Design and Numerical Simulation of Biomimetic Structures to Capture Particles in a Microchannel

The study of separating different sizes of particles through a microchannel has been an interest in recent years and the primary attention of this study is to isolate the particles to the specific outlets. The present work highly focuses on the design and numerical analysis of a microchip and the microparticles capture using special structures like corrugated dragonfly wing structure and cilia walls. The special biomimetic structured corrugated wing is taken from the cross-sectional area of the dragonfly wing and cilia structure is obtained from the epithelium terminal bronchioles to the larynx from the human body. Parametric studies were conducted on different sizes of microchip scaled and tested up in the range between 2–6 mm and the thickness was assigned as 80 µm in both dragonfly wing structure and cilia walls. The microflow channel is a low Reynolds number regime and with the help of the special structures, the flow inside the microchannel is pinched and a sinusoidal waveform pattern is observed. The pinched flow with sinusoidal waveform carries the particles downstream and induces the particles trapped in desired outlets. Fluid particle interaction (FPI) with a time-dependent solver in COMSOL Multiphysics was used to carry out the numerical study. Two particle sizes of 5 µm and 20 µm were applied, the inlet velocity of 0.52 m/s with an inflow angle of 50° was used throughout the study and it suggested that: the microchannel length of 3 mm with corrugated dragonfly wing structure had the maximum particle capture rate of 20 µm at the mainstream outlet. 80% capture rate for the microchannel length of 3 mm with corrugated dragonfly wing structure and 98% capture rate for the microchannel length of 2 mm with cilia wall structure were observed. Numerical simulation results showed that the cilia walled microchip is superior to the corrugated wing structure as the mainstream outlet can conduct most of the 20 µm particles. At the same time, the secondary outlet can laterally capture most of the 5 µm particles. This biomimetic microchip design is expected to be implemented using the PDMS MEMS process in the future.

Landscape Evolution as a Diversification Driver in Freshwater Fishes

The exceptional concentration of vertebrate diversity in continental freshwaters has been termed the “freshwater fish paradox,” with > 15,000 fish species representing more than 20% of all vertebrate species compressed into tiny fractions of the Earth’s land surface area (<0.5%) or total aquatic habitat volume (<0.001%). This study asks if the fish species richness of the world’s river basins is explainable in terms of river captures using topographic metrics as proxies. The River Capture Hypothesis posits that drainage-network rearrangements have accelerated biotic diversification through their combined effects on dispersal, speciation, and extinction. Yet rates of river capture are poorly constrained at the basin scale worldwide. Here we assess correlations between fish species density (data for 14,953 obligate freshwater fish species) and basin-wide metrics of landscape evolution (data for 3,119 river basins), including: topography (elevation, average relief, slope, drainage area) and climate (average rainfall and air temperature). We assess the results in the context of both static landscapes (e.g., species-area and habitat heterogeneity relationships) and transient landscapes (e.g., river capture, tectonic activity, landscape disequilibrium). We also relax assumptions of functional neutrality of basins (tropical vs. extratropical, tectonically stable vs. active terrains). We found a disproportionate number of freshwater species in large, lowland river basins of tropical South America, Africa, and Southeast Asia, under predictable conditions of large geographic area, tropical climate, low topographic relief, and high habitat volume (i.e., high rainfall rates). However, our results show that these conditions are only necessary, but not fully sufficient, to explain the basins with the highest diversity. Basins with highest diversity are all located on tectonically stable regions, places where river capture is predicted to be most conducive to the formation of high fish species richness over evolutionary timescales. Our results are consistent with predictions of several landscape evolution models, including the River Capture Hypothesis, Mega Capture Hypothesis, and Intermediate Capture Rate Hypothesis, and support conclusions of numerical modeling studies indicating landscape transience as a mechanistic driver of net diversification in riverine and riparian organisms with widespread continental distributions.

Big Data Mining Method of Marketing Management Based on Deep Trust Network Model

Through big data mining, enterprises can deeply understand the consumer preferences, behavior characteristics, market demand and other derived data of customers, so as to provide the basis for formulating accurate marketing strategies. Therefore, this paper proposes a marketing management big date mining method based on deep trust network model. This method first preprocesses the big data of marketing management, including data cleaning, data integration, data transformation and data reduction, and then establishes a big data mining model by using deep trust network to realize the research on the classification of marketing management data. Experimental results show that the proposed method has 99.08% accuracy, the capture rate reaches 88.11%, and the harmonic average between the accuracy and the recall rate is 89.27%, allowing for accurate marketing strategies.

Singularities in the gravitational capture of dark matter through long-range interactions

We re-examine the gravitational capture of dark matter (DM) through long-range interactions. We demonstrate that neglecting the thermal motion of target particles, which is often a good approximation for short-range capture, results in parametrically inaccurate results for long-range capture. When the particle mediating the scattering process has a mass that is small in comparison to the momentum transfer in scattering events, correctly incorporating the thermal motion of target particles results in a quadratic, rather than logarithmic, sensitivity to the mediator mass, which substantially enhances the capture rate. We quantitatively assess the impact of this finite temperature effect on the captured DM population in the Sun as a function of mediator mass. We find that capture of DM through light dark photons, as in e.g. mirror DM, can be powerfully enhanced, with self-capture attaining a geometric limit over much of parameter space. For visibly-decaying dark photons, thermal corrections are not large in the Sun, but may be important in understanding long-range DM capture in more massive bodies such as Population III stars. We additionally provide the first calculation of the long-range DM self-evaporation rate.

An estimate of the amount of geological CO2 storage over the period 1996-2020

Existing centralised databases of industrial-scale CCS report various characteristics including capture capacities but do not specify the amount of CO2 stored from commercial CCS facilities. We review a variety of publicly available sources to estimate the amount of CO2 that has been captured and stored by operational CCS facilities since 1996. We organise these sources into three categories broadly corresponding to the associated degree of legal liability or auditing. Data were found for twenty commercial-scale facilities, indicating a combined capture capacity of 36 MtCO2 per year. Combining data from all three categories suggests that approximately 27 MtCO2 of this was stored in the subsurface in 2019. However, considering only categories 2 and 1 of reporting, storage estimates for 2019 reduce to 25 MtCO2 and 11 MtCO2, respectively. Nearly half of the projects investigated here are reporting injection rates close to their originally proposed capture rate capacity. Our data also show that between 1996 and 2020, 196 Mt of CO2 has been cumulatively stored, combining data for all three categories. The database presented here provides further insight into the factors influencing performances of CCS operations and the data can be used to parameterise energy system models for analysing plausible scaleup trajectories of CCS.

Human Motion Posture Detection Algorithm Using Deep Reinforcement Learning

To address problems of serious loss of details and low detection definition in the traditional human motion posture detection algorithm, a human motion posture detection algorithm using deep reinforcement learning is proposed. Firstly, the perception ability of deep learning is used to match human motion feature points to obtain human motion posture features. Secondly, normalize the human motion image, take the color histogram distribution of human motion posture as the antigen, search the region close to the motion posture in the image, and take its candidate region as the antibody. By calculating the affinity between the antigen and the antibody, the feature extraction of human motion posture is realized. Finally, using the training characteristics of deep learning network and reinforcement learning network, the change information of human motion posture is obtained, and the design of human motion posture detection algorithm is realized. The results show that when the image resolution is 384 × 256 px, the motion pose contour detection accuracy of this algorithm is 87%. When the image size is 30 MB, the recognition time of this method is only 0.8 s. When the number of iterations is 500, the capture rate of human motion posture details can reach 98.5%. This shows that the proposed algorithm can improve the definition of human motion posture contour, improve the posture detailed capture rate, reduce the loss of detail, and have better effect and performance.

Design strategies for oxy-combustion power plant captured CO2 purification

This study presents a novel design and techno-economic analysis of processes for the purification of captured CO2 from the flue gas of an oxy-combustion power plant fueled by petroleum coke. Four candidate process designs were analyzed in terms of GHG emissions, thermal efficiency, pipeline CO2 purity, CO2 capture rate, levelized costs of electricity, and cost of CO2 avoided. The candidates were a classic process with flue-gas water removal via condensation, flue-gas water removal via condensation followed by flue-gas oxygen removal through cryogenic distillation, flue-gas water removal followed by catalytic conversion of oxygen in the flue gas to water via reaction with hydrogen, and oxy-combustion in a slightly oxygen-deprived environment with flue-gas water removal and no need for flue gas oxygen removal. The former two were studied in prior works and the latter two concepts are new to this work. The eco-technoeconomic analysis results indicated trade-offs between the four options in terms of cost, efficiency, lifecycle greenhouse gas emissions, costs of CO2 avoided, technical readiness, and captured CO2 quality. The slightly oxygen-deprived process has the lowest costs of CO2 avoided, but requires tolerance of a small amount of H2, CO, and light hydrocarbons in the captured CO2 which may or may not be feasible depending on the CO2 end use. If infeasible, the catalytic de-oxygenation process is the next best choice. Overall, this work is the first study to perform eco-technoeconomic analyses of different techniques for O2 removal from CO2 captured from an oxy-combustion power plant.

The predator-dependent replicator dynamics

Ecological interactions are central to understanding evolution. For example, Darwin noticed that the beautiful colours of the male peacock increase the chance of successful mating. However, the colours can be a threat because of the increased probability of being caught by predators. Eco-evolutionary dynamics takes into account environmental interactions to model the process of evolution. The selection of prey types in the presence of predators may be subjected to pressure on both reproduction and survival. Here, we analyze the evolutionary game dynamics of two types of prey in the presence of predators. We call this model \textit{the predator-dependent replicator dynamics}. If the evolutionary time scales are different, the number of predators can be assumed constant, and the traditional replicator dynamics is recovered. However, if the time scales are the same, we end up with sixteen possible dynamics: the combinations of four reproduction’s games with four predation’s games. We analyze the dynamics and calculate conditions for the coexistence of prey and predator. The main result is that predators can change the equilibrium of the traditional replicator dynamics. For example, the presence of predators can induce polymorphism in prey if one type of prey is more attractive than the other, with the prey ending with a lower capture rate in this new equilibrium. Lastly, we provide two illustrations of the dynamics, which can be seen as rapid feedback responses in a predator-prey evolutionary arm’s race.

Economic Model Predictive Control for Post-Combustion CO2 Capture System Based on MEA

For the post-combustion CO2 capture (PCC) system, the time variability of the economic performance is key to the production process of such an actual industrial process. However, the performance index used by the conventional model predictive control (MPC) does not reflect the economy of the production process, so the economic cost function is used instead of the traditional performance index to measure the economy of the production process. In this paper, a complete dynamic model of the PCC system is constructed in Aspen Plus Dynamics. The effectiveness of the model is verified by dynamic testing; subspace identification is carried out using experimental data, a state-space equation between flue gas flow and lean solvent flow; the CO2 capture rate is obtained; and dynamic models and control algorithm models of accused objects are established in Matlab/Simulink. Under the background of the environmental protection policy, an economic model predictive control (EMPC) strategy is proposed to manipulate the PCC system through seeking the optimal function of the economic performance, and the system is guaranteed to operate under the economic optimal and excellent quality of the MPC control strategy. The simulation results verify the effectiveness of the proposed method.

Three-Dimensional TID Hardening Design for 14 nm Node SOI FinFETs

The fin field-effect transistor (FinFET) has been the mainstream technology on the VLSI platform since the 22 nm node. The silicon-on-insulator (SOI) FinFET, featuring low power consumption, superior computational power and high single-event effect (SEE) resistance, shows advantages in integrated circuits for space applications. In this work, a rad-hard design methodology for SOI FinFETs is shown to improve the devices’ tolerance against the Total Ionizing Dose (TID) effect. Since the fin height direction enables a new dimension for design optimization, a 3D Source/Drain (S/D) design combined with a gate dielectric de-footing technique, which has been readily developed for the 14 nm node FinFETs, is proposed as an effective method for SOI FinFETs’ TID hardening. More importantly, the governing mechanism is thoroughly investigated using fully calibrated technology computer-aided design (TCAD) simulations to guide design optimizations. The analysis demonstrates that the 3D rad-hard design can modulate the leakage path in 14 nm node n-type SOI FinFETs, effectively suppress the transistors’ sensitivity to the TID charge and reduce the threshold voltage shift by >2×. Furthermore, the rad-hard design can reduce the electric field in the BOX region and lower its charge capture rate under radiation, further improving the transistor’s robustness.