Deep Reinforcement Learning (RL) is often criticised for being data inefficient and inflexible to changes in task structure. Part of the reason for these issues is that Deep RL typically learns end-to-end using backpropagation, which results in task-specific representations. One approach for circumventing these problems is to apply Deep RL to existing representations that have been learned in a more task-agnostic fashion. However, this only partially solves the problem as the Deep RL algorithm learns a function of all pre-existing representations and is therefore still susceptible to data inefficiency and a lack of flexibility. Biological agents appear to solve this problem by forming internal representations over many tasks and only selecting a subset of these features for decision-making based on the task at hand; a process commonly referred to as selective attention. We take inspiration from selective attention in biological agents and propose a novel algorithm called Selective Particle Attention (SPA), which selects subsets of existing representations for Deep RL. Crucially, these subsets are not learned through backpropagation, which is slow and prone to overfitting, but instead via a particle filter that rapidly and flexibly identifies key subsets of features using only reward feedback. We evaluate SPA on two tasks that involve raw pixel input and dynamic changes to the task structure, and show that it greatly increases the efficiency and flexibility of downstream Deep RL algorithms. DespiteWe propose that these limitations of using pre-learned representations can be overcome by applying a selective attention mechanism that is inspired by biological agents. Selective attention in biological agents acts as a gating mechanism, whereby the firing of neurons that encode an attended feature increase, while the firing rates of neurons encoding a non-attended feature decrease . This gaIn recent years, Deep RL researchers have shown a lot of interest in attention mechanisms. Most notably, self-attention and the transformer architecture have been applied in Deep RL to push the state-of-the-art further . At its Selective Particle Attention   the number of feature combinations could be extremely large, (ii) reward feedback may be sparse, and (iii) changes in the environment may occur without warning. To address these challenges we present a novel algorithm termed on (SPA) A, which We evaluate 22.1In end-to-end Reinforcement Learning (RL) representations of complex high dimensional inputs, such as pixel values, are learned using a sparse reward signal. This leads to low sample efficiency and representations that are highly specialised for a single task. One way to circumvent these problems is to learn low dimensional representations using other objective functions that have a denser training signal. These representations can then be passed to a Deep RL algorithm, which reduces the complexity of the problem faced by the Deep RL algorithm and leaves the underlying representations unperturbed for future tasks.In line with this theory, several previous works have used autoencoders to learn low dimensional representations of high dimensional inputs. These low dimensional representations are then used to form the state space of an RL algorithm . Crucialfast and flexible method for attending to subsets of latent features. By only attending to a subset of features, those not important for the current task can be ignored by the Deep RL algorithm. This further improves efficiency because the dimensionality of the problem is reduced and also further improves flexibility because if the task changes then a new subset of features can be attended to without entirely overwriting the previously learned function.While applying Deep RL algorithms to latent representations has seen many successes in recent years, we believe this process can be improved further by incorporating a 2.2In Deep Reinforcement Learning (RL) attention to subsets of features is typically achieved by multiplying each feature by a value between 0 and 1. A value closer to 1 means that more ‘attention’ is paid to that feature. While this simple filtering process is effective, the difficulty lies in inferring the attention values for a given task. This is particularly difficult when the only feedback is in the form of a sparse, delayed reward signal. The most popular approach for learning a vector of attention values is known as self-attention . Self-atDespite these successes we believe that self-attention is not an ideal mechanism for selecting subsets of features to be used by a Deep RL algorithm. The primary benefit of self-attention is its ability to relate different parts of the input regardless of how far apart they are. This is particularly useful when trying to solve problems where the input is a sequence  or when Attempts to learn an attention vector without backpropagation have been limited. Most notably, 2.3Filtering-based approaches have been widely used to deal with uncertainty and non-linear dynamics in Markov processes . In the In the present work we suggest that particle filters have several properties that also make them a good candidate for rapidly and dynamically attending to subsets of pre-learned features. Firstly, they use approximate inference, which is important because the number of feature combinations to consider can be prohibitively large. Secondly, particle filters are able to deal with non-linear dynamics and non-gaussian distributions, which are typical of RL problems. Finally, and most importantly, particle filters rely on sequential importance sampling rather than backpropagation. This means that they can be updated rapidly and dynamically in comparison to approaches such as self-attention.33.1Our approach is heavily inspired by selective attention in biological agents. Selective attention is the process of selecting subsets of pre-existing features for decision-making and is thought to be the responsibility of the Pre-Frontal Cortex (PFC) . For exattention . These vttention  that canttention . Each pattention . It alsottention .The states of each particle are updated based on (1) how accurately the combination of features predicts future reward and (2) the magnitude of activation of individual features. The first step reflects top-down attention and is based on a theoretical proposal from neuroscience that humans learn to attend to the features that are most predictive of reward . For exa3.23.2.1attention vector to perform soft-attention. The attention vector has In order to filter the pre-learned representations for the Deep RL algorithm, Selective Particle Attention  VGG-16 . Similaro backup . For theo backup , which hMore details of these algorithms can be found in the 44.1In order to test the effectiveness of SPA we designed two tasks that require an agent to select actions based on raw pixel inputs and to switch attention without warning in response to changing task demands. In both tasks the score used to evaluate the agent was the achieved reward because this was the value the algorithm was tasked with optimising. The first task consisted of multiple choice questions using images from the entire Caltech 101 data set , which cThe purpose for this task design was two-fold. Firstly, the agent has to work out on each trial which image is associated with a reward based on the features of the ‘target’ category. For example, if the target category is ‘soccer ball’ then the agent has to learn to attend to features that are indicative of round shapes in order to obtain reward. Secondly, because the ‘target’ category changes every 50 trials, the agent also has to adapt to this change in reward structure by dynamically shifting its attention to the features of another category using only reward feedback.To ensure that the agent did not learn to remember specific exemplars in the data set we had training and test phases. During the training phase the agent was allowed to update the parameters of the Deep RL network in response to reward feedback but during the test phase it was not. Therefore the only parameters the agent was allowed to change during the test phase was the attention values using the particle filter. Importantly, the test phase used images from the chosen object categories that were not presented during training. In total, the training phase consisted of 200 blocks of 50 trials and the test phase consisted of 10 blocks of 50 trials.As a baseline to measure the effectiveness of our proposed attention mechanism we compared the performance of While the dynamic attention mechanism of Both the training and test results suggest that The results shown in Bottom-up attention aside, having a few highly active features is only useful for the multiple choice task if they help to discriminate between the different image categories. As seen in 4.2www.pygame.org). In the game, the agent controlled a grey block at the bottom of the screen and could move it either left or right at each time step. An object, which could vary in shape and colour, was generated every second at the top of the screen and moved downwards to the bottom of the screen in a straight line. The goal of the agent was to collect objects by colliding with them as they reached the bottom of the screen. Importantly, the agent was only rewarded for collecting objects that had a specific colour and/ or shape, which encourages the agent to attend to specific features that are indicative of reward. Agents were trained for 2000 episodes, with each episode lasting 60 s.Having demonstrated the effectiveness of We explored two different variations of this basic game configuration. In the first variation, the agent was given a reward of ＋1 for collecting objects of a certain shape and a reward of 0 for all other shapes A. This tIn addition to the reward function, other aspects of the environment can change such as the set of perceptual observations experienced by the agent. In RL this is commonly referred to as a change in state space. The second variation of the object collection game therefore explored the ability of te space B. For thAs with the multiple choice task, we compared n update  was thenUpon the change in reward function after 1000 episodes, 5Learning latent representations for Deep Reinforcement Learning (RL) can lead to many benefits including a lower dimensional input, access to a denser training signal and representations that are useful for multiple tasks. Typically, when latent or pre-learned representations are used for Deep RL all of the extracted features are used to learn a policy. However this naive approach can be improved upon by only passing the features that are relevant for the current task to the Deep RL algorithm. This improves learning efficiency because the dimensionality of the input is reduced further and also improves flexibility because there is less interference between features when the task changes. Despite these benefits, mechanisms for attending to subsets of features in Deep RL tend to rely on backpropagation which makes them slow to learn and inflexible to changes in task. As a result, this negates the many potential benefits of being able to rapidly and dynamically attend to subsets of features. To solve this problem we proposed a novel algorithm, termed Selective Particle Attention  or a supervised learning problem where the network has to predict the reward associated with each image. as MNIST  or CIFARas MNIST . HoweverInterestingly, in order to successfully apply d adults , as the Part of the reason for the design of the object collection game was that it allowed for the rewarded shape to be easily changed during learning. This change can be manipulated to correspond to a change in the reward function or state space. We found that  changed . Neverth5.2We believe the ability of 5.3The key component of The movement step of the particle filter is guided by bottom-up attention, which introduces a bias towards the most active features. This is equivalent to a prior over the hypothesis space, favouring hypotheses with relatively few features. Interestingly, this is consistent with findings in neuroscience that people tend to make decisions based on individual features before reasoning about objects that involve more complex combinations of features . SimilarThe observation step of the particle filter is guided by top-down attention. This process involves evaluating different hypotheses based on their ability to predict future reward . It has ulations .5.4The effectiveness of l stream . This isl stream . Having The updating of the particle filter in on tasks . From thThe weights of VGG-16 are the result of extensive pre-training on an image classification task known as Imagenet . ImportaThis dependence of ntations  could fu6We have presented a novel algorithm, termed Selective Particle Attention (The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Hyperbaric welding is used for different steels in many underwater applications as a repair welding process. A difference between wet and dry welding processes can be made. Due to the increased ambient pressure, these processes have special features inherent in the process that influence the cooling and penetration behaviour. The positive use of these effects outside underwater applications is currently rarely addressed in science and application. The presented work establishes these advantages on the basis of a higher strength structural steel and characterizes the effects on the microstructure of a joined S700MC steel and on the mechanical properties of the joint. It will be shown that a hyperbaric environment can be used to orient the weld more towards the depth of the sheet. Furthermore, it will be shown that this change leads to modified cooling, which in itself influences the mechanical properties of the weld seam. A distinction is made between different types of underwater welding processes, for example, welding in dry and wet environments2. The equipment for welding in a dry atmosphere can be very complex, similar to a mini habitat for manual welding3. Furthermore, the research activity around underwater welding has been increasing in the last decade2, Fig. Welding under hyperbaric environmental conditions has been used for many years in underwater production and repair. Due to the place of execution, under water, very complex support equipment is sometimes necessary for the execution of the weld seam4. Furthermore, it was shown that an increased ambient pressure results in a significant reduction in the arc length at the same welding voltage9. In addition to the influence on current and voltage, an influence of the increased pressure on the spatter formation could also be shown10.One topic in research is the process influence of the water-containing atmosphere and the increased ambient pressure15. Akselsen et al. showed that suitable toughness values for different pressure levels can be achieved using different types of filler metal for X70 pipeline high strength steel16. Furthermore, they showed that the penetration depth of the weld is increased due to an increased pressure16, and they estimate some practical implications, such as filling root gaps and a reduced layer count. They proposed a first theory for the increased penetration and focused on flow effects in the melt. The stated that the flow is directed downwards to form a deeper penetration due to the elevated pressure.In the field of materials, a clear influence of the hydrogen content on the microstructure morphology, surface roughness and crack behavior of high-strength steels, which are mostly investigated, was shown17 and duplex stainless steels19 under hyperbaric process conditions has been investigated. For aluminum alloys, a beneficial usage of hyperbaric welding in the lower pressure ranges up to 10 bar can be assumed for the reduction of pores, a key problem regarding the welding of aluminum17.In addition, the range of materials included in the various investigations is also becoming increasingly broad. For example, the welding of copper or aluminum alloys20 and for the welding of cladded multimaterial pipelines21.For some materials, there is a call for more investigations, such as the welding of molybdenum alloys under hyperbaric process conditionsHowever, a positive benefit in welding production due to an increased ambient pressure has been little addressed in research, but it is of interest since a significant increase in the energy density in the arc can be realized by the arc shortening effect and a corresponding possible voltage increase. In addition, the increased ambient pressure causes the arc to constrict and thus increases the local energy density.22. Bunaziv et al. showed a similar effect for welding with the CMT mode at elevated ambient pressures5.An increased energy density leads to a deeper penetration of the weld seam, as Dutra showed for a modified welding process23 and Bunaziv et al. showed for a modified welding process5. The arc constriction is, in addition, dependent on the shielding gas mixture used24, as for normal GMA welds. Azar modelled and validated the thermal cooling cycles and found an influence of the pressure on the cooling t8/5-time but classified this effect as minor25. In other studies, the effect on the cooling time can be considered significant. An altered cooling time will lead to different mechanical properties in the weld metal and the HAZ.The increase in the energy density of the arc should result in an increased welding depth and changed solidification conditions, as Azar et al. predictedThe work presented in this paper is intended to shift the focus of hyperbaric welding processes away from underwater welding towards a potential improvement of conventional welding processes. For this purpose, knowledge of the underlying relationships between the welding process boundary conditions, the ambient pressure and the geometric and mechanical weld metal and HAZ (Heat affected zone) properties is crucial. Therefore, in the work presented hereafter, a model is derived for the dependence of the weld geometry on the set variables during hyperbaric welding. The derived statistical model will give an overview of the relationship between the welding depths and weld metal hardness. The focus of the interpretation of the literature and the results in the discussion of the findings will focus on a beneficial use of a hyperbaric process environment in welding without a water-containing setup.To investigate the beneficial use of hyperbaric welding in welding production, a hyperbaric chamber with an external wire feed was realized Figs.  and 3. TFor the following investigations, the ambient pressure of 2 bar to 16 bar, as well as the welding voltage and the welding current, were measured according to a statistical design of experiments in the non-synergy operation mode of the welding power source. Argon was fed into the test chamber as a shielding gas and to top up the pressure to the desired pressure for the experiment.The chemical composition of the base material used, an S700MC low-alloyed high strength steel, was determined by optical emission spectroscopy. Table 26.As is usual for fine-grained structural steels, microalloys with the elements titanium, vanadium and niobium as well as a larger proportion of manganese are present. The microstructure of S700MC is very fine-grained, ferritic and has a pronounced rolling texture Fig. 26.Figure26. The displayed values are the mean values for the three specimens tested.Larger blocky titanium nitride precipitates (yellowish) are visible throughout the whole microstructure at larger magnifications. Tensile tests were carried out to determine the quasi-static mechanical properties of the test material Table . TherefoThe chemical composition of the weld filler metal was also determined by optical emission spectrometry on two-dimensional build-up welds with 4 layers and is shown in Table The welding tests were carried out following a statistical D-optimal design of experiments (DoE), as shown in Table The welding speed was set to 30 cm/min. The welding machine used was an EWM Alpha Q 551. The wire diameter was 1.2 mm, and argon was used as the shielding gas.17. These results are supported by findings from Xue et al. presented in27. The maximum welding depth is reached at high welding voltages and welding currents.The derived statistical model is shown in Fig. 28, a local maximum for the welding depth as a function of welding voltage and welding current, can be assumed and will form beyond the test area shown, Fig. These results and findings from studies with aluminum22 described in their work. This buried arc has also been seen by visual observation. A buried arc is a welding arc that can occur by welding currents above 500 A30. This arc burns beneath the surface of the wok piece .In this case, due to the increased ambient pressure, a more finger-shaped seam geometry in comparison to weld seams at lower pressures occurred, Fig. 17. To prove this, some single runs but welds of 15 mm sheets are presented in Sect. “16 that the molten pool flow is shaped to produce a deeper penetration should explain the effects described here and in the literature. Baba et al. showed that a buried arc can significantly reduce the number of welding passes required30. Due to the constriction of the arc by the increased pressure, the buried arc can also occur at currents below 500 A. The typical downward extended form of the buried arc already occurs at 350 A and a pressure of 8 bar, Fig. This arc also leads to a more finger-shaped bead geometry and can lead to the usage of smaller angles in weld seam preparation, which can lead to fewer layers that need to be welded for multirun welds5.Figure Due to the greater welding depth, shown in Fig. 33. For lower energy levels, a slight decrease in grain size can be anticipated, which is also a typical phenomenon for this type of filler. However, a quantitative determination of the grain size of weld seams remains inconclusive. Figure The weld seams show a microstructure of acicular ferrite typical for the filler metal used. In addition, grain boundary ferrite is formed for higher joining energy levels. The found microstructure is typical for this type of filler metal23. This is supported by the results. The differences in the microstructure due to different cooling show up for higher energy welding processes, Fig. Azar et al. predicted a change in the microstructure for higher welding energy and increased ambient pressureIn summary, it can be said that the microstructure is typical for this filler material and that there are hardly any quantifiable differences between the different energy levels and pressures.34.The results for the characterization of the microstructure coincide with those for the hardness of the welds. To determine the hardness of the weld metal, a microhardness series (HV0.2) was placed from the base metal into the weld metal, with 10 indentations made in the weld metal. The values given in Table 30. Furthermore, they have shown that the necessary weld preparation can also be reduced. The stabilisation of the buried arc at increased pressure shown in the previous chapters demonstrates the potential of the process for welding production, especially for use in the range below 500 A welding current.To demonstrate the positive effects of a hyperbaric process environment for welding fabrication, bars with a sheet thickness of 15 mm made of S700MC were joined with the aid of a backing plate also made of S700MC. To increase the possible weld depth beyond the welding depth selected in the statistical model, a Y-seam preparation with a 5 mm web and an opening angle of 40° was selected. 35 V and 500A (wire feed of 22.3 m/min) were set as process variables. Figure To determine the cooling times, type-K thermocouples were spotted at a distance of 3 mm to the left and right of the weld seam preparation when welding the butt joints. They were located in the middle of the weld length to be welded.8/5 times on the ambient pressure. Furthermore, the corresponding energy per unit length, which was determined from the average values of welding current and welding voltage, which were also recorded, is shown in Fig. 25, but their cooling times are 2 s relatively fast, which is typical for underwater applications. In this case, the shown cooling times are in the range between 10 and 20 s, which is typical for “normal” welding of this type of base material. An influence on the arc efficiency was also proposed by Farrell for hyperbaric TIG welding of duplex stainless steels in19.Figure The presented work shows that an increased ambient pressure can lead to a deeper and more finger-like weld seam characteristic, which can be used in a non-underwater environment for a reduced angle in weld seam preparation and a reduced layer count due to a deeper penetration of the weld. In addition, the dependency of the arc energy input due to the change in the arc efficiency has been shown, but this needs to be further investigated. The presented results are in line with the existing literature.The possible reduction of weld seams and reduction of the weld seam preparation have been exemplarily shown for a single seam joint of a 15 mm thick plate with a 40° Y-shaped weld seam preparation with 5 mm.Furthermore, statistical models were set up for the hardness and penetration as a function of the process values. The derived statistical model can help to identify the needed process values of the welding process, including the ambient pressure for a targeted welding depth per layer.By stabilising the buried arc through the increased ambient pressure, the number of welding layers required for thick-walled components can be significantly reduced. However, further development steps are necessary for the implementation of hyperbaric welding on thick-walled components. Nevertheless, it could also be shown that the pressures required to stabilise the buried arc are relatively low and that it should therefore be possible to implement a concept for locally increasing the pressure without a chamber.In addition to the presented investigations, the use of a mixture of 82% argon and 18% carbon dioxide as the shielding gas is planned. The determination of the influence of ambient pressure on toughness and other mechanical properties is also pending. In this case a detailed study of the HAZs microstructure is planned as well. Furthermore, the next large step is the development of an out-of-chamber hyperbaric welding torch to utilize the effects shown.


Humans are social animals and the interpersonal bonds formed between them are crucial for their development and well being in a society. These relationships are usually structured into several layers (Dunbar’s layers of friendship) depending on their significance in an individual’s life with closest friends and family being the most important ones taking major part of their time and communication effort. However, we have little idea how the initiation and termination of these relationships occurs across the lifespan. Mobile phones, in particular, have been used extensively to shed light on the different types of social interactions between individuals and to explore this, we analyse a national cellphone database to determine how and when changes in close relationships occur in the two genders. In general, membership of this inner circle of intimate relationships is extremely stable, at least over a three-year period. However, around 1–4% of alters change every year, with the rate of change being higher among 17-21 year olds than older adults. Young adult females terminate more of their opposite-gender relationships, while older males are more persistent in trying to maintain relationships in decline. These results emphasise the variability in relationship dynamics across age and gender, and remind us that individual differences play an important role in the structure of social networks. Overall, our study provides a holistic understanding of the dynamic nature of close relationships during different stages of human life. In this paper we examine the dynamics of relationship growth and decay with respect to ego age and gender using real world data from mobile phone records. We compare these turnovers with respect to different types of relationships. Analysing communication patterns through such mobile phone datasets is a valuable way to gain insight into the social networks of humans12 along with their behavioural patterns caused by seasonal and geographical changes13, daily rhythms16 and mobility patterns18 among others 21.Human societies are built up in part out of personal social networks. These networks, consisting of nodes as individuals and edges representing relationships between them, evolve through the addition and deletion of nodes, reflecting the formation of new bonds and the dissolution of old ones. We know relatively little about the rates of churn in networks and the dynamics of relationship change – despite the fact that these are fundamental aspects of social networks. Previous research has shed some light on the temporal evolution of relationships that are manifestations of different stages of human life 22. This fractal structure forms a series of layers in the network that reflect the emotional closeness of the individuals involved26. Studies on networks formed through social media, such as Facebook and Twitter have shown layered structures similar to offline face-to-face networks 27, and similar patterns have been reported from massive multiplayer online games 28. The numbers 1.5, 5, 15, 50, and 15029 cumulatively represent the number of intimate friendships, close friendships, best friends, good friends and generic friends, respectively. Note that we use the term ‘friends’ here in a generic sense to include extended family as well as more conventional friends. Friends may be lost or gained in each of these layers31. The size of egocentric networks has been observed to be higher for younger people and becomes more stable as they grow older32. The emotional closeness of an ego or focal individual with its alters  is inversely proportional to the number of alters in the layer, with the innermost layer of around 5 alters  consisting of the most intimate friendships and are the closest of all34. This layer commonly includes parents, siblings or close friends as well as romantic partners from the first layer and is the set of people the ego would rely on for advice, help and emotional support. The ego invests most of her or his social time in this group of alters (or close friends), with the ~ 5 members of this layer accounting for ~ 40% of ego’s total social time31.Human social networks have a layered structure, defined by the fractal structure of the Dunbar number7. This unique mobile phone dataset contains call detail records (CDRs) and demographic information of anonymized users that have remained loyal to the service provider for a period of three years35. Survey data based on questionnaires to study social networks provide a more realistic approach but are limited by the subjectivity of answers, bias in recollection and the number of individuals included for the studies. Mobile phone datasets have become a popular way of studying large-scale social networks36 and can provide a more objective view on communication patterns37. However they are inevitably limited by the fact that they ignore other modes of communication 38. The dataset used in the current study predates the popularity of smartphones as well as most social media and since mobile phone numbers are usually exchanged between trusted members of a community, we can reasonably assume that communications include a sub-network of known individuals. More importantly, perhaps, it has been demonstrated that online ties correlate with offline ties since friends tend to communicate with friends in proportion to their emotional closeness irrespective of the medium of communication38. While both approaches to studying social networks have their pros and cons, the ability to study large scale data helps us to gain insight into the network at a macroscopic level. Phone call datasets provide us with very large samples, thus enabling us to study significant events such as relationship formation and decay that normally occur at low frequencies and are hence difficult to study using more conventional self-report methods. It is important to be clear that our concern focuses on understanding the nature of the relationship itself, not the mode of communication whereby the relationship is maintained. A mobile phone calling dataset provides us with a convenient and rigorous way of assessing relationship quality simply because it requires two individuals to be willing to invest time in each other.In our study, we examine the closest relationships (“the support clique”) of the friendship network by studying their calling activities through mobile phone communication data of a particular service provider from a southern European countryMany studies of social networks focus either on the overall top-down properties of networks  or on the triadic structure of relationships. Our focus, however, is on the properties of individual ties, and for this a different bottom-up approach that focuses on the individual ties themselves rather than on the properties of sets of ties. Note also that we are not concerned here to evaluate the causes of relationship change. We are simply concerned to determine how frequently close relationships change, and whether this differs between the two genders and across major age cohorts among adults. Also we note that here, we are not concerned about changes in the relationships of under-17s due to not having enough data in this age cohort.In the present study, we aim to determine two things: first, how frequently do relationships within this core grouping change and, second, how does this vary with age and gender. Since phone calls involve a commitment of time, a high rate of calling activity between a pair of individuals can be reasonably assumed to indicate a close relationship between them, while a lower calling rate would indicate a weak tie. By the same token, we interpret a decline in calling rate as indicating the termination of the relationship. If the decline is sudden , we interpret this as indicating a falling out. If call frequency declines more gradually, it suggests a gradual loss of interest.In this study we have considered only those relationships between pairs of mobile phone users that form a close bond over a span of three years or which decay from a close bond (see Methods). A pair of service subscribers is considered to have a close bond between them if both of them are in the top five rank of each others’ social networks. By definition, these two users fall within the first two Dunbar layers of their respective networks. The ranks of users in all pairs have been calculated in their respective egocentric social networks and are grouped according to their gender (see the Methods section). The same-gender interactions are termed as female-female (FF) and male-male (MM) ego-alter friendships while the opposite-gender interactions are termed female-male (FM) and male-female (MF) ego-alter friendships, respectively.Figure We observe the same trend in Fig. In order to investigate the turnover in friendship networks we compared the calling activities between three different age groups: young adults (from 17 years to 21 years), adults (from 25 years to 35 years) and the middle-aged (from 45 to 55 years). The young adult cohort consists of individuals that go through major changes in their lives due to education or starting work, whereas the adult cohort spans the period associated with marriage and reproduction (note that divorce and re-marriage is less common in this Catholic country), and the middle-aged cohort identifies that period in life when the social focus is mainly centred around older children, grandchildren and the extended family. When the age difference between an ego and an alter is less than or equal to 10 years, then we deem it a “peer” interaction and if the difference is between 20 to 40 years of age then we deem it to be a “non-peer” interaction. The total number of pairs in each of the gender and age based groups are given in Supplementary Tables Overall, the low frequencies with which close relationships form and decay in a three year period is an indicator of how stable these inner core relationships typically are. Nonetheless, there are some significant patterns in these data. The percentage of relationship formation (3.1 ± 0.7%) in opposite gender friendships for the female egos belonging to the young adult cohorts is almost half of their corresponding decay percentage (6.1 ± 0.9%): in effect, they lose close opposite gender relationships over time without replacing them, although the rate at which this happens is low (roughly 1% of relationships per year). In contrast, the male egos in the young adult cohort have similar values for both formation (6.0 ± 1.4%) and decay (7.7 ± 1.5%) indicating that they make friends as easily as they lose them. A similar pattern is observed for female egos in the adult cohort as well for opposite gender relationships. The rates at which relationships form  in the adult cohort are almost half of their decay percentages . Thus, the male and female egos behave similarly in the adult group but behave differently in the young adult group.For same gender friendships, we observe that the formation and decay percentages balance each other out in the young adult group for both female  and male  egos, with rates of turnover that are higher than in the adult and middle-aged cohorts. We also observe that they have a distinctively higher decay than formation rates, particularly in the adult group with the percentages of the formation being  {7.3 ± 0.4%, 7.7 ± 0.5%} and the percentage of the decay being {11.1 ± 0.5%, 10.9 ± 0.6%} for female and male egos, respectively. For the middle-age cohorts the percentages of the relationship formation and decay for all gender groups are in balance with values of the formation being {6.6 ± 1.0%, 5.2 ± 1.0%, 4.1 ± 0.6%, 3.3 ± 0.5%} and the decay being  {5.9 ± 0.9%, 4.7 ± 1.0%, 3.1 ± 0.5%, 3.4 ± 0.5%} for FF, MM, FM, MF friendships, respectively, where the gender on the left is the ego and gender on the right is the alter. In other words, the turnover is high in the younger cohorts in all gender groups and becomes smaller as individuals age.When the users interact with their non-peers see Fig. , we obseTo explore these patterns in more detail in the adult cohort, Fig. Figure 40 In general, the females tend to have fewer and more close relationships than males as can be seen through the comparison of the ranks in Fig. 29.In this study we have analysed population level mobile phone communication data from a Southern European country to investigate relationship changes in different age cohorts. Firstly, in same gender relationships we observe that females tend to have higher ranks in female egocentric networks compared to males in male egocentric networks. This observation is consistent with known qualitative differences in same gender communicationSecondly, the overall low percentages for the formation/decay of relationships in the network as observed in Figs. 43. The same pattern is not observed in the case of young adult male egos. This could also be due to the fact that guardianship of the family over young adult females probably tends to be exercised over a longer period than young adult males in a society44.Thirdly, the percentages of the formation/decay of close relationships are higher in the young adult and adult cohorts than the middle-aged cohort, indicating more stable friendships in the latter. The young adults in this age group are usually more mobile, socially and economically, and this may be expected to impact on the stability of their relationships. We notice that, particularly for opposite gender friendships, the females have a lower rates of formation than decay, indicating that they may be more meticulous in choosing male friends45: in this population, the mean age at marriage at the time the phone data were sampled was 29 years, with first reproduction following soon afterwards46. Moving out of the parental home to establish an independent home life, combined with the arrival of children, marks a major social transition and upheaval48. We also observe that the percentages of the formation and decay eventually balance out in the middle-aged cohorts thus indicating that this age group has achieved some degree of stability in their social lives.Fourthly, marked differences in the percentages of relationship formation and decay in the case of 25-35 year old adults for opposite gender friendships for both the male and female egos likely reflects the impact of reproduction40 that comes into play when the mothers start taking an especial interest in their children’s lives when grandchildren come along49.Finally, we observe from Fig. Additionally, we observed differences in the calling activity patterns of the opposite-gender peer and non-peer interactions among the adult and middle-age cohorts during the formation and decay of relationships. Our analyses show that after a close bond is formed between opposite gender peers in the adult cohort, it is the females that systematically tend to call their male counterparts more often. However, when the middle-age cohorts interact with their peers, then the males call their female counterparts more for both the cases when relationships are being formed and when they are decaying. Since we do not expect these kinds of relationships to happen between siblings, this could probably represent romantic or platonic friendships. In addition, we observe a distinctly higher call rate by the adult cohorts to their older counterparts when relationships are being formed. The formation of relationships in this group may be indicative of those being formed between an individual and their in-laws might reflect the fact that the adult cohort makes more effort in trying to establish a relationship with their older counterparts since younger age groups are generally more active technologically than older age groups.25. While longitudinal studies using questionnaire-based data through Add Health50 can portray the different types of relationships actually involved, we believe that our cohort-based study using mobile phone data provides a complementary approach that gives us insights objectively about the gender preferences and their churn on a scale that allows rare but significant events to be examined.Since, all the users are from the same service provider that had a 20% market share in the country, this dataset may only capture a proportion of the true support clique. However, irrespective of whether this is the case, our focus is on the set of closest relationships that an ego has rather than a specific number. Precisely how we define this subset is less important than that our sample represents a small inner core of an individual’s total network. That said, any tendency for friends and family to gravitate towards the same service provider as a result of special deals or local availability, etc., will increase the likelihood that we capture their actual network 5-layer. Indeed, analyses of the structure of complete egocentric networks from this dataset without making any presumptions about structure yield a layer of exactly this size27.In conclusion, our analyses of mobile phone data for a large population establish that the inner core Dunbar layer of friendships are stable with very few relationships moving out or entering this core. In our analyses, we find that during the younger stages of adult life people mostly lose friends, with young adults having slightly more unstable friendships as compared to older adults. However, friendships seem to become more stable during middle-age after people settle down with families and stable careers. Additionally, young females tend to be picky while choosing male friends, but after they form a close bond, they tend to be slightly more active in maintaining those relationships. Even though modes of communication through social media platforms have become more prevalent nowadays, we expect churn to occur in the latter networks irrespective of the technology used to service them. This is expected as the layered structures in friendship networks are based on emotions rather than memoryThe dataset used in this study was obtained from a southern European service provider consisting of mobile phone call detail records (CDRs) of the service users for a period of three years from January, 2007 to December, 2009. The call details of the users were anonymised by the service provider by attaching a unique identifier for each of the users such that the privacy of the customer remains protected and cannot be traced back to the individuals themselves. The CDRs include a list of the caller and callee with time stamps and dates. The call durations for each call are provided only for the year 2007. The users’ age and gender along with the postal code are also listed in the dataset. A total of 644,170 remained loyal to the service provider for all three years with a total of 1,143,718 unique links between them. The users that appear or disappear in the dataset within this time period of three years were not considered to avoid confusions over ties that might already have been present in an ego’s network but through a different service provider.n in the superscript represents any rank lower than 5) then we consider only the changes in ranks of the relationships as shown by arrows in a schematic diagram in Fig. The rank of a user in another user’s network has been calculated in the following way. We consider all the alters that the user has called or received calls from on a monthly basis and have ordered them according to the total number of Incoming/Outgoing calls in a decreasing order. Alters having the highest number are ranked one and so on. The ranks are then calculated for each of the 36 months being considered and then averaged on a yearly basis. Next, we consider only those links with users having top ranks between 1 to 5 mutually in each others networks in the year 2007 and 2009. This is because we intend to study formation or decay of very close relationships that form the ”support clique” of the Dunbar layers. Thus, any pair that starts off with a mutual top 5 rank and then decays over the span of three years to lower ranks and any pair that starts of at any rank but ultimately ends with top 5 rank in the year 2009 have been considered. In such a scenario, if we represent all pairs having top 5 ranks as N and R represents the total number of calls and the numerical value of the yearly averaged ranks of the pairs of users in each others egocentric networks respectively. Similarly, a decay in relationship is defined to occur if calling activity decreases and yearly averaged rank gets demoted each year.Next, we define a formation of a close relationship to occur between two pairs of users if the calling activity between them increases and yearly averaged ranks for both the users gets elevated each year:This filtering of the data leads to 8,637 pairs of users who end up forming a close relationship in 2009 and 10,310 pairs whose relationship decayed over three years. The age distribution of alters of the egos considered in the three different categories  are shown in Supplementary Figs. Supplementary Information.


The lack of understanding about the brain's reaction processes in perceiving touch and separation between skin and object surfaces is a barrier to the development of existing brain‐computer interface technologies and virtual haptics. These technologies are limited in their ability to advance. It leaves prosthesis users with a limited amount of tactile information that they can feel. This study aims to determine whether distinct surface aspects of various items trigger different reactions from the brain when friction is removed from the surface.When friction is suddenly removed from the surface of an item, a technique called event‐related potential, (ERP) is used to study the features of people's EEGs. It is done after the subject has actively explored the object's surface. A 64‐channels EEG collecting system was utilized to acquire EEG data from the individuals.  The event‐related potentials for friction removal were generated using the Oddball paradigm, and the samples consisted of sandpaper with three distinct degrees of roughness. We utilized a total of 20 participants, 10 of whom were male, and 10 of whom were female, with a mean age of 21 years.It was discovered that the P3 component of event‐related potentials, which is essential for cognition, was noticeably absent in the friction withdrawal response for various roughnesses. It was the case regardless of whether the surface was smooth or rough. Moreover, there was no statistically significant difference between the P1 andP2 components, which suggests that the brain could not recognize the surface properties of objects with varying roughness as the friction withdrawal was being performed.It has been demonstrated that tactile recognition does not occur after friction withdrawal. The findings of this paper could have significant repercussions for future research involving the study of haptic perception and brain‐computer interaction in prosthetic hands. It is a step toward future research on the mechanisms underlying human tactile perception, so think of it as preparation. The tip of the finger skin has no less than 600 nerve fibers per square millimeter, making the finger very sensitive to sensory objects.Losing function in one or both hands can result in significant physical infirmity and even mental disability. Each year, thousands of people suffer the consequences of upper‐limb paralysis or of amputation caused by a traumatic event or disease.When does the perception happen? Some people think it happens when a finger is pressed on the object. While others might think it happens after touch.Traditional neuromolecular biology and neurobiochemistry are not enough to observe the brain's response when the sense of touch occurs. The reason is that we cannot think of a change in people's thinking simply as a change in a particular part of the brain tissue, cells, or neurotransmitters. In 1965, Sutton proposed the concept of event‐related potentials (ERPs), recording brain‐evoked potentials on the surface of the skull using average superposition technology to reflect the neurophysiological changes of the brain during the cognitive process.The direct research on the relationship between surface characteristics and electroencephalogram is still in its infancy. Through a series of experiments, Chen et al.22.1The experimental samples A, B, and C are three kinds of sandpaper with different roughness: 600 mesh; 1000 mesh; 3000 mesh. The sample is cut to a size of 30 mm × 30 mm.2.2The sample is placed in a self‐made tactile stimulation presentation device (Patent Authorization No. CN 108664903 A), as shown in Figure When each sample is transposed to the specified starting point, a trigger signal is sent to the Electrical Geodesics Incorporated (EGI) 64‐channel ERP acquisition system  via synchronizer (DB9 Pinout).The subjects are 21‐year‐old undergraduates or postgraduates, 10 males and 10 females, all right‐handed, and have no hand injury.The classical experimental mode in ERP experiments include Go/Nogo,2.3The experimental data are analyzed offline. The steps are as follows: merge behavior data and EEG preview; removal of electrooculogram artifacts; subparagraph; baseline correction; removal of artifacts; superposition averaging.In data processing, a bandwidth of 0.05–30 Hz was selected to remove the electrooculogram artifacts. EEG recorded the whole experimental process. The main components of ERPs were analyzed in this paper, including P1, P2 and P3. P1 is a significant positive component with an incubation period of about 100 ms. It usually appears on the occipital side of the temporo‐occipital area and is affected by attention, reflecting the characteristics of physical stimulation. P2 is a significant positive component with a latency of about 200 ms, which reflects task‐related processing. P3 has been studied most for its correlation with cognition. Its families include P3a and P3b, and the incubation period is about 300 ms or even longer.33.1In the ERP topography shown in Figure 3.2According to the ERP topographic map, it can be seen that the ERPs induced by the removal of friction are concentrated in the frontal, parietal, and temporal lobes. So, the electrode from the frontal lobe to the parietal lobe was selected, and the typical ERP waveform was observed, as shown in Figure In practical experience, people often think that cognition occurs after the fingers leave the object's surface. It is more likely due to the cognitive process through memory rather than the cognitive process directly induced by the surface features of objects. Hopfingera et al.It should be noted that the previous tactile ERP experiments provide evidence that both nontarget and target stimuli could produce P3 components.Figure Current brain‐computer interface technology cannot make the brain recognize the feeling of separation between the prosthetic limb and the object surface. If we want to express the surface features of virtual objects and use bionic skin to “read” the surface texture, the key lies in understanding the tactile perception mechanism of the human brain. Human fingertips rely on a large number of tactile sensors to receive surface texture information. In contrast, tactile sensors such as Pacinian Corpuscle are sensitive to vibration signals, which shows that mechanical vibration signals are important for perceiving and distinguishing textures.4This paper only studies the withdrawal of friction in passive touch. Contrary to passive tactile sense, active tactile sense allows volunteers to produce psychological expectations before exploring the surface characteristics of objects. This distinction has a great influence on some typical components of ERPs and should be discussed.This paper is just an experiment on the dimension of the tactile texture. For volunteers, roughness tests are relatively simple. If you switch to other slightly harder dimensions, it is also worth verifying if the cognitive process continues until the finger leaves the sample.This paper only shows that there is no significant correlation between the withdrawal friction and the cognition through EEG analysis. It is also worth discussing whether the cognitive process of humans will continue to form memories and feedback to P3 for cognition after the withdrawal of friction and complete the complete whole process of cognition.In this paper, ERPs evoked by the friction withdrawal when exploring the object's surface are studied and analyzed to find out whether the cognition will still occur after the withdrawal of friction. It turns out that P3 of the ERPs were missing when the friction was withdrawn. As a result, it can be considered that the cognitive process doesn’t occur. This paper is of great significance to studying haptic representation and perception of humanoid robots, which contributes to the further study of haptic perception mechanisms in the future.The authors declare no conflict of interest.The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Xuzhou Central Hospital . Informed consent was obtained from all subjects involved in the study.


Medical Adhesive Related Skin Injuries can arise from topically applied medical devices, especially in those with fragile skin, including the elderly and premature infants. The purpose of this study was to compare gentleness and reapplication of two pulse oximetry sensors .Eighteen healthy subjects aged 65 years and older were enrolled in the gentleness trial, and 20 healthy subjects (18–69 years) were enrolled in the reapplication trial. For the gentleness trial, trans‐epidermal water loss (TEWL) measurements were made at five sites on each forearm at three time points . Total amount of protein adhered to each device was also determined. For the reapplication trial, a series of 180° peel tests were performed to observe the forces required to detach the sensor from the skin.p < 0.05). Both were significantly greater than control (p < 0.05). Further, protein analysis revealed that the amount of protein removed was significantly less with OxySoftN compared to MaxN (p < < 0.0001). Differences in loss of adhesion of the tail region between the two sensors were demonstrated, with OxySoftN depreciating at a much slower rate compared with MaxN.TEWL rates in the tail region were significantly greater with MaxN compared to OxySoftN at T1 (The OxySoftN sensor appears to be gentle, even on fragile skin, based on reduced strain on the skin during removal. Further, it demonstrated the ability to withstand several reapplications without functional loss in adhesion. Elderly and premature infants are at particular risk for skin injuries based on their fragile skin.Pulse oximetry is a standard of care that is routinely used in the NICU on premature infants.Measurement of gentleness of an adhesive is based on measuring the disruption of the skin barrier. A consensus group of key opinion leaders recommends both trans‐epidermal water loss (TEWL) and the amount of protein removed as highly objective measures to clinically assess and document MARSI.The purpose of the current study was to compare the gentleness and reapplication of two pulse oximetry sensors, OxySoftN and MaxN . OxySoftN uses a new silicone‐based skin‐contacting adhesive, whereas MaxN uses an acrylic adhesive in the optical region and synthetic rubber adhesive in the tail region. The ability to reapply the sensor is characterized through peel force measurements on skin. The gentleness is characterized through TEWL and protein removal. Significant benefits were observed with the silicone‐based patient‐side adhesive.2Two studies comparing different attributes of OxySoftN and MaxN in a pair‐wise fashion were carried out on healthy volunteers and performed in accordance with the Declaration of Helsinki under protocols that were duly approved by an independent review board .All volunteers were fully informed about the purpose of the study in which they were participating and the associated instrumental methods. Upon signing an informed consent document, they were instructed to stop the use of all potentially moisturizing products  on their arms for the 3 days prior to the study day. Upon arrival at the clinic, any hair within the test sites, which could interfere with the procedures, was removed using a gentle surgical prep clipper. Both volar forearms were then wiped with an alcohol swab and allowed to dry for at least 2 min before mapping out the test sites using a nontoxic marker.2.1Eighteen (18) healthy subjects  were enrolled in the gentleness study. All were aged 65 years or older, with the assumption being that they might be more likely to have more fragile skin than those below age 65 years.2.2The test products for both studies were provided by Medtronic . Both devices are pulse oximetry sensors that are very similar in design, consisting of an optical region that houses the sensor and an associated fabric tail, as shown in Figure 2.3This was a 1‐day study in which subjects remained at the clinic for approximately 10 h.TEWL measurements were made using a recently calibrated cyberDERM RG1 Evaporimeter System  with dual TEWL Probes that were manufactured by Cortex Technology  and available in the US through cyberDERM, Inc. . All the measurements were performed in an environmentally controlled room with temperature at 20–22°C and relative humidity at 40%–60%. Measurements were taken after a 30‐min acclimatization period, with the test areas on the volar forearm fully exposed. The TEWL rate was calculated as the average value over a 20 s period after steady‐state conditions had been reached.Measurements were made on the five (5) sites that have been mapped out on each arm as labeled in Figure T0: Baseline (prior to the first application)T1: After 4 h of wear (1st application of devices)T2: After 4 h of wear (2nd application of devices)TEWL measurements were taken at the following time points:tm Protein Test.At T1, after the devices had been worn for approximately 4 h, the devices were manually removed and retained for reapplication for the second wear period. After the 30‐min post‐removal TEWL measurements were completed, the same devices were reapplied to the same sites as at the baseline visit. Approximately 25% of the MaxN sensors showed some signs of lifting while this never was a problem with the OxySoftN sensors. In a few cases, there were concerns that the MaxN device may become detached completely, and these sensors were replaced during the second wear period. At T2, the devices were again manually removed and then sent to Nelson Labs , where the total amount of protein adhered to each device was determined using the Micro BCA2.4This was a 1‐day study in which the subjects remained at the clinic for approximately 2 h.The computerized cyberDERM 180° peel tester Figure  is desigThe OxySoftN and MaxN devices were run in a pairwise fashion, with one being assigned to the lateral side and the other to the medial side of the forearm according to the randomized treatment map, with the optical sensor region always positioned toward the wrist as shown in Figure A nontoxic marker was used to place fiducial marks on the subject's skin to ensure the device was placed parallel to the major axis of the forearm. The subject's arm was then positioned within the Peel Test Apparatus such that the test site surface was in plane with the horizontal path that the clamp would take during the run. A micro projector attached to the apparatus overlaid an image of the peel traverse to ensure proper alignment. Extreme care was also taken to have the subject always have his fingers properly placed and arm fully extended so that the tension across the skin surface remained reasonably constant during the entire series of peels for that individual.One caveat with the 180° peel test is that observed forces arise not only from detaching the adhesive device from the skin but also from the device bending back upon itself. Since both test devices consist of a flexible lead strip and a stiffer section with electrodes, the observed plot, as shown in Figure As the adhesive under the optical region is the same as the adhesive on the tail for both devices, the increase in values seen in the optical region is not an increase in adhesion but rather an increase in force of removal due to the work required to bend the stiff components. Nevertheless, we do want to analyze this portion of the peel test because the effectiveness of the device depends upon the optical sensors being firmly attached to the skin. Thus, in addition to confirming visually that the sensor is still firmly attached to the skin, we also determined the work required to remove the optical region by using the Riemann left sum as an approximation of the area under the curve within the optical region.2.5p‐value of <0.05 was considered statistically significant in all tests. Continuous variables are presented as mean ± standard deviation, unless otherwise noted.The data were processed using GraphPad statistical software. A two‐sided For the TEWL results, the appropriate values were pooled by individual subject to provide a mean value for the optical region and tail regions of the two sensors and the corresponding control for each time point. A repeated measure ANOVA with Tukey‐Kramer Multiple Comparisons Test was then run on the net change from baseline to determine the degree of significance at T1 and T2.t‐test was employed. A value of 1.1 gm was given to those samples falling below the detection limits of the Micro BCAtm assay.For protein adhered to each device, a paired For the Peel Test, the appropriate values for the optical and tail regions were calculated from profiles obtained from both devices for each of the 18 serial cycles of removal and reattachment from each subject. These were then compiled, and the mean values plotted as a function of cycle number. The resulting trendlines, which show the dynamics of decreasing adhesion with increased number of reapplications, were analyzed for goodness of fit using the MS Excel toolbox.33.1Mean TEWL rates are summarized in Figure In comparing results from the optical region of the OxySoftN and MaxN sensors, it was found that at both T1 and T2, TEWL rates were significantly elevated with both devices compared to the control sites. However, no significant differences were found to exist between the two sensors at either time point.p < 0.05). The TEWL rates were also significantly greater with the MaxN sensor compared to OxySoftN at T1 (p < 0.05).In comparing the results from the tail region of the OxySoftN and MaxN sensors, it was found that at T1, TEWL rates were significantly elevated for both the OxySoftN and MaxN sensors when compared to the control (p < 0.01). TEWL rates were not significantly increased for the OxySoftN sensors when compared to control (p > 0.05).In comparing the results obtained at T2 from the tail region, it was found that TEWL rates were significantly elevated only for the MaxN sensors when compared to the control (p < < 0.0001), as shown in Figure Further, the amount of protein removed was significantly less with the OxySoftN sensor compared to the MaxN sensor (1.3 ± 0.4 ug/ml vs. 10.1 ± 2.9 μg/ml, 3.2Successful completion of all 18 reapplication trials was seen with the OxySoftN sensor. However, in 60% of cases, the MaxN sensor lost adhesion prior to the 18th trial. To gain a better insight into this observation, we compiled the calculated values for the 18 sequential runs sorted by code for the 20 individuals who participated in this study. The group averages for each device obtained from the tail and optical regions were then plotted as a function of the sequential run number Figures . The resIt should be appreciated that interpreting the data obtained from the optical region is complicated, as factors other than the adhesion can impact the work required to remove the device from the skin. Chief among these is that this region contains the sensors, causing it to be stiffer, and thus more work is required to bend the region back on itself to achieve the 180° pull. Nevertheless, we feel that it is the loss of adhesion that is primarily responsible for the observed behavior.4The silicone‐based adhesive of the OxySoftN sensor does not appear to disrupt the skin barrier function, as evidenced by the TEWL results. Although TEWL rates were significantly elevated in those sites to which either the OxySoftN or MaxN sensors were attached compared to the intact skin of the control sites, it is important to understand that the degree of change, although statistically significant, is clinically very modest and iin‐line with previous findings.The protein removal results also demonstrated that the OxySoftN sensor removes much less skin than the MaxN. The amount of protein removal was significantly different, and the real difference is probably even larger, as the OxySoftN fell below the detection limit of the test. Lower dilution ratios than those used here would increase the concentration of protein in the assay, potentially allowing for a lower detection limit of protein on future adhesives. The results from the protein assay demonstrate that the OxySoftN adhesive removed less skin than the MaxN sensor, corroborating the difference in TEWL seen between the two devices in the sensor region.Peel force testing demonstrated that the adhesive on the OxySoftN sensor far outlasts the MaxN sensor. The OxySoftN adhesive was able to reposition up to 18 times, whereas the MaxN lost adhesion more quickly. The decay rate of adhesion can be estimated through the fitted parameters. The adhesion of OxySoftN decayed by 1.1% per reposition for both the tail and optical region. The typical lifetime of an adhesive is 1–3 days. Assuming the sensor is removed and placed at a new site every 4 h, 18 reapplications would not be uncommon. Here, OxySoftN retained adhesion over 18 removals and, on average, decreased by approximately 20%. This would also suggest that limit for re‐adhering the OxySoftN sensor was not achieved, even at 18 repositions.The reapplication testing was completed in rapid succession and therefore did not have a 4‐h dwell time, as would be expected in the NICU. However, increasing the dwell time would likely not impact the results for OxySoftN but increase the decay rate for the MaxN. Traditional adhesives, such as the MaxN, exhibit increased peel forces and reduced reapplication with longer dwell times as the adhesive can flow and fully wet the skin.Skin‐contacting adhesives form physical bonds with the skin, which requires the adhesive to be in close contact with the skin.4.1The study was designed as an initial investigation into the benefits of a new silicone‐based patient‐side adhesive for pulse oximetry sensors. Gentleness of the adhesive was assessed on an elderly population with the intent to target fragile skin. However, no screening was performed on subjects to characterize fragile skin, and no age restrictions were used for the reapplication study. Further, pediatric patients were not included in the study population. The protein assay results for the silicone‐based adhesive were at the detection limit. Hence, the expected amount of protein removed by the silicone‐based adhesive would be lower than what was measured.The 180° peel test was used to assess reapplication. This testing assumes that stiffness is the same throughout the device backing. Although the stiffness between the optical and tail regions did differ, as seen in the bimodal peel profile, the adhesive coverage was uniform across the respective devices. Further, the decay curves for the tail and optical regions demonstrated the same dynamics.The sensor dwell time during the gentleness study was 4 h, timing that may be seen in the clinical setting. However, due to the time restraints of completing 18 reapplication trials, the dwell time of the sensors during this testing was only a few minutes. Clear differences between the two sensors were seen at six reapplications, demonstrating the superiority of the OxySoftN to MaxN in terms of reapplications.5Silicone adhesive can be a good solution for strong, yet gentle adhesives that are repositionable. The current study demonstrates that the OxySoftN sensor made of silicone had greater repositionability and less skin removal compared to the traditional MaxN sensor with acrylic and synthetic rubber adhesives. TEWL shows that there was not damage to the skin barrier function during removal of the device. Protein removal results show that they are more sensitive to changes in adhesives than TEWL and that OxySoftN removes a significantly less amount of skin compared to MaxN. Reapplication results show that the decay curves of the adhesives are markedly different and that the OxySoftN can withstand 18 repositions to skin, potentially more. These three metrics combined show that the new adhesive is gentler, as well as having the ability to stay adhered to the patient for a larger number of repositions.Jacob Dove and Derek Moody are employees of Medtronic. Other authors have no conflict of interest.


Cornu aspersum, using the appetitive Pavlovian Conditioning procedure of tentacle lowering. Snails experienced an odorous conditioned stimulus (CS) without any consequence before being conditioned with food. The conditioned stimulus preexposure occurred in the same context than the conditioning and the test context or in the different context. The study was performed in two replicas in which the photoperiod was defined by level of illumination and time of day (circadian replica) or was defined only by light (light replica). Both replicas showed that the CS preexposure in the same context as conditioning produced a delay in the acquisition of the conditioned response (CR). However, when the CS preexposure took place in a different context than the conditioning context, an equivalent level of CR as that observed in controls without preexposition to CS was shown. These results are congruent with context specificity of LI and they provide the first evidence of this phenomenon in terrestrial mollusks. Learning processes and theories involved in this phenomenon are also debated in the paper.The present study was conducted to assess the context specificity of latent inhibition (LI) in the snail  In appetitive Pavlovian Conditioning, a CS becomes associated with the taste and/or the nutritive properties of an Unconditioned Stimulus (US), usually food. It is possible to modulate the strength of this association by manipulating the variables effective in standard conditioning paradigms. One of the most relevant variables is the experience with the CS previous to conditioning. When the conditioned stimulus (CS) is repeatedly exposed to a neutral stimulus subsequent conditioning is retarded when that stimulus is used as a CS  and light (the light replica). In both replicas, it was expected that subjects which were preexposed to the CS1 in the same context as conditioning would show a delay in the conditioning acquisition, congruent with LI. In addition, subjects preexposed to the CS1 in a different context than the conditioning context were expected to show the acquisition of the CS-US association, congruent with context specificity of LI.The present study was designed to determine the context specificity of LI in snail Cornu aspersum, which were collected from the wild in a garden from the small town of Noreña (Asturias). They lived grouped among the garden stones and their food was the green leaves of the ferns present in their habitat. They were manually collected from their habitat and taken directly to the laboratory, where they were maintained and prepared for each experimental replica.The subjects used in this study were the common snails 52 adult snails were employed, with a mean shell diameter of 25.94 mm (range 20–32 mm) for the circadian replica, whereas 53 adult snails with a mean shell diameter of 29.06 mm (range 22–33 mm) were used for the light replica. Snails were individually housed in plastic cages (50 × 50 × 100 mm) with air holes. The house boxes were placed in a room with a constant temperature of 22 ºC and a reversed 12 light/dark cycle, starting at 06:00 am. They were given access to a small amount of water and ad libitum food, which was composed of corn grains for poultry, and prior to the start of the experiment, they were food-deprived for 10 days. At the end of the experiment, snails were given food ad libitum (corn grains) and placed back into the wild, but in a different garden, 50 km away from the place where they were collected to avoid their recapture.La Casa de Los Aromas, 2 ml/L of distilled water) were used as the CSs, and carrot was used as the US. The pieces of carrot had a mean diameter of 27 mm (range 22–29 mm) and were 1 mm thick.The experimental set was a plastic perforated surface  placed 65 mm above the surface of a table and the experimental room was maintained at 22 ºC. The context stimuli were two types of lights to reproduce the light/dark context in both replicates of the experiment. A white light (LED 5.5 W) was used as the light context, whereas a red light (LED 3 W) was used as the dark context, given that prior research established the snail’s spectral sensitivity range in 390–580 nm Barker , which iIn this experiment, tentacle lowering was measured as the CR by one observer, who was not aware of the group to which each subject belonged. This measure consisted of counting in real time the number of times the left tentacle descended below an imaginary line, drawn horizontally just above the head of the snail Ungless , 2001.same context-preexposure group were preexposed to the odour used during conditioning (CS1) in the same context as the conditioning and the tests. On the contrary, subjects from different context-preexposure group were preexposed to the CS1, but in a different context than the conditioning and the tests. From same context-no preexposure group, subjects were preexposed to a different odour from that used during conditioning (CS2) in the same context as the conditioning and the tests. Finally, subjects from different context-no preexposure group were preexposed to the CS2 in the different context. The role of the odours was not counterbalanced, so the odour of mango was used as CS1 and the odour of coconut was used as CS2.Snails were randomly divided into four groups based on the context in which subjects received the preexposure and the kind of stimulus presented during the preexposure. Subjects from i.Pre-Test1 or CS2 for 2 min. This odour was placed below the perforated surface in a dish containing four cotton pads and each one was impregnated with 2 ml of the solution.In this phase, the tentacle lowering response was measured for each subject individually. Based on the group to which they belonged, snails were exposed to CSii.Preexposure1 or CS2) was presented for 2 min. The odour was placed in the same way as pre-test and 6 trials were made during the day with an intertrial interval (ITI) of 58 min.During the preexposure phase, the odour  for 2 min. A piece of carrot was placed in front of snail whereas the odour was placed in the same way as in the previous phases. 3 trials were performed during the day with an ITI of 58 min . Data management and analysis was performed using SPSS v21 .The number of times that subjects lowered the left tentacle during the pre-tests and tests was measured. One-way analysis of variance was used in the pre-test analysis. Also, the repeated-measures ANOVAs were carried out to observe if there were any differences in the counterbalanced training context and to analyse the main results shown in Fig. same context-preexposure. This group presented a lower conditioning level than the rest of the groups in the first two tests, this difference being higher in test 2. Nevertheless, CR for the same context-preexposure group increased during test 3, showing an equivalent level of conditioning to the other groups. This description was corroborated by the statistical analyses. The same effect was observed in Panel B, but during test 1 the CR of the of same context-no preexpsoure group was higher than the CR for the rest of the groups. However, this difference was not significant as the statistical analyses show.Figure 1 preexposure or CS2 preexposure) and the training context (if the conditioning and the tests were performed in the light or in the dark context) as the between-subjects factors.The first set of analyses examined the effect of context counterbalancing to see if there were any significant differences in the CR between-subjects which received the conditioning and the tests in the light context and the subjects which received the conditioning and the tests in the dark context. A repeated-measures ANOVA was carried out with the pre-test and the tests as the within-subjects factor and the preexposure context (if the preexposure was made in the same context as conditioning or in the different context), the stimulus preexposure  and the context of the preexposure phase (light or dark context). According to Fig. The results for both experimental replicas suggested that subjects showed different CR levels depending on the stimulus preexposed CS1 or CS and the P = 0.026; light replica test1 MDS 3.96 ± 0.35, test2 MDS 5.26 ± 0.28, P < 0.001) and test 1 and test 3 . Nevertheless, there were no significant differences between test 2 and test 3 .In both replicas, it was shown that there existed significant differences between test 1 and test 2  in which the tentacle-lowering mean of same context—preexposure group was lower than the tentacle-lowering mean of same context- no preexposure group. These results indicated that there were not equivalent CR in the same context-preexposure groups with respect to those that received another CS and it is congruent with the LI effect.In test 2, both replicas showed a significant effect of the interaction between the preexposure context and the stimulus preexposure. This interaction was analysed with the Bonferroni pairwise comparisons. It showed significant differences between same context-preexposure group and different context-preexposure group , so the results suggest that there was an effect of the context involved in the LI performance, which is congruent with context specificity of LI.In addition, there were significant differences in the tentacle-lowering mean between P = 0.536; light replica P = 0.779) between the tentacle-lowering mean of the different context—preexposure group and the mean of the different context—no preexposure group . These results indicate that there were equivalent conditioning levels in the different context-preexposure groups with respect to those that received another CS.However, the analyses did not show significant differences  between the same context-no preexposure and different context- no preexposure . These results indicate that there were equivalent conditioning levels in both control groups.Also, there were no significant differences . This effect takes place regardless of the replica. Therefore, both context cues (the photoperiod or the light) were equally effective.According to Fig. Cornu aspersum, using the Pavlovian Conditioning of tentacle lowering procedure. The experiment presented here had two replicas: the circadian replica, in which the photoperiod (determined by the hour of the day and the illumination) was used as context; and the light replica, in which only the light was used as context. The second one (the light replica) was performed to reproduce the results observed in the circadian replica and simplify the procedure.The purpose of the current study was to determine the context specificity of LI in the snail 1 in the same context as the conditioning and the tests showed the lowest CR mean during the tests 1 and 2. Nevertheless, in test 3, these subjects reached an equivalent conditioning level as the rest of the groups. These results showed a delay of the conditioning acquisition which can be considered an instance of LI phenomenon. The second major finding was that subjects which were preexposed to the CS1 in a different context than the conditioning and the tests showed an equivalent level of CR throughout the three tests as subjects which were preexposed to the CS2. In addition, during the first two tests, these subjects presented a higher CR level than subjects which were preexposed to the CS1 in the same context as the conditioning and the tests. These results support the idea that a context change during the preexposure of the CS1 affects LI. The present study is, therefore, the first attested evidence about context specificity of LI in terrestrial mollusks and can be added to the only study we are aware of that found contextual specificity of LI in an invertebrate species . Thus, to understand complex learning phenomena as LI, it is necessary to broaden the range of both learning procedures and subject species.


Existing studies of the Indonesian Throughflow have focused on the Makassar Strait and the exit straits, where the upper thermocline currents carry North Pacific waters to the Indian Ocean. Here we show, using mooring observations, that a previous unknown intermediate western boundary current (with the core at ~1000 m depth) exists in the Maluku Sea, which transports intermediate waters (primarily the Antarctic Intermediate Water) from the Pacific into the Seram-Banda Seas through the Lifamatola Passage above the bottom overflow. Our results suggest the importance of the western boundary current in global ocean intermediate circulation and overturn. We anticipate that our study is the beginning of more extensive investigations of the intermediate circulation of the Indo-Pacific ocean in global overturn, which shall improve our understanding of ocean heat and CO Here the authors use in situ mooring data in the Maluku Channel and the Lifamatola Passage of the Indonesian seas to show that a western boundary current transports Antarctic water from the South Pacific to the Indian Ocean. The salinity minimum is just the upper boundary of the AAIW layer. The layer down to ~27.5 σθ is generally identified as the AAIW, which covers thickness of several hundred meters10. Considering the strong mixing at the entrance of the Indonesian seas12, the part of the water masses below the North Pacific Intermediate Water 13 and above the top of the Upper Circumpolar Deep Water 14 is suggested to be dominated by the AAIW movement.Observations have shown that the Antarctic Intermediate Water (AAIW) generated in the Sub-Antarctic Front zone of the Southern Ocean is transported into the equatorial western Pacific by the New Guinea Coastal Undercurrent (NGCUC)nd5 Fig. . The AAI17. Due to the threshold depth as shallow as 680 m within the Makassar Strait, the AAIW in the western Pacific is not connected with the Indian Ocean circulation below this threshold through the Makassar Strait. In the eastern Indonesian seas, the sill depths of the Maluku Sea and Lifamatola Passage are as deep as 2000 m. Sparse historical hydrography measurements have shown a salinity minimum larger than that of the NPIW at about 300 m depths in the Maluku Channel, suggesting the intrusion of the lower part of the South Pacific Tropical Water  into the Indonesian seas15. A salinity minimum layer at 600–700 m depths in the northern Maluku Sea, with the salinity minimum as high as 34.58 psu, might suggest the presence of AAIW mixed with the surrounding waters18. However, the path of the AAIW intrusion into the Indonesian seas has never been disclosed.Existing studies of the Indonesian Throughflow (ITF) have focused on the currents in the Makassar Strait and the exit straitsIn this work, we identify a previous unknown intermediate western boundary current (WBC) in the Maluku Sea and through the Lifamatola Passage, using in situ mooring observations. The current carries the AAIW from the western equatorial Pacific into the Seram-Banda Seas between 450 m and 1800 m, and contributes about 1.36 Sv to the total ITF.Maluku Channel mooring observations. Since 2014, a major Western Pacific Ocean Circulation-Indonesian Throughflow mooring array has been constructed by the Institute of Oceanology of the Chinese Academy of Sciences in the western Pacific and the Indonesian seas to measure the transports and water mass properties of the ITF, especially in the eastern route, in connection with the western Pacific and Indian Ocean circulation. The moorings inside the Indonesian seas were deployed and maintained using the Indonesian R/V Baruna Jaya VIII, in collaboration with the Research Center for Oceanography of Indonesian Institute of Sciences [now the National Research and Innovation Agency (BRIN)]. Three moorings  were maintained in the Maluku Channel during November 2014 through November 2016  was deployed in a water depth of 2100 m, located upstream of the historical mooring site of van AkenThe Maluku channel and the LF moorings form a set of synchronous current measurements at both of the entrance and exit of the southern Maluku Sea, which allow for examination of intermediate depth circulation and connections. In addition, three moorings were deployed in the Talaud-Halmahera Channel  during December 2016 and September 2018 to measure the exchange between the Maluku Sea and the western Pacific Ocean. Each mooring was equipped with an upward-looking ADCP at 450 m and a few RCMs at 250 m, 750 m, 1000 m, and 1800 m. Unfortunately, the lower part of the TH3 mooring was not recovered in the November 2017 cruise. Data of new deployment at the same site after October 2018 are used in the following analysis instead.−1 at the core depth of 1000 m. This intermediate flow along the western boundary has been indicated by historically current measurements at 750 m and 1250 m at a location very close to M0122. But the western intensification of the channel currents was not recognized and mentioned. The fact that the core depth of this current is deeper than the threshold depth of the Makassar Strait underlines the uniqueness and importance of this intermediate WBC in connecting the Pacific and Indian Ocean intermediate-depth circulation.The mooring data at M01 have shown a persistent southward intermediate WBC between 450 m and 1800 m flowing into the southern Maluku Sea through the western channel Fig. , and a wDestination and origin of the intermediate WBC. We suggest that the Maluku Channel intermediate WBC should reach the Lifamatola Passage and continue southward, since the latter is the only deep opening to the southern Indonesian seas. The LF mooring observations indeed show that the mean currents below the depth of 450 m are southward into the Seram and Banda Seas above the 95% confidence level Fig. .We further suggest that the Maluku Channel intermediate WBC comes from the inflow through the northern TH Channel Fig. . Althougθ and the salinity S is used to identify the water mass of the AAIW in the equatorial western Pacific. All of the hydrographic data were collected using a SBE 911 plus CTD instrument onboard of the R/V Baruna Jaya VIII. The θ–S relations of the water masses in the TH channel and in the western Maluku Channel during the mooring maintenance cruises are similar to that of the AAIW in the 133°E section in the equatorial western Pacific Ocean, with a salinity minimum around the isopycnal σθ = 27.2 kg m−3 , larger than the southward transport of 1.36 Sv in the Lifamatola Passage in the same depth range. In comparison, the Makassar Strait mean transport between 450 m and 760 m depths calculated from mooring data is about 1.7 Sv, assuming the nonslip condition, which is the mixture of the NPIW and lower SPTW from the North Equatorial Subsurface Current24. The AAIW is too deep to enter the Makassar Strait in general. The transports through the Lifamatola Passage were estimated across a section of the shortest distance between the coasts using freeslip and nonslip boundary conditions on the western and eastern coast, respectively, to simulate the western intensification of the currents  Fig. , the intWe used the OFES model simulation to estimate the uncertainty of the WBC transports. The WBC transports between 600 m and 1200 m based on the simulated and the interpolated M01 velocity in the model are calculated to be 2.08 Sv and 1.75 Sv, respectively, in the western Maluku Channel Fig. . The sim20, which shows deep overflow into the abyssal Seram and Banda Seas along the thalweg in the southeastern direction and weak northwestward currents above 1250 m. The difference between the two observations can be explained by the precipitous descending of temperature and salinity contours deeper than 1500 m from upstream down the slope of the Lifamatola threshold  anomalies in the box 170°W–120°W, 5°S–5°N, using Reynolds OIv2 SST analysis data. The anomalies are calculated relative to a monthly climatological seasonal cycle based on the years 1982–2005. The monthly climatology is linearly interpolated to determine weekly anomalies. Spatial averaging of the gridded analysis was weighted by surface area.32. The AGCs above the 800 m are calculated using the dynamic height calculation, referenced to the AGCs at 800 m. In this study, we use the average AGCs from January 2004 to December 2016 to describe the AAIW movement.The gridded Argo data used in this study include salinity and temperature profiles on a 1° longitude × 1° latitude horizontal grid and in 58 vertical levels from 2.5 m to 1975 m in monthly archives spanning the time period from January 2004 to December 2016. The absolute geostrophic currents (AGCs) are calculated from the monthly gridded Argo profiles between 800 m and 2000 m using the P-vector methodSupplementary Information