Statistical Characterization Research Articles

Statistical and source characterization of the 2023 Kahramanmaraş Türkiye earthquake sequence

AbstractWe studied seismic features of the 2023 Kahramanmaraş Türkiye earthquake sequence by analyzing the spatiotemporal behavior of the b- and -p values and source characteristics of the mainshocks. We complemented our study by determining the regional stress field. The b-values in the region varied from 0.45 to 1.15. We observed a slight b-value decrease (Δb≈0.2) months before the two mainshocks. Our results exhibited complex b-value patterns on the fault planes and regular aftershock productivity rates (1.14 < p < 1.25). We compare static stress drop estimates derived from effective fault dimensions to those of finite-fault dimensions. Total effective stress drops (the sum of the stress drops of all fault segments) for the earthquake doublet were almost identical (~ 2.05 MPa), while those from finite-fault dimensions are somewhat lower (0.35 and 0.96 MPa). Based on a complete stress drop case, effective seismic efficiency was 0.65 and 0.43 for both mainshocks. The amount of partial stress drop was used to discriminate between different stress drop models. No clear model is discerned for the first mainshock, but a partial or even complete stress drop, assuming finite-fault dimensions, is supported by our measurements. Stress drop estimations derived from spectral analysis (25.46 and 34.39 MPa) agreed with global studies but larger than finite and effective fault estimates. Stress inversion results indicated that in the fault segments where the mainshocks occurred, the orientation of principal axes was consistent with a strike-slip regime. Conversely, the normal-faulting regime dominates adjacent areas of the main fault system.

Characterizing Long-term Astroclimate Parameters at the Muztagh-Ata Site in the Pamir Plateau with ERA5 and MERRA-2 Data

Abstract The Muztagh-Ata site, an excellent high-altitude ground-based astronomical observing site was discovered and monitored in the eastern part of the Pamir Plateau in the southwestern Xinjiang Uygur Autonomous Region of China. This site has been systematically monitored using various observational parameters since the spring of 2017. Yet, the site lacks long-term monitoring and statistical characterization of key variables such as: precipitable water vapour (PWV) and air temperature. These factors directly influence whether a site is suitable for hosting large ground-based telescopes across optical, infrared, submillimeter, and millimeter wavelengths in later stages. In this study, we utilized atmospheric reanalysis datasets from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and ERA5, the fifth edition of the European Centre for Medium-Range Weather Forecasts (ECMWF). These datasets were combined with local observations from the weather station at the Muztagh-Ata site. Following the validation of ground-based and satellite data, we conducted a comparative analysis of PWV and temperature trends at the Muztagh-Ata site over a period of 24 years. The weighted annual mean nighttime temperature and PWV increase at rates of 0.18 ∼ 0.38 ○C decade−1 and 0.02 ∼ 0.15 mm decade−1, respectively. The nighttime PWV slightly decrease during the winter with rates of −0.01 ∼ −0.03 mm decade−1. This findings reveal that the PWV and temperature variation patterns at the Muztagh-Ata site are consistently stable, particularly in the results derived from the ERA5 dataset. The comprehensive conditions at this site are highly suitable and advantageous for hosting large optical, infrared, submillimeter, and millimeter wavelengths telescope installations.

Characterisation and calibration of multiversal methods

AbstractMultiverse Analysis is a heuristic for robust multiple models estimation where data fit many connected specifications of the same abstract model, instead of a singular or a small selection of specifications. Differently from the canonical application of multimodels, in Multiverse Analysis the probabilities of the specifications to be included in the analysis are never assumed independent of each other. Grounded in this consideration, this study provides a compact statistical characterisation of the process of elicitation of the specifications in Multiverse Analysis and conceptually adjacent methods, connecting previous insights from meta-analytical Statistics, model averaging, Network Theory, Information Theory, and Causal Inference. The calibration of the multiversal estimates is treated with references to the adoption of Bayesian Model Averaging vs. alternatives. In the applications, it is checked the theory that Bayesian Model Averaging reduces both error and uncertainty for well-specified multiversal models but amplifies errors when a collider variable is included in the multiversal model. In well-specified models, alternatives do not perform better than Uniform weighting of the estimates, so the adoption of a gold standard remains ambiguous. Normative implications for misinterpretation of Multiverse Analysis and future directions of research are discussed.

Power Brownian motion: an Ornstein-Uhlenbeck lookout

Abstract The well-known Ornstein-Uhlenbeck process (OUP) is the central go-to Gaussian model for statistical-equilibrium processes. The recently-introduced power Brownian motion (PBM) is a Gaussian model for diffusive motions, regular and anomalous alike. Using the Lamperti transform, this paper establishes PBM as the ‘diffusion counterpart’ of the OUP. Namely, the paper shows that PBM is for diffusive motions what the OUP is for statistical-equilibrium processes. The intimate parallels between the OUP and PBM are explored and illuminated via four main perspectives. 1) Statistical characterizations. 2) Kernel-integration with respect to Gaussian white noise. 3) Spatio-temporal scaling of the Wiener process. 4) Langevin stochastic dynamics driven by Gaussian white noise. To date, the prominent Gaussian models for anomalous diffusion are fractional Brownian motion (FBM), and scaled Brownian Motion (SBM). Due to its intimate OUP parallels, due to the ‘anomalous features’ it displays, due to the fact that it encompasses SBM, and following a detailed comparison to FBM: this paper argues the case for PBM as a central go-to Gaussian model for regular and anomalous diffusion.

Enhanced Transdermal Delivery of Cilnidpine Via Ultradeformable Vesicle Loaded Patch: Statistical Optimization, Characterization and Pharmacokinetic Assessment.

The study aimed to address the limitations of oral delivery and enhance the bioavailability of Cilnidipine (often prescribed as antihypertensive drug) (CND) through the development of transdermal patches containing ultra-deformable transferosomes. CND, known for its low oral bioavailability and adverse effects, was encapsulated in transferosomes using a thin film hydration method. Seventeen formulations were made (using Box Behnken Design), varying Soya lecithin, Tween-80, and rotary evaporator's speed, and evaluated for vesicle size, polydispersity index (PDI), and entrapment efficiency (EE %). The better formulation was selected based on these parameters and incorporated into transdermal patches. Physicochemical properties, in-vitro and ex-vivo permeation, and skin irritancy studies were conducted on the patches. Pharmacokinetic studies were conducted using male Wistar albino rats. The study found that the developed transferosomal formulations had vesicle sizes between 185 nm and 401 nm, entrapment efficiency (EE%) between 63% and 92%, and zeta potential ranging from -52 mV to -20 mV. Both in-vitro and ex-vivo permeation studies showed that transferosomal formulations provided significantly better drug permeation than plain Cilnidipine patches, with increased permeation linked to higher PEG-400 concentrations. The transferosomal patches did not cause skin irritation. The optimized formulation exhibited a higher % drug release (85.7±1.5%). In pharmacokinetic studies using male Wistar albino rats, the transferosomal patch CTP-17 demonstrated a higher maximum concentration (Cmax) of 1565.068 mcg/ml and a greater area under the curve (AUC) of 13225.352 μg h/ml compared to oral administration. The study concludes that the transferosomal patches of CND offer a promising approach for effective transdermal delivery, potentially improving hypertension management for prolonged periods in a controlled manner.

Dynamical Consequence of Shadows Cast to the Outer Protoplanetary Disks. I. Two-dimensional Simulations

There has been increasing evidence of shadows from scattered light observations of outer protoplanetary disks (PPDs) cast from the (unresolved) disk inner region, while in the meantime these disks present substructures of various kinds in the submillimeter. As stellar irradiation is the primary heating source for the outer PPDs, the presence of such shadows thus suggests inhomogeneous heating of the outer disk in azimuth, leading to a “thermal forcing” with dynamical consequences. We conduct a suite of idealized two-dimensional disk simulations of the outer disk with azimuthally varying cooling prescription to mimic the effect of shadows, generally assuming the shadow is static or slowly rotating. The linear response to such shadows is two-armed spirals with the same pattern speed as the shadow. Toward the nonlinear regime, we find that shadows can potentially lead to the formation of a variety of types of substructures including rings, spirals, and crescents, depending on viscosity, cooling time, etc. We have conducted systematic and statistical characterization of the simulation suite, and as thermal forcing from the shadow strengthens, the dominant form of shadow-induced disk substructures change from spirals to rings, and eventually to crescents/vortices. Our results highlight the importance of properly modeling the dynamical impact of inhomogeneous stellar irradiation, while calling for more detailed modeling incorporating more realistic disk physics.

A discrete extension of the exponential type II distribution: statistical characterizations, reliability analysis, and Bayesian vs. non-Bayesian inferences for random right-censored and complete count data

A discrete extension of the exponential type II distribution: statistical characterizations, reliability analysis, and Bayesian vs. non-Bayesian inferences for random right-censored and complete count data

Statistical characterization of electricity use profile: Leveraging data analytics for stochastic simulation in a smart campus

On the path to energy transition, advanced metering infrastructures have been installed in distribution systems to support sustainability goals, generating a substantial volume of electricity consumption data that are essential for planning and management studies. Additionally, given the stochastic nature of electricity consumption, understanding and quantifying statistical properties such as data distribution, normality, stationarity, and autocorrelation are crucial for the development of more sustainable systems and the enhancement of building performance. In this context, this paper presents a statistical methodology for assessing key aspects of electricity consumption in buildings on a smart campus, which is an initiative originated on university campuses that integrates sustainable energy systems, efficient electrical infrastructure, and data-driven technologies to establish a sustainable learning environment. Using 28 months of electricity consumption data from a Brazilian smart campus, Electricity Use Profile models are developed and several hypothesis tests and probability distribution fittings are conducted to extract statistical features from the models of 128 buildings. The results indicate that each building exhibits unique statistical properties that cannot be generalized, emphasizing the need for data analysis for each building before using the data in decision-making processes.

Size-Resolved Shape Evolution in Inorganic Nanocrystals Captured via High-Throughput Deep Learning-Driven Statistical Characterization.

Precise size and shape control in nanocrystal synthesis is essential for utilizing nanocrystals in various industrial applications, such as catalysis, sensing, and energy conversion. However, traditional ensemble measurements often overlook the subtle size and shape distributions of individual nanocrystals, hindering the establishment of robust structure-property relationships. In this study, we uncover intricate shape evolutions and growth mechanisms in Co3O4 nanocrystal synthesis at a subnanometer scale, enabled by deep-learning-assisted statistical characterization. By first controlling synthetic parameters such as cobalt precursor concentration and water amount then using high resolution electron microscopy imaging to identify the geometric features of individual nanocrystals, this study provides insights into the interplay between synthesis conditions and the size-dependent shape evolution in colloidal nanocrystals. Utilizing population-wide imaging data encompassing over 441,067 nanocrystals, we analyze their characteristics and elucidate previously unobserved size-resolved shape evolution. This high-throughput statistical analysis is essential for representing the entire population accurately and enables the study of the size dependency of growth regimes in shaping nanocrystals. Our findings provide experimental quantification of the growth regime transition based on the size of the crystals, specifically (i) for faceting and (ii) from thermodynamic to kinetic, as evidenced by transitions from convex to concave polyhedral crystals. Additionally, we introduce the concept of an "onset radius," which describes the critical size thresholds at which these transitions occur. This discovery has implications beyond achieving nanocrystals with desired morphology; it enables finely tuned correlation between geometry and material properties, advancing the field of colloidal nanocrystal synthesis and its applications.

Binomial Line Cox Processes: Statistical Characterization and Applications in Wireless Network Analysis

Binomial Line Cox Processes: Statistical Characterization and Applications in Wireless Network Analysis

Integrated bioprocessing and genetic strategies to enhance soluble expression of anti-HER2 immunotoxin in E. Coli

Immunotoxins are widely applied for cancer therapy. However, bacterial expression of immunotoxins usually leads to the formation of insoluble and non-functional recombinant proteins. This study was aimed to improve soluble expression of a novel anti-HER2 immunotoxin under the regulation of the trc promoter in Escherichia coli by optimization of the cultivation conditions using response surface methodology (RSM). To conduct RSM, four cultivation variables (i.e., inducer concentration, post-induction time, post-induction temperature, and medium recipe), were selected for statistical characterization and optimization using the Box-Behnken design and Design Expert software. Based on the developed model using the Box-Behnken design, the optimal cultivation conditions for soluble expression of anti-HER2 immunotoxin were determined to be 0.1 mM IPTG for induction in the LB medium at 33 °C for 18 h. The expressed immunotoxin was successfully purified using affinity chromatography with more than 90% purity and its bioactivity was confirmed using cell-based ELISA. Technical approach developed in this study can be generally applied to enhance the production yield and quality of recombinant proteins using E. coli as the gene expression system.

Enhanced polyhydroxyalkanoate production from Mesobacillus aurentius: Statistical optimization, characterization and industrial application

Synthetic plastics pose a major environmental threat and it is necessary to produce an alternative biopolymer. In the current study, the production of polyhydroxybutyrate (PHB) by Mesobacillus aurentius was enhanced using Response surface methodology (Box–Behnken design). This study explores the potential of aquabiofloc systems as a source of polyhydroxyalkanoates (PHB)-producing bacteria. The optimized medium conditions, as determined by Response Surface Methodology (RSM), included 18.68 g of sucrose, 4.0 g of yeast, an incubation period of 69.57 h, and a pH of 7.1. The ANOVA results revealed that the model developed for predicting PHA yield was highly significant (p < 0.05). The predicted PHA yield was 63.12%, while the experimental yield was 65.35%. The maximum production of PHA was obtained with sucrose and yeast as carbon and nitrogen sources. The extracted polymer was characterized using UV, FTIR, 1H NMR, and SEM-EDAX analysis confirming the polymer to be PHB. The thermal stability of the produced PHA showed degradation temperatures ranging from 310 °C. The mechanical properties of the extracted PHA were also assessed, demonstrating tensile strength and viscosity of 22.4 MPa and 1.23 MPa. respectively. The antimicrobial activity of the produced PHA was evaluated, demonstrating significant inhibitory effects against both Gram-positive and Gram-negative bacterial strains as well as fungal strains. The Cytotoxicity assessment in HepG2 cells indicated that PHB is less toxic in nature. The findings highlight the promising role of marine bacteria, Mesobacillus aurentius, in the development of environmentally friendly biopolymers. This bacterium represents a novel candidate for PHB production, offering a potential alternative to petroleum-based plastics.

Noise characterization analysis of dynamic dual-energy CT and its advantage in suppressing statistical noise

Objective.Multi-energy CT conducted by photon-counting detector has a wide range of applications, especially in multiple contrast agents imaging. However, static multi-energy (SME) CT imaging suffers from higher statistical noise because of increased energy bins with static energy thresholds. Our team has proposed a dynamic dual-energy (DDE) CT detector model and the corresponding iterative reconstruction algorithm to solve this problem. However, rigorous and detailed analysis of the statistical noise characterization in this DDE CT was lacked.Approach.Starting from the properties of the Poisson random variable, this paper analyzes the noise characterization of the DDE CT and compares it with the SME CT. It is proved that the multi-energy CT projections and reconstruction images calculated from the proposed DDE CT algorithm have less statistical noise than that of the SME CT.Main results.Simulations and experiments verify that the expectations of the multi-energy CT projections calculated from DDE CT are the same as those of the SME projections. Still, the variance of the former is smaller. We further analyze the convergence of the iterative DDE CT algorithm through simulations and prove that the derived noise characterization can be realized under different CT imaging configurations.Significance.The low statistical noise characteristics demonstrate the value of DDE CT imaging technology.

Statistical Optimization and Characterization of Nimodipine Transferosomes

Background: Nimodipine is a vasodilator that is used for the prevention of cerebral vasospasm after subarachnoid hemorrhage. The oral and intravenous administration of the drug is associated with undesirable side effects. So, transdermal delivery using lipid-based nanovesicles, also known as transferosomes, can be thought of as an alternative. Objective: To optimize the formulation of transferosomes using the statistical design of experiments, with the aim of obtaining the most suitable transferosomes for the transdermal delivery of nimodipine. Methods: In the Box-Behenken statistical design, the independent variables were the quantities of nimodipine, phospholipon 90%, and sodium deoxycholate, while the dependent variables were the vesicle size, entrapment efficiency for nimodipine and its flux through the rat's skin. The optimized formulation was characterized through transmission electron microscopy and the deformability index. Results: The optimized formulation of transferosomes suggested by the software consisted of 30 mg nimodipine, 150 mg phospholipon 90% and 15 mg sodium deoxycholate. The resulted values were 248 nm for vesicles size, 81% for entrapment, and 476 μg/cm2/h. Under transmission electron microscopy, transferosomes appeared as vesicles, with a 0.98 deformability index for the optimized formula. Conclusions: Nimodipine can be formulated as transferosomes and efficiently applied for transdermal delivery.

Hardware-Efficient Configurable Ring-Oscillator-Based Physical Unclonable Function/True Random Number Generator Module for Secure Key Management.

The use of physical unclonable functions (PUFs) linked to the manufacturing process of the electronic devices supporting applications that exchange critical data over the Internet has made these elements essential to guarantee the authenticity of said devices, as well as the confidentiality and integrity of the information they process or transmit. This paper describes the development of a configurable PUF/TRNG module based on ring oscillators (ROs) that takes full advantage of the structure of modern programmable devices offered by Xilinx 7 Series families. The proposed architecture improves the hardware efficiency with two main objectives. On the one hand, we perform an exhaustive statistical characterization of the results derived from the exploitation of RO configurability. On the other hand, we undertake the development of a new version of the module that requires a smaller amount of resources while considerably increasing the number of output bits compared to other proposals previously reported in the literature. The design as a highly parameterized intellectual property (IP) module connectable through a standard interface to a soft- or hard-core general-purpose processor greatly facilitates its integration into embedded solutions while accelerating the validation and characterization of this element on the same electronic device that implements it. The studies carried out reveal adequate values of reliability, uniqueness, and unpredictability when the module acts as a PUF, as well as acceptable levels of randomness and entropy when it acts as a true random number generator (TRNG). They also illustrate the ability to obfuscate and recover identifiers or cryptographic keys of up to 4096 bits using an implementation of the PUF/TRNG module that requires only an array of 4×4 configurable logic blocks (CLBs) to accommodate the RO bank.

Statistical characterization of marine wind structure over wind-turbine-relevant heights in tropical cyclones

Tropical cyclones (or typhoons, hurricanes) may influence the power production and structural integrity of offshore wind turbines. However, understanding of tropical cyclone wind structure over turbine-relevant heights remains limited. Based on observations from a wind lidar and a meteorological mast at a planned offshore wind farm site, this paper investigates the marine wind characteristics, e.g., wind speed shear, wind direction veer, and turbulence intensity, over turbine-relevant heights in tropical cyclones. Statistical distributions of the marine wind characteristics are analyzed, and their variations with upstream fetch conditions and hub-height wind speeds are examined. Some noteworthy features of the marine wind structure in tropical cyclones are observed, such as the median wind veer rate of 0.018°/m and veer angle of 2.2° across the turbine rotor heights, different wind shear coefficients and wind veer rates for upper and lower rotors, and levelling off or decrease of wind shear coefficient and turbulence intensity with increasing wind speed at hub-height wind speed over 25 m/s. The outcome of this study is expected to provide useful information for design and operation of offshore wind turbines, marine platforms, and other ocean structures in tropical cyclone-prone regions.

A reliability analysis framework coupled with statistical uncertainty characterization for geotechnical engineering

Reliability analysis plays an important role in the risk management of geotechnical engineering. For the random field-based method, it is expected that the uncertainty characterization of geo-material parameters and the realization of random field can be integrated effectively. Moreover, as the increase in measured data size is generally difficult in the field investigation of geotechnical engineering due to limitation of budget and time etc., the statistical uncertainty resulting from sparse data should be paid great attention. Therefore, taking the determination of hyper-parameters for Bayesian-based conditional random field as the breakthrough, this study proposed a reliability analysis framework to achieve the expectation above. In this proposed reliability analysis framework, the present characterization method of statistical uncertainty is improved by setting the lognormal distribution as the prior distribution of scale of fluctuation (SOF). Subsequently, the performance of statistical uncertainty characterization method is tested by a set of unconfined compressive strength (UCS) database about rocks. Then, a case study about the stability analysis of slope is employed to demonstrate the beneficial effect of the proposed reliability analysis framework. It is found that the uncertainty in both the realization of random field and the reliability analysis results can be significantly mitigated by the proposed reliability analysis framework.

Combined interpretation of objective firearm evidence comparison algorithms using Bayesian networks.

Traditionally, firearm and toolmark examiners manually evaluate the similarity of features on two bullets using comparison microscopy. Advances in microscopy have made it possible to collect 3D topographic data, and several automated comparison algorithms have been introduced for the comparison of bullet striae using these data. In this study, open-source approaches for cross-correlation, congruent matching profile segments, consecutive matching striations, and a random forest model were evaluated. A statistical characterization of these automated approaches was performed using four datasets of consecutively manufactured firearms to provide a challenging comparison scenario. Each automated approach was applied to all samples in a pairwise fashion, and classification performance was compared. Based on these findings, a Bayesian network was empirically learned and constructed to leverage the strengths of each individual approach, model the relationship between the automated results, and combine them into a posterior probability for the given comparison. The network was evaluated similarly to the automated approaches, and the results were compared. The developed Bayesian network classified 99.6% of the samples correctly, and the resultant probability distributions were significantly separated more so than the automated approaches when used in isolation.

Complexity of High-Dimensional Identity Testing with Coordinate Conditional Sampling

We study the identity testing problem for high-dimensional distributions. Given as input an explicit distribution \(\mu\) , an \(\varepsilon \gt 0\) , and access to sampling oracle(s) for a hidden distribution \(\pi\) , the goal in identity testing is to distinguish whether the two distributions \(\mu\) and \(\pi\) are identical or are at least \(\varepsilon\) -far apart. When there is only access to full samples from the hidden distribution \(\pi\) , it is known that exponentially many samples (in the dimension) may be needed for identity testing, and hence previous works have studied identity testing with additional access to various “conditional” sampling oracles. We consider a significantly weaker conditional sampling oracle, which we call the \(\mathsf{Coordinate\ Oracle}\) , and provide a computational and statistical characterization of the identity testing problem in this new model. We prove that if an analytic property known as approximate tensorization of entropy holds for an \(n\) -dimensional visible distribution \(\mu\) , then there is an efficient identity testing algorithm for any hidden distribution \(\pi\) using \(\widetilde{O}(n/\varepsilon)\) queries to the \(\mathsf{Coordinate\ Oracle}\) . Approximate tensorization of entropy is a pertinent condition as recent works have established it for a large class of high-dimensional distributions. We also prove a computational phase transition: for a well-studied class of \(n\) -dimensional distributions, specifically sparse antiferromagnetic Ising models over \(\{+1,-1\}^{n}\) , we show that in the regime where approximate tensorization of entropy fails, there is no efficient identity testing algorithm unless \(\mathsf{RP}=\mathsf{NP}\) . We complement our results with a matching \(\Omega(n/\varepsilon)\) statistical lower bound for the sample complexity of identity testing in the \(\mathsf{Coordinate\ Oracle}\) model.

Impact of Non-Residential Users on the Energy Performance of Renewable Energy Communities Considering Clusterization of Consumptions

Renewable energy communities foster the users’ engagement in the energy transition, paving the way to the integration of distributed renewable energy sources. So far, the scientific literature has focused on residential users in energy communities, thus overlooking the opportunities for industrial and commercial members. This paper seeks to bridge this gap by extending the analysis to the role of non-residential users. The proposed methodology develops an effective clustering approach targeted to actual non-residential consumption profiles. It is based on the k-means algorithm and statistical characterization based on relevant probability density function curves. The employed clusterization procedure allows for effectively reducing a sample of 49 real industrial load profiles up to 11 typical consumption curves, whilst capturing all the relevant characteristics of the initial population. Furthermore, a peer-to-peer sharing strategy is developed accounting for distributed and shared storage. Three scenarios are considered to validate the model with different shares of non-residential users, and the results are then evaluated by means of shared energy, self-consumption, and self-sufficiency indices. Moreover, the results show that the integration of a large non-residential prosumer in a REC may increase the self-sufficiency of residential members by 8.2%, self-consumption by 4.4%, and the overall shared energy by 37.3%. Therefore, the residential community consistently benefits from the presence of non-residential users, with larger users inducing more pronounced effects.