Large language models (LLMs) have significantly reshaped different walks of business. To meet the increasing demands for individualized railway service, we develop LLM4Rail - a novel LLM-augmented railway service consulting platform. Empowered by LLM, LLM4Rail can provide custom modules for ticketing, railway food & drink recommendations, weather information, and chitchat. In LLM4Rail, we propose the iterative "Question-Thought-Action-Observation (QTAO)" prompting framework. It meticulously integrates verbal reasoning with task-oriented actions, that is, reasoning to guide action selection, to effectively retrieve external observations relevant to railway operation and service to generate accurate responses. To provide personalized onboard dining services, we first construct the Chinese Railway Food and Drink (CRFD-25) - a publicly accessible takeout dataset tailored for railway services. CRFD-25 covers a wide range of signature dishes categorized by cities, cuisines, age groups, and spiciness levels. We further introduce an LLM-based zero-shot conversational recommender for railway catering. To address the unconstrained nature of open recommendations, the feature similarity-based post-processing step is introduced to ensure all the recommended items are aligned with CRFD-25 dataset.

Wheat yellow rust, caused by the fungus Puccinia striiformis, is a critical disease affecting wheat crops across Britain, leading to significant yield losses and economic consequences. Given the rapid environmental changes and the evolving virulence of pathogens, there is a growing need for innovative approaches to predict and manage such diseases over the long term. This study explores the feasibility of using deep learning models to predict outbreaks of wheat yellow rust in British fields, offering a proactive approach to disease management. We construct a yellow rust dataset with historial weather information and disease indicator acrossing multiple regions in England. We employ two poweful deep learning models, including fully connected neural networks and long short-term memory to develop predictive models capable of recognizing patterns and predicting future disease outbreaks.The models are trained and validated in a randomly sliced datasets. The performance of these models with different predictive time steps are evaluated based on their accuracy, precision, recall, and F1-score. Preliminary results indicate that deep learning models can effectively capture the complex interactions between multiple factors influencing disease dynamics, demonstrating a promising capacity to forecast wheat yellow rust with considerable accuracy. Specifically, the fully-connected neural network achieved 83.65% accuracy in a disease prediction task with 6 month predictive time step setup. These findings highlight the potential of deep learning to transform disease management strategies, enabling earlier and more precise interventions. Our study provides a methodological framework for employing deep learning in agricultural settings but also opens avenues for future research to enhance the robustness and applicability of predictive models in combating crop diseases globally.

Several times a day, Francesca Simon, the author of the Horrid Henry children's books, gets out her phone to check the weather – not just for where she is, but where friends and family live, where she has been on holiday, where she was brought up. I find them quite magical," she said. With about 10 locations logged, her friends make fun of her "weather porn" habit. This week, Simon discovered she shared a weather app fixation with Queen Camilla when the pair discussed a miserable summer's day at a charity event. "[Camilla] said everybody teases her … so we were laughing at our mutual obsession," Simon said. It is an obsession shared by millions. If you are going on holiday, planning a summer barbecue, worrying about your garden or suffering from hay fever, you are likely to check an app at least daily for the latest forecast. The apps give much more localised and detailed information than traditional weather forecasts, including wind speeds and the percentage chance of rain, in ...

Residential Load Profile (RLP) generation and prediction are critical for the operation and planning of distribution networks, especially as diverse low-carbon technologies (e.g., photovoltaic and electric vehicles) are increasingly adopted. This paper introduces a novel flow-based generative model, termed Full Convolutional Profile Flow (FCPFlow), which is uniquely designed for both conditional and unconditional RLP generation, and for probabilistic load forecasting. By introducing two new layers--the invertible linear layer and the invertible normalization layer--the proposed FCPFlow architecture shows three main advantages compared to traditional statistical and contemporary deep generative models: 1) it is well-suited for RLP generation under continuous conditions, such as varying weather and annual electricity consumption, 2) it demonstrates superior scalability in different datasets compared to traditional statistical models, and 3) it also demonstrates better modeling capabilities in capturing the complex correlation of RLPs compared with deep generative models.

Traffic accidents are one of the biggest challenges in a society where commuting is so important. What triggers an accident can be dependent on several subjective parameters and varies within each region, city, or country. In the same way, it is important to understand those parameters in order to provide a knowledge basis to support decisions regarding future cases prevention. The literature presents several works where machine learning algorithms are used for prediction of accidents or severity of accidents, in which city-level datasets were used as evaluation studies. This work attempts to add to the diversity of research, by focusing mainly on concentration of accidents and how machine learning can be used to predict hotspots. This approach demonstrated to be a useful technique for authorities to understand nuances of accident concentration behavior. For the first time, data from the Federal District of Brazil collected from forensic traffic accident analysts were used and combined with data from local weather conditions to predict hotspots of collisions. Out of the five algorithms we considered, two had good performance: Multi-layer Perceptron and Random Forest, with the latter being the best one at 98% accuracy. As a result, we identify that weather parameters are not as important as the accident location, demonstrating that local intervention is important to reduce the number of accidents.

Accurate shared micromobility demand predictions are essential for transportation planning and management. Although deep learning models provide powerful tools to deal with demand prediction problems, studies on forecasting highly-accurate spatiotemporal shared micromobility demand are still lacking. This paper proposes a deep learning model named Interactive Convolutional Network (ICN) to forecast spatiotemporal travel demand for shared micromobility. The proposed model develops a novel channel dilation method by utilizing multi-dimensional spatial information (i.e., demographics, functionality, and transportation supply) based on travel behavior knowledge for building the deep learning model. We use the convolution operation to process the dilated tensor to simultaneously capture temporal and spatial dependencies. Based on a binary-tree-structured architecture and interactive convolution, the ICN model extracts features at different temporal resolutions, and then generates predictions using a fully-connected layer. The proposed model is evaluated for two real-world case studies in Chicago, IL, and Austin, TX. The results show that the ICN model significantly outperforms all the selected benchmark models. The model predictions can help the micromobility operators develop optimal vehicle rebalancing schemes and guide cities to better manage the shared micromobility system.

Technologically speaking, we live in a time of plenty. Today, I can ask a chatbot to render The Canterbury Tales as if written by Taylor Swift or to help me write a factually inaccurate autobiography. With three swipes, I can summon almost everyone listed in my phone and see their confused faces via an impromptu video chat. My life is a gluttonous smorgasbord of information, and I am on the all-you-can-eat plan. But there is one specific corner where technological advances haven't kept up: weather apps.

Optimizing management practices and selecting the best cultivar for planting play a significant role in increasing agricultural food production and decreasing environmental footprint. In this study, we develop optimization frameworks under uncertainty using conditional value-at-risk in the stochastic programming objective function. We integrate the crop model, APSIM, and a parallel Bayesian optimization algorithm to optimize the management practices and select the best cultivar at different levels of risk aversion. This approach integrates the power of optimization in determining the best decisions and crop model in simulating nature's output corresponding to various decisions. As a case study, we set up the crop model for 25 locations across the US Corn Belt. We optimized the management options (planting date, N fertilizer amount, fertilizing date, and plant density in the farm) and cultivar options (cultivars with different maturity days) three times: a) before, b) at planting and c) after a growing season with known weather. Results indicated that the proposed model produced meaningful connections between weather and optima decisions. Also, we found risk-tolerance farmers get more expected yield than risk-averse ones in wet and non-wet weathers.

Deep learning (DL), also known as deep structured learning or hierarchical learning, is a subset of machine learning. It is loosely based on the way neurons connect to each other to process information in animal brains. To mimic these connections, DL uses a layered algorithmic architecture known as artificial neural networks (ANNs) to analyze the data. By analyzing how data is filtered through the layers of the ANN and how the layers interact with each other, a DL algorithm can'learn' to make correlations and connections in the data. These capabilities make DL algorithms an innovative tool with the potential to transform healthcare.

In the drive towards full autonomy, vehicle makers and solutions providers must tackle a nearly impossible variety of factors. Some of these, often necessarily, are more attention-grabbing than others but there are numerous ones just as critical that don't typically appear in auto technology headlines. One is the weather, which is somewhat odd because information about weather and its effect on road conditions has been crucial ever since humankind has climbed into a car. In our'assisted driving' age, that need remains acute and experts say it will be more so as we move up the levels of autonomy. Fortunately, with increasingly robust vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) technology, weather data and forecasts can be delivered ever more effectively.