TM .To report on the use of surface guided imaging during frameless intracranial stereotactic radiotherapy with automated delivery via HyperArcTM and were monitored for intrafraction motion by the AlignRT®  surface imaging (SI) system. Immobilization was with the EncompassTM  aquaplast mask device. AlignRT® log files were correlated with trajectory log files to correlate treatment parameters with SI reported offsets. SI reported offsets were correlated with gantry angle and analyzed for performance issues at non‐zero couch angles and during camera‐pod blockage during gantry motion. Demographics in the treatment management system were used to identify race and determine if differences in SI reported offsets are due to skin tone settings.All patients received intracranial radiotherapy with HyperArcA total of 981 fractions were monitored over 14 months and 819 were analyzed. The median AlignRT® reported motion from beginning to the end of treatment was 0.24 mm. The median offset before beam on at non‐zero couch angles was 0.55 mm. During gantry motion when camera pods are blocked, the median magnitude was below 1 mm. Median magnitude of offsets at non‐zero couch angles was not found to be significantly different for patients stratified by race.Surface image guidance is a viable alternative to scheduled mid‐treatment imaging for monitoring intrafraction motion during stereotactic radiosurgery with automated delivery. The treatment planning system (TPS) component within Eclipse  allows for high‐quality SRT planning with limited dosimetry experience and a more streamlined workflow. Pre‐defined angles are selected for treatment and isocenter selection is used to ensure no collision risk during treatment. Patient immobilization must use the Encompass SRS Immobilization System  for proper collision mapping and radiosurgery specific normal tissue objective. To monitor for intrafraction motion, HyperArcTM allows MV imaging at designated waypoints during treatment. Stopping the automated delivery to acquire and review images increases the length of the treatment and may increase the chance for patients to move. An alternative to mid‐treatment imaging is surface imaging, which uses optical tracking of the patient's surface to monitor for intrafraction motion.HyperArcRecently, with the technological advancements in optical reconstruction and projection techniques, surface imaging has become increasingly more popular as a non‐invasive, non‐radiographic form of image guidance. Surface imaging (SI) systems use a combination of real‐time optical and laser‐based imaging techniques that have been shown to properly position patients,TM. In this study, we examine the magnitude of translational intrafraction motion from beginning to the end of treatment, the magnitude of SI reported offsets at non‐zero couch angles, the impact of gantry motion on performance with respect to camera blockage, and the effect of skin tone on reported offset.This study presents the largest cohort to date of patient data captured via surface guided imaging during SRT delivered with HyperArc2TM. Surface imaging was performed with AlignRT®, also known as the Optical Surface Monitor System . In this study, all treatments were monitored with AlignRT® v5.1 which includes Advanced Camera Optimization (ACO). We have previously reported on system performance before and after the utilization of ACO.All treatments were performed on an Edge linear accelerator  equipped with HyperArcTM requires the use of the Encompass mask; therefore, all patients in this study were immobilized with the Encompass system which includes both a posterior molded head support and anterior open view mask compatible with surface imaging. The open‐face portion of the mask was wired during simulation to enable viewing of the open‐face region within the treatment planning system. The open‐face region was then contoured, the eye regions removed, and the resulting structure was exported to AlignRT® to be used as the region of interest (ROI) for SGRT. Prior to treatment, therapist turn on the projectors  for a minimum of 10 min to allow the cameras to reach thermal equilibrium. AlignRT requires that the user select the skin tone setting per patient. Skin tone setting is selected per the discrection of the user based on visual inspection of the patient. During treatment, each patient underwent radiographic imaging with both kV orthogonal imaging and cone‐beam CT (CBCT).HyperArcTM has optional MV waypoints to monitor for intrafraction motion, these were not utilized in favor of monitoring with SI. SI reported offsets, called Real Time Deltas (RTDs) in AlignRT®, were monitored during treatment for patient motion. If the magnitude (MAG) of translational RTDs exceeded patient‐specific thresholds , the patient was returned to the reference position at couch zero. This was done by manually stopping the beam and not by use of gating thresholds and beam holds. If the MAG at couch zero was under the threshold, treatment was resumed. If RTDs remained above the threshold at the reference position, radiographic imaging was performed, shifts performed, and treatment was resumed.After radiographic alignment, a reference surface was captured in AlignRT®, and treatment was initiated. While HyperArcLog files from AlignRT® continuously record RTDs from the patient’s reference position throughout treatment. SI system logs were correlated with information from the ARIA database  and linear accelerator trajectory log files. The SI log files and trajectory log files were synchronized using the initial beam‐on flag in each file.This enabled syncing RTDs with gantry angle toassess changes in RTDs with respect to the camera pod blockage by the gantry. The left and right camera pods were assumed to be at least partially blocked by the gantry at angles 303° ± 15° and 57° ± 15°.The user selected skin tone setting is not documented in the SI log file; therefore, demographics in ARIA were used to identify the race of each patient as a surrogate for skin tone. Patients were classified in the following groups per ARIA: White, Black, or not‐specified (NS). Spot checks of skin tone settings were performed and confirmed that skin tone and race were correlated. This was done to study the impacts of SI skin tone settings on performance. We compared the magnitude of RTDs between the three groups to determine if there were differences due to suboptimal camera exposure settings. RTDs before beam on at non‐zero couch angle and the end of treatment were evaluated for the three patient cohorts.Over 14 months, 981 fractions from 324 patients of SRT were analyzed. The average treatment time, defined as the interval from first beam‐on to the final beam‐off, was 2.85 min (range 1.62–7.95 min). Of the 981 fractions observed, 83 were omitted due to the inability to locate the corresponding trajectory log file. Twenty‐one additional fractions were omitted due to abnormal termination of treatment which caused a discontinuity in SI log file analysis. Twenty‐five were omitted due to a discordance between the SGRT and trajectory log beam on flags of >3 s. Thirty‐two (3.8%) fractions contained mid‐treatment imaging due to SI reported patient motion and were omitted due to discontinuity in RTDs due to new reference surface capture. Note that some fractions had multiple reasons for omission which left 844 fractions from 281 patients for analysis.3P = 0.127) using a Wilcoxon rank‐sum test. Statistical analysis was not performed for the NS group due to the limited number of patients. Figure TM. The frequency of utilized couch angles is shown in Fig. Table Fractions with mid‐treatment imaging were omitted from the analysis in Table 4The focus of this study was to analyze SI reported offsets with automated delivery; therefore, SI logs with a single reference capture were required. Patients with multiple reference captures were excluded from the analysis due to a discontinuity in SI logs which prevented comparison of patient position from the initial reference surface capture to the end of treatment. Per our previously reported workflow,TM. This study excludes functional SRT patients  since they are treated with the virtual cone technique developed at our institution.TM; therefore, only four eligible patients were not treated with HyperArcTM during the period of this study. Three were due to not being simulated in the Encompass mask, and one was ineligible due to the lesion being located outside of the collision‐free zone required for HyperArcTM delivery.Not all stereotactic radiotherapy patients are treated with HyperArcAlignRT® enables setting patient‐specific thresholds for reported out of tolerance RTDs or gating the beam. Our clinic does not enable gating via SI, rather RTDs are monitored by the treatment team, comprising a radiation oncologist, neurosurgeon, physicist, and therapists. If intrafraction motion tolerances are exceeded, the beam is manually stopped. The tolerance for returning to the reference position is typically 1 mm but could be modified for patient‐specific attributes . In a previous study with traditionally delivered SRT, 4.3% of patients had CBCT confirmed patient movement while this study reports 2.2%.Anecdotal reports for SI system users indicated performance issues that may be attributed to patient‐specific characteristics. Skin tone was chosen as an attribute to investigate due to camera performance being dependent on proper exposure settings. Note that AlignRT® requires the user to specify the skin tone of the patient. During the time of this study, the software had three choices for skin‐tone: fair, mid, and dark. The current version of AlignRT® Advance v6.2  now has five available settings for skin‐tone. This choice sets the exposure times of the cameras and are aimed to improve the variations that can affect the performance of RTDs due to skin tone. Darker skin tones will absorb more of the projected speckle pattern; therefore, longer exposure time is needed to allow sufficient light to reach the sensor. If the optimal skin tone is not selected, there is a risk of under or over‐exposing the image resulting in inferior surface tracking. A hypothesis was that relying on users to select skin tone settings could result in suboptimal performance; however, our data did not find a difference between the RTDs before beam‐on at non‐zero couch angles for the two largest cohorts of patients studied.Similar to a previous study, RTDs at non‐zero couch angles were larger than RTDs observed at the end of treatment, indicating SI system performance is still sub‐optimal at non‐zero couch angles. Additionally, the largest component of the translation magnitude continues to be from offsets reported in the longitudinal direction as shown in Table TM users have the option of utilizing mid‐treatment imaging via the electronic portal imaging device (EPID) to take MV images at designated time points. This is referred to as waypoint imaging and is scheduled to be taken before beam‐on at non‐zero couch angles. While waypoint imaging can be utilized by clinics with and without surface imaging, its utility is restricted due to the MV images being restricted to AP/PA imaging; therefore, vertical offsets are not reported. Waypoint imaging is also vulnerable to variability between users and sensitive to the region of interest set if auto‐matching is utilized.HyperArcAnother benefit of SI monitoring is continuous logging of RTDs throughout treatment rather than at specified radiographic imaging time‐points. This allows for aggregate data analysis of camera performance at all couch and gantry angles utilized clinically. Analysis of SI system logs with trajectory logs allows correlating RTDs with the corresponding gantry angle. Figure 5TM patients. Surface image offers sub‐millimeter accuracy with real‐time measurements and no loss of treatment efficiency. SI offsets observed during HyperArcTM are on the order of offsets observed during traditionally delivered SRT. Analysis in conjuction with linear accelerator trajectory logs can be used to assess system performance when camera pods are blocked by the gantry and enable an aggregate analysis of patient‐reported offsets during automated delivery.Surface image guidance is a viable alternative to scheduled mid‐treatment x‐ray imaging for HyperArcElizabeth L. Covington made substantial contributions to the conception and design of the work; the acquisition, analysis, and interpretation of data for the work; drafting the work and revising it critically for important intellectual content; gave final approval of the version to be published; and agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Dennis N. Stanley made substantial contributions to the interpretation of data for the work; revising it critically for important intellectual content; gave final approval of the version to be published; and agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.John B. Fiveash made substantial contributions to the interpretation of data for the work; revising it critically for important intellectual content; gave final approval of the version to be published; and agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Evan M. Thomas made substantial contributions to the interpretation of data for the work; revising it critically for important intellectual content; gave final approval of the version to be published; and agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Samuel R. Marcrom made substantial contributions to the interpretation of data for the work; revising it critically for important intellectual content; gave final approval of the version to be published; and agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Marcus Bredel made substantial contributions to the interpretation of data for the work; revising it critically for important intellectual content; gave final approval of the version to be published; and agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Christopher D. Willey made substantial contributions to the interpretation of data for the work; revising it critically for important intellectual content; gave final approval of the version to be published; and agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Kristen O. Riley made substantial contributions to the interpretation of data for the work; revising it critically for important intellectual content; gave final approval of the version to be published; and agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Richard A. Popple made substantial contributions to the conception and design of the work; the acquisition, analysis, and interpretation of data for the work; revising it critically for important intellectual content; gave final approval of the version to be published; and agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Elizabeth L. Covington has received honorarium and travel assistance from VisionRT. Evan M. Thomas, Richard A. Popple, and John B. Fiveash have received honoraria from Varian Medical Systems which are outside of the scope of this article. University of Alabama‐Birmingham has educational and research grants from Varian Medical Systems.


To the Editor,We wish to thank Honore et al.  for theiIn our study the time of onset of RRT was recorded in 17/22 patients and only 7 patients  had RRT performed within 24 h of study baseline. Considering the small number of patients in the study who had RRT within 24 h of baseline, we believe that this would not affect the overall results.Specifically for patients undergoing continuous RRT, using P-PENK, P-NGAL or any other biomarker that could be removed from circulation by RRT as a risk marker might lead to an underestimation of risk and should be interpreted with caution. We completely agree with Honore et al. that studies assessing the performance of prognostic biomarkers in this specific patient group are lacking and should be targeted for future studies.


This was conducted in a reflectron (Jordan), modified in the laboratory. Due to the large number of fragments, special care has been taken into the calibration of the system, in the simultaneity between the laser pulse and the sample, and the associated electronics to ensure that produced fragment spectra arise from the interaction laser-sample. We emphasize the next aspects of the method:  Specifications tableWe used a pulsed valve and a skimmer to generate a supersonic molecular beam in a collision free system. Between the valve and the skimmer there is an 10 cm stainless steel extension with a 2 mm conical termination inside the chamber, that allowed the adiabatic gas to expand closer to the skimmer. That extension allows cluster formation 9 and 1010 W·cm−2 were achieved under these experimental conditions.The laser radiation  was focused into the interaction region using a 15 cm focal length lens. The diameter at the focal point was 80.0 µm. Radiation intensities between 10A sample of liquid nitromethane from Sigma-Aldrich (purity ~99%) was used. The nitromethane vapor pressure was 3.7 kPa (27.75 torr) at 20°C. The sample was heated at a constant temperature of 28°C and introduced by a pulse valve into the ionization chamber in the gas phase.The nitromethane gas and laser pulse interact at 90° . The interaction region was located between two polarized electrodes separated by 6 mm with a circular mesh of 90% transparency and a 1 cm diameter. The positive fragments formed at the nanosecond laser pulse were accelerated to the free field region of the R-TOF mass spectrometer by applying an extraction voltage to the electrodes, in the present case it was 1.5 keV −6 torr with an open valve (175 µs) and a base pressure of 10−8 torr by the operation of two turbo molecular pumps (Pfeiffer Vacuum and Agilent) while the ions were produced. Once the fragment spectra were obtained it is necessary to identify the correspondent peak to a specific fragment.After the ions passed the drift zone, they were directed to the detector, which was a 25 mm dual microchannel plate (Chevron). The signals from the microchannel plate were routed to a fast preamplifier VT 120 (ORTEC) and a picosecond time analyzer (Mod. 9308 ORTEC). A computer (software PTA-32 ORTEC) collected and processed the data according to the arrival time of the mass/charge fragments. The operating pressure was 2 × 10The electronics of the detection system allows practically any ions to be recorded, since it is the time difference that differentiate the masses. In the multichannel system, the temporal scan can be done from very short time (400 ns) to very long time (2.7 years) using around 65 thousand channels. The detector is a microchannel plate (MCP), which receives the signal of ions, when hits the detector surface and the small electric charge which is produced goes to the preamplifier and converted to a voltage pulse. The response is of the order of nanoseconds.−6 segThe time interval for that single charge mases - 6 seg and the detection time is 10−9 seconds. Microchannel plate is therefore able to differentiate the times of arrival of each ion. On the other hand, the electronic response time is 10−12 seconds. So, an excellent resolution to transform the current generated by the ions to analog and finally digital signal is guaranteed.The difference in time of arrival to the detector for ions with 1 amu and 2 amu is ~ 10 The All authors have participated in the conception and design and writing of the article, and have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.


In this study, we present the use of an internet of things (IoT) analytics platform service to mimic real-time pipeline monitoring and determine the location of damage on a pipeline. Pressure pulses, based on the principle of vibration in pipes are used for pipeline monitoring in this study. The principle of time delay between pulse arrivals at sensor positions is also adopted in this study. An Arduino and a Wi-Fi module were combined, programmed and used to produce a wireless communication device which communicates with the ThingSpeak internet of things (IoT) analytics platform. A total of five channels were created on the platform to collect data from the five sensors that were used in the experimental test rig that made use of wireless communication device. Signal data was collected once every 15 s and all the channels were updated every 2 min. ThingSpeak provided instant visualizations of data posted by the wireless communication device. Online analysis and processing of the data was performed as it came in. A second test rig was built that made use of a data logger for processing of data. The measured velocity of pulse propagation using the data logger and air as transport fluid was 355 m/s. The computed estimates of event location for the 50 measurements taken ranged between 4.243 m and 4.246 m. This had a scatter of just 3 mm against the actual measured event location of 4.23 m. The experimental results obtained showed that the performance of the wireless communication device compared satisfactorily with the data logger and is capable of detecting the location of damage on real pipelines when used for real time monitoring.Using this communication device and an analytics platform, real-time monitoring of pipelines can be carried out from any location in the world on any internet-enabled device. Tight twists in the line do not permit the inflexible exoskeleton of the pig to go throughBlockages brought about by silt and pollutants may go about as hindrances to the way of a pig .Pipes may have various widths which forbid the section of these torpedo-like structures .There are valves in the pipeline that forever discourage the entry of anything besides a gas or liquid .There might be no immediate passage into a line .Pipelines are considered as the major globally recognised means of transporting petroleum products. These pipelines are usually damaged as a result of natural events  or due to third party activities . Methods of monitoring of pipelines include intermittent appraisal of pipelines, use of pipeline integrity management systems, on-the-ground and air surveillance of pipelines by security forces. High cost, planning complexity, and lack of proper access routes are major drawbacks for these monitoring methods. A great challenge that pipeline operators have faced in the past has been that of real-time monitoring of pipelines that is not restricted to control rooms in a particular location. Previous research works on pipeline monitoring have rarely focused on real time transmission and monitoring of damage data wirelessly to an Internet-of-Things (IoT) platform. Previous research works have focused on the design/development of intelligence pipeline inspection gauges (pigs) to achieve this . AvailabThe process of pigging is a very expensive one. Estimates have shown that pipeline monitoring and inspection by pigs can cost as much as $56,000 per km of pipeline . Taking The use of an Arduino, Wi-Fi module and the ThingSpeak IoT platform is advocated for to achieve real time monitoring of a pipeline from anywhere in the world.Most times, damages caused by impulsive events generate a pressure pulse that propagates in both directions through the fluid in the pipe. Detection and measurement of these pressure pulses can be carried out at points remote from the event. These measured pulses contain information about the event and can be used to monitor these pipelines to detect and locate damages along the pipeline.Methods of pipeline line monitoring from previous research exists. These methods have their various merits and demerits. The negative pressure wave (NPW) method of pipeline monitoring was used by Junxiao et al.  in carryVibration-based methods for pipeline monitoring have been found to be very effective. The method of time delay between pressure pulse arrivals was found to be very effective in the location of leaks on a pipeline . Due to The oil and gas sector has been slow to embrace IoT technology despite having pipelines and refining facilities, and instrumentation on drilling rigs for decades. Only recently has the extraction industry begun to work with modern IoT . This chIt was referenced in  that cooIoT can play several roles in monitoring of pipelines. Operational data from electric submersible pump can be monitored to detect potential failure and automatically stop the pump to prevent damage, and in-turn notify operators to repair or replace the pump based on current machine and maintenance models . IoT canThe upsides of embracing IoT for pipeline monitoring are various. Joshi  expresseTo the best of the author’s knowledge, not much works exits in the use of IoT for monitoring of petroleum pipelines but related works to the scope of this study exists. Notably, Cheddadi et al.  proposedPipeline monitoring and inspection technologies have garnered significant attention across the globe. In this study, sensor networks were used to determine the location of bursts, leaks and other anomalies (damages) in general pipeline systems using pressure pulses based on the principle of vibration in pipes. The principle of time delay between pulse arrivals at sensor positions was adopted in this study. The competitive advantage of this research work and its contribution to knowledge lies in its ability to perform real-time damage location through the use of a combination of wave propagation, an active sensor network, a wireless data transmission system, and an Internet of Thing (IoT) platform. With this system, the transmission of damage data captured by sensors to the monitoring room is achieved wirelessly, thereby making the monitoring of pipelines in real-time from any location in the world possible.To the best of the authors knowledge, previous research works on pipeline monitoring have rarely focused on real time transmission and monitoring of damage data wirelessly to an Internet-of-Things (IoT) platform. Additionally, the investigators utilized an Arduino and Wi-fi-module to produce a wireless communication device to capture pressure pulses from sensors on pipes on an experimental test rig and transmit these pulse data wirelessly to the ThingSpeak analytics platform. The following section discusses the data and methods utilized for this study. The third section discusses the findings based on methods adopted. The fourth section outlines the conclusions drawn from this study.Flexible polyethylene hose pipeA test rigThe major materials that were used for this work are:TCAM piezoelectric sensors  for detection of propagated pulses along pipeline.Pulse generator for generation of sharp fronted pressure pulses in the pipe.Pico Log 1012  for recording and processing of signals from pressure pulses at specified sampling rateWireless communication device for processing and transmission of signal data received from sensors wirelessly to the ThingSpeak IoT analytics platform.Laptop computer: Connected to data acquisition module for processing of and visualisation of data.The experimental setups for this work are shown in Figs. A wireless communication device was developed to carry out wireless transmission of signal data from the sensors to the ThingSpeak IoT analytics platform. The device consisted of an Arduino and a Wi-Fi module which were integrated with the sensors that were placed on the pipelines. The Arduino was programmed to communicate with the ThingSpeak IoT analytics platform thereby sending signal data to the platform for visualization and analysis.An Arduino is an open-source electronics platform based on easy-to-use hardware and software. Inputs like light on a sensor, a finger on a button, or a Twitter message can be read by an Arduino board. These inputs are then turned it into an output like activating a motor, turning on an LED, publishing something online . The HwaThe ESP01 ESP8266 Wi-Fi module was used for developing the wireless communication device. ESP8266 is a standalone transceiver that is of minimal cost which can be adopted in an IoT structure. This Wi-Fi module enables internet connection to sensors and other application specific devices through its general purpose input/output (GPIO) pins. It is very user friendly with minimal development up-front, and minimal loading during runtime. It is designed to occupy minimal printed circuit board (PCB) area and connects with the server by means of the TCP or UDP communication protocol .A pressure pulse is generated when a pipe is damaged and this pressure pulse propagates in both directions away from the location of the damage. Both pressure pulses are eventually reflected when they reach the boundaries of the pipelines. This work looked at the monitoring of vibration-based events on a pipeline considering a situation with air flowing through the pipe. This required the use of sensors on opposite sides of the event. Using a pressure measurement sampled at a high frequency at several points along the pipe, the travel times of the pressure pulses can be found.x1, x2, x3, and x4 from some boundary. The arrival times of some generated pulses caused by a damage-inducing impulsive event occurring at an unknown location is recorded as t1, t2, t3, and t4 by the four sensors. Sensor 3 or 4 may be used to determine the location of the event on the pipe shown in Fig. Cp in the form of a pressure pulse through the fluid filled pipe in both directions.The pulse arrival times in a pipe and the sensor positions along the pipe can be used to monitor a pipe and also determine the location of an event along a pipe. These events could either be caused by drilling, impact, explosion, etc. In Fig. This pulse propagation velocity can be measured from the arrival of pulses at the same side of the event which are sensors 3 and 2, or sensors 2 and 1 as in the case shown in Fig. To determine the arrival times of signals at each sensor, the cross correlation technique was used.ThingSpeak is an IoT platform service that enables live data streams to be viewed and analyzed from sensor devices in the cloud . This plA total of five channels were created on the ThingSpeak platform to collect data from the five sensors that were used in the experimental test rig. Signal data was collected once every 15 s and all the channels were updated every 2 min. ThingSpeak provided instant visualizations of data posted by the wireless communication devices. Online analysis and processing of the data was performed as it came in. Figure The right side of the diagram depicts the algorithm development associated with the IoT application. Here insight was made into the collected data by performing historical analysis on the data.To validate the theory of event location as discussed above; two experimental flow loops as shown in Fig. delay). The pulse propagation velocity was calculated as [xab = distance between two selected sensors.Sensor 1 was located at 2.24 m from the event location; sensor 2 was located at 0.68 m from the event location; sensor 3 was located at 3.68 m from the event location; sensor 4 was located at 6.68 m from the event location, and sensor 5 located at 12.684 m from the event location. Pressure pulses were generated at specific pressures severally and measurements made for each amount of pressure set in the pulse generator. The velocity of the propagated pulse was determined experimentally based on measured pressure at two sensors. These measured pulses were cross-correlated using the Matlab® software to estimate the delay between arrival times  gave the location of the event. x21 is the known distance between sensor 1 and 2, while t12 is the measured time delay between pulse arrivals at sensors 1 and 2 which was obtained using the cross-correlation technique. With the aid of the written Matlab® code, a consistency in the computed event location estimates was observed. The computed estimates were in the range of between 4.243 and 4.246 m, and having a scatter of just 3 mm. The actual measured location of the event on the test rig was 4.23 m.The Matlab® m-code language was used in the calculation of the event location of the test rig’s pipe with air as the transport fluid. The location of the real event is the point of entrance of the original pressure pulse into the main pipe and it was determined from measurements made at sensors 1 and 2. The tee connection is the actual source of the pulses and formed the basis of all calculations about the location of the event. Equation A wireless communication device was incorporated into the experimental test rig as shown in Fig. The free student license of the ThingSpeak platform was used for this work and as a result, there was a 15 s delay in the transfer of pulse data to the platform. To achieve a one second transfer of pulse data on the platform, a professional license was required. Using a pulse generator, sharp fronted pulses were generated into the pipe and these captured pressure pulses were transmitted wirelessly via the wireless communication device to the ThingSpeak platform. Figure The experimental setup was same as before with sensor 2 closest to the tee connection and taken as the damage location. On the ThingSpeak platform, sensor 1 readings were displayed on field 1 while sensor 2 readings were displayed on field 5. Sensor 3, 4 and 5 readings were displayed on fields 3, 2 and 5 respectively on the ThingSpeak platform.Based on the sensor locations on the experimental set, a pressure pulse from the pulse generator would get to sensor 2 first, then to sensors 1, 3, 4, and 5 respectively. This was as a result of their respective distances from the pulse generator. The results of these experiments confirm the aforementioned and also the effectiveness of the wireless communication device.For the case of pressure of 1.0 bar as in Fig. A total of 15 tests were repeated using five different pressure readings on the pulse generator and the wireless communication device, and the results were similar. A typical screen display of the ThingSpeak platform is as shown in Fig. A wireless communication device was developed for transmission and processing of measured pressure pulses wirelessly to an IoT analytics platform (ThingSpeak) for real time monitoring. Tests rigs were built with static air as the transport fluid to validate the efficacy of the developed transmission device. Based on a previously developed method of locating the position of an intrusion event on a pipe that relies on the time delay between pulse arrivals at two sensors, a difference of 20 mm only was recorded between the computed and actual event locations when a data logger was used for capturing and transmission of sensor data. This was carried out as a way of validating the theory of pipeline monitoring based on pulse arrival times at sensors along the pipe. The wireless communication device was then used for capturing and transmission of data from sensors on the test rig’s pipe to the ThingSpeak platform. The results obtained were similar to those obtained using the data logger.A low-cost pipeline monitoring system with the ability to perform real-time damage detection, location, and allows you to view the results of the measured pressure pulses in real time on a computer system or even on a smart phone from any location in the world was developed.


Metab Eng, 13(6), 745–755) with additional constraints, regularization and analysis of uncertainty. Instead of using estimated uptake or secretion rates, concentration measurements are used directly to avoid an amplification of measurement errors by numerical differentiation. It is shown that the regularized DMFA for EM method is significantly more robust against measurement noise than methods using estimated rates. The confidence intervals for the estimated reaction rates are obtained by bootstrapping. For the selection of a set of EM for a given st oichiometric model, the DMFA for EM method is combined with a multiobjective genetic algorithm. The method is applied to real data from a CHO fed‐batch process. From measurements of six fed‐batch experiments, 10 EM were identified as the smallest subset of EM based upon which the data can be described sufficiently accurately by a dynamic model. The estimated EM reaction rates and their confidence intervals at different process conditions provide useful information for the kinetic modeling and subsequent process optimization.In macroscopic dynamic models of fermentation processes, elementary modes (EM) derived from metabolic networks are often used to describe the reaction stoichiometry in a simplified manner and to build predictive models by parameterizing kinetic rate equations for the EM. In this procedure, the selection of a set of EM is a key step which is followed by an estimation of their reaction rates and of the associated confidence bounds. In this paper, we present a method for the computation of reaction rates of cellular reactions and EM as well as an algorithm for the selection of EM for process modeling. The method is based on the dynamic metabolic flux analysis (DMFA) proposed by Leighty and Antoniewicz  of metabolic network are used as reactions. An algorithm for the selection of EM ensures, that the model complexity is kept as low as possible. The concept is applied to data of a CHO fed‐batch fermentation. Soons et al.  use a coFor the selection and fitting of kinetics, estimates of the cell‐specific EM reaction rates are needed. Several algorithms that have been proposed for this analysis use measured cell‐specific fluxes of medium components  by Leighty and Antoniewicz  for the It is shown that the regularized DMFA for EM method is more robust against measurement noise than other methods which are based upon the estimation of cell specific fluxes and that tuning of the algorithm is straightforward by using cross‐validation.(1)Before the EM are analyzed, the original DMFA problem with additional constraints to account for the irreversibility of certain reactions provides the best possible fit for a given metabolic network and therefore can be used as a benchmark. A suitable selection of EM should exhibit a comparable fit to the data. If the quality‐of‐fit is sufficient, the selection of EM can be carried out, otherwise essential reactions in the metabolic network are missing.(2)geometrical reduction in which geometrically similar EM are discarded from the possibly large set of possible EM.A first reduction is carried out by means of (3)DMFA for EM method. The two objectives of the multiobjective GA are to optimize the fit of the predictions to the measured data and to minimize the size of the employed subset of the EM To explore the trade‐off between a good fit to the data and a low number of EM which is preferable because it reduces the model complexity.For a further reduction, a multiobjective genetic algorithm (GA) is used together with the The selection of a subset of EM which are suitable for dynamic process modeling is determined by the following procedure:After a set of EM has been found, the pdf of the corresponding EM reaction rates over time at different process conditions can be evaluated by a bootstrap method in which the estimation is repeated with resampled measurements. This information is useful for selecting and fitting kinetic expressions of the reactions by statistical methods. A quantification of the uncertainty of the estimated cell‐specific rates is necessary as the magnitude of this uncertainty varies considerably during the course of a fermentation process. Figure DMFA for EM problem, (b) adding linear constraints to the original DMFA approach, (c) showing how the bounded DMFA method can be used for the evaluation of a metabolic network, and (d) calculating confidence intervals of the specific reaction rates.Some elements of this procedure were already published presented in  and the related new formulations (bounded DMFA and DMFA for EM) is given. Furthermore, the evaluation of the confidence intervals of the DMFA estimates and the regularization are explained.In this section, a short introduction of the original DMFA method by Leighty and Antoniewicz  (which w2.1ν¯(t):P is the  stoichiometric matrix, ν¯(t) the cell‐specific flux vector, and Xv(t) is the concentration of viable cells. The volumetric flux vector V¯(t) is:The differential equations that govern the evolution of the concentrations of the species in the reaction medium in a batch process  can be calculated from the time‐dependent vector of fluxes in the metabolic network, ν¯(t). The steady state assumption for the internal metabolites in metabolic flux analysis (MFA) postulates that the derivatives of the internal concentrations are zero. This results in a, usually under‐determined, system of linear equations:N is the  stoichiometric matrix. Under this assumption, V_(t) can be calculated from volumetric rates of the so‐called free fluxes U¯(t):(N) denotes that N⋅K=0. All fluxes which satisfy Equation  are piece‐wise linear over time between inflection points Tj, such that Equation (Uk(Tj) can then be determined by solving the unbounded DMFA problem (5):Leighty and Antoniewicz  assume tEquation  has a sicˆi(tj) in Equation (Uk(Tj). The number of inflection points is usually chosen manually according to the expected profiles of the volumetric rates which are computed by this method.Each Equation  can be ccˆi(tj) therefore are smoothened and the influence of the measurement noise is averaged out to a certain extent. The smoothing of the estimated concentration profile is a consequence of the limited flexibility of the assumed piece‐wise linear volumetric reaction rates. For further details, the reader is referred to Leighty and Antoniewicz  which fulfill the steady state assumption and irreversibility constraints can also be expressed as a non‐negative linear combination of reaction rates of EM, assembled in the vector R¯(t)  EM reaction rates over time R¯(t) is obtained by solving the DMFA for EM problem (9):s.t.Rk(Tj) is the value of the (volumetric) reaction rate of EM k at the inflection point Tj, nr is the number of EM and nT the number of inflection points. Each cˆi(tj) can be computed as a linear combination of all estimated quantities Rk(Tj). The cell‐specific reaction rates r¯(t) can be obtained from the volumetric rates R¯(t) by:The (volumetric) fluxes 2.2Rk(Tj) result in only small changes of the value of the objective function, as many EM with a similar stoichiometry exits. To overcome this problem, a penalty term is added to the cost function:Xv is the concentration of viable cells and α is a scalar weighting factor which optimal value is found by cross‐validation. With this additional term, the L2‐norm of the cell‐specific rates is penalized. Penalizing cell‐specific rates is preferred over penalizing volumetric rates as unwanted and unrealistic behavior on the cellular level is less likely to occur. Otherwise, unrealistically high cell‐specific rates might be obtained from the DMFA for EM method in the beginning or at the end of a process where the concentration of viable cells is low. This criterion is also used by Schwartz and Kanehisa  may lead to ill‐conditioned problems, so that even large changes in Kanehisa  for the 2.3Classical methods for the estimation of confidence intervals like the Cramer‐Rao lower bound are problematic in systems with many parameters from which a few might be unobservable, which can happen in the DMFA method when a large number of reactions are involved. Additionally, constraints on the reaction rates cannot be taken into account.We therefore propose to use a bootstrap method instead. Here, the measurements are resampled from their expected probability density functions. Often, the probability density functions of the measurements can be assumed to be Gaussian distributed and the variances can be estimated or are available from sensor data. Alternatively, these uncertainties can be estimated by a maximum likelihood estimator.estimation bias by the DMFA method:Tj must be randomized.The position of the inflection time‐points α must be zero or small such that numerically ill‐conditioned estimations can be carried out without strongly influencing the result.The regularization coefficient The estimation of the reaction rates with the DMFA method can then be repeated using the sampled data. However, two major changes have to be made to get rid of the From the sampled estimates of the reaction rates and their confidence intervals are obtained.2.4The different variants of the DMFA method can be used to identify a set of active EM directly from measurement data and thus enable the modeler to select a suitable subset of EM for a dynamic process model.bounded DMFA and DMFA for EM for the complete set of EM are both based on the steady state assumption for the internal metabolites and take the irreversibility of reactions into account. The minimum SSR, which can be calculated from Equations   bounded Equation ) are del(1)DMFA for EM method can be used to ensure that no significant EM is removed. The algorithm is described in the appendix.A geometrical reduction, which discards similar EM from the original set based on their cosine similarity. This algorithm can be used for a preliminary reduction if the initial number of EM is very large. The SSR value which is calculated using the (2)multiobjective GA which considers as objectives the SSR value and the number of EM in the set.A For the selection of a subset for a process model, two algorithms are proposed:ξi for each EM. The variable ξi determines whether the corresponding EM is part of the subset or not. For each selected subset, problem (9) is solved using a linear solver to obtain the corresponding SSR value. The optimization problem can be written formally as:The degrees of freedom which the GA optimizes are binary decision variables SSR which results from an optimized selection of EM as a function of the cardinality of the set of EM. The Pareto front helps the modeler to decide how many EM are necessary to capture the measured behavior of the process with an acceptable model error. The SSR value approaches the SSR value of the bounded DMFA problem when the number of EM in the subset increases.The resulting Pareto front describes the SSR value of the resulting sets of EM give an indication of the expected quality‐of‐fit and of the complexity of the process model which is based on this set of reactions. Practically, one will search for the lowest number of EM which still provide a desired fit to the data.Thus, before any kinetic expression is fitted, the 3DMFA for EM method that was presented above with other commonly used techniques that are based on the analysis of cell‐specific uptake or secretion rates, namely Poolman et al. , the total cell density, concentrations of the components of the medium, and dissolved oxygen‐sensor information were available and used. For confidentiality reasons, not more details can be given and not all measured components can be shown in the following.The concept for the choice of an EM reaction set was applied to data‐sets from six fed‐batch fermentations of a CHO culture. One experiment was excluded from this procedure and used as a validation data set for the model which was built using the chosen set of EM and fitted to the other five data sets. For brevity, we will show only three of the remaining five experiments in the following which represent the most “extreme” responses of the process to different set‐points for the pH value and glucose levels. Measurements of the viable cell density  and bounded DMFA (7), the fluxes of the reactions in the network and their confidence intervals were estimated by minimizing the difference between the estimated concentration profile and the (resampled) measured data  over the number of EM. In each evaluation of the objective function of the multiobjective GA, the DMFA for EM method was used for all experiments. The number of inflection points was set to 5. Figure SSR values for different reduced sets of EM together with the SSR of the unbounded and bounded DMFA problem for the training data. In this study, the MATLAB® implementation gamultiobj was used in which the NSGA‐II algorithm is utilized. Custom mutation and crossover functions were created to account for the binary variables ξi . The coSSR value of the bounded DMFA problem provides the lower limit of the SSR values for the subsets of EM. This lower bound is only dependent on the network itself and not on the number and the choice of the EM. The geometrical reduction turned out to be a suitable algorithm for reducing large sets of EM without a loss of physiologically important EM, but if smaller subset sizes are aimed at, the evolutionary algorithm gives much better results.The SSR value . The fit to the data of one experiment for four out of ten different concentration profiles is shown for different selections of EM subsets in Figure bounded DMFA method. For <10 reactions, the approximation becomes significantly worse. With <10 EM, essential pathways seem to be missing and the data cannot be reconstructed well enough. The stoichiometry of the 10 EM is shown in Table Oxic‐ and anoxic conversion of glucose with‐ and without lactate formationDifferent yields for biomass and/or product formationUtilization of different nitrogen sourcesReutilization of by‐products like lactate.From the Pareto front of the multiobjective evolutionary algorithm, it is easily possible to select a subset of EM for a process model. The set should be as small as possible but with an acceptable 4.3ai and bi were estimated from replicates of the experiments. The sampled estimates cˆ for four different components in three different experiments are shown in  should express only the significant influences.The information about reaction rates and their confidence intervals is very useful for the modeling of the process, as it enables the modeler to differentiate between significant and nonsignificant influences on the reaction rates. The kinetic functions for the reaction rates The results of the real‐world example showed that the evaluation of the confidence intervals is especially useful for fitting specific rate equations to estimates as the observability of these rates heavily depends on the state of the process. In this example, the range of the confidence intervals comprises several orders of magnitude.rˆ(Θ) to estimated rates of EM r by solving:Θ is the parameter vector and σ(ti) is the standard deviation of the estimate which can be obtained from the bootstrap samples of the estimate r(t). Only with a reliable estimate of σ, it is possible to fit and compare meaningful kinetics based on a statistical measure as, for example, the Akaike information criterion.In an earlier contribution , for example, by back‐propagation. Here also, the standard deviation of the rates σ(t) can be used for the weighting of the values at different time‐points. If this information is neglected, the identified rate expressions would be corrupted by unreliable estimation noise, especially in beginning and at the end of the fed‐batch process where the observability of the specific reaction rates is bad due to a low viable cell density.Also black‐box models like MLP for reaction kinetics of the selected EM can be fitted to the estimated reaction rates With the selected EM and fitted kinetics, the dynamic process model for fermentation processes is ready to be used for process design, optimization, model‐predictive control, or other purposes.5This paper presents methods for the selection of small sets of EMs and for the estimation of reaction rates from noisy concentration measurements. We propose extensions to the method of Leighty and Antoniewicz  which heIt could be shown in a simulation study that the DMFA for EM method is superior for estimating EM reaction rates from noisy concentrations measurements.The presented algorithm for the selection of small subsets of EM was used to select a suitable set of EM for a model of a CHO fed‐batch fermentation using real‐world data. It could be shown that the metabolism can be described by 10 EM with an acceptable accuracy at the experimental conditions.Using a bootstrap resampling method, the confidence intervals of the EM reaction rates of this set were calculated. This information can then be used for the choice and fitting of kinetic equations.


Surrogate-based optimization of distillation columns using an iterative Kriging approach is investigated. Focus is on deterministic global optimization to avoid suboptimal local minima. The determination of optimal setups and operating conditions for ideal and non-ideal distillation columns, leading to mixed-integer nonlinear programming problems, serve as case studies. It is found that the optimization using the adapted Kriging approach yields similar results compared to the direct global optimization of the original problem in the ideal case, while it leads to a huge improvement compared to a multistart local optimization approach in the non-ideal case. Rigorous optimization of distillation columns is of major interest in the chemical process industry due to its high economical impact. Due to the presence of discrete and continuous decision variables this leads to mixed-integer nonlinear programs (MINLP). Standard local optimization or stochastic optimization approaches can not guarantee that the optimum found by the optimizer is the global one. Alternatively, deterministic global optimization based on convex relaxations within a branch and bound framework has become an interesting approach for solving such problems, e.g., see the recent textbook by Locatelli and Schoen  for an iTo overcome this problem, different solution approaches were recently proposed. Quirante et al.  suggesteThe outline of the paper is as follows: The general concept of Kriging interpolation is briefly explained in Sect. Kriging models can be used to approximate complex mathematical models of real world processes. During the last years they gained increasingly more interest from engineers from different fields, such as chemical engineering, e.g. see  and 11]11]. The reference pointskth reference point coincides with the kth unit vector c and the reference points must be chosen such that matrix c and appropriate reference points are available, e.g. see [Given a vector-valued function mentclass2pt{minime.g. see .i, parameter c. In summary, In case of entclass1pt{minimaollowing , values Halton sequence [As the choice of the reference points may greatly influence the accuracy, it is important to use a space filling approach instead of using randomly generated data points. Otherwise clustering in unimportant regions of the model may occur and can result in a surrogate model of poor quality. In this work, a sequence  is used After a first Kriging model is generated, it is optimized using the deterministic global optimization software BARON . FollowiThis refinement approach is related to trust region methods that are used as a numerical solving strategy to compute locally optimal solutions for non-linear optimization problems. Trust region methods are based on an iterative procedure in that an approximation model of the original problem is solved in each step. In each iteration the corresponding approximation model is restricted to a certain sub-region usually containing the solution of the previous iteration. The size of the sub-regions may depend on the assumed model quality estimated with information from previous steps. We refer to the work  for a reAll Kriging models constructed through our computations are implemented as MINLPs and solved using the GAMS 24.6.1 framework with the deterministic global optimization software BARON 15.9.22., Cplex 12.6.3 is used as LP/MIP subsolver and CONOPT 3.17A is utilized as NLP subsolver. The calculations are carried out on a Linux PC with 3.40 GHz Intel Core i7-6700 CPU and 16 GB memory.As a first case study an ideal four-component distillation column with variable number of stages and a variable feed stage is chosen. A sketch of such a distillation column is depicted in Fig. a, b and c, and become more restrictive for the separation task from a to c. The concrete parameter setting defining each test instance is provided in Mertens et al. [The case study deals with three different mixtures that are separated with three different product specifications each. This leads to a total number of nine test instances. The mixtures are labeled by numbers 1, 2 and 3 where the difficulty of the separation task increases from mixture 1–3. The product specifications are given by the purity requirements on the distillate, i.e. on the mixture consisting of the two more volatile components, and on the bottom product, i.e. the mixture consisting of the two less volatile components. Different product specifications are labeled by letters s et al. .D in mol/s and by the vapor flow rate V in mol/s. For the known optimal solutions to our test instances, these characteristic properties are summarized in Table Note that feasible solutions to the considered MINLPs represent feasible distillation column designs of the corresponding separation tasks. A characterization of each such designs is given by the length entclass1pt{minimaV and D, and Algorithm 1 described in Sect. s et al.  and the entclass1pt{minimaentclass1pt{minimaNote that in most cases the first solutions obtained do not meet the constraints due to the inaccuracy of the first Kriging model. However, after the refinement of the sampling region the constraints are met by the found optimal solutions.Strict comparison of computation times of the different approaches is not possible due to different hardware and optimization software configurations. However, it was observed that the reference model typically could not be solved within 10 h, whereas the reformulated model with some tailor made bound tightening strategies was most of the time solved in less than an hour. For the detailed statistics we refer to Mertens et al. . The comIn the previous section focus was on separation of ideal mixtures with constant relative volatilities. Next, a highly non-ideal azeotropic mixture with variable volatilities is considered.From Sect. zTake the transformed states The assumption of positive derivatives in  and 10)10) alwayOne example for such a mixture is the distillation of Toluene, Methanol and Methylbutyrate , which if is set to 1.8 mol/s and the feed composition mole fraction B is the bottom flow rate in mol/s. The same objective function as in the previous example is used. The degrees of freedom are the distillate flow rate To model non-ideal phase behavior, several activity coefficient models, such as UNIQUAC and Wilson , may be Since global optimization with the rigorous model or its reformulation like in the ideal case is not possible within reasonable time anymore, local optimization is applied as a reference. For this, the local multi-start heuristic provided by BARON is applied with 5000 starting points. The results are displayed in the second row of Table The Kriging models have been fitted using 1190 sampling points. A cross validation of the first Kriging model (case “SurrogateD and V. After sampling around the obtained optimum and generating the second Kriging model (“SurrogateSince the operating conditions found during the reference optimization “Local” are far away from the optimal operating conditions for “Surrogatementclasspt{minimaD within a 25 % range and Note that the values for The purity of Toluene achieved with the operating conditions of “SurrogateBased on the solution “LocalThe computation times for each optimization are given in seconds in the column “time” of Table In this work, two case studies concerning the distillation of multi-component mixtures have been conducted, where global optimization techniques have been applied to surrogate models of the distillation columns investigated.It was shown that the reformulation developed earlier by the authors is not always applicable in the case of a distillation of a mixture with non-ideal VLE, rendering the problems unsolvable within a reasonable amount of time for standard deterministic global optimization software. In these cases, the iterative global optimization of surrogate models is a good alternative, which yields better optima than ordinary local solver and in some cases comes close to the global optimum. It can, however, not be guaranteed that the solution obtained by the optimization of these surrogate models is the actual global optimum or lies in a close neighborhood of it.Further work will be concerned with more advanced adaptive sampling methods and algorithms for the generation of global models.


However, measurement of the SWCC is often very time consuming, inaccurate and requires a lot of effort. In order to determine an accurate SWCC, we used HYPROP2. This method article extensively describes the topics which were not covered well by the instrument's manual such as collecting soil samples, use of the HYPROP refill unit, degassing water prior to degassing the tensio shafts and other procedures. Advice is provided in terms of better handling of the equipment to receive all four phases of an optimal measuring curve. Following the step-by-step procedure mentioned in this article would provide a high-quality SWCC. Our measurements were performed on both clay loam and sandy loam soils to show differences in the SWCC. We found that the upper tensio shaft took longer to cavitate for sandy loam soil compared to the clay loam soil.  This method explains the use of HYPROP  as an alternative technique to the conventional methods of measuring Soil water characteristic curve (SWCC) and unsaturated hydraulic conductivity of soil ,18. MeasHYPROP2 measures water potential at different soil saturation levels with the help of two tensiometers . This device is a more advanced version of HYPROP that has more robust and precise pressure transducers. It also introduces a faster optical monitoring of the measurements through a LED ring for better visualization of the current status of the device There are studies where results obtained from HYPROP have also been compared with the results from conventional methods or to different models. Öztürk et al. The step-by-step procedure to collect soil samples and to run HYPROP2 measurements is discussed in the later sections of this article.Soil samples were collected from two privately-owned farms. The first one is near Blissfield, Michigan. The soil type is predominantly Ziegenfuss clay loam, which is classified as a poorly drained soil  . The faFor both farms, soil samples were collected at two different locations with three replicates at each location. It is important to note that soil samples must remain undisturbed during collection. Four tools  are needIn this section, we will explain the steps required to prepare the HYPROP2 for determining the SWCC.The first step in operating HYPROP2 is degassing water. A vacuum bottle was filled with deionized (DI) water. The tube coming out of the vacuum bottle was connected to the vacuum mount of the HYPROP2 system, and the vacuum mount was connected to the vacuum pump  degassing the device using the HYPROP refill unit and (ii) degassing using syringes. Meter Group recommended degassing using the HYPROP refill unit because it is more accurate. Alternative to the HYPROP refill unit is manual degassing using syringes, which creates challenges to degas the water completely. This manual method requires a lot of labor, and chances of error is more than using the automated HYPROP refill unit.The HYPROP manual gives a fair instruction about how to degas the device manually using syringes but does poorly on explaining the degassing process using HYPROP refill unit. The scope of this article is to give a better understanding of how the HYPROP refill unit works in degassing the tensio shafts and HYPROP sensor unit.Assembly: This method involves the use of a high-performance vacuum pump that can generate a vacuum pressure of around 0.85 to 0.90 bars, which can degas the sensor unit and both of the tensio shafts. The total arrangement consisted of four instruments connected to each other  cannot be achieved, proper degassing will not be accomplished. Also, even if the system reaches full vacuum, the user needs to check if it can still hold the vacuum after the pump is turned off. The purpose of using the pump is to allow the system to reach full vacuum and then retain it for a couple of hours even if the pump is not in action. If the system is not able to hold the vacuum, there is definitely a leak in any of the refill unit components. A leak can happen in different ways such as a damaged vacuum bottle, the tubes may not be pushed all the way in through the ports of the vacuum mount and beaker mount, or the pressure valve attached to the beaker mount may be open. Thus, proper attention needs to be directed to these details.After degassing the sensor unit using the HYPROP2 refill unit, the soil sample was saturated with water. The HYPROP manual suggests saturating the soil sample in degassed water. But in reality, the water for saturating soil sample solely depends on the purpose of the experiment. For agricultural applications, the soil core needs to be saturated in subsurface drainage water to simulate water movement in the soil matrix. Thus, we started saturating the soil cores in subsurface drainage water (collected from the on-farm sites). The required time of saturating different kinds of soils may vary .Table 1SA step-by-step saturation process of the soil sample is shown in . The whiOnce the soil was saturated, these two programs were used to determine the SWCC: (i) HYPROP-View and (ii) HYPROP-Fit. HYPROP-View was used during the beginning of the experiment to select the saving directories, selection of the HYPROP device, and to determine the data collection frequency from the sensor unit. HYPROP-Fit was used at a later stage to find the stop point and air entry point of the sample and further evaluation of the results. HYPROP-Fit allowed to select different computational methods for evaluation of results, and to export the results to the computer. Both programs were fairly easy to use, and we recommend using the HYPROP2 manual for more explanation.Once degassing was done, the valve connected to the beaker mount was opened very slowly. If the valve was opened too quickly, a sudden pressure shock could damage the pressure sensors in the sensor unit. Then, the tensio shafts were removed from the adapters. Once the tension shafts have been removed from the adapters, it is very important to keep the tensio shafts hydrated, so putting the silicone caps on them is a good practice. The user can also add degassed water on ceramic tips of the tensio shafts from time to time to prevent them from drying. The respective connection ports for each shaft is already drawn on the sensor unit.When the tensio shafts are completely degassed and their ceramic tips are fully wet, they are ready to be screwed in the sensor unit. The HYPROP manual suggests that it takes about 9 turns for the tensio shafts to get sealed in the ports. From our experience, that was not always the case, or it was hard to measure exact 9 turns . Thus, aThe tensio shafts are very sensitive and can get damaged by the slightest negligence while operating, so it is good to occasionally check whether the tensio shafts are working properly. An easy step to check whether the tensio shafts are functioning is to add small drops of water on the ceramic tip of the tensio shafts. The refilling wizard window in the HYPROP-View program will show the change in pressure due to addition of water droplets. If the pressure for both tensio shafts go back to zero in a little while, then the tensio shafts are functioning properly. This is to check the zero potential of the tensio shafts. Another way to check is to dry the ceramic tip with paper towel and to keep an eye to the pressure values. In this case, the pressure values should readily increase to the atmospheric air pressure.Precautions: One important precaution before starting the experiment with HYPROP2 is to check whether offset recalibration is necessary. METER Group sets a narrow range of values for HYPROP sensors to function efficiently. If HYPROP has not been used for some time, the offset values of the electronic sensors may drift from the values set by METER Group. In these circumstances, the user needs to check for offset recalibration. From our experience, it was seen that during consecutive experiments the offset recalibration was unnecessary. However, the user should always check whether calibration is needed. Also, the HYPROP manual does a good job in explaining this calibration process. After degassing the sensor unit, the user needs to connect it with the provided USB adapter. Then, HYPROP-View program needs to be started. After clicking on the “refilling wizard”, the HYPROP sensor should be selected. Then, the software will check whether calibration is required. If calibration is needed, clicking the button “set zero value” will recalibrate the HYPROP sensors.When the tensiometers were fully functioning and the soil sample was saturated, i.e., a shiny surface appeared on top of the soil core , it was Once holes were made with the auger, there was a chance that air would enter those holes, so it was imperative to fill the holes again with water using the droplet syringe. At this stage, the soil was fully saturated and ready to be assembled to the sensor unit. Before assembling the soil core to the sensor unit, the silicone caps from the tensio shafts were removed. Then, the sensor unit was inverted and slowly placed close to the holes in the soil core. Once both tensio shafts were aligned with their respective holes in the soil core, the tensio shafts along with the sensor unit was slowly penetrated to the soil core , and the soil will easily fall off the sampling ring. To address this, the sampling ring was cleaned properly with a brush so that the tray could catch all the grains of the soil sample. The tray was placed in an oven for 24 h at 105C. After drying, the soil sample were weighed again. The dry soil weight was calculated by subtracting the empty aluminum tray weight from the combined weight of soil and tray after drying. This dry weight was recorded in the HYPROP-Fit program to measure the volumetric water content HYPROP measurements were conducted for both clay loam and sandy loam soils. The results for both soils are shown in Stopping the measurements section).The water tension did not rise immediately at the beginning of the measurements, but rather both tensio shafts showed a very gradual increase and it was difficult to distinguish their pressure difference until about first 3 h of the measurements . At 2 kPeS is effective saturation (cm3/cm3), h is pressure head (cm), ϴ is the volumetric water content , rϴ is the residual water content (cm3/cm3) and sϴ is the saturated water content (cm3/cm3). Additionally, α is the shape parameter related to the inverse of the air entry pressure (cm−1), n is the shape parameter that controls both the bending of the retention curve at the air-entry region and the asymptotic curvature towards the residual water content, and m is the additional shape parameter which equals to 1 – (1/n).HYPROP-Fit program lets the user choose different models to analyze the HYPROP2 measurements. The default one is the Van Genuchten model (1980) where volumetric water content of the soil is calculated by the following equationsIt is also possible to select 4 other models  in HYPROP2 for analysis, but we used the Van Genuchten α = 0.0730, n = 1.661, m = 1– (1/n) = 0.398, θs = 0.371 and θr = 0.280Thus, the equation of volumetric water content for clay loam soil is calculated ash = 63.1 cm, and pF=1.8), 33 kPa  and 1500 kPa  to be 31.3%, 29.1% and 28.1%, respectively. These values are important for irrigation timing, and can be used as an input for hydrological models such as DRAINMOD HYPROP-Fit program also provides estimates of field capacity and permanent wilting point. Field capacity is estimated at 33 kPa matric potential. The water content at permanent wilting point is estimated at 1500 kPa. The above model provides the water content at 6 kPa  = 0.494, θs = 0.386 and θr = 0.138Using the Van Genuchten-Mualem h = 63.1 cm, and pF=1.8), 33 kPa  and 1500 kPa  to be 31.3%, 29.1%% and 28.1%, respectively.This model gives the water content at 6 kPa (This study provides an extensive guideline for using HYPROP2 including collecting an undisturbed soil sample and using the instrument to determine an accurate SWCC. The HYPROP2 manual does not give good instructions on how the soil samples should be collected and how to saturate those samples in water based on one's research needs. Also, this article talks about the degassing process using the HYPROP refill unit. Even though the optimal curve is expected from HYPROP2 based on the instrument manual, a suboptimal curve can also be used to determine the SWCC. This method is highly efficient in degassing water and to generate good results. Anyone correctly following this method article should be able to replicate the results obtained in this study.


Increasingly, microbeams and microcrystals are being used for macromolecular crystallography (MX) experiments at synchrotrons. However, radiation damage remains a major concern since it is a fundamental limiting factor affecting the success of macromolecular structure determination. The rate of radiation damage at cryotemperatures is known to be proportional to the absorbed dose, so to optimize experimental outcomes, accurate dose calculations are required which take into account the physics of the interactions of the crystal constituents. The program RADDOSE‐3D estimates the dose absorbed by samples during MX data collection at synchrotron sources, allowing direct comparison of radiation damage between experiments carried out with different samples and beam parameters. This has aided the study of MX radiation damage and enabled prediction of approximately when it will manifest in diffraction patterns so it can potentially be avoided. However, the probability of photoelectron escape from the sample and entry from the surrounding material has not previously been included in RADDOSE‐3D, leading to potentially inaccurate does estimates for experiments using microbeams or microcrystals. We present an extension to RADDOSE‐3D which performs Monte Carlo simulations of a rotating crystal during MX data collection, taking into account the redistribution of photoelectrons produced both in the sample and the material surrounding the crystal. As well as providing more accurate dose estimates, the Monte Carlo simulations highlight the importance of the size and composition of the surrounding material on the dose and thus the rate of radiation damage to the sample. Minimizing irradiation of the surrounding material or removing it almost completely will be key to extending the lifetime of microcrystals and enhancing the potential benefits of using higher incident X‐ray energies. Consideration of the absorbed dose allows MX experiments to be planned so the sample dose does not exceed a dose limit at which the integrity of the sample is severely compromised.A widely used metric that has been useful in providing a reproducible “Such calculations show that when a 100 μm sized crystal containing no heavy atoms is irradiated by 12.4 keV (≡1 Å) X‐rays, only ~2% of the incident beam will interact with the crystal in any way.Einc), and a similar method was employed by Cowan and NaveEinc higher than the ~12.4 keV routinely used (20–30 keV Einc was predicted to be optimum) for small crystals, providing other experimental factors remain optimized . More recent Monte Carlo simulationset al. predicted a significant improvement on increasing incident energy from 12.4 to 26 keV for crystals of 5 μm or smaller, provided that a detector efficient at these higher energies is used.et al.et al.In terms of deleterious effects, the photoelectrons are particularly damaging, with a 12 keV photoelectron potentially giving rise to ~500 further ionizations.Einc above 20 keV. These three RADDOSE versions were written in Fortran and were designed for a scenario in which the beam was bigger than the crystal, thus totally bathing it for the whole duration of the data collection.To allow the experiments mentioned above to be intercomparable, the absorbed dose must be calculated and specified. A convenient and freely available tool used by many experimenters to estimate doses over the last 16 years has been the RADDOSE and RADDOSE‐3D series of programs, which have been evolving and becoming more sophisticated with time. RADDOSE v1However once X‐ray beams were routinely smaller than the crystals, this approach was no longer appropriate, and the current software, RADDOSE‐3D,Earlier versions of RADDOSE and RADDOSE‐3D had assumed that photoelectrons and Compton recoil electrons lose all of their energy where they are produced, until the recent incorporation of photoelectron escape into RADDOSE‐3D.This paper reports an extension of RADDOSE‐3D to perform full Monte Carlo simulations of a rotating crystal during MX experiments, which comprehensibly track the path and energy loss of photoelectrons and Compton recoil electrons produced both in the crystal and the surrounding material. This version (version 4), which includes an option for estimating time‐resolved doses for experiments using femtosecond pulses at Free Electron Lasers2et al.RADDOSE‐3D is an open source Java program that provides a full three‐dimensional representation of an MX experiment. The program divides the crystal into voxels and simulates its interaction with the X‐ray beam via a strategy described by the user . The user describes a single crystal in the input and the program can simulate several data collection strategies via the wedge input block, with each wedge being associated with the most recently defined beam. A more complete description of the RADDOSE‐3D input is given by Zeldin 2.1The input for the Monte Carlo simulation is the same as that of RADDOSE‐3D with a few extra specifiers, as described below and shown in Figure In the “beam” block, a polychromatic beam can be defined by specifying the full width half maximum (FWHM) of the energy spread of the beam.2.2y‐axis corresponding to the rotation axis of the goniometer, the z‐axis corresponding to the beam direction, and the x‐axis being the remaining axis. The x‐ and y‐coordinates of the photon are assigned within the tophat or Gaussian beam profile by random inverse transform sampling, where a random number u between 0 and 1 is chosen and the largest value on the x‐axis of a cumulative distribution function such that P(x) ≤ u is returned. The photon is placed in front of the crystal, with the z‐coordinate corresponding to the start of the surrounding material . The energy of the photon is then assigned, which is based on systematic inverse transform sampling of a Gaussian distribution if a polychromatic beam is defined, or it is given the user specified energy if no beam energy FWHM is defined.For the Monte Carlo simulations Figure , each phλphoto, λcomp, and λtot, respectively) by taking the inverse of the sum of all the atomic cross sections, σjx, in the crystal, as shown in Equation Nj is the number of atoms of element j in the unit cell of the crystal, denoted as the volume V. For each element j, the XCOM databaseσjphoto and σjcomp).For the composition of the material currently traversed by the photon, the mean free path lengths (MFPLs) until the next ionizing interaction are then computed for the photoelectric and Compton effects, as well as for the total  database.For a photon interacting with either the crystal or the surrounding material, random inverse transform sampling is used to determine the type of interaction (Compton or photoelectric), according to the relative probabilities for the occurrence of each type of event. If this is photoelectric absorption, the initial photoelectron energy is set to X value. The Y and Z direction vector values are chosen randomly, with the condition that the magnitude of the vector is always 1. The photoelectrons (and any other electrons that are produced) are tracked using a polar coordinate system, with the coordinate transformations being calculated using Equation θ is the polar angle φ is the azimuthal angle The beam polarization direction biases the distribution of the initial emission direction of the photoelectron so that it is not random. The horizontal polarization of synchrotron radiation  results Ecomp) and angular emission direction (θcomp) are computed using Equations m is the rest mass of an electron, c is the speed of light, and a random angle in radians is chosen for the angular deflection of the photon, θpho, where Ecomp to the electron, the Compton scattered photon is no longer tracked. The error caused by this is negligible since the total (Compton effect + photoelectric) MFPL in pure water is 2.07 mm for a 10 keV photon, there is only a <0.5% chance of another interaction in 10 μm of material. In addition, for a 10 keV photon, the Compton effect MFPL in pure water is ~30 times higher than that of the photoelectric effect, so the probability of a Compton event in the first place is very low.For a Compton scattering event, the recoil electron energy . This uses the collision stopping power which is the average energy loss per unit path length as a result of Coulomb collisions with bound atomic electrons,The amount of energy lost by the electron over distance For every photon simulated, the crystal is assumed to be pristine . This means that the dose is likely to be slightly overestimated, as it is in standard RADDOSE‐3D calculations. However, models have shown that for an “average protein,” there is an average of one ionization per atom at a dose of ~400 MGy.Dn, is calculated using the following equation:Jn is the energy deposited in voxel n, Vn is the voxel volume, and ρ is the sample density.After all photons have been simulated, the energy deposited in each voxel is divided by the ratio of the number of photons simulated compared with the number incident on the crystal in the actual experiment. The final dose absorbed by a voxel, 2.3The Monte Carlo simulations in RADDOSE‐3D give several output dose metrics, including the average dose across all voxels in the crystal  and the average dose across all voxels that fall within the beam area . Also output is a “RADDOSE‐3D style” dose calculated analytically from the relevant equations, where all energy from the photoelectric and Compton effects remains in the sample. The Monte Carlo “RADDOSE‐3D style” dose is routinely compared to the dose output by default RADDOSE‐3D calculations  which do not use Monte Carlo methods. If there is a significant difference, a warning is displayed and the user is advised to increase the number of photons that are being simulated in the Monte Carlo procedure. The average diffraction weighted dose (DWD)33.1A systematic analysis was performed to determine in what situations the RADDOSE‐3D Monte Carlo simulations should be used to calculate dose to provide a more accurate value than the default RADDOSE‐3D calculations.The size of sample in which photoelectron escape from the crystal becomes significant was tested in the absence of surrounding material Figure .Einc = 12 keV. The higher the beam energy, the greater the reduction in dose when including photoelectron escape, since the photoelectrons will have on average higher energy and hence travel further. For Einc = 30 keV, there is a 20% reduction in dose for crystals smaller than 50 μm. Figure Einc, with an ~81% reduction in dose on simulating photoelectron escape for a 1 μm crystal at Einc = 12 keV compared to a ~ 91% reduction in dose for Einc = 20 keV compared with the no escape case. The potential advantages of using higher beam energies in MX for microcrystals is explored further in Dickerson and Garman.The larger the crystal, the lower the chance of photoelectron escape, with a greater than 20% reduction in dose for crystals smaller than 10 μm and an A similar trend will be seen for microbeams that are smaller than the crystal, since photoelectrons will escape the irradiated volume. However, in spite of the reduction in dose for microbeams Figure , the dif3.2Einc = 12 keV, and 8.7 μm for Einc = 20 keV  will depend largely on: crystal size, beam size, beam energy, and the size and composition of the surrounding material. The smaller the size of the crystal or the beam, the bigger the difference between the dose values. This difference gets larger as beam energy increases, and at what crystal size this becomes significant for different beam energies can be obtained from the results shown in Figure However, the difference between the doses will be reduced from the values displayed in Figure 4.2Einc will also be abolished. The increase in background and rate of radiation damage progression will be particularly detrimental to microcrystals, so it is important to reduce the negative effects of the surrounding material as much as possible.As well as increasing the level of background, irradiation of the surrounding material can greatly increase the dose and thus the rate of radiation damage progression as a result of ionizations from photoelectrons produced in the surrounding. If a large enough volume of surrounding material is irradiated, the advantage of increased photoelectron escape when using higher Einc can also be realised, since the thickness of the irradiated surrounding material is more likely to be less than the photoelectron range in this case. There will thus be a greater number of photoelectrons escaping rather than entering the sample.If heavy atoms with a large photoelectric cross section are present in the mother liquor, backsoaking should be attempted to remove as many of these as possible to reduce the number of photoelectrons entering from the surroundings. An oil based cryoprotectant may also be beneficial, provided that it is composed of elements with a lower atomic number than oxygen. The next step would be to match the beam size to the crystal to ensure that the entire crystal is irradiated but as little surrounding material as possible is. However, the surrounding material in front and behind the crystal will still be irradiated, and also material around the sides of the crystal will be in the tails of the Gaussian intensity profile of the beam. It is most important to minimize irradiation of the surrounding material around the sides of the sample in the axis of the horizontal beam polarization vector, since photoelectron emission is biased along this axis so these photoelectrons will have the highest probability of entering the sample. If the beam size can be well matched to the crystal size, the increased photoelectron escape from using higher The ideal strategy to minimizing the negative effects of the surrounding material would be to reduce the thickness of it as much as possible without dehydrating the crystal. Sample grids have been designed to remove as much mother liquor as possible4.3y axis, the “AngleP” input to alter the angle about the z‐axis, and “AngleL” to change the angle about the x‐axis. If the crystal is not necessarily centered in the middle of a Gaussian beam, the “startoffset” input can be used to translate the crystal off center.Given the large differences in calculated doses between different crystal orientations shown in Table As highlighted in Section 4.4et al.,Einc of ~26 keV, with a decrease in diffraction efficiency occurring as Einc is increased beyond this due to a drop in the detection efficiency of a CdTe detector and the increasing Compton scattering cross section. The present study explored the effect further, with a focus on the effect of the surrounding material. It is clear from the results presented in Figures As discussed in Dickerson 5https://github.com/GarmanGroup/RADDOSE-3D/releases.An extension to RADDOSE‐3D has been presented which allows users to perform Monte Carlo simulations that take into account the redistribution of photoelectrons and Compton recoil electrons from the crystal and surrounding material. This provides more accurate dose estimates for MX experiments conducted with microbeams and/or microcrystals than were previously available. The simulation results highlight the important role that the surrounding material can play in the dose absorbed by the crystal and thus in the rate of radiation damage progression. It is vitally important to reduce irradiation of the surrounding material to increase the diffraction lifetime of microcrystals in order to realise the benefits of higher incident X‐ray beam energies. Lastly, if orientation can be controlled, the Monte Carlo simulations can allow identification of the best crystal orientation to maximize diffraction efficiency. The program is freely available and can be found at In summary, the new RADDOSE‐3D MC simulation capability of the program allows researchers to estimate absorbed doses for their planned experimental parameters prior to the X‐ray irradiation of their crystals. Data collection conditions can then be adjusted in order to optimize the data quality when using microbeams and microcrystals.Joshua Dickerson: Conceptualization; investigation; methodology; software; writing‐original draft; writing‐review and editing. Elspeth Garman: Conceptualization; funding acquisition; investigation; project administration; software; supervision; writing‐original draft; writing‐review and editing.Data S1 Supporting informationClick here for additional data file.