Supplemental Digital Content is available in the text Antibiotic stewardship (ABS) programs intend to improve outcomes of nosocomial infections and to counteract the emergence of further antimicrobial resistances. At the anesthesiologic-neurosurgical intensive care unit (ICU) of the University Medical Center Regensburg (Germany) we implemented a standard operating procedure (SOP) with clear instructions for the preanalytical handling and storage of microbiological samples. We intended to find out whether the instructions given in the SOP led to a higher rate of ideal material being sent to the laboratory and to overall better quality of the received results.We retraced retrospectively all samples taken in cases of suspected pneumonia, urinary tract infection, bloodstream infection, catheter infection associated with a central venous or arterial catheter and ventriculitis due to external ventricular drainage as well as all smears taken for the screening for multi-resistant bacteria within a time period of 1 year before to 1 year after the implementation of the SOP.In the case of suspected pneumonia and urinary tract infection, large amounts of ideal material were sent to the microbiological laboratory. A remarkable improvement after the implementation of the SOP, however, could only be observed regarding the number of urine samples taken from older urinary catheters, which was significantly lower in the “SOP group”. Samples for microbiological diagnostics were taken much more often in the daytime, although storage of the probes did not lead to worse results.Concrete instructions enable adequate preanalytical handling of microbiological probes. However, we could not recognize substantial improvements probably due to a preexisting high process quality on the ICU. Microbiological diagnostics during the night shift has to be improved. The present study was initiated as part of an ABS program.Antibiotic stewardship (ABS) programs aim for a targeted use of antibiotics, not only to improve the outcome of infectious diseases in hospitalized patients but also to counteract the emergence of further antimicrobial resistances. Microbiological diagnostics is an essential determinant for achieving the goals of ABS programs, A particular potential for improvement should be given for microbiological laboratory diagnostics in cases of suspected urinary tract infections.,11The quality of results of microbiological diagnostics is significantly influenced by the preanalytical handling of the samples. This important aspect is often addressed only marginally in the present guidelines or in ABS programs in general, although the demand of precise instructions regarding microbiological laboratory diagnostics seems to be of great importance considering the limited knowledge of technical aspects.With the objective of improving the preanalytical handling of microbiological samples, a standard operating procedure (SOP) containing clear instructions, for example concerning the selection of proper sample material or the handling of samples in the case the transport to the laboratory is delayed, got implemented at the anesthesiologic-neurosurgical intensive care unit (ICU) of the University Medical Center Regensburg (Germany).The aim of the present study was to investigate whether the implementation of the SOP had a notable positive effect on the quality of results of microbiological samples.2The anesthesiologic-neurosurgical intensive care unit of the University Medical Center Regensburg is attended by physicians from the department of anesthesiology and the department of neurosurgery. The primary patient population is made up of postoperative or postinterventional patients of the departments of neurosurgery and neuroradiology, as well as patients with spontaneous intracranial hemorrhage or craniocerebral trauma. But within the framework of the occupancy concept for intensive care patients at the University Medical Center Regensburg also patients from other disciplines are accepted if necessary.On May 1, 2018 an SOP for the preanalytical handling of microbiological samples for the diagnostics for bacterial and mycotic infections was implemented  as well as all smears taken for the screening for multi-resistant bacteria were retraced within a time period of 1 year before to 1 year after the implementation of the SOP. This way, a “historical group” including the samples taken prior to the implementation of the SOP  and a “SOP group” with samples taken afterwards  were generated. An evaluation of routinely stored data and documents in 3 software applications took place: The patient data management system of the intensive care unit , the clinical SAP system  and the system for statistics and analytics of the hospital hygiene .2.2Samples taken from children and adolescents under the age of 18 years were excluded from the analysis. Similarly, postoperative and postinterventional patients with uncomplicated clinical course and a length of stay at the intensive care unit of less than 24 hours were not considered in the analysis.2.3The study was conducted in accordance with the approval from the local ethics committee (18-1028-101).2.4t test or Mann–Whitney U test. Categorical variables were indicated as frequency and comparison between the groups was performed using Chi-Squared-test. P values below .05 were considered statistically significant. In this retrospective study, no a priori sample size calculation was performed. Instead, all patients available in the pre-defined period were included to maximize the power of the study.Data were statistically analyzed using software . Metric data were presented as mean ± standard deviation provided that normal distribution was given, or as median and interquartile range (IQR) provided that not. According to the given distribution of data, testing among the groups was performed using Student 33.1P = .012). The patients of both groups had the same median length of stay of 4 days . Neurosurgical patients still made up the biggest part of both groups after subtraction of the above mentioned uncomplicated postoperative cases .During the observation period 640 patients in the historical group and 708 patients in the SOP group were treated at the anesthesiologic-neurosurgical intensive care unit of the University Medical Center Regensburg. In the historical group 245 patients (38.3%) and in the SOP group 294 patients 41.5%) were excluded from further analysis due to the character of their stay being solely postoperative or postinterventional, of less than 24 hours duration and without any complications. Therefore 395 cases in the historical group and 414 cases in the SOP group remained . In 44 cases of the, in total, 75 positive samples of the historical group and in 33 of the 56 positive samples of the SOP group, exactly 1 type of germ was detected . Two types were found in 27 samples in the historical and 18 samples in the SOP group . And 3 or more types were merely detected in 4 and 5 samples . Thirty three of the 139 samples of the historical group (23.7%) and 28 of the 93 samples of the SOP group (30.1%) had to be temporarily stored prior to transportation to the microbiological laboratory. Even when only looking at these cases separately, the frequency of positive findings did not differ between the 2 groups . An overview over the detected germs and their frequency taken all respiratory tract samples from the 2 groups together is provided in Supplement S1, http://links.lww.com/MD2/A350.A total of 139 samples from the patients’ respiratory tract were sent to the microbiological laboratory in the historical group and 93 such samples were sent in the SOP group. In every case the material had been obtained correctly in accordance with the SOP. The frequency of positive findings did not differ between the 2 groups observed: 75 out of the 139 samples in the historical group (54.0%) vs 56 out of the 93 samples in the SOP group . In the historical group 12 and in the SOP group 16 urine samples were sent to the microbiological laboratory although either no urinalysis had existed beforehand or a urinalysis had been conducted but showed no typical signs of a urinary tract infection . The frequency of positive findings regarding microbiological analysis of the urine did not differ between the 2 groups . Solely 1 pathogenic germ was found in 10 samples of the historical and 15 samples of the SOP group . Two types of pathogenic germs could be detected in 4 samples of both the historical and the SOP group . Three or more types of pathogenic germs were cultivated from the material of 2 samples of the historical group, but of no sample of the SOP group . Also, when looking only at those cases, where the urine could not be sent immediately to the laboratory but had to be temporarily stored , the frequency of positive findings did not differ in a statistically significant manner . The frequency of the detected germs in the urine samples of both groups is shown in Supplement S2, http://links.lww.com/MD2/A351.In case of a suspected urinary tract infection, in total 24 samples in the historical group and 36 samples in the SOP group were sent to the microbiological laboratory. In all of these cases, correctly, native urine was gathered rather than using dip-slide cultural media. But, in the SOP group significantly more frequently either fresh urine or urine from a urinary catheter having been inserted within the past 24 hours reached the laboratory . In contrast, blood cultures were frequently taken from vascular catheters having been introduced longer than 24 hours ago . In all transmitted blood cultures, germs could be identified in 13 out of the 65 samples in the historical group and in 17 out of the 115 samples in the SOP group . Among these, 1 type of pathogenic germ was found in 11 of the 13 positive blood cultures of the historical group and in 15 of the 17 blood cultures of the SOP group . Two types of pathogenic germs had been cultured from 2 samples of the 2 groups . None of the analyzed blood cultures showed 3 or more types of germs. Temporary storage was necessary in 22 of the 65 blood cultures (33.8%) of the historical group and in 28 of the 115 blood cultures (24.3%) of the SOP group. These cases showed no difference in the frequency of positive findings among the 2 groups . The identified germs from all positively tested blood cultures are shown in Supplement S3, http://links.lww.com/MD2/A352 together with their frequency.Blood cultures for diagnostics in cases of suspected bloodstream infection were taken 33 times in the historical group  and 58 times in the SOP group . Transmittal of correct material  was rare in both groups . Twenty two of the 58 individual samples of the historical group and 7 of the 23 samples of the SOP group were found to be positive . Of these, in 18 cases in the historical group and in 5 cases of the SOP group only 1 type of germ was detected . Two types of germs were cultured from 4 and 1 sample, respectively , while only 1 sample from the SOP group contained 3 different types of germs.In the historical group, 25 cases of suspected vascular infection associated with a central venous or arterial catheter led to the transmittal of material to the microbiological laboratory , in the SOP group 11 cases . Correct material  was obtained in 10 of the 25 cases in the historical and in 5 of the 11 cases in the SOP group . In Supplement S4, http://links.lww.com/MD2/A353 the cultured germs and their frequency in the cases of suspected catheter-associated infection are depicted.16 of the 58 samples of the historical group (27.6%) and 9 of the 23 samples of the SOP group (39.1%) had to be temporarily stored prior to their transmission to the laboratory. Here, germs were detected in 8 samples of the historical and 3 samples of the SOP group . Positive findings resulted from 4 samples of the historical and 5 samples of the SOP group . In both groups, each positive sample always showed only 1 type of germ. 4 samples of the historical as well as the SOP group had to be temporarily stored at the intensive care unit. In the historical group, a pathogenic germ was cultivated from 2 of these samples, in the SOP group from 1 . The positive samples of both groups contained Staphylococcus hominis in 4 cases, Staphylococcus epidermidis in 3 cases and methicillin-sensitive Staphylococcus aureus in 2 cases.Material from patients with the suspected diagnosis of ventriculitis due to EVD was sent to the microbiological laboratory in 28 cases in the historical group  and in 27 cases in the SOP group . Correct material  reached the laboratory in 18 cases of both groups . Supplement S5, http://links.lww.com/MD2/A355 portrays the detected multi-resistant germs from the samples of both groups.Smears to screen for multi-resistant bacteria requiring patient isolation were taken from various patients prior to transferring them to an external hospital or rehabilitation center. In the historical group, 490 samples were taken from 119 patients in this context, in the SOP group 603 samples from 156 patients. Comparing the frequency of positive findings between the 2 groups, the level of significance was just missed  when cultivation of a pathogen fails or when numerous pathogens are cultivated, especially in urine or cerebrospinal fluid samples or in blood cultures. The degree of adherence to the instructions of the SOP on the modalities of storing samples that could not be directly sent for microbiological diagnostics remains unclear. The undetectable effect on the quality of results in these samples could therefore be due to the same treatment of the samples of the 2 groups. A further important point could have contributed to the lack of significant differences between the 2 groups: because of their participation in an ABS program, the ICU staff had regularly received feedback from the microbiological laboratory in the case of incorrectly handled samples for several years. This feedback is likely to have continuously improved process quality, despite the lack of an SOP on this topic at the time.5The aspect of preanalytical sample handling in microbiological diagnostics should be given higher priority in ABS programs. The creation of an SOP can be helpful in this regard. In the present study, however, hardly any relevant effects could be shown by the implementation of such an SOP. A noteworthy aspect was that microbiological samples were mainly obtained in the daytime during the acceptance periods of the bacteriological laboratory. We see an urgent need for action here, since it must be assumed that nosocomial infections in intensive care units manifest themselves independently of the time of day. As a matter of principle in the case of suspected infection, the aim should be to immediately preserve specific material for microbiological diagnostics and then start a calculated anti-infective therapy right away.Conceptualization: Martin Kieninger, Bernd Salzberger, Thomas Holzmann.Data curation: Bärbel Kieninger, Thomas Holzmann.Formal analysis: Martin Kieninger, Florian Zeman.Investigation: Andreas Mandlinger.Methodology: Florian Zeman, Thomas Holzmann.Project administration: Martin Kieninger, Thomas Holzmann.Software: Florian Zeman.Writing – original draft: Martin Kieninger, Nina Doblinger.Writing – review & editing: Nina Doblinger, Bärbel Kieninger, Sylvia Bele, Bernd Salzberger, Wulf Schneider-Brachert, Bernhard Graf, Florian Zeman, Thomas Holzmann.


The Rurikids were the reigning house of Rus’, its principalities and,ultimately the Tsardom of Russia, for seven centuries: from the IX to the endof the XVI century. According to the Primary Chronicle , the main chronicle of Rus’, the Rurik dynasty was founded by theVarangian prince Rurik, invited to reign in Novgorod in 862, but still there isno direct genetic evidence of the origin of the early Rurikids. This research,for the first time, provides a genome-wide paleogenetic analysis of boneremains belonging to one of the Rurikids, Prince Dmitry Alexandrovich(?–1294), the son of the Grand Prince of Vladimir Alexander YaroslavichNevsky (1221–1263). It has been established that his Y chromosome belongsto the N1a haplogroup. Most of the modern Rurikids, according to theirgenealogies, belonging to the N1a haplogroup, have the most similar variants ofY chromosomes to each other, as well as to the Y chromosome of Prince DmitryAlexandrovich. Genome-wide data of the medieval and modern Rurikidsunequivocally indicates that they belong to the N1a haplogroup of the Ychromosome, starting at least from the XI century (since the time of PrinceYaroslav the Wise). All the other alleged Rurikids, both ancient and modern,being carriers of other haplogroups , possess high heterogeneity ofthe sequence of Y chromosomes, meaning that we cannot confirm their commonancestry. The most probable ancestors of Prince Dmitry Alexandrovich in themale line were the men who left the burial ground Bolshoy Oleny Island on thecoast of the Kola Peninsula about 3,600 years ago. The reconstruction of thegenome of Prince Dmitry Alexandrovich indicates the contribution of threeancestral components to his origin: (1) the early medieval population of theeast of Scandinavia from the island of Oland, (2) representatives of the steppenomadic peoples of the Eurasian steppes of the Iron Age or the early medievalpopulation of central Europe (steppe nomads from the territory of Hungary), and(3) the ancient East-Eurasian component. Reliable statistics were also obtainedwhen the Scandinavians were replaced with the Medieval Russian Slavicpopulations of the XI century. Thus, for the first time, we have shown thecomplex nature of interethnic interactions in the formation of the nobility ofmedieval Rus’ on the example of the ancient Rurikid. The application of paleogenetic methods when studying the genetic identity andorigin of the medieval Russian nobility is one of the most productive amongmany modern scientific approaches capable of expanding and verifying theexisting knowledge about the Medieval Russian society, its ethnic composition,and political organization. Meanwhile, the remains of the Rurikids, the mostancient reigning family whose members were the major actors in the history ofRussia in the IX–XVI centuries, remain almost untouched by paleogeneticresearches. The XI– XII centuries Rurikids’ haplogroups werereconstructed based on genetic materials of modern individuals whose genealogy,according to historical data, ascends to Rurik with different degrees ofreliability . The accThe existence of a “blind spot” in the study of the genomes of theMedieval Russian nobility in many aspects is due to the complexity ofpersonally identifying the remains of the Rurikids and other aristocraticfamilies in the necropolises of the X–XIV centuries. It is well knownthat the names of the buried were not indicated in any way on funerarystructures, sarcophagi, or tombstones until the beginning of the XV century.The location of princely burial grounds is established by annalistic messages,synodics of the XVI–XVII centuries, taking into account the latertradition of church veneration of many representatives of the princely family.Archaeological research of burial grounds in Medieval Russian churches and theanthropological study of bones are the main ways of identifying noble burials,but the conditions of necropolises do not always allow for such identification.The long use of necropolises, the practice of placing new burials over oldones, moving the revered remains during their examination in the XV–XIXcenturies, and, finally, the removal of relics in the course of theanti-religion campaigns in Soviet times led to part of the princely remains ofthe XI–XIV centuries from the burial places in the Medieval Russianchurches getting lost, or the remains not being reliably matched with certainhistorical persons whose graves were disposed in these necropolises. Oneexample of the use of bone remains in order to conduct the genetic analysis ofthe Rurikids whose membership in the princely ruling family cannot be verifiedby archaeological data is the study of the presumed remains of Prince GlebSvyatoslavich from the Chernigov Transfiguration Cathedral – a skullfound during repair works at the temple without archaeological documentation.Thus, the few burial places of the Rurikids with bone remains that can bereliably attributed to the princely family, based on the archaeological data,anthropological definitions, and a set of historical evidence acquire specialsignificance. To such trustworthy burial places belongs the burial site ofprince Dmitry Alexandrovich, found in the southern altar apse in thesouth-eastern part of the Transfiguration cathedral in Pereslavl-Zalessky(Supplementary 1).DNA isolation and genomic library preparationAll experiments with aDNA were carried out in “a clean room”– a room specially equipped for these purposes at the Federal ResearchCenter “Fundamentals of Biotechnology” of the Russian Academy ofSciences (Skryabin Institute of Bioengineering).DNA was isolated from the bone remains found in the ruined sarcophagus of theTransfiguration cathedral in Pereslavl-Zalessky. According to the historicalinformation on the burial place, archeological data and anthropologicaldefinitions, these remains belong to the son of Prince Alexander YaroslavichNevsky – Prince Dmitry Alexandrovich (Supplementary 1). The remains arecharacterized by good preservation of bone tissue, which is typical for remainsfound long after burial out of contact with the ground, suggesting a ratherlate episode of destruction of the sarcophagus. To isolate aDNA from thesamples provided for genetic analysis, we obtained three portions of bonepowder weighing 20, 50, and 80 mg from the metacarpal bone of the hand, rotula,and navicular bone of the foot, respectively. DNA was isolated by magneticseparation using buffer D – that of the Dabney method , isopropanol, 0.12 M sodium acetate, and 0.05% (v/v)Tween 20) and silica-coated magnetic beads [The resulting DNA was used to prepare libraries of single-stranded DNAfragments using the ACCEL-NGS 1S Plus DNA Library Kit according to the original protocol but with minor modifications: for the stepsproviding strand elongation and sample indexing, uracil-tolerant polymerase was used. To assess the content of endogenousDNA, test sequencing of the constructed libraries of lowcoverage DNA fragmentswas carried out, approximately 3–4 million single reads per sample (50 bplong). For the sample with the best preservation of the genetic material (highendogeneity and the presence of C > T substitutions at the 5’ ends ofDNA fragments), an additional library was prepared from the same DNA extractand pre-treated with a mixture of uracil-DNA glycosylase (UDG) and endonucleaseVIII . The mixThe MyBaits Expert Human Affinities Prime Plus Kit (Daicel Arbor Biosciences)was used for subsequent enrichment of the genome regions of interest.Biotinylated single-stranded DNA probes from the kit cover single nucleotidepolymorphisms (SNPs) from the panel “1240K capture” , 46,000 Bioinformatics analysisTo remove contaminating DNA reads from the sequencing data, we used the BBDuksoftware  includedAligned reads were filtered to ensure a minimum display quality of 20 usingsamtools view (version 1.9) .IndexinPostmortem DNA damage patterns were analyzed using the MapDamage2 software, which oK) in the range of 4–12; the run with the lowestcross-validation error was selected to plot the graph of population admixture.To determine the genetic clustering of the NEV2.3 sample among the ancientsamples known at the time of the study presented in the Allen Ancient DNAResource (AADR) panel , theADMTable 1 ofSupplementary 2. The following parameters were set by default:lsqproject: YES, numoutlieriter; 0, shrinkmode; and YES for the smartpcaanalysis. Mitochondrial haplotypes were determined using the HaploGrep program[For principal component analysis (PCA), the smartpca tool from the EIGENSOFTpackage was used. Ancient samples were projected onto the first two componentsof the modern samples. A list of samples is presentedin  program. DetermiThe HIrisPlex-S online tool , 22, 23DNA isolation and genomic library preparation from a modern human bloodsampleTo avoid the bias caused by the method of library preparation, we sequenced asample of a modern Rurikid (sample with an identifier Olgovich3), according tohis genealogy. Informed consent to participate in the genetic research wasobtained from the modern Rurikid before the start of any research procedures.Genomic DNA was isolated from 200 μl of blood using a Magen DNA blood minikit according to the protocol. Overall, 1 μg of genomic DNA was used tofragment the sample to an average DNA fragment size of 200 nucleotides on aCovaris platform. The library for subsequent whole genome sequencing wasprepared according to the instructions in the NEBNEXT DNA UltraII (NEB) kit.Sequencing was carried out on a HiSeq1500 platform . In total,115,564,028 reads of 150 nucleotides were generated. Mapping was performedusing BWA (v0.7.17) on the hg19/GRCh37 reference human genome, followed by theremoval of PCR duplicates. For further identification of SNPs, we used104,109,318 reads generated on the bcftools software.Genetic testing of the alleged modern Rurikids was carried out in thecommercial laboratory FamilyTreeDNA in Houston (USA), in the laboratory ofhuman population genetics at the Research Centre of Medical Genetics namedafter the Academician N.P. Bochkov (Moscow) and in the laboratory ofevolutionary genetics at the Research institute of medical genetics (Tomsk).The results of the genetic testing were provided by Seslavin A.N., Volkov V.G.and Maksimov N.G., the leaders of the international research project“Rurikovichi. The genome of Russian princes.” Some of the resultswere presented as bam files ,part as a list of SNPs of the Y chromosome (Olgovich2 and Olgovich5). Allparticipants donated their genetic data to be used in this project.Discovery of the remains of Prince Dmitry Alexandrovich in theTransfiguration Cathedral in Pereslavl-Zalesskyth Novgorod chronicle, the Moscow chronicle of thelate XV century, Voskresenskaya and Nikonovskaya chronicles) contain directreferences to the fact that he was buried in the Transfiguration Cathedral ofPereyaslavl-Zalessky  [The architectural and archaeological team of the Institute of Archeology of theRussian Academy of Sciences, led by V.V. Sedov, examined the alleged burialsite of Prince Dmitry Alexandrovich in Pereslavl-Zalessky. Prince DmitryAlexandrovich (?–1294) was the second son of Prince AlexanderYaroslavich, who inherited the Principality of Pereslavl after hisfather’s death 1263). At different times Prince Dmitry possessed theNovgorod and the great Vladimir principalities. He died on Volok Lamsky on hisway to Pereslavl from Tver, and he was buried in Pereslavl . At the same time, a number of chronicles(including the 43. At dif 170 p.) ,28. ThePaleogenetic analysis of bone remains from the sarcophagusTo isolate aDNA, bone samples  of an adult individual, presumably Prince Dmitry Alexandrovich(identification number Nev2), were collected. Bone powder weights were obtainedfrom the samples with the corresponding identification numbers Nev2.1, Nev2.2,and Nev2.3, from which DNA was isolated and libraries of single-strandedfragments were prepared for the initial shotgun sequencing in order to assessendogeneity. Sequencing results are presented in Supplementary 3.The Nev2.3 sample was characterized by the highest proportion of endogenous DNAand the frequency of cytosine to thymine substitutions at the 5’ ends ofaDNA fragments and was selected for further in-solution enrichment andsequencing. The frequency of C to T substitutions around the 5´ ends ofthe sequences are presented in the figure in Supplementary 4.Table 1).As a result of assessing thelevel of contamination of the sample according to such parameters as the degreeof heterozygosity of mtDNA and X chromosome(Table 1 andTable 2of Supplementary 5), no contamination of the sample was detected.As a result of genome-wide sequencing of the library of Nev2.3 aDNA fragments,more than 15 million reads were generated and 532,154 single nucleotidepolymorphisms (SNPs) were identified. The data suggested that the genomebelonged to a man, his mitochondrial haplogroup was F1b, and the Y chromosomehaplogroup was N1a (Prediction of the phenotypic traits of Prince Dmitri Alexandrovich basedon genetic dataP = 0.962), hair colorwas dark (P = 0.810) or brown (P = 0.555),and skin tone was intermediate (0.635), that is, neither light nor dark.Prediction results for the phenotypic properties of sample NEV2.3 are shown inTable 2.We have investigated the sample NEV2.3, presumably belonging to Prince DmitryAlexandrovich, son of Prince Alexander Yaroslavich Nevsky, and managed toidentify the single-nucleotide polymorphisms that allow us to predict hisphenotype with a reasonable probability: hair color, skin color, and eye color.Eye color was most likely to be brown  and threeancient descendants of Rurik , 31, 32.For the phylogenetic positioning of the Y chromosome, we used all the samplesavailable in the Allen Ancient DNA Resource (AADR) database  carryingFig. 1)originating from various noble families:Mstislavich2 (M), Mstislavich3 (M), Mstislavich4 (M), Mstislavich5 (M),Yurievich1 (Yu), Yurievich2(Yu), Yurievich3 (Yu), Olgovich2 (O), Olgovich3 (O),and Olgovich5 (O). M stands for the Mstislaviches, heirs of the branch of theprincely Monomakh family descended from Prince Mstislav Vladimirovich(1076–1132); Yu, for the Yurieviches, a branch of the Rurikids derivedfrom the great prince of Kiev Yurii Dolgorukii († 1157); and O, for theOlgoviches, the heirs of the middle line of Chernigov princes, the descendantsof Oleg “Gorislavich” Svyatoslavich († 1115). Hereinafter wewill use the symbols M, O, and Yu (these branches are reproduced inSupplementary 7) in order to attribute the samples to the genealogic branch ofRurikids. The detailed genealogy of the Rurikids is provided in Supplementary 8.As a result of phylogenetic positioning, the Y chromosome of Prince DmitryAlexandrovich was clustered together with the Y chromosomes of alleged modernRurikids , published under theidentification number VK542 . The Y cFig. 2).It turned out that the Y chromosome of the alleged prince IzyaslavIngvarevich Lutsky, although it belongs to the haplogroup R1a, does not clustertogether with the samples of contemporary representatives of the Rurikids: thesamples of Mstislavich1 (M), Olgovich1 (O), and Olgovich4 (O). Moreover,Mstislavich1 is clustered separately from Olgovich1 and Olgovich4. We shouldmention that these samples do not cluster with other “Vikings” withhaplogroup R1a whose remains were found in Gnezdovo (VK466), Staraya Ladoga, and Kurevanikha (VK160) [For carriers of the haplogroup R1a of the modern representatives of theRurikids and samples from the AADR database, an analysis was carried out usingthe same algorithm as for carriers of the haplogroup N1a belonging to the N1a haplogroup and Prince Dmitry Alexandrovich havehighly similar Y chromosomes. The aggregate of genome-wide data on the medievaland modern Rurikids unequivocally indicates that they belong to the N1ahaplogroup of the Y chromosome, starting at least from the XIth century (sincethe time of Prince Yaroslav the Wise). All the other prospective Rurikids, bothancient and modern, being carriers of other haplogroups , possesshigh heterogeneity of the sequence of Y chromosomes; we cannot, therefore,confirm their common ancestry.Search for archaeological samples with the Y chromosome sequences closestto Prince Dmitry Alexandrovichmd millennium BC, located in the Kola district of the Murmanskregion .Previously, using these samples as anexample, the gene flow of the peoples of Siberia (East Eurasian component) tothe North and East of Europe was shown [The Y chromosome of Prince Dmitry Alexandrovich, in addition to the modernRurikids, is clustered in the same branch with the ancient people from BolshoyOleny Island (Russia_Bolshoy), a burial ground dating back to the middle of the2as shown .A high Fig. 3 andin Table 1 of Supplementary 9.We studied the contribution of the genome of people from Bolshoy Oleny Islandto the formation of the medieval population living in the Baltic territories ofmodern Finland, Denmark, Sweden, and Norway. For this purpose, we used thegenomes of the “Vikings” published in the 2020 Margaryan article indicated a significant flow of East Eurasian genes into the“Vikings” with the N1a haplogroup . Theresults are presented in Table 1 ofSupplementary 10). To achieve a successful modeling, a fourthsource was added, which was represented by the eastern samples of the XiongnuIron Age (about 100 BC–50 AD) or samples of the Bolshoy Oleny Island. Itturned out that the Scandinavian populations were modeled equally effectively(p > 0.05) using both the Xiongnu[Table 2 Supplementary10). Their genetic contribution to these populations wasas follows: to Ladoga – 4.7% Xiongnu and 4.7% Bolshoy Oleny Island; andto EstoniaIA – 6.5% Xiongnu and 8.4% Bolshoy Oleny Island.The hypothesis that the people from Bolshoy Oleny Island are one of the optimalproxy populations for four-ways admixture was tested by repeating the modelingthat was performed by Margaryan et al .Some Vi Xiongnu and BolsThus, it is clear that the gene pool of medieval “Vikings,”representing a significant part of Northern Europe (island and mainland), cameinto being partly through a flow of genes from Siberia, and the male ancestorsof Prince Dmitry Alexandrovich were, with a high probability, men who left theBolshoy Oleny burial ground island on the coast of the Kola Peninsula about3,600 years ago.Analysis of mtDNA of Prince Dmitry AlexandrovichThe mitochondrial haplogroup of Prince Dmitry Alexandrovich was identified asF1b1. This haplogroup refers to the East Eurasian cluster, and itsrepresentation at different frequencies in the gene pools of most of thepreviously studied ancient and modern populations of the Baikal region andadjacent territories of Central Asia is noted , 37, 38.It is rather difficult to interpret the origin of the mitochondrial haplogroupof Prince Dmitry Alexandrovich, since for almost all historical epochs there isan increased variability and “diversity” of the mitochondrialcomposition of the female part of the groups of the ancient population. This isdue to the fact that marriages of an official and unofficial natureconcentrated representatives of completely different genetic lines in onegeographical locus. When examining the history of dynasties, it is important tokeep in mind that the attraction of women of various backgrounds as abeneficial or forced political move is a widespread phenomenon. Thus, the F1bmitochondrial group of Prince Dmitry Alexandrovich can be associated both withthe ancient northern flow from the territory of Siberia (East Eurasiancomponent)  and withResults of PCA analysisTable 1 ofSupplementary 2). A simplified version of these results is reproducedin Fig. 4 . The location of ancient and modern genomes on the PCA mapcorrelates with the geographical coordinates of the correspondingarchaeological sites (Pearson correlation 0.76). The PC1 axis mainly coincideswith the West– East direction; and the PC2, with the North–South.The genome of Prince Dmitry Alexandrovich  holds an intermediate position between the European and CentralAsian clusters. The ancient samples closest in time to Prince DmitryAlexandrovich belong to an early medieval population of Central Europe, theAvars steppe nomads of the late period; for example, the Hungary_ LateAvar (IDI16741) [The principal component analysis (PCA) was used to assess the geneticaffinities of the genome of Prince Dmitry Alexandrovich to other known ancientand modern populations. The results of the PCAanalysis for 740 samples areshown in the figure in Supplementary 11 . Archaeologists often define the Avars asCaucasoids, suggesting that only a small ruling stratum, the elite, retained apronounced Mongoloid feature. The recently published genomes of ancientindividuals of the Avar period demonstrate their genetic heterogeneity; on theprincipal component plot, the studied samples are scattered over the entirewedge from the populations of Western Eurasia to the populations of NortheastAsia . DespiteAdmixture analysisK from 6 to 12. The resultsof the Admixture analysis in simplified form with the number of ancestralpopulations equal to six (K = 6) are shownin Fig. 5.Representatives of ancient populations are shown whose genomes wereused for modeling the genetic origin of the prince.Analysis of the genetic origin of Prince Dmitry Alexandrovich was carried outusing the Admixture method. Supplementary 12 presents the results of anAdmixture analysis with parameters When decomposing the genome of Prince Dmitry Alexandrovich into ancestralcomponents, it should be noted its genetic similarity to representatives of theearly medieval population of the east of Scandinavia, the“Vikings,” which may militate in favor of the version of the“Vikings” (Scandinavian) origin of Rurik, the ancestor of theprincely family called Rus’, which the Chronicle directly indicates. Hereand below, we use the term “Vikings” in quotation marks to showthat this is a heterogeneous and complex European population in its historicalformation, united only by the way of life and habitat.K equal to6–12.Comparison of the genome of Prince Dmitry Alexandrovich with the genomes of theScandinavian populations of the “Viking” Age, including those fromthe territory of modern Russia ,indicatThus, based on PCA data, Admixture analysis, and information on mitochondrialDNA, it can be argued that Prince Dmitry Alexandrovich had a significanteastern component in his genome. This distinguishes him from the early medievalpopulation of the east of Scandinavia, the “Vikings,” and themedieval Slavic sample from Vladimir (Sungir6), but it makes him closer to theancient population of Finland, the Kola Peninsula, and the early medievalpopulation of central Europe, which includes a well-known component of steppenomads. Probably, this contribution came from both the male and female lines,which corresponds to the routes of ancient migration from Siberia to the northof Europe and migrations from Siberia in the first millennium BC – thefirst millennium AD along the Eurasian steppe corridor.Modeling the genome of Prince Dmitry Alexandrovich from the genomes ofancestral populationsAfter analyzing the results of the PCA and Admixture analysis, as well asavailable historical information, we selected populations that couldparticipate in the formation of the genome of Prince Dmitry Alexandrovich: thegenomes of the early medieval population of the east of Scandinavia;representatives of the Iranian-speaking nomads of the Eurasian steppes of theIron Age, and the population of the early Middle Ages of Central Europe, whichincludes a well-known component of the steppe nomads and samples of individualsrepresenting the ancient East Eurasian component. To assess the contribution ofthe Slavic component to the genome of Prince Dmitry Alexandrovich, samples ofthe Medieval Russian population of the XI century from the rural necropolis ofthe Shekshovo settlement in Suzdal Opolye and an individual of the XII centuryfrom the territory of modern Vladimir (Sungir6) were used, 44.SevModeling of the genome of Prince Dmitry Alexandrovich using the genomesof the “Vikings”. QpWave-analysis determined that aminimum of three ancestry sources were sufficient to model the genome of PrinceDmitry Alexandrovich (Supplementary 13). Exploring all possible combinations ofthree sourc es from the selected list was accomplished using the qpAdm tool.Results of the modeling of the genome of Prince Dmitry Alexandrovich(p greater than 0.05) are presented in Supplementary 14(Table 1).The genome of Prince Dmitry Alexandrovich can be successfullymodeled as a three-way admixture of the “Vikings,” steppe nomads,and ancient East Eurasian components. For example, one of such models includes46.6% of the early medieval population of the east of Scandinavia(Sweden_EarlyViking); 39.6% of the early medieval population of central Europe,which includes the known component of the steppe nomads (Hungary_LateAvar); and13.8% of the Russia_IA source as the third component (Iron Age sample from theterritory of the Republic Altai). Substitution of Russia_IA with Iron Agesamples from the territory of Finland (Finland_Levanluhta) or the Bolshoy OlenyIsland in the Kola district of the Murmansk region produced a reliablethree-way admixture model. These third components  have the ancient East Eurasian component incommon. According to recent studies, people of that ancestry arrived in theKola Peninsula more than 3,500 years ago from Siberia[Fig. 6A and inSupplementary 14 (Table 1). Siberia and mixeFig. 6B).The Sweden_ EarlyViking population is represented by threesamples from the village of Bode (Böde) on the island of Öland anddated around the period between the VII and VIII centuries AD . The analysis of strontium isotopes for these samples[Among “Vikings” Sweden_EarlyViking was the largest contributingpopulation to the genome of Prince Dmitry Aleksandrovich 46.6%). For other“Vikings” this proportion did not exceed 9%. The minimumcontribution is made by the Estonia_ EarlyViking population: 2.7%(6.6%. Forp= 0.64) modeled as a three-way admixture between Europeanhunter-gatherers, Neolithic farmers, and steppe pastoralists (Supplementary14 Table 2),while the Oland_Sweden_Viking group could not be modeled as amixture of these ancestral populations (p = 0.01). There arealso significant differences in the modeling of these two groups of“Vikings” using a single source group, which are Iron Agepopulations from the territory of Europe or a sample of the Medieval Russianpopulation of the XI century from the territory of modern Vladimir, Russia(Supplementary 14 Table 3).In an extended analysis, all “Viking” population groups were usedto test the single-source model for the Sungir6 sample.Those populations of “Vikings” thatprovide reliable values of F4-statistics for this model are concentrated in thenorthern part of Europe, Ireland and Iceland, while none of the southernpopulations fits into such a model. These results raised the question of therelationship of the Scandinavian population groups to the Slavs in the periodfrom the VI to the XI centuries.Our qpAdm analysis showed a significant genetic difference between theOland_Sweden_EarlyViking group and other samples from Oland Island, designatedas Oland_Sweden_Viking: the first one can be successfully.The model provides a fits onlywith early Iron Age nomads from the Tien Shan region, perhaps, due to theirgenetic profile: high proportion (70%) of the Late Bronze Age steppepastoralists, 25% of the South Siberian hunter-gatherer component, and 5% ofthe component associated with the Neolithic population of Iran[A statistically reliable genetic model of the genome of Prince DmitryAlexandrovich was also obtained by replacing the early medieval population ofCentral Europe (including a component of steppe nomads) with Iranian-speakingnomads of The Eurasian steppes in The Iron Age . The results are shownin  of Iran. This grModeling the genome of Prince Dmitry Alexandrovich using the genomes ofthe Medieval Russian Slavic populationFig. 4).We hypothesize that alternative models – replacing the population of“Vikings” with Medieval Russian Slavic populations – willlikely also provide a fit. Since the pagan Slavic tradition practiced cremationuntil the end of the X century, we used samples of the medieval Russianpopulation of the XI century from the rural necropolis of the settlement ofShekshovo9 in the Suzdal Opolye and an individual of the XI century from theterritory of modern Vladimir, Russia (Sungir6), 43.TheFig. 8 and in Supplementary14, Table 6).Replacing the Sungir6 with Shekshovo9 as a source, the proportionof the Medieval Russian Slavic population amounts to 18.7%; the nomadic IronAge steppe peoples (Kyrgyzstan_TianShan_Saka) – 78.2%; and Russia_IA_2– about 3%.In the slightly differentthree-source modeling with the Hungarian Avars, Slavic Medieval Russian andRussia IA – the proportion reached 76,2%, 10,8%, and 13,1%,correspondingly. The decrease in the Slavicproportion can be explained by the partial compensation of Slavic origin by theAvars individuals who were used for modeling. Their origin suggests 80% of thelocal East European source and only 20% of the Central Asian one (ID I16741)[We successfully modeled Prince Dmitry Alexandrovich’s ancestry as beingderived from populations related to the Sungir6 sample – 19.7%; Iron Agenomadic steppe peoples – 73.8% for Kyrgyzstan_ TianShan_Saka; and theIron Age from the territory of the present-day Altai Republic – 6.5% forRussia_IA( I16741).Thus, modeling of the genome of Prince Dmitry Alexandrovich indicates thecontribution of three ancestral sources to its origin: (1) the early medievalpopulation of the east of Scandinavia from the island of Oland, (2) the steppenomadic peoples of the Eurasian steppes of the Iron Age or the early medievalpopulation of central Europe (steppe nomads from the territory of Hungary), and(3) the East Eurasian component. Alternative models, replacing the populationof “Vikings” with the Medieval Russian Slavic populations(Shekshovo9 and Sungir6) also provide a fit.Paleogenetics and mathematical statistics provided an opportunity to discussthe origin of the Rurikids and design a reliable tool to attribute remains withdisrupted documentation to this ruling family.An analysis of the genealogical tree of the Rurikids showed that the modernindividuals of this family, who have a Y chromosome clustered with PrinceDmitry Alexandrovich’s Y chromosome, belong to three different branches– the Olgoviches, Mstislaviches, and Yuryeviches. Thus, the N1ahaplogroup of the Y chromosome characterizes all three branches of the tree,suggesting that their first common ancestor, Prince Yaroslav the Wise, was acarrier of N1a haplogroup also.The mitochondrial haplogroup of Prince Dmitry Alexandrovich was determined asF1b1, which may point to the contribution of eastern populations to his genome.This hypothesis is also supported by autosomal data (PCA and Admixture).Although the main genetic makeup of Dmitry Aleksandrovich can be attributed tothe Scandinavians/Slavic/European populations our results provide clearevidence of the input of the Eastern genetic component. This is in line withhistorical data: marriages of Russian princes with the daughters of thePolovtsian khans from the end of the XI century were a common practice thatcemented allied relations and political interaction , 47. DmiOur results raise some questions on Rurikids’ genetic history. The mostobvious of them are (i) how to explain the presence of the “Easterncomponent” in the genome of Dmitry Aleksandrovich and (ii) were there agenetic connection between Scandinavians and Slavs in the pre Rurik era? Theanswers may come after a systematic paleogenomic study of new, reliablydocumented paleoanthropological materials from the territory of Russia.


The aim of this research was to obtain new polymer composites with a silicone rubber matrix, having favorable mechanical and functional properties. They contained admixtures in the amount of 10% by weight of expanded graphite (EG) or birch bark (BB). Additionally, some composites contained magnetic particles in the form of carbonyl iron in the amount of 20% by weight. The tensile strength, water absorption, frost resistance, surface contact angle, and free surface energy were examined. Microscopic images were taken using the SEM method and the content of some elements in selected microareas was determined using the EDXS method. In the study, a constant magnetic field with magnetic induction B was used, by means of which the properties and structure of polymer composites were modified. Scientific research in the field of polymers is the driving force behind the progress of civilization. Smart materials are able to respond to external stimuli, such as magnetic fields, with significant changes in their properties. The magnetic field affects not only chemical reactions, but also the crystallographic structure and physicochemical properties of the final products. Owing to their unique properties, such materials can be used in the space industry, automotive industry, or electrical engineering. The aim of this research is to use various types of admixtures and to influence the polymerization process with a constant magnetic field, thus expanding the spectrum of possibilities for designing materials with new, modified, and improved physicochemical properties. Polymer composites are commonly used, among others, in the chemical, construction and electrical industries, and therefore, an increasing demand for such materials has been observed for many years. Thus, developing new materials with similar or better properties than those currently produced has become extremely important. The applied constant magnetic field with magnetic induction B was an additional external factor that modified both the properties and structure of polymer composites. The effect of a constant magnetic field on the polymerization processes of polymer composites is relatively little known and rarely described in the literature.3O4) and crystalline cellulose in the amount of 10%, 20%, and 30% w/w as well as starch in the amount of 10% w/w. Moreover, cellulose and starch fillers were added to provide partial biodegradability to the composites. Scanning electron microscopy linked with EDS and XRD techniques was used to characterize the chemical composition and structure of polymer composites, respectively. It was found that the polymer composites obtained in a constant magnetic field with a different magnetic induction B displayed changes of such properties as tensile strength, resistance to bending and impacts, water absorbability, frost resistance, and chemical resistance to acids and alkali. Thus, a magnetic field can be used as an external factor that allows materials with designed properties to be obtained, and in addition, natural additives accelerate the biodegradation processes of the composites.Miękoś et al.  have invw/w. As admixtures, expanded graphite and powdered graphite (20% w/w) were also used. During polymerization, the composites were placed between the poles of an electromagnet. In this case, under a constant magnetic field with magnetic induction B = 0.5 T, changes in some properties were detected. In particular, the hardness of the composite with an admixture of birch bark increased approx. 12% from 24.01 to 26.96 N/mm2 and with an admixture of yellow dextrin, it increased approx. 15% from 26.12 to 29.93 N/mm2. Moreover, the water absorbability of the pristine resin decreased from 0.18 to 0.13%, and that of the composite with powdered graphite decreased from 0.48 to 0.46%. Such changes in properties, often beneficial, are of great importance from the point of view of the potential use of these materials.Miękoś et al.  and Fu e2, Al2O3, and Fe2O3, and carbon nanomaterials, e.g., carbon black, carbon nanotubes, and graphene, are widely used as additives, which increase the high-temperature resistance of silicone rubber composites  and 6 [GUM(B)OL-1(Fe)(EG)] (Table 5) were selected to present detailed studies of the influence of a constant magnetic field with a wide range of magnetic induction B varying from 0 to 1 T on the water absorbability of polymer composites. These dependencies are shown in The tests demonstrated that the magnetic induction of B = 0.5 T was the most effective in reducing the water absorbability of composite 4 GUM(B)OL-1(Fe)(BB) in a constant magnetic field . The samThe results of the frost resistance tests of the six polymer composites are presented in The reference sample 1 GUM(B)OL-1(−)(−) containing silicone rubber showed a frost resistance of 0.3%, both when it was prepared in a constant magnetic field B = 0.5 T and without a magnetic field. The deterioration of frost resistance, i.e., a reduction in weight loss of the sample, was the most significant for sample 3 with birch bark as an admixture. The frost resistance decreased 18 and 22 times for the samples prepared without and with a constant magnetic field with induction B = 0.5 T, respectively. An improvement in frost resistance, i.e., almost a two-fold decrease in the weight loss of the sample, was found for sample 2 with carbonyl iron as an admixture prepared in a constant magnetic field.In order to examine the impact of a constant magnetic field with a magnetic induction B in the range of 0–1 T on the frost resistance of polymer composites, the samples 4 GUM(B)OL-1(Fe)(BB) and 6 GUM(B)OL-1(Fe)(EG) were selected. The results of the studies are presented in The obtained results show that in all the ranges of magnetic inductions used, the frost resistance of polymer composite 4 GUM(B)OL-1(Fe)(BB) with carbonyl iron and birch bark as an admixture is higher, i.e., the weight loss of the sample is lower, than that of polymer composite 6 GUM(B)OL-1(Fe)(EG) with carbonyl iron and expanded graphite as an admixture. In addition, the frost resistance of polymer composite 6 GUM(B)OL-1(Fe)(EG) does not change significantly up to a magnetic induction B of 0.8 T. However, when the magnetic induction B was equal to 1 T, the frost resistance of this polymer composite increased by about 20%.For the study of the influence of a constant magnetic field on tensile strength in the static tensile test of polymer composites, composite 6 GUM(B)OL-1(Fe)(EG) (Table 5) was selected.The composites were obtained in a constant magnetic field within the range of 0–1 T. In addition to determining how magnetic induction affects the mechanical strength and elongation of polymer composites, it has also been demonstrated that the composites achieved anisotropic properties in a constant magnetic field. Anisotropic properties depend on the direction of testing these features; hence, the composites were stretched in different directions. A sample with dimensions of 8 cm × 5 cm × 0.5 cm obtained in a constant magnetic field was used for the tests. The method of mechanical tensile treatment of the composites is shown in mσ stress values in the composite material, it was found that they are much higher when the direction of tensile force F is perpendicular to the direction of magnetic induction B (By studying the uction B . Under am). In this case, the influence of the direction of tensile forces F relative to the direction of action of the magnetic induction B vector was determined , and if θ > 90°, then the wettability of the surface by the liquid is low (hydrophobic surface).The determinations involved measurements of six measuring drops of the following liquids: water, diiodomethane, and glycerin. On the basis of these measurements, the values of the surface wetting angle and the surface free energy of the composites were determined. For the purpose of studies of the influence of a constant magnetic field on the surface wetting angle of polymer composites within the range of magnetic flux density values (B = 0.0–1.0 T), polymer composites 4 GUM(B)OL-1(Fe)(BB) and 6 GUM(B)OL-1(Fe)(EG) were selected. The results of the measurements of the wetting angle of the surface by water θ are shown in On the basis of the conducted research, the constant magnetic field was found to cause an increase in the surface wetting angle of polymer composite 4 GUM(B)OL-1(Fe)(BB) with an admixture of carbonyl iron and birch bark by 25.3%, while for composite 6 GUM(B)OL-1(Fe)(EG) with the admixture of carbonyl iron and expanded graphite, the increase was 19.6%. The wettability of the surface of these composites was demonstrated to decrease due to the increase in the hydrophobicity of the samples.Surface free energy (SFE) is one of the thermodynamic quantities describing the equilibrium state of atoms in the surface layer of a specific material. Surface free energy reflects a specific state of imbalance in intermolecular interactions present at the interface of the phases of two media. The surface free energy is equal to the work needed to form a unit of area during the separation of two equilibrium phases in a reversible isothermal process. To present studies of the influence of a constant magnetic field on the surface free energy of polymer composites within the range of magnetic induction (B = 0–1 T), composites 4 GUM(B)OL-1(Fe)(BB) and 6 GUM(B)OL-1(Fe)(EG) were also selected. The results of the calculations of surface free energy are shown in As a result of the conducted research, the constant magnetic field was found to cause a decrease in the surface free energy of polymer composite 4 GUM(B)OL-1(Fe)(BB) of 34%, and 29.4% in the case of composite 6 GUM(B)OL-1(Fe)(EG). This was due to the fact that as a result of the action of the constant magnetic field, the surface of the material was less rough, the filler grains and polymer particles were more packed, and there was less free space in between. Ferromagnetic particles aligned themselves in the direction of the magnetic induction vector, so that other particles, even diamagnetic ones, were also arranged in a certain order , similar changes can be observed. The water absorbability (nw) in a constant magnetic field with magnetic induction B = 0.5 T decreases from 0.68% to 0.49%. This is confirmed by the increase in the surface wetting angle θ of the composite from 93.8 to 104.0°. This is also confirmed by mechanical strength tests. With a more favorable effect of the force F relative to the direction of magnetic induction B (F⊥B), the maximum stress in the composite (σm) increases from 0.35 to 0.48 Pa. At the same time, the tensile deformation (Ɛm) is reduced from 35.0 to 27.0%. Therefore, the effect of a constant magnetic field involved a reduction in the roughness of the material surface and more dense packing of the filler grains and polymer particles so that there was less free space between them.For composite 4 [GUM(B)OL-1(Fe)(BB)], there is a decrease in water absorbability (n−6–10−2 mm) , the electrostatic Coulomb forces and Van der Waals forces predominate, while when the particles are larger (0.1–1 mm), the capillary forces begin to prevail. Hydrodynamic forces become important only when a constant magnetic field is applied. The magnetic particles contained in the polymer, in order to achieve large magnetic moments, align themselves in the direction of the external magnetic field. The intermolecular interaction forces cause the particles to attract each other, which leads to their aggregation in complex networks, shortening the distance between them, and thus stiffening the material. The change in the internal energy U or enthalpy H in the magnetic field is due to the direction of charges in the material or ions in the liquid, as well as the disappearance of the insulating thermal barrier.The effect of a constant magnetic field on atoms manifests itself in the form of stresses in the crystal lattice. In liquids, however, magnetic fields acting on both electrons and ionized atoms cause dynamic effects, one of which is the volumetric motion of the medium. In turn, the movement of the masses causes the fields to be modified. Thus, we are dealing with a coupled system of matter and fields. During the polymerization of composites, an interaction between the liquid and solid phases takes place. Capillary interactions (attractive forces), Van der Waals interactions (attractive forces), electrostatic Coulomb interactions in the double layer (repulsive forces), expansion pressure (repulsive forces), and hydrodynamic interactions occur between grains. When the particles are small in size , thanks to which it was possible to change the properties of composites in a constant magnetic field.The following components were used for the preparation of polymer composites: GUMOSIL B silicone rubber ; OL-1 hardener ; EG 290 expanded graphite with high resistance to corrosion, elevated temperatures, electromagnetic radiation and compression ; dried birch bark, with a high content of cellulose, containing betulin with anticancer, antiviral (HIV-1), anti-inflammatory, and antiallergic properties , Ogrodowa 87a/3) . GUMOSIL B silicone rubber matrix polymer composites additionally contained magnetic particles with ferromagnetic properties in the form of carbonyl iron in the amount of 20% w/w), the filler  and the OL-1 hardener for silicone rubber. The whole was mixed using a mechanical stirrer at a speed of 300 rpm for 180 s. The mixed liquid mass of the sample was placed in molds prepared according to the PN-EN ISO 10210:2018-1 standard [All the test samples were prepared according to the same procedure. After the sample composition was developed, the individual components were weighed and added in the following order: GUMOSIL B silicone rubber, magnetic particles in the form of carbonyl iron :mn is the mass (in g) of the sample saturated with distilled water and m is the mass (in g) of the dry sample. The frost resistance of the samples was tested as follows. The samples dried to constant weight were immersed completely in distilled water for 24 h. After their removal from distilled water, the samples were placed on tissue paper for 30 min to dry and then placed in the freezer at −20 °C for 24 h. After removal from the freezer, the samples were immersed completely in distilled water again for 24 h, then removed from the water, air-dried for 48 h, and weighed. The percentage damage of sample S was defined as the relative loss of its mass calculated from Equation (2):m1 is the mass (g) of the dried test sample and m2 is the mass (g) of the sample dried at the end of the test. There are many different methods of testing frost resistance. The frost resistance tests presented in this paper were based on the PN-EN 206+A1:2016-12 [Mechanical tensile strength was tested using a Zwick/Roell Z050 type, KL 0.05 testing machine, with a 50 kN measuring head, a tensile speed of 50 mm/min, and a speed of 5 mm/min for the module . The tensile strength was tested in accordance with the PN EN ISO 527-1  and PN E:2016-12  and PN-8:2016-12  standardw/w of expanded graphite (EG) or birch bark (BB). Some polymer composites were additionally modified with magnetic particles in the form of carbonyl iron (Fe) in the amount of 20% w/w. In the work, a constant magnetic field with magnetic induction B was used to modify the properties and structure of the composites. As a result of the research on the water absorbability of the composites, it was found that the polymer composites with the admixtures of birch bark and birch bark with carbonyl iron were characterized by the highest water absorbability of 29.4%. A 10-fold increase in the water absorbability of the composite was observed, and a 20-fold increase in the case of the composite obtained in a constant magnetic field. The best effect was obtained in a constant magnetic field (B = 0.5 T) for the composite with an admixture of EG 290 expanded graphite, where the water absorbability of the composite decreased from 0.4647% to 0.0875%, i.e., five times. The deterioration of frost resistance (weight loss) was the greatest for the composite containing an admixture of birch bark. Frost resistance deteriorated 18 times for a sample obtained without a magnetic field and 22 times for a sample obtained in a constant magnetic field with B = 0.5 T. An almost two-fold frost resistance improvement  occurred for a composite containing an admixture of carbonyl iron, especially under the influence of a constant magnetic field. In addition to the observation that the value of magnetic induction B affects the ultimate tensile strength and elongation of polymer composites, it was also demonstrated that the composites acquired the properties of anisotropic materials in a constant magnetic field. On examination of the stress values σm of the composites, it was found that they are much higher when the direction of tensile force F is perpendicular to the direction of magnetic induction B during the polymerization of the composite. The difference in the value of stress in the material, with the action of a constant magnetic field, e.g., B = 0.5 T, reached approx. 55%, while the difference in the deformation of the material was approx. 35%. The constant magnetic field increased the surface wetting angle of the carbonyl iron and birch bark polymer composite by 25.3%, and that of the composite with an admixture of carbonyl iron and expanded graphite by 19.6%. The wettability of the surface became lower, and thus the surface of the composites became more and more hydrophobic. On the other hand, the constant magnetic field reduced the surface free energy of the polymer composite with an admixture of carbonyl iron and birch bark by 34%, and that of the composite with an admixture of carbonyl iron and expanded graphite by 29.4%. The composites obtained in a constant magnetic field had visible, larger clusters of magnetic particles, which were arranged into chains. On the basis of SEM microscopic images, divided into microareas by the EDXS method, chemical composition analysis was performed. The dominant component in many microareas was silicon (Si) due to its contribution to silicone rubber. Some of the microareas demonstrated the presence of carbonyl iron particles arranged in chains. The quantitative content of carbon (C) was not taken into account in the studies, because this component comes from both expanded graphite and silicone rubber, so it would be overestimated.This paper presents the results of our research on new polymer composites based on silicone rubber matrix. The polymer composites contained admixtures in the amount of 10% A constant magnetic field, by affecting the multiphase structure of the polymer composite, changes the thermodynamic equilibrium of the material. Under the influence of a constant magnetic field, thermodynamic functions such as enthalpy and internal energy can change. Thanks to the use of magnetic particles, e.g., carbonyl iron, the shape of polymer composites can be controlled. They were distributed in the polymer and converted the energy of the magnetic field into heat. The appropriate temperature could be achieved by changing the density of the magnetic particles and the value of magnetic induction.The only limitation in the use of a constant magnetic field to obtain samples of polymer composites may be the space between the pole pieces of an electromagnet producing a constant magnetic field with a specific magnetic induction B. This space determines the dimensions of the individual samples of polymer composites intended for testing.


Compared to pure PLA, the diffusion coefficients of the O2, CO2, and N2 of the blends with 30% PBF decreased by 75%, 122%, and 188%, respectively. Our simulation results are in good agreement with the actual experimental results.Three blends of Poly (PBF) and Poly(lactic acid) (PLA) blends were modeled using molecular dynamics simulations, with PBF contents of 10%, 20%, and 30%, respectively. The study investigated the compatibilities of the blends, as well as the mechanical and gas barrier properties of the composite systems. The molecular dynamics simulation results show that: (1) PLA and PBF have good compatibility in the blend system; (2) the optimal toughness modification was achieved with a 20% PBF content, resulting in a 17.3% increase in toughness compared to pure PLA; (3) the barrier properties of the blend for O In addition, reducing the cycle time and increasing the recycling rate of bio-based polyesters remain great challenges. However, from the point of view of disposal methods for waste petroleum-based polyester products, such as incineration, landfill, and dumping into the sea [Bio-based polyesters are increasingly being used in various areas such as tissue engineering, food packaging engineering, and drug delivery, owing to their excellent mechanical, crystalline, and gas barrier properties ,2,3. Bio the sea ,8,9, bioPolylactic acid (PLA) is one of the most promising bio-based polyesters for industrial applications. PLA can be obtained by lactic acid polycondensation or lactide ring-opening polymerization, and the end products of its degradation are carbon dioxide and water . PLA hasRecently, there has been considerable interest in the bio-based monomer called 2,5-furandicarboxylic acid , which is a renewable diacid and considered as an alternative to benzodicarboxylic acid . 2,5-FDCComputational simulations are being increasingly used in various fields, particularly in materials engineering and pharmaceutical engineering. These simulations enable us to design models and study the physical and chemical properties of materials. The use of computational simulation software in simulating reactions provides a theoretical basis for experiments, reduces costs, and increases safety. In recent years, molecular dynamics and computational simulations have been extensively used by polymer modifiers to study polymer modification, yielding meaningful theoretical predictions and scientific rationale. Fojtíková  investigIn this study, we used Materials Studio (MS) software to carry out PBF modification of PLA based on molecular dynamics (MD). We analyzed the compatibility of PLA with PBF and the effect of different PBF contents on the toughness and gas barrier properties of PLA at the microscopic level. The properties of polymer materials exhibit diversity due to the complexity and multi-level nature of polymer chain structures. We used a molecular dynamics simulation to study the properties of PLA and PBF blends from a microscopic perspective, which is conducive to accelerating the research and development of PLA-based special performance materials. At the same time, studying the relationship between the polymer structure and gas permeability at the molecular level will help to understand and explore the microscopic mechanism of the polymer gas barrier in essence.In this study, all simulated processes used the COMPASSII force field. Periodic boundary conditions were applied in all directions of the polymer box. The schematic diagram of periodic boundary conditions is shown in This study was based on molecular dynamics principles and investigated the structure and properties of PLA/PBF blends at the atomic scale, where the structural diagram of the material is shown in 3 to ensure that the polymer chains had sufficient relaxation space in the box. Too large an initial density could potentially lead to irrational structures, such as entanglement and overlap between chains. The constructed polymer model requires energy minimization, annealing, and molecular dynamics simulations to obtain an equilibrium amorphous polymer model. The energy-minimized O2, N2, and CO2 were added to the polymer model according to the above steps for the study of the polymer gas barrier properties.Polymer models were constructed using energy-balanced PLA and PBF chains, with ten chains added to each model, depending on the computer capabilities. Pure PLA, PBF, and PLA/PBF blends in the ratios of 9/1, 8/2, and 7/3 were constructed as shown in First, the constructed polymer model was subjected to geometry optimization with 25,000 iterations. PLA, PBF, and three blend systems underwent 25,000 iterations of geometric optimization calculations, and their energy reached a stable state. The energy convergence curves of AF10, AF20, and AF30 are shown in The model with the lowest energy after annealing was used in the next step of the MD simulation. MD simulations were carried out in the following three steps by alternating between the canonical ensemble (NVT) and NPT ensembles. First, the polymer model was simulated for 1000 ps in the NVT (T = 298 K) ensemble, releasing any unreasonable tension that may be present in the polymer. The temperature change of the blend system during the dynamic simulations of NVT ensemble is shown in After the stable configuration was obtained, the compatibility between materials was first studied, because the compatibility of materials is one of the key factors that effectively improve the performance of composite materials. Good compatibility between materials can often better retain the original advantages of the two materials, so it produces good synergy. Thereafter, the mechanical properties, free volume, and diffusion coefficient of the material were studied.We also calculated the density of PLA and PBF through molecular dynamics simulation and compared it with experimental data. As shown in To accurately investigate the effect of PBF on PLA and the compatibility of the two, the properties of the blends were investigated using solubility parameters δ and intermolecular pair correlation functions g(r), where intermolecular pair correlation functions are also called radial distribution functions. The solubility parameter is an important physical quantity that reflects the intermolecular forces of a material and can be used to determine the compatibility of two materials. Hildebrand put forward the concept of solubility parameters, but Hildebrand solubility parameters are only applicable to non-polar materials and are often used to predict the compatibility of the solvent and solute, where Equation (1) defines the solubility parameter ,25. On tcoh is the cohesive energy, V is the mixing volume of the material, and CED is the cohesive energy density.In Equation (1), EDδ, Pδ, and Hδ represent the dispersion solubility parameter, polar solubility parameter, and hydrogen bonding solubility parameter, respectively.In Equation (2), elecδ and vdWδ represent the solubility parameters of electrostatic and van der Waals, respectively.In Equation (3), According to the results in 2. Based on the principle of similar solubility, we believe that the two materials are compatible [The solubility parameters of PLA and PBF are very similar, with a mpatible ,31.The radial distribution function g(r) was used to analyze the compatibility of the two materials more accurately. The radial distribution function, also known as the intermolecular pair correlation function, is the probability of finding particle A at a distance r from particle B, and g(r) can reflect the structural characteristics of the material at the microscopic level and reveal the nature of interparticle interactions in the material . The expA and NB are the atomic numbers of materials A and B in the system, respectively, ρA is the average number density of atoms in the molecular chain of material a, rij is the distance between atoms in different molecular chains of materials A and B, and δ is the Dirac function.In Equation (4), NThe relationship between the radial distribution functions of the intramolecular C–C pairs of pure PLA and the three blends and the composition of the blends is shown in ij of the material was calculated. The stiffness matrix of the PLA is shown in Equation (5). The Lamé constant of the material was calculated using a stiffness matrix. The Lamé constants of the PLA stiffness matrix are λ = 2.0261 and μ = 1.4711.In the Fortite module, the stiffness matrix CE), shear modulus (G), and bulk modulus (K) of the material can be obtained using the Lamé constants obtained using Equation (6) [The tensile modulus (tion (6) .(6)E=μ. Long et al.  experime2, N2, and CO2, respectively [2 and N2 are very similar; therefore, their free volume distributions are similar. The FFV of O2, N2, and CO2 tends to decrease and then increase with an increasing PBF content, but they are all smaller than the FFV in pure PLA. This indicates that the diffusion behavior of the three gases is limited in the blend, whereas it is easier to diffuse in PLA.The size of the free volume plays a very important role in the diffusion behavior of gas molecules in polymers. The free volume is the space wherein gas molecules can reach when diffusing. In the MS simulations, the free volume was obtained using the hard sphere probe method. The unoccupied space in the constructed box was divided by the total volume of the box to obtain the free volume fraction (FFV) in the model. In general, the larger the FFV, the greater the diffusion coefficient of the diffusing molecules in the model. We calculated the free volumes of polymers and blends through simulations in the Atom Volume & Surface tool in MS. The radii of the hard sphere probes were set to 1.52 Å, 1.53 Å, and 1.65 Å, corresponding to the kinetic radii of Oectively ,38. The MSD) can be calculated via tabulated analysis [t.In the Forcite module, the mean square displacement (analysis . The Einanalysis . TherefoMSD of O2, N2, and CO2 in the pure polymer and co-blends and obtained the diffusion coefficients of small gas molecules in the polymer by MSD to assess the gas barrier properties of the materials using the diffusion coefficients. The results of the gas diffusion coefficient calculations for pure PLA, pure PBF, and the three blends are shown in 2, N2, and CO2 gases gradually improve, with AF30 demonstrating the best barrier properties. The results of the barrier properties of PLA, PBF, and PLA/PBF to O2 and CO2 measured by Long’s experiment [We calculated the periment  are show2, CO2, and N2 by 75%, 122%, and 188%, respectively.In this study, we investigated the compatibility, mechanical properties, and gas barrier properties of PLA and PBF blends using a molecular dynamics simulation. The simulation results show that PBF and PLA have good compatibility, and that the addition of PBF can significantly increase the gas barrier of PLA while improving its toughness. Specifically, adding 20% PBF improved the toughness of PLA by 17.32%, and adding 30% PBF improved the barrier properties of PLA against OMore importantly, by comparing with the experimental data, our simulation results are in good agreement with the actual experimental results, which shows that molecular dynamics simulation can be used as an effective tool for predicting the mechanical and barrier properties of polymer materials.


Wheat pests and diseases are one of the main factors affecting wheat yield. According to the characteristics of four common pests and diseases, an identification method based on improved convolution neural network is proposed. VGGNet16 is selected as the basic network model, but the problem of small dataset size is common in specific fields such as smart agriculture, which limits the research and application of artificial intelligence methods based on deep learning technology in the field. Data expansion and transfer learning technology are introduced to improve the training mode, and then attention mechanism is introduced for further improvement. The experimental results show that the transfer learning scheme of fine-tuning source model is better than that of freezing source model, and the VGGNet16 based on fine-tuning all layers has the best recognition effect, with an accuracy of 96.02%. The CBAM-VGGNet16 and NLCBAM-VGGNet16 models are designed and implemented. The experimental results show that the recognition accuracy of the test set of CBAM-VGGNet16 and NLCBAM-VGGNet16 is higher than that of VGGNet16. The recognition accuracy of CBAM-VGGNet16 and NLCBAM-VGGNet16 is 96.60 and 97.57%, respectively, achieving high precision recognition of common pests and diseases of winter wheat. It is estimated that the occurrence area of wheat pests and diseases will reach 54 million hm2 in 2022. The overall situation is biased, including 27.3 million hm2 diseases and 26.7 million hm2 pests.Wheat is one of the main food crops in China, and pests and diseases are a major problem in wheat production that seriously affects the yield and quality of wheat ,2. AccorTraditional wheat pest identification mainly relies on staff patrols or the machine vision technology for auxiliary identification. Machine vision technology is an efficient method for automatic detection of crop pests and diseases, and its core is image processing. Currently, there is no general separation theory in image processing, resulting in insufficient scalability of the algorithm. In addition, the use of machine vision technology to identify winter wheat pests and diseases still has problems, such as complex treatment process, high labor cost, strong subjectivity, and difficulty in timely detection of large-scale infection of pests and diseases. In recent years, with the continuous development of artificial intelligence technology, deep learning  has gradAs a representative model of deep learning, convolutional neural network (CNN) can automatically extract the features of diseases and pests, simplify the identification process of winter wheat pests and diseases, reduce labor costs, greatly improve the accuracy and stability of pests and diseases identification, and improve the identification efficiency. It can effectively promote the informatization and intelligence of agriculture, which is of great significance for the stable development of agriculture. For the identification of tomato pests and diseases, Fuentes et al.  added vaAccording to the research status of deep learning in the identification of pests and diseases, most of them mainly focus on large-leaf plants , while that for small-leaf plants is relatively rare. To this end, taking the winter wheat as an object, a standardized dataset for the four common pest and disease samples collected in the natural environment of the test field is built. A traditional CNN model for the identification of winter wheat pests and diseases is constructed, and the migration learning technology and attention mechanism are introduced for improvement. Finally, the accurate identification of winter wheat common pests and diseases in the natural environment is realized.22.11) Dataset sorting and division. The dataset contains five categories: healthy wheat, aphid wheat, powdery mildew wheat, leaf rust wheat, and stripe rust wheat. The dataset contains 1,003 samples from different periods of wheat growth. Some samples are shown in According to the most common sample division ratio (8:2), each type of dataset is randomly divided into training sets and test sets. The composition of the datasets is shown in 2) Preprocessing. To meet the input sample size requirements of the CNN model, the nearest neighbor image interpolation method is used to uniformly process the original image to two specifications of 224 × 224 (pixels) and 227 × 227 (pixels) images. Normalization processing refers to obtaining the mean values of all samples in the dataset on the red, green, and blue channels separately, and then subtracting the mean values of each channel from each sample to obtain the standardized image. Normalization of images can effectively avoid gradient explosion during training.3) Data enhancement. The pre-processed image is enhanced to reduce the light interference. The image enhancement method based on scientific experiments and analysis, Retinex series algorithm, is selected to enhance the image. The dataset comes from the agricultural and water teaching practice base of North China University of Water Resources and Electric power, and is collected in the natural environment. The winter wheat variety is Xinhuamai 818, a new variety developed by Henan Agricultural University in 2018. The standardization of datasets includes sample sorting, preprocessing, and data enhancement.It can be seen from 2.2F1-score, and accuracy of the proposed algorithm are 93.06, 93.17, 92.82, and 93.14%, respectively. The results verify that the raised algorithm significantly ameliorates the classification property of clothing image algorithms.1) AlexNet network model, which won the championship in the 2012 image recognition contest with 57.1% accuracy and 80.2% top-5 recognition rate, has established the core position of CNN in image classification algorithm  proposed2) VGGNet  network 3) Inception-V3  network Considering the environmental conditions of hardware equipment, the following three convolutional neural network models with low requirements for computer hardware and excellent image classification effect are selected to realize the classification and recognition training of common pest and disease images of winter wheat.2.31) Add random noise and random filtering. The dataset in this article was collected in the natural environment of the experimental field, so the background is relatively complex and there is also a lot of noise and light wave interference. Adding random noise and random filtering can reduce the interference of complex backgrounds on training results and better improve the generalization ability of the model. In this work, several common filters are selected, and random numbers are generated to make random selection among median filter, Gaussian blur filter, and other types.2) Perform random rotation and random offset. Due to the lack of unified regulations on the target position during the sampling process, and the existence of multi-angle issues such as positive and negative, far and near, this article randomly rotates and offsets the samples to improve the model’s generalization ability to target position changes.3) Add random color jitter. The collection of samples in the afternoon, on both sunny and cloudy days, will have significant differences in color. Therefore, random color jitter will be added to improve the model’s generalization ability to lighting factors. This article expands the sample by generating random numbers to randomly select from several changes in image saturation, brightness, contrast, and sharpness.Due to the problem of small dataset size and uneven distribution of sample sizes among different types in the manually collected basic dataset in this article, this section introduces data augmentation technology to expand the basic dataset, mainly using the following methods.2.41) Freezing source model refers to freezing the parameters of other layers of the source model during training, and modifying and training only the parameters of the last fully connected layer according to the classification number of the new dataset . At this2) Fine-tuning source model refers to fine-tuning the parameters of the source model during training. According to different fine-tuning layers, the fine-tuning source model can be divided into fine-tuning part layers and fine-tuning all layers .Due to the small size of the dataset, it cannot meet the training of large network models, and the transfer learning technology  is intro2.5CBAM module is a mixed attention module proposed by Sanghyun Woo et al. in 2018. This module integrates the channel attention mechanism and spatial attention mechanism, and gives different weights to each channel and each position of the feature map, respectively. In the channel domain, it enhances relevant channels and suppresses irrelevant channels; in the spatial domain, it filters out useful features and suppresses useless features. Its structure is shown in It can be seen from 2.6N is the category of datasets, N = 5, 1) Accuracy (2) The precision rate (3) The recall rate (F1 (4)The accuracy, precision rate of each classification, recall rate, and 2.7The test machine is MacBook Air, with a CPU 1.6 GHz dual-core Intel Core i5, a memory of 8 GB, and a hard disk capacity of 256 GB. The operating system is macOS Monterey; the deep learning framework is Tensor Flow-CPU; the programming language is Python with 3.7.9 version, and the editor is PyCharm; the image processing library is cv2 and PIL, and the environment management software is Anaconda3.33.1Based on the normalized dataset of winter wheat pests and diseases, this section realizes the identification of pests and diseases using AlexNet, VGGNet16, and Inception-V3 traditional CNNs. The models with the highest accuracy in all iterations of each network are selected as the final model of each network. To improve the training speed of the model, the training sets are trained in batches. The corresponding hyper parameters of each network model are shown in The parameters of Adam optimizer in the Table are set as beta_1 = 0.9 and beta_2 = 0.99. The initial learning rate of the attenuation learning rate method is set to 0.01, and the attenuation is 1/2 every 10 iterations. The learning rate of the constant learning rate method is always 0.01.F1 is 0.6835, it has the worst recognition on aphids; when F1 is 0, the recognition accuracy of powdery mildew is also low; when F1is 0.2772, the recognition accuracy is poor. (2) The accuracy of the test set of VGGNet16 model is 69.14%, and the recognition effect on leaf rust is the best. When F1 is 0.7605, the recognition effect on aphids is as poor as that of AlexNet model, and that on powdery mildew is also low, with F1 being only 0.2527. The effect of VGGNet16 model is better than AlexNet model. (3) The accuracy of Inception-V3 model is 60.37%. Its recognition effect on healthy wheat is the best, and F1 is 0.6952. The recognition effect on aphids is as poor as that of AlexNet model, and that on powdery mildew is also low, with F1 being only 0.2437. (4) In the three network models, VGGNet16 has the highest accuracy and is selected as the basic network.From 3.2F1 of aphids and powdery mildew is relatively low. Combined with the number of samples in each category, it is found that the recognition effect is positively correlated with the number of samples, as shown in From the experimental results in the previous section, it can be seen that the It can be seen from To ensure that the proportion of each sample in each category is consistent, and that the number of samples in each category after expansion is relatively balanced, this study randomly expands all samples in each category by multiple. The composition of the expanded dataset is shown in The model is trained based on the expanded training set and the hyper-parameters corresponding to the VGGNet16 model in F1 changes from 0.0 to 0.3999. F1 of powdery mildew changes from 0.2527 to 0.3484, which is increased by 0.0957. This shows that the expanded training set samples can well solve the problem of low F1 recognition of aphids and powdery mildew using this model.3.3To solve the problem that the dataset is too small to train large models, the public large crop pests and diseases dataset Plant-Village is selected for migration learning of VGGNet16. First, the VGGNet16 model is fully trained based on the Plant-Village dataset, and then the number of neurons in the last fully connected layer of the model is modified. The training is continued on the winter wheat pests and diseases dataset. A total of four experimental protocols are designed according to the transfer learning technology, as shown in In Four transfer learning models are trained based on the expanded training set. To maintain consistency, the hyper-parameter setting of the transfer learning experiment is consistent with VGGNet16. The evaluation indicators of the test set corresponding to the four groups of transfer learning experiments are shown in As shown in 3.4Since the dataset is sampled in the nutritional growth and reproductive growth stage of wheat, the color and characteristics of wheat pest and disease samples in this stage are quite different and the background of pest and disease samples in natural environment is complex. To this end, the CBAM-VGGNet16 and NLCBAM-VGGNet16 models are further designed. The addition of attention mechanism can enhance the role of relevant features during model training in the channel domain. At the same time, the interference of irrelevant location features on the classification model in the spatial domain can be suppressed. The VGGNet16 network diagram before and after improvement is shown in Single A1) of single pest and disease and the identification accuracy (A1) of the test sets. In addition, the A-ResNet50 [In this section, the CBAM-VGGNet16 and NLCBAM-VGGNet16 network models are trained based on the expanded dataset and the experimental parameters are consistent with VGGNet16. The model was evaluated using the identification accuracy  In the three network models trained based on the improved dataset, VGGNet16 has the highest accuracy and is selected as the basic network for further optimization.2) The training set is expanded by data expansion technology, so that the number of samples in each category is balanced, and the problem of low recognition accuracy of aphids and powdery mildew in the recognition of VGGNet16 model is solved.F1 of each category, the fine-tuning source model is better than the freezing source model. When more layers are involved in fine-tuning, the better the recognition effect of the model will be. The transfer learning method of fine-tuning all layers has the best effect, and the recognition accuracy can reach 96.02%.3) The transfer learning experimental scheme of freezing source model, fine-tuning part of layers and fine-tuning all layers is designed. Compared with its recognition accuracy and the 4) The recognition effect of CBAM-VGGNet16 and NLCBAM-VGGNet16 based on attention improvement is better than that of VGGNet16. The accuracy rate of the test set of CBAM-VGGNet16 is 96.60%, and that of NLCBAM-VGGNet16 is 97.57%, which realizes the high-precision identification of common pests and diseases of winter wheat.Aiming at the common pests and diseases of wheat, the dataset of winter wheat pests and diseases is standardized. The VGGNet16 CNN model is trained based on data expansion and migration learning technology, and finally the high-precision identification of common wheat pests and diseases is realized.