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We investigate the existence of local minimizers for the nonlinear Schrödinger (NLS) equation with localized nonlinearity on noncompact metric graphs. In the absence of ground states, we prove that normalized local minimizers of the NLS equation do exist under suitable topological and metric assumptions of the graphs. In particular, we provide a criterion for the existence of local minimizers for the NLS equation in this article. Our results rely on the variational method and an application of Gagliardo-Nirenberg inequalities.

Despite remarkable educational gains of minorities, ethnoracial wage inequality persists and has even expanded among highly educated workers. Conventional explanations for this inequality are primarily derived from comparing workers across different educational levels and are less salient for understanding inequalities within the highly educated workforce. This study examines a previously overlooked source of ethnoracial inequality among highly educated workers: vertical mismatch between workers’ educational level and the education requirements for their occupation. Using a longitudinal sample of college graduates from the Survey of Income and Program Participation, we find that vertical mismatch accounts for a large part of racial/ethnic and nativity wage inequality. Specifically, highly educated minorities (especially blacks and Hispanics) and immigrants (especially those holding a foreign degree) are disproportionately channeled into mismatched jobs and subsequently consigned to such positions. Also, highly educated Hispanics and Asians, as well as foreign-educated immigrants, face greater wage penalties of vertical mismatch. The findings offer new insights into a key source of ethnoracial and nativity stratification.

The major monoamine neurotransmitters, serotonin (5-HT) and catecholamines (i.e., norepinephrine (NE), epinephrine (E), and dopamine (DA)), are critical to the nervous system function, and imbalances of the neurotransmitters have been connected to a variety of diseases, making their measurement useful in a clinical setting. A simple, rapid, robust, sensitive, and specific LC-MS/MS method has been developed and validated for the simultaneous quantitation of urinary serotonin and catecholamines with low cost, which is ideal for routine clinical applications. A simple extraction from complex urine was accomplished using tailored solid phase extraction incorporating phenylboronic acid complexation on a 96-well HLB microplate for the sample extraction and resulted in significantly improved throughput, selectivity, and extraction recovery. Compared to 1–10 mL of urine typically used, this method required only 10 μL. A rapid chromatographic elution with a total cycle time of 6 min per sample compared to reported run times of 19–75 min was achieved on a PFP column. The sensitivity of l and 2 ng mL −1 for the detection of low abundant E and NE combined with the high coverage of 1024 ng mL −1 for DA enabled the multi-analyte detection of these biogenic amines in a single run. Good linearity (2.0–512, 1.0–512, 4.0–1024, and 4.0–1024 ng mL −1 for NE, E, DA, and 5-HT, respectively), accuracy (87.6–104.0%), precision (≤8.0%), extraction recovery (69.6–103.7%), and matrix effect (87.1–113.1% for catecholamines and 63.6–71.4% for 5-HT) were obtained. No autosampler carryover was observed. The analytes were stable for 5 days at 20 °C, 14 days at 4 °C, and 30 days at −20 °C and five freeze–thaw cycles. The easy sample preparation, rapid LC, and multi-analyte MS detection allow two 96-well plates of samples to be extracted within 2 h and analyzed on an LC-MS/MS system within 24 h. The applicability and reliability of the assay were demonstrated by assessment of the reference interval for authentic urine specimens from 90 healthy individuals. Graphical abstract A simple, rapid, robust, sensitive and specific LC-MS/MS method combined with a dual functional solid phase extraction has been developed and validated for the simultaneous extraction and quantitation of monoamine neurotransmitters in human urine with low cost

Human urine, which is high in nutrients, acts as a resource as well as a contaminant. Indiscriminate urine discharge causes environmental pollution and wastes resources. To elucidate the research status and developmental trajectory of source-separated urine (SSU) treatment and recovery, this study was based on the Web of Science Core Collection (WOSCC) database and used the bibliometric software VOSviewer and CiteSpace to conduct a comprehensive and in-depth bibliometric analysis of the related literature in this field. The findings revealed a general upward trend in SSU treatment and recovery from 2000 to 2023. The compendium of 894 scholarly articles predominantly focused on the disciplines of Environmental Sciences, Environmental Engineering, and Water Resources. China and the USA emerged as the foremost contributors. Keyword co-occurrence mapping, clustering, and burst analysis have shown that the recovery of nitrogen and phosphorus from urine is currently the main focus, with future prospects leaning toward the retrieval of biochemicals and chemical energy. This study systematically categorizes and compares the developmental status, current advancements, and research progress in this field. The findings of this study provide a valuable reference for understanding developmental pathways in this field of research.

In order to study the recommendation system of digital media based on semantic classification, the CF-LFMC algorithm based on semantic classification is proposed. Firstly, the traditional algorithm is analyzed. Aiming at some problems existing in the traditional algorithm, a clustering algorithm model based on term meaning and collaborative filtering algorithm is designed by combining the collaborative filtering algorithm and project-based clustering algorithm. Before analyzing sparse data, the cold start and timeliness of the traditional algorithm are improved. Secondly, the performance comparison of three cosine similarity calculation methods of experimental IBCF algorithm, the performance comparison between CF-LFMC algorithm and IBCF algorithm, and the performance comparison between CF-LFMC algorithm and CF-LFMC algorithm without the time function is carried out. The clustering value N = 10 in the CF-LFMC algorithm is taken as the experimental result; MAE values of both algorithms decrease with the increase of the nearest neighbor number k. When the number of nearest neighbors is small, MAE values of the two algorithms are close to each other. As the number of nearest neighbors increases, the accuracy of the algorithm does not improve significantly, and the calculation cost of the algorithm will increase with the increase of the number of nearest neighbors, so the number of nearest neighbors between 20 and 30 is more appropriate. CF-LFMC shows better accuracy, and the CF-LFMC algorithm improved by the time function has improved the accuracy, which is better than the traditional algorithm in accuracy.

The rapid development of neuro-inspired computing demands synaptic devices with ultrafast speed, low power consumption, and multiple non-volatile states, among other features. Here, a high-performance synaptic device is designed and established based on a Ag/PbZr 0.52 Ti 0.48 O 3 (PZT, (111)-oriented)/Nb:SrTiO 3 ferroelectric tunnel junction (FTJ). The advantages of (111)-oriented PZT (~1.2 nm) include its multiple ferroelectric switching dynamics, ultrafine ferroelectric domains, and small coercive voltage. The FTJ shows high-precision (256 states, 8 bits), reproducible (cycle-to-cycle variation, ~2.06%), linear (nonlinearity <1) and symmetric weight updates, with a good endurance of >10 9 cycles and an ultralow write energy consumption. In particular, manipulations among 150 states are realized under subnanosecond (~630 ps) pulse voltages ≤5 V, and the fastest resistance switching at 300 ps for the FTJs is achieved by voltages <13 V. Based on the experimental performance, the convolutional neural network simulation achieves a high online learning accuracy of ~94.7% for recognizing fashion product images, close to the calculated result of ~95.6% by floating-point-based convolutional neural network software. Interestingly, the FTJ-based neural network is very robust to input image noise, showing potential for practical applications. This work represents an important improvement in FTJs towards building neuro-inspired computing systems. Brain-inspired computing demands high-performance synapses. Here, the authors report a subnanosecond ferroelectric tunnel junction with 256 conductance states, 10 9 endurance, and 5.3 fJ/bit energy consumption, satisfactory to build synaptic devices.

Next-generation non-volatile memories with ultrafast speed, low power consumption, and high density are highly desired in the era of big data. Here, we report a high performance memristor based on a Ag/BaTiO 3 /Nb:SrTiO 3 ferroelectric tunnel junction (FTJ) with the fastest operation speed (600 ps) and the highest number of states (32 states or 5 bits) per cell among the reported FTJs. The sub-nanosecond resistive switching maintains up to 358 K, and the write current density is as low as 4 × 10 3  A cm −2 . The functionality of spike-timing-dependent plasticity served as a solid synaptic device is also obtained with ultrafast operation. Furthermore, it is demonstrated that a Nb:SrTiO 3 electrode with a higher carrier concentration and a metal electrode with lower work function tend to improve the operation speed. These results may throw light on the way for overcoming the storage performance gap between different levels of the memory hierarchy and developing ultrafast neuromorphic computing systems. Memristor devices based on ferroelectric tunnel junctions are promising, but suffer from quite slow switching times. Here, the authors report on ultrafast switching times at and above room temperature of 600ps in Ag/BaTiO3/Nb:SrTiO3 based ferroelectric tunnel junctions.

MIRACL is a multilingual dataset for retrieval across 18 languages that collectively encompass over three billion native speakers around the world. This resource is designed to support monolingual retrieval tasks, where the queries and the corpora are in the same language. In total, we have gathered over 726k high-quality relevance judgments for 78k queries over Wikipedia in these languages, where all annotations have been performed by native speakers hired by our team. MIRACL covers languages that are both typologically close as well as distant from 10 language families and 13 sub-families, associated with varying amounts of publicly available resources. Extensive automatic heuristic verification and manual assessments were performed during the annotation process to control data quality. In total, MIRACL represents an investment of around five person-years of human annotator effort. Our goal is to spur research on improving retrieval across a continuum of languages, thus enhancing information access capabilities for diverse populations around the world, particularly those that have traditionally been underserved. MIRACL is available at .

Celastrol is a quinone methyl triterpenoid monomeric ingredient extracted from the root of Tripterygium wilfordii . Celastrol shows potential pharmacological activities in various diseases, which include inflammatory, obesity, cancer, and bacterial diseases. However, the application prospect of celastrol is largely limited by its low bioavailability, poor water solubility, and undesired off-target cytotoxicity. To address these problems, a number of drug delivery methods and technologies have been reported to enhance the efficiency and reduce the toxicity of celastrol. We classified the current drug delivery technologies into two parts. The direct chemical modification includes nucleic acid aptamer–celastrol conjugate, nucleic acid aptamer–dendrimer–celastrol conjugate, and glucolipid–celastrol conjugate. The indirect modification includes dendrimers, polymers, albumins, and vesicular carriers. The current technologies can covalently bond or encapsulate celastrol, which improves its selectivity. Here, we present a review that focalizes the recent advances of drug delivery strategies in enhancing the efficiency and reducing the toxicity of celastrol.