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The wind resource assessment community has long had the goal of reducing the bias between wind plant pre‐construction energy yield assessment (EYA) and the observed annual energy production (AEP). This comparison is typically made between the 50% probability of exceedance (P50) value of the EYA and the long‐term corrected operational AEP (hereafter OA AEP) and is known as the P50 bias. The industry has critically lacked an independent analysis of bias investigated across multiple consultants to identify the greatest sources of uncertainty and variance in the EYA process and the best opportunities for uncertainty reduction. The present study addresses this gap by benchmarking consultant methodologies against each other and against operational data at a scale not seen before in industry collaborations. We consider data from 10 wind plants in North America and evaluate discrepancies between eight consultancies in the steps taken from estimates of gross to net energy. Consultants tend to overestimate the gross energy produced at the turbines and then compensate by further overestimating downstream losses, leading to a mean P50 bias near zero, still with significant variability among the individual wind plants. Within our data sample, we find that consultant estimates of all loss categories, except environmental losses, tend to reduce the project‐to‐project variability of the P50 bias. The disagreement between consultants, however, remains flat throughout the addition of losses. Finally, we find that differences in consultants' estimates of project performance can lead to differences up to $10/MWh in the levelized cost of energy for a wind plant.

Abstract Sleep-tracking devices, particularly within the consumer sleep technology (CST) space, are increasingly used in both research and clinical settings, providing new opportunities for large-scale data collection in highly ecological conditions. Due to the fast pace of the CST industry combined with the lack of a standardized framework to evaluate the performance of sleep trackers, their accuracy and reliability in measuring sleep remains largely unknown. Here, we provide a step-by-step analytical framework for evaluating the performance of sleep trackers (including standard actigraphy), as compared with gold-standard polysomnography (PSG) or other reference methods. The analytical guidelines are based on recent recommendations for evaluating and using CST from our group and others (de Zambotti and colleagues; Depner and colleagues), and include raw data organization as well as critical analytical procedures, including discrepancy analysis, Bland–Altman plots, and epoch-by-epoch analysis. Analytical steps are accompanied by open-source R functions (depicted at https://sri-human-sleep.github.io/sleep-trackers-performance/AnalyticalPipeline_v1.0.0.html). In addition, an empirical sample dataset is used to describe and discuss the main outcomes of the proposed pipeline. The guidelines and the accompanying functions are aimed at standardizing the testing of CSTs performance, to not only increase the replicability of validation studies, but also to provide ready-to-use tools to researchers and clinicians. All in all, this work can help to increase the efficiency, interpretation, and quality of validation studies, and to improve the informed adoption of CST in research and clinical settings.

Topology optimization (TO), a numerical technique to find the optimal material layout with a given design domain, has attracted interest from researchers in the field of structural optimization in recent years. For beginners, opensource codes are undoubtedly the best alternative to learning TO, which can elaborate the implementation of a method in detail and easily engage more people to employ and extend the method. In this paper, we present a summary of various open-source codes and related literature on TO methods, including solid isotropic material with penalization (SIMP), evolutionary method, level set method (LSM), moving morphable components/voids (MMC/MMV) methods, multiscale topology optimization method, etc. Simultaneously, we classify the codes into five levels, from easy to dicult, depending on their diculty, so that beginners can get started and understand the form of code implementation more quickly.

Visible Light Communication (VLC) is a transformative paradigm poised to revolutionize the automotive and numerous other sectors. As the demand for high data rates and low latency applications grows, the limited bandwidth of standard white LED-based lamps—typically restricted to a few MHz—presents a significant bottleneck. While high-order modulation schemes like Quadrature Amplitude Modulation (QAM) offer superior spectral efficiency, their computational complexity often hinders real-time implementation. Consequently, the existing literature lacks experimental validation of low-latency real-time VLC links. This work addresses this challenge by proposing a modified algorithm that is implemented in a resource-efficient QAM modulator/demodulator (MODEM) for an FPGA. The algorithm includes the synchronization loop. The proposed MODEM is available as open-source code and provides a scalable foundation for researchers to explore low-latency real-time VLC links. Experimental results demonstrate successful 2, 4, and 6 Mb/s links using 4-, 16-, and 64-QAM constellations, respectively, over a white-phosphor-power LED. We measured a latency of less than 1.3 μs.

This work investigates crystallization modeling by modifying an open-source computational fluid dynamics code OpenFOAM. The crystallization behavior of high-density polyethylene (HDPE) is implemented according to theoretical and experimental literature. A number of physical interdependencies are included. The cavity is modeled as deformable. The heat transfer coefficient in the thermal contact towards the mold depends on contact pressure. The thermal conductivity is pressure- and crystallinity-dependent. Specific heat depends on temperature and crystallinity. Latent heat is released according to the crystallization progress and temperature. Deviatoric elastic stress is evolved in the solidified material. The prediction of the cavity pressure evolution is used for the assessment of the solution quality because it is experimentally available and governs the residual stress development. Insight into the thermomechanical conditions is provided with through-thickness plots of pressure, temperature and cooling rate at different levels of crystallinity. The code and simulation setup are made openly available to further the research on the topic.

The possibilities of applying the pure Lagrangian vortex methods of computational fluid dynamics to viscous incompressible flow simulations are considered in relation to various problem formulations. The modification of vortex methods—the Viscous Vortex Domain method—is used which is implemented in the VM2D code developed by the authors. Problems of flow simulation around airfoils with different shapes at various Reynolds numbers are considered: the Blasius problem, the flow around circular cylinders at different Reynolds numbers, the flow around a wing airfoil at the Reynolds numbers 104 and 105, the flow around two closely spaced circular cylinders and the flow around rectangular airfoils with a different chord to the thickness ratio. In addition, the problem of the internal flow modeling in the channel with a backward-facing step is considered. To store the results of the calculations, the POD technique is used, which, in addition, allows one to investigate the structure of the flow and obtain some additional information about the properties of flow regimes.

Abstract Analyzing scored sleep is a fundamental prerequisite to understanding how sleep changes between health and disease. Classically, this is accomplished by manually calculating various measures (e.g. percent of non-rapid eye movement sleep) from a collection of scored sleep files. This process can be tedious and error-prone, especially when studies include large animal numbers or involve long recording sessions. To address this issue, we present SleepInvestigatoR, a versatile tool that can quickly organize and analyze multiple scored sleep files into a single output. The function is written in the open-source statistical language R and has a total of 25 parameters that can be set to match a wide variety of experimental needs. SleepInvestigatoR delivers a total of 23 unique measures of sleep, including all measures commonly reported in the rodent literature. A simple plotting function is also provided to quickly graph and visualize the scored data. All code is designed to be implemented with little formal coding knowledge, and step-by-step instructions are provided on the corresponding GitHub page. Overall, SleepInvestigatoR provides the sleep researcher a critical tool to increase efficiency, interpretation, and reproducibility in analyzing scored rodent sleep. Graphical Abstract Graphical Abstract

Significance: Active implants require batteries as power supply. Their lifetime is limited and may require a second surgical intervention for replacement. Intracorporal energy harvesting techniques generate power within the body and supply the implant. Solar cells below the skin can be used to harvest energy from light. Aim: To investigate the potential of subdermal solar energy harvesting. Approach: We evaluated global radiation data for defined time slots and calculated the output power of a subdermal solar module based on skin and solar cell characteristics. We assumed solar exposure profiles based on daily habits for an implanted solar cell. The output power was calculated for skin types VI and I/II. Results: We show that the yearly mean power in most locations on Earth is sufficient to power modern cardiac pacemakers if 10 min midday solar irradiation is assumed. All skin types are suitable for solar harvesting. Moreover, we provide a software tool to predict patient-specific output power. Conclusions: Subdermal solar energy harvesting is a viable alternative to primary batteries. The comparison to a human case study showed a good agreement of the results. The developed code is available open source to enable researchers to investigate further applications of subdermal solar harvesting.

In this article, we develop a simple mathematical GNU Octave/MATLAB code that is easy to modify for the simulation of mathematical models governed by fractional-order differential equations, and for the resolution of fractional-order optimal control problems through Pontryagin’s maximum principle (indirect approach to optimal control). For this purpose, a fractional-order model for the respiratory syncytial virus (RSV) infection is considered. The model is an improvement of one first proposed by the authors in 2018. The initial value problem associated with the RSV infection fractional model is numerically solved using Garrapa’s fde12 solver and two simple methods coded here in Octave/MATLAB: the fractional forward Euler’s method and the predict-evaluate-correct-evaluate (PECE) method of Adams–Bashforth–Moulton. A fractional optimal control problem is then formulated having treatment as the control. The fractional Pontryagin maximum principle is used to characterize the fractional optimal control and the extremals of the problem are determined numerically through the implementation of the forward-backward PECE method. The implemented algorithms are available on GitHub and, at the end of the paper, in appendixes, both for the uncontrolled initial value problem as well as for the fractional optimal control problem, using the free GNU Octave computing software and assuring compatibility with MATLAB.

This work presents AudioSet-Tools , a modular and extensible Python framework designed to streamline the creation of task-specific datasets derived from Google AudioSet. Despite its extensive coverage, AudioSet suffers from weak labeling, class imbalance, and a loosely structured taxonomy, which hinder its applicability in machine listening workflows. AudioSet-Tools addresses these issues through configurable taxonomy-consistent label filtering and class rebalancing strategies. The framework includes automated routines for data download and transformation, enabling reproducible and semantically consistent dataset generation for pre-training and downstream fine-tuning of deep learning models. While domain-agnostic, we showcase its versatility through AudioSet-EV , a curated subset focused on emergency vehicle siren recognition — a socially relevant and technically challenging use case that highlights structural and semantic gaps in the AudioSet taxonomy. We further provide an extensive comparative benchmark of AudioSet-EV against state-of-the-art emergency vehicle corpora. All source code and datasets are openly released on GitHub and Zenodo , fostering transparency and reproducibility in real-world audio signal processing research.