Search Results (169)

As a key node of the energy internet, the park-level integrated energy system undertakes the dual functions of improving energy supply reliability and promoting low-carbon development in the transformation of the global energy structure. The need to simultaneously meet terminal energy demand and market regulation requirements constrains operational optimization due to factors such as energy price fluctuations. Future research should focus on supply–demand coordination mechanisms and energy efficiency improvement strategies to advance the high-quality development of such systems. To this end, this study constructs a collaborative optimization framework integrating demand response based on a dual-compensation mechanism and dynamic multi-energy pricing and incorporates it into a Stackelberg game-based low-carbon economic dispatch model. By incorporating a dynamic multi-energy pricing mechanism, the model coordinates and optimizes the interests of the upper-level park integrated energy system operator (PIESO) and the lower-level park users. On the supply side, the model couples a two-stage power-to-gas (P2G) device with a stepwise carbon trading mechanism, forming a low-carbon dispatch system enabling source–grid–load coordination. On the demand side, an integrated demand response mechanism with dual compensation is introduced to enhance the coupling intensity of multi-energy flows and the adjustability of price elasticity. The simulation results show that, compared with traditional models, the proposed optimization framework achieves improvements in three dimensions: carbon emissions, economic benefits, and user costs. Specifically, the carbon emission intensity is reduced by 28.04%, the operating income of the PIESO is increased by 29.53%, and the users’ energy consumption cost is decreased by 13.05%, which verifies the effectiveness and superiority of the proposed model. Full article

Researchers have devised numerous methods to model intricate behaviors in phenomena that unfold in multiple stages. This work focuses on a specific category of piecewise hybrid terminal systems characterized by delay. To account for hereditary memory effects, which are absent in standard integer-order systems, our framework partitions the time interval into two distinct phases. The initial phase employs the classical derivative, while the subsequent phase utilizes the Atangana–Baleanu–Caputo (ABC) fractional derivative. We establish conditions that guarantee both the existence and uniqueness of solutions through the application of suitable fixed-point arguments. Furthermore, Hyers–Ulam (H-U) stability is investigated to ascertain the robustness and reliability of the derived solutions. To exemplify these theoretical findings, we present a fractional-order tuberculosis treatment model that incorporates the development of drug resistance, alongside a general numerical example. Numerical simulations reveal that changes in the fractional order influence the dynamic behavior of the disease. Full article

Composite interphase spacers are essential components in ultra-high-voltage (UHV) transmission lines to suppress conductor galloping. This study investigates the first reported case of a core-rod fracture in a 500 kV composite spacer and elucidates its degradation mechanism through multi-scale characterization, electrical testing combined and electric field and mechanical simulation. Macroscopic inspection and industrial computed tomography (CT) show that degradation initiated at the unsheltered high-voltage sheath–core interface and propagated axially, accompanied by continuous interfacial cracks and void networks whose volume ratio gradually decreased along the spacer. Material characterizations indicate moisture-driven glass-fiber hydrolysis, epoxy oxidation, and progressive interfacial debonding. Leakage current test further indicates humidity-sensitive conductive paths in the degraded region, confirming the presence of moisture-activated interfacial channels. Electric-field simulations under two shed configurations demonstrated that local field intensification was concentrated within 20–30 cm of the HV terminal, where the sheath and core surface fields increased by approximately 9.3% and 5.5%. Mechanical modeling demonstrates a pronounced bending-induced stress concentration at the same end region. The combined effects of moisture ingress, electrical stress, mechanical loading, and chemical degradation lead to the decay-like fracture. Improving sheath hydrophobicity, enhancing interfacial bonding, and optimizing end-fitting geometry are recommended to mitigate such failures and ensure the long-term reliability of UHV composite interphase spacers. Full article

Tidal flats play a vital role in coastal ecosystems by supporting biodiversity, mitigating natural hazards, and functioning as blue carbon reservoirs. However, monitoring their geomorphological changes remains challenging due to high turbidity, shallow depths, and tidal variability. Conventional approaches—such as satellite remote sensing, acoustic sounding, and topographic LiDAR—face limitations in resolution, accessibility, or coverage of submerged areas. Airborne bathymetric LiDAR (ABL), which uses green laser pulses to detect reflections from both the water surface and seabed, has emerged as a promising alternative. Unlike traditional discrete-return data, full waveform analysis offers greater accuracy, resolution, and reliability, enabling more flexible point cloud generation and extraction of additional signal parameters. A critical step in ABL processing is waveform decomposition, which separates complex returns into individual components. Conventional methods typically assume fixed models with three returns (water surface, water column, bottom), which perform adequately in clear waters but deteriorate under shallow and turbid conditions. To address these limitations, we propose an adaptive progressive Gaussian decomposition (APGD) tailored to tidal flat environments. APGD introduces adaptive signal range selection and termination criteria to suppress noise, better accommodate asymmetric echoes, and incorporates a water-layer classification module. Validation with datasets from Korea’s west coast tidal flats acquired by the Seahawk ABL system demonstrates that APGD outperforms both the vendor software and the conventional PGD, yielding higher reliability in bottom detection and improved bathymetric completeness. At the two test sites with different turbidity conditions, APGD achieved seabed coverage ratios of 66.7–70.4% and bottom-classification accuracies of 97.3% and 96.7%. Depth accuracy assessments further confirmed that APGD reduced mean depth errors compared with PGD, effectively minimizing systematic bias in bathymetric estimation. These results demonstrate APGD as a practical and effective tool for enhancing tidal flat monitoring and management. Full article

In multibeam low-Earth-orbit (LEO) satellite systems, precoding has emerged as a key technology for mitigating co-channel interference (CCI) and for improving spectral efficiency (SE). However, its practical implementation is challenged by the difficulty of acquiring reliable instantaneous channel state information (iCSI) and by the high computational complexity induced by large-scale antenna arrays, making it incompatible with fixed codebook-based beamforming schemes commonly adopted in operational systems. In this analysis, we propose a beamspace transmission framework leveraging statistical CSI (sCSI) and achieves reduced computational complexity compared with antenna-domain precoding designs. Specifically, we first propose a low-complexity beam selection algorithm that selects a small subset of beams for each user terminal (UT) from a fixed beamforming codebook, using only the UTs’ two-dimensional (2D) angular information. To suppress CCI among beams, we then derive a beamspace weighted minimum mean square error (WMMSE) precoding scheme based on the equivalent beamspace channel matrix. The derivation employs an sCSI-based WMMSE (sWMMSE) formulation derived from an upper bound approximation of the ergodic sum rate, which provides a tighter estimate than the expected mean square error (MSE)-based lower bound approximation. Simulation results demonstrate that the proposed sCSI-based beamspace transmission scheme achieves a favorable trade-off between performance and computational complexity. Full article

To address the conflicts between traditional composite slab reinforcement layouts and supports—which adversely affect construction quality and efficiency—and to fill the theoretical gap regarding end connections without projecting bars in terms of interface shear transfer, staged flexural behavior, and anchorage reliability, a grooved end-connection configuration for composite slabs is proposed. In this configuration, the longitudinal bars of the precast slab do not extend beyond the slab end. The precast slab end is formed with a recessed–protruding profile; the longitudinal bars are exposed within the groove, where additional reinforcement is pre-embedded (with a diameter not less than the area-equivalent of the longitudinal bars that would otherwise extend into the support). After erection, the additional bars are extended using straight-thread sleeves; short longitudinal bars within the groove are tied to the bottom longitudinal bars. Both the extended additional bars and the short longitudinal bars are anchored into the support by at least 5d and pass the support centerline. To evaluate the global flexural behavior of slabs with grooved end-connections, a two-span, full-scale specimen was tested under static loading. Failure characteristics, crack initiation and propagation, ultimate capacity, deflection, and ductility were investigated. The results indicate that, in the full-scale two-span test, the service load was 11.35 kN/m² (approximately 13.5% higher than the design value of 10.0 kN/m²); the midspan deflection was about L/110 (smaller than the L/50 limit); the first cracking and the pronounced nonlinearity inflection point occurred at approximately 4.25 kN/m² and ≥9.35 kN/m², respectively; and the maximum crack width was 1.66 mm. The test was terminated prior to reaching the durability and deformation limits, after which the load was increased to 22.20 kN/m². The specimen exhibited a ductile flexural failure governed by tensile reinforcement yielding; the top concrete did not crush, no shear failure was observed at the ends, and no delamination occurred at the composite interface, demonstrating favorable global flexural performance. Full article

With the rapid growth of e-commerce and rising demand for faster, reliable last mile delivery, optimizing the spatial layout of terminal logistics facilities is critical. This paper proposes a two-stage location framework for mini-depots and lockers considering spatiotemporal customer demand. In the first stage, Affinity Propagation (AP) clustering identifies candidate mini-depot locations and locker layouts based on temporal and spatial demand characteristics. In the second stage, an Adaptive Heuristic Electric Eel Foraging Optimization (AHEEFO) determines the optimal mini-depot location strategy to minimize total cost. A dataset of 1157 Beijing customer points, including latitude, longitude and demand information, is used for model validation. Results show that Scenario 2, with dispersed demand, outperforms Scenario 1 and traditional strategies in both total cost and customer satisfaction; dispersed demand can be effectively supported via crowdsourced delivery and locker layout, whereas concentrated demand requires more professional courier resources. Comparative experiments reveal AP clustering is more stable, reducing clustering-stage cost by 13.57% compared with K-means, and AHEEFO outperforms other algorithms in cost optimization, computational efficiency, and significance tests under random demand surges. Finally, the sensitivity analysis highlights the effects of different algorithmic and operational parameters, offering valuable insights for both managerial practice and academic research. Full article

The standing seam metal roof system is wind-sensitive due to its light weight and decreasing stiffness as the span increases, and in recent years there have been a number of wind-exposed damages to the structures where these roof systems have been applied. In order to study the wind-uplifted resistance reliability of different types of standing seam metal roof systems, and then to evaluate their safety level, a reliability analysis framework was developed. The proposed approach integrates the Latin Hypercube Sampling–Monte Carlo Simulation (LHS–MCS) method to assess the wind-uplifted resistance reliability of standing seam metal roof systems. Taking Jinan Yaoqiang International Airport Terminal Building’s standing seam Al-Mg-Mn roof system and Urumqi Tianshan International Airport Transportation Center’s standing seam Al-Zn-plated steel roof system as the objects of research, the research was carried out from the aspects of wind uplift test, wind tunnel test, finite element simulation, and wind-uplifted resistance reliability analysis. The study shows the following: the wind-uplifted resistance bearing capacity of the roof systems is significantly affected by the width of the roof panel, the spacing of the fixed support, the thickness of the roof panel, and the diameter of end interlocking; the effects of the differences in structural parameters and roof types are eliminated by the introduction of a damage index, and the failure forms of different types of roof systems can be unified, and the corresponding limit state function can then be deduced; based on the LHS–MCS method, the reliability indexes of the two common types of standing seam metal roof systems were obtained to be 3.0975 and 3.2850, respectively, which are lower than the requirements of the code for the first safety level, and it is recommended that reinforcement measures be prioritized at the connection points between roof panel and support, such as reducing the spacing of the fixed support or decreasing the diameter of end interlocking, to improve the structural safety. The above study can provide a reference for the safety level assessment, wind resistant design, and sustainable operation and maintenance of different types of standing seam metal roof systems. Full article

We report an efficient, high-yield method for synthesizing dumbbell-shaped conjugates composed of gold nanoparticles (AuNPs) connected by double-stranded (ds) DNA. The dsDNA, bearing terminal thiol groups, was covalently attached to two AuNPs to form uniform constructs comprising either 15 nm or 25 nm particles bridged by 38 base pairs (bp) or 100 bp dsDNA. The dumbbells were purified by gel electrophoresis and exhibited high stability, remaining intact for several days in pure water or buffers at ambient temperature. Deposition onto solid substrates followed by drying, however, led to their partial structural collapse. TEM imaging showed that deposition on carbon grids typically yielded dumbbell structures with interparticle gaps of only 1–2 nm, suggesting that the dsDNA bridge contracts during deposition and drying. However, deposition on polylysine-coated mica for AFM imaging preserved the native geometry, with the gaps consistent with the expected DNA length. Our results reveal that deposition significantly affects the structure and integrity of dsDNA bridges in dumbbell constructs, highlighting the importance of appropriate substrate and surface coating selection for reliable characterization of DNA properties in dried dumbbells. Full article

In Low Earth Orbit (LEO) satellite communication systems, providing reliable data transmission for ultra-high-speed mobile terminals faces severe challenges from dramatic Doppler effects and fast time-varying channels. Orthogonal Time Frequency Space (OTFS) modulation is a promising technique for high-mobility Low Earth Orbit (LEO) satellite communications, but its performance is often limited by inaccurate Channel State Information (CSI) prediction and suboptimal resource allocation, particularly in dynamic channels with coupled parameters like SNR, Doppler, and delay. To address these limitations, this paper proposes an adaptive OTFS frame configuration scheme based on multi-domain channel prediction. We utilize a Long Short-Term Memory (LSTM) network to jointly predict multi-dimensional channel parameters by leveraging their temporal correlations. Based on these predictions, the OTFS transmitter performs two key optimizations: dynamically adjusting the pilot guard bands in the Delay-Doppler domain to reallocate guard resources to data symbols, thereby improving spectral efficiency while maintaining channel estimation accuracy; and performing optimal power allocation based on predicted sub-channel SNRs to minimize the system’s Bit Error Rate (BER). The simulation results show that our proposed scheme reduces the required SNR for a BER of $1\times {10}^{-3}$ by approximately 1.5 dB and improves spectral efficiency by 10.5% compared to baseline methods, demonstrating its robustness and superiority in high-mobility satellite communication scenarios. Full article

This paper presents a comprehensive reliability analysis framework for the Chris–Jerry (CJ) lifetime distribution under an improved adaptive progressive Type-II censoring plan. The CJ model, recently introduced to capture skewed lifetime behaviors, is studied under a modified censoring structure designed to provide greater flexibility in terminating life-testing experiments. We derive maximum likelihood estimators for the CJ parameters and key reliability measures, including the reliability and hazard rate functions, and construct approximate confidence intervals using the observed Fisher information matrix and the delta method. To address the intractability of the likelihood function, Bayesian estimators are obtained under independent gamma priors and a squared-error loss function. Because the posterior distributions are not available in closed form, we apply the Metropolis–Hastings algorithm to generate Bayesian estimates and two types of credible intervals. A comprehensive simulation study evaluates the performance of the proposed estimation techniques under various censoring scenarios. The framework is further validated through two real-world datasets: one involving rainfall measurements and another concerning mechanical failure times. In both cases, the CJ model combined with the proposed censoring strategy demonstrates superior fit and reliability inference compared to competing models. These findings highlight the value of the CJ distribution, together with advanced censoring methods, for modeling lifetime data in physics and engineering applications. Full article

To enhance the energy efficiency of liquefied natural gas (LNG) terminals, this study developed a full-process dynamic simulation model using Aspen HYSYS (hereinafter referred to as HYSYS) to accurately replicate the time-varying energy consumption characteristics of key processes, including unloading, tank boil-off gas (BOG) management, recondensation, and vaporization for send-out. Through dynamic analysis of the impact of different operating conditions on the energy consumption of critical equipment, methane content and compressor outlet pressure were identified as sensitive factors, and multivariable interaction effects were quantified. Combining the Particle Swarm Optimization (PSO) algorithm to optimize equipment operating parameters and incorporating constraints such as equipment start-stop frequency and flare emissions, process improvements were achieved, including intelligent pre-cooling during unloading, multi-mode vaporization coupling, and model predictive control for storage tanks. Safety response logic under extreme conditions was also enhanced. Field validation results show that the optimized system reduces total energy consumption by 18.5%, with a relative error between simulated and field data of ≤13%. Daily equipment start-stop cycles decreased from five to two times, and flare emissions were reduced from 25 kg/h to 12 kg/h. Within a 95% confidence interval, the total energy consumption prediction fluctuated by ±4.2%, demonstrating good model stability. This study provides reliable technical support for energy-efficient operation of LNG terminals. The proposed multivariable interaction analysis and safety control strategies under extreme conditions further enhance the engineering applicability of the optimization framework. Full article

Network reliability refers to a probabilistic measure of a network system’s ability to maintain its intended service functionality within a specified time interval and under given operating conditions. Let $\Omega (n,m)$ be the set of all simple two-terminal networks on n vertices and m edges. If each edge operates independently with the same fixed probability $p\in [0,1]$, then the two-terminal reliability, denoted by $R_2(G,P))$, is the probability that there exists a path between two target vertices s and t. For a given number of vertices n and edges m, there are some graphs within $\Omega (n,m)$ that have higher reliability than others, and these are known as extremely optimal graphs. In this work, we determine the sets of extremely optimal graphs in two classes of two-terminal network with sizes $m={\displaystyle \frac{n(n-1))}{2}}-2$ and $m={\displaystyle \frac{n(n-1))}{2}}-3$, consisting of 2 and 5 networks, respectively. Moreover, we identify one class of graphs obtained by deleting some edges among non-target vertices in the complete two-terminal graph, and we count the number of graphs of this class with size ${\displaystyle \frac{n(n-1)}{2}}-\lfloor {\displaystyle \frac{n-2}{2}}\rfloor \le m\le {\displaystyle \frac{n(n-1))}{2}}-1$ by applying the Pólya counting principle. Full article

Smart logistics systems generate massive amounts of data, such as images and videos, requiring real-time processing in edge clusters. However, the edge cluster systems face performance bottlenecks in reception and forwarding high-concurrency data streams from numerous smart terminals, resulting in degraded processing efficiency. To address this issue, a novel high-performance data stream model called CBPS-DPDK is proposed. CBPS-DPDK integrates the DPDK framework from Intel corporations with a content-based publish/subscribe model enhanced by semantic filtering. This model adopts a three-tier optimization architecture. First, the user-space data plane is restructured using DPDK to avoid kernel context switch overhead via zero-copy and polling. Second, semantic enhancement is introduced into the publish/subscribe model to reduce the coupling between data producers and consumers through subscription matching and priority queuing. Finally, a hierarchical load balancing strategy ensures reliable data transmission under high concurrency. Experimental results show that CBPS-DPDK significantly outperforms two baselines—OSKT (kernel-based data forwarding) and DPDK-only (DPDK). Relative to the OSKT baseline, DPDK-only achieves improvements of 37.5% in latency, 11.1% in throughput, and 9.1% in VMAF; CBPS-DPDK further increases these to 51.8%, 18.3%, and 11.2%, respectively. In addition, compared with the traditional publish–subscribe system NATS, CBPS-DPDK maintains lower delay, higher throughput, and more balanced CPU and memory utilization under saturated workloads, demonstrating its effectiveness for real-time, high-concurrency edge scenarios. Full article

In automated container terminals (ACTs), rolling bearings of equipment serve as crucial power transmission components, and their performance directly determines the operational efficiency, reliability, and service life of the entire equipment. Rolling bearing fault detection and diagnosis are key means to improve production efficiency, reduce the safety risks, and achieve sustainable development of equipment in ACTs. However, existing rolling-bearing diagnosis models are vulnerable to environmental noise and interference, depressing accuracy and raising misclassification, and they seldom achieve both noise robustness and a lightweight design; robustness usually increases complexity, while compact networks degrade under low signal-to-noise ratios. Therefore, this paper proposes a noise-robust, lightweight, and interpretable deep learning framework for fault detection and diagnosis of rolling bearings in automated container terminal (ACT) equipment. The framework comprises four coordinated components, including Time-Domain Filter, Frequency-Domain Filter, Physical-Feature Extraction module, and Classification module, whose joint optimization yields complementary time–frequency representations and physics-aligned features, and fuses into robust diagnostic decisions under noisy and non-stationary environments. The first component highlights impulsive transients, the second component emphasizes harmonic and sideband modulation, the third module introduces two differentiable and rolling bearing-signal-informed objectives to align learning with characteristic bearing signatures by weighted-average kurtosis and an Lp/Lq-based envelope-spectral concentration index, and the last module integrates multi-layer convolutional neural networks (CNN) and Deep Kolmogorov–Arnold Networks (DeepKAN). Finally, two public datasets are employed to estimate the model’s performance, and results indicate that the proposed method outperforms others. Full article