The phoretic mechanisms at stake in the propulsion of asymmetric colloids have been the subject of debates during the past years. In particular, the importance of electrokinetic effects on the motility of Pt-PS Janus sphere was recently discussed. Here, we probe the hydrodynamic flow field around a catalytically active colloid using particle tracking velocimetry both in the freely swimming state and when kept stationary with an external force. Our measurements provide information about the fluid velocity in the vicinity of the surface of the colloid, and confirm a mechanism for propulsion that was proposed recently. In addition to offering a unified understanding of the nonequilibrium interfacial transport processes at stake, our results open the way to a thorough description of the hydrodynamic interactions between such active particles and understanding their collective dynamics. In order to predict the behaviours of self-propelled particles it is important to understand the fluid disturbances they generate. Here the authors measure the flow-fields around active particles and show that they are in agreement with theoretical predictions which take into account electrokinetic effects. In particular, it has enabled experimental efforts to mimic functions inspired by cellular biology, such as cargo transport or chemical sensing, which might lead to radically new medical applications. From a theoretical point of view, such objects do not obey the laws of equilibrium statistical physics, and the description of their non-equilibrium dynamics remains a major challenge. Understanding these non-equilibrium phenomena could help us develop a new paradigm in engineering by designing emergent behaviour.The ability to design active colloids and to accurately control their motion in a fluid environment is one of the cornerstones of modern physical sciences, and has motivated a significant amount of work over the past years6. Although the field gradients can be externally imposed on the phoretic particles, a most interesting situation arises when the particle generates them itself. For instance, the particle can bear an “active site”, which degrades solute particles present in the bulk. The resulting asymmetric distribution of reaction products creates a nonzero slip velocity at the surface of the particle and yields self-propulsion10.Phoretic transport has emerged as a prominent mechanism for non-equilibrium activity. It has been known for many decades that the interactions between a particle immersed in a fluid and an inhomogeneous field—which can be chemical concentration, temperature, or electrostatic potential—can lead to force-free and torque-free propulsion11 and insulator spheres with only one metal-coated hemisphere12 that are able to decompose hydrogen peroxide with Pt as a catalyst. It was shown that bimetallic swimmers are able to decompose fuel simultaneously at both ends. Such an electrochemical process results in an electron transfer across the object, and the associated proton movement in the solution permits self-electrophoretic propulsion. However, despite extensive studies, the phoretic mechanisms at stake in Janus sphere propulsion are not yet well established, and have been the subject of recent debates. Although their constitutive material  is an insulator, recent experimental investigations have revealed that electrokinetic effects need to be taken into account together with diffusiophoretic effects to fully describe the propulsion mechanism16. The emergence of such effects is permitted by the inhomogeneity of Pt coating, which is thicker at the pole of the colloid than at its equator. We recently proposed a mechanism where the motility of the Pt-PS sphere is linked to closed current loops, which start at the equator of the particle and end at its pole . This approach has previously provided insight in to flow fields around stagnant Janus colloids actuated by AC electric fields21 had travelled to the top of the cuvette and could be seen translating parallel to the glass surface. After a period some of the Janus spheres became stuck to the surface, probably due to impurities on the surface. We recorded images of the Janus spheres moving through the field of tracer particles. We note that focussed at the top of the cuvette the bulk of the tracer particles are not in focus. This provided a background level of illumination that enabled us to image the non-fluorescent Janus spheres, where they appeared as a transparent disc with a shadow covering one-half of the disc indicating the position and rotation of the Pt cap. For both the moving and stuck colloids the Janus particle appeared as a “half-moon” shape consistent with the near surface alignment effect reported in ref. 17. This was confirmed by image analysis that found the polar angle to be close to 90°. The observation that the static colloids also showed this alignment relative to the substrate, suggests they were translating along the surface before becoming stuck. Tracking of the tracer particles has been described before22 but the Janus spheres required a different approach. We used pattern matching to obtain the center position of a Janus sphere and the angle of rotation of its Pt cap. This data was further analysed as described in the Methods section to arrive at flow fields averaged over the observation of many different colloids. Figure An optical cuvette filled with a mixture of tracer particles and Pt-coated Janus colloids was prepared, and the tracer particles at the top of the cuvette were brought into focus. After a few minutes equilibration time some of the gravitactic Janus sphere swimmer particlesa = 2.5 μm polystyrene particles were symmetrically coated in Pt metal. This was achieved using the solution phase salt reduction method that has been previously described, performed without the Pickering emulsion mask step, resulting in a uniformly Pt-coated colloid23. Tracer motion was again observed and quantified as described above both in the presence and absence of 10 wt% H2O2.To assess phoretic effects in a closely related system that is expected to only produce a chemical gradient, and no flow field, u around the Janus colloid is the solution of the Stokes problem:In the low Reynolds number limit, the flow field uph originates from a non-zero tangential slip velocity at the surface of the colloid that arises from the current loops16, denoted as v(θ) is written as an expansion on Legendre polynomials Pn:We first present the solution of these equations in free space, neglecting the nearby solid wall at the top of the experimental cell (see below for a discussion regarding this approximation). The phoretic contribution to the total flow field Bn (which have the dimension of a velocity) are not known a priori and depend on the details of the phoretic mechanisms taking place at the surface of the colloid.The coefficients u = uph + umono, where uph is the phoretic contribution and umono is the contribution from the force monopole that maintains the colloid steady in the laboratory frame of reference. The contribution from the force monopole f is computed by enforcing the steadiness of the colloid in the laboratory frame of reference. The flow field around the colloid when it is freely moving is obtained in a similar fashion, but without the contribution from the force monopole and subject to the boundary condition U = 5.37 ± 1.64 μm s−1, and this value was used to normalise the presented data.In the situation where the Janus swimmer is stuck to the glass slide, the total flow field may be written as us and around a freely moving colloid um in the equatorial plane. They only depend on the sets of Legendre coefficients r, θ) and the parameters {Bn}. We assume that the sums over the Legendre polynomials are truncated at some order N (here we use N = 5). We use the functions determined analytically (see Methods) as fitting functions for the experimental data, so that we obtain four sets of fit parameters B1,…, B5. The data sets are shown in the inset of Fig. θ = π/2 and π, and has a maximum between π/2 and π.In the Methods section, we give the expression of the velocity field around the stuck colloid 17For further applications, it can be useful to use the simplified formθ. Taking the expression of the slip velocity given in Eq. .See Fig. n in Eq. , we findθ. These reactivity variations can be related to the Pt-coating thickness (d) variations12 shown in Fig. d can be calculated by a simple geometric model: dmax is 10 nm in our experiment, and π–θ gives the angle between the incident Pt “rays” during metal evaporation and the surface normal vector for the sphere). Using an exponential fit , which leads to a flux of protons in the surrounding fluid. The rate of production of these charged species has not yet been determined.It is also interesting to compare this simplified slip-velocity profile, which is consistent with the results obtained from the above fitting procedure to the full experimental flow field, with estimated pole to equator changes in Pt surface reactivity as a function of We show the experimentally measured velocity fields for tracer particle motion in Fig. 24. In regions of low velocity, such as in the fluid surrounding the inactive side of the Pt-PS particle, Brownian translations of the tracer particles influence the streamlines producing stochastic detail, whereas the catalytically induced velocity dominates elsewhere producing smooth streamlines. The flow field around the moving colloid is consistent with the stuck colloid  as a frv(θ) and near a wall with no-slip has not been established analytically. Alternatively, Spagnolie and Lauga25 proposed to represent the flow field at position r around a squirmer as the superposition of the flow fields created by elementary singularities located at the centre of the colloid r0 , and the starred quantities their mirror-image   (Eq. ), we cany)) Fig. . As expe17. The flow fields around a stuck or a moving swimmer for other candidate slip velocity profiles do not show a similarly good agreement in comparison . A profile of slip velocity that would be uniform on the Pt hemisphere and zero on the PS hemisphere will yield velocity profiles that are closer to the ones observed experimentally .The very good quantitative agreement between the components of the velocity measured analytically and experimentally shows that the simple shape of the slip velocity 12. The Janus spheres are coated on one hemisphere with a thin evaporated layer of Pt metal with a maximum thickness of 10 nm. To the suspension we added 3 μL of a 1.0 wt% dispersion of green fluorescent polystyrene spheres (radius = 0.25 μm) to act as tracer particles. After vigorous shaking to fully disperse the tracer particles the cuvette was placed on the stage of a Nikon Eclipse LV100 microscope fitted with a Nikon 20 × 0.45 N.A. objective. Operating in fluorescence mode using the blue excitation band of a Nikon B2A filter cube the tracer particles at the top of the cuvette were brought into focus. The calculated depth of field for this optical arrangement is 3.1 μm, and the focal plane is manually positioned to capture the motion of the tracer colloids at a depth corresponding to the equatorial region of the Janus colloid. While some tracer particles that are nearer to the substrate will also be in focus, these rapidly move either out of focus and so do not contribute significantly to the flow field, or back towards the equatorial region. We recorded images (512 × 512 pixels) of the Janus spheres moving parallel to the glass surface and through the field of tracer particles using an Andor Neo camera at a frequency of 100 Hz.Our experimental system consisted of a 4 cm × 1 cm × 0.1 cm glass cuvette filled with a very dilute suspension of We used the pattern matching functions of the LabView graphical programming language to obtain the center position of a Janus sphere and the angle of rotation of its Pt cap. Tracer particle and Janus sphere trajectories were smoothed to suppress Brownian noise. We then calculated the frame-by-frame vectors along the tracer particle trajectories relative to the position and angle of the Janus sphere. The vectors were then placed on a 0.5μm grid with the Janus sphere at the origin and an angle of rotation of 0°. Where multiple vectors occupied the same grid position an average vector was calculated. We used 1240 s of video data of 25 Janus spheres and 1260 s of video data of 21 Janus spheres to generate the average flow field around the translating and stuck Janus spheres, respectively. The streamlines depicted on the flow fields ,umono is the contribution from the force monopole that maintains the colloid steady in the laboratory reference of frame.The total flow field around a stuck Janus swimmer readsuph is the solution of the Stokes problem  islem (Eq.  and 2)  uph is t28, the total flow field then writesUsing the known expression of The flow field at the surface of the colloid isImposing Ignoring the contribution coming from the force monopole and solving the Stokes problem with the boundary conditionWe start from the expression of the flow field in terms of the Legendre coefficients associated to the slip velocity (Eq.  for the ing Eqs.  and 17)\documentEach of these functions is used to fit the experimental data (for v(θ) is shown on the main plot of Fig. The reconstructed For each situation (moving colloid and stuck colloid), the experimental data consists in two sets of measurements d in Eq.  if the cd in Eq.  if the cn is the number of data points.The error is estimated asSupplementary Information


When realising future fusion reactors, their stationary burning must be maintained and the heat flux to the divertor must be reduced. This essentially requires a stationary internal transport barrier (ITB) plasma with a fast control system. However, the time scale for determining the position of the foot point of an ITB is not clearly understood even though its understanding is indispensable for fast profile control. In this study, the foot point of the electron ITB (eITB) was observed to be reformed at the vicinity of a magnetic island when the island started to form. In addition, the enhanced confinement region was observed to expand during the eITB formation according to the radial movement of the magnetic island toward the outer region. Compared to the time scales of the local heat transport, the faster time scales of the movement of the eITB foot point immediately after island formation (~0.5 ms) suggest the importance of the magnetic island for plasma profile control to maintain stationary burning. For this, the stationary internal transport barrier (ITB) plasma, the pressure gradient of which sharply changes in the core region, and the fast control system of the ITB plasma profile are considered essential and effective. The time scale for determining the position of the foot point of an ITB is not clearly understood even though its understanding is indispensable for fast profile control. Therefore, a deeper understanding of the physical mechanism in determining the ITB profile, especially the radial propagation of ITB region, is necessary for maintaining stationary ITB plasma and profile control.The key issues in realising future fusion reactors involve the maintenance of stationary burning and reduction of heat flux to the divertor with high-pressure core plasma5. A similar mechanism showing the effect of a magnetic island on the plasma transport has also been observed through numerical simulations such as gyrokinetic calculations6. In ref. 7, the simulation showed that transport is reduced in the core plasma region adjacent to the magnetic island because of reduction in micro-turbulence, which is caused by the increase in flow shear around the magnetic island. In addition, the ITB location, at which a large temperature gradient appears, is found to be strongly correlated to the rational surface. Kishimoto et al.8 theoretically studied the ITG mode simulation to explain the reduction in transport near the minimum q area on the rational surface. This simulation predicts the suppression of turbulent transport around the minimum q surface due to the discontinuity of the phase relationship in the global wave structure across the minimum q surface. This discontinuity is more efficiently established with the increase in the flow shear and the decrease in the curvature of q at the minimum q surface9. In stellarator/heliotron plasmas, the eITB is thought to be characterised by the transitions of the neoclassical transport between the “ion root” , usually negative) and the “electron root” (with a large positive Er); these are based on a bifurcation mechanism13. Although the effects of the neoclassical transport and existence of the rational surface and magnetic island for the eITB have already been investigated experimentally and theoretically, the interaction between these three mechanisms has not yet been investigated. The specific characteristics of Heliotron J, i.e., low magnetic shear and high flexibility, based on the magnetic configuration allow us to control low-order rational surfaces within the rotational transform profile, and therefore study how the magnetic topology affects eITB formation. Here we report the structure formation of the eITB region immediately after the formation of a magnetic island. We also discuss the expansion of the improved confinement region following the movement of the magnetic island.The formation of the electron ITB (eITB), especially the determination of its foot point, has been reported to be affected by the neoclassical transport and existence of a rational surface and magnetic island. In the Large Helical Device (LHD) experiments, the magnetic island located near the foot point of the eITB contributes to the formation of the eITB by producing radial electric field shear at the magnetic island boundaryne), which fulfils the condition of low ne , ne, and the plasma current as well as the Te and ne profiles measured through the Nd:YAG Thomson scattering (YAG-TS) system17 at 210, 240, and 300 ms. The eITB formation, which is characterized by a peaked Te profile shape was observed from t = 200 to 330 ms. When the plasma current increased up to 0.7 kA at t = 223 ms, a sudden jump was observed in Te at r/a ~ 0.1 in the electron cyclotron emission (ECE) signal. This increase does not represent the transition to the eITB formation because the eITB was already formed before the rapid rise in the ECE signal. The YAG-TS measurements show that the enhanced confinement region in the eITB expands at the transitive increase.The effect of rational surface and/or magnetic island on the profiles of eITB plasmas was investigated in Heliotron J under constant electron density  of each point were estimated by fitting the curve of the ECE signals as follows:The expansion speed of the enhanced confinement region was investigated using radially separated ECE channels. Figure Te profiles before and after the rapid rise in Te are shown in Fig. Te, the eITB foot point rapidly moved toward the outer region within several hundreds of microseconds, resulting in a delay in the rise of Te toward the outer region. Here, the effective electron thermal diffusivity  calculated before and after the movement of the foot point using the TRAVIS code18 are both localised inside r/a = 0.1 with almost the same shape. Therefore, the transitive expansion of the enhanced confinement region by the eITB during the current ramp-up cannot be explained by the change of the ECH absorption because Te and ne at the resonance zone do not change. A “structural formation” around the eITB foot point is considered to cause the rapid movement of that foot point.The Pinj ~ 270 kW). As the condition of the eITB formation depends on ne, ne was controlled to be fixed through gas puffing for different magnetic configurations. Figure n/m = 4/7 rational surface is important because it is a candidate in which the magnetic island is produced owing to the n = 4 toroidal periodicity of the vacuum magnetic field of Heliotron J. The n/m = 4/7 is larger at n/m = 5/9 ~ 0.556), at which the possibility of the formation of a magnetic island is low, around the n/m = 4/7 rational surface, the structural formation is only related to that particular rational surface. This result strongly suggests that the movement of the eITB foot point is affected by the existence of a magnetic island instead of a rational surface. Figure Te profiles at four time intervals  in the same discharge as shown in Fig. Te profiles were observed to be flattened in the vicinity of the eITB foot point with increase in the toroidal current . The radial position of the Te flattening moves outward with the movement of the n/m = 4/7 rational surface. This movement is consistent with the calculated position of the n/m = 4/7 rational surface, suggesting the existence of a magnetic island. Note that we confirmed experimentally that the flat structure of Te did not appear in plasmas without rational surfaces.To clarify the role of the rational surface and/or magnetic island in the structural formation, the eITB formations for other magnetic configurations with rotational transform values of 20. The n/m = 4/7 rational surface was produced around r/a ~ 0.37 at ~1.5 kA (t = 300 ms). The calculation shows that the eITB can be formed without the rational surface. The formation time of the n/m = 4/7 rational surface is consistent with the structure formation time. The calculation also shows the outward movement of the eITB, which is synchronous with the movement of the rational surface with the increase in current. Figure Te profiles, and the positions of n/m = 4/7 rational surface estimated through neoclassical calculation as a function of the plasma current. Before the structure formation, the eITB foot point was kept at the same location. When the plasma current reached 0.7 kA, the location of the eITB foot point jumped to the outside of the plasma from r/a ~ 0.13 to r/a ~ 0.23. After the structure is formed, the location continued moving outward from r/a ~ 0.2 to r/a ~ 0.4 with the increase in current. The eITB foot points are located slightly inside the position of the n/m = 4/7 rational surface. However, the changes in the ne profiles are insignificant during the discharge. Although the central Te slightly decreases at the end of the discharge, the kinetic stored energy increases after the structure is formed owing to the expansion of the enhanced confinement area.The rotational transform profile, including bootstrap current for the magnetic configuration for which vacuum q rational surface are the drivers of ITB formation/evolution30:Te profiles, which represent the characteristics of the magnetic island, were observed to be flattened in the vicinity of the eITB foot point with increase in the toroidal current  the foot point of eITB moves transitively outward when the island starts developing; (ii) after the structure formation, the foot point continues to move outward following the movement of the magnetic island and the improved confinement region expands. These results show a synergy effect of the eITB and magnetic island for the expansion of the improved confinement region. The time scales of the movement of the eITB foot point immediately after the island formation (~0.5 ms) are much faster than that of the local heat transport, suggesting the importance of the magnetic island for the plasma profile control to maintain stationary burning.l, m) = , where l and m are the pole number and pitch numbers of the helical coil, respectively. The major and averaged minor radii are 1.2 and 0.1–0.2 m, respectively31. The magnetic-field strength is 1.25 T and the working gas is deuterium. As the magnetic configuration of Heliotron J has low magnetic shear, the vacuum rotational transform profile is almost flat and the value of central Pinj ~ 270 kW, single pass absorption ratio is above ~90%). The absorbed power of ECH was estimated by the TRAVIS code18. The plasma current ramps up to 1.5 kA and is mainly driven by a bootstrap current. An electron cyclotron (EC) current was not driven because of the parallel refractive index of The experiments were performed on Heliotron J, which is a medium-sized helical-axis heliotron device with a periodicity of (Te) and density (ne) profile in plasma. This method measures the scattering light due to electrons in the plasma. The Te and ne are obtained from the Doppler broadening and absolute intensity of scattering light, respectively. In this study, we measured the radial profiles for the  Te and  ne by means of the Nd:YAG Thomson scattering (YAG-TS) system17. The system consists of two 50 Hz lasers each of 550 mJ, large collection optics, and 25 radial channel interference polychromators . This measurement system achieves a S/N ratio of ~50 for low-density plasma (ne ~ 0.5 × 1019 m−3).Thomson scattering of laser light is a popular method of measuring electron temperature  is optically thick to measure the temperature of bulk electron.The intensity of electron cyclotron emission (ECE) from the plasma is proportional to


As the specialty of Andrology expands it is important to establish the most important studies that have shaped, and continue to shape, current research and clinical practice. Bibliometric analysis involving a citation rank list is an established means by which to identify the published material within a given field that has greatest intellectual influence. This bibliometric analysis sought to identify the 100 most influential manuscripts in Andrology, as well as the key research themes that have shaped contemporary understanding and management of andrological conditions.The Thompson Reuters Web of Science citation indexing database was interrogated using a number of search terms chosen to reflect the full spectrum of andrological practice. Results were ranked according to citation number and further analysed according to subject, first and senior author, journal, year of publication, institution and country of origin.n = 11), followed by the New England Journal of Medicine (n = 10). The most common theme represented within the top 100 manuscripts was erectile dysfunction (n = 46), followed jointly by hypogonadism and male factor infertility (n = 24 respectively).The Web of Science search returned a total of 24,128 manuscripts. Citation number of the top 100 articles ranged from 2819 to 218 (median 320). The most cited manuscript  reported the prevalence and risk factors for erectile dysfunction (ED) in the Massachusetts Male Ageing Study. The Journal of Urology published the highest number of manuscripts (Erectile dysfunction should be considered the most widely researched, published and cited field within andrological practice. This study provides a list of the most influential manuscripts in andrology and serves as a reference of what comprises a ‘highly citable’ paper for both researchers and clinicians. Andrology is the medical specialty that focuses on many aspects of male health, comprising a wide range of conditions of the male reproductive system, as well as urological pathologies that are specific to men. Although the clinical science had been studied for many years, it was not until after the introduction of the term ‘andrology’ in 1951 that scientists and clinicians from a diverse range of backgrounds began to refer to themselves as ‘andrologists’ [The generation of a citation rank list is one method of identifying the published material within a given field that has greatest intellectual influence . A citatA number of clinical disciplines have employed citation analysis to determine the most influential articles in their field. These include entire specialties such as general surgery , plasticThe Thompson Reuters Web of Science citation indexing database was interrogated using the method previously described by Ellul et al. . As andrThe search was conducted on 5th June 2018 and included all manuscripts published in the English language from 1900 onwards. Results were subsequently ranked by citation number. Final interrogation of the database was independently performed by two assessors (NB and TE). The 100 most cited articles were further evaluated according to subject, first and senior author, journal, year of publication, institution and country of origin. The 2016 impact factor of each journal was also identified from the Journal Citation Reports dataset . In ordeThe Web of Science search returned a total of 24,128 manuscripts. Table n = 41), as demonstrated in Fig. The 100 most cited manuscripts were published over a broad time period, with the greatest proportion between 2000 and 2009 (n = 11), including the most cited article by Feldman et al. [Table n et al. , and accn = 66), followed by the United Kingdom (n = 12) and Canada (n = 5). The Massachusetts General Hospital was the institution with the greatest number of manuscripts (n = 7), followed jointly by the Cleveland Clinic and New England Research Institute (n = 6 respectively), all of which are based in the USA. RC Rosen [n = 5), including the most cited article [The country with the greatest number of publications was the United States of America , followed jointly by hypogonadism and male factor infertility (n = 24 respectively). Figure Figure The citation rate of the top 10 manuscripts ranged from 118 to 45, as shown in Table This study is the first bibliometric analysis to identify and analyse the most influential manuscripts in the field of andrology. A range of topics were represented within the top 100, which reflects the spectrum of clinical andrological practice as well as the variation in the specialist backgrounds of andrological practitioners and researchers. Similarly, there is significant overlap with other medical specialties including, but not limited to, endocrinology. Despite this overlap, the most prevalent theme of publication was the assessment and/or treatment of patients with ED, constituting 46 papers within the top 100. This included the most cited article by Feldman, et al.  describiThe self-administered erectile function questionnaire reported in the Feldman, et al. study differed from the now more commonly used International Index of Erectile Function (IIEF), which was first described three years later in 1997 by Rosen, et al. . This laFourteen manuscripts in the top 100 focussed specifically on the treatment of ED. These ranged in age from the article by Brindley in 1983 examining the effects of intra-cavernosal alpha-blockade , to thatn = 66), followed by the United Kingdom (n = 12) and Canada (n = 5). This disparity between the USA and other countries may reflect the subspecialisation that occurs in contemporary American Urological practice compared with other geographical regions [A significant majority of manuscripts were published by authors in the United States of America . Interestingly, impact factor did not necessarily correlate with the most cited papers. For example, the second most cited paper by Rosen, et al.  was publThe main limitation of bibliometric analysis is the potential for a number of types of bias. Firstly, disproportionate citation may result from institutional bias, language biases, self-citation or powerful person bias. In addition, older manuscripts may receive more citations due to the length of time in which they are in the public domain. Although the use of citation rate attempts to control for this, it may take a number of years for influential manuscripts to accrue citations due to the publication lead-time for their citing manuscript. A further limitation is the inclusion of only first and senior authors, and the institution of the first author. It is possible that several first authors will have co-authored other papers in the top 100 and therefore be underrepresented in the current study. Finally, searching based on title means a small number of manuscripts that have key andrological themes without pertaining to these in their title may not have been identified.This list of highly cited papers identifies the topics and authors that have made the most impact in the discipline of andrology over the last century. There is a clear predominance of manuscripts focusing on the treatment and pathophysiology of ED, which should therefore be considered the most widely researched, published and cited field within andrological practice. This study provides a reference of what may be considered as the most influential papers in andrology and serves as a inidcation of what comprises a ‘highly citable’ manuscript for both researchers and clinicians.


According to the Euro Health Consumer Index, the Dutch healthcare system is the champion of Europe. Yet we are living for longer, prosperity is increasing and the population is growing. If we do not continue to adjust our healthcare system to these trends, medical expenses in the Netherlands will double to € 174 billion (in 2040). We are also facing job market difficulties in healthcare. We risk a shortfall of 125 thousand employees in 2022. It is therefore time to restructure healthcare. Not tomorrow but today. Healthcare will increasingly be organised around the day-to-day lives of patients—at home or work, with offline and online healthcare seamlessly matching up to each other. The shortage of personnel also demands a different attitude to healthcare provision. As a health insurer we can help to improve healthcare, for instance by giving healthcare providers the financial capacity to organise their care differently. Or by conducting independent research so that conclusions can be drawn on the legal criterion of ‘the state of science and practice’. This is how it was possible, in conjunction with the Dutch Cardiology Centres, the Netherlands Society of Cardiology and FocusCura, to include the ‘Hartwacht’ telemonitoring service in health insurance policies. According to the Euro Health Consumer Index, the Dutch healthcare system is the champion of Europe. Scores for aspects such as accessibility, the outcome of care and patient rights clearly demonstrate this. Yet we are living for longer, prosperity is increasing and the population is growing. If we do not continue to adjust our healthcare system to these trends, medical expenses in the Netherlands will double to € 174 billion in 2040. We are also facing growing job market difficulties in healthcare. If we fail to act, we risk a shortfall of 100 to 125 thousand employees in 2022. These forecasts are very clear too. It is therefore time to restructure healthcare—not tomorrow but today.Restructuring healthcare requires a new way of thinking and the use of new resources. Healthcare will increasingly be organised around the day-to-day lives of patients—at home or work, with offline and online healthcare seamlessly matching up to each other. e‑Health will play an ever more prominent role in this transition. The shortage of personnel also demands a different attitude to healthcare provision. The emphasis will increasingly be on encouraging self-reliance rather than taking over. The ability of patients to take control and the power of digital resources will be used to the full here.The pace of change needs to increase.The shift of healthcare to the home has been ongoing for a while now. Although our life expectancy is rising ever higher, the number of hospital admissions has stayed the same over the past 35 years . This isThere are plenty of pilot schemes and positive initiatives: from digital consultations between GPs and specialists, telemonitoring of chronically ill patients to the use of robotics and domotica in care of the elderly. Yet upscaling is slow in getting going. Many new initiatives lack sufficient evidence for their effectiveness. They first need to meet the ‘state of science and practice’ before the process of including them in basic insurance policies can be initiated. Telemonitoring and e‑consultations are currently financed in an ad-hoc manner because they do not yet come under the services of the Dutch Healthcare Authority (NZa). Moreover, not all healthcare providers are embracing these innovations, even though they may well be in the best interests of patients. We therefore have a long way to go on all fronts. An important player who can operate as a flywheel here is the medical specialist.As a health insurer we can also help to improve healthcare, for instance by giving healthcare providers financial capacity to organise their care differently. Or perhaps by conducting independent research so that conclusions can be drawn on the legal criterion of ‘the state of science and practice’. This is how it was possible, in conjunction with the Dutch Cardiology Centres, the Netherlands Society of Cardiology and FocusCura, to include the ‘Hartwacht’ telemonitoring service in health insurance policies.Each year, 20,000 Zilveren Kruis policyholders end up in hospital with chronic heart failure. Total expenses on an annual basis: € 25.5 million. Among this group are 3,800 patients who are actually admitted to hospital; an essential and undesirably intrusive situation for patients. Moreover, clinical admission is expensive; 19% of the patients with chronic heart failure account for 60% of total medical expenses (€ 15.5 million). e‑Health and digitisation of heart care could potentially prevent 30% of these admissions via telemonitoring [As patients send values such as blood pressure and heart rate to a cardiologist via an app, the quality of life is improving considerably for various patient groups . At the same time, cardiologists obtain detailed insight into the patient’s condition, outliers in the health status of patients are prevented and there are fewer patients visiting A&E.A number of problems can therefore be tackled at the same time using e‑Health. Waiting times at outpatient departments will be reduced, specialist assistance will be used where really needed and patients will be given a better idea of their own medical conditions. Telemonitoring of chronic heart patients increases productivity and cuts expenses as algorithms take over some of the tasks. The quality of life of patients improves and healthcare is structured more efficiently, while the quality of healthcare improves due to more frequent measurements. This is why it is our ambition to offer this to patients in all regions of the Netherlands.The policy and parameters have already been developed so nothing now stands in the way of further upscaling. Furthermore, the Dutch Healthcare Authority has relaxed the rules on telemonitoring for next year . The bal


This environment contains a set of rigid static objects representing a selection of one-dimensional local shape features embedded in a 3D space: an edge, a flat and a convex surface. The proposed algorithm simultaneously optimizes main perception-action loop components: feature extraction, integration of features over time, and the control strategy, while continuously acquiring data online. Inspired by the Recurrent Attention Model, we formalize the target task of haptic object identification in a reinforcement learning framework and reward the learner in the case of success only. We perform a multi-module neural network training, including a feature extractor and a recurrent neural network module aiding pose control for storing and combining sequential sensory data. The resulting haptic meta-controller for the rigid 16 × 16 tactile sensor array moving in a physics-driven simulation environment, called the Haptic Attention Model, performs a sequence of haptic glances, and outputs corresponding force measurements. The resulting method has been successfully tested with four different objects. It achieved results close to 100% while performing object contour exploration that has been optimized for its own sensor morphology.Haptic exploration is a key skill for both robots and humans to discriminate and handle unknown objects or to recognize familiar objects. Its active nature is evident in humans who from early on reliably acquire sophisticated sensory-motor capabilities for active exploratory touch and directed manual exploration that associates surfaces and object properties with their spatial locations. This is in stark contrast to robotics. In this field, the relative lack of good real-world interaction models—along with very restricted sensors and a scarcity of suitable training data to leverage machine learning methods—has so far rendered haptic exploration a largely underdeveloped skill. In robot vision however, deep learning approaches and an abundance of available training data have triggered huge advances. In the present work, we connect recent advances in  While the sense of touch is central to human life, tactile capabilities of robots are currently hardly developed. This stark contrast becomes even more apparent if one compares touch and vision: while good camera sensors have become affordable and ubiquitous items and huge image and video databases together with deep learning have brought computer vision close (some would argue on par) to human vision –3, compaOne reason is the very limited maturity of tactile sensors as compared with human skin. A second and deeper reason is that touch differs from vision in an important way: while looking at an object leaves its state unaffected, touch requires physical contact, coupling the sensor and the object in potentially complex and rich ways that usually also change the position, orientation or even the shape of the object. Human haptics makes active and sophisticated use of this richness to lend us skills such as haptic exploration, discrimination, manipulation and more. Large parts of these tasks are hard or impossible to model sufficiently accurately to replicate them on robots, thereby calling again for machine learning approaches similar to those that were highly successful in vision. However, the highly interactive nature of touch makes not only the learning problem itself much more difficult but also creates a problem for the availability of meaningful training data, since information about interactive haptics is much harder to capture in databases of static tactile patterns. As a consequence, learning approaches for the modality of interactive touch are still largely in their infancy and tactile skills enabling robots to establish and control rich and safe contact with objects or even humans are still a largely unsolved challenge which severely limits the use of robots in both domestic and industrial applications.In this work we focus on using machine learning for the synthesis of one central and important haptic skill: the discrimination of unknown object shapes through a sequence of actively controlled haptic contacts between a sensor and the object surface. Our approach builds on recent advances that show how a deep network can be made to learn to integrate a sequence of visual observations to discriminate visual patterns. We extend this approach from the visual to the haptic domain and—by taking inspiration from insights about the organization of haptical exploration in humans—we create a potentially interesting new bridge between a computational understanding of interactive touch in robotics and in human haptics.exploratory procedures (EPs) , corresponding to the range in which the output of the neural network lies. Due to the location of the pressure-sensitive surface on only one side of the Myrmex, rotations are performed within the range φ ∈ . Further rotation will not yield contact information between the object and the sensor surface. The acquired pressure information is employed not only to classify the given object but also to determine the next position and orientation of the sensor in the next exploration step.During training and classification, the agent is always presented with one out of four objects. It explores the restricted object space with the sensor by performing a predefined number of haptic glances. In order to learn an exploration policy that is independent of the object’s pose within the global coordinate system, we introduce r = 1 for a correctly classified object and a reward of r = 0 otherwise. We then use the standard formulation of a Markov decision process defined by the tuple , where S denotes the set of states and A the set of admissible actions. PA is the set of transition matrices, one for each action a ∈ A with matrix elements a from state γ the discount factor and S0 ⊆ S is the set of starting states. The goal is to find an optimal policy π: S → A that maximizes the discounted future rewardReinforcement learning is a well-known class of machine learning algorithms for solving sequential decision-making problems through maximization of a cumulative scalar-valued reward function . To formγ ∈  and the standard deviation σ(θ) of the Gaussian θ is referring to the corresponding weights of the model that are necessary to generate the desired output, which is in this case μ or σ. The extent of exploration of the location policy is given by the size of the Gaussian’s standard deviation σ. While for large σ, the raw location of the glance, given by μ, is imprecise, the location has more precision for smaller σ.The features of the LSTM are propagated through a linear layer that outputs the mean μ and σ for the position and for orientation. The used activation function for the output layers are chosen to limit the resulting values to a reasonable range. While the tanh is used as the activation function to generate the mean within the desired range, the softplus function . Thus, if q is sampled outside this range, it is resampled. The new pose vector is then given asTo ensure that the location and position of the sensor remain within the predefined space around the to-be-classified object and also that the orientation remains within its boundaries, the sampled values of the Gaussians location network, but also propagated through a different linear layer that is then used for classification. To achieve this, the softmax-function is utilized to encode the predicted class-affiliation of the current object o in a probability density S glances, using the current set of weights θt at training step t. For classification, the class o with the highest probabilityIn order to classify a given object, the generated feature vector of the LSTM is not only transferred to the o are uniformly chosen from the set of all four available objects. The target loss function r = 1, and r = 0 otherwise. The predicted probability of correctly identifying the target object o after S glances is then given as categorical cross-entropy can be used to compute the loss.For each training step, a new batch of size 64 is generated, where the to-be-classified objects μx and μφ of the location component of the policy, the characteristic eligibility as outlined in σx and σφ are learned by applying For learning the means π(o) gives the computed classification probability that the to-be-classified object is object o, while yo is 1 if o corresponds to the correct object and 0 otherwise.The function β controls the contribution of the different parts of the update. While for β = 1 both parts of the update contribute equally to the weight update, a smaller factor of β < 1 assigns more resources to the classification part. For β = 0, the part of the update that involves the location network is completely omitted  and orientations φ ∈  that are sampled from a uniform distribution. For training, only the classification part of β is set to 0.The second part of the evaluation is dedicated to the pose control by the location network. We evaluate it during the learning process, and compare the results against a model with a random location choice. To this end, we omit the location network and provide the model with new locations In the third part of the evaluation, the different approaches for combining the tactile information with its corresponding location (What & Where) are compared.3 steps. In order to measure the performance after a certain number of training steps, the training is stopped. This is followed by estimation of the mean classification accuracy of 100 newly generated batches, using the currently available policy. In our experiments, the “classification accuracy” or “classification performance” is defined as the probability of the model to correctly classify the current object. To obtain a statistically correct measure of the accuracy, each experiment is repeated 10 times. For the final evaluation, the mean accuracy of these experiments is computed with the standard deviation of the mean as the accuracy measure error.All models are trained for 50 ⋅ 10random search  the respective position of the sensor within the location space and φ ∈  the angle. For each object the recording of the tuples do starts with the position x = −1 and the orientation φ = −π/2. These two parameters are then both incremented with a step size of Δx = 0.01 and Δφ = π ⋅ 0.01, leading to 201 × 201 prerecorded data-points do for each object. The complete dataset has then a size of roughly 161 ⋅ 103 data-points that can be picked to approximate the sensor pose generated by the location network. For a new pair  generated by the network, the closest data-point do is selected from the pre-recorded data set.In order to perform quick optimization and testing, we conducted multiple experiments on a pre-recorded dataset Do see  generateπrloc substitutes the location network with a random location generator. πMLP substitutes the LSTM unit with a linear layer of the same size with a ReLu as its activation function. As the neural network is now built out of linear layers only, it can be seen as a multi-layer perceptron (MLP). In the last column, labeled 〈πMLP〉, the classification performance of πMLP is evaluated by averaging over all conducted glances.The main results of the conducted experiments are summarized in The “full model” Column 2 presents the results of the random location policy. It starts from the same performance as the full model (since the first glance is random in both policies) and from there approaches its asymptotic performance more slowly, making its performance inferior when only 2 to 6 glances can be invested. Thus, our model is able to learn to efficiently extract important information when the number of possible interactions with the given object are limited.πMLP is optimized based only on the last glance, and therefore does not improve after two glances.If the recurrent LSTM unit is replaced with a linear layer of the same size (column 3), the classification accuracy does not rise beyond 67%, constituting the worst result. Due to missing recurrent connection, and the fact that the accuracy is evaluated only after the last glance, the MLP-based architecture However, by averaging its output according toπrloc and the full model πrloc. Employing the LSTM to represent the sequence history yields better performance in comparison to the memory-less architecture πMLP. Here, the location network only slightly improves the location w.r.t. the task-relevant information with the increasing number of glances. In comparison with πMLP〉 that accumulates individual classification decisions may be due to the averaging out of noise with the increasing number of glances. Therefore, both recurrence and an optimized location control are likely to be necessary ingredients of an efficient haptic exploration model in our scenario. These results may be constrained by the simplicity of the 3D shapes considered in the experiment. For an extensive evaluation of the proposed approach, the creation of data sets with a greater number of different objects would be necessary, including stimuli that are more challenging to differentiate without an optimized control strategy. For the described case, we expect that the efficiency and accuracy trends would become more evident.The performed evaluations demonstrated that the proposed model is able to classify the objects with an accuracy of nearly 100% by actively acquiring an optimized sequence of tactile sensor measurements. In this approach the data is generated on-the-fly by haptic interaction with the environment, performed by means of haptic glances and directed by the history of previous tactile events. The results of the conducted experiments show that the full network architecture The network architecture The modularity of our model should, however, provide the functionality to adapt to more complex sensor devices as different modules of the HAM just have to be extended to cope with the increasing number of control dimensions. In the current configuration, our model has a total number of 741248 trainable weights. Within the simplest case, for each additional trainable parameter that is provided to the low-level haptic glance controller an additional output stream with at least one additional linear layer (with e.g. 64 neurons) has to be added to the location network. While this procedure does not necessarily lead to a significant increase in the number of trainable weights, too many additional control parameters might exceed the memory and processing capacity of the LSTM network. The LSTM network contains—in contrast to the linear layers—a large amount of the trainable weights. A necessary amplification of its size or the addition of a second LSTM network in order to increase performance might have a higher impact on the model’s size and its training time. While the designed hybrid loss might be a reasonable approach when only two control parameters have to be adapted, a higher number could slow down the convergence process of the model. One possible way out of this dilemma might be to separately train the classification and control part of the HAM with different loss functions that share the achieved reward.haptic glances. Provided a pose parameter as an input, a floating tactile sensor array touches the surface at the specified location and yields the resulting pressure vector. We have trained a meta-controller network architecture to perform an efficient haptic exploration of 3D shapes by optimally parametrizing the haptic glance controller to perform a sequence of glances and identify 3D objects. Tests of the architecture have been successfully performed in a physics-driven simulation environment.In this work we have proposed the first implementation of a controller, inspired by the concept of The structure of the meta-controller includes a mechanism that accumulates the data acquired during execution of the task and parameterizes the future haptic glances based on the optimized representation of this data. However, the current mechanism performing this temporal integration—based on an LSTM and inspired by the functionality of the working memory—may not be sufficient for an execution of a more complex task consisting of multiple task stages, such as e.g. haptic search, or a contact-rich object manipulation. In such tasks, it may be necessary to save the representation of data existing in the working memory to a long-term memory, from which this information could be retrieved at a later stage in the task execution. To this end, the meta-controller needs to communicate with an extra structure, based on e.g. hashing, such as Neural Turing Machine  to accesTo support our claim that the resulting policy can enable a robot equipped with a tactile sensor to perform efficient object identification by touch, we see performing tests with a (simulated) robot platform, equipped with a Myrmex tactile sensor array as our next task. Furthermore, we will extend our experimental design with the second curvature dimension and, corresponding to this, an extra degree of freedom in our haptic glance controller.Due to the fact that the pose is sampled from a Gaussian distribution, it is highly unlikely that the same position or orientation is sustained during the exploration. Therefore, the current approach results in a jumpy energy-inefficient exploration trajectory which makes a more energy-efficient policy desirable. Consequently, the meta-controller optimization should be extended to enable a smoother trajectory generation. This may be possible by a careful shaping of the reward function or a further refinement of the location network.Beyond performing haptic object identification, we believe that the developed procedure may be applied to enable a robot to perform complex manipulation tasks that heavily rely on haptics. Execution of a more complex tasks such as above-mentioned haptic search commonly involve multiple types of strategies, targeting exploration of different types of haptic features, e.g. movability or rigidity. This may be possible by implementing a set of low-level haptic glance controllers characterized by different parameterizations and outputs accompanied by a gating mechanism that enables the overall model to switch between them.S1 Codehttp://gazebosim.org/.The simulation software is available under the following link: (DOCX)Click here for additional data file.S2 Codehttps://github.com/ubi-agni/gazebo_tactile_plugins.Code of the tactile simulation is available under the following link: (DOCX)Click here for additional data file.S3 Codehttp://www.ros.org/.The software is available under the following link: (DOCX)Click here for additional data file.S4 Codehttps://github.com/ros-simulation/gazebo_ros_pkgs/blob/kinetic-devel/gazebo_plugins/src/gazebo_ros_hand_of_god.cpp.The plugin is available under the following link: (DOCX)Click here for additional data file.S5 Codehttp://doi.org/10.4119/unibi/2936475.A link to the source code can be found at: (DOCX)Click here for additional data file.S1 VideoA visualization of the contact information in Gazebo.(MP4)Click here for additional data file.S2 Videohttps://www.youtube.com/watch?v=CftpCCrIAuw.The video presents design and applications with MHSB (DOCX)Click here for additional data file.S3 VideoThe video describes the experimental setting and illustrates the functionality of the haptic glance controller during object exploration.(MP4)Click here for additional data file.S1 Projecthttps://ni.www.techfak.uni-bielefeld.de/node/3574.Project web-site is available under the following link: (DOCX)Click here for additional data file.S1 Datasethttp://doi.org/10.4119/unibi/2936475.The recorded dataset is available at: (DOCX)Click here for additional data file.


Wireless capsule endoscopy (WCE) is an effective technology that can be used to make a gastrointestinal (GI) tract diagnosis of various lesions and abnormalities. Due to a long time required to pass through the GI tract, the resulting WCE data stream contains a large number of frames which leads to a tedious job for clinical experts to perform a visual check of each and every frame of a complete patient’s video footage. In this paper, an automated technique for bleeding detection based on color and texture features is proposed. The approach combines the color information which is an essential feature for initial detection of frame with bleeding. Additionally, it uses the texture which plays an important role to extract more information from the lesion captured in the frames and allows the system to distinguish finely between borderline cases. The detection algorithm utilizes machine‐learning‐based classification methods, and it can efficiently distinguish between bleeding and nonbleeding frames and perform pixel‐level segmentation of bleeding areas in WCE frames. The performed experimental studies demonstrate the performance of the proposed bleeding detection method in terms of detection accuracy, where we are at least as good as the state‐of‐the‐art approaches. In this research, we have conducted a broad comparison of a number of different state‐of‐the‐art features and classification methods that allows building an efficient and flexible WCE video processing system. As color and texture are the main features to explore bleeding frame candidates, this paper is focusing on color detection in the red‐green‐blue (RGB) color space and various texture features. Experimental analysis depicts that this method is capable of performing bleeding detection with the performance achieved at least as good as the state‐of‐the‐art techniques. At the same time, this paper provides the broad comparison and analysis of the different state‐of‐the‐art features and classification methods in terms of their usability for building the efficient and flexible WCE video processing systems. The remainder of this paper is organized as follows: Section 2 provides a short survey of the related works found in the literature. Section 3 describes our methodology and the proposed algorithm. Results and discussions are presented in section 4. Finally, in section 5, we present our conclusions and provide directions for future work.2Bleeding is very a common abnormality found in the GI tract. Many researchers have contributed to detecting this with high‐performance classifiers. It is crucial to detect bleeding at an early age since it is a precursor for inflammatory bowel diseases such as Crohn’s disease and UC. Figures Baopu LiFurthermore, Liu and GuPixel‐level methods are supposed to be more accurate in order to classify bleeding and nonbleeding pixel samples efficiently. YuanTo summarize, researchers have studied to analyze each and every frame of WCE video sequences to detect the frames with a pathological alteration. These experiments have been performed by using various image processing and pattern recognition techniques to generate proper frame characteristics, for example, computing color and texture features using various color models. These characteristics define the classification on the basis of frame pixels and frame regions for discrimination between normal and abnormal tissue structure. In our previous work, we have developed an algorithm to extract color feature for ulcer using statistical feature analysis.3The bleeding detection technique proposed in this paper is shown in Fig. 3.1i and j are the horizontal and vertical indices in the frame, respectively.In the GI tract, there can be areas that are both under‐ and over‐illuminated, and which are, therefore, cannot be processed. For example, a large air bubble packet3.21τ = 0.3 and 2τ = 0.7, respectively. Morphological dilation is then applied to dilate the detected information. If A is a frame after masking operation and B is the structural element, then dilation of A by B is defined as followsWe encounter various false results due to the presence of edge information in frames which may lead to a wrong detection. These edges are basically intestinal folds, and the ambiguous edging is caused by a random vector of camera’s view direction. To eliminate this information, we are using a canny operator.B to its origin, and the reflection is shifted by z. For this experiment, we have chosen the structuring element of dilation B to be a square with a three‐pixel width. Morphological erosion is performed later to remove few bleeding pixels wrongly detected to enhance final result . After removal of dark spots, as shown in Fig. The feature vector used as an input for our SVM‐based detection approach is defined as C1, C2, C, C4, C5.3.5Texture is a very useful feature for a wide range of use cases in image processing and classification tasks. It is generally assumed that the human visual system uses textures for recognition and interpretation of visual input. In general, color is usually a pixel property while texture can only be measured from a group of pixels.° with a range of 1 (nearest neighbor) was used in this work. The 22 texture descriptions extracted from each of the gray tone spatial dependence matrices are presented in Table One of the good candidates for the texture analysis is a statistical method of examining texture that considers the spatial relationship of pixels is the gray‐level co‐occurrence matrix (GLCM). GLCM is a matrix that is defined over a frame to be the distribution of co‐occurring pixel values  at a given offset, also known as the gray‐level spatial dependence matrix. The GLCM functions characterize the texture of a frame by calculating how often pairs of pixel with specific values and in a specified spatial relationship occur in a frame, creating a GLCM, and then extracting statistical measures from this matrix.p is the ‐th entry in a normalized gray‐tone spatial‐dependence matrices, x(i)p is the i‐th entry in the marginal probability matrix obtained by summing the rows of gp, N is the number of distinct gray levels in the quantized frame, and HX and HY are the entropies of xp and yp.The entire textural features are extracted from the gray‐tone spatial‐dependence matrices. The equations, which define a set of 22 measures of textural features, are presented in Table P. Thus, the sum of squares will be high.The feature T1 is also called Energy or Uniformity, which is a measure of homogeneity of a frame. A similar scene will contain only a few gray levels, giving a GLCM with only a few but relatively high values of T2 is the entropy function, which is the randomness or the degree of disorder present in the frame. The value of entropy is significant when all elements of the co‐occurrence matrix are the same and small when elements are unequal. Inhomogeneous scenes have low first order entropy, while a similar scene has high entropy. In dissimilarity, (T3) the weights with which GLCM probabilities are multiplied, increase linearly away from the diagonal .The features T3‐T6, T18, and T19 are the smoothness statistics, which use a weighted distance from the main diagonal of the GLCM . The inverse difference moment (IDM) (T6) is also called homogeneity, and it measures the local homogeneity of a frame. The IDM feature obtains the measures of the closeness of the distribution of the GLCM elements to the GLCM diagonal. IDM has a range of values to determine whether the frame is textured or nontextured. Homogeneity measures how close the distribution of elements in the GLCM is to the diagonal of GLCM. As homogeneity increases, the contrast, typically, decreases.μx,μy,σx,andσy parameters/values are the mean and standard deviation of xP and yP. If the frame has horizontal textures, the correlation in the direction of 0° degree is often more significant than those in other directions.The correlation (T7) feature measures how correlated a pixel is to its neighborhood. Correlation is a measure of gray level linear dependence between the pixels at the specified positions relative to each other. Feature values range from −1 to 1, these extremes indicating perfect negative and positive correlation, respectively. The i + j” frame is created, having a range of integer intensities from 0 to 2(gN − 1). The i + ju value is computed and stored for the first neighborhood of the frame and is subsequently updated as the neighborhood is moved by one pixel. When the cluster shade is high, the frame is asymmetric.The cluster shade (T9) is a measure of the skewness of the matrix and catches the perceptual concepts of uniformity p value found within the window. High maximum probability values occur if one combination of pixels dominates the pixel pairs in the window.Cluster prominence (T10) is also a measure of asymmetry.Variance (T12) is also known as Sum of Squares and is a measurement of heterogeneity which is strongly correlated to first order statistical variable such as standard deviation. The variance increases when the gray level values differ from their mean. It also includes the average calculated over the sum of adjacent pixels (T13), the variance calculated over the sum of the adjacent pixels (T14), the variance over the difference between adjacent pixels (T16), the entropy on the sum of the adjacent pixels (T15) and the entropy on the difference of the adjacent pixel (T17).x and y. It is an invariant under a change of parameterization x’ = f(x), y’ = f(y), and reduces to the classical correlation coefficient when p is normal.The informational coefficient of correlation (T18 and T19) is a function of the joint probability density function p of the two variables Q. It expresses the singular value characterization for the finite‐valued random variables.The maximal correlation coefficient (T20) defines the square root of the second largest Eigenvalues of the matrix 3.6The color and texture features described earlier are designed to extract the different local characteristics at a level of individual pixels. In this work, we are using the machine‐learning‐based two‐class‐classification methods to differentiate between two different pixel groups. The first group includes all pixels that show bleeding‐related findings, like fresh and old blood, open wounds, etc. These findings are typically colored in shades of red: from bright red for fresh blood to dark brown for old blood residuals. At first glance,The second group of pixels is associated with different findings that are not‐bleeding‐related, like normal GI tract tissue, stool masses, food leftovers, bubbles, instruments, water, over‐ under‐illuminated areas, etc. All these “normal” findings can be colored in more or less random colors, but they can also be colored in shades of red, for example, food leftovers, some types of fecal masses, some types of normal GI tract tissue, etc. In contrast to the first group of pixels, for this group of pixels, the texture is an important characteristic that allows the system to distinguish between different types of findings, and in combination with the color information, the texture‐related local characteristics are an essential input for machine‐learning methods to perform pixel‐level classification. Detailed pixel‐perfect classification is essential because the bleeding areas can be both big and very small and, at the same time, the state‐of‐the‐art WCE devices have a relatively low spatial resolution. Thus, an evaluation of each pixel is essential for high‐performance bleeding detection in this scenario.In the proposed pixel‐level classification approach, we use Random Tree (RT),4This research has obtained its novelty through collaboration with the medical experts (experienced endoscopists) from Endoscopy unit at University of Malay Medical Center (UMMC), Kuala‐Lumpur, Malaysia. The data collection has been performed at their medical center especially to carry out this research. In order to validate our results, the experts provided the ground truth data in the form of frame labels (bleeding/nonbleeding) for all the collected frames and the pixel‐level bleeding areas segmentation masks , , and  named "All", "Five top", and "Five different" respectively in the following tables. All the color and texture features were extracted for each meaningful pixel  of the input frames using the corresponding equations described in sections 3.4 and 3.5. The features extracted were combined using the simple early‐fusion approach when the resulting pixel's feature vector made as a simple enumeration of all the feature values used. The machine‐learning approaches used are RT, RF, and LMT; all have been proven Table To investigate the validity and potential of the proposed feature combinations for the real‐world bleeding detection approaches, we have performed twofold cross‐validation of the algorithms using the previously created training and test datasets. The validation results are depicted in Table However, the measured difference between the best performing RF‐based bleeding detection runs with all the texture, and the  texture features is almost nonnoticeable with the difference value of 0.001, thus using of only these five texture features in combination with five color features recommended for reducing the classification computation complexity. In our future work, after collecting and annotating of a more significant number of bleeding frames form a broader range of patients and bleeding cases, we are going to re‐evaluate the value of the different texture features using the statistical methods in order to find the combination of texture features with the best possible performance and computational complexity balance for this use‐case.A comparison of our bleeding detection method to the best state‐of‐the‐art methods is depicted in Table Generally speaking, a fair comparison of the different two‐class classification approaches is difficult with the widely used PREC, REC, specificity, accuracy, and even F‐Measure scores because none of these metrics assess the imbalances in the positive and negative samples in the datasets, as well as the sizes of the datasets. In the case of a fully balanced dataset with the equal number of positive and negative samples, the MCC value measured is equal to the F‐Measure value. With the increase of the level of the dataset imbalance, the MCC value becomes lower with the limit of zero for the fully unbalanced dataset. Thereby, comparative analysis of the F‐Measure and MCC score can also be used to estimate the dataset balance for the results obtained using the nonpublic and not well‐described datasets. Thus, the only metrics that can efficiently be used for the direct performance comparison of the different methods on the different datasets is MCC, and we, therefore, invite all the researches to report this metrics or a whole set of TP, TN, FP, and FN values enabling a computation of all the metrics can be used for the method's comparison.5In this paper, we presented a developed automated bleeding detection algorithm that detects the frame with bleeding as well as pixels that are associated with bleeding areas. We briefly describe the related work and the base ideas of our detection approach. We introduced the color and texture features used for frames analysis and presented both our approaches using either color or features and the combined color‐texture‐based approach. The novelty of the best‐performing detection approach includes a combination of the best color and texture features used. A detailed evaluation of the frame‐ and pixel‐level bleeding detection has been performed. The experimental results displayed a good performance of our bleeding detection method in terms detection accuracy at least as good as state‐of‐the‐art approaches. Not only that the novelty of the proposed method promises the higher accuracy, provides a broader comparison of distinctive state‐of‐the‐art features, and various classification methods, alongside with the detection method performance measurement using a comprehensive combination of metrics. The conducted experimental studies confirmed the importance of the features combination even for the relatively simple case of GI tract bleeding detection. Using of both the color and texture features is required for the highest detection performance.For the future work, we plan to extend the sets of texture and color features used in our classification approach and to perform a more in‐depth statistical analysis of the value of different features for the classification performance. Next, we plan to extend the methods presented in this paper for WCE ulcer frames analysis in order to support UC and inflamed areas detection and localization. Finally, using our previous successful experienceThe authors declare that they have no conflict of interests.


The Chain Fracture Zone is a 300‐km‐long transform fault that offsets the Mid‐Atlantic Ridge. We analyzed new multibeam bathymetry, backscatter, gravity, and magnetic data with 100% multibeam bathymetric data over the active transform valley and adjacent spreading segments as part of the Passive Imaging of the Lithosphere Asthenosphere Boundary (PI‐LAB) Experiment. Analyses of these data sets allow us to determine the history and mode of crustal formation and the tectonic evolution of the transform system and adjacent ridges over the past 20 Myr. We model the total field magnetic anomaly to determine the age of the crust along the northern ridge segment to better establish the timing of the variations in the seafloor fabric and the tectonic‐magmatic history of the region. Within the active transform fault zone, we observe four distinct positive flower structures with several en échelon fault scarps visible in the backscatter data. We find up to −10 mGal residual Mantle Bouguer Anomaly in the region of the largest positive flower structure within the transform zone suggesting crustal thickening relative to the crustal thinning typically observed in fracture zones in the Atlantic. The extensional/compressional features observed in the Chain Transform are less pronounced than those observed further north in the Vema, St. Paul, and Romanche and may be due to local ridge segment adjustments. Variations in the seafloor fabric and crustal thickness with age suggest time variable magmatic productivity at the ridge segmentsThe crustal thickness in the Chain Transform Fault does not appear thin, likely due to transpression or a high degree of serpentinizationThe transpressive features in the Chain Transform are muted compared to more northerly transforms, likely due to ridge segment adjustments For example, a small degree of transtension and subsequent flexure has been suggested as the mechanism for the transverse ridge visible on 10‐ to 25‐Myr seafloor in the Vema Fracture Zone  in March 2016 and the other aboard the RRS Discovery (DY072) in March 2017, which were the deployment and recovery cruises for the Passive Imaging of the Lithosphere Asthenosphere Boundary experiment . The regions of the active ridge were determined by mapping the region of high backscatter intensity, which we infer to be strong backscatter from fresh basalt flows in the topographic lows of the median valleys Figure .2.2Total field magnetic data were collected on both cruises along the ship tracks. We focused on one nearly W‐E ridge perpendicular profile across the northern ridge segment from MGL16‐02. This is because the entire gridded data set, collected in short nearly N‐S and S‐N ridge parallel tracks, appears to have artifacts owing to a ship track spacing that was broad compared to the scale of the magnetic anomaly. We did not collect a long profile along the southern ridge flanks. To calculate the magnetic anomaly, we removed the International Geomagnetic Reference Field from each data point  was measured on both MGL16‐02 and DY072 using a Bell BMG3 gravimeter and a Lacoste Romberg gravimeter, respectively, at a sample rate of 1 s. Gravity ties were performed before and after both cruises and the data corrected for drift over the duration of the cruises. The data were Eötvös corrected, and then smoothed with a 300‐s moving average filter to reduce the effects of waves and small ship accelerations. We then subtracted the International Gravity Formula from the data. Prior to gridding, time periods where the ship was turning, or accelerating, were removed from the data set. The crossover errors for the surveys and the satellite derived free air gravity anomaly  from the FAA by subtracting the predicted gravity for the gridded multibeam bathymetry data and a constant thickness crust using the method of Parker . We set  Divins, , and in  Divins, .3 and a thermal expansion coefficient of 2.5 × 10−5 °C−1. The gravity anomaly arising from the thermal density contrast was calculated by summing the upward continued contribution from each 1‐km depth slice in the grid. This anomaly was then subtracted from the MBA to create the rMBA. For 1‐D studies across the ridge, the rMBA was calculated in a similar manner with the same parameters, but the thermal model was calculated from the half‐space cooling model , further corrects the MBA for the gravity anomaly associated with the predicted thermal structure of the aging lithosphere . We then apply a low pass Gaussian shaped filter in the wavenumber domain, with a half‐width of 35 km. We also solved for the lateral density variation of a 6‐km crust to explore the potential effects of serpentinization within the transform system. In this case, we downward continued the rMBA to approximately the midcrustal depth of 6,000 m and assumed a constant thickness layer of 6,000 m to solve for change in density in Parker's equations rather than topography  in the west and lower to average densities in the east (−18 kg/m3) relative to the average densities of the surrounding crust produced at the ridge segments.We also translated the rMBA to lateral density variations within the upper 6 km of the crust, assuming that the crust is constant thickness Figure b to exam4Variations in seafloor fabric and inferred crustal thickness with seafloor age suggest significant changes in melt productivity and tectonism through time in the region. A change from abyssal hill type fabric to detachment fault type morphology is generally associated with a change in the style of crustal formation from magmatically robust to more tectonically dominated mid‐ocean ridge spreading  between 10 and 11 Ma. The northern ridge advance into the transform zone associated with this offset may have caused the transpression that resulted in the large eastern flower structure around this time. The offsets visible in the boundary faults are well correlated with step overs in the fault scarps within the transform zone itself and with the presence of positive flower structures Figures . Thus, tBased on the rMBA, there are likely significant changes in the crustal structure within the transform. It is likely that the crust is thinner immediately adjacent to the northern ridge segment in the west and the southern transform valley wall centered at 14°W based on the positive rMBA, which indicates a mass excess in the region. This is consistent with thinner crust with mantle peridotite at shallower depth. Serpentinization will reduce the density of the mantle by up to 27% for total serpentinization of peridotite (Carlson & Miller, 3 lateral density variation (Figure Beneath the positive flower structures, the rMBA values are close to the background level of ±5 mGal or are negative beneath the large eastern flower structure. Near the three western flower structures, the rMBA is markedly more positive on the ridge segments on either side and thin crust and/or higher densities are inferred. Similarly, for the largest flower structure in the east the crust on the adjacent southern ridge segment is inferred to be thin. In regions beneath the flower structures the crust could have been thickened relative to the adjacent thin crust of the ridge segments due to transpression, or the density could have been lowered by serpentinization that masks crustal thinning or some combination. Both are likely. Transpression increases crustal thickness. In a similar tectonic setting, serpentinized mylonitic periodities have been recovered from the surface of a positive flower structure in the St. Paul Transform (Maia et al., n Figure b we obsen Figure . High‐reThe inferred changes in crustal structure in the transform fault valley contrast with the commonly held view that slow spreading produces thin crust in the mid‐Atlantic transform zones (White et al., In the broader context of the other large offset transform faults in the equatorial Atlantic, which saw major readjustment and deformation events 10 Ma, the tectonic features in the Chain Transform are muted. Around ~10 Ma the Vema Fracture Zone, saw the construction of its transverse ridge, with a rotation in the strike of the abyssal hill fabric by 10° anticlockwise (Bonatti et al., 5We present results from a marine geophysical survey of the Chain Transform and the adjacent ridge segments. We find that there are variations with time in the magmatic productivity of the individual ridge segments, inferred from seafloor fabric and gravity analysis. The ridge segments change modes between abyssal hill production and detachment fault production within 40 km of the transform zone. In addition, we find strong evidence for crustal thickening or a substantial amount of serpentinization of the crust inferred from the gravity anomalies and shallow bathymetry within the active transform zone, mostly in the form of a large positive flower structures. The variations in crustal thickness or serpentinization and ridge segmentation, magmatic productivity, and transpression within the transform do not appear to be associated with a significant deformation event at 10 Ma. observed at large offset transforms further north along the Mid‐Atlantic Ridge. The only deformation event that may have been related to this event is the formation of the large eastern positive flower structure, which was produced by a local ridge segment adjustment. This suggests the Chain Transform may be the southern extent of these deformation events.


To assess the construct validity and reliability of themotivation section of the Motivated Strategies for Learning Questionnaire inSaudi Arabia.A cross-sectional study using the Motivated Strategies forLearning Questionnaire (MSLQ) was conducted. The MSQL has essentially two sections: a motivation section and alearning strategies section.  Themotivation section, which consists of 31 items, was used.  A total of 146 medical students who were allmale completed the questionnaire. Confirmatory factor analysis was used to test the hypothesised factorstructure, and to identify the validity and reliability of the motivation sectionof the MSQL.    A selected group of fit statistics showed that thehypothesised model did not fit the sample data fairly well.  The Intrinsic Goal Orientation subscaleconsisted of 4 items (α = 0.75), the Extrinsic Goal Orientation subscaleconsisted of 4 items (α = 0.78), the Task Value subscale consisted of 6 items(α =0.86), the Control of Learning Beliefs consisted of 4 items (α =0.78), theSelf-Efficacy for Learning and Performance consisted of 8 items (α =0.89), andthe Test Anxiety consisted of 5 items (α =0.77).It is concluded that the hypothesised model did notfit the data well. This may suggest that the motivation section of the MSQL maynot work for Saudi Arabian students. However, this could be due to the factthat the sample data available on this study did not represent female students.Further work is required to establish this. Limitations of the study arediscussed. Since social and cultural issues can be threats to the validity of test scores,A cross-sectional study was used to investigate the hypothesised model of the motivation section of the MSLQ. To achieve this, a total of 146 medical students in the University of Prince Sattam Bin Abdulaziz was recruited using a convenience sampling strategy. The University has recruitment targets for male students. For that reason, all of the participants of the study were male. They were in the second to the sixth year of their studies., -The motivation section of the MSLQ was used in this study includes 31 items answered on a seven-point Likert–type scale , see Prior to undertaking the investigation, ethical clearance was obtained from the Research Ethical Committee in the University of Prince Sattam Bin Abdulaziz, College of Medicine, Saudi Arabia. In order to protect research participants, the confidentiality and anonymity of participants and their data were preserved.  To collect data in a professional manner, we sent the questionnaire to the department of education at the University in order to print and distribute our survey questionnaires to the students. To gather an adequate sample size, the data were collected in different places. For example, some students completed the questionnaires in class time, and other students completed the questionnaire in the department of education.,Confirmatory Factor Analysis of the responses was performed using the statistical packages SPSS and AMOS (version 22). This procedure was used both to measure the construct validity of the academic motivation section of the MSLQ and to test the fit of the hypothesised model to the sample data. To evaluate the construct validity of the questionnaire and assess whether the data fit the empirical model, a CFA was performed.  The following fit statistics were used to identify the model fit: the chi-square statistic .The single-headed arrowsrepresent the factor loadings (regression), and the bidirectional curved arrows represent factor correlations (factor covariance for unstandardized solutions). The path diagram also shows relationships among variables. The numbers “1” in the diagram reflect that the regression coefficient has been fixed to 1. As we hypothesised a six-factor model is confirmed in the academic motivation of the MSLQ, the model was tested using fit statistics.  The results of fit statistics for the one-factor model and the six- factor model are presented in , -This study set out with the aim of assessing the academic motivation section of the MSLQ, using the CFA approach, in order to monitor and enhance teaching and learning among Saudi Arabian medical students.  The results of this study show the academic motivation section of the MSLQ has satisfactory internal consistency reliability. Contrary to exceptions, this study did not approve the hypothesised model of the academic motivation section of the MSLQ to be used for Saudi Arabian medical students. Alternatively stated, the model may not work for monitoring and improving teaching and learning in medicine.  Alternatively, our data sample may not represent the whole student given that female students are not included in this study. However, these results match those observed in the previous studies.Finally, a number of important limitations need to be considered. First, the study is limited by the lack of data on female students.  Secondly, the data only came from a single institute. Therefore, the generalisability of these results is limited to the target population. Finally, the major limitation of this study is the low sample size.   These limitations mean that study results need to be interpreted cautiously.The present study was designed to measure the construct validity of the motivation section of the MSLQ and to assess its hypothesised model. The results of this study show the motivation section of the MSLQ is a reliable measure. However, the data do not fit the hypothesised model and therefore the questionnaire may not be valid. The reliability of scale score may encourage researchers to conduct further studies in order to obtain a clear picture of the MSLQ.  This research has generated some questions in need of future investigations.   For example, a future study with more focus on both genders is suggested. In addition to this, a large sample size could provide a clear picture of both sections of the MSQL for Saudi Arabian medical students.The authors declare that they have no conflict of interest.