Search Results (143)

Organoid technology has emerged as a revolutionary tool in cancer research, offering physiologically accurate, three-dimensional models that preserve the histoarchitecture, genetic stability, and phenotypic complexity of primary tumors. These self-organizing structures, derived from adult stem cells, induced pluripotent stem cells, or patient tumor biopsies, recapitulate critical aspects of tumor heterogeneity, clonal evolution, and microenvironmental interactions. Organoids serve as powerful systems for modeling tumor progression, assessing drug sensitivity and resistance, and guiding precision oncology strategies. Recent innovations have extended organoid capabilities beyond static culture systems. Integration with microfluidic organoid-on-chip platforms, high-throughput CRISPR-based functional genomics, and AI-driven phenotypic analytics has enhanced mechanistic insight and translational relevance. Co-culture systems incorporating immune, stromal, and endothelial components now permit dynamic modeling of tumor–host interactions, immunotherapeutic responses, and metastatic behavior. Comparative analyses with conventional platforms, 2D monolayers, spheroids, and patient-derived xenografts emphasize the superior fidelity and clinical potential of organoids. Despite these advances, several challenges remain, such as protocol variability, incomplete recapitulation of systemic physiology, and limitations in scalability, standardization, and regulatory alignment. Addressing these gaps with unified workflows, synthetic matrices, vascularized and innervated co-cultures, and GMP-compliant manufacturing will be crucial for clinical integration. Proactive engagement with regulatory frameworks and ethical guidelines will be pivotal to ensuring safe, responsible, and equitable clinical translation. With the convergence of bioengineering, multi-omics, and computational modeling, organoids are poised to become indispensable tools in next-generation oncology, driving mechanistic discovery, predictive diagnostics, and personalized therapy optimization. Full article

Background: Chronic pain affects nearly one in five adults worldwide and is increasingly recognized not only as a disease but as a potential risk factor for neurocognitive decline and dementia. While some evidence supports this association, existing systematic reviews are static and rapidly outdated, and none have leveraged advanced methods for continuous updating and robust uncertainty modeling. Objective: This protocol describes a living systematic review with dose–response Bayesian meta-analysis, enhanced by artificial intelligence (AI) tools, to synthesize and maintain up-to-date evidence on the prospective association between any type of chronic pain and subsequent cognitive decline. Methods: We will systematically search PubMed, Embase, Web of Science, and preprint servers for prospective cohort studies evaluating chronic pain as an exposure and cognitive decline as an outcome. Screening will be semi-automated using natural language processing models (ASReview), with human oversight for quality control. Bayesian hierarchical meta-analysis will estimate pooled effect sizes and accommodate between-study heterogeneity. Meta-regression will explore study-level moderators such as pain type, severity, and cognitive domain assessed. If data permit, a dose–response meta-analysis will be conducted. Living updates will occur biannually using AI-enhanced workflows, with results transparently disseminated through preprints and peer-reviewed updates. Results: This is a protocol; results will be disseminated in future reports. Conclusions: This living Bayesian systematic review aims to provide continuously updated, methodologically rigorous evidence on the link between chronic pain and cognitive decline. The approach integrates innovative AI tools and advanced meta-analytic methods, offering a template for future living evidence syntheses in neurology and pain research. Full article

Effective production control requires aligning strategic planning with real-time execution under dynamic and stochastic conditions. This study proposes a scalable dual-agent Deep Reinforcement Learning (DRL) framework for the joint optimisation of Work-In-Process (WIP) control and job sequencing in flow-shop environments. A strategic DQN agent regulates global WIP to meet throughput targets, while a tactical DQN agent adaptively selects dispatching rules at the machine level on an event-driven basis. Parameter sharing in the tactical agent ensures inherent scalability, overcoming the combinatorial complexity of multi-machine scheduling. The agents coordinate indirectly via a shared simulation environment, learning to balance global stability with local responsiveness. The framework is validated through a discrete-event simulation integrating agent-based modelling, demonstrating consistent performance across multiple production scales (5–15 machines) and process time variabilities. Results show that the approach matches or surpasses analytical benchmarks and outperforms static rule-based strategies, highlighting its robustness, adaptability, and potential as a foundation for future Hierarchical Reinforcement Learning applications in manufacturing. Full article

This work proposes a novel design configuration for an elasto-poro-hydrodynamic damper (EPHD damper) that consists of an imbibed, soft, elastic, porous material enclosed by a rubber membrane. The core innovation lies in the device’s ability to collect and re-imbibe expelled fluid during decompression, ensuring potential functionality and durability across repetitive loading cycles. Damping is achieved through the synergy of three mechanisms: friction of the membrane and of the piston with solid boundaries, squeeze flow inside the porous layer, and compression of the poro-elastic structure. The EPHD damper’s behavior was evaluated both theoretically and experimentally through quasi-static, low-speed compression tests, with dynamic evaluation being reserved for future work. A numerical model successfully validated stress-deformation behavior against experimental data, with a simplified analytical model providing a good approximation. The study also identifies that the piston–membrane friction coefficient significantly influences the EPHD damper’s performance. These findings provide a valuable framework for optimizing the design and expanding its potential application to repetitive damping systems. Full article

This paper deals with static stability in planar grasps of an object by multiple fingers. Differently from previous research, we focus on the case that each finger has redundant links and joints. Based on contact constraints between the object and fingers, the relationships among displacements of object’s pose, contact positions, and joint positions are formulated. Using the constraints, the redundant joints are reduced to independent parameters. The relationship between the displacement and reaction torque of each joint is modeled as a linear spring, and potential energy of the grasp is formulated. Not only for frictionless sliding contact but also for pure rolling contact, we derive stable conditions on the contact positions and joint positions. Based on the conditions, partially differentiating the potential energy, a wrench (force and moment) vector and a stiffness matrix applied to the object by each finger are derived. Summing up the wrenches and matrices of all the fingers, we obtain a wrench vector and a stiffness matrix of the grasp, and we evaluate the grasp stability. Because of our analytical formulation, grasp parameters such as local curvatures at contact points, joint stiffnesses, etc., are explicitly included in the derived matrices. Partially differentiating the wrenches and matrices by the grasp parameters, we clarify effects of the parameters on the stability. Moreover, the difference between the frictionless sliding contact and pure rolling contact is derived in the wrench vector and the stiffness matrix. Using numerical examples, we validate our analysis. Full article

This work is framed as an application of static and small exponential random graph models for complex networks in multiple layers. This document revisits the small network and exhibits its potential. Examining the bibliography reveals considerable interest in large and dynamic complex networks. This research examines the application of small networks (50,000 population) for analyzing global commerce, conducting a comparative graph structure of the tariffs, and importing multilayer networks. The authors created and described the scenario where the readers can compare the graph models visually, at a glance. The proposed methodology represents a significant contribution, providing detailed descriptions and instructions, thereby ensuring the operational effectiveness of the application. The method is organized into five distinct blocks (Bn) and an accompanying appendix containing reproduction notes. Each block encompasses a primary task and associated sub-tasks, articulated through a hierarchical series of steps. The most challenging mathematical aspects of a small network analysis pertain to modeling and sample selection (sel_p). This document describes several modeling tasks that confirm that sel_p = 10 is the best option, including modeling the edges and the convergence and covariance model parameters, modeling the node factor by vertex names, Pearson residual distributions, goodness of fit, and more. This method establishes a foundation for addressing the intricate questions derived from the established hypotheses. It provides eight model specifications and a detailed description. Given the scope of this investigation, a historical examination of the relationships between different network actors is deemed essential, providing context for the study of actors engaged in global trade. Various analytical perspectives (six), encompassing degree analyses, diameter and edges, hubs and authority, co-citation and cliques in mutual and collapse approaches, k-core, and clustering, facilitate the identification of the specific roles played by actors within the importation network in comparison to the tariff network. This study focuses on the Latin American and Caribbean region. Full article

In the era of digital transformation, understanding and quantifying the mechanisms by which management strategies influence organizational performance is a critical yet insufficiently addressed challenge. Existing analytical models often overlook the intertwined temporal dependencies, cross-frequency interactions, and heterogeneous contextual factors that shape strategic impacts in real-world settings. To address these limitations, we propose TFHA (Time–Frequency Harmonic Attention), a unified framework that integrates frequency-domain pattern decomposition, temporal context encoding, and multi-view representation learning to analyze and forecast strategy-driven performance outcomes in an interpretable manner. Specifically, a Fourier Frequency Attention module captures multi-scale periodic patterns underlying strategic behaviors, while a temporal feature embedding component encodes both static calendar effects and dynamic, event-triggered fluctuations. Furthermore, a Contrastive Time–Frequency Representation Enhancement module aligns semantic, behavioral, and quantitative perspectives to produce robust, context-aware representations. Experiments on four real-world datasets from digital tourism management platforms demonstrate that TFHA reduces MAE by up to 18.5% compared with strong baselines such as Autoformer, Informer, and ETSformer, while exhibiting strong robustness under input perturbations and cross-domain generalization. These results highlight TFHA’s potential as both a predictive tool and an analytical lens for revealing the time–frequency dynamics underpinning the effectiveness of digital brand management strategies in tourism contexts. Full article

The accuracy of modal superposition methods for determining displacement or strain field of structures largely depends on the selection of modes relevant to its deformation. Analytical methods for modal selection have been developed to minimise errors in reconstructing deformation through a linear combination of modal shapes. This study constitutes an initial step towards the development of structural health-monitoring algorithms for large engineering machines, where continuous monitoring of strain and stress, assuming a linear elastic field, is critical. The focus is on selecting modes that significantly contribute to the reconstruction of static deformation of structures. A detailed analytical approach, derived from established structural dynamics principles, leads to the formulation of modal selection criteria. These criteria are based on two fundamental quantities from dynamic and elastic theory: the modal participation factor and internal strain potential energy. Three criteria are introduced: the directional participation factor criterion (DPFC), the global participation factor criterion (GPFC), and the internal strain potential energy criterion (ISPEC). While DPFC and GPFC rely on displacements, ISPEC uses strains. The methods are validated through a case study involving a rectangular plate subjected to various loads, demonstrating their applicability to complex deformation scenarios, which require the combination of multiple modes to fully describe the static deformation. The proposed criteria are formulated for linear elastic systems and are therefore applicable, in principle, to plate-like components, machine casings, thin structural panels, and certain civil and aerospace panels, under the assumptions of small strains, linear constitutive behaviour, and validity of modal superposition. The approach also represents a first step towards the integration of modal selection with machine learning for structural health-monitoring applications and presents a computational cost significantly lower than that of full finite element analyses. Full article

Maxwell’s equations epitomize our knowledge of standard electromagnetic theory in vacuums and matter. Here, we report the clearcut results of an extensive, ongoing investigation aiming to mathematically digest Maxwell’s equations in virtually all problems based on the three standard building units, dielectric and magnetic, found in practice (i.e., spheres, cylinders and plates). Specifically, we address the static/quasi-static case of a linear, homogeneous and isotropic dielectric and magnetic sphere subjected to a DC/low-frequency AC external scalar potential, (vector field, ), of any form, produced by a primary/free source residing outside the sphere. To this end, we introduce an expansion-based mathematical strategy that enables us to obtain immediate access to the response of the dielectric and magnetic sphere, i.e., to the internal scalar potential, (vector field, ), produced by the induced secondary/bound source. Accordingly, the total scalar potential, = + (vector field, = + ), is immediately accessible as well. Our approach provides ready-to-use expressions for and ( and ) in all space, i.e., both inside and outside the dielectric and magnetic sphere, applicable for any form of (). Using these universal expressions, we can obtain and ( and ) in essentially one step, without the need to solve each particular problem of different () every time from scratch. The obtained universal relation between and ( and ) provides a means to tailor the responses of dielectric and magnetic spheres at all instances, thus facilitating applications. Our approach surpasses conventional mathematical procedures that are employed to solve analytically addressable problems of electromagnetism. Full article

Against the backdrop of ongoing degradation of ecosystem services and the increasing demand for sustainable development, the scientific delineation of ecological management zones has become a critical means by which to balance human wellbeing and ecological conservation. This study takes Jiangxi Province as the research area and selects four typical ecosystem services—food production, water supply, carbon storage, and soil retention—to systematically evaluate their supply–demand relationships from both static and dynamic dimensions. By introducing the entropy weight method to construct a comprehensive supply–demand index and integrating a coupling coordination degree model with a four-quadrant dynamic evolution model, this paper proposes a coupled “static–dynamic” analytical framework. The findings reveal significant spatial heterogeneity in various ecosystem services; high-supply areas are concentrated in the southern and peripheral mountainous regions while demand is closely linked to population distribution, exhibiting a pattern of high demand in the central areas and high supply in the peripheral areas. Our supply–demand matching analysis uncovers a distinct gradient distribution characterized by core imbalance and peripheral coordination, with prominent supply–demand conflicts in urban expansion areas and enhanced coordination in peripheral ecological barrier zones. Based on these insights, we divide Jiangxi Province into five types of ecological management zones: Degraded Restoration, Conflict Mitigation, Coordination Enhancement, Potential Development, and Maintenance Conservation, with tailored management strategies proposed for each zone type. As a result, this study not only provides scientific support for regional ecological spatial optimization but also offers a new methodological paradigm for ecosystem services management. Full article

This study demonstrates the effectiveness of the differential transform method (DTM) in solving complex solid mechanics problems, focusing on static analysis of beams under various loads and boundary conditions. For cantilever beams (BSM1), DTM provided exact polynomial solutions for deflections and slopes: a cubic solution for concentrated end loads, a quadratic distribution for applied moments, and a fourth-degree polynomial for uniformly distributed loads, all matching established theoretical results. For simply supported beams (BSM2), DTM yielded solutions across two intervals for midspan concentrated forces, though required corrective terms for applied moments due to discontinuities. Under uniform loading, the method produced precise polynomial solutions with maximum deflection at midspan. Key advantages include DTM’s high-precision analytical solutions without additional approximations and its adaptability to diverse loading scenarios. However, for cases with pronounced discontinuities like concentrated moments, supplementary methods (e.g., Green’s functions) may be needed. The study highlights DTM’s potential for extension to nonlinear or dynamic problems, while software integration could broaden its engineering applications. This study demonstrates, for the first time, how DTM yields exact polynomial solutions for Euler–Bernoulli beams under discontinuous loads (e.g., concentrated moments), overcoming limitations of traditional numerical methods. The method’s analytical precision and avoidance of discretization errors are highlighted. Traditional methods like FEM require mesh refinement near discontinuities (e.g., concentrated moments), leading to computational inefficiencies. DTM overcomes this by providing exact polynomial solutions with corrective terms, achieving errors below 0.5% with only 4–5 series terms. Full article

This study addresses the critical challenge of optimizing coal pillar width in burst-prone mines with thick, hard roof strata, balancing resource recovery, roadway stability, and coal burst mitigation. Through integrated analytical modeling and rigorously calibrated numerical simulations, the research reveals the complex interplay between pillar width, roof mechanics, and stress redistribution. Key findings demonstrate that pillar width dictates roof failure mechanics and energy accumulation. The case study indicates that increasing the coal pillar width from 6 m to 20 m shifts the tensile fracture location from solid coal toward the pillar center, migrates shear failure zones closer to roadways, and relocates elastic strain energy accumulation to the pillar area. This concentrates static and dynamic loads directly onto wider pillars upon roof fracture, escalating instability risks. A risky coal pillar width is identified as 10–20 m, where pillars develop severe lateral abutment pressures perilously close to roadways, combining high elastic energy storage with exposure to roof fracture dynamics. Conversely, narrow pillars exhibit low stress concentrations and limited energy storage due to plastic deformation, reducing burst potential despite requiring robust asymmetric support. Strategic selection of narrow or wide pillars provides a safer pathway. The validated analytical–numerical framework offers a scientifically grounded methodology for pillar design under hard roof conditions, enhancing resource recovery while mitigating coal burst risks. Full article

AC/DC parallel transmission is a critical approach for large-scale centralized transmission. Existing assessments of power transfer capability in AC/DC corridors rarely incorporate comprehensive security and stability constraints, potentially leading to overestimated results. This paper investigates a grid-forming renewable energy system integrated via AC/DC parallel transmission. First, the transmission section’s power transfer limit under N-1 static security constraints is determined. Subsequently, analytical conditions satisfying synchronization and frequency stability constraints are derived using the equal area criterion and frequency security indices, revealing the impacts of AC/DC power allocation and system parameters on transfer capability. Finally, by integrating static security, synchronization stability, and frequency stability constraints, an operational region for secure AC/DC power dispatch is established. Based on this region, an optimal power allocation scheme maximizing the corridor’s transfer capability is proposed. The theoretical framework and methodology enhance system transfer capacity while ensuring AC/DC parallel transmission security, with case studies validating the theory’s correctness and method’s effectiveness. Full article

This study proposes an integrated framework that combines nonlinear stochastic vibration analysis with reliability assessment to address the safety issues of cable systems under damage conditions. First of all, a mathematical model of the damaged cable is established by introducing damage parameters, and its static configuration is determined. Using the Pearl River Huangpu Bridge as a case study, the accuracy of the analytical solution for the cable’s sag displacement is validated through the finite difference method (FDM). Furthermore, a quantitative relationship between the damage parameters and structural response under stochastic excitation is developed, and the nonlinear stochastic dynamic equations governing the in-plane and out-of-plane motions of the damaged cable are derived. Subsequently, a Gaussian Radial Basis Function Neural Network (GRBFNN) method is employed to solve for the steady-state probability density function of the system response, enabling a detailed analysis of how various damage parameters affect structural behavior. Finally, the First-Order and Second-Order Reliability Method (FORM/SORM) are used to compute the reliability index and failure probability, which are further validated using Monte Carlo simulation (MCS). Results show that the severity parameter η shows the highest sensitivity in influencing the failure probability among the damage parameters. For the system of the Pearl River Huangpu bridge, an increase in the damage extent δ from 0.1 to 0.4 can reduce the reliability-based service life of by approximately 40% under fixed values of the damage severity and location, and failure risk is highest when the damage is located at the midspan of the cable. This study provides a theoretical framework from the point of stochastic vibration for evaluating the response and associated reliability of mechanical systems; the results can be applied in practice with guidance for the engineering design and avoid potential damages of suspended cables. Full article

Rapid urbanization and demographic shifts present significant challenges to spatial justice in green space provision. Traditional static assessments have become increasingly inadequate for guiding park planning, which now requires a dynamic, future-oriented analytical approach. To address this gap, this study incorporates population dynamics into urban park planning by developing a dynamic evaluation framework for park accessibility. Building on the Gaussian-based two-step floating catchment area (Ga2SFCA) method, we propose the human-population-projection-Ga2SFCA (HPP-Ga2SFCA) model, which integrates population forecasts to assess park service efficiency under future demographic pressures. Using neighborhood-committee-level census data from 2000 to 2020 and detailed park spatial data, we identified five types of population change and forecast demographic distributions for both short- and long-term scenarios. Our findings indicate population decline in the urban core and outer suburbs, with growth concentrated in the transitional inner-suburban zones. Long-term projections suggest that 66% of communities will experience population growth, whereas short-term forecasts indicate a decline in 52%. Static models overestimate park accessibility by approximately 40%. In contrast, our dynamic model reveals that accessibility is overestimated in 71% and underestimated in 7% of the city, highlighting a potential mismatch between future population demand and current park supply. This study offers a forward-looking planning framework that enhances the responsiveness of park systems to demographic change and supports the development of more equitable, adaptive green space strategies. Full article