Search Results (8,332)

Purpose: Tumor location influences survival in bladder cancer, potentially due to genetic heterogeneity driven by distinct embryological origins and structural compositions. We investigate location-specific somatic gene alterations (GAs) and their potential clinical implications in muscle-invasive bladder cancer (MIBC). Methods: We explored the role of the intra-bladder tumor location in determining survival and underlying genetic alterations in MIBC patients using multiple large independent databases. We analyzed the tumor location’s impact on survival using the Surveillance, Epidemiology, and End Results (SEER) database and validated these findings using cBioPortal (CBP), which also contains gene sequencing data, enabling a comparison of GA frequency by tumor location. We investigated GA combinations to identify potential synthetic lethal (SL) combinations and co-occurrence signatures for survival prediction. Using the ROC Plotter database, we explored how significantly altered genes affect the response to immune checkpoint inhibitors (ICI). Results: An analysis of 6712 SEER and 570 CBP patients revealed significant (p < 0.001) differences in overall survival stratified by tumor location, with trigone tumors showing the worst survival. Genomic analysis identified 35 genes with location-specific alteration frequencies. Three of these genes, CDKN2A, SPTAN1, and BIRC6, were significantly predictive of ICI response, and three genes were uniquely associated with a specific location: BPTF (anterior wall), RYR1, and OBSCN (dome). Furthermore, we identified 349 SL pairs from the 35 significantly altered genes, and a co-occurrence analysis revealed two novel gene pairs associated with improved survival. Conclusions: Intra-bladder tumor location determines survival and distinct genetic profiles in MIBC. These location-specific alterations predict ICI response and identify novel synthetic lethal targets, guiding precision oncology. Full article

Background/Objectives: Current clinical evidence suggests that cannabidiol (CBD) demonstrates therapeutic potential in the management of chronic pain, particularly in conditions involving inflammation. However, its therapeutic potential is severely limited by poor oral bioavailability, extensive first-pass metabolism, and the need for frequent high-dose administration, which compromises patient adherence and tolerability. Long-acting injectable (LAI) delivery systems offer a strategy to overcome these limitations by providing sustained plasma concentrations and reducing dosing frequency. This study aimed to develop and optimise CBD-loaded poly (lactic-co-glycolic acid) (PLGA) microspheres for LAI delivery and to evaluate poly(2-ethyl-2-oxazoline) (POx) as a functional and biocompatible alternative to the conventionally used poly (ethylene glycol) (PEG). Methods: CBD-loaded microspheres were prepared using emulsion–solvent evaporation technique. The formulations were optimised based on entrapment efficiency (EE), drug loading (DL), particle size distribution, surface morphology, thermal behaviour, in vitro release kinetics, and cytocompatibility using NIH 3T3 fibroblasts. Multiple in vitro release methodologies, including dialysis bag, shaking-flask, and USP Apparatus IV, were evaluated to identify the most discriminative and practical approach for long-term release assessment. Results: The optimised POx-based microspheres demonstrated superior control over particle size, yielding significantly smaller and more uniform particles compared with PEG-based microspheres (124 ± 1.47 µm vs. 218 ± 13.5 µm, respectively). Differential scanning calorimetry (DSC) confirmed molecular dispersion of CBD within the polymer matrix. In vitro release studies demonstrated sustained drug release over 20 days. Conclusions: POx represents a promising alternative to PEG for the formulation of CBD-loaded PLGA microspheres, offering enhanced physicochemical stability and biological compatibility. This platform supports the development of safe and effective long-acting injectable CBD therapies and consideration of POx as an alternative to PEG. Full article

Against the backdrop of global warming, changes in the frequency and intensity of freeze–thaw cycles in cold regions profoundly impact soil physical structure. This review examines the mechanisms by which freeze–thaw cycles influence soil aggregate stability and pore structure evolution, focusing on revealing their synergistic evolution patterns. Results indicate that ice crystal growth during freeze–thaw processes directly disrupts soil cementation systems through expansion pressure and wedging effects, leading to aggregate disintegration and pore restructuring. This process is not unidirectional but forms a coupled feedback cycle of “ice crystal action–aggregate disintegration–pore restructuring.” Aggregate stability governs the initial pore restructuring, while the pore structure, in turn, influences aggregate stability by regulating water migration and colloidal dynamics. Responses of soil aggregates and pore structures to freeze–thaw cycles are comprehensively regulated by multiple factors, including soil physicochemical properties, freeze–thaw parameters, and anthropogenic disturbances. This synergistic evolution mechanism profoundly impacts soil water and heat transport, nutrient cycling, and erosion resistance. The paper also identifies current research gaps in regional coverage, cross-scale coupling, and in situ monitoring techniques. It envisions future efforts integrating multi-scale observations with intelligent technologies to deepen understanding of freeze–thaw-driven soil structure evolution mechanisms, thereby providing theoretical support for sustainable agriculture and ecological conservation in cold regions. Full article

Under global climate change, flood processes exhibit significant non-stationarity due to multiple driving factors, rendering traditional frequency analysis methods based on stationarity assumptions inadequate for accurate risk assessment. This study, focusing on the Kuitun River Basin and utilizing observed data from the Jiangjunmiao Hydrological Station (1959–2014), develops a joint design approach that addresses both non-stationarity and multivariate dependence. The approach integrates the Generalized Additive Model for Location, Scale, and Shape (GAMLSS) with copula functions and employs a parametric bootstrap to quantify the impacts of marginal parameter estimation and sample size uncertainty on design floods. The results indicate that flooding in the Kuitun River is influenced by precipitation, temperature, and snowmelt, with summer precipitation having the greatest impact. Marginal parameter uncertainty is significantly amplified at high return periods, and the confidence intervals of design values expand as the return period increases. In the joint framework, the OR criterion is more sensitive to parameter perturbations, with the 100-year flood peak and flood volume design values approximately 24.2% and 19.8% higher than those of the AND criterion, respectively. Increasing the sample size significantly reduces uncertainty; when the sample size increases from 56 to 500, the HDR area and confidence interval width decrease by approximately 60–70%, and the stability of joint flood design estimates improves significantly. The research findings can provide a scientific basis and technical support for flood analysis and risk management in the Kuitun River Basin under changing environmental conditions. Full article

Serial flow cytometry was recently introduced as a method that can estimate measurement uncertainty (i.e., imprecision, the coefficient of variation of repeated measurements of individual particles) independent from population characteristics. Replication of light sources and detectors at multiple sites along a flow cytometer’s microchannel requires more equipment and can complicate detector synchronization. Here, we introduce amplitude modulation to encode each region of a serial cytometer with a unique carrier frequency, which enables demultiplexing of the combined signal incident on a single photodetector by fast Fourier transform (FFT) peak magnitude. To facilitate validation of detection, matching, and uncertainty quantification of fluorescence signals, we designed a microfluidic amplitude modulation (AM) serial flow cytometer that has ground truth detectors on individual regions (serial cytometry) in parallel with the combined channel detection for AM demultiplexing. With this report, we present metrics for event detection and dynamic range, prevalence and processing of overlapping detections, region-decoding accuracy, process yield, and uncertainty quantification on a brightness ladder of calibration microspheres. Despite being operated with reduced light intensities, the AM cytometer was capable of high-fidelity performance in comparison to conventional serial cytometry. For events above the detection limit, over 97% were analyzed. Both conventional and AM serial cytometers achieved median imprecisions in the range of 0.53% to 2.1% after outlier removal, which was well below the inherent intensity distribution of any of the microsphere subpopulations. Overall, AM cytometry supports uncertainty quantification and temporal analyses of serial cytometry data with a reduced number of photodetectors, which offers simplification of chip design with multiple measurement regions and wide-field detectors. Full article

Existing research has demonstrated that meta-learning methods hold considerable promise in addressing the challenges posed by few-shot object detection. However, remote sensing scenarios present two major challenges. The sparse features of small objects provide insufficient support information for query enhancement, and significant morphological variations caused by lighting and viewpoint differences hinder intra-class consistency capture via direct alignment in few-shot learning. To address these challenges, we propose a generative meta-learning detection framework. The framework first introduces a Dynamic Relation Dual-Stream Network to achieve dynamic support-query feature alignment through joint modeling of evolutionary and relational features, thereby enhancing representation in few-shot conditions. Second, an Optimal Transport-based Generative Meta-Learner is developed to mitigate feature distribution bias via generative augmentation in latent space. Additionally, an Orthogonal Frequency Decomposition Head is incorporated to adaptively separate query features into low-frequency contour and high-frequency detail components, effectively suppressing background noise interference. Experiments on multiple remote sensing datasets demonstrate that the proposed method achieves consistent performance gains over leading baseline methods in various few-shot settings. Its effectiveness is further validated across different backbone networks, highlighting strong generalization in few-shot remote sensing object detection. Full article

UAVs are widely used for all-weather, round-the-clock security inspections in urban and industrial areas. However, pure visible-light systems fail at night or in adverse weather conditions, while pure infrared methods are limited by thermal noise, low spatial resolutions, and high false alarm rates. Multispectral images render the task of object detection highly reliable and robust by providing complementary target feature information. This study suggests a frequency-based cross-attention transformer (FCAT) for multispectral object detection as a solution to this issue. This approach collects cross-modal complementary characteristics, effectively learns and integrates global contextual information via the cross-attention mechanism, and greatly increases multispectral object detection accuracy. At the same time, spatial-domain features are mapped to the frequency domain via the Fourier transform, and the scaled dot product attention is estimated via element-wise product operations, which break through the limitation of traditional spatial-domain matrix multiplication and effectively reduce the computational cost of the model. Additionally, this study independently builds a multi-scene multi-time climate visible–infrared dataset (OPVM-VIRD), which contains 20,025 target instances, to address the issue of the lack of all-weather cross-spectral data in object detection tasks from the perspective of UAVs. Experimental findings from the OPVM-VIRD, M3FD, and FLIR datasets demonstrate that our proposed approach outperforms prevailing state-of-the-art multispectral object detection algorithms on public benchmarks, while the FCAT model achieves an mAP50 score of 94.7% on our custom-built dataset—10.8% higher than ICAF. At the same time, the number of FCAT parameters is 85.26 M, which is significantly lower than that of mainstream models, such as ICAF. Therefore, the FCAT is a change detection strategy with strong model generalization abilities, and it has important application value in the all-day and all-weather security patrol of cities and industrial parks carried out by UAVs. Full article

Accurate carbon price forecasting is vital for risk management but is hindered by high volatility and sensitivity to external shocks. Existing multivariate models typically overlook unstructured news sentiment, failing to capture irrational fluctuations driven by market public opinion. To address this, this paper proposes VBN-Net, a hybrid model integrating carbon-specific news sentiment with Variational Mode Decomposition (VMD). Two core innovations are presented: First, a multi-modal input mechanism combines structured financial data with unstructured carbon news sentiment to effectively capture policy-driven shocks. Second, a Sequential Beluga Whale Optimization strategy is designed to adaptively optimize feature engineering in steps. Unlike conventional approaches, the VBN-Net first employs VMD for denoising and frequency decomposition, and then optimizes the fusion weights of news sentiment across different frequency components derived from multi-source news. This strategy effectively overcomes the subjectivity of manual parameter selection, providing high-quality features for a fixed CNN-BiLSTM backbone. By integrating VMD-based denoising with optimized multi-source news fusion, the model achieves consistent performance improvements across multiple evaluation metrics. The empirical findings validate the effectiveness of the proposed model in enhancing forecasting performance, thereby providing a reliable analytical tool for participants in the carbon market. Full article

Predictive maintenance (PdM) and predictive quality monitoring (PQM) increasingly rely on data-driven condition monitoring using vibration and related signals. However, real-world deployment often faces domain drift across machines, operating regimes, and sensing conditions, while only a few labeled target samples are available. This combination of distribution shift and label scarcity creates a substantial deployment gap for models trained in a single setting. This paper proposes a physics-informed few-shot learning (PI-FSL) domain adaptation framework that is among the first to combine episodic metric learning with soft physics-consistency regularization to improve cross-domain generalization. The framework integrates CWT-based time–frequency encoding, relation-based episodic classification, physics-consistency constraints at representation and signal levels, and PSD-guided episodic sampling within a unified adaptation pipeline. We evaluated PI-FSL under explicit few-shot transfer scenarios on tool-wear and bearing-condition-monitoring datasets. On the Bosch benchmark, PI-FSL achieved an F1 = 0.960 (balanced accuracy = 0.961) for cross-machine transfer and an F1 = 0.907 (balanced accuracy = 0.901) under a combined machine-operation shift. A cross-dataset evaluation across tool-wear and multiple bearing-fault benchmarks under a unified two-way five-shot protocol further demonstrated a competitive and transferable performance. PI-FSL achieved the best average macro-F1 and a balanced accuracy, with the largest margin on PU bearing transfer (macro-F1, 0.663 vs. 0.590; balanced accuracy, 0.710 vs. 0.634). The ablation results showed that few-shot fine-tuning is the main contributor, while physics regularization provides an additional stabilizing gain under transfer. These findings support PI-FSL as a practical episodic framework for robust cross-domain condition monitoring across heterogeneous industrial datasets under realistic drift and limited labels. Full article

The transition towards sustainable educational campuses requires robust energy management strategies that integrate operational oversight with advanced analytics. This paper presents a campus-scale electricity monitoring system at the University of Algarve, designed to support the institution’s sustainability goals through continuous monitoring, data reliability, portability, and scalability to handle concurrent high-frequency campus-wide telemetry. The system consolidates heterogeneous meters into a unified platform, enabling precise tracking of energy consumption and photovoltaic generation. Beyond operational efficiency, the platform incorporates a data-driven analytical layer featuring short-term forecasting using Prophet, chosen for its computational scalability, and a hybrid anomaly detection scheme combining forecast residuals with Isolation Forest. These capabilities facilitate the early identification of waste and abnormal consumption patterns, directly contributing to energy conservation and carbon footprint reduction. Validated across multiple buildings, the system demonstrates both portability to different energy profiles and high data continuity, reducing the cognitive load on facility managers. By providing a reproducible blueprint for intelligent energy monitoring, this work supports institutions in their pursuit of energy efficiency and sustainable development, aligning operational practices with broader environmental objectives. Full article

This paper investigates the transition behaviour and stochastic resonance phenomenon in a class of bifurcation systems with a symmetric piecewise smooth potential, induced by a control parameter, under the combined influence of multiplicative white noise, additive white noise, and a periodic force. As the control parameter increases, the symmetric piecewise smooth potential of the system evolves from tristability to bistability. To study stochastic resonance in this system, an approximate Fokker–Planck equation is first derived based on Novikov’s theorem and the Fox approximation method. Subsequently, the approximate stationary probability density of the system is obtained from the Fokker–Planck equation, revealing the occurrence of a stochastic P-bifurcation. Then, within the bistable and multistable regimes, the effects of the bifurcation parameter, multiplicative noise intensity, and additive noise intensity on the mean first passage time (MFPT) are analysed. Finally, based on the mean first passage time, the response amplitude for stochastic resonance is derived via linear response theory, and the influences of the bifurcation parameter, noise intensities, correlation time, and signal frequency on the response amplitude are examined. In the bifurcation regime, the correctness of the expressions is verified numerically. It is found that multistability reduces the mean first passage time, and stochastic resonance is further analysed using the Fokker–Planck equation. Full article

This study examined lower secondary students’ self-reported use of generative artificial intelligence (GenAI) for schoolwork across four modalities (text, image, audio, and video), and tested how attitudes grounded in the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) relates to usage. A total of 312 grade 7–9 students from Slovenian primary schools participated in the study. The final analytic sample comprised 229 students (47.2% female; M_age = 13.2 years) who reported at least minimal familiarity with GenAI. Students completed an online questionnaire assessing frequency of tool use and five attitude components (teacher support, perceived usefulness, perceived ease of use, experience, and attitudes toward learning with GenAI). Text generation tools were used more frequently than image, audio, or video generation tools. Text tool use was higher among ninth graders than seventh and eighth graders. Text tool use correlated positively with perceived usefulness, perceived ease of use, and experience, and negatively with attitudes toward learning with GenAI. In multiple regression, only perceived usefulness uniquely predicted text tool use, with attitudes explaining 13.7% of variance. Findings indicate that GenAI uptake is currently text-centric and primarily associated with perceived usefulness/performance expectancy, while perceived teacher support (facilitating conditions) shows weak links to use. Full article

Background: Carbapenem-resistant Enterobacterales (CRE) are a global public health concern, with carbapenem-resistant Klebsiella pneumoniae (CR-Kp) recognised as the highest-priority pathogen. This study aimed to investigate the epidemiological features of CRE isolates throughout the COVID-19 pandemic in Buenos Aires, Argentina. Methods: A prospective study was conducted in two hospitals from 2019 to 2022, recovering all CRE from inpatients. Antimicrobial susceptibility was performed by automated and/or manual tests, according to CLSI. β-lactamases detection was performed using Multiplex PCR and MALDI-TOF MS. Kp typing was assessed by multiplex PCR and/or MLST based on WGS. Results: 22% (359/1594) were CRE, predominantly CR-Kp. Overall, high non-susceptibility (NS) rates were observed in both centres. NS remained largely stable in HA, except for a significant increase in colistin NS, whereas HB showed a rise in NS to multiple antimicrobials over time. A significant shift from multidrug-resistant to extensively drug-resistant and difficult-to-treat phenotypes was observed across the study periods. Out of 359 CRE, bla_KPC was confirmed in 141, bla_NDM in 170, and bla_KPC + bla_NDM in 20 isolates. Before the COVID-19 pandemic, KPC was the main carbapenemase in HB, while NDM was already the prevalent one in HA. In 2022, both enzymes showed similar prevalence. bla_KPC-2 and bla_NDM-5 were the prevalent alleles in K. pneumoniae. Before the COVID-19 pandemic, K. pneumoniae epidemiology varied by hospital, characterised by clonal diversity; however, in 2022, CG258-tonB79 drove the epidemiology in both hospitals. Conclusions: A more extensive resistance phenotype among CRE was evidenced throughout the COVID-19 pandemic, driven by carbapenemase-producing K. pneumoniae. NDM-5 and KPC-2 were the main carbapenemases identified. A temporal shift in carbapenemase prevalence was observed in each hospital, converging in similar frequencies of KPC and NDM by 2022 across both centres. This scenario was driven by the active dissemination of K. pneumoniae ST258. Full article

We investigate the potential of frequent repeat imagery acquired by the PlanetScope Dove small satellite constellation to overcome temporal and spatial limitations in automated surface topography mapping. While individual PlanetScope Dove stereo pairs produce low-quality Digital Surface Models (DSMs) with large height uncertainties, the high temporal frequency enables multiple DSMs to enhance accuracy through multiple-pair image matching. We present a fully automated SETSM framework by improving the quality of PlanetScope Dove DSMs based on SETSM Multi-Pair Matching Procedure (SETSM MMP). This framework enhances stereo pair quality through an optimized stereo pair selection by sequential conditional filtering and a Weighted Stereo Pair Index (WSPI). A novel inter-plane vertical coregistration, which minimizes scaling errors between single stereo pair DSMs, was developed to improve consistency and accuracy in DSM quality without reference surfaces. Applied to the cloud-obscured Pantasma crater region in Nicaragua, the optimized stereo pair selection automatically selects well-defined stereo pairs. The inter-plane vertical coregistration without existing reference surfaces achieves up to a 43% Root Mean Square Error (RMSE) reduction and 26% improvement in distribution within a 5 m vertical error. DSM quality correlated strongly with tile size, stereo pair convergence angle, asymmetric angle and terrain-dependent scale variability. The proposed framework provides fully automatic, high quality PlanetScope Dove DSMs without Ground Control Points (GCPs). Full article

Unmanned aerial vehicles (UAVs) have emerged as a promising platform for supporting integrated sensing, computing, and communication (ISCC) functionality in Internet of Things (IoT) applications. This paper investigates a UAV-enabled ISCC network, where the UAV performs radar sensing and onboard edge computing with the computational assistance of ground access points (APs). Given the limited onboard energy, ensuring energy-efficient operation of UAVs is crucial to support the long-term sustainability of network performance. In this paper, we define computing efficiency as the ratio between the total number of successfully processed computational bits and the overall UAV energy consumption, under the constraint of a required sensing threshold. To maximize this performance metric, this paper jointly optimizes the beamforming vector, the CPU frequency, and the trajectory of the UAV. This optimization problem is modeled as a Markov decision process (MDP) and solved using a deep reinforcement learning (DRL) approach based on a memory mechanism. Specifically, a long short-term memory (LSTM) and twin delayed deep deterministic policy gradient (TD3)-based trajectory design and resource allocation (LTTDRA) algorithm is proposed. LSTM units are integrated into the actor and critic to effectively capture the temporal correlations in dynamic environments, thereby enhancing policy stability and accelerating algorithm convergence. The reward function is meticulously designed to alleviate sparse-penalty effects and learn high-performance strategies in complex environments with multiple constraints. Extensive simulations are conducted under various settings and network scenarios, and the results consistently indicate that the proposed approach substantially outperforms the baseline schemes. Full article