Modern laundry detergents work better in cold water. Breakthroughs, discoveries, and DIY tips sent every weekday. You may wash your clothes with hot water--many people do. That could be a mistake. Modern washing machines, and modern laundry detergent, are designed to work well at cold temperatures.

Heat pumps (HPs) have emerged as a cost-effective and clean technology for sustainable energy systems, but their efficiency in producing hot water remains restricted by conventional threshold-based control methods. Although machine learning (ML) has been successfully implemented for various HP applications, optimization of household hot water demand forecasting remains understudied. This paper addresses this problem by introducing a novel approach that combines predictive ML with anomaly detection to create adaptive hot water production strategies based on household-specific consumption patterns. Our key contributions include: (1) a composite approach combining ML and isolation forest (iForest) to forecast household demand for hot water and steer responsive HP operations; (2) multi-step feature selection with advanced time-series analysis to capture complex usage patterns; (3) application and tuning of three ML models: Light Gradient Boosting Machine (LightGBM), Long Short-Term Memory (LSTM), and Bi-directional LSTM with the self-attention mechanism on data from different types of real HP installations; and (4) experimental validation on six real household installations. Our experiments show that the best-performing model LightGBM achieves superior performance, with RMSE improvements of up to 9.37\% compared to LSTM variants with $R^2$ values between 0.748-0.983. For anomaly detection, our iForest implementation achieved an F1-score of 0.87 with a false alarm rate of only 5.2\%, demonstrating strong generalization capabilities across different household types and consumption patterns, making it suitable for real-world HP deployments.

Soft pneumatic fingers are of great research interest. However, their significant potential is limited as most of them can generate only one motion, mostly bending. The conventional design of soft fingers does not allow them to switch to another motion mode. In this paper, we developed a novel multi-modal and single-actuated soft finger where its motion mode is switched by changing the finger's temperature. Our soft finger is capable of switching between three distinctive motion modes: bending, twisting, and extension-in approximately five seconds. We carried out a detailed experimental study of the soft finger and evaluated its repeatability and range of motion. It exhibited repeatability of around one millimeter and a fifty percent larger range of motion than a standard bending actuator. We developed an analytical model for a fiber-reinforced soft actuator for twisting motion. This helped us relate the input pressure to the output twist radius of the twisting motion. This model was validated by experimental verification. Further, a soft robotic gripper with multiple grasp modes was developed using three actuators. This gripper can adapt to and grasp objects of a large range of size, shape, and stiffness. We showcased its grasping capabilities by successfully grasping a small berry, a large roll, and a delicate tofu cube.

Our household only recently discovered the glorious, hands-free reality of a AI-enabled, self-emptying robot vacuum, one that scooted expertly around our apartment, busting dust for weeks on end with little input from us. But even with our trusty Roomba busting dust left and right, we knew something was missing: The bot was only sweeping, not mopping. We'd long dismissed the idea of a robot mop--after all, the earliest ones did little more than drag a damp cloth across the floor. But robot mopping tech has advanced at a furious pace, with the latest robot vacuum-and-mop combos boasting mop heads that apply downward pressure and lift themselves to avoid carpets, while base stations can clean the mop pads themselves--with hot water, no less. Could a vacuum-and-mop robot really measure up to a standard stick mop?

Utilizing solar energy to meet space heating and domestic hot water demand is very efficient (in terms of environmental footprint as well as cost), but in order to ensure that user demand is entirely covered throughout the year needs to be complemented with auxiliary heating systems, typically boilers and heat pumps. Naturally, the optimal control of such a system depends on an accurate prediction of solar thermal production. Experimental testing and physics-based numerical models are used to find a collector's performance curve - the mapping from solar radiation and other external conditions to heat production - but this curve changes over time once the collector is exposed to outdoor conditions. In order to deploy advanced control strategies in small domestic installations, we present an approach that uses machine learning to automatically construct and continuously adapt a model that predicts heat production. Our design is driven by the need to (a) construct and adapt models using supervision that can be extracted from low-cost instrumentation, avoiding extreme accuracy and reliability requirements; and (b) at inference time, use inputs that are typically provided in publicly available weather forecasts. Recent developments in attention-based machine learning, as well as careful adaptation of the training setup to the specifics of the task, have allowed us to design a machine learning-based solution that covers our requirements. We present positive empirical results for the predictive accuracy of our solution, and discuss the impact of these results on the end-to-end system.

A New York lawyer could face discipline after it was discovered a case she cited was generated by artificial intelligence and did not actually exist. The 2nd U.S. Circuit Court of Appeals ordered lawyer Jae Lee to its grievance panel last week after discovering she used OpenAI's ChatGPT to research prior cases for a medical malpractice lawsuit but failed to confirm whether the case she was citing actually existed, according to a report from Reuters. The attorney included the fictitious state court decision in an appeal for her client's lawsuit claiming that a Queens doctor botched an abortion, according to the report, leading the court to order that Lee submit a copy of the decision that the lawyer later found she was "unable to furnish." The lawyer's conduct "falls well below the basic obligations of counsel," the 2nd U.S. Circuit Court of Appeals concluded in its disciplinary review, which was sent to Lee. Lee would later admit to using a case that was "suggested" to her by ChatGPT, a popular AI chatbot, and failing to verify the results herself. The lawyer's decision to use the popular application comes even though experts have warned against such practices, noting that AI is a relatively new technology that also is well-known for "hallucinating" false or misleading results.

Everyone is looking for a way to slash their heating bills amid soaring energy prices and the deepening cost-of-living crisis. Now, a British start-up has come up with a new way of doing so using a method that may seem a little bizarre to some -- by fitting a computer server to a household's hot water tank. Heata claims its shoebox-sized device could help Britons save around £150 a year on their energy bills, while small companies can also make use of the computer power available on the servers rather than them being in a large data centre. As the computer gets hot, the tank takes waste heat away from it and uses this to warm water for showers, baths and washing up. Each unit can deliver up to 4.8kWh of hot water per day, the company says -- approximately 80 per cent of the hot water required in an average UK household. As many people will know, laptops and computers can get very hot when running for long periods, with internal fans used to cool them down.

After a long day of work, you might feel tired or exhilarated. Either way, you are affected by what happened to you in the past. Accelerator magnets are no different. What they went through – or what went through them, like an electric current – affects how they will perform in the future. Without understanding a magnet's past, researchers might need to fully reset them before starting a new experiment, a process that can take 10 or 15 minutes.

Artificial intelligence has "tremendous potential" to support and accelerate a reliable and lowest-cost energy transition, a new report by the World Economic Forum has revealed. Through its high-tech applications, AI can integrate renewable energy resources into the power grid, support an autonomous electricity distribution system and open up new revenue streams for demand-side flexibility, WEF said in its Harnessing Artificial Intelligence to Accelerate Energy Transition report compiled in collaboration with BloombergNEF and Deutsche Energie-Agentur (dena) – the German energy agency. AI can create substantial value for the global energy transition, the report said. Every 1 per cent of additional efficiency in demand will create $1.3 trillion in value between 2020 and 2050 due to reduced investment needs, according to BloombergNEF's net-zero scenario modelling. This could be achieved by enabling greater energy efficiency and flexing demand. "In energy, we are only seeing the beginning ...

Artificial Intelligence (AI) is smart, but it could do better. The software development industry is constantly working to push algorithmic logic beyond the scope of the current computer processing envelope and create new ways for computers to'think' and emulate human beings. We have of course progressed significantly onward from the fanciful notions of AI that were characterized in the Sci-Fi movies of the 1980s. Largely as result of access to massively more powerful processors and massively larger (and eminently accessible) datasets -- and as a result of cloud computing and modern approaches to database management, we can now create an impressive amount of smartness in the AI that we now develop. But AI needs to get smarter.