Search Results (921)

In a high-dimensional classification dataset, feature selection is crucial for improving classification performance and computational efficiency by identifying an informative subset of features while reducing noise, redundancy, and overfitting. This study proposes a novel metaheuristic-based ensemble feature selection approach by combining the complementary strengths of Modified Binary Particle Swarm Optimization (MBPSO) and Binary Dynamic Grey Wolf Optimization (BDGWO). The proposed MBPSO–BDGWO ensemble method is specifically designed for high-dimensional classification problems. The performance of the proposed MBPSO–BDGWO ensemble method was rigorously evaluated through an extensive simulation study under multiple high-dimensional scenarios with varying correlation structures. The ensemble method was further validated on several real datasets. Comparative analyses were conducted against single-stage feature selection methods, including BPSO, BGWO, MBPSO, and BDGWO, using evaluation metrics such as accuracy, the F1-score, the true positive rate (TPR), the false positive rate (FPR), the AUC, precision, and the Jaccard stability index. Simulation studies conducted under various dimensionality and correlation scenarios show that the proposed ensemble method achieves a low FPR, a high TPR/Precision/F1/AUC, and strong selection stability, clearly outperforming both classical and advanced single-stage methods, even as dimensionality and collinearity increase. In contrast, single-stage methods typically experience substantial performance degradation in high-correlation and high-dimensional settings, particularly BPSO and BGWO. Moreover, on the real datasets, the ensemble method outperformed all compared single-stage methods and produced consistently low MAD values across repetitions, indicating robustness and stability even in ultra-high-dimensional genomic datasets. Overall, the findings indicate that the proposed ensemble method demonstrates consistent performance across the evaluated scenarios and achieves higher selection stability compared with the single-stage methods. Full article

Horizontal wells combined with multi-stage fracturing are key techniques for extracting tight oil formation. However, due to the ultra-low permeability and porosity of reservoirs, energy depletion occurs rapidly, necessitating external supplements to sustain production. During the hydraulic fracturing process, large volumes of fracturing fluid are injected into reservoirs, increasing its pressure to a certain extent. However, due to the oil-wet nature of the formation, the fracturing fluid cannot penetrate the rock, failing to enhance oil recovery during the shut-in period. Surfactant-based nanofluids have been introduced as fracturing fluid additives to reverse rock wettability, thereby boosting imbibition-driven recovery. Although the imbibition has been studied to inspire the tight oil recovery, few studies have demonstrated the imbibition in enhanced fossil hydrogen energy, which further promotes the imbibition recovery. In this paper, complex nanofluid dispersions (CND) have been proved to enhance the tight reservoir pressure. Through contact angle and imbibition experiments, it is shown that CND can transform oil-wet rock to water-wet, reduce the adhesion of oil, and improve the ultimate oil recovery through the imbibition effect. Then, core flow testing experiments were conducted to show CND can decrease the flow resistance and improve the swept area of the injected fluid. In the end, pressure transmission tests were conducted to show CND can enhance the formation energy and production after fracturing. Results demonstrate that CND enables the fracturing fluid to travel further away from the hydraulic fractures, thus decreasing the depletion of tight formation pressure and maintaining a higher oil production rate. Results help optimize the design of the hydraulic fracturing of tight offshore reservoirs. Full article

Waterproofing systems frequently experience performance degradation during long-term service due to material aging and structural deformation, thereby necessitating localized repair interventions. The bonding interface between newly applied and existing membrane materials is a critical determinant of repair effectiveness. In this study, the aging-dependent repair performance of three representative waterproof membrane systems was systematically investigated using peel strength testing, low-temperature flexibility assessment, and interfacial morphology analysis under thermal–oxidative aging for 2, 5, 14, and 28 days. The results demonstrate that the homogeneous repair system based on ultra-thin reinforced self-adhesive polymer-modified bituminous membranes exhibits superior overall performance, maintaining the highest peel strength with only minor degradation even after 28 days of accelerated aging. In contrast, the polymeric butyl self-adhesive membrane subjected to homogeneous repair exhibited rapid adhesion degradation after 14 days, whereas the heterogeneous repair system showed improved stability during intermediate aging stages. Low-temperature flexibility testing further revealed that root-resistant bituminous membranes exhibited a slower aging rate, with a cracking temperature increase of 7 °C after 28 days, compared to a 10 °C increase observed for ultra-thin self-adhesive membranes. These quantitative findings provide clear guidance for the selection of appropriate repair membrane systems under varying aging conditions in waterproofing engineering, particularly for maintenance and rehabilitation applications. Full article

Accurate photovoltaic (PV) power forecasting is critical for the stable operation of power systems. Existing methods rely solely on historical data, which significantly decline in forecasting accuracy at 3–4 h ahead. To address this problem, a novel ultra-short-term PV power forecasting method based on temporal attention-variable parallel fusion encoder network is proposed to enhance the stability of forecasting results by incorporating Numerical Weather Prediction data to correct temporal predictions. Specifically, independent encoding modules are constructed for both historical power sequences and future NWP sequences, enabling deep feature extraction of their respective temporal characteristics. During the decoding phase, a two-stage coupled decoding strategy is employed: for 1–8 steps predictions, the model relies solely on temporal features, while for 9–16 steps horizons, it dynamically fuses encoded information from historical power data and future NWP inputs. This approach allows for accurate characterization of future trend dynamics. Experimental results demonstrate that, compared with conventional methods, the proposed model reduces the average normalized root mean square error (NRMSE) at 4th ultra-short-term forecasting by 0.50–5.20%, while it improves the $R^2$ by 0.047–0.362, validating the effectiveness of the proposed approach. Full article

The Peak Age of Information (PAoI) quantifies the freshness of updates used in cyber-physical systems (CPSs), realized within the Internet of Things (IoT) paradigm, encompassing devices, networks, and control algorithms. Consequently, PAoI is a critical metric for real-time applications enabled by Ultra-Reliable Low Latency Communication (URLLC). While highly useful for system evaluation, the direct analysis of this metric is complicated by the correlation between the random variables constituting the PAoI. Thus, it is often evaluated using only the mean value rather than the full distribution. Furthermore, since CPS communication technologies like Wi-Fi or DECT-2020 involve multiple processing stages, modeling them as tandem queueing systems is essential for accurate PAoI analysis. In this paper, we develop an analytical model for a DECT-2020 network segment represented as a two-phase tandem queueing system, enabling detailed PAoI analysis via Laplace–Stieltjes transforms (LST). We circumvent the dependence between generation and sojourn times by classifying updates into four mutually exclusive groups. This approach allows us to derive the LST of the PAoI and determine the exact Probability Density Function (PDF) for $M\left|M\right|1\to M\left|M\right|1$ system. We also calculate the mean and variance of the PAoIs and validate our results through numerical experiments. Additionally, we evaluate the impact of different service time distributions on PAoI variability. These findings contribute to the theoretical understanding of PAoI in tandem queueing systems and provide practical insights for optimizing DECT-2020-based communication systems. Full article

Incorporating coarse aggregates into ultra-high-performance concrete (UHPC-CA) can reduce material costs, yet reliably predicting its strength-related behavior and overall performance remains challenging. This study examines UHPC-CA through a two-stage orthogonal experimental program comprising 18 mixtures with coarse aggregate, fly ash, and hybrid fiber reinforcements (steel, polypropylene, and composite fibers). Microstructural characterization using scanning electron microscope (SEM) and X-ray computed tomography (X-CT) was conducted to assess interfacial features and crack evolution and to link these observations to the measured mechanical response. Experimentally, fiber reinforcement markedly enhanced post-cracking performance. Compared with the fiber-free control mixture, the optimal hybrid configuration increased flexural strength from 6.9 to 23.5 MPa and compressive strength from 60.1 to 90.5 MPa. The steel–composite fiber system outperformed the steel–polypropylene system, which is consistent with the tighter composite-fiber interfacial bonding observed by SEM/X-CT and supports the feasibility of partially substituting steel fibers. An artificial neural network (ANN) model trained on 50 mixtures and evaluated on 10 unseen mixtures achieved an R² of 0.9703, an MAE of 1.22 MPa, and an RMSE of 2.11 MPa for compressive strength prediction, enabling sensitivity assessment under multi-factor coupling. Overall, the proposed experiment–characterization–modeling framework provides a data-driven basis for performance-oriented mix design and rapid screening of UHPC-CA. Full article

This study aimed to elucidate the structure–activity relationship between the structural characteristics of three plant-derived polysaccharides, Lycium barbarum polysaccharide (LBP), citrus pectin (CP) and peach gum polysaccharide (PGP), and their prebiotic functionalities. Structural analysis indicated that LBP exhibited a medium molecular weight and was rich in galactose and rhamnose, which contributed to its high uronic acid content, strong antioxidant activity, and sustained fermentation profile with enhanced butyrate production. In contrast, CP, with its low molecular weight and neutral linear glucan backbone, was rapidly utilized by gut microbiota, leading to accelerated propionate accumulation. Meanwhile, PGP, characterized by an ultra-high molecular weight and a highly branched arabinogalactan configuration, acted as a specific substrate that promoted mid- to late-stage fermentation and significantly increased butyrate yield, highlighting its prebiotic property driven by structural complexity. The functional differences among these polysaccharides were determined by their monosaccharide composition, molecular weight distribution, and chain conformation. These findings provide a scientific basis for the targeted development of plant-derived prebiotics aimed at specific metabolic functions. Full article

Positron Emission Tomography (PET) is undergoing a profound transformation. Driven by the convergence of highly sensitive long-axial field-of-view (LAFOV) total-body PET systems and an expanding portfolio of targeted radiopharmaceuticals, PET is progressively evolving beyond its traditional role in oncologic diagnosis and staging. Ultra-sensitive scanners enable whole-body imaging with markedly reduced radiotracer doses, rapid acquisition times, and true dynamic multiparametric imaging across all organs simultaneously. In parallel, molecularly targeted radioligands support tumour phenotyping, theranostic applications, and personalized dosimetry. Together, these advances position PET as a systemic imaging platform capable of interrogating whole-body tumour biology, guiding precision therapies, and potentially enabling early detection or surveillance strategies in selected high-risk populations. This narrative review summarizes the technological foundations of total-body PET, reviews current clinical and translational applications, discusses opportunities and limitations for early detection and surveillance, and outlines a research and implementation roadmap to responsibly translate this paradigm into clinical oncology. Full article

Metal Injection Molding (MIM) is an established, high-precision manufacturing route for small, geometrically complex metallic components, integrating polymer injection molding with powder metallurgy. State-of-the-art feedstock systems, such as Catamold (polyacetal-based), enable catalytic debinding performed in furnaces operating under ultra-high-purity nitric acid atmospheres (>99.999%). The subsequent thermal stages pre-sintering and sintering are carried out in continuous controlled-atmosphere furnaces or vacuum systems, typically employing inert (N₂) or reducing (H₂) atmospheres to meet the specific thermodynamic requirements of each alloy. However, incomplete decomposition or secondary volatilization of binder residues can lead to progressive hydrocarbon accumulation within the sinering chamber. These contaminants promote undesirable carburizing atmospheres, which, under austenitizing or intercritical conditions, increase carbon diffusion and generate uncontrolled surface carbon gradients. Such effects alter the microstructural evolution, hardness, wear behavior, and mechanical integrity of MIM steels. Conversely, inadequate dew point control may shift the atmosphere toward oxidizing regimes, resulting in surface decarburization and oxide formation effects that are particularly detrimental in stainless steels, tool steels, and martensitic alloys, where surface chemistry is critical for performance. This review synthesizes current knowledge on atmosphere-induced surface deviations in MIM steels, examining the underlying thermodynamic and kinetic mechanisms governing carbon transport, oxidation, and phase evolution. Strategies for atmosphere monitoring, contamination mitigation, and corrective thermal or thermochemical treatments are evaluated. Recommendations are provided to optimize surface substrate interactions and maximize the functional performance and reliability of MIM-processed steel components in demanding engineering applications. Full article

Vehicular ad hoc networks (VANETs) must simultaneously satisfy stringent reliability, latency, and sustainability targets under highly dynamic urban and highway mobility. Existing solutions typically optimise one or two dimensions (link stability, clustering, or energy) but lack an integrated, adaptive mechanism that fuses heterogeneous metrics while remaining lightweight and deployable. This paper introduces a VANET routing protocol named SYMPHONY (Synergistic Hierarchical Metric-Fusion and Predictive Hybrid Optimization for Network Yield) that operates in three coordinated layers: (i) a compact neighbourhood filtering stage that reduces forwarding scope and eliminates transient relays, (ii) a cluster layer that elects resilient cluster heads using fuzzy energy-aware metrics and backup leadership, and (iii) a global inter-cluster optimizer that blends a GA-reseeded swarm metaheuristic with a stability-aware pheromone scheme to produce multi-objective routes. Crucially, SYMPHONY employs an ultra-lightweight online weight-adaptation module (contextual linear bandit) to tune metric fusion weights in response to observed rewards (packet delivery ratio, end-to-end delay, and Green Performance Index). We evaluated the proposed routing protocol SYMPHONY versus strong modern baselines across urban and highway scenarios with varying density and resource constraints. The results demonstrate that SYMPHONY improves packet delivery ratio by up to 12–18%, reduces latency by 20–35%, and increases the Green Performance Index by 22–45% relative to the best baseline, while keeping control overhead and per-node computation within practical bounds. Full article

Background/Objectives: Immunotherapy has significantly improved clinical outcomes for patients with late-stage melanoma, yet a substantial portion of patients fail to respond to these treatments. The variability in responses to immunotherapy, both among individual patients and across different melanoma subtypes, underscores the need to explore the influence of circulating factors such as oxylipins on therapeutic outcomes. This study investigated the relationship between serum oxylipin profiles and response to immune checkpoint inhibitor therapy in melanoma subtypes to identify potential metabolic biomarkers for treatment response. Methods: In a retrospective cohort study, serum samples from 43 stage III and stage IV melanoma patients treated at the University of Colorado Hospital from 2010 to 2023 were analyzed via ultra-high-pressure liquid chromatography-mass spectrometry. Melanoma patients were treated with anti-PD-1 monotherapy or combination immune checkpoint inhibitor therapy, and response was assessed using RECIST 1.1 criteria. Results: We determined that global oxylipin metabolite profiles are largely uniform pre- and post-treatment across melanoma subtypes, including cutaneous, acral, mucosal, and uveal melanoma. Prostaglandin J₂ was more abundant in rare melanoma subtypes, including acral, mucosal, and uveal melanoma, compared to cutaneous melanoma. Conclusions: Despite limited variation in serum oxylipin molecular species by subtype and response status, we observed significant differences in prostaglandin J₂, which could serve as a potential biomarker for immune checkpoint inhibitor therapy response in melanoma. Full article

China is the world’s largest producer of acrylic fiber, and the wastewater generated from its production contains a significant amount of biologically refractory organic pollutants. However, comprehensive screening studies on organic compounds in such wastewater remain limited, which hampers effective wastewater treatment and ecological risk management to some extent. In this study, high-resolution mass spectrometry (HRMS) was combined with comprehensive two-dimensional gas chromatography (GC×GC) and ultra-performance liquid chromatography, along with multiple characterization techniques—including proton nuclear magnetic resonance spectroscopy, infrared spectroscopy, and fluorescence spectroscopy—to qualitatively analyze organic compounds present in wastewater from four stages of wet-spun acrylic fiber production: acrylonitrile mixed wastewater, polymerization wastewater, spinning wastewater, and final mixed wastewater. The results indicated that sulfonate esters, various other esters, alkanes, heterocyclic compounds, aromatic compounds, and substances containing multiple conjugated systems were commonly present across all four sample types, potentially contributing to the poor biodegradability of the wastewater. Additionally, a higher abundance of volatile organic compounds was detected in the mixed wastewater, while acrylonitrile appeared to be more concentrated in the spinning wastewater. The complementary use of spectral analysis, proton nuclear magnetic resonance, and HRMS provided a robust analytical foundation for identifying organic pollutants in acrylic fiber production wastewater. Full article

We report on the realization of a platform for trapping and manipulating individual ⁸⁸Sr atoms in optical tweezers. A first cooling stage based on a blue shielded magneto-optical trap (MOT) operating on the ${|}^1{\mathrm{S}}_0⟩\to {|}^1{\mathrm{P}}_1⟩$ transition at $461 \mathrm{n}\mathrm{m}$ enables us to trap approximately 4 × 10⁶ atoms at a temperature of 6.8 mK. Further cooling is achieved in a narrow-line red MOT using the ${|}^1{\mathrm{S}}_0⟩\to {|}^3{\mathrm{P}}_1⟩$ intercombination transition at 689 nm, bringing 5 × 10⁵ atoms down to $5\mu \mathrm{K}$ and reaching a density of 4 × 10¹⁰ cm³. Atoms are then loaded into 813 nm tweezer arrays generated by crossed acousto-optic deflectors and tightly focused onto the atoms with a high-numerical-aperture objective. Through light-assisted collision processes we achieve the collisional blockade, which leads to single-atom occupancy with a probability of about 50%. The trapped atoms are detected via fluorescence imaging with a fidelity of $99.986\left(6\right)\%$, while maintaining a survival probability of $97\left(2\right)\%$. The release-and-recapture measurement provides a temperature of $12.92\left(5\right)\mu \mathrm{K}$ for the atoms in the tweezers, and the ultra-high-vacuum environment ensures a vacuum lifetime higher than 7 min. These results demonstrate a robust alkaline-earth tweezer platform that combines efficient loading, cooling, and high-fidelity detection, providing the essential building blocks for scalable quantum simulation and quantum information processing with Sr atoms. Full article

At the current stage, indoor Ultra-Wideband (UWB) positioning systems often encounter challenges in achieving high localization accuracy under non-line-of-sight (NLOS) conditions within workshop environments when employing the Double-Sided Two-Way Ranging (DS-TWR) algorithm. To address this issue, a positioning optimization method based on the DS-TWR algorithm is proposed. By streamlining message exchanges between nodes, the method reduces node energy consumption and shortens ranging time, thereby enhancing system energy efficiency and response speed. Furthermore, to improve positioning accuracy in workshop NLOS environments, an Adaptive Kalman Filtering algorithm is introduced. This algorithm dynamically evaluates the influence of obstruction information caused by NLOS conditions on the covariance of observation noise and adaptively adjusts the filtering gain of the signals accordingly. Through this approach, the system can effectively eliminate invalid positioning information in signals, mitigate the adverse effects of NLOS conditions on positioning accuracy and achieve more precise localization. Experimental results demonstrate that the proposed optimization algorithm achieves substantial performance improvements in both static and dynamic positioning experiments under workshop NLOS conditions. Specifically, the algorithm not only enhances system positioning accuracy but also further strengthens the real-time ranging precision of the DS-TWR algorithm. Full article

Based on the modified Hamilton–Ostrogradsky principle, a mathematical model of a distributed-parameter high-voltage HVAC line that includes lightning shield wires is proposed. A partial differential equation of a five-wire power line is produced as a result. Therefore, a methodology for looking for boundary conditions of a long line equation in the five-wire version is proposed here. A mathematical model is introduced as an example of a section of a power line that consists of a high-voltage long line that includes shield wires operating in an equivalent concentrated-parameter power system presented in its circuit version. The system is described with both partial and ordinary derivative differential equations. Poincaré boundary conditions of the third type are applied to solve the state equations of the object discussed. A discrete line model is thus presented, described with ordinary differential equations based on the well-known straight-line method. Transient processes across the system are analysed exactly at the moment of a lightning strike against a shield wire in the middle section of the line. To this end, a mathematical lightning strike model is developed by means of cubic spline interpolation. The original system of differential equations is integrated into the implicit Euler method, considering the Seidel method. The end results of the computer simulation are presented graphically and analysed. The results show the effectiveness of the proposed method of analysing transients across ultra-high-voltage lines that include lightning protection wires and can serve as accurate calculations of power supply lightning protection at the stages of design and production. Full article