In swarm-robotics foraging, the purpose of task allocation is to adjust the number of active foraging robots dynamically based on the task demands and changing environment. It is a difficult challenge to generate self-organized foraging behavior in which each robot can adapt to environmental changes. To complete the foraging task efficiently, this paper presents a novel self-organized task allocation strategy known as the dynamic response threshold model (DRTM). To adjust the behavior of the active foraging robots, the proposed DRTM newly introduces the traffic flow density, which can be used to evaluate the robot density. First, the traffic flow density and the amount of obstacle avoidance are used to adjust the threshold which determines the tendency of a robot to respond to a stimulus in the environment. Then, each individual robot uses the threshold and external stimulus to calculate the foraging probability that determines whether or not to go foraging. Simulation and physical experiments are carried out to evaluate the performance of the proposed method. The experimental results show that the DRTM is superior to and more efficient than the adaptive response threshold model (ARTM) in swarm foraging.

Background We aimed to compare the retinal, optic disc vascular density (ODVD) values, and acircularity index (AI) of patients with idiopathic macular telangiectasia type 2 (IMT) and healthy individuals using the optical coherence tomography angiography(OCTA) device. Methods The study included 39 patients with IMT and 37 healthy controls. The OCTA findings of the patients and controls were examined. Results The total, parafoveal and perifoveal vascular density of the superficial capillary plexus, choriocapillaris blood flow, inside-disc ODVD, retinal nerve fiber layer (RNFL), and retinal thicknesses were found to be statistically significantly lower and the foveal avascular zone value was statistically significantly higher in the IMT group compared to the control group (p=0.001, p=0.01, p=0.02, p=0.01, p=0.009, p=0.002, p=0.02, respectively).There was a statistically significant negative correlation between best-corrected visual acuity (BCVA) and AI (p=0.02), a statistically significant positive correlation between peripapillary vascular density and BCVA(p=0.04). Conclusion We consider that the lower retinal, choriocapillaris, and ODVD values, retinal and RNFL thicknesses in the patients with IMT compared to the controls were due to vascular damage, remodeling, fibrosis, proliferation, and Müller cell damage. Ellipsoid zone defect, AI, and peripapillary vascular density are important indicators in the evaluation of visual acuity in these patients.

Polychlorinated phenoxathiins (PCPTs), polychlorinated dibenzothiophenes (PCDTs), and polychlorinated thianthrenes (PCTAs) are sulfur analogues of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/DFs). Chlorothiophenols(CTPs) and chlorophenols (CPs) are key precursors to form PCTA/PT/DTs, which can form chloro(thio)phenoxy radical, sulfydryl/hydryl-substituted phenyl radical and (thio)phenoxyl diradicals. The available radical/radical PCTA/DT formation mechanism failed to explain the higher concentration of PCDTs than that of PCTAs under the pyrolysis or combustion conditions. Thus in this work, a detailed thermodynamics and kinetic calculations were carried out to investigate the pre-intermediates formation for PCTA/PT/DTs from radial/molecule coupling of 2-C(T)P with their key radical species. Our study found that the radial/molecule mechanism can thermodynamically and kinetically contribute to the gas-phase formation of PCTA/PT/DT/s. The S/C coupling modes to form thioether-(thio)enol intermediats are preferable over the O/C coupling modes to form ether-(thio)enol intermediats. Thus, although the radial/molecule coupling of chlorophenoxy radical with 2-C(T)P have no effect on the PCDD/PTs formation, the radial/molecule coupling of chlorothiophenoxy radical with 2-C(T)P play an important role in the PCDT/PT formation. Most importantly, the pre-PCDT intermediates formation pathways from the coupling of sulfydryl/hydryl-substituted phenyl radical with 2-C(T)P and the coupling of (thio)phenoxyl diradicals with 2-C(T)P are more favorable to pre-PCTA/PT intermediates formation pathways from the coupling of chlorothiophenoxy radical with 2-C(T)P, which can give reasonable explanation for the high PCDT-to-PCTA ratio in the environment.

A space charge density wireless measurement system based on the idea of distributed measurement is proposed for collecting and monitoring the space charge density in an ultra-high-voltage direct-current (UHVDC) environment. The proposed system architecture is composed of a number of wireless nodes connected with space charge density sensors and a base station. The space charge density sensor based on atmospheric ion counter method is elaborated and developed, and the ARM microprocessor and Zigbee radio frequency module are applied. The wireless network communication quality and the relationship between energy consumption and transmission distance in the complicated electromagnetic environment is tested. Based on the experimental results, the proposed measurement system demonstrates that it can adapt to the complex electromagnetic environment under the UHVDC transmission lines and can accurately measure the space charge density.

The research objectives was to determine the plants vegetation profile in forest revegetation and Mount Arjuna springs. The method used was descriptive method. While based on the techniques and tools used to research, the author uses the survey method to obtain facts that occur in the research area, namely in the area around the springs of Arjuna mount, Pasuruan, East Java, Indonesia. Data obtained in the field are then processed and analyzed using GIS, ArcView 3.3 and Google Earth programs. The results of the study explain the profile of plant vegetation, namely plant stratification, bird wealth, taxonomic wealth and plant density have a significant effect on forest revegetation and springs in Arjuna mount. The forest vegetation profile has a positive and significant effect on the preservation of springs. The better the profile of forest vegetation, the preservation of springs will be better, and vice versa.

The petroleum industry produces thousands of barrels of oilfield waters from the initial stage driven by primary production mechanisms to the tertiary stage. These produced waters contain measurable amounts of oil in water emulsions, the exact amounts being determined by the chemistry of the crude oil. To meet strict environmental regulations governing the disposal of such produced waters, demulsification to regulatory permissible levels is required. Within the electric double layer theory, coupled with the analytical solutions to the Poisson Boltzmann Equation, continuum electrostatics approaches can be used to describe the static and electrokinetic properties of such emulsion systems. Therefore, theoretical understanding of the stability of oil in water emulsions within such fundamental concepts provides reliable approaches to demulsification. In this paper, we have used theoretical approaches, based on zeta potential and surface charge density models to determine the stability of oil in water emulsions for different crude oil samples. Accordingly, we have used literature based data on the chemistry of the crude oils, and have ranked the order of emulsion stability based on criterial established by zeta potential plots for the oil. Our theoretical calculations show that where the isoelectric points of crude oil samples are closer to each other, the degree of ionization plots versus pH indicate close tends for salinities at varying pH of oilfield waters. Based on the chemistry of crude oil samples, the most efficient and cost-effective means of demulsification is by reducing produced water pH to values closer to the average point of zero charge pH values

This paper addresses the issues of how we can quantify structural information for nonparametric distributions and how we can detect its changes. Structural information refers to an index for a global understanding of a data distribution. When we consider the problem of clustering using a parametric model such as a Gaussian mixture model, the number of mixture components (clusters) can be thought of as structural information in the model. However, there does not exist any notion of structural information for nonparametric modeling of data. In this paper we introduce a novel notion of {\em kernel complexity} (KC) as structural information in the nonparametric setting. The key idea of KC is to combine the information bias inspired by the Gini index with the information quantity measured in terms of the normalized maximum likelihood (NML) code length. We empirically show that KC has a property similar to the number of clusters in a parametric model. We further propose a framework for structural change detection with KC in nonparametric distributions. With synthetic and real data sets we empirically demonstrate that our framework enables us to detect structural changes underlying the data and their early warning signals.

Control of autonomous robot motion in radial mass density field is presented. In that sense the robot motion is described as the function of the radial mass density parameters. The radial mass density field is between the maximal radial mass density and the minimal radial mass density. Between these two limited values one can use n points (n = 1, 2, . . . nmax) and calculate the related radial mass density for each point. The radial mass density is maximal at the minimal gravitational radius and minimal at the maximal gravitational radius. This conclusion is valid for Planck scale, but also for the scales that are less or higher of that one. Using the ratio of the Planck mass and Planck radius it is generated energy conservation constant with value κ = 0.99993392118. Further, in this theory it is possible to connect Planck’s and gravitational parameters as functions of the maximal (or minimal) radial mass density. In that sense the autonomous robot motion in radial mass density field is important for the control of the robot motion at micro and nano scales.

Recently, due to the decrease in labor force, increase in labor costs, and the desire for improved quality of life, research on robots has been actively conducted to address these issues. However, it is currently difficult to find robots that physically interact with humans. The reason is that the actuators of robots do not have high torque density on their own. To solve this problem, high torque density motors, such as proprioceptive actuators, are being researched. However, the torque density is still insufficient for physical interaction with humans, so we have developed a motor with higher torque density. However, high torque density motors have the disadvantage of lower speed characteristics due to increased Back-EMF. Therefore, to address the deterioration of speed characteristics in the developed motor, we applied the Independent 3-Phase winding structure to improve the speed characteristics. Consequently, through comparison with Y-Connection and Delta-Connection, we propose the most suitable winding structure for high torque density motors intended for physical interaction with humans.

Redox-flow batteries are rechargeable batteries that store energy in two liquid electrolytes, separated by a membrane. During charging and discharging, the electrolytes flow through the membrane and undergo chemical reactions that generate or consume electrical energy. These batteries are known for their scalability and long cycle life and are versatile energy storage technologies developed to enhance efficiency and lower costs. Deep Eutectic Solvents (DES) are eco-friendly alternatives to traditional electrolytes, and their properties have been studied to improve the performance of redox-flow batteries. We investigated choline chloride/ethylene glycol (ChlCl/Eg) mixtures with LiPF6 salt at four concentrations through experimental and MD simulations. The thermophysical and transport properties of the mixture, including density, diffusion coefficient, viscosity, and ionic conductivity, were calculated across a temperature range of 298.15–398.5 K. Our findings indicate that an increase in salt concentration leads to a decrease in diffusion coefficients and ionic conductivity, while increasing the viscosity of the deep eutectic solvent. This research provides valuable insights into the behavior of DES mixtures and can aid in the design and optimization of DES-based processes for use in redox-flow batteries.

NiO-based nanomaterials have attracted a great deal of interest in view of various applications, that have stimulated the implementation of various synthetic approaches to control their chemico-physical properties as a function of the desired end-use. In this regard, their bottom-up preparation starting from suitable precursors plays an important role, but a molecular level in-sight into their reactivity remains an open challenge. In this study, we focus on the fragmentation of Ni(II) diketonate-diamine adducts, of interest as precursors for Ni(II) oxide systems, by com-bined electrospray ionization mass spectrometry (ESI-MS) with multiple collisional experiments (ESI-MSn) and theoretical calculations. The joint analyses revealed common features in the frag-mentation pattern of the target compounds: i) in the first-fragmentation, all three complexes form analogous base-peak cations by losing a negatively charged diketonate; in these cations, Ni-O and Ni-N interactions are stronger and the Ni positive charge is lower than in the neutral complexes; ii) the tendency of ligand electronic charge to migrate towards Ni further increases in the subsequent fragmentation processes, leading to the formation – in all three cases – of a tetracoordinated Ni environment involving an interesting cation-π intramolecular interaction.

This study comprehensively investigates Al2O3's mechanical properties, focusing on fracture toughness, surface energy, Young's modulus, and crack propagation. The density functional theory (DFT) is employed to model the vacancies in Al2O3, providing essential insights into this material’s structural stability and defect formation. The DFT simulations reveal a deep understanding of vacancy-related properties and their impact on mechanical behavior. In conjunction with molecular dynamics (MD) simulations, the fracture toughness and crack propagation in Al2O3 are explored, offering valuable information on material strength and durability. The surface energy of Al2O3 is also assessed using DFT, shedding light on its interactions with the surrounding environment. The combination of DFT and MD simulations provides a robust framework for a comprehensive understanding of Al2O3's mechanical properties, with implications for material science and engineering applications.

In this study, alfalfa silage with different packing densities (500 kg/m3, 600 kg/m3 and 700 kg/m3) were set up under outdoor high temperature and indoor normal temperature. The chemical composition, fermentation quality and microbial community of alfalfa silage were analyzed. The results showed that DM and WAC content decreased with the increase of density. When the density is 600kg/m3, CP content reaches the maximum and ADF content is the lowest. Temperature has a certain effect on alfalfa silage. The content of Lactobacillus plantarum fermented at high temperature is lower than that fermented at normal temperature. At the same time, the content of Lactobacillus plantarum decreased with the increase of packing density. However, the content of Lactobacillus plantarum was the lowest when the packing density was 600 kg/m3 at high temperature. In summary, this study explained the effects of different temperature and density on alfalfa fermentation quality and microbial community, and clarified that the density should be reasonably controlled within 600 kg/m3 during alfalfa silage, providing theoretical support for production practice.

The pressing process and density profile of particleboards produced with addition of waste wood containing phenol-formaldehyde (PF) resin was presented. The particles were melamine-urea-phenol-formaldehyde (MUPF) resinated. Recovered particles were obtained from post-industrial waste plywood. The press closing time recorded during the pressing of mats containing only recovered particles in the core layer (100%), at a unit pressure of 3 MPa, in which the face layer rate was 50%, resin load for face and core layer was 12% and 10% was shorter by 29% than for the industrial particle mats. Regardless of the level of variability of independent factors the overheating time of mats to a temperature of 100 °C, containing recovered particles was 10% - 20% shorter than the time of overheating mats with industrial particles. The greatest impact (52%) on maximum density of the face layer of particleboards was demonstrated by the content of the recovered particles and the resin load (32%). The maximum density area of the face layer was located closer to the surface in particleboards produced with a higher (80%, 100%) content of the recovered particles, a higher (i.e. 12% and 10%, respectively, for face and core layer) resin load, a lower (35%) face layer rate and a higher (3 MPa ) unit pressure.

A study on flywheel geometries is presented here with the use of finite element modelling (FEM) simulations. The study analyses the stress behaviour of flywheel rotors subject to the rotational speed that creates the maximum stress that the flywheel can support, and then calculates the rotational energy mass ratio (energy density) for each geometry. The obtained geometry was Gaussian section for the Flywheel rotor. The used material for the simulations is carbon fibre Hexcel UHM 12000. The best results were a rotational speed of ≈279.000 rpm and rotational energy density of ≈ 440 Wh/kg for a single disk, but this keeps the total energy low. To increase the total energy, Flywheels with 2 and 3 rotors were analysed. Then the stability under rotation of these Flywheels were analysed, some instability was found and a solution was presented.

The research on the distribution of rural buildings is one of the fundamental works of urban-rural development in Vietnam. Adopting Mask R-CNN deep learning framework and collecting sub-meter remote sensing images, this research used a remote sensing interpretation model of rural buildings trained based on East Asian characteristics of rural buildings and successfully recognized about 2.87 million rural buildings in 34 Vietnamese provincial administrative districts with a total area of rural buildings of 2,492 million square meters. The reliability of the identification results was verified by manual detection and quantitative statistics, and a multi-scale database of rural buildings in Vietnam based on individual rural buildings was created. Based on the database, this paper analyzes the distribution characteristics of rural buildings and summarizes characteristics of rural buildings distribution at the country, regional, and provincial scales. The identification results lay the foundation for the next study of urban-rural relations in Southeast Asia and the construction of a basic database on villages.

Multi-board electronic cases with high density and high power modules are widely used in industrial power supply management. With the improvement of case performance and miniaturization requirements, heat dissipation becomes one of the important factors to be considered in the design process. First,The existing small electronic thermal design methods focus on heat dissipation structure or heat source layout optimization,and ignores on-load test for modeling analysis. Second,The selected power module has on-load power consumption, resulting in relatively low calorific value and the effectiveness of thermal design cannot be verified. Third, The thermal lacks Intelligent monitoring and feedback control mechanism. In order to solve these problems, this paper designs a kind of heat dissipation case with intelligent temperature control based on high-power and high-density power supply array. Based on the extremely miniaturization design principle, we adopt the composite heat dissipation mode based on conduction and supplemented by forced air cooling . The case is made of magnesium and aluminum alloy with a perforated design. Finally, we compare and analyze with the existing cooling design. The results show that the case is smaller in volume, and the cooling performance parameters are slightly better than the existing case. Under the condition of high-density and high-power design, The output power of the whole system is not less than 10kw and the lowest packing-level density is not less than 47w/cm2 with high reliability, portability and practicability. It also provides technical support and prototype support for the standardized design of similar power arrays.

Changes in the atomic and electronic structure of silicon carbide 3C-SiC (β-SiC), resulting from lead adsorption, have been studied within the density functional theory. The tests were conducted for three representative surfaces with varying degrees of lead coverage. Results indicate that chemisorption occurs, with the strongest binding, found on the hexagonal surface (111) in interaction with three dangling bonds. The adsorption energy rises with increasing coverage, especially as the surface approaches saturation. Analysis of the data was focused on parameters important for corrosion processes such as bond distances, binding energies, Bader charges and charge density difference.

One of the strategies to reduce the contents of the low density lipoproteins (LDLs) in a blood is a hemoperfusion, in which case they are selectively retracted from plasma by an adsorber located outside the patient body. Recently, a photo-controllable smart surface was developed for this purpose characterised by high selectivity and reusability [C.Guo et al., ACS Applied Materials & Interfaces 2022, DOI:10.1021/acsami.2c07193]. We present a mesoscopic model for such a setup involving the azobenzene-containing polymer brush and the model LDL particles. The latter comprise an uniform spherical core covered by a shell of elongated particles representing phospholipids. The system is studied using the coarse-grained molecular dynamics simulation. We examined the dependences of the binding energy on both the length of polymer chains and the grafting density of a brush, and established optimal conditions for the adsorption. These are explained by a competition between the concentration of azobenzenes and phospholipids in the same spatial region, flexibility of polymer chains, and excluded volume effects.

The false codling moth (FCM), Thaumatotibia leucotreta (Meyrick), is believed to have originated from Ethiopia and sub-Saharan Africa. Currently, this pest has extensively spread and is found in most parts of Africa, with records in approximately 40 countries in over 100 host plant species. Despite Thaumatotibia leucotreta being the leading cause of interceptions of Capsicum and cut flowers exported by Kenya to the European Union, information on abundance and damage levels inflicted on capsicum is limited. The objective of the study was to assess the abundance and damage levels of T. leucotreta on capsicum in the selected counties in Lower Eastern Kenya (Kitui, Machakos, and Makueni counties). Higher T.leucotreta larval density per farm was recorded in Kitui County compared to other counties. In farms with capsicum only (not intercropped with other crops), the mean number of FCM larvae was relatively higher in Kitui. Farming practices such as the use of uncertified seeds and seedlings and the excessive use of pesticides may be the major contributors to high larval incidence in Kitui County.

Phase synchronization of weakly coupled limit cycle oscillators are related to the stability of the zero solution of the reduced-order dynamics of phase differences, represented by a systems of differential equations on a hypertorus. Using Rantzer's density function, a dual form of Lyapunov function, we propose a method to certify almost global stability of an equilibrium on a hypertorus. We show that the proposed method can certify robustness of phase synchronization of all-to-all and weakly coupled limit cycle oscillators with respect to disturbances in phases. The method leverages sum of squares polynomial optimization to construct the certification function.

This study investigated the development of Aedes aegypti density in houses of the urban locality of Poblado Miguel Alemán Valdes, in the Sonora state of Mexico, after application of 1% propoxur paint as full wall coverage (IP) and targeted indoor painting (IP 1m) in comparison to IRS with propoxur 70% WP (full wall coverage). The 1% propoxur paint was applied by the homeowners by brushing and rolling at the recommended dose of 1L/8 m², equivalent to 1.5 g a.i./m², while IRS was conducted by professionals with Propoxur 70% WP at a dose of 1 g a.i./m². Adult mosquito surveys were conducted in a random sample of houses in each block one week before the interventions and at week 1, month 1 to 4, month 6, month 9 and month 12 post-interventions. All three propoxur based treatments provided similar reductions (43.7%, 44.9% and 41.3% for IP, IP 1m and IRS respectively) in the fraction of houses positive in female Aedes aegypti resting indoor and outdoor as one year average of 8 follow up surveys. Indoor resting density of Aedes females during the one-year evaluation was reduced by 77.5% through IP followed by IP 1m with 64.2% reduction and 30% reduction with IRS. Culex mosquitoes’ interior density was affected as well by the insecticide treatments with similar average reductions for IP 1m (50.0%) and IRS (57.8%) in comparison with control. Aedes breeding was impacted by the insecticide paint in similar extent for both interventions, expressed by a substantial reduction of the House Index (20.1% IP, 31.2% IP 1m) and especially the Container Index (51.8% IP, 61.7% IP 1m) during the one-year surveys in comparison to control. In contrast, IRS treated block experienced an increase in both indexes. However, despite IP and IP 1m impacted in Aedes adult and immature indexes with noticeable reductions, the differences in all cases were not significative among the different insecticide treatments. The low sample size and mosquito population levels may have influenced the statistical outcomes. More than 80% of the interviewed residents were satisfied with the effectiveness of the paint and IRS treatments. The determination of the blood cholinesterase activity of tested individuals after the use of the carbamate paint and IRS in this study did not exceed acceptable inhibition limits. This study suggests that the application of propoxur paint by homeowners as full house coverage or as targeted indoor painting can be a safe and accepted intervention method for density reduction of Aedes aegypti populations in urban environments.

Three genera of very large volant birds existed for most of the Pliocene: the Pelagornithidae seabirds; the large North American Teratornithidae and the stork Leptoptilos falconeri in Africa and Asia. All became extinct around 3 Ma. The reasons for their demise are puzzling, as the Pelagornithidae had a world-wide evolutionary history of more than 50 Ma, smaller teratorns were still extant in the Holocene and smaller stork species are still globally extant. Extant large birds have a common critical takeoff airspeed suggesting a biomechanical limit in terms of power, risk and launch speed, and simulations of the flight of these extinct species suggest that at 1 bar they would have exceeded this value. Estimates for the Late Pliocene atmospheric density are derived from marine and terrestrial isotopes as well as resin chemistry, both approaches suggesting a value of about 1.2 bar, which drops to present levels during the period 3.3 to 2.6 Ma, thus a loss in atmospheric density may have caused biomechanical and ecological stress contributing to their extinction and/or development of smaller forms. This hypothesis is examined in terms of a possible mechanism of atmospheric mass loss and how this would be seen in the geological record. At 1.2 bar all the extinct species present takeoff airspeeds similar to large extant volant birds and which match the expected power and kinetic energy levels.

The COVID-19 pandemic of 2020 changed the way we interact and engage in commerce at a fundamental level. Social distancing and stay-at-home orders leave businesses and cities wondering what economic activity will look like in the future. Given a likely reduction in face-to-face interactions, it is important to better understand how social interactivity influences economic outcomes. Here we measure the effect of social interactions in the workforce on patent production and economic efficiency. We decompose U.S. occupations into individual work activities, determine which of those activities are associated with face-to-face interactions, and reaggregate the labor force of each U.S. metropolitan statistical area (MSA) into a metric of social interactiveness. We then calculate each MSA’s density of social work activities and find that this measure is more highly correlated with an MSA’s per capita patent production than simple population density. This suggests that density of face-to-face interactions is the important driver of a city’s rate of invention. We close by exploring analogies between the development of cities and the development of stars, suggesting ways these analogies may help frame future research on cities.

Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the recognition of characters, compound characters, and word because of complex structure. For this kind of complex pattern recognition problem, it is always difficult to decide which feature extraction and classifier would be the best choice. Moreover, it is also true that different feature extraction and classifiers offer complementary information about the patterns to be classified. Therefore, combining feature extraction and classifiers, in an intelligent way, can be beneficial compared to using any single feature extraction. This study proposed the combination of HOG +zonal density with SVM to recognize the Brahmi words. Keeping these facts in mind, in this paper, information provided by structural and statistical based features are combined using SVM classifier for script recognition (word-level) purpose from the Brahmi words images. Brahmi word dataset contains 6,475 and 536 images of Brahmi words of 170 classes for the training and testing, respectively, and the database is made freely available. The word samples from the mentioned database are classified based on the confidence scores provided by support vector machine (SVM) classifier while HOG and zonal density use to extract the features of Brahmi words. Maximum accuracy suggested by system is 95.17% which is better than previously suggested studies.

Understanding the physical properties of ultramafic rocks is important for evaluating awide variety of petrologic models of the oceanic lithosphere, particularly upper mantle and lower crust. Hydration of oceanic peridotites results in increasing serpentine content, which affects lithospheric physical properties and the global bio/geochemical cycles of various elements. In understanding tectonic, magmatic and metamorphic history of the oceanic crust, interpreting seismic velocities, rock composition and elastic moduli are of fundamental importance. In this study we show that as serpentine content increases, density decreases linearly with a slope of 7.85. We also correlate increase in serpentine content with a linear decline in shear, bulk and Young’s moduli with slopes of 0.48, 0.77, 0.45 respectively. Our results show that increase in serpentine content of lower crust and forearc mantle could decrease elasticity of lithospehere and result in break-offs. Therefore tectonic processes at peridotite rich slow spreading ridges may be strongly affected by serpentine content, particularly serpentinization may be responsible for discontinuities in thin crust, and formation of weak fault zones.

To generate a probability density function (PDF) for fitting probability distributions of real data, this study proposes a deep learning method which consists of two stages: (1) a training stage for estimating the cumulative distribution function (CDF) and (2) a performing stage for predicting the corresponding PDF. The CDFs of common probability distributions can be adopted as activation functions in the hidden layers of the proposed deep learning model for learning actual cumulative probabilities, and the differential equation of trained deep learning model can be used to estimate the PDF. To evaluate the proposed method, numerical experiments with single and mixed distributions are performed. The experimental results show that the values of both CDF and PDF can be precisely estimated by the proposed method.

The dislocation density tensors of thin elastic shells have been formulated explicitly in terms of the Riemann curvature tensor. The formulation reveals that the dislocation density of the shells is proportional to KA3=2, where K is the Gauss curvature and A is the determinant of metric tensor of the middle surface.

Water treatment efficiency of several filter media such as perlite, modified (silicated) perlite, zeolite and sand were studied on wastewater. This study contains 2 distinct experiments which the first one emphasize on the modified perlite’s performance in removing turbidity which has been gone through 3 phases including low turbidity model water, mid turbidity and high turbidity model water; the second experiment underlines the differentiation between 3 evolutionary material based on perlite which undergoes the filtration process with certain turbid model water. It was dedicated that modified perlite removed more than 90% of turbidity and it functions better than other materials with high turbidity (more than100 NTU). The experiments have been conducted with sodium silicate perlite, normal perlite and synthetic zeolite perlite in order to determine the optimized material which is able to be substituted with perlite in the filtration process.

For traffic management under sudden disaster in high-density areas, the first and foremost step is to prevent traffic congestion in the disaster-affected area by traffic flow control, as to provide enough and flexible traffic capacity for emergency evacuation and emergency rescue. Catastrophe border identification is the foundation and the key to traffic congestion prediction under sudden disaster. This paper uses a mathematical model to study the regional traffic flow in the high-density area under sudden fire disaster based on the Cusp Catastrophe Theory (CCT). The catastrophe border is identified by fitting the CCT-based regional traffic flow model to explore the stable traffic flow changing to the instable state, as to provide a theoretical basis for traffic flow manage and control in disaster-affected areas, and to prevent the traffic flow being caught into disorder and congestion. Based on VISSIM simulator data by building simulation scenarios with and without sudden fire disaster in a Sudoku traffic network, the catastrophe border is identified as 439pcu/lane/h, 529pcu/lane/h, 377pcu/lane/h at 5s, 10s, 15s data collection interval respectively. The corresponding relative precision, which compares to the method of Capacity Assessment Approach (CAA), is 89.1%, 92.7% and 76.5% respectively. It means that 10s data collection interval would be the suitable data collection interval in catastrophe border identification and regional traffic flow control in high-density area under sudden fire disaster.

In the current manuscript we assess to what extent X-ray photoelectron spectroscopy is a suitable tool for probing the dipoles formed at interfaces between self-assembled monolayers and metal substrates. To that aim, we perform dispersion-corrected, slab-type band-structure calculations on a number of biphenyl-based systems bonded to a Au(111) surface via different docking groups. In addition to changing the docking chemistry (and the associated interface dipoles), also the impacts of polar tail-group substituents and varying dipole densities are investigated. We find that for densely-packed monolayers the shifts of the peak positions of the simulated XP-spectra are a direct measure for the interface dipoles. In the absence of polar tail-group substituents they also directly correlate with adsorption-induced work-function changes. At reduced dipole-densities this correlation deteriorates, as work function measurements probe the difference between the Fermi-level of the substrate and the electrostatic energy far above the interface, while core level shifts are determined by the local electrostatic energy in the region of the atom from which the photoelectron is excited.

The textbook analysis of vacuum energy density (VED) in flat spacetime follows from Pauli’s lectures of 1951, in which quantum vacuum is modeled as a reservoir of free harmonic oscillators. In his lectures, Pauli shows that deriving a nearly vanishing VED is contingent upon fulfilling three corollary conditions called polynomial-in-mass-constraints. The goal of this work is to evaluate Pauli’s constraints against the Standard Model parameters and the Higgs mechanism of spontaneous symmetry breaking.

We analyzed the peak spring tidal current speeds, annual mean tidal power densities (TPD) and annual energy production (AEP) obtained from experiment 06.1, referred as the "HYCOM model" throughout, of the three dimensional (3D), global model HYCOM in an area covering the Baja California Pacific and the Gulf of California. The HYCOM model is forced with astronomical tides and surface winds alone, and therefore is particularly suitable to assess the tidal current and wind-driven current contribution to in-stream energy resources. We find two areas within the Gulf of California, one in the Great Island Region and one in the Upper Gulf of California, where peak spring tidal flows reach speeds of 1.1 meters per second. Second to fifth-generation tidal stream devices would be suitable for deployment in these two areas, which are very similar in terms of tidal in-stream energy resources. However, they are also very different in terms of sediment type and range in water depth, posing different challenges for in-stream technologies. The highest mean TPD value when excluding TPDs equal or less than 50 W/m² (corresponding to the minimum velocity threshold for energy production) is of 172.8 W/m², and is found near the town of San Felipe, at (lat lon) = (31.006 -114.64); here energy would be produced during 39.00% of the time. Finally, wind-driven currents contribute very little to the mean TPD and the total AEP. Therefore, the device, the grid, and any energy storage plans need to take into account the periodic tidal current fluctuations, for optimal exploitation of the resources.

The aerospace industry develops prognosis and health management algorithms to ensure better safety on board. Particularly for in-flight controls where jamming is dreaded. For that, vibration signals are monitored to predict future defect occurrences. However, time series are not labeled according to severity level, and the user can only assess the system health from the data mining procedure. To that extent, we developed a clustering algorithm using a deep neural network core. We encoded the time series into pictures to be feed into an artificially trained neural network: U-NET. From the segmented output, one-dimensional information on cluster frontiers is extracted and filtered without any parameter selection. Then, a kernel density estimation finally transforms the signal into an empirical density. Ultimately, a Gaussian mixture model extracts the latter independent components. The method empowered us to reveal different degrees of severity faults in the studied data, with their respective likelihood without prior knowledge. We compared it to state-of-the-art machine learning algorithms. However, internal clustering results evaluation for time series is an open question. As state-of-the-art indexes were not producing relevant results, we built a new indicator to fulfill this task. We applied the whole method to an actuator consisting of an induction machine linked to a ball screw. This study lays the groundwork for future training of diagnosis and prognosis structures in the health management framework.

Iterative Stochastic decoding is an alternative to standard fixed-point decoding of Low-density parity check codes (LDPC) to reduce inter-node routing. In this paper, we propose a Flexible Field-Programmable Gate Array (FPGA)-Based Stochastic decoding (SD) hardware architecture for LDPC codes in the Fifth-Generation (5G) New Radio (NR) Standard that supports decoding of set of various code rates. This decoder’s runtime flexibility is desirable to switch a better performing code rate automatically based on the channel conditions without the extra time needed for reprogramming of FPGA. An offline design method is implemented to generate the hardware description language (HDL) code description of the decoder for the required code-rate set, which is further synthesized and integrated into the Xilinx Kintex-7 series FPGA board to determine the hardware resource utilisation and processing throughput. The Synopsys design tools were employed during both the simulation and synthesis stages, in combination with TSMC 65-nm CMOS standard cell technology, to facilitate comparative analysis. Compared with state-of-the-art designs, the proposed architecture reduces hardware utilization by up to 26% and achieved energy efficiency by 52%.

The world is moving to the next phase of the energy transition with high penetrations of renewable energy. Flexible and scalable redox flow battery (RFB) technology is expected to play an important role in ensuring electricity network security and reliability. Continuous performance improvements will further enhance their value by reducing parasitic losses and maximizing available energy conversion over broader operating conditions. Concentration overpotentials from poor internal reactant distribution at high and low states of charge (SOC) limit power densities and are thus an important area of investigation. However, efforts to address these coupled electrochemical phenomena can compromise mechanical performance. Modelling and simulation of cell design innovations have shown it is possible to reduce losses from pump energy while increasing the availability of active species where required. The combination of wedge-shaped cells with static mixers investigated in this paper can reduce pressure drop and improve energy efficiency. Toroidal vanadium redox flow battery (VRB/VRFB) designs incorporating this innovation are presented for further development to improve community engagement with the technology.

This paper concentrates on the general birth-death processes with two different types of catastrophes. The Laplace transform of transition probability function for birth-death processes with two-type catastrophes are is successfully expressed with the Laplace transform of transition probability function of the birth-death processes without catastrophe. The first effective catastrophe occurrence time is considered. The Laplace transform of its probability density function, expectation and variance are obtained.

Two beneficial characteristics of aircraft generators are having armature windings and PM excitation sources placed on the stator side (maintenance and thermal management) and having a clear-cut rotor without permanent magnets or excitation windings (vulnerable at high speeds due to associated centripetal mechanical stresses). Within this framework, a doubly-salient permanent magnet (DSPM) generator was conceived by optimizing the stator size and rotor structure to minimize the torque ripple and maximize the RMS voltage value per turn of each generator phase. A Multi-Objective Design Strategy based on a 2D finite element analysis was used, being set to have characteristics closest to optimal for a Boeing 767 turbine, that is, the necessary electromotive force for a required power of 90 kW at 3000 rpm, feeding a simplified Boeing 767 electrical power distribution system. The results show that the machine could not deliver the required power at 3000 rpm since the 2D finite elements analysis demonstrates that the 2D model gives optimistic results when compared with the 3D FEM model. However, with a 3D finite element analysis of the machine feeding the aircraft load, it was seen that the machine’s efficiency is 92%, suggesting this machine can be a plausible solution.

The possibility of using graphane monolayer crystals as the electrode material is becoming popular. Graphane is stable at room temperature and has large surface area, but its chemical inertness hinders its direct interactions with Li ions. In this study, we performed density functional theory calculations to study the energetic stability, structural and electronic properties of Li on graphane with various CH divacancy configurations (v12, v13 and v14 ). The results show that adsorption of Li atom reduces the formation energy of the CH divacancy configurations. The Li-v12 is most stable with the highest binding energy of 3.25 eV/Li and relaxes to in-plane with other C atoms. Altering the Li charge state to have Li−1-v12 or Li+1 -v12 affects the energetic stability and electronic characters of Li-v12 . The Li−1-v12 (Li +1-v 12 ) enhances (reduces) the binding force between Li and v 12 configuration, and furthermore it improves (deteriorates) the conductivity of the structure. Further investigation of graphane with vacancies is encouraged due to these intriguing observations, as it holds promise for potential utilization as an electrode material.

In the natural environment, sand accumulation and desertification can influence the efficiency of photovoltaic arrays. In this study, PV module output characteristics were examined under different sand particle size, varying sand densities and inclination angles, and at wind speeds of 5m/s, 10m/s, and 15m/s. Both theoretical and experimental results showed that the output power of the module declines with increasing sand density. As the sand particle size increases, the module maximum output power rises and then stabilizes. The two control variables, namely sand density and sand particle size, influence the module temperature and light transmittance distinctly. Contrary to the impact of sand density on the module filling factor curve, the module filling factor exhibits varying trends under different sand particle sizes. Additionally, the pattern of the module filling factor as a function of sand density contrasts with that of the module temperature under analogous conditions. Specifically, when the sand density on the PV module surface increases from 0 to 40g/m², there is a 32.2% reduction in maximum output power. At a wind speed of 15m/s, this reduction was found consistent across all wind speed categories. Furthermore, the trend of the module filling factor rel-ative to sand accumulation density contrasts with the module temperature trend under analogous conditions. Notably, the filling factor reached its apex at a sand accumulation density of 35g/m², which corresponded to the module temperature's nadir during the experiment.

This study examines the wind shear coefficient (WSC) values at three coastal wind sites located in the southern region of Balochistan, Pakistan: Pasni, Ormara, and Jiwani. These WSC values were obtained using 10-minute measured wind speed data at heights of 20, 40, and 60 meters above ground level (AGL). Since wind measurements are typically recorded at lower heights due to cost and resource constraints, extrapolation techniques were employed to estimate wind speeds at higher altitudes. However, using a constant WSC value for extrapolation may lead to significant errors between extrapolated and actual wind speed measurements, impacting the energy output of wind turbines. To evaluate the effect of WSC on energy yield, the study employed power curves and frequency distributions for 2MW and 1.5MW wind turbines. Additionally, wind power density was calculated using air density derived from measured air temperature and surface pressure data, covering two years period from November 2016 to August 2018. The overall mean WSC values were found to be 0.076 at Pasni, 0.094 at Jiwani, and 0.053 at Ormara. The study further investigated the seasonal, monthly, and diurnal variations of WSC. For assessing wind resources at a height of 60m, the study utilized Wind Roses, wind power density, and Weibull parameters. Comparing the actual WSC values presented in this paper with those obtained using the 1/7 power law and measured data at 60m AGL, the energy yield from the wind turbines showed reduced output and capacity factor.

The general-purpose Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC) at CERN includes the hadronic calorimeter to register the energies of the charged and neutral hadrons produced in the proton-proton collisions at the LHC at a center of mass energy 13.6 TeV. The calorimeter is located inside the superconducting solenoid of 6 m in diameter and 12.5 m in length creating the central magnetic flux density of 3.8 T. For operating optimally in the high pileup and high radiation environment of the High Luminosity LHC, the existing CMS endcap calorimeters will be replaced with a new high granularity calorimeter (HGCal) comprising of an electromagnetic and a hadronic section in each of the two endcaps. The hadronic section of the HGCAL will include 44 stainless steel absorber plates with a relative permeability value well below 1.05. The volume occupied by 22 plates in each endcap is about 21 m3. The calculation of the axial electromagnetic forces on the absorber plates is a crucial element in designing the mechanical construction of the device. With a three-dimensional computer model of the CMS magnet, the axial forces on each absorber plate are calculated and the dependence of forces on the central magnetic flux density value is presented. The method of calculation and the obtained results are discussed.

This study aims to assess the interface shear adhesion behaviour between compacted clay and a metallic surface. A new testing approach was developed in this study for this purpose. The proposed method is simple and requires neither advanced equipment nor special test procedures, and thus represents an improvement on existing practice in this field. The experimental program involves determining the interface shear adhesion strength of reconstituted Kaolin clay/metallic surface where the Kaolin clay testing specimens were compacted dynamically at different energy levels and moisture contents. In general, the results show that the interface shear adhesion strength increases as the dry density of the clay increases, whereas it decreases as the moisture content increases. Furthermore, the results in this study reveal a unique multistage interfacial shear adhesion strength behaviour as the moisture content changes that could be related to the compaction curve of the clay. The observed behaviour, in this study, could be interpreted in terms of the effect of clay dry density and moisture content on the contact area and moisture-induced capillary adhesion at clay particles-continuum interface surface.

Plasma plays an important role in semiconductor processes. With the recent miniature and integration, the control of plasma became essential for the success in critical dimension of a few nanometers and etch narrow and deep holes with high aspect ratios. Recently, the etching process has reached physical limitations due to a significant increase in wafer surface temperature under the elevated amount of RF power, affecting not only the warpage phenomenon but also etching uniformity and etching profiles. Therefore, the plasma characteristics are identified using invasive single Langmuir probe (SLP) for wafer temperature diagnosis. Optical data is obtained through a non-invasive optical emission spectroscopy (OES) and the plasma parameters are derived to compare and verify with the SLP. Two variables, electron temperature and electron density, are substituted for the heat flux formula to derive the heat flux according to its location. Using a wafer-type temperature sensor, check the derived heat flux values and compare trends. Such studies are expected to be able to calculate heat flux values in real time, anticipate wafer temperatures, and solve existing problems.

Voids in face-centered cubic (fcc) metals are commonly assumed to form by the aggregation of vacancies; however, the mechanisms of vacancy clustering and diffusion are not fully understood. In this study, we use computational modeling to provide a detailed insight into the structures and formation energies of primary vacancy clusters, mechanisms and barriers for their migration in bulk copper, and how these properties are affected at simple grain boundaries. The calculations were carried out using Embedded Atom Method (EAM) potentials and Density Functional Theory (DFT) and employed the Site-Occupation Disorder code (SOD), the Activation Relaxation Technique nouveau (ARTn) and the Knowledge Led Master Code (KLMC). We investigate stable structures and migration paths and barriers for clusters of up to six vacancies. Migration of vacancy clusters occurs via hops of individual constituent vacancies with di-vacancies having a significantly smaller migration barrier than mono-vacancies and other clusters. This barrier is further reduced when di-vacancies interact with grain boundaries. This interaction leads to the formation of self-interstitial atoms and introduces significant changes into the boundary structure. Tetra-, penta-, and hexa-vacancy clusters exhibit increasingly complex migration paths and higher barriers than smaller clusters. Finally, the direct comparison with the DFT results shows that EAM can accurately describe the vacancy-induced relaxation effects in the Cu bulk and in grain boundaries. Significant discrepancies between the two methods were found in structures with a higher number of low-coordinated atoms, such as penta-vacancies and di-vacancy absortion by grain boundary. These results will be useful for modeling the mechanisms of diffusion of complex defect structures and provide further insights into the structural evolution of metal films under thermal and mechanical stress.

Research have been conducted in many countries around the world to assess air quality during COVID-19 pandemic, especially during lockdown period, some of these studies found an increase or decrease in some pollutants. This paper investigates the impact of COVID-19 on seven air pollutants (i.e., PM_2.5, PM₁₀, NO₂, O₃, SO₂, H₂S, CO) from the period January 2020 to December 2020 in the State of Kuwait. Kuwait is a desert country located in the north-eastern part of the Arabian Peninsula, and the northeast of the Arabian Gulf (Persian as it is sometimes called). Several analytical methods were conducted, such as spatial analysis (spatial interpolation) to study the distribution of the studied variables. The data was also statistically analysed (time series analysis - Kernel density) to study the temporal changes. The analysis also included applying air quality index to the data. We found that concentrations for the pollutants decreased during the pandemic due to the decrease of anthropogenic sources including such as traffic and petroleum activities, but the concentration for PM_2.5 increased, mostly because of the transported dust coming with the northwest winds prevailing in Kuwait from the Arabian deserts and Iraq.

Maize (Zea mays L.) production in West and Central Africa is constrained by drought, low soil-N and Striga infestation. Breeders in the region have developed and commercialized extra-early and early-maturing hybrids (E-EH and EH), which combine high yield potentials with tolerance/resistance to the three stresses. Hybrids of both maturity groups are new to the farmers; thus, the urgent need to recommend appropriate agronomic practices for these hybrids. We investigated the responses of four hybrids belonging to extra-early and early-maturity groups to plant density (PD) and nitrogen (N) application in five agroecologies. The EHs consistently out-yielded the E-EHs in all the five agroecologies. The hybrids showed no response to N-fertilizer application above 90 kg ha-1. All interactions involving N had no significant effect on all traits except in few cases. The E-EHs and EHs had similar response to PD; their grain yield decreased as PD increased. Contrarily, flowering was delayed and expression of some other agronomic traits such as plant and ear aspects became poorer with increased PD. Optimal yield was obtained at approximately 90 kg N ha-1 and 66,666 plants ha-1. Most of the measured traits indicated high repeatability estimates (i.e. ≥ 60) across the N levels, PDs and environments. Evidently, the hybrids were intolerant of high PD.

Nicotine is a pharmacologically active component of tobacco which adversely affects the male reproductive system and fertility and Icariin (ICA) is the main active ingredient of Epimedium herba which has been used to treat several male reproductive problems. This study was aimed at investigating the protective or ameliorative effect of ICA against reproductive toxicity induced by intraperitoneal injection of nicotine in mice. Forty male mice were randomly divided into 4 groups: control, nicotine (0.75 mg/kg intraperitoneally), icariin (ICA, 75 mg/kg), and icariin plus nicotine (ICA + nicotine) group. After 35 days of treatment, the mice were weighed, sacrificed, and their reproductive organs were collected and examined for further studies. In the nicotine-treated group, epididymal sperm density and serum testosterone concentrations significantly decreased relative to the control group. Nicotine also caused oxidative damage as shown by significant reduction in the activities of antioxidant enzymes and an elevation in Malondialdehyde (MDA) levels. Icariin on the other hand, improved the reduction in sperm characteristics, hormone levels, and activities of antioxidant enzymes alterations observed in the nicotine treated mice. These findings indicate that the nicotine-induced reproductive toxicity and oxidative damages on male reproductive tissues can be effectively attenuated by icariin.

The objective of this study was to determine the association of physiological responses and root distribution patterns on yield of the second ratoon cane and the relationships among these traits. Seventeen sugarcane genotypes were planted in a randomized complete block design with four replications. The second ratoon crop was evaluated for germination percentage, cane yield, SPAD chlorophyll meter reading (SCMR), chlorophyll fluorescence, relative water content (RWC), specific leaf area (SLA) and stomatal conductance. Root length density (RLD) was evaluated by auger method. The root samples were divided into upper soil layer and lowers soil layers to study root distribution patterns. Sugarcane genotypes were significantly different for RLD, germination percentage and cane yield. Root distribution patterns were classified into three groups based on the RLD. High RLD between plants in the upper soil layers at 90 DAH was positively correlated with high germination, whereas high RLD between rows in the lower soil layers at 90 and 270 DAH was associated with high cane yield. RWC at 90 DAH and stomatal conductance at 180 DAH were closely related to germination percentage, whereas chlorophyll fluorescence and stomatal conductance at 180 DAH were closely related to cane yield.

The relative dislocation density of aluminum and copper samples is quantitatively measured using linear Resonant Ultrasound Spectroscopy (RUS). For each metallic group, four samples were prepared with different thermomechanical treatments in order to induce changes in their dislocation densities. The RUS results are compared with Nonlinear Resonant Ultrasound Spectroscopy (NRUS) as well as Second Harmonic Generation (SHG) measurements. NRUS has a higher sensitivity by a factor of two to six and SHG by 14% to 62%. The latter technique is, however, faster and simpler. As main a result we obtain a quantitative relation between the changes in the nonlinear parameters and the dislocation density variations, which in a first approximation is a linear relation between these differences. We also present a simple theoretical expression that explains the better sensitivity to dislocation content of the nonlinear parameters with respect to the linear ones. X-Ray diffraction measurements, although intrusive and less accurate, support the acoustics results.

β-dicalcium silicate (β-Ca₂SiO₄, or β-C₂S in cement chemistry notation) is one of the most important minerals in cement. An improvement of its hydration rate would be the key point for developing environmentally friendly cements with lower energy consumption and CO₂ emissions. However, there is a lack of fundamental understanding on the water/β-C₂S surface interactions. In this work we aim to evaluate the water adsorption on three β-C₂S surfaces at the atomic scale using density functional theory (DFT) calculations. Our results indicate that thermodynamically favorable water adsorption takes place in several surface sites, with a broad range of adsorption energies (−0.78 to −1.48 eV), depending on the particular mineral surface and adsorption site. To clarify the key factor governing the adsorption, the electronic properties of water at the surface were analyzed. The partial density of states (DOS), charge analysis, and electron density difference analyses suggest a dual interaction of water with β-C₂S (100) surface: a nucleophilic interaction of the water oxygen lone pair with surface calcium atoms, and an electrophilic interaction (hydrogen bond) of one water hydrogen with surface oxygen atoms. Despite the elucidation of the adsorption mechanism, no correlation was found between the electronic structure and the adsorption energies.

In mega cities such as Seoul in South Korea, it is very important to protect the city from the flooding even for the short time of period due to the enormous amount of economic damage. In impervious area of the city, stormwater pipe network is commonly applied to discharge rainfall to the outside of catchment. Therefore, the stormwater pipe network in urban catchment should be carefully designed to discharge the runoff quickly and efficiently. In this study, different types of structures in stormwater pipe network were evaluated using the relationship between the peaks rainfall and runoff in urban catchments in South Korea. More than 400 historical rainfall events were applied in five urban catchments to estimate peak runoff from different type of network structures. Linear regression analysis was implemented to estimate peak runoffs. The coefficient of determination of the regressions were higher than 0.9 which means the regression model represent very well the relationship between the two peaks. However, the variation of the prediction becomes large as the peak rainfall increases and the variation become even larger when the network structure is branched. Therefore, it depends on the structure of stormwater pipe network. When the water paths in the pipe network is unique (branched network), the increased amount of rainfall is congested around the rainwater inlets and the uncertainty of peak runoff prediction is increased. If there are many possible water paths depending on the amount of discharge (looped network), the increased rainfall is discharged more quickly through the many water paths. This can be a way to represent the reliability of the stormwater pipe network. The structures of stormwater pipe network is evaluated using drainage density which is the length of pipes over the unit catchment area and 95% confidence interval. As a result, the 95% confidence interval is increased as the drainage density is increased because the accuracy of peak runoff prediction is decreased. As mentioned earlier, because the looped networks have many alternative water flowing paths, elimination time of rainfall from the catchments become short, the 95% confidence interval become narrow, and the reliability of peak runoff prediction become high. Therefore, it is beneficial to install looped stormwater pipe network within the affordable budget. It is important factor to determine the amount of complexity in stormwater pipe network to decrease the risk of urban flooding.

Novel high energy density materials N14 (1,6-dihydro-1,2,3,3a,4,5,5a,6,7,8,8a,9,10,10a-tetradecazapyrene) and N18 (1,2,2a,3,4,4a,5,6,6a,7,8,8a,9,10,10a,11,12a-octadecazacoronene) were designed, and their structures, detonation performance and stabilities were calculated employing density functional theory (DFT). Calculations reveals that they have a good balance between high energy and stability. Their energy gaps between LUMO and HOMO are all lower than that of TATB, while their impact sensitivity h_50% is estimated close to that of RDX. Concerning energy, detonation performance of the N14 (P = 43.6 GPa, D = 10040 m/s, Q = 2214 cal/g) and the N18 (P = 37.4 GPa, D = 9400 m/s, Q = 2114 cal/g) are comparable to CL-20.

Deadwood represents a source of nutrients, carbon and water for metabolism within forest ecosystem. Nutrients are mobilized due to the decomposition of wood, which is a long-term process that can be best studied by analysing environmental data on a temporary scale. Our study provides physico-temporal data on the downed logs of three major tree species in European temperate forests: Abies alba Mill., Fagus sylvatica L. and Picea abies (L.) Karst. Time since death was obtained using tree censuses (repeated for 40 years) and dendrochronology for each single downed log, the oldest being 75 years old. Standard laboratory methods were used for the determination of wood density and moisture changes. F. sylvatica was decomposed rapidly in the initial phase – mass loss was 50% during the 5 years after death, while A. alba and P. abies lost 13% and 16%, respectively. Downed logs of F. sylvatica contained 391 kg of water per m³, while these of P. abies 279 kg. A log-transformed linear model was created that shows the dependence of time since death on mass loss. According to the model, F. sylvatica had the shortest total decomposition time (39 years), followed by A. alba (58 years) and P. abies (86 years).

The atherosclerosis, a chronic and inflammatory disease that occurs when there are high levels of low-density lipoprotein (LDL) on plasma. This important risk factor for development of cardiovascular disease (CVD) is the main cause of death worldwide. MicroRNAs have recently emerged as potential biomarkers and therapeutic target for lipid metabolism disorders. In this review, we will provide profile of surrounding miRNAs that have demonstrated being regulators of PCSK9, LDLR and APOB100 genes. Recent work has identified the mir-148, mir-128, mir-27a/b, mir-185, mir-301, mir-130 as important regulators of this pathway because they decrease supply of LDL receptors through interaction with PCSK9. Inhibition of LDLR expression cause elevation of plasma LDL levels which induces atherosclerosis. While mir-30c, mir-122, mir-34 decrease MTTP, which promotes degradation of APOB100 preventing assembly and secretion of VLDL. We conclude that, when overexpressed, mir-148a, mir128 and mir-27a/b, mir-122 and mir-34 are related to decrease in LDLR, facilitating occurrence of atherosclerosis. While mir-30 has been linked to decreased atherosclerosis. Detection of miRNAs profile could be used in the future as a biomarker for disturbs linked to c-LDL uptake and in future anti-miRNAs therapies may be used in the treatment of atherosclerosis.

Powder bed fusion (PBF) process is a metal additive manufacturing process which can build parts with any complexity from a wide range of metallic materials. PBF process research has predominantly focused on the impact of only a few parameters on product properties due to the lack of a systematic approach for optimizing a large set of process parameters simultaneously. The pivotal challenges regarding this process require a quantitative approach for mapping the material properties and process parameters onto the ultimate quality; this will then enable the optimization of those parameters. In this study, we propose a two-phase framework for optimizing the process parameters and developing a predictive model for 316L stainless steel material. We also discuss the correlation between process parameters -- i.e., laser specifications -- and mechanical properties and how to achieve parts with high density (> 98%) as well as better ultimate mechanical properties. In this paper, we introduce and test an innovative approach for developing AM predictive models, with a relatively low error percentage of 10.236% that are used to optimize process parameters in accordance with user or manufacturer requirements. These models use support vector regression, random forest regression, and neural network techniques. It is shown that the intelligent selection of process parameters using these models can achieve an optimized density of up to 99.31% with uniform microstructure, which improves hardness, impact strength, and other mechanical properties.

Brushless Direct Current Motors are significantly improved for using many electrical applications. The design investigation is increased year by year and most of the motor design researchers find out the superior efficiency of the BLDC motor. The traditional motor consumed more electrical power than BLDC motor. The design of BLDC motors that are compact, small, easy to control, save energy and light weight. Due to this fact, the design of BLDC motor should be analyzed to improve the motor characteristics performance. The research focuses on the design of BLDC motor performance using Taguchi Method and Response Surface Methodology (RSM) for Finite Element Method (FEM). Firstly, the Taguchi Method is applied to identify the optimization point of design parameters and consider using the reference model of actual BLDC electric fan motor with a rated power of 26Watts design. Based on the Taguchi Method to create the RSM is used to two-step optimization design performance using in FEM analysis. The performances of motor design proposed approach to provide the effeteness for motor design investigator. In this investigation aims the better structural performances in output power, torque, efficiency and torque ripple. The final design of proposed model achieved an energy efficiency is 10% higher than the reference design of prototype modes, the output power result is 10W higher this reference design. We can reduce the loss of the proposed motor design. Furthermore, the maximum torque can also increase the 0.032Nm of that design. The design simulation results were produced and studied by the FEM analysis of JMAG software. From the evaluation results, the configurations of reference model design are compared with the proposed design by using the three methods of motor design optimization showed the best characteristics for performance of motor investigation design.

This study explores optimizing Synthetic Aperture Radar (SAR) satellite constellation scheduling for multi-imaging missions in densely targeted areas using an in-house developed Modified Dynamic Programming (MDP) algorithm. By employing Mixed-Integer Linear Programming (MILP) to define the mission planning problem, the research aims to maximize observation of high-value targets within restricted planning horizons. Numerical simulations, covering a wide range of target numbers and satellite configurations, reveal the MDP algorithm’s superior mission allocation efficiency, enhanced success rates, and reduced revisit times, compared to the Greedy algorithm. The findings underscore the MDP algorithm’s improved operational efficiency and planning robustness for complex imaging tasks, demonstrating significant advancements over traditional approaches.

Bipolar disorder is a severe mood disorder and is one of the top 20 reasons of disability in the world. It causes a huge burden on society. In this study, the prediction models of bipolar disorder were constructed based on the concept of knowledge distillation. The input data consisted of patients of bipolar disorder and matched controls, all of which were selected from the open database MIMIC. The method of kernel density estimation (KDE) was exploited to generate probability density functions (PDF) which identify distributions of input data. The PDF values referred to as the soft labels were combined with the input data to construct the prediction models of bipolar disorder using decision tree and artificial neural network respectively. According to the evaluation results, indicators for identifying positive samples of bipolar disorder were improved. Meanwhile, the indicators for identifying negative samples have also been advanced. In addition, the branching attributes selected by the decision trees can be mapped back to specific disease diagnoses, which are all associated with bipolar disorder. In conclusion, using KDE to generate the soft label information of the input data can make knowledge distillation work and has improved the performances of prediction models for bipolar disorder.

The hard open problem of finding the lower bound for the Fokker Planck Kolmogorov’s time-dependent controller parameter probability density function is addressed for the first time in literature in this work. This revolutionary exposition will put control theory and other related inter-disciplinary fields to a higher level towards contemporary control theory. Notably, based on the influential role of control theory in both engineering and industry, this paper will be of great value to all engineering and industry professionals who seek to know more about advanced trends within control theory settings. On the other remit of the spectrum, Fokker Planck Kolmogorov(FPK) equations are of high importance to physicists as well as mathematicians, based on their multiple applicability to multi-interdisciplinary fields of human knowledge, and beyond. So, this by default adds more taste and credibility to this study. Concluding thoughts are included at the end of the work along with a difficult outstanding problem and the direction of future work.

Cellulose nanofibrils (CNFs) are materials with high aspect ratio. Typically, TEMPO-oxidised, or cationic CNFs consist of long fibrils (up to 2 μm) with very low thickness (less than 10 nm). Derived from their high aspect ratio, CNFs form strong hydrogels with high elasticity at low concentrations. Thus, CNF suspensions appear as an interesting rheology modifier to be applied in cosmetics, paints, foods, as a mineral suspending agent, among other applications. The high viscosity results from the strong 3D fibril network, which is related to good fibrillation of the material, allowing the nanofibrils to overlap. The overlap concentration (c*) was found to vary from ca. 0.13 to ca. 0.60 wt.% depending on the type and intensity of the pre-treatment ap-plied during the preparation of the CNFs. The results confirm the higher tendency for the fibres treated with CHPTAC (cationic) and TEMPO to form a 3D network, resulting in the lowest c*. For the TEMPO-oxidised CNF suspensions, it was also found that aggregation is improved at acidic pH conditions due to lower charge repulsion among fibrils, leading to an increase in the suspension viscosity, as well as higher apparent yield stresses. TEMPO CNF suspensions with low content of carboxylic groups tend to precipitate at moderate acidic pH values.

In this study, modified red mud after phosphorus adsorption was used as the adsorbent, and hydrochloric acid and deionized water were used as desorbents to desorb phosphorus. The components in the adsorbent were optimized based on density functional theory, and adsorbent and desorbent models were established. Molecular dynamics simulation was performed to determine the phosphorus concentration before and after desorption, interaction energies, radial distribution function(RDF), mean-square displacement(MSD), and diffusion coefficient. The Monte Carlo method was used to simulate the desorption isotherm, desorption site, heat of desorption, and desorption energy. Simulation results showed that deionized water could only desorb phosphorus on the adsorbent surface, and the stability of the system deteriorated upon adding hydrochloric acid. Hydrochloric acid destroyed the ionic and hydrogen bonding between the O atoms in H2PO4− and reactive metal and oxygen atoms in the activated red mud particles. Moreover, the van der Waals force decreased considerably. The ionic and hydrogen bonds between H2PO4− and the surface of activated red mud particles were broken by hydrochloric acid, which accelerated the desorption of phosphorus from the adsorbent surface. The interaction between hydrochloric acid and phosphorus accelerated the diffusion, which decreased the adsorption capacity. Moreover, the desorption capacity increased with increasing temperature.

The surface configurations of the low-index facets of a set of spinel oxides are investigated using DFT+U calculations to derive surface energies and predict equilibrium nanoparticle shapes via the Wulff construction. Two very different conditions are investigated, corresponding to application either in heterogeneous catalysis or in electrocatalysis. First, the bare stoichiometric surfaces of NiFe₂O₄, CoFe₂O₄, NiCo₂O₄, ZnCo₂O₄ spinels are studied to model their use as high-temperature oxidation catalysts. Second, focusing attention on the electrochemical oxygen evolution reaction (OER), and on the CoFe₂O₄ inverse spinel as the most promising OER catalyst, we generate surface configurations by adsorbing OER intermediates, and, in an innovative study, we recalculate surface energies taking into account adsorption and environmental conditions, i.e., applied electrode potential and O₂ pressure. We predict that, under OER operating conditions, (111) facets are dominant in CoFe₂O₄ nanoparticle shapes, in fair agreement with microscopy measurements. Importantly, in the OER case we predict a strong dependence of nanoparticle shape upon O₂ pressure. Increasing O₂ pressure increases the size of the higher-index (111) and (110) facets at the expense of the (001) more catalytically active facet, whereas the opposite occurs at low O₂ pressure. These predictions should be experimentally verifiable and help define the optimal OER operative conditions.

To achieve a micro-destructive and rapid measurement of the wood density of standing trees, this study investigated the feasibility of using the micro-drilling resistance method, formulated a mathematical model, explored the potential for a unified model across multiple tree species, and analyzed the influence of moisture content on measurement accuracy. First, 231 tree cores and drill resistance data were sampled from Pinus massoniana, Cunninghamia lanceolate, and Cryptomeria fortunei. The basic density and moisture content of each core were measured, and the average value of each resistance data record was calculated. Second, the average drill resistance, the natural logarithm of average drill resistance, and absolute moisture content were used as independent variables, while the wood basic density was used as the dependent variable. Third, the total model of the three tree species and sub-model for each tree species were established through a stepwise regression method. Finally, the accuracy of each model was compared and analyzed with that of using the average basic density of each tree species as an estimated density. The results revealed that the estimation accuracy of the sub-model for each tree species was 1.670 percentage points higher than the average value of each tree species used as the density estimation value for these tree species. Additionally, except for Cryptomeria fortunei, the natural logarithm of drill resistance significantly influenced the wood density model at a significance level of 0.05. Moreover, moisture content significantly affected the total model and sub-models of Pinus massoniana at a significance level of 0.05. The estimation accuracy of the total mathematical model was 2.125 percentage points lower than that achieved using the average basic density of each tree species to predict their density. The results indicated the feasibility of using the micro-drilling resistance method to measure the wood density of standing trees. Moreover, the relationship between wood density and drill resistance did not follow a linear pattern, and moisture content slightly influenced the drill needle resistance. Furthermore, the establishment of a mathematical model for each tree species was deemed essential. This study provides valuable guidance for measuring the wood density of standing trees through the micro-drilling resistance method.

Penrose’s singularity from 1965 predicted that in the center of the black hole space-time curvature is infinite and consequently gravity force there is infinite. In 2014 NASA measured universal space has Euclidean shape. This means that stellar objects cannot curve universal space and that the space-time singularity model has no physical existence. In the center of black holes energy density of superfluid space is so low that the electromagnetic properties of space are changed. This causes electromagnetic forces between the nucleus and orbiting electrons to become unstable. In the center of a black hole, atoms are falling apart into elementary particles. Black holes are rejuvenating systems of the universe, they transform old matter into fresh energy in the form of elementary particles. Astrophysical jets are the outcome of this process.

Milkfish (Chanos chanos) locally called Bangus in the Philippines, is a popular food fish in terms of production and consumption in the Philippines, Indonesia and Taiwan. This study characterized a typical grow-out milkfish farm in the Davao region in terms of socioeconomic profile, farming practices, and simple costs and returns. Three sites were chosen to conduct this study, mainly in Mati, Pantukan and Panabo in Davao region where N=43 farm workers provided information on their daily farming practices. Farms are commonly stocked with 16,000 fingerlings, harvesting occurs after every 6 months with six feeding times in a day. Moreover, workers were paid an average of Php 10,000 per month while the company had a gross income of Php 930,000 per cropping cycle. The feed cost shares 76% of the overall operational cost with a 27% return on investment for the first year of operation. Climate change impacts (e.g. higher sea surface temperature, unpredictable weather and flooding) and deteriorating water quality conditions were seen as bigger threats. Other results showed a positive relationship between stocking density and volume harvested (df=1, MS=0.0907, F=40.13, P=0.000) and income (df=1, MS=0.048, F=21.22, P=0.000). The study also showed the need for cheaper feed substitute and better benefits to farm workers.

Fuzzy Rule Interpolation (FRI) offers an innovative methodology for inferring outcomes from data points that match no established rules in a sparse fuzzy rule base. Generally, a traditional fuzzy rule-based system suffers from the inability to perform inference while facing an unmatched observation, given a sparse fuzzy rule base that is derived from insufficient data or inadequate human experience and that fails to cover parts of a specific problem domain. Fortunately, starting with a sparse rule base, Fuzzy Rule Interpolation (FRI) can help to formulate interpolated rules specifically in instances where observations fail to activate any existing rule. An interpolated rule gives a result called consequent, which is related to the unmatched observation so that the inference system will not fail to generate an outcome corresponding to the observation as the output. Conventionally, these interpolated rules will be discarded after obtaining outcomes. However, valuable information may be potentially embedded within those discarded rules addressing the limitation regarding the coverage of the original sparse knowledge space. Moreover, without exploiting the potential value of the interpolated rules, the fuzzy inference system based on a sparse rule base will never improve its efficiency and robustness in the long run. Therefore, this underscores the necessity for Dynamic Fuzzy Rule Interpolation (D-FRI), which aims to collate interpolated rules that cover particular knowledge spaces absent in the sparse rule base. Intuitively, those frequently appeared interpolated rules are considered valuable because future observations are more likely to hit the respective problem areas. Promoting selected interpolated rules to the sparse rule base can enhance the overall coverage of the knowledge space and increase the inference efficiency over time. This paper introduces a framework for Dynamic Fuzzy Rule Interpolation that integrates the widely used Transformation-based Fuzzy Rule Interpolation (T-FRI) with an effective form of clustering of interpolated results. Experimental findings validate the effectiveness of this approach.

Two-dimensional (2D) bismuth oxyhalides (BiOX) have attracted much attention as potential optoelectronic materials. Expecting for their application diversity, we herewith systematically investigate the tunable properties of 2D BiOX by using first principles calculations. Their electronic and optical properties can be modulated by changing the number of monolayers, applying strain, and/or varying the halogen composition. The bandgap shrinks monotonically and approaches the bulk value, the optical absorption coefficient increases, and the absorption spectrum redshifts, as the layer number of 2D BiOX increases. Carrier transport property can be improved by applying tensile strain, and the ability of photocatalytic hydrogen evolution can be obtained by applying compressive strain. General strain engineering will be effective in linearly tune the bandgap of BiOX in a wide strain range. Strain together with halogen composition variation can tune the optical absorption spectrum to be on demand in the range from visible to ultraviolet. It suggests that that 2D BiOX materials have potential serving as tunable novel photodetectors, can be used to improve clean energy techniques, and are prospective in the field of flexible optoelectronics.

The urban environment induces stress on trees and the impact can be identified by observing the condition of the crown. The aim of this study is to correlate the variables crown density (Cdn), crown transparency (Ctr) and dieback (Cdie) with the following vegetation indices: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI2), blue normalized difference vegetation index (BNDVI), green normalized difference vegetation index (GNDVI), green-red vegetation Index (GRVI) and red-green-blue vegetation index (RGBVI) of the tree crown located in urban green areas, as well as chlorophyll fluorescence (Fv/Fm) as an indirect indicator of tree health condition. A total of 549 trees were evaluated, represented in 24 families, 36 genera and 53 species; the variables had average values of 67.96 % in Cdn, 35.19 % in Ctr and 1 % in Cdie. Correlations were found between Fv/Fm, NDVI and BNDVI. NDVI and BNDVI correlated with variables such as Cdn and Ctr, mainly in species such as Ligustrum lucidum, Jacaranda mimosifolia and Fraxinus uhdei. Therefore, it is possible to evaluate tree health condition of trees in urban green environments through the identification of unfavorable conditions at the crown level by using vegetation indices for some of the species studied.

This paper focuses on the impact of defects density and carrier capture cross-section area in the electron transport material (ETM), hole transport material (HTM), and absorber layers on the performance of perovskite solar cells and quantum efficiency (QE). Furthermore, the impact of defects density at the interface between ETM/absorber and absorber/HTM is also studied. SCAPS-1D software is used in the current study in determining solar cell performance. The proposed perovskite solar cell structure is a planar FTO/TiO2/ CH3NH3PbI3/ Cu2O. The results indicated that increasing the defect density in the absorber layer significantly affects cell performance, while in ETM and HTM layers, the cell parameters remain unaffected. It is also found that the defect capture cross-section has a similar behavior to the defect density in the main layers (ETM, absorber, and HTM). In addition, it is observed that by increasing the defects density in the ETM/absorber and absorber/HTM interfaces layer, the cell parameters FF, Jsc, and PCE have been slightly decreased, with no effect on Voc. Moreover, it is also noted that the quantum efficiency QE is sharply reduced. Finally, this paper introduced the correlation between the defect density and the capture cross-section, which is the first attempt to find such a relationship in perovskite solar cells to the knowledge of the authors.

This work reports the first nanocrystalline SnON (7.6 % Nitrogen content) n-type nanosheet Field-Effect Transistor (nFET) with transistor’s effective mobility (µeff) as high as 357 and 325 cm2/V-s at electron density (Qe) of 5×1012 cm-2 and ultra-thin body thickness (Tbody) of 7 and 5 nm, respectively. At the same Tbody and Qe, these µeff values are significantly higher than single crystalline Si, InGaAs, thin-body Si-on-Insulator (SOI), two-dimensional (2D) MoS2 and WS2. New discovery of slower µeff decay rate at high Qe than SiO2/bulk-Si universal curve was found, owing to one order of magnitude lower effective field (Eeff) by more than 10 times higher dielectric constant () in channel material, which keeps the electron wave-function away from the gate-oxide/semiconductor interface and lowers the gate-oxide surface scattering. In addition, the high µeff is also due to the overlapped large radius s-orbitals, low 0.29 mo effective mass (me*) and low polar optical phonon scattering. SnON nFETs with record-breaking µeff and quasi-2D thickness enable potential monolithic three-dimensional (3D) integrated circuit (IC) and embedded memory for 3D biological brain-mimicking structures.

A significant portion of the harvested land base in western Canada is becoming old enough or entering a phase where thinning is a legitimate option. A comprehensive review of the existing knowledge of commercial thinning (CT) treatments applied to pine and spruce-dominated stands in Alberta was conducted, with particular regard to the intensity, the timing of interventions, method and impacts on crop tree growth responses. Although the geographical focus of this review is Alberta, information on this topic is more complete in other areas of North America, and Europe, where there is a long history of density management. In areas of eastern North America, our review revealed that CT from below, with tree removal levels from 27 to 43% of the basal area could increase the merchantable wood volume from 11 to 60 m3 ha-1, depending on stand age and intensity of thinning. For Alberta conditions, and considering the risks, we conclude that commercial thinning basal area removal should be in the range of 25 to 40%, depending on a variety of factors such as species, wind firmness and insect or disease incidence and risk. Thinning too aggressively and/or too late will increase the blowdown risk but the literature is fairly consistent in suggesting that live crown ratios should be >40% to maximize the chance of growth response and minimize the blowdown risk. In cases where stands are also threatened by stressors such as drought, wind, and insect or disease outbreaks, CT treatments likely offer the potential at limiting the overall risk, but localized knowledge and experience are critical. It is intended that the information presented may support ongoing and future research trials and growth and yield (G&Y) model development with regard to potential CT treatments to apply and the likely results of practical application to commercial forestry.

Phytoseiidae inhabit a wide range of herbs, shrubs and trees. Aesculus hippocastanum is an important ornamental tree in Europe and is likely reservoir of these mites. We therefore assessed the species composition and the spatial and seasonal variability in the abundance of Phytoseiidae in city parks in South Bohemia, Czech Republic. Leaf samples were randomly collected from horse chestnut tree branches at eight sites, five times during the vegetation season in 2013. The mites were collected by washing technique and mounted on slides for identification. In total, 13,903 specimens of phytoseiid mites were found, and eight species were identified: Amblyseius andersoni, Euseius finlandicus, Kampimodromus aberrans, Neoseiulella tiliarum, Phytoseilus macropilis, Paraseiulus talbii, Paraseiulus triporus and Typhlodromus (Typhlodromus) pyri. Paraseiulus talbii and P. macropilis were recorded on the leaves of horse chestnut trees for the first time in the Czech Republic in this study. The predominant species was E. finlandicus (96.25%). The number of mites per compound leaf was, on average, 2.53, 10.40, 23.54, 11.59 and 9.27 on the sampling dates in each month between May and September, respectively. The mite density was significantly affected by the sampling site and date.

Low-dimensional carbon-based (LDC) materials have attracted extensive research attentions in electrocatalysis because of their unique advantages such as structural diversity, low cost, and chemical tolerance. They have been widely used in a broad range of electrochemical reactions to relief environmental pollution and energy crisis. Typical examples include hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), carbon dioxide reduction reaction (CO₂RR), and nitrogen reduction reaction (NRR). Traditional “trial and error” strategies seriously slowed down the rational design of electrocatalysts for these important applications. Recent studies show that the combination of density functional theory (DFT) calculations and experimental research is capable of accurately predicting the structures of electrocatalysts, thus could reveal the catalytic mechanisms. Herein, current well-recognized collaboration methods of theory and practice are reviewed. The history of modern DFT, commonly used calculation methods, and basic functionals are briefly summarized. Special attention is paid to descriptors that are widely accepted as a bridge links the structure and activity, and the breakthroughs for high-volume accurate prediction of electrocatalysts. Importantly, correlating multiple descriptors are used to systematically describe the complicated interfacial electrocatalytic processes of LDC catalysts. In addition, machine learning and high-throughput simulations are crucial in assisting the discovery of new multiple descriptors and reaction mechanisms. This review will guide the further development of LDC electrocatalysts for extended applications from the aspect of DFT computations.

We present large eddy simulations of a mid-latitude open ocean front using a modified state of the art computational fluid dynamics code. We investigate the energy and information fluxes at the submesoscale/small-scale range in the absence of any atmospheric forcing. We find submesoscale conditions (Ro∼1, Ri∼1) near surface, within baroclinic structures related with partially imbalanced frontogenetic activity. Near surface, the simulations show a significant scale coupling on scales larger than ∼103(m). This is manifested as a strong direct energy cascade and intense mutual communication between scales, where the latter was evaluated using an estimator based on Mutual Information Theory. At scales smaller than ∼103(m) the results show near-zero energy flux, however, at this scale range, the estimator of mutual communication still shows values corresponding with a significant level of communication between them. This fact motivates to investigate the nature of the self-organized turbulent motion at this scale range with weak energetic coupling but where communication between scales is still significant and to inquire into the existence of synchronization or functional relationships between scales with emphasis on eventual underlying non-local processes.

Global malaria epidemiology has changed in the last decade with a substantial increase in cases and death being recorded. Over 90% of global cases and deaths occur in Sub-Saharan Africa (SSA). Tanzania accounts for about 4% of all cases and deaths reported in recent years. It is believed that several factors contribute to the resurgence of malaria, parasite resistance to antimalarials and mosquito resistance to insecticides being at the top of the list. The presence of sub-microscopic infections poses a significant challenge to malaria rapid diagnostic tests (mRDT), particularly in low-endemic areas. Our cross-sectional surveys in Handeni and Moshi, Tanzania assessed the effect of low parasite density on mRDT. A significant difference (P˂0.001) in malaria prevalence by mRDT, light microscopy (LM) and nested polymerase chain reaction (PCR) was found among age groups. In comparison to all other groups, school-age children (5-15 years) had the highest prevalence of malaria. Based on the results of this study, mRDT may miss up to 6% of cases of malaria mainly due to low-density parasitaemia. Routinely used mRDT will likely miss the sub-microscopic parasitemia which will ultimately contribute to the continued spread of malaria and hinder efforts to control and eliminate it.

In the study of materials and macromolecules by first-principle methods, the bond order is a useful tool to represent molecules, bulk materials and interfaces in terms of simple chemical concepts. Despite the availability of several methods to compute the bond order, most applications have been limited to small systems because a high spatial resolution of the wave function and an all-electron representation of the electron density are typically required. Both limitations are critical for large-scale atomistic calculations, even within approximate density-functional theory (DFT) approaches. In this work we describe our methodology to quickly compute delocalization indices for all atomic pairs, while keeping the same representation of the wave function used in most compute-intensive DFT calculations on high-performance computing equipments. We describe our implementation into a post-processing tool, designed to work with Quantum ESPRESSO, a popular open-source DFT package. In this way we recover a description in terms of covalent bonds from a representation of wave function containing no explicit information about atomic types and positions.

The selectivity of electrochemical sensors to ascorbic acid (AA), dopamine (DA), and uric acid (UA) remains an open challenge in the field of biosensing. In this study, the selective mechanisms for detecting AA, DA, and UA molecules on the graphene and graphene oxide substrates were illustrated through the charge population analysis from the DFT calculation results. Our substrate models contained the 1:10 oxygen per carbon ratio of reduced graphene oxide, and the functionalized configurations were selected according to the formation energy. Geometry optimizations were performed for the adsorption of AA, DA, and UA on the pristine graphene, epoxy-functionalized graphene, and hydroxyl-functionalized graphene at the DFT level with vdW-DF2 corrections. From the calculations, AA was bound to both epoxy and hydroxyl-functionalized GO with relatively low adsorption energy, while DA was adsorbed stronger to the electronegative epoxy groups. The strongest adsorption of UA to both types of functional groups corresponded to the largest amount of electron transfer through the pi orbitals of UA. Local electron loss created local electric fields that opposed the electron transfer during an oxidation reaction. Our analysis agreed with the results from previous experimental studies and provide insight into other electrode modifications for electrochemical sensing.

One-dimensional chain of core-shell pairs connected by ideal springs enables design of the metamaterial demonstrating the negative effective density and negative specific thermal capacity. We assume that the molar thermal capacity of the reported metamaterial is governed by the Dulong-Petit law in its high temperature limit. The specific thermal capacity depends of the density of the metamaterial; thus, it is expected to be negative, when the effective density of the chain is negative. The range of the frequencies enabling the effect of the negative thermal capacity is established. Dependence of the effective thermal capacity on the exciting frequency for various core/shell mass ratios is elucidated. The effective thermal capacity becomes negative in the vicinity of the local resonance frequency ω0 in the situation when the frequency ω approaches ω0 from above. The effect of the negative effective thermal capacity is expected in metals in the vicinity of the plasma frequency.

Active galactic nuclei (AGNs) are throwing in the interstellar space huge jets of energy in the form of elementary particles. The calculation of the energy density of space in the centre of the black hole with the mass of the Sun shows that in the space-time singularity of such a black hole energy density of space there is so low that atoms become unstable and fall apart into elementary particles. In this sense, AGN is a rejuvenating system of the universe. It transforms its own old matter into fresh energy in the form of jets.

The main goal of this article is to demonstrate the impact of environmental data on the spreading of Covid-19. In this research, data has been collected from 70 cities/provinces that are affected by Covid-19. Here, environmental data refers to temperatures, humidity and population density in each of these cities/provinces. This data has been analyzed using statistical models such as Poisson, Quasi-Poisson and negative Binomial. It is found that a negative Binomial regression model is the best fit for our data. Our results reveal that average high temperature is the vital factor to slow down the spread of Covid-19. In addition, higher population density found to be an important factor for the quick spreading of Covid-19 where it is quite impossible to maintain the social distance and the virus can spread easily.

To realize high response and selectivity gas sensor in detecting dissolved gases in transformer oil, in this study, the adsorption of four kinds of gases (H2, CO, C2H2 and CH4) on Pd-graphyne as well as the gas sensing properties evaluation were investigated. The energetically favorable structure of Pd doped γ-Graphyne was first studied, including the comparison of different adsorption sites and discussion of electronic properties. Then, the adsorption of these four molecules on Pd-graphyne was explored. The adsorption structure, adsorption energy, electron transfer and electron distribution, the band structure and density of states were calculated and analyzed. The results show that the Pd atom prefers to be adsorbed on the middle of three C≡C bonds and the band gap is smaller. The CO adsorption exhibits the largest adsorption energy and electron transfer and brings obvious change to the structure and electron properties to Pd-graphyne. Because of the conductance decrease after adsorption CO and acceptable recovery time at high temperature, the Pd-graphyne can be promising gas sensing materials to detect CO with high selectivity. This work offers theoretical support for the design of nanomaterials based gas sensor using novel structure for industrial application.

One ambitious objective of Integrated Computational Materials Engineering (ICME) is to shorten the materials development cycle by using computational materials simulation techniques at different length scales. In this regard, the most important aspects are the prediction of the microstructural evolution during material processing and the understanding of the contributions of microstructural features to the mechanical response of the materials. One possible solution to such a challenge is to apply the Phase Field (PF) method because it can predict the microstructural evolution under the influence of different internal or external stimuli, including deformation. To accomplish this, it is necessary to take into account plasticity or, specifically, non-homogeneous plastic deformation, which is particularly important for investigating the size effects in materials emerging at the micron length scale. In this work, we present quasi-2D simulations of plastic deformation in a face centred cubic system in a finite strain formulation. Our model consists of dislocation-based strain gradient crystal plasticity implemented into a PF code. We apply this model to study the influence of grain size on the mechanical behavior of polycrystals, which includes dislocation storage and annihilation. Furthermore, the initial state of the material before deformation is also considered. The results show that a dislocation-based strain gradient crystal plasticity model can capture the Hall-Petch effect in many aspects. The model reproduced the correct functional dependence of the flow stress of the polycrystal on grain size without assigning any special properties to the grain boundaries. However, the predicted Hall-Petch coefficients are significantly smaller than those found typically in experiments. In any case, we found a good qualitative agreement between our findings and experimental results.

Bianchi type III string cosmological models (massive and geometric) in presence of magnetic field with vacuum energy density following the techniques used by Letelier (1979) and Stachel (1980) are obtained. We find that the energy conditions (dominant as well as weak) given by Hawking and Ellis (1974) are satisfied for the models. Also the solutions obtained satisfy conservation equation for massive string with magnetic field and vacuum energy density. The models in general, represent anisotropic phase of space-time due to the presence of string. However, the models isotropize at late time and in special case. It has also been shown that the magnetic field is directly linked with the matter. For string dust model, the state finder parameters {r,s} are in excellent agreement with Planck results (Sahni et al. (2003)). The physical and geometrical aspects of the models with special cases are also discussed.

Many dioecious plants have sex chromosomes that are cytologically heteromorphic, but about half of species lack cytological differences between males and females and are thus homomorphic. Very little is known about the size and content of the non-recombining sex-determining region (SDR) in these species. Here, we assess the size and content of the SDR of the diploid dioecious herb Mercurialis annua, which has homomorphic sex chromosomes and shows signatures of mild Y-chromosome degeneration. We used RNAseq to identify new Y-linked markers for M. annua. Twelve of 24 transcripts with male-specific and male-biased expression could only be PCR-amplified from males and are thus Y-linked. We found a further six Y-linked sequences that were present in males but not females using genome capture data from multiple populations. We used the Y-linked sequences to identify and sequence 17 sex-linked bacterial artificial chromosomes (BACs), which form 11 groups of non-overlapping sequence, covering a total sequence length of about 1.5 Mb. Content analysis of this region suggests it is enriched for repeats, has a low gene density and contains few candidate sex-determining genes. The BACs map to a subset of the sex-linked region of the genetic map, which is estimated to be at least 14.5 Mb. This is substantially larger than estimates for other dioecious plants with homomorphic sex chromosomes, especially given the small genome size of M. annua. Our data provide a rare, high-resolution view of the homomorphic Y chromosome of a dioecious plant.

The improvement of mechanical properties of cantala fiber and its composites. Treatments including alkali, silane, and the combination of both were carried out to modify the fiber surface. The influence of chemical treatments on fiber properties such as the degree of crystallinity and tensile strength was investigated. A variety of short cantala fiber reinforced rHDPE composites were produced by hot press, and the effect of fiber treatment on the flexural strength of composites was observed. SEM observations also carried out to highlight these changes. The result shows that alkali treatment improves tensile strength and tensile modulus of alkali treated fiber (NF12) which was predicted as a result of the enhancement of the cellulose crystallinity. In contrast, the tensile strength and tensile modulus of silane (SF05) and alkali-silane treated fiber (NSF05) decreased compared to untreated fiber (UF) which is caused by the addition of amorphous material. The tensile strength of alkali-silane treated fiber (NSF05) was lower than alkali treated fiber (NF12), but the composites prepared with NSF05 showed the highest increment of flexural strength of 25.9%. This may be due the combination of alkali and silane treatment helped in the better formation of fiber-matrix interface adhesion.

Cryptocurrencies have obtained a crucial position in the international financial landscape. The cryptocurrency market has been perceived as a highly volatile market since the inception of Bitcoin. This study investigates the relevant performance of extreme value models (EVM) in estimating the Value-at-Risk (VaR) of Bitcoin and Ethereum returns. The extreme value mixture models, GPD-Normal-GPD (GNG) and GPD-KDE-GPD models are fitted to the returns of Bitcoin and Ethereum and the Kupiec likelihood backtesting procedure is performed on the VaR estimates to assess the fits. Both models’ results showed that the fits were a much more decent representation of the observed data when compared to the Normal distribution. The backtesting results showed that the GPD-KDE-GPD model’s fit was superior to that of the GPD-Normal-GPD for both sets of returns at all VaR risk levels except at the 99% level. The results of this study may assist with understanding the dynamics and risks associated with cryptocurrencies and can serve as a beneficial tool for decision-making and risk management to investors, traders, financial institutions and many other participants in the cryptocurrency ecosystem.

This work is the first in literature to tackle the difficult open problem of determining the upper bound and threshold theorem for the TDCDP (time-dependent controller parameter) of the (Fokker Planck Kolmogorov) probability density function. This revolutionary exposition will put control theory and other related inter-disciplinary fields to a higher level towards contemporary control theory. Notably, based on the influential role of control theory in both engineering and industry, this paper will be of great value to all engineering and industry professionals who seek to know more about advanced trends within control theory settings. On the other remit of the spectrum, Fokker Planck Kolmogorov(FPK) equations are of high importance to physicists as well as mathematicians, based on their multiple applicability to information theory, graph theory, data science, finance, economics, and beyond. So, this by default adds more taste and credibility to this study. This leads by nature to introducing a different flavor to this ground-breaking research by highlighting the impact of Fokker Planck Kolmogorov(FPK) to revolutionize crypocurrency,which have received its name because it uses encryption to verify transactions, a new debatable digital payment system that doesn't rely on banks to verify transactions. It’s a peer-to-peer system that can enable anyone anywhere to send and receive payments. The paper ends with closing remarks combined with some challenging open problems and the next phase of research.

To map Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV 2) spreading and evaluate immune responses variations against this virus, it was essential to locally set up efficient serological tests. The SARS-CoV2 immunogenic proteins were very expensive and not affordable mainly for Low and Middle Income Countries (LMICs). For this purpose, the commonly used antigen, Re-ceptor-Binding Domain (RBD) of Spike S1 protein (S1RBD), was produced using the the Baculovirus Expression Vector System (BEVS) . During the current study, the expression of S1RBD was monitored using western blot under different culture conditions. Different parameters were studied: Multiplicity Of Infection (MOI), cell density at infection and harvest time. Hence, optimal conditions for efficient S1RBD production were identified: MOI 3; cell density at infection 2-3x 106 cells/mL and time post-infection (tpi or harvest time) of 72h and 72-96 h successively for expression in shake-flasks and 7L-bioreactor. A high production yield of S1RBD varying between 4mg and 70 mg per liter of crude cell culture supernatant was achieved, respectively, in shake-flasks and in 7L-bioreactor. Moreover, the produced S1RBD showed an excellent antigenicity potential against COVID-19 patient sera evaluated by western blot. Thus, additional serological assays, such as ELISA, were developed using the purified S1RDB (published by other research groups).

Data collected at the Cascais tide gauge, located on the west coast of Portugal Mainland, have been analyzed and sea level rise rates have been updated. Based on a bootstrapping linear regression model and on polynomial adjustments, time series are used to calculate different empirical projections for the 21^st century sea level rise, by estimating the initial velocity and its corresponding acceleration. The results are consistent to an accelerated sea level rise, showing evidence of a faster rise than previous century estimates. Based on different numerical methods of second order polynomial fitting, it is possible to build a set of projection models of relative sea level rise. Appling the same methods to regional sea level anomaly from satellite altimetry, additional projections are also built with good consistency. Both data sets, tide gauge and satellite altimetry data, enabled the development of an ensemble of projection models. The relative sea level rise projections are crucial for national coastal planning and management since extreme sea level scenarios can potentially cause erosion and flooding. Based on absolute vertical velocities obtained by integrating global sea level models, neo-tectonic studies and permanent Global Positioning System (GPS) station time series, it is possible to transform relative into absolute sea level rise scenarios, and vice-versa, allowing the generation of absolute sea level rise projection curves and its comparison with already established global projections. The sea level rise observed at the Cascais tide gauge has always shown a significant correlation with global sea level rise observations, evidencing relatively low rates of composed vertical land velocity from tectonic and post-glacial isostatic adjustment, and residual synoptic regional dynamic effects rather than a trend. An ensemble of sea level projection models for the 21st century is proposed with its corresponding probability density function, both for relative and absolute sea level rise for the west coast of Portugal Mainland.

The main aim of this paper is to analyse the performance of different intelligent compaction measurement values (ICMVs) and compare them with spot test measurements for asphalt pavement. To accomplish this, a two-layer asphalt testbed was constructed using an instrumented double-drum roller. All ICMVs [compaction meter value ( ), compaction control value ( ), roller-integrated stiffness ( ), and vibratory modulus ( )] were calculated using the same accelerometer data collected during the construction of the asphalt testbed. A novel method using the cross-correlation technique was developed to estimate the phase lag between the excitation force of the roller drum and the ground reaction, which is required for and calculations. The analysis of ICMV data showed a strong correlation between and , while and demonstrated a strong correlation only when double jump of the roller drum did not occur. Further, none of the ICMVs recorded during the last roller pass of asphalt compaction showed an acceptable correlation with asphalt densities [measured using a non-nuclear density gauge (NNDG)], mainly due to the influences of the underlying support and asphalt temperature on ICMVs. A correction method to decouple the influence of underlying support on was developed and validated using existing data from the literature. The applicability of this method for different field scenarios was demonstrated using numerical modelling.

Abstract: Dispersal strategies of a species can affect its invasion success. Investigation into the dispersal strategies of invasive species in relation to different factors helps our understanding of the invasion mechanisms and provides knowledge for population management and invasion evaluation. Tetranychus ludeni Zacher (Acari: Tetranychidae) is an invasive species, which is native to Europe but now cosmopolitan. Here, we examined the effects of age and density on dispersal in mated females. Our results show that older females with more eggs were more likely to disperse and moved longer distances than younger ones with fewer eggs. Older females spread most of their eggs out of the natal habitats and over longer distances, which reduced competition and increased offspring fitness. Females significantly increased dispersal probability and distance with the increase of population density to avoid crowding. The synchronization of dispersal and reproduction, along with the positive density-dependent dispersal strategy, may facilitate habitat colonization and invasion speed of T. ludeni.

In this paper, four probability functions are compared with the purpose of establishing a methodology to improve the accuracy of the estimated wind energy in a desert city in northwestern Mexico. For the statistical modeling, 3 time series of wind speed data were used, corresponding to the years 2017, 2018, and 2019, recorded with a sonic anemometer at a sampling frequency of 10 Hz. From these, the analysis is performed with different stationarity periods (5, 30, 60, and 600 seconds). The estimation of the parameters characterizing the probability density functions was performed using different methods; the statistical models were evaluated by the coefficient of determination and the Nash-Sutcliffe efficiency coefficient, while their accuracy by the measured quadratic error, the mean square error, the mean absolute error, and the mean absolute percentage error. It was found that Weibull with the energy pattern factor method and Gamma using the method of moments were the probability density functions that best describe the statistical behavior of the wind speed and, therefore, better estimate the energy generated, so it is concluded that the proposed methodology will allow having greater confidence, both in the estimation of the wind speed and in the small wind energy available for its use.

Using density functional theory (DFT), the density of states of NH4V3O8 (NVO) was analyzed pre and post-oxygen defects (Od) formation. The findings revealed a reduced bandgap in NVO after Od introduction, emphasizing the role of Od in enhancing conductivity of the material, thus improving its electrochemical attributes. Through the water bath method, both NVO and its oxygen-deficient counterpart, (NH4)2V10O25⸱8H2O (NVOd), were synthesized as potential cathode materials for aqueous zinc-ion batteries (AZIBs). Experimental outcomes resonated with DFT predictions, highlighting the beneficial role of oxygen defects in boosting electrical conductivity. Notably, the refined material displayed a remarkable capacity of 479.3 mAh g-1 at 0.1 A g-1, underscoring its promise for advanced energy storage solutions.

Density functional theory (DFT) and coupled cluster theory (CCSD(T)) calculations are performed to investigate the geometric, electronic structures and chemical bonding of a series of Cu-doped zinc oxide clusters, CunZn3O3 (n = 1-4). The structural evolution of CunZn3O3 (n = 1-4) clusters are elaborated in our work. The planar seven-membered ring of CuZn3O3 cluster plays an important role in the structural evolution, that is, the Cu atom, Cu dimer (Cu2) and Cu trimer (Cu3) anchor on the CuZn3O3 cluster. The aggregate behavior of Cu atoms on Zn3O3 may offer insight into the study of Cu/ZnO-based catalysts. Additionally, it is found that the CunZn3O3 clusters become more stable as the Cu content (n) increases. Bader charge analysis points out that as the Cu atoms doping, the reducibility of Cu aggregation (Cun-1) on the CuZn3O3 cluster increases. Combined with the d-band centers and the surface electrostatic potential (ESP), the reactivity and the possible reaction sites of CunZn3O3 (n = 1-4) clusters are also illustrated.

Magnesium, as one of the most abundant cation in the human body, plays an important role in both physiological and pathological processes. In this study, it was shown that a promising biomedical material Mg-substituted hydroxyapatite (Mg-HA) can be synthesized via fast mechanochemical method. For this method, the nature of magnesium-contained carriers was shown to be important. When using magnesium oxide as a source of magnesium, the partial insertion of magnesium cations into the apatite structure occurs. In contrast, when magnesium hydroxide or monomagnesium phosphate is used, a single-phase Mg-HA is formed. Both experimental and theoretical investigations showed that an increase in the Mg content leads to a decrease in the lattice parameters and unit cell volume of Mg-HA. Density functional theory calculations showed the high sensitivity of the lattice parameters towards the crystallographic position of the calcium site substituted by magnesium. It was shown experimentally that insertion of magnesium cations decreases the thermal stability of the hydroxyapatite. The thermal decomposition of Mg-HA leads to the formation of a mixture of stoichiometric HA, magnesium oxide and Mg-substituted tricalcium phosphate phases.

The purpose of this study is to identify the different effects exerted by planting density and nitrogen fertilization on high-salinity Reclaimed Paddy Fields(RPF) and General Paddy Fields(GPF), and to find the amount of fertilization and the planting density suitable for the growth of forage rice in each paddy field. In both experimental locations, there was no significant change in the soil due to N treatment, but in the case of RPF, electrical conductivity(EC) decreased significantly from more than 5dS/m to up to 2.87dS/m during the yellow ripe stage due to the influence of floods and concentrated precipitation on the fields. In all soils, as both the amount of N treatment and the planting density increased, there was a proportional relationship in which the number of tillers and the dry weight also increased, with the occurrence of lodging also being increased. The dry weight, as expected, was 1.5 times higher at a planting distance of 10 cm, rather than 16 cm. In addition, in both locations N treatment led to an increase in the dry weight, but when N treatment reached 400kg/ha (2.0), the dry weight decreased instead. Moreover, although there was no clear difference in feed value according to N treatment, in RPF, neutral detergent fiber(NDF) was higher than 60%, RFV was less than 98, and TDN was also low, confirming that the quality of rice was higher in GPF.

When describing the tribological behaviour of technical surfaces, the need for full length scale microtopographic characterization often arises. The self-affine of surfaces and the characterization of self-affine by a fractal dimension and its implantation into tribological models are commonly used. The goal of our present work was to determine the frequency range of fractal behaviour of surfaces by analysing the microtopographic measurements of a brake plunger. We also wanted to know if the bifractal and multifractal behaviour can be detected in real machine parts. As a result, we developed a new methodology for determining the fractal range boundaries to separate the nano- and micro-roughness. In order to reach our goals, we used atomic force microscope (AFM) and stylus instrument to provide measurements in wide frequency range (19 nm - 3 mm). As a result of the power spectral density (PSD) based fractal evaluation, it was found that the examined surface could not be characterized by a single fractal dimension and that we developed a methodology for stopping the margin of validity of each fractal dimension, and we also developed a methodology for determining the limits of validity of certain fractal dimensions.

Large epidemiological studies show U-shaped relationships between high-density lipoprotein cholesterol (HDL-C) levels and all-cause mortality in individuals without atherosclerotic cardiovascular diseases (ASCVD). Association in those with ASCVD by sex is unclear. We examined the association between HDL-C levels and 10-year all-cause mortality in subjects (≥40 years of age) with ASCVD using the 2010 National Health Insurance Service and the National Death Registry of Korea. We categorized HDL-C levels into three groups (low: <40 mg/dL for males, <50 mg/dL for females; high: 40-90 mg/dL for males, 50-90 mg/dL for females; extremely high: >90 mg/dL) and 10 mg/dL intervals. We conducted a sex-stratified and adjusted Cox proportional hazards analysis. Out of 1,711,548 individuals (54% female, mean age 61.4 years), 10-year mortality was observed in 218,252 (12.8%). Males had a higher mortality rate than females (16.2% vs 9.8%; P<0.001). When adjusting for age, body mass index, LDL-cholesterol, triglycerides, hypertension, diabetes, smoking, and alcohol consumption, the low and extremely high HDL-C groups had significantly higher hazard ratios for 10-year mortality compared to the high HDL-C group in males [1.183 (1.166-1.199), 1.359 (1.288-1.434)] and in females [1.153 (1.138-1.169), 1.095 (1.029-1.167)]. The frequency distribution bars for the 10-year mortality rate showed sex-specific nadirs of 50-59 mg/dL in males and 70-79 mg/dL in females. In this ASCVD cohort, the extremely high HDL-C (>90 mg/dL) group had 35.9% and 9.5% higher 10-year mortality risk than the high HDL-C group for males and females, respectively. There was a slightly U-shaped relationship between baseline HDL-C levels and 10-year mortality rate, with earlier inflection in males than in females.

Early spaceborne laser altimetry mission development starts in pre-phase A design, where diverse ideas are evaluated against mission science requirements. A key challenge is predicting realistic instrument performance through forward modeling at arbitrary spatial scale. Analytical evaluations compromise accuracy for speed, while radiative transfer modeling is not applicable at global scale due to computational expense. Instead of predicting arbitrary properties of a lidar measurement, we develop a baseline theory to predict only the distribution of uncertainty specifically for the terrain elevation retrieval based on terrain slope and fractional canopy cover features through a deep neural network gaussian mixture model, also known as a mixture density network (MDN). Training data was created from differencing geocorrected GEDI L2B elevation measurements with 32 independent reference lidar datasets in the contiguous U.S. from the National Ecological Observatory Network. We trained the MDN and selected hyperparameters based on regional distribution predictive capability. On average, the relative error of equivalent standard deviation of predicted regional distributions was 15.9%, with some anomalies in accuracy due to generalization and insufficient feature diversity and correlation. As an application, we predict the percent of elevation residuals of a GEDI-like lidar within a given mission threshold from 60°S to 78.25°N, which correlate to qualitative understanding of prediction accuracy and instrument performance.