Arsenic contamination of groundwater is a major public health concern worldwide. The problem has been reported mainly in southern Asia and, especially, in Bangladesh. Slow-sand filters (SSF) augmented with iron were proven to be a simple, low-cost and decentralized technique for the treatment of arsenic-contaminated sources. In this research, three pilot-scale SSF (flowrate 6 L·h−1) were tested regarding their capability of removing arsenic from groundwater in conditions similar to those found in countries like Bangladesh (70 µg As(III) L−1, 26 °C). From the three, two filters were prepared with mixed media, i.e., sand mixed with corrosive iron matter (CIM filter) and iron-coated sand (ICS filter), and a third conventional SSF was used as a reference. The results obtained showed that the CIM filter could remove arsenic below the World Health Organization (WHO) guideline concentration of 10 µg·L−1, even for inlet concentrations above 150 µg·L−1. After 230 days of continuous operation the arsenic concentration in the effluent started increasing, indicating depletion or saturation of the CIM layer. The effluent arsenic concentration, however, never exceeded the Bangladeshi standard of 50 µg·L−1 throughout the whole duration of the experiments.
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Purpose: To determine whether using different combinations of kVp and mAs with additional filtration can reduce the effective dose to a paediatric phantom whilst maintaining diagnostic image quality.Methods: 27 images of a paediatric AP pelvis phantom were acquired with different kVp, mAs and additional copper filtration. Images were displayed on quality controlled monitors with dimmed lighting. Ten diagnostic radiographers (5 students and 5 experienced radiographers) had eye tests to assess visual acuity before rating the images. Each image was rated for visual image quality against a reference image using 2 alternative forced choice software using a 5-point Likert scale. Physical measures (SNR and CNR) were also taken to assess image quality.Results: Of the 27 images rated, 13 of them were of acceptable image quality and had a dose lower than the image with standard acquisition parameters. Two were produced without filtration, 6 with 0.1mm and 5 with 0.2mm copper filtration. Statistical analysis found that the inter-rater and intra-raterreliability was high.Discussion: It is possible to obtain an image of acceptable image quality with a dose that is lower than published guidelines. There are some areas of the study that could be improved. These include using a wider range of kVp and mAs to give an exact set of parameters to use.Conclusion: Additional filtration has been identified as amajor tool for reducing effective dose whilst maintaining acceptable image quality in a 5 year old phantom.
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This paper reviews the literature for lowering of dose to paediatric patients through use of exposure factors and additional filtration. Dose reference levels set by The International Commission on Radiological Protection (ICRP) will be considered. Guidance was put in place in 1996 requires updatingto come into line with modern imaging equipment. There is a wide range of literature that specifies that grids should not be used on paediatric patients. Although much of the literature advocates additional filtration, contrasting views on the relative benefits of using aluminium or copper filtration, and theireffects on dose reduction and image quality can vary. Changing kVp and mAs has an effect on the dose to the patient and image quality. Collimation protects adjacent structures whilst reducing scattered radiation.
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The Smart Current Limiter is a switching DC to DC converter that provides a digitally pre-set input current control for inrush limiting and power management. Being able to digitally adjust the current level in combination with external feedback can be used for control systems like temperature control in high power DC appliances. Traditionally inrush current limiting is done using a passive resistance whose resistance changes depending on the current level. Bypassing this inrush limiting resister with a Mosfet improves efficiency and controllability, but footprint and losses remain large. A switched current mode controlled inrush limiter can limit inrush currents and even control the amount of current passing to the application. This enables power management and inrush current limitation in a single device. To reduce footprint and costs a balance between losses and cost-price on one side and electromagnetic interference on the other side is sought and an optimum switching frequency is chosen. To reduce cost and copper usage, switching happens on a high frequency of 300kHz. This increases the switching losses but greatly reduces the inductor size and cost compared to switching supplies running on lower frequencies. Additional filter circuits like snubbers are necessary to keep the control signals and therefore the output current stable.
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Several studies have suggested that precision livestock farming (PLF) is a useful tool foranimal welfare management and assessment. Location, posture and movement of an individual are key elements in identifying the animal and recording its behaviour. Currently, multiple technologies are available for automated monitoring of the location of individual animals, ranging from Global Navigation Satellite Systems (GNSS) to ultra-wideband (UWB), RFID, wireless sensor networks (WSN) and even computer vision. These techniques and developments all yield potential to manage and assess animal welfare, but also have their constraints, such as range and accuracy. Combining sensors such as accelerometers with any location determining technique into a sensor fusion systemcan give more detailed information on the individual cow, achieving an even more reliable and accurate indication of animal welfare. We conclude that location systems are a promising approach to determining animal welfare, especially when applied in conjunction with additional sensors, but additional research focused on the use of technology in animal welfare monitoring is needed.
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In sports, inertial measurement units are often used to measure the orientation of human body segments. A Madgwick (MW) filter can be used to obtain accurate inertial measurement unit (IMU) orientation estimates. This filter combines two different orientation estimates by applying a correction of the (1) gyroscope-based estimate in the direction of the (2) earth frame-based estimate. However, in sports situations that are characterized by relatively large linear accelerations and/or close magnetic sources, such as wheelchair sports, obtaining accurate IMU orientation estimates is challenging. In these situations, applying the MW filter in the regular way, i.e., with the same magnitude of correction at all time frames, may lead to estimation errors. Therefore, in this study, the MW filter was extended with machine learning to distinguish instances in which a small correction magnitude is beneficial from instances in which a large correction magnitude is beneficial, to eventually arrive at accurate body segment orientations in IMU-challenging sports situations. A machine learning algorithm was trained to make this distinction based on raw IMU data. Experiments on wheelchair sports were performed to assess the validity of the extended MW filter, and to compare the extended MW filter with the original MW filter based on comparisons with a motion capture-based reference system. Results indicate that the extended MW filter performs better than the original MW filter in assessing instantaneous trunk inclination (7.6 vs. 11.7◦ root-mean-squared error, RMSE), especially during the dynamic, IMU-challenging situations with moving athlete and wheelchair. Improvements of up to 45% RMSE were obtained for the extended MW filter compared with the original MW filter. To conclude, the machine learning-based extended MW filter has an acceptable accuracy and performs better than the original MW filter for the assessment of body segment orientation in IMU-challenging sports situations.
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In practice, faults in building installations are seldom noticed because automated systems to diagnose such faults are not common use, despite many proposed methods: they are cumbersome to apply and not matching the way of thinking of HVAC engineers. Additionally, fault diagnosis and energy performance diagnosis are seldom combined, while energy wastage is mostly a consequence of component, sensors or control faults. In this paper new advances on the 4S3F diagnose framework for automated diagnostic of energy waste in HVAC systems are presented. The architecture of HVAC systems can be derived from a process and instrumentation diagram (P&ID) usually set up by HVAC designers. The paper demonstrates how all possible faults and symptoms can be extracted on a very structured way from the P&ID, and classified in 4 types of symptoms (deviations from balance equations, operational states, energy performances or additional information) and 3 types of faults (component, control and model faults). Symptoms and faults are related to each other through Diagnostic Bayesian Networks (DBNs) which work as an expert system. During operation of the HVAC system the data from the BMS is converted to symptoms, which are fed to the DBN. The DBN analyses the symptoms and determines the probability of faults. Generic indicators are proposed for the 4 types of symptoms. Standard DBN models for common components, controls and models are developed and it is demonstrated how to combine them in order to represent the complete HVAC system. Both the symptom and the fault identification parts are tested on historical BMS data of an ATES system including heat pump, boiler, solar panels, and hydronic systems. The energy savings resulting from fault corrections are estimated and amount 25%. Finally, the 4S3F method is extended to hard and soft sensor faults. Sensors are the core of any FDD system and any control system. Automated diagnostic of sensor faults is therefore essential. By considering hard sensors as components and soft sensors as models, they can be integrated into the 4S3F method.
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It is of utmost importance to collect organic waste from households as a separate waste stream. If collected separately, it could be used optimally to produce compost and biogas, it would not pollute fractions of materials that can be recovered from residual waste streams and it would not deteriorate the quality of some materials in residual waste (e.g. paper). In rural areas with separate organic waste collection systems, large quantities of organic waste are recovered. However, in the larger cities, only a small fraction of organic waste is recovered. In general, citizens dot not have space to store organic waste without nuisances of smell and/or flies. As this has been the cause of low organic waste collection rates, collection schemes have been cut, which created a further negative impact. Hence, additional efforts are required. There are some options to improve the organic waste recovery within the current system. Collection schemes might be improved, waste containers might be adapted to better suit the needs, and additional underground organic waste containers might be installed in residential neighbourhoods. There are persistent stories that separate organic waste collection makes no sense as the collectors just mix all municipal solid waste after collection, and incinerate it. Such stories might be fuelled by the practice that batches of contaminated organic waste are indeed incinerated. Trust in the system is important. Food waste is often regarded as unrein. Users might hate to store food waste in their kitchen that could attract insects, or the household pets. Hence, there is a challenge for socio-psychological research. This might also be supported by technology, e.g. organic waste storage devices and measures to improve waste separation in apartment buildings, such as separate chutes for waste fractions. Several cities have experimented with systems that collect organic wastes by the sewage system. By using a grinder, kitchen waste can be flushed into the sewage system, which in general produces biogas by the fermentation of sewage sludge. This is only a good option if the sewage is separated from the city drainage system, otherwise it might create water pollution. Another option might be to use grinders, that store the organic waste in a tank. This tank could be emptied regularly by a collection truck. Clearly, the preferred option depends on local conditions and culture. Besides, the density of the area, the type of sewage system and its biogas production, and the facilities that are already in place for organic waste collection are important parameters. In the paper, we will discuss the costs and benefits of future organic waste options and by discussing The Hague as an example.
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Neighborhood image processing operations on Field Programmable Gate Array (FPGA) are considered as memory intensive operations. A large memory bandwidth is required to transfer the required pixel data from external memory to the processing unit. On-chip image buffers are employed to reduce this data transfer rate. Conventional image buffers, implemented either by using FPGA logic resources or embedded memories are resource inefficient. They exhaust the limited FPGA resources quickly. Consequently, hardware implementation of neighborhood operations becomes expensive, and integrating them in resource constrained devices becomes unfeasible. This paper presents a resource efficient FPGA based on-chip buffer architecture. The proposed architecture utilizes full capacity of a single Xilinx BlockRAM (BRAM36 primitive) for storing multiple rows of input image. To get multiple pixels/clock in a user defined scan order, an efficient duty-cycle based memory accessing technique is coupled with a customized addressing circuitry. This accessing technique exploits switching capabilities of BRAM to read 4 pixels in a single clock cycle without degrading system frequency. The addressing circuitry provides multiple pixels/clock in any user defined scan order to implement a wide range of neighborhood operations. With the saving of 83% BRAM resources, the buffer architecture operates at 278 MHz on Xilinx Artix-7 FPGA with an efficiency of 1.3 clock/pixel. It is thus capable to fulfill real time image processing requirements for HD image resolution (1080 × 1920) @103 fcps.
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In this article a generic fault detection and diagnosis (FDD) method for demand controlled ventilation (DCV) systems is presented. By automated fault detection both indoor air quality (IAQ) and energy performance are strongly increased. This method is derived from a reference architecture based on a network with 3 generic types of faults (component, control and model faults) and 4 generic types of symptoms (balance, energy performance, operational state and additional symptoms). This 4S3F architecture, originally set up for energy performance diagnosis of thermal energy plants is applied on the control of IAQ by variable air volume (VAV) systems. The proposed method, using diagnosis Bayesian networks (DBNs), overcomes problems encountered in current FDD methods for VAV systems, problems which inhibits in practice their wide application. Unambiguous fault diagnosis stays difficult, most methods are very system specific, and finally, methods are implemented at a very late stage, while an implementation during the design of the HVAC system and its control is needed. The IAQ 4S3F method, which solves these problems, is demonstrated for a common VAV system with demand controlled ventilation in an office with the use of a whole year hourly historic Building Management System (BMS) data and showed it applicability successfully. Next to this, the influence of prior and conditional probabilities on the diagnosis is studied. Link to the formal publication via its DOI https://doi.org/10.1016/j.buildenv.2019.106632
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