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In this study, a flexible capacitive pressure sensor based on filter paper substrate is proposed. The electric double layer (EDL) structure was constructed through the introduction of ionic liquid (IL), and the pressure-sensing was realized by using the change of ion-electron interface contact. The paper-based iontronic pressure sensor possesses a high sensitivity of 221.9 kPa−1, which is hundreds of times higher than that of the capacitive pressure sensors with conventional parallel plate structure, as well as an ultra-low detection limit of 2 Pa. The sensor exhibits an enormous application potential in healthcare wearable electronics and human-machine interface equipment. [ABSTRACT FROM AUTHOR]

Thermal aging and ultraviolet (UV) irradiation are used as methods to prepare samples of kraft paper spanning its full lifecycle within a high-voltage transformer, from new to end of life. The mechanical break strain is correlated with the degree of polymerization (DP), representing a progressive deterioration of mechanical properties during aging. Subsequent exposure to oil flow mimicking both normal plant operation and reclamation activities shows that aged paper exposed to high rates of flow experiences noticeable surface roughening. Despite this, no measurable erosion occurs even after long-term temperature/flow cycling in used mineral oil containing a notable particulate content. This indicates that kraft paper is unlikely to be eroded by normal reclamation activities, even in a severely aged asset. This confirms the validity of using this approach to extend plant life. [ABSTRACT FROM AUTHOR]

IEEE CAS Society is pleased to congratulate the following recipients of the 2024 IEEE Circuits and Systems Society Awards. The Society is especially proud and happy to honor their outstanding accomplishments and we look forward to their continued involvement and close contact with the CAS Society. [ABSTRACT FROM AUTHOR]

As this Special Section goes to publication, semiconductor manufacturing in the United Status, and globally, continues to expand at a seemingly torrid pace. Assisted by government funding and driven in part by artificial intelligence workloads that gobble up increasing amounts of data center computing capacity, Intel and TSMC fabs are going up in Arizona, TI and Samsung fabs are coming to Texas, and Micron has big plans in New York. Unfortunately, just like those flying cars we were once promised, AI has not yet eliminated the need for the skilled trades and engineers required to build and successfully operate a fab. As a result, workforce development has become an important part of the increasingly complex semiconductor manufacturing process: Where are the thousands of engineers the semiconductor industry needs to staff these new fabs going to come from? How can we make more students excited about science and engineering? While the Guest Editors don’t have all the answers, we are happy that ASMC contributes to the solution by actively supporting student presentations and posters and annually recognizing the best student paper of the conference. And, maybe some day, the artificial intelligence systems that semiconductor manufacturing has enabled will give us those Star Wars or Star Trek robots that can build fabs and make chips too. [ABSTRACT FROM AUTHOR]

The thermal index for thermally upgraded Kraft paper was determined for a series of five mineral insulating liquids and two natural esters using IEEE Standard C57.100 Annex A.4.2 and compared with the values in IEEE Standard C57.91. [ABSTRACT FROM AUTHOR]

The long-term operation of dry-type transformer bushings at high temperatures can impact the performance of their primary insulation material. This article presents an experimental study of the effect of thermal aging time on electrical treeing in the epoxy resin-impregnated paper. The experimental results show that the electrical treeing pattern along the middle path of the electric field and the interlayer creepage along the average direction of the electric field are closely related to the aging time. Under the effect of thermal aging at 150 °C, the tree-starting voltage of epoxy resin-impregnated paper tends to increase and decrease as the aging time increases. An analysis and discussion of the test results in conjunction with scanning electron microscope (SEM) and energy dispersive spectroscopy (EDS) tests suggest that the destruction of the cross-linked network of the epoxy resin-impregnated paper at 150 °C is a fundamental reason for the significant decrease in the material’s tree-starting voltage and the change in electrical treeing morphology and interlayer creepage. Long-term operation at high temperatures can seriously impact the primary insulation performance of dry-type transformer bushings and requires attention. [ABSTRACT FROM AUTHOR]

This issue of IEEE Vehicular Technology Magazine includes our first invited paper. In the next issues, we plan to publish a series of invited papers on selected topics of high interest that should drive and become a reference for the community. The invited papers will provide a vision on the evolution, open challenges, and trends for technologies of high-expected impact. [ABSTRACT FROM AUTHOR]

The semiconductor industry is the core industry of the information age. As a key link in the semiconductor industry, wafer fabrication plays a key role in its development. In the testing stage of the wafer, each die of the wafer is detected and marked, and a wafer map with a certain spatial pattern can be formed. The analysis and classification of these spatial patterns can identify the cause of wafer defects, thereby improving production yield. However, as wafer size increases, line widths become smaller, etc., the probability of a mixed defect mode wafer pattern increases. Moreover, the mixed defect mode wafer map is more difficult to identify and classify than the single defect mode wafer map. Therefore, this paper proposes an improved deep convolutional neural network (DCNN) structure model for the recognition and classification of mixed defect pattern wafer maps. From the perspective of increasing the width of the DCNN, the improved network structure can avoid problems such as over-fitting and limited extraction of features due to the continuous deepening of the DCNN. The network is called Multi-Path DCNN (MP-DCNN) structure. The experimental results show that the proposed Multi-Path DCNN structure has better performance and higher classification accuracy than existing methods. [ABSTRACT FROM AUTHOR]

In recent years, the establishment of a substantial amount of academic groups on scientific social network has brought new opportunities for the collaboration among researchers. In this situation, conducting paper recommendation to these academic groups is of terrific necessity in that it can further facilitate group activities. However, when producing group recommendation, existing methods fail to make full use of the abundant group information, from which a great deal of valuable information can be inferred to facilitate the recommendation performance. In addition, those methods tend to assign an equal weight to each group member when aggregating their recommendations, which is unreasonable in practice. Although some improvements have been made to remedy this problem by assigning different weights to group members, they fail to take into account the reliabilities of group members. Therefore, a group-oriented paper recommendation method based on probabilistic matrix factorization and evidential reasoning (GPMF_ER) is proposed in this article to tackle these problems. More specifically, the group and paper content information are integrated into the probabilistic matrix factorization model to enhance the accuracy of individual recommendation. Afterward, evidential reasoning rule is introduced in the aggregation step to consider both the weights and reliabilities of group members. Extensive experiments have been conducted on the real world CiteULike dataset and the results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The total/partial oil change treatment can affect the furfural partitioning in oil-paper-pressboard insulation, resulting in poor accuracy of furfural analysis. Moreover, due to the high cost of total oil change, partial oil change is more practical during transformer operation. This paper investigates the furfural partitioning in the oil-paper-pressboard insulation under partial oil change conditions. First, the initial furfural partitioning equation with non-oil change is proposed based on the obtained furfural content in oil, paper, and pressboard. Then, the modified furfural partitioning model under partial oil change condition is established by introducing a correction factor. Moreover, a preliminary correction scheme for the furfural partitioning model considering the influence of moisture and acid is provided. Finally, furfural content in oil-Kraft paper and oil-thermally upgraded Kraft (TUK) paper insulation are discussed, and results indicate that the proposed modified furfural partitioning model is not suitable for the oil-TUK paper insulation. This work can provide theoretical support for furfural analysis of oil-immersed transformers. [ABSTRACT FROM AUTHOR]

Measuring the furan concentration in transformer oil is one of the most common methods of monitoring and evaluating insulation paper and estimating its degree of polymerization (DP). Here, methods for measuring furan concentration, especially ultraviolet-visible (UV-Vis) spectroscopy method, are reviewed on the old transformers oil. The results show that the excessive turbidity of old oils and noise at low wavelengths are the main disadvantages of this method, which reduce the measurements accuracy. The following solutions are proposed to overcome these disadvantages: 1) diluting insulation oil with hexane to reduce absorption intensity and 2) using average absorption at a given wavelength to reduce noise impact. These solutions are applied to the oil samples of ten old distribution transformer. It is observed that the correlation between 2-furaldehyde (2-FAL) concentration and average absorption intensity in 190–600 nm wavelength range is equal to ${R}^{{2}} =0.98$. A mathematical equation for calculating 2-FAL concentration based on average absorption is presented. It has been shown that to reduce the test time, a 190–400 nm range is the optimal measurement wavelength range. The results show that the average difference between the values of DP in the proposed method and the direct measurement method is equal to 77.5. [ABSTRACT FROM AUTHOR]

We report our recent progress in quantitative aging assessment of the oil-impregnated-paper (OIP) equipment (i.e., degree of polymerization, DP) by near infrared spectroscopy (NIRS). The NIRS database is built by incorporating 8 types of insulating paper and total 478 differently aged samples. We propose an improved PCA-RBF-NN model to address the nonlinear correlation between DP of insulating paper and spectra, and hence strengthening the prediction accuracy for field assessment. In the improved model, the principle component analysis (PCA) and the filtering layer are two essential procedures for eliminating the noises and unrelated information from the spectra. The field practices show that the improved PCA-RBF-NN model owns better performance than the classic PLS model and general RBF-NN model on the disassembled bushing (RMSE: 56 vs 109 vs 124) and transformer (RMSE: 50 vs 237 vs 244), respectively. The NIRS powered by the improved algorithm can provide a rapid solution to the aging condition assessment of the OIP power equipment in the field. [ABSTRACT FROM AUTHOR]

The ecological environment is increasingly damaged due to the impact of mineral oil extraction and leakage. As a kind of sustainable resource, a vegetable oil transformer has bright application prospects in future. However, the stability of vegetable oil has always been a problem to be solved. In addition, the metallic copper inside the transformer has a certain catalytic effect on the deterioration of vegetable oil. In this article, a copper–oil–paper insulation model is established. Based on the molecular dynamics simulation method, the catalytic mechanism of copper on the deterioration of oil-paper insulation is revealed from the microscopic level. Then, the effects of temperature, contact area, and oxygen on the catalytic effect of copper are discussed. The results show that copper could accelerate the decomposition of oil-paper insulation by attracting H atoms and O atoms in oil-paper. The increase in temperature accelerates the progress of the catalytic reaction and aggravates the deterioration of oil-paper insulation. The oxygen concentration has an important influence on the catalytic reaction of copper. As the oxygen concentration increases, the catalytic effect of copper is weakened. The abovementioned research could provide some theoretical reference for further exploration of effective oil-paper insulation deterioration protection technology. [ABSTRACT FROM AUTHOR]

Novel designs of paper-based bandstop frequency-selective surfaces (FSSs) are presented in this article. The characteristics of the paper-based honeycomb structure are investigated first. One folded dipole resonator with first-order bandstop response is analyzed and further printed on the sidewalls of the honeycomb structure accordingly. A second-order response with two cascade FSSs is proposed with controllable bandwidth by the coupling coefficient of the resonators with the aid of the equivalent circuit model. Two paper-based FSS samples were fabricated and measured to demonstrate the validity of the proposed design strategies. Benefiting from the merits of lightweight and strong mechanical properties, the proposed paper-based FSS is a potential candidate for application in radar systems and aircraft. [ABSTRACT FROM AUTHOR]

The 2024 IEEE Conference on Artificial Intelligence was held from 25-27 June at the Marina Bay Sands Conference Centre in Singapore (Figure 1). IEEE CAI 2024, the second edition of this conference and exhibition, emphasised AI applications and key AI verticals impacting industrial technology and innovation. Similar to the inaugural event in June 2023 at Santa Clara, California, the 2024 edition was co-sponsored by the IEEE Computational Intelligence Society (as the lead organisation for the first two editions), IEEE Computer Society, IEEE Signal Processing Society, and IEEE Systems, Man, and Cybernetics Society. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms, especially evolutionary algorithms, have been frequently used to find near optimal solutions to combinatorial optimization problems. The evaluation of such algorithms is often conducted through comparisons with other algorithms on a set of benchmark problems. However, even if one algorithm is the best among all those compared, it still has difficulties in determining the true quality of the solutions found because the true optima are unknown, especially in dynamic environments. It would be desirable to evaluate algorithms not only relatively through comparisons with others, but also in absolute terms by estimating their quality compared to the true global optima. Unfortunately, true global optima are normally unknown or hard to find since the problems being addressed are NP-hard. In this paper, instead of using true global optima, lower bounds are derived to carry out an objective evaluation of the solution quality. In particular, the first approach capable of deriving a lower bound for dynamic capacitated arc routing problem (DCARP) instances is proposed, and two optimization algorithms for DCARP are evaluated based on such a lower bound approach. An effective graph pruning strategy is introduced to reduce the time complexity of our proposed approach. Our experiments demonstrate that our approach provides tight lower bounds for small DCARP instances. Two optimization algorithms are evaluated on a set of DCARP instances through the derived lower bounds in our experimental studies, and the results reveal that the algorithms still have room for improvement for large complex instances. [ABSTRACT FROM AUTHOR]

He is author of the books "Artificial Neural Networks for Modelling and Control of Non-linear Systems" (Springer) and "Least Squares Support Vector Machines" (World Scientific), co-author of the book "Cellular Neural Networks, Multi-Scroll Chaos and Synchronization" (World Scientific) and editor of the books "Nonlinear Modeling: Advanced Black-Box Techniques" (Springer), "Advances in Learning Theory: Methods, Models and Applications" (IOS Press) and "Regularization, Optimization, Kernels, and Support Vector Machines" (Chapman & Hall/CRC). [ABSTRACT FROM AUTHOR]

The generation and distribution of furfural are significantly affected by moisture content in the insulating paper. However, the diffusion mechanism of furfural under moisture effect is not clear, hence, this study investigates the effect of moisture on the diffusion characteristics of furfural by combining macro experiment and micro molecular dynamics simulation. The simulation results demonstrate that the increased moisture content in paper accelerates the diffusion of furfural from paper to oil, which is also proved by the experimental data. Then, the results of interaction energy analysis indicate that the increase of moisture enhances the van der Waals force between oil and furfural. The present findings are expected to improve the theoretical level of furfural analysis, which will facilitate the aging evaluation of the transformer insulating paper based on furfural. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

The space charge effect of transformer insulating paper will distort its internal electric field, and its space charge effect will be more prominent under extremely complex working conditions. At present, the micromechanism of charge transport and electric field distortion characteristics of paperboard are not clear under complex working conditions. So in this article, based on the bipolar carrier transport (BCT) model and the dynamic carrier balance model, the effects of high temperature, aging degree, moisture content, and bent degree on charges accumulation in paperboard are studied under dc high voltage by numerical simulation and the measured data needed for simulation. The results show that the higher temperature strengthens the carrier migration, deepens the injection depth of the charge, and aggravates the distortion of electric field intensity in paperboard. The more extended the aging time of paperboard, the greater the internal trap density and the deeper the trap depth, which lead to charges migration difficultly. Increasing water content in the paperboard will accelerate space charges to reach a steady state, while further increasing it will slow down this process, which is related to the shallow trap getting deeper. When the critical part of paperboard is extruded, many traps are introduced, resulting in charges accumulation seriously. The research results can reference the design of paperboard and the maintenance of transformer insulation system. [ABSTRACT FROM AUTHOR]

Chemical mechanical planarization (CMP) is vital for ensuring chip fabrication uniformity at nanometer scales. The emergence of a series of phenomenological CMP process models (Stine et al., 1997; Gbondo-Tugbawa, 2002; Xie, 2007; Vasilev, 2011) suggests that the existing model upgrade approach is largely based on a change in phenomenological model assumptions, demanding deep insights into complex process mechanisms and protracted period for accuracy improvements. To tackle this issue, this paper proposes a decoupling regression strategy for model upgrades. This strategy employs a data-driven approach to enhance the coupling relationships within the model, facilitating continuous improvement of simulation accuracy based on the existing model. It is capable of achieving improvements in model accuracy even in scenarios where modelers lack insight into complex process mechanisms. We validate our method by upgrading the Density Step Height (DSH) model to the Extend-DSH model to address poor erosion predictions at the 28nm node. Comparing model predictions with silicon data reveals that the Extend-DSH model aligns better with the measured data, reducing the root mean square error from 159.31Å to 6.89Å and increasing the coefficient of determination from -0.83561 to 0.6058, showcasing the effectiveness of the proposed chip-level CMP model upgrade method grounded in the decoupling regression strategy. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for Virtual Metrology (VM) in plasma etch processes based on analysis of all time and wavelength samples of Optical Emission Spectroscopy (OES) signals. The new method flattens each OES signal into a single vector, after which Singular Value Decomposition (SVD) is performed on the matrix formed by vectors of flattened OES signals in the training dataset. Low rank SVD projections of flattened and standardized OES recordings served as inputs for Ridge Regression, Artificial Neural Network, and Random Forest based VM models. A VM study is then conducted on a dataset gathered from a major 300 mm wafer fabrication facility, showing that the use of newly proposed SVD-based OES features consistently outperformed benchmark VM model features. Additional analysis of feature importance performed based on the analytically tractable Ridge Regression VM model form demonstrated distinct time-frequency patterns of OES signal portions that were highly informative for prediction of relevant Critical Dimensions, clearly justifying the need to use the entire OES signals for VM. [ABSTRACT FROM AUTHOR]

Personality determines various human daily and working behaviours. Recently, a large number of automatic personality computing approaches have been developed to predict either the apparent or self-reported personality of the subject based on non-verbal audio-visual behaviours. However, most of them suffer from complex and dataset-specific pre-processing steps and model training tricks. In the absence of a standardized benchmark with consistent experimental settings, it is not only impossible to fairly compare the real performances of these personality computing models but also makes them difficult to be reproduced. This paper presents the first reproducible audio-visual benchmark to provide a fair and consistent evaluation of eight existing personality computing models (e.g., audio, visual and audio-visual) and seven standard deep learning models on both self-reported and apparent personality recognition tasks. Building upon a set of benchmarked models, we also investigate the impact of two previously-used long-term modelling strategies for summarising short-term/frame-level predictions on personality computing results. We comprehensively investigate all benchmarked models on two publicly available datasets, ChaLearn First Impression and UDIVA self-reported personality datasets, and conclude: (i) apparent personality traits, inferred from facial behaviours by most benchmarked deep learning models, show more reliability than self-reported ones; (ii) visual models frequently achieved superior performances than audio models on personality recognition; (iii) non-verbal behaviours contribute differently in predicting different personality traits; and (iv) our reproduced personality computing models generally achieved worse performances than their original reported results. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals have been widely studied in human emotion recognition. The majority of existing EEG emotion recognition algorithms utilize dozens or hundreds of electrodes covering the whole scalp region (denoted as full-channel EEG devices in this paper). Nowadays, more and more portable and miniature EEG devices with only a few electrodes (denoted as few-channel EEG devices in this paper) are emerging. However, emotion recognition from few-channel EEG data is challenging because the device can only capture EEG signals from a portion of the brain area. Moreover, existing full-channel algorithms cannot be directly adapted to few-channel EEG signals due to the significant inter-variation between full-channel and few-channel EEG devices. To address these challenges, we propose a novel few-channel EEG emotion recognition framework from the perspective of knowledge transfer. We leverage full-channel EEG signals to provide supplementary information, available online, for few-channel signals via a transfer learning-based model CD-EmotionNet, which consists of a base emotion model for efficient emotional feature extraction and a cross-device transfer learning strategy. This strategy helps to enhance emotion recognition performance on few-channel EEG data by utilizing knowledge learned from full-channel EEG data. To evaluate our cross-device EEG emotion transfer learning framework, we construct an emotion dataset containing paired 18-channel and 5-channel EEG signals from 25 subjects, as well as 5-channel EEG signals from 13 other subjects. Extensive experiments show that our framework outperforms state-of-the-art EEG emotion recognition methods by a large margin. [ABSTRACT FROM AUTHOR]

Research on social psychology has revealed the existence of an affective mechanism in a human group, which is the group members spread their emotions to one another, the emotions of the group members form the group emotion, and the group emotion as a powerful force shapes the group members’ emotions. Current group emotion recognition methods focus on how the emotions of the group members form the group-level emotion but rarely take into account how the group emotion feeds back to the group members instantaneously. This paper proposes a new graph convolutional network architecture to characterize this unique affective mechanism for group emotion recognition. We regard the group members as the nodes of the graph and introduce a pseudo node into the graph to represent the role of the group. This paper uses graph convolutional networks to model the emotional interactions within the group from a static image and constructs an effective emotional representation at the group level for recognition. Experiment results on three widely used datasets for group emotion recognition show that our proposed method achieved superior performance in terms of recognition accuracy compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Workers with demanding jobs are at risk of experiencing mental stress, leading to decreased performance, mental illness, and disrupted sleep. To detect elevated stress levels in the workplace, studies have explored stress measurement from physiological, psychological, and behavioral perspectives. This paper reviews the assessment methods and strategies for mitigating mental stress in the workplace and provides recommendations for early detection and mitigation of mental stress. Among the modalities, Electroencephalography (EEG), Electrocardiography (ECG) and Galvanic Skin Response (GSR) were found to be the most used in assessing mental stress in the workplace. Nevertheless, these modalities are sensitive to motion artifacts and are difficult to be integrated into real work environments. To further improve stress level assessment in the workplace, multimodality integration with a reduced number of sensors such as EEG, GSR and Functional near infrared spectroscopy (fNIRS) can be utilized. This would lead to developing strategies for stress management in real-time. Furthermore, combining EEG with fNIRS would improve source localization of mental stress. To mitigate stress, we recommend developing a closed loop system that incorporates brain data acquisition systems and machine learning with physical stimulations such as audio Binaural Beats Stimulation and/or Transcranial Electric Stimulation. [ABSTRACT FROM AUTHOR]

Methanol is considered to be a new chemical marker to estimate the aging state of transformer cellulose insulation paper. The key to improve the assessment accuracy is to establish the precise relationship between the concentration of methanol in oil and degree of polymerization (DP) of insulation paper. The previous research in this area has shortcomings and needs to be further improved. In this article, first, an aging assessment basic model (temperature 20 °C, moisture content of insulation paper 0.637%) is established based on the theoretical derivation of cellulose degradation kinetics, and is calculated by 130 °C thermal aging experiment. Furthermore, the effects of different temperatures (20 °C–90 °C) and moisture contents of insulation paper (0.6%–3.4%) on methanol in oil are studied by diffusion experiment. Based on the above effects, the temperature and moisture correction factors are established by mathematical reduction. What is more, a modified model is proposed by integrating the basic model and the correction factors. Eventually, the verification experiment is randomly prepared to test the accuracy of modified model and the result shows that the relative error of DP is within 7%. The physical meaning and accuracy of aging assessment are improved by model derivation and modification of influencing factors. [ABSTRACT FROM AUTHOR]

Although the circulating flow of insulating oil can enhance heat dissipation in converter transformers, it can also influence the space charge distribution in oil–paper composite insulation. In this article, the oil–paper composite insulation between flat electrodes was used as the experimental model. Based on the pulsed electroacoustic (PEA) method, we investigated the effect of uncharged flowing oil on the space charge distribution in composite insulation under a dc electric field and polarity reversal. Under a dc electric field, as the oil flow velocity increased, the amount of interface charge between the oil and paper first increased and then decreased. During the polarity reversal process, the field distortion was aggravated at slower oil flows. These experimental results were explained in terms of the effect of oil flow on ionized charges in the oil. [ABSTRACT FROM AUTHOR]

Due to rapid temperature rise and insulation moisture, bubbles can generate in the oil-paper insulation of oil-immersed power transformers, which can reduce the dielectric strength of oil-paper insulation and even bring about the risk of insulation breakdown. In order to accurately evaluate the conditions of bubble formation, the physical process of bubble formation in oil-paper insulation is studied in this article. Considering the effects of water vaporization, gas dissolution in oil, and moisture migration on bubble formation, a bubble formation prediction model is constructed. Based on Henry’s law, the dissolution equilibrium of gas in oil is taken into consideration and the upper boundary of the initial temperature of bubble effect (ITBE) envelope is obtained in this model. Based on the moisture equilibrium in oil-paper insulation, the bubble formation process caused by the emergence and rapid evaporation of free water is analyzed, and then, the lower boundary of the ITBE envelope is obtained in this model. The model results show that the moisture content of the pressboard and the presence of free water have a great impact on ITBE. Specifically, the higher the moisture content, the lower the ITBE value. In addition, with the further increase of moisture content, the upper and lower boundaries of the ITBE envelope gradually tend to be the same. However, once free water is present on the surface of cellulose, ITBE decreases rapidly to below 120 °C. The model built in this article is of great significance to study the temperature limit of the power transformer. Hence the ITBE envelope can provide an important theoretical reference for reducing the risk of bubble formation during power transformer operation. [ABSTRACT FROM AUTHOR]

Healthy sleep is an important indicator of good physical and mental health. The current technology employed for monitoring sleep quality is typically expensive and invasive, which demands innovations in respiration monitoring. In addition, recent effort in developing wearable respiration sensors has faced great challenges in concurrently obtaining low cost, high stretchability, and high sensitivity. Herein, we demonstrate a simple fabrication strategy to construct low-cost, stretchable, skin-breathable and ultrasensitive respiration sensors using pencil drawing, electrode pasting, and laser cutting methods. With smart designs, the sensors exhibited high sensitivity, good stability, decent skin-breathability and capability to detect respiration rate and depth in real-time. The sensors fabricated by the simple low-cost method in this study can be utilized to measure sleep quality, providing warning signals for apnea and sleep disorders. The proposed technology holds promise for developing cost-effective wearable ultrasensitive sensors for sleep quality monitoring. [ABSTRACT FROM AUTHOR]

This paper investigates the behavior of interface charges between oil and oil immersed paper insulations in unaged and aged samples. Combined accelerated thermal and electrical aging experiments of oil immersed paper insulations are conducted to obtain aged oil and aged paper samples. The interface charges between oil and paper samples with different aging status are measured with the Pulsed Electro-acoustic (PEA) method. Zeta potentials in samples with different aging status are determined. It is shown that the amount of interface charge increases with the aging duration, and does not follow the prediction of Maxwell-Wagner-Sillars (MWS) law. This is explained in terms of the increase of the interface charge barrier due to the effect of electric double layer. It is proposed that interface charge may act as an optional characterization parameter for the assessment of oil immersed paper insulations. [ABSTRACT FROM AUTHOR]

A growing body of evidence suggests that the ethanol dissolved in the oil can be used as an indicator to effectively monitor the aging behavior of insulating paper. In this study, thermal aging experiments are performed on oil-paper systems with different initial moisture contents and aging temperatures, and then a gas chromatography-mass spectrometer equipped with an automatic headspace sampler is used to test the ethanol contents of the oil samples. The variations and correlations of ethanol contents with temperature, moisture and aging degree are analyzed. The experimental results show that the ethanol contents in oil had a strong correlation with temperature, and is also affected by the cellulose hydrolysis. In addition, a preliminary lifespan assessment of the oil-paper systems under laboratory conditions is conducted by establishing the relationship between ethanol and the degree of polymerization. Then, the time-temperature superposition method is applied to the relationship, and a lifespan model with any temperature is determined according to the ethanol kinetic equation. Based on the model, further observation of the fitting parameters in the case of the two moisture contents (0.8%, 2.3%), the goodness of fit reached higher values of 0.961 and 0.973, respectively. The results of this promising model can help us to a better understanding of ethanol indicator to condition assessment for the transformer paper insulation. [ABSTRACT FROM AUTHOR]

The carbon oxides (CO2 and CO) in the insulating oil of power transformers come from the thermal aging of cellulosic insulating paper; the CO2/CO ratio can reflect the aging state of cellulosic insulating paper. At present, the formation mechanisms of CO2 and CO in oil-immersed power transformers are still unclear. In this article, the ReaxFF reaction force field is used to simulate the thermal aging process of cellulosic insulating paper, and the main reaction pathways of CO2 and CO molecules formed by cellulose pyrolysis are obtained. The study finds that the amount of CO2 and CO generated is related to temperature. When the pyrolysis temperature continues to rise, the amount of CO2 generated shows a downward trend, while the amount of CO generated continues to increase, and the CO2/CO ratio continues to decrease. The simulation results are consistent with the previous experimental results. Through pathway tracking, it is found that the CO2 and CO molecules are mainly derived from certain specific atoms of cellulose molecules. The COSMO-RS module is used to analyze the change trend of the solubility of CO2 and CO in insulating oil with temperature. The results show that the solubility of CO2 and CO decreases with the increase of insulating oil temperature, which provides theoretical support for accurate application of carbon oxides aging evaluation index at different temperatures. [ABSTRACT FROM AUTHOR]

Timely identification of the aging stage of the transformer can effectively ensure the safe operation of the transformer and prevent the aging fault of the transformer. This work proposes the aging diagnosis of transformer using the Raman spectroscopic recurrence plot combined with learning vector quantization (LVQ) neural network. Oil-paper insulation samples at different aging stages are obtained by accelerated thermal aging tests. Acquisition of Raman spectra of insulating oil is from 800 to 3000 cm−1 and converts to the Raman spectroscopic recurrence plot. The gray-level cooccurrence matrix (GLCM) is used to extract the characteristic information of the Raman spectroscopic recurrence plot. The optimal number of neurons in competitive layer of LVQ neural network is determined by the experimental method. A transformer aging diagnosis model based on the Raman spectroscopic recurrence plot is established. The results show that the diagnostic accuracy of the proposed diagnostic model reached 95%. The Raman spectroscopic recurrence plot has more abundant aging “fingerprint” characteristic information and the proposed diagnostic method shows potential for monitoring oil-paper insulation. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

It is known that 1,2,3 benzotriazole (BTA) can effectively limit the oil flow electrification in oil–paper insulation used in HVAC transformers, but it is still unclear whether BTA is applicable to converter transformers. Thus, the effects of BTA on interface charge behaviors and charge migration in oil–paper system under dc stress are investigated in this article. The experimental results show that BTA reduces the interface barrier between oil and oil-immersed paper by altering the charge distribution of the electric double layer (EDL) at the oil–paper interface, which is beneficial to the operation stability of converter transformers in the condition of polarity inversion. However, it is also found that the BTA additive promotes the charge accumulation at oil–paper interface and enhances the local electric field by increasing the carrier mobility of oil–paper insulation. The experimental results in this article provide references for the selection of insulating oil additives for converter transformers. [ABSTRACT FROM AUTHOR]

This paper focuses on the in-depth analysis of the physical mechanism of the broadband frequency domain spectroscopy of oil-paper insulation. The complex permittivity shows two low-frequency dispersion phenomena, and the real part of the complex conductivity exhibits two slope changes under the synergistic effect of aging and moisture. The two slope changes are mainly attributed to the migration characteristics of water molecules and polar aging products with a degradation of the oil-paper insulation, resulting in the transition of the frequency dependence between the DC conductivity and AC conductivity. When the oil-paper insulation deteriorates seriously, the imaginary part of the dielectric modulus shows double relaxation peaks. Physical models of parasitic capacitance and molecular polarity interface polarization are proposed to reveal the physical mechanism of the double relaxation peaks. The left peak of the double relaxation peaks is caused by the accumulation of water molecules and polar aging products at the electrode/insulation interface, and the right peak is due to the charge disturbance of the cellulose molecular chain surface. This paper can provide theoretical support and technical guidance for the condition diagnosis and evaluation of oil-paper insulation. [ABSTRACT FROM AUTHOR]

Transistor random threshold voltage variations due to process fluctuations seriously affects the stability of Static Random Access Memory (SRAM). In this paper, a SRAM bit transistors threshold voltage $({Vth})$ deviation monitoring scheme and system is proposed. This scheme ingeniously achieves on-chip measurement of all transistors threshold voltages without altering compact SRAM bit array layout. Control signal strategies and Transistor ${Vth}$ Determination Circuit (TVDC) for different types of Devices Under Test (DUTs) have been proposed. The system is implemented using a 65 nm CMOS process with a core area of 0.01875mm2. Through Monte Carlo analysis, the Weighted Average (WA) difference of the proposed scheme and the direct measurement method is not more than 10mV, and the Root Mean Square Error (RMSE) difference is not more than 3mV. This system can also effectively detect the cell position of the transistor threshold voltage mismatch simulated by modifying the substrate voltage. For SRAM arrays of different scales, the method proposed in this paper has area efficiency and flexible reconfigurability. [ABSTRACT FROM AUTHOR]

It is now possible to detect dangerous arcing faults in the paper insulation of transformers and distinguish them from arcing faults in oil, using new sub-zones of Duval Pentagons 1 and 2. [ABSTRACT FROM AUTHOR]

The intent of this work is to evaluate the influence of cellulose insulation paper on gassing tendency of transformer oil under electrical discharge conditions. For comprehensive enumeration, insulation oil was thermally stressed at 115°C as per ASTM D 1934 with a controlled thermal aging history. The thermally aged oils were considered for gassing tendency evaluation as per ASTM D 6180 (5 hours, 10 kV AC) in the presence of cellulose. Different cellulose Kraft paper proportions including 0, 10, 20, and 30% of aged oil were introduced in to discharge cell. Different cellulose proportions and aging conditions were considered to understand the impact of cellulose while understanding the decomposition aspects of insulation. Diagnostic measurements with those including dissolved gas analysis, moisture, interfacial tension, turbidity, dissolved decay contents, acidity, and dissipation factor were performed before and after gassing tendency evaluation. It was found that, cellulose paper aids significantly in increasing dissolved gases and moisture in oil during electrical discharge. Importantly, diagnostic measurements have shown the ability of paper to absorb certain decomposition products during the discharge process. [ABSTRACT FROM AUTHOR]

For extra-high-voltage high-temperature superconducting (HTS) cables, not only high electrical insulation performance but also low dielectric loss is required. Therefore, we focus on a porous synthetic paper with lower dielectric loss, e.g., Tyvek polyethylene nonwoven fabric, than that of polypropylene laminated paper (PPLP), which has been used for a conventional HTS cable insulation system. In this paper, we compare partial discharge inception characteristics for different liquid nitrogen/synthetic paper composite insulation systems. The partial discharge inception electric field strength in the composite insulation system depends on the electric field strength in the porous area of synthetic paper. [ABSTRACT FROM AUTHOR]

This work presents a single-use, disposable, paper sensor for blood urea measurement, directly from whole-blood sample. The sensor is compact, inexpensive and can be used even by an unskilled person. It is a part of a small, hand-held meter which adopts an impedimetric sensing technology using graphite electrodes. Here, the urea concentration of the blood is estimated in five different levels from the measured impedance of the whole-blood sample. Only, 0.2 mL of blood is required for each measurement and the response time is only 30 s. The look up table and pre-calibration are discussed here with detailed experimental results and analysis. The work also discusses the design of an electronic interface and an embodiment to clamp and hold the sensor in the measuring platform. The proposed prototype of blood-urea meter will provide a point-of-care diagnosis and monitoring for the house-held patient. [ABSTRACT FROM AUTHOR]

Selective detection and quantification of biomarkers related to human diseases are essential for preventive healthcare. Surface-enhanced Raman scattering (SERS) spectroscopy is a powerful analytical tool offering high sensitivity. However, the success of this promising analytical tool relies on the ability to effectively fabricate SERS substrate. Herein we have demonstrated a plasmonic paper-based flexible substrate (PPFS) for SERS sensing. In situ growth of silver nanostructures (AgNS) on the paper-based substrate was achieved by using a simple one-step silver mirror reaction (SMR). FESEM and TEM results depicts that the increasing silver ion content influences the morphology (growth of multifacets), as well as size of AgNS. Further, the PPFS substrate was tested with Rhodamine-6G (Rh-6G) dye and an attomole sensitivity with a LOD of 4.54 $\times \,\,10^{-{18}}$ M was achieved. Further, two biomarkers, lactic acid (LA) and uric acid (UA) were detected on the PPFS substrate, with ${\mu }\text{M}$ and pM sensitivity, having LOD values of 0.6 $\times \,\,10^{-{6}}$ and 0.3 $\times \,\,10^{-{12}}$ M respectively. Above detection levels for UA on PPFS is two orders better than reported values, whereas for LA it is comparable with reported substrates. Finally, UA, LA and their mixtures were tested on PPFS and results compared with commercial substrate. The performance of PPFS were found better in all cases, thus, multifaceted AgNS paper based PPFS offers the potential to be used as a biosensor for detection of various biomarkers from body fluids, responsible for the detection of the critical disease for preventive health care. [ABSTRACT FROM AUTHOR]

The article titled "Call for Papers: IEEE ETHICS-2025" discusses the theme of the 2025 IEEE ETHICS conference, which is "Emerging Technologies, Ethics, and Social Justice." The conference aims to explore various topics related to the ethical and social implications of emerging technologies, such as automation, AI ethics, social innovation, and the future of work. It also highlights the connection between emerging technologies and UN Sustainable Development Goals, as well as the importance of teaching AI ethics. The article provides information on the submission deadline for papers and poster abstracts. [Extracted from the article]

Frequency-domain dielectric spectroscopy (FDS) is widely used in the insulation assessment of oil-immersed power equipment, and the moisture in insulating paper could be qualitatively predicted by mathematical approximation. However, due to the still lots unexplored underlying mechanisms, until now, there is still no convincing and physical method for the diagnosis of moisture content. With the aid of spectral decomposition methods, this article aims to explore the effects and mechanism of moisture on the independent dielectric response spectra. First, the measured dielectric response curve is unfolded and reconstructed by derivation of the real part curve. Second, the independent polarization and conductance spectra of damp samples are extracted, and then, the relationships between moisture content in insulating paper and spectral parameters are established quantitatively. The results show that moisture introduces a new polarization in the midfrequency region at room temperature, and both polarization and conductance increase with the moisture content. The multiple adsorption model and the proton exchange model of moisture are employed to illustrate the changes of the polarization and the conductance spectra, respectively. This research provides a methodological reference for the moisture assessment of oil–paper insulation. [ABSTRACT FROM AUTHOR]

High-frequency (HF) oil-immersed transformers with small size and light weight have been paid wide attention by researchers. Its oil–paper insulation frequently withstands HF over-voltages at frequencies of several kilohertz and above, resulting in the too early failure of oil–paper insulation. In order to investigate the effect of HF on oil–paper insulation, this article investigates the partial discharge (PD) characteristics of oil–paper insulation under HF stress. The experimental results show that a frequency-induced inflection point is existed near 10 kHz and the PD parameters of HF voltages is much higher than that ac voltages. Besides, the theoretical mechanism is expounded to interpret and understand the formation process of frequency-induced inflection points more easily. It can be found that the change in frequency causes the change in the charge decay time constant and the residual charge on the insulating surface, resulting in the formation of the frequency-induced inflection point at HF. These results might contribute to determine the characteristics of PDs between HF voltages and ac voltages. It can provide references for the research and production of HF transformers. [ABSTRACT FROM AUTHOR]