Search Results (40)

Structured illumination microscopy (SIM) with π/2 phase-shift modulation traditionally relies on frequency-domain computation, which greatly limits processing efficiency. In addition, the illumination regime inherent in structured illumination techniques often results in poor visual quality of reconstructed images. To address these dual challenges, this study introduces DM-SIM-LLIE (Differential Low-Light Image Enhancement SIM), a novel framework that integrates two synergistic innovations. First, the study pioneers a spatial-domain computational paradigm for π/2 phase-shift SIM reconstruction. Through system differentiation, mathematical derivation, and algorithm simplification, an optimized spatial-domain model is established. Second, an adaptive local overexposure correction strategy is developed, combined with a zero-shot learning deep learning algorithm, RUAS, to enhance the image quality of structured light reconstructed images. Experimental validation using specimens such as fluorescent microspheres and bovine pulmonary artery endothelial cells demonstrates the advantages of this approach: compared with traditional frequency-domain methods, the reconstruction speed is accelerated by five times while maintaining equivalent lateral resolution and excellent axial resolution. The image quality of the low-light enhancement algorithm after local overexposure correction is superior to existing methods. These advances significantly increase the application potential of SIM technology in time-sensitive biomedical imaging scenarios that require high spatiotemporal resolution. Full article

In this paper, general Runge–Kutta–Nyström (GRKN) methods are developed and analyzed, tailored for second-order initial value problems of the form $y^{″}=L{y}^{\prime }+My+g\left(t\right)$, where $L,M\in {\mathbb{R}}^{n\times n}$ are constant matrices with $n\ge 1$. The construction of embedded pairs of orders $6\left(4\right)$ and $7\left(5\right)$, suitable for adaptive integration strategies, is emphasized. By utilizing rooted tree theory and recent simplifications for linear inhomogeneous systems, symbolic order conditions are derived, and efficient schemes are designed through algebraic and evolutionary techniques. Numerical tests verify the superiority of our new derived pairs. In particular, this work introduces novel embedded GRKN pairs with reduced-order conditions that exploit the linearity and structure of the underlying system, enabling the construction of low-stage, high-accuracy integrators. The methods incorporate FSAL (First Same As Last) formulations, making them computationally efficient. They are tested on representative physical systems in one, two, and three dimensions, demonstrating notable improvements in efficiency and accuracy over existing high-order RKN methods. Full article

Ultrasonic wave attenuation in biological tissues arises from complex interactions between mechanical, structural, and fluidic properties, making it essential to identify dominant mechanisms for accurate simulation and device design. This work introduces a novel integration of experimentally measured tissue parameters into time-explicit nonlinear acoustic wave simulations, in which the equations are directly solved in the time domain using an explicit solver. This approach captures the full transient waveform without relying on frequency-domain simplifications, offering a more realistic representation of ultrasound propagation in heterogeneous media. The study estimates both sound diffusivity and viscous damping parameters (dynamic and bulk viscosity) for a broad range of ex vivo tissues (skin, adipose tissue, skeletal muscle, trabecular/cortical bone, liver, myocardium, kidney, tendon, ligament, cartilage, and gray/white brain matter). Four regression models (power law, linear, exponential, logarithmic) were applied to characterize their frequency dependence between 0.5 and 5 MHz. Results show that attenuation is more strongly driven by bulk viscosity than dynamic viscosity, particularly in fluid-rich tissues such as liver and myocardium, where compressional damping dominates. The power-law model consistently provided the best fit for all attenuation metrics, revealing a scale-invariant frequency relationship. Tissues such as cartilage and brain showed weaker viscous responses, suggesting the need for alternative modeling approaches. These findings not only advance fundamental understanding of attenuation mechanisms but also provide validated parameters and modeling strategies to improve predictive accuracy in therapeutic ultrasound planning and the design of non-invasive, tissue-specific acoustic devices. Full article

This study introduces an amphibious spherical robot equipped with a dual-propulsion system (ASR-DPS) and investigates its water-surface motion characteristics. Due to its distinctive spherical geometry, the robot exhibits markedly different hydrodynamic behavior compared to conventional vessels. A comparative analysis of the frontal wetted area is performed, followed by computational fluid dynamics (CFD) simulations to assess water-surface performance. The results indicate that the hemispherical bow increases hydrodynamic resistance and generates large-scale vortex structures as a consequence of intensified flow separation. Although the resistance is higher than that of traditional hulls, the robot’s greater draft and dual-propulsion configuration enhance stability and maneuverability during surface operations. To validate real-world performance, standard maneuvering tests, including circle and zig-zag maneuvers, are conducted to evaluate the effectiveness of the propeller-based propulsion system. The robot achieves a maximum surface speed of 1.2 m/s and a zero turning radius, with a peak yaw rate of 0.54 rad/s under differential thrust. Additionally, experiments on the pendulum-based propulsion system demonstrate a maximum speed of 0.239 m/s with significantly lower energy consumption (220.6 Wh at 60% throttle). A four-degree-of-freedom kinematic and dynamic model is formulated to describe the water-surface motion. To address model uncertainties and external disturbances, two control strategies are proposed: one employing model simplification and the other adaptive control. Simulation results confirm that the adaptive sliding mode controller provides precise surge speed tracking and smooth yaw regulation with near-zero steady-state error, exhibiting superior robustness and reduced chattering compared to the baseline controller. Full article

This paper proposes a novel sparse twin parametric insensitive support vector regression (STPISVR) model, designed to enhance sparsity and improve generalization performance. Similar to twin parametric insensitive support vector regression (TPISVR), STPISVR constructs a pair of nonparallel parametric insensitive bound functions to indirectly determine the regression function. The optimization problems are reformulated as two sparse linear programming problems (LPPs), rather than traditional quadratic programming problems (QPPs). The two LPPs are originally derived from initial L1-norm regularization terms imposed on their respective dual variables, which are simplified to constants via the Karush–Kuhn–Tucker (KKT) conditions and consequently disappear. This simplification reduces model complexity, while the constraints constructed through the KKT conditions— particularly their geometric properties—effectively ensure sparsity. Moreover, a two-stage hybrid tuning strategy—combining grid search for coarse parameter space exploration and Bayesian optimization for fine-grained convergence—is proposed to precisely select the optimal parameters, reducing tuning time and improving accuracy compared to a singlemethod strategy. Experimental results on synthetic and benchmark datasets demonstrate that STPISVR significantly reduces the number of support vectors (SVs), thereby improving prediction speed and achieving a favorable trade-off among prediction accuracy, sparsity, and computational efficiency. Overall, STPISVR enhances generalization ability, promotes sparsity, and improves prediction efficiency, making it a competitive tool for regression tasks, especially in handling complex data structures. Full article

This study investigates reformulation in a substandard variety of Italian, italiano popolare, from the early 20th Century, focusing on a collection of letters and postcards from semi-literate Sicilian peasants during World War I. The analysis identifies three reformulation markers: cioè, anzi, and vuol dire. These markers are affected by hypercorrection, interference, and structural simplification, reflecting the sociolinguistic dynamics of italiano popolare. Additionally, the study of these markers sheds light on the relationships between reformulation and related discourse functions, namely paraphrase, correction, addition, and motivation. By positioning occurrences of reformulation along a continuum between the spoken and written mode, the findings suggest that this discourse function is employed more as a rhetorical strategy that characterizes planned written texts, rather than as a feature of disfluency that is typical of unplanned speech. Ultimately, examining reformulation in italiano popolare provides valuable insights into the relationship between sociolinguistic variation and language change in the beginning of the 20th Century, a key phase in the spread of Italian as a national language. Full article

The seismic design of underground or aboveground structures is commonly based on the free-field assumption, which neglects the interaction between underground structures–soil–aboveground structures (USSI). This simplification may lead to unsafe or overly conservative, cost-intensive designs. To address this limitation, a series of shaking table tests were conducted on a coupled USSI system, in which the underground component consisted of a subway station connected to tunnels through structural joints to investigate the “city effect” on-site seismic response, particularly under long-period horizontal seismic excitations. Five test configurations were developed, including combinations of one or two aboveground structures, with or without a subway station. These were compared to a free-field case to evaluate differences in dynamic characteristics, acceleration amplification factors (AMFs), frequency content, and response spectra. The results confirm that boundary effects were negligible in the experimental setup. Notably, long-period seismic inputs had a detrimental impact on the field response when structures were present, with the interaction effects significantly altering surface motion characteristics. The findings demonstrate that the presence of a subway station and/or aboveground structure alters the seismic response of the soil domain, with clear dependence on the input motion characteristics and relative structural positioning. Specifically, structural systems lead to de-amplification under high-frequency excitations, while under long-period inputs, they suppress short-period responses and amplify long-period components. These insights emphasize the need to account for USSI effects in seismic design and retrofitting strategies, particularly in urban environments, to achieve safer and more cost-effective solutions. Full article

Explainable Machine Learning (XML) in high-stakes domains demands reproducible methods to aggregate feature importance across multiple models applied to the same structured dataset. We propose the Weighted Importance Score and Frequency Count (WISFC) framework, which combines importance magnitude and consistency by aggregating ranked outputs from diverse explainers. WISFC assigns a weighted score to each feature based on its rank and frequency across model-explainer pairs, providing a robust ensemble feature-importance ranking. Unlike simple consensus voting or ranking heuristics that are insufficient for capturing complex relationships among different explainer outputs, WISFC offers a more principled approach to reconciling and aggregating this information. By aggregating many “weak signals” from brute-force modeling runs, WISFC can surface a stronger consensus on which variables matter most. The framework is designed to be reproducible and generalizable, capable of taking important outputs from any set of machine-learning models and producing an aggregated ranking highlighting consistently important features. This approach acknowledges that any single model is a simplification of complex, multidimensional phenomena; using multiple diverse models, each optimized from a different perspective, WISFC systematically captures different facets of the problem space to create a more structured and comprehensive view. As a consequence, this study offers a useful strategy for researchers and practitioners who seek innovative ways of exploring complex systems, not by discovering entirely new variables but by introducing a novel mindset for systematically combining multiple modeling perspectives. Full article

Male contraception remains an unmet need. Na,K-ATPase α4 (NKA α4), a specific Na⁺/K⁺ transporter of the sperm flagellum, is an attractive target for male contraception. NKA α4 is critical for sperm motility and fertility, and its deletion in male mice causes complete infertility. Our previous structure–activity relationship (SAR) studies on a cardenolide scaffold identified a highly selective, safe NKAα4 inhibitor, but its complex, heavily hydroxylated structure posed challenges for modification and optimization. To address this, we employed a structural simplification strategy to synthesize novel steroidal and non-steroidal analogs and examined their effects on NKAα4 inhibition and sperm motility. Both series reduced sperm motility (up to ~50%), with IC₅₀ values in the picomolar range. Compounds 13 and 45 displayed specificities for NKAα4 over NKAα1, did not affect sperm viability, and showed no reversibility in vitro. Notably, 45, featuring a hexahydronaphthalene core and a benzyltriazole moiety at C5, exhibited potent, highly selective NKAα4 inhibition, reduced sperm motility in vitro and in vivo, and blocked fertilization in vitro. This highlights 45 as a promising lead for non-hormonal male contraception and indicates that the newly generated series of compounds possess the key characteristics needed for further development as potential non-hormonal male contraceptive agents. Full article

Tablet surface defects are typically controlled by visual inspection in the pharmaceutical industry. This is an insufficient response variable for knowledge-based formulation and process development, and it results in rather limited robustness of the control strategy. In this article, we present an analytical method for the quantitative characterization of visual tablet surface defects. The method involves analysis of the tablet surface by a digital microscope to obtain optical images and three-dimensional surface scans. Pre-processing procedures are applied for the simplification of the data to allow the detection of the imprint characters and tablet surface structures by a Faster R-CNN object detection model. Geometrical variables like perimeter and area were derived from the results of the object detection model and statistically analyzed for a selected number of tablets. The analysis allowed the development of product-specific acceptance criteria by a small reference dataset, and the quantitative evaluation of sticking, picking, chipping, and abrasion defects. The method showed high precision and sensitivity and demonstrated robust detection of visual tablet surface defects without false negative results. The image analysis was automated, and the developed algorithm can be operated by a simple routine on a standard computer in a few minutes. The method is suitable for industrial use and enables advancements in industrial formulation and process development while providing a novel opportunity for the quality control of visual tablet surface defects. Full article

The floodplain of a large river plays a crucial role in the river’s ecosystem and serves as an essential microhabitat for river fish to complete their life history events. Over the past four decades, the floodplain represented by the Jingjiang section in the middle reaches of the Yangtze River has experienced a significant reduction in area, complexity, and diversity of fish microhabitats. This study quantitatively analyzed the dynamic changes and geomorphological structure of the floodplain in the Jingjiang reach (JJR) of the Yangtze River using satellite remote sensing images and high-resolution unmanned aerial vehicle (UAV) optical images. We built an enhanced U-Net model incorporating both the CBAM and SE parallel attention mechanisms to classify these images and identify environmental structural units. The accuracy of the enhanced model was 16.39% higher compared to original U-Net model. At the same time, the improved normalized difference water index (mNDWI), enhanced vegetation index (EVI), and normalized difference vegetation index (NDVI) were utilized to extract the flood frequency of the floodplain and analyze the area changes of the floodplain in the JJR. The trend of the flood area in the JJR during the flood season was consistent with the overall trend of flood areas in the flood season, which generally exhibits a downward tendency. In 2022, the floodplain of the JJR underwent substantial anthropogenic disturbances, with 40% of its area comprising anthropogenic environmental units. Compared to historical periods, the impervious surface within the floodplain has increased annually, while ecological units such as riparian forests and trees have gradually diminished or even disappeared, leading to a simplification of structural complexity. These findings provide a critical background and robust data foundation for the protection and restoration of fish habitats and the formulation of strategies for fish population reconstruction in the Yangtze River. Full article

The vibration control in structural design has long been a critical area of study, particularly in mitigating undesirable resonant vibrations using dynamic vibration absorbers (DVAs). Traditional approaches to tuning DVAs have relied on complex mathematical models based on Newtonian or Euler–Lagrange equations, often leading to intricate systems requiring simplification of the analysis of multi-degree-of-freedom structures. This paper introduces a novel modeling approach for analyzing DVAs based on the concept of global admittance, which stems from mechanical admittance and network simplifications. This model streamlines the representation of structures with DVAs as one-degree-of-freedom systems coupled with a global admittance function, which emulates additional damping coupled to the primary structure. In this work, global admittance functions are determined by the independent analysis of the mechanical networks of the DVA, restructuring the process of obtaining the system’s transfer function. The model was validated using different classical DVA configurations, demonstrating total accuracy in its applicability across designs concerning conventional modeling. Our most remarkable finding was that the dimensionless function, ${\gamma }_g\left(\mathsf{\Omega }\right)$, resulting from the global admittance, partially decouples the dynamics of the DVAs from the primary structure, facilitating the implementation of passive vibration control strategies in more realistic structural models. Additionally, this work establishes a significant advancement in vibration control analysis, providing a flexible tool for control strategies in real-world structural systems. Full article

While informal settlements have been extensively studied in the Global South, their counterparts in the Global North remain under-researched, despite their critical role in shaping urban morphology. This paper introduces “Resident-Centered Narrative Mapping”, a framework designed to uncover micro-morphological knowledge through the lived spatial experiences of marginalized residents. By examining the epistemological question “whose morphology?”, this study critiques conventional urban morphological methods, which often disregard spatial practices embedded in the everyday lives of marginalized communities. Focusing on a marginalized lilong settlement in downtown Shanghai, this research work integrates critical cartography with ethnographic fieldwork to develop a micro-morphological mapping process centered on resident narratives. This process, structured around the phases of finding, inscription, and simplification, demonstrates how residents’ daily practices actively shape and reconfigure their built environment. This study offers an alternative perspective to understand the dynamic processes of urban renewal in informal settlements and emphasizes the dialectical relationship between resident-driven spatial practices and the transformation of the urban form. By broadening urban morphology’s methodological framework, this research provides insights into how resident-driven mapping can inform localized regeneration strategies. The findings highlight the potential for marginalized communities to shape urban regeneration policies, advocating for inclusive, resident-centered development. Full article

This article provides a comprehensive analysis of Jordan’s energy transition, integrating regulatory, infrastructural, and social aspects to advance the nation’s journey toward achieving the Sustainable Development Goals (SDGs), particularly in clean energy, innovation, and infrastructure. Utilizing regression analysis and data from 447 households, this study defines the interdependence of policy and infrastructure in solar energy adoption, identifying tariff structures as a primary influencer. The current net metering policy, which limits compensation to 80% of exported energy, results in lengthy payback periods, contrasting with Morocco’s successful 100% feed-in tariff model and its shorter payback period. This comparative perspective, examining Morocco, Egypt, and the UAE, identifies effective renewable strategies. Those underline this study’s global relevance, particularly in promoting equitable access and infrastructural modernization. The article’s practical dimension is another major asset. Beyond diagnosing challenges such as deficiencies in battery storage and urban–rural disparities in subsidy access, the authors propose concrete reforms like licensing simplification, tariff indexing, and energy storage development. That dual academic and applied value positions this study as a crucial resource for shaping Jordanian energy policy and aiding other developing nations in their renewable energy pursuits. By filling a research gap, this article quantitatively assesses the interaction between regulatory policy and infrastructure, which are often separately studied, while the use of random sampling enhances the validity of its statistical inferences. Overall, this research contributes significantly to the broader discourse on renewable energy transitions within the MENA region and beyond, aligning policy, technology, and equity to support Jordan’s sustainable energy efforts. Full article

With the rapid development of lightweight network models and efficient hardware deployment techniques, the demand for real-time semantic segmentation in areas such as autonomous driving and medical image processing has increased significantly. However, realizing efficient semantic segmentation on resource-constrained embedded platforms still faces many challenges. As a classical lightweight semantic segmentation network, ENet has attracted much attention due to its low computational complexity. In this study, we optimize the ENet semantic segmentation network to significantly reduce its computational complexity through structural simplification and 8-bit quantization and improve its hardware compatibility through the optimization of on-chip data storage and data transfer while maintaining 51.18% mIoU. The optimized network is successfully deployed on hardware accelerator and SoC systems based on Xilinx ZYNQ ZCU104 FPGA. In addition, we optimize the computational units of transposed convolution and dilated convolution and improve the on-chip data storage and data transfer design. The optimized system achieves a frame rate of 130.75 FPS, which meets the real-time processing requirements in areas such as autonomous driving and medical imaging. Meanwhile, the power consumption of the accelerator is 3.479 W, the throughput reaches 460.8 GOPS, and the energy efficiency reaches 132.2 GOPS/W. These results fully demonstrate the effectiveness of the optimization and deployment strategies in achieving a balance between computational efficiency and accuracy, which makes the system well suited for resource-constrained embedded platform applications. Full article