Intensity Values Research Articles

Three-Dimensional Cell Culture Micro-CT Visualization within Collagen Scaffolds in an Aqueous Environment.

Among all of the materials used in tissue engineering in order to develop bioequivalents, collagen shows to be the most promising due to its superb biocompatibility and biodegradability, thus becoming one of the most widely used materials for scaffold production. However, current imaging techniques of the cells within collagen scaffolds have several limitations, which lead to an urgent need for novel methods of visualization. In this work, we have obtained groups of collagen scaffolds and selected the contrasting agents in order to study pores and patterns of cell growth in a non-disruptive manner via X-ray computed microtomography (micro-CT). After the comparison of multiple contrast agents, a 3% aqueous phosphotungstic acid solution in distilled water was identified as the most effective amongst the media, requiring 24 h of incubation. The differences in intensity values between collagen fibers, pores, and masses of cells allow for the accurate segmentation needed for further analysis. Moreover, the presented protocol allows visualization of porous collagen scaffolds under aqueous conditions, which is crucial for the multimodal study of the native structure of samples.

Impacts of the land use transition on ecosystem services in the Dongting Lake area

Urbanization-induced land use transitions (LUTs) result in a decline in ecosystem services, which has implications for regional ecological security. In order to explore the relationship between ecosystem services and land use transition, this paper utilizes the InVEST model, a geographically weighted regression (GWR) model, to examine the impact of land use transition on ecosystem services in the Dongting Lake area (DLA). The results showed that 1) with the change in urbanization development, the average values of land use transition intensity (LUI) in 2000, 2010, and 2020 are 237.99, 235.82, and 238.92, respectively. Land use dynamics (LUD) show a tendency to increase and then decrease, with average values of 5.58 and 5.62 for the periods 2000–2010 and 2010–2020, respectively, and the transformation of land use shows obvious spatio-temporal heterogeneity. 2) Habitat quality and carbon sequestration showed a downward trend. In contrast, food supply followed an upward trend; soil conservation (SC) and water yield (WY) services initially increased and decreased later. The overall spatial changes in habitat quality and carbon sequestration appear to be insignificant. Food supply shows significant differences in the plains compared to other areas, while soil conservation and water yield service show significant changes in places other than the DLA. 3) From 2000 to 2020, land use transition dynamics, population density, GDP density, night lighting, and transition intensity had mainly negative effects on ecosystem services. Only the Normalized Vegetation Index (NDVI) showed a positive effect on ecosystem services. The results of the research will provide valuable references for the development and implementation of spatial ecological restoration planning and land use policies in the national territory.

Balloonless spinning spindle head shape optimisation

Abstract The article presents a procedure for assessing and selecting the shape of the ring spinner spindle head that is optimum for yarn manufacturing using the balloonless system, considering multiple criteria. This procedure consists of two stages: the Pareto-optimal method and the distance function method. Twelve spindle head variants were designed and manufactured of 100Cr6 bearing steel composed of C (0.93–1.05%), Si (0.15–0.35%), Mn (0.25–0.45%) along with aluminium alloys EN AW-6060 (AlSi1MgMn) containing Si (0.70–1.3%), Mg (0.6–1.2%), Mn (0.4–1.0%) and EN AW-6060 (AlMgSi) containing Si (0.30–0.60%) and Mg (0.35–0.60%). The shapes of the heads were developed on the basis of the design solutions used previously by the leading ring-spinning machine and spinning spindle manufacturers. Four design variants of spindle heads, made of AlMgSi wrought aluminium alloy, have a unique shape that enables shaping their teeth (notches) by extrusion on a hydraulic press. The assessment criteria were production cost per unit, head weight, maximum surface hardness HV0.1, and four yarn technological quality criteria: maximum yarn tension values during the formation of the base, body and head of the package, and the tension amplitude. The assessment values for the criteria obtained through calculations and measurements were normalized. Then, a set of Pareto-optimal solutions was determined using seven criteria. Due to the fact that this set consisted of six variants, the distance function was used to select the best one. The best design variant of the head is the one corresponding to the lowest distance function value. In our case, the variant is a unique shape, made of AlMgSi alloy, with teeth thickened on the outer diameter of the head, ensuring the lowest yarn tension value at the height during the formation of the package.

A Saturation Artifacts Inpainting Method Based on Two-Stage GAN for Fluorescence Microscope Images.

Fluorescence microscopic images of cells contain a large number of morphological features that are used as an unbiased source of quantitative information about cell status, through which researchers can extract quantitative information about cells and study the biological phenomena of cells through statistical and analytical analysis. As an important research object of phenotypic analysis, images have a great influence on the research results. Saturation artifacts present in the image result in a loss of grayscale information that does not reveal the true value of fluorescence intensity. From the perspective of data post-processing, we propose a two-stage cell image recovery model based on a generative adversarial network to solve the problem of phenotypic feature loss caused by saturation artifacts. The model is capable of restoring large areas of missing phenotypic features. In the experiment, we adopt the strategy of progressive restoration to improve the robustness of the training effect and add the contextual attention structure to enhance the stability of the restoration effect. We hope to use deep learning methods to mitigate the effects of saturation artifacts to reveal how chemical, genetic, and environmental factors affect cell state, providing an effective tool for studying the field of biological variability and improving image quality in analysis.

Stylization of Oral Elements in the Novel Women. Voices. By Jasmina Musabegović

This paper aims to analyze and interpret the linguistic and stylistic means by which the world of the novel Women. Voices. by Jasmina Musabegović is built. The paper analyzes and interprets the novel’s spoken language stylization, as its main stylistic feature. It is based on the assumption that orality, as a linguistic-stylistic feature of the novel, is primarily realized at the syntactic-stylistic level. In this direction, the elements of expressive syntax, such as spoken (oral) sentence length, elliptical, nominative, and fragmented sentences, as well as syntactic repetitions and accumulations, are especially explored. The mentioned syntactic-stylistic means are investigated to point out their stylistic potential in the revitalization of the tonal, rhythmic, intonation, and intensity values of the spoken language, based on which novelistic voices are generated as the narrative subject of the novel. Also, exclamatory and expressive interrogative sentences are explored in the paper as significant means of conveying emotional-expressive (affective) and intensifying the semantic values of a sentence. In doing so, it is particularly indicated that interrogative sentences, in addition to appearing as elements of expressive syntax, have a significant opening (cataphoric) value in developing the novel's narrative content. In addition, the paper points out the stylistic possibilities and values of the vernacular idiom in the stylization of novelistic expression with folk expression.

A computed tomography-based limit analysis approach to investigate the mechanical behavior of the human femur prone to fracture

The paper proposes a refined CT-based FE modelling strategy that implements a limit analysis numerical procedure, namely the Elastic Compensation Method (ECM), to estimate a lower bound to the collapse load of a human femur. In particular, the model geometry was obtained from CT images by segmentation of a fresh-frozen human cadaveric femur that was discretized with second-order tetrahedral 3D finite elements. A yield criterion of Tsai–Wu-type, expressed in principal stress space, was adopted to model the bone tissues for which the strength limit values in tension, compression and shear are computed locally from the femoral density distribution also derived from CT images. The developed CT-based numerical technique showed the ability to predict, at least for the examined femur for which the experimental collapse load is available, a lower bound to the collapse load. The proposed approach seems a promising and effective tool that could be adopted into clinical practice to predict the fracture risk of human femur starting from patient-specific data given by medical imaging.

KLASIFIKASI HASIL MRI TUMOR OTAK DENGAN EKTRAKSI FITUR GRAY LEVEL CO-OCCURANCE MATRIX (GLCM)

An important part of the body is the brain which is the source of all the body's organs in the skull cavity. Brain tumors are one of the diseases that can attack it. Detection of brain tumors is one aspect that is considered important in medical diagnosis. This research aims to implement GLCM (Gray Level Co-occurrence Matrix) feature extraction on MRI images of brain tumors and to find the best algorithm performance for detecting brain tumors using these MRI images. The data used in this research is public data originating from kaggle.com. The feature extraction process in pictures used in this research is GLCM, which has the function of calculating the frequency of pixel intensity values ​​that are spaced between images using parameters 0o, 45o, 90o, and 135o. The next stage in this research is to carry out preprocessing steps and then look for classification values ​​from the MRI results using the Naïve Bayes, C4.5, and Neural Network algorithms. The results obtained show that Naïve Bayes has the best algorithm performance compared to C4.5 and Neural Network, namely with an accuracy of the Naïve Bayes algorithm of 96.8%, while for the C4.5 algorithm it is 41.5% and the Neural Network is 38.25%. Apart from this, this study proves that GLCM feature extraction has proven effective in capturing texture information from MRI images which is very important in brain tumor classification.

Chlorophyll content estimation and ripeness detection in tomato fruit based on NDVI from dual wavelength LiDAR point cloud data

Tomato (Solanum lycopersicum L.) is a climacteric fruit exhibiting the ripening pattern with change in colour from green to red. Normalized difference vegetation index (NDVI) derived from spectral-optical analysis has been previously employed in ripeness analysis of various fruits based on its strong correlation to ripening-related chlorophyll content. In this study, light detection and ranging (LiDAR) laser scanner was applied to gain 3D tomato point clouds aimed at estimating the spatially resolved chlorophyll content and ripeness class of tomato fruit. Freshly harvested tomatoes, capturing six ripeness classes (mature green, breaker, turning, pink, light red, and red) according to USDA colour standard, were analysed using a linear conveyor mounted sensor system consisting of two LiDAR units measuring position and return signal strength intensity at 660 nm and 905 nm. Fruit point clouds were pre-processed including geometric correction considering the curvature of each tomato sample, removing highest intensity areas at the specular highlighted spots, calibration of intensity values using standard black and white colour coated boards, and calibration on attenuation of the opaque samples. Particularly, for gaining the attenuation coefficient (μ), 9 opaque synthetic models with known attenuation were used. Obtained μ660 and μ905 were merged to gain NDVILiDAR. Chemically analysed chlorophyll content of tomato samples was correlated to μ660 and NDVILiDAR, whereas low correlation appeared for μ905 with a coefficient of determination (R2) of 0.58, 0.60, and 0.06, respectively. Regression models were used to estimate the total tomato chlorophyll content and the lowest root mean square error (RMSE) was found for μ660 (RMSE = 4.97 mg (100 g dry mass)−1) followed by NDVILiDAR (RMSE = 5.22 mg (100 g dry mass)−1), and μ905 (RMSE = 53.50 mg (100 g dry mass)−1). Histograms of NDVILiDAR and μ660 were extracted from each tomato point cloud and utilized to construct PLS-DA model for tomato ripening class prediction considering the spatial chlorophyll distribution. The overall accuracies for NDVILiDAR and μ660 were 70 % and 68 %, respectively, in leave-one-out cross-validation for visually defined colour classes. The LiDAR-based approach could support selective detection of ripe fruit in robots for harvesting and postharvest handling.

Multimodal Image Fusion Workflow Incorporating MALDI Imaging Mass Spectrometry and Microscopy for the Study of Small Pharmaceutical Compounds.

Multimodal imaging analyses of dosed tissue samples can provide more comprehensive insights into the effects of a therapeutically active compound on a target tissue compared to single-modal imaging. For example, simultaneous spatial mapping of pharmaceutical compounds and endogenous macromolecule receptors is difficult to achieve in a single imaging experiment. Herein, we present a multimodal workflow combining imaging mass spectrometry with immunohistochemistry (IHC) fluorescence imaging and brightfield microscopy imaging. Imaging mass spectrometry enables direct mapping of pharmaceutical compounds and metabolites, IHC fluorescence imaging can visualize large proteins, and brightfield microscopy imaging provides tissue morphology information. Single-cell resolution images are generally difficult to acquire using imaging mass spectrometry but are readily acquired with IHC fluorescence and brightfield microscopy imaging. Spatial sharpening of mass spectrometry images would thus allow for higher fidelity coregistration with other higher-resolution microscopy images. Imaging mass spectrometry spatial resolution can be predicted to a finer value via a computational image fusion workflow, which models the relationship between the intensity values in the mass spectrometry image and the features of a high-spatial resolution microscopy image. As a proof of concept, our multimodal workflow was applied to brain tissue extracted from a Sprague-Dawley rat dosed with a kratom alkaloid, corynantheidine. Four candidate mathematical models, including linear regression, partial least-squares regression, random forest regression, and two-dimensional convolutional neural network (2-D CNN), were tested. The random forest and 2-D CNN models most accurately predicted the intensity values at each pixel as well as the overall patterns of the mass spectrometry images, while also providing the best spatial resolution enhancements. Herein, image fusion enabled predicted mass spectrometry images of corynantheidine, GABA, and glutamine to approximately 2.5 μm spatial resolutions, a significant improvement compared to the original images acquired at 25 μm spatial resolution. The predicted mass spectrometry images were then coregistered with an H&E image and IHC fluorescence image of the μ-opioid receptor to assess colocalization of corynantheidine with brain cells. Our study also provides insights into the different evaluation parameters to consider when utilizing image fusion for biological applications.

Restructuring agricultural land use in Ukraine. Directions for development

The intensity of agricultural land use in Ukraine was analyzed based on soil quality and the values of the land-use intensity (LUI) index and the land-use capacity (LUC) index. In the administrative districts of Cherkasy region, the LUI index ranged from 0.07 to 1.0, and the LUC index ranged from 0.01 ha per 1000 UAH of gross output to 0.19 ha per 1000 UAH, which points to considerable difference in land-use intensity and agricultural land-use capacity. New approaches should be sought to improve the management and use of agricultural land. Functional land use areas should be restructured, in particular, by implementing non-traditional methods of agricultural land use. The results of the study indicate that land-use functions should be modified over an area of 5,343,900 ha, where 2,049,900 ha should be converted to crop rotations for soil protection, 208,000 ha – to protective forest margins; and 2,878,000 ha – to non-traditional forms of agricultural land use. Motives: The purpose of the article was to propose a methodological approach for assessing the intensity of agricultural land use and to suggest directions for land-use restructuring to improve its ecological status. Aim: In the proposed methodical approach, the intensity of agricultural land use was assessed with the land-use intensity (LUI) index and the land-use capacity (LUC) index. To reduce plowing and increase land-use intensity, the functions of degraded arable land should be modified over an area of 5,343,900 ha, where 2,049,900 ha should be converted to crop rotations for soil protection, 208,000 ha – to protective forest margins; and 2,878,000 ha – to non-traditional forms of agricultural land use. Results: The article substantiates the need to modify the functions of arable land over an area of 5,343,900 ha, where 2,049,900 ha should be converted to crop rotations for soil protection, 208,000 ha – to protective forest margins; and 2,878,000 ha – to non-traditional forms of agricultural land use.

A novel hybrid enhanced oil recovery technique to enhance oil production from oil-wet carbonate reservoirs by combining electrical heating with nanofluid flooding

Enhanced Oil Recovery provides a promising technique to maximise fossil fuel recovery from existing resources, and when used in conjunction with Carbon Capture and Storage/Utilisation provides a way to support a transition to alternative cleaner fuels. A hybrid Enhanced Oil Recovery method by a combination of electrical heating and nanofluid flooding was applied to oil-wet carbonate reservoirs and assessed in terms of the oil production, zeta potential, contact angle, pellet compaction, interfacial tension, and pH values. The hybrid technique consisted of a combination of direct current (up to 30 V) and iron oxide (Fe2O3) or magnesium oxide (MgO) nanofluids. Both nanofluids were injected into oil-wet Austin chalk – our laboratory model of an oil-wet carbonate reservoir – and then electrical heating was started, or vice versa. Introducing electrical heating first increased oil recovery by up to 27% in seawater compared to 16% in deionised water. When Fe2O3 nanofluid was injected, oil recovery further increased to 32% in seawater and 24% in deionised water. The contact angle and zeta potential decreased from 124° to 36° and from −24.4 to −23.7 mV, respectively, when nanofluid was injected in seawater, leading to better nanofluid stability and penetration into the carbonate rock as shown by increased pellet porosity from 6.6% to 14.8%. Moreover, it was found that the interfacial tension was reduced from 72 to 32.7 mN/m in the pre-magnetised samples with Fe2O3 NPs injection compared to 33.2 mN/m in the samples with MgO injection. It was found from our experiments that the effect of the generated electricity on the surface charge was of a temporary nature as the zeta potential of the rock returned to its original value as soon as the power was disconnected. The mechanism underlying the hybrid Enhanced Oil Recovery EOR technique from the laboratory findings was found to be based on electrowetting and nanofluid adsorption. Results indicate that the technique is promising for further improving oil recovery and securing energy supply during the transition to net zero.

Gray-tone appearances on 4-hour delayed gadolinium-enhanced magnetic resonance imaging indicate severe inner ear pathology and symptoms in sudden sensorineural hearing loss.

Hybrid of reversed image of positive endolymph signal and negative image of perilymph signal (HYDROPS) in delayed gadolinium-enhanced magnetic resonance imaging (MRI) typically depicts normal inner ear as "white-tone" and endolymphatic hydrops as "black-transparent" appearances, whereas ears with auditory and vestibular disorders are occasionally depicted as "gray-tone." This study aimed to investigate the pathological basis of sudden sensorineural hearing loss (SSNHL) patients with "gray-tone" appearances on HYDROPS. Delayed gadolinium-enhanced MRI examinations were conducted on 29 subjects with unilateral SSNHL. We mainly analyzed positive perilymph image (PPI) and positive endolymph image (PEI), which were components HYDROPS. On PPI, signal intensity (SI) values extracted from the cochlear and vestibular region of interest (ROI) were higher in the SSNHL ears with dizziness/vertigo symptom at the first visit compared to the healthy ear. Additionally, the PPI/PEI enhancement pattern in the vestibule was associated with a high prevalence of hearing and vestibular deteriorations at the first visit and poor hearing improvement after treatment. Enhancement on PPI/PEI may result from leakage of gadolinium into the inner ear following breakdown of the blood-labyrinth barrier, with high SI being correlated with the amount of leakage. Particularly, a significant leakage into the endolymphatic space, defined as PPI+/PEI+, indicates severe inner ear pathology. Ultimately, we emphasize that the "gray-tone" appearance in the inner ear on HYDROPS comprises enhancements on both PPI and PEI and propose a new classification for evaluating SSNHL Peri- and Endolymphatic image Enhancement pattern in Delayed gadolinium-enhanced MRI (SPEED). 4.

On-Chip Capture, Raman-Silent Polymer Labeling, and Digital Mapping Analysis of Escherichia coli O157:H7 in Beverages All-in-One.

Escherichia coli O157:H7 is one of the most susceptible foodborne pathogens, easily causing food poisoning and other health risks. It is of great significance to establish a quantitative method with higher sensitivity and less time consumption for foodborne pathogens analysis. The Raman-silent signal has a good performance for avoiding interference from the food matrix so as to achieve accurate signal differentiation. In this work, we presented a preparation-mapping all-in-one method for digital mapping analysis. We prepared a functionalized Raman-silent polymer label of Escherichia coli O157:H7, which was captured on a porous 4-mercaptophenylboric acid@Ag foam chip. To improve accuracy and widen the detection range, a digital mapping quantitative strategy was employed in data extraction and processing. By transfer mapping information into digitized statistical results, the limitation of obtaining reproducible intensity values just by randomly selected spots on the substrate can be addressed. With a wide linear range of 1.0 × 101-1.0 × 105 CFU mL-1 and a limit of detection of 4.4 CFU mL-1, this all-in-one method had good sensitivity performance. Also, this method achieved good precision and selectivity in a series of experiments and was successfully applied to the analysis of beverage samples.

Understanding the surface tension reduction of the water/air interface due to the synergistic effect of the SDS-CTAB mixture: A molecular dynamics simulation study

Using molecular dynamics simulation, the surface tension is calculated and compared with experimental data to provide a microscopic interpretation of the synergistic effect on water surface tension reduction caused by a binary mixture composed of anionic and cationic surfactants. For the anionic and cationic surfactants, the sodium dodecyl sulphate (SDS) and the cetyl trimethylammonium bromide (CTAB) were used, respectively. Besides the electrostatic attraction between the anionic and cationic surfactants, resulting in a synergistic effect, other properties were analysed to study the reduction of the surface tension from a molecular level. For different SDS:CTAB composition of distinct properties of the pseudobinary mixture, such as the hydrogen bonds, order of the tails, and the arrangement of the surfactants at the interface were calculated. Those properties present different behaviours at the maximum and minimum values of the surface tension. The CTAB tails present a higher order than the SDS tails at all SDS:CTAB compositions however, the first ones show a maximum order at the minimum in the surface tension. The excess configurational energy was also calculated and shows a minimum value in the same composition as the minimum value of the surface tension.

Novel insight into the feed conversion ratio in laying hens and construction of its prediction model

Feed efficiency (FE) is an important economic factor in poultry production, and feed conversion ratio (FCR) is one of the most widely used measures of FE. Factors associated with FCR include genetics, the environment, and other factors. However, the mechanisms responsible for FCR in chickens are still less well appreciated. In this study, we examined the pattern changes of FCR, then delved into understanding the mechanisms behind these variations from both genetic and environmental perspectives. Most interestingly, the FCR at the front section of henhouse exhibited the lowest value. Further investigation revealed that laying rate in the high FCR (HFCR) group was lower than that in the low FCR (LFCR) group (P < 0.05). Cortisol, total antioxidant capacity (TAOC), and IgG levels in the LFCR group were significantly lower than those in the HFCR group (P < 0.05), while BUN level was significantly higher than that in the HFCR group (P < 0.05). We identified a total of 67 and 10 differentially expressed genes (DEGs) associated with FCR in ovarian and small intestine tissues, respectively. Functional enrichment analysis of DEGs revealed that they might affect FCR by modulating genes associated with salivary secretion, ferroptosis, and mineral absorption. Moreover, values for relative humidity (RH), air velocity (AV), PM2.5, ammonia (NH3), and carbon dioxide (CO2) in the LFCR group were significantly lower than those in the HFCR group (P < 0.05). Conversely, value for light intensity (LI) in the LFCR group was significantly higher than that in the HFCR group (P < 0.05). Correlation analysis revealed a positive correlation between FCR and RH, AV, PM2.5, NH3, and CO2, and a negative correlation with LI. Finally, the FCR prediction model was successfully constructed based on multiple environmental variables using the random forest algorithm, providing a valuable tool for predicting FCR in chickens.

A Self-foaming Strategy to Construct Small Mo2C Nanoparticles Decorated 3D Carbon Foams as Superior Electromagnetic Wave Absorbing Materials with Strong Corrosion Resistance.

3D macroporous carbon-based foams are always considered as promising candidates for high-performance electromagnetic (EM) wave absorbing materials due to the collaborative EM contribution and salutary structure effect. However, the uneven distribution of heterogeneous EM components and the cumbersome preparation process have become key issues to hinder their performance improvement and practical popularity. Herein, the fabrication of 3D carbon foam decorated with small and highly dispersed Mo2C nanoparticles is realized by an innovative self-foaming strategy. The foaming mechanism can be attributed to the decomposition of nitrate during the softening process of organic polymers. The good dispersion of Mo2C nanoparticles boosts interfacial polarization significantly. After regulating the content of Mo2C nanoparticles, the optimal Mo2C/CF-x exhibits good EM absorption performance, whose minimum reflection loss intensity value can reach up to -72.2dB, and effective absorption bandwidth covers 6.7GHz with a thickness of 2.30mm. Very importantly, the resultant Mo2C/CF-x exhibits hydrophobicity and strong acidic anticorrosion, and a long-time treatment in HCl solution (6.0molL-1) produces negligible impacts on their EM functions. It is believed that this extraordinary feature may render Mo2C/C foams as qualified and durable EM wave absorbing materials (EWAMs) under rigorous conditions.

Methane-Water Interfacial Tension in Nanopores: A Dissipative Particle Dynamics Study.

Imbibition of fracturing fluid in deep shale nanopores has a significant effect on shale gas production. One of the key parameters affecting imbibition is the interfacial tension of the methane-water system. However, studies on the methane-water interfacial tension in nanopores are very limited, and obtaining the accurate value of the methane-water interfacial tension at the nanoscale is difficult and time-consuming. In this work, a dissipative particle dynamics simulation model was built to study the methane-water interfacial tension in nanopores. This model provides reliable access to methane-water interfacial tension for deep shales under high-temperature, high-pressure conditions at low computation cost. It can be easily used to compute the methane-water interfacial tension in nanopores or the confined space in wide application scenarios. A sensitivity study of methane-water interfacial tension on a variety of factors was conducted. Results demonstrate that under high-pressure conditions, the increase in pressure leads to the rise of interfacial tension. When pressure increases from 20 to 120 MPa, interfacial tension rises from 0.0275 to 0.12 N/m, which contributes to the severe imbibition of fracturing fluid in deep shales. The confinement effect was observed by investigating the influence of pore size. Interfacial tension almost remains unchanged in pores smaller than 7 nm because most of the confined space is occupied by interface layer molecules in these pores. When pore size increases from 7 to 15 nm, the confinement effect is reduced. The interfacial tension experiences a growth from 0.1155 to 0.27 mN/m. Compared with pressure and pore size, the effect of temperature on interfacial tension can be neglected during deep shale gas production.

A network analysis of the interrelationships between depression, anxiety, insomnia and quality of life among fire service recruits.

Research on the mental health and quality of life (hereafter QOL) among fire service recruits after the end of the COVID-19 restrictions is lacking. This study explored the network structure of depression, anxiety and insomnia, and their interconnections with QOL among fire service recruits in the post-COVID-19 era. This cross-sectional study used a consecutive sampling of fire service recruits across China. We measured the severity of depression, anxiety and insomnia symptoms, and overall QOL using the nine-item Patient Health Questionnaire (PHQ-9), seven-item Generalized Anxiety Disorder scale (GAD-7), Insomnia Severity Index (ISI) questionnaire, and World Health Organization Quality of Life-brief version (WHOQOL-BREF), respectively. We estimated the most central symptoms using the centrality index of expected influence (EI), and the symptoms connecting depression, anxiety and insomnia symptoms using bridge EI. In total, 1,560 fire service recruits participated in the study. The prevalence of depression (PHQ-9 ≥ 5) was 15.2% (95% CI: 13.5-17.1%), while the prevalence of anxiety (GAD-7 ≥ 5) was 11.2% (95% CI: 9.6-12.8%). GAD4 ("Trouble relaxing") had the highest EI in the whole network model, followed by ISI5 ("Interference with daytime functioning") and GAD6 ("Irritability"). In contrast, PHQ4 ("Fatigue") had the highest bridge EI values in the network, followed by GAD4 ("Trouble relaxing") and ISI5 ("Interference with daytime functioning"). Additionally, ISI4 "Sleep dissatisfaction" (average edge weight = -1.335), which was the central symptom with the highest intensity value, had the strongest negative correlation with QOL. Depression and anxiety were important mental health issues to address among fire service recruits in the post-COVID-19 era in China. Targeting central and bridge symptoms identified in network analysis could help address depression and anxiety among fire service recruits in the post-COVID-19 era.

Rough bed hydrodynamics under wave-current interactional flow field: double averaging approach

ABSTRACT The effect of surface wave on the double-averaged (DA) current-only flow properties is examined over two different types of rough beds comprising hemispherical elements with regular and staggered arrangements. For each case, the relative roughness spacing of p/r = 4 is maintained, where p = pitch distance and r = hemisphere height. The changes in turbulence characteristics against the water depth are analysed for superimposed surface waves propagating along the current with 1 and 2 Hz frequencies. The effect of regular and staggered roughness arrangements on different turbulence characteristics is explored. Results demonstrate that the DA stream-wise velocity decreases and increases at the outer region and within the roughness region, respectively, due to wave propagation. Owing to the surface wave, the DA intensity and form-induced intensity increase above the roughness layer and decrease within the bottom boundary layer. The values of form-induced intensities are smaller (greater) than the DA turbulence intensities at each considered flow depth within the roughness (outer) layer. For the entire flow depth, the values of DA Reynolds stress decrease due to wave interactions along a steady current. Again, the turbulence is observed to be close to isotropic for the current-only flow at the outer zone, while the flow transits to an anisotropic state with wave imposition on current. Further, the computed values of the turbulence indicator and dispersion functions are used for the identification of the level of turbulence and dispersion at each flow depth for the locations upstream and downstream of a hemisphere in the hemispherical bed.

Experimental and numerical studies of the processes of spraying coal-water slurries with pneumatic nozzle

The article presents the results of spraying tests of highly viscous coal-water slurries with a pneumatic nozzle and numerical studies of the structure of the gas-drop jet. An increase in the viscosity of coal-water slurries was ensured by introducing a third component into its composition – industrial waste (pyrogenetic liquid). The moderate effect of the slurry viscosity on the average droplet size of coal-water slurries in the jet is due to the high efficiency of the aerodynamic characteristics of the pneumatic nozzle. Introduction of pyrogenetic liquid in the amount of 20 % by weight into the composition of coal-water slurry significantly increases its viscosity. The value of the surface tension of such a slurry is more than doubled. The increase in the average droplet size was no more than 25 % when pyrogenetic liquid content in the composition of coal-water slurry was over 10 % by weight. The average droplet size of coal-water slurries varied in the range from 180 to 210 microns in the study area of gas droplet jet. The results of experimental and numerical studies have shown good convergence. High air velocities at short distances from the nozzle ensure high crushing efficiency of the jet of coal-water slurry. The breakdown of the boundary layer of the liquid from the periphery of the droplets of coal-water slurries and its subsequent destruction was observed at short distances from the nozzle. The values of the Weber number were 100<We<350. At considerable distances from the nozzle (more than 0.5 m), droplets of coal-water slurries were subjected to vibrational crushing (We<50).