Pedestrians represent a population of vulnerable road users who are directly exposed to complex traffic conditions, thereby increasing their risk of injury or fatality. This study first constructed a multidimensional indicator to quantify pedestrian exposure, considering factors such as Point of Interest (POI) attributes, POI intensity, traffic volume, and pedestrian walkability. Following risk
行人是易受伤害的道路使用者群体，他们直接暴露在复杂的交通状况下，从而增加了受伤或死亡的风险。本研究首先构建了一个多维指标来量化行人暴露，其中考虑了兴趣点（POI）属性、兴趣点强度、交通流量和行人步行能力等因素。风险

Urbanization increases electricity demand due to population growth and economic activity. To meet consumer's demands at all times, it is necessary to predict the future building energy consumption. Power Engineers could exploit the enormous amount of energy-related data from smart meters to plan power sector expansion. Researchers have made many experiments to address the supply and demand imba
城市化带来的人口增长和经济活动增加了电力需求。为了随时满足消费者的需求，有必要预测未来的建筑能耗。电力工程师可以利用智能电表提供的大量能源相关数据来规划电力部门的扩张。为解决供需失衡问题，研究人员进行了许多实验。

Drones are an increasingly popular choice for wildlife surveys due to their versatility, quick response capabilities, and ability to access remote areas while covering large regions. A novel application presented here is to combine drone imagery with neural networks to assess mortality within a bird colony. Since 2021, Highly Pathogenic Avian Influenza (HPAI) has caused significant bird mortali
无人机因其多功能性、快速反应能力以及能够进入偏远地区并覆盖大片区域的能力，越来越多地成为野生动物调查的首选。本文介绍的一项新应用是将无人机图像与神经网络相结合，以评估鸟类群落内的死亡率。自 2021 年以来，高致病性禽流感已造成大量鸟类死亡。

Reliable early forecasting of extreme summer air temperatures is essential for effectively managing and mitigating the socioeconomic damage caused by thermal disasters. Numerical weather prediction models have become valuable tools for forecasting air temperature; however, they incur high computational costs, resulting in coarse spatial resolution and systematic bias owing to imperfect parametr
可靠的夏季极端气温早期预报对于有效管理和减轻热灾害造成的社会经济损失至关重要。数值天气预报模式已成为预报气温的重要工具，但其计算成本较高，空间分辨率较低，且由于参数不完善而存在系统性偏差。

Agricultural drought (AD) is disastrous to crop production and plant growth. The prediction of AD with sufficient lead time is helpful for developing agricultural water strategy, particularly under the context of global warming. However, the previous studies mainly focused on short lead times (1-6 months) and only used 3 or less variables to predict AD through copula models. In this study, a no
农业干旱（AD）对作物生产和植物生长具有灾难性影响。在充足的提前期预测农业干旱有助于制定农业用水战略，尤其是在全球变暖的背景下。然而，以往的研究主要集中在较短的提前期（1-6 个月），并且仅使用 3 个或更少的变量通过 copula 模型来预测 AD。在本研究中，一个无

Deep learning (DL) methods have recently garnered attention from the climate change community for being an innovative approach to downscaling climate variables from Earth system and global climate models (ESGCMs) with horizontal resolutions still too coarse to represent regional- to local-scale phenomena. In the context of the Coupled Model Intercomparison Project phase 6 (CMIP6), ESGCM simulat

Spatially aggregated data on socio-demographic groups often fail to capture the population's spatial heterogeneity in cities. This poses challenges for urban planning, particularly when addressing the needs of groups such as migrants or families with children. Moreover, the commonly provided aggregated units, such as census tracts, vary in size and across data sources. Existing literature on di

Energy is of paramount importance for the world, and it is a fundamental driver of economic growth and development. Industries, businesses, and households rely on energy for even a small task. Due to its high demand, a significant portion of the global population still lacks access to reliable and affordable energy sources. Many industries and sectors continue to waste significant amounts of en

Energy consumption in buildings contributes to over a third of global energy consumption and 28% of greenhouse gas emissions. With urbanization and population growth, rising building energy demand can lead to environmental degradation. While significant renewable resources are used to generate electricity to mitigate environmental problems, demand-side management remains crucial for achieving n

Long-term load forecasting (LTLF) models play an important role in the strategic planning of power systems around the globe. Obtaining correct decisions on power network expansions or restrictions based on predictions help substantially reduce the power grid infrastructure costs. The classical approach of LTLF is limited to the usage of artificial neural networks (ANN) or regression-based appro
长期负荷预测（LTLF）模型在全球电力系统的战略规划中发挥着重要作用。根据预测做出正确的电网扩展或限制决策，有助于大幅降低电网基础设施成本。传统的 LTLF 方法仅限于使用人工神经网络（ANN）或基于回归的方法。

Simple Summary We advanced a deep learning model that significantly enhances the detection and population estimation of feral pigeons in the dynamic urban landscape of Hong Kong, employing computer vision techniques. The inherent challenges associated with pigeon concealment within complex urban structures and their high mobility necessitate a robust and effective strategy. Our improved model,
简单摘要 我们利用计算机视觉技术推进了一个深度学习模型，该模型显著增强了对香港动态城市景观中野鸽子的检测和种群数量估计。鸽子隐藏在复杂的城市结构中，而且具有很高的流动性，这些固有的挑战要求我们采取一种稳健有效的策略。我们改进了模型、

Objective: The purpose of this study is to analyze the texture characteristics of chronic non-bacterial osteomyelitis (CNO) bone lesions, identified as areas of altered signal intensity on short tau inversion recovery (STIR) sequences, and to distinguish them from bone marrow growth-related changes through Machine Learning (ML) and Deep Learning (DL) analysis. Materials and methods: We included
研究目的本研究旨在分析慢性非细菌性骨髓炎（CNO）骨病变的纹理特征（在短头绪反转恢复（STIR）序列上被识别为信号强度改变的区域），并通过机器学习（ML）和深度学习（DL）分析将其与骨髓生长相关变化区分开来。材料与方法我们纳入了