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THR-DMPOM model for reliable high-precision evaluation to enhance real-time performance management and optimize resource allocation
The role of metacognition in fibromyalgia impact and psychological distress: a cross-sectional study in Turkish women with fibromyalgia
Quantitative characterization of thermo-optically modulated microbend loss in silica fibers for high-temperature deep-well telemetry
Abstract To mitigate the nonlinear amplification of fiber micro-bend loss in high-temperature deep-well environments (303.15 ~ 483.15 K), this study establishes an equivalent refractive index model integrating thermal, mechanical, and optical fields. The model incorporates thermo-optic effects(TOEs) directly into the micro-bend equivalent refractive index expression, facilitating unified modeling of coupled thermal and bending perturbations. Finite element eigenmode analysis was employed to quantify the synergistic effects of bending radius (0.1 ~ 1.0 mm) and temperature on radiation leakage and mode coupling. Results demonstrate that temperature does not act as an independent loss term. Instead, it reconfigures waveguide confinement by modulating the core-cladding refractive index profile, thereby altering mode-coupling pathways. Multimode fiber (MMF) exhibits oscillatory loss governed by inter-modal phase matching, with attenuation coefficients displaying non-monotonic temperature dependence. Conversely, single-mode fiber (SMF) demonstrates threshold-type loss surge at temperatures T ≥ 440 K. Spectral and phase analyses reveal that MMF undergoes significant phase decorrelation and topological discontinuity under stochastic mode coupling, whereas SMF maintains phase continuity even under high-loss conditions, demonstrating superior coherence stability. Furthermore, wavelength scanning shows that long wavelengths (> 1.6 μm) significantly enhance SMF micro-bend sensitivity, while the stochastic coupling in MMFs is better suited for short-distance, intensity-modulated telemetry. This study establishes a quantitative correlation between temperature, bending, and wavelength, providing essential physical criteria and engineering guidelines for fiber selection and loss compensation in extreme downhole environments.
Photoacoustic device fingerprints induce bias in deep learning models
Abstract Deep learning (DL) models developed for established medical imaging modalities have shown increasing performance and reliability as a result of scaling efforts. In contrast, model development for emerging modalities such as photoacoustic imaging (PAI) remains challenged by data sparsity, which limits model generalizability and raises the susceptibility to bias. While recent studies in PAI have started to investigate subject-related confounders, the impact of hardware-related confounders remains unexplored, posing a critical risk for failure in multicentric deployment scenarios. We are the first to provide a multicentric analysis of hardware-induced bias in PAI. We analyzed device-specific characteristics in images from four device instances and two peripheral artery disease studies, and trained DL models to classify device origin and disease under varying levels of device–health correlations in the data. We showed that 1) multiple instances of the same PAI device type embed identifiable fingerprints in the images, 2) that DL models can leverage these fingerprints to reach $$100\,\%$$ accuracy in device detection and critically, 3) when a correlation between device instance and health status is present, models trained for disease diagnosis exploit these device-specific signatures as shortcuts, thereby producing biased and clinically misleading predictions. This research highlights the risk of overestimating algorithm performance when such confounding is overlooked, emphasizing the importance of bias evaluation and explainable artificial intelligence methods to identify potential shortcuts, finally enabling multicentric PAI studies.
Assessing Zn12O12 nanocage for the elimination of ciprofloxacin from drinking water using DFT simulations
Evolutionary game-theoretic modeling of electricity market dynamics for elastic load optimization
Analysis of stability and chaotic trajectories in nonlinear fluid wave interactions under forcing effects
Extended Maxwell equations derived from spacetime geometry
Abstract This paper presents a unified relativistic description of electromagnetic and gravitational interactions in the classical regime, addressing the fundamental question of why like charges repel while like masses attract. We formulate a flat spacetime geometry tailored to special relativity and introduce intrinsic operations of spacetime product and spacetime curl, yielding a concise formalism arising directly from relativistic spacetime properties. Based on three axiomatic postulates—relativistic properties of spacetime, energy conservation, and standard charge condition—we rigorously derive four extended Maxwell equations and their corresponding force formulas by employing our formalism, without ad hoc parameters or phenomenological fitting. These simultaneously recover electromagnetic and gravitational interactions, along with their mirror-symmetric counterpart, thereby naturally resolving the aforementioned fundamental question. A thought experiment demonstrates consistency with the principle of relativity. We propose a unified charge system to facilitate the unified description. The framework remains strictly classical, does not incorporate spin and radiative-reaction effects, and applies to gravity in quasi-charge conditions where the motion of matter can be treated as a conserved Lorentz four-vector current. This axiomatic derivation provides a geometrically grounded unified description of long-range interactions and a transparent foundation for theoretical extensions.
Stochastic Grey Wolf Optimization for Hyperparameter Tuning of LSTM and RNN Models in Energy Forecasting
Abstract Accurate photovoltaic (PV) power forecasts are required to support the stable and efficient incorporation of solar power into modern electric power grids. Even though the use of recurrent neural network models such as RNNs and LSTMs for time series forecasting has proven successful, these model’s ability to make accurate predictions is heavily influenced by proper hyper parameter selection. Therefore, the goal of this research is to introduce a stochastic grey wolf optimizer (SGWO) based hyper-parameter optimization system for RNN and LSTMs used for predicting PV power. A stochastic grey wolf optimizer will be added to the basic grey wolf optimizer to enhance the search capabilities of the algorithm. This new stochastic grey wolf optimizer introduces randomness into the optimization process which can help prevent premature convergence and increase exploration within the problem space. The performance of the proposed SGWO-based system will be tested on a real world PV data set. The comparison will include results from manual tuning, random searching, and standard GWO. Performance metrics will consist of root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) and will be used to evaluate how well each of the algorithms performed when making out-of-sample predictions. Results from testing showed that the SGWO-LSTM configuration produced the highest total out-of-sample prediction accuracy; MAE = 0.018, RMSE = 0.041, R 2 = 0.978.
Synergistic oxidation and adsorption of arsenic(III) by epigallocatechin gallate activated iron(III) peroxydisulfate system
Comparative performance of the ahmed and vertucci systems in classifying mandibular premolar canal morphology: a Bayesian and information-theoretic analysis
IDMBD: Intelligent Diagnostic Modelling of Bipolar Disorder at its early onset
Abstract The proposed study presents an Intelligent Diagnostic Modelling of Bipolar Disorder (IDMBD), which represents an underlying framework operating on a Mobile Computing Device (MCD). The approach is meant to acquire multi-modal heterogeneous features in order to profile the participants more effectively with the highest granularity. IDMBD adopts a standard scale for forecasting the criticality state of Bipolar Disorder (BD). The feature-based profiling is carried out based on five discrete attributes collected by MCD, which, after undergoing a series of analytical processing, is subjected to a deep neural network-based learning model. An extensive evaluation is being carried out in multiple combinations of both machine learning and deep learning models to find that IDMBD exhibits optimal performance when integrated with Random Forest and Decision Tree with respect to multiple performance metrics.
Age-related changes in saccade behavior
Unraveling the mechanism of action of the PepGAT peptide against Candida albicans through proteomics approaches
Prevalence, species diversity, and antimicrobial resistance profile of Listeria monocytogenes in retail meat and dairy products in Southern Ethiopia
Deep learning-based artificial intelligence can improve the diagnosis of small bowel obstruction: stratified comparison study and hierarchical Bayesian model
Dual-adaptive imputation graph neural network for knowledge-aware recommendation
Abstract Recommender systems play a vital role in enhancing user experience by efficiently delivering personalized and relevant content. While knowledge graph-based recommender systems effectively alleviate the data sparsity and cold-start challenges of traditional approaches, they still suffer from two major limitations: (1) insufficient utilization of the user-item interaction matrix and (2) suboptimal integration of heterogeneous knowledge graph signals with collaborative information. In this work, we propose DAIGNN (Dual-Adaptive Imputation Graph Neural Network), a novel recommendation framework designed to overcome these limitations through three key innovations. First, we introduce a similarity-driven imputation mechanism that constructs an Imputation Graph using pseudo-ratings, thereby enhancing graph connectivity and significantly reducing data sparsity. Second, we incorporate multiple auxiliary information sources on both the user and item sides, enabling DAIGNN to capture richer contextual and relational semantics beyond conventional user-item interactions. Third, we develop a dual-adaptive feature fusion mechanism that learns optimal fusion weights to dynamically integrate heterogeneous information from multiple graph sources. Extensive experiments conducted on four real-world datasets demonstrate the superior effectiveness of DAIGNN. On average, it achieves a 3.1% improvement in AUC and a 2.0% improvement in F1-score over state-of-the-art baselines, confirming its robustness across diverse settings.
Abiotic synthesis of RNase-resistant phosphodiester and pyrophosphate-linked polymers via thermodynamically controlled wet-dry cycling
Biotype dynamics of the common house mosquito Culex pipiens in the anthropogenic environment in winter: a citizen science approach
Abstract The two biotypes of the mosquito species Culex pipiens s.s. (biotype pipiens and biotype molestus ) differ in overwintering strategy. Biotype pipiens enters diapause, whereas biotype molestus remains active year-round. In the Netherlands, flooded crawlspaces may provide sheltered development sites for biotype molestus in winter. We therefore investigated the effects of human population density and crawlspace flooding on the presence of Cx. pipiens s.s. biotypes and their hybrids using citizen-submitted mosquito samples via the platform ‘Muggenradar.’ Mosquitoes were categorized by population density and crawlspace condition. In addition, biotypes and feeding patterns were identified using molecular analyses. Descriptive analyses indicated that biotype molestus was more abundant in areas with high population density (24.1% at ’low’ versus 58.6% at ‘high’), while biotype pipiens dominated in areas of low population density (19.5% at ‘high’ versus 63.3% at ‘low’). Similarly, we found an association of biotype molestus with flooded crawlspaces (68.4% of specimens). However, these patterns were not fully supported by a multivariate analysis. Blood meal analyses revealed that nearly all biotype molestus (98.2%) and hybrid mosquitoes (100%) fed on humans. Surprisingly, six blood-engorged biotype pipiens were collected. We conclude that anthropogenic environments may influence the occurrence of Cx. pipiens s.s. biotypes and hybrids in winter.