Defining the start and assessing the intensity of influenza seasons are essential to ensure timely preventive and control measures and to contribute to the pandemic preparedness. The present study aimed to determine the epidemic and intensity thresholds of influenza season in Tunisia using the moving epidemic method.We applied the moving epidemic method (MEM) using the R Language implementation (package “mem”). We have calculated the epidemic and the different intensity thresholds from historical data of the past nine influenza seasons (2009‐2010 to 2017‐2018) and assessed the impact of the 2009‐2010 pandemic year. Data used were the weekly influenza‐like illness (ILI) proportions compared with all outpatient acute consultations. The goodness of the model was assessed using a cross validation procedure.The average duration of influenza epidemic during a typical season was 20 weeks and ranged from 11 weeks (2009‐2010 season) to 23 weeks (2015‐2016 season). The epidemic threshold with the exclusion of the pandemic season was 6.25%. It had a very high sensitivity of 85% and a high specificity of 69%. The different levels of intensity were established as follows: low, if ILI proportion is below 9.74%, medium below 12.05%; high below 13.27%; and very high above this last rate.This is the first mathematically based study of seasonal threshold of influenza in Tunisia. As in other studies in different countries, the model has shown both good specificity and sensitivity, which allows timely and accurate detection of the start of influenza seasons. The findings will contribute to the development of more efficient measures for influenza prevention and control. Although in most cases, it leads to an increased number of consultations, it may cause severe illness and death especially among high‐risk groups. In fact, the World Health Organization (WHO) has recently updated global estimates to more than 3 million severe cases and from 290 000 to 650 000 respiratory deaths due to influenza each year.Ongoing monitoring and assessment of seasonal influenza are therefore essential to ensure early warning of epidemics and tailored preventive and control measures in real‐time. The last 2009 pandemic revealed many deficiencies in most countries' influenza surveillance systems, especially the capacity to estimate the severity of the season in a timely manner. For this reason, the WHO has progressively developed a framework on pandemic influenza severity assessment (PISA) and recommended member states to apply the proposed tools and measures.Various mathematical and statistical models have been developed to establish thresholds for influenza activity and study the dynamics of the disease.In Tunisia, influenza surveillance was first based on the virological surveillance ensured by the National Influenza Centre (NIC) recognized by the WHO since 1980 and supported by the Primary Health Care Direction of Ministry of Health. Starting from the late 1990s, the epidemiological surveillance was established through the network of Influenza‐like illness (ILI) sentinel sites at primary healthcare centers in the 24 governorates of the country. This network was progressively improved mainly by reducing the number from 268 in 1999 to 113 ILI sites in 2014, better representativeness and training of all staff involved in the surveillance.Given the importance of seasonality and intensity levels in influenza severity assessment, our study aimed to determine the epidemic and intensity thresholds of influenza season in Tunisia by applying the moving epidemic method (MEM) based on ILI historical surveillance data of the last 9 years (2009‐2018).22.1Influenza surveillance in Tunisia is carried out each year from 1st October (week 40) to 30th April (week 18) over a period of 30 weeks. Data collection is based on standardized forms of weekly aggregated data of ILI cases. These paper forms are sent from ILI sites at the local level to the regional directions in each governorate then to the Primary Health Care Direction at the national level. Aggregated data forms consist of general information including ILI site, governorate, the number of ILI cases and the total number of outpatients by gender and age groups . In Tunisia, case definition of ILI was an outpatient with fever (≥38°C) and cough or sore throat with onset less than 5 days prior to presentation in the absence of a specific diagnosis.2.2–Baseline: below the epidemic threshold–Low level: between the epidemic and the medium thresholds–Medium level: between the medium and the high thresholds–High level: between the high and the very high thresholds–Very high level: above the very high thresholdWe applied the moving epidemic method (MEM) to establish epidemic and intensity thresholds, based on previous publications and the WHO's interim guidance for influenza severity assessment.For the present work, we have described the epidemic and intensity thresholds from historical data of a period beginning in October 2009 and ending in April 2018.The epidemic period is defined as the period of weeks with increased weekly values in a season. The periods of weeks before and after the epidemic period represent the pre‐epidemic and post‐epidemic periods, respectively. The epidemic threshold is the value which defines the start of the epidemic period while intensity thresholds represent the values marking the limit of the intensity levels. Besides, epidemic percentage is the sum of values in the epidemic period over the total sum of values of the whole influenza season, which reflects the coverage percentage of the epidemic period.2.3The goodness of the model was assessed using a cross validation procedure. This procedure is based on the extraction of each season from the historical series and using it as "a target season", for which we calculate the beginning and end of the epidemic period. Subsequently, the pre‐ and post‐epidemic thresholds are calculated on the basis of the remaining seasons and excluding the target season. These steps were repeated for all the available seasons.Values of the target season inside and outside of the defined epidemic period were compared to the thresholds calculated using all historical information but the target season.Aiming to evaluate the performance of the epidemic threshold to detect epidemics, we studied the sensitivity (Se), specificity (Sp), the positive predictive value (PPV) and the negative predictive value (NPV). The sensitivity consists of the model's ability to categorize epidemic weeks while the specificity is the model's ability to categorize non‐epidemic weeks. According to the cross‐validation analysis described by Vega et al,The number of weeks of the epidemic above the threshold, divided by the number of weeks above the threshold is the positive predictive value, expressing the proportion of epidemic weeks correctly classified by the model. On the other hand, the negative predictive value consists of the number of non‐epidemic weeks below the threshold, divided by the number of weeks below the threshold, corresponding to the proportion of non‐epidemic weeks correctly classified by the model.To optimize the goodness of the model, we also looked at the optimum slope parameter to find the value that maximizes the sensitivity and specificity. It is an inner parameter ranging from 2% to 4%.33.1The beginning, the end and the extent of the epidemic seasons differed from one season to another Table .In most cases, influenza epidemic started around week 46. The first activity of influenza epidemic was observed in 2017‐2018 at week 43 and the latest one in 2012‐2013 at week 49.Influenza epidemic during the pandemic year 2009‐2010 started at week 48 and ended at week 6. The duration of epidemic seasons ranged from 11 weeks (2009‐2010 season) to 23 weeks (2015‐2016 season). Epidemic percentage varied between 74.77% and 77.52%, corresponding to a high coverage of the epidemic period.Almost all the seasons were one‐wave seasons except 2010‐2011 and 2014‐2015 with more than one wave. Most often, epidemic peaks were observed between week 3 and week 9. However, some seasons peaked earlier (week 51 in 2009‐2010 and 2017‐2018) and other seasons peaked later (week 12 in 2015‐16). The epidemic seasons seem to follow a pattern with a rapid increase at the beginning and a slowly decrease at the end of the season.Besides, Figure Except the pandemic season which was considerate of a very high intensity, most of the seasons were described as low. This was useful to characterize the dynamics of influenza over time.3.2The different thresholds and intensity levels were determined using two models; one including and the other excluding the 2009‐10 pandemic season.When including the pandemic season, the average duration of influenza epidemic during a typical season was 20 weeks Figures  and 3A. As a result of this analysis, epidemic threshold was 8.99% if we include the pandemic season and post‐epidemic threshold was 8.25%.Medium, high and very high intensity thresholds were 10.48%, 17.69%, and 22.3%, respectively .This choice was largely motivated by the type of data available by the Tunisian influenza surveillance system. In fact, the basic requirements of the MEM are simple and reliable epidemiological data for a time period between 5 and 10 years, preferably ILI data.The MEM is a tool developed to better understand annual influenza epidemics and assess the epidemic status and intensity on a weekly basis.The determined epidemic threshold with the exclusion of the pandemic season was 6.25%. It showed a very high sensitivity (85%) and a high specificity (69%). However, when including 2009‐10, the threshold increased to 8.99% with a sensitivity and specificity of 39% and 87%, respectively. The different levels of intensity were also affected with a considerable increase. This is understandable since there was higher ILI rates registered during this year and thus higher pre‐ and post‐epidemic thresholds comparing to the other seasons. This confirms the importance of the pandemic year in the analysis and the need to exclude it when estimating the parameters and indicators of epidemic threshold.The majority of authors used ILI consultations in primary healthcare settings expressed per 100 000 population Applying the MEM to define the thresholds also allowed us to visualize influenza activity in Tunisia for the past nine seasons. Overall, influenza seasons seem to be mostly one‐wave, homogenous with a seasonal pattern. Epidemics usually start between the 43rd and the 49th week and last from 11 to 23 weeks. The duration of epidemic seasons was comparable to the range reported in other studies (6‐25 weeks)The specificity of the determined epidemic threshold was lower than its sensitivity. Sensitivity is important for detecting epidemics but specificity is crucial to avoid false alerts. In fact, once an epidemic is declared, the media's interest increases and prevention and control measures are implemented, especially vaccination campaigns and antiviral use.Besides, it is important to underline that the specificity of the model is related to the case definition used. The lower is the specificity of the case definition, the lower is the specificity of the model. Although a new case definition was used since 2014, the changes enhanced sensitivity without greatly compromising the specificity.This low specificity may represent the main limitation of our study. Most authors concluded to models with very high specificity. In Vega's study about establishing thresholds in 19 European countries, the lowest specificity was 89.6% in Kazakhstan with an overall specificity of 95.5%.The circulation of a new influenza virus, as it was observed during the 2009‐2010 pandemic in many countries, may also generate abnormal epidemiological data and falsely positive results.In these situations, additional virological data are necessary to confirm the start of the epidemic period, especially that our results showed a better performance of the MEM model excluding the pandemic season than the one including this season.5In summary, the moving epidemic method is a simple method offering a flexible procedure to calculate epidemic thresholds based on historical epidemiological data. Its strength lies in its ability to also determine different intensity thresholds useful to the weekly monitoring of the season's intensity.Our study is the first mathematically based study of seasonal threshold of influenza in Tunisia using historical ILI weekly data. The determined epidemic threshold was 6.25%, differing from the threshold of 10% adopted until now. The high sensitivity and specificity of this threshold in the detection of epidemics make it robust and reliable.Indicating the start and assessing the intensity of influenza seasons remain a high priority for Ministries of Health, not only at the national level for timely preventive and control measures but also at the international level by contributing to the pandemic preparedness. The next step is therefore to implement the use of the determined epidemic threshold for public health purposes with monitoring the next seasons.


Using nine geometric parameter combinations, three-dimensional finite element simulations were performed, and these were used to evaluate the deformed features and the geometric compatibilities on the spur gear structure and the internal spline feature. Based on these comparative evaluations using the numerically simulated results, it is shown that the dimensional requirements of the target shape can be satisfied with the shoulder angle combination of  for .Parametric investigations related to shoulder angle on tool geometry for a combined cold extrusion of a drive shaft, which consisted of spur gear and internal spline structures, were conducted through three-dimensional FE (finite element) simulations. The drive shaft was required to be about 92.00 mm for the face width of the top land on the spur gear part and roughly 22.70 mm for the groove depth of the internal spline section. AISI 1035 carbon steel material with a diameter of 50.00 mm and a length of 121.00 mm was spheroidized and annealed, then used as the initial billet material. A preform as an intermediate workpiece was adopted to avoid the excessive accumulation of plastic deformation during the combined cold extrusion. Accordingly, the cold forging process involves two extrusion operations such as a forward extrusion and a combined extrusion for the preform and the drive shaft. As the main geometric parameters influencing the dimensional quality and the deformed configuration of the final product, the two shoulder angles of  In various cold working processes, a combined forging is classified as an operation that merges similar or different sequences together into a single procedure ,2,3; it This study introduces a drive shaft, which is a torque-carrying metal component used in industrial hydraulic pumps. This mechanical-structural metal member has a three-dimensional complicated aspect wherein two different functional structures such as a spur gear and an internal spline are merged into the shaft ,14. Specθ1 on the extrusion die for the preform forging and θ2 on that for the combined extrusion were selected as the main geometric variables. That is, the practically applicable variables for θ1 were assumed to be 30°, 45°, and 60°, and those for θ2were also supposed to be 30°, 45°, and 60°. With nine allowable combinations related to the geometric variables on the extrusion dies, FEM-based numerical simulations were performed to investigate the effects of the geometric variables on the product quality and the dimensional compliance. A series of geometric comparisons between the designed drive shaft and the simulated results were conducted with respect to the deep groove within the internal spline having the irregular hexadecagonal cross-section, as well as the sixteen tooth profiles of the spur gear. The results indicated that the drive shaft was well deformed and that the required specifications were closely satisfied when the parameter combination set of  was . As a brief summary, the results showed that preform forging and combined extrusion could successfully be adopted to visualize the drive shaft used for the hydraulic pumps with a good consistency regarding the dimensional requirements.Based on the prior results related to the systematic approach to the process design for a drive shaft , in ordeOne of the most important factors in manufacturing a drive shaft with a high degree of geometric difficulty is the reasonable selection of a suitable forging process. Based on the required product characteristics, a thermal working condition for hot and warm and cold forging processes must be selected and the specific forging process for realizing the target shape also has to be defined and applied. In view of these, the spur gears are typified to include many teeth and profiles on a circumferential surface of a round shaft; the internal splines are identified with several grooves or ridges in the shaft ,18.In terms of mechanical robustness and structural integrity, the metal forged components must satisfy certain crucial characteristics such as smooth tooth faces to secure a sufficient contact surface, a precise gear profile to transmit fitly the external torque into the mechanical member, a high wear resistance against friction and contact behaviors, a high mechanical-structural strength to endure the torsional load induced by the external torque, and a high reliability and durability . From thThe combined cold extrusion process investigated in this study consisted of the forward and the backward extrusions. Here, the forward extrusion was defined as the actualization of the sixteen-tooth spur gear, while the backward one was defined as realizing the internal spline with the irregular hexadecagonal cross-sectional configuration. However, when the cylindrical-type initial billet is directly deformed to the final target configuration as illustrated in θ1) must be induced from the preform. In consideration of these facts, the preform was designed as shown in Before designing the preform for obtaining the drive shaft, the geometric feature of the lower shaft shown in As a mechanical-structural member of the industrial hydraulic pump, the drive shaft must provide a high mechanical and structural strength against torque, high corrosion, and wear resistances, as well as high endurance characteristics, without any inherent defects such as voids or micro-cracks. In various mechanical-structural ferrous metals, the appropriate selection of a workpiece material is necessary to satisfy the requirements listed above. According to these considerations, an AISI 1035 (JIS-G4051-S35C) cold-drawn round bar with a diameter of 50.0 mm, which is widely applied to the shaft, gear, bolt, sprocket, and so forth, was chosen as the raw billet material.Because the forward extrusion and combined extrusion are successively conducted and the whole extrusion length is quite long, the workpiece material must ensure forgeability and ductility. In many applications of the cold forging process, the raw billet materials have been annealed to soften the yield and the ultimate strengths as well as improve the elongation characteristics ,10,19,20The tensile properties of AISI 1035 carbon steel, which were obtained from the uni-axial tensile tests, are summarized in θ1 and θ2. Because the drive shaft has disparate structures such as the spur gear and the internal spline, combined with the fact that the forged product quality is more influenced than that derived from other process parameters, the shoulder angles were selected as the actually applicable parameters for simultaneously obtaining the forward extruded tooth profile and the backward extruded deep groove by combined extrusion. The cold forging process is generally regarded to be a quasi-static process, so the influence of the strain rate is typically ignored. In view of this, a series of elastic recovery behaviors of the workpiece material during cold forging can already be neglected, so the dimensional variation can be disregarded as well. This means that the geometries of the drive shaft eventually match the tool configurations.As shown in θ1) in θ2) in Therefore, the geometric variables of the drive shaft can be identically regarded as the shoulder angles on the extrusion dies (middle), as illustrated in As the cold forging process consisted of the forward and combined extrusion, these separated forging operations were necessarily both individual FEM-based numerical simulation models. In other words, two FE models must be constructed, such as one related to the forward forging process for the preform and the other related to the combined extrusion operation for the drive shaft. The numerical simulation model for the forward extrusion to realize the preform is displayed in TM, and as these procedures were defined as quasi-static processes, the effect of the strain rate was neglected. In order to investigate and verify the effects of the geometric parameters on the product quality and the dimensional compliance, the practically applicable variables for θ1 were assumed to be 30°, 45°, and 60°, and those for θ2 were also supposed to be 30°, 45°, and 60°. Because there was obviously expected to be an interrelationship between these process variables, a series of parametric investigations with various combinations of the shoulder angles were pursued, and the results are summarized in The friction characteristics on the contact interfaces between the forging tool components and the workpiece were considered as the shear friction behavior with a coefficient of 0.098, and the workpiece was coated with a phosphophyllite coating ,19,21. Tθ1) on the middle die for the preform extrusion was selected among the three values of 30°, 45°, and 60°, three forward extrusion simulations using the initial billet in The numerical simulation of the forward extrusion was first carried out in order to obtain the preform using the spheroidizing annealed AISI 1035 workpiece with a diameter of 50.00 mm and a height of 121.00 mm. The forward extrusion process was defined as having the one-sixteenth cyclic planar symmetric condition, as illustrated in Regarding the results obtained from the forward extrusion simulation with a shoulder angle of 30°, θ1) of the preform being given as 30°, 45°, or 60°. It was then revealed that the cold forging load decreases as the shoulder angle is increased. It suggests that the large shoulder angle leads to a decrease in the cold forging load due to the smoother metal flows around the extrusion die during the forward extrusion operation.The simulated results with regards to the forward extrusion for obtaining the preform show that the effective stresses and strains were typically distributed, and that the maximum values were similarly positioned around the extruded neck region of the deformed preforms. In particular, it was observed that the effective stress and strain levels, as well as the maximum values, tended to decrease with an increasing shoulder angle. These facts can be clarified by predicting the forward extrusion loads. θ1 and θ2were predefined as depicted in θ1) was considered to be derived from the preform extrusion. Accordingly, the combined extrusion simulations were performed using the three preforms with different shoulder angles (θ1). As summarized in θ2) on the extrusion die (middle) for shaping the irregular hexadecagonal deep groove and the sixteen-tooth profile of the drive shaft was also chosen among 30°, 45°, and 60°, nine FE models with the same boundary conditions were built up in total.This combined extrusion operation for the drive shaft was regarded to be responsible for forming the irregular hexadecagonal deep groove with a depth of about 22.7 mm at the upper head part; it was simultaneously responsible for shaping the sixteen-tooth spur gear structure with an extruded length of roughly 92.0 mm. In order to validate the effects of the geometric parameters on the product quality of the drive shaft, both the shoulder angles of θ1, θ2), such as , , and , are presented in θ1 and θ2 for the preform and the drive shaft were identified with those on the middle dies for the preform operation and the combined extrusion, respectively. In order to determine whether or not the combined extrusion simulation was completed successfully, the reference length was selected as the backward extruded height of about 28.50 mm on the upper head region because it was hard to gauge the deep groove depth of roughly 22.70 mm.As mentioned previously, the inherent residual stress resulting from the forward extrusion operation in The effective stress and strain distributions for case 1 with the shoulder angle combination of  are depicted in θ2), and this was consistent with the results of the forward forging for the preform. These characteristics can also be explained by the forging load properties of the combined extrusion, as shown in On the whole, aside from that area, the effective stress and strain were felicitously distributed around the internal spline and the spur gear sections. Moreover, it should be noted in the numerical simulation results that the stress and strain tended to decrease with an increasing shoulder angle (θ1 and θ2 on the extrusion (middle) dies were conducted as shown in In this study, the parametric investigations with regards to both shoulder angles of θ2) on the combined extrusion die was also found to be 30°—that is, with the variable sets of , , and —the sinking depths on the deep grooved upper head part  were predicted to be roughly 3.23 mm, as large and undesirable values. Regarding the end shape of the extruded tooth, in the cases with the shoulder angle combinations of , , and , the tooth height as the sum of the addendum and the dedendum on the spur gear was unevenly distributed with an error of over 1.00 mm .All of the numerical simulations were conducted with the nine combination sets summarized in θ2 = 30°) tended to induce the severe sinking phenomenon, and the large one (θ2 = 60°) tended to cause the insufficient face width of the tooth. Further, it is shown that the short face width of the tooth is derived from the large angle (θ1 = 60°). Ultimately, the most applicable geometric parameters for actualizing the required configuration and dimension provided in θ1 = 45°, θ2 =45°).The results derived from these compatibility evaluations were summarized in θ2) of the extrusion die was 45° with the preform obtained by the angle (θ1) of 45°. As shown in O-O’ on the bottom land of the spur gear geometry was used to trace the deformation history during the combined extrusion. When the preform was compressed by the deep grooving punch, deformed shapes per increments of every 10% based on a total punch stroke of about 88.28 mm—as predicted in Because the main geometries of the drive shaft were actually realized by the combined extrusion consisting of the forward extrusion and backward extrusion, the deformation history was traced in terms of the operation sequence of both extrusions in the progress of the cold forging process, when the shoulder angle . Then, the proposed combined extrusion for shaping the drive shaft was also verified to be compatible. In order to investigate the dimensional relevance, the numerically simulated features of the preform and drive shaft were used for comparison with the designed features. That is, the preform obtained from the forward forging simulation shown in Regarding the results obtained from the numerical investigations on the preform forging and the combined extrusion for realizing the drive shaft, the applicable geometric parameters that can satisfy the required configuration and dimensions, as shown in Further, In this study, a drive shaft that two distinctive features such as the spur gear feature and the internal spline geometry were merged into a single shaft was introduced. In particular, the internal spline structure had the details of an irregular hexadecagonal groove with a depth of about 22.70 mm, while the spur gear geometry had a sixteen-tooth profile with a face width of nearly 92.00 mm. In order to realize the drive shaft by the cold forging operations, and by using the spheroidized and annealed AISI 1035 workpiece and three-dimensional one-sixteen (1/16) FE models with a cyclic planar symmetric condition, a series of parametric investigations related to the shoulder angle of each extrusion die were performed through a series of FEM-based numerical simulations. Based on the results extracted from the FEM-based numerical simulations, compliance evaluations of the dimensional specifications required for the drive shaft were carried out. The results obtained from these parametric investigations can be summarized as follows:(1) Due to the geometric distinctiveness of the drive shaft in which the internal spline structure and the spur gear feature were positioned almost adjacent to each other, the combined extrusion process was used for the concurrent cold forging. Furthermore, to avoid excessive plastic deformation of the drive shaft, the preform was applied as an intermediate billet.θ1 = 45° and θ2 = 45° with respect to the shoulder angles was chosen.(2) Through a series of evaluations of the effects of the shoulder angles on the product qualities of the numerically visualized preform and realized drive shaft, it was ensured that the preform and the drive shaft can be felicitously fabricated when the geometric combination of (3) In terms of the dimensional relevance, it was verified that the numerical simulation results have a meaningful dimensional accuracy in the range of acceptable variations in view of the main shapes and the required dimensions.(4) It was conclusively confirmed that the drive shaft, which was composed of the spur gear and the internal spline, can be adequately produced through the combined cold extrusion process proposed in this study.(5) Based on the results visualized with the numerical simulations and parametric investigations, a series of experimental verifications including the design and fabrication of the cold forging tool components for the prototyping and mass production of the drive shaft will be introduced in the near future.


Unmanned Aerial Vehicles (UAVs) have been widely applied for pesticide spraying as they have high efficiency and operational flexibility. However, the pesticide droplet drift caused by wind may decrease the pesticide spraying efficiency and pollute the environment. A precision spraying system based on an airborne meteorological monitoring platform on manned agricultural aircrafts is not adaptable for. So far, there is no better solution for controlling droplet drift outside the target area caused by wind, especially by wind gusts. In this regard, a UAV trajectory adjustment system based on Wireless Sensor Network (WSN) for pesticide drift control was proposed in this research. By collecting data from ground WSN, the UAV utilizes the wind speed and wind direction as inputs to autonomously adjust its trajectory for keeping droplet deposition in the target spraying area. Two optimized algorithms, namely deep reinforcement learning and particle swarm optimization, were applied to generate the newly modified flight route. At the same time, a simplified pesticide droplet drift model that includes wind speed and wind direction as parameters was developed and adopted to simulate and compute the drift distance of pesticide droplets. Moreover, an LSTM-based wind speed prediction model and a RNN-based wind direction prediction model were established, so as to address the problem of missing the latest wind data caused by communication latency or a lack of connection with the ground nodes. Finally, experiments were carried out to test the communication latency between UAV and ground WSN, and to evaluate the proposed scheme with embedded Raspberry Pi boards in UAV for feasibility verification. Results show that the WSN-assisted UAV trajectory adjustment system is capable of providing a better performance of on-target droplet deposition for real time pesticide spraying with UAV. Crop protection UAVs have achieved rapid development in recent years for their high pesticide spraying efficiency and strong environmental adaptation. Meanwhile, greater demands have also been made for precision pesticide spraying along with the advancement of precision agriculture. Pesticide droplet deposition is a critical indicator for measuring the effect of precision pesticide spraying. The wind speed and wind direction in meteorological conditions have greater influences on the deposition and drift of the pesticide droplets, in comparison to the flying height of the UAV and the droplet size, etc. In particular, the crosswind-induced pesticide drift, which can be defined as the process whereby the pesticide droplet disappears or re-settles after being carried out of the target area by the airflow perpendicular to its motion, is a principal factor affecting aerial spraying . Since tDeposition and drift models of UAV spraying droplets have been established through wind tunnel experiments and field experiments to predict the drift distance and deposition distribution under various meteorological conditions, as well as to evaluate according to weather station data whether a particular time is suitable for pesticide spraying to help select the appropriate operational parameters for UAV spraying ,4,5,6. HThe wireless sensor network has been vigorously developed in the field of precision agriculture such as crop condition monitoring, precision fertilization and irrigation. With the cost decline in various agricultural sensors, crop growth condition monitoring will certainly be popularized in all stages of agricultural production. Environmental data collected in the working area can be sent to the UAV in real time by the wireless sensor network. Then, the UAV can reduce pesticide drift through adaptive route adjustments according to changes in meteorological conditions. Moreover, leaf wetness sensors, which detect the distribution of droplet deposition, can feedback the spraying effect to the UAV and help in ensuring accurate spraying .Collecting sensor node data as a relay or mobile sink. UAVs help in performing time-sensitive tasks ,9,10,11,Assisting in locating the WSN nodes ,26,27.Performing wireless charging for the WSN nodes ,29,30.Dispersing the WSN nodes in large areas .Rapid development has been witnessed in research concerning the UAV-WSN system in recent years. In UAV-WSN systems, UAVs mainly assist WSN in performing the following four functional categories: On the other hand, the WSN enable UAVs to operate better in different fields. Di Gennaroa et al.  proposedPesticide drift was controlled by optimizing the spraying route of the crop protection UAV using deep Q-network (DQN) and particle swarm optimization (PSO).A pesticide droplet drift model that includes wind speed and wind direction as parameters was developed and adopted to simulate and compute the drift distance of pesticide droplet.Communication latency between the UAV and WSN has been measured and proven that it can meet the real-time requirements of the system.A LSTM-based wind speed prediction model and an RNN-based wind direction prediction model were established to deal with the problem of failing to acquire the latest wind speed and direction data due to the delayed communication or temporary disconnection with the ground node.An embedded development system was built to run the wind speed and wind direction prediction models as well as the PSO trajectory optimization algorithm based on the Raspberry Pi 4B+. It has also been proven that this approach can satisfy the real-time requirements of the practical application.With the view to controlling pesticide drift, this study consults previous research findings to design a system that adjusts the UAV flight route in line with the wind direction and wind speed monitored by the ground wireless sensor network. Contributions made by this study are summarized as follows:The rest of this paper is organized as follows. In Due to the drifting effect of natural wind, it is possible for pesticide droplets to be blown out of the target area, leading to not only poor droplet deposition within the target area, but also pollution to the surrounding environment. In response to this problem, a UAV trajectory adjustment scheme has been proposed, as shown in It is assumed that a crop field is divided into adjacent operation zones by taking the UAV spray swath as the width of each operation zone and the straight-line distance of the UAV flying in a route changing period as the length of the operation zone. In this study, the route changing period is set as 10 s, so as to match the wind change on real time basis. When the spraying starts in an operation zone, the UAV sends a broadcast message to the ground sensor nodes, requesting the wind speed and wind direction. In the case of long operation zone, querying the sensor nodes located in the next operation zone is needed and can be performed by using multi-hop links wireless communication between UAV and ground WSN. As the operation zone is relatively short in this research , the wind data from the sensor node in the current operation zone is also suitable for the next operation zone trajectory adjustment. The wind speed and wind direction are sampled every 1 s. At the other times, ground sensor nodes switch into a sleeping state and regularly wake up for messages. The sensor nodes that receive the UAV message transmit the latest wind data to the UAV in one hop. The UAV then takes the average of the acquired sensor data to obtain the wind information. By this means, communication delay can be decreased compared with the multi-hop transmission. To make sure at least one sensor node can respond to the UAV’s requests at any time, the distance between adjacent nodes in an operation zone or among neighboring zones is set as nearly 50 m for a network of the ZigBee protocol, while the node sleep time is set as 30 ms. On receiving the wind data, the DQN algorithm or PSO algorithm are applied to generate the route changing parameters for the next operation zone, which are illustrated in detail in There are two constraint conditions for successfully executing the route changing process. One important condition is that the computational cost (runtime) of acquiring the route changing parameters should be lower than the time required for spraying a single operation zone. The other condition is that the wind data should be received by the UAV in time. In the event of packet loss that prevents the timely wind information from being received by the UAV, two backup settings can be used: (i) In the case of wind data missing in a short time, the wind speed and direction is predicted based on the previously received sensor data, which will be discussed in Served by the data from field wireless sensors, the crop protection UAV accepts droplet drift and deposition data as feedback and adjusts its flight route in real time based on the wind speed and wind direction data of the farmland during the spraying process. In this approach, a closed control loop is formed, which requires a mechanism to collect the environmental status information (wind speed and direction). This can also be analogized to accumulating experience through learning and exploration, before gradually adjusting the strategy  through environmental feedback .Reinforcement learning  is a leaThe UAV employs cattle ploughing reciprocation and is capable of spraying the whole farmland with an effective spraying width. The default spraying trajectory is set along the central axis of each operation zone, with wind speed and direction sensors arranged in each operation zone as needed. If feedback on droplet deposition is needed from the sensor during reinforcement learning, both sides of the central axis in the operation zone will be configured with two rows of wireless sensor nodes and leaf wetness sensors at equal distances. If the droplet deposition feedback is not needed, then the wind speed and direction sensors will be arranged as desired, rather than placed in symmetry.U represents the position of the UAV, UAV is an intelligent learning agent that can perceive its environment in multidimensional states. Therefore, it is important to select the states which have direct association with trajectory optimization effect for observation. However, if too many states are selected, an excessively large state space will be formed, leading to calculation overload and reducing the convergence speed. If insufficient states are selected, some of the important environment information will be lost, hindering the UAV from learning the optimal strategy. The following state spaces were constructed on the basis of the acquirable sensor data:During the reinforcement learning, the agent selects an action from the set of optional actions according to a certain strategy to be executed upon observing the current states  of the environment. The determination of the optional action space exerts a crucial influence on the agent’s learning process. In this study, the UAV action is to advance a step by selecting a certain steering angle at the current position and to fly in parallel with the central axis of the working area after adjusting to an appropriate position  is the set threshold, and a (a > 0) is a constant. Algorithm 1 shown below describes the DQN algorithm used for the UAV trajectory adjustment.Algorithm 1 DQN-based UAV Trajectory Adjustment1:Initialize: Initialize an experience replay memory with capacity D2:Q with random weight       Initialize the online network 3:Q′ with the weight       Initialize the target network 4:for episode Mdo5:  Initialize the activity state of UAV6:for t = 1 to Tdo  8:7:  According to current state 9:  Receive reward 10:D  Store transition :DQN is a reinforcement learning method that approximates the value function Q using a neural network. The neural network structure constructed in this paper, as shown in The droplet drift model, which focuses on the drift distance of pesticide droplets under the influence of various factors, has important guiding significance for adjusting the pesticide spraying trajectory in this study. Rotor wind field, UAV flight height and speed, wind speed and direction as well as droplet size and nozzle pressure are critical factors affecting droplet drift. Because this study is focused on the method of adjusting the spraying route based on real-time wind speed and direction, a simplified droplet drift model that depends primarily on wind speed and direction was proposed, while the UAV model, flight height and speed, as well as the droplet size and nozzle pressure were set as fixed options.Y (m) is the drift distance of the droplet deposition center, and The regression equation for the drift distance of the droplet deposition center obtained via wind tunnel experiments  can be eIt can be seen from The above conclusion describes the influence of wind speed on the drift of the droplet deposition center when the UAV is hovering. Since the actual flying operation of the UAV should be considered in this study, the influence of the flight speed of UAV on the droplet drift should be also taken into account. Notice that pesticide droplets drift toward the opposite of UAV flight direction. For a M234-AT model quadrotor fitted with a pressure nozzle made by Lechler GmbH , the relationship between the drift and the distance behind the UAV, given that the nozzle position is 0.5 m, the distance between nozzles is 0.25 m, the flight height is 1.5 m, and the flight speed 6 m/s, can be expressed as :(8)y=41.Using the above equation, Substituting nozzle pressure Then, substituting Equations (11) and (12) into Equations (13) and (14), the simplified pesticide droplet drift model can be obtained:Particle swarm optimization is a kind of self-adaptive random algorithm based on group hunting strategy featuring fast convergence rate, as well as easiness in programming and implementation since the optimized function is not required to be differentiable, derivable, and continuous. It is hence perfect for solving a high-dimensional UAV trajectory optimization problem that demands high real-time performance but tolerates lower-precision solutions.GDfor iteration = 1 to do2: if expected conditions are met then3:    break4:      end if5:    for each particle ido6:    7:       Calculate the fitness value according to the function (19)if the fitness value is the best of the particle then8:       9:         Store the position of the corresponding particle in the particleend if10:      if the fitness value is the best of the swarm then11:      12:        Store the position of the corresponding particle in the swarmend if13:      end for14:    15:  Update the particles’ velocities and positionsend for16: 17: Output the best particleAlgorithm 2 describes the optimization process. First, by initializing the particle swarm, each particle has a random initial position and initial speed. Then, the maximum number of iterations of the algorithm is set. A possible solution should be detected when the maximum number of iterations or the given conditions is met. Fitness values of all particles are obtained through optimizing the function The DQN and PSO algorithms proposed in this paper perform trajectory optimization in accordance with wind speed and direction data sent by the ground wireless sensor network to the UAV. Nevertheless, isolated nodes and networks might occur due to the large farmland area, causing the UAV to fail to receive the wind speed and direction data. Moreover, large communication latency might be caused by re-transmission because of packet loss, affecting the real-time operation of the trajectory adjustment mechanism. At this point, a method that can be used for quickly predicting the wind speed and direction is required. For this reason, a short-term wind speed and direction prediction model is established to predict the wind speed and direction in the area to be sprayed when the communication latency becomes large.Based on the collected wind direction and wind speed data, LSTM wind speed prediction and RNN wind direction prediction were selected in this study after comparing the wind speed and direction prediction effects of a fully connected neural network, one-dimensional convolutional neural network, recurrent neural network (RNN), and long and short-term memory network (LSTM). The wind speed and wind direction prediction model is designed into a three-input and single-output network structure, and its inputs are two-dimensional tensor composed of wind speed and wind direction data, as well as average humidity data and average temperature data.The wind speed prediction model consists of wind speed and direction feature extraction layer, humidity feature extraction layer, temperature feature extraction layer, feature stitching layer and wind speed vector prediction layer. The feature extraction layer is comprised of an LSTM layer and dropout layer. Specifically, the LSTM layer involving several neurons is used for extracting shallow features of sequence data. Dropout discards neurons, with the probability of The wind direction prediction model consists of the wind speed and direction feature extraction layer, humidity feature extraction layer, temperature feature extraction layer, feature stitching layer, and wind direction vector prediction layer. The feature extraction layer is comprised of the RNN layer and dropout layer. The number of neurons in the RNN layer in the wind direction prediction model is twice the number of neurons in the LSTM layer in the wind speed prediction model. Similarly, compared with the wind speed prediction model, the wind direction prediction model also increases the number of neurons in the feature stitching layer without performing feature compression. The wind direction sequence value over a short term in the future is the output of the wind direction prediction model.In this section, the performances of two proposed trajectory adjustment algorithms are evaluated in simulation using the collected wind speed and direction data and the predicted wind speed and direction data, respectively.A STM32F103ZET6 single-chip microcomputer, a RS-FXJT05-V05 anemometer and an SD card were adopted for environmental data monitoring. The meteorological data of certain areas in the university from 20 June 2019 to 20 September 2019 were collected at the data collection cycle of 1 s. Parts of the collected wind speed data are shown in 10 s are set as the cycle period of trajectory adjustment. In the meantime, each 10 consecutive wind speeds and direction values are included in a set for one cycle period of trajectory adjustment. Concerning the reward design of the DQN algorithm, the concentration difference of the leaf wetness sensors on both sides of the central axis as mentioned in After 300 times of training, the curve of cumulative reward value with the number of training is shown in Y(t) represents the Bezier curve; After the algorithm convergence, the adjusted UAV spraying route is shown in f  was applied for neural network training and inference. We executed the simulation on the Ubuntu 16.04 operation system with 2 GB NVIDIA GeForce GT 750 M graphics and Intel CORE i7-4500U (8 G).Route adjustment (RA) is not performed under the real wind speed and direction values, i.e., pesticide spraying along the central axis of the work area.Route adjustment is conducted by replacing the wind speed and direction values in the next 10 s with the current wind speed and direction value.Route adjustment is conducted with the predicted wind speed and direction values for the next 10 s.Route adjustment is performed with the predicted wind speed and direction values when the mean variation of wind speeds in the past 10 s is greater than the threshold. Otherwise, the current wind speed and direction value will take place of the wind speed and direction values in the next 10 s for trajectory adjustment.Next, the results of DQN-based and PSO-based trajectory optimization are compared with the following four types of wind speed and direction data: The mean variation of wind speeds refers to the average sum of the difference of the wind speed minus the average wind speed per second in the past 10 s. According to changes in collected wind speed and direction data, the threshold Different types of wind speed and direction data are applied to PSO-based trajectory optimization, as shown in The average spraying accuracy by using different wind speed and direction data in the DQN and PSO trajectory optimization is presented in In this section, the communication time between UAV and WSN is tested, as well as the feasibility of running the PSO-based trajectory adjustment on the embedded hardware is verified.CC2630 Zigbee modules were used in this test for networking, one of which was placed on the DJI Drone Phantom IV and configured as a coordinator, as shown in During the experiments, it was found that without obvious obstacles, the received signal became weak and the communication was affected when the plane projection distance between the UAV and the nearest node on the ground exceeds 80 m. In the following tests, ground nodes are arranged at the four corners of a rectangle with 50 m in length and 30 m in width. When the UAV flies at a speed of 5 m/s with the respective altitude of 5 m and 10 m, it starts from the midair above node A, and flies to node B, node C, and node D in turn, and finally returns to node A without hovering. The UAV sends a data request to the ground nodes during flighty. A retransmission mechanism is adopted in the data transmission of nodes. And the UAV will send a new data request upon receiving data from all nodes. A total of 120 rounds of data collection are carried out in a single flight. Communication latency of nodes A and B and the number of requests sent by the coordinator to the node in each data collection are presented in The average communication latency between the UAV and the average number of requests to each node in each data collection is shown in The wind speed and direction prediction model and the PSO-based trajectory adjustment algorithm are operated on the Raspberry Pi 4B+ (as shown in 3000 sets of data are randomly selected from the collected wind speed and direction data set. 10 wind speed and direction values in consecutive time are included in each set of data, and the time corresponding to each set of wind speed and direction values is a cycle of trajectory adjustment. The running time of the test is stipulated as follows: It starts by receiving current wind data, then predicting wind speed and direction, and it is not stopped until parameters of PSO-based trajectory adjustment is output. The running time for obtaining parameters of trajectory adjustment of the first 1000 sets of data is shown in A system was designed for adjusting the spraying trajectory of a crop protection UAV based on data obtained from ground wireless sensors with the view to controlling pesticide drift. First of all, a UAV droplet drift model under the influence of wind speed and direction was established. On this basis, the UAV spraying trajectory was optimized using DQN and PSO algorithms, respectively. The simulation results show that both algorithms can decrease the droplet drift dramatically with the reduction in the drift distance of the droplet deposition center by up to 50%.Moreover, LSTM and RNN were applied for predicting wind speed and wind direction, respectively, so as to address the problem of missing the latest wind speed and direction data caused by communication latency or lack of connection with the ground nodes. The simulation results show that using the predicted wind speed and direction value for trajectory optimization can further enhance the pesticide drift control effect in comparison to using merely the current wind speed and direction value for trajectory optimization, when the average wind speed change is greater than 1 m/s in a short period of time in the past. At last, the implementation of the UAV route adjustment system was discussed. The communication test between the UAV and the ground WSN proves that the communication latency is, in general, within 0.5 s. Also, the average time consumed for running the wind speed and direction prediction model and the PSO-based trajectory optimization algorithm on the Raspberry Pi 4B+ is 0.229 s, indicating that the UAV trajectory adjustment system can meet the real-time requirement in the pesticide spraying operation.Researches concerning the following aspects will be carried out. Firstly, since the droplet drift model used in this study is only for one UAV model, the droplet drift and deposition in more mainstream UAV models and nozzles under the influence of wind speed and direction should be studied in the future. Moreover, relevant research on the relationship between the deposit amounts on the leaf wetness sensor and the deposition time would be carried out, in order to provide the operation guiding for UAV trajectory adjustment or support the analysis of deposition indicators upon spraying. In addition, the accuracy of wind direction and wind speed sensors also affect the accuracy of trajectory adjustment. The wind direction sensors used in this study can only acquire wind directions in eight orientations. In order to reduce the error, sensors with higher accuracy are required. Finally, the flight control system of the UAV will be developed, so that the trajectory adjustment method can be verified in the field test.


Dactylopius opuntiae), unlike the fine cochineal (Dactylopius coccus), in prickly pear crops of farmers leads to consider it as one of the major pests for this crop. In this study, we present the implementation of an optical setup that ensures the measurement of the in-vivo fluorescence spectra of wild cochineals ranging in size from 440 to 1190 µm in their natural habitat achieved by developing a reproduction model adopted from available literature. It was observed that in-vivo fluorescence spectra of these insects were comprised in the spectral region of 570–760 nm, showing a proportional dependence between the fluorescence intensity emitted and the cochineal size. In addition, we have considered other spectral parameters to perform the comparison between fluorescence spectra of the different cochineal sizes. These results provide the basis for the development of novel methodologies and equipment aimed towards the early detection of this pest in prickly pear crops from its early growth stages (nymph I and II).It is known that the harmful presence of the wild cochineal ( It is known that the quality of the dye produced by this insect depends on if it is from the fine cochineal or the wild one. The fine cochineal, Dactylopius coccus, is distinguished by an external white dusty coating called coccerin3 while wild cochineal, Dactylopius opuntiae, shows an external cottony white cover5. In contrast with fine cochineal, the wild cochineal does not have a high economic value in food, textile, cosmetics and pharmaceutical industries. On the other hand, its presence in prickly pear and edible cactus farming leads to negative effects to such an extent that it is considered as one of the main insect pests attacking prickly pear crops in several areas of Mexico and other countries11. Specifically, problematic are the females, who from their first instar, also known as “crawler”, they move on the surface of the plant looking for fresh cladodes to feed. Once the crawler introduces its buccal apparatus into the plant tissue, it will not move again, constantly sucking the sap from the cladode12, bringing as a consequence the premature fall of cladodes and fruits before their maturation and eventually the death of the plant14.The cochineal is an insect that feeds off the prickly pear cactus and from which a red pigment commonly known as carmine is extracted whose use dates back to prehistoric times15 is when the insect reaches adulthood. The latter means that at this time the cochineal has already fed, reproduced and deposited eggs on the crop; as a result, it is too late for its control, thus favoring the spread of the pest. Therefore, given the high resolution of optical methods and their intrinsic diversity, they might be excellent candidates for the early and efficient detection of the wild cochineal in cactus crops, thus contributing to timely pest monitoring with the consequent reduction of pesticides and other materials that are currently used in the control and treatment of pests and their effects.In general, the farmers notice that their crops are infected by this pest when the cochineal is able to be observed with the naked eye, having at that moment a size of 3.4 mm long by 2.1 mm wide approximately, which according to the study of Rodrigo et al.16, which is the main chemical component of carmine, has been widely studied by means of optical spectroscopy for chemical applications20. This component was used in these investigations from commercial products available from companies such as Sigma Aldrich and was always diluted in solvents such as water, methanol, boron and hydrochloric acid. In these studies, research on different issues of the fluorescence of this compound is reported.Carminic acid21. Its concentration has been studied depending on different factors. For example, Rodríguez et al.22 showed that the weight of this insect and the age of the cladode on which they feed cause variations in the carminic acid concentration present in ex-vivo cochineal. Later Flores et al.21 studied the variation in the concentration of carminic acid with the different maturation stages of ex-vivo cochineal through capillary electrophoresis and flow cytometry in the measurement of the total fluorescence of the carminic acid globules of this insect. In contrast to these two studies, in a recent investigation, Cárdenas23 reported the observation of in-vivo fluorescent microscopic images of wild cochineal. This result was obtained when a laser pointer emitting at 532 nm was used as excitation source, while in-vivo fluorescence observation was performed by placing a band pass filter  in one of the ocular tubes of the microscope  to which a color CMOS camera  was attached23.However, carminic acid is found also naturally within the cochineal as a part of the hemolymph of this insectTo the best of our knowledge, there are no existing research articles that report in-vivo measurement of the fluorescence spectrum of cochineal (fine or wild). Therefore, the detailed investigation of in-vivo cochineal fluorescence microscopy and spectroscopy would contribute to a better comprehension of the scope of the fluorescence of this insect as a basic principle for its early detection in the prickly pear cactus fields.In this research an optical setup that ensures measuring the in-vivo fluorescence spectrum of wild cochineal in its natural habitat is reported. The optical setup essentially consists of a miniature fiber optic spectrometer, a dichroic mirror and two microscope objectives, one of which ensures the “punctual” excitation over the cochineal and the other concentrates the fluorescent radiation in the optical fiber connected to said spectral device. Additionally, the dependence of the fluorescence spectrum of this insect with its size in the early developmental stages is also studied. It was possible to measure the fluorescence spectrum of wild cochineals sized between 440 and 1190 µm, observing that the maximum emission wavelength oscillates in the range of 620–667 nm owing possibly to the movement generated by the stimulation of the excitation light over the live-female cochineal, while the spectral emission band was practically the same for all sizes (570–760 nm). In addition, it was found that in this band, the fluorescent spectral intensity level rises with the increase in the size of the cochineal. We proposed and tested a simple and inexpensive but useful fluorescence standard for adjusting the optical setup and optimizing the fluorescence detected signals previously to the spectra recording process.The spectral data acquired from the samples are plotted as shown in Fig. It is noticeable that each spectrum shown in Fig. In order to distinguish details such as the shape and the difference of fluorescence intensity between the three spectra, corresponding to the different sized cochineals, only these 3 spectra were plotted in Fig. In order to confirm the reliability of our results in this proof of concept and for statistical purposes, we have repeated these measurements in five additional sets of cochineals of similar sizes to those above reported. As it is shown in Fig. It is observed from Table In the case of the analysis of the spectral parameters for comparing fluorescence spectra, we found a similar behavior for sets 1, 4 and 5 with comparable bandwidth values very close to each other, while for sets 2 and 3 the bandwidth values were different showing a minimum value for the small cochineal as well as the same bandwidth for the medium and large cochineals in both cases, as shown in Table This study allowed us to determine maximum emission wavelength ranges to each repeatability set: 628.71–636.42 nm for set 1, 644.09–674.50 nm for set 2, 644.09–651.74 nm for set 3, 628.71–659.35 nm for set 4 and finally 628.71–644.09 nm for set 5.Figure As can be seen in Fig. On the other hand, we have proposed the carmine colored pencil as the fluorescence standard in this study, due to the absence of a commercial one in our laboratory. We measured 10 fluorescence spectra of the carminic acid standard to study its stability over time (data no shown). The temporal stability obtained by these measurements as well as the nearness of its maximum peak (624 nm) with the peaks of fluorescence emission of the cochineals, allows us to suggest using this carmine colored pencil as a standard for in-vivo fluorescence measurements of cochineals in our experimental setup. In this scenario, such fluorescence standard is mainly used during the aligning step of the optical set up to maximize the fluorescence signal recorded at the spectrometer. Besides, this standard is easy to use for comparison purposes.Figure 2. These climatic parameters  were obtained from the UPT weather station .Lastly, but not less important, we have ensured the availability of cochineals to perform measurements at different time periods through a cochineal reproduction model fundamentally based on the infection of healthy cladodes through points of contact with a cladode already infected with the pest. This method allows us to keep this pest in constant development supported by the fact that, as observed in our research, an infected cladode with the pest on both sides is enough to contaminate 4 to 5 healthy adult cladodes. By having cladodes planted in pots, this method is manageable and allows them to be exposed to sunlight for the proper growth of the pest according to its life cycle and be returned to the laboratory to perform the fluorescence measurements at a temperature range from 20 to 25 °C as well as a relative humidity range from 42 to 80% during a time of 30 min for each cochineal set, as observed on Table Dactylopius opuntiae) in their natural habitat was presented in this work. The main objective of the paper was a proof of concept for measuring the above-mentioned spectrum of wild cochineals ranging from small to large sizes (440–1190 µm).An optical setup through which was demonstrated the existence of in-vivo fluorescence spectrum in different sized wild cochineals , which could produce certain excessive heating on the cochineal surface and volume generating discomfort and causing them to move in order to avoid such stimulus. Therefore, the excitation light spot could randomly strike on the dorsal structure of the cochineal and generate a fluorescence spectrum which may vary during the typical integration time used in this study (200 ms), resulting in a range of spectra to be defined for each sample measured. We consider these small variations of spectra, due to the cochineal movement, by performing 10 measurements of the fluorescence spectrum for a single cochineal and reporting its average value and standard deviation. For statistical purposes, we have repeated these measurements 5 times more by using cochineals sets of similar sizes to those reported in this work, as shown in Fig. We found that for a measurement set composed by three live-female cochineals of sizes ranging between 440 and 1190 µm, the emission fluorescence spectra of each insect show different peaks of maximum intensity in 666, 621 and 644 nm for the cochineals classified as small, medium and large, respectively. We believe this difference in the position of maximum fluorescence emission peaks, could have its origin due to the experiment being in-vivo and these organisms, although attached to the cactus, could have some freedom of movement31. During the movement of the cochineal, due to the stimulus generated by the laser focus, the excitation light spot can strike on different sections of the surface and this could lead to differences in the fluorescence signal collected by the microscope objective. The collected signal may depend on the light spot-objective distance, the angle of incidence with respect to the normal of the dorsal surface, among other factors. In this sense, it was found that the intensity level of each spectral band of fluorescence emission rises as the size of the studied cochineal increases. Essentially, the method used for comparing fluorescence spectra in this work consisted on a graphical testing based on the spectral parameters . This standard showed acceptable spectral intensity stability during fluorescence measurements. Therefore, we propose that it could be used as a fluorescence standard of carminic acid for future studies related to this topic.We also consider relevant to highlight here, that a cylindrical segment of a carmine colored pencil was proposed and tested as a fluorescence standard for this optical setup, due to the absence of a commercial one in our laboratorysee Fig. a, the sp24 when detected fluorescence emission of this solution at 670 nm by its excitation at 420 nm. Similarly, Cerovic et al.25 reported that chlorophyll dissolved in methanol has its fluorescence emission peak approximately at 685 nm when it was excited at a wavelength of 450 nm. Finally, another similar result is shown on the website of Prahl and Jacques26 about the research of Weber27 where the chlorophyll dissolved in diethyl ether has a fluorescence emission peak at 666 nm when it was excited at a wavelength of 614 nm.It is relevant to mention that, as far as we know, this is the first study which investigates and specifies the spectral band of in-vivo fluorescence emission of the wild cochineal in its natural habitat and compares it with the fluorescence spectrum of cladode. In this study we have demonstrated the existence of in-vivo fluorescence of the cladode through the fluorescence emission spectrum of the portion of the cladode in which the cochineals of different sizes were parasitizing. As was appreciated in Fig. 24 mention that “Normally, the chlorophyll and other pigments in intact chloroplasts act as energy transducers and do not fluoresce”, which is not observed in our results. For this reason, in this study the existence of the in-vivo chlorophyll fluorescence is also demonstrated by means of the emission spectrum of the cladode portion presented in Fig. Dissimilar to our finding, Murphy and DavidsonDespite the maximum intensity of fluorescence emission of the cladode at 686 nm, being 3.5 times higher than the corresponding fluorescence emission of the large cochineal at 644 nm, the inclusion of an adequate band pass filter in a future field instrument, allowing only an emission band centered in this last wavelength to pass, must serve to visualize the desired in-vivo fluorescence of cochineal. This approach could become in the main element for the early detection of cochineal pest in crops by farmers. Therefore, it is proposed to use a commercial filter  with central wavelength in 630 nm which adequately blocks both the excitation wavelength and the fluorescence emission wavelength of the cladode portion. Particularly in this study, it turns out to be of great importance to include this kind of optical device to discriminate the unwanted wavelengths and to carry out the in-vivo detection of the wild cochineal through fluorescence techniques with the aim of being part of the instrumentation that is going to be used in the crops by the farmers and the specialized staff. The transfer of this technology to adequate conditions such as those of a prickly pear field could be implemented by a portable optoelectronic device based on the results reported in this research work. Essentially, the prototype could include the use of a special optical system, a ring of LEDs that provides the excitation beam at the appropriate wavelength, and the display of cochineals by visual and/or assisted detection by a two-dimensional sensor coupled to an LCD mini-screen. This device will provide a useful tool for farmers all over the world that face the problem of early detection of this that affect prickly pear fields.34, such as malathion, parathion, among others35. Therefore, a consequent economic savings for prickly pear farmers and/or prickly pear producers of the state of Hidalgo and other producing regions of this crop can be achieved. As well as applications of this technique worldwide, where the existence of the problem with the pest of the cochineal is known.The research we have presented in this study, provides basic elements that support the working principle of a novel method aimed at the in-vivo detection of the wild cochineal pest by incorporating fluorescence techniques, in order to design and develop field instruments that help to monitor and determine the presence of this kind of pest in its early growth stage in prickly pear crops. Hence, it contributes to the reduction of chemical pesticides used in the fighting of this pest, by detecting the cochineal when it has not developed its white waxy coating on the outside of its body that protects it from predators and provides resistance at chemical pesticidesIn this section, the methodology for the fluorescence spectra acquisition of the in-vivo cochineal in its natural ambient is described. The optical setup used is our own design that guarantees the detection of low levels of fluorescence present in this study. In addition, the wild cochineal reproduction model employed to ensure the existence of the samples is described. This study proposes a fluorescence standard based on a commonly used disk-shaped carmine colored pencil segment because of the non-availability of a commercial one in our laboratory.x, y and z, (7) a 5 × microscope objective lens, (8) multi-mode optical fiber, (9) a miniature fiber optic spectrometer and (10) computer equipment. The previous elements were arranged as shown in Fig. The optical setup for detecting fluorescence spectra consists of (1) a power supply, (2) an excitation source, (3) a dichroic mirror, (4) a 10 × microscope objective lens, 5) a homemade sample-holder, (6) a mechanical positioning device with micrometric movements in  a homema19, and as was demonstrated by Cárdenas23, this wavelength allows the excitation of the fluorescence of natural carminic acid from wild cochineal in-vivo. The dichroic mirror  is tilted at an angle of 45° with respect to the direction of laser beam propagation allowing the excitation light to be reflected towards the aperture of the 10 × microscope objective lens  which in turn focuses this radiation on a plane where the sample is placed. The specimen to be studied is fixed on the sample holder which in turn is magnetically coupled to the mechanical positioning device , that together with a micrometric base  allows the cochineal to be placed at the focal point of the excitation beam. Both the fraction of the fluorescent light emitted isotropically by the cochineal and the fraction of the light reflected diffusely by this insect enter the 10 × objective lens and leave it as a collimated beam towards the dichroic mirror. The first fraction of light is transmitted approximately 85% to the second microscope objective lens  while the second one is strongly blocked by the dichroic mirror. A 400 µm diameter optical fiber  is placed at the focal distance from this latter microscope objective to collect the fluorescence radiation and transmit it to the input port of the miniature fiber optic spectrometer  which allows for the spectral decomposition of the fluorescence radiation for its later processing. This spectrometer is connected through a USB port to a desktop computer where the BWSpec  software is installed, provided by the same manufacturer of this device, by means of which the acquisition of the spectra is carried out with an integration time of 200 ms. This software allows the user to save the spectra in plain text files (.txt) for further processing of the acquired information.The power supply  provides a constant voltage of 3.5 V to the excitation source. The excitation source is a commercial laser pointer (KG-303-6) emitting at a wavelength of 532 nm and nominal power of 1 W. This wavelength is found within the absorption band of carminic acid diluted in methanol23.The use of the dichroic mirror in the optical setup allows for adequate discrimination of the excitation and emission wavelengths. It shows about a 90% reflection in the range of 380–535 nm, the range in which the wavelength of the excitation beam is found (λexc = 532 nm), whereas for wavelengths in the range of 565–800 nm it presents an 85% transmission. We expect that the in-vivo cochineal fluorescence emission should be mainly located in this last spectral region, based on the findings reported in previous research works for solutions of carminic acid in methanolAs a part of this research, we proposed the use of a segment from a carmine colored pencil  as a fluorescence standard due to the absence of a commercial one in our laboratory. This homemade fluorescence standard was used to calibrate the spectra acquisition optical setup. The standard was obtained by cutting a 5 mm segment of the carmine colored pencil with a razor blade  to achieve a finer cut. Then, this segment was situated inside a plastic piece in an annulus-shape to provide protection and handling during fluorescence measurements. Finally, its face of greater diameter was polished with fine grit sandpaper  to get a smooth plane surface. The opposite face was adhered to the sample-holder while the polished one was placed perpendicular to the optical axis of the 10 × objective lens so that the excitation light impinges on it allowing the optimization of the fluorescence signal recorded by the optical setup.In order to test the temporal stability of the carminic acid fluorescence standard, a continuous acquisition of fluorescence spectra was performed by using the optical setup previously shown in Fig. The cochineal samples used in this research come from a cladode infected with this pest donated by farmers from the Sociedad Cooperativa Productora Agropecuaria de Nopal Tlanalapa S.C. de R. L. de C.V., from the state of Hidalgo, Mexico.For its survival, this pest was reproduced in the Biomedical Optics Laboratory of the Polytechnic University of Tulancingo, according to the reproduction model illustrated in Fig. As observed, in a plastic pot with a diameter approximately of 20 cm, two healthy cladodes were planted with the cladode infected with the cochineal pest placed between them. The spread of the pest is carried out through the points of contact between cladodes, which in this case were the thorns of each specimen, the separation distance between them was 1.5–3 cm approximately. Since the first stage of cochineal pest is mobile, during migration to other parts of the cladode, some cochineals fell to the ground infecting it from the base.4 and we note that in our reproduction model both sides of the infected cladode have about 30 colonies conformed of 1–4 adult cochineal per side, then it is assumed that the spread of the pest towards the healthy cladodes with an infected cladode is enough to contaminate. This was visually verified by observing that during the course of two days both cladodes were already contaminated. In other variants of the reproduction model it was verified that a cladode with the pest is enough to infect four to five adult cladodes approximately of similar dimensions to those used in this study. This reproduction procedure allows us to have several groups of pots where the cochineals are in different maturation stages. The reproduction model proposed in this work is similar to the previously reported reproduction methods29 where the planting of the cladodes is also done in pots, however, the pest infestation is carried out in a different way from the one proposed in this work.If we consider that in literature it has been reported that during oviposition females deposit about 419 eggsIn order for the pest to maintain an appropriate lifecycle, the pots are taken out the laboratory to expose the cladodes to sunlight during the day, taking care to avoid the rain and predators that could put the cochineals at risk. In the evening, approximately at 6 o’clock, the pots are returned to their resting place in the laboratory for their care, conservation and adequate control for this study. To be specific, in this study a photoperiod of 9 ± 1 h of direct solar radiation was stablished, to later leave the pots inside the laboratory in absolute darkness, until the next day. This photoperiod was applied for 3 months, for covering the life cycle of the cochineals (90 days approx.). A script development in MATLAB R2014a  software was performed to process the solar radiation detected by the UPT weather station , as shown in Fig. 2], corresponding to each graph of this figure.Note that the photoperiod was established in the time range of 09:00–18:00 h, (0–540 min in graphs), corresponding to the time in which the reproduction model was exposed to direct sunlight, so that at the end of the day was placed again inside the laboratory. Figure The values of temperature and humidity, external (blue line) and internal (red line), recorded by the UPT weather station during the fluorescence spectra acquisition period for each set presented in this work, were processed in a script developed in MATLAB R2014a  software. In Fig. www.weatheronline.mx) and by the UPT weather station . The values, corresponding to temperature and humidity recorded by the UPT weather station, are the result of an average of the data plotted in Fig. The measurement periods of each cochineal set, comprise around 35 min, where most of the cases were performed from 18:10 to 20:15 h. In this figure, data of temperature and humidity recorded by the UPT weather station were plotted, both external and internal with respect to the CIDETyP  building where the Biomedical Optics Laboratory is located. In Table Observe that, in Table 23. We assumed that the cochineals are infesting the cladode, in other words feeding from the sap, given their morphological features and size observed and compared with those referred in the available literature4.For this study 3 live-female cochineals of different sizes were selected, of around 480 µm for the small cochineal, 540 µm for the medium cochineal and 790 µm for the largest cochineal. These sizes were determined with a script developed in MATLAB R2014a  software reported in the work of CárdenasIn order to prepare the biological samples to be studied spectrally, a segment of approximately 6 mm long was cut from one of the infected cladodes planted in the laboratory. The three cochineals previously classified in different sizes were found in this portion as shown in Fig. The in-vivo fluorescence spectra measurements from the cochineals were performed using the optical setup described at the beginning of this section under laboratory conditions of complete darkness, at a temperature range from 20 to 25 °C as well as a relative humidity range from 42 to 80%. Additionally, it was necessary to turn on the excitation laser 30 min before the measurements began in order to achieve a stable fluorescence signal. During this time the output of the laser was obstructed with a dark piece of cardboard to avoid photodegradation of the sample, which was already placed in the sample-holder. Subsequently, the sample was approached to the edge of a dark colored, 374 µm thin plastic sheet, located just at the focal distance of the objective lens. Knowing the thickness of this sheet allows for the sample to be placed at the focal distance of the microscope objective once the plastic sheet is removed by moving properly the sample with the micrometric mechanism along the z-axis. This guarantees that recorded spectra of the cochineal are carried out “correctly”, that is, when the excitation focal point is on the highest part of the surface of this insect.Once in this location, 10 fluorescence spectra were acquired from each one of the cochineals selected and located in the cladode portion. Three displacements of the sample holder were performed on the z-axis, moving the sample away from and then back toward to the focus plane of the laser excitation beam. Finally, these fluorescence spectra were averaged using a script development in MATLAB R2014a  software, obtaining a smoothed average spectrum of each cochineal as well as the standard deviation between them.Figure p values < 0.05 were considered to be statistically significant.Ten fluorescence spectra were acquired from each cochineal of different size that was present in the biological samples. These spectra were processed with a script developed by the authors in MATLAB R2014a  software, obtaining a smoothed average spectrum of each cochineal as well as its standard deviation. Figures for comparative fluorescence spectra of cochineals of different sizes show average values of fluorescence intensity and its standard deviation (errors bars). Control of reference fluorescence spectra of the background medium (cladode) and a homemade fluorescence standard were taken in all the experiments. Experiments were replicated in five sets of biological samples containing cochineals of similar sizes as is shown in Fig. On the other hand, in order to support the comparison between fluorescence spectra mentioned in this work, we have processed and collected spectral information  for each set of fluorescence spectra by using the MATLAB function “corrcoef”. For statistical purposes, we have processed values of fluorescence intensity at three additionally wavelengths in 620 nm, 660 nm and 760 nm (data not shown), in order to obtain a linear fit and to calculate linear correlation coefficients.In order to support the hypothesis of this work by determining how the cochineal size modifies the intensity of in-vivo fluorescence spectra, we have developed a script of our ownership in MATLAB R2014a  software for achieving to fit this intensity as a depending function of the cochineal size in a linear model as observed in Fig.


Most mitral valve repair techniques provide excellent surgical results by removing regurgitation, but all of these techniques simultaneously reduce posterior valve mobility. A comprehensive biometric study of the mitral valve apparatus will provide landmarks that would help improve this posterior valve mobility.Thirty one (31) human hearts have been studied, from 14 women and 17 men. The characteristics of the studied sample were analyzed descriptively. The difference in means of the variables between women and men were tested using a Student t test. Correlations between the different measures were determined by simple regression analysis. Mean values are shown with ± 1 standard deviation and the limit of significance was set at 0.05.p > 0.05). There was no correlation between these different measured values (p > 0.05).The mean weight of the hearts was 275.3 ± 2.4 g. The anteroposterior diameter of the mitral annulus was 29.3 ± 1.22 mm, the intertrigonal distance was 25.2 ± 3.50 mm and the anterior leaflet to posterior leaflet ratio was 1.9 ± 0.10, the length of the chordae A2 = 19.4 ± 1.15 mm and P2 = 14.5 ± 0.85 mm. The length of the anterior papillary muscle averaged 30.9 ± 7.20 mm and that of the posterior one 30.0 ± 8.75 mm. The comparison of the different values measured between women and men showed no statistically significant difference (A perfect knowledge of anatomy and biometry is therefore essential to offer alternative techniques that reproduce the real anatomy and physiology with a complete reconstruction of the mitral valve. The first person to describe the mitral valve in detail was Leonardo da Vinci in the late 1400's. Subsequently it was Andreas Vesalius 1514–1564), reported by , who cla14–1564, We wanted to understand if it is possible, based on a complete study of anatomy and biometry, to have a procedure that would allow to preserve the movement of the valve leaflets after a repair plasty.The objective of this article is to undertake a comprehensive biometric study of the main chordaes , the mitral annulus and the papillary muscles in the human heart, to look for benchmarks that would help surgeons to improve their techniques, to determine if the average of the measurements in women is different from that of men, and to look for a correlation between the different values measured as well as for a correlation between age and each of these values.the antero-posterior diameter which goes from the middle of the anterior mitral annulus (base of implantation of the anterior leaflet) thus passing through the center of A2 and P2 to that of the posterior ring (base of implantation of the posterior leaflet).The inter-commissural diameter of the mitral ring which goes from the anterior commissure (limit between A1 and P1) to the posterior commissure (limit between A3 and P3).The intertrigonal distance which goes from the posterior or right trigone  to the anterior or left trigone . A fibrous part connects the two trigones on which is inserted the mitral ring (base of the anterior leaflet).The length of the anterior leaflet which goes from the middle of the anterior mitral annulus (base of implantation of the anterior leaflet) to the free edge of the anterior leaflet, thus passing through the center of A2.The length of the posterior leaflet which goes from the middle of the posterior mitral annulus (base of implantation of the posterior leaflet) to the free edge of the posterior leaflet, passing through the center of P2.The length of the marginal cordaes which goes from the point of the head of the papillary muscle concerned to the free edge of the valve.The length of the papillary muscles which goes from the base of implantation of the papillary muscle on the ventricle to the top of the head of the papillary muscle concerned.8-The distance between the papillary muscles and the annulus which goes from the top of the head of the papillary muscle concerned to the mitral annulus.The distance between the papillary muscles and the apex which goes from the base of implantation of the papillary muscle concerned on the ventricle to the apex.Thirty one (31) human hearts have been studied, from 14 women and 17 men, with a mean age of 79 ± 10 and extremes of 50 and 97 years old. Each of these hearts was fixed with 10% formalin. Prior to examination of each heart, the aorta and pulmonary artery were resected 1 cm above the valve commissure. The resection was also performed at the foot of the superior and inferior vena cava and each heart was weighed. To verify the absence of mitral regurgitation, each valve was inspected through the left atrium opened at its left edge, while saline solution was injected under pressure through the aorta. Then the left border of the left ventricle was also opened to reveal the mitral annulus, anterior valve, posterior valve, chordaes and papillary muscles. These different elements of the mitral valve were examined and the main chordaes were counted per valve. The following measurements were carried out:The number of heads per papillary muscle and the number of chordae per papillary muscle were also counted. See Figs. Sample characteristics were analyzed in a descriptive way, which allowed us to collect means and standard deviations for the quantitative variables. The normality of the chord lengths was verified by the Shapiro–Wilk test, Concerning gender, the difference in the means of the variables between women and men were tested using a Student t-test. Correlations between the different measures were determined by simple regression analysis. Mean values are shown with ± 1 standard deviation, and the limit of significance was set at 0.05.Statistical analysis was performed using SAS software version 7.1.Terminology The three segments of the anterior leaflet are named: A1 (anterior), A2 (median) and A3 (posterior). The three segments of the posterior leaflet has the opposite segments P1 (anterior), P2 (median) and P3 (posterior), A , P .The aortic annulus was oval in shape, flattened in its portion adjacent to the aortic annulus between the two fibrous trigons in all our patients. Its anteroposterior diameter averaged 29.3 ± 1.22 mm and its intercommensural diameter averaged 33.8 ± 1.37 mm. The intertrigonal distance measures 25.2 ± 3.50 mm and the anterior to posterior leaflet ratio was 1.9 ± 0.10 . We did not find any correlation between these different measured values (p > 0.05) or between age and these measured values (p > 0.05).Comparison of our different measured values between women and men showed no statistically significant difference (p > 0.05). We did not find any correlation between these different measured values (p > 0.05) or between age and these measured values (p > 0.05).Dissection of these hearts and measurement was uneventful for all specimens. All dissected subjects had a mitral leaflet , mitral annulus, tendon chordaes, and the two papillary or pillar muscles . Each papillary muscle provides chordaes to each of the corresponding halves of the two valves. Comparison of our different measured values between females and males showed no statistically significant difference . These chordaes were first measured by TEE, immediately preoperatively, and then by they were measured by manual measurement intraoperatively. The measured distance is the distance from the tip of the papillary muscle to the free edge of the nonprolapsed valve. As a result, it was found that the two types of measurements differed by 1 mm on average in favor of the ultrasound measurement. The average of the manual measurement is 23 mm. That of the ultrasound measurement is 24 mm.These results of manual measurement of mitral valve chordae length intraoperatively, with a stopped, hypothermic and bloodless heart are superposable to our results of mitral valve chordae length measurements in the human cadaver.This manual intraoperative measurement, contrary to the transesophageal echographic measurement, should be reserved for patients with a contraindication to transesophageal echography: thoracic radiotherapy, esophageal pathology , unstable lesions of the cervical spine, massive hematemesis, evolving upper GI hemorrhagic lesion, gastric or esophageal perforation, recent esophageal surgery, situation leading to a benefit-risk assessment  .When replacing native chordaes with artificial chordaes during mitral plasty procedures, the surgeon is always confronted with the determination of the ideal length of the artificial chordaes. If the replacement chordaes are short, excessive tension can cause tissue rupture at the sutures. If they are very long, they cannot transmit enough tension during a cardiac cycle. Therefore, it is preferable and useful to have accurate values of the length of the native healthy marginal chordaes in order to adapt this length to the artificial neochordaes to be implanted.The principle is to implant the neochordaes with the right length, between the tip of the head of the papillary muscle and the free edge of the valve. It is not necessary to resect the broken or elongated chorae. There is therefore no risk for secondary or tertiary chordaes if these chordaes have the same implantation on the papillary muscle.In all cases, a homothetic reduction in a flexible ring must be systematically performed as in other plastic surgery techniques.For this homothetic reduction, there is a relationship between our measurements and the choice of annuloplasty rings. Indeed, for each ring model, the manufacturers provide a specific sizer, which is used as a tool to determine the size of the annuloplasty ring. These sizers most often have a handle holding a D-shaped platform that includes two notches. The dimensions of the platform and the distance between the notches are used to determine the ring size. Annuloplasty rings are labeled with even  or odd sizes . In even rings, the distance between the sizer notches typically indicates the distance between the commissures, whereas in odd rings, the distance between the sizer notches indicates the intertrigonal distance. The platform surface is used to measure either the bare surface or the entire surface of the mitral leaflets. The septolateral diameter of the platform is used to measure the height of the anterior mitral leaflet. The quality of the annuloplasty is assessed by the coaptation height of the valve leaflets and the morphology of the closure line of these leaflets.Nowadays, there is also the catheter-based mitral prolapse repair , which iSeveral authors , 17, havAbitch et al. , demonstCarvalho et al. , studiedThe limitation of this method of chordae substitution is that it is not sufficient when it comes to Barlow's disease. In this case, the diamond resection of the excess of tissue localized in relation to the ring and the free edge of the valve, associated with the closure of slits and indentations are necessary.We expect to find in the future, possibilities to make this technique of chordae substitution sufficient and efficient to all complex mitral prolapses including Barlow's disease. It is in this context that we should consider continuing this biometric work in the laboratory and in the operating room.The technique of replacing the native chordaes by artificial chordaes allows to reproduce the real anatomy and physiology with conservation of the movement of the two valves, thanks to a complete reconstruction of the mitral valve apparatus. A perfect mastery of anatomy and biometry is therefore essential.