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This study examined the role of intermittent illumination/dark conditions coupled with MnO2-ammendments to regulate the mobility of As and Fe in flooded arsenic-enriched soils. Addition of MnO2 particles with intermittent illumination led to a pronounced increase in the reductive-dissolution of Fe(III) and As(V) from flooded soils compared to a corresponding dark treatments. A higher MnO2 dosage (0.10 vs 0.02 g) demonstrated a greater effect. Over a 49-day incubation, maximum Fe concentrations mobilized from the flooded soils amended with 0.10 and 0.02 g MnO2 particles were 2.39 and 1.85-fold higher than for non-amended soils under dark conditions. The corresponding maximum amounts of mobilized As were at least 92 % and 65 % higher than for non-amended soils under dark conditions, respectively. Scavenging of excited holes by soil humic/fulvic compounds increased mineral photoelectron production and boosted Fe(III)/As(V) reduction in MnO2-amended, illuminated soils. Additionally, MnO2 amendments shifted soil microbial community structure by enriching metal-reducing bacteria (e.g., Anaeromyxobacter, Bacillus and Geobacter) and increasing c-type cytochrome production. This microbial diversity response to MnO2 amendment facilitated direct contact extracellular electron transfer processes, which further enhanced Fe/As reduction. Subsequently, the mobility of released Fe(II) and As(III) was partially attenuated by adsorption, oxidation, complexation and/or coprecipitation on active sites generated on MnO2 surfaces during MnO2 dissolution. These results illustrated the impact of a semiconducting MnO2 mineral in regulating the biogeochemical cycles of As/Fe in soil and demonstrated the potential for MnO2-based bioremediation strategies for arsenic-polluted soils.

More clinical evidence is needed regarding the relative priority of treatments for brain metastases (BMs) from EGFR/ALK-negative/unselected non-small cell lung cancer (NSCLC). PubMed, EMBASE, Web of Science, Cochrane Library, and ClinicalTrials.gov databases were searched. Overall survival (OS), central nervous system progression-free survival (CNS-PFS), and objective response rate (ORR) were selected for Bayesian network meta-analyses. We included 25 eligible randomized control trials (RCTs) involving 3,054 patients, investigating nine kinds of treatments for newly diagnosed BMs and seven kinds of treatments for previously treated BMs. For newly diagnosed BMs, adding chemotherapy, EGFR-TKIs, and other innovative systemic agents (temozolomide, nitroglycerin, endostar, enzastaurin, and veliparib) to radiotherapy did not significantly prolong OS than radiotherapy alone; whereas radiotherapy + nitroglycerin showed significantly better CNS-PFS and ORR. Surgery could significantly prolong OS (hazard ratios [HR]: 0.52, 95% credible intervals: 0.41–0.67) and CNS-PFS (HR: 0.32, 95% confidence interval: 0.18–0.59) compared with radiotherapy alone. For previously treated BMs, pembrolizumab + chemotherapy, nivolumab + ipilimumab, and cemiplimab significantly prolonged OS than chemotherapy alone. Pembrolizumab + chemotherapy also showed better CNS-PFS and ORR than chemotherapy. In summary, immune checkpoint inhibitor (ICI)-based therapies, especially ICI-combined therapies, showed promising efficacies for previously treated BMs from EGFR/ALK-negative/unselected NSCLC. The value of surgery should also be emphasized. The result should be further confirmed by RCTs.

Developing thermally activated delayed fluorescence (TADF)‐active silver clusters with near‐unity quantum efficiency is of practical importance in cutting‐edge optoelectronic devices, but remains a tremendous challenge due to the difficulty of de novo synthesis and uncertainty of properties. Herein, we demonstrate a lattice modulation on parent TADF‐ active silver cluster, achieving TADF‐driven photoluminescence quantum yield (PLQY) from 12 % to near‐unity. Systematic experimental and calculated results reveal that the lattice modulation effectively lowers the singlet‐triplet splitting (ΔEST) from 718 to 549 cm‐1, thereby facilitating thermally activated reverse intersystem crossing: T5→S5, leading to extremely efficient TADF by surpassing both phosphorescence and non‐radiative decay, thus boosting the near‐unity PLQY. Such high PLQY is extremely rare in the TADF‐active silver clusters and even in the whole noble‐metal clusters. This research showcases an unparalleled example of lattice modulation to realize near unity PLQY of TADF‐active silver clusters. [ABSTRACT FROM AUTHOR]

In the context of carbon neutrality and emission reduction goals, energy consumption optimization in the oil and gas industry is crucial for reducing carbon emissions and improving energy efficiency. As a key component in drilling operations, optimizing the energy consumption of drilling pumps has significant potential for energy savings. However, due to the complex and variable geological conditions, diverse operational parameters, and inherent nonlinear relationships in the drilling process, accurately predicting energy consumption presents considerable challenges. This study proposes a novel Long Short-Term Memory Attention model for precise prediction of drilling pump energy consumption. By integrating Long Short-Term Memory (LSTM) networks with the Attention mechanism, the model effectively captures complex nonlinear relationships and long-term dependencies in energy consumption data. Comparative experiments with traditional LSTM and Convolutional Neural Network (CNN) models demonstrate that the LSTM-Attention model outperforms these models across multiple evaluation metrics, significantly reducing prediction errors and enhancing robustness and adaptability. The proposed model achieved Mean Absolute Error (MAE) values ranging from 5.19 to 10.20 and R2 values close to one (0.95 to 0.98) in four test scenarios, demonstrating excellent predictive performance under complex conditions. The high-precision prediction of drilling pump energy consumption based on this method can support energy optimization and provide guidance for field operations. [ABSTRACT FROM AUTHOR]

Despite recent advances in cluster‐based catalysis for organic synthesis, the substrate scope of reactions catalyzed by metal nanoclusters is typically not superior to previously established catalytic systems. Herein, we develop new atomically precise copper nanoclusters for protosilylation, with scope expanding to alkenes and simple enynes that were not suitable for prior synthetic methodologies with traditional copper complexes. The involvement of a second copper center in the metal kernel during the migratory insertion step is thought to be responsible for the expanded scope. In addition, the reaction is highly compatible with water and can be carried out in open air rather than under inert gas protection. Mechanistic studies suggest that the cluster‐catalyzed protosilylation proceeds in the absence of silyl radicals. The current findings demonstrate the potential of using metal nanoclusters for practical and sustainable chemical synthesis. [ABSTRACT FROM AUTHOR]

Deep and offshore drilling operations face complex geological formations, uncertain formation pressures, and narrow safety density windows, making them susceptible to lost circulation risks. To address these challenges, this paper introduces an innovative, intelligent lost circulation monitoring model that incorporates geological lithology information. This model not only utilizes real-time drilling parameters, but also encodes geological information such as rock type as inputs to the model. By combining these key lithological features, the model can comprehensively assess wellbore stability and reduce the lost circulation risks. In this paper, the Conditional Tabular Generative adversarial network (CTGAN) model is used to enhance the data of small-sample risk data, which can effectively expand the data distribution space and improve the performance of the model. This paper conducts a comparative analysis of intelligent monitoring results using artificial neural networks (ANNs), long short-term memory (LSTM), and temporal convolutional networks (TCNs). The results show that the TCN achieves an identification accuracy of 93.7%. Furthermore, the analysis reveals that the inclusion of lithology information significantly enhances the model's performance, resulting in a 7.1% increase in accuracy. The false alarm rate of the model can be reduced by 10.2%, considering the fluctuation of the logging curve caused by the on/off condition of the pump. This indicates that the introduction of lithology information and the condition of the pump on−off provide advantages in monitoring and identifying lost circulation risks, enabling a more precise assessment of wellbore stability and a reduction in lost circulation incidents. The method of lost circulation monitoring proposed in this paper provides an important safety guarantee for the oil drilling industry. [ABSTRACT FROM AUTHOR]

Rate of penetration (ROP) is crucial for evaluating drilling efficiency, with accurate prediction essential for enhancing performance and optimizing parameters. In practice, complex and variable downhole environments pose significant challenges for mechanistic ROP equations, resulting in prediction difficulties and low accuracy. Recently, data-driven machine learning models have been widely applied to ROP prediction. However, these models often lack mechanistic constraints, limiting their performance to specific conditions and reducing their real-world applicability. Additionally, geological variability across wells further hinders the transferability of conventional intelligent models. Thus, combining mechanistic knowledge with intelligent models and enhancing model stability and transferability are key challenges in ROP prediction research. To address these challenges, this paper proposes a Mechanism-Data Fusion and Transfer Learning method to construct an intelligent prediction model for ROP, achieving accurate ROP predictions. A multilayer perceptron (MLP) was selected as the base model, and training was performed using data from neighboring wells and partial data from the target well. The Two-stage TrAdaBoost.R2 algorithm was employed to enhance model transferability. Additionally, drilling mechanistic knowledge was incorporated into the model's loss function as a constraint to achieve a fusion of mechanistic knowledge and data-driven approaches. Using MAPE as the measure of accuracy, compared with conventional intelligent models, the proposed ROP prediction model improved prediction accuracy on the target well by 64.51%. The model transfer method proposed in this paper has a field test accuracy of 89.71% in an oilfield in China. These results demonstrate the effectiveness and feasibility of the proposed transfer learning method and mechanistic–data integration approach. [ABSTRACT FROM AUTHOR]

Humans demonstrate remarkable abilities to predict physicalevents in complex scenes. Two classes of models for physicalscene understanding have recently been proposed: “IntuitivePhysics Engines”, or IPEs, which posit that people make pre-dictions by running approximate probabilistic simulations incausal mental models similar in nature to video-game physicsengines, and memory-based models, which make judgmentsbased on analogies to stored experiences of previously en-countered scenes and physical outcomes. Versions of the lat-ter have recently been instantiated in convolutional neural net-work (CNN) architectures. Here we report four experimentsthat, to our knowledge, are the first rigorous comparisonsof simulation-based and CNN-based models, where both ap-proaches are concretely instantiated in algorithms that can runon raw image inputs and produce as outputs physical judg-ments such as whether a stack of blocks will fall. Both ap-proaches can achieve super-human accuracy levels and canquantitatively predict human judgments to a similar degree,but only the simulation-based models generalize to novel sit-uations in ways that people do, and are qualitatively consis-tent with systematic perceptual illusions and judgment asym-metries that people show.

Near infrared (NIR) emitter with circularly polarized phosphorescence (CPP), known as NIR CPP, has emerged as a key part in the research of cutting‐edge luminescent materials. However, it remains a challenge to obtain nanoclusters with NIR CPP activity. Here, we propose an asymmetric transformation approach to efficiently synthesize two pairs of chiral silver nanoclusters (R/S‐Ag29 and R/S‐Ag16) using an achiral Ag10 nanocluster as starting material in the presence of different concentration chiral inducer (R/S)‐1,1′‐binaphthyl‐2,2′‐diyl hydrogenphosphate (R/S‐BNP). R/S‐Ag29, formed in the low‐concentration R/S‐BNP, exhibits a unique kernel‐shell structure consisting of a distorted Ag13 icosahedron and an integrated cage‐like organometallic shell with a C3 symmetry, and possesses a superatomic 6‐electron configuration (1S2|1P4). By contrast, R/S‐Ag16, formed in the high‐concentration R/S‐BNP, features a sandwich‐like pentagram with AgI‐pure kernel. Profiting from the hierarchically chiral structures and superatomic kernel‐dominated phosphorescence, R/S‐Ag29 exhibits infrequent CPP activity in the second near‐infrared (975 nm) region, being the first instance of NIR‐II CPP observed among CPL‐active metal nanoclusters. This study presents a new approach to reduce the difficulty of de novo synthesis for chiral silver nanomaterials, and facilitates the design of CPP‐active superatomic nanoclusters in NIR region. [ABSTRACT FROM AUTHOR]