Experimental Data Research Articles

Accurate mathematical modeling of electronic load harmonics using machine learning software

This paper presents accurate mathematical models for several power system harmonics problems using an efficient machine learning tool, Eureqa. Three previously published research papers that included actual field measurements have been chosen to compare and assess the harmonics mathematical models presented in this paper. Following that, mathematical models of the output parameters as functions of the input parameters have been developed based on these data. For all these different research works and experiments, a total of 17 mathematical models have been built using basic curve fitting tools. Most of these proposed models couldn’t fit the experimental data appropriately. Considerable error is observed for several models. In this paper, all these 17 problems are formulated using Eureqa software utilizing the same data presented in the discussed research works. Very accurate fitting capability to the experimental data is achieved using Eureqa, where almost near zero error is reached for the majority of the proposed models. The maximum mean absolute error (MAPE) among all developed models was 0.13% as opposed to 24% for the models presented in the literature.

Arousal as a universal embedding for spatiotemporal brain dynamics.

Neural activity in awake organisms shows widespread and spatiotemporally diverse correlations with behavioral and physiological measurements. We propose that this covariation reflects in part the dynamics of a unified, multidimensional arousal-related process that regulates brain-wide physiology on the timescale of seconds. By framing this interpretation within dynamical systems theory, we arrive at a surprising prediction: that a single, scalar measurement of arousal (e.g., pupil diameter) should suffice to reconstruct the continuous evolution of multidimensional, spatiotemporal measurements of large-scale brain physiology. To test this hypothesis, we perform multimodal, cortex-wide optical imaging and behavioral monitoring in awake mice. We demonstrate that spatiotemporal measurements of neuronal calcium, metabolism, and brain blood-oxygen can be accurately and parsimoniously modeled from a low-dimensional state-space reconstructed from the time history of pupil diameter. Extending this framework to behavioral and electrophysiological measurements from the Allen Brain Observatory, we demonstrate the ability to integrate diverse experimental data into a unified generative model via mappings from an intrinsic arousal manifold. Our results support the hypothesis that spontaneous, spatially structured fluctuations in brain-wide physiology-widely interpreted to reflect regionally-specific neural communication-are in large part reflections of an arousal-related process. This enriched view of arousal dynamics has broad implications for interpreting observations of brain, body, and behavior as measured across modalities, contexts, and scales.

Catalytic growth in a shared enzyme pool ensures robust control of centrosome size.

Accurate regulation of centrosome size is essential for ensuring error-free cell division, and dysregulation of centrosome size has been linked to various pathologies, including developmental defects and cancer. While a universally accepted model for centrosome size regulation is lacking, prior theoretical and experimental works suggest a centrosome growth model involving autocatalytic assembly of the pericentriolar material. Here we show that the autocatalytic assembly model fails to explain the attainment of equal centrosome sizes, which is crucial for error-free cell division. Incorporating latest experimental findings into the molecular mechanisms governing centrosome assembly, we introduce a new quantitative theory for centrosome growth involving catalytic assembly within a shared pool of enzymes. Our model successfully achieves robust size equality between maturing centrosome pairs, mirroring cooperative growth dynamics observed in experiments. To validate our theoretical predictions, we compare them with available experimental data and demonstrate the broad applicability of the catalytic growth model across different organisms, which exhibit distinct growth dynamics and size scaling characteristics.

Research on optimization of electrical parameters of EDM based on a neural network algorithm

An electrical Discharge Machine (EDM) is an effective spark machine that passes the sparks to get the desired shape by performing metal fabrication. In the EDM process, materials are removed between the two electrodes by transferring the workpiece’s high electric voltage and dielectric liquid. The voltage between the two electrodes is gradually increased to break down the dielectrics and remove the materials from the surface. EDM utilizes several parameters to regularize the material removal rate during this process. EDM changes its parameter for every experiment to minimize the Average Tool Wear Rate (ATWR). The existing techniques utilize a neural model to optimize the EDM parameter. However, the traditional approaches fail to perform the fine-tuning process that affects the Material Removal Rate (MRR) and ATWR. Therefore, meta-heuristics optimization techniques are incorporated with the neural model to enhance the EDM parameter optimization process. In this work, the EDM experimental data have been collected and processed by the learning process to create the training pattern. Then, the test data is investigated using the Backpropagation Neural Model (BPM) to propagate the neural parameters. The BPM model is integrated with the Butterfly Optimization Algorithm (BOA) to select the search space’s global parameters. This analysis clearly shows an 8.93% maximum prediction rate, 0.023 minimum prediction rate and 2.83% mean prediction rate while investigating the different testing patterns compared to other methods.

Energy and exergy performances of low-density polyethylene plastic particles assisted by microwave heating.

Plastic waste can exist naturally for hundreds of thousands of years and harm humans, animals, and the environment. In this study, the energy and exergy performances (absorbed energy, energy efficiency, absorbed exergy, and exergy efficiency) of LDPE (low-density polyethylene) plastic particles assisted by microwave heating based on the experimental data as affected by microwave power, feeding load, and chamber volume were evaluated and analyzed. The results showed that as the microwave power raised from 500 to 900 W, the feeding load changed from 10 to 30g, and the chamber volume decreased from 200 to 100ml, (a) the absorbed energy at the heating time of 60min increased from 19.73kJ, 5.84kJ, and 22.71kJ to 37.69kJ; (b) the energy efficiency for the whole heating process increased from 1.10%, 0.32%, and 1.26% to 2.09%; (c) the absorbed exergy at the heating time of 60min increased from 0.308, 0.091, and 0.091 to 0.724kJ; and (d) the exergy efficiency for the whole heating process increased from 0.017, 0.005, and 0.023 to 0.040%, respectively.

Investigation of the Phase Mechanism Behaviors of Fe-Cr-Ni alloy by Molecular Dynamics Simulation

In the presented study, Fe-Cr-Ni ternary alloy system, which is classified as medium entropy alloys, was modelled using molecular dynamics (MD) simulation method. Model system was built at specific concentration ratios in accordance with the crystal lattice structures in the phase diagrams. The potential energy function based on the Grujicic-Zhou (GZ) type embedded atom method (EAM) was chosen as the potential function suitable for the system. The phase transformation mechanisms of the model system were investigated by applying heating-cooling processes on the most stable structures. In these processes, thermodynamic parameters such as temperature, volume, potential energy and density were calculated. In addition, the phase transformation mechanism and structural properties were analysed using radial distribution functions (RDF). Three-dimensional pictures of MD cells and the number of crystal structures were obtained using the visualization and analysis software via the atomic positions obtained during the transformations. In all these processes, the results obtained by the MD calculation method were interpreted and compared with the experimental data.

Graphene Oxide Decorated with Nickel Cobaltite Nanoparticles as an Adsorbent for Cationic Methyl Green Dye: Kinetic, Isotherm, and Thermodynamic Studies

‎تم في هذه الدراسة ، تزيين ‏رقائق أكسيد الجرافين (‏GO‏) بجسيمات ‏كوبلتيت النيكل النانوية ‏NiCo2O4‎‏(‏NC‏) عن طريق الترسيب في ‏الموقع ، وتم استخدام المتراكب ‏المحضر (‏NC: GO‏) كسطح ماز لإزالة ‏صبغة الميثيل الخضراء ( ‏MG‏) من ‏المحاليل المائية. تم التحقق من ‏التغطية الناجحة لأوكسيد الجرافين ‏بجزيئات كوبلتيت النيكل النانوية ‏‏(‏NC‏) باستخدام دراسات ‏FT-IR‏ وحيود ‏الأشعة السينية (‏XRD‏). كانت أحجام ‏الجسيمات البلورية لل ‏NC‏ و ‏ لل ‏GO‏ المزين ب NCهي ‏‎10.53‎‏ نانوميتر ‏‏9.30 نانومتر على التوالي. تم دراسة ‏تأثير العديد من العوامل التجريبية ‏، بما في ذلك زمن التلامس ، وكمية ‏السطح الماز ، ودرجة الحرارة. كان ‏وقت التلامس الأمثل وكمية السطح 120 ‏دقيقة و 3 مجم / لتر على التوالي. ‏تتلائم بيانات الامتزاز بشكل أفضل مع ‏ايزوثيرم ‏Freundlich‏. تم استخدام ‏أربعة نماذج حركية لتتبع عملية ‏الامتزاز: معادلة زائفة من الدرجة ‏الأولى ، ومعادلة زائفة من الدرجة ‏الثانية ، ومعادلة الانتشار داخل ‏الجسيمات ، ومعادلة بويد. أظهرت ‏نمذجة البيانات التجريبية أن حركية ‏الامتزاز كانت ممثلة بشكل جيد ‏بنموذج الرتبة الثانية الزائفة (‏R2 ‎‎= 0.9945‎‏) مع معدل ثابت سرعة يبلغ 3.2 ‏‏× 10 - 3 (جم / مجم. دقيقة). يتم ‏امتصاص صبغة ‏MG‏ تدريجيًا بواسطة ‏الجسيمات النانوية ‏NC‏ من خلال ‏الانتشار داخل الجسيمات ويتم ‏الاحتفاظ بها بعد ذلك في مسام أصغر. ‏أظهرت قيم التحليل الديناميكي ‏الحراري أن عملية امتزاز صبغة ‏MG‏ ‏كانت ماصة للحرارة بطبيعتها ، و ‏تلقائية وعملية الامتزاز فيزيائية. الكلمات المفتاحية: الانتشار داخل الجسيمات ، المسافات البينية، النموذج الزائف من الدرجة الأولى ، معادلة بويد

Network Subgraph-based Method: Alignment-free Technique for Molecular Network Analysis

Background: Comparing directed networks using the alignment-free technique offers the advantage of detecting topologically similar regions that are independent of the network size or node identity. Objective: We propose a novel method to compare directed networks by decomposing the network into small modules, the so-called network subgraph approach, which is distinct from the network motif approach because it does not depend on null model assumptions. Methods: We developed an alignment-free algorithm called the Subgraph Identification Algorithm (SIA), which could generate all subgraphs that have five connected nodes (5-node subgraph). There were 9,364 such modules. Then, we applied the SIA method to examine 17 cancer networks and measured the similarity between the two networks by gauging the similarity level using Jensen- Shannon entropy (HJS). Results: We identified and examined the biological meaning of 5-node regulatory modules and pairs of cancer networks with the smallest HJS values. The two pairs of networks that show similar patterns are (i) endometrial cancer and hepatocellular carcinoma and (ii) breast cancer and pathways in cancer. Some studies have provided experimental data supporting the 5-node regulatory modules. Conclusion: Our method is an alignment-free approach that measures the topological similarity of 5-node regulatory modules and aligns two directed networks based on their topology. These modules capture complex interactions among multiple genes that cannot be detected using existing methods that only consider single-gene relations. We analyzed the biological relevance of the regulatory modules and used the subgraph method to identify the modules that shared the same topology across 2 cancer networks out of 17 cancer networks. We validated our findings using evidence from the literature.

Direct visualization of emergent metastatic features within an ex vivo model of the tumor microenvironment.

Metabolic conditions such as hypoxia, nutrient starvation, and media acidification, together with interactions with stromal cells are critical drivers of metastasis. Since these conditions arise deep within tumor tissues with poor access to the bloodstream, the observation of nascent metastases in vivo is exceedingly challenging. On the other hand, conventional cell culture studies cannot capture the complex nature of metastatic processes. We thus designed and implemented an ex vivo model of the tumor microenvironment to study the emergence of metastatic features in tumor cells in their native 3-dimensional (3D) context. In this system, named 3MIC, tumor cells spontaneously create ischemic-like conditions, and it allows the direct visualization of tumor-stroma interactions with high spatial and temporal resolution. We studied how 3D tumor spheroids evolve in the 3MIC when cultured under different metabolic environments and in the presence or absence of stromal cells. Consistent with previous experimental and clinical data, we show that ischemic environments increase cell migration and invasion. Importantly, the 3MIC allowed us to directly observe the emergence of these pro-metastatic features with single-cell resolution allowing us to track how changes in tumor motility were modulated by macrophages and endothelial cells. With these tools, we determined that the acidification of the extracellular media was more important than hypoxia in the induction of pro-metastatic tumor features. We also illustrate how the 3MIC can be used to test the effects of anti-metastatic drugs on cells experiencing different metabolic conditions. Overall, the 3MIC allows us to directly observe the emergence of metastatic tumor features in a physiologically relevant model of the tumor microenvironment. This simple and cost-effective system can dissect the complexity of the tumor microenvironment to test perturbations that may prevent tumors from becoming metastatic.

Manufacturability of functionally graded porous β-Ti21S auxetic architected biomaterials produced by laser powder bed fusion: Comparison between 2D and 3D metrological characterization.

Functionally graded porous structures (FGPSs) are attracting increasing interest in the manufacture of prostheses that benefit from lower stiffness and optimized pore size for osseointegration. In this work, we explore the possibility of employing FGPSs with auxetic unit cells. Their negative Poisson's ratio was exploited to reduce the loss of connection between prosthesis and bone usually occurring in standard implant loaded under tension and therefore undergoing lateral shrinking. In addition, to further improve osseointegration and mitigate stress shielding effects, auxetic FGPSs were fabricated in this work using a novel β-Ti21S alloy characterized by a lower Young's modulus compared to traditional α + β Ti alloys. Specifically, two different auxetic FGPSs with aspect ratio equal to 1.5 and angle θ of 15° and 25° with a relative density (ρr) gradient of 0.34, 0.49, 0.66 and of 0.40, 0.58, 0.75 were designed and printed by laser powder bed fusion. The 2D and 3D metrological characterization of the as-manufactured structures was compared with the design. 2D metrological characterization was carried out using scanning electron microscopy analysis, while for the 3D characterization, X-ray micro-CT imaging was used. An undersizing of the pore size and strut thickness in the as-manufactured sample was observed in both auxetic FGPSs. A maximum difference in the strut thickness of -14 and -22% was obtained in the auxetic structure with θ = 15° and 25°, respectively. On the contrary, a pore undersizing of -19% and -15% was evaluated in auxetic FGPS with θ = 15° and 25°, respectively. Compression mechanical tests allowed to determine stabilized elastic modulus of around 4 GPa for both FGPSs. Homogenization method and analytical equation were used and the comparison with experimental data highlights a good agreement of around 4% and 24% for θ = 15° and 25°, respectively.

Impact of Mesenchymal Stem Cells on the Gut Microbiota and Microbiota Associated Functions in Inflammatory Bowel Disease: A Systematic Review of Preclinical Evidence on Animal Models.

Inflammatory bowel disease (IBD) is a global health problem in which gut microbiota dysbiosis plays a pivotal pathogenic role. Mesenchymal stem cells (MSCs) therapy has shown promising application prospects for its powerful immune regulation and tissue repair ability. Recent experimental data suggest that MSCs also regulate the composition of gut microbiota. The current review analyzed, for the first time, the research data linking MSCs and gut microbiota modulation in IBD models aiming at assessing the role of gut microbiota in MSCs repair of IBD. A comprehensive and structured literature search was performed up to January 2023 on the PubMed, Web of Science, and Scopus databases. The quality and risk of bias assessment followed the PRISMA guidelines and SYRCLE's tool. A total of nine pre-clinical studies on animal models were included. Although the dose and route of MSCs applied were quite heterogeneous, results showed that MSCs displayed protective effects on intestinal inflammation, including mice general assessment, immunoregulation, and intestinal barrier integrity. Meanwhile, studies showed positive effects on the composition of gut flora with MSCs administration, which had been characterized by restoration of Firmicutes/ Bacteroides balance and reduction of Proteobacteria. The beneficial bacteria Akkermansia, Bifidobacterium, and Lactobacillus were also distinctly enriched, and the pathogenic bacteria Escherichia-Shigella was conversely decreased. The alpha and beta diversity were also regulated to resemble those of healthy mice. Microbial metabolic functions, such as biosynthesis of secondary bile acid and sphingolipid metabolism, and some biological behaviors related to cell regeneration were also up-regulated, while cancer function and poorly characterized cellular function were down-regulated. Current data support the remodeling effect on gut microbiota with MSC administration, which provides a potential therapeutic mechanism for MSCs in the treatment of IBD. Additional studies in humans and animal models are warranted to further confirm the role of gut microflora in MSCs repairing IBD.

Comparative study of experimental and DFT calculations for 3-cinnamoyl 4-hydroxycoumarin derivatives.

Computational research plays an important role in predicting the chemical and physical properties of biologically active compounds important in future structural modifications to improve or modify biological activity. This research focuses on quantum chemical and spectroscopic investigations properties of synthesized 4-hydroxycoumarin derivatives. Quantum chemical calculations were obtained using B3LYP, HF, and M06-2x level methods with the 6-31++G (d,p) basis set. Afterward, IR, 1H, 13C, UV-Visible experimentally parameters were compared with the results obtained using the B3LYP/6-31+G*(d) basis set of the molecules to be able to characterize the structures. Based on the quantum chemical calculations compound with acetamido group on the phenyl ring is the most reactive, and compound with nitro substituent is the least reactive and the the strongest electrophile among tested compounds. With the exception of compounds with dimethylamino group, all other compounds have a pronounced tautomer between OH and C = O group. The calculated and experimental values are in agreement with each other. The molecular structure in the ground state of six 3-cinnamoyl 4-hydroxycoumarin derivatives was optimized using density functional theory. The observed and computed values were compared and it can be concluded that the theoretical results were in good linear agreement with the experimental data.

Activation of Polycystin-1 Signaling by Binding of Stalk-derived Peptide Agonists.

Polycystin-1 (PC1) is the membrane protein product of the PKD1 gene whose mutation is responsible for 85% of the cases of autosomal dominant polycystic kidney disease (ADPKD). ADPKD is primarily characterized by the formation of renal cysts and potential kidney failure. PC1 is an atypical G protein-coupled receptor (GPCR) consisting of 11 transmembrane helices and an autocatalytic GAIN domain that cleaves PC1 into extracellular N-terminal (NTF) and membrane-embedded C-terminal (CTF) fragments. Recently, signaling activation of the PC1 CTF was shown to be regulated by a stalk tethered agonist (TA), a distinct mechanism observed in the adhesion GPCR family. A novel allosteric activation pathway was elucidated for the PC1 CTF through a combination of Gaussian accelerated molecular dynamics (GaMD), mutagenesis and cellular signaling experiments. Here, we show that synthetic, soluble peptides with 7 to 21 residues derived from the stalk TA, in particular, peptides including the first 9 residues (p9), 17 residues (p17) and 21 residues (p21) exhibited the ability to re-activate signaling by a stalkless PC1 CTF mutant in cellular assays. To reveal molecular mechanisms of stalk peptide-mediated signaling activation, we have applied a novel Peptide GaMD (Pep-GaMD) algorithm to elucidate binding conformations of selected stalk peptide agonists p9, p17 and p21 to the stalkless PC1 CTF. The simulations revealed multiple specific binding regions of the stalk peptide agonists to the PC1 protein including an "intermediate" bound yet inactive state. Our Pep-GaMD simulation findings were consistent with the cellular assay experimental data. Binding of peptide agonists to the TOP domain of PC1 induced close TOP-putative pore loop interactions, a characteristic feature of the PC1 CTF signaling activation mechanism. Using sequence covariation analysis of PC1 homologs, we further showed that the peptide binding regions were consistent with covarying residue pairs identified between the TOP domain and the stalk TA. Therefore, structural dynamic insights into the mechanisms of PC1 activation by stalk-derived peptide agonists have enabled an in-depth understanding of PC1 signaling. They will form a foundation for development of PC1 as a therapeutic target for the treatment of ADPKD.

Learning-Assisted Fast Determination of Regularization Parameter in Constrained Image Reconstruction.

To leverage machine learning (ML) for fast selection of optimal regularization parameter in constrained image reconstruction. Constrained image reconstruction is often formulated as a regularization problem and selecting a good regularization parameter value is an essential step. We solved this problem using an ML-based approach by leveraging the finding that for a specific constrained reconstruction problem defined for a fixed class of image functions, the optimal regularization parameter value is weakly subject-dependent and the dependence can be captured using few experimental data. The proposed method has four key steps: a) solution of a given constrained reconstruction problem for a few (say, 3) pre-selected regularization parameter values, b) extraction of multiple approximated quality metrics from the initial reconstructions, c) predicting the true quality metrics values from the approximated values using pre-trained neural networks, and d) determination of the optimal regularization parameter by fusing the predicted quality metrics. The effectiveness of the proposed method was demonstrated in two constrained reconstruction problems. Compared with L-curve-based method, the proposed method determined the regularization parameters much faster and produced substantially improved reconstructions. Our method also outperformed state-of-the-art learning-based methods when trained with limited experimental data. This paper demonstrates the feasibility and improved reconstruction quality by using machine learning to determine the regularization parameter in constrained reconstruction. The proposed method substantially reduces the computational burden of the traditional methods (e.g., L-curve) or relaxes the requirement of large training data by modern learning-based methods, thus enhancing the practical utility of constrained reconstruction.

Analyzing the Nuclear Structure of 13O-13B and 13N-13C Mirror Nuclei

In the context of the shell model, electromagnetic transitions were used to analyze the nuclear structure of a mirror nucleus with the same mass number, A = 13. The shell model investigation was performed by calculating the root mean square (rms) for the proton, neutron, matter and charge radii, occupancies, the excitation energies, as well as electromagnetic moments, using the elements of a body density matrix of the PSDMOD two-body effective interactions carried out in the psdpn-shell model space. The present results adopted the harmonic oscillator's single particle eigen functions and Hartree-Fock approximation. In addition, the effect of core polarization was added using the effective charge and effective g factors to calculate electromagnetic moments. To acquire a fair analysis of the information, the core polarization has to be present. The outcomes were compared with experimental data

Analytic Prediction of Rotor Broadband Noise with Serrated Trailing Edges with Applications to Urban Air Mobility Aircraft

Theoretical predictions for broadband noise generated by rotors with serrated trailing edges are presented. This study extends Lyu and Ayton’s model for serrated trailing-edge noise to account for factors such as finite span length, the accurate acoustic attenuation rate of trailing-edge noise, modified spanwise wavenumber, and the proper scattering factor. This new model is implemented in the rotorcraft broadband noise tool, UCD‐QuietFly. The validation results demonstrate a satisfactory level of agreement with the experimental data for serrated trailing‐edge noise of a wing section, a hovering rotor, and a forward‐flight rotor. Next, the effects of serration parameters, such as the number of serrations, serrations starting radial location, serration geometry, and serration height, on rotor broadband noise are investigated. For example, it is observed that sine-wave, chopped peak, or sawtooth serration shapes provide the most significant noise reduction among the various shapes considered. Finally, broadband noise reduction through serrations is applied to two urban air mobility aircraft scenarios: a six‐passenger quadrotor and a quiet helicopter. For the quadrotor, serrated trailing edges show a noise reduction potential of over 9 dB, with higher reductions observed at higher tip speeds and fewer blades. A quiet helicopter equipped with serrated blades in forward flight achieves a maximum noise reduction of over 10 dB in the forward direction.

Treatment of Reactive Orange 16 Dye-Bearing Wastewater by Electro-Fenton Process with Stainless-Steel Electrodes: Statistical Optimization and Operational Analysis

Abstract: In the current work, the Electro-Fenton (EF) based Reactive Orange 16 (RO16) dye treatment was studied and compared with central composite (CC) and Taguchi design (TD) statistical optimization tools. Color removal (RC) and COD decay (RCOD) were chosen responses for the effect of pH (A), electrolysis time (B), initial dye concentration (C), and current density (D). The facecentred CC design and L16 orthogonal array were used in the experimental procedures. At optimal conditions, the coefficient of determination (R2) values of 0.99 for CC and 0.97 for TD suggest statistical significance and good model agreement. The results of the ANOVA and Prob. > F values supported the model’s successful experimental data fitting. Taguchi method was found as an appropriate methodology for parameter percentage contributions with fewer experimental runs. Moreover, the S/N ratio charts proved to be a successful CC design replacement. The current density and pH were found to be the most important factors for the EF process. A higher biodegradability (BOD5/COD) and minimum iron concentration (0.45 mg/L) in the effluent sludge demonstrated good environmental disposal suitability. In the last, the effect of various inhibitors/scavengers (SO4 −2, PO4 −3, EDTA, etc.) on the EF process performance was also carried out.

Multi-Node Feature Learning Network Based on Maximum Spectral Harmonics-to-Noise Ratio Deconvolution for Machine Condition Monitoring

Since the cyclostationarity in vibration signals is the key to judge the rotating machine health state, spectral harmonics-to-interference ratio (SHIR) has been used to construct single node feature learning network (SHIR-based blind deconvolution, BD-SHIR) to realize condition monitoring of machine. However, BD-SHIR still has several obvious limitations, including the need for prior fault information and the tendency to fall into local optimal solutions which will affect its feature learning and state monitoring performance. Therefore, this paper proposes a multi-node feature learning network based on BDSHIR, which does not rely on prior information, multi-node adaptive BDSHIR (MABD-SHIR). It uses each filter in the 1/3 binary tree filter bank to design each initial node (filter) in the learning network correspondingly, and introduces an adaptive fault characteristic frequency (FCF) detection process. Since this initialization can give some filters close to the fault resonant band, iterations starting from these nodes are more likely to detect the precise FCF and converge to the optimal solution. Finally, MABD-SHIR can obtain multiple local optimal solutions, among which the one with the largest SHIR is generally closer to the global optimal solution than the one obtained by BD-SHIR. Simulation and experimental data of defective rotating machinery verify the effectiveness of MABD-SHIR in machinery condition monitoring and fault feature learning. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the problems that the rotating machine condition monitoring based on BDSHIR is easy to prematurely fall into local optimal solution and requires prior fault information. These problems can be effectively solved by extending BDSHIR from single node structure to multi-node structure and introducing adaptive period detection technique (A-DPT). The core idea is to start iterations from multiple initial filters and to detect the FCF adaptively during each iteration. Multiple features conforming to filtered signals’ SHIR maximization can be finally learned. Therefore, in this paper, MABD-SHIR is proposed, which is equivalent to a multi-node feature learning network. Since the objective function of BD is usually non-convex, MABD-SHIR can obtain multiple local optimal solutions without requiring prior FCFs. This is more likely to achieve a global optimal solution than BDSHIR, which starts iteration from only one initial filter and ends up with one local optimal solution. In addition, the objective function of MABD-SHIR has lower mathematical complexity, which reduces the difficulty of optimization.

Online course as a means of developing master's professional competencies: results of a pedagogical experiment

The article proposes a solution to a scientific problem, which is to substantiate the feasibility of using online courses in the professional training of masters in the pedagogical university, to develop an online course and experimentally test its effectiveness as a means of developing the professional competencies of masters in the speciality 014~Secondary Education (Mathematics). The author used the following methods of scientific research: pedagogical experiment, observation (for systematic perception and analysis of learning activities, motivation and personal growth of students in the context of blended learning), testing (to determine the level of logical thinking and development of students’ professional competences), questionnaire (to monitor the ergonomics and efficiency of the online course and the Mathematics teacher’s e-portfolio), expert evaluation of the developed online course, and methods of mathematical statistics (for proper processing of experimental data and verification of the online course’ effectiveness). The results of the control stage of the study indicate a positive trend in the development of mathematical, information and educational, methodological, psychological and pedagogical, and professional and technological competencies of students in the experimental group. The final test of the online course was chosen as a tool for determining the level of mathematical competencies of the masters in the experimental group. The reliability of the assessment of the masters’ professional competencies was confirmed by testing the hypothesis of the normal distribution of test results according to the Shapiro-Wilk test. In addition, a close correlation between students' academic achievement level in an online course and the level of logical thinking development was substantiated using the Student’s t-test. Prospects for further research include the development of online courses on Mathematics teaching methods with an emphasis on methodical, professional, and technological competencies; a detailed study of the impact of the latest information technologies (artificial intelligence, virtual and augmented reality, web programming, etc.) on the development of students’ information and educational professional competences in distance and blended learning.

Ribonanza: deep learning of RNA structure through dual crowdsourcing.

Prediction of RNA structure from sequence remains an unsolved problem, and progress has been slowed by a paucity of experimental data. Here, we present Ribonanza, a dataset of chemical mapping measurements on two million diverse RNA sequences collected through Eterna and other crowdsourced initiatives. Ribonanza measurements enabled solicitation, training, and prospective evaluation of diverse deep neural networks through a Kaggle challenge, followed by distillation into a single, self-contained model called RibonanzaNet. When fine tuned on auxiliary datasets, RibonanzaNet achieves state-of-the-art performance in modeling experimental sequence dropout, RNA hydrolytic degradation, and RNA secondary structure, with implications for modeling RNA tertiary structure.