Cocoon sorting is one of the most labor-demanding activities required both at the end of the agricultural production and before the industrial reeling process to obtain an excellent silk quality. In view of the possible relaunch of European sericulture, the automatization of this production step is mandatory both to reduce silk costs and to standardize fiber quality. The described research starts from this criticality in silk production  to identify amelioration solutions. To this aim, the automation of this activity was proposed, and a first prototype was designed and built. This machinery is based on the use of three cameras and imaging algorithms identifying the shape and size of the cocoons and outside stains, a custom-made light sensor and an AI model to discard dead cocoons. The current efficiency of the machine is about 80 cocoons per minute. In general, the amelioration obtained through this research involves both the application of traditional sensors/techniques to an unusual product and the design of a dedicated sensor for the identification of dead/alive pupae inside the silk cocoons. A general picture of the overall efficiency of the new cocoon-sorting prototype is also outlined. Bombyx mori L. The insect secretes the silk filament to build a cocoon, in which the larva completes its metamorphosis. Silk thread obtainment is the result of a series of agricultural and industrial processes, among which mulberry cultivation for silkworm feeding, larval rearing from the eggs to the spinning stage, cocoon processing and reeling are the most prominent. Without any human intervention, the moth would develop inside its self-assembled silk case and eventually emerge from it by softening the protein glue (sericin), which makes the cocoon close-knit, through an enzymatic process. The silk industrial chain usually devitalizes the insect by hot hair at the pupal stage, while it is still in the cocoon, to avoid damages to the silk shell that can be, therefore, preserved, for a long time. Afterwards, the reeling process takes place, and the commercial silk skein is produced by unwinding the cocoon thread. To produce superior raw silk, cocoons should be assessed for their silk shell rate out of the whole cocoon, the length of the silk filament and the percentage of defective cocoons. Defects of cocoons are determined by many external factors and can be grouped into defects of cleanliness, which are the most important (inside stained cocoons/dead cocoons and outside stained cocoons) and defects of shape and texture   were produced at the CREA–AA, Sericulture Laboratory of Padua, where they hatched. Then, the larvae were reared until the third instar at a collective nursery farm and distributed to different producers for the rearing of the fourth and fifth instars and commercial cocoon spinning. After harvesting, cocoons were manually sorted into different categories (from the first to the third grade), according to their shape, dimensions, and color, by the same farmers and, thereafter, brought to CREA for drying. After the drying process, different cocoon samples were prepared by randomly picking cocoons from the different producers and dividing them into the following categories:-Shape and size, using a camera and imaging algorithms;-Outside stains, using two opposite-direction-mounted cameras and imaging algorithms;-Amount of light passing through, using a custom-made light sensor and AI model.The prototype was based on an opto-electronic machine and was conceived to operate through three main sections using three cameras and one light sensor. The selection parameters were set as follows:This sorting machine A was buiFurthermore, pneumatic actuators for the final and initial selections were activated by solenoid valves also controlled by the same PLC as above .The three cameras installed on the prototype were connected to a main personal computer (PC) via one ethernet cable each, passing through a power over ethernet (PoE) switch, and they received trigger signals to take pictures at a certain time. Preliminary operations done prior to the acquisition process ere made with the graphical user interface (GUI) designed and managed in the Java programming language; furthermore, using the same GUI, it was possible to monitor various parameters, such as the number of photos taken, status of the cameras, status of the analog to digital converter (ADC), and so on, or doing other operations like starting or stopping the acquisition B.All these tasks on the main PC were achieved using multithreading programming techniques, and they were performed while storing at the same time all the synced images from the cameras in addition to the raw data from the matrix sensor. Syncing between PC, custom light sensor and PLC was carried out with the Modbus ethernet communication protocol, which was implemented on the Arduino Portenta H7 using an appropriate software library.The hierarchy we chose, based on the Modbus communication protocol, can be summarized by establishing one client represented by the PC and two servers, represented by the Arduino and the PLC. Between the PC and the PLC, there was a Boolean type of data exchange, which was conceived to coordinate the movement of all the mechanical parts. On the other hand, a correlation between the light sensor and the PC was built to allow Boolean data to handle the timing of acquisition, thus later using the same bus to pass raw data to the PC’s ModBus registers.The whole mechanical construction was made possible by the extensive use of aluminum profiles, enabling a high level of modularity without compromising the structural strength. The conveyor belt was based upon an unusual design, and for this reason, a custom-made model manufactured in Italy was used in this project C. The prototype was built to operate on a horizontal type of motion, starting from a vertical feeder that catches and transports silkworm cocoons towards a 45 degree slide. The first camera, placed at the end of the slide, was used to discard cocoons whose shape was not adequate by the action of two pneumatic actuators mounted perpendicularly to the camera. Cocoons that were considered shape-approved fall by gravity into specifically designed small capsules fixed to a horizontal conveyor belt. Using this conveyor belt, the small capsule with one cocoon inside advanced to the next two cameras, which were specialized in stain selection. These cameras were the same model as the shape-selection one, while the difference was in the software used to determine this parameter and in the placement. In fact, one camera faced the cocoon from below and the other from above; this arrangement was used to frame two different faces of the same sample, thus reducing any possible reading error, such as mistaking the state of the cocoon. The last selection station was dedicated to the discrimination of alive vs. dead pupae inside the cocoons and was made by a complex sensor based on photodiodes for which a custom PCB and software were designed. After all the processing was done, the machine could send individual samples to different containers according to the different quality grades through a pneumatic-oriented slide placed at the end of the conveyor belt.The prototype applied different approaches for the detection of the selection traits.This step consisted of a station where cocoons were selected according to their shape. A digital camera was placed about twenty centimeters above the cocoon resting space, and in between, there was a light-emitting diode (LED) light made by Banner, model LEDWO50M, which could project white light on-axis with the camera lens to produce even lighting on the surface of the object. Images were acquired by an optoelectronic system based on a GigE Vision Camera Manta G-504B/C with a 5 megapixel (MP) Sony ICX655 RGB sensor operating at a resolution of 1292 × 964 pixels. Cocoon shape and size were extracted through an image analysis protocol .Cocoon outlines were extracted by segmenting the original images in the following way. The first image’s HSB coordinates were extracted, and the matrix M was obtained as:With rgb2gray = 0.2989 × H + 0.5870 × S + 0.1140 × B. Note that this is the same linear combination used to obtain the luminance from the RGB colorimetric space along with the ITU-R Recommendation BT.601.The HSB color space was chosen because it is very efficient in emphasizing the edges in pictures. In particular, the B component simulated the change in illumination from a white source, which was, of course, enhanced across the edges of an object. The H and S components were used to distinguish the inner side of the border from the other one. They indeed changed significantly going from the cocoon to the background.The obtained matrix M was finally normalized to the maximum, and the result transformed to uint8 format and binarized using the Otsu threshold . After binarization, a total of 180 points  equally angularly spaced (one point every π/90 rad) from the centroid were digitized along the outline of 1006 cocoons. Coordinates were aligned by generalized Procrustes analysis . Cocoon-The Z-score of a variable x is defined as (X-m)/s where m is the mean value of x and s is its standard deviation. The Z-score is needed to compare variables having very different support.Given the extreme sample imbalance (81% well-shaped cocoons and 19% bad-shaped cocoons) of the original data, a new dataset of 356 samples was built, joining together the 178 bad-shaped cocoons and 178 well-shaped cocoons randomly selected among the 773 well-shaped cocoons belonging to the original dataset.The obtained sample dataset was divided into two subsets: the training (70%) and the test (30%) set. This ratio results indeed in the best training-to-testing trade off with respect to overfitting and underfitting. The results were validated through cross validation, averaging over 100 iterations on the whole original dataset of 951 cocoons.Size was measured by counting the pixels belonging to the cocoon outline and the whole cocoon selected according to a threshold criterion. Cocoons with areas smaller and larger than a fixed value, therefore, were discarded.For the stain-selection phase, two cameras were placed about twenty centimeters apart from the moving slot. The first camera framed the cocoon from above, giving an image of the top part.Subsequently, the second camera instead framed the same cocoon from below, therefore giving an image that represented the bottom part. Considering that spots can occur on the opposite side with respect to that examined by the camera, acquiring and labelling two images from two different viewpoints led to a better representation of the cocoon surface. Images were acquired by two optoelectronic systems, both based on a GigE Vision Camera Manta G-504B/C with a 5 megapixel (MP) Sony ICX655 RGB sensor operating at a resolution of 1292 × 964 pixels. The first stain-selection camera used a LED light made by Banner, model LEDWO50M, while the second camera used two warm white LED strips powered at 24 V and mounted in parallel to the conveyor-belt direction of motion .Given the emergent difference in the color of stains with respect to the cocoon surface, to observe the presence of stains outside the cocoons, an empirical imaging procedure was applied. A fixed threshold approach was implemented on the HSB colorimetric space.In order to sort the stain-free cocoons, images were filtered with two sets of threshold values on the HSB colorimetric space; these values were used according to the different illumination conditions and are reported in 2, it means that the cocoon presents stains. The percentages of correct classification for both top- and bottom-camera positions were then extracted.If the filtered image contains objects with an area larger than 144 pixelsThis custom-made sensor is designed to read the light that is projected from a small LED panel placed above the cocoons. The sensor’s matrix external box was made with a 3D printer, as was the box for the Arduino and power management logic board, and was designed to fit in the specific mounting space; moreover, single sensors were isolated among themselves by a 3D-printed grille placed on the matrix itself. This design was adopted to prevent light leaks between sensors, because this behavior could cause false readings . The con2 of surface. The Arduino Portenta H7 was then able to read the voltage given by every photodiode using a single channel of the ADC working at 10-bit resolution . This setup, which involved only one ADC, was designed to save time during the acquisition process and was implemented by using two fast 74HC4051D multiplexers made by Nexperia driven by four control busses each.In detail, the sensor was based on the TEMD5510FX01 photodiode arranged matrix of 7 × 5 units, covering about 1749 mmEvery one of the 35 sensor readings was then performed by selecting first the row and then the column of the desired “pixel” using the control busses and eventually reading the Arduino Portenta’s ADC to store the value afterwards. Every pixel’s value was then saved in a vector inside Arduino’s flash memory and later wrote on the designated ModBus registers located inside the main PC.The 35 signals obtained by the photodiode matrix were aligned, standardized through the Z-score and processed with a logistic regression algorithm for the 366 cocoons. The sample dataset was divided into two subsets: the training (80%) and test (20%) sets. The percentage of correct classification was extracted, and the results validated through cross validation averaging over 100 iterations.The software operated in two modes. The first was the acquisition of images from the camera and the matrix photodiode sensor to sample the cocoons for the construction of the model. The images from the three GigE Vision Cameras were stored on a disk, a folder for each camera, in the bmp format. The information from the photodiode sensor was stored in CSV format, in its folder. Each file was stored reporting the acquisition timestamp, the name of the camera and a progressive number that identified the acquisition, necessary to trace a cocoon in the sequence: shape and size, top outside stained, lower bottom outside stained and dead cocoons.The second operating mode was the selection of the cocoons, with the use of the models described in points 2.2, 2.3 and 2.4, which were integrated into the software, exporting a library, one for each model, using the Matlab compiler. The image from the camera applied the relative algorithm of the model, returning if the cocoon was fit or not, for its related class. This procedure was applied for all the three cameras, except for the photodiode sensor, whose model was applied internally to the software without the use of external libraries, implementing a method that calculates the formula with the relative weights generated by the model.The prototype showed different performances according to the various selection parameters considered. At the beginning of the testing phase, due to the lightness of the cocoons and to avoid the excessive movement of the cocoons in their slot, which might, in turn, cause some alterations in image acquisition, the production frequency of the system was only 30 cocoons per minute. Nevertheless, as the prototype has constantly been improved in terms of hardware and software, at the moment the optimizations and tests carried out allowed the system to reach a frequency up to 80 cocoons per minute.2 as oversized cocoons. However, the machine showed low performances (29.6% of correct classification) in selecting small-size cocoons , mainly due to the cocoons being vertically positioned inside their housing places (In cocoon size selection (oversize or undersize cocoons), the machinery reached optimum performances for oversize cocoons, classifying 87.8% of cocoons larger than 450 mmg places . By elimWell-shaped cocoons were selected through logistic regression applied on the Z-score of the EFA coefficients as input variables as described in The stained cocoon selection was done through the fixed-threshold processing of the top-view camera images, which showed a percentage of incorrect classification of 7.9% out of 852 unstained samples. This can be considered a good result if compared with the 14.6% error rate characterizing the manual procedure. The false-negative percentage (stained cocoons marked as unstained) was 22.9%. These failures in staining identification can be entirely attributed to the position of the spot on the side of the cocoon not directly caught by the camera due to its position. The fixed-threshold processing for the bottom camera showed a percentage of incorrect classification of 5.9% on 859 unstained cocoons and a false-negative percentage of 20.7%. This error was attributed to the position of the spot on the cocoon with respect to the camera even in this case. Results are summarized in The performances of the prototype in dead-cocoon selection based on the logistic regression model from the photodiode matrix are summarized in Tp (Tn), and Fp (Fn) are the number of true-positive (negative) and false-positive (negative) cases. We consider positive cases undersized or oversized, not well-shaped, and stained and dead cocoons. The threshold values eventually used are also reported.In order to show the impact of each step on the overall process, the performances of the models used for the multi-step classification method are summarized in Cocoon sorting is performed by farmers and perfected at the reeling plant, because of the importance of processing regular-size and perfectly undirtied cocoons in the reeling basins. This aspect is of basic relevance to obtain the 5A–6A (top level) , and alsThe final output of the prototype should be compared to the manual output obtainable by subjective selection performed by the farmers. According to the sampling data of the sericultural laboratory of Padua, gained throughout the 5-year duration of the Serinnovation project, manual accuracy in cocoon sorting is rarely better than 80%, ranging from 75% for bad harvests and inexperienced farmers to 95% for good farmers and optimal harvesting seasons (authors’ unpublished data). In addition, the sorting efficiency of a trained operator in cocoon sorting is about 40–50 cocoons/minute (authors’ unpublished data). According to the real sorting efficiency of the prototype, the machine is a little bit slower than an experienced operator (30 vs. 45 cocoons/minute); however, the prototype has the chance of increasing its performance through a machine learning process, and the theorical speed is higher than the peak speed of the operator (80 vs. 50 cocoons per minute). Furthermore, it should be outlined that an operator is not capable of maintaining the full capacity and peak speed for many hours, and this is the reason why the farmers’ manual sorting process is imprecise. Therefore, both the number of resting pauses increases (therefore the average sorting speed decreases) or the efficiency in sorting diminishes considerably.Therefore, the data obtained in These solutions are different considering the various quality parameters we took into account. In case of the errors in sorting cocoons according to their size, the failure of the system is almost always related to undersized cocoons when their largest diameter is not aligned to the longest axes of the cradles; this happens when they fall into the cradle in a vertical position rather than lying down. The possible solution may be to rely on two passages or to improve the cradle mouth so that cocoons are obliged to be better oriented in the same cradle. However, at the moment, 90% success for this parameter can be considered quite a good achievement. The prototype showed quite a poor performance in shape classification. This section of the machine is more difficult to set, as cocoons are not perfectly the same in their shape but are more or less rounded at tips and more or less constricted in the middle; in addition, their surface’s evenness is also dependent on many environmental factors and is rarely completely homogenous.Moreover, the difficulty of sourcing bad-shaped cocoons resulted in a great sample imbalance with dramatic drawbacks on the model accuracy. The latter point can be overcome by collecting more samples and re-training the model. Luckily enough, only a very irregular cocoon shape affects the reeling process. Nevertheless, even in this case, the success ratio of cocoon sorting can be improved by improving lighting and making the background color more uniform . Concerning stained cocoons, the error ratio is quite low and acceptable, although this criticality can be almost eliminated, especially for the bottom camera, by changing the illumination conditions. The live vs. dead pupae selection in the cocoons is the most critical point, as supposed, for which an innovative and unique sensor was specifically designed. Further improvements can be obtained by reducing the light leakage by drastically reducing the height of the grid supporting cocoons and intensifying the illumination; in addition, to increase the sensitivity of the sensor, a higher-resolution ADC can be adopted. Unfortunately, cocoons with dead pupae in the reeling basin greatly affect the general reelability performances and the quality of the overall silk filament by impacting water cleanliness and therefore the other non-defective cocoons reeled together with the defective ones. This point appears, therefore, to be the most prominent issue on which to focus the next research efforts.The concept on which this prototype is based is to combine cameras and classic approaches with custom sensors specifically realized from patents ,23.Generally speaking, this first prototype permitted the study of the patented idea in more detail and to individuate practical aspects on which further research is advisable.Silk textile processing represents an agro-based industry engaged in manufacturing primary (cocoons) and secondary (silk skeins) products of agriculture. Like other industrial chains, it needs the standardization of the raw material to obtain high-quality goods for consumers. As cocoons are obtained through a biological activity from living organisms, they cannot be perfectly homogenous, and farmers should intervene in their selection through a high-labor and cost-demanding manual method. The mechanization of this operation can lower the production costs consistently and contribute to increasing the quality of the obtained silk. Therefore, the prototype described in this paper might be enlisted among the current efforts in reintroducing sericultural activity in Europe through regional and European projects based on the transfer of research innovations into practice and aimed at increasing the added value of the fiber intended for use in the textile luxury market or new smart applications. This prototype is a good workbench for future applications developed with new sensors or new AI algorithms, and it offers a chance to consistently improve the actual techniques used in cocoon sorting. Moreover, From the performance perspective, is possible to improve the accuracy of the machine, increasing the length of the belt, thus adding more control stations for cocoons processing. The speed of the selection process can be increased by adding one or more machines in parallel, all synced with each other, leading to a higher cocoons-per-minute rate.


The surface-enhanced Raman scattering (SERS) spectra of piperidine adsorbed on silver/chloride colloids were studied by a combined density functional theory (DFT)/time dependent DFT (TD-DFT) approach. The mechanism of chemical enhancement on the Raman signals is due to at least two contributions: the first comes from the changes in the molecular force constants and the dynamic polarizabilities of the normal modes, when the molecule is chemisorbed. DFT calculations satisfactorily reproduce the SERS spectra of piperidine adsorbed on silver, showing that the species formed on the silver particle is a complex formed by a deprotonated piperidine linked to a silver cation. A second contribution to the SERS chemical enhancement is due to a resonance Raman effect occurring when the wavelength of the Raman excitation falls within the electronic excitation band of the molecule/metal complex. Actually, the SERS spectra of piperidine show a significant dependence on the wavelength of the laser excitation, with a marked enhancement in the green-light region. TD-DFT calculations on the most-probable complex explain this behavior, because a strong excitation band of the complex is calculated in the green spectral region. This pinpoints that a resonance between the exciting radiation and the absorption band of this complex is responsible for this enhancement effect. In this way, one can observe the Raman spectra of molecular sub-monolayers adhering to metal surfaces of appropriate roughness, also without the formation of chemical bonds between the molecules and metal. The electromagnetic contribution plays a predominant role for the SERS enhancement because the chemical mechanism usually improves the enhancement factor only up to 102.SERS (surface-enhanced Raman scattering) effect ,2 providHowever, the chemical contribution plays an important role in the SERS spectral pattern, because strong ligand/metal interactions can provoke significant frequency shifts of the SERS bands with respect to the normal Raman spectra of the non-adsorbed molecules, as well as significant changes in the relative intensities. To observe a chemical SERS enhancement, it is necessary that the molecules approach up to distances of about 2 Angstroms to the metal surface, where defects are present as active sites in the chemisorption process. The chemical enhancement is due mostly to two contributions: one non-resonant (static), due to the perturbation in the electron density of the chemisorbed molecule, which can intensify the dynamic polarizabilities of some normal modes of the complex formed by ligand molecules and active sites of the metal surface; another of charge-transfer that can be evidenced as Raman resonance by varying the electrode potential or the exciting radiation. In particular, this resonance Raman effect occurs when the wavelength of the Raman excitation falls within the electronic excitation band of the molecule/metal complex. The first contribution, which affects both positions and relative intensities of the SERS bands differently, can be simulated well by DFT  calculations on the basis of the ground state of the model systems as molecules linked to metal adatoms, also recently performed ,6,7,8,9.Here, we have studied the chemical effect in the SERS spectra of piperidine adsorbed on Ag colloidal nanoparticles. Piperidine is a saturated heterocyclic molecule, which can chemically interact with metal through the lone pair of the nitrogen atom. However, this investigation is made more complex by the fact that piperidine can occur in liquid state in two different conformations, when the hydrogen atom linked to nitrogen is either in equatorial or in axial conformation with respect to the average plane of the molecule. In the gas phase, the equatorial conformation was found more stable , as well3  to excess NaBH4 , aged a week to prevent the formation of reduction products [−3 M concentration, in order to improve SERS enhancement. Piperidine . For the SERS spectra also the 457.9 nm and 488.0 nm lines of the ArUV−vis absorption spectra of colloidal suspensions were obtained in the 200–800 nm region by using a Cary 5 Varian spectrophotometer.+ cation, whereas to model the Ag(0) atom, we resorted to unrestricted calculations with spin multiplicity equal to 2. In TD-DFT calculations we considered up to 12 excited states of both singlet and triplet multiplicity .DFT calculations were carried out using the GAUSSIAN 09 package . OptimizFor the sake of completeness, we also checked two other levels of theory to investigate the most likely complex: (1) adopting a mixed basis set composed by Pople’s 6-311++G for the organic molecule and Lanl2DZ for the silver cation; (2) adopting the CAM-B3LYP /Lanl2DZ The binding energies for the piperidine-silver bonds of the different models of the complex were calculated taking into account the BSSE (basis-set superposition error) correction: in these cases, however, no implicit solvent by polarizable continuum model was used.−5 M concentration to a colloidal suspension of silver nanoparticles, activated by the presence of chloride anions, which provide a strong increase in the SERS signal of the adsorbed organic molecules. In this regard, it is important to underline that the chemical enhancement mechanism plays an essential role in the SERS spectra when the surface of the silver nanoparticles is activated by the presence of halide anions, in particular chlorides. These latter, when adsorbing on the metal, promote the formation of efficient active sites [Piperidine was added in 10ve sites ,25,26,27The SERS spectrum of piperidine adsorbed on Ag nanoparticles is reported in A DFT study could effectively reproduce the SERS spectrum of piperidine by considering model complexes where the molecule is linked to silver adatoms or adclusters through the lone pair of the nitrogen atom. In the past, it was shown that in the colloids activated by halide anions the surface-active sites were positively charged, so the SERS spectral patterns often appeared very similar to those shown by normal Raman spectra of the corresponding Ag (I) coordination compounds ,34,35,36Molecule linked through the lone pair of nitrogen to a silver atom;Molecule linked to a silver ion, because there can be positively charged active sites on the surface of Ag nanoparticles activated by coadsorbed chloride anions;Piperidine deprotonated due to a surface reaction with a positive active site, even if the pKa of the molecule in water is 10.45 (at 25°) and the pH of the colloid is around 9; this possible reaction can be schematized as in For this purpose, we have considered different complexes:+ and Anion-Ag+, we considered the possibility that piperidine binds to silver both in the axial position and in the equatorial position, due to an interconversion between conformers. Thus, we performed DFT calculations to obtain structures and Raman spectra of silver complexes, as shown in + bonds were much larger  than those corresponding to the complexes with the neutral molecule, whereas the energy differences between axial and equatorial conformations were almost negligible as reported.For each of the three complexes, called Pipe-Ag, Pipe-AgThe Raman spectra calculated for these six possible complexes are reported in + ion . Moreover, it should be noted that the strongest interaction between molecule and metal occurred for this complex, with the shortest N-Ag distance (2.111 Å) and the largest molecule→metal charge-transfer (−0.6932 |e|). In addition, the agreement between calculated Raman observed SERS frequencies was satisfactory, as shown in + equatorial complex, along with the normal modes corresponding to the prominent SERS bands, which were attributable to ring deformation modes. The N-Ag distance remained unchanged in the vibrations, except for the two bands at lower frequencies.The complexes in axial conformation were not able to reproduce the SERS spectrum; in particular, the only complex capable of effectively simulating the observed SERS spectrum, as regards both band positions and intensities, was that in which the anion of deprotonated piperidine in equatorial conformation was linked to an AgFurthermore, we performed further calculations to assess the robustness of our computational approach.(1)+ equatorial complex, identified as the most likely anchoring geometry, and we performed B3LYP calculations but with a mixed basis set made up of Pople’s 6-311++G for the organic anion and LanL2DZ for the metal cation: we did this in order to check if a larger basis set could improve the results presented above.We considered the Anion-Ag(2)On the same complex, we also adopted a CAM-B3LYP/LanL2DZ level of theory to check if long-range electronic correlation  could improve the agreement with the experiments.In both cases, the latter agreement was actually worse than with the standard B3LYP/LanL2DZ approach used in most of this paper. Indeed, the average difference between computed and experimentally observed frequencies increased, and some intense SERS bands could not be assigned, as shown in (3)5+) in order to better model the silver surface. This model did not show significant improvement in the agreement with SERS spectra, in comparison with calculations with a single Ag+. This result, reported in We performed B3LYP/LanL2DZ calculations on piperidine anion linked to a larger adcluster . On the other hand, it was evident that the chemical effect played a relevant role in the case of piperidine adsorbed on silver, since the SERS spectrum was very different from the normal Raman spectrum, in both positions and intensities of the bands.A marked intensification of the SERS signal by excitation in the region of green radiation (500–550 nm) was observed. This intensification cannot be attributed to the electromagnetic effect linked to plasmon resonance, because no plasmon band was observed in the green light region of the absorption spectrum of the Ag colloid in the presence of piperidine, as shown in + equatorial complex, which we selected as the most probable. A strong electronic excitation band occurred in the green region, as shown in Therefore, we performed a TD-DFT calculation on the Anion-AgFinally, the transition calculated around 540 nm was totally attributed to the excitation between the HOMO and LUMO orbitals, which, as shown in We studied the SERS spectra of piperidine adsorbed on silver/chloride colloids by means of combined DFT and TD-DFT calculations, analyzing the chemical effect on the Raman enhancement of the molecule. This effect, in fact, is particularly important for silver colloids activated by the coadsorption of chloride anions, as it provides a marked interaction between the molecules and active sites of the metal surface. In the specific case of piperidine, the importance of this effect is shown by the marked differences observed between the normal Raman spectrum and the SERS spectrum of the molecule.The chemical enhancement mechanism is due to at least two contributions, both related to the formation of molecule/metal complexes. The first contribution derives from the changes in both the molecular force constants and the dynamic polarizabilities of the normal modes, when the molecule is chemisorbed. Hence, significant differences occur in both positions and intensities of the observed SERS bands. In the present work, we analyzed these spectral features by DFT calculations, by considering model complexes with the piperidine molecules in two possible conformational situations, axial and equatorial, linked to neutral or positively charged silver adatom. We also took into account the possible deprotonation of piperidine when adsorbing on a positive metal surface. On the other hand, XPS studies previously ascertained that a significant amount of Ag (I) is really present on the surface of the silver nanoparticles activated by chloride ions . Here, oAs a conclusion, we showed how DFT calculations, along with a time-dependent approach, were able to account for the SERS spectra of piperidine, in particular for both the contributions to the chemical enhancement mechanism. In the future, a further improvement with experimental data may be achieved by adopting computationally more burdensome ab initio molecular dynamics simulations, along with specifically purposed tools, such as, for instance, the TRAVIS software  or appro


The PEC measurements showed that at higher concentrations, the use of higher magnetic fields increases the PHJ values, and lower magnetic fields cause a decrease in PHJ when using the higher nanoparticle concentrations.Hematite is considered a promising photoanode material for photoelectrochemical water splitting, and the literature has shown that the photoanode production process has an impact on the final efficiency of hydrogen generation. Among the methods used to process hematite photoanode, we can highlight the thin films from the colloidal deposition process of magnetic nanoparticles. This technique leads to the production of high-performance hematite photoanode. However, little is known about the influence of the magnetic field and heat treatment parameters on the final properties of hematite photoanodes. Here, we will evaluate those processing parameters in the morphology and photoelectrochemical properties of nanostructured hematite anodes. The analysis of thickness demonstrated a relationship between the magnetic field and nanoparticles concentration utilized to prepare the thin films, showing that the higher magnetic fields decrease the thickness. The  The HRTEM images were acquired at the FTO/α-Fe2O3 interface  is the irradiance spectrum of the light source used for the photoelectrochemical measurements, and A(λ) is the absorbance spectrum obtained from UV-vis spectroscopy.The optical properties of the hematite thin films were characterized by UV-vis spectroscopy. The ic field a, the coic field b, and thld (Jabs c 4]. Ac. AcJabs The process of forming thin films in the presence of higher magnetic fields promotes a higher-density nanoparticle film (compacted), and the process of sintering and removing the organic ligand can increase the porosity of the film, a factor associated with a decrease in PHJ values, as depicted in PHJ decreasing in the case of higher nanoparticle concentrations in lower magnetic fields. We observed that optimal performance was achieved with a lower concentration deposition or greater magnetic fields. To obtain thicker films with higher nanoparticle concentrations, a stronger magnetic field must be applied. Conversely, at lower concentrations, weaker magnetic fields can be used.The relationship between the efficiency’s dependence on the magnetic field and concentration and the photocurrent density is essentially identical. The magnetic field had a two-step effect on the performance of hematite thin films. At higher magnetic fields, all thin films exhibited a similar efficiency of approximately 7.9%, whereas the film with the lowest concentration achieved an efficiency of 9.3%. In contrast, for lower magnetic fields, the efficiency of these films was highly dependent on their concentration during preparation. The efficiency of the thin films prepared at high concentrations in weaker magnetic fields was poor. To achieve greater efficiency at different nanoparticle concentrations, it is necessary to employ strong magnetic fields; if a weaker magnetic field is employed, lower nanoparticle concentrations are required. Another possible explanation for the effect of magnetism is the presence of a magnetic field that aligns magnetic nanoparticles, which, when the material is converted to hematite, promotes the film’s texturing in the <110> directions, resulting in excellent photoelectrochemical properties. Future research will focus on the dip-coating rise and fall velocity, which could affect the formation of thin films, as well as how the presence of a magnetic field influences this process.PHJ and efficiency performance, regardless of the nanoparticle concentration. Applying a weaker magnetic field requires a lower nanoparticle concentration to activate a greater PHJ and efficiency.Our findings indicate that the magnetic-field-assisted CND method produces superior hematite thin-film control and high-performance PEC devices. Moreover, we believe that the magnetic field deposition process is a viable alternative to the active hematite benchmark efficiency. The magnetite nanoparticles that were synthesized were monodispersed with a narrow size distribution, presenting a stable colloidal solution and forming a ferrofluid. A comparison of the film thicknesses in relation to the applied magnetic field demonstrated that the magnetic field intensity could be used to regulate the formation and thickness of these films. In the presence of a stronger magnetic field, the SEM analysis revealed a reduction in the size of the pores. Intriguingly, the PEC results revealed that a stronger magnetic field is required to achieve a higher


Parameter estimation is extremely important for a radar jammer. With binary phase shift keying (BPSK) signals widely applied in radar systems, estimating the parameters of BPSK signals has attracted increasing attention. However, the BPSK signal is difficult to be processed by traditional time frequency analysis methods due to its phase jumping and abrupt discontinuity features which makes it difficult to extract PN (PN) codes of the BPSK signal. To solve this problem, a two-step PN codes estimation method based on sparse recovery is introduced in this paper. The proposed method first pretreats the BPSK signal by estimating its center frequency and converting it to zero intermediate frequency (ZIF). The pretreatment transforms phase jumps of the BPSK signal into the level jumps of the ZIF signal. By nonconvex sparsity promoting regularization, the level jumps of the ZIF signal are extracted through an iterative algorithm. Its effectiveness is verified by numeric simulations and semiphysical tests. The corresponding results demonstrate that the proposed method is able to estimate PN codes from the BPSK signal in serious electromagnetic environments. With the development of electronic warfare, the radar jammer has become one of the most important equipment of the modern war . It sensPossessing low probability of interception and strong anti-interference ability ,14,15, bRecently, some methods for PN codes estimation of the BPSK signal have been proposed. By synchronous demodulation, ref.  succeedsThe difficulty of PN codes estimation lies in the lack of appropriate signal analysis methods. The phase jumping and abrupt discontinuity features of BPSK signals lead the instantaneous frequency to an impulse function that is present at all frequencies. The current signal analysis methods, such as the short-time Fourier transform, Wigner–Ville distribution , waveletA novel PN codes estimation method based on NCTVR is proposed, and its corresponding optimization function is established.An iterative algorithm based on the forward–backward splitting algorithm is proposed to solve the NCTVR.The proposed method is verified by numeric simulations and semiphysical tests.Inspired by the previous works, a novel two-step PN codeS estimation method based on nonconvex total variation regularization (NCTVR) is introduced for BPSK signal. The NCTVR-based method first pretreats the BPSK signal by estimating its center frequency with an interpolation to Fourier coefficients (IFCs) frequency estimator  and convTo make the novelty of the proposed method more clearly, the difference among our method and the previous works are given in The remainder of this paper is organized as follows. The mathematical model is given in In modern electronic warfare, the radar jammer is widely applied for protecting owned units from the detection or guidance of the hostile radar systems. The principle of the radar jammer is shown in When the receiver of the jammer captures a radar signal, it first quadrature downconverts the captured signal into intermediate frequency with a local oscillator. Then, a pair of high-speed analog-to-digital converters (ADCs) with sampling rate c(n), For a BPSK signal, parameter estimation can be performed with a minimum mean square error estimatorIn this paper, a novel NCTVR-based PN code estimation method is proposed instead of the minimum mean square error estimator shown in (2) to reduce the complexity of the parameter estimation of a BPSK signal. The proposed method, whose diagram is shown in Here, we select an IFC estimator to estimate the center frequency of the digital intermediate frequency signal. To decouple the center frequency and phase jumps of the BPSK, the digital intermediate frequency signal is frequency doubled by subtracting the square of the real part of the digital intermediate frequency signal from the square of its imaginary part. The frequency doubling signal is denoted asδ. For this purpose, the two DFT coefficients between Assuming the total number of samples N is an integral power of 2 (This can be achieved by zero padding), the index of the bin with the largest magnitude after FFT is The noise terms will affect the accuracy of the residual, which is analyzed later. Finally, the estimated result of center frequency can be expressed asIts estimation accuracy can be improved by updating the value of The initial phase of the frequency doubling signal is estimated by the Fourier coefficients at Namely, According to (9), the estimated result of the initial phase The final ZIF signal obtained after secondary downconversion can be expressed asAfter secondary downconversion, the ZIF signal is sent to the NCTVR filter. To facilitate the analysis, we assume that Then, PN code estimation can be formulated as the following optimization problem:Compared with the L1 norm, the proposed MC penalty promotes sparsity more effectively and accurately preserves the amplitude of the piecewise-constant signal. Shown in When z play a role in enhancing sparsity. The extra computational complexity of MC penalty is (18), which includes twice matrix multiplications and a soft threshold function. The flow chart of NCTVR is shown in According to the above definitions, z is equal to zero, (19) is a classical TVR problem. The classical TVR problem can be solved with an L1-norm-based regularization penalty term, but it has a limitation that tends to underestimate the amplitudes of signal discontinuities. Therefore, we introduce a minimax-concave penalty function to improve the TVR problem. The sequence z, resulting from the minimax-concave penalty function, enhances the sparsity of the ZIF signal and makes it more robust to noise.The essence of the PN code estimation method proposed in this paper is to extract level jumps from a noise-polluted ZIF signal. According to (18) and (19), when As observed in z can be considered as the response of a Laplacian operator (LAPO) on the PN codes Therefore, the sequence Assume that As the last item of (21) is a higher order infinitesimal of variance As our initial phase estimation method is still based on Fourier coefficients, it variance of the estimated center frequency can be expressed asAccording to (18) and (19), NCTVR detects the PN codes with the first and second derivatives of the ZIF signal. The detection threshold is decided by the two regularization parameters The performance of the NCTVR-based PN codes estimation method is tested by numerical simulation and semiphysical in this section. In our simulation scenario, there is only one BPSK radar that works in the X band with a carrier frequency of 10 GHz. Its chipping width and code length are 50 ns and 31, respectively. The reconnaissance receiver, working under the SNR varying from −10~10 dB, is able to accept signals ranging from 10 GHz to 11 GHz by using a local oscillator of 10.5 GHz and high-speed ADCs with a 1 GHz sampling rate. As the distance between the radar and reconnaissance receiver changes slowly in a short time, the amplitude change of the received signal can be ignored. When the BPSK signal enters the reconnaissance receiver, it is quadrature downconverted and sampled to a digital intermediate frequency signal. When processing the obtained digital intermediate frequency signal, the iterations of the IFCs and NCTVR are 2 and 100, respectively, with regularization parameters where The effect of the pretreatment is estimating the center frequency and initial phase of the digital intermediate frequency signal and converting it to ZIF. The standard deviation of the center frequency and initial phase under different SNRs is shown in The simulation results of the NCTVR with different SNRs are shown in Estimation performance of the MC penalty and L1 norm is shown in To more intuitively reflect the estimation accuracy of the NCTVR, 5000 simulations under different SNRs are carried out, and the average correlation coefficients between the estimation results and the original PN codes are selected as the basis for evaluation. From the simulation results shown in To verify the performance of NCTVR, semiphysical tests are carried out in an anechoic chamber. In our experiment, the BPSK radar is 30 m away from the reconnaissance receiver. Its transmitting signal, with a 10 GHz carrier frequency and a chipping width of 20 ns, is captured and downconverted by an N8201A, and the obtained intermediate frequency signal, whose center frequency is approximately 100 MHz, is sampled by an M9203A digital receiver with a sampling rate of fs = 1 GHz. The final digital intermediate frequency signal is processed on a PC. The results of the NCTVR are shown in In this paper, a novel PN codes estimation method based on NCTVR is proposed for radar jammer. the NCTVR-based method first converts the digital intermediate frequency signal into ZIF with a pretreatment based on IFCs and secondary downconversion. This turns phase jumps of the BPSK signal into level jumps of the ZIF signal. Then, an MC penalty based NCTVR filter is introduced to extract level jumps of the ZIF signal. By adopting the MC penalty function instead of the L1-norm-based penalty, the NCTVR can extract accurately the level jumps of the ZIF signal under a serious SNR. Its estimation accuracy is over 70%, even under SNR −10 dB. Compared with the existing PN code estimation method, the advantage of the proposed algorithm is that it possesses a competitive estimation accuracy and do not need any priories. The results of the simulations and semiphysical tests show that the proposed method works well on a radar jammer.However, the proposed method needs an iterative algorithm to solve the optimistic problem, which affects its real-time performance. Therefore, our future work includes solving the NCTVR by ADMM and adopting the deep-ADMM-net to accelerate its speed.


M = 15.35, SD = 1.677) from a high school located in a rural town in northern Spain gave information on their physical activity (PA) levels in two different periods. Linear mixed models were used to examine these changes. The results indicated that vigorous physical activity (VPA) and the metabolic equivalent of task (MET) significantly increased between the two periods. A growth tendency of several components of fitness  was also observed. Finally, the results indicated that belonging to a sports club and getting involved in more VPA better explained the development in cardiovascular and muscle fitness between the two time points. Thus, the results of this study highlighted the relevance of membership in a sports club and vigorous PA in order to mitigate the potential negative effect of social distancing measures on physical fitness.The purpose of this study was to analyze the changes in physical activity and physical fitness between the beginning of the first academic year after a confinement (November 2020) and the beginning of the second academic year after a confinement  in a cohort of adolescents. Moreover, the evolution of physical fitness after controlling for physical activity was examined. A total of 687 students ( On 7 January 2020, the World Health Organization (WHO) identified a new coronavirus that constituted a global health emergency. Experts determined that person-to-person transmission happened during the incubation period of the virus . TherefoThere is a strong link between physical activity and health ,4. For iPhysical fitness levels are important predictors of health outcomes such as cardiovascular and skeletal health or mental wellbeing . ParticuBased on the above, one purpose of this study was to analyze the changes in physical activity between the beginning of the first academic year after a confinement (November 2020) and the beginning of the second academic year after a confinement  in a cohort of adolescents. A second aim was to investigate whether physical fitness varied between these two time measurement points. Finally, the third goal was to examine the evolution of physical fitness after controlling for physical activity levels and belonging to a sports club during this period.M = 15.35, SD = 1.77) from a high school located in a rural environment in the north of Spain. The inclusion criteria were age ≥ 11 years old and enrolled in the high school that was selected to conduct the study; participants also had to declare that they were in good health. Those students who reported a physical limitation were exempted from performing the tests involving the affected parts.The sample consisted of 687 compulsory education and baccalaureate students , aged between 11 and 19 years old (This was a quantitative within-subject study design. All participants took part in two measurement points (T1 and T2) and the outcomes were compared between them. A multilevel approach was used. In this study, physical activity levels and physical fitness were measured twice in a cohort of adolescents from a rural population (at the beginning of the first academic year after a confinement (November 2020) and at the beginning of the second academic year after a confinement . First, approval was obtained from the ethics committee of the university where the study was conducted. Subsequently, the research was introduced to the administration team of the participating school and the parents of the students; written consent was obtained from all of them. Students filled out a questionnaire that included questions about physical activity and socio-demographic information. Several fitness tests were then used to measure six components of their physical fitness  as well as their coordination. All tests used in this study belonged to the Eurofit fitness test battery. The physical fitness measurements were registered by the two physical education teachers of the high school, who had more than 10 years of experience in registering physical fitness measures. All tests were performed in the gym of the high school under the supervision of the teachers of the students at two time points (November 2020 and November 2021). In order not to influence the measurement process, each student was evaluated by the same evaluator at the two time points. Thus, each physical education teacher registered the physical fitness measures of their own students. All students were informed that the information would be treated confidentially. They were also informed that their participation was voluntary. The first wave of data was collected in the third week of November 2020, a period when physical activity and sports were regulated by the authorities and several activities were not allowed . The quarantine finished on 24 April 2020; from then, several stages of de-escalation measures were established such as a relaxed confinement or limitations to the practices and official tournaments of sports clubs. One year afterward, participants were asked again about the physical activity they were involved in and physical fitness was measured again (November 2021). The study respected the key ethical values and followed the principles of the World Medical Declaration of Helsinki.Physical Activity. The International PA Questionnaire (IPAQ) [www.ipaq.ki.se accessed on 5 November 2022) of the short form  was used [e (IPAQ)  is an inOther measures. The other measures included in the questionnaire were gender, age, and membership of a sports club .Trunk flexion. Sit-and-reach (SAR) measured the flexibility of the trunk. Initial position: the barefoot performer was required to sit in front of a box with their legs fully extended and the soles of their feet in full contact with the wall of a box. Procedure: the subject bent their trunk forward with straight legs and extended their arms and the palms of their hands to a ruler, trying to reach as far as possible. Final position: the subject reached out and held that position for at least one to two seconds whilst the distance was recorded in centimeters and millimeters. The best of two results was registered.Standing long jump test (broad jump). This was a test of explosive leg power. Initial position: the performer was required to stand straight, with their feet slightly apart and their toes behind a starting line. Development: the participants had to gain momentum to jump by bending their legs and pushing with their arms from behind to in front; the participant then attempted to jump as far as possible, landing on both feet without falling backward. Final position: At the moment of landing, the performer had to reach a maximum distance without losing balance. Assessment of the test: The total jump distance was the sum of three component distances; the takeoff distance, the flight distance, and the landing distance. The best of two results was registered.Crunches in 30 s (A30S). Sit-ups (SUP) measured the strength endurance of the abdominal muscles. Initial Position: the performer was in a supine position with their legs bent at 90° and their feet slightly apart with their fingers intertwined behind their neck; an assistant held the feet to fix them to the ground. Procedure: upon hearing the start signal, the performer had to try to attempt as many repetitions as possible—always touching the knees with the elbows and their back on the mat—and the evaluator counted the number of repetitions aloud. Completion: when 30 s had passed, the observer indicated to the performer that the test had finished. Assessment of the test: the number of repetitions performed correctly was registered.Course Navette (CN). This 20 m shuttle run test (20 m SR) measured the aerobic cardiovascular endurance or cardiorespiratory fitness. Initial position: the participants were behind a starting line, one meter away from each other. Procedure: A tape recorder was started. Upon hearing a sound signal, the participants moved to the opposite line (50 m), crossing it and waiting for the next sound signal. The participants had to follow the rhythm marked by the tape recorder for as long as possible. The speed increased incrementally. The test ended when the participants could not reach the opposite line before the sound of the subsequent warning on two consecutive occasions. Evaluation of the test: the completed stages were registered and the last stage announced before the performers abandoned the test was registered.Medicine ball throw (3 kg) (MBT3K). The medicine ball throw (MBT) measured the explosive force of the extensor muscles of the upper limbs, trunk, and lower limbs. Initial position: The students stood behind a marked line with their feet shoulder-width apart, holding the ball behind their heads with both hands. Procedure: the ball was thrown as far as possible and the participants stepped forward over the line after the ball was released. Assessment: The participants had three attempts; the best score was registered (centimeters).50 m race (50MR). This measured the displacement speed, maximum cyclic speed, or alactic anaerobic power. Initial position: the students stood behind the starting line of a prepared track. Procedure: At the sound of the word “ready”, the arm of the evaluator was raised and the students adopted an alert position. At the sound of the word “go”, the arm of the evaluator descended, the running began, and a stopwatch was started. Students attempted to run the 50 m as fast as possible without slowing down. Ending: when the finish line was crossed, the timer was stopped. Assessment of the test: the students repeated the test 2 times with a 10 m break between them; the best time of the two attempts made was recorded.Coordination test. Eye–hand coordination test (E-HCT). Participants had to throw a ball to a vertical wall with one hand and catch the ball correctly with the other hand as frequently as possible in 30 s. The participants were free to use overhand, underhand, or a combination of both for the throwing and catching. Subsequently, the participants were able to use their best motor performance strategies. The participants practiced throwing and catching the ball six times before the first attempt.All data were analyzed using the statistical package SPSS, version 27.0 . Descriptive analyses were performed and linear mixed models were used because the standard errors of the estimated model parameters were not underestimated (reducing the risk of inflation of type I errors) and missing data were not a problem . The outThe models were tested taking VPA, MPA, LPA, and the METs as the dependent variables and linear time , sex , and age as the predictor variables. The models were also tested taking the fitness dimensions as the dependent variables and the following as the predictor variables: Model A—linear time , sex , and age; Model B—linear time , sex , age, PA , and belonging to a sports club .p < 0.001) between T1 and T2 whereas MPA and LPA remained stable. Linear mixed analyses  showed tIn the physical fitness tests, in Model A   and the beginning of the second academic year post-confinement  in a cohort of adolescents living in a rural population in Spain. The results of this study indicated that VPA and the METs significantly increased between the two periods whereas MPA and LPA remained stable. Several studies have detected a decrease in physical activity due to confinements ,13,14, eExamining the evolution of physical activity and fitness in adolescents is relevant in this context because both physical activity and fitness are positively related to health ,29. Due The results also indicated a growth between the two periods in several components of fitness. For instance, the scores of the following components were significantly higher at the beginning of the second year post-confinement: upper body power, strength endurance , cardiovascular fitness, flexibility, and eye–hand coordination. These results suggested that during the confinement, physical fitness and coordination could have been negatively affected due to the quarantine decreed by the government ,20. HoweThe results of the present study provide new information because gender, age, and physical activity performed during the first and the second year post-confinement were controlled in the statistical analysis. Thus, a second model was tested where the variables of physical activity and membership in a sports club were included. The results indicated that the growth between T1 and T2 disappeared in the MBT and CN tests. In these two cases, minutes/week of VPA and PA performed in a sports club largely mitigated the effects of confinement. Therefore, the results of this study were in line with the findings of López-Bueno et al. , suggestThis study provides a new insight into how COVID-19 home confinements and other social distancing measures have affected physical fitness in a sample of Spanish adolescents. After one year of less restrictive social distancing measures, MVPA increased as well as a few capacities of fitness, particularly upper body muscular power, strength endurance, flexibility, cardiovascular fitness, and eye–hand coordination. However, it should be highlighted that after controlling for physical activity and belonging to a sports club, the results indicated that getting involved in more VPA and the commitment to a sports club better explained the development in upper body muscular power and cardiovascular fitness. Long periods of free movement restrictions may negatively affect cardiorespiratory fitness and health ,30. HoweThere are a few limitations of this study that must be addressed. First, this study did not provide a baseline for the impact of COVID-19 on adolescents, but examined the evolution of physical activity and physical fitness between two periods . Second, the participants came from a rural environment and a generalizability to other contexts cannot be established. Another limitation of this study was that the physical activity measures were obtained via a questionnaire; data collected through accelerometers could enrich the results. As the pandemic and school closures began in early March 2020, having a pre-test of physical activity closer to this date would have been ideal. However, it is important to note the radical reductions in physical activity reported in other studies due to the strict lockdown measures. Finally, field tests were employed in this study to assess physical fitness; laboratory measures could provide more valid results. Overall, this study provided preliminary data exemplifying the evolution in physical activity and physical fitness of adolescents after the lockdown measures due to the COVID-19 pandemic. Future research should explore the long-term effects of these variables.Previous research has observed that muscle and cardiovascular fitness decreased or its evolution was paralyzed in adolescents during the 2019–2020 school years. The results of this study are encouraging and suggest that both males and females tended to recover their physical fitness two years after the lockdown measures. The results indicated a growth between the first and the second year of the pandemic in five of the seven fitness tests. The findings indicated both males and females exhibited mean increases in upper body muscular power, strength endurance of the abdominal muscles, flexibility, cardiovascular fitness, and eye–hand coordination. However, the relevance of physical activity in the physical fitness of adolescents should be highlighted. The results suggested that being involved with sports clubs and performing higher levels of VPA seemed to be related to better cardiovascular and muscle fitness after the two years since the beginning of the so-called “new normality”. As the effects of COVID-19 could be prolonged, future research could focus on understanding why sports participation saw a greater association with these health-related fitness markers. This would be beneficial in this context in order to propose effective solutions to encourage physical activity and membership in sports clubs or associations. Public support for children and families should be considered to encourage the positive effects of physical activity in youths, and guidelines to ensure the maintenance of physical activity in adolescents should be established if similar situations are repeated in the future.


Finally, the performance of the improved method is quantitatively evaluated in terms of the denoising quality and efficiency using the signal-to-noise ratio, root-mean-square error, sample entropy and running time.There are high- and low-frequency noise signals in a microseismic signal that can lead to the distortion and submersion of an effective waveform. At present, effectively removing high- and low-frequency noise without losing the effective signal of local waveform spikes remains a challenge. This work addresses this issue with an improved wavelet adaptive thresholding method. Because a denoised signal conceptually approximates the minimum error, a dynamic selection model is established for the optimal threshold. On this basis, an adaptive correction factor  Catastrophic rockburst can cause rock fragments to fly and rock masses to collapse over a large area, which seriously affects normal construction operations and threatens worker safety. Microseismic monitoring technology is an effective early warning method for rockburst monitoring4, based on real-time captured rock catastrophe evolution signals, to obtain information on seismic parameters for the timely and accurate monitoring of malignant rockburst patterns. In deep rock engineering operations, when accompanied by mechanical vibration, bottom noise and other signals, the signals captured by a microseismic monitoring system are mixed with noise signals, resulting in large errors in the initial arrival of microseismic events and source parameter data, which seriously affects mining and the accurate prediction and early warning of rockburst disaster precursor information. Therefore, the development of monitoring signal denoising processing is an important component of accurate rockburst precursor information acquisition.Rockbursts often occur in deep rock mass engineering projects such as tunneling5, Mousavi et al.6, and Cecilia Dip et al.4 developed a denoising model using a conventional digital filter to achieve noise signal suppression. Iqbal et al.7 and Nasr et al.8 proposed a signal filtering method based on SVD that removes the noise signal in the monitoring signal. The methods based on conventional digital filtering and SVD are mainly used to eliminate high-frequency noise signals9. Empirical mode decomposition is a new signal time–frequency processing method that realizes the extraction of pure signals through the processes of polluted signal decomposition, high-frequency signal filtering and signal reconstruction11. However, the empirical mode decomposition algorithm produces modal aliasing, boundary effects and other phenomena when processing signals12, which easily deform and distort the signal waveform. To compensate for the deficiency of the empirical mode decomposition denoising algorithm13, multiple model combination methods such as SVD14, EEMD-SVD and ELM15 have been successively proposed for signal denoising research. The results show that the denoising effect of the combination method is better than that of a single method, but the combination method increases the complexity of the model and reduces the noise removal efficiency.In the microseismic signal acquisition process, the effective signal of the microseismic waveform can be interfered with and drowned out by noise signals of different frequencies. To obtain pure microseismic signals, Iqbal et al.16, signal denoising17, image processing19, fault detection20 and other fields. The wavelet thresholding algorithm is suitable for nonsmooth or nonperiodic signal denoising, with good amplitude preserving and denoising effects, and is therefore suitable for denoising microseismic signals22. In the wavelet thresholding algorithm, the selection of the threshold function is critical, and the commonly used threshold functions are the hard threshold function and soft threshold function. Using the hard threshold function to denoise a waveform will cause jitter in the local area of the processed waveform and damage the effective waveform. Using the soft threshold function to denoise the waveform is smoother, but the peak signal-to-noise ratio (PSNR) of the processed waveform is smaller than that of the waveform processed by the hard threshold function because the wavelet coefficients change greatly. Currently, optimizing threshold functions is a popular direction in signal processing. Bayer Fábio et al.23, Lu et al.24 improved the mathematical model of the threshold function, which effectively improved the signal-to-noise ratio of the signal. Zhu et al.25 proposed a multiple-threshold denoising method with multiple denoising rules for microseismic signals, which effectively removed the noise signal from the original monitoring signal. The above method is effective in eliminating noise and improves the signal-to-noise ratio of the signal. However, the microseismic signals collected in the field are mixed with noise signals of different frequencies, and it is difficult for the above method to suppress the complex noise at low frequencies in the microseismic signals by shrinking the high-frequency wavelet coefficients9, which makes the waveform take-off position blurred.The wavelet thresholding algorithm is a mainstream algorithm for signal denoising processing and has been used in laser waveform processingTo effectively remove the noise of different frequencies, retain the effective signal in the prominent area and clearly highlight the take-off position of the waveform, a wavelet adaptive threshold denoising method suitable for microseismic signal denoising is proposed in this paper. The method is optimized in three aspects—standard variance, threshold function and wavelet coefficient estimation function—to achieve the separation of mixed high- and low-frequency noise in microseismic signals and to provide support for the accurate acquisition of seismic source parameters and the prediction of rockburst.Microseismic monitoring technology is an important means of rockburst disaster monitoring and early warning. Due to the bottom noise and other interference, the microseismic waveform collected by a geophone is often drowned out by other noise signals, as shown in Fig. 25. When affected by the noise signal, the waveform take-off is ambiguous, which makes the initial arrival time error of the microseismic waveform large, resulting in inaccurate location of the microseismic event, as shown in Fig. In microseismic monitoring technology, the accurate pickup of microseismic signals at the initial arrival time is one of the keys to ensure the source positioning accuracyHow to effectively remove the high- and low-frequency noise while retaining the useful signal is the difficulty of signal denoising. The wavelet threshold denoising method mainly denoises by suppressing the useless signal and enhancing the useful signal and has the function of retaining the local peak of the waveform  and suppressing the interference noise. Research has shown that after the signal has been processed by wavelet transform, there is a difference in the size of wavelet coefficients corresponding to the effective signal and the noisy signal; therefore, the purpose of denoising can be achieved by setting a threshold value. The specific process is as follows: first, the original signal is decomposed by wavelet transform to obtain wavelet coefficients. Figure Wavelet base selection: in practical applications, completely ideal wavelets do not exist. Generally, compactly supported wavelets are selected or appropriate wavelet bases are selected according to the characteristics of the signal.j-th layer and the unified threshold, respectively. Where N is the number of wavelet coefficients of each layered detail signal and Cd is the detail coefficient.Threshold calculation: The waveform denoising results obtained by selecting different thresholds are also different. As shown in Eqs.  and 2),,2), theya is equal to 0, it is the mathematical expression of the hard threshold; when parameter a is equal to 1, it is the mathematical expression of the soft threshold.wj,k are wavelet estimation coefficients and wj,k represent wavelet coefficients.Wavelet threshold function selection: the threshold function plays the role of modifying the wavelet coefficients. The commonly used threshold functions include hard and soft threshold functions. Equation  is the mIn the process of wavelet threshold denoising, the rational selection of the wavelet base, threshold and threshold function is very important and determines the quality of signal denoising.To overcome the shortcomings of the traditional wavelet threshold algorithm, a wavelet adaptive threshold method suitable for denoising microseismic waveforms is proposed. The method is mainly improved from the following aspects:The mean and median are typical statistics that reflect the overall situation of the sample, representing the mean and median levels of the sample, respectively. When the sample data have skewed distribution characteristics, the median can make up for the shortcoming that the mean is easily affected by extreme values and more accurately reflect the overall situation of the sample data. According to the overall situation of the original microseismic monitoring data, the median is introduced to improve the standard deviation. The improved standard variance expression is:26, denoising using a uniform threshold strategy can impair the useful signal. To solve this problem, the threshold determination method in bad data repair is improved and introduced into the field of mine microseismics27, and the denoised signal is used to approximate the minimum error to establish a threshold model with adaptive characteristics. The steps for the model to dynamically prefer the optimal threshold are as follows:①P. The elements of P are the absolute values of the wavelet coefficients of the j-th layer arranged according to the size relationship. The value of ri is calculated using Eq. ..j-th lay30. The analysis shows that the waveform deformation and damage in the protruding area are caused by the excessive contraction of the effective signal; the setting of parameter a is too large. To combine the advantages of both soft and hard thresholding methods, Ge et al.31 and Qiao et al.32 set the parameter a in Eq. . As shown in Fig. According to the hard threshold and soft threshold functions, when x-values to denoise the six sets of test signals in turn. As shown in Fig. SNR values of the six groups of curves in (a) first increase, then slightly decrease, and then remain unchanged with the increase of x value, and the x value corresponding to the peak value is 1/2; (b) The RMSE value of the six groups of curves decrease with the increase of x value, then slightly increase and then stabilize, and the corresponding x value of the peak value is 1/2. Therefore, the value of x is determined to be 1/2.An improved wavelet thresholding model is established using a cyclic approach calling 1/6, 1/5…, 2 as a according to the wavelet coefficients of each decomposition layer, the adaptability, robustness and accuracy of wavelet coefficient correction of different decomposition layers can be improved. The improved wavelet coefficient estimation function for the j-th layer based on Eq.  and, according to the index value, find the corresponding element in P and record it as CDmin;Step 6 Call the standard deviation and CDmin value, combine Eqs. (ine Eqs.  and 9),,Step 6 CStep 7 Establish an ‘if’ statement according to Eq. , median-based standard variance value with mean-based aj (Median-Mean), mean-based standard variance value with mean-based aj (Mean-Mean), and the formula based on the standard variance value of the mean with aj based on the median (Mean-Mean), four denoising models are established to denoise the contaminated signals, and the denoising results are shown in Table Based on the unstable and aperiodic characteristics of microseismic signals, this paper introduces the median and improves the expressions of the standard variance and correction coefficient aj proposed in this paper can effectively improve the noise removal effect.As seen in Table aj added in this paper, the models with and without coefficient aj are used to denoise the polluted signal. As shown in Fig. aj is not increased, the noise is not completely eliminated because λj is not effectively corrected, which is shown in regions ① and ③; the sudden change point of the curve obtained by the model without increasing coefficient aj has a large error with the original curve. In region ②, as the curve obtained by the model without increasing coefficient aj is outside the contour of the local spike of the original signal curve, the useful signal is eliminated. In region ④, the curve obtained by the model without increasing coefficient aj is within the contour of the original signal curve. When coefficient aj is added, the model obtains the corresponding best correction coefficient according to the wavelet coefficient of each decomposition layer, as shown in Fig. λj ≥ wj,k ≥ -λj, wj,k are judged as wavelet coefficients generated by the noisy signal and set to 0. Otherwise, wj,k are judged as wavelet coefficients generated by the useful signal, and λj is corrected with coefficient aj of the j-th layer as the adjustment scale, thus obtaining the estimated value of wavelet coefficients. As shown in Fig. aj, the mutation point of the curve obtained by the model with coefficient aj is closer to the original curve. In area ②, the model with the added coefficient increases the denoising scale under the correction of aj, effectively removing the residual noise signal, and the obtained denoising signal curve is shrunk compared with the model without adding coefficient aj. In region ④, the model with the added coefficient reduces the denoising scale under the correction of aj, avoiding the loss of useful signal, and the obtained denoising signal curve is amplified compared with the model without adding coefficient aj. In regions ② and ④, the curve obtained by adding coefficient aj has the highest degree of agreement with the original signal curve. This shows that the addition of coefficient aj is of great significance to the denoising method proposed in this paper.To verify the effectiveness of the coefficient The traditional wavelet threshold method, Jing-yi’s method, Huai-Lian’s method and the method of this paper are used to eliminate the random noise in the polluted signal. As shown in Fig. SNR is increased by 0.218 times, and the RMSE is reduced by 0.251. Compared with Jing-yi's method24 and Huai-lian's method9, the SNR value is increased by 0.150 and 0.005 times, respectively, and the RMSE value is decreased by 0.143 and 0.006, respectively. The sample entropy value is 0.0056. The time used for denoising one time is 0.408 s, which is lower than the other three denoising methods, and the denoising efficiency is high.The denoising effect of the method proposed in this paper is the best, as shown in Table To quantitatively evaluate the effectiveness and applicability of the denoising model, the microseismic simulation signal and its polluted signal were generated. As shown in Fig. aj. The results are shown in Table To further verify the effectiveness of the method proposed in this paper, noise elimination processing of the polluted signal of the microseismic simulation signal is carried out by using the five models established in Section "aj and the original signal is large; in region ②, the curve obtained by the model without adding coefficient aj is within the contour of the original signal curve, indicating that the model denoising scale is too large, so that some useful signals are eliminated. To effectively eliminate the noise signal, the Median-Median model (the model with the added coefficient aj) decomposes the polluted signal into 8 layers and obtains the best correction coefficient a for each layer, as shown in Fig. aj. In area ②, compared with the curve obtained by the model without adding coefficient aj, the curve obtained by the median-median model is expanded as a whole under the action of coefficient aj, and the degree of coincidence with the change trend of the original curve is higher.As shown in Fig. After filtering with four different methods, the noise signal reduction effect is remarkable. As shown in Fig. SNRs (DBs) of Jing-yi’s method, Huai-liang’s method and the method proposed in this paper are increased by 0.305, 0.38 and 0.43 times, respectively. The error  between the denoised signal processed by the method proposed in this paper and the original signal is the smallest, which is 0.870. Among the four methods, although the method proposed in this paper takes the longest time to remove noise, it meets the requirements of denoising efficiency for microseismic signals. The sample entropy value of the signal after denoising using the proposed method in this paper is only 0.0078, indicating that it contains very little noise signal. Therefore, the proposed denoising method can effectively remove mixed high- and low-frequency noise signals.As shown in Table 9 that Huai-liang's method has a good denoising effect, which further highlights the first break of noisy microseismic records. As shown in Figs. Rock rupture and blasting signals are common microseismic signals in deep underground engineering. Affected by the engineering environment, the signals collected by the microseismic monitoring system in the field are more complex than the synthesized simulation signals. In this section, on the premise of setting the same wavelet basis function and number of decomposition layers, the method proposed in this paper and Huai-liang's method are used to denoise the typical rock fracture and blasting microseismic signals collected in mines. It can be seen in the literature34. Therefore, using the traditional fixed threshold denoising strategy will cause problems such as incomplete removal of noise signals and loss of useful signals. To compensate for the deficiency of the fixed threshold denoising strategy and effectively eliminate the noise signal in a microseismic monitoring signal, the traditional wavelet threshold denoising method is improved as follows: (1) The optimal threshold dynamic selection model is established. To make the denoised signal approach the minimum error, the model calculates the unified threshold and adaptive threshold of wavelet coefficients in each decomposition layer and automatically compares and selects the best threshold according to certain rules, which can eliminate the mixed noise signal to the greatest extent. (2) Due to the complexity and diversity of the noise signal, as shown in the curves obtained from the model without incremental coefficients aj in Figs. aj is proposed. The coefficient can accurately reflect the noise intensity according to the wavelet coefficients of each decomposition layer and effectively modify the denoising scale (The wavelet threshold denoising method provides an ideal tool for signal filtering. The research shows that in the process of wavelet decomposition, the amplitude of the wavelet coefficients of the useful signal remains stable and does not change with the change in the number of layers of wavelet decomposition; the amplitude of the wavelet coefficients of the noise signal exhibits attenuation changes with the increase in the number of wavelet decomposition layersng scale .aj preserves the morphological characteristics of the original signal to the greatest extent and effectively solves problems such as residual local peak noise signals and the loss of useful signals in the waveform mutation area. According to the comparison in Tables Compared with the traditional methods of hard threshold, soft threshold and half threshold, the proposed method breaks through the limitation of fixed threshold and has adaptive characteristics. The quantitative evaluation of synthetic signals shows that it can eliminate both high- and low-frequency noise at the same time and can make the waveform jump position clear. To address the problem of useful waveform damage in the mutation region, the noise intensity of each decomposition layer is proposed to further modify the denoising scale. As shown in Figs. An optimal threshold dynamic selection model is established. The model can automatically select the best threshold from the unified threshold and adaptive threshold of each decomposition layer according to certain conditions so that the denoised signal is infinitely close to the minimum error, realize the simultaneous elimination of mixed noise signals of different frequencies in microseismic monitoring signals, and make the waveform take off clearly.An adaptive correction coefficient reflecting the noise intensity is proposed. This coefficient uses the 1/2 power value of the absolute value of the wavelet coefficient of each decomposition layer to the ratio of the amplitude to reflect the noise intensity of each decomposition layer, ensures that the wavelet coefficient of each decomposition layer modifies the denoising scale according to its own conditions, and retains the useful information of the local peak area of the waveform to the greatest extent.Based on the optimal threshold dynamic selection and adaptive correction coefficient method, a new denoising model is established. The synthetic signal and monitoring signal test show that, compared with the traditional wavelet threshold denoising method and the method proposed by Jing-yi, the proposed method can significantly improve the signal-to-noise ratio of microseismic signals and is suitable for the real-time processing of complex noise in microseismic signals.To compensate for the shortcomings of the traditional wavelet threshold method, this paper proposes a new wavelet threshold method suitable for the denoising of microseismic signals by optimizing and improving the traditional method to determine the threshold strategy and wavelet coefficient estimation function. Its main conclusions are as follows:Supplementary Information.