Lifeboat Foundation

The Driving Training Based Optimization (DTBO) algorithm, proposed by Mohammad Dehghani, is one of the novel metaheuristic algorithms which appeared in 2022⁸⁰. This algorithm is founded on the principle of learning to drive, which unfolds in three phases: selecting an instructor from the learners, receiving instructions from the instructor on driving techniques, and practicing newly learned techniques from the learner to enhance one’s driving abilities^81,82. In this work, DTBO algorithm is used, due to its effectiveness, which was confirmed by a comparative study⁸³ with other algorithms, including particle swarm optimization⁸⁴, Gravitational Search Algorithm (GSA)⁸⁵, teaching learning-based optimization, Gray Wolf Optimization (GWO)⁸⁶, Whale Optimization Algorithm (WOA)⁸⁷, and Reptile Search Algorithm (RSA)⁸⁸. The comparative study has been done using various kinds of benchmark functions, such as constrained, nonlinear and non-convex functions.

Lyapunov-based Model Predictive Control (LMPC) is a control approach integrating Lyapunov function as constraint in the optimization problem of MPC^89,90. This technique characterizes the region of the closed-loop stability, which makes it possible to define the operating conditions that maintain the system stability^91,92. Since its appearance, the LMPC method has been utilized extensively for controlling a various nonlinear systems, such as robotic systems⁹³, electrical systems⁹⁴, chemical processes⁹⁵, and wind power generation systems⁹⁰. In contrast to the LMPC, both the regular MPC and the NMPC lack explicit stability restrictions and can’t combine stability guarantees with interpretability, even with their increased flexibility.

The proposed method, named Lyapunov-based neural network model predictive control using metaheuristic optimization approach (LNNMPC-MOA), includes Lyapunov-based constraint in the optimization problem of the neural network model predictive control (NNMPC), which is solved by the DTBO algorithm. The suggested controller consists of two parts: the first is responsible for calculating predictions using a neural network model of the feedforward type, and the second is responsible to resolve the constrained nonlinear optimization problem using the DTBO algorithm. This technique is suggested to solve the nonlinear and non-convex optimization problem of the conventional NMPC, ensure on-line optimization in reasonable time thanks to their easy implementation and guaranty the stability using the Lyapunov function-based constraint. The efficiency of the proposed controller regarding to the accuracy, quickness and robustness is assessed by taking into account the speed control of a three-phase induction motor, and its stability is mathematically ensured using the Lyapunov function-based constraint. The acquired results are compared to those of NNMPC based on DTBO algorithm (NNMPC-DTBO), NNMPC using PSO algorithm (NNMPC-PSO), Fuzzy Logic controller optimized by TLBO (FLC-TLBO) and optimized PID controller using PSO algorithm (PID-PSO)⁹⁵.

Jim Clarke, Director of Quantum Hardware at Intel Labs, discusses how chemistry and physics drive the development of qubits in these unique systems. These systems will bring mind-blowing computing power to the world in the next decade and beyond.

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Inventing a new, faster way to produce sustainable, self-dyed leather alternatives is a major achievement for synthetic biology and sustainable fashion. Professor Tom Ellis

Synthetic chemical dyeing is one of the most environmentally toxic processes in fashion, and black dyes – especially those used in colouring leather – are particularly harmful. The researchers at Imperial set out to use biology to solve this.

Researchers at the University of Maryland genetically modified poplar trees to produce high-performance, structural wood without the use of chemicals or energy-intensive processing. Made from traditional wood, engineered wood is often seen as a renewable replacement for traditional building materials like steel, cement, glass and plastic. It also has the potential to store carbon for a longer time than traditional wood because it can resist deterioration, making it useful in efforts to reduce carbon emissions.

But the hurdle to true sustainability in engineered wood is that it requires processing with volatile chemicals and a significant amount of energy, and produces considerable waste. The researchers edited one gene in live poplar trees, which then grew wood ready for engineering without processing.

The research was published online on August 12, 2024, in the Journal Matter.

For decades, microbiologists like Weiss thought of antibiotic resistance as something a bacterial species either had or didn’t have. But “now, we are realizing that that’s not always the case,” he said.

Normally, genes determine how bacteria resist certain antibiotics. For example, bacteria could gain a gene mutation that enables them to chemically disable antibiotics. In other cases, genes may code for proteins that prevent the drugs from crossing bacterial cell walls. But that is not the case for heteroresistant bacteria; they defeat drugs designed to kill them without bona fide resistance genes. When they’re not exposed to an antibiotic, these bacteria look like any other bacteria.

Leading The Next Wave Of Innovation In Drug Discovery, To Modulate Any Target, Every Time — Dr. P. Ryan Potts, Ph.D., VP and Head, Induced Proximity Platform, Amgen.

Dr. Ryan Potts, Ph.D. is Vice President and Head, Induced Proximity Platform at Amgen (https://www.amgen.com/science/researc…) which is focused on novel ways to bring two or more molecules in close proximity to each other to tackle drug targets that are currently considered “undruggable.” He also leads Amgen’s Research \& Development Postdoctoral Fellows Program (https://www.amgen.com/science/scienti…).

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A catalyst that significantly enhances ammonia conversion could improve wastewater treatment, green chemical and hydrogen production.

CU Boulder scientists have found how ions move in tiny pores, potentially improving energy storage in devices like supercapacitors. Their research updates Kirchhoff’s law, with significant implications for energy storage in vehicles and power grids.

Imagine if your dead laptop or phone could be charged in a minute, or if an electric car could be fully powered in just 10 minutes. While this isn’t possible yet, new research by a team of scientists at CU Boulder could potentially make these advances a reality.

Published in the Proceedings of the National Academy of Sciences, researchers in Ankur Gupta’s lab discovered how tiny charged particles, called ions, move within a complex network of minuscule pores. The breakthrough could lead to the development of more efficient energy storage devices, such as supercapacitors, said Gupta, an assistant professor of chemical and biological engineering.

This finding may reinvent the understanding of chemical components, geography episodes and the history ofthe moon.