Search

Abstract Metro systems play an important role in reducing urban traffic congestion and promoting the sustainable development of urban transport in megacities. With the expansion of a metro network, transfer stations are necessary for increasing the service connectivity of a metro network. An accurate estimation of transfer passenger flow can help improve the operations management of a metro system. This study proposes a data‐driven methodology for estimating the transfer passenger flow volume of each transfer station in a metro network by mining smart card data. The estimated transfer passenger flow data are visualized to show the spatial‐temporal distribution characteristics of metro transfer passenger flow. The case study results of the Shenzhen Metro network demonstrate that the proposed data‐driven methodological framework is very effective in estimating different types of transfer passenger flows, such as total transfer passenger flow, hourly transfer passenger flow, and inbound and outbound transfer flows at each transfer station. The spatial‐temporal distribution characteristics of transfer passenger flow can be very useful for designing effective and efficient passenger flow management measures to ensure the safe and efficient operation of a metro system.

Metro systems play an important role in reducing urban traffic congestion and promoting the sustainable development of urban transport in megacities. With the expansion of a metro network, transfer stations are necessary for increasing the service connectivity of a metro network. An accurate estimation of transfer passenger flow can help improve the operations management of a metro system. This study proposes a data‐driven methodology for estimating the transfer passenger flow volume of each transfer station in a metro network by mining smart card data. The estimated transfer passenger flow data are visualized to show the spatial‐temporal distribution characteristics of metro transfer passenger flow. The case study results of the Shenzhen Metro network demonstrate that the proposed data‐driven methodological framework is very effective in estimating different types of transfer passenger flows, such as total transfer passenger flow, hourly transfer passenger flow, and inbound and outbound transfer flows at each transfer station. The spatial‐temporal distribution characteristics of transfer passenger flow can be very useful for designing effective and efficient passenger flow management measures to ensure the safe and efficient operation of a metro system. [ABSTRACT FROM AUTHOR]

Several factors contribute to the increase in irrigation demand: population growth, demand for higher value-added products, and the impacts of climate change, among others. High-quality water is essential for irrigation, so knowledge of water quality is critical. Additionally, water use in agriculture has been increasing in the last decades. Lack of water quality can cause drip clog, a lack of application uniformity, cross-contamination, and direct and indirect impacts on plants and soil. Currently, there is a need for more automated methods for evaluating and monitoring water quality for irrigation purposes, considering different aspects, from impacts on soil to impacts on irrigation systems. This work proposes a data-driven method to address this gap and implemented it in a case study in the PCJ river basin in Brazil. The methodology contains nine components and considers the main steps of the data lifecycle and the traditional machine learning workflow, allowing for automated knowledge extraction and providing important information for improving decision making. The case study illustrates the use of the methodology, highlighting its main advantages and challenges. Clustering different scenarios in three hydrological years (high, average, and lower streamflows) and considering different inputs (soil-related metrics, irrigation system-related metrics, and all metrics) helped generate new insights into the area that would not be easily obtained using traditional methods.

Several factors contribute to the increase in irrigation demand: population growth, demand for higher value-added products, and the impacts of climate change, among others. High-quality water is essential for irrigation, so knowledge of water quality is critical. Additionally, water use in agriculture has been increasing in the last decades. Lack of water quality can cause drip clog, a lack of application uniformity, cross-contamination, and direct and indirect impacts on plants and soil. Currently, there is a need for more automated methods for evaluating and monitoring water quality for irrigation purposes, considering different aspects, from impacts on soil to impacts on irrigation systems. This work proposes a data-driven method to address this gap and implemented it in a case study in the PCJ river basin in Brazil. The methodology contains nine components and considers the main steps of the data lifecycle and the traditional machine learning workflow, allowing for automated knowledge extraction and providing important information for improving decision making. The case study illustrates the use of the methodology, highlighting its main advantages and challenges. Clustering different scenarios in three hydrological years (high, average, and lower streamflows) and considering different inputs (soil-related metrics, irrigation system-related metrics, and all metrics) helped generate new insights into the area that would not be easily obtained using traditional methods. [ABSTRACT FROM AUTHOR]

An automated vehicle storage and retrieval system (AVS/RS) is a widespread automated warehouse solution that hosts hundreds of stock-keeping units (SKU) and counts thousands of incoming and outgoing unit loads corresponding to a sequence of time-dependent storage and retrieval transactions. AVS/RS ensures high storage density, reduced cycle time, and high productivity. This study introduces and applies an original data-driven comparative and competitive multi-scenario methodology to measure and control the performance of a multi-deep tier-captive AVS/RS. This original methodology measures and controls the impact of lane depth (1), assignment strategy (2), opening strategy (3), and dispatching strategy (4) on the storage capacity, system throughput, and space efficiency in the design and configuration of an AVS/RS. The proposed methodology was applied to a real case study, demonstrating that the combination of the four leverages significantly affects system performance. [ABSTRACT FROM AUTHOR]

L'industria del design, caratterizzata da un modello di supply chain lineare, necessita di un cambiamento verso un approccio più consapevole. Questo articolo esplora il potenziale della co-creazione e della progettazione circolare per affrontare questa sfida, coinvolgendo i consumatori nel processo di design, promuovendo la creazione di prodotti più duraturi, minimizzando gli sprechi e massimizzando l'efficienza delle risorse durante l'intero ciclo di vita del prodotto. L'articolo, in conclusione, presenta alcuni casi studio di designer che hanno adottato con successo questo approccio, sviluppando prodotti sostenibili e di tendenza.

The promise of data-driven methodology in various computer disciplines has been shown by the many real-world implementations of methods based on machine learning (ML) over the last couple of decades. ML is finding its way into the computer curriculum in higher education, and an increasing number of organizations are introducing it into computer education in grades K-12. Researching how agency and intuition grow in these situations is critical as computational learning becomes increasingly common in K-12 computer instruction. However, knowing the difficulties associated with teaching algorithmic learning through grades K-12 presents an even more difficult barrier for computer education research, given the difficulties educators and schools now face in integrating traditional learning. This article describes the prospects in data mining schooling for grades K-12. These developments include adjustments to philosophy, technology, and practice. The research addresses several distinctions that K-12 computer educators should consider while addressing this problem and places the current results into the broader context of computing education. The research focuses on crucial elements of the fundamental change needed to properly incorporate ML into more comprehensive K-12 computer courses. Giving up on the idea that rule-based, "traditional" programming is necessary for next-generation computational thinking is a crucial first step. [ABSTRACT FROM AUTHOR]

The secure operation of the transmission grid is of primary importance for any power system operator. However, the introduction of new technologies, market deregulation, and increasing levels of interconnectivity have made the analysis and assessment of power system security both more challenging and essential than ever. In this context, data-driven-based methodologies are being increasingly employed to classify and anticipate insecure future states, and make inferences on potential triggers of undesired operational conditions. This paper provides a comprehensive and systematic review of this fast-moving research area and covers data-driven-based methodologies deployed in both static and dynamic security assessment. Particular attention is paid to recent trends, such as the use of spatiotemporal feature selection algorithms and the increasing research activity in short-term voltage stability and frequency stability, which are not yet widely assessed as a collective in the existing literature.

This paper uses a thorough case study experiment to examine the real-world applications of IoT-driven innovations within the context of Industry 5.0. The factory floor has a temperature of 32.5°C, a warehouse humidity of 58%, and a safe pressure level of 102.3 kPa on the manufacturing line, according to an analysis of IoT sensor data. A 5.7% decrease in energy use was made possible by the data-driven strategy, as shown by the office's CO2 levels falling to 450 parts per million. The case study participants, who had a varied range of skills, were instrumental in the implementation of IoT, and the well-organized schedule guaranteed a smooth deployment. Key Industry 5.0 indicators, such as +2% in production efficiency, -5.7% in energy usage, -29% in quality control flaws, and +33.3% in inventory turnover, show significant gains. Key metrics evaluation, data-driven methodology, case study, Industry 5.0, IoT-driven innovations, and revolutionary potential are highlighted by these results.

Organic electrochemical transistors (OECTs) show abundant potential in biosensors, artificial neuromorphic systems, brain-machine interfaces, etc With the fast development of novel functional materials and new device structures, OECTs with high transconductance (g _m > mS) and good cycling stabilities (> 10,000 cycles) have been developed. While stability characterization is always time-consuming, to accelerate the development and commercialization of OECTs, tools for stability prediction are urgently needed. In this paper, OECTs with good cycling stabilities are realized by minimizing the gate voltage amplitude during cycling, while a remaining useful life (RUL) prediction framework for OECTs is proposed. Specifically, OECTs based on p(g2T-T) show tremendously enhanced stability which exhibits only 46.1% on-current (I _ON ) and 33.2% peak g _m decreases after 80,000 cycles (53 min). Then, RUL prediction is proposed based on the run-to-failure (RtF) aging tests (cycling stability test of OECTs). By selecting two aging parameters (I _ON and peak g _m ) as health indicators (HI), a novel multi-scale feature fusion (MFF) method for RUL prediction is proposed, which consists of a long short-term memory (LSTM) neural network based multi-scale feature generator (MFG) module for feature extraction and an attention-based feature fusion (AFF) module for feature fusion. Consequently, richer effective information is utilized to improve the prediction performance, where the experimental results show the superiority of the proposed framework on multiple OECTs in RUL prediction tasks. Therefore, by introducing such a powerful framework for the evaluation of the lifetime of OECTs, further optimization of materials, devices, and integrated systems relevant to OECTs will be stimulated. Moreover, this tool can also be extended to other relevant bioelectronics.

In this study, a data-driven methodology for the reliability analysis of natural gas compressor units is developed, and both the historical failure data and performance data are employed. In this methodology, firstly, the reliability functions of the catastrophic failure and degradation failure are built. For catastrophic failure, the historical failure data are collected, and the rank regression model is utilized to obtain the reliability function of the catastrophic failure. For degradation failure, a support-vector machine is employed to predict the unit's performance parameters, and the reliability function of the degradation failure is determined by comparing the performance parameters with the failure threshold. Finally, the reliability of the compressor unit is assessed and predicted by integrating the reliability functions of the catastrophic failure and the degradation failure, and both their correlation and competitiveness are considered. Furthermore, the developed methodology is applied to an actual compressor unit to confirm its feasibility, and the reliability of the compressor unit is predicted. The assessment results indicate the significant impact of the operating conditions on the precise forecasting of the performance parameters. Moreover, the effects of the value of the failure threshold and the correlation of the two failure modes on the reliability are investigated. [ABSTRACT FROM AUTHOR]

This article advances a critical approach to the analysis of social policy texts drawing on the philosophical perspectives of hyperrealism, surrealism, ethics, and Critical Discourse Analysis. Drawing on official government texts and speeches on the continuing development of Singapore's education policy, the paper examines the way metaphors of flexibility, diversity, choice, and opportunity are used within an evolving ideological context that work to continually produce truth conditions as justifications for inequality. In doing this, the analysis foregrounds a functional aspect of policy metaphors as divisive mechanisms of neo-liberalism through associating individuals with the appearance of discriminatory forms of economic materiality which does not in fact exist in reality. [ABSTRACT FROM AUTHOR]

Background: Potential therapy and confounding factors including typical co‐administered medications, patient’s disease states, disease prevalence, patient demographics, medical histories, and reasons for prescribing a drug often are incomplete, conflicting, missing, or uncharacterized in spontaneous adverse drug event (ADE) reporting systems. These missing or incomplete features can affect and limit the application of quantitative methods in pharmacovigilance for meta-analyses of data during randomized clinical trials. Methods: Data from patients with hypertension were retrieved and integrated from the FDA Adverse Event Reporting System; 134 antihypertensive drugs out of 1131 drugs were filtered and then evaluated using the empirical Bayes geometric mean (EBGM) of the posterior distribution to build ADE-drug profiles with an emphasis on the pulmonary ADEs. Afterward, the graphical least absolute shrinkage and selection operator (GLASSO) captured drug associations based on pulmonary ADEs by correcting hidden factors and confounder misclassification. Selected drugs were then compared using the Friedman test in drug classes and clusters obtained from GLASSO. Results: Following multiple filtering stages to exclude insignificant and noise-driven reports, we found that drugs from antihypertensives agents, urologicals, and antithrombotic agents (macitentan, bosentan, epoprostenol, selexipag, sildenafil, tadalafil, and beraprost) form a similar class with a significantly higher incidence of pulmonary ADEs. Macitentan and bosentan were associated with 64% and 56% of pulmonary ADEs, respectively. Because these two medications are prescribed in diseases affecting pulmonary function and may be likely to emerge among the highest reported pulmonary ADEs, in fact, they serve to validate the methods utilized here. Conversely, doxazosin and rilmenidine were found to have the least pulmonary ADEs in selected drugs from hypertension patients. Nifedipine and candesartan were also found by signal detection methods to form a drug cluster, shown by several studies an effective combination of these drugs on lowering blood pressure and appeared an improved side effect profile in comparison with single-agent monotherapy. Conclusions: We consider pulmonary ADE profiles in multiple long-standing groups of therapeutics including antihypertensive agents, antithrombotic agents, beta-blocking agents, calcium channel blockers, or agents acting on the renin-angiotensin system, in patients with hypertension associated with high risk for coronavirus disease 2019 (COVID-19). We found that several individual drugs have significant differences between their drug classes and compared to other drug classes. For instance, macitentan and bosentan from endothelin receptor antagonists show major concern while doxazosin and rilmenidine exhibited the least pulmonary ADEs compared to the outcomes of other drugs. Using techniques in this study, we assessed and confirmed the hypothesis that drugs from the same drug class could have very different pulmonary ADE profiles affecting outcomes in acute respiratory illness. Funding: GJW and MJD accepted funding from BioNexus KC for funding on this project, but BioNexus KC had no direct role in this article.

In this work, we propose and develop data-driven explicit state-space based fault detection, isolation and estimation filters that are directly identified and constructed from only the available system input–output (I/O) measurements and through only the estimated system Markov parameters. The proposed methodology does not involve a reduction step and does not require identification of the system extended observability matrix or its left null space. The performance of our proposed filters is directly related to and linearly dependent on the Markov parameters identification errors. The estimation filters operate with a subset of the system I/O data that is selected by the designer. It is shown that our proposed filters provide an asymptotically unbiased estimate by invoking a low order filter as long as the selected subsystem has a stable inverse. We have derived the estimation error dynamics in terms of the Markov parameters identification errors and have shown that they can be directly synthesized from the healthy system I/O data. Consequently, our proposed methodology ensures that the estimation errors can be effectively compensated for. Finally, we have provided several illustrative case study simulations that demonstrate and confirm the merits of our proposed schemes as compared to methodologies that are available in the literature. [ABSTRACT FROM AUTHOR]

Relocated I-frames are a key type of abnormal inter-coded frame in double compressed videos with shifted GOP structures. In this work, a frame-wise detection method of relocated I-frame is proposed based on convolutional neural network (CNN). The proposed detection framework contains a novel network architecture, which initializes with a preprocessing layer and is followed by a well-designed CNN. In the preprocessing layer, the high-frequency component extraction operation is applied to eliminate the influence of diverse video contents. To mitigate overfitting, several advanced structures, such as 1 × 1 convolutional filter and the global average-pooling layer, are carefully introduced in the design of the CNN architecture. Public available YUV sequences are collected to construct a dataset of double compressed videos with different coding parameters. According to the experiments, the proposed framework can achieve a more promising performance of relocated I-frame detection than a well-known CNN structure (AlexNet) and the method based on average prediction residual. [ABSTRACT FROM AUTHOR]