Structure Of Space Research Articles

Efficient H2 evolution over MoS2-NiS2/g-C3N4 S-scheme photocatalyst with NiS2 as electron mediator

Low-cost transition metal sulfides are commonly utilized as photocatalysts for H2 production owing to their exceptional conductivity and superior specific surface area. In this study, MoS2-NiS2 nanoflowers with multidimensional space structure was obtained through the sulphuration reaction between S2- and NiMoO4 under Kirkendall effect during hydrothermal reaction. Subsequently, MoS2-NiS2 was loaded on the surface of g-C3N4 nanosheets using a solvent self-assembly strategy to form 3D MoS2-NiS2/g-C3N4 heterojunctions. The experimental results demonstrate that the H2 production rate of 20 wt% MoS2-NiS2/g-C3N4 reaches 22153 μmol·g-1·h-1 under a 300 W Xe lamp irradiation, which is 59.5 and 201.4-folds than MoS2-NiS2 and g-C3N4, and surpasses 20 wt% MoS2/g-C3N4 (211 μmol·g-1·h-1) and 20 wt% NiS2/g-C3N4 (6183 μmol·g-1·h-1). The XPS and Superoxide radical capture experiments demonstrate that the charge transfer between MoS2-NiS2 and g-C3N4 follows the S-scheme route, and NiS2 functions as an electron mediator, facilitating the transfer of electrons from MoS2 to g-C3N4 to consume the holes, which enhances the efficiency of H2 evolution reaction on g-C3N4. Furthermore, the S-scheme heterojunction of MoS2-NiS2/g-C3N4 with the multidimensional geometric structure can provide abundant active sites for catalytic reactions, this presents a promising approach for the development of cost-effective and high-performance g-C3N4-based heterojunctions. This work offers distinctive perspectives on the trajectory of renewable H2 energy development.

Perithonsillar and parapharyngeal abscess: microbiological and morphological features

Introduction. The most dangerous local complication of acute and exacerbations of chronic inflammatory diseases of the pharynx is the formation of abscesses in the structure of the cellular spaces of the neck. Paratonsillar abscess (PTA) is the most common abscessing lesion. In turn, parapharyngeal abscess (PFA) is often found in patients with PTA as its complication.Aim. To study the clinical, microbiological and pathomorphological features of parapharyngeal complications in patients with PTA.Materials and methods. The analysis of medical records of 50 patients with diagnoses of parathonsillar and parapharyngeal abscesses who underwent inpatient treatment at the otorhinolaryngological department of the N.I. Pirogov State Clinical Hospital No. 1 in the period from September 2021 to April 2023 was carried out. The studied patients were divided into 2 groups. In group 2 of patients (n = 25; F 10, M 15; average age -37.2 years ± 10.8 years) an isolated PTA was diagnosed. In the 2 group of patients (n = 25; F 10, M 15, the average age was 44.6 years ± 14.8 years) PTA was complicated by the development of PFA.Results and discussion. Associations of facultative anaerobic and obligate anaerobic microorganisms were found in all pus aspirates obtained from PTA and PFA. Monocultures of microorganisms were not isolated in any case. The detection of Fusobacterium necrophorum was associated with pronounced necrotic changes in the tissues of the tonsils and surrounding structures; Peptostreptococcus anaerobius with purulent melting of fiber tissue; Veillonella dispar with pronounced infiltrative processes in tissues combined with oral pathology in patients.Conclusions. The most common microorganisms in patients with PTA and PFA were Streptococcus pyogenes and Fusobacterium necrophorum. The activity of Fusobacterium necrophorum is associated with a more severe course of the inflammatory process in the near-pharyngeal space.

Deep reinforcement learning for collision avoidance of autonomous ships in inland river

Due to the characteristics of large inertia, large time delay, and nonlinearity in ship motion, the maneuverability of ships is an important issue related to the safety of ship navigation. The manipulation of ships significantly affects the safety of ship navigation, and its importance will gradually increase with the development of autonomous ships. Collision accidents are common due to the complexity of inland waterways and density of the ships. Herein, an autonomous learning framework with deep reinforcement learning (DRL) is constructed for autonomous driving tasks. The state space, action space, reward function and neural network structure of DRL are designed based on the ship’s maneuvering characteristics and control requirements. A DDPG algorithm is then used to implement the controller. Finally, some representative route segments of the inland waterway is selected for simulation research based on the virtual simulation environment. The designed DRL controller can quickly converge from the training and learning process to meet the control requirements. By comparing with the previous experimental results, the effectiveness of this algorithm is verified. Therefore, this study provides a reference for future research on the collision avoidance technology of autonomous ships in inland rivers.

Active noise control of refrigerator based on cascaded notch feedback algorithm

Refrigerators bring convenience to people, but the noise they produce can also affect people’s lives. Refrigerator noise is dominated by low-frequency noise, and the active noise control method is more effective for this kind of noise. However, the existing active noise control methods for refrigerators do not take into account factors such as the limited space and complex structure of the refrigerator compressor room, and external interference signals will be introduced to affect the noise reduction performance during the error signal collection. Given that, this paper proposes the cascade notch feedback algorithm, which can well reduce the influence of external interference signals to better achieve the refrigerator noise reduction. The algorithm consists of the cascaded notch filtering algorithm and the feedback algorithm. The cascade notch filtering algorithm is formed by cascading the adaptive notch filtering algorithm, which is used to deal with the main frequency and harmonic noise generated by the refrigerator. The feedback algorithm consists of a robust algorithm for dealing with external interference signals introduced during error signal collection. The simulation experiment proves that the algorithm has advantages in terms of noise reduction and computational cost compared with the adaptive notch filtering algorithm and the improved algorithm. The experimental test platform is set up to carry out the actual refrigerator noise reduction experiments under different conditions. The algorithm has the effect of noise reduction under different robust algorithms.

Updating surrogate models in early building design via tabular transfer learning

Surrogate models provide estimations of design performance objectives, which can yield rapid feedback in early building design. However, constructing parametric models and generating data to train surrogate models is time-consuming. Once established, designers are discouraged from modifying the structure of the design space, which may require additional simulation and retraining. To address this limitation, we propose a new workflow that incorporates a tabular transfer learning approach, coupled with a random walks sampling technique. This approach preserves the knowledge from the initial dataset, thus reducing the number of simulations required to update the surrogate model when new variables are introduced. Through a façade design space case study with a daylighting performance objective, we demonstrate that fewer samples are needed to update the surrogate model and achieve adequate model performance compared to classical interpretable classifiers when only a few new samples are possible or desired. As the number of new samples increases, the performances of some classical methods using traditional sampling improve to eventually meet or surpass the tabular transfer learning method.

Research on trajectory planning based on deep reinforcement learning

Abstract For trajectory planning in a three-dimensional continuous space with multiple complex constraints, traditional optimal control methods face significant challenges, such as long computation times and poor dynamic performance. Using deep reinforcement learning algorithms for training agents in flight trajectory planning can effectively improve the dynamic performance of the planning algorithms. This paper focuses on training agents for UAV emergency landing and obstacle avoidance trajectory planning based on deep reinforcement learning algorithms. Within the reinforcement learning framework, we design the training environment, state space, action space, reward function, and neural network structure. To address common issues in reinforcement learning, such as poor generalization and slow training speed, we improve the training process using the concept of curriculum learning. Comparisons with pseudospectral methods demonstrate that deep reinforcement learning can effectively handle trajectory planning tasks in real flight environments, achieving trajectory quality close to that of pseudospectral methods while offering superior real-time performance.

Parametric design of curved hydrocyclone using data points and its separation enhancement mechanism

Based on parametric design of curves using data points, two novel hydrocyclones were designed: Spline-Curved-Cone hydrocyclone (H2) and Expassoc-Curved-Cone hydrocyclone (H3). These designs are improvements on the traditional biconical hydrocyclone (H1). Numerical simulations and experimental validation by PIV measurement were used to investigate the effects of cone section profiles on the flow characteristics and separation efficiency. The results showed both H2 and H3 achieved higher separation efficiency than H1. Specifically, the highest efficiency of H3 increased by 24.71%, and that of H2 increased by 16.22% compared with H1. It was also found the curved design of cone section profile directly affects the tangential velocity distribution and pressure distribution of the flow field inside hydrocyclones. H3 exhibited better flow field stability and highest separation efficiency due to its optimized cone section space and flow structure. This study provides scientific basis and data support for the optimization and industrial application of the cylinder-on-cone hydrocyclones.

Regulating Catalytic Oxidation Enantiomers Behavior by Imparting Chiral Microenvironment in Zr-Based Metal-Organic Frameworks.

Chiral inversions of enantiomers have significantly different biological activities, so it is important to develop simple and effective methods to efficiently identify optically pure compounds. Inspired by enzyme catalysis, the construction of chiral microenvironments resembling enzyme pockets in the pore space structure of metal-organic frameworks (MOFs) to achieve asymmetric enantioselective recognition and catalysis has become a new research hotspot. Here, a super-stable porphyrin-containing material PCN-224 is constructed by solvothermal method and a chiral microenvironment around the existing catalytic site of the material is created by post-synthesis modifications of the histidine (His) enantiomers. Experimental and theoretical calculations results show that the modulation of chiral ligands around Zr oxide clusters produces different spatial site resistances, which can greatly affect the adsorption and catalytic level of the enantiomeric molecules of tryptophan guests, resulting in a good enantioselective property of the material. It provides new ideas and possibilities for future chiral recognition and asymmetric catalysis.

A general adaptive unsupervised feature selection with auto-weighting

Feature selection (FS) is essential in machine learning and data mining as it makes handling high-dimensional data more efficient and reliable. More attention has been paid to unsupervised feature selection (UFS) due to the extra resources required to obtain labels for data in the real world. Most of the existing embedded UFS utilize a sparse projection matrix for FS. However, this may introduce additional regularization terms, and it is difficult to control the sparsity of the projection matrix well. Moreover, such methods may seriously destroy the original feature structure in the embedding space. Instead, avoiding projecting the original data into the low-dimensional embedding space and identifying features directly from the raw features that perform well in the process of making the data show a distinct cluster structure is a feasible solution. Inspired by this, this paper proposes a model called A General Adaptive Unsupervised Feature Selection with Auto-weighting (GAWFS), which utilizes two techniques, non-negative matrix factorization, and adaptive graph learning, to simulate the process of dividing data into clusters, and identifies the features that are most discriminative in the clustering process by a feature weighting matrix Θ. Since the weighting matrix is sparse, it also plays the role of FS or a filter. Finally, experiments comparing GAWFS with several state-of-the-art UFS methods on synthetic datasets and real-world datasets are conducted, and the results demonstrate the superiority of the GAWFS.

A New Structure of Random Approach Normed Space via Banach Space

The goal of this research is to define the convergent sequence in A-random approach space and sequentially convergent are discussed and the cluster point, open and closed ball and linear transformation. We are going to explain a new structure of Random approach normed space via Banach space in and discussed all the relations between metric space in this research.

Utilizing unsupervised machine learning to uncover novel phenogroups within the broad QRS complex

Abstract Background Conduction disease can manifest as a broad QRS complex on the ECG. Broad QRS is conventionally categorised as left bundle branch block (LBBB) and right bundle branch block (RBBB) or non-specific intraventricular conduction delay (IVCD), based on QRS morphology. These subgroups were coined over a century ago and have been relevant for heart failure with reduced ejection fraction and cardiac resynchronization therapy (CRT). However, there may be more precise phenogroups underlying broad QRS complexes. Purpose Using unsupervised machine learning to define novel broad QRS phenogroups and characterise them using clinical variables and disease outcomes. Methods We extracted 51 relevant features using a Variational Autoencoder from 47,456 median-beat ECGs with broad QRS intervals (QRS ≥ 130ms) from a secondary care cohort. These were input into Discriminative Dimensionality Reduction via Learning a Tree (DDRTree), a clustering method that models the continuum of heterogeneity by projecting the underlying distribution as a tree structure in a low-dimensional space, while assigning distinct branch/cluster labels. Results Six distinct phenogroups were identified within broad QRS morphologies (Figure-1). Two branches were RBBB dominant (Branch 1: more males, high-comorbidity; Branch 6: low-comorbidity/healthy-RBBB). Two branches were LBBB dominant (Branch 4: mixed IVCD with CHB burden; Branch 5: more females, CRT responders). Two branches were IVCD/mixed-phenotype (Branch 2: IVCD-dominant, high risk of future cardiovascular events; Branch 3: average/core branch). Within LBBB, Branch 4 and 5 showed similar trends with improving echocardiography profiles and CRT response when adjusted for covariates, each compared against the core branch. Branch 5 was distinct due its high risk of future HF (HR 1.19 [1.07-1.33] p < 0.01), cardiovascular death (HR 1.35 [1.20-1.50] p < 0.0001), all-cause mortality (HR 1.08 [1.01-1.16] p < 0.05) and CHB (HR 1.35 [1.16-1.56] p < 0.0001) (Figure-2). Conversely within RBBB, Branch 1 and 6 showed stark differences across various traits. Branch 1 captured a high-risk profile associated to higher risk of cardiovascular death (HR 1.37 [1.24-1.52] p < 0.0001), all-cause mortality (HR 1.20 [1.12-1.29] p < 0.0001) and CHB (HR 1.15 [1.01-1.32] p < 0.05) (Figure-2), while Branch 6 retained a low-risk phenotype. The tree dimensions also revealed relevant trends in cardiac anatomy and valvular dysfunction; the top-left region in Figure-1 associated with higher left ventricular ejection fraction and lower left ventricular end-diastolic dimension, lower left ventricular end-systolic dimension and a greater CRT response. Conclusion Through unsupervised methods we have identified conduction disease phenogroups with additive value for disease prognosis and CRT response prediction beyond traditional LBBB and RBBB labels. These novel phenogroups may help guide precision care for patients with a broad QRS complex.Figure-1Figure-2

Preserving text space integrity for robust compositional zero-shot learning via mixture of pretrained experts

In the current landscape of Compositional Zero-Shot Learning (CZSL) methods that leverage CLIP, the predominant approach is based on prompt learning paradigms. These methods encounter significant computational complexity when dealing with a large number of categories. Additionally, when confronted with new classification tasks, there is a necessity to learn the prompts again, which can be both time-consuming and resource-intensive. To address these challenges, We present a new methodology, named the Mixture of Pretrained Expert (MoPE), for enhancing Compositional Zero-shot Learning through Logit-Level Fusion with Multi Expert Fusion Module. The MoPE skillfully blends the benefits of extensive pre-trained models like CLIP, Bert, GPT-3 and Word2Vec for effectively tackling Compositional Zero-shot Learning. Firstly, we extract the text label space for each language model individually, then map the visual feature vectors to their respective text spaces. This maintains the integrity and structure of the original text space. During this process, the pre-trained expert parameters are kept frozen. The mapping of visual features to the corresponding text spaces is subject to learning and could be considered as multiple learnable visual experts. In the model fusion phase, we propose a new fusion strategy that features a gating mechanism that adjusts the contributions of various models dynamically. This enables our approach to adapt more effectively to a range of tasks and data sets. The method’s robustness is demonstrated by the fact that the language model is not tailored to specific downstream task datasets or losses. This preserves the larger model’s topology and expands the potential for application. Preliminary experiments conducted on the UT-Zappos, AO-Clever, and C-GQA datasets indicate that MoPE performs competitively when compared to existing techniques.

Synthesis and observation of optical skyrmionic structure in free space

Synthesis and observation of optical skyrmionic structure in free space

Research on the Green Construction Technology of Stilt Houses Based on the Climate Adaptation of Transitional Seasons

Stilt houses are extremely adaptable to terrain and climate. However, current indoor thermal environment research in transitional seasons is not prominent. Therefore, in this research, a typical stilt house in southwest China was chosen as the research object and analyzed by combining Climate Consultant climate analysis software and field measurement data. The results showed that the indoor thermal stability of a stilt house was excellent during the transition season, and the attic had the most obvious climate regulation, with the maximum temperature difference between indoors and outdoors being 9 °C when the outdoor temperature was the highest. The difference between the mean radiant temperature and the average air temperature was only 0.04 °C, and the radiant effect of the enclosure on the interior was small. The indoor relative humidity ranged from 63.2% to 85.1%, showing high relative humidity, but the fluctuation was relatively stable. Stilt floors did not play a significant role in climate regulation during the transition season, and the semi-open space structure was more prone to moisture accumulation when the outdoor humidity was high. Regarding practical application, the climate adaptation strategies of shading, cooling, and dehumidification were applied in the transition season, but dehumidification was ineffective.

Vortices on cylinders and warped exponential networks

We study 3d N=2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {N}=2$$\\end{document}U(1) Chern–Simons-Matter QFT on a cylinder C×R\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C\ imes \\mathbb {R}$$\\end{document}. The topology of C gives rise to BPS sectors of low-energy solitons known as kinky vortices, which interpolate between (possibly) different vacua at the ends of the cylinder and at the same time carry magnetic flux. We compute the spectrum of BPS vortices on the cylinder in an isolated Higgs vacuum, through the framework of warped exponential networks, which we introduce. We then conjecture a relation between these and standard vortices on R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbb {R}^2$$\\end{document}, which are related to genus-zero open Gromov–Witten invariants of toric branes. More specifically, we show that in the limit of large Fayet–Iliopoulos coupling, the spectrum of kinky vortices on C undergoes an infinite sequence of wall-crossing transitions and eventually stabilizes. We then propose an exact relation between a generating series of stabilized CFIV indices and the Gromov–Witten disk potential and discuss its consequences for the structure of moduli spaces of vortices.

3D-3C measurements of flow reversal in small sessile drops in shear flow

Sessile drops play an important role in nature and the operation of many technical and biological systems as for instance fuel cells, cleaning processes, lab-on-a-chip devices and single-cell analysis. Nonetheless, their dynamic behavior in a shear flow is still not fully understood (e. g. detachment mechanism). Challenges exist regarding the precise simulation of two-phase flows as well as difficulties in conducting flow measurements with sufficient temporal resolution of more than 1 kHz. In this article, we present the first three-dimensional flow measurements in strongly oscillating drops stemming from a shear flow by using a monocular 3D localization microscope based on a Double-Helix Point Spread Function combined with Particle Tracking Velocimetry. The high temporal resolution and the large measurement volume - in terms of microscopy - make it possible to measure the time- and phase-averaged flow in small drops with Bond numbers (Bo) smaller than 1. Water drops were placed in an air flow channel and measurements were conducted for Reynolds numbers (Red) from about 750 to 1700. Our measurements show that the results of previous investigations for drops with Bo>1 concerning vortex pattern and flow reversal inside the drop apply for smaller drops as well. In addition, we are able to reveal the three-dimensional flow structure and multiple vortex-pattern in time and space. We discover a periodic 3D vortical flow pattern that corresponds to the first and second eigenfrequency of the drop. Moreover, we demonstrate the potential of adaptive optics to correct measurement errors stemming from time-varying light refraction when conducting measurements through the fluctuating drop surface, in particular for opaque substrates. The results may help in understanding the coupling of inner and outer flow for sessile drops in shear flow which allows for an analysis of the onset motion of these drops, i.e. drop removal. Removal of sessile drops plays a crucial role in many applications, which includes among other things the water management of fuel cells where small drops with Bo<1 predominantly occur.

Aboveground and belowground sizes are aligned in the unified spectrum of plant form and function

Understanding the global variation of plant strategies is essential for unravelling eco-evolutionary processes and ecosystem functions. Variation in ten fundamental aboveground and fine-root traits is summarised in four dimensions, the first of which relates to aboveground plant size. However, there is no consensus about how root size fits within this scheme. Here, we add rooting depth and lateral spread, compiling a set of twelve key traits that define the fundamental investments of plants in growth, reproduction, and survival. We examine whether the inclusion of root size alters the dimensionality and structure of trait correlations defining plant functional strategies. Our results show that including root size traits does not alter the fundamental structure and dimensionality of the plant functional space, regardless of trait completeness and phylogenetic relatedness. Plant size defines a single continuum of allometric investments at the global scale, independent from leaf and root economic strategies.

Fractional Einstein–Gauss–Bonnet Scalar Field Cosmology

Our paper introduces a new theoretical framework called the Fractional Einstein–Gauss–Bonnet scalar field cosmology, which has important physical implications. Using fractional calculus to modify the gravitational action integral, we derived a modified Friedmann equation and a modified Klein–Gordon equation. Our research reveals non-trivial solutions associated with exponential potential, exponential couplings to the Gauss–Bonnet term, and a logarithmic scalar field, which are dependent on two cosmological parameters, m and α0=t0H0 and the fractional derivative order μ. By employing linear stability theory, we reveal the phase space structure and analyze the dynamic effects of the Gauss–Bonnet couplings. The scaling behavior at some equilibrium points reveals that the geometric corrections in the coupling to the Gauss–Bonnet scalar can mimic the behavior of the dark sector in modified gravity. Using data from cosmic chronometers, type Ia supernovae, supermassive Black Hole Shadows, and strong gravitational lensing, we estimated the values of m and α0, indicating that the solution is consistent with an accelerated expansion at late times with the values α0=1.38±0.05, m=1.44±0.05, and μ=1.48±0.17 (consistent with Ωm,0=0.311±0.016 and h=0.712±0.007), resulting in an age of the Universe t0=19.0±0.7 [Gyr] at 1σ CL. Ultimately, we obtained late-time accelerating power-law solutions supported by the most recent cosmological data, and we proposed an alternative explanation for the origin of cosmic acceleration other than ΛCDM. Our results generalize and significantly improve previous achievements in the literature, highlighting the practical implications of fractional calculus in cosmology.

Graph Regularized Sparse L2,1 Semi‐Nonnegative Matrix Factorization for Data Reduction

ABSTRACTNon‐negative Matrix Factorization (NMF) is an effective algorithm for multivariate data analysis, including applications to feature selection, pattern recognition, and computer vision. Its variant, Semi‐Nonnegative Matrix Factorization (SNF), extends the ability of NMF to render parts‐based data representations to include mixed‐sign data. Graph Regularized SNF builds upon this paradigm by adding a graph regularization term to preserve the local geometrical structure of the data space. Despite their successes, SNF‐related algorithms to date still suffer from instability caused by the Frobenius norm due to the effects of outliers and noise. In this paper, we present a new SNF algorithm that utilizes the noise‐insensitive norm. We provide monotonic convergence analysis of the SNF algorithm. In addition, we conduct numerical experiments on three benchmark mixed‐sign datasets as well as several randomized mixed‐sign matrices to demonstrate the performance superiority of SNF over conventional SNF algorithms under the influence of Gaussian noise at different levels.

Gentrius: Generating Trees Compatible With a Set of Unrooted Subtrees and its Application to Phylogenetic Terraces.

For a set of binary unrooted subtrees generating all binary unrooted trees compatible with them, i.e. generating their stand, is one of the classical problems in phylogenetics. Here, we introduce Gentrius-an efficient algorithm to tackle this task. The algorithm has a direct application in practice. Namely, Gentrius generates phylogenetic terraces-topologically distinct, equally scoring trees due to missing data. Despite stand generation being computationally intractable, we showed on simulated and biological datasets that Gentrius generates stands with millions of trees in feasible time. We exemplify that depending on the distribution of missing data across species and loci and the inferred phylogeny, the number of equally optimal terrace trees varies tremendously. The strict consensus tree computed from them displays all the branches unaffected by the pattern of missing data. Thus, by solving the problem of stand generation, in practice Gentrius provides an important systematic assessment of phylogenetic trees inferred from incomplete data. Furthermore, Gentrius can aid theoretical research by fostering understanding of tree space structure imposed by missing data.