Additional Error Research Articles

Accelerated Simulation of Multi-Electrode Arrays Using Sparse and Low-Rank Matrix Techniques.

Modeling of Multi-Electrode Arrays used in neural stimulation can be computationally challenging since it may involve incredibly dense circuits with millions of interconnected resistors, representing current pathways in an electrolyte (resistance matrix), coupled to nonlinear circuits of the stimulating pixels themselves. Here, we present a method for accelerating the modeling of such circuits while minimizing the error of a simplified simulation by using a sparse plus low-rank approximation of the resistance matrix. Specifically, we prove that thresholding of the resistance matrix elements enables its sparsification with minimized error. This is accomplished with a sorting algorithm implying efficient O (N log (N)) complexity. The eigenvalue-based low-rank compensation then helps achieve greater accuracy without adding significantly to the problem size. Utilizing these matrix techniques, we accelerated the simulation of multi-electrode arrays by an order of magnitude, reducing the computation time by about 10-fold, while maintaining an average error of less than 0.3% in the current injected from each electrode. We also show a case where acceleration reaches at least 133 times with additional error in the range of 4%, demonstrating the ability of this algorithm to perform under extreme conditions. Although the techniques presented here are used for simulations of photovoltaic retinal prostheses, they are also immediately applicable to any circuit involving dense connections between nodes, and, with modifications, more generally to any systems involving non-sparse matrices. This approach promises significant improvements in the efficiency of modeling the next-generation retinal implants having thousands of pixels, enabling iterative design with broad applicability.

Comparison of continuous temperature measurement methods in the intensive care unit: standard bladder catheter measurements versus non-invasive transcutaneous sensors.

The purpose of this study was to compare a wearable system for body core temperature measurement versus bladder and tympanic thermometers in an intensive care setting. The question was, if continuous non-invasive sensors in the intensive care unit represent an alternative to current standard methods of invasive continuous bladder temperature measurement methods?Between May and September 2023, a comparative investigation involving 112 patients was conducted in a 20-bed surgical intensive care unit to assess various temperature probes, including those placed in the tympanic tube, bladder, and skin. To achieve this, a wireless non-invasive sensor system provided by greenTEG AG, Switzerland, was affixed to different body locations (clavicular and lateral chest) of each catheterized patient (equipped with a temperature probe) admitted to the intensive care unit. Furthermore, tympanic temperatures were recorded at specified intervals. The measurement duration ranged from a minimum of six hours to a maximum of six days, resulting in the analysis of a total of 355 simultaneous temperature measurements.In this study, a wearable temperature measurement system attached to two different body sites revealed a consistent negative bias compared to bladder temperature. In addition, the measurements were particularly influenced by body constitution. The tested system in all patients showed a mean absolute error (MAE) of 0.45°C for the lateral chest and 0.50°C for the clavicular position. Tympanic measurements had a mean absolute error of 0.35°C. In patients with body mass index (BMI) ≥ 25 the MAE increased to 0.5°C for the lateral chest and 0.56°C for the clavicular position. In contrast, the tympanic measurement had a reduced MAE of 0.32°C, which is well below this threshold when compared to bladder measurements.In conclusion the investigated system did not meet the clinically relevant acceptance criteria and showed low precision in correctly identifying fever episodes compared to invasive temperature probes, however its main advantage lies in its continuity and non-invasiveness. This makes it a potential alternative to intermittent tympanic measurement devices. In this study we were able to show, that in at least one subset of patients, the non-invasive and continuous device demonstrated a precision comparable to tympanic measurements.The accuracy of all non-invasive methods was lower than in previous studies, suggesting that the use of bladder temperature as reference and user related variations may have introduced additional errors.

Planck data revisited: Low-noise synchrotron polarization maps from the WMAP and Planck space missions

Observations of cosmic microwave background (CMB) polarization, essential for probing a potential phase of inflation in the early Universe, suffer from contamination by polarized emission from the Galactic interstellar medium. This work combines existing observations from the WMAP and space missions to make a low-noise map of polarized synchrotron emission that can be used to clean forthcoming CMB observations. We combine DR5 K, Ka and Q WMAP maps with PR4 30 GHz and 44 GHz LFI maps and WMAP K, Ka and Q maps with LFI 30 GHz and 44 GHz maps, using weights that near-optimally combine the observations as a function of sky direction, angular scale, and polarization orientation. We publish well-characterized maps of synchrotron $Q$ and $U$ Stokes parameters at $ = 30$ GHz and an angular resolution of 1 degree. A statistical description of uncertainties is provided with Monte-Carlo simulations of additive and multiplicative errors. Our maps are the most sensitive full-sky maps of synchrotron polarization to date, and are made available to the scientific community on a dedicated website.

Non-classical measurement error in instrumental variables estimation: An application to the medical care costs of obesity.

Estimates of the impact of body mass index and obesity on health and labor market outcomes often use instrumental variables estimation (IV) to mitigate bias due to endogeneity. When these studies rely on survey data that include self- or proxy-reported height and weight, there is non-classical measurement error due to the tendency of individuals to under-report their own weight. Mean reverting errors in weight do not cause IV to be asymptotically biased per se, but may result in bias if instruments are correlated with additive error in weight. We demonstrate the conditions under which IV is biased when there is non-classical measurement error and derive bounds for this bias conditional on instrument strength and the severity of mean-reverting error. We show that improvements in instrument relevance alone cannot eliminate IV bias, but reducing the correlation between weight and reporting error mitigates the bias. A solution we consider is regression calibration (RC) of endogenous variables with external validation data. In simulations, we find IV estimation paired with RC can produce consistent estimates when correctly specified. Even when RC fails to match the covariance structure of reporting error, there is still a reduction in asymptotic bias.

Half-wave voltage controllable optical voltage sensor with arbitrary electric field direction modulation*

Abstract An optical voltage sensor with an arbitrary electric field direction modulation (AEFDM) mode is proposed to increase the half-wave voltage. The mode is realized by heterogeneous electrodes arranged in a center-symmetric way, and generates an electric field with a direction at an arbitrary angle to the light propagation direction. The finite element method and coupling wave theory are used to design and optimize the electrodes and field distribution. The experimental results show that heterogeneous electrodes and AEFDM mode are able to regulate the sensor’s half-wave voltage in a range from 1 to 30 times (47–1409 kV), with 0.5 class accuracy and 99.99% linearity. This novel AEFDM method proposes a new electrode shape and position, reducing temperature drift to 50% compared to solid-state voltage divider method.

Estimating error rates for single molecule protein sequencing experiments.

The practical application of new single molecule protein sequencing (SMPS) technologies requires accurate estimates of their associated sequencing error rates. Here, we describe the development and application of two distinct parameter estimation methods for analyzing SMPS reads produced by fluorosequencing. A Hidden Markov Model (HMM) based approach, extends whatprot, where we previously used HMMs for SMPS peptide-read matching. This extension offers a principled approach for estimating key parameters for fluorosequencing experiments, including missed amino acid cleavages, dye loss, and peptide detachment. Specifically, we adapted the Baum-Welch algorithm, a standard technique to estimate transition probabilities for an HMM using expectation maximization, but modified here to estimate a small number of parameter values directly rather than estimating every transition probability independently. We demonstrate a high degree of accuracy on simulated data, but on experimental datasets, we observed that the model needed to be augmented with an additional error type, N-terminal blocking. This, in combination with data pre-processing, results in reasonable parameterizations of experimental datasets that agree with controlled experimental perturbations. A second independent implementation using a hybrid of DIRECT and Powell's method to reduce the root mean squared error (RMSE) between simulations and the real dataset was also developed. We compare these methods on both simulated and real data, finding that our Baum-Welch based approach outperforms DIRECT and Powell's method by most, but not all, criteria. Although some discrepancies between the results exist, we also find that both approaches provide similar error rate estimates from experimental single molecule fluorosequencing datasets.

The possibility of using the DKS-AT1123 dosimeter for radiation monitoring of medical electron accelerators with the energy of more than 10 MeV

In the Russian Federation, there is a constant increase in the number of radiation medical installations with electron accelerators. Over the past 4 years, their number has increased 2.5 times. These installations contain pulsed electron accelerators that generate pulsed bremsstrahlung radiation with a maximum energy from 6 to 21 MeV. Currently, there are no devices designed for dosimetry of the pulsed photon radiation with energy of more than 10 MeV in the state register of measuring instruments of the Russian Federation. The most widely used radiation monitoring device for pulsed electron accelerators is the DKS-AT1123 X-ray and gamma radiation dosimeter designed for dosimetry of pulsed bremsstrahlung radiation with an energy of up to 10 MeV. The purpose of this work is to evaluate the possibility of using this device for dosimetry of pulsed bremsstrahlung radiation with a maximum energy of up to 20 MeV. The authors calculated the energy spectra of bremsstrahlung radiation for a point source with a maximum energy of 20 MeV behind flat concrete screens with a thickness of 1 m, 2 m and 3 m by the Monte Carlo method using the GEANT4 calculation program. The energy dependence of the registration efficiency of the DKS-AT1123 dosimeter was extrapolated to the energy range of 10–50 MeV in the kerma-approximation without taking into account the energy transfer by secondary electrons. It was assumed that it corresponds to the energy dependence of the total mass attenuation coefficient for the absorbed energy of gamma quanta in water. Using conversion coefficients for converting the fluence of monoenergetic photons into the effective dose rate at an anterior-posterior radiation incidence on the human body, real dose rates were calculated, and using the energy dependence of the dosimeter readings, the predicted results of measuring the unit dose rate with the DKS-AT1123 dosimeter behind a concrete protection with a thickness of 1, 2 and 3 m were obtained. It is shown that the maximum expected underestimation of the measurement results will not exceed 40% and practically does not depend on the thickness of the concrete shield in the thickness range from 1 to 3 m. To account for this underestimation, it is necessary to use the value of additional measurement error due to the energy dependence of the sensitivity of this device for the photon radiation energy of more than 10 MeV, equal to 70%. This makes it possible to use the measurement results obtained using this dosimeter to adequately characterize the state of radiation safety during operation of pulsed electron accelerators with a maximum energy of up to 20 MeV. It is possible to use a correction factor to the measurement results equal to 1.63 ±0.04 to compensate for this underestimation. The proposed approach can be used to create a methodology for using this dosimeter for radiation monitoring of medical electron accelerators with the energy of up to 20 MeV, if there are correction factors for radiation protection configurations and radiation energies encountered in practice.

Vulnerability to semantic and phonological interference in normal aging and amnestic mild cognitive impairment (aMCI).

To determine whether the increased vulnerability to semantic interference previously observed in amnestic mild cognitive impairment (aMCI) is specifically associated with semantic material or if it also affects other types of material, suggesting generalized executive and inhibitory impairment. Seventy-two participants divided into two groups (33 aMCI, 39 normal control [NC]) matched for age and education were included. They completed a comprehensive neuropsychological examination, the French version of the Loewenstein Acevedo Scale for Semantic Interference and Learning (LASSI-L; semantic interference test), and a homologous experimental phonological test, the phonological interference and learning test. Independent sample t tests, mixed analysis of variance (ANOVA), and analysis of covariance (ANCOVA) on memory and interference scores were conducted to compare memory and interference in both conditions for both groups. For memory scores, results revealed significant main effects of group (NC > aMCI) and condition (semantic > phonological) and significant interactions (poorer performance in the semantic condition for aMCI). aMCI committed more phonological false recognition errors, were disproportionately more vulnerable to retroactive semantic interference, and showed a higher percentage of intrusion errors associated with proactive semantic interference than NC. To our knowledge, this is the first study to compare vulnerability to interference in aMCI and normal aging with two similarly designed semantic and phonological word list learning tasks. Taken together, our results suggest that aMCI present with broad difficulties in source memory and inhibition, but that impaired deep semantic processing results in additional semantic intrusion errors during proactive interference and impacts their ability to show good recall after an interference list (greater semantic retroactive interference). Results are discussed according to the level-of-processing and activation/monitoring theories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Methodology for Measuring the Dose Rate of Pulsed Bremsstrahlung Radiation using Gamma Radiation Dosimeters with Geiger-Muller Counter

Introduction. Industrial monitoring of pulsed bremsstrahlung radiation is associated with a number of challenges. Russia produces only three dosimeters that can be used for measuring pulsed bremsstrahlung radiation with a pulse duration of less than 10 gs. These dosimeters, in addition to being rather expensive, have a number of significant restrictions on the energy range (10 MeV) and the minimum pulse duration (10 ns). The DKG-RM1621 dosimeter with a Geiger-Muller counter can be used for dosimetry of photon radiation with energies up to 20 MeV. However, this device is not intended for dosimetry of pulsed radiation.Aim. Development of a methodology for conducting radiation monitoring of pulsed bremsstrahlung radiation sources using dosimeters with Geiger-Muller counters.Materials and methods. In 2021, measurements of the dose rate of pulsed bremsstrahlung radiation with a maximum energy of 3.0 MeV at pulse repetition rates of 50, 100, 150, 200, 250, 300, and 400 Hz were carried out using DKS- AT1123 (as a reference) and MKS-AT117M dosimeters with a Geiger-Muller counter.Results. A technique was developed for correcting the results of measuring the dose rate of pulsed bremsstrahlung radiation by a dosimeter equipped with a Geiger-Muller counter, which allows the dose rate of pulsed bremsstrahlung radiation to be measured with an additional error of less than 15 % in a practically significant range of dose rates. For the MKS- AT117M dosimeter at a pulse repetition rate of 400 Hz, this value was 320 gSv/h, which is sufficient for most practical tasks in radiation monitoring.Conclusion. The feasibility and possibility of successful application of dosimeters with Geiger-Muller counters for dosimetry of pulsed bremsstrahlung radiation using the proposed measurement technique with a limitation on the maximum measured dose rate is shown.

Comparison of Depth-Related Visuomotor Task Performance in Uniocular Individuals and in Binocular Controls With and Without Temporary Monocular Occlusion.

Do one-eyed (uniocular) humans use monocular depth cues differently from those with intact binocularity to perform depth-related visuomotor tasks that emulate complex activities of daily living? If so, does performance depend on the participant's age, duration of uniocularity and head movements? Forty-five uniocular cases (age range 6-37years; 2.4months-31.0years of uniocularity) and 46 age-similar binocular controls performed a task that required them to pass a hoop around an electrified wire convoluted in depth multiple times, while avoiding contact as indicated by auditory feedback. The task was performed with and without head restraint, in random order. The error rate and speed were calculated from the frequency of contact between the hoop and wire and the total task duration (adjusting for error time), respectively, all determined from video recordings of the task. Head movements were analyzed from the videos using face-tracking software. Error rate decreased with age (P < 0.001) until the late teen years while speed revealed no such trend. Across all ages, the error rate increased and speed decreased in the absence of binocularity (P < 0.001). There was no additional error reduction with duration of uniocularity (P = 0.16). Head movements provided no advantage to task performance, despite generating parallax disparities comparable to binocular viewing. Performance in a dynamic, depth-related visuomotor task is reduced in the absence of binocular viewing, independent of age-related performance level. This study finds no evidence for a prolonged experience with monocular depth cues being advantageous for such tasks over transient loss of binocularity.

Exact optimisation of spatiotemporal monitoring networks by p‐splines with applications in groundwater assessment

AbstractThis paper develops methods to optimise the sampling strategy for monitoring networks which have fixed locations with regular sampling but with only a proportion of these locations to be used on each sampling occasion. This creates the need for a dynamic spatiotemporal sampling design which makes optimal choices of the locations to be sampled on each occasion. This is a commonly occurring scenario in many environmental settings where there is an existing network of monitoring stations and sampling can be expensive. The particular context of optimisation of an existing groundwater monitoring network is discussed in the paper. The standard design criteria of integrated variance (IV) and variance of the integral (VI) are adapted to the spatiotemporal setting. p‐spline models are shown to allow exact computation of IV and VI, in the case of the additive errors, and a very good approximation of IV in the case of multiplicative errors. The speed of these exact computations allows the globally optimal sampling design to be identified efficiently. In the standard case of additive errors, the design criteria are able to exploit information across time. The only information needed is the location of sampling points, not the values sampled. This contrasts with the case of multiplicative errors where the design criteria are also influenced by the observed response data. Simulated and real examples are used to illustrate the results throughout.

Private Query Release via the Johnson-Lindenstrauss Transform

We introduce a new method for releasing answers to statistical queries with differential privacy, based on the Johnson-Lindenstrauss lemma. The key idea is to randomly project the query answers to a lower dimensional space so that the distance between any two vectors of feasible query answers is preserved up to an additive error. Then we answer the projected queries using a simple noise-adding mechanism, and lift the answers up to the original dimension. Using this method, we give, for the first time, purely differentially private mechanisms with optimal worst case sample complexity under average error for answering a workload of $k$ queries over a universe of size $N$. As other applications, we give the first purely private efficient mechanisms with optimal sample complexity for computing the covariance of a bounded high-dimensional distribution, and for answering 2-way marginal queries. We also show that, up to the dependence on the error, a variant of our mechanism is nearly optimal for every given query workload.

Robust Quantum Gates against Correlated Noise in Integrated Quantum Chips.

As quantum circuits become more integrated and complex, additional error sources that were previously insignificant start to emerge. Consequently, the fidelity of quantum gates benchmarked under pristine conditions falls short of predicting their performance in realistic circuits. To overcome this problem, we must improve their robustness against pertinent error models besides isolated fidelity. Here, we report the experimental realization of robust quantum gates in superconducting quantum circuits based on a geometric framework for diagnosing and correcting various gate errors. Using quantum process tomography and randomized benchmarking, we demonstrate robust single-qubit gates against quasistatic noise and spatially correlated noise in a broad range of strengths, which are common sources of coherent errors in large-scale quantum circuits. We also apply our method to nonstatic noises and to realize robust two-qubit gates. Our Letter provides a versatile toolbox for achieving noise-resilient complex quantum circuits.

Sample-optimal classical shadows for pure states

We consider the classical shadows task for pure states in the setting of both joint and independent measurements. The task is to measure few copies of an unknown pure state &amp;#x03C1; in order to learn a classical description which suffices to later estimate expectation values of observables. Specifically, the goal is to approximate Tr(O&amp;#x03C1;) for any Hermitian observable O to within additive error &amp;#x03F5; provided Tr(O2)&amp;#x2264;B and &amp;#x2016;O&amp;#x2016;=1. Our main result applies to the joint measurement setting, where we show &amp;#x0398;&amp;#x007E;(B&amp;#x03F5;&amp;#x2212;1+&amp;#x03F5;&amp;#x2212;2) samples of &amp;#x03C1; are necessary and sufficient to succeed with high probability. The upper bound is a quadratic improvement on the previous best sample complexity known for this problem. For the lower bound, we see that the bottleneck is not how fast we can learn the state but rather how much any classical description of &amp;#x03C1; can be compressed for observable estimation. In the independent measurement setting, we show that O(Bd&amp;#x03F5;&amp;#x2212;1+&amp;#x03F5;&amp;#x2212;2) samples suffice. Notably, this implies that the random Clifford measurements algorithm of Huang, Kueng, and Preskill, which is sample-optimal for mixed states, is not optimal for pure states. Interestingly, our result also uses the same random Clifford measurements but employs a different estimator.

Enhancing stock volatility prediction with the AO-GARCH-MIDAS model.

Research has substantiated that the presence of outliers in data usually introduces additional errors and biases, which typically leads to a degradation in the precision of volatility forecasts. However, correcting outliers can mitigate these adverse effects. This study corrects the additive outliers through a weighting method and let these corrected values to replace the original outliers. Then, the model parameters are re-estimated based on this new return series. This approach reduces the extent to which outliers distort volatility estimates, allowing the model to better adapt to market conditions and improving the accuracy of volatility forecasts. This study introduces this approach for the first time to generalized autoregressive conditional heteroskedasticity mixed data sampling (GARCH-MIDAS) models, so as to establish an additional outliers corrected GARCH-MIDAS model (AO-GARCH-MIDAS). This pioneering approach marks a unique innovation. The research employs a diverse array of evaluation methods to validate the model's robustness and consistently demonstrates its dependable performance. Findings unequivocally reveal the substantial influence of outliers on the model's predictive capacity, with the AO-GARCH-MIDAS model exhibiting consistent superiority across all evaluation criteria. Additionally, while the GARCH model showcases stronger estimation capabilities compared to the GARCH-MIDAS model, the latter demonstrates heightened predictive prowess. Notably, regarding variable selection, the results underscore the greater predictive informational value inherent in realized volatility over other low-frequency factors.

Machine learning and global vegetation: random forests for downscaling and gap filling

Abstract. Drought is a devastating natural disaster, during which water shortage often manifests itself in the health of vegetation. Unfortunately, it is difficult to obtain high-resolution vegetation drought impact information that is spatially and temporally consistent. While remotely sensed products can provide part of this information, they often suffer from data gaps and limitations with respect to their spatial or temporal resolution. A persistent feature among remote-sensing products is the trade-off between the spatial resolution and revisit time: high temporal resolution is met with coarse spatial resolution and vice versa. Machine learning methods have been successfully applied in a wide range of remote-sensing and hydrological studies. However, global applications to resolve drought impacts on vegetation dynamics still need to be made available, as there is significant potential for such a product to aid with improved drought impact monitoring. To this end, this study predicted global vegetation dynamics based on the enhanced vegetation index (evi) and the popular Random forest (RF) regressor algorithm at 0.1°. We assessed the applicability of RF as a gap-filling and downscaling tool to generate global evi estimates that are spatially and temporally consistent. To do this, we trained an RF regressor with 0.1° evi data, using a host of features indicative of the water and energy balances experienced by vegetation, and evaluated the performance of this new product. Next, to test whether the RF is robust in terms of spatial resolution, we downscale the global evi: the model trained on 0.1° data is used to predict evi at a 0.01° resolution. The results show that the RF can capture global evi dynamics at both a 0.1° resolution (RMSE: 0.02–0.4) and at a finer 0.01° resolution (RMSE: 0.04–0.6). Overall errors were higher in the downscaled 0.01° product compared with the 0.1° product. Nevertheless, relative increases remained small, demonstrating that RF can be used to create downscaled and temporally consistent evi products. Additional error analysis revealed that errors vary spatiotemporally, with underrepresented land cover types and periods of extreme vegetation conditions having the highest errors. Finally, this model is used to produce global, spatially continuous evi products at both a 0.1 and 0.01° spatial resolution for 2003–2013 at an 8 d frequency.

Evaluation of LES-based time-decoupled model-predictive control in different wind farm layouts

Very recently, Janssens and Meyers (Wind Energy Sci., vol. 9, 65–95, 2024) proposed a time-decoupled model-predictive control (TD-MPC) framework for wind farm power optimization using a control model based on coarse-grid large-eddy simulation (LES). In a receding-horizon fashion, by computing the controls for the next time window based on a prediction of the flow (so ahead of time), they achieved (almost) real-time computational speed. The current paper further explores the potential of this approach in the context of dynamic yaw control, under the assumption of exact state knowledge. To that end, the LES-based, TD-MPC framework is validated on a reference wind farm for three different wind directions, for which we observe gains ranging from 4 (for staggered configurations) to 42% (for aligned configurations w.r.t. the wind direction). The LES-based controller always outperforms a Betz optimal controller and a static (wake model based) yaw controller at near real-time speed. An analysis of the yaw angles reveals that the framework is very effective in steering the wakes away from downstream turbines in upstream and downstream regions, synchronized to the turbulent inflow. By comparing the performance of the LES-based controllers (based on the actuator disc turbine model) against a reference simulation that uses a more accurate actuator sector model, we illustrate the robustness of the approach in the presence of the additional model error.

Population pharmacokinetics of intraperitoneal irinotecan and SN-38 in patients with peritoneal metastases from colorectal origin.

Peritoneal metastases (PM) are common in patients with colorectal cancer. Patients with PM have a poor prognosis, and for those who are not eligible for cytoreductive surgery (CRS) with or without hyperthermic intraperitoneal chemotherapy (HIPEC), palliative chemotherapy is currently the only option. Recently, we conducted a phase I trial (INTERACT) in which irinotecan was administered intraperitoneally (IP) to 18 patients ineligible for CRS-HIPEC. The primary objective was to evaluate covariates influencing the PK profile of irinotecan and SN-38 after IP administration. Secondly, a population PK model was developed to support the further development of IP irinotecan by improving dosing in patients with PM. Patients were treated with IP irinotecan every 2 weeks in combination with systemic FOLFOX-bevacizumab. Irinotecan and SN-38 were measured in plasma (588 samples) and SN-38 was measured in peritoneal fluid (267 samples). Concentration-Time data were log-transformed and analyzed using NONMEM version 7.5 using FOCE+I estimation. An additive error model described the residual error, with inter-individual variability in PK parameters modeled exponentially. The final structural model consisted of five compartments. Weight was identified as a covariate influencing the SN-38 plasma volume of distribution and GGT was found to influence the SN-38 plasma clearance. This population PK model adequately described the irinotecan and SN-38 in plasma after IP administration, with weight and GGT as predictive factors. Irinotecan is converted intraperitoneal to SN-38 by carboxylesterases and the plasma bioavailability of irinotecan is low. This model will be used for the further clinical development of IP irinotecan by providing dosing strategies.

Prediction of Errors in the Field of Additive Manufacturing Technology

Additive manufacturing, also known as 3D printing, allows the formation of complex geometric structures layer by layer. Predicting errors in this process may help identify potential problems in a timely manner and minimise waste. A human may detect an additive manufacturing error, but cannot provide continuous monitoring or real-time correction. The article is focused on the design of a camera system design for online monitoring of the 3D printing process with the task of detecting process errors arising during 3D printing of objects. The article describes the methodology for tracking the occurrence of process errors in 3D printing, which are identified in the OctoPrint Nexus AI plug-in environment for the subsequent application of a suitable solution to minimize the occurrence of defects. The application of a real-time process monitoring system including the ability to correctly predict anomalous behaviour in the context of artificial intelligence has proven to be an appropriate solution to that particular problem.

Transferring energy signatures across space and time to assess their viability for rapid urban energy demand estimation

Energy signatures offer a rapid bottom-up energy modelling tool that relates thermal building space heating and cooling demand to ambient temperature. While simplified from a building physics perspective, energy signatures can easily exploit existing buildings’ operational datasets for their calibration, fitting individual buildings or archetypes. To assess their viability in modelling buildings’ heating and cooling demand at urban or larger scales, it is important to assess whether the same building model expressed as an energy signature could be used under different weather conditions, through spatial (location) or temporal (future weather) transfer. As energy demand depends on environmental factors other than temperature, a systematic error quantification from transferring energy signatures across different climatic contexts is required. These errors are assessed by comparing signature-based estimates to EnergyPlus simulations of a single-family home and an office building archetype exposed to current and future climate across eight US climate zones. Energy signatures are confirmed to be a valid approach if sufficient heating and cooling data is used for their identification. On an annual basis, signature-based estimates result in an error of approximately ± 30 % if the signature is spatially transferred, which can exceed 75 % for unsuitable source locations. When energy signatures are temporally transferred, an annual error of only ± 5–10 % is expected. At a daily resolution, the inherent error from applying the energy signature method is significantly larger than the additional error introduced through a spatiotemporal transfer of the energy signature.