Search Results (132)

This study employs a combined approach of theoretical calculation and numerical simulation to systematically optimize the impeller of a turbine expander, the core component of a 10-ton/day hydrogen liquefaction system. First, based on thermodynamic analysis and one-dimensional calculations, a three-factor four-level orthogonal experiment optimizes the parameters of reaction degree, radius ratio, and blade height ratio. Building upon this foundation, the influence of two-dimensional meridional profiles on impeller efficiency is investigated to establish design criteria. Subsequently, the effects of three-dimensional parameters including tip clearance, blade count, and blade thickness on performance are analyzed. Finally, the impact of rotational speed and flow rate on efficiency is explored, identifying high-efficiency operational ranges. Through multi-parameter collaborative optimization, an impeller configuration achieving low outlet temperature (53.67 K) and high efficiency (about 93.6%) is obtained, providing critical references for designing high-efficiency turbine expanders in hydrogen liquefaction systems. Full article

Accurately identifying the load status of a ball mill is critical for optimizing grinding efficiency and ensuring operational stability. However, the one-dimensional vibration signals collected from ball mills exhibit strong non-stationarity and a high degree of entanglement between multi-scale local transient features and long-range temporal evolution patterns. To address this, rather than relying on a purely black-box approach, this paper introduces a novel Deep Multi-scale Spatial–Temporal Feature Decoupling Network (DMSTFD-Net) guided by a clear feature decoupling philosophy to enhance model interpretability. The core of DMSTFD-Net lies in its hierarchical collaborative feature refinement mechanism. It first utilizes a one-dimensional residual network (ResNet) to adaptively capture and preliminarily decouple multi-scale spatial characteristics from the raw signal. Subsequently, the extracted high-level feature sequences are fed into a bidirectional gated recurrent unit (Bi-GRU) to decouple high-order temporal dynamic patterns. Experiments on a multi-condition dataset demonstrate that the proposed network achieves a state-of-the-art accuracy of 97.65%. Furthermore, dedicated cross-condition experiments and t-SNE visualizations validate the framework’s effectiveness. The results confirm that DMSTFD-Net provides a powerful, robust, and more interpretable solution for ball mill load identification. Full article

Rolling bearings are one of the core components of industrial equipment. Owing to the rapid development of deep learning methods, a multitude of data-driven remaining useful life (RUL) estimation approaches have been proposed recently. However, several challenges persist in existing methods: the limited accuracy of traditional data-driven models, instability in sequence prediction, and poor adaptability to diverse operational environments. To address these issues, we propose a novel prognostics approach integrating three key components: time-intrinsic mode functions-derived feature representation (TIR) sequences, a one-dimensional temporal feature convolution–reallocation network (TFCR) with a flexible configuration scheme, and a physics-based Kalman filtering method. The approach first converts denoised signals into TIR-sequences using windowed empirical mode decomposition (EMD). The TFCR network then extracts hidden high-dimensional features from these sequences and maps them to the initial RUL. Finally, physics-based Kalman filtering is applied to enhance prediction stability and enforce physical constraints, producing refined RUL estimates. The experimental results based on the XJTU-SY dataset show the superiority of the proposed approach and further prove the feasibility of this method in bearing RUL estimation. Full article

Against the background of traffic conflicts arising due to the highly concentrated population in high-density cities, this study aims to systematically identify the core transport needs of patients awaiting medical treatment; based on the theory of the Kano model, we construct a measurement system relating to patient transport needs when awaiting medical treatment that encompasses multiple levels. Taking 10 large general hospitals in Guangzhou as samples, this study collected data through questionnaires and auxiliary interviews, using the importance–sensitivity analysis method to accurately measure the degree of patient needs for each influencing factor of the transport environment for medical treatment. The study found that, among the primary needs (core basic needs), the perfection of public transport (which directly affects the convenience of medical care) is the core need with the highest degree of demand. Among the second-level needs (refined categorised demand levels), specifically relating to important attributes (essential needs), priority attention should be given to patient diversion, hospital–city connection, and corridor settings. As concerns the high value-added one-dimensional attributes (desired needs), focus should be placed on controlling health and safety distances and guiding the flow of medical treatment, while for high glamour attributes (glamour needs), primary consideration should be given to crowd distribution, stopping and resting, and direct access to the ground floor. The group difference analysis (grouped by emotional state, transport mode, and group type) showed that the first-level demand sensitivity ranking was highly consistent, and the second-level demand for urban connectivity, convenient transfer, and direct underground access were also common priorities. This study is the first to introduce the Kano model into the analysis of high-density urban healthcare transport systems, providing a clear basis for the grading of demand for the design of the transport environment for patients’ medical care. This is of great practical value for alleviating congestion and improving the resilience of emergency response in mega-cities in relation to medical care. Full article

The emergence of bacterial resistance has spurred an urgent need to develop effective alternatives to traditional antibiotics. Phenylethyl alcohol from plants exhibits potential antimicrobial properties, but its efficacy is limited due to its compromised dispersion in water and structural stability in ambient conditions. Herein, for the first time, a polymerization-induced self-assembly strategy was employed to obtain different morphological nanogels with phenylethyl alcohol moieties as hydrophobic cores through in situ reversible addition–fragmentation chain-transfer (RAFT) polymerization. The well-defined copolymers of PTEG_x-co-PPMA_y with controllable molecular weights and narrow polydispersity were confirmed by a combination of techniques. The generated phenylethyl alcohol-based nanogels demonstrated potent antibacterial activity, particularly PTEG₃₀-co-PPMA₇₀ with a one-dimensional linear architecture, which achieved a minimum inhibitory concentration of 62 μg mL⁻¹ against E. coli. SEM revealed membrane disruption as the bactericidal mechanism, highlighting enhanced efficacy against Gram-negative bacteria due to structural differences in cell envelopes. This study establishes a robust platform for designing phenylethyl alcohol-based nanogels with controllable structures toward achieving potent antimicrobial performance, offering a promising strategy for combating bacterial resistance while addressing the dilemma of conventional antibiotic drug systems. Full article

One-dimensional GaAs/InGaAs core–shell nanowires (NWs) with reverse type-I band alignment are promising candidates for next-generation optoelectronic devices. However, the influence of composition gradients and atomic interdiffusion at the core–shell interface on their photoluminescence (PL) behavior remains to be clarified. In this work, GaAs/In_xGa_1−xAs NW arrays with different indium (In) compositions were prepared using molecular beam epitaxy (MBE), and their band alignment and optical responses were systematically investigated through power and temperature-dependent PL spectra. The experiments reveal that variations in the In concentration gradient modify the characteristics of potential wells within the composition graded layer (CGL), as reflected by distinct PL emission features and thermal activation energies. At elevated temperatures, carrier escape from these wells is closely related to the observed PL saturation and emission quenching. These results provide experimental insight into the relationship between composition gradients, carrier dynamics, and emission properties in GaAs/InGaAs core–shell NWs, making them promising candidates for high-performance nanoscale optoelectronic device design. Full article

To address the high false alarm rate of intrusion detection systems based on distributed acoustic sensing (DAS) for power cables in complex underground environments, an innovative GRT-Transformer multimodal deep learning model is proposed. The core of this model lies in its distinctive three-branch parallel collaborative architecture: two branches employ Gramian Angular Summation Field (GASF) and Recursive Pattern (RP) algorithms to convert one-dimensional intrusion waveforms into two-dimensional images, thereby capturing rich spatial patterns and dynamic characteristics and the third branch utilizes a Gated Recurrent Unit (GRU) algorithm to directly focus on the temporal evolution features of the waveform; additionally, a Transformer component is integrated to capture the overall trend and global dependencies of the signals. Ultimately, the terminal employs a Bidirectional Long Short-Term Memory (BiLSTM) network to perform a deep fusion of the multidimensional features extracted from the three branches, enabling a comprehensive understanding of the bidirectional temporal dependencies within the data. Experimental validation demonstrates that the GRT-Transformer achieves an average recognition accuracy of 97.3% across three typical intrusion events—illegal tapping, mechanical operations, and vehicle passage—significantly reducing false alarms, surpassing traditional methods, and exhibiting strong practical potential in complex real-world scenarios. Full article

This study investigates the coupling mechanism between a parabolic notch and dislocations in one-dimensional (1D) hexagonal piezoelectric quasicrystals (PQCs) based on the theory of complex variable functions. By applying perturbation techniques and the Cauchy integral, analytical solutions for complex potentials are derived, yielding closed-form expressions for the phonon–phason stress field and electric displacement field. Numerical examples reveal several key findings: significant stress concentration occurs at the notch root, accompanied by suppression of electric displacement; interference patterns between dislocation cores and notch-induced stress singularities are identified; the J-integral quantifies distance-dependent forces, size effects, and angular force distributions reflecting notch symmetry; and the energy-driven dislocation slip toward free surfaces leads to the formation of dislocation-free zones. These results provide new insights into electromechanical fracture mechanisms in quasicrystals. Full article

The statistical mechanics of structured particles with arbitrary size and shape adsorbed onto discrete lattices presents a longstanding theoretical challenge, mainly due to complex spatial correlations and entropic effects that emerge at finite densities. Even for simplified systems such as hard-core linear k-mers, exact solutions remain limited to low-dimensional or highly constrained cases. In this review, we summarize the main theoretical approaches developed by our research group over the past three decades to describe adsorption phenomena involving linear k-mers—also known as multisite occupancy adsorption—on regular lattices. We examine modern approximations such as an extension to two dimensions of the exact thermodynamic functions obtained in one dimension, the Fractional Statistical Theory of Adsorption based on Haldane’s fractional statistics, and the so-called Occupation Balance based on expansion of the reciprocal of the fugacity, and hybrid approaches such as the semi-empirical model obtained by combining exact one-dimensional calculations and the Guggenheim–DiMarzio approach. For interacting systems, statistical thermodynamics is explored within generalized Bragg–Williams and quasi-chemical frameworks. Particular focus is given to the recently proposed Multiple Exclusion statistics, which capture the correlated exclusion effects inherent to non-monomeric particles. Applications to monolayer and multilayer adsorption are analyzed, with relevance to hydrocarbon separation technologies. Finally, computational strategies, including advanced Monte Carlo techniques, are reviewed in the context of high-density regimes. This work provides a unified framework for understanding entropic and cooperative effects in lattice-adsorbed polyatomic systems and highlights promising directions for future theoretical and computational research. Full article

The lead-cooled fast reactor (LFR), a Generation IV nuclear system candidate, presents unique neutronic characteristics distinct from pressurized water reactors. Its neutron spectrum spans wider energy ranges with fast neutron dominance, exhibiting resonance phenomena across energy regions. These features require a fine energy group structure for fuel lattice calculations, significantly increasing computational demands. To balance local heterogeneity modeling with computational efficiency, researchers across the world adopt fuel assembly equivalence methods using 1D cylindrical models through volume equivalence principles. This approach enables detailed energy group calculations in simplified geometries, followed by lattice homogenization for few-group parameter generation, effectively reducing whole-core computational loads. However, limitations emerge when handling strongly heterogeneous components like structural/control rods. This study investigates the 1D equivalence method’s accuracy in lead–bismuth fast reactors under various fuel assembly configurations. Through comprehensive analysis of material distributions and their equivalence impacts, the applicability of the one-dimensional equivalence approach to fuel assemblies of different geometries and material types is analyzed in this paper. The research further proposes corrective solutions for low-accuracy scenarios, enhancing computational method reliability. This paper is significant in its optimization of the physical calculation and analysis process of a new type of fast reactor component and has important engineering application value. Full article

Fabricating efficient photocatalysts that can be used in solar-to-fuel conversion and to enhance the photochemical reaction rate is essential to the current energy crisis and climate changes due to the excessive usage of nonrenewable fossil fuels. To attain high photo-to-chemical conversion efficiency, it is important to fabricate cost-effective and durable catalysts with high activity. One-dimensional cadmium sulfides (1D CdS), with higher surface area, charge carrier separation along the linear direction, and visible light harvesting properties, are promising candidates for converting solar energy to H₂, reducing CO₂ to commodity chemicals, and remediating environmental pollutants. The main disadvantage of CdS is photocorrosion due to the leaching of S²⁻ ions during the photochemical reactions, and further charge recombination rate leads to low quantum efficiency. Therefore, the implementation of core–shell heterostructured morphology, i.e., the growth of the shell on the surface of the 1D CdS, which offers unique features such as protection of CdS from photocorrosion, a tunable interface between the core CdS and shell, and photogenerated charge carrier separation via heterojunctions, provides additional active sites and enhanced visible light harvesting. Therefore, the viability of the core–shell synthesis strategy and synergetic effects offer a new way of designing photocatalysts with enhanced stability and improved charge separation in solar energy conversion systems. This review highlights some critical aspects of synthesizing 1D CdS core–shell heterostructures, underlying reaction mechanisms, and their performance in photoredox reactions. Finally, some challenges and considerations in the fabrication of 1D CdS-based core–shell nanostructures that can overcome the current barriers in industrial applications are discussed. Full article

This paper presents KlyH, a new 1D (one-dimensional) large-signal simulation software for klystrons, designed to deliver efficient and accurate simulation and optimization tools. KlyH integrates a Fortran-based dynamic link library (DLL) as its computational core, which employs high-performance numerical algorithms to rapidly compute critical parameters such as efficiency, gain, and bandwidth. Compared with traditional 1D simulation tools, which often lack open interfaces and extensibility, KlyH is built with a modular and open architecture that supports seamless integration with advanced optimization and intelligent design algorithms. KlyH incorporates multi-objective optimization frameworks, notably the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Optimized Multi-Objective Particle Swarm Optimization (OMOPSO), enabling automated parameter tuning for efficiency maximization and interaction length optimization. Its bandwidth-of-klystron-analysis module predicts gain and output power across operational bandwidths, with optimization algorithms further enhancing bandwidth performance. A Java-based graphical user interface (GUI) provides an intuitive workflow for parameter configuration and real-time visualization of simulation results. The open architecture also lays the foundation for future integration of artificial intelligence algorithms, promoting intelligent and automated klystron design workflows. The accuracy of KlyH and its potential for parameter optimization are confirmed by a case study on an X-band relativistic klystron amplifier. Discrepancies observed between 1D simulations and 3D PIC (three-dimensional particle-in-cell) simulation results are analyzed to identify model limitations, providing critical insights for advancing high-performance klystron designs. Full article

Background: Logistics and transport, core of many business processes, are continuously optimized to improve efficiency and market competitiveness. The paper describes a modular coordination of vehicle routing and bin packing problems that enables independent instances of the problems to be joined together, with the aim that the vehicle routing solution satisfies all the constraints from real-world applications. Methods: The vehicle routing algorithm is based on an adaptive memory procedure that also incorporates a simple, one-dimensional bin packing problem. This preliminary packing solution is refined by a complex, three dimensional bin packing for each vehicle to identify the infeasible packages. The method iteratively adjusts virtual volumes until reaching near-optimal routes that respect bin-packing constraints. Results: The coordination enables independent applications of an adaptive memory procedure to vehicle routing and a genetic algorithm approach to bin packing while joining them in a computationally tractable way. Such a coordinated approach is applied to a frequently used public benchmark and proven to provide commensurate costs while significantly lowering algorithm complexity. Conclusions: The proposed method is further validated on a real industrial case study and provided additional savings of 14.48% in average daily distance traveled compared to the current industrial standard. Full article

The present study focuses on the development of a diagnostic system for measuring radiated power and core soft X-ray intensity emissions with the goal of detecting a broad spectrum of photon energies emitted from the central plasma region of the DEMO tokamak. The principal objective of the diagnostic apparatus is to deliver a comprehensive characterization of the radiation emitted by the plasma, with a particular focus on estimating the radiated power from the core region. This measurement is essential for determining and monitoring the power crossing the separatrix, which is a critical parameter controlling overall plasma performance. Since diagnostics rely on line-integrated measurements, the application of tomographic reconstruction techniques is necessary to extract spatially resolved information on core plasma radiation. This contribution presents the development of numerical algorithms addressing the problem of radiation tomography reconstruction. A robust and computationally efficient method is proposed for reconstructing the spatial distribution of plasma radiated power, with a view toward enabling real-time applications. The reconstruction methodology is based on a linear model formulated using a set of predefined basis functions, which define the radiation distribution within a specified plasma cross-section. In the initial stages of emissivity reconstruction in tokamak plasmas, it is typically assumed that the radiation distribution is dependent on magnetic flux surfaces. As a baseline approach, the plasma radiative properties are considered invariant along these surfaces and can thus be represented as one-dimensional profiles parameterized by the poloidal magnetic flux. Within this framework, the reconstruction method employs an approximation model utilizing three sets of basis functions: (i) polynomial splines, as well as Gaussian functions with (ii) sigma parameters and (iii) position parameters. The performance of the proposed method was evaluated using two synthetic radiated power emission phantoms, developed for the DEMO plasma scenario. The results indicate that the method is effective under the specified conditions. Full article

The rapidly evolving field of functional yarns has garnered substantial research attention due to their exceptional potential in enabling next-generation electronic textiles for wearable health monitoring, human–machine interfaces, and soft robotics. Despite notable advancements, the development of yarn-based strain sensors that simultaneously achieve high flexibility, stretchability, superior comfort, extended operational stability, and exceptional electrical performance remains a critical challenge, hindered by material limitations and structural design constraints. Here, we present a bioinspired, hierarchically structured core-sheath yarn sensor (CSSYS) engineered through an efficient dip-coating process, which synergistically integrates the two-dimensional conductive MXene nanosheets and one-dimensional silver nanowires (AgNWs). Furthermore, the sensor is encapsulated using a yarn-based protective layer, which not only preserves its inherent flexibility and wearability but also effectively mitigates oxidative degradation of the sensitive materials, thereby significantly enhancing long-term durability. Drawing inspiration from the natural architecture of plant stems—where the inner core provides structural integrity while a flexible outer sheath ensures adaptive protection—the CSSYS exhibits outstanding mechanical and electrical performance, including an ultralow strain detection limit (0.05%), an ultrahigh gauge factor (up to 744.45), rapid response kinetics (80 ms), a broad sensing range (0–230% strain), and exceptional cyclic stability (>20,000 cycles). These remarkable characteristics enable the CSSYS to precisely capture a broad spectrum of physiological signals, ranging from subtle arterial pulsations and respiratory rhythms to large-scale joint movements, demonstrating its immense potential for next-generation wearable health monitoring systems. Full article