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Researchers face difficulties in finding relevant papers to their research interest as the number of scientific publication is rapidly increasing on the web. Scientific paper recommenders have emerged as a leading solution to help researchers by automatically suggesting relevant and useful publications. Several approaches have been proposed on improving recommender systems. However, most existing approaches depend on priori user profiles, and thus they cannot recommend papers to new user. Furthermore, the existing approaches utilize non-public contextual information, and thus it cannot adequately find similarities between papers due to copyright restrictions. Also, the existing approaches consider only single level paper-citation relation to identify similarities between papers. Considering the above challenges, this paper presents a collaborative filtering based recommendation approach for scientific papers that does not depend on priori user profiles and which utilizes only public contextual information. Using citation context, we utilized 2-level paper-citation relations to find hidden associations between papers. The rational underlying this approach is that, two papers are co-occurred with same cited paper(s) and two papers are co-occurring with same citing paper(s) are significantly similar to some extent. To evaluate the performance of the proposed approach, publicly available datasets are used to conduct extensive experiments. The experimental results demonstrate that the proposed approach has significantly outperforms the baseline approaches in terms of precision, recall, F1, mean average precision, and mean reciprocal rank, which are commonly used information retrieval metrics. The novelty of this study is that, with the proposed approach, researchers are able to find relevant and useful publications over the internet regardless of their previous research experiences and research area.

The NIST selection process for standardizing Post-Quantum Cryptography Mechanisms is currently running. Many papers already studied their theoretical security, but the resistance in deployed device has not been much investigated so far. In particular, fault attack is a serious threat for algorithms implemented in embedded devices. One particularly powerful technique is to use safe-error attacks. Such attacks exploit the fact that a specific fault may or may not lead to a faulty output depending on a secret value. In this paper, we investigate the resistance of various Post-Quantum candidates algorithms against such attacks.

Most insulation in the power transformers consists of oil and cellulose (paper/pressboard). Mineral oil has been used as an insulating liquid for over a hundred years. However, mineral oil is a non-biodegradable oil and has a serious impact on the environment and the non- renewable energy resources. Therefore, it is important to replace this oil with another alternative insulating liquid, and PFAE was introduced in the year 2006 in Japan. In addition, the ability of an insulating system also depends on the thickness of the monomolecular cellulose fiber on the kraft paper. Hence, it is necessary to determine the appropriate thickness of kraft paper for use as an insulator on the power transformer for this type of oil. This project presents paper-oil (PFAE) charging and discharging current characteristics. The study was carried out through the implementation of experiments in the laboratory. Polarization and Depolarization Current (PDC) test has been carried out to test the conductivity and response function of the insulating materials. It was found that the thickness of kraft paper affecting the capacitance value and the PDC measurement of the tested model. In both dry and wet conditions, the sample of two layers of kraft insulation paper shows the best insulation status as it has the lowest conductivity compared to the other samples. It can be concluded that it is necessary to determine the ideal thickness of insulating paper to ensure that the power transformer has the best insulation.

In this work, Schottky diodes based on amorphous indium-gallium-zinc-oxide (IGZO) were fabricated on cellulose microfiber paper substrate. Silver lines used as the Schottky barrier were printed, in parallel to thermally evaporated Cr/Au ohmic contact, using a dispense printer. The morphological and electrical characteristics of the devices are presented. The fabricated diodes exhibited rectification ratios ranging from 3.4 to 34.9 at ±1V with ON voltages that range from 1.1V to 1.4 V, for device lengths (Ag to Au distance) from 145 µm to 894 µm. The diodes were characterized in a temperature range between 25°C and 80°C. They showed a decrease of the ON current when increasing temperature, which is mainly attributed to the change of the cellulose microstructure. Indeed, an opposite of the ON current behavior was registered when the diode was realized on a polyimide substrate. The realized flexible paper-based diodes offer a potential promising choice for printed environmental-friendly electronics.

The industry of disposables has undergone a huge revolution in this century owing to the busy lifestyles and technological advancements in its production and manufacturing process. Paper cups are one of the widely used products in disposable industry. It has multi utility options that is it can be used to drink water as well as beverages, soups, liquids etc. This paper entirely focuses on increasing the quality factor of production and manufacturing process of these paper cups by adding additional circuitry to the existing machinery to reduce waste of raw materials as well as the per unit cost involved in wastage of electricity. This research work is based on using proximity sensors to sense the materials and molded paper cups infront of machinery and reduce the electric wastage through a relay Mechanism assembly. This is a PLC based live project example of effectively increasing the overall efficiency of the manufacturing plant

For the detection of product color uniformity and seam texture defects in the production process of test paper, detection methods are proposed respectively. For the detection of the color uniformity, a uniformity detection method based on dynamic light correction is proposed. The illumination correction image is used for color dynamic compensation of the test paper image to solve the influence of uneven illumination on the test paper. For the detection of texture defects such as seams, a texture defect detection method based on gray gradient aggregation intensity is proposed. This method uses one-way Gaussian convolution to strengthen features, and then calculates the gradient, divides the region and calculates the gradient extremum aggregation strength to judge texture defects, which can quickly and efficiently find seam defects. The experimental results show that the above methods have obvious effects on the color uniformity and seam detection of the test paper. The color uniformity accuracy rate is 96%, the seam detection accuracy rate is 97%. The total detection time of a single test paper is about 90ms. The algorithm is real-time and accurate, and can be used in actual production detection.

Life expectancy of power transformers is limited by the integrity of insulating cellulosic paper wound around transformer winding. The insulating paper is subjected to thermal, electrical and mechanical stresses. There are three main factors causing degradation in the paper, i.e. moisture, oxygen and temperature. In this work, the degradation rate due to temperature is only discussed. That means that transformer life could be shortened or extended by adjusting loads which results in higher or lower temperature conditions. The temperature, namely the hot-spot temperature is typically used to estimate the relative ageing rate and loss of insulation life. Being able to estimate the hot-spot temperature accurately given load and weather data helps operators to plan about transformer replacement properly. This could also reduce costs of early replacement due to a conservative estimation. In this work, thermal models have been developed using machine learning techniques to learn thermal behavior from historical data. The data are comprised of the hot-spot temperature estimated by winding temperature indicator (WTI), load profiles, ambient temperature, rainfall, wind speed and direction and solar radiation. The proposed method has been validated using data for five 400kV/275kV, four 400kV/132kV and two 275kV/66kV autotransformers and has been shown to work effectively.

In the screening area of the paper mill has the high density impurities which are removed from the pulp and it is stored in the machine chest. The pulp in the machine chest is directly transferred to the head box without removing any medium density sand particles which affects the quality of the pulp. Only if the pulp entering the paper machine is clean and well-mixed with additives a good quality of paper will be produced. So in between the pulp tank and the pulp receiver a medium density cleaner is installed. The pulp in the machine chest is allowed to flow through the round hopper. Due to the circulatory motion and by centrifugal force the high density sand particles will settle in the bottom of the hopper. Due to the centripetal force the pulp is recovered from the hopper. The sand particles that settled in the bottom of the hopper are removed by the periodic opening and closing of the solenoid valves at regular interval of time. The medium density impurities are removed by creating hydro cyclone. These sequential operations are automated and controlled through PLC. So the overall plant efficiency is increased. Since the medium density impurities are removed this ensures the good quality paper production.

Electromagnetic Fault Injection (EMFI) is a well known method of introducing faults for security analysis of digital devices. Such faults can be seen as analogous to the faults which are known to naturally occur in digital devices, a known problem with designing safety-critical systems.Numerous standards have been developed for safety-critical systems, including the development of standards for increasing the rate of naturally occurring faults using particle sources. In this work, we demonstrate that desktop EMFI tooling can be used to accomplish similar testing, but with more control, effectively speeding up the evaluation process.We demonstrate using EMFI tooling for safety evaluation to recreate a highly publicized safety issue present in an automotive ECU – one that could not easily be recreated with other techniques.

Although testing software is paramount to safeguard our digitizing society, students are reluctant to consider a career in the field. A reason could be that dedicated courses on software testing are rare. However, even when such a course exists, students perceive testing as a boring, unrewarding and even dogmatic chore. For more than 10 years, we have been teaching a software testing course at the Vrije Universiteit in Amsterdam. Driven by our belief that an abundant exposure to software bugs makes good testers, we experimented with many ideas to engage students and make them love the topic. The most unorthodox, yet effective interventions we are proud of, were: (1) to scare students by analyzing past, software-related accidents, such as Therac-25 or Boeing 737-MAX; (2) to thrill them using bug-hunting gamification, enabled by the in-house developed VU-BugZoo; (3) to trust them an end-To-end testing of safety-critical software-intensive systems, such as model trains, automatic insulin pumps and even radiotherapy facilities, and (4) to inspire their career, by opening a dialog with test professionals from industry. The result is a mature course, read yearly by 50 computer science graduates, where almost 80% of the participants find the topic interesting and challenging, and 40% consider a future carrier in testing. These positive results make us confident that we found a formula that works. In this position paper, we would like to share our innovative ideas and lessons learned. Also in the future, we will stay committed to educate enthusiastic and responsible software testers.

In this paper, we obtain samples of thermal aging transformer insulation paper at 6 time points through an accelerated thermal aging test that simulates the actual operating state of the transformer. The Raman spectroscopy diagnosis model of transformer insulating paper aging is proposed. The model combines an autoencoder and a linear discriminant analysis algorithm to extract the characteristics of the Raman spectrum, and uses a Naive Bayes classification method to divide the aging stage of the insulating paper. Through the Raman spectroscopy diagnosis model of transformer insulation paper aging designed in this paper, the Raman spectroscopy characteristic quantity extracted by the AE-LDA method can effectively reflect the aging characteristic information of the insulating paper, and the Naive Bayes classification algorithm is used to predict. Research shows that the model has a high predictive accuracy for the assessment of the aging state of insulating paper, and can effectively diagnose the aging state of transformer oil-paper insulation. The Raman spectroscopy used in the diagnosis of the aging of insulating paper has good aging discrimination ability and good application prospects, and provides a new idea for the diagnosis of the aging state of transformer oil-paper insulation.

Because the traditional insulation paper state detection method has some shortcomings, this paper proposes a non-destructive detection method, which uses the texture feature value of insulation paper image to establish a relationship with the degree of polymerization (DP) of insulation paper state, and then indirectly estimates the aging state of insulation paper. Firstly, the insulation paper images in different aging states are collected, and the texture feature values of the images are extracted by gray histogram, gray level co-occurrence matrix and gray level run-length matrix; then, the texture feature values are selected by correlation; then, the validity of the selected texture feature values to represent the insulation paper state is verified by support vector machine (SVM); finally, the multiple linear regression method is used. The relationship between the degree of polymerization and the texture feature value is established and verified by the actual results. The results show that the texture feature value has a good fitting relationship with the degree of polymerization, which verifies the effectiveness of this method in evaluating the state of insulating paper.

The ageing status of the oil-paper insulation is closely related to the operation of the transformers. Recently, near infrared spectroscopy (NIRS) is used to identify the ageing condition of oil-paper insulation for it is a more rapid and nondestructive detection than the traditional viscometry method. However, the identification accuracy of existing quantitative analysis method of NIRS are still insufficient. This paper proposed an improved Linear discriminant analysis (LDA) method to classify the spectra and to identify the ageing condition of the insulating paper. Based on DP measurement, the insulating paper specimens prepared in laboratory are divided into varying ageing groups. The principle of LDA method is introduced. The classic LDA method has been applied to identify the ageing status of specimens respectively from 4 ageing groups and 3 ageing groups. The results show that LDA has a better performance in classifying four groups than three groups. To achieve an accurate ageing condition identification with balanced accuracy of the model and enough categories of ageing status, the improved LDA method is proposed. The identification results show that the improved method has satisfactory performance and indicates great application potentials in the ageing assessment of the oil-paper impregnated insulation.

Prediction and detection of skin diseases have always been a challenging and critical task for health care professionals. There are many clinics and skin practitioners who are offering their services at exorbitant costs. At the same time, skin condition prevails in our country in every part. In the current scenario majority of the skincare practitioners are using traditional methods to diagnose the disease which may take a considerable amount of time. Skin Diseases are serious problems in recent times as it is a matter of environmental factors, socioeconomic factors, lack of full diet, etc. This paper is a comparative study about understanding various skin diseases related to normal skin issues as well as cosmetology. A comparison between oily, dry, and normal skin is also projected in this study. A survey of different papers is done on basis of technologies used, results with accuracy, ethical conduct, number of diseases diagnosed, datasets, etc are done.

In this paper the optimal surface area of an air-to-air plate heat exchanger is determined. The analysis is based on input data obtained by measurement in real operating conditions in board producing paper machine in Belgrade, Serbia. For that purpose effectiveness-NTU method together with previously defined cost functions was applied, taking into account seasonal variations in the parameters of exhaust and fresh atmospheric air. In addition, the total net savings in the consumption of primary energy sources in the factory that can be achieved by applying an optimised system for the use of waste heat were calculated and a simple payback period of the investment was determined.

The purpose of this short position paper is to stimulate a community wide discussion on how situation awareness (SA) technologies may best support the decisions made during pandemics. Currently much of SA is directed towards the critical information needed to make epidemiological and public health decisions; as important as this is, an epidemic also requires doctors and front line medical professional to make numerous decisions regarding individualized patient care. The question is how can SA contribute to both the rapid collection, analysis, and sharing of critical patient information among medical personnel, and then across all levels of decision-making from individual patients to public health policy?

This paper describes approaches to the use of wearables and related monitoring and diagnostic tools to glean and present data to patients and their caregivers in the form of Personal Health Dashboard. Raw data from devices such as fitness watches, blood glucose and blood pressure monitors can be analyzed by machine learning algorithms ensembles over the cloud or at the edge, and the resulting patterns can be presented to allow for actionable responses. The advent of low-cost computational, sensing, monitoring and diagnostic tools allow for cost-effective solutions that can have wide adaptability. The paper draws from prior and ongoing research conducted by the team to present these positions in the advancement of healthcare.