Socio-economic constructs and urban topology are crucial drivers of human mobility patterns. During the coronavirus disease 2019 pandemic, these patterns were reshaped in their components: the spatial dimension represented by the daily travelled distance, and the temporal dimension expressed as the synchronization time of commuting routines. Here, leveraging location-based data from de-identified mobile phone users, we observed that, during lockdowns restrictions, the decrease of spatial mobility is interwoven with the emergence of asynchronous mobility dynamics. The lifting of restriction in urban mobility allowed a faster recovery of the spatial dimension compared with the temporal one. Moreover, the recovery in mobility was different depending on urbanization levels and economic stratification. In rural and low-income areas, the spatial mobility dimension suffered a more considerable disruption when compared with urbanized and high-income areas. In contrast, the temporal dimension was more affected in urbanized and high-income areas than in rural and low-income areas. Studying human mobility during the coronavirus disease 2019 pandemic, the authors observe asynchronous temporal dynamics of people’s movements and compare this with spatial mobility changes. Once decoded and analysed, this digital fingerprint provides a new ground to portray urban dynamics11. In particular, the sequence of locations gathered from mobile phone devices via call detail records (CDR) and location-based service (LBS) data offers a unique opportunity to assess a broad time span of people’s urban activities in almost real time, overcoming the limitations of surveys and censuses13. CDR and LBS give us information about people’s daily motifs across urban locations14, their attitude in exploring different places15, the route of their commutes17 and the purpose of their urban journey18. The location of people in cities is predictable19 and is strictly connected with the circadian rhythms of social activities20, as well as home and work locations. The spatio-temporal variability of commuting patterns21 is intertwined with the mode of journey22, the population density 23 and the socio-economic status26.The places we visit28. They have been used to inform public health policymakers assessing the spread of a disease across the population31. Recently, during the coronavirus disease 2019 (COVID-19) pandemic, LBS metrics have become a proxy to evaluate the effectiveness and effects of mobility restriction policies enforced by local governments worldwide35. Using aggregated mobility data, researchers around the world can develop models to study and predict transmission dynamics38, investigate the impact and effectiveness of restriction policies and re-opening strategies47, and analyse the effects of these policies on the local economy, ethnic and socio-economic groups52. Moreover, coupling the mobility data with the socio-economic and ethnicity groups from the census, it is possible to estimate the socio-economic impact of such restrictions in each different community57.CDR and LBS contribute to the continuous creation of snapshots of citizens’ mobility patterns and represent a needed instrument to provide valuable insights on population dynamics in circumstances that urge rapid response58 and/or the location visited47). In both cases, using de-identified data from mobile phone users, the authors employ the radius of gyration to assess the spatial differences in mobility. The temporal analyses are restricted to assessing trip duration of changes in spatial mobility over time. These studies do not address synchronized mobility patterns or other temporal aspects of human mobility during the pandemic. In addition, both works were published in the early stages of the pandemic, so mobility changes in the same population during different lockdowns could not be studied. Besides the spatial patterns, human whereabouts follow temporal regularities driven by physiology, natural cycles and social constructs59. Few studies have explored these regularities aiming to characterize temporal components and classify people according to weights on these components59 or to uncover the emergency social phenomena such as the ‘familiar strange’60. In the context of the pandemic, it was found that morning activity started later, evening activity started earlier and temporal behavioural patterns on weekdays became more similar to weekends61. Since urban mobility patterns are built upon the space–time interaction62, it is vital also to study both dimensions of mobility to shed light on the mechanisms behind the changes in human mobility during the COVID-19 pandemic.The majority of the current literature is focusing on the changes in the spatial dimension of mobility during the COVID-19 pandemic, that is, if citizens are changing the patterns of their whereabouts in terms of magnitude , we employ the radius of gyration65 as a spatial metric. The radius of gyration was chosen for being a well-known metric applied to measure human mobility69. This measure was used during COVID-19 to gauge the general population’s compliance with mobility restrictions70, inform policymakers on their decisions47 and reveal differences in the impact on different socio-economic groups and minorities74. Besides the regularities in the spatial dimension, human mobility patterns also exhibit a high degree of temporal regularity64. These regularities are related to circadian rhythms59 and commuting for work75, study75 or shopping purposes76, for example. In our work, to gauge the temporal dimension of human mobility, we defined the mobility synchronization metric to quantify the co-temporal occurrence of the daily mobility motifs. It mainly measures the regularities linked to synchronize work schedules, that is, people leaving home around the same time to go to work. Increased synchronized mobility leads to augmented social contact rates, which elevate the risk of transmission of infectious diseases60. Hence, mobility synchronization can provide relevant insights to policy markers during the pandemic. Combining these spatio-temporal metrics gives us an idea of how far infectious individuals could potentially travel and how many people they could be in contact with .We can assess the space–time interactionIn this Article, leveraging LBS data from de-identified mobile phone users who opted-in to anonymous location sharing for research purposes, we study how citizen mobility patterns changed from January 2019 to February 2021 across the United Kingdom. As the pandemic unfolded, we observed changes in the duration and frequency of trips and disentangled how each mobility dimension was affected. At the lifting of each restrictions, the spatial mobility dimension recovered faster than the temporal dimension. The space–time components drift their trends during the second lockdown to finally align back after the third lockdown. Trips are defined in our paper as the event in which a user leaves their geo-fenced home area. For each trip, we registered the total time spent outside before returning home, if the trip included a green area, and the distance travelled.gov.uk/government/statistics/national-travel-survey-2018, accessed on 1 June 2023). Meanwhile, in urbanized areas, the synchronicity of the daily activity was dissolved possibly by the rise of asynchronous communing patterns 78.We coupled the mobility dimensions with the urbanization, unemployment, occupation and income levels from the census at the local authorities level. Rural and urban areas manifest opposite trends. In rural areas, the lockdowns affect more the spatial dimension, where the locations of human activities are spread apart and consequently the trips are longer  as a proxy to gauge the impact of the pandemic on the mobility patterns of income/occupation groups. We noticed that areas with elevated concentration of population on low-income routine occupations had the most substantial reduction in the spatial and temporal dimensions of mobility.Throughout this section, we define the spatial dimension of human mobility as the span of the citizen’s movement, that is, the length of the trips. This dimension is gauged using the radius of gyration, which quantifies how far from the centre of a user’s mobility the visited geographical locations are spread. In the temporal dimension, we are interested in measuring co-temporal events linked to collective, synchronized behaviours. This dimension is estimated with the mobility synchronization metric that represents temporal regularities related to when people tend to leave their residences at regular time period. Our goal is to quantify how containment measures  affected travel rhythms of the populations. The intervals are identified through the analysis of the strongest frequency components in Fourier spectra of the out-of-home trips time series. For this analysis, we use the trip data aggregated hourly. More information on these metrics is available in gov.uk/government/collections/slides-and-datasets-to-accompany-coronavirus-press-conferences, accessed on 1 June 2023). Given the discrepancies in the implementation of the different lockdown across each country in the United Kingdom, an analysis of the local/regional impact of the pandemic is included in We analysed LBS data from January 2019 to February 2021 in the United Kingdom. This period includes the three lockdowns announced by the United Kingdom’s prime minister . Figure We employed the concept of residual activity79) and the characteristics and pre-existing social vulnerabilities of urban and rural areas, as found in previous works93During the first lockdown, urban areas presented an increase in expected mobility. In contrast, rural areas have a negative trend. However, during the second and third lockdown, an opposite scenario emerges. Rural local authorities increased the expected residual activity, and urban areas decreased it. These differences can be driven by the change in the mobility restriction policies .The results of the radius of gyration Fig.  and the Analysing the trends of the radius of gyration and mobility synchronization compared with the baseline of 2019, we can notice some differences in the urban–rural and spatio-temporal response of the local authorities , available at ons.gov.uk/employmentandlabourmarket, accessed on 1 June 2023).As discussed, the number of trips has decreased across all the urban–rural groups during the pandemic. Since work-related activities often create the necessity to leave home, the rise in home working and unemployment rate contributes to the reduction in the mobility levelsWe divided the time series of the radius of gyration and mobility synchronization into pre-pandemic (from April 2019 to February 2020) and pandemic (from April 2020 to February 2021) periods, and we analysed the Kendall Tau correlation between them and unemployment claimant count.97. However, this result can be due to a rise in the unemployment rate and the spatial mobility levels before the pandemic. For the pandemic period, although the lockdowns have reduced the radius during specific periods, we see in Fig. At first glance, the positive correlation between the radius of gyration and the unemployment rate Fig.  seems toWhile the correlation between the spatial dimension of mobility and unemployment drops only marginally, preserving the positive sign and affecting more urban than rural areas, the correlation between the time dimension of mobility and unemployment drops considerably, becoming negative and impacting more rural districts than the urban Fig. .The same explanation applies to mobility synchronization in the pre-pandemic period. During the pandemic, we can see that the spatio-temporal dimensions display different patterns between May and November, and the temporal one does not present the same steep recovery as the spatial between April and August 2020. The oscillations in the patterns of the temporal dimension impacted the correlation with unemployment, making it negative.98. In this sense, we use the English NS-SEC  to gauge the response of employment relations and conditions of occupations to the pandemic. Using this classification, we also have insights into the connection between the spatio-temporal mobility and wealth level. This analysis is important since wealth and economic segregation are linked to differences in mobility patterns and response to human mobility restrictions under the COVID-19 pandemic33.We argued that work trips contribute to the creation of our mobility patterns. Moreover, the type of occupation, among other socio-demographic characteristics, also influences those patternsIn the NS-SEC classification, classes related to managerial occupations exhibit strong positive correlation with income as depicted in Fig. Moreover, Fig. In all scenarios depicted in Fig. 99 and the purpose100 of the trips were also observed during the pandemic. Researchers found that these changes vary accordingly to income levels100 and could be related to the emergence of new habits101.Besides the difference in the time–space facets of human mobility analysed, changes in the duration99.To assess the changes in the duration of the trips, we measured the time elapsed when the user left their home geo-fencing area and entered it again. We can further disaggregate the trips by classifying them as work-related and other types based on their starting time. Comparing the duration of the trips in a week with no mobility restriction Fig. 79. After that, we can see a substantial difference in the amount of green areas trips in rural and urban areas. Although one could argue that this could indicate a preference towards more natural leisure trips in rural areas, further investigation is required to identify the reasons behind these differences.Besides the trips’ duration, another relevant aspect being analysed is the impact on the type of trips. We study the differences in leisure-related trips before and during the pandemic to assess this impact. Here these trips are estimated on the basis of examining trips that include green areas such as parks, sports facilities and play areas. In a period without any mobility restriction measures, we expect to see an increase in these trips from the end of winter until the end of summer. This number should start decreasing as when the winter approaches. As shown in Fig. 102). A vital contribution of our work lies in applying two metrics to disentangle the changes in mobility’s temporal and spatial dimensions. Using the radius of gyration, we could identify that the effect of the first lockdown was more substantial than the other ones in changing the spatial characteristics of citizens’ movement. Among the reasons that could lead to this result, we can mention more strict policies adopted in the first lockdown and the lockdown duration85. However, further investigation is needed to obtain more pieces of evidence to support these hypotheses.The accurate estimation of spatio-temporal facets of human mobility and gauging its changes as a reflection of the pandemic and mobility restriction policies is critical for assessing the effectiveness of these policies and mitigating the spread of COVID-19  and the spatio-temporal human mobility metrics adopted. We noticed that rural areas presented a more considerable reduction in spatial patterns compared with urban ones. At the same time, urban areas were more impacted in their temporal dimension than rural ones. These differences observed in the urban–rural classification are correlated with the population density of the regions and can affect the impact of the mobility restriction measures.85. Similarly, changes were also observed concerning the duration of work-related trips and the number of trips to green areas. These differences were also observed at urbanization and socio-economic levels.We also observed changes in the response of regions regarding their unemployment rates and populations income. For the unemployment rates, we observed that before the COVID-19 pandemic, the unemployment claimant count was positively correlated with the radius of gyration and mobility synchronization in the majority of the areas. However, during the pandemic, this correlation became weaker for the radius of gyration and negative for mobility synchronization. Less urbanized areas tend to have a lower spatial correlation and a higher temporal correlation with unemployment than more urbanized areas. Using the NS-SEC classification as a proxy to assess the response of different income and work groups, we observed that low-income routine/semi-routine occupations were the groups that presented the greatest reduction in their radius and synchronization. Moreover, the changes in the mobility restriction policies after the first lockdown had an impact on these groups, which can be the reason behind the greatest impact on the temporal dimension of mobility81. Moreover, our results indicate that different groups  experience and respond to these policies differently. These results were also found by similar works99 and provide insight to policymakers to design strategies that consider each group’s particularities.Regarding our work’s contribution to policymakers, we argue that the spatio-temporal metrics employed in this study help to assess mobility changes before and after the implementation of policies, as seen in the first and second lockdowns with flexible stay-at-home policiesIn summary, the analysis of the spatial dimension of human mobility coupled with the insights from the study of the temporal dimension allows us to characterize the impact of policies such as stay-at-home and school closures on the population of different areas/socio-economics. These differences suggest that each group experiences, in a particular way, the emergence of asynchronous mobility patterns primarily due to the enforcement of mobility restriction policies and new habits .r2 value equal to 0.775 was obtained . Similarly, the analysis of the unemployment claimant rate, the urban–rural classification of English local authorities92 and the NS-SEC105 used the data published by the ONS publicly available under the terms of the Open Government Licence. The remaining socio-economic data utilized aggregated data from the UK Census of 2011 (ref. 106), which is available for download at the InFuse platform also under the terms of the Open Government Licence. For the green area study, we used the Ordnance Survey Open Greenspace dataset to obtain information on the locations of public parks, playing fields, sports facilities and play areas . Our analysis did not include categories related to religious grounds, such as burial grounds or churchyards.The Income study employed data from the report on income estimates for small areas in England in 2018 provided by the ONS64. It can be described as the characteristic distance travelled by a user u during a period and is calculated as followsNu represents the unique locations visited by the user, i and i. The mobility value of each region is the median value of the radius of gyration of the users within a temporal window of 8 days centred around a given day.We conducted the study of the radius of gyration (RG) following the definition of Gonzalez et al.107, and it is used to highlight differences between the measured behaviour of the local authorities compared with their expected behaviour. For a given local authority i is calculated as followsa−norm(t) is the normalized activity averaged over all local authorities under at each particular time, and Z-score metrict, μabs is the mean activity of all local authorities under the same urban–rural classification of i at a specific time, and σabs represents the standard deviation of all local authorities under the same urban–rural classification of ai,j at a particular time.The concept of residual mobility activity displayed in Fig. The mobility synchronization is not limited to conventional commuting routines. It can happen at any time of the day in which people tend to perform certain activities. For example, school teachers, healthcare professionals and other routine or semi-routine occupations tend to have defined times reserved for specific activities . To have a more accurate portrait of the mobility synchronization patterns, instead of analysing it as a concentration of trips around certain hours, we define the mobility synchronization as the total magnitude in the periodicity in the out-of-home trips.ψ(t), the discrete wavelet transform can be described asj and k are integers that represent, respectively, the scale and the shift parameters. For the Fourier transform, a discrete transform of the signal xn, for n = 0…N − 1 is:K = 0…N − 1 and ei2πkn/N− represents the Nth roots of unity.First, using 2019 as a baseline, we analyse the wavelet and Fourier spectra to determine the expected strongest frequency components in the mobility regularity. For a given mother wavelet 108 corresponding to the 12 h, 8 h and 6 h. The generalized Lomb–Scargle periodograms is calculated asyi represents the N measurements of a time series at time ti, ω is frequency and τ can be obtained fromEmploying these two transforms, we found that the mobility patterns are characterized by five main periods, namely 24 h, 12 h, 8 h and 6 h (Supplementary Fig. The mobility synchronization is a value between 0 and 1, where higher values for a given period indicate that more people left their homes simultaneously. In the context of the pandemic, high mobility synchronization can be translated into a potential increase in the likelihood of being exposed or exposing more people to the virus due to the large number of people moving simultaneously.Further information on research design is available in the Supplementary InformationSupplementary Figs. 1–9, discussion and Table 1.Reporting SummaryPeer Review File


Database records contain useful information, which is readily available, but, unfortunately, limited compared to the source (publications). Our study reviewed the text fragments supporting the association between the biological macromolecules and diseases from Open Targets to map them on the biological level of study . We screened records using a dictionary containing terms related to the selected levels of study, reviewed 600 hits manually and used machine learning to classify 31,260 text fragments. Our results indicate that association studies between diseases and macromolecules conducted on the level of DNA and RNA prevail, followed by the studies on the level of proteins and metabolites. We conclude that there is a clear need to translate the knowledge from the DNA/RNA level to the evidence on the level of proteins and metabolites. Since genes and their transcripts rarely act in the cell by themselves, more direct evidence may be of greater value for basic and applied research. Therapeutic targets are biomacromolecules whose behavior could be modulated by the active ingredient of medication to correct a certain pathological state . TherefoOne of the essential requirements for a biomacromolecule to serve as the therapeutic target is its involvement in disease-related processes. Causal relationships and associations between biomacromolecules and diseases are studied extensively using different experimental methods on the level of DNA/RNA, proteins and metabolites and are covered by the scientific literature. In turn, scientific literature may be processed and summarized using automated or semi-automated approaches to provide the overall association score between the particular disease and the biological entity (biomacromolecule). Resources like Open Targets provide such information for the researchers, who can use it to prioritize potential targets and plan their biomedical studies, benefiting from the existing collective knowledge on the subject without an independent extensive literature search.Nevertheless, information loss inevitably occurs during the process of summarization. In the case of literature-based judgment on the association between a biological entity and disease in Open Targets, it occurs too: data on the biological level of study is absent. I.e., it is not explicitly stated what exactly was studied in the context of disease: gene and its variants (DNA), transcript (mRNA), a final gene product (protein), or related organic compound (metabolite).In our study, we applied a well-established systematic review methodology to map ,7 the schttps://osf.io/9w5x3/?view_only=f4fee9d348a2406fa3d2e0e9c75f83ff (accessed on 3 December 2022)), registration number is h82a4. It is important to note that not all scientific articles had been manually reviewed and mapped, but only their fragments contained in the materials of the Open Targets platform  as an acetylated {protein} in PC3 prostate cancer cells. [Aurora B] is regulated by acetylation/deacetylation during mitosis in prostate cancer cells”.Does the text fragment allow us to judge the category of the experimental method?Are highlighted terms sufficient to judge the category of experimental method?Is the specific level of the study mentioned in the text fragment?Text fragments were loaded into the in-house developed Tool for Categorization of Short Text Fragments, TCSTF . TCSTF wAnswering these three questions allows us to judge the level of study (question 3) and get the characteristics of the text fragments and terms found in the text fragment .Using this tool, the belonging to the previously defined category was validated or invalidated by the consensus decision of the authors of this paper  to 0.73 (category proteins). Classifiers were used to predict the category for all the fragments which previously had passed dictionary-based screening.Technical realization. Open Targets data were downloaded and processed using R-scripts [https://www.php.net/manual/en/intro.xmlreader.php, accessed on 9 April 2023) tidyverse [https://inkscape.org/, accessed on 9 April 2023).-scripts  written -scripts , jsonlit-scripts  and tidy-scripts . Data fridyverse . R packaidyverse  was usedidyverse . R packaidyverse  and diagDictionary-based screening allowed us to elucidate 31,260 text fragments presumably describing the experimental studies on DNA and RNA, protein, and metabolite levels see .These results align with the current view of biomedical studies: analyzing nucleic acids is probably the most accessible and high-throughput option since polymerase chain reaction (PCR) allows one to study small quantities of biological material fast with affordable equipment . WorkingGiven that our dictionary has different numbers of terms in each category, we adjusted the results given in Using the results of the dictionary-based screening, we selected a small but diverse subset of the text fragments (two hundred in each previously defined category) and reviewed them manually. The results are depicted in As can be seen from Does the text fragment allow us to judge the category of the experimental method?Are highlighted terms sufficient to judge the category of experimental method?Also, during the manual review, we determined additional characteristics of the text fragments and terms by answering the following questions:The results for the twenty terms from DNA/RNA, proteins and metabolites categories associated with the biggest prevalence of positive  and negative (lower ten) answers are given in From As one can see from Overall, the manual review of the text fragments allowed us to precisely map the small fraction (600) of text fragments to the DNA/RNA, protein, or metabolite level of study. These results could be of value on their own or be used as the training sets for the automated review conducted using machine learning techniques, the latter path we choose. Moreover, the analysis of these results, combined with the associated terms from the dictionary used during the screening stage, allowed us to highlight the ways to optimise the screening stage for further studies.The results of the machine learning review of the 31,260 retrieved text fragments are summarized in According to Methods of genomics and transcriptomics are still the most accessible and mature ones. They allow monitoring of the molecules both in high-throughput and targeted modes. Thus, such methods are the most often used.Despite the start of the postgenomic era and recent progress in proteomics  and metaThe number of biological macromolecules associated with diseases via metabolites is limited .Overall, the results of the mapping indicate the prevalence of the association studies between biological macromolecules and diseases conducted on the level of nucleic acids (DNA or RNA) over the protein and metabolite levels of the study. Probably, there are the following main factors contributing to this situation:Our results suggest that given the number of genomic and transcriptomic evidence of the role of distinct biological macromolecules in pathology, there is a clear need to translate them into evidence on the level of proteins and metabolites ,8. FurthOur systematic mapping review of the text fragments from Open Targets describing relations between biological macromolecules and diseases has shown the prevalence of the knowledge obtained while studying the DNA/RNA variants and levels. Therefore, translating such knowledge on the level of proteins and metabolites may be proposed to improve the reliability of the association between potential therapeutic targets and diseases.


With the continuous development of flexible electronics, multi-functional device integration, artificial intelligence technology, etc., flexible wearable sensors are playing an increasingly important role in people’s daily motion monitoring. However, current flexible wearable sensors have problems such as low accuracy, poor real-time performance, and poor stability in motion monitoring, which seriously hinder the better application of flexible wearable sensors and are not conducive to the collection and monitoring of motion signals. To this end, this paper designed a flexible wearable sensor motion monitoring system and tested its performance through the preparation and performance research of new functional polymer conjugated materials. The research results show that the motion monitoring system designed based on the new functional polymer conjugated material flexible wearable sensor has good monitoring accuracy and real-time performance. When the output data is 500 pieces, the running monitoring accuracy reaches 95.4%, and the monitoring feedback time is 0.321 s; the high jump movement monitoring accuracy rate reaches 97%, and the monitoring feedback time is 0.287 s; the long jump movement monitoring accuracy rate reaches 96%, and the monitoring feedback time is 0.296 s. This shows that the flexible wearable sensor motion monitoring system of this paper has better performance and can meet the current demand for accuracy and real-time motion monitoring. This study highlights the impact of new functional polymer conjugated materials on flexible wearable sensors, helping to further solve the deficiencies of flexible wearable sensors in sports monitoring and promote their better development. As current computer technology develop rapidly, the application of flexible wearable sensors is becoming increasingly widespread . In termThanks to the continuous development of current science and technology, flexible strain sensors have potential application value in electronic skin, medical monitoring, human-computer interface and other fields. The key challenge of applying strain sensors to human motion monitoring is to achieve wide strain range and full range high sensitivity simultaneously . Wu Qi sHowever, some scholars have different ideas. They have studied the photoelectrochemical properties and application fields of conjugated materials and believed that conjugated materials have certain application value in sensor design. MacFarlane Liam R considered that the development of π-conjugated polymers supplied an entry point for various new functional organic materials. He introduced methods for synthesizing π-conjugated polymer nanoparticles and explained the applications of the resulting nanoparticles in electronics and optoelectronics, biomedical imaging and treatment, photocatalysis and sensing . Koenig,To better ameliorate the performance of flexible wearable sensors and enable them to perform better motion monitoring, this article analyzes the selection and preparation of new functional polymer conjugated materials and combines previous scholars’ discussions on sensor technology and motion monitoring to optimize the design of flexible wearable sensors. Through empirical research, it is found that the wearable sensor designed in this article has higher accuracy, better stability and real-time performance for motion monitoring. Compared with traditional sensors, the innovation of sensor design in this article lies in paying attention to the unique properties of new functional polymer conjugated materials and applying them to optimize the performance of sensors. This helps to diminish the shortcomings of sensors and ultimately better promote motion monitoring. The main framework structure of this study is shown in Functional polymer conjugated materials are compounds with repetitive structures composed of alternating double single bonds on the main chain of polymers. This arrangement makes the bonding and anti bonding molecular orbital nonlocalized along the main chain of the molecule . When π This article refers to the preparation method of dense fluorene conjugated polymer materials, and uses chemical materials such as 1-bromo-2-ethylhexane and anhydrous potassium carbonate to prepare functional polymer conjugated materials . Due to First, 1-Bromo-2-ethylhexane is added to a 250 mL single neck bottle and then 100 mL of N,N-Dimethylformamide is added. Under the ice bath condition, N-bromosuccinimide is added in batches and it is determined by thin-layer chromatography. After all raw materials are converted into a single bromine product, the reaction is stopped, extracted with ether and washed with a lot of water. The organic phase could be dried through Anhydrous magnesium sulfate. The concentrated crude product is chromatographed on a silica gel column and Petroleum ether is used as eluent to obtain Compound 1 with a yield of 90%. Then, Compounds 1, 3-Thiophenylboronic acid, Tetra (triphenylphosphine) palladium, 80 mL Toluene and 40 mL Sodium carbonate are successively added to a 250 mL double neck flask. They are stirred, heated and refluxed and they all react under the action of nitrogen. The reaction is monitored by thin-layer chromatography until the reaction stops overnight and then the crude product is obtained by water washing, Petroleum ether extraction, separation, organic phase combination, drying, reduced pressure concentration. Then chromatography is carried out on the silica gel column, with Petroleum ether/ethyl acetate as eluent and finally Compound 2 is obtained, with the yield of 82%. Eventually, Compound 2 is added to a 250 mL single neck bottle and then 100 mL chloroform is added. Under the condition of ice bath, N-bromosuccinimide is added in batches and determined by thin-layer chromatography. After all raw materials are converted into a single bromine product, the reaction is stopped, extracted with chloroform. A large amount of water is used to wash and the organic phase is dried with Anhydrous magnesium sulfate. After concentration, it is chromatographed on a silica gel column. Finally, Compound 3 is obtained with Petroleum ether as eluent in 89% yield. The main chemical materials required for the experiment are shown in The instruments and models involved in the preparation of materials are shown in After the synthesis of three compounds, the compound performance should be tested by scanning electron microscope, thermogravimetric analyzer and diffractometer. The scanning electron microscope image is shown in First, a small piece of film sample is pasted on the conductive tape and then it is pasted on the sample table. The sample is vacuumed and sprayed with platinum and then the shape of the sample is observed with scanning electron microscope. Meanwhile, the surface composition of the sample is analyzed and the X-ray diffraction pattern is obtained. Then thermogravimetric analysis is carried out and 10 mg of compound powder is selected and tested under nitrogen environment. It is necessary to adjust the temperature range to 25–800°C and heat at a rate of 10°C per minute. X-ray diffraction (XRD) method: Finally, place the sample on the sample table and perform diffraction analysis using an X-ray diffractometer. According to specific experimental requirements, set the scanning step to 0.02° and the scanning range to 2–80°. In X-ray diffraction analysis, the focus is usually on the smaller angle range, as X-ray diffraction typically occurs at smaller angles. The setting of step size will affect the accuracy and resolution of the experiment. Meanwhile, due to the fact that the detector of the selected X-ray diffractometer may not be able to receive scattered X-rays with an angle greater than 80°, its scanning range is set at 2–80°. And the setting of step size is also limited to some extent by the accuracy of the instrument. If the step size is too large, it may cause data distortion or loss. Therefore, based on specific experimental requirements and instrument limitations, we have set such a scanning range and step size.Additionally, this article also tests the optical properties of the compound and the results are shown in Known from Flexible wearable sensors have broad application prospects in human motion monitoring, health restoration, human-computer interaction and other fields . It can The composition of a sensor usually consists of three parts, namely: sensitive components, conversion components and signal conditioning conversion circuits . BecauseThis article uses flexible polymer materials as the matrix material and conjugated materials with excellent electrical properties as conductive fillers to prepare flexible wearable sensors with high strain and sensitivity through different methods . In thisCopper tape is used as a conductive material in flexible sensors to transmit the sensor’s sensing signal to the readout circuit. The strain tolerance of copper strip refers to the range of change in its resistance value when subjected to a certain strain. This strain tolerance is an important parameter of copper strip, as it directly affects the sensitivity and data accuracy of the sensor. When the copper strip is subjected to strain , its resistance value will change. This change is due to the change in the geometric shape and size of the copper strip under strain, resulting in a change in resistance. The smaller the strain tolerance of the copper strip, the greater the change in its resistance value when subjected to a certain strain. For flexible sensors, the strain tolerance of copper strips is an important performance indicator. If the strain tolerance of the copper strip is too large, it will cause the resistance value of the sensor to not change significantly when subjected to strain, thereby affecting the sensitivity and data accuracy of the sensor. On the contrary, if the strain tolerance of the copper strip is too small, it will cause a significant change in the resistance value of the sensor when subjected to small strains, which will also affect the data accuracy of the sensor. When designing sensors, it is also necessary to consider the impact of the strain tolerance of copper strips on sensor performance to ensure the accuracy and stability of the sensor’s readout.After designing a flexible wearable sensor using functional polymer conjugated materials, the performance of the designed sensor should be tested. This article tests the tensile sensing performance of a sensor wrapped in conjugated materials to evaluate its stability, repeatability and sensitivity in small-scale tensile applications.Within a certain period of time, a constant current is applied to the fibers at both ends of the sensor to make the deformation of the fiber 30% and then release it evenly and slowly. Meanwhile, the voltage on both sides of the fiber is measured when repeatedly pulling and loosening at a frequency of 30 Hz and the resistance value on the fiber is calculated in combination with Ohm’s law. The results are shown in The horizontal axis represents time and the vertical axis represents the relative rate of change in resistance.According to The motion monitoring system composed of flexible wearable sensors in this article is a wireless motion monitoring system with high reliability and real-time performance. Due to the fact that people’s normal lives cannot do without exercise, a fast data collection device must be used to achieve a better exercise experience, which poses greater challenges to the motion monitoring system. Therefore, the structure of a motion monitoring system should be designed based on flexible wearable sensors to ameliorate system performance and better satisfy current demands. The system design mainly includes the connection and communication between sensors and data acquisition devices.Flexible wearable sensors can exhibit excellent sensing characteristics when in contact with the human body . HoweverTo effectively express the changes in joints and posture during human movement, the collected data should be expressed in an intuitive and efficient manner. Assuming that in sample collection, each sample can simultaneously obtain raw perceptual information from 10 perceptual units. If Owing to different human body movements and varying degrees of joint bending, the response of sensors may also vary. Additionally, the sensitivity of the sensors prepared varies due to the degree of encapsulation of functional polymer conjugated materials. This results in different sensitivity of the obtained sensors to human motion posture and joint bending, leading to different response ranges of the sensors. To solve this problem, the original data must be standardized. Therefore, this article uses two maximum vectors to normalize the truncated filtered data, converting the response values of each sensor to the same dimension.By using the above processing, the original perception data under different motion postures and joints can be mapped to the range of . At this point, the sampled data for each frame can be represented by the formula:When collecting data on human motion posture and joints, the measurement results may have some noise due to small changes in the human body and jitter of fiber optic sensors. To filter out these noises and better express the characteristics of human motion in the data, the exponential weighted average method is chosen to filter the data . The expAmong them: The data is filtered by the above method, so that the response value at the current time point is not only related to the original data obtained by sampling, but also includes the response value obtained in a previous period of time. By assigning different weights to the data through exponential weighting and then summing and averaging them, the actual response value after filtering at the current time can be obtained. This method can not only eliminate the fluctuation of data caused by partial noise, but also alleviate the excessive response caused by external environmental factors (such as sudden large stretching/releasing). The formula can be expressed as:Among them: This article takes the fiber sensor of the prepared functional polymer conjugated material as the core sensing unit, supplemented by devices such as microprocessors and analog-to-digital converters, to develop a motion monitoring system that can monitor human motion in real-time. Among them, the adaptive program formed by truncation filtering and data normalization can effectively solve measurement errors caused by differences in human posture and joint bending amplitude and also effectively diminish the negative effects caused by factors such as initial resistance and sensitivity differences of sensors. The testing interface of the motion monitoring system is shown in Considering that the motion monitoring system in this article needs to meet wearability requirements, the selected hardware has a smaller volume and lighter weight. Especially for the selection of batteries, it is extremely important. The selected battery in this article has a height of 59.0 mm and a diameter of 32.3 mm. A protective board circuit is installed in the battery. When the battery experiences overcharging, discharging or short circuit, the protective circuit can isolate the battery from the circuit. The volume and thickness of the entire battery are very small. The purpose of doing this is to reduce the thickness of the final product and enable it to directly attach to the clothing. The system uses a flexible sensor made of functional polymer conjugated materials, which can monitor the real-time state of human motion and provide a basis for further analysis of human motion status.After designing a flexible wearable sensor motion monitoring system based on new functional polymer conjugated materials, the performance of the flexible wearable sensor in motion monitoring should be evaluated to verify the performance of the system, such as accuracy, stability and real-time performance, thereby offer more basis to the practical application of new functional polymer conjugated materials in flexible wearable sensor motion monitoring.To better validate the performance of the sports monitoring system, this article selects sophomore students from a local university’s physical education college as the research object. The flexible wearable sensor is used to collect the output data of students’ daily activities and movements, forming a sports data set. There are a total of 5000 data in this data set and 500 data for running, high jump and long jump training.Accuracy is the most important criterion for measuring the performance of motion monitoring systems. The higher the system accuracy, the better the system performance, which is more conducive to promoting scientific training and analysis. This article tested the accuracy of the system in monitoring running, high jump and long jump training under different output data conditions. The results are depicted in The horizontal axis represents the amount of output data and the vertical axis represents the accuracy of system monitoring when outputting different amounts of data.According to In addition, the accuracy of motion monitoring using the method proposed in this paper was compared with that of references 4, 5, and 7 when the output data was 500. The results are shown in From In a system, stability is the key to ensuring high-quality, efficient and high-precision output. Stability is also an significant indicator for measuring the performance of motion monitoring systems. The better the stability of a flexible wearable sensor motion monitoring system, the more conducive it is to monitoring the training status and situation. This article tested the stability of the motion monitoring system. The results are depicted in The horizontal axis represents the output data volume and the vertical axis represents the system stability when outputting data volume.Seen from In order to better highlight the stability of the method proposed in this paper for motion monitoring, the stability of the motion monitoring system was also tested when the output data was 500. The results are shown in From Besides the above performance, the real-time performance of the motion monitoring system is also very important. A motion monitoring system with better real-time performance can constantly monitor its motion status, detect abnormal data in a timely manner and offer early warnings, which helps to achieve scientific training and increase exercise efficiency. The real-time performance of the motion monitoring system is shown in The horizontal axis represents the amount of output data and the vertical axis represents the time required for system feedback when outputting different amounts of data.Known from Motion monitoring is a complex and multidimensional analytical process. Therefore, the training monitoring of athletes requires a large sports monitoring system as support, and sports monitoring is a process of quantitative analysis of physiological indicators. In this process, it is necessary for various types of sensors to work together and perform their respective functions. The performance improvement of each type of sensor can lead to a leapfrog improvement in the performance of the entire motion monitoring system.The flexible wearable sensor of the novel functional polymer conjugated material in this article can analyze the heart and muscles during exercise by real-time monitoring of electrophysiological signals, thereby providing support for exercise rehabilitation and motion analysis. With the integration of various disciplines, the performance and functionality of sensors have greatly improved. Sensors based on electrophysiological signals can obtain more accurate electrophysiological signals and apply them to various types of motion analysis. To obtain more accurate electrophysiological signals, the most important thing is the preparation of sensor electrodes. The rationality of electrode structure design has a significant impact on the signal-to-noise ratio of the obtained signal. It is best for electrodes to have a good combination in terms of biocompatibility, softness, adhesion, conductivity, and breathability. This article is based on a flexible wearable sensor made of novel functional polymer conjugated materials, which can accurately monitor specific physiological signals. At the same time, it relies on an adaptive program formed by truncation filtering and data normalization to achieve data processing and denoising. With the development of intelligent manufacturing, the diversity and integration of sensors can be greatly improved. Integrating different sensors together can effectively solve the problem of single factor detection of sensors, thus providing more possibilities for multivariate motion monitoring.Because of the continuous improvement of chemical material preparation methods, the application of conjugated materials is also becoming increasingly widespread. However, in the field of sensors, the application of conjugated materials still needs further exploration. The research direction of this article was the study of flexible wearable sensors for motion monitoring based on novel functional polymer conjugated materials. Firstly, the relevant background of its research was introduced, followed by a review of the strengths and weaknesses of previous scholars. Afterwards, this article provided a detailed analysis of the preparation and performance of novel functional polymer conjugated materials and then explored the preparation methods and performance of sensors. Based on this, combining sensors based on novel functional polymer conjugated materials, this paper designed the latest flexible wearable sensor motion monitoring system and tested its performance. The research results showed that the motion monitoring system designed in this article had good accuracy, stability and real-time performance. It could accurately and stably monitor the exercise training situation and provide timely feedback, thereby improving the effectiveness of exercise training and ensuring the smooth progress of sports. It had good practical application value. However, there are also shortcomings in the research. Due to practical limitations, the data set composed in this article is relatively small, which may have a certain influence on the final research results when selecting small data samples during system testing. In addition, the sensors prepared in this article still have significant room for improvement. In subsequent research, better and more complete conductive networks can be constructed to enhance their strain range, Meanwhile, the system is difficult to monitor small signals in the human body. In the future, more sensitive conductive networks such as crack structures and fold structures can be constructed to achieve this.


In this study, the problems encountered in radar cross-section (RCS) measurement experiments utilizing a dielectric barrier discharge (DBD) plasma system are examined and an effective solution is proposed. A DBD plasma system generates heat due to the high bias voltage required for plasma generation. The thermal-induced structural deformation of the DBD structure caused by this high voltage and its impact on RCS measurements are analyzed. In addition, techniques for minimizing the thermal-induced deformation and compensation methods for addressing the minimized deformation are proposed. Furthermore, RCS measurements are conducted on two kinds of DBD structures using the proposed method to experimentally demonstrate the improved agreement between the simulation and measurement results. For both structures, the RCS experimental results are in very good agreement with the simulation results, which enables accurate plasma characterization. In conclusion, it can be expected that the proposed method can be used to provide more accurate RCS measurements on various DBD structures that generate high heat. Due to the direct impact on the survivability of weapon systems, the significance of low-observable techniques has increased in the modern era of highly advanced military technologies. Since World War II, significant attention has been given to low-observable technologies in the RF/microwave frequency bands, specifically aimed at reducing the radar cross-section (RCS) of various targets ,5,6,7,8.The initial approach to RCS reduction involves the utilization of radar-absorbing materials (RAMs) or radar-absorbing structures (RASs), which absorb incoming radar signals and effectively decrease them ,11,12,13As another method for reducing the RCS, plasma-based technologies have recently attracted attention because of their ability to absorb and attenuate electromagnetic energy . CompareA previous study confirmed the stealth effect by applying an inductively coupled plasma generator to the S-shaped inlet of an aircraft and analyzing the reduction in the RCS through measurements . It was The dielectric barrier discharge (DBD) plasma generator, on the other hand, is relatively thin and generates less heat during plasma generation compared to inductively coupled plasma (ICP) ,19,20,21The aim of this paper is to analyze and address the effects of heat generated by DBD plasma generation systems. A comprehensive system was developed to evaluate the deformation of the DBD plasma generator structure due to the heat generated during plasma generation. Through measurements using a thermal imaging camera, the heat generated by the DBD plasma generator was quantitatively identified. Then, a high-resolution laser was used to measure the degree of heat-induced deformation. Through our study, specific solutions were proposed to mitigate the deformation, and in experiments, the heat was reduced by more than two times, and the deformation was minimized by 43%. Furthermore, a simple method for compensating for the error caused by this minimized deformation is presented.To demonstrate the validity of the proposed thermal compensation method, RCS measurement experiments were conducted on the structure presented in  and a neAs plasma is a lossy medium, the RCS can be reduced by attenuating the radar signal as it propagates through. However, the DBD plasma generator used in this paper is a typical generator, a parallel plate capacitor. Therefore, the top electrode that first encounters the radar signal must be configured with a frequency-selective surface (FSS) that allows a specific frequency band signal to pass through it, enabling electromagnetic waves to reach the plasma. The DBD plasma generator structure designed with this principle in mind is shown in X-band. The bottom electrode, with dimensions of 200 × 200 mmThe top electrode was constructed by arranging cross-dipole cells, designed to pass band in the ioned in , but alsX-band and a vector network analyzer (VNA) 37237D from Anritsu with a time-gating function to measure reflections from the generator were used.The experimental setup for measuring the RCS is shown in The RCS measurement process used in this study is shown in Equation . In EquaIn addition, an experimental setup was constructed to measure the thermal deformation of the DBD plasma generator. A camera with manual focus was aligned outside the vacuum chamber to analyze the thermal effects of the DBD plasma generator. Several photos were taken from the top and sides of the chamber to capture different perspectives.A schematic diagram of the measurement setup to analyze the thermal effects of the DBD plasma generator is shown in A schematic representation of the deformation of a DBD structure captured using a high-resolution camera when plasma is generated by applying voltage to the DBD generator system is shown in To address the issue of heat-induced deformation in the acrylic holder of the conventional DBD plasma generator, the use of an aluminum heat sink is proposed. As depicted in Utilizing the aluminum heat sink support, the heat of the DBD plasma generator was effectively dissipated to the surroundings, resulting in a temperature reduction to 52 °C during the measurements. This temperature is more than twice as low as in the previous setup. Additionally, the expansion of the upper electrode was measured using MicroEpsilon’s laser displacement sensor OptoNCDT 1420. The results showed that the expansion of the upper electrode due to heat was reduced from 2.8 ± 0.3 mm to 1.6 ± 0.3 mm.With the use of an aluminum heat sink, the deformation of the DBD plasma generator due to heat was dramatically reduced, but it still existed. To reduce the impact of this deformation and achieve more accurate plasma analysis, the following error compensation method was used: Using the previously measured inflation of the top electrode of 1.6 mm, the RCS of the proposed DBD structure with (The fabricated DBD generator is shown in X-band.A comparison between the simulation and measurement results of the RCS before and after the proposed error compensation method was implemented is presented in The results of the RCS reduction in the plasma on state compared to the plate in the proposed structure are displayed in Furthermore, to verify the proposed error compensation method, in addition to the DBD plasma generator structure in X-band.The simulated and measured results of the RCS and RCS reduction considering the implementation of the proposed error compensation method in the type 2 DBD structure are compared in By applying the heat effect compensation method described in Equation , it becoThe newest reported plasma RCS reduction techniques are presented in In this study, the FSS was designed assuming the absence of the plasma. However, it is anticipated that the effectiveness would be further increased if the FSS is designed with the presence of the plasma. In this case, the electromagnetic properties of the plasma must be characterized accurately. This aspect remains a topic for future research and improvement.To summarize this work, a method is presented to accurately determine the extent of RCS reduction due to plasma by minimizing thermal deformation and compensating for the effects of the deformation that nevertheless occurs. The validity of the method is verified by the good agreement between the compensated experimental and simulation results. Furthermore, the method can be utilized to design a generator to compensate for the effect of the deformation. However, its effectiveness is uncertain because the RCS of a distended structure is generally higher than that of a flat structure.The aim of this paper was to analyze and address the effects of heat generated by DBD plasma generation systems. A comprehensive system for evaluating the deformation of the DBD structure resulting from the heat generated during plasma generation was developed. A solution for mitigating this issue was proposed, and it was demonstrated, through experiments, that the heat can be reduced by more than two times. Furthermore, a straightforward method for compensating for errors caused by the minimized deformation was presented. This compensation technique enables a more accurate analysis of the RCS effect induced by plasma, thus improving the precision of the measurements. To validate the efficacy of the proposed approach, two types of DBD generator structures were fabricated, and RCS measurements were performed using these structures. Remarkably, the results showed excellent agreement when compared to the simulated data for both DBD structures. This strong correlation between the simulation and measurement results provides further evidence of the accuracy and reliability of the proposed method. In conclusion, the proposed method offers a promising and practical solution for obtaining more accurate RCS measurements in various DBD structures that generate significant amounts of heat. By addressing the heat-induced deformation and implementing compensation techniques, we can effectively improve the precision and reliability of RCS measurements. These advancements have the potential to greatly contribute to the field of DBD plasma systems and facilitate more accurate characterization and analysis in a wide range of applications.


This paper proposes a real-time semantics-driven infrared and visible image fusion framework (RSDFusion). A novel semantics-driven image fusion strategy is introduced in image fusion to maximize the retention of significant information of the source image in the fusion image. First, a semantically segmented image of the source image is obtained using a pre-trained semantic segmentation model. Second, masks of significant targets are obtained from the semantically segmented image, and these masks are used to separate the targets in the source and fusion images. Finally, the local semantic loss of the separation target is designed and combined with the overall structural similarity loss of the image to instruct the network to extract appropriate features to reconstruct the fusion image. Experimental results show that the RSDFusion proposed in this paper outperformed other comparative methods on both subjective and objective evaluation of public datasets and that the main target of the source image is better preserved in the fusion image. Due to technical limitations of hardware devices or optical imaging, images captured with a single type of sensor and recording device can only capture partial information and cannot effectively and comprehensively describe the scene being imaged . For exaInfrared and visible images have become a popular means for image fusion due to their powerful complementary information. In recent years, many infrared and visible image fusion algorithms have been proposed, which broadly fall into two categories: traditional algorithms and deep learning-based algorithms. Traditional image fusion algorithms mainly include methods based on multiscale transform ,8, methoCurrent image fusion algorithms based on deep learning can effectively integrate infrared and visible images, but there are still several issues that need to be addressed. First, existing algorithms tend to use global fusion to process source image information, using the same processing scheme for each local target in the source image, without considering the importance of each target in the source image under different tasks, resulting in mediocre results in the local fusion of fusion images. Currently, some studies have constrained the network to perform local fusion by introducing pixel saliency, but pixel saliency does not allow efficient detection of the target objects in an image to be achieved. Second, the loss functions of existing image fusion networks mainly focus on the overall structural loss of the image and global pixel loss. Structural loss mainly refers to the loss of detail texture between the source image and the fusion image, while global pixel loss mainly refers to the loss of pixel size between the source image and the fusion image. However, neither of these two loss types can fully reflect the loss of pixels on locally important targets of the image.To address the above issues, this paper introduces semantic segmentation into the image fusion task and proposes a semantics-driven framework for real-time infrared and visible image fusion. Specifically, we add the corresponding infrared and visible semantically segmented images to the original infrared and visible dataset to extract the target mask. After generating fusion images, the main semantic targets in the source and fusion images are extracted using the target mask, and the loss of the source and fusion images on the main semantic targets is calculated to constrain the fusion images to maximize the retention of local target features in the source images. In addition, this paper also designs semantic loss based on local target objects that can accurately reflect the pixel loss of the fusion image on local target objects.This paper proposes a real-time semantics-driven framework for infrared and visible image fusion that makes full use of semantic information to have the fusion images retain clearer semantic targets;This paper proposes local target content loss that guides the fusion network to locally fuse the same targets extracted from infrared and visible images, effectively improving the local target quality of the fusion images;Experimental results show that the proposed algorithm outperforms existing popular fusion algorithms in both subjective visualization and objective evaluation.The main contributions of this paper can be summarized as follows:The rest of the paper is structured as follows: This section reviews the available popular image fusion algorithms and provides a brief introduction to semantic segmentation.With deep learning techniques gaining ground in image processing, deep learning-based methods have been widely used in image fusion tasks. Among them, AE-based frameworks mainly pre-train a self-encoder on some large datasets to achieve feature extraction and image reconstruction and then integrate the features to achieve image fusion using some hand-designed fusion strategies. For example, Li et al. proposed the DenseFuse method , which hCNN-based frameworks are end-to-end image fusion frameworks that can effectively avoid the disadvantages of handcrafted fusion strategies. Liu et al. first proposed a CNN-based fusion method using 16 × 16 paired image blocks to train the network to build decision maps , which fGAN-based frameworks are also end-to-end image fusion frameworks that avoid hand-designed fusion strategies using adversarial learning between generators and discriminators. Ma et al. first proposed FusionGAN . This frIn general, both traditional and deep learning-based approaches emphasize the quality and metrics of the fusion image as a whole while ignoring the importance of the main target object. In practice, the information of interest is what needs to be observed, while the information of disinterest is what can be relatively ignored. Therefore, the targets in the image are segmented and assigned appropriate weights according to the needs of different real tasks. Semantic segmentation is a quality technique for dividing targets in each region of the image.Semantic segmentation is one of the fundamental tasks in computer vision today, and its main function is to let the computer segment an object based on the semantics in the image and determine what and where the object is at the pixel level. In recent years, semantic segmentation methods based on deep learning have attracted much attention due to their excellent performance and powerful generalization capabilities. First, Long et al. proposed a semantic segmentation method, FCN , which iThis section introduces the proposed semantics-driven infrared and visible light image fusion framework, RSDFusion.Image fusion is a technique for extracting and integrating important information from source images. The key to this technique is how to select significant information in the source images. In different scenarios, different target information has different importance. For example, in autonomous driving, information on people, vehicles, and roads is more important than other information. In other words, the targets in the source image need to be weighted differently according to different scenes. In this context, semantic segmentation techniques can help to select the important targets in the source image. Therefore, we designed a semantics-driven image fusion network using semantic segmentation, so that the fusion image can more effectively preserve the semantic information of important targets in the source image according to the needs.The general framework of our proposed RSDFusion is shown in Meanwhile, Then, the fusion image target . This function ensures that the fusion image has the same range of variation as the input image.We used the semantic segmentation network SegFormer to semantically segment the infrared and visible dataset RoadScene and manually correct the segmented images to finally obtain a new infrared and visible dataset, RRS (RoadScene-Seg), with semantically segmented images. The SegFormer network is a lightweight segmentation network based on a transformer and multilayer perceptron that has the advantages of few parameters, fast training, and being powerful. The structure of the SegFormer network is shown in A total of 1908 images are included in the RSS dataset. Each image set contains an infrared image, a visible image, an infrared semantically segmented image, and a visible semantically segmented image. The segmented source image contains a large number of semantic objects, but not all of them are necessary. Therefore, semantics-based image fusion is concerned with how to select the necessary semantic target objects. In this paper, we argue that the targets of the source image should be suitable to the work to be performed. For different usage scenarios, the appropriate key target objects should be selected. For example, the segmentation of people, cars, and roads is crucial in autonomous driving. Therefore, in this image fusion process, the semantic targets of people, cars, and roads are given higher priority and are the important semantic target objects to be retained in the final fusion image. In addition, the semantic segmentation images of infrared and visible images are very different, and some semantic objects only exist in infrared or visible images. Therefore, to avoid semantic dropouts of important targets from the source images, we use masks for people and cars in infrared images, masks for roads and plants in visible photos, and masks for the sky in infrared and visible images.To better preserve the source image’s overall structure and enhance the fusion image’s semantic information, we propose a new loss function. It is mainly divided into global structural loss  Datasets: We performed quantitative and qualitative experiments on our algorithms using the RoadScene and TNO datasets. The RoadScene dataset is an infrared and visible light dataset for autonomous driving containing 221 image pairs including people, vehicles, and roads. The TNO dataset is currently the most classic dataset for infrared and visible light image fusion tasks, and it consists of 60 image pairs from different military scenes taken with cameras at different wavelengths.(2) Evaluation metrics: Fusion image performance is mainly divided into two categories: subjective evaluation and objective evaluation. Subjective evaluation is generally based on the visual perception of the user. Usually, the more infrared salient targets and visible detailed textures contained in the fusion image there are, the better the user’s subjective evaluation is. Objective evaluations typically use suitable quantitative metrics to assess the performance of the fusion network. In this paper, the following six objective metrics were selected: entropy (EN) , standarL is the total number of gray levels and The mathematical formula of EN, which is a measurement of the amount of information in an image, is as follows:MI is a metric that quantifies how much information has passed from the source to the fusion image. It mainly computes the correlation between source and fusion images. The MI mathematical formula is as follows:SD is a metric of a fused image’s contrast and distribution. The SD mathematical formula is as follows:SSIM is a metric that measures the structural similarity between the fusion image and the source image, and its formula is shown in Equation ; its ranVIF is a metric that quantifies the amount of information shared by the fusion and source images using the human visual system and natural scene statistics. The VIF mathematical formula is as follows:SCD is a metric for assessing the information richness within a fusion image that measures the difference between the source and fusion images. The SCD mathematical formula is as follows:RSDFusion was compared with nine of today’s most popular methods: two traditional algorithms, namely, MDLatLRR  and GTF (3) Training setup: We used the RoadScene dataset to train our RSDFusion model. From it, 180 pairs of infrared and visible images were selected; then, a semantic segmentation network was used to segment these images. Each set of images after segmentation contained an infrared image, an infrared semantic image, a visible image, and a visible semantic image. A sliding window of 256 × 384 with a step size of 32 was used to crop the images to obtain more training images. After cropping, a total of 1908 image sets were obtained for training. In the test, 21 pairs of typical images were chosen from the TNO and RoadScene datasets for comparison experiments. In addition, the hyperparameters in network training were defined as follows: the training batch size was 16; the number of iterations was 10; the learning rate was 5 × 10We compared RSDFusion with nine other methods on the RoadScene dataset to evaluate our performance.(1) Qualitative comparison: To visually compare the performance difference between our method and other algorithms, we selected two pairs of typical source images from the RoadScene dataset for subjective evaluation, both of which contained semantic segmentation objects to be extracted and focused on in subsequent evaluations and which were captured in the daytime and nighttime, respectively. The results of the compared algorithms are shown in As shown in (2) Quantitative comparison: A total of 21 pairs of images from the RoadScene dataset were selected for quantitative comparison. The objective comparison results of each method on six metrics are shown in In summary, extensive experimental controls on the RoadScene dataset show that our algorithm has superior semantic target object extraction and detailed texture retention, and the generated fusion images have superior human visual effects. We attribute this to the following factors: First, we accurately capture the extracted targets using semantic loss, which improves the network’s control over local images. Second, we constrain the fusion image to retain more detailed texture information of the source image while ensuring the extraction of the focal target using the global structural loss function.To evaluate the generalization performance of the model, further comparisons were performed on the TNO dataset.(1) Qualitative comparison: Two pairs of typical source images were selected from the TNO dataset for subjective evaluation; they were taken in clear and foggy scenes at night, respectively. Their comparison results are shown in (2) Quantitative comparison: A total of 21 pairs of images from the TNO dataset were selected for quantitative comparison. The objective evaluation results are shown in In image fusion tasks, operational efficiency is a significant factor in evaluating the performance of image fusion models. In this paper, the designed fusion model, RSDFusion, is a lightweight real-time network that must have efficient operational efficiency to support real-time image fusion. The efficiency of RSDFusion and nine other methods was tested on the RoadScene and TNO datasets in the same hardware environment. As shown in In our model, semantic loss and structural loss are significant components of the training network. To verify the soundness of the designed semantic and structural loss functions, we trained the fusion model without semantic and structural loss, respectively. As shown in In In this paper, we propose a new real-time semantics-driven infrared and visible image fusion framework, RSDFusion. The framework introduces semantic segmentation into image fusion, divides the source image into several semantic objects, designs an adapted loss function for its important semantic target objects, and combines it with global structural loss to drive network training. As a result, the fusion images we generate not only preserve the important targets of the source images but also retain a large amount of the texture detail information of the source images. The method also has the limitations that the generality of the task needs to be improved and the fusion network needs to be retrained when the target of interest for the task changes. In extensive qualitative and quantitative experiments on the RoadScene and TNO datasets, our fusion method RSDFusion outperformed other popular methods in both subjective visualization and objective metric measures. Furthermore, the efficient operation of our algorithm supports the real-time fusion of source images.


This article delves into the complex world of quantum games in multi-agent settings, proposing a model wherein agents utilize gradient-based strategies to optimize local rewards. A learning model is introduced to focus on the learning efficacy of agents in various games and the impact of quantum circuit noise on the performance of the algorithm. The research uncovers a non-trivial relationship between quantum circuit noise and algorithm performance. While generally an increase in quantum noise leads to performance decline, we show that low noise can unexpectedly enhance performance in games with large numbers of agents under some specific circumstances. This insight not only bears theoretical interest, but also might have practical implications given the inherent limitations of contemporary noisy intermediate-scale quantum (NISQ) computers. The results presented in this paper offer new perspectives on quantum games and enrich our understanding of the interplay between multi-agent learning and quantum computation. Both challenges and opportunities are highlighted, suggesting promising directions for future research in the intersection of quantum computing, game theory and reinforcement learning. As the vanguard of computational theory and machine learning increasingly intersects with the intricacies of quantum mechanics, a profound shift in our understanding of complex systems is emerging. At this crossroads lies the study of quantum games and multi-agent learning, a field poised to revolutionize myriad sectors through the marriage of quantum computing, game theory and reinforcement learning. This research elucidates this profound transformation by delving into the intricate dynamics of quantum games involving multiple learning agents.Game theory, a cornerstone of modern economics and numerous branches of social sciences, provides an insightful mathematical framework for analyzing strategic scenarios and deciphering decision-making complexities in interactive situations. The framework of game theory, first articulated by von Neumann and Morgenstern , has becIt is critical to emphasize that Nash proved  that alltum game , despite in PPAD . This meMeanwhile, Multi-Agent Reinforcement Learning (MARL) is a field concerned with how autonomous agents can learn and adapt to their environment to optimize their collective behavior. MARL, paired with game theory, has driven advances in autonomous driving , drone fRecent articles have ventured into Quantum Machine Learning  and QuanThe present discussion deviates from this more common route, probing the behavior of classical agents within quantum games. There are precedents that examine classical multi-agent learning within quantum games. Ref.  presentsN players. The learning agents display proficient adaptation and, notably, are able to easily and methodically highlight when the performance of agents playing in quantum games surpasses that in their classical counterparts.The core of our study introduces an algorithm in which agents selfishly and independently utilize the Adam algorithm  to adjusIn addition to exploring the learning dynamics in quantum games, this work delves into the effects of quantum noise on algorithm performance. Contrary to the common belief that quantum noise is often regarded as a performance impediment , our anaThe findings of this work enhance our understanding of quantum game dynamics and open avenues for practical applications. The successful performance of the quantum game learning algorithm and its uncovering of the counterintuitive potential of quantum noise to enhance system performance provide meaningful contributions to quantum computing, strategic decision-making and machine learning. By enabling more accurate modeling of complex interactions and improved optimization strategies, this research brings us a step closer to realizing the vast potential of quantum computing in real-world scenarios.The remainder of this paper is organized as follows. The goal of game theory is to analyze decision-making systems involving two or more players cooperating or not with each other. An important feature in games is that the reward one player receives depends not only on the strategy she chooses, but also on other player strategies. It is well known that a game is defined by three elements: players, strategies and rewards. This work is based on N players’ games with two pure strategies each. The rewards are defined by a The following N-player two-strategy games are considered throughout the article: the minority problem , which rEWL [first step involves assigning a quantum state to each of the possible strategies. In the case of two strategies, for example, in the prisoners’ dilemma, cooperate →defect→second step is to create a quantum circuit where each player is assigned a qubit that starts in state third step is to create an entangled state between all players. This is done by applying the entangling operator N represents the number of players and To model quantum games, we followed the EWL  protocolEWL . The firfourth step, every player chooses her most suitable strategy individually and independently. This is done by modifying the state of her own qubit locally. To do this, every player applies one or more one-qubit gates, modifying the state of her qubit. A general one-qubit gate [In the bit gate  is a unififth step is to apply the operator J conjugate transpose) after the strategies of the players. Finally, the sixth step consists of measuring the state of the qubits to read the classical outputs of the circuit and, therefore, the final action of each player. The readouts are used as inputs of the pay-off matrix to determine the players’ rewards.We can already highlight the fact that while classic players are limited to only two pure strategies , quantum players can choose among an infinite number of pure strategies, that is, any combination of real value for the three parameters One last thing to add is the fact that we are going to replace the three-parameter general one-qubit gate γ)Rn^(δ) . Having In this section, the algorithm employed by the agents for learning optimal actions in the quantum games previously presented is elucidated. The algorithm operates on a decentralized basis, with each player solely attempting to maximize their own long-term reward. Although players do not share any information with their opponents about the game they are engaged in or the rewards they receive, they have to share their actions with other participants.Before delving into the algorithmic description, it is important to define a few crucial concepts. First, each player’s strategy is represented as the matrix product of three parameter-dependent (actions) quantum rotation gates: The output quantum state of the quantum circuit is then computed as N columns. Each row corresponds to a specific combination of actions chosen by the players, while each column represents the reward received by each player. This matrix representation serves as a succinct summary of all rewards assigned in the game for every possible combination of actions. As an illustrative example, a generic two-player game with two actions can be represented by the following matrix, where the pairs All N-player games with two actions, as mentioned above, can be represented by a matrix of Using the previously defined concepts, a reward vector can be computed. This is achieved by multiplying the output state probability vector by the game matrix. The mathematical representation of this operation is given by It is now possible to outline the model-free policy-based algorithm employed by the players to update their strategies based on the rewards received in each iteration. Here, the term policy refers to a function used by the agent to select actions given the current reward received . Every pUpon calculation of the gradients, the players’ actions could be directly adjusted in the direction of increasing rewards using stochastic gradient ascent (SGD) straightforwardly as in , where αm is an estimate of the first moment (the mean) of the gradients and v is an estimate of the second moment (the uncentered variance) of the gradients. For further information on the Adam algorithm, please refer to the bibliography [However, the Adam algorithm surpasses vanilla SGD and RMSProp by offering adaptive learning rates that adjust for each action individually to navigate the reward function, employing moving averages (as RMSProp), but also incorporates the calculation of moment estimation. Although it needs additional computations, which are defined next:iography .The gradient calculation method and the Adam algorithm both involve various hyperparameters. These values have been meticulously studied, resulting in the identification of an optimal set of values. These optimal values are adopted for the remainder of the article, ensuring consistency and optimal results in subsequent analyses and discussions.Finally, a block diagram depicting the comprehensive system that includes both the learning agents and the quantum game environment can be found in J. This scenario corresponds to the classical version of the game.Games without entanglement, achieved with a value of J, which corresponds to the fully quantum version of the game.Games with entanglement, achieved with a value of This section presents the results obtained from applying the previously described algorithm to the general-sum games mentioned earlier. Graphical representations of the rewards for each player over 1,000,000 iterations are provided for two scenarios:The outcomes are presented in this way to verify the algorithm’s proper functionality—the classical scenario should behave as expected—and to compare the dynamics and performance of the players between the classical and quantum cases. This comparison can reveal any noteworthy shifts in behavior or strategy that emerge in the transition from a classical to a quantum framework.This analysis begins with the minority game. The results are indeed compelling, although a more specific description of the minority game is beneficial for our analysis. As framed in our environment, the minority game for N players provides each player with two options (0 and 1), while a reward of  players .However, for the four-player game, the classical case converges to a reward of Let us now turn our attention to The results of this game are certainly more striking. All players in the classical game converge  to their Nash equilibrium, resulting in a total reward of N) quickly converge to the value of As seen in Finally, we focus on the volunteer game, as depicted in In this final scenario, we observe that the behaviors of both the classical and quantum cases are nearly identical for every N. Rewards consistently converge to the expected value of the classical Nash equilibrium All the computer code required to reproduce the results reported in this section can be found in the Github repository shared in the “This chapter examines how the inherent noise in quantum computers might affect the performance of the learning algorithm. This analysis is a first step towards understanding the practical implications of deploying such algorithms in real-world quantum computational environments, where noise is an unavoidable factor.J, all players could apply their gates to their qubits in an ideal channel, without any type of noise affecting the vulnerability of the quantum system. Given the difficulty of meeting such a condition, it becomes necessary to undertake a modeling approach to take into account this situation accurately.In The noise model chosen for our study is the depolarizing channel, primarily because it effectively models both bit-flip and phase-flip errors. While the depolarizing channel captures the essential features of numerous real-world noise processes, it is a simplified model. In order to represent all the nuances of noise in some specific quantum devices, further fine-tuning is needed. The state of the quantum system of one qubit after this noise is del this  is by adThis means that the quantum state in each channel will remain intact with probability For this analysis, our focus will be on the Platonia game in its quantum variant  achieves a higher reward than a rational player (zero noise). However, such behavior would not be particularly noteworthy. For example, this is observed in the classic prisoners’ dilemma , where rA plausible explanation for this phenomenon might stem from the fact that when the number of players increases, the reward function becomes more complex and, if the algorithm operates in a noiseless setup, it can become trapped in local maxima. However, a minuscule amount of noise in the quantum circuits might suffice to remove the system from these local maxima, allowing exploration of other actions with higher rewards. Importantly, the noise level needs to be low enough to not harm the overall learning process and therefore the performance of the system.This research has probed the intricate dynamics of quantum games involving multiple agents, shedding light on the complex relationships between quantum noise, system performance and learning efficacy. It has been shown that gradient-based strategies in a multi-agent setting present intriguing characteristics when quantum mechanics are taken into account.The proposed quantum learning algorithm has shown robust performance across various game configurations. Furthermore, the investigation of quantum noise effects uncovered a unique behavior that might have far-reaching implications. As expected, increased noise caused player performance in games involving fewer participants to deteriorate. However, strikingly, a slight amount of noise appeared advantageous in games with six and seven players, seemingly facilitating escape from local maxima and enabling exploration of more rewarding actions. This discovery stimulates discussion on the potential leverage of system noise, a common challenge in contemporary quantum computers, to enhance performance.The insights from this study hold substantial implications for the real-world application of quantum computing. For example, in economic and financial models where strategic interaction and learning play critical roles, the exploitation of quantum effects could lead to more realistic and accurate predictions. In machine learning and artificial intelligence, quantum enhancements might allow for more efficient solution exploration and optimization. Finally, in communication networks, the principles of quantum games could enable the development of more robust quantum-enhanced communication systems.The exploration of quantum games with multi-agent learning in this research has only scratched the surface. There is a wealth of complex dynamics and fascinating phenomena to be explored in this burgeoning field. Further research is anticipated to elucidate these exciting frontiers, advancing our understanding of quantum games and their potential applications.