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Wind speed prediction is crucial for precisely wind power forecasting and reduced maintenance costs. Highland regions, which possess a considerable wind potential, present complex meteorological conditions, making wind speed prediction challenging. Traditional weather forecasting relies on complex statistical methods and extensive prior knowledge. While recent deep learning models have improved prediction accuracy, they often assume uniform influence weight structure, limiting model effectiveness. This study introduces an enhanced Conditional Local Convolution Recurrent Network (CLCRN) model to improve spatiotemporal wind speed forecasting using multidimensional meteorological inputs such as temperature, pressure, and dew point, alongside wind components. This model addresses uniform influence model weight issue by redesigning convolution kernels to better capture local meteorological features and integrating multiple influencing factors. Our model consistently achieves lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values across various prediction intervals (3, 6, 9, and 12 h) compared to other models, supported by the meteorological station data from 2019 to 2021. Furthermore, the spatial distribution of the local convolution weights aligns with local wind velocity patterns in Inner Mongolia, enhancing model interpretability. These results demonstrate potential for practical applications in renewable energy planning and wind dynamics simulation.

As Android is a popular a mobile operating system, Android malware is on the rise, which poses a great threat to user privacy and security. Considering the poor detection effects of the single feature selection algorithm and the low detection efficiency of traditional machine learning methods, we propose an Android malware detection framework based on stacking ensemble learning—MFDroid—to identify Android malware. In this paper, we used seven feature selection algorithms to select permissions, API calls, and opcodes, and then merged the results of each feature selection algorithm to obtain a new feature set. Subsequently, we used this to train the base learner, and set the logical regression as a meta-classifier, to learn the implicit information from the output of base learners and obtain the classification results. After the evaluation, the F1-score of MFDroid reached 96.0%. Finally, we analyzed each type of feature to identify the differences between malicious and benign applications. At the end of this paper, we present some general conclusions. In recent years, malicious applications and benign applications have been similar in terms of permission requests. In other words, the model of training, only with permission, can no longer effectively or efficiently distinguish malicious applications from benign applications.

Nanofabrication on soft polymeric surfaces is an essential process in many fields, for example, chip manufacturing, microfluidics, high efficiency solar cells, and anticounterfeiting. In order to achieve these applications, various nanofabrication methods have been explored. Among them, nanoimprint lithography (NIL) has drawn worldwide attention because of its cheap and fast processability. In this minireview, an overview of azopolymer‐based NIL is provided. Since their discovery, azopolymers have demonstrated versatile photoresponsive characteristics due to their unique physical and chemical properties that originate from the photoisomerization of azobenzene chromophores. As such, two aspects are reported in this minireview. On the one hand, various azopolymers showing photofluidization and photoswitchable glass transition temperatures have been developed, thus facilitating methodological advancements in NIL. On the other hand, these on‐demand NIL methods provide greater opportunities for azopolymer‐based applications, such as templating of optics, directional photo‐manipulation of nanopatterns, and micro photo‐actuators. Also the challenges are discussed that remain in this field. Imprint with light: This Minireview is focused on azopolymer‐based nanoimprint lithography (NIL). Recently, azopolymers with photoswitching‐Tg and azopolymers with fast photoisomerization‐cycle have been used in NIL, offering delicate methods in this area. In turn, NIL has helped to develop azopolymers with designable nanostructure for various applications. In this Minireview, these two aspects are involved.

Enfortumab vedotin (EV) is an antibody-drug conjugate (ADC) that has been approved by the FDA for patients with locally advanced or metastatic urothelial carcinoma (UC). This study presents a comprehensive pharmacovigilance analysis of the post-marketing safety profile of EV in the real-world based on the US Food and Drug Administration Adverse Event Reporting System (FAERS). Adverse event (AE) reports regarding EV between January 2020 and December 2023 were obtained from the FAERS database. The standardized MedDRA query (SMQ) narrow search AEs on the preferred term (PT) level were used. Disproportionality analysis was performed to identify the AE signals for EV with the reporting odds ratio (ROR), proportional reporting ratio (PRR), multi-item gamma Poisson shrinker (MGPS), and Bayesian confidence propagation neural network (BCPNN). A total of 2,216 reports regarding EV were included in the present study. SMQ analysis results indicated that a stronger strength signal was found in severe cutaneous adverse reactions, retroperitoneal fibrosis, and peripheral neuropathy. A total of 116 significant disproportionality PTs referring to 14 system organ classes (SOCs) were retained by disproportionality analysis, with 49 PTs not listed on the EV drug label. Frequently reported EV-related AEs included rash, peripheral neuropathy, decreased appetite, alopecia, and pruritus. The time to onset of the majority of EV-related AEs was within 30 days (66.05%), with only 0.73% events occurring after 1 year. The disproportionality analysis highlights that dermatologic toxicity and peripheral neuropathy were the major AEs induced by EV. The potential AEs not listed on the drug label were mainly related to gastrointestinal, hepatic, and pulmonary events. Further research is needed to confirm and explore the EV-related AEs in clinical practice.

Background: Apalutamide is a new drug class, which is approved to treat prostate cancer (PCa). The aim of our study was to assess the safety profiles of apalutamide in real-world through data mining of the United States Food and Drug Administration Adverse Event Reporting System (FAERS). Method: We included adverse event (AE) reports regarding apalutamide submitted to the FAERS from 2018 quarter 1 (2018Q1) to 2022 quarter 1 (2022Q1). Disproportionality analyses, including reporting odds ratio (ROR), were performed to identify the signals of AEs in patients receiving apalutamide. A signal was detected if the lower limit of the 95% confidence interval (CI) of ROR >1 and at least 3 AEs were reported. Results: The FAERS database documented 4,156 reports regarding apalutamide from 1 January 2018, to 31 March 2022. A total of 100 significant disproportionality preferred terms (PTs) were retained. Frequently observed AEs in patients receiving apalutamide included rash, fatigue, diarrhea, hot flush, fall, weight decreased, hypertension. The most significant system organ class (SOC) was “skin and subcutaneous tissue disorders”, which mainly consisted of dermatological adverse events (dAEs). The additional AEs observed with the significantly signal contain lichenoid keratosis, increased eosinophil count, bacterial pneumonia, pulmonary tuberculosis, hydronephrosis. Conclusion: Our findings provide valuable evidence for apalutamide safety profile in the real-world, which could help clinicians and pharmacists to enhance their vigilance and improve the safety of apalutamide in clinical practice.

China established the Tiered-network Healthcare Delivery System (THDS) in 2015 to address the disproportionate number of patients attending tertiary hospitals relative to primary- or secondary-care institutions. Although the reported number of outpatients visiting tertiary hospitals is slowly decreasing, numerous patients choose to visit them regardless of their disease's severity. To effectively implement the THDS, this article explored the relationship between patients' sociodemographic and belief characteristics and their healthcare-seeking behavioral decision-making in China. Data obtained through questionnaires were analyzed using decision tree and logistic regression models to explore outpatients' characteristics and medical decision-making using comprehensive feature data. Moreover, further statistical analyses were conducted on the outpatient data obtained from the regional population health platform in Jiaxing, China. The decision tree model revealed that whether outpatients have medical insurance is the primary factor guiding their healthcare-seeking behaviors, with those without medical insurance more likely to choose primary or secondary hospitals to treat minor diseases. For those with medical insurance, profession is the main factor, with industrial workers more inclined to choose primary or secondary hospitals for minor diseases. The logistic regression analyses revealed that outpatients without insurance and who were not freelancers or individual owners were more likely to choose primary or secondary hospitals for minor diseases. Further statistical analysis of the data from the Jiaxing population health platform showed that, for minor or general diseases, outpatients without medical insurance and employed as farmers tended to choose primary and secondary hospitals over tertiary hospitals. The three analyses yielded consistent results: in China, medical insurance and patients' profession are the most important factors guiding outpatients' healthcare-seeking behaviors. Accordingly, we propose that the government should focus on economic reforms to increase outpatients' visits to primary and secondary hospitals and diagnosis-related groups' payment of medical insurance to decrease the admittance of patients with minor diseases in large tertiary hospitals. Meanwhile, the government should correct patients' belief prejudice about selecting hospitals through corresponding publicity.

Dust storms are destructive extreme weather events that are difficult to detect because of their large spatial distributions and temporal movement characteristics. The Mongolian Plateau is one of the most important dust sources and poses a serious threat to dust storm disasters in north-east Asia. Understanding the transboundary transport pathways and meteorological driving forces of dust is crucial for dust storm prevention and control. We selected one extreme dust storm event that occurred from 19th to 24th March 2023, in the southern Mongolian Plateau, identified its spatial-temporal transport pathways, and analyzed the impact of multiple meteorological components on dust storm occurrences. The daily distribution of dust storms was recognized firstly by using a normalized difference dust index supported by MOD09GA data. ERA-5 hourly data were used to analyze the spatiotemporal variations of meteorological components. By integrating data on the daily distribution of dust storms with the time-series trends of meteorological components, this study identified dewpoint temperature, air temperature, wind components, surface pressure, and precipitation as the key variables influencing the formation and propagation of dust storms. The results showed that dust storm occurrence is associated with a reduction in dewpoint temperature and increase in air dryness. Low-pressure systems are important precursors of dust storm formation, and strong winds drive their propagation and spread. The reversal of these meteorological trends reduces their intensity and extent, while increased precipitation at the end of a dust storm event helps settle dust. These findings provide crucial insights into the meteorological driving forces and predictability of dust storms on the Mongolian Plateau, and can be utilized in other geographical areas prone to dust storm.

Nitrogen incorporation is fundamental in organic synthesis for functional materials, pharmaceuticals, and agrochemicals. While C–N bond formation has gained great progress, N-N bond construction remains challenging: existing methods rely on prefunctionalized precursors, and direct coupling is hindered by narrow substrate scope and poor reactivity control. Although transition-metal-catalyzed nitrene strategies exhibit high efficacy for intermolecular N–H insertion, nitrene-mediated N–N coupling under metal-free conditions has rarely been explored. Herein, we report a broadly applicable sulfilimine-based approach for selective N–N coupling. By leveraging sulfilimines as nitrene precursors, we achieve controlled release of highly reactive nitrene intermediates, enabling intermolecular N-N coupling with amines. This strategy provides an alternative pathway for nitrene-mediated N-H insertion reaction over conventional methods, providing a streamlined route for constructing complex N–N-containing architectures. Transition-metal-catalyzed nitrene strategies exhibit high efficacy for intermolecular N–H insertion, but nitrene-mediated N–N coupling under metal-free conditions has rarely been explored. Herein, the authors report the selective N–N coupling using sulfilimines as nitrene precursors.

Objective. To evaluate the diagnostic efficacy of MDA-MB-231 triple-negative breast cancer with 125I-labeled pHLIP (Var7) by single-photon emission computed tomography/computed tomography (SPECT/CT) imaging. Methods. The binding fraction of [125I]I-pHLIP (Var7) and MDA-MB-231 cells was measured at pH 7.4 and pH 6.0, and tumor-bearing mice were subjected to small-animal SPECT/CT imaging studies. Results. At pH=6.0, the binding fractions of [125I]I-pHLIP (Var7) and MDA-MB-231 cells at 10 min, 40 min, 1 h, and 2 h were 1.9±0.1%, 3.5±0.1%, 6.3±0.8%, and 6.6±0.3%, respectively. At pH=7.4, there was no measured binding between [125I]I-pHLIP (Var7) and MDA-MB-231 cells. Small-animal SPECT/CT imaging showed clearly visible tumors at 1 and 2 h after injection. Conclusions. [125I]I-pHLIP (Var7) could bind to MDA-MB-231 cells in an acidic environment, and small-animal SPECT/CT imaging showed clear tumors at 1 and 2 h after probe injection.

Ultra-low cycle fatigue (ULCF) damage is one of the main failure modes of steel bridge piers when subjected to severe earthquake. However, existing experimental and numerical studies aiming at ULCF damage of steel piers almost adopt the uniaxial loading strategy, which is different from the real seismic motion. To make up for this problem, the ULCF behavior of steel piers under horizontal bidirectional cyclic loads was investigated in this paper. A two-level zooming analytical system was presented first to save computational cost of the ULCF assessment of steel piers. Its applicability and cost-efficiency were numerically and experimentally verified through detailed elaboration. And based on this, a series of numerical work of piers under horizontal bi-directional and unidirectional cyclic loads were carried out. The relationship between the ductile durability and pier parameters was studied with the help of a micro-damage mechanism-based ULCF assessment method and an energy assumption-based evaluation index. Results showed that compared with the unidirectional loads, bi-directional loads could bring about significant deterioration in the ULCF resistance of piers. The relative ductility capacity of steel piers under bi-directional cyclic loads is approximately consistent as width-to-thickness ratio and slenderness ratio change, and enhances as axial compression ratio increases.