Liquid enriched with gas does not contain bubbles strongly scattering visible light but its refractive index changes significantly near the electrodes. The change of the refractive index is a collective effect of bulk nanobubbles with a diameter smaller than 200 nm. Any alternative explanation fails to explain the magnitude of the effect. Spatial structure of the cloud is investigated with the optical lever method. Its dynamics is visualised observing optical distortion of the electrode images or using differential interference contrast method. The cloud covers concentric electrodes, in a steady state it is roughly hemispherical with a size two times larger than the size of the electrode structure. When the electrical pulses are switched off the cloud disappears in less than one second. The total concentration of gases can reach very high value estimated as 3.5 × 10 Nanobubbles (NBs) are nanoscopic gaseous domains than can exist on solid surfaces or in the bulk of liquids. They attracted significant attention in the last decade –3 due toet al. [et al. [et al. [et al. [et al. [2 NBs around hydrophilic particles was observed with synchrotron technique by Pan et al. [Ohgaki et al.  produced [et al.  for smal [et al.  observed [et al.  proposed [et al. –24 hydron et al. . AlthougjF ∼ 100 A/cm2 than that for normal electrolysis performed by DC voltage  electrolysis of water (see  for a re 1 A/cm2 ). High s/cm2 [μs . Only NB/cm2 [μs , 30. It 2 and O2 gases inside of NBs. Normally, the gases cannot interact spontaneously at room temperature but in the NBs, where the surface-to-volume ratio is large, some surface channels for the reaction can be opened [The reason for the fast dynamics is not different physical properties of the bubbles but rather ability of the gases to interact. A significant part of the gases disappears in phase with the change of polarity , 30 as oe opened . Generate opened , 33.2 merge with those containing O2 and disappear in the reaction. If the overpressure becomes large (comparable with 1 bar), short-lived microbubles (MBs) visualized with a stroboscope appear in the chamber [The NBs containing only hydrogen or only oxygen are collected in the system. They produce overpressure in a microchamber closed with a flexible membrane. The overpressure was measured using the calibrated membrane deflection . When th chamber . They li chamber . These p chamber , 35 thatIn this paper we investigate a state of liquid highly supersaturated with hydrogen and oxygen gases. With different experimental techniques we probe a cloud of gas above the electrodes and demonstrate that it can be controlled by electrical means. We argue that the gas in the cloud exists in the form of NBs.2SO4 per 100 g H2O) is used as the electrolyte. In contrast with previous works where platinum [Sodium sulfate solution in Milli-Q water (15 g of Naplatinum  or coppeplatinum  electrodμm). Circular shape of the electrodes provides a better localisation of the gas produced by the electrochemical process. The internal electrode has a diameter of 300 μm and the external diameter of the structure is 700 μm. The contact lines originally insulated by the photoresist are covered additionally by a compound Omnivisc 1050 to provide better chemical resistivity.The electrodes were patterned using a standard lithography . Each series is triggered manually. The electrodes were observed in a microscope Mitutoyo FS70  with a mounted camera Nikon 1 J4 to record videos at a frame rate of 60 fps. A similar video was made with a thin (150 μm) glass floating above the electrodes to avoid possible surface buckling.loud see  we applyOptical lever. Optical lever method allows investigation of a space distribution of the gas cloud around the electrodes. The scheme of measurements is presented in 3. A laser beam (λ = 632 nm) passes through the cuvette parallel to the sample surface and is focused in the liquid volume. If the beam passes through the cloud, it is deflected due to the change of the refractive index of the liquid enriched with the gas. This deflection is measured by a four-section photodiode (PD). The cuvette with the sample can be moved independently along all three axis. By moving the cuvette along the laser beam (X axis) one can align the beam waist above the central electrode. Choosing the vertical position of the cuvette (Z axis) we can probe the cloud on different distances from the sample. A minimal vertical distance of 500 μm between the focal spot and the sample is limited by spurious reflections. The Z coordinate can be chosen with an accuracy of 2 μm within the range of 2 mm.Y direction, the beam waist scans above the electrode structure. The deflection of the beam is an integral effect of the change of the refractive index along the beam but the main contribution comes from the region nearby the beam waist. The beam passed through the cuvette is collected by a focusing lens on the PD. Scanning in the Y direction is realized by an electrodynamic driver, which gives smooth, well controllable, and repeatable motion.When the cuvette moves in Differential interference contrast. Differential interference contrast (DIC) microscopy uses [opy uses  two mutuμm. The phase difference between the two images is encoded in the colormap (period λ/2). Since DIC is sensitive to only one direction of the phase gradient, the images were obtained for two orthogonal orientations of a splitting prism in the Nomarski objective.The AP voltage pulses were modulated by the sawtooth signal with a period of 5 s to observe the dynamics of the gas distribution. In this case the concentration of the gas increases, reaches a maximum, and decreases again. The interference pattern is observed in white incoherent light with a Nomarski objective 10x. The displacement of the two orthogonally polarized images was 30 Vibrometer. Short-time dynamics of the cloud can be visualised with a laser Doppler vibrometer, which measures the rate of change of the phase difference between the beam passed through the liquid and reflected from the sample and the reference beam reflected from a reference mirror within the instrument. We use a Polytec MSA-400 instrument. The laser beam (λ = 632 nm) with a spot size of ∼ 5 μm is focused on the sample. A series of pulses are applied to the sample and the voltage, current, and vibrometer signal are recorded synchronously. We used different number of pulses ∼ 1000 and the beam is focused in different points on the structure to probe the gas concentration locally.It has to be noted that interpretation of the vibrometer data is not straightforward and has to be taken with care. This is because not only change in the refractive index but also movement of the liquid both give contribution to the signal. On the time scale > 1 ms it is impossible to separate the two contributions.Application of a constant potential to the electrodes results in the formation of titanium oxide on the positive electrode, which reduces the current and the electrochemical process stops. The oxide gives gold or copper-like color to the anode that is explained by the light interference rather than stoichiometric defects in the oxide . ApplicaU = 10 V and f = 200 kHz. In previous investigations of the AP regime, where Pt, Au, Cu, or W electrodes were used, each current pulse was well fitted by the time dependence [I(t) = IF + I1et/τ − , where IF is the Faraday current and the second term describes the charging-discharging effects with a relaxation time τ. For Ti/Al electrodes this is not exactly the case as one can see in IF is estimated as 60 mA.The AP voltage pulses and the corresponding current response are shown in pendence  I(t) = In around the electrodes. Although we are not able to observe individual NBs due to fast dynamics, it is possible to make a definite statement that the change in n is induced by the gas presenting in liquid in the form of the NBs. This statement is based on the following experimental facts: (i) observed n is always smaller than the refractive index of the solution ns; (ii) the difference ns − n is so large (up to 0.26) that it can be explained only by a significant amount of gas densely packed in NBs; (iii) absence of scattering of the visible light shows that the gas is collected in nanoobjects with a size smaller than 200 nm; (iv) the local temperature near the electrodes increases not more than 6.5°C that excludes vapor as a possible origin of the refractive index change. The data supporting these facts are discussed below.All the experimental methods described in this paper visualise the change in the refractive index fi (gas in our case) we are using the Bruggeman effective medium approximation . It deat = 15 s but panel (b) shows the same electrodes at t = 17.6 s. First, one can notice that the diameter of the central electrode is slightly smaller in panel (b). The variation of the diameter is especially well visible in the video. Second, the central electrode in (b) is out of focus as one can see in the insets between the panels. D of the central electrode ΔD/D with time. Each series of pulses results in the decrease of the visible electrode size up to 4.5% and when the series is over the size gets back to its original value. Variation of the size for the third series of pulses is zoomed in panel (d). After 2.5 s the size saturates at some value. After the last pulse the size returns to its original value faster than in 1 s. The circles defining the peripheral electrode also shrink but in less degree than the central electrode as can be seen in panel (d).The strongest distortion is observed in the center of the structure and it becomes smaller to the periphery. The effect can be explained by the gas generated in the electrochemical process. We can imagine that the gas produced by the process is distributed in a cloud of a lens-like shape: the layer in the center is thicker than the layer at the periphery. The refractive index of this lens is smaller than that for the surrounding liquid. One would see a similar situation examining an object via a plano-concave lens: the imaginary image of the electrodes would look smaller than it is. An alternative explanation could be that the surface of liquid rises up above the electrodes. This possibility can be excluded since we observe the same situation when a glass is floating above the electrodes.h. From a simple geometrical optics one can deduce the relation between the relative change of the diameter and the refractive index of the layer  cannot be reached in the experiment since we do not observe microbubbles as in [n one can estimate as follows. Suppose that the gas in liquid is collected in clusters (nanobubbles) approximately of similar size. Due to surface tension these clusters are spherical. The maximal volume fraction of gas in such a system is given by the fraction of close-packing of equal spheres that is fg ≈ 0.74. The corresponding minimal value of the refractive index is nmin = 1.09. Using this nmin and the observed value ΔD/D ≈ 0.045 we find from hmin ≈ 780 μm. As the ultimate upper limit we use H < (3 − 0.5 − 0.1) mm that is the maximal liquid layer minus the thickness of the substrate and the sticking tape. It corresponds to the maximal refractive index nmax = 1.25. Thus, according to fg < 0.74.To estimate the amount of gas that can produce this level of distortion let us assume that the gas-enriched layer above the center of the electrode structure has a thickness ayer see ΔDD=-, where α ≈ 0.024 K−1 [2SO4. As one can see from T ≈ 6.5° K. This temperature rise is small enough to exclude a significant volume fraction of vapor, which could influence the refractive index. On the other hand, temperature increase of the solution on 6.5° K will reduce the refractive index less than 0.001 [It is natural to assume that the optical distortion is due to the gas produced by the electrochemical process. However, one could imaging an alternative explanation when the Faraday current heats up the liquid locally that results in the change of the refractive index. Since the conductivity of electrolytes depends linearly on temperature it can be used to estimate the local temperature near the electrodes. The amplitude of the current is related to the temperature increase Δ.024 K−1  is the tan 0.001 , while tfg = 0.74, when many gaseous clusters merge. The stroboscope images [If we increase the amplitude of the driving voltage above 12 V, clearly audible clicking sounds will appear, which were described earlier  for simie images  or fast e images  show theY direction (see Y axis). In all cases the signal of the PD responsible for the beam defection towards the sample (segment “Down”) does not change and remains within the noise level. Sectors “Left” and “Right” have their maxima at the distances +250 μm and −250 μm from the center, respectively, indicating a finite size of the cloud. We define the edges of the cloud as the distance where the signal of “Left” and “Right” sectors is half of the maximum that is 500–600 μm. Sector “Up” has the maximal signal in the center of the electrode structure. The total lateral size of the cloud at the height h = 500 μm is estimated as L = 1000–1200 μm that is in agreement with what we observe with the optical distortion method.The optical lever experiment gives information on the space structure of the gas cloud around the electrodes. It is known that for a nonhomogeneous medium the beam is deflected in the direction of a larger refractive index. In this experiment the laser beam is going parallel to the sample surface at some vertical distance from this surface and the beam scans along tion see . When thh = 500, 600, and 700 μm, respectively. One can see that the magnitude of all the signals is slowly reduced with the increasing distance. When the distance is above 1000 μm the signals drops down fast  in the liquid. The amount of generated gas is smaller than that in the optical distortion case because much smaller Faraday current IF ∼ 2 mA is passed through the electrolyte. Small current is necessary to keep the response of the PD linear.To estimate a homogeneous refractive index n as see Δn≈Δxl21x about 40 μm. Using x one finds for the noise level in n a value of δn = 3 × 10−4.The sensitivity of the system can be estimated from the noise level shown in Change of the differential interference contrast with time due to the pulses modulated by the sawtooth profile is presented in Panel (a) shows the contrast when the amplitude of the pulses is minimal (zero). One can see that in the overlapping regions the phase difference is zero. Some contrast exists only in the upper part of the picture, where the structure is covered by the compound that has a height gradient. Panel (b) shows the contrast when the amplitude of the pulses is half of its maximum value. The central electrode in its center is still black because there is no gradient of the refractive index. However, at the maximal amplitude (panel (c)) some structure appeared in the central part of the electrode that arise due to inhomogeneous current distribution and liquid flow induced by the moving NBs (see below). We observed similar structures in the optical lever data too.n is less straightforward and we do not provide here a specific number for n.The data demonstrate qualitatively the gas distribution in the cloud that is in agreement with what we observe with the optical distortion and optical lever methods. However, the interpretation of the gradient of π ≈ 200 nm. Occasionally microscopic bubbles are formed due to presence of defects. They strongly scatter light, and can be easily recognised in the signal as shown in f = 200 kHz as shown in As we already mentioned the interpretation of the vibrometer data is more complicated because the method is sensitive not only to the change of the refractive index but also to the liquid movement. With this method we can see the change of the refractive index only on a short time scale. Nevertheless, the vibrometer data are important in two aspects. First, the data have a regular character as one can see in Increase of the gas concentration in the cloud is responsible for smooth evolution of the signal as shown in panel (a). When the pulses are switched on the thickness of the gas-enriched layer increases with time and the signal is negative because the optical path is reduced. New gas clusters produced near the electrodes push the old ones further away generating movement of the liquid. This movement contributes to the signal increasing the optical path. The liquid flow is the reason for the minimum in the signal. When the pulses are switched off the signal evolution is determined by the liquid flow. In the total time domain the signal is shown in All our observations show that the refractive index changes due to a significant volume fraction of gas, which exists in the electrolyte near the electrodes. We see that the refractive index is reduced that is unambiguously follows from the optical distortion experiment. In this experiment the image of electrodes shrinks in response to the applied AP pulses that is possible only if the refractive index of the cloud is smaller than that for the surrounding liquid. The same is true for the optical lever experiment, for which the beam is always deflected out of the electrode structure.N produced by one series of pulses in the optical distortion experiment isIF ≈ 60 mA, τ = 4 s, and e is the charge of electron. At normal conditions this gas would fill a volume as large as 47 mm3. The other gas that could appear in the electrolyte is vapor but the temperature rise is too low to have any appreciable amount of vapor inside of the liquid. No other gases can be produced in the process in appreciable amounts.We cannot determine from our data the chemical nature of the gas that produces significant change of the refractive index. However, there are no doubts that large amount of hydrogen and oxygen is produced by the Faraday current. The total number of molecules n since the current pulses increase the temperature near the electrodes only on 6.5° K. The only reasonable explanation for the change of n in the optical distortion experiment is the gas produced electrochemically.The reduction of the refractive index in the distortion experiment was estimated in between 0.1 and 0.26. Any impurity in the solution cannot produce this large change. One could imagine the situation when the amount of salt in the electrolyte varies around the electrodes. However, if the salt concentration drops from 1 mol/L to zero, the refractive index decreases only on 0.02 . MoreoveD is the gas diffusion coefficient and t is the time, the gas concentration is estimated as jF is the Faraday current density. The latter is jF ∼ IF/A ≈ 85 A/cm2, where A = 2.25 × 10−8 m2 is the area of the central electrode. This is an average value of the current density but the local value near the edge of the electrode can be a few times larger [D = 4.5 × 10−9 m2/s one finds that just in 50 μs the gas concentration reaches the value ng ∼ 5.6 × 1026 m−3, which corresponds to the supersaturation S = ng/nsat ∼ 1200, where the saturated concentration for H2 is nsat = 4.7 × 1023 m−3.To answer the question in which form this gas exists in the liquid, let us estimate first the local supersaturation above the electrode. The gas produced by the process diffuses away from the electrode. Within the diffusion layer of thickness s larger . Using aμs long single polarity pulse manifests itself in the form of nanobubbles covering the negative electrode homogeneously already in 20 μs. These NBs scatter light and are visible as a haze. After 100 μs they form well visible microbubbles and their dynamics becomes slow. It is interesting to note that although the NBs cover a significant area of the electrode the current is not reduced. It means that the nucleation most of the bubbles happens above the surface. Indeed, this is possible only due to the homogeneous nucleation when surface defects do not play a crucial role in the nucleation.This huge supersaturation reduces the barrier for bubble nucleation and bubbles are generated homogeneously in the diffusion layer. It was demonstrated  that hyd2 or only O2 are collected in the system and are responsible for the change of the refractive index in our experiments. Therefore, we conclude that the gas in the liquid exists in the form of NBs and the main reason for this is extremely high supersaturation produced by the electrochemical process in AP regime.A somewhat different scenario is realized for alternating polarity pulses because both gases are produced above the same electrode and these gases are able to interact. When a NB contains mixture of gases they react to each other apparently via surface dominating processes , 29, 31.The next question is why the NBs are not grow to the size strongly scattering the visible light? Two bubbles containing different gases can exchange by the content due to gas diffusion in the liquid . In this process one bubble disappears at all but the other one is not growing because the gas that enters in the bubble disappears in the reaction. If the pulses are switched off and the gas is not produced, this effect reduces the total gas concentration as one can see in μs or less for supersaturations ∼ 1000 reached in the AP electrochemical process.The discussion above shows that it is hardly possible to visualize individual NBs with the existing methods because they are not only very small but also live a short time. These NBs probably are not different from the bulk NBs produced mechanically , 17, butfg and the ideal gas law for hydrogen concentration one hasr is the bubble size, γ is the surface tension of the liquid, P0 is the atmospheric pressure, and kT is the temperature in energy units. Taking as reasonable values fg = 0.5 and r = 50 nm one finds nH2 ≈ 2.3 × 1026 m−3, which corresponds to the supersaturation with hydrogen SH2 ≈ 495. The concentration of oxygen is two times smaller than nH2 and the supersaturation is SO2 ≈ 152 (saturated concentration for O2 is 7.7 × 1023 m−3). These values of S are smaller than those achieved near the electrodes because significant part of the produced gas turns back to water, but anyway, the level of supersaturation in the cloud is very high.Nanobubbles open the possibility for a high effective supersaturation of liquids. Since the gas in NBs is densely packed due to a high Laplace pressure, liquid can accept much more gas than in the form of microscopic bubbles where the Laplace pressure does not dominate. We can estimate the level of effective supersaturation in the cloud using the result of the distortion experiment. Using the definition of the volume gas fraction V is the volume of one NB, then the average volume per one bubble in the cloud is V/fg. For the optical distortion experiment 0.28 < fg < 0.74 and one can say that the state of the material is rather a nanofoam than a liquid. This is the reason why the refractive index strongly deviates from that of liquid. With the increase of the voltage amplitude the Faraday current becomes larger and the volume fraction of gas increases. At U = 12 V this fraction apparently reaches its maximal value fg = 0.74 because short-lived microbubbles start to appear in the system, which explode with well audible sound. This effect was described earlier [If  earlier  for the IF ≈ 2 mA was much smaller than in the optical distortion experiment, where IF ≈ 60 mA. In the former case we have found much smaller gas fraction fg ≈ 0.01 than in the latter one. It clearly demonstrates that the density of NBs in the cloud is defined by the Faraday current as one could expect for an electrochemical process.In the optical lever experiment the Faraday current π, where λ = 600 nm is a typical wavelength of light. At the same time it has a significantly different refractive index, which is smaller than the index of the surrounding liquid. The cloud can be roughly represented as a hemispherical segment based on the electrodes and having the diameter two times larger than the external diameter of the electrodes.In this paper we visualized a cloud of NBs using different experimental techniques. The most important property of the cloud is that in spite of a significant gas content it does not scatter visible light demonstrating absence of objects with a size larger than λ/When the voltage pulses are applied to the electrodes the cloud is growing with a rate defined by the electrochemical current but then it reaches a steady state. The latter is explained as a balance between new bubbles produced by the current and the bubbles merging and disappearing in the combustion reaction. When the pulses are switched off the cloud disappears for less than a second. This process is controlled only by merging of the bubbles.Here we demonstrated that the cloud of bulk NBs can be very dense and it can be controlled electrically. The density of the cloud is defined by the Faraday current. It can be important for applications ranging from cleaning to applications in microfluidic devices.S1 FigY-coordinate for four different beam distances from the substrate. The signal is going down fast when the vertical distance is larger than 800 μm.Signal of “Up” segment as a function of (JPG)Click here for additional data file.S2 FigSignal on the PD segments “Up” and “Down” versus the position of the laser spot on the PD surface. Coordinate “0” corresponds to the center of the PD. The curves are used as the calibration curves.(JPG)Click here for additional data file.S3 FigU = 18 V averaged over a period of 5 μs. The left panel (a) shows the response to 2500 pulses triggered at t = 0. The signal jumps up and down in the beginning and at the end of the series. No scattering on microbubbles is observed. Similar raw signal but of smaller magnitude, corresponds to Raw signal of the vibrometer (velocity) for a very high driving voltage (JPG)Click here for additional data file.S1 VideoU = 10 V and f = 200 kHz without a glass floating above the electrodes. The frame rate is 60 fps and the frame size is 1920 × 1080 px. The video with a glass floating above the electrodes is looking very similar and is available on the request. The corresponding voltage and current are shown in Video was recorded at (AVI)Click here for additional data file.S2 VideoU = 8 V and f = 400 kHz at a frame rate of 30 fps. The frame size is 1280 × 1024 px. The voltage is modulated by the sawtooth profile with a period of 5 s.File was recorded at (AVI)Click here for additional data file.S3 VideoThe same as (AVI)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.


SDMs) and assess the effect of competitors on the distribution and abundance of the species of interest. It is often difficult to validate the accuracy of this approach with available data. Here, we simulate virtual species that experience competition. In these simulated datasets, we can unambiguously identify the effects that competition has on a species' distribution. We then fit SDMs to the simulated datasets and test whether we can use the outputs of the SDMs to infer the true effect of competition in each simulated dataset. In our simulations, the abiotic environment influenced the effects of competition. Thus, our SDMs often inferred that the abiotic environment was a strong predictor of species abundance, even when the species' distribution was strongly affected by competition. The severity of this problem depended on whether the competitor excluded the focal species from highly suitable sites or marginally suitable sites. Our results highlight how correlations between biotic interactions and the abiotic environment make it difficult to infer the effects of competition using SDMs.A fundamental goal of ecology is to understand the determinants of species' distributions . Competition among species  is common in nature and it would be tremendously useful to quantify its effects on species' distributions. An approach to studying the large‐scale effects of competition or other biotic interactions is to fit species' distributions models ( For our purposes, SDMs are statistical models that seeks to estimate the probability of occurrence or abundance of a species of interest using independent variables describing the environments where the species could be observed . These densities can change across the spatial gradient x , or over time t . Throughout this paper, species 1 will be considered the focal species and species 2 the competitor. The “local” terms in Equations . Increasing the density of the competitor reduced the population growth rate of the focal species. The term α12 describes the per capita harm that the competitor inflicts on the focal species. When α12 = 0, the competitor has no effect on the focal species. As α12 increased, the competitor progressively inflicts more harm to the focal species. Equivalent terms describe the change in density of the competitor at the same location. For example, r2 is the growth rate of the competitor when it is rare and in the absence of the focal species, just as r1 is the growth rate of the focal species. In our simulations however, we assume that r1 or r2 does not vary with x because the outcome of competition rarely depends on r1 or r2 n1 and nquations . When th2 Case, .At a single location, our model can produce several different outcomes including competitive exclusion, where one species eliminates the other; stable coexistence, where the two species can survive at a single location indefinitely; or a priority effect (unstable coexistence), where either species can eliminate the other, depending on the initial densities of each species, which were specified at the start of each simulation.Da indicate that individuals disperse more frequently.We assumed that some individuals disperse passively among nearby locations. This is represented by the “dispersal” terms in Equations  and 1b)1b). HighK1, K2). To describe these changes in the carrying capacity of the focal species, we use the equation:m1 describes how the carrying capacity of species 1 changes across the region (m1 is a slope). The parameter b1 represents the carrying capacity of species 1 when x = 0 . We define x = 0 as the point where the focal species' carrying capacity is 0 and define x = 1 as the point where the focal species' carrying capacity is 1 , in others simulations competition was weak and stable coexistence was possible (α12 = 0.75), and in most of our simulations competition was strong , making priority effects possible. We assumed that the strength of competition was constant across environmental gradients because the range limits often depend on the joint effects of K1 and α12. This makes it simpler to study range limits by investigating changes in carrying capacity , weak dispersal (Da = 0.1), and strong dispersal (Da = 10).We chose parameter values that allowed competition to produce several distinct effects including competitive exclusion and stable coexistence as well as unstable coexistence. We used five different values for the per capita effect of competition from the competitor on the focal species. In some simulations, the competitor had no effect on the focal species , and in a fourth simulation, the focal species had a higher initial density than the competitor (1 vs. 0.01).x = −1,000 to x = 1,000. This wide range of sampling ensured that many sites were outside of the fundamental niche of the focal species . However, most of our figures only depict samples from ~x = 0 to x = 1,000, the range of conditions where the focal species was present. To represent ecologist's uncertainty in the actual density of species found at a given site, we generated a random number from a Poisson distribution with a mean equal to the population density at this site . However, for 535 datasets, competition eliminated one of the species from the entire region. We chose to simulate all combinations of parameter values because there was no obvious way to systematically simulate the full range of possible species' distributions generated by our dataset while ensuring that both species were abundant. These datasets were ignored in subsequent analyses because it made little sense to estimate the effects of competition on a species' distribution when one of the competing species is absent. As such, 965 simulated datasets were considered for SDM analyses.2.2n1 or n2) and the environment E along with the competitor.We applied SDM procedures recommended in Elith and Franklin ; Elith aBecause the exploratory analyses suggested that the relationship between the focal species and the environment and the competitor were linear, we used GLMs, to estimate the impact the environment and the competitor had on the focal species. This approach is particularly useful for examining the importance of biotic interactions as the resulting parameter estimates are easy to interpret Model ignoring the competitor: glmModel including the competitor: D2 by Guisan and Zimmermann  in D2 when the competitor is included, in addition to the environment, and the regression coefficient (βcompetitor) associated with the other species, which is an estimate of how the density of the focal species changes when the density of the competitor increases. We selected these two statistics because the amount of deviance explained and parameter estimates are two common ways to evaluate the importance of a variable in regression models  describing the environmental conditions at a given location. To study the effect of competition in the SDM, we used the proportion of deviance explained by the SDM weighted by residual degrees of freedom. This statistic has been called mmermann . This stCrawley, . Our appCrawley, .D2 for 886 of the datasets and an estimate of βcompetitor for 721 of the simulated datasets.Of the 965 simulated datasets on which SDMs were calculated, it sometimes happened that one of the species was rare enough to interfere with model fitting. As a result, we have information on 2.3D2 attributed to the environment is small. In these simulations, a great deal of deviance should be attributed to the density of the competitor. These same simulations should show negative values of βcompetitor, indicating that the SDM predicts that the density of the focal species is lower in locations where the density of the competitor is high.If SDMs can identify the effect of competition on species' distributions, then simulations where the competitor excluded the focal species from many locations should result in SDMs where the R2) from linear models for the relationships between the importance of competition and SDM parameters. This is a measure of how much of the variability in the importance of competition can be explained by the SDM output. In our case, a R2 of 1 indicates that all of the variability in the importance of competition can be explained using output from SDMs, while a value of, say, 0.01 indicates that only a small amount of the variability in the importance of competition (1%) can be explained by the output of the SDMs. Following accepted usage, we do not assume a priori that the relationships in our study are linear; instead, we ask how much variability can be attributed to a linear relationship . An index used to infer the importance of competition should ideally be linear so that a change in the output of the SDMs can easily be interpreted as a change in the importance of competition Figure . As suchChesson, .b is a parameter akin to an intercept term in a regression ensuring that the value of the standard deviation after applying the link function does not become zero, which would cause singularity problems when calculating likelihoods. In this analysis, b was fixed to 0.01. This type of additive model allows for the smoothed estimate to changes in the mean and standard deviation of the model. We present expected degrees of freedom  for both the mean and standard deviation. These models were constructed using the gam function in the mgcv package in R.To determine whether it would be easier to summarize information from this analysis using nonlinear relationships, we also fit Gaussian local‐scale additive models, which have two link functions, one for the means and one for the standard deviations of the models. The identity link function was used for the models' mean, while a log link function Equation  was usedTo relax the assumption of linearity, we investigated how often information derived from SDMs agreed with the true effect of competition. More specifically, we determined the probability that in a randomly selected pair of simulations, information from SDMs would lead us to believe (incorrectly) that competition is more important in one simulation when it is actually more important in the other. This probability is an alternative summary of the information in Kendall's tau .3The relationship between the importance of biotic interactions in our simulations and the importance of biotic interactions inferred by SDMs was highly nonlinear, and on occasion highly variable Figure . Thus, iR2 values from the linear model (dark gray dashed lines) were typically low (<.09), indicating that only a small amount of the variability in the importance of competition could be predicted using summary statistics derived from SDMs  tended to be those where the focal species and its competitor rarely encountered one another. These were often simulations where the competitor had a small effect on the distribution of the focal species . As such, values of βcompetitor dramatically lower than zero were a poor guide for the importance of competition.Simulations where the estimated effect of the competitor on the focal species was unusually strong  between the effects of the abiotic environment and the effects of competition, and multicollinearity is well known to affect variable selection and prediction in SDMs  and found that only one species occurred in some regions while both species occurred together in other regions. The authors could contrast the distributions of each species across regions to infer the effect of competition.We emphasize that our simulations were designed to systematically explore different qualitative outcomes of competition; they cannot be interpreted as representing common outcomes of competition in nature. Our results showed that it could be difficult or impossible to infer the importance of competition using SDMs. We showed that problems could occur with or without dispersal, when the two species coexist stably at some locations or whether a priority effect allows each species to exclude the other from the same location. But we do not know how commonly these conditions are found in nature. In other words, the larger number of simulations (combinations of parameters) for which it was difficult to detect competition effects is not necessarily proportional to the kinds of competitive interactions found in nature. In cases like Figure Depending on the application, the challenges of empirically identifying an effect of competition on species' distributions may represent either a modest caveat or a severe limitation on the usefulness of SDMs to study biotic interactions. When using SDMs to describe where a species is present . We believe that this simplification is appropriate even though simulations in one dimension sometimes miss outcomes of competition that emerge in two spatial dimensions. Two‐dimensional models of competition produce different dynamics when dispersal and competition interact in complex ways , even though competition eliminates the focal species from the center of the gradient (intermediate values of x; see figure 1 in Austin, In our simulations, we assumed that the carrying capacities of each species changed linearly across the study region. If the changes in carrying capacity were nonlinear, additional complications could emerge. For example, it would be possible for the focal species to persist at the edges of the study region (high and low values of In some cases, it may be possible to use SDMs to infer the importance of competition when additional information is available. If two species have a response to a single gradient, this may mask the effect of their competition, but SDMs typically include multiple environmental predictors, invoking the concept of the Hutchinsonian hypervolume (Elith & Franklin, Inferences regarding the effect of competition on species' distributions may also require multiple lines of evidence from natural and designed information on species' traits, experiments, and phylogenies (Pigot & Tobias, None declared. Click here for additional data file.


The data presented in this article are related to the research article entitled “Discrete Adjustment to a Changing Environment: Experimental Evidence”   Specifications TableValue of the data•These data are valuable for analyzing how people form and update forecasts.•The experiment defines the subjects’ objective function and rewards subjects for their performance, thus researchers can treat the decision-maker's objective as known.•The experiment controls what information subjects have about the task at each point in time, enabling researchers to test hypotheses about how subjects process the information in real time.•Subjects control how quickly to perform the task and how often to update their estimates, hence the data can be used for studies of response time, the nature of adjustment dynamics, and the relationship between accuracy and speed in performing a task.•The experiment yields a large number of observations per subject, allowing for analysis both within and across subjects.1The dataset contains the outcomes of a laboratory experiment 22.1On a computer program, subjects are presented with the interface shown in Every session begins with the slider set at 0% and without any rings displayed on the interface. The subject begins with a guess and adjusts the position of the slider to indicate his or her estimate. When the subject is satisfied with her current estimate, she can click on the button “NEXT” at which point a new ring is drawn randomly from the box and displayed on the screen. Only the latest ring is displayed on the screen at any given moment. This process is repeated until the session ends (after 1000 ring draws).2.2The subject's reward is given by the quadratic scoring rule 2.3p of drawing a green ring from the box is drawn from the uniform distribution on the unit interval. After each ring draw, there is a constant probability equal to 0.5% of a box change. If there is a box change, the new probability of drawing a green ring is also an independent draw from the uniform distribution on the unit interval. The subjects are not told when a change in the box occurs, but they are told in advance that the box might change, and they know the probability of a regime change and the distribution from which the new box is drawn. Ricci and Gallistel The initial probability 2.4


The purpose of the present study was to test an idea of and describe a concept of a novel method of detecting defects related to horizontal nonuniformities in ultrasound equipment. The method is based on the analysis of ultrasound images collected directly from the clinical workflow. In total over 31000 images from three ultrasound scanners from two vendors were collected retrospectively from a database. An algorithm was developed and applied to the images, 150 at a time, for detection of systematic dark regions in the superficial part of the images. The result was compared with electrical measurements  of the transducers, performed at times when the transducers were known to be defective. The algorithm made similar detection of horizontal nonuniformities for images acquired at different time points over long periods of time. The results showed good subjective visual agreement with the available electrical measurements of the defective transducers, indicating a potential use of clinical images for early and automatic detection of defective transducers, as a complement to quality control. The methods can be summarized as simple tests, tissue‐mimicking phantom tests and commercial electronic transducer tests. A simple and effective method is the “paper‐clip method”.Visual inspection of the image uniformity using a tissue‐mimicking phantom is used in quality assurance to detect both vertical and horizontal nonuniformities.Electronic transducer testers such as FirstCall aPerio  and ProbeHunter  are testers to which the transducer is connected. Pulses are sent element‐wise toward a target in water and the echoes are evaluated. The result from the test is comprehensive and typically contains, among other parameters, the sensitivity for all elements individually presented as bar graphs. If an element has zero sensitivity, the element is nonfunctional.The existing methods described above for testing for defective transducers are associated with limitations. Firstly, they all require access to the equipment (or at least the transducer) and are time‐consuming. This requires personal resources for performing the tests and the equipment is furthermore unavailable for clinical use. Secondly, when checking for defects, the time from failure to detection could in worst case be up to the checking interval, and a defect may therefore be unnoticed for a long time. Finally, intermittent failures may not be detected at all by the described methods.The purpose of the present study was to address the limitations described above by testing an idea of and describing the concept of a novel method of detecting defects related to horizontal nonuniformities in ultrasound equipment. The method is based on the analysis of ultrasound images collected directly from the clinical workflow and applicable to, e.g., linear array transducers, as used in the present study.22.AThe proposed method is based on the fact that the anatomical information in an image varies for every clinical image, whereas the darker region corresponding to a defective transducer is present in all images produced by the transducer. Thus, by averaging many images or determining their median, a more or less homogeneous background is obtained even from clinical images, since the anatomical variations tend to cancel each other, whereas the systematic darker streaks — present in all images — remain and become easier to detect than in a single image.Basing a method of detecting nonuniformities on clinical images introduces new possibilities for quality control. As the images can be collected directly after they are stored clinically, the quality control can be performed without intervening with the equipment and at any time point. The most straightforward approach seems to be to calculate a median uniformity image for visual assessment based on a number of images that have been stored in a database. In Fig. For continuous monitoring of the system, a more advanced approach could be to automatically analyze the latest produced images for horizontal uniformity aberrations. Such an analysis could be a complement to the normal quality assurance program and notify the service organization of a detected artifact and thus lead to earlier detection of defects. The details of an example of such an analysis are given in the present paper. The purpose of the paper was to describe the details of this first attempt in a case study based on three ultrasound systems and to test if it may be possible at all. The results are qualitative and a follow‐up study is planned for a quantitative evaluation of a larger number of ultrasound systems and cases.2.BThe method for visual assessment and an implementation of an automated analysis was applied to stored images for three scanners, with one defective transducer each. Four FirstCall measurements, revealing different kinds of defects, were available for comparison. The scanners were used in radiological departments for general imaging and the images were stored as 8‐bits RGB (Red Green Blue) images in DICOM  format. The scanners, the transducer types and the number of images used retrospectively for the automated analysis are shown in Table This retrospective study using clinical images was approved by the Regional Ethical Review Board. All image handling and analysis were performed automatically using in‐house developed MATLAB  applications. Images from a chosen scanner/transducer combination were imported and selected. The B‐mode part in the stored images was extracted from the surrounding information and was resized so that all extracted B‐mode images had the same size. The extracted and resized B‐mode images were then placed in an image stack, used as input to an algorithm developed for automatic detection of defects. The detection was based on construction of a curve representing systematic darker streaks in the images.2.CImages from a certain scanner, distinguished with the DICOM tag “StationName”, were retrieved from the archive. The transducer type was determined by the DICOM tag “TransducerData” for the Philips scanner. For the GE scanners, the transducer information in the images was used instead since the DICOM tag was empty for these images. Images containing Doppler curves and side‐by‐side images were rejected. This was determined by checking if the DICOM tag “SequenceOfUltrasoundRegions” had more than one item or if the size of the image had more columns than the ones just containing one image. Color Doppler images without curves were included.2.D.The stored images had surrounding information around the B‐mode image — fields containing patient name, time, etc. — and the only information needed was the grayscale B‐mode image. To extract the B‐mode image from the surrounding information, the image was first converted from color to gray. The largest connected area of nonzero pixels was then selected using the MATLAB's Image Processing Toolbox functions “bwconncomp” and “regionprops”. The rest of the pixels in the image were set to zero. The coordinates for the outer corners of the selected area were then determined see Fig.  and the To check if the image was zoomed laterally, the physical aperture of the transducer was compared with the physical width of the B‐mode image, using the left and right X values and the DICOM tag “PhysicalDeltaX”. If the physical width of the B‐mode image was inside the range of 49.5 and 51.5 mm, the B‐mode image was extracted, otherwise the image was rejected . Finally, the extracted B‐mode images were resized in order to get the same size for all extracted images using the MATLAB's Image Processing Toolbox function “imresize” (bicubic interpolation). The width of the resized images was chosen to be equal to the number of elements in the transducer. The height of the resized images was chosen to be 500 pixels for both transducers in order to get decent resolution of the depth. This resulted in image sizes of 336 × 500 for ML 6‐15 and 256 × 500 for L12‐5.2.EThe idea of the automatic analysis was to develop an algorithm that uses the information in the superficial part of a large number of images to create a curve with a length equal to the number of columns in the resized images . When the algorithm detects darker regions above chosen thresholds, these positions in the curve are replaced by positive values that represent the dark regions in width and strength. When the area under this systematic dark region (SDR) curve reaches a chosen threshold, this could be used for a notification that the transducer or the scanner needs an extra check. A 3D plot of the SDR curves could be used to monitor the condition of a system and see if an accepted aberration increases or remains the same.stack images, are shown. The algorithm combines three different types of analyses, described by the green, red, and blue paths in the figure. Each of the three paths creates a curve with a length equal to the number of columns in the resized images and all the values are set to zero as default. The values are changed only if dark regions are determined as aberrations by the algorithm. The largest value of the three curves at each position is chosen for the SDR curve. In the green path, dark streaks that are narrow and situated in the same lateral position in many images are detected. The red path detects wider darker streaks that are more diffuse and not necessarily have the same lateral position of the dark valleys in the images in the stack. Both the green and red paths use subtraction of a polynomial fit to compensate for baseline variations. This approach is not successful if the darker regions appear in the horizontal endpoints of the images since the polynomial fit adopts to the endpoints of the curves. The blue path has the sole purpose to detect darker regions in the horizontal endpoints of the images. The algorithm was implemented in the following way.In Fig. select randomly chosen images (without replacement) from the image stack. This was repeated Nrep times, resulting in Nrep median images. A column‐wise‐mean (CWM) of rows Rupper – Rlower was calculated for each of the median images, resulting in Nrep CWM curves. The curves were inverted and a polynomial fit of order Opoly,green was fitted to and subtracted from each of the curves. A peak detection was performed using the MATLAB's Signal Processing Toolbox function “findpeaks” (default settings) for each of the curves. The function returned the position, height, and width of all peaks that were found. The means of the heights and widths of the peaks were calculated. All peaks that had a higher mean value of the height than the threshold Tgreen were selected.First, the mean of each row was subtracted for each row in all images. For the green path, a median image was calculated based on NThe heights and the widths of the n selected peaks were used to create a vector, containing Gaussian curves for the selected peaks at the correct position, see eq. stack images were used to calculate a median image. Pixels between rows Rupper and Rlower were used to calculate the CWM curve, as above. The CWM curve was inverted and for the blue path all values above Tblue for the first and last Pinclude percent of the values in the inverted CWM curve were selected, the rest were set to zero. For the red path, a polynomial fit of order Opoly,red was fitted to and subtracted from the curve as baseline compensation. All values above Tred were selected, the rest were set to zero.For the red and blue paths in Fig. exclude percent of the SDR curve were set to zero. In order to obtain a scalar measure of the nonuniformity of the system, the area under the SDR curve was finally calculated using trapezoidal numerical integration. The area under the curve was normalized by the number of elements in the transducer. An example of an SDR curve is shown in Fig. stack = 150) selected from Case 1 for creating the SDR curve.At each position, the largest value from the three curves was selected to create the SDR curve. To avoid the darker streaks in the borders of the images that appear for fully functional transducers, the first and last PThe algorithm was applied to the image data described in Table stack = 150, Nselect = 15, Nrep = 100, Rupper = 1, Rlower = 19, Opoly,green = 6, Opoly,red = 6, Tgreen = 2, Tred = 5, Tblue = 10, Pinclude = 25, and Pexclude = 2. These values were selected after empirical testing with the aim to get a result that agreed reasonably well with the FirstCall measurements for the three cases and for other scanners that were investigated but not used in the present paper, but a proper optimization of the parameters was not conducted. The depth distal to the transducer using these values was 19/500 (approximately 4%) of the extracted image depth, regardless of the depth of the image.The values of the variables used were N3The amount of extracted and rejected images of the image extraction subroutine for the different cases is presented in Table The retrospective SDR curves for the three cases are shown as 3D plots in Fig. exclude percent in the algorithm.For Case 1, there were two FirstCall measurements, the second one performed 16 months after the first one see Fig. . Two of For Case 2, it is possible to follow how the defect emerged in both width and strength in Fig. For Case 3 in Fig. The area under the SDR curves for the three cases are shown in Fig. 4In this paper, a novel method for assessment of the horizontal uniformity of ultrasound systems, based on the use of a large number of clinical images, has been presented. An initial algorithm for automatic detection of systematic vertical dark regions in the superficial part of the images produced by linear array transducers has also been described. The algorithm was applied to three different cases from two vendors/transducers, where four FirstCall measurements were available for comparison. In total 31409 images were used in the analysis by the algorithm, 150 at a time. By visual assessment of the 3D plot of the SDR curves in Fig. The algorithm used in the present paper has similarities to the one described in IEC Technical Specification.stack, was set to 150 images in the present paper. The higher Nstack, the more robust the algorithm becomes for systematic defects that are present in all Nstack images, but the longer it takes to replace the images in the stack and thus the longer it may take for a new defect to be detected. Nstack was set to 150 in order to get illustrative SDR curves for the cases with few false positives, but this number may be reduced for a good balance between occasional false positives and an early detection of defects. To detect intermittent defects it is also preferable to have a smaller image stack. However, a single measurement of a phantom or a FirstCall measurement may not detect intermittent defects. Sipilä et al.The number of images used in the image stack for the construction of each SDR curve, Nstack = 150 as used in the present paper, and thus the average number of days for a defect to fully affect the SDR curve, was approximately 12, 13, and 22 days, respectively. The investigated transducers were used in 46%, 14%, and 25% of the images produced by each system for Cases 1–3, respectively. The proportion of images in the image stack containing the defect that is needed to affect the SDR curve depends on the defect characteristics. This has not been investigated, but naturally affects the time for detection.Since the time from when a defect occurs to when it is discovered should be as short as possible, Mårtensson et al.Using the area under the SDR curve is a simple way to implement an automatic detection of defects. More advanced ways could involve applying weight functions that amplify the central part of the SDR curve more than the peripheral part or that emphasize wider streaks more than narrow ones, etc. In this way, the clinical relevance of the scalar measure might be increased.The described method has been developed for stored images. One condition for this approach to be successful is that a given transducer is always connected to the same scanner when used, since there normally is no individual information  in the DICOM tags regarding the transducer; just the type for the Philips machine and none for the GE machines. In the used cases the images were retrospectively collected from a database and there was no possibility to be certain that the same individual transducers had been used for all images. However, the pattern in the SDR curves makes it reasonable to assume that this was the case, since the dark regions appeared in the same lateral positions over time. Generally, if the transducer often is shifted between systems, the method is not suitable. However, if a transducer is defective and connected to another system that is also monitored, the defect will eventually be detected in the new system. Similarly, if the defect remains when transducers are switched, the defect can be assumed to be internal and maybe be tied to a specific port. In this way, the method can be a supplement if the defects are intermittent or if it is difficult to localize the origin of the defect. However, if the intermittent defects occur during periods of time short relative to the total time to fill the image stack, the present method may have difficulties detecting these defects.An issue when extracting the images was that logotypes and scales sometimes partly interfered on the largest connected region of nonzero pixels. This was handled by detecting the vertical borders in different ways for the two examined manufacturers, and would probably have to be customized for other manufacturers. The used method extracted the whole image and resized both depth and width to get the same number of pixels for both width and depth for all extracted images. This approach makes aberrations in vertical bands not detectable, since different depths are mixed in the median uniformity image. Logotypes and markers set by the users also affected the outcome of the method, since all logotypes and markers are part of the images. A result of this disadvantage can be seen for Case 3 in Fig. Darker streaks in the image caused by a defective transducer are most easily detected in the superficial part of the image. If an image is zoomed axially, the darker region caused by the defect of the transducer will decrease with depth, but if the image is zoomed laterally, the defect of the transducer and the dark region in the image unfortunately no longer correlate laterally. Therefore, laterally zoomed images were rejected. This exclusion criteria also rejected images if the X values were estimated erroneously by the extraction algorithm.All scanner settings that affect beam forming, image processing, and possible algorithms that the manufacturers have built into the scanner to compensate for defects in the equipment, affect the clinical image and thus the median uniformity image. If spatial compounding is used, this will probably impact performance of the method to be less sensitive in a similar way as spatial compounding impacts visibility of defects when checking horizontal uniformity using a phantom. For the images used in the present paper, there was no information available whether spatial compounding was used or not. Defects that can be detected using clinical images are also present in the final processed clinical images, which is an advantage if the only purpose is to detect aberrations in the images. However, the major advantages of using stored clinical images are that this can be done automatically and that there is no need to get admission to the equipment — only access to the database where the images are stored is required. If some kind of curves are presented, like the ones in Fig. Assessment of the image uniformity as part of a quality assurance program is used for other imaging modalities as well, such as e.g., computedFinally, to use the information available in clinical images for automated detection of changes in important parameters has been described for other areas and purposes in the field of medical imaging. Examples are automated detection of changes in patient exposure5A method of using clinical images for assessment of horizontal uniformity in ultrasound imaging has been introduced. The method is applicable to, e.g., linear array transducers, as used in the present study. An algorithm for automatic detection of horizontal nonuniformities has been described and tested on more than 31000 clinical images from three systems in a case study. Subjectively, the algorithm seemed to be robust, as visually similar SDR curves were obtained for images acquired at different time points over long periods of time. Furthermore, the results showed good visual agreement with available electrical measurements of the defective transducers, indicating a potential use of clinical images for early and automatic detection of defective transducers, as a complement to quality assurance.The authors declare no conflict of interest.


Improving the sustainability in agriculture is nowadays an important challenge. The automation of irrigation processes via low-cost sensors can to spread technological advances in a sector very influenced by economical costs. This article presents an auto-calibrated pH sensor able to detect and adjust the imbalances in the pH levels of the nutrient solution used in hydroponic agriculture. The sensor is composed by a pH probe and a set of micropumps that sequentially pour the different liquid solutions to maintain the sensor calibration and the water samples from the channels that contain the nutrient solution. To implement our architecture, we use an auto-calibrated pH sensor connected to a wireless node. Several nodes compose our wireless sensor networks (WSN) to control our greenhouse. The sensors periodically measure the pH level of each hydroponic support and send the information to a data base (DB) which stores and analyzes the data to warn farmers about the measures. The data can then be accessed through a user-friendly, web-based interface that can be accessed through the Internet by using desktop or mobile devices. This paper also shows the design and test bench for both the auto-calibrated pH sensor and the wireless network to check their correct operation.  Precision farming helps farmers to monitor [Over the last decades, traditional horticulture and greenhouse-growing have experienced an important evolution due to the advent of more environmentally friendly alternatives. Dry season periods and climate change generate a low storage nutrient solution quantity . For thi monitor  and cont monitor  and the  monitor , in some monitor , the useResearch  describiThis paper presents the design, implementation and testing of a smart system for bicarbonate control in irrigation for hydroponic precision farming that may help to improve the water quality in hydroponic agriculture. In addition, the process of calibration and measurement is controlled by a middleware (developed in Java) focused on reducing the quantity of false data acquired. The data is stored in a database (DB) that provides farmers with a customized auto-plan to control particular diseases, leading to a more sustainable agriculture. The system is also characterized by low operating and managing costs due to the use of auto-cleaner probes, an auto-calibration pH test  and an auto-acid injection dosage of the nutrient solution. In addition, the monitoring system provides historical and real-time values of different relevant environmental parameters, generating statistics that may help farmers to make specific measures and decisions to improve fertilizer treatments and water quality. This system is part of the multimedia platform, PLATEM PA (Multimedia Technology Platform for precision agriculture) . PLATEM The usefulness of our technological solution can be shown by the following example involving a farmer working on the indoor production of tomatoes for a small horticulture cooperative. He controls the greenhouse parameters daily. He has at his disposal a historical dataset that describes the weekly recommended water times, water pH levels, and water electrical conductivity (EC) levels for tomato production over a one-year period. These recommendations have been determined with a rule-learning algorithm based on a classification model consisting of 65 rules that specify the levels of bicarbonates, indoor ultraviolet (UV) light and temperature, water time, fertilization and pest control.Following the use of these rules, this farmer was not satisfied because of poor results. He deletes some rules which seem to be artifacts of the data and introduces new rules that represent well-known relationships between acidity levels and watering time that were omitted by the learning algorithm. After verifying the increase in accuracy of the edited rule set with a holdout dataset, he deploys the model. If the farmer had to rely on commodity rule learning systems, he probably would have had to export the rule learning results via XML, and then import the results to a scoring engine. The system presented in this paper allows this task to be accomplished through a web-based platform, the PLATEM multimedia platform, where the farmer can share his results with the rest of the cooperative farmers’ platform group.The main reason for focusing our work on hydroponic agriculture  is becauThe rest of paper is organized as follows: Over the last few years, different researchers and companies have proposed solutions for monitoring the state of crops (fields and greenhouses) by using sensors. Such sensors, when deployed throughout an area of crops, allow us to distinguish the specific necessities of each area which improves the management of the field and addresses possible problems accurately. This section shows some of the most important papers related to hydroponic agriculture. The section is organized as follows: First, the section presents the latest contributions in smart greenhouse control systems for hydroponic agriculture. After that, we include some works related to the use of WSN and sensor communication in agriculture. Finally, this section discusses platforms where farmers can share data regarding to farming and crops and how to add a smart core to multimedia platforms. We also describe the advantages of our system compared to the aforementioned literature.3, K, and H ions in hydroponic solutions. WSNs based on Wi-Fi standards (IEEE 802.11 a/b/g/n), Bluetooth and ZigBee have extensively been used in different fields for monitoring different parameters [In , the autrameters , applianrameters  and for rameters , in addirameters . The sysrameters . Anotherrameters . ZigBee There has been previous research ocused in the field of precision agriculture in which the development of sensors and the remote management of irrigation programs has been presented. A previous article about the H2020 Smart-Akis project presented knowledge about the contributions of European Farming Technologies and commercial technologies . AdditioAs the related literature has shown, there are several systems that are currently applied in hydroponic agriculture, including devices to control the pH, acidity or nutrient auto-dosage. However, all of them are applied in to water source without taking into account the current state of water that is already in the gutters that provide the nutrient solution to the plants. Our system measures the current acidity level in the gutters and adjusts the value of the new nutrient solution that will be provided to the plants to maintain an optimum acidity value at all times. This task is not performed by existing systems. In addition, we use a WSN to gather data from several points in the facility and verify that the nutrient solution throughout the entire facility is the adequate for its crops.Our smart system presents several advantages with respect to existing solutions. First, unlike other systems, it provides a complete system, both hardware and software, so compatibility issues are minimized. Furthermore, this kind of development avoids the collection of data from or through third-party  cloud-based platforms. Second, it uses non-proprietary communication technology (NRF24L01), which eases the integration of both nodes and third-party gathering data with auto-recalibrated sensors. Third, thanks to the use of standard NRF24L01 transceivers , the depThis section describes the proposed system and its operation. The network architecture is also presented as well as the devices used to implement the nodes.The acidity of a nutrient solution is measured with a pH sensor. The pH level of the nutrient solution in this study requires a mildly acidic nutrient solution with a pH level ranging from 5.5 to 5.7 for optimum neutralization of bicarbonates and salts. The total pH scale ranges from 4.5 to 14, where 7.0 is considered the neutral pH value. A liquid solution with a pH lower than 7 is considered to be an acidic solution, while solutions with a pH greater than 7 are basic or alkaline solutions. This sensor gives us a signal related to the hydrogen ion concentration measured by a pH electrode. The isopotential point pH of 7.00 (0 mV), measured on a range from 0 to 14, has an accuracy lower than 15 mV. The relationship between the pH value and the output voltage is E = 59.16 (mV/pH). The pH sensor is connected to a wireless node sensor that provides the necessary electronics to control the different hardware parts through micro-pumps which take the different solutions and liquids from containers. In order to obtain accurate measurements, the sensor needs to be periodically calibrated. To this end, three ready-to-use pH reference buffer solutions to perform decalibration and provide distilled water to clean the probe are required. Firstly, we need to clean the glass bulb of the electrode. Next, the pH sensor has to be turned to calibration mode. After injecting the calibration solution into a beaker, the electrode is immersed on it with a micro-pump. 2 levels in the water, so they can also affect the pH. If the presence of algae is significant, the pH will increase because the CO2 will be removed from the solution. Bacteria can transform certain forms of nitrogen, thus having the effect of increasing the acidity. Large amounts of CO2 in the air generate more CO2 in the nutrient solution and vice versa. For this type of problem, it may be advisable to measure the solution every 2 days. Finally, we must consider that in medium–high temperature conditions, when the pH of the nutrient solution is in the range of 7.5, the correct absorption of nutrients by the plant is affected.There are different parameters and factors that cause an increase in the pH value over time. For example, if the water contains wastewater, the pH can increase by 1 point in a week. This type of change occurs over a long period of time so it is easy to act on. On the other hand, bacteria and microorganisms change the COThe auto-calibrated pH sensor is a precision agriculture system which is part of a WSN, and a smart framework software supports the control of irrigation process on greenhouses. This subsection is focused on the design of the algorithm.3) is present at pH levels between 4.5 and 8.3, and the carbonate ion (CO3) is present in the nutrient solutions with pH levels above 8.3. The calcium carbonate salts have very low solubility so, once the precipitate has been produced, it is very difficult to restore the pH value of the water. The best way to prevent this problem is to carry out continuous treatment with commercial acids that neutralize and destroy the bicarbonates found in the nutrient solution. In this way, we can avoid their combination with calcium ions in the nutrient solution or with those that are added as nutrients. The amount of acid that needs to be incorporated into the nutrient solution depends on the volume of water that is to be treated and the bicarbonate content of the nutrient solution Excess acid will produce a drop in the pH level to a value that could be lethal to crops.The main function of the control algorithm is to detect the presence of carbonates or bicarbonates in the nutrient solution which determines the pH values. The bicarbonate ion ):a, pH and EC. The goal of our system is to neutralize bicarbonates to optimize the irrigation functionality. The density increases because the purer the acid is, the greater the amount of neutralization required.It keeps a record of the main parameters that affect the development of irrigation and fertilization, through the ability to use smart core decision processes to identify deficiencies in water quality values, by using a wide range of devices connected in a WSN.It assists in preventing crop diseases through predictive models by obtaining ad-hoc alerts from the farm. Specifically, the algorithm automates the detection of both primary and secondary quality water tests and sends alerts to the users. It is possible to use WSN as a greenhouse controller . The algIt helps to reduce and prevent environmental pollution, reuse wastewater for irrigation and increase the auto-management of crop systems, in accordance with sustainable Common Agricultural Rural Development rules promoted by the European Union in Common Agricultural Policy.This system was also conceived to help farmers to monitor the quality of nutrient solutions and to increase the quality of crops produced by presenting information about parameters and recommendations to support decision-making. Thus, the auto-calibrated pH sensor covers three basic needs:Because the main function of our algorithm is to obtain a pH between 5.5 and 6, we must leave 0.5 a sensor and EC sensor to the server which is in charge of controlling the entire facility and is directly connected to the Internet. The data is stored in a DB placed in the cloud. The owners can access the stored data through personal devices in order to check historical gathered data and confirm the correct operation of different systems.As we mentioned before, the designed pH sensor is integrated into WSN architecture that monitors several greenhouses containing hydroponic systems. Manual interpretation is a time consuming and expensive task. For these reasons, we introduced a WSN for gathering data. In this case, we selected the NRF24L01 transceiver from the Nordic Semiconductor Company. The communications chip chosen was an 8-bit Atmel which is characterized by its low power consumption. We have found some proposals and recommendations regarding the use of the NRF24L01 transceiver, but in all of them, we can only see the part of the data transmission from a node to its coordinator. In previous work with the NRF24L01 transceiver, the communications protocol pattern was based on sending the data from the node to the mesh network so that it eventually reached the coordinator. These networks have mainly been developed in WSNs to send data about variables. In our mesh network proposal, the routing method was based on the node identifiers, rather than using a prefixed routing table. Finally, the nodes were protected by IP66 boxes.The main goal of middleware software is to analyze historic data related to the acid density ratio and to modify the acidity values of the nutrient solution to ensure the correct growth of the crops. The DB contains the reference values shown in As Algorithm 1 shows, the system firstly checks the wireless connectivity and nodes’ status. In this phase, the system determines if there are any problems related to the battery level or any other hardware issues. If the values from the sensors are out of range, the system starts the auto-clean process and the calibration tasks. The next step is data gathering, and if these values are inside the normal range, the data is wirelessly sent to the middleware located in the server. When the middleware receives new data, it starts to compare the new values with the historic data and values of equivalent dosages preconfigured in the algorithm. After processing the data, the value of acid to be added to the nutrient solution is calculated. At this point, the acid micro-pumps are enabled to inject the amount of acid required. Finally, the system rechecks the pH level to verify that neutralization process has been successfully performed.Algorithm 1. Pseudocode of Flow Diagram(1) Get data;(2) Start intelligent diagnostic Rule Engine;(3) Determine the current status of the pH, electrical conductivity (EC) and acidity parameters based on the rules and sensor data;(4) If the current state and the historical state are different, then   (5) The current status is regarded as the historical status, and the current time is recorded;(6) Otherwise   (7) Compute the state of the battery;      (8) If the duration is within the limit, then      (9) Intelligent diagnosis is delivered;     (10) Otherwise        (11) Auto-clean the sensor and inject new water sample        (12) Go to (2)      (13) End      (14) Otherwise      (15) Go to (1)     (16) End(17) End(0) WSN Conectivity OK;The designed middleware/decision system is implemented in Java Drools and monitored by PHP and HTML5 languages, and the service is provided services through the Internet. Users can remotely login into the system via the Internet, browse and query all of the information, including historical sensor data and alert information, real-time sensor data and alert information, expert knowledge, intelligent diagnoses and other services. Regarding the storage of the collected data, a MySQL database was selected, since it is widely used and there is a strong user community behind it. Moreover, the back-end and front-end servers chosen were compatible with such a database.This section shows the results of the developed system, focusing on three important aspects: the quality of the collected data, the communication network and the power consumption of nodes.The system was used in tomato crops and lettuce crops placed in the same small greenhouse . The choice of these crops was because they are some of the most suitable for cultivation with this technique .3 of density. This means that in order to provide a According to the values shown in In order to see how the pH sensor is able to neutralize a dose of water by providing the volume of required acid to neutralize the sample, we progressively added 5 +. When the value of acidity changes, there is an ion combination that generates important changes in water. In concrete, the increase of carbonate in water generates changes in water temperature, water conductivity and the presence of HStarting from the initial pH values of the water sample, after adding the first doses of acid, in the resulting solution, most of the protons initially react with the carbonates in the water, but some of them remain free in the water. The results are shown in graphs in accordance with the reference table for acid dosages in relation to pH. This causes a decrease in pH.Finally, we test two possible cases, i.e., the evolution of the pH level when the auto-calibrated pH sensors and our algorithm are not used and when the designed system is used see . The expIn order to determine the network’s performance, we have deployed the network shown in Due to climate change or the presence of vegetation between the nodes and coordinator node, it is possible for errors to occur. After capturing the network traffic during 70 min, we detected a very low error rate of 1.6%.The radio system supports an automatic confirmation function (autoACK), activated by default, in which the receiving radio changes to transmission mode after reception and recognizes the reception of data. This helps to maintain a bidirectional channel.A sends to BA sends to CB answers AA already knows that B has the right message.C answers AOne already knows that C has the right message.In the first test, a simple scenario is created with three nodes, A, B and C, with the following exchange of messages:Finally, The benefits of the automation of the process of taking measures are the reduction in the maintenance costs of the facilities and the improvement in the analysis process which with the classic manual analysis methods, can support 20 min of processing per sample. Acid data acquisition should be done every 45–50 min during watering. In this way, we can ensure the quality of the nutrient solution.This paper has presented a smart auto-calibrated pH sensor allowing the data gathering and adjustment of the quality of nutrient solutions to optimize watering in precision agriculture. Specifically, our smart auto-calibrated system supports decision-making to control false data acquisition. The sensor is controlled by a wireless node that is part of a WSN which is controlled by decision-making middleware . Our sysThe information collected by our WSN is presented through a user-friendly web portal for management and visualization. The web-based system can be accessed from any computer, tablet or smartphone with the only requirement being having a browser and an Internet connection. To sum up, all results obtained confirm that smart middleware is a good tool for auto-control of data and remote management of greenhouses to automate the detection of blind drippers and water quality.After testing and analyzing the results, we conclude that the use of our system can help farmers to better control the process of watering. One of the main benefits for farmers is that this system will reduce the worktime required for cleaning and calibrating sensors in large areas dedicated to indoor horticulture production and will enhance the autonomy of greenhouses. Vertical agriculture could reduce the impact of the effects on the environment . Our solThe proposed system allows data gathering for decision support in farming. It helps users to easily make decisions in greenhouse production by providing descriptive, predictive and prescriptive analytics.For future work, we will focus our efforts on providing more precise definitions of other parameters that can also affect water quality irrigation with the purpose of adding them to our real-time monitoring systems. We also want to check the integration of new rules into algorithms in autonomous irrigation controllers without human interaction. We will carry out a study to evaluate if our developed smart sensor could be a solution for basic commercial systems on the market.


The specialty of Neurology is faced with a fundamental problem of economics: supply and demand. The projected increase in provider supply is unlikely to keep up with projected increases in patient-care demand. Many large academic centers have used residents to meet this patient-care demand. However, the conflict between education of residents and patient-care needs has created a hindrance to both of those missions. Many specialties have been using advanced practice clinicians (APCs) to help address the need for patient care. In the setting of a residency program, this availability of APCs can help to alleviate patient-care demands for the resident and allow for better allocated educational time. Neurology has not historically been a popular choice for APCs and a standardized educational curriculum for a Neurology APC has not been established.The authors share an example curriculum recently implemented for training new inpatient Neurology APCs. This curriculum includes a 12-week program complete with rotations through various subspecialties and proposes fundamental lecture topics for use in education. The authors share their expectations for clinical duties that evolve over the course of the 12-week program in conjunction with expectations for increasing clinical knowledge as well as efficiency in system utilization.The addition of APCs to support a busy inpatient Neurology practice has obvious beneficial implications but the integration and education of this new staff must be structured and well-designed to support the confidence of the APC in both their knowledge and their role as an indispensable member of the care team. The current number of US neurologists is considered unacceptable in view of the increase in age-related neurologic diseases in the population. Therefore, many large academic medical centers have relied on neurology trainees such as residents and fellows to help with meeting the needs of the population. However, this has created a conflict between education and patient care responsibilities whereby the clinical workload of a resident interferes with the educational requirements mandated during residency . This coIn many contexts, APCs are already involved in the provision of neurological services. The American Academy of Neurology (AAN) reported that APCs were employed in 39% of neurology practices with a higher percentage of them used in the hospital setting .Neurology is a satisfying career selection for APCs according to the AAN survey; job satisfaction was noted to be high with low turnover rates coupled with average time working in neurology being more than seven years . The aboCurrent APC education may not involve adequate exposure to neurology to ensure that high-quality patient care can be provided without changes to the traditional curriculum. The Accreditation Review Commission on Education for the Physician Assistant, Inc. (ARC-PA) requires the following areas for the preclinical curriculum: anatomy, physiology, pathophysiology, pharmacology, and genetic and molecular mechanisms of disease. In the clinical curriculum, the required areas are emergency medicine, family medicine, general internal medicine, general surgical care, geriatrics, pediatrics, prenatal care, and women’s health. Training programs for advanced practice nursing are far more variable as there are numerous types of programs available. Historically, education for APNs has been described as “uncoordinated and sporadic, resulting in programs that have been inconsistent in academic preparation, duration, theory base, and practice requirements” .There are a number of specialty post-graduation programs for PAs, but only a single program for neurology exists Table  6]. The. The6]. Our hospital is an academic medical center with 551 licensed beds, over 29,000 admissions per year with over 960,000 emergency room visits per year. We have about 1500 inpatient neurology admissions per year and about 20,000 outpatient neurology visits per year. Our department has been utilizing APCs in the outpatient setting for over a decade. We had expanded our outpatient APC employment over the last two years with a large focus on improving access to Neurology. This expansion along with the development of an access clinic was successful in reducing lead time for new patient visits from about one year down to several weeks .Given our intradepartmental success with the use of APCs, and faced with a growing inpatient census, we saw an opportunity to incorporate APCs into the inpatient setting to decompress the workload on our residency program. The APC would work alongside the residents and perform history and physicals, write orders, complete pre-rounding, write progress notes, prepare discharge summaries, and address any urgent patient issues. Our inpatient service line is separated in two teams, one dedicated to stroke and the other to general neurology; each typically comprised of two residents and one attending. The APCs would ultimately alternate between the two service lines every four weeks, expanding each team to at least four providers.Our approach to curriculum development was to integrate two APCs into our inpatient neurology program which was already comprised of 12 residents (four per post graduate year). The orientation and training program was designed by a team of providers from within the neuroscience group at our hospital. This team was comprised of physicians and APCs representing both Neurology and Neurosurgery. The premise of creating such a curriculum was to ensure that our APCs would be given the basic fundamentals to practice neurology in a supervised capacity. In our outpatient experience, we had experienced difficulties in competency which was felt to be related to lack of a standardized educational curriculum.We created an orientation and training program for this position that would take place over 12 weeks. The APC will rotate through all necessary aspects of the service to provide adequate introductory exposure. These aspects included Stroke Service, General Service, Epilepsy Monitoring Unit, Outpatient Neurology Clinic, Neurophysiology Lab, Interventional Neuroradiology, and Acute Inpatient Rehab. At the start of orientation, the APC is exposed to a Neurologic Examination tutorial video and in person training. During each day, in addition to clinical responsibilities, there is some combination of training, certification, focused lectures, and reference articles for review. Pertinent clinical certification and training included National Institutes of Health Stroke Scale (NIHSS), Modified Rankin Scale (MRS), and brain death evaluation. Since there is a Neurology residency at our facility, we encourage the APC to attend the daily residency lectures ad-lib. We developed additional targeted lectures for introductions to all conditions and diseases that are frequently encountered on our inpatient service. A list of topics by week can be found in Table A detailed timeline of the clinical duties for the entire 12 weeks is provided in Table During the orientation timeframe, routine short written multiple choice tests were given corresponding to the teaching and reading being assigned that week. Each test was 10 questions long and test topics included stroke, headache, epilepsy, neuromuscular disease, and two tests on other general neurology. Testing was used to assess acquisition and integration of clinical knowledge. Testing was reviewed in person between the APC and the Neurologist responsible for the orientation. When knowledge gaps were identified, additional resources were dedicated to close said gaps. At the conclusion of the orientation process, the APC no longer had any required additional lectures to attend but was invited to continue to attend any and all Neurology resident lectures that were being given.Identifying the sources to procure the most salient and reliable information can be difficult. We recommend using the most up to date guideline statements where applicable . Internally cultivated lectures are indispensable for transmission of knowledge and also help the APC to learn the preferences of their colleagues when it comes to subtle variations in practice. Where guideline statements and lectures don’t suffice it is reasonable to provide references to high-quality literature that focus on evidence-based medicine.No formal evaluation process took place but an ongoing dialogue was kept between the primary supervisor and the APC during 12 weeks of the orientation and training program. The most well-liked parts of the program were reported to be the detailed calendar of articles to review and references for additional training and certification and the lecture series. The two APCs who have completed our program believe the training to have been valuable both from an educational standpoint but also from an operational standpoint; allowing time for a new hire to adjust to the work-flow and intricacies of a new hospital system resulted in satisfaction amongst the APCs who felt better equipped to function as an independent member of the team at the completion of orientation. From the supervisor’s perspective, the periodic short knowledge-based testing was valuable to identify potential shortcomings in the educational process. Both APCs who completed the program demonstrated excellent acquisition of knowledge as assessed by this testing during the orientation process. However, both APCs scored poorly on testing related to dementia by comparison to other topics.In our opinion, teaching on rounds is crucial to the cultivation of knowledge not only during the orientation but also across the continuum of employment. However, we believe that bedside teaching alone cannot effectively cultivate a well-rounded APC and therefore it is our opinion that having a structured schedule with focused high yield topics is imperative to this orientation process.In any teaching scenario, feedback plays a critical role in the learning process. Feedback was given by the supervising physician; this person was involved in the screening and hiring process, and was identified as the point of contact for the APC. We sought to provide feedback via face-to-face meetings every two weeks. These meetings were kept informal and brief, but allowed for the APC to provide feedback on such things as clinical load, value of lectures, teaching on rounds, and overall experience. Prior to the meetings feedback was collected from residents, attending physicians, and other staff members on any thoughts or concerns they had about the APC.Supervision and oversight of the APC training process was a dualistic approach with a formally designated supervisor as mentioned above providing remote oversight, while each attending on service provided direct supervision and oversight on a day to day and patient to patient basis. The APC was granted variable levels of independence based on the comfort level of both the attending physician and the APC. This independence is heavily influenced by patient complexity and APC experience; at any point, the attending should be available to provide direct oversight but if both the attending and the APC agree, then the patient can be seen by the APC alone.To date, we have had two APCs complete this training process which is a limitation in our ability to fully evaluate the merits of such a training program. Furthermore, since the inpatient APC position is new for our department, we do not have any comparison of an APC who did not complete this type of curriculum training. As more APCs complete our program we will plan to gather their feedback on the efficacy of this curriculum in order to continue to modify and update our program to reflect any shortcomings of our current structure. One of the shortcomings that has already been identified is related to the relatively low scores on testing related to the topic of dementia. In part, this can be explained by the virtue of dementia being largely an outpatient-related condition but also our program currently lacks a specialist in Cognitive and Behavioural Neurology. Due to these relatively low scores, we have added additional reference material and lectures on this topic to enhance the knowledge in this area.Standardizing an orientation and training program for new hire APCs can ensure that each individual is provided with a similar foundation of knowledge and skills such that integration into a subspecialty, such as neurology, can be accomplished despite the overall lack of post-graduate training for APCs. It is our experience that the APCs find this time period focused on education and training to be a valuable and positive experience. In our institution, we are now exploring a partnership with our affiliated Physician Assistant School to provide clinical experience in Neurology during the clinical phase of the degree program to enhance the pre-graduate exposure to Neurology as a subspecialty.