X-ray diffraction and scanning electron microscopy were used to study changes in the structure of amorphous alloys under deformation by high-pressure torsion and multiple rolling. The change in mean nearest neighbor distance (the radius of the first coordination sphere) under deformation was determined. During deformation, shear bands are formed in amorphous alloys, which are regions of lower density compared to the surrounding undeformed amorphous matrix. Shear bands are zones of increased free volume, in which crystallization processes are facilitated. The change in the proportion of free volume under deformation of various types was estimated. The formation of shear bands leads to the appearance of steps on the surface of the samples. The number of shear bands and the surface morphology of deformed amorphous alloys were determined by the type of deformation and the physical properties of the material. The results obtained are discussed within the concept of free volume in the amorphous phase. Amorphous metal alloys have an unusual structure for alloys and a number of excellent physical properties ,2. They It is known that the structure of shear bands is characterized by enhanced free volume content ,40,41,45The investigation of structural changes during deformation processing in combination with the analysis of steps on the surface  can enable estimation of the proportion of deformed material. This work aimed to study changes in the structure of amorphous alloys during deformation.77Si13B10 alloy is a high-induction amorphous magnetic material that serves as the basis for Finemet-type nanocrystalline materials and belongs to the group of the most widely studied ferromagnetic amorphous alloys [62.5Cu22.5Al10Fe5 alloy refers to the most common alloys that can be obtained as a bulk metallic glass [The structure of amorphous Al-, Fe-, and Zr-based alloys was studied. These alloys are among the most widely studied amorphous and nanocrystalline materials and belong to different groups of alloys. Amorphous Al-Ni-Y and Al-Ni-La alloys are typical representatives of a group of light high-strength materials from the Al-TM-RE group  ,21,34,37s alloys ,12,18,36ic glass ,28,29,3862.5Cu22.5Al10Fe5 alloy were prepared as ribbons and rods . Ingots of the Zr62.5Cu22.5Al10Fe5 alloys were made via arc melting using pure (>99.9%) components in a pure argon atmosphere with a Ti getter. The BMG samples were prepared through quenching into a copper mold. The diameter of the rods was 8 mm. The ribbons of all the studied compositions  were prepared via melt spinning onto a rapidly rotating wheel. The cooling rate was about 106 K/s. The thickness of the Zr62.5Cu22.5Al10Fe5 ribbons was about 50 μm. Ingots of the Al-Ni-La(Y) alloys (6–10 at.% Ni and 2–5 at.% La or Y) were prepared via arc melting in eliminated argon from elemental Al (99.99%) and Ni (99.9%) and the Al3La(or Y) (99.7%) compound. Amorphous ribbons with a thickness of about 50 μm and a width of 15 mm were obtained via melt spinning in a helium atmosphere. Alloy ingots with nominal compositions of Fe77Si13B10 were prepared through the arc melting of a mixture of Fe (99.9%), Si (99.9%), and B (99.9%); they were melted three times to homogenization before melt spinning in a high-purity argon atmosphere. The width of the ribbons was 1 cm, and the thickness was about 30 μm. The samples were deformed by high-pressure torsion (HPT) and multiple rolling at room temperature. In the first method (HPT), the specimen was placed between two punches, the lower of which was rotated. The degree of deformation varies along the sample radius. Areas near the edge of the sample (in the periphery) are subjected to the greatest deformation; the center of the sample remains practically non-deformed. During repeated rolling, the near-surface regions of the sample are deformed to the greatest extent, while the regions located far from the surface are deformed to a lesser extent. The value of true deformation under HPT is noticeably greater than under multiple rolling. The BMG samples were cut into disks with a thickness of 0.5 mm and polished before deformation. The deformation was carried out at a pressure of 4–6 GPa at a rate of 1 rotation per minute. Cold rolling of the samples was performed in a VEB Schwermaschinenbau four-roll mill using the multiple rolling technique, with the number of rolling passes being 1–150. The level of true deformation was calculated by Formula (1) [r is the radius of a sample,h0 is the initial thickness of a sample, and h is the thickness of a sample after deformation. The samples were subjected to isothermal annealing in a temperature range of 100–300 °C for different time intervals.The compositions of the amorphous alloys after melt quenching are given below. The composition of the alloys was monitored using local X-ray microanalysis by means of a Zeiss Supra 50VP scanning electron microscope. The accuracy of determining composition for all components, except for boron, was about 0.1%. Samples of the amorphous Zrmula (1) ,57:(1)e=88Ni6Y6 alloy after annealing (1) and rolling (2), obtained using Cu Kα (left curves) and CoKα (right curves) radiation.The structure of the samples was investigated by transmission electron microscopy (TEM), scanning electron microscopy (SEM), and X-ray diffraction (XRD) with Cu Kα and Co Kα radiations. Standard computer programs were used for processing the X-ray diffraction spectra . X-ray diffraction studies were carried out on Siemens D-500 and RigakuSmartLab SE diffractometers, with the scanning step being 0.02°. Initial studies were carried out using Cu Kα radiation (wavelength λ = 1.5406 Å). However, since an important part of the investigation was the analysis of the diffuse peak profile (its width and position), X-ray diffraction studies were also carried out using Co Kα radiation. The wavelength of Co Kα radiation (λ = 1.789 Å) is longer than that of Cu Kα radiation, which allows for “stretching” of the diffuse maximum and the ability to study it more carefully. It can be seen that the right maxima are wider than the left ones, and the changes in the positions of peaks 1 and 2 in the right part of the figure are more pronounced. It is easy to calculate that, in the region where the first diffuse maxima exist (in the angular region 30 < 2θ < 60), the change in the corresponding interplanar (or interatomic) spacing is 1.431 Å for Cu Kα radiation and 1.675 Å for Co Kα radiation. This means that the change in the interplanar distance per 1° (angular degree) is 0.056 Å and 0.048 Å for Co Kα and Cu Kα radiation, respectively. This means that under the same collimation conditions, the difference in the accuracy of determining the position of the diffuse maximum when using Co Kα radiation is 17% higher than when using Cu Kα radiation.Ab and Bb are the amplitudes of coherent scattering of atoms A and B, Ac and Bc are the atomic concentrations of components A and B, AbA + cBbB,<b> = c and Q = 4π sin θ/λ .To obtain more information about the structure of the amorphous phase, the partial radial distribution functions can be calculated. For a binary alloy A-B, this means the calculation of several functions for the AA, AB, BA, and BB pairs of atoms. The structure factor calculated from the experimental scattering curves for a two-component alloy is the following:AA(Q), SAB(Q),S and BB(Q)S corresponding to the pair correlations of the A-A, A-B, and B-B components. It is clear that to determine the partial structural factors, it is necessary to obtain a system of three equations with different values of Ab or Bb, which requires several independent experiments, such as the use of radiation of different types .This function is a superposition of the partial functions There are various ways of defining the total structure factor S(Q), which are called the Faber–Ziman and Bhatia–Thornton formalisms. According to Faber and Ziman , partial1 (the mean nearest neighbor distance), was determined from the position of the main maximum of the scattering curve using the Ehrenfest equation [λ is the wavelength of the radiation used and θ is the angular position of the maximum of the scattering curve.The radius of the first coordination sphere, Requation :2R1 sin def and Rini are the radii of the first coordination sphere of the deformed and initial (as-prepared) samples, respectively.To estimate the proportion of free volume, ΔV, the well-known formula was used:After quenching, all alloys were amorphous. N is the total number of atoms per volume unit, F(S) is the scattering amplitude, ρ(R) is the number of atoms per volume unit located at distance R from the selected atom, ρ0 is the average number of atoms per volume unit, and S is the magnitude of the wave vector. The sequence of maxima of the function I(S) is determined by the sequence of maxima of the function sin (SR)/SR. This function has peaks at SR values of 7.73, 14.06, 20.46, etc. It is easy to determine that the radius of the first coordination sphere (the mean nearest neighbor distance) is the following:Determination of the mean nearest neighbor distance in the amorphous phase was based on the following measurements. As is known, the prevalence of frequency scattering by the amorphous phase is determined by the following formula :(5)I(S)=This means that the radius of the first coordination sphere can be determined from the value of the wave vector corresponding to any maximum of the scattering intensity curve using Formula (2). Since the first maximum of the scattering curve is the most intense, its position is usually used to determine the radius of the first coordination sphere.62.5Cu22.5Al10Fe5 alloy immediately after quenching. It is typical. Below we will analyze the region near the first diffuse maximum.62.5Cu22.5Al10Fe5 alloy determined by Formula (3) is R1 = 2.998 Ǻ. After deformation by the high-pressure torsion method , a slight shift of the diffuse maximum to the region of smaller angles is observed. 62.5Cu22.5Al10Fe5 alloy before (1) and after (2) deformation (the region of the first diffuse maximum). The curves are shifted along the y-axis for clarity. It can be seen that the peak shift is small, but the results are reproducible with repeated surveys. After deformation, the radius of the first coordination sphere is R1 = 3.003 Ǻ. The increase in the radius of the first coordination sphere after deformation is ΔR1 = 0.005 Ǻ.The radius of the first coordination sphere of the amorphous Zr62.5Cu22.5Al10Fe5 alloy is the same as for the rods. After deformation by the HPT method (30 rotations), R1 = 3.028 Ǻ and the change is ΔR1 = 0.025 Ǻ.Similar results were obtained in the study of samples of the same alloy when prepared as a ribbon. The radius of the first coordination sphere for the ribbons of the amorphous Zr77Si13B10 ribbons before (1) and after (2) deformation (the region of the first diffuse maximum). The values of the radius of the first coordination sphere calculated from the XRD patterns are R1 = 2.488 Ǻ for the initial amorphous alloy and R1 = 2.492 Ǻ for the deformed sample . Thus, the value calculated from the shift of the diffuse maximum is ΔR1 = 0.004 Ǻ. As can be seen from Similar results were also obtained in an investigation of amorphous Fe-based alloys. 77Si13B10 alloy increases to ΔR1 = 0.007 Ǻ. After this treatment, the samples remain amorphous. Similar results were also observed for bulk Zr-based alloys [An increase in the degree of deformation (by increasing the number of anvil rotations) leads to a greater change in the radius of the first coordination sphere. With an increase in the number of anvil rotations from one to five, the change in the radius of the first coordination sphere in the Fed alloys .77Si13B10 alloy, after rolling, the changes were noticeably smaller and amounted to ΔR1 = 0.001 Ǻ.Thus, HPT deformation promotes an increase in mean nearest neighbor distance in both bulk amorphous alloys and amorphous ribbons. In the case of the above amorphous Fe87Ni8La5 amorphous alloy after rolling (thickness change (ho-h)/ho = 35%). It is clearly seen that it is asymmetric and, obviously, is a superposition of two maxima.A more complicated situation arises in the study of amorphous Al-based alloys. The issue is related to the fact that, during the deformation of the amorphous phase of Al-TM-RE alloys , amorphous phase decomposition (separation) often occurs and a heterogeneous amorphous structure is formed. This heterogeneous amorphous structure consists of regions enriched or depleted in terms of one of the alloy components. In this case, the observed shift of the diffuse maximum to smaller angles is due to the appearance of amorphous regions enriched in the component with the largest atomic size . It should be noted that the approach considered here is valid, first of all, for metallic glasses. In a number of oxide glasses or in one-component systems (liquid Sn or Ge); therefore, the origin of peak asymmetry may be more complicated. 87Ni8La5 alloy annealed at 150 °C for 5 h. As in the XRD pattern of the rolled sample, the diffuse maximum is asymmetric and is a superposition of two maxima. The different position of the two subpeaks indicates that the sample contains regions that differ in composition. Based on an analysis of the atomic size and composition of metallic glass, it is easy to conclude that the two diffuse maxima correspond to regions enriched and depleted in yttrium (and/or nickel). Curves 3 and 4 in 88Ni6Y6 alloy annealed at 100 °C for 30 h (1) and rolled by 40% (2). For this alloy, the values of ΔR1 given in Using Formula (4) and the values of the radii of the first coordination sphere and their changes, it is possible to determine the change in the free volume in the amorphous structure that results from deformation by the high-pressure torsion (HPT) and multiple rolling (MR) methods. The data obtained are listed in The value of the radius of the first coordination sphere, of course, depends on the size of the alloy atoms, and its change depends on the degree of deformation. It should be noted that a change in the free volume value also depends on the conditions for obtaining the sample. When preparing amorphous alloys using the melt spinning method, the structure of the melt actually freezes. Since the structure and density of the melt depend on temperature, the density of the amorphous phase will also depend on the temperature of the melt before quenching and the cooling rate. The value of the frozen free volume in this case will also be determined by these parameters, as well as by the prehistory of the sample before deformation onset. For example, during aging, structural relaxation processes are possible, leading to a decrease in free volume ,69,70,711) determined above is averaged as follows:a and b are the volume fractions of the unchanged amorphous phase and shear bands, respectively. Since the volume fraction of shear bands is small, the change in the average radius of the first coordination sphere1 = 0.008 Ǻ. An estimate of the change in the radius of the first coordination sphere provides a value of ~0.3%. This corresponds to an increase in free volume, ΔV, of about 0.9%, which is consistent with known data regarding changes in the structure of amorphous alloys during HPT [As mentioned above, changes in free volume during the deformation of amorphous alloys are due to the formation of shear bands , which depends on both the degree of deformation and the elastic constants of the material. After deformation, the structure of the sample is actually a nanoglass consisting of amorphous regions with the amorphous phase that did not change under deformation and amorphous regions of reduced density (shear bands). Therefore, the radius of the first coordination sphere  and shear bands (b), differ significantly: a >> b. This means that the changes in the structure in the shear bands are much larger than those observed. It is these large changes that lead to an increase in the diffusion coefficient in these regions of several orders of magnitude, as noted in [It should be emphasized that the data on changes in the radii of coordination spheres and changes in density ) are average values. The values of coefficients noted in .77Si13B10 and Al88Ni10Y2 alloys. The surfaces of the as-prepared alloys are smooth and do not contain any features. As noted above, during deformation, steps are formed on the surface of the sample that characterize the places where shear bands emerge on the surface of the sample. The number of shear bands, their morphology, and their location also depend on the elastic constants of the material, as well as the type and degree of deformation. 77Si13B10 samples after rolling. After the first rolling pass, steps are formed on the surface, and their formation occurs unevenly. Thus, One can see that the surfaces of the deformed samples contain numerous steps corresponding to shear bands. The size and number of steps depend on the type and degree of deformation, as well as on the chemical composition of the alloy. The results of studying the surface morphology of deformed amorphous alloys show that the number of shear bands increases with the degree of deformation, which is indicated by the change in material volume due to deformation. Such an increase in the number of steps with an increase in the degree of deformation is illustrated in 77Si13B10 alloy deformed by rolling  was estimated using direct measurements from the images. It was approximately 2100 μm in the image. If we assume that each step was formed as a result of the shear band coming to the surface, that the shear bands have the same thickness throughout the sample, and that they do not multiply inside the sample, the volume fraction of material (ν) in the shear band can be estimated. The volume fraction of the amorphous phase in the shear bands can be determined from Formula (9):0 (ρ0 is the density of the undeformed amorphous phase). This means that the change in density is 0.13%, which agrees with the XRD data . It manifests itself in XRD patterns as a shift of the diffuse maximum to the region of smaller angles. The change in the proportion of free volume depends on the type and degree of deformation, as well as on the elastic constants of the material. The results obtained are an example of such changes and are not sufficient for an exhaustive analysis of the effect of the degree and type of deformation on the value of free volume and its dependence on the listed parameters. This requires wider research on a large group of amorphous alloys deformed under different conditions.62.5Cu22.5Al10Fe5, Fe77Si13B10, and Al-Ni-La(Y) alloys prepared via melt quenching in the form of ribbons and/or rods (BMG) and further deformed by the high-pressure torsion and multiple rolling methods were studied. Both methods of deformation lead to the formation of steps on the sample surface due to the formation of shear bands . These shear bands are zones of enhanced free volume (lower density). The formation of shear bands leads to an increase in the average radius of the first coordination sphere of the amorphous phase. The change in mean nearest neighbor distance in an amorphous structure depends on the conditions of the preparation of the material, the type and degree of deformation, and the physical constants of the material .Changes in the structure of amorphous Zr


Decision support and decision making systems find increasingly more applications in various modes of transport. In this connection, criteria of movement safety assessment must be established for manned, unmanned, and autonomous vehicles, bearing in mind the specifics of each transport mode. Distance is one of the basic criteria. In sea transport these include the closest point of approach and ship’s domain understood as an area that should remain free of other objects. The authors examine the relationship between the declared domain and the actually maintained effective domain in a restricted area. They define the coefficient describing this relationship for different ship’s relative bearings in the form of a mathematical function. The formulated relationship allows determining an effective passing distance based on the declared, i.e., assumed passing distance. The determined relationship also enables identification of the declarative domain based on the effective one. These relationships may be used in decision support systems of manned ships, in remote ship control centres and decision-making systems of autonomous ships for the assessment of ship movement safety, planning collision avoidance manoeuvres and generation of safe trajectories. Safe navigation requires continuous analysis and assessment of the situation by using various navigational aids available on board. Navigational situation analyses and assessment must be based on specific criteria. In restricted areas, the parameters commonly used in the process of situation analysis and assessment are insufficient. Their use in restricted waters is usually very difficult and requires a lot of experience in interpretation and practical application. Replacement of these parameters by an elliptical domain of the ship with the possibility of defining its basic parameters would significantly improve the process of assessing navigational safety. Electronic Chart Display and Information Systems (ECDIS) and simpler Electronic Chart Systems (ECS) offer the capabilities of defining and introducing new parameters that are complementary to or replace those currently used. The ship domain seems to attract more interest as a tool for analyzing and assessing navigational situations. The concept of declarative domain was introduced in , but theKnowledge of the relationships between ship domains identified in tests will allow one to determine the distance at which the ships will actually pass each other on any relative bearing, based on the passing distance assumed and desired by the navigator. This will lead to the creation of an effective domain model based on the declarative domain.The essential factors affecting the shape and size of ship domain include ship size and speed and the type and parameters of the water area. The ship domain, as a criterion of navigational safety assessment, is of particular importance in areas of high vessel traffic intensity, where the ship has limited manoeuvring possibilities due to the occurrence of physical and conventional restrictions.In the currently used and newly introduced devices and systems, the ship domain can be practically used for:current analysis and assessment of the situation;planning of anti-collision manoeuvres;use in navigational decision support systems.There are several methods of determining the ship domain. These are: real tests, simulation tests, expert questionnaire surveys, analytical methods, and artificial intelligence methods. The article presents the results of simulation tests conducted for the development of the effective domain model and expert-based survey for the determination of the declarative domain. The results were summarized by formulating a universal relationship between the declarative domain and the effective domain of the ship.The concept of ship domain appeared in the literature in the early 1970s. In 1971 Fujii and Tanaka published the results of research on the capacity of fairways . Those aThe first scientific publication entirely dedicated to the ship domain was published in 1975 . She defShip domains were determined based on observations and recordings of data from radars or Automatic Radar Plotting Aid (ARPA) devices installed on ships, land-based Vessel Traffic Systems (VTS) or research centres. The common development of electronic position determination systems and satellite global positioning systems in the early 1990s resulted in data recording and ship domain determination using fixed positions of ships. Such domains are referred to as effective domains. The wide access to Automatic Identification System (AIS) data enabled the tracking of real trajectories of ships, analysis of the domains, and the determination of safety levels. Hansen et al. presented the results of research using recorded AIS data in the form of density charts, where the length of the ship was used as a relative unit of distance . SimilarIn 1993 Zhao, Wu, and Wang  presenteDomains determined by analytical methods constitute a separate group. Liu et al.  presenteToday, many research centers around the world are working on autonomous ships , 26. ProTwo restricted areas were chosen for the questionnaires and simulation tests: the Singapore Strait and the Dover Strait. Both shipping areas, in terms of size, traffic intensity, and the presence of traffic separation schemes, are similar, and most navigators are familiar with them from experience. They are known for the highest vessel traffic intensity in the world. Creating the models of declarative and effective domains, the authors examined the impact of ship size and speed on the shape and size of the domain. In all the tests, the minimum, mean and maximum domains were determined. Our analysis included absolute domains  and relaIn both types of research three models of ships of different sizes were used, further referred to as large, medium and small ships .To examine the impact of ship size, three groups of scenarios and questionnaires were prepared:large ship encounters–a large ship with a large ship;medium ship encounters–involving two medium size ships;small ship encounters–a small ship meets another small ship.To examine the impact of ship speed variants, three separate groups of scenarios and questionnaires were prepared (six encounters in each group):low-high speed–manoeuvring ship proceeds at a speed approximately twice slower than the non-manoeuvring ship steaming “full ahead”;equal speeds–both ships move initially at the same speed;high-low speed–manoeuvring ship proceeds at ‘manoeuvring full ahead’, approximately twice faster than that of the non-manoeuvring ship’s speed.To determine declarative domains, the questionnaire-based survey was conducted among participants in training courses at the Marine Officer Training Centre, the Maritime University of Szczecin, currently employed in shipping companies as operational or management level deck personnel. The questionnaires used to collect data for the article were voluntary and conducted anonymously among navigators improving their professional qualifications during supplementary training courses. The questions referred to the passing distance of ships on selected heading angles that the navigators wished to maintain in a collision avoiding manoeuvre. The research was conducted for different encounter situations. Eight characteristic, easy to identify relative bearings were selected: 000°, 045°, 090°, 135°, 180°, 225°, 270°, 315°. A total of 153 questionnaires were collected, each containing three sets of declared distances at a/m relative bearings for different ship size and speed relations. Finally, 459 individual ship domains for three ship sizes and different speed relations were collected. Calculations were carried out in nautical miles converted to meters, and using the relative distance units (a multiple of ship length), where distances found in a questionnaire form were divided by the respective ship length. The questionnaires are included in the The obtained sets of points were placed on collective charts in the so-called radar system, presenting distances as the function of relative bearings for all the tested angles  laboratory. In simulation tests, similarly to questionnaire surveys, only experienced deck officers and captains took part. In the tests, ship positions were recorded at the highest maximum frequency. The problem of determining the effective domain is described in detail in . Examplex0 –displacement of the centre along the axis x;y0 –displacement of the ellipse centre along the axis y;a—length of the semi-major axis;b–length of the semi-minor axis.y, x system, commonly used in shipping.Figs The authors in  examine The absolute declarative and effective domains of a medium ship are shown in In a preliminary analysis of relationships between declarative and effective domains, where both domains were drawn on one chart, for all the sizes and speeds, it was found that identifying a simple function relationship would not be easy. The difficulty arises due to the shape, proportion, and displacement of ellipses’ centres of each domain. Besides, on some relative bearings the declarative distances are not greater than the effective ones, as could have been assumed after a superficial analysis.The algorithm of determining relationships presenting a detailed description of the adopted method is shown in dD and dE on relative bearings, for i∈<000°; 360°>.An auxiliary diagram for the analysis of relationships between domains is shown in The relevant analysis requires preliminary verification of the consistency of the data held. Some doubts concerned the ambiguous interpretation of the relative bearing used for the comparison of distances. We checked whether the processing of data in the present form would not be subject to ambiguity and would not lead to incorrect conclusions.x0, y0, a, b. This enabled, after plotting the elliptical domains, to compare the distances di on each relative bearing RBi, that were direct equivalents of the radius r and angle θ applied to plot an ellipse. Each RBi should be interpreted as relative for the domain under consideration because it is measured from its centre not overlapping the centre of another domain and the centre of the coordinate system. The direct comparison of such distances would lead to the derivation of only apparent relationship burdened with significant errors, depending on the type of the investigated domain. The coefficient defined and described further in this article (declarative distance to effective distance on i-th relative bearing) would take this form:The ellipses of all domains were written in the database as four previously indicated parameters (used in the ellipse equation): δ on a relative bearing i;riD—apparent declarative passing distance on a relative bearing i;riE—apparent effective passing distance on a relative bearing i (earing i .Due to the nature of the data (displaced ellipse centres relative to the coordinate system centre), to avoid ambiguities in Eq  that migRB = 090°. However, from the centre of the effective domain perspective, point E of the domain line crossing is visible on RB≅120°. From the centre of the declarative domain the point D of the domain line crossing the straight line pp is seen on RB≅095°, while the radiuses rE and rD indicate points E’ and D’ on apparent relative bearings 090°.The displacement of the domain ellipses’ centres seen in RBi, we solved the system of three Eq .xiD, yiD), and effective ellipse points  for the same relative bearing RBi from the common centre of the system—ship’s position —not the geometric centre of one of the elliptical domains. Only such pairs of coordinates of the examined domain points permit unequivocal comparison of the distances determined by those points.As a result, we obtained the corresponding coordinates of the declarative ellipse points (τ (5) as the function of the relative bearing .The blue colour marks the values of declarative distances, purple—effective distances, while the differences of distances on the given δi—coefficient δ on relative bearing i;diD—calculated declarative passing distance on relative bearing i;diE—calculated effective passing distance on relative bearing i  is shown in B = 1 for simpler interpretation of the chart . Similar relationships were found for relative domains by analysing the coefficient δW.Intermediate results were analysed in detail. The coefficients B and relative coefficient δW were plotted in one diagram for all types of ships and speed variants , individually identified for each domain. We found that this will be a more precise method than a simple division of subsequent absolute distances by the appropriate length of the ship.Further in the analysis of changes in the coefficient δ values, the absolute coefficient δvariants . Both cuThe obtained curve blue in  demonstrThe model function of the sum of sines is described by the general Eq :y=∑i=1n—number of terms of the equation in the series (1≤n≤8);ai—amplitude constant;bi—frequency constant;ci—phase constant for each term  of the equation.δ = f(RB), a three-term function of the sum of sines (8).The approximation function was analysed for various number of terms. The fitting was done in variants from n = 1 to n = 8 terms . Due to the sufficient fit, the chosen function was δ on the relative bearing RB assumes the form (9):Then the function dependence of the coefficient RB—relative bearing;a1, b1, c1, a2, b2, c2a3, b3, c3—function coefficients Click here for additional data file.S2 Appendix(PDF)Click here for additional data file.S3 Appendix(PDF)Click here for additional data file.


Since the discovery of camelid heavy-chain antibodies in 1993, there has been tremendous excitement for these antibody domains (VHHs/sdAbs/nanobodies) as research tools, diagnostics, and therapeutics. Commercially, several patents were granted to pioneering research groups in Belgium and the Netherlands between 1996–2001. Ablynx was established in 2001 with the aim of exploring the therapeutic applications and development of nanobody drugs. Extensive efforts over two decades at Ablynx led to the first approved nanobody drug, caplacizumab (Cablivi) by the EMA and FDA (2018–2019) for the treatment of rare blood clotting disorders in adults with acquired thrombotic thrombocytopenic purpura (TPP). The relatively long development time between camelid sdAb discovery and their entry into the market reflects the novelty of the approach, together with intellectual property restrictions and freedom-to-operate issues. The approval of the first sdAb drug, together with the expiration of key patents, may open a new horizon for the emergence of camelid sdAbs as mainstream biotherapeutics in the years to come. It remains to be seen if nanobody-based drugs will be cheaper than traditional antibodies. In this review, I provide critical perspectives on camelid sdAbs and present the promises and challenges to their widespread adoption as diagnostic and therapeutic agents. Immunoglobulins (Igs) or antibodies are the key elements of the adaptive immune system. These Y-shaped bifunctional glycoproteins can specifically recognize non-self-antigens, thus forming a molecular communication bridge with other elements of the immune system to neutralize and eliminate foreign pathogens. In response to signals from the innate immune system, Igs are developed through a precise molecular mechanism and are first displayed on B lymphocytes’ membrane and then secreted into bodily fluids. The adaptive immune response paradigm has been reasonably conserved in mammals, though antibody structure and isotype variations do exist across species . In humaThese domains adopt a conserved Ig fold structure which consists of two layers of anti-parallel β-plated sheets closely packed with hydrophobic amino acid side-chains, resulting in a β-barrel or sandwich-like structure ,8. The CCamelus dromedarius (Arabian camel), C. ferus (wild Bactrian camel), C. bactrianus (Bactrian camel), Lama glama (llama), L. guanaco (guanaco), Vicugna (vicugna), and V. pacos . In the 1990s, a research team at the Vrije Universiteit Brussel (VUB), led by Professor Raymond Hamers, analyzed the total and fractionated sera obtained from healthy dromedaries in Morocco and observed smaller IgG subclasses that seemed to lack the light chains. The first set of data published in 1993 showed that dromedaries’ serum, besides the conventional IgG , contains two smaller immunoglobulin fractions  lacking the light chains and the first heavy-chain constant domain (named thereafter: heavy-chain-only antibodies or HCAbs) and contributed up to 75% of total serum IgG  ,15. AddiFollowing the discovery of HCAbs in camelid species, the presence of a similar class of heavy-chain immunoglobulin, named IgNAR, was found in the adaptive immune system of a distantly related species of nurse shark and reported by Greenberg and co-workers in 1995 . ImmunolResolving the apo crystal structure of the VHH and a VHH:lysozyme complex revealed rather unique features of an anti-lysozyme VHH where the N-terminal segment of CDR3 penetrated into the catalytic site of lysozyme and the CDR3 C-terminal end folded over the former VL interface, shielding some of the remaining hydrophobic area  in the VHH. This type of antibody:antigen interaction required a major shift from the previously established canonical structures for human CDR1 and had hardly ever been observed before with conventional antibodies ,25,26,27To draw a comprehensive picture of VHH:antigen interactions, a recent structural study on 105 VHH-antigen crystal structures with resolution <3 Å revealed that VHHs tend to recognize more rigid, concave, conserved, and structured epitopes enriched with a combination of aromatic, polar, charged, and hydrophobic amino acids. This study also found that CDR3 was involved in about 50% of all interactions whereas CDR2 and CDR1 contribute to 20% and 13% of all interactions, respectively. Most importantly, the share of non-CDR residues reached 16% of all interactions and was located in four distinct regions on the VHH scaffold , namely,We believe that evolutionary removal of the VL domain established the VHH as a novel antigen-binding domain where the conventional role of CDRs has been somewhat modified and the binding interactions are extended to non-CDR regions. At the same time, a number of evolutionary mechanisms such as extending the length and complexity of amino acids in both CDR1 and CDR3 loops, participation of non-CDR regions including residues in FR2 and “CDR4”  in antigen binding and also shaping the CDR3 loop, and increasing the rate of somatic hypermutation which is concentrated, but not limited, to CDR regions that have been applied to enlarge the VHH repertoire diversity in the absence of the VL domain . ConsequGenomic studies, cDNA sequencing, and next-generation sequencing of camelid heavy-chain antibody repertoires have shown that HCAbs possess unique functions in the camelid adaptive immune system and emerged through common ancestry with a precise molecular evolutionary mechanism. The highlights of these findings are: (a) the first constant domain (CH1) is spliced out in HCAbs, leading to direct attachment of recombined VHH exon to the hinge region with various lengths 0–27 aa); (b) specific amino acid substitutions are imprinted in the camelid genome in FR2, primarily on the side of the VH that contacts the VL in classical antibodies, including at Kabat positions: V37F/Y, G44E, L45R, and W47G , as well as in some other positions in FR1 and FR3  which would contact the former CH1 domain in conventional IgGs; and (c) the length of CDR regions (loops) is expanded in HCAbs and deviates from the canonical loop structures established for human and mouse Igs [ aa; (b) Immunogenetic studies have shown that camelid HCAbs have preserved unique evolutionary mechanisms to diversify the VHH paratope repertoire, including: (a) a relatively large pool of VHH minigene segments that recombine with two smaller DH and JH minigenes along with a higher rate of insertions/deletions at the V-D-H junctions; (b) higher incidence of recombination signal sequence (RSS) in FR3 region (residue 76-78) underpinning the phenomenon of gene replacement which involve recombination activating-gene (RAG) protein; (c) higher rates of somatic hypermutation in CDRs, including a novel hot spot hypermutation codon (TAY) in CDR1, and further extending to non-CDR regions, in particular to FR3; (d) acquirement of additional cysteine residues within the FR2 and CDR region; and (e) participation of residues in FR2 region both in interacting with antigen and shaping the CDR3 loop ,38,39,40In regard to the evolutionary emergence of HcAbs, several hypotheses have been proposed though it is difficult to draw solid conclusions. The most plausible explanation for the existence of HCAbs is that there is an evolutionary advantage associated with this class of antibodies. The unique paratope structure, which is smaller and with a more prolate shape than conventional mAbs, may provide better access to cryptic epitopes . Indeed, the number of reported VHHs to be enzyme inhibitors or virus neutralizers well exceeds that of similar reports for conventional antibodies. Given the relatively recent discovery of VHHs in comparison to mAbs, this finding likely indicates that these domain antibodies are well-adapted to target a few sites of vulnerability by recognizing highly conserved epitopes among different strains of viruses  ,42,43,44igh locus: IGHV1 ; IGHV2 ; and IGHV3 . All the identified VHH genes in alpaca are homologous to the human IGHV3 and clustered based on sequence similarity into six subsets [Genomic studies have shown that both classical and HCAb variable domains (VH and VHH) are encoded by IGV genes, with a distinct but intermingled set of genes for VHs and VHHs. These minigenes recombine with the same IGD and IGHJ minigenes to generate a single VDJ gene fragment for transcription for both VH and VHH. There is an identical VH and VHH gene organization between camelid species with an estimated number of 17 VHH minigenes for alpaca and 42 VHH minigenes for dromedary . Homolog subsets . cDNA se subsets ,47. Sequ subsets  showed t subsets .+ on dendritic cells by phage panning of an immune llama library [Additionally, cDNA library analysis of the llama IgG repertoire identified a set of V genes with a distinct leader signal and a high degree of homology to clan II (human VH4 family). These VH genes do not have the imprint of VHH solubilizing substitutions in FR2; however, the VDJ product can be rearranged to encode either the heavy chain of conventional antibodies or HCAbs. Therefore, these V domains could contribute to the diversity of both conventional and heavy-chain repertoire. Due to the high homology (79–89%) to human VH4, it is suggested that these antigen-specific VHs could bypass the humanization step often performed on VHHs for human applications. This potential was first demonstrated by the identification of VH4 domains with antigen-binding specificity and good solubility behavior against CD11c library .The constant domain is encoded by three distinct genes in dromedaries. IGHG1 encodes the conventional IgG1 constant domain with the CH1 domain. Heavy-chain IgG2 and IgG3 isotypes are encoded by IGHG2 and IGHG3. These isotypes harbor point mutations at the 5′-end of the CH1-hinge intron (G to A) which abolishes the existing consensus splicing site (GT) in the IGHG1 gene, leading to the deletion of the CH1 domain in the course of mRNA splicing ,36,39,46Nature report, the group examined the binding properties of a standalone VH domain with its Fv counterpart (VH:VL) derived from a mouse mAb (D1.3) or from an amplified VH library made from the spleen of a mouse immunized against lysozyme. The VH domains showed specific binding to the target antigen, albeit with 10 times lower affinity which may be improved by additional engineering. However, the major shortcomings were solubility and stability of the VH domains which required further genetic manipulation. Due to these shortcomings, the idea of generating sdAbs did not captured sufficient commercial interest at that time. The discovery of HCAbs by Hamers–Casterman and colleagues in camelid species in 1989 re-ignited the idea of single-domain antibodies. Due to the physical absence of a light chain, these domain antibodies have a natural, built-in design to overcome the solubility and aggregation behaviors observed with mouse and human VH domains. It took a few years of intense investigation to demonstrate that these smaller antibody formats in camelid blood are not artifacts or disease-driven antibodies (known previously as heavy-chain immunoglobulin diseases) which were reported in humans [Nature in 1993 [With technological advances in the 1980s and the identification of the genes encoding Ig molecules and the molecular mechanism of Ig development in B cells , the iden humans ,57. Conf in 1993 . Antibod in 1993 .The first movement toward using these domain antibodies as potential therapeutics started about a decade later when Ablynx, a biopharmaceutical/VIB-funded spin-off company, was established in December 2001. The major scientific developments in the first decade included: (a) extensive studies on the VHH sequences obtained by cDNA libraries and defining VHH families ,17,47; (The second decade of VHH development (2003–2013) started with the exponential growth of publications and patent applications mainly by VIB and other subsidiary companies in Belgium, as well as by more than 50 institutions around the globe . The majwww.sanofi.com (accessed on 11 February 2022)) [clinicaltrials.gov (accessed on 18 February 2022)).Since 2014, several major developments have dramatically increased research activities around the camelid VHHs. Most importantly, the first positive phase I trial results of Ablynx’s lead therapeutic domain antibody (Caplacizumab) was published and established the proof-of-concept for the first application of a nanobody in medicine. Around the same time, more VHH-based therapeutics were advanced into clinical trials and Ablynx expanded its collaborations with large biopharmaceutical companies such as Merck, Boehringer Ingelheim, Sanofi, etc., with more than 20 pre-clinical and clinical programs. Another development was the expiry of a main camel patent in 2014 in Europe and in 2017 in the US. The turning point in the history of Ablynx occurred in January 2018 when French drugmaker Sanofi announced that it had acquired the Belgium biotech company at a price of $4.8 billion. Following this and toward the end of 2018, the first nanobody drug  received EMA approval  and entered the market (y 2022)) ,76. Caply 2022)) . As intey 2022)) ,78. For y 2022)) . As of 2There are a number of approaches to isolate camelid sdAbs against a target of interest. The starting point is to capture the heavy-chain repertoire of a camelid and build a library for screening purposes. Currently, the dominant approach is to immunize camelid species and build the library based on the repertoire of heavy-chain immunoglobulins . HoweverOne advantage of camelid VHH antibody library construction from immunized animals is that, by using a proper set of primers, the cloned immunized VHH repertoire almost mirrors the heavy-chain repertoire which has been matured in vivo. On the contrary, single-chain variable fragment (scFv) library construction requires the artificial joining of VH and VL domains by a synthetic linker which may lead to challenges with stability and proper folding, while also losing the original VH-VL pairing, requiring a larger library size to capture all combinations. Being a single domain, VHHs bypass a number of complexities related to the library construction, panning, and expression of conventional antibody fragments such as Fab and scFv as these domain antibodies do not require synthetic assembly of VH and VL domains or chain association. The VHH libraries are also genetically stable and fold efficiently when displayed on phage or other selection platforms. Additionally, their expression is less cumbersome in bacterial systems as they do not require complex folding machinery. We regularly obtain 10–100 mg/L of purified VHHs in bacterial systems, and this is considered the norm in many published works ,89. As mThere are a number of inherent features which make VHHs ideal for many applications, such as cancer therapy (as immunotoxin or radioisotope conjugates) and tumor imaging. The small size of VHHs gives them a unique advantage for rapid extravasation, deep tumor tissue penetration, and rapid tissue/blood clearance ,97. The There have been a number of studies on the pharmacokinetic (PK) and pharmacodynamic (PD) profiling of VHHs, with the most comprehensive one reported on caplacizumab by Sanofi . The PK Additional features of VHHs include their high thermostability (high refolding capacity) and relative resistance to harsh environments such as the GI tract and chemical denaturation, non-physiological pH, and high pressure. These features make them ideal reagents for oral and aerosol therapy, antibody-drug conjugates, and immobilization on microchip biosensors ,112,113.Today, VHHs have entered almost all biological research fields and there are countless reports on the isolation and characterization of the VHHs against numerous disease biomarkers specific to cancer, biochemical and hematological disorders, neuroinflammation, and infectious pathogens ,76. As mAnother potential application of VHHs is the treatment of neurodegenerative diseases. The strong target specificity of antibody-based therapeutics is a major advantage compared to small molecule drugs for the treatment of neurodegenerative diseases. However, the delivery of protein therapeutics to the brain has proven challenging, since only a small fraction of injected mAbs (0.1–0.2%) reaches the brain due to the tight blood–brain barrier (BBB) . BispeciSalmonella enterica) [VHHs are ideal antigen-binding domains for intracellular expression particularly due to relatively simple folding requirements in the reducing environment of the cytoplasm. For such applications, a pre-selection strategy is required to choose those VHHs with no additional cysteines and better folding capacity. These features, combined with the preferred recognition of conformational or discontinuous epitopes, make VHHs very promising tools to visualize and monitor the conformational state of proteins inside cells. VHHs could also be used to disrupt cellular pathways, and target certain disease targets inside the cells and intracellular pathogens. There are multiple approaches by which VHHs could be delivered inside cells: these include disruption of the cellular membrane by physical methods such as microinjections, electroporation the use of cell-penetrating peptides and polycationic resurfacing, DNA transfection, liposome-mediated mRNA delivery, or viral transfer of DNA into cells . For insnterica) ,139,140.The innate modular and monomeric nature of VHHs makes them ideal building blocks for the generation of bi- and multispecific antibodies and for multidomain functional molecules such as those used in CAR-T and other cell therapies ,76,78. Tclinicaltrials.gov (accessed on 27 February 2022)).The bispecific antibody market is growing very fast and is valued at over $20 billion USD. Blinatumomab was the first bispecific T-cell engager (BiTE), which uses two scFv fragments to target CD19 on a cancer cell and CD3 on a T cell, and was developed by Amgen and approved in 2014 . SimilarApplications of VHHs as a building block to engage other types of immune cells, such as natural killer (NK) cells in bispecific formats have also been demonstrated ,148,149.www.opig.stats.ox.ac.uk (accessed on 29 February 2022)) from more than 40 research groups around the world, including 65 patent applications to protect SARS-CoV-2 sdAbs. One highlight was the work by Güttler and colleagues where 45 VHHs were isolated from an immune alpaca library with all VHHs shown to block viral infection [E. coli. This finding paves the way toward expressing this difficult-to-express protein in a simple and low-cost bacterial system for future vaccine production [Infectious diseases and emerging pathogens are other important research areas in which VHHs have unique applications. As discussed earlier, structural studies on VHH:antigen complexes showed that VHHs tend to target more rigid, conserved, and structured epitopes. This has important implications for the development of species cross-reactive VHH therapeutics against infectious disease targets which constantly mutate to escape the host immune system. Large numbers of VHHs against bacterial and viral pathogens have been isolated and published, with a number of them in different phases of clinical trials ,150. Durnfection . It was oduction , and mayDespite the many advantages mentioned above, there are some limitations related to further engineering of the VHHs or certain applications where VHHs are not ideally suited. The single-domain nature of VHHs, with about 110–130 amnio acids, makes the involvement of each and every residue more significant when compared to the two-domain structure of scFvs. Consequently, highly accurate engineering along with modeling studies for the purpose of, for example, humanization will be required in order to preserve the integrity of the VHH domain. Our experiences on VHH humanization have shown that only a limited number of modification is allowed, and a complete humanization often led to a drastic decrease of affinity, hampering the stability of the scaffold, and/or reduction of expression yield .VHH scaffolds may not also be optimally designed to detect or capture small molecules in vitro and in vivo. The dominant convex surface topology of a VHH scaffold could not ideally accommodate molecular interactions when compared to the flat or concave topologies provided by conventional antibody fragments such as scFvs and Fabs. Although repeated immunization of llamas and alpacas has resulted in binders with low to high nanomolar affinities against several haptens and small molecules ,158,159,Another limitation in working with camelid species is logistics. The camelid species may not be readily accessible to every researcher and animal husbandry and immunizations per se require special facilities. For some immunization procedures such as DNA immunization, it is important to use more than one animal and analyze the consistency or heterogeneity of immune responses. Outbred llamas are not ideal for this type of assay establishment. We and others have used multiple DNA immunization procedures in llamas and it is very difficult to evaluate the efficiency of each methodology and its reproducibility. Despite these difficulties, VHHs with high affinity and specificities against difficult-to-access targets such as membrane proteins have been reported with no detailed immune response analysis. It seems that the lack or presence of a polyclonal immune response against the target of interest is not a valid indicator for library construction in llamas following DNA immunization ,163,164.https://www.researchandmarkets.com/reports/5319143/monoclonal-antibodies-mabs-global-market-report (accessed on 21 February 2022)). As of December 2021, over 100 mAbs and antibody fragments have been approved by the FDA in the USA and/or by the EMA in Europe for different disease indications ranging from cancer to autoimmune and chronic inflammatory diseases to infectious diseases [The first therapeutic mAb, muromonab-CD3 (trade name: Orthoclone OKT3) for the prevention of kidney transplant rejection was approved by FDA in 1986 for human use. This is a little more than a decade following the invention of hybridoma technology by Kohler and Milestein ,169,170.diseases ,171.The lack of restrictive IP on the original hybridoma technology for production of mAbs was a major incentive to explore the great potential of monospecific biologics in various field of research and to develop effective research tools, diagnostic reagents and therapeutics. Advances in molecular biology and immunology in the 1980s provided the opportunities to clone antibody genes from hybridoma cells/immunized B lymphocytes and express them in prokaryotic and eukaryotic systems. Therefore, “second generation” of antibodies including Fabs and scFvs were made known. This, along with the introduction of phage display technology in the late 1980s, opened new windows of opportunity to isolate antigen-specific antibody fragments from phage-displayed libraries with direct access to their genetic materials ,173,174.A survey of the literature clearly shows that camelid VHHs are the “third-generation” of antibodies that have many biotechnological advantages over the conventional antibody fragments (Fab and scFv) and are forecasted to dominate the biological markets in the years to come. Indeed, the commercial applications of VHHs for non-medical applications appeared first in the market before its therapeutic application (caplacizumab), which was approved by EMA and FDA and entered the market in 2019 ,177,178.Other than their potential application as research tools, which would complement the role of mAbs, extensive efforts by many research groups around the world have been performed to isolate sdAbs for diagnostic and therapeutic applications. A countless number of excellent reviews have summarized the application of sdAbs as research reagents and medicine following the establishment of Ablynx back in 2001 ,99,121. After almost three decades of the discovery of camelid heavy-chain antibodies by professor Hamers and his research team in Belgium, countless research articles and patents have been published and the commercial fruit of their discovery is gradually entering the pharmaceutical market. It is noteworthy to mention that the initial picture after the first observation of HCAbs in Belgium was quite gloomy and it took several years of extensive research to demonstrate that these antibodies were naturally-derived and not a disease by-product. However, as cDNA sequencing and genomic and structural data accumulated, we were able to piece the puzzle together and draw a beautiful picture of what nature had designed, now known as “VHHs” or “camelid sdAbs” or “nanobodies” ,18,78. I


Rosa damascena is extensively used as a key natural ingredient in the perfume and cosmetic industries. However, the productivity and quality of rose oil are a big concern from the old plantation. It is hypothesized that rejuvenation of old rose plantations through ground-level pruning at right time could improve the yield of flowers and the quality of essential oil. Consequently, a field trial was led-out with 10 treatment conditions encompassing two pruning systems (ground-level pruning and ground-level pruning followed by top pruning at the end of December) and five different months of ground-level pruning (June–October) to understand the best pruning practices. In this experiment, the flower yield ranged from 18.32 to 62.40 q ha−1, and oil content varied from 0.035 to 0.049% under different pruning systems and months of pruning. Ground-level pruned plants, irrespective of the month, registered statistically (p ≤ 0.05) more flower yield (618.62 and 473.29 g bush−1) compared with ground-level pruning followed by top pruned plants in both seasons. The average across the pruning system, ground-level pruning in October registered statistically (p ≤ 0.05) greater yield of flowers (709.10 and 605.13 g bush−1) compared with the ground-level pruning from June to August. Despite significant variations in flower yield among the treatments, the percentage share of the major compounds particularly β-citronellol+nerol and geraniol in the essential oil were not affected (p ≥ 0.05) by the pruning month and pruning system. Thus, the finding suggests that the production from the old plantation of R. damascena can be improved by ground-level pruning during October under mild-temperate conditions.The essential oil of  Rosa damascena, commonly known as damask rose, is a most important essential-oil-bearing plant. This species belongs to the family of Rosaceae. Owing to low oil concentration in flowers and the absence of synthetic replacements, the market price of natural rose oil is very high. This crop is commercially cultivated in many countries like Bulgaria, Turkey, Morocco, Iran, Egypt, China, Russia, and India among others. The major uses of rose essential oil are preparations of high-grade perfume and cosmetic products. Thus, the demand for natural rose oil is steadily increasing day by day. In 2018, globally the market size of rose oil was about USD 278.7 million, and it is projected to expand at a CAGR of 6.8% for the prediction period of 2019–2025  had been made by Riedel et al.  study the effect of ground-level pruning on the flower yield of overaged bushes of damask rose; (2) to understand the combined effects of the pruning system and time of ground-level pruning on the yield of flower, essential oil concentration, and quality.The monoculture is mostly practiced in damask rose for maximizing the yield. However, the productivity abruptly declines after 10–12 years of plantation due to depletion of soil health, and abiotic and biotic stress, which may often cause the abandonment of plantations. The overaged bush in old plantations can be rehabilitated by applying different agronomic or horticultural practices. To rejuvenate overaged plantations, rose bushes may be uprooted and re-planted. Nevertheless, this infers an unproductive phase for up to 3 years that is commercially challenging for smallholders. An alternative way to restore higher yield quicker than new planting is the rejuvenation of old bushes by ground-level pruning. In ground-level pruning, all the old shoots are cut at ground level or near ground level. It is hypothesized that in this type of pruning system all the unproductive and dry shoots caused by different biotic and abiotic stresses will be removed, consequently, new vigorous shoots will come out from the crown or roots. An attempt at rehabilitation pruning for cacao  that is positioned at 1,393 m altitude from the mean sea level. The reaction of the experimental soil was acidic with a pH value of 6.2. Organic carbon content was 1.13%. The inherent soil nitrogen (N), phosphorus (P), and potassium (K) contents in the experimental site were also estimated. The available N, P, and K were 260.5, 13.3, and 389.6 kg haR. damascena had a plantation geometry of 1.5 m X 0.75 m. The experiment was conducted for two consecutive years. During both years, the crop was fertilized with N, P, and K at 200, 43.7, and 83 kg ha−1, respectively. The experiment was conducted under rainfed conditions; thus, additional irrigation water was not applied during the investigation. Other cultural practices were followed as per standard recommendation for R. damascena for the western Himalayan conditions. The experiment was performed in a randomized block design (RBD) with the two factors during 2015–2016, and the experiment was again repeated during 2016–2017 cropping season to validate the first-year results. The first factor was the type of pruning with two levels [(i) ground-level pruning (P1) and (ii) ground-level pruning followed by top pruning (P2)]; the second factor was different months of ground-level pruning . Accordingly, the total treatment combinations were 10 (two types of pruning x five pruning times). First, ground pruning was done in the first week of June since the flower appeared up to the first week of May in the western Himalayan conditions. In the case of ground-level pruning followed by top pruning, the top pruning was done at the end of December. In the case of ground-level pruning, plants were pruned one time, while for ground-level pruning followed by top pruning, plants were pruned two times (ground pruning at different months and top pruning during the end of December). All the treatments were replicated three times. Thus, experimental units were 30 (10 treatment combinations x 3 replications). In the case of ground-level pruning, the plants were pruned at 5–8 cm from ground level, whereas only the apical 5–10 cm portion was removed for top pruning.In this study, the 10-year-old plantation of −1), the weight of individual flowers (g flower−1), and the yield of flowers (g bush−1) were recorded on a day-to-day basis. The flower yield per hectare (ha) has been expressed in quintal (q) units  throughout the manuscript. The flower harvesting was started on the 9th and 8th of April in 2016 and 2017, respectively, and it was continuing up to 38 days. To prevent the losses of volatile compounds, flowers were harvested in the early morning (6:00–7:30 AM) by manual hand plucking.For the recording of treatment-wise yield data, two plants were randomly chosen from all the treatments from all the replications. The chosen plants were marked for the collection of day-to-day data. From the opening date of flower harvesting to the end of flowering, the data on the number of flowers . These distillation conditions were the same as those earlier reported  which was coupled with an AOC-5000 auto-injector, and DB-5  fused silica capillary column (30 m × 0.25 mm and a film thickness 0.25 μm). The programmed temperature was from 70 to 220°C (4 and 5 min) with a stepwise increase in temperature at the rate of 4°C min−1 for 5 min; the injector temperature was 240°C and interface temperature was 250°C, respectively. The ionization voltage used was 70 eV with 800–50 amu acquisition mass range, and the carrier gas used was helium, whose flow rate was 1.1 ml per min. Homologous series of n-alkanes (C8–C24) was used to calculate the retention indices (RI) of all volatile components. Then, for the identification of rose oil compounds, the calculated values of retention indices (RI) and their mass spectra were matched with the database of the NIST-MS library  analysis of the rose oil extracted from fresh petals of y Stein, . After t−1 and held for 5 min; the injector temperatures were 240 and 250°C, respectively. Nitrogen was used as the carrier gas with a velocity of 1.05 ml min−1. The quantification of compounds was done through peak area normalization, and the response factor was fixed equal to one for each identified component.Gas chromatographic (GC) analysis of the rose oil samples was performed by a Shimadzu GC-2010 gas chromatograph  which is equipped with a flame ionization detector (FID) and DB-5 capillary column . The operating conditions, particularly, the temperature was set from 70°C (4 min) to 220°C with a stepwise increase in temperature at the rate of 4°C minp = 0.05. Statistica 7 software was further used to develop a correlation matrix to establish the relationships among the yield and its attributes and different compounds of essential oil. Principal component analysis (PCA) was also executed to appraise the influences of treatments on the chemical constituents of R. damascena essential oil and for grouping the treatment.The relevant data collected from this experiment for consecutive 2 years (2015–2016 and 2016–2017) were subjected to the analysis of variance (ANOVA) by Statistica 7 software to test the effects of the treatment. A two-factorial RBD was adopted in this experiment. The treatment means were differentiated with the help of LSD (least significant difference) value at R. damascena in per unit area is largely governed by the number of flowers per bush and individual flower weight. In this experiment, the number of flowers per bush was significantly (p ≤ 0.05) changed by the pruning types and month of pruning (−1) by about 22 and 27% compared with ground-level followed by top pruning in 2015–2016 and 2016–2017, respectively (−1) was equal with both the pruning systems (p ≤ 0.05) increased with the ground-level pruning by about 21 and 34% compared with ground-level followed by top pruning in 2015–2016 and 2016–2017, respectively. Because of the higher number of flowers per bush and individual bush yield, the significantly (p ≤ 0.05) higher flower yield (54.44 and 41.65 q ha−1) was recorded with the ground-level pruning compared with ground-level followed by top pruning in both years (44.94 and 30.88 q ha−1). The low flower yield with double pruning (ground-level followed by top pruning) system may be because the plant was not able to store enough metabolic food for future use or not ready for second mechanical stress in form of pruning after ground-level pruning. The sprouting ability in pruned plants may be clarified as compensatory growth capacity due to the “coordination theory”  effect on the number of flowers per bush, individual bush yield, and flower yield per unit area , and the October pruning (just before winter) resulted in the greatest flower yield (62.40 and 53.25 q ha−1). The lowest flower yields (25.86 and 18.32 q ha−1) were registered with June pruning (just after completion of the reproductive phase). This low yield with early ground-level pruning (June) was probably because the stored metabolic food was utilized for vegetative growth before winter dormancy.The effects of ground-level pruning on flower yield were found significant  between the pruning system and the month of pruning, and the utmost yields (67.89 and 62.81 q ha−1) were registered with ground-level pruning during October  changed during both years (−1) was changed significantly (p ≤ 0.05) due to different pruning systems and months of pruning  of essential oil in flowers were not significantly (th years . However pruning . Average pruning . This gr pruning  rather t pruning . This im−1) was positively correlated with the flower number (No. bush−1) with r values of 1.00 (p ≤ 0.01) and 0.99 (p ≤ 0.01) in the first and second years, respectively (p ≤ 0.05 and 0.42) suggests that the number of flowers per bush may be a more important yield attribute for affecting overall flower yield. The absolute positive correlation (r = 1.00) was found between flower yield (q ha−1) and flower yield per bush. The essential oil concentration was weakly correlated with flower weight, the number of flowers per bush, and flower yield. However, the oil yield (kg ha−1) exhibited a strong and positive correlation with the number of flowers per bush  and flower yield  as reported by Pal et al. , eicosane , heneicosane , and tricosane . The results also suggested that tricosane is strong and positively correlated with nonadecene , eicosane , and heneicosane .Although 17 compounds have been identified in the essential oil during both years , the corR. damascena in both years. The results from the PCA analysis revealed that the component first and second (PC1 and PC2) collectively accounted for 61.84 and 69.48% of the total variations for the years 2015–2016 and 2016–2017, respectively , P1M3 (ground-level pruning during August), P1M4 (ground-level pruning during September), and P2M1 (ground-level during June followed by top pruning) are separated by PC1 from other treatments, and these treatments are in the positive coordinate  and P2M4 (ground-level during September followed by top pruning) are separated from the rest of the treatments  was performed by using 17 compounds of essential oil that were extracted from fresh petals of ectively . The eigectively . In addiordinate . In 2017inate  on chemical compositions, a separate analysis was done (p ≥ 0.05) by pruning management, except eugenol in the second year and methyl eugenol in the first year (p ≥ 0.05) by the sole effect of pruning systems like partial and complete pruning.In this study, a total of 29 compounds were identified in both seasons , and thewas done . In Tablrst year . Irresperst year . Overallrst year  who repop ≥ 0.05) changed by the time of ground-level pruning, except for 4-terpineol and methyl eugenol in the first year (p ≥ 0.05). Similarly, the geraniol content was about 12–22% lower in the September-pruned plants than in the June-pruned plants without any statistical differences (p ≥ 0.05). The trends of nonadecane and heneicosane contents were irregular, and not similar over the years. However, the overall concentrations of hydrocarbons  were higher in the second year.Average over the pruning system, the compositions of essential oil were not statistically (rst year . The majR. damascena. The flower yield was significantly (p ≤ 0.05) increased with the ground-level pruning by about 21–34% compared with ground-level followed by top pruning. Among the months of ground-level pruning, October pruning (just before winter) resulted in the utmost flower yield (62.40 and 53.25 q ha−1). Similarly, essential oil yields were 29–47% greater with ground-level pruning than ground-level followed by top pruning. Thus, this agronomic strategy is a sensible recommendation, to some extent, for the restoration/ rejuvenation of the low-productive old plantation of R. damascena. Additional study is required to elucidate the effects of below-ground competition, plant nutrients, and soil environment on regrowth, the yield of flowers, and the concentration and quality of essential oil of R. damascena. The results demonstrated in this investigation were conducted at 1,393 m mean sea level and mild-temperate conditions. The ideal time for ground-level pruning may be different in other environmental conditions.The most significant finding of this 2-year field experiment is that only ground-level pruning during October can significantly increase flower yield without losing the quantity and quality of essential oil from the old plantation of The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author.S and MM: data collection, conducting the experiment, essential oil extraction, identification of compounds, and manuscript writing. BT: data collection and essential oil extraction. PP: conceive the ideas, statistical analysis and interpretation, and manuscript writing. All authors contributed to the article and approved the submitted version.This study was completed under project no. HCP-0007 (Aroma Mission), which was funded by CSIR, Government of India.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Deep learning models have been widely used in data-driven bridge structural damage diagnosis methods in recent years. However, these methods require training and test datasets to satisfy the same distribution, which is difficult to satisfy in practice. Domain adaptation transfer learning is an efficient method to solve this problem. Most of the current domain adaptation methods focus on close-set scenarios with the same classes in the source and target domains. However, in practical applications, new damage caused by long-term degradation often makes the target and source domains dissimilar in the class space. For such challenging open-set scenarios, existing domain adaptation methods will be powerless. To effectively solve the above problems, an adversarial auxiliary weighted subdomain adaptation algorithm is proposed for open-set scenarios. Adversarial learning is introduced to proposed an adversarial auxiliary weighting scheme to reflect the similarity of target samples with source classes. It effectively distinguishes unknown damage from known states. This paper further proposes a multi-channel multi-kernel weighted local maximum mean discrepancy metric (MCMK-WLMMD) to capture the fine-grained transferable information for conditional distribution alignment . Extensive experiments on transfer tasks between three bridges verify the effectiveness of the algorithm in open-set scenarios. With the rapid development of artificial intelligence technology, deep learning has been widely studied and applied in the field of bridge structural damage diagnosis. A large number of deep learning-based bridge damage diagnosis techniques ,9,10,11 Traditional deep learning models have achieved good results when the training (source domain) and test (target domain) datasets obey the same distribution . HoweverDeep transfer learning frameworks based on domain adaptation methods expect to learn shared features from the source and target domains to transfer damage knowledge ,20,21, wThe application of TL in structural health monitoring (SHM) is an emerging field. The use of TL to solve the classification problem of vision-based SHM is becoming a new research direction . In roadFrom the discussion of the existing studies mentioned above, we know the following:(1) The existing research has achieved good results in the close-set scenario; i.e., the source and the target domains have the same class space. However, in the actual bridge diagnosis scenario, new damages (unknown class) that are not included in the source domain classes often appear due to the degradation of the bridge structure; i.e., the class space of the source domain is a subset of the target domain (open-set) ,42. Ther(2) In the existing intelligent diagnosis methods based on TL, the domain adaptation method mainly learns the global domain shift to align the marginal distribution of the source and target domains without considering the relationship between the corresponding sub-domains (a sub-domain contains the samples within the same class). This confuses the data and the discrimination structure. As a result, fine-grained information of each class may be lost . Figure Motivated by the aforementioned issues and to promote the successful application of intelligent bridge damage diagnosis in open-set scenarios, this paper proposes a new intelligent structural damage diagnosis method for bridges, namely, an intelligent bridge diagnosis method based on adversarial auxiliary weighted subdomain adaptation network (AWSDN). A multi-channel multi-scale feature extractor is designed to expand the width of the feature extraction network to obtain deeper and multi-scale features. To isolate target outlier samples and prevent the negative transfer caused by these outliers, adversarial learning is introduced to the proposed adversarial auxiliary weights for samples in the target domain to describe the similarity between samples in the target and source domains. Furthermore, the multi-channel multi-kernel weighted local maximum mean discrepancy (MCMK-WLMMD) is proposed to effectively align the conditional distribution between correlated subdomains, i.e., subdomain adaptation. The main contributions of this paper are summarized as follows:(1) Effectively solving the challenging open-set domain adaptation problem in bridge damage diagnosis, which is rarely studied in the existing literature.(2) An adversarial weighting method is proposed for target samples by using adversarial training on the domain classifier and feature extractor. Negative transfer is avoided by isolating outlier class values with the help of adversarial auxiliary weights.(3) MCMK-WLMMD aims to measure the distribution discrepancy between correlated subdomains in a shared class space to obtain fine-grained transferable information for more efficient domain adaptation.(4) Extensive experiments on the dataset of three bridges verify the effectiveness and superiority of the proposed method.This paper is organized as follows. The problem of TL is introduced in ith sample of the source domain, and ith sample in the source domain (C is the number of sample labels). Accordingly, unlabeled data obtained from other working conditions or bridges are called the target domain jth sample of the target domain. The source domain data are collected under the probability distribution This study focuses on the problem of open-set domain adaptation in bridge damage diagnosis. Usually, the labeled data obtained from a bridge or model are used as the source domain This paper aims to build a data-driven deep transfer model that can learn invariant features from source and target domains for bridge damage diagnosis in open-set scenarios. The deep transfer model can not only identify the target outliers as unknown classes but also accurately classify the target samples belonging to the shared classes.A parameter-free discrepancy metric called MMD is often used in many transfer tasks. It can estimate the distribution discrepancy between the different domains. The mathematical formula is as follows:MMDH Status Recognition Module (SR): This includes a feature extractor and a state recognizer. The feature extractor consists of a CNN and a multi-channel parallel multi-scale extractor (MPME) to automatically learn higher-level multi-scale features from input samples in different domains. The health status is determined by the status recognition based on the features extracted by the extractor.(2) Adversarial Auxiliary Domain Classifier (DC): This takes the features learned by the feature extractor as input and predicts the domain labels of the features. Adversarial learning is introduced to use adversarial training between the domain classifier and feature extractor, meaning that the domain classifier cannot distinguish the domain classes of the features. Then, the prediction error of this domain classifier is used to describe the similarity of the target-domain samples with the source domain, i.e., the indicator of the adversarial auxiliary weight. Using this auxiliary weight indicator, the outlier samples (unknown class samples) of the target domain can be separated.(3) Sub-Domain Adaptation Module with MCMK-WLMMD (SA): A sub-domain adaptation with multi-channel multi-kernel weighted local maximum mean discrepancy (MCMK-WLMMD) is proposed to align the conditional distribution for diagnosis acknowledgement transfer.(4) Outlier Classifier (OC): This is used to identify the outlier classes (unknown classes) and shared classes of the target domain.m, n, and L are set as 5, 2, and 256, respectively.The vibration signal of the bridge in this study is a one-dimensional signal, and thus a 1D-CNN was selected. The 1D-CNN model used in status recognition is mainly composed of one input layer, five convolutional layers, five pooling layers, two fully connected layers, and one output layer. In the status recognition module, the first 13 layers are feature extractors, and the last layer is the health status classifier. The parameters of CNN and MPME are presented in kth target-domain sample, which represents the likelihood that the sample belongs to the shared class. This paper calculates the weights through two strategies, namely adversarial auxiliary weighting and inter-domain distance metric weighting.For unlabeled target domains, this paper proposes a weighting scheme assisted by adversarial learning. A. Adversarial Auxiliary Weighting.ith sample of source and target domains are expressed in Equation CJS=Equation .(7)WL,iC. Joint Target Instance Weighting.In this study, the proposed target instance weight combines the adversarial auxiliary weight with the inter-domain distance metric weight, i.e., the joint target instance weight expressed as Equation . In addiMost of the studies using domain adaptation for TL ,43,44,45A. Weighted Local Maximum Mean Difference (WLMMD).c, respectively. Note that both c. To solve the above problems, the local distribution of the two sub-domains in the source and target domains is used in this study. By minimizing the local distribution discrepancy between the two sub-domains of the source and target domains, sub-domain adaptation  is achieved. However, in unsupervised TL, the samples of the target domain are unlabeled, which makes it difficult to use the MMD to align the conditional distribution between the source and target domains. In response to this situation, the WLMMD is proposed to achieve proper alignment. WLMMD uses the output of the training network Equation  is obtaiEquation , the disquation  functions to enhance the representation ability of the mapping function. The multi-kernel function (MK) is given by Equation . TherefoEquation , where zEquation , the conEquation .(12)κ≜{As shown in The outlier classifier OC is designed to accurately identify outlier states in the target domain. Since the samples in the target domain are unlabeled data, this study proposes pseudo-outlier labels for the target samples to train the outlier classifier. As described in Equation , where dEquation . When th(1)Minimizing the status recognition error in the source domain;(2)Maximizing the error of the domain classifier;(3)Minimizing the error of the sub-domain adaptation with MCMK-WLMMD;(4)Minimizing the error of the outlier classifier.The training optimization objective of the AWSDN mentioned in this study consists of four parts:In the status recognition module, because there are sufficient damage state labeled data in the source domain, the training of the AWSDN model on the source domain adopts the supervised learning mode. After the model completes the feature extraction, the status recognition loss minAs shown in Equation .(18)maxThe sub-domain adaptation with MCMK-WLMMD is proposed to reduce the condition distribution discrepancy between the source and target domains, i.e., minimizing the error of sub-domain adaptation (Equation .(19)minOutlier classifier is designed to recognize the new damage in the target domain; thus, the fourth objective is to minimize the error of the outlier classifier, i.e., Equation .(20)minBased on the above information on optimization, the overall optimization objective is to maximize the error of the domain classifier under the premise that the sum of the errors of the status recognition with sub-domain adaptation and outlier classifier in their respective domains is minimized. Thus, the final object is to minimize Equation .(21)LosAfter establishing the training optimization object, AWSDN can use the fast gradient descent algorithm (SGD) to train the proposed method. To complete the training process by minimizing From 2008 to 2019, an intelligent bridge structural health monitoring system (IBSHM) was developed to gather vibration data and evaluate the healthy condition automatically and in real time. A schematic of this system is shown in To promote the successful application of intelligent damage diagnosis of bridges with unlabeled data, this paper introduces experiments on the Kando Bridge, the simulation model of the Kando Bridge, and the Seiran Bridge to verify the reliability, superiority, and transfer ability of AWSDN. We used three data sets obtained from three different bridges  to perform transfer damage diagnosis experiments and data analysis.The data-sets of these three bridges  were established to carry out the research on deep TL damage diagnosis. Thus, the three datasets were distributed in three different domains, and the data distribution in each domain was different. In these experiments, acceleration sensors with 200 Hz sampling frequency were deployed on bridges. For example, in the Kando Bridge experiment, 300 iterations of data collection by 15 wireless sensors and 300 iterations of data collection by 15 wired sensors were performed for each damage and excitation situation. In total, 400 samples was selected randomly from these 600 samples for data analysis. The acceleration data of one sensor (in the case 1 of damage type III) is shown in Data-set A was obtained from a test bridge named Kando Bridge. The old Kando Bridge was built in 1964 in Izumo, Shimane Prefecture, Japan. Since the new Kando Bridge was built, the old bridge was abandoned and used for various damage diagnosis experiments. Experiments were conducted to acquire undamaged and damaged bridge data. The bridge is a steel girder bridge with a concrete deck slab. Before we artificially damaged the bridge, we collected data to serve as the measurement of the undamaged condition . To obtain significant vibration data in the damage position, we deployed sensors beside the potential damage in this experiment. We then induced damage to the bridge and collected measurement data, which were considered to represent the damage condition.Damage was inflicted at three different locations in two cases, as listed in The detailed information of these damage types is as follows:(1) Type I: The bearing between the abutment and bridge deck was damaged, as shown in (2) Type II: The gusset plate of the bridge deck located between piers P1 and P2 was damaged, as shown in (3) Type III: The concrete ceiling and the reinforcement were scratched out to damage the deck of the bridge, as shown in After measuring the non-damaged Kando Bridge, we performed the above three types of artificial damage to the bridge. Because the most common excitation in normal bridges is a running car, apart from ambient excitation, a loaded moving car was used as an external excitation. For damage type I, II, and III, the sampling frequency of the acceleration sensors was 200 Hz.Dataset B was obtained from the simulation model of the Kando Bridge. The model is shown in The experimental bridge was called Seiran Bridge. This bridge satisfied all the demand conditions that we required: made of steel, with deterioration problems, many crossing cars, and easy placement of the bridge sensor module. In other words, this bridge is typical of Japan’s bridge problems. Damages include corrosion and deterioration at three different places, as indicated in (1) Type I: The bearing between the abutment and bridge deck was corroded, as shown in (2) Type II: The steel frame of the bridge deck located between piers P1 and A2 was corroded, as shown in (3) Type III: The concrete ceiling of the deck was deteriorated as shown in In this study, to evaluate the damage recognition performance of our proposed method in the open set scenarios, the source domain only covers part of the damage states in different transfer tasks. The open-set transfer tasks are listed in To verify the effectiveness and superiority of the AWSDN in open-set scenarios, this study also uses other models to conduct a comparative analysis. The related models are presented in (1) CNN (M1): A deep learning method based on the same scheme as the CNN in AWSDN. It is a supervised learning method only considering the source domain as a loss function. Compared with M1, it aims to illustrate the improvement of the deep transfer damage diagnosis method.(2) Open-set support vector machine  : OSVM ut(3) OSVM-MMD (M3): OSVM-MMD combines transfer learning techniques with OSVM. The transfer learning is achieved by minimizing the MMD metric between the source and target domains to improve the identification of OSVM.(4) Deep TL  : To demo(5) DCTLN (M5) : This isjth sample in the target domain, and jth sample in the target domain.In this study, to evaluate performance, target outliers are treated as unknown classes. As can be observed in (1) AWSDN achieved the best results for all open-set transfer tasks. The high accuracy rate highlights the superior generalization ability and robustness of AWSDN. It can effectively address the challenging open-set transfer damage diagnosis and provide more accurate diagnosis results.(2) In the open-set damage diagnosis, because M1 (CNN) is trained only through source domain data and ignores the discrepancy in the distribution of data collected under different bridges or different environments, the results of M1 are far from ideal. In addition, the samples of the target outlier classes lead to a significant increase in the recognition error rate of M1. However, AWSDN reduces the distribution discrepancy and separates the samples of the target outlier classes by proposing an adversarial auxiliary weighted sub-domain adaptation module to obtain a better classification accuracy. Experimental results show that, compared with traditional CNN (M1), the classification accuracy of AWSDN is increased by (3) Compared with the effective approach for outlier detection (OSVM), the diagnosis accuracy of the two methods with transfer learning and outlier detection (OSVM-MMD and AWSDN) is significantly better. The reason is that OSVM-MMD and AWSDN reduce the distribution discrepancy between the source and target domains through the domain adaptive method, while OSVN cannot well address the domain shift. By combining MMD-based domain adaptation with OSVM, OSVM-MMD not only extracts domain-invariant features, but also separates outliers, which greatly improves the diagnostic accuracy.(4) The deep TL method DDC (M4) and DCTLN (M5) show better diagnostic performance than M1 because of the use of domain adaptation to reduce the domain distribution discrepancy. However, since DDC and DCTLN do not consider the negative transfer caused by outlier samples in the target domain, their diagnostic accuracy is greatly reduced. Therefore, the diagnosis results of these two deep transfer learning methods are significantly worse than AWSDN. Experimental results show that, compared with DDC (M4) and DCTLN (M5), the classification accuracy of AWSDN is increased by approximately P and the recall rate R of label c are expressed in Equations (23) and (24) respectively.c. c, but the true label is not c. c, but the predicted label is not c.(5) t-SNE [To reveal the ability of AWSDN to align the features of the same label samples (sub-domains) in different domains, t-SNE . These fIn this study, deep transfer learning was introduced for the first time in the field of bridge structural damage diagnosis to promote the successful application of intelligent bridge damage diagnosis in open-set scenarios. To solve the distribution discrepancy between domains in the open-set scenarios, an adversarial auxiliary weighted subdomain adaptation-based deep transfer learning model was proposed. A sub-domain adaptation module based on MCMK-WLMMD was proposed to obtain domain-invariant features. To prevent the negative transfer caused by the outliers, an adversarial auxiliary weighting mechanism was proposed to obtain the instance-level weights of the target-domain samples, which were used to describe the similarity of target-domain samples with the source. An experimental study of open-set deep transfer damage diagnosis was also conducted. From the results, the following conclusions were drawn:(1) First, compared with the deep learning based intelligent structure damage diagnosis method without the transfer learning and outlier classes isolation, our proposed method has a higher recognition accuracy in open-set scenarios.(2) Second, our proposed adversarial auxiliary weighted sub-domain adaptation with MCMK-WLMDD is superior to the domain adaptation of other widely used TL methods in terms of minimizing the distribution discrepancy between different domains.(3) Finally, our proposed method can extend the network trained with labeled data obtained from one bridge to classify the unlabeled data with unknown classes in the open-set scenarios. This will promote the practical application of the transfer damage diagnosis of bridges.