Core Challenges Research Articles

SGFormer: Semantic Graph Transformer for Point Cloud-Based 3D Scene Graph Generation

In this paper, we propose a novel model called SGFormer, Semantic Graph TransFormer for point cloud-based 3D scene graph generation. The task aims to parse a point cloud-based scene into a semantic structural graph, with the core challenge of modeling the complex global structure. Existing methods based on graph convolutional networks (GCNs) suffer from the over-smoothing dilemma and can only propagate information from limited neighboring nodes. In contrast, SGFormer uses Transformer layers as the base building block to allow global information passing, with two types of newly-designed layers tailored for the 3D scene graph generation task. Specifically, we introduce the graph embedding layer to best utilize the global information in graph edges while maintaining comparable computation costs. Furthermore, we propose the semantic injection layer to leverage linguistic knowledge from large-scale language model (i.e., ChatGPT), to enhance objects' visual features. We benchmark our SGFormer on the established 3DSSG dataset and achieve a 40.94% absolute improvement in relationship prediction's R@50 and an 88.36% boost on the subset with complex scenes over the state-of-the-art. Our analyses further show SGFormer's superiority in the long-tail and zero-shot scenarios. Our source code is available at https://github.com/Andy20178/SGFormer.

The unified tradeoff model.

Evidence is steadily mounting that attribute-based models offer a more accurate description of intertemporal choices than traditional alternative-based models. Among the attribute-based models, the tradeoff model offers the broadest coverage of research findings, but at the cost of considerable complexity: There now are various instantiations of the model dealing with partially overlapping universes of choice options and preference patterns. Moreover, there are reports of preference patterns in intertemporal decisions about monetary losses that contradict all attribute-based models proposed so far. Taking stock of these core challenges, and all other evidence, we develop an account of intertemporal choice, the unified tradeoff model, that is simpler, yet more comprehensive, than all currently available versions of the tradeoff model taken together. It borrows extensively from its predecessors, but it introduces a new element, time bias, that enables it to accommodate an extraordinarily broad range of preference patterns, and also generate new predictions that contradict all existing models of intertemporal choice. We report four studies that test and confirm its predictions regarding delay, interval, sign, and magnitude dependence in choices between single-dated outcomes, and a fifth study that tests and confirms its predictions regarding the relation between delay preference in choices that only involve single-dated payments and duration preference in choices that also involve sequences of payments. Having subjected the unified tradeoff model to an elevated risk of disconfirmation, we discuss its parsimony and scope in relation to yet other phenomena, most notably, preference patterns in consumption decisions, the final frontier for attribute-based models. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Digital twinning of temperature fields for modular multilayer multiphase pipeline structures

The temperature field of oil and gas wells in the field of petroleum engineering presents a core problem and challenge in the digital twin framework due to its ultra-long-distance and highly variable structural characteristics. The varying wellbore cross-sectional structures with depth make it difficult to establish an effective and generalized analytical model for heat transfer. In this study, we propose, for the first time, a method to automate the construction of multi-layered and multi-component heat transfer models by using a general computational model based on non-steady-state single-phase structural modules. This method enables the automated generation of complex multi-layered and multi-component heat transfer models, thereby achieving the construction of a generalized model for temperature field characterization with varying wellbore cross-sectional structures over ultra-long distances. Utilizing this modeling approach, we validate the proposed method through case studies using actual wellbore temperature field data. The results demonstrate the lightweight and efficient computational analysis of temperature field information under non-steady-state conditions.

An empirical study of the performance of the sixth generation ports model with smart ports with reference to major container ports in mainland China

Since the UNCTAD proposed the initial port model, called the first generation port model (1GP) in 1994, its adaptation and transformation have evolved, reflecting not only the evolution of port operations and technological advancements, but also the resilience of ports to natural disasters and supply disruptions. Recently, the trio of COVID-19, digitalization, and decarbonization (CDD) has emerged as core challenges whilst providing numerous opportunities for building adaption and resilience for the port industry. In light of this, the sixth generation ports (6GP) model has emerged. This paper addresses crucial research gaps by empirically testing the 6GP model, emphasizing the impact of emerging factors of the CDD, including 1) the lack of empirical testing for the 6GP model and the emphasis on the CDD; 2) the exploration of factors contributing to the significant development of Chinese container ports; 3) the investigation into how the CDD lead to the reversal of the port devolution continuum path. In doing so, an empirical study has been conducted, focusing on six major container ports in mainland China, all of which are among the world’s top 10 container ports in terms of container throughput. It evaluates the importance of the criteria of the 6GP model with the performance of the six ports. The findings of this research are discussed for managerial and policy implications, enriching the existing literature on port devolution, and providing the port industry with a holistic view of port development and strategy in the post-pandemic period.

Scalable Fault-Tolerant Quantum Technologies with Silicon Color Centers

The scaling barriers currently faced by both quantum networking and quantum computing technologies ultimately amount to the same core challenge of distributing high-quality entanglement at scale. In this Perspective, a novel quantum information-processing architecture based on optically active spins in silicon is proposed that offers a combined single technological platform for scalable fault-tolerant quantum computing and networking. The architecture is optimized for overall entanglement distribution and leverages color-center spins in silicon (T centers) for their manufacturability, photonic interface, and high-fidelity information-processing properties. Silicon nanophotonic optical circuits allow for photonic links between T centers, which are networked via telecom-band optical photons in a highly connected graph. This high connectivity unlocks the use of low-overhead quantum error-correcting codes, significantly accelerating the time line for modular scalable fault-tolerant quantum repeaters and quantum processors. Published by the American Physical Society 2024

Biopsychosocial determinant of quality of life of older adults in Pakistan and Canada.

The rapidly rising average age of the older adults has brought various global healthcare challenges. A core challenge is how to enhance their quality of life (QoL). The objective of the current study was to test the significance of biopsychosocial determinants of quality of life of older adults in Pakistan and Canada. A cross-sectional survey was carried out on a conveniently approached purposive sample of 1,005 older adults (Pakistani = 557 and Canadian = 448) of age range between 60 years and 80 years. The data were collected via demographic datasheet, World Health Organization Quality of Life Brief Scale, Health and Lifestyle Questionnaire, General Self-Efficacy Scale, Rosenberg Self-Esteem Scale, and Berlin Social Support Scale. The results of hierarchical regression analysis showed that biopsychosocial factors (viz., health and lifestyle, chronic illness, self-efficacy, self-esteem, and social support) significantly predicted (R 2 = .27, and.68) quality of life of older adults in Pakistan and Canada, respectively, after controlling the demographic variables. Significant differences were found between Pakistani and Canadian older adults on biopsychosocial factors: Canadian older adults scored significantly higher on health and lifestyle, self-efficacy, and quality of life, and older adults in Pakistan scored significantly higher on self-esteem and social support. A significant amount of better QoL of older adults can be achieved through enhancing the biopsychosocial correlates of their QoL, both in Pakistan and Canada.

Research on E-commerce Logistics Distribution Path Optimization Based on Ant Colony Algorithm

With the rapid development of e-commerce industry, the efficiency of logistics and distribution has become a key constraint to its further development. In this paper, we explore the optimization of e-commerce logistics and distribution paths through the ant colony algorithm, aiming to improve the distribution efficiency and reduce the cost. The study firstly analyzes the core elements and challenges of e-commerce logistics and distribution paths, and then describes in details about the ant colony algorithm and its application to path optimization. By constructing a model adapted to the characteristics of e-commerce distribution, we use the ant colony algorithm to optimize a variety of different sizes and complexities of distribution paths. By constructing a model adapted to the characteristics of e-commerce distribution, we use the ant colony algorithm to optimize a variety of distribution paths with different sizes and complexities, and the experimental results show that the algorithm can effectively reduce the total distribution distance and cost. In the test cases, we observe that the distribution paths are shortened by 15\% on average, and the distribution cost is reduced by about 20\%. These results prove the effectiveness and feasibility of the ant colony algorithm in the optimization of e-commerce logistics distribution paths. This study provides a new idea for e-commerce logistics distribution path optimization, which is of great significance for improving logistics efficiency and reducing operation cost.

Experimental and virtual testing of bone-implant systems equipped with the AO Fracture Monitor with regard to interfragmentary movement.

Introduction: The management of fractured bones is a key domain within orthopedic trauma surgery, with the prevention of delayed healing and non-unions forming a core challenge. This study evaluates the efficacy of the AO Fracture Monitor in conjunction with biomechanical simulations to better understand the local mechanics of fracture gaps, which is crucial for comprehending mechanotransduction, a key factor in bone healing. Through a series of experiments and corresponding simulations, the study tests four hypotheses to determine the relationship between physical measurements and the predictive power of biomechanical models. Methods: Employing the AO Fracture Monitor and Digital Image Correlation techniques, the study demonstrates a significant correlation between the surface strain of implants and interfragmentary movements. This provides a foundation for utilizing one-dimensional AO Fracture Monitor measurements to predict three-dimensional fracture behavior, thereby linking mechanical loading with fracture gap dynamics. Moreover, the research establishes that finite element simulations of bone-implant systems can be effectively validated using experimental data, underpinning the accuracy of simulations in replicating physical behaviors. Results and Discussion: The findings endorse the combined use of monitoring technologies and simulations to infer the local mechanical conditions at the fracture site, offering a potential leap in personalized therapy for bone healing. Clinically, this approach can enhance treatment outcomes by refining the assessment precision in trauma trials, fostering the early detection of healing disturbances, and guiding improvements in future implant design. Ultimately, this study paves the way for more sophisticated patient monitoring and tailored interventions, promising to elevate the standard of care in orthopedic trauma surgery.

Measuring Commonality in Recommendation of Cultural Content to Strengthen Cultural Citizenship

Recommender systems have become the dominant means of curating cultural content, significantly influencing the nature of individual cultural experience. While the majority of academic and industrial research on recommender systems optimizes for personalized user experience, this paradigm does not capture the ways that recommender systems impact cultural experience in the aggregate, across populations of users. Although existing novelty, diversity, and fairness studies probe how recommender systems relate to the broader social role of cultural content, they do not adequately center culture as a core concept and challenge. In this work, we introduce commonality as a new measure of recommender systems that reflects the degree to which recommendations familiarize a given user population with specified categories of cultural content. Our proposed commonality metric responds to a set of arguments developed through an interdisciplinary dialogue between researchers in computer science and the social sciences and humanities. With reference to principles underpinning public service media (PSM) systems in democratic societies, we identify universality of address and content diversity in the service of strengthening cultural citizenship as particularly relevant goals for recommender systems delivering cultural content. We develop commonality as a measure of recommender system alignment with the promotion of a shared cultural experience of, and exposure to, diverse cultural content across a population of users. Moreover, we advocate for the involvement of human editors accountable to a larger value community as a fundamental part of defining categories in the service of cultural citizenship. We empirically compare the performance of recommendation algorithms using commonality with existing utility, diversity, novelty, and fairness metrics using three different domains. Our results demonstrate that commonality captures a property of system behavior complementary to existing metrics and suggests the need for alternative, non-personalized interventions in recommender systems oriented to strengthening cultural citizenship across populations of users. Moreover, commonality demonstrates both consistent results under different editorial policies and robustness to missing labels and users. Alongside existing fairness and diversity metrics, commonality contributes to a growing body of scholarship developing “public good” rationales for digital media and machine learning systems.

Online semi-supervised active learning ensemble classification for evolving imbalanced data streams

Concept drift is a core challenge in classification tasks of data streams. Although many drift adaptation methods have been presented, most of them assume that labels of all data are available, which is impractical in many real-world applications. Additionally, the absence of label makes the imbalance ratio of an imbalanced data stream difficultly being obtained in time, providing the inaccurate guidance for resampling and causing poor generalization. To tackle the joint challenges, an online semi-supervised active learning method is proposed to classifier imbalanced data streams with concept drift. A newly-arrived data is first added to the sliding window, and then assigned a pseudo label in terms of its nearest cluster. Meanwhile, semi-supervised clustering algorithm offers its predicted label. Based on the above two predictive labels, cluster-based query strategy provides the criteria for the evaluation and selection of representative instances. More especially, the uncertainty and importance of instances are defined to synthetically evaluate its representativeness. After obtaining true labels of typical ones, ensemble classifier is updated by all instances in current sliding window. Experimental results on 13 synthetic and real data streams indicate that the proposed method outperforms six comparative methods on both G-mean and Recall under various labeling budgets.

PUBLIC MANAGEMENT OF THE DEVELOPMENT OF THE HIGHER EDUCATION SYSTEM IN UKRAINE

The article considers core challenges and problems of public management of Ukrainian higher education system within the global landscape of higher education development. Systemic analysis of factors influencing higher education system in Ukraine is carried out based on complex vision, including social and political factors. Overall trends and practices observed in higher education of different countries is carried out, with outlining of the state-of-the art and prospects of Ukrainian higher education on the background of this landscape. Penta helix model is mentioned as one of potentially effective directions to be included in public management of Ukrainian higher education system development.

Ultra-dense Motion Capture: An exploratory full-automatic approach for dense tracking of breast motion in 4D.

Understanding the dynamic deformation pattern and biomechanical properties of breasts is crucial in various fields, including designing ergonomic bras and customized prostheses, as well as in clinical practice. Previous studies have recorded and analyzed the dynamic behaviors of the breast surface using 4D scanning, which provides a sequence of 3D meshes during movement with high spatial and temporal resolutions. However, these studies are limited by the lack of robust and automated data processing methods which result in limited data coverage or error-prone analysis results. To address this issue, we identify revealing inter-frame dense correspondence as the core challenge towards conducting reliable and consistent analysis of the 4D scanning data. We proposed a fully-automatic approach named Ulta-dense Motion Capture (UdMC) using Thin-plate Spline (TPS) to augment the sparse landmarks recorded via motion capture (MoCap) as initial dense correspondence and then rectified it with a sophisticated post-alignment scheme. Two downstream tasks are demonstrated to validate its applicability: virtual landmark tracking and deformation intensity analysis. For evaluation, a dynamic 4D human breast anthropometric dataset DynaBreastLite was constructed. The results show that our approach can robustly capture the dynamic deformation characteristics of the breast surfaces, significantly outperforms baselines adapted from previous works in terms of accuracy, consistency, and efficiency. For 10 fps dataset, average error of 0.25 cm on control-landmarks and 0.33 cm on non-control (arbitrary) landmarks were achieved, with 17-70 times faster computation time. Evaluation was also carried out on 60 fps and 120 fps datasets, with consistent and large performance gaining being observed. The proposed method may contribute to advancing research in breast anthropometry, biomechanics, and ergonomics by enabling more accurate tracking of the breast surface deformation patterns and dynamic characteristics.

Challenges for conceptualising otherness

Otherness is a deceptively simple concept. Ostensibly it refers to someone else, who is, in an ultimate sense, unknowable. But, there are many ways in which the self-other boundary is blurred. First, self is already other from the standpoint of the other. Second, in so far as perspective taking is possible, there is some otherness within the self, and some self within the other. Third, when people talk and think they routinely move between the perspectives of self and other, changing and shifting perspectives, and leveraging one perspective against the other. Overall, the core challenge for conceptualising otherness is that it does not exist without the self. Otherness is not ‘in’ the perspective of the other rather it is a two-sided relational quality that arises between the shifting perspectives of self and other.

Artificial intelligence in neurology: opportunities, challenges, and policy implications.

Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation. AI's potential to advance personalized precision neurology and global brain health directives hinges on resolving core challenges across four pillars-models, data, feasibility/equity, and regulation/innovation-through concerted pursuit of targeted recommendations. Paramount actions include swift, ethical, equity-focused integration of novel technologies into clinical workflows, mitigating data-related issues, counteracting digital inequity gaps, and establishing robust governance frameworks balancing safety and innovation.

Graph neural network recommendation algorithm based on improved dual tower model

In this era of information explosion, recommendation systems play a key role in helping users to uncover content of interest among massive amounts of information. Pursuing a breadth of recall while maintaining accuracy is a core challenge for current recommendation systems. In this paper, we propose a new recommendation algorithm model, the interactive higher-order dual tower (IHDT), which improves current models by adding interactivity and higher-order feature learning between the dual tower neural networks. A heterogeneous graph is constructed containing different types of nodes, such as users, items, and attributes, extracting richer feature representations through meta-paths. To achieve feature interaction, an interactive learning mechanism is introduced to inject relevant features between the user and project towers. Additionally, this method utilizes graph convolutional networks for higher-order feature learning, pooling the node embeddings of the twin towers to obtain enhanced end-user and item representations. IHDT was evaluated on the MovieLens dataset and outperformed multiple baseline methods. Ablation experiments verified the contribution of interactive learning and high-order GCN components.

Feature selection applied to QoS/QoE modeling on video and web-based mobile data services: An ordinal approach

Nowadays, mobile service providers perceive the user experience as a reliable indicator of the quality associated to a service. Given a set of Quality of Service (QoS) factors, the aim is to predict the Quality of Experience (QoE), measured in terms of the Mean Opinion Score (MOS). Although this problem is receiving much attention, there are still some challenges that require more research in order to find effective solutions for meeting user’s expectation in terms of service quality. A core challenge in this topic refers to the analysis of the contribution of each factor to the QoS/QoE Model. In this work, we study the mapping between QoS and QoE on video and web-based services using a machine learning approach. For such purpose, we design a lab-testing methodology to emulate different cellular transmission network scenarios. Then, we address the problem of inducing a predictive model and identifying relevant QoS factors. Results suggest that bandwidth is a key factor when analyzing user’s perception of service quality.

Towards an Approach to Co‐Execute System Models at the Enterprise Level

ABSTRACTIndustry 4.0, the Internet of Things, and large‐scale system‐to‐system interactions are driving digital transformation in the industry. Model‐based systems engineering (MBSE) is one of the core paradigms behind this transformation. MBSE practices are widely applied to enterprise (including system of systems and mission) architectures, which become a crucial part of successful digital transformation. The core challenge today is not only how digital continuity can be maintained by connecting different layers of models (such as system models to system‐of‐systems models), but also how to perform detailed analysis and simulation at the enterprise level model. This paper studies Systems Modeling Language (SysML®) as the standard language to model systems, Unified Architecture Framework (UAF) as the framework, Unified Architecture Framework Modeling Language (UAFML) as the language to model enterprise architectures and proposes an approach for end‐to‐end co‐execution of the integrated enterprise model.

Optimal Control of Temporal Networks With Variable Input and Node-Source Connection.

Many networked systems built upon real-life physical or social interactions have time-varying connections among individual units, where the temporal changes in connectivity and/or interaction strength lead to complicated dynamics. The temporal network model was proposed in the form of controlled linear dynamical systems acting in an ordered sequence of time intervals. One of the core challenges in network science is the control of networks and the optimization of the control strategy. However, most canonical frameworks for solving optimal control problems were established for static networks featuring constant topology. New theories and techniques are yet to be developed for the temporal networks, with an important case being that the input and the source-node connection are both variables. In this work, by formulating a quadratic energy cost without solving the Riccati differential equation, we show that the control effort can be reduced substantially by improving either the system trajectories or the input matrices. The two approaches are further combined in a coordinate descent framework, integrating linearly constrained quadratic programming, and a projected gradient descent method. Taken together, the results underline the potential of temporal networks as energy-efficient control systems and present strategies to improve the control input. Moreover, the proposed algorithms can serve as a starting point for future engineering of real-world temporal networks.

Individual learning paths mastering teachers’ professional vision

IntroductionPromoting a professional vision of teaching as a key factor of teachers’ expertise is a core challenge for teacher professionalization. While research on teaching has evolved and successfully evaluated various video-based intervention programs, a prevailing emphasis on outcome measures can yet be observed. However, the learning processes by which teachers acquire professional vision currently remain a black box. The current study sought to fill this research gap. As part of a course dedicated to promoting a professional vision of classroom management, students were imparted knowledge about classroom management that had to be applied to the analysis of authentic classroom videos. The study aimed to determine the variety of individual strategies that students applied during their video analyses, and to investigate the relationship between these and the quality of the students’ analyses, measured by their agreement with an experts’ rating of the video clips.MethodsThe sample comprised 45 undergraduate pre-service teachers enrolled in a course to acquire a professional vision of classroom management. By applying their imparted knowledge of classroom management, students engaged in the analysis of classroom videos to learn how to notice and interpret observable events that are relevant to effective classroom management. Implementing a learning analytical approach allowed for the gathering of process-related data to analyze the behavioral patterns of students within a digital learning environment. Video-based strategies were identified by conducting cluster analyses and related to the quality of the students’ analysis outcomes, measured by their concordance with the experts’ ratings.ResultsWe gained insight into the learning processes involved in video-based assignments designed to foster a professional vision of classroom management, such as the areas of interest that attracted students’ heightened attention. We could also distinguish different approaches taken by students in analyzing classroom videos. Relatedly, we found clusters indicating meticulous and less meticulous approaches to analyzing classroom videos and could identify significant correlations between process and outcome variables.DiscussionThe findings of this study have implications for the design and implementation of video-based assignments for promoting professional vision, and may serve as a starting point for implementing process-based diagnostics and providing adaptive learning support.

Mapping the sustainability branding field: emerging trends and future directions

Purpose This study aims to offer the intellectual structure and dynamics of the sustainability branding field, involving the identification of influential authors and journals, current and emerging themes, theories, methods, contexts and future research directions. Design/methodology/approach The study conducted a bibliometric approach of 1,509 articles retrieved from Scopus to analyze the evolution of the knowledge of sustainability branding and suggest future research. The analysis used various methods such as performance analysis, keyword analysis, cluster analysis and bibliographic coupling. Findings The topics of corporate image, philanthropy and stakeholder pressures were core in the foundation phase. Then rose the topics of sustainable development goals and global supply chains. Green marketing and the new paradigms of circularity, ethical consumerism and hyperconnected societies emerged more recently. Six thematic clusters represent the field’s knowledge structure: (1) corporate branding and reputation, (2) sustainable business development, (3) sustainable branding and ethical consumption, (4) corporate social responsibility, (5) brand equity and green marketing and (6) sustainability branding in hospitality and tourism. Practical implications This paper provides readers with an overview of sustainability branding core themes, key contributions and challenges, which can be used as a toolkit for brand management studies and practice. Originality/value The study’s uniqueness lies in bibliometric analysis (combined with network analysis and science mapping techniques) of the sustainability branding field from the identification and evolution of the thematic clusters to propose future research directions.