Limited Conditions Research Articles

Rolling bearing fault diagnosis based on multi-scale weighted visibility graph and multi-channel graph convolution network

Current data-driven fault diagnosis methods are prone to overfitting and a decrease in accuracy when working with only a limited number of labeled samples. Additionally, existing graph neural network-based fault diagnosis methods often fail to comprehensively utilize both global and local features. To address these challenges, we propose a rolling bearing fault diagnosis method based on multi-scale weighted visibility graph and a multi-channel graph convolutional network (MCGCN). Our approach converts vibration signals into multiple weighted graphs from the perspective of geometric meaning and extracts local node feature information and global topology information of graphs using MCGCN. Experimental results demonstrate that our method achieves excellent performance under both sufficient and limited data conditions, providing a promising approach for real-world industrial bearing fault diagnosis.

GeLC-FAIMS-MS workflow for in-depth middle-down proteomics.

Middle-down proteomics (MDP) is an analytical approach in which protein samples are digested with proteases such as Glu-C to generate large peptides (>3kDa) that are analyzed by mass spectrometry (MS). This method is useful for characterizing high-molecular-weight proteins that are difficult to detect by top-down proteomics (TDP), in which intact proteins are analyzed by MS. In this study, we applied GeLC-FAIMS-MS, a multidimensional separation workflow that combines gel-based prefractionation with LC-FAIMS MS, for deep MDP. Middle-down peptides generated by optimized limited Glu-C digestion conditions were first size-fractionated by polyacrylamide gel electrophoresis, followed by C4 reversed-phase liquid chromatography separation and additional ion mobility fractionation, resulting in a significant increase in peptide length detectable by MS. In addition to global analysis, the GeLC-FAIMS-MS concept can also be applied to targeted MDP, where only proteins in the desired molecular weight range are gel-fractionated and their Glu-C digestion products are analyzed, as demonstrated by targeted analysis of integrins in exosomes. In-depth MDP achieved by global and targeted GeLC-FAIMS-MS supports the exploration of proteoform information not covered by conventional TDP by increasing the number of detectable protein groups or post-translational modifications (PTMs) and improving the sequence coverage.

ПОВЫШЕНИЕ НАГРУЗОЧНОЙ СПОСОБНОСТИ МЕЛКОМОДУЛЬНОЙ ЧЕРВЯЧНОЙ ПЕРЕДАЧИ

The study objective is to analyze possible ways to increase the load capacity and develop a worm gear that meets the following requirements and working conditions: minimum weight, high reliability, maintenance-free, short-term operation mode, limited lubrication conditions, the ability to work at low temperatures. The task to which the paper is devoted is to increase the load capacity and the wear resistance of worm gears. The load capacity is defined as the possibility of transmitting the greatest torques with the smallest overall dimensions. The research methods are based on the theory of strength, theory of elasticity, mechanics of contact interaction. The novelty of the work is in finding effective design and technological ways to increase the load capacity, developing a modification of the worm gear and testing the method for calculating the strength of the transmission with a globoidal worm.
 Research results: a review and analysis of the results of studies concerning the modification of gear, working conditions, lubrication, manufacturing technology, wear; possible methods of increasing the bearing capacity of a small-module worm gear are considered; a modification of the gear with a globoidal worm is developed; a method of strength calculation is tested.
 Conclusions: the method of strength calculation and the method of modification of the globoidal gear allow improving the quality characteristics of worm gears – weight and size characteristics, reliability, resource, ease of operation.

Experimental research and optimization of a thermoelectric generator excited by pulsed combustion mode under limited heat dissipation for combined heat and power supply

In this paper, a scheme of pulse combustion mode with pulse input of gas is proposed to solve the problems of low output electrical performance and energy conversion efficiency of combustion-based thermoelectric systems under limited heat dissipation conditions, which is based on experiments. The characteristics of the thermoelectric system show that pulse combustion mode can increase the instantaneous temperature difference, so as to achieve the excitation effect on the output power. The relationships among output power, system efficiency, excitation intensity and pulse input parameters, and the geometry of thermoelectric modules are obtained. The influence of pulse input parameters on the excitation intensity is interactive, and top dead center of pulse inlet power is the parameter that has the greatest influence on the performance. Compared to the constant combustion mode, the pulse combustion mode can increase the output power by up to 28.3%. A maximum output power of 7.15 W with air-cooled heat dissipation and a maximum system efficiency of 3.26% were experimentally obtained, which is a 30% increase in efficiency over current air-cooled heat dissipation thermoelectric systems. Therefore, this paper can provide solutions for more practical power supply, provide effective and feasible guidance for the study of thermoelectric systems, and provide useful insights into the electrical properties of thermoelectric systems.

In-situ characterisation of fluvial dune morphology and dynamics under limited sediment supply conditions, Seine River, France

Dune dynamics and associated sedimentary fluxes have been estimated over a period of a week in the river Seine characterised by limited sediment supply. Distributed into two corridors, typical medium and small dunes associated with fluvial environments have been characterised from multibeam data acquired on four dates, 23-29 March 2021, after a limited peak flood. Dune morphometric parameters have been quantified on cross-dune 1 m spacing of bed elevation profiles and dune migration rates have been calculated by analysing successive multibeam acquisitions. Evidence of limited sediment supply conditions have been recognised from areas uncovered by mobile sediment in the troughs between medium dunes. Under such conditions, bedload transport associated with dune migration has been estimated using three methods: differential bathymetry, dune tracking and the Van Rijn empirical formula using a correction factor accounting for limited sediment supply conditions. The three methods provide similar bedload fluxes (less than an order of magnitude difference), and the small differences are related to input data uncertainties and the capability of these methods to capture sediment transport processes, such as sediment bypassing dunes. Based on field observations, this paper brings new constrains to the relationships between dune morphological parameters, migration rates and sedimentary fluxes and environmental parameters (sediment grain size, flow velocity and water depth) under limited supply conditions in a real river.

Cross domain fault diagnosis based on generative adversarial networks

Data-driven fault diagnosis utilizing deep learning algorithms is currently a topic of great interest. Without proper training, data-driven models usually fail to generalize on operating conditions different from the ones used in the training set. The majority of domain adaptation research for machinery fault diagnosis focuses on the transfer between limited working conditions for the same machine. In real-life applications, machines operate under a wide range of operating conditions and the data are mostly available for healthy conditions with seldom failures. Hence, models generated from controlled experiments do not usually generalize well under substantial domain shifts. To address this issue, this paper proposes a semi-unsupervised domain adaptation approach for cross-machine fault diagnosis which integrates model optimization and Generative Adversarial Networks (GANs) to bridge the gap between source and target domains. Experiments of transferring between two bearing data sets show that the proposed method is able to effectively train an optimized model that generalizes on both the source and target domains, and train a generator that learns the source domain probability distribution to substitute for larger domain shifts.

Root growth and physiological responses in wheat to topsoil and subsoil compaction with or without artificial vertical macropores

The process of soil compaction can cause various stresses on roots, ultimately limiting their growth and development within the soil. Understanding this phenomenon in real-world conditions can be challenging since the growth of roots is influenced by the soil environment. To investigate this issue, four experiments were conducted to examine the impact of topsoil (two in pots: with clay loam and sandy loam soils under two soil water regimes) and subsoil (in rhizobox: one with clay loam soil and the other with sandy loam soil, containing artificial vertical macropores) compaction on the relationship between edaphic factors and the physiological response of wheat roots. The topsoil compaction reduced root length, volume, and weight by 30–50% and the root diameter by ∼15% compared to the non-compact soil. The effect was reduced in the soil with higher clay content (clay loam), especially under the limited soil water condition. Plant physiological responses were adversely affected by compaction with a reduction in plant height. The transpiration rate was highly impacted (21–47% reduction) with the build-up of intercellular CO2 content in leaves (13–31%), especially with limited water applications. Root growth was severely restricted (>60%) in the compact subsoil layer, although the surface area and volume of roots increased in the overlying non-compact layer. Naturally occurring or artificial vertical macropores acted as escape channels, facilitating the roots to pass through the compact subsoil and grow abundantly in the loose soil below. However, plants in field conditions encounter a mix of loose and compact soil zones. By studying how roots respond to this soil heterogeneity, we can develop strategies to reduce the negative effects of soil compaction.

Social Vulnerability Analysis to Disaster COVID-19 in Central Java

The COVID-19 disaster has socio-economic impacts in various regions, including Central Java. COVID-19 has the potential to exacerbate social vulnerability due to the limited internal conditions of individuals to survive. This study tries to analyze social vulnerability based on indicators of individual conditions demographically, socio-economically, and the ability to access service facilities which can be exacerbated by the potential danger of COVID-19. This study aims to analyze the level of social vulnerability and the spatial and temporal distribution of the COVID-19 disaster in Central Java. This study uses a quantitative analysis method by calculating Principle Component Analysis (PCA) and Social Vulnerability Index (SoVI) to identify the level of social vulnerability in Central Java. Analysis of the level of the COVID-19 disaster uses cumulative positive case data which is analyzed spatially and temporally using ArcGIS. This study shows that the higher the SoVI value in a region, the higher the level of individual social vulnerability in that region. The results showed that the highest level of social vulnerability to the COVID-19 disaster in Central Java was the cities of Semarang, Cilacap and Banyumas which were influenced by the conditions of individual vulnerability and the number of positive cases of COVID-19.

Prescribed Performance Tracking Control of Lower-Triangular Systems with Unknown Fractional Powers

This paper is concerned with the tracking control problem for the lower-triangular systems with unknown fractional powers and nonparametric uncertainties. A prescribed performance control approach is put forward as a means of resolving this problem. The proposed control law incorporates a set of barrier functions to guarantee error constraints. Unlike the previous works, our approach works for the cases where the fractional powers, the nonlinearities, and their bounding functions or bounds are totally unknown; no restrictive conditions on the powers, such as power order restriction, specific size limitation or homogeneous condition, are made. Moreover, neither the powers and system nonlinearities nor their bounding functions or bounds are needed. It achieves reference tracking with the preassigned tracking accuracy and convergence speed. In addition, our controller is simple, as it does not necessitate parameter identification, function approximation, derivative calculation, or adding a power integrator technique. At the end, a comparative simulation demonstrates the effectiveness and advantage of the proposed approach.

Challenges and Remedial Strategies in Land Tax Collection in Medan Belawan: Qualitative Analysis with NVivo and Matrices of Framework

This research delves into the challenges and effectiveness of land and building tax (PBB) collection in Medan Belawan District over the period from 2019 to 2022. Through the utilization of Nvivo 11 software, the research employs qualitative methods, including interviews and data analysis, to uncover the intricacies of tax collection challenges in Medan Belawan District. The results aim to inform policymakers and tax authorities, providing insights to enhance the effectiveness of tax collection and promote taxpayer compliance. The findings reveal persistent shortcomings in achieving predetermined revenue targets, with various obstacles hindering the collection process. Non-delivery of Tax Payable Notices (SPPT) to taxpayers, inconsistent taxpayer residence data, limited economic conditions, and a lack of awareness among citizens regarding the significance of tax payments contribute to the inefficiency. To address these issues, the District has implemented measures such as checking taxpayer residence data, issuing warnings through environmental heads, and conducting outreach activities. However, the overall effectiveness of tax collection remains suboptimal. The study suggests that disparities in taxpayer data, insufficient awareness, and economic challenges are primary factors contributing to this ineffectiveness. Further efforts are recommended, including improved training programs, an incentive system based on work discipline, and increased community outreach to enhance understanding and compliance.

Method to Calculate the Limiting and Optimal Conditions of the Chemical and Energy Engineering Process of Calcination of Lump and Pelletized Phosphate Ore Raw Materials on the Grate of a Traveling-Grate Roasting Machine

Method to Calculate the Limiting and Optimal Conditions of the Chemical and Energy Engineering Process of Calcination of Lump and Pelletized Phosphate Ore Raw Materials on the Grate of a Traveling-Grate Roasting Machine

Adversarial Autoencoder Network for Hyperspectral Unmixing.

Spectral unmixing (SU), which refers to extracting basic features (i.e., endmembers) at the subpixel level and calculating the corresponding proportion (i.e., abundances), has become a major preprocessing technique for the hyperspectral image analysis. Since the unmixing procedure can be explained as finding a set of low-dimensional representations that reconstruct the data with their corresponding bases, autoencoders (AEs) have been effectively designed to address unsupervised SU problems. However, their ability to exploit the prior properties remains limited, and noise and initialization conditions will greatly affect the performance of unmixing. In this article, we propose a novel technique network for unsupervised unmixing which is based on the adversarial AE, termed as adversarial autoencoder network (AAENet), to address the above problems. First, the image to be unmixed is assumed to be partitioned into homogeneous regions. Then, considering the spatial correlation between local pixels, the pixels in the same region are assumed to share the same statistical properties (means and covariances) and abundance can be modeled to follow an appropriate prior distribution. Then the adversarial training procedure is adapted to transfer the spatial information into the network. By matching the aggregated posterior of the abundance with a certain prior distribution to correct the weight of unmixing, the proposed AAENet exhibits a more accurate and interpretable unmixing performance. Compared with the traditional AE method, our approach can greatly enhance the performance and robustness of the model by using the adversarial procedure and adding the abundance prior to the framework. The experiments on both the simulated and real hyperspectral data demonstrate that the proposed algorithm can outperform the other state-of-the-art methods.

Microsurgery for drug resistance epilepsy due to temporal lobe lesions in a resource limited condition: a cross-sectional study.

The study was a retrospective and prospective cross-sectional study of 35 cases of drug-resistant temporal lobe epilepsy. All patients were operated on by the primary author between May 2018 and September 2022. The study evaluated various factors including clinical characteristics, electroencephalogram, magnetic resonance imaging, surgical outcomes, and histopathology. The success rate of the surgeries (74.3%) is similar to those reported in high-income countries. 51.4% underwent selective amygdalohippocampectomy for cases that localized to the mesial temporal lobe. Lateral/neocortical lesions underwent lesionectomy (48.6%). Our study found a complication rate of 17.1%: meningitis (8.5%), trainset focal paralysis (2.9%), and soft tissue infection (5.7%). There were no mortalities. The article showcases an international collaborative effort that demonstrates the possibility of providing highly effective and safe surgical care for temporal lobe epilepsy even in low-resource environments. The authors hope that this model can be replicated in other areas with similar resource limitations.

Three-Dimensional Cellular Automaton for Modeling of Self-Similar Evolution in Biofilm-Forming Bacterial Populations

Bacterial populations often form colonies and structures in biofilm. The paper aims to design suitable algorithms to simulate self-similar evolution in this context, specifically by employing a hybrid model that includes a cellular automaton for the bacterial cells and their dynamics. This is combined with the diffusion of the nutrient (as a random walk), and the consumption of nutrients by biomass. Lastly, bacterial cells divide when reaching high levels. The algorithm computes the space-time distribution of biomass under limited nutrient conditions, taking into account the collective redistribution of nutrients. To achieve better geometry in this modified model approach, truncated octahedron cells are applied to design the lattice of the cellular automaton. This allows us to implement self-similar realistic bacterial biofilm growth due to an increased number of inner relations for each cell. The simulation system was developed using C# on the Unity platform for fast calculation. The software implementation was executed in combination with the procedure of surface roughness measurements based on computations of fractional dimensions. The results of the simulations qualitatively correspond to experimental observations of the population dynamics of biofilm-forming bacteria. Based on in silico experiments, quantitative dependencies of the geometrical complexity of the biofilm structure on the level of consumed nutrients and oxygen were revealed. Our findings suggest that the more complex structure with a fractal dimension of the biofilm boundaries (around 2.6) corresponds to a certain range of nutrient levels, after which the structure degenerates and the biofilm homogenizes, filling the available space provided and tending towards a strictly 3D structure. The developed hybrid approach allows realistic scenario modeling of the spatial evolution of biofilm-forming bacterial populations and specifies geometric characteristics of visualized self-similar biofilm bacterial structures.

СЕЙСМОСТІЙКІСТЬ ВЕЛИКОРОЗМІРНОГО РЕЗЕРВУАРУ ДЛЯ ЗБЕРІГАННЯ МЕТАНОЛУ

The paper contains the methods of analytical modeling of the seismic loads at the large-sized tanks in the form of impulsive and convective hydrodynamic pressures of the liquid. Have been performed the simulation of the stress-strain state of the tank by the finite element method basis on the developed mathematical models of seismic impact. The simulation takes into account the distortion of the tank’s geometry, obtained as a result of long-term operation and reduction in the strength of tank structures due to corrosive wear. Have been considered the generally accepted foreign method for calculating seismic resistance for assessing the strength and durability of tank’s building structures. The method is adapted to the requirements of national standards. Have been revealed that the most dangerous state for the tank is seismic action with a horizontal direction cosine as a result of the analysis of possible design situations. Have been performed the numerical calculations using the proposed method of analytical modeling of seismic impact for a tank with a volume of 20 000 m3, built in the Yuzhny city, Odessa region. Have been determined the estimated height value of the excited wave, which can appear on the surface of methanol during a seismic shock. Have been proven that in order to preserve the integrity of the roof structures, as well as to ensure the seismic resistance of other structures, the filling of the tank with the product should be limited to 61% of the total design capacity. Have been verified the most overloaded sections of the shell, which are the supporting layers of the side wall, as well as the most probable limit conditions during a seismic shock (buckling).Have been developed the technological regulations for further safe operation of tank based on seismic resistance and outlined directions for further scientific research on this topic.

An Improved Lightweight Dense Pedestrian Detection Algorithm

Due to the limited memory and computing resources in the real application of target detection, the method is challenging to implement on mobile and embedded devices. In order to achieve the balance between detection accuracy and speed in pedestrian-intensive scenes, an improved lightweight dense pedestrian detection algorithm GS-YOLOv5 (GhostNet GSConv- SIoU) is proposed in this paper. In the Backbone section, GhostNet is used to replace the original CSPDarknet53 network structure, reducing the number of parameters and computation. The CBL module is replaced with GSConv in the Head section, and the CSP module is replaced with VoV-GSCSP. The SloU loss function is used to replace the original IoU loss function to improve the prediction box overlap problem in dense scenes. The model parameters are reduced by 40% and the calculation amount is reduced by 64% without losing the average accuracy, and the detection accuracy is improved by 0.5%. The experimental results show that the GS-YOLOv5 can detect pedestrians more effectively under limited hardware conditions to cope with dense pedestrian scenes, and it is suitable for the online real-time detection of pedestrians.

Sibling cannibalism in the false widow spider is dependent on spiderling density and the reliable availability of fresh prey

AbstractAdult female spiders lay batches of eggs in silken egg sacs, and after hatching, the spiderlings live for transient periods in their mother's web before dispersing. Sibling cannibalism is frequently observed among spiderlings of many species under conditions of food deprivation. Here, we conducted assays in small Petri dishes with different densities of newly hatched (second instar) spiderlings of the false widow spider, Steatoda grossa, using a split‐clutch design. Prey (freshly killed fruit flies) availability was manipulated both numerically and temporally. Offspring from 10 different females were separated as siblings into densities of two, four, or eight spiderlings per Petri dish and these were provided with either 0 flies (starvation control), two flies, four flies, or eight flies that were replenished weekly or every 3 weeks. A further control was conducted with solitary spiderlings in Petri dishes deprived of flies. The number of surviving spiderlings per Petri dish was counted every 3 days until only one remained (or until death of the solitary spiderling). Our results show that the rate of cannibalism was lower with increasing spiderling density and when fresh flies were replenished more frequently, whereas the number of flies that were provided did not affect cannibalism. In S. grossa, juvenile cannibalism occurs primarily under conditions of extreme food limitation, although in synanthropic habitats where the spider is abundant, it may be an adaptive strategy owing to the potential scarcity of prey. Under certain conditions, cannibalism in spiderlings is adaptive by eliminating competitors and providing nutrient‐rich food.

Thiamine (vitamin B1) helps to regulate wheat growth and yield under water limited conditions by adjusting tissue mineral content, cytosolutes and antioxidative enzymes

Thiamine (vitamin B1) helps to regulate wheat growth and yield under water limited conditions by adjusting tissue mineral content, cytosolutes and antioxidative enzymes

Line solitons, lumps, and lump chains in the (2+1)-dimensional generalization of the Korteweg–de Vries equation

This article focuses on the exploration of novel soliton molecules for the (2+1)-dimensional Korteweg–de Vries equation. Specifically, Hirota bilinear form is derived through Bell polynomial method, and the hybrid solution comprising one lump soliton and an arbitrary number of line solitons or/and lump chains is derived through the introduction of a long wave limit and new constraint conditions between the parameters of the N-soliton solutions and velocity resonance. The paper presents both analytical and graphical demonstrations of a range of interactions, showcasing the propagation of nonlinear localized waves. The findings of this study could contribute to a better understanding of physical phenomena associated with the propagation of nonlinear localized waves.

Fonsecaea pedrosoi produces ferricrocin and can utilize different host iron sources

The survival of living organisms depends on iron, one of the most abundant metals in the Earth's crust. Nevertheless, this micronutrient is poorly available in our aerobic atmosphere as well as inside the mammalian host. This problem is circumvented by the expression of high affinity iron uptake machineries, including the production of siderophores, in pathogenic fungi. Here we demonstrated that F. pedrosoi, the causative agent of the neglected tropical disease chromoblastomycosis, presents gene clusters for siderophore production. In addition, ten putative siderophore transporters were identified. Those genes are upregulated under iron starvation, a condition that induces the secretion of hydroxamates, as revealed by chrome azurol S assays. RP-HPLC and mass spectrometry analysis allowed the identification of ferricrocin as an intra- and extracellular siderophore. F. pedrosoi can grow in different iron sources, including the bacterial ferrioxamine B and the host proteins ferritin, hemoglobin and holotransferrin. Of note, addition of hemoglobin, lactoferrin and holotransferrin to the growth medium of macrophages infected with F. pedrosoi enhanced significantly fungal survival. The ability to produce siderophores in iron limited conditions added to the versatility to utilize different sources of iron are strategies that certainly may contribute to fungal survival inside the host.