Microfluidic devices have been widely used for biological and cellular studies. Microbioreactors for three-dimensional (3D) multicellular spheroid culture are now considered as the next generation in in vitro diagnostic tools. The feasibility of using 3D cell aggregates to form multicellular spheroids in a microbioreactor with U-shaped barriers has been demonstrated experimentally. A barrier array is an alternative to commonly used microwell traps. The present study investigates oxygen and glucose concentration distributions as key parameters in a U-shaped array microbioreactor using finite element simulation. The effect of spheroid diameter, inlet concentration and flow rate of the medium are systematically studied. In all cases, the channel walls are considered to be permeable to oxygen. Necrotic and hypoxic or quiescent regions corresponding to both oxygen and glucose concentration distributions are identified for various conditions. The results show that the entire quiescent and necrotic regions become larger with increasing spheroid diameter and decreasing inlet and wall concentration. The shear stress (0.5–9 mPa) imposed on the spheroid surface by the fluid flow was compared with the critical values to predict possible damage to the cells. Finally, optimum range of medium inlet concentration (0.13–0.2 mM for oxygen and 3–11 mM for glucose) and flow rate (5–20 μL/min) are found to form the largest possible multicellular spheroid (500 μm), without any quiescent and necrotic regions with an acceptable shear stress. The effect of cell-trap types on the oxygen and glucose concentration inside the spheroid was also investigated. The levels of oxygen and glucose concentration for the microwell are much lower than those for the other two traps. The U-shaped barrier created with microposts allows for a continuous flow of culture medium, and so improves the glucose concentration compared to that in the integrated U-shaped barrier. Oxygen concentration for both types of U-shaped barriers is nearly the same. Due to the advantage of using U-shaped barriers to culture multicellular spheroids, the results of this paper can help to choose the experimental and design parameters of the microbioreactor. Microfluidic systems have been used as practical and powerful tools for cell studies and can replace conventional in vitro systems and even animal models ,2. TheseVarious methods exist for the formation of spheroids on a chip such as floating on a nonadhesive surface, the hanging-drop method, suspension in spinner flask, acoustic, magnetic or electric force driven methods and mechanical trapping ,12,13. MHypoxia and necrosis are essential phenomena in tumors, which may occur in in vitro spheroid culture . GeneralSpheroid diameter is one of the critical parameters that affect the distribution of oxygen and nutrients inside the spheroid. The size of a spheroid can be controlled by cell density and fluid flow during cell seeding process as well as the culture duration. Curcio et al.  investigAnother parameter that affects oxygen concentration distribution is oxygen permeability of the culture chamber or channel wall. Anada et al.  experimeThe trap and aggregate shapes also affect the oxygen and glucose concentration distributions inside the aggregates. We recently examined this parameter numerically . The resThis paper aims to numerically investigate oxygen and glucose concentration distributions in multicellular spheroid entrapped in a three-dimensional microfluidic device with an oxygen-permeable wall, an integrated U-shaped barrier or a U-shaped barrier created with microposts or microwell. The effects of all these parameters, namely the spheroid diameter, inlet concentration, media volume flow rate and trap type, on oxygen and glucose concentrations are investigated using finite element approach. Based on glucose and oxygen concentration distributions, hypoxic or quiescent and necrotic zones are identified for each case that can be very useful for designing high-performance microbioreactors.We assumed that the upper channel walls and U-shaped barriers are made of the commonly used elastomer polydimethylsiloxane (PDMS), which is permeable to oxygen. The channel lower wall is made of PDMS but placed on an impermeable surface like glass surface. Furthermore, assuming other obstacles in a row, both surfaces are considered to be symmetrical. In this model, the medium flow through the channel and around the spheroid was considered as a steady, incompressible, three-dimensional and laminar flow. The medium has hydrodynamic properties similar to those of water at 37 °C. The fluid flow is governed by the continuity and Navier–Stokes equations   and zero-pressure are imposed at the inlet and outlet, respectively. The values of simulation parameters are presented in There are generally two different approaches for modelling a multicellular aggregate, viz. discrete approach and continuum approach. Each approach has its own advantages and limitations. The discrete or cell-based approach is computationally complex and is suitable for modeling the dynamic process of tumor growth, as cell interaction plays an important role. The continuum approach is a macroscopic (top-down) model based on a spatially-averaged standpoint. This approach is a suitable for considering epigenetic variation such as environmental variables. Since the main aim of the present study is to determine the oxygen and nutrient distributions available from the external environment of the spheroid, we adopted the continuum approach, which can be coupled with the floand concentration distribution around the spheroid. In addition, our model is based on off-chip technique. Therefore, dynamic tumor growth plays a marginal role. Accordingly, we take into account the effect of cells by considering the Michaelis–Menten reaction terms for both oxygen and glucose.K is the diffusion coefficient of oxygen, c is concentration, t is time, and R is the reaction term. To investigate the distribution of oxygen concentration, convection–diffusion equation in the medium (Equation  without J is diffusion mass flux) and concentration jump due to the difference in the partition coefficients  and areas of necrosis. Similar to the previous section, we investigated the effect of inlet concentration, spheroid diameter and medium flow rate on these regions. The border of these regions are identified by sc (IVD)  used in An increasing spheroid diameter, decreasing inlet concentration and medium flow rate lead to the growth of the quiescent and necrotic region. Small spheroids and high inlet concentration and a medium flow rate reduce these zones.x–z plane of the spheroid too.Similar to the previous section, a 4% decrease in the spheroid diameter, a 20% decrease in The quiescent and necrotic regions due to deficiency of oxygen are located at the lower part of the spheroid. The quiescent and necrotic zones due to deficiency of glucose tend to locate at the back of the spheroid, where it is not exposed to direct flow. The reason is that the barrier is permeable to oxygen and not to glucose. This feature increases the risk of damage to cells due to lack of glucose. These regions are also nonspherical due to asymmetric glucose concentration distribution around the spheroid. D = 400 μm, Q = 5 μL/min, This section investigates the effect of a trap type with a different design on the distribution of oxygen and glucose concentration in the spheroid and the amount of shear stress on its surface. The general geometry is similar to the one shown in The time-dependent solution for no medium flow was achieved for different spheroid diameters. The glucose concentration in all domains was obtained by solving the steady problem under D = 500 μm). Over time, the low concentration of glucose appears in some region and the possibility of necrosis increases as the time passes.In this study, the oxygen and glucose concentration distributions were assumed to be independent from each other. One can consider the effects of oxygen-glucose coupling on the simulation results. Also, the oxygen and glucose consumption rates of the multicellular aggregates were modelled based on Michaelis–Menten reaction terms. Thus, depending on the cell type and heterogeneity of the cell clusters, each corresponding constant should be pre-determined precisely based on empirical values. Finally, the effect of tumor growth or multicellular spheroid growth was not considered here. The growth equations can be coupled with concentration equations of oxygen and glucose. As explained before, there is a discrete approach to model a multicellular aggregate. This approach can be used to model tumor growth considering cell interactions, which are ignored here due to the mentioned assumptions. The spheroid can be modeled as a porous medium to consider the effect of culture medium flow inside the aggregate. All of the above can help to improve the prediction of experimental results to increase the accuracy.In an attempt to provide a predictable platform for in vitro assays, continuity, momentum and convection–diffusion equations were solved numerically in a microfluidic bioreactor with different cell traps and oxygen permeable walls. The effect of various parameters affecting such microfluidic system, which is suitable for culturing and formation of multicellular spheroids, were evaluated. The results showed that increasing spheroid diameter decreased the concentration of glucose and oxygen inside the spheroid due to the enhancement of oxygen consumption. Subsequently, increasing the medium volume flow rate increased the concentrations of both glucose and oxygen. The reduction of inlet concentration can affect the concentration distribution, and may even lead to quiescence or necrosis especially for large spheroid diameters. Therefore, the flow of culture medium cannot be stopped for a long period of `time. Accordingly, a continuous flow is more appropriate for this system if large spheroids are cultured. Increasing the spheroid diameter decreased inlet concentration and medium flow rate, and consequently, the hypoxic or quiescent and necrotic regions are expanded. The deviation of streamlines due to the U-shaped barrier decreased the amount of shear stress on the multicellular spheroids such that the maximum shear stress applied on a spheroid surface approaches 9 mPa. Oxygen and glucose concentrations inside the spheroid for a system with any of the U-shaped barriers (either the integrated U-shaped barrier or the one created with the microposts) are much higher than those in the microwell. The flow created in the gaps between the microposts increases the glucose concentration compared to that in the integrated counterpart. Due to the permeability of the U-shaped barriers, the concentration of oxygen is high and almost the same for both types of the U-shaped traps. The results of this study can serve as a guideline for a microbioreactor with better design and control the parameters affecting the formation and culture of multicellular spheroids within such microfluidic platforms.


In densely populated areas, surface waters are affected by many sources of pollution. Besides classical pollutants like nutrients and organic matter that lead to eutrophication, micropollutants from various point- and non-point sources are getting more attention by water quality managers. For cost-effective management an integrated assessment is needed that takes into account all relevant pollutants and all sources of pollution within a catchment. Due to the difficulty of identifying and quantifying sources of pollution and the need for considering long-term changes in boundary conditions, typically substantial uncertainty exists about the consequences of potential management alternatives to improve surface water quality. We therefore need integrated assessment methods that are able to deal with multiple objectives and account for various sources of uncertainty.This paper aims to contribute to integrated, prospective water management by combining a) multi-criteria decision support methods to structure the decision process and quantify preferences, b) integrated water quality modelling to predict consequences of management alternatives accounting for uncertainty, and c) scenario planning to consider uncertainty from potential future climate and socio-economic developments, to evaluate the future cost-effectiveness of water quality management alternatives at the catchment scale. It aims to demonstrate the usefulness of multi-attribute value functions for water quality assessment to i) propagate uncertainties throughout the entire assessment procedure, ii) facilitate the aggregation of multiple objectives while avoiding discretization errors when using categories for sub-objectives, iii) transparently communicate the results. We show how to use such multi-attribute value functions for model-based decision support in water quality management.We showcase the procedure for the Mönchaltorfer Aa catchment on the Swiss Plateau. We evaluate ten different water quality management alternatives, including current practice, that tackle macro- and micropollutants from a wide spectrum of agricultural and urban sources. We evaluate costs and water quality effects of the alternatives under four different socio-economic scenarios for the horizon 2050 under present and future climate projections and visualize their uncertainty. While the performance of alternatives is catchment specific, the methods can be transferred to other places and other management situations. Results confirm the need for cross-sectoral coordination of different management actions and interdisciplinary collaboration to support the development of prospective strategies to improve water quality.  •Model based decision support is needed for integrative water quality management.•Multi-criteria decision support methods help developing water quality assessments.•We evaluate alternatives that reduce agricultural and urban sources of pollution.•We illustrate tradeoffs between costs and water quality including uncertainty.•Scenario analysis helps evaluating robustness of outcomes to future developments. PollutiMulti-criteria decision analysis (MCDA) offers numerous methods to support structured decision making and to combine multiple criteria to an overall assessment . These mIn the field of water quality management, a current review documents large variability in the effectiveness of different management alternatives  that areIn this paper, we introduce a model-based decision support framework for water quality management that combines the assessment of water quality management alternatives with uncertain consequences regarding multiple objectives with scenario planning to account for impacts of future developments that are out of scope of water quality management. We therefore explicitly deal here with two major sources of uncertainty, the future development of external boundary conditions (climate and socio-economic development) and uncertainty of model predictions about the consequences of management alternatives conditional on the boundary conditions .We use multi-attribute value theory  to formuWe illustrate the framework with an application to a catchment on the densely populated Swiss Plateau that is affected by urban and agricultural point and non-point source pollution . In thisWhile the outcomes of this case study will be very case specific and context dependent, the decision support framework and assessment methods accounting for uncertainties of model predictions and of changing boundary conditions are largely independent of the application case and therefore transferable to other cases. With this study we aim for demonstrating how to facilitate a cross-sectoral coordination of long-term water quality management in face of large uncertainties with the help of multi-criteria decision theory.22.1value focused thinking . In the next step, the fundamental objectives are identified that should be fulfilled by the management alternatives. These should include all important decision criteria to be considered. The objectives are structured in form of a hierarchy to facilitate the discussion among stakeholders and the quantification of preferences.measurable value function , a dominance analysis can reveal, if there are any alternatives that are equal or inferior to others in all aspects and inferior in at least one aspect, even without knowing the full preference structure . However2.2locations of uncertainty, which are quantified by the water quality model, cover input, model structure, and parameter uncertainty  conditions and statistical uncertainty regarding the water quality model outcomes and costs. The nature of uncertainty is dominated by epistemic uncertainty (imperfect knowledge) but also includes natural variability of the system .To predict future consequences of management alternatives regarding costs and water quality, we need to account also for future changes in boundary conditions that are out of scope of local water quality management. These include climate change that will affect hydrological conditions as well as socio-economic changes that may affect management costs and sources of pollution. Since it is difficult to predict the local effects of both climate change and socio-economic changes for a time-horizon of several decades, we use scenarios that try to cover different realizations of the future without specifying how probable they are . We use ertainty . The lev2.32 with currently 24000 inhabitants living in five municipalities. Intensive agriculture (57%), forest (15%) and urban settlements (11%) are most important land uses  to the worst case to be expected in Swiss rivers . The legal thresholds, as defined by the Swiss water protection legislation, correspond to a value of 0.6 on the value scale .On the higher levels of the objectives hierarchy , we haveMost pronounced differences between the aggregation functions exist when one of the sub-objectives is very good and the other is very bad. For the examples given in We considered four possibilities for confronting water quality effects and costs: (1) To elicit a case specific value function for costs and the trade-offs between costs and water quality effects from stakeholders and decision makers , (2) to Under the current policy system in Switzerland it is unrealistic to expect that water quality managers will have a combined budget from which they can choose the most suitable management alternative in each catchment. Usually, for each type of management alternative (e.g. addressing agriculture or urban wastewater) there is a separate  policy, which defines targets, budget constraints and financing instruments. However, even in this situation an integrated water quality assessment is needed, because these sectoral policies still usually require a spatial prioritization of measures, which profits from cross-sectoral coordination, and because the managers should be informed about the effects they can expect, which requires an overview about changes regarding all sources of pollution.We therefore consider the fourth option the most interesting for our case study. However, depending on the policy process, the other three options might be suitable as well and would be straightforward to implement.utility  couplings were used from the ENSEMBLES data archive to train weather generators for the study site . The div2.92.9.1A detailed description of the water quality model used for predicting the effects of management alternatives under future scenarios is given in 2.9.2For the purpose of estimating the costs of management alternatives under different socio-economic scenarios, it is assumed that specific management alternatives are declared mandatory within the Mönchaltorfer Aa study area. A detailed description of the cost estimation procedure for each management alternative is provided in the Supplementary material. The costs of urban management alternatives are expressed on an annual basis. For each urban management alternative, we use the most reliable existing data or market prices and provide the minimum, maximum and best cost estimate. The minimum and maximum cost estimates can be considered as the lower and upper bounds of the confidence intervals around the best estimate and are used for the uncertainty analysis. The costs of the agricultural management alternatives can occur due to: (i) a decrease in agricultural income and in workload or (ii) an increase in workload, which reduces the productivity of farming. In the former case, the cost of an alternative represents the difference between the contribution margin under the current conditions and the contribution margin under the new conditions. In the latter case, the costs are estimated as the loss in agricultural income earned per working hour for any workload that exceeds that of the reference situation.3In the following sections, we present the consequences of the management alternatives on water quality (3.1), costs (3.2) and trade-offs between water quality effects and costs (3.3). In addition we discuss the prerequisites to transfer the approach to other management cases (3.4).3.13.1.1A deficit analysis for the current state reveals that all sites that are influenced by wastewater treatment plants  are affected by nitrate and phosphate pollution . In addiThe uncertainty assessment for the catchment outlet  for curr3.1.24 is already good to high under current practice and is unaffected by the management alternatives. The sub-objective PO4 has poor to good state under current practice but is unaffected by the management alternatives. NO3 is bad to moderate under current practice and significantly improved only by WWTP and All to a good state, while NatPark leads to a minor improvement. Similarly, the overall nutrient assessment has a poor to moderate state for current practice and is significantly improved by WWTP and All to a moderate to good state. The overall water quality assessment is poor to moderate for current practice. The alternatives StoreVol, PermPav, RainRet, BufZone show no effect, the alternatives BanBioc and also WWTP, OrgFarm, and NatPark lead to minor improvements, and only All leads to significant improvement to a good state. Because the respective measures are linked to different policy and management fields  this result supports the need for cross-sectorial coordination to achieve a good water quality status. The tools presented here can help communicating expected outcomes including the respective uncertainties in a transparent manner to stakeholders of the different sectors.We first compare the assessment results for the different management alternatives for the present climate and the Status quo scenario at the catchment outlet : Only thNext, we compare the influence of climate and socio-economic scenarios on the effects of the management alternatives on the overall water quality assessment .Fig. 9PeBoth climate and socio-economic scenarios have rather minor influences on the overall water quality assessment but may affect the ranking of management alternatives based on median values, due to the small differences between some of the alternatives. In general, under future climate the results are expected to be slightly worse than under present climate. While the moderate growth and decline scenarios lead to very similar results compared to the Status Quo, the exponential growth scenario reduces the chance of reaching moderate or good state for all single management alternatives, while the combined alternative All performs similarly to the other socio-economic scenarios. In general, the effectiveness of the management measures appears fairly robust against the potential socio-economic and climate changes considered in this study .3.2The relevance of absolute costs depends on the overall economic situation and the population size, which is reflected in the different socio-economic scenarios. Therefore, the costs presented here  are expr3.3v > 0.6) in all future scenarios, it comes with comparably high costs  is the only alternative that can be expected to lead to a good water quality (i.e. median gh costs . Only th3.4utility (From a conceptual point of view, the suggested water quality management framework can be applied to other places and/or other management questions. However, this requires a consideration of each step of the decision support process  as descrutility , with ot4Water quality management is a typical example for environmental decision making that has to deal with multiple objectives, many different alternatives, large uncertainties in the prediction of their consequences, and has to account for fairly long-term changes in boundary conditions that cannot directly be influenced by local management, such as socio-economic development or changing climate conditions. For water quality management decisions, it is important to be informed about the changes in substance concentrations that can be expected from different management alternatives. However, since usually many different substances have to be considered that stem from various sources of pollution, an integrated assessment is necessary that helps dealing with the multi-objective nature of the decision problem and helps propagating and visualizing the associated uncertainties. Since the decisions affect many stakeholders, a transparent communication of the decision basis is of particular importance. With this study, we introduced a framework to combine the use of multi-attribute value functions for integrated assessment with water quality modelling and scenario planning. We illustrated its suitability to help informing stakeholders, policy makers, and the public about the decision problem with a full account of the uncertainties of expected outcomes.A central element of the presented framework is to use a continuous value scale for each sub-objective. This avoids the propagation of discretization errors from the lowest to the highest level of the objectives hierarchy, but still allows translating the values into color-coded quality classes, which have a familiar meaning to the decision makers, at all levels of the objectives hierarchy. This allows choosing the appropriate level of detail for a transparent discussion of the effects of different management alternatives with stakeholders, policy makers, or the public.While the methods and visualizations used are generalizable to other water quality management decisions, the outcomes of the case study are very catchment specific, because they depend on the local conditions and the contributions of the different sources of pollution, which may vary substantially between catchments. Our results show that, especially when uncertainty at the lowest level of the objectives hierarchy is large and varies between alternatives, it is very insightful to propagate the uncertainty to the value scale and translate it into water quality classes that have a clear meaning for policy makers and managers. The combination with scenario planning for boundary conditions that are hard to predict allows evaluating the robustness of the performance of different management alternatives to future changes.This study confirms the need for a cross-sectoral coordination between different management actions to achieve larger ecological effects , even ifNone.


In recent years, there has been an increase in the number of reported incidents of child-to-parent violence (CPV); however, this type of intra-family violence remains vastly understudied compared with other forms of family violence. The aim of this study is to analyze the relationship between family communication and verbal CPV through the mediation of adolescent perceived stress. The sample consisted of 2399 Spanish students of both genders between the ages of 11 and 20 years. Results show that problematic family communication is a risk factor for the presence of verbally abusive behavior towards parents, with a direct and indirect relationship through perceived stress. Open family communication is presented as a protective factor against verbally abusive behavior due to a negative relationship with perceived stress. Results point to a mediating role of perceived stress, which would explain the mechanism which links the quality of family communication to verbal violence towards parents. Implications of these results are discussed. In recent years, there has been a sharp increase in the number of reported incidents of a, to date, little studied type of intra-family violence, such as child-to-parent violence (CPV) ,2,3. TheCPV or parent abuse is defined as any act committed by children that makes parents feel threatened, intimidated, and controlled . The varThis research lies within the framework of the ecological systems theory of human development ,10 and, Among the multiple components of the family context, the communication between its members is considered a good indicator of how well the family system functions . AccordiIn relation to CPV, some studies have indicated that open communication is a protective factor against CPV ,25. StudStress perceived by adolescents (the extent to which they appraise that their demands exceed their ability to cope) is a potential mediating variable to consider in this relationship, given its consistent link with violent behavior in adolescents ,29,30 anInstead, most of the studies either focus their analysis on individual factors or are centered around family characteristics as explanatory contextual elements of CPV . From anA total of 2399 Spanish adolescents of both sexes  took part in this study. The students, aged from 11 to 20 years old , came from four schools in the regions of Valencia and Andalusia. In each region, participants were selected using a stratified cluster sampling method [Firstly, a letter was sent to the selected schools explaining the research project. The school principal was subsequently contacted by telephone and the detail of the project was explained. Consent forms were sent to the parents, along with a letter from the principal explaining the nature of the research. After obtaining the relevant permissions, a seminar was held with the teaching staff of each school to explain the objectives and scope of the study. The research was carried out by a group of trained and experienced researchers to provide students with the necessary support to successfully complete the questionnaires. We explained the goals of the study to the students, informed them that participation was voluntary and anonymous, and required their consent. Participants filled out the scales in their usual classrooms during a regular classroom period. The order of administration of the instruments was counterbalanced in each class and in each school. The ethics committee of the hosting university (University of Miguel Hernández) granted ethical approval. The study met the ethical values required for research on human beings, respecting the basic principles included in the Helsinki Declaration.We used the parent-adolescent communication scale (PACS) , adaptedWe used the Spanish version  of the pThe child version of conflict tactics scales (CTS2) by Straus and Douglas  was usedThe statistical program SPSS Amos v.19  was used. Missing values were calculated using the linear interpolation method. Firstly, the descriptive statistics of the observable variables  and the bivariate correlations between them were calculated. Secondly, a structural equation model was then used to analyze the relationships between the variables using a two-step process, as recommended by Anderson and Gerbing . The meaAn analysis of structural equations was carried out to test the hypothesized relations between the variables. The tested model was identified as each latent variable had at least two indicators ,52. The In order to confirm the measurement model, a confirmatory factorial analysis was carried out. An oblique model encompassing all of the latent variables of the theoretic structural model was used. Bearing in mind that the Mardia coefficient was high (61.42), the model fit was tested with a maximum likelihood method, together with standard bootstrapping with 500 resamples. This process offers an average of the estimations derived from the samples obtained from the bootstrap and their standard errors.Furthermore, the bootstrapping process compares the values estimated without bootstrapping with the averages obtained from the bootstrap resampling in order to gauge the level of bias. The confidence intervals  of the regression weightings and the standardized regression weightings indicate that the estimated values were considerably different from 0, bearing in mind that the lack of normality does not affect the estimations .2/df = 7.92. All the parameters were significant (p < 0.01), and all latent variables correlated between them, with values between −0.40 (communication problems with perceived stress) and 0.61 .The results of the measurement model were acceptable under the proposed cut points, and the estimated parameters possessed significant values given that they did not include a 0 value. Specifically, the fit indices were: CFI = 0.92; IFI = 0.92; RMSEA = 0.08; SRMSR = 0.06; df = 59; XThe structural equation model (SEM) calculated proposed that each of the two latent exogenous variables (open communication and communication problems) separately influenced the individual latent endogenous variable (perceived stress) and the verbal CPV. In addition, a covariation relation was observed between the two family latent exogenous variables. The individual latent endogenous variable in turn influenced the verbal CPV latent endogenous variable. The covariances and omegas obtained from the estimated parameter in the SEM are presented in p < 0.01); however, we found that open communication was not directly related with verbal CPV. This led to a respecification of the model, and the path open communication–verbal CPV was fixed to 0. This model also showed an adequate goodness-of-fit (see p < 0.01). The respecified model is represented in The results of the structural equation analysis showed an adequate goodness-of-fit, which is presented in p < 0.01) and β IND = 0.04 , respectively. It should be noted that this mediation would be partial in the case of the relation between communication problems and verbal CPV, given the existence of a direct relationship between both variables (β = 0.39).A test of the indirect effects was calculated on the respecified model. We observe that perceived stress seems to mediate the relations between open and problematic communication and verbal CVP, as indicated by the significant indirect effects in both cases : β IND = −0.07 , the results showed that this type of communication was both directly and indirectly (through the mediation of perceived stress) related with verbal CPV. The direct relationship may arise in adolescents who perceive that their parents do not listen to them and are excessively critical, resulting in them resorting to verbal violence as an automatic response. In effect, it has been observed that adolescents who believe they matter less to their families will more likely threaten or engage in intrafamilial violence . This reThe indirect relationship arose from the role of perceived stress in the relation between family communication and verbal CPV. According to these results, it seems that those adolescents who perceived more problems in the communication with their parents perceived greater stress in their lives and also displayed greater amounts of verbal violence toward their parents. Inversely, open communication between parents and adolescents was associated with less perceived stress in adolescents and consequently less verbal CPV. These results point to a mediating role of perceived stress, which would explain the mechanism linking the quality of family communication to verbal CPV.We can interpret these results from the theoretical framework developed by Olson et al. . In theiThis study has some limitations which are necessary to point out. Firstly, causal relationships could not be established between the variables due to the transversal character of the research. The results obtained point to a possible explanatory mechanism of stress in the relationship between family communication and CPV. Only the availability of longitudinal data collected in future research would allow us to confirm the direction of this relationship between variables. Secondly, the data has been collected solely through self-report. The collection of data from the parents about their communication with their children could be of great use. Despite this, with regard to self-reports in behavioral problems, previous studies have indicated the reliability of self-reports compared with information obtained from parents ,62. FinaIn conclusion, this study contributes to the current knowledge of CPV from an ecosystemic perspective due to our simultaneous consideration of individual and family variables. The results partly indicate that the characteristics of family communication have an influence on the presence of CPV, given that they affect the levels of perceived stress by the adolescent. Moreover, problematic family communication is directly related to verbal CPV. Hence, two aspects can be outlined regarding the scope of CPV intervention. Firstly, any intervention would need to focus on the family context with the object of promoting open communication among its members, in which ideas and feelings can be freely expressed and communication related problems, such as excessive criticism or negativity, can be reduced. Secondly, there may be a need for a parallel intervention focused on developing adequate stress management strategies in adolescents.


Noise suppression capacity in multiple-input multiple-output (MIMO) sonar signal processing is derived under the assumption of white Gaussian noise. However, underwater noise mainly includes white Gaussian noise and colored noise. There exists a certain correlation between the noise signals received by each MIMO sonar array element. The performance of traditional direction-of-arrival (DOA) estimation methods decreases obviously in complex marine noise. In this paper, we propose a marine environment noise suppression method for MIMO applied to multiple targets’ DOA estimation. The noise field can be decomposed into a symmetric noise component and an asymmetric noise component. We use the covariance matrix imaginary component to pre-estimate the signal sources, then use the dimension reduction transformation to reconstruct the real component of the covariance matrix. The Toeplitz technique is utilized to reduce the correlation of the reconstructed covariance matrix. Thus, the subspace decomposition-based techniques such as multiple signal classification (MUSIC) can be used for multiple targets’ DOA estimation. To reduce the computational complexity of the methods, search-free direction-finding techniques such as the estimation of signal parameters via rotational invariance techniques (ESPRIT) can be utilized. As a result, the proposed methods can achieve better direction-finding performance in the condition of limited snapshots with lower computational cost. The corresponding Cramer-Rao bound (CRB) is deduced and the signal-to-noise ratio (SNR) gain obtained by dimension reduction processing is discussed. Simulation results also show the superiority of the proposed method over the existing methods. Multiple-input multiple-output (MIMO) sonar is a system that consists of the transmit array and the receive array. Both arrays employ multiple sensors. The transmit array emits orthogonal waveforms and the receive array completes the echo signal acquisition ,2,3. TheDirection-of-arrival (DOA) estimation of multiple targets is corrupted by complex marine noise at the receiver sensors. DOA estimation accuracy improvement is one of the most important issues to be overcome in the application of MIMO sonar ,15. ManyTherefore, an effective method to improve the noise suppression ability of multi-target DOA estimation is the main contribution of this paper. We proposed a new method for the accurate DOA estimation in the case of complex noise. The essence of this method is to improve the accuracy of direction finding, minimize the computational complexity and reduce the number of snapshots required. The ability to successfully reduce the number of snapshots is because this method is less sensitive to snapshots. It is worth mentioning that the probability of the target resolution of this method increases faster than traditional methods. Simultaneously, this method avoids the freedom loss caused by decoherent processing. We can simulate complex marine environments by adding colored noise to white Gaussian noise. In the actual noise field, the proportion of the symmetric noise component is much larger than that of the asymmetric noise component ,39. ConsThe paper is organized as follows. In Notation: Consider a uniform linear array (ULA) of co-located MIMO sonar, equipped with a transmit array of elements , we can The The components of spatial noise include white Gaussian noise and colored noise. It can be regarded as the superposition of discrete planar waves generated by several noise sources. Compared to actual noise, the error decreases as the number of noise sources increases. According to statistical characteristics, if we add as many noise sources as possible, we can gain an environmental noise field that is closer to the true value. As shown in For simplicity, we take two symmetric noise sources, By using matched filtering on echo signals generated by the orthogonal signals Because of the orthogonality between the echo signals, the covariance matrix of the transmitting signal can be simplified into the identity matrix By vectorizing Equation (3), we can obtain the output end signal sample vector after matched filtering.The main goal is to suppress the influence of noise in the direction of arrival estimation, especially the symmetric component of the spatial noise. In an actual acoustic environment, the symmetric noise component in the noise component has a large influence on the real part of the covariance matrix and has little effect on the imaginary part. By using the imaginary part DOA estimation method, the real part of the echo signal covariance matrix is removed, and the influence of symmetric noise on the direction-finding performance of the algorithm is reduced. To further analyze the covariance matrix, we consider the idea of dimension reduction transformation and obtainConsidering Equation (15), the two parts of the equation can be regarded as the covariance matrix of the signal matrix with the incident angle of The noise component can be considered as the superposition of mutually independent plane waves generated by several noise sources. The larger the number of noise sources, the closer the noise is to the real noise. In this paper, we decompose the spatial noise component into several narrowband noises with the quantity of To simplify the calculation, in The spatial correlation function of It is worth noting that the imaginary part of Let the real part of the covariance matrix Because of the numerical difference and complex noise in the array elements, the covariance matrix is an approximate Toeplitz matrix. In this case, when we do eigen-decomposition on the covariance matrix, the division of the signal subspace and noise subspace is not clear, which will lead to a performance decline in the DOA estimation of MUSIC algorithm. This phenomenon is particularly pronounced in the case of low SNR, the direction-finding result shows worse performance compared with the traditional MUSIC method. Therefore, we can modify the real part By averaging the diagonal elements of the matrix We can obtain the modified envalues . On the Remove the real part of the covariance matrix Construct Reduce the coherence of Construct Compute the noise subspace and the signal subspace, finally estimating DOAs.Up to now, we have achieved the noise suppression method for DOA estimation in co-located MIMO sonar. We show the major steps of the proposed method as follows:The choice of the twice DOA estimation methods in the steps will be discussed in the following section.In this section, we analyze the performance of the noise suppression technique for MIMO sonar DOA estimation. For simplicity, RC-STIM is used to represent the noise suppression method we proposed.In the following subsection, we assume a uniform linear transmit array consists of The RC-STIM method we proposed needs to be combined with the appropriate DOA estimation algorithm to complete direction-finding. Considering the characteristics of the MIMO sonar array, we adopt the MUSIC algorithm and the ESPRIT algorithm. For achieving the effect of suppressing noise, we need to perform two DOA estimation operations. In order to obtain the computational complexity of the proposed algorithm, we can analyze the complexity of the traditional MUSIC algorithm (hereinafter referred to as MUSIC). The computational complexity of the MUSIC algorithm is mainly affected by the covariance matrix calculation, eigenvalue decomposition (EVD) and spectral peak search calculation. Compared with the MUSIC algorithm, the proposed method requires higher computational complexity. On the other hand, the dimension reduction transformation can help to reduce the computational complexity. For convenience, we abbreviate the MUSIC-based dimension reduction method as RC-MUSIC. While Similarly, we can get the computational complexity of the ESPRIT-based RC-STIM algorithm. The abbreviated definition of RC-ESPRIT is the same as above. Different from the MUSIC algorithm, the ESPRIT algorithm avoids the computational load brought by spectral peak search and the calculating speed is more prominent than the MUSIC-based methods. Hence, the total computational complexity of the proposed method isIn our framework, a useful statistical bound for evaluating the limiting DOA estimation performance is the CRB. In this section, we analyze the CRB of MIMO sonar DOA estimation accuracy. It can be derived from the considered reconstructed covariance matrix, i.e., the model expressed by Equation (27). It is worth mentioning that the proposed MIMO sonar model with the reconstructed covariance matrix differs from the traditional MIMO sonar model given in Equation (4) due to the fact that the reduced dimension transformation method is used in Equation (27). Because of the irrelevance of noise and signals, the covariance matrix of noise can be written asto Refs. ,43, we dAssume that the noise source signals that generate colored noise are irrelevant, therefore, the covariance matrix of colored noise can be modeled as Throughout our simulations, we adopt the co-located MIMO array. We assume that the transmit and receive arrays are both uniform linear arrays with half wavelength spacing of each element and In this paper, we use the proposed covariance matrix reconstruction and dimension reduction method to improve the noise suppression ability. At this time, the subspace decomposition-based techniques such as MUSIC and the search-free direction-finding techniques such as ESPRIT can be used for multiple targets’ DOA estimation. In all simulations, unless otherwise stated, all methods are computed based on 500 independent runs.We evaluate the DOA estimation performance of our algorithms and present the root-mean-square error (RMSE) asIt can be seen from Furthermore, we consider that the DOA estimation is considered to be solved when the following is satisfiedIt is worth noticing that, in the case of low SNR, the MUSIC-based RC-STIM algorithm has a strong suppression of colored noise, while the ESPRIT-based RC-STIM algorithm can effectively reduce the impact of the white Gaussian noise. Considering the computational complexity and noise suppression, we can try to combine the proposed two methods. The computational complexity of the proposed methods has been discussed above. To access a higher DOA estimation accuracy and lower computational complexity to improve the algorithm performance, we utilize ESPRIT to carry out the preliminary DOA estimation before covariance matrix construction processing, and take the advantage of MUSIC in final DOA estimation to ensure the target direction-finding precision. Another option is to reverse the two methods. From the above, the computational complexity of this method isSince the computational complexity of the algorithms is related to the snapshots, we reduce the snapshots to The influence of symmetric noise component on algorithm performance for multiple targets DOA estimation is proposed. A group of methods for noise suppression is introduced. The essence of the aforementioned methods is covariance matrix reconstruction and dimension reduction. Unlike the previous methods that directly utilize the covariance matrix to do DOA estimation, a priori processing, i.e., pre-estimation, is used to decrease the effect of the symmetric noise component. Dimension reduction processing can help to reduce the computational complexity. MUSIC and ESPRIT can then be used for multiple targets’ DOA estimation. The ESPRIT-based methods are more advantageous in terms of reducing computational complexity. Simultaneously, Method (37) allows us to achieve a better DOA estimation performance in the lower SNR region. Performance analysis of the proposed noise suppression methods and comparison to the existing MIMO sonar techniques are given. The computational complexity of the proposed methods can be controlled by the selection of the snapshots and the prior DOA estimation algorithm. The performance of the MUSIC-based RC-STIM method and Method (37) outperform the other methods. Simulation results show the superiority of the two proposed methods over the existing methods.In future work, we intend to further optimize the noise suppression algorithm model, reduce the impact of the pre-estimation result on the DOA estimation accuracy. On the other hand, the pre-estimation result can be utilized to design the transmit beam space to improve SNR. Thus, the DOA estimation accuracy can be improved.


The tool implements specific design rules as it considers the user’s needs and fabrication requirements. The compliance of the meanders generated by the proposed tool is confirmed by fabricating the generated designs and comparing whether the resulting devices indeed realize the desired specification. To this end, two case studies are considered: first, the realization of dedicated fluidic resistances and, second, the realization of dedicated mixing ratios of fluids. The results demonstrate the versatility of the tool regarding application and technology. Overall, the freely accessible tool with its flexibility and simplicity renders manual drawing of meanders obsolete and, hence, allows for a faster, more straightforward design process.Microfluidics continues to bring innovation to the life sciences. It stimulates progress by enabling new ways of research in biology, chemistry, and biotechnology. However, when designing a microfluidic device, designers have to conduct many tasks by hand—resulting in labor-intensive processes. In particular, when drawing the design of the device, designers have to handle re-occurring entities. Meander channels are one example, which are frequently used in different platforms but always have to fit the respective application and design rules. This work presents an online tool which is capable of  Microfluidics is an emerging field which provides technological advances to the life sciences. It is an enabling technology when it comes e.g., to biological cell studies ,2,3, higDespite these promises, the design process of microfluidics is still in its infancy. In fact, most designs are entirely derived by hand, which requires the consideration of a substantial number of interdependencies affecting the intended behavior (Note that recently an automatic method for determining the required resistances of channels for certain applications have been proposed in ). This, Computer-Aided Design (CAD)—sometimes also called Microfluidic Design Automation (MDA)—are becoming essential. This can nicely be seen when looking at the microelectronics industry, which, nowadays, heavily depends on tools that allow for  modeling and simulating electrical circuits as well as to consecutively derive a physical design with fabrication details in an automated fashion.In order to make microfluidics more accessible , methods for This motivates the development of similar CAD methods and tools for the microfluidic domain, which will aid the design process, predict the designed functionality, and/or perform tasks in an automated way. Adequate tools will increase the accessibility, reduce the costs of the design process, and allow for a short time-to-market ,18. CorrHowever, currently, there is no (or not yet a) standardized design process for microfluidics. This can be explained by the rich set of available platforms which utilize different physical effects and technologies  and thermanually draw the meander channel for their respective application  and design rules . Up to now, microfluidic designers still have to use design tools such as AutoCAD, Solid Edge, or Inkscape to draw the meander design consisting of lines and radii [However, even though an integrated design flow aided by CAD methods does not yet exist . The tool allows designers to automatically generate meander channels with a desired fluidic resistance for their respective needs and by taking restrictions from the fabrication process into account. The Meander Designer generates the desired design of the channel and outputs this design in the widely applicable, vector-based Scalable Vector Graphics (SVG) file format . This allows for integrating and connecting the meander channel into the overall design and gives designers the highest flexibility with respect to the applied design process as well as to the used fabrication technology .This work presents a tool called Realizing dedicated fluidic resistances: This case study considers the application of generating meanders with a dedicated fluidic resistance ranging from 10 to Realizing dedicated mixing ratios of fluids: This case study considers the application of generating fluidic mixtures of different ratios. Therefore, a design is used containing two generated meanders with a dedicated resistance ratio, which eventually results in the desired mixing ratio. The mixing ratios 20:20, 40:20, 30:10, and 40:10 are tested, which also represent the used fluidic resistances in In order to confirm the compliance of the obtained designs , a set of meanders is generated and fabricated for the following two case studies:The obtained measurements taken from the fabricated devices confirm that the meanders automatically generated by the Meander Designer indeed yield designs that can directly be used as entities in the overall design.The remainder of this paper is structured as follows: This section describes the Meander Designer tool. Therefore, this section first states the problem description and afterwards provides technical details of the online tool.As reviewed in the introduction, meanders are central entities in microfluidic designs because they can be used to delay the flow , realizeR for a meander channel with a rectangular cross section depends on its length L, width W, height H, and the viscosity In particular, the fluidic resistance of the meander is important as it impacts the pressure-flow relation. The fluidic resistance fined by (1)R=αμLBesides embodying a particular resistance value, a meander needs to comply with specific design rules and ideally make full use of a given chip space while providing needed connectivity to the rest of the design.Designing a meander with a desired fluidic resistance and additionally considering all those constraints makes the design a cumbersome task. For example, already a slight change in the length of the meander significantly affects the desired resistance (cf. Equation ).Meander Designer is developed, which allows for automatically generating meander designs for the designer’s specific needs and constraints. Therefore, the designer only has to providethe desired resistance,the viscosity of the used fluid,the desired width/height ratio of the meander boundary,the channel width and height (information of the channel cross section),the fabrication constraints such as a lateral channel distance and a minimum bend radius,the inlet and outlet positions, as well asan optional correction factor in the form of a constant or first order function.In an effort to automate this task , a tool called the meander design as a Scalable Vector Graphics (SVG) file ,the resulting channel length,the resulting channel volume,the resulting boundary size of the meander (the width and height), as well asthe logging file, which serves as a documentation of the generated meander.Using this input, the tool generates a meander design in a fully-automated fashion. This includesFurthermore, the Meander Designer allows to account for actual and non-ideal fabrication results in the form of a correction factor. For example, in the process of soft lithography, the fabrication result e.g., of the channel width depends on a variety of parameters. Main influences are the photomask, the exposure step, the development step, and various tempering steps. The channel height also underlies variations due to coating, tempering, and development steps. Therefore, the fabrication result can vary and depends on a series of influences. This complexity of dependent and independent influences makes it difficult to account for in the design process.Therefore, an optional correction factor is introduced to the Meander Designer. The correction factor enables the designer to account for the deviation of the in-house fabrication process that is at hand. More precisely, the designer is given the possibility to specify the correction factor in the form of a constant or as a first order function. Then, a meander which should implement the desired resistance has to be fabricated with the corrected resistance value in order to compensate the deviation. The corrected resistance is calculated as follows when a first order function is used:Equation  represenhttp://iic.jku.at/eda/research/meander_designer/. In the following, the input and output masks are described. Moreover, a brief overview of the tool’s internals is provided.The Meander Designer is developed as an online tool which makes it broadly accessible for microfluidic designers and allows them to generate meanders without requiring any local installations. The tool is implemented in JavaScript in combination with the Bootstrap library and embedded within an HTML webpage. Users can access the tool through the link By pressing the green button “Design” the tool starts the design process. Therefore, the provided input parameters and Equation  are usedOverall, the Meander Designer allows for automating the tedious task of manually designing a meander and is suited to seamlessly fit into any design step regardless which technological process is utilized. After generating a meander design, the obtained SVG file can be imported into the design tool. In the design tool, the generated meanders can easily be integrated and connected to the rest of the design.In order to confirm the compliance of the proposed tool, case studies are conducted for which the obtained designs are fabricated and measured. The experimentally determined results are compared to the initial specification in the tool. This section briefly describes the applied fabrication process (soft lithography with master fabrication and chip fabrication).A glass substrate  with a thickness of 1.0 mm is cleaned with acetone, isopropanol, and deionized water. The substrate is consecutively dried in a nitrogen stream. A dehydration bake at For the experiment, 33.0 g of PDMS  in a ratio of 10:1 is prepared and thoroughly mixed with an electric stirrer for 5 min. The compound is degassed in a vacuum chamber for 45 min. The PDMS is poured onto the previously fabricated master mold creating a layer thickness of 4 mm. The PDMS is cured in a convection oven at This section describes the setup of the two conducted case studies that utilize the Meander Designer covered in The first case study considers the realization of dedicated fluidic resistances. Therefore, the Meander Designer is used to generate designs of meanders which have resistances that range between 10 to Note that, when generating these meanders using the Meander Designer, the variations resulting from the used fabrication process and how they affect the resistances of the meanders were unknown. Therefore, no correction factor in the Meander Designer was initially used.For each fluidic resistance, four copies of the generated meander are fabricated. Then, the volumetric flow rate through each of these meanders is measured. Therefore, a pump applies increasing pressures ranging from 0 mbar to 1000 mbar using a step width of 50 mbar. In order to also consider hysterical effects, the pressure is decreased with the same step width. This procedure is repeated for all four meander copies. By taking the quotient between the pressure and the volumetric flow rate, the fluidic resistances of the meanders are determined. Furthermore, the measurement setup eliminates errors in the periphery supply and the measurement equipment. Details on this measurement setup are described in The second case study considers the realization of dedicated mixing ratios. Therefore, the Meander Designer is applied for generating for each design realizing a certain mixing ratio two meanders. These two meander designs realize a fluidic resistance ratio, which eventually implements a certain mixing ratio. For example, to realize a mixing ratio of 1:1 of two fluids having the same properties, the resistances of both meanders have to be equal  is used. A pressure pump drives both fluids and the resistance of the meander determines the amount of flow through the respective meander. For measuring the mixing ratio, pictures are taken where the width of the red colored stream and the width of the blue colored stream are measured pixel-wise with an image processing software. The mixing ratio is determined by the ratio of two pixel values. Details on this measurement setup are described in This section presents and discusses the results which are obtained in the two case studies.no correction factor has been applied in the Meander Designer because the correction factor was unknown for the fabrication process at hand. x-axis , while the y-axis provides the actually obtained  values of the resulting (fabricated) design. All obtained results are denoted by black points, while the ideal match between the measured and the desired resistances are additionally added in terms of orange points which serve as reference.For the evaluation of the first case study, the generation (and fabrication) of meanders is considered, which realize different fluidic resistances ranging from 10 to y-axis). The deviation is defined by the ratio between the measured and the desired meander resistance . However, still, it can be observed that, for all desired resistance values except for The rest of this case study aims to demonstrate how the correction factor allows for decreasing the deviation between the measured and the desired resistance value of the meander. Therefore, first the obtained measurements are used to derive a correction factor for the applied fabrication process  mixing ratios. The obtained coefficient of determination is equal to For the evaluation of the second case study, the mixing ratios of the two differently dyed fluids are measured. Recall that these mixing ratios are produced by resistance ratios of meander channels. Overall, both conducted case studies and the obtained measurements taken from the fabricated devices confirm that the designs automatically generated by the Meander Designer precisely implement the input specification and, hence, can directly be used in designs.http://iic.jku.at/eda/research/meander_designer/) and generating the meander channels in the open SVG file format, the tool can be used by a large group of users from the microfluidic domain. The conducted case studies and the obtained results proved that the tool is capable to produce absolute fluidic resistance values as well as mixing ratios using relative fluidic resistance ratios. The obtained meanders can be utilized to generate precise fluidic devices, internal mixing ratios or to set the ideal operation point of a fluidic network. The flexibility of the tool to adjust it to any given technological process renders it very employable in any design process. Overall, the tool provides an important step towards the automation of the design process for microfluidic devices.This work proposed a tool for automatically generating meander designs. This tool renders the manual drawing of meanders obsolete and, hence, allows to speed up the design of microfluidic devices. By providing the Meander Designer as an online tool (accessible through the link


The postpartum period is an important period for preventive strategies as common maternal and child health risks may become manifest. Women with a lower socioeconomic status tend to have lower maternal empowerment. Increasing their risks of adverse maternal and child health outcomes. This study aims to assess the effectiveness of a primary care level intervention. Delivered to maternity care assistants, aiming to increase maternal empowerment postpartum.This study is part of the Dutch nationwide “Healthy Pregnancy 4 All-2” (HP4All-2) program, which aims to identify vulnerable mothers and young children at risk of adverse health outcomes, and subsequently improve their care. This program targets women from deprived neighborhoods.A pragmatic cluster randomized controlled trial will be undertaken in 12 maternity care organizations. Maternity care organizations in urban municipalities (i.e. the clusters) will be randomized to either a systematic risk assessment during pregnancy with emphasis on identification of non-medical risk factors for adverse maternal and neonatal health outcomes, and subsequent adaptation of care towards a client-tailored approach during pregnancy and the postpartum period, or solely the systematic risk assessment. The primary outcome is the prevalence of a low maternal empowerment score postpartum. Secondary maternal outcomes cover health-related quality of life, postnatal depression, smoking, alcohol consumption, illicit drug use. Finally, maternal and neonatal health care utilization postpartum are recorded. All outcomes will be analyzed according to the intention-to-treat principle, using multi-level mixed effects models.The study will contribute to evidence regarding the effectiveness of client-tailored, risk-based maternity care to increase maternal empowerment postpartum.6311, registered 03-27-2017.Netherlands Trial Registry (NTR)  The postpartum period, defined by the World Health Organization (WHO) as the period from childbirth to the 42nd day following delivery, is an important period for preventive strategies as common maternal and child health problems become apparent. Health problems include maternal and neonatal infections, postpartum hemorrhage, neonatal respiratory and feeding problems, and psychological problems of varying severity . The purWomen with a lower socioeconomic status (SES) are more at risk of developing adverse maternal and child health outcomes within both western and non-western countries. The resulting inequity in maternal and child health outcomes already starts before birth and extends into early childhood –7. In adIn the Netherlands, a national protocol is used during pregnancy to assess the need for maternity care postpartum, expressed in hours of care . This DuThis study is part of the Dutch nationwide “Healthy Pregnancy 4 All-2” (HP4All-2) program, which aims to improve the identification of and care for mothers and young children at risk of adverse health outcomes . HP4All-We designed a pragmatic C-RCT in six municipalities in the Netherlands to assess the effectiveness of a complex intervention to promote maternal empowerment in the postpartum period. The intervention under study consists of a systematic risk assessment during pregnancy, with emphasis on identifying non-medical risk factors for adverse maternal and neonatal health outcomes, in conjunction with client-tailored care during pregnancy and the postpartum period based on the obtained risk profile. We used the SPIRIT statement for clinical trial protocols to guide reporting of our protocol .This study is embedded in the HP4All-2 program that aims to reduce perinatal health inequalities and to improve the care for young children and their mothers . Six outParticipation of maternity care organizations is based on several factors: their willingness to participate, their capacity (defined as the expected number of eligible women to be cared for during the study period), and their ability to allow a sufficient number of MCAs to participate throughout the study period (sufficiency is based on the expected number of participants included). To increase cooperation of the participating organizations in the study, we aligned our protocol with their standard working schedules. Such an alignment for example would allow that the timing of our intervention, which starts during a scheduled home visit, differs regarding weeks of gestation.MCAs from the participating organizations will act as local researchers after completing the training sessions. They will inform potential participants about the study and obtain written informed consent. They also serve as the main point of contact between the research team and the participants, and have a key role in sustaining participant recruitment and participant participation throughout the study period.Inability to conduct a scheduled home visit for all participants.All pregnant women to be cared for by participating maternity care organizations, with a scheduled home visit during pregnancy, are eligible and invited to take part in the study.Unwillingness to sign a written informed consent form.The intervention consists of two dimensions: 1) a systematic risk assessment during pregnancy with emphasis on identification of non-medical risk factors for adverse maternal and neonatal health outcomes, and 2) subsequent adaptation of maternity care towards a client-tailored approach during pregnancy and the postpartum period.The first part is based on an electronic risk assessment questionnaire and covers six different domains related to individual level SES and psychological factors. All assessed factors are known to be associated with adverse health outcomes during pregnancy, childbirth or early childhood. The assessment consists of three main components: 1) the validated Mind2Care (M2C) tool , 2) the The MCA will review and discuss the identified risk factor(s) upon completion of the electronic assessment by the participant. Specific risk factors are linked to specific care pathways, which guide the MCA into taking appropriate action, and organize client-tailored care during pregnancy and the postpartum period. Care pathways consist of different steps a MCA can take to initiate a risk-guided provision of care in collaboration with professionals from different echelons. These steps can include communication with an involved community midwife or obstetrician but also with professionals from the public health sector. Care pathways are tailored in order to accommodate practice variations within maternity care organizations to fit local protocols and habits.Three large health insurers in the Netherlands agreed to additional fund a home risk intake  for all study participants rather than for primiparous women alone. Prior to inclusion of participants, MCAs from both the intervention and the control arm will be trained extensively to use the renewed risk assessment. Each pair of clusters within a municipality will be trained together. The training consists of two sessions of three hours and will be led by a well-established Dutch educational agency together with the executing investigator (JL). For each cluster, the first training will take place at the start of the study and the second refresher training will be scheduled three months after the start.The training program is focused on providing various communication skills for health care professionals that will build their confidence to address risks within a participant’s social situation and lifestyle . The following key topics were covered during the training program: creating opportunities for constructive conversations, empowering women to take control and manage their personal situation, and asking additional questions to identify the need of a participant regarding her health care provision. In addition, this training program covers group exercises to enhance effective and respectful engagement, in order to correctly include participants in a research trial, and in order to answer participants’ questions regarding the study protocol effectively.The second training was set up to refresh the knowledge and skills obtained in the first training and to evaluate the feasibility of the study protocol. If deemed necessary by the investigator team, minor adjustments in study protocol may be made to address any issues raised. Prior to the start of the study, all staff working in the administrative or the management sector of every participating organization receives training by the executing investigator of the research team (JL). This training focuses on the study protocol and all administrative tasks related to participant engagement with the study .Prior to the start of the intervention during the postpartum period, all MCAs working for maternity care organizations allocated to the intervention arm receive an additional three-hour training session directed towards the subsequent step of a tailored care approach, once the risk profile is known. This training session is led by another well-established Dutch educational agency together with the executing investigator of the research team (JL).The content of this training includes the rationale for the study; background information highlighting the importance of the social gradient in perinatal and postpartum health, a group conversation of the contribution of the risk factors evaluated to this gradient, and skills training. Skills training is focused on different topics: shaping knowledge of the social gradient in health to create and encourage a preventive approach in care, creating confidence to address a participant’s social situation and lifestyle, and skills to increase practical and emotional social support. In addition, the timing of the obtained skills during the early postpartum period and the subsequent preventive approaches will be discussed in-group exercises. The obtained skills should strengthen a participant’s own capabilities in taking care of herself, her baby, and her family, thereby increasing maternal empowerment.To adequately compare the intervention and the control group with regard to their medical and non-medical risk factors during pregnancy, the risk assessment during pregnancy is performed in both study arms. The MCA in the control arm is however instructed not to discuss the detected risks with the participant upon completion of the risk assessment or to discuss the findings with other health care professionals. One exception to this blinding of participants is made for the risk factor “suicidal thoughts”, given the severity of this risk identification. In the control group the risk assessment will be followed by conventional care during pregnancy and the postpartum period.The primary outcome is the prevalence of a low maternal empowerment score postpartum, defined as a score beneath the 20th centile of all empowerment scores within the control arm Table . MaternaSecondary outcome measures address maternal health-related quality of life, maternal depression, maternal smoking, alcohol consumption, drug use, and maternal and neonatal health care utilization postpartum. All individual outcome measures are summarized in Table The adherence to the study protocol within the intervention arm will be assessed quantitatively using questionnaires filled out by the MCAs who provided the care in the early postpartum period. This questionnaire will be filled out for each participant separately and assesses the MCAs’ knowledge of the maternal risk factors obtained during pregnancy and their knowledge of the applied client-tailored care during pregnancy and the postpartum period.In addition we will assess the effectiveness of the implementation process and the adherence to the study protocol in both arms. The effectiveness of the implementation process within maternity care organizations will be assessed quantitatively with a questionnaire addressed to professionals working in the administrative or management sector of each maternity care organization.Potentially eligible women receive a study flyer when registering with one of the participating maternity care organizations. During a scheduled home visit (i.e. the intake), the participant will fill out the structured risk assessment electronically. At the end of the standard maternity care provision period, a second questionnaire will be filled out assessing the primary outcome and a number of secondary outcomes Table . A thirdCalculation of sample size is based on the presumed effect of the intervention on the primary outcome at the individual level. The intervention is considered to be effective when the prevalence of a low empowerment score, is reduced by 50%; in other words, our sample size is calculated to determine a proportion difference from a low empowerment score at the 20th centile towards the 10th centile.A previous study using the MEQ, in an unselected population of 2675 women in the postpartum period, showed a mean score over four domains of 3.70, with a left-skewed distribution (25th centile 3.46 and 75th centile 3.88) . We hypoThe intervention will be implemented at the cluster level (i.e. (groups of) maternity care organizations), while the intervention effect will be assessed at the participant level. We expect a small variance in provided care between clusters as is common in primary care practices . The IntWe account for a lost to follow-up of 33% (i.e. from inclusion to assessment of primary outcome). The target sample size is therefore set at 856 (i.e. 1.5*(2.91*196)) participants per arm.To achieve adequate participant enrolment, an inclusion target per month will be set for each organization. Monitoring of this target will be communicated on monthly bases with all local researchers. Not achieving this target is seen as the impediment of effective recruitment and action will be taken to improve the effectiveness in recruiting eligible participants. An example of such action would be evaluating the barriers for participant inclusion, evaluating engagement barriers, and providing additional support for MCAs.We will perform allocation by clusters, with the maternity care organization(s) as the randomization unit, to minimize possible contamination effects among professionals. Each cluster was randomized to the intervention arm or the control arm by a statistician not involved in the implementation of the trial and blind to the identity of the clusters and the incorporated maternity care organizations.Maternity care organizations agreed to participate, and to accept any allocation, prior to the randomization procedure.Study information to participants strictly contains information related to the allocated arm, without mentioning the randomized design of the trial or the changes in the provided care during pregnancy and the postpartum period.No interim analyses are planned, and all outcomes will be analyzed following data collection.All analyses will be performed according to the intention-to-treat principle.Descriptive statistics will be presented, and formal inference will be based on hypothesis testing with two-sided statistical significance assessed at the 5% level.Multiple imputation using chained equations will be used to account for missing data in baseline characteristics. We will analyze the effect of the intervention on our primary and secondary outcome measurements using multilevel mixed-effects linear or logistic regression analyses with an assumed random effect for each cluster.A data monitoring committee (DMC) is not installed, as the ethics committee deemed it as unnecessary, because the risks for participants are negligible. All aspects of the intervention fall within the scope of conventional maternity care.The funding source has no role in the design of the study, and neither in the collection and management of the data.With this C-RCT we aim to systematically increase the identification of non-medical risk factors during pregnancy, and use the obtained risk profile for improved client-tailored care during the postpartum period. We envisage that this intervention creates an additional opportunity for MCAs to empower women during the postpartum period. Increased maternal empowerment has the potential to enhance their self-efficacy, their quality of life, and their child’s well-being during the postpartum period , 13.To our knowledge this is the first multicenter trial of client-tailored, risk-guided maternity care, creating a unique opportunity to investigate its potential to improve maternal empowerment. In concordance with the overall aim of the HP4All-2 program, this study targets women from deprived neighborhoods, aiming to reduce adverse maternal and neonatal outcomes of those who are at greatest risk.There are several challenges in the execution of this complex C-RCT that merit discussion. This C-RCT is embedded in a complex existing collaborative setting involving multiple municipalities and maternity care organizations and their professionals. Particular challenges include maintaining engagement in 17 sites spread across the country, engaging staff groups employed by different organizations, and motivating MCAs from different organizations to change their routine working practices and adapt to a more risk-guided provision of care. In addition, there is an unequivocal registration of the personalized care undertaken by maternity care professionals during the postpartum.To conclude, this C-RCT is the first trial in the Netherlands aiming to empower women during the postpartum period. Providing a risk assessment, with emphasis on non-medical risks related to SES and psychopathology, facilitates client-tailored care and may contribute to prevention of adverse health outcomes for mother and child.


If the signal strength obtained from sonar is higher than the predefined detection threshold, it is considered as a candidate for target tracking. This detection threshold is a parameter that affects the detection probability of targets and the distribution of clutter measurements, so it is important to determine a proper threshold to improve target tracking performance. There are various techniques for adjusting the detection threshold. To apply these techniques, it is assumed that the probability density functions of the signal strength for clutter are known in advance. However, in a real environment, the probability density function of the signal strength is unknown in general. In this paper, we propose a detection threshold control method using extremum seeking control in realistic environments. The extremum seeking control is a method used in complex nonlinear systems. We propose a new structure for extremum seeking control that is applicable to detection processes with nonlinear characteristics. This structure is used to adjust the detection threshold of the received signal amplitude to make the estimated clutter measurement density converge to a designed clutter measurement density to achieve the best target tracking performance in the current environment. Simulation studies for the proposed extremum seeking control applied to target tracking in an unknown clutter signal distribution demonstrate the effectiveness and improved target tracking performance. Signals acquired from sonar are used as measurements for track initiation and track updating if the signal amplitude is higher than the specified detection threshold. The clutter refers to the non-target random detections generated by random objects, thermal noise effects, reverberation, etc., that are generally random in number, location, and intensity. The sonar acquires the clutter measurements in addition to the target measurements, so that the target tracking performance is deteriorated by these clutter measurements. Therefore, to improve the target tracking performance in a cluttered environment, data association ,2,3,4 thHowever, there is trade-off in varying the detection threshold. If the detection threshold is increased, the number of generated clutter measurements is small, and the clutter measurement density is low; however, the detection probability of targets is also low. Conversely, lowering the detection threshold increases the detection probability of the target, but the clutter measurement density increases .Techniques such as “CFAR” for determining an appropriate detection threshold in the presence of such trade-offs have been proposed ,9. HowevIn this paper, we propose a detection threshold control method applying extremum seeking control, assuming that the probability density function of the signal strength for clutter is unknown . The tarThe clutter measurement density is calculated by estimating the specific spatial volume for each measurement through SCMDE  proposed in .In order to verify the effectiveness of the proposed method, a series of simulation runs for target tracking with unknown clutter distribution information is executed. The simulation scenarios include constant velocity motions in homogeneous clutter environments and a maneuvering motion in a heterogeneous clutter environment. To improve the tracking performance, the interacting multiple model (IMM)  structurThe rest of this paper is organized as follows. The problem statement for modeling and target tracking algorithm is presented in A target tracking algorithm based on the Kalman filter is used to estimate the state variable of the target being tracked. To use this algorithm, it is necessary to design the measurement model and kinetic model of the target to be tracked mathematically. In addition, since the measurement information obtained from the sonar includes not only the target measurements, but also the clutter measurements (unwanted information), a clutter measurement distribution model is needed. To perform target tracking in the presence of clutter, it is necessary to use data association to distinguish between the target measurements and the clutter measurements. However, target tracking algorithms based on data association do not have a means to judge whether the track is tracking the target or clutter. To overcome these problems, data association target tracking algorithms based on track management have been proposed. Track management is a technique for assigning the concept of “target existence probability”, which is a means of evaluating tracks, to each track and determining whether to perform target tracking based on the target existence probability. In this section, the target dynamics, measurements obtained from the sensor, clutter measurement model, and IMM-IPDA  are descx and y are the position components and The target for this study is a surface ship moving on shallow water such that the target state dynamic equation is modeled in a two-dimensional Cartesian coordinate system with the target state For maneuvering target dynamics, the state transition matrix ectively .(3)Fk1=Here, The active sonar measures both the range Here, k. A collection of all measurements up to time k can be expressed as the measurement set In this paper, the conversion method  that conm represents the number of clutter measurements obtained from the sensor in the surveillance region. Since the distribution of clutter measurements in the surveillance region is non-homogeneous, the clutter measurement density is calculated by estimating the clutter measurement density as an unknown parameter.The number of clutter measurements in the surveillance region follows the Poisson distribution. It is expressed as follows.The probability distribution of the clutter signal strength is assumed to follow a Chi-squared, Rayleigh, or Weibull distribution in most tracking systems, and each probability distribution is respectively expressed below .(12)PsCb is a scale parameter, and cis a shape parameter.Here, In this paper, the probability distribution of clutter signal strength is modeled as “exponential-type” with unknown parameter For the exponential distribution, Since the probability distribution of clutter signal strength modeled in exponential form has a large tail, the probability of false alarm can change drastically even with small changes in the detection threshold.Data association techniques such as IPDA are essential because the measurements obtained by the active sonar involve both clutter measurements and target measurements. The IPDA algorithm utilizes the target existence probability as a track quality measure. The target existence probability is calculated recursively based on the received measurements at each scan. The track management of IPDA determines confirmation and termination of the tracks based on the updated target existence probability. The presence of false tracks constrains the application of the IMM algorithm for tracking targets in cluttered environments such that the IMM-IPDA algorithm is proposed. The IMM-IPDA algorithm consists of an interaction step, a prediction step, a validated measurement selection step, an update step, and a combination step, inside a single sampling time.k are indexed by n and m, respectively. The mode change between consecutive scans is assumed to follow the Markov process. The mixed state vector In the interaction step, the predicted mode probability is calculated by using the mode transition probability N is the dynamic model number, and The predicted mode probability is:The diagonal elements represent the probability that the dynamics model of the previous time is the same dynamic model at the next time.In the prediction step, a Kalman filter prediction step is performed for each dynamic model.The prior target existence probability k, and k.Here, In the validated measurement selection step, the validation gate is set around the predicted state vector of each dynamic model, and then, the measurements in this area are used for track state update. Measurement The likelihood for the validated measurement of the mth dynamic model and:In the update step, the posterior mode probability of each dynamic model Data association probability The measurement likelihood ratio The data association probability for each dynamic model The posterior state vector The posterior target existence probability is calculated using the measurement likelihood ratio In the combination stage, the final output of the track is calculated. The estimate of the track can be obtained from the Gaussian mixture for all dynamic models using the mode probability of the dynamic model as a weight.The goal is to adjust the detection threshold so that the estimated clutter measurement density extremum ,13. The There are two input values. The inverse of the estimated clutter measurement density The role of the above function compensates for the difference between the two inputs. That is, the inverse value of the estimated clutter measurement density should not be much different from the inverse value of the desired clutter measurement density. The output from the compandor comes out in square form, multiplied by In this structure, the input is the inverse of the clutter measurement density and the output is the detection threshold value. The role of are wave . AMax isThe sign block at the bottom of the two parts is:The role of the sign block is to determine whether to increase or decrease the detection threshold at the next scan based on the inverse of the estimated clutter measurement density at the current scan. For example, if the inverse of the estimated clutter measurement density is greater than the inverse of the desired clutter measurement density, this means that the estimated clutter measurement density at the current scan is less than the desired clutter measurement density. Therefore, the clutter measurement density at the next scan must be increased. To increase the clutter measurement density, the number of measurements is greater than the current scan, and the detection threshold After extracting the information with a signal strength higher than the initial threshold, the clutter measurement density of the measurements is estimated. The estimated clutter measurement density is used to calculate the data association probability and the target existence probability in data association for the tracking filter. Then, the estimated clutter measurement density value is entered as the extremum seeking control input, and the detection threshold at the next scan is calculated.Before performing detection threshold control using extremum seeking control on-line for target tracking, a table for the desired clutter measurement densities should be prepared. The algorithm summarized in Algorithm 1 below is performed sequentially through specified off-line procedures in advance to find the desired clutter measurement densities for the tracking environment. The desired clutter measurement density is obtained as a function of Algorithm 1 Find the desired clutter measurement density.1:Select 2:fordo3: 4: 5: 6:ifthen 7:fordo  8:fordo   9:    IMM-IPDA of the clutter signal strength was uncertain, were conducted to validate the effectiveness of the proposed method. In each scenario, both the fixed detection threshold commonly used in practice and the proposed adaptive detection threshold were simulated for comparative analysis.In the first scenario, two constants The target started from position cking of , respectThe performance index of the target tracking algorithm was performed through false track discrimination. The target existence probability, which was calculated using the likelihood ratio in data association, was used for the evaluation. A track with a target existence probability higher than a predetermined confirmation threshold value was defined as a confirmation track, and the FTD process was performed for each of the confirmed tracks during every scan. The FTD process played a role in distinguishing among these confirmed tracks whether the actual target was being tracked or the clutter was being tracked. If it satisfied the following equation, it was classified as a confirmed false track that was tracking the clutter:Here, M and x and y, respectively.In this simulation, To analyze whether the estimated clutter measurement density closely follows the desired clutter measurement density when adjusting the detection threshold through extremum seeking control, the value of To analyze the effect of the initial detection thresholds on the tracking performance, the simulation with varying the initial detection thresholds was performed. The initial detection thresholds were selected as Scenario 2 was designed for checking the robustness of the proposed extremum seeking control algorithm by introducing a heterogeneous clutter environment (two regions with different In In this paper, we proposed a detection threshold control method using extremum seeking control, considering the nonlinearity characteristics of the detection process, as well as an environment in which the probability density function of the signal strength for clutter was unknown. Since there was a trade-off between the detection probability of the target and the clutter measurement density, it is important to determine an appropriate detection threshold with optimal target tracking performance. To achieve optimal target tracking performance, the estimated clutter measurement density must converge to the desired clutter measurement. We conclude that the use of the IPDA algorithm with the proposed extremum seeking control was effective in improving the tracking performance in surveillance regions with a homogeneous clutter distribution, as the unknown clutter measurement density can be adjusted to the desired clutter measurement density by the proposed extremum seeking control algorithm. For maneuvering target tracking in heterogeneous clutter environments with unknown clutter distributions, IMM-IPDA with the proposed extremum seeking control produced better performance than the conventional methods with constant thresholds. This flexible feature contributes to its applicability for practical implementation.