Optimization Procedure Research Articles

Improving the estimation of countermovement jump height from force plate recordings by considering the interaction between multiple procedural steps: An optimisation approach.

Force plates are used as standalone measurement systems in research and practice to evaluate metrics such as jump height. Calculating jump height involves multiple procedural steps, but previous investigations aiming to improve calculation procedures have only considered the influence of a single procedural step in isolation. The purpose of this study was to investigate if considering the interacting influence of multiple procedural steps in conjunction would impact the accuracy of jump height calculated from force plate recordings. An optimisation procedure was used to determine the combination of filter type, filter order, filter cut-off, integration start point and instant of take-off, that would minimize the root mean squared difference between force plate calculated jump height and a kinematic criterion. The best filter approach was a fifth order Butterworth filter with a 6 Hz cut-off frequency or a third order Chebyshev filter with a 5 Hz cut-off frequency. The best starting point for integration was approximately 0.25 s prior to the onset of the jump and the instant of take-off was best identified by finding the first instant that the force-time signal decreased by the magnitude of system weight. The presented optimisation technique provides an improved quantitative approach to develop standard procedures.

Combining SDS-PAGE to capillary zone electrophoresis-tandem mass spectrometry for high-resolution top-down proteomics analysis of intact histone proteoforms.

Mass spectrometry (MS)-based top-down proteomics (TDP) analysis of histone proteoforms provides critical information about combinatorial post-translational modifications (PTMs), which is vital for pursuing a better understanding of epigenetic regulation of gene expression. It requires high-resolution separations of histone proteoforms before MS and tandem MS (MS/MS) analysis. In this work, for the first time, we combined SDS-PAGE-based protein fractionation (passively eluting proteins from polyacrylamide gels as intact species for mass spectrometry, PEPPI-MS) with capillary zone electrophoresis (CZE)-MS/MS for high-resolution characterization of histone proteoforms. We systematically studied the histone proteoform extraction from SDS-PAGE gel and follow-up cleanup as well as CZE-MS/MS, to determine an optimal procedure. The optimal procedure showed reproducible and high-resolution separation and characterization of histone proteoforms. SDS-PAGE separated histone proteins (H1, H2, H3, and H4) based on their molecular weight and CZE provided additional separations of proteoforms of each histone protein based on their electrophoretic mobility, which was affected by PTMs, for example, acetylation and phosphorylation. Using the technique, we identified over 200 histone proteoforms from a commercial calf thymus histone sample with good reproducibility. The orthogonal and high-resolution separations of SDS-PAGE and CZE made our technique attractive for the delineation of histone proteoforms extracted from complex biological systems.

Ultrasound‐assisted and landmark‐based nusinersen delivery in spinal muscular atrophy adults: A retrospective analysis

AbstractIntroduction/PurposeNusinersen, the first treatment approved for all spinal muscular atrophy (SMA) types, is administered intrathecally through lumbar puncture. We used ultrasound assistance or a landmark‐based technique to access the lumbar intrathecal space in adult SMA patients. This study aimed to evaluate the technical success and adverse events (AEs) in such patients using either technique over a long observation period.MethodsFifty‐one adult patients received 507 consecutive interlaminar nusinersen administrations. Patients presented with both ‘uncomplicated spines’ or ‘complicated spines’; two patients had previous back surgery. Technical success and AEs were recorded using either technique. A generalised linear mixed model was applied to evaluate predictors of technical success and complications.ResultsAn overall success rate of 99.6%, with only two procedures failing to reach the intrathecal space, and an overall optimal procedure rate of 90.3% have been reported. A total of 455 procedures (89.7%) were uneventfully performed. One (0.2%) case of severe AE (puncture of a bulky abdominal annexal cyst) was recorded. Twenty‐seven episodes (5.3%) of post‐dural puncture headache (PDPH) and 24 episodes (4.7%) of radicular or back pain, both successfully treated with medical therapy, have also been reported. Technical success was significantly associated with ‘complicated spines’ (P = 0.022) and the use of ultrasound assistance (P = 0.01), and the use of ultrasound was the only independent predictor of uncomplicated procedures (P = 0.007).DiscussionIn adult patients with SMA both landmark‐based and ultrasound‐assisted techniques are safe and effective even in the long term. The use of assistance is associated with technical success and can predict uncomplicated procedures.ConclusionOur results support the use of ultrasonography in order to improve the success and reduce the burden of nusinersen intrathecal administration.

A gradient-based strategy to construct efficient heat conduction path for arbitrary configurations

The heat conduction optimization problem is a widely investigated abstraction of various heat dissipation problems in industry and electronics cooling. This paper studies the optimization strategy that produces optimized heat conduction path made of high thermal conductivity materials, with the objective of lowering the hot spot temperature or improving temperature uniformity. The automatic differentiation (AD) technique is a general-purpose method to calculate the partial derivatives of any continuous function composed by elementary functions with respect to multiple independent variables. It is introduced to calculate the gradient of the objective function with respect to the thermal conductivity field, so that the optimization procedure can be guided directly towards the minimization of the desired objective function. This paper investigated the configurations of both continuous-value thermal conductivity and discrete-value thermal conductivity. Results indicate that the present optimization strategy is able to handle various types of boundary conditions and objective functions. The present method is compared with the iterative method based on variational principle (Var), showing that the AD method performs better than the Var method in most situations.

Developing flood mapping procedure through optimized machine learning techniques. Case study: Prahova river basin, Romania

Study regionPrahova river basin located in the central-southern region of Romania. Study focusThis study aims to assess the susceptibility to flooding by using state-of-the-art machine learning and optimization procedures. To achieve this goal, we employed ten flood-related variables as independent variables in our machine learning models. These variables include slope angle, convergence index, distance from the river, elevation, plan curvature, hydrological soil group, lithology, topographic wetness index, rainfall, and land use. We used 158 flood locations as dependent variables in the training of four hybrid models: Deep Learning Neural Network-Statistical Index (DLNN-SI), Particle Swarm Optimization-Deep Learning Neural Network-Statistical Index (PSO-DLNN-SI), Support Vector Machine-Statistical Index (SVM-SI), and Particle Swarm Optimization-Support Vector Machine-Statistical Index (PSO-SVM-SI). Utilizing the Statistical Index method, we calculated coefficients for each flood predictor class or category. New hydrological insights for the regionThe PSO-DLNN-SI model demonstrated the best performance, achieving an AUC-ROC curve of 0.952. It's worth noting that the application of the PSO algorithm significantly enhanced the model's performance. Additionally, it's crucial to highlight that approximately 25 % of the study region exhibits a high to very high susceptibility to flood events. Taking into account the very precise results of the models applied in the present study, we can state that from a hydrological point of view, the current research contributes to a better understanding of the intensity with which floods can affect the different areas of the Prahova river basin.

Multidisciplinary treatment of giant thymoma, paving the way to complete surgical resection: a case report

BackgroundA multidisciplinary treatment approach is recommended for patients with extensive, advanced, or recurrent thymomas. However, detailed treatment strategies, such as chemotherapy regimens and optimal surgical procedures, are still under debate.Case presentationWe report a case of gigantic locally advanced thymoma. A 70-year-old male was referred to our hospital following the detection of abnormal chest shadows. Chest X-ray and computed tomography (CT) scans revealed a 21-cm mass in the anterior mediastinum, encircling the pulmonary hilum and extending into the left thoracic cavity. PET/CT showed increased 18F-fluorodeoxyglucose uptake at the tumor site. Based on a trans-percutaneous CT-guided needle biopsy, the tumor was diagnosed as a Type B2 thymoma at the clinical IIIA stage. The patient underwent four cycles of preoperative induction chemotherapy, including cisplatin, doxorubicin, and methylprednisolone (CAMP), resulting in a partial response; the tumor shrank to 12 cm and FDG uptake decreased. Considering the patient’s age and comorbidities, we performed total thymectomy, along with partial resections of the parietal, mediastinal and visceral pleura, pericardium, and left upper lobectomy. This approach achieved complete histological resection, mitigating the risk of recurrence. Pathological analysis confirmed a thymoma, ypT3 (lung) N0M0 stage IIIA, with no malignancy in the pericardial or pleural effusions. No recurrence was detected 9 months post-surgery.ConclusionsWe report a case of giant thymoma successfully treated with multidisciplinary strategy. Surgical treatment alone may not have achieved complete resection, but after inducing significant tumor shrinkage with preoperative CAMP therapy, we were able to achieve complete resection. This treatment strategy may be effective in large thymoma cases.

Impacts of the cyromazine emergence inhibition procedure of Bactrocera dorsalis on Fopius arisanus: implications for the biological control of diptera pest.

Fopius arisanus (Sonan) is an important egg-pupal endoparasitic wasp of Bactrocera dorsalis (Hendel). As traditional method of sorting nonparasitic B. dorsalis from parasitic wasps is labor-intensive, requires specific equipment and poses the risk of spreading fertile hosts, the development of a more convenient, economical and safe sorting procedure is important. The optimal cyromazine emergence inhibition procedure (CEIP) involved facilitating the pupation of B. dorsalis mature larvae (Bdml) in 3 mg kg-1 cyromazine sand substrate (CSS) for 48 h. When the Bdml that had been exposed to F. arisanus during the egg stage were treated with 3-7 mg kg-1 CSS for 48 h, no negative effects on the emergence parameters of parasitoids were observed. Treatment with 3-4 mg kg-1 CSS had insignificant effects on the biological and behavioral parameters of F. arisanus. However, treatment with 5-6 mg kg-1 CSS adversely affected the fecundity and antennating activity of the wasps; specifically, 6 mg kg-1 CSS negatively affected the lifespan and flight ability of wasps. Fortunately, no transgenerational effects on these parameters were observed in the progeny. Regarding the nutrient reserves of both sexes of F. arisanus, significant dose-dependent effects were observed. Moreover, 5-6 mg kg-1 CSS significantly reduced the protein and carbohydrate content in F. arisanus; in particular, 6 mg kg-1 CSS notably reduced the lipid content. CEIP provides a more flexible, economical and safe mass-release program for F. arisanus. In addition, it has profound implications for the biological control of other dipteran pests. © 2024 Society of Chemical Industry.

Genetically-Based Active Flow Control And PIV Measurements Of A Circular Cylinder Wake

In the field of flow manipulation, considerable attention is devoted to controlling the wake of bluff bodies, which are characterized by the phenomenon of vortex shedding. Specifically, the control of the wake behind a single circular cylinder has garnered substantial research interest due to its engineering applications, such as reducing aerodynamic drag in the automotive and aerospace industries. Therefore, in the current work, through the application of the planar PIV technique, the fluid dynamic behavior of a circular cylinder wake controlled via synthetic jets (SJ) and Machine Learning (ML) methods is analyzed and discussed. ML algorithms are used to determine the optimal voltage signal for the loudspeaker, which effectively minimizes the aerodynamic drag of the cylinder. This approach overcomes the limitations associated with the a priori assumption of a parametric waveshape. In particular, the gradient-enriched machine learning control (gMLC) algorithm is chosen as optimization tool to search the best open-loop control policy for aerodynamic drag reduction. The cost function for this problem has been defined considering both the drag reduction contribution and the electrical power spent for the actuation. Initial optimization tests are performed by minimizing a cost function considering only the reduction in aerodynamic drag. Subsequently, a term related to the electrical power consumption for actuation is incorporated into the cost function. Once obtained the optimal control policies for these cases (hereinafter renamed CC1 and CC2, respectively), the physical mechanism responsible for reducing the aerodynamic drag is investigated more closely by analyzing the effect of the control laws on the turbulent flow statistics. Figure 1 reports a comparative assessment of the CC1 and CC2 with the baseline configuration and the configuration controlled by a sinusoidal law (CCS). The baseline configuration is characterized by an extended region of localized turbulent fluctuations in the far wake, where the time-averaged signature of von Kármán vortices is found. In contrast, all the controlled configurations show a global reduction in wake turbulence, with a peak of turbulent energy at the rear stagnation point due to the momentum injection from the SJ actuator. Notably, the CCS and CC2 configurations share similar flow field morphologies, while the CC1 configuration shows a greater reduction in turbulent fluctuations compared to the other controlled cases. This control law also achieves the largest drag reduction among all configurations studied reporting a 9.7% of drag reduction, compared to CCS and CC2, which exhibit similar reductions of 8.4% and 8.5%, respectively. Therefore, the optimization procedure has proven the capability of finding control policies able to outperform the traditional sinusoidal control widely studied in literature.

Correction to: Optimal and Maximin Procedures for Multiple Testing Problems

Correction to: Optimal and Maximin Procedures for Multiple Testing Problems

Leveraging Optimal Sparse Sensor Placement to Aggregate a Network of Digital Twins for Nuclear Subsystems

Nuclear power plants (NPPs) require continuous monitoring of various systems, structures, and components to ensure safe and efficient operations. The critical safety testing of new fuel compositions and the analysis of the effects of power transients on core temperatures can be achieved through modeling and simulations. They capture the dynamics of the physical phenomenon associated with failure modes and facilitate the creation of digital twins (DTs). Accurate reconstruction of fields of interest (e.g., temperature, pressure, velocity) from sensor measurements is crucial to establish a two-way communication between physical experiments and models. Sensor placement is highly constrained in most nuclear subsystems due to challenging operating conditions and inherent spatial limitations. This study develops optimized data-driven sensor placements for full-field reconstruction within reactor and steam generator subsystems of NPPs. Optimized constrained sensors reconstruct field of interest within a tri-structural isotropic (TRISO) fuel irradiation experiment, a lumped parameter model of a nuclear fuel test rod and a steam generator. The optimization procedure leverages reduced-order models of flow physics to provide a highly accurate full-field reconstruction of responses of interest, noise-induced uncertainty quantification and physically feasible sensor locations. Accurate sensor-based reconstructions establish a foundation for the digital twinning of subsystems, culminating in a comprehensive DT aggregate of an NPP.

A semi-analytical approach to wire arc additive manufacturing simulation for deposition sequence optimisation

ABSTRACT Categorised as a directed energy deposition process, wire arc additive manufacturing (WAAM) is a promising technology for fabricating large-scale structures and components across various industries. However, the quality of WAAM parts depends on the tool trajectory, as it affects the temperature distribution. Ideally, temperature-related issues, such as overheating, are minimised by choosing an optimal deposition sequence. Selecting the deposition sequence solely on intuition is an unreliable approach, particularly when dealing with complex and irregular parts. This paper introduces a simplified WAAM simulation (SWS) model, which was embedded into an optimisation procedure for determining the optimal deposition sequence of thin-walled WAAM parts. The proposed method is based on a semi-analytical function, which was calibrated and validated using thermal histories obtained from finite element simulations. The findings showed that the proposed SWS model can reproduce the average thermal history at predefined positions within a sample part with minimal computational effort. Moreover, the optimisation successfully identified optimal deposition sequences for producing the sample part regarding a given optimisation criterion. The outcomes of this research contribute to the tool trajectory planning process for WAAM parts. An optimal deposition sequence improves WAAM part quality, increasing the usability of WAAM technology in the production industry.

Leveraging Optimal Sparse Sensor Placement to Aggregate a Network of Digital Twins for Nuclear Subsystems

Nuclear power plants (NPPs) require continuous monitoring of various systems, structures, and components to ensure safe and efficient operations. The critical safety testing of new fuel compositions and the analysis of the effects of power transients on core temperatures can be achieved through modeling and simulations. They capture the dynamics of the physical phenomenon associated with failure modes and facilitate the creation of digital twins (DTs). Accurate reconstruction of fields of interest (e.g., temperature, pressure, velocity) from sensor measurements is crucial to establish a two-way communication between physical experiments and models. Sensor placement is highly constrained in most nuclear subsystems due to challenging operating conditions and inherent spatial limitations. This study develops optimized data-driven sensor placements for full-field reconstruction within reactor and steam generator subsystems of NPPs. Optimized constrained sensors reconstruct field of interest within a tri-structural isotropic (TRISO) fuel irradiation experiment, a lumped parameter model of a nuclear fuel test rod and a steam generator. The optimization procedure leverages reduced-order models of flow physics to provide a highly accurate full-field reconstruction of responses of interest, noise-induced uncertainty quantification and physically feasible sensor locations. Accurate sensor-based reconstructions establish a foundation for the digital twinning of subsystems, culminating in a comprehensive DT aggregate of an NPP.

A two-layer heuristic for patient sequencing in the operating room theatre considering multiple resource phases

In this paper, we study the surgery scheduling problem in the operating room theatre. The problem considers the sequencing of patients and calculation of their start times with splitting of surgeries into resource phases to facilitate the efficient use of different types of resources. We propose a dedicated two-layer heuristic to compose an operational patient and resource schedule. The first optimisation layer applies an evolutionary heuristic to devise patient schedules while considering the scheduling of the operating surgeons and rooms. This step employs a machine-learning mechanism predicting the feasibility of chromosomes, which improves the algorithm’s efficiency and effectiveness, and relies on novel local search operators to find high-quality solutions. The second layer devises the schedule of the other resources using a decomposition-based heuristic. Computational experiments are conducted to show the performance of the proposed two-layer heuristic and validate its design choices. We benchmark the proposed algorithm with other optimisation procedures and show the contribution of considering multiple resource phases for real-life decision-making.

Unveiling SAR target recognition networks: Adaptive Perturbation Interpretation for enhanced understanding

Deep neural networks (DNNs) have obtained remarkable achievements in various vision tasks. However, DNNs’ mechanism remains obscure, especially in synthetic aperture radar (SAR) target recognition. To clearly understand DNNs’ mechanism, some interpretation algorithms are proposed to provide a saliency map to visualize DNN’s decisions, which can be categorized into (1) propagation-based methods, (2) activation-based methods, and (3) perturbation-based methods. Unfortunately, each of them has some imperfection in producing saliency maps. The saliency maps produced by propagation-based methods are sensitive to SAR speckles. Activation-based methods have difficulty producing a saliency map where the highlighted region covers the entire feature. Perturbation-based methods tend to forfeit intricate details of the target in saliency map during optimization. To mitigate the above limitations, this paper proposed Adaptive Perturbation Interpretation (API), with two aims: (1) to improve the interpretability of SAR recognition networks and (2) to recover the fine-grained lost during the optimization procedures of perturbation-based methods. Specifically, we first introduce a Multi-information Perturbation Optimization Module (MPOM) to retain the target’s local characteristics while constraining saliency map’s sparsity by configuring regularization terms. Additionally, we further introduce a Feature Compensation Module (FCM) to recover the fine-grained feature lost during the optimization process in MPOM. Extensive experiments are implemented on SAR interpretation benchmark datasets, MSTAR and SARAIRcraft1.0, the results demonstrate the superiority of API to other state-of-the-art methods.

Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics.

We develop ∂-HylleraasMD (∂-HyMD), a fully end-to-end differentiable molecular dynamics software based on the Hamiltonian hybrid particle-field formalism, and use it to establish a protocol for automated optimization of force field parameters. ∂-HyMD is templated on the recently released HylleraaasMD software, while using the JAX autodiff framework as the main engine for the differentiable dynamics. ∂-HyMD exploits an embarrassingly parallel optimization algorithm by spawning independent simulations, whose trajectories are simultaneously processed by reverse mode automatic differentiation to calculate the gradient of the loss function, which is in turn used for iterative optimization of the force-field parameters. We show that parallel organization facilitates the convergence of the minimization procedure, avoiding the known memory and numerical stability issues of differentiable molecular dynamics approaches. We showcase the effectiveness of our implementation by producing a library of force field parameters for standard phospholipids, with either zwitterionic or anionic heads and with saturated or unsaturated tails. Compared to the all-atom reference, the force field obtained by ∂-HyMD yields better density profiles than the parameters derived from previously utilized gradient-free optimization procedures. Moreover, ∂-HyMD models can predict with good accuracy properties not included in the learning objective, such as lateral pressure profiles, and are transferable to other systems, including triglycerides.

Synthesis of HAp by Means of Sonoprecipitation Method.

Biomaterials, like hydroxyapatite (HAp), are the subject of many scientific investigations. Their specific application, however, is determined by the form and some characteristic features of the resulting material. Synthesis methods and optimization procedures leading to a product of predetermined characteristics are therefore of great interest. To broaden the existing knowledge, sonoprecipitation was investigated as a potential method for the production of nanosized HAp particles. The research was carried out in a static mixer (STM) immersed in the ultrasonic bath. The influence of operating conditions, e.g., ultrasonic power PUS (εUS), ultrasonic frequency (fUS), and unit mixing power (εmix), was investigated in terms of nucleation intensity, product quality, and characteristics (particle size distribution (PSD), mean size, shape, etc.). As a result, the optimal conditions for the HAp nanoparticles synthesis (mean size: d~150 nm; length: L1~250 nm; width: L2~80 nm) in the form of needles/whiskers/rods-similar to the shape of the HAp present in natural human bones, free from agglomerates, with negligible signs of particle destruction-were determined. The formation of HAp of smaller sizes (d ≤ 100 nm) and more compact shapes (L1~155 nm, L2~90 nm), useful in bone regeneration processes, was also discussed.

Optimization of steam parameters in double-pressure heat recovery steam generators

Double-pressure heat recovery steam generators are installed in medium-size combined cycle power plants. For a fixed total heating surface area of heat recovery steam generator, the net power output of the power plant depends on steam pressures and temperatures. This paper presents a method of determining the optimal set of steam parameters in double-pressure heat recovery steam generators without reheating and with reheating. The mathematical models of both heat recovery steam generators are presented, and the optimization procedure is described. For given exhaust gas temperature and total heating surface area, the optimal steam parameters of both heat recovery steam generators that maximize net power output can be found subjected to the constraint that the steam quality at turbine outlet is not less than 88 %. Simulation results show that reheating increases optimal net power output by 2.4 % and exhaust gas temperature by about 80 °C.

Optimal Transport Map With Prescribed Direction Indicator for Seismic Full‐Waveform Inversion

AbstractThe quadratic Wasserstein (W2) metric has been proposed as a promising misfit function to mitigate cycle‐skipping phenomena in full‐waveform inversion. Mathematically, we demonstrate that the smoothness of the W2‐based adjoint source is two orders of magnitude higher than that based on L2‐norm, which guarantees a larger convergence radius of related inverse problems. However, the oscillatory characteristics of seismic signals and subsequent operations of transforming them into probability densities would decrease the accuracy of the optimal transport map T(t) and exacerbate the nonconvexity of the misfit function. To tackle these challenges, we propose the concept of prescribed direction indicator, which indicates the properly matching direction from predictions to observations, in order to correct inaccurate T(t). 1D synthetic examples suggest that reasonable bijection can be constructed through the proposed method. Numerical experiments demonstrate that it works well during optimization procedures, including enlarging the convergence radius of the inverse problem, improving the computational efficiency and enhancing the reliability of inversion results.

A novel highly sensitive inner filter effect-based fluorescence immunoassay with quantum dots for bioanalysis of zolbetuximab, a monoclonal antibody used for immunotherapy of gastric and gastroesophageal junction adenocarcinoma

Zolbetuximab (ZOL) is a groundbreaking monoclonal antibody targeting CLDN 18.2, a cancer cell surface protein. It is a first-in-class therapy for gastric and gastroesophageal junction adenocarcinoma. However, there is currently any immunoassay available for bioanalysis of ZOL, hindering its pharmacokinetic studies, therapeutic monitoring, and safety profile refinement. To address this gap, this study presents the development and validation of a novel highly sensitive inner filter effect-based fluorescence immunoassay (IFE-FIA) with quantum dots (QDs) as a probe. This assay enables the quantitative determination of ZOL in plasma samples. The assay involved non-competitive capturing of ZOL from the samples using a specific antigen (CLDN 18.2 protein) immobilized on assay plate microwells. A horseradish peroxidase (HRP)-labelled anti-human IgG was used to measure the immune complex. The assay's detection system relies on the formation of a light-absorbing colored product through an HRP-catalyzed oxidative reaction with the substrate 3,3′,5,5′-tetramethylbenzidine. This light absorption efficiently quenched the fluorescence of QDs via the IFE. The measured fluorescence signals corresponded to the concentrations of ZOL in the samples. The conditions of the IFE-FIA and its detection system were refined, and the optimum procedures were established. Following the guidelines of immunoassay validation for bioanalysis, the assay was validated, and all the validation criteria were acceptable. The assay demonstrates high sensitivity, accurately quantifying ZOL at concentrations as low as 10 ng/mL in plasma samples, with acceptable precision. Importantly, it avoids interferences from endogenous substances and plasma matrix. The recoveries in spiked human plasma ranged from 96.8 % to 104.5 %, with relative standard deviations of 4.1 %–6.5 %. The proposed IFE-FIA represents a valuable tool for quantifying ZOL in clinical settings, enabling assessment of its pharmacokinetics, therapeutic drug monitoring, and safety profile refinement.

HyperMAML: Few-shot adaptation of deep models with hypernetworks

Few-Shot learning aims to train models which can adapt to previously unseen tasks based on small amounts of data. One of the leading Few-Shot learning approaches is Model-Agnostic-Meta-Learning (MAML), which learns the general weights of the meta-model that are later adapted to downstream tasks. However, MAML’s main limitation lies in that the update procedure is realized by gradient-based optimization, which cannot always modify weights to the essential level in one or even a few iterations. Moreover, using many gradient steps results in time-consuming optimization and inference procedures. In this paper, we propose HyperMAML, a novel generalization of MAML, where the update procedure is also a part of the model. Namely, we replace gradient descent with a trainable Hypernetwork which updates the weights. Consequently, the model can generate significant updates whose range is not limited to a fixed number of gradient steps. Experiments show that HyperMAML outperforms MAML in most cases and performs comparably to state-of-the-art techniques in standard Few-Shot learning benchmarks.