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Background The screening of biological hazards, chemical contaminants and allergens in food products is a major concern of food industry since they can cause serious illnesses and even lead to death. Therefore, there is a significant interest in developing low-cost, robust, and rapid detection methods for food safety and quality analysis. Conventional methods provide accurate analytical characteristics, but require well-equipped laboratories, trained personnel, costly reagents, and complicated sample preparation procedures prior to the detection in order to obtain reliable results. Hence, they are not always ideal for on-site food surveillance. Scope and approach This paper discusses the fundamentals of microfluidic paper-based analytical devices (μPADs) and reviews the recent progress in their usage for screening biological hazards, harmful chemical residues, and allergens in food products to systematically evaluate the strengths and weaknesses of this technology. It then covers future trends and useful strategies that could be employed in μPADs design and fabrication procedures to address the critical challenges in equipment-free food analysis. Key findings and conclusions μPADs offer promising avenues to revolutionize food safety and quality analysis with merits of equipment-free sample preparation and detection at low-cost. Despite the advancement in the microfluidic technology, the manufacturing of user-friendly sample-to-answer μPADs still requires intensive research studies to be employed for robust and sensitive food safety and quality analysis.

In recent times, it has been witnessed that wireless systems based on IoT-based have developed rapidly in various sectors. The IoT (Internet of Things) is the network in which physical devices, equipment, sensors and other objects can communicate among themselves without human involvement. The WSN (Wireless Sensor Network) is a central component of the IoT, which has proliferated into several different applications in real-time. The IoT and WSNs now have various critical and non-critical applications impacting nearly every area of our everyday life. WSN nodes are usually small and battery-driven machines. Thus, the energy effective data aggregation techniques that increase the lifespan of the network are highly significant. Various approaches and algorithms for energy-efficient data aggregation in IoT-WSN systems were presented. This paper reviews the literature with specific attention to aspects of wireless networking for the preservation of energy and aggregation of data.

Triboelectric nanogenerators (TENGs) have been investigated as a promising device to harvest mechanical energy into electricity. At present, paper-based triboelectric nanogenerators (P-TENGs) have been widely researched because paper features with lightweight, low cost, abundance, renewability and biodegradability. In this paper, the operating principles and operation modes of TENGs, the development progress and performance enhancement routes of P-TENGs are comprehensively reviewed. Additionally, the overview, challenges and opportunities of P-TENGs applications are also presented.

Activating flux is made up of inorganic material mixed with a volatile medium. The objective of this study is to identify and review the literature in activating flux, and different type of welding techniques. For that purpose, a structured review method is adopted and the Scopus database is used as a primary source for the selection of papers. The papers were identified using the string term of “Activating flux” OR “Activated flux” in the title of papers. And a total of 238 papers were identified for the study. Besides the above few other papers were also reviewed and the materials used, the type of flux used, and the findings of different combinations were highlighted in the form of tables. The findings of the study provide a comprehensive understanding of the different type of welding techniques focusing on TIG welding, and the different combination of flux used and their impact was summarized.

Space Resource Utilization could reduce the mass, cost, and risks of future space activities. The many nonlinear and compounding effects mean that seemingly insignificant concept of operations choices can invalidate the conclusions of sophisticated analyses. Here we successively relax self-imposed constraints in a recent publication to demonstrate that the proximity of a market and the benefits of aerobraking cannot be omitted.

Microfluidic paper-based analytical devices (μPADs) have attracted much attention over the past decade. They embody many advantages, such as abundance, portability, cost-effectiveness, and ease of fabrication, making them superior for clinical diagnostics, environmental monitoring, and food safety assurance. Despite these advantages, μPADs lack the high sensitivity to detect many analytes at trace levels than other commercial analytical instruments such as mass spectrometry. Therefore, a preconcentration step is required to enhance their sensitivity. This review focuses on the techniques used to separate and preconcentrate the analytes onto the μPADs, such as ion concentration polarization, isotachophoresis, and field amplification sample stacking. Other separations and preconcentration techniques, including liquid-solid and liquid-liquid extractions coupled with μPADs, are also reviewed and discussed. In addition, the fabrication methods, advantages, disadvantages, and the performance evaluation of the μPADs concerning their precision and accuracy were highlighted and critically assessed. Finally, the challenges and future perspectives have been discussed.

This study explored the relationship between syntactic complexity and rhetorical functions and the relationship between syntactic complexity and level of move/step realization in learner-generated research paper introductions. We created a corpus of 79 introductions written by undergraduate L2 students at intermediate to high-intermediate levels. We annotated the introductions for moves and steps, measured their syntactic complexity at the global, clausal, and phrasal levels, and rated their level of move/step realization across four levels (no, low, intermediate, and high). We conducted one-way MANOVA, one-way ANOVA, Pearson correlation, and stepwise multiple linear regression analyses. We found sentences realizing Reviewing items of previous research function to be significantly longer than other steps in Move1 and to contain significantly more clausal and phrasal complexity features. Sentences performing Claiming centrality function also demonstrated significantly more phrasal complexity features as measured by complex nominals per clause. We also found a higher level of phrasal complexity in introductions with a higher level of move/step realization, which can be attributed to introductions including Step1 and Step3, rather than containing all steps. Our findings imply that there is value in developing explicit instructions on how to use complex structures in performing rhetorical steps in research paper introductions. © 2021 Elsevier Ltd

Summary Skin-interfaced wearable electronics can find a broad spectrum of applications in healthcare, human-machine interface, robotics, and others. The state-of-the-art wearable electronics usually suffer from costly and complex fabrication procedures and nonbiodegradable polymer substrates. Paper, comprising entangled micro- or nano-scale cellulose fibers, is compatible with scalable fabrication techniques and emerges as a sustainable, inexpensive, disposable, and biocompatible substrate for wearable electronics. Given various attractive properties (e.g., breathability, flexibility, biocompatibility, and biodegradability) and rich tunability of surface chemistry and porous structures, paper offers many exciting opportunities for wearable electronics. In this review, we first introduce the intriguing properties of paper-based wearable electronics and strategies for cellulose modifications to satisfy specific demands. We then overview the applications of paper-based devices in biosensing, energy storage and generation, optoelectronics, soft actuators, and several others. Finally, we discuss some challenges that need to be addressed before practical uses and wide implementation of paper-based wearable electronics., Graphical abstract, Biodevices; Bioelectronics; Electronic materials

The main objective of this project was to assess an inkless method to collect reference (exemplar) fingerprints as a low-cost, portable, and hygienic technique; for temporary storage and as an alternative to the conventional black ink method. The tested inkless approach requires alcohol-based hand sanitizer gel as the medium and thermal paper as the substrate. The final quality and clarity of fingerprints were examined and compared between the two methods, especially on the basis of medium/substrate and biological sex effects. Two different evaluation systems of visual quality (clarity) were conducted to provide more reliable results: a quality score scheme (QS) and metrics from the FBI's Universal Latent Workstation (ULW). Fingerprints were obtained from 20 individuals (11 males and 9 females) between the ages of 20 and 40; a total of 240 impressions were evaluated. The results provide evidence that the inkless method produces identifiable fingerprints of equivalent quality to the standard ink method, although significantly better for males. The potential of the proposed technique as an alternative to the conventional ink method is proven and corroborated by the two independent systems of visual quality assessments.

The benefits of many breakthroughs in biotechnology, health, and medicine have often been restricted by the high cost and the limited availability of tests and application devices. There is a need for low-cost bioassays for biotechnological applications and health diagnostics. To facilitate quality health care to resource-limited regions, the point-of-care devices needs to be affordable, sensitive, user-friendly, rapid, and robust. Paper is an ideal ingredient to develop disposable bioassay devices. Bioactive papers can be used to develop paper-based analytical devices for clinical diagnosis, assessing organ dysfunction, and detecting infectious diseases and microorganisms. Bioactive papers can also be used as antibacterial papers in healthcare and food packaging. At present the paper diagnostics is a multibillion-dollar market. Considering the product range and applications in health sector the market potential of bioactive papers is very high. Synchronized scientific and industrial research is needed to develop high quality and consistent commercial products from laboratory proof of concept.

Nitrite is one of the most common carcinogens in daily food. Its simple, rapid, inexpensive, and in-field measurement is important for food safety, based on the requirements of the standard from Codex Alimentarius Commission and China. Using polyacrylonitrile (PAN) and thin layer silica gel (SG), p-aminophenylcyclic acid (SA) and naphthalene ethylenediamine hydrochloride (NEH), as carriers and chromogenic agents, respectively, PAN-NSS as nitrite color sensor is proposed. After fixing and protecting of SA and NEH with layer-upon-layer PAN, the validity period of the test paper can be prolonged from 7 days to more than 30 days. The reproducibility of PAN-NSS preparation is ensured by electrospinning. Combined with PAN-NSS, deep convolutional neural network (DCNN) and APP as a visual monitoring platform, which has the functions of rapid sampling, data processing and transmission, intuitive feedback, etc., and provides a fully integrated detection system for field detection.

Electrochemical paper-based analytical devices (ePADs) are useful analytical devices that serve as point-of-care testing (POCT) devices for various clinical biomarkers in view of their simplicity, portability, and low-cost format. However, multistep reagent manipulation usually restricts the performance of the device for end users. Herein, we developed a sequential ePAD for sequential immunosensing fluid delivery by integrating dual flow behaviors (fast-flow/delayed) within a single paper platform for the simultaneous detection of hepatitis B surface antigen (HBsAg) and hepatitis C core antigen (HCVcAg). In the present work, a fast-flow channel was used for the automated washing of unbound antigens, while a delayed channel was created to store a redox reagent for further electrochemical analysis with a single buffer loading (the analysis time can be completed within 500 s). Hence, the undesirable complex procedure of multi-step reagent manipulation is scarcely needed by the user. The detection limit of the proposed ePAD was as low as 18.2 pg mL−1 for HBsAg and 1.19 pg mL−1 for HCVcAg. In addition, this proposed ePAD was also proven to be effective in real clinical sera from patients to verify its biological applicability. The ePAD sensor shows high promise as an easy-to-use, portable, and extendable sensor for other multiplex biological assays.

Human muscles can sense external stimuli and generate forces as well. To emulate these capabilities, we design a paper-based transducer capable of both sensing and actuation. Utilizing the origami technique, we develop a soft transducer with attributes of simple structure, easy fabrication and low cost. The origami transducer can function as a deformable capacitive sensor to measure contract forces/pressures. It can achieve a sensitivity up to 0.051 kPa −1, comparable to soft capacitive sensors in the literature. During cyclic tests up to 1000 cycles, this soft sensor exhibits excellent repeatability and negligible hysteresis, thus enabling a high accuracy. On the other hand, this origami transducer can act as a soft actuator to generate haptic feedback. The voltage-induced output force can be 0.4 N, comparable to haptic devices based on soft actuators in the literature. This origami transducer is finally demonstrated for application to breath monitoring of a subject, functioning as both a wearable sensor and actuator. It is believed that paper-based origami transducers can offer a unique option to soft robots and wearable devices, due to their simple design, low cost, and capability for simultaneous sensing and actuation.

Over the past few years, researchers have focused on stimulus-responsive soft materials and their applications in the design of soft actuators. The key factor of the soft actuators to achieve various applications is the programmable and controllable shape deformations. Here, a versatile and simple surface patterning method consisted of pencil drawing and pasting polymer films is proposed to design the soft actuators with programmable deformations. Due to the thermal expansion effect and the hygroexpansion effect, the graphite paper/polymer actuator performs a bending motion with a curvature of 1.4 cm−1 under light irradiation and 1.2 cm−1 under high humidity. Owing to the convenience of the patterning method, different patterns on both two surfaces of the paper substrate are designed without effort. Thus, through tuning the grayscale of the graphite on one surface of the paper, three types of T-shaped actuators can be obtained with different deformations under light irradiation. In addition, by pencil drawing or hydrophobically modifying specific patterns on the two surfaces of the paper, three types of strip-shaped actuators are also proposed to demonstrate that different 3D shape deformations can be obtained under different stimuli. Finally, a helix-shaped actuator inspired by the natural plant tendrils is fabricated by the thermal-induced shaping method. The helix-shaped actuator performs twisting/untwisting motion under different stimuli and can be used as a grasping robot. These results demonstrate the diversified programmability of the graphite paper/polymer actuator. Hence, the simple and versatile programming methods of soft actuators have great potential to be used in the field of biomimetic applications, intelligent robotics, and lab-on-paper devices.

This paper analyses over 89,000 documents between scientific journals and international patents in order to identify main technologies for material pyrolysis (i.e. fluidized bed, hot balls, microwave, plasma, and laser), compare them in terms of technical performances and discover future trends. The predictive model for future trends is based on the evolutionary model of the TRIZ theory, with particular attention to the evolutionary law, “from macro to micro”, which explains how technical systems evolve towards increasingly smaller, controlled, and resource-efficient interactions. In our case, we applied the focus to the interface between the heat source and the pyrolyzed raw material. The proposed evolutionary model showed a substantial alignment with chronological trends, and the model was consistent with both patent trends and those of the scientific literature. To make the analysis quantitatively more robust, a comparison of the different technologies has also been introduced in terms of percentage distribution and the number of patents/papers referring to the different classes of heating rates and reaction temperatures and types of flash/fast/intermediate/slow reaction. Finally, a quantitative metric based on the innovation index has been adopted, to take into account the number of citations normalized on the years of publication. The main outputs of this study identified that radiations-based pyrolysis, involving microscopic interaction, seem more interesting in term of technical performances (i.e. heating rate and reaction temperature), although their technological growth has yet to occur, unlike fluidized bed and hot balls, which typically work at a macroscopic level and seem to have already reached maturity. According to a bibliometric index, laser corpus resulted three time more innovative then fluidized bed reactors papers, especially for heating rates and reactions temperature.

It is generally accepted that the latest research findings are published as research papers in journals. Social network analysis (SNA) can offer a perspective from which to analyze technological developments. This work analyzes the development of lithium-ion batteries (LIBs) and electrolytes by SNA based on papers from 2011 to 2015. Three types of social networks, country-, institution-, and research paper-based networks were used. Country-based network analysis shows that major ten countries have a close relationship with respect to technology development. Institution-based network analysis revealed that 48 top institutions publish the most prolifically and that a “small-world” effect exists within the network, indicating excellent knowledge transfer among institutions through citations. Several institutions occupy the center position within the network and have a prominent influence in knowledge transfer as judged by centrality analysis. Research paper-based social networks, consisting of high-citation articles, are grouped into six technology clusters, which exhibit a broad range of knowledge transfer processes regardless of the battery and electrolyte types, indicating underlying closely interwoven relationships. The knowledge transfer path can describe the technology evolution in detail. An overview of technological development status can be thus obtained by this proposed three-level analysis method.

The optical model for reflectance lightness of stereo paper product surface is proposed, which could promote color reproduction study of paper model products and improving color reproduction quality. The reflectance lightness was measured using non-contact measurement method with spectroradiometer, CS2000A, that can measure stereo paper product surface lightness and tristimulus. Once inputting the paper product parameters of surface angle and tone, the model will predict the reflectance lightness for stereo paper product surface. After analyzing statistical information of light transmission, paper product surface reflectance lightness can be obtained. The results showed that prediction model is versatile and easy to use, besides the model quite approached to realistic paper product surface. The optical model simulation system could sufficiently accomplish reflectance lightness, which provides a novel way to predict and reproduce printing quality.

Scientific output, as measured in research published annually, has seen a consistent growth for decades now. As more manuscripts are submitted for publication each year, new publishing venues appear – often as increasingly specialised offshoots of existing journals and conferences. This situation presents scholars with a wealth of publishing venues to consider and choose from for their manuscripts. Surprisingly, we find that the most cited papers are not necessarily found in the highest-ranked journals, while the best conferences dominate in this regard. We find it intriguing that popular Computer Science conferences act like a vacuum of attention, centralising all good publications, while journals are carried less by their renown and thus can attract strong manuscripts even at a low rank. But to what extent does a venue imply a paper’s recognition and popularity? We propose a new approach and processing pipeline, in which we project a heterogeneous publication network, learn representations for papers and classify said papers according to their 2-year citation success. This framework provides a groundwork for a venue recommendation system, a tool for improving paper’s scientific recognition through a better choice of a publishing venue.

Creatinine is a clinically significant analyte used to diagnose kidney condition. However, the literature still lacks in creatinine sensors fulfilling point-of-care testing requirements. In this paper, we have developed colorimetric paper-based creatinine sensors adhering to point-of-care testing principles. The signal readout is accomplished with a smartphone modified with 3D-printed elements and processed with a self-written application compromising computer vision algorithm for automatic detection of the colored zone. Two colorimetric methods – routinely used Jaffe method and an alternative one with 3,5-dinitrobenzoic acid were both tested and compared. Hue channel intensity from HSV color space and green channel from RGB color space was used as the analytical signal in Jaffe and 3,5-dinitrobenzoate method, respectively. For both kinds of sensors the linear range of the response covered the range significant for urinary analysis, with precision, expressed as RSD, below 5%. Limit of quantification for Jaffe method was 1.05 mmol·L−1 whereas it was 0.82 mmol·L−1 for 3,5-dinitrobenzoate method. The utility of the developed sensors to selectively quantify creatinine in undiluted urine was proved using artificial urine samples and the obtained recoveries were in the range from 70 to 129 %.

Objective Providing instruction for assembly tasks is essential in modern manufacturing industry, as well as in households for customers that buy products to be assembled at home. Recent technological developments might be able to assist in completing an assembly task faster and more accurately. The aim of this study was to evaluate whether performance and usability differs when instructions for an assembly task are presented on digital glasses versus paper. Methods Participants (n = 63) completed one of three versions of an assembly task (between-subject-design) with LEGO® bricks: (1) with paper instruction (P), (2) with text instructions presented stepwise via digital glasses (GT), (3) with stepwise text and auditory instruction (in parallel) on digital glasses (GA). Outcome measures on performance were completion time and errors. Furthermore, usability was measured by the User Experience Questionnaire, the Standardized Usability Questionnaire, the Post-Study Usability Questionnaire, and cognitive processing skills were assessed by the Trail Making Test and different versions of the Eriksen Flanker Task. Analyses were adjusted for the confounding factors age, gender, experience with glasses and LEGO, and problems with instruction. Results Findings indicate that task completion was faster with the paper instructions compared to both versions of instruction via digital glasses (GT, GA). We observed no difference in accuracy and usability between the instructions. “Novelty” was rated higher for instructions for both GT and GA, compared to P. Discussion Results show that instructions on digital glasses may not always be more effective for assembly than the traditional paper-based instructions. Further studies are necessary to investigate whether effectiveness may depend on task complexity, target group, experience of the user with task and device, and how the information is presented.

A single-sheet 3-dimensional paper-based microfluidic analytical device (3D-μPAD) has a user-friendly compact format that eliminates any potential for user alignment error compared to the stacking and folding format. However, achieving sophisticated analysis is still a challenge because forming a complex 3D microfluidic network sufficient for performing analysis in a single sheet of paper is difficult. Here, we present the first report of automatic multistep digital analysis in a single-sheet 3D-μPAD created by controlling the penetration of different wax inks into paper. Simple printing of different wax inks with different spreading properties produces different dimension microchannels in paper. This controllable change in the dimensions of the microchannels results in different flow rates of fluid in each microchannel. Based on the spatial resolution of the microchannels, our strategy enables programming of microfluidic flow through manipulation of the fluidic time delay. This approach creates 3D-μPADs capable of sequential fluid delivery and multistep biochemical reactions with single loading of a sample solution. The methodology can be further expanded to combination with digital assays to measure the concentration of a target analyte by simply counting the number of colored bars at a fixed time. It does not require any external instruments to analyze the measurement of the target analyte concentration. The significance of this work lies in the promising potential for automatic multistep digital assays in a low-cost and easy-to-use paper-based format that can be integrated with a roll-to-roll process for commercial-scale manufacturing.

Machine learning methods are being successfully applied in various domains of human activity, including optics. Can they become a universal instrument capable of improving user aspect of photonic devices? Is their application a necessary element of such improvement? Answers to fundamental questions arising from application of machine learning methods in ultrashort-pulsed lasers are discussed. Future prospects and current fundamental limitations of such methods are analysed.

Highlights • Evidence synthesis (including systematic reviews, scoping reviews, and more) involves many complementary areas of expertise: methods, information retrieval, statistics, and clinical knowledge. • Few individual peer reviewers have all the requisite knowledge to evaluate all aspects of evidence synthesis manuscripts. • Segmented peer review as proposed by Dinakaran et al offers a good solution to the challenges of rigorously peer reviewing evidence synthesis manuscripts.

Identifying a forged printed document with scanned evidence can be a challenge. Microscopic printing is showing random shape which depends on the printing source as well as printing material. This paper presents a statistical analysis of the printing patterns under a microscopic scale, analyses the effect of printing direction, printing substrate (uncoated and coated paper), and printing technology (conventional offset, waterless offset, and electrophotography). The analysis shows a negligible effect of printing direction, yet, using the shape descriptor indexes, the printing materials and technologies are distinguishable under a microscopic scale. As a result, the algorithms based on Support Vectors Machine and Random Forest are developed, with shape descriptor indexes as features, for printing source identification. Both proposed algorithms, equally, achieve a high classification accuracy rate, over 92% accuracy with complex geometric-shape patterns. Thanks to the lightweight and efficiency of the Support Vectors Machine, the study shows promising applications for real-world and potential implementation in the Internet of Things devices.

The operation of continuous pulp digesters play a pivotal role in the profitability of a pulp mill. Particularly, process faults due to the non-ideal flow behaviour in the digester have direct impact on the pulp quality, production rate and chemical utilization of the plant. The study begins with an introduction on process faults in continuous reactors by explicitly focusing on continuous pulp digesters. Operational data from a commercial pulp digester is analysed to examine process abnormalities. Subsequently, the extended Purdue model is modified to simulate non-ideal flows related continuous digester faults. The affect of different process faults on important process variables are analysed with the help of simulation studies. In the second part of the paper, a hybrid fault diagnostics approach based on first-principle digester model and Bayesian network is proposed and validated. Additionally, the methodology of developing the diagnostics approach are also described.

In our current world, active display technology creates a glut of energy demand to meet its illumination needs. This demand can be stymied by using reflective display technologies that require no active illumination, with some examples including: electronic paper using electrophoretic motion of ink particles, electrowetting of water/oil droplets, and interferometric modulators, all of which have been commercialized. However, due to the lack of active light sources, it is difficult to implement these reflective display technologies in low light environments (e.g. nighttime display). In this work, we report an experimental observation of multiple concentric circular rainbows from reflective microscale concave interfaces (MCIs), which are introduced by the reflection of optical rays within a polymer-embedded microsphere. Exit rays from a single edge and opposite edges will introduce completely different interference mechanisms depending on the illumination and observation conditions, which will result in different angle-dependent colors. By clarifying the mechanism behind this coloration phenomenon, as well as quantitatively mapping the generated color, the implementation of the MCI in smart signs and pixelated displays are demonstrated, showing angle-dependent color-changing reflected images that can be observed over a wide spatial angle range. This structural material will serve as a building block for the development of new platforms for light-matter interactions, on-chip sensors, anti-counterfeiting tools, and passive and smart color reflective displays. Intriguingly, we also demonstrate a smart MCI traffic sign for both visible and infrared wavelengths that will introduce extra signals for pattern and image recognition in order to enhance the safety of future autopilot/autonomous systems.

With the rapid development of emerging technologies, research on technology adoption is valuable for improving the reception efficiency of new technologies. This paper uses bibliometric analysis methods and visualization software to explore the research stages, the hot issues, the limitations of the research and the future fronts. A total of 11,706 documents were collected as the original data using the retrieval formulas. Sorted by the numbers of citations, the top 10% of the documents are extracted as the top-cited documents. First, combining software and bibliometric analysis, we obtain the annual development trends and identify the productive countries/regions, the influential institutions and the high-yielding authors. In addition, we overview the representative authors and their works in the development of technology adoption. Then, we conduct keyword analysis and burstiness analysis to identify the academic hotspots, research fronts and limitations in this field. The results show that increasingly more researches have shifted from theories to applications. Technology adoption has multiple applications in the fields of online shopping, mobile learning, mobile banking, e-learning and health care delivery. Finally, we draw conclusions on the bibliometric findings and forecast the future research trends of technology adoption. In the future, more researches will focus on the impacts of age, gender, culture, and the environment on technology adoption.

The ability to engineer biological systems, whether to introduce novel functionality or improved performance, is a cornerstone of biotechnology and synthetic biology. Typically, this requires the generation of genetic diversity to explore variations in phenotype, a process that can be performed at many levels, from single molecule targets (i.e., in directed evolution of enzymes) to whole organisms (e.g., in chassis engineering). Recent advances in DNA synthesis technology and automation have enhanced our ability to create variant libraries with greater control and throughput. This review highlights the latest developments in approaches to create such a hierarchy of diversity from the enzyme level to entire pathways in vitro, with a focus on the creation of combinatorial libraries that are required to navigate a target's vast design space successfully to uncover significant improvements in function.

The power transformer analysis focused internal fault identification is important for the energy efficiency in all the countries, the partial discharge is one of the main failure modes, it affects the remaining life until the failure. In this research article, the new approach by using soft techniques as support vector machine (SVM) and tree models (TM) allow to detect partial discharge with high accuracy (97.55%); dissolved gas analysis (DGA) for the quality of the oil-Kraft, degradation and evaluate the remaining life. An important achieve is the highest accuracy for the thermal influence in oil degradation (100%). Finally, this research article has provided an algorithm for the highest accuracy obtained from the DGA for oil and Kraft degradation with a crosscheck process by using the cluster analysis.

Automatic sleep stage scoring based on deep neural networks has come into focus of sleep researchers and physicians, as a reliable method able to objectively classify sleep stages would save human resources and simplify clinical routines. Due to novel open-source software libraries for machine learning, in combination with enormous recent progress in hardware development, a paradigm shift in the field of sleep research towards automatic diagnostics might be imminent. We argue that modern machine learning techniques are not just a tool to perform automatic sleep stage classification, but are also a creative approach to find hidden properties of sleep physiology. We have already developed and established algorithms to visualize and cluster EEG data, facilitating first assessments on sleep health in terms of sleep-apnea and consequently reduced daytime vigilance. In the following study, we further analyze cortical activity during sleep by determining the probabilities of momentary sleep stages, represented as hypnodensity graphs and then computing vectorial cross-correlations of different EEG channels. We can show that this measure serves to estimate the period length of sleep cycles and thus can help to find disturbances due to pathological conditions., Highlights • Artificial intelligence leads to a paradigm shift in sleep research. • Sleep stage scoring with deep neural networks is robust and reliable. • Neural networks find embeddings of EEG raw data. • Probabilities of momentary sleep stages can be visualized as hypnodensity graphs. • Cross-correlations of sleep stage probability vectors provide an estimate of sleep cycle period length.

The architecture, engineering, and construction (AEC) industry is undergoing a substantial change, and the technological advancements are shaping the future of this industry. Over years, numerous tools have been developed based on building information modeling (BIM) and the Internet of Things (IoT) to manage a variety of tasks and activities within different construction and operational phases in the AEC industry. Recently, with the advent of novel internet-enabled technologies in providing the ability of real-time connectivity, researchers have started to develop virtual design and construction (VDC) and digital twin approaches to remotely monitor, control, and optimize different work progresses and activities in real-time at any work level in the AEC industry. This paper presents a reference model to illustrates the main advantages and applications of VDC and digital twin, and to demonstrate their trends and anticipated cost savings in the AEC industry at a global level. These technological advancements along with augmented reality (AR) and virtual reality (VR) can offer the unique benefit of real-time monitoring of the current status as well as predicting the future of any physical structure at any level of work. The global demand and usage of these technological advancements will lead to great cost savings in the AEC industry which would be US$950 million in design and construction and US$400 million in operation and maintenance phases in the non-residential AEC industry in 2025. This paper also discusses the future challenges of VDC and digital twin in the industry. As a main line of future research, these technologies can simultaneously be utilized with artificial intelligence to enable human-centered decision making to automate and optimize work progresses. Such integrations of VDC, digital twin, artificial intelligence, and human-centered approaches will shape the future of the AEC industry and provide numerous research opportunities in this domain.

Simultaneous Localization and Mapping (SLAM) is the foundation of autonomous navigation for unmanned systems. The existing SLAM solutions are mainly divided into the visual SLAM(vSLAM) equipped with camera and the lidar SLAM equipped with lidar. However, pure visual SLAM have shortcomings such as low positioning accuracy, the paper proposes a visual-inertial information fusion SLAM based on Runge-Kutta improved pre-integration. First, the Inertial Measurement Unit (IMU) information between two adjacent keyframes is pre-integrated at the front-end to provide IMU constraints for visual-inertial information fusion. In particular, to improve the accuracy in pre-integration, the paper uses the Runge-Kutta algorithm instead of Euler integral to calculate the pre-integration value at the next moment. Then, the IMU pre-integration value is used as the initial value of the system state at the current frame time. We combine the visual reprojection error and imu pre-integration error to optimize the state variables such as speed and pose, and restore map points' three-dimensional coordinates. Finally, we set a sliding window to optimize map points' coordinates and state variables. The experimental part is divided into dataset experiment and complex indoor-environment experiment. The results show that compared with pure visual SLAM and the existing visual-inertial fusion SLAM, our method has higher positioning accuracy.

Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques. Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the field. Plant phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red, green and blue (RGB) imaging, thermal imaging, chlorophyll fluorescence imaging (CFIM), hyperspectral imaging, 3-dimensional (3-D) imaging or high resolution volumetric imaging. This paper provides an overview of imaging techniques and their applications in the field of plant phenotyping. This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification. In this paper, information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods. This paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural (2-D and 3-D), physiological and temporal trait estimation, and classification studies in plants.

Enforcing admired machine learning approaches to huge data enhanced novel issues for researchers. Conventional libraries could not suitably fulfil the requirement of complex model with wide variety of data and system parameters. Therefore new methodologies are required performing the computation on more than one machine over distributed environment. Some distributed frameworks on huge data such as MapReduce and TensorFlow have been deployed to solve various machine learning problems in heterogeneous distributed environment. The objective of this paper is providing a wide variety of useful information about platforms, approaches, problems, datasets, and optimization approaches in distributed systems. So researchers have been used the beneficial information to develop new approaches for efficient machine learning. This paper also covers the various formats of data like structured, semi structured and unstructured big data. A brief review of previous works is also represented in text and tabular format to provide motivation to the researchers for developing new paradigm of distributed computing environment.

Solar energy, wind energy etc. are the main sustainable energy sources used around the world. These sustainable energy sources are used in day-to-day life in various forms for numerous purposes. One of the highly used sustainable energy sources is the solar energy which with the help of photovoltaic (PV) cells can be made use for the generation of electrical power. However, these sources generate less voltage values and thus it requires a high gain converter. In this paper, a modified Luo with flyback DC to DC converter is presented that assures high voltage gain, single switch, less stress across the components and less ripple current. Furthermore, the simulations along with the hardware prototype for the presented converter is carried out and presented in this paper.

The primary objective of university education is to provide students with the required information and skills to enable them to make a successful contribution to the national development effort. In order to evaluate the level of expertise and skill of students, this preparation includes frequent examination and appraisal in the form of tests. Manual exams are carried out by most universities; Post Graduate and Under Graduate in India. Due to multiple uses made of the outcomes of the students, these examinations have high stakes. Consequently, the experiments became subject to different types of malpractice. Test malpractice affects the success of students in exams and leaves the scores/grades earned inaccurate, hence a challenge to examination bodies and the nation's education system (India). These are all disturbing signs that pose a danger to the nation. Examination malpractice has eaten deep into the Indian culture of education. Therefore, the object of this paper is to explore the problem of examination malpractice in India. The paper covered relevant measures, procedure setting/implementation and suggestions for this action. And finally, a model is proposed to stop malpractices in the examination. This IoT based model is used for detecting any metallic object or object in a range more than normal IR proximity sensor range. The block diagram of proposed model and circuit diagram for that model discussed in detail. This proposed model may be used for any investigation in the universities examinations.

The Secured exams are one of the foremost troublesome challenges in impersonation Judgment. For any Educational Institution, conducting an online authenticated examination is a major task due to its diverse security components. The main objective of our paper is to provide maximum security and to avoid the malpractice due to impersonation which are happening during the conduct of the examinations. The main objective of our paper is to provide maximum security and to avoid the impersonation and it is related to malpractices which are happening during the conduct of the examinations. The methodology we have adapted deals with the automation process of all the manual verification processes which are done. This is done by the combination of IoT and Image Processing technology. The verification of image and fingerprint confirms the authenticity of the person. The final result is displayed in the IoT database which reduces man power and time consumption. This paper has proposed a technique which eliminates all the errors and illegal activities which happen at the initial stage of the examination process. This technique has proved to provide a better security rather than the manual checkup process.

In the Internet of Things (IoT) the physical objects network is described as “things” linked to sensors, software and other technologies to connect and share information through the Internet with other devices and systems. The floods lead to large losses of life and property in several countries. However, the absence of sufficient technology results in further death and property losses as a result of flooding in developed countries. This is because of a lack of flood alert systems. The objective of this paper is to monitor the flood situation and provide a text message warning in the event of a threat. The main aim of this paper is to detect rising river water levels at a safe distance away from the railways and to allow the respective authorities to take appropriate measures by means of SMS. A sensor is a system that senses and reacts to certain physical data. The 8051 microcontrollers were produced by Intel in 1981. The microcontroller is 8-bit. It has 40 DIP pins, 4 kb of ROM stores and 128 bytes of RAM and two timers and 16 bits. It is equipped with 2 timers. There are four parallel 8-bit ports that are programmable and adjustable as necessary. The hardware device that uses GSM mobile technology for the provision of an information link with a remote network is a GSM modem or a GSM module. A float switch is a level sensor type, a fluid control system that is used inside a tank. A condenser is a tool that stores electrical energy in an electric field. It's a passive electronic feature with two terminals. A LED is a semiconductor light source that transmits light through the flowing current. Liquid Crystal Display stands for LCD. It is a thin, flat display device used in a variety of electronics. A digital electronic level shifter is a system used in the transformation of signs from one logic or voltage domain to another, also known as logic level shifter or voltage level Translation. A diode is an electronic component with a two-terminal that mainly conducts current in one direction, with low resistance and high resistance in one direction. The aim of this paper is to detect early Flood and give early warnings to Needy people.

In the recent years the utilization of cloud to store and process the big data is increased rapidly. Security is the major concern faced by the big data in cloud environment. This paper proposed the authentication scheme for preserving the security and privacy of the big data in cloud environment. This paper considered the medical big data processing from the telemedicine system as the big data. The medical big data consists of huge volumes of medical records which are stores in the cloud. In the proposed mechanism follows mutual authentication and performs encryption operation between user and cloud environment. The proposed authentication mechanism has less computational load compared to the existing algorithms.

This paper is a unique effort to resolve the scaling issues of machine learning using the multi-node environment of Big Data. For this purpose, it incorporates the concept of machine learning with big data. Machine learning is a branch of Artificial Intelligence which trains the computers to learn without being explicitly programmed. Machine learning works on the development of computer programs and software that work according to the input dataset. In machine learning, classification is used to identify the class of instances. In this the category or class or a group provides the new observation to which it belongs on the basis of the training dataset. Training datasets contain those instances whose class is already known. An algorithm that implements classification is known as a classifier. The main aim of this paper is to evaluate how different classifiers work on two different datasets, one is a real time dataset of MP Election nomination (2018), while the other is a standard Breast Cancer dataset, released by University of Wisconsin (1995). There are various classifiers available like Decision Trees, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Naive Bayes etc. Different classifiers work differently on different datasets, giving different accuracy according to the kind and the size of the dataset. So we have used different classifiers for our work. When we were applying machine learning classifier on a real-time dataset, i.e. MPSEC dataset, Accuracy goes down. Similarly, these classifiers apply to Standard Breast Cancer dataset then results show outstanding accuracy. This research work shows the exciting patterns to study the case of a real-time and standard dataset using different machine learning classifiers with a combination of big data. This research work also incorporates the concept of parallelization using a multi-nodes environment which is able to deal with scalability issues.

Road surface irregularities not only has an impact on consistency of road, but also affects driver safety, automobile mechanical stability, and fuel consumption. To order to evaluate road roughness and spot potholes, many solutions have been suggested to remotely track the road surface quality. Some of these methods use an embedded mobile accelerometer to take a crowd sensing view to feel the surface of road. Although crowd sensing has various advantages such as ubiquity and less cost, it has some sensitivity to the problems generated by structures made by human, actions of the driver, and characteristics of the road surface which cannot be identified road anomalies. For this purpose, this paper suggests method of deep learning which helps us (a) to identify the different types of surface of road and (b) to differentiate potholes automatically from destabilizations in the crowd sensing-based application sense arising from speed bumps or driver behavior. This paper study and implement different deep learning frameworks in particular: convolutionary neural networks, LSTM networks, and computational structures for reservoirs. The tests were performed using knowledge from the real world, and the results revealed good precision in solving both of the problems.

As a result of accidents or other injuries, bone fractures are becoming more common in our country. According to the India market survey study, fracture cases are becoming more common in Indian hospital records. The incidence rising by over 4.4 lakh in the last three decades and projected to reach over 6 lakh by 2020. The authors of this paper attempted to explain various forms of fracture detection techniques. This paper is folded into six halves. First, we will go through the introduction and data preparation step. Second, we discussed related work on fracture detection so far. Third, we look at different feature extraction methods that may be used to diagnose bone fractures. Fourth, we look at both traditional and deep learning-based methods for detecting bone fractures. Fifth, we looked at performance evolution approaches for determining the correctness of various algorithms. Sixth, we go through the many concerns and obstacles that researchers confront when working with fracture detection. The majority of authors are only concerned about whether the bone is broken or not, with very few concentrating on the classification of bone fractures. This paper aims to aid researchers in developing models that can automatically detect and classify fractures in human bones by providing a preliminary decision support system.

Malware propagation by adversaries has witnessed many issues across the globe. Often it is found that malware is released in different countries for monetary gains. With the proliferation of malware spreading activities, it is made possible that now we have malware patterns that are used for training machine learning models. Thus machine learning became indispensable for malware detection. The traditional machine learning models have limitations in performance as the training depth is limited. The emergence of deep learning models paved way for more training possibilities and improvement in detection accuracy with least false positives. This paper reviews literature on deep learning techniques that are used for malware detection. The deep learning methods used for malware detection include CNN, RNN, LSTM and auto encoders. LSTM is found to have memory in the cell to have better possibilities. Auto encoders are found to have better unsupervised approach with encoding and decoding to arrive at abnormalities (malware) detection. There are many contributions found using machine learning and deep learning towards Android malware detection. This paper provides knowledge that leads to further research in deep learning which is essential to improve the state of the art.

In this paper Telematics and employments of Telematics in vehicles is presented. Advancement is developing every year and is used by planners of the structure in various edges for the duration of regular day to day existence. Telematics system is making in different perspectives. Apparently it gives a lot of organizations in human lives. It makes life snappier and lessening setbacks in vehicle. Telematics data gives the reason to perceiving how far, how rapidly and underneath what conditions an individual drives, similarly as a foundation for logically propelled data showing improvement. This limit is developing new and innovative things that even more correctly esteem peril and attract beneficial new customers. By then, it explains how this structure works. It is like manner tends to the favored viewpoint and block of the system. This paper in like manner fuses the issues related to the telematics structure. Finally, it will see logical examination and wrap up this paper.

The concept of Cyber Physical System (CPS) is widely used in different industries across the globe. In fact, it is the holistic approach towards dealing with cyber space and physical environments that do have inter-dependencies. In the existing systems, there was a separate approach for security of the two worlds (cyber and physical). However, it does not provide necessary security when security is employed independently. The research in this paper identifies the need for integrated security for CPS. Besides it throws light into different security challenges associated with CPS and the countermeasures that existed based on machine learning and deep learning techniques that come under Artificial Intelligence (AI) and data science. From the review of literature, it is understood that data science perspective is suitable for protecting CPS with required adaptive strategy. This paper provides several useful insights related to security analysis of CPS using machine learning. It paves way for further investigation and realize a comprehensive security framework to protect CPS from internal and external cyber-attacks.

This paper targets to transmit industrial data from one sensor node to another sensor node, wirelessly and securely. However as the nodes have Internet access and are typically positioned in an unsupervised context with minimal individual intervention; security and seclusion are the prime challenges in Industrial Wireless Sensor Networks (IWSN). To use IWSN, a user must be able to get real time information straight from the source nodes and this is accomplished by Radio Frequency Identification (RFID). It yields a cost-effective solution of identification problems, but it frequently suffers from diverse safety and privacy difficulties. This paper resolves the issues of RFID systems using Global System for Mobile Communications (GSM) module that generates a One-Time Password (OTP) for accessing the data, and thereby ensures user's privacy and security in Industrial Wireless Sensor Network. Furthermore, eight evaluation criteria have been met by the proposed system surpassing the existing systems.

Over a last few decennary free space optical communication (FSO) have become more interesting alternative to the radio frequency for communication. The FSO is emanating technic that have been found the applications in different place of shorter & longer distance communication. The any technology, of FSO has been worked that much good in the several applications rather than other. In this paper we will examine FSO from different angles. The analyses this paper accompany of the technology has shown that FSO technologies inherent strengths and it lack the use of in ground cables which make it much expeditious & cheaper to installing, and the facts that will operates in the unlicensed spectrum, it will be removed & installed elsewhere and it has relatively high bandwidth. Specifically, this paper found that because FSO uses air as a communication medium, its performance & reliability are restricted by fog, rain, snow and dust. Maximum range of FSO may be in kilometers only. The applications that FSO technology seems most suited to is clear this pap rather and short distance connection establishment, like interrelation to broadband networks backbone & backbone links bet this papered building in the metropolitan area networks (MAN) or the campus area networks (CAN).

The first thing addressed in this paper is Plant fruit recognition and its feature extraction, it plays vital role in Agriculture. The point is to construct an exact, quick and solid framework utilizing CNN realities. Automatic fruit detection may reduce human efforts (counting number of fruits and manual identification). The second thing addressed in this paper is plant classification to avoid similarities in many plant fruits. Fruit classification may help fruit sellers to identify and to differentiate various kind of fruits having same similarities. The proposed framework has applied convolutional Neural Net (CNN) to the undertakings of distinguishing natural fruit pictures. In any case, deep learning has been shown as of late to be an extremely incredible picture identification procedure, and CNN is a best in class way to deal with deep learning.