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The utilization of nanotechnology to enhance the properties of liquids has been underway for several decades. The systematic incorporation of nanoparticles into the base liquids has demonstrated its effectiveness in improving the thermoelectrical properties of insulating liquids. Vegetable-based liquids have emerged as potential alternatives to mineral-insulating oil due to the environmental concerns associated with the latter, despite their excellent electrical properties. Despite the environmental friendliness and health safety attributes of vegetable-based liquids, there are yet some limitations like low ionization resistance, high dielectric losses, low volume resistivity, and poor oxidation stability. These limitations are currently being addressed using different nanoparticles by the addition of an appropriate concentration to the base liquids. Though there is development in the synthesis of nanofluids using different nanoparticles for transformer insulation, no nanoparticle has been declared as one with ultimate performance. This review presents a comprehensive examination of recent findings in the literature concerning the use of nanofluids for transformer insulation, focusing specifically on the challenge of long-term stability. The review addresses various aspects including the characterization of nanoparticles, types of nanoparticles employed for enhancing insulating liquids, methods for preparing nanofluids, strategies for improving nanofluid stability, the impact of nanoparticles on vegetable-based insulating liquids, and the existing challenges associated with nanofluids. The report investigates these topics extensively, presenting a thorough analysis of the subject matter. It aims to provide valuable insights to researchers in this field and to encourage the exploration of sustainable and environmentally friendly insulating liquids suitable for green transformers.

The advent of collaborative robotics in industry has created a closer collaboration between humans and robots. This has led to the need to optimally schedule human and robot tasks to be robust enough to handle variability induced by time-related operator errors caused by the inability to accurately forecast the stochastic nature of human behavior. This article proposes an explicit scheme for tackling time-related variability in human tasks online in applications where humans intervene at a given time in the collaborative workspace. The planning problem is reformulated as a Travelling Salesman Problem combined with a 0/1-Knapsack Problem in order to actively define robot behavior when there is an unmodelled shift in the human execution time sequence. The method uses a two-level adaptation scheme. The first one (offline) inputs the predicted human behaviour in terms of time required for different activities at each work cycle, and then computes an overall task schedule to minimize the robot's operation time and idle time. The second one (online) involves the real-time detection of the human's timing to either stop the prescribed plan or enhance it in order to minimize robot and human idle times, thereby optimizing the sense of ease and fluency in the interaction. The system is simulated in different scenarios where the human predicted time is set to be wrong, and thus the system needs to account for such variation. The effect of the human predicted time on the task schedule is presented and helps to demonstrate the effectiveness of the proposed approach in dealing with human variability without prior modeling knowledge of the human task time distribution.

The rapid development of self-driving vehicles requires integrating a sophisticated sensing system to address the various obstacles posed by road traffic efficiently. While several datasets are available to support object detection in autonomous vehicles, it is crucial to carefully evaluate the suitability of these datasets for different weather conditions across the globe. In response to this requirement, we present a novel dataset named the Canadian Vehicle Datasets (CVD). Subsequently, we present deep learning models that use this dataset. The CVD comprises street-level videos which were recorded by Thales, Canada. These videos were collected with high-quality cameras mounted on a vehicle in the Canadian province of Quebec. The recordings were made during daytime and nighttime, capturing weather conditions such as hazy, snowy, rainy, gloomy, nighttime and sunny days. A total of 10000 images of vehicles and other road assets are extracted from the collected videos. A total of 8388 images were annotated with corresponding generated labels 27766 with their respective 11 different classes. We analyzed the performance of the YOLOv8 model trained using the existing RoboFlow dataset. Then, we compared it with the model trained on the expanded version of RoboFlow using the proposed weather-specific dataset, CVD. Final values of improved accuracy of 73.26 %, 72.84 %, and 73.47 % (Precision/Recall/mAP) were reported upon adding the proposed dataset. Finally, the model trained on this diverse dataset exhibits heightened robustness and proves highly beneficial for both autonomous and conventional vehicle operations, making it applicable not only in Canada but also in other countries with comparable weather conditions.

In the era of Industry 4.0, digital transformation has spurred the swift advancement of technologies, including intelligent predictive maintenance scheduling, prognostics and health management. The accurate prediction of remaining useful life plays a crucial role in these technologies as it extends power equipment’s safe operational duration and decreases the maintenance costs associated with unforeseen shutdowns. Also, the increased accessibility of data for monitoring system conditions has paved the way for the more immense adoption of machine learning models in prognostics and health management for power transformers. At the moment, with the ever-increasing demand for electricity, there is a corresponding increase in the degradation processes of power transformers. Transformers insulation system and more importantly, the paper insulation happens to be the principal part where the degradation is prominent. Therefore, an accurate prediction of the insulating paper condition through its degree of polymerization is required to guarantee the reliability of power transformers. In this regard, the predictions, reliability, and health monitoring of this power equipment can be actualized by modeling the degradation of transformer insulation paper through several machine learning frameworks. In this view, this review paper has been drafted not only to serve as a guide for researchers interested in the fields of transformer insulation system fault prognosis but also to offer insights into potential research directions as existing literature in modeling and evaluating transformer paper insulation is presented.

A key factor in ensuring the efficient and safe operation of power transformers is the early and accurate diagnosis of incipient faults. Among the tools available to achieve this goal, dissolved gas analysis (DGA) is widely used by power transformers' maintenance professionals. It is a preventive maintenance tool, used for condition monitoring, fault diagnosis and unplanned outage prevention. With the development of artificial intelligence (AI), many intelligent‐based methods using AI tools have been proposed in the literature for DGA data interpretation. Although these methods achieve high diagnostic accuracies and improve DGA efficiency, they are generally complicated and the research documented in these publications is difficult to replicate. Traditional DGA‐based methods are simple, easy to understand and implement, and widely used by power transformers' maintenance professionals. Many methods proposed in recent years overcome the limitations of the pioneer methods and are increasingly effective. The authors present a detailed and comprehensive literature review of the traditional DGA‐based methods for mineral oil‐immersed power transformer faults diagnosis. This review also addresses ways to improve the efficiency of the available traditional methods. Some pitfalls that need to be taken into account to improve the efficiency of the DGA‐based diagnostic methods are also presented.

Superhydrophobic coatings can be a suitable solution for protecting vulnerable electrical infrastructures in regions with severe meteorological conditions. Regenerative superhydrophobicity, the ability to regain superhydrophobicity after being compromised or degraded, could address the issue of the low durability of these coatings. In this study, we fabricated a superhydrophobic coating comprising hydrophobic aerogel microparticles and polydimethylsiloxane (PDMS)-modified silica nanoparticles within a PDMS matrix containing trifluoropropyl POSS (F-POSS) and XIAMETER PMX-series silicone oil as superhydrophobicity-regenerating agents. The fabricated coating exhibited a static contact angle of 169.5° and a contact angle hysteresis of 6°. This coating was capable of regaining its superhydrophobicity after various pH immersion and plasma deterioration tests. The developed coating demonstrated ice adhesion as low as 71.2 kPa, which remained relatively unchanged even after several icing/de-icing cycles. Furthermore, the coating exhibited a higher flashover voltage than the reference samples and maintained a minimal drop in flashover voltage after consecutive testing cycles. Given this performance, this developed coating can be an ideal choice for enhancing the lifespan of electrical insulators.

As the focus tilts toward online detection methodologies for transformer oil aging, bypassing challenges associated with traditional offline methods, such as sample contamination and misinterpretation, fiber optic sensors are gaining traction due to their compact nature, cost-effectiveness, and resilience to electromagnetic disturbances that are typical in high-voltage environments. This study delves into the sensitivity analysis of intensity-modulated plastic optical fiber sensors. The investigation encompasses key determinants such as the influence of optical source wavelengths, noise response dynamics, ramifications of varying sensing lengths, and repeatability assessments. Our findings highlight that elongating sensing length detrimentally affects both linearity response and repeatability, largely attributed to a diminished resistance to noise. Additionally, the choice of the optical source wavelength proved to be a critical variable in assessing sensor sensitivity.

Many power transformers throughout the world are nearing or have gone beyond their theoretical design life. Since these important assets represent approximately 60% of the cost of the substation, monitoring their condition is necessary. Condition monitoring helps in the decision to perform timely maintenance, to replace equipment or extend its life after evaluating if it is degraded. The challenge is to prolong its residual life as much as possible. Dissolved Gas Analysis (DGA) is a well-established strategy to warn of fault onset and to monitor the transformer’s status. This paper proposes a new intelligent system based on DGA; the aim being, on the one hand, to overcome the conventional method weaknesses; and, on the other hand, to improve the transformer diagnosis efficiency by using a four-step powerful artificial intelligence method. (1) Six descriptor sets were built and then improved by the proposed feature reduction approach. Indeed, these six sets are combined and presented to a Kohonen map (KSOM), to cluster the similar descriptors. An averaging process was then applied to the grouped data, to reduce feature dimensionality and to preserve the complete information. (2) For the first time, four direct Multiclass Support Vector Machines (M-SVM) were introduced on the Generic Model basis; each one received the KSOM outputs. (3) Dempster–Shafer fusion was applied to the nine membership probabilities returned by the four M-SVM, to improve the accuracy and to support decision making. (4) An output post-processing approach was suggested to overcome the contradictory evidence problem. The achieved AUROC and sensitivity average percentages of 98.78–95.19% (p-value < 0.001), respectively, highlight the remarkable proposed system performance, bringing a new insight to DGA analysis.

The aim of this research is to predict the mechanical properties along with the behaviors of standard insulating paper used in power transformers under thermal aging. This is conducted by applying an artificial neural network (ANN) trained with a multiple regression model and a particle swarm optimization (MR-PSO) model. The aging of the paper insulation is monitored directly by the tensile strength and the degree of polymerization of the solid insulation and indirectly by chemical markers using 2-furfuraldehyde compound content in oil (2-FAL). A mathematical model is then developed to simulate the mechanical properties (degree of polymerization (DPV) and tensile index (Tidx)) of the aged insulation paper. First, the datasets obtained from experimental results are used to create the MR model, and then the optimizer method PSO is used to optimize its coefficients in order to improve the MR model. Then, an ANN method is trained using the MR-PSO to create a nonlinear correlation between the DPV and the time, temperature, and 2-FAL values. The acquired results are assessed and compared with the experimental data. The model presents almost the same behavior. In particular, it has the capability to accurately simulate the nonlinear property behavior of insulation under thermal aging with an acceptable margin of error. Since the life expectancy of power transformers is directly related to that of the insulating paper, the proposed model can be useful to maintenance planners.

The use of geophysical data to accurately determine water levels is demonstrated for an aquifer within the Saint-Narcisse moraine in the Mauricie region of southeastern Québec, Canada. Two numerical simulations were conducted using FEFLOW, one based on regional piezometric data and the other using geophysical data; the data were acquired through transient electromagnetic (TEM), electrical resistivity (ERT), and ground-penetrating radar (GPR) surveys. The three-dimensional geological and groundwater flow model was based on data from 94 boreholes, 5 stratigraphic cross-sections, and 20 TEM, 6 ERT (~1.44 km) and 4 GPR (~0.97 km) surveys. Both numerical analyses confirmed the simulated water levels, and the root mean square errors obtained from the piezometric data and the multiple geophysical techniques were similar at 3.81 and 2.76 m, respectively. Through a discrete modeling approach, this study shows that groundwater levels estimated using geophysical tools and methods and those determined by direct observation are comparable. The outcome illustrates how geophysical data can complement direct observations to provide additional hydraulic information to hydrologic modellers. Geophysical surveys provide an extensive set of soft data that can be leveraged to improve groundwater flow models and determine groundwater levels, particularly in areas characterized by limited direct piezometric information. L’utilisation de données géophysiques pour déterminer avec précision les niveaux d’eau est démontrée pour un aquifère situé dans la moraine de Saint-Narcisse dans la région de la Mauricie au sud-est du Québec, Canada. Deux simulations numériques ont été effectuées à l’aide de FEFLOW, l’une basée sur des données piézométriques régionales et l’autre sur des données géophysiques; les données ont été acquises par des levés électromagnétiques transitoires (TEM), de résistivité électrique (ERT) et de radar à pénétration de sol (GPR). Le modèle tridimensionnel géologique et d’écou

This paper introduces a lean noise-cancelling sturdy-MASH delta-sigma ADC, achieved through the implementation of a noise-shaping SAR second stage, the use of which instead of the usual delta-sigma stage, eliminates, by virtue of its inherent feedforward mechanism, the quantization noise of the first stage. Moreover, any mismatch-induced error of the noise-shaping SAR gets shaped by the first stage, circumventing the need for strict capacitor matching or calibration. The elimination of noise-cancelling and/or calibration circuitry, as well as the inclusion of the area-efficient noise-shaping SAR qualify this topology as lean. Simulations demonstrate a proof-of-concept design achieving 107.7 dB SQNR for an OSR of 14 over a 1 MHz bandwidth.

The effect of multipass friction stir processing (FSP) on the microstructure and mechanical properties of an AlSi10Mg alloy produced by laser-powder bed fusion was investigated. FSP was performed at a rotational speed of 950 rpm and traverse speed of 85 mm/min. The results indicated that FSP destroyed the coarse grain structure in the as-built AlSi10Mg by generating fine and equiaxed grain structures with shear texture components of A∗ 1 (111) 112 and A∗2 (111) 112, in addition to causing fragmentation and refinement of the Si networks. FSP reduced the tensile strength slightly but significantly improved ductility. One-pass FSP exhibited superior mechanical properties compared with the two- and three-pass scenarios. The higher strength of the one-pass sample was attributed to the strengthening mechanisms induced by the Si particles, which were grown by repeated FSP. The higher ductility of the one-pass sample was explained using the kernel and grain average misorientations. Furthermore, the post-FSP microstructural evolution and fracture behavior of the samples were discussed.

This study investigates the effects of microcylindrical pillar geometry on surface wettability and ice nucleation time. The cylindrical micropillars were designed and then fabricated on silicone rubber surfaces by micromachining to create a template, followed by direct replication using a compression molding method. This approach offers an efficient, nontoxic, and low-cost means of producing micro-nanoscale roughness on surfaces. We tested the wetting (i.e., contact angle and contact angle hysteresis) and anti-icing (i.e., ice nucleation time) properties of the patterned silicone rubber surfaces having different combinations of pillar diameter and interpillar spacing. According to our experimental results for a limited range of pillar diameters (80 and 110 ) and center to center spacing (pitch; 125–300 ), decreasing the diameter and increasing the space of micro-cylindrical pillars may result in the Cassie wetting until a threshold value. Beyond this pillar diameter/pitch threshold, Wenzel wetting occurred. Surfaces characterized by pillars of different diameter and pitch also increased the freezing delay by reducing the area of ice–substrate contact and the heat transfer between the water droplet and the surface. We demonstrate that the properties of superhydrophobic and anti-icing surfaces can be controlled by altering the geometry of micropillars across the surface.

In this article, some investigations on the prebreakdown and breakdown phenomena of low pour point insulating liquids under negative lightning impulse (LI) voltage are reported. The tested liquids include mineral oil (MO), a typical synthetic ester (TSE), and two low-pour point synthetic esters. These liquids underwent accelerated thermal aging. The nonaged and aged samples were subjected to LIs using a point-plane electrode arrangement. The discussions are focused on the initiation of partial discharges, propagation of streamers, and breakdown behavior in the nonaged and aged liquids. The investigated parameters include inception voltage, LI breakdown voltage, streamer acceleration voltage, and streamer velocity. The results are supported by the oscillographs of the light activity that is recorded during the discharge process. The prebreakdown phenomenon noticed in the TSE vis-à-vis mineral insulating oil is in line with the existing literature. Importantly, it is noticed that the inception and breakdown voltages of the nonaged low pour point synthetic esters are similar to nonaged MO. In addition, the inception and breakdown voltages of the aged low pour point synthetic esters are noticed to be higher than that of the aged MO. These results add to the arguments in favor of replacing MOs in power transformers.

Erosional incidents have heightened the necessity of studies regarding rock mass erosion in unlined dam spillways. Enhanced comprehension of hydraulic erodibility necessitates an investigation into the geomechanical and hydraulic aspects of erosional phenomena. Controlled blasting is commonly employed to establish unlined spillways in rock masses, and this process results in irregularities along the spillway surface profile. Recent research has identified key geometric parameters of rock masses that impact erosion in unlined spillways, such as joint opening, dip and dip direction, and joint spacing. However, the effect of spillway surface irregularities on hydraulic parameters remains uncertain. Numerous studies have examined the surface roughness of rock at the millimeter scale within the domain of hydraulic engineering. Despite these efforts, a noticeable gap persists in our understanding of how surface irregularities specifically exert influence over hydraulic parameters. Currently, there is a lack of a clear equation or methodology to incorporate irregularities into hydraulic erosive parameters. The main aim of this study is to show how such irregularities affect the hydraulic parameters. This study is dedicated to emphasizing the importance of considering these irregularities. Building upon the findings obtained, the core aim of this research is to facilitate the formulation of an equation in future investigations that effectively accounts for these irregularities when calculating hydraulic erosive parameters. To assess the significance of surface irregularities in unlined spillways, computational fluid dynamics (CFD) with ANSYS-Fluent software was employed to analyze 25 configurations of spillway surface irregularities and their effects on various factors, including pressure (total, dynamic, and static pressures), shear stress, flow velocity, and energy. The findings indicated that irregularities significantly influenced the hydraulic parameters. Specifically

In this article, lightning impulse breakdown behaviour of two low pour point synthetic ester liquids is presented in comparison to a typical synthetic ester at both positive and negative polarities. Traditional mineral insulating oil has been also considered for reference purposes. A detailed breakdown behaviour analysis of the four test liquids under a non-uniform field (medium gap, point-plane electrode system) and quasi-uniform field (smaller gap, U-plane electrode system) is envisaged. The lightning impulse breakdown measurements based on the source voltage waveforms and light activity during the discharge process are presented. The Weibull breakdown failure rates and streamer velocity during the breakdown of different liquids for all the cases (+/- polarities and both electrode configurations) are reported in support of the discussions. In the case of non-uniform fields, the lightning breakdown voltage of the low pour point liquids is found to be higher than typical synthetic esters and is comparable to mineral oil under both polarities. While in the case of quasi-uniform field, the lightning breakdown voltage of the low pour point liquids is found to be lower than mineral oil and comparable to the typical synthetic ester under both polarities. These findings add to limited knowledge on the application of esters in cold countries and allow insulation designers to estimate the behaviour of the low pour point synthetic ester liquids under lightning conditions.

The detection of On-Load Tap-Changer (OLTC) faults at an early stage plays a significant role in the maintenance of power transformers, which is the most strategic component of the power network substations. Among the OLTC fault detection methods, vibro-acoustic signal analysis is known as a performant approach with the ability to detect many faults of different types. Extracting the characteristic features from the measured vibro-acoustic signal envelopes is a promising approach to precisely diagnose OLTC faults. The present research work is focused on developing a methodology to detect, locate, and track changes in on-line monitored vibro-acoustic signal envelopes based on the main peaks extraction and Euclidean distance analysis. OLTC monitoring systems have been installed on power transformers in services which allowed the recording of a rich dataset of vibro-acoustic signal envelopes in real time. The proposed approach was applied on six different datasets and a detailed analysis is reported. The results demonstrate the capability of the proposed approach in recognizing, following, and localizing the faults that cause changes in the vibro-acoustic signal envelopes over time.

Frequency response analysis (FRA) is being employed worldwide as one of the main methods for the internal condition assessment of transformers due to its capability of detecting mechanical changes. Nonetheless, the objective interpretation of FRA measurements is still a challenge for the industry. This is mainly attributable to the lack of complete data from the same or similar units. A large database of FRA measurements can contribute to improving classification algorithms and lead to a more objective interpretation. Due to their destructive nature, mechanical deformations cannot be performed on real transformers to collect data from different scenarios. The use of simulation and laboratory transformer models is necessary. This research contribution is based on a new method using Finite Element Method simulation and a lumped element circuit to obtain FRA traces from a laboratory model at healthy and faulty states, along with an optimization method to improve capacitive parameters from estimated values. The results show that measured and simulated FRA traces are in good agreement. Furthermore, the faulty FRA traces were analyzed to obtain the characterization of faults based on the variation of the lumped element’s parameters. This supports the use of the proposed method in the generation of faulty frequency response traces and its further use in classifying and localizing faults in the transformer windings. The proposed approach is therefore tailored for generating a larger and unique database of FRA traces with industrial importance and academic significance.

Considered as the heart of electrical power transmission and distribution networks, power transformers are essential part of the electricity transmission grid. Among the condition monitoring and fault diagnosis tools for these machines, dissolved gas analysis (DGA) has proven its effectiveness in their early detection and classification of faults. Up to date, many methods have been proposed in the literature for the interpretation of DGA data, classified into traditional and intelligent methods. This paper proposes a two-steps hybrid method, which uses the strengths of both methods. The approach uses the evolutionary k-means clustering algorithm based on the genetic algorithm for subset formation and subset analysis by human expertise. In the diagnostic procedure, to determine the condition of a sample, the subset to which it belongs is first identified and then the corresponding diagnostic sub-model is applied. The proposed method has been implemented with 595 DGA data, tested on 254 DGA data and validated on the International Electrotechnical Commission (IEC) TC10 database. Their performances were evaluated and compared with existing traditional, intelligent and hybrid methods. From the results obtained with the IEC TC10 database, the newly proposed approach depicts the best overall diagnosis accuracies. Indeed, the best performance is achieved with the proposed method compared to other models in the literature, with diagnostic accuracy of 98.29% compared to 88.89% of the Gouda triangle method, to 88.03% of the Hyosun Corporation gas ratio method or to 86.32% of the three ratios technique.

The degradation of the insulation system in liquid-filled power transformers is a serious concern for electric power utilities. The insulation system’s ageing is accelerated by moisture, acids, oxidation products, and other decay particles (soluble and colloidal). The presence of these ageing by-products is detrimental to the insulation system and may further lead to premature ageing and serious consequences. The ageing mechanisms of oil-paper insulation are complex, highly interrelated, and strongly temperature-dependent. The operating temperature of the transformer insulating system has a direct relationship with the loading profile. The major aspect that is witnessed with the fluctuating temperatures is moisture migration and subsequent bubble evolution. In other words, gas bubbles evolve from the release of water vapor from the cellulosic insulation wrapped around the transformer windings. The models presented in the existing standards, such as the IEC Std. 60076-7:2018 and the IEEE Std. C57.91:2011, are mainly based on the insulation temperature, which acts as a key parameter. Several studies have investigated the moisture dynamics and bubbling phenomenon as a function of the water content in the paper and the state of the insulation system. Some studies have reported different prototypes for the estimation of the bubble inception temperatures under selected conditions. However, there are various attributes of the insulation system that are to be considered, especially when expanding the models for the alternative liquids. This paper reviews various evaluation models reported in the literature that help understand the bubbling phenomenon in transformer insulation. The discussions also keep us in the loop on the estimation of bubbling behavior in alternative dielectric liquids and key attributable factors for use in transformers. In addition, useful tutorial elements focusing on the bubbling issue in transformers as well as some critical analyses are addressed f

A new strategy is proposed to modify the grain structure and crystallographic texture of laser-powder bed fusion AlSi10Mg alloy using multi-pass friction stir processing (FSP). Accordingly, 1–3 passes of FSP with 100% overlap were performed. Scanning electron microscopy and electron backscattered diffraction were used for microstructural characterization. Continuous dynamic recrystallization and geometric dynamic recrystallization are the governing mechanisms of grain refinement during FSP. The stir zones have bimodal grain structures containing large and fine grains. The multi-pass FSP caused a considerable increase in the volume fraction of the large-grained area in the stir zone, which contained higher values of low-angle boundaries and sharp shear texture components of B(11¯2)[110] and B¯¯¯(1¯12¯)[1¯1¯0]. The formation of low-energy grain boundaries in the stir zone and alignment of the low-energy crystallographic planes with the surface of the sample made the strategy of using multi-pass FSP a promising candidate for corrosion resistance enhancement in future studies. Moreover, the detailed evolution of the grains, texture components, grain boundaries, and Si particles is discussed.

Insulation cellulose paper is a basic measure for a power transformer’s remaining useful life, and its advantageous low cost, electrical, and mechanical properties have made it an extensive insulation system when impregnated in a dielectric liquid. Cellulose paper deteriorates as a result of ageing due to some chemical reactions like pyrolysis (heat), hydrolysis (moisture), and oxidation (oxygen) that affects its degree of polymerization. The condition analysis of cellulose paper has been a major concern since the collection of paper samples from an operational power transformer is almost impossible. However, some chemicals generated during cellulose paper deterioration, which were dissolved in dielectric liquid, have been used alternatively for this purpose as they show a direct correlation with the paper’s degree of polymerization. Furthermore, online and non-destructive measurement of the degree of polymerization by optical sensors has been proposed recently but is yet to be available in the market and is yet generally acceptable. In mitigating the magnitude of paper deterioration, some ageing assessments have been proposed. Furthermore, researchers have successfully enhanced the insulating performance of oil-impregnated insulation paper by the addition of various types of nanoparticles. This study reviews the ageing assessment of oil-paper composite insulation and the effect of nanoparticles on tensile strength and electrical properties of oil-impregnated paper insulation. It includes not only significant tutorial elements but also some analyses, which open the door for further research on the topic.

In recent years, permafrost warming has adversely affected the performance of infrastructure projects in northern Canada. The common construction practice in these permafrost-underlain areas is to use the pile foundation for building and infrastructure rather than using shallow foundations. Thus, the influence of warming permafrost on the loading capacity of pile foundations is critical for evaluating forthcoming changes under a warming climate. The present study was performed to assess the influence of temperature and loading rate on the adfreeze strength of piles in permafrost soil. A series of uniaxial compressive tests on wooden piles drilled into frozen soil were carried out at various temperatures, mimicking the conditions in warming permafrost. The results demonstrated that an increase in strain rate led to an increase in the adfreeze strength of piles. Moreover, the wooden piles exhibited peak adfreeze strength at a displacement of less than 2 mm for most temperature and deformation rate combinations. Interestingly, although the adfreeze strength of piles increased as temperatures cooled below −1°C, a transition phase between −3°C and −4.5°C was observed in which the adfreeze strength decreased. It could be noted that failure occurred in the wooden piles at temperatures colder than −5°C rather than frozen soil failure at these temperatures. These variations in the adfreeze strength were related to soil temperature, unfrozen water content, and the adhesive bonds at the interface between the frozen soil and pile.

The fluids studied in this section are EG106 SAE Type IV and Polar Guard Advance Type IV. EG 106 is an ethylene glycol-based product, and PG is a propylene glycol-based product. For each test, 16.2 g of the product was placed on a small aluminum plate with a thickness of about 1.6 mm. The tests were conducted at-5 C, and the cold chamber ventilation was set at 60. During this test, several snow deposits of the same quantity were applied to each product every 2 minutes

The emerging needs of human beings are pushing manufacturing companies from mass production to mass customization. The occurrence of these new challenges leads to a change of scenario where the robot no longer works isolated from human to a scenario in which the robot collaborates with the human in the same workspace (collaborative robotics). Wearable sensors using inertial measurement unit (IMU) are widely used to capture human upper body gestures in which the set of gesture being recognize is very large. However, foot gesture approach is starting to gain some places in applications where human’s hands are occupied when interacting with robots. This study presents an insole-based foot gesture recognition method for cobot operation mode selection. The insole is composed of an IMU and four force sensors. The classification algorithm uses a support vector machine (SVM) classifier based on features extracted by means of dynamic time warping (DTW) applied to only one reference gesture signal. Five human participants are used for the dataset. As a case study, the system was interfaced in real time (real-time classification algorithm) using a Simulink 2020a scheme with Universal Robots UR5 (5 kg payload). The worst-case recognition accuracy is around 88%. The algorithm is able to adequately discriminate between 10-foot gestures by means of a wearable insole sensor incorporated into the insole. Moreover, this study shows that, the control gesture can accurately be recognized from other current activities such as walking, turning, climbing the stairs, and similar.