Search Results (3,641)

In the era of data elements, it is extremely necessary and practically important to analyze network characteristics and evolutionary trends in academic research collaboration in the field of data assets research, which can provide valuable insights for promoting deep cooperation of scholars and enhancing their collaborative efficiency. However, existing studies on data assets research rarely delve into key differentiating characteristics and core thematic priorities between Chinese and international samples of collaboration networks. Based on bibliometric methods and social network analysis, a systematic comparative analysis between Chinese collaboration networks and international collaboration networks is conducted via CiteSpace software by using core literature from the China National Knowledge Infrastructure (CNKI) and Web of Science Core Collection, developed by Clarivate Analytics (WoS). Through observation, we find that the number of publications in this field has reached a preliminary scale with distinct differences in research focus and collaborative features between cooperation networks in China (CNCs) and international cooperation networks (ICNs). In recent years, Chinese samples have primarily focused upon research themes related to data value realization, such as data rights confirmation, data assets accounting, and data trusts. The overall connectivity of CNCs seems relatively weak, and a stable core author group has not formed, while collaborations in CNCs are predominantly localized and short-term. In contrast, international samples in recent years have mainly addressed the contextual application of data assets, exhibiting a collaboration network characterized by multi-center, interdisciplinary, and cross-institutional synergy, while core authors in ICNs are closely interconnected and their connectivity and structure are generally stronger than those of CNCs. Full article

Backgrounds: Adverse Childhood Experiences (ACEs) are linked to early and long-lasting mental health issues and somatic multimorbidity. Emerging evidence suggests ACEs may also accelerate physical frailty in old age. This study examines the association between ACEs and sarcopenia, an ageing-related disease and core component of frailty. Methods: Baseline data from the Canadian Longitudinal Study on Aging (CLSA), including 25,327 participants aged 45–85 years (50.3% female sex) were analyzed. Sarcopenia was defined using the revised European Working Group of Sarcopenia in Older People (EWGSOP2) guidelines. ACE were assessed via the Childhood Experiences of Violence Questionnaire and the National Longitudinal Study of Adolescent to Adult Health Wave III questionnaire, covering eight ACE categories. Multiple logistic regression models examined the association between the number of ACE count and sarcopenia, which were adjusted for age, sex, education, income, and ethnicity. Results: Given a significant interaction between age and ACE (p < 0.01), analyses were stratified into four age groups (45–54, 55–64, 65–74, and 75–85 years). A significant association only emerged in the oldest group (75–85 years; OR = 0.93 [95% CI: 0.86–1.00], p = 0.043), but this result was in the opposite direction we hypothesized. Sensitivity analyses confirmed findings across different operationalisations of ACE and sarcopenia. Conclusions: Higher ACE exposure was not associated with sarcopenia in middle aged and older adults. The unexpected protective association in the oldest-old subgroup may reflect survival bias. Age-stratified longitudinal studies are needed to clarify this relationship. Full article

This paper addresses the ill-posed problem in calculating subgroup parameters for resonance self-shielding within nuclear reactor physics. The conventional Pade approximation method often yields negative subgroup cross-sections lacking physical meaning due to its treatment of overdetermined nonlinear systems, making the subgroup transport equations unsolvable. To overcome this, an optimized Pade approximation method is proposed: a resonance factor criterion is used to select energy groups requiring calculation; a systematic procedure dynamically traverses background cross-section combinations starting from a minimal subgroup number, incrementally increasing it until solutions meeting accuracy constraints with positive parameters are found; and, given the insufficiency of background points, a high-resolution resonance integral table is constructed, particularly for ranges exhibiting significant cross-section variations. Numerical validation confirms the method eliminates negative parameters, ensures physical validity, and significantly improves accuracy across benchmark cases including typical fuel pins, burnt pellets, and Gd-bearing lattices. This approach effectively resolves the ill-posedness of the traditional method, offering a more robust and precise subgroup resonance treatment for high-fidelity core neutronics. Full article

This study presents a numerical investigation into the heart valve through a fluid–structure interaction (FSI) framework using a two-dimensional, steady-state, Newtonian flow assumption. While simplified, this approach captures core biomechanical effects and provides a baseline for future extension toward non-Newtonian, pulsatile, and three-dimensional models. The analysis focuses on the influence of magnetic field intensity characterized by the Hartmann number (Ha) and flow regime defined by the Reynolds number (Re) on critical hemodynamic parameters, including wall shear stress (WSS), velocity profiles, and pressure gradients in the valve region. The results demonstrate that stronger magnetic fields significantly stabilize intravalvular flow by suppressing recirculation zones and reducing flow separation distal to valve constrictions, offering protective hemodynamic benefits and serving as a non-invasive method to modulate vascular behavior and reduce the risk of cardiovascular pathologies such as atherosclerosis and hypertension. Full article

Space debris is an increasingly severe problem in the space industry. According to projections, the number of satellites will increase from the current 10,000 to 100,000 by 2030, specially in LEO orbits. This significant rise in the number of satellites threatens space sustainability, forcing satellites to perform more maneuvers to avoid impacts or leading to the production of more and more space debris due to collisions (Kessler Syndrome). Consequently, substantial efforts have been made to detect and track space debris, leading to the development of the current catalogs. However, with existing technology, detecting and tracking small debris remains challenging. In order to improve the current system, several proposals of Space-Based Situational Awareness (SBSA) have been made. These proposals involve satellites equipped with telescopes to detect space debris and determine their orbits. Unlike prior works, focused primarily on detection rates, this research aims to quantify their accuracy in orbit determination as a function of observation duration, the number of observers, and sensor precision. The Unscented Kalman Filter (UKF) is employed as the core estimation algorithm, leveraging both simulated single-case analyses and Monte Carlo simulations to evaluate system performance under various configurations and uncertainties. The results indicate that a constellation of at least three observers with high-precision instruments and sub-kilometer positioning accuracy can reliably estimate debris orbits within an observation period of 4–7 min, with the mean error in position and velocity obtained being 2.2–3 km and 3–4 m/s, respectively. These findings offer critical insights for designing future SBSA constellations and optimizing their operational parameters to address the growing challenge of orbital debris. Full article

Studying comprehensive performance is fundamental for the effective utilisation of broomcorn millet (Panicum miliaceum L.) germplasm resources and breeding of new varieties. However, compared with other major crops, research on broomcorn millet germplasm resources is limited, and the trait variations of broomcorn millet are unclear. In this study, three qualitative and seven quantitative traits of 933 broomcorn millet core collections were analysed to provide the basis for improving utilisation of broomcorn millet germplasm resources. The seed colour was a strong phenotypic trait and had eight variants. The 933 resources exhibited three panicle types: lateral (74.5%), scattered (18.4%), and compact (7.1%). They exhibited two inflorescence colours: green (54.7%) and purple (45.3%). Pearson’s correlation analysis revealed that 1000-seed weight significantly correlated with plant height, length of panicle, and number of main stem segments. The period of duration positively correlated with 1000-seed weight but negatively correlated with the number of uniserial panicles. Cluster analysis based on the quantitative traits indicated that all resources were divided into three groups, and each group had its respective characteristics. The analysis of core germplasm resources of broomcorn millet in this study provided a basis to explore excellent genes and for breeding of excellent varieties. Full article

In the field of mobile crowdsensing (MCS), a large number of tasks rely on the participation of ordinary mobile device users for data collection and processing. This model has shown great potential for applications in environmental monitoring, traffic management, public safety, and other areas. However, the enthusiasm of participants and the quality of uploaded data directly affect the reliability and practical value of the sensing results. Therefore, the design of incentive mechanisms has become a core issue in driving the healthy operation of MCS. The existing research, when optimizing long-term utility rewards for participants, has often failed to fully consider dynamic changes in trustworthiness. It has typically relied on historical data from a single point in time, overlooking the long-term dependencies in the time series, which results in suboptimal decision-making and limits the overall efficiency and fairness of sensing tasks. To address this issue, a credibility-aware incentive mechanism based on long short-term memory and proximal policy optimization (CIM-LP) is proposed. The mechanism employs a Markov decision process (MDP) model to describe the decision-making process of the participants. Without access to global information, an incentive model combining long short-term memory (LSTM) networks and proximal policy optimization (PPO), collectively referred to as LSTM-PPO, is utilized to formulate the most reasonable and effective sensing duration strategy for each participant, aiming to maximize the utility reward. After task completion, the participants’ credibility is dynamically updated by evaluating the quality of the uploaded data, which then adjusts their utility rewards for the next phase. Simulation results based on real datasets show that compared with several existing incentive algorithms, the CIM-LP mechanism increases the average utility of the participants by 6.56% to 112.76% and the task completion rate by 16.25% to 128.71%, demonstrating its significant advantages in improving data quality and task completion efficiency. Full article

Motivated by China’s new urbanization and ecological civilization construction initiatives, the Shenyang Municipal Committee has recently has proposed an ambitious goal of advancing the construction of a Park City with northern characteristics. The scientifically planned urban landscape is essential for balancing ecological protection with sustainable development,. This plan is crucial for driving the realization of the Park City initiative. This study employed ArcGIS 10.8 and Fragstats 4.2 to systematically examine land use transitions and landscape pattern dynamics in Shenyang’s main urban area (2000–2020). The results indicated that Shenyang’s urban core has experienced significant southward expansion across the Hun River over the last two decades. This expansion resulted in a substantial increase in constructed land of 490.84 km² (from 15.78% to 29.19% in total coverage). Conversely, cultivated land, forest land, and grassland exhibited negative dynamic rates of −0.99%, −0.54%, and −1.02%, respectively, with 76.89% of cultivated land converted to construction land. Landscape pattern indices revealed intensified fragmentation: the number of patches rose by 163, while the largest patch area, landscape aggregation index, and contagion index decreased by 16.74%, 0.40%, and 5.84%, respectively. However, the landscape division index increased by 0.12%, with Shannon’s diversity index and evenness index increasing by 0.19 and 0.11, respectively. These metrics demonstrated the positive correlation between urbanization intensity and landscape pattern alterations. The examination of the dynamic land use patterns in Shenyang integrated seven crucial indicators to assess the development of the emerging Park City. Results indicated challenges including urban land expansion, cultivated land loss, limited resources, and uneven green space distribution. The findings revealed the negative correlation between land use pattern evolution and Park City requirements. The research suggested strategies at the macro-, meso-, and micro-scales to address these issues and reconcile urbanization pressures with sustainable Park City development in Shenyang. Full article

Since 2008, the Chinese government has been intensively implementing policies to control plastic pollution. This study employs text mining and scientific statistical methods to quantitatively analyze 223 policy documents spanning the period from 2008 to 2025. The novelty of this study is associated with the analysis of temporal coherence, content complementarity and subject collaboration in the field of policy aimed at limiting the use of plastic and the development of a universal methodology for the design of complex environmental policies. The results show that policy releases peaked in 2008 and 2020. The average number of policies issued by each province in the eastern region is the highest, with 6.8 items. In terms of policy content, A3, B3, and C3 are the most prominent policy objectives, means and safeguard measures, respectively. The synergy of policy content indicates that market-oriented policies have stronger implementation, for example, the synergy strength between A2 and B2 is 0.7467. The synergy between legislative and enforcement policies is insufficient. For example, the synergy strength between A1 and B1 is only 0.2903. The regional synergy decreases from southwest to northeast. The northeast region lags comprehensively. In terms of time, the similarity of policy texts between 2021 and 2023 remained stable (0.3256–0.3666). The three-dimensional framework offers an approach to policy synergy evaluation. It indicates that the core governance framework of the Chinese Government has strong continuity. This study also has positive value for global environmental protection, such as reducing plastic pollution. Full article

As a core component in quantum cryptography, Quantum Random Number Generators (QRNGs) face dual critical challenges: insufficient randomness enhancement and limited compatibility with post-processing algorithms. This study proposes an Adaptive Feedback Compensation Algorithm (AFCA) to address these limitations through dynamic parameter feedback and selective encryption strategies. The AFCA dynamically adjusts nonlinear transformation intensity based on real-time statistical deviations, retaining over 50% of original bits while correcting local imbalances. Experimental results demonstrate significant improvements across QRNG types: the Monobit Test p-value for continuous QRNGs increased from 0.1376 to 0.9743, and the 0/1 distribution deviation in discrete QRNGs decreased from 7.9% to 0.5%. Compared to traditional methods like von Neumann correction, AFCA reduces data discard rates by over 55% without compromising processing efficiency. These advancements provide a robust solution for high-security quantum communication systems requiring multi-layered encryption architectures. Full article

This study presents a reconfigurable optical convolutional neural network (CNN) architecture that integrates a crossbar switch network into a smart-pixel-based optical CNN (SPOCNN) framework. The SPOCNN leverages smart pixel light modulators (SPLMs), enabling high-speed and massively parallel optical computation. To address the challenge of data rearrangement between CNN layers—especially in multi-channel and deep-layer processing—a crossbar switch network is introduced to perform dynamic spatial permutation and multicast operations efficiently. This integration significantly reduces the number of processing steps required for core operations such as convolution, max pooling, and local response normalization, enhancing throughput and scalability. The architecture also supports bidirectional data flow and modular expansion, allowing the simulation of deeper networks within limited hardware layers. Performance analysis based on an AlexNet-style CNN indicates that the proposed system can complete inference in fewer than 100 instruction cycles, achieving processing speeds of over 1 million frames per second. The proposed architecture offers a promising solution for real-time optical AI applications. The further development of hardware prototypes and co-optimization strategies between algorithms and optical hardware is suggested to fully harness its capabilities. Full article

Background/Objectives: Cows produce antibodies with ultralong CDRH3 segments (ulCABs) that contain a disulfide-stabilized knob domain. This domain is connected to the globular core of the antibody by a β-strand stalk. In the crystal structures, the stalk protrudes from the core in an extended conformation and presents the knob at its distal end. However, the rigidity of this topology has been questioned due to the extensive crystal packing present in most ulCAB crystal structures. To gain more insight into the dynamics of ultralong CDRH3s, we performed a comparative molecular dynamics (MD) study of 19 unique ulCABs. Methods: For all 19 systems, one-microsecond MD simulations were performed in explicit solvent. The analyses included an investigation of the systems’ conformational stability and the dynamics of the knob domain as well as an energetic analysis of the intramolecular knob interactions. Results: The simulations show that the extended stalk–knob conformation observed in the crystal structures is not preserved in solution. There are significant differences in the degree of knob dynamics, the orientations of the knobs, the number of flexible stalk residues, and the frequency of the motions. Furthermore, interactions between the knob and the light chain (LC) of the ulCABs were observed in about half of the systems. Conclusions: The study reveals that pronounced knob dynamics is a general feature of ulCABs rather than an exception. The magnitude of knob motions depends on the system, thus reflecting the high sequence diversity of the CDRH3s in ulCABs. The observed knob–LC interactions might play a role in stabilizing distinct knob orientations. The MD simulations of ulCABs could also help to identify suitable knob fragments as mini-antibodies by suggesting appropriate truncation points based on flexible sites in the stalks. Full article

A polyelectrolyte (PE) chain in the vicinity of an oppositely charged surface can exhibit a discontinuous transition from the adsorbed to the desorbed state once the electrostatic attractive interactions are not strong enough to overcome the entropic losses caused by the PE-surface adsorption. In the context of PE–protein interactions, the heterogeneity of the charge distribution and the effects of a low dielectric permittivity underneath the surface are crucial. Studies of the combined effects of these two properties are very sparse, especially in the spherical geometry; we thus fill this gap here. We study the adsorption of PE chains onto spherical particles with heterogeneously charged surfaces, with the main focus on the critical-adsorption conditions and the effects of a low-dielectric core. Metropolis Monte Carlo simulations are employed, with the PE exploring the phase-space around the binding particle in the canonical ensemble. Two adsorption–desorption transitions are observed when the particle possesses a net charge of the same sign as that of the PE, resulting in nonmonotonic behavior of the critical charge density required for the PE–particle electrostatically driven adsorption. An increased affinity between the PEs and low-dielectric particles with variable heterogeneous charge distributions is observed, in contrast to the behavior detected for homogeneous low-dielectric particles. This higher affinity occurs when the Debye screening length in the solution becomes comparable to the dimensions of a patch of the opposite sign to the PE. A number of real-life applications of the considered PE–particle system is presented in the discussion, in particular regarding the properties of the complex formation between various PEs and globular proteins featuring a dipolar-type distribution of electric charges on their surfaces, such as insulin and bovine serum albumin. Full article

Vibrating feeders are used to empty bulk materials from storage bins, to feed and dispense materials into weighing bins or dispensers, or to feed materials evenly and smoothly into downstream equipment. The harmonic oscillation of the trough can be provided by an electromagnetic oscillator, which consists of an electromagnet consisting of a core and a coil with a given number of coil turns and armature. The aim of this paper has been to verify whether the working condition of the vibrating feeder, i.e., its fault-free operation and the ability to transport the required mass amount of material, can be described on a basis of the measured vibration values using acceleration sensors. This paper describes three experimental methods that allow us with the use of force sensors to measure the adhesion force of the electromagnet and the deformation force of the bent leaf springs, and the use of acceleration sensors to measure the vibration on the trough and on the steel frame of the vibrating feeder. The highest average value of the effective vibration velocity (56.7 mm·s⁻¹) in the horizontal plane was measured on a steel frame of a vibrating feeder using FR4 Epoxy leaf springs with a stiffness of 47.8 N·mm⁻¹ and a weight of 2.57 kg of conveyed material per trough. The lowest average value of the effective vibration velocity (24.6 mm·s⁻¹) has been measured at a weight of 5.099 kg of material conveyed on the trough. We can state that from the analysis of the measured vibration velocities transmitted to the steel frame of the vibrating feeders, it is possible to monitor the partial phases of their operation and diagnose any faults that may occur. It is also possible to determine whether the optimal amount of bulk material is being loaded onto the trough. Full article

The black-box nature of deep machine learning hinders the extraction of knowledge in science. To address this issue, a proposal for a neural network (k-net) based on the Kolmogorov–Arnold Representation Theorem is presented, pursuing to be an alternative to the traditional Multilayer Perceptron. In its core, the algorithmic nature of neural networks lies in the fundamental symmetry between forward-mode and reverse-mode accumulation techniques, both of which rely on the chain rule of partial derivatives. These methods are essential for computing gradients of functions, an operation that is at the core of the training process of neural networks. Automatic differentiation addresses the need for accurate and efficient calculation of derivative values in scientific computing; procedural programs are thus transformed into the computation of the required derivatives at the same numerical arguments. This work formalizes the algebraic structure of neural network computations by framing the training process within the domain of hyperdual numbers. Specifically, it defines a Kolmogorov–Arnold-inspired neural network (k-net) using dual numbers by extending the univariate functions and their compositions that appear in the representation theorem. This approach focuses on computation of the Jacobian and the ability to implement such procedures algorithmically, without sacrificing accuracy and mathematical rigor, while exploiting the inherent symmetry of the dual number formalism. Full article