Probabilistic Method to Fuse Artificial Intelligence-Generated Underground Utility Mapping.

Utility as-built plans, which typically provide information about underground utilities' position and spatial locations, are known to comprise inaccuracies. Over the years, the reliance on utility investigations using an array of sensing equipment has increased in an attempt to resolve utility as-built inaccuracies and mitigate the high rate of accidental underground utility strikes during excavation activities. Adapting data fusion into utility engineering and investigation practices has been shown to be effective in generating information with improved accuracy. However, the complexities in data interpretation and associated prohibitive costs, especially for large-scale projects, are limiting factors. This paper addresses the problem of data interpretation, costs, and large-scale utility mapping with a novel framework that generates probabilistic inferences by fusing data from an automatically generated initial map with as-built data. The probabilistic inferences expose regions of high uncertainty, highlighting them as prime targets for further investigations. The proposed model is a collection of three main processes. First, the automatic initial map creation is a novel contribution supporting rapid utility mapping by subjecting identified utility appurtenances to utility inference rules. The second and third processes encompass the fusion of the created initial utility map with available knowledge from utility as-builts or historical satellite imagery data and then evaluating the uncertainties using confidence value estimators. The proposed framework transcends the point estimation of buried utility locations in previous works by producing a final probabilistic utility map, revealing a confidence level attributed to each segment linking aboveground features. In this approach, the utility infrastructure is rapidly mapped at a low cost, limiting the extent of more detailed utility investigations to low-confidence regions. In resisting obsolescence, another unique advantage of this framework is the dynamic nature of the mapping to automatically update information upon the arrival of new knowledge. This ultimately minimizes the problem of utility as-built accuracies dwindling over time.

Paintable Silicone-Based Corrugated Soft Elastomeric Capacitor for Area Strain Sensing.

Recent advances in soft polymer materials have enabled the design of soft machines and devices at multiple scales. Their intrinsic compliance and robust mechanical properties and the potential for a rapid scaling of the production process make them ideal candidates for flexible and stretchable electronics and sensors. Large-area electronics (LAE) made from soft polymer materials that are capable of sustaining large deformations and covering large surfaces and are applicable to complex and irregular surfaces and transducing deformations into readable signals have been explored for structural health monitoring (SHM) applications. The authors have previously proposed and developed an LAE consisting of a corrugated soft elastomeric capacitor (cSEC). The corrugation is used to engineer the directional strain sensitivity by using a thermoplastic styrene-ethylene-butadiene-styrene (SEBS). A key limitation of the SEBS-cSEC technology is the need of an epoxy for reliable bonding of the sensor onto the monitored surface, mainly attributable to the sensor's fabrication process that comprises a solvent that limits its direct deployment through a painting process. Here, with the objective to produce a paintable cSEC, we study an improved solvent-free fabrication method by using a commercial room-temperature-vulcanizing silicone as the host matrix. The matrix is filled with titania particles to form the dielectric layer, yielding a permittivity of 4.05. Carbon black powder is brushed onto the dielectric and encapsulated with the same silicone to form the conductive stretchable electrodes. The sensor is deployed by directly painting a layer of the silicone onto the monitored surface and then depositing the parallel plate capacitor. The electromechanical behavior of the painted silicone-cSEC was characterized and exhibited good linearity, with an R2 value of 0.9901, a gauge factor of 1.58, and a resolution of 70 µÎµ. This resolution compared well with that of the epoxied SEBS-cSEC reported in previous work (25 µÎµ). Its performance was compared against that of its more mature version, the SEBS-cSEC, in a network configuration on a cantilever plate subjected to a step-deformation and to free vibrations. Results showed that the performance of the painted silicone-sCEC compared well with that of the SEBS-cSEC, but that the use of a silicone paint instead of an epoxy could be responsible for larger noise and the under-estimation of the dominating frequency by 6.7%, likely attributable to slippage.

Review of Artificial Intelligence Applications for Virtual Sensing of Underground Utilities.

Accurately identifying the location and depth of buried utility assets became a considerable challenge in the construction industry, for which accidental strikes can cause important economic losses and safety concerns. While the collection of as-built utility locations is becoming more accurate, there still exists an important need to be capable of accurately detecting buried utilities in order to eliminate risks associated with digging. Current practices typically involve the use of trained agents to survey and detect underground utilities at locations of interest, which is a costly and time-consuming process. With advances in artificial intelligence (AI), an opportunity arose in conducting virtual sensing of buried utilities by combining robotics (e.g., drones), knowledge, and logic. This paper reviewed methods that are based on AI in mapping underground infrastructure. In particular, the use of AI in aerial and terrestrial mapping of utility assets was reviewed, followed by a summary of AI techniques used in fusing multi-source data in creating underground infrastructure maps. Key observations from the consolidated literature were that (1) when leveraging computer vision methods, automatic mapping techniques vastly focus on manholes localized from aerial imagery; (2) when applied to non-intrusive sensing, AI methods vastly focus on empowering ground-penetrating radar (GPR)-produced data; and (3) data fusion techniques to produce utility maps should be extended to any utility assets/types. Based on these observations, a universal utility mapping model was proposed, one that could enable mapping of underground utilities using limited information available in the form of different sources of data and knowledge.

Structural Health Monitoring of Fatigue Cracks for Steel Bridges with Wireless Large-Area Strain Sensors.

This paper presents a field implementation of the structural health monitoring (SHM) of fatigue cracks for steel bridge structures. Steel bridges experience fatigue cracks under repetitive traffic loading, which pose great threats to their structural integrity and can lead to catastrophic failures. Currently, accurate and reliable fatigue crack monitoring for the safety assessment of bridges is still a difficult task. On the other hand, wireless smart sensors have achieved great success in global SHM by enabling long-term modal identifications of civil structures. However, long-term field monitoring of localized damage such as fatigue cracks has been limited due to the lack of effective sensors and the associated algorithms specifically designed for fatigue crack monitoring. To fill this gap, this paper proposes a wireless large-area strain sensor (WLASS) to measure large-area strain fatigue cracks and develops an effective algorithm to process the measured large-area strain data into actionable information. The proposed WLASS consists of a soft elastomeric capacitor (SEC) used to measure large-area structural surface strain, a capacitive sensor board to convert the signal from SEC to a measurable change in voltage, and a commercial wireless smart sensor platform for triggered-based wireless data acquisition, remote data retrieval, and cloud storage. Meanwhile, the developed algorithm for fatigue crack monitoring processes the data obtained from the WLASS under traffic loading through three automated steps, including (1) traffic event detection, (2) time-frequency analysis using a generalized Morse wavelet (GM-CWT) and peak identification, and (3) a modified crack growth index (CGI) that tracks potential fatigue crack growth. The developed WLASS and the algorithm present a complete system for long-term fatigue crack monitoring in the field. The effectiveness of the proposed time-frequency analysis algorithm based on GM-CWT to reliably extract the impulsive traffic events is validated using a numerical investigation. Subsequently, the developed WLASS and algorithm are validated through a field deployment on a steel highway bridge in Kansas City, KS, USA.

Multi-Time Resolution Ensemble LSTMs for Enhanced Feature Extraction in High-Rate Time Series.

Systems experiencing high-rate dynamic events, termed high-rate systems, typically undergo accelerations of amplitudes higher than 100 g-force in less than 10 ms. Examples include adaptive airbag deployment systems, hypersonic vehicles, and active blast mitigation systems. Given their critical functions, accurate and fast modeling tools are necessary for ensuring the target performance. However, the unique characteristics of these systems, which consist of (1) large uncertainties in the external loads, (2) high levels of non-stationarities and heavy disturbances, and (3) unmodeled dynamics generated from changes in system configurations, in combination with the fast-changing environments, limit the applicability of physical modeling tools. In this paper, a deep learning algorithm is used to model high-rate systems and predict their response measurements. It consists of an ensemble of short-sequence long short-term memory (LSTM) cells which are concurrently trained. To empower multi-step ahead predictions, a multi-rate sampler is designed to individually select the input space of each LSTM cell based on local dynamics extracted using the embedding theorem. The proposed algorithm is validated on experimental data obtained from a high-rate system. Results showed that the use of the multi-rate sampler yields better feature extraction from non-stationary time series compared with a more heuristic method, resulting in significant improvement in step ahead prediction accuracy and horizon. The lean and efficient architecture of the algorithm results in an average computing time of 25 µµs, which is below the maximum prediction horizon, therefore demonstrating the algorithm's promise in real-time high-rate applications.

Hybrid Carbon Microfibers-Graphite Fillers for Piezoresistive Cementitious Composites.

Multifunctional structural materials are very promising in the field of engineering. Particularly, their strain sensing ability draws much attention for structural health monitoring applications. Generally, strain sensing materials are produced by adding a certain amount of conductive fillers, around the so-called "percolation threshold", to the cement or composite matrix. Recently, graphite has been found to be a suitable filler for strain sensing. However, graphite requires high amounts of doping to reach percolation threshold. In order to decrease the amount of inclusions, this paper proposes cementitious materials doped with new hybrid carbon inclusions, i.e., graphite and carbon microfibers. Carbon microfibers having higher aspect ratio than graphite accelerate the percolation threshold of the graphite particles without incurring into dispersion issues. The resistivity and strain sensitivity of different fibers' compositions are investigated. The electromechanical tests reveal that, when combined, carbon microfibers and graphite hybrid fillers reach to percolation faster and exhibit higher gauge factors and enhanced linearity.

Soft Elastomeric Capacitor for Angular Rotation Sensing in Steel Components.

The authors have previously proposed corrugated soft elastomeric capacitors (cSEC) to create ultra compliant scalable strain gauges. The cSEC technology has been successfully demonstrated in engineering and biomechanical applications for in-plane strain measurements. This study extends work on the cSEC to evaluate its performance at measuring angular rotation when installed folded at the junction of two plates. The objective is to characterize the sensor's electromechanical behavior anticipating applications to the monitoring of welded connections in steel components. To do so, an electromechanical model that maps the cSEC signal to bending strain induced by angular rotation is derived and adjusted using a validated finite element model. Given the difficulty in mapping strain measurements to rotation, an algorithm termed angular rotation index (ARI) is formulated to link measurements to angular rotation directly. Experimental work is conducted on a hollow structural section (HSS) steel specimen equipped with cSECs subjected to compression to generate angular rotations at the corners within the cross-section. Results confirm that the cSEC is capable of tracking angular rotation-induced bending strain linearly, however with accuracy levels significantly lower than found over flat configurations. Nevertheless, measurements were mapped to angular rotations using the ARI, and it was found that the ARI mapped linearly to the angle of rotation, with an accuracy of 0.416∘.

Smart Graphite-Cement Composite for Roadway-Integrated Weigh-In-Motion Sensing.

Smart multifunctional composites exhibit enhanced physical and mechanical properties and can provide structures with new capabilities. The authors have recently initiated a research program aimed at developing new strain-sensing pavement materials enabling roadway-integrated weigh-in motion (WIM) sensing. The goal is to achieve an accurate WIM for infrastructure monitoring at lower costs and with enhanced durability compared to off-the-shelf solutions. Previous work was devoted to formulating a signal processing algorithm for estimating the axle number and weights, along with the vehicle speed based on the outputs of a piezoresistive pavement material deployed within a bridge deck. This work proposes and characterizes a suitable low-cost and highly scalable cement-based composite with strain-sensing capabilities and sufficient sensitivity to meet WIM signal requirements. Graphite cement-based smart composites are presented, and their electromechanical properties are investigated in view of their application to WIM. These composites are engineered for scalability owing to the ease of dispersion of the graphite powder in the cement matrix, and can thus be used to build smart sections of road pavements. The research presented in this paper consists of electromechanical tests performed on samples of different amounts of graphite for the identification of the optimal mix in terms of signal sensitivity. An optimum inclusion level of 20% by weight of cement is obtained and selected for the fabrication of a plate of 30 × 15 × 5 cm3. Results from load identification tests conducted on the plate show that the proposed technology is capable of WIM.

A Weigh-in-Motion Characterization Algorithm for Smart Pavements Based on Conductive Cementitious Materials.

Smart materials are promising technologies for reducing the instrumentation cost required to continuously monitor road infrastructures, by transforming roadways into multifunctional elements capable of self-sensing. This study investigates a novel algorithm empowering smart pavements with weigh-in-motion (WIM) characterization capabilities. The application domain of interest is a cementitious-based smart pavement installed on a bridge over separate sections. Each section transduces axial strain provoked by the passage of a vehicle into a measurable change in electrical resistance arising from the piezoresistive effect of the smart material. The WIM characterization algorithm is as follows. First, basis signals from axles are generated from a finite element model of the structure equipped with the smart pavement and subjected to given vehicle loads. Second, the measured signal is matched by finding the number and weights of appropriate basis signals that would minimize the error between the numerical and measured signals, yielding information on the vehicle's number of axles and weight per axle, therefore enabling vehicle classification capabilities. Third, the temporal correlation of the measured signals are compared across smart pavement sections to determine the vehicle weight. The proposed algorithm is validated numerically using three types of trucks defined by the Eurocodes. Results demonstrate the capability of the algorithm at conducting WIM characterization, even when two different trucks are driving in different directions across the same pavement sections. Then, a noise study is conducted, and the results conclude that a given smart pavement section operating with less than 5% noise on measurements could yield good WIM characterization results.

Numerical Investigation of Auxetic Textured Soft Strain Gauge for Monitoring Animal Skin.

Recent advances in hyperelastic materials and self-sensing sensor designs have enabled the creation of dense compliant sensor networks for the cost-effective monitoring of structures. The authors have proposed a sensing skin based on soft polymer composites by developing soft elastomeric capacitor (SEC) technology that transduces geometric variations into a measurable change in capacitance. A limitation of the technology is in its low gauge factor and lack of sensing directionality. In this paper, we propose a corrugated SEC through surface texture, which provides improvements in its performance by significantly decreasing its transverse Poisson's ratio, and thus improving its sensing directionality and gauge factor. We investigate patterns inspired by auxetic structures for enhanced unidirectional strain monitoring. Numerical models are constructed and validated to evaluate the performance of textured SECs, and to study their performance at monitoring strain on animal skin. Results show that the auxetic patterns can yield a significant increase in the overall gauge factor and decrease the stress experienced by the animal skin, with the re-entrant hexagonal honeycomb pattern outperforming all of the other patterns.

Use of flexible sensor to characterize biomechanics of canine skin.

BACKGROUND: Suture materials and techniques are frequently evaluated in ex vivo studies by comparing tensile strengths. However, the direct measurement techniques to obtain the tensile forces in canine skin are not available, and, therefore, the conditions suture lines undergo is unknown. A soft elastomeric capacitor is used to monitor deformation in the skin over time by sensing strain. This sensor was applied to a sample of canine skin to evaluate its capacity to sense strain in the sample while loaded in a dynamic material testing machine. The measured strain of the sensor was compared with the strain measured by the dynamic testing machine. The sample of skin was evaluated with and without the sensor adhered. RESULTS: In this study, the soft elastomeric capacitor was able to measure strain and a correlation was made to stress using a modified Kelvin-Voigt model for the canine skin sample. The sensor significantly increases the stiffness of canine skin when applied which required the derivation of mechanical models for interpretation of the results. CONCLUSIONS: Flexible sensors can be applied to canine skin to investigate the inherent biomechanical properties. These sensors need to be lightweight and highly elastic to avoid interference with the stress across a suture line. The sensor studied here serves as a prototype for future sensor development and has demonstrated that a lightweight highly elastic sensor is needed to decrease the effect on the sensor/skin construct. Further studies are required for biomechanical characterization of canine skin.

Concrete Crack Detection and Monitoring Using a Capacitive Dense Sensor Array.

Cracks in concrete structures can be indicators of important damage and may significantly affect durability. Their timely identification can be used to ensure structural safety and guide on-time maintenance operations. Structural health monitoring solutions, such as strain gauges and fiber optics systems, have been proposed for the automatic monitoring of such cracks. However, these solutions become economically difficult to deploy when the surface under investigation is very large. This paper proposes to leverage a novel sensing skin for monitoring cracks in concrete structures. This sensing skin is constituted of a flexible electronic termed soft elastomeric capacitor, which detects a change in strain through changes in measured capacitance. The SEC is a low-cost, durable, and robust sensing technology that has previously been studied for the monitoring of fatigue cracks in steel components. In this study, the sensing skin is introduced and preliminary validation results on a small-scale reinforced concrete beam are presented. The technology is verified on a full-scale post-tensioned concrete beam. Results show that the sensing skin is capable of detecting, localizing, and quantifying cracks that formed in both the reinforced and post-tensioned concrete specimens.

Introduction to State Estimation of High-Rate System Dynamics.

Engineering systems experiencing high-rate dynamic events, including airbags, debris detection, and active blast protection systems, could benefit from real-time observability for enhanced performance. However, the task of high-rate state estimation is challenging, in particular for real-time applications where the rate of the observer's convergence needs to be in the microsecond range. This paper identifies the challenges of state estimation of high-rate systems and discusses the fundamental characteristics of high-rate systems. A survey of applications and methods for estimators that have the potential to produce accurate estimations for a complex system experiencing highly dynamic events is presented. It is argued that adaptive observers are important to this research. In particular, adaptive data-driven observers are advantageous due to their adaptability and lack of dependence on the system model.

An Experimental Study on Static and Dynamic Strain Sensitivity of Embeddable Smart Concrete Sensors Doped with Carbon Nanotubes for SHM of Large Structures.

The availability of new self-sensing cement-based strain sensors allows the development of dense sensor networks for Structural Health Monitoring (SHM) of reinforced concrete structures. These sensors are fabricated by doping cement-matrix mterials with conductive fillers, such as Multi Walled Carbon Nanotubes (MWCNTs), and can be embedded into structural elements made of reinforced concrete prior to casting. The strain sensing principle is based on the multifunctional composites outputting a measurable change in their electrical properties when subjected to a deformation. Previous work by the authors was devoted to material fabrication, modeling and applications in SHM. In this paper, we investigate the behavior of several sensors fabricated with and without aggregates and with different MWCNT contents. The strain sensitivity of the sensors, in terms of fractional change in electrical resistivity for unit strain, as well as their linearity are investigated through experimental testing under both quasi-static and sine-sweep dynamic uni-axial compressive loadings. Moreover, the responses of the sensors when subjected to destructive compressive tests are evaluated. Overall, the presented results contribute to improving the scientific knowledge on the behavior of smart concrete sensors and to furthering their understanding for SHM applications.

Customization of an atomic force microscope for multidimensional measurements under environmental conditions.

Atomic force microscopy (AFM) is an analytical surface characterization tool that reveals the surface topography at a nanometer length scale while probing local chemical, mechanical, and even electronic sample properties. Both contact (performed with a constant deflection of the cantilever probe) and dynamic operation modes (enabled by demodulation of the oscillation signal under tip-sample interaction) can be employed to conduct AFM-based measurements. Although surface topography is accessible regardless of the operation mode, the resolution and the availability of the quantified surface properties depend on the mode of operation. However, advanced imaging techniques, such as frequency modulation, to achieve high resolution, quantitative surface properties are not implemented in many commercial systems. Here, we show the step-by-step customization of an atomic force microscope. The original system was capable of surface topography and basic force spectroscopy measurements while employing environmental control, such as temperature variation of the sample/tip, etc. We upgraded this original setup with additional hardware (e.g., a lock-in amplifier with phase-locked loop capacity, a high-voltage amplifier, and a new controller) and software integration while utilizing its environmental control features. We show the capabilities of the customized system with frequency modulation-based topography experiments and automated voltage and/or distance spectroscopy, time-resolved AFM, and two-dimensional force spectroscopy measurements under ambient conditions. We also illustrate the enhanced stability of the setup with active topography and frequency drift corrections. We believe that our methodology can be useful for the customization and automation of other scanning probe systems.

Trophoblast stem cell marker gene expression in inner cell mass-derived cells from parthenogenetic equine embryos.

Although putative horse embryonic stem (ES)-like cell lines have been obtained recently from in vivo-derived embryos, it is currently not known whether it is possible to obtain ES cell (ESC) lines from somatic cell nuclear transfer (SCNT) and parthenogenetic (PA) embryos. Our aim is to establish culture conditions for the derivation of autologous ESC lines for cell therapy studies in an equine model. Our results indicate that both the use of early-stage blastocysts with a clearly visible inner cell mass (ICM) and the use of pronase to dissect the ICM allow the derivation of a higher proportion of primary ICM outgrowths from PA and SCNT embryos. Primary ICM outgrowths express the molecular markers of pluripotency POU class 5 homeobox 1 (POU5F1) and (sex determining region-Y)-box2 (SOX2), and in some cases, NANOG. Cells obtained after the passages of PA primary ICM outgrowths display alkaline phosphatase (AP) activity and POU5F1, SOX2, caudal-related homeobox-2 (CDX2) and eomesodermin (EOMES) expression, but may lose NANOG. Cystic embryoid body-like structures expressing POU5F1, CDX2 and EOMES were produced from these cells. Immunohistochemical analysis of equine embryos reveals the presence of POU5F1 in trophectoderm, primitive endoderm and ICM. These results suggest that cells obtained after passages of primary ICM outgrowths are positive for trophoblast stem cell markers while expressing POU5F1 and displaying AP activity. Therefore, these cells most likely represent trophoblast cells rather than true ESCs. This study represents an important first step towards the production of autologous equine ESCs for pre-clinical cell therapy studies on large animal models.

Soft Elastomeric Capacitor for Strain and Stress Monitoring on Sutured Skin Tissues.

Sutures are ubiquitous medical devices for wound closures in human and veterinary medicine, and suture techniques are frequently evaluated by comparing tensile strengths in ex vivo studies. Direct and nondestructive measurement of tensile force present in sutured biological skin tissue is a key challenge in biomechanical fields because of the unique and complex properties of each sutured skin specimen and the lack of compliant sensors capable of monitoring large levels of strain. The authors have recently proposed a soft elastomeric capacitor (SEC) sensor that consists of a highly compliant and scalable strain gauge capable of transducing geometric variations into a measurable change in capacitance. In this study, corrugated SECs are used to experimentally characterize the inherent biomechanical properties of canine skin specimens. In particular, an SEC corrugated with a re-entrant hexagonal honeycomb pattern is studied to monitor strain and stresses for three specific suture patterns: simple interrupted, cruciate, and intradermal patterns. Stress is estimated using constitutive models based on the Fractional Zener and the Kelvin-Voigt models, parametrized using a particle swarm algorithm from experimental data and results from a validated finite element model. Results are benchmarked against findings from the literature and show that SECs are valuable for clinical evaluation of tensile force in biological skins. It was found that both the ranking of suture pattern performance and the sutured skin's Young's modulus using the proposed approach agreed with data reported in the literature and that the estimated stress at the suture level closely matched that of an approximate finite element model.

[Social actors and phenomenologic modelling]. / Les acteurs sociaux et la modélisation phénoménologique.

The phenomenological approach has a quasi-monopoly in the individual and subjectivity analyses in social sciences. However, the conceptual apparatus associated with this approach is very restrictive. The human being has to be understood as rational, conscious, intentional, interested, and autonomous. Because of this, a large dimension of human activity cannot be taken into consideration: all that does not fit into the analytical categories (nonrational, nonconscious, etc.). Moreover, this approach cannot really move toward a relational analysis unless it is between individuals predefined by its conceptual apparatus. This lack of complexity makes difficult the establishment of links between phenomenology and systemic analysis in which relation (and its derivatives such as recursiveness, dialectic, correlation) plays an essential role. This article intends to propose a way for systemic analysis to apprehend the individual with respect to his complexity.

RNA/DNA ratios in American glass eels (Anguilla rostrata): evidence for latitudinal variation in physiological status and constraints to oceanic migration?

During their larval leptocephalus phase, newly hatched American eels undergo an extensive oceanic migration from the Sargasso Sea toward coastal and freshwater habitats. Their subsequent metamorphosis into glass eel is accompanied by drastic morphological and physiological changes preceding settlement over a wide geographic range. The main objective of this study was to compare RNA/DNA ratios and condition factor among glass eels in order to test the null hypothesis of no difference in physiological status and metabolic activity of glass eels at the outcome of their oceanic migration. This was achieved by analyzing glass eel samples collected at the mouth of 17 tributaries covering a latitudinal gradient across the species distribution range from Florida to Gaspésie (Québec). Our main observations were (i) a latitudinal increase in mean total length; (ii) a latitudinal variation in mean RNA/DNA ratios, which was best explained by a quadratic model reaching its minimum in the central range of sampling locations; and (iii) a latitudinal variation in Fulton's condition factor, which was best explained by a quadratic model reaching its maximum in the central range of sampling locations. Below we discuss the possible links between latitudinal variation in glass eel physiological status and variable energetic and environmental constraints to oceanic migration as a function of latitudinal distribution.

Induced pluripotent stem cell lines derived from equine fibroblasts.

The domesticated horse represents substantial value for the related sports and recreational fields, and holds enormous potential as a model for a range of medical conditions commonly found in humans. Most notable of these are injuries to muscles, tendons, ligaments and joints. Induced pluripotent stem (iPS) cells have sparked tremendous hopes for future regenerative therapies of conditions that today are not possible to cure. Equine iPS (EiPS) cells, in addition to bringing promises to the veterinary field, open up the opportunity to utilize horses for the validation of stem cell based therapies before moving into the human clinical setting. In this study, we report the generation of iPS cells from equine fibroblasts using a piggyBac (PB) transposon-based method to deliver transgenes containing the reprogramming factors Oct4, Sox2, Klf4 and c-Myc, expressed in a temporally regulated fashion. The established iPS cell lines express hallmark pluripotency markers, display a stable karyotype even during long-term culture, and readily form complex teratomas containing all three embryonic germ layer derived tissues upon in vivo grafting into immunocompromised mice. Our EiPS cell lines hold the promise to enable the development of a whole new range of stem cell-based regenerative therapies in veterinary medicine, as well as aid the development of preclinical models for human applications. EiPS cell could also potentially be used to revive recently extinct or currently threatened equine species.