Search Results (3,085)

Feature selection in high-dimensional datasets presents significant computational challenges, particularly in domains with large feature spaces and limited sample sizes. This paper introduces FL-SBA, a novel metaheuristic algorithm integrating fractional calculus enhancements with Laguerre operators into the Secretary Bird Optimization Algorithm framework for binary feature selection. The methodology incorporates fractional opposition-based learning utilizing Laguerre operators for enhanced population initialization with non-local memory characteristics, and a Laguerre-based binary transformation function replacing conventional sigmoid mechanisms through orthogonal polynomial approximation. Fractional calculus integration introduces memory effects that enable historical search information retention, while Laguerre polynomials provide superior approximation properties and computational stability. Comprehensive experimental validation across ten high-dimensional gene expression datasets compared FL-SBA against standard SBA and five contemporary methods including BinCOA, BAOA, BJSO, BGWO, and BMVO. Results demonstrate FL-SBA’s superior performance, achieving 96.06% average classification accuracy compared to 94.41% for standard SBA and 82.91% for BinCOA. The algorithm simultaneously maintained exceptional dimensionality reduction efficiency, selecting 29 features compared to 40 for competing methods, representing 27% improvement while achieving higher accuracy. Statistical analysis reveals consistently lower fitness values (0.04924 averages) and stable performance with minimal standard deviation. The integration addresses fundamental limitations in integer-based computations while enhancing convergence behavior. These findings suggest FL-SBA represents significant advancement in metaheuristic-based feature selection, offering theoretical innovation and practical performance improvements for high-dimensional optimization challenges. Full article

Galactic cosmic ray (GCR) radiation is a major barrier to human space exploration beyond Earth’s magnetic field protection. Mesenchymal stem cells (MSCs) are found in all organs and play a critical role in repair and regeneration of tissue. We engineered bone marrow-derived MSCs and evaluated their response to ionizing radiation exposure. Epidermal growth factor receptor (EGFR) expression by certain types of cancers has been shown to induce radioresistance. In this study, we tested the feasibility of transfecting MSCs to overexpress EGFR (eMSC-EGFR) and their capacity to tolerate and recover from X-ray exposure. Quantitative real-time PCR (qRT-PCR) and immunoblotting results confirmed the efficient transfection of EGFR into MSCs and EGFR protein production. eMSC-EGFR maintained characteristics of human MSCs as outlined by the International Society for Cell & Gene Therapy. Then, engineered MSCs were exposed to various dose rates of X-ray (1–20 Gy) to assess the potential radioprotective role of EGFR overexpression in MSCs. Post-irradiation analysis included evaluation of morphology, cell proliferation, viability, tumorigenic potential, and DNA damage. eMSC-EGFR showed signs of radioresistance compared to naïve MSCs when assessing relative proliferation one week following exposure to 1–8 Gy X-rays, and significantly lower DNA damage content 24 h after exposure to 4 Gy. We establish for the first time the efficient generation of EGFR overexpressing MSCs as a model for enhancing the human body to tolerate and recover from moderate dose radiation injury in long-term manned space travel. Full article

As large common areas, cruise ship atriums affect passenger comfort and HVAC efficiency. Due to their complexity and high occupancy, maintaining a suitable thermal environment is difficult. Experimental measurements, thermal load analysis, and CFD simulation are used to assess and improve the atrium’s summer thermal climate. Experimental data supported the use of the RNG $k$-$\epsilon$ turbulence model to forecast airflow and temperature. To meet the cooling demand of 28,784 W, a supply air volume of 10,742 m³/h was required. Various air-supply methods were evaluated for temperature distribution, airflow velocity, PMV, and air age. Larger diffusers and better air dispersion increased temperature homogeneity, air age, and comfort. Redistributing airflow to corridors reduced localized overheating but raised core temperatures, whereas adding diffusers without boosting supply volume caused interference. The configuration with larger diffuser areas and equilibrated airflow maintained a temperature of 21–23 $°\mathrm{C}$, a PMV of −0.1 to 0.1, an air velocity of 0–0.3 $\mathrm{m}/\mathrm{s}$, and an average air age of 350 s. The findings provide theoretical and engineering guidance for energy-efficient HVAC systems in cruise ship atriums and other large public spaces. Full article

Shunting locomotives exhibit a wide and unpredictable range of power profiles. This unpredictability makes it impossible to rely on offline optimizations or predictive methods combined with online optimization. To maintain optimal performance across this broad range of operating conditions, the online control strategy must be robust. This article proposes a robust method to determine the optimal parameter combinations for an online energy management strategy of a hybrid fuel cell battery shunting locomotive, ensuring optimality across all scenario conditions. The first step involves extracting a statistically representative subspace for simulation, both in terms of parameter combinations and scenario conditions. A response surface model (numerical twin) is then constructed to extrapolate results across the entire space based on this simulated subspace. Using this model, the optimal solution is identified through metaheuristic algorithms (minimization search). Finally, the proposed solution is validated against a set of expert-defined scenarios. The result of the methodology ensures robust optimization across an infinite number of scenarios by minimizing the impact on both the fuel cell and the battery, without increasing mission costs. Full article

We propose MUSE++, an advanced and lightweight speech enhancement (SE) framework that builds upon the original MUSE architecture by introducing three key improvements: a Mamba-based state space model, dynamic SNR-driven data augmentation, and an augmented multi-objective loss function. First, we replace the original multi-path enhanced Taylor (MET) transformer block with the Mamba architecture, enabling substantial reductions in model complexity and parameter count while maintaining robust enhancement capability. Second, we adopt a dynamic training strategy that varies the signal-to-noise ratios (SNRs) across diverse speech samples, promoting improved generalization to real-world acoustic scenarios. Third, we expand the model’s loss framework with additional objective measures, allowing the model to be empirically tuned towards both perceptual and objective SE metrics. Comprehensive experiments conducted on the VoiceBank-DEMAND dataset demonstrate that MUSE++ delivers consistently superior performance across standard evaluation metrics, including PESQ, CSIG, CBAK, COVL, SSNR, and STOI, while reducing the number of model parameters by over 65% compared to the baseline. These results highlight MUSE++ as a highly efficient and effective solution for speech enhancement, particularly in resource-constrained and real-time deployment scenarios. Full article

Deep space navigation, defined as spacecraft position tracking beyond the lunar orbit, presents significant challenges due to the extremely weak Global Navigation Satellite System (GNSS) signals and severe signal attenuation over interplanetary distances. Traditional terrestrial systems, such as NASA’s Deep Space Network (DSN) and ESA’s ESTRACK, rely on Very Long Baseline Interferometry (VLBI) for angular positioning. However, these systems are limited by relatively short baselines, atmospheric distortions requiring extensive calibration, and reduced line-of-sight (LOS) availability due to Earth’s rotation. Because VLBI angle measurements require at least two simultaneously visible stations, the measurement duty cycle is inherently constrained. This research proposes a complementary deep space navigation approach using space-based interferometry, in which radio signals from the spacecraft are received and cross-correlated onboard Geostationary Earth Orbit (GEO) satellites. By replacing terrestrial VLBI stations with dual GEO platforms, the method significantly extends the effective baseline, removes atmospheric phase errors, and provides near-continuous visibility to deep space targets. Unlike Earth-based systems, GEO-based interferometry maintains persistent mutual visibility between stations, enabling higher measurement availability and more flexible mission support. A complete system model is presented, including the principles of dual-frequency phase-based angular tracking and a structured error budget analysis. Theoretical error analysis indicates that the GEO-based system achieves a total angular error better than 4 nanoradians—within the same order of magnitude as terrestrial VLBI. In particular, the space-based architecture nearly doubles the geometric availability for interferometric tracking while eliminating the need for atmospheric calibration. These results support the feasibility of the GEO-based VLBI concept and motivate continued research, including detailed simulations, hardware implementation, and field validation. Full article

The conservation and transmission of cultural heritage are enduring drivers of sustainable development. As a significant form of cultural heritage, temples play a vital role in maintaining urban historical continuity and embodying local culture. This study investigated the landscape roles of temples within the ancient city of Chongqing. Drawing primarily on sources such as the “Chongqing Fuzhi Quantu” (Complete Map of Chongqing Prefecture) from the Qing Dynasty, it identifies 79 temples in historical Chongqing. Employing Historical Geographic Information Systems (HGIS), the study reveals the multi-scale distribution characteristics of these temples and their interaction mechanisms with the urban spatial structure. The findings indicate that: (1) The development of Chongqing’s temples is closely linked to the stratification process of urban historical landscapes, serving as historical markers reflecting urban culture; (2) The distribution of temples in Qing-dynasty Chongqing exhibited significant correlations with the mountain-river environment and topography, forming clusters at key urban nodes while demonstrating spatial differentiation based on their attributes; (3) the landscape roles of temples in the ancient Chongqing city by guiding the urban landscape order, shaping city landmarks, and anchoring collective memories. Through the interrelated interactions across multiscale spaces, they collectively shaped the urban imagery. The study aims to provide practical recommendations for urban heritage conservation, cultural tourism, and sustainable development. Full article

The dynamic confrontation between medical image-encryption technology and cryptanalysis enhances the security of sensitive healthcare information. Recently, Lai et al. proposed a color medical image-encryption scheme (LG-IES) based on a 2D Logistic-Gaussian hyperchaotic map (Applied Mathematics and Computation, 2023). This paper identifies that the LG-IES suffers from vulnerabilities stemming from the existence of equivalent keys and the linear solvability of the diffusion equation, enabling successful attacks through crafted chosen-plaintext attacks and known-plaintext attacks. For an $M\times N$ image, a system of linear equations with rank r can be constructed, resulting in a reduction of the key space from $2^{32\times M\times N}$ to $2^{32\times (M\times N-r)}$. To address these security flaws, the improved ILG-IES integrates the SHA-3 Edge-Pixel Filling Algorithm (SHA-3-EPFA), which includes plaintext-related SHA-3 hashing for parameter generation, a chaos-driven 3 × 3 × 3 Unit Rubik’s Cube rotation to achieve cross-channel fusion, and edge-pixel filling rules for diffusion encryption. ILG-IES outperforms LG-IES in attack resistance (resists CPA/KPA/differential attacks) while maintaining comparable security indicators (e.g., NPCR 99.6%, UACI 33.5%) to reference schemes. In future work, SHA-3-EPFA can be embedded as an independent module into most permutation-diffusion-based image-encryption systems, offering new perspectives for securing sensitive color images. Full article

The inherent multiscale nature of objects poses a fundamental challenge in remote sensing object detection. To address this, feature pyramids have been widely adopted as a key architectural component. However, the effectiveness of these pyramids critically depends on the sampling operations used to construct them, highlighting the need to move beyond traditional fixed-kernel methods. While conventional interpolation approaches (e.g., nearest-neighbor and bilinear) are computationally efficient, their content-agnostic nature often leads to detail loss and artifacts. Recent dynamic sampling operators improve performance through content-aware mechanisms, yet they typically incur substantial computational and parametric costs, hindering their applicability in resource-constrained scenarios. To overcome these limitations, we propose Lurker, a learned and unified resampling kernel that supports both upsampling and downsampling within a consistent framework. Lurker constructs a compact source kernel space and employs bilinear interpolation to generate adaptive kernels, enabling content-aware feature reassembly while maintaining a lightweight parameter footprint. Extensive experiments on the DIOR and DOTA datasets demonstrate that Lurker achieves a favorable trade-off between detection accuracy and efficiency, outperforming existing resampling methods in terms of both accuracy and parameter efficiency, making it especially suitable for remote sensing object detection applications. Full article

Work performed in confined and low-height spaces (NSLH) is relatively common across several industries, yet it has not been adequately addressed from an ergonomic perspective. Such activities require workers to adopt awkward postures, most often with the trunk bent and rotated, while handling loads positioned at varying distances from the body. These conditions lead to rapid fatigue, musculoskeletal strain, and, in the long term, may cause serious health disorders. Traditional ergonomic risk assessment methods, such as REBA, RULA, or QEC, were initially applied in these situations; however, the results were unsatisfactory. Their broad applicability and reliance on calculation tables that incorporate factors irrelevant to NSLH tasks prevent them from providing an accurate evaluation of ergonomic risks in these environments. To overcome these limitations, a new assessment method, RALH (Risk Assessment for Narrow Spaces with Low Height), was developed. The method aims to evaluate ergonomic risks in contexts where workers cannot maintain an upright posture, resulting in significant stress on the spinal column, particularly in the lumbar and cervical regions. The RALH methodology incorporates parameters such as trunk inclination, trunk rotation, load weight, distance between the body and the load, exposure duration, and the worker’s physical fitness. A dedicated software tool, ERGO Agent—RALH, was designed to implement this methodology, providing structured data collection, parameter normalization, and ergonomic risk calculation. Case studies, including distribution agents working inside van cargo compartments, demonstrated that the method produces accurate and objective results. Beyond diagnosis, RALH also supports the development of preventive strategies, such as equipment optimization, task allocation, worker training, and physical conditioning. Overall, the RALH method is a practical tool for improving occupational health and efficiency in NSLH environments, where traditional ergonomic approaches are insufficient. Full article

Detection of changes in remote sensing relies on clean multi-temporal images, but cloud cover may considerably degrade image quality. Cloud removal, a critical image-restoration task, demands effective modeling of long-range spatial dependencies to reconstruct information under cloud occlusions. While Transformer-based models excel at handling such spatial modeling, their quadratic computational complexity limits practical application. The recently proposed Mamba, a state space model, offers a computationally efficient alternative for long-range modeling, but its inherent 1D sequential processing is ill-suited to capturing complex 2D spatial contexts in images. To bridge this gap, we propose the multi-pattern scanning Mamba (MPSM) block. Our MPSM block adapts the Mamba architecture for vision tasks by introducing a set of diverse scanning patterns that traverse features along horizontal, vertical, and diagonal paths. This multi-directional approach ensures that each feature aggregates comprehensive contextual information from the entire spatial domain. Furthermore, we introduce a dynamic path-aware (DPA) mechanism to adaptively recalibrate feature contributions from different scanning paths, enhancing the model’s focus on position-sensitive information. To effectively capture both global structures and local details, our MPSM blocks are embedded within a U-Net architecture enhanced with multi-scale supervision. Extensive experiments on the RICE1, RICE2, and T-CLOUD datasets demonstrate that our method achieves state-of-the-art performance while maintaining favorable computational efficiency. Full article

During orbital operations, spaceborne microstrip antennas are continuously exposed to solar radiation and the cold thermal sink of space, enduring extreme temperature variations. These extreme temperature variations induce significant thermal stress, which leads to deformation in spaceborne antennas, inevitably degrading their operational performance. To address this issue, an optimized design method for antenna array structure based on strain compensation is proposed in this paper. The proposed method uses the COMSOL Multiphysics 6.2 to analyze thermal-structural-electromagnetic coupling behavior of spaceborne microstrip arrays under extreme temperature conditions. The simulation quantifies the thermal-strain distribution. Accordingly, different slits are introduced in regions of high-strain concentration, effectively redistributing the strain to minimize thermal deformation. This optimized configuration maintains superior electrical performance while significantly enhancing thermal stability. Both simulation and measurement results verify the effectiveness of the proposed optimization design method. Notably, the proposed method offers a novel solution for mitigating thermal-induced performance degradation in spaceborne antenna systems without requiring active thermal control. Full article

This study proposes a discrete multi-agent Q-learning framework for the online tuning of PID controllers in continuous dynamic systems with limited observability. The approach treats the adjustment of each PID gain ($k_p$, $k_i$, $k_d$) as an independent learning process, in which each agent operates within a discrete state space corresponding to its own gain and selects actions from a tripartite space (decrease, maintain, or increase its gain). The agents act simultaneously under fixed decision intervals, favoring their convergence by preserving quasi-stationary conditions of the perceived environment, while a shared cumulative global reward, composed of system parameters, time and control action penalties, and stability incentives, guides coordinated exploration toward control objectives. Implemented in Python, the framework was validated in two nonlinear control problems: a water-tank and inverted pendulum (cart-pole) systems. The agents achieved their initial convergence after approximately 300 and 500 episodes, respectively, with overall success rates of $49.6\%$ and $46.2\%$ in 5000 training episodes. The learning process exhibited sustained convergence toward effective PID configurations capable of stabilizing both systems without explicit dynamic models. These findings confirm the feasibility of the proposed low-complexity discrete reinforcement learning approach for online adaptive PID tuning, achieving interpretable and reproducible control policies and providing a new basis for future hybrid schemes that unite classical control theory and reinforcement learning agents. Full article

Guided bone regeneration membranes are essential for bone formation. While non-resorbable membranes require removal surgery, resorbable membranes such as poly (lactic-co-glycolic acid) PLGA are widely used; however, issues with animal-derived components and degradation control have been identified. A novel bilayer membrane composed of synthetic poly (L-lactic acid-co-ε-caprolactone) (PBM) was developed, offering prolonged degradability and elasticity. This study compared the wound-healing effects of PBM and PLGA membranes in vivo and in vitro experiments. In vivo, maxillary molars were extracted from rats, and membranes were placed over the sockets. Healing was evaluated histologically at 1, 2, 3, 4 and 8 weeks. In vitro, oral epithelial cells and fibroblasts were seeded on both sides of PBM. Adhesion and permeability of the membranes were assessed. In vivo, both groups displayed similar mucosal healing. However, PBM preserved a clear bone-soft tissue boundary. In vitro, the surface of PBM supported significantly greater oral epithelial cell adhesion than the reverse side, with no differences for fibroblasts. Both sides of PBM exhibited better protein permeability compared to PLGA. PBM maintained distinct bone-soft tissue separation in rat extraction sockets, suggesting its potential as an effective space maintainer in guided bone regeneration. Further studies are warranted to investigate the mechanisms underlying these favorable properties. Full article

The sustainable exploitation of geothermal energy is often challenged by issues such as groundwater level decline and thermal attenuation. This study focuses on the sandstone thermal reservoir in Linqing City, Shandong Province. A three-dimensional thermo-hydro-mechanical (THM) multi-field coupling numerical model is developed to simulate the evolution of geothermal water levels and temperature fields under varying reinjection rates. The model was validated against observed water level and temperature data, showing maximum deviations of 1.62 m and 0.6 °C. Simulation results indicate that increasing the reinjection rate mitigates water-level decline but accelerates thermal breakthrough, expanding the low-temperature zone. At a 100% reinjection rate, the minimum temperature at the bottom of the thermal reservoir decreases to 63.6 °C, and the low-temperature area extends to 11.61 km². Moderate reinjection rates help to slow thermal energy loss while maintaining reservoir pressure and stabilizing water levels. This study reveals the dual effects of reinjection rate on the balance of geothermal system and puts forward suggestions on optimizing well spacing according to the simulated advance rate of cold waterfront, so as to ensure sustainable thermal recovery. It provides theoretical basis and numerical simulation support for reinjection strategy optimization and well spacing design of similar geothermal fields in Linqing and North China Plain. Full article