Data Gathering System Research Articles

Intelligent Fault Diagnosis in Industrial Machinery: Leveraging AI with LSTM Autoencoder for Enhanced Fault Detection

Machinery Fault Detection (MFD) is an important process in contemporary industrial systems, where it predicts possible physical failures before they lead to a serious problem. This uses multiple technologies to monitor machine statuses (algorithms, data gathering systems and sensors) Using a servo-motor driven actuator for deployment, the Locking Mechanism is pre-assembled into an OEM ATE and will enable predictive failure mode identification (via monitoring and warnings of operational parameters i.e., vibration, temperature or auditory signals in-built to MFD systems) leading to Prophylactic maintenance before critical bottlenecks can occur. The dataset we used in our study was collected from Kaggle and it is called the SpectraQuest Machinery Fault Simulator (MFS) Alignment-Balance-Vibration (ABVT). We used LSTM Autoencoder, KNN, SVM and DNN to analyzed the data. Our LSTM Autoencoder model was very accurate and achieved a precision, recall, accuracy and F-score of 99%. We worked on very large scale datasets. It will help the system detect faults and predict their evolution over time, so you save maintenance costs and increase production in your factory. More research on the practical efficiency of these models in real-time across different industrial settings can create a path towards improved and scalable MFD solutions.

Continuous data-gathering system on migrants: a critical review

The investigation examines the continuous system as one of the two most common forms of migrant data-gathering today. The system is defined as a set of tools that obtains migration data on changes in the registration of the place of previous residence within the country or immigration record, including the total number of individuals entering or leaving the country and the type of visa issued to them. The strengths of the continuous system are noted, which lie in its ability to generate data on a regular basis and be useful to the authorities at the national level during the management of migration challenges and forecasting of various processes. The factors that influence the rejection of this data-gathering form today are highlighted, such as: 1) difficulty of conducting a comparative analysis between countries; 2) blurring of the entry category, which makes its unification problematic; 3) issues with monitoring of persons with unsettled (legal) status; 4) lack of recording of undocumented individuals in the continuous record of migrants; 5) system bias in focusing on emigration, ignoring the dimension of immigration. The idea about the lower level of use of the continuous system in our time due to the need for large financial resources, technological support, real agreement and coordination of joint actions between the governments of national states is substantiated as an unlikely scenario, taking into account the political, economic, cultural and social, etc. differentiation in the world. Attention is paid to the issue of prospects for the implementation of a continuous data-gathering system in the future based on the analysis of recent research and changes in the status of migration in the context of sustainable development. It is emphasized that, given the current challenges, the form will be used in the future, continuously recording data on displaced persons, but not claiming primacy in the process of systematizing information on global flows of migrants.

Learning-based methods for adaptive informative path planning

Adaptive informative path planning (AIPP) is important to many robotics applications, enabling mobile robots to efficiently collect useful data about initially unknown environments. In addition, learning-based methods are increasingly used in robotics to enhance adaptability, versatility, and robustness across diverse and complex tasks. Our survey explores research on applying robotic learning to AIPP, bridging the gap between these two research fields. We begin by providing a unified mathematical problem definition for general AIPP problems. Next, we establish two complementary taxonomies of current work from the perspectives of (i) learning algorithms and (ii) robotic applications. We explore synergies, recent trends, and highlight the benefits of learning-based methods in AIPP frameworks. Finally, we discuss key challenges and promising future directions to enable more generally applicable and robust robotic data-gathering systems through learning. We provide a comprehensive catalog of papers reviewed in our survey, including publicly available repositories, to facilitate future studies in the field.

Retrospective data-gathering system on migrants and its generic methodological limitations: critical review

Retrospective data-gathering system on migrants and its generic methodological limitations: critical review

Maternal and perinatal health research during emerging and ongoing epidemic threats: a landscape analysis and expert consultation

IntroductionPregnant women and their offspring are often at increased direct and indirect risks of adverse outcomes during epidemics and pandemics. A coordinated research response is paramount to ensure that this...

Seismic survey of steeply inclined coal seams using the vertical data acquisition system

Prior to the establishment of a coal mine, a high-resolution three-dimensional (3D) seismic survey was carried out. For seismic exploration of steep coal seams with an inclination angle more than 45°, the selection of the data gathering system is essential. The time disparity that remains when the traditional acquisition technology acquires 3D seismic data will be rather significant. The relationship between coal seam inclination, remaining time difference, and reflection coefficient is investigated in light of the presence of steeply inclined coal seams. When the direction of the generation surveying line is parallel to the coal seam, it is suggested as a vertical data acquisition system. To create a vertical data gathering system, the point is situated on the down-dipping side of the coal seam and a predetermined distance is kept between shooting points and detection points. A vertical data collection system was used to implement 3D seismic data acquisition in the Muli Coalfield in northern Qinghai. As a solid foundation for the future processing and analysis of 3D seismic data, valuable observation data with high resolution were successfully obtained. The study came to the conclusion that a vertical data collecting system is helpful in promoting effective coal mine output.

Research and Development Investment of Would-Be Star Biotech Firms in a Financial Crisis

While biotech firms have comparative advantage in innovative and quick drug discoveries to big pharmaceutical companies, the so-called “valley of death,” or the negative-profit cleft is becoming deeper and longer. So what is the necessary research and development (R&D) investment of future successful biotech firms if their financial condition is negative profit in financial crisis related with coronavirus or real estate? We will use an open data source, the United States Securities and Exchange Commission's Electronic Data Gathering, Analysis, and Retrieval system database. The main finding obtained through the analysis of a Bayesian Markov chain Monte Carlo regression model suggests that fiscal year (FY)2008 negative-profit and FY2018 Star—the highest total shareholders’ equity values in FY2018 Pareto distribution—biotech firms’ R&D investment productivity was higher than that of FY2008 positive-profit and FY2018 Star counterparts. And there was a clear difference between would-be Star and would-be non-Star biotech firms in their R&D investment characteristics during financial crises.

Legal particularities of AI technology usage in real-world data formation

In recent decades, technological progress has contributed to a consistent change in approaches to organizing the provision of medical care in various regions of the world. Electronic data-gathering systems make it possible to create extensive information databases about the health status of the population of certain territories or entire states. The introduction of technological solutions based on the use of artificial intelligence (AI) technologies makes it possible to provide a systematic analysis of large volumes of information, as well as to develop new treatment methods of life-threatening diseases. The use of AI technologies not only has significant potential for improving the organization of medical care, but also brings essential risks of human rights restriction, it may also form discriminatory practices or even cause harm to health. The authors demonstrate the importance of AI technologies in improving separate stages of medical care and the circulation of healthcare technologies, and also present various approaches to defining the notion of “artificial intelligence”, that is a crucial element in specifying the object of legal regulation. The article systematizes the list of threats and challenges to human security associated with the use of AI technologies. The development of legal regulation of this sphere at the national (United States of America) and supranational (European Union) levels is analyzed, and also the main directions of development of this field in the Russian Federation are presented.

Effective decision support systems in clinical practice and prevention: literature review

Clinical decision support systems (CDSS) often outperform human capabilities for processing a large amount of information, dramatically simplifying the work of specialists and avoiding medical errors. The implementation of such systems is a complex task that requires high-tech developments. The annual increase in the development of such systems has a geometric progression. However, it is unclear if most of them will be integrated into clinical practice and recommendations. The use of CDSS to address various disease diagnosis, treatment, and prevention issues is demonstrated, and possible linkages between scientific clinical observations and CDSS are examined. Currently, many data gathering and processing systems use machine learning algorithms and convolutional technologies to create CDSS, resulting in data that exceeds the ability of human thinking to determine the logic of recommended decisions. This study presents the most studied modern CDSS, the possibilities of their application, and the implementation issues.

A smart and sustainable framework for millet crop monitoring equipped with disease detection using enhanced predictive intelligence

Today, there is a visible reduction in productivity of common crops like rice and wheat in agricultural sector. At the same time, millet crops have emerged as an alternative to counter global food security issues because of their adaptability and nutritional value that can combat malnutrition. Also, because of its low moisture requirement and tolerance to extreme climatic conditions, it is perceived as a stable cereal. But its cultivation is affected by diseases like rust and blast, thereby causing harm to the farming economy. Conventional operational methods of disease detection require regular manual intervention which are also costly. The correctness of expert suggestions is also under scrutiny. Thus, a sustainable, robust and low cost modern approach for millet crop monitoring and disease detection is required. This research aims to develop a smart and sustainable framework by integrating Internet of things (IoT) and deep learning (DL). In the presented framework, a sensory module based automated crop health data gathering system with an improved deep learning-assisted intelligent disease recognition model is developed for the millet crop. Sensors collect data from millet farmland transfer the crop data readings to the cloud server for storage and Raspberry Pi for further detection. Any abnormality in reading leads to an alert notification communicated to the farmer. A hybrid predictive model, Customized Convolutional neural network (Customized-CNN) model works with the Raspberry Pi to predict the presence of blast and rust disease symptoms in millet. The proposed sustainable model is implemented, and it generates productive outcomes. The recorded accuracy, precision, recall, and f-score values with the customized CNN model were 98.8%, 98.2%, 97.4%, and 97.7%, respectively. The training and testing delays were only 67 s and 88 s, respectively. A sample of yearly sensor readings was generated to show the accuracy and reliability of the model’s data collection. Also, the model proved to be scalable, as it gave noteworthy performance with diverse crops. The evaluation shows the reliability of the model, and it can be used by farming societies to enhance millet cereal yield in a cost-effective way.

Specifics of the research program for offshore fields: The case of the Piltun-Astokh oil and gas condensate field of the Sakhalin II project

This paper describes some features of data gathering program for offshore field with comparison to onshore field-analogue. The findings are based on well and reservoir management experience that was gained during the Piltun-Astokh field development operated under the Sakhalin II project. The paper describes general elements of well and reservoir management system and current snapshot of data gathering and research activities on the Piltun-Astokh offshore field. Additionally, data gathering and analysis life cycle is described, and the importance of integrated data analysis is noted.

Fragmented Securities Regulation, Information-Processing Costs, and Insider Trading

Using a unique setting where stand-alone banks submit filings to bank regulators instead of the U.S. Securities and Exchange Commission (SEC), we examine the consequences of fragmented securities regulation for information-processing costs and opportunistic insider trading. We find the market reaction to insider-trading filings on FDICconnect is less timely than to those on SEC’s Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, suggesting FDICconnect generates higher information-processing costs. We also find only large investors trade more on insider-trading filings on FDICconnect than on insider-trading filings on SEC EDGAR, thus extracting benefits from the delayed market reaction to insider-trading filings on FDICconnect. Finally, we find increased insider selling in stand-alone banks prior to negative earnings news, suggesting insiders’ opportunistic use of private information. These findings collectively suggest regulatory fragmentation undermines market efficiency and distorts the level playing field. This paper was accepted by Suraj Srinivasan, accounting. Funding: This work was supported by the Eugene Lang Junior Faculty Fellowship. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4903 .

Demonstrating Retrieval of Financial Information in XBRL

The Securities and Exchange Commission (SEC) has upgraded the existing system of Electronic Data Gathering, Analysis, and Retrieval (EDGAR), to Interactive Data Electronic Applications (IDEA) platform, using eXtensible Business Reporting Language (XBRL). In January 2009, the SEC issued its final mandate for XBRL adoption and the conversion target dates for all firms. This conversion enables users to retrieve listed companies’ financial statement information at two levels, document and the data element, compared to the document level alone under the existing system of EDGAR. Beneficial to all users, this change is particularly important to resource-strapped entities such as small businesses, and universities. The result is user friendly for students researching information. With simply the industry standard tool of Microsoft Excel, without expensive proprietary XBRL modules, detailed information is now available, quickly, efficiently and at minimum cost. This paper explains the basic concept of XBRL and demonstrates the ease of the retrieval process in Microsoft Excel.

Accuracy of badminton swing action recognition based on fractional time network

To improve the recognition accuracy of badminton players’ swing movements, this study proposes a single inertial sensor based method for badminton swing movement recognition. This article proposes a badminton racket-mounted data gathering system with a single inertial sensor and proposes a real-time motion data flow-based window segmentation technique to capture motion data. On this basis, a two-layer classifier recognition model based on C4.5 Decision Tree (C4.5 T) algorithm and Random Forest (RF) method is constructed to recognize swing technical actions. Using the C4.5 T to identify the swing style of athletes; The RF method is used to recognize the swing technical action. The final experiment showed that the method studied achieved a recognition accuracy of 95.36% for six common swing movements. The proposed model has good application prospects in the recognition of badminton swing movements. However, due to the limitations of the experimental conditions, the recognition effect of this method on more complex swing movements needs to be further verified.

Pharmacological Basis for Antispasmodic, Bronchodilator, and Antidiarrheal Potential of Dryopteris ramosa (Hope) C. via In Vitro, In Vivo, and In Silico Studies.

Background:Dryopteris ramosa is used as an old treatment for several diseases. D. ramose fronds are eaten to treat gastrointestinal (GIT) issues and as an antibiotic. However, there is a dearth of literature justifying its traditional use. Aims and objectives: the current work used biological and molecular docking studies to support traditional usage and elucidate D. ramosa's multitarget mechanism. Materials and methods: Bioactive compounds were docked in silico. Force displacement transducers coupled with a power lab data gathering system examined the effects of compounds on rabbit jejunum, trachea, and aorta tissues. Albino mice and rats were used for in vivo studies. Results: Bioactive compounds interacted with inflammation, asthma, and diarrhea genes, according to in silico studies. D. ramosa crude extract (Dr.Cr) calmed impulsive contractions and K+ (80 mM)-provoked contractions in the jejunum and tracheal tissue dose-dependently, showing the presence of the Ca++ channel-blocking (CCB) effect, further verified by the rightward parallel shift of CRCs equivalent to verapamil. Polarity-based fractionation showed spasmolytic activity in Dr.DCM and muscarinic receptors mediated spasmogenic activity in the Dr.Aq fraction. Dr.Cr vasoconstricted the aortic preparation, which was totally blocked by an angiotensin II receptor antagonist. This suggests that Dr. Cr's contractile effect is mediated through angiotensin receptors. In rats and mice, it showed anti-inflammatory and antidiarrheal action. Conclusion: This study supports the traditional medicinal uses of D. ramosa against GIT disorders and may be an important therapeutic agent in the future.

Investigation of laser-textured triboelectric nanogenerator for vibration sensing of machine tools

The breakdown of any industrial mechanical system can be predicted and identified using vibration sensing. Piezoelectric material-based vibration sensors are commercially available, but their use is limited by their reliance on external power sources and intricate data-gathering systems. Recently, contact electrification-based triboelectric nanogenerators (TENGs), which are reliable, affordable, and lightweight devices, have been developed as vibration sensors. The TENG is a high-voltage output device; however, its lower current output restricts its practical applications. In this work, we report a novel laser texturing technique for output enhancement of polytetrafluoroethylene (PTFE)- and aluminum (Al)-based TENG for machinery vibration sensing applications. An Nd3+: YAG pulse laser was used for texturing the PTFE sheet. A 50% spatial spot overlap with laser fluences of 10 and 50 J cm−2 was chosen to investigate the impact on the TENG electrical output. As compared to pristine TENG, the open-circuit voltage and short-circuit current of laser-textured (LT) TENG increased from 308 V to 368 V and 12.64 µA to 19.16 µA, respectively. The TENG device was attached to a lathe and a milling machine to sense the change in vibration state with respect to various machining parameters. Moreover, the proposed LT performance-enhanced TENG has excellent potential and broad applications in the fields of machinery monitoring, fault detection, and the Internet of Things and Industry 4.0.

The Real Effects of Modern Information Technologies: Evidence from the EDGAR Implementation

ABSTRACTUsing the implementation of the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system from 1993 to 1996 as a shock to information dissemination technologies, we examine how a significant reduction in disclosure processing costs affects the real economy. We find that the EDGAR implementation leads to an increase in corporate investment and that this effect is concentrated in value firms. We provide evidence that improved equity financing and enhanced managerial incentives are likely the underlying mechanisms. Specifically, the EDGAR implementation leads to an increase in a firm's stock liquidity, a decrease in the cost of equity capital, and an increase in the level of equity financing. Consistent with the monitoring effect of broad information dissemination, the EDGAR implementation leads to an increase in a firm's operating performance. Our findings suggest that it is important to consider information dissemination beyond information production when examining the real effects of corporate disclosures.

Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks

This paper provides a comprehensive and systematic review of fault localization methods based on artificial intelligence (AI) in power distribution networks described in the literature. The review is organized into several sections that cover different aspects of the methods proposed. It first discusses the advantages and disadvantages of various techniques used, including neural networks, fuzzy logic, and reinforcement learning. The paper then compares the types of input and output data generated by these algorithms. The review also analyzes the data-gathering systems, including the sensors and measurement equipment used to collect data for fault diagnosis. In addition, it discusses fault type and DG considerations, which, together with the data-gathering systems, determine the applicability range of the methods. Finally, the paper concludes with a discussion of future trends and research gaps in the field of AI-based fault location methods. Highlighting the advantages, limitations, and requirements of current AI-based methods, this review can serve the researchers working in the field of fault location in power systems to select the most appropriate method based on their distribution system and requirements, and to identify the key areas for future research.

Novel modified ANFIS based fuzzy logic model for performance prediction of FRCM-to-concrete bond strength

Construction repairs have used fiber-reinforced cement mortar (FRCM). Concrete and FRCM bond strength usually outweigh mechanical criteria. Nevertheless, testing complex bonds like the FRCM and concrete bond takes time, money, and errors. This study employed fuzzy logic (FL) based on the adaptive neuro-fuzzy inference system (ANFIS) to simplify and reliably estimate the FRCM-to-concrete bond strength (CBS) by modeling complicated and non-linear systems computationally efficiently. The models take six inputs: concrete splice length, stirrup cross-sectional area to spacing, longitudinal tension bar area to effective cross section, compressive strength, relative rib area, and minimum concrete cover. The model outputs concrete steel bar bond strength. The FLANFIS model predicts FRCM-to-CBS using the tensile testing results of the 10 specimens (5 concrete and 5 FRCM). Data instruct the model and measure its precision. This article defines the ANFIS-based FRCM-to-CBS. This research will employ 5 concrete specimens and 5 FRCM specimens, totaling 0.05 m^3 of concrete and FRCM mix. 0.25 kg of adhesive bonds the sample. Grip, control, and data gathering systems are employed with a 1 kN tensile testing equipment. This research comprises preparing concrete and FRCM specimens, bonding with adhesive, and tensile testing. The FL-ANFIS model predicts FRCM-to-CBS with a high coefficient of determination (R2) of 0.995 and a strong correlation coefficient (r) of 0.982 in training. The pattern predicted accurately with RMSE of 0.264 and MAE of 0.196. This paper shows that FL-ANFIS can predict steel bar bond strength in concrete quickly and accurately. The pattern reduces waste, design costs, and time.

Evidence, computation and AI: why evidence is not just in the head

Can scientific evidence outstretch what scientists have mentally entertained, or could ever entertain? This article focuses on the plausibility and consequences of an affirmative answer in a special case. Specifically, it discusses how we may treat automated scientific data-gathering systems—especially AI systems used to make predictions or to generate novel theories—from the point of view of confirmation theory. It uses AlphaFold2 as a case study.