Reference Model Research Articles

Enhancing thermodynamic consistency: Clarification on the application of asymmetric activity model in multi-component chromatographic separation

The single-component Mollerup model, with over 40 direct applications and 442 citations, is the most widely used activity model for chromatographic mechanistic modeling. Many researchers have extended this formula to multi-component systems by directly adding subscripts, a modification deemed thermodynamically inconsistent (referred to as the reference model). In this work, we rederived the asymmetric activity model for multi-component systems, using the van der Waals equation of state, and termed it the multi-component Mollerup model. In contrast to the reference model, our proposed model accounts for the contributions of all components to the activity. Three numerical experiments were performed to investigate the impact of the three different activity models on the chromatographic modeling. The results indicate that our proposed model represents a thermodynamically consistent generalization of the single-component Mollerup model to multi-component systems. This communication advocates adopting of the multi-component Mollerup model for activity modeling in multi-component chromatographic separation to enhance thermodynamic consistency.

The Composition of Earth's Lower Mantle

Determining the composition of Earth's lower mantle, which constitutes almost half of its total volume, has been a central goal in the Earth sciences for more than a century given the constraints it places on Earth's origin and evolution. However, whether the major element chemistry of the lower mantle, in the form of, e.g., Mg/Si ratio, is similar to or different from the upper mantle remains debated. Here we use a multidisciplinary approach to address the question of the composition of Earth's lower mantle and, in turn, that of bulk silicate Earth (crust and mantle) by considering the evidence provided by geochemistry, geophysics, mineral physics, and geodynamics. Geochemical and geodynamical evidence largely agrees, indicating a lower-mantle molar Mg/Si of ≥1.12 (≥1.15 for bulk silicate Earth), consistent with the rock record and accumulating evidence for whole-mantle stirring. However, mineral physics–informed profiles of seismic properties, based on a lower mantle made of bridgmanite and ferropericlase, point to Mg/Si ∼ 0.9–1.0 when compared with radial seismic reference models. This highlights the importance of considering the presence of additional minerals (e.g., calcium-perovskite and stishovite) and possibly suggests a lower mantle varying compositionally with depth. In closing, we discuss how we can improve our understanding of lower-mantle and bulk silicate Earth composition, including its impact on the light element budget of the core. ▪The chemical composition of Earth's lower mantle is indispensable for understanding its origin and evolution.▪Earth's lower-mantle composition is reviewed from an integrated mineral physics, geophysical, geochemical, and geodynamical perspective.▪A lower-mantle molar Mg/Si of ≥1.12 is favored but not unique.▪New experiments investigating compositional effects of bridgmanite and ferropericlase elasticity are needed to further our insight.

Enabling self-service technology (SST) using an intelligent chatbot to advance the online apparel co-design process

ABSTRACT Apparel co-design in online has emerged as a promising approach to enhancing customer experience by offering customised apparel. However, effectively communicating accurate product requirements through product configurators for co-design is a challenge for non-specialists. This study proposes an approach to improve the online apparel co-design by developing a natural language-based, self-service technology-enabled chatbot to effectively elicit customer requirements. The study was based on sports bra as a case study, with a Wizard of Oz approach used to characterise the chatbot requirements through a newly developed website. Thematic analysis was conducted on chatlogs generated by thirty participants who co-designed a sports bra with the assistance of a product expert. Key findings were used to develop a reference model based on a morphological box, which helps design and develop a chatbot on product configurators in the online apparel retail industry to enhance experience through effective customer requirement elicitation.

Optimal Controller Identification for multivariable non-minimum phase systems

This work deals with data-driven control for non-minimum phase (NMP) systems, where the goal is to design a controller for a plant whose model is unknown by using a batch of input–output data collected from it. The approach is based on the Model Reference paradigm, where a transfer function matrix – the reference model – is used to specify the desired closed-loop performance. The NMP systems issue in Model Reference approaches is a well-known problem in control literature and it is no different in data-driven methods. This work explains how to adapt the formulation of the Optimal Controller Identification (OCI) method to cope with this class of systems. Considering a convenient parametrization of the reference model and a flexible performance criterion, it is possible to identify the NMP transmission zeros of the plant along with the optimal controller parameters, as it will be shown. Both diagonal and block-triangular reference model structures are treated in detail. Simulation examples show the effectiveness of the proposed approach.

An AprilTags-Based Approach for Progress Monitoring and Quality Control in Modular Construction

Traditional approaches to modular construction progress monitoring and quality control with stringent and tight tolerances for on-site and off-site assembly processes are usually based on 3D laser scanning, but the high equipment costs associated with acquiring point clouds have economic ramifications. This paper provides the details of a new and inexpensive method through the integration of AprilTags and an ordinary phone. By using AprilTags instead of QR codes to label modules, progress management is achieved through the rapid identification and association of multiple modules based on a single image. Moreover, a virtual multi-view vision algorithm based on AprilTags is proposed to generate 3D reverse models of the construction site; the quality result can be acquired by comparing the offset and rotation values of the reverse model and the BIM model. Finally, all the algorithms are validated through comparing the reverse models with the reference models made with 3D printing and 3D laser scanning, which verifies the accuracy and efficiency of the proposed method.

Transforming Interactive Educational Content into Immersive Virtual Reality Learning Objects

Currently, the integration of immersive virtual reality applications into learning management systems is limited, which disadvantages kinesthetic learners, as the majority of learning material is mostly written or visual in nature. This research presents a method on how to transform existing conventional learning material into immersive virtual reality and integrate it into learning management systems, demonstrating it with a pilot study and an immersive virtual reality application prototype. The prototype application uses WebXR technology and provides integration into the Moodle learning management system using the Sharable Content Object Reference Model standard. The developed learning object was evaluated using the System Usability Scale, Web-based Learning Tool evaluation scale and the igroup Presence Questionnaire. The results of the pilot study revealed that the learning object performed above average in all tested fields and is preferable to established non-IVR solutions.

Scheduling optimization of shared energy storage and peer-to-peer power trading among industrial buildings

Constructing distributed photovoltaic systems on industrial building rooftops and establishing adaptive microgrid energy flow scheduling models are effective means of achieving full deployment and consumption of photovoltaics. The operational modes and stakeholders involved in shared energy storage and peer-to-peer trading differ significantly, influencing both the energy flow scheduling and on-site consumption rates of microgrids. In this study, a dual-objective function model with multiple constraints was designed, and particle swarm optimization was applied to seek optimal energy storage capacity. Pareto optimal solution sets under dual objectives with conflicting interests were explored, and comparative analyses of energy flow scheduling under shared energy storage and peer-to-peer trading microgrid modes were conducted. Furthermore, the daily cost-effectiveness of these two modes was evaluated. The results indicated that stakeholders with conflicting interests difficult to achieve win–win outcomes in energy scheduling. The integration of peer-to-peer trading not only reduced shared energy storage capacity by 18% but also achieved local consumption rates of 62% and 100% in summer and winter, respectively. Peer-to-peer trading had a minimal impact on user power costs, yet it increases power revenues by 32% and 235% in summer and winter, respectively, thereby reducing the net expenditure of the users. The results of this study provide a reference for microgrid energy flow scheduling models involving entities of uncommon interest in industrial parks.

Spatial-temporal variations in evapotranspiration across the continental United States: An atmospheric water balance perspective

Accurate estimation of terrestrial evapotranspiration (ET) is crucial for understanding land–atmosphere interactions, but is challenging on the regional scales. The water balance approach, including terrestrial and atmospheric water balance (TWB and AWB), provides a simple tool for estimating regional ET. This study estimated ET values based on the AWB approach (ETAWB) over the continental United States (CONUS) for the period 1979–2021. Validations using TWB-based ET estimates (ETTWB) suggest that ETAWB is accurate. ETAWB demonstrates comparable interannual variability with the other three long-term ET products over the CONUS and is consistent with ETTWB in the majority of basins. The 43-year CONUS averaged ETAWB is 548 ± 26 mm/year, with higher values in the eastern region and the coastal regions in the western CONUS, and lower values in the western arid regions. During 1979–2021, increasing trends of ETAWB are observed in the eastern CONUS, while decreasing trends occurred in the western regions. The intercomparison between ETAWB, GLEAM, Noah, and ERA5 illustrates similar spatial patterns and linear trends. In the water-limited arid basins, ETAWB anomalies over time show strong agreements with precipitation anomalies. The results of this study indicate that the AWB approach provides reasonable regional-to-continental terrestrial ET estimates over the CONUS, serving as a reference for hydrological and climatic modeling.

Semantic Web-Based Energy-Efficient Design of Dual-Mode Vehicles Based on Deep Neural Networks

Dual-Mode Vehicles (DMVs) represent a new and sustainable solution to the problems of urban mobility, and energy consumption. This proposed work of a DMV can operate without a conventional battery. Instead, it uses a combination of energy management technology, renewable energy systems, and other energy sources to ensure continuous operation. The efficiency and the working statistics of such DMV could also be sufficiently monitored through semantic techniques. The user can freely choose the type of working of the vehicle based on the current inputs given by the users. These semantics serve a crucial role in establishing a reference model for seamless data exchange between the trained Bald Eagle Search (BES) algorithm and the deep neural network (DNN). This proposed work has been implemented and the performance has been analyzed concerning the State Of Charging (SOC), generator voltage, and DC-DC converter voltage. The proposed work yields higher SOC and voltage than other existing schemes.

Adaptive variable impedance force/position hybrid control for large surface polishing

PurposeThis paper aims to propose a hybrid force/position controller based on the adaptive variable impedance.Design/methodology/approachFirst, the working space is divided into a force control subspace and a position subspace, the force control subspace adopts the position impedance control strategy. At the same time, the contact force model between the robot and the surface is analyzed in this space. Second, based on the traditional position impedance, the model reference adaptive control is introduced to provide an accurate reference position for the impedance controller. Then, the BP neural network is used to adjust the impedance parameters online.FindingsThe experimental results show that compared with the traditional PI control method, the proposed method has a higher flexibility, the dynamic response accommodation time is reduced by 7.688 s and the steady-state error is reduced by 30.531%. The overshoot of the contact force between the end of robot and the workpiece is reduced by 34.325% comparing with the fixed impedance control method.Practical implicationsThe proposed control method compares with a hybrid force/position based on PI control method and a position fixed impedance control method by simulation and experiment.Originality/valueThe adaptive variable impedance control method improves accuracy of force tracking and solves the problem of the large surfaces with robot grinding often over-polished at the protrusion and under-polished at the concave.

An efficient learning based approach for automatic record deduplication with benchmark datasets

With technological innovations, enterprises in the real world are managing every iota of data as it can be mined to derive business intelligence (BI). However, when data comes from multiple sources, it may result in duplicate records. As data is given paramount importance, it is also significant to eliminate duplicate entities towards data integration, performance and resource optimization. To realize reliable systems for record deduplication, late, deep learning could offer exciting provisions with a learning-based approach. Deep ER is one of the deep learning-based methods used recently for dealing with the elimination of duplicates in structured data. Using it as a reference model, in this paper, we propose a framework known as Enhanced Deep Learning-based Record Deduplication (EDL-RD) for improving performance further. Towards this end, we exploited a variant of Long Short Term Memory (LSTM) along with various attribute compositions, similarity metrics, and numerical and null value resolution. We proposed an algorithm known as Efficient Learning based Record Deduplication (ELbRD). The algorithm extends the reference model with the aforementioned enhancements. An empirical study has revealed that the proposed framework with extensions outperforms existing methods.

Conceptual foundations of Tech-Driven logistics and supply chain management for economic competitiveness in the United Kingdom

The concept paper explores the transformative potential of technology in enhancing the logistics and supply chain sector to bolster the UK's economic competitiveness. This executive summary highlights the paper's primary objectives, theoretical frameworks, and anticipated outcomes, focusing on the integration of advanced technologies to streamline supply chain operations and drive economic growth. This paper aims to provide a comprehensive framework for understanding how technology can be leveraged to optimize logistics and supply chain management in the UK. It underscores the importance of a robust and efficient supply chain as a cornerstone of economic competitiveness, enabling businesses to reduce costs, improve delivery times, and enhance overall operational efficiency. Central to this paper is the exploration of various technological innovations that can revolutionize supply chain management. It discusses the impact of emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, and big data analytics on the logistics sector. These technologies enable real-time tracking, predictive analytics, enhanced transparency, and improved decision-making processes, which are crucial for maintaining a competitive edge in the global market. The paper delves into theoretical models and frameworks that support the integration of technology into supply chain management. It examines concepts such as the Digital Supply Chain (DSC) model, which emphasizes the interconnectedness of digital technologies in creating a seamless supply chain network. Additionally, the paper explores the Supply Chain Operations Reference (SCOR) model, which provides a structured approach to measuring and improving supply chain performance through technology-driven metrics and best practices. Addressing the practical challenges of implementing tech-driven logistics, the paper highlights issues such as cybersecurity risks, high initial investment costs, and the need for skilled labor. It proposes solutions like fostering public-private partnerships, investing in cybersecurity measures, and developing training programs to build a tech-savvy workforce capable of managing advanced supply chain systems. The expected outcomes of tech-driven logistics and supply chain management include increased operational efficiency, reduced operational costs, improved customer satisfaction, and enhanced agility in responding to market changes. These improvements can significantly contribute to the UK's economic competitiveness, positioning it as a leader in the global supply chain landscape. The paper provides a strategic roadmap for integrating advanced technologies into the logistics sector. By leveraging these technologies, the UK can enhance its supply chain efficiency, drive economic growth, and maintain a competitive advantage in the global economy. The paper calls for concerted efforts from policymakers, industry leaders, and academia to foster an environment conducive to technological innovation and supply chain excellence. Keywords: Logistic, Supply Chain, Technology, Cyber Security, UK.

Water quality ensemble prediction model for the urban water reservoir based on the hybrid long short-term memory (LSTM) network analysis

ABSTRACT The water quality of drinking water reservoirs directly impacts the water supply safety for urban residents. This study focuses on the Da Jing Shan Reservoir, a crucial drinking water source for Zhuhai City and the Macau Special Administrative Region. The aim is to establish a prediction model for the water quality of drinking water reservoirs, which can serve as a vital reference for water plants when formulating their water supply plans. In this research, after smoothing the data using the Hodrick-Prescott filter, we utilized the long short-term memory (LSTM) network model to create a water quality prediction model for the Da Jing Shan Reservoir. Simulation calculations reveal that the model's fitting degree is consistently above 60%. Specifically, the prediction accuracy for pH, dissolved oxygen (DO), and biochemical oxygen demand (BOD) in the water quality prediction model aligns with actual results by more than 70%, effectively simulating the reservoir's water quality changes. Moreover, for parameters like pH, DO, BOD, and total phosphorus, the relative forecasting error of the LSTM model is less than 10%, confirming the model's validity. The results of this study offer an essential model reference for predicting water quality for the Da Jing Shan Reservoir.

Approximating printed circuit heat exchangers dynamics in a sCO2 RCBC as a low-order system via an optimal time constant

The low-order approximation emerges as a promising method for capturing the dynamic behavior of Printed Circuit Heat Exchangers (PCHEs) in sCO2 Recompression Closed Brayton Cycles (RCBCs). However, two primary challenges hinder widespread adoption: concerns regarding feasibility and the determination of key parameters, such as the time constant. To address these challenges, this study first affirms the feasibility of approximating PCHE's dynamic behaviors with a low-order system. In specific, the validity of first-order approximation on the dynamics of high-temperature recuperator (HTR) with mild non-linear property variations is initially assumed. However, a discrepancy is noticed under various operating conditions by employing the same time constant after being compared with the reference model (SCOPE). Then, a novel concept of the optimal time constant, τop, is introduced to underscore the influence of operating conditions on parameter accuracy. Through extensive parameter sweeping, the impact of operating conditions on approximation error is evaluated from two perspectives. First, utilizing τop effectively reduces approximation errors to acceptable levels. The mean squared error remains below 2°C2 for mass flow rate fluctuations below 32.5 kg/s, and below 4°C2 for temperature variations below 40°C. Second, the optimal time constant is significantly influenced by mass flow rate, notably affected by geometry configuration, and minimally influenced by hot inlet temperature. Additionally, this study provides insights for design optimization and control strategy selection, as the findings highlight the trade-off between response speed and thermal performance, and the relatively rapid response of sCO2-PCHEs to temperature control, compared with mass flow rate control.

Hybrid intelligent models for predicting weekly mean significant wave heights

Accurate predictions of significant wave heights are crucial in the field of marine and ocean engineering. This paper presents two hybrid intelligent models, EN-1 and EN-2, that combine random forest (RF) and extreme learning machine (ELM) methods through direct linear weighting and gene expression programming-based nonlinear weighting methods, respectively, for the prediction of weekly mean significant wave heights. The performances of the EN-1 and EN-2 models were compared with those of 11 reference models. The results showed that the coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percentage error (MAPE) of the EN-1 and EN-2 models were 0.9850, 0.0567, 0.0736 and 3.7107%, 0.9940, 0.0335, 0.0487 and 2.2373%, respectively, for the training datasets and 0.9802, 0.0627, 0.0847 and 4.1461%, 0.9754, 0.0707, 0.0966 and 4.2614%, respectively, for the testing datasets, indicating that the EN-1 and EN-2 models have good predictive performance and can be effectively used for the prediction of weekly mean significant wave heights. In addition, a sensitivity analysis was conducted to investigate the influence of the input variables on the model performance.

DNA Methylation-derived biological age and long-term mortality risk in subjects with type 2 diabetes

BackgroundIndividuals with type 2 diabetes (T2D) face an increased mortality risk, not fully captured by canonical risk factors. Biological age estimation through DNA methylation (DNAm), i.e. the epigenetic clocks, is emerging as a possible tool to improve risk stratification for multiple outcomes. However, whether these tools predict mortality independently of canonical risk factors in subjects with T2D is unknown.MethodsAmong a cohort of 568 T2D patients followed for 16.8 years, we selected a subgroup of 50 subjects, 27 survived and 23 deceased at present, passing the quality check and balanced for all risk factors after propensity score matching. We analyzed DNAm from peripheral blood leukocytes using the Infinium Human MethylationEPIC BeadChip (Illumina) to evaluate biological aging through previously validated epigenetic clocks and assess the DNAm-estimated levels of selected inflammatory proteins and blood cell counts. We tested the associations of these estimates with mortality using two-stage residual-outcome regression analysis, creating a reference model on data from the group of survived patients. ResultsDeceased subjects had higher median epigenetic age expressed with DNAmPhenoAge algorithm (57.49 [54.72; 60.58] years. vs. 53.40 [49.73; 56.75] years; p = 0.012), and accelerated DunedinPoAm pace of aging (1.05 [1.02; 1.11] vs. 1.02 [0.98; 1.06]; p = 0.012). DNAm PhenoAge (HR 1.16, 95% CI 1.05–1.28; p = 0.004) and DunedinPoAm (HR 3.65, 95% CI 1.43–9.35; p = 0.007) showed an association with mortality independently of canonical risk factors. The epigenetic predictors of 3 chronic inflammation-related proteins, i.e. CXCL10, CXCL11 and enRAGE, C-reactive protein methylation risk score and DNAm-based estimates of exhausted CD8 + T cell counts were higher in deceased subjects when compared to survived.ConclusionsThese findings suggest that biological aging, as estimated through existing epigenetic tools, is associated with mortality risk in individuals with T2D, independently of common risk factors and that increased DNAm-surrogates of inflammatory protein levels characterize deceased T2D patients. Replication in larger cohorts is needed to assess the potential of this approach to refine mortality risk in T2D.

Changes of in vivo electrical conductivity in the brain and torso related to age, fat fraction and sex using MRI

This work was inspired by the observation that a majority of MR-electrical properties tomography studies are based on direct comparisons with ex vivo measurements carried out on post-mortem samples in the 90’s. As a result, the in vivo conductivity values obtained from MRI in the megahertz range in different types of tissues (brain, liver, tumors, muscles, etc.) found in the literature may not correspond to their ex vivo equivalent, which still serves as a reference for electromagnetic modelling. This study aims to pave the way for improving current databases since the definition of personalized electromagnetic models (e.g. for Specific Absorption Rate estimation) would benefit from better estimation. Seventeen healthy volunteers underwent MRI of both brain and thorax/abdomen using a three-dimensional ultrashort echo-time (UTE) sequence. We estimated conductivity (S/m) in several classes of macroscopic tissue using a customized reconstruction method from complex UTE images, and give general statistics for each of these regions (mean-median-standard deviation). These values are used to find possible correlations with biological parameters such as age, sex, body mass index and/or fat volume fraction, using linear regression analysis. In short, the collected in vivo values show significant deviations from the ex vivo values in conventional databases, and we show significant relationships with the latter parameters in certain organs for the first time, e.g. a decrease in brain conductivity with age.

Population-adjusted national rankings in the Olympics

Ranking countries in the Olympic Games by medal counts clearly favors large-population countries over small ones, while ranking by medals-per-capita produces national rankings with very small population countries on top. We discuss why this happens, and propose a new national ranking system for the Olympics, also based upon medals won, which is inclusive in the sense that countries of widely-varying population can achieve high rankings. This population-adjusted probability ranking ranks countries by how much evidence they show for high capability at Olympic sports. In particular, it ranks countries according to how improbable their medal counts would be in an idealized reference model of the Games which posits that all medal-winning nations have equal propensity per capita for winning medals. The ranking index U is defined using a simple binomial sum. Here we explain the method, and we present population-adjusted national rankings for the last three summer Olympics (London 2012, Rio 2016 and Tokyo 2020, held in 2021). If the advantages of this ranking method come to be understood by sports media covering the Olympics and by the interested public, it could be widely reported alongside raw medal counts, thus adding excitement and interest to the Olympics.

Investigating off-road vehicle lateral stability with integrated chassis control

This study investigates the improvement of off-road vehicle lateral stability by integrated control of active rear steering (ARS) and rear differential braking (RDB) and how the performance of such systems compares on smooth and rough roads. The ARS and RDB controllers comprise a sliding mode controller (SMC) for which critical design choices are the SMC reference model, SMC gain and integration rule. Findings include that the kinematic model reference error is preferred over the phase plane location error on both terrains, the SMC gain is terrain dependent, and the rear axle slip angle is the preferred integration rule over the stability index (SI) on both terrains. The study also found that RDB, and to a lesser degree ARS, tends to improve on the baseline vehicle path-following ability for a double lane change (DLC) manoeuvre on both terrains, but RDB has a larger loss of speed compared to ARS. The Rear axle slip angle was found to be a terrain-dependent tuneable integration rule to combine ARS and RDB, and this resulted in a control system that has the good path-following ability of RDB but the low loss of speed associated with ARS after tuning.

Prognostic Value of Integrated Assessment of Cardiac Magnetic Resonance-Derived Global Coronary Flow Reserve and Cardiopulmonary Exercise Testing-Derived Peak Oxygen Consumption in Patients With Acute Myocardial Infarction.

Background: This study investigated the prognostic value of cardiovascular magnetic resonance (CMR)-derived global coronary flow reserve (G-CFR) in addition to cardiopulmonary exercise testing (CPET) variables in patients with acute myocardial infarction (AMI). Methods and Results: We investigated 127 patients with AMI who underwent primary or urgent percutaneous coronary intervention (PCI) and post-intervention CMR and CPET. The incidence of major cardiac and cerebrovascular events (MACCE), defined as all-cause death, recurrent non-fatal myocardial infarction, re-hospitalization due to congestive heart failure, and stroke, was evaluated (median follow-up, 2.8 years). Patients with MACCE (n=14) had lower ejection fraction (EF) (50 [43-59] vs. 58 [51-63]%; P=0.014), lower G-CFR (1.74 [1.19-2.20] vs. 2.40 [1.61-3.66]; P=0.008), and lower peak oxygen consumption (V̇O2) (15.16±2.64 vs. 17.19±3.70 mL/kg/min; P=0.049) than patients without MACCE. G-CFR<2.33 and peak V̇O2 <15.65 mL/kg/min (cut-off values derived from receiver operating characteristic curve analyses) were significantly associated with the incidence of MACCE (log-rank test, P=0.01). The combination of low G-CFR and low peak V̇O2 improved risk discrimination for MACCE when added to the reference clinical model including age, male sex, post-PCI peak creatine kinase, EF, and left anterior descending artery culprit lesion. Conclusions: G-CFR and peak V̇O2 showed incremental prognostic information compared with the reference model using historically important clinical risk factors, indicating that this approach may help identify high-risk patients who suffer subsequent adverse events.