Optima Neuroscience (United States)

Many engineering applications rely on numerical methods to simulate complex physical systems, which often entail prohibitive computational costs. Additionally, problems involving parametrized domains require the generation of a computational mesh for each configuration, which is an expensive and error-prone process. Surrogates based on proper orthogonal decomposition can alleviate this burden providing rapid predictions of partial differential equations (PDEs) solutions, but they rely on explicit geometric parametrizations and shared reference meshes, requirements rarely satisfied in practice. In this paper, we present a machine-learning-based workflow for predicting PDEs solutions and derived quantities in the context of geometric parametrization, without explicit knowledge of the geometric parameters or a common discretization of the geometries. The workflow is composed of two encoding blocks that independently encode the geometries and the associated outputs, and a mapping block that learns the relationship between the two encodings. The main contribution of this work is the introduction of a regularization method for INR-based encoders, designed to preserve the structure of the encoded spaces in the learned latent space. The workflow was tested on two problems. The prediction of one-dimensional signals (radial and thrust force) on a vertical axis turbine blade with varying thickness and curvature, and the prediction of the two-dimensional pressure coefficient fields associated with geometric variations of the Royal Aircraft Establishment airfoil 2822 (RAE2822). The experimental results demonstrate that the proposed regularization substantially enhances the overall predictive accuracy, reducing the prediction error of unseen configurations by one order of magnitude compared to more standard approaches.

Student-centered approach to preparing Junior Academy of Sciences (MAN) projects in mathematics is substantiated, combining sound topic selection with bringing results to a reproducible state. The paper proposes explicit topic-selection criteria (student comprehensibility, amenability to algorithmic treatment, visualizability, attainable scholarly novelty, practical relevance) and shows how to turn them into a step-by-step research trajectory – from problem formulation and a concise literature review to prototype implementations, computational checks, interpretation, manuscript preparation, and defense. Special emphasis is placed on types of novelty realistic for MAN level: constructing new catalogs and bounds for small parameters, improving algorithms, and validating known hypotheses on new parameter ranges, with all steps accompanied by transparent success metrics. Building on analyzed works, a minimal reproducibility standard is outlined for mathematical MAN projects: unambiguous definitions and notation; explicit assumptions, statements, and scope; illustrative examples/ counterexamples; tables documenting test parameters; and a brief description of verification procedures (which steps were executed, stopping criteria, and counting methods). Visualizations are treated as evidence rather than decoration: a “key figure” carries a self-contained caption and can be reproduced by the stated sequence of steps (e.g., diagrams of first/second neighborhoods in graphs; coverage maps of pairs in lottery problems; comparative plots contrasting naïve enumeration with improved constructions). A concise assessment rubric for students, supervisors, and juries keeps attention on relevance, novelty, correctness of arguments/constructions, reproducibility, and figure quality. The approach is illustrated by two representative cases in discrete mathematics. First, Seymour’s Second Neighborhood Conjecture: using properties of potential counterexamples (density, diameter ≥ 3, penalty-function filters, and the vertex-weighted equivalence) to narrow the search space, formalize pruning conditions, and build informative neighborhood diagrams. Second, combinatorial coverings for lottery problems: demonstrating algorithmic construction of coverings and the alignment of upper and lower bounds (including realistic parameters such as 6-out-of-36), enabling rigorous justification of optimal solutions and independent verification. Both cases show how to balance accessibility with scholarly novelty, ensure reproducibility, and prepare results for peer-reviewed publication and a successful defense.

Abstract This paper presents a field-proven methodology that eliminates routine flaring during well testing operations. The objective is to challenge the long-standing industry practice of flaring hydrocarbons during flowback and cleanup phases, and to demonstrate that full test objectives production logging, sampling, cleanup, and data acquisition can be achieved without combustion. The case study is based on a high-H₂S gas condensate well in a remote region of Kazakhstan. To enable flare-free well testing, a novel configuration was deployed combining conventional well test equipment with a portable multiphase boosting system (BoostFlow™), capable of handling variable gas volume fractions (GVF) from 0 to 100%. Produced fluids were routed to a test separator where free water was efficiently removed, while the oil and gas phases were simultaneously pressurized and transported to a remote processing facility. This setup eliminated the need for conventional flare systems and on-site hydrocarbon handling. The operation was performed in a high-H₂S gas condensate reservoir with minimal surface infrastructure, where seamless integration of the booster into the test flowline was essential to ensure stable, controlled flowback and safe pressure management. The operation successfully achieved all standard well testing objectives, including flowback, fluid sampling, and production evaluation, while significantly reducing routine flaring. The integrated multiphase boosting system demonstrated robust performance, maintaining stable operation across a wide range of flow conditions. Notably, the unit sustained continuous operation at 100% gas volume fraction for more than 24 hours, while also delivering consistent performance under low-GVF, liquid-rich conditions. This operational flexibility enabled seamless management of transient flow regimes commonly encountered during well cleanup and test sequences. Successful execution validates the technical feasibility of eliminating flaring from well testing without compromising data integrity or operational objectives. This case provides a proven, scalable model for future applications in sour gas, gas condensate, and remote-field settings, particularly where infrastructure is limited or ESG-driven performance targets are in effect. This paper presents a well testing approach that eliminates routine flaring, challenging long-standing operational norms. Through a first-of-its-kind field application in Kazakhstan, it demonstrates that comprehensive well testing can be performed without combustion. The methodology offers practical value for operators seeking to align with ESG goals and evolving emissions regulations, while maintaining full test integrity in technically demanding, sour gas environments.

Medical monitors (MMs) play a critical and often underrecognized role in ensuring patient safety, regulatory compliance, and scientific integrity during clinical trials. Particularly in early-stage pharma, biotechnology, and medical device development, the MM serves as an independent safeguard against bias and unanticipated risks. This editorial outlines the rationale, responsibilities, and operational value of the MM role, emphasizing its function in document review, real-time safety oversight, and regulatory alignment. Through illustrative case studies and current best practices, we argue that the independence and objectivity of the MM is a strategic business necessity, not merely a regulatory checkbox.

Proposal DescriptionGene therapies hold tremendous potential, yet manual operations, product loss, and high costs impede patient access to vital treatments. The fill/finish processes are pivotal in optimizing drug product availability. Precision is paramount in formulating, filling, and packaging the therapies to preserve their efficacy and integrity. These challenges are leveraging advanced technologies like the adaptable Optima FillCell. This flexible and modular system streamlines operations, reducing reliance on manual labor and mitigating the risk of errors and contamination. Real-time monitoring and data analysis provide crucial insights, empowering manufacturers to optimize production parameters and minimize wastage, ultimately increasing the number of viable drug vials. By enhancing efficiency and maximizing overall yield.

A system using aerial vehicles to autonomously locate and retrieve ground packages of various colors and shapes within a designated area was developed. This system was notable for its use of an innovative rotor configuration that offered a higher degree of control compared to traditional designs. To accurately identify and track the packages, a multi-sensor approach combining vision and Light Detection and Ranging technologies was implemented, integrating data into an extended Kalman filter framework for precise position and velocity estimates. An electro-permanent magnet was employed to securely grab and release the packages, which have a ferrous material. The process of path planning and collision avoidance was conducted in a decentralized manner, leveraging a shared global map among the airborne vehicles. This paper details system technical design, including the integration of various technologies, and shares insights and outcomes derived from its application.

Актуальность. Анофтальм требует постоянного мониторинга для комфортного ношения глазного протеза. Существующие классификации недостаточно учитывают анатомические изменения, что затрудняет реабилитацию. Новая классификация необходима для улучшения хирургии и протезирования. Цель исследования. Разработать классификацию анофтальмической орбиты с учетом состояния соединительных и костных тканей. Материалы и методы. Исследование включало 457 пациентов с анофтальмом различной этиологии, проходивших осмотр и диагностику. Результаты. Обнаружены различия в состоянии век, конъюнктивальной полости и костей орбиты у пациентов с врожденным и приобретенным анофтальмом. Заключение. Предложенная классификация способствует более точной дифференциации и реабилитации пациентов с анофтальмом.

The anisotropic continuum stored energy density (ACSED) functional is applied for accurate constitutive modeling of biological tissues and finite element implementation without the isochoric—volumetric split, the anisotropic—isotropic split, or the anisotropic invariant split. Related stress and elasticity tensors in the reference and current configurations are worked out. A new kinematic model is derived based on the tangent Poisson’s ratio as a cubic polynomial function of stretch. The ACSED model, along with the kinematic model, accurately fits uniaxial extension test data for compressible human skin, bovine articular cartilage, and human aorta samples.