Search

Objective Our aim was to develop an efficient search strategy for prognostic studies and clinical prediction guides (CPGs), optimally balancing sensitivity and precision while independent of MeSH terms, as relying on them may miss the most current literature. Materials and Methods We combined 2 Hedges-based search strategies, modified to remove MeSH terms for overall prognostic studies and CPGs, and ran the search on 269 journals. We read abstracts from a random subset of retrieved references until ≥ 20 per journal were reviewed and classified them as positive when fulfilling standardized quality criteria, thereby assembling a standard dataset used to calibrate the search strategy. We determined performance characteristics of our new search strategy against the Hedges standard and performance characteristics of published search strategies against the standard dataset. Results Our search strategy retrieved 16 089 references from 269 journals during our study period. One hundred fifty-four journals yielded ≥ 20 references and ≥ 1 prognostic study or CPG. Against the Hedges standard, the new search strategy had sensitivity/specificity/precision/accuracy of 84%/80%/2%/80%, respectively. Existing published strategies tested against our standard dataset had sensitivities of 36%–94% and precision of 5%–10%. Discussion We developed a new search strategy to identify overall prognosis studies and CPGs independent of MeSH terms. These studies are important for medical decision-making, as they identify specific populations and individuals who may benefit from interventions. Conclusion Our results may benefit literature surveillance and clinical guideline efforts, as our search strategy performs as well as published search strategies while capturing literature at the time of publication.

Agriculture is the important aspect of country economy. Farmers employ laborers for various purposes of works to be carried out on their farm. One of the important works is to make a ridge and furrow in their agricultural fields. In general, farmers use tractors, plowing machines, and other manual methods for making ridges and furrows in the agricultural field which increases the cost of operation to produce ridges and furrows. Further, the manual method of producing ridges and furrows needs more labor and takes more number of days to complete the work. Work. It also needs a lot of physical work. [1] . Plowing machines are costly and use power. This machine is highly economical and efficient which would replace the existing manual method of removal of furrows. Further, the machine is operated by freely available solar energy. The new machine is capable of reducing the physical work and also speed up the removal of wastages along the furrow. This furrow formation machine is mainly designed and fabricated to dug the tunnel and to form the furrows along the boundary of the land and along both the sides of the crops.

In production, poor ergonomic environments not only lead to increased workloads and health hazards for employees but also tend to reduce efficiency and quality. Recently, the human-cyber-physical (HCP) system has been proposed and widely studies to meet human capabilities and limitations. However, most existing frameworks are still not adaptive enough to integrate humans into a smart production environment due to a lack of real-time individual human factors digitalization. This research proposes an HCP workstation model for comprehensive assembly resources digitalization and autonomous interaction by CPS enabling technologies. Based on this, an adaptive control system has been developed for proactive ergonomic risk mitigation. Computer vision is deployed for real-time individual ergonomic evaluation and a prototype has been set up for functional validation.

We have a tendency to enter an incredibly new age in computer technology. In the cloud, IoT can be a type of "universal world neural network" connecting various objects. IoT can be a display of intelligent connected devices and networks consisting of sensitive machines communicating with alternative machines, environments, artifacts, infrastructures, and human activity. Radio Frequency Identification (RFID) and detector network technologies may emerge to meet this new challenge. Therefore, a huge amount of data is produced, stored, and information is transformed into helpful acts that will make our lives much easier and safer. Internet connectivity is, however, provided to citizens on networks and their mobile devices in most nations, meaning that the transmission of data across the network is also much simpler and less costly.

The aim of this study is to design a fully automatic Sundry board machine for reduction of congestion in the cardboard making process and increase the quality and quantity of production. For this purpose, the manual cutting and transfer of sheet is replaced by automated mechanism. The mechanisms include the incorporation of the cutting arrangement in the cutting drum itself, deployment of air pressure to separate the sheet edge from the cutting drum, a cross cutting mechanism and a mechatronic system to control these all mechanism. The initial review is done on the semi-automatic machine followed by the requirement definition. The required components are brainstormed and individually developed. A time based relation is designed such that a microcontroller can be programmed later on for the synchronism of all the components. Thisrelation is visually represented and the entire system is now ready for assembling.

The dirty paper wiretap channel, also referred to as the Gaussian wiretap channel with noncausal channel state at the transmitter, plays an important role in wireless physical layer security. In this paper, the dirty paper wiretap channel is revisited by considering actions on the channel state, i.e., the transmitter generates an action sequence that affects the formation of the channel state. Lower and upper bounds on the secrecy capacity of this model are proposed, and numerical results indicate that the actions on the state help to increase the achievable secrecy rate of the dirty paper wiretap channel. The study of this paper offers a new option for enhancing the secrecy rates of the state-dependent wiretap channel models.

This article describes the device design and fabrication of two different configurations (flow-through and stopped-flow) of a sequential fluid delivery platform on a microfluidic paper-based device. The developed device is capable of storing and transporting reagents sequentially to the detection channel without the need for external power. The device comprises two components: an origami folding paper (oPAD) and a movable reagent-stored pad (rPAD). This 3D capillary-driven device eliminates the undesirable procedure of multiple-step reagent manipulation in a complex assay. To demonstrate the scope of this approach, the device is used for electrochemical detection of biological species. Using a flow-through configuration, a self-calibration plot plus real sample analysis using a single buffer introduction are established for ascorbic acid detection. We further broaden the effectiveness of the device to a complex assay using a stopped-flow configuration. Unlike other electrochemical paper-based sensors in which the user is required to cut off the device inlet or rest for the whole channel saturation before measurement, herein a stopped-flow device is carefully designed to exclude the disturbance from the convective mass transport. As a proof of concept, multiple procedures for electrode modification and voltammetric determination of serotonin are illustrated. In addition, the research includes an impedimetric label-free immunosensor for α-fetoprotein using the modified stopped-flow device. The beneficial advantages of simplicity, low sample volume (1 μL), and ability to perform a complex assay qualify this innovative device for use with diverse applications.

Headbox is a critical sub-process of paper machine. Headbox is a two input and two output process with significant loop interaction. Quality of the paper produced depends on its operation. Hence, its precise control is required to produce good quality paper. This paper presents design of proportional integral derivative (PID) controller for headbox using particle swarm optimization (PSO) technique. Performance and robustness of PSO - PID controller have been compared with conventional PID controllers on the basis of time response, frequency response and performance indices (ISE, IAE, ITSE and ITAE).

Chemical and biological sensing are crucial tools in science and technology. Plasmonic nanoparticles offer a virtually limitless number of photons for sensing applications, which can be available for visual detection over long periods. Moreover, cellulosic materials, such as paper, represent a versatile building block for implementation of simple, yet valuable, microfluidic analytical devices. This mini review outlines the basic theory of nanoplasmonics and the usability of paper as a nanoplasmonic substrate exploiting its features as a (bio)sensing platform based on different mechanisms depending on localized surface plasmon resonance response. Progress, current trends, challenges and opportunities are also underscored. It is intended for general researchers and technologists who are new to the topic as well as specialist/experts in the field.

Food safety and access to systematic approaches for ensuring detection of food hazards is an important issue in most developing countries. With the arrival of paper-based analytical devices (&micro, PADs) as a promising, rapid, easy-to-use, and low-cost analytical tool, we demonstrated a simple microfluidic-based titration study for the analysis of packaged fruit juices. Similar, to the titration experiments using traditional glassware in chemistry laboratories, in this study the titration experiments were developed using paper microfluidics for the analysis of several analytes such as pH, vitamin C, sugars, and preservatives present in the packaged fruit juices. The allergen found commonly in dairy based mixtures and the non-pathogenic biochemical component responsible for food spoilage in cider based fruit juices were also determined. The results obtained using paper microfluidics were compared with those obtained using a conventional spectrophotometric technique. Finally, a paper microfluidics based multiplexed sensor was developed for the analysis of common nutritional ingredients, an allergen, and a non-pathogenic byproduct present in packaged fruit juices on a single platform. Overall, the results presented in this study reveal that the proposed paper microfluidic assisted colorimetric multiplexed sensor offers a quick and reliable tool for on-spot routine analysis for food safety applications.

BackgroundPeriodic demographic health surveillance and surveys are the main sources of health information in developing countries. Conducting a survey requires extensive use of paper-pen and manual work and lengthy processes to generate the required information. Despite the rise of popularity in using electronic data collection systems to alleviate the problems, sufficient evidence is not available to support the use of electronic data capture (EDC) tools in interviewer-administered data collection processes. ObjectiveThis study aimed to compare data quality parameters in the data collected using mobile electronic and standard paper-based data capture tools in one of the health and demographic surveillance sites in northwest Ethiopia. MethodsA randomized controlled crossover health care information technology evaluation was conducted from May 10, 2016, to June 3, 2016, in a demographic and surveillance site. A total of 12 interviewers, as 2 individuals (one of them with a tablet computer and the other with a paper-based questionnaire) in 6 groups were assigned in the 6 towns of the surveillance premises. Data collectors switched the data collection method based on computer-generated random order. Data were cleaned using a MySQL program and transferred to SPSS (IBM SPSS Statistics for Windows, Version 24.0) and R statistical software (R version 3.4.3, the R Foundation for Statistical Computing Platform) for analysis. Descriptive and mixed ordinal logistic analyses were employed. The qualitative interview audio record from the system users was transcribed, coded, categorized, and linked to the International Organization for Standardization 9241-part 10 dialogue principles for system usability. The usability of this open data kit–based system was assessed using quantitative System Usability Scale (SUS) and matching of qualitative data with the isometric dialogue principles. ResultsFrom the submitted 1246 complete records of questionnaires in each tool, 41.89% (522/1246) of the paper and pen data capture (PPDC) and 30.89% (385/1246) of the EDC tool questionnaires had one or more types of data quality errors. The overall error rates were 1.67% and 0.60% for PPDC and EDC, respectively. The chances of more errors on the PPDC tool were multiplied by 1.015 for each additional question in the interview compared with EDC. The SUS score of the data collectors was 85.6. In the qualitative data response mapping, EDC had more positive suitability of task responses with few error tolerance characteristics. ConclusionsEDC possessed significantly better data quality and efficiency compared with PPDC, explained with fewer errors, instant data submission, and easy handling. The EDC proved to be a usable data collection tool in the rural study setting. Implementation organization needs to consider consistent power source, decent internet connection, standby technical support, and security assurance for the mobile device users for planning full-fledged implementation and integration of the system in the surveillance site.

Paper-based sensor is a new analytical technique applicable in healthcare monitoring, environmental monitoring, food quality control, etc. Currently, these technologies are drawing great attention due to ease of fabrication, cost-effectiveness, and their simple, portable, and disposable nature. The inherent qualities of paper, such as availability in large quantities, cheap, lightweight, and biodegradability, are being exploited to develop sustainable devices. The application of paper as a substrate material in litmus paper was dated back to seventeenth century. The semiquantitative detection of glucose in urine and immune chromatographic paper test strips for pregnancy test kits are examples of paper-based sensing devices. Today, microfluidic paper-based sensors with advanced designs involving complex 3D geometrics for handling multiples analytes are under investigation. It is foreseen that advancements in the fabrication and analytical techniques will help in improving the accuracy and sensitivity of paper-based sensors. Different types of detection approaches used in microfluidic paper-based devices are colorimetry, luminescence, electrochemical detection, fluorescence, and surface-enhanced Raman scattering.

Paper-based devices has gained widespread applications with its unique properties like affordability, rapid detection, user-friendliness, sensitivity, specificity etc. These low costs devices are considered as powerful tool for point of care testing because of the inherent microfluidic nature, disposability, flexibility, and ability to store and analyze the sample. With the advancement in fabrication techniques and the requirement of real-time diagnosis and health monitoring, paper-based devices also emerged in the field of wearable diagnostic applications. Depending upon the biocompatibility, paper-based devices are integrated with different materials to be used as in vitro and in vivo wearable devices. In this chapter, we will cover the principles of the paper-based devices along with the evolution of the wearable devices and also briefly review the recent development in the wearable diagnostic devices.

Brazilian Forensic Scientists are frequently asked about ballpoint pen manuscripts chronology in documents suspected to be forged. In the recent years, many methods like quantification of 2-PE (2-phenoxyethanol) have been developed on this subject. The validation of these methods only recently has been a concern between scientific forensic community. Researchers studied the behavior of ink on ageing, but few of them concerned about the influence of the paper on the extraction of 2-PE. This study performed the figures of merit evaluation of the quantification of 2-PE using GC/MS, testing the main parameters involved in the reliability of the method (linearity, repeatability, limits of detection and quantification, accuracy and robustness). After, based on a full factorial design with four factors and two repetitions, the authors tested kind of paper, grammage of paper, ink color and three ink ages, to verify the paper influence on the quantity of 2-PE from ink, trough GC/MS analysis. The merit parameters evaluation showed that the method is linear, precise, accurate and robust. The results for the effect of paper showed main effects of the factors and the existence of interactions effects between the kind of paper and paper grammage, and between the kind of paper and other factors.

Game-based learning represents a promising alternative to teach computing in higher education. This paper presents “BDD Assemble!”, a paper-based game proposal for teaching Behavior Driven Development (BDD) competences. For this, the proposed game and the evaluation approach with software engineering students are described. As a result, a simple, interactive and colaborative game was provided, able to teach BDD concepts in a practical, competitive and fun way.

Paper-based analytical devices (PADs) are simple, convenient, and portable devices that can detect selected substances and quickly display results. PADs are being adopted at increasing rates for use in an expanding array of fields including biochemistry, drug test assaying, and environmental analysis. Compared with traditional analytical methodology, PADs provide several advantages including the following: (1) the capacity for easy mass production; (2) portability; (3) low cost; and (4) small sample volume requirements. These features position PADs as significantly useful tools in rural areas and developing countries, where sophisticated laboratories are limited. In addition to these advantages, PADs offer great potential for integration with the Internet and new artificial intelligence technology. Integrating what may be called “lab-on-a-paper” technology with the Internet of Things offers new opportunities for individuals, in the lab, in the field, and even at home, to carry out advanced examinations and analyses of a variety of things including point-of-care health diagnostics, health status, and environmental quality. In this way, PADs can significantly improve quality of life for a great number of people.

Many companies across several industries have made the switch from paper to electronic documentation with the goals of improving efficiency, lowering costs, and providing employees with ways to easily access important information all at their fingertips. In the aviation industry, Electronic Flight Bags (EFBs) have transformed all required inflight, paper documentation into digital formats that are easily accessible to pilots on the flight deck. Unfortunately, in the development of first generation EFB documentation applications, and many other document-rich applications in industry, little attention has been paid to human factors design principles, resulting in applications that are inefficient and can burden user performance. This paper describes a usability analysis that was performed on a current EFB application used by a major U.S. air carrier, and provides redesign recommendations to increase usability of the EFB and streamline pilot performance. These results provide insight into potential problems to avoid in future flight deck application design, as well as for applications that serve to digitize paper-based process. Additionally, the results provide guidelines to help ensure effective and efficient user interfaces.

Mass unbalance is a major concern in modern day rotating machinery. For single and double disc rotor bearing systems, model based techniques have been used to identify the unbalance. The aim of this research is to predict the unbalance in a similar way for a paper machine’s tube roll using its virtual model. In this case study, the rotor is a large diameter continuous tube with thin walls and without any mounted disc. To identify the unbalance magnitude and phase, modal expansion and equivalent load minimization by least squares is used. The distributed equivalent load is sorted using two different methods. The first method considers only the isolated maximum load. In the second method, the load at all the nodes are taken into account. This first method is tested on a single disc rotor bearing system from literature and the unbalance parameters were predicted with good accuracy. However, for the paper machine’s tube roll, due to its continuous structure, the equivalent load is distributed more evenly across the rotor. Therefore the second method is able to predict the unbalance parameters with better accuracy.

The fabrication and performance of a wearable paper‐based chemiresistor for monitoring perspiration dynamics (sweat rate and sweat loss) are detailed. A novel approach is introduced to measure the amount of aqueous solution in the order of microliters delivered to the sensor by monitoring a linear change in resistance along a conducting paper. The wearable sensor is based on a single‐walled carbon nanotubes and surfactant (sodium dodecylbenzenesulfonate) nanocomposite integrated within cellulose fibers of a conventional filter paper. The analytical performance and the sensing mechanism are presented. Monitoring sweat loss in the human body while exercising is demonstrated using the integration of a wireless reader and a user‐friendly interface. By addressing the barriers of cost, simplicity, and the truly in situ demanding measurements, this unique wearable sensor is expected to serve in the future in many different applications involving the on‐body detection of biofluids, such as a monitoring tool of dehydration levels for athletes as well as a tool for enhancing the sport performance by providing an accurate recovery of the hydration status in daily exercises.

Triboelectric nanogenerators (TENGs) have been investigated as a promising methodology to harvest mechanical energy into electricity. The matching impedance and the transient property of TENGs are important for their practical application. In this work, we proposed novel paper-based methodology for investigation of TENGs. First, we propose a novel method for triggering transience of pencil-on-paper (POP)-TENGs using household microwave oven and commercial graphite pencil. Moreover, a fast, simple and cost-effective approach for measuring the matching impedance of TENG based on paper variable resistor is proposed. The results of this work are beneficial for simple, fast and cost-effective functionalization of TENGs. Keywords: Triboelectric nanogenerator (TENG), Matching impedance

Food safety remains one of the most important issues in most countries and the detection of food hazards plays a key role in the systematic approach to ensuring food safety. Rapid, easy-to-use and low-cost analytical tools are required to detect chemical hazards in foods. As a promising candidate, microfluidic paper-based analytical devices (μPADs) have been rarely applied to real food samples for testing chemical hazards, although numerous papers have been published in this field in the last decade. This review discusses the current status and concerns of the μPAD applications in the detection of chemical hazards in foods from the perspective of food scientists, mainly for an audience with a background in mechanical and chemical engineering who may have interests in exploring the potential of μPAD to address real-world food safety issues.

Tetracycline is widely used as medicine for disease treatments and additives in animal feeding. Unfortunately, the abuse of tetracycline inevitably causes tetracycline residue in animal-origin foods. Though classical methods can detect tetracycline in high sensitivity and precision, they often rely on huge and expensive setups as well as complicated and time-consuming operations, limiting their applications in rapid and on-site detection. Here, we propose a handheld inkjet printing paper chip based smart tetracycline detector: tetracycline can be determined by inkjet printing prepared paper chip based enzyme-linked immunosorbent assay (ELISA) with the advantages of high sensitivity, excellent specificity and low cost, moreover, a smartphone based paper chip reader and application is designed for automatically determining tetracycline with simple operations, high precision and fast speed. The smart tetracycline detector with a compact size of 154 mm × 80 mm × 50 mm and self-supplied internal power can reach a rather low detection limit of ~0.05 ng/mL, as proved by practical measurements. It is believed the proposed handheld inkjet printing paper chip based smart tetracycline detector is a potential tool in antibiotic sensing for routine uses at home and on-site detection in the field.

SaaS (Software-as-a-Service) is a cloud computing model, which is sometimes referred to as “on-demand software”. Existing SaaS platforms are investigated before building new distributed SaaS platform. The service data mining and evaluation on existing SaaS platforms improve our new SaaS platform. For SaaS that provide various APIs, we analysis their website data in this paper by our data mining method and related software. We wrote a crawler program to obtain data from these websites. The websites include Baidu API and ProgrammableWeb API. After ETL (Extract-Transform-Load), the obtained and processed data is ready to be analyzed. Statistical methods including non-linear regression and outlier detection are used to evaluate the websites performance, and give suggestions to improve the design and development of our API website. All figures and tables in this paper are generated from IBM SPSS statistical software. The work helps us improve our own API website by comprehensively analyzing other successful API websites.

Technology is shaping our lives in a multitude of ways. This is fuelled by a technology infrastructure, both legacy and state of the art, composed of a heterogeneous group of hardware, software, services, and organisations. Such infrastructure faces a diverse range of challenges to its operations that include security, privacy, resilience, and quality of services. Among these, cybersecurity and privacy are taking the centre-stage, especially since the General Data Protection Regulation (GDPR) came into effect. Traditional security and privacy techniques are overstretched and adversarial actors have evolved to design exploitation techniques that circumvent protection. With the ever-increasing complexity of technology infrastructure, security and privacy-preservation specialists have started to look for adaptable and flexible protection methods that can evolve (potentially autonomously) as the adversarial actor changes its techniques. For this, Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) were put forward as saviours. In this paper, we look at the promises of AI, ML, and DL stated in academic and industrial literature and evaluate how realistic they are. We also put forward potential challenges a DL based security and privacy protection system has to overcome. Finally, we conclude the paper with a discussion on what steps the DL and the security and privacy-preservation community have to take to ensure that DL is not just going to be hype, but an opportunity to build a secure, reliable, and trusted technology infrastructure on which we can rely on for so much in our lives.

PURPOSE Generalizability, reproducibility and objectivity are critical elements that need to be considered when translating machine learning models into clinical practice. While a large body of literature has been published on machine learning methods for segmentation of brain tumors, a systematic evaluation of paper quality and reproducibility has not been done. We investigated the use of “Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis” (TRIPOD) items, among papers published in this relatively new and growing field. METHODS According to PRISMA a literature review was performed on four databases, Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL) and Web of science core-collection first in October 2020 and a second time in February 2021. Keywords and controlled vocabulary included artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. The publications were assessed in order to the TRIPOD items. RESULTS 37 publications from our database search were screened in TRIPOD and yielded an average score of 12.08 with the maximum score being 16 and the minimum score 7. The best scoring item was interpretation (item 19) where all papers scored a point. The lowest scoring items were the title, the abstract, risk groups and the model performance (items number 1, 2, 11 and 16), where no paper scored a point. Less than 1% of the papers discussed the problem of missing data (item 9) and the funding of research (item 22). CONCLUSION TRIPOD analysis showed that a majority of the papers do not score high on critical elements that allow reproducibility, translation, and objectivity of research. An average score of 12.08 (40%) indicates that the publications usually achieve a relatively low score. The categories that were consistently poorly described include the ML network description, measuring model performance, title details and inclusion of information into the abstract.

Catalogue of billions of phrases from 107 million papers could ease computerized searching of the literature. Catalogue of billions of phrases from 107 million papers could ease computerized searching of the literature.

This companion paper is to support the replication of paper "Campus3D: A Photogrammetry Point Cloud Benchmark for Outdoor Scene Hierarchical Understanding", which was presented at ACM Multimedia 2020. The supported paper's main purpose was to provide a photogrammetry point cloud-based dataset with hierarchical multilabels to facilitate the area of 3D deep learning. Based on this provided dataset and source code, in this work, we build a complete package to reimplement the proposed methods and experiments (i.e., the hierarchical learning framework and the benchmarks of the hierarchical semantic segmentation task). Specifically, this paper contains the technical details of the package, including file structure, dataset preparation, installation package, and the conduction of the experiment. We also present the replicated experiment results and indicate our contributions to the original implementation.

In our MM'20 paper,, we presented a Kalman filter-based approach for prediction of head motion in 6DoF. The proposed approach was employed in our cloud-based volumetric video streaming system to reduce the interaction latency experienced by the user. In this companion paper, we present the dataset collected for our experiments and our simulation framework that reproduces the obtained experimental results. Our implementation is freely available on Github to facilitate further research.

In this paper we reproduce experimental results presented in our earlier work titled "Describing Subjective Experiment Consistency by $p$-Value P-P Plot" that was presented in the course of the 28th ACM International Conference on Multimedia. The paper aims at verifying the soundness of our prior results and helping others understand our software framework. We present artifacts that help reproduce tables, figures and all the data derived from raw subjective responses that were included in our earlier work. Using the artifacts we show that our results are reproducible. We invite everyone to use our software framework for subjective responses analyses going beyond reproducibility efforts., Comment: Please refer to the original publication: https://dl.acm.org/doi/10.1145/3474085.3477935 Related paper: https://dl.acm.org/doi/abs/10.1145/3394171.3413749 or arXiv:2009.13372

The industry of disposables has undergone an astronomically immense revolution in this century owing to the diligent lifestyles and technological advancements in its engenderment 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 acclimated to imbibe water as well as beverages, soups, liquids etc. This paper entirely fixates on incrementing the quality factor of engenderment and manufacturing process of these paper cups by integrating adscititious circuitry to the subsisting machinery to truncate waste of raw materials as well as the per unit cost involved in wastage of electricity. There is a necessity to improvise the quality of overall production parameters using automation techniques. Replacing the whole machinery is a very high costing implementation hence economic feasibility of automation can be achieved by adding an additional circuitry and improving the overall perfoemance. This research work is predicated on utilizing proximity sensors to sense the materials and molded paper cups in front of machinery and minimize the electric wastage through a relay Mechanism assembly. This is a PLC predicated live project example of efficaciously incrementing the overall efficiency of the manufacturing plant

This chapter focuses on cost control by analyzing the intelligent control system of waste paper deinking production line, and finally designs a solution. In the aspect of control index, machine learning has designed a solution that pays less attention to the benefit of a single control loop and focuses on maximizing the overall benefit of the production line. In the aspect of control demand, according to the complexity of waste paper deinking production line, a distributed control solution is designed Finally, according to the dynamic fuzziness in machine learning system, the basic concept of dynamic fuzzy machine learning is constructed by using dynamic fuzzy set The dynamic fuzzy machine learning model is described and the related learning algorithm is given. Finally, a simulation example is given, and satisfactory results are obtained by comparing with the elastic BP algorithm RPROP of neural network.

Facebook, Twitter, Instagram, and LinkedIn are all popular online social media sites these days. Everyone uses different social media platforms, from children to adults. The use of these social media applications is increasing, which leads to a rise in social media crime. Here is where the word “fake profiles” comes into play; these fake profiles are responsible for distributing misleading details about a particular user or attempting fraudulent activities with bad intentions. Many researchers have been carried in response to the issue of fake profiles that exist in any social media application. There is research available in different directions to get a real problem solution. This paper summarizes all of the research on fake profile identification in social media platforms. This paper gives survey of work, indicating whether the user profile is fake or real.

Paper dating analysis is an important research direction of document inspection, which is widely used in the inspection and identification of cultural relics and ancient books. This article focuses on the analysis of paper dating in ancient books, and proposes a non-destructive inspection and analysis method based on paper fiber texture images. Aiming at the global stacking morphology of paper fibers and the local features of specific types of fiber morphology, we propose a neural network-based hybrid texture feature extraction and representation method: On the one hand, we use convolutional networks to obtain global features of fiber texture; at the same time, we designed a method of extracting and representing local fiber morphological features based on the attention mechanism. By mixing the above two types of features, we realize the extraction and representation of paper fiber features. Furthermore, we use the GRU(Gate Recurrent Unit) model to establish a paper dating time series model and design a new loss function. In order to verify the method, this paper selects 36 domestic books published from 1950 to 2000, and uses the document checker VSC 6000 to collect paper texture images as a dataset, and verifies the effectiveness of the proposed method on this dataset. Experiments prove that the method proposed in this paper has achieved ideal results in paper dating analysis.

The facility layout is a strategic design that is used for a long time. All manufacturing industries must pay attention to the right layout to increase the productivity of the industry. A prayer paper manufacturing industry located in the Tanjung Morawa area, Medan has an error in the placement of raw materials and the placement of production machines, so that the distance from the temporary warehouse to the printing and cutting work stations is far apart, causing high material handling costs. Seeing these problems, research was carried out to improve the layout of the facilities and redesign. The method used for this research is Systematic Layout Planning (SLP), which is one of the methods used to regulate a workplace in a factory by using two areas with high frequency and logical relationships with each other. And the Computerized Relative Allocation of Facilities Technique (CRAFT) Algorithm is a repair program, which is a program that looks for optimal design by making gradual improvements to the layout. CRAFT evaluates the layout by interchanging departmental locations. Inputs required for the CRAFT algorithm include initial layout, data flow or frequency of movement, cost data per unit distance, and the number of departments that do not change or remain. The CRAFT method is usually applied using Quantitative Systems (QS) software. By comparing the layout between SLP and CRAFT, the optimal result is obtained using the SLP method by reducing the distance between departments by 1.407 meters or a distance efficiency of 39.91%.