Search Results (7,939)

Quantitative operational data mining and control strategies are essential for energy-saving regulation in intelligent District Heating Systems (DHSs). However, due to complex industrial environments and heterogeneous sensor networks, operational heating data often face a “zero ground-truth labels” bottleneck, rendering conventional residual-based unsupervised quality control algorithms ineffective without calibration baselines. To resolve this, this paper proposes an unsupervised multivariate data quality assessment framework constrained jointly by physical and statistical topologies. The framework evaluates time-series data streams across five dimensions: completeness, typicality, consistency, uniqueness, and timeliness. At the statistical topology level, the Mahalanobis distance identifies the spatiotemporal distribution center of multivariate variables, replacing traditional accuracy metrics with statistical typicality to enable self-consistent quantification without ground truth. At the physical topology level, coupled logical relations between primary and secondary heating networks are extracted as rigid first-principles constraints. An information entropy weight method then adaptively determines indicator weights to eliminate subjective biases. Full-sample validation was conducted using real-world SCADA data across a complete heating season from a regional network zone (46 heat exchange stations). The network-wide average data quality score reached 0.905, confirming overall control-loop input readiness, though specific stations exhibited cascading degradation from localized physical faults. This framework systematically reveals data quality heterogeneity in complex DHS and provides a generalizable theoretical baseline for Industrial Internet of Things (IIoT) data cleansing under zero-ground-truth conditions. Full article

This study systematically investigates the microstructure, physical properties, and sealing capacity of mudstone under thermal action using scanning electron microscopy (SEM), high-pressure mercury intrusion, breakthrough pressure testing, and diffusion coefficient analysis. Pore-throat structure, porosity, permeability, breakthrough pressure, and diffusion coefficient were measured at 25, 300, 600, 900, and 1200 °C, while additional SEM observations were conducted at 100, 200, 400, and 500 °C. Based on the integration of these discrete measurements and supplementary SEM observations, the evolution of mudstone sealing capability was interpreted as follows. (1) At 25–200 °C, mineral dehydration exposes primary pores, with porosity and permeability increasing gradually while strong sealing capacity is maintained. (2) At 200–400 °C, clay mineral dehydration, thermal expansion, and secondary fracture development increase pore connectivity, resulting in a sharp decline in sealing capability. (3) At 400–1200 °C, mineral structure collapse, pore-fracture connection, and mineral melting further increase porosity and permeability, leading to progressive sealing failure. (4) The threshold temperature for mudstone sealing failure is inferred to be approximately 400 °C. (5) Mudstone affected by temperatures below 200 °C is recommended as an effective caprock for underground coal gasification chambers. This study provides theoretical guidance for caprock selection and safe operation in underground coal gasification engineering. Full article

Carbonic anhydrases (CAs) are a class of zinc-containing metalloenzymes widely present in the biological world, catalyzing the reversible hydration of CO₂ to form HCO₃⁻ and H⁺. These enzymes play essential roles in pH homeostasis, gas exchange, metabolic regulation, and virulence expression in pathogens. In fungi, CAs mainly belong to the α- and β-classes and have undergone extensive diversification during evolution. In plant pathogenic fungi, the functions of CAs have extended beyond traditional metabolic roles, evolving into key “environmental adaptation and virulence regulatory factors.” This review takes Magnaporthe oryzae as a model organism and integrates recent advances in CA research across various microorganisms. It systematically summarizes the classification diversity, structural features, subcellular localization, and biological functions of fungal CAs. Particular emphasis is placed on the molecular profile, mitochondrial localization, physical interaction network, and multiple functional roles of the MoCA family members in conidial development, appressorium formation, oxidative stress response, HCO₃⁻ homeostasis, nitrogen metabolism, and mitochondrial energy metabolism. Based on these findings, this study proposes a multi-layered analytical framework integrating CA molecular characteristics, mitochondrial functional regulation, and fungal pathogenicity. It explores the potential of targeting fungal CAs for the development of novel selective fungicides and highlights key research directions, aiming to provide theoretical insights into plant-fungal interactions and innovative strategies for disease control. Full article

Quantum metrology promises sensitivity beyond classical strategies, yet it remains unsettled how quantum-enabled precision should scale with physical resources and how to interpret quantum advantage. We provide a physically grounded resource accounting that clarifies the true Heisenberg limit and resolves apparent super-Heisenberg paradoxes. We demonstrate that the Heisenberg limit is best viewed as an information-theoretic manifestation of the quantum speed limit. We illustrate these ideas with a simple, super-resolving phase estimation protocol based on Rabi oscillations in two-level atoms driven on an m-photon resonance. In this setting, the phase error scales as $n^{-m/2}$, where n is the average photon number. Recasting metrological sensitivity through quantum dynamical speed limits yields operational bounds that reconcile such super-resolution strategies with the standard Heisenberg interpretation and identify the relevant resources in the norm of the generator. We also revisit the common attribution of the NOON state’s $1/n$ scaling to quantum entanglement. We show that such an attribution is not generic and the Heisenberg $1/n$ scaling does not, by itself, certify entanglement as the enabling resource. Full article

Lead (Pb²⁺) pollution poses a severe threat to the ecological environment and human health due to its high toxicity, bioaccumulation, and refractory nature. Traditional treatment technologies for lead-contaminated wastewater, such as chemical precipitation, ion exchange, and membrane separation, often face limitations, including secondary pollution, high costs, and high energy consumption. In contrast, adsorption has emerged as a promising alternative technology with advantages such as a simple process, high efficiency at low concentrations, and renewability. Biomass-based hydrogels and their composite systems, as novel green adsorbent materials, combine the abundant functional groups of natural biomass with the structural stability, high porosity, and recoverability of hydrogels through a three-dimensional cross-linked network, offering unique advantages for lead ion adsorption. Depending on their composition, these systems range from fully biodegradable pure biopolymer networks to partly biodegradable or biomass-containing composites incorporating inorganic, carbon-based, or metal–organic framework (MOF) materials. This paper systematically reviews the latest research progress on cellulose, lignin, sodium alginate, chitosan, starch-based hydrogels, and their composite systems for lead (Pb²⁺) adsorption. First, the structural characteristics, cross-linking mechanisms, and functional modification strategies of various biomass hydrogels are introduced. Then, the adsorption mechanisms of Pb²⁺, including multiple modes of action such as coordination complexation, ion exchange, electrostatic interaction, and physical adsorption, are systematically analyzed. The adsorption performance of different material systems is compared in detail. The regeneration and recycling performance, as well as the potential practical applications, of the materials are evaluated. On this basis, the main challenges in current research are summarised: balancing adsorption capacity and mechanical strength, achieving selective adsorption in actual wastewater, improving regeneration efficiency, and optimizing costs. In addition, future development directions for biomass hydrogel adsorbent materials are discussed, including the design of multi-functional composite materials, the development of intelligent, responsive hydrogels, engineering-scale-up, and life-cycle assessment. This review aims to provide a theoretical framework and technical roadmap for the rational design of high-performance, sustainable hydrogel adsorbents and to promote their engineering application for the treatment of lead-contaminated wastewater. Full article

Mechanized harvesting of subsurface crops has long been confronted with the critical engineering dilemmas of high damage rates and high impurity rates. Traditional taxonomic classification methods based on botanical families and genera fail to provide effective guidance for the engineering research and development of harvesting machinery. From an engineering perspective, this paper proposes a novel classification logic that categorizes subsurface crops into three major types based on their soil burial depth and physical distribution characteristics: shallow-soil clustered growth type (0–20 cm), mid-soil scattered growth type (20–40 cm), and deep-soil vertically rooted type (>40 cm). The harvesting bottlenecks of representative crops within these strata, including potato, onion, peanut, sweet potato, cassava, and yam, are systematically elucidated. Furthermore, this review provides an in-depth analysis of the current state of frontier core technologies, such as bionic drag reduction excavation, flexible multi-stage separation, microscopic discrete element method (DEM) simulation, kinematic optimization, and AI-based visual perception. This paper aims to reveal the common bottlenecks in subsurface crop harvesting and prospect future developmental trends centered on the deep integration of machinery and agronomy as well as intelligent perception and adaptation, thereby providing a solid theoretical foundation and engineering reference for the innovation of global agricultural machinery. Full article

The overhead ground wire (OGW) may fracture at the suspension clamp under short-circuit faults, posing a serious threat to the safe operation of transmission lines. However, the dominant damage mechanism—whether Joule heating or arc discharge—remains unclear. This study investigates a 110 kV OGW breakage accident through combined experimental and numerical approaches. Fracture analysis using scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) revealed composite damage featuring both melting and tensile necking, with no fatigue characteristics. A real-scale short-circuit test platform was constructed, which, for the first time, directly captured intense arc discharge phenomena inside the suspension clamp during current flow. A multi-physics finite element model was then developed to decouple and quantify the thermal contributions of Joule heating and arc heating. Results show that Joule heating alone raises the local temperature to only 49.27 °C—far below the melting points of aluminum (660 °C) and steel (1450 °C). In contrast, arc heating elevates the temperature to over 26,000 °C locally, causing rapid melting of aluminum strands and heating of the steel core above 1450 °C within milliseconds. This extreme heat reduces the effective load-bearing cross-section and tensile strength, ultimately leading to fracture under normal operating tension. The findings demonstrate that arc discharge, rather than Joule heating, is the decisive factor in such failures. This study provides a quantitative theoretical basis for fault protection and hardware design optimization of overhead transmission lines. Full article

Flow patterns in vertical wellbores are highly complex and variable. Classical theoretical models for pressure drop prediction frequently yield significant errors when applied to different geological blocks. However, existing correction models suffer from incomplete coverage of flow patterns. Therefore, it is necessary to develop new pressure drop prediction models specifically for gas–liquid two-phase flow in vertical wellbores under various flow patterns. This study conducted physical experiments on gas–liquid two-phase flow in vertical wellbores, successfully reproducing four typical flow patterns—bubbly flow, slug flow, churn flow, and annular flow—and determining their transition boundaries. Based on the experimental data, a systematic comparison was performed among four classic flow pattern discrimination models: Aziz, Beggs–Brill, Mukherjee–Brill, and Ansari. The results indicated that the Aziz model demonstrates superior applicability for identifying vertical flow patterns. To address the substantial prediction errors of the Aziz model in pressure drop calculations, the liquid holdup ratio and friction factor under different flow patterns were manually corrected using the experimental data, yielding a new pressure drop prediction model (Model 1). Furthermore, the particle swarm optimization (PSO) algorithm was introduced to further automatically optimize and fit the model parameters, thereby establishing another new pressure drop prediction model (Model 2) specifically tailored for different flow patterns. Validated against the experimental measured data, the new Model 2 achieved a Mean Absolute Percentage Error (MAPE) of 11.9% and a Root Mean Square Error (RMSE) of 1.9 kPa. Further verified by independent validation experiments outside the calibration dataset, Model 2 achieves an average prediction error of 12.0%, maintaining stable and high prediction accuracy. In comparison, the Aziz model and the new Model 1 yielded MAPE/RMSE values of 50.6%/5.3 kPa and 18.2%/4.9 kPa, respectively. The errors of the new Model 2 are markedly lower than those of the aforementioned models, demonstrating its superior accuracy in calculating pressure drops under various flow patterns. These findings provide a more accurate and reliable theoretical model for pressure drop calculation in gas–liquid two-phase flow within vertical wellbores, thereby laying a solid foundation for numerical simulation history matching and subsequent production forecasting. Full article

We introduce a new method of dimensional analysis based on complete systems of units, such as the metric and Planck systems, in which fundamental dimensionless constants arise naturally. In fact, it is the reformulated Planck system that communicates its dimensionless constants to the metric or any other system. The method reveals additional complex dynamical scales and physical effects beyond those amenable to conventional dimensional analysis. We formulate our strategy in simple settings involving pairs of seemingly unrelated constants, and then we extend the analysis to more complicated cases involving combinations of three to five well-known universal constants. In constructions involving several unrelated constants, the method captures increasingly complex effects and places two or more disparate physics areas into a single framework connecting them by never-before-seen combinations of fundamental dimensionless constants, such as the fine-structure constant and the gravitational coupling constant. Thus, this method provides a pathway to blending descriptions of two or more fundamental interactions that have so far eluded a consistent theoretical formulation. Full article

Despite the small mass fraction typically observed for the interstellar medium, dust plays a significant role as a key component of galaxies, affecting a wide range of properties. This review focuses specifically on how dust grains influence interstellar chemical abundances and on the processes that regulate the evolution of the galactic dust budget. I describe the main physical processes regulating dust evolution, including production by stars and other sources, destruction in supernova shocks and interstellar growth, and the ways in which they are included in galactic chemical evolution models. I discuss the main effects of interstellar dust on the abundances measured in various high-redshift systems that include Damped Lyman $\alpha$ absorbers, detected along the lines of sight of distant quasars and in the absorption spectra of Gamma Ray Burst afterglows. I discuss the measure of dust masses in galaxies and review its global budget, evaluated through the study of the evolution of the comoving dust mass density, for which I present an up-to-date compilation of data chosen from the literature. Interstellar dust growth plays a critical role in regulating the dust budget, for which I present a list of evidence both in favour of it and against. The dust budget at high redshift is one aspect that requires attention to drive significant progress in the future, along with the investigation of the properties of dust in local, low-metallicity systems. Our poor theoretical knowledge of basic aspects related to dust evolution evidences the need for a new high-sensitivity space telescope operating in the far-infrared regime, still awaited by the community since the demise of Herschel. Full article

The conventional description of time-varying media assumes that electromagnetic fields evolve according to fixed continuity conditions during parameter jumps. However, recent discussions have made it clear that such continuity conditions are not physical constraints, but are determined by the microscopic processes that underlie the modulation. However, a unified theoretical framework capable of systematically describing different continuity conditions is still lacking. By treating continuity rules as tunable parameters and incorporating them into a unified time-varying theoretical framework, the scope of time-varying metamaterials is expanded to encompass non-resonant reflectionless wave amplification without momentum bandgaps, reversible conversion between propagating waves and static fields, etc. Hence, in this work, wave phenomena previously considered impossible become attainable, opening a new dimension for controlling light–matter interactions through time-varying media. Full article

Rapid progress in quantum computing threatens the long-term security of classical cryptographic primitives, and with them the integrity of contemporary blockchain systems that rely fundamentally on computational hardness assumptions. Hence, quantum-native blockchain architectures have emerged as a conceptual pathway toward information-theoretic disturbance detectability. Two influential approaches have emerged in the literature. The temporal GHZ-state blockchain provides disturbance-detectable tamper sensitivity through entanglement in time, whereas the weighted quantum-hypergraph blockchain achieves high encoding efficiency through phase-based quantum representations of classical information. However, each addresses only part of the problem. In this work, we introduce a hybrid quantum blockchain framework whose primary novelty is the integration of phase-encoded classical data representation with recursively generated temporal GHZ entanglement within a single blockchain architecture. Rather than proposing a new encoding scheme or a new temporal-entanglement construction, the framework combines both mechanisms found in the literature and introduces a corresponding verification procedure for validating phase-encoded temporally entangled blocks. This architecture preserves the physics-based measurement-disturbance detectability of temporal entanglement while enabling more efficient classical-to-quantum data encoding inspired by hypergraph-based phase weighting. The result is a conceptual blockchain model that simultaneously enhances tamper sensitivity and encoding efficiency, providing a coherent foundation for future research on secure and practical quantum-era ledger systems. Full article

Background: The preservation of intrinsic capacity (IC) is a global public health priority, and digital health technologies offer promising avenues for managing functional decline. However, the longitudinal impact of specific smart monitoring devices on IC trajectories remains underexplored, and theoretical debates persist regarding whether digital interventions exacerbate health inequalities (the “digital divide”) or mitigate them (the “compensatory effect”). Methods: Using a balanced panel of 2432 older adults from the China Longitudinal Aging Social Survey (CLASS 2020–2023), we conducted wave-specific latent profile analyses based on five standardized WHO intrinsic-capacity domains. Modal class assignment and cross-wave profile matching were subsequently used to describe profile stability and transitions. The longitudinal protective association of baseline smart blood pressure (BP) monitor use with IC trajectories was evaluated using multinomial logistic regression with Inverse Probability Weighting (IPW). Exploratory structural path analysis was conducted to examine a potential mediating pathway. Results: The baseline latent profile analysis identified three profiles: Robust Capacity (80.2%), Cognitively Impaired (10.9%), and Sensory Impaired (8.8%). The Sensory Impaired class exhibited strong path dependence, with 62.79% of participants remaining in the same state over three years. Baseline estimates revealed that smart BP monitor usage was significantly associated with a reduced relative risk of transitioning into or remaining in the Sensory Impaired profile (RRR = 0.557, 95% CI: 0.325–0.955). The IPW analysis yielded a directionally consistent estimate (RRR = 0.696), although the association was attenuated and was not statistically significant (p = 0.162). Subgroup and pathway analyses yielded non-significant but suggestive findings, indicating that protective associations might be concentrated among vulnerable groups, particularly women and individuals with lower educational attainment. Conclusions: Smart physiological monitoring interventions demonstrate a longitudinal protective association against the persistence of physical frailty. Rather than definitively bridging the digital divide, targeted medical-grade digital tools may serve as compensatory resources for specific vulnerable subpopulations. These observational associations highlight the need for further quasi-experimental testing before integrating specific digital interventions into community-based chronic care ecosystems. Full article

The rapid growth of electric vehicle (EV) adoption presents significant challenges for power system stability while creating new opportunities for decentralized energy management. Peer-to-peer (P2P) energy networks have emerged as a promising approach for transforming EVs from passive loads into coordinated grid assets. This paper presents a comprehensive review of EV-P2P integration through a three-layer architectural framework that systematically connects physical infrastructure, market mechanisms, and intelligent control strategies. The Physical Layer reviews how V2X technologies and bidirectional charging enable EVs to operate as flexible storage resources and ancillary service providers. The Transactional Layer reviews on blockchain-based platforms, auction mechanisms, and game-theoretic models for secure energy trading. The Intelligence Layer reviews advanced control strategies, including decentralized optimization methods such as the Alternating Direction Method of Multipliers (ADMM) and Deep Reinforcement Learning. Collectively, the reviewed studies demonstrate that these approaches enable EVs to operate as flexible loads, distributed storage resources, and ancillary service providers, while improving energy trading efficiency, reducing operating costs, and alleviating network congestion under simulated operating conditions. Despite these promising results, a substantial gap remains between simulation-based studies and practical implementation. Future research should prioritize integrated pilot projects to evaluate scalability, interoperability, cybersecurity, and regulatory compliance under realistic operating conditions. Full article

Unmanned Aerial Vehicles (UAVs) are seeing increased use in agricultural settings due to their potential to be integrated with systems for applying pesticides. They can target specific areas while offering the potential to reduce chemical waste and improve application efficiency. However, this means that spray deposition efficiency is strongly influenced by rotor-induced downwash, which affects droplet transport, drift, and uniformity. This study presents a combined computational and experimental investigation of downwash–spray interactions in a hexacopter platform. CFD is used to predict the performance of various sprayer configurations that differ in the number, spacing, and positioning of nozzles. Rotor-induced airflow is modeled using an actuator disk approach in ANSYS Fluent 2025, and spray behavior is predicted using the Discrete Phase Model. Pure water was used as the working fluid for both the CFD simulations and experimental validation to ensure consistency between numerical and physical testing conditions. Numerical results indicate that a two-nozzle under-rotor setup maximizes performance characteristics such as deposition area, density, and uniformity for the designed agricultural UAV, providing a theoretically effective deposition area of 9.375 m², an effective application rate of 0.03387 mL/m², and a coefficient of variation of 45.3%. Compared to the best-performing boom configuration, this represents an approximately 13.5% improvement in spray uniformity. These results are validated through experimental testing using a modular UAV sprayer system and deposition measurements obtained from water-sensitive paper in controlled indoor conditions, achieving a droplet size of 502 µm, swath width of 1.8 m, 0.8% area coverage, and a coefficient of variation of 36.5%. While differences were observed between predicted and measured droplet size distributions, the CFD and experimental results demonstrated similar trends in spray coverage and deposition uniformity. Future work will refine simulations to better match experimental conditions and investigate canopy interaction, crosswind effects, and field-scale performance. Full article