Search Results (154,848)

The rapid advancement of deep learning (DL) has fundamentally transformed intelligent communication systems, leading to a rapid proliferation of survey papers covering wireless communications, optical networks, vehicular systems, integrated sensing and communication (ISAC), and emerging 6G technologies. Although these surveys provide valuable insights within their respective domains, they remain largely fragmented, employ inconsistent taxonomies, lack systematic cross-domain comparisons, and do not provide a unified perspective on the evolution of AI-enabled communication systems. Consequently, researchers face increasing difficulties in identifying common design principles, evaluating methodological trends, and understanding how different communication domains are converging toward AI-native networking. To overcome these limitations, this paper presents a comprehensive meta-survey that systematically analyzes, compares, and synthesizes existing survey literature on DL for intelligent communication systems. Specifically, the proposed meta-survey (i) establishes a unified taxonomy spanning communication domains, learning paradigms, network layers, and DL architectures, (ii) introduces a unified AI-driven communication pipeline that maps representative solutions from diverse communication domains into a common framework, (iii) performs a comprehensive cross-domain comparative analysis to identify methodological strengths, research trends, technical challenges, and remaining gaps, and (iv) provides a technology-oriented roadmap highlighting future research directions and maturity levels toward AI-native communication systems. By integrating these complementary perspectives, this work offers a holistic reference that facilitates knowledge transfer across communication domains and supports the design of next-generation AI-native communication networks. Full article

Fine-scale site selection of urban parks serves as a core measure to guarantee spatial equity of public service spaces for residents and advance the sustainable development of urban ecological spaces. Among relevant tasks, balancing the supply and demand of green spaces stands out as an essential foundation for maintaining long-term stability of urban human well-being and ecosystems. Green-space supply is defined as the stock of various existing urban parks within the city, while green-space demand is quantified via grids generated based on residential communities in Changsha. Existing research on urban park site selection lacks a full-process coupled framework, fails to accommodate differentiated layout demands for multi-level parks, and struggles to reconcile the sustainable operation and long-term ecological empowerment of urban green-space systems. Taking the main urban area of Changsha as the research scope, this study divides the study area into grid units to analyze the spatial differentiation of green-space accessibility and identify service blind zones. The XGBoost model is adopted to predict areas suitable for green-space construction, and the NSGA-III algorithm is applied to realize collaborative multi-objective optimization covering service efficiency, ecological benefits, and land development costs. The results reveal that the 15-min walking coverage of community parks in central Changsha only reaches 57.29%. Respectively, 34.52% and 41.04% of residential communities record accessibility levels below the municipal average of urban parks and forest parks, with prominent shortages of green-space supply in peripheral urban areas. This study optimizes and screens twenty-eight candidate sites for community parks, twelve candidate sites for urban parks, and eight candidate sites for forest parks. The proposed scheme effectively narrows the gap in green-space accessibility across the whole city and coordinates ecological conservation with land development costs. Compared with research relying on a single model or two-stage coupling frameworks, this paper constructs a systematic workflow spanning supply–demand status assessment to multi-objective layout decision-making, enabling differentiated optimized layout of multi-tiered parks. The integrated framework effectively enhances the spatial resilience and resource utilization efficiency of urban green-space systems, facilitates high-quality and sustainable upgrading of urban living environments, and provides a referable innovative approach for multi-level urban park arrangement and refined multi-objective planning. Full article

To systematically investigate the effects of C-shaped and rectangular steel frames on the seismic performance of assembled composite shear walls, this paper, based on the validation of existing pseudo-static test results, employs ABAQUS software to establish refined finite element models, and carries out parametric analyses on C-shaped steel-frame composite shear walls (CSCSWs) and rectangular steel-frame composite shear walls (RSCSWs). With shear-span ratio, axial-load ratio, boundary frame steel plate thickness, and concrete strength grade as variables, a total of 28 numerical models are designed to systematically examine the influence laws of each parameter on bearing capacity, ductility, energy dissipation capacity, and failure modes, and to reveal the performance differences in the confinement mechanisms of the two cross-sectional types. The results indicate that: as the shear-span ratio decreases from 3.0 to 1.0, the bearing capacity increases by up to 171%, but the ductility drops by up to 43%, and the failure mode shifts from flexure-dominated to shear-dominated; increasing the steel plate thickness can simultaneously enhance bearing capacity and ductility, with the peak load increasing by up to 52% and cumulative energy dissipation by over 110%, the mechanism being the synergistic enhancement of the flexural contribution of the boundary frame and the passive confinement effect on the core concrete; increasing the axial-load ratio can improve bearing capacity by about 24%, but significantly impairs ductility and energy dissipation capacity, and it is recommended that the design axial-load ratio be controlled between 0.26 and 0.43; the concrete strength grade has a limited effect on bearing capacity, and as the strength increases, brittle characteristics emerge, leading to a ductility decrease of about 12%; therefore, provided that the strength requirements are met, enhancing the concrete strength grade should not be taken as the primary technical approach for improving the seismic performance of such structures. Comparing the two cross-sectional types, the rectangular cross-section, by providing more uniform and effective lateral confinement, exhibits superior bearing capacity, ductility, and energy dissipation to the C-shaped cross-section across the entire parameter domain, and its performance advantages are more pronounced under conditions of high axial-load ratio and large shear-span ratio. Full article

Background/Objectives: Cognitive impairment and neurological dysfunction in Multiple Sclerosis are increasingly understood as consequences of altered brain network connectivity. Magnetoencephalography offers high temporal resolution for investigating functional connectivity patterns, which may underlie various clinical symptoms. This exploratory pilot study aims to characterize preliminary FC patterns at rest and during cognitive processing in MS patients with a high fatigue burden compared with healthy controls using MEG, complemented by a systematic review of existing MEG literature in MS. Methods: In this pilot study, MEG was utilized to investigate connectivity in MS patients and HCs. Data were acquired during resting-state and a digitized version of the Symbol Digit Modalities Test. A systematic review was also conducted following PRISMA guidelines to synthesize the current evidence on frequency-specific MEG connectivity in MS. Results: MS patients exhibited a connectivity pattern paralleling the directionality of neuronal slowing, described in the spectral power MS literature, during resting-state, characterized by reduced interhemispheric coherence in alpha and beta bands alongside region-specific increases in frontal–parietal and parietal–occipital alpha coherence, reflecting a bidirectional spatial reorganization. Conversely, during the SDMT task, the MS group demonstrated significant compensatory recruitment with increased coherence in theta and gamma bands (specifically in frontal and striatal circuits). The systematic review of 44 MEG studies corroborated these findings, highlighting a consistent trend of frequency-dependent dysconnectivity that aligns with clinical load and disease pathology. Conclusions: We found that in MS, network alterations are state-dependent, shifting from reduced connectivity at rest to increased coherence during cognitive effort. These preliminary findings suggest that MEG-based connectivity may serve as a functional neuroimaging biomarker of underlying network reorganization, for monitoring early cognitive disease progression in MS. Full article

Background/Objectives: GWASs have identified more than 250 prostate cancer (PCa) predisposition loci, predominantly in European and partly Asian cohorts. The Kazakh population is markedly under-represented in international genetic studies, limiting existing risk models. This study aimed to analyze the distribution of 102 PCa-associated single-nucleotide polymorphism (SNP) genotypes and alleles and to identify reliable population-specific associations with PCa risk in Kazakh men. Methods: This retrospective case–control study included 941 Kazakh men (476 with histologically confirmed PCa and 465 cancer-free controls). Genomic DNA extracted from peripheral blood was genotyped with TaqMan^® OpenArray^® technology on a QuantStudio 12K Flex system. Associations were assessed by Pearson’s χ² test and logistic regression, with genotypic and allelic odds ratios (OR) and 95% confidence intervals (CI). Two-step multiple-testing correction (Bonferroni and Benjamini–Hochberg false-discovery rate, FDR) was applied. Predictive models were built using classification and regression trees (CART) and stepwise logistic regression. Results: Of 102 SNPs, 39 showed nominally significant genotypic differences; 12 remained significant after Bonferroni correction and 2 after FDR (14 in total). Several of the corrected loci were significant at both the genotypic and allelic level. Allelic ORs ranged from 0.37 (protective rs10187424 T allele) to 4.81 (rs1545985). A parsimonious seven-SNP autosomal logistic-regression model achieved an apparent AuROC of 0.84 (10-fold cross-validated 0.82); adding age as a covariate raised discrimination to 0.87. Ten of the fourteen significant loci remained significant after age adjustment, and six of these formed a core signal robust to both age imbalance and genotyping-quality concerns. Conclusions: This first large-scale SNP-association study in Kazakh men shows allele-frequency profiles resembling East Asian rather than European populations, confirming the need for population-specific genetic risk-assessment tools. The seven-SNP model showed high discriminatory power in the training set and requires external validation before clinical application. Full article

Upper-limb motor dysfunction resulting from neurological disorders severely limits patients’ activities of daily living and social participation. Pneumatic upper-limb rehabilitation robots have emerged as a promising intervention owing to their inherent compliance, lightweight design, and high power-to-weight ratio, which facilitate safe, repetitive, and home-based training. Despite these advantages, extensive clinical translation remains hindered by challenges including actuator hysteresis, nonlinear dynamics, limited accuracy in intention recognition, and inconsistent clinical evaluation metrics. This review systematically examines recent advancements in pneumatic upper-limb rehabilitation robots across four critical dimensions: structural design, human–robot interaction, control strategies, and clinical translation. We comparatively analyze rigid exoskeletons, soft wearable devices, and rigid–soft hybrid configurations based on output capability, motion accuracy, comfort, and clinical applicability. The findings suggest that while rigid systems offer high precision and soft systems maximize safety, rigid–soft hybrid architectures represent a critical developmental trend for balancing motion accuracy with interaction compliance. Furthermore, the review evaluates multimodal sensing techniques (e.g., EMG, EEG, and IMUs) for motion intention decoding and training state monitoring, alongside conventional, adaptive, and artificial intelligence-driven control methods aimed at compensating for pneumatic nonlinearity and improving real-time response. Current clinical evidence indicates that these systems effectively enhance upper-limb function and muscle strength, particularly in post-stroke rehabilitation; however, existing trials are frequently constrained by small sample sizes, short interventions, and heterogeneous protocols. Future research must prioritize rigid–soft hybrid architectures, robust multimodal sensor fusion, digital twin-assisted assessment, adaptive intelligent control, and standardized home-based rehabilitation platforms. Ultimately, this comprehensive review provides a concise reference for the design optimization and clinical deployment of next-generation pneumatic rehabilitation systems. Full article

Simultaneous and stochastic optimization of open-pit mining complexes at the medium-term level aims to maximize expected profits, manage technical risk for integrated value chains, and enhance the operational feasibility of the long-term plan while still meeting its targets to achieve long-term value. To address two key operational feasibility challenges over a twelve-month horizon, the proposed framework integrates two features that provide a more detailed operational evaluation during decision-making than existing methods. The first involves explicit tracking of shovel movements to align optimized extraction sequences with the operational capabilities of loading equipment. The second uses high-order simulation to produce probability distributions for processing plant responses based on geometallurgical properties of the material being scheduled and selected operating modes. To efficiently optimize schedules with this detailed evaluation, a solution method is proposed using an online machine learning model to rank moves in a metaheuristic search procedure. The framework is demonstrated using a gold mining complex and results show realistic extraction sequences with an increase in metal production and cashflow when compared to a regression-based processing model. Full article

Formic acid (HCOOH), the most abundant carboxylic acid in the troposphere, modulates rainwater acidity, aerosol water uptake, and the oxidative capacity of remote atmospheres, yet its global budget remains poorly constrained. Herein, we present the first HCOOH total-column retrieval from thermal–infrared (TIR) measurements of the Thermal And Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer-2 (TANSO-FTS-2) on board GOSAT-2, providing an early-afternoon observational perspective that complements existing morning low-Earth-orbit and geostationary HCOOH products. The Optimal Estimation retrieval sequentially fits the surface state, the atmospheric background (temperature, water vapor and ozone), and the HCOOH profile in a 1104–1109 cm${}^{-1}$ microwindow centered on the ${\nu }_6$ Q-branch, with a radiance-ratio-scaled a priori that adapts to each scene. Averaging-kernel diagnostics concentrate the sensitivity in the 500–900 hPa layer with degrees of freedom for signal of approximately 1.05 under enhanced-emission conditions. For a 2019–2020 Australian bushfire case, including HCOOH in the state vector reduces the mean spectral residual from −0.327 K to 0.033 K. Independent evaluation against 113 time-coincident Toronto NDACC FTIR overpasses gives R = 0.95 and a zero-intercept slope of 2.12 for raw FTIR versus GOSAT-2. Applying the GOSAT-2 a priori and averaging kernel to the FTIR profiles changes the slope to 0.77 and reduces the RMSE to $0.23\times {10}^{16}$ molec cm${}^{-2}$; this one-sided smoothing is treated only as a sensitivity diagnostic. Monthly global maps for December 2019 and June 2020 show cross-sensor consistency with the IASI/MetOp-B ANNI-HCOOH product at R = 0.83 and 0.76. Over East Asia during April–June 2023, GOSAT-2 correlates with FY-4B/GIIRS at R = 0.90 (April) and R = 0.65 (June), with coherent three-sensor daily variability. These satellite comparisons are treated as cross-sensor consistency assessments rather than independent validation. GOSAT-2 consistently reports lower columns, a sensitivity-limited tendency consistent with a priori dominance under weak signals, limited information content, a narrow retrieval window, and differences among retrieval frameworks. The current product is a first demonstration for cloud-free daytime land scenes; this domain defines its sampling scope and representativeness but is not interpreted as a direct cause of the lower columns. The product offers a traceable GOSAT-2 TIR observational constraint on tropospheric HCOOH for future multi-platform synergy. Full article

Cilium is among the most widely deployed Container Network Interfaces (CNIs), serving as the default CNI in the Google Kubernetes Engine. It extends standard Kubernetes NetworkPolicy (KNP) with two additional types—CiliumNetworkPolicy (CNP) and CiliumClusterwideNetworkPolicy (CCNP)—each with distinct semantics. When all three coexist in a cluster, the resulting composition is difficult to reason about formally, leading to misconfiguration and security incidents. Existing verification tools, KANO and VeriKube, address subsets of the problem but share two critical limitations: neither provides a formal denotational semantics that precisely characterizes the three-layer composition, nor a canonical representation enabling policy-equivalence checking with completeness guarantees. We close this gap with three contributions. First, we develop the first formal denotational semantics for Cilium’s three-layer composed policy—KNP (additive), CNP (deny-wins), CCNP (cluster-override)—and prove that the composite function is Hyper-Rectangular Piecewise-Constant (HRPC)-like. Second, we construct a Reduced Ordered Interval Decision Diagram (ROIDD) for the composite policy space and prove a canonicity theorem—canonical for a fixed field order—enabling policy-equivalence checking as structural isomorphism in O(|ROIDD|) time. Third, we develop certified conflict-detection algorithms for shadow, redundancy, and cross-layer conflict anomalies across all three layers with formal proofs of soundness and completeness. Experimental evaluation on synthetic policies confirms zero mismatches between ROIDD evaluation and ground-truth brute force; detection of shadow, redundancy and cross-layer anomalies at precision and recall of 1.000, scored against exhaustive enumeration of the entire packet space; agreement with a live Cilium v1.19.5 data plane on every probe of a scenario built to exercise each clause of the composite semantics; ROIDD compression ratios of 5–15× over the unshared decision tree on the compressed evaluation domain; and low-microsecond (0.74–1.95 µs) per-packet lookup latency that is independent of policy size. A native C++ implementation, evaluated on the same policy dataset, reconstructs the identical decision-diagram structure and classifies each packet in under 60 ns—roughly 30× faster than the Python reference—confirming that sub-microsecond classification is inherent to the algorithm rather than an artifact of the implementation language. Full article

Lost circulation is a major technical bottleneck restricting safe and efficient while-drilling plugging operations. Polyacrylamide gel has become a widely used plugging material in drilling engineering. Unlike rigid, cement-like plugging materials, the gel system formed in this study does not develop a hardened, consolidated structure capable of anchoring or binding the drill bit during subsequent drilling operations, thereby eliminating the risk of bit-sticking. Nevertheless, the gel possesses sufficient elastic (viscoelastic) structural strength—reflected in its storage modulus (G′)—to effectively resist deformation and displacement under differential pressure, thereby providing reliable fracture-sealing performance, which effectively prevents pipe-sticking risks. However, high-concentration PAM molecular chains easily stretch and entangle in aqueous solution, triggering an abnormal increase in initial viscosity and poor pumpability. Although Ca²⁺ can inhibit the premature water absorption and thickening of PAM to maintain system fluidity, an excessively high Ca²⁺ concentration will suppress the hydrolysis of Al³⁺ and hinder the formation of hydroxyaluminum—the key crosslinking component of the gel system. To solve the above contradiction, a CaCl₂-tolerant delayed-release crosslinker was synthesized. ZnO was selected as a carrier to adsorb and immobilize polynuclear hydroxyaluminum complexes hydrolyzed from an inorganic aluminum crosslinker at 70 °C, realizing the controlled delayed release of the crosslinker. The microstructures and chemical bonding were characterized by SEM elemental mapping, FT-IR and ²⁷Al MAS NMR. The results confirm that abundant aluminum species are uniformly loaded on the ZnO surface to form stable Zn–O–Al covalent bonds, and the loaded aluminum exists mainly in the form of hydroxyaluminum. With increasing temperature, the Zn–O–Al bonds gradually break and slowly release hydroxyaluminum species. A novel delayed crosslinking gel system was ultimately optimized, composed of 6% CaCl₂, 10.4% PAM and 3% ZnO loaded with polynuclear hydroxyaluminum. The system exhibits excellent delayed gelation behavior, with a fluidity loss time longer than 120 min and a gelation time over 200 min. It maintains favorable fluidity within 30–90 °C, and the formed gel shows a stable elastic modulus (G′) and viscous modulus (G″). Moreover, the system achieves a plugging rate of more than 90% and a breakthrough pressure above 5 MPa, demonstrating superior comprehensive plugging performance for while-drilling plugging applications. Full article

Non-rigid image registration is a fundamental problem in medical imaging and a representative example of continuous transformation modeling in image processing. Diffeomorphic registration methods, such as Large Deformation Diffeomorphic Metric Mapping (LDDMM) and its PDE-constrained variants (PDE-LDDMM), provide mathematically grounded formulations with strong geometric guarantees for transformation quality. However, existing approaches face persistent trade-offs between numerical stability, accuracy, and computational efficiency. Recent work has explored implicit neural representations (INRs) and neural ordinary differential equations (NODEs) as flexible neural representations for modeling continuous transformations. Despite their increasing adoption, their practical behavior and limitations in diffeomorphic registration remain insufficiently understood. In this paper, we present a unified formulation of INR- and NODE-based registration methods within LDDMM and PDE-LDDMM, enabling a systematic and controlled comparison across architectures, sampling strategies, and numerical solvers. Our analysis reveals fundamental trade-offs between these approaches. In particular, we show that MLP-based INR formulations introduce significant computational overhead and rely on sampling strategies that can degrade smoothness and lead to the increased occurrence of non-diffeomorphic transformations at higher resolutions. Moreover, these approximations do not fully alleviate the computational cost, with some variants exceeding the costs of expensive classical optimization-based methods. In contrast, NODE-based formulations and downsampling strategies consistently provide transformations with more controlled Jacobian extrema while maintaining competitive computational performance. Among the evaluated methods, the original NODE-LDDMM and NODE-PDE-LDDMM formulations achieve the most favorable trade-offs between registration accuracy, geometric consistency, and computational efficiency. These findings provide clear insights into the design of neural representations for continuous transformation modeling, with practical implications for diffeomorphic registration and computational anatomy applications. Full article

The extracellular matrix (ECM) in cancer differs from healthy tissue in structure, composition, and mechanical properties. Matricellular proteins (MCPs) play important roles in shaping ECM architecture during tissue remodeling. This narrative review, combined with a bioinformatics analysis, examines six major MCP families—Fasciclins, Tenascins, Thrombospondins, Small Leucine-Rich Proteoglycans, the SPARC family, and the CCN family—through a mechanobiological lens in bladder cancer. It summarizes current knowledge on the mechanical regulation of MCP expression, their effects on matrix stiffness, and their contributions to bladder cancer progression. Analyses of public datasets reveal that stromal cells are the predominant source of MCPs in the tumor microenvironment. Furthermore, mechanical upregulation and involvement in the formation of stiff ECM highlight MCPs as important players in a mechanotransduction feedback loop. While most MCPs exert pro-tumorigenic effects on bladder cancer cells, several display context-dependent or anti-tumorigenic activities. Existing studies have primarily focused on the isolated effects of MCPs on bladder cancer cell lines in two-dimensional systems or simple subcutaneous xenograft models. Both approaches fail to capture the context-dependent nature of MCPs and their involvement in ECM formation. These findings underscore the need for future studies to investigate the complex effects of MCPs on bladder cancer progression. Full article

This study investigates how the integration of artificial intelligence reshapes the professional identity of university English teachers within higher education. Against the backdrop of global digital transformation, AI presents both disruptive potential and a significant ‘adaptation crises’ for educators. Using Bronfenbrenner’s Ecological Systems Theory as an analytical framework, this research systematically reviews the existing literature to address three objectives: (1) identify directions and typologies of teacher identity transformation; (2) analyse multilayered ecological influential factors; and (3) examine core challenges teachers face and their corresponding coping strategies. The findings indicate that professional identity is dynamically reconstructed across nested ecosystems, from micro-level classroom interactions to chronological level sociocultural contexts. This study advances an integrative perspective on the complex technology–teacher relationship, highlighting that successful identity transformation requires coordinated support across all ecological levels. Theoretical and practical implications are discussed to facilitate sustainable teacher development in the face of AI. Full article

Centralized hot water systems in university dormitories can provide significant energy flexibility through demand response (DR). Thus far, however, existing studies have mainly focused on system-level optimization and have failed to provide a quantitative framework that accounts for individual users’ DR participation behavior and the associated uncertainty. In this paper, we develop a data-driven approach to evaluate demand-side flexibility and quantify the uncertainty that arises as a result of user participation. A clustering-based stochastic load prediction model is proposed and validated using real operational data, serving as the baseline for DR load shifting. Four DR strategies for students are designed based on time-of-use pricing and/or academic credit incentives. A survey of nearly 1000 students is used to calibrate participation probabilities, while a binomial distribution model characterizes the uncertainty of user participation, allowing us to derive the probability distribution and expected value of the system’s flexibility potential. Compared with the no-DR baseline, the combined price–credit incentive yields the highest flexibility, achieving a peak-shaving rate of 62.66% and thus significantly outperforming the price-only strategy. Notably, the academic credit incentive alone increases students’ willingness to participate more effectively than price signals. Furthermore, when the number of participating users exceeds 648, the fluctuation range of the estimated flexibility potential falls below 10.5%, enabling stable flexibility evaluation with a moderately large user sample. Full article

The growing integration of gamification and digital technologies in higher education has led to increasing interest in students’ perceptions of gamified learning environments. While existing research predominantly focuses on the effectiveness of gamification in enhancing motivation, engagement, and learning outcomes, less attention has been paid to differences between students’ expectations and actual experiences. This study addresses this gap by comparing the perceptions of students with and without prior experience with gamified learning. A quantitative survey was conducted among 189 engineering students from a technical university using a structured questionnaire based on a five-point Likert scale. Two groups were compared: students with prior experience in gamified learning and those without such experience. Data were analyzed using descriptive statistics, reliability analysis, the Mann–Whitney U test and Cliff’s Delta. The findings suggest that prior experience was associated primarily with differences in perceived learning interest. A statistically significant difference was identified only for this dimension, with students who had prior experience reporting significantly higher levels of agreement regarding the ability of gamification to make learning more interesting. For the remaining dimensions, including motivation, engagement, involvement, enjoyment of competitions, and distraction, differences between the groups were observed but were not statistically significant. Reliability analysis indicated higher internal consistency among students with prior experience. However, no formal statistical comparison of the Cronbach’s alpha coefficients was performed, and this finding should therefore be interpreted with caution. The study compares the perceptions of students with and without prior experience of gamified learning and discusses the findings from a theoretical perspective. Rather than directly measuring expectations, the findings provide a conceptual interpretation of how prior experience may be associated with students’ perceptions of gamified learning and offer implications for the design and implementation of gamification in higher education. Full article