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Semantic Sensor Web (SSW) links the semantic web technique with the sensor network, which utilizes sensor ontology to describe sensor information. Annotating sensor data with different sensor ontologies can be of help to implement different sensor systems’ inter-operability, which requires that the sensor ontologies themselves are inter-operable. Therefore, it is necessary to match the sensor ontologies by establishing the meaningful links between semantically related sensor information. Since the Swarm Intelligent Algorithm (SIA) represents a good methodology for addressing the ontology matching problem, we investigate a popular SIA, that is, the Firefly Algorithm (FA), to optimize the ontology alignment. To save the memory consumption and better trade off the algorithm’s exploitation and exploration, in this work, we propose a general-purpose ontology matching technique based on Compact co-Firefly Algorithm (CcFA), which combines the compact encoding mechanism with the co-Evolutionary mechanism. Our proposal utilizes the Gray code to encode the solutions, two compact operators to respectively implement the exploiting strategy and exploring strategy, and two Probability Vectors (PVs) to represent the swarms that respectively focuses on the exploitation and exploration. Through the communications between two swarms in each generation, CcFA is able to efficiently improve the searching efficiency when addressing the sensor ontology matching problem. The experiment utilizes the Conference track and three pairs of real sensor ontologies to test our proposal’s performance. The statistical results show that CcFA based ontology matching technique can effectively match the sensor ontologies and other general ontologies in the domain of organizing conferences.

Deep learning models, such as recurrent neural network (RNN) models, are suitable for modeling and forecasting non-stationary time series but are not interpretable. A prediction model with interpretability and high accuracy can improve decision makers’ trust in the model and provide a basis for decision making. This paper proposes a double decomposition strategy based on wavelet decomposition (WD) and empirical mode decomposition (EMD). We construct a prediction model of high-order fuzzy cognitive maps (HFCM), called the WE-HFCM model, which considers interpretability and strong reasoning ability. Specifically, we use the WD and EDM algorithms to decompose the time sequence signal and realize the depth extraction of the signal’s high-frequency, low-frequency, time-domain, and frequency domain features. Then, the ridge regression algorithm is used to learn the HFCM weight vector to achieve modeling prediction. Finally, we apply the proposed WE-HFCM model to stationary and non-stationary datasets in simulation experiments. We compare the predicted results with the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models.For stationary time series, the prediction accuracy of the WE-HFCM model is about 45% higher than that of the ARIMA, about 35% higher than that of the SARIMA model, and about 16% higher than that of the LSTM model. For non-stationary time series, the prediction accuracy of the WE-HFCM model is 69% higher than that of the ARIMA and SARIMA models.

Background Infertility is one of the prominent public health concerns nationwide. Neutrophils, despite their established significance as vital players in both inflammatory and immune processes, have been studied scarcely in terms of their effect on female infertility. The present study aimed to determine the prevalence of neutropenia among women of reproductive age in the U.S. to contribute valuable insights to the broader context of reproductive health. Methods The present study was designed as a cross-sectional investigation. The data of 5,250 female participants aged 18–45 years were obtained from the National Health and Nutrition Examination Survey (NHANES) conducted between the years 2013 and 2020. The representativeness of the population was ensured by conducting statistical assessments based on NHANES weights. A logistic regression model was established to assess the hematologic parameters across the distinct populations stratified according to age, ethnicity, smoking status, and infertility. Multivariate logistic regression was performed next, and weighted odds ratios along with the 95% confidence interval values were calculated, which assisted in predicting the prevalence of neutropenia among the female participants. Results The data of a total of 5,250 female participants, representing a multiracial population of 51.17 million in the United States, were analyzed in the present study. Meanwhile, the estimated neutropenia incidence was 7.09% (95% CI: 6.16−8.01%), which indicated a prevalence among approximately 36.2 million U.S. citizens. In comparison to white subjects, black subjects exhibited a significantly lower average leukocyte count, with a mean difference (MD) of 1.16 × 10 9 /L ( P  < 0.001), along with a lower neutrophil count (MD: 1.09 × 10 9 /L; P  < 0.001). It is noteworthy that a substantial decrease was noted in the distribution graphs of both neutrophil and leukocyte counts among the black subjects. Moreover, compared to non-smokers in the racial populations, including white, Mexican American, and black people, the smokers exhibited significantly elevated mean leukocyte count and mean neutrophil count. The logistic regression analysis indicated an elevated risk of neutropenia among black individuals and females with infertility. Conclusions Neutropenia appears to have a higher prevalence in the general population compared to that acknowledged previously. The findings of the present study indicated association between neutropenia and infertility. This highlighted the importance of directing increased attention toward neutropenia in the context of both research and clinical practice.

The synthesis of multi-carbon (C 2+ ) fuels via electrocatalytic reduction of CO, H 2 O using renewable electricity, represents a significant stride in sustainable energy storage and carbon recycling. The foremost challenge in this field is the production of extended-chain carbon compounds (C n , n ≥ 3), wherein elevated * CO coverage (θ co ) and its subsequent multiple-step coupling are both critical. Notwithstanding, there exists a “seesaw” dynamic between intensifying * CO adsorption to augment θ co and surmounting the C-C coupling barrier, which have not been simultaneously realized within a singular catalyst yet. Here, we introduce a facilely synthesized lattice-strain-stabilized nitrogen-doped Cu (LSN-Cu) with abundant defect sites and robust nitrogen integration. The low-coordination sites enhance θ co and concurrently, the compressive strain substantially fortifies nitrogen dopants on the catalyst surface, promoting C-C coupling activity. The n-propanol formation on the LSN-Cu electrode exhibits a 54% faradaic efficiency and a 29% half-cell energy efficiency. Moreover, within a membrane electrode assembly setup, a stable n-propanol electrosynthesis over 180 h at a total current density of 300 mA cm −2 is obtained. The transformation of CO and H 2 O into C 2+ fuels using renewable electricity represents a significant stride in carbon recycling. Here, the authors introduce a plasma-treated Cu catalyst, achieving high CO coverage and promoted C-C coupling ability for efficient n-propanol formation.

Electrochemical conversion of CO 2 into value-added chemicals holds promise to enable the transition to carbon neutrality. Enhancing selectivity for a specific hydrocarbon product is challenging, however, due to numerous possible reaction pathways of CO 2 electroreduction. Here we present a Cu–polyamine hybrid catalyst, developed through co-electroplating, that significantly increases the selectivity for ethylene production. The Faradaic efficiency for ethylene production is 87% ± 3% at −0.47 V versus reversible hydrogen electrode, with full-cell energetic efficiency reaching 50% ± 2%. Raman measurements indicate that the polyamine entrained on the Cu electrode results in higher surface pH, higher CO content and higher stabilization of intermediates compared with entrainment of additives containing little or no amine functionality. More broadly, this work shows that polymer incorporation can alter surface reactivity and lead to enhanced product selectivity at high current densities. Electrochemical conversion of CO 2 into value-added chemicals holds promise to enable the transition to carbon neutrality, but enhancing the selectivity toward a specific hydrocarbon product remains a challenging task. Now, the authors present a Cu–polyamine hybrid catalyst that achieves Faradaic efficiency of 87% for ethylene and full-cell energy efficiency of 50%.

Atomic structure and electronic band structure are fundamental properties for understanding the mechanism of superconductivity. Motivated by the discovery of pressure-induced high-temperature superconductivity at 80 K in the bilayer Rud-dlesden-Popper nickelate La 3 Ni 2 O 7 , the atomic structure and electronic band structure of the trilayer nickelate La 4 Ni 3 O 10 under pressure up to 44.3 GPa are investigated. A structural transition from the monoclinic P 2 1 / a space group to the tetragonal I 4/ mmm around 12.6–13.4 GPa is identified, accompanied by a drop of resistance below 7 K. Density functional theory calculations suggest that the bonding state of Ni 3 d z 2 orbital rises and crosses the Fermi level at high pressures, which may give rise to possible superconductivity observed in resistance under pressure in La 4 Ni 3 O 10 . The trilayer nickelate La 4 Ni 3 O 10 shows some similarities with the bilayer La 3 Ni 2 O 7 and has unique properties, providing a new platform to investigate the underlying mechanism of superconductivity in nickelates.

Background Numerous studies have demonstrated restless legs syndrome (RLS) might worsen motor and non-motor symptoms in patients with Parkinson’s Disease (PD). However, research into the effects of concurrent RLS on the function of the autonomic nervous system remains limited. Our study particularly focused on its effects on the autonomic nervous system. Method From October 2022 to February 2025, 392 patients with PD were continuously included in our study. PD patients were categorized into those with RLS and those without RLS, based on the criteria established by the International Restless Legs Syndrome Study Group (IRLSSG). A variety of questionnaires were utilized to evaluate the severity of symptoms in PD patients, including the King’s Parkinson’s Disease Pain Scale (KPPS), Parkinson’s Disease Sleep Scale (PDSS), and the Scales for Outcomes in Parkinson’s Disease for Autonomic Dysfunction (SCOPA-AUT), among others. Result Our research included 98 patients (25.0%) who met the IRLSSG diagnostic criteria for RLS. The concurrent RLS in PD patients was significantly related to KPPS scores KPPS scores [OR = 1.049, 95%CI:1.007–1.093, P  = 0.021], thermoregulatory subscores [OR = 1.275, 95%CI:1.007–1.615, P  = 0.044] and PDSS scores [OR = 0.978, 95%CI:0.963–0.993, P  = 0.004]. Moreover, the Restless Leg Syndrome Rating Scale (RLSRS) scores in PD with RLS group were positively associated with Pittsburgh Sleep Quality Index (PSQI) scores [β = 0.312, 95%CI:0.031–0.683, P  = 0.032]. Conclusion 1.PD patients experiencing more severe pain symptoms, more severe sleep disorders, and more severe dysfunction of the thermoregulatory system were at a higher risk of developing RLS. Among these factors, pain score was the most effective predictor of concurrent RLS. 2. PD patients with RLS who had poorer sleep quality tended to have a more severe RLS.

In this study, a quantitative detection method of pipeline cracks based on a one-dimensional convolutional neural network (1D-CNN) was developed using the time-domain signal of ultrasonic guided waves and the crack size of the pipeline as the input and output, respectively. Pipeline ultrasonic guided wave detection signals under different crack defect conditions were obtained via numerical simulations and experiments, and these signals were input as features into a multi-layer perceptron and one-dimensional convolutional neural network (1D-CNN) for training. The results revealed that the 1D-CNN performed better in the quantitative analysis of pipeline crack defects, with an error of less than 2% in the simulated and experimental data, and it could effectively evaluate the size of crack defects from the echo signals under different frequency excitations. Thus, by combining the ultrasonic guided wave detection technology and CNN, a quantitative analysis of pipeline crack defects can be effectively realized.

The bacterial strain WD-2, which was capable of efficiently degrading prochloraz-manganese, was isolated from soil contaminated with prochloraz-manganese, selected through enrichment culturing and identified as Bacillus cereus. Test results indicated that the optimal temperature and pH for bacterial growth were 35-40°C and 7.0-8.0, respectively. The highest degradation rate was above 88-90% when the pH was 7.0~8.0 and reached a maximum value (90.7%) at approximately 8.0. In addition, the bacterium showed the greatest growth ability with an OD600 of 0.805 and the highest degradation rate (68.2%) when glucose was chosen as the carbon source, while the difference in nitrogen source had no obvious influence on bacterial growth. The degradation rate exceeded 80% when the NaCl concentration was 0~2% and the rate reached 89.2% at 1%. When the concentration was higher than 7%, the growth of WD-2 and the degradation of prochloraz-manganese were found to be inhibited, and the degradation rate was merely 8.5%. The results indicated that strain WD-2 was able to effectively degrade prochloraz-manganese and might contribute to the bioremediation of contaminated soils.

To address the core challenges of multivariate nonlinear coupling and long-term temporal dependency in 4D UAV trajectory prediction, this study proposes an innovative model named KAN-Former. On a 21-dimensional multimodal UAV dataset, KAN-Former achieves statistically significant improvements over all baseline models, reducing the mean squared error (MSE) by 8.96% compared to the standard Transformer and by 2.66% compared to the strongest physics-informed baseline (PITA), while decreasing the mean absolute error (MAE) by 7.43% relative to TimeMixer/PatchTST. The model adopts a collaborative architecture with two key components: first, a “vertical–horizontal” cross-dimensional attention mechanism—where the vertical branch models physical correlations among multivariate variables using hierarchical clustering priors, and the horizontal branch employs a blockwise dimensionality reduction strategy to efficiently capture long-term temporal dynamics; second, it represents the first application of Kolmogorov–Arnold decomposition in trajectory prediction, replacing traditional feedforward networks with learnable combinations of B-spline basis functions to approximate high-dimensional nonlinear mappings. Ablation studies verify the effectiveness of each module, with the KAN module alone reducing MSE by 6.59%. Moreover, the model’s feature clustering results align closely with UAV physical characteristics, significantly improving interpretability. The demonstrated improvements in accuracy, interpretability, and computational efficiency make KAN-Former highly suitable for real-world applications such as real-time flight control and air traffic management, providing reliable trajectory forecasts for decision-making systems. This work offers a new paradigm for trajectory prediction in complex dynamic systems, successfully integrating theoretical innovation with practical value.