Researchers developed an AI model combining physics and observations to reconstruct the Earth's mantle history. The model accurately recreated past temperatures and deep-mantle flow with high accuracy, indicating that combining complementary geophysical information is essential for recovering realistic mantle convection histories.
A team of researchers at North Carolina State University has created a novel approach to optimize vaccine distribution by combining machine learning with column generation. This method accelerates run-time for the optimization model by 79.1% while maintaining high-quality solutions.
A new mathematical approach using optics helps computers solve larger, more complex optimization problems by reducing computational demands. The framework can be applied to various real-world challenges, including facility placement and data clustering, with potential benefits for a carbon-neutral future.
A new optimization framework helps food banks deliver food more efficiently by accounting for variables such as food availability and household demand. The tool has been incorporated into an app that can also be used by businesses to address delivery logistics challenges.
Researchers developed a machine learning framework that accurately predicts and optimizes biochar production from algae, identifying temperature as the dominant control on biochar yield. The model achieved strong agreement with experimental results and was able to pinpoint key factors influencing biochar production.
Researchers developed MatAgent, an AI framework that leverages a large language model to design new inorganic materials. The system uses natural language reasoning and explains its decisions in plain language, making the design process more efficient and transparent.
A study compares five DNA foundation language models across 57 diverse datasets to identify their strengths and weaknesses in predicting gene expression, identifying genomic components, and detecting harmful mutations. The findings highlight the importance of selecting appropriate models based on specific genomic tasks.
A new project aims to develop a computationally efficient model that accurately predicts how additive manufacturing process parameters influence the solidification microstructure of binary alloy solidification. This will enable optimization of additively manufactured parts with confidence in critical industries.
The book explores foundational and advanced principles of modeling concurrent control systems using Petri nets, focusing on building reliable, verifiable systems where concurrency plays a central role.
A new book introduces a structure that balances efficiency and fairness in optimization models, examining real-world effects of different approaches. It suggests that truly optimal results are fair ones, promoting informed and ethical choices in algorithm-driven world.
Dan M. Frangopol, a pioneer in life-cycle civil engineering, has been recognized by the International Association of Structural Safety and Reliability (IASSAR) for his sustained service to the organization. He is the inaugural recipient of the Distinguished Service Award, established in 2013.
A USC-led study shows that a quantum annealer outperforms classical algorithms in finding near-optimal solutions to complex problems. The researchers used a D-Wave Advantage processor and implemented error suppression techniques to overcome noise limitations.
A research team from the University of South China has developed a novel algorithm to optimize radiation-shielding design in nuclear reactors. The algorithm, based on a reference-point-selection strategy, efficiently solves many-objective optimization problems and provides optimized shielding solutions for new types of reactors.
Machine learning (ML) techniques can identify materials with high synthesis feasibility and suggest suitable experimental conditions. Computational models derived from thermodynamics and kinetics enhance predictive performance and interpretability of ML models, optimizing experimental design and increasing synthesis efficiency.
Researchers found that placing naloxone kits in transit stations could improve availability and save more lives. By optimizing distribution strategies using mathematical models, the team discovered that just 60 kits at 650 locations could cover over half of opioid poisonings in Vancouver.
The authors introduce advanced methodologies for integrating maintenance strategies and structural health monitoring to extend infrastructure service life. Topics include data-driven decision-making, multi-objective optimization, cost-benefit analysis, and the role of data analytics in managing uncertainties.
Researchers at Tokyo University of Science have developed a new method called black-box forgetting, which enables selective removal of unnecessary information from large pre-trained AI models. This approach enhances model efficiency and improves privacy by reducing computational resources and information leakage.
A new study published in JAMA found that pulmonary vein isolation combined with ethanol infusion of the vein of Marshall significantly improved freedom from atrial arrhythmias within 12 months. This approach outperformed traditional pulmonary vein isolation alone in reducing AF recurrence rates.
A team of researchers has developed a proposal for an urban goods distribution network in Barcelona, focusing on micro-hubs at existing public transport stations. The algorithm optimizes delivery routes using cargo bikes and electric vans to reduce greenhouse gas emissions and improve air quality.
Researchers developed a model to project Italy's energy storage needs for a renewable energy system, accounting for daily and seasonal fluctuations. The model suggests that increasing short-term energy storage capacity is critical for decarbonizing the power sector.
Researchers developed HypOp, a framework using unsupervised learning and hypergraph neural networks to solve combinatorial optimization problems significantly faster. The framework can also tackle certain problems that prior methods cannot effectively solve.
Engineers developed a material that mimics human bone for orthopedic femur restoration, providing optimized support and protection from external forces. This innovative approach uses machine learning, optimization, and 3D printing to create a fully controllable computational framework.
Researchers have introduced an optimization technique that accelerates Bayesian inference without requiring extensive user effort. This new automated method achieves more accurate results faster than another popular approach and offers reliable uncertainty estimates to help scientists understand when to trust their predictions.
Yu Yang's NSF-funded research aims to reduce vehicle emissions and promote the use of electric bikes and scooters by developing socially informed traffic signal control systems. The project involves a three-pronged method that uses low-cost mobile air-quality sensing, spatial-temporal graph diffusion learning, and reinforcement learnin...
Osaka University researchers have developed an AI-driven algorithm to control indoor heating and cooling systems, achieving significant energy savings of up to 30%. The system learns the symbolic relationships between variables, including power consumption, based on a large dataset, ensuring comfortable temperatures despite winter cond...
Researchers developed a divide & conquer approach to leads-to model checking, mitigating the state explosion problem and improving performance. The technique, DCA2L2MC, divides the reachable state space into smaller sub-state spaces, making it feasible for large-scale systems.
A team of researchers at Texas A&M University is developing a new method for understanding metal behavior under extreme conditions using metal cutting, a traditional manufacturing tool. The process involves shearing or deforming the metal to extreme levels under high rates and can provide fundamental information on material strength an...
A team of researchers from Rensselaer Polytechnic Institute has developed a system to optimize TV ad scheduling, resulting in a 3-5% revenue increase for networks. The model combines mathematical programming and machine learning to assign ads to specific breaks and positions.
Researchers created adaptive optical phantoms by combining multiple pigments to mimic target tissue's optical properties, successfully validating them in extensive experiments. The new platform enables broader band spectra for emerging hybrid modalities and novel instruments.
A new computational tool can generate an optimal design for a complex fluidic device without requiring manual assumptions about its shape. The system uses anisotropic materials to represent tiny voxels, allowing it to create smooth curves and intricate designs that other methods cannot.
Researchers have developed a scalable, fully coupled quantum-inspired processor that can solve optimization problems efficiently. The system uses an array calculator approach to divide calculations among multiple chips, reducing data transmission and increasing performance.
Xiu Yang, a 2022 NSF CAREER award recipient, is working on an algorithmic approach to model and overcome hardware errors in quantum computing. He aims to enable the technology to achieve its promise of unparalleled speed in solving complex problems.
Researchers from Shibaura Institute of Technology have developed a novel low-cost method for refining boron using ultrasonication, resulting in 95% pure MgB2 superconductors with improved magnetic properties. This breakthrough could make cheap superconductors a reality soon.
Researchers found that computer assistance in design leads to better solutions but compromises creativity and user agency. In a virtual reality experiment, novice designers outperformed their human-led counterparts when using an optimized approach.
A new study employs computer algorithms to design multimaterial structures mimicking natural designs for efficient actuators and energy absorbers. The approach enables the creation of sustainable devices with reusable and fully recoverable energy dissipators.
Researchers from academia and industry will converge at Lehigh University to discuss innovative solutions for optimizing efficiency and resiliency in the global supply chain. The workshop aims to leverage machine learning for prescriptive analytics, enabling proactive optimization of supply chain operations.
Researchers explored optimizing disease control in prisons using rapid tests, identifying the optimal strategy to minimize costs and reduce infection. The study found that switching between full and no testing depends on various parameters, including contagion rates and test sensitivity.
Researchers developed a technique to automatically search for simulation configurations that test various behaviors of automated driving systems. The proposed method uses evolutionary computation to discover configurations leading to specific features of driving behaviors, such as high acceleration and deceleration.
Scientists develop a quantum annealing framework to solve the long-standing problem of ion diffusion in solids. The approach shows promising results, especially when compared to other computational techniques, and could expand materials science.
Zhao and Cheng are working on a project to develop new gradient-free methods for training various types of deep neural networks. They aim to create an algorithmic and theoretical framework for model parallelization based on gradient-free optimization, as well as efficient distributed workflow systems.
A study from Michigan State University and the Max Planck Institute suggests that a partisan Congress is more productive than bipartisan groups. The research used mathematical programming models to analyze coalitions of lawmakers, finding that partisanship often helps bills pass into law.
A new mathematical optimization model introduced in the INFORMS journal Transportation Science can reduce extreme flight delays by as much as 20-30% on average, while increasing crew salary costs by only 2-3%. This approach allows airlines to balance delay reduction with buffer placement costs.
A team of researchers has developed a mathematical model to calculate the cost - time and energy - to complete a task based on the number of drones and recharging stations available. The model considers the energy required for each drone to complete its portion of the task and fly to a charging station as needed.
Researchers at North Carolina State University have developed a flow-based high-throughput screening technology for optimizing hydroformylation reactions. The new technique significantly reduces testing time to about 30 minutes, while also minimizing human interaction with toxic gases and saving money.
A University of Texas at Arlington researcher is working on a way to overcome the mathematical and physical barriers to make optimization of the US power grid a reality. The research focuses on increasing efficiency, reliability and security while reducing costs.
A team of Lehigh engineers has created a novel method called AMIGO that considers multiple elements and is the first to factor in so many aspects. The algorithm demonstrates its effectiveness on transportation network recovery in an imagined post-earthquake San Diego, finding near-optimal solutions in a small number of trials.
The UW optimization algorithm, RDIS, breaks down complex problems into smaller chunks, solving them exponentially faster. In protein design and self-driving car applications, RDIS performs significantly better than existing methods, accurately mapping images into realistic spaces.
A study on local salespeople's pricing discretion reveals that central, optimized pricing yields higher profits (20%) than local pricing (11%), improving overall profitability. The hybrid approach balances HQ analytic capabilities with field deal-specific information.
Researchers at MIT developed an algorithm to optimize optimization algorithms, guaranteeing the best possible solution for complex engineering problems. By using a Gaussian smoothing technique, they generate a sequence of simpler problems that progressively add complexity, ensuring convergence to a global minimum.
Topology optimization enables creation of patient-specific, case-by-case designs for tissue-engineered bone replacements in facial reconstruction. The technique accounts for variables like blood flow and chewing forces to optimize structure and function.
Researchers at Carnegie Mellon University have devised a new process that improves the efficiency of ethanol production, resulting in significant cost savings. The innovative design uses a multi-column system and energy recovery network to reduce steam consumption, leading to an 11% decrease in manufacturing costs.
Dr. Éva Tardos received the George B. Dantzig Prize for her groundbreaking work on network-flow algorithms, approximation algorithms, and combinatorial auctions. Her research focuses on efficient methods for solving optimization problems in graphs and networks.
The recipients of the 2006 Lagrange Prize are recognized for their groundbreaking papers on nonlinear programming without a penalty function and global convergence of a filter-SQP algorithm. These works introduced the filter idea, which has led to the development of effective nonlinear optimization codes.