Search Results (4,101)

Image enhancement in complex scenes is challenging due to the frequent coexistence of multiple degradations caused by adverse weather, imaging hardware, and transmission environments. Existing datasets remain limited to single or weather-specific degradation types, failing to capture real-world complexity. To address this gap, we introduce the Robust Multi-Type Degradation (RMTD) dataset, which synthesizes a wide range of degradations from meteorological, capture, and transmission sources to support model training and evaluation under realistic conditions. Furthermore, the superposition of multiple degradations often results in feature maps dominated by noise, obscuring underlying clean content. To tackle this, we propose the Feature Filter Transformer (FFformer), which includes: (1) a Gaussian-Filtered Self-Attention (GFSA) module that suppresses degradation-related activations by integrating Gaussian filtering into self-attention; and (2) a Feature-Shrinkage Feed-forward Network (FSFN) that applies soft-thresholding to aggressively reduce noise. Additionally, a Feature Enhancement Block (FEB) embedded in skip connections further reinforces clean background features to ensure high-fidelity restoration. Extensive experiments on RMTD and public benchmarks confirm that the proposed dataset and FFformer together bring substantial improvements to the task of complex-scene image enhancement. Full article

Hemodynamics significantly impact the biology of endothelial cells (ECs) lining the blood vessels. ECs are exposed to various hemodynamic forces, particularly frictional shear stress from flowing blood. While physiological flows are critical for the normal functioning of ECs, abnormal flow dynamics, known as disturbed flows, may trigger endothelial dysfunction leading to atherosclerosis and other vascular conditions. Such flows can occur due to sudden geometrical variations and vascular abnormalities in the cardiovascular system. In the current study, a microfluidic system was used to investigate the impact of different flow conditions (i.e, normal vs. disturbed) on ECs in vitro. We particularly explored the relationship between specific flow patterns and cellular pathways linked to oxidative stress and inflammation related to atherosclerosis. Here, we utilized a 2D cell culture perfusion system featuring an immortalized human vascular endothelial cell line (EA.hy926) connected to a modified peristaltic pump system to generate either steady laminar flows, representing healthy conditions, or disturbed oscillatory flows, representing diseased conditions. EA.hy926 were exposed to an oscillatory flow shear stress of 0.5 dynes/cm² or a laminar flow shear stress of 2 dynes/cm² up to 24 h. Following flow exposure, cells were harvested from the perfusion chamber for quantitative PCR analysis of gene expression. Reactive oxygen species (ROS) generation under various shear stress conditions was also measured using DCFDA/H2DCFDA fluorescent assays. Under oscillatory shear stress flow conditions (0.5 dynes/cm²), EA.hy926 ECs showed a 3.5-fold increase in the transcription factor nuclear factor (NFκ-B) and a remarkable 28.6-fold increase in cyclooxygenase-2 (COX-2) mRNA expression, which are both proinflammatory markers, compared to static culture. Transforming growth factor-beta (TGFβ) mRNA expression was downregulated in oscillatory and laminar flow conditions compared to the static culture. Apoptosis marker transcription factor Jun (C-Jun) mRNA expression increased in both flow conditions. Apoptosis marker C/EBP homologous protein (CHOP) mRNA levels increased significantly in oscillatory flow, with no difference in laminar flow. Endothelial nitric oxide synthase (eNOS) mRNA expression was significantly decreased in cells exposed to oscillatory flow, whereas there was no change in laminar flow. Endothelin-1 (ET-1) mRNA expression levels dropped significantly by 0.5- and 0.8-fold in cells exposed to oscillatory and laminar flow, respectively. ECs subjected to oscillatory flow exhibited a significant increase in ROS at both 4 and 24 h compared to the control and laminar flow. Laminar flow-treated cells exhibited a ROS generation pattern similar to that of static culture, but at a significantly lower level. Overall, by exposing ECs to disturbed and normal flows with varying shear stresses, significant changes in gene expression related to inflammation, endothelial function, and oxidative stress were observed. In this study, we present a practical, optimized system as an in vitro model that can be employed to investigate flow-associated diseases, such as atherosclerosis and aortic aneurysm, thereby supporting the understanding of the underlying molecular mechanisms. Full article

In this paper, we investigate the integrability and solution structure of the semi-discrete KP equation. A bilinear Bäcklund transformation, Lax pair, and nonlinear superposition formula are systematically derived, establishing the integrability of the system. Furthermore, one- and two-periodic wave solutions are constructed using Hirota’s method combined with Riemann theta functions. By means of a rigorous limiting procedure, the asymptotic behavior of the periodic wave solutions is analyzed, and the connection between periodic wave and soliton solutions is established. These results not only enrich the solution structure of the semi-discrete KP equation but also provide new perspectives on periodic phenomena in discrete integrable systems. Full article

This work investigates the interplay between the Mellin transform and Lambert transforms to derive several novel results. In particular, we establish new inversion formulae for the Lambert transforms along with a Plancherel-type identity. Additionally, we explore the implications of these findings, highlighting their relevance to Salem’s equivalence and potential connections with the Riemann hypothesis. Full article

Medical image segmentation has been a central research focus in deep learning, but methods based on convolutions have limitations in modeling the long-range validity of images. To overcome this issue, hybrid CNN-Transformer architectures have been explored, with SwinUNet being a classic approach. However, SwinUNet still faces challenges such as insufficient modeling of relative position information, limited feature fusion capabilities in skip connections, and the loss of translational invariance caused by Patch Merging. To overcome these limitations, the architecture RE-XswinUnet is presented as a novel solution for medical image segmentation. In our design, relative position biases are replaced with rotary position embedding to enhance the model’s ability to extract detailed information. During the decoding stage, XskipNet is designed to improve cross-scale feature fusion and learning capabilities. Additionally, an SCAR Block downsampling module is incorporated to preserve translational invariance more effectively. The experimental results demonstrate that RE-XswinUnet achieves improvements of 2.65% and 0.95% in Dice coefficients on the Synapse multi-organ and ACDC datasets, respectively, validating its superiority in medical image segmentation tasks. Full article

As tourism increasingly drives the revitalization of traditional villages, rural spaces are undergoing a transformation from functional living areas to spaces for cultural display and leisure. This shift has amplified the spatial usage discrepancies between multiple stakeholders, such as tourists and villagers, highlighting conflicts in spatial resource allocation and behavior path organization. Using Wulin Village, a typical example of a Minnan overseas Chinese village, as a case study, this paper introduces social network analysis to construct a “spatial–behavioral” dual network model. The model integrates both architectural and public spaces, alongside behavior path data from villagers and tourists, to analyze the spatial structure at three scales: village-level network completeness, district-level structural balance, and point-level node vulnerability. The study integrates two dimensions—architectural space and public space—along with behavioral path data from both villagers and tourists. It reveals the characteristics of spatial structure under the intervention of multiple behavioral agents from three scales: village-level network completeness, district-level structural balance, and point-level node vulnerability. The core research focus of the spatial network includes the network structure of architectural and public spaces, while the behavioral network concerns the activity paths and behavior patterns of tourists and villagers. The study finds that, at the village scale, Wulin Village’s spatial network demonstrates good connectivity and structural integrity, but the behavior paths of both tourists and villagers are highly concentrated in core areas, leading to underutilization of peripheral spaces. This creates an asymmetry characterized by “structural integrity—concentrated behavioral usage.” At the district scale, the spatial node distribution appears balanced, but tourist behavior paths are concentrated around cultural nodes, such as the ancestral hall, visitor center, and theater, while other areas remain inactive. At the point scale, both tourist and villager activities are highly dependent on a few high-degree, high-cluster nodes, improving local efficiency but exacerbating systemic vulnerability. Comparison with domestic and international studies on cultural settlements shows that tourism often leads to over-concentration of spatial paths and node overload, revealing significant discrepancies between spatial integration and behavioral usage. In response, this study proposes multi-scale spatial optimization strategies: enhancing accessibility and path redundancy in non-core areas at the village scale; guiding behavior distribution towards multifunctional nodes at the district scale; and strengthening the capacity and resilience of core nodes at the point scale. The results not only extend the application of behavioral network methods in spatial structure research but also provide theoretical insights and practical strategies for spatial governance and cultural continuity in tourism-driven cultural villages. Full article

In recent years, renewable energy generation (RPG) has experienced rapid growth, and large-scale hydro–wind–photovoltaic storage (HWPS) bases have been progressively developed in southwest China, where hydropower resources are abundant. Ensuring the small-signal stability of such large-scale integrated systems has become a critical challenge. While considerable research has focused on the small-signal stability of grid-connected wind, photovoltaic, or energy storage systems (ESSs), studies on the stability of large-scale HWPS bases remain limited. Moreover, emerging grid codes require power electronic devices to maintain synchronization under unbalanced grid conditions. The time-varying rotating transformations introduced by positive-sequence (PS) and negative-sequence (NS) control render the conventional Park transformation ineffective. To address these challenges, this study develops a linear time-periodic (LTP) model of a large-scale HWPS base using trajectory linearization. Based on Floquet theory, the impacts of RPG station and ESS control parameters on system stability are analyzed. The results reveal that under the considered scenario, these control parameters may induce oscillations over a relatively wide frequency range. Specifically, low PLL and DVC bandwidths (BWs) are associated with the risk of low-frequency oscillations, whereas excessively high BWs may lead to sub-synchronous oscillations. The validity of the analysis is verified through comparison with time-domain simulations of the nonlinear model. Full article

This study presents a pulse generator employing a saturable pulse transformer (SPT) in conjunction with a fast recovery diode, integrated within an all-solid-state pulse-forming network (PFN). The saturation inductance of the SPT serves as a component of the initial LC section of the PFN, thereby contributing to the preservation of output waveform integrity. The secondary energy storage capacitor is charged through the primary circuit and the SPT, subsequently discharging into the load under the regulation of the SPT. An increase in the SPT’s transformation ratio corresponds to a rise in its saturated inductance, which in turn prolongs the pulse rise time. To mitigate this effect, a fast recovery diode is incorporated to sharpen the pulse front. Specifically, upon saturation of the SPT, current reverses through the fast recovery diode, effectively short-circuiting the load. When the inductor current attains a predetermined threshold, the diode reverts to reverse cut-off and rapidly switches off, enabling the PFN to discharge swiftly into the load and generate a high-voltage pulse characterized by a rapid rising edge. Furthermore, augmenting the number of secondary windings on the SPT—each connected to a PFN module—and arranging multiple PFNs in series facilitates an increase in output voltage. Experimental evaluations demonstrated that a three-stage PFN pulse generator attained a peak voltage of −16.9 kV on an 80 Ω matched load, with pulse currents exceeding 200 A while maintaining a 19 ns front edge. These results indicate that the proposed approach is effective for producing high-voltage, narrow pulses with rapid rise times. Additionally, the pulse power generator is capable of delivering repetitive pulses of −16.9 kV at a frequency of 20 kHz in burst mode. Full article

Abrasive belt wear has an important impact on the dimensional accuracy and surface quality of parts. Accurate quantitative measurement of abrasive belt wear is an important basis for optimizing grinding process parameters, but also a very challenging task for abrasive belts with randomly distributed abrasive particles. In this paper, a quantitative method of determining wear state based on the life cycle surface images of the abrasive belt is proposed to evaluate its material removal ability in the grinding process. For blunted abrasive particles with extremely irregular shapes, TransUNet with a hybrid encoding of a CNN and transformer is adopted to obtain strong representation of complex features and high-precision segmentation boundaries. Three other U-net-based semantic segmentation networks are compared to prove the effectiveness of the trained TransUNet model. The number and area of blunted abrasive particles were calculated by connected domain and statistical methods. The proportion of worn abrasive particles and the wear area ratio when the service life of the abrasive belt is exhausted are about 74.29% and 3.06%, respectively. Full article

Palestinian higher education institutions operate under extraordinary challenges due to military occupation, including movement restrictions, resource limitations, and institutional closures, requiring university leaders to develop innovative approaches to maintain academic standards. This study explores how deans in Medicine, Law, and Engineering—the first programs established in Palestine—navigate these constraints through strategic management practices that transform them into strategic capabilities. Using semi-structured interviews, we conducted in-depth qualitative research with three program deans who possess a comprehensive understanding of their faculties’ organizational dynamics and adaptive strategies. Thematic analysis revealed several key management approaches, including the strategic adaptation of curricula to meet community needs while maintaining global standards; deliberate investment in faculty development and international collaborations; the integration of critical pedagogy that connects academic learning with social responsibility; and the successful advancement of gender equity in traditionally male-dominated fields. Deans created innovative solutions, including community-based medical training, legal clinics addressing occupation-related cases, and engineering projects focused on local infrastructure challenges. These findings demonstrate how educational leaders transform systemic constraints into opportunities for pedagogical innovation and institutional strengthening. This research contributes new theoretical insights into management practices in crisis-affected higher education, demonstrating how strategic leadership can sustain academic excellence despite severe restrictions. The insights offer valuable lessons for university administrators worldwide facing similar challenges, particularly in conflict-affected regions where educational continuity is threatened. Full article

Digital mining has been evolving in recent years under the Industry 4.0 paradigm. In this sense, technological tools such as sensors aid the management and operation of mining projects, reducing the risk of accidents, increasing productivity, and promoting business sustainability. DT is a technological tool that enables the integration of various Industry 4.0 technologies to create a virtual model of a real, physical entity, allowing for the study and analysis of the model’s behavior through real-time data collection. A digital twin of an underground mine is a real-time, virtual replica of an actual mine. It is like an extremely detailed “simulator” that uses data from sensors, machines, and personnel to accurately reflect what is happening in the mine at that very moment. Some of the functionalities of an underground mining DT include (i) accurate geometry of the real physical asset, (ii) real-time monitoring capability, (iii) anomaly prediction capability, (iv) scenario simulation, (v) lifecycle management to reduce costs, and (vi) a support system for smart and proactive decision-making. A digital twin of an underground mine offers transformative benefits, such as real-time operational optimization, improved safety through risk simulation, strategic planning with predictive scenarios, and cost reduction through predictive maintenance. However, its implementation faces significant challenges, including the high technical complexity of integrating diverse data, the high initial cost, organizational resistance to change, a shortage of skilled personnel, and the lack of a comprehensive, multi-layered conceptual framework for an underground mine digital twin. To overcome these barriers and gaps, this paper proposes a strategy that includes defining an advanced, multi-layered conceptual framework for the digital twin. Simultaneously, it advocates for fostering a culture of change through continuous training, establishing partnerships with specialized experts, and investing in robust sensor and connectivity infrastructure to ensure reliable, real-time data flow that feeds the digital twin. Finally, validation of the advanced multi-layered conceptual framework for digital twins of underground mines is carried out through a questionnaire administered to a panel of experts. Full article

The evaluation of the fracturing effect of coalbed methane (CBM) wells is crucial for the efficient development of CBM reservoirs. Currently, studies focusing on the evaluation of the hydraulic fracture stimulation effect of coal seams and the integrated analysis of “drilling-fracturing-monitoring” are relatively insufficient. Therefore, this paper takes three drainage and production wells in the coalbed methane block on the northwest wing of the Xiangxia anticline in the Bijie Experimental Zone of Guizhou Province as the research objects. In view of the complex geological characteristics of this area, such as multiple and thin coal seams, high gas content, and high stress and low permeability, the paper systematically summarizes the results of drilling and fracturing engineering practices of the three drainage and production wells in the area, including the application of key technologies such as a two-stage wellbore structure and the “bentonite slurry + low-solid-phase polymer drilling fluid” system to ensure wellbore stability, low-solid-phase polymer drilling fluid for wellbore protection, and staged temporary plugging fracturing. On this basis, a study on microseismic signal acquisition and tomographic energy inversion based on a ground dense array was carried out, achieving four-dimensional dynamic imaging and quantitative interpretation of the fracturing fractures. The results show that the fracturing fractures of the three drainage and production wells all extend along the direction of the maximum horizontal principal stress, with azimuths concentrated between 88° and 91°, which is highly consistent with the results of the in situ stress calculation from the previous drilling engineering. The overall heterogeneity of the reservoir leads to the asymmetric distribution of fractures, with the transformation intensity on the east side generally higher than that on the west side, and the maximum stress deformation influence radius reaching 150 m. The overall transformation effect of each well is good, with the effective transformation volume ratio of fracturing all exceeding 75%, and most of the target coal seams are covered by the fracture network, significantly improving the fracture connectivity. From the perspective of the transformed planar area per unit fluid volume, although there are numerical differences among the three wells, they are all within the effective transformation range. This study shows that microseismic fracture monitoring technology can provide a key basis for the optimization of fracturing technology and the evaluation of the production increase effect, and offers a solution to the problem of evaluating the hydraulic fracture stimulation effect of coal seams. Full article

Nucleic acid aptamers leverage defined tertiary structures for precise molecular recognition, positioning them as transformative biomedical tools. We engineered AP1-F, a G-quadruplex (G4)-structured aptamer that selectively binds membrane-anchored nucleolin (NCL) non-permeabilizing, overcoming a key limitation of conventional probes. Microscale thermophoresis confirmed nanomolar affinity to NCL. By means of rigorous optimization, AP1-F attained a greater than ten-fold fluorescence signal ratio between malignant and normal cells in co-cultures, exceeding the extensively researched AS1411. Dual-channel flow cytometry demonstrated over 98.78% specificity at single-cell resolution within heterogeneous cell populations, owing to AP1-F’s unique membrane localization—unlike AS1411’s intracellular uptake, which elicited erroneous signals from cytoplasmic NCL. Competitive binding experiments and Laser Confocal Imaging confirmed that AP1-F specifically identifies cancer cells by binding to the NCL recognition site on the membrane. In pathological sections, AP1-F exhibited a 40.5-fold fluorescence intensity ratio between tumor and normal tissue, facilitating accurate tissue-level differentiation. Significantly, it delineated molecular subtypes by associating membrane NCL patterns with morphometric analysis: luminal-like MCF-7 displayed consistent staining in cohesive clusters, whereas basal-like MDA-MB-468 revealed sporadic NCL with irregular outlines—characteristics imperceptible to intracellular-targeted antibodies, thus offering subtype-specific diagnostic insights. This combination biochemical–morphological approach accomplished subtype differentiation with a single-step, non-permeabilized process that maintained lower cytotoxicity and tissue integrity. AP1-F enhances diagnostic accuracy by utilizing spatial confinement to eradicate intracellular interference, connecting molecular specificity to intraoperative margin evaluation or biopsy categorization. Full article

Digital marketing has become a game-changer by combining cutting-edge technologies, insights into how customers behave, and applicability across industries to change how businesses plan and how they interact with customers. Digital marketing is a key part of being competitive, sustainable, and innovative in a world where more and more people are using the internet and social media. Even though this subject is important, the study of it is still scattered, which shows that there is a need to systematically map out its intellectual structure. This research utilizes a bibliometric and topic modeling methodology, analyzing 4722 publications sourced from the Scopus database, including the string “Digital Marketing”. The authors employed Latent Dirichlet Allocation (LDA), a method from Natural Language Processing, to discern latent study themes and Vosviewer 1.6.20 for bibliometric analysis. The results explore ten main thematic clusters, such as digital marketing and blockchain, applications in the health and food industries, higher education and skill enhancement, machine learning and analytics, small and medium-sized enterprises (SMEs) and sustainability, emerging trends and ethics, sales transformation, tourism and hospitality, digital media and audience perception, and consumer satisfaction through service quality. These clusters show that digital marketing is becoming more interdisciplinary and is becoming more connected to ethical and technological issues. The report finds that digital marketing research is changing quickly because of artificial intelligence (AI), blockchain, immersive technology, and reflect it with a digital business environment. Future directions encompass the expansion of analyses to new economies, the implementation of advanced semantic models, and the navigation of ethical difficulties, thereby guaranteeing that digital marketing fosters both business progress and public welfare. Full article

Arable field abandonment is a major driver of landscape change in rural areas worldwide. It is defined as the cessation of agricultural activities and the withdrawal of agricultural management on land. This study examined arable land abandonment and subsequent land use and land cover (LULC) changes in Gotyibeni, Manqorholweni, Mawane, and Melani villages over a 20-year period. The aim was to understand these changes and how rural livelihoods and social relationships within and between households were perceived to have transformed following the LULC shifts. Landsat 5, 7, 8, and 9 multispectral imageries with a 30 m spatial resolution were analysed for two periods (i.e., 2000–2010 and 2010–2020). Five land cover classes were mapped: arable fields, grasslands, homestead gardens, residential areas, and shrublands. Post-classification change detection revealed a steady decline in arable fields, largely replaced by grasslands, shrublands, and residential areas. User accuracy was above 80% across all LULC maps, providing confidence in the LULC results. To link these spatial changes with social outcomes, 97 households that had abandoned field cultivation were purposively selected across the four villages. Semi-structured interviews were conducted to capture household experiences. Findings showed that reduced field cultivation was perceived to undermine household economic status, with households increasingly dependent on government social grants amid high unemployment. In addition, weakened social connections and shifts in cultural practices were reported. Overall, the study demonstrated how combining satellite imagery with community perspectives provides a comprehensive understanding of rural arable land abandonment and its consequences. Full article