Smart Data Research Articles

Analyzing the impact of the EVFTA on the importation of transportation vehicles, parts, and components into Vietnam from the EU: A SMART model approach

This study examines the impact of the EU-Vietnam Free Trade Agreement (EVFTA) on Vietnam’s imports of transportation vehicles, parts, and components from the EU. Using the SMART model and data from UN COMTRADE and Vietnamese government agencies, the analysis shows modest increases in import values. Growth rates are 0.23% with the EVFTA alone and 0.21% when the Regional Comprehensive Economic Partnership (RCEP) is also considered. These findings reveal different effects among EU member states and product categories, especially in HS group 87 (vehicles) and HS group 89 (ships and boats), where trade creation is notable for Germany, Slovakia, and Italy. However, the study also finds significant trade diversion, with $361.91 million shifting from other regions to the EU. Despite these modest gains, the EVFTA positively impacts Vietnam’s industrial growth and green energy transition by enabling the import of advanced EU products. The research emphasizes the importance of considering how agreements like the RCEP influence the broader economic effects of the EVFTA, as some benefits shift to RCEP countries like Japan, South Korea, and China.

Malaysian Rainwater Harvesting System for In-House Power Generation

Energy harvesting monitoring systems have become more important as the Internet of Things (IoT) have grown. An intelligent system to monitor rainwater harvesting at UNITEN COE BN is being designed and developed in this study. Rainwater harvesting operations will be improved by developing an intelligent system. Monitoring techniques are studied, and sensors are designed for simulation. Smart rainwater harvesting systems are designed and implemented in this study, contributing to the field of smart monitoring systems. Rainwater collection, storage and usage are monitored and analyzed with smart sensors and data acquisition systems. Water turbine speed, voltage, and rainfall intensity are monitored by sensors in the developed system. Data from sensors is processed in Python GUIs. Visual displays allow users to monitor the rainwater harvesting system remotely. Durability and infrastructure compatibility are considered when selecting materials. It is found that smart rainwater harvesting system performance and reliability can be improved through simulation testing and validation. The study concluded that, storm water resources can be optimized by accessing real-time information.

LSPR-susceptible metasurface platform for spectrometer-less and AI-empowered diagnostic biomolecule detection

In response to the growing demand for biomolecular diagnostics, metasurface (MS) platforms based on high-Q resonators have demonstrated their capability to detect analytes with smart data processing and image analysis technologies. However, high-Q resonator meta-atom arrays are highly sensitive to the fabrication process and chemical surface functionalization. Thus, spectrum scanning systems are required to monitor the resonant wavelength changes at every step, from fabrication to practical sensing. In this study, we propose an innovative dielectric resonator-independent MS platform that enables spectrometer-less biomolecule detection using artificial intelligence (AI) at a visible wavelength. Functionalizing the focused vortex MS to capture gold nanoparticle (AuNP)-based sandwich immunoassays causes the resulting vortex beam profiles to be significantly affected by the localized surface plasmon resonance (LSPR) occurring between AuNPs and meta-atoms. The convolutional neural network algorithm was carefully trained to accurately classify the AuNP concentration-dependent focused vortex beam, facilitating the determination of the concentration of the targeted diagnostic biomolecule. Successful in situ identification of various biomolecule concentrations was achieved with over 99 % accuracy, indicating the potential of combining an LSPR-susceptible MS platform and AI for continuously tracking various chemical and biological compounds.

A Modified EMD Technique for Broken Rotor Bar Fault Detection in Induction Machines.

Induction machines (IMs) are commonly used in various industrial sectors. It is essential to recognize IM defects at their earliest stage so as to prevent machine performance degradation and improve production quality and safety. This work will focus on IM broken rotor bar (BRB) fault detection, as BRB fault could generate extra heating, vibration, acoustic noise, or even sparks in IMs. In this paper, a modified empirical mode decomposition (EMD) technique, or MEMD, is proposed for BRB fault detection using motor current signature analysis. A smart sensor-based data acquisition (DAQ) system is developed by our research team and is used to collect current signals wirelessly. The MEMD takes several processing steps. Firstly, correlation-based EMD analysis is undertaken to select the most representative intrinsic mode function (IMF). Secondly, an adaptive window function is suggested for spectral operation and analysis to detect the BRB fault. Thirdly, a new reference function is proposed to generate the fault index for fault severity diagnosis analytically. The effectiveness of the proposed MEMD technique is verified experimentally.

Hyperspectral image reconstruction for predicting chick embryo mortality towards advancing egg and hatchery industry

As the demand for food surges and the agricultural sector undergoes a transformative shift towards sustainability and efficiency, the need for precise and proactive measures to ensure the health and welfare of livestock becomes paramount. In the egg and hatchery industry, hyperspectral imaging (HSI) has emerged as a cutting-edge, non-destructive technique for fast and accurate egg quality analysis, including detecting chick embryo mortality. However, the high cost and operational complexity compared to conventional RGB imaging are significant bottlenecks in the widespread adoption of HSI technology. To overcome these hurdles and unlock the full potential of HSI, a promising solution is hyperspectral image reconstruction from standard RGB images. This study aims to reconstruct hyperspectral images from RGB images for non-destructive early prediction of chick embryo mortality. Initially, the performance of different image reconstruction algorithms, such as HRNET, MST++, Restormer, and EDSR were compared to reconstruct the hyperspectral images of the eggs in the early incubation period. Later, the reconstructed spectra were used to differentiate live from dead embryos eggs using the XGBoost and Random Forest classification methods. Among the reconstruction methods, HRNET showed impressive reconstruction performance with MRAE of 0.0955, RMSE of 0.0159, and PSNR of 36.79 dB. This study motivated the idea that harnessing imaging technology integrated with smart sensors and data analytics has the potential to improve automation, enhance biosecurity, and optimize resource management towards sustainable agriculture 4.0.

Exploring the Potential of a Smart Ring to Predict Postoperative Pain Outcomes in Orthopedic Surgery Patients.

Poor pain alleviation remains a problem following orthopedic surgery, leading to prolonged recovery time, increased morbidity, and prolonged opioid use after hospitalization. Wearable device data, collected during postsurgical recovery, may help ameliorate poor pain alleviation because a patient's physiological state during the recovery process may be inferred from sensor data. In this study, we collected smart ring data from 37 inpatients following orthopedic surgery and developed machine learning models to predict if a patient had postsurgical poor pain alleviation. Machine learning models based on the smart ring data were able to predict if a patient had poor pain alleviation during their hospital stay with an accuracy of 70.0%, an F1-score of 0.769, and an area under the receiver operating characteristics curve of 0.762 on an independent test dataset. These values were similar to performance metrics from existing models that rely on static, preoperative patient factors. Our results provide preliminary evidence that wearable device data may help control pain after orthopedic surgery by incorporating real-time, objective estimates of a patient's pain during recovery.

Transitioning practices of water utilities from reactive to proactive: Leveraging Australian best practices in digital technologies and data analytics

Developments in technology and data analytics bring opportunities for water utilities to proactively adapt their practices to better manage pressing challenges, such as water losses, ageing assets, and urban water scarcity due to climate change. Despite the increase in commercial digital technologies for monitoring urban water, wastewater and stormwater systems, digital transformation in water utilities has been occurring much slower than in other businesses (e.g., the energy sector). Leveraging from Australian best practices, this study aims to facilitate water utility’s digital transformation by establishing a targeted research agenda and a continuous improvement process for the successful implementation, operation, and improvement of smart water technologies. Four case studies on 1) automated and digital water consumption metering, 2) early warning of water mains breaks, 3) early detection of sewerage blockages and spills, and 4) smart rainfall reuse and flood mitigation are used to exemplify the potential benefits and hurdles of adopting digital technologies in water utilities, while highlighting pressing areas needing further research and development. Key research areas identified include: 1) development frameworks to map water utilities capacity and needs for ranking priority areas of investment, 2) improvements in sensor hardware, 3) integration of big, multi-sourced data and cybersecurity, 4) increase in generality of smart data applications for various field conditions (i.e., reduce the need for or level of customization), and 5) data-informed proactive asset management and water pricing. Key barriers to be overcome include 1) lack of internal capacity to develop, operate and integrate smart applications, 2) the risk conservative culture of water utilities when facing innovation, and 3) lack of operation standards among water utilities to promote benchmarking. It is imperative that water utilities actively drive the digital transformation process by perpetuating collaborations with research centres and the private sector, while simultaneously increasing in-house capacity through investments in skilled personnel and training.

Utility of AI Based Soil Testing in Agriculture

ABSTRACT The soil plays crucial role in any kind of agricultural crops. Depending upon the different ingredients of the soil the nature of crops to be farmed is decided.Optimizing agricultural practices is crucial for countries like India to address economic growth and escalating food demands. Historical challenges stemming from diverse weather conditions, geographical variations, conventional farming methods, and economic uncertainties have hindered improved crop yields. The dearth of information on crop yield productivity has also resulted in significant economic losses. In response, the adoption of advanced agricultural technologies such as smart farming, digital agriculture, and Data Analytics has become imperative. These innovations provide valuable insights into factors influencing crop yields, best-fit crops for a certain area, and enabling accurate predictions. With precise crop yield forecasts, farmers can develop tailored cultivation plans, implement efficient crop health monitoring systems, and manage yields effectively, thereby transforming agriculture into a highly profitable venture. This paper delves into the applications of technological advancements in agriculture, focusing on areas such as Digital Agriculture, Crop Management, and Crop Protection, and Data Analytics, with the aim of contributing to the optimization of agricultural practices and bolstering the economic impact of the agricultural sector. KEYWORDS: Agriculture, Artificial Intelligence, Machine learning, crop prediction

IoT based energy aware local approximated MapReduce fuzzy clustering for smart healthcare data transmission

IoT based energy aware local approximated MapReduce fuzzy clustering for smart healthcare data transmission

The robust SmartEnsembleNet: A game changer in finger knuckle biometrics

SmartEnsembleNet represents a major leap forward in the domain of biometric identification, focusing on the relatively underexplored area of finger knuckle biometrics. This research enhances the effectiveness and uniqueness of biometric systems by leveraging the distinct and secure features of finger knuckle patterns. As a complement to existing biometric techniques like fingerprints and facial recognition, it adds an additional layer of security and reliability. Addressing the challenges faced by finger knuckle identification, such as image acquisition, environmental effects, and aging, SmartEnsembleNet introduces a pioneering multi-layered ensemble learning strategy that combines eight diverse machine learning models. These models, trained via a 5-fold cross-validation on datasets from IIT Delhi and PolyU, demonstrate significant improvements in accuracy, precision, recall, and F1-score. A distinctive feature of SmartEnsembleNet is its innovative meta-model, streamlining the creation of a meta-dataset. This dataset is created by applying bilinear interpolation to the predicted probabilities from the top-performing model, and it is further refined by subtracting these interpolated probabilities from the validation data. This iterative process generates ‘smart data’, which serves as the basis for training the ultimate ‘smart model’. Logistic regression is chosen for its outstanding performance. This method, applied uniformly to both training and testing phases, considerably enhances classification accuracy in finger knuckle biometrics, establishing new benchmarks. SmartEnsembleNet’s performance undergoes a detailed evaluation using the Friedman test and Nemenyi post-hoc analysis, guaranteeing a selection process grounded in statistical rigor. With its resilience to noise and adaptability under various conditions, SmartEnsembleNet not only addresses existing gaps in finger knuckle biometric research but also pioneers future advancements, providing a versatile and secure solution impactful for both academia and high-security scenarios.

Smart Data Harvesting in Cache-Enabled MANETs: UAVs, Future Position Prediction, and Autonomous Path Planning

The task of gathering data from nodes within mobile ad-hoc wireless sensor networks presents an enduring challenge. Conventional strategies employ customized routing protocols tailored to these environments, with research focused on refining them for improved efficiency in terms of throughput and energy utilization. However, these elements are interconnected, and enhancements in one often come at the expense of the other. An alternative data collection approach involves the use of Unmanned Aerial Vehicles (UAVs). In contrast to traditional methods, UAVs directly collect data from mobile nodes, bypassing the need for routing. While existing research predominantly addresses static nodes, this paper proposes an evolutionary based, innovative path selection approach based on future position prediction of caching enabled mobile ad-hoc wireless sensor network nodes for UAV data collection, aimed at maximizing node encounters and gathering the most valuable information in a single trip. The proposed technique is evaluated for different movement models and parameter configurations.

Investigating the synergy of Blockchain and AI: enhancing security, efficiency, and transparency

ABSTRACT Integrating Blockchain and Artificial Intelligence can change business processes, promising improvements in Security, efficiency, and transparency. This proposed research investigates the synergy between these two technologies, investigating how their convergence can change business operations. AI’s data analysis and decision-making ability, coupled with Blockchain’s Security and transparent data sharing, sets the stage for innovative solutions across diverse sectors. The study aims to explore this integration’s potential benefits and challenges, emphasizing the need for further exploration and development. By conducting a systematic literature review, this research will analyze current findings to provide comprehensive insights into how Blockchain and AI can work together to improve business processes. This investigation uses AI to enhance Blockchain’s capabilities through smart contract automation, data analysis, and decision-making, while Blockchain offers a secure and transparent framework for AI operations. The research seeks to answer critical questions about improving Blockchain networks’ Security and privacy using AI, using Blockchain to enhance AI systems’ transparency and accountability, and using AI to improve Blockchain network performance and scalability. Beyond this research, the Author aims to contribute to the Blockchain and AI integration literature, offering a base for future research.

Adherence to single inhaler triple therapy and digital inhalers in Chronic Obstructive Pulmonary Disease: a literature review and protocol for a randomized controlled trial (TRICOLON study)

BackgroundMedication non-adherence is a significant problem in patients with Chronic Obstructive Pulmonary Disease (COPD). Efforts to address this issue are receiving increased attention. Simplifying treatment by prescribing single-inhaler triple therapy (SITT) as an alternative to multi-inhaler triple therapy (MITT) or with smart inhalers are often considered potential solutions. However, the actual impact of these innovations on adherence and clinical outcomes is unclear.MethodsTo address this knowledge gap we first conducted a literature review focusing on two research questions: 1) the difference in adherence between SITT and MITT users in COPD, and 2) the effect of smart inhalers on adherence in COPD. Separate searches were conducted in PubMed and two authors independently assessed the articles. In addition, we present a protocol for a study to acquire knowledge for the gaps identified.ResultsTo address the first research question, 8 trials were selected for further review. All trials were observational, i.e. randomized controlled trials were lacking. Seven of these trials showed higher adherence and/or persistence in patients on SITT compared with patients on MITT. In addition, four studies showed a positive effect of SITT on various clinical outcomes. For the second research question, 11 trials were selected for review. While most of the studies showed a positive effect of smart inhalers on adherence, there was considerable variation in the results regarding their effect on other clinical outcomes.The TRICOLON (TRIple therapy COnvenience by the use of one or multipLe Inhalers and digital support in ChrONic Obstructive Pulmonary Disease) trial aims to improve understanding regarding the effectiveness of SITT and smart inhalers in enhancing adherence. This open-label, randomized, multi-center study will enroll COPD patients requiring triple therapy at ten participating hospitals. In total, 300 patients will be randomized into three groups: 1) MITT; 2) SITT; 3) SITT with digital support through a smart inhaler and an e-health platform. The follow-up period will be one year, during which three methods of measuring adherence will be used: smart inhaler data, self-reported data using the Test of Adherence to Inhalers (TAI) questionnaire, and drug analysis in scalp hair samples. Finally, differences in clinical outcomes between the study groups will be compared.DiscussionOur review suggests promising results concerning the effect of SITT, as opposed to MITT, and smart inhalers on adherence. However, the quality of evidence is limited due to the absence of randomized controlled trials and/or the short duration of follow-up in many studies. Moreover, its impact on clinical outcomes shows considerable variation. The TRICOLON trial aims to provide solid data on these frequently mentioned solutions to non-adherence in COPD. Collecting data in a well-designed randomized controlled trial is challenging, but the design of this trial addresses both the usefulness of SITT and smart inhalers while ensuring minimal interference in participants' daily lives.Trial registrationNCT05495698 (Clinicaltrials.gov), registered at 08–08-2022. Protocol version: version 5, date 27–02-2023.

Smart data analysis V2: A user-friendly software for non-statisticians.

Data analysis can be accurate and reliable only if the underlying assumptions of the used statistical method are validated. Any violations of these assumptions can change the outcomes and conclusions of the analysis. In this study, we developed Smart Data Analysis V2 (SDA-V2), an interactive and user-friendly web application, to assist users with limited statistical knowledge in data analysis, and it can be freely accessed at https://jularatchumnaul.shinyapps.io/SDA-V2/. SDA-V2 automatically explores and visualizes data, examines the underlying assumptions associated with the parametric test, and selects an appropriate statistical method for the given data. Furthermore, SDA-V2 can assess the quality of research instruments and determine the minimum sample size required for a meaningful study. However, while SDA-V2 is a valuable tool for simplifying statistical analysis, it does not replace the need for a fundamental understanding of statistical principles. Researchers are encouraged to combine their expertise with the software's capabilities to achieve the most accurate and credible results.

Proactive Drive Failure Prediction for Cloud Storage System Through Semi-Supervised Learning

Proactive drive failure prediction can help operators handle the failing drives in advance, enhancing the storage system dependability. SSD and HDD failure prediction techniques are currently evolving towards a semi-supervised approach. In this paper, we are dedicated to enhancing the methodology for semi-supervised drive failure prediction from these aspects: design more powerful, robust yet generic models, mine drive failure modes and make the prediction model interpretable. Specifically, we propose two semi-supervised drive failure prediction models, DFP-VL and DFP-FL, from the perspective of reconstructing SMART data and learning SMART data's probability density, respectively. They capture the pattern of healthy drives, and failing drives can be detected when they deviate from the normal pattern. We mine two failure modes, slow deterioration and dramatic deterioration. Based on that, we design two failure detectors, ”ThresholdDetector” and ”HybridDetector”, to determine whether a drive deviates from the normal pattern. We evaluate the proposed methods on both SSD and HDD SMART data. DFP-VL is generic yet effective and can interpret the predicted results. DFP-FL has better performance than DFP-VL, but it cannot interpret the results. ThresholdDetector has low complexity and can detect most failing drives. HybridDetector has high complexity and can further improve the detection performance.

The conceptual model of Indonesian palm oil supply chain based on blockchain

Abstract Indonesia is the world’s largest producer and consumer of palm oil. However, the palm oil supply chain faces numerous challenges related to actors, traceability, transparency, and sustainability. To address these issues, a conceptual model for a blockchain-based palm oil supply chain in Indonesia is proposed. The model aims to leverage the decentralized and immutable nature of blockchain technology. It incorporates components such as smart contracts, distributed ledgers, and data sharing mechanisms to enhance traceability, transparency, and sustainability in the palm oil supply chain. The research utilized the SSM (Soft System Methodology) and CATWOE (Customer, Actor, Transformation, Worldview, Owner, Environment) methodologies. The actors involved in the palm oil supply chain include smallholders, plantation companies, third-party suppliers, middlemen, traders, palm oil mills, refineries, distributors, retailers, and consumers. The blockchain conceptual model is structured based on a sustainable supply chain model that considers economic, social, and environmental aspects. Blockchain transactions regulate the process of supply chain actors, while stakeholders such as the government, financial institutions, palm oil research organizations, NGOs, and consumers are involved. The blockchain-based supply chain model is expected to address the key issues of the palm oil supply chain, aiming for a sustainable supply chain system. By implementing this model, it is anticipated that the palm oil industry can achieve greater accountability, transparency, and environmental sustainability.

A Review of Edge Computing Technology and Its Applications in Power Systems

Recent advancements in network-connected devices have led to a rapid increase in the deployment of smart devices and enhanced grid connectivity, resulting in a surge in data generation and expanded deployment to the edge of systems. Classic cloud computing infrastructures are increasingly challenged by the demands for large bandwidth, low latency, fast response speed, and strong security. Therefore, edge computing has emerged as a critical technology to address these challenges, gaining widespread adoption across various sectors. This paper introduces the advent and capabilities of edge computing, reviews its state-of-the-art architectural advancements, and explores its communication techniques. A comprehensive analysis of edge computing technologies is also presented. Furthermore, this paper highlights the transformative role of edge computing in various areas, particularly emphasizing its role in power systems. It summarizes edge computing applications in power systems that are oriented from the architectures, such as power system monitoring, smart meter management, data collection and analysis, resource management, etc. Additionally, the paper discusses the future opportunities of edge computing in enhancing power system applications.

The Construction of Fuzzy Prediction Model of Stock Price Rise and Fall Based on Machine Learning Technology

In recent years, the use of smart data analysis method to predict the stock price is financial technology; important issues in the field of finch. However, there are many technical indicators and human subjective factors will affect the stock price forecast, so we must effectively grasp the important influence indicators to improve the accuracy of stock price forecast. Therefore, this study uses four machine learning algorithms to predict and analyze the stock price fluctuation through the screening process of technical indicators, and then selects the important technical indicators. In addition, due to the uncertainty and fuzziness of the attributes of technical indicators and human subjective judgment, this study uses the fuzzy inference method to construct the fuzzy inference system to predict the rise and fall of stock price, and proposes the prediction method of the range of the rise and fall of stock price. Finally, this paper makes an empirical analysis on the stock price data of three companies. The results show that the accuracy of stock price forecast is more than 82.13%, and the average accuracy of stock price forecast is more than 83%. Therefore, the fuzzy inference prediction system proposed in this study not only has the theoretical basis, but also can effectively predict the trend and range of stock price, which has practical value and contribution to investors.

Smart Agriculture Applications and Data Analysis with Industry 4.0

The world’s food demand is predicted to exceed its food supply significantly in the coming periods. Many physical factors, such as climate change in access to water, change the production amount and food prices. Efficient resource utilization can be achieved by developing smart agricultural applications using the IoT and image processing techniques. For this purpose, in the proposed study, a smart agriculture application was developed using IoT technology with image processing. In the proposed image processing algorithm, the crops’ images were processed, weight estimates were made, and their numbers were taken. The data we collect instantly can be uploaded to the cloud, and the data collected on the cloud side can be analyzed over the mobile application. Thanks to data analysis with the mobile application we have developed, we can see the current status of the crops and make instant decisions, and it also plays a role in determining market prices. With all these studies, it has been seen that agriculture can be digitalized with the technologies we use, and thus, productivity in agriculture can increase significantly.

Patient-perceived barriers to early initiation of prenatal care at a large, urban federally qualified health center: a mixed-methods study

BackgroundEarly initiation of prenatal care is widely accepted to improve the health outcomes of pregnancy for both mothers and their infants. Identification of the various barriers to entry into care that patients experience may inform and improve health care provision and, in turn, improve the patient’s ability to receive necessary care.AimThis study implements a mixed-methods approach to establish methods and procedures for identifying barriers to early entry to prenatal care in a medically-vulnerable patient population and areas for future quality improvement initiatives.MethodsAn initial chart review was conducted on obstetrics patients that initiated prenatal care after their first trimester at a large federally qualified health center in Brooklyn, NY, to determine patient-specified reasons for delay. A thematic analysis of these data was implemented in combination with both parametric and non-parametric analyses to characterize the population of interest, and to identify the primary determinants of delayed entry.ResultsThe age of patients in the population of interest (n = 169) was bimodal, with a range of 15 – 43 years and a mean of 28 years. The mean gestational age of entry into prenatal care was 19 weeks. The chart review revealed that 8% recently moved to Brooklyn from outside of NYC or the USA. Nine percent had difficulty scheduling an initial prenatal visit within their first trimester. Teenage pregnancy accounted for 7%. Provider challenges with documentation (21%) were noted. The most common themes identified (n = 155) were the patient being in transition (21%), the pregnancy being unplanned (17%), and issues with linkage to care (15%), including no shows or patient cancellations. Patients who were late to prenatal care also differed from their peers dramatically, as they were more likely to be Spanish-speaking, to be young, and to experience a relatively long delay between pregnancy confirmation and entry into care. Moreover, the greatest determinant of delayed entry into care was patient age.ConclusionOur study provides a process for other like clinics to identify patients who are at risk for delayed entry to prenatal care and highlight common barriers to entry. Future initiatives include the introduction of a smart data element to document reasons for delay and use of community health workers for dedicated outreach after no show appointments or patient cancellations.