Calibration Process Research Articles

Modelling beach volume changes caused by moderate and weak hydrodynamic conditions

The goals of this study were the calibration of the XBeach model for moderate and weak hydrodynamic conditions in 2D mode and the determination of the volumetric changes induced by them. An evaluation of model performance was made based on the Brier Skill Score (BSS), the visual match of the profile shape (VMS), the absolute volumetric change error (m3/m) and the relative volumetric change error (%). An analysis of accuracy in determining volumetric changes in the dune coast, with XBeach applied, provided an average absolute error in determination of volumetric changes of 4.0 m3/m for a significant storm and 1.5 m3/m for moderate and weak hydrodynamic conditions in a 2D model calibration process. Simulations of morphological changes caused by moderate and weak hydrodynamic conditions for seven timeframes classified into three groups have been run in 2D mode. Within a validation process, the average absolute error in the determination of volumetric changes in the beach was ranging from 0.64 m3/m to 2.42 m3/m depending on the group of hydrodynamic conditions. A high value of the correlation coefficient (R) between the measured and modelled balance of volumetric changes (m3/m) for all timeframes – 0.79 and for the ‘strongest’ group no. 3 – 0.91 were revealed. For the remaining groups, i.e., group no. 1 and no. 2 of hydrodynamic conditions, no such correlation was observed. Furthermore, the temporal analysis of the sum of beach volumes proved that a rising trend was observed over 4 months. Thus, moderate and weak hydrodynamic conditions dominated the accumulation within the area of study. XBeach reflected this trend well, producing a difference between the measured and modelled sum of volumes at the end of the timeframe of 7.27 m3/m, which is close to the volume determination error by the model.

A comprehensive method for error separation in hydrological modelling

AbstractEvaluation metrics play a pivotal role in the calibration process of hydrological models, serving as objective functions that directly influence the final values of model parameters and significantly affect users' perceptions of model performance. However, the choice and interpretation of evaluation metrics are subjective; therefore, this study provides a more objective framework for assessing model performance. This paper initially explored the applicability of various commonly used evaluation metrics, providing an overview of their limitations. Following this, we decomposed errors by analysing their physical significance and geometric representation in scatter plots, categorizing them into systematic and unsystematic errors. Through the decomposition and derivation of the Nash–Sutcliffe efficiency (NSE) formula, we established the quantitative relationship among various evaluation metrics. The soil and water assessment tool (SWAT) model was utilized to simulate monthly runoff in the Baishan basin (China), for the period 1994–2017, with NSE serving as the objective function for calibration. Our findings are consistent with previous studies, indicating that the model tends to slightly underestimate high flows while significantly overestimating low flows. Further analysis through error decomposition and the examination of relationships among various evaluation metrics revealed that unsystematic errors were dominant during the spring snowmelt runoff period, while systematic errors prevailed in the dry season. By evaluating the runoff series based on the magnitude of runoff or by categorizing it according to seasons and months, a more stringent assessment of the model's performance was achieved. These findings not only highlight the necessity for careful selection of evaluation metrics but also underscore the significance of our methodological advancements in enhancing hydrological model precision and reliability.

MRCNN: Multi-input residual convolution neural network for three-dimensional reconstruction of bubble flows from light field images

Accurate measurement of bubbles in air-water two-phase flows holds immense significance in the realm of thermal hydraulics assessments within nuclear reactors. Nevertheless, conventional bubble measurement techniques grapple with challenges encompassing system intricacy, limited real-time capabilities, and inaccuracies stemming from their inherent two-dimensional (2-D) nature. In response, we pioneered an innovative three-dimensional (3-D) analysis approach that leverages light field (LF) imaging diagnosis and deep learning algorithms. Unlike traditional 2-D reconstruction methods, our approach enables direct computation of bubble depth from LF images using digital refocusing technology. Following calibration, a seamless transformation is established between the camera coordinate system and the real-world coordinate system using a sharpness evaluation algorithm. This calibration process ensures precise alignment of refocused images with real-world positions. Subsequently, fully automated and highly accurate computations of bubble depth are realized from input images via the incorporation of a multi-input residual convolution neural network (MRCNN). The limitations of traditional two-dimensional imaging techniques are effectively addressed by this methodology, resulting in a reduction in measurement errors. The study confirms the feasibility of employing LF imaging diagnosis and deep learning algorithms for bubble measurements in an air-water two-phase flow, offering a significant improvement over traditional methods.

Finite Element Modeling and Calibration of a Three-Span Continuous Suspension Bridge Based on Loop Adjustment and Temperature Correction.

Precise finite element modeling is critically important for the construction and maintenance of long-span suspension bridges. During the process of modeling, shape-finding and model calibration directly impact the accuracy and reliability. Scholars have provided numerous alternative proposals for the shape-finding of main cables in suspension bridges from both theoretical and finite element analysis perspectives. However, it is difficult to apply these solutions to suspension bridges with special components. Seeking a viable solution for such suspension bridges holds practical significance. The Nanjing Qixiashan Yangtze River Bridge is the first three-span suspension bridge in China. To maintain the configuration of the main cable, the suspension bridge is equipped with specialized suspenders near the anchors, referred to as displacement-limiting suspenders. It is the first suspension bridge in China to use displacement-limiting suspenders and their anchorage system. Taking the suspension bridge as a research background, this paper introduces a refined finite element modeling approach considering the effect of geometric nonlinearity. Firstly, based on the loop adjustment and temperature correction, the shape-finding and force assessment of the main cables are carried out. On this basis, a nonlinear finite element model of the bridge was established and calibrated, taking into account factors such as pylon settlement and cable saddle precession. Finally, the static and dynamic characteristics of the suspension bridge were thoroughly investigated. This study aims to provide a reference for the design, construction and operation of the three-span continuous suspension bridge.

Uncertainty Calculation as a Service: Integrating Cloud-Based Microservices for Enhanced Calibration and DCC Generation.

The calibration industry is renowned for its diverse and sophisticated equipment and complex processes, which necessitate innovative solutions to keep pace with rapidly advancing technology. This paper introduces an enhancement to an existing microservice-based cloud architecture, aimed at effectively managing the inherent complexity within this field. The enhanced architecture seamlessly integrates various equipment types and communication technologies, aligning diverse stakeholder expectations into a unified system that ensures efficient and accurate calibration processes. It highlights the integration of microservices to facilitate various methods of uncertainty calculation and the generation of digital calibration certificates (DCCs). A case study on RF power measurement illustrates the practical application and benefits of the enhanced architecture. Although initially focused on RF power measurement, the flexible architecture allows for future expansions to accommodate new standards and measurement techniques. The enhanced system offers a comprehensive approach to managing data flow from calibration equipment to the final generation of DCCs, utilizing cloud-based services for efficient data processing. As a future direction, this extension sets the groundwork for broader applicability across multiple measurement types, ensuring readiness for upcoming advancements in metrology.

A Markov cohort model for Endoscopic surveillance and management of Barrett’s esophagus

Barrett's esophagus is an asymptomatic precursor to esophageal adenocarcinoma. Its rising incidence due to lifestyle factors, coupled with healthcare costs, requires cost-effective alternatives for surveillance. We propose a decision-analytic Markov cohort model to simulate Barrett's esophagus's natural progression to esophageal adenocarcinoma using TreeAge Pro. Health states include metaplasia (non-dysplastic Barrett's esophagus), low-grade dysplasia, high-grade dysplasia, and esophageal adenocarcinoma. Triplicates of these health states represent one non-stratified and two risk-stratified cohorts for devising risk-based strategies. A cycle length of six months and a time horizon of 35 years, totaling 70 cycles, is considered. Model inputs are derived from literature and, when unavailable from an extensive local database of 1087 patients (5081 person-years) from March 2003–2021, cleaned and analyzed with Rstudio (R version 3.6.3). Specific tests included descriptive statistics, Cox-proportional hazard models, and graphing. A seven-step calibration process is performed for risk-stratified and non-stratified groups simultaneously to match the progression to high-grade dysplasia and esophageal adenocarcinoma. This allows comparison between risk- and non-risk-based strategies. The calibration process included input parameterization, optimization, goodness of fit calculation, selection of sets meeting convergence criteria, and integration into probabilistic sensitivity analysis. This process generated 10,187 sets of transition probabilities, with 4358 meeting convergence criteria, ensuring equal model outputs in all groups. Mortality was 10.7% for cancer-related deaths, matching literature values. This process provides a robust framework for evaluating Barrett's esophagus progression and management strategies, supporting informed decision-making in healthcare.

A genetic algorithm-based method to modulate the difficulty of serious games along consecutive robot-assisted therapy sessions

Background and Objective:One of the biggest challenges during neurorehabilitation therapies is finding an appropriate level of therapy intensity for each patient to ensure the recovery of movement of the affected limbs while maintaining motivation. Different studies have proposed adapting the difficulty of exercises based on psychophysiological state, based on success rate, or by modeling the user’s skills. However, all studies propose solutions for a single session, requiring a calibration process before using it in each session. We propose a dynamic adaptation method that can be used during different rehabilitation sessions, without the need for recalibration between sessions. Methods:The adaptation architecture is based on a genetic algorithm that aims to maintain a certain score level and to motivate the user to move. The method has been evaluated with two serious games for five sessions using a rehabilitation robot. A common initial evaluation was made for all the users involved in the study, and the game parameters that best suited each user from the previous session were introduced as the starting point of the next session. In addition, the desired score rate was lowered between sessions to increase the difficulty level. The psychophysiological state of the users was measured based on the Self-Assessment Manikin test, as well as different cardiorespiratory and galvanic skin response signals were analyzed. Results:The adaptation architecture proposed can find those game parameters that maximize the user movement for both games. In one of the games, the score rate set for each session is followed with high fidelity. The degree of personalization in the games increases between sessions as the dispersion of the game parameters grows. The Self-Assessment Manikin test and the physiological signals results would indicate that the psychophysiological state remains equal between sessions despite an increase in game difficulty. Conclusions:The genetic algorithm-based game adaptation has proven efficacy in maximizing the therapy performance through the sessions without needing recalibration. It also can be concluded that the design of the game influences the adaptation performance. Additionally, adaptive game design facilitated by our method does not significantly impact players’ emotional or physiological states.

Improved motor imagery training for subject's self-modulation in EEG-based brain-computer interface.

For the electroencephalogram- (EEG-) based motor imagery (MI) brain-computer interface (BCI) system, more attention has been paid to the advanced machine learning algorithms rather than the effective MI training protocols over past two decades. However, it is crucial to assist the subjects in modulating their active brains to fulfill the endogenous MI tasks during the calibration process, which will facilitate signal processing using various machine learning algorithms. Therefore, we propose a trial-feedback paradigm to improve MI training and introduce a non-feedback paradigm for comparison. Each paradigm corresponds to one session. Two paradigms are applied to the calibration runs of corresponding sessions. And their effectiveness is verified in the subsequent testing runs of respective sessions. Different from the non-feedback paradigm, the trial-feedback paradigm presents a topographic map and its qualitative evaluation in real time after each MI training trial, so the subjects can timely realize whether the current trial successfully induces the event-related desynchronization/event-related synchronization (ERD/ERS) phenomenon, and then they can adjust their brain rhythm in the next MI trial. Moreover, after each calibration run of the trial-feedback session, a feature distribution is visualized and quantified to show the subjects' abilities to distinguish different MI tasks and promote their self-modulation in the next calibration run. Additionally, if the subjects feel distracted during the training processes of the non-feedback and trial-feedback sessions, they can execute the blinking movement which will be captured by the electrooculogram (EOG) signals, and the corresponding MI training trial will be abandoned. Ten healthy participants sequentially performed the non-feedback and trial-feedback sessions on the different days. The experiment results showed that the trial-feedback session had better spatial filter visualization, more beneficiaries, higher average off-line and on-line classification accuracies than the non-feedback session, suggesting the trial-feedback paradigm's usefulness in subject's self-modulation and good ability to perform MI tasks.

Detecting naphthenic acids in oil sands process water with microbial electrochemical sensor: Impact of inoculum sources and quorum sensing autoinducer

This study presents the inaugural demonstration of a microbial electrochemical cell (MXC)-based biosensor for naphthenic acids (NAs) detection in oil sands process water (OSPW) from a mining site. During the calibration process, different operating parameters were evaluated, including standing times (3 – 12 hours) and charging durations (5 – 20 minutes) within the charging-discharging cycles. The observations ascertained that a combination of a 12-hour standing period followed by a 15-minute charging period yielded the optimal current output when measuring NA concentrations ranging from 4.98 to 24.9 mg/L in real OSPW samples (diluted to concentrations of 20–100 %). Subsequently, incorporating diverse inoculum sources from MXCs led to significant signal output improvements, with one sustained on a mixture of cyclohexane carboxylic acids, commercial naphthenic acids, and real OSPW, and another exposed to acetate medium for over four years. Moreover, integrating quorum sensing (QS) autoinducers (10 μM) increased the current generation, highlighting the potential for more sensitive detection capabilities. Ultimately, our MXC biosensor could enable a cost-effective and rapid detection of OSPW NAs, offering a potential alternative for environmental surveillance.

Deep-Learning-Guided Electrochemical Impedance Spectroscopy for Calibration-Free Pharmaceutical Moisture Content Monitoring.

The moisture content of pharmaceutical powders can significantly impact the physical and chemical properties of drug formulations, solubility, flowability, and stability. However, current technologies for measuring moisture content in pharmaceutical materials require extensive calibration processes, leading to poor consistency and a lack of speed. To address this challenge, this study explores the feasibility of using impedance spectroscopy to enable accurate, rapid testing of moisture content of pharmaceutical materials with minimal to zero calibration. By utilizing electrochemical impedance spectroscopy (EIS) signals, we identify a strong correlation between the electrical properties of the materials and varying moisture contents in pharmaceutical samples. Equivalent circuit modeling is employed to unravel the underlying mechanism, providing valuable insights into the sensitivity of impedance spectroscopy to moisture content variations. Furthermore, the study incorporates deep learning techniques utilizing a 1D convolutional neural network (1DCNN) model to effectively process the complex spectroscopy data. The proposed model achieved a notable predictive accuracy with an average error of just 0.69% in moisture content estimation. This method serves as a pioneering study in using deep learning to provide a reliable solution for real-time moisture content monitoring, with potential applications extending from pharmaceuticals to the food, energy, environmental, and healthcare sectors.

Pre-Launch Calibration of the Bidirectional Reflectance Distribution Function (BRDF) of Ultraviolet-Visible Hyperspectral Sensor Diffusers

An Ultraviolet-Visible Hyperspectral Sensors (UVS) instrument is an ultraviolet-visible imaging spectrograph equipped with two-dimensional charge-coupled device detectors. It records both the spectrum and the swath perpendicular to the flight direction, offering a wide 112° swath. This configuration enables global daily ground coverage with high spatial resolution. The absolute values of in-orbit solar irradiance can be evaluated using the bidirectional reflectance distribution function (BRDF), with the measurement accuracy directly affecting the accuracy of constituent inversion. This paper outlines the calibration process for the BRDF of the UVS, detailing the calibration methods and equipment used. It also proposes a BRDF model and discusses key coefficients. The accuracy levels of the UVS in the UV1, UV2, and VIS channels were 2.162%, 2.162%, and 2.173%, respectively.

Calibration guide for watershed modeling with distributed groundwater modeling: application for the SWAT+ model

ABSTRACT We present a new calibration strategy guide for coupled surface–subsurface hydrologic models. The goal is to reduce bias in environmental modeling and make calibration processes more consistent. The strategy guide enhances model efficiency and accuracy by focusing on changes to channel, soil, landscape, and aquifer properties. This approach is particularly beneficial when simulating important hydrologic features like baseflow and peaks and across different watersheds. Developed using the Soil and Water Assessment Tool (SWAT+) and the new gwflow module in six diverse US watersheds, each with unique hydrologic characteristics, the guide encourages improved calibration through routine use. It addresses specific issues related to the temporal distribution of streamflow and offers a robust method for enhancing model performance across a wide range of hydrologic conditions. By employing parameter estimation and sensitivity analysis, the approach ensures rigorous and objective calibration of SWAT+ and other models that include land, soil, aquifer, and water processes, leading to more accurate hydrologic modeling.

Improving Radiometric Block Adjustment for UAV Multispectral Imagery under Variable Illumination Conditions

Unmanned aerial vehicles (UAVs) equipped with multispectral cameras offer great potential for applications in precision agriculture. A critical challenge that limits the deployment of this technology is the varying ambient illumination caused by cloud movement. Rapidly changing solar irradiance primarily affects the radiometric calibration process, resulting in reflectance distortion and heterogeneity in the final generated orthomosaic. In this study, we optimized the radiometric block adjustment (RBA) method, which corrects for changing illumination by comparing adjacent images and from incidental observations of reference panels to produce accurate and uniform reflectance orthomosaics regardless of variable illumination. The radiometric accuracy and uniformity of the generated orthomosaic could be enhanced by improving the weights of the information from the reference panels and by reducing the number of tie points between adjacent images. Furthermore, especially for crop monitoring, we proposed the RBA-Plant method, which extracts tie points solely from vegetation areas, to further improve the accuracy and homogeneity of the orthomosaic for the vegetation areas. To validate the effectiveness of the optimization techniques and the proposed RBA-Plant method, visual and quantitative assessments were conducted on a UAV-image dataset collected under fluctuating solar irradiance conditions. The results demonstrated that the optimized RBA and RBA-Plant methods outperformed the current empirical line method (ELM) and sensor-corrected approaches, showing significant improvements in both radiometric accuracy and homogeneity. Specifically, the average root mean square error (RMSE) decreased from 0.084 acquired by the ELM to 0.047, and the average coefficient of variation (CV) decreased from 24% (ELM) to 10.6%. Furthermore, the orthomosaic generated by the RBA-Plant method achieved the lowest RMSE and CV values, 0.039 and 6.8%, respectively, indicating the highest accuracy and best uniformity. In summary, although UAVs typically incorporate lighting sensors for illumination correction, this research offers different methods for improving uniformity and obtaining more accurate reflectance values from orthomosaics.

Experimentally Calibrated Numerical Modeling for Performance-Based Seismic Design of Low-Frequency Rocking Electrical Cabinets

ABSTRACT In this paper, the performances of two electrical cabinet specimens during shake table tests are described in detail in terms of the observed damage and their measured peak transient responses. The measured responses of the specimens are then used to calibrate simplified numerical models of the two specimens. The calibrated numerical models are used to derive fragility functions to illustrate their usefulness in performance-based seismic design applications. The calibration process and the basic concept of the numerical models can be used for calibrating similar numerical models for other typologies of similar floor anchored non-structural elements that have a rocking failure mode.

Simulation experiment design for calibration via active learning

Simulation models often have parameters as input and return outputs to understand the behavior of complex systems. Calibration is the process of estimating the values of the parameters in a simulation model in light of observed data from the system that is being simulated. When simulation models are expensive, emulators are built with simulation data as a computationally efficient approximation of an expensive model. An emulator then can be used to predict model outputs, instead of repeatedly running an expensive simulation model during the calibration process. Sequential design with an intelligent selection criterion can guide the process of collecting simulation data to build an emulator, making the calibration process more efficient and effective. This article proposes two novel criteria for sequentially acquiring new simulation data in an active learning setting by considering uncertainties on the posterior density of parameters. Analysis of several simulation experiments and real-data simulation experiments from epidemiology demonstrates that proposed approaches result in improved posterior and field predictions.

The patterns of potential evapotranspiration and seasonal aridity under the change in climate in the upper Blue Nile basin, Ethiopia

Evapotranspiration is one of the determinant components of the hydrological process, highly influenced by climate change due to the increase in atmospheric temperature at global and regional scales. This study was designed to evaluate the extent to which climate change affects the Potential Evapotranspiration (PET) and the consequent Aridity Index (AI) in the high-emission scenario of Representative Concentration Pathways (RCPs) in the Gilgel Abay, Ribb, Gumara, and Megech watersheds using six Global Climate Models in the 2011–2040, 2041–2070, and 2071–2100 relative to the 1971–2000 (baseline period). The average PET is simulated using the Soil Water Assessment Tool (SWAT) model. Penman-Monteith and Hargreaves methods were used in the computation of PET using the water balance technique, and the Hargreaves method was found more efficient in calibration and validation processes. The Aridity Index (AI) of watersheds is calculated using the ratio of precipitation and potential evapotranspiration. The study revealed that the change in annual average PET is showing an increasing pattern in the three time periods, and the highest rate of changes in Megech, Gilgel Abay, Ribb, and Gumara, watersheds are 16.66%, 15.53%, 14.68%, and 13.46%, respectively in the 2071–2100 time period. Seasonally, the highest rate of change in PET is 20.37% (September), 19.29% (April), 17.46% (March), and 17.02% (March) in the Megech, Gilgel Abay, Ribb, and Gumara, respectively. Similarly, the seasonal highest change in Aridity Index (AI) is also likely to be observed in the 2071–2100 in which in the dry season, it accounts –0.303 (March), –0.299 (March), –0.285 (April), and –0.276 (April) in the Ribb, Gumara, Gilgel Abay, and Megech, respectively, whereas in the rainy season, the change is 0.263, 0.258, 0.238, and 0.211 in the Gilgel Abay, Gumara, Ribb, and Megech, respectively. In general, due to the rising atmospheric temperature, the amount of moisture during dry seasons in the headwater catchments of the upper Blue Nile basin is expected to deplete in the 21st century. Therefore, it is highly recommended to use different climate change adaptation mechanisms including adopting suitable physical and biological water conservation techniques to enhance the amount of water stored in the subsurface and joining the groundwater during the rainy season.

An automated framework for material property calibration in loudspeaker simulation model

The shift to virtual meetings, online classes, and remote work has established a new norm, leading to a surge in the use of virtual communication platforms such as Zoom and Microsoft Teams. This shift has increased the demand for high-quality headsets and speakerphones, emphasizing the need for clear, superior audio quality. The process of calibrating material properties typically relies on repetitive simulations guided by experts' intuition, presenting challenges in establishing new Finite Element Models (FEMs) of loudspeakers, as it requires the repeated identification of material property values. We present a systematic framework for calibrating the mechanical material properties of loudspeaker drivers, a crucial prerequisite for developing accurate FEMs of loudspeakers. Specifically, we propose a statistically-driven approach to replace the conventional manual calibration process, which typically relies on multiple simulations guided by expert intuition. Efficient Global Optimization (EGO) is applied to address the expensive optimization problems of loudspeaker simulation. To tackle the curse of dimensionality, the objective function is decomposed into several functions based on effective parameter groups using Global Sensitivity Analysis (GSA) results. The parameters of the FEM are then calibrated to the reference data from the Lumped Parameter Model (LPM) using the decomposed-reduced objective function, providing the calibrated parameters for the loudspeaker simulation. By implementing this novel approach, even individuals without prior knowledge or experience in loudspeaker material properties can effectively and reliably obtain the necessary data for finite element modeling.

Machine learning modeling for fuel cell-battery hybrid power system dynamics in a Toyota Mirai 2 vehicle under various drive cycles

Electrification is considered essential for the decarbonization of mobility sector, and understanding and modeling the complex behavior of modern fuel cell-battery electric-electric hybrid power systems is challenging, especially for product development and diagnostics requiring quick turnaround and fast computation. In this study, a novel modeling approach is developed, utilizing supervised machine learning algorithms, to replicate the dynamic characteristics of the fuel cell-battery hybrid power system in a 2021 Toyota Mirai 2nd generation (Mirai 2) vehicle under various drive cycles. The entire data for this study is collected by instrumenting the Mirai vehicle with in-house data acquisition devices and tapping into the Mirai controller area network bus during chassis dynamometer tests. A multi-input - multi-output, feed-forward artificial neural network architecture is designed to predict not only the fuel cell attributes, such as average minimum cell voltage, coolant and cathode air outlet temperatures, but also the battery hybrid system attributes, including lithium-ion battery pack voltage and temperature with the help of 15 system operating parameters. Over 21,0000 data points on various drive cycles having combinations of transient and near steady-state driving conditions are collected, out of which around 15,000 points are used for training the network and 6,000 for the evaluation of the model performance. Various data filtration techniques and neural network calibration processes are explored to condition the data and understand the impact on model performance. The calibrated neural network accurately predicts the hybrid power system dynamics with an R-squared value greater than 0.98, demonstrating the potential of machine learning algorithms for system development and diagnostics.

Forecasting the Usage of Bike-Sharing Systems through Machine Learning Techniques to Foster Sustainable Urban Mobility

Bike-sharing systems can definitely contribute to the achievement of sustainable urban mobility. In spite of this potential, their planning and operation are not free of difficulties. The main operational problem of bike-sharing systems is the unbalanced distribution of bicycles over the service region, resulting in zones where bicycles are scarce and zones where bicycles accumulate. In order to provide an acceptable level of service, the operator needs to carry out repositioning movements, which are costly. Bike-sharing repositioning optimization solutions have been developed that rely on the estimation of the expected number of requests and returns at each location. Errors in this prediction are directly transferred to suboptimal repositioning solutions. For this reason, the development of methodologies able to accurately forecast bike-sharing usage is an issue of great concern. This paper deals with this problem using machine learning regression methods, which yield usage predictions from inputs such as historical usage and meteorological data. Three different machine learning regression techniques have been analyzed (i.e., random forest, gradient boosting, and artificial neural networks) and applied to a case study based on the New York City bike-sharing system. This paper describes the variables of the models and their calibration processes. Results are analyzed and compared in order to determine which one of the three techniques and under what conditions is the most adequate. Comparisons are not only made in terms of accuracy but also with respect to the applicability of the algorithms. Results indicate that, given the similar accuracy of all methods, the simpler calibration process of the random forest technique makes it advisable for most applications.

Dynamic range and optimization strategies for radiochromic film calibration using gradient radiation fields.

This investigation aimed to optimize gradient positioning for radiochromic film calibration to facilitate a uniform distribution of calibration points. The study investigated the influence of various parameters on gradient dose profiles generated by a physical wedge, assessing their impact on the field's dose dynamic range, a scalar quantity representing the span of absorbed doses. Numerical parameterization of the physical wedge profile was used to visualize and quantify the impact of field size, depth, and energy on the dynamic range of dose gradients. This concept enabled the optimization of the gradient positioning and estimation of the necessary number of exposures for the desired calibration dose range. An optimization algorithm based on histogram bin height minimization was developed and presented. The maximum dynamic range was achieved with a 20 20 field size at 5 cm depth. Optimization of wedge gradient positioning yielded the most uniform dose distribution with 7 exposures for the [1,10] Gy range and 8 exposures for the [1,20] Gy range. Film calibration using gradients centered at 1.6, 3, 3.5, and 7 Gy central axis (CAX), obtained through optimized gradient positioning, was showcased. The presented work demonstrates the potential for an improved film calibration process, with efficient material utilization and enhanced dosimetric accuracy for clinical applications. While the method was described for the use of a physical wedge, the methodology can be easily extended to the use of a more convenient dynamicwedge.