Based on the current global pandemic statistic data, here we developed a logistic probability function configured SEIR model to analyse the COVID-19 outbreak and estimate its transmission pattern under different “anticipate- or delay-to-activate” policy response scenarios in containing the pandemic. We found that the potential positive effects of stringent pandemic P&C measures would be almost canceled out in case of significantly delayed action, whereas a partially procrastinatory wait-and-see control policy may still be able to contribute to containing the degree of epidemic spread although its effectiveness may be significantly compromised compared to a scenario of early intervention coupled with stringent P&C measures. A  The COVID-19 pandemic has led to serious socioeconomic consequences and could generate far-reaching impacts on global public health governanceBased on a retrospective investigation of epidemic growth, this study aims to build a new hybrid epidemic modeling framework to facilitate our understanding of which key variables may exercise significant influences in simulating and forecasting different scenarios of COVID-19 infections across a number of representative countries. Our study time span covers the first phase of the epidemic development, i.e., from the initial novel coronavirus outbreak in late January through to the end of March 2020 when the pandemic had rampaged through the majority of the world's populous nations.According to WHO 2020-3-25), 136 countries have implemented measures that significantly intervened international traffic as defined under Article 43 of the International Health Regulations (2005) as of March 25. This pandemic has posed a daunting challenge to global health governance 020-3-25,. Over 16Science, 2020; Rasmussen, Clinical Infectious Diseases 2020time effect into pandemic control when introducing and enforcing national responses based on previous experiences learned through the global collaboration in combatting COVID-19 , home-isolation, and social distancing by national health authorities, whereas more stringent and radical policies such as city and nationwide lockdown, internal traffic restriction, and outbound/inbound travel ban, were only launched to contain the spread when the pandemic situation had already passed a critical point which accelerated interpersonal transmission and spread of the epidemic with national health system's capacity facing serious saturation challenges and domestic medical resources being rapidly exhausted. Unconventional control and prevention policies such as lockdown and strengthened confinement policies has been continued to be generalized through all European countries since the serious outbreak in Italy around mid-February. The effectiveness of stringent P&C measures is highly influenced by the public authority's degree of epidemic monitoring and enhanced management of the health system. Another critical factor is related to the degree of public compliance , which lWhilst many European countries and a number of states in the US are considering imposing further lockdowns to effectively tackle the COVID-19 crisis, an underlying question as to global pandemic governance relates to the timing of adopting preventive and interventionist measures, i.e., when the national health authority should implement such radical P&C measures as city or even region-wise lockdown together with rigorous isolation & confinement policies. In this paper, we provide a retrospective estimate of the scale of the pandemic spread under different scenarios of variation in key influencing parameters with a hybrid model. We have developed a new hybrid model of infectious disease transmission based on a configured epidemiology SIR  scenario represents a trajectory where the government decides to maintain their modestly aggressive containment policy which reflects the To illustrate the epidemic growth pattern, our modeling will primarily focus on local dynamics of each epidemic event in a specific place in contrast with the pandemic, which resulted from spreading of the disease worldwide. The utility of this modeling logic is two-fold. (1) The careful examination of a local epidemic in a country is a prerequisite of understanding the underlying mechanism of the formation of a COVID-like pandemic; and (2) It allows us to draw useful lessons from inter-country comparison in terms of appropriateness of epidemic control measures and timing of government intervention in the light of better coordination when facing a global public health crisis, which is a sine qua non-condition of effective governance of a highly contagious epidemic which is further complexified by international movement and the context of globalized and interconnected economy. This study selected two subgroups as the modeling base, i.e., developed countries  and developing countries , given the fact that all of them were heavily struck by the coronavirus, whereas markedly different outcomes were produced over the course of pandemic development.Our modeling results indicate that early-stage preventive measures are the most effective way to contain the pandemic spread. In addition, appropriate state interventions in macro-management of human and socio-economic resources, i.e., enforcing social distancing and quarantines, and setting up special hospital facilities, are all essential to constrain the global transmission of the virulent infection. With the rapid spread of the novel coronavirus worldwide, this pandemic is no longer a single country's affair, but is on the way to develop into a global security concern requiring cooperation and control of all countries . To do sA number of prior studies have addressed this issue around prevention effectiveness by using different modeling paradigms , 21, manS) to the infected state (I). The transition state from the infected to the removed is defined as a constant variable since isolation is the main concern which has little influence on the death and recovery of patients. The estimate of the infected cases is obtained with the combination of the SIR model and the calculation of the infection rate from each iteration, then the loss function is calculated as the mean squared error (MSE) between the estimate and actual infected cases. The mathematical expression of the infection rate is expressed by a logistic function:First, the BP network is used to train the model which is a regression problem, where we use China's case as the baseline model. The model inputs are the active infected cases and death rate in China from February 15, 2020 to March 15, 2020, here, we define: total affected cases = active affected cases + removed cases, removed cases = death + recovered. We select the fitted objective as the infection rate from the susceptible state  can be derived. We use the real data from February 15, 2020 to March 15, 2020 in China published by WHO to train the BP-SIR model, and the fitted model estimate is compared with the actual data, shown in Two prevention control strategies are used as follows:α, bias, and c parameter settings for modeling prediction.Business as usual or delayed P&C policy, adopted by the government currently , which is embodied in the bias are set based on the situation of Wuhan from January 23 to February 28, and the forecast days are dependent on the days of delay in the “delay” experiment , while the other parameters are kept unchanged (strict quarantine).Stringent policy case, where countries began to take strict quarantine measures similar to Hubei Province, China from March 16, 2020. In the model, the values of α and It can be seen from th day  and the 19th day , the so called “inflection point,” with current isolation measure adoption and strict quarantine measure adoption similar to Wuhan district, respectively. The peak value of the active affected cases in the BAU epidemic control strategy is about 2.5 times higher than that under the strict prevention control strategy. We used the real data from March 15, 2020 to March 18, 2020 to test the developed model and the estimate results with active infected cases and total infected cases. Based on this baseline model, we present in the next section the projected infection cases in four major countries which experienced sharp increases since the widespread outbreak outside China in early March, namely the US, Italy, Germany, and France.From The modeling results of the infection cases and deaths caused by COVID-19 in the four countries under four counterfactual “delay” scenarios with the assumption that the number of the infected at the early stage of the epidemic is relatively small, whether in a country with a large population or with a small population, are presented in per se is a critical factor in shaping the infection and death curves across all countries affected by the coronavirus outbreak, regardless of whether the government has decided to take strong  or moderate actions  at the initial stage of the outbreak.It can be clearly seen that the timing In other words, if a government envisages taking strong policies to tackle the COVID-19 epidemic, the decision of whether it chooses to implement the policy immediately or to delay in activating stringent P&C measures such as city lockdown and mandatory self-quarantine, may lead to stark differences in epidemic spread in terms of number of infections and deaths. Delaying the epidemic control policy may result in irreversibly large impacts on the infected countries as a result of the overwhelming peak of the number of infections and saturated medical resources. For instance, in the case of a strong policy scenario in France, doubling the delay period (from 2 to 4 weeks) of implementing the stringent quarantine measures would increase the peak number of active infection cases by a factor of more than four.Under the current circumstances of rapid transmission of the coronavirus disease, all the countries in the world are obliged to take strong actions to minimize the irreversible damage and to avoid a global heath calamity. As such, taking a strong policy as early as possible is an optimal strategy as any delays may result in irreversible damages. The epidemic distribution curves may be effectively flattened by timely intervention of public authority coupled with stringent P&C measures. The peak infections would increase exponentially if the immediate subsequent weeks following the outbreak were forgone without an appropriate pandemic control policy. Stringent P&C measures may allow all countries to significantly reduce both infected cases and number of deaths caused by COVID-19 across all four countries. However, in the case of significant delay, the stringent policy measures are less likely to flatten the infection cases' distribution curves.Our hybrid modeling results clearly indicate the paramount importance of taking timely preventive measures in public health systems when dealing with such a contagious pandemic as COVID-19 given its high person-to-person transmission risk, as disastrous consequences would be generated if the optimal window of intervention were forfeited at the early stage of outbreak, in particular in densely populated areas  with scarce medical resources. It is of paramount importance to cut off virus contamination channels through social networks and interpersonal spread at the beginning of epidemic outbreak.per se in accelerating the arrival of peak of infection . In other words, the positive effects of stringent P&C measures would be canceled out in the case of significantly delayed action, whereas a moderately delayed control policy may still be able to contribute to containing the degree of epidemic spread although its effectiveness may be significantly compromised compared to a scenario of early intervention coupled with stringent P&C measures. A procrastinatory activation strategy adopted by the government and health authority to deal with the uncertainly of COVID-19 pandemic development at the early stage of outbreak turned out to be a worse strategy from the optimal control perspective, as significant irreversible damage would be produced in this case.In particular, timing is more important than the degree of policy stringency More importantly, early actions can significantly help flatten the shape of distribution of contamination. This will allow society to reduce transmissibility and severity which are the two most critical factors that determine the effect of an epidemic . The morAnother painful lesson from the coronavirus pandemic is that several large-population nations have been seriously impacted due to weak state intervention and loose control of interpersonal contact during the early stage of epidemic outbreak, and the situation worsened after the global pandemic as a result of saturation of medical resources and unchecked disobedience of lockdown measures. Typical examples are Brazil and India, both experienced a large number of deaths over the course of pandemic development even after vaccination became widely available in early 2021. From a retrospective viewpoint, the modeling findings of this research may offer some important implications for global public health governance, in particular for the cooperation and coordination of epidemic containment measures in the most vulnerable countries with weak medical responses to COVID-19-like global pandemic spread. The would-be outbreak in African and middle-eastern countries may draw some useful lessons from the major affected countries and regions, in particular the US and European experiences  in termsThe main purpose of preventive intervention is to eliminate or minimize the source of infections, to cut off the route of transmission, and to protect the susceptible population, i.e., reducing public gatherings and close interpersonal contact, good hygiene practice such as wearing masks outdoors and washing hands frequently, canceling entertainment, social and religious gathering activities, limiting traffic and mobility, strengthening quarantine at transportation hubs such airports, railways, and bus stations, temporarily closing schools and public places, and carrying out thorough disinfection when necessary . In addiItaly, as the second most seriously affected country  by the pandemic outside of China at the early stage of the coronavirus outbreak, has taken strict prevention measures similar to the Chinese pattern, i.e., closing down seriously infected towns and regions in order to take control of interpersonal spread. However, the current situation is far from being controlled effectively due to non-strict implementation of confinement and lack of stringent measures, such as home isolation and total suspension of urban mobility. Since the confinement started a week ago, it is reported that people are still moving around within the isolation zones, and cost-efficient self-protection measures such as face mask wearing have not been generalized to minimize the risk of person-to-person transmissions through respiratory airways. In addition, the situation was further complicated by the overload on local health systems due to a sharp increase in infected people in a short period of time and local medical capacity soon becoming saturated. The national intervention efficacy may not sustain without timely external support from third parties such as the European Union. The comprehensive mobilization and support pattern during the Wuhan Crisis at the beginning of February in China may provide a useful blueprint of efficient intervention for both the European Union and Italian decision-makers.In addition, the effective containment of COVID development relies heavily on whether the people in a concerned country respect the guidelines and recommendations provided by the public health authority and scientists, e.g., wearing mask, social distancing, avoiding public gatherings, and respecting curfew and lockdown instructions. Such ignorance and disobedience behavior should be seriously avoided, otherwise the situation may become critical and the infected number could increase out of control in a very short period of time. The wild spread of the disease in the US and Brazil after the pandemic and the recent human tragedy produced in India demonstrate the importance of regulating individual conduct, and that the social costs of tolerating anarchical individual freedom can be astronomical. In comparison, the rigorous compliance of the public with the government P&C guidelines has allowed China to successfully cope with the epidemic since the outbreak. The growth of total confirmed cases and reported deaths in China was brought under control since the Wuhan lockdown was lifted in April 2020 and remains relatively stable over the last 12 months, as shown in However, it is worth mentioning that adapted economic policy measures have to be put in place to accompany the stringent P&C implementation to facilitate people's obedience without losing their jobs or sacrificing family income, and specific financial resources or funds must be mobilized to provide a minimal surviving condition for those who struggle in isolation. Strict measures such as lockdown may face legitimate challenge if people's basic daily needs in impacted areas cannot be duly addressed. The lack of financial and social support was the primary reason why countries like Mexico, Brazil, India, and most of Africa have not succeeded in any effective lockdown policies so far. The government could choose to move directly into what should be done to assure an early global lockdown, and the coordinative and obedient action could save both lives and moneyResearch efforts from both public health institutions and research organization across the world need to be effectively coordinated in order to provide timely advice to WHO and national governments, while the latter must be clear-eyed and sober about the underlying pattern of pandemic spread dynamics and possible evolution, and calibrate our policy and strategy accordingly. In this regard, effective isolation and quarantine measures can help minimize the uncontrolled spread of virus in the early stage of outbreak. For instance, in Singapore, anyone who is subject to a mandatory stay-at-home notice  may face severe judicial punishment if she/he violates the notice's requirement. Likewise, the similar experience in Wuhan, the epicenter of COVID-19, shows that the city lockdown policy brought significant benefits in terms of reducing new confirmed cases and mortality only after strict traffic control and compulsory isolation P&C measures came into effect 2 weeks following the lockdown notice on January 23.In order to optimize the efficacy of pandemic prevention and control measures, implementation guidelines need to be formulated for effective infectious disease control, while coordinating personnel from health care, public security, transportation, and social service departments of the local community. An integrated resources management and allocation system needs to be established to allow local medical and social workers to jointly complete the work of screening and controlling the sources of infection. The success of joint implementation of the pandemic control guidelines depends upon the response of emergency planning such as cutting off the route of transmission and protecting the susceptible population with isolation and treatment of infectious diseases , 31.who.int.Publicly available datasets were analyzed in this study. This data can be found here: JL and YZ conceived and designed the study. JL and LY set up the experiments. LY and YZ collected the data and ran the model. JL, YZ, and JZ analyzed the data. JL, YZ, LY, and ZC interpreted the results and wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Infrared images of power equipment play an important role in power equipment status monitoring and fault identification. Aiming to resolve the problems of low resolution and insufficient clarity in the application of infrared images, we propose a blind super-resolution algorithm based on the theory of compressed sensing. It includes an improved blur kernel estimation method combined with compressed sensing theory and an improved infrared image super-resolution reconstruction algorithm based on block compressed sensing theory. In the blur kernel estimation method, we propose a blur kernel estimation algorithm under the compressed sensing framework to realize the estimation of the blur kernel from low-resolution images. In the estimation process, we define a new  Infrared thermal imaging technology is an important technical means to ensure the safe and reliable operation of power equipment ,2. With Super-resolution (SR) aims to reconstruct a high-quality image rement y . SR is apolation ,5,6. In polation  proposedpolation  proposedpolation ,10. The polation  proposedpolation  proposedpolation  used thepolation , the autpolation  introducpolation  improvedHowever, most of the above-mentioned super-resolution methods assume that there is no blur in the imaging process or that the blur kernel is known. According to the actual imaging model, it is known that the above two assumptions are not reasonable. In actual situations, blur often exists, and the blur kernel is generally unknown, or only partial prior information is known ,18. TherBased on the above discussion, we propose a maximum a posterior (MAP)-based blind super-resolution algorithm by using the CS theory. First, the basic CS super-resolution model is combined with the blind image deblurring theory, and a blur kernel estimation algorithm under the CS framework is proposed, thereby realizing the estimation of the blur kernel from the low-resolution image. In the estimation process, through the analysis of the significant edge intensity changes before and after the image is blurred, a new As a brand-new signal sampling theory, CS enables signal reconstruction to break through the Nyquist sampling law and greatly reduces the number of sampling points required for accurate signal recovery . The basriterion , the sigThe image is recorded as a two-dimensional signal Considering the cost of sensors and the pressure of data transmission, it is difficult to directly obtain clear, HR infrared images for condition monitoring and fault diagnosis of power equipment. Therefore, it is necessary to use SR technology on the LR images collected by low-precision sensors. Then, the input signal It can be seen from the image degradation model that Since the blurring process is common during image acquisition and is generally unknown ,18, it iThe extraction effect of the significant edges of an image is directly related to the accuracy of the blur kernel estimation. The current common methods mostly use the timation ,27,28,29The gradient distribution of an infrared image has strong sparsity both before and after blurring. However, we noticed that the gradient value of the image generally shows a decreasing trend in the process of blurring, which is especially obvious in the part of the large gradient value. Through the gradient operators In simple terms, this phenomenon is caused by the weighted summation of pixel values that occur in the blurring process. From In order to eliminate the small gradients while enhancing the significant edges of the blurred infrared image so that the intermediate latent image in the reconstruction process can move closer to the clear infrared image, and to improve the accuracy of the blur kernel estimation, a new calculation method was defined as follows:According to the MAP theory, the blur kernel estimation objective function can be expressed as:  L1 norm ; here, wr kernel . The lasIn addition, in order to ensure the calculation efficiency of the blur kernel estimation, we did not stretch the image into a column vector in the original compressed sensing before the calculation. The reason is that if the image is not divided into blocks but directly stretched as a column vector for calculation, the downsampling matrix will be too large and the calculation speed will be greatly reduced. If it is divided into blocks, corresponding boundary problems will be introduced. Therefore, we modified the original model slightly, constructing a row sampling matrix In order to solve Equation (10) quickly, our strategy was to adopt the half quadratic splitting algorithm  and the The solution of Since the model (12) contains the lacement . The mapAfter the calculation of Shrinking by hard threshold, the solutions of Fixing Shrinking by soft threshold, the solutions of According to the definition of the The solution result shows that by using the duced in .For the subproblem accurate ; therefoproaches ,30,33, wSince the blur kernel ested by ,30, we dAlgorithm 1: Blur Kernel Estimation Algorithm.Input: Blurred image generate the initial value of each variablefor  repeat solve for repeatsolve for repeatsolve for repeatsolve for until until until  until  solve for  end forOutput: blur kernel In order to facilitate the operation of the BCS algorithm, we generated a circular Toeplitz matrix to convert the convolution operation between the blur kernel Suppose After the matrices It should be noted that the extension of In this paper, the relationship between HR images and LR images proposed by combining CS theory and the image degradation model is shown in (5). In this section, we will establish an SR image reconstruction model based on this relationship and propose an optimized solution algorithm, named two-step total variation sparse iteration (TwTVSI). We introduce the total variation (TV) regular term into the objective function of The original objective function of CS super-resolution based on the principle of image degradation is as follows:As the compressed sensing SR reconstruction of the entire image not only takes up too much storage space but also has a very high computational complexity, we adopted the BCS method to divide the image into several small image blocks and performed SR reconstruction for each small image block separately. We divided the image in the following way:However, the reconstruction method of BCS will introduce a “blocking effect” to the SR image. Therefore, in addition to the sparse prior, we further introduce prior information into Equation (29) to constrain the solution and improve the quality of the reconstructed image based on the MAP theory. Since the image is also sparse in the gradient domain, we introduced the TV regular term in the objective function to perform the minimum variational constraint on the whole image so as to achieve the purpose of eliminating the blocking effect of the reconstructed image. The calculation method of image total variation is as follows:We blocked the image and introduced the TV regular term. The objective function can be rewritten as follows:In the process of optimizing Equation (32), Using the Lagrangian multiplier method, (33) can be transformed into (34):Since we adopt the SR reconstruction method of BCS, when performing row–column direction difference operation, it will involve the cross-operation of the edge elements between adjacent image blocks. Through derivation, the difference operation between adjacent image blocks can be represented by a periodic sparse dictionary matrix, which greatly speeds up the operation speed while ensuring the accuracy. Let Then, the calculation methods of As for h method .In summary, the two-step total variation sparse iteration algorithm flow is shown in Initialize Perform the first iteration of the TV constraint: solve for Perform the second iteration of the Sparse constraint; according to (40), obtain Determination: stop iteration if Output: reconstructed HR image The specific calculation method of each step is as follows:Our proposed algorithm introduces the TV regular term into the Our test environment parameters were as follows: Intel(R) Core(TM)i5-9300H CPU @2.40GHz; memory: 16.00GB; operating system: Windows 10; MATLAB R2019a. We obtained the following fixed parameters through repeated experiments and adjustments: duced in , and itsIn order to make the experimental results more convincing, we separately reconstructed artificially synthesized infrared images of power equipment and actual infrared images of power equipment. We compared our method with the methods proposed by Keys , Shao 1, MichaelEntropy represents the uniformity of a system in physics. The more uniformly a system is distributed, the greater its information entropy is. The concept of image information entropy is derived from this, which can be defined as follows:To verify the effectiveness of our proposed posed in ,21. We uIn addition, we did not directly process the infrared image in RGB mode, but rather converted it to YCbCr mode, where Y is the brightness component, Cb is the blue chroma component, and Cr is the red chroma component. Since the human eye is more sensitive to the Y component, the visual difference caused by the subtle changes in the other two components is extremely small; thus, all operations of our proposed method and comparison method were performed on the Y component. Using the blur kernel estimated by the Y component, the Cb and Cr components were reconstructed by nonblind deblurring  and cubiWe selected 11 different infrared images of power equipment and artificially synthesized them into LR blurred images. Below, we show the HR images reconstructed using different methods for one of the LR blurred images. For the remaining 10 images, we give the reconstruction results of different methods in the form of evaluation parameters.A Gaussian blur kernel with a size of As shown in The remaining 10 infrared images were also artificially synthesized using the It can be seen from In order to verify the effectiveness of the When performing SR modeling based on the CS theory, we introduced a blur matrix to deconvolute and deblur the reconstructed image. In this section, we analyze the effect and computing time of the TwTVSI algorithm to verify the effectiveness of the improved model and the designed algorithm. At present, infrared images are still generally processed in the central place of control, but now the emergence of 5G communications, cloud computing, cloud platforms, distributed computing and other technologies provides many new possibilities for the processing and analysis of infrared images in the future. For example, it is no longer limited to processing infrared images at a specific location, the raw data can be uploaded to the cloud platform through 5G communication, and then call the computing power through cloud computing to complete the image processing. Alternatively, we can use distributed computing, call on local idle computing power, complete the analysis and processing of the image, and upload the final evaluation results and original data. The evaluation results are uploaded to the operation and maintenance platform for operation and maintenance personnel to read, and the original data is directly stored in the database for future data analysis, which realizes the further distribution of functions and reduces the platform’s data processing and interaction pressure. After providing the cloud platform access port for operation and maintenance personnel, they can accurately grasp the operating status of the equipment through mobile phones and computers at any time and any place, and then complete the operation and maintenance work according to the platform’s prompts.Infrared thermal imaging technology is an important technical means to ensure the safe and reliable operation of power equipment, through which faults in power equipment can be detected in time. In order to resolve the problem of insufficient resolution and definition of infrared images, we proposed a blind SR algorithm based on the theory of CS.In order to complete the blind SR, the image blur kernel needed to be estimated first. We combined the basic CS SR model with the blind image deblurring theory and then proposed a blur kernel estimation algorithm under the CS framework to realize the estimation of the blur kernel from LR images. In the estimation process, we defined a new In the SR image reconstruction part, we added a blur matrix to the original CS model, thereby constructing a CS super-resolution model based on the principle of image degradation. The sparse base in the model is a specific sparse dictionary about the infrared image of the power equipment, which was obtained using the high-dimensional dictionary training method. The sparse dictionary can enhance the sparse representation of the image and improve the SR reconstruction effect. Finally, according to the theory of BCS, we designed the TwTVSI algorithm, which can effectively process the CS super-resolution model with the sparse dictionary. TwTVSI also can eliminate the “blocking effect” so as to complete high-quality SR reconstruction of infrared images. The SR reconstruction experiments with blurred LR infrared images verified the effectiveness of the blind SR method that we proposed. The experiments proved that our method can better adapt to the actual application in and demand of the electric power industry. Our method is helpful in allowing operation and maintenance personnel to accurately grasp the operating status of power equipment and for improving the reliability of power system operation.


To evaluate macro- and microstructural integrity of the AF, tractography based on the constrained spherical deconvolution model was performed. The AF in the left and right hemispheres were then manually reconstructed using a modified 3-segment model (Current evidence strongly suggests that the arcuate fasciculus (AF) is critical for language, from spontaneous speech and word retrieval to repetition and comprehension abilities. However, to further pinpoint its unique and differential role in language, its anatomy needs to be explored in greater detail and its contribution to language processing beyond that of known cortical language areas must be established. We address this in a comprehensive evaluation of the specific functional role of the AF in a well-characterized cohort of individuals with chronic aphasia (nt model , and a mnt model . The nor Subsequently, connections between cortical areas have become as critical as the areas themselves in modern models of language processing (The arcuate fasciculus (AF) is a white matter bundle that connects the frontal and temporal lobes within each cerebral hemisphere by passing dorsally beneath the parietal lobe. The AF was already included in the first classic model of language processing proposed by Wernicke at the end of the 19th century , and todocessing .Though the AF was originally purported to support repetition of verbal information , contempNumerous recent investigations in clinical populations underscore a more multifaceted role of the AF in language processing. In addition to delineating the role of the AF in the left hemisphere, limited reports have indicated that this tract in the right hemisphere may play a compensatory role for language processing. First, Part of the confusion regarding the functional role of the AF in language processing is most likely due to its undefined anatomy. Most studies to date still consider the AF as one indivisible uni-functional entity in accordance with classical language models . Recent While the 3-segment model is the most common one, other models of the tract have been proposed as well. For example, Another related factor contributing to diverse findings might be the use of different methods and metrics to infer tract damage and delineate its specific role. Lesion load e.g., , resultsGiven the importance of the AF complex for language processing and outstanding questions concerning its anatomy and function, the overarching goal of the current study is to provide a comprehensive functional evaluation of the AF complex in both ipsilesional and contralesional hemispheres in a cohort of individuals with chronic post-stroke aphasia. For the first time, both the model by (1)Explore whether damage to different segments of the AF  contributes differentially to language processing in individuals with chronic post-stroke aphasia.(2)Investigate whether damage to the AF is predictive of language impairment beyond damage to known cortical language regions.(3)Establish whether variations in the AF in the right  hemisphere contribute to language outcomes in aphasia.age = 63.7 1.4 years, from 40 to 83 years of age). All participants except 3 were strongly right-handed based on the Edinburgh Handedness Inventory (time post–onset = 96.693.1 months). Patients in this sample presented with a wide range of speech and language deficits, some performing within normal limits on the WAB, but still complaining of residual naming and/or comprehension deficits (see “Results” section for more information). All patients signed IRB-approved consent forms and were tested in accordance with the Helsinki Declaration.For years, the Center for Aphasia and Related Disorders at VANCHCS has been seeing stroke patients who complain of speech and language deficits. Those with left hemisphere lesions who participate in research at the Center undergo neuroimaging and comprehensive language testing. In the last several years, a HARDI-diffusion-weighted imaging sequence has been added to our scanning protocol, yielding a group of thirty-three successively scanned participants with aphasia  following a left hemisphere stroke  sequences were collected with the following parameters: TR = 17600 ms, TE = 93.6 ms, flip angle = 90°, The participants’ lesions were traced directly onto the patient’s native T1-weighted images using MRIcro/MRIcron software . During We then estimated lesion load to three cortical language areas: frontal, temporal, and parietal cortex. These areas were defined based on the Harvard-Oxford Cortical Structural Atlas in FSL . The frowww.mr-startrack.com, Diffusion-weighted imaging data were first preprocessed using a fieldmap correction for susceptibility induced distortions . The size of the STG ROI depended on the individual’s brain anatomy.• AF-MTG – fibers extending between the posterior MTG and the frontal lobe, defined by a manually drawn ROI placed in the white matter underneath the posterior MTG cortex on the sagittal slices and the Frontal ROI. Again, the size of the MTG ROI depended on the individual’s brain anatomy.• AF temporal pole – fibers extending from the frontal lobe all the way to the anterior part of the temporal lobe, including the temporal pole, defined by an Temporal Pole ROI and the Frontal ROI. The Temporal Pole ROI was a 2D disk placed on the coronal slice in the middle of the temporal lobe behind the STG and MTG ROIs. The AF temporal pole branch did not include fibers terminating in the posterior STG or MTG cortex.• For each segmented tract, we extracted tract volume and mean Hindrance Modulated Orientation Anisotropy HMOA, . HMOA isFirst, we correlated tract metrics  of all tract segments for both hemispheres with language measures while taking relevant demographic variables and lesion volume into account. Beyond outlined tract segments, we also included overall AF volume  in the correlation analysis to evaluate the overall role of the AF in language. To account for multiple comparisons, we adjusted the significance level in the correlation analyses for each tract with the False Discovery Rate (FDR) correction . Second,Prior to running any analyses, we screened our behavioral and neuroimaging data for outliers. For behavioral data, we looked at WAB AQ scores  and for neural data, we examined lesion volume. Boxplots of these two variables are presented in the t-tests showed that volume and HMOA measures in the left  hemisphere were significantly lower compared to the right  hemisphere for all AF segments (p < 0.001), except for HMOA of AF posterior (p = 0.066).For the remaining 32 participants descriptive statistics for language scores and tract metrics are presented in See t-test (for gender). Age and time post-onset were not significantly related to any language or tract indices (p > 0.05). Significant differences in performance based on gender were observed for the auditory comprehension subtests of the WAB and the CYCLE-R, with female participants obtaining higher scores (p < 0.05). Lesion volume was significantly negatively related to WAB and CYCLE-R subtest scores; it also correlated strongly with both tract metrics (p < 0.05). Thus, to conserve statistical power, based on empirical evaluation of association patterns we included gender and lesion volume as covariates in the correlational analyses below. Additionally, given the substantial variability in post-onset times amongst participants and the fact the time post-onset has been a factor in other studies  and stroke-related  covariates had an impact on test performance and/or tract indices based on Pearson correlations and independent-samples  studies , we feltThe results of this partial correlational analysis are presented in The observed relationship between HMOA and language scores further extended these findings with most of the correlations surviving the stringent corrections for multiple comparisons. AF long and AF posterior were related to most of the language measures, while all three branches of the AF within the Glasser and Rilling model were again associated with naming.In contrast to the left AF tracts, the right hemisphere tracts did not show consistent or strong relationships with language measures, and the few significant correlations did not survive the corrections for multiple comparisons . Results of the regression analyses for language scores where the significant role of tracts was observed beyond that of cortical language areas are presented in Next, we ran a series of multiple regression analyses to determine whether residual tract integrity of the AF complex in the left hemisphere was related to language outcome beyond damage to specific frontal and temporal cortical language areas. In the first step of this hierarchical regression analysis, lesion load to frontal, temporal, and parietal language areas along with gender and time post-onset were entered as covariates. Lesion volume was not entered into the analysis since it correlated strongly with lesion load to language areas. In the second step, either normalized volume (Step 2a) or HMOA (Step 2b) of the three segments of the left AF according to the Catani model were entered into the analysis using the forward procedure  and BNT , as well as the AF temporal pole HMOA to naming scores on the BNT  could be established. These findings underscore the role of the long-range temporal-frontal connections, particularly those extending to the anterior part of the temporal lobe, in naming ability.Next, we conducted the same analysis using the Glasser and Rilling AF segmentation. Here, only significant contributions of the AF temporal pole normalized volume to naming scores on the WAB (Adj. The current study explored how stroke-induced damage to the AF impacts language processing abilities in individuals with chronic post-stroke aphasia. We segmented the AF in both hemispheres using two anatomical models: the modified 3-segment model by The AF is a large, dense tract that connects numerous frontal and temporal language regions. In this study, reconstructions of the AF included connections not described in existing models. Specifically, when using the segmentation according to the In the current study, damage to the AF was associated with a broad range of aphasic language deficits. As expected, the correlations between tract and language metrics significantly diminished once lesion volume was taken into account, as lesion volume was strongly associated with both indices. With larger lesions, volume of long associative fibers is more likely to be reduced and, at the same time, more pronounced behavioral deficits are expected. Accordingly, similar to lesion symptom mapping analyses , investiWhen the AF segments within the Current results indicated that HMOA is a sensitive measure of tract integrity reflecting important aspects of residual function. While the two tract measures, volume and HMOA, largely index similar properties, as shown by their high intercorrelations, particularly in the left hemisphere where the relationship is largely driven by the underlying stroke lesion, they are not identical. Volumetric measurements reflect how much of the tract is still left, while HMOA is more indicative of the integrity of the residual tract, approximating how well the remaining parts of the tract are able to function. For a comprehensive evaluation of structural connectivity these measures need to be used in tandem. Together, observed patterns of associations between both tract metrics and language scores are highly consistent with multiple previous studies on the functional role of the AF , and shoThe current study was the first one to explore the functionality of the AF according to the Further, for the first time, the contribution of the AF to language processing was comprehensively investigated while damage to relevant cortical language areas was systematically taken into account. Beyond the contribution of these classical frontal, posterior temporal, and inferior parietal language areas, the volume of the long segment of the AF and particularly the AF temporal pole segment was an important predictor for lexical-semantic retrieval. With HMOA measurements of tract integrity, it was demonstrated that the anterior and posterior segments of the left AF are also important for overall language abilities. Specifically, AF anterior, similar to previous findings  is cruciOverall, the current study highlighted that the contribution of the long segment of the AF to language might support more general language functions than specific ones (for a related argument see In the current study, we did not find any clear contribution of the right hemisphere AF segments to language performance. The overall pattern of correlations between tract metrics of the right AF and language scores was very sparse and not easily generalizable, with none of them surviving the FDR correction for multiple comparisons. This finding is consistent with previous stroke studies that also failed to find a coherent relationship between the right hemisphere AF and language . A potenThe current study had several limitations that should be addressed in future investigations. Although we used a number of specialized language tests such as the CYCLE and BNT, we relied on the WAB for a number of measures. Supplemental and more linguistically sensitive tests may be useful in further isolating the specific and unique roles of the AF and its segments in language processing.In terms of tract reconstructions, we did not explore all possible segmentations of the AF. With our tractography algorithm it was not possible to segment the AF according to It is also possible that idiosyncratic reorganization and compensation in chronic stroke led to varied effects, particularly given the large variability in post-onset times and that many of the participants in the current study had only very mild residual language deficits. Even though we accounted for heterogenous post-onset times in our analyses, this variability might have masked differential effects at different stages of recovery, such as contribution of the right hemisphere areas and tracts in early stages post-stroke e.g., . To bettIn sum, our findings lend support to the broader role of the AF in language processing, with particular emphasis on comprehension and naming, and point to the posterior segment of this tract as being most crucial for supporting residual language abilities. The findings stand in contrast to the dorsal-ventral models of language processing that ascribe a specific role to the dorsal stream and AF in particular, limiting its role to repetition abilities and comprehension at the sentence level. Current results highlight the importance of considering additional tract segments that should also be incorporated into future models of language processing and underscore the importance of considering different anatomical subdivisions of given tracts.dronkers@berkeley.edu.The datasets presented in this article are not readily available: Research data are not shared per Department of Veteran Affairs privacy regulations. Requests to access the datasets should be directed to ND, The studies involving human participants were reviewed and approved by the VA Northern California Health Care System IRB. The patients/participants provided their written informed consent to participate in this study.MI contributed to conception, design of the study, developed the diffusion data processing pipeline, performed the statistical analysis, and wrote the original draft of the manuscript. AZ and MI performed the tractography and lesion segmentations, and subsequent data coding. AT developed the MRI scanning protocols and subsequent raw data processing algorithms and provided funding for the project. JB facilitated behavioral data collection and organized the database. ND contributed to conception, design of the study, funding acquisition, and oversaw execution of the project. All authors contributed to manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In this study, new low-cost neck-mounted sensorized wearable device is presented to help farmers detect the onset of calving in extensive livestock farming by continuously monitoring cow data. The device incorporates three sensors: an inertial measurement unit (IMU), a global navigation satellite system (GNSS) receiver, and a thermometer. The hypothesis of this study was that onset calving is detectable through the analyses of the number of transitions between lying and standing of the animal (lying bouts). A new algorithm was developed to detect calving, analysing the frequency and duration of lying and standing postures. An important novelty is that the proposed algorithm has been designed with the aim of being executed in the embedded microcontroller housed in the cow’s collar and, therefore, it requires minimal computational resources while allowing for real time data processing. In this preliminary study, six cows were monitored during different stages of gestation , both with the sensorized wearable device and by human observers. It was carried out on an extensive livestock farm in Salamanca (Spain), during the period from August 2020 to July 2021. The preliminary results obtained indicate that lying-standing animal states and transitions may be useful to predict calving. Further research, with data obtained in future calving of cows, is required to refine the algorithm. The study and monitoring of livestock has always been a subject of great interest. Indeed, quantitative measurement of animal behaviour is an important tool for understanding their reproduction, survival, welfare, and interaction with other animals . Animal Every year an average of 8.5% of perinatal calves are lost due to natural abortions, stillbirths, and complications during parturition  [An automated solution based on cow data collection from sensors could provide better calving predictions. This will allow the farmer to better identify those cows that require intensive supervision and to focus on caring for cows with upcoming calving, reducing possible risks and improving the health and wellbeing of the animals.Internet of things (IoT), an already mature and effective technology, can help improve the efficiency and productivity in agriculture and livestock production systems . IoT hasPrecision livestock farming (PLF) aims to manage individual animals by continuously monitoring their health, welfare, production/reproduction and/or environmental impact in real time . This isDifferent physiological and behavioural parameters associated with calving can be monitored through sensors. The analysis of the internal temperature and its evolution, usually measured in the vulva, the rectum, or the rumen of the animal, is one of the most accurate calving predictors . It has An exhaustive meta-analysis of the different publications related to calving detection in cows is showed in . It concAnother indicator of calving is the increase in lying bouts (LB). This behaviour is associated with the restlessness that the animal feels due to the imminency of the calving. The frequency of lying bouts and their mean duration increase greatly as the calving event approaches, starting already 48 h before and being maximized on the day of calving . This inLying bouts can be easily identified using a leg-mounted accelerometer , but detTail-raising patterns have been observed to change in the 24 h prior to calving ,31 and cIn calving prediction by traditional methods, the farmer makes a visual inspection of the cow to know its status. This is an error-prone task in which even experts may fail to provide an accurate prediction of calving date. In extensive farming, animals move freely in a wide area, which makes it more difficult for the farmer to properly monitor and manage pregnant cows. An automated solution based on cow data collected from sensors and processed by algorithms, can provide better delivery predictions than visual observation. This will allow the farmer to have a more accurate estimation of the expected calving date of the cow and to identify those cows that require intensive supervision due to the proximity of calving. It will make possible both reducing the workload of farmers, who can focus on caring for cows with upcoming calving and improving the health of the cow.In this paper a low-cost neck-mounted sensorized wearable device was designed and an algorithm was developed to detect the onset of calving of cows in extensive livestock farming. This work was divided in three main phases: (1) development of a wearable solution for data collection based on different sensors; (2) data collection in extensive livestock farming by using the aforementioned solution and human-based observations; and (3) development of algorithms to detect the onset of calving and creation of a decision function based on the frequency and duration of lying and standing behaviour (lying bouts).This paper is organized as follows. The goal of the first phase of the study is the development of a solution for recording large quantities of behavioural information, which will later be combined with human observation of the animals. The captured information will be used for the development, validation, and quantification of algorithms for calving prediction.A new low-cost sensorized wearable device was developed and integrated into a collar, which can be placed around the cow’s neck. The developed device incorporates three sensors: an Inertial Measurement Unit (IMU), a GNSS and a thermometer. Human observers helped with monitoring, labelling, and recording the animals’ state using our own development PC software. The designed collars were tested on cows from an extensive livestock farm in Salamanca (Spain), during the period August 2020–July 2021.The sensorized wearable device is a collar which is placed on the animal’s neck. The collar houses a nRF52840-dongle  microcontroller, three sensors , a microSD card breakout board  for data storage and lithium batteries. ® Cortex™-M4 CPU which supports short-range wireless standards including BLE. For measuring the cow’s body temperature, a DS18B20 digital thermometer with a temperature range of −55 to 125 °C (accuracy ± 0.5 °C) is placed in such a way that contact between the skin of the cow and the sensor occurs. The microcontroller acquires the 9–12 bits configurable Celsius temperature measurements using a unique 1-wire interface, which only requires one port pin for the communication. The IMU integrated in the collar is the ICM-20948 , which is a low-power 9-axis motion tracking device embedding a 3-axis gyroscope, a 3-axis accelerometer, and a 3-axis compass. A SAM-M8Q  receiver is used for precise geographic positioning of the animal and is configured to work in PSM (Power Save Management) mode to minimize power consumption. Both the IMU and the GNSS communicate with the nRF52840 microcontroller using I2C at 400 KHz. The microSD card, which allows the storage of data from the sensors, communicates with the microcontroller using SPI. Four lithium ion NCR18650GA  batteries of 3.7 V and 3350 mA were used to power the device.The microcontroller nRF52840 is a small, low-cost device built around the 32-bit ARM® 2+). The polymer selected was ABS, a low-cost plastic material with good mechanical properties. The cover box has two areas  were collected at 1 Hz. The orientation of the IMU axis is shown in Initial tests were performed using BLE 4.2 for the communication between the board and a computer where data was registered. Due to high power consumption, however, it was decided to store the information onboard instead, using a microSD card. This approach increased the average collar battery life to up to 15 days.Each datapoint includes the information from the sensors and a timestamp, using the format described in Although the data transmission is no longer performed wirelessly, the collar still makes use of BLE: during collar initialization a timestamp will be exchanged between the collar and the laptop, which allows for later data synchronization. A keepalive message is also sent periodically (every minute), from collar to laptop, using BLE, to allow the human observer monitoring the cow to detect if the collar is still operative or another action is required .Collar initializationDirect visual observations were used to collect states and actions of cows in their natural environment at the cattle farm. A PC-based software a was desDuring collar initialization, a timestamp is sent to the collar using BLE. This timestamp will be used for the synchronization of the sensor data collected by the collar and the states and actions of the cows recorded by the human observer.Collar management bAccording to the European Commission, individual identification and registration of bovine livestock is mandatory to ensure full traceability and, consequently, enhance food safety and better safeguard animal health. The application uses an alpha-numeric code as the cowl’s ID. Similarly, each device is identified by a unique 64-bit collar ID, which corresponds to the serial number of the microcontroller housed in the collar.During operation, it was noted that human observers sometimes had difficulty identifying cows when recording actions, especially over long distances or when animals were close to each other. To solve this, each manufactured collar was printed with a different colour  which would not be registered in the general behaviour tag set, where the neutral state can happen both standing and lying (if the cow is doing nothing else).Import data:Once the operator recovers the collar and obtains the sensor data files stored in the microSD, the application allows the generation of a final file, using the timestamp in both files to combine and synchronize the data from the collar with the data from the file containing the visual observation data (stored in the PC). This synchronized data set is used to develop our own parturition prediction algorithm described in For this study, ten collars were developed. However, cow monitoring was limited to a maximum of three animals simultaneously in order to improve the quality of the annotated data. The rest of the produced collars were saved as replacements, to minimize dead times and data loss in case a collar stopped working. The monitored beef cattle belong to an extensive livestock farm located in the municipality of Carrascal de Barregas, in the province of Salamanca, Spain.During the study, collars were fitted to six cows see . For eleThe schema of data acquired with the collars is shown in Previous research work related to monitoring of pregnant cows around calving,  and , has been focused on the period immediately around the time of calving . This approach however is not practical for application where monitoring is less frequent, such as extensive livestock farms where large herds are held. Therefore, our research was focused on the long-term monitoring of the pregnant animals, which extended up to two months after calving. Using this approach, we could analyse the individual behavioural change during different stages of pregnancy, which previous research has proved can differ greatly between individuals .The labels used for data annotation are presented in The distribution of the annotated actions is presented in General behaviour  action pFor this reason, a second set of labels  only accThe annotated data from human observations show a small delay between the change of action of the cow and the annotation of this new action due to the response time of the observer and their need to operate the annotation software. For this reason, the datapoints recorded two minutes before an action change were ignored. This time period was chosen as a balance between loss of data and minimizing mislabelled data in the dataset. As cows did not change actions with high frequency in the recorded labels, two minutes resulted in enough certainty without losing a significative volume of data for each action.As discussed previously, the sensor readings from the collars and the annotations from the observers can be joined using the timestamp of each data point to form the final dataset.Non-annotated data from the devices. These data have been proved to be useful for unsupervised and semi supervised learning tasks.General behaviour annotated data . This daStanding/lying behaviour annotated data . This daThree datasets were generated using the recorded data:The experiments and algorithms have been developed using Python 3.7  along seCalving prediction is the main objective of this pilot study. To usefully notify parturition, it is necessary to detect it with enough anticipation using a low-memory algorithm suited for the microcontroller.The number of transitions of the animal between lying and standing has been empirically proved to be a good indicator of parturition in different studies ,40,41. TIn  is indicOur algorithm for cow posture classification (lying/standing) based on accelerometer readings has been implemented based on an heuristic threshold . This ap, the following algorithm has been developed:First, a rectangular sliding window of the last 15 min is used to count the number of readings in the Y-axis between a given superior and inferior threshold, Next, This binary function Finally, with this discrete transition signal To achieve “a low-memory algorithm”, the accelerometer data is resampled from 17.6 Hz to 0.27 Hz as our experiments have proven that this data rate is enough for the algorithm. This way, memory requirements during the sliding window operations decreases and battery life of the device increases. Based on these appreciations and given the series of readings from the Y-axis of the accelerometer at discrete timestamps As indicated in As indicated in The heuristic-based algorithm we have developed traces lying bouts with enough resolution to detect their increase in a time frame, which can be used to detect cow calving. This is accomplished without the need to calculate a calving indicator index that requires tracking the step count and the time spent lying down by the animal as in , mainly A very important aspect of the developed calving detection algorithm is its simplicity. This algorithm must be programmed in the microcontroller housed in the collar placed on the cow’s neck, which represents a significant limitation in the microcontroller’s computing and memory fields. Furthermore, a coarse estimate of the calving date in the receiving end of the calving prediction is also useful to use in conjunction with the algorithm, as it ensures the rejection of any naturally inviable false positive from the algorithm .The dataset used in previous studies ,40, usuaAlthough the proposed algorithms of this study are focused on calving prediction, the developed sensorized wearable device and the collected data enable the development of different algorithms that could be of great help to farmers both in extensive and intensive livestock. Additionally, the generalist design of the presented wearable device could be equally helpful to develop hardware solutions oriented to different animals, benefiting their caretakers with the localization data from the collar, the suite of sensors that it incorporates, and other algorithms that could be implemented within the device.In this paper, a low-cost neck-mounted sensorized wearable device to continuous long-term monitoring cow data has been presented. To incorporate the ability to detect cow parturitions with enough anticipation using only this device an algorithm has been developed. This algorithm can detect an increment in the number of times the cow stands up and lies down (lying bouts) using a signal calculated from the accelerometer’s readings. To test the proposed algorithm, data collection using the cow neck-mounted devices and annotations from in-situ human observers has been carried out for eleven months (August 2020–July 2021). The data gathered by the neck-mounted collars correspond to six different cows monitoring in extensive livestock farming.This preliminary study , provides evidence that cow approaching parturition shows an increase in lying bouts behavioural pattern that can predict calving on average two hours before calving. To confirm these results, however, more pregnant cows need to be monitored and further research is required to refine this algorithm. The long-term character of the data acquired will allow for individualization of the thresholds for calving detection by calculating the baseline “restlessness level” of a particular animal. This could be used by the collar to generate an alert system to warn the farmer of the onset of calving.The low-cost of this device  would benefit most large livestock holdings by greatly reducing the number of hours the human experts must manually monitor individual animals. Economies of scale would allow the unitary price of the collar to be lowered even more, which would allow massive adoption even for the monitoring of large herds. However, smaller farms with less resources would also benefit from the reduced cost of the device, which lowers the entry barrier into a technology such as this, thus allowing for its adoption.Future work will focus in two separate points. The first one, data related, requires the acquisition of calving data from a larger number of cows to validate the developed algorithm and to develop a new data driven algorithm that learns from the available data from different cows . This new algorithm could be dedicated to the classification of standing and lying behaviour or to the detection of the birth event as a time series task. Furthermore, the data already gathered from the animals long before calving and therefore related mostly to the normal activity of the animal would allow for the development of algorithms that analyse cow behaviour . A deviation of the normal behaviour of a particular animal due to sickness, heat, or an abortion could be signalised to the farmer.The second point englobes the development of a new neck-mounted collar with IMU, GNSS and wireless low energy, and long-range communication using the LoRa protocol. This wireless communication would allow farmers to physically localize cows in extensive livestock farming, as well as receiving notifications of the detected parturition event, reducing the workload associates with parturition, and preventing dystocia in unattended calving. The energy autonomy of the device is an aspect of great importance in terms of its practical utility. In the tests carried out, it has been identified as an improvement parameter. For this, solar-powered technology is being incorporated in order to increase overall autonomy.


Compared to conventional internal combustion engines, electric vehicles can generate a wider variety of longitudinal behaviors based on their high-performance motors and regenerative braking systems. The longitudinal behavior of a vehicle affects the driver’s driving satisfaction. Notably, each driver has their own driving style and as such demands a different performance for the vehicle. Therefore, personalization studies have been conducted in attempts to reduce the individual driving heterogeneity and thus improve driving satisfaction. In this respect, this paper first investigates a quantitative characterization of individual driving styles and then proposes a personalization algorithm of accelerating behavior of electric vehicles. The quantitative characterization determines the statistical expected value of the personal accelerating features. The accelerating features include physical values that can express acceleration behaviors and display different tendencies depending on the driving style. The quantified features are applied to calculate the correction factors for the target torque of the traction motor controller of electric vehicles. This driver-specific correction provides satisfactory propulsion performance for each driver. The proposed algorithm was validated through simulations. The results show that the proposed motor torque adjustment can reproduce different acceleration behaviors for an identical accelerator pedal input.As worldwide vehicle CO Figurt values . While tter 2020 . In 2030The ever-intensifying regulations pertaining to emissions and fuel economy have resulted in the recent growth of the Electric Vehicle (EV) market ,6,7,8,9.The relatively large number of early adopters of EVs makes it a suitable topic for personalization research. There are various factors that affect consumers’ adoption of EVs ,18,19,20Personalization in the automobile field is still a relatively recent research issue. Customization of the automobile exterior, as well as personalization of the position of the driver’s seat and side mirrors are common issues. Currently, EVs have two main application areas of personalization: the personalization of driving performance and the personalization of driver assistance systems ,26,27,28Personalization of products, which means changing something or designing a component for someone specific, is not a new concept, but advances in information and web technologies have generated considerable interest in various fields over the past 20 years. It allows service and product providers to extend personalization from one-on-one personal relationships with individual customers or users to automated services based on the analysis of data collected according to the personal tastes and behaviors of numerous users .The general concept of personalization is intuitive, but the understanding and goals of personalization vary among different disciplines and researchers. Here, we will focus on personalization in terms of human–machine interaction and see this interaction as a means of making skills more acceptable and useful for people. This is particularly important in the field of advanced driver assistance systems, where the goal is not only to improve the driving experience, but also to improve driving safety and prevent accidents caused by human error.Since such a system can only be effective when used in practice, it becomes necessary to gain the driver’s confidence, meet expectations for driving behavior, and not annoy the driver with irrelevant recommendations and precautions. However, driver expectations vary from driver to driver and within individual drivers, depending on the driver’s condition and the driving situation. Therefore, driver assistance and related systems appear to be particularly suitable for personalization.In general, personalization is classified as either explicit or implicit personalization. Explicit personalization requires users to explicitly change their system by specifying their preferences and choosing the specific system settings that work best for them. Such an adaptive system limits the user’s choices to the system’s suggestions, but allows direct control. The main drawback of these systems includes limitations in the number of settings that the user can intuitively understand. For more complex support functions, it is very difficult to design settings, for example, as potential stings can go far beyond simple sporty and comfortable driving modes. In addition, the combined effect of setting several parameters at the same time is very opaque to the user. Conversely, implicit personalization observes a user’s behavior without directly requesting preferences from the user and derives a user model for predicting user preferences or behaviors based on these user data.Over the years, most of the implicit personalization research in the automotive field has been conducted to improve the Advanced Driver Assistance System (ADAS). ADAS was conventionally designed to guarantee a level of general performance that satisfies unspecified individuals. However, an assistance system that is optionally used by a specific user, such as Adaptive Cruise Control (ACC), must satisfy a user’s personal preferences ,32,33,34Most previous studies pertaining to practical applications of personalization have proposed a set of predefined values by categorizing drivers into several groups ,36,37,38Therefore, several studies have proposed driver models that directly map vehicle states through deterministic equations into control inputs, such as speed, acceleration, and pedal operation, based on an individual’s driving style ,39,40,41This paper primarily focuses on approaches for personalizing EV driving performance, which belongs to the category of implicit personalization, in order to model users through driver behavior observations.Compared to conventional ICEVs, it is relatively easy to personalize the performance of EVs. In particular, there are distinct differences in terms of longitudinal behavior. The longitudinal movement can be roughly divided into acceleration and deceleration. In the case of acceleration, it is easier to secure a higher performance in EVs than ICEVs. EVs consist of a much simpler powertrain and have an inherent advantage, as they use an electric motor instead of a standard engine. The electric drive motor has a wide range of performance coverages, with a fast response and high torque at low speed. In addition, as the transmission becomes unnecessary, linear control is possible, so performance adjustments are easier to apply. In the case of deceleration, a regenerative braking system can be used in addition to hydraulic brakes in EVs. The regenerative braking system is capable of adjusting its strength, enabling various deceleration performances to be realized.In general, driving mode functions are offered in mass-production EVs. These functions typically have three or more options, and the EV’s deceleration performance is adjusted according to the option selected by the driver. The options are determined within the range of vehicle performance and are individually tuned by the developer during development. However, because the number of options is limited, they cannot reflect all the preferences for all drivers.To add variety to these options, a new technology was proposed to allow drivers to customize the vehicle’s performance themselves ,50. The This paper proposes the personalization of the accelerating performance of EVs in a driver’s manual driving condition. The fundamental process for personalization is to model the driving behaviors based on driver-specific data ,52,53,54The EV powertrain control system is personalized based on driver characteristics. General EV powertrain control systems utilize look-up tables for the target torque, which have been calibrated through propulsion motor experiments. The values of the look-up tables are commonly designed to guarantee the stable acceleration of EVs. Notably, these values can change the acceleration performance of the vehicle through corrections. As such, if the correction is performed according to an individual driver’s needs, driving satisfaction for each driver can be increased ,56,57. TTo clarify the contribution of this study, the featured aspects of this study are summarized as follows. We propose a correction method for the target torque calculated using the look-up table of the motor rotation speed and the accelerator pedal position under the condition of manual driving. The target torque table was calibrated through repetitive experiments to provide standard acceleration performance; previous studies added correction look-up tables based on vehicle specifications to compensate for the target torque. The limitations of these approaches were that the correction look-up tables consider only the target motor’s own operation range and provide discrete correction results. To overcome these limitations, the proposed method determines correction factors based on personal driving features, such as the target driver’s preferred maximum acceleration and maximum jerk. To ensure stability, the proposed correction factor can reflect various driver preferences at a high degree of freedom, while limiting the range of each factor according to the target motor specifications.The remainder of this paper is organized as follows: In order to design the personalization algorithm, drivers’ personal driving data were analyzed. The real driving data for driver modeling were collected through vehicle tests with various in-vehicle sensors. A free-driving scenario refers to a situation in which the ego-vehicle runs freely without obstacles around it. In the free-driving scenario, since there is no disturbance affecting the speed of the ego-vehicle, the driver can operate the vehicle as they wish. Therefore, by analyzing the vehicle behavior in the free-driving scenario, it is possible to investigate the driver’s preferred driving style. However, even if there are no obstacles around the vehicle, driving behavior may be affected according to information pertaining to traffic or other external influences on the road. For example, the vehicle may start after being stopped by a stop signal from a traffic light, or it may accelerate again after decelerating during a right turn at an intersection. In such a situation, the driver’s propensity for acceleration, deceleration, and stopping can change.In this paper, we focus on the situation when the vehicle accelerates from a stopped position under free-driving conditions. Individual driver characteristics during acceleration are then used to personalize the acceleration behavior of the EVs through quantification in The driving route was set, as shown in To effectively analyze the driving data acquired when the vehicle starts from a standstill in a free driving scenario, specific conditions are defined. The acceleration section and initial acceleration section are defined in all driving data. The situation in which the feeling of acceleration felt by the driver in the acceleration section is dominant is during the initial acceleration. Therefore, we defined the initial acceleration section as the section in which the slope of the APS is positive. The initial acceleration section is considered as a transition period for obtaining the desired acceleration by the driver. In this section, the driving torque gradually increases until reaching a peak, and the vehicle acceleration also reaches a peak. During this process, the driver feels a great sense of acceleration and jerk that is not felt in other acceleration scenarios.Based on data from the initial acceleration section, the driving features were defined. The driving features include physical values that can effectively express the initial acceleration tendencies of the vehicle. Three driving features were defined: maximum acceleration, maximum jerk, and initial jerk, as shown in The determined driving features were statistically analyzed for each driver. To effectively compare the actual driving data for each driver, a non-parametric density estimation method, Kernel Density Estimation (KDE), was used. Due to the nature of the driving data, based on the driver’s simple change of mind, there is a lot of uncertainty that needs to be expressed parametrically. One driver characteristic, the kernel density function of the maximum acceleration, appears differently for the five drivers, as seen in the first graph in To express the distribution of the driving feature as one quantitative value, we used the median of the data distribution. The median value of the data has the advantage of minimizing the influence of the extreme values, compared to the commonly used arithmetic mean. In addition, to determine the standard of distribution of each driving feature, we checked the feature distribution of all the drivers and defined its median value as the reference value, as shown in the second graph of For each driver, a relative driving tendency may be estimated based on the reference value. For example, the 3rd driver has a characteristic value most similar to all drivers, whereas the 5th driver prefers an exceptionally high maximum acceleration. To effectively express the differences in propensity between drivers, the features were relatively compared. The driving feature deviation is defined as each driver’s median value over the total driver’s reference value, based on the following equation:All driving feature deviations calculated using this equation are shown in The longitudinal behavior of EVs is a result of traction control and braking control, according to human-drivers’ pedal operations . In partIn this study, the accelerator pedal is assumed to be the control input that the driver manipulates to achieve the desired longitudinal behavior of the vehicle. In other words, the driving style appears only through the behavior of the vehicle, and the accelerator pedal is only a variable that varies depending on the style.The accelerator pedal has a decisive influence on setting the target torque within the motor controller. The simplified calculation process of the target motor torque is shown in We designed the personalization algorithm of the target motor torque to be used between the LUT of the initial target torque and the rate limiter. The algorithm consists of set-point scaling and the feedback controller. The set-point scaling factor primarily corrects the magnitude of the target torque according to the driver. The feedback controller then manipulates the transient behavior of the target torque by adjusting the reactivity of the controller.The set-point scaling factor is calculated based on the vehicle’s acceleration and the feature deviation of each driving feature, as in the following equation.The scaling factor takes the role of modulating the initial target torque according to the driver’s preference. The maximum acceleration is the dominant limiting factor used to determine the value of the scaling factor, as they are both related to APS sensitivity. The APS sensitivity refers to how the absolute value of the APS affects the target torque. As shown in The feedback controller ultimately affects the jerk by adjusting the dynamics of the required torque. The feedback controller was designed as a PI controller, and each P gain and I gain are scheduled according to the driver. In the case of a driver who wants more aggressive acceleration, the two gains become larger, and for a slightly milder driver, the gains decrease. Specifically, the scaling factor of the 1st driver is reduced compared to the general driver, and as the dynamics are slowed by the feedback controller the vehicle’s response to the acceleration input is slowed.To validate the personalized motor controller, a simulation environment including an ego-vehicle was modeled based on the target vehicle, a Hyundai KONA EV, using the vehicle simulation software IPG CarMaker. IPG CarMaker enables detailed model configurations at the vehicle powertrain level, as shown in The IPG CarMaker-based simulation environment was integrated with the actual driving data to validate the personalized target torque of the traction motor controller. The purpose of this simulation was to prove that when the same APS profile was used as an input, the behavior of the vehicle changed according to the driver due to personalization of the target torque, as shown in In the case of a general EV with no personalization algorithm, the driver controls the APS feedback to achieve the desired longitudinal behavior. As a result of the APS feedback, the aggressive drivers need more dynamic pedal operation than the milder drivers. However, if the performance of the EV motor controller is automatically set to suit a specific driver, even an aggressive driver should be able to obtain their desired vehicle behavior using only normal pedal operation. To verify this assumption, the most average driver’s APS profile was used as the input for these simulations. Here, we use the 3rd driver’s profile as the average driver, based on the results of the driving data analysis.For an identical APS profile, the personalization algorithm corrects the torque using the scaling factor and feedback controller set for each driver. The result is then compared to the actual driving data of the corresponding driver to evaluate the torque correction performance. For example, the personalization algorithm configured to the mildest 1st driver and the APS profile of the normal 3rd driver are integrated in order to replicate the driving behavior of the 1st driver’s actual data.The proposed algorithm was confirmed to reproduce different vehicle behaviors through the scaling factor and the feedback controller, as shown in The difference in the results of each driver is similar to the driving feature deviation values determined through the quantification of driving behavior. In the case of maximum acceleration, the feature deviation of the 5th driver is 1.43. The feature deviation of the 5th driver derived from the case shown in Since there are two APS profiles in which different drivers drove under similar driving conditions, not only the magnitude but also the overall behavior are different. For example, there is a difference in both the period maintaining the maximum jerk in the initial acceleration section, and in the period maintaining the maximum acceleration. Despite the different APS profiles, the acceleration in the simulation results that was corrected through the personalization algorithm is similar to that of the experimental results. The acceleration behavior in the initial acceleration section is particularly well replicated, though the speed of the vehicle and the torque of the drive motor also appear very similar.To quantitatively evaluate the acceleration behavior in the simulations regarding the performance of the proposed algorithm, a statistical analysis was performed in the same manner as for the driving behavior quantification process. The personalization algorithm for each driver was simulated for all acceleration scenarios under which the actual drivers were driving, and the KDE was applied to the results. The maximum acceleration of the 1st driver was correctly replicated by the proposed algorithm, though there was a small error of 2.18% between the two medians. Finally, in the result of the 5th driver, the driver’s preference was exceptionally different, so the replication performance was slightly lower than the other two drivers, but still displayed a high accuracy (error of 2.81%).This paper quantitatively characterized individual drivers’ features and implemented a personalization algorithm of the EV’s accelerating performance under a driver’s manual driving. The driver characteristics quantification algorithm derives the expected statistical value for each driving feature. This approach focuses on determining one representative value for one driving feature representing a driver’s personal preference. The driving features are physical values that can express longitudinal driving behaviors among vehicle states, and display different tendencies depending on the driving pattern. In order to personalize longitudinal vehicle behavior, the quantified driving features are applied to the traction motor controller of the EVs. The traction motor controller calculates the target torque based on the accelerator pedal position in order to reflect the driver’s intentions. In this process, we utilized the quantified driving features to calculate the correction factors of the target torque. This driver-specific correction provided satisfactory propulsion performance for each driver. The proposed algorithm was then validated through simulation studies consisting of a precise EV model. The validation results confirmed that the proposed motor controller could reproduce different acceleration behaviors for identical accelerator pedal inputs. Through a comparison between the experimental and simulation data, the distributions of each driving feature displayed a high similarity. In the case of the maximum acceleration, errors in the distribution medians were lower than 3%.The proposed target torque personalization satisfactory improves individual driving. The target torque table is generally calibrated through experiments to satisfy standard acceleration performance. The previous research introduced look-up tables of the correction factors to adjust the torque within a range of motor specifications. The limitations of the previous research were that the correction look-up tables depended on the motor’s operation range and showed discrete correction results. To overcome these limitations, the proposed algorithm automatically calculates the correction factors through personal driving data analysis. Furthermore, to prevent stability problems, the proposed correction factor is limited according to the target motor specifications. The adjustment of the EV’s output torque greatly affects not only vehicle performance but also driving safety. For example, if the driving tendency a driver selected as a personalization target is excessively aggressive, it is not necessary to compensate for the driver unconditionally, but to offer support within the range that guarantees driving safety. Even if it is possible to tune the motor to achieve higher acceleration according to the specifications of the EV, safety must be ensured with a certain margin, and a service that recommends improving driving style rather than personalization to drivers outside the range must be provided. In addition to the acceleration performance discussed in this paper, personalization in the ADAS field has safety as the top priority, and research is being conducted to provide a personalized function for each driver within the possible range.