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The emphasis of this study lied in the impact of tree planting spacing around the teaching building on the indoor wind environment. CFD simulations of the indoor wind environment of teaching building affected by trees were performed utilizing the ANSYS Fluent software using the standard k-ε model with additional source terms. Deciduous broad-leaved trees and coniferous trees were selected as representative tree species for comparison. Five different tree planting spacings were arranged outside the teaching building, and the indoor airflow velocity distribution and pressure distribution were simulated. Then the effects of these different tree layout forms on the indoor ventilation efficiency of the building were compared and analyzed. The results showed that the sum of the total ventilation flow rate in the classrooms rose with the increase of the spacing between trees. However, due to the different location relationship between the tree canopy and the classrooms, the ventilation efficiency of each classroom showed differences. As for tree species, the blocking effect of tall deciduous broad-leaved trees on indoor ventilation was more obvious than that of coniferous trees. This study will have guiding significance for the layout design of vegetation around the building and creating a good indoor ventilation environment.

Planting trees around buildings has always been used as one of the most common viable landscaping strategies in schools. However, the impact of trees on natural ventilation inside the building has been neglected without investigation. The emphasis of this study related to the impact of outdoor trees around the academic building on indoor ventilation. Numerical simulations of the indoor wind environment of the academic building affected by trees were performed utilizing the k-ε model with additional source terms. The numerical model was also validated by measured data. Two kinds of trees were selected, they are camphor and metasequoia. Camphor is a kind of broad-leaved tree and metasequoia is a kind of coniferous tree. 26 simulation cases with six different tree canopy spacings were conducted. These results showed that the outdoor trees had great influences on the natural ventilation performance of the academic building. Compared with the case without the trees, the highest decrement in ventilation flow rate could be up to 31.97% in this study. For the cases of classrooms with horizontal distribution, the ages of air of the classrooms became fresher with the increase of the canopy spacing. While for the cases of classrooms with vertical distribution, the canopy spacing had fewer effects on the natural ventilation performances. It was also found that the blocking effects of camphor on indoor ventilation were higher than that of metasequoia. The average ventilation flow rate in cases with metasequoia was increased by 14.89% compared to the cases with camphor. This study could provide guidance for the layout design of trees around the building.

With an increase in energy consumption globally, Fischer-Tropsch (FT) synthesis is a good alternative for producing fuels and chemicals from coal, natural gas or biomass. Among them, coal to liquids has been put into production in countries that have large coal reserves. In this process, Fe-based catalysts are commonly used due to their earth abundance, comparatively wide operation range and ready availability to handle low H2/CO ratio from coal. Despite their extensive applications, the kinetic and mechanistic understandings of Fe carburization and FT reaction on Fe-carbides are relatively limited due to the complexity of the phase composition of the applied catalysts. This review summarizes the current state of knowledge of FT synthesis on Fe-carbide with an emphasis on the underlying mechanism. Specifically, the employment of a model catalyst, such as Raney Fe, could provide a convenient way to furnish kinetic information regarding Fe carburization and subsequent FT reaction. A major challenge for further understanding catalytic reactions occurring at the Fe-carbide surface is correlating FT activity and selectivity to a specific active site. To address this issue, the advancements of both DFT calculations and surface science techniques are highly demanded.

This paper presents a fast transient response low-dropout (LDO) regulator with all-NPN push–pull buffer in 0.6-μm bipolar process. In order to improve the transient response, an all-NPN push–pull buffer is proposed. Based on single Miller capacitance (SMC), the use of the all-NPN push–pull buffer overcomes the shortcomings of the equivalent series resistance (ESR) that requires strict output capacitor types. Besides, the proposed merging structure of bandgap reference and error amplifier not only improves the transient response, but also simplifies the circuit and reduces the output noise. Implemented and fabricated in a 0.6-μm bipolar process, the proposed LDO regulator occupies an active area of 1.6 mm 2 . The measured maximum load current is 200 mA, and the circuit can work at the load current of 300 mA. Moreover, the measured line regulation and load regulation are 0.8 mV/V and 0.09 mV/mA, respectively.

Review ranking is pivotal in e-commerce for prioritizing diagnostic and authentic feedback from the deluge of user-generated content. While large language models have improved semantic assessment, existing ranking paradigms face a persistent trade-off in long-context settings. Pointwise scoring is efficient but often fails to account for list-level interactions, leading to miscalibrated top-\\(k\\) rankings. Listwise approaches can leverage global context, yet they are computationally expensive and become unstable as candidate lists grow. To address this, we propose Residual Listwise Preference Optimization (RLPO), which formulates ranking as listwise representation-level residual correction over a strong pointwise LLM scorer. RLPO first produces calibrated pointwise scores and item representations, then applies a lightweight encoder over the representations to predict listwise score residuals, avoiding full token-level listwise processing. We also introduce a large-scale benchmark for long-context review ranking with human verification. Experiments show RLPO improves NDCG@k over strong pointwise and listwise baselines and remains robust as list length increases.

The proliferation of in-the-wild videos has greatly expanded the Video Quality Assessment (VQA) problem. Unlike early definitions that usually focus on limited distortion types, VQA on in-the-wild videos is especially challenging as it could be affected by complicated factors, including various distortions and diverse contents. Though subjective studies have collected overall quality scores for these videos, how the abstract quality scores relate with specific factors is still obscure, hindering VQA methods from more concrete quality evaluations (e.g. sharpness of a video). To solve this problem, we collect over two million opinions on 4,543 in-the-wild videos on 13 dimensions of quality-related factors, including in-capture authentic distortions (e.g. motion blur, noise, flicker), errors introduced by compression and transmission, and higher-level experiences on semantic contents and aesthetic issues (e.g. composition, camera trajectory), to establish the multi-dimensional Maxwell database. Specifically, we ask the subjects to label among a positive, a negative, and a neutral choice for each dimension. These explanation-level opinions allow us to measure the relationships between specific quality factors and abstract subjective quality ratings, and to benchmark different categories of VQA algorithms on each dimension, so as to more comprehensively analyze their strengths and weaknesses. Furthermore, we propose the MaxVQA, a language-prompted VQA approach that modifies vision-language foundation model CLIP to better capture important quality issues as observed in our analyses. The MaxVQA can jointly evaluate various specific quality factors and final quality scores with state-of-the-art accuracy on all dimensions, and superb generalization ability on existing datasets. Code and data available at https://github.com/VQAssessment/MaxVQA.

Providing high-quality item recall for text queries is crucial in large-scale e-commerce search systems. Current Embedding-based Retrieval Systems (ERS) embed queries and items into a shared low-dimensional space, but uni-modality ERS rely too heavily on textual features, making them unreliable in complex contexts. While multi-modality ERS incorporate various data sources, they often overlook individual preferences for different modalities, leading to suboptimal results. To address these issues, we propose MRSE, a Multi-modality Retrieval System that integrates text, item images, and user preferences through lightweight mixture-of-expert (LMoE) modules to better align features across and within modalities. MRSE also builds user profiles at a multi-modality level and introduces a novel hybrid loss function that enhances consistency and robustness using hard negative sampling. Experiments on a large-scale dataset from Shopee and online A/B testing show that MRSE achieves an 18.9% improvement in offline relevance and a 3.7% gain in online core metrics compared to Shopee's state-of-the-art uni-modality system.

Text-to-image diffusion models are typically trained to optimize the log-likelihood objective, which presents challenges in meeting specific requirements for downstream tasks, such as image aesthetics and image-text alignment. Recent research addresses this issue by refining the diffusion U-Net using human rewards through reinforcement learning or direct backpropagation. However, many of them overlook the importance of the text encoder, which is typically pretrained and fixed during training. In this paper, we demonstrate that by finetuning the text encoder through reinforcement learning, we can enhance the text-image alignment of the results, thereby improving the visual quality. Our primary motivation comes from the observation that the current text encoder is suboptimal, often requiring careful prompt adjustment. While fine-tuning the U-Net can partially improve performance, it remains suffering from the suboptimal text encoder. Therefore, we propose to use reinforcement learning with low-rank adaptation to finetune the text encoder based on task-specific rewards, referred as \\textbf{TexForce}. We first show that finetuning the text encoder can improve the performance of diffusion models. Then, we illustrate that TexForce can be simply combined with existing U-Net finetuned models to get much better results without additional training. Finally, we showcase the adaptability of our method in diverse applications, including the generation of high-quality face and hand images.

Cross-View Geo-Localization (CVGL) focuses on identifying correspondences between images captured from distinct perspectives of the same geographical location. However, existing CVGL approaches are typically restricted to a single view or modality, and their direct visual matching strategy lacks interpretability: they only determine whether two images correspond, without explaining the rationale behind the match. In this paper, we present GLEAM-C, a foundational CVGL model that unifies multiple views and modalities by aligning them exclusively with satellite imagery. Our framework improves training efficiency through optimized implementation and achieves accuracy comparable to prior modality-specific CVGL models via a novel two-phase training strategy. To address interpretability, we further propose GLEAM-X, a novel task that combines cross-view correspondence prediction with explainable reasoning enabled by multimodal large language models (MLLMs). We construct a bilingual benchmark using commercial MLLMs to generate training and testing data, and refine the test set through rigorous human revision for systematic evaluation of explainable cross-view reasoning. Together, GLEAM-C and GLEAM-X form a comprehensive CVGL pipeline that integrates multi-modal, multi-view alignment with interpretable correspondence analysis, unifying accurate cross-view matching with explainable reasoning and advancing Geo-Localization by enabling models to better Explain And Match. Code and datasets used in this work will be made publicly accessible at https://github.com/Lucky-Lance/GLEAM.