Search Results (29,152)

We formulate the Quantum Information Copy Time (QICT) framework for conserved charges under strictly local quantum dynamics and isolate its logically strongest consequence. The theorem-level core is a receiver-optimised variational speed-limit inequality: after projection away from the conserved zero mode, the copy time is bounded from below by the inverse square root of a Liouvillian-squared receiver susceptibility times a local encoding seminorm. This statement is written in a finite-volume operator framework and does not require a diffusive ansatz. We then examine what follows only after additional infrared assumptions. Under a single diffusive slow-mode hypothesis, the variational inequality reduces to the practical scaling relation used in the benchmark computations. That reduction is treated as conditional and is stress-tested numerically rather than promoted by rhetoric. Within the anomaly-free Abelian span relevant for one Standard-Model-like generation, hypercharge selection is elevated to theorem-level status; by contrast, minimal gauge-algebra uniqueness remains explicitly conditional on additional model-selection axioms. The remainder of the manuscript is organised as an explicitly documented closure programme built on top of this core. In that closure, a gauge-coded QCA construction, a microscopic benchmark for the transport normalisation, and an electroweak matching convention are combined to produce a resonance-centred Higgs-portal singlet-scalar mass band together with direct-detection, invisible-width, and relic-consistency checks. These latter results are presented as model-dependent consequences of an explicit closure ansatz rather than as deductions from locality alone. Full article

Children are among the most sensitive groups to air pollution. This study focuses on Chinese children aged 0–16 years, integrating six waves of tracking data from the China Family Panel Studies (CFPS, 2012–2022), the ChinaHighAirPollutants (CHAP) dataset, and MOD11A1 land surface temperature (LST) data, covering 20,241 samples across 25 provinces. Using the eXtreme Gradient Boosting–SHapley Additive exPlanations (XGBoost-SHAP) framework, we quantified the relative contributions of fine particulate matter (PM_2.5), ozone (O₃), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and climate factors to children’s respiratory disease risk. The overall area under curve (AUC) was 0.6765, with urban and rural sub-models achieving 0.6576 and 0.6864, respectively. SHAP analysis revealed that the temporal variable ranked first, reflecting population-level improvements from 2012 to 2022; age ranked second, with a 70.1% prevalence in the 0–6 age group. Rural PM_2.5 contribution was approximately 1.68 times that of urban areas; the O₃ effect showed opposite directions between urban (risk) and rural (protective association) settings; solid fuel contribution in rural areas was approximately 2.25 times the urban level. Regional clustering analysis identified differentiated environmental drivers across five geographic types. These findings provide a quantitative basis for differentiated regional prevention strategies. Full article

Translational neuroscience relies on both in vitro slice recordings and in vivo recordings. Their spontaneous population dynamics are observed under decisively different conditions, and across independent experiments, there is typically no clear neuron-to-neuron correspondence. Here, we formulate a one-step-ahead, 1 ms binned, bidirectional transfer task between in vitro and in vivo multineuronal spike trains and provide a standardized evaluation procedure for generation across markedly different recording preparations. We train an autoregressive transformer on 1 ms binned, 128-unit binary sequences and introduce Dice loss to directly optimize spike-event overlap under extreme class imbalance, comparing it with Binary Focal Cross-Entropy (γ = 2.0). Across 12 mouse datasets (6 in vitro HD-MEA sessions and 6 in vivo Neuropixels sessions), the method achieves strong within-domain performance and remains above chance for cross-domain generation (ROC-AUC 0.70 ± 0.09 for in vitro → in vivo; 0.80 ± 0.10 for in vivo → in vitro). Because spike events are rare, we report Precision–Recall curves and PR-AUC alongside ROC-AUC to reflect minority-event quality. The present results should be interpreted as predictive generation under preparation/domain shift rather than as direct evidence of preserved causal biological dynamics; whether the framework also reflects features such as E/I balance or oscillatory structure remains an important question for future validation. To our knowledge, this is the first demonstration of bidirectional, time-resolved generation between unpaired in vitro and in vivo population spike trains without assuming cell correspondence, and the framework can be adapted to other sparse neural event data and related event-based datasets when domain-specific validation criteria are defined. Full article

Preeclampsia remains a major cause of maternal and perinatal morbidity and mortality worldwide, yet progress in biomarker discovery and predictive modeling has translated only modestly into clinically meaningful risk stratification. Over the past two decades, numerous biomarkers and predictors reflecting placental–angiogenic dysfunction, maternal cardiovascular maladaptation, and inflammatory–metabolic stress have been proposed, alongside increasingly sophisticated statistical and machine learning approaches. However, many predictive strategies continue to treat preeclampsia as a single disease entity and rely on static thresholds applied at isolated gestational time points. Accumulating biological and clinical evidence instead suggests that preeclampsia represents a heterogeneous syndrome composed of partially overlapping mechanistic phenotypes whose relative contributions vary across pregnancy and across individuals. In this narrative review, we argue that further progress in prediction is likely to depend less on the identification of additional biomarkers and more on how biological heterogeneity and temporal dynamics are integrated into predictive frameworks. We synthesize current evidence supporting multimarker approaches, phenotype-informed frameworks, and longitudinal risk trajectories that conceptualize prediction as a dynamic process rather than a binary classification task. We also examine the complementary roles of classical statistical models and machine learning, emphasizing that calibration, external validation, interpretability, transportability, and clinical usability are essential, alongside discrimination, for successful clinical implementation. Finally, we outline key research priorities for the next generation of predictive studies, including mechanistically grounded phenotyping, dynamic risk updating across gestation, rigorous evaluation across diverse populations, and explicit linkage of risk stratification to preventive interventions and clinical decision-making. Together, these directions support a shift toward an integrative, longitudinal, and clinically anchored approach to preeclampsia prediction. Full article

Chitosan, owing to its abundant amino and hydroxyl functional groups, serves as an effective biosorbent for the removal of toxic metal(loid) ions from water. This review summarizes recent advances in chitosan-based adsorbents specifically for arsenate (As(V)) and copper ions (Cu(II)), with an emphasis on adsorption mechanisms and electrospun nanofiber technologies. A conceptual “charge adaptation–structure synergy” model is proposed to elucidate the distinct adsorption behaviors of chitosan toward anionic and cationic substances: under acidic conditions, As(V) adsorption is dominated by electrostatic attraction to protonated amino groups, whereas at pH values near or above the pKa, Cu(II) removal proceeds via synergistic chelation involving deprotonated amino and hydroxyl groups. Competitive and synergistic interactions in binary systems, particularly between As(V) and coexisting anions such as phosphate, are also discussed. Notably, the kinetic advantages of electrospun chitosan nanofibers are highlighted, with equilibrium times shortened from several hours to approximately 0.5–2.6 h. Key challenges and future research directions are further discussed, including scalable manufacturing and the treatment of complex wastewater matrices. Full article

During offshore oilfield development, traditional injection–production processes commonly suffer from delayed regulation, low operational efficiency, and heavy reliance on manual intervention. Achieving real-time diagnosis of injection–production anomalies and dynamic optimization under complex geological conditions and harsh marine environments represents a core scientific challenge. This study presents the development and field deployment of an intelligent cable-controlled injection–production integrated management system. The work is positioned as an application- and system-oriented study, focusing on addressing practical challenges in offshore oilfield operations through the integration of established machine learning techniques into a cohesive operational platform. The system employs a cloud-native microservice architecture and integrates nine functional modules, enabling closed-loop management from data acquisition to intelligent decision making. Key methodological contributions include: (1) a weighted ensemble model combining Random Forest and SVM for blockage diagnosis, balancing global feature learning with boundary sample discrimination to achieve 92% diagnostic accuracy; (2) a Bayesian fusion framework that integrates static geological priors with dynamic sensitivity analysis for probabilistic quantification of injector–producer connectivity, achieving 85% identification accuracy with rigorous uncertainty propagation; and (3) a three-stage human–machine collaborative mechanism that substantially reduces anomaly response latency while ensuring field safety. Field application in Bohai oilfields demonstrates that the system shortens the injection–production response cycle by approximately 42%, reduces anomaly response time from over 72 h to less than 2 h (a 97% reduction), decreases water consumption per ton of oil by 27.6%, and increases injection–production uptime by 11.3 percentage points. This study provides an interpretable, extensible, and closed-loop technical solution for intelligent offshore oilfield development, with future directions including digital twin predictive simulation and reinforcement learning for real-time optimization. Full article

This paper considers geometry-aware ground user clustering and Cluster Center Optimization for beam pointing and scheduling in adaptive multibeam Geostationary Earth Orbit (GEO) satellite networks. By grouping ground users, beams can be directed toward user clusters to maximize satellite throughput. We propose GeoClust, a polynomial-time geometric user clustering algorithm for adaptive multibeam GEO satellite networks, using a geometric set-cover approach that explicitly balances link signal-to-interference-plus-noise ratio (SINR) and hopping overhead. The algorithm employs a Boyle–Dykstra projection-based cluster center update within an alternating optimization framework, combined with nearest-center membership updates, to enforce the cluster-radius constraint while ensuring feasibility and provable convergence. It also achieves near-linear throughput scaling with increasing number of RF chains. Numerical evaluations on real-world population data show that, under heavy traffic conditions, our approach more than doubles the zero outage and median user rates compared to benchmark schemes. Full article

Air pollution is one of the fundamental environmental problems that directly threaten public health, ecosystems, and sustainable urban life in regions with high industrialization and urbanization density. This study aims to investigate whether the air pollution dynamics in Gaziantep and Kilis, two neighboring cities in Turkey, exhibit distinctive city-specific characteristics in their time series. In this context, Dynamic Time Warping (DTW) distance matrix and hierarchical clustering approaches were applied to compare the temporal behavior of pollutants from daily time series of PM10, SO₂, CO, and O₃ measurements across provinces between 2021 and 2025. Random Forest (RF), XGBoost, and Support Vector Machines (SVM) models were then developed to examine the separability of cities based solely on pollutant concentrations. The results revealed that the RF and XGBoost models successfully classified the two cities with over 93% accuracy. Additionally, SHAP analysis was used to interpret the contribution of each pollutant within the classification models, indicating that PM10 and SO₂ have relatively higher importance in distinguishing between the two cities. It should be noted that SHAP provides model-based interpretability rather than a direct representation of physical or atmospheric mechanisms. The findings suggest that pollutant time series may exhibit statistically distinguishable structures even between neighboring cities. Full article

Pneumatic transmission lines play a critical role in the dynamic performance of soft robotic actuation systems, yet their behaviour is difficult to capture using conventional integer-order (IO) models. In long, slender pipelines, compressibility, viscothermal losses, and wave propagation give rise to distributed damping and non-exponential relaxation dynamics that are not well represented by finite-dimensional models. This paper presents a control-oriented, experimentally validated fractional-order (FO) modelling framework for pneumatic pipeline dynamics under closed-end boundary conditions. Models are calibrated using measured step-response data from a 13.2 m pipeline, with all parameters—including the fractional order—identified through a unified optimisation procedure. In addition to global fitting accuracy, model performance is evaluated using control-relevant metrics, including effective delay, initial slope and early transient behaviour, and early-time error. The results show that FO models provide a more compact and structurally consistent representation of long-memory dynamics while improving the accuracy of control-relevant features compared to their IO counterparts. These findings demonstrate that fractional dynamics offer a physically meaningful and practically useful framework for modelling pneumatic transmission lines, with direct implications for high-performance control design in soft robotic systems. Full article

Acute traumatic aortic injury (ATAI) is a rare but life-threatening consequence of blunt trauma that requires prompt diagnosis and accurate imaging assessment. This review presents an imaging-based approach to ATAI, with emphasis on computed tomography angiography (CTA) as the first-line modality for diagnosis, grading, treatment planning, and follow-up. CTA enables the detection of both direct and indirect signs while also allowing for the assessment of lesion severity, extent, and associated findings that may influence management. Familiarity with common mimics and anatomic variants improves diagnostic confidence and helps avoid false positive interpretations. Careful protocol optimization, including multiphasic acquisition, bolus timing, and postprocessing reconstructions, can further enhance image quality and diagnostic performance. Recognition of patient-related and technical CTA artifacts, along with strategies to reduce them, including the selective use of ECG-gated CTA, may further decrease diagnostic uncertainty. We also discuss the complementary roles of emerging CT technologies and magnetic resonance angiography in selected patients. Finally, we review current classification systems, imaging-guided management, post-treatment surveillance, and potential complications. Awareness of ATAI imaging findings, protocol optimization, and diagnostic pitfalls is essential for accurate interpretation and effective multidisciplinary care. Full article

Background: Employee voice, as a bottom-up proactive behavior, is crucial for organizational development. However, sustaining employee voice over time remains a shared challenge for both practice and research. Among various influencing factors, supervisor feedback, due to its central role in organizational interactions, serves as a key source of decision-making information affecting employees’ subsequent voice intention. Nevertheless, existing research predominantly focuses on the unidirectional effects of supervisor feedback, often overlooking the bidirectional nature of leader–subordinate interactions. In reality, the effectiveness of supervisor feedback ultimately depends on its congruence with the subordinate’s psychological expectations. Methods: This study integrates person–environment fit theory and role identity theory to investigate how the congruence between subordinates’ expected feedback and supervisors’ actual feedback influences subsequent voice behavior. Through two studies—a scenario-based experiment with 201 participants and a retrospective questionnaire survey with 212 participants—we employed polynomial regression and response surface analysis to examine four feedback congruence patterns. Results: In congruent situations, the “expected positive–actual positive” combination promotes subsequent voice behavior more effectively than the “expected negative–actual negative” combination. In incongruent situations, the “expected negative–actual positive” combination is more effective in promoting subsequent voice than the “expected positive–actual negative” combination. Furthermore, voice role identity mediates the relationship between feedback congruence and subsequent voice behavior, revealing a key psychological mechanism. Implications: This study moves beyond a direct antecedent framework by focusing on the congruence between feedback expectations and reality, thereby deepening the theoretical understanding of the dynamics of voice. By empirically demonstrating how congruent and positive feedback strengthens employees’ internal identity as contributors, it provides practical insights for organizations aiming to foster a sustainable voice climate. Full article

To systematically review the research progress on unmanned aerial vehicle (UAV) formation control, this paper proposes a mission-driven full-lifecycle analysis architecture. The architecture summarizes the core scenarios and key technologies involved in the three main stages: formation assembly, formation maintenance, and formation reconfiguration. Moreover, a comprehensive evaluation framework is established that covers pre-event, in-event, and post-event phases from the perspectives of resilience, robustness, reliability, and vulnerability. The interrelationships among these four dimensions are explained in terms of time, function, and design. Finally, this paper identifies current research gaps and practical challenges in terms of algorithms, evaluation methodologies, and real-world deployment verification, and outlines future development directions. Full article

Medicine–food homology (MFH) substances, which possess both medicinal and edible properties, have garnered widespread attention in the global health context of the new era. The MFH industry has experienced explosive growth and has gradually become a key supporting aspect of TCM modernization. However, due to the pollution of the modern environment, the content of pollutants in MFH products has been increasing, raising concerns regarding quality, safety, and efficacy control. Traditional quality-analysis technologies struggle to meet the needs of rapid on-site detection because of their dependence on large instruments and the complexity of operation. This dilemma has propelled advances in sensor technology. With its advantages of high sensitivity, real-time detection, and portability, sensor technology has become a key technical support for quality control and supervision in the field of MFH. In this review, we comprehensively categorize the mainstream sensor types used for analysis in the field of MFH, including intelligent sensors, optics, electrochemistry, biosensors, etc. This review outlines their research status, elaborates on their primary application directions and corresponding core technologies, discusses current challenges (including stability, interference, and cost), and presents future perspectives. Overall, sensor-based technologies offer a promising and scalable solution for the quality control of MFH products, addressing critical challenges such as stability, interference, and cost. With ongoing advances in intelligent sensing, optics, electrochemistry, and biosensing platforms, these methods are poised to play an increasingly vital role in ensuring the safety, efficacy, and quality consistency of MFH products amid growing environmental pressures. Full article

Given China’s strategic need to alleviate poverty and promote high-quality development in the Yellow River Basin, in this paper, we adopt the unique perspective of ecological migration to dynamically analyze changes in the spatial structure, spatial differentiation, trajectory, and formation mechanism of multidimensional poverty. This study finds the following: (1) In recent years, multidimensional poverty in the contiguous poverty-stricken areas represented by Liupan Mountain in Ningxia has shown a tendency to change from overall poverty to partial poverty. (2) The influence of rural per capital net income on multidimensional poverty has been gradually slowing down over time, which reflects the evolution of the concentrated contiguous poverty-stricken areas represented by the Liupan Mountain area in Ningxia from absolute poverty to relative poverty. (3) Geographical capital and economic development exert a high degree of direct impact on multidimensional poverty. However, as key carriers of spatial reconstruction, ecological migration is not a direct first-order input factor. Instead, it indirectly influences the spatial reconstruction of poverty by reshaping the distribution of population, housing, cultivated land, and infrastructure, with its effects reflected in core indicators such as per capita cultivated land and ecological vulnerability. Establishing a long-term poverty alleviation mechanism for advantageous industries, building a multidimensional education system for poverty reduction, and implementing ecological migration are important pathways to alleviate and eliminate multidimensional poverty in this region. Full article

Lithium-ion batteries (LIBs) have become the prominent energy storage technology because of their high specific energy, longer lifespan, and excellent efficiency. Traditional lithium extraction processes are energy intensive and time-consuming. Direct lithium extraction (DLE) methods provide a more sustainable and efficient alternative. This review offers a comprehensive overview of lithium-ion battery resources and direct lithium extraction methods. The detailed discussion of the DLE methods, which include adsorption, ion exchange, solvent extraction, membranes separation, and electro-chemical systems is presented. A comprehensive analysis of the recent technological advances of the direct lithium extraction processes in terms of technology readiness levels, and commercial potential is reported. The advantages and the technical challenges of the DLE methods are also reported. Finally, the review outlines the artificial intelligence outlook of the DLE processes. The review aims to provide deeper insights into the limitations and the opportunities of DLE methods towards crucial future research efforts for lithium-ion batteries advancements. Full article