The paper deals with the gas-phase formation of nanoparticles that is a fundamental process responsible for the condensed matter in the Universe, which also attracts attention due to its involvement in the particle synthesis for various nanotechnology applications. Previously reported results on MgO nano-oxides formed by Mg combustion showed a unique phenomenon coined “the condensation stagnation” that is the occurrence of critical clusters with suppressed growth. Here we focus on the effect of an external ionizer on this condensation growth stagnation. We show that the condensation stagnation occurring in the Mg particle flame subjected to a positive ion flux is similar to that in the unaffected flame. In contrast, applying negative charging significantly influences the state of stagnation of the system, i.e., no critical clusters are observed in the products sampled from the flame. The discovered critical behavior of the state of stagnation is explained in terms of the heat transfer between the condensed MgO nanoparticles and the surrounding gas, which efficiency depends on the sign of the nanoparticle charge. This dependence of the heat transfer efficiency on the nanoparticle charge is a new fundamental effect that should become the basis for accurate modeling in two-phase high-temperature systems. For research purposes, the process can be realized in metal-containing flames6. The formed gaseous metal suboxides further condense generating nano-oxides7. It is also the basis of flame synthesis of various nanomaterials.Being a fundamental process responsible for creating the condensed matter in the Universe8. Due to that high value of the released energy, its dissipation, which is an essential condition of the nanoparticle growth, is a complex process that can lead to critical phenomena in the evolution of generated particulate10. The energy dissipation mechanisms have not be paid enough attention in the current models describing nanoparticle formation. Historically, it is related to the apparently obvious assumption on the thermal equilibrium between generated nanoparticles and the bath gas, which implies the efficient heat transfer on the particle-gas interface11. On the contrary, an inefficient heat transfer can lead to a critical nanoparticle evolution during formation that is caused by an impossibility to dissipate the condensation energy. Thus, the question on heat transfer on the nanoparticle-gas interface is a key issue for fundamental understanding of the particulate generation from a gas.The distinctive peculiarity of the process is a relatively high energy release corresponding to the transition from a gaseous to a condensed (liquid or solid) state that is on the order of 5 eV per condensing molecule13. It allows for ruling out the formation mechanisms alternative to condensation such as coalescence, which is historically considered as the leading growth process11.It should be emphasized that the Mg flame is probably an optimal system to study the gas-phase formation of nano-oxides. The MgO nanoparticles formed as a result of combustion have the perfect cubical shape that evidences the solid state of material during the growth13. Two groups of particles occur in combustion products, specifically, mature cubes of sizes distributed around 30–40 nm, and shapeless particles of about 6–7 nm typical size. We call those shapeless particles “critical clusters”. There is the distinctive gap between the particle size distributions, i.e., there are no particles in the size range between ~ 10 nm and ~ 15 nm. The phenomenon is based on peculiarities of the heat transfer between the MgO nanoparticle and bath gas. At a low energy accommodation coefficient (EAC)15 describing efficiency of the heat transfer between the condensed body and the gas in the free-molecular regime, the nanoparticle becomes thermally isolated from the gas. Then, overheating of a critical cluster upon an arrival of a single oxide molecule leads to a loss of saturation conditions required to sustain condensation13. It is this overheating that does not allow for the condensation growth of critical cluster resulting in their stagnation.In our previous work, analyzing products sampled from a single Mg particle flame we discovered and addressed a unique phenomenon coined “the condensation stagnation”15 provide a guidance on the sought stagnation control. The nanoparticle charging can be a mechanism of the stagnation switch.Based on understanding of its mechanism that is related to heat transfer, the stagnation can be controlled by varying the EAC. In particular, a significant increase of the EAC should lead to the stagnation disappearance due to the loss of stagnation conditions related to the thermal isolation of critical clusters. The recent author’s results on the EAC comprehension15, and, therefore, to the loss of the thermal isolation at these conditions. This demonstration of the condensation stagnation control by varying the EAC is a key step in creating the accurate approach to the description of the gas-phase nanoparticle formation.In this paper we focus on the effect of external charging on the condensation stagnation and demonstrate the phenomenon control depending on the sign of charging. In particular, it is shown that the stagnation disappears under negative charging that corresponds to the theory prediction on the EAC increase3. Also, the stagnation reduction would result in the condensation enhancement. That enhanced condensation is, in particular, preferred in propulsion applications in order to shift the process from the nozzle toward the combustor, which leads to increase of the condensation energy contribution to the system thrust16.Besides the fundamental importance for comprehending nanoparticle condensation growth in high-temperature systems, the discovered stagnation-related phenomena have practical implications. A possibility to affect that condensation stagnation would undoubtedly allow for controlling the final properties of generated particulate such as the PSD, which is beneficial for a fine tailoring of nanoparticle morphology in the flame synthesis17 is not applicable. The problem is related to the flame disturbance and the relatively long residence time of the probe during this type of sampling. It could be solved by using an alternative sampling procedure that allows for a short transmission electron microscope (TEM) grid residence time in the flame. If this were the case, the TEM images would represent the actual particle morphology, which is not affected by the above-mentioned drawbacks. Our previously developed experimental setup was proposed for instant deposit of particles on a TEM grid for following visualization on high resolution TEM13.The purpose of the experiments was to obtain a snapshot of the flame-generated particulates that could be further examined. In order to get reliable data, the traditional approach to the nanoparticle sampling using a thermophoretic probe18 was utilized) are presented in Fig. The typical TEM images of samplescollected from a neutral Mg flame , as well as flames exposed to “positive” or “negative” external charging (a home-made ionizerN)≡(ΔN)1/2.The PSD of the particulates collected from the negative flame is presented in Fig. Besides mature cubical particles of tens of nanometers size, sub-ten nanometer-size clusters are present in the neutral and positive flames. Those clusters do not exist in the negative flame;The 10–15 nm size particles are present in the negative flame instead of sub-ten nanometer-size clusters that occur in the neutral and positive flames.The following observations can be summarized for further analysis:13. The phenomenon was coined “stagnation of condensation growth”. Its mechanism is based on the thermal isolation of clusters from the bath gas. That thermal isolation is possible at a low EAC, which is an efficiency of conductive heat transfer on the interface, i.e., the ratio of the energy transferred upon a single molecule collision to that at the full accommodation. Note that in literature two terms are used for the same, namely, the energy accommodation coefficient and the thermal accommodation coefficient19. In our opinion, since it deals with energy, the term “energy accommodation coefficient” is more accurate to describe the process. Under thermal isolation of clusters with the bath gas, the arrival of a single condensing oxide molecule leads to the cluster overheating. At the elevated cluster temperature, the surrounding oxide vapor becomes non-saturated being supersaturated before that single condensation event. As a result, the condensation growth of clusters is impossible and the stagnation occurs.The occurrence of shapeless sub-ten nanometer-size clusters in the neutral Mg particle flame has previously been discovered and addressed13); this Figure is also reproduced in Methods section). This explanation requires fundamental understanding of reasons why and how the EAC increases in the negative flame and is likely unaffected in the positive flame.Based on the previous knowledge, it is natural to assume that the disappearance of sub-ten nanometer-size clusters in the negative flame originates from the loss of conditions that sustain their occurrence in the neutral flame. That loss of conditions can be possible if the EAC increases reducing the thermal isolation of clusters from the bath gas. In its turn, this enables a single condensation event without the significant cluster overheating that would have led to the loss of supersaturation. Upon the loss of conditions sustaining the stagnation, clusters can further grow via condensation, which results in the appearance of particles of 10–15 nm size. Those particles are seen in the negative flame only. Then, it is natural to assume that they originate from grown clusters, which stagnate in the neutral and positive flames. It should be noted that the size of these 10–15 nm particles being grown from stagnating clusters fills the gap in the PSD resulting from the condensation stagnation in the neutral flame see Fig. 13; this 15. Two mechanisms contribute to the EAC, specifically, the phonon-related channel that occurs in any material, and the electron-related channel, which is possible only in relatively-high electrically conductive materials. The phonon-related component of the EAC is vanishing at high temperatures14, and it is the charge independent. The electron-related component occurring only in metal-like materials is sensitive to the particle charge15. At the positive nanoparticle charge, the Coulomb blockage prevents electron from reaching the particle surface that suppresses the electron-related component of the EAC. At the negative nanoparticle charge, the effect is absent resulting in the high EAC.Our explanation is based on the recent EAC conceptA relatively high energy is released during nano-oxide condensation from the gas phase, which in the case of MgO:8 that is the equivalent of 6.8 eV per condensing molecule resulting in unusual nanoparticle properties revealed in our previous work. In particular, it was demonstrated that nano-oxide particles formed in the flame exhibit metal-like electrical conductivity20. The latter justifies a need to consider the electron-related component of the EAC along with the phonon-related component while describing the nano-oxide heat transfer with the bath gas. Note that within the flame nano-oxides acquire positive charge due to the thermionic emission of electrons caused by the high particle temperature. Then, the effective EAC, α, can be expressed asαph, and the electron component, αel, depends on the positive charge, Z, as15µ that controls the electron component of the EAC ke and kB being the electrostatic and Boltzmann constants, accordingly, and e being the elementary charge. Thus, the EAC depends on the acquired charge asis about 660 kJ/molαph = 0.003, and at µ = 3 that is estimated at the cluster size D ~ 6 nm and the particle temperature T ~ 2500 K. The boundary between the EAC areas where either the condensation growth (higher EAC) or the condensation stagnation (lower EAC) occurs is also plotted for convenience. For illustrative purposes the boundary is shown at α = 0.004. Following the Fig. Z ≥ 6 should stagnate.Figure N- and ΔN0 are the number of clusters that carry the charge providing the EAC value favorable for stagnation in the negative and neutral flames, correspondingly. Assuming that external charging reduces the particle charge by 1, one can conclude that if the clusters with the charge Z ≥ Z* stagnate in the neutral flame then the clusters carrying the charge Z ≥ (Z* + 1) before the ion exposure would stagnate in the negative flame. Thus,N(Z) being the function describing the particle charge distribution.Like other physical values, the particle charge magnitude also has some distribution. Then, one can estimate the effect of external negative charging on stagnation. It can be quantified as the ratio of the number of clusters that stagnate in the negative and neutral flames.η < 1, which is generally means the stagnation reduction. Using the Gaussian distribution of the particle charge σ = 1, at Z* = 6 one can get η = 0.08. This low value of η can be interpreted as the stagnation suppression/disappearance corresponding to the above observations. Thus, this illustrative estimate supports a feasibility of the effect of external negative charging on the EAC, which is required to explain the experiment.From Eq. , one can2 in Eq.  and choov being the convection velocity and L being the flame diameter.In order to analyze an ionizer capability to affect the charge balance of the flame, the ionizer current, n, and the flame volume occupied, V ≡ πL3/6, asThe flame-associated charge is related to the charge number density, Then,n = 1018 m−3, and L = 6 mm (twice the burning particle size) and v = 40 cm/s21, we obtain Ifl = 1.2 µA. Thus, the ionizer current is comparable with the flame current, which means that the ionizer is capable of affecting the flame charge balance.At the typical flame charge number density The major finding of the current work is the demonstration of a disappearance of the condensation growth stagnation in the Mg particle flame exposed to external negative charging. The phenomenon can be explained based on the strong variation of the energy accommodation coefficient depending on the nanoparticle charge. This EAC control is a fundamental result that needs a close attention while modeling two-phase systems at high temperatures. The effect of the EAC variation is expected to be strong in metal-containing flames due to a high energy release during metal combustion that sustains high temperatures within the system. The thermionic emission of electrons from nano-oxides formed in the system is an internal mechanism of the EAC change. The EAC can be also affected by external charging. The condensation stagnation phenomenon and the possibility to control it can have important implications in high-temperature particulate-generating systems when a fine tailoring of particle morphology is essential.22 that controls the rate of nano-oxide growth is also of general nature. Then, the results could be extrapolated from Mg to other metals. A Mg flame studied in this paper is just an optimal system allowing for separating effects relevant to condensation, which is possible due to the perfect cubical shape of MgO nanoparticles.It should be emphasized that the relatively high energy release during nano-oxide condensation, which is the reason for phenomena considered in this work, is general for metal-containing flames. Furthermore, the condensation energy dissipation by condense-luminescence17 is not applicable. The issue is related to the flame disturbance and the relatively long residence time of the probe during sampling. The problem can be overcome utilizing the sampling that allows for a short TEM grid residence time. If the residence time of the grid within the flame is short enough, TEM images would represent the actual particulate morphology that is not affected by the flame disturbance due to the sampling.The goal of experiments was to obtain a snapshot of the flame-generated particulate that could be further examined. The common approach is to deposit particles on a transmission electron microscope (TEM) grid, and then, to analyze it with TEM. In order to get reliable data, the traditional approach to nanoparticle sampling using a thermophoretic probe12 in all experiments. The sampling was performed in about 2 s after ignition to ensure flame stabilization. MgO nanoparticles were sampled from the unaffected flame and from the flame, to which the unipolar emission of ions was applied. Both the negative and positive ion emission were utilized and generated by a home-made ionizer capable of producing both positive and negative ions18. The distance from the ionizer to the burning particle was about 5 cm. The whole set-up was placed on a metal plate that was electrically grounded. Five sampling runs were performed for each charging scenario to confirm repeatability of the nanoparticle morphology peculiarities.The experimental set-up is sketched in Fig. The MgO nanoparticles deposited on the grids were analyzed with TEM . Obtained TEM images were visually examined. The specimen collected from the negative flame was processed to obtain the PSD. That PSD illustrates the nanoparticle morphology difference concluded from TEM images with naked eyes.N/N in the PSD Figure. The PSD of the particulate collected from the negative flame was compared with the PSD reported for the neutral flame  that is also shown in Fig. In order to obtain the PSD, logarithms of nanoparticles sizes measured from TEM images were combined in a single-column database and further processed using the built-in Origin “Frequency Count” procedure to separate data among bins. The bin size of 0.1  was chosen in order to get 10 size intervals contributing to the PSD. A number of particles in a given bin was divided by the total number of particles, which is reported as Δsee Fig. 13 that i


Emergency Department (ED) boarding is related to in-hospital patients' discharge since no beds will be available for receiving ED patients if there is a delay for patients in the yard leaving the hospital. New techniques implemented in hospital institutions, such as digital signatures to facilitate clerical work improve these processes. We evaluated the impact of expediting patients' discharge after medical orders with the number of patients with an unplanned hospital admission from the Hospital Out Clinic directed to ED for waiting for an available bed in a public tertiary hospital in Brazil.We conducted a quasi-experimental study before and after an intervention. It consisted of an encrypted digital signature to reduce clerical work and expedite the patient's release from the institution after medical discharge. We used an interrupted time-series analysis based on administrative data  from 2011 to 2020.We enrolled 210,496 patients admitted to the hospital from January 2011 to December 2020. Of those, 69,897(33%) composed the group after the intervention. There was no difference between the groups' gender, age distribution, the proportion of surgical patients, or in-hospital stay (≤ 7 or > 7 days). The interrupted time series analysis for the time from medical order to effectively hospital discharge showed an immediate change in level , but no a difference in the slope of the behavior of the post-intervention curve . For the number of patients directed to ED, we observed no immediate change in level , but a difference in the slope of the behavior of the post-intervention curve .Reducing clerical work and expediting patient discharge was associated with decreased potential boarders to ED. Emergency Department (ED) boarding is the output of several processes . One of Our institution is a Brazilian tertiary referral public teaching hospital with 4,5 million inhabitants in the northeast region of Sao Paulo. We have around 3,000 external visits (HOC) every weekday, and a common problem is unscheduled hospital admission directed to ED [Our hospital implemented an in-house developed Electronic Health Record (EHR), which continuously actualizes and allows us to customize processes. We use business process management (BPM) tools to implement any change in the EHR and succeed in other areas of study as sepse . We receOtherwise, the patient discharge after medical order was the final step and took seven hours on average, mainly due to clerical work required by Brazilian regulations. We hypothesized that reducing this time would be possible using a nurse encrypted digital signature (DS) as a trigger in the EHR to allow safe discharge. Changing this step would also imply more beds available sooner, avoiding directing patients to ED. Besides, it would interrupt several procedures, such as sending meals and medications to the floor after the patient has left the hospital.We conducted a quasi-experimental study before and after an intervention to expedite the patient's release from the institution after medical discharge. We performed the analysis based on administrative data  from January 1, 2011, to December 31, 2020.We used interrupted time-series analysis (ITS) to evaluate the discharge process intervention after medical order in the direction of unscheduled hospital admissions to the ED. We extract the data from our Electronic Medical Record (EMR), provided by the Clinical Hospital of the Ribeirão Preto Medical School of the University of São Paulo – Brazil. Our institution's Ethical Committee approved the study .We designed the BPM current process before Fig. A and aftWe chose to include data from 2011 onwards since our EHR started to function that year. We performed a sensitivity analysis reducing the time for three years prior to and post-intervention, and there was no change in the results. Based on that and the consistency of the nurse discharge process after the medical order in our institution, we decided to keep the baseline results. We excluded data after December 31, 2020, due to our institution's COVID-19 (2019 Novel Coronavirus Disease) pandemic changes . Our modWe also recorded the average number of daily patients directed to ED by month during the same study time and treated them equally with ITS to evaluate the intervention's impact.p-values in β2 to indicate an immediate treatment effect and in β3 to demonstrate a treatment effect over time. Comparing groups follows the same rules but adds interaction terms [We used the itsa command routine in Stata 15 ® to conduct ITS analysis and construct graphics. The itsa command routine estimates the parameters according to equation Y_t = β_0 + β_1 T + β_2 X_t + β_3 TX_t + _t. It measured the aggregated outcome variable at each equally spaced time point t, T is the time since the start of the study, Xt is an indicator variable representing the intervention, and TX_t is an interaction term. In a single-group study, β0 represents the intercept or starting level of the outcome variable. β1 is the slope or trajectory of the outcome variable until the introduction of the intervention. β2 represents the change in the outcome level immediately following the intervention's introduction. β3 represents the difference between preintervention and postintervention slopes of the outcome. Thus we look for significant on terms .We enrolled 210,496 patients admitted to the hospital from January 2011 to December 2020. Of those, 69,897 (33%) patients composed the group after the intervention. There was no difference between the groups' gender, age distribution, the proportion of surgical patients, or in-hospital stay (≤ 7 or > 7 days). The interrupted time series analysis for the time from medical order to virtually hospital discharge showed an immediate change in level , but not a difference in the slope of the behavior of the post-intervention curve . There was no change in the results with stratification by in-hospital stay , but a difference in the slope of the behavior of the post-intervention curve  – Fig. In histograms, we presented the number of patients discharged by the day's hour Fig. . Before We showed that a simple intervention based on an encrypted digital signature as a trigger of events in an EHR could reduce the time to discharge after medical order around a median of 3.6 h by reducing clerical work. Furthermore, this change was immediate and sustained after the intervention. Finally, we showed a slight and persistent decrease in the number of patients directed to ED waiting for an available bed on the floor.Reducing boarders in the ED is a complex problem. The phenomenon is associated with several factors and different intensities depending on the institution studied. Therefore, identifying the leading causes to solve is paramount. In our institution, unscheduled hospital admission indicated to HOC patients was a workable BPM evaluation problem. According to Brazilian legislation regarding clerical work, the main problem was guaranteeing safe discharge after medical order . Before www.advisory.com) estimated a reduction of 0.25 days in the duration of hospital stay for an institution of 600 beds would provide an extra 25 beds available in 24 h. This amount of beds is what we should expect to accommodate the unscheduled hospital admissions of HOC patients.This intervention had an immediate and sustained effect, reducing 3.6 h of hospital bed turnover. In 2013, The Advisory Board Company  to direct patients to the ED instead of looking for an available bed on the floor. Second, each specialty has some designated beds for their use in our institution. The "new" bed that the intervention provided was not always that of the specialty for patients needing in-hospital admission. We corrected that by implementing an allocation beds central dashboard and guaranteed the teams that "their" beds would be available again in 24 h if they "lend" them to the specialty in need . Third, Besides the impact of expediting the patient discharge, another positive result was that the patients left the hospital earlier (15h00 to 16h00). Our patients come from small cities and depend on working-hour public transportation to go home after discharge. If the discharge occurs after 17h00, there is no public transportation, the patient has to remain in the hospital up to the following day, and we could not use the bed for another patient waiting in the ED. This problem is aggravated during weekends when this public transportation is not available. We attributed the bimodal distribution before the intervention to the nurse duty shift. The shift to the left and assuming a normal time delay distribution for the patients leaving the hospital after the intervention corroborates this. Other authors showed similar findings , 11.Our approach to avoiding boarder by expediting discharge after medical order is a pull forward strategy since the available bed will "pull" patients waiting for admission to the floor. Another process recently reviewed in the literature is a push-forward one, represented by the transfer of patients to a lounge on the floor, so the floor staff will try to guarantee an available bed . This puOur findings follow others. Mustafa et al. 2016) demonstrated that the patients directed to ED from HOC due to an unscheduled admission have a solid association with crowding. Furthermore, patients with delayed discharge occupied up to 15% of the hospital beds 016 demon. Silva eIdeally, following patients' flow in a thorough output model, including more active measures such as a dedicated person, would bring more immediate results in reducing boarders . Neverth


To the Editor:The author reported no conflicts of interest.Journal policy requires editors and reviewers to disclose conflicts of interest and to decline handling or reviewing manuscripts for which they may have a conflict of interest. The editors and reviewers of this article have no conflicts of interest.The It is estimated that less than 5% of patients with small cell lung cancer (SCLC) are amenable to surgical resection.In the staging system introduced by the Veterans Administration Lung Cancer Study Group, limited SCLC was originally defined as tumor volume that could be treated within a tolerable single radiotherapy port.P < .001).P = .07). Stratification of patients according to specific pN stage in the study by Zhou and colleaguesInterestingly, Zhou and colleaguesThe authors are to be congratulated for analyzing a relatively large cohort of patients undergoing surgery for SCLC, and their study contributes to the existing body of evidence on the surgical management of this disease. Prospective trials are anticipated to answer many of the remaining questions regarding the role of adjuvant therapies in resected SCLC.


Homo erectus to gradual change in their habitat has been developed and studied. The core assumptions of the hypothesis and the model scenario are the pre-adaptation of our ancestors to occupy the ecological niche that they constructed for themselves by having evolved displaced communication and a rudimentary tool manufacture, two features allowing them to use a new, concentrated and abundant resource—megafauna carrion—on the savannahs replacing arboreal habitats owing to the drying climate of East Africa at about 2 Ma. The shift in diet required coordinated cooperation by the hominin scavengers confronted with concurrent predators. Power scavenging compelled displaced symbolic communication featuring a limited semantic range; syntax was not yet required. We show that phenotypic evolution on the accuracy of information transfer between cooperating hominins is a necessary and sufficient condition for the population of agents to survive the diet shift. Both the individual and the group fitness of the hominin horde increased with the accuracy of their protolanguage, with decreasing time allocated to foraging and thus more time left for culture.A dynamic model and an agent-based simulation model implementing the assumptions of the confrontational scavenging hypothesis on early protolanguage as an adaptive response of This article is part of the theme issue ‘Human socio-cultural evolution in light of evolutionary transitions’. Note that the default value of the maximum amount of meat carried per head (Emeatmax = 1000) is half of the maximum energy reserve of an adult agent (Emax = 2000—see §2b); so the maximum meat load represents about a quarter of the body mass of an agent .The model follows the fate of a single simulated horde on the individual level over several thousand generations on an hourly basis. There are two types of hominin agents in the model: ‘adults’ and ‘children’. For the sake of simplicity, genders are not specified in either group. At any point in time the current number of adult individuals is denoted by ferences . Worthy ferences . More spferences . The fooamp site . Also, aamp site . We assu (b)H. erectus  they bring into the campsite in the evening, and from the remaining food (if any) the next morning—see details later. The daily energy use of an idle agent is Edaily = 100 units day−1 for adults and half of that for children. There is an energy cost for movement: Emove = 1 unit per 12 min of walk . Communication has a small daily cost as well: Ecomm = κC units day−1, where C is communication ability (0 ≤ C ≤ 1) and κ = 10 is a factor of proportionality.For the sake of simplicity, harvested and consumed resources, as well as the actual energy reserves of each individual, are measured in arbitrary units of energy. Also, the actual values of the model parameters of energy intake and utilization are arbitrary, but consistent with the results of studies estimating the corresponding data for us e.g. –32). The. TheH. eFC, and that of bushes is FB = 1500, the difference representing the higher energy density of carrion. The default value is FC = 100; this is one of the main parameters of the model. Berries on a bush are replenished at a rate of R = 300 units day−1. The maximum energy content of a bush is 300 units, while the energy content of carcasses follows a uniform distribution drawn from the range  units, representing 30 to 45 times the energy reserve of an adult hominin, i.e. the energy content of a carcass is 15 to 22.5 times larger than that of a hominin agent. This amounts to a mass of meat of about 1 metric tonne per carcass, which is consistent with the size range of mega-mammalian species  interval. This choice guarantees that ρ = 0 only for Cspeaker = Clistener = 1, and it can be large if Cspeaker = 0 and/or Clistener = 0. The perceived distorted level of danger is The information on which the decision of the recruits on joining a team is based is the risk of death associated with the actual mission, the exact measure B). Bravery is determined by hunger: iC joins the team with probability iC. This makes group formation stochastic  and can result in groups of size less than optimal for the mission.A listener can join the butcher team being recruited if his perceived risk B). An unsuccessful scout (one unable to recruit even a single team member) gives up and becomes either available for recruitment or a scout.If more than one scout is successful in a single day (i.e. if more than one carcass was discovered the day before), multiple butchering teams are assembled one by one, starting with the best-communicating scout and followed by the others in order of communication skill. After the first team is complete, the next scout starts recruiting, etc. until there remain no more applicants or scouts. A butcher team assembled in the morning sticks together until it returns to the campsite in the evening. The rest of the horde (those not in butchering teams) remain at the campsite or leave for gathering/scouting, depending on their actual motivation level ( (d)N < Nmax) or they leave the horde . Adult individuals die with a constant E hits 0, which occurs to an initially well-fed agent failing to obtain food for 20 consecutive days) or to carnivore attacks while butchering and transporting meat to the campsite. The death rate from carnivore attacks is proportional to the distance s between the carcass and the campsite: C of the parent with an additive phenotypic mutation term drawn from a normal distribution with 0 mean and σ variance.Children at age 10 reach adulthood and either they join the active core of the horde if there is a place for them , but most of them are fixed by the spatio-temporal scale of the process or constrained into narrow ranges of values by straightforward assumptions. The rest of the fixed parameters regard the energy balance of the hominin population and the environment. These are basically set by an arbitrary scaling assumption, but they are canonical among each other: the arbitrary choice of value for one determines the feasible range of the others. The only evolving variable of the model is t = 20 000 and ends at t = 30 000 years in all simulations. These dates mark the borders between the three phases  of the simulated time period, which covers 120 000 years in most cases (200 000 if needed for reaching a stationary state). The shift in the availability of the two basic resources, i.e. the decrease in berry and the simultaneous increase in carrion supply, initiates an evolutionary change in the communication ability of the hominin agents, but the actual increase of communication skills starts later than the environmental change, and it also extends into the carrion phase. Communication approaches its maximum accuracy in a nearly uniform fashion across the horde  from the horde during the carrion phase. Electronic supplementary material, figure S1b shows that the average energy level corresponding to the average fitness of the individuals increases from 1170 units in the berry phase to about 1250 in the carrion phase. The approximately 10% increase is due to the energetically more favourable situation in the carrion phase (targeted exploitation of high-density food patches).The average energy obtained by a solitary (adult) agent in the berry phase is about 325 units per day, while the a shows the temporal change in the number of deaths per year due the two external mortality factors (starvation and predation). Deaths per year by starvation are more than three times more frequent in the berry phase (approx. 15) than in the carrion phase (approx. 4.5). Children's death rate due to starvation falls even lower in the carrion phase (b). This difference, along with increased resource availability and the consequential fertility gain in the carrion phase, is the primary selective force driving the transition. Even though the number of deaths due to the second external mortality factor—predation—is small, approx. 1.0), it represents a potent selective force towards the evolution of accurate communication. Misunderstanding the actual level of predation risk during butchering missions is detrimental in both directions: underestimating the risk results in extra predation mortality, whereas overestimating it induces unjustified cowardice preventing optimal team recruitment, and thus causes relative starvation. That is, the death rate due to predation is about half of the cost of selection for better communication skills within the horde (selection load). The other half of the selection load is the indirect loss of resource benefits due to cowardice, which is obviously effective but difficult to quantify.  and children (under 10 years) are 22 and 5 years, respectively. By taking only the natural mortality of rate Since mega-herbivore carcasses are relatively large compared with the transport capacity of the average butchering team , butchering teams rarely have sufficient personnel to carry all the meat available from a carcass back to the campsite. The average of team saturation  increases during the transition phase roughly in proportion to the average accuracy of communication within the horde, its average reaching about 80% at the stationary state  number of young adults  who are forced to leave the horde. Figure 3b). In the carrion phase less time is spent foraging. The remaining ‘free time’ could be allocated to developing new technologies, raising children, socializing and creating culture—all these activities indirectly contributing to both individual and group fitness. This effect is not yet considered so that the free time thus obtained is wasted in the model, but it is safe to assume that the obvious benefits of the time used to create culture could also significantly contribute to human evolution, including the perfection of language skills.An interesting aspect of the transition from berries to carrion as the main resource of the population is the change in the time that an average agent spends foraging. Considering ‘laggards’ with zero foraging time and also the smaller number of steps taken by butchers compared with gatherers, the difference is substantial between the two phases b. In theElectronic supplementary material, movies S1, S2 and S3 visualize the activities of the horde in the two phases. Electronic supplementary material, movie S1 animates 15 days in the berry phase, zoomed on the neighbourhood of the campsite. Electronic supplementary material, movie S2 follows 50 days in the carrion phase; electronic supplementary material, movie S3 is a zoomed slow-motion version of electronic supplementary material, movie S2, with relevant events marked.The standard parameter set was assembled with feasible spatial and temporal scales of the Palaeolithic scene in mind so that the foraging range and the demographic features like the average lifespan and the birth rate in the hominin horde take reasonable values. The parameters characterizing the environment  and the time scale of the evolutionary acquisition of protolanguage skills (the phenotypic mutation rate of the accuracy of communication) are relevant and potentially important with respect to the focal question: at what values of these parameters can we expect sustained persistence of the population with displaced communication evolving and spreading under changing ecological conditions?τ) is indifferent as long as it exceeds a reasonable minimum allowing communication skills to evolve in the horde featuring a certain rate of phenotypic mutations (σ) . At σ = 0.005, the minimum of τ is about 20.000 years. The total supply of carrion biomass available within the spatial range of the horde (FCSC) has to exceed a minimum in order to keep the population viable during the transition and the carrion phases . Average carcass size SC  has a threefold effect on population growth through the chance of finding, butchering and transporting carcasses, as well as on protolanguage evolution through the need to organize all these activities . Electronic supplementary material, figure S3 shows that the main demographic effect of a successful diet shift is the spectacular increase in the steady-state number of children in the horde in the carrion phase. With the size of the horde at its limit, the surplus of juveniles all become emigrants, the number of whom is obviously related to the group fitness of the horde.Electronic supplementary material, §S1.3 gives a detailed description of the actual screening results, which we summarize here briefly. The length of the transition period  and Parasites  are always present, whereas Gatherers (G) are optional. Results are presented for groups of two and larger , with or without kin selection and/or special assortment of S by recruitment (assumed to depend on communication), with linear, convex and concave benefit functions. Relatedness and recruitment are always helpful, by either making invasion of the G–P community by S possible, or at least enlarging the basin of attraction for S (with a mutant tail of P). Note that, unlike the simulations above, these models account for infinitely many groups of limited size. Naturally, diminishing fruits and more frequent carcasses help the transition to S.. 4Homo erectus, using both Oldowan and Acheulean technology, had the means and the tools [The question that this model attempted to answer is a simple yes-or-no one: could the acquisition of a rudimentary form of displaced symbolic communication support the persistence of early hominin populations amidst the drastic climate change driving them from their previous arboreal habitat out to the savannah? Our answer to the question is a definite ‘yes’, provided that the assumptions behind the model scenario are correct. Did a large herbivore megafauna indeed spread over African savannahs  while fohe tools  at theirhe tools ,38 that H. erectus [H. erectus is at least about 2.04–1.95 million years old, and that the analysis of other hominin species there and the high taxonomic diversity of also non-hominin species indicate a period of climatic variability [H. erectus faced challenges that exerted strong selection pressure on the population. A recent review explains: ‘Among primates, terrestrial species live in larger groups with more and bigger males than arboreal taxa, particularly those that live in savannah versus forest environments; they also have higher frequencies of counter-attacks against known predators, and if this pattern was followed as early hominins became committed bipeds, it could have led to confrontational scavenging. If hominins discovered carcasses in different locations on the landscape than where the stone needed to process those carcasses were located, as well as in different locations than preferred plant foods, scavenging may have selected for characteristics including social cooperation, planning depth and detailed mental mapping’ [According to the confrontational scavenging hypothesis, protolinguistic ‘Adam’ belonged to  erectus . It is riability . In thismapping’ , p. 11.H. erectus at about 1.9 Ma [H. erectus use increased energy, reduce its chewing efficiency, and sleep safely on the ground without fire? Second, How could a cooked diet have been introduced to a raw-foodist, mid-Pleistocene Homo without having major effects on its evolutionary biology? Satisfactory answers to these questions will do much to resolve the tension between archaeological and biological evidence [Confrontational scavenging must have been intermingled with primary scavenging, hunting of smaller prey, and gathering . A long-t 1.9 Ma , which nt 1.9 Ma . A problevidence . Confronevidence ).c). Abundant and easily accessible meat supplies make it possible for quite inaccurately communicating agents to survive, either as laggards avoiding participation in butchering missions altogether and subsisting on the leftover which is still abundant, or as members of small teams of butchers running a low risk in butchering and transporting meat because many small carcasses are easy to find close to the campsite. Accurate communication is not indispensable for the ecological success of the horde in times of easy access to resources. On the other hand, the accuracy of communication (C) evolves to its highest possible equilibrium value on the verge of extinction, when reliable information about concentrated resource patches (carcasses) and the cooperative actions of large teams are vital to avoid starvation. This explains why the most severe resource shortage that the population can still survive results in the highest accuracy of communication, especially when the scarce food supply is distributed in a few large lumps. The reason for surviving these unfavourable conditions and avoiding famine is precisely the communication skill evolved. The cross-check for the role of displaced communication in population survival is that not allowing C to mutate above 10% of its maximum value during the simulations inevitably leads to extinction during the transition phase.It is striking how tightly the survival of the population in our model is dependent on the agents evolving communication skills to perfection under any reasonable ecological condition . The onla), which results in lower death rates due to starvation, but this improvement is partly compensated by the increase in deaths due to predation during scavenging missions. The latter is the selection load of evolving accurate communication —a possible large-scale side effect of which could have been the first out-of-Africa dispersal wave of H. erectus [All the variables related to individual and group fitness are improved by the time the transition from the berry to the carrion phase is completed. Of course, this model does not consider any effect of the resource shift on the basic fitness of the hominins due to direct changes in diet or indirect ones through extra time becoming available for manufacturing tools and for cultural activities—factors with likely significant positive effects on life expectancy. What improves with the transition in the model is the average energy level of the agents (electronic supplementary material, figure S1nication . Improve erectus .A different agent-based model  found thH. erectus and ensure the survival of its populations, with vocalizations slowly taking over [Finally, we make a few remarks on the supposed structure of a minimal protolanguage ,49 that ing over . The fining over  that a sing over  with coming over .


Open source in-situ environmental sensor hardware continues to expand across the globe for a variety of applications. Sensor-management systems typically perform three fundamental tasks: sample sensors at a specified time or period, save data onto retrievable media, and switch power to components on and off in between sample cycles to conserve battery energy and increase field operation time. These tasks are commonly accomplished through integrating separate off-the-shelf components into the desired system such as: power relays, SD card hardware, Real-Time Clocks (RTCs), and coin cell batteries. To enable faster prototyping, the Openly Published Environmental Sensing Lab abstracted all of these requirements into a single printed circuit board (PCB), Hypnos, that can be included in any project to achieve these commonly-required capabilities: powering on and off connected sensors on a schedule and logging collected data to the removable SD card. The hardware is laid out in a “Feather” form factor, a popular configuration in the open-source hardware community, to easily mate with other industry standard products. The onboard RTC acts as an alarm clock that wakes a user-attached microprocessor from low-power sleep modes in between sample cycles. By integrating all these components into a single PCB, we save cost while significantly reducing physical system size. The design as well as a suite of code functions that enable the user to configure all the Hypnos board features are detailed. In situ sensor systems translate environmental phenomena at their field site into data that are logged to media (often SD card) to be retrieved for research. Although the variety of what can be measured and for what purposes vary widely, there are certain tasks that are common among a majority of these different projects. Such examples are high-precision time keeping for scheduling sample cycles, saving collected data, as well as powering sensors on and off to conserve power. These tasks are normally done through different hardware-- which commonly require three different pieces: real time clocks (RTCs), SD card memory for data storage, as well as power relays for turning peripherals on and off. While many microprocessors are capable of going into low-power sleep modes to conserve battery, the sensors and other system components may continue to draw current during down-time, wasting battery. Power management in sensor deployment is crucial for creating a long-lasting battery life since the energy resources are limited A project aimed toward creating a low cost environmental monitoring system is Smart Environmental Monitoring and Analysis Technologies (SEMAT) Another method that is commonly used to conserve power is using a power relay. The OPEnS lab has used Adafruit’s Mini Relay FeatherWing, which was designed with the Feather footprint to allow easy installation to circuits with other Feather boards. The problem with this is it uses a latching mechanism. Since this type of design has moving parts, the stability of the component may not last in the long term. There are other types of power relays that do not use a latching mechanism and instead use MOSFET circuitry such as power controllers from Sparkfun and DFRobot. Using MOSFETs provides more stability to the circuit because they do not rely on moving parts. However, these products typically do not use the Feather footprint design, making the integration more difficult than the FeatherWing Relay for Feather based systems.The goal when creating the Hypnos board was to be able to preserve power and extend battery life, as well as easily support existing sensor peripherals along with Adafruit’s Feather microcontroller. The Feather can be easily programmed through Arduino, a program that can connect hardware and software. The wide use of Arduino has also developed a variety of documentation and information available online. This key feature is the main reason why many electronic deployments use Arduino The Hypnos board provides all three of these common services  in one PCB. The Hypnos high-precision DS3231 RTC is used to track time in between sample cycles as well as queue an interrupt signal as a wake-up alarm for the microprocessor. A 3.3 V coin cell battery may be inserted to keep the RTC powered while the rest of the system is asleep, allowing the RTC to remain powered to send its interrupt signal and keep track of time. A μSD holder is built in so users are able to log their data to a long-term memory and retrieve it when convenient. Power can be turned on and off at the user’s discretion, allowing the user freedom to take measurements as needed. Using the Hypnos board allows users to condense their electronics to one PCB-- reducing the amount of required electronics-- rather than connecting three different types of hardware for the same purpose.The Hypnos board is a 2x1 inch PCB with two sets of pin connections, a real time clock, and a μSD chip holder. It is designed based on the Feather pinout specification made by Adafruit The Hypnos combines a DS3231 real-time clock with a μSD slot. Adafruit provides an Adalogger FeatherWing that is similar, but instead features a PCF8523, a less accurate RTC, and a μSD slot. For each least significant bit (LSB), the PCF8523 introduces an offset ranging from 4.069 ppm to 4.34 ppm based on the 32.768 kHz clock Adafruit’s website. If telemetry is desired, the user would attach the appropriate Feather hardware to the system and program it accordingly.The Hypnos board does not have a microprocessor, which is why the Hypnos is a peripheral for a microprocessor board. We use a Feather M0 (SAMD12) board in this particular application. The Hypnos provides power switching for the sensors and a medium for the SD card for data logging. The Feather M0 microprocessor turns the Hypnos’ power rails on and off via GPIO, sets the Hypnos DS3231 alarms via i2c, communicates with the sensors, and logs the data to the Hypnos’ SD card through SPI communication. The Hypnos DS3231 interrupt pin is attached to a GPIO pin on the Feather M0 to wake the microprocessor from sleep mode. The Feather M0 has built-in i2c, SPI, and GPIO pins that are designed to support communication protocols to attached sensors and the Hypnos peripherals. Many other microprocessors meet these requirements to use the Hypnos as well. Adafruit has a series of Feathers that accomplish additional tasks such as LoRa communication, WiFi connection, Bluetooth, and more that can be found on Because the Hypnos does not sample the sensors themselves , the range of sensors that can be supported by the Hypnos is limited by the attached microcontroller. The Hypnos i2c line also includes optional pull-up resistors that may increase performance since the Feather M0 does not come with attached resistors Wire library to interface with i2c devices with Arduino has no time-out functions and possible infinite while loop conditions. The Hypnos itself does not contain failsafe mechanisms in the case of an i2c or SPI bus crash during operation or sleep. However solutions to these kinds of issues are well known and documented. The Feather M0 (SAMD21), as well as many other microprocessors contain software watchdog timers that can be configured to reset and restore the system in these circumstances Serial bus hanging, memory overflow, and hard faults can cause embedded systems to crash. For example, a commonly used ●Low power control circuit designed for sensor package.●Combination of high-precision real-time clock DS3231 with μSD slot for data logging.●FeatherWing footprint for stackable modules.The Hypnos has the following overall capabilities:Hypnos V3.2.brd: The file that can be loaded into AutoDesk EAGLE to get the layout of the board.Hypnos V3.2.sch: The file that can be loaded into AutoDesk EAGLE to get the schematic of the circuit.Hypnos V3.2_Gerber.zip: The Hypnos PCB manufacturing files that can be loaded into Gerblook for free viewing of the Hypnos board Hypnos V3.2_centroid.csv: Pick-n-place file to send for PCB assembly services.* price will vary with source”. Additionally, there are other tools that are needed to build the Hypnos board such as a soldering iron, lead-free solder, solder paste, flux, a printed stencil of the Hypnos , as well as a reflow oven or heat gun. Alternatively, if a reflow oven is not available, a toaster oven can be used in its place. The printed stencils are available through the CAD files and are used to apply solder paste accurately on the Hypnos PCB. A Hypnos Build Guide video is available for visual instructions.The hardware materials that are needed to assemble the Hypnos board are listed in the Bill of Materials in Section “Bill of MaterialsAssembling the Hypnos will use the reflow oven for the top components but will require hand soldering for the back components. If a reflow oven is not available, hand soldering and reflowing with a heat gun is another option which will be explained in Section“Reflow process”. The layout for the Hypnos board is shown below in Using the printed stencil, align the Hypnos underneath ensuring that the holes of the stencil line up with the solder pads of the Hypnos. Solder paste will only be applied to the front face of the Hypnos. Apply a small amount of solder paste to each hole-- making sure that the solder paste is on the solder pads and does not smear when the stencil is removed.There are two options for determining the placement of each component: looking at a picture of a finished Hypnos board shown in  below orThe μSD card module has three main components: a diode, a 100 nF capacitor, and the μSD card holder shown in The RTC module contains the RTC, a 100 nF capacitor, as well as a push button that sends an interrupt signal. Again, place the components so the metal ends are touching the pads of the Hypnos board. The RTC is designed to have a notch at the top of the module. Place the RTC so the notch is on the top side. Also be careful to place the RTC so each of the pins are touching only one pad. The orientation of the capacitor and the push button does not matter as long as they are touching the pads. Since these components are rectangular, there are only two configurations where they would touch the pads. The way they are oriented from there does not matter. While most of the components do not have a polarity, there are certain components that do, such as the LED and the large P-channel MOSFET shown in Place the LED so the green lining is facing the outside of the board. The large P-channel MOSFET should be placed so the circle is in the bottom right corner. The Hypnos board has an asterisk that indicates this shown in reflow oven or a standard toaster oven. Although the RTC is temperature sensitive, it can handle temperatures below 180 °C.After placing the components on the Hypnos board, place the PCB into the reflow oven and heat it up according to the low-temperature solder reflow graph in If a reflow or toaster oven is not available, the top components can also be hand soldered with the addition of a heat gun. It is recommended that components are soldered one at a time starting with the μSD holder since it has many pins that may be difficult to solder with a soldering iron. Apply solder onto the pads that the μSD holder connects to first. Then, place the μSD holder and use the heat gun to heat up the solder to create a connection. Once the board is cooled, the remaining components can be hand soldered one at a time with a similar technique of applying solder to the pads first and then placing the components. The soldering iron can be used to heat up the solder instead of the heat gun for the rest of the process.Once the reflow process is complete and the Hypnos board is cooled, hand solder the remaining components to the back side as shown in Solder desired headers to connect to a Feather M0 board (both on Hypnos and Feather). When deciding which headers to solder on, it is important to think about how the Feather and Hypnos will connect to the sensor or sensor board. Note that the power rails on the Hypnos are shown in The Feather will stack on top (or on the bottom) of the Hypnos. As As mentioned before in Section “Solder headers onto Adafruit feather M0 and hypnos”, there are 3 main ways to connect the Hypnos in relation to the Feather and sensor/sensor board: stacking the Hypnos between the Feather and sensor board, using a Feathering Doubler or Tripler to connect all the electronics, or stacking the Feather and Hypnos and wiring the sensor to the Hypnos rails.The second configuration is shown in The third configuration is stacking the Hypnos on top of a Feather and wiring the sensor wires to the Hypnos as shown in In all of these examples, the Hypnos was placed depending on the needs of the project. In the first example, the Hypnos was placed between the Feather and sensor board. Placing the Hypnos between the two will prevent access to the two push buttons, but it was required for the circuit to work. The second and third example had the Hypnos stacked on top of the Feather which allowed the sensor wires to be easily hooked up to the power rails on the Hypnos. This configuration also allows access to the push button in the event the reset and interrupt buttons are needed.Wiring the sensor wires to the Hypnos will be similar to wiring them through a Feather with the only difference being where the sensor’s power wire is connected. Normally, the sensor voltage wire would be wired to a relay. With the Hypnos, the power wire will be connected to a voltage rail on the Hypnos which are rated 3.3 V, 5 V, or the external voltage rail which is noted in The remaining wires will be connected to the pins they would normally be connected to the Feather. There are some exceptions to this rule-- meaning there are some pins that the user will not be able to use to wire their sensor which are pins 5 and 6 on the Hypnos. These pins are reserved for controlling the power rails. There are other pins that are reserved for Hypnos functionality such as pins 10 and 12 which are the chip select and interrupt pins respectively. These are noted in the pin diagram in The external battery source can be connected to have a voltage output of the same rating with the added benefit of the power relay. The battery source would be connected to the + V Input and GND pins on the left side listed in Loom which enhances the Hypnos programming process. By using this library, programming and prototyping with the Hypnos becomes much easier because most of the functionality of time keeping, data collecting, and data logging is done automatically. The repository for this library can be found in the OPEnS Loom Zenodo page and the example code within Loom to use the Hypnos can be found in Examples > Lab Examples > Hypnos_SD or Hypnos_SD_Sleep in the Loom Zenodo repository.The OPEnS lab uses an open source Arduino library called In Loom, there are built in functions that make programming easier. One aspect is the configuration json file that will read which sensors are being used. By including the SD and RTC in this json file, the program will recognize these components in the circuit. Once the system knows the components, there are other functions that record data from the sensors as well as log them to the μSD.The program will be uploaded to the Feather M0 and the Hypnos will be attached through headers. The program will turn on the 3.3 V and 5 V power rails, initialize the RTC and SD, and take measurements taken by the Feather (including the time from the RTC and any sensor measurements) and log it to the SD.Garrysblog’s GitHub repository and the SD functionality was based on Adafruit’s SD example. The values read from an analog pin are also read and written to a text file on the SD card. Note that the “SD.h” and “RTClib.h” libraries are necessary to compile the code. Installing Arduino should include the “SD.h” library, but the “RTClib.h” needs to be downloaded from Adafruit’s GitHub repository. Instructions on installing libraries can be found on Arduino’s Installing Additional Arduino Libraries page.•Is the appropriate board profile selected?•Is the SD plugged in? Does it use FAT16 or FAT32 format?•Are the necessary libraries correctly installed?The following code is used to operate the Hypnos’ SD, RTC, and power rail functionality without Loom. The RTC functionality was forked from There are some potential safety issues with using the Hypnos board. Just like with any circuit, be sure to double check that the wiring from the sensors to the Hypnos board is correct. One important thing to keep in mind is to connect the external battery source correctly on the + V rail. Reversing this could potentially damage both the sensor as well as the Feather. Using the + V rail gives the user a lot of options to work with, however, there is a limit to the + V rail. To be safe, the highest voltage that should be attached to the + V rail is 24 V. The MOSFET is not designed to handle higher voltages so hooking up a higher voltage source may damage the circuit.Data was taken from a project, Sitkanet, which has used the Hypnos board The duration of the deployment over these tests were different, which can be seen through the difference in packets charted. However, the data can be compared through relative voltage levels. There was a 5 min interval in between samples for both tests.For a comparative range of voltages (3.76/3.77 V to 3.53/3.51 V), the system using the Hypnos took 82,282 samples while the system without the Hypnos took 3,050 samples-- meaning the trial with the Hypnos took over 25x more samples with the same amount of battery usage. This can be seen in the chart from the much steeper slope for the No Hypnos data. With the reduced sleep current (current between sample times), much less power is wasted and the battery levels last longer as shown in The work does not use any human or animal subjects.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


The development of the so-called theranostics approach, in which imaging information are used to define a personalized therapeutic strategy, is driving the increasing use of radionuclides in nuclear medicine. They are artificially produced either in nuclear reactors, charged particle accelerators, or using radionuclide generators. Each method leads to radioisotopes with different characteristics and then clinical utility. In the first two cases they are extracted from stable or radioactive target bombarded with a particle beam. After extraction/purification of the target, the radionuclides, either implanted on solid or in liquid form, needs to be transported to a centralized production site, a radiopharmacy or an hospital. The transport of needed radioactive material must obey strict rules. For a radionuclide, a limit in activity that it is possible to transport has been established for each type of allowed packages. For type A package these limits are called A1  and A2 . However, these limits can be easily reached if the activity to transport is high or if the radionuclide of interest is a “non–conventional” one. Indeed, for many radionuclides, there are no available/tabulated A1 and A2 and, in these cases, a very conservative set of values is imposed. This is in particular the case for some of the non-conventional radionuclide of interest in medicine (as for example Tb-149 or Tb-161). The non-tabulated values, and in general the A1/A2 limit, can be evaluated following the so-called Q-system and using Monte Carlo calculations. In the present work, we have used the MCNPX Monte Carlo code to evaluate dose rate values in different exposure scenarios. This has allowed us to determine A1/A2 coefficients for several non-conventional radionuclides of interest for medical applications. The developed technique can be extended easily to other radionuclides and can be adapted in case of changes in regulatory rules. A1 and A2 that are used to determine the activity limit for the transportation with each type of container. In particular A1 means the activity value for special form radioactive material , while A2 is the activity limit for radioactive material other than special form. Type A containers allow the transport of radioisotopes with activity below A1 or A2. Type B packages are required when the activities to transport are higher than the value A1 or A2 and lower than 3,000 A1,2 (for shipment by plane). The definition of those activity limits for each radionuclide is made through the so-called Q-System model. It consists in a methodology in which a series of accidental exposure scenarios are used to quantify the hazard of different type of radiations. The development of the method started in the late ‘70, it has been reviewed during the years and still under study. The actual regulation, and the literature in general, still suffer of a lack of knowledge concerning those limits. For some radionuclides, indeed, there are no available/tabulated A1 and A2 and in these cases a very conservative set of values is used  Regulation for the Safe Transport of Radioactive material describes different types of packages for the transport of radioactive material in relationship to the associated risk arising from the activity and the physical form of the radioactive material contained in the package. For each radionuclide the regulation defines two values, called  is used . They arAn impelling example of the necessity of new calculations is the case of the terbium isotopes and in particular Tb-149 and Tb-161.A2 value of 9E-05 TBq (90 MBq) for alpha emitter nuclide must be used. For research purposes, involving for example the treatment of a series of mice, few hundred MBq would be needed   for unknown beta emitters is applied in case of non-special form radioactive material. A single patient injection would require the use of several GBq . Also in this case there is no tabulated values and the generic Aeral GBq . As prevA1 and A2 through Monte Carlo techniques in the evaluation of the dose rate coming from the defined exposure routes, giving suggestions for possible modifications of the transportation values associated to radionuclides of potential medical interest.It is, then, necessary to add more complete and accurate information on the transport limit of given radionulicde taking into account the real hazard coming from the nuclide spread during an accident. The non-tabulated values can be obtained combined the method defined by the Q-system with Monte Carlo simulations. The present study presents this approach by determining In the following paragraphs the main principles/hypothesis of the Q-system method are reported as described in the Appendix 1 of the IAEA Safety guide No. SSG-26 .Under the Q-System, a series of exposure routes are considered, each of which may lead to radiation exposure  of a person in proximity of the damaged type A package involved in a severe transport accident causing the release of some of the content. The dosimetric routes are illustrated in the QA for external dose due to photons,QB for external dose due to beta emission,QC for internal dose due to inhalation,QD for skin contamination and ingestion dose from beta emission,QE for submersion dose due to gaseous isotopes.QC, QD and QE values are not relevant. Consequently, the A1 value, for special form materials, corresponds to the minimum value between QA and QB.Special form radioactive materials are able to retain eventual gas or fragments of the source following an accident due to their characteristics of certified sealed capsule. For this reason, the scenarios defined by A2 is the minimum among the five Q values, since all the scenarios are possible.For non-special form radioactive materials, instead, the source is not necessarily sealed: QA value is determined by the consideration of the external radiation dose due to the gamma or the X-rays to the whole body of a person exposed near a type A package following an accident. In this scenario the source is considered placed at 1 m from the person and the shield is assumed completely lost during the accident. In the revised Q-system, the information from the gamma emission spectrum for the radionuclides are coming from the ICRP Publication 38 (1984) and for the calculations the source is considered isotropic and pointlike. The QA values are given by:The γ is the effective dose rate coefficient for the radionuclide, C is the conversion factor determining the units for QA (10−12 since Q are given in TBq) and A is the activity of the source (1 Bq).where D is the reference dose of 0.05 Sv (50 mSv), t is the exposure time of 0,5 h (30 min), DRCIncluding all these values in the previous equation we obtain:pt is the effective dose rate coefficient for the radionuclide at a distance of 1 m in air (Sv Bq−1 h−1). A (non-exhaustive) list of dose and dose rate coefficients may be found in Table II.2 Appendix II of the IAEA Safety Guide ;– (e/X)i is the yield of photons of energy Ei per disintegration of the radionuclide (Bq s)−1;– Yi is the energy of the photon (MeV);– E– d is the distance in air (1 m) from the source;en/ρ)Eiμ is the mass energy absorption coefficient in air for photons of energy Ei (cm2 g−1);– (i is the linear attenuation coefficient in air for a photon of energy Ei (cm−1);– μi,d) is the air Kerma buildup factor for photons of energy Ei and distance d of 1m;– BQB value is determined as the beta dose to the skin of a person exposed following an accident involving a type A package. The shielding of the transport package is not assumed to be completely lost in the accident as for the previous case, but a residual shielding factor for beta emitters , included in the 1985 Edition of the Transport Regulations, is considered. Contrary to the gamma radiation, the electrons of the source can strongly interact with the materials around it and so the presence of a residual shielding can contribute to absorb the radiation (and to reduce part of the dose).The QB is calculated by using the complete beta spectra for the radionuclides of ICRP Publication 38  of 0.5 Sv;– t is the exposure time of 0.5 h;β is the equivalent skin dose rate coefficient for the radionuclide;– DRCQB (10−12 since the Q are given in TBq).– C is a conversion factor that determines the units for QB can be calculated from:Thus, including in the equation the correct factors, the β is the equivalent skin dose rate coefficient for beta emission at a distance of 1 m in air from the self-shielded material (Sv Bq−1 h−1). Dose and dose rate coefficients may be found in Table II.2 of Appendix II (where ėendix II .The dose rate coefficient is defined as:with:maxβ is the shielding factor computed at the maximum energy of the beta spectrum (see more details below);– SFair is the dose at 1 m (in air) per disintegration (MeV g−1 Bq−1s−1);– J– C is a numerical conversion constant.air is computed as:The factor Jwhere:– n is the number of beta particles emitted per disintegration;−1s−1);– N(E) is the number of electrons emitted with energy between E and E+dE  is the dimensionless dose distribution that represents the fraction of emitted energy deposited in a spherical shell of radius r/rE;– j(r/rE + d(r/rE) is as tabulated by Cross et al. :The self- shielding of the package was taken to be a smooth function of the maximum energy of the beta spectrum ] , it is aConsidering also the limiting doses, this leads to an expression for the contents limit based on inhalation of the form:where:– D is the reference dose of 0.05 Sv;−6 is the fraction of the inhaled content of the package;– 10inh is the dose coefficient for inhalation;– DCQC (10−12 TBq/ Bq).– C is the conversion factor that determines the units of QC value can be calculated as follow:Using these factors and coefficients, the inh is the effective dose coefficient for inhalation of the radionuclide (Sv/Bq). Values for ėinh may be found in Tables II, III of Appendix II the Safety Series n.115  of 0.5 Sv;−3 is the fraction of the package content distributed per unit area of the skin (m−2);– 10skin is the equivalent skin dose rate coefficient for skin contamination;– DRC4 s (5 h);– t is the exposure time of 1.8 × 10QD (set to 1).– C is a conversion factor that determines the units for QD can be evaluated as:With those factors, skin is the equivalent skin dose rate per unit activity and unit area of the skin (Sv s−1 TBq−1 m2). dose and dose rate coefficients may be found in Table II.2 of Appendix II  was 1.44 10−3QE (m−3). Submersion dose coefficients for effective and skin dose are given in the Federal Guide n.12  is calculated as:While the where:DLeff and DLskin are the dose criteria for effective dose  and equivalent dose to the skin , respectively;– m−3 per Bq;– TIAC is the time-integrated activity concentration in air per unit activity released which was set to 2.6 Bq s QE (10−12);– C is the conversion factor that determines the units for hsubeff and hsubskin are the submersion dose coefficient for effective dose and skin equivalent dose, respectively (Sv Bq−1s−1m3), provided by IAEA TS-G-1.1 Xm=where:Xm is the derived value of A1 or A2 in case of a mixture;– – f(i) is the fraction of activity or activity concentration of the radionuclide i in the mixture;A1 or A2 value for the radionuclide i.– X(i) is the Rounding method:A1 and A2 values are rounded up or down to the nearest significant figure.The Q values are quoted to 2 significant digits whereas The methodology described in the previous sections implies the use of analytic formulae or empiric coefficients and relies in some cases on the approximation of integral equations. Moreover, the information on the isotopes' spectra are based on old libraries dated 1984-94.pt, ėb, ḣskin, ėinh.A good alternative is represented by the use of Monte Carlo simulations to evaluate directly the dose rate parameters to use in the formulae for the calculation of the Q values: ėThis method avoids the solution of complex equations and takes into account all the phenomena involved in the interaction of the source's particles with the matter and the surrounding air, giving a realistic evaluation of the dose in the single accidental scenarios. It will include the recent nuclear physics interaction cross sections of the particles as well as effects like Bremsstrahlung that has not been fully included in the current Q-system. However, the basic principles, like the geometrical factors and the radiological criteria of the current Q system, remain.The Monte Carlo computer program MCNPX  has beenEach nuclide is characterized by a typical spectrum of emission. A procedure that allows a fast calculation for each nuclide without the need to set a different MC code for each of them has been used: the dose rate values is computed for monoenergetic particles sources; then, using the typical spectra characteristics (energy distribution and branching ratio of the particles emitted in the decay), the effective dose rate is associated to each radionuclide.The applied method is similar in all the cases/scenarios and it is composed by the following main steps:Step 1:–      The geometry reproducing as close as possible the accidental scenario described by the Regulation for the single Q value is modeled in the MCNPX code;Step 2:–      A pointlike source of beta or gamma particles of 1 Bq is set up (in the origin) and its emission considered isotropic and monoenergetic. The spectra of energies simulated goes from 0.01 to 5 MeV for gamma and 0.1 to 5 MeV for electrons and positrons. The number of primaries used is 1.0E+07.–      The dose rate for the defined active/detection area and associated to the single energy with emission probability of 100% is evaluated using the MCNPX F8 tally;Step 3:–      Using the spectra of each isotope, the dose rates associated to the single energies are calculated;–      The dose rate for each i-th particle energy composing the spectra is weighted by the relative effective Branching Ratio;–      When the emitted particle energy is not present in the simulated data set, a linear interpolation is done for that particular energy bin;–      For monoenergetic spectra the total dose rate is given by the arithmetic sum of the single dose rates weighted by the probability of decay:–      In case of continuum spectra  the dose rate is coming from the trapezoidal integration rule of the data set.–      If the isotope is characterized by both monoenergetic and a continuous spectra, the dose rate is the sum of the two components.Step 4:–      The obtained dose rate coefficient is used to calculate the Q value under study using the formulae presented in the previous section.In all calculations the dose rate is relative not only to the primary particles emitted from the source, but also to the effect of the secondary particles, coming from the elastic and inelastic scattering with the surrounding materials. Unlike the analytical calculations, these effects can be easily taken into account using the Monte Carlo method.skin, involved in the calculation of the QC value, is linked to the dose rate released to the organs of the respiratory tract. The complexity related to the needs to understand the fractional deposition and the chemical affinity in each sector of the respiratory organs for each radionuclide, led us to use the values of hskin defined in the ICRP119 publication  for this study. The main reason is that the gaseous form of radioactive medical isotopes to transport is very rare. Moreover, the QE calculations imply the knowledge of the isotopes concentration on the air volume of the room with the time and the need to simulate the dose for the general human phantom.The recalculation of the dose coefficient QA, QB and QD values, keeping the ones defined in the Regulation for QC and QE unchanged for the final comparison.This study focuses in particular on the re-calculation of the control group of Isotopes for whom the dose coefficients that appear in the equations for the Q values are tabulated in the IAEA Safety Guide. A comparison between the listed coefficients and the ones simulated in this study have been done to validate the method.The Monte Carlo method have been initially tested for a pt, ėb, ḣskin, ėinh) for some nuclides who present non-tabulated Q values and generic limits of transport.The procedure have been then applied to evaluate the dose rate coefficients  is placed, in air, at 1 m from the source: the active area is represented by a spheric shell with inner radius of 1 m and thickness of 0,30 m composed by water. The reason of this material choice is due to the similar density and composition of water with the human body . The thiThe values of the simulated dose rates with the energy for the monoenergetic gamma sources are plotted in the pt dose rate factor is given by the sum of the dose rate associated to the single energies (Ḋ(Ei)) weighted by their relative probability of emission (I(Ei)).Considering the gamma spectra for each isotope (energy and associated branching ratio), the ėQA factor is then evaluated. The results of simulation for the dose rate coefficient ėpt and the relative QA values for the chosen control group and for the other nuclides of interest are reported in the Using the Equation 2 the QB value is determined by the beta dose to the skin of a person exposed during an accident involving a type A package containing special form material. A residual shielding factor (SF) for beta emitters is considered.The The geometry reproduced in MCNPX is reported in QA calculation  , while aculation .The simulated dose rate to the skin for the single energy positron and electron source is reported in the graph below . The eneFor energy values 0.3–0.5 MeV we can observe that the dose rate increases up to a peak. Here the source-detector distance (1 m in air + 0.07 mm of water + 0.04 mm of water detector in this case) corresponds to the maximum depth at which the incident electrons with those energies are repeatedly scattered and penetrate into the target while losing their energy. Increasing the energy, the electrons ranges become higher and they will go through the detector depositing only a fraction of their energy. Above 2 MeV the behavior can be assumed linearly decreasing. The choice of the binning reflects this behavior: small bin is used to sample the peak region and a larger one in the linear decreasing region and at the end of the curve tail.QA value.Positrons and electrons have basically the same behavior (same deposited energy) in the skin tissue. There is a density effect correction coefficient that differentiates the collision stopping power of the two charged particles . For posThe dose rate is given by the result of the sum of two factors: the dose coming from the continuum beta specrum  the dose rate values are weighted by the probability of emission (BR).The calculation of the adimensional SF follows the method established in the IAEA regulation: if the isotope under study presents particles with energies higher than 2 MeV, the shielding factor is set to 3, otherwise it will depend on the maximum beta energy of the beta spectra (Equation 8). In case the isotope presents only monoenergetic electrons, a shielding factor of 3 is chosen a priori, independently from the spectra.QD factor is related to the accidental scenario in which the dose is transferred to the person due to the handling of the damaged Type A package.The The geometry reproduced in the code is reported in the The source is now at contact with the skin and the area of detection is still represented by a spherical shell with thickness of 0.04 mm and at a depth 0.07 mm. The skin composition is the same than the one reported in the skin dose factor calculation is similar to the one used for the coefficient ėb except for the absence of the shielding factor effect.The method of the hAs first step, the dose rate for the single energies (with 100% of branching ratio) is evaluated. The results of the simulations are reported in The range of chosen energies goes from 0.06 (minimum energy to have electrons with range comparable to the skin thickness) to 4 MeV.b case, it is possible to distinguish three regions in the dose rate behavior as a function of the energy.As for the previous ėIn the first region, the dose rate increase up to a maximum value corresponding to the energy for which the electrons range is equal to the source-skin derma distance. For higher energies, the range of the electrons increases at the expense of the deposited dose in the detection area. Then the second region is characterized by an exponential decay of the dose rate values. Starting from 1 MeV, it is possible to assume a linear decreasing behavior, corresponding to the third region. The choice of the energy bin for the spectra reflects this trend: small bins are used to sample the first two regions, while a larger one is used for the curve tail.Subsequently, the spectra of the isotopes under study are retrieved. Once again, the dose rate coming from the (n) monoenergetic electron of the spectra is given by the sum of the single contribution to the dose (Ḋ(Ei)) weighted by the relative probability of emission (BR). The contribution to the dose coming from the continuum spectra is given by the trapezoidal integration of the single contribution always weighted by their relative probability of emission.where:p, ėb, hskin, ėd, the relative Q values and the A1 and A2 limits compared with the ones specified in the IAEA Safety Guide. Three graphs can be useful to visually compare the Monte Carlo sets of data with the Regulatory ones and make some conclusions.The entire set of results of the Monte Carlo method described in the previous paragraph are summarized in the control group, for which the IAEA values are available and tabulated. The two graphs in A1 and A2 . There are two exceptions:The first 10 cases represent what we called the A1 is represented by the case of Ca-47 for which the recalculated value is smaller than the one in the Regulation. The explanation is found in the different ėb dose coefficients. A reason for this discrepancy could be the use of different nuclear data sets for the beta decay of this radionuclide and the daughter included in the calculation (Sc-47).The exception for A2 seems to be represented by the case of Be-7. As said previously, the A2 value is given by the minor of all the Q values. In the case of the Monte Carlo method, the limiting factor for the Be-7 is imposed by the QD value (2.90E+00 TBq), almost two orders of magnitude lower than the tabulated one (1.0E+03 TBq).An exception for QD results to be a value higher than 103 TBq, QD shall be limited to 103 TBq. Applying this rules, the A2 for Be-7 becomes limited by the gamma dose rate coefficient and equal to: 2.09 TBq.Actually in the Regulation it is assumed that if A1 and A2 that have no value in IAEA tables and for which generic transport limits must be used. The relative errors of the simulations are always lower than 1%  and not reported in the tables and the graph.The MC method is able to well reproduce the scenarios, the hypothesis and mostly the physics behind the Regulation. Moreover, those results allowed us to validate the MC simulation codes and apply them to obtain a dataset of A1 and 0,02 TBq for A2 , two orders of magnitude.The gap between the regulatory values and the simulated ones is more evident in the case of the 1/A2 the calculated values are respectively, 8.56E-01 TBq and 4.54E-01TBq. Applying the MC method we would observe that:For the two alpha emitters with generic transport limits, Tb-149 and Bi-213, the AA1 limits are respectively, 2 and 4 times higher than the generic one (2.0E-01 TBq);the A2 values are four orders of magnitude higher than what is prescribed by the Regulation (9E-05 TBq)the recalculated A, the dose from gamma source exposure.in the case of Tb-149 the limiting value is coming from the QB, due to the beta dose to the skin.for Bi-213, instead, the lower of the Q values is the Qb and ḣskin.The values listed in the previous tables are also in good agreement with the ones obtained, for the same group of isotopes, from a working group of the Radiation Protection group at CERN. The main differences with the present study is the use of Fluka as the Monte Carlo software used for the calculations  and geomThe development of new techniques of production of exotic radionuclides to use in systemic radiotherapy and imaging yields to the development of new containers to transport them. The radionuclides suitable for nuclear medicine purposes are characterized by short half-life. They are generally produced in nuclear reactors, cyclotrons or other accelerator facilities.In the context of the transport the  isotope's half life is an important factor: considering the time needed for transport from the point of production to the laboratories for the chemical saparation and the labeling (sometime those two are not in the same place) and then the transport of the final product to the hospital, the initial activity to be transported shall be much higher then the one actually used at the patients level.Once the samples are irradiated, they shall undergo a series of chemical treatments before being coupled to biological substances to be injected in humans or animals for preclinical studies.From the place of irradiation the samples containing the desired radionuclide is shipped to a chemical laboratory. The final product can be then used in the same place of production or it can be shipped again to other places like hospitals, imaging center or other research institutes.Appropriate packages are needed to move the irradiated samples. In the first phase of this path the sample to transport is characterized by a high level of activity, generally due also to the presence of radioactive contaminants collected at the same time.Due to the hours or days spent for the travel and the needs to take into account the decay of the radionuclides, the activities to transport suitable for the radiopharmaceutical production sometimes exceeds the values defined for the type A containers or industrial packages imposed by the IAEA. This higher hazard involves the use of more complex and safety demanding packages, called type B containers.The value of activity to transport, different for each radionuclide, is the quantity defining the type of package to use for transport.The International Atomic Energy Agency established a method, the so-called Q-system, based on different kind of exposures during an accident involving the damage of a transport container.Those values are most of the time general and not based on specific calculations. Moreover, the nuclear data refers to not updated database and references to the used ones are difficult to identify.A1 and A2 based on the Q-system as set by IAEA has been described. It has been used as a basis of an alternative method of calculation making use of Monte Carlo techniques and in particular of the software MCNPX to evaluate dose rate parameters in specific scenarios.The use of the Monte Carlo method for the evaluation of the transport limit A1 and A2: 8.56E-01 TBq) are two orders of magnitude higher then the one prescribed by the regulation . While for Tb-161 applying the Monte Carlo method it would be possible to gain one order of magnitude for A1 (from 1.0E-01 extabilished from the regulation to 7.1E+00 TBq) and A2 (from 2.0E-02 extabilished from the regulation to 5.9E-01 TBq).This method has been validated with a control group of nuclides with known/tabulated Q values. The results of the simulations, also in agreement with the ones obtained by other working groups, would allow an increase of the generic tabulated values. Among the analized cases we can cite the ones regarding two of the Terbium isotopes used in nuclear medicine: Tb-149 and Tb-161. For Tb-149, the recalculated values  and a study of the dose due to the submersion accidental scenario  in case of gaseous sources. This may be done including in the simulations the information on the ICRP human phantom.A future development and improvement of these calculations must include Monte Carlo simulations to quantify the alpha emitter's hazard (for the QB calculations, the geometry and the material composing the shield associated to this calculation.Additional study is needed also to better determine the Shielding Factor included in the Recently an international working group managed by IAEA has been created with the aim of improve and update the Q-System method and databases . A new vThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.MM wrote the first draft of the manuscript. All authors contributed to the discussion concerning the results included in the manuscript and to its revision and read, approving the submitted version, and added comments to the discussion part, method presented and the results section.This research project has been supported by a Marie Sklodowska-Curie Innovative Training Network Fellowship of the European Commission's Horizon 2020 Program under contract number 642889, MEDICIS-PROMED. The Cyclotron Arronax was supported by CNRS, Inserm, INCa, the Nantes University, the Regional Council of Pays de la Loire, local authorities, the French government and the European Union. This work has been, in part, supported by a grant from the French National Agency for Research called Investissements d'Avenir, Equipex Arronax-Plus no ANR-11-EQPX-0004, Labex IRON no ANR-11-LABX-18-01 and ISITE NExT no ANR-16-IDEX-007.SA was employed by Naogen Pharma. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.