Global Frame Research Articles

Electric Vehicle Import/Export Flows in Maritime Ports: Are Local Stakeholders Monitoring their Arrival?

Within the global climate change risk mitigation frame, automotive OEMs are progressively replacing ICE-powered cars with electric vehicles in their model ranges. This strategic move is deeply impacting every level of their supply chains, including maritime ports. To shed some light, in the absence of relevant academic literature, on how closely local stakeholders are monitoring this evolution, multiple port authorities’ and logistic service providers’ institutional websites, as well as auto industry-related web sources, have been browsed (Q4 2023), looking for comments on the arrival of electric vehicles in North American and European auto ports. Study shows that contexts differ from one region to another, even from one port/one stakeholder group to another, leading to various attitudes ranging from spontaneous early movers to hyper-cautious followers.

Multimodal Sensemaking and Sensegiving Processes of Discursive Threat Appraisal in Environmental Crisis Communication

ABSTRACTThis study investigates how multimodal sensemaking and sensegiving processes of discursive threat appraisal are employed by global movements in their environmental crisis communication when disclosing how corporate commitments are failing to reduce the global plastic pollution. The theoretical framework includes perspectives on global sticky crises, threat appraisal, framing, crisis sensemaking and sensegiving, and multimodal discourse. Empirically, a series of brand audits reports of #breakfreefromplastic global movement are systematically examined. The analytical focus transcends the usual monomodal approach and is directed towards the multimodal interplay that builds strategic discourses through images and texts. Findings details, on the one hand, the multimodal discursive sensemaking processes of content enrichment and appeal bolstering as well as their outcomes related to the building of common sense, new sense, and non‐sense; on the other hand, they clarify how prognostic, diagnostic, and motivational framing tasks are accomplished in environmental crisis sensegiving by the global movement. This study provides insights useful to corporations when reevaluating their own crisis sensemaking and sensegiving in corporate communications and, ultimately, contribute to changing risky corporate behaviour toward more effective and ethical environmental crisis prevention and communication.

The Many Faces of Environmental Security

This review surveys recent evidence on environmental security, bringing diverse approaches to the subject and evidence relating to different environmental issues into conversation with one another. We focus on the five environmental issues most commonly viewed as having conflict or security effects: climate change, water, forests and deforestation, biodiversity and conservation, and mining and industrial pollution. For each issue, we consider evidence along three dimensions: the impacts of environmental variables on violent conflict, the conflict impacts of policy and development interventions vis-à-vis these environmental issues, and their global policy framing and institutionalization. Through this, we draw particular attention to the poverty and/or inconsistency of the evidence relating to environmental variations, which stands in stark contrast to the extensive evidence on policy and development interventions; noting that policymakers have been much more concerned with the former theme than the latter, we call for this imbalance to be addressed.

Self-Localization of Anonymous UGVs Using Deep Learning from Periodic Aerial Images for a GPS-Denied Environment

This work concerns the autonomous navigation of non-holonomic ground mobile robots in a GPS-denied environment. The objective was to locate, in a global frame, without GPS, anonymous ground mobile robots starting from two consecutive aerial images captured by a single fixed webcam. The effectiveness of deep learning by a MultiLayer Perceptron in an indexed localization was compared to the methods studied in previous works. The ability of a robot to determine the position of other non-indexed robots was also performed. The structure and parameters of the network and the choice of the points taken into account during the learning phase to obtain a local optimum are presented. The results, obtained from simulated and experimental data, are compared to those obtained with more classical methods for different sampling periods (time between images).

Postural Strategies Used While Donning a Simulated xEMU Spacesuit

Our goal is to further understand how a simulated extravehicular mobility unit (xEMU) spacesuit affects the relative movement of one’s body segments. The effect of the xEMU spacesuit on gait is not yet fully understood. Here, gait was examined in terms of postural strategies, defined by the absolute angle with standard deviation (AAD) and the anchoring index (AI). The AAD values allowed the measurement of the absolute angles of body segments and their standard deviation, whereas the AI provided a measure of how stable a body segment was relative to a global reference frame and the inferior body segment. The body segments examined were the head, thorax, lumbar, and pelvis segments of 17 participants (26.53 ± 6.51 years old). The configurations tested included unsuited, or using a xEMU Vest or a hard upper body torso (HUT) for four walking conditions: eyes open/closed, with either forward or backward walking. The AAD values of the xEMU Vest were insignificant compared to those of the unsuited condition. The HUT significantly affected the AAD values compared to the unsuited condition. The AI for the HUT also indicates a new unique postural strategy being employed by the HUT group that was not previously observed.

Israel's War on Gaza in a Global Frame

Israel's War on Gaza in a Global Frame

Motion focus global–local network: Combining attention mechanism with micro action features for cow behavior recognition

The use of machine vision technology for recognizing cow behavior plays a crucial role in daily management, health monitoring, and breeding and reproduction in dairy farming, making it an essential component of modern smart agriculture. This paper presents a novel dual-stream network model, the Motion Focus Global-Local Network (MFGN), for analyzing cow video data. The dual-stream network consists of a global spatiotemporal feature stream and a fine motion feature stream. The global spatiotemporal feature stream extracts key frames to remove redundant information and utilizes a Transformer network for global spatio-temporal feature extraction, reflecting the dynamic changes in cow videos and the temporal relationships between video frames. The fine motion feature stream, based on frame differencing of cow videos, uses focal convolution to capture subtle movements of cows, enhancing the focus on minor behavioral changes. To evaluate the performance of the proposed model, video data samples were collected from eight cows marked on their bodies and heads at an Australian farm site (CSIRO Armidale), including a total of 1715 video sequences across three behavior categories. The model achieved recognition accuracies of 98.1% for drinking, 95.5% for grazing, and 49.3% for other behaviors, with an overall average recognition accuracy of 79.4%, representing a 7.4% improvement over the classic TSN model. Overall, the MFGN network effectively extracts and integrates global spatiotemporal features with fine motion features from cow video data, modeling both the overall sequence characteristics and focusing on local motion details, achieving precise behavioral recognition. This research not only enhances the accuracy of cow behavior recognition but also provides new technological means for precise management in modern smart agriculture, with broad industry application potential to improve farm efficiency, profitability, and disease control and prevention.

BP3M: Bayesian Positions, Parallaxes, and Proper Motions Derived from the Hubble Space Telescope and Gaia Data

We present a hierarchical Bayesian pipeline, BP3M, that measures positions, parallaxes, and proper motions (PMs) for cross-matched sources between Hubble Space Telescope (HST) images and Gaia—even for sparse fields (N * < 10 per image)—expanding from the recent GaiaHub tool. This technique uses Gaia-measured astrometry as priors to predict the locations of sources in HST images, and is therefore able to put the HST images onto a global reference frame without the use of background galaxies/QSOs. Testing our publicly available code in the Fornax and Draco dwarf spheroidal galaxies, we measure PMs that are a median of 8–13 times more precise than Gaia DR3 alone for 20.5 < G < 21 mag. We are able to explore the effect of observation strategies on BP3M astrometry using synthetic data, finding an optimal strategy to improve parallax and position precision at no cost to the PM uncertainty. Using 1619 HST images in the sparse COSMOS field (median nine Gaia sources per HST image), we measure BP3M PMs for 2640 unique sources in the 16 < G < 21.5 mag range, 25% of which have no Gaia PMs; the median BP3M PM uncertainty for 20.25 < G < 20.75 mag sources is 0.44 mas yr−1 compared to 1.03 mas yr−1 from Gaia, while the median BP3M PM uncertainty for sources without Gaia-measured PMs (20.75 < G < 21.5 mag) is 1.16 mas yr−1. The statistics that underpin the BP3M pipeline are a generalized way of combining position measurements from different images, epochs, and telescopes, which allows information to be shared between surveys and archives to achieve higher astrometric precision than that from each catalog alone.

From the global South to the human rights stage: A study of global frame resonance using a comparative case of Women, Life, Freedom and Bloody November in Iran

From the global South to the human rights stage: A study of global frame resonance using a comparative case of Women, Life, Freedom and Bloody November in Iran

Motion In-Betweening via Deep ∆-Interpolator.

We show that the task of synthesizing human motion conditioned on a set of key frames can be solved more accurately and effectively if a deep learning based interpolator operates in the delta mode using the spherical linear interpolator as a baseline. We empirically demonstrate the strength of our approach on publicly available datasets achieving state-of-the-art performance. We further generalize these results by showing that the ∆-regime is viable with respect to the reference of the last known frame (also known as the zero-velocity model). This supports the more general conclusion that operating in the reference frame local to input frames is more accurate and robust than in the global (world) reference frame advocated in previous work.

Automated extrinsic calibration of solid-state frame LiDAR sensors with non-overlapping field of view for monitoring indoor stockpile storage facilities

Several industrial and commercial bulk material management applications rely on accurate, current stockpile volume estimation. Proximal imaging and LiDAR sensing modalities can be used to derive stockpile volume estimates in outdoor and indoor storage facilities. Among available imaging and LiDAR sensing modalities, the latter is more advantageous for indoor storage facilities due to its ability to capture scans under poor lighting conditions. Evaluating volumes from such sensing modalities requires the pose (i.e., position and orientation) parameters of the used sensors relative to a common reference frame. For outdoor facilities, a Global Navigation Satellite System (GNSS) combined with an Inertial Navigation System (INS) can be used to derive the sensors’ pose relative to a global reference frame. For indoor facilities, GNSS signal outages will not allow for such capability. Prior research has developed strategies for establishing the sensor position and orientation for stockpile volume estimation while relying on multi-beam spinning LiDAR units. These approaches are feasible due to the large range and Field of View (FOV) of such systems that can capture the internal surfaces of indoor storage facilities.The mechanical movement of multi-beam spinning LiDAR units together with the harsh conditions within indoor facilities (e.g., excessive humidity, wide range of temperature variation, dust, and corrosive environment in deicing salt storage facilities) limit the use of such systems. With the increasing availability of solid-state LiDAR units, there is an interest in exploring their potential for stockpile volume estimation. Despite their higher robustness to harsh conditions, solid-state LiDAR units have shorter distance measurement range and limited FOV when compared with multi-beam spinning LiDAR. This research presents a strategy for the extrinsic calibration (i.e., estimating the relative pose parameters) of installed solid-state LiDAR units inside stockpile storage facilities. The extrinsic calibration is made possible using deployed spherical targets and a complete, reference scan of the facility from another LiDAR sensing modality. The proposed research introduces strategies for: 1) automated extraction of the spherical targets; 2) automated matching of these targets in the solid-state LiDAR and reference scans using invariant relationships among them; and 3) coarse-to-fine estimation of the calibration parameters. Experimental results in several facilities have shown the feasibility of using the proposed methodology to conduct the extrinsic calibration and volume evaluation with an error percentage less than 3.5% even with occlusion percentages reaching up to 50%.

Конечно-элементное моделирование нелинейных задач упругости в абсолютных узловых координатах на неструктурированных шестигранных сетках

We consider a finite element approach to solving the problem of elasticity theory in terms of absolute nodal coordinate formulation (ANCF), in which large body displacements are described in a global reference frame without using any local coordinate system. The main feature of the method is the absence of gyroscopic effects and, as a result, constancy of the mass matrix and the vector of the generalized force of gravity. In contrast to the traditional ANCF approach, the sets of nodal degrees of freedom of the finite element are formed only on the basis of absolute coordinates of nodes, which allows us to solve the problem using, in particular, unstructured hexahedral meshes. To construct the stiffness matrix, we apply a second-order automatic differentiation algorithm, which ensures its symmetrical form (Hessian matrix) and is analytically accurate in calculating the derivative. This approach also makes it possible to carry out calculations for models of hyperelastic materials without the corresponding Piola-Kirchhoff tensor. It has been shown that within the framework of discretization of the equation of motion, along with the well-known Newmark numerical integration scheme, it is possible to use the HTT-α scheme, which is unconditionally stable, second-order accurate and dissipative for high frequencies. We present several examples of solving static and dynamic elasticity problems for compressible and incompressible models of hyperelastic materials, in which the functions of internal energy density of the body are specified in terms of the deformation gradient.

Seismic behavior of high-rise modular buildings with simplified models of inter-module connections

Inter-module connections serve as crucial components, horizontally and vertically connecting individual modules to form modular buildings. This paper presents a component-based model that divides inter-module connections into vertical and horizontal connection components, facilitating the simplification of shear and axial behaviors for input into the global numerical model. Initially, experimental studies and numerical simulations were conducted to investigate the shear and axial behavior of these connection components, following which simplified connection component models were derived. Subsequently, a component-based model of inter-module connections was established, in which the shear and axial properties of the vertical and horizontal components were represented by nonlinear connector elements. Finally, the proposed component-based model was incorporated directly into the global frame models of high-rise steel modular buildings, and the seismic behavior was evaluated. The accuracy of the proposed component-based model was investigated by comparing the natural vibration characteristics and global building responses of high-rise steel modular buildings with traditional empirical models established based on assumptions of rigid or pinned fixities. The results reveal that traditional empirical models fail to accurately represent the discontinuous properties of inter-module connections, resulting in an overestimation of structural lateral stiffness. In contrast, the component-based model accounts for the actual load-transfer mechanisms of vertical and horizontal components, providing a more accurate understanding of the natural vibration characteristics and seismic response of high-rise steel modular buildings. The proposed component-based model establishes a critical link between isolated connection behavior and structural response assessment for high-rise steel modular buildings, making it well-suited for accurately predicting global building responses.

Framing of Russia-Ukraine War: Comparisons of Global News

This study examined the news framing of the Russia-Ukraine war in global news media outlets. Using the framing theory, a literature review of prominent news frames covering wars and conflicts was drawn. Two hypotheses were set for the major news frames more than others and the relationship between the emphasis on the frames and news organizations’ national interests. Based on predicted framing themes, news frames were obtained from topic modeling with 48,364 news stories. An analysis with a verification sample was implemented to compare the topics with news publishers’ national interests. Five news frames from topic modeling were found: humanism, politics, peace, war, and the economy. In the coverage of the news about the war by global news organizations, the humanism and peace frames were related to Ukraine, whereas the war frame was associated with Russia. The relationship between national interests and pronounced news frames found that European news emphasized humanism more than other regions. The national interest of news outlets was reflected in the news of the war. Study implications and suggestions in terms of news framing and national interests were discussed.

Sensitivity of principal component analysis outcomes to data pre-processing conditions when quantifying trial-to-trial variability in whole-body kinematics

This study investigated whether modes of variance in trial-to-trial whole-body kinematic variability identified by principal component analysis (PCA) were consistent across data pre-processing conditions generated from a common dataset. Comparisons made included 1) when trajectory data was expressed in a global vs. local reference frame; 2) when the number of landmarks used to represent whole-body motion differed, and; 3) whether input trajectory data were normalized to participant stature. Varying data pre-processing conditions prior to PCA does not bias the total variance identified. However, it can influence how modes of variance are dispersed across PCs, which in turn, can influence interpretation.

Novel Lessons In NW

Abstract: With a mixture of structural homage and ironic reference, Zadie Smith’s fragmentary, polyvocal novel NW (2012) cannily adapts the imperatives of the classic bildungsroman from a nineteenth-century national-industrial context to a contemporary global frame. Within NW ’s four main narratives of formation, education and individual ambition serve not to cultivate and fulfill, but to frustrate, fail, or fragment. As a counterpoint to its negative assessment of education and linear progression, however, NW ’s thorough manipulation of the logic of formation highlights the novel’s capacity to reorient readers’ modes of attention and empathy as conditioned by alterity, to use Dorothy Hale’s term. Drawing on Smith’s essays, I suggest that NW ’s experimental novelistic techniques present alternative forms of education for the reader: a range of indeterminate and uncertain “lessons,” dependent on singular encounters with the text.

TYPES OF RISKS WITHIN PROJECTS

The realization of a project requires the application of stages, which are often prone to the appearance of risks. They can affect, negatively or positively, the evolution of a project, from all points of view. A good identification and framing of risks can lead to their effective management, throughout the life of a project, in order to implement an effective project. Through the present research, we consider the global framing of risks, to support their effective management, for the benefit of projects.

Planar Reconstruction of Indoor Scenes from Sparse Views and Relative Camera Poses

Planar reconstruction detects planar segments and deduces their 3D planar parameters (normals and offsets) from the input image; this has significant potential in the fields of digital preservation of cultural heritage, architectural design, robot navigation, intelligent transportation, and security monitoring. Existing methods mainly employ multiple-view images with limited overlap for reconstruction but lack the utilization of the relative position and rotation information between the images. To fill this gap, this paper uses two views and their relative camera pose to reconstruct indoor scene planar surfaces. Firstly, we detect plane segments with their 3D planar parameters and appearance embedding features using PlaneRCNN. Then, we transform the plane segments into a global coordinate frame using the relative camera transformation and find matched planes using the assignment algorithm. Finally, matched planes are merged by tackling a nonlinear optimization problem with a trust-region reflective minimizer. An experiment on the Matterport3D dataset demonstrates that the proposed method achieves 40.67% average precision of plane reconstruction, which is an improvement of roughly 3% over Sparse Planes, and it improves the IPAA-80 metric by 10% to 65.7%. This study can provide methodological support for 3D sensing and scene reconstruction in sparse view contexts.

Postural Change of the Annual Cicada (Tibicen linnei) Helps Facilitate Backward Flight.

Cicadas are heavy fliers well known for their life cycles and sound production; however, their flight capabilities have not been extensively investigated. Here, we show for the first time that cicadas appropriate backward flight for additional maneuverability. We studied this flight mode using computational fluid dynamics (CFD) simulations based on three-dimensional reconstructions of high-speed videos captured in a laboratory. Backward flight was characterized by steep body angles, high angles of attack, and high wing upstroke velocities. Wing motion occurred in an inclined stroke plane that was fixed relative to the body. Likewise, the directions of the half-stroke-averaged aerodynamic forces relative to the body (local frame) were constrained in a narrow range (<20°). Despite the drastic difference of approximately 90° in body posture between backward and forward flight in the global frame, the aerodynamic forces in both flight scenarios were maintained in a similar direction relative to the body. The forces relative to the body were also oriented in a similar direction when observed during climbs and turns, although the body orientation and motions were different. Hence, the steep posture appropriated during backward flight was primarily utilized for reorienting both the stroke plane and aerodynamic force in the global frame. A consequence of this reorientation was the reversal of aerodynamic functions of the half strokes in backward flight when compared to forward flight. The downstroke generated propulsive forces, while the upstroke generated vertical forces. For weight support, the upstroke, which typically generates lesser forces in forward flight, is aerodynamically active in backward flight. A leading-edge vortex (LEV) was observed on the forewings during both half strokes. The LEV's effect, together with the high upstroke velocity, increased the upstroke's force contribution from 10% of the net forces in forward flight to 50% in backward flight. The findings presented in this study have relevance to the design of micro-aerial vehicles (MAVs), as backward flight is an important characteristic for MAV maneuverability or for taking off from vertical surfaces.

A fast and high precision multi-robot environment modeling based on M-BFSI: Bidirectional filtering and scene identification method

This article designs and implements a fast and high-precision multi-robot environment modeling method based on bidirectional filtering and scene identification. To solve the problem of feature tracking failure caused by large angle rotation, a bidirectional filtering mechanism is introduced to improve the error-matching elimination algorithm. A global key frame database for multiple robots is proposed based on a pretraining dictionary to convert images into a bag of words vectors. The images captured by different sub-robots are compared with the database for similarity score calculation, so as to realize fast identification and search of similar scenes. The coordinate transformation from local map to global map and the cooperative SLAM exploration of multiple robots is completed by the best matching image and the transformation matrix. The experimental results show that the proposed algorithm can effectively close the predicted trajectory of the sub-robot, thus achieving high-precision collaborative environment modeling.