This paper investigates a textile material of low surface mass for its protection against electromagnetic radiation (EMR), which is suitable for composite structures of garments, and for technical and interior applications. The shielding effectiveness against EMR of fabric knitted from polyamide threads coated with silver, measured in the frequency range of 0.9 GHz to 2.4 GHz, indicated a high degree of protection. The key contribution of the paper is the evaluation of the stability of the shielding properties against EM radiation after applying apolar and polar solvents, in synergy with the cyclic process parameters of wet and dry cleaning. The results of the study confirmed the decline in the shielding effectiveness after successive cycles of material treatment with dry and wet cleaning. The effect of wet cleaning in relation to dry cleaning is more apparent, which is due to the damage of the silver coating on the polyamide threads in the knitted fabric. The increased awareness of EMR has led to the worldwide introduction of new regulations for manufacturers of electrical and electronic devices, which must now comply with the electromagnetic compatibility requirements (EMC requirements). The need to set limits for the EM radiation of electrical and electrical devices  that radiate EM energy in different frequency ranges aims to minimise the possibility of interference with radio and wired communications. The lifespan and efficiency of electronic devices can be increased by their protection against electromagnetic interference ,3,4,5,6.absorption,reflection,secondary reflection.When they hit the surface of a material, EM rays cause the charge in the material to oscillate. This forced oscillation of the charge acts as an antenna and results in reflection, whereas the other part is converted into thermal energy due to the oscillation. This kind of signal loss is known as attenuation due to absorption. Thus, the protective property of the material against EMR is based on the reflection against the conductive surface and the absorption in the conductive volume. Part of the wave is reflected, while the rest is transmitted and weakened as it passes through the medium .The combined effect of losses through reflection and absorption determines the effectiveness of the protective properties of the material, depending on its electrical and magnetic properties, surface and interior conductivity properties, material thickness, material composition, abrasion, and degree of processing .SE). The shielding effectiveness of the conductive barrier SE in dB is the sum of loss of reflection (R), loss of absorption (A), and the loss of secondary reflection (rR), and is calculated according to Equation (1):The ratio of the level of the electric field at a certain distance from the source without protection (shield) and the level of the electric field with protection is defined as the shielding effectiveness , while the protective properties of EMR were monitored by testing the properties of the shielding on the frequencies of 0.9 GHz, 1.8 GHz, 2.1 GHz, and 2.4 GHz.The specifications of the shielding electrically-conductive knitted fabric made of polyamide (PA) yarn coated with silver (Ag) are presented in This functional knitted fabric can either be incorporated as a functional interlining in clothing or used to make children’s clothing due to its soft touch and antimicrobial properties, made possible by the silver. The non-stick type of interlining with protective properties against EM radiation is put between the base material and the lining, thus forming part of the composite structure of the garment. Garments are exposed to various mechanical and physicochemical influences, which makes it necessary to objectively evaluate the protective properties of their materials or composite structures before and after exposure to different frequencies. This is an important factor for the assessment of the service life of a garment with added value, which in this analysis is the protection against EMR (electromagnetic radiation).The functional material (PA/Ag) is exposed to a cyclic treatment with a polar solvent (water) in wet cleaning, and an apolar solvent (perchloroethylene) in dry cleaning. These physicochemical processes were conducted through the synergy of solvents and the process parameters of the Sinner’s circle: chemistry, mechanical agitation, temperature, and time . Wet cleThe surface of the PA/Ag fabric was analysed before and after the cyclic treatment with an apolar solvent in dry cleaning (P) and a polar solvent in wet cleaning (W) under Sinner’s circle parameters; the samples were observed after the 1st, 3rd, 5th, 7th and 10th cycle. Despite the silver content in the PA/Ag fabric, all of the samples were coated with gold and palladium for 90 s using Emitech Mini sputter coater SC7620 ). The observation of the samples’ surface was performed with the SE detector of the scanning electron microscope FE-SEM, MIRAIILMU, Tescan, Czech Republic, under a magnification of 500×.temperature 23 ± 1 °C,relative humidity 50 ± 10%.The shield properties of the tested samples were investigated using a method described in detail elsewhere , under ta measuring instrument: NARDA SRM 3000,an HP 8350 B signal generator,an IEV horn antenna: Industrija za elektrozveze (Telecommunication Industry), Ljubljana, Type A12,a wooden frame, in which a sample of PA/Ag material of 1 m × 1 m was placed.According to the recommendations of the IEE-STD 299-97 , MIL STDE0) measured without the fabric and the EM field intensity (E1) with the material placed between the radiation source and the measuring device.The EM protection factor was determined as the ratio between the EM field intensity (SE (dB) was calculated according to the following Equation (2):E0 is the field level without protection (shield),E1 is the field level with protection (shield).The shielding effectiveness 0 represents the initial shielding effectiveness of the PA/Ag knitted fabric,SEP represents the shielding effectiveness of the PA/Ag knitted fabric after the 1st, 3rd, 5th, 7th, and 10th dry cleaning cycles,SEW represents the shielding effectiveness of the PA/Ag knitted fabric after the 1st, 3rd, 5th, 7th, and 10th wet cleaning cycles.SEThe change in the shielding effectiveness of the PA/Ag knitted fabrics after the 1st, 3rd, 5th, 7th, and 10th cycles of dry and wet cleaning is expressed using Equations (3) and (4):The cyclic exposure of the PA/Ag material to solvents in synergy with the process parameters led to a change in the material thickness tested according to EN ISO 5084: 2003, as shown in Due to the presence of amide bonds in the macromolecules, PA fibres can form hydrogen bonds, owing to which they have a better ability to absorb moisture   in synergy with the dry cleaning process parameters could be seen after five cycles, which can be attributed to the successive cycles. With the increase in the number of cycles on the knitted fabric, greater longitudinal damage of the silver coating on the threads was noticed, which intensified in the 10th cycle. The irregular shape of the damage and the appearance of ruptures on the silver coating indicate a more intense influence of mechanics as a process factor, which led to fractures of the material. Such local damage excludes the influence of solvents, which would act more evenly over the entire surface. The changes in the surface of the PA/Ag fabric under the influence of the polar solvent (W) and wet cleaning process parameters were visible after the 3rd cycle. The change dynamics were more intense compared to the dry cleaning (P). Additionally, irregular incrustations could be seen on the sample surface after 10 cycles of wet cleaning (W_10), which indicate the interaction of some of the substances in the process. The SEM images indicate that the polar solvent, in synergy with the process parameters of wet cleaning, caused a higher degree of longitudinal and irregular local damage to the PA/Ag fabric compared to the apolar solvent and the process parameters of dry cleaning.The examination of the durability of the protective properties of elastic yarns with metal coatings has confirmed that the changes in the conductive properties during washing depend on the type of coating. The obtained results deviate from the research conducted on silver-coated textiles after washing in 25 cycles; the silver coating remained almost unchanged .The polyamide, as a pure polymer, exhibits non-conductive properties, while the coating with Ag enhanced the electrical conductivity of the material and increased its shielding effectiveness .The shielding effectiveness (SE) of the face and reverse side of the PA/Ag fabric before the solvent treatment with process parameters at frequencies of 0.9 GHz, 1.8 GHz, 2.1 GHz, and 2.4 GHz are shown in The protective properties of the face and reverse side of the untreated PA/Ag samples at all of the frequencies are almost identical, as shown in The first cycle of the PA/Ag fabric treatment with the dry cleaning (P) and wet cleaning (W) reduced the degree of protection at 0.9 GHz. The wet cleaning (W) had a stronger influence compared to the dry cleaning (P), while the largest difference of SE properties was confirmed after 3 cycles. The almost linear and parallel decline of the SE properties continues after the 5th, 7th, and 10th cycle .The first cycle of the PA/Ag fabric treatment with the dry cleaning (P) and wet cleaning (W) solvents, in synergy with process parameters, reduced the degree of protection at 1.8 GHz. The wet cleaning had a stronger influence compared to the dry cleaning; the largest difference of SE properties was found after the 7th cycle .The first cycle of the PA/Ag fabric dry cleaning and wet cleaning reduced the degree of protection at 2.1 GHz. The wet cleaning had a stronger influence compared to the dry cleaning. The largest difference in SE properties was found after the 1st cycle, and the almost linear and parallel decline in the SE properties continued after the 3rd, 5th, 7th, and 10th cycle .Textile materials characterized by a shielding effectiveness (SE) of >20 dB are acceptable for industrial applications , meaningThe SE differences of the treated PA/Ag fabric caused by the physicochemical influence of dry and wet cleaning in relation to the untreated fabrics are shown in Based on the obtained efficiency differences (dSE) at all frequencies, a more progressive influence of the wet cleaning on the decrease of the SE value compared to the dry cleaning could be clearly noticed. The largest efficiency differences were found at 2.4 GHz. The impact of the process parameters in wet and dry cleaning on dSE correlated well with the surface observation of the SEM images.The initial surface damage of PA/Ag fabrics is observed after the 5th dry cleaning. The obtained dSE values of the PA/Ag fabric through five cycles of dry cleaning at all frequencies are almost equal. The differences in dSE between the lower and higher frequencies are noticeable after the 7th and 10th cycles of dry cleaning.The subsequent cycles of wet cleaning affect larger differences at all frequencies compared with the 1st cycle. The initial surface damage of the Ag layer on the PA fabric observed after the 3rd wet cleaning cycle caused a great decrease in the dSE value, especially at a frequency of 2.4 GHz.It has been confirmed that the damage of the fabric surface layer in the wet cleaning process affects the decrease in SE values. The obtained results for the samples treated in the wet cleaning process were not in accordance with the results presented in , in whicPolyamide knitted fabric made of silver-coated thread possesses an optimal electromagnetic shielding effectiveness in the frequency range from 0.8 GHz to 2.4. GHz. The protective factor, minimal weight and thickness are promising characteristics for clothing, interior, and technical applications. The initial SE properties were changed under repeated cycles of dry and wet cleaning. The increased number of wet and dry cleaning cycles caused a linear drop of the SE values at 0.9 GHz, 1.8 GHz, 2.1 GHz, and 2.4 GHz. The SEM images indicated the damage of the silver coating on the polyamide yarn. The degradation was more noticeable after the wet cleaning than the dry cleaning.


The LARES (LAser RElativity Satellite) was built by the Italian Space Agency (ASI) and launched on 13 February 2012 by the European Space Agency. It is intended for studying the Lense–Thirring effect resulting from general relativity as well as for geodynamic studies and satellite geodesy. The satellite is observed by most ground laser stations. The task of this work is to determine the station coordinates and to assess the quality of their determination by comparison with the results from the LAGEOS-1 and LAGEOS-2 satellites. Observation results in the form of normal points  were downloaded from the EUROLAS Data Center for the period from 29 February 2012 to 31 December 2015. Seven-day orbital arcs were computed by the NASA GSFC GEODYN-II software, determining the coordinates of seventeen selected measuring stations. The average Root Mean Square (RMS) (15.1 mm) of the determined orbits is nearly the same as for LAGEOS (15.2 mm). The stability of the coordinates of each station (3DRMS) is from 9 mm to 46 mm  with the uncertainty of determining the coordinates of 3–11 mm (LAGEOS 2–7 mm). The combined positioning for the LARES + LAGEOS-1 + LAGEOS-2 satellites allows for the stability of 5–18 mm with an uncertainty of 2–6 mm. For most stations, this solution is slightly better than the LAGEOS-only one. The LARES (LAser RElativity Satellite) is mainly intended to study the Lense–Thirring effect resulting from general relativity ,2,3. ThiWhy is the LARES the most useful low-altitude geodetic satellite to co-establish the ITRF? Among the geodetic satellites, the basis for determining the coordinates of the stations are the LAGEOS-1 and LAGEOS-2. Etalon-1 and Etalon-2 satellites are also used for this purpose, but due to their high orbit  there are too few laser measurements to improve the quality of the coordinates determined from LAGEOS. The list of current passive geodetic satellites, which are in the shape of a sphere for more accurate determination of the center of mass of the satellite, is presented in Among the satellites presented in The LARES satellite, built by the Italian Space Agency (ASI), was launched on 13 February 2012 from the ESA Kourou Center in French Guiana using the Vega VV01 rocket. The satellite is moving in a circular orbit at a distance of 1450 km with an inclination of 69.5 degrees over a period of 1.9 h. The satellite is only intended for laser observations. It is a sphere with a diameter of 36.4 cm and a mass of 386.8 kg (the densest object in the solar system). On the surface it has 92 corner cube retroreflectors with a diameter of 38.1 mm and a depth of 27.9 mm each. The satellite is observed by most ground laser stations.−4 m2/ kg (the densest known object in the solar system (15.3 g/cm3)), its circular orbit (an eccentricity of 0.0008). The main advantages of LARES satellite for its use in satellite geodesy are as follows: its spherical shape, very well-defined center of mass, high mass (M) , small cross section (A) , small cross-section to mass, the ratio A/M = 2.69 × 10Its disadvantage is its low satellite orbit 1450 km), which entails a significant impact due to high harmonics of the Earth’s gravity field (Earth’s gravity field coefficients up to 100 × 100), earth albedo, and residual atmospheric drag [0 km, whiThe orbit of the LARES and SLR station coordinates have been determined using the NASA GSFC GEODYN-II orbital software  from theFrom among the force models and parameters presented in Due to the non-convergence of the iterative process, 21 arcs were rejected, mainly due to the low number of normal points (in the most cases below 900 per arc), especially during the winter time. The list of all SLR stations that were observing the LARES in the period from 29 February 2012 to 31 December 2015 is given in To obtain the most accurate results, three scenarios for the selection of core stations were tested, the results of which formed the orbit of the LARES. The tests were performed using the parameters and models given in Scenario 1: 15 SLR stations selected on the basis of the quality assessment and the number of normal points of the LAGEOS were used.Scenario 2: all 40 stations that observed the LARES during the period were used.Scenario 3: based on the results of scenario 2, the 17 best stations were selected as the core stations for creating the orbit of the LARES.The evaluation of the obtained results was carried out for each station based on the number of normal points, the average 3DRMS of coordinates determined, and the continuity of the observations. The best results were achieved for scenario 3 and these 17 stations were used as the core stations for further analysis , bold.The geocentric coordinates of the stations computed by the NASA GSFC GEODYN-II software were processed to the topocentric coordinates North, East, and Up by means of the Borkowski transformation . The resTo compare the results of the LARES with the LAGEOS-1 and LAGEOS-2 satellites, the same weekly arcs for LAGEOS from the same stations were computed using the GEODYN-II software. Parameters and models of the orbital software are provided in . The resThe number of normal points for the three satellites  for each station on the whole period of this study is presented on Examples of the determined North, East, and Up components for the LARES and LAGEOS satellites for the Zimmerwald and Yarragadee stations are shown in The most important element for the future inclusion of the LARES satellite in ITRF is the determination of the station coordinates from normal equations for all three satellites combined (LARES + LAGEOS-1 + LAGEOS-2).i is the arc number, X is value of component X for each arc, n is the number of arcs. The RMS of the components Y and Z are computed analogously. The total RMS for all components  is computed from the Formula (2):X, Y, Z) and the final result (2) has to be the same.The best parameter that determines the quality of the station coordinates is the average 3DRMS of the three components  (Equations (1) and (2)):The 3DRMS for each station and the three solutions is presented in σ) as follows (3):X, Y, Z are average standard deviations of the determined components X, Y, Z. The results presented in The precision of coordinate determination is defined by 3D standard deviation  with an indication of small improvement for the solution from three satellites. For Yarragadee station , both soWe assessed the quality of station coordinates determined from the results of laser observations of the LARES satellite, noting that they are much lower than in the case of LAGEOS satellites. The station coordinates obtained from LARES are additionally dependent on the continental location of the station. Positive results were obtained for the results from combined solution of data from the LARES, LAGEOS-1, and LAGEOS-2. The results are even slightly better than those from LAGEOS. Very good agreement between average Range Bias and orbital RMS was found for all three satellites. The average orbital RMS for the LARES is ±15.1 mm for all stations and is nearly the same as for the LAGEOS.The accuracy of the station coordinates, defined as the average 3DRMS position for the LARES, changed from 9.5 mm to 45.9 mm, while for the LAGEOS satellites it changed from 5.2 mm to 15.4 mm, similar to the combined solution from three satellites  from 5.2 mm to 18.3 mm . The staPosition uncertainty, defined as the average 3D standard deviation of the coordinate determination for individual stations for LARES, is in the range of 3.4 mm to 11.1 mm. For LAGEOS, it is much better at 1.9 mm to 6.7 mm. For LARES + LAGEOS, the best result, it is from 1.7 mm to 5.7 mm.An important problem with the LARES observations is the lack of normal points in some weeks, especially in winter time, which causes the need to remove arcs due to the non-convergence iterative process. For some of the 40 stations there are too few normal points per week, which results in rejection of arcs due to the need to meet the criteria of quantity  and quality (<average 3 sigma). The result is that only 20% of all normal points for some stations will be left.Will the results from many geodetic passive satellites   allow foIn the near future, there is a plan to launch the LARES 2 into an orbit similar to the LAGEOS satellites  , which wThe combined solution from three satellites (LARES + LAGEOS-1 + LAGEOS-2) is better than the LAGEOS-only option. It is a good signal for including the LARES satellite and, in the future, LARES-2 to ITRF also, but this work needs additional activity of ILRS analysis centers.


The output value of mulberry in spring was greater than that in summer and autumn, and the production efficiency of mulberry carbon in spring was higher than that in summer and autumn. The ecological environment of the mulberry production industry can be improved by increasing the resources of carbon sequestration and reducing the source of production input. (3) In general, the photosynthetic carbon sink of mulberry is greater than the total carbon emission and presents a positive externality to the ecological environment.Carbon footprint refers to the greenhouse gas emissions of an activity during the whole life cycle or a specific period of time. Mulberry is an important cash crop. Thus, establishing a standardized accounting method for the carbon footprint of mulberry production and analyzing its carbon emission scenarios is important in correctly understanding the impact of mulberry production on the environment. Using the life cycle assessment method and on the basis of the statistical data of mulberry production of urban farmers in Haining City, China, in 2014–2016, this study calculates and evaluates the carbon footprint of mulberry production. Results show the following. (1) Indirect carbon emissions is the main part of total carbon emissions, accounting for 85%–88% of total carbon emission, and industrial inputs (fertilizers and pesticides) are the main cause of carbon emissions. (2) The total carbon emissions per hectare in 2016 (6550.73 kgce/hm China is now facing a veritable environmental challenge . Climate2 concentrations of the world and China in 2017 were 402.2 ± 2.8 and 405.0 ± 3.0 ppm, respectively, representing an increase of 2.2 and 2.6 ppm from 2016, respectively. These values were basically the same as the annual average absolute increases (2.2 and 2.4 ppm) of the world and China in the past eight years (2010–2017). On August 30, 2019, the Ministry of Ecology and Environment of China held a press conference and pointed out that China’s carbon emission intensity in 2018 was 45.8% lower than that in 2005. This report indicates the rapid growth of greenhouse gas emissions and serves as a solid foundation for the realisation of the 13th Five-Year Plan to tackle climate change and the implementation of the national independent contribution by 2030. China’s carbon emission intensity has been effectively alleviated.China attaches great importance to tackling climate change and actively and effectively controlling greenhouse gas emissions. According to the China Greenhouse Gas Bulletin 2018, the annual average COAgriculture is not only a source of greenhouse gas emissions but also the industry which is most vulnerable to the impact of climate change . A stronLow carbon agriculture is one of the objectives of modern agricultural development. Therefore, the accounting of carbon emissions from agriculture and the analysis of its carbon emission scenarios are the basis for the implementation of low carbon agricultural technology transformation, carbon trading, carbon labelling, and other management measures. Carbon footprint accounting and evaluation have become one of the hot spots in low carbon agriculture research ,13,14,15The calculation and evaluation of carbon footprint are carried out using life cycle assessment (LCA). LCA is a bottom–up accounting method, which can be applied to the relative microscale accounting and evaluation of carbon footprint. It can cover all stages of production and the entire process, from production source to consumption and the disposal of production waste. LCA is a ‘cradle to grave’ accounting method ,23. The Scholars at home and abroad use LCA to calculate and analyze the carbon footprint of agricultural production ,33,34,35Mulberry is not only an important economic crop, but also an important material basis for silk production and the manufacture of textiles and clothing. In the production process of mulberry trees, greenhouse gas emissions are produced to varying degrees. However, no scholars have yet accounted for the carbon footprint of mulberry production. Haining City, located in Taihu Lake Basin, is an important base for mulberry production and silk production in China. Therefore, this paper takes Haining City of Zhejiang Province as an example to study the carbon footprint of mulberry production stage. In this paper, the carbon emission of mulberry and its related indicators are calculated, the carbon emission scenarios are analyzed, the impact of mulberry production on the environment is deeply understood, and it is of great significance to seek the green production way of sericulture agriculture. In this paper, LCA method is used to calculate and analyze the carbon footprint of mulberry production stage, and a set of standardized carbon footprint accounting system for mulberry production is constructed. The carbon footprint of mulberry production stage is comprehensively evaluated from the perspective of ecological function, social function and economic function. The results should provide a reference for accounting and evaluating the carbon footprint of mulberry production stage and lay a foundation for the final realization of low carbon planting.This paper is organized as follows. 2. The minimum planting area of mulberry is 0.0667 hm2, and the maximum is 0.2333 hm2; the average density is 12,150 plants/hm2. Considering the difference in technical levels, we sum up and average the data on labor, chemical fertilizers, pesticides, and manure according to the investigation. The data collection period is from January 2014 to December 2016.The data used in this research are from the statistical survey of mulberry planting farmers in the jurisdiction  of Haining Agricultural Economic Bureau of China. The planting mode in this area is based on farmers’ families, they are under the guidance of technicians engaged in standardized planting, fertilization, irrigation, and branch digging. The survey data involves 15 mulberry growers in one year, with a total planting area of 1.764 hmUnder the LCA method, the carbon footprint of the mulberry production stage is calculated as follows: set the system boundary, define the greenhouse gases, establish the calculation formula and interpret the result.In this study, the system boundary is set as the whole process of the agricultural production stage of adult mulberry that has been put into production. According to the statistical caliber of the mulberry survey data of the Haining Agricultural Economic Bureau of China, the whole process of mulberry production is divided into two stages: spring (from March 20 to May 20), summer and autumn (from June 10 to October 20). The harvest period of summer and autumn includes two stages: mid-autumn mulberry and late autumn mulberry. The two growth stages of mulberry in a year need the input of chemical fertilizers, pesticides and labor. The geographical coordinates of Haining City, China are 30°15′–30°35′ north latitude and 120°18′–120°52′ east longitude see . It beloGreenhouse gas emissions are divided into direct carbon emissions and indirect carbon emissions. The carbon dioxide produced by workers’ respiration and manure stacking is direct carbon emission. Methane and nitrous oxide are produced by manure during natural stacking. The carbon dioxide produced by pesticide spraying and chemical fertilizer application belong to indirect carbon emission. Therefore, greenhouse gases are defined as carbon dioxide, nitrous oxide, and methane. The boundary of the carbon footprint accounting system in the mulberry production stage is shown in 2O emissions from farmland soil. According to the production process of mulberry and the research results of Tian Yun [CE is the total carbon emission (kgce/hm2), iCE is the carbon emission of each production input (kgce/hm2), iσ is the carbon emission parameters of each production input i , iC is the amount of each production input i, NCE20 represents carbon footprint caused by direct N2O emissions from application of N fertilizer (kgce/hm2); NC, quantity of N fertilizer (kg/hm2) applied for mulberry production; σN2O, emission factor of N2O emission induced by N fertilizer application (tN2O–N t−1 N fertilizer); 44/28, the molecular weight of N2 in relation to N2O; 298, net global warming potential (GWP) in a 100-year horizon; 12/44, the molecular weight of CO2 in relation to CE [Carbon emissions from agricultural production refer to the total greenhouse gas emissions from the production, transportation, and use of inputs such as electricity for irrigation, chemical fertilizers, pesticides, and agricultural plastic film during the whole production process from sowing to harvesting, as well as the direct NTian Yun  and ChenTian Yun , the calCS is the carbon absorption amount of photosynthesis carbon sink (kgce/hm2); s is the carbon absorption rate of photosynthesis carbon (kgce/kg), referring to the carbon required for each synthetic unit of organic matter; EP is the economic output of crops (kg), which refers to the quality of mulberry leaves with economic use produced during planting and growth; and θ is the water content ratio of the economic output part of mulberry, indicating the ratio of crop water content to the whole mulberry leaf quality; X is the economic coefficient of mulberry, which is the ratio of the total amount of organic matter produced and accumulated through photosynthesis between the marketable mulberry leaf and the whole growth period of mulberry .Photosynthesis carbon sink is calculated as follows:NCE is the net carbon emission equivalent (kgce/hm2); the net value of the total carbon emission minus the photosynthesis carbon sink is measured, with CE as the total carbon emission equivalent (kgce/hm2) and CS as the photosynthesis carbon sink (kgce/hm2).Net carbon emission is calculated as follows:In the study, the nutrient content of the compound fertilizer (GB15063-2009) used in mulberry production is 45%, that of calcium magnesium phosphate (GB20412-2006) is 12%, that of urea (GB2440-2001) is 46%, and that of ammonium bicarbonate (GB1888-2008) is 17%. According to relevant research results, the parameters of carbon emission and photosynthesis carbon sink are selected herein. According to the actual agricultural production in China, the relevant parameter values in formulas (1)–(3) are shown in With reference to the research results of Song Bo  and TianS is the land carbon intensity (kgce/m2), CE is the total carbon emission (kgce/hm2) and M is the land area (m2). The large S value means a large amount of carbon emission per unit land area.Land carbon intensity represents the carbon emission per unit planting area of mulberry trees and the carbon emission per square meter soil of the production system. The calculation formula isKC is the ecological efficiency of carbon and is a dimensionless index; CS and CE are the carbon sink of photosynthesis (kgce/hm2) and total carbon emission (kgce/hm2), respectively. KC > 1 shows that the carbon emission of mulberry production is less than the carbon sink and that mulberry production have positive externalities to the ecological environment. The larger the value is, the higher the sustainability of the production system will be. Given 0 ≤ KC < 1, the greater the carbon emission of mulberry production is, the closer the value of KC to 0 will be, and the lower the sustainability of the production system is. When KC = 1, the carbon emission of mulberry production is equal to carbon sink; otherwise, the state of the system is neutral.Carbon ecological efficiency refers to the ratio of photosynthetic carbon to total carbon emissions produced in mulberry production. It is one of the indicators used to evaluate the sustainability of mulberry production. The calculation formula isCarbon production efficiency refers to the ratio of economic output and carbon emission of mulberry. It is an efficiency index used to measure the economic output of unit carbon emission in a mulberry production system. The calculation formula isPC is the carbon production efficiency (kg/kgce), and EP and CE represent the economic output (kg/hm2) and total carbon emission (kgce/hm2) of mulberry, respectively. PC reflects the economic output per unit of carbon emission of the production system. A large PC value means a high economic output per unit of carbon dioxide emission.In the formula, JC is the carbon economic efficiency (Yuan/kgce); T and CE are the total output value (Yuan/hm2) and total carbon emission (kgce/hm2), respectively. JC describes the economic benefits per unit of carbon emission of the production system. A large JC value indicates great economic benefits per unit of carbon dioxide emission.Carbon economic efficiency is the ratio of the total output value of mulberry to carbon emission. It measures the economic benefits brought by each unit of carbon emission of a mulberry production system. The calculation formula is as follows:According to the statistical survey data of the Haining Agricultural Economic Bureau of China, formulas (1)–(3) are used to calculate the carbon footprint and evaluation index values of the mulberry planting area per hectare in 2014 .2 land were 5617.92 kgce, including 810.43 kgce (14.43%) for direct carbon emissions and 4807.49 kgce (85.57%) for indirect carbon emissions. Indirect carbon emissions exceed those of direct carbon emissions. The carbon emissions of each production input are in the descending order of nitrogen, manpower, phosphate, manure, potash and pesticide. Among them, the carbon emissions generated by chemical fertilizer are far greater than the sum of the carbon emissions of other production inputs . It is the largest source of greenhouse gas emissions in the section, with a contribution rate of 54.36%, shows that mulberry production is highly dependent on chemical fertilizers because in the mulberry production stage, chemical fertilizers should be applied before and after each picking period. In addition, mulberry has large leaves and high leaf yield. It also requires large amounts of water and chemical fertilizer for cultivation.2O gas. Compared with other greenhouse gases, N2O has the characteristics of high potential for warming, long time in the atmosphere and destruction of the ozone layer, and its negative effects are more obvious [In addition, the denitrification of nitrogen fertilizer in the soil will directly release a large amount of N obvious . The car2, which is the third largest emission source in mulberry production stage. The carbon emission of manure is 60.43 kgce/hm2; manure is mainly used for composting after harvesting in late autumn to increase the organic matter in soil and improve the soil structure. In addition, the heat generated by the fermentation of manure during composting can make the seeds of weeds in the manure of livestock and poultry inactivate, avoid the overgrowth of weeds after application and increase the mulberry output in the next year. The carbon emission of pesticides is 7.81 kgce/hm2. Pesticides should be sprayed at all stages to prevent pests because mulberry leaves are good in quality and rich in nutrition and are thus easily damaged by leaf-eating pests.The mechanization degree of mulberry planting industry in Haining is relatively low, and labor input is relatively large. The labor carbon emission is 750 kgce/hm2, approximately twice that of in summer and autumn. The total photosynthesis carbon sink in 2014 is 7665.17 kgce/hm2, which is more than the total carbon emission. The net carbon emission is −2047.33 kgce/hm2. The net carbon emission is negative, and the carbon ecological efficiency is greater than 1, indicating that the carbon emission is less than the photosynthesis carbon sink and that mulberry production has a positive externality to the ecological environment. The carbon intensity of the land is 0.56 kgce/m2, signifying that the carbon emission of mulberry production per 1 m2 planting area is 0.56 kgce. The carbon ecological efficiency is 1.36, indicating that the carbon sink of photosynthesis is 1.36 unit for every unit of carbon emission produced in mulberry production. The carbon production efficiency is 7.58 kg/kgce, showing that the economic output of 7.58 kg can be obtained for every 1 kgce of mulberry production. The carbon economic efficiency is 9.10 Yuan/kgce, indicating that the economic value of mulberry production is 9.10 Yuan per 1 kgce of carbon emission.In spring, the photosynthesis carbon sink is 5066.55 kgce/hmDuring mulberry production, farmers in different years have different inputs and outputs. Therefore, the carbon footprint, carbon intensity and carbon efficiency of mulberry production in different years are also different. According to the field survey data obtained from the random sampling of mulberry production in different years, the carbon footprint, photosynthesis carbon sink, carbon intensity and carbon efficiency of mulberry from 2014 to 2016 are calculated using formulas (1)–(3).2, followed by 5729.64 kgce/hm2 in 2015 and 5617.92 kgce/hm2 in 2014. The total carbon emissions in 2016 were significantly higher than those in 2015 and 2014. By comparing the carbon emissions of various production inputs in 2014–2016, it can be seen that the direct carbon emissions in these three years are slightly different. As far as the carbon footprint brought by labor is concerned, because the mulberry planting industry in Haining city adopts the planting mode based on farmers’ families, the annual labor input is almost unchanged, and the annual carbon emission is almost the same, close to 750 kgce/hm2. At the same time, with the development of China’s economy and science and technology, the use of manure in Haining City has been kept at a relatively low level, and the fluctuation range of its use is not large.As shown in 2 mulberry tree put into production, farmers applied 1366.64 kg of nitrogen fertilizer. In 2015, for every 1 hm2 mulberry tree put into production, farmers applied 1395.92 kg of nitrogen fertilizer. However, the amount of nitrogen input in 2016 became 1640.73 kg/hm2. The increased use of nitrogen fertilizer not only resulted in a significant increase in carbon emissions, but also in N2O emissions caused by denitrification in the soil. Total carbon emissions due to increased use of nitrogen fertilizer increased by about 17.54 percent in 2016 compared with 2015. In summary, total carbon emissions in 2016 were significantly higher than in the previous two years, mainly due to increased use of nitrogen fertilizers.By comparing the use of production inputs over the past three years, we found that the use of nitrogen fertilizer changed greatly. In 2014, for every 1 hm2.As far as the carbon footprint of chemical fertilizer is concerned, the proportions of chemical fertilizer applications in all years were close, with nitrogen fertilizer as the most dominant, followed by phosphorus and potassium. The main reason is that mulberry leaves grow rapidly in spring, summer and autumn; hence, chemical fertilizers should be applied before and after picking, whereas nitrogen fertilizer can promote leaf hypertrophy. The greatest pesticide carbon footprint was recorded in 2016, followed by those in 2014 and 2015; nevertheless, the overall values were similar. This result is explained as follows. Before the sprouting of mulberry in spring, summer, and autumn, the same dose of pesticide is sprayed to prevent pests. The numbers of trees planted per hectare in all years were also similar, with the average being 12,150 plants/hm2 in 2015, followed by 7665.17 kgce/hm2 in 2014 and 6571.53 kgce/hm2 in 2016. The annual carbon sink of photosynthesis in 2016 is significantly lower than the previous two years. The reason is that in the summer and autumn of 2014, mulberry was damaged by mulberry borer pests, the whole process of mulberry production in 2016 was damaged by mulberry borer pests and high temperature, and the mulberry leaf loss was serious. As a result, the photosynthetic carbon sink per hectare in 2014 was lower than that in 2015. Moreover, because spring is the high-yielding period of mulberry production, with the output being much higher than the output value of mulberry in summer and autumn, the photosynthetic carbon sink per hectare in 2016 was significantly lower than that in the previous two years.The carbon footprint evaluation indexes of mulberry production in different years are shown in 2, followed by 5729.64 kgce/hm2 in 2015, and 5617.92 kgce/hm2 in 2014. As a result, in terms of net carbon emissions, in 2015, the maximum is −2132.71 kgce/hm2, followed by −2047.33 kgce/hm2, in 2016, the minimum is −20.80 kgce/hm2, and the net carbon emissions are significantly less than the previous two years. The reason is that under the premise of the impact of mulberry leaf yield, the input of fertilizers, manure and pesticides in 2016 was higher than that in the previous two years. Moreover, the two-way surplus of carbon emission and carbon sink of photosynthesis made the net carbon emission in 2016 much lower than that in the previous two years. But in general, the net carbon emissions in 2014–2016 were all negative, indicating that agricultural production can bring positive externalities to the environment.Combined with The land carbon intensity in 2016 was significantly higher than that in 2015 and 2014. In terms of carbon ecological efficiency, it were almost unchanged in 2014 and 2015, and significantly greater than 1, indicating that mulberry production brought significant positive externalities to the environment. In 2016, the carbon ecological efficiency decreased significantly due to the reduction of photosynthesis carbon sink and the significant increase of carbon emission. The value of carbon ecological efficiency is slightly greater than 1, which can still bring positive externalities to the environment. At the same time, due to the impact of natural disasters, the economic output and economic benefits per unit of carbon footprint generated by emissions in 2016 decreased significantly. Overall, the carbon ecological efficiency, carbon production efficiency and carbon economic efficiency in 2015 are the best in three years.In general, the total carbon emissions of the mulberry production stage are mainly indirect carbon emissions, and industrial inputs are the main cause of carbon emissions, accounting for 85%–88% of the total carbon emissions. The carbon emissions of each production input were arranged in decreasing order as follows: Fertilizer, labor, manure, and pesticide. The output value of mulberry in spring is larger than that in summer and autumn. In sum, the total carbon emission of mulberry in the production stage is less than the carbon sink of photosynthesis, has positive externalities on the ecological environment, the net carbon emission is negative, the carbon ecological efficiency is greater than 1. The reasonable control of the proportion of ‘industrial inputs’ in mulberry production can reduce the total carbon emissions and improve the positive externalities.2, followed by 5729.64 kgce/hm2 in 2015 and 5617.92 kgce/hm2 in 2014. The main carbon emission of mulberry in each year originated from chemical fertilizer. In terms of photosynthesis carbon sink, it ranked first in 2015 at 7862.21 kgce/hm2. The net carbon emission in 2016 was the lowest, and the carbon production efficiency was inferior. The ecological environment of the mulberry industry can be improved by increasing the carbon sink resources and reducing the pollution of production inputs. An increase in output can also improve the positive externality of carbon emissions, more advanced transplantation and branching techniques can increase the yield of mulberry leaves, this need to be more carefully managed.According to the analysis of the carbon footprint of mulberry production in different years, the carbon emission of mulberry in 2016 was the highest at 6550.73 kgce/hmThe evaluation index analysis shows that the land carbon intensity of mulberry production is small, indicating that the carbon emission per square meter of land is low and that the carbon ecological efficiency value is greater than 1. This result indicates that the total carbon emission of mulberry production is less than the photosynthesis carbon sink, which has a positive externality to the ecological environment.The scattered planting pattern of farmers is greatly affected by weather, diseases, and insect pests. Despite the guidance of technicians, the farmers still applied fertilizer at will. Advanced technical guidance for farmers’ planting process, reasonable control of the use of ‘industrial inputs’ in the mulberry production process and precise fertilization can reduce the pollution sources of production inputs and effectively reduce the total carbon emissions. Advanced and centralized large-scale planting mode can improve productivity, uniform management allows precise fertilization, reduce carbon emissions and further improve the positive and external carbon emissions of mulberry production.This paper has potential limitation. The limitation is: the economic coefficient of mulberry used in the photosynthesis carbon sink estimation model of mulberry production process in this paper is taken from the experience value of experts on mulberry production in China. However, China is a vast country, there are some limitations in using the average economic coefficient of Chinese mulberry production to represent the mulberry production level of Haining City. In the future research, we should further build a more targeted economic coefficient value suitable for Haining City.


The paper presents the results of research on the tribological properties of spark-plasma-sintered Al-SiC composites. Composites with contents of 50 and 70 wt.% SiC were prepared. The sintering process was carried out using an HP D 25/3 spark plasma sintering furnace under vacuum, at the sintering temperature of 600 °C and compaction pressures of 50 and 80 MPa, respectively. The heating rate was 100 °C/min and the holding time was 10 min. Composites with a density of 91–100% were obtained. The tribological properties of the composites were evaluated based on weight loss and the coefficient of friction using a block-on-ring tribotester. Along with the weight percentage of SiC and compaction pressure, the sliding distance, and load during the tribological test were considered. Both the weight percentage of SiC and compaction pressure affected the tribological behavior of Al-SiC composites. It was found that the wear resistance was higher when a lower compaction pressure and a smaller amount of reinforcing phase (50 wt.%) were used. The oxide film is continuous, pore-free, and prevents further oxidation of Al. For this reason, a continuous film of Al2O3 can be treated as a positive factor; however, during sintering, this thermodynamically stable oxide film covering individual Al particles acts as a barrier to the formation of metal-to-metal bonds, necessary for the formation of necks between the particles, and their growth, as well as to the volumetric diffusion processes responsible for shrinkage during sintering [2O3 powder is much higher than those of Al-based powders [Aluminum (Al) powders are commonly used to produce composites reinforced with hard ceramic particles. The basic problem that occurs when sintering Al powder is its strong affinity for oxygen, resulting in the surface of the powder particles being covered with a thin film of oxide that creates a barrier to forming a permanent intermetallic bond between the sintered powder particles. In addition, oxygen reacts with some ceramics to form intermetallic phases affecting the quality of the metal–ceramic interface and, thus, the final properties of the composite ,2,3. Al The sintering process parameters have a strong influence on the change in the dimensions of the sintered compacts during the process and the creation of additional process products resulting from the reaction between the components of the sintered material, as well as the gas coming from the atmosphere. Thus, this influences changes in the microstructure of the sintered material. In the classical powder metallurgy route, nitrogen is the most favorable atmosphere for sintering Al ,4,5,6. AMany researchers have studied the effect of SiC reinforcement on the wear behavior of Al-SiC composites. An increase in wear resistance was observed with an increase in SiC content ,19,20,21Al-based composites reinforced with ceramic particles are produced by many methods, both by casting and by classical powder metallurgy techniques ,22,23,242O3 in the microstructure and also the presence of Al4C3 carbide. The formation of Al4C3 carbide in sintered Al-SiC composites was also revealed by Zhang et al. [2O4 continuous phase. Additionally, the coefficient of friction and specific wear rate were reduced, which is explained by the increase in the mechanical properties and also by the transition of the wear mechanism from a combination of adhesive and fatigue wear to abrasive wear. In [In the literature on composites based on Al or Al alloys, there is little information on the SPS process, especially concerning composites with a high content of the SiC reinforcing phase. Literature data neither provide uniform data for the sintering of such composites by using the SPS technique nor the tribological properties. Leszczyńska et al.  presenteg et al. . Van Trig et al.  studied wear. In , the autThe results presented herein provide information about the tribological properties and wear mechanisms depending on the amount of reinforcing phase (SiC) and SPS process parameters. Due to different amounts of the SiC phase (50 and 70 wt.%) and different compaction pressures (50 and 80 MPa), the investigated composites have different microstructures and properties, which has a direct impact on the tribological properties of these materials. The fabricated composite samples were based on Al with different amounts of reinforcement in the form of SiC particles (50 and 70 wt.%). The process of air gas atomization was employed to produce the Al powder particles , which had an average size of <63 µm. Nearly 70% of powder particles in the powder fraction had sizes within the 32–63 μm range, whereas the remaining ones were below 32 μm. SiC powder particles  with an average size of 60–80 µm in the form of particulates were added in quantities of 50 and 70 wt.% to the alloy matrix. The morphology of the starting powder materials can be seen in −2 mbar at the sintering temperature of 600 °C, and compaction pressures of 50 and 80 MPa. The temperature was monitored by an optical pyrometer through a nonthrough hole in the upper punch. The distance between the point of temperature measurement and the sample was 5 mm. The heating rate was 100 °C/min and the holding time was 10 min. The on:off ratio of pulsed current was set at 125:5 (ms/ms). Cylindrical-sintered compacts with dimensions of 40 mm in diameter and 10 mm in thickness were produced.A homogenous powder mixture was produced by mixing pure Al powder with the SiC powder in a Turbula T2F shaker-mixer for 30 min. The sintering process was carried out using an HP D 25/3  spark plasma sintering furnace under a vacuum of 5 × 10The microstructure of the composites was investigated with a GX 51  light microscope (LM) and an SU-70  Schottky-type electron gun scanning electron microscope (SEM) with a Thermo Scientific NORAN System 7 X-ray microanalysis system (EDS) . Examinations were carried out at various magnifications. The Brinell hardness (HB) was measured using a 2.5 mm-diameter carbide ball and a force of 612.92 N applied on mirror-shine-polished specimen surfaces, and the Brinell hardness (HB) was measured. The tribological properties of the Al-SiC composites were determined by testing 20 × 4 × 4 mm specimens in a block-on-ring T-05  tribotester  under drThe specimen (1) was secured in a specimen holder equipped with a hemispherical insert that ensures appropriate contact between the specimen and a rotating ring (2). The specimen’s wear surface was situated perpendicular in relation to the pressing direction. The double lever system transmits load L by pressing the specimen against the ring to the accuracy of ±1%. The ring is rotated at a perpetual rotating speed. A counter-specimen (a rotating ring) 49.5 mm in diameter made of heat-treated 100Cr6 bearing steel having a hardness of 53 HRC was employed. All the dry-condition wear tests were performed at a perpetual rotating speed amounting to 136 rpm throughout the test. The load throughout the test was also maintained at the same level; two loads were applied: 100 and 200 N. Before the test, samples were prepared in terms of surface quality (an appropriate roughness—below 1 µm) and thoroughly degreased by rinsing in a Struers degreasing agent; after drying, they were weighed and tested immediately afterward. After the test, the sample was also washed, dried, and weighed. The weight loss was determined from the difference in weight. As the initial samples are characterized by different weights, in order to accurately characterize the differences, the weight losses are converted to %. Changes in the coefficient of friction over time were ascertained for the run-in period (25 m) and the overall sliding distance (1000 m). The friction force and load were measured to calculate the coefficient of friction. The wear surface was analyzed by a scanning electron microscope in order to ascertain the wear mechanisms.3 for Al-50SiC and Al-70SiC, respectively. In the case of composites containing 50 wt.% SiC, regardless of the compaction pressure used, a material with a density close to the theoretical one was obtained. In the case of composites containing 70 wt.% SiC, the sintered compacts produced at the higher compaction pressure (80 MPa) were characterized by a higher density. The determined densities were 90.68% and 92.72%, respectively, for the sintered compacts produced using the compaction pressures of 50 and 80 MPa.It was possible to distribute the reinforcing phase particles evenly and prevent SiC agglomerates from forming due to the use of a SiC reinforcing phase having a particle size nearly identical to the matrix material, as well as owing to mixing the powders additionally in a Turbula T2F shaker-mixer. Locally, especially in the composites with 70 wt.% SiC, pores/voids are visible. They can also be SiC particle chipping, formed during preparation of the metallographic specimens. Regardless of the compaction pressure and SiC content, the sintered compacts were characterized by a good connection at the Al/SiC interface, which is particularly well-visible in the micrographs taken at high magnification .The tribological properties are presented in The changes in the coefficient of friction presented in 2O3 were also observed on the surface of the Al matrix, which confirms the presence of a wear mechanism involving oxidation, typical for this type of material, regardless of the friction conditions. In the case of the composite sintered under the compaction pressure of 80 MPa, oxides concentrated on the edge of the smearing Al matrix (Analysis of the surface after friction presented in l matrix f. Only aIncreasing the content of the strengthening phase to 70 wt.% changed the wear mechanisms during the tribological test . The smeThe microcracks are also characteristic, both in the direction of friction and transverse; they were the result of adhesive wear. There are visible effects of abrasive wear, also related to the smaller amount of Al on the surface of the composite during tribological contact, which were not so effectively removed by the plastic deformation of Al. Attention should also be paid to the large gaps present on the surface after friction of the Al-70SiC composite sintered under the compaction pressure of 80 MPa. Chipping was located in the SiC areas, which confirms the previous discussions regarding maintaining the coefficient of friction in these materials. These gaps were related to the presence of boundaries between the SiC particles that had not been sintered, and the increased compaction pressure additionally caused stresses in these areas.The effective distance between the SiC particles was reduced owing to the higher concentration of strengthening-phase particles in the matrix, resulting from a higher applied compaction pressure, which improved not only the density but also the hardness.During the run-in period, significant differences in the coefficient of friction were found, especially in the case of the composites with 70 wt.% SiC. In the composites sintered with the lower compaction pressure of 50 MPa, the initial course of friction depended on the applied load, while in those sintered under 80 MPa, an almost identical linear course and stabilization of the coefficient of friction occurred, which prove the better fixation of the reinforcing phase in the matrix, regardless of the increased load during the test.The determined coefficient of friction for the sintered compacts with the 50 wt.% SiC content was usually higher than that for the sintered compacts with the 70 wt.% SiC content, which resulted from the role of the Al matrix in the friction process. The presence of Al in the friction pair changed the mechanisms occurring there; the role of adhesion increased, which influenced the increase in the coefficient of friction. Additionally, increasing the compaction pressure applied during sintering adversely affected the coefficient of friction.The wear mechanisms of the investigated composites mainly depended on the content of the strengthening phase, the compaction pressure used during SPS, and to the least extent on the applied load in the friction pair.The following conclusions can be made from the conducted study:


At present, in the field of video-based human action recognition, deep neural networks are mainly divided into two branches: the 2D convolutional neural network (CNN) and 3D CNN. However, 2D CNN’s temporal and spatial feature extraction processes are independent of each other, which means that it is easy to ignore the internal connection, affecting the performance of recognition. Although 3D CNN can extract the temporal and spatial features of the video sequence at the same time, the parameters of the 3D model increase exponentially, resulting in the model being difficult to train and transfer. To solve this problem, this article is based on 3D CNN combined with a residual structure and attention mechanism to improve the existing 3D CNN model, and we propose two types of human action recognition models  and Attention Residual 3D Network (AR3D)). Firstly, in this article, we propose a shallow feature extraction module and improve the ordinary 3D residual structure, which reduces the parameters and strengthens the extraction of temporal features. Secondly, we explore the application of the attention mechanism in human action recognition and design a 3D spatio-temporal attention mechanism module to strengthen the extraction of global features of human action. Finally, in order to make full use of the residual structure and attention mechanism, an Attention Residual 3D Network (AR3D) is proposed, and its two fusion strategies and corresponding model structure  are introduced in detail. Experiments show that the fused structure shows different degrees of performance improvement compared to a single structure. With the continuous development of deep neural networks and the promotion of human action recognition application scenarios, the research into human action recognition based on deep neural networks has become a key field in recent years. At present, deep neural networks are mainly divided into two branches in the field of video-based human action recognition: one is the use of 2D CNN for feature extraction, represented by Two-Stream CNN , which uHowever, the existing work still has some shortcomings, which are mainly reflected in the following aspects: (1) The extraction of spatio-temporal features with the Two-Stream CNN model is independent, and it is easy to ignore its intrinsic connection, which affects the final performance of recognition; (2) When using the Two-Stream network to recognize human action, it is generally impossible to use a single RGB image. It is necessary to preprocess the RGB picture to extract time series features, such as the extraction of optical flow and the representation of motion trajectories, which increases the cost of model training indirectly. (3) Although the 3D CNN can extract spatio-temporal features of video sequences, compared with a 2D convolution operation, the model parameters of the 3D convolution operation increase exponentially, which makes it difficult to train and transfer the model; (4) The 3D convolution operation cannot distinguish between background features and human action features during feature extraction, and at the same time, the associated features before and after the series data are not captured, which makes the recognition of the model vulnerable to environmental factors. This reduces the recognition performance of the model.With the aim of resolving the the above shortcomings, in this article, we use 3D CNN as a basis combined with residual structure and attention mechanism to improve upon the existing 3D CNN model and propose our human action recognition models. To summarize, our contributions in this paper are as follows: (1) We design a model for shallow and deep parts, extract the shallow features and deep features separately; (2) We improve the 3D residual module by decoupling the 3D convolution kernel; (3) The 3D attention mechanism is used to reduce the sensitivity of the model to changes in the background environment; (4) The experiment explores different fusion methods of the 3D residual module and the 3D attention mechanism module; (5) With only RGB images as input and without large-scale pre-training, the accuracy of the our models reaches a relatively high level.With the continuous development of industries such as intelligent surveillance, human–computer interaction and video retrieval, the research into video-based human action recognition has attracted the considerable attention of the academic community. In recent years, the CNN has achieved success in various fields; not surprisingly, human action recognition methods based on CNNs also have proven to be very effective and are becoming a key area of research.Simonyan et al.  first prIn addition to Two-Stream CNN, 3D CNN is another important method for human action recognition. Ji et al.  prsentedThe attention mechanism in deep learning refers to the mode of human attention thinking, which is able to quickly retrieve high-value information from massive amounts of information using limited attention resources. In recent years, the attention mechanism has been used in various network models and research fields ,13,14,15However, the spatial attention mechanism, SENet and CBAM all mainly highlight the features of action from the current frame itself, without considering the internal relationship between consecutive frames. Therefore, this article addresses this shortcoming and proposes a 3D attention mechanism see  to captuAs the depth of the neural network model continues to increase, the model becomes prone to degradation and the gradient disappears. In order to solve these problems, He et al.  proposedUCF101 and HMDB51 are two commonly used benchmark video datasets and were utilized in this study. Both of them are available online ,20. UCF1The deep learning model proposed in this paper belongs to the category of 3D CNN, which generally uses continuous multi-frame RGB pictures as the input data of the model, so the original video dataset needed to be preprocessed before model training. This mainly included the following steps: (1) Video frame conversion: A video frame conversion program was used to obtain picture frames for all videos in each category and save them in the corresponding video folder, and the total number of picture frames after each video conversion were counted; (2) Dataset division: We divided the UCF101 and HMDB51 datasets according to the five categories mentioned above, meaning that the training record was regenerated for each video by using the labeling program. The format was as follows: picture frame folder path → category number; (3) Making the dataset: We used the the Dataset API provided by TensorFlow to encapsulate the data set corresponding to step (2) as a dataset object. The encapsulation process included operations such as random scramble, data enhancement, cropping and normalization of the image data. The main purpose of this was to enhance the diversity of the dataset and prevent the model training from overfitting.Tran et al.  proposedIt can be seen from Since the structures of the input layer, fully connected layer and classification layer of the deep learning-based human action recognition model were basically the same, the following text mainly focuses on the principle and corresponding structure of the feature extraction module. In this section, we first introduce the 3D SFE-Module and the 3D Residual Module in the R3D model.The 3D SFE-Module refers to the structural design of the C3D model [It can be seen from In the designed 3D SFE-Module, two operations were mainly carried out—3D convolution and 3D pooling—to extract shallow spatio-temporal features. The 3D convolution operation refers to the use of a three-dimensional convolution kernel Calculate the mean value of each input batch of data. Assuming that the batch input data are (2)Solve the variance (3)Use the mean (4)Perform scale transformation and translation operations on the normalized sample The identity transformation addition operation is used to add the input received by the residual structure to the output result of the residual structure after being transformed by the identity mapping, and finally the added result is applied to the ReLU activation function to increase its output nonlinearity.In video-based human action recognition, since the input is a consecutive multi-frame image sequence, in addition to the features of the action itself, some connections between consecutive multi-frames will remain, as shown in In From In the attention weight extraction part, we first performed a In the feature extraction part, we first used a In order for the 3D attention mechanism structure to be embedded in any network structure, it is necessary to ensure the consistency of its input and output dimensions. Therefore, before the final attention feature x received by the 3D attention structure to the attention feature map y as the input of the next module of the neural network. The purpose of this design was to reduce the possibility of gradient disappearance when adding this module to the deep network.In the addition operation part, referring to the design of the 3D residual structure, we directly added the original input In summary, the specific implementation of the 3D attention mechanism module can be abstracted as the following equations (see Equations –8)):(5)):(5)8)):In this section, we present the fusion of the 3D Residual Module and 3D Attention Module designed in the previous sections, creating a brand new module to capture richer deep feature information.We proposed two fusion strategies: strategy 1 fused the 3D attention mechanism into the connection of the identity transformation of the 3D residual structure, while strategy 2 fused the 3D attention mechanism to the output of the 3D residual structure. The difference between these two fusion strategies was mainly the location at which the 3D attention mechanism was added. Thus, the models that use these two strategies are collectively referred to as the Attention Residual 3D Network (AR3D), and the model generated by strategy 1 is named AR3D_V1, while that generated by strategy 2 is named AR3D_V2.The DFE Module of the AR3D_V1 used fusion strategy 1 to fuse the 3D attention mechanism into the connection of the identity transformation of the 3D residual structure; the corresponding architecture is shown in x, it passed through a 3D attention module to obtain the feature y through the ReLU function, as shown in the following equation:A represents the attention feature extraction operation (see Equations (Assuming that the input received by this module was quations –8) for  for x, iThe DFE-Module of the AR3D_V2 model was obtained by using fusion strategy 2. The specific structure is shown in From x, and then the 3D residual module output its corresponding residual feature y would be obtained .A represents the attention feature extraction operation, and the specific process is shown in Equations (Assuming that the input received by this module was Equation ) and theEquation ).(11)xrquations –8)..x, and tAll experiments in this article were based on the Ubuntu 18.04 bionic system, the CPU was Intel Xeon E5-2620v4 and the GPU was a GeForce RTX 2080 Ti. The development toolwasis Sublime Text3, the programming language was Python3, the image processing library was OpenCV4.1.1, and the deep learning framework was TensorFlow and Keras.n, the predicted category of a certain sample The purpose of this article was to improve the performance of human action recognition models; thus, the measured indicators were based on the accuracy of recognition. Assuming that the sample size of a certain dataset is n samples and the mini-batch size is b, then the entire dataset is divided into The model in this article used the Mini-Batch Gradient Descent (MBGD) method during training, where mini-batch refers to randomly selecting a training subset from the training dataset. Assuming that a certain training set contains The input of the customized model in this article was a continuous 16-frame RGB picture; the picture size was uniformly cropped to All the parameters of the comparison model in this paper adopted the default settings in the original paper, which was done to ensure the fairness and effectiveness of the comparison experiment.The baseline models included 3D-ConvNet  and C3D Other models included mainly provided the principles and structures to which this article has referred in the design of the DFE-Module, as well as some successful evolution models in recent years. A summary of the comparison model is shown in In It can be seen from It also can be seen from In this part, we evaluate the efficiency of our methods and compare them with other state-of-the-art methods. The experiment was performed on UCF101 and HMDB51 datasets. As shown in When the DFE Module was a 3D residual structure, the R3D model in this article was slightly worse than the AR3D model. Our analysis showed that the 3D attention mechanism effectively improves the performance. When the DFE Module was a fusion of attention and residual information, the performance of AR3D_V2 was better than AR3D_V1, which shows that the sequential fusion of the attention mechanism and the residual structure is better than adding the attention mechanism to the connection of the identity mapping, mainly because the latter changes the characteristics of the residual and loses the feature information extracted by the Residual 3D module.In this article, we propose AR3D, a 3D CNN architecture for video-based human action recognition. In this network, our approach includes a 3D residual structure and 3D attention mechanism structure. This network architecture has two advantages: one is the introduction of a 3D residual structure with decoupling operations, which enables us to build a deep 3D network structure to improve the accuracy of human action recognition; the other is the improvement of the 3D attention mechanism module, taking into account the internal relationship between consecutive frames and providing an attention mechanism for the network. Experimental results show that our AR3D model can achieve good performance.


To the Editor—The World Health Organization (WHO) declared COVID-19 a pandemic on March 11, pointing to >118,000 cases of coronavirus pneumonia worldwide.1 On the last day of 2019, China reported some cases of pneumonia with unknown etiology in Wuhan. Approximately 7 days later, gene sequencing revealed that the etiologic agent was a coronavirus, which was subsequently named SARS-Cov-2.2 As of March 18, 2020, >218,000 infected patients and 8,900 deaths had been reported, and the virus had reached 173 countries.34 Preparedness comprises activities that began before the crisis; its goal is to create infrastructure and to empower public health workers. Alert plans comprise activities conducted to detect and verify the outbreak, and response activities during the crisis focus on controlling the problem.5To control an outbreak, every country needs to have preparedness, alert, and response plans.6 Today, 60 days after the first case, statistics show that the peak of infection has passed. A total of 8,413 cases have been confirmed, and the number of new cases has reached <100 for the fourth day in a row.3 In this study, we describe the outbreak response and preparedness activities that Korea implemented to control the COVID-19 epidemic.The first individual with COVID-19 in Korea was detected on January 20, 2020.7The outbreak alert system in Korea has 4 levels: (1) attention to the outbreak, in which the government began to monitor and prepare; (2) caution when the outbreak entered the country and the government operates cooperation system; (3) alerts regarding the spread of infection to other areas and initiation of the response system; and (4) mobilization of a nationwide response system as the outbreak spread and became severe.8 while the source was not yet clear, Korea started screening and implemented a quarantine plan at the airports. Those who had visited Wuhan in the previous 14 days were required to complete a health questionnaire and to self-quarantine for 14 days. If fever or respiratory symptoms appeared, they were required to call the Korea Centers for Disease Control and Prevention (KCDC).3Four days after the notification of new cases in China,6 resulting in the elevation of the infection alert level from blue (attention) to yellow (caution). In-depth epidemiological studies were conducted, and all contacts were followed for 14 days. These individuals were isolated and tested if any symptoms appeared, and all of the places where the case patients had gone  were disinfected.3 On February 21, when epidemiologic studies revealed 2 main sources of transmission, those places were defined as “special care zones” where a specialized team focused on controlling transmission,9 and the alert level was elevated to the highest (severe).10On January 20, the first case of COVID-19 pneumonia, which was detected in the airport screening station, was confirmed,10Rapid diagnosis and widespread testing were other areas of focus in Korea. The proportion of confirmed to suspected cases varied from 0.5 in the initial days to 3.9 in the peak days. Early detection helped Korea eliminate the infection from the community and restrict it to health facilities, which is an essential aspect of outbreak response. Also, research teams started their work in the very early days to develop rapid tests, treatments, and vaccines. From January 31 onward, the 6-hour test was distributed in some health facilities, and from February 7 onward, all health facilities all around the country had this test.10Moreover, the KCDC started reporting the situation from January 20 onward to provide accurate and real-time data. These reports included the number of confirmed cases and patients under investigation, history of confirmed cases, and prevention advice for the public. The number of the KCDC call center has been mentioned in almost every report, and Koreans were asked not to travel to China and Wuhan, to avoid public outdoor activities, to cough or sneeze safely, and to wear masks when visiting a health center. Besides, the guideline for management and screening get updated whenever needed; travel to Wuhan which was in the definition of suspected cases where changed to travel to china, and finally omitted.Altogether, the main goal of outbreak response in Korea was prevention of entrance of COVID-19 and at the same time, inhibition of the spread of the virus throughout the country. These goals were achieved through 3 main strategies: (1) containment and mitigation based on outbreak situation; (2) Risk communication to attract community participation; and (3) science-based and fact-driven actions.


BRCA1/2 gene testing is a difficult, expensive, and time-consuming test which requires excessive work load. The identification of the BRCA1/2 gene mutations is significantly important in the selection of treatment and the risk of secondary cancer. We aimed to develop an algorithm considering all the clinical, demographic, and genetic features of patients for identifying the BRCA1/2 negativity in the present study. An experimental dataset was created with the collection of the all clinical, demographic, and genetic features of breast cancer patients for 20 years. This dataset consisted of 125 features of 2070 high-risk breast cancer patients. All data were numeralized and normalized for detection of the BRCA1/2 negativity in the machine learning algorithm. The performance of the algorithm was identified by studying the machine learning model with the test data. k nearest neighbours (KNN) and decision tree (DT) accuracy rates of 9 features involving Dataset 2 were found to be the most effective. The removal of the unnecessary data in the dataset by reducing the number of features was shown to increase the accuracy rate of algorithm compared with the DT. BRCA1/2 negativity was identified without performing the BRCA1/2 gene test with 92.88% accuracy within minutes in high-risk breast cancer patients with this algorithm, and the test associated result waiting stress, time, and money loss were prevented. That algorithm is suggested be useful in fast performing of the treatment plans of patients and accurately in addition to speeding up the clinical practice. Machine learning is a computer-based predictive method or an estimation algorithm which makes an assumption of a hypothesis and uses this assumption for the estimation of the unknown condition using various mathematical and statistical methods. Thanks to this association between mathematics and computer science, the difficulties on creating and calculating the statistical models from large datasets including billions of data are resolved . MachineThe studies on the use of machine learning algorithms in medicine are particularly based on the evaluations associated with the medical screening systems , 12. TheBRCA1/2 genes [BRCA1/2 genes are tumor suppressing genes and have a role in response to cellular stress through the activation of the DNA repair processes. The pathogenic mutations in BRCA1/2 genes show that breast cancer has hereditary characteristic and increases the risk of getting breast cancer diagnosis [BRCA1 mutation and 69% of women with pathogenic BRCA2 mutation are diagnosed with breast cancer at a stage of life. In addition, researchers reported that approximately 40% of women with pathogenic BRCA1 mutation and approximately 26% of women with pathogenic BRCA2 mutation develop secondary breast cancer in the contralateral 20 years after the first diagnosis of breast cancer [BRCA1/2 gene mutations, the fast practice, and concluding of the gene analysis are highly important in planning the treatment and identifying the secondary cancer risk. The completion of the study of this test which is difficult, expensive, and long-lasting and therefore stressful for patients is significantly important in the rapid selection of the treatment protocols and rapid change of treatment when required.The cause of the breast cancer is still unknown, and there is no available efficient prevention method against breast cancer. Therefore, early diagnosis or the identification of the present risk are the most important steps in treatment, and in the prevention of breast cancer . Importa/2 genes . BRCA1/2iagnosis . Althougt cancer . TherefoBRCA1/2 negativity in the literature; in addition, there is no available algorithm that describes the BRCA1/2 negativity by only evaluating the clinical and demographic data of patients without performing the BRCA1/2 gene test in high-risk breast cancer patients with a family history. There is a need for algorithms which identify the BRCA1/2 gene mutation negativity in a short time and enabling the planning treatment rapidly by reducing the health costs and workload of cancer genetics polyclinic and laboratory. Therefore, an algorithm which identifies the individuals with negative BRCA1/2 gene mutation in short time was developed in the study by considering all the clinical, demographic, and genetic features of the total of 2070 patients.There is no algorithm which identifies the BRCA1/2 mutation without having a BRCA1/2 gene test. The experimental dataset with 2070 individuals were created and pre-processing step was started with the feature selection. Datasets obtained in the feature selection were independently performed in pre-processing procedure, and were normalized and numeralized for developing classification models. The training and testing dataset were generated for the supervised machine learning models for detection of the BRCA1/2 negativity. All the work flow diagram for machine learning algorithm is presented in The study flow was arranged as follows in the study conducted to develop an algorithm in order to determine the negative cases for BRCA1/2 genetic testing between 1999 and 2019, was created. Some part of this raw data is presented in BRCA1/2 genes, family history, and receptor condition; however, the modifiable factors were the use of oral contraceptive, alcohol consumption, hormone therapy, breast feeding, and smoking.A dataset, including all the clinical, demographic, and genetic features of 2070 breast cancer patients who presented to the Cancer Genetics polyclinic in the Cancer Genetics Division of the Oncology Institute in Istanbul University for BRCA1/2 negativity or there were unnecessary, irrelevant, and invalid features in the dataset. Then, the raw dataset consisting of 125 features was evaluated using Pearson's correlation method using the Statistical Package for the Social Sciences (SPSS) v.21, and statistically significant (p < 0.05) features were selected. After the feature selection stage, two different datasets were generated for 1460 and 1532 breast cancer patients and their 26 and 9 variables. The patient and feature data of the datasets are given in Feature selection is a method which may be used for removing the unnecessary, irrelevant, and invalid features which have no contribution to the accuracy of a predictive model. The raw dataset of 2070 high-risk breast cancer patients included 125 modifiable and unmodifiable parameters. First, the raw dataset was performed feature selection because there were numerous variables which did not affect the After Pearson's correlation analysis, the statistically most significant 9 features and their associations in the raw dataset are given in Two different datasets obtained in the feature selection procedure were independently performed in a preprocessing procedure and were normalized and numeralized for developing classification models of the dataset for the machine learning algorithms. The nonnumerical data included in the dataset during this procedure were numeralized. The numerical data were degraded between 0 and 1 for normalization. zi indicates the normalized i data. xi is normalized between 0 and 1 interval by division with the value between min(x) and max(x) after deduction from the minimum x value included in the dataset to be normalized.All data were numeralized and normalized for obtaining more successful results in the machine learning algorithm.k nearest neighbours (KNN), support vector machines (SVM), and decision tree (DT) machine learning algorithms, and classification models were generated for the detection of the BRCA1/2 negativity. k nearest neighbour is a supervised machine learning technique. k performs the classification procedure in accordance with the nearest neighbour. SVM classifies the samples by generating the optimum hypersurface which differentiates the samples in the space. DT is a supervised machine learning technique which performs classification by generating a decision tree.After the preprocessing procedure, the first dataset with 1460 patients and 26 features and the second dataset with 1532 patients and 9 features were, respectively, evaluated using the In the scope of the study, both 2 datasets that were normalized and numeralized were divided into two groups: 67% as the training dataset and 33% as the test dataset. Three different classification models were developed with training of KNN, SVM, and DT algorithms using the training dataset. The performances of the developed classification models were measured using the test data and were comparatively presented.BRCA1/2 negativity. The number of features was reduced to two different datasets as 26 and 9 using the experimental dataset dimension reduction procedure. All the learning algorithms were separately performed to both 2 datasets, and the success rates were measured. The success rates of algorithms used in developing the classification models for Dataset 1 and Dataset 2 are given in The raw dataset involving 125 features of 2070 high-risk breast cancer patients was used for the detection of the The performance of the algorithm was identified by studying the machine learning model with the test dataset. Accordingly, 9 features involving dataset 2 was found to be the most effective for KNN and DT accuracy rates compared with the 26 features involving Dataset 1. However, no difference was detected between both datasets for SVM rates. We found that the rem006Fval of the unnecessary data by decreasing the number of features in the dataset significantly increased the accuracy rate of the algorithm compared with the decision tree. As shown in BRCA1/2 gene positivity have been studied to be measured with various statistical methods in high-risk individuals for breast cancer. However, the developed statistical models could only identify the possibility of BRCA1/2 positivity and could not estimate the possibility of BRCA1/2 positivity with high accuracy [BRCA1 positivity, and some could only identify the risk for specific age intervals [The risk of being diagnosed with breast cancer and accuracy , 30. Somntervals –33.BRCA1/2 gene polymorphisms and familial cancer history. The clusters corresponding to different profiles were identified using an accurate mathematical model for estimating the cluster of the individual in this analysis method. The individuals were evaluated in accordance with this clustering method, and the possibility of getting breast cancer of the individual was found to be higher in the second decade of life using this mathematical method if the healthy individual had 2 relatives diagnosed with breast cancer in their 20s [Doncescu and Kabbaj developed a model which could estimate the breast cancer development in accordance with the age of the individual using the machine learning techniques. The age of being diagnosed with breast cancer of the individual is estimated in this modelling based on the heir 20s .BRCA1/2 single-nucleotide substitution variants with unknown clinical significance using the probabilistic neural network and deep neural network-stacked autoencoder [KhajePasha et al. developed an algorithm which predicted the clinical importance of the oencoder . As a coBRCA1/2 gene expression analysis using the machine learning algorithms in their study. Researchers investigated the BRCA1/2 gene expression level using the machine learning algorithms in that study. The algorithms used in the study were the sequential algorithm based on confusion matrix, DCKSVM, and HRBFNN and the prediction algorithms. The HRBFNN algorithm was proven to be effective in analysing the BRCA1/2 gene expression in the breast cancer dataset in that investigation. Researchers emphasized that it could be made stronger using the other computer-aided algorithms for improving the productivity [Sumitha investigated whether breast cancer could be diagnosed with uctivity .BRCA1/2 gene test used in the clinical practice is difficult and requires excessive workload and is more expensive and time-consuming compared with the other tests. Longer time of testing causes stress for the patient. In addition, the identification of BRCA1/2 gene mutation is significantly important for selection of treatment and identification of the risk of the possibility of the secondary cancer. All the above-mentioned algorithms can identify the breast cancer risk in healthy individuals in specific categories and can provide the possibility of carrying BRCA1/2 gene mutation. However, the rate of the BRCA1/2 gene mutation carriers is approximately 15%, and 85% of the high-risk patients are unnecessarily tested. Enabling to obtain the clinical results of this expensive, difficult, and long-term taking test and selection of the accurately correct individuals for testing in clinical practice will facilitate the routine practice and reduce the health costs. In addition, selection of BRCA1/2 gene mutation-negative individuals among the high-risk breast cancer patients is significantly important for reducing the workload and health costs and reducing the stress of the patient and for fast selection of the treatment.The BRCA1/2 gene mutation in a very short time in our study targeting to solve these problems. BRCA1/2 negativity was identified in 92.88% accuracy with no need for BRCA1/2 gene testing within minutes in high-risk breast cancer patients using this algorithm and stress of waiting for the test result; time and money loss were prevented. We suggest that this algorithm will be useful in the rapid and accurate performing of the treatment planning of the patients in addition to accelerating the clinical practice.We evaluated all the clinical, demographic, and genetic features of 2070 patients and developed an algorithm which identifies the