Density Variations Research Articles

Kinetic description of flow detachment at a smooth micro-step: the near-free-molecular regime

We study the pressure-driven steady gas flow, imposed by temperature or density gradients, over a backward-facing step in a two-dimensional microchannel. Focusing on the near-free-molecular regime of high Knudsen (Kn) numbers, the problem is analyzed asymptotically based on the Bhatnagar, Gross and Krook kinetic model, and supported by numerical Discrete Velocity Method and Direct Simulation Monte Carlo calculations. The wall conditions are formulated using the Maxwell model, superposing specular and diffuse surface conditions. The asymptotic solution contains the leading-order free-molecular description and a first-order integral representation of the near-free-molecular correction. Our results indicate that flow separation at the step can occur at arbitrarily large (yet finite) Knudsen numbers in channels with specular surfaces (i.e., having an accommodation coefficient of α=0), driven by temperature differences between the inlet and outlet reservoirs. It is then shown that detachment is significantly suppressed by density variations between reservoirs and partially diffuse surfaces (with α≳0.3). While the mass flow rate in a specular channel decreases with decreasing Kn≫1 in a density-driven setup (in line with the Knudsen Paradox), it increases in a temperature-driven flow. The results are obtained for arbitrary differences between the inlet and outlet reservoir equilibrium properties, and are rationalized using the linearized problem formulation.

The Characterization of the Alcoholic Fermentation Process in Wine Production Based on Acoustic Emission Analysis

The present experimental study assessed the viability of utilizing an acoustic emission signal as a monitoring instrument to predict the chemical characteristics of wine throughout the alcoholic fermentation process. The purpose of this study is to acquire the acoustic emission signals generated by CO₂ bubbles to calculate the must density and monitor the kinetics of the alcoholic fermentation process. The kinetics of the process were evaluated in real time using a hydrophone immersed in the liquid within the fermentation tank. The measurements were conducted in multiple fermentation tanks at a winery engaged in the production of wines bearing the Rioja Denomination of Origin (D.O.) designation. Acoustic signals were acquired throughout the entirety of the fermentation process, via a sampling period of five minutes, and stored for subsequent processing. To validate the results, the measurements obtained manually in the laboratory by the winemaker were collected during this stage. Signal processing was conducted to extract descriptors from the acoustic signal and evaluate their correlation with the experimental data acquired during the process. The results of the analyses confirm that there is a high linear correlation between the density data obtained from the acoustic analysis and the density data obtained at the laboratory level, with determination coefficients exceeding 95%. The acoustic emission signal is a valuable decision-making tool for technicians and winemakers due to its sensitivity when describing variations in kinetics and density during the alcoholic fermentation process.

An Innovative 3D-IDE Design and Adaptive Signal Extraction Algorithm for Efficient Ovarian Cancer Detection.

This work presents a facile, ultrasensitive, and selective chemiresistive biosensor assisted by an adaptive signal extraction algorithm (ASEA) for detecting vimentin, a potential biomarker for ovarian cancer detection. The low-cost device, fabricated on a PCB substrate through sacrificial copper etching, features a 3D-IDE design with interwoven comb-like structures mimicking the natural symmetry of a droplet. An unequal count of positive and negative concentric circle fingers ensures a uniform, higher electric field over the sensor's surface, as verified by COMSOL Multiphysics 3D simulation. This optimal electric field elegantly reflects changes in the IV characteristics, even with minor variations in surface charge density from probe-target interactions. Graphene oxide, functionalized with a heterobifunctional linker, serves as the sensing nanomaterial. A detailed study examines the device's response with interdigitated gaps from 30 to 150 μm. A wider interdigitated gap introduces greater variability in the response across different voltage levels. To address this, the Python-based ASEA meticulously scans the entire voltage range, isolating the segment of the signal that best balances both the intensity and extension for optimal expression. ASEA boosts the limit of detection (LOD) by five times for sensors with gaps of over 100 μm. The biosensor achieves a minimum LOD of 9.45 fg mL-1 and a highest sensitivity of (ΔR/R) 477 ng mL-1 cm2. The biosensor exhibits excellent selectivity, strong resistance to interfering proteins, and commendable stability, showcasing its potential for on-field and point-of-care testing.

Variation of Packing Densities and Liquid Film Thicknesses in Alkali Solution and Water in Natural Pozzolan Calcium Hydroxide Pastes and Mortars

The characteristic properties of concrete depend on the quality of mortar. In order to improve the properties of hardened concrete, the mortar needs to have good performance. The property of mortar is affected by wet packing density, liquid film thickness, type and quality of binder, gradation of fine aggregates, and binder-to-fine-aggregate ratio. The commonly used alkali solutions to produce polymer mortar and concrete are cursed by carbon dioxide emissions. This study used alkali solution and water to investigate the variation of packing densities and film thicknesses on paste and mortar mixes. of natural pozzolan and calcium hydroxide at a ratio of 75P25CH and standard sand. The standard proctor compaction tools and procedures were adopted to attain the maximum values of packing density of 0.937 for mortar mixed with alkali solution, 0.919 for mortar mixed with water, 0.906 for paste mixed with alkali solution, and 0.829 for paste mixed with water. For the case of film thicknesses, the optimum values obtained were 0.548 μm for mortar mixed with alkali solution, 0.248 μm for mortar mixed with water, 0.398 μm for paste mixed with alkali solution, and 0.09 μm mixed with water. The results indicate that alkali solution produced higher packing density and liquid film thickness than water for both paste and mortar, and the packing density and liquid film thickness increase with the increase of the number of compacting blows with 9 blows to 36 blows. The study recommends the establishment of standard compaction effort and procedure.

Variation in coral rubble cryptofauna is scale-dependent and driven by small-scale habitat characteristics

Motile cryptofauna living in dead coral rubble represent some of the greatest biodiversity and basal energetic resources on tropical coral reefs. Yet we know relatively little about how and why coral rubble cryptofauna communities change over space and time. As human impacts increase the degradation of living hard corals to dead coral rubble on many reefs worldwide, understanding the communities that will succeed in these degraded environments and the factors paramount to their success becomes increasingly central to coral reef ecology and conservation. Using a remote and uninhabited oceanic atoll in the Pacific Ocean, we quantified the natural spatial variability in motile cryptofauna diversity and community structure in coral rubble across scales (m to km) and tested whether variability at smaller scales could be explained by gradients in microhabitat. We show that coral rubble cryptofauna communities are most variable at intra-site scales (m) rather than inter-site scales (100s m) or between reef zones (km scales). We also show that a substantial amount of variation in cryptofauna density (55%) and phyla-level community structure (31%) is explained by small-scale habitat characteristics, specifically the substrate type below the rubble and the variability in macroalgal cover on individual rubble pieces. Our findings highlight the need to study small-scale processes that are relevant to motile cryptofauna and their community interactions if we are to elucidate the structuring forces of these diverse cryptic assemblages on coral reefs.

Radial variation of fiber morphology and wood density of the commercial species Drypetes sp. and Myroxylon balsamum

Studying the radial variation of wood density, an essential biophysical property that reflects the quality of commercial species in tropical forests, is crucial. Understanding how these variations relate to wood anatomy provides valuable insights. In this study, we evaluated fiber morphology and radial density variation using X-ray densitometry in two commercial species from southeastern Peru. Ten trees from each species, Drypetes sp. and Myroxylon balsamum (Peru balsam), were analyzed. Fiber characteristics were assessed using macerated tissue, and density profiles were obtained via X-ray densitometry. The results indicate that in Drypetes sp., density decreases from the pith to the bark, whereas Myroxylon balsamum shows no significant radial variation. These findings are important for the efficient use and processing of these species.

Interannual Variation of Stomatal Traits Impacts the Environmental Responses of Apple Trees.

Stomata are fundamental to plant-water relations and represent promising targets to enhance crop water-use efficiency and climate resilience. Here, we investigated stomatal density (SD) variation in 269 apple accessions across 3 years (2019-2021), which demonstrated significant differences between accessions but consistency over time. We selected 2 subsets of 20 accessions, each with contrasting SD: high stomatal density (HSD; 370-500 mm-2) and low stomatal density (LSD; 192-316 mm-2). SD groups were compared in stomatal function, leaf physiology and crop productivity across two seasons (2021-2022). LSD had lower stomatal conductance (gs) and higher intrinsic water-use efficiency in both years (p < 0.05). Hotter and drier conditions in 2022 reduced gs similarly in both groups (-22% HSD, -21% LSD), but also created a difference in net carbon assimilation (Anet) that was not present in 2021 (HSD + 1.7 μmol CO2 m-2 s-1, p < 0.05). LSD constraints on Anet were reflected in carbon isotope discrimination (δ13C, p < 0.001) and annual decline in fruit yield (-35%, p < 0.001). Our results demonstrate the suitability of SD as a trait to improve WUE, but also identifies a trade-off between water savings and productivity, which requires consideration for breeding.

Dynamic RU-Next: Advancing liver and tumor segmentation with enhanced deep learning architecture

Liver cancer is a significant worldwide health issue, making accurate liver tumor identification essential for diagnosis and treatment planning. However, achieving precise tumor segmentation in CT scans is complex due to structural, boundary, and density variations. The Dynamic RU-Next (DRuN) model is introduced to address these challenges, integrating a ResNeXt backbone for enhanced feature extraction with a Dynamic U-Net decoder utilizing pixel shuffle up-sampling. Two variants are tested on the publicly available LiTS dataset: DRuN-50 (ResNeXt-50 backbone) and DRuN-101 (ResNeXt-101 backbone). Both models demonstrated improved performance compared to existing methods, with DRuN-50 and DRuN-101 achieving accuracies of 98.33% and 98.88% for liver delineation, and 96.66% for tumor delineation. The respective dice scores are 98.41% and 98.96% for the liver and 96.29% and 95.38% for tumor segmentation. Additionally, these models process each image in 1.036 s and 1.075 s, respectively.

Learning Signed Hyper Surfaces for Oriented Point Cloud Normal Estimation.

We propose a novel method called SHS-Net for point cloud normal estimation by learning signed hyper surfaces, which can accurately predict normals with global consistent orientation from various point clouds. Almost all existing methods estimate oriented normals through a two-stage pipeline, i.e., unoriented normal estimation and normal orientation, and each step is implemented by a separate algorithm. However, previous methods are sensitive to parameter settings, resulting in poor results from point clouds with noise, density variations and complex geometries. In this work, we introduce signed hyper surfaces (SHS), which are parameterized by multi-layer perceptron (MLP) layers, to learn to estimate oriented normals from point clouds in an end-to-end manner. The signed hyper surfaces are implicitly learned in a high-dimensional feature space where the local and global information is aggregated. Specifically, we introduce a patch encoding module and a shape encoding module to encode a 3D point cloud into a local latent code and a global latent code, respectively. Then, an attention-weighted normal prediction module is proposed as a decoder, which takes the local and global latent codes as input to predict oriented normals. Experimental results show that our algorithm outperforms the state-of-the-art methods in both unoriented and oriented normal estimation.

Oxidation behavior and mechanism of 4H-SiC surface by holes in electrochemical and photoelectrochemical systems

4H-SiC serves as an ideal substrate for fabricating high-performance power electronic devices, and the extremely low polishing efficiency is a key constraint to its promotion and application. To explore the oxidation behavior and mechanism of 4H-SiC surfaces can help to achieve efficient surface oxidation and improve the polishing efficiency. Therefore, in this work, a comparative analysis between electrochemical oxidation and photoelectrochemical oxidation of the 4H-SiC substrate surface is carried out. The variations of oxidation current density, surface morphology, oxide thickness, and surface hardness of 4H-SiC substrate are investigated by oxidation experiments, nanoindentation tests, and linear sweep voltammetry (LSV) measurements. Based on the study about the oxidation behavior of 4H-SiC surface by holes and the energy band theory, benzoic acid is introduced as a fluorescence probe to reveal the principles of low-concentration hole induced electrochemical oxidation and high-concentration hole dominated photoelectrochemical oxidation. Evolution models for the oxidation of 4H-SiC surfaces by these two methods are developed, and the differences in oxidation mechanisms are clarified. Photoelectrochemical oxidation is approximately 3 times more efficient than electrochemical oxidation, the former can form a dense and uniform oxide layer. The results of this work provide insights into achieving efficient polishing of 4H-SiC substrate.

The Drivers of Sustainable Development in South African Economy: The Role of Female Advancement

Contemporary societies have challenges in achieving sustainable development, while African economies remain quite distant from this ideal course for sustainable growth. Likewise, official statistics in South Africa show a wide gap between male and female involvement in economic activities. Given this, the study investigates the connection between three dimensions of sustainable development and female advancement in South Africa using auto-regressive disturbed lags (ARDL), Structural Vector autoregressive (SVAR) and Ganger non-causality approach. The three dimensions of sustainable development considered in this study are economic, environmental and social, proxies by adjusted net national savings, carbon emissions and adjusted net national income per capita, respectively. The female advancement indicators are proxies by employment, wages and salaries, and secondary school enrolment. The result from the ARDL shows that female advancement indicators promote short (SR) and long-run (LR) economic sustainability. Also, female wages and salaries have a positive relationship with carbon emissions in the SR but not significantly in the LR. At the same time, female employment and secondary school enrolment have a positive relationship with carbon emissions in the SR but are negative in the LR. The primary causes affecting long-term environmental sustainability are GDP per capita, financial development, population density, trade openness and urbanisation. Also, female advancement indicators promote social sustainability, especially employment, wages and salaries. More so, the SVAR results show that the forecasting error shock of GDP has minimal effect on carbon emission with notable variation in the population density. The causality result shows a unidirectional relationship between economic sustainability and female secondary school enrolment, environmental sustainability and female secondary school enrolment, and social sustainability and female secondary school enrolment, all flowing to female secondary school enrolment. Based on the findings, the study recommended improving female educational standards and increasing involvement in economic participation.

The Inherited Crustal Structure and Lithospheric Thermal Field Beneath the Sea of Marmara (NW Türkiye): Observations From 3D Gravity Modeling and Seismic Tomography Analysis

AbstractThe North Anatolian Fault (NAF) extends for over 1,000 km across Türkiye and poses significant seismic hazard in the region. The Main Marmara Fault (MMF) segment of the NAF in the Sea of Marmara (NW Türkiye), exhibits along‐strike segmentation in its interseismic strain accumulation. Constraining the lithospheric configuration below the MMF is critical to understand its segmentation and assessing seismic hazard in the area. We present a new 3D lithospheric‐scale density of the Sea of Marmara, that combines gravity modeling and seismic tomography analysis. Using forward and inverse gravity modeling with free‐air gravity data and available constraints of geological units we derived the intra‐crustal density structure. Shear‐wave velocity tomography models provided insights into the temperature and density configuration of the uppermost mantle, and the geometry of the 1330°C isotherm. Our results highlight significant crustal density variations: lower‐density crust in the Sakarya Zone and Strandja Massif, and denser crust below the Istanbul Zone, which overlies a relatively hotter lithospheric mantle. This lithospheric configuration reflects both ongoing tectonic processes and inheritance from past geological events, including the drifting of the Istanbul Zone crustal block and the signature of past subduction events. The extent of the Istanbul Zone denser crust spatially correlates with the locked segment of the MMF. The bimaterial nature of the fault segment likely influences its interseismic and coseismic behavior. The denser, stiffer Istanbul Zone crust would promote interseismically locked conditions in contrast to the adjacent, more compliant crustal block and could result in asymmetric rupture with a preferred directivity.

Modeling of interfacial diffusion in adjacent flows of polymer films

Adjacent flow of two polymeric fluids occurs in many industrial processes. Under these processes, entangled polymer chains usually undergo extensional flow and shear flow deformation fields, rendering orientation and stretching within polymer chains. In the present paper, the chain stretching ratio and interfacial diffusion in a symmetric bilayer film in the isothermal adjacent flows in a coextrusion process are modeled using the double constraint release with chain stretching model. Extension-dominant and shear-dominant flows are considered separately for ease of the modeling process. Also, the impact of entanglement density on the reptation relaxation is investigated to determine the entanglement density variation and its effect on the stretching ratios and interfacial diffusion. Our findings show that extension-dominant and shear-dominant deformation fields have different impacts on polymer chain stretching, affecting polymer interfacial chain diffusion. Our findings show that while shear flow with the strength of 30 s−1 increases the stretching ratio by 28%, extensional flow with the same strength increases the stretching ratio by 60% of the maximum stretching ratio for polystyrene chains with an average molecular weight of 200 km/mol at 175 °C. Our results show that initial entanglement density is effective on the chain stretching only in the transition step before chains reach equilibriums. This study highlights the impact of flow conditions and chain configuration in polymers on engineering the diffusion of polymer chains at the interface of layered configurations.

APoX-M: Accelerate deep point cloud analysis via adaptive graph construction

Graph-based deep learning point cloud processing has gained increasing popularity but its performance is dragged by the dominating graph construction (GC) phase with irregular computation and memory access. Existing works that accelerate GC by tailoring architecture for a single GC algorithm fail to maintain efficiency because they neglect the best GC algorithm variation incurred by the point-cloud density variation in changing scenarios. Therefore, we propose APoX-M, a unified architecture with an adaptive GC scheme that can identify the optimum GC approach according to the point cloud variation. We also provide better memory management and scheduling optimizations for better performance. Experiments indicate that APoX-M achieves higher performance and energy efficiency over existing accelerators.

Numerical investigation on failure modes and TDA-based mitigation measure of jointed rigid pipes under ground subsidence

Jointed rigid pipes are vulnerable to permanent ground deformation (PGD). Compressible and lightweight materials, such as tire-derived aggregate (TDA), geofoams and straws, can be placed around buried pipes to reduce the loads from PGD and minimize the risk of pipe failures. In this study, a calibrated three-dimensional finite element model incorporating a modified Mohr-Coulomb model for simulating the strain softening behavior of surrounding soils is employed to analyze the failure modes of jointed rigid pipes under ground subsidence with various crossing scenarios, where the fault plane intersects different positions of the pipeline. Then, the efficiency of TDA mitigation in reducing the risk of pipe failure is evaluated, considering variations in geometry, size and density of TDA zone. It is found that the most detrimental fault-pipe crossing position locates at 3/4L of the pipe barrel, where excessive bending moment can lead to longitudinal cracking. TDA mitigation mainly prevents structural failure of pipelines, while its influence on the kinematics of joints is negligible. The most pronounced loading reduction for pipelines is achieved by employing TDA with a lower degree of compaction, combined with the burial configuration of full-surrounded layout pattern of TDA.

Theoretical study on electrochemical and thermal behavior of GNP incorporated anode materials for lithium-ion batteries

In this paper, a theoretical investigation of the electrical and thermal performance of lithium-ion batteries employing an unexplored graphene nanoplatelets (GNP) incorporated lithium titanate (LTO-GNP) anode and lithium manganese oxide cathode is demonstrated by using the COMSOL Multiphysics modeling and simulation. The GNP proportions in the LTO active material matrix are varied from 0 to 3 wt%, to assess the improvements in cell potential, state of charge (SOC), variation in internal temperature, discharging capacity, and battery power density across four selected electrodes. The simulation results have indicated the positive effects of increasing the anode’s GNP concentration on the battery performance, alongside temperature-dependent open-circuit voltage (OCV) and SOC outputs of the battery. The analysis of the effects of thermal conductivity on heat accumulation and dissipation within the battery has indicated that the anode with 3 wt% of GNP consistently performed superior to the other electrodes by providing uniform temperature distribution and enhanced electrical performance by promoting better discharge capacity and power density. The LTO anode with 3 wt% GNP has shown 0.2V more cell potential and also has 12.5% improved thermal conductivity when compared to LTO anode without GNP, contributing to better heat distribution inside the battery. The simulation also emphasized the importance of optimal GNP loading in the Li-ion battery anode to attain more uniform temperature distribution with improved battery health and efficiency.

Electrocoagulation for nickel, chromium, and iron removal from mine water using aluminum electrodes

High global demand for nickel metal has contributed significantly to the growth of the nickel mining industry in Indonesia. This growth has a positive multiplier effect on the economy, with the potential to affect aquatic life and humans owing to the high levels of chromium, nickel, and iron in mine water. Therefore, this study aims to develop an electrocoagulation (EC) reactor to remove nickel, chromium, and iron from mine water. This study used a continuous reactor and aluminum electrodes with variations in current density (3.378, 6.757, and 10.135 mA/cm2) and inflow (0.3, 0.5, and 1 L/min). The results showed that the operating scenario with a current strength of 6 A and an inflow of 0.3 L/min had a removal efficiency of 86.89 % nickel, 99.51 % chromium, and 80.61 % iron with a charge loading value of 11,194 F/ma3 and Reynolds number of 39. These results are expected to provide valuable information for the development of an effective EC technology, thereby demonstrating its potential for the removal of metals from nickel mine water.

Variation in flower morphology associated with higher bee diversity in urban green spaces.

Urbanization is a leading threat to biodiversity, but scientifically informed management of urban ecosystems can mitigate negative impacts. For wild bees, which are declining worldwide, careful consideration of flower choice in public and private green spaces could help preserve their diversity. While floral density and species richness are both linked to wild bee diversity, the mechanisms underlying these relationships are not fully understood. Here, we tested two hypotheses relating the influence of floral trait composition to bee species richness, which we have termed the within-trait diversity and optimal floral trait hypotheses. Specifically, we assessed whether variation in bee richness relates to variation in the weighted variance (trait diversity) and mean (optimal trait) of floral traits observed in urban green spaces across the city of Montreal, Canada. Our analyses focused on two floral traits relating to pollinator feeding success: nectar sugar concentration and corolla length. After accounting for variation in floral density among sites, bee richness was positively related to community-weighted variance in corolla length, supporting the within-trait diversity hypothesis. These findings suggest that management practices that increase the diversity of flower morphologies in urban green spaces can promote the persistence of wild bee communities in cities.

Contact-Free Online Monitoring of Bioreactor Cell Cultures with Magnetic Resonance Relaxometry.

Frequent, low-latency measurements of bioreactor culture growth are critical for achieving maximum culture efficiency and productivity. Typical cell density and viability measurements are made by manually removing a sample from the culture, but this approach is both slow and unsuitable for small culture volumes, which cannot support frequent destructive sampling. In this work, automated magnetic resonance relaxometry measurements of a sealed bioreactor system are used to estimate the cell density and provide qualitative information about the culture in near real-time. The system detects variations in cell density in minutes, enabling rapid intervention that would be impossible with the once-daily measurements taken by a traditional sampling-based culture analysis system.

Spatiotemporal Distribution, Bioaccumulation, and Ecological and Human Health Risks of Polycyclic Aromatic Hydrocarbons in Surface Water: A Comprehensive Review

The release of polycyclic aromatic hydrocarbons (PAHs) by human energy exploitation and excessive environmental use has caused substantial environmental contamination. These compounds bioaccumulate in aquatic environments and translocate through the food chain, posing risks to health and environmental safety. To better understand the risks of PAHs in surface water and food chains, this review summarizes their distribution, concentration levels, sources, and toxicity in various surface water environments. It also examines how PAH bioaccumulation affects aquatic organisms and human health. Globally, PAHs have been detected in both aquatic environments and organisms with an increasing trend. Human activity is the main cause of PAH contamination. The results revealed a distinct geographical distribution of PAH risk influenced by population density, industrial development, climate, and seasonal variations. PAHs are found in remote areas, indicating their medium- and long-range transport by atmospheric dispersion. PAHs bioaccumulate in aquatic organisms and cause direct and indirect toxic effects via biomagnification. PAH bioaccumulation is directly correlated with aquatic pollution. This study also emphasizes the carcinogenicity of compounds such as benzo[a]pyrene, identifying occupational and environmental exposure frequencies as key risk factors. This study enhances our understanding of the dynamics of multiple PAHs in aquatic ecosystems and their health effects, thereby contributing to environmental sustainability.