Developing strategies to mitigate or to adapt to the threats of floods is an important topic in the context of climate changes. Many of the world’s cities are endangered due to rising ocean levels and changing precipitation patterns. It is therefore crucial to develop analytical tools that allow us to evaluate the threats of floods and to investigate the influence of mitigation and adaptation measures, such as stronger dikes, adaptive spatial planning, and flood disaster plans. Up until the present, analytical tools have only been accessible to domain experts, as the involved simulation processes are complex and rely on computational and data-intensive models. Outputs of these analytical tools are presented to practitioners  on maps or in graphical user interfaces. In practice, this output is only used in limited measure because practitioners often have different information requirements or do not trust the direct outcome. Nonetheless, literature indicates that a closer collaboration between domain experts and practitioners can ensure that the information requirements of practitioners are better aligned with the opportunities and limitations of analytical tools. The objective of our work is to present a step forward in the effort to make analytical tools in flood management accessible for practitioners to support this collaboration between domain experts and practitioners. Our system allows the user to interactively control the simulation process , while a realistic visualization allows the user to mentally map the results onto the real world. We have developed several novel algorithms to present and interact with flood data. We explain the technologies, discuss their necessity alongside test cases, and introduce a user study to analyze the reactions of practitioners to our system. We conclude that, despite the complexity of flood simulation models and the size of the involved data sets, our system is accessible for practitioners of flood management so that they can carry out flood simulations together with domain experts in interactive work sessions. Therefore, this work has the potential to significantly change the decision-making process and may become an important asset in choosing sustainable flood mitigations and adaptation strategies. Climate changes already have drastic implications for rainfall and ocean levels, and the situation is likely to worsen , have put a multi-layer safety approach into place as a framework for the development of these mitigation and adaptation strategies EU . The mulOne of the main challenges for following a multi-level safety approach is the integrated decision-making process it requires. This means that different practitioners, related to the three layers mentioned above, have to collaborate and find integrated solutions for flood management. These practitioners include policy analysts or political decision-makers from municipalities, water boards, and provinces or representatives from fire and police departments, hospitals, and energy companies.http://www.deltaressystems.com/hydro/product/108282/sobek-suite) or Mike11 (http://www.mikebydhi.com), can be used to gain insight into the consequences of floods under different climate scenarios or alternative measures in terms of water depths, flow velocities, or damages. They offer predictions by simulating the physical processes involving various area features such as elevation and roughness resistance and external forces such as storm events and dam breaches  are usually only presented with resulting maps. Typically, these indicate the impacts of individual flood hazards or the results of risk analyses, in which multiple hazard scenario’s, each having a certain likelihood, are combined into risk maps  we present a working system and show the utility of our approach, and 2) we explain the technical contributions needed to achieve a working solution. The paper offers an overview of our system and its possibilities, focusing on interaction and visualization. We also give a description of the novel techniques that have been used. The results of two tests are presented.In this section, we explain how the system was developed and give an overview of the key features and their technical backgrounds. For more detailed descriptions, we provide references to other publications.The system presented here builds upon a prototype developed between 2011 and 2014, involving researchers and engineers from Delft University of Technology, Deltares, and Nelen & Schuurmans.The key features of the system are based on user requirements that were derived from 13 semi-structured interviews among policy analysts of the regional water board Hoogheemraadschap van Delfland (The Netherlands), conducted in 2011 and reported in Leskens et al. . The intBased on these user requirements, the key features of our system focus on a realistic visualization of floods and interactivity to enable practitioners to explore various options for flood mitigation and adaptation measures rapidly, together with domain experts. To ensure technical reliability , our system combines a high spatial resolution  and the inclusion of all relevant processes  Stelling .2) and resolution . Making an interactive system with a realistic visualization for such large areas is a very challenging goal.There are other flood analysis tools available that provide comparable features and user interfaces , with a resolution of around 15–50 points per square meter. These points, colored via aerial images, ensure recognition of the area and its features  in high detail. Water properties, based on the simulations, are projected realistically into this 3D visualization. For example, flow directions are visualized by moving waves and water depth is displayed by adjusting the light extinction. Furthermore, the system supports stereo rendering to create the illusion of depth, if the corresponding equipment is available. Extensive interactive device support gives users the possibility to navigate the 3D world with ease. This also allows us to adapt the system’s navigation to the cognitive and motoric capacities of different audiences . Our system builds upon an efficient out-of-core rendering system that displays large-scale light detection and ranging (LiDAR) data  visualizations, in which the impacts of flood hazards can only be viewed directly from above, the 3D visualization shows the impacts also from the side. Flood depths can therefore be examined without the use of a legend in which typically different shadings of blue are used to indicate the depth of a flood. The basis for this realistic 3D visualization is data from Light detection and range (LiDAR) scans coupled to output of water simulations. LiDAR data is collected by a technology that measures distance by illuminating a target with a laser and analyzing the reflected light. The result of these LiDAR scans, usually carried out by helicopters, are points specified in three spatial dimensions  and annotations can be transferred directly to the virtual 3D LiDAR model. The on-the-spot visualization of concurrent changes on 3D topographic models of comparably sized datasets has not been demonstrated before within the scientific geospatial community. Our solution also allows highlighting of areas via common user interfaces, such as Lizard, Google Maps, or Open Street Maps to ensure a good acceptance of the system among the various users. Such possibilities can be helpful for discussions by directly visualizing the impact of the suggested solutions  files, which are readily produced with the abovementioned software packages. These 2D polygonal definitions are shared among various users, who can then concurrently define modifications.Technically, the approach modifies the LiDAR points on the fly by restricting the changes to the points that are currently visible to the observer in the 3D visualization software. Hereby, we avoid treating the entire data set, which would be too large to allow for interactive rates. Each LiDAR point is localized on the fly via a hierarchical structure build from the user-defined polygonal areas in the KML file  and their disadvantageous infiltration properties lead to water accumulating on the pavement.In order to naturally embed precipitation information in a realistic 3D visualization, we developed a method to render, dynamically update and animate clouds according to measurements provided by the Koninklijk Nederlands Meteorologisch Instituut (KNMI). Figure The rendering of large-scale water bodies is particularly challenging. No previous solution existed to directly render the results of hierarchical simulation processes Stelling , despiteThe challenge is that the simulation relies on an approximation in the form of an adaptive quadtree, which respects water properties. These data sets, even in a hierarchical representation, are too large to be displayed entirely at real-time rates. Hence, an efficient display method is needed to simplify the representation. This adaptive scheme should be based on the viewing distance to add details only in the proximity to the observer, where they are needed.The problem relates to terrain-rendering solutions, but previous work used a uniformly sampled height field , the vertices are displaced to represent the water levels and the recovered values are interpolated across each grid cell, which enables us to apply a texture-based wave-synthesis algorithm  can contribute to a better accessibility of flood analysis tools for practitioners and support collaborative flood analyses with domain experts, we carried out two tests. The first was aimed at the usability of our system for individual practitioners who are no domain experts. The second was a use case to test whether our system could support collaborative flood analysis with practitioners and domain experts in a real-world application.http://www.ahn.nl) together with satellite imagery. The data was further augmented by object databases and soil maps provided by the Dutch Water Boards and conversion tables to transform land use into infiltration rates, as well as roughness, interception, permeability, and porosity values . All had normal or corrected-to-normal vision. While they were not familiar with the system and goals, they all had experience in working with computers.The experiment took around 15 min per participant, including instructions, test, and subjective feedback. It consisted of two parts: (1) a comparison between a simulated flood hazard represented with a illustration directly from above (2D) and a helicopter view (3D) Fig.  and (2) Which visualization is best suitable to estimate damages to houses? Which visualization is best suitable to estimate loss of lives? Which visualization is best suitable to estimate whether evacuation is necessary? Does the 3D visualization have added value in respect to the 2D visualization and, and if so, what is this added value? [open question]For part 1 of the experiment, a flood was simulated in a study area in Amsterdam called Watergraafsmeer. The calculated maximum water depths of this simulation where projected in two ways. First, our 3D visualization system was applied, in which the user could make a realistic helicopter flight over the flooded area. Second, a conventional 2D visualization was applied, consisting of a digital map (Google), in which the maximum water depths where projected that could only be viewed directly from above see Fig. . We showIn part 2 of the experiment, the participants were asked to use the interaction possibilities to study the area Watergraafsmeer in Amsterdam. First, an introduction to our system was given to explain how the simulation display works. Second, the participants were given the opportunity to familiarize themselves with the simulation display. No explicit time limit was given for this. When they indicated that they understood how the simulation display had to be operated, they were asked to give an answer to the following question: which streets in the study area will be inaccessible for cars after a rain shower of 100 mm in 1 h? The time it took to answer this question was then measured.We used an Intel Core i7 at 2.67 GHz processor, 6 GB of main memory, and an NVIDIA Quadro FX380 card with a 20-in. Samsung 2233RZ  screen, and the participants used a classic three-button mouse device for interaction. For the 3D visualization, the main device uses an Intel Xeon 6 core hyperthreaded processor @ 3.2 GHz, 12 GB of main memory, and an NVIDIA GeForce GTX 680 with a common screen  attached.The responses to the closed question in part 1 of the test  are listed in the table below , located north of Amsterdam and lies approximately 3 m under sea level. It is protected by dikes from water in the IJsselmeer/Markermeer-lake. The area is inhabited by approximately 400,000 people.The West-Friesland area is a large, flood prone area , the Waterboard, municipalities, agriculture, business, project developers, energy providers, health service, fire department, and an insurance company. These stakeholders covered most of the parties involved in choosing mitigation measures. Apart from five domain experts from a consultancy company and the water board who where used to applying analysis tools, most of the other participants were not used to involvement in the application of analysis tools.The work session yielded a number of measures that were proposed by the participants and could be directly tested with our system. The measures were assigned to responsible stakeholders for further elaboration. An overview of these measures is listed in Table The results of our user study show that our system is accessible and usable for users who are not domain experts like hydraulic engineers or model specialists. First, the 3D visualization helped most of the participants to better understand the consequences of a flood scenario in terms of damages, loss of life, and the urgency to evacuate in respect to the 2D visualization. In the open question about the added value of the 3D visualization over the 2D visualization, all participants agreed that the 3D visualization helped them to better imagine what a flood means for an area. Second, it was shown that these non-expert participants all were able to use our interactive simulation display. Moreover, they were able to carry out an expert analysis about the availability of roads after a heavy rain event within minutes. With conventional models, which have specialist user interfaces and require hours of computation time, this would not have been possible.The case study showed that our system was usable for collaborative application by practitioners and domain experts. The simulation display was effectively used on the level of at least three aspects. First, it enabled practitioners from different backgrounds to understand what floods meant for their specific field of profession. Second, it helped these practitioners to contribute in suggesting suitable solutions, following the multi-level safety approach. Third, the simulation display helped the practitioners to retrieve direct feedback on the effectiveness of their suggested solutions.Our results show that our system is accessible for practitioners of flood management such that they can carry out flood analysis together with domain experts in interactive work sessions. However, one should be careful to draw general conclusions about the appreciations of our system. In our case, the simulation display and 3D visualization were mainly used for a general exploration of solutions which on the short term have no direct financial or political consequences. The impact of the decisions and the time pressure for making these decisions were therefore low. It is expected that our system will be used differently when time pressure and consequences are high. Experiences show that decision-makers in these circumstances highly value the accuracy of the outcomes of a model (Brugnach et al. We expect that interactive modeling can contribute to a better understanding of uncertainties in model outputs among decision-makers as they can directly examine if current data were used in the model setup. They can also see how suggested solutions are translated into the model and therefore better understand the scope of the outcomes. Still, a calibration and validation with real measured data, as applied in common model practices, is always advisable. However, in environmental problems related to climate changes, the future is unpredictable and the nature of the relationship between processes is sometimes unknown (Leedal et al. Regarding the technical aspects of our studies, we report that our system is efficient enough to be executed on a standard desktop PC, as reported in Section In this paper, we presented our system for analyzing flooding scenarios. We showed that, despite the complexity of the involved models and the size of the involved data sets, our system is accessible for practitioners of flood management such that they can carry out flood analysis together with domain experts in interactive work sessions. In particular, the realistic 3D visualization helps practitioners, who are no domain experts, to better estimate the scale as well as the impact of a flood. The simulation display allows them to interact with the system and to explore complex flooding scenarios. Both results have been demonstrated in a user study and a case study, showing that our system represents an important step ahead towards the closer involvement of practitioners in the analysis driving the decision-making process for developing mitigation and adaption strategies for the threats of floods.The accessibility of our system can also be underlined by the fact that a modified version was recently installed in two museums in The Netherlands, namely the Delft Science Center, as well as the Watersnoodmuseum in Zeeland. The two installations rely on our 3D visualization system to illustrate the impact of the 1953 flooding, which led to an enormous destruction of large parts of the Netherlands. Our solution gives people a direct access to flood-related information and offers insights into decision-making for flood mitigation and adaption strategies.


Hyalomma ticks or by contact with infected blood or tissue. First cases of the disease were reported in Pakistan in 1976 but regular outbreaks have been observed since the year 2000. A huge agricultural base with more than 175 million livestock, the concomitant presence of Hyalomma ticks and a lack of precautionary measures to prevent transmission lead to a considerable risk for exposed populations to contract CCHF in Pakistan. At the same time, secondary cases contracted by nosocomial transmission are reported from hospitals.Crimean-Congo haemorrhagic fever (CCHF) is a potentially fatal disease endemic in Pakistan. The causative virus is transmitted by the bite of Here we present an outbreak of CCHF with four of six patients succumbing to the disease before the suspicion for CCHF was raised. Importantly, the main clinical features of these cases were gastrointestinal symptoms without any clinical signs of bleeding. Only the last two patients in this outbreak presented with typical signs of bleeding disorder and were then confirmed being infected by CCHF. Confirmation of diagnosis was done at the National Institute of Health by real-time RT-PCR.This case series highlights the importance of early clinical suspicion for CCHF in exposed individuals and the need for improved precautionary measures against the spread of CCHF within the Pakistani population and hospitals.The online version of this article (10.1186/s40249-018-0499-z) contains supplementary material, which is available to authorized users. Please see Additional file Nairovirus) belongs to the Bunyaviridae family and was first isolated in Congo in 1956 [Hyalomma ticks or by direct contact with infected blood and tissue [Crimean-Congo haemorrhagic fever (CCHF) is a potentially deadly disease with mortality of up to 70% , 2. The  in 1956 , 5. The CCHF is endemic in Pakistan, where the climatic conditions are suitable for the transmitting ticks, hence allowing the spread of the virus –10. WithThe clinical presentation of CCHF can range from self-limiting flu-like to life threating symptoms , 22. If Karak is a southern district of Khyber Pakhtunkhwa (KPK) Province. In May 2017, a cluster of four deaths was reported from Ambiri Village in the district of Karak. In total six patients had presented with severe diarrhoea, nausea and vomiting. Initially, this was suspected to be an outbreak of a yet undiagnosed gastrointestinal disease. Two of the patients later also developed signs of severe bleeding disorder. Within four days, four out of the six patients had died. Based on the rapid evolvement of disease, fatalities in apparently healthy individuals and the unknown cause of death an investigation of the outbreak was started. The investigating team collected all available information and health data, which are presented below. The case definitions used during the investigations are shown in Table On 26th May, 2017, a 49 years old male patient, from Ambiri Village demonstrated predominant gastrointestinal symptoms of nausea and diarrhoeas accompanied by fever. He was treated at home by paramedics but his condition deteriorated and he died suddenly after experiencing heavy diarrhoeal episodes. Since he was not taken to a hospital, further investigations could not be done. According to his family he has been involved in the slaughter of a cow before the onset of symptoms.On 28th May, 2017, another male patient, 40 years old, from the same village and neighbour of the first case, developed diarrhoea accompanied by excessive nausea and vomiting and was therefore referred to a local hospital. He was admitted and routine investigations were performed. The investigating team did however not get access to the results of these laboratory investigations. According to the treating physician and the attendants of the patient, no clinical signs of bleeding were observed. In the meantime, the patient’s 15 years old son similarly developed progressive vomiting and diarrhoea and was taken to the hospital. Symptoms were still considered to be due to common gastroenteritis and hence both patients received rehydration therapy. As the clinical state of the patient worsened further, case 2 was referred to a tertiary care hospital in Peshawar. He did not survive the transfer and the corpse was then transported back to the village for funeral rituals. His son, who was case 3 in this investigation, was sent home, despite of his bad state, to attend last rituals of the funeral of his father. There he similarly died within the following days.On 29th May, 2017, a fourth person developed gastrointestinal symptoms and was initially treated by a local physician for gastroenteritis and dehydration in Karak. Due to his worsening clinical condition, he was referred to the Lady Reading Hospital Peshawar. There, he was transferred from the emergency unit to the gastroenterology department. Before further investigations were done, the patient died on 31st May.The fifth case was a 53 years old male and had been involved in butchering and skinning of the same cow, as the other patients. He developed similar gastrointestinal symptoms as well as additional haemorrhagic signs (melena) and also was admitted to Lady Reading Hospital Peshawar. His blood specimen was sent to the hospitals laboratory for routine investigations and additionally to the National Institute of Health (NIH) for detection of the Dengue NS-1 antigen, CCHF virus antigen and genomic RNA, using a real-time reverse transcriptase polymerase chain reaction (RT-PCR) . Further details about his treatment could not be obtained, but he did survive the acute episode of disease.The outbreak investigation revealed that a terminally ill cow had been slaughtered in the village of the patients and that the meat had been distributed to all the 110 households of the village. The gastrointestinal symptoms of the cases were the main presenting feature so the initial hypothesis had been that the illness might be associated with the ingestion of the cow meat. But the investigation team determined that approximately more than 300 people had eaten some of the meat but only six had developed symptoms. All of these six patients had also been involved in slaughtering of the cow, handling the carcass and skinning of the animal. Even though great efforts were made to retrospectively obtain samples from all of the presented patients, PCR diagnosis of CCHF virus infection was done in just two of the cases. Diagnosis of the other four patients was based on clinical symptoms, history of patients and close link to the confirmed cases. The predominant symptoms in the presented cases were of gastrointestinal type.The investigating team visited the village and it was found that in total there were 21 contacts who had been exposed to a high risk of contracting CCHF by handling the infected cow. This included family members and relatives of the patients, health workers involved in patient care, a veterinary doctor who had had contact with the cow, as well as those involved in the slaughter and skinning of the cow. Blood was taken from all the 21 contacts and sent to NIH for CCHF testing. All of the contacts results turned out negative.Within this case report several of the challenges are presented that health care personal has to face when confronted with CCHF in Pakistan. These include problems with clinical suspicion and availability of diagnostic services for CCHF. At present only two facilities - NIH in Islamabad and the Agha Khan Hospital in Karachi - are capable of diagnosing CCHF. However, sending specimens to these facilities from peripheral hospitals is costly and the lack of monetary resources can constitute an important barrier in adequate laboratory assessment. Secondly, results will take several days until being available. This poses an important operational challenge as the patient may have already have succumbed to the disease and further transmission of the virus may have occurred either in the health facility or within the family of the patient.Over the past decades CCHF has been reported only sporadically in different parts of the country and almost all previously reported CCHF cases in Pakistan presenting to hospitals were characterized by haemorrhagic symptoms , 29, 30.Diarrheal diseases are highly prevalent in Pakistan during the summer seasons – a period with increased risk for contracting CCHF due to suitable climatic conditions for the transmitting ticks. As diarrhoea can be an early clinical sign of CCHF, it is important for health care workers in CCHF endemic areas to maintain a high level of suspicion. To take a rigorous history of contact and exposure to animals, travel details and profession of patients presenting with diarrheal diseases is an important step when establishing differential diagnoses. If the history points to a possible infection with CCHF, the disease should be ruled out immediately. Considering CCHF as differential diagnosis early in the clinical course is also paramount for use of adequate personal protective measures, which are necessary to prevent nosocomial transmission of the disease –34. To aCurrently there is no surveillance system in place to report the disease early, especially in Baluchistan, an underdeveloped region of Pakistan. In Khyber Pakhtunkhwa Province, however, a new initiative has recently been launched with the name of Integrated Disease Surveillance and Response System (IDSRS) . The maiClearly this case series has several limitations, including a lack of testing of biological samples and specific details or laboratory results or history of the patients. Conversely, this is also an appropriate reflection of real life settings in local hospitals in rural Pakistan and particularly in resource limited regions of Western Pakistan. It underlines the importance of early clinical suspicion of this rare but dangerous disease when diagnostic possibilities are far off and doctors may have to rely on basic examination and clinical experience. It also presents how traditional habits, such as unprotected slaughtering of cows in groups or religious ceremonies influence disease dissemination and outcome.In summary, this outbreak reminds us that CCHF may present with rather unspecific symptoms and can have a rapidly fatal course of disease. Therefore, awareness of the disease has to be raised. Trainings for health workers focussing on diagnosis and treatment but also on notifying health authorities about new cases should become a priority in these affected regions.Additional file 1:Multilingual abstracts in the five official working languages of the United Nations. (PDF 718 kb)


Ankylosed teeth may have a significant esthetic and functional impact especially at the anterior segment of the upper arch. Treatment of ankylosed teeth is challenging. The objective of this case report is to describe a clinical case in which an ankylosed tooth was treated with the use of osteogenic distraction associated with simplified orthodontic biomechanics. A 17-year-old female Caucasian patient presented with a Class II malocclusion, severe maxillary dental crowding, moderate mandibular dental crowding, anterior open bite, upper midline deviation to the right, and upper right central incisor in infraocclusion due to ankylosis. Treatment involved the use of the ankylosed tooth as anchorage for the distalization of the right upper segment to correct the Class II malocclusion and to create space prior to surgery. After one week of surgical osteotomy, traction of the tooth and bone segment was initiated with the use of intermaxillary elastics. The ankylosed tooth was moved to the desired position. Bone formation and mucogingival tissue adaptation were observed. Thus, esthetic and functional improvement was achieved. Osteogenic distraction associated with simplified orthodontic biomechanics is an alternative to the treatment of ankylosed teeth which can replace the use of distractor screws, making treatment simpler and more accessible. Tooth ankylosis is the fusion of cementum with the alveolar bone. It can be caused by genetic predisposition, local metabolic changes, dental trauma , or replDue to the failure of movement with normal vertical dentoalveolar growth, ankylosed teeth and their gingival margin remain infrapositioned relative to adjacent teeth, which is an important esthetic problem, especially in the anterior region of the arch , 5. TreaOD is a tissue engineering technique in which the gradual separation of surgically sectioned bone edges results in the generation of a new bone . It is aAfter osteotomy and placement of a distraction appliance, the bone fragment is kept stable during a latency phase. This allows initial bone healing in the gap between the edges of the fracture and a soft callus is formed . At the The objective of this case report is to describe a clinical case in which an ankylosed maxillary central incisor was moved to the desired position with OD using simplified orthodontic biomechanics.The patient was a 17-year-old Caucasian female who wanted a more esthetic smile through orthodontic treatment. She had a Class II division 1 malocclusion, severe crowding in the maxillary arch, moderate crowding in the mandibular arch, anterior open bite, upper midline deviation to the right, and the maxillary right central incisor in infraversion . The panThe patient and her parents were asked about any history of trauma. They reported that the patient had suffered a fall in childhood, which could have generated ankylosis of the right upper central incisor.″ segmented wire together with 5/16 medium elastics with 50g of force on each side, 16 hours per day, but it was not successful  wire .″ × .025″ wire was placed in the upper arch (Once the Class II molar relationship was corrected and the spaces were opened, a segmented stainless steel .019per arch  and a siThe patient was then submitted to surgery. Two vertical vestibular relaxing incisions were performed distal to the upper canines, and a horizontal incision was performed superiorly, in the alveolar mucosa. After the opening of the flap, vestibular periosteum detachment was performed. The mucosa and the periosteum in the palatal region were maintained intact in order to preserve blood irrigation. The osteotomy was performed with drill and chisel mesially, distally, and apically to the root of tooth 11. A chisel was used to release the bone fragment, which remained connected only by the palatal periosteum. After that, the vestibular periosteum and the flap were repositioned and the suture was performed.″ × .025″ wire with in and out bends for 6 months.After 7 days of latency, OD was initiated using intermaxillary elastics with a force of 320 g . The patConsidering the less invasive technique used and taking into account the difficulty in dealing with a case of dental ankylosis in the upper anterior region, the methods used were successful for both the functional and esthetic aspects. Infrapositioned teeth may be part of a malocclusion and the possibility of ankylosis must be considered in these cases . HoweverTreatment of ankylosed teeth is challenging, especially at the anterior segment of the upper arch. One option is the surgical luxation in an attempt to break the fusion between the cementum and the bone. However, a repair process that usually results in recurrence of the ankylosis follows this procedure .Another treatment alternative is tooth extraction and replacement with a prosthetic tooth or an implant. The problem related to this approach is the bone defect that is frequently present due to the failure of vertical alveolar growth associated with the ankylosis . In addiOther approaches involve corticotomy, osteotomy, and OD . OD is tConsidering this, we opted for OD using simple orthodontic biomechanics with the use of intermaxillary elastics. Dolanmaz et al.  also useKofod et al.  argue thAnother aspect that must be considered is the fact that OD using screws allows only unidirectional movements. The use of the arch wire or intermaxillary elastics to distract the bone fragment allows the movement of the tooth and bone in three dimensions, which can lead to a more esthetic result. Kinzinger et al.  describe″ × .025″ TMA cantilever was used. The ankylosed tooth was used as anchorage for the distalization biomechanics, as suggested by Isaacson et al. [The opening of space so that a bone fragment of adequate size can be obtained is fundamental to guarantee the maintenance of the blood supply and the success of the procedure , 10, 13.n et al. . Anothern et al. .Growing patients should be warned about the possibility of relapse, since the OD corrects the dental positioning but does not treat the ankylosis. Thus, the lack of vertical growth of the alveolar bone will persist. In such cases, delaying treatment until growth has ceased must be considered . For patIn the clinical case presented, the patient did not show signs of loss of pulp vitality of the ankylosed tooth. However, this is a possible complication of this type of treatment .The treatment resulted in correct positioning of the ankylosed tooth along with the supporting tissues, thus providing better function and smile esthetics. For the clinical case presented, osteogenic distraction with a simplified orthodontic biomechanics was an alternative to using distractor screws, making the treatment simpler and more accessible.


While smart metering applications have initially focused on energy and gas utility markets, water consumption has recently become the subject of increasing attention. Unfortunately, despite the large number of solutions available on the market, the lack of an open and widely accepted communication standard means that vendors typically propose proprietary data collection solutions whose adoption causes non-trivial problems to water utility companies in term of costs, vendor lock-in, and lack of control on the data collection infrastructure. There is the need for open and interoperable smart water metering solutions, capable of collecting data from the wide range of water meters on the market. This paper reports our experience in the development and field testing of a highly interoperable smart water metering solution, which we designed in collaboration with several water utility companies and which we deployed in Gorino Ferrarese, Italy, in collaboration with CADF (Consorzio Acque Delta Ferrarese), the water utility serving the city. At the core of our solution is SWaMM (Smart Water Metering Middleware), an interoperable wireless IoT middleware based on the Edge computing paradigm, which proved extremely effective in interfacing with several types of smart water meters operating with different protocols. The efficient use of natural resources is increasingly important, especially with the current trends towards a larger population mainly concentrated in urban environments (smart cities and megacities) ,2. SeverThere is a growing interest in smart metering applications , that haHowever, so far, research has dedicated a limited effort to the investigation of smart water metering solutions. This is unfortunate, because smart water metering presents interesting challenges both at the scientific and at the engineering levels. More specifically, we can identify three main problems to address.First, lacking an open and widely accepted communication standard in the smart water metering market, most companies manufacturing smart water meters propose proprietary solutions. In addition, the smart metering market is quickly evolving from the communications perspective: while a few years ago smart meters only used the Wireless M-Bus communication protocol, more recent meter also adopt LoRa, and the first NarrowBand IoT (NB-IoT) solutions are emerging. This results in vendor and protocol lock-in issues for water utilities, which might lead to high expenditures (both CAPEX and OPEX) and ultimately hinder innovation.Second, a significant part of the water meters installed at the end user level are of the traditional mechanical type, designed to display water consumption visually and read by a human operator. These “dumb” meters have a very large installation base and still represent a cheap and well tested solution. As a result, they are likely to play a non-negligible role in the water utility market for (at least) the near future. This means that smart water metering solutions designed for large scale deployment need to have the capability to operate with dumb meters, possibly implementing sophisticated automated reading solutions.Finally, there is the need to address practical aspects connected to real-life deployment of large scale smart water metering solutions. More specifically, how to address radio frequency propagation issues and how to set the reading frequency (a trade off between observability and energy consumption) represent issues of major importance. Reports on practical knowledge developed on the field, which are unfortunately, very rare at the moment of this writing, would help designers to build improved smart water metering solutions.This paper attempts to bridge this gap by introducing a comprehensive smart water metering solution, which we designed in collaboration with several Italian water utility companies to address specifically the peculiarities and requirements of the smart water utility market. This paper also reports on our extensive field testing experiences. Our solution leverages the Edge Computing approach to distribute data pre-processing functions in proximity of the water meters and takes advantage of modern Commercial Off The Shelf (COTS) IoT technologies, both at the hardware and at the (open source) software level, to reduce costs and facilitate interoperability.At the core of our solution is SWaMM (Smart Water Metering Middleware), an interoperable wireless IoT middleware that is capable of interfacing with a wide range of smart water meters operating with different protocols. SWaMM Edge Gateway devices running the SWaMM Edge component collect water consumption data from the smart meters, with a dynamically tunable frequency that allows tradeoffs between observability and energy saving, preprocess the data, transcoding it if needed and aggregating it to consolidate transmissions, and forward it to the SWaMM Cloud platform, which further analyzes the data to return useful consumption pattern information both to the users and to the water utility.Our solution was thoroughly tested in the context of two field experiments. First, we deployed SWaMM to perform the smart water metering of all residential houses in Gorino Ferrarese, Italy. In collaboration with CADF (Consorzio Acque Delta Ferrarese), the water utility serving the city, all the mechanical water meters in Gorino Ferrarese were replaced with smart ones. In addition, we deployed several gateways to retrieve consumption data from the smart water meters and forward it to the SWaMM Cloud platform.In addition, we tested the capabilities of our solution to integrate with an existing water metering infrastructure by deploying SWaMM to implement the smart water metering of selected residential homes in Ferrara, Italy. More specifically, we selected several residences in Ferrara that represented frequent use cases  with different water meters.From the initial experimentation to the deployment in the field, SWaMM has shown a remarkable interoperability and was capable of interfacing with a wide range of water meters. Not only SWaMM interfaced with smart meters adopting either the Wireless M-Bus or the LoRa communication protocols but also with traditional mechanical meters retrofitted with an Optical Reader Kit that we designed to integrate SWaMM with older water metering installations. Finally, the analysis of the data collected implemented by the SWaMM platform has proven very effective in detecting leakages.The rest of the paper is organized as follows. Both providers and consumers present common interests in the smart water metering market . On the With respect to the energy and gas markets, smart water metering presents important criticalities. In fact, water usually tends to be much cheaper than other commodities , and thus, resulting in less investments and efforts in the market. Another interesting criticality is that the water meters are usually located in underground and/or protected areas (inspection pits), which do not have an electricity coverage for obvious safety reasons. This results in systems that need to be battery powered and extremely energy efficient to provide at least an acceptable average lifetime period. In addition, these underground areas may also present radio frequency propagation issues, which significantly reduces the strength and the range of wireless transmissions. However, the increasing reduction of water availability  due to sDespite smart water metering being a relatively recent practice, there are several smart water meters available on the market. The first generation of smart water meters adopted low power short-range wireless protocols, such as Wireless M-Bus, that operates over the unlicensed spectrum (169 or 868 MHz in Europe). These meters were designed to operate in Remote Meter Reading (RMR) systems, in which data collection can be performed without a dedicated networking infrastructure: operators equipped with portable receivers collect data in the proximity of the smart meters either in walk-by or drive-by mode. RMR systems eliminate the need for physical access or visual inspection of smart meters but do not allow either real-time or automated consumption monitoring.More recently, a second generation of smart water meters that leverage on low-power long-range wireless protocols, such as LoRa (Long Range), which is designed for a wider communication range both in urban and extra-urban environments, hit the market ,15. ThesUnfortunately, despite the large number of solutions available on the market, the lack of an open and widely accepted communication standard pushed vendors to propose proprietary data collection solutions. The adoption of proprietary solutions presents water utility companies with several significant problems in term of costs, vendor lock-in, and lack of control on the data collection infrastructure.In fact, vendors typically propose proprietary and often very expensive gateways, that need to be acquired in addition to smart meters and whose cost might account for a non negligible portion of capital expenditure for water utility companies that want to implement an AMR system. This is further exacerbated in case short-range water meters are employed. In fact, while nothing prevents short-range smart meters to be adopted within AMR systems, their limited communication radius would require the deployment of a large number of gateways. Thorough planning and field testing would be needed in order to minimize the number of gateways required to collect data from all the smart meters, and in some cases the deployment of ad hoc signal repeaters to extend the radio coverage of smart meters would be required.In addition, due to the lack of an open standard, smart meter vendors have typically adopted proprietary protocol specifications to transmit metering data over the network. As new smart meters need to be compatible with the existing monitoring solution, this may easily result in vendor and communication protocol lock-in problems. Lock-in risks are particularly high at the moment of this writing, in which smart water metering could arguably be classified at the “early adopters” stage of the technology life cycle. In fact, solutions on the market are rapidly evolving with respect to lower energy consumption and longer communication range, and the first NarrowBand IoT (NB-IoT) proposals are emerging. As a result, choosing proprietary solutions offered by a vendor now might hinder or preclude the adoption of significantly more convenient water meters from a different vendor in a few years.https://oms-group.org/en/download4all/oms-specification/) radio protocol and a proprietary protocol, which usually can provide some additional information. Furthermore, on the metering market it is common for manufacturers to provide proprietary platforms designed to receive data from different types of smart meters using proprietary radio protocol (for their own smart meters) and also to collect data using the OMS protocol, and thus, enabling data collection from smart meters that support open standard. A representative example of a similar solution is the one provided by Sensus, which makes use of the proprietary radio protocol SensusRF to collect metering data from Sensus and other types of smart meters communicating via the OMS protocol and operate on frequencies on the 868 MHz band. However, such solution, equipped only with 868 MHz antennae, cannot receive messages from smart meters that operates on different frequencies, such as the WaterTech meters that communicates on frequencies on the 169 MHz band.Finally, some manufacturers have developed smart meters that are able to communicate both through an open OMS (Open Meter Standard) , or fractions of m3, on the consumption wheel of the water meter’s dial. Therefore, this information must be read on the water meter’s dial and it refers to the total volume (cumulative consumption) of water transited since the installation of the water meter.For many decades, water meter manufactures have used only basic physical principles of measurements in the design of water meters. For instance, the Italian legislation forced, until a few years ago, water utilities and meter manufactures to adopt and develop metering instrumentation based exclusively on mechanical/dynamic measurement principles. This regulations and the low manufacturing costs of this type of water meters reflected in water metering parks mainly composed of traditional “dumb” measuring instruments. Most of the time, this instrumentation is composed of a turbine and mechanical gears. The water passing through the meter makes move the turbine, which is connected to mechanical gears that transform the rotational speed of the turbine in a flow measurement. This measure is then expressed in cubic meters . For example, single-jet meters are designed for residential accounts, multiple jet meters are used both on residential and industrial accounts, and Woltmann meters are used mainly for industrial accounts.https://sensus.com/products/hri-mei/), which is wire connected to a battery-powered Wireless M-Bus radio module (https://sensus.com/products/sensus-pulse-rf/).Since the smartness of a metering instrumentation is a characteristic associated to the ability of transmitting metering data and not to the instrumentation itself, it becomes possible to enable “smartness” even on traditional mechanical meters. In fact, in recent years, water metering manufactures have introduced communication modules capable of reading data from a pulse emitter connected to the mechanical meter and transmitting over a wireless radio protocol, e.g., Wireless M-Bus. For example, Sensus produces a mechanical water meter capable of integrating a pulse emitter  revolution have provided inexpensive and high quality tools that represent a terrific foundation upon which to build a comprehensive and interoperable smart water metering solution capable of operating with different types of meters using different open wireless protocols and data formats.With this regards, wireless IoT communication solutions represent compelling key enabling technologies for smart metering applications. In addition to the already mentioned Wireless M-Bus and LoRa protocols, which are being increasingly adopted by smart water metering manufacturers, there are a range of wireless IoT protocols that might be relevant for smart metering applications, with different capabilities in term of allowed bitrate, range of operation (distance), number of supported devices, and power consumption .Within those, IEEE 802.15.4, and its full stack proprietary extension ZigBee, is widely adopted in low power networks and provides transmissions capabilities over a limited range (10–50 m) and it supports different network topologies: star, peer-to-peer, and cluster . BluetooWith the only notable exception of Sigfox, all these solutions allow to perform information exchange at the edge of the network on the unlicensed spectrum, and consequently to realize wireless smart metering solutions without depending on the support of network providers/operators or paying licensing fees .In addition, modern Commercial Off-The-Shelf (COTS) hardware solutions are inexpensive and battle tested, ranging from powerful Single Board Computers (SBC) based on ARM microprocessors, which can run full operating systems such as Linux, to very low power ARM microcontrollers. The former could be used as a platform for the development of interoperable gateways; the latter can be used for energy constrained applications, for instance for signal repeating or optical reading on the smart water meter side. From the communication perspective, many messaging protocols, such as Message Queue Telemetry Transport (MQTT) and Advanced Message Queuing Protocol (AMQP), have been developed as open standards and have been thoroughly designed and tested in a plethora of real life applications. They could be employed for reliable communications in smart water metering applications. Finally, a plethora of COTS open source software components, including implementations of MQTT and AMQP messaging systems, can be easily reused for IoT applications. Those components would be particularly well-suited for the realization of smart water metering systems, enabling a rapid application development approach and an impressive reduction in time to market.Edge Computing is a relatively recent paradigm that allows IT service developers and providers to allocate some data-processing tasks at the edge of the network on the top of different edge devices , with the potential of significantly reducing services’ latencies and improve the quality of those services, and thus, enabling a better utilization of both hardware resources and network bandwidth by limiting the communications with Cloud Computing platforms. We designed SWaMM in accordance with the Edge Computing paradigm to enable data processing directly at the edge of the network, thus, exploiting the available computational resources used for the collection of the metering data and also reducing the number and the size of data transmissions over the cellular network.According to the Edge Computing paradigm, IoT applications run on devices at the edge  to exploit the proximity of users and data and provide better QoS and QoE to the users of those applications. Exploiting the proximity of users and devices is one of the key ideas behind the realization of Edge Computing solutions. In fact, by processing partially or completely the data gathered by IoT sensors and devices at the edge is possible to create more effective and responsive services, thus, enabling a better utilization of both hardware resources and network bandwidth by limiting the communications with Cloud Computing platforms.3) using different wireless protocols on configured radio frequency channels. Instead, the dumb water meters illustrated in At the other end, we have multiple SWaMM Edge Gateways running at the edge  processing at the edge, we used relatively powerful SBCs (Raspberry Pi 3) as a hardware platform for the realization of the gateways. In fact, SWaMM Edge Gateways can also analyze data from dumb water meters using OCR techniques directly at the edge to enable data collection from old meters, which do not have automated reading capabilities, thus, avoiding to transmit pictures over the cellular network to a Cloud Computing platform for elaboration.The gateways store and pre-process the metering data at the edge before uploading via WiFi or the cellular network (3G/4G/LTE) the aggregated data to an application running on a Cloud Computing platform, which makes further elaborations of the data by means of algorithms for consumer profiling, leakages and fault detection, and so on. Data about consumption, alarms, and profiling are then made available to the end-users of the monitoring system (utility management and consumers) via a personalized web application.With this design, we tried to achieve a comprehensive solution, which enables an automated metering data collection from different water meters available on the market and enable interoperability from a water utilty perspective that need to collect and elaborate data from its entire metering park. SWaMM offers capabilities that result in a detailed monitoring of the water distribution system capable of detecting district and/or accounts leakages, and thus, lets water utilities to optimize the water distribution system and schedule maintenance interventions.We designed SWaMM using open source technologies with the aim of creating an open solution to tackle the entire flow of a smart metering system from data collection to data elaboration. Furthermore, we exploited edge computing solutions by developing ad hoc gateways for the processing and aggregation of the metering data at the edge. In addition, having intelligent devices at the edge also enables the possibility of a granular management of the smart meters, thus, allowing to easily control all the components of the metering system.The SWaMM Edge Gateway is a data collection and elaboration kit capable of easily integrating with a wide spectrum of different types of smart meters and wireless protocols located at the edge of the network. The kit allows to collect and aggregate metering data from the nearby smart meters, to run OCR algorithms on pictures collected from old water meters, and to publish the data processed at the edge to the SWaMM elaboration platform running on the Cloud.Logging for application monitoring and collection of the data;Threading and Asyncio, for the correct execution of the multithreaded application;Pyserial for interfacing with serial ports;Sqlite3 for interfacing with the SQLite relational database;Pickle for serializing and deserializing Python objects.https://opensource.google.com/projects/tesseract) software capabilities of analyzing and retrieving a text from an image, in this case the cumulative consumption value [At the core of the application subsystem is the Water Management Python (WMPy) module, a library specifically designed to interface the SWaMM gateway with the wireless communication kits: Wireless M-Bus and LoRa. This library provides functionalities to deal with event management, configuration, devices monitoring, and communications management (sending and receiving messages over Wireless M-Bus and/or LoRa). The support for other meter types requires the development of a new plugin component for the Wireless protocol manager which provides the support for the specific communication protocol and data format used by that meter. In addition, another important piece composing the WMPy library is the OCR module. This module is capable of running OCR algorithms to detect the cumulative consumption of a water meter by processing the picture of its consumption wheel. To this end, we leveraged on the Google Tesseract OCR  to the Cloud Computing platform.Then, on the top of the Application Subsystem we have several other software modules to implement data collection and aggregation. In detail, the more important modules are:Finally, processing the metering data is not computationally expensive since it requires only to parse and elaborate the received message. Furthermore, messages are received and buffered/stored on an in-memory buffer waiting to be processed, and thus, avoiding to lose metering data. Finally, possible limitations regarding the number of supported smart water meters mainly depend on protocol specifications, collision and interference avoidance mechanisms, and radio frequency propagation issues, e.g., the presence of obstacles that can block the propagation of the wireless transmissions ,23.To enable data collection from old water meters we specifically designed the SWaMM Optical Reader Kit (ORK). The SWaMM ORK is a sort of smart cap that can be assembled on different type of dumb water meters. The SWaMM ORK provides “smartness” to old dumb meters, which do not support other smart reader mechanisms e.g., a pulse emitter, by means of a radio module that transmits the picture of the consumption wheel visible on the water meter. In detail, To realize SWaMM ORK we developed the illustrated prototype by analyzing the factor form of the most common used water meters to make this cap suitable for collecting data on them. As illustrated in SWaMM ORK can be configured to take a snapshot of the meter’s consumption wheel with a desired frequency, e.g., each day, and to transmit the resulting JPEG image to the nearby SWaMM Edge Gateway through the LoRa radio module for elaboration. Before transmitting the picture, the SWaMM ORK converts the JPEG image in gray scale in order to reduce the size of the data to be transmitted over LoRa.The Business Logic, RBAC (Role Based Access Control) and CRM (Customer Relationship Management)/ERP (Enterprise Resource Planning) integration components are the business logic layer of the SWaMM platform. The Business Logic module implements the business logic of the platform and provides the management functions of gateways, including (re)configuration and firmware upgrades.The RBAC module implements the authorization mechanisms to access the various functions offered by the SWaMM middleware, using an approach that allows the SWaMM platform to realize different access levels, e.g., SWaMM system operators, providing a specific subset of functionality depending on the user’s role. This module is quite important considering the sensibility of water consumption information. In order to preserve the customers’ privacy only authorized subjects can have access to sensible data.The ReST API  component provides an access interface to the SWaMM platform functions specially designed to allow integration with other platforms and software systems. More precisely, the functions of the SWaMM platform will be accessible through the Web using an API based on the ReST architectural paradigm. To this regard, we report some example on how to interface with the SWaMM ReST API in This will not only automate the export of metering data collected in an appropriate format based on XML (eXtensible Markup Language), JSON (JavaScript Object Notation) or CSV , thus, facilitating integration with sophisticated and powerful Big Data analysis tools, but also accessing “programmatically” all the functions of the platform, including the management of the SWaMM Edge Gateways. The SWaMM platform also provides advanced reporting functions, able to create, both on demand and in an automated way at scheduled deadlines, detailed technical documents that effectively highlight the state of the monitored accounts.Finally, the Data Analysis module is the module responsible for data elaboration. In particular, this module analyzes the collected and aggregated data using fault and leakage detection algorithms that we specifically designed for the management of water distribution networks. Then, users can access the elaborated results via the Web Application, which exposes a user-friendly interface to inform about consumption and the presence of leakages on the monitored accounts.The SWaMM Broker is the component of the wireless metering middleware that deals with all aspects related to the management of interactions between the gateways and the SWaMM elaboration platform. The SWaMM Broker allows a spatial and temporal decoupling of the communications between SWaMM Edge Gateways at the edge and the SWaMM platform running on the Cloud, which makes the platform significantly more robust, since the various components can continue to work even in case of temporary component failures.In addition, we believe that the adoption of a broker-based solution has many advantages, including the ability to transmit metering data and commands over different communication protocols, such as AMQP (Advanced Message Queuing Protocol) or MQTT (Message Queuing Telemetry Transport), and the ability to enable an efficient communication between software components with remarkably different performance. Furthermore, the SWaMM Broker also takes care of saving and storing the data by interacting with the DataBase Management System. We chose to use MQTT because of its simplicity and its small overhead. In fact, a single server can support thousands of remote clients. Furthermore, MQTT allows high scalability from the point of view of the development and management of applications. We chose RabbitMQ server side because we found it reliable and fully configurable, and thus, well suited to be installed on a Cloud Computing platform. At the same time we chose Mosquitto at the client side because is open source, well documented and supported, and it is the state of the art for implementing MQTT client applications.In addition, the SWaMM platform makes use of the broker to interact with the SWaMM Edge Gateways by sending management commands for tuning the behaviour of the smart meters associated to the gateways. The broker exchanges both data and command messages via MQTT in order to guarantee the reliability of the platform, thus, limiting errors due to components and network failures.gateway/management/update topic. Then, at the reception of this message, gateways will download and install the update locally.Finally, MQTT topics follow the style illustrated in Within the GST4Water project (Green Smart Technology for Water), we thoroughly evaluated our solution and proved the validity of SWaMM in the city of Gorino Ferrarese  ,25. As phttps://sensus.com/products/iperl-international/), which has a built-in Wireless M-Bus module that we configured to transmit on the 868 MHz frequency a meter every 15 min. Instead, with regards to the SWaMM Edge Gateways, we configured the Raspberry Pi 3 with the IMST (iM871A-USB) USB Wireless M-Bus dongle to receive the Wireless M-Bus messages from the smart meters. In particular, we expected to experience radio frequency propagation issues due to the locations of these smart meters. In fact, as usually in Italy, these smart water meters were located in underground inspection pits as the one shown in Part of these tests focused on the evaluation of Wireless M-Bus smart water meters to identify protocol performances, the number of required gateways, and other configurations. In detail, we used the Sensus iPerl smart water meter , which mainly occurs during the day, alternating with periods when water consumption decreases to zero (mostly during the night). Instead, if an account is characterized by a leak, the smart water meter will detect a continuous consumption, which will be marked as leakage by the monitoring system . Thus, the algorithm is set up to assess the presence/absence of leakages inside residential houses by looking for non-consumption in certain periods of the day. Examples of detected leakages are the ones depicted in On the other hand, water utility managers have the opportunity to analyze the collected data to optimize the management of the water supply network and to reduce real water loss levels, by applying the water balance method using the high resolution data. In fact, managers can estimate the leak level of the distribution network by monitoring the incoming and outgoing discharges from well-defined portions of the distribution network (using Supervisory Control And Data Acquisition (SCADA) systems) and by combining this information with the consumption trend of all users belonging to the same area. For example, In addition, the water utility has the opportunity to identify faulty smart meter devices immediately, reducing the apparent water losses due to measurement errors. For example, We conducted several tests to evaluate the performance and the quality of the OCR module in detecting the cumulative consumption on pictures taken from different types of water meters. Considering the wide variety of water meters deployed on the field, we consider for the scope of these tests only three types of water meters, which are the most common used in the city of Ferrara, Italy. During these tests, we evaluated SWaMM ORK and the efficiency of the OCR module on dumb water meters put into operation, to verify its functionality during the time. To this end, we realized a simplified hydraulic network, as the one illustrated in On the SWaMM Edge Gateway side, the OCR module is initially instructed on the position of the digits to process each one individually. Initially, we used the standard Tesseract engine of Google Tesseract 4.0. Unfortunately, applying the standard Tesseract engine on the meter’s digits did not produce extremely brilliant results. A first cause was due mainly to the fact that Tesseract’s neural network is trained to identify all the possible characters commonly used for writing  for a multitude of fonts. In our case, instead, the interest have to be focused only for the specific fonts used on the water meters’ consumption wheels. Another fundamental motivation is depicted in For this reason, we decided to re-train the Tesseract LSTM neural network, using the pictures collected from the dumb water meters as a training dataset. After two days of system operation, we collected about 1400 images for each dumb meter, which included both entire digits and “digit transactions”. Using the re-trained neural network, we achieved an accuracy of the results between 80 and 90 percent.However, we tried to further improve the text recognition quality results. To this end, we designed a specific software component that works following the Tesseract processing, implementing an algorithm we called “validation process”, which tries to improve the precision of the results obtained using Tesseract. We developed this validation algorithm according to the suggestions of the water utilities we collaborated with during the development and field testing of SWaMM. This algorithm tries to improve the text recognition accuracy by limiting the identification of result not corresponding to the reality, using the know-how of the typical average water consumption of residential accounts. In this way, the algorithm can validate or possibly correct the Tesseract results. For instance, in the event that a proposed result is not compatible with domestic consumption patterns, the validation algorithm runs a series of checks to identify what caused the possible recognition error. Errors can be differentiated in single digit recognition error  or multi-digit recognition error . In the first case, the algorithm tries to correct the possible error by leveraging on the Tesseract capabilities, while in case of substantial errors, the algorithm adopts a more conservative approach and keeps the value of the previous correct reading since it is not possible to interpret the result of Tesseract processing.3, between the Tesseract results and the correct ones. At the same way, deltaP represents the sum of the differences, expressed in m3, between the validation process results and the correct ones.To demonstrate the functionality and utility of the validation process algorithm, we conducted a series of tests for the dumb meter of type 1, each time selecting 40 different images that depicted the consumption trends of this meter. In 3 using only the re-trained Tesseract, to only 5 m3 of average error for the validation process. These results are acceptable for water bill calculation. In fact, considering that billing procedures are usually performed monthly, or sometimes even bimonthly, the amount of readings pertaining to the billing time window tend to even out the error in a single cumulative water consumption reading. Therefore, an average error of 5 m3 for a single cumulative water consumption reading is relatively small and well within the acceptance window.As shown in process_time function  of the Python time module that the entire processing of the consumption wheel’s picture (image elaboration and OCR processing) takes about 1261 ms (milliseconds) on the Raspberry Pi 3. This means that a SWaMM Edge Gateway can process around 7100 readings from Optical Reading Kits per day. Considering one or two readings per meter per day  this means that a gateway could respectively support 7100 and 3550 water meters. However, radio frequency propagation issues and bandwidth limitations impact on the amount of data that can be transmitted and consequently reduce the number of water meters that can be served by a single gateway. These results seem to confirm the validity of the retrofitting solution in those situations in which replacing a traditional water meter with a new one is difficult. Therefore, the OCR processing does not present a limiting factor on the SWaMM Edge Gateway.Finally, we calculated using the In the near future, the growing need for environmental sustainability is expected to push the need for smart metering solutions that avoid the waste of natural resources and increase the population awareness. To this end, smart water metering solutions provide many advantages for both consumers and water utilities. On the one hand, consumers can look over the time series of totalized and incremental consumption and the volume of water required during the last 24 h, thus, allowing real-time monitoring of their consumption and consequently to adopt more environmental friendly (virtuous) behaviors, thus, avoiding bad habits and waste of water. In addition, consumers can benefit of a more transparent water bill and avoid possible inconveniences caused by refund procedures, since the consumption is always determined on the basis of real data instead of a general forecast. Furthermore, this solution offers consumers the opportunity to receive an alarm message when the smart water metering platform detects leaks within the consumer’s property, and thus, reducing waste of water even more.Another advantage of using smart water metering platform is that the collected data can be used to implement water demand forecasting models for the entire district monitored areas. As a result, the general utility would be able to optimise the distribution network management by controlling the pressures of the distribution network and ensuring a more efficient management of the entire water district . In addition, a smart monitoring system also allows to reduce energy consumption and water losses, which are usually connected to an excessive amount of pressure in the distribution network. Finally, water utilities can make use of the collected data to plan marketing strategies, provide feedbacks on their water consumption or recommendations on personalized water-saving practices.Our extensive field testing experience with SWaMM showed that the design of an efficient and automated smart water metering solution requires an intelligent analysis of the performance of wireless protocols, the optimal placement of gateways, and a thorough testing and evaluation phase. As we reported in the previous sections, some wireless protocols may perform significantly better than others when physical limitations can interfere with data collection procedures. On the other hand, protocols that enable longer ranges usually require a bigger power consumption, and thus, limiting the average lifetime of smart meters.In the smart water metering market, interoperable wireless middleware solutions such as SWaMM, based on open technologies, both at the software and the hardware levels, and capable of dealing with different communication protocols and data formats, represent a key enabling technology. In fact, unlike stovepiped closed proprietary systems, SWaMM does not force to use a specific type of water meter, neither at the radio communication protocol nor at the meter type (smart or dumb) level, and allows water utilities to choose among different water metering technologies and to switch to different vendors. We believe that this capability will gain importance over the next years when new manufacturers will enter into the smart metering market, thus, influencing the heterogeneity in metering parks composition.Finally, the use of SWaMM might open the market to 3rd party companies that operate between consumer and water utilities. These companies can operate as intermediary and offer metering solutions for collecting and elaborating metering consumption data, and thus, opening scenarios where the metering infrastructure is sold as a service.Smart metering is an active topic in research literature. Many efforts have been done to propose open and interoperable solutions for the smart metering of water and other commodities. This work  analyzesWith focus on wireless protocols for smart metering solutions,  providesIn , SimmhanIn , the autA smart water metering architecture is discussed in . In thisSWaMM differs from the other solutions by proposing a comprehensive wireless middleware for smart water metering, which tackles the entire information flow of the metering data, from the sensing to the elaboration. Furthermore, we designed SWaMM to be open and interoperable with a wide range of wireless protocols, thus, enabling the integration of more reliable and innovative solutions.We presented a comprehensive smart water metering solution that aims at tackling the heterogeneity of protocols and standards in a competitive and proprietary market and to avoid vendor and protocol lock-in. The core of this solution is SWaMM, a wireless IoT middleware for the collection and elaboration of metering data from a wide range of smart water meters. In addition, purposely developed Optical Reading Kits allow to integrate SWaMM with dumb water meters. SWaMM was thorougly tests on the field, also in the context of a full scale deployment in Gorino Ferrarese, Italy as part of the GST4Water project. The results obtained demonstrate the effectiveness of our middleware in reducing the waste of commodities over the water distribution network, by means of an accurate tracking and monitoring of consumptions.Finally, let us note that, while SWaMM was specifically designed for smart water metering applications, its flexible design allows the middleware to be easily adopted for the smart metering of other commodities. In fact, SWaMM Edge Gateways could be used to collect data from other smart meters such as electricity and gas, as far as these smart meters will use supported wireless protocols. We believe that other IoT low power wireless protocol such as NB-IoT should be investigated and proved on the field. With this regard, we specifically designed SWaMM to be open and interoperable to future protocol extensions.In addition to the experimentation with new water meter models and more sophisticated water leakage detection algorithms, future works will include the development of a dedicated OCR solution for dumb water meters that could provide better accuracy with respect to Tesseract, and the realization of a significantly improved version of the Optical Reader Kit, based on a low-power computing platform that allows it to operate for around 2 years on a couple of AA batteries.


Photographs of an AHS from a pulmonary vein, containing large numbers of bubbles, were selected in 1‐min sequences over a period of 7 min, starting 18 min after decompression from 1,013 kPa. This showed bubble detachment, coalescence and expansion, as well as competition for dissolved gas between bubbles. There was greater expansion of peripheral than of central bubbles. We suggest that the dynamics of decompression bubbles on the surface of the blood vessel may be the closest approximation to true decompression physiology, and as such can be used to assess and calibrate models of decompression bubbles. We further discuss the implications for bubble size in the venous circulation.We found that lung surfactant leaks into the bloodstream, settling on the luminal aspect of blood vessels to create active hydrophobic spots (AHS). Nanobubbles formed by dissolved gas at these AHS are most probably the precursors of gas micronuclei and decompression bubbles. Sheep blood vessels stretched on microscope slides, and exposed under saline to hyperbaric pressure, were photographed following decompression Bubble formation on luminal aspect of blood vessels are the best experimental model for decompression bubbles, their interactions and dynamics. On detaVast amounts of literature have been devoted to the attempt to establish the source and stability of gas micronuclei as the precursors of decompression bubbles. The production and stability of nanobubbles on a hydrophobic surface presented a solution to the long sought‐after gas micronuclei from which decompression bubbles develop. We have demonstrated that surfactant leaks from the lung into the bloodstream, settling on the luminal aspect of blood vessels to create active hydrophobic spots (AHS). Nanobubbles formed by dissolved gas at these AHS are most probably the precursors of gas micronuclei and decompression bubbles. In previously reported studies , and not with the tissue across the vessel wall. Small bubbles with a high internal pressure (the Laplace equation) and high gas tension would have been expected to transfer gas to larger bubbles having a lower gas tension Ostwald, . Thus, sCompetition for the dissolved gas among bubbles growing in close proximity to one another was examined in theoretical studies by Karapantsios, Kostoglou, Divinis, & Bontozoglou  and by V3.2The time to the first appearance of a bubble at an AHS is varied. In Figure Bubbles detached from the blood vessels in the experimental setup, most probably when buoyancy equaled the intermembrane adhesion force Arieli, . This yiIn conclusion, some of the currently used algorithms for the construction of decompression tables, such as the varying permeability model, based as it is on the theoretical considerations of Yount and Hoffman , are fouNo conflicts of interest, financial or otherwise, are declared by the author.R.A. drafted manuscript, edited and revised, and approved final version of manuscript.


Until recently, other than in-person election observation, there have been few quantitative methods for determining the integrity of a democratic election. Here we present a machine learning methodology for identifying polling places at risk of election fraud and estimating the extent of potential electoral manipulation, using synthetic training data. We apply this methodology to  Assuring that an election was run in a free and fair manner has been the focus of a significant body of research. Much of that research, especially efforts to document the integrity of elections outside the United States, has utilized direct in-person election observation methods , 2. In-pThe desire for a less-costly, quantitative, and broad coverage means of detecting potential election fraud has led researchers to propose a number of quantitative approaches for detecting irregularities using different methods for anomaly detection . Some haa priori knowledge about the forms that election manipulation may take in a particular context, and may fail to detect many forms of potential fraud.These various approaches for detecting potential election fraud all suffer from different methodological problems . These rThese methodological issues have led some scholars to advocate the use of machine learning approaches for detecting potential election fraud –21. TherOur approach differs from those in previous studies. While we use a supervised machine learning ensemble approach (Random Forest), we lack a priori labeling of voting precincts for whether they may have been affected by manipulation—and the form and extent of that electoral manipulation. Thus, we develop a relatively naïve hierarchical model, that we use to estimate what a “clean” election might look like across all of the voting precincts in the election we study, which forms our “clean” synthetic training data. Based on our substantive knowledge of the election, and on past studies of election fraud, we focus on two forms of manipulation: ballot-box stuffing and vote stealing. We then use our a priori and theory-driven knowledge about the potential forms and extent of potential election fraud to generate synthetic examples of voting precincts exposed to manipulation. We fit the model using cases labeled as ‘clean’ and ‘at risk’ in the synthetic training data, then apply that trained model to national data from the 2015 national elections in Argentina. With this method we are able to locate polling precincts resembling training examples susceptible to manipulation or error, and to estimate the extent of this potential electoral manipulation. This approach is discussed in detail below, where we also provide a variety of validation analyses.mesa) level [mesas where votes were cast for the larger parties and the combined “other” party. We remove mesas with less than 100 total ballots cast, as these very small mesas have highly variable turnout and vote share results. After removing these cases, we have 90,012 mesas from 24 provinces . A summary of the voting results from these mesas is shown in We use data from the first round of the 2015 presidential election in Argentina at the polling station (a) level . This daWe focus on the 2015 election as it took place in the context of widespread distrust in the integrity of the electoral process. A recent scholarly publication presented evidence suggestive of election irregularities in the previous presidential election . Uncertamesas into clean or at-risk categories. We first generate clean synthetic data using a mixed effects regression. We use the vote share of each party as the dependent variable, and incorporate demographic variables as well as whether FPV won a majority of votes in 2011 as fixed effects, while allowing the intercepts to vary by department. We use this model to predict the expected clean vote share for each party, given their demographics and results from 2011.We generate synthetic clean and at-risk data to train a supervised classification model that can be used on the actual election data to classify One concern is that the actual vote shares on which we train the multilevel model may themselves be tainted by some type of manipulation, and hence the model predictions may not accurately reflect an entirely “clean” distribution. However, by using potentially manipulated data as the “clean” baseline for the model, it tempers the overall distribution of our predictor variables, which makes extreme values less unlikely. As our models seek anomalous behaviors in the turnout and vote shares, this tempering means that our model is more conservative in detecting voting locations that may be at risk for manipulation or error.Next, we alter the clean synthetic data in two ways that fall into one of the two hypothetical fraudulent behaviors: (1) vote stealing (VS), where agents from one of the major parties steal votes from other parties by transferring some of these votes to the perpetrating party and destroying the rest, and (2) ballot box stuffing (BBS), where party agents artificially inflate the votes for their party. Here we focus on the FPV, the incumbent Peronist party, for two reasons. First, a previous study found that FPV monitors may have interfered with election results in areas covered by their party monitors in past elections . Second,at-risk synthetic examples as follows. We choose two separate parameters that dictate the probability of any mesa being tainted by VS and BBS, and two additional parameters that determine the extent of VS or BBS in tainted mesas. The extent of VS is given by the proportion of votes from other parties being converted to FPV votes. The extent of BBS is given by the proportion of abstention votes counted as FPV votes. We then use a random process to choose which at-risk mesas face which type of potential fraud, and the extent of potential fraud in affected mesas. As the next step in our synthetic data generation, we subtract the potential stolen votes from the other parties and give a portion of them to FPV, and add additional votes to FPV to simulate ballot box stuffing. On average, synthetic mesas at risk of VS show relatively small turnout rates, whereas those at risk of BBS have high turnout rates. Both types of simulated manipulation show higher vote shares for the FPV. Distributions of turnout and the vote share of each party are shown in We generate We then use the labeled synthetic data to train a model that can be used on actual election data to see whether there is evidence suggesting that a voting precinct is at risk of VS or BBS. The outcome variable of the model has three classes: clean, risk of VS, or risk of BBS. The predictors are turnout and the vote shares of each party. We use Random Forest to train this model ; Random mesa is clean or at risk of manipulation, correctly predicting 97% of the clean synthetic examples.To test the performance of our model, we divide the synthetic data into 10 folds, train the model on 9 folds, and examine its performance on the remaining one fold of data saved for testing. We average the prediction accuracy for the 10 tests and show the performance in mesa into one of the three categories: clean, at risk of BBS, or at risk of VS.We next train a Random Forest model on the full set of synthetic data to utilize all available information. mesas in the prediction, as well as the distribution of turnout and vote shares. Then we evaluate the sensitivity of results to changes in key parameters used to generate the synthetic data—the probability an at-risk mesa is tainted with potential fraud or error and the extent of the manipulation in at risk mesas.We first present the prediction results using a set of discretionary parameters. We show the proportion of clean and at risk mesas that are predicted to be clean and at risk in the actual data, using the Random Forest model trained on randomly generated synthetic data drawn from hypothetical clean and at-risk distributions. In generating the synthetic data, we initially assume that 1/3 of at-risk mesas are affected by BBS and VS, respectively, and that these two forms of fraud are exclusive events. We also assume that, on average, 3/4 of voter abstention in mesas at risk of BBS is counted as votes for the incumbent, and that 1/2 of votes cast for opposition parties in mesas at risk of VS are counted as votes for the incumbent. Setting these last two parameters at large levels helps limit the chance that clean mesas closely resembling the at-risk data will by chance be classified as at-risk. When simulation parameters are held at these baseline levels, 86.3% of mesas are predicted to be clean in the actual data. In total, 13.7% of mesas are classified as at-risk; 12.3% are found at risk of BBS and only 1.4% at risk of VS.mesas. Similar to the synthetic data, mesas at risk of BBS have much higher turnout, while turnout is the lowest in mesas at risk of VS. While VS and BBS have somewhat similar effects on the vote share of the FPV in the synthetic data, mesas at risk of VS show much stronger support for FPV than mesas at risk of BBS. Unsurprisingly, the risk of VS is significantly associated with lower vote shares for all other parties, whereas the risk of BBS is associated with decreased support for Cambiemos more than the other parties. Overall, the distributions of turnout and vote shares in the actual data using model predicted categories are consistent with the distributions in the synthetic data.mesas vary markedly across Argentinean provinces, as provinces differ much in terms of demographic composition. Figs We next looked at the relationship between predictions and basic demographics of the departments where voting precincts are located. The department-level demographic information was drawn from the 2010 national population census. This analysis suggests that while there is little difference between the aggregate demographics of mesas labeled clean and at risk of BBS, mesas labeled at risk of VS are located in less urbanized departments, with populations experiencing greater need and higher incidence of illiteracy , 68% of mesas are labeled as clean; however, when we assume that FPV engages in much more substantial BBS , the model labels 95% of mesas as clean. While this may seem counterintuitive, it is what we expect to see in a simulation like this: when we increase the proportions of at-risk mesas subject to VS and BBS, the predicted percentages of clean mesa decrease, as we are allowing fewer mesas to be clean. When we increase the extent of VS or BBS in tainted synthetic mesas, however, only mesas with exceedingly large turnout or FPV vote share can be detected by the model as fraudulent, leading to conservative predictions where most mesas are labeled clean.Finally, we evaluate the sensitivity of our predictions to changes in the set of four key parameters used in generating the synthetic training data, holding other parameters constant. mesas or precincts) to use for forensic purposes, so researchers will need some type of synthetic data. As we discuss above, researchers can use historical demographic and political data, along with substantive or theoretical knowledge about the forms that possible election manipulation or error might take in a particular election, to develop the sort of synthetic training data that we deploy in this paper. These training examples can then be used to identify electoral locations at risk of possible election error or fraud and to estimate the overall extent of potential electoral manipulation, as we illustrated using data from a recent election in Argentina. Clearly, this is an important area for future research; users of forensic methods like these will need reliable guidance about how to develop synthetic training data.The approach we use in this paper relies upon synthetic training data to generate precinct-level predictions of fraud risk. Both the use of synthetic data and generation of granular predictions are important contributions of our work. For supervised machine learning tools to be useful for election forensics, the analyst needs training data of some form. It is rare for the analyst to have a list of labeled units Click here for additional data file.


A life cycle assessment (LCA) of feminine hygiene products was completed with three samples considered representative of sanitary pads, tampons and menstrual cups. Gabi LCA software was used to organize inventory data with the ILCD (v.1.0.10) life cycle impact assessment method used to determine mid-point and normalized impact scores. Data from the ecoinvent database, and literature were used to complete the assessment. The dataset includes product details , life cycle inventory (LCI) data and life cycle impact assessment (LCIA) results for all mass and energy flows. Hait and Powers [1] used the data in a comparative LCA. The dataset is an MS Excel spreadsheet that has separate tabs for each of the three products for both LCI and LCIA steps in the LCA. Specific weights and materials of construction of each of the three products are also included in the spreadsheet. These were used to create the life cycle flows for each of the products, which are included in this DIB article , Fig. 3.2Materials that comprise these sanitary products were determined from manufacturer specifications and literature resources. Key resources include: Kimberly-Clark ; Barman Four life cycle stages were assessed within the LCA to provide a cradle to grave analysis of each menstrual product: raw materials production, product manufacture, transportation and use, and disposal. The functional unit of the impact analysis was one year of coverage for one woman. This amounted to an average of 240 pads or tampons or 1/10th of a menstrual cup. The weight of the basic components of each sample were measured directly (average of 10 items). Other assumptions related to materials and their production are identified in the dataset. Sensitivity analyses to assess uncertainty included the number of units used per year, resources used for electricity production, the relative weight of polyethylene and cellulose in the pad's absorbent core and the inclusion of an applicator for the tampon. Gabi LCA Software was utilized to create life cycle models of the three products studied. Each of the gray box processes incorporate other input and output elementary flows that are compiled by the software to create one composite inventory list with all elementary flow inputs and outputs into the system. For example, water is required in the production of both polyethylene and polypropylene. Water is not included in the life cycle diagram for readability. This elementary flow is included as part of the polyethylene or polypropylene production process. Because many processes required water, the Gabi software sums the water demand in each process and compute a total water requirement.Some of the processes within the feminine hygiene product plans did not have inventories completed down to the elementary flow level. For such processes that were not completely defined by elementary flows, a life cycle diagram plan was created to describe that processes life cycle by elementary flows. These extra plans were then nested into the feminine hygiene product plans as individual processes. This plan nesting allows the data from the flows required in those required plans to be pulled into the feminine hygiene product plan. Thus, a comprehensive inventory was completed, without requiring every process required in one plan such as the figure below. Instead, the Gabi LCA software continues to work as if there are largely extended plans, without requiring that pictorial representation.A search function within GaBi allows the user to view all relevant inventories and select the best suited inventory to describe the material and energy of interest. Criterion for selection of the inventory for this study was based on trying to select the best available representative inventory. If multiple options of inventories that described the material and energy flow accurately were available, preference was shown for analyses done in the United States. If US inventories were not available, alternative inventories were selected, in decreasing order of preference, from a global average, European average, or other country-specific analysis. If there were multiple methods of producing a material or energy flow, a technology mix or production mix was selected in preference to one specific method.The inventories included in the Gabi database are not all cradle-to-grave and thus are supplemented with further material and energy process flows. Additionally, not all material and energy flows required in the production of menstrual products are included in the GaBi database. Therefore, the ecoinvent (3.3) database was used to fill in such data gaps. The most relevant study was selected by finding the LCA completed for the material that has properties most similar to that of the material used in the feminine hygiene product. Again, preference was shown for US studies and if not available, global averages. Once selected, the study provides a spreadsheet of all material and energy inputs and outputs for manufacture of the material. The data gleaned from ecoinvent was imported into feminine hygiene product inventory models in Gabi to ensure relevant material and energy processes were described as precisely as feasible.Some inputs into the life cycle of the feminine hygiene products were excluded from the scope of this work. These materials were not easily characterized in the available database and composed a small portion of the product by weight. Based on the LCI of raw materials used to manufacture the products, only 0.76%, 0.36%, and 3.47% of the weight of tampons, sanitary pads, and menstrual cups, respectively were omitted from the analysis.The Gabi software was also used to determine mid-point and normalized mid-point categories for each of several indications. ILCD methodology  for mid-