Hunt for North Carolina woman who traveled to idyllic Caribbean island without husband then vanished... as he issues heartbreaking plea She let criminals run riot over $1bn skyscraper development for YEARS. Now LA's mayor has the audacity to hail the clean-up of city's infamous'graffiti towers' John was devastated to learn he had melanoma that had spread to his lungs, liver and kidney. He had just one symptom - and it WASN'T on his skin Alleged stabber's astonishingly casual response to being arrested as he lounges in IHOP and sips drink as cops try to cuff him: 'One moment' Married elite NYC lawyer, 45, is caught red-handed with underling, 29, in passionate Central Park rendezvous... then tracked down at $2.3m home he shares with wife nearby Will Harry and William call a truce and both attend tribute event to mark 30th anniversary of Diana's death? I blew up my ten-year marriage after a single night with my husband's friend. People will judge me - but here's why I don't regret it German police hunt member of'Islamist scene' after driver ploughed van into crowds at Berlin Pride event in'brutal attack' leaving at least one person dead and 16 injured Monster 20-foot great white named'Big Rose' spotted as shark sightings surge 1,000% off US coast Rap star 21 Savage's 14-year-old'nephew' kills himself after accidentally shooting younger sister Trump doubles down on threat to build'North America Air Filter Barrier' between the US and Canada to block wildfire smoke Inside new life of Lindsay Clancy's husband and fertility doctor wife: Family reveals touching texts, intimate wedding details and happy photos... as ex stands trial for murder of their three children Caitlin Clark and Angel Reese lead glamorous arrivals at WNBA All-Star Game as league's biggest stars stun on orange carpet Chilling moment Florida man kills drinking buddy before casually dumping his body off a pier and kicking victim's shoes in behind him Bruce Springsteen is a'bitter old man' slammed by fellow rockers for'dissing his own country': Insiders reveal what's behind shift that turned The Boss into The BORE Margot Robbie looks chic in a yellow silk co-ord as she joins her mother Sarie Kessler on a girls' trip to Paris READ MORE: How long can you REALLY keep your leftovers in the fridge?

In the field of Material Science, effective information retrieval systems are essential for facilitating research. Traditional Retrieval-Augmented Generation (RAG) approaches in Large Language Models (LLMs) often encounter challenges such as outdated information, hallucinations, limited interpretability due to context constraints, and inaccurate retrieval. To address these issues, Graph RAG integrates graph databases to enhance the retrieval process. Our proposed method processes Material Science documents by extracting key entities (referred to as MatIDs) from sentences, which are then utilized to query external Wikipedia knowledge bases (KBs) for additional relevant information. We implement an agent-based parsing technique to achieve a more detailed representation of the documents. Our improved version of Graph RAG called G-RAG further leverages a graph database to capture relationships between these entities, improving both retrieval accuracy and contextual understanding. This enhanced approach demonstrates significant improvements in performance for domains that require precise information retrieval, such as Material Science.

Discovering new superionic materials is essential for advancing solid-state batteries, which offer improved energy density and safety compared to the traditional lithium-ion batteries with liquid electrolytes. Conventional computational methods for identifying such materials are resource-intensive and not easily scalable. Recently, universal interatomic potential models have been developed using equivariant graph neural networks. These models are trained on extensive datasets of first-principles force and energy calculations. One can achieve significant computational advantages by leveraging them as the foundation for traditional methods of assessing the ionic conductivity, such as molecular dynamics or nudged elastic band techniques. However, the generalization error from model inference on diverse atomic structures arising in such calculations can compromise the reliability of the results. In this work, we propose an approach for the quick and reliable evaluation of ionic conductivity through the analysis of a universal interatomic potential. Our method incorporates a set of heuristic structure descriptors that effectively employ the rich knowledge of the underlying model while requiring minimal generalization capabilities. Using our descriptors, we rank lithium-containing materials in the Materials Project database according to their expected ionic conductivity. Eight out of the ten highest-ranked materials are confirmed to be superionic at room temperature in first-principles calculations. Notably, our method achieves a speed-up factor of approximately 50 compared to molecular dynamics driven by a machine-learning potential, and is at least 3,000 times faster compared to first-principles molecular dynamics.

The typical benchmark evaluations have begun to However, it remains unclear if the model's fall short and do not cover the nuances of LLMs' responses bear useful meaning - whether the model abilities (Zoph et al., 2022). Did the model provide understands the topic or is responding probabilistically a certain answer simply because of the huge purely based on training data. TruthfulQA amount of similar text it saw during training? Or (Lin et al., 2021) comes close to assessing a did the model register a piece of knowledge and use model's understanding of the world but it is designed that to answer the question? It is impossible to tell to exploit the imitative weaknesses of models them apart without analyzing the training dataset, and relies on a model's elaborate response and which, given the current trend, is not available for text-matching metrics. In contrast, our work intends most models. Current RAG (Retrieval Augmented to extract knowledge and understanding from Generation) systems rely on LLM's prompt memory LLMs without intentionally tricking or confusing to register some facts and expect the model the model.

Molecule-text modeling, which aims to facilitate molecule-relevant tasks with a textual interface and textual knowledge, is an emerging research direction. Beyond single molecules, studying reaction-text modeling holds promise for helping the synthesis of new materials and drugs. However, previous works mostly neglect reaction-text modeling: they primarily focus on modeling individual molecule-text pairs or learning chemical reactions without texts in context. Additionally, one key task of reaction-text modeling -- experimental procedure prediction -- is less explored due to the absence of an open-source dataset. The task is to predict step-by-step actions of conducting chemical experiments and is crucial to automating chemical synthesis. To resolve the challenges above, we propose a new pretraining method, ReactXT, for reaction-text modeling, and a new dataset, OpenExp, for experimental procedure prediction. Specifically, ReactXT features three types of input contexts to incrementally pretrain LMs. Each of the three input contexts corresponds to a pretraining task to improve the text-based understanding of either reactions or single molecules. ReactXT demonstrates consistent improvements in experimental procedure prediction and molecule captioning and offers competitive results in retrosynthesis. Our code is available at https://github.com/syr-cn/ReactXT.

Heat, Ventilation and Air Conditioning (HVAC) systems play a critical role in maintaining a comfortable thermal environment and cost approximately 40% of primary energy usage in the building sector. For smart energy management in buildings, usage patterns and their resulting profiles allow the improvement of control systems with prediction capabilities. However, for large-scale HVAC system management, it is difficult to construct a detailed model for each subsystem. In this paper, a new data-driven room temperature prediction model is proposed based on the k-means clustering method. The proposed data-driven temperature prediction approach extracts the system operation feature through historical data analysis and further simplifies the system-level model to improve generalization and computational efficiency. We evaluate the proposed approach in the real world. The results demonstrated that our approach can significantly reduce modeling time without reducing prediction accuracy.

A thorough regulation of building energy systems translates in relevant energy savings and in a better comfort for the occupants. Algorithms to predict the thermal state of a building on a certain time horizon with a good confidence are essential for the implementation of effective control systems. This work presents a global Transformer architecture for indoor temperature forecasting in multi-room buildings, aiming at optimizing energy consumption and reducing greenhouse gas emissions associated with HVAC systems. Recent advancements in deep learning have enabled the development of more sophisticated forecasting models compared to traditional feedback control systems. The proposed global Transformer architecture can be trained on the entire dataset encompassing all rooms, eliminating the need for multiple room-specific models, significantly improving predictive performance, and simplifying deployment and maintenance. Notably, this study is the first to apply a Transformer architecture for indoor temperature forecasting in multi-room buildings. The proposed approach provides a novel solution to enhance the accuracy and efficiency of temperature forecasting, serving as a valuable tool to optimize energy consumption and decrease greenhouse gas emissions in the building sector.

The large amount of data collected in buildings makes energy management smarter and more energy efficient. This study proposes a design and implementation methodology of data-driven heating, ventilation, and air conditioning (HVAC) control. Building thermodynamics is modeled using a symbolic regression model (SRM) built from the collected data. Additionally, an HVAC system model is also developed with a data-driven approach. A model predictive control (MPC) based HVAC scheduling is formulated with the developed models to minimize energy consumption and peak power demand and maximize thermal comfort. The performance of the proposed framework is demonstrated in the workspace in the actual campus building. The HVAC system using the proposed framework reduces the peak power by 16.1\% compared to the widely used thermostat controller.

Sci-fi fans will know the Terminator was only a ruthless killing machine because of its effortless ability to heal itself after damage. Now, engineers at Cornell University in New York may be well on their way to recreating this remarkable self-healing ability. The experts have created a robot capable of detecting when and where it has been damaged and then restoring itself on the spot. The small soft robot, which resembles a four-legged starfish, uses light to detect changes on its surface that are created by cuts. For self-healing to work, the robot must be able to identify that there is something that needs to be fixed.

Proportional-integral-derivative (PID) control underlies more than $97\%$ of automated industrial processes. Controlling these processes effectively with respect to some specified set of performance goals requires finding an optimal set of PID parameters to moderate the PID loop. Tuning these parameters is a long and exhaustive process. A method (patent pending) based on deep reinforcement learning is presented that learns a relationship between generic system properties (e.g. resonance frequency), a multi-objective performance goal and optimal PID parameter values. Performance is demonstrated in the context of a real optical switching product of the foremost manufacturer of such devices globally. Switching is handled by piezoelectric actuators where switching time and optical loss are derived from the speed and stability of actuator-control processes respectively. The method achieves a $5\times$ improvement in the number of actuators that fall within the most challenging target switching speed, $\geq 20\%$ improvement in mean switching speed at the same optical loss and $\geq 75\%$ reduction in performance inconsistency when temperature varies between 5 and 73 degrees celcius. Furthermore, once trained (which takes $\mathcal{O}(hours)$), the model generates actuator-unique PID parameters in a one-shot inference process that takes $\mathcal{O}(ms)$ in comparison to up to $\mathcal{O}(week)$ required for conventional tuning methods, therefore accomplishing these performance improvements whilst achieving up to a $10^6\times$ speed-up. After training, the method can be applied entirely offline, incurring effectively zero optimisation-overhead in production.