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This paper presents the design and development of a proof of concept (PoC) open-source data logger system for wireless data acquisition via Wi-Fi aimed at bench testing and fault detection in combustion and electric engines. The system integrates multiple sensors, including accelerometers, microphones, thermocouples, and gas sensors, to monitor critical parameters, such as vibration, sound, temperature, and CO2 levels. These measurements are crucial for detecting anomalies in engine performance, such as ignition and combustion faults. For combustion engines, temperature sensors detect operational anomalies, including diesel engines operating beyond the normal range of 80 °C to 95 °C and gasoline engines between 90 °C and 110 °C. These readings help identify failures in cooling systems, thermostat valves, or potential coolant leaks. Acoustic sensors identify abnormal noises indicative of issues such as belt misalignment, valve knocking, timing irregularities, or loose parts. Vibration sensors detect displacement issues caused by engine mount failures, cracks in the engine block, or defects in pistons and valves. These sensors can work synergistically with acoustic sensors to enhance fault detection. Additionally, CO2 and organic compound sensors monitor fuel combustion efficiency and detect failures in the exhaust system. For electric motors, temperature sensors help identify anomalies, such as overloads, bearing problems, or excessive shaft load. Acoustic sensors diagnose coil issues, phase imbalances, bearing defects, and faults in chain or belt systems. Vibration sensors detect shaft and bearing problems, inadequate motor mounting, or overload conditions. The collected data are processed and analyzed to improve engine performance, contributing to reduced greenhouse gas (GHG) emissions and enhanced energy efficiency. This PoC system leverages open-source technology to provide a cost-effective and versatile solution for both research and practical applications. Initial laboratory tests validate its feasibility for real-time data acquisition and highlight its potential for creating datasets to support advanced diagnostic algorithms. Future work will focus on enhancing telemetry capabilities, improving Wi-Fi and cloud integration, and developing machine learning-based diagnostic methodologies for combustion and electric engines. [ABSTRACT FROM AUTHOR]

This paper presents the design and development of a proof of concept (PoC) open-source data logger system for wireless data acquisition via Wi-Fi aimed at bench testing and fault detection in combustion and electric engines. The system integrates multiple sensors, including accelerometers, microphones, thermocouples, and gas sensors, to monitor critical parameters, such as vibration, sound, temperature, and CO2 levels. These measurements are crucial for detecting anomalies in engine performance, such as ignition and combustion faults. For combustion engines, temperature sensors detect operational anomalies, including diesel engines operating beyond the normal range of 80 °C to 95 °C and gasoline engines between 90 °C and 110 °C. These readings help identify failures in cooling systems, thermostat valves, or potential coolant leaks. Acoustic sensors identify abnormal noises indicative of issues such as belt misalignment, valve knocking, timing irregularities, or loose parts. Vibration sensors detect displacement issues caused by engine mount failures, cracks in the engine block, or defects in pistons and valves. These sensors can work synergistically with acoustic sensors to enhance fault detection. Additionally, CO2 and organic compound sensors monitor fuel combustion efficiency and detect failures in the exhaust system. For electric motors, temperature sensors help identify anomalies, such as overloads, bearing problems, or excessive shaft load. Acoustic sensors diagnose coil issues, phase imbalances, bearing defects, and faults in chain or belt systems. Vibration sensors detect shaft and bearing problems, inadequate motor mounting, or overload conditions. The collected data are processed and analyzed to improve engine performance, contributing to reduced greenhouse gas (GHG) emissions and enhanced energy efficiency. This PoC system leverages open-source technology to provide a cost-effective and versatile solution for both research and practical applications. Initial laboratory tests validate its feasibility for real-time data acquisition and highlight its potential for creating datasets to support advanced diagnostic algorithms. Future work will focus on enhancing telemetry capabilities, improving Wi-Fi and cloud integration, and developing machine learning-based diagnostic methodologies for combustion and electric engines.

Topology optimization (TO), a numerical technique to find the optimalmaterial layoutwith 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, fromeasy to difficult, depending on their difficulty, so that beginners can get started and understand the form of code implementation more quickly. [ABSTRACT FROM AUTHOR]

Abstract 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.

This article describes the open-source C++ code VM2D for the simulation of two-dimensional viscous incompressible flows and solving fluid-structure interaction problems. The code is based on the Viscous Vortex Domains (VVD) method developed by Prof. G. Ya. Dynnikova, where the viscosity influence is taken into account by introducing the diffusive velocity. The original VVD method was supplemented by the author's algorithms for boundary condition satisfaction, which made it possible to increase the accuracy of flow simulation near the airfoil's surface line and reduce oscillations when calculating hydrodynamic loads. This paper is aimed primarily at assessing the efficiency of the parallelization of the algorithm. OpenMP, MPI, and Nvidia CUDA parallel programming technologies are used in VM2D, which allow performing simulations on computer systems of various architectures, including those equipped with graphics accelerators. Since the VVD method belongs to the particle methods, the efficiency of parallelization with the usage of graphics accelerators turns out to be quite high. It is shown that in a real simulation, one graphics card can replace about 80 nodes, each of which is equipped with 28 CPU cores. The source code of VM2D is available on GitHub under GNU GPL license. [ABSTRACT FROM AUTHOR]

There are many ways to use open source code to implement digital standards for scholarly journal publishing. However, providing digital services using open-source code can be a challenge, especially for small and local academic society journals. This paper provides some critical examples of using some of the many open-source code resources available to the public. Journal Article Tag Suite (JATS) Extensible Markup Language (XML) has been established as an essential tool, and is now used by most journals for digital publication. JATS XML can be converted to other viewer formats, including Extensible Hypertext Markup Language, PubReader, and EPUB 3.0. It can also be used to create dynamic interactive PDFs. It can be converted to other XMLs, incluing Crossref XML, PubMed XML, and DOAJ XML. Open-source code published on GitHub, National Information Standards Organization, and the US National Library of Medicine can be used for Crossref XML deposition for digital object identifier and Crossmark stamp registration. These examples of open-source code need to be implemented on journal websites to provide local academic journal publishers with various critical functions. This paper provides instructions on the best ways to realize these digital standards so that journal content can be provided to readers in a more friendly and effective way.

Membrane-based dehumidification is a promising solution for building applications because of its low cost and limited energy consumption. Developing an efficient and cost-effective open-source code simulation tool is important for optimizing and evaluating such devices in HVAC applications. This paper describes a physics-based model, which accounts for the fundamental heat and mass transfer between a humid-air vapor stream on the feed side and a flowing stream on the permeate side of a membrane. The developed model comprises two mass transfer submodels—a microstructure model and a performance map model—and adopts a segment-by-segment method for discretizing heat and mass transfer governing equations for flow streams on the feed and permeate sides of a membrane. The model can simulate dehumidifiers and energy recovery ventilators with parallel-flow, cross-flow, and counter-flow configurations, and the predictions compare reasonably well with the measurements. The model was used to evaluate the effect of membrane microstructure parameters and membrane surface deflection factors, as well as to investigate the performance of combined dehumidification and energy recovery exchangers. The model and C++ open-source codes are expected to become a fundamental tool in analyzing future membrane-based dehumidification systems.

The object of research in the paper is a built-in refactoring mechanism in the Swift programming language. Swift has gained a lot of popularity recently, which is why there are many new challenges associated with the need to support and modify the source code written in this programming language. The problem is that the more powerful refactoring mechanism that can be applied to Swift is proprietary and cannot be used by other software. Moreover, even closed-source refactoring software tools are not capable of performing more complex queries. To explore the possibilities of expanding the built-in refactoring, it is suggested to investigate the software implementation of the sourcekit component of the Swift programming language, which is responsible for working with «raw» source code, and to implement new refactoring action in practice. To implement the research plan, one refactoring activity that was not present in the refactoring utilities (adding an implementation of the Equatable protocol) was chosen. Its implementation was developed using the components and resources provided within the sourcekit component. To check the correctness and compliance with the development conditions, several tests were created and conducted. It has been discovered that both refactoring mechanisms supported by the Swift programming language have a limited context and a limited scope and application. That is why the possibility of expanding the functionality should not be based on the local level of code processing, but on the upper level, where it is possible to combine several source files, which often happens in projects. The work was directed to the development of the own refactoring action to analyze and obtain a perfect representation of the advantages and disadvantages of the existing component. As a result, a new approach to refactoring was proposed, which will allow solving the problems described above. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Recently, multicomponent alloys have been studied for hydrogen storage because of their vast compositional field, which opened an exciting path for designing alloys with optimized properties for any specific application, in a properties-on-demand approach. Since the experimental measurements of hydrogen storage properties are very time-consuming, computational tools to assist the exploration of the endless compositional field of multicomponent alloys are needed. In a previous work reported by Zepon et al. (2021), a thermodynamic model to calculate pressure-composition-temperature (PCT) diagrams for body-centered-cubic (BCC) multicomponent alloys was proposed. In the present work, we implemented this model in an open-source code with an user-friendly interface to calculate PCT diagrams for BCC multicomponent alloys having any of the following elements: Mg, Al, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Zr, Nb, Mo, Pd, Hf, and Ta. The open-source code aims to allow the use of the thermodynamic model for alloy design as well as to encourage other researchers to improve the inputs and the initial thermodynamic model. As an example of application of the model for alloy design, the code was employed to investigate the effect of different metals (M) on the PCT diagrams of Ti 0.3 V 0.3 Nb 0.3 M 0.1 alloys. • Thermodynamic model for BCC multicomponent alloys was implemented in a code. • The code is open-source and was developed in Python language. • PCT diagrams of BCC multicomponent alloys can be calculated. • The user-friendly code may assist the alloy design for hydrogen storage. [ABSTRACT FROM AUTHOR]

The study's purpose was to assess the reliability of the LIS2DH12 in two different positions, using the commercial sensor Actigraph GT9X as a reference instrument. Five participants completed two gait tests on a treadmill. Firstly, both sensors were worn on the wrist and around the thigh. Each test consisted of a 1 min walk for participants to become accustomed to the treadmill, followed by a 2 min trial at ten pre-set speeds. Data from both sensors were collected in real-time. Intraclass correlation coefficient (ICC) was used to evaluate the equality of characteristics obtained by both sensors: maximum peaks, minimum peaks, and the mean of the complete signal (sequence of acceleration values along the time) by each axis and speed were extracted to evaluate the equality of characteristics obtained with LIS2DH12 compared to Actigraph. Intraclass correlation coefficient (ICC) was extracted, and a standard deviation of the mean was obtained from the data. Our results show that LIS2DH12 measurements present more reliability than Actigraph GT9X, ICC > 0.8 at three axes. This study concludes that LIS2DH12 is as reliable and accurate as Actigraph GT9X Link and, therefore, would be a suitable tool for future kinematic studies. [ABSTRACT FROM AUTHOR]

We present a new spectral analysis method for the identification of periodic signals in geophysical time series. We evaluate the power spectral density with the adaptive multitaper method, a nonparametric spectral analysis technique suitable for time series characterized by colored power spectral density. Our method provides a maximum likelihood estimation of the power spectral density background according to four different models. It includes the option for the models to be fitted on four smoothed versions of the power spectral density when there is a need to reduce the influence of power enhancements due to periodic signals. We use a statistical criterion to select the best background representation among the different smoothing + model pairs. Then, we define the confidence thresholds to identify the power spectral density enhancements related to the occurrence of periodic fluctuations (γ test). We combine the results with those obtained with the multitaper harmonic F test, an additional complex‐valued regression analysis from which it is possible to estimate the amplitude and phase of the signals. We demonstrate the algorithm on Monte Carlo simulations of synthetic time series and a case study of magnetospheric field fluctuations directly driven by periodic density structures in the solar wind. The method is robust and flexible. Our procedure is freely available as a stand‐alone IDL code at https://zenodo.org/record/3703168. The modular structure of our methodology allows the introduction of new smoothing methods and models to cover additional types of time series. The flexibility and extensibility of the technique makes it broadly suitable to any discipline.Key Points: Our technique provides a robust estimate of the continuous background of colored Power Spectral DensityThis method uses a combination of spectral and harmonic statistical tests to identify periodic fluctuationsThere are multiple options for the method of Power Spectral Density smoothing and the background model [ABSTRACT FROM AUTHOR]

Various surface-wave exploration methods have become increasingly important tools in investigating the properties of subsurface structures. Inversion of the experimental dispersion curves is generally an indispensable component of these methods. Accurate and reliable calculation of partial derivatives of surface-wave dispersion curves with respect to parameters of subsurface layers is critical to the success of these approaches if the linearized inversion strategies are adopted. Here we present an open-source MATLAB package, named SWPD (Surface Wave Partial Derivative), for modeling surface-wave (both Rayleigh- and Love-wave) dispersion curves (both phase and group velocity) and particularly for computing their partial derivatives with high precision. The package is able to compute partial derivatives of phase velocity and of Love-wave group velocity analytically based on the combined use of the reduced delta matrix theory and the implicit function theorem. For partial derivatives of Rayleigh-wave group velocity, a hemi-analytical method is presented, which analytically calculates all the first-order partial differentiations and approximates the mixed second-order partial differentiation term with a central difference scheme. We provide examples to demonstrate the effectiveness of this package, and demo scripts are also provided for users to reproduce all results of this paper and thus to become familiar with the package as quickly as possible. [ABSTRACT FROM AUTHOR]

While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug–target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred. [ABSTRACT FROM AUTHOR]

Pupil dilation is an effective indicator of cognitive and affective processes. Although several eyetracker systems on the market can provide effective solutions for pupil dilation measurement, there is a lack of tools for processing and analyzing the data provided by these systems. For this reason, we developed CHAP: open-source software written in MATLAB. This software provides a user-friendly graphical user interface for processing and analyzing pupillometry data. Our software creates uniform conventions for the preprocessing and analysis of pupillometry data and provides a quick and easy-to-use tool for researchers interested in pupillometry. To download CHAP or join our mailing list, please visit CHAP's website: http://in.bgu.ac.il/en/Labs/CNL/chap. [ABSTRACT FROM AUTHOR]

We present an open-source adaptive front-tracking solver for biological capsules in viscous flows. The membrane elastic and bending forces are solved on a Lagrangian triangulation using a linear Finite Element Method and a paraboloid fitting method. The fluid flow is solved on an octree adaptive grid using the open-source platform Basilisk. The Lagrangian and Eulerian grids communicate using an Immersed Boundary Method by means of Peskin-like regularized Dirac delta functions. We demonstrate the accuracy of our solver with extensive validations: in Stokes conditions against the Boundary Integral Method, and in the presence of inertia against similar (but not adaptive) front-tracking solvers. Excellent qualitative and quantitative agreements are shown. We then demonstrate the robustness of the present solver in challenging cases featuring extreme membrane deformations, very large computational domains and high volume fractions. Moreover, we illustrate the capability of the solver to simulate inertial capsule-laden flows in complex STL-defined geometries, opening the door for bioengineering applications featuring large three-dimensional channel structures. The source code and all the test cases presented in this paper are freely available. • Front Tracking/Immersed Boundary method to simulate a 3D flow laden with deformable capsules in a complex geometry. • Extension of the method to adaptive mesh refinement on Cartesian octree grids. • Implementation in the open-source software Basilisk. • Complex geometries are considered via STL files. • Comprehensive validations to verify the robustness and accuracy of the method. [ABSTRACT FROM AUTHOR]

String kernels have successfully been used for various NLP tasks, ranging from text categorization by topic to native language identification. In this paper, we present a simple and efficient algorithm for computing various spectrum string kernels. When comparing two strings, we store the p -grams in the first string into a hash table, and then we apply a hash table lookup for the p -grams that occur in the second string. In terms of time, we show that our algorithm can outperform a state-of-the-art tool for computing string similarity. In terms of accuracy, we show that our approach can reach state-of-the-art performance for polarity classification in various languages. Our efficient implementation is provided online for free at http://string-kernels.herokuapp.com . [ABSTRACT FROM AUTHOR]

In this paper, an open-source toolbox that can be used to accurately predict the distribution of the major physical quantities that are transported within a proton exchange membrane (PEM) fuel cell is presented. The toolbox has been developed using the Open Source Field Operation and Manipulation (OpenFOAM) platform, which is an open-source computational fluid dynamics (CFD) code. The base case results for the distribution of velocity, pressure, chemical species, Nernst potential, current density, and temperature are as expected. The plotted polarization curve was compared to the results from a numerical model and experimental data taken from the literature. The conducted simulations have generated a significant amount of data and information about the transport processes that are involved in the operation of a PEM fuel cell. The key role played by the concentration constant in shaping the cell polarization curve has been explored. The development of the present toolbox is in line with the objectives outlined in the International Energy Agency (IEA, Paris, France) Advanced Fuel Cell Annex 37 that is devoted to developing open-source computational tools to facilitate fuel cell technologies. The work therefore serves as a basis for devising additional features that are not always feasible with a commercial code.

This paper presents a method for estimating environmental contours in an arbitrary number of dimensions. The method, referred to as Direct-IFORM, does not require a model for the joint distribution of the variables. It can therefore be applied in higher dimensions without degradation in performance. The method involves multiple univariate analyses under rotations of the axes to estimate return values in various directions, which are used to form a contour. The method accounts for serial correlation in the observations, which removes the positive bias that occurs when this is neglected. An efficient open-source code is provided for estimating Direct-IFORM contours. A four-dimensional example is presented for contours of wind speed, wave height, wave period and wind-wave misalignment direction. The application of the method to the design of offshore wind turbines is discussed. • Method presented for estimating contours without need to fit joint distribution. • Method applicable in higher dimensions without degradation in performance. • Method accounts for serial correlation in observations, reducing positive bias. • Open source code in MATLAB and Python provided for estimating Direct-IFORM contours. • Example presented for 4-dimensional contours of wind and wave parameters. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a Reformulation-Linearization Technique-based open-source optimization software for solving polynomial programming problems (RLT-POS). We present algorithms and mechanisms that form the backbone of RLT-POS, including constraint filtering techniques, reduced RLT representations, and semidefinite cuts. When implemented individually, each model enhancement has been shown in previous papers to significantly improve the performance of the standard RLT procedure. However, the coordination between different model enhancement techniques becomes critical for an improved overall performance since special structures in the original formulation that work in favor of a particular technique might be lost after implementing some other model enhancement. More specifically, we discuss the coordination between (1) constraint elimination via filtering techniques and reduced RLT representations, and (2) semidefinite cuts for sparse problems. We present computational results using instances from the literature as well as randomly generated problems to demonstrate the improvement over a standard RLT implementation and to compare the performances of the software packages BARON, COUENNE, and SparsePOP with RLT-POS. [ABSTRACT FROM AUTHOR]

The article carries out the theoretical basis, the usefulness, and validation of the computational program geonetDigitizer (licensed as an open-source code as BSD-2) in order to apply as a toolbox for the programming language MATLAB©. The program geonetDigitizer was designed for the digitalization of plots expressed in the spherical projections Wulff and Lambert, which is of extensive use in structural geology and geomechanics. The reasons of presenting the program as an open code has the purpose to recover historical data presented in those projections, as also to recover old and modern information presented in that projections. All of these will reduce the possible phenomenon known as Digital Dark Age. To validate the code, it is presented here two examples, where real known values are compared with their corresponding values obtained by the process of digitizing. Finally, in the appendix is presented two application examples. [ABSTRACT FROM AUTHOR]

The advent of detailed hydrodynamic model simulations of urban flooding has not been matched by improved capabilities in flood exposure analysis which rely on validation against observed data. This work introduces a generic, building-level flood exposure analysis tool applying high resolution flood data and building geometries derived from hydrodynamic simulations performed with the 2D hydrodynamic flood modelling software CityCAT. Validation data were obtained from a survey of affected residents following a large pluvial flood event in Newcastle upon Tyne, UK. Sensitivity testing was carried out for different hydrodynamic model and exposure tool settings and between 68% and 75% of the surveyed buildings were correctly modelled as either flooded or not flooded. The tool tends to underrepresent flooding with a better performance in identifying true negatives (i.e. no flooding observed with no flooding modelled) compared to true positives. As higher true positive rates were accompanied by higher false positive rates, no single scenario could be identified as the optimal solution. However, the results suggest a greater sensitivity of the results to the classification scheme than to the buffer distance applied. Overall, if applied to high resolution flood depth maps, the method is efficient and suitable for application to large urban areas for flood risk management and insurance analysis purposes. • A flood exposure analysis tool which uses high resolution inundation grids and detailed building geometries for cities avoiding aggregation of data. • The tool is validated against observed data collected after a large pluvial flood event. • Developed as a Python-based Jupyter notebook the code can be easily adapted and extended to accommodate specific user needs. • Decision making support tool for stakeholders in flood risk management. [ABSTRACT FROM AUTHOR]