Multiple Reference Research Articles

Path Tracking for Electric Mining Articulated Vehicles Based on Nonlinear Compensated Multiple Reference Points Linear MPC

Path Tracking for Electric Mining Articulated Vehicles Based on Nonlinear Compensated Multiple Reference Points Linear MPC

The role of hydropower in decarbonisation scenarios

An increased penetration of renewable energy sources is essential for the energy transition. A major role will be played by wind and solar, as they are widely available. Hydropower is another crucial resource, currently covering large shares of power generation (e.g., Norway, Italy, Brazil). Despite little expected growth, in a context of increasing electrification, improved integration of hydropower can play a critical role thanks to programmable operation. This work addresses the modelling of hydropower flexibility in energy system models and analyses the impact of hydropower operation on CO2 emission-constrained scenarios. To implement the study, a detailed dataset of the Italian programmable hydroelectric plants is created, using open-source information, covering location, rated power, and storage capacity. Inflow timeseries are derived from historical operational data. These new sets of data are employed in OMNI-ES (a multi-node, multi-sector, and multi-vector energy system model) to study optimal configurations and operation of the Italian energy system in decarbonisation scenarios, such as net-zero-CO2 and Fit-for-55 targets. Considering different operational strategies and multiple historical reference years (impacting the inflow), results demonstrate significant changes in hydropower behaviour and highlight its relevance as zero-carbon resource in terms of both power and energy output, influencing the installation of other technologies.

Shakespeare and Covid Drama in This England (Winterbottom, 2022)

This article considers the significance of different Shakespearean allusions in a political docudrama miniseries This England (2022), directed for Sky by Michael Winterbottom and scripted by Winterbottom and Kieron Quirke. The action focuses on the first crucial months in England after the outbreak of the Covid-19 pandemic, offering a panoramic view of the nation under duress as a newly formed government, with Boris Johnson at its helm, mishandles the crisis. The article seeks to explain the presence of multiple Shakespearean references, from the title alone, through numerous direct quotations to the casting of Kenneth Branagh as Johnson. Shakespearean traces play a pivotal, though confusing, role as they both critique the actions of the government and its leader by offering an ironic framing device while increasing the viewer’s sympathy for its central protagonist via the presence of a Shakespearean celebrity.

Realistic 3D Simulators for Automotive: A Review of Main Applications and Features.

Recent advancements in vehicle technology have stimulated innovation across the automotive sector, from Advanced Driver Assistance Systems (ADAS) to autonomous driving and motorsport applications. Modern vehicles, equipped with sensors for perception, localization, navigation, and actuators for autonomous driving, generate vast amounts of data used for training and evaluating autonomous systems. Real-world testing is essential for validation but is complex, expensive, and time-intensive, requiring multiple vehicles and reference systems. To address these challenges, computer graphics-based simulators offer a compelling solution by providing high-fidelity 3D environments to simulate vehicles and road users. These simulators are crucial for developing, validating, and testing ADAS, autonomous driving systems, and cooperative driving systems, and enhancing vehicle performance and driver training in motorsport. This paper reviews computer graphics-based simulators tailored for automotive applications. It begins with an overview of their applications and analyzes their key features. Additionally, this paper compares five open-source (CARLA, AirSim, LGSVL, AWSIM, and DeepDrive) and ten commercial simulators. Our findings indicate that open-source simulators are best for the research community, offering realistic 3D environments, multiple sensor support, APIs, co-simulation, and community support. Conversely, commercial simulators, while less extensible, provide a broader set of features and solutions.

A newsvendor model with multiple reference points: Target-setting for aspirational newsvendors

Prospect theory posits that the determination of an outcome as a gain or loss hinges upon the reference points, thereby exerting a substantial influence on the decision-making processes of individuals. These reference points can encompass both external targets and internal aspirations (self-goals), forming two potential candidates. Despite a growing body of evidence showcasing the concurrent impact of multiple reference points on decision-makers’ choices, scant attention has been accorded to this phenomenon within the realm of operations management literature. In light of this, the present study delves into the influence wielded by an external target, functioning as a reference point, on the decisions and profitability of newsvendors who also harbor a well-defined aspiration (self-goal) reference point. The study introduces two distinct archetypes of newsvendors: the balanced newsvendors, adept at harmonizing personal needs with target attainment and thus navigating decisions influenced by dual reference points; and the focused newsvendors, singularly fixated on target realization and thereby governed by a sole target reference point. Our findings reveal that the effects of target-setting in boosting expected profits on both categories of newsvendors are contingent upon the specific value attributed to their self-goal. The implications of target-setting can either yield advantages or drawbacks, contingent upon the self-goal. This asymmetry in outcomes holds true for both balanced and focused newsvendors, with the former exhibiting a higher likelihood of deriving benefits from target-setting. Moreover, an exploration of scenarios wherein a newsvendor’s self-goal may undergo updates underscores the potential for further enhancement in the performance of balanced newsvendors.

Temporal relation transformer for robust visual tracking with dual-memory learning

Recently, transformer trackers mostly associate multiple reference images with the search area to adapt to the changing appearance of the target. However, they ignore the learned cross-relations between the target and surrounding, leading to difficulties in building coherent contextual models for specific target instances. This paper presents a Temporal Relation Transformer Tracker (TRTT) for robust visual tracking, providing a concise approach to modeling temporal relations by dual target memory learning. Specifically, a temporal relation transformer network generates paired memories based on static and dynamic templates, which are reinforced interactively. The memory contains implicit relation hints that capture the relations between the tracked object and its immediate surroundings. More importantly, to ensure consistency of target instance identities between frames, the relation hints from previous frames are transferred to the current frame for merging temporal contextual attention. Our method also incorporates mechanisms for reusing favorable cross-relations and instance-specific features, thereby overcoming background interference in complex spatio-temporal interactions through a sequential constraint. Furthermore, we design a memory token sparsification method that leverages the key points of the target to eliminate interferences and optimize attention calculations. Extensive experiments demonstrate that our method surpasses advanced trackers on 8 challenging benchmarks while maintaining real-time running speed.

Modeling Polymer Microencapsulation Processes Using CFD and Population Balance Models

Computational fluid dynamics (CFD) modeling has emerged as a valuable tool for investigating complex processes like microencapsulation. This paper aims to validate the ability of CFD simulations to predict particle size distribution in a polymer microencapsulation process. The CFD modeling approach employed a Eulerian multiphase framework, incorporating a discrete population balance model to track the evolution of the droplet population. A realizable k-ε turbulence model and a multiple reference frame strategy were utilized to capture the system’s flow dynamics. The results reveal that while the CFD simulations align well with experimental data at higher agitation speeds (>10,000 rpm), discrepancies arise at lower speeds (<7500 rpm), indicating a challenge in accurately capturing turbulent viscous regimes. Despite these challenges, the CFD model demonstrates robust predictive capabilities for droplet formation and distribution in microencapsulation processes, validated by error margins within the acceptable limits. The validated model can be used as a reliable tool to guide experimental efforts and optimize process parameters, contributing to an enhanced understanding and control of microencapsulation processes.

Relation between the geometric shape and rotation of Galactic globular clusters

We homogeneously measured the elliptical shapes of 163 globular clusters (GCs) using the on-sky distribution of their cluster members and the third data release of the ESA mission Gaia (DR3). The astrometry enables the differentiation of stars within clusters from those in the field. This feature is particularly valuable for clusters located in densely populated areas of the sky, where conventional methods for measuring the geometry of the GCs are not applicable. The median axial ratio of our full sample is 〈b/a〉 = 0.935−0.090+0.033 and 0.986−0.004+0.009 for the subset of 11 GCs previously studied based on Hubble Space Telescope imaging. We investigated whether the minor axis of the ellipses can be interpreted as a pseudo-rotation axis by comparing it to measurements of cluster rotation. Using the radial velocities from Gaia, we detected rotation for three clusters, NGC 5139, NGC 104, and NGC 6341, and observed an alignment between the pseudo-rotation axis and the 2D projection of the real rotation axis. To expand the set of clusters for which rotation has been detected, we analyzed multiple literature references. Depending on the reference used for comparison, we observed an alignment in between 76% to 100% of the clusters. The lack of an alignment observed in some clusters may be linked to different scales analyzed in various studies. Several studies have demonstrated that the orientation of rotation varies with the distance from the center. We estimate that the next Gaia release will increase the number of stars with radial velocities in GCs from ~10 000 in Gaia DR3 to ~55 000 in Gaia DR4. This will enable the measurement of rotation and ellipticities at identical angular scales for additional clusters, which will help us to clarify whether the previously mentioned alignment occurs in all clusters.

Accelerating wheat improvement through trait characterization: advances and perspectives.

Wheat (Triticum spp.) is a primary dietary staple food for humanity. Many wheat genetic resources with variable genomes have a record of domestication history and are widespread throughout the world. To develop elite wheat varieties, agronomical and stress-responsive trait characterization is foremost for evaluating existing germplasm to promote breeding. However, genomic complexity is one of the primary impediments to trait mining and characterization. Multiple reference genomes and cutting-edge technologies like haplotype mapping, genomic selection, precise gene editing tools, high-throughput phenotyping platforms, high-efficiency genetic transformation systems, and speed-breeding facilities are transforming wheat functional genomics research to understand the genomic diversity of polyploidy. This review focuses on the research achievements in wheat genomics, the available omics approaches,and bioinformatic resources developed in the past decades. Advances in genomics and system biology approaches are highlighted to circumvent bottlenecks in genomic and phenotypic selection, as well as gene transfer. In addition, we propose conducting precise functional genomic studies and developing sustainable breeding strategies for wheat. These developments in understanding wheat traits have speed up the creation of high-yielding, stress-resistant, and nutritionally enhanced wheat varieties, which will help in addressing global food security and agricultural sustainability in the era of climate change.

Understanding lived experiences and perceptions of perinatal depression in Nigeria: a qualitative evidence synthesis

Background Perinatal depression is a common mental health disorder which spans during and after pregnancy. Unfortunately, there is poor healthcare-seeking behaviour for this treatable condition. Additionally, maternal healthcare workers sometimes overlook the signs and symptoms, failing to diagnose it adequately. This review aims to provide a framework for understanding the in-depth perceptions and implementation gaps surrounding perinatal depression in Nigeria. Methods This study employed a rigorous qualitative evidence synthesis methodology to gather and analyse both published and unpublished qualitative research on the lived experiences and perceptions of perinatal depression in Nigeria. The research was conducted by searching multiple electronic databases, citation chaining, and checking reference lists. The studies were then screened by title, abstract, and full text, and the quality of the included studies was evaluated using the Critical Appraisal Skills Programme (CASP) checklist for appraising a qualitative study. The data extracted from these studies were then synthesized using the ‘best-fit’ framework method, which combines deductive and inductive approaches to analysis. Results 10 eligible papers were identified. Data were organised into themes eight themes mapped to an already existing framework: Recognitions of perinatal mental distress, labelling of mental distress, symptom patterns, social norms, roles and expectations, supernatural factors, physical/bodily issues, coping strategies, help-seeking from professionals/medical institutions. No study was excluded as a result of methodological limitations. Conclusions The review highlights the significance of integrating mental health services into routine perinatal care and the need for comprehensive interventions that consider sociocultural factors. The findings emphasize the importance of increasing awareness and education among women and healthcare providers to improve early detection and intervention. Healthcare professionals play a crucial role in identifying and managing perinatal depression; therefore, it is essential to provide them with comprehensive and regular training.

Comprehensive drought risk assessment and mapping in Taiwan: An ANP-ANN ensemble approach

This study aims to comprehensively evaluate and map the risk of drought in Taiwan by employing a combination of two powerful models, the Analytic Network Process (ANP) and Artificial Neural Network (ANN). This innovative approach utilizes an ensemble learning method, where ANP constructs a logical network and assigns weights to various indicators. Subsequently, ANN leverages these weights to train the model effectively. A total of twenty indicators were incorporated into the analysis to create a holistic drought risk map for Taiwan. These indicators are thoughtfully categorized into three essential components: hazard, exposure, and vulnerability, providing a well-defined representation of drought risk. The trained ANN model showcases remarkable accuracy and performance, boasting values of 0.940 for accuracy, 0.946 for precision, 0.938 for recall, 0.942 for the F1 score, and 0.923 for the Kappa Index. These results unequivocally affirm the model's effectiveness in predicting drought risk. Furthermore, the final drought risk map underwent rigorous validation through fieldwork and statistical data. The validation process yielded high accuracies, ranging from 0.717 to 0.851, for assessing damage to crops, converted damaged areas, and estimated value product loss. This validation, conducted against multiple reference data sources, underscores the map's reliability and its alignment with various goodness-of-fit criteria. In summary, this study underscores the potency of the ANP-ANN ensemble approach, with the trained ANN model proving its robustness in swiftly predicting drought risk across diverse ecological and socioeconomic scenarios.

Deep video compression based on Long-range Temporal Context Learning

Video compression allows for efficient storage and transmission of data, benefiting imaging and vision applications, e.g. computational imaging, photography, and displays by delivering high-quality videos. To exploit more informative contexts of video, we propose DVCL, a novel Deep Video Compression based on Long-range Temporal Context Learning. Aiming at high coding performance, this new compression paradigm makes full use of long-range temporal correlations derived from multiple reference frames to learn richer contexts. Motion vectors (MVs) are estimated to represent the motion relations of videos. By employing MVs, a long-range temporal context learning (LTCL) module is presented to extract context information from multiple reference frames, such that a more accurate and informative temporal contexts can be learned and constructed. The long-range temporal contexts serve as conditions and generate the predicted frames by contextual encoder and decoder. To address the challenge of imbalanced training, we develop a multi-stage training strategy to ensure the whole DVCL framework is trained progressively and stably. Extensive experiments demonstrate the proposed DVCL achieves the highest objective and subjective quality, while maintaining relatively low complexity. Specifically, 25.30% and 45.75% bitrate savings on average can be obtained than x265 codec at the same PSNR and MS-SSIM, respectively.

Improved sub-genomic RNA prediction with the ARTIC protocol.

Viral subgenomic RNA (sgRNA) plays a major role in SARS-COV2's replication, pathogenicity, and evolution. Recent sequencing protocols, such as the ARTIC protocol, have been established. However, due to the viral-specific biological processes, analyzing sgRNA through viral-specific read sequencing data is a computational challenge. Current methods rely on computational tools designed for eukaryote genomes, resulting in a gap in the tools designed specifically for sgRNA detection. To address this, we make two contributions. Firstly, we present sgENERATE, an evaluation pipeline to study the accuracy and efficacy of sgRNA detection tools using the popular ARTIC sequencing protocol. Using sgENERATE, we evaluate periscope, a recently introduced tool that detects sgRNA from ARTIC sequencing data. We find that periscope has biased predictions and high computational costs. Secondly, using the information produced from sgENERATE, we redesign the algorithm in periscope to use multiple references from canonical sgRNAs to mitigate alignment issues and improve sgRNA and non-canonical sgRNA detection. We evaluate periscope and our algorithm, periscope_multi, on simulated and biological sequencing datasets and demonstrate periscope_multi's enhanced sgRNA detection accuracy. Our contribution advances tools for studying viral sgRNA, paving the way for more accurate and efficient analyses in the context of viral RNA discovery.

Virtual feedback control of interior road noise based on headrest loudspeakers

This paper analyzes the factors that affect the noise reduction performance of feedback control based on remote microphone technique (RMT) and how these factors can be exploited to improve the control performance. First, simulations were conducted to compare the noise reduction performance of employing feedback control directly at the virtual microphone (located at the ear position) and on the physical microphone (located on the headrest), as well as the RMT-based feedback control system. Then the impact of the delay of the virtual secondary path and the coherence between physical and virtual signals on the noise reduction performance of the RMT-based feedback control system was analyzed. It is found that the noise reduction performance can be improved by reducing the delay of the virtual secondary path or increasing the number of physical microphones. Finally, experiments of a dual-channel control system conducted inside an electric car cabin demonstrate that the feedback control strategy based on RMT achieves a binaural noise reduction of 3.4 dBA when employing 4 physical microphones to estimate sound pressure at the ear positions. This approach achieves similar performance but is more cost-effective than a feedforward system using multiple reference sensors.

Preparing Teachers for Linguistically Diverse Classrooms—A Systematic Review on Interventions and Intersectional Perspectives

Teachers need to be well-equipped to work with diverse groups of students from various linguistic backgrounds. Following the PRISMA guidelines, this systematic review analyzed international empirical research on interventions aiming at preparing secondary pre-service teachers for teaching linguistically diverse students. The dataset includes papers from 2012 to 2022 retrieved through the electronic databases ERIC, Education Source, and Scopus. The goal of this review was twofold, namely (1) to investigate how interventions contribute to the development of strategies and skills for teaching linguistically diverse students and (2) to analyze which of the identified interventions considered aspects of intersectionality and, more specifically, in what sense. Multiple intervention strategies, such as assessment tools, self-inquiry, or practice experiences, were found. Regarding the targeted skills of pre-service teachers, this review identified the emerging themes of Methods and Instruction, Critical and Reflective Thinking, as well as Linguistic Awareness. While the term intersectionality was hardly used explicitly, several studies implicitly addressed intersectional matters. Single-issue approaches, which considered multiple diversity referents in an additive rather than an interconnected way, occurred most frequently. This review underlines the need for interventions in teacher education that are based on a broader understanding of linguistic diversity and emphasizes the importance of employing critical and intersectional perspectives.

BLEND: Probabilistic Cellular Deconvolution with Automated Reference Selection.

Cellular deconvolution aims to estimate cell type fractions from bulk transcriptomic and other omics data. Most existing deconvolution methods fail to account for the heterogeneity in cell type-specific (CTS) expression across bulk samples, ignore discrepancies between CTS expression in bulk and cell type reference data, and provide no guidance on cell type reference selection or integration. To address these issues, we introduce BLEND, a hierarchical Bayesian method that leverages multiple reference datasets. BLEND learns the most suitable references for each bulk sample by exploring the convex hulls of references and employs a "bag-of-words" representation for bulk count data for deconvolution. To speed up the computation, we provide an efficient EM algorithm for parameter estimation. Notably, BLEND requires no data transformation, normalization, cell type marker gene selection, or reference quality evaluation. Benchmarking studies on both simulated and real human brain data highlight BLEND's superior performance in various scenarios. The analysis of Alzheimer's disease data illustrates BLEND's application in real data and reference resource integration.

SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning

Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for transcriptome-wide association studies (TWAS). To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies show that SR-TWAS improves power, due to increased training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real studies identify 6 independent significant risk genes for Alzheimer’s disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson’s disease (PD) for substantia nigra tissue. Relevant biological interpretations are found for these significant risk genes.

CFD Analysis of Aerodynamic Characteristics in a Square-Shaped Swarm Formation of Four Quadcopter UAVs

The aerodynamic behavior of a square-shaped formation of four quadcopter UAVs flying in a swarm is investigated in detail through three-dimensional computer simulations utilizing Computational Fluid Dynamics (CFD) methodology. The swarm configuration comprises four UAVs positioned with two in the upper row and two in the lower row along the same propeller axes. The flow profile generated by the UAV propellers rotating at 10,000 revolutions per minute is analyzed parametrically using the Multiple Reference Frame (MRF) technique. UAVs within the swarm are positioned at 75 cm from the motion centers of adjacent propellers. This distance, the effects of horizontally and vertically positioned UAVs on each other, and the collective behavior of the swarm are thoroughly examined. Pressure, velocity, and turbulent kinetic energy values are meticulously analyzed. This research represents a milestone in understanding the aerodynamic characteristics of UAV swarms and the optimization of swarm performance. The findings highlight effective factors in swarm flights and their consequences for UAVs. Additionally, the article describes the “near-UAV phenomenon”. Furthermore, the methodology developed for CFD simulations provides an approach to analyzing close flight scenarios and evaluating their performance in various swarm configurations. These achievements contribute to the future development of UAV technology.

Multi-species cryoEM calibration and workflow verification standard.

Cryogenic electron microscopy (cryoEM) is a rapidly growing structural biology modality that has been successful in revealing molecular details of biological systems. However, unlike established biophysical and analytical techniques with calibration standards, cryoEM has lacked comprehensive biological test samples. We introduce a cryoEM calibration sample that is a mixture of compatible macromolecules that can be used not only for resolution optimization but also provides multiple reference points for evaluating instrument performance, data quality, and image processing workflows in a single experiment. This combined test specimen provides researchers a reference point for validating their cryoEM pipeline, benchmarking their methodologies, and testing new algorithms.

An efficient pre-analysis and optimization generation method for reference curves of automated fiber placement path planning

Complex curved composite components often rely on multiple reference curve algorithms for path planning in automated fiber placement. However, the reference curves are typically manually drawn. Moreover, designing the reference curves follows an iterative planning-analysis-improvement process, which can be inefficient. A new approach for the automatic pre-analysis and optimized generation of reference curves for fiber placement is proposed in this paper to enhance the efficiency of reference curve analysis and generation. Firstly, a pre-analysis algorithm for reference curves based on triangular meshes is proposed. This algorithm analyzes the theoretical geodesic curvature and angular deviation of the path before its planning. Subsequently, a comprehensive evaluation index for reference curve generation is formulated based on the pre-analysis algorithm, and the reference curve is optimized using genetic algorithms. The results demonstrate that the pre-analysis algorithm accurately computes the steering radius distribution of the path. Areas with over-limit steering radius can be eliminated while maintaining angular deviations within 10° by utilizing optimized reference curves for path planning.