Lognormal Function Research Articles

Amazonian aerosol size distributions in a lognormal phase space: characteristics and trajectories

Abstract. This study introduces a first glance at Amazonian aerosols in the N–Dg–σ phase space. Aerosol data, measured from May 2021 to April 2022 at the Amazon Tall Tower Observatory (ATTO), were fitted by a multi-modal lognormal function and separated into three modes: the sub-50 nm, the Aitken (50–100 nm), and the accumulation modes. The fit results were then evaluated in the N–Dg–σ phase space, which represents a three-dimensional space based on the three lognormal fit parameters. These parameters represent, for a given mode i, the number concentration (Ni), the median geometric diameter (Dg,i), and the geometric standard deviation (σi). Each state of a particle number size distribution (PNSD) is represented by a single dot in this space, while a collection of dots shows the delimitation of all PNSD states under given conditions. The connections in ensembles of data points show trajectories caused by pseudo-forces, such as precipitation regimes and vertical movement. We showed that all three modes have a preferential arrangement in this space, reflecting their intrinsic behaviors in the atmosphere. These arrangements were interpreted as volumetric figures, elucidating the boundaries of each mode. Time trajectories in seasonal and diurnal cycles revealed that fits with the sub-20 nm mode are associated with rainfall events that happen in the morning and in the afternoon. But in the morning they grow rapidly into the Aitken mode, and in the afternoon they remain below 50 nm. Also, certain modes demonstrated well-defined curves in the space, e.g., the seasonal trajectory of the accumulation mode follows an ellipsoid, while the diurnal cycle of the sub-50 nm mode in the dry season follows a linear trajectory. As an effect of the precipitation on the PNSDs and vice versa, N and Dg were found to increase for the sub-50 nm mode and to decrease for the Aitken and accumulation modes after the precipitation peak. Afternoons with precipitation were preceded by mornings with larger particles of the accumulation mode, whose Dg was ∼ 10 nm larger than in days without precipitation. Nevertheless, this large Dg in the morning seems to influence subsequent rainfall only in the dry season, while in the wet season both N and Dg seem to have the same weight of influence. The observed patterns of the PNSDs in the N–Dg–σ phase space showed to be a promising tool for the characterization of atmospheric aerosols, to contribute to our understanding of the main processes in aerosol–cloud interactions, and to open new perspectives on aerosol parameterizations and model validation.

Probability density distributions of morphological parameters of barchan dunes and their responses to the climate environment

Barchan dunes are a common aeolian geomorphology in sand seas on Earth and Mars. They occur primarily as barchan dune groups. Obvious regional differences occur in the size distribution of dune swarms in different dune fields because of sediment grain size, regional airflow, sand flux, and surface conditions. To explore dune size distributions in different barchan dune fields and clarify the effects of climate on barchan dune morphometry, we used high-resolution remote sensing images and extended time-series meteorological data. Barchan dune fields were located in the southeast edge of the Taklimakan Desert, the western and southern parts of the Qaidam Basin Desert, the Gonghe Basin Sandy Land, and the sand belt connecting the Badain Jaran Desert and the Tengger Desert. Our results revealed the probability density distribution of morphological parameters of barchan dunes, correlations among parameters, and responses to wind regime. The main conclusions are: (1) Probability density distributions of the lengths of windward and lee slopes, the lengths of the two horns, and the dune width, height, and spacing in different deserts followed a log-normal distribution function. (2) Good correlations were found among the dune morphological parameters; width and height, windward slope length and width, and height and spacing showed positive proportionality, and the basal area and the product of width and height were linearly correlated in a double logarithmic coordinate system. (3) Good correspondence was found between dune asymmetry and regional wind conditions, which can be explained by Tsoar's model. Morphological parameters of barchan dunes were linearly and positively correlated with the drift potential. These results provide essential data for further study of the morphological thresholds for dune-dune interactions and elucidation of the mechanisms of interaction among barchan dunes.

Empirical Fragility Analysis of Haitian Reinforced Concrete and Masonry Buildings

This study develops empirical fragility curves for concrete and masonry buildings in Haiti, utilizing data from the 2021 earthquake. A dataset of 3527 buildings from the StEER database, encompassing a diverse range of building types, is used. These buildings types include reinforced concrete structures with masonry infills, confined masonry buildings, reinforced masonry bearing walls, and unreinforced masonry bearing walls. Shakemaps from the USGS are utilized to assess the earthquake’s intensity at each building, with the peak ground acceleration (PGA) as the intensity measure. Damage is classified into five distinct states: no damage, minor, moderate, severe, and partial or total collapse. For each of these states, the corresponding probabilities of exceedance are calculated, and log-normal cumulative distribution functions were fitted to those data to produce empirical fragility curves. The results show a notable similarity in performance among the four types, each having high probability of failure even under low-intensity earthquakes. Total fragility curves (including all four building types) are developed subsequently and they are convolved to the probabilistic seismic hazard map of Haiti to assess the seismic risk. This includes estimating the annual probability of partial/total collapse and the probability of partial/total collapse in the event of 475-year and 2475-year earthquakes. The results indicate a significant risk, with up to 64% probability of collapse in certain areas for the 2475-year earthquake and a probability of collapse of 15% for a 475-year earthquake. These findings underscore the critical vulnerability of Haiti’s buildings to seismic events and the urgent need for their retrofit.

Determination of meaningful block sizes for rockfall modelling

The determination of the so-called design block is one of the central elements of the Austrian guideline for rockfall protection ONR 24810. It is specified as a certain percentile (P95–P98, depending on the event frequency) of a recorded block size distribution. Block size distributions may be determined from the detachment area (in situ block size distribution) and/or from the deposition area (rockfall block size distribution). Deposition areas, if present, are generally accessible and measurable without technical aids. However, most measuring methods are subjective, uncertain, not verifiable, or inaccurate. Also, rockfall blocks are often fragmented due to the preceding fall process. The in situ block size distribution is (also) required for meaningful rockfall modelling. The statistical method seems to be the most efficient and cost-effective method to determine in situ block size distributions with many blocks within the whole range of block sizes. In the current literature, joint properties are often described by the lognormal and exponential distribution functions. Today, we can model synthetic rock masses on the basis of discrete fracture networks. They statistically describe the geometric properties of the joint sets. This way, we can carry out exact rock mass block surveys and determine in situ block size distributions. We wanted to know whether the in situ block size distributions derived from the synthetic rock mass models can be described by probability distribution functions, and if so, how well. We fitted various distribution functions to three determined in situ block size distributions of different lithologies. We compared their correlations using the Kolmogorov–Smirnov test and the mean-squared error method. We show that the generalized exponential distribution function best describes the in situ block size distributions across various lithologies compared to 78 other distribution functions. This could lead to more certain, accurate, verifiable, holistic, and objective results. Further investigations are required.

Optimal power flow of thermal-wind-solar power system using enhanced Kepler optimization algorithm: Case study of a large-scale practical power system

In the current century, electrical networks have witnessed great developments and continuous increases in the demand for fossil fuels based energy, leading to an excessive rise in the total production cost (TPC), as well as the pollutant (toxic) gases emitted by thermal plants. Under this circumstances, energy supply from different resources became necessary, such as renewable energy sources (RES) as an alternative solution. This latter, however, characterized with uncertainty nature in its operation principle, especially when operator system wants to define the optimal contribution of each resource in an effort to ensure economic and enhanced reliability of grid. This paper presents an Enhanced version of Kepler optimization algorithm (EKOA) to solve the problem of stochastic optimal power flow (SOPF) in a most efficient way incorporating wind power generators and solar photovoltaic with different objective functions, the stochastic nature of wind speed and solar is modeled using Weibull and lognormal probability density functions respectively. To prove the effectiveness of the proposed EKOA, various case studies were carried out on two test systems IEEE 30-bus system and Algerian power system 114-bus, obtained results were evaluated in comparison with those obtained using the original KOA and other methods published in the literatures. Thus, shows the effectiveness and superiority of the efficient EKOA over other optimizers to solve complex problem. The incorporation of RES resulted in a significant 2.39% decrease in production cost, showcasing EKOA’s efficiency with a $780/h, compared to KOA’s $781/h, for IEEE 30-bus system. For the DZA 114-bus system revealed even more substantial reductions, with EKOA achieving an impressive 12.6% reduction, and KOA following closely with a 12.4% decrease in production cost.

An optimal power flow solution for a power system integrated with renewable generation

<abstract> <p>Integrating Green Renewable Energy Sources (GRES) as substitutes for fossil fuel-based energy sources is essential for reducing harmful emissions. The GRES are intermittent and their integration into the conventional IEEE 30 bus configuration increases the complexity and nonlinearity of the system. The Grey Wolf optimizer (GWO) has excellent exploration capability but needs exploitation capability to enhance its convergence speed. Adding particle swarm optimization (PSO) with excellent convergence capability to GWO leads to the development of a novel algorithm, namely a Grey Wolf particle swarm optimization (GWPSO) algorithm with excellent exploration and exploitation capabilities. This study utilizes the advantages of the GWPSO algorithm to solve the optimal power flow (OPF) problem for adaptive IEEE 30 bus systems, including thermal, solar photovoltaic (SP), wind turbine (WT), and small hydropower (SHP) sources. Weibull, Lognormal, and Gumbel probability density functions (PDFs) are employed to forecast the output power of WT, SP, and SHP power sources after evaluating 8000 Monte Carlo possibilities, respectively. The multi-objective green economic optimal solution consisted of 11 control variables to reduce the cost, power losses, and harmful emissions. The proposed method to address the OPF problem is validated using an adaptive IEEE bus system. The proposed GWPSO algorithm is evaluated by comparing it with PSO and GWO optimization algorithms in terms of achieving an optimal green economic solution for the adaptive IEEE 30 bus system. This evaluation is conducted within the confines of the same test system using identical system constraints and control variables. The integration of a small SHP with WT and SP sources, along with the proposed GWPSO algorithm, led to a yearly cost reduction ranging from <bold>$\$$19,368</bold> to <bold>$\$$30,081</bold>. Simulation findings endorsed that the proposed GWPSO algorithm executes fruitfully compared to alternative algorithms regarding a consistent convergence curve and robustness, proving its potential as a viable choice for achieving cost-effective solutions in power systems incorporating GRES.</p> </abstract>

Optimization of open optical communication with multiple UAV over non homogenous turbulent paths

The article is devoted to optimizing communication through open atmospheric channels with many UAV by using inhomogeneous turbulent paths. A probabilistic assessment of the total signal loss in the communication system between the central communication node and a group of UAV located at the same distance from this node is analyzed. The case is considered when the scintillation indices in the subzones of optical communication with the UAV are different, which leads to unequal signal losses in the corresponding optical channels. The target functional is compiled by using a certain integral of the lognormal probability density function, which includes the newly introduced function of the losses dependence from the scintillation index. An analytical dependence of these indicators, characterizing the worst case when the total losses in the network reach a maximum, is presented. Keywords optical communications, turbulence, UAV, optimization, atmosphere

Effect of deformation heterogeneity on development of geometrically necessary dislocation density in heterogeneous-structured CrMnFeCoNi high-entropy alloy

Heterogeneous-structured high-entropy alloys (HEAs) overcome strength-ductility trade-off, demonstrating potential for a wide range of applications in aerospace, defense and military fields. In this work, the evolution of geometrically necessary dislocation (GND) density in a heterogeneous-structured CrMnFeCoNi HEA is investigated at different strains. This HEA consists of fine- and coarse-grained domains. GND density measurements are performed using electron backscatter diffraction. GND density increases significantly in the fine-grained domains while increases slightly in the coarse-grained domains. The disparity of GND density between the coarse and fine grains indicates a distinct deformation gradient, which is related to back-stress strengthening. At 7% strain, the GND density in the fine-grained domains reaches parity with that in the coarse-grained domains, indicating a transition from single slip to multiple slip. The local misorientation distributions are described by a lognormal function.

Modelling diameter at breast height distribution of jack pine and black spruce natural stands in eastern Canada

Individual tree diameter at breast height (DBH) distribution is an important information for forest management planning. Forest managers obtain the DBH data either by field measurements or estimations using predictive models. However, probability distribution models are still lacking or need improvement. Therefore, we aimed to construct and fit diameter distribution models that reflect forest structure and composition change. We evaluated gamma, log-normal, and Weibull probability distribution functions (PDFs) for two commercially important tree species, black spruce ( Picea mariana (Mill) B.S.P.) and jack pine ( Pinus banksiana Lamb), grown in natural stands across Ontario, Canada. We modelled the parameters of the distributions as a function of stand-level variables for these species. We used DBH data from 735 permanent sample plots. Our results showed that all three evaluated PDFs reflected observed DBH distribution. We demonstrated that the moment-based recovered parameters could represent the maximum likelihood-estimated parameters precisely, and parameters of the PDFs can be modelled as a function of stand-level dynamic covariates. The models unbiasedly predicted the PDF parameters DBH means and DBH classes. The R2 of the model fit ranged between 0.35 and 0.98 for the predicted parameters and 0.90 and 0.97 for the predicted DBH.

(Invited) Formation and Characterization of Fe-Silicide Nanodots for Optoelectronic Application

Light emission from group-IV based nanostructures has been attracting much attention in the field of Si-based photonics because of its potential to combine photonic processing with electronic processing on a single chip [1]. Among various Fe-silicides, semiconducting β-FeSi2 shows light emission in the near-infrared region [2, 3] and is one of promising materials in optoelectronic application from viewpoints of tuning emission energy and improving luminous efficiency by means of quantized structures. In this work, we focus on low temperature formation of β-FeSi2 nanodots (NDs) over ~1011 cm-2 in areal density on SiO2 and the characterization of light emission properties of β–FeSi2 NDs.After conventional wet-chemical cleaning steps of p-type Si(100) substrates, a ~300-nm-thick SiO2 layer was grown at 1000°C in dry O2 and followed by dipping shortly in a dilute HF solution to make uniform surface termination with Si-OH bonds. After that, Fe films in the thickness range from 1.0 to 3.0 nm were formed on the OH-terminate SiO2 layer by electron beam evaporation without any extra heating. And then, the Fe–films were exposed to remote plasma of pure H2 (H2-RP) at 400°C to form highly-dense Fe-NDs [4]. Subsequently, so-prepared Fe-NDs were exposed to pure SiH4 at 400°C while controlling the amount of SiH4 exposure by duration and pressure.AFM images taken after exposure of Fe-films to H2-RP confirm the high-density formation of well-defined NDs. In the cases of H2-RP exposure of 1.3-nm-thick and 3.0-nm-thick Fe-films, the areal dot densities as high as ~1.1 × 1011 and ~1.3 × 1011 cm−2 were obtained, respectively, in which the average dot-heights of self-assembled Fe-NDs were determined to be ~5.3 and ~10.9 nm, respectively, by fitting a log-normal function to each measured size distribution. Notice that AFM images taken after SiH4 exposure show almost no change in the areal dot density while a slight increase in the average dot height. From cross-sectional transmission electron microscope images, we have also verified the formation of distorted-spherical shape NDs with a lattice spacing of ~0.18 nm associated with β-FeSi2. And the result is well consistent with the chemical composition of NDs after SiH4 exposure as evaluated by x-ray photoelectron spectroscopy. In addition, with 976 nm light excitation using a semiconductor laser, the NDs after the SiH4 exposure show stable photoluminescence (PL) signals in the energy region from ~0.7 to ~0.9 eV even at room temperature. On the other hand, no PL signals were detected from the NDs before the SiH4 exposure and after Ar anneal at 400°C. Also, when the size of the Fe-silicide NDs was increased from ~5.3 to ~10.9 nm in average dot height by increasing the initial Fe-film thickness, a distinct red-shift in PL was clearly observed. Thus, the observed PL can be interpreted in terms of radiative recombination of photogenerated carriers through quantized states of β-FeSi2 NDs. These results indicate that SiH4 exposure to Fe-NDs formed on SiO2 is a very promising technique for the low-temperature fabrication of luminescent FeSi2 NDs.AcknowledgementThis work was supported in part by Grant-in-Aid for Scientific Research (A) 21H04559 of MEXT Japan, and the Cooperative Research Project Program of the Research Institute of Electrical Communication, Tohoku University.

Reliability Assessment of Statistical Distributions for Analyzing Dielectric Breakdown Strength of Polypropylene

Various statistical distributions, such as Weibull, log-normal, and exponential functions, are frequently employed to interpret the dielectric breakdown (BD) strength data of insulating materials, including cross-linked polyethylene, low-density polyethylene, polypropylene (PP), and polyethylene. This study aimed to determine a suitable statistical distribution for analyzing the dielectric BD strength data of PP insulators before and after thermal degradation. Dielectric BD strength tests were conducted on thermally deteriorated PP insulators under various degradation conditions. Additionally, a coefficient of determination was employed to assess the compatibility between the dielectric BD strength data and the statistical distribution of PP insulators before and after thermal degradation. The test results indicate that the coefficient of determination for alternating current BD strength data was 0.955 in the log-normal distribution before degradation and 0.929 in the Weibull distribution after degradation. Consequently, in the analysis of the PP insulation breakdown data, the log-normal distribution was found to be suitable for data before degradation, while the Weibull distribution was deemed suitable for data after degradation. These results can lead to lower errors in the power system design process, enhancing reliability when analyzing the BD strength data of insulation materials.

Study of convective air turbulence based on the probability distribution function of angle of arrival fluctuations

This study investigates the probability density function (PDF) of angle of arrival (AoA) fluctuations of a laser beam propagating through convective air turbulence in two settings: indoor turbulence and atmospheric surface layer. The indoor turbulence is generated by an electric heater with a 50 cm × 100 cm surface area placed horizontally on the bottom. The atmospheric turbulence experiments are conducted over a level at 60 cm height from an asphalted area using a slightly divergent laser beam that propagates along 350 m. Image motion monitoring of four sub-apertures installed in front of the receiving telescope aperture is used to record the AoA fluctuations and their PDFs over the entrance pupil at the sub-aperture locations for both types of turbulence. The observations are made in two perpendicular directions to the propagation of the laser beam. The experimental PDFs of the AoA fluctuations are fitted with Gaussian, Lognormal, Weibull, and Gamma distribution functions. The results show that the Weibull and Gamma models are more consistent with the experimental data for the indoor convective air and atmospheric turbulence respectively. For the indoor convective turbulence, the full width at half maximum and the peak location of the measured PDFs increase with the heater temperature, as well as the goodness of fit, which saturates at high temperatures.

Improved predictions of permeability properties in cement-based materials: A comparative study of pore size distribution-based models

Permeability is a fundamental physical property of a cement-based material and is closely related to its pore structure. This study developed an improved model for permeability prediction of cementitious materials with a function of the pore size distribution (PSD), in which PSD is simulated from single and compound lognormal distribution functions, respectively. The efficiency of the improved model is demonstrated by calculating permeability for mortars and pastes available in the literature. It is found that, first, for cementitious materials with unimodal PSD, the single lognormal distribution function is adequate to describe the pore structures. In contrast, for cementing mixtures presenting multimodal PSD, the compounded lognormal distribution function is highly recommended to cover a wide range of pore components. Second, the permeability model based on the compound PSD can predict well permeability to gas intrusion, whereas the single PSD-based model is applicable for calculating permeability via water intrusion. Furthermore, slippage effect and water saturation degree are introduced in the PSD-based model. Results prove that the corrected gas permeability model from the compound PSD performs well in estimating the gas permeability in unsaturated states. Finally, the compound PSD-based model is extended by considering varying porosities and relative humidities, which can be used to quantitively describe permeability when cementitious materials undergoing pore structure degradation induced by durability issues.

Asymmetrical fixed-bed breakthrough curve modelling: Comparing simplistic, log-modified, fractal-like, and probability distribution function models

Column adsorption studies frequently adopt classical breakthrough curve models such as semi-mechanistic Bohart-Adams and Thomas models. They are logistic functions which simulate symmetrically sigmoidal curves centrosymmetric at 50% breakthrough; consequently, they fail and estimate erroneous results when dealing with asymmetric breakthrough curves. This work analyses and compares the trend of how log-modified, fractal-like modified, and probability distribution function models simulate asymmetric breakthrough curves with varied extents of tailing. We generated the asymmetric breakthrough curves by varying the bed depth: 6 mm, 9 mm, and 12 mm on a glass microcolumn of 100 mm length and 5.5 mm inner diameter at a fixed flow rate of 1 mL/min using 10 mg/L bisphenol-A as adsorbate and polyaniline as adsorbent. The asymmetrical curves were a consequence of polyaniline’s low specific surface area of 6.96 m²/g, heterogeneous particle size distribution, slow intraparticle and surface diffusion. The log-Bohart-Adams and fractal-like Bohart-Adams models perfectly fit the curves regardless of symmetry although log-Bohart-Adams performed subpar with increased tailing. The fractal-like Bohart-Adams model accurately estimated the breakthrough times of 13 min, 26 min, and 90 min for the 6 mm, 9 mm, and 12 mm bed depths with a critical bed depth of 5.65 mm. The normal and Gompertz probability distribution functions were unable to adapt to asymmetry of the curves owing to their fixed inflection points at c/cc = 0.5 and 0.37 respectively. Although the Weibull, log-normal, and log-Gompertz functions have floating inflection points, only log-Gompertz function had a satisfactory fit (Radj2>0.98) regardless of symmetry.

Constraining the Properties of Black Hole Seeds from the Farthest Quasars

Over 60 yr after the discovery of the first quasar, more than 275 such sources are identified in the epoch of reionization at z > 6. JWST is now exploring higher redshifts (z ≳ 8) and lower-mass (≲107 M ⊙) ranges. The discovery of progressively farther quasars is instrumental to constraining the properties of the first population of black holes (BHs), or BH seeds, formed at z ∼ 20–30. For the first time, we use Bayesian analysis of the most comprehensive catalog of quasars at z > 6 to constrain the distribution of BH seeds. We show that the mass distribution of BH seeds can be effectively described by combining a power law and a lognormal function tailored to the mass ranges associated with light and heavy seeds, assuming Eddington-limited growth and early seeding time. Our analysis reveals a power-law slope of and a lognormal mean of . The inferred values of the Eddington ratio, the duty cycle, and the mean radiative efficiency are , , and , respectively. Models that solely incorporate a power law or a lognormal distribution within the specific mass range corresponding to light and heavy seeds are statistically strongly disfavored, unlike models not restricted to this specific range. Our results suggest that including both components is necessary to comprehensively account for the masses of high-redshift quasars, and that both light and heavy seeds formed in the early Universe and grew to form the population of quasars we observe.

Fragility functions for corroded reinforced concrete columns

Fragility functions for structural members play an important role in the FEMA P-58 methodology for the seismic performance assessment of structures. In this study, fragility functions were developed for aged reinforced columns with corroded reinforcing bars. To this end, an experimental database containing 120 specimens subjected to repeated cyclic loads was compiled from 20 previous studies. The experimental specimens were categorized into three groups: no corrosion damage (NCD), low corrosion damage (LCD), and high corrosion damage (HCD). These damage states were identified by experimental hysteretic responses in terms of the drift angle, which are linked to common methods of repair, including structural repair, structural enhancement, and component replacement. The obtained drift angles were used to fit the log-normal fragility functions of the NCD, LCD, and HCD columns. The effect of reinforcement corrosion was examined by comparing the fragility functions of uncorroded and corroded columns. According to the principles suggested by FEMA P-58, the developed fragility functions were mostly judged to have a high level of quality. The fragility medians of the corroded columns showed a clear decreasing trend along with an increasing degree of corrosion damage. However, the corrosion of the reinforcing bars had a limited effect on the fragility dispersion. The maximum differences in the exceedance probability were calculated by comparing the fragility curves for the LCD and NCD columns, which were greater than 20 % for all concerned damage states. For the HCD columns, the corrosion of the reinforcing bars led to the maximum difference in the exceedance probability, even beyond 50 %, compared to the results for the NCD columns. The above results reveal the significant effect of the corrosion of the reinforcing bars. The specific fragility functions developed for the corroded reinforced concrete columns will facilitate the seismic loss and resilience assessment of aging reinforced concrete building structures.

Investigating the Mesh Size Selectivity of Olive Flounder (Paralichthys olivaceus) Gillnets for Fisheries Resource Management in the East Sea

This study aimed to evaluate the mesh selectivity of gillnets used in olive flounder (Paralichthys olivaceus) fishing by examining the distribution of total length (TL) across various mesh sizes. A marine fishing experiment was conducted at Gajin Harbor in Goseong County, Gangwon Province, employing experimental gear with four different mesh sizes: 90, 105, 135, and 150 mm. Mesh selectivity analysis involved a comparative assessment of selectivity curve methods through normal, log-normal, and bi-normal functions. Among these, the bi-normal function demonstrated the best fit for estimating the mesh selectivity curve within the SELECT model. The critical finding was the net size at which 50% of the minimum landing size (35 cm in TL) of olive flounder was selected, ranging from 108.7 to 121.1 mm. According to the bi-normal function curve, this size was estimated as 114.0 mm. Our findings indicate that using a mesh size larger than the theoretically estimated size in mesh selectivity analysis can further decrease the catch of immature individuals. These findings provide essential data for devising strategies to efficiently utilize and manage olive flounder resources.

Integrating renewable energy and V2G uncertainty into optimal power flow: A gradient bald eagle search optimization algorithm with local escaping operator

AbstractHere, a new approach is proposed for solving the optimal power flow (OPF) problem in transmission networks using a Gradient Bald Eagle Search Algorithm (GBES) with a Local Escaping Operator (LEO). The method takes into account uncertainty of the renewable energy sources (wind energy and photovoltaic systems) and Vehicle‐to‐Grid (V2G) in the stochastic OPF problem. To improve the efficiency of the proposed technique and enhance its local exploitation capability, the LEO method's selection features are utilized. Monte Carlo methods are employed to estimate the generation costs of the renewable sources and PEVs and study their feasibility. The uncertainty of the renewable sources and PEVs is represented by Weibull, lognormal, and normal probability distribution functions (PDFs). The GBES approach is experimentally compared with well‐known meta‐heuristics using twenty‐three different test functions, and the results indicate its superiority over BES and other recently developed algorithms. Furthermore, the proposed method's effectiveness is evaluated using IEEE 30‐bus test system under various scenarios, and the simulation results demonstrate that it can effectively address OPF issues considering renewable energy sources and V2G, providing superior optimal solutions compared to other algorithms.

Some statistical remarks on GRBs jointly detected by Fermi and Swift satellites

ABSTRACT We made statistical analysis of the Fermi GBM and Swift BAT observational material, accumulated over 15 yr. We studied how GRB parameters (T90 duration, fluence, and peak flux) that were observed by only one satellite differ from those observed by both. In the latter case, it was possible to directly compare the values of the parameters that both satellites measured. The GRBs measured by both satellites were identified using the k-nearest neighbours algorithm in Euclidean distance. In the parameter space we determined the direction in which the jointly detected GRBs differ most from those detected by only one of the instruments using linear discriminant analyses. To get the strength of the relationship between the parameters obtained from the GBM and BAT, a canonical correlation was performed. The GBM and BAT T90 distributions were fitted with a linear combination of lognormal functions. The optimal number of such functions required for fit is two for GBM and three for BAT. Contrary to the widely accepted view, we found that the number of lognormal functions required for fitting the observed distribution of GRB durations does not allow us to deduce the number of central engine types responsible for GRBs.

EMPIRICAL VULNERABILITY ANALYSIS OF RAILWAY BRIDGE SEISMIC DAMAGE BASED ON 2022 MENYUAN EARTHQUAKE

A 6.9 magnitude earthquake at a depth of 10 km struck Menyuan County, Haibei Prefecture, Qinghai Province, China, on January 8, 2022. This earthquake damaged some railway bridges on the Lanzhou-Xinjiang Passenger Dedicated Line. This study combines relevant historical earthquake damage experience, considers the effects of earthquake intensity, site soil classification, superstructure type, foundation failure factor, number of spans, and total bridge length, and develops empirical formulas for seismic damage prediction of railway bridges using ordinal logistic regression model in SPSS software. The seismic damage matrix, as were the anticipated multi-intensity mean damage index and the empirical vulnerability curve based on the two-parameter lognormal distribution function, were generated on this basis. According to the conclusions, although the suggested particular equations and vulnerability curves do not apply to the remainder of the region owing to geographical uniqueness, the technical approach is valid. It may be used as a reference for seismic damage prediction and vulnerability evaluation in other regions. The empirical vulnerability analysis based on the earthquake damage prediction matrix derived from the regression analysis can provide reasonable and fast forecasts before the next earthquake.