Single Perspective Research Articles

Advances in MRI Research for First-Episode Schizophrenia: A Selective Review and NSFC-Funded Analysis.

The causes of schizophrenia remain unclear, and research has been hindered by the lack of quantifiable standards. However, magnetic resonance imaging (MRI) is addressing these challenges, revealing critical neurobiological details and emphasizing its importance in both evaluation and treatment. First, we reviewed the progress of research on structural MRI (sMRI), functional MRI (fMRI), multimodal/multiomics analysis, artificial intelligence, and neuromodulation in first-episode schizophrenia (FES) over the past 5 years. Second, we summarize the current state of schizophrenia research funded by the National Natural Science Foundation of China (NSFC) to facilitate academic exchange and cooperation both domestically and internationally. sMRI has identified early neurodevelopmental biomarkers in FES patients, and fMRI has highlighted functional abnormalities across disease stages. Multimodal/multiomics analysis has revealed complex brain-neurobiology interactions. Neuromodulation techniques, which directly modulate neural activity in specific brain regions, offer promising long-term benefits for stabilizing conditions and enhancing patients' quality of life. NSFC-funded analysis shows China is increasing its funding for schizophrenia research, though funding distribution remains uneven. The research focus has shifted from a single perspective on brain structure and function to multichannel, multimodal comprehensive analysis methods. This progress has driven the integration of machine learning-driven multiomics research, aiming to construct disease classification models, explore disease mechanisms, and guide treatment from multidimensional and interdisciplinary perspectives. MRI technology has provided new perspectives for the diagnosis and treatment of schizophrenia, especially the neurobiological foundations of the disease. Support from the NSFC provides a scientific and financial basis for future research and treatment, heralding scientific discoveries and technological innovations in this field and bringing hope to schizophrenia patients.

Cross-View Multi-Scale Re-Identification Network in the Perspective of Ground Rotorcraft Unmanned Aerial Vehicle

Traditional Re-Identification (Re-ID) schemes often rely on multiple cameras from the same perspective to search for targets. However, the collaboration between fixed cameras and unmanned aerial vehicles (UAVs) is gradually becoming a new trend in the surveillance field. Facing the significant perspective differences between fixed cameras and UAV cameras, the task of Re-ID is facing unprecedented challenges. In the setting of a single perspective, although significant advancements have been made in person Re-ID models, their performance markedly deteriorates when confronted with drastic viewpoint changes, such as transitions from aerial to ground-level perspectives. This degradation in performance is primarily attributed to the stark variations between viewpoints and the significant differences in subject posture and background across various perspectives. Existing methods focusing on learning local features have proven to be suboptimal in cross-perspective Re-ID tasks. The reason lies in the perspective distortion caused by the top-down viewpoint of drones, and the richer and more detailed texture information observed from a ground-level perspective, which leads to notable discrepancies in local features. To address this issue, the present study introduces a Multi-scale Across View Model (MAVM) that extracts features at various scales to generate a richer and more robust feature representation. Furthermore, we incorporate a Cross-View Alignment Module (AVAM) that fine-tunes the attention weights, optimizing the model’s response to critical areas such as the silhouette, attire textures, and other key features. This enhancement ensures high recognition accuracy even when subjects change posture and lighting conditions. Extensive experiments conducted on the public dataset AG-ReID have demonstrated the superiority of our proposed method, which significantly outperforms existing state-of-the-art techniques.

Dual-perspective fusion for word translation enhancement

Most Bilingual Lexicon Induction (BLI) methods retrieve word translation pairs by finding the closest target word for a given source word based on cross-lingual word embeddings (WEs). However, we find that solely retrieving translation from the source-to-target perspective leads to some false positive translation pairs, which significantly harm the precision of BLI. To address this problem, we propose a novel and effective method to improve translation pair retrieval in cross-lingual WEs. Specifically, we apply a fusion of both source-side and target-side perspectives throughout the retrieval process to alleviate false positive word pairings that emanate from a single perspective. Moreover, in translation scenarios using Large Language Models (LLMs), we propose fusing the LLMs perspective with the BLI model perspective to enhance LLM’s translation capability. On benchmark datasets of BLI, our proposed method achieves competitive performance compared to existing state-of-the-art (SOTA) methods. It demonstrates effectiveness and robustness across six experimental languages, including similar language pairs and distant language pairs, under both supervised and unsupervised settings.

Overview of Terrain Traversability Evaluation for Autonomous Robots

ABSTRACTTraversability evaluation is the foundation and core of unmanned platforms for scene understanding and autonomous navigation, whose successful completion relies on the analysis of the platform's characteristics and the semantic and geometric features of the surrounding environment. This topic has been reviewed by many literatures, which are characterized by a single perspective and lack comprehensive evaluation frameworks. Thus, the concept and developmental trajectory of traversability evaluation are initially outlined in this paper, distinguishing it from other issues, while constructing an evaluation framework based on two categories: direct assessment and downstream task assessment. Subsequently, traversability evaluation methods are classified based on multiple dimensions, including sensor types, robot types, usage scenarios, and learning approaches. On the basis of the constructed evaluation framework, comparisons are made among existing algorithms in terms of performance and runtime. Subsequently, a summary is provided on commonly used features and their mainstream computation methods in terrain evaluation. Additionally, open‐source data sets in this field and projects for scene construction and algorithm validation are compiled and organized. Finally, an analysis is conducted on the development direction and trends, emphasizing the urgent need to establish standardized evaluation systems and comparison baselines. Furthermore, it is imperative that various environmental and platform information be comprehensively integrated into algorithms, while also ensuring that simulation, demonstration, and exploration are incorporated into a unified framework to enhance the robot's learning capability.

Cumulative Interpersonal Risk, Non-Suicidal Self-Injury, and Suicide Attempts in Early Adolescence: Between-Person Differences and within-Person Effects.

Poor interpersonal relationships are one of the risk factors for non-suicidal self-injury (NSSI) and suicidal behaviors among adolescents. Previous studies have mostly focused on a single interpersonal perspective, and it remains uncertain how the cumulative interpersonal risk across time and domains interacts with NSSI and suicide. The current study employed cross-lagged panel models (CLPM) and random intercept cross-lagged panel models (RI-CLPM) to investigate the interaction among cumulative interpersonal risk, NSSI, and suicide attempts at both the between-person and within-person levels, while examining sex differences in these relationships. A total of 1038 Chinese adolescents (47.0% girls; baseline Mage = 12.35 years, SD = 0.52) participated in a four-wave longitudinal survey with 6-months intervals. The results showed that at the between-person level, there were reciprocal predictive relationships between cumulative interpersonal risk, NSSI and suicide attempts. At the within-person level, changes in NSSI affected changes in cumulative interpersonal risk, and suicide attempts exhibited a reciprocal relationship with cumulative interpersonal risk and NSSI, with significant sex differences observed. These findings underscore the stable and trait-like associations between cumulative interpersonal risk, NSSI and suicide attempts. Given these findings, to reduce the occurrence of suicide among adolescents, it is essential to target interventions aimed tat enhancing their interpersonal adaptability.

Why the great surges of developments are different: a coupling analysis of the socio-political paradigm and the techno-economic paradigm

PurposeThe “problem-ridden present” is indubitably related to the prevalence of neoliberalism, but it is also when the fifth technological revolution took place. In this special time, what should be done to achieve the coupling of Socio-political Paradigmand Technoeconomic Paradigm through the adaptive transformation of socio-political paradigm to avoid the resurfacing of the “problem-ridden present”?Design/methodology/approachThe obvious difference between the efficacy of the fifth technological revolution and the previous four indicates that the Techno-economic Paradigm of Neo-Schumpeterian school is not sufficient enough to explain the institutional obstacles. No matter institutional or technological, any single perspective would not be adequate to explain the fundamental cause of the “problem-ridden present”.FindingsFrom a historical materialism standpoint, the structure and progress of the techno-economic paradigm are inevitably influenced by the socio-political paradigm, and only when the techno-economic paradigm and the socio-political paradigm are coupled, the potential of the wave of technological revolution can be “shared release.”Originality/valueThe coupling analysis of the Socio-political Paradigm and the Technoeconomic Paradigm is of great enlightening value to the understanding of the economic development in the Digital Economy era.

Agreeing to Disagree: Translating Representations to Uncover a Unified Representation for Social Robot Actions

Researchers and designers of social robots often approach robot control system design from a single perspective; such as designing autonomous robots, teleoperated robots, or robots programmed by an end-user. While each design approach presents a tradeoff between some advantages and limitations, there is an opportunity to integrate these approaches where people benefit from the best-fit approach for their use case. In this work, we propose integrating these seemingly distinct robot control approaches to uncover a common data representation of social actions defining social expression by a robot. We demonstrate the value of integrating an authoring system, teleoperation interface, and robot planning system by integrating instances of these systems for robot storytelling. By relying on an integrated system, domain experts can define behaviors through end-user interfaces that teleoperators and autonomous robot programmers can use directly thus providing a cohesive expert-driven robot system.

A spatially explicit drift foraging model quantifies salmon rearing habitat enhancement

Salmonid rearing habitat restoration is often intended to increase juvenile prey abundance, predator refugia, and physical conditions that enhance growth, development, and survival. A common restoration evaluation approach is to quantify habitat quality via physical metrics such as depth and velocity. However, such an approach does not account for the bioenergetic implications of habitat occupancy. We developed a spatially explicit predictive framework to assess net rate of energy intake (NREI) for juvenile Chinook salmon (Oncorhynchus tshawytscha) experiencing pre-existing and post-project conditions in a restored side channel complex. Energetic submodels from existing juvenile salmonid drift foraging literature are used in combination with site-specific macroinvertebrate drift data and a 2D hydraulic model. We address the following question: does our model predict increased net available energy for foraging juveniles in a restored channel condition compared to pre-existing conditions under varying discharges, thermal conditions, and seasonality? Observed macroinvertebrate drift data was extrapolated to the model domain via random forest models. Modeled post-project conditions showed statistically significant improvements in foraging opportunity compared to existing conditions, with depth ranked as the most important NREI predictor. The novel framework presented here improves upon previous applications that model drift from a single fish perspective and generalize prey density.

A Comprehensive Survey on Cyber-Physical System Security in the Internet of Things (IoT): Addressing and Solutions

Background: Cyber-Physical Systems (CPSs) integrate computing, control, and communication technologies, bridging cyberspace and the physical world to enhance critical infrastructure and safety-critical systems. Existing surveys often address CPS security from a single perspective, necessitating a more comprehensive approach. Methods: This paper presents a thorough review of CPS security from three perspectives: the physical domain, the cyber domain, and the cyber-physical domain. We examine attacks on physical components like sensors, cyber-attacks targeting CPSs, and integrated cyber-physical attacks. For each domain, we analyse corresponding detection and defence mechanisms. Results: Our review reveals that CPSs face significant security threats across all domains. In the physical domain, attacks on sensors can disrupt system operations, but various defences are available. In the cyber domain, CPSs are vulnerable to malware, hacking, and denial-of-service attacks, with several detection and defence strategies in place. The cyber-physical domain highlights complex threats where cyber-attacks cause physical damage, requiring comprehensive security approaches. Conclusion: By examining CPS security from multiple perspectives, this review provides a holistic understanding of current threats and defence mechanisms. It identifies future research directions to enhance CPS security, aiming to better protect critical infrastructure against evolving cyber threats.

Adaptive 360° video timeline exploration in VR environment

Timeline control is a crucial interaction during video viewing, aiding users in quickly locating or jumping to specific points in the video playback, especially when dealing with lengthy content. 360°videos, with their ability to offer an all-encompassing view, have gradually gained popularity, providing a more immersive experience compared to videos with a single perspective. While most 360°videos are currently displayed on two-dimensional screens, the timeline design has largely remained similar to that of conventional videos. However, virtual reality (VR) headsets provide a more immersive viewing experience for 360°videos and offer additional dimensions for timeline design. In this paper, we initially explored 6 timeline design styles by varying the shape and interaction distance of the timeline, aiming to discover designs more suitable for the VR environment of 360°videos. Subsequently, we introduced an adaptive timeline display mechanism based on eye gaze sequences to optimize the timeline, addressing issues like obstructing the view and causing distractions when the timeline is consistently visible. Through two studies, we first demonstrated that in the 360°space, the three-dimensional timeline performs better in terms of usability than the two-dimensional one, and the reachable timeline has advantages in performance and experience over the distant one. Secondly, we verified that, without compromising interaction efficiency and system usability, the adaptive display timeline gained more user preference due to its accurate prediction of user timeline needs.

Fuzzy Clustering Guided by Spectral Rotation and Scaling.

Representing data in different spaces becomes more powerful and suitable for solving downstream learning tasks. The membership degrees obtained through fuzzy C-means (FCM) clustering cannot capture data structures sufficiently, as they represent samples from a single Euclidean geometrical perspective. To address this issue, we propose a novel fuzzy clustering model guided by spectral rotation and scaling (FCSR). In FCSR, both spectral embeddings and membership degrees are considered as new representations of data. They can complement each other from different perspectives which enables the model to engage more structural properties of the data. The process of solving the problem of membership degrees not only inherits the merits of traditional FCM but also preserves data neighborhood structures revealed by the spectral decomposition based on an affinity matrix. Furthermore, to improve the adaptability and extensibility of FCSR, the projected and kernel versions of FCSR (FCSR-P and FCSR-K) are formed. We demonstrate that FCSR-P is suitable for high-dimensional scenarios and FCSR-K can improve the linear separability among data. Extensive experiments conducted on various well-known data sets illustrate the validity of the proposed ideas.

From the Lens of Constructivism: The Culture & History of Johor in International Relations via the Johor Heritage Foundation

Why are certain state actors actively promoting their cultural and historical heritage to others?. While many scholars in the field of international relations (IR) often stress the importance of national interest in understanding a state or country's behaviour, IR is a multifaceted field that cannot be fully comprehended from a single perspective. Therefore, this article explores the significance of culture and history within international relations by examining key concepts of Constructivism, an IR theory, with Johor, a prominent southern state of Malaysia, as a case study. The primary objective of this article is to explore the Constructivism perspective on the role of Johor's culture and history in its international relations. Additionally, the article comprehensively discusses the role of the Johor Heritage Foundation (JHF), a statutory body under the state government, in preserving the culture and history of this state. For findings, Johor through JHF is a significant state which highly emphasized on the importance of culture and history within its rapid development. The qualitative methodology and relevant themes were analysed and categorised to illustrate the different components of the role of Johor's culture and history within international relations from the perspective of Constructivism.

Research on the Configuration Path of Innovation Performance of Strategic Emerging Enterprises

As vehicles for implementing innovation-driven strategies, the strategic emerging industries are crucial for enhancing national competitiveness and sustainable development. Improving innovation performance in these industries has been a central focus of academic research. Notably, existing studies have primarily analyzed the net effects from a single perspective. This study examined 261 strategic emerging Chinese enterprises listed on the A-share market. Utilizing the Technology–Organization–Environment framework and fuzzy set qualitative comparative analysis, this study explores the impact paths and mechanisms of the coupling configurations of technology, organization, and environment to enhance enterprises’ innovation performance from a configuration perspective. We discovered that, first, no single antecedent condition is necessary to achieve high enterprise innovation performance. However, increasing the level of digital transformation and intensity of innovation investments universally results in high innovation performance. Second, the technological, organizational, and environmental conditions exhibit “multiple concurrency”, forming diverse configurations that drive enterprise innovation performance; hence, the driving paths of enterprise innovation performance are varied. Third, four schemes exist for achieving high innovation performance in strategic emerging enterprises: environment-driven under technological dominance, technology–organization driven type, organization-driven under technological–environmental dominance, and technology–organization–environment co-driven type. Exploring the synergistic paths driving innovation performance from a configuration perspective enhances our understanding of the complex interactions among multiple factors in improving such performance. This provides significant theoretical and practical implications for enterprises aiming to improve their innovation performance.

Cylinder object 6-DOF pose estimation via single perspective circle on cylindrical surface

Abstract Pose estimation is an important research topic in computer vision. In this paper, a novel method for six-degree-of-freedom (6-DOF) pose estimation of cylindrical objects via a single perspective circle on cylindrical surface is proposed. Unlike traditional methods that rely on planar circular features, a circular feature target which is affixed to the side of cylindrical objects is utilized. The geometric properties of the spatial curve formed by its contour are leveraged for pose estimation. Specifically, the imaging model of the spatial curve is established to derive the general equation of its projection curve. The initial pose is determined based on the perspective principle, and the final optimized pose is obtained with the least squares method. To avoid local optima, a verification and correction method is proposed to enhance estimation accuracy. Synthetic and physical experiments demonstrate that the proposed method can achieve real-time, high-precision pose estimation for cylindrical objects, exhibiting robustness against occlusion and noise interference.

A High-Resolution Remote Sensing Road Extraction Method Based on the Coupling of Global Spatial Features and Fourier Domain Features

Remote sensing road extraction based on deep learning is an important method for road extraction. However, in complex remote sensing images, different road information often exhibits varying frequency distributions and texture characteristics, and it is usually difficult to express the comprehensive characteristics of roads effectively from a single spatial domain perspective. To address the aforementioned issues, this article proposes a road extraction method that couples global spatial learning with Fourier frequency domain learning. This method first utilizes a transformer to capture global road features and then applies Fourier transform to separate and enhance high-frequency and low-frequency information. Finally, it integrates spatial and frequency domain features to express road characteristics comprehensively and overcome the effects of intra-class differences and occlusions. Experimental results on HF, MS, and DeepGlobe road datasets show that our method can more comprehensively express road features compared with other deep learning models (e.g., Unet, D-Linknet, DeepLab-v3, DCSwin, SGCN) and extract road boundaries more accurately and coherently. The IOU accuracy of the extracted results also achieved 72.54%, 55.35%, and 71.87%.

Multi-Scale Risk-Informed Comprehensive Assessment Methodology for Lithium-Ion Battery Energy Storage System

Lithium-ion batteries (LIB) are prone to thermal runaway, which can potentially result in serious incidents. These challenges are more prominent in large-scale lithium-ion battery energy storage system (Li-BESS) infrastructures. The conventional risk assessment method has a limited perspective, resulting in inadequately comprehensive evaluation outcomes, which impedes the provision of dependable technical support for the scientific appraisal of intricate large-scale Li-BESS systems. This study presents a novel Li-BESS-oriented multi-scale risk-informed comprehensive assessment framework, realizing the seamless transmission of assessment information across various scales. The findings from a previous smaller-scale analysis serve as inputs for a larger scale. The evaluation process of this method is more scientifically rigorous and yields more comprehensive results compared to assessment technologies just relying on a single perspective. By utilizing the proposed comprehensive assessment methodology, this study utilized the emergency power supply of nuclear power plants (NPPs) as an application scenario, demonstrating the complete implementation process of the framework and conducting a comprehensive assessment of Li-BESS feasibility as an emergency power source for NPPs. Our findings propose a novel paradigm for the comprehensive assessment of Li-BESS, which is expected to serve as a scientific foundation for decision-making and technical guidance in practical applications.

Segmentation of multi-view video speed extraction method that can cross lane dividing lines

Aiming at the problem that the existing video traffic parameter extraction methods are overly dependent on manual annotation and a single perspective cannot effectively correct the dynamic driving deviation of vehicles on site, a multi-perspective video traffic parameter extraction method that can segment and cross lane dividing lines is proposed. The method consists of an automatic annotation point generation module and a multi-perspective correction module. The automatic annotation point generation module realizes the process of automatically generating annotation points by constructing a reference block based on equal-length lane dividing lines. The multi-perspective correction module proposes multiple mapping methods between vehicles and lane dividing lines and a correction speed measurement method based on the average speed probability density function to correct the two types of deviations generated by vehicles during dynamic driving. Experimental results on public data sets and measured data sets show that the vehicle speed extraction accuracy of the proposed method is better than other speed measurement methods, and it has a certain degree of universality.

Comparison of the Achieved and Expected Competence Level of New Graduate Nurses: Two Cross-Sectional Studies.

To explore and compare the self-perceived competence level of new graduate nurses with the expected competence level that nurse managers believe new graduate nurses should have in general. Two cross-sectional empirical studies. Two independent cross-sectional studies were carried out in 12 tertiary hospitals in September 2020 and November 2021, respectively. Participants were 1017 new graduate nurses with less than 2 years of work experience and 306 nurse managers who completed an online survey. The score of self-perceived nursing competence of new graduate nurses was 74.77 ± 14.35, and the expectation of nurse managers was 74.21 ± 15.04. The values of 18 items were significantly different between the two of them (p < 0.05), and there were only three items showed higher expectations of nurse managers than the self-rated values of new graduate nurses. Results of latent profile analysis revealed that this cohort of new graduate nurses could be divided into three subgroups, named risky competent (11.1%), medium competent (46.0%) and advanced competent (42.9%). When comparing, the nurse managers' expected competence level was between the value of 'medium competent' group and 'advanced competent' group. Our study adopted a dual perspective comparison of nurse managers and new graduate nurses, which broke through the limitations of the previous single perspective study of new graduate nurses' nursing competence. This study highlights the importance of the consistency in new graduate nurses' nursing competence from the perspectives of new graduate nurses and nurse managers. This study showed a generally good match of actual nursing competence level of new graduate nurses and the expected level from nurse managers. While there was significant individual heterogeneity in the competence score of new graduate nurses, three subgroups were identified through the individual-centred analysis of latent profile analysis. Therefore, Nurse managers need to systematically adjust the standardized training programs and teaching methods for new graduate nurses according to the population characteristics and take precise interventions to improve nursing competence of new graduate nurses. We adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. No Patient or Public Contribution.

In vivo Multi-perspective 3D + t Ultrasound Imaging and Motion Estimation of Abdominal Aortic Aneurysms.

Time-resolved three-dimensional ultrasound (3D + t US) is a promising imaging modality for monitoring abdominal aortic aneurysms (AAAs), providing their 3D geometry and motion. The lateral contrast of US is poor, a well-documented drawback which multi-perspective (MP) imaging could resolve. This study aims to show the feasibility of in vivo multi-perspective 3D + t ultrasound imaging of AAAs for improving the image contrast and displacement accuracy. To achieve this, single-perspective (SP) aortic ultrasound images from three different angles were spatiotemporally registered and fused, and the displacements were compounded. The fused MP had a significantly higher wall-lumen contrast than the SP images, for both patients and volunteers (P < .001). MP radial displacements patterns are smoother than SP patterns in 67% of volunteers and 92% of patients. The MP images from three angles have a decreased tracking error (P < .001 for all participants), and an improved SNRe compared to two out of three SP images (P < .05). This study has shown the added value of MP 3D + t US, improving both image contrast and displacement accuracy in AAA imaging. This is a step toward using multiple or large transducers in the clinic to capture the 3D geometry and strain more accurately, for patient-specific characterization of AAAs.

The polyphonic principle in the novelistic thinking of Olga Tokarczuk

AbstractThis study explores the influence of the musical principle of polyphony in the novelistic thinking of the Polish writer Olga Tokarczuk, with a particular focus on the structure of her novel Primeval and Other Times. This principle is manifested in various facets of the novels’ composition, their characters and their themes.In the introductory part of this text, I apply the concept of intermediality to show how the polyphonic principle straddles the boundary-line between music and literature. As in music, also in literature the polyphonic principle is rooted in the principle of plurality: there is no single truth, no single perspective or way of looking at things, and each thought always exists in counterpoint with another thought. This creates a genuine multiplicity of voices each with equal status, which may exist in various relationships with each other (contrast, mirroring, divergence, complementing, etc.), though none of them represents a definitive truth.The main focus in this study is on the novel Primeval and Other Times. When analyzing it, I apply the same approach as could be used for a complex polyphonic structure—specifically, a four-voice fugue with four clearly defined themes: space, time, God, and people. I trace how each of these themes is gradually developed through an elaborate counterpoint: (space: time, space: God, space: people, etc.), and I also set out to present a synthesis—because each voice (theme), despite its independence and equality of status, forms an integral part of the whole, contributing to the text’s meaning-making.