Additional Function Research Articles

A novel role of the fission yeast sulfiredoxin Srx1 in heme acquisition.

The transporter Str3 promotes heme import in Schizosaccharomyces pombe cells that lack the heme receptor Shu1 and are deficient in heme biosynthesis. Under microaerobic conditions, the peroxiredoxin Tpx1 acts as a heme scavenger within the Str3-dependent pathway. Here, we show that Srx1, a sulfiredoxin known to interact with Tpx1, is essential for optimal growth in the presence of hemin. The expression of Srx1 is induced in response to low iron and repressed under iron repletion. Coimmunoprecipitation and bimolecular fluorescence complementation experiments show that Srx1 interacts with Str3. Although the interaction between Srx1 and Str3 is weakened, it is still observed in tpx1Δ mutant cells or when Str3 is coexpressed with a mutant form of Srx1 (mutD) that cannot bind Tpx1. Further analysis by absorbance spectroscopy and hemin-agarose pull-down assays confirms the binding of Srx1 to hemin, with an equilibrium constant value of 2.56 μM. To validate the Srx1-hemin association, we utilize a Srx1 mutant (mutH) that fails to interact with hemin. Notably, when Srx1 binds to hemin, it partially shields hemin from degradation caused by hydrogen peroxide. Collectively, these findings elucidate an additional function of the sulfiredoxin Srx1, beyond its conventional role in oxidative stress defense.

Impact of growth conditions on the nitrogen, protein, colour and amino acid profiles of the cultured macroalga, Palmaria palmata

The impact of growth conditions on the nitrogen, protein, colour and amino acid profiles of Palmaria palmata (from 2 locations) were investigated. Laboratory cultures of P. palmata were grown over a period of 18 days with and without nutrient addition, i.e., with culture medium (F/2) and urea (0.05 or 0.10 g L−1). The total nitrogen (TN) content of cultured P. palmata originating from longlines harvested from Cork increased with the addition of culture medium (F/2) and urea (0.05 g L−1) in comparison to the initial sample (4.15 ± 0.05 vs 1.73 ± 0.03%) while biomass from Galway increased with addition of culture medium (F/2) and urea (0.10 g L−1) in comparison to the initial sample (3.72 ± 0.05 vs 2.43 ± 0.06%). The protein nitrogen (PN) and non-protein nitrogen (NPN) of cultured P. palmata originating from Galway also increased when grown with the addition of nutrients. Nutrient enrichment during growth influenced the colour characteristics (lightness L*, redness a*, yellowness b* and colour difference ΔE) and the electrophoretic protein profile. Correlations between TN and ΔE (r = 0.945) for Cork originated sample, PN and ΔE (r =-0.944) and NPN and ΔE (r = 0.734) for Galway originated sample were observed. In-gel digestion followed by UPLC-MS/MS demonstrated that RuBiSCo, phycoerythrin and allophycocyanin expression in P. palmata was enhanced with nutrient addition. Amino acid profile and score analysis showed variation as a function of nutrient addition. Overall, nutrient inclusion in the growth medium provides a strategy for the enhanced production of high-quality protein by P. palmata.

Real-world implementation and evaluation of a Model Predictive Control framework in an office space

In this work, Model Predictive Control (MPC) is experimentally implemented on a Heating, Ventilation and Air Conditioning (HVAC) system of a large-scale office space. As controller model, a physics-based Modelica building model is calibrated based on historic data pursuing an iterative, nonlinear optimization approach. For the calibration period with a horizon of seven weeks, the calibrated model exhibits a high model accuracy with a Root Mean Square Error (RMSE) of 0.49 °C between the measured and estimated room temperature. The MPC toolchain includes modules for state estimation and forecasts of disturbances quantities (such as outdoor temperature, solar radiation including calculation of the direct and diffuse fraction, supply temperatures and occupancy). The MPC execution comprises the operation of heating based on radiators and floor heating (via regulation of valve openings) as well as shading of three Venetian blind systems (via regulation of vertical position and slat inclination angle). The experiment is conducted during a heating period with a duration of three weeks from October 21 to November 11, 2022. The heating actuators are controlled considering their typical dynamics and take into account the night setback during unoccupied office periods. User acceptance of the automated shading control is included through additional cost function terms for the shading operation. The field test reveals the predictive control capabilities of the proposed MPC toolchain in a real-life scenario, demonstrating energy-efficient building operation and total average discomfort of 0.53 Kh/d. The MPC formulation provides flexibility regarding adjustability of the control towards energy efficiency, thermal comfort, daylight transmission and non-oscillating shading control. Finally, the disturbance forecast accuracies for outdoor temperature and the solar radiation quantities are evaluated and the MPC control performance is compared against a conventional control approach.

Double voltage vector model predictive control for grid-connected cascade H-bridge multilevel inverter with fixed switching frequency

Model predictive control (MPC) is an outstanding control method which can achieve superior dynamic response, nonlinear control and multi-objective collaborative control. However, because of unfixed switching frequency, the harmonic spectrum of the output current is dispersed, which would make it difficult to design the output filter. In this work, a double voltage vector model predictive control (DVV-MPC) algorithm for grid-connected cascade H-bridge (CHB) multilevel inverter is presented. The algorithm not only has the advantages of MPC but also produces fixed the switching frequency of the inverter. It is realized by a modulation stage with two adjacent voltage vectors in one switching cycle. And the duty cycle of the associated voltage level is obtained by minimizing an additional cost function. This method is suitable for the cascades of n cells inverter. For reducing the computation burden and eliminating the computation delay, adjacent regions prediction method and time-delay compensation method are applied to the inverter. Finally, in this paper, the proposed strategy is applied to a single-phase grid-connected 7-level CHB inverter. Simulation and experiment are executed to show the advantages of the proposed control algorithm in dynamic state and steady state.

Limit Theorems for Additive Functionals of Continuous-Time Lattice Random Walks in a Stationary Random Environment

For a continuous-time lattice random walk $X^\Lambda=\set{X^\Lambda_t,t\ge 0}$ in a random environment $\Lambda$, we study the asymptotic behavior, as $t\rightarrow \infty$, of the normalized additive functional $c_t\int_0^{t} f(X^\Lambda_s)ds$, $t\ge 0$. We establish a limit theorem for it, which is similar to that in the non-lattice case, under less restrictive assumptions.

Iterative Backpropagation Method for Efficient Gradient Estimation in Bilevel Network Equilibrium Optimization Problems

Network optimization or network design with an embedded traffic assignment (TA) to model user equilibrium principle, sometimes expressed as bilevel problems or mathematical programs with equilibrium constraints (MPEC), is at the heart of transportation planning and operations. For applications to large-scale multimodal networks with high dimensional decision variables, the problem is nontrivial, to say the least. General-purpose algorithms and problem-specific bilevel formulations have been proposed in the past to solve small problems for demonstration purposes. Research gap, however, exists in developing efficient solution methods for large-scale problems in both static and dynamic contexts. This paper proposes an efficient gradient estimation method called Iterative Backpropagation (IB) for network optimization problems with an embedded static TA model. IB exploits the iterative structure of the TA solution procedure and simultaneously calculates the gradients while the TA process converges. IB does not require any additional function evaluation and consequently scales very well with higher dimensions. We apply the proposed approach to origin-destination (OD) estimation, an MPEC problem, of the Hong Kong multimodal network with 49,806 decision variables, 8,797 nodes, 18,207 links, 2,684 transit routes, and 165,509 OD pairs. The calibrated model performs well in matching the link counts. Specifically, the IB-gradient based optimization technique reduces the link volume squared error by 98%, mean absolute percentage error (MAPE) from 95.29% to 21.23%, and the average GEH statistics from 24.18 to 6.09 compared with the noncalibrated case. The framework, even though applied to OD estimation in this paper, is applicable to a wide variety of optimization problems with an embedded TA model, opening up an efficient way to solve large-scale MPEC or bilevel problems. Funding: The study is supported by IVADO Postdoctoral Fellowship scheme 2021, HSBC 150th Anniversary Charity Programme HKBF17RG01, National Science Foundation of China (71890970, 71890974), General Research Fund (16212819, 16207920) of the HKSAR Government, and the Hong Kong PhD Fellowship.

Advanced multi-wall carbon nanotube-optimized surfactant-polymer flooding for enhanced oil recovery

In this captivating study, we explored the fascinating potential of carbon nanotube-based nanofluids in improving oil recovery efficiency and transforming interfacial tension. To accomplish this, we acquired high-purity multi-walled carbon nanotubes from Plasma Chem Gmbh, ensuring a purity level exceeding 95 %, which underwent additional functionalization and experimentation to explore their impact. The functionalization of multi-wall carbon nanotubes with ammonium hydroxide and hydrogen peroxide generated diverse surface groups, reduced van der Waals interactions, and increased carbon nanotube dispersibility through enhanced interactions with polymers or solvents. The modified multi-wall carbon nanotubes were characterized using Raman analysis, HRTEM, and zeta potential. Varying concentrations of altered multi-wall carbon nanotube-based fluids were prepared to study their impact on surface tension using a Surface tensiometer. Adsorption studies were conducted to minimize surfactant loss. Contact angle experiments using a Goniometer helped alter the rock surface's wetting characteristics. Finally, the core flooding experiment using two pore volumes of the nanofluid with the optimal concentration of mMWCNT with SDS and 1000 ppm of polymer calculates the oil recovery factor. The study's findings revealed that the modified multi-wall carbon nanotubes significantly reduced surface and interfacial tension by approximately 59 % and 56 %, respectively, at the optimum concentration. Moreover, the nanotubes successfully minimized surfactant loss by about 49.3 %. The contact angle experiments demonstrated that the modified nanotubes transformed the rock surface from oil-wet to water-wet, as evidenced by a substantial reduction of approximately 54 % in the contact angle value. In conclusion, the core flooding experiment, employing a chemical slug composed of SDS at CMC, modified multi-wall carbon nanotubes, and industrial-grade polymer, yielded a remarkable oil recovery factor of approximately 70 % of the original oil in place.

Aire drives steroid hormone biosynthesis by medullary thymic epithelial cells.

Thymic epithelial cells (TECs) produce glucocorticoids, which antagonize negative selection of autoreactive thymocytes and promote a competent T cell antigen-specific repertoire. To characterize their source, we generated a knock-in reporter mouse in which endogenous Cyp11b1, the final enzyme in de novo production of active glucocorticoids, was fluorescently tagged with mScarlet. Here, we find that Cyp11b1 is expressed in medullary TECs (mTECs) but not cortical TECs or other cells in the thymus. A distinct characteristic of mTECs is the presence of Aire, a transcription factor that drives expression of tissue-restricted antigens (TRAs) important for establishing immune tolerance. Cyp11b1 expression was highest in Aire+ mTECs, lower in post-Aire mTECs, and absent in mTECs of Aire-deficient mice. Transcriptomic analyses found that multiple enzymatic biosynthetic pathways are expressed specifically in mTECs and are also Aire dependent. In particular, we found that the thymus expresses messenger RNA for enzymes that catalyze production of many bioactive steroids and that glucocorticoids and sex steroids were secreted by cultured thymi. Expression of the transcripts for these genes and production of their final steroid products were markedly reduced in the absence of Aire. Thus, in addition to its well-established role in inducing TRAs that promote negative selection, Aire has an additional and contrary function of inducing glucocorticoids that antagonize negative selection, which together may expand and enhance the TCR repertoire. Furthermore, because Aire drives expression of multiple enzymes responsible for production of other non-gene-encoded bioactive molecules, it might have yet other roles in thymus development and function.

Functionalization of in vivo tissue-engineered living biotubes enhance patency and endothelization without the requirement of systemic anticoagulant administration.

Vascular regeneration and patency maintenance, without anticoagulant administration, represent key developmental trends to enhance small-diameter vascular grafts (SDVG) performance. In vivo engineered autologous biotubes have emerged as SDVG candidates with pro-regenerative properties. However, mechanical failure coupled with thrombus formation hinder translational prospects of biotubes as SDVGs. Previously fabricated poly(ε-caprolactone) skeleton-reinforced biotubes (PBs) circumvented mechanical issues and achieved vascular regeneration, but orally administered anticoagulants were required. Here, highly efficient and biocompatible functional modifications were introduced to living cells on PB lumens. The 1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine-N-methoxy (DMPE)-PEG-conjugated anti-coagulant bivalirudin (DPB) and DMPE-PEG-conjugated endothelial progenitor cell (EPC)-binding TPS-peptide (DPT) modifications possessed functionality conducive to promoting vascular graft patency. Co-modification of DPB and DPT swiftly attained luminal saturation without influencing cell viability. DPB repellent of non-specific proteins, DPB inhibition of thrombus formation, and DPB protection against functional masking of DPT's EPC-capture by blood components, which promoted patency and rapid endothelialization in rat and canine artery implantation models without anticoagulant administration. This strategy offers a safe, facile, and fast technical approach to convey additional functionalization to living cells within tissue-engineered constructs.

In-depth analysis of the artistic expression of paper-cutting elements in interior space design

Abstract To deeply analyze the performance of paper-cutting elements in interior space design, this paper explores the artistic expression of paper-cutting elements. The BIM model is constructed based on the TVL algorithm, and a noise-reducing style migration method with an additional total variable loss function is used to fuse the texture features of a specific paper-cutting style so that the paper-cutting texture is generated in the target object area more intensively. The algorithm is supplemented with a U-shaped network structure to obtain the mask map of the target object, modify and expand the ScanNet training data set, and input a content map and a style map to generate the stylized paper cutout image automatically. The results show that compared with other elements, the stability of paper-cut elements reaches 96.31%, the image smoothness reaches 89.34%, and the retention of significant regions reaches 93.26%. In modern design, the peak signal-noise of paper-cut elements improved by 13.418d on average, the structural similarity also improved by 0.34807, and the recognition rate of paper-cut elements in the furnishing category reached 95%. Applying paper-cut art in interior space design can give interior furnishing design better visual effects, provide inspiration and ideas for interior space design innovation, and contribute to improving aesthetics.

Transitional Functions: The Emergence of the Anticipatory Transition in Nineteenth-Century Sonatas

In sonata form, the transition module has specific primary functions and labels any other uncommon ones as deformations or, rather, idiosyncrasies. Considering the difference in compositional styles and techniques used between the eighteenth and nineteenth centuries, the latter undergoes a trend explored by some nineteenth-century composers: a development in its transition module, which ostensibly confining it to an idiosyncratic feature or a lower-level default of the transition (Hepokoski and Darcy, 2011). I argue that composers utilized this technique in transitions to foreshadow themes in subsequent modules, yielding an additional function of the transition. This paper examines nineteenth-century sonatas, providing an analytic overview of my findings and introducing a concept I call the “Anticipatory Transition,” offering an additional function to the transition–—distinct from other concepts by previous scholarship. Building off the work of Schenker's concept of “linkage technique” and its contribution to the development of new themes in subsequent thematic modules (Smith, 2007), as well as Schmalfeldt's adoption of Dahlhaus’s’ “processual” ideas (Schmalfeldt, 2011; Carro, 2020), the anticipatory transition is employed through two avenues: first, a literal “copy-and-paste,” and second, a “spun-out” transition. I analyze four case studies that utilize anticipatory transitions in nineteenth-century sonatas. It is important to examine the function of transitions more deeply and explore other possible outcomes because the material within can inform other thematic modules and contribute to the cohesiveness of the work. My analyses demonstrate that anticipatory transitions—profound than mere deformations and limitations—offer a deeper sense of unity and a broader perspective within sonata form.

Indigo reduction by using microorganism: Comparison of reducing power between Dietzia sp. KDB1 and yeast (Saccharomyces cerevisiae)

AbstractThis study aims to develop microbial indigo reduction by comparing the reducing power between Dietzia sp. KDB1 and yeast (Saccharomyces cerevisiae) at some reduction conditions. Oxidation–reduction potential (ORP), colour strength (K/S), and fluorescence intensity (FI) were applied to evaluate the reduction level as a function of microorganism concentration, pH, and glucose addition at 30°C. For both of the microbial systems, ORP decreased further −20 mV and K/S increased more than two by adding glucose. The reduction level became higher slightly by adding glucose, which played a role of metabolite in both of the reduction systems. Overall, reduction level of the yeast system was about a half of KDB1 system at the same reduction condition of 45 mL volume, 15 g/L natural indigo, 6 g/L reducing agents, pH 10.0, 30°C and glucose added.

Generating functions and large-charge expansion of integrated correlators in \U0001d4a9 = 4 supersymmetric Yang-Mills theory

We recently proved that, when integrating out the spacetime dependence with a certain integration measure, four-point correlators leftlangle {mathcal{O}}_2{mathcal{O}}_2{mathcal{O}}_p^{(i)}{mathcal{O}}_p^{(i)}rightrangle in \U0001d4a9 = 4 supersymmetric Yang-Mills theory with SU(N) gauge group are governed by a universal Laplace-difference equation. Here {mathcal{O}}_p^{(i)} is a superconformal primary with charge p and degeneracy i. These physical observables, called integrated correlators, are modular-invariant functions of Yang-Mills coupling τ. The Laplace-difference equation is a recursion relation that relates integrated correlators of operators with different charges. In this paper, we introduce the generating functions for these integrated correlators that sum over the charge. By utilising the Laplace-difference equation, we determine the generating functions for all the integrated correlators, in terms of the initial data of the recursion relation. We show that the transseries of the integrated correlators in the large-p (i.e. large-charge) expansion for a fixed N consists of three parts: 1) is independent of τ, which behaves as a power series in 1/p, plus an additional log(p) term when i = j; 2) is a power series in 1/p, with coefficients given by a sum of the non-holomorphic Eisenstein series; 3) is a sum of exponentially decayed modular functions in the large-p limit, which can be viewed as a generalisation of the non-holomorphic Eisenstein series. When i = j, there is an additional modular function of τ that is independent of p and is fully determined in terms of the integrated correlator with p = 2. The Laplace-difference equation was obtained with a reorganisation of the operators that means the large-charge limit is taken in a particular way here. From these SL(2, ℤ)-invariant results, we also determine the generalised ’t Hooft genus expansion and the associated large-p non-perturbative corrections of the integrated correlators by introducing λ = p {g}_{YM}^2 . The generating functions have subtle differences between even and odd N, which have important consequences in the large-charge expansion and resurgence analysis. We also consider the generating functions of the integrated correlators for some fixed p by summing over N, and we study their large-N behaviour, as well as comment on the similarities and differences between the large-p expansion and the large-N expansion.

SARS-CoV-2 Virus-like Particles with Plasmonic Au Cores and S1-Spike Protein Coronas.

The COVID-19 pandemic has stimulated the scientific world to intensify virus-related studies aimed at the development of quick and safe ways of detecting viruses in the human body, studying the virus-antibody and virus-cell interactions, and designing nanocarriers for targeted antiviral therapies. However, research on dangerous viruses can only be performed in certified laboratories that follow strict safety procedures. Thus, developing deactivated virus constructs or safe-to-use virus-like objects, which imitate real viruses and allow performing virus-related studies in any research laboratory, constitutes an important scientific challenge. Such species, called virus-like particles (VLPs), contain instead of capsids with viral DNA/RNA empty or synthetic cores with real virus proteins attached to them. We have developed a method for the preparation of VLPs imitating the virus responsible for the COVID-19 disease: the SARS-CoV-2. The particles have Au cores surrounded by "coronas" of S1 domains of the virus's spike protein. Importantly, they are safe to use and specifically interact with SARS-CoV-2 antibodies. Moreover, Au cores exhibit localized surface plasmon resonance (LSPR), which makes the synthesized VLPs suitable for biosensing applications. During the studies, the effect allowed us to visualize the interaction between the VLPs and the antibodies and identify the characteristic vibrational signals. What is more, additional functionalization of the particles with a fluorescent label revealed their potential in studying specific virus-related interactions. Notably, the universal character of the developed synthesis method makes it potentially applicable for fabricating VLPs imitating other life-threatening viruses.

Evaluation and Application of Surrounding Rock Stability Based on an Improved Fuzzy Comprehensive Evaluation Method

Ensuring the stability of surrounding rock is crucial for the safety of underground engineering projects. In this study, an improved fuzzy comprehensive evaluation method is proposed to accurately predict the stability of surrounding rock. Five key factors, namely, rock quality designation, uniaxial compressive strength, integrality coefficient of the rock mass, strength coefficient of the structural surface, and groundwater seepage, are selected as evaluation indicators, and a five-grade evaluation system is established. An improved analytic hierarchy process (IAHP) is proposed to enhance the accuracy of the evaluation. Using interval numbers rather than real numbers in constructing an interval judgment matrix can better account for the subjective fuzziness and uncertainty of expert judgment. Subjective and objective weights are obtained through IAHP and coefficient of variation, and the comprehensive weight is calculated on the basis of game theory principles. In addition, trapezoidal and triangular membership functions are employed to determine the membership degree, and an improved fuzzy comprehensive evaluation model is constructed. The model is then used to determine the stability of the surrounding rock based on the improved criterion. It is applied to six samples from an actual underground project in China to validate its effectiveness. Results show that the proposed model accurately and effectively predicts the stability of surrounding rock, which aligns with the findings from field investigations. The proposed method provides a valuable reference for evaluating surrounding rock stability and controlling construction risks.

An automatic quadrature method for semi-infinite integrals of exponentially decaying functions and its Matlab code

For the integral of a function f over a semi-infinite interval [0,∞) with f(x) decaying exponentially as x→∞, an automatic quadrature scheme is constructed and implemented in Matlab. Three limit formulae of Clenshaw–Curtis-type rules in our recent work (Numerical Algorithms, vol.90, 2022, pp.3–30) are exploited that converge fast for analytic functions. Each formula is expressed by a weighted infinite sum of function values at nodes and so its truncated finite sum is used. The best of the three formulae in accuracy is used as an approximation whose error is estimated with other two formulae and no additional function evaluations, since nodes of the other formulae are included in those of the best one. Numerical results indicate that our automatic scheme is better in performance than the QUADPACK routine for several types of test integrals. Particularly, it copes quite well with oscillatory integrals including the sinusoidal function and/or the Bessel function that are known to be difficult to approximate.

Multi-objective optimization and evaluation of a water-saving scenario to produce hydrogen and freshwater in an innovative biomass-assisted plant

With global communities being susceptible to adverse impacts of greenhouse gases, hydrogen has substantially drawn the attention of scientists to investigate it as a promising fuel. Hence, the present investigation aims to conduct an exhaustive thermodynamic analysis of an innovative biomass-assisted system driven by the Brayton cycle through multi-objective optimization to detect the different optimal scenarios for generating and storing hydrogen. This system contains a Brayton cycle as a prime mover, steam gasification of biomass and three-stage compressors to generate and store hydrogen, and multi-effect distillation to produce freshwater. The ultimate goal of this study is to use the generated steam through the produced freshwater as a steam agent of the gasifier to conclude a water-efficient scenario in producing hydrogen. To discern the impact of the chosen design parameters on the performance of the system, an exhaustive parametric study has been carried out. Afterward, multi-objective optimization via the non-dominated sorting genetic algorithm has been undertaken to find the optimal values by regarding the financial constraints. Tri-objective optimization results demonstrated that optimum values of total cost rate, exergy efficiency, and hydrogen production rate are 1.43 $/s, 47.56%. , and 0.12 kg/s. Besides, by regarding the consumption of water as an additional objective function, the results demonstrate that by reducing consumed water up to 47.5%, the generated hydrogen only decreases by 8%.

Appointed-Time Dynamic Average Consensus: A Holistic Planning and Multi-Step Control Framework

Most existing solutions to the dynamic average consensus (DAC) problem assume access to the full dynamic of reference signals or their derivatives. Such constraints are more stringent and impose restrictions on use. Hence, we revisit the DAC problem in this paper and present its solution by developing a holistic planning and multi-step control (HPMSC) framework. Here, the HPMSC allows the agents to achieve DAC in an appointed settling time without involving any other parameters, which is called appointed-time dynamic average consensus (AT-DAC). Under the HPMSC framework, a sampling-data-based AT-DAC controller is designed, which guarantees that agents can track the average value of dynamic reference signals at a pre-appointed time. Compared with the existing results on DAC, a nice highlight of this paper is the implementation of zero-error DAC with weaker conditions imposed on the reference signals, without the need to know the upper bounds on their derivatives and without additional common function conditions. Finally, the performance validation of theoretical results is demonstrated by a numerical simulation.

Novel robust approach–based dynamic global terminal synergetic tracking control for uncertain second-order systems: Wind turbine system and power converter applications

This article provides a systematic approach for synthesizing a novel robust synergetic control mechanism. This controller employs a dynamic global terminal synergetic surface with additional sinusoidal function. The overall macrovariable gives the system a rapid approaching rate, maintains the finite time stability, and provides a chattering-free control law. The stability analysis is validated for any uncertain second-order system via indirect approach of Lyapunov theorems. Furthermore, a hyperbolic tangent nonlinear disturbance observer is designed in order to estimate any lumped disturbance. The feasibility and the effectiveness of the proposed composite control law is tested and evaluated through simulations for simple model, wind turbine system and DC-DC buck converter. Finally, the simulation results show that the presented dynamic global terminal synergetic control–based hyperbolic tangent nonlinear disturbance observer can effectively reject the disturbances, enhance the robustness, and guarantee the tracking accuracy and rapidity.

IMPLEMENTATION OF TECHNOLOGY FOR IMPROVING THE QUALITY OF SEGMENTATION OF MEDICAL IMAGES BY SOFTWARE ADJUSTMENT OF CONVOLUTIONAL NEURAL NETWORK HYPERPARAMETERS

Background. The scientists have built effective convolutional neural networks in their research, but the issue of optimal setting of the hyperparameters of these neural networks remains insufficiently researched. Hyperparameters affect model selection. They have the greatest impact on the number and size of hidden layers. Effective selection of hyperparameters improves the speed and quality of the learning algorithm. It is also necessary to pay attention to the fact that the hyperparameters of the convolutional neural network are interconnected. That is why it is very difficult to manually select the effective values of hyperparameters, which will ensure the maximum efficiency of the convolutional neural network. It is necessary to automate the process of selecting hyperparameters, to implement a software mechanism for setting hyperparameters of a convolutional neural network. The author has successfully implemented the specified task.
 Objective. The purpose of the paper is to develop a technology for selecting hyperparameters of a convolutional neural network to improve the quality of segmentation of medical images..
 Methods. Selection of a convolutional neural network model that will enable effective segmentation of medical images, modification of the Keras Tuner library by developing an additional function, use of convolutional neural network optimization methods and hyperparameters, compilation of the constructed model and its settings, selection of the model with the best hyperparameters.
 Results. A comparative analysis of U-Net and FCN-32 convolutional neural networks was carried out. U-Net was selected as the tuning network due to its higher quality and accuracy of image segmentation. Modified the Keras Tuner library by developing an additional function for tuning hyperparameters. To optimize hyperparameters, the use of the Hyperband method is justified. The optimal number of epochs was selected - 20. In the process of setting hyperparameters, the best model with an accuracy index of 0.9665 was selected. The hyperparameter start_neurons is set to 80, the hyperparameter net_depth is 5, the activation function is Mish, the hyperparameter dropout is set to False, and the hyperparameter bn_after_act is set to True.
 Conclusions. The convolutional neural network U-Net, which is configured with the specified parameters, has a significant potential in solving the problems of segmentation of medical images. The prospect of further research is the use of a modified network for the diagnosis of symptoms of the coronavirus disease COVID-19, pneumonia, cancer and other complex medical diseases.