Multidimensional Characteristics Research Articles

Integrated proteogenomic and metabolomic characterization of papillary thyroid cancer with different recurrence risks

Although papillary thyroid cancer (PTC) has a good prognosis, its recurrence rate is high and remains a core concern in the clinic. Molecular factors contributing to different recurrence risks (RRs) remain poorly defined. Here, we perform an integrative proteogenomic and metabolomic characterization of 102 Chinese PTC patients with different RRs. Genomic profiling reveals that mutations in MUC16 and TERT promoter as well as multiple gene fusions like NCOA4-RET are enriched by the high RR. Integrative multi-omics analyses further describe the multi-dimensional characteristics of PTC, especially in metabolism pathways, and delineate dominated molecular patterns of different RRs. Moreover, the PTC patients are clustered into four subtypes (CS1: low RR and BRAF-like; CS2: high RR and metabolism type, worst prognosis; CS3: high RR and immune type, better prognosis; CS4: high RR and BRAF-like) based on the omics data. Notably, the subtypes display significant differences considering BRAF and TERT promoter mutations, metabolism and immune pathway profiles, epithelial cell compositions, and various clinical factors (especially RRs and prognosis) as well as druggable targets. This study can provide insights into the complex molecular characteristics of PTC recurrences and help promote early diagnosis and precision treatment of recurrent PTC.

The effects of knowledge base characteristics on patent pledge financing: the moderating effects of technological munificence and dynamism

Purpose This study aims to examine how the knowledge breadth and depth of a patent affect its likelihood of being pledged. It also seeks to explore whether these relationships change diversely in different technological environments. Design/methodology/approach A complementary log-log model with random effects was conducted to test the hypotheses using a unique data set consisting of 348,927 invention patents granted by the China National Intellectual Property Administration from 1985 to 2015 belonging to 74,996 firms. Findings The findings reveal that both knowledge breadth and depth of a patent positively affect its likelihood of being pledged. Furthermore, the knowledge breadth and depth entail different degrees of superiority in different technological environments. Research limitations/implications This study focuses on the effect of an individual patent’s knowledge base on its likelihood of being selected as collateral. It does not consider the influence of the overall knowledge characteristics of the selected patent portfolio. Practical implications Managers need to pay attention to patents’ knowledge characteristics and the changes in technological environments to select the most suitable patents as collateral and thus improve the success rate of pledge financing. Originality/value This study explores the impact of multidimensional characteristics of knowledge base on patent pledge financing within a systematic theoretical framework and incorporates technological environments into this framework.

Solvent-free synthesis of self-regenerative superhydrophobic film from silicone waste with drag-reduction and selective oil absorption behaviour

In the present work, we devised an ecological solution for recycling silicone waste into functionalized films possessing superhydrophobicity with low drag and oil adsorption characteristics. We used two ecological approaches to transform silicone waste into nanoparticles with inherently distinct surface characteristics. These spheroidal silica nanoparticles, with an average size of 150 nm, showed hydrophilic and hydrophobic characteristics. The detailed characterization confirmed hydrophobic silica being grafted with –CH3 oligomers compared to hydrophilic counterparts. These silica nanoparticles were used to fabricate flexible superhydrophobic films through a solvent-free approach. Films synthesized using hydrophilic particles showed better de-wetting characteristics with advancing angle > 150°, low hysteresis (<5°) and sliding angle (<5°) at an optimum volumetric concentration. High de-wetting stability and low adhesion of ∼ 20 μN with water droplet were noticed for these films due to homogenous particle distribution, leading to significant protrusions and favourable line tension. High van der Waals interaction energy aided better mixing of hydrophilic particles in the matrix than the hydrophobic counterpart. This also resulted in high mechanical strength ∼ 7 MPa (matrix strength ∼ 3 MPa) and better robustness under different harsh conditions, sustaining more than ten months of weathering and one month of immersion. Films showed real-time self-regeneration capability, retaining high de-wetting due to surface chemistry and morphology rejuvenation. The film also displayed extremely low drag, promoting water droplet mobility at ∼ 0.5 m/s wind velocity and oil–water separation capability. The fluorine-free flexible superhydrophobic films synthesized using silicone waste exhibited multi-dimensional characteristics for wide-range applications.

Diversified evolutionary patterns of surface urban heat island in new expansion areas of 31 Chinese cities

As a hot area of population inflow in the process of urbanization, the urban expansion area faces rapid growth of surface urban heat island (SUHI). However, the multi-dimensional evolutionary characteristics of SUHI in urban expansion areas are still unclear. Through analyzing the evolution of SUHI range ratio, mean intensity and maximum intensity, in this study we identified the comprehensive evolutionary pattern of summer SUHI in urban expansion areas of 31 major cities in China during 2000–2018, and further investigated the corresponding dominant influencing factors. The results showed that the SUHI range and intensity in urban expansion areas exhibited a significant increasing trend in 81% and 71% of the cities respectively during the summer daytime. The cities with declining SUHI range and intensity were distributed in northwestern high-altitude areas with low economic level, while the cities with dominant increases in the SUHI range were distributed in eastern low-altitude areas with high economic level. Climate conditions and population distribution were the dominant influencing factors of intensity-dominated increasing and balanced increasing types, respectively. The intensity and range two-dimensional increasing type had experienced the most severe SUHI growth, with large proportion of secondary industry as the main influencing factor. This study highlighted the importance of multi-dimensional characteristics of SUHI evolution, which provided a new insight to understand SUHI change in urban expansion areas and associated mitigation measures.

Sudden crisis events and firms' stock price fluctuations: A multidimensional heterogeneity perspective

AbstractThis study takes listed companies that have experienced sudden crisis events as samples. The event study method and multiple regression analysis are further adopted to explore the relationship between sudden crisis events and the stock price fluctuations of listed companies from the perspective of multidimensional characteristics. Findings reveal that sudden crisis events have a significant impact on stock price fluctuations. However, the firms' response frequency and response time to crisis events can exert a buffering role in their adverse association. Further analysis shows that the stock price fluctuations also vary with different heterogeneous characteristics including crisis event types, ownership types, firm competitiveness and firm size. The multidimensional analysis targeting the impact of sudden crisis events on the stock market is conducive to helping firms grasp the rhythm of crisis events and further implement some practical and feasible crisis management measures.

그림책 독서치료가 중년여성의 행복감에 미치는 효과

The purpose of the study was to verify that the multi-dimensional characteristics of picture book bibliotherapy work as a treatment tool appropriate for the biological, environmental, and psychological characteristics of middle-aged women, thus enhancing their sense of happiness. The research method was a Heterogeneous Control Group Pre- and Post-test design. We organized a total of 15 women aged 40 to 65 years old, living in city B, into an experimental group of 8 members and a control group of 7 members. From July 15, 2022 to September 2, 2022, picture book bibliotherapy was conducted with the experimental group only once a week for 8 weeks, for 150 minutes each week. The collected data was carried out Two-Way Analysis of Variance and Analysis of Covariance. As a result of the study, the Experimental Group showed a statistically significant increase in both Overall Happiness Scores and the 6 Sub-factors Scores when it is compared to the control group.

A Spatiotemporal Deep Learning-Based Smart Discovery Approach for Marine Pollution Incidents from the Data-Driven Perspective

Marine pollution incidents (MPI) are often a dynamic process of time and space interaction. Currently, the monitoring of MPI is basically realized by manual analysis from expert experience. Such working mode has an obvious time lag, and is not useful for timely disposal. As a result, intelligent algorithms that can make quick discovery for MPI from massive monitoring data remain a practical demand in this field. Considering that monitoring elements generally have multi-dimensional characteristics and spatiotemporal causal relationships, this work develops a spatiotemporal deep learning-based smart discovery approach for MPI from the data-driven perspective. In particular, a systematic preprocessing workflow is developed for the spatiotemporal monitoring data, which facilitates following feature extraction. Then, a spatiotemporal convolution neural network structure is developed to extract features from original spatiotemporal monitoring data. On this basis, the discovery results of MPI can be output via neural computing structures. Taking the polluting marine oil spill incident in the Bohai Sea in eastern China as a case study, this work carries out a simulation application and its result analysis. The obtained simulation results can reveal the proper performance of the proposal.

A model for new media data mining and analysis in online English teaching using long short-term memory (LSTM) network.

To maintain a harmonious teacher-student relationship and enable educators to gain a more insightful understanding of students' learning progress, this study collects data from learners utilizing the software through a network platform. These data are mainly formed by the user's learning characteristics, combined with the screen lighting time, built-in inertial sensor attitude, signal strength, network strength and other multi-dimensional characteristics to form the learning observation value, so as to analyze the corresponding learning state, so that teachers can carry out targeted teaching improvement. The article introduces an intelligent classification approach for learning time series, leveraging long short-term memory (LSTM) as the foundation of a deep network model. This model intelligently recognizes the learning status of students. The test results demonstrate that the proposed model achieves highly precise time series recognition using relatively straightforward features. This precision, exceeding 95%, is of significant importance for future applications in learning state recognition, aiding teachers in gaining an intelligent grasp of students' learning status.

A review of carbon-based catalysts and catalyst supports for simultaneous organic electro-oxidation and hydrogen evolution reactions

Producing organic electro-oxidation and hydrogen evolution reactions (HER) simultaneously in an electrolytic cell is an appealing method for generating valuable chemicals at the anode while also producing H2 at the cathode. Within this framework, the task of designing energy-saving electrocatalysts with high selectivity and stability is a considerable challenge. Carbon-based catalysts, along with their supports, have emerged as promising candidates due to their diverse sources, large specific surface area, high porosity and multidimensional characteristics. This review summarizes progress from 2012 to 2022, in the use of carbon-based catalysts and their supports for organic electrooxidation and HER. It delves into outer-sphere electrooxidation mechanisms involving molecule-mediated oxidation and oxidative radical coupling reactions, as well as inner-sphere electrooxidation mechanisms, encompassing both acidic and alkaline electrolytes. The review also explores prospective research directions within this domain, addressing various aspects such as the design of electrocatalytic materials, the study of the relationship between the structure and properties of electrocatalysts, as well as examining their potential industrial applications.

Collaborative Optimization of Learning Team Formation Based on Multidimensional Characteristics and Constraints Modeling: A Team Leader-Centered Approach via E-CARGO

With the massive popularization of e-learning, collaborative learning via learning teams has become indispensable to enhancing the learning efficiency and learning quality of overall learners. The team leader usually plays a key role in collaborative learning. However, the existing research ignores the key characteristics of learners and constraints relevant to e-learners when identifying appropriate team leaders and compatible members. A novel collaborative optimization approach to learning team formation is proposed based on a refined learner model and the environments—classes, agents, roles, groups, and objects (E-CARGO) model. With the proposed approach, a learner is modeled by combining 5-D characteristics (i.e., cognitive ability, leadership, sociability, learning style, and personality) and three types of constraints (e.g., conflicts, genders, and the number of members), and an assessment mechanism is designed to measure the comprehensive abilities of learners for identifying an ideal team leader and selecting the team members for a team. By innovatively introducing the role-based collaboration theory and E-CARGO model, the leader-centered learning team formation problem is formalized as a collaborative optimization problem. The mathematical model and the constraint relations are established for this problem, which is solved based on the IBM CPLEX package. Finally, a case study and experiments demonstrate that the proposed approach is efficient and feasible, in favor of improving the satisfaction degree of learners.

Impact of urban land development on carbon emission performance based on a multidimensional framework

Massive carbon emissions resulting from global urbanization have been one of the main triggers of climate warming. Because of its scale and representativeness, China's urbanization has significantly affected the world's socioeconomic system. Therefore, a comprehensive understanding of the urban land development (ULD) process and its impact on carbon emission performance (CEP), using cities in China as an example, is crucial for achieving global climate goals. Thus, using 245 cities in China as examples, a new explanatory framework was developed to understand the multidimensional characteristics of ULD and their impact on CEP. The average ULD increased from 0.266 to 0.299 and stabilized from 2000 to 2019, with the gap between cities continuously narrowing. Average CEP increased from 0.634 to 0.663 and then decreased to 0.556, presenting an increasing distribution pattern from northwest to southeast. The coupling level between ULD and CEP decreased from the east coast inland. More than half of the cities retained their coupling levels, and 3.67% achieved leapfrog development. The higher the initial CEP level, the weaker the positive effect of an increase in ULD level on the coupling score. In addition, the influences of the multidimensional characteristics of ULD on CEP showed significant spatiotemporal heterogeneity at the country scale. Thus, this study proposes that the effect of the ULD on CEP is phased, which is highly correlated with the evolution of the coupling level. These findings highlight the necessity for city managers to incorporate the differential impacts of the multidimensional characteristics of ULD on CEP in the formulation of carbon emission reduction policies.

Mobility difference index: a quantitative method for detecting human mobility difference

ABSTRACT Differences in human mobility reflect temporal variations and spatial differences in urban spaces, including regional functions, physical environments, and geographical sentiments. Accurately quantifying these differences is critical for understanding and managing cities. However, existing measurement methods overlook the spatial distribution of population movement, which limits the ability to compare spatial differences in human mobility. Separate treatment of the spatial distribution, flux, and distance of human movement increases the complexity and uncertainty of understanding geographic phenomena. Therefore, we propose a flow-based location measure, termed the mobility difference index (MDI), that fuses multidimensional movement characteristics to quantify temporal variations and spatial differences in human mobility. The method quantifies the differences in human mobility by calculating the minimum transformation cost between two sets of origin-destination flows based on optimal transport theory. Simulation experiments confirmed the advantage of the MDI in perceiving the multidimensional characteristics of human movement, particularly regarding spatial distribution. We examined mobile signaling and positioning data from Wuhan and found that the proposed MDI could effectively identify the spatial and temporal dependencies of variations in human mobility and heterogeneous effects of spatial semantics and distance on spatial differences in human mobility.

A Diagnostic Method for Open-Circuit Faults in DC Charging Stations Based on Improved S-Transform and LightGBM

The open-circuit fault in electric vehicle charging stations not only impacts the power quality of the electrical grid but also poses a threat to charging safety. Therefore, it is of great significance to study open-circuit fault diagnosis for ensuring the safe and stable operation of power grids and reducing the maintenance cost of charging stations. This paper addresses the multidimensional characteristics of open-circuit fault signals in charging stations and proposes a fault diagnosis method based on an improved S-transform and LightGBM. The method first utilizes improved incomplete S-transform and principal component analysis (PCA) to extract features of front- and back-stage faults separately. Subsequently, LightGBM is employed to classify the extracted features, ultimately achieving fault diagnosis. Simulation results demonstrate the method’s effectiveness in feature extraction, achieving an average diagnostic accuracy of 97.04% on the test dataset, along with notable noise resistance and real-time performance. Additionally, we designed an experimental platform for diagnosing open-circuit faults in DC charging station and collected experimental fault data. The results further validate the effectiveness of the proposed method.

Multidimensional characteristics of musculoskeletal pain and risk of hip fractures among elderly adults: the first longitudinal evidence from CHARLS.

Hip fractures are a major public health concern among middle-aged and older adults. It is important to understand the associated risk factors to inform health policies and develop better prevention strategies. Musculoskeletal pain is a possible implicating factor, being associated with physical inactivity and risk of falls. However, the association between musculoskeletal pain and hip fractures has not been clearly investigated. A nationally representative sample of the Chinese population was obtained from the China Health and Retirement Longitudinal Study (CHARLS). The study collected patient information on their demographic characteristics, socioeconomic status, other health-related behavior, and history of musculoskeletal pain and hip fractures. Univariate and multivariate analyses were conducted to investigate the factors influencing the risk of hip fracture, including factors related to the individual and to musculoskeletal pain. P for trend test was performed to assess the trend of each continuous variable. The robustness and bias were assessed using the bootstrap method. Restricted cubic spline regression was utilized to identify linear or non-linear relationships. Among the 18,813 respondents, a total of 215 individuals reported that they have experienced a hip fracture. An increased risk of hip fracture was associated with the presence of waist pain and leg pain (P < 0.05), as well as with an increased number of musculoskeletal pain sites (P < 0.05). For individuals aged 65 and above, a significant association was found between age and the risk of hip fracture (P < 0.05). Furthermore, respondents with lower education level had a higher risk of hip fracture compared to those with higher education levels (P < 0.05). In the Chinese population, the risk of hip fracture was found to be associated with both the location and extent of musculoskeletal pain, as well as with other factors such as age and demographic characteristics. The findings of this study may be useful for informing policy development and treatment strategies, and provide evidence for comparison with data from other demographic populations.

Mining public behavior patterns from social media data during emergencies: A multidimensional analytical framework considering spatial–temporal–semantic features

AbstractStudying human behavioral patterns from social media data is an important part of emergency management. However, the multidimensional characteristics of social media data have rarely been fully utilized. This study proposes a multidimensional analytical framework for social media user behavior that integrates time–geographic–semantic features. The framework defines the spatiotemporal semantic multidimensional relationship of social media user behavior and maps it into a time–geographic–semantic (TGS) cube, based on which a TGS‐weighted similarity measure was created. We then applied a spectral clustering algorithm to cluster the trajectories of the user behavior. Subsequently, a prefix‐projected pattern growth algorithm was used to mine frequent semantic patterns from the clustering results and analyze their spatiotemporal distribution characteristics. Taking the COVID‐19 pandemic as a case study, we analyzed Weibo user behavior in China from January 9 to March 10, 2020. The results showed that the clustering of TGS similarity was better than that of the commonly used edit distance on real and longest common subsequences. Five semantic patterns of public responses were identified during the COVID‐19 pandemic. The semantic patterns of categories 1, 2, 4, and 5 were “spindle‐shaped,” meaning that their core semantics were stable and concentrated on one or several topics despite the frequent semantic changes in the middle stage. Category 3 was “wave‐shaped,” indicating that their semantics fluctuated between serval topics during the pandemic. This discovery shows that the framework is suitable for analyzing and comprehensively understanding public behavior during pandemic emergencies. This framework has good universality and great potential for extension to other emergencies.

Financial Inclusion and the Contribution of Fintech in Middle-Income Countries: An Empirical Investigation across a Panel of Countries in Africa and Asia

This paper explores the contribution of financial services to financial inclusion amidst digitization and Fintech growth. It proposes an inclusion index based on African and Asian countries from 2010 to 2019, examining multidimensional characteristics and determinants of financial inclusion. The analysis reveals modest progress, with only eight countries nearing a 0.6 inclusion level, while the rest fall below 0.50. Econometric regression tests highlight a positive relationship between financial inclusion, bank cards, and mobile money, suggesting that digitization of banks has bolstered inclusion. Connectivity and digital payment transactions are also found to be significant factors positively associated with inclusion. Notably, the variable representing Fintech usage stands out as the most significant contributor to financial inclusion.

세계 내에서 관계하는 몸으로 바라본 위약효과

Placebo effects are intriguing phenomena in which beneficial healing effects happen despite the administration of a placebo, which has no active substances or involves inert procedures. Multi-dimensional characteristics have been involved in placebo effects, exhibiting various and non-specific modalities. The two most representative well-known explanatory models on placebo effects, expectancy and conditioning theories, which have perspectives rooted in Cartesian dualism that separate mind and body, show inherent limitations and problems in sufficiently explaining placebo effects. In order to elucidate placebo effects which shows non-separative, connective and relating aspects, new models capable of embracing and explaining such aspects are required. This study attempted to examine how placebo effects could happen in our bodies by means of studies based upon phenomenology and enactivism as models to account for the connective, combining, and interactive relations between various dimensions. This study elucidates the mechanisms of placebo effects using the phenomenological and various enactive concepts. At the heart of connections and relations between several heterogenous dimensions, relating bodies as embodied beings are closely involved in the dynamic interactions with our mind and environments. Our bodies where health, illness, and healing processes through medical practices including placebo effects take place could be considered as the very relation itself in which our internal and external worlds encounter and interact. This study suggests that continual establishment and verification of new theories concerning these relations, which are manifested in placebo effect, are required to deal persuasively with the topic, and the need for appropriate utilization of the relational aspects of placebo effects.

Social Dynamics and Intelligence of Killer Whales (Orcinus Orca)

The present study undertakes a comprehensive investigation into the behavioral and social complexity of Orcinus orca, commonly referred to as killer whales. Utilizing a multimodal approach that combines acoustic analysis, behavioral observation, and social network mapping, this research addresses four primary dimensions: the species' specialized communication techniques, intricate social structures, cooperative hunting strategies, and evidence for cultural transmission. Our analysis reveals that killer whales employ diverse acoustic and non-acoustic signals, enabling intricate communication networks essential for social bonding and collaborative behaviors. Social structures within pods are observed to be highly hierarchical, often influenced by matrilineal lineages, thereby indicating advanced social organization. Cooperative hunting strategies differ depending on prey type and local environmental conditions, reflecting a high degree of behavioral adaptability and specialization. The study also furnishes compelling evidence for the cultural transmission of learned behaviors across generations, suggesting a complexity often attributed solely to primates. Understanding these multidimensional characteristics holds substantial implications for the broader marine biology animal cognition field, especially for conserving this apex predator. However, it is essential to note the limitations of this study, including potential observer bias, restricted sample size due to observational limitations, and the inconclusive nature of some findings on cultural transmission. Further research is imperative, both to validate and extend these results. Advancements in remote sensing technologies and machine learning algorithms offer promising avenues for future research, which is pivotal for developing nuanced and effective conservation strategies for Orcinus orca in an era of rapidly changing oceanic ecosystems.

Molecular Simulation of Nitrogen Adsorption in Multidimensional Nanopores and New Insights into the Inversion of Pore Size Distribution for Gas Shale

Low-temperature nitrogen adsorption is a widely used method for the research and evaluation of gas shale’s pore structure. The existing interpretation method, utilizing gas adsorption isotherms to obtain pore size distribution (PSD), is always based on the one-dimensional geometry model, while the void space of gas shale has strong multi-dimensional characteristics. It is necessary to investigate the nitrogen condensation and evaporation behavior in multidimensional structures. In this study, a series of two-dimensional and three-dimensional models based on ink-bottle pores were constructed. A hybrid molecular simulation approach combining grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) is proposed to simulate the low-temperature nitrogen adsorption isotherms. Three aspects have been analyzed in detail. Compared with the conventional understanding that the threshold of cavitation in the ink-bottle pore only relates to throat diameter, this study discloses a wider and more comprehensive range of conditions of cavitation that considers both the throat length and diameter. As pore spaces of shale samples consist of many complex interconnected pores, the multi-stage ink-bottle pore model is more suitable than the single ink-bottle pore model to similarly reproduce the wider cavitation pressure range. A more universal parameter is proposed that quantitatively unifies the influence of cavity diameter and length on condensation pressure and has good applicability in cavities with different shapes. This work quantitatively studies the nitrogen adsorption isotherms of three-dimensional complex nanopore structures using molecular simulation and provides a reasonable explanation for the low-temperature nitrogen adsorption isotherms of gas shale.

Low-carbon development in China's transportation sector: Multidimensional characteristics and policy implications

Although low-carbon development of transportation sector is crucial for global deep decarbonization, the multidimensional characteristics (e.g., inequality, complex network and efficiency-related drivers) carbon intensity (CI) in the sector at sub-national level is still lacking in-depth evaluation for an emerging economy in particular. Here, using social network analysis, inequality decomposition and production-theoretical decomposition analysis with a focus on regional heterogeneity, the study comprehensively investigated the complex network, inequality and novel drivers related to efficiency and technological change of CI for transportation sectors across 30 provinces in China over 1997–2019. We found that first, the strict spatial correlation structure of CI in the transportation sector was gradually broken and the inter-provincial correlation were enhanced. Second, the inequality of CI declined where gasoline, diesel oil, kerosene and raw coal were the main contributors. Third, the potential carbon factor mainly promoted the reduction of CI while the impact of potential energy intensity exhibited an "inverted U″ trend over the period. Forth, the group technical change effects of energy usage and CO2 emission first promoted the decline of CI and then inhibited it. The study highlights the importance of the multidimensional characteristics in the transportation sector when formulating CI-related policies according to the local conditions.