Search Results (2,308)

Amidrapid advances in intelligent medicine, decoding brain activity from electroencephalogram (EEG) signals has emerged as a critical technical frontier for brain–computer interfaces and medical AI systems. Given the inherent spatial resolution limitations of an EEG, researchers frequently integrate functional magnetic resonance imaging (fMRI) to enhance neural activity representation. However, fMRI acquisition is inherently complex. Consequently, efforts increasingly focus on cross-modal transformation methods that map EEG signals to fMRI data, thereby extending EEG applications in neural mechanism studies. The central challenge remains generating high-fidelity fMRI images from EEG signals. To address this, we propose a diffusion model-based framework for cross-modal EEG-to-fMRI generation. To address pronounced noise contamination in electroencephalographic (EEG) signals acquired via simultaneous recording systems and temporal misalignments between EEGs and functional magnetic resonance imaging (fMRI), we first apply Fourier transforms to EEG signals and perform dimensionality expansion. This constructs a spatiotemporally aligned EEG–fMRI paired dataset. Building on this foundation, we design an EEG encoder integrating a multi-layer recursive spectral attention mechanism with a residual architecture.In response to the limited dynamic mapping capabilities and suboptimal image quality prevalent in existing cross-modal generation research, we propose a diffusion-model-driven EEG-to-fMRI generation algorithm. This framework unifies the EEG feature encoder and a cross-modal interaction module within an end-to-end denoising U-Net architecture. By leveraging the diffusion process, EEG-derived features serve as conditional priors to guide fMRI reconstruction, enabling high-fidelity cross-modal image generation. Empirical evaluations on the resting-state NODDI dataset and the task-based XP-2 dataset demonstrate that our EEG encoder significantly enhances cross-modal representational congruence, providing robust semantic features for fMRI synthesis. Furthermore, the proposed cross-modal generative model achieves marked improvements in structural similarity, the root mean square error, and the peak signal-to-noise ratio in generated fMRI images, effectively resolving the nonlinear mapping challenge inherent in EEG–fMRI data. Full article

With the expansion of unmanned aerial vehicles (UAVs) into complex three-dimensional (3D) terrains for reconnaissance, rescue, and related missions, traditional path planning methods struggle to meet multi-constraint and multi-objective requirements. Existing swarm intelligence algorithms, limited by the “no free lunch” theorem, also face challenges when the standard Information Acquisition Optimizer (IAO) is applied to such tasks, including low exploration efficiency in high-dimensional search spaces, rapid loss of population diversity, and improper boundary handling. To address these issues, this study proposes a Multi-Strategy Enhanced Information Acquisition Optimizer (MEIAO). First, a Levy Flight-based information collection strategy is introduced to leverage its combination of short-range local searches and long-distance jumps, thereby broadening global exploration. Second, an adaptive differential evolution operator is designed to dynamically balance exploration and exploitation via a variable mutation factor, while crossover and greedy selection mechanisms help maintain population diversity. Third, a globally guided boundary handling strategy adjusts out-of-bound dimensions to feasible regions, preventing the generation of low-quality paths. Performance was evaluated on the CEC2017 (dim = 30/50/100) and CEC2022 (dim = 10/20) benchmark suites by comparing MEIAO with eight algorithms, including VPPSO and IAO. Based on the mean, standard deviation, Friedman mean rank, and Wilcoxon rank-sum tests, MEIAO demonstrated superior performance in local exploitation of unimodal functions, global exploration of multimodal functions, and complex adaptation on composite functions while exhibiting stronger robustness. Finally, MEIAO was applied to 3D mountainous UAV path planning, where a cost model considering path length, altitude standard deviation, and turning smoothness was established. The experimental results show that MEIAO achieved an average path cost of 253.9190, a 25.7% reduction compared to IAO (341.9324), with the lowest standard deviation (60.6960) among all algorithms. The generated paths were smoother, collision-free, and achieved faster convergence, offering an efficient and reliable solution for UAV operations in complex environments. Full article

Promoting breakthrough innovation is a critical strategy for overcoming technological bottlenecks and addressing “chokepoint” challenges, especially for emerging economies. This paper constructs a two-tier innovation network comprising collaborative R&D and technology transaction subnetworks. Using panel data from Chinese A-share listed companies between 2008 and 2022, we empirically examine the impact of network embeddedness on firm breakthrough innovation in the artificial intelligence industry and explore the moderating effect of enterprise digitalization. The results reveal a U-shaped relationship between embeddedness breadth and breakthrough innovation, and an inverted U-shaped relationship between embeddedness depth and breakthrough innovation. The heterogeneous resource acquisition mediates these nonlinear relationships. As a firm’s digitalization intensity increases, the U-shaped and inverted U-shaped relationships between embeddedness dimensions and breakthrough innovation are significantly amplified. This study deepens our understanding of the mechanisms and boundary conditions by which network embeddedness affects firm innovation and provides new theoretical insights for fostering breakthrough innovation in emerging economies. Full article

Background/Objectives: Autism spectrum disorder (ASD) is a neurodevelopmental disorder typically characterized by impaired social communication. Previous reports have postulated gut microbiota to be an important non-genetic factor affecting ASD-like phenotypes in mice, as germ-free (GF) mice show impaired social communication. Results: In this study, we identified hippuric acid (HA) as a metabolite generated via a gut microbiome-dependent mechanism that plays a role in the acquisition of social behavior during mouse development. We discovered that oral or intraperitoneal HA administration to GF mice normalizes their social behavior. Furthermore, HA administration restored oxytocin expression in the hypothalamic paraventricular nucleus and secretin expression in the subfornical organ, suggesting that HA may activate the secretin–oxytocin system to influence the social behavior of mice. Conclusions: These findings indicate that HA may serve as an important gut microbiome-dependent mediator affecting the brain mechanisms involved in regulating social behavior. Full article

The microbiome of howler monkeys is being studied as a potential indicator of forest health. This explorative research aimed to analyze the microbiome, antibiotic resistance genes, and virulence factors of the howler monkey (Alouatta seniculus) in two Colombian Andean forests. A total of six samples were collected from three monkeys in two different forests. The samples were processed and sequenced using 16S rRNA V3-V4 metabarcoding and shotgun metagenomics. No significant differences in microbial diversity were observed between locations. A total of 43 possible resistance genes were identified, 11 of which were associated with plasmids, while 66 virulence genes were detected. The bacterial genera with the highest number of resistance genes were Escherichia and Enterococcus, whereas Escherichia and Citrobacter exhibited the highest number of virulence factors. The bacteria were predominantly resistant to fluoroquinolones, macrolides and beta-lactams, while adherence was the dominant virulence mechanism. This exploratory study suggests that the locations provide similar habitats for howler monkeys and that the presence of resistance genes is primarily due to intrinsic bacterial resistance mechanisms and natural resistance in wild populations despite the environmental presence of bacterial genera with resistance genes and virulence factors. However, acquisition through interaction with domestic animals was not evaluated. Full article

Coastal erosion represents a pervasive issue affecting numerous coastal regions, stemming from both natural phenomena and anthropogenic activities. Notably, a substantial proportion, approximately 70%, of sandy beaches globally exhibit a retreating trend. This study aims to clarify the coastal erosion dynamics that have undergone significant transformation in recent decades, exerting a profound impact on the coastal systems along the Italian peninsula. Specifically, this study investigates a segment of the Lazio coastline corresponding to the Foce Verde—Rio Martino beach area in the Latina municipality. Geographic Information System (GIS) software, such as ArcGIS Pro 3.5.0, was employed for geospatial data acquisition, enabling the precise delineation and documentation of shoreline fluctuations within this coastal expanse spanning from 2003 to 2019 (the inclusion criteria for the core research period of the Bachelor’ s thesis, along with the graduation year). The principal objective of this investigation is to furnish a comprehensive overview of the metamorphosis observed in the Latina coastline during the specified temporal interval. This analysis will encompass an evaluation of the coastal defense mechanisms employed, encompassing both “hard” (engineered structures) and “soft” (natural or nature-based) interventions, within this temporal context. Full article

Vocational training can lead to higher employment rates and improved incomes, particularly for young women in low-resource settings like Kampala’s slums. Despite these benefits, further research is needed to understand the full impact and mechanisms of vocational training on youth in low-resource environments. In 2022, a focus group project, part of a larger study, involved 60 women aged 18 to 24, recruited from three Youth Support Centers operated by the Uganda Youth Development Link (UYDEL) in Kampala. Six focus groups (about 10 women in each group) were held to explore urban stress and how vocational training might mitigate social and environmental stressors and improve mental health. Data analysis conducted using NVivo software identified five key themes: economic benefits, skill development, building confidence and self-esteem, improved social and behavioral well-being, and enhanced lifestyle and quality of life. This formative research underscores that vocational training benefits young women, highlighting outcomes such as job acquisition, financial empowerment, and skill development. Additionally, self-esteem and confidence development emphasize the training’s role in fostering mental health and agency and addressing gender inequality. These findings underscore the value of vocational training in enhancing the mental health and overall well-being of young women and suggest areas for future research for how to best optimize and scale these programs in low-resource settings. Full article

The efficacy of data-driven automatic target recognition (ATR) algorithms relies on the prior knowledge acquired from the target sample set. However, the lack of knowledge of high-value unknown target samples hinders the practical application of existing ATR models, as the acquisition of this SAR imagery is often challenging. In this paper, we propose an out-of-distribution knowledge inference-based approach for the implementation of open-set-recognition tasks in SAR imagery. The proposed method integrates two modules: out-of-distribution feature inference and a knowledge-sharing retrain mechanism. First, the proposed out-of-distribution feature inference module aims to provide the requisite prior knowledge for the ATR model to effectively recognize unknown target samples. Furthermore, the aforementioned module also employs a compact feature extraction scheme to mitigate the potential overlap between the constructed out-of-distribution feature distribution and the known sample feature distribution. Finally, the proposed method employs the novel knowledge-sharing retraining mechanism to learn prior knowledge of unknown SAR target samples. Several experimental results show the superiority of the proposed approach based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. Some ablation experiments also demonstrate the role of each module of the proposed approach. Even when one category of target sample information is completely absent from the training set, the recognition accuracy of the proposed approach still achieves $90.31\%$. Full article

Grouted sleeve connections are widely employed in the substructures of prefabricated bridges. After installation, the grout filling condition inside the sleeves cannot be directly inspected, while grouting defects may significantly compromise the mechanical performance of the piers. This study investigates the feasibility of using the non-destructive impact-echo method to detect grouting defects in sleeves. Finite element simulation was conducted to analyze the influence of the distance between the impact point and the signal acquisition point on detection accuracy, revealing that a distance of 40–60 mm yields optimal results. Experimental findings demonstrate that the method can effectively identify grouting defects in double-row sleeves, although it cannot precisely locate the defective sleeve. A novel analytical approach is proposed, using the thickness frequency and its modes of fully grouted specimens as a benchmark. By comparing thickness frequencies at different measurement points, grout quality can be intuitively evaluated. Validation using a six-sleeve model with varying grouting densities confirmed the method’s effectiveness in detecting grouting defects in non-boundary sleeves and its practical applicability in engineering. Full article

Therapeutic resistance remains a critical barrier in oncology, frequently leading to cancer relapse after initial treatment response. Growing evidence suggests the presence of drug-tolerant persisters (DTPs), a rare subpopulation of cancer cells that survives chemotherapy by entering a reversible specific adaptation. Unlike classical cell resistance, the DTP phenotype is independent of genetic changes and maintained through dynamic regulatory mechanisms. DTPs are phenotypically heterogeneous and can exhibit stem-like and quiescent cell phenotypes, non- or slow proliferation, and remarkable plasticity due to a di-pause-like state and executing epithelial–mesenchymal transition (EMT) or transdifferentiation processes. Despite advances in research, the molecular mechanisms underlying DTPs’ biology and their role in cancer relapse remain only partially understood. The review summarizes the current progress in processes that lead to the acquisition of cellular persistence status, which, in turn, constitute areas of vulnerability that can be exploited in cancer therapy. We highlight anti-DTP therapeutic strategies, including epigenetic modification, cell signaling and transcriptional regulation, metabolic reprogramming, and modification of cell interactions within the tumor microenvironment. Furthermore, we focus on the potential role of nanomaterials in the combat against DTPs. Nanoparticles not only act as part of the drug delivery process, enabling precise DTP targeting and enhancing intracellular drug accumulation, but their intrinsic properties can also be used to eradicate DTPs directly or by enhancing the effectiveness of other therapeutic strategies. The integrated approach offers strong potential to eliminate tumor persistence, prevent recurrence, and improve long-term patient outcomes beyond conventional therapies. Full article

In bone conduction (BC) hearing, sound is transmitted directly to the cochlea via skull vibrations, bypassing the outer and middle ear. This provides a therapeutic option for patients with conductive or mixed hearing loss and single-sided deafness. Although finite-element models have advanced understanding of the mechanisms underlying BC, progress toward personalized treatment strategies remains limited by a lack of standardized, experimentally validated, subject-specific models. This study proposes a hierarchical validation framework to support the development and validation of individualized computational models of the human head under BC stimulation. The framework spans four anatomical levels: system, subsystems, structures, and tissues. This approach enables systematic acquisition of data from intact cadaver heads down to isolated material domains. To demonstrate the applications of the framework, an experimental study was conducted on a single cadaver head, targeting three levels: the intact head (system), extracted bone pieces (structures), and isolated cortical layers (tissues). Subsystems were not addressed. High-resolution photon-counting computed tomography (CT) and energy-integrating cone-beam CT were used to acquire anatomical data. One-dimensional laser Doppler vibrometry was used to capture vibrational responses of bone pieces and cortical layers under wet and dry conditions. Representative results were analyzed to assess the impact of preparation state on resonance behavior. Comparative analysis showed that photon-counting CT provided superior structural resolution compared with energy-integrating cone-beam CT, particularly at the full-head (system) level. Vibrational measurements at the structure and tissue levels from the same anatomical region revealed broadly consistent resonance vibration patterns, enabling comparison of resonance frequencies. The influence of hydration state and thickness reduction on vibrational behavior was highlighted. The proposed framework provides a scalable methodology for validation of subject-specific BC models with the potential for more accurate BC simulations based on the hypothesis of functional variability rooted in anatomical variability. Obvious use cases would include the development of improved hearing aid designs and personalized treatments. In parallel, a successful correlation of anatomical and functional variability can serve as inspiration for design principles of metamaterials. Full article

The industrialization of using crop straw as energy is currently hindered by systemic bottlenecks, including high collection and storage costs, a poorly coordinated industrial chain, and underdeveloped market mechanism. This study takes Guangdong province as a case study to construct a tripartite evolutionary game model on the transition of straw to energy among the government, enterprises, and farmers. Different from previous studies that focused on the strategy of penalizing the open burning of straw by farmers, this work investigated the cooperation of farmers for straw removal from field, the operational strategies of enterprises for straw utilization as energy, and the selection of government-guided incentive policies. It analyzes the behavioral evolution of these stakeholders under various incentive policies and cooperative scenarios. Numerical simulations were performed to identify the system’s evolutionary stable strategies and assess the potential of expanding straw for energy utilization. It indicated that mild government intervention could lead to a stable equilibrium through facilitating the removal of straw from fields and the utilization of straw as energy by enterprises. Farmers were sensitive to the fluctuation of acquisition price, and their willingness to cooperate would be negatively impacted by a large-scale price reduction. Enterprise expansion was exposed to significant risk under intensive policy intervention. The feasible pathway to increase the proportion of straw utilization as energy in Guangdong began at a small scale. Under mild incentive policies, a scenario targeting a 20% increase was more likely to achieve a market equilibrium for large-scale production than that targeting a 55% increase. The government should draw up positive incentive policies to promote the utilization of straw as energy. By guiding farmers in straw removal from the field and improving the energy enterprises’ competitiveness, the government should curb irrational industry expansion and corporate speculation, and shift from investment support to incentive policies. Meanwhile, the ecological construction of industry and supply chains should be enhanced, and the scale should be used to reduce the high supply-side costs of the straw. It would overcome the central barrier to the commercialization of straw utilization as energy. This work sets an example for conducting dynamic analysis of multi-stakeholder interactions for straw utilization. Full article

Hyperspectral imagers on satellites obtain the fine spectral signatures that are essential in distinguishing one material from another but at the expense of a limited spatial resolution. Enhancing the latter is thus a desirable preprocessing step in order to further improve the detection capabilities offered by hyperspectral images for downstream tasks. At the same time, there is growing interest in deploying inference methods directly onboard satellites, which calls for lightweight image super-resolution methods that can be run on the payload in real time. In this paper, we present a novel neural network design, called Deep Pushbroom Super-Resolution (DPSR), which matches the pushbroom acquisition of hyperspectral sensors by processing an image line by line in the along-track direction with a causal memory mechanism to exploit previously acquired lines. This design greatly limits the memory requirements and computational complexity, achieving onboard real-time performance, i.e., the ability to super-resolve a line in the time that it takes to acquire the next one, on low-power hardware. Experiments show that the quality of the super-resolved images is competitive with or even surpasses that of state-of-the-art methods that are significantly more complex. Full article

Recirculating Aquaculture Systems (RAS) represent a high-density, controlled-environment fish farming method that requires constant monitoring of critical water quality parameters to ensure high water quality and fish stock health. Manual monitoring is labor-intensive and prone to error, creating a significant risk of catastrophic loss. This work presents the design and implementation of an automated monitoring system built on a Raspberry Pi platform that integrates multiple sensors (temperature, pH, conductivity, water level, and pumps’ functionality) to provide continuous, real-time data acquisition. A key feature is a software-based outlier rejection algorithm that enhances data integrity, and the code is freely available on the GitHub platform for further development. The collected data has been published on the ThingsBoard IoT platform for visualization and historical analysis via the HTTPS protocol. Furthermore, the system implements a proactive alerting mechanism using the Pushover notification service to deliver instant mobile alerts when parameters deviate from predefined thresholds. Commercial solutions cost in the order of thousands of euros, have high maintenance and operational costs, and pose integration and compatibility challenges. This solution provides a reliable, scalable, and cost-effective method for maintaining optimal conditions in a RAS, with hardware costs of less than EUR 150. Full article

The gut microbiota of Drosophila melanogaster offers a simplified yet powerful system to study conserved mechanisms of host–microbe interactions. Unlike the highly complex mammalian gut microbiota, which includes hundreds of species, the fly gut harbors a small and defined community dominated by Lactobacillus and Acetobacter. Despite its low diversity, this microbiota exerts profound effects on host physiology. Commensal bacteria modulate nutrient acquisition, regulate insulin/TOR signaling, and buffer dietary imbalances to support metabolic homeostasis and growth. They also influence neural and behavioral traits, including feeding preferences, mating, and aggression, through microbial metabolites and interactions with host signaling pathways. At the immune level, microbial molecules such as peptidoglycan, acetate, uracil, and cyclic dinucleotides activate conserved pathways including Imd, Toll, DUOX, and STING, balancing antimicrobial defense with tolerance to commensals. Dysbiosis disrupts this equilibrium, accelerating aging, impairing tissue repair, and contributing to tumorigenesis. Research in Drosophila demonstrates how a low-diversity microbiota can shape systemic host biology, offering mechanistic insights relevant to human health and disease. Full article