Big Field Research Articles

Battery health management in the era of big field data

Battery health management in the era of big field data

Modeling the impact of BDA-AI on sustainable innovation ambidexterity and environmental performance

Data has evolved into one of the principal resources for contemporary businesses. Moreover, corporations have undergone digitalization; consequently, their supply chains generate substantial amounts of data. The theoretical framework of this investigation was built on novel concepts like big data analytics—artificial intelligence (BDA-AI) and supply chain ambidexterity’s (SCA) direct impacts on sustainable supply chain management (SSCM) and indirect impacts on sustainable innovation ambidexterity (SIA) and environmental performance (EP). This study selected employees of manufacturing industries as respondents for environmental performance, sustainable supply chain management, big data analytics, artificial intelligence, and supply chain ambidexterity. The results from this study show that BDA-AI and SCA significantly affect SSCM. SSCM has significant associations with SIA and EP. Finally, SIA has a significant impact on EP. According to the results indicating the indirect impacts, BDA-AI has significant indirect relationships with SIA and EP by having SSCM as the mediating variable. Furthermore, SCA has significant indirect associations with SIA and EP, with SSCM as the mediating variable. Additionally, both BDA-AI and SCA have significant indirect associations with EP, while SIA and SSCM are mediating variables. Finally, SSCM has an indirect association with EP while having SIA as a mediating variable. The findings of this paper provide several theoretical contributions to the research in sustainability and big data analytics artificial intelligence field. Furthermore, based on the suggested framework, this study offers a number of practical implications for decision-makers to improve significantly in the supply chain and BDA-AI. For instance, this paper provides significant insight for logistics and supply chain managers, supporting them in implementing BDA-AI solutions to help SSCM and enhance EP.

Real-Time Automatic M-Mode Echocardiography Measurement With Panel Attention.

Motion mode (M-mode) echocardiography is essential for measuring cardiac dimension and ejection fraction. However, the current diagnosis is time-consuming and suffers from diagnosis accuracy variance. This work resorts to building an automatic scheme through well-designed and well-trained deep learning to conquer the situation. That is, we proposed RAMEM, an automatic scheme of real-time M-mode echocardiography, which contributes three aspects to address the challenges: 1) provide MEIS, the first dataset of M-mode echocardiograms, to enable consistent results and support developing an automatic scheme; For detecting objects accurately in echocardiograms, it requires big receptive field for covering long-range diastole to systole cycle. However, the limited receptive field in the typical backbone of convolutional neural networks (CNN) and the losing information risk in non-local block (NL) equipped CNN risk the accuracy requirement. Therefore, we 2) propose panel attention embedding with updated UPANets V2, a convolutional backbone network, in a real-time instance segmentation (RIS) scheme for boosting big object detection performance; 3) introduce AMEM, an efficient algorithm of automatic M-mode echocardiography measurement, for automatic diagnosis; The experimental results show that RAMEM surpasses existing RIS schemes (CNNs with NL & Transformers as the backbone) in PASCAL 2012 SBD and human performances in MEIS.

If you want to learn about real behaviour, measure real behaviour

We argue that research aiming to understand or change human behavior must measure real behavior, not just behavioral intentions, to draw valid conclusions. The work highlights the well-established gap between people’s intentions and behavior across various tourism and hospitality contexts. Methodologically, we encourage authors not to rely on behavioral intentions and instead measure real behavior. We provide an overview of methods available to capture real behavior either automatically or manually in tourism contexts. The article also introduces a special issue in the Journal of Sustainable Tourism that showcases the measurement of real environmentally significant tourist behavior using diverse methods, such as biometric techniques, big data analytics, field observations, and experiments. We conclude by discussing five issues that prevent studies form drawing causal conclusions about behavior, namely, (i) reliance on behavioral intentions; (ii) a sample that does not reflect the population of interest; (iii) errors in measuring latent psychological constructs; (iv) consumer hypocrisy and social desirability bias; and (v) situational factors and habits. By advocating for a transition towards measuring real behavior, the article and the special issue aim to increase the validity and impact of research seeking to understand human behavior and drive effective behavior change for addressing global challenges.

Tightly-Secure Two-Tier Signatures on Code-Based Digital Signatures with Chameleon Hash Functions

In the current landscape where quantum algorithms pose a significant threat to conventional digital signature algorithms, code-based digital signature algorithms have emerged as the primary focus of ongoing research in post-quantum cryptography. Digital signatures play a pivotal role in ensuring non-repudiation and authentication, making them an indispensable cryptographic technique. The vulnerability of most digital signature algorithms to quantum attacks have prompted a significant surge in research on code-based digital signature algorithms, which have emerged as a prominent field within post-quantum cryptography. There are generally three distinct approaches to constructing code-based digital signature algorithms: (1) Developing an algorithm that follows the inverse process of the code-based public-key encryption algorithm; (2) Utilizing zero-knowledge identification algorithms in conjunction with the Fiat–Shamir paradigm to formulate a signature algorithm; (3) Constructing a specialized subset of the syndrome space as the foundation for the digital signature algorithm. Chameleon Signature is a non-interactive signature that operates on the hash and signature paradigm, exhibiting comparable efficiency to conventional schemes. Its distinct advantage lies in the fact that the owner of the public key does not necessarily require access to the corresponding secret key within the Chameleon hash algorithm. Notably, Chameleon signatures possess an inherent characteristic of non-transferability, with their validity ascertainable solely by designated recipients. This paper introduces the first Chameleon hash function based on both KKS and HFE schemes, showcasing its superiority over traditional schemes through rank metrics and big fields for enhanced security. The deployment of Chameleon hash functions within hash-and-sign signature schemes introduces a nuanced layer of security and verification flexibility. This study elucidates the implications of integrating Chameleon hash functions into the recipient’s public key infrastructure, highlighting the dual capability it affords authorized parties for secure and adaptable verification processes, alongside mechanisms for the detection of unauthorized alterations.

Farmers’ Group Coaching and Agricultural Extension Regarding Soil and Water Conservation in Nagari Cupak, West Sumatra

Nagari Cupak is a sub-district in Solok Regency with a big rice field that can be developed to increase farmers’ wealth. The development of rice field potential in Nagari Cupak can be conducted with agricultural extension to farmers. One of the outreach topics that can deliver to farmers is the importance of the efficient functioning of farmer groups (as a learning class, as production units, and as a place to cooperate). In this situation, the topic regarding conserving water and soil in rice fields for efficient rice production in Nagari Cupak is another one that may be discussed with farmers. The agricultural extension was carried out in Jorong Panyalai, Nagari Cupak, Gunung Talang District, on Thursday, 10 August 2023. The method used in community service is the lecture method. Furthermore, it continued with a question-and-answer session. The object of this community service is to join farmer groups in Nagari Cupak, which consists of 18 farmer groups. These activities showed that the farmers did not optimise the group’s function because they only used it as a gathering place. Farmer groups also shared the issues they frequently encountered, and solutions were offered. In addition, agricultural extension activities were continued with practice regarding soil and water conservation on land provided by the Nagari Cupak Government. The farmers’ persistence can be seen in their enthusiasm for this extension activity until the end of the extension. In order to enable farmers to practice soil and water conservation on their own, they also requested that Extension Officers continue to support them.

An Economical Sensor-Based Automated Plant Watering System for Smart Irrigation

Lack of water results in a reduction in photosynthesis in plants and crops. Plants are significantly affected when the soil water level is depleted thereby resulting in low production in the agricultural sector which can lead to a reduction in food supply. This problem makes water supply and management highly necessary for plants to grow effectively. Over the past century, the rate at which water usage has increased globally has increased at a rate higher than that of population growth. Particularly in dry regions, an increasing number of regions are now using more water than they can sustainably provide. The traditional watering techniques, such as sprinklers and flood mode, are ineffective and those not supply the ideal needs of plants because some plants do not need excess water to grow. This also results in water wasting. These ineffective irrigation systems often provide habitat for organisms that carry illnesses. Research has established that excessive water spillage and stagnant water can be habitat for mosquitoes which can also be dangerous to human health. Therefore, watering plants must be done intelligently, by developing an intelligent system that can regulate the content of water needed to water a plant in accordance with their needs and the moisture content of the soil. This will also reduce the spillage of water. In this study, an automated plant watering system that monitors the soil's moisture level to determine when watering plants is necessary was developed using hardware components such as Arduino UNO (Microcontroller), soil moisture sensor, pump, relays, wires, sprinkler and software instructions to drive the hardware components. An economical watering system was designed. The system was tested and can be effectively used in small gardens and big crop fields.

Examining the transformation of postindustrial land in reversing the lack of urban vitality: A paradigm spanning top-down and bottom-up approaches in urban planning studies

A distinct rise of global interest in the role of cultural and creative industries in post-industrial urban development calls for the transformation of our industrial heritage into creative clusters. Despite many factories being removed in deindustrialization, some enterprises still preserve and reuse the former industrial lands. However, the inconclusive judgments on the performances of creative clusters call for an in-depth examination of the underlying mechanism of transforming dilapidated urban industrial spaces. In addition, few empirical studies have revealed the commonalities and differences of market entities employing both the top-down and bottom-up approaches during the redevelopment.In this article, a multilevel transformation, and the consequent spatial performance in three postindustrial lands in Beijing have been examined based on big data analysis, field observation and in-depth interviews. We reached three conclusions. First, in addition to the angle of participatory planning, the deep exploration of specific market entities as an intermediate joint among different parties in protecting and reusing the postindustrial lands uncovers a distinct perspective of urban revitalization. Second, the finding of the enterprises' role in mitigating the conflicts between bottom-up and top-down approaches further testifies to a less-dichotomous perspective in urban regeneration studies. Last, though the examination of the multilevel transformation mechanism reflecting the difference between state and nonstate enterprises in property-led management, a generic paradigm behind the trend of relying on enterprises in revitalizing the large-scale postindustrial lands is revealed.

Progress and evolution of hotspots in butterfly diversity research in green spaces.

Ecosystems depend on biodiversity; therefore, protecting biodiversity is beneficial for the development of ecosystems. Butterflies are indicator species that respond quickly to environmental changes and reflect environmental conditions. Butterfly diversity is a crucial evaluation indicator of habitat quality in green spaces. We used CiteSpace and Bibliometrix to conduct a bibliometric analysis of research relating to butterfly diversity in green spaces. Based on 538 papers published from 2002-2022, we systematically reviewed the status, frontiers, and hotspots for research on butterfly diversity in green spaces. Our results showed that: (1) Research on butterfly diversity in green spaces has consistently demonstrated an upward trend between 2002-2022. Studies on this subject have garnered attention from researchers worldwide, with notable interest and contributions from scholars in the United States. (2) Early studies focused on butterfly habitat preferences and extinction concerns, and later articles appeared on the influence of external environmental factors (land use, climate change, plants, etc.). (3) Three main research topics received much attention between 2002 and 2022: biodiversity conservation, butterfly habitats, and the relationship between butterfly diversity and its influencing factors. (4) The relationship between green spaces and butterfly diversity (landscape features, vegetation features, and human activities) was discussed; these factors on butterfly communities should be considered in the planning and constructing of future green spaces. (5) Two significant future directions have been identified: more research on the impact of external factors and a need for more technical integration with the big data field. Future research on butterfly diversity in green spaces should adopt a more multi-scale, multi-disciplinary approach and aim to enhance the practicality and guidance of research findings.

Trans-Amplifying RNA Vaccines Against Infectious Diseases: A Comparison with Non-Replicating and Self-Amplifying RNA.

The recent COVID-19 pandemic as well as other past and recent outbreaks of newly or re-emerging viruses show the urgent need to develop potent new vaccine approaches, that enable a quick response to prevent global spread of infectious diseases. The breakthrough of first messenger RNA (mRNA)-based vaccines 2019 approved only months after identification of the causative virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), opens a big new field for vaccine engineering. Currently, two major types of mRNA are being pursued as vaccines for the prevention of infectious diseases. One is non-replicating mRNA, including nucleoside-modified mRNA, used in the current COVID-19 vaccines of Moderna and BioNTech (Sahin et al., Nat Rev Drug Discov 13(10):759-780, 2014; Baden et al., N Engl J Med 384(5):403-416, 2021; Polack et al., N Engl J Med 383(27):2603-2615, 2020), the other is self-amplifying RNA (saRNA) derived from RNA viruses. Recently, trans-amplifying RNA, a split vector system, has been described as a third class of mRNA (Spuul et al., J Virol 85(10):4739-4751, 2011; Blakney et al., Front Mol Biosci 5:71, 2018; Beissert et al., Mol Ther 28(1):119-128, 2020). In this chapter we review the different types of mRNA currently used for vaccine development with focus on trans-amplifying RNA.

Research on Management and Optimization of Big Data Computing Engine Based on Cloud Native Technology IT Architecture

In the wave of digital transformation, we are in a data-driven era. With the explosive growth of data volume, traditional big data processing platforms are facing more and more challenges, including insufficient computational efficiency and resource utilization. At the same time, cloud native technology has emerged as a key factor in filling this gap due to its flexibility, automated management, and cross platform compatibility. Although cloud native technology has broad prospects, its application in the field of big data is still in its early stages, and there are still many problems that need to be solved. Among them, the unity of cluster management, the collaboration between operation and development, and the efficiency of technology architecture transformation are the main challenges currently faced. To address these issues, we will conduct in-depth research around cloud native technology, big data computing engine technology, and containerized services, exploring and implementing a big data computing engine management and optimization solution based on cloud native technology. By proposing and implementing cloud native deployment methods, containerized construction methods, and a one-stop big data application management and development platform, the aim is to provide new ideas and methods for solving many problems in traditional big data computing engine management, and to contribute to the development and innovation of the big data field.

Identification of the characteristic visual field loss patterns in optic neuropathies

Visual fields support physicians in the diagnosis and progression tracking of optic neuropathies, enabling the visualization of the site of the optic nerve or optic pathways damage. Furthermore, the identification of the underlying patterns of different optic neuropathies can strengthen the diagnosis or help the clinicians for the patient's prognosis. Different abnormalities have been described and associated to neuroophthalmological diseases, such as central and ceco‐central scotomas related to hereditary optic neuropathies as Leber and dominant optic atrophy diseases which are mostly affected in the macular retinal ganglion cells and the papillomacular bundle. Altitudinal defects are usually associated with ischemic optic neuropathies, and in the big field of glaucoma the most commonly abnormalities are the arcuate and the nasal step.Related to this topic, artificial Intelligence is gaining traction, implementing, and testing different approaches to the automatic detection of visual field loss patterns in distinct diseases. For example, application of archetype analysis for the detection of glaucoma progression or for the assessment of efficacy in idiopathic intracranial hypertension treatment trial.

Performance Comparison of Indoor Humidity Modeling: A Novel Hybrid Model vs. the Simplified Calculation Model by EN ISO 13788

Ventilation is essential for maintaining indoor air quality and human comfort, particularly in buildings with mechanical ventilation systems. However, modeling the complex relationship between ventilation rate and measurable indoor environmental parameters, such as humidity, poses significant challenges. Current methods for modeling this relationship are often inefficient. For example, data-driven models frequently require sufficient information on ventilation, which may not be available for constant air volume (CAV) systems. On the other hand, simulation programs can be time-consuming and require expert engagement. In this study, we present two simplified calculation models, including a hybrid model developed by the authors, and a well-known model introduced by EN ISO 13788. We compare their performance in evaluating the impact of ventilation rate on indoor humidity. The hybrid model uses a novel analytical method to upgrade a single-input regression model (which considers only outdoor humidity as input) to enable the evaluation of indoor humidity based on both outdoor humidity and ventilation rate. We used the commercial TRNSYS program to model two scenarios: increasing the reference ventilation rate and decreasing the reference ventilation rate. The results showed that both models matched TRNSYS simulations with satisfactory accuracies, but the hybrid model demonstrated superior performance to the EN ISO 13788 model in both scenarios. The hybrid model’s higher accuracy and hybrid features make it better suited for big data analysis and field studies.

A Vector Field Visualization Method for Trajectory Big Data

With the rapid growth of trajectory big data, there is a need for more efficient methods to extract, analyze, and visualize these data. However, existing research on trajectory big data visualization mainly focuses on displaying trajectories for a specific period or showing spatial distribution characteristics of trajectory points in a single time slice using clustering, filtering, and other techniques. Therefore, this paper proposes a vector field visualization model for trajectory big data, aiming to effectively represent the inherent movement trends in the data and provide a more intuitive visualization of urban traffic congestion trends. The model utilizes the motion information of vehicles to create a travel vector grid and employs WebGL technology for vector field visualization rendering. The vector field effects are effectively displayed by generating many particles and simulating their movements. Furthermore, this research also designs and implements congestion trend point identification and hotspot congestion analysis, thus validating the practicality and effectiveness of trajectory big data vector field visualization. The results indicate that compared to traditional visualization methods, the vector field visualization method can demonstrate the direction and density changes in traffic flow and predict future traffic congestion. This work provides valuable data references and decision support for urban traffic management and planning.

SSTP: Stock Sector Trend Prediction with Temporal-Spatial Network

In financial big data field, most existing work of stock prediction has focused on the prediction of a single stock trend. However, it is challenging to predict a stock price series due to its drastic volatility. While the stock sector is a group of stocks belonging to the same sector, and the stock sector index is the weighted sum of the prices of all the stocks in the sector. Therefore the trend of stock sector is more stable and more feasible to predict than that of a single stock. In this paper, we propose a new method named Stock Sector Trend Prediction (SSTP) to solve the problem of predicting stock sector trend. In SSTP method, we adopt the Relative Price Strength (RPS) to describe the trend of the stock sector, which is the relative rank of stock sector trend. In order to learn the intrinsic probability distribution of the stock sector index series, we construct the multi-scale RPS time series and build multiple independent fully-connected stock sector relation graphs based on the real relationship among stock sectors. Then, we propose a Temporal-spatial Network (TSN) to extract the temporal features from the multi-scale RPS series and the spatial features from the stock sector relation graphs. Finally, the TSN predicts and ranks the trends of the stock sector trend with the temporal-spatial features. The experimental results on the real-world dataset validate the effectiveness of the proposed SSTP method for the stock sector trend prediction.

Densification Mechanism and Natural Gas Accumulation Process, Triassic Xujiahe Formation, Hechuan Area, Sichuan Basin, China

Abstract We have witnessed a skyrocketing development of tight gas in China. However, all developed gas field in China face the obstacle of the decrease of production. The Xujiahe Formation in Sichuan Basin shows a promising future for the next big gas field. In this work, we will shed light on the densification mechanism and natural gas accumulation process of Triassic Xujiahe Formation, Hechuan Area, Sichuan Basin through physical experiments and theory analysis. We describe the reservoir characteristics. The diagenesis types and characteristics of sandstone reservoirs in the promising future of the next big gas field. In this work, we will shed light on the densification mechanism and natural gas accumulation process of Triassic Xujiahe Formation, Hechuan Area, Sichuan Basin through physical experiments and theory analysis. We describe the reservoir characteristics. The diagenesis types and characteristics of sandstone reservoirs in the second Member of Xujiahe Formation in Hechuan area are analyzed by means of rock thin section, cast thin section, and mercury injection. By observing the microscopic characteristics, types, and homogenization temperature distribution of fluid inclusions, the accumulation period of natural gas in Hechuan gas field is given, and the accumulation process of natural gas is revealed. The results show that (1) the natural gas charging the tight sandstone reservoirs of the Upper Triassic Xujiahe Formation in Hechuan area is a continuous process. The gas accumulation period can be roughly divided into two stages; (2) combining with the analysis of the hydrocarbon accumulation period of time, we put forward the natural gas accumulation model of the Xujiahe Formation, Hechuan area: the natural gas accumulation is accompanied by formation densification.

Sentiment Analysis of the Consumer Review Text Based on BERT-BiLSTM in a Social Media Environment

In this paper, a BERT-BiLSTM-based consumer review text sentiment analysis method in the e-commerce big data field is proposed. First, the unlabeled text is trained using the BERT training model for the language introduced in the deep learning, and then the pre-training model of the text data is delivered by the learning textual features and data to extract deeper vectors. Second, the BiLSTM model is applied to simultaneously obtain contextual information so as to illustrate optimal textual features. Finally, a corresponding sentiment analysis model relative to the consumer review text is constructed by combining the BERT model with BiLSTM to better merge the context for classifying sentiment and improving the final feature vector accuracy for the sentiment classification results. Simulated by experiments, the method proposed in this paper was compared with another three methods using the same data set. The results obtained indicate that the proposed method has the highest precision, recall, and F1-Measure, and the values reach 92.64%, 90.32%, and 91.46%, respectively.

Multi Disease Detection Using Deep Learning

Abstract: Chronic diseases are the main reason for increase in mortality rate. Heart disease, cancer, diabetes, stroke, and arthritis all are chronic diseases. NCDs have a more mortality rate than other diseases. Each year, approximately 5.8 million Indians die from NCDs; globally, 41 million die from chronic diseases. Investment and prevention are two important needs for chronic diseases. It is important to discover the solution for chronic diseases. The management of these types of illnesses includes early detection, patient care, and related services. Because of the explosion of medical data, data management will become a difficult challenge. Deep learning [9] is important in big data fields such as the medical industry. Deep learning is a advancement of machine learning that is capable of performing different tasks. Chronic diseases are diagnosed using different technologies, and that is why deep learning is used to provide the best treatment to patients, resulting in a low mortality rate.

Rational selection of the oxygen source for atomic layer deposition Al2O3 insulators

Insulating gate dielectric is an important component of thin film transistors (TFT) and non-volatile memories, whose properties directly affect the electrical performance of the devices. In this study, the Al2O3 insulators were prepared by atomic layer deposition (ALD) using different oxygen sources and their properties were systematically studied. Based on the two-step oxidation method (H2O + O3), the Al2O3 thin films exhibited a small gate leakage current and a big breakdown field of 9 MV/cm. However, the use of the O3 increased the C-related impurities causing a large sweep hysteresis voltage of the TFTs, i.e., 8.65 V and 4.66 V for that with Al2O3 (O3) and Al2O3 (H2O + O3), respectively. Attributed to a small amount of impurities in the Al2O3 (H2O) insulators as well as the appropriate amount of Al element diffusion at the Al2O3/ZnO interface, the TFTs using Al2O3 (H2O) as insulators presented a high mobility of 36.1 cm2V−2s−1, a subthreshold swing of 0.25 V/dec and a small sweep hysteresis voltage of 0.42 V. Therefore, the properties of the ALD-Al2O3 films can be easily controlled by changing the type of the oxidizer. The Al2O3 thin films with H2O as oxidant can be used as the insulators of the TFTs, while the Al2O3 thin films based on O3 have great potential as the charge trap layer of the memories.

Enhanced Embedded AutoEncoders: An Attribute-Preserving Face De-Identification Framework

Nowadays, face recognition technology has been dramatically improved by the advances in deep learning and big data fields. However, this also poses grand challenges in protecting personal identity information in intelligent applications of the Internet of Things (IoT). Existing methods based on the K-Same algorithm have low effectiveness for protecting personal identity while preserving face attributes. In this paper, we propose an attribute-preserving face de-identification framework called Enhanced Embedded AutoEncoders to address this problem. Our framework consists of three parts: a Privacy Removal Network, a Feature Selection Network and a Privacy Evaluation Network. The main purpose of our framework is to ensure that the Privacy Removal Network is capable of discarding information involving identity privacy and retaining desired face attributes for certain prediction applications. In order to achieve this goal, the design of the Privacy Removal Network is crucial. Specifically, we employ two different autoencoders, one of which is embedded within the other. Extensive experimental results show that our framework outperforms existing methods by an average of 3.42%-26.22% in terms of data utility under comparable face de-identification performance, which indicates that the proposed framework can not only effectively retain face attributes but also protect personal identity well.