Engineering Sciences

143 items

arXiv Sep 26, 2026
The work introduces the concept of an organizational memory for agentic business process execution—a shared, human-governed, agent-consumable reference layer of organization-specific procedural knowledge—and derives requirements (R1–R9), an architecture covering memory curation and runtime consumption, and an instantiation based on process atoms; in a purchase-to-pay proof-of-concept, the Policy Compliance Rate averaged over 10 scenarios with four runs each reached 88% with GPT-4.1 and 95% with Claude Sonnet 4.5 for the memory-equipped agent, versus 70% and 80% for RAG and 30% for the no-knowledge base setup with both models.
The work introduces the concept of an organizational memory for agentic business process execution—a shared, human-governed, agent-consumable reference layer of organization-specific procedural knowledge—and derives requirements (R1–R9), an architecture covering memory curation and runtime consumption, and an instantiation based on process atoms; in a purchase-to-pay proof-of-concept, the Policy Compliance Rate averaged over 10 scenarios with four runs each reached 88% with GPT-4.1 and 95% with Claude Sonnet 4.5 for the memory-equipped agent, versus 70% and 80% for RAG and 30% for the no-knowledge base setup with both models.
SAP Signavio proposes an "organizational memory" architecture that lifts LLM agents' policy compliance in a procurement process from 30% to 88%–95%
The work introduces the concept of an organizational memory for agentic business process execution—a shared, human-governed, agent-consumable reference layer of organization-specific procedural knowledge—and derives requirements (R1–R9), an architecture covering memory curation and runtime consumption, and an instantiation based on process atoms; in a purchase-to-pay proof-of-concept, the Policy Compliance Rate averaged over 10 scenarios with four runs each reached 88% with GPT-4.1 and 95% with Claude Sonnet 4.5 for the memory-equipped agent, versus 70% and 80% for RAG and 30% for the no-knowledge base setup with both models.
The work introduces the concept of an organizational memory for agentic business process execution—a shared, human-governed, agent-consumable reference layer of organization-specific procedural knowledge—and derives requirements (R1–R9), an architecture covering memory curation and runtime consumption, and an instantiation based on process atoms; in a purchase-to-pay proof-of-concept, the Policy Compliance Rate averaged over 10 scenarios with four runs each reached 88% with GPT-4.1 and 95% with Claude Sonnet 4.5 for the memory-equipped agent, versus 70% and 80% for RAG and 30% for the no-knowledge base setup with both models.
The University of Ha’il Journal of Science (UOHJS) Sep 26, 2026
Using a qualitative literature review plus multi-case comparison of three operating projects above 100 MW — Hornsdale Power Reserve (100 MW/129 MWh), Gateway Energy Storage (250 MW) and Victorian Big Battery (300 MW) — the study analyzes how battery energy storage systems (BESS) provide frequency regulation, peak shaving, renewable firming, voltage support and black start in smart grids, and identifies drivers such as public–private partnerships, FCAS and arbitrage revenue and green-bank financing, alongside barriers such as thermal-runaway fires, cooling-system failure, siting disputes, missing regulatory standards and high initial capital cost.
Using a qualitative literature review plus multi-case comparison of three operating projects above 100 MW — Hornsdale Power Reserve (100 MW/129 MWh), Gateway Energy Storage (250 MW) and Victorian Big Battery (300 MW) — the study analyzes how battery energy storage systems (BESS) provide frequency regulation, peak shaving, renewable firming, voltage support and black start in smart grids, and identifies drivers such as public–private partnerships, FCAS and arbitrage revenue and green-bank financing, alongside barriers such as thermal-runaway fires, cooling-system failure, siting disputes, missing regulatory standards and high initial capital cost.
Three 100 MW-class battery storage case studies show frequency-regulation revenue and policy financing drive deployment, while thermal-runaway fires and missing standards hold it back
Using a qualitative literature review plus multi-case comparison of three operating projects above 100 MW — Hornsdale Power Reserve (100 MW/129 MWh), Gateway Energy Storage (250 MW) and Victorian Big Battery (300 MW) — the study analyzes how battery energy storage systems (BESS) provide frequency regulation, peak shaving, renewable firming, voltage support and black start in smart grids, and identifies drivers such as public–private partnerships, FCAS and arbitrage revenue and green-bank financing, alongside barriers such as thermal-runaway fires, cooling-system failure, siting disputes, missing regulatory standards and high initial capital cost.
Using a qualitative literature review plus multi-case comparison of three operating projects above 100 MW — Hornsdale Power Reserve (100 MW/129 MWh), Gateway Energy Storage (250 MW) and Victorian Big Battery (300 MW) — the study analyzes how battery energy storage systems (BESS) provide frequency regulation, peak shaving, renewable firming, voltage support and black start in smart grids, and identifies drivers such as public–private partnerships, FCAS and arbitrage revenue and green-bank financing, alongside barriers such as thermal-runaway fires, cooling-system failure, siting disputes, missing regulatory standards and high initial capital cost.
arXiv Sep 25, 2026
The work proposes an autonomous robotic ultrasound framework driven by an LLM agent that retrieves guideline steps from scanning handbooks, reasons over current observations and scanning state, and dynamically invokes tools for trajectory planning, robot execution, contact adjustment, voice guidance, and trajectory refinement, with reinforcement-learning (PPO) fine-tuning to improve reasoning quality and the correctness of tool selection and parameterization; validated verbally on 10 unseen ultrasound scanning guidelines, fine-tuning raised step-wise accuracy from 0.6512 to 0.8973 and overall success rate from 0.5384 to 0.
The work proposes an autonomous robotic ultrasound framework driven by an LLM agent that retrieves guideline steps from scanning handbooks, reasons over current observations and scanning state, and dynamically invokes tools for trajectory planning, robot execution, contact adjustment, voice guidance, and trajectory refinement, with reinforcement-learning (PPO) fine-tuning to improve reasoning quality and the correctness of tool selection and parameterization; validated verbally on 10 unseen ultrasound scanning guidelines, fine-tuning raised step-wise accuracy from 0.6512 to 0.8973 and overall success rate from 0.5384 to 0.
An RL-fine-tuned small model lets a robot follow ultrasound guidelines to scan gallbladder, spine, and kidney autonomously
The work proposes an autonomous robotic ultrasound framework driven by an LLM agent that retrieves guideline steps from scanning handbooks, reasons over current observations and scanning state, and dynamically invokes tools for trajectory planning, robot execution, contact adjustment, voice guidance, and trajectory refinement, with reinforcement-learning (PPO) fine-tuning to improve reasoning quality and the correctness of tool selection and parameterization; validated verbally on 10 unseen ultrasound scanning guidelines, fine-tuning raised step-wise accuracy from 0.6512 to 0.8973 and overall success rate from 0.5384 to 0.
The work proposes an autonomous robotic ultrasound framework driven by an LLM agent that retrieves guideline steps from scanning handbooks, reasons over current observations and scanning state, and dynamically invokes tools for trajectory planning, robot execution, contact adjustment, voice guidance, and trajectory refinement, with reinforcement-learning (PPO) fine-tuning to improve reasoning quality and the correctness of tool selection and parameterization; validated verbally on 10 unseen ultrasound scanning guidelines, fine-tuning raised step-wise accuracy from 0.6512 to 0.8973 and overall success rate from 0.5384 to 0.
SciPost Physics Sep 25, 2026
AllShowers is a continuous normalizing flow generative model with a Transformer architecture that simulates calorimeter showers for electrons, photons, and charged and neutral hadrons in the highly granular ILD detector using a single model, covering a wide range of incident energies and angles without retraining and surpassing previous single-particle-type models in hadronic shower fidelity.
AllShowers is a continuous normalizing flow generative model with a Transformer architecture that simulates calorimeter showers for electrons, photons, and charged and neutral hadrons in the highly granular ILD detector using a single model, covering a wide range of incident energies and angles without retraining and surpassing previous single-particle-type models in hadronic shower fidelity.
AllShowers unifies calorimeter shower simulation for electrons, photons, and charged and neutral hadrons in one generative model, surpassing prior single-particle models on hadronic showers
AllShowers is a continuous normalizing flow generative model with a Transformer architecture that simulates calorimeter showers for electrons, photons, and charged and neutral hadrons in the highly granular ILD detector using a single model, covering a wide range of incident energies and angles without retraining and surpassing previous single-particle-type models in hadronic shower fidelity.
AllShowers is a continuous normalizing flow generative model with a Transformer architecture that simulates calorimeter showers for electrons, photons, and charged and neutral hadrons in the highly granular ILD detector using a single model, covering a wide range of incident energies and angles without retraining and surpassing previous single-particle-type models in hadronic shower fidelity.
International Journal of Robust and Nonlinear Control Sep 25, 2026
This paper presents the composite adaptive control barrier function (CaCBF) algorithm for nonlinear control-affine systems with linear parametric uncertainty, deriving the adaptation law from a composite energy function that integrates a logarithmic safety barrier, a control Lyapunov function, and a parameter-error term; it proves forward invariance of the safe set for all bounded parameters without persistence of excitation, robustness of the safety guarantee to bounded state-derivative measurement errors, uniform ultimate boundedness of all closed-loop signals, and that the CaCBF admissible control set always contains the robust counterpart as a subset; simulations of adaptive cruise control, an omnidirectional robot, and a planar drone traversing a narrow gate show CaCBF recovers the pe
This paper presents the composite adaptive control barrier function (CaCBF) algorithm for nonlinear control-affine systems with linear parametric uncertainty, deriving the adaptation law from a composite energy function that integrates a logarithmic safety barrier, a control Lyapunov function, and a parameter-error term; it proves forward invariance of the safe set for all bounded parameters without persistence of excitation, robustness of the safety guarantee to bounded state-derivative measurement errors, uniform ultimate boundedness of all closed-loop signals, and that the CaCBF admissible control set always contains the robust counterpart as a subset; simulations of adaptive cruise control, an omnidirectional robot, and a planar drone traversing a narrow gate show CaCBF recovers the pe
Composite adaptive control barrier functions couple parameter estimation with safety in one energy function, recovering in three simulations the safe operating space robust methods give up
This paper presents the composite adaptive control barrier function (CaCBF) algorithm for nonlinear control-affine systems with linear parametric uncertainty, deriving the adaptation law from a composite energy function that integrates a logarithmic safety barrier, a control Lyapunov function, and a parameter-error term; it proves forward invariance of the safe set for all bounded parameters without persistence of excitation, robustness of the safety guarantee to bounded state-derivative measurement errors, uniform ultimate boundedness of all closed-loop signals, and that the CaCBF admissible control set always contains the robust counterpart as a subset; simulations of adaptive cruise control, an omnidirectional robot, and a planar drone traversing a narrow gate show CaCBF recovers the pe
This paper presents the composite adaptive control barrier function (CaCBF) algorithm for nonlinear control-affine systems with linear parametric uncertainty, deriving the adaptation law from a composite energy function that integrates a logarithmic safety barrier, a control Lyapunov function, and a parameter-error term; it proves forward invariance of the safe set for all bounded parameters without persistence of excitation, robustness of the safety guarantee to bounded state-derivative measurement errors, uniform ultimate boundedness of all closed-loop signals, and that the CaCBF admissible control set always contains the robust counterpart as a subset; simulations of adaptive cruise control, an omnidirectional robot, and a planar drone traversing a narrow gate show CaCBF recovers the pe
Scientific Reports Sep 25, 2026
Using historical general user proposals from three beamlines at Oak Ridge National Laboratory's Spallation Neutron Source (EQ-SANS, CNCS, POWGEN), the study has large language models judge every pair of proposals within a run cycle, converts the win-lose outcomes into rankings with the Bradley-Terry model, and finds that LLM rankings correlate positively with human rankings (Spearman ρ about 0.2-0.8, rising to ≥0.5 after 10% outlier removal), show no statistically significant difference from human review in identifying proposals with high publication potential, cost over two orders of magnitude less, and support linear-complexity proposal similarity analysis via embedding models.
Using historical general user proposals from three beamlines at Oak Ridge National Laboratory's Spallation Neutron Source (EQ-SANS, CNCS, POWGEN), the study has large language models judge every pair of proposals within a run cycle, converts the win-lose outcomes into rankings with the Bradley-Terry model, and finds that LLM rankings correlate positively with human rankings (Spearman ρ about 0.2-0.8, rising to ≥0.5 after 10% outlier removal), show no statistically significant difference from human review in identifying proposals with high publication potential, cost over two orders of magnitude less, and support linear-complexity proposal similarity analysis via embedding models.
LLM pairwise comparison of historical proposals at three Spallation Neutron Source beamlines ranks them in positive correlation with human ranking, matches human reviewers at flagging high-publication-potential proposals, and costs over two orders of magnitude less
Using historical general user proposals from three beamlines at Oak Ridge National Laboratory's Spallation Neutron Source (EQ-SANS, CNCS, POWGEN), the study has large language models judge every pair of proposals within a run cycle, converts the win-lose outcomes into rankings with the Bradley-Terry model, and finds that LLM rankings correlate positively with human rankings (Spearman ρ about 0.2-0.8, rising to ≥0.5 after 10% outlier removal), show no statistically significant difference from human review in identifying proposals with high publication potential, cost over two orders of magnitude less, and support linear-complexity proposal similarity analysis via embedding models.
Using historical general user proposals from three beamlines at Oak Ridge National Laboratory's Spallation Neutron Source (EQ-SANS, CNCS, POWGEN), the study has large language models judge every pair of proposals within a run cycle, converts the win-lose outcomes into rankings with the Bradley-Terry model, and finds that LLM rankings correlate positively with human rankings (Spearman ρ about 0.2-0.8, rising to ≥0.5 after 10% outlier removal), show no statistically significant difference from human review in identifying proposals with high publication potential, cost over two orders of magnitude less, and support linear-complexity proposal similarity analysis via embedding models.
SIAM Journal on Scientific Computing Sep 25, 2026
The authors develop and evaluate a data-free, physics-informed method for learning solution operators: after a finite element discretization, a multilayer perceptron (MLP) maps problem data parameters (boundary conditions, coefficients, right-hand sides, etc.) to the finite element degrees of freedom, with a loss function given by the expected energy functional, plus a parallelizable training algorithm that can use random batches of mesh elements; on a parametric Poisson problem, a Gaussian random field coefficient problem, and a nonlinear neo-Hookean elastic beam, learning errors are small (e.g., for the parametric problem with the full mesh and a 4·512 network, mean relative energy error 2.0e-4, L2 0.0020, H1 0.
The authors develop and evaluate a data-free, physics-informed method for learning solution operators: after a finite element discretization, a multilayer perceptron (MLP) maps problem data parameters (boundary conditions, coefficients, right-hand sides, etc.) to the finite element degrees of freedom, with a loss function given by the expected energy functional, plus a parallelizable training algorithm that can use random batches of mesh elements; on a parametric Poisson problem, a Gaussian random field coefficient problem, and a nonlinear neo-Hookean elastic beam, learning errors are small (e.g., for the parametric problem with the full mesh and a 4·512 network, mean relative energy error 2.0e-4, L2 0.0020, H1 0.
Learning nonlinear finite element solution operators with MLPs and energy minimization yields small learning errors on parametric Poisson, Gaussian random field, and nonlinear elasticity cases, and speeds up Newton's method when the network prediction is used as an initial guess
The authors develop and evaluate a data-free, physics-informed method for learning solution operators: after a finite element discretization, a multilayer perceptron (MLP) maps problem data parameters (boundary conditions, coefficients, right-hand sides, etc.) to the finite element degrees of freedom, with a loss function given by the expected energy functional, plus a parallelizable training algorithm that can use random batches of mesh elements; on a parametric Poisson problem, a Gaussian random field coefficient problem, and a nonlinear neo-Hookean elastic beam, learning errors are small (e.g., for the parametric problem with the full mesh and a 4·512 network, mean relative energy error 2.0e-4, L2 0.0020, H1 0.
The authors develop and evaluate a data-free, physics-informed method for learning solution operators: after a finite element discretization, a multilayer perceptron (MLP) maps problem data parameters (boundary conditions, coefficients, right-hand sides, etc.) to the finite element degrees of freedom, with a loss function given by the expected energy functional, plus a parallelizable training algorithm that can use random batches of mesh elements; on a parametric Poisson problem, a Gaussian random field coefficient problem, and a nonlinear neo-Hookean elastic beam, learning errors are small (e.g., for the parametric problem with the full mesh and a 4·512 network, mean relative energy error 2.0e-4, L2 0.0020, H1 0.
SIAM Journal on Scientific Computing Sep 25, 2026
This work systematically derives a family of algorithms for the LTLT (L unit lower triangular, T skew-symmetric tridiagonal) triangular tridiagonalization of a skew-symmetric matrix X using the FLAME methodology, presents unpivoted and pivoted blocked right-looking, left-looking, and fused variants, identifies new level-2 and level-3 BLAS-like operations, and implements them with BLIS 2.0 packing mechanisms and OpenMP parallelism; experiments show the best implementations greatly exceed the performance of the only known prior software, PFAPACK and Pfaffine, while matching or exceeding related symmetric factorization software.
This work systematically derives a family of algorithms for the LTLT (L unit lower triangular, T skew-symmetric tridiagonal) triangular tridiagonalization of a skew-symmetric matrix X using the FLAME methodology, presents unpivoted and pivoted blocked right-looking, left-looking, and fused variants, identifies new level-2 and level-3 BLAS-like operations, and implements them with BLIS 2.0 packing mechanisms and OpenMP parallelism; experiments show the best implementations greatly exceed the performance of the only known prior software, PFAPACK and Pfaffine, while matching or exceeding related symmetric factorization software.
FLAME-derived LTLT factorization algorithms for skew-symmetric matrices, with fused BLAS-like operations, greatly outperform PFAPACK and Pfaffine
This work systematically derives a family of algorithms for the LTLT (L unit lower triangular, T skew-symmetric tridiagonal) triangular tridiagonalization of a skew-symmetric matrix X using the FLAME methodology, presents unpivoted and pivoted blocked right-looking, left-looking, and fused variants, identifies new level-2 and level-3 BLAS-like operations, and implements them with BLIS 2.0 packing mechanisms and OpenMP parallelism; experiments show the best implementations greatly exceed the performance of the only known prior software, PFAPACK and Pfaffine, while matching or exceeding related symmetric factorization software.
This work systematically derives a family of algorithms for the LTLT (L unit lower triangular, T skew-symmetric tridiagonal) triangular tridiagonalization of a skew-symmetric matrix X using the FLAME methodology, presents unpivoted and pivoted blocked right-looking, left-looking, and fused variants, identifies new level-2 and level-3 BLAS-like operations, and implements them with BLIS 2.0 packing mechanisms and OpenMP parallelism; experiments show the best implementations greatly exceed the performance of the only known prior software, PFAPACK and Pfaffine, while matching or exceeding related symmetric factorization software.
IEEE Spectrum Sep 25, 2026
A 15-person multidisciplinary team from ITESO, Universidad Jesuita de Guadalajara (engineering students, faculty advisors, and IEEE Guadalajara Section volunteers) developed RoboMeshA through the EPICS in IEEE initiative, a portable all-in-one educational platform that brings robotics, computer vision, and AI experiences into high school classrooms lacking robotics laboratories; students connect via a web browser to manually interact with the robot or use its control modes to watch it move and detect and avoid obstacles, the team has built two units and partnered with CETI Colomos and Prepa ITESO high schools to validate the platform in classroom settings, and it is developing a modular coupling framework so that four RoboMeshA robots can operate together.
A 15-person multidisciplinary team from ITESO, Universidad Jesuita de Guadalajara (engineering students, faculty advisors, and IEEE Guadalajara Section volunteers) developed RoboMeshA through the EPICS in IEEE initiative, a portable all-in-one educational platform that brings robotics, computer vision, and AI experiences into high school classrooms lacking robotics laboratories; students connect via a web browser to manually interact with the robot or use its control modes to watch it move and detect and avoid obstacles, the team has built two units and partnered with CETI Colomos and Prepa ITESO high schools to validate the platform in classroom settings, and it is developing a modular coupling framework so that four RoboMeshA robots can operate together.
Mexico ITESO team builds portable robotics teaching platform RoboMeshA under EPICS in IEEE, with two units built and classroom validation at high schools
A 15-person multidisciplinary team from ITESO, Universidad Jesuita de Guadalajara (engineering students, faculty advisors, and IEEE Guadalajara Section volunteers) developed RoboMeshA through the EPICS in IEEE initiative, a portable all-in-one educational platform that brings robotics, computer vision, and AI experiences into high school classrooms lacking robotics laboratories; students connect via a web browser to manually interact with the robot or use its control modes to watch it move and detect and avoid obstacles, the team has built two units and partnered with CETI Colomos and Prepa ITESO high schools to validate the platform in classroom settings, and it is developing a modular coupling framework so that four RoboMeshA robots can operate together.
A 15-person multidisciplinary team from ITESO, Universidad Jesuita de Guadalajara (engineering students, faculty advisors, and IEEE Guadalajara Section volunteers) developed RoboMeshA through the EPICS in IEEE initiative, a portable all-in-one educational platform that brings robotics, computer vision, and AI experiences into high school classrooms lacking robotics laboratories; students connect via a web browser to manually interact with the robot or use its control modes to watch it move and detect and avoid obstacles, the team has built two units and partnered with CETI Colomos and Prepa ITESO high schools to validate the platform in classroom settings, and it is developing a modular coupling framework so that four RoboMeshA robots can operate together.
arXiv Sep 24, 2026
Using Fisher information and random matrix theory, the authors derive a closed-form expression for the noise-induced error of chaotic/diffusive reconstructive spectrometers, linking the variance bound σ²_ε Tr[(AᵀA)⁺] to the spectral correlation length Γ_corr, mean transmittance T₀, and the numbers of frequency channels N and measurement channels M, establish conditions for super-resolution, and validate the theory with a random matrix model and full-wave FDTD simulations, where the predicted optimal on-chip cavity size is about 17.9 µm.
Using Fisher information and random matrix theory, the authors derive a closed-form expression for the noise-induced error of chaotic/diffusive reconstructive spectrometers, linking the variance bound σ²_ε Tr[(AᵀA)⁺] to the spectral correlation length Γ_corr, mean transmittance T₀, and the numbers of frequency channels N and measurement channels M, establish conditions for super-resolution, and validate the theory with a random matrix model and full-wave FDTD simulations, where the predicted optimal on-chip cavity size is about 17.9 µm.
Random matrix theory yields a closed-form noise-error bound for reconstructive spectrometers and predicts a 17.9 µm optimal cavity in on-chip FDTD
Using Fisher information and random matrix theory, the authors derive a closed-form expression for the noise-induced error of chaotic/diffusive reconstructive spectrometers, linking the variance bound σ²_ε Tr[(AᵀA)⁺] to the spectral correlation length Γ_corr, mean transmittance T₀, and the numbers of frequency channels N and measurement channels M, establish conditions for super-resolution, and validate the theory with a random matrix model and full-wave FDTD simulations, where the predicted optimal on-chip cavity size is about 17.9 µm.
Using Fisher information and random matrix theory, the authors derive a closed-form expression for the noise-induced error of chaotic/diffusive reconstructive spectrometers, linking the variance bound σ²_ε Tr[(AᵀA)⁺] to the spectral correlation length Γ_corr, mean transmittance T₀, and the numbers of frequency channels N and measurement channels M, establish conditions for super-resolution, and validate the theory with a random matrix model and full-wave FDTD simulations, where the predicted optimal on-chip cavity size is about 17.9 µm.

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