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The explosive growth of tasks on crowdsourcing platforms has intensified information overload, making it difficult for workers to spot lucrative bids; yet mainstream recommenders inherit a user-independence assumption from e-commerce and therefore overlook the real-time competition among workers, which degrades ranking stability and accuracy. To bridge this gap, we propose the Adaptive Parameter Exponential Decay Algorithm (APAED), which first produces base relevance scores with an offline neural model and then injects a competition-aware exponential decay whose strength is jointly determined by the interquartile range of each worker’s score list (global factor) and the live bid distribution of every task (local factor). This model-agnostic adjustment explicitly quantifies competitive intensity without handcrafted features and can be paired with any backbone recommender. Experiments on a real-world dataset comprising 25,643 tasks and 19,735 workers show that APAED cuts the residual RMSE of HR@10 from 9.575×10−4 to 5.939×10−4 (−38%) and that of MRR from 2.920×10−4 to 0.736×10−4 (−75%), substantially reducing score fluctuations across epochs and consistently outperforming four strong neural baselines. These results confirm that explicitly modeling worker competition yields more accurate and stable task recommendations in crowdsourcing environments.

The generation of second-order sidebands and its associated group delay is an important subject in optical storage and switch. In this work, the efficiency of second-order sideband generation in a coupled optomechanical cavity system with a cubic nonlinear harmonic oscillator is theoretically investigated. It is found that the efficiency of second-order sideband generation can be effectively enhanced with the decrease in decay rate of optomechanical cavity, the increase in coupling strength between two cavities and the power of probe field. The slow light effect (i.e., positive group delay) is also observed in the proposed optomechanical cavity system, and can be controlled with the power of control field.

Cathode materials have impeded the development of aqueous Zn batteries (AZBs) for a long time due to their low capacity and poor cycling stability. Here, a “two birds with one stone” strategy is devised to optimize the Ni–Co hydroxide cathode material (NCH) for AZBs, which plays an essential role in both composition adjustment and morphology majorization. The F-doped Ni–Co hydroxide (FNCH) exhibits a unique nanoarray structure consisting of the 2D flake-like unit, furnishing abundant active sites for the redox reaction. A series of analyses prove that FNCH delivers improved electrical conductivity and enhanced electrochemical activity. Contributing to the unique morphology and adjusted characteristics, FNCH presents a higher discharge-specific capacity, more advantageous rate capability and competitive cycling stability than NCH. As a result, an aqueous Zn battery assembled with a FNCH cathode and Zn anode exhibits a high capacity of 0.23 mAh cm−2 at 1 mA cm−2, and retains 0.10 mAh cm−2 at 10 mA cm−2. More importantly, the FNCH–Zn battery demonstrates no capacity decay after 3000 cycles with a conspicuous capacity of 0.15 mAh cm−2 at 8 mA cm−2, indicating a superior cycling performance. This work provides a facile approach to develop high-performance cathodes for aqueous Zn batteries.

Tube hydroforming (THF) is a unique forming technique, which can transform metal tubes into complex hollow parts using hydraulic fluid as the forming medium. The initial non-uniform thickness of as-received practical tubes significantly affects their formability in the hydroforming process. A forming limit diagram (FLD), also called as the forming limit curve (FLC), is often adopted to evaluate the forming behaviour of sheet metals in plastic forming processes to avoid forming failures. The purposes of this research are fivefold, namely to establish the FLCs of tubular blanks with varied initial thickness deviations in tensile and compressive strain states by means of finite element (FE) modelling of THF, to construct a non-uniform geometric model for practical tubes, to analyse the impact of initial thickness deviation on the FLCs, to clarify the differences in the FLCs obtained using three different instability criteria, and to validate the proposed non-uniform geometric model by conducting hydro-bulging experiments. Results show that it is possible to accurately predict the FLCs of practical tubes with initial non-uniform thicknesses using FE simulation combined with the proposed non-uniform geometric model. We found that the displacement of FLCs occurred in the major- and minor-strain coordinates because the thickness is initially not uniform; however, no significant discrepancies were observed in the FLCs obtained using the three instability criteria. The proposed approach, which combines FE simulation with a non-uniform geometric model, can be easily employed to predict the ultimate strains of tubes with initial non-uniform thicknesses in THF to avoid forming defects.

T cells become dysfunctional when they encounter self antigens or are exposed to chronic infection or to the tumour microenvironment 1 . The function of T cells is tightly regulated by a combinational co-stimulatory signal, and dominance of negative co-stimulation results in T cell dysfunction 2 . However, the molecular mechanisms that underlie this dysfunction remain unclear. Here, using an in vitro T cell tolerance induction system in mice, we characterize genome-wide epigenetic and gene expression features in tolerant T cells, and show that they are distinct from effector and regulatory T cells. Notably, the transcription factor NR4A1 is stably expressed at high levels in tolerant T cells. Overexpression of NR4A1 inhibits effector T cell differentiation, whereas deletion of NR4A1 overcomes T cell tolerance and exaggerates effector function, as well as enhancing immunity against tumour and chronic virus. Mechanistically, NR4A1 is preferentially recruited to binding sites of the transcription factor AP-1, where it represses effector-gene expression by inhibiting AP-1 function. NR4A1 binding also promotes acetylation of histone 3 at lysine 27 (H3K27ac), leading to activation of tolerance-related genes. This study thus identifies NR4A1 as a key general regulator in the induction of T cell dysfunction, and a potential target for tumour immunotherapy. Tolerant T cells display characteristic patterns of gene expression and epigenetics that are distinct from other types of T cells and are orchestrated by the transcription factor NR4A1.

SNAREs (soluble N‐ethylmaleimide‐sensitive factor attachment protein receptors) facilitate docking and fusion of vesicles with their target membranes, playing a crucial role in vesicle trafficking and exocytosis. However, the spatial assembly and roles of plasma membrane (PM)‐associated SNAREs in phytopathogen development and pathogenicity are not clearly understood. In this study, we analysed the roles and molecular mechanisms of PM‐associated SNARE complexes in the banana Fusarium wilt fungus Fusarium oxysporum f. sp. cubense tropical race 4 (FocTR4). Our findings demonstrate that FocSso1 is important for the fungal growth, conidiation, host penetration and colonization. Mechanistically, FocSso1 regulates protein secretion by mediating vesicle docking and fusion with the PM and hyphal apex. Interestingly, a FocSso1–FocSec9–FocSnc1 complex was observed to assemble not only at the fungal PM but also on the growing hyphal apex, facilitating exocytosis. FocSso2, a paralogue of FocSso1, was also found to form a ternary SNARE complex with FocSec9 and FocSnc1, but it mainly localizes to the PM in old hyphae. The functional analysis of this protein demonstrated that it is dispensable for the fungal growth but necessary for host penetration and colonization. The other subunits, FocSec9 and FocSnc1, are involved in the fungal development and facilitate host penetration. Furthermore, FocSso1 and FocSnc1 are functionally interdependent, as loss of FocSso1 leads to mis‐sorting and degradation of FocSnc1 in the vacuole and vice versa. Overall, this study provides insight into the formation of two spatially and functionally distinct PM SNARE complexes and their involvement in vesicle exocytosis to regulate development and pathogenicity of FocTR4. Two SNARE complexes Sso1–Sec9–Snc1 and Sso2–Sec9–Snc1 were identified in Fusarium oxysporum f. sp. cubense that coordinately facilitate docking and fusion of exocytic vesicles with the plasma membrane in a spatially specific way.

Aqueous electrolytes, while conferring inherent safety advantages, inevitably induce hydrogen‐evolution corrosion, resulting in nonuniform Zn deposition and shortened cycle life. Herein, a novel electrolyte with buffering function is designed to modulate ion behavior and stabilize interface pH. The introduced additive acts as a cushion maskant (CM) that spontaneously adsorbs onto the Zn metal surface, displacing interfacial water molecules and thereby suppressing corrosion. Simultaneously, its coordination with Zn2+ homogenizes the Zn2+ flux to promote uniform deposition. Moreover, the protonation/deprotonation equilibria of CM within the electrolyte buffer local pH fluctuations, stabilizing the interfacial microenvironment. Consequently, a beneficial solid electrolyte interphase (SEI) is established, which further shields the Zn anode, enhances interfacial stability, and markedly improves cycling durability. Accordingly, Zn//Zn symmetrical cells in CM‐containing electrolyte can realize exceptional lifespan for 2800 h at 2 mA cm−2 and 970 h even at 10 mA cm−2. In addition, CM demonstrates the superior practical applicability in Zn//I2 full cells for long‐term and rate tests. Zn//I2 pouch full cell can operate for 150 mAh with CM. This study offers a distinctive and comprehensive strategy for stabilizing the Zn anode. A rationally engineered buffer electrolyte is devised, in which molecules acting as a cushion maskant (CM) spontaneously adsorb on the Zn anode to modulate ion behavior and suppress corrosion while its protonation–deprotonation equilibria buffer interfacial pH, markedly prolonging cycle life.

Forming limit is one of the important indexes to evaluate the formability of materials. In a variety of methods of evaluating the formability of materials, the forming limit diagram (FLD) is the most intuitive and effective, and the most widely used. There are many methods to obtain the forming limit diagram. This paper mainly introduces the methods for obtaining the forming limit diagram of the material defects, and material defects are classified into three categories: geometric defects, defects in organization structure and material constitutive defects, and the methods for obtaining forming limit diagram based on these three kinds of defects is analyzed and summarized.

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks but remain fundamentally static, unable to adapt their internal parameters to novel tasks, evolving knowledge domains, or dynamic interaction contexts. As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck, necessitating agents that can adaptively reason, act, and evolve in real time. This paradigm shift -- from scaling static models to developing self-evolving agents -- has sparked growing interest in architectures and methods enabling continual learning and adaptation from data, interactions, and experiences. This survey provides the first systematic and comprehensive review of self-evolving agents, organizing the field around three foundational dimensions: what, when, and how to evolve. We examine evolutionary mechanisms across agent components (e.g., models, memory, tools, architecture), categorize adaptation methods by stages (e.g., intra-test-time, inter-test-time), and analyze the algorithmic and architectural designs that guide evolutionary adaptation (e.g., scalar rewards, textual feedback, single-agent and multi-agent systems). Additionally, we analyze evaluation metrics and benchmarks tailored for self-evolving agents, highlight applications in domains such as coding, education, and healthcare, and identify critical challenges and research directions in safety, scalability, and co-evolutionary dynamics. By providing a structured framework for understanding and designing self-evolving agents, this survey establishes a roadmap for advancing more adaptive, robust, and versatile agentic systems in both research and real-world deployments, and ultimately sheds light on the realization of Artificial Super Intelligence (ASI) where agents evolve autonomously and perform beyond human-level intelligence across tasks.

G protein-coupled receptors （GPCRs） are involved in all human physiological systems where they are responsible for transducing extracellular signals into cells. GPCRs signal in response to a diverse array of stimuli including light, hormones, and lipids, where these signals affect downstream cascades to impact both health and disease states. Yet, despite their importance as therapeutic tar- gets, detailed molecular structures of only 30 GPCRs have been determined to date. A key challenge to their structure determination is adequate protein expression. Here we report the quantification of protein expression in an insect cell expression system for all 826 human GPCRs using two different fusion constructs. Expression char- acteristics are analyzed in aggregate and among each of the five distinct subfamilies. These data can be used to identify trends related to GPCR expression between dif- ferent fusion constructs and between different GPCR families, and to prioritize lead candidates for future structure determination feasibility.