Browse Articles

Characteristics of fetal facial expression changes using artificial intelligence: a pilot study

Effect of boric acid on the color stability and mechanical properties of 3D-printed permanent resins

Elevated synaptic PKA activity and abnormal striatal dopamine signaling in Akap11 mutant mice, a genetic model of schizophrenia and bipolar disorder

Seasonality affects key physiological pathways in Pontastacus leptodactylus during an Aphanomyces astaci epidemic outbreak

Association of noninvasive tests of liver fibrosis with chronic kidney disease in MASLD: a systematic review and meta-analysis

Impact of common variants on brain gene expression from RNA to protein to schizophrenia risk

Beyond biodiversity: the role of Paramuricea clavata forests in shaping taxonomic and functional diversity

Differential misestimation of maximum fluorescence yields can confound efforts to optimize photosynthesis

Bridging the gap between subduction dynamics and the long-term strength of the Sunda megathrust

Exploring the predictive performance of deep learning for fracturing fluid flowback and shale gas production

Abstract The big data system has been developed to optimally combine numerical-model predictions with actual measurements from the gas shale play to create the best estimates of current shale gas conditions and their uncertainties, improving our ability to forecast and understand the shale gas production variations. However, considering the hydraulic fracturing fluid flowback in predicting shale gas production introduces new challenges. For example, complexities in the flow of gas-water two-phase fluid defy traditional numerical simulation, while flowback are complicated by strong environmental disruptions, and shale gas production encounters substantial noise interference. Here, we developed CNN-Transformer, a production and fluid flowback predicted system using deep learning, by integrating a convolutional neural network (CNN) and a Transformer network. CNN’s receptive field focus irregular observational data on the local region correlation of the sequence, while Transformer network extracts the state information of historical production and flowback volumes from previous time frames, and predicts future time states, aiming to capture temporal patterns. We contrast the performance of CNN-Transformer, CNN-LSTM, CNN-GRU-AM models in predicting shale gas production, flowback volume and the relationship between the two. We show that the CNN-Transformer outperforms the other models, with R²=0.644 and RMSE = 0.1424, compared to CNN-GRU-AM (R²=0.6068, RMSE = 0.1513) and CNN-LSTM (R²=0.5727, RMSE = 0.1618) in predicting flowback volume. We conclude that CNN-Transformer (R 2  = 0.72, RMSE = 0.3824) markedly reduces analysis error of the shale gas production, outperforming both CNN-LSTM (R 2  = 0.4911, RMSE = 0.44912) and CNN-GRU-AM (R 2  = 0.5705, RMSE = 0.4249). We deem our results to lay a foundation for further debates on striking a balance between fracturing fluid flowback volume and prediction of shale gas production.

Alterations in the gastric and small intestinal microbiota of mice exposed to short-term and long-term hypoxia

Field manipulation of Weyl modes in an ideal Dirac semimetal

AI powered multi feature fusion framework for retrieving images using color, texture and shape descriptors

Images of social support reminders, but not learned safety signals, reduce long-term fear

Abstract Although previously considered to simply be a form of safety signals, reminders of social-support-figures have recently been shown to have a distinct combination of effects: inhibiting fear while present, like safety signals do, but also enhancing the extinction of fear, in contrast to safety signals, which impair fear extinction. In this direct comparison of these cues, we confirmed this divergent pattern of effects and revealed that while safety signals inhibit fear by reducing expectation that a fearful event will occur, social support reminders inhibit fear via an alternative pathway, possibly by reducing perceived fearfulness of the event itself. These results substantiate the differentiation of social support reminders from safety signals and contribute to their still-being-discovered effects, paving the way for a possible new understanding of the potential benefits of these reminders for extinction-based therapies. Indeed, while safety signals are harmful to fear extinction processes and the therapies that rely on them, social support reminders may be uniquely beneficial—both decreasing the discomfort associated with completing such therapies by inhibiting fear while present, as well as enhancing therapeutic outcomes by enhancing fear extinction. Thus, reminders of our closest others may represent a simple, inexpensive, and non-invasive method to boost current therapies.

Region-specific defect engineering of Bi2W1-xO6-γ induces nanoscale electric fields and surface active-sites for enhanced visible-light oxidation of salt-lake flotation agents

Abstract Breaking the limitations of conventional defect engineering, this work pioneers region-specific dual-defect engineering in Bi 2 WO 6 . By precisely tailoring tungsten (W) and oxygen (O) vacancies at nanoscale spatial domains-W vacancies at the edges and O vacancies at the center-a spatially asymmetric defect configuration is achieved. This configuration induces a synergistic “defect dipole” effect, amplifying the internal electric field by 2.74 times while simultaneously enriching surface-active sites. As a result, the photocatalytic efficiency is dramatically enhanced, achieving complete oxidation of recalcitrant flotation agents-octadecylamine (ODA) and 4-dodecylmorpholine (DMP)-within just 2 h of visible light irradiation, which is 3.6 times faster than that of pristine Bi 2 WO 6 . Additionally, the generation of reactive species ( $$\cdot {{\rm{O}}}_{2}^{-}$$ ⋅ O 2 − , $${}^{1}{\rm{O}}_{2}$$ O 2 1 , and h⁺) is significantly boosted by factors of 8.98, 5.55, and 20.02, respectively, highlighting the material’s remarkable reactivity. Photoelectrochemical analyses reveal a remarkable 290% increase in charge separation efficiency. This enhancement is further supported by an improved O 2 adsorption capacity, which promotes the formation of reactive oxygen species involved in the degradation process. Impressively, the engineered Bi₂W₁₋ₓO₆₋ᵧ exhibits outstanding performance in real-world industrial wastewater treatment under solar irradiation, demonstrating its practical viability. Overall, this work establishes a new paradigm in photocatalysis by integrating precise nanoscale defect engineering with enhanced electrostatic modulation.

Irrelevant region preserving for counterfactual image manipulation

Identifying resonant frequencies of viruses for microwave-based detection and inactivation of pathogenic viruses

Visualizing the translation landscape in human cells at high resolution

Human settlement water security in Northern Xinjiang China based on DPSIR model

Enhancing sustainability in soldering: effects of recycled welding slag and rice husk ash on microstructural, thermal, and mechanical properties of Sn–Cu composite solders

Abstract This study investigated the microstructural, thermal and mechanical properties of a series of plain and composite lead-free solder systems (Sn–0.7Cu − xWS; Sn–0.7Cu − xRHA; x = 0, 6, and 12 wt%), as well as sustainable reinforcing solutions. The microstructural characteristics of the present alloys were investigated using scanning electron microscopy (SEM), energy dispersive X-ray spectrometry (EDS), electron probe microanalysis (EPMA), and X-ray diffraction (XRD). Moreover, thermal properties of the present composites were investigated utilizing differential scanning calorimetry (DSC). Images obtained from the SEM demonstrate that welding slag (WS) and rice husk ash (RHA) nanoparticles are uniformly distributed within the composite material. The findings showed that the incorporation of WS and RHA nanoparticles markedly altered the microstructure of these alloys and improved their thermal and mechanical properties. It was observed that the mechanical properties of the Sn-0.7Cu-xRHA composites were superior to those of the Sn-0.7Cu-xWS composites. Additionally, compared to other composites, the Sn-0.7Cu-6RHA composites exhibited significantly higher levels of tensile strength and hardness, while the Sn-0.7Cu-12RHA composites revealed superior thermal characteristics. It also focused on the environmental benefits and potential challenges of using recycled agricultural waste and welding slag in Sn-0.7Cu solders from a life cycle assessment perspective.