Search Results (4,518)

Underwater acoustic (UWA) communication supports many critical applications but still faces several physical-layer signal processing challenges. In response, recent advances in machine learning (ML) and deep learning (DL) offer promising solutions to improve signal detection, modulation adaptability, and classification accuracy. These developments highlight the need for a systematic evaluation to compare various ML/DL models and assess their performance across diverse underwater conditions. However, most existing reviews on ML/DL-based UWA communication focus on isolated approaches rather than integrated system-level perspectives, which limits cross-domain insights and reduces their relevance to practical underwater deployments. Consequently, this systematic literature review (SLR) synthesizes 43 studies (2020–2025) on ML and DL approaches for UWA communication, covering channel estimation, adaptive modulation, and modulation recognition across both single- and multi-carrier systems. The findings reveal that models such as convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and generative adversarial networks (GANs) enhance channel estimation performance, achieving error reductions and bit error rate (BER) gains ranging from ${10}^{-3}$ to ${10}^{-6}$. Adaptive modulation techniques incorporating support vector machines (SVMs), CNNs, and reinforcement learning (RL) attain classification accuracies exceeding 98% and throughput improvements of up to 25%. For modulation recognition, architectures like sequence CNNs, residual networks, and hybrid convolutional–recurrent models achieve up to 99.38% accuracy with latency below 10 ms. These performance metrics underscore the viability of ML/DL-based solutions in optimizing physical-layer tasks for real-world UWA deployments. Finally, the SLR identifies key challenges in UWA communication, including high complexity, limited data, fragmented performance metrics, deployment realities, energy constraints and poor scalability. It also outlines future directions like lightweight models, physics-informed learning, advanced RL strategies, intelligent resource allocation, and robust feature fusion to build reliable and intelligent underwater systems. Full article

This study investigates how differing national research governance models impact academic performance by comparing European Research Council (ERC) grant winners in the social sciences and humanities from France and the Netherlands. Situated within the broader context of centralized versus decentralized research systems, the analysis aims to understand how these structures shape publication trends, thematic diversity, and collaboration patterns. Drawing on Scopus and SciVal data covering 9996 publications by 305 ERC winners between 2019 and 2023, we employed a multi-method approach, including latent Dirichlet allocation for topic modeling, compound annual growth rate analysis, and co-authorship network analysis. The results show that neuroscience, climate change, and psychology are dominant domains, with language and linguistics particularly prevalent in France and law and political science in the Netherlands. French ERC winners are more likely to be affiliated with national or sectoral institutions, whereas in the Netherlands, elite universities dominate. Collaboration emerged as a key success factor, with an average of four co-authors per publication and network analyses revealing central figures who bridge topical clusters. International collaborations were consistently linked with higher visibility, while single-authored publications showed limited impact. These findings suggest that institutional context and collaborative practices significantly shape research performance in both countries. Full article

Accurate and rapid prediction of structural responses to seismic loading is critical for ensuring structural safety. Recently, there has been active research focusing on the application of deep learning techniques, including Physics-Informed Neural Networks (PINNs) and Long Short-Term Memory (LSTM) networks, to predict the dynamic behavior of structures. While these methods have shown promise, each comes with distinct limitations. PINNs offer physical consistency but struggle with capturing long-term temporal dependencies in nonlinear systems, while LSTMs excel in learning sequential data but lack physical interpretability. To address these complementary limitations, this study proposes a hybrid LSTM-PINN model, combining the temporal learning ability of LSTMs with the physics-based constraints of PINNs. This hybrid approach allows the model to capture both nonlinear, time-dependent behaviors and maintain physical consistency. The proposed model is evaluated on both single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) structural systems subjected to the El-Centro ground motion. For validation, the 1940 El-Centro NS earthquake record was used, and the ground acceleration data were normalized and discretized for numerical simulation. The proposed LSTM-PINN is trained under the same conditions as the conventional PINN models (e.g., same optimizer, learning rate, and loss structure), but with fewer training epochs, to evaluate learning efficiency. Prediction accuracy is quantitatively assessed using mean error and mean squared error (MSE) for displacement, velocity, and acceleration, and results are compared with PINN-only models (PINN-1, PINN-2). The results show that LSTM-PINN consistently achieves the most stable and precise predictions across the entire time domain. Notably, it outperforms the baseline PINNs even with fewer training epochs. Specifically, it achieved up to 50% lower MSE with only 10,000 epochs, compared to the PINN’s 50,000 epochs, demonstrating improved generalization through temporal sequence learning. This study empirically validates the potential of physics-guided time-series AI models for dynamic structural response prediction. The proposed approach is expected to contribute to future applications such as real-time response estimation, structural health monitoring, and seismic performance evaluation. Full article

A novel barna-like virus was found to be associated with field-collected Afrina sporoboliae plant-parasitic nematodes. The positive-sense, single-stranded RNA genome of this virus, named Afrina barna-like virus (AfBLV), comprises 4020 nucleotides encoding four open reading frames (ORFs). ORF 1 encodes a protein product spanning a transmembrane, a peptidase, and VPg domains, whereas an overlapping ORF 2 encodes an RNA-dependent RNA polymerase (RdRP). ORF2 may be expressed via a −1 translational frameshift. In phylogenetic reconstructions, the RdRP of AfBLV was placed inside a separate clade of barna and barna-like viruses related to but distinct from the genera in the Solemoviridae and Alvernaviridae families, within the overall lineage of Sobelivirales. ORF 3 of AfBLV encodes a protein product of 206 amino acids (aa) long with homology to a putative protein encoded by a similarly positioned gene of an uncharacterized virus sequence identified previously as Barnaviridae sp. ORF 4 encodes a 161 aa protein with no significant similarities to sequences in the GenBank databases. AfBLV is the first barnavirus found in a nematode. Sequence comparisons of the AfBLV genome and genomes of other barna-like viruses suggested that a recombination event was involved in the evolution of AfBLV. Analyses of the phylogeny of RdRPs and genome organizations of barna-like and solemo-like viruses support the re-classification of Barnavirus and Dinornavirus genera as members of the Solemoviridae family. Full article

Background and Objectives: Uterine cancer is the most common gynaecologic malignancy in developed countries, yet the psychosocial sequelae of treatment are incompletely described. This prospective, single-centre study quantified six-month changes in the quality of life (QoL) and perceived stress in women with newly diagnosed uterine cancer and explored clinical moderators of change. Methods: Participants completed four validated self-report questionnaires: the 36-item Short-Form Health Survey (SF-36), the 26-item World Health Organization Quality-of-Life-BREF (WHOQOL-BREF), the 30-item EORTC QLQ-C30 and the 10-item Perceived Stress Scale (PSS-10) before therapy and again six months after surgery ± adjuvant chemoradiation. Subgroup analyses were performed for stage (FIGO I–II vs. III–IV). Results: Mean SF-36 Physical Functioning improved from 58.7 ± 12.1 to 63.1 ± 12.6 (Δ = +4.4 ± 7.3; p = 0.000, d = 0.36). PSS declined from 24.1 ± 5.6 to 20.8 ± 5.4 (Δ = −3.3 ± 5.0; p < 0.001, d = 0.66). The WHOQOL-BREF Physical and Psychological domains rose by 4.4 ± 6.9 and 3.5 ± 7.3 points, respectively (both p < 0.01). EORTC QLQ-C30 Global Health increased 5.1 ± 7.6 points (p < 0.001) with parallel reductions in fatigue (−5.4 ± 9.0) and pain (−4.8 ± 8.6). Advanced-stage patients showed larger reductions in stress (ΔPSS −3.5 ± 2.5 vs. −2.3 ± 2.3; p = 0.036) but similar QoL gains. ΔPSS correlated inversely with ΔWHOQOL Psychological (r = −0.53) and ΔSF-36 Mental Health (r = −0.49) and positively with ΔEORTC Global Health (r = −0.42) (all p < 0.001). Conclusions: Over six months, multimodal uterine cancer treatment was associated with clinically meaningful QoL improvements and moderate stress reduction. Greater stress relief paralleled superior gains in psychological and global health indices, highlighting the importance of integrative survivorship care. Full article

Ships play a crucial role in modern society, serving purposes such as marine transportation, tourism, and exploration. Malfunctions or defects in the main engine, which is a core component of ship operations, can disrupt normal functionality and result in substantial financial losses. Consequently, early fault diagnosis of abnormal engine conditions is critical for effective maintenance. In this paper, we propose a deep hybrid model for fault diagnosis of ship main engines, utilizing exhaust gas temperature data. The proposed model utilizes both time-domain features (TDFs) and time-series raw data. In order to effectively extract features from each type of data, two distinct feature extraction networks and an attention module-based classifier are designed. The model performance is evaluated using real-world cylinder exhaust gas temperature data collected from the large ship low-speed two-stroke main engine. The experimental results demonstrate that the proposed method outperforms conventional methods in fault diagnosis accuracy. The experimental results demonstrate that the proposed method improves fault diagnosis accuracy by 6.146% compared to the best conventional method. Furthermore, the proposed method maintains superior performanceeven in noisy environments under realistic industrial conditions. This study demonstrates the potential of using exhaust gas temperature using a single sensor signal for data-driven fault detection and provides a scalable foundation for future multi-sensor diagnostic systems. Full article

Single image super-resolution is the inverse problem of reconstructing a high-resolution image from its low-resolution counterpart. Although recent Transformer-based architectures leverage global context integration to improve reconstruction quality, they often overlook frequency-specific characteristics, resulting in the loss of high-frequency information. To address this limitation, we propose the Dual Frequency Adaptive Network (DFAN). DFAN first decomposes the input into low- and high-frequency components via Stationary Wavelet Transform. In the low-frequency branch, Swin Transformer layers restore global structures and color consistency. In contrast, the high-frequency branch features a dedicated module that combines Directional Convolution with Residual Dense Blocks, precisely reinforcing edges and textures. A frequency fusion module then adaptively merges these complementary features using depthwise and pointwise convolutions, achieving a balanced reconstruction. During training, we introduce a frequency-aware multi-term loss alongside the standard pixel-wise loss to explicitly encourage high-frequency preservation. Extensive experiments on the Set5, Set14, BSD100, Urban100, and Manga109 benchmarks show that DFAN achieves up to +0.64 dBpeak signal-to-noise ratio, +0.01 structural similarity index measure, and −0.01learned perceptual image patch similarity over the strongest frequency-domain baselines, while also delivering visibly sharper textures and cleaner edges. By unifying spatial and frequency-domain advantages, DFAN effectively mitigates high-frequency degradation and enhances SISR performance. Full article

This study presents the design and development of a structurally optimized mobile robotic system with a machine learning-based energy-efficient path planning framework. Topology optimization (TO) and finite element analysis (FEA) were applied to reduce structural weight while maintaining mechanical integrity. The optimized components were manufactured using Fused Deposition Modeling (FDM) with ABS (Acrylonitrile Butadiene Styrene) material. A custom power analysis tool was developed to compare energy consumption between the optimized and initial designs. Real-world current consumption data were collected under various terrain conditions, including inclined surfaces, vibration-inducing obstacles, gravel, and direction-altering barriers. Based on this dataset, a path planning model was developed using machine learning algorithms, capable of simultaneously optimizing both energy efficiency and path length to reach a predefined target. Unlike prior works that focus separately on structural optimization or learning-based navigation, this study integrates both domains within a single real-world robotic platform. Performance evaluations demonstrated superior results compared to traditional planning methods, which typically optimize distance or energy independently and lack real-time consumption feedback. The proposed framework reduces total energy consumption by 5.8%, cuts prototyping time by 56%, and extends mission duration by ~20%, highlighting the benefits of jointly applying TO and ML for sustainable and energy-aware robotic design. This integrated approach addresses a critical gap in the literature by demonstrating that mechanical light-weighting and intelligent path planning can be co-optimized in a deployable robotic system using empirical energy data. Full article

With the continuous development of the Internet, people have put forward higher requirements for the stability and availability of the network. Although we constantly strive to take measures to avoid network failures, it is undeniable that network failures are unavoidable. Therefore, in this situation, enhancing the stability and reliability of the network to cope with possible network failures has become particularly crucial. Therefore, researching and developing high fault protection rate intra-domain routing protection schemes has become an important topic and is the subject of this study. This study aims to enhance the resilience and service continuity of networks in the event of failures by proposing innovative routing protection strategies. The existing methods, such as Loop Free Alternative (LFA) and Equal Cost Multiple Paths (ECMP), have some shortcomings in terms of fast fault detection, fault response, and fault recovery processes, such as long fault recovery time, limitations of routing protection strategies, and requirements for network topology. In response to these issues, this article proposes a new routing protection scheme, which is an intra-domain routing protection scheme based on the minimum cross-degree backup path. The core idea of this plan is to find the backup path with the minimum degree of intersection with the optimal path, in order to avoid potential fault areas and minimize the impact of faults on other parts of the network. Through comparative analysis and performance evaluation, this scheme can provide a higher fault protection rate and more reliable routing protection in the network. Especially in complex networks, this scheme has more performance and protection advantages than traditional routing protection methods. The proposed scheme in this paper exhibits a high rate of fault protection across multiple topologies, demonstrating a fault protection rate of 1 in the context of real topology. It performs commendably in terms of path stretch, evidenced by a figure of 1.06 in the case of real topology Ans, suggesting robust path length control capabilities. The mean intersection value is 0 in the majority of the topologies, implying virtually no common edge between the backup and optimal paths. This effectively mitigates the risk of single-point failure. Full article

Single-image super-resolution (SISR) plays a critical role in enhancing visual quality for real-world applications, including industrial inspection and embedded vision systems. While deep learning-based approaches have made significant progress in SR, existing lightweight SR models often fail to accurately reconstruct high-frequency textures, especially under complex degradation scenarios, resulting in blurry edges and structural artifacts. To address this challenge, we propose a Dense Residual Fused Attention Network (DRFAN), a novel lightweight hybrid architecture designed to enhance high-frequency texture recovery in challenging degradation conditions. Moreover, by coupling convolutional layers and attention mechanisms through gated interaction modules, the DRFAN enhances local details and global dependencies with linear computational complexity, enabling the efficient utilization of multi-level spatial information while effectively alleviating the loss of high-frequency texture details. To evaluate its effectiveness, we conducted ×4 super-resolution experiments on five public benchmarks. The DRFAN achieves the best performance among all compared lightweight models. Visual comparisons show that the DRFAN restores more accurate geometric structures, with up to +1.2 dB/+0.0281 SSIM gain over SwinIR-S on Urban100 samples. Additionally, on a domain-specific rice grain dataset, the DRFAN outperforms SwinIR-S by +0.19 dB in PSNR and +0.0015 in SSIM, restoring clearer textures and grain boundaries essential for industrial quality inspection. The proposed method provides a compelling balance between model complexity and image reconstruction fidelity, making it well-suited for deployment in resource-constrained visual systems and industrial applications. Full article

The Retabit platform is a data-driven tool designed to bridge the gap between building rehabilitation and urban regeneration by integrating energy, economic, and social dimensions into a single framework. Leveraging multiple public data sources, the platform provides actionable insights to local and national authorities, public housing agencies, urban planners, energy service providers, and research institutions, helping to align renovation initiatives with broader urban transformation goals and climate action objectives. The platform consists of two main components: Analyse, for examining building conditions through multidimensional indicators, and Plan, for designing and simulating renovation projects. Retabit contributes to more transparent and informed decision-making, encourages collaboration across sectors, and addresses long-term sustainability by incorporating participatory planning and impact evaluation. Its scalable structure makes it applicable across diverse geographic areas, policy contexts, and domains linked to sustainable urban development. Full article

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are a deadly subtype of soft tissue sarcoma for which effective therapeutic options are lacking. Currently, the best treatment for MPNSTs is complete surgical resection with wide negative margins, but this is often complicated by the tumor size and location and/or the presence of metastases. Radiation or chemotherapy may be combined with surgery, but patient responses are poor. Targeted treatments, including small-molecule inhibitors of oncogenic proteins such as mitogen-activated protein kinase kinase (MEK), cyclin-dependent kinases 4 and 6 (CDK4/6), and Src-homology 2 domain-containing phosphatase 2 (SHP2), are promising therapeutics for MPNSTs, especially when combined together, but they have yet to gain approval. Immunotherapeutic approaches have been revolutionary for the treatment of some other cancers, but their utility as single agents in sarcoma is limited and not approved for MPNSTs. The immunosuppressive niche of MPNSTs is thought to confer inherent treatment resistance, particularly to immunotherapies. Remodeling an inherently “cold” tumor microenvironment into a “hot” immune milieu to bolster the anti-tumor activity of immunotherapies is of great interest throughout the cancer community. This review focuses on novel therapeutics that target dysregulated factors and pathways in MPNSTs, as well as different types of immunotherapies currently under investigation for this disease. We also consider how certain therapeutics may be combined to remodel the MPNST immune microenvironment and thereby generate a durable anti-tumor immune response to immunotherapy. Full article

Recent advancements in Earth observation technologies have accelerated remote sensing (RS) data acquisition, yet cross-domain semantic segmentation remains challenged by domain shifts. Traditional unsupervised domain adaptation (UDA) methods often rely on computationally intensive and unstable generative adversarial networks (GANs). This study introduces elevation-aware domain adaptation (EADA), a multi-task framework that integrates elevation estimation (via digital surface models) with semantic segmentation to address distribution discrepancies. EADA employs a shared encoder and task-specific decoders, enhanced by a spatial attention-based feature fusion module. Experiments on Potsdam and Vaihingen datasets under cross-domain settings (e.g., Potsdam IRRG → Vaihingen IRRG) show that EADA achieves state-of-the-art performance, with a mean IoU of 54.62% and an F1-score of 65.47%, outperforming single-stage baselines. Elevation awareness significantly improves the segmentation of height-sensitive classes, such as buildings, while maintaining computational efficiency. Compared to multi-stage approaches, EADA’s end-to-end design reduces training complexity without sacrificing accuracy. These results demonstrate that incorporating elevation data effectively mitigates domain shifts in RS imagery. However, lower accuracy for elevation-insensitive classes suggests the need for further refinement to enhance overall generalizability. Full article

The increasing demand for high-resolution subsurface imaging has driven significant advances in geophysical inversion methodologies. Despite the availability of various software packages for electrical resistivity tomography (ERT), time-domain induced polarization (TDIP), and seismic refraction tomography (SRT), significant challenges remain in selecting optimal regularization parameters and in the effective incorporation of prior information into the inversion process. In this study, we propose new strategies to address these critical issues by developing versatile and flexible tools for electrical and seismic tomographic data inversion. Specifically, we introduce two automated procedures for regularization parameter selection: a full loop method (fixed-λ optimization) where the regularization parameter is kept constant during the inversion process, and a single-inversion approach (automaticLam) where it varies throughout the iterations. Additionally, we present a novel constrained inversion strategy that effectively balances prior information, minimizes data misfit, and promotes model smoothness. This approach is thoroughly compared with the state-of-the-art methods, demonstrating its superiority in maintaining model reliability and reducing dependence on subjective operator choices. Applications to synthetic, laboratory, and real-world case studies validate the efficacy of our strategies, showcasing their potential to enhance the robustness of geophysical models and standardize the inversion process, ensuring its independence from operator decisions. Full article

The whitefly Bemisia tabaci (Hemiptera: Aleyrodoidea) causes direct feeding damage to crop plants and transmits pathogenic plant viruses, thereby threatening global food security. Although whitefly-infecting RNA viruses are known and proposed as biocontrol agents, no insect DNA virus has been found in any member of Aleyrodoidea. Using rolling circle amplification (RCA) of viral DNA from whiteflies collected from crop fields in Morocco, followed by Illumina sequencing of the RCA products, we found a novel insect single-stranded (ss) DNA parvovirus (family Parvoviridae) in addition to plant ssDNA geminiviruses transmitted by whiteflies. Based on its genome organization with inverted terminal repeats and evolutionarily conserved proteins mediating viral DNA replication (NS1/Rep) and encapsidation (VP), encoded on the forward and reverse strands, respectively, we named this virus Bemisia tabaci ambidensovirus (BtaDV) and classified it as a founding member of a new genus within the subfamily Densovirinae. This subfamily also contains three distinct genera of ambisense densoviruses of other hemipteran insects (Aphidoidea, Coccoidea, and Psylloidea). Furthermore, we provide evidence for the genetic variants of BtaDV circulating in whitefly populations and for its partial sequences integrated into the B. tabaci genome, with one integrant locus potentially expressing a fusion protein composed of viral Rep endonuclease and host DNA-binding domains. This suggests a long-term virus-host interaction and neofunctionalization of BtaDV-derived endogenous viral elements. Full article