The European Union’s 7th Environmental Action Programme (EAP) aims to assess and minimize environmental health risks from the use of hazardous chemicals by 2020. From this angle, policy questions like whether an implemented policy to reduce chemical exposure has had an effect over time, whether the health of people in specific regions or subpopulations is at risk, or whether the body burden of chemical substances  varies with, for example, time, country, sex, age, or socio-economic status, need to be answered. Indicators can help to synthesize complex scientific information into a few key descriptors with the purpose of providing an answer to a non-expert audience. Human biomonitoring (HBM) indicators at the European Union (EU) level are unfortunately lacking. Within the Horizon2020 European Human Biomonitoring project HBM4EU, an approach to develop European HBM indicators was worked out. To learn from and ensure interoperability with other European indicators, 15 experts from the HBM4EU project (German Umweltbundesamt (UBA), Flemish research institute VITO, University of Antwerp, European Environment Agency (EEA)), and the World Health Organization (WHO), European Core Health Indicator initiative (ECHI), Eurostat, Swiss ETH Zurich and the Czech environmental institute CENIA, joined and contributed to a workshop, held in June 2017 at the EEA in Copenhagen. First, selection criteria were defined to evaluate when and if results of internal chemical exposure measured by HBM, need to be translated into a European HBM-based indicator. Two main aspects are the HBM indicator’s relevance for policy, society, health, and the quality of the biomarker data . Secondly, an approach for the calculation of the indicators was designed. Two types of indicators were proposed: (‘sum) indicator(s) of internal exposure’ derived directly from HBM biomarker concentrations and ‘indicators for health risk’, comparing HBM concentrations to HBM health-based guidance values (HBM HBGVs). In the latter case, both the percentage of the studied population exceeding the HBM HBGVs (PE) and the extent of exceedance (EE), calculated as the population’s exposure level divided by the HBM HBGV, can be calculated. These indicators were applied to two examples of hazardous chemicals: bisphenol A (BPA) and per- and polyfluoroalkyl substances (PFASs), which both have high policy and societal relevance and for which high quality published data were available . European HBM indicators help to summarize internal exposure to chemical substances among the European population and communicate to what degree environmental policies are successful in keeping internal exposures sufficiently low. The main aim of HBM indicators is to allow follow-up of chemical safety in Europe. To inform the safe management of chemical substances in Europe and hence protect human health against chemicals related adverse health effects, 27 countries are currently collaborating in the human biomonitoring initiative HBM4EU (Human Biomonitoring for Europe) . HBM4EU HBM4EU aims to coordinate and advance human biomonitoring in Europe . HBM4EU Indicators are tools to illustrate complex scientific information using a few carefully selected variables, and thereby communicate the main messages to a non-expert audience . They seHuman biomonitoring (HBM) is situated at the intersection of environment and human health . EnvironIndicators are influenced by the value judgement on the question they are designed to provide an answer to. The value judgement is not objective but is related to societal norms of what is ‘desirable’ or ‘undesirable’, which may be affected by political or cultural preferences and related ethical reasoning. In relation to the value judgement implied in the question asked , an indicator ideally has to have a transparent connection with a normative statement . Such a Presenting HBM data in the form of an indicator informs strategy and policy development in the area of chemicals management on the one hand, while at the same time, it may serve to check progress towards policy targets already set (normative statements). HBM indicators may provide valuable information on the progress of national and international activities on environment and health, including those led by the European Parliament, the European Commission (EC), OECD (Organisation for Economic Co-operation and Development), and WHO. They may also provide knowledge on the progress on the UN (United Nations) sustainable development goals (SDGs) that have integrated health as an outcome of sustainable development goals no. 3  , no. 6 (90 or the P95) of the internal exposure levels  of a representative sample of individuals from the general population. It highlights exposure in specific subpopulations vs. the population as a whole. Monitoring trends in human chemical exposure can be related to chemicals in environmental compartments, food, or consumer products. This ‘result’ indicator can be compared with a level below which no adverse health effect is expected . In that case, a judgement is made on the ‘Margin of Internal Exposure’  and it can be named a ‘HBM indicator for health risk’.The terminology of indicators varies in the fields in which they are applied. Eurostat differentiates between ‘result’ and ‘impact’ indicators. Measurement of the concentration of a substance in blood or urine could be defined as ‘HBM indicator of internal exposure’, and is an example of a result indicator. It does not give information on the hazard of the chemical, at which level an effect will occur (potency) or of the risk. Changes of internal exposure levels with time, age, sex, and geographical location can be evaluated. Furthermore, a comparison can be made between HBM values of specific sub-populations on the one hand and the so-called HBM reference values (HBM RV) representing the high-end , as well as experts involved in indicator development from Eurostat, European Core Health Indicators (ECHI), World Health Organization (WHO) Europe, the World Health Organization (WHO), Swiss ETH Zurich and the Czech environmental institute CENIA. The following items were on the agenda: (i) to discuss and receive inputs on criteria for selecting the HBM4EU indicators and on calculation methods; (ii) to discuss the possible use of HBM indicators in relation to the policy targets they could support; (iii) to get an overview of how other chemical indicators are developed and which objectives they support; (iv) to discuss how the indicators support the communication to various target audiences. Each of the participants presented examples, methodologies, and reflections on indicators that were developed within their domains. After the workshop, a manuscript was prepared and work package leaders of HBM4EU responsible for work packages on mixtures and on modelling were invited to comment. Selection criteria were defined to evaluate if there was a need to inform on specific HBM results by means of a European HBM indicator. The criteria were discussed in the workshop. The selection criteria were partly based on those used by: ECHI, the Norman Networks indicators for chemicals in water, and the environmental indicators of the EEA.Two chemical substance groups (being handled within HBM4EU) were chosen as a case study. Grouping of substances in HBM4EU was considered if: (i) common analytical methods can be used to analyse multiple substances in one matrix; (ii) the substances have similar uses, with the possibility of substitution within the group; (iii) the substances have a similar toxicological profile. For the bisphenols priority group, bisphenol A (BPA) was selected as the lead substance. For the per- and polyfluorinated alkyl substances (PFASs), the following lead substances were selected: perfluorooctanesulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), and perfluorohexane sulfonate (PFHxS). N = 1844). Samples were collected in 2011. Urinary BPA was analysed after enzymatic hydrolysis in samples of mothers (<45 years old) and their children (5–12 years old) (N = 639) from only six European countries [The BPA data used in this manuscript were published data from the EU FP-7 and Life + project DEMOCOPHES. It was a European-wide HBM pilot study in which urine samples from children and their mothers were sampled in 17 European countries (EU-FP7) . Additiountries ,17,18. DTo illustrate the indicators, widely accepted health-based guidance values such as the HBM-I value from the German Human Biomonitoring Commission  were useHBM Indicator of Internal Exposure50). For single substances, P50 and P95 values were used to calculate HBM indicators representing exposure levels in the whole population, or stratified parts of the population ). Indicators often use available aggregated data, such as summary statistics of measurements  for PCBs (polychlorinated biphenyls) and PBDEs (polybrominated diphenyl ethers) [To calculate an indicator for a group of substances at population level, the P ethers) . In the x value. In the current paper, another grouping variant was also used in which the concentrations of perfluorinated alkyl acids (PFAAs) being a subgroup of the larger group of PFASs, were summed per year in each of the age categories in which it was measured. The average result indicator was then calculated by taking a simple average of the age groups. Weighting factors, taking into account the age distribution of the whole population, were not applied, since it would introduce a bias on the PHBM Indicator for Health Riskn being the number of samples that have HBM-exposure levels above the HBM HBGV and N being the total number of samples. Two other types of HBM indicators were calculated, intrinsically including an evaluation against HBM HBGVs. The first one is the ’Percentage of population  exceeding the HBM HBGV , and the second is the on-population level ’Extent of exceedance’ . The percentage of the population exceeding the HBM HBGV is given as: x percentile by the HBM HBGV:The extent of exceedance (EE) describes the extent by which the HBM HBGV is exceeded. It is calculated by dividing the Px the HBM HBGV is exceeded. Below 1, there is no exceedance for the Px.A value >1 means that for the percentile P50 and P95. Whatever Px is selected for use in the extent of exceedance indicator, more information on how the distribution of the HBM data (including other percentiles) relate to the HBM HBGV can be given in the background information of the indicator.Consensus was found in calculating the extent of exceedance indicator (EE) of single substances for P50 and P95 as bars and the HBM HBGVs were included as line plots. Alternatively, indicators were plotted against, for instance, time (producing a time-trend) and/or were stratified according to other descriptive parameters , depending on the available information and the policy questions. Visualisation and graphical presentation of the indicators were not thoroughly discussed and lay out may still change.Data were visualized using Excel. The graphs visualise PInspired by existing work on HBM and other indicators, a set of criteria for how to select substances to create HBM-indicators was developed see . Two maiThe discussion of the experts resulted in the conclusion that an indicator needs to ‘answer’ questions of policy and society. It can be seen as a road sign for policy making. It is a simplification that helps users to absorb information quickly but this also means that it increases the amount of context needed to interpret the information in a correct way. De-contextualisation is one of the most common and yet complex risks in the use of indicators. Selection of exposure to a chemical using HBM data includes weighing off the compounds’ relevance to the availability of qualitative data. Overall, during the Copenhagen HBM indicator workshop it was agreed upon that indicators should have policy relevance, data should be available, that exposure burden from a given chemical should be linked to certain health effects, and each indicator should be unique, comparable at international level, and be clear and easy to interpret. However, after the workshop, further discussions led to a consensus in the group that the link to specific health effects would be useful for some but would not be an essential prerequisite for all HBM-indicators. An increase of the concentration of a chemical in a system  may be an early warning signal of a build-up of chemical exposure. Two chemical groups, BPA and PFASs, were selected as examples for this paper, going through the process of selection (relevance and data availability) and calculation of the indicators. BPA has a high EU policy and societal relevance , mainly 50 and P95 values. These values are typically reported in scientific studies, and by presenting both, both average and higher-exposed individuals are visible in the graph. The 95th percentile has been selected in many studies as a based percentile to derive reference values (RVs) from HBM studies [From the Copenhagen HBM indicator workshop there was consensus to calculate indicators for P studies . From a p < 0.05) higher values were observed in children from Slovenia and significantly lower levels were found in Sweden. For the mothers, significantly higher concentrations were observed in Belgium and Denmark, while levels in Sweden and Slovenia were significantly lower [50 country indicators pictured in For the used data on urinary BPA, Covaci et al. (2015) reported quite similar levels in children and mothers, and the levels in the different countries were close together . They fuly lower . The sma50, a factor of 4.8 higher) and children . The levels were significantly higher in children compared to their mothers for PFOS  and PFOA   . This waAn alternative indicator for exposure that could be used for a group of substances was calculated from PFOS, PFOA, PFNA, and PFHxS data from the Swedish population  . This tyBoth the strength and the weakness of a group indicator is that it just simplifies total chemical exposure, which can be followed over time, and which may warrant further investigations or risk management options. The group result indicator has the advantage that it can include substances that have replaced phased-out compounds. This indicator may therefore be used to survey regrettable substitution. Indeed, in the context of PFASs, this feature is very relevant, as in recent years perfluoropolyethers (PFPEs) and their carboxylates and fluorotelomer alcohols and thio-ethers and (polymer) derivatives thereof have substituted many of the PFOA and PFOS uses . They ar50 or P95, comparison with HBGV allows for an estimation of the adverse health outcome risk. This type of indicator is a subcategory of an environmental performance indicator (EPI). The extent of exceedance (EE) indicator can also be seen as a distance to target indicator or a margin of  exposure indicator [Aside from calculating simply the Pndicator ,33,34. Indicator . Other indicator ,36) may ndicator ). Showinndicator ,39 and tndicator , as it i50 and P95 values for each country were below the HBM HBGVs. Calculating the percentage of the population with levels above the HBM-I value (PE), this was <5% for the Danish children and the Belgian mothers and children (derivation based on Max. and P95 levels). For other countries, this was 0% for both children and mothers. The extent of exceedance indicator (EE), based on the ratio of the 95% percentile over the HBM-I guidance value, was <1. As the outcome of this indicator is largely influenced by the number of samples studied and the percentile selected for comparison, it is informative to provide the value of N together with the indicator. The numbers of participants per country of the BPA study are given in For the results of BPA reported by Covaci et al. (2015) , it can 50 for PFOA and PFOS the HBM-I value was exceeded 1.5–1.7 times while based on P95 for PFOA and PFOS the exceedance was 1.7–3.2 times over the HBM-I value. For the Danish PFOA and PFOS, it was clear from x used to calculate the EE involves a value judgement, which may depend on the policy question, on risk management options, as well as on more scientific issues. A lower percentile, such as P50, ‘neglects’ half of the population, which would not be represented in the EE. When using the P95 for the calculation, one might, on the other hand, include persons with extreme habits or even with genetic/biological diseases. Altogether, some examples of issues that could be considered in making the choice for a specific (higher or lower) percentile are: chronic character and severity of health outcomes ; biological half-life in humans, with short-lived metabolites possibly leading to excretion peaks [The choice of the percentile Pon peaks ; focus oThis demonstrates clearly that the selection of health-based guidance values and percentiles selected for comparison have a strong normative reference frame. This should be clearly explained in a transparent way, as this is driven by societal values that may shift over time and place.50 or P95, for the indicator are relatively independent of LOQ, whereas lower percentiles of the dose distribution may be influenced by differences in LOQ . HBM4EU will establish a network of laboratories within Europe that will produce high quality and comparable HBM data for selected priority substances. A stringent quality assurance quality control process is being installed. HBM data should be comparable when data from different populations are compared. The biomarkers should be analysed in the same type of sample tissue and chemical analysis of the biosamples should provide comparable results. However, this cannot always be guaranteed currently. In contrast to environmental monitoring or food and feed monitoring, human biomonitoring of the general population is not embedded in a legal framework at EU level. Internal and external quality assessment schemes and standard operating procedures are often available for well-established traditional biomarkers, but they are not obligatory and do not exist for newer biomarkers. Analytical methods may differ among studies, as well as pre- and post-sampling protocols. Sensitivity of analytical methods may differ among labs. This may have implications on the number of samples that are above the limit of quantification (LOQ) and influences the distribution of the reference data of the study population. The use of higher percentiles, such as PTo have a reliable estimate of the percentiles with a reasonable narrow confidence interval, the International Federation of Clinical Chemistry (IFCC) recommends having valid data from at least 120 individuals from the study population . This shIndicators derived from HBM data are complex because they must take account factors such as the variability in susceptibility in individuals and variability in co-exposures.To compare HBM datasets from two study populations living, for example, in different geographic areas, one should make sure that the other determinants of exposure in the two study populations are comparable in order to prevent misinterpretation of differences in exposure between the study areas. Statistical models using individual HBM data can adjust for differences in the characteristics of the study populations. This is not possible if the indicator has to be constructed from already aggregated datasets. The use of individual data has the advantage that they can be stratified and aggregated into population subgroups, such as age and sex or geographical regions. This allows specific research and/or policy questions, relating to, for example, social, economic, and equity issues, to be answered. The use of individual datasets requires, however, that an informed consent has been signed by the individual study persons, and that it covers use of the data for that or a comparable purpose. Compliance is also needed with the General Data Protection Regulation  for safe transfer and analysis of the individual data together with other datasets without risking that the natural identity of individuals can be revealed. Indicators should be built with data that represent the target population. This requires that the group of interest is sufficiently represented in the study population at the same frequency as it is in the target population. As participation rates differ, minorities and individuals of low socio-economic class are often underrepresented in population studies. Studies might also be designed for specific vulnerable sub-populations, such as pregnant women and children. This introduces a selection bias that, to some extent, can be solved with the use of weighing factors that can compensate for differences in participation rates among groups of interest. As mentioned earlier, health-based guidance values (HBGV) may shift over time as more scientific knowledge and data become available to improve analysis of exposure response associations. Moreover, the discovery of the capacity of certain synthetic chemicals to cause health effects at low-level exposure is ongoing . The impact of long-term low-level exposure is still a knowledge gap . The derBoth in the EU and internationally e.g., in the United Nations (UN) and the WHO, there is a wish to improve the evidence base liking environmental quality to human health. HBM exposure and health risk indicators are informative, as they are positioned on the continuum between exposure and health. However, associated trends between HBM indicators and environmental or health indicators should always be interpreted carefully, as such ecological associations do not prove causal linkage. In the field of environment and health, there are three European Core Indicator lists, namely the Environmental Core Indicators of the European Environmental Agency (EEA) ,49, the HBM indicators are additional tools to follow specific chemicals in the pressure-state-impact and response framework. They help to provide a more comprehensive view of the fate of exogenous chemicals that enter the human body. It is important for HBM and environmental indicators that the same substance(s) is (are) measured and grouped in the same way. This may be a challenge, because environmental indicators typically are linked to specific policies, which may group chemicals differently. Examples are specifically PFOA or the total organic fluorine in blood, or Tributyltin and/or tin (Sn) in biota. Depending on the substance and its suspected sources and routes of exposure, it would be relevant to compare different types of environmental indicators . Unfortunately, there is a lack of monitoring and, consequently, indicators lack for chemicals in most articles and products in the EU. IPCHEM, the EU Information Platform for Chemical Monitoring , hosts rIn real life, individuals are exposed to complex mixtures of man-made chemicals every day. This is complex, as each individual will be exposed to a different mixture and no simple methods are available to monitor the presence of a mixture in the individual and to assess its impact. Hence no HBM-based indicators for exposure to mixtures are available yet, although the policy demand is clear . The 7thHBM indicators of combined exposure to chemicals can serve as a surveillance tool to identify subgroups of individuals that are highly exposed to multiple chemicals, such as people that live in the vicinity of polluted hot spot locations. This information can be used to prioritise further studies to elucidate, for example, the risk and/or sources of pollutions, which can inform the risk management. The HBM indicator of PFAS is an example of such an indicator. When developing HBM indicators for risk assessment, a concern is whether it can be justified to sum chemicals with different or unknown modes of toxicological action. Furthermore, the potential for synergistic or antagonistic action among the compounds has to be taken into account with a mathematical formulation that would introduce biochemical interaction terms (factors) when constructing the cumulative exposure indicator reported. When information of toxicity is lacking for a chemical, REACH allows the toxicity of a chemical with ‘similar’ structure or properties to be used, which is termed read-across. It remains to be further discussed how read-across can be applied to make HBM indicators for groups of structurally similar chemicals for which there is limited information on modes of actions.Human biomonitoring (HBM) indicators are easily interpretable tools used to monitor internal exposure to (a) chemical substances in various dimensions in an absolute sense  or in a relative sense, for example, referred to statistically-derived reference levels in the general population or normalised to some levels regarded as safe such as the HBM HBGV . As such, HBM indicators can convey messages to policy makers on: (1) the exposure burden from chemicals, which may help to prioritize and inform risk management and future policy making, and (2) can highlight the success or failure of regulatory actions that have been taken. Consequently, HBM indicators can be used as a governance instrument to deliver information about the progress that has been made in reaching policy objectives, such as minimizing the exposure burden and the health impact of chemicals on vulnerable sub-populations or the whole European population. For observing changes in chemical exposure burden, it is necessary to collect data periodically, which will allow to consider both the current chemical exposure status and to interpret it in the light of the historical time trends. As a result of an expert workshop, HBM indicators were developed for exposure to single substances and to a group of substances and for health risk. HBM indicators for exposure are those that take into account the measured exposure, which may or may not be benchmarked against HBM reference values (HBM RVs) for the general population. Time trends, spatial trends, stratification by age and gender, socioeconomic status, etc., can be displayed in relation to the data availability and policy goal, if present. When HBM health-based guidance values (HBGVs) are available, measured exposure can be benchmarked against these and inform risk assessment . The percentage of the population exceeding the HBGV (PE) and the extent of exceedance of HBGV (EE) can be calculated. Which percentage (percentile) of the population that is used in calculating the HBGV PE and EE depends of the level of protection wanted, and therefore involves value judgement. As there may be different preferences, the decision would benefit from a societal and political debate, and therefore more information and transparency on the distribution of the individual HBM data should be given.Overall, the workgroup agreed that the indicator should contain a clear and easy to understand description. The policy questions also largely determine the type and design of the indicator. Generally, the indicator can be expressed in number format or infographics to order to increase the readability. Taken all together, the main aim of HBM indicators is to allow straight forward follow-up of European chemical safety.Indicators should be seen as communication tools that should be able to close the gap between the science community, chemical risk managers, the public health community, and the public at large. They reduce complexity through their ability to be used in simple messages that should be clear and understandable for a large audience. The three angles of HBM indicators—going downstream to describe the health effects of the measured levels of chemicals in humans, going upstream to identify environmental and other sources which can be reduced, and digging deeper to understand if certain groups are at risk—lend themselves well to creating a compelling narrative of chemicals impact on human lives. Throughout the work with indicators and their construction, it is therefore essential to keep in mind whether one representation or another facilitates or hampers the communication to the target audience. The hope is that such discussions will help to raise awareness across Europe and will make more people interested in making smart choices in relation to chemicals , whether


We have calculated that with the world population projected to increase from 7.5 billion in 2017 to 9.8 in 2050, the next generation (within 33 years) will produce 12,000–13,000 Mt of plastic, and that the yearly consumption will reach 37–40 kilos of plastic per person worldwide. One of the branches of the plastics industry is the production of plastics for agriculture e.g., seed trays and pots. In this paper, novel metakaolin-based geopolymer composites reinforced with cellulosic fibres are presented as an alternative to plastic pots. Materials can be dedicated to agricultural applications, provided they have neutral properties, however, geopolymer paste and its final products have high pH. Therefore, a two-step protocol of neutralisation of the geopolymer foam pots was optimised and implemented. The strength of the geopolymer samples was lower when foams were neutralised. The reinforcement of geopolymers with cellulose clearly prevented the reduction of mechanical properties after neutralisation, which was correlated with the lower volume of pores in the foam and with the cellulose chemical properties. Both, neutralisation and reinforcement with cellulose can also eliminate an efflorescence. Significantly increased plant growth was found in geopolymer pots in comparison to plastic pots. The cellulose in geopolymers resulted in better adsorption and slower desorption of minerals during fertilisation. This effect could also be associated with a lower number of large pores in the presence of cellulose fibres in pots, and thus more stable pore filling and better protection of internal surface interactions.  The exponential increase of the world’s population and production intensity causes a lot of environmental problems, amongst them, an unparalleled volume of generated waste ,2. The pOne of the branches of the plastics industry is the production of plastics for agriculture and horticulture e.g., seed trays and pots that are a staple in most greenhouse cultivations. A small sized company can process 10,000 tonnes of plastic per year for a total production of about 20–50 million pots, and it is estimated that around 500–800 million plastic plant pots are used every year in each European country. Many pots are simply discarded after plants have been planted in the ground. Considering all of the above data, biodegradable alternatives to plastic plant pots are a challenge for producers. Fibrous bio-based materials such as coir, manure, peat, rice hull, straw and wood pulp, or even recycled paper can be pressed into the shape of a plant pot. However, most of these alternative containers for growing plants can easily be damaged, and in some cases more resistant materials are required for outdoor planting. As an effect of fires in France between 1970–1973, which involved common organic plastics, the interest in alternative non-flammable materials has increased , and has4 and AlO4 tetrahedra sharing oxygen corners. If foaming agents  are added into the geopolymer paste during its consolidation, lightweight, and resistant geopolymer foams with pore sizes ranging from nanometers to a few millimetres, and a total porosity of up to 90% can be obtained . However, the comparison between metakaolin [2).The X-ray diffractograms of the geopolymer foams  showed ttakaolin  and geopMaterials can be designated to agricultural applications, provided they have neutral properties. Amongst others, one of the key factors is pH. The desirable pH in the range of 6.0–7.5 is acceptable for optimum plant growth, whereas pH > 8.3 and pH < 4.5 is too alkaline and too acid, respectively. First, plants in the pots made from materials for which pH is beyond the optimal range could be directly damaged . Second,The geopolymers did not show significant changes in appearance and organoleptic properties after immersion in acid solution. However, both the neutralisation process and the reinforcement of geopolymers with cellulose eliminated any efflorescence—white salt deposits on or near the surface of pots—which can occur after 90 days of aging . The micThe strength of geopolymer samples, determined using a Schmidt hammer, was slightly lower when G foams were previously neutralised . ReinforFirst, it can be the effect of a lower susceptibility of cellulose to the acidic agent when compared to the geopolymers. Second, the total volume of pores correlates with the density and the strength of the geopolymer foam. It is generally agreed that large pores or voids weaken the structure more than small pores in the geopolymer material, which is in agreement with the larger pore size in the G and GM foams than in the GC and GCM foams. Additionally, the coalescence of macropores observed in the G and GM foams can cause a decrease in the flexural strength found in these samples. Third, the presence of small cracks in the G foam (but not in GC) exposed to the acidic agent, can further increase the susceptibility of the basic geopolymer foams G. Fourth, the strength of the geopolymer material depends on the matrix strength . UniformAltogether, this implies the feasibility of using cotton fibres to mitigate brittle failure in geopolymers and their protection from the negative effect of acidic agents .The requirements for application of the geopolymer pots in plant cultivation are not only the properties that allow them to be used as an alternative to plastic pots. Better plant growth in the geopolymer pots was also expected due to the higher air permeability of the pots, and their mineral composition. Therefore, germination and growth of spring wheat in geopolymer pots was tested versus plant growth in a plastic pot . Much faTaking into consideration the simple chemical composition of the pots, which does not guarantee plant nutrition during whole vegetation, and the fact that the soil in pots loses nutritional properties with time due to nutrient uptake by plants, it is necessary to fertilise plants during the growing season. Therefore, the ion adsorption and desorption in the geopolymer foam were tested. The pots were soaked for 24 h in the solution of standard fertiliser, and 10 subsequent rinses were performed, which was the equivalent of watering the plants in growing conditions . The resHowever, it is important to note that plants in the geopolymer pots needed more water than in plastic pots. The geopolymer pots easily evaporate the excess water, due to the fact that water can be transported from the internal pot walls to the external walls through capillaries in the material. This is unsuitable due to economic and environmental reasons, therefore additional treatments of the outside wall of the pots  should be considered to obtain fully functional geopolymer pots for industrial production and cultivation of a large number of plants in greenhouse conditions.2O2). The excellent potential of a two-stage neutralisation method was demonstrated, which allows the use of geopolymers for the production of pots with approximately pH 7, and thus, for plant cultivation. Geopolymers can be reinforced with cellulose fibres, which protect the geopolymer structure during the neutralisation process  and improves the chemical, physical, and mechanical properties of geopolymer foams. Low-cost geopolymer foam materials could be produced from metakaolin and sand, using an alkali activation process (NaOH) and foaming agents (HGreat adsorption/desorption properties allow soaking the geopolymer foam pots in a mineral solution for a long-term fertilisation effect. Geopolymer foams reinforced with cellulose fibres were found to distribute the macroelements and supply them to plants in a more uniform way as well as to prevent them being washed-out during watering. The positive impact on plant growth was observed when they were planted in geopolymer foam pots, as they had greater growth compared to plants that had been grown in plastic containers. In conclusion, bio-geopolymer pots are a suitable alternative to plastic pots.


The Public and Private Partnership (PPP) model has been used to provide public services and goods. In China, local governments are willing to use the PPP model in many public services, such as integrated river management (IRM) projects, due to ease fiscal budget and the improved access to technology from the private sector. However, there has not been any specific discussion in the literature for applying the PPP model to IRM projects. In this study, we find that the PPP model results in the non-standardization of IRM projects. Our research paper builds the PPP operation framework for IRM projects. Our findings suggest that while the environmental quality evaluation system created in contracts for government payment seems to be optimal for protecting the public interest, it actually strains the partnership between the two parties and so its implementation should be considered on a case by case basis. Since the history of IRM projects using the PPP model is short, the actual performances of these types of projects has not yet been demonstrated. Local governments should be cautious about adopting the PPP model for such projects, and private companies should be cautious about their involvement. Our research will garner more scholarly attention to the application of the PPP model in complex projects. In China, river pollution is an area of public concern. The rapid development of urbanization and the inefficiency of treatment facilities in rural areas have led to the influx of large amounts of pollutants into rivers. These pollutants include not only COD, ammonia, and nitrogen, but also heavy metals, which seriously affect the water quality and food safety ,2,3. TheIn order to standardize PPP projects and encourage local governments to carry out PPP projects, the National Development and Reform Commission (NDRC) and Ministry of Finance of the People’s Republic of China (MOF) have established the PPP information platform and statistical system. The MOF, the government budget expenditure management department, offers a comprehensive statistical scale of PPP projects. The PPP projects database can be found in China’s PPP Integrated Information Platform Project Database, which is maintained by China’s PPP Center and was established by the MOF. The 8th Report from China’s PPP center shows that there were 14,424 projects in total, with an accumulated investment of 18.2 trillion Yuan (RMB) .After nearly two years of practice, and with the support of the Chinese central government establishing policies used to promote the PPP model, more local governments preferred to use the PPP model for comprehensive water environment management projects, as well as the management of river and urban black smelly water management . Some ofThere is much criticism of the PPP model, including the facts that it can be used to hide government debt and promote unreasonable risk allocation, lacks sustainable supervision, and results in inaccurate demand forecasts ,12,13, lThe rise of the PPP model can be attributed to its use as a financing tool; it allows for the introduction of social capital investments to ease the pressure on a large number of infrastructure investment expenditures, solving government budget deficits. Another reason for the PPP model’s use is that the public obtains private technology and operational management capabilities to improve the quality and efficiency of public services ,15.Previous studies have generated many definitions of the PPP model, and even some debate, but there is no unified PPP model definition because of the various political, economic, legal, cultural, and industrial backgrounds found around the world . The PPPThe openness of the public sector results in the number of PPP application areas and whether PPP is suitable in certain public sectors. The threshold implemented by the government for the private sector should be considered. Based on the incomplete contract and property rights theory, Hart  concludeIn the water sector, privatization or the PPP model has been widely used in many countries, so there are many studies that examine the link between ownership and performance. Tong et al.  gave a cEven though enough studies could guide the government and private sector in their execution of certain PPP projects, some barriers exist to prevent local governments from successfully applying the PPP model ,31, suchThere is currently no detailed analysis on the conditions and implications of the PPP model in IRM projects. We used existing cases and created a comprehensive analysis using the key contents of the PPP model.In China, the first PPP project was perhaps the Laibin B power station, where a foreign firm from the private sector took part in the project . In ordeChina’s PPP center classifies IRM projects under eco-construction and environmental protection. In order to facilitate the research, it was necessary to define the PPP model for an IRM project. The concept of IRM is not new, as it is the most important management concept and engineering measure for dealing with water pollution in recent years. Integrated measures are mainly implemented in the upper, middle, and lower reaches of rivers. Engineering measures control polluting from the source to the end of the river and mainly include pollution interception, sewage treatment, river dredging and remediation, wetland construction, ecological restoration, and other types of projects.Therefore, based on the Project Database, we chose “water environmental management, river basin management, and river ecological restoration” as the keywords to select projects. We set the criteria for the case selection because some projects do not really belong to environmental management; they are only clean-up actions that can temporarily improve the quality of the river and do not accomplish environmental restoration.For quantitative and qualitative analysis, we collected contracts and the tender notices of the studied projects and then used content analysis to extract information from these records. The content analysis focused on public and private concerns. Based on the content analysis results, we then developed our discussion and conclusions from the perspectives of the local government and private sector.Nearly 50 projects were collected, but some projects were removed because of unmatched objects or little information. Since PPP projects related to environmental governance have been able to receive government budget approval, some land development projects were packaged with environmental projects and utilized the PPP model.We collected information from 18 projects from 2014 to 2017, and there was no specificity in terms of the area and scale. The first case of an IRM project using the PPP model appeared in 2014 in a project for the Nanming River. The first case based on the environmental quality performance evaluation method involved the NaKao River. Before the Nanming River project, the government implemented projects that were contracted out one by one, rather than bundling all the projects that affected the river quality improvement to one company in the private sector. As Grimsey et al. [There are few cases of a contract duration exceeding 20 years, even from the removed cases; the minimum contract period for samples in this study was 10 years. Local governments consider the contract duration mainly in terms of the annual amount of the local government budget that can be arranged and the payback period for the private sector investment. The long-term contractual period actually appears to be monopolized for a certain period of time, which increases the government’s risk and undermines the public interest.The project implementation agency is regulated by the Tendering Law and should give priority to open tendering to obtain bidders for public services. In practice, local governments adopt open tendering and competitive negotiation to choose private sector businesses, that the agency uses open tendering as the main way . There aSince local governments do not have a legal representative to directly invest the SPV as a stakeholder, a local government-owned company serves as the representative of the public sector. The regulation implemented by the government allows state-owned company as private sector for PPP projects. Generally, public and private entities set up the SPV using equity cooperation. The private sector has the responsibility for the project financing.After the bidding process is organized by the government, the private sector business that wins the bid should make a contract with the government. Generally, the PPP project contract consists of two parts: a cooperation agreement signed by the government and private sector and a franchise or service agreement signed by the government and SPV. Both documents have legal validity. Then, the SPV obtains the franchise rights to finance, design, construct, and operate the project, and transfers control of the project to the local government at the end of the contract period. During the bidding process, negotiation over the special terms of the contract is complicated because it determines how the risk is distributed between the parties. Besides the risk allocation, the PPP model of an IRM project should specify how evaluate the service provided by the private sector and how pay according to the assessment results.Under the principle of fairness, local governments generally authorize third-party agencies and environmental monitoring agencies to evaluate the operational effectiveness of PPP projects. If the evaluation results achieve the contractual requirements, the local governments comply with the contract to pay; if the project objectives cannot be achieved, a third party analyzes the reasons and then makes a judgement of which party is responsible .There is no doubt that IRM belongs to the public service. Since specific levels of environmental quality are quantifiable, it is reasonable for private parties to provide environmental improvement services under the PPP model. Bundling is the basic characteristic of the PPP model. The PPP model for IRM projects includes bundling the building and operational stages, as well as bundling different projects together, such as sewage treatment, pollution interception, landscaping, or pure management investment. There is an indirect relationship between the asset operation output and public good, i.e., sewage treatment assets treat polluted water to meet standards, but that does not mean that the river environment can meet the quality standard since there are many issues that affect the environment.Due to the bundling of multiple projects together, the cases we examined had larger investments, which made their outcomes more uncertain than those of general PPP projects. The governance measures for each river vary due to its economic development, pollution sources, and functional zoning. In addition, the implementation of the PPP model requires the contributions of the local budget, project management, and other factors, making it difficult for local governments to carry out IRM PPP projects because they test the government’s comprehensive management capabilities. Before introducing the PPP model for IRM projects, local governments should research the fiscal budgeting, risk control, performance appraisal, and private returns. Engaging a professional PPP consulting company to assist local governments is necessary. Local governments should also study long term project management issues, and attempt to predict possible challenges and take measures to address them . They shThe survey also shows that not all local governments have adopted the PPP model in carrying out IRM projects. Non-standardization, over-complexity, and the excessive capabilities of local government’s project management may be factors that hinder the promotion of the PPP model. Therefore, when considering the adoption of the PPP model, local governments should be cautious and examine the actual context of the project.Since IRM projects include various types of technologies, they require higher level skills from the private sector. When selecting the appropriate private sector, local governments require that private sector should have a history of operating cases, engineering qualifications, and financing capabilities. It is difficult for a single company to meet all of the requirements. There are 2 solutions to these issues: businesses can engage in a consortium bid, where multiple private firms with different functions present joint bids, or firms can merge into a kind of comprehensive environmental management service provider, with financing, technical services, construction, and operations for the project. Another way is that through equity acquisition, a form of group company owning various companies with different functions during the whole life of a project, and thus enhancing competitiveness.As mentioned before, state-owned companies also belong to the private sector in China. State-owned companies have relatively lower financing costs and undertake more risks, making them more competitive than purely private companies. Local governments that choose state-owned companies could reduce their political risks. The proportion of state-owned companies in the private sector that have bid on PPP projects has gradually increased, not only in the area of IRM. Many discussions about this issue have mentioned that the risk of local government investment has not been substantially transferred, and it will undermine the development of the private economy.Risk allocation is the core of PPP projects. However, no matter how risks are transferred from government to private entities, the government still undertakes the ultimate responsibility for a project’s operational risk . From thTherefore, contracts alone, which are often imperfect and incomplete, are not enough, and local governments should continuously supervise the private sector . Some ofThe cases examined in this research suggest that local governments have already applied various payment methods based on performance evaluations. After introducing the performance appraisal system for IRM PPP projects, local governments should pay more attention to the improvement of the river’s environmental quality, rather than the quality of the individual project. However, some local governments still adopt traditional payment methods. In these cases, local governments have adopted two main payment methods in practice: (1) single service fees that are totally based on the Performance Evaluation (P1) and a (2) 2-phase payment method consisting of usability service fees and operational and maintenance performance service fees (P2), where the government pays for the availability asset and then pays for the service when the river quality meets the standard.However, P2 has attracted the attention of some experts and policy-makers, who argue that usability service fees lead to the overinvestment at the building stage and that governments should use the single service fees. Since P2 is an innovative approach that only emerged in the past 3 years, the previous papers have not discussed the argument from the perspective of microeconomics. Under the incomplete contract method , at the Our research shows that the payment method, choice of the private sector firms and ownership structure of the SPV, and differences among local governments make the PPP Model non-standardized.Local people are the taxpayers, and also the terminal PPP project payers, whose welfare should be considered by the government when it implements the PPP model. For IRW projects, the sewage treatment price can directly reflect the social cost, while other fees from the parts of IRM project packages are harder to collect from local people because they are difficult to calculate. Using the open tender system, the government can choose the lowest cost bidder to achieve value for its money. Online information has shown that it is hard to find obvious evidence that the PPP model would increase or decrease the cost of IRM projects. The disadvantage of a price war is that winner has the possibility to cut the building or operating costs, which then affects the quality of the project and the operational outcome.However, there are two signals that should not be ignored. In order to win the bid, private sector firms often use price wars to maliciously compete with prices below the investment cost to obtain the project franchise. They then take advantage of their monopoly of the project to force the government to raise prices or give other support, which is available to the firms through the price increase clause in the contract. Another signal is that the market supervisor issues a policy to allow the local government to dynamically adjust water treatment fees. Since the application of the PPP model IRM projects has a short history, it is hard to estimate the cost-effectiveness of the PPP model using the available cases. There are no academic papers that discuss this issue.This research provides the evidence that IRM projects can be used with the PPP model. With the background of the PPP boom, local governments that implement the PPP model in various public service areas can solve the problems of excessive local government debt and increase the efficiency of project operations, as well as transform government management methods.This paper summarizes a PPP operational framework in applicability, which could provide a reference for local governments that want to implement IRM projects using the PPP model or private sector that want to participate in PPP projects. Although the basic operational framework of the PPP model is mature and commonly practiced, it is difficult to find information about it in IRMS projects because of the non-standardized nature of the project. Therefore, before choosing the PPP model for IRM projects, local governments should not excessively consider the advantages of the PPP model and understand that the PPP model makes projects more complex. Both parties have to seriously consider whether the partnership building in PPP models is proper, rather than focusing on the short-term benefits.The adoption of an environmental quality performance appraisal system is optimal, but it increases the difficulty of risk allocation between 2 parties. If both parties do not have sufficient experience, this method should be used with caution because the government still has the ultimate responsibility for environmental quality. The local government should supervise the whole lifecycle of a PPP project, and as SPV’s minor shareholder can yet be a way to perform duties.Since this new type of PPP model is still being explored, the research in this paper was superficial, and there were deficiencies in the data and analysis. This research can, however, provide a reference for local governments and private sector to cooperate via the PPP model, and the complex PPP model could attract more scholars’ attention and result in further research.


Wireless sensor networks, as an emerging paradigm of networking and computing, have applications in diverse fields such as medicine, military, environmental control, climate forecasting, surveillance, etc. For successfully tackling the node localization problem, as one of the most significant challenges in this domain, many algorithms and metaheuristics have been proposed. By analyzing available modern literature sources, it can be seen that the swarm intelligence metaheuristics have obtained significant results in this domain. Research that is presented in this paper is aimed towards achieving further improvements in solving the wireless sensor networks localization problem by employing swarm intelligence. To accomplish this goal, we have improved basic versions of the tree growth algorithm and the elephant herding optimization swarm intelligence metaheuristics and applied them to solve the wireless sensor networks localization problem. In order to determine whether the improvements are accomplished, we have conducted empirical experiments on different sizes of sensor networks ranging from 25 to 150 target nodes, for which distance measurements are corrupted by Gaussian noise. Comparative analysis with other state-of-the-art swarm intelligence algorithms that have been already tested on the same problem instance, the butterfly optimization algorithm, the particle swarm optimization algorithm, and the firefly algorithm, is conducted. Simulation results indicate that our proposed algorithms can obtain more consistent and accurate locations of the unknown target nodes in wireless sensor networks topology than other approaches that have been proposed in the literature. As an emerging paradigm of networking and computing, wireless sensor networks (WSNs) have been relevant and applicable in diverse fields such as medicine, military, environmental control, climate forecasting, surveillance, etc. Consistent development and advances in networks have significantly extended and enabled broad application of WSNs. Recently, WSNs have been integrated with other concepts, like with the concept of the internet of things (IoT) .A WSN is a network infrastructure that consists out of vast number of minuscule, diminutive, inexpensive autonomous devices denoted as sensor nodes, which monitor and detect the environment in order to compile data . The datThere are numerous challenges in WSNs’ implementation process, such as node localization, coverage, energy consumption of sensor nodes, data routing issues, etc. Despite all of these challenges and issues, the most significant one is determining the location of sensor nodes. In order for data to be effectively collected from the environment, the locations of all sensor nodes ought to be known and valid. If altered, the data cannot be relevant for other utilization and processes. Because of the terrain deployment and different outdoor climates and indoor environments, some sensor nodes cannot be reached, and for that problem there has to be a communication and connection structure where at each moment all sensor nodes are visible and obtained for collecting and transferring data for future use. There are some approaches that consider using global positioning system (GPS) for each sensor node that is deployed, but it can result in malfunction of the nodes, energy consumption for data transfer and node usage, as well as cost and size.Considering these challenges and deployment structure in environment that is monitored, the preferred alternative is to localize deployed nodes. The WSNs localization problem can be defined as the process of finding exact locations of the unknown target sensor nodes that are randomly deployed in the monitoring environment by using the existing precise locations of sensor nodes, denoted as anchor nodes, which are utilized to obtain the distance and positions of target nodes with methods such are triangulation , time ofSince the WSNs are often deployed in indoor environments that contain walls, interference, multi-path effect, humidity and temperature variations, it is important to emphasize significance of node localization in such surroundings. In such scenarios, sensors could find their positions inside a floor of a building using wireless local area network (WLAN) technology . In thisD) monitoring environment. Each distributed sensor node has a transmission range r. The WSN can be defined as an Euclidean graph V represents the group of sensor nodes i-th and the j-th sensor node is r. Unknown target nodes are denoted as the set of M, and localized nodes, whose positions are acquired with localization technique, are represented as the set of L. Anchor nodes are defined as set N, with positions  of the tree growth algorithm (TGA) and the elephant herding optimization (EHO) swarm intelligence metaheuristics . We have applied proposed improved swarm algorithms in solving the node localization problem in WSNs and enhanced results that have been previously obtained by other swarm intelligence approaches that have been tested on the same localization problem instance. When tackling node localization, we have included a multi-stage localization approach, which indicates that all unknown sensor devices are localized in multiple stages of the algorithm’s execution.According to the literature survey, there are no previous implementations of any TGA based algorithm for the node localization problem in WSNs, while the original EHO has been previously adapted for tackling similar (not the same) problem instances ,19.The scientific contribution of this paper is twofold: improvements in solving the WSNs localization problem are established and the original versions of the TGA and EHO swarm intelligence metaheuristics are improved.According to the above statements, the basic research question that is addressed in this paper can be formulated as follows: “Is it possible to achieve further improvements in solving the WSNs localization problem by applying swarm intelligence metaheuristics?”.The details of all algorithm’s implementations, as well as the details of the control parameters’ adjustments and experimental conditions, are fully provided in this paper, so the researcher who wants to implement proposed approaches and to run simulations has more than enough information to do this on her/his own.The rest of the paper is organized as follows. In In this section we present literature review of swarm intelligence approaches for node localization in WSNs. Due to its importance on solving NP hard problems, at the beginning of this section we also show brief review of genetic algorithms application for this problem.Real life problems such as WSNs node localization, transportation problems, portfolio optimization, can be modeled as optimization tasks. Optimization presents one of the most researched domains. Metaheuristic algorithms are widely implemented for practical NP hard problems, as can be seen in the literature.Metaheuristics inspired by the nature can be in general divided into two groups: evolutionary algorithms (EA) and swarm intelligence. One of the most well-known representatives of EA is genetic algorithm (GA). GA proved to be capable of solving large number of NP hard problems, including problems from the domain of WSNs.In Ref. , the autOne of the first swarm intelligence approaches that has been presented in the literature was particle swarm optimization (PSO) . PSO metOne of the swarm intelligence metaheuristics with most implementations for various NP-hard optimization tasks is artificial bee colony (ABC), which has many applications for both, benchmark , and praMonarch butterfly optmization (MBO) is a relatively novel swarm algorithms approach that was proposed in 2015 by Wang and Deb . In thisCuckoo search (CS) algorithm, which was inspired by the obligate brood parasitism of some cuckoo species, was developed in 2009 by Yang and Deb . This swIn the review section of this paper it is also worth mentioning that the swarm algorithms were also applied to other problems from the WSNs domain. For example, in Ref. , a pigeoIn this section of the paper we give mathematical formulation of the node localization problem instance that we utilized in the conducted research.The objective of node localization in WSNs is the assessment of coordinates of the unknown target sensor nodes randomly distributed in the monitoring environment, with the goal to minimize the objective function. The evaluation of target node position is concluded with the range-based distributed localization technique, which is performed in two phases: the ranging phase and the position estimation phase.For assessing the distance between target nodes and anchor nodes in the first phase, the intensity of the received signal was considered. Due to the fact that the signal is corrupted by Gaussian noise, the precise measurements could not be obtained. In the second phase, positions of target nodes are estimated by using geometric approach, trilateration method. The position estimation phase uses the previous obtained information from the ranging phase. Many different techniques for calculating the positions (coordinates) of target nodes exist, however in this particular problem statement, the trilateration method was utilized. Due to the measurement imprecision in these two phases, swarm intelligence algorithms can be implemented to minimize the localization error.D) WSN monitoring environment, M target sensor nodes and N anchor nodes are randomly deployed, with the transmission range R. Measured distance between each target and anchor node are revised with the Gaussian noise variable. For each target node, the distance between anchor nodes in its range is calculated by using equation In a two-dimensional (2Equation :(1)di=(The variance of Equation :(2)σd2=ABCR, and with distance n. By utilizing the trigonometric principles of sines and cosines, the position of the target node can be calculated. This minimizes the error between actual distance and the estimated distance. The trilateration method is depicted in In the trilateration method for estimating the position of the unknown sensor node, the target node is defined as localized if there are minimum three anchor nodes with the known coordinates N presents the total number of anchor nodes within transmission range of target node that is subject to localization.The swarm intelligence metaheuristics which are implemented in this research are executed independently for each localizable target node to obtain its position. The agents are initialized within centroid of the anchor nodes that are located within the range of the localizable target node by using the following expression:R of the localazable target node).Furthermore, the swarm intelligence algorithms aim to find the coordinates Equation .(4)fEL=1The efficiency of the localization algorithm is measured by the average localization error As stated in In our TGA’s implementation, the balance between exploration and exploitation is dynamically adjusted during the algorithm’s execution. Our proposed approach is named dynamic search TGA (dynsTGA).In this section, we present both the original (that was devised and implemented before our research) and improved version of the TGA, along with the critical analysis and performance discussion.The TGA algorithm, created by Armin Cheraghalipour and Mostafa Hajiaghaei-Keshteli in 2017 , was insAt the starting point of the TGA algorithm’s execution, the entire population of solutions is separated into three populations (subpopulations), defined as N solutions that are randomly allocated is generated amongst upper and lower paramaters’ bounds, with the fitness value that is calculated for each individual. After this, the entire population of N solutions is arranged with respect to the fitness value, where the current best solution j-th iteration is defined. The local search for all the individuals in the subpopulation i:i-th solution in the subpopulation j (old solution i) and i), respectively, The TGA algorithm is executed in the following order. First, the initial population of Equation , where iWhen the new solution is created, the greedy selection is conducted between the old and the new solution, where all the solutions which values are lower are rejected from the population.Afterwards all the solution from the Equation  is consiWhen distance Equation :(8)y=λxAll the solutions Equation .(9)TN2jN, where it combines N.When it comes to the subpopulation N solutions are pointed and chosen as the initial population for the next iteration of the TGA algorithm’s run. For this process the roulette wheel selection process is employed.The created extended population Every swarm intelligence metaheuristics performs the search guided by two basic processes: the exploitation (intensification) and exploration (diversification). The algorithm will obtain satisfying results only if the trade-off between these two process is well adjusted.If the balance between diversification and intensification is not properly adjusted, the algorithm may fail to obtain satisfying results according to two different scenarios. In the first case, if the balance is set in favor to exploitation, the metaheuristics may converge to the sub-optimal part of the search space in early iterations. As a consequence, the search process may be trapped in the local optimum. This behavior is in the literature known as the premature convergence. In the second scenario, if the exploitation exploration balance is adjusted in favor to exploration, in most cases, the algorithm will find the part of the search region where an optimal or satisfying solutions reside, but will fail to hit those solutions due to the deficiency in exploitation power.In the basic implementation of the TGA approach, the trade-off between intensification and diversification is adjusted by using two control parameters Both parameters, In the original TGA implementation, all three parameters are static during the whole course of algorithm’s execution.By performing experimental tests on standard unconstrained benchmarks, we came to the conclusion that the performance in terms of convergence speed and the solution’s quality of the original TGA approach can be improved by introducing dynamical adjustments of the values for In the early iterations of algorithm’s execution, the value of the parameter Equation ). The baAlso, in the beginning of the algorithm’s execution, the number of solutions that are removed from the population . We camWith the introduction of dynamic behavior of Algorithm 1 Pseudo code of the dynsTGA metaheuristicsInitialization. Generate random initial population, set the iteration counter t to 1, the value of maximum iteration number N1 by usi   Apply greedy selection mechanism to choose between the old and the new solutionend for    Remove   Generate   Evaluate all solutions in the population and sort them according to their fitness valueN best solutions as initial population for the next iteration  Choose   Recalculate the values of end while return The best solution from the population Similarly, as in the case of the TGA metaheuristics, as the second step to achieve the goal of making further improvements in solving the WSNs localization problem by applying swarm algorithms, we improved original EHO metaheuristics by hybridization.In this section, we present both the original (that was devised and implemented before our research) and hybridized version of the EHO, along with the critical analysis and performance discussion.Elephant herding optimization (EHO) algorithm, proposed and created by Wang et al. in 2015 for solving global optimization tasks , was insBecause of this independence and structural communication between elephants in the same clan, and outside of the clan, there are two different environments: the first environment where elephants communicate and function under the influence of a matriarch, and the second environment where male calves leave the herd. These environments are defined as updating and separating operators.N indicates the number of sensor nodes randomly distributed in the monitoring field. Each population is detached into n clans.In the population, each individual is presented as an integer number vector with the dimension j in each clan Then each solution n clans. The updating operator is defined by changing the position of each solution j in the clan j in the clan j in the clan The population is divided into Equation :(10)xneD indicates the total dimension of the search space, following is the calculation of the center of the clan d-th of the elephant individual As The separating operator can be modeled as:By conducting empirical tests on standard unconstrained and constrained benchmarks of the basic EHO metaheuristics, we have noticed some deficiencies in the algorithms’ performance.As already mentioned above, robustness and solution’s quality of any swarm intelligence approach depends on the balance between the exploitation and the exploration. In the original EHO approach, the process of intensification is performed by the updating operator, and the new position (solution) depends of the current best solution in the population (see Equations –12)). T). T12)).According to the performed practical tests on standard unconstrained and constrained benchmarks, we concluded that the basic EHO metaheuristics suffers from inappropriate trade off between exploitation and exploration. Utilization of the separating operator, which performs exploration, is not enough, and this balance is shifted towards exploitation. As a consequence of inadequately established balance, in some runs, the algorithm could not hit region of the search space where an optimal solution resides, and it gets stuck in a suboptimal domain. Such behavior has direct implications to the worse mean values of the objective function that is subject to the optimization process. On the other hand, in some runs, the original EHO is lucky, and manages to find the right part of the search space. Thus, we concluded that the basic EHO has two drawbacks: the lack of exploration power and inadequate balance between exploration and exploitation.To overcome these deficiencies, we hybridized EHO with the well-known ABC swarm intelligence metaheuristics . We intr to Ref. .For describing solution in the population, in our hybridized approach we used Equation .By incorporating the At the beginning of algorithm’s execution, since for modeling exploration two mechanisms are used, the trade off of exploitation-exploration is slightly more shifted towards exploration. The basic assumption is that at these early iterations, the algorithm still did not find the right part of the search space, and more exploration is needed. However, in later iterations, with the preposition that the algorithm has converged to an optimal region, more intensive exploitation is needed and the balance should be shifted towards exploitation. In order to accomplish such behavior, the value of the We named our approach hybridized EHO (HEHO). The pseudo-code is given in Algorithm 2.Algorithm 2 Pseudo code of the HEHO algorithmInitialization. Generate the individuals in population; divide population into n clans; calculate fitness for each individual; set generation counter  whiledo   Arrange all solutions according to their fitnessfor all clans do  for all solution j in the clan do       Update Equation     Select and obtain better solution between     Update Equation     Select and retain better solution between end for   end for  for all clans do     Replace the worst solution in clan Equation    Replace the solutions that violate Equation end for    Analyze population and calculate fitness  Recalculate the value of the end while return the best solution among all clans The experimental section of this paper is divided into two parts. In the first part, since the WSNs localization problem can be formulated as bound-constrained optimization, we show testing results and analysis of the dynsTGA and HEHO metaheuristics for standard bound-constrained (unconstrained) optimization benchmarks. In the second part we show empirical results for the WSNs localization problem along with comparative analysis with other state-of-the-art metaheuristics, theoretical analysis, and discussion.As already mentioned in Since the WSN localization problem belongs to the group of NP hard problems, if devised approaches perform better than the original ones in the case of unconstrained benchmarks, the logical assumption is that they will also obtain better results in the case of WSNs localization.Details of some benchmark functions that were used for validation of our approaches are given in All tests for both algorithms are executed in 30 independent algorithm runs, each starting with different pseudo-random number seed.N) was set to 100, and the values for For evaluating performance of the TGA and dynsTGA we employed similar control parameter settings as in Refs. ,55. The The dynsTGA approach utilized similar values to the control parameters described above, with few modifications. In the first 210 iterations, the values of Comparative analysis between the original TGA and dynsTGA for unconstrained tests is given in As can be seen from n was set to 5, and the number of solutions in each clan Both approaches EHO and HEHO were adjusted as follows: the number of clans to Refs. ,57. EachIn the case of the HEHO approach, the initial value of the Comparative analysis between the original EHO and the HEHO for standard bound-constrained benchmarks is given in From and HEHO , it can In the final part of testing dynsTGA and HEHO metaheuristics on standard bound-constrained benchmarks, we performed comparative analysis with other state-of-the-art algorithms that have been tested on the same problem instances. We also wanted to perform side-by-side comparison between the dynsTGA and HEHO approaches, so we included results for both proposed approaches into single comparison table.Comparative analysis was performed with the iterative best performance algorithm (IBPA), largest absolute difference algorithm (LADA), taboo search (TS), and weighted superposition attraction (WSA) metaheuristics. Simulation results for the algorithms that are included in comparative analysis were taken from Refs. ,62.Comparative analysis is shown in From the presented comparative analysis it can be concluded that on average the dynsTGA metaheuristics performs better than all other approaches included in comparisons. For example, in the case of the Moreover, as it has already been pointed out in In order to make better insights into the proposed algorithms’ performance, convergence speed graphs for In this subsection, we first present the setup of experimental environment for the WSN localization problem and the control parameters’ adjustments for EHO, HEHO, TGA, and dynsTGA metaheuristics that were employed in the conducted experiments. After we show details of conducted simulations, as well as comparative analysis with other state-of-the-art approaches found in the literature that were tested on the same problem instance under the same experimental conditions.In this and the subsequent subsection, we have provided exhaustive details of the control parameters’ adjustments and experimental conditions, so researchers who wants to implement the proposed approaches and to run simulations have more than enough information to do this on their own.U represents unit of measurement. Within this monitoring environment, static target sensor nodes and anchor nodes with coordinates . Two esN and the maximum generation number , the size of population  in Ref. . In Ref. in Ref. , swarm iN) was set to 25, the values for The basic TGA and dynsTGA control parameters were adjusted as follows: initial population size  and sensor nodes (N). We wanted to perform more detailed analysis of robustness and solutions’ quality of proposed approaches. In all tests the value of In the second set of experiments, we utilized different numbers of target nodes , the worst performing metaheuristics is EHO, while the algorithm that obtained the best solution quality is the dynsTGA. Metaheuristics HEHO and TGA generate nearly same results in all test instances.A visualization of the results in the test with 50 target and 15 anchor nodes, in the case when all presented approaches managed to localize all 50 unknown sensors, is given in As already mentioned above, in Ref.  the implBy carefully analyzing For example, in tests on problem instances with {50 targets, 15 anchors}, {100 targets, 25 anchors}, and {150 targets and 35 anchors} the BOA approach on average obtained localization errors of 0.30, 0.24, and 0.55, respectively. For the same tests, the dynsTGA-based localization localized unknown targets with the localization errors of 0.25, 0.22, and 0.47, which is significantly better than in the case of BOA. At the same time, on average, both algorithms managed to localize the same number of target nodes.Contrarily, the HEHO metaheuristics, which outperform the PSO and FA approaches, for the same problem instances, obtained localization error of 0.37, 0.35, and 0.78, respectively. Thus, the HEHO-based localization estimated the locations of unknown targets with lesser accuracy when compared to the dynsTGA approach.Side-by-side comparisons of convergence speed between the dynsTGA and the HEHO on problem instances with {50 targets, 15 anchors}, {100 targets, 25 anchors}, and {150 targets and 35 anchors} are shown in From the presented figures, it can be clearly seen that the dynsTGA obtains much better performance and convergence speed than the HEHO approach. In the case of the problem instance with 150 targets and 35 anchor nodes , in earlFinally, we have also performed additional experiments with varying transmission range, number of anchor nodes, and number of generations. We wanted to measure the impact of experimental conditions changes on the obtained results and to better establish performances of the proposed metaheuristics. As in the previously conducted experiments, results are averaged over 30 independent algorithms’ runs, each starting with different pseudo-random number seed.In the experiments with varying transmission range, we executed algorithms several times with different values for anchor nodes’ transmission range  on the problem instance with 50 target nodes. Visual representation of the obtained results  is given in With the goal to measure the performances of the proposed approaches with greater accuracy, we have also performed experimental simulations with varying number of generations on the WSN topology with 50 target and 15 anchor nodes. With the increase in generation number, the number of localized nodes also increases, and the localization error decreases. The dynsTGA-based localization obtained the least localization error compared to other proposed approaches and proved to be the best metaheuristics when tackling this problem. However, as it can be seen from The research presented in this paper is aimed towards achieving further improvements in solving the WSNs localization problem by employing swarm intelligence algorithms. To accomplish this goal, we have improved the basic versions of the TGA and EHO swarm intelligence metaheuristics and applied them in determining the locations of unknown sensor nodes with greater accuracy and precision than other similar algorithms.The scientific contribution of this paper is twofold: improvements in solving the WSNs localization problem are established and the original versions of the TGA and EHO swarm intelligence metaheuristics are improved.In the basic TGA, with the goal to enhance exploitation-exploration balance, we introduced dynamic behavior and devised dynsTGA metaheuristics. We have also enhanced the original EHO approach by incorporating the Both proposed algorithms (dynsTGA and HEHO) were first tested on standard unconstrained (bound-constrained) benchmarks with the objective to measure improvements over the original (unmodified) versions.With the goal to improve the localization accuracy of unknown targets, we have conducted empirical experiments on different sizes of sensor networks ranging from 25 to 150 target nodes, whose distance measurements are corrupted by Gaussian noise. To establish whether we accomplished our objective, we performed comparative analysis with other state-of-the-art swarm intelligence algorithms, the BOA, PSO, and FA, which have been already tested on the same localization problem instance and under the same experimental conditions. As a general conclusion, the dynsTGA metaheuristics showed the best performance, solution quality, and robustness when tackling the node localization problem and improved results of the previously tested algorithms.All the details, including descriptions of the algorithms’ implementations, experimental environment setup, and control parameters’ adjustments, are fully provided, so researchers who wants to implement the proposed approaches and to conduct simulations on their own could do so based on the information provided in this paper.As part of the future research in this domain we will adapt and test the proposed metaheuristics on other problems and challenges from the domain of WSNs as well. Moreover, we will also implement and improve other swarm approaches and adapt them for tackling the same an similar problems that should be addressed in WSNs implementation.Since many challenges from the WSNs research area belong to the group of NP hard optimization, and due to the fact that the swarm intelligence metaheuristics proved to be robust methods for solving these kinds of problems, this area of research is prosperous and many opportunities for improvements exist. First, we will try to adapt the dynsTGA and the HEHO metaheuristics, which are proposed in this paper, for other WSNs problems, such as energy-based localization, deployment, coverage, and routing. Moreover, we also plan to perform experiments with different kinds of WSNs, like indoor, under water, under ground, and networks with mobile sensor nodes.On the other hand, in the swarm intelligence research domain, there are also many opportunities for improvements. In general, swarm algorithms may be improved by applying minor and/or major changes/modifications. Minor improvements may be implemented by changing the value or behavior of control parameters, and/or changing some components of the exploitation or exploration search equations. Major improvements are in general performed by applying hybridization with other algorithms or metaheuristics.In this context, we also plan to conduct research on existing swarm intelligence approaches improvements, and to adapt them to different kinds of WSNs challenges. When WSNs and swarm intelligence are combined, a huge research potential exists and this potential should be exploited and investigated.


On-chip microplasmas have previously been used in designing a compact and portable device for identifying pollutants in a water sample. By exciting a liquid sample with a high energy microdischarge and recording the spectral wavelengths emitted, the individual elements in the liquid are distinguishable. In particular, this study focuses on cesium, a contaminant from nuclear incidents such as the collapse of the nuclear power plant in Fukushima, Japan. This article shows that not only can the presence of cesium be clearly determined at concentrations as low as 10 ppb, but the relative concentration contained in the sample can be determined through the discharges’ relative spectral intensity. In March 2011, a 9.0 magnitude earthquake and the resulting 15-m tsunami caused the collapse of the Fukushima I Nuclear Power Plant in Fukushima, Japan, leading to a radiological incident requiring monitoring. Radioactive cesium, a waste product of nuclear power, demands concern as it is water-soluble and capable of affecting plants, animals, and water that we consume . This eqCurrent methods of cesium detection generally require sample collecting and testing at off-site facilities . HoweverOur method of cesium detection employs microplasmas for on-site testing and monitoring of cesium contamination after a radiological incident. Microplasmas have earned a wide range of applications: from a nanotorch capable of generating very localized microplasma for new methods of controlled direct-write micro and nanofabrication  to electThe designed device is shown in 2 by 2 mm were etched into the ceramic substrate via sandblasting. The cathode and anode were patterned to a Cr film by spinning on S1813 photoresist at 2250 rpm, annealing at 115 The device is fabricated similarly to the device discussed by Sweeney et al. . A microThe Identification of Molecular Spectra [Concentrations of NaCl, CuCl, Ca(NOf 3000 V . For allf 3000 V , The Ide Spectra , and NIS Spectra  were useIn order to obtain a baseline emission spectra, a sample of simulated seawater with 3.5% salinity was tested. rganisms . The mosrganisms . A samplA concentration of 3 mL CsOH to 10 mL deionized water was tested to obtain to the emission spectra seen in In order to verify that cesium is in fact being observed and to show the ease at which the concentration of cesium can be determined relative to another sample, the emission spectra of samples with different CsOH to deionized water concentrations were measured. A comparison of spectra obtained from two samples with different concentrations can be seen in A calibration curve for cesium was also obtained and is depicted in The microdevice described in this article was shown to detect cesium contamination through observation of a spectral peak at 853.9 nm in samples with concentration as low as 10 ppb. Furthermore, the device allows for the differentiation of cesium to other elements, both naturally occurring and contaminants. By exciting a liquid sample with a high energy microdischarge and recording the spectral wavelengths emitted, the individual elements in the liquid become distinguishable. Through the matching of multiple peaks to known cesium spectra, the presence of cesium in a water sample is determined with certainty. Comparing the intensity of responses from samples provides a clear method in determining the concentration of cesium in relation to one another. Through this method, it is not possible to differentiate between cesium isotopes contained in the sample. However, this method provides a first-response style, on-site means for testing and monitoring water contamination. This device also provides a means for testing for the arrival of contamination propagating from the incident site in the days following the incident. Samples can later be shipped to laboratories for further testing. In the event of a disaster similar to the Fukushima collapse, this device, with its low cost of manufacturing and mobility, would be ideal for on-site detection of radioactive cesium contamination in seawater.


Up to 64% of individuals who have experienced a first episode of psychosis (FEP) have used cannabis, and 30% of these have a cannabis use disorder , 2. CannThe answer appears to be no. Young people enrolled in early intervention programs do not have lower levels of cannabis or other drug use than those who receive treatment as usual . In a reCM is an evidence-based intervention in which individuals receive tangible rewards for submitting objective evidence of drug and alcohol abstinence . Our groFindings from the CIRCLE trial raise several considerations for future research. First, for CM to be effective, the magnitude and frequency of rewards for abstinence must be sufficient to decrease use. The unique developmental and psychiatric profiles of those with FEP mean that typical CM schedules might require modification to improve the intervention efficacy . Second, a conclusive trial of CM for cannabis or other substance use requires the combination of self-report and a biochemically confirmed outcome in both the CM and control groups. Third, while we have observed CM-associated reductions in psychiatric hospitalizations, our work investigated treatment as usual in public mental health centers in the USA. The CIRCLE trial, however, investigated the impact of CM on hospitalizations within the context of specialized FEP treatment, which is designed to reduce acute psychiatric care utilization . Thus, iIn the past decade, early intervention programs for FEP have revolutionized the treatment of schizophrenia. This momentum provides the opportunity to integrate interventions that address cannabis and substance use problems early in the course of illness, with the goal of preventing the negative effects of substance use on people with psychosis. With the increasing availability of high potency THC products, and the global movement towards decriminalization, cannabis use presents a formidable and important area of focus for FEP treatment providers. Studies such as the CIRCLE trial indicate that developing an effective treatment for cannabis use in this population will not be easy. A one-size-fits-all approach is unlikely to be effective given the homogeneity of cannabis use  in young people with FEP. Effective strategies are also needed to prevent people with FEP from using cannabis in the first place. Future research focusing on implementation and dissemination issues is needed to close the gap between research and practice improvement. As a field, we should strive to develop truly integrated early interventions for psychosis and substance use to help improve the lives of young people with, or at high risk of, these commonly co-occurring disorders.