Primary care is a key element of health care systems and addresses the main health problems of the population. Due to the demographic change, primary care even gains in importance. The knowledge of the patients’ preferences can help policy makers as well as physicians to set priorities in their effort to make health care delivery more responsive to patients’ needs. Our objective was to describe which aspects of primary care were included in preference studies and which of them were the most preferred aspects.In order to elicit the preferences for primary care, a systematic literature search was conducted. Two researchers searched three electronic databases  and conducted a narrative synthesis. Inclusion criteria were: focus on primary health care delivery, discrete choice experiment as elicitation method, and studies published between 2006 and 2015 in English language.We identified 18 studies that elicited either the patients’ or the population’s preferences for primary care based on a discrete choice experiment. Altogether the studies used 16 structure attributes, ten process attributes and four outcome attributes. The most commonly applied structure attribute was “Waiting time till appointment”, the most frequently used process attribute was “Shared decision making / professional’s attention paid to your views”. “Receiving the ‘best’ treatment” was the most commonly applied outcome attribute. Process attributes were most often the ones of highest importance for patients or the population. The attributes and attribute levels used in the discrete choice experiments were identified by literature research, qualitative research, expert interviews, or the analysis of policy documents.The results of the DCE studies show different preferences for primary health care. The diversity of the results may have several reasons, such as the method of analysis, the selection procedure of the attributes and their levels or the specific research question of the study. As the results of discrete choice experiments depend on many different factors, it is important for a better comprehensibility of the studies to transparently report the steps undertaken in a study as well as the interim results regarding the identification of attributes and levels.The online version of this article (doi:10.1186/s12913-017-2433-7) contains supplementary material, which is available to authorized users. According to the World Health Organization’s (WHO’s) declaration of Alma-Ata, primary care is a key element of health care systems. As it addresses the main health problems in the community and often serves as the patients’ first level of contact with the health care system, primary care is highly important to all citizens . PrimaryPreferences for medical care can be defined as “statements that indicate the importance of specific aspects of clinical behavior of care providers or the organization of care”  is a common technique to elicit preferences for health care services or technologies. DCEs are based on Lancaster’s  theory aThere are several discrete choice experiments focusing on patient or public preferences for primary care and GPs. Those DCEs include attributes like “Waiting time till appointment” or “Length of the consultation”. The levels for the attribute waiting time could be “same day”, “one day” and “two days”, for example. If “Price” is one of the attributes of the DCE, it is possible to calculate the willingness to pay for the other attributes. As a DCE is an attribute-based method of measuring preferences, the identification of appropriate attributes is an essential task . AttribuThe aim of this systematic review is (1) to provide an overview of the attributes and attribute levels used in the discrete choice experiments and of how they were selected and (2) to reveal which attributes of primary care are most important for patients and the population.We conducted a systematic review of published studies reporting stated preferences, particularly discrete choice experiments for primary health care in OECD countries . The respondents were faced with a maximum of eight different choice sets in most surveys. The sample size was <500 in six and >1000 in seven of the studies. Eight studies had a response rate between 40% and 60%. Three studies reported a response rate of less than 40% and two studies of more than 60%. In these two studies [Table  studies , 33 the  studies , 28, 31.A crucial aspect of discrete choice experiments is the selection of attributes and associated levels that adequately outline the health service a study focuses on. As there are no accurate rules to determine attributes and levels presented in a DCE, we examined how they were selected by the researchers.n = 10 studies) and qualitative research, such as focus groups or semi-structured interviews (n = 8 studies). Other options to identify attributes are discussions with experts (n = 3), an analysis of policy documents (n = 2) or selection of attributes based on attributes used in former studies (n = 3). There are some studies that used several of these methods to identify relevant attributes. Gerard [Table . Gerard  for examents n =  or selec. Gerard  performen = 8), and “Waiting time in the practice” (n = 6). Among the process attributes, the one most commonly used was “Shared decision making / professional’s attention paid to your views” (n = 6). Only a limited number of studies applied outcome attributes. “Chance of receiving the ‘best’ treatment” was used twice and “Chance contact relieves anxiety” as well as “Likelihood of having illness cured” were each used in one study. The levels of the mentioned primary care attributes vary widely. Some attributes like “Care provider” have quite similar levels across the DCE studies (doctor versus nurse practitioner or practice nurse or primary care team), other attributes like “Waiting time till appointment” have very diverse levels . All levels and associates attributes are shown in Table Table The 18 studies are based on a wide range of attributes concerning primary health care. As diverse as the used attributes, so are the results of the preferences measures. Table Based on the results of the regression analyses conducted in the identified studies the attribute “Care provider” was the most important in 4 of the DCEs , 25, 28.Overall, this systematic review of preference studies has identified a considerable number of attributes which effect the organization of primary health care. Moreover, the results of the studies show different preferences for primary health care. Overall 8 studies identify a structure attribute and 8 studies a process attribute as being most important for the respondents. In 2 studies the most significant attribute is an outcome attribute. However, it should be noted that those are absolute numbers, not relative ones. As there are 4 studies that use only structure attributes, all in all there is a lower chance for a process attribute to be the most important one. In addition, there are only 4 studies that include an outcome attribute, so that the chance for those ones to be most significant is even lower. Relative to the number of studies including the attribute dimension in question, a process attribute is most often the one of highest importance, i.e. the one with the highest ß-coefficient in the regression analysis.In general, the selection of attributes strongly depends on the aim of a study. If a study for example focuses on preferences for appointment booking systems or out-of-hours services, it is evident that the study uses rather structure attributes than process ones. However, what is remarkable is that only a few studies use outcome attributes. Those using outcome attributes have quite different study objectives, such as models of primary care or primary care consultations in general. In two of the four studies that use an outcome attribute it turns out to be the most important one. In both studies it is the same attribute, which is “Receiving best treatment”.Comparing the attributes used in the studies and their results there does not seem to be a pattern according to the studys’ objective or origin. The diversity of the results may have different other reasons: The study results may particularly be biased for example by (1) the specific research question of the study or aim of the study or by (2) the selection procedure of the attributes and their levels.Primarily, the chosen attributes and, thus, the preferences elicited by the DCE depend on the specific research question. Even if the study objective is the same, the precise issue might differ. Pedersen et al.  and TurnMost of the studies followed the recommendations of for example the ISPOR or other authors concerning the selection procedure of the attributes and levels, which should be supported by evidence like literature reviews, qualitative research or other scientific methods. Although the process of selecting the relevant attributes is highly important for a preference study, there are no precise guidelines on how to translate the literature search or the previous qualitative work into the final attributes and their levels . Amaya-AThis review of the literature has some limitations. First, the results of a DCE can be influenced by reasons other than the research question or the attribute selection, which cannot all be discussed in this article. Those reasons might be the inclusion of an opt-out option , 36 or tFurthermore, due to the exclusion of non-English language articles, some relevant studies might not have been included in this review. Furthermore, it is possible that the use of other search terms would have led to other search results.Finally, because the results of a DCE depend on various factors such as research question, selection of attributes and levels and also on method of data analysis, it is not possible to directly compare their results.This review gives an overview over the attributes used in DCE studies that measure preferences for primary care. The results of discrete choice experiments are quite diverse and difficult to compare with each other, because they depend on many different factors, such as the research question, the process of selecting relevant attributes or the method of analysis. In order to achieve a high comprehensibility it is important to transparently report all steps undertaken in a DCE as well as the interim results – especially of the literature research and the qualitative pilot study. This could, for example, be done by publishing the results of the literature search or the qualitative work, or by adding them to an (electronic) appendix. As the final selection of the attributes out of the range of all possible attributes is inherently less transparent and rather interpretative and driven by different interests, the prior steps should be documented as clearly and reproducibly as possible.Furthermore, this review can be helpful for researchers planning to conduct a DCE in the field of primary health care because it gives a broad overview of attributes and levels used in DCEs in the past 10 years. It also highlights which attributes and dimensions of care provision are important to patients.Although the results of the DCE studies are not directly comparable, DCEs generally give relevant information on patient preferences within the context of the study setting and can support political decisions that take into account the patients’ perspective.Additional file 1:Systematic Review Protocol. (DOC 31 kb)Additional file 2:Search strategy. (DOC 34 kb)


Impairment of hand and finger function after stroke is common and affects the ability to perform activities of daily living. Even though many of these coordination deficits such as finger individuation have been well characterized, it is critical to understand how stroke survivors learn to explore and reorganize their finger coordination patterns for optimizing rehabilitation. In this study, I examine the use of a body-machine interface to assess how participants explore their movement repertoire, and how this changes with continued practice.Ten participants with chronic stroke wore a data glove and the finger joint angles were mapped on to the position of a cursor on a screen. The task of the participants was to move the cursor back and forth between two specified targets on a screen. Critically, the map between the finger movements and cursor motion was altered so that participants sometimes had to generate coordination patterns that required finger individuation. There were two phases to the experiment – an initial assessment phase on day 1, followed by a learning phase (days 2–5) where participants trained to reorganize their coordination patterns.Participants showed difficulty in performing tasks which had maps that required finger individuation, and the degree to which they explored their movement repertoire was directly related to clinical tests of hand function. However, over four sessions of practice, participants were able to learn to reorganize their finger movement coordination pattern and improve their performance. Moreover, training also resulted in improvements in movement repertoire outside of the context of the specific task during free exploration.Stroke survivors show deficits in movement repertoire in their paretic hand, but facilitating movement exploration during training can increase the movement repertoire. This suggests that exploration may be an important element of rehabilitation to regain optimal function. Stroke often results in impairments of upper extremity, including hand and finger function, with 75% of stroke survivors facing difficulties performing activities of daily living , 2. CritTypical approaches to hand rehabilitation emphasize repetition  and funcTo address this issue, there has been a greater focus on directly facilitating the learning of new coordination patterns. Specifically, in hand rehabilitation, virtual tasks  have been examined as a way to train finger individuation , 21. In In order to test the hypothesis that exploration of novel coordination patterns can improve overall movement repertoire, I used a body-machine interface , 31 to eParticipants were 10 chronic stroke survivors with mild to moderate impairment of the hand. Inclusion criteria were: a single stroke (>6 months prior to participation), and some movement of the fingers (Chedoke-McMaster > 3). Exclusion criteria were: visual or cognitive deficits that would prevent them for performing the task. Data from one participant (S05) was excluded because of technical difficulties with wearing the glove on the paretic hand. The information of the remaining 9 participants is provided in Table The task for the participants was to wear a data glove  and control a cursor on the screen –33 Fig.. The datMapping finger movements to cursor motion:0 represents an offset term designed to position the cursor on the center of the screen, wI , wM , wR , wL represent the weighting coefficients on the index, middle, ring and little fingers, and sI , sM , sR , sL represent the “normalized” signal values from the four fingers measured by the data glove. The normalized signal values were obtained by dividing the raw glove signal values from the data glove by the range of motion for that finger . The range of motion for each finger on each day was obtained for each individual prior to the task, where they were simply asked to flex and extend the fingers as much as possible. This normalization was done to ensure that all finger motions contributed equally to cursor motion, and that all participants would still be able to perform the task regardless of their actual range of motion .The cursor was controlled by finger movements as follows: The data glove signals from the metacarpophalangeal (MCP) joints of four fingers , were mapped on to the horizontal position of the cursor on the screen using the following equation:I , wM , wR , wL) is referred to as the “map” (since it maps finger movements to cursor movement) and determines the set of coordination patterns that can be used to perform the task. The reason there are a set of coordination patterns (and not one unique coordination pattern) is that this mapping is redundant – i.e. it maps 4 degrees of freedom (movement of each finger) to 1 degree of freedom . This means that there are multiple finger postures that can be used to get the cursor to any particular location on the screen.The vector W =  anI , wM , wR , wL): IM + RL , IM  and IM-RL  , middle (M), ring (R), and the little (L) fingers. On day 1, participants performed the cursor control task using three different maps that differed in the weighting coefficients (w-1) Fig. . The thrThe three conditions were presented in randomized order and participants performed the task both with their paretic and their non-paretic hand. The order of which hand they started with was also randomized. Each map consisted of having to reach 50 targets. In between blocks (when the maps were changed), participants were simply informed that the task difficulty could change, and that they should continue to do the task to the best of their ability, and might have to move their fingers in a different way if necessary.For days 2–5, participants came in twice a week to practice the novel coordination pattern in the cursor control task. To examine how participants could reorganize their coordination pattern, participants practiced a block of 100 trials where the map was changed Fig. . ParticiExploration blocks: In addition to learning the map, to examine if the training influenced their movement repertoire outside of the task, I had participants perform a “free-exploration” task where they were asked to make as many different motions with the fingers for 1 min. To ensure that I was capturing the full movement repertoire, I recorded a total of four 1-min trials - 2 before the training, and 2 after training in each session. This exploration block was a measure of the total movement repertoire. No feedback was provided to the participant during or at the end of each exploration block.Task performance in the cursor control task was measured by the following metrics: (i) number of targets successfully reached, (ii) movement time per target , and (iii) normalized path length. . The normalized path length is a measure of the degree of control participants had over the cursor, and was used to examine if changes in movement time were simply due to changes in movement speed (in which case there should be no change in the path length).To quantify the degree to which different coordination patterns were explored, I used the data from the IM-RL block (i.e. the difficult map that required finger individuation) and performed a principal components analysis (PCA) on the normalized data glove signals of the four fingers. The PCA was computed using the covariance matrix, which meant that the relative amplitudes of the finger movements was preserved. The variance accounted for (VAF) by the first principal component (PC1) was used as a measure of the movement repertoire used. A greater percentage of variance in PC1 indicates a smaller movement repertoire, and conversely, a smaller percentage indicates a larger movement repertoire. This metric is based on studies that used other dimensionality reduction techniques to quantify the complexity of the movement repertoire , 36.Similar to the initial assessment phase, I quantified task performance using the number of targets reached, and the movement time per target. In addition because the map changed during the course of trial, the movement time was computed in each of 10 trial blocks .(i)reorganization time – because the map changed during the course of the trial, the reorganization time was computed as the movement time in trial block 5  – the last block of the map change, when the movement times were typically the largest.(ii)Change in PC1 – to compute a measure of how much the coordination pattern changed, I used the dot product to find the angle between the PC1 in the first trial block (i.e. 1–10), and the PC1 for every subsequent trial block. I used the maximum angle during the entire trial as a measure of the reorganization in the coordination pattern. A value close to 0 degrees indicates little reorganization from the original pattern, whereas a value close to 90 degrees indicates a large change in the coordination pattern.The reorganization in coordination patterns during the task was computed using the following measures:I computed PCA on the data from each of the 4 free exploration blocks each day. The VAF by PC1 was averaged across the four blocks to get a measure of the movement repertoire outside of the task.Because participants were quite heterogeneous in their movement impairment (and therefore in their ability to perform the task), I used the Friedman’s test (a non-parametric test of the repeated measures ANOVA) to examine changes in movement time.For all dependent variables in the learning phase, comparisons were made between the first and last session (i.e. day 2 vs day 5) using the Wilcoxon signed-rank test (a non-parametric version of the paired t-test). The significance level was set at α = 0.05.For the paretic hand, four participants were not able to complete the full set of targets in the IM-RL map , and one participant (S01:13 targets) in the IM map. Except these, participants were able to complete the full set of 50 targets in the remaining conditions.p = .008). Post hoc pairwise comparisons indicated that the movement times in the IM-RL map was significantly longer than the IM + RL map (p = .007). None of the other comparisons were significantly different from each other. The same pattern of results was also observed on the non-paretic hand . The magnitude of the difference on the non-paretic hand was much smaller than that observed on the paretic hand.I examined the movement time for the paretic and non-paretic hand during all 3 tasks  and the unaffected hand  indicating that longer movement times were strongly associated with higher path lengths (and therefore differences in movement time were not simply a matter of speed differences).To examine if differences in movement time were due to differences in controlling the cursor, I also examined the path length   and the VAF accounted for PC1 (01) Fig. . This coOne participant (S04) who attended the initial assessment phase was not able to participate in the learning phase of the study – as a result, the learning phase included only 8 participants.p = .036).The number of targets achieved in each of the 4 sessions, and the corresponding movement time is shown in Fig. Sample data from one individual during the first session (Day 2) in the learning phase is shown in Fig. p = .046)  showed a statistically significant decrease in the reorganization time from the first to the last session of training  Fig. .Fig. 7Rep = .093)  – however there was no significant change in the angle from the first to the last session  Fig. .p = .020), indicating that participants who had greater hand function were able to reorganize their coordination pattern to a greater extent.There was also a significant correlation between the average change in PC1 (averaged across the first and last sessions) and the box and block test score Fig. . Becauser = −.868, p = .005) , indicating an increase in the movement repertoire with training  Fig. . In addiing Fig. .Fig. 8SaTo put the 8% drop in VAF in context, I used data from a previous study , where 1The aim of this study was to examine how stroke survivors explore and reorganize finger coordination patterns. Participants used a body-machine interface that mapped their finger movements in different ways to the control of the cursor. The map between finger and cursor motion was changed without the knowledge of the participants to get participants to explore different coordination patterns to perform the task. I found the following results: (i) stroke survivors have difficulty when the mapping requires finger individuation, and the amount of exploration observed was associated with clinical tests of hand function, (ii) with 4 sessions of learning, participants were better able to reorganize their coordination patterns to perform the task requiring individuation, and (iii) this training was also associated with a modest improvement in overall movement repertoire outside of the task.Our first finding that stroke survivors had difficulty with finger individuation is consistent with the literature , 9. MoveThe second finding was that with training, participants showed improvements in reorganizing their coordination pattern when performing the task. Participants who were unable to complete the full set of targets at the first session increased the number of targets that they were able to reach by the last session, and there was also an overall drop in the movement time. Reorganization times also decreased with practice, indicating participants were more efficient in their exploration after practice. The reorganization was also correlated with their initial hand function – this was indicated by a correlation between the change in PC1 angle and the initial Box and Blocks score. These results are consistent with evidence that stroke results in altered muscle synergies, indicating reduced movement repertoire  and a reFinally, I also found changes in movement repertoire during the free exploration task after training (as quantified by the PC1 VAF). In the free exploration task, there was no cursor to control, but instead participants were simply asked to explore the repertoire of finger movements. Here, there were two observations – (i) the initial movement repertoire (the PC1 VAF) was correlated to the Box and Blocks test, indicating that initial hand function was associated with movement repertoire, and (ii) I found a decrease in VAF after 4 sessions of training, suggesting that movement repertoire improves when participants learn to explore and reorganize their finger coordination when learning the cursor control task  this study was done in chronic stroke survivors who were generally several years past the stroke, making the change in the course of a few weeks less likely due to other factors, and (ii) unlike functional tests where there is clearly repeated effects of exposure, I found improvements in movement repertoire during the free exploration task, which was unrelated to the cursor control task. A second limitation was that although I was able to recruit participants with a wide range of impairment levels, all participants had to be able to wear the data glove, and have at least some volitional movement of the fingers, which resulted in a rather small sample size that potentially limits the generalizability of the findings. However, the same approach could be used in principle with more severe motor impairments using assistive robots that can aid finger movements .In conclusion, stroke survivors show deficits in movement repertoire that can be assessed through motor exploration. With training, participants were able to better explore novel coordination patterns outside the movement repertoire, and that also resulted in an increase in the movement repertoire. These results point out that exploration may be a critical tool to improve hand dexterity.


For many years, silicon micro-strip detectors have been successfully used as tracking detectors for particle and nuclear physics experiments. A new application of this technology is to the field of particle therapy where radiotherapy is carried out by use of charged particles such as protons or carbon ions. Such a treatment has been shown to have advantages over standard x-ray radiotherapy and as a result of this, many new centres offering particle therapy are currently under construction around the world today. The Proton Radiotherapy, Verification and Dosimetry Applications (PRaVDA) consortium are developing instrumentation for particle therapy based upon technology from high-energy physics.x–u–v configuration to be suitable for fast proton tracking with minimal ambiguities.The characteristics of a new silicon micro-strip tracker for particle therapy will be presented. The array uses specifically designed, large area sensors with technology choices that follow closely those taken for the ATLAS experiment at the HL-LHC. These detectors will be arranged into four units each with three layers in an pCT). This will aid the accurate delivery of treatment doses and in addition, the tracker will also be used to monitor the beam profile and total dose delivered during the high fluences used for treatment.The sensors will form a tracker capable of tracing the path of ~200 MeV protons entering and exiting a patient allowing a new mode of imaging known as proton computed tomography (90Sr source in the laboratory and a 60 MeV proton beam at the Clatterbridge Cancer Centre.We present here details of the design, construction and assembly of one of the four units that will make up the complete tracker along with its characterisation using radiation tests carried out using a  Typically, the standard approach for radiotherapy involves the use of x-ray beams, a technique that was first developed for medicine by physicists. Radiotherapy today, still remains a field that continues to go hand in hand with developments in physics and engineering Proton imaging: instrumental to the planning of any program of radiotherapy is a good imaging modality that can deliver accurate information on the patient׳s anatomy, and in particular the accurate location of the target volume. For proton therapy, this is carried out by an x-ray CT scan from which the proton stopping power of the tissue can be derived and the necessary range of the treatment beam calculated. During this conversion from x-ray imaging to proton stopping power, an uncertainty in the proton range is introduced, of order 1–3 mm pCT), a device that can accurately measure the trajectory and energy loss of protons as they pass through an object for many different angular projections is needed pCT scanner using silicon detectors for both the tracking and range (energy) measurements of protons , or by moving the imaging system itself. The current design of the tracker will demonstrate that a completely solid state system offering high-precision directional information on the path of protons can be used in conjunction with an energy-range measurement to perform a pCT scan tons see  [16], [12Silicon micro-strip sensor: each silicon micro-strip detector has a nominal thickness of 150 μm and is made from n-in-p silicon. The detector contains 2048 strips in total, 1024 read out on each side of the detector by eight ASICs  to allow for high occupancy, and the chip operates at a frequency of 104 MHz and its front-end amplifier with a shaping time of 30 ns. This corresponds to four times the average cyclotron frequency for the energy range of interest (60–200 MeV) at the facilities that will be used. There are two modes that can be used to acquire data: treatment mode and patient imaging or pCT mode. In treatment mode all strips are read out at ~100 μs intervals to allow sampling of the beam distribution for quality assurance (QA) purposes and dosimetry during the high fluences used during patient treatment. In pCT mode, it is possible to read up to four channels per ASIC with signal over threshold for the expected nominal beam spill repetition rate of 26 MHz. This allows for accurate tracking of multiple protons through the system at any one time. The hybrid PCB which can be seen in 3Tests with minimum ionising particles: preliminary tests with the assembled tracking unit were carried out in the lab using minimum ionising particles (MIPs) from a 90Sr source with a activity of 37 MBq. Although the tracker was never designed to track such particles, it served as a useful diagnostic during assembly to see, for each layer, if the signals were visible above the noise and could be read out with the DAQ. This test was repeated after construction of the tracking unit in order to verify that signals could be seen separately in each of the three layers  in order to optimise the spacing of the layers and prevent crosstalk. Such tests provided valuable information for adjustments of the electronics and improvement of the DAQ software, as well as allowing sensor/ASIC characteristics to be extracted during times when tests at an accelerator were not possible. Using these methods, the number of dead or noisy strips turned off by the DAQ was found on average to be −. This signal is less than half of what will be expected in the final application when beams of protons with energies ranging from 60 to 200 MeV are used and thus, optimisation of the system for detection of MIPs allowed the system to be prepared for tests in a proton beam. In 90Sr source is shown in the three layers of a tracking unit formed from the data of six strip halves. This data was collected for several thousand frames in the treatment mode readout  and allowed the testing of all channels in both the first and second threshold. This can be seen in the blue and red histograms respectively, set at their default values of 84 and 200 mV. which correspond to the end of the first threshold  locations of the protons for the 30 mm beam at a beam current of 7 pA can be seen for data in the first threshold in x,y) locations calculated. A two-dimensional hit map of the beam using these locations can be seen in pCT scan.During the experiment, a beam of protons with a width of 30 mm was used and its fluence monitored by means of an ionisation chamber. The chamber (connected to an electrometer) was situated a few cm after the nozzle in front of the detector  in conjunction with an energy-range measurement of the protons will also be assessed. This is necessary in order to know whether it will be possible to demonstrate that a pCT scan is possible with a fully solid state system of this kind.We have presented here the assembly and first results of a three layer tracking unit that will be deployed along with three other units for particle therapy applications. The results presented here show that both modes of data acquisition and both thresholds are operational and that tracking between the layers is possible. The complete tracker will be capable of fast particle tracking with minimal ambiguities and can therefore give useful measurements for dosimetry and beam QA, as well as providing the necessary directional information on the path of individual protons during a


Internal gravity waves are generated as adjustment radiation whenever a sudden change in forcing causes the atmosphere to depart from its large-scale balanced state. Such a forcing anomaly occurs during a solar eclipse, when the Moon’s shadow cools part of the Earth’s surface. The resulting atmospheric gravity waves are associated with pressure and temperature perturbations, which in principle are detectable both at the surface and aloft. In this study, surface pressure and temperature data from two UK sites at Reading and Lerwick are examined for eclipse-driven gravity wave perturbations during the 20 March 2015 solar eclipse over northwest Europe. Radiosonde wind data from the same two sites are also analysed using a moving parcel analysis method, to determine the periodicities of the waves aloft. On this occasion, the perturbations both at the surface and aloft are found not to be confidently attributable to eclipse-driven gravity waves. We conclude that the complex synoptic weather conditions over the UK at the time of this particular eclipse helped to mask any eclipse-driven gravity waves.This article is part of the themed issue ‘Atmospheric effects of solar eclipses stimulated by the 2015 UK eclipse’. The shadow causes a transient cooling in the Earth’s atmosphere of up to a few degrees Celsius along the path of the umbra, which is the region of the Earth that experiences a total eclipse. Within the umbra, the solar disc becomes completely covered for a period of a few minutes, blocking all solar radiation with the exception of that emitted from the Sun’s corona. A smaller reduction in solar radiation and temperature can also be observed within the penumbra, which is the area where a partial eclipse is experienced.An important consequence of the cooling effect of the Moon’s shadow during a solar eclipse was first hypothesized by Chimonas & Hines . It was A number of investigators have found evidence of eclipse-driven pressure changes using sensitive micro-barometers. Measurements during the 7 March 1970 solar eclipse across the USA revealed pressure perturbations with a dominant period of 89 min, together with weaker perturbations with periods in the range from 15 to 57 min . During The present study examines temperature and pressure perturbations during the total solar eclipse that occurred over northwest Europe on 20 March 2015. The umbra tracked across the Atlantic and Arctic Oceans, where the only landmasses experiencing totality were the Faroe Islands, and Svalbard in the Arctic Circle. The penumbra, however, was experienced over most of northern Europe, with 85% totality across the British Isles and the Scandinavian Peninsula. Our study aims to examine whether gravity waves generated by the eclipse are detectable in surface temperature and pressure measurements from two sites in the UK: the Reading University Atmospheric Observatory (RUAO), which is located at 51.43° N, 0.93° W and experienced 85% totality, and the UK Met Office Lerwick Observatory, which is located at 60.13° N, 1.18° W on the Shetland Isles and experienced 97% totality. In addition, measurements above the surface are analysed from radiosonde balloons launched at the same two sites, to determine whether any eclipse-generated gravity waves are detectable in the upper atmosphere.2.(a)At the Reading Observatory, surface temperature was measured using a 100 Ω platinum resistance thermometer that was located inside a Stevenson screen. In addition, surface pressure was measured using a Druck precision barometer that is sensitive to 1 Pa and is identical to that used by Aplin & Harrison . At the et al. [To examine rapid fluctuations in the surface measurements, both pressure and temperature time series were detrended using a smoothing spline to remove any relatively slow background variability. To minimize tapering effects around the edges of the selected time window, the smoothing spline was fitted to 6 h of data surrounding the eclipse and was then truncated down to 3 h. The residual data were then smoothed using a 10-point moving Savitzky–Golay method similar to that used by Zerefos et al. . To examet al.  were takAs discussed in §1, there may be other meteorological sources of gravity waves present. To attempt to determine whether any of the pressure perturbations detected are eclipse-driven, one can examine the wave propagation direction and speed to see whether any gravity waves are propagating away from the umbra. Information about the wave direction and speed can be calculated from the difference in the wind vectors between minimum and maximum pressure perturbations .(b)Given that the source of the gravity waves that are generated during an eclipse is in the upper atmosphere, and given that gravity waves propagate through the atmosphere, it may be possible to detect the gravity waves using radiosondes suspended from weather balloons. Such radiosondes have been used previously to investigate the properties of gravity waves in the troposphere and stratosphere using the temperature and horizontal wind component profiles ,21.Technological advances in the last 15 years have allowed radiosonde data to be archived at 1 s resolution. Therefore, vertical velocities can now be inferred from the radiosonde’s ascent speed and archived in addition to horizontal velocities. Vertical velocity variations in radiosonde profiles have previously been attributed to gravity waves  opening ω of a gravity wave [f is the Coriolis parameter, N is the Brunt–Väisälä frequency and α is given byx′, y′ and z′ are the three-dimensional displacements of an air parcel that is moving under the influence of the gravity wave. To a first approximation, the radiosonde can be considered to move as a passive air parcel, to which the following parcel displacement method analysis can be applied. To calculate the displacement perturbations from the radiosonde, the ascent speed and horizontal wind components are first smoothed to remove high-frequency instrumental noise. For this study, data spanning heights of 13–17 km are selected, as these were the heights at which maximum obscuration of the solar disc was observed by the radiosonde [To give an example of our analysis methodology, ity wave  can be cdiosonde  and are u, v and w wind components to calculate the background velocities x and y were integrated from the launch site and z was integrated from a height of 13 km. The mean displacements were then subtracted from the true displacements to yield the displacement perturbations, x′, y′ and z′. In order to select the points at which to perform the analysis, local maxima along the variable S were chosen, whereS. In the case of no gravity waves being present, the alignment of troughs and peaks would be random across all three displacement dimensions. S, at least two peaks of the displacement dimensions are in phase. In cases where a third is not in agreement, it is often 90° out of phase, adding more confidence that the radiosonde is detecting a gravity wave.A second-order polynomial was fitted to the ent with . The bacN was calculated from the thermodynamic variables measured by the radiosonde over a 250 m height window usingθ is the potential temperature, the over-bar denotes an average over the height window, and g is the acceleration due to gravity. N is a measure of static stability, with N2<0 indicating that the air is hydrostatically unstable, thereby removing the restoring force that is required for gravity-wave propagation.Finally, ω/f [U′ is calculated. The temperature perturbation T′ is calculated in an identical method to the horizontal wind perturbations. For a gravity wave, T′ is ±90° out of phase with U′, hence by taking the vertical derivative of T′ the phase shift is removed and both quantities will have either the same or opposing signs. Hence, the sign of η can be used to deduce wave propagation, whereη is positive (negative), then the wave propagates (anti) parallel to the positive bearing of the major ellipse axis [U′ is sampled over the same height window. These are then averaged over the height range of the detected hodograph.The characteristics of a monochromatic internal gravity wave can be inferred from a sounding by removing the background wind speeds and then plotting the horizontal velocity perturbations on a hodograph. For an ideal gravity wave, the hodograph would take the shape of an ellipse. By fitting an ellipse to an observational hodograph, the frequency of the gravity wave can be found. Specifically, the ratio of the major to minor axes of the fitted ellipse is proportional to ω/f . This reω/f . Fortunapse axis , where tpse axis  have alsThe moving parcel method is a useful tool as it enables the frequency of inertia-gravity waves to be inferred even if there is an incomplete or partial ellipse observed on the hodograph. However, as the moving parcel method uses the relationship between the vertical and horizontal components, it cannot be used to calculate wave propagation direction. The aim is to combine the strengths of these two methods to reliably gather as much information as possible about the inertia-gravity waves present in the atmosphere.3.The path of the 20 March 2015 solar eclipse transited across the North Atlantic Ocean as depicted in (a)The surface pressure chart  from the(b)−2 in solar radiation from 0900 to 1000 UTC during the eclipse, although this reduction would have been much larger in clear-sky conditions.For the entire duration of the solar eclipse, Reading was under the region of stratiform cloud that is shown in a shows that the surface pressure decreased by about 0.7 hPa during the eclipse. Although the timing of the start of the pressure decrease coincides with the decrease in solar radiation during the eclipse, the pressure drop is probably not eclipse-induced but rather caused by the ridge of high pressure becoming less dominant. The atmospheric cooling effect of the eclipse can be seen in figure 8c, which shows a 0.3°C surface cooling during the course of the eclipse. The cooling started at about 0900 UTC, when the solar radiation began to drop.Figure 8b,d. A Savitzky–Golay 10-point smoothing was applied, followed by a Lomb periodogram. Visual inspection indicates a 0.05°C drop in temperature between 0930 and 1000 UTC. This drop could be indicative of an eclipse-driven gravity wave, because a temperature change of this magnitude cannot be attributed to incoming solar radiation variability, which is minimal as shown in The corresponding detrended data points are shown in figure 8b shows a significant peak at a period of about 35 min, in good agreement with our initial visual inspection of the time series. The detrended surface pressure also shows a peak at periods of 45–50 min. However, in order to determine whether or not these peaks are attributable to the eclipse, we must assess the likelihood of such perturbations occurring on a non-eclipse day. Therefore, a similar analysis was performed on data recorded at the same time of day on the days both immediately preceding and following the eclipse. Figure 9a,c shows Lomb periodograms of the detrended surface temperature and pressure data from 19 and 21 March, respectively. It can be seen that on 19 March there are peaks in the temperature and pressure in the period range 45–60 min. On 21 March, there is a temperature peak in the period range 30–60 min, but for pressure the dominant peak occurs at a period of 20 min. In figure 10b, compass plots show the direction of wave propagation calculated from pressure and wind information assuming every perturbation in pressure is related to a wave. On the eclipse day, there are a large proportion of detected waves propagating to the south, which could be eclipse-driven waves propagating away from the umbra. However, when examining the days surrounding the eclipse in figure 10a,c, where waves propagate in multiple directions, there is little confidence in exclusively attributing the waves to the eclipse. Therefore, for the surface measurements made at Reading during the eclipse, we are unable to distinguish the temperature and pressure perturbations from those caused by other meteorological phenomena.The Lomb periodogram of the detrended surface temperature data in figure 9(c)b, visual inspection indicates that a wave form could be present, but the reduced sensitivity of the barometer at Lerwick compared with the barometer at Reading makes the detection exercise more difficult.A low-pressure system was passing over the Lerwick Observatory on 20 March (discussed in §3a), suggesting that it may be difficult to detect any eclipse-driven waves. Surface pressure and temperature from 0800 to 1100 UTC are shown in figure 11a,b. The Lomb periodograms show temperature and pressure perturbations with periods of about 55–60 min during the eclipse. There is also a smaller peak at a period of approximately 30 min. For the non-eclipse day shown in figure 12b, there is a pressure perturbation peak at a period of approximately 30 min, indicating that there is no evidence to attribute the peak on eclipse day to the eclipse. There is a small peak at periods of about 60–90 min on 19 March, but the peak carries less statistical significance than the one detected on 20 March. Wave speed and direction were calculated for Lerwick between 0800 and 1100 UTC for both 19 and 20 March, as for Reading; however, for both days the derived values are random and do not show anything significant that would imply an eclipse-driven wave. The results are shown in the electronic supplementary material, figure S2.As in §3c, Lomb periodograms were constructed from the detrended surface pressure and temperature data collected at Lerwick for both 19 and 20 March, and are shown in figure 12(d)a,b. As only two ascents were made, it is difficult to deduce whether the wave periods that were detected during the solar eclipse were caused by it. However, the periodicities that were deduced were typical of those of gravity waves in the lower stratosphere.Radiosonde launches made from the Lerwick and Reading observatories had reached the lower stratosphere when the eclipse was at its highest totality. The moving parcel analysis that was presented in §2b was performed on the data between heights of 13 and 17 km. At Reading, radiosondes were launched on the mornings of both 19 and 20 March. The period calculated from the moving parcel technique and hodographic technique is shown in figure 13a–f presents time series of the radiosondes launched and the time periods derived from the moving parcel and hodographic analysis. A range of time periods is detected, with the majority similar to those of internal gravity waves. There is no evidence to suggest that the solar eclipse generated any distinctly different gravity-wave activity. The variability in the time periods for each ascent could be attributable simply to synoptic conditions and their interactions with the tropopause. During the ascent for the eclipse, only two complete ellipses were observed in the hodographs. Coupled with the large variability in propagation direction observed over the 48 h period, it is impossible to deduce whether the eclipse generated any gravity waves that were propagating away from the source of the eclipse. Propagation directions are shown in the electronic supplementary material, figures S3 and S4.At Lerwick, six radiosondes were launched over a 48 h period, being a mix of research and operational launches. Figure 14b and 14a show large differences. In both cases, the moving parcel method appears to underestimate the period when compared to that of the hodograph, which may be attributed to errors in the moving parcel calculation method ). ). b and equation ), vertic4.(a)The results presented in §3b,c do not allow us to attribute any of the oscillations in surface pressure and temperature unambiguously to internal gravity waves generated as adjustment radiation in response to the cooling effect of the solar eclipse. At Reading, pressure and temperature perturbations were detected with periodicities of 30–55 min, which is similar to the periodicities observed in previous studies. A lack of variability in the cloud base height and solar radiation during the eclipse do suggest that the perturbations are not associated with weather systems. Wave propagation directions show little evidence to support exclusive eclipse-driven gravity waves at Reading. There may have been an eclipse-driven wave present, but it is impossible to detect it among other perturbations in pressure and temperature.In ,11, spec(b)In §3d, the moving parcel and the hodographic techniques that were used to infer the periodicities of gravity waves in the lower stratosphere give time periods similar to those expected from the literature . Howeveret al. [−2, which caused a small cooling of 0.3°C. When compared to the solar radiation change of 600 W m−2 recorded during the 1999 solar eclipse [Chimonas  theorizeet al.  reported eclipse ,12 and t−1, which is attributable to synoptic-scale weather.Other factors that could have affected the magnitude of the cooling are the time of day and year. During the August 1999 eclipse, the Sun’s zenith angle would have been lower and the eclipse was later in the day, causing larger cooling effects. In ,9,12, pret al. [Inadequate cooling near the surface could explain the absence of eclipse-induced pressure perturbations, but in the lower stratosphere there is less obscuration of solar radiation due to fewer clouds and less absorption and scattering. The lower stratosphere contains the ozone layer, which at high latitudes contains higher ozone concentrations at lower altitudes (15–20 km) . This imet al.  showed t5.This study has found little substantial evidence to suggest that pressure and temperature perturbations caused by eclipse-induced gravity waves could have been present over the UK during the 20 March 2015 solar eclipse. Contributions from the weather conditions, time of day and geographical location all obscured the interpretation of the perturbations, making them difficult to distinguish from perturbations caused by normal synoptic-scale influences. In particular, insufficient cooling due to cloud cover and the time of year could be a major factor as to why distinctive pressure perturbations were not detected. The results agree with other studies seeking eclipse-induced gravity waves, such as  and thosin situ measurements.Further work might concentrate on making similar observations during a future solar eclipse. In particular, the solar eclipse of 21 August 2017 across the USA offers greater potential for detecting eclipse-induced gravity waves, because it will occur in the summer months at lower latitudes. In addition, a simple adjustment to the payload of the weather balloon could allow measurements to be made higher up in the atmosphere. This adjustment would improve the detection prospects for eclipse-induced internal gravity waves, because the density drop-off with increasing altitude causes wave amplitudes to grow quasi-exponentially with height in order to conserve kinetic energy. Finally, the moving parcel method used to derive frequencies could be further improved upon by a more comprehensive comparison with wind profiler observations of gravity waves with


To examine care characteristics related to desirable care as reported by formal and informal caregivers in Dutch Psycho‐geriatric nursing homes for patients with dementia.Qualitative exploratory study.The sample consisted of four nursing homes. In each home, semi‐structured interviews were conducted with a manager, a quality advisor or head nurse, a daily care supervisor and an informal caregiver. The findings were analysed by labelling and coding the text fragments.The 16 semi‐structured interviews contained 60 discussion items. The 16 items that were shared by the four interviewee categories were clustered into the following six major themes: good quality of care; poor quality of care; elements of a vision; extra hands; bureaucracy; and formal caregivers. Since the start of the study presented in this article, complaints were reported about the nursing‐home care for patients with dementia, which resulted in newspaper rankings of the best and the poorest homes. It is important to be aware of those complaints.According to these rankings, some nursing homes perform very well, some have a poor performance and the majority show average performance. Pondering this wide range of judgements on the quality of care, the following question arises: what are the characteristics that determine the positive appreciation of formal caregivers, the patients and their informal caregivers?The six care themes that arose in the interviews were: good quality of care; poor quality of care; elements of a vision; extra hands; bureaucracy; and formal caregivers.Initial evidence supports the validity of the six characteristics as themes to be discussed in the nursing‐home care for patients with dementia.Discussion between formal caregiver, informal caregiver and the patient about the patients' needs is deemed necessary.Attitudes of caregivers may be subject to change if caregivers are aware of complaints that informal caregivers express.The quality of care should be evaluated in structured discussions by the formal caregivers and the informal caregivers of patients with dementia, with reference to individual needs.Training and education, according to the theme quality of care, may be necessary to help  caregivers manage individual needs of patients.1Nursing homes in the Netherlands provide psycho geriatric care to patients with dementia and other disorders in the function of the brain. The care in the nursing homes is subsidized by the national government, with the patient making a financial contribution as well. There are two kinds of nursing homes: small‐scale nursing homes and large‐scale nursing homes. The organization of care in small‐scale nursing homes differs from the care in large‐scale homes. In small‐scale nursing homes, the formal caregiver is responsible for a group of patients and their family representatives. In large‐scale nursing homes a group of formal caregivers is responsible for all the patients and the care is delivered using a task‐centered approach. In 2008, 36,934 patients with dementia were admitted to 368 small‐scale and large‐scale nursing homes in the Netherlands. The mean age of this group was 83 years  and has recently also started doing so via the Patients' Federation  collects and provides data on care in nursing homes via their website “Choose better”  were analysed by a Dutch newspaper  and PubMed – for instruments used nationally and internationally for measuring the quality of care. Literature on family participation in nursing‐home care was also analysed. The reason for the literature study was to discover which instruments were up to date. The terms for the literature searches were: Psychogeriatric And dement* AND care AND nursing home AND elderly AND measuring AND instrument 1997–2011. Table 2.1The design of this qualitative, exploratory study was influenced by the results of the ranking of 350 nursing‐homes reported by the newspaper, which used the CQ‐I and ZI questionnaires  a manager [M]; (ii) a quality advisor or head nurse [Q]; (iii) a daily care supervisor [S]; and (iv) an informal caregiver [I] . Thus 16 caregivers were interviewed in total. These interviews with the caregivers took about 60 min each and questions were similar for all categories. The semi‐structured interviews started with an open question: “In your opinion, what is the dependent or independent care variable that would let your nursing home stay in this position in the classification by the newspaper?”2.3In this study, two nursing homes that were classified as among the best and two that were classified as among the poorest were selected for an interview visit by the researcher. The management of the four homes were approached and asked to participate in the interviews. All participants were asked for consent. The choice for interviews was made as a way of having objective face‐to‐face contact with the formal and informal caregivers of the patients with dementia  compared to 35 times (poorest homes).Good quality of careAttention: [I] “The nurses have to work very fast. They are busier than before with all kinds of things at the expense of the attention for the patients.” Attitude: [Q] “According to research done in the organization, important factors are: keep responsibility in the teams, let team members alert one another to ensure the right attitude.” Satisfaction: [M] “The patients want to live in a nice and safe environment, to eat well and have clean rooms. The families give these elements a high score.”Poor quality of careAggressive patients: [S] “As the supervisor responsible for the daily care, we can give less attention to the patients. As an example: when a patient is aggressive they cannot walk outside. We have to give them medication.” Inspection: [I] “It is not known what kind of performance indicators the Inspectorate of the Ministry of Health used in this nursing home. Based on my professional experience, the Inspectorate monitors files. They check if the right papers are in the files with regard to events and decrees of the court.” Complaints: [M] “Patients do have a culture of complaining, but they do not call the complaints desk.”Elements of a visionSmall‐scale: [Q] “Important reasons for establishing small‐scale nursing homes are, for example, a feeling of engagement among the family and less staff absenteeism.” Multi‐Disciplinary Consultation: [S] “The family cannot attend the Multi‐Disciplinary Meeting of professionals to give their input. The daily care supervisor talks to the informal caregiver of the patient with dementia before and after the Multi‐Disciplinary Meeting.” Admission: [I] “Mother would have liked to live independently, but she was diabetic and wasn't eating. It took a long time before she signed for the admission.” Vision: [Q] “To give structure to the patients for their safety, we want the patients to be themselves.” Care plan: [S] “In the first contact between the doctor and the family, we started with discussing the care plan.”Extra handsFamily: [M] “In our institution, we have critical care family assistance; for example a daughter moved to this region and admitted her mother to this institution nearby. She helps a lot.” Volunteers: [I] “Not everybody can cope with the fact that the same thing is said three times in 5 min.”BureaucracyData: [M] “The way the Ministry looks at the data of a care home differs from the way patients experience their well‐being. The Inspectorate is more likely to plan their audits when there has been an incident in a facility in another part of the Netherlands.” Classification:[Q] “Because of the successful classification, the project “small scale living” was ended and the budget was cut, which is a pity for the facility. They cannot give the same attention to the patients as they did before in the project.”Formal caregiversEducation: [Q] “Within the organization, an education program has been developed, which has not yet been implemented in the unit included in the ranking.”To get a better understanding of what is seen as important in the best nursing homes, a selection of remarks of the six major themes (a–f) in the interview transcriptions is given below. We also added the citations concerning the original 16 characteristics and – between brackets – the interviewee category concerned: [M] a manager; [Q] a quality advisor or head nurse, [S] a daily care supervisor and [I] an informal caregiver of a patient with dementia. The choice of the characteristic and category citations was made by the researcher, and was based on the importance of the characteristic and differences between caregiver categories. In this way the variation in citations and between categories was shown:Citations about good quality of care are given in Table When comparing the remarks by the interviewees in the best homes and those in the poorest homes, we found some differences. The number of times family is mentioned by the health‐care workers responsible for the daily care in the two best homes is 14 and 11 times, respectively, compared with 22 and twice, respectively, in the poorest homes. The managers in both the best and the poorest homes also often mentioned the importance of family, 13 and 3 times, respectively, in the best homes compared with 11 and 6 times, respectively, in the poorest homes.4This study aimed to find characteristics for desirable care as reported by formal and informal caregivers. Six themes concerning desirable care emerged based on 16 characteristics used in the interview transcriptions. The six themes are: good quality of care; poor quality of care; elements of a vision; extra hands; bureaucracy; formal caregivers. These themes emerged from the data, however, the judgement of caregivers can be influenced by dissatisfaction  concerning quality of care in the nursing homes was used  researcher. Also, the national – assessment centre, where they assess if a person is eligible for long‐term care support and which has a lot of data, was not willing to share information. The ranking by the newspaper with the best and poorest nursing homes based on the results of the CQ‐I and ZI became therefore the entrance into the homes  caregivers involved in the care for patients with dementia. Most patients with dementia are not able to stand up for themselves and are dependent on others to take care of them. Validation of the six themes is necessary to provide insight into formal and informal caregivers' opinions about the desirable care in nursing homes for patients with dementia.The authors declare that they have no competing interests.


This paper reports on the feasibility of using a novel and robust microwave sensing technique to analyse and detect silver materials in an aqueous solution. Two products are tested, namely: silver chloride and silver oxide. The study mainly focused on indicating the difference between them and also any change in the size/size distribution of the sample. A microwave sensor designed previously is utilised to identify the potential of the technique to carry out the analysis. The results are presented as microwave spectrums that are the material response to microwaves. The results have shown that the technique has reasonably indicated the change in material type as well as size distribution. The results also show that these curves are distinguishable and can be related to the material and the change in its size. It is concluded that there is a potential of extending this technique to determine various other properties of silver products. The study suggests a design and development of a bespoke unit as a dedicated analysis tool and to address any anomalies arising from the current feasibility. This will have a huge industrial benefit in terms of cost reduction and time associated with the industrial analysis of silver material. It is crucial in any manufacturing industry to determine the process variables rapidly and accurately. This proof of concept study focused on introducing a new and innovative method of quality control and assessment of silver materials during the manufacturing process. It investigated the potential of using a non-invasive microwave sensing technique to determine the properties of silver based materials of various types and to instantaneously spot a difference between them. The robustness of the technique demonstrated the potential of it to develop as a real-time option in the situations where timely results are crucial. The investigation was conducted in partnership with AmesGoldsmith UK Ltd, a manufacturer of silver based products. The company was keen on exploring cost effective, robust and reliable ways of characterising the silver based products during the manufacturing process in terms of various properties. The challenging part of the study was to use the microwave sensing technique for the samples in an aqueous solution, which is the case during the manufacturing process of silver products at AmesGoldsmith UK Ltd, based on the fact that microwaves can be absorbed in water due to its high dielectric losses.Currently, AmesGoldsmith UK Ltd has no method in place to monitor the quality of the product during the manufacturing process. They carry out testing after the manufacturing is complete which is time consuming, laborious to carry out and often the results show inaccuracy and inconsistency in the process. The potential of investigating an efficient technique capable of monitoring the process in real-time would be a significant benefit to the company for quality control and validation of the finished product.This investigation has not only provided an innovative approach of microwave analysis and its application to characterise the silver based products in aqueous solution but also has introduced a firm ground to the industrial partner with sufficient information on the potential of developing it further to not only monitor the manufacturing process but also to explore its potential to be used to monitor the properties of the material. These properties may include particle size, particle size distribution, contamination, etc. during the manufacturing process. Although this work presents a proof of concept study at this stage, the ultimate aim in the future would be to design and develop a bespoke sensor unit to implement the microwave sensing and monitoring.Microwave permittivity measurements and sensing has been much used in the research for material characterisation and detecting the dielectric properties of various substances. The technique has been established over a period of more than 40 years , 2. Due Due to its versatile nature, the technique has been implemented in various industrial applications such as to determine the dielectric properties of various substances , 13, 14,Microwave sensors can be designed and developed in various sizes, shapes and types depending on the application type. This includes cavity resonators, Flexible antennas/sensors, patch antennas, etc. , 13. Mic11) and a transmitted signal also known as transmission coefficient (S21). The Vector Network Analyser (VNA) can be used to generate microwave signals. Since permittivity relates to the material’s ability to transmit an electric field, it is a complex quantity and changes with changing frequency. It takes into consideration both the energy stored by the material called dielectric constant ε′, as well as any losses of energy termed as dielectric loss factor ε″. These two quantities together are called scattering parameters also referred to as S-parameters. By considering how these parameters change at discrete frequency intervals, the change can be linked to the material type, its composition, concentrations of the constituents, size/size distribution, etc. in the sample [Permittivity is a measurement of change in an electric field due to the presence of the material. It is dependent on the material’s ability to polarise in response to the applied field. The microwave analysis technique can be used to provide unique signal spectrums of two important quantities, i.e. a reflected signal also known as reflection coefficient  mode has its electric component in the direction of the propagation of wave whereas the transverse electric (TE) mode has its magnetic component in the direction of propagation of wave [mnl mode can be calculated using the Eq.  Group of Liverpool John Moores University (LJMU). Since it was a proof of concept study, the work explored the potential of using microwave sensing as a technique to monitor the properties of silver material during the manufacturing process in aqueous solution. Hence, the focus was on the technique and not the sensor itself to assess if it can be developed potentially as a technique to carry out the analysis.To understand the interaction of microwaves with the sample under test it was important to provide the inner dimensions of the resonator cavity and the arrangement of electromagnetic waves inside the cavity, representing the modes at which the measurements were taken. The modes and the field pattern was simulated using a COMSOL Multiphysics simulation tool and the results are presented. The schematic diagram of the cylindrical cavity’s side view, its inner dimensions, sample and coupling structure is shown schematically in Fig. 21 measurement was in the range of 6–6.25 and 7.1–7.16 GHz whereas for S11 measurement was in the range of 7.95–8 GHz. The experimental resonant peaks in the case of the empty cavity for the measurement frequencies were at 6.1530, 7.1329 and 7.9792 GHz. The simulated resonant peaks were identified at 6.1785, 7.165 and 8.104 GHz. The results of the simulations were used to understand the interaction of microwaves with the test samples at these frequencies to see how the peaks were obtained despite the silver products being in a lossy medium (aqueous solution). The simulations results of the resonant frequencies and their respective modes are presented in Fig. Since, the cavity was not designed for the current work, COMSOL simulations of the resonant cavity filled with air were carried out. The simulations were carried out at the experimental measurement frequencies to compare the experimental and simulated results. These frequencies were identified through a series of experiments conducted and presented in the Sect. 220, TM320 and TM420.It can be seen from Fig. A cavity resonator can be classed as a black box with EMW (microwaves) entering the cavity from the input port, interacting with the material to characterise it and leaving out of the output ports. The cavity utilised in the current work and the experimental setup of the sensing system is shown in Fig. This initial feasibility study made an effort to see how efficiently a microwave sensing technique differentiated between various silver based products inside the aqueous solutions. Further information such as difference in the particle size and a particle size distribution was also indirectly investigated in this study. Although, the size distribution was not quantified at this stage of the feasibility, this area can be explored in detail in the follow up study. The overall size distribution of various samples is presented in Table Two types of silver materials, silver oxide and silver chloride, supplied by AmesGoldsmith UK Ltd were used in the study. The samples were prepared and tested in 15 mL polypropylene tubes as shown in Fig. The samples tested are listed in Table 220, TM320 and TM420 is given in Table Before carrying out the experimental measurements, it was useful to calculate the theoretical resonant frequencies of the empty cavity at the measurement frequencies using the Eq.  from Secc is for the empty cavity, index s is for the cavity loaded with sample and v is the volume. The result obtained for the dielectric constant and dielectric loss of the silver oxide sample in aqueous solution is presented in Table It was important to study the influence of silver in aqueous solution by measuring the dielectric properties of silver product. This was also to help in making comparisons between the experimental and simulated results  for silver oxide and the microwave frequency change shows the strong characteristic response of silver oxide powder as noticed in [Previous study by Ateeq et al.  has showr to the  in the cticed in . Silver ticed in . This al11 (reflection co-efficient) and S21 (transmission coefficient) was carried out over the full range of spectrum, 9 kHz–13.6 GHz, with the instrument calibrated. This was to identify and highlight the resonant peaks and the area of interest for further investigation and detailed experimental analysis. The frequency was then narrowed down to a small range depending on the response of the material to microwaves. Figure 11 between 9 kHz and 13.6 GHz frequency.Initially the sensor measurements of both SAfter careful analysis of the full spectrum it was found that 6–9 GHz section of the spectrum can be further investigated and is encircled in Fig. 21 measurement between 9 kHz and 13.6 GHz. Again, the area of interest was selected based on careful study of the full range frequency response. The encircled section in Fig. Figure 11 and S21 measurements between 6 and 9 GHz is shown in Figs. The VNA instrument was recalibrated between 6 and 9 GHz to take further measurements of the highlighted areas in Figs. 21 measurements from Fig. The transmission co-efficient in Fig. 11 measurements in Fig. In case of SBy analysing Fig. The shift of the frequency from the empty cavity to the empty tube sample was 0.9 MHz to the left showing the introduction of polypropylene tube in the cavity. The rest of the comparison was performed between the control empty tube sample and the silver material samples.The shift for both the silver chloride samples was around 7.4 MHz to the right showing an increase in the frequency. It is significant and shows the demonstrable shift in the frequency and distinction between the empty tube and silver chloride sample in aqueous solution. When looking at the result of repeated sample, the shift from the control sample was around 7.7 MHz. Also the difference between the original sample and re-measurement was minute, i.e. 0.3 MHz which is pretty negligible and could be built into the design of a bespoke unit.The shift for silver oxide samples was in between 5.3 and 6.2 MHz to the right from the control sample, i.e. empty tube. This difference is again significant, and the technique being sensitive, is large enough to demonstrate the potential of the technique to analyse silver oxide samples in aqueous solution. When this shift was compared with silver chloride sample the difference was more than 2.1–1.2 MHz between them. The peaks for silver oxide samples were between the control and silver chloride samples. Again this value is large enough in comparison to the sensitivity of the technique to demonstrate the potential of differentiating between the two different types of Silver products, in this case silver chloride and silver oxide.In addition to the change in the frequency, significant change in the amplitude was observed between the two silver types as well as in comparison of them to the control sample of empty tube as seen in Fig. When the measurements for the silver oxide samples was analysed, the peaks for the samples silver oxide-1, silver oxide-2, silver oxide-3 and silver oxide-4 were detected at the frequencies 6.1580, 6.1574, 6.1580 and 6.1583 GHz respectively. On repetition of the sample silver oxide-1 the peak frequency was at 6.1577 GHz. This slight change again could be attributed to the change in the size distribution facing the incident wave and may need further investigation to prove it. The results of the above measurements and Fig. 21 measurements between 7.1 and 7.16 GHz in Fig. The shift in the frequency of silver chloride samples in comparison to the control sample is around 2.7 MHz towards right showing the increase in the frequency. Again, the shift is significant and along with the change in amplitude in comparison to the control sample demonstrates that the microwave sensing technique is detecting the difference between the silver chloride and control sample.The shift in the spectrum of silver oxide to the right was around 2.3 MHz which is a high reading on the basis that the technique is very sensitive. The difference between the silver chloride and silver oxide sample is around 0.4 MHz. Although it is tiny and could be negligible, in conjunction with the amplitude change in comparison to silver chloride sample could prove significantly important to differentiate between the two types of silver products. This capability of considering both the frequency shift and amplitude needs to be built into the design of the sensor.As stated earlier, in addition to the change in the frequency observed, a significant difference in the amplitudes between the samples exists. The bespoke sensor for the purpose can be designed to take this into consideration within an acceptable range of amplitude change.When the SQ’ of the cavity. Figure Q factor cavity can increase the sensitivity of detection as well as characterisation of the material and can easily be analysed.In summary, the results between the frequency ranges of 6–6.25 GHz were more effective in comparison to the frequency range of 7.1–7.16 GHz in differentiating between the silver products. In addition, the results in Figs. The initial results discussed above show the potential of the microwave sensing to be developed as a technique to analyse Silver products in aqueous solution. However, a bespoke sensor unit is required to minimise the error and to maximise the detection capability by keeping the sensitivity at balance. This can be achieved through step by step modelling and the design of a microwave sensor.11 measurements, it was worth considering the effectiveness of microwave sensors using the reflection co-efficient measurements. The re-calibrated measurement results for the frequency 7.95–8 GHz is shown in Fig. 21 measurements, however, there is a change in the amplitude with low quality factor ‘Q’ measurement results, specifically in the case of silver oxide.Although prominent but less consistent results in terms of repetition were obtained from S11 measurements need to be further examined and careful consideration should be given to the design of the new sensor. However, it would be of concern if the measurements need to be carried out using S11 (reflection co-efficient) mode which may not be the case for this application because repeatable and consistent results are obtained in the case of S21 measurements. Hence, better results might be achieved if the design of the sensor is carried out around the transmission coefficient measurements. For the silver oxide, the measurements results of silver oxide-1, silver oxide-2 shows peaks at 7.9784 GHz whereas silver oxide-3 and silver oxide-4 shows peaks at the frequencies 7.9785 GHz. When the silver oxide-1 sample was repeated the peak value is at 7.9784 GHz which is similar to the original. The shift in the case of silver oxide from the control sample is around 5.7 MHz which shows the capability of the sensor detection and is very reasonable in terms of resonant peaks occurrences.As seen in Fig. 11 measurements, the only difference observed was the change in the amplitude of every sample. If it is important to design a sensor around S11 detection the sensitivity and accuracy can be looked into and could possibly be reduced by the optimum design of the bespoke unit.In case of S11 measurement may not be accurate and may not be a good choice for the future sensor development for characterising the silver products in aqueous solution.The theoretical resonant peak for an empty cavity calculated in Table 21 parameter. Only the S21 parameter was considered due to the evident difference in the experimental results of the response frequencies to various silver materials. Simulated S11 parameter was measured but the resonant frequency was detected at around 8.1 GHz in comparison to the experimental resonant frequency of 7.9792 GHz. This was a significant difference between the experimental and simulated calculations, similar to the variation between the theoretical and experimental calculations. Hence, to avoid the confusion to the reader the results are not here. For the purpose of this study only two samples were simulated for S21, i.e. empty cavity, and cavity loaded with the silver oxide sample. The measured value of the permittivity . This means that at higher frequencies the speed of dipole rotation is insufficient and cannot match with the changing AC bias [This behaviour has been observed by some previous research studies such as Kumar and Sharma  and Min  AC bias . However AC bias  also fou AC bias . In the  AC bias , 33. In 21 measurements. The results have shown that the technique is capable of detecting various silver based products and display microwave spectrums of them. However, detailed design work is required to develop a dedicated unit with a full potential to not only minimise the error but also to include possibly a detection of multiple properties of the material and linking them back to the material itself. Potentially, the technique can be further developed as an alternative to conventional time consuming physical and chemical testing methods. The biggest advantage would be the cost and time savings associated with the technique if available to the Silver products industry.An initial feasibility study was carried out on the use of microwave sensing technique as a potential method to analyse silver materials and their properties in aqueous solution. The scope of this preliminary study was mainly focused on differentiating between various silver products such as silver chloride and silver oxide as well as indirect detection of change in the size distribution between the two products. Microwave sensing was found to be a robust, instantaneous, cost effective and repeatable technique and has a potential to analyse the silver material which is of great benefit to the industry. The results were promising exhibiting accuracy and repeatability , the attributes required for any industrial application, especially in the case of transmission co-efficient, SMicrowave sensing technique demonstrated its capability to spot individual samples of silver chloride and silver oxide and showed the difference between them. If further research work is carried out, the spectrums captured from the dedicated sensor designed can be stored in a database to create a wealth of microwave spectrums representing certain material properties.11 measurements. This could be attributed to the variation in the size distribution percentage in the sample or the way the sample(s) was re-inserted, i.e. different side of the tube facing the incident microwave. To develop an effective microwave sensor, both of these parameters should be considered and combined together to relate it to a certain type of sample. Further work in the analysis could also possibly make this technique more intelligent by relating the changes in the microwave response to the specific properties of silver materials. This could be achieved through artificial intelligence techniques.The differences were not only spotted in the frequency shift but also in the amplitudes between silver chloride and silver oxide samples. It was also found that on repeat measurements of the similar material, i.e. silver oxide-1, silver oxide-2, silver oxide-3, silver oxide-4 or silver chloride-1, silver chloride-2, the amplitude slightly changed, especially in the case of SAt AmesGoldsmith UK Ltd, the silver products produced have fewer size distribution range. However, if a sensor is developed for wider range of particle size and size distribution detection addressing the wider silver based industry, different combinations of particle size and particle size distribution may provide similar resonant frequency and quality factor Q. This can be addressed by implementing advance post analysis/processing techniques such as artificial intelligence and neural networks analysis in the processing of the results obtained to distinctively identify various products and relate them to the products specific properties.11 parameter that has a simulated resonant peak detected at higher frequency than the experimental result and hence was not considered for further simulation analysis.The simulated and experimental results almost provided comparable resonant peaks at the measurement frequency range, showing the resemblance between the simulated and experimental results. The exception was the measurement of S11 measurement.In terms of theoretical measurements, the theoretical calculations of the resonance for the measurement frequencies in the 6 and 7 GHz frequency range was close to the experimental outcome. Again, the slight difference was observed in the case of the resonant peak of SA non-conventional phenomena of increase in frequency with permittivity was observed and could be attributed to the interfacial polarisation or significant dependence of samples on temperature or frequency parameters as discussed in Sect. As mentioned earlier, there is a need to capture all the data and built up a database for the future reference to allow the potential development of the analysis technique up to a prototype level. Pulling out the data and making comparisons between various set of data would also allow to target anomalies in the existing technique and help in optimising the development of the new sensor system for more accurate and predictable results.A thorough study of smaller sections of microwave spectrum and individual peaks helped in exploring and assessing the potential of developing microwave sensing as a technique to differentiate between the silver samples. This brief investigation also pointed out the capability of extending this technique and carry out further research work to see if the technique can enable the characterisation of the material including particle size, particle size distribution, contamination, etc. to name a few. Some of the conclusions and recommendations are listed below:Development of microwave sensing technique could bring a significant benefit to the silver manufacturing industry. However, the key is to develop the sensor and sensing technique that can target the industry requirements in a simple manner. Empowering the silver industry with instantaneous analysis technique can reduce the time spend on the analysis through conventional methods, can reduce the labour intensive work, improve the quality of the product and reduce the costs associated with the conventional analysis techniques in a precise manner. The cost effectiveness can be achieved by focusing the analysis around a narrow band of frequency.