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This paper presents a broadband, simple, re-useable and low-cost approach for noninvasive sweat monitoring using a passive microwave circuit and a cellulose filter paper as a superstrate. The proposed sensor is composed of filter paper with the ability to absorb the sample liquid under test (LUT) placed on top of a coplanar waveguide (CPW) transmission line. Various samples of sodium chloride (NaCl) solutions with concentrations in the range of 0.01-2 mol/L and models of artificial sweat are used to test the proposed sensor. The difference in transmission coefficient (S21) between dry and wet states is used to determine the concentrations of tested solutions in the band of 1-6 GHz. The sensor detects concentrations as low as 0.01 mol/L (0.58 g/L) and quantities as low as 137 μL with a maximum sensitivity of 46.7 dB/g/L. The proposed sensor presents a simple approach to sample and characterize liquids with enhanced sensitivity and consistent performance using microwave signals.

Background: Although some have noted that combining technical and marketing content is precarious, technical communication professionals are increasingly involved in content marketing, which includes the creation of white papers. Literature review: The little existing literature on white papers provides conflicting guidance about managing the combination of technical and marketing content. Both soft-sell and hard-sell marketing approaches have been recommended. One source of such inconsistent guidance may be the lack of agreement about definitions. Research on print advertisements has described hard and soft selling as multidimensional rather than binary aspects of persuasive appeals. Research question: Which dimensions of hard- and soft-sell appeals are predominant in white papers? Research methodology: To complete our descriptive study, we collected a corpus of documents labeled as white papers in TechRepublic, and then selected and trained three raters to complete a series of judgments about dimensions of persuasive appeals in the corpus. We aggregated those ratings, calculating the mean and standard deviation for the dimensions to describe their distribution across the corpus. Results/discussion: Overall, hard-sell dimensions were more prevalent than soft-sell dimensions. However, the soft-sell category of “implicitness” was also dominant. Conclusion: Our results demonstrate the value of treating hard and soft selling as multidimensional, complementary, and combinatory marketing appeals that allow, for example, a single white paper to be both “subjective” (soft sell), and “precise” (hard sell), or both “creative” (soft sell) and “informative” (hard sell).

A 3D modeling system with all-inclusive functionality is too demanding for a casual 3D modeler to learn. There has been a shift towards more approachable systems, with easy-to-learn, intuitive interfaces. However, most modeling systems still employ mouse and keyboard interfaces, despite the ubiquity of tablet devices and the benefits of multi-touch interfaces. We introduce an alternative 3D modeling and fabrication paradigm using developable surfaces, inspired by traditional papercrafting, and we implement it as a complete system designed for a multi-touch tablet, allowing a user to fabricate 3D scenes. We demonstrate the modeling and fabrication process of assembling complex 3D scenes from a collection of simpler models, in turn shaped through operations applied to virtual paper. Our fabrication method facilitates the assembly of the scene with real paper by automatically converting scenes into a series of cutouts with appropriately added fiducial markers and supporting structures. Our system assists users in creating occluded supporting structures to help maintain the spatial and rigid properties of a scene without compromising its aesthetic qualities. We demonstrate several 3D scenes modeled and fabricated in our system, and evaluate the faithfulness of our fabrications relative to their virtual counterparts and 3D-printed fabrications.

Transformer insulation paper is a key indicator for transformer remaining operational life. Paper decomposition is evaluated using the degree of polymerization (DP) which calls for samples of insulation paper from operating transformers. Since collecting such paper samples is extremely difficult, other indicators have been used to indirectly reveal the DP value of insulation paper. This includes dissolved gases in transformer oil such as carbon oxides and hydrocarbon gases, furan compounds, methanol, ethanol, and moisture along with some oil characteristics such as interfacial tension. However, for the same oil sample, these individual parameters lead to different DP values. This is attributed to the lack of accuracy of the established mathematical and artificial intelligence models correlating DP with each of the above mentioned individual parameters. This paper presents a self-learning method to estimate the DP value of transformer insulation paper based on multiple transformer oil aging parameters. The proposed method comprises data processing, fuzzy c-means and linear regression. Results reveal that estimating the DP value based on multiple aging parameters is more accurate than estimating it using one single parameter as per the current practice. The proposed method not only helps to understand the correlation between multiple oil aging parameters and the DP value of paper insulation, but also promotes the establishment of more accurate life assessment models for power transformers based on these oil aging parameters.

Traction transformers are one of the most important equipment in the traction power supply system. Thermal aging of transformer insulation system determines its reliable life. To achieve an estimation of the insulation life of traction power transformers operated under real load condition, the effects of traction loads on the thermal aging of oil-paper insulation in traction transformers were investigated. First, the design principles for the accelerated thermal aging experiment were studied and proposed by considering magnitude (i.e., load rate) and frequency (i.e. spare time and lasting time) of the traction load. Then, an experimental system was established in the laboratory to conduct extensive accelerated thermal aging experiments, in which the traction loads were mimicked by using positive temperature coefficient (PTC) heating plates and a programmable logic controller (PLC). Finally, the experimental results were analyzed and discussed to reveal the effects of traction load on the thermal aging of oil-paper insulation in traction transformers.

In the last decade, much effort has been invested in using methanol as an oil-soluble chemical marker for assessing the condition of insulating paper. The use of this marker as paper life index presents many advantages particularly with new transformers insulated with thermally upgraded papers. However, until now, no interpretation model has been available for its extensive use by the transformer community. In this paper, a methanol-based interpretation model is presented for the first time using postmortem paper analysis on core-type transformers. This model allows the evaluation of the average degree of polymerization of a transformer's cellulose winding. Furthermore, threshold values based on the methanol concentration are given using this approach. Finally, model validation was performed on a limited number of papers taken from transformers being assessed in the repair shop.

The papers in this special issue were submitted to all the conferences organized by IEEE Technology and Engineering Management Society (TEMS) in a given calendar year. In 2017, IEEE TEMS initiated its flagship conference TEMSCON (held in Silicon Valley), which serves as rebranding from its original International Technology Management Conferences (ITMC). Following that, in 2018, two more conferences were initiated globally, one in Asia known as IEEE TEMS International Symposium on Innovation and Entrepreneurship (ISIE), and one in Europe as IEEE TEMS International Conference on Technology and Entrepreneurship (ICTE). For this special issue, we invited authors attending any of the three aforementioned conferences in 2018 to submit full manuscripts for reviews, and the present special issue with eleven papers is the result of a careful selection following our usual rigid double-blinded peer-review process.

The International Technology Roadmap for Wide-Bandgap Power Semiconductors (ITRW) has four distinct technical working groups, each providing its own perspective on the technology and industrial drivers for the adoption of wide-bandgap (WBG) power semiconductors in power electronics. This article summarizes the progress toward an initial roadmap for the packaging and integration working group of the ITRW.

Paper surfaces under the microscopic view are observed to be formed by intertwisted wood fibers. Such structures of paper surfaces are unique from one location to another and are almost impossible to duplicate. Previous work used microscopic surface normals to characterize such intrinsic structures as a “fingerprint” of paper for security and forensic applications. In this work, we examine several key research questions of feature extraction in both scientific and engineering aspects to facilitate the deployment of paper surface-based authentication when flatbed scanners are used as the acquisition device. We analytically show that, under the unique optical setup of flatbed scanners, the specular reflection does not play a role in norm map estimation. We verify, using a larger dataset than prior work, that the scanner-acquired norm maps, although blurred, are consistent with those measured by confocal microscopes. We confirm that, when choosing an authentication feature, high spatial-frequency subbands of the heightmap are more powerful than the norm map. Finally, we show that it is possible to empirically calculate the physical dimensions of the paper patch needed to achieve a certain authentication performance in equal error rate (EER). We analytically show that log(EER) is decreasing linearly in the edge length of a paper patch.

The existing plethora of document classification techniques exploits different data sources either from the content or metadata of research articles. Various journal publishers like Springer, Elsevier, IEEE, etc., do not provide open access to the content of research articles, whereas metadata is freely available there. Metadata like title, keyword, and abstract can serve as a better alternative to the content in various scenarios. In the current literature, researchers have assessed the role of some of the metadata individually. We believe that the collective contribution of metadata parameters can play a significant role in classifying research papers. This paper presents a comprehensive evaluation of the role of metadata, individually as well as in combinations to achieve the objective of research paper classification. Moreover, we have classified the research articles into ACM hierarchy root categories (e.g. general literature, hardware, software, etc.). In this comprehensive evaluation, we have assessed all the possible combinations of metadata features against different classifiers such as Random Forest, K Nearest Neighbor, and Decision Tree. The results of this research reveal that the title & keywords combination outperforms other combinations with an F-measure score of 0.88.

In this paper, the nature of thermally upgraded Kraft electrical insulating papers and their characterization by measurement of nitrogen content is discussed. A historical context and explanation of the basics of the various chemical methods used to measure nitrogen content in electrical papers is presented. The Kjeldahl and Dumas methods for quantifying nitrogen content were compared using six insulating papers in different laboratories in an effort to correlate the results obtained with both techniques. The results provide an insight into the viability of the Dumas combustion method as a proposed industry standard test for measuring the nitrogen content of paper. Moreover, data on the accuracy, reproducibility, and repeatability of the Dumas method is provided for the analysis of thermally upgraded electrical papers.

This paper studies the secure transmission in a multiuser broadcast channel where only the statistical channel state information of the eavesdropper is available. We propose to apply secret dirty-paper coding (S-DPC) in this scenario to support the secure transmission of one user and the normal (unclassified) transmission of the other users. By adopting the S-DPC and encoding the secret message in the first place, all the information-bearing signals of the normal transmission are treated as noise by potential eavesdroppers and thus provide secrecy for the secure transmission. In this way, the proposed approach exploits the intrinsic secrecy of multiuser broadcasting and can serve as an energy-efficient alternative to the traditional artificial noise (AN) scheme. To evaluate the secrecy performance of this approach and compare it with the AN scheme, we propose two S-DPC-based secure transmission schemes for maximizing the secrecy rate under constraints on the secrecy outage probability (SOP) and the normal transmission rates. The first scheme directly optimizes the covariance matrices of the transmit signals, and a novel approximation of the intractable SOP constraint is derived to facilitate the optimization. The second scheme combines zero-forcing dirty-paper coding and AN, and the optimization involves only power allocation. We establish efficient numerical algorithms to solve the optimization problems for both schemes. Theoretical and simulation results confirm that, in addition to supporting the normal transmission, the achievable secrecy rates of the proposed schemes can be close to that of the traditional AN scheme, which supports only the secure transmission of one user.

Frequency Domain Spectroscopy (FDS) is proposed as a suitable diagnostic method in this paper for implementation on energized oil-paper insulation systems. In present FDS tests, the measurement method is only designed for offline applications. To overcome the deficiencies with the offline testing, this work investigates the feasibility of performing FDS measurements on energized transformers with the view to developing an online diagnostic tool using neutral connections as injection and measurement paths. In the proposed technique, a low-pass filter system is designed to remove the high frequency noise as well as the power frequency signals present in the measured neutral currents. The technique has been successfully applied in a laboratory environment on transformers with oil-paper insulation systems with different operational ages and is feasible for single-phase as well as 3-phase transformers with the accessible neutral points. Compared with the conventional FDS results, a nonlinear dielectric phenomenon is observed from the energized insulation systems as higher dissipation factor Tanδ values primarily due to the increased losses under power frequency voltage excitation. The degree of the nonlinearity is found to correlate well with the degradation status of insulation systems under test.

Flexible In2O3 nanowire transistors gated by microporous SiO2-based solid electrolytes are fabricated on paper substrates at room temperature. Low-voltage (1.0 V) operation of these devices is realized owing to the large electric-double-layer capacitance of (1.73 μF/cm2 at 20 Hz) of the microporous SiO2 solid electrolytes, which were deposited at room temperature. The subthreshold swing, current on/off ratio, and field-effect mobility of the paper-based nanowire transistors are estimated to be 74 mV/decade, 1.7×106, and 218.3 cm2/V·s, respectively. These low-voltage paper-based nanowire transistors show promise for use in portable flexible paper electronics and low-cost portable sensors.

This paper presents a wavelet texture analysis of microscopy images acquired from artificially-aged samples of power transformer winding insulation paper. Oil-impregnated Kraft paper samples are prepared for experimental tests where they are thermally stressed in an oven at temperatures above what would normally occur in operation. This accelerated ageing test arrangement is used to produce a set of paper samples with varying levels of insulation deterioration. From the paper samples, microscopy images of the paper surface are obtained using a standard optical microscope. These microscopic images are analyzed using a texture analysis method that utilizes a two-dimensional wavelet transform to extract detailed information from the wavelet decomposition coefficient matrices. These features are analyzed to assess how the texture of paper changes in response to thermal deterioration and ageing. The results suggest that the texture features are correlated to the commonly-used degree of polymerization test. Additionally, statistical classification is performed on the wavelet texture features using both supervised and unsupervised machine learning methods. The results demonstrate that differentiation between oil-impregnated paper samples with different levels of thermal degradation using wavelet texture analysis is effective.

Aging of transformer paper insulation is commonly investigated through indirect measurements conducted in the oil, e.g. furanic compounds. In addition to the conventional indicators, recent investigations showed that alcohols like methanol and ethanol in oil could be used as paper aging indicators. This paper focuses on the applicability of these new paper aging indicators in a novel, gas-to-liquid (GTL) technology based transformer oil through an accelerated laboratory aging experiment. The experiment was conducted at 120 °C for up to 280 days. A conventional mineral oil was also tested as a reference. Kraft paper aged in both oil types showed similar reductions in degree of polymerization (DP) and tensile strength (TS). Amount of methanol in oil is higher than that of 2-FAL in oil when DP is over 375, confirming the promise of using methanol as an early paper aging indicator. Both the new paper aging indicator, methanol and the conventional paper aging indicator, 2-FAL are applicable for the GTL oil without any modifications. In addition, oil only aging experiments showed that oil does produce methanol but it is negligible compared with oil paper aging experiment; however the amount of ethanol measured in the present oil paper aging experiment is believed to be originated from oil oxidation rather than from paper aging.

Over the last decade, the rapid development in health care industry and security has driven innovations in the field of wearable electronics and smart sensors. This report demonstrates the fabrication of molybdenum diselenide (MoSe2) by a facile, low-cost and scalable hydrothermal synthesis method followed by a vacuum filter deposition on cellulose paper. The fabricated device is utilized as a pressure and strain sensor. The fabricated device is passivated using polydimethylsiloxane (PDMS), and also, PDMS film enhances the flexibility of the device. The calculated sensitivity of pressure sensor is ~0.3 kPa−1, which is comparable and relatively better than the pressure sensor fabricated using sophisticated techniques. Furthermore, the device exhibited significant of merit for strain sensing with a gauge factor of ~6.84. The fabricated device showed excellent resilience in 500 cycle bending tests, which promises the robustness of the device. As an application, the fabricated sensor is used to develop a wireless keyboard by integrating the sensor on fingertips of an individual and a real-time human motion monitoring application in which the data are transmitted wirelessly to a smartphone with a dedicated android application. The successful fabrication of MoSe2/paper-based cost-effective, flexible, and biodegradable pressure-strain sensor shows tremendous applications in future intelligent user interface systems, human security, and personal health care monitoring.

Objective: Early stage diagnosis of sepsis without overburdening health services is essential to improving patient outcomes. Methods: A fast and simple-to-use platform that combines an integrated circuit with paper microfluidics for simultaneous detection of multiple-metabolites appropriate for diagnostics was presented. Paper based sensors are a primary candidate for widespread deployment of diagnostic or test devices. However, the majority of devices today use a simple paper strip to detect a single marker using the reflectance of light. However, for many diseases such as sepsis, one biomarker is not sufficient to make a unique diagnosis. In this work multiple measurements are made on patterned paper simultaneously. Using laser ablation to fabricate microfluidic channels on paper provides a flexible and direct approach for mass manufacture of disposable paper strips. A reusable photodiode array on a complementary metal oxide semiconductor chip is used as the transducer. Results: The system measures changes in optical absorbance in the paper to achieve a cost-effective and easily implemented system that is capable of multiple simultaneous assays. Potential sepsis metabolite biomarkers glucose and lactate have been studied and quantified with the platform, achieving sensitivity within the physiological range in human serum. Conclusion: We have detailed a disposable paper-based CMOS photodiode sensor platform for real-time simultaneous detection of metabolites for diseases such as sepsis. Significance: A combination of a low-cost paper strip with microfluidic channels and a sensitive CMOS photodiode sensor array makes our platform a robust portable and inexpensive biosensing device for multiple diagnostic tests in many different applications.

Several miniaturized viscometers, or microviscometers, have been developed exploiting numerous rapid prototyping techniques. Among them, paper microstrips, famously known as microfluidic paper-based analytical devices ( $\mu $ PADs), have become popular due to their cost-efficacy, simple fabrication, fast response, and easily disposable. Many fabrication methods are existing to develop paper microstrips. Herein, an alternative fabrication method is proposed where fused deposition modeling (FDM)-based 3-D printer (3DP) has been employed using polycaprolactone (PCL) filament. F, image processing has been utilized to measure viscosity in such microfluidic domain. Viscosity was calculated by measuring the time taken by the fluid to cover a fixed length between two spots in the microchannel based on the programed and color-coded regions-of-interest. The image processing program was developed considering the change in the gray scale in the virtual region of interests (ROIs) in the microchannel during the fluid flow in the paper microstrips. A 3-D printed handheld platform, containing raspberry pi with on-board camera and display, was developed to execute the image processing and automate the entire work flow. In the proposed device, the accuracy was measured to be >92%.

In this study, aiming to characterize the dynamic polarization behavior of water molecules affected by hydrogen bonding with oil-paper insulation, we performed dielectric spectroscopy on oil-paper insulation with moisture contents of 0.4–4.7% in the terahertz band (0.3–3.3 THz). A relaxation-resonance integrated polarization model considering water-participant hydrogen bond (WHB) effects is established to describe the complex dielectric constant of oil-paper insulation with moisture. The theoretical model is in good agreement with the experimental results. By analyzing the relationship between the polarization parameters and moisture content, three states of WHBs are found. The WHB type, strength and disorder show different characteristics with increased moisture. Furthermore, molecular simulation studies are carried out, and the calculation results provided a persuasive argument for the analysis of terahertz dielectric spectroscopy. This research reveals an important new strategy for studying the polarization behavior of water molecules affected by oil-paper insulation as an experimental supplement to molecular simulation. The proposed test and analysis method render effective and sustainable progress in practical applications of rapid nondestructive evaluation and 2D imaging of moisture content distributed in oil-paper insulation.

With the global trend to establish smart distributed-generation grids, the level of AC-voltage harmonics is significantly increasing due to power electronic converters interfacing renewable energy sources with the grid. Such harmonics will not only degrade the power quality and increase the losses in the electricity grids but will also have a severe impact on oil impregnated paper (OIP) in power transformer bushings. While much attention has been given to investigate the deterioration of OIP under thermal and electrical stresses, the impact of voltage harmonics on the OIP structure is still uncertain and calls for more investigations. Therefore, this paper is aimed at investigating the variations of the multilevel structure of an OIP aged under high levels of AC-voltage harmonics. In this context, degree of polymerization of the aged OIP is measured to reveal the changes of OIP secondary structure. Also, X-ray diffraction, deuteration treatment and Fourier transform infra-red spectroscopy are conducted to analyze the changes of the OIP aggregate structure during the aging process. Further experiments are performed to investigate the variation of the microscopic appearance, thermal behavior and dielectric properties, including scanning electronic microscopy, thermal gravity analysis, differential scanning calorimetry and frequency domain spectroscopy. This investigation on the variation of the multilevel structure of aged OIP not only promotes further understanding to the aging behavior and mechanism of OIP under AC-voltage harmonics, but also proposes crystallinity and accessibility as two new effective aging indicators for the OIP structure.

The eight papers in this special section were presented at the IEEE International Solid-State Circuits Conference (ISSCC)that was held in San Francisco, California, from February 16 to February 20, 2020.

In this article, we put forward the substantial challenges in cyber resilience in the domain of autonomous systems and outline foundational solutions to address these challenges. These solutions fall into two broad themes: resilience-by-design and resilience-by-reaction . We use several application drivers from autonomous systems to motivate the challenges in cyber resilience and to demonstrate the benefit of the solutions. We focus on some autonomous systems in the near horizon (autonomous ground and aerial vehicles) and also a little more distant (autonomous rescue and relief). For resilience-by-design , we focus on design methods in software that are needed for our cyber systems to be resilient. In contrast, for resilience-by-reaction , we discuss how to make systems resilient by responding, reconfiguring, or recovering at runtime when failures happen. We also discuss the notion of adaptive execution to improve resilience, execution transparently and adaptively among available execution platforms (mobile/embedded, edge, and cloud). For each of the two themes, we survey the current state, and the desired state and ways to get there. We conclude the paper by looking at the research challenges we will have to solve in the short and the mid-term to make the vision of resilient autonomous systems a reality. This article came out of discussions that started at the NSF-sponsored Grand Challenges in Resilience Workshop held at Purdue in 2019 with the co-authors contributing to going into the depth of the issues and then this article.

Millimeter-wave imaging may be used to assist in\ud the problem of wastepaper recognition during the sorting and\ud recycling process. Currently, there are a variety of techniques,\ud which are used within materials recovery facilities to sort waste\ud paper and card; however, improvements in sorting capability can\ud be realized using a multiple sensors to provide complementary\ud (orthogonal) data. This paper discusses the issues related to\ud the processes, which are currently used for sorting of recycled\ud wastepaper and the phenomenology for millimeter-wave imaging\ud of a wastepaper material. Experimental measurements and\ud images in the millimeter wave band are presented, which support\ud the proposed application.

Mobile and wireless communications are playing an irreplaceable role in our daily life. 5G is highly expected and can be considered as the convergence of mobile and wireless communications. This topic becomes one of the hottest research topics recently, and papers on this topic have been paid much attention. Table I lists the top ten papers published in the IEEE Transactions on Microwave Theory and Techniques (TMTT) in 2019 with the highest numbers of Full Text View since published. Seven papers are about millimeter-waves (mm-waves), and because mm-waves multiple-input–multiple-output (MIMO) will be adopted globally, the 24.25–27.5-GHz band and 27–29.5-GHz bands are now approved worldwide as the standard 5G communication bands. The most popular paper is published by Asbeck et al. [item 1) in the Appendix] for mm-wave power amplifiers.

Oil soluble chemical markers such as methanol, ethanol and 2-furaldehyde for assessing the condition of insulating paper still present many challenges for an accurate interpretation in real transformers. Indeed, many conceptual parameters such as design (shell vs. core) or type of cooling are needed for a more accurate interpretation of the data. Moreover, similarly to water, the measured marker concentrations in oil are temperature-dependent, i.e. an existing partition phenomenon between the oil and the solid insulation modify the solubility of the markers, thus changing their apparent concentrations in the oil. Consequently, to follow the real trend of these species during the transformer’s service life, it is crucial to correct their concentrations at a specific temperature, as is done for the water content. Knowing these facts and in order to calibrate a predictive model, Hydro-Quebec decided to access equipment when dismantled, which enables a large amount of paper to be sampled from different sections of the windings. This allows for a more accurate representation of the transformer paper conditions in relation to transformer design. The paper condition obtained by measuring the degree of polymerization in accordance with the presence of chemical markers is a valuable process. We believe that it is possible to better understand the behavior of the paper insulation and to assess markers concentration thresholds using the oil analysis. This article discusses the recent experience in this field with specific cases.

Due to the increasing size of s (s), their design and optimization phases (i.e., ) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some trade-off between design quality and computational overhead. ML can build powerful models that have successfully been employed in related domains. In this survey, we categorize how () may be used and is used for design-time and run-time optimization and exploration strategies of s. A meta-study of published techniques unveils areas in that are well-explored and underexplored with, as well as trends in the employed algorithms. We present a comprehensive categorization and summary of the state of the art on for. Finally, we summarize remaining challenges and promising open research directions.

A Midwestern pulp and paper mill had arc-flash ratings around 100 cal/cm2 on 50 480-V unit substations. New relays were installed to replace the existing protection and add light detection to improve arc-flash detection times. During a couple of one-day outages, sample installations took place. During the cold mill outage, 44 installations were completed over 12 days by working around the clock. Old relays were removed, new plates were mounted and wired in, and fiber sensors were routed through the buswork and into the breaker cubicles. Updated arc-flash studies show an average arc-flash rating reduction of 90%.

The 2019 International Conference on Modeling and Analysis of Semiconductor Manufacturing (MASM 2019) was part of the 2019 Winter Simulation Conference (WSC) that was held December 8–11 in National Harbor, MD, USA. The MASM conferences provide a forum for the exchange of ideas and industrial innovations between researchers and practitioners from around the world involved in modeling and analysis of complex high-tech manufacturing systems. While the papers from MASM conferences are of high quality and are often cited in journal papers, they are limited in size. As a premier journal for topics covered in the MASM conferences, IEEE T ransactions on S emiconductor M anufacturing , agreed to consider extended article submissions based on the 21 papers accepted and presented at the MASM 2019 conference for a Special Section of an upcoming issue of the journal. were sessions on Scheduling and Dispatching, Fab Scheduling, Maintenance and Engineering, Capacity and Production Planning, Qualification and Wafer-to-Order Allocation, Semiconductor Manufacturing Models, and Semiconductor Modeling Representations. Dr. Sabine Herlitschka, Chief Executive Officer and Chief Technology Officer of Infineon Technologies Austria AG, gave an excellent keynote talk titled “A European View on Future Semiconductor Industry Needs for MASM.” An archive of the MASM 2019 papers can be found at https://informs-sim.org/wsc19papers/by_area.html .

Transformer oil, insulating paper, high voltage direct current (HVDC) bushings and on-load tap changer (OLTC) are essential parts of converter transformers. When the power flow of the HVDC system is reversed or a sudden outrage occurs, the charge field will deform the dielectric internal electric field, thus easily leading to the occurrence of discharge or insulation damage. In this case, it is necessary to carry out the characterization of the oil–paper interface to avoid these consequences. This paper analyses the interface charge accumulation and dissipation characteristics of an oil–paper insulation system under DC and polarity reversal voltages. In this paper, induced electrification was used to charge the composite oil–paper insulation. The amplitude of DC and polarity reversal voltages applied in this paper was 4 kV, and paper with thicknesses of 0.08 and 0.13 mm was used. This research offers a theoretical exploration to optimize the transformer oil– paper insulation structure and prevent damage to its insulation systems. From the research on different layers and thicknesses of paper, it can be concluded that the decay is fast in the initial period and then becomes slower with the lapse of time until finally the decay curve tends to become flat. With an increase in the number of the paper layers, the interface charge density decreases. So in real life multilayer paper is used to avoid failures owing to its structural weakness. Regarding the DC polarity reversal effect, increasing the thickness of the paper is equivalent to adding paper layers. The dissipation rate of the positive interface charge is quicker than that of the negative one. In the polarity reversal test, the dissipation rate becomes smaller as the reversal time gets longer.

Various miniaturized viscometers have been developed utilizing several fabrication methods. Among them, microfluidic paper-based analytical devices ( $\mu $ PADs) are becoming popular due to their fabrication ease, cost-effectiveness, and the fact that the flow can be carried out using the embedded capillaries themselves. Mostly, $\mu $ PADs are reported to be fabricated by a solid-ink printer, which has significantly high capital and operational cost. To overcome such drawbacks, a novel rapid prototyping method has been proposed, wherein the formation of the hydrophobic regions was created by polycaprolactone (PCL) filament using a 3-D printer. To leverage this, $\mu $ PAD as a viscometer, velocity, or time between two points with known distances was required, which was carried out by an amperometric approach, established by fabricating the integrated screen-printed electrodes intersecting the microchannel of the $\mu $ PAD. The time measurement was fully automated by a microcontroller, and the relative viscosity was calculated by comparing the time taken by the reference fluid with that of a test fluid to cover a known length. Such integrated, automated, and low-cost paper-based microviscometer was leveraged to measure and analyze the viscosities of various milk variants, which has an accuracy of >92%.

In this paper, we present a new scheme for implementing virtual keyboards, which uses only two to four motion-recognition rings per hand and a two-dimensional keyboard template (e.g., an A4 size paper with printed key positions). It has the benefit of portability, customizability, and low-cost when compared with existing approaches. Essentially, we have shown that wearing two wireless IoT rings on the middle phalanges of two fingers of each hand, users can input the alphabetic characters into a computing device by typing on a flat paper on a desk, and potentially in mid-air. We have demonstrated that two rings are sufficient in capturing the gestures and motions of all fingers in a typing hand for keystrokes recognition. A single wireless IoT ring, which weighs 7.8 grams, consists of a Bluetooth low energy (BLE) unit, a micro inertial measurement unit (mIMU), and a cell battery. The 3-axes attitude angles and the Z-axis acceleration of each ring are adopted as the features for keystroke recognition. The overall keystroke recognition accuracy rate can reach as high as 94.8% when two IoT rings are worn by a user on each hand; this accuracy rate increases to 98.6%, when four rings are worn on each typing hand.

This study explores the method of applying surface enhanced Raman scattering (SERS) in diagnosing the aging stage of oil-paper insulation equipment in the power system, which provides access to the on-line mornitoring of electrical equipment. Oil-paper samples in different aging stages were obtained by accelerated aging experiments. Effective Raman signals of insulating oil were collected by using the self-made hexagonal sliver nano-plates SERS substrates and the self-assembled Raman detection platform. For the SERS data of oil-paper insulation samples, kernel entropy component analysis (KECA) was applied to extract the Raman spectral features and support vector machine (SVM) was used to recognize the the aging stage of oil-paper insulation. The results demonstrate that the KECA-SVM method exhibits a good diagnostic capacity and the recognition accuracy of the propsed method reached 81.43 % (57/70). In summary, a new spectral method is proposed to diagnose the aging stage of oil-paper insulation, which lays a foundation for the actual diagnosis of the aging condition in running oil-paper insulation equipment by Raman spectroscopy.

Many papers have been written about the phenomenon of arc flash, the causes of arc flash, how to mitigate specific arc flash problems, and how to design arc flash issues out from the beginning. This paper will discuss the overall process of completing an engineering incident energy analysis and remediation project in a pulp and paper mill. Most mills are not fortunate enough to receive sufficient funding to do the whole study and a remediation process all at once. This paper explores how a Midwestern pulp and paper mill worked over several years to get through the process of the study, remediation efforts, and the establishment of an overall electrical safety program.

The papers in this special issue were presented at the 2018 17th International Workshop on Data Mining in Bioinformatics (BIOKDD), held in conjunction with the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. The Workshop was held on August 20, 2018 in London, UK.

The papers in this special section were presented at the 2019 IEEE International Symposium on Circuits and Systems (ISCAS 2019), that was held in Sapporo, Hokkaido, Japan from May 26 to 29, 2019.

Continuous monitoring of breathing flow is an essential but often poorly utilized predictor that is causing poor patient outcome. A low-cost, light-weight, easy-to-use, reliable, and disposable breathing sensor is required to bypass the limitations of existing conventional sensors, which are bulky, expensive, and often require experts to handle. In this article, a low-cost, inkjet-printed graphene sensor on a disposable glossy photo-paper substrate is presented for breathing-rate monitoring. The sensor architecture consists of a graphene nanoparticle based thin functional layer on top of a silver nanoparticle based interdigitated conductive pattern. A standard office inkjet-printer was configured with nanoparticle inks and a printed circuit board design software was utilized for the layouts. The sensor was tested in a laboratory environment, and its data were analyzed for different breathing patterns. An empirical model of the sensor was developed using the Cole–Cole impedance model. Test results showed successful detection of breathing rates for different breathing patterns. The prototype sensor provides a low-cost, disposable, and practical solution for frequent breathing pattern recognition.

Introduction: The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. However, most patterns of diseases are based on old datasets and stepwise algorithms that provide limited accuracy. Improving diagnostic accuracy of the ECG can be done by applying machine learning algorithms. This requires taking existing scanned or printed ECGs of old cohorts and transforming the ECG signal to the raw digital (time (milliseconds), voltage (millivolts)) form. Objectives: We present a MATLAB-based tool and algorithm that converts a printed or scanned format of the ECG into a digitized ECG signal. Methods: 30 ECG scanned curves are utilized in our study. An image processing method is first implemented for detecting the ECG regions of interest and extracting the ECG signals. It is followed by serial steps that digitize and validate the results. Results: The validation demonstrates very high correlation values of several standard ECG parameters: PR interval 0.984 +/−0.021 (p-value < 0.001), QRS interval 1+/− SD (p-value < 0.001), QT interval 0.981 +/− 0.023 p-value < 0.001, and RR interval 1 +/− 0.001 p-value < 0.001. Conclusion: Digitized ECG signals from existing paper or scanned ECGs can be obtained with more than 95% of precision. This makes it possible to utilize historic ECG signals in machine learning algorithms to identify patterns of heart diseases and aid in the diagnostic and prognostic evaluation of patients with cardiovascular disease., We present a MATLAB-based tool and algorithm that converts a printed or scanned ECG into a digitized ECG signal. Validation on 30 ECG scanned curves more than 95% precision. This makes it possible to utilize historic ECG signals in machine learning algorithms to identify patterns of heart diseases and aid in the diagnosis and evaluation of patients with cardiovascular disease.

The three papers in this special section were presented at the 2019 CVPR conference that was held in Long Beach CA, in June of 2019.

The interaction between technology and healthcare has a long history. However, recent years have witnessed the rapid growth and adoption of the Internet of Things (IoT) paradigm, the advent of miniature wearable biosensors, and research advances in big data techniques for effective manipulation of large, multiscale, multimodal, distributed, and heterogeneous data sets. These advances have generated new opportunities for personalized precision eHealth and mHealth services. IoT heralds a paradigm shift in the healthcare horizon by providing many advantages, including availability and accessibility, ability to personalize and tailor content, and cost-effective delivery. Although IoT eHealth has vastly expanded the possibilities to fulfill a number of existing healthcare needs, many challenges must still be addressed in order to develop consistent, suitable, safe, flexible, and power-efficient systems that are suitable fit for medical needs. To enable this transformation, it is necessary for a large number of significant technological advancements in the hardware and software communities to come together. This keynote paper addresses all these important aspects of novel IoT technologies for smart healthcare-wearable sensors, body area sensors, advanced pervasive healthcare systems, and big data analytics. It identifies new perspectives and highlights compelling research issues and challenges, such as scalability, interoperability, device-network-human interfaces, and security, with various case studies. In addition, with the help of examples, we show how knowledge from CAD areas, such as large scale analysis and optimization techniques can be applied to the important problems of eHealth.

Oil-paper compound insulation has been widely used in power transformers for quite a long time because of its good performance. The paper insulation degrades under the combined stresses of thermal, electrical and chemical stresses during routine operations, resulting in the reduction of its degree of polymerization (DP) and hence its mechanical and electrical strength. Space charges in a dielectric material have a close relation to its electrical performance. Therefore, in this work, the effect of paper-aged states (with different DP values) on the space charge behavior in oil-impregnated paper insulation is investigated, and the pulsed electro-acoustic (PEA) method is used for the space charge distribution measurement. In order to achieve this purpose, new oil-impregnated paper samples are thermally-accelerated aged for different time interval to obtain different DP values, and then the residual oil in each sample is removed, and the Kraft paper samples are dried in a vacuum dry oven. After that, all paper samples are impregnated with new oil, and PEA measurements are performed at room temperature. The experimental results show that, when the external dc voltage is applied on the oil-paper sample, homo-charge injection occurs for all samples. The samples with different aging states exhibit quite different space charge characteristics for both voltage-on and voltage-off conditions. When the aged states are worse, for voltage-on condition, more charges are induced at cathode and anode, as well as more space charges are accumulated in the bulk. For voltage-off condition, the space charge decay rate shows no obvious change for different aged samples.

This special section presents an updated version of the paper that won the first prize award in the ITU (International Telecommunication Union) Kaleidoscope conference (K-2020).

Wireless sensors and networks are used only occasionally in current control loops in the process industry. With rapid developments in embedded and highperformance computing, wireless communication, and cloud technology, drastic changes in the architecture and operation of industrial automation systems seem more likely than ever. These changes are driven by ever-growing demands on production quality and flexibility. However, as discussed in "Summary," there are several research obstacles to overcome. The radio communication environment in the process industry is often troublesome, as the environment is frequently cluttered with large metal objects, moving machines and vehicles, and processes emitting radio disturbances [1], [2]. The successful deployment of a wireless control system in such an environment requires careful design of communication links and network protocols as well as robust and reconfigurable control algorithms.

Aviation is often seen as an innovative sector eager to seek technological solutions. It is often assumed that aircraft cockpits have panels with electronic displays and many buttons, some labeled "auto." While this might be the case for commercial aviation (Figure 1), the vast majority of noncommercial civil aircraft cockpits look nothing like these modern cockpits. Non-commercial civil aircraft, known as general aviation (GA), typically have cockpits that mainly consist of analog instruments that show the heading of the aircraft, the altitude, and the air speed (Figure 2). While flying, the pilots make calculations using a compass and an aviation calculator. Due to the slow entry rate of new, glass-cockpit aircraft [1], the composition of the GA aircraft fleet is not going to change any time soon.

This paper focuses on the common mistakes made by students at the early stages of research when writing scientific papers, thesis and reports. It points out mistakes to be avoided and the basic rules that can be inferred by reading a few scientific documents but are not usually clearly written and that we, as professors, end up teaching over and over again. The outcome of this paper will be, hopefully, that we will not need to correct the same common mistakes again and you, as a student, will have a faster lane to publishing. Starting with the abstract: it is composed of a single paragraph, does not contain acronyms or references and describes in short the work, main highlights and points out the results or main conclusions obtained from the work being presented. The abstract is an independent part of the paper and commonly has a character or word limit that you need to respect. It can be read as a ``stand-alone'' and the paper starts in the introduction, meaning that the introduction is not the sequence of the abstract and it can have some text in common if needed.

THE YEAR IS 2028. IT'S 8 P.M. ON A Wednesday night and you're famished. You're staring wistfully at the only remaining item in your refrigerator: a package of sausages with an unappetizing grayish hue. Ugh. Did they always look like that? Are they still safe to eat? In 2018, you'd have to rely on your sense of smell and take a gamble. But in 2028, you might simply wave your smartphone over the package. The smartphone interrogates the package's embedded sensor, which measures the concentration of gases associated with meat decomposition. The smartphone displays the message "Safe to eat within the next 20 hours," and then offers a list of recipes for cooking with sausages. Too hungry to bother with the recipes, you tear open the package, toss the sausages into a frying pan, and discard the package-along with its sensor technology.

I would like to introduce three wonderful papers to all our readers. Q. Xu and Y. Zhao from the Nanjing University of Aeronautics and Astronautics, China; X. Shen and K. Chen from the Nanjing Rongce Testing Technology Ltd, China; and Y. Huang from the University of Liverpool, U. K., authored the first paper "Absorption Cross Section Measurement of a Vehicle in Reverberation Chamber for Quick Estimation of Field Strength". For electromagnetic immunity testing of a vehicle, a reverberation chamber (RC) loaded with the vehicle-under-test has to be calibrated so that the necessary input power to generate the required field strength can be determined. In this paper, the average absorption cross section (ACS) of a vehicle in the frequency range of 80 MHz — 24 GHz is measured so that the required electric field strength in the RC loaded with a car can be estimated before the RC is constructed. Once the typical ACS of a car is known, one only needs to measure the Q factor of an unloaded RC and the Q factor of the RC loaded with a car can be predicted. This is very useful before the RC is constructed, as once the RC is built the unloaded Q factor cannot be increased.