Donald Trump signed four executive orders on Tuesday aimed at reviving coal, the dirtiest fossil fuel that has long been in decline, and which substantially contributes to planet-heating greenhouse gas emissions and pollution. Environmentalists expressed dismay at the news, saying that Trump was stuck in the past and wanted to make utility customers "pay more for yesterday's energy". The US president is using emergency authority to allow some older coal-fired power plants scheduled for retirement to keep producing electricity. The move, announced at a White House event on Tuesday afternoon, was described by White House officials as being in response to increased US power demand from growth in datacenters, artificial intelligence and electric cars. Trump, standing in front of a group of miners in hard hats, said he would sign an executive order "that slashes unnecessary regulations that targeted the beautiful, clean coal".

FIRST ON FOX: The Department of Energy, the Department of the Interior and the Environmental Protection Agency are set to announce a bevy of new actions Tuesday afternoon that will "unleash" coal energy following President Donald Trump's expected signature on an executive order reinvigorating "America's beautiful clean coal industry," Fox News Digital learned. "The American people need more energy, and the Department of Energy is helping to meet this demand by unleashing supply of affordable, reliable, secure energy sources -- including coal," Department of Energy Secretary Chris Wright said in a Tuesday statement provided to Fox News Digital. "Coal is essential for generating 24/7 electricity generation that powers American homes and businesses, but misguided policies from previous administrations have stifled this critical American industry," he said. "With President Trump's leadership, we are cutting the red tape and bringing back common sense." Trump is expected to sign an executive order Tuesday afternoon that will cut through red tape surrounding the coal industry, including directing the National Energy Dominance Council to designate coal as a "mineral," end a current pause to coal leasing on federal lands, promote coal and coal technology exports, and encourage the use of coal to power artificial intelligence initiatives, Fox News Digital learned of the upcoming executive order.

A triangular structural panel data model with additive separable individual-specific effects is used to model the causal effect of a covariate on an outcome variable when there are unobservable confounders with some of them time-invariant. In this setup, a linear reduced-form equation might be problematic when the conditional mean of the endogenous covariate and the instrumental variables is nonlinear. The reason is that ignoring the nonlinearity could lead to weak instruments As a solution, we propose a triangular simultaneous equation model for panel data with additive separable individual-specific fixed effects composed of a linear structural equation with a nonlinear reduced form equation. The parameter of interest is the structural parameter of the endogenous variable. The identification of this parameter is obtained under the assumption of available exclusion restrictions and using a control function approach. Estimating the parameter of interest is done using an estimator that we call Super Learner Control Function estimator (SLCFE). The estimation procedure is composed of two main steps and sample splitting. We estimate the control function using a super learner using sample splitting. In the following step, we use the estimated control function to control for endogeneity in the structural equation. Sample splitting is done across the individual dimension. We perform a Monte Carlo simulation to test the performance of the estimators proposed. We conclude that the Super Learner Control Function Estimators significantly outperform Within 2SLS estimators.

Flammability index (FI) and cone calorimetry outcomes, such as maximum heat release rate, null me to igni null on, total smoke release, and fi re growth rate, are cri null cal factors in evalua null ng the fi re safety of polymers. However, predic null ng these proper null es is challenging due to the complexity of material behavior under heat exposure. In this work, we inves null gate the use of machine learning (ML) techniques to predict these fl ammability metrics. We generated synthe null c polymers using Synthe null c Data Vault to augment the experimental dataset. Our comprehensive ML inves null ga null on employed both our polymer descriptors and those generated by the RDkit library. Despite the challenges of limited experimental data, our models demonstrate the poten null al to accurately predict FI and cone calorimetry outcomes, which could be instrumental in designing safer polymers. Addi null onally, we developed POLYCOMPRED, a module integrated into the cloud based MatVerse pla null orm, providing an accessible, web based interface for fl ammability predic null on. This work provides not only the predic null ve modeling of polymer fl ammability but also an interac null ve analysis tool for the discovery and design of new materials with tailored fi re resistant proper null es. 2

Abstract-- F orce estimation is the core indicator for evaluating the performance of tactile sensors, and it is also the key technical path to achieve precise force feedback mechanisms. This study proposes a design method for a visual tactile sensor (VBTS) that integrates a magnetic perception mechanism, and develops a new tactile sensor called MagicGel. The sensor uses strong magnetic particles as markers and captures magnetic field changes in real time through Hall sensors. On this basis, MagicGel achieves the coordinated optimization of multimodal perception capabilities: it not only has fast response characteristics, but also can perceive non-contact status information of home electronic products. I. INTRODUCTION With the rapid advancement of tactile sensor technology, its crucial role in robotics, automation systems, and human-computer interaction has become increasingly evident. Tactile sensors enhance a robot's ability to perceive its environment, equipping the robot with more precise and intelligent operational capabilities. In the field of flexible operation and human-computer interaction, accurate tactile perception is the key to realizing core functions such as bionic grasping and force-controlled interaction. Traditional tactile sensors are mostly based on piezoresistance, capacitance or piezoelectric principles, which can achieve quantitative force perception. However, they have significant limitations in spatial resolution, dynamic response range and force estimation accuracy. J Shan and J Zhao are co-first authors of the article.

In this paper, we reproduce the experimental results presented in our previous work titled "Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems," which was published in the proceedings of the 31st ACM International Conference on Multimedia. This paper aims to validate the effectiveness of our proposed method and help others reproduce our experimental results. We provide detailed descriptions of our preprocessed datasets, source code structure, configuration file settings, experimental environment, and reproduced experimental results.

The colonization of Mars presents extraordinary challenges, including radiation exposure, low atmospheric pressure, and toxic regolith. Recent advancements in synthetic biology and genetic engineering offer unprecedented opportunities to address these obstacles by utilizing terrestrial extremophiles and engineered organisms. This paper examines the potential for creating symbiotic relationships between terrestrial microbes and hypothetical Martian life forms, should they exist, to support a sustainable human presence on Mars. Inspired by natural examples of endosymbiosis, such as mitochondria and chloroplasts, we propose methods to engineer life forms capable of enduring Martian conditions. Key components include experimental designs, laboratory simulations, and bioengineering approaches essential to this endeavor. The ethical, political, and technological challenges of introducing engineered life to Mars are critically evaluated, with an emphasis on international collaboration and robust planetary protection policies. This research underscores engineered symbiosis as a transformative strategy for enabling life to adapt and thrive on Mars while advancing humanity's aspirations for interplanetary habitation and exploration. By addressing these challenges, this work highlights a path toward sustainable life on Mars, reflecting both scientific ingenuity and ethical stewardship.

Fatigue life characterizes the duration a material can function before failure under specific environmental conditions, and is traditionally assessed using stress-life (S-N) curves. While machine learning and deep learning offer promising results for fatigue life prediction, they face the overfitting challenge because of the small size of fatigue experimental data in specific materials. To address this challenge, we propose, DeepOFormer, by formulating S-N curve prediction as an operator learning problem. DeepOFormer improves the deep operator learning framework with a transformer-based encoder and a mean L2 relative error loss function. We also consider Stussi, Weibull, and Pascual and Meeker (PM) features as domain-informed features. These features are motivated by empirical fatigue models. To evaluate the performance of our DeepOFormer, we compare it with different deep learning models and XGBoost on a dataset with 54 S-N curves of aluminum alloys. With seven different aluminum alloys selected for testing, our DeepOFormer achieves an R2 of 0.9515, a mean absolute error of 0.2080, and a mean relative error of 0.5077, significantly outperforming state-of-the-art deep/machine learning methods including DeepONet, TabTransformer, and XGBoost, etc. The results highlight that our Deep0Former integrating with domain-informed features substantially improves prediction accuracy and generalization capabilities for fatigue life prediction in aluminum alloys.

Secretary of Energy Chris Wright discusses the economic impact of lowering energy prices, why energy is essential for artificial intelligence dominance, American LNG exports and possible U.S. operation of Ukrainian nuclear plants. Rep. August Pfluger, R-Texas, said that his constituents are feeling optimistic once again about the future of the oil and gas industry in his district and beyond. The Republican represents parts of central Texas that are critical to the industry, including the Permian Basin, as the Trump administration has famously promised to "drill, baby, drill." "Think about the hardworking men and women of the Permian Basin, or the Bakken or the Marcellus, or any other producing area. President Biden said, 'What you do is evil. You producing oil and gas is evil.' I mean, they basically demonized them," he told Fox News Digital in a recent interview.

Additive Manufacturing (AM) is a promising solution for handling the complexity of fabricating soft robots. However, the AM of hyperelastic materials is still challenging with limited material types. Within this work, pellet-based 3D printing of very soft thermoplastic elastomers (TPEs) was explored. Our results show that TPEs can have similar engineering stress and maximum strain as Ecoflex OO-10. These TPEs were used to 3D-print airtight thin membranes (0.2-1.2 mm), which could inflate up to a stretch of 1320\%. Combining the membrane's large expansion and softness with the 3D printing of hollow structures simplified the design of a bending actuator that can bend 180 degrees and reach a blocked force of 238 times its weight. In addition, by 3D printing TPE pellets and rigid filaments, the soft membrane could grasp objects by enveloping an object or as a sensorized sucker, which relied on the TPE's softness to conform to the object or act as a seal. In addition, the membrane of the sucker was utilized as a tactile sensor to detect an object before adhesion. These results suggest the feasibility of 3D printing soft robots by using soft TPEs and membranes as an interesting class of materials and sensorized actuators, respectively.