Mixed Distribution Research Articles

Approaches for Behavior Intensity Estimation in Groups of Heterogeneous Individuals: Precision and Applicability for Data with Uncertainty

In socially oriented areas, there arises the problem of assessing the cumulative characteristics of behavior, such as intensity, that are realized in groups of individuals. All individuals vary in their behavior and the available data is limited and may be associated with significant uncertainty: only a few episodes may be known and only a few individualsi the group may be observed. Mathematical models of behavior are used for estimation of key characteristics of the behavior. One of them is based on the gamma–Poisson point process, that reflects the heterogeneity of individuals in a form of a mixing distribution. This general model allows to formulate several methods of frequency estimation: the Cox regression, estimation of the copula parameter, and a posteriori inference in Bayesian belief networks. The aim of the paper is to assess their determine the precision of these methods based on the Kantorovich–Rubinstein distance between estimates and the true distribution of the desired parameter. The analysis of assumptions of those methods allows to formulate rules, that allow to chose the appropriate method in various sutuations of data availability. It has been shown that the copula-based approach provides the most accurate estimates and has the mild assumptions for the number of observed objects, but it cannot take into account external factors that may influence the behavior. Among methods that can take into account process covariants, estimates based on a posteriori inference in hybrid Bayesian belief networks have the highest precision. The paper considers a method for quantification of a hybrid BBNs with the approximation of mixtures of truncated exponents, that is data-demanding at the stage of calculating a priori estimates. However, it is noted that there are other approaches to setting hybrid BSDs in which a priori estimates can be set completely expertly.

Research on the P‐S‐N Curve Fitting Method of Notched Specimens Considering Small Sample Properties

ABSTRACTAiming at the issue of fatigue test data for large‐scale mechanical components of building steel are very limited, a method for fitting P‐S‐N curves under small sample data of notched specimens is proposed to predict fatigue life. First, a fatigue life subsample augmented and its reliability assessment method are established, based on Bayesian hierarchical modeling and modified Monte Carlo method. Second, a clustering combination weighting method is proposed, to define weights of hidden variables of the binomial mixture Weibull distribution, and the expectation–maximization algorithm is used to determine probability density function of the distribution. Finally, the P‐S‐N curves under various failure probabilities are fitted with Weibull distributed life models, and the convergence and prediction accuracy of the different models are compared. The results show that the fatigue data of small samples can be predicted better by using mixed Weibull distribution, and the fitting P‐S‐N curve is more reliable and accurate.

Research on Multi-Parameter Error Model of Backcalculated Modulus Using Abaqus Finite Element Batch Modeling Based on Python Language

The error in modulus backcalculation is a crucial component in validating the rationality and reliability of results for engineering applications. The objective of this study is to identify the theoretical limitations associated with backcalculated modulus errors under typical parameter uncertainties and to determine the primary factors contributing to these errors. Firstly, using the actual measurements or data from the Long-Term Pavement Performance (LTPP) project, the statistical distributions of errors for typical parameters in the modulus backcalculation model were determined. Subsequently, a factor level table for orthogonal experimental design was developed, leading to the construction of 81 orthogonal design experimental schemes and their corresponding theoretical pavement structure models based on the actual error distributions. The deflection responses of 81 theoretical pavement structure models were then computed using an ABAQUS finite element batch analysis method devised in Python. Furthermore, a multi-parameter error model for modulus was established using multiple linear regression and variance analysis. Finally, the theoretical limitations of modulus errors under actual errors were analyzed. The results show that the errors of thickness, load amplitude and load frequency follow a normal distribution, while the distribution of backcalculated modulus errors follows an approximate mixed Gaussian distribution. When the errors of multiple parameters are combined randomly, the modulus errors range from −100% to 595%, and the probability of the modulus errors being less than 15% is highest in the asphalt surface layer, followed by the subgrade, and then the base and subbase layers. Within the same error range, the modulus error is random. However, with different error ranges, the overall level of modulus error increases in proportion to the size of those ranges. Compared to factors such as thickness, load amplitude, and load frequency, the errors in deflections have a highly contribution rate on the modulus errors exceeding 99%.

Exploring the Spatiotemporal Heterogeneities in Urban Vitality Through Scalable Proxies from Mobile Data

Jane Jacob’s concepts of urban vitality and diversity have become prevailing urban planning philosophies in most countries for making cities more livable. Recent changes in demographics and the impacts of COVID-19 have exacerbated the economic and social challenges that cities commonly face, particularly spatiotemporal heterogeneities. Being able to understand these heterogeneities in scalable approaches is fundamental to tackling these challenges in cities. Therefore, this article aims to provide a new form of scalable estimation of urban vitality by using the de facto population. Instead of merely adopting static statistical information such as morphological characteristics in areas, we leverage dynamic factors such as internal mobility flows and energy use intensity as proxies for the spatiotemporal dynamics of indoor and outdoor behaviors of crowds. In this way, we combine dynamic attributes and static features to describe the patterns of urban vitality, which are directly related to spatiotemporal dynamics in urban places. We utilize GNSS-based mobile data and building energy usage intensity as dynamic proxies along with static data such as land use mix and age distribution. To better capture spatial heterogeneity, we use a Multiscale Geographically Weighted Regression (MGWR) model to identify the relationships between the de facto population and the dynamic and static factors. Drawing from the factors determining urban vitality, this article provides policy implications for alleviating spatiotemporal urban imbalances. These data-driven implications can fill the technical knowledge gaps in establishing planning strategies for achieving urban sustainability while enhancing overall subjective livability.

Differences in Mathematical and Verbal Achievement Between Girls and Boys: The Heuristic Potential of the Structural Typing Approach in Large‐Scale Studies

ABSTRACTThe results of total testing from the years 2015–2022 on the mathematical and verbal achievement of Lithuanian pupils (N ≈ 250,000) are presented. These are the standardised tests from grades 4 to 12. The K‐Means method has discovered six types of achievement. The highest achievement type is dominated by girls (61.1%) who perform well on both mathematical and verbal tasks. The lowest achievement type is dominated by boys (57.4%) who solve both mathematical and verbal tasks extremely poorly. Each of these types makes up 1/5 of the population, and the gap between the means of their groups is about 2.5 standard deviations. The remaining four types of achievement are in the 20th to 80th percentile and make up about 60% of the population. Differences in means within the same type between mathematic and verbal achievement average 0.85 standard deviations or span one quartile. Gender differences are clearly visible in this subgroup: boys solve mathematical tasks better and verbal tasks worse; girls solve verbal tasks better and mathematical tasks worse. Big data may form a mixed distribution. It is appropriate to first discover the basic types of achievement and only then look for gender‐specific differences. Such a type‐building approach is heuristically superior to the conventional approach of working only with the mixed dataset.

Bayesian mixture model for accurate assessment of monthly maximum wind speed: A case study in Gwadar

Assessment of monthly maximum wind speed (MMWS) is essential in various environmental disciplines, including architectural risk assessment, climatology, renewable energy sources, building design, and agricultural activities. The distribution of wind speed is an important module of its development. Presently, several mixing distributions have been suggested for fitting the theoretical maximum wind speed distributions. However, the drawback of various distribution elements is the outcome of numerous fitting presentations, and it is challenging to choose the best form of single distribution to employ when building a hybrid model. In order to solve those difficulties, utilizing the mixtures of Sine-skewed-von Mises distributions (MSS-VM) with various prior parameter distributions is extended in the present study. In this research, the assessment of MMWS employing the maximum-likelihood estimation (MLE) method is taken into consideration for the wind speed data from the Pakistan province of Baluchistan Gwadar station. The results of the Anderson Darling (AD), Chi-square, BIC, and AIC tests, as well as the Kolmogorov Smirnov (KS) test, indicated that MSS-VM was the best distribution for the Gwadar station. The suggested models automatically find the optimum number of elements, in addition to having more appropriate performance. The outcomes demonstrate that MSS-VM performance is more suitable than other heterogeneous and single-distribution models. The design estimations derived using the Bayesian mixture distribution provide a sense of the highest wind speed that will occur across a particular area. It is therefore crucial for designers and policymakers in the planning, designing, and building of various structures.

Heterogeneous lamella mixed grain structures of strength-ductility synergy in medium strain rate rolled AZ31 magnesium sheets

The increase in strength of Mg and its alloys, like other metals, usually couples with a large reduction in ductility, which limits its widely application. A novelty heterogeneous lamella mixed grain structures could effectively achieve high strength-ductility synergy and are obtained by medium-strain rate rolling with high reduction in single-pass which is an industrial process that can be easily scaled up for large-scale production at low cost, i.e. tensile strength of ∼279 MPa and elongation of ∼21.2 % in low alloying magnesium alloy (AZ31). This novelty heterogeneous lamella mixed grain structure displays fine grain lamella and mixed grain lamella alternative distribution, and twins exist inside mixed grain lamella. During deformation, the twinning occurs in the mixed grain lamella and the heterogeneous lamella mixed grain structures have high Schmid factor (SF) for non-basal slip, resulting in the excellent plasticity. The strength is mainly provided by the fine grain lamella and the twins inside mixed grain lamella. The back stress induced by the heterogeneous structure could dynamically achieve strength and ductility. This work provides a new sight to design the heterogeneous lamella mixed grain structure to obtain different combination of strength and plasticity by adjusting grain size distribution, twin density in mixed grain lamella, aspect ratio of coarse grain and misorientation among coarse grain, fine grain and twin.

The Two Component Generalized Finite Erlang Mixture and its Properties and Special Cases

This research proposes the construction of a two-component generalized finite Erlang mixture with four parameters. Three distinct cases of this mixture are presented, differing in their mixing weights and corresponding component probability (Erlang) distributions. Special cases of these mixtures, including the one-, two-, and three-parameter finite Erlang mixtures, have also been derived. The statistical properties examined for these mixtures include the distribution function, survival function, hazard function, moment generating function, raw and central moments, mean, variance, coeffcient of skewness, coeffcient of kurtosis, and order statistics. Parameter estimation for the finite Erlang mixtures was conducted using both the method of moments and maximum likelihood estimation. Furthermore, the 4-parameter mixed Erlang distributions were applied to a real dataset on the relief times of patients receiving an analgesic, to evaluate their goodness of fit. The results demonstrate the potential of these mixtures to provide robust modeling for empirical data, suggesting their applicability in various statistical and practical contexts.

Influence of Vegetation Phenology on the Temporal Effect of Crop Fractional Vegetation Cover Derived from Moderate-Resolution Imaging Spectroradiometer Nadir Bidirectional Reflectance Distribution Function–Adjusted Reflectance

Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products are being increasingly used for the quantitative remote sensing of vegetation. However, the assumption underlying the MODIS NBAR product’s inversion model—that surface anisotropy remains unchanged over the 16-day retrieval period—may be unreliable, especially since the canopy structure of vegetation undergoes stark changes at the start of season (SOS) and the end of season (EOS). Therefore, to investigate the MODIS NBAR product’s temporal effect on the quantitative remote sensing of crops at different stages of the growing seasons, this study selected typical phenological parameters, namely SOS, EOS, and the intervening stable growth of season (SGOS). The PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product. The Anisotropic Flat Index (AFX) was further employed to explore the influence of vegetation type and mixed pixel distribution characteristics on the BRDF shape under different stages of the growing seasons and different FVC; that was then combined with an NDVI spatial distribution map to assess the feasibility of using the reflectance of other characteristic directions besides NBAR for FVC correction. The results revealed the following: (1) Generally, at the SOSs and EOSs, the differences in PCCs before vs. after the NBAR correction mainly ranged from 0 to 0.1. This implies that the accuracy of FVC derived from MODIS NBAR is lower than that derived from MODIS Surface Reflectance. Conversely, during the SGOSs, the differences in PCCs before vs. after the NBAR correction ranged between –0.2 and 0, suggesting the accuracy of FVC derived from MODIS NBAR surpasses that derived from MODIS Surface Reflectance. (2) As vegetation phenology shifts, the ensuing differences in NDVI patterning and AFX can offer auxiliary information for enhanced vegetation classification and interpretation of mixed pixel distribution characteristics, which, when combined with NDVI at characteristic directional reflectance, could enable the accurate retrieval of FVC. Our results provide data support for the BRDF correction timescale effect of various stages of the growing seasons, highlighting the potential importance of considering how they differentially influence the temporal effect of NBAR corrections prior to monitoring vegetation when using the MODIS NBAR product.

On Dagum-Pareto mixture distribution: properties, simulation and application in insurance

The most principal subject in actuarial science is the modeling of claims amount. There are two types of distributions associated with two types of risk. We speak of light-tailed distributions as opposed to heavy-tailed distributions or extreme value distribution. Among the distributions of the amount of heavy tail claim is: Weibull, Log-Normal, Pareto and Burr “Also named DAGUM”. Many attempts were implemented in expanding the classes of mixed and compound distributions. In this article, we present a new distribution by mixing Dagum distribution (α, λ, r) and Pareto distribution (α, λ) named Dagum-Pareto mixture distribution. Some of statistical properties of this new distribution are obtained which include reliability function, hazard function, skewness, kurtosis and quantile function. Also we use the maximum likelihood method to estimate the model parameters. A Simulation study was carried out to examine the performance and accuracy of the maximum likelihood estimates of the proposed distribution. An application of the new model to no-life insurance real data set is presented to illustrate its usefulness and applicability. In our case, it is proven that Dagum-Pareto mixture distribution provides better fit compared to others existing probability distributions using some goodness of fit measures.

Preparation of carbon nanotube composite solder and analysis of the thermal properties

Abstract The solder layer constitutes a crucial component of IGBT power modules. It endures significant thermal stress during operation, and the magnitude of this stress correlates with the coefficient of thermal expansion. To prevent fatigue damage to the solder layer during operation, it is necessary to reduce the solder’s coefficient of thermal expansion, thereby alleviating the impact of thermal stress. In this paper, the method of adding carbon nanotube powder to solder is proposed to solve this problem. Firstly, the relationship between thermal stress and the coefficient of thermal expansion is analyzed, and then the mechanical ball milling method is employed to prepare composite solder with a 0.05% carbon nanotube content. Scanning electron microscopy observation of the prepared composite solder reveals homogeneous mixing and uniform distribution of carbon nanotubes on the solder particle surfaces. Furthermore, thermal expansion coefficient tests were conducted on the sintered carbon nanotube composite solder, revealing a 16.2% reduction in the coefficient due to the incorporation of carbon nanotubes.

Exploring tribological and corrosion properties of Al-5052/SiC+CeO2 hybrid surface composites fabricated using friction stir processing

In this study, advanced aluminium metal matrix hybrid surface composites were synthesized using the five-pass friction stir processing (FSP) technique. A blend of Hybrid reinforcement particles, silicon carbide and cerium oxide, were incorporated into the Al-5052 matrix at varying SiC: CeO2 ratios of 100:0, 75:25, 50:50, 25:75, and 0:100. The multi-pass FSP methodology ensured thorough mixing and uniform nanoparticle distribution, resulting in tailored microstructures. The optimal 75:25 SiC: CeO2 ratio exhibited superior microstructural, anti-corrosion, and tribological properties, with the lowest wear rates of 8.3 × 10−9 and 8.8 × 10−9 mm3/m at 15 N and 25 N loads, respectively, and the lowest corrosion rate of 8.06 mm per year compared to other hybrid blends.

Comparative analysis of percutaneous vertebroplasty and kyphoplasty in the treatment of Stage III Kummell’s disease without neurological symptoms: a retrospective study

ObjectiveThis study analyzes the safety and efficacy of percutaneous vertebroplasty (PVP) and percutaneous kyphoplasty (PKP) treatments for Stage III Kummell’s disease without neurological symptoms, comparing the advantages and disadvantages of these two minimally invasive surgical methods.MethodsA retrospective analysis was conducted on 53 patients with non-neurological Stage III Kummell’s disease treated with PVP and PKP at our hospital from December 2018 to January 2023. Patients were divided into PVP (25 cases) and PKP (28 cases) groups based on the surgical method. There were no significant differences in general preoperative data between the two groups (all p > 0.05), ensuring comparability. The study compared surgical duration, volume of bone cement injected, distribution pattern of bone cement, rate of bone cement leakage, and preoperative, postoperative, and final follow-up scores of Visual analogue scale(VAS) and Oswestry disability index(ODI). Additionally, relative anterior height of the injured vertebrae, and Cobb angle of deformity, along with their changes at preoperative, postoperative, and final follow-up stages were calculated and analyzed.ResultsNo significant preoperative differences were observed between the groups (p > 0.05). The PKP group had longer surgeries, higher cement volumes (p < 0.001), and lower leakage rates (p < 0.05), with primarily chunky cement distributions versus mixed distributions in the PVP group. No complications other than cement leakage occurred. VAS and ODI scores showed no significant changes at various time points (p > 0.05) but improved significantly from preoperative (p < 0.001). Both groups saw improved vertebral heights and Cobb angles post-surgery (p < 0.05), with more significant improvements in the PKP group (p < 0.05). Over time, both groups experienced gradual vertebral height loss and increased Cobb angles, more pronounced in the PKP group (p < 0.05). At the final follow-up, there were no statistical differences in vertebral height and Cobb angle between the two groups (p > 0.05).ConclusionThe study evaluates the safety and efficacy of PVP and PKP for Stage III Kummell’s disease without neurological symptoms, comparing the merits of both minimally invasive techniques.

Investigation on Enhancement of AP/HTPB/Al Composite Solid Propellant Combustion Characteristics via DC Electric Field

ABSTRACT To investigate the influence mechanisms of DC electric fields on the combustion of solid propellants, a numerical model for the combustion characteristics of AP/HTPB/Al composite solid propellant under the DC electric field is established. Effects of the applied electric field on combustion flame structure, combustion surface temperature distribution, and mixing fraction distribution are analyzed. The results indicate that the applied electric field enhances the mixing effect of the propellant gas phase decomposition products, thereby altering the flame structure and bringing it closer to the combustion surface. The application of the electric field leads to an increase in the combustion surface temperature by 20–70 K. The effect of the negative electric field on the combustion surface temperature and burning rate is more significant compared to that of the positive electric field. The results provide valuable theoretical guidance in the field of solid engine combustion control.

Effect of turbulent mixing on the formation of intermediate nepheloid layer over the northern continental slope of the Andaman sea

An intermediate nepheloid layer (INL) serves as an important conduit for the cross-slope transport of particulate matter, including organic carbon, biological nutrients and other lithogenic minerals. Despite extensive reports on the substantial sediment influx from the Ayeyarwady River into the northern continental slope of the Andaman Sea (AS), the transport route and fate of these river-borne sediments remain poorly understood, due to lack of in situ observations of turbid INL over the slope. In this study, we present direct evidence of an INL over the northern continental slope of the AS during the winter of 2019/2020, accompanied by enhanced mid-water turbulent mixing. Mooring measurements reveal energetic internal tides with high-mode vertical structure in the study region; and beam-like structures of internal tides are observed, which could be responsible for the enhanced mid-water turbulent mixing coinciding with the INL. Moreover, available microstructure profiles reveal energetic turbulent mixing with bottom-intensified turbulent diffusivity over the study area. Numerical experiments suggest that inhomogeneous distribution of turbulent mixing over the continental slope could induce local convergence of the upwelling transport in the upslope direction, resulting in an intrusion from the boundary to the interior and consequently promoting the INL formation. The discovery of the INL and its mixing-driven generation mechanism provide new insights into sediment transport dynamics over the northern continental slope of the AS.

A Prediction Method for City Traffic Noise Based on Traffic Simulation under a Mixed Distribution Probability

Predicting and assessing urban traffic noise is crucial for environmental management. This paper establishes a traffic noise simulation method based on microscopic traffic simulation, utilizing a traffic simulation under a mixed distribution probability combining normal and exponential distributions. This method integrates a single-vehicle noise prediction model to compute the spatial distribution of noise. Comparison with empirical data demonstrates that the proposed model effectively predicts the level of traffic noise. The accuracy of the model is validated through comparison with measured data, showing minimum and maximum errors of 3.60 dB(A) and 4.37 dB(A), respectively. Additionally, the noise spatial results under microscopic traffic models are compared with those under line source models, revealing that the proposed model provides a more detailed and realistic noise spatial distribution. Furthermore, the noise variation patterns between stable and time-varying traffic flows are investigated. Results indicate that noise levels fluctuate under stable traffic flow, whereas under time-varying traffic flow, noise values exhibit a stepped change.

Experimental Study of High-Diodicity Inlet Effects On Rotating Detonation Combustor Performance and Operability

Abstract Implementing high-diodicity inlets is critical to reduce backflow and mitigate the pressure loss across the injector in RDCs. Experiments on air injection diodicity were conducted in a non-premixed RDC for both cold-flow and reacting conditions. The introduction of a Tesla-like diode impacted operating modes and injector dynamics, but the extent of that effect depended on the throat-to-combustor area ratio. A smaller ratio mitigated the impact of the diode on detonation characteristics, while a larger ratio extended the operating range of stronger wave modes. The diode stabilized RDC operation through an increased static pressure drop, but limited performance most likely due to poor reactant mixing and local equivalence ratio distribution. Cold-flow tests showed a higher diodicity for the diode, which may contribute to higher pressure gain in reacting experiments. A modified diodicity formulation based on reacting flow measurements was introduced, suggesting that a multi-metric approach can be useful to assess injector performance. High-diodicity air inlets could be useful tools for reducing total pressure loss and controlling operating modes, but careful consideration is required to limit adverse effects on processes like reactant mixing.

Diffusing on Two Levels and Optimizing for Multiple Properties: A Novel Approach to Generating Molecules with Desirable Properties.

In the past decade, Artificial Intelligence (AI) driven drug design and discovery has been a hot research topic in the AI area, where an important branch is molecule generation by generative models, from GAN-based models and VAE-based models to the latest diffusion-based models. However, most existing models pursue mainly the basic properties like validity and uniqueness of the generated molecules, a few go further to explicitly optimize one single important molecular property (e.g., QED or PlogP), which makes most generated molecules little usefulness in practice. In this paper, we present a novel approach to generating molecules with desirable properties, which expands the diffusion model framework with multiple innovative designs. The novelty is two-fold. On the one hand, considering that the structures of molecules are complex and diverse, and molecular properties are usually determined by some substructures (e.g., pharmacophores), we propose to perform diffusion on two structural levels: molecules and molecular fragments respectively, with which a mixed Gaussian distribution is obtained for the reverse diffusion process. To get desirable molecular fragments, we develop a novel electronic effect based fragmentation method. On the other hand, we introduce two ways to explicitly optimize multiple molecular properties under the diffusion model framework. First, as potential drug molecules must be chemically valid, we optimize molecular validity by an energy-guidance function. Second, since potential drug molecules should be desirable in various properties, we employ a multi-objective mechanism to optimize multiple molecular properties simultaneously. Extensive experiments with two benchmark datasets QM9 and ZINC250k show that the molecules generated by our proposed method have better validity, uniqueness, novelty, Fr´echet ChemNet Distance (FCD), QED, and PlogP than those generated by current SOTA models. The Code of D2L-OMP is available at https://github.com/bz99bz/D2L-OMP.

Phylogenomic analyses and comparative genomic studies of Thermus strains isolated from Tengchong and Tibet Hot Springs, China.

Genus Thermus is the main focus of researcher among the thermophiles. Members of this genus are the inhabitants of both natural and artificial thermal environments. We performed phylogenomic analyses and comparative genomic studies to unravel the genomic diversity among the strains belonging to the genus Thermus in geographically different thermal springs. Sixteen Thermus strains were isolated and sequenced from hot springs, Qucai hot springs in Tibet and Tengchong hot springs in Yunnan, China. 16S rRNA gene based phylogeny and phylogenomic analyses based on concatenated set of 971 Orthologous Protein Families (supermatrix and gene content methods) revealed a mixed distribution of the Thermus strains. Whole genome based phylogenetic analysis showed, all 16 Thermus strains belong to five species; Thermus oshimai (YIM QC-2-109, YIM 1640, YIM 1627, 77359, 77923, 77838), Thermus antranikianii (YIM 73052, 77412, 77311, 71206), Thermus brokianus (YIM 73518, 71318, 72351), Thermus hydrothermalis (YIM 730264 and 77927) and one potential novel species 77420 forming clade with Thermus thalpophilus SYSU G00506T. Although the genomes of different strains of Thermus of same species were highly similar in their metabolic pathways, but subtle differences were found. CRISPR loci were detected through genome-wide screening, which showed that Thermus isolates from two different thermal locations had well developed defense system against viruses and adopt similar strategy for survival. Additionally, comparative genome analysis screened competence loci across all the Thermus genomes which could be helpful to acquire DNA from environment. In the present study it was found that Thermus isolates use two mechanism of incomplete denitrification pathway, some Thermus strains produces nitric oxide while others nitrious oxide (dinitrogen oxide), which show the heterotrophic lifestyle of Thermus genus. All isolated organisms encoded complete pathways for glycolysis, tricarboxylic acid and pentose phosphate. Calvin Benson Bassham cycle genes were identified in genomes of T. oshimai and T. antranikianii strains, while genomes of all T. brokianus strains and organism 77420 were lacking. Arsenic, cadmium and cobalt-zinc-cadmium resistant genes were detected in genomes of all sequenced Thermus strains. Strains 77,420, 77,311, 73,518, 77,412 and 72,351 genomes were found harboring genes for siderophores production. Sox gene clusters were identified in all sequenced genomes, except strain YIM 730264, suggesting a mode of chemolithotrophy.Through the comparative genomic analysis, we also identified 77420 as the genome type species and its validity as novel organism was confirmed by whole genome sequences comparison. Although isolate 77420 had 99.0% 16S rRNA gene sequence similarity with T. thalpophilus SYSU G00506T but based on ANI 95.86% (Jspecies) and digital DDH 68.80% (GGDC) values differentiate it as a potential novel species. Similarly, in the phylogenomic tree, the novel isolate 77,420 forming a separate branch with their closest reference type strain T. thalpophilus SYSU G00506T.

Adaptive stacked species distribution modelling: Novel approaches to large scale quantification of blue carbon to support marine management

This study introduces a novel concept of ‘Adaptively Stacked’ Species Distribution Models (AS-SDMs) to predict blue carbon habitat distribution, abundance, carbon stocks, and carbon sequestration potential in Orkney. AS-SDMs are built from Weighted Boosted Regression Trees (WBRTs) that adaptively stack blue carbon sediment thickness, sediment carbon content, and sequestration potential to predicted abundance. A novel method to describe substrate types by relative inputs of mud, sand, and gravel is detailed that better characterises the determining factors of seagrass, maerl, and horse mussel abundance. This study also introduces a novel use of indexes to mitigate double counting issues of mixed species distribution models.Seagrass, maerl, horse mussel, and mixed seagrass and maerl (SGM) habitats are estimated to cover a maximum area of 657 km2 in Orkney, have a total sediment carbon stock of 16 Mt. C, and sequester 6000 t C yr−1. Applying a conservative threshold of 50 % abundance to habitat predictions, six key potential areas of blue carbon offset projects are identified. These areas cover just over 9 km2, have a total carbon stock of 330,000 t C, and sequester 330 t C yr−1. When applied to UK carbon credit value, assuming integration with voluntary markets and compliance with accreditation criteria, the habitats in these areas have a potential value of £24.5 million. If applied as annual values, these areas have carbon stocks with a potential value of £0.93 million yr−1 and a carbon sequestration potential value of £24,000 yr−1.