Real-time Approach Research Articles

Time-dependent quantum/continuum modeling of plasmon-enhanced electronic circular dichroism.

In this work, we present a multiscale real-time approach to study the plasmonic effects of a metal nanoparticle (NP) on the electronic circular-dichroism (ECD) spectrum of a chiral molecule interacting with it. The method is based on the time-evolution of the molecule's time-dependent wavefunction, expanded in the eigenstates of a perturbed Hamiltonian. A quantum description of the molecular system is coupled to a classical representation of the NP via a continuum model. The method is applied to methyloxirane and peridinin at various distances (1, 3, and 5nm) with respect to a gold NP surface. While no remarkable effect is observed for methyloxirane at any studied distance, an enhancement appears when the peridinin lies at 1nm and the pulse is linearly polarized perpendicularly to the molecular axis, with the ECD signal centered at 4.1eV increased by a factor of around 20. These results are rationalized looking at the gap between the plasmonic peak of the NP at around 2.5eV and the molecular excitations: the smaller the gap between molecular and plasmonic excitations, the larger the plasmonic enhancement of the ECD signal. Moreover, ECD peaks are selectively enhanced due to the favorable coupling between the pulse polarization and the combined effect of electric and magnetic dipole moments. This approach allows one to go through the electronic structure and dynamics of chiral molecules for obtaining a realistic description of plasmon-mediated ECD spectra, e.g., paving the way to applications to molecules of biological relevance interacting with nanostructures of experimental interest.

Single and multi-objective real-time optimisation of an industrial injection moulding process via a Bayesian adaptive design of experiment approach

Minimising cycle time without inducing quality defects is a major challenge in injection moulding (IM). Design of Experiment methods (DoE) have been widely studied for optimisation of injection moulding, however existing methods have limitations, including the need for a large number of experiments within a pre-determined search space. Bayesian adaptive design of experiment (ADoE) is an iterative process where the results of the previous experiments are used to make an informed selection for the next design. In this study, an experimental ADoE approach based on Bayesian optimisation was developed for injection moulding using process and sensor data to optimise the quality and cycle time in real-time. A novel approach for the real-time characterisation of post-production shrinkage was introduced, utilising in-mould sensor data on temperature differential during part cooling. This characterisation approach was verified by post-production metrology results. A single and multi-objective optimisation of the cycle time and temperature differential (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta T$$\\end{document}) in an injection moulded component is proposed. The multi-objective optimisation techniques, composite desirability function and Nondominated Sorting Genetic Algorithm (NSGA-II) using the Response Surface Methodology (RSM) model, are compared with the real-time novel ADoE approach. ADoE achieved almost a 50\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} reduction in the number of experiments required for the single optimisation of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta T$$\\end{document}, and an almost 30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} decrease for the optimisation of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta T$$\\end{document} and cycle time together compared to composite desirability function and NSGA-II. The optimal settings identified by ADoE for multiobjective optimisation were similar to the selected Pareto optimal solution found by NSGA-II.

Real-time executable platoon formation approach using hierarchical cooperative motion planning framework

While connected and automated vehicle (CAV) platooning holds promise for enhancing traffic efficiency and reducing energy consumption, we still lack efficient algorithms for guiding the local movements of CAVs to form a platoon on a road due to significant computational and control challenges. This study addresses this gap by designing a real-time executable Hierarchical and Recursive Platoon Formation (HR-PF) framework tailored to mixed flow traffic conditions that encompass both Human-Driven Vehicles (HDV) and CAVs. The HR-PF framework comprises three hierarchical mathematical models (modules) designed to optimize platoon formation while considering both macroscopic traffic conditions and microscopic traffic safety. Module-I formulates a mixed integer quadratic program to determine the timing, location, and state of platoon formation. It is further extended to a mixed integer nonlinear program so that we can also select the optimal size of the target platoon. Module-II designs a Hybrid State-Lattice Motion planner to generate optimal trajectory references for CAVs to approach the target platoon state, ensuring microscopic traffic safety. Module-III develops longitudinal and lateral controllers to enable CAVs to track trajectory references accurately. These models function recursively at varying frequencies to balance mathematical rigor with practical application. Numerical experiments demonstrate that HR-PF facilitates efficient platoon formation in real-time across diverse traffic scenarios and road geometries while sustaining traffic efficiency. Furthermore, the performance of platoon formation is affected by surrounding traffic density and CAV penetrations, with prompt formation observed under LOS C and D traffic environments and slightly more traffic impacts under LOS E and F. These findings provide robust support for exploring advanced platoon formation and platooning strategies for CAVs under complicated traffic environments.

Time-dependent abinitio molecular-orbital decomposition for high-harmonic generation spectroscopy.

We propose a real-time time-dependent abinitio approach within a configuration-interaction-singles ansatz to decompose the high-harmonic generation (HHG) signal of molecules in terms of individual molecular-orbital (MO) contributions. Calculations have been performed by propagating the time-dependent Schrödinger equation with complex energies, in order to account for ionization of the system, and by using tailored Gaussian basis sets for high-energy and continuum states. We have studied the strong-field electron dynamics and the HHG spectra in aligned CO2 and H2O molecules. Contribution from MOs in the strong-field dynamics depends on the interplay between the MO ionization energy and the coupling between the MO and the laser-pulse symmetries. Such contributions characterize different portions of the HHG spectrum, indicating that the orbital decomposition encodes nontrivial information on the modulation of the strong-field dynamics. Our results correctly reproduce the MO contributions to HHG for CO2 as described in the literature experimental and theoretical data and lead to an original analysis of the role of the highest occupied molecular orbitals HOMO, HOMO-1, and HOMO-2 of H2O according to the polarization direction of the laser pulse.

A Real-Time Approach for Error Detection in 𝜇PMU Measurements

A Real-Time Approach for Error Detection in 𝜇PMU Measurements

A Novel Normalized Quantitative Real-Time PCR Approach for Ensuring Roe Deer (Capreolus capreolus) Meat Authenticity in Game Meat Foods

Roe deer meat is a prized game product in many European countries. However, concerns exist regarding the accuracy of the amount of declared roe deer in processed game meat foods. This study aimed to develop a reliable method for the detection and quantification of roe deer in commercialized game meat products. A TaqMan probe-based quantitative real-time PCR (qPCR) assay was designed, targeting a single-copy 120-bp region of the roe deer agouti signaling protein (ASIP) encoding gene. The method employed the normalized ∆Cq approach to establish a calibration curve for roe deer detection and quantification within 0.05–50% (w/w) in complex raw and processed matrices. The method proved to be specific for roe deer identification, achieving limits of detection and quantification of 0.04 ng of roe deer DNA and 0.05% (w/w) of roe deer in simulated pâté. Following validation with blind samples, highlighting the precision and trueness of the approach, the assay was applied to 46 market samples from four European origins (Poland, Portugal, France, and Spain). The analysis revealed significant discrepancies between declared roe deer content and actual levels in all roe deer labeled products. The global analysis of results, combining the previous survey on red deer species with present roe deer data, identified 61% of mislabeled/adulterated samples due to the absence of deer species, substitution of roe deer with red deer, substitution of fallow deer with other deer species and red deer with pork, and undeclared addition of roe deer. This study demonstrates the effectiveness of the developed qPCR method for accurate roe deer meat authentication in foods, showing its usefulness as a tool for routine food inspection to ensure labeling compliance.

Optimization of Real-Time Control Approach: Number, Placement, and Proportional–Integral–Derivative Control Rules of Flow Control Devices in Distributed Flood Routing

Climate change, through more frequent extreme weather events, and urban sprawl, by increasing runoff, are two critical threats to drainage networks, impacting both public health and property. Augmenting drainage networks to withstand additional stress by enlarging conduits or constructing new detention facilities requires a significant financial investment. The goal of this study is to enhance urban resilience by optimizing real-time control (RTC) systems for drainage networks that optimize the flow control devices (FCDs), which could mitigate the need to invest in major construction costs. RTC is an approach that can help mitigate flooding in urban areas. This study is the first to optimize feedback controllers in SWMM, as well as the first to simultaneously optimize the number, location, and proportional–integral–derivative (PID) controllers for FCDs through two nested genetic algorithms (GAs), and especially within a unified environment (i.e., Python), which led to more efficient management of the process, thereby enhancing the efficiency of urban drainage network optimization. This study examined the impact of optimized RTC on the urban drainage network (UDN) in a part of New Orleans, LA, USA, under 1-, 2-, 5-, and 10-year storm events. The optimized RTC resulted in an improvement of up to 50% in network performance during a design storm. The results demonstrate the applicability in an urban environment where storms, flooding, and financial investments are critical to the management of stormwater drainage.

Adaptive hybrid prediction model for adapting to data distribution shifts in machining quality prediction

Abstract The accuracy of data-driven intelligent prediction for machining quality relies on the training samples. However, in actual applications, the continuous operation of machining equipment leads to gradual distribution shifts between the process data and the training samples for modeling. The shifts result in a degradation in the performance of predictive model, previous studies have often overlooked this issue. To tackle with the intricate problem, this research proposes a real-time model optimization approach. Firstly, a method for detecting machining data distribution shifts based on the two-sample Kolmogorov–Smirnov test is proposed. Then, an adaptive hybrid prediction model (AHPM) capable of real-time optimization is developed. This model consists of a deep neural network (DNN) and a broad learning system (BLS). DNN plays a primary role in prediction within the hybrid model with excellent generalization capability. BLS quickly completes optimization prior to DNN with its unique parameter update mechanism to compensate for prediction loss. Experimental results indicate that AHPM achieves the shortest optimization time while maintaining high accuracy, with post-optimization error reduction rates for mean squared error, mean absolute error, and mean absolute percentage error all exceeding 10%. In the test of application to actual machining cases, accuracy improved by 8.88% compared to traditional methods without optimization.

The Influence of Social Change on Human Resource Management Strategies in the Digital Era

Social change in the digital era has had a significant impact on various aspects of life, including the world of work and human resource management (HR). This study aims to explore the influence of social change on HR management strategies in the digital era, as well as identify the challenges and opportunities that arise in the process of organizational adaptation. Using a qualitative approach and literature review method, this study analyzes the latest scientific literature from various trusted sources, including academic journals, textbooks, and industry reports. The results of the study indicate that social change has driven transformation in various aspects of HR management, including recruitment, retention, employee development, performance management, and organizational culture. Digitalization, shifting workforce demographics, and changing employee expectations are the main factors influencing HR strategies. Key findings include: (1) adoption of AI and big data technologies in the recruitment and selection process, (2) increased focus on work flexibility and work-life balance, (3) transformation of employee development programs towards a continuous learning and personalization model, (4) shift from traditional performance management to a more agile and real-time feedback-based approach, and (5) the importance of building an inclusive and adaptive organizational culture. The study also uncovers the ethical and social challenges that arise from the digitalization of HR practices, as well as the importance of bridging the digital divide in the workforce. In conclusion, successful organizations are those that are able to adapt their HR strategies to meet the challenges of the digital era, while maintaining a focus on the human factor as the core of effective HR management.

RCCNet: A Spatial-Temporal Neural Network Model for Logistics Delivery Timely Rate Prediction

In logistics service, the delivery timely rate is a key experience indicator, which is highly essential to the competitive advantage of express companies. Prediction on it enables intervention on couriers with low predicted results in advance, thus ensuring employee productivity and customer satisfaction. Currently, few related works focus on couriers’ level delivery timely rate prediction, and there are complex spatial correlations between couriers and road districts in the express scenario, which makes traditional real-time prediction approaches hard to utilize. To deal with this, we propose a deep spatial-temporal neural network, RCCNet to model spatial-temporal correlations. Specifically, we adopt Node2vec, which can encode the road network-based graph directly to capture spatial correlations between road districts. Further, we calculate couriers’ historical time-series similarity to build a graph and employ graph convolutional networks to capture the correlation between couriers. We also leverage historical sequential information with long short-term memory networks. We conduct experiments with real-world express datasets. Compared with other competitive baseline methods widely used in industry, the experiment results demonstrate its superior performance over multiple baselines.

Exploring the Utility of a Real-Time Approach to Characterising Within-Person Fluctuations in Everyday Stress Responses.

Few studies have measured components of stress responses in real time-an essential step in designing just-in-time interventions targeting moments of risk. Using ecological momentary assessment (EMA), we characterised stress response components to everyday stressors, including reactivity (the response following a stressor), recovery (the return towards baseline), and pile-up (the accumulation of stressors) (RRPs) by quantifying the dynamics of response indicators (i.e., subjective stress, negative affect, and perseverative cognition). To determine the utility of these novel measures in capturing and characterising acute moments of the stress response, this study evaluated the proportion of variance in RRPs attributed to (1) between-person, (2) between-days, and (3) within-day (momentary) levels. Healthy adults (n=123; aged 35-65, 79% women, 91% non-Hispanic White) participated in a 14-day study assessing stress response via EMA 6times a day. RRPs were constructed from 10,065 EMA reports. Multilevel models with moments nested within days nested within persons were used to partition variance in the RRPs. Reactivity and recovery indicators captured the most variation within-days (i.e., across moments; range 76%-80% and 87%-89%, respectively), with small amounts of variance between-person. For pile-up, variation was mostly observed between-days (range 60%-63%) and between-persons (range 27%-31%). In contrast, raw measures of stress response reflected substantial between-person (range 32%-54%) and within-day (range 34%-53%) variance. These results demonstrated that a person-specific approach to measuring stress response components (i.e., RRPs) can capture the dynamic within-person variation in stress response, as it occurs in real time, making it well-suited for use in novel just-in-time interventions targeting moments of risk.

LIVE MRP (Material Requirements Planning) Impact on Supply Chain

Live Material Requirements Planning (Live MRP) refers to an advanced, real-time approach to managing and optimizing inventory, production, and procurement processes. Unlike traditional MRP systems that update periodically (e.g., daily, weekly), Live MRP integrates dynamic data from across the supply chain and production processes, providing instantaneous updates to material needs, production schedules, and procurement activities. This capability allows companies to respond more swiftly to market fluctuations, production disruptions, and changing customer demands, enhancing operational efficiency, reducing lead times, and improving decision-making

A Digital Twin Based Real-Time Augmented Vision System Approach for Healthcare Applications

A Digital Twin Based Real-Time Augmented Vision System Approach for Healthcare Applications

Enhancing Real-Time Emotion Recognition in Classroom Environments Using Convolutional Neural Networks: A Step Towards Optical Neural Networks for Advanced Data Processing

In contemporary academic settings, end-of-semester student feedback on a lecturer’s teaching abilities often fails to provide a comprehensive, real-time evaluation of their proficiency, and becomes less relevant with each new cohort of students. To address these limitations, an innovative feedback method has been proposed, utilizing image processing algorithms to dynamically assess the emotional states of students during lectures by analyzing their facial expressions. This real-time approach enables lecturers to promptly adapt and enhance their teaching techniques. Recognizing and engaging with emotionally positive students has been shown to foster better learning outcomes, as their enthusiasm actively stimulates cognitive engagement and information analysis. The purpose of this work is to identify emotions based on facial expressions using a deep learning model based on a convolutional neural network (CNN), where facial recognition is performed using the Viola–Jones algorithm on a group of students in a learning environment. The algorithm encompasses four key steps: image acquisition, preprocessing, emotion detection, and emotion recognition. The technological advancement of this research lies in the proposal to implement photonic hardware and create an optical neural network which offers unparalleled speed and efficiency in data processing. This approach demonstrates significant advancements over traditional electronic systems in handling computational tasks. An experimental validation was conducted in a classroom with 45 students, demonstrating that the level of understanding in the class as predicted was 43–62.94%, and the proposed CNN algorithm (facial expressions detection) achieved an impressive 83% accuracy in understanding students’ emotional states. The correlation between the CNN deep learning model and the students’ feedback was 91.7%. This novel approach opens avenues for the real-time assessment of students’ engagement levels and the effectiveness of the learning environment, providing valuable insights for ongoing improvements in teaching practices.

Development of Software Interface for AI-Driven Weed Control in Robotic Vehicles, with Time-Based Evaluation in Indoor and Field Settings

Herbicide blanket application is widely practiced by farmers to chemically control weeds in the field, thereby enhancing productivity and improving crop quality. However, their repetitive use also has caused several negative issues on the environment and human health. To overcome these negative issues precision site-specific weed control can be used to reduce the herbicide application by targeted application on only weed areas. Hence, a robotic platform utilizing NVIDIA Jetson embedded device as control and processing unit was developed and tested on lab and field conditions for weed control. The average time taken for data acquisition and artificial intelligence-based computer vision tasks was tested under both lab and field conditions. Moreover, a grid map creation algorithm using YOLOv4 deep learning algorithm was evaluated to control the nozzles of the robotic platform. Integrating all these components together enabled real-time weed management approaches, enhancing the precision and efficiency of herbicide application Independent paired t-tests reveal that there is no significant difference in computational time between lab and field testing. Conversely, independent paired t-test revels that there is significant difference in image size between lab and field testing. The reduction of herbicide application based on grid map was obtained 79 to 80 %, which does not include YOLOv4 algorithm failure and system synchronization failure. The study suggests the importance of field testing for real-time applications of the robotic platform by using deep learning computer vision methods for weed control.

Tensor product random matrix theory

The evolution of complex correlated quantum systems such as random circuit networks is governed by the dynamical buildup of both entanglement and entropy. We here introduce a real-time field theory approach—essentially a fusion of the GΣ functional of the SYK model and the field theory of disordered systems—engineered to microscopically describe the full range of such crossover dynamics: from initial product states to a maximum entropy ergodic state. To showcase this approach in the simplest nontrivial setting, we consider a tensor product of coupled random matrices, and compare to exact diagonalization. Published by the American Physical Society 2024

Integrating Fintech-Driven Technical Analysis into Value Investing: A Study of Moving Averages in Cryptocurrency Market Dynamics

This research explores the application of fintech-driven technical analysis, specifically focusing on moving averages within value investing. Using Coinbase as a case study, the study examines the effectiveness of moving averages in volatile market conditions where traditional metrics are less applicable. Historical stock data was exported from Yahoo Finance and analyzed using Excel, emphasizing the 30 vs. 10 moving average combination. This combination was chosen to balance short-term and long-term market perspectives. The analysis revealed that while moving averages effectively highlight trends and momentum shifts, they are lagging indicators and require supplementary analysis to account for external factors such as news and regulatory changes. Despite not being a precise predictive tool, moving averages provided valuable insights into market behavior, supporting the argument that fintech tools can complement traditional value investing strategies by offering real-time analysis and dynamic approaches to market assessment.

Spatial participatory planning for urban logistics: A GIS-enhanced Real-Time Spatial Delphi approach

Effective urban logistics planning plays a crucial role in effectively managing infrastructure and transportation networks, contributing to the mitigation of negative externalities associated with last-mile deliveries and fostering city sustainability. Among the various facets of supply chain management, selecting appropriate locations in urban areas for logistics facilities, such as automated parcel lockers and smart loading bays, stands out. However, the complexity of decision-making in this context, involving diverse stakeholders with potential conflicting objectives, poses a significant challenge in reaching a common consensus. This paper proposes an innovative approach to address this issue by adopting a spatial participatory process that combines GIS models and Real-Time Spatial Delphi to identify suitable locations for two logistic facilities. The case study is the city of Dublin (Ireland), where an Urban Living Lab of the H2020 project SENATOR is based. A modified Delphi is used, first gathering expert opinions on preferences and objectives. These insights are then used to develop a spatial analysis using GIS, ranking areas according to the expert preferences. These outcomes form the basis for the Real-Time Spatial Delphi, where stakeholders and experts working on the project collaboratively engage to achieve a spatial consensus on the territory. The findings of this study provide valuable information for stakeholders, policymakers, local authorities, and governmental bodies to implement specific actions. Through the active involvement of key actors in the decision-making process, this approach ensures that the selected locations for logistics facilities align with the needs of all parties involved.

Modulating subjective pain perception with decoded Montreal Neurological Institute-space neurofeedback: a proof-of-concept study.

Pain is a complex emotional experience that still remains challenging to manage. Previous functional magnetic resonance imaging (fMRI) studies have associated pain with distributed patterns of brain activity (i.e. brain decoders), but it is still unclear whether these observations reflect causal mechanisms. To address this question, we devised a new neurofeedback approach using real-time decoding of fMRI data to test if modulating pain-related multivoxel fMRI patterns could lead to changes in subjective pain experience. We first showed that subjective pain ratings can indeed be accurately predicted using a real-time decoding approach based on the stimulus intensity independent pain signature (SIIPS) and the neurologic pain signature (NPS). Next, we trained participants (n = 16) in a double-blinded decoded fMRI neurofeedback experiment to up- or downregulate the SIIPS. Our results indicate that participants can learn to downregulate the expression of SIIPS independently from NPS expression. Importantly, the success of this neurofeedback training was associated with the perceived intensity of painful stimulation following the intervention. Taken together, these results indicate that closed-loop brain imaging can be efficiently conducted using a priori fMRI decoders of pain, potentially opening up a new range of applications for decoded neurofeedback, both for clinical and basic science purposes. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

A Multi-View Real-Time Approach for Rapid Point Cloud Acquisition and Reconstruction in Goats

The body size, shape, weight, and scoring of goats are crucial indicators for assessing their growth, health, and meat production. The application of computer vision technology to measure these parameters is becoming increasingly prevalent. However, in real farm environments, obstacles, such as fences, ground conditions, and dust, pose significant challenges for obtaining accurate goat point cloud data. These obstacles lead to difficulties in rapid data extraction and result in incomplete reconstructions, causing substantial measurement errors. To address these challenges, we developed a system for real-time, non-contact acquisition, extraction, and reconstruction of goat point clouds using three depth cameras. The system operates in a scenario where goats walk naturally through a designated channel, and bidirectional distributed triggering logic is employed to ensure real-time acquisition of the point cloud. We also designed a noise recognition and filtering method tailored to handle complex environmental interferences found on farms, enabling automatic extraction of the goat point cloud. Furthermore, a distributed point cloud completion algorithm was developed to reconstruct missing sections of the goat point cloud caused by unavoidable factors such as railings and dust. Measurements of body height, body slant length, and chest circumference were calculated separately with deviation of no more than 25 mm and an average error of 3.1%. The system processes each goat in an average time of 3–5 s. This method provides rapid and accurate extraction and complementary reconstruction of 3D point clouds of goats in motion on real farms, without human intervention. It offers a valuable technological solution for non-contact monitoring and evaluation of goat body size, weight, shape, and appearance.