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1. VLA-Cache: Towards Efficient Vision-Language-Action Model via Adaptive Token Caching in Robotic Manipulation

Author

Xu, Siyu, Wang, Yunke, Xia, Chenghao, Zhu, Dihao, Huang, Tao, and Xu, Chang

Subjects
Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Vision-Language-Action (VLA) model can process instructions and visual perception to directly generate actions as output in an end-to-end fashion due to its strong multi-modal reasoning capabilities. While the performance of VLA models is promising, their computational cost can be substantial. This raises challenge for applying them on robotics tasks, which requires real-time decision-making to respond quickly to environmental changes. Since robotic control involves sequential decision-making, the visual input often exhibits minimal variation between successive steps. A natural idea is to reuse the computational results of unchanged visual tokens from the last step. Motivated by this idea, we propose VLA-Cache, an efficient vision-language-action model. VLA-Cache incorporates a token-selection mechanism that compares the visual input at each step with the input from the previous step, adaptively identifying visual tokens with minimal changes. The computational results for these unchanged tokens are then reused in subsequent steps via KV-cache, thereby significantly improving the efficiency of the VLA-Cache model. Experimental results on both simulation (e.g., LIBERO benchmark and SIMPLER) and real-world robot valid VLA-Cache can achieve practical acceleration with minimal sacrifice in success rate.

Published
2025

2. Mitigating Object Hallucinations in Large Vision-Language Models via Attention Calibration

Author

Zhu, Younan, Tao, Linwei, Dong, Minjing, and Xu, Chang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Large Vision-Language Models (LVLMs) exhibit impressive multimodal reasoning capabilities but remain highly susceptible to object hallucination, where models generate responses that are not factually aligned with the visual content. Recent works attribute this issue to an inherent bias of LVLMs where vision token attention map has a fixed correlation with spatial position, and propose to mitigate this issue by reordering visual tokens. However, we find that different LVLMs exhibit different correlations between attention and spatial position, which makes the existing solution difficult to generalize to other LVLMs. To address this issue, we first introduce a training-free solution, Uniform Attention Calibration (UAC), that estimates the bias from single meaningless input image and applies a calibration matrix to rectify attention imbalances. To further alleviate the bias, we relax the assumption of single meaningless input in UAC and introduce a fine-tuning solution, Dynamic Attention Calibration (DAC), that enforces the consistent outputs wherever the object locates in the image via a plug-and-plays module. Comprehensive experiments across multiple benchmarks demonstrate that UAC and DAC significantly reduce object hallucination while improving general multimodal alignment. Our methods achieve state-of-the-art performance across diverse LVLM architectures on various metrics.

Published
2025

3. AI Scaling: From Up to Down and Out

Author

Wang, Yunke, Li, Yanxi, and Xu, Chang

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

AI Scaling has traditionally been synonymous with Scaling Up, which builds larger and more powerful models. However, the growing demand for efficiency, adaptability, and collaboration across diverse applications necessitates a broader perspective. This position paper presents a holistic framework for AI scaling, encompassing Scaling Up, Scaling Down, and Scaling Out. It argues that while Scaling Up of models faces inherent bottlenecks, the future trajectory of AI scaling lies in Scaling Down and Scaling Out. These paradigms address critical technical and societal challenges, such as reducing carbon footprint, ensuring equitable access, and enhancing cross-domain collaboration. We explore transformative applications in healthcare, smart manufacturing, and content creation, demonstrating how AI Scaling can enable breakthroughs in efficiency, personalization, and global connectivity. Additionally, we highlight key challenges, including balancing model complexity with interpretability, managing resource constraints, and fostering ethical development. By synthesizing these approaches, we propose a unified roadmap that redefines the future of AI research and application, paving the way for advancements toward Artificial General Intelligence (AGI).

Published
2025

4. Generative Physical AI in Vision: A Survey

Author

Liu, Daochang, Zhang, Junyu, Dinh, Anh-Dung, Park, Eunbyung, Zhang, Shichao, and Xu, Chang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Generative Artificial Intelligence (AI) has rapidly advanced the field of computer vision by enabling machines to create and interpret visual data with unprecedented sophistication. This transformation builds upon a foundation of generative models to produce realistic images, videos, and 3D or 4D content. Traditionally, generative models primarily focus on visual fidelity while often neglecting the physical plausibility of generated content. This gap limits their effectiveness in applications requiring adherence to real-world physical laws, such as robotics, autonomous systems, and scientific simulations. As generative AI evolves to increasingly integrate physical realism and dynamic simulation, its potential to function as a "world simulator" expands-enabling the modeling of interactions governed by physics and bridging the divide between virtual and physical realities. This survey systematically reviews this emerging field of physics-aware generative AI in computer vision, categorizing methods based on how they incorporate physical knowledge-either through explicit simulation or implicit learning. We analyze key paradigms, discuss evaluation protocols, and identify future research directions. By offering a comprehensive overview, this survey aims to help future developments in physically grounded generation for vision. The reviewed papers are summarized at https://github.com/BestJunYu/Awesome-Physics-aware-Generation.

Published
2025

5. High-speed readout for direct light orbital angular momentum photodetector via photoelastic modulation

Author

Yang, Dehong, Xu, Chang, Lai, Jiawei, Fan, Zipu, Liang, Delang, Wang, Shiyu, Cheng, Jinluo, and Sun, Dong

Subjects
Physics - Optics, Physics - Applied Physics
Abstract

Recent progress in direct photodetection of light orbital angular momentum (OAM) based on the orbital photogalvanic effect (OPGE) provides an effective way for on-chip direct electric readout of orbital angular momentum, as well as large-scale integration focal-plane array devices. However, the recognition of OAM order from photocurrent response requires the extraction of circular polarization-dependent response. To date, the operation speed of such detector is currently at the minute level and is limited by slow mechanical polarization modulation and low OAM recognition capability. In this work, we demonstrate that the operation speed can be greatly improved via electrical polarization modulation strategy with photoelasitc modulator accompanied by phase-locked readout approach with lock-in amplifier. We demonstrate an operation speed of up to 1 kHz with this new technology in the mid-infrared region (4 {\mu}m) on an OAM detector using multilayer graphene (MLG) as photosensitive material. In principle, with new modulation and readout scheme, we can potentially increase the operation speed to 50.14 kHz with a PEM that operates at a state-of-the-art speed. Our work paves the way toward high-speed operation of direct OAM detection devices based on OPGE effect and pushes such technology to a more practical stage for focal plane array applications., Comment: 27 pages,5 figures

Published
2025

6. TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts

Author

Huang, Yu-Hao, Xu, Chang, Wu, Yueying, Li, Wu-Jun, and Bian, Jiang

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Time series generation models are crucial for applications like data augmentation and privacy preservation. Most existing time series generation models are typically designed to generate data from one specified domain. While leveraging data from other domain for better generalization is proved to work in other application areas, this approach remains challenging for time series modeling due to the large divergence in patterns among different real world time series categories. In this paper, we propose a multi-domain time series diffusion model with domain prompts, named TimeDP. In TimeDP, we utilize a time series semantic prototype module which defines time series prototypes to represent time series basis, each prototype vector serving as "word" representing some elementary time series feature. A prototype assignment module is applied to extract the extract domain specific prototype weights, for learning domain prompts as generation condition. During sampling, we extract "domain prompt" with few-shot samples from the target domain and use the domain prompts as condition to generate time series samples. Experiments demonstrate that our method outperforms baselines to provide the state-of-the-art in-domain generation quality and strong unseen domain generation capability., Comment: AAAI 2025

Published
2025

7. Color Correction Meets Cross-Spectral Refinement: A Distribution-Aware Diffusion for Underwater Image Restoration

Author

Chang, Laibin, Wang, Yunke, Du, Bo, and Xu, Chang

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Underwater imaging often suffers from significant visual degradation, which limits its suitability for subsequent applications. While recent underwater image enhancement (UIE) methods rely on the current advances in deep neural network architecture designs, there is still considerable room for improvement in terms of cross-scene robustness and computational efficiency. Diffusion models have shown great success in image generation, prompting us to consider their application to UIE tasks. However, directly applying them to UIE tasks will pose two challenges, \textit{i.e.}, high computational budget and color unbalanced perturbations. To tackle these issues, we propose DiffColor, a distribution-aware diffusion and cross-spectral refinement model for efficient UIE. Instead of diffusing in the raw pixel space, we transfer the image into the wavelet domain to obtain such low-frequency and high-frequency spectra, it inherently reduces the image spatial dimensions by half after each transformation. Unlike single-noise image restoration tasks, underwater imaging exhibits unbalanced channel distributions due to the selective absorption of light by water. To address this, we design the Global Color Correction (GCC) module to handle the diverse color shifts, thereby avoiding potential global degradation disturbances during the denoising process. For the sacrificed image details caused by underwater scattering, we further present the Cross-Spectral Detail Refinement (CSDR) to enhance the high-frequency details, which are integrated with the low-frequency signal as input conditions for guiding the diffusion. This way not only ensures the high-fidelity of sampled content but also compensates for the sacrificed details. Comprehensive experiments demonstrate the superior performance of DiffColor over state-of-the-art methods in both quantitative and qualitative evaluations.

Published
2025

10. Peer-to-Peer Transactions of Multiple Prosumers Based on Incomplete Information of Bayesian Game

Author

Tang, Wanxin, Jin, Xiaolong, Jia, Hongjie, Mu, Yunfei, Yu, Xiaodan, Xu, Chang, Jing, Zhipeng, Ma, Liang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Yang, Qingxin, editor, and Li, Jian, editor

Published
2025
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11. Dynamic Monitoring Platform for Overall Temperature Rise of Dry-Type Power Transformers Based on Digital Twin Technology

Author

Chu, Ziqing, Zhu, Sa, Xu, Chang, Ni, Runqing, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Yang, Qingxin, editor, and Li, Jian, editor

Published
2025
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12. ConGeo: Robust Cross-View Geo-Localization Across Ground View Variations

Author

Mi, Li, Xu, Chang, Castillo-Navarro, Javiera, Montariol, Syrielle, Yang, Wen, Bosselut, Antoine, Tuia, Devis, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Leonardis, Aleš, editor, Ricci, Elisa, editor, Roth, Stefan, editor, Russakovsky, Olga, editor, Sattler, Torsten, editor, and Varol, Gül, editor

Published
2025
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13. Active Generation for Image Classification

Author

Huang, Tao, Liu, Jiaqi, You, Shan, Xu, Chang, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Leonardis, Aleš, editor, Ricci, Elisa, editor, Roth, Stefan, editor, Russakovsky, Olga, editor, Sattler, Torsten, editor, and Varol, Gül, editor

Published
2025
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14. Training-Free Composite Scene Generation for Layout-to-Image Synthesis

Author

Liu, Jiaqi, Huang, Tao, Xu, Chang, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Leonardis, Aleš, editor, Ricci, Elisa, editor, Roth, Stefan, editor, Russakovsky, Olga, editor, Sattler, Torsten, editor, and Varol, Gül, editor

Published
2025
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15. Visual Prompting via Partial Optimal Transport

Author

Zheng, Mengyu, Hao, Zhiwei, Tang, Yehui, Xu, Chang, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Leonardis, Aleš, editor, Ricci, Elisa, editor, Roth, Stefan, editor, Russakovsky, Olga, editor, Sattler, Torsten, editor, and Varol, Gül, editor

Published
2025
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43. Preparation and performance comparison of low‐dielectric epoxy resins cured by naphthalene‐ and phenyl‐based active esters.

Author

Zhu, Youwei, Liu, Jiaming, Qin, Jingjing, Wang, Dingsong, Li, Wanyan, Xu, Chang‐An, and Liang, Liyan

Subjects
DIELECTRIC properties, DIELECTRIC loss, PERMITTIVITY, TENSILE strength, THERMAL expansion, EPOXY resins
Abstract

The poor dielectric properties of epoxy resins limit their application in microelectronics, and active ester curing agent is an effective means to enhance the dielectric properties of epoxy resins. However, the phenyl active ester curing resins nowadays have the problem of low mechanical properties. In this work, a novel naphthalene‐based active ester‐cured resveratrol epoxy resin system (REP/NDA) was prepared for the first time. Compared with the phenyl‐active ester‐cured epoxy resin (REP/PDA), the naphthyl‐active ester prepared epoxy resin has obvious advantages in mechanical properties. The experimental results indicated a tensile strength measurement for REP/NDA at 91.9 MPa, the tensile strength of REP/PDA was 65.3 MPa, and the tensile strength of REP/NDA was 141% of that of REP/PDA. The prepared REP/NDA epoxy resin exhibits favorable dielectric properties, evidenced by a dielectric constant of 3.02 at 10 MHz and a dielectric loss of 0.0042, very good thermal stability (T5% of 379°C), excellent water absorption (only 0.49% for 7 days from 2 to 8°C) and good dimensional stability (coefficient of thermal expansion below Tg of 77 ppm). The first synthesis of naphthalene‐based active ester curing agent offers a reference for creating new low dielectric epoxy resin materials that work out exceptionally. [ABSTRACT FROM AUTHOR]

Published
2025
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44. Probing the special angle in twisted bilayer MoS2 via angle-dependent scanning tunneling microscopy studies.

Author

Liu, Xueying, Liu, Shiying, Xiao, Yulong, Xu, Chang, Wu, Jiaxin, Li, Kaihui, Li, Si-Yu, and Pan, Anlian

Abstract

Twisted transition metal dichalcogenides (TMDs) homo-bilayers host unique quantum properties, which can be tuned by interlayer twist angle θ. However, the systematic evolution of their typical electronic properties with respect to the twist angle θ, which is crucial for identifying the “special angle” analogous to the “magic angle” of twisted bilayer graphene in correlation physics studies, remains incompletely understood. Here, via scanning tunneling microscopy (STM) and spectroscopy (STS), we investigate the variation of the moiré potential, flat band, and layer polarization characteristics across a wide range of twist angle θ in twisted bilayer MoS2 (TB-MoS2). The moiré potential of the valence band exhibits a non-monotonic variation with θ, peaking at a maximum value up to 204 meV at θ∼1.7°. Concurrently, at the same θ, the bandwidth of the flat band at the ΓV point of the valence band attains its minimum, precisely signifying the “special angle” θc∼1.7° in TB-MoS2. Interestingly, layer polarization in the moiré superlattice is spatially visualized through the distribution of local density of states (LDOS) at the energies of both ΓV and KV points of the valence band, where the polarization degree at the ΓV point demonstrates a close dependency on θ. Our findings deepen understanding of twist-angle effect in TMDs, advancing both fundamental physics and practical application. [ABSTRACT FROM AUTHOR]

Published
2025
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45. Significance of <italic>TYMS</italic> Polymorphism rs3819102 as a Prognostic Marker for Nonsmoking Lung Cancer Patients of the Han Ethnicity in China.

Author

Du, Wei, Xu, Chang, Cheng, Zhiyuan, Sun, Zhenyu, Guo, Shicheng, Li, Qiang, Song, Yuanlin, Shen, Bo, Bao, Yang, and Wu, Junjie

Subjects
*SINGLE nucleotide polymorphisms, *CANCER patients, *CANCER prognosis, *CHINESE people, *LUNG cancer
Abstract

Introduction: With high incidence and mortality rates, lung cancer is now one of the most common cancers in the world. The 5-year survival rate of lung cancer patients is very low, and predicting the prognosis of lung cancer patients and using it to develop treatment strategies and interventions is important for prolonging the survival time of patients. Folate metabolism involves various aspects such as methylation of DNA, RNA, proteins, lipids, etc. Disorders of folate metabolism are closely related to cardiovascular diseases, immunodeficiencies, tumors, etc., and TYMS is a key enzyme in the folate metabolic pathway. We investigated and analyzed the relationship between single nucleotide polymorphism rs3819102 synergistic clinical features in the TYMS and prognosis in lung cancer. Methods: A total of 888 Han Chinese patients with primary lung cancer were recruited between January and November 2009 (10 months), including Changhai Hospital Affiliated to the Naval Military Medical university (Second Military Medical University) and Taizhou Institute of Health Sciences of Fudan University. Of these, 49 were excluded due to incomplete data collected for various reasons. The study was approved by the Ethics Committee of the School of Life Sciences, Fudan University, and written informed consent was obtained from all participating subjects. This study does not include minors. Genomic DNA was extracted from patient blood samples using the Qiagen Blood Kit (Qiagen, Chatsworth, California) and genotyped using SNPscan technology. The association between TYMS polymorphism rs3819102 and prognostic was analyzed by the Kaplan-Meier (KM) analysis, log-rank test, and Cox proportional-hazards model. Results: In the Han nationality nonsmoking patients in China, compared with AA + AG genotype, the GG genotype (GG vs. AA + AG: adjusted hazard ratio = 1.69, 95% confidence interval: 1.00–2.83, p = 0.048401) of rs3819102 conferred a worse prognosis. TYMS rs3819102 A > G mutation shortened lung cancer patients’ survival and worse prognosis. Conclusion: TYMS rs3819102 may be a prognostic factor for deterioration in lung cancer patients. [ABSTRACT FROM AUTHOR]

Published
2025
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46. GmWRKY33a is a hub gene responsive to brassinosteroid signaling that suppresses nodulation in soybean (Glycine max).

Author

Yang, Mingliang, Lei, Chengjun, Ma, Chao, Hou, Xiuming, Yao, Mingming, Mi, Liang, Liu, Enliang, Xu, Linli, Wang, Shukun, Liu, Chunyan, Chen, Qingshan, Xin, Dawei, Xu, Chang, and Wang, Jinhui

Subjects
TRANSCRIPTION factors, NITROGEN fixation, ROOT formation, ROOT-tubercles, BRASSINOSTEROIDS
Abstract

Brassinosteroids (BRs) are key phytohormones influencing soybean development, yet their role in symbiosis remains unclear. Here, the RNA-Seq was used to identify important gene associated with BRs and symbiotic nitrogen fixation, and the function of candidate gene was verified by transgenic hairy roots. The result shows that the RNA-Seq analysis was conducted in which BR signaling was found to suppress nodule formation and many DEGs enriched in immunity-related pathways. WGCNA analyses led to the identification of GmWRKY33a as being responsive to BR signaling in the context of symbiosis establishment. Transgenic hairy roots analyses indicated that GmWRKY33a served as a negative regulator of the establishment of symbiosis. The qRT-PCR analysis confirmed that BR signaling upregulates GmWRKY33a , leading to nodulation suppression and activation of soybean immune responses. In summary, our research revealed that BR suppresses root nodule formation by modulating the immune signaling pathway in soybean roots. We further identified that GmWRKY33a, a crucial transcription factor in BR signaling, plays a negative role in the symbiotic establishment. [ABSTRACT FROM AUTHOR]

Published
2025
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47. Effect of intraoperative noise isolation on postoperative nausea and vomiting in patients undergoing gynecological laparoscopic surgery: protocol for a randomized controlled trial.

Author

Fu, Chong, Xia, Fan, Yan, Zihan, Xu, Han-bing, Zhao, Wei-ming, Lei, Yi-shan, Xu, Chang, Huo, Wen-wen, Tao, Duo-duo, Wang, Juan, Shan, Xi-sheng, Peng, Ke, Liu, Hong, Ji, Fu-hai, and Liu, Hua-yue

Subjects
PREVENTION of surgical complications, VOMITING prevention, NOISE control, LAPAROSCOPIC surgery, STATISTICAL sampling, RANDOMIZED controlled trials, INTRAOPERATIVE care, LONGITUDINAL method, GYNECOLOGIC surgery, NAUSEA
Abstract

Background: Postoperative nausea and vomiting (PONV) are common complications following general anesthesia, particularly in gynecological laparoscopic surgeries. This study aims to evaluate the effect of intraoperative noise isolation on PONV incidence. Method: This single-center, prospective, randomized controlled trial will enroll 192 adult patients undergoing laparoscopic gynecological surgery. Patients will be randomly assigned in a 1:1 ratio and stratified by age into either the control group (Group C), without noise-cancelling headphones, or a noise reduction group (Group NR), using noise-cancelling headphones from anesthesia induction until the end of surgery. All patients will receive intraoperative dexamethasone and ondansetron prophylaxis. The primary outcome is the incidence of PONV within 48 h post-surgery. Secondary outcomes include PONV severity at 24 and 48 h, antiemetic use, pain scores, need for rescue analgesia, Quality of Recovery-15 (QoR-15) scores, Richards-Campbell Sleep Questionnaire (RCSQ) scores, hemodynamic interventions, extubation time, length of stay in PACU and hospital, adverse events (hypertension, hypotension, bradycardia, tachycardia, desaturation after extubation, postoperative shivering, emergence agitation, allergic reactions, severe arrhythmias arrhythmia, cardiac arrest, hypothermia), patient satisfaction, and postoperative complications based on the Postoperative Morbidity Survey (POMS). Analyses will be conducted using modified intention-to-treat (mITT) and per-protocol (PP) populations. Discussion: We hypothesize that intraoperative use of noise-cancelling headphones will reduce PONV incidence in patients undergoing gynecological laparoscopic surgery. The findings could enhance postoperative care protocols for thoracoscopic gynecological procedures. Trial registration: Chinese Clinical Trial Registry (ChiCTR2400087460). [ABSTRACT FROM AUTHOR]

Published
2025
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48. Molecular ferroelectric self-assembled interlayer for efficient perovskite solar cells.

Author

Xu, Chang, Hang, Pengjie, Kan, Chenxia, Guo, Xiangwei, Song, Xianjiang, Xu, Chenran, You, Guofeng, Liao, Wei-Qiang, Zhu, Haiming, Wang, Dawei, Chen, Qi, Hong, Zijian, Xiong, Ren-Gen, Yu, Xuegong, Zuo, Lijian, and Chen, Hongzheng

Subjects
DIPOLE interactions, PHYSICAL & theoretical chemistry, SOLAR cells, PEROVSKITE, PRODUCTION sharing contracts (Oil & gas)
Abstract

The interfacial molecular dipole enhances the photovoltaic performance of perovskite solar cells (PSCs) by facilitating improved charge extraction. However, conventional self-assembled monolayers (SAMs) face challenges like inadequate interface coverage and weak dipole interactions. Herein, we develop a strategy using a self-assembled ferroelectric layer to modify the interfacial properties of PSCs. Specifically, we employ 1-adamantanamine hydroiodide (ADAI) to establish robust chemical interactions and create a dipole layer over the perovskite. The oriented molecular packing and spontaneous polarity of ferroelectric ADAI generate a substantial interfacial dipole, adjusting band bending at the anode, reducing band misalignment, and suppressing charge recombination. Consequently, our formamidinium lead iodide-based conventional PSC achieves efficiencies of 25.13% (0.06 cm2) and 23.5% (1.00 cm2) while exhibiting enhanced stability. Notably, we demonstrate an impressive efficiency of 25.59% (certified at 25.36%) in a 0.06 cm2 area for the inverted champion device, showcasing the promise of ferroelectric SAMs for PSCs performance enhancement. The conventional approach with applying self-assembled monolayer suffers from limited interface coverage and weaker dipole interactions. Here, authors employ ferroelectric molecule to construct a dipole layer, achieving certified efficiency of 25.36% for inverted perovskite solar cells. [ABSTRACT FROM AUTHOR]

Published
2025
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50. Stubble Management Plays a Greater Role than Tillage Practice in Shaping Soil Microbiome Structure and Function.

Author

Xu, Chang, Toh, Ruey, Li, Jishun, Zhou, Yi, and Denton, Matthew D.

Subjects
AGRICULTURAL conservation, SUSTAINABLE agriculture, SOIL structure, WHEAT straw, RESOURCE availability (Ecology)
Abstract

Tillage and stubble management play crucial roles in conservation agriculture, exerting a considerable influence on soil properties. This study aims to focus on the gaps in our understanding of how tillage and stubble management interact to affect the taxonomic and functional structure of the soil microbiome. Soil samples were collected from a long-term field trial implementing no tillage (NT) and conventional tillage (CT) with stubble retention and removal. Metagenomic sequencing facilitated the assembly of a gene catalog comprising 4.36 billion non-redundant genes. Stubble management markedly altered both the taxonomic and functional composition of the prokaryotic community, the addition of stubble caused a significant increase in Proteobacteria, but a decrease in Chloroflexi compared with no stubble. The key prokaryotic species and gene functions contributing most to the dissimilarity of the prokaryotic communities between the treatments with and without stubble were identified, including Rhodospirillum sp. Stubble retention increased the availability of carbon resources in the soil, resulting in a higher proportion of genes functional for metabolic activity and plant–pathogen interactions. However, tillage practice did not influence the structure or diversity of the soil prokaryote community. Our findings identify the target microbial species for future isolation, enabling the development of eco-friendly biofertilizers to promote sustainable agriculture. [ABSTRACT FROM AUTHOR]

Published
2025
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