divided into six categories, i.e., cross layered routing protocol, quality of service aware routing protocol (QoS), cluster-based routing protocol, temperature-aware routing approaches, postural movement-based routing protocol and energy-aware routing routing protocol.The QoS routing protocols work based on QoS metrics. The QoS routing is a modular-based protocol where synchronization and network density make it difficult to enhance the QoS protocol. In WBAN, patients’ data  is considered very important and transmitted efficiently and securely based on QoS metrics. The QoS routing protocols can be divided into two sub-categories, such as delay-tolerant and reliability-based protocol, where the reliability-based protocol enhances the throughput and minimize the delay and delay-tolerant protocol assure the packets delivery in time. The following are the few QoS-based routing protocols presented such as RL-QRP protocol , ENSA-BACommunication in WBAN is affected by the postural movement of the human body. The communication between nodes is usually disconnected by frequent body movement. Several research studies have been made in order to define the cost function for optimal routing to sink. Some of the postural movement-based routing protocols are ETPA , PSR 3030, DVRPLMaximizing energy conservation of nodes is a challenge in WBAN. The size of the battery in WBAN is compact and small with little energy consumption. Several energy-aware routing protocols have been proposed in order to preserve the power for the extended life of wireless networks. Many of the existing power-saving approaches are used to evade message headers, decreasing the frequency of sending network control messages and redundant retransmissions. A few of energy-aware routing protocols are Co-CEStat , MEPF 2, RSSI 22, DARE , ESR 3333, and SIn body sensor network designing, interference and absorption of antenna radiation are the crucial tasks. In WBAN, the human body can be affected by the radiation emitted for the sensors implant in or on the human body . In 36]36], authM2E2 , RE-ATTEM2E2 , (TMQoS)M2E2 , M-ATTEMM2E2 , ATTEMPTM2E2 , HPR [40M2E2 , RAIN [4M2E2 , LTRT [4M2E2 , LTR [44M2E2 . Table 2In WBAN, temperature and energy are the main factors to be evaluated. Cluster-based routing is the type of WBAN routing which aims to decrease the energy consumption and maximize the life-cycle of a network. In cluster-based routing, the node energy consumption is minimized by breaking the networks into portions, and each portion represents a cluster. These clusters are used for a communication head using a cluster head (CH). The CH is responsible for forwarding, aggregating, and collecting the data. The existing cluster routing protocols discussed in the literature include hybrid indirect transmission (HIT) , AnyBodyThe cross-layer routing protocol is mainly used for the development of wireless sensors networks. The fundamental responsibility of the cross-layer approach is to provide synchronization among the layer without affecting the MAC layer functionality. Moreover, cross-layer routing protocols significantly enhance the network performance and resolve issues related to network and MAC layer. Cross-layer routing protocols are divided into four categories, i.e., Biocomm and Biocomm-D , TICOSS Griggs et al.  presenteIn WBAN, sensor nodes of a minimal size are usually implanted in or on the human body. The heat-rise of implanted sensors nodes and the energy consumption are the two significant challenges required to be dealt through efficient routing. As aforementioned, these WBAN enabled blockchain approaches did not consider the temperature-rise and energy-consumption for next-hop selection, which ultimately damages adjacent body tissues in case of continual transfer of physiological data. Moreover, none of the existing approaches minimized energy conservation to avoid frequent replacement of implanted nodes in WBAN. Additionally, many of the strategies discussed in the literature review are related to the management of health records or data sharing among different healthcare providers. Regardless, not any of the thermal/energy-aware approaches will address minimum heat-rise and maximize energy conservation in WBAN using hyperledger fabric. To the best knowledge of the author, there has been no functional routing protocol that is used to optimized heat-rise and energy conservation in WBAN based on blockchain to enhance network life-span built so far.Reliable and stable data transmission with low energy consumption and the least temperature-rise of each sensor node is significant in WBAN. One of the primary goals of this research is to intelligently operate single-hop and multihop transmission to enhance the network efficiency in domains of energy and temperature. In our proposed model, the sink is planted in the middle of the human body, as shown in Every node in the network are fixed and implantedThe sink node’s responsibility is to acquired data from all the neighbouring sensor nodes. The software and hardware resources are sufficient with a steady energy supply.The transmission range and power are static for every sensor.The sink node is the final destination of each sensor node.Each sensor node is generated by the fixed size of the packet.The multihop path is used to forward the data to the destination.Optimum path selection is made by considering two constraints of WBAN, i.e., temperature- rise and energy consumption of a node from source to destination.WBAN is a heterogeneous network, the deployment of nodes in/on the body is application dependent; therefore, in our scheme, we are taking 9 or 12 implanted sensor nodes. The nodes at a distance of one-hop from the sink can communicate directly with the sink while distant nodes carry out their communication using intermediate nodes (multihop). We have taken some assumptions in our proposed routing protocol listed below:Energy consumption and temperature-rise issues are addressed in our approach by selecting the forwarding node with the minimum temperature and maximum energy. Moreover, the node having a temperature more than the threshold value of both energy and temperature is also avoided.Blockchain is a fundamentally transparent platform where the interaction between users and smart contracts modeled as crypto-signed but unencrypted transactions is visible to all participants in the blockchain network. The core functionality of blockchain technology is to provide solutions for the sharing of sensitive data. Data is only accessible to a limited number of recipients. The blockchain data is only unlocked due to the cryptographic artifacts. The privacy of the data is important, so due to the blockchain, this can be achieved when designing a new platform with an additional layer of encryption for the enhancement of the patient’s vital signs data. We developed a customized solution that enables user- and group-based secret sharing by utilizing access control rules based on hospital privacy and security requirements so that data can be shared across the user of the system in the healthcare ecosystem.The proposed blockchain-enabled ATEAR routing protocol is comprised of two modules, i.e., the ATEAR routing protocol and blockchain. The perseverance of energy and evasion of temperature-rise are the primary concerns for the WBAN network life. To achieve efficiency and stability of the network, these two challenges need to be dealt with collectively. The potential problems are related to send data packet (from source to destination) with less energy consumption and less temperature for every communication. The existing routing approaches for WBAN addressed these issues individually. However, frequent communication using an optimal route with high energy and low temperature will ultimately result in energy exhaustion and overheating of that route or death of a node. In this research, this problem is resolved by presenting a protocol “ATEAR—Adaptive Thermal-/Energy-Aware Routing in WBAN”. This protocol provides the mechanism of selection of optimal multihop paths among multiple available routes to avoid frequent partitioning of WBAN. This optimization is achieved by discovering multiple relay nodes from the source to destination, and each node is given a cost value based on the temperature and energy metric. Every time before sending the data, the forwarding node is selected depending upon the cost function. Thus, communication will be carried out using multiple options of the nodes avoiding a single route or node used continuously. ATEAR is a reactive source protocol initiated by the source node to forward the data using alternative options. Our proposed ATEAR scheme constitutes two phases, i.e., the initialization phase and data transmission phase.The initialization process is initiated by the destination node to obtain the distance of each node from destination in terms of the number of hops. This phase involves the organization of the network into levels. At the end of this phase, each node is assigned a ring level value; this value of the level indicates its distance from the sink. At the beginning of this phase, a packet is broadcast by the sink with a ring level value of 0 in the packet, and this packet is received by all of its neighbours. The nodes that receive this packet will update the ring level field by incrementing 1 in the ring level value and rebroadcast the packet. This operation of rebroadcasting and updating of the ring level value continues until the end of this phase until each node acquires its ring level. The format of the setup packet in initialization phase includes sourceID, destinationID, temperature, ringlevel, and energy. The sourceID indicates the unique ID of the node which broadcasts the setup packet. Destination ID represents the destination address, energy and temperature field indicates the energy and temperature of the sender node.In the initialization phase, the primary goal is to generate the ring levels for all nodes—every node broadcast this setup packet to explore its neighbours. On receiving the setup packet, a sensor node considers itself in level n if the hop count is n from the sink. If a node receives a packet with a smaller value of ring level, then it will update according to the new ring level. The algorithm of the initialization phase is given below, and the flow chart is shown in This phase is responsible for acquiring available sensors nodes distance from sink with the current statistics of temperature and energy of every node. The appropriate route selection is based on the stability of route, which depends on the node energy and temperature. The multiple forwarding nodes are available, the cost is evaluated, and selection of node depends upon this calculated cost value. The algorithm of the initialization phase is given below, and the flow chart is shown in Algorithm 1.Algorithm 1: Initialization PhaseIn this proposed protocol, the data is forwarded using relay nodes rather than of having direct communication between the source node and destination node to reduce energy consumption and temperature rise. The source node broadcasts the data packet with its ring level. On receiving the packet, the ring level of the relay node is compared with the received data packet ring level. For instance, if the value of the relay node ring level is less and the ID of the relay node, then the packet is processed and broadcast; otherwise, the data packet will be discarded. This operation will continue until the packet is destined to the sink node. If the ring level value of the received data packet is less than the neighbour node ring level value, then the energy, temperature, and IP address are stored by the neighbour node and discard the packet. Afterwards, the information is used to select the optimal next hop for the forwarding data packet.The neighboring nodes only received data packets form the current node whose path in terms of distance to destination is more than itself (current node). Hence, only relay nodes (Pr) among the neighboring nodes accept the setup packet which fulfills Equation .(1)d.(2).(2)5).Algorithm 2: Data transmission phaseIn this section, we have discussed the simulation setup and the obtained results of the proposed blockchain-enabled WBAN routing protocol. For simulation, we have used OMNeT++, a simulator tool, for the performance evaluation and comparison with other related routing approaches. OMNeT++ , the simIn this study, two different scenarios by changing the number of nodes were considered to analyze the performance of the WBAN network. We have considered 9 or 12 sensor nodes that are implanted in the human body. A static and rechargeable sink was placed at the center of the body, whose responsibility is to collect and forward all sensed data from all the sensor nodes to the external server or PDA. However, communication afar from the sink was not considered for this protocol. We assumed all the sensor nodes were homogeneous in terms of their computational power, sensing ability, initial energy level, and the temperature. The packet size considered in this work was of 296 bits. In order to analyze the performance, the proposed protocol was compared with the existing Multipath Ring Routing protocol. In our proposed protocol, the selection of the relay node was premeditated and is based on residual energy and temperature of the node. The simulation setup along with the energy values and temperature values are given in Table 4Average Throughput: Throughput is the rate at which information was sent through the network.Temperature rise: Represents the increase in the temperature of each node during data transmission.Energy Consumption: Energy consumed during the simulation time.Simulation time: The time period during which a simulation ran over the simulatorAverage Temperature rise: Represents the average increase of temperature for the overall network.Average Energy Consumption: Average energy consumption indicates the average decrease or consumed during the overall network during simulation time.Evaluation metrics used for the proposed work are given here:Average energy consumption over different simulation times is shown in Temperature variation indicates the change in temperature , read throughput, transaction throughput, read latency, and transaction latency. We used Hyperledger Caliper, an open-source simulation tool used to investigate the performance of blockchain applications . In ordeSimilarly, the average read latency was computed by changing the send rate from 500 to 3000 tps using different machine resources. It can be observed from Endorser peers endorsed the transaction when it was proposed. The endorser peer function was executed when the specific chaincode was triggered and endorsed the transaction before it was committed. In Hyperledger Fabric, the ordering of transaction was performed by ordering node along with the other nodes to form ordering service. In The attaining of good health facility and security of electronic medical records is the most demanding need for mankind, and thus, the WHO emphasized on the fundamental rights of human beings to attain quality healthcare. In recent times, the healthcare system is experiencing transference from a traditional approach to a modernized patient-oriented approach. In a modernized patient-oriented approach, the implementation of the information technology for the exchange of patients’ personal data with ease of communication is in the interest of an individual’s health through telemonitoring and telemedicine systems. Keeping in view all associated advantages, telemonitoring has been recognized as a promising area of research by using the latest tools to transform raw data into useful data. In WBAN, avoiding overheating of the nodes and energy preservation are the two most vital features need to consider for the stability of WBAN. Considering the unique essence of WBAN along with the routing and security issues, a blockchain-enabled routing protocol ATEAR and its improved version Enhanced ATEAR is presented in this work. To testify the significance of the presented protocol, several experiments on the basis of temperature, energy, latency, and throughput are carried out. The results generated from the simulations are compared with the existing routing technique MRRP. The results show a better performance of ATEAR and Enhanced ATEAR than MRRP, TARA, and SHR. The demonstrated results show the temperature rise and energy depletion in MRRP is impracticable for WBAN. Moreover, the proposed blockchain-enabled WBAN platform is based on the network and resolve the existing challenges of WBAN, i.e., scalability, identity, and data security. Temperature and energy, on the other hand, didn’t cause any damage to the tissues due to the fact that the threshold always lies within the threshold. Additionally, the overall performance of the enhanced ATEAR is improved due to energy harvesting and temperature thresholding. The throughput is also considerably improved in the proposed work than in MRRP, TARA, and SHR due to the even distribution of energy and temperature The presented protocols are for fixed implanted sensors with no mobility. Regarding our future work, we are planning to extend the proposed protocol to deal with the different body postures, i.e., the postural movement of the body will be considered along with the maintained temperature and energy of the network.