Distribution Model Research Articles

0.2-mm-Step Verification of the Dual Gaussian Distribution Model with Large Sample Size for Predicting Tap Success Rates

The Dual Gaussian Distribution Model can be utilized for predicting the success rates of tapping targets. However, previous studies have shown that the prediction error increases to as much as 10 points, where "points" represent the percentage difference between the observed and predicted values of the tap success rate, particularly for a small target width W such as 2 mm. We hypothesize that this could be due to the experimental designs with sparse W levels performed by few participants, rather than the model itself. Our experiment involving horizontal and vertical bar targets with W = 2-8 mm (step: 0.2 mm) performed by more than 180 participants showed that the maximum prediction errors were relatively small: 2.769 and 3.185 points, respectively. Furthermore, the correlation between W and the prediction error was statistically small (Pearson's |r| < 0.2), and W was not a significant contributor to changing prediction errors (p>0.05). As these results do not support the concerns that the Dual Gaussian Distribution Model has an issue when used with small targets, the development of applications and refined models is encouraged to continue.

A Systematic Literature Review of Insurance Claims Risk Measurement Using the Hidden Markov Model

In the rapidly evolving field of insurance, accurate risk measurement is crucial for effective claims management and financial stability. Therefore, this research presented a systematic literature review (SLR) on insurance claims risk measurement using the Hidden Markov Model (HMM). Bibliometric analysis was conducted using VOSviewer 1.6.20 and ResearchRabbit software to map research trends and collaboration networks in this topic. This review explored the implementation of the HMM in predicting the frequency and severity of insurance claims, with a focus on the statistical distribution methods used. In addition, the research emphasized the influence of the number of hidden states in the HMM on claims behavior, both in terms of frequency and magnitude, and provided interpretations of these hidden dynamics. Data sources for this review comprised three databases, namely, Scopus, ScienceDirect, and Dimensions, and additional papers from a website. The article selection process followed updated PRISMA 2020 guidelines, resulting in twelve key papers relevant to the topic. The results offered insights into the application of the HMM for forecasting the frequency and severity of insurance claims and opened avenues for further investigation on distribution models and hidden state modeling.

Dissymmetrization in eudialyte-group minerals. I. A model of ordered cation arrangement in the crystal structure of amableite-(Ce) using the P3 symmetry

The crystal structure of the recently discovered eudialyte group mineral, amabellite-(Ce) Na15[(Ce1.5Na1.5)Mn3]Mn2Zr3£Si[Si24O69(OH)3](OH)2 · H2O, found in the hyperagpaitic pegmatite of the Saint-Amable massif (Canada), has been solved by X-ray structural analysis within the space group R3. Amabellite-(Ce) is a member of the eudialyte group with the lowest calcium content and differs from other members of this group by the dominance of lanthanides in the part of the edge-sharing octahedra of the six-membered ring. The unit cell parameters of the mineral are: a = 14.1340(2), c = 30.378(1) Å, V = 5255.6(3) ų. A model of the cation distribution in the crystal structure of amabellite-(Ce) within the low-symmetry space group P3 has been proposed. The obtained 162 independent positions were refined in the isotropic-anisotropic approximation of atomic displacements using 3968 F 3σ(F), R = 4.6%. Despite the fairly close results, the transition from space group R3 to P3 allows for more detailed information on the local distribution of a number of elements over the framework positions. A comparison was made between the crystal structure models of amabellite within the symmetries R3 and P3, as well as other low-calcium eudialyte group minerals previously studied within several space groups.

Enhancing regional sustainable industrialisation and poverty reduction post‐COVID. The case of the common market for the eastern and southern Africa region

AbstractIndustrialisation is important for attaining economic development in an economy as well as other macroeconomic objectives, such as economic growth and low levels of unemployment. This research aimed to examine how the Common Market for Eastern and Southern Africa can use industrialisation for effective poverty reduction. The research adopted the panel autoregressive distributed lag model. Data were sourced from the World Bank from 1994 to 2022. The results demonstrated that social security, remittances, industrial development and development aid are crucial factors that can be leveraged to reduce poverty in the COMESA region. It was established that if industrialisation increases by 1%, poverty will decrease by 19% in the long run. It was suggested that trade barriers be removed, and modern technology be harnessed to ensure the value‐benefit of the beneficiation of raw materials for effective poverty reduction.

Mapping habitat suitability of invasive crayfish in aridland riverscapes: Virile crayfish (Faxonius virilis) in the Lower Colorado River Basin, USA

Correlative species distribution modeling (SDM) is an important tool to predict distributions of invasive species. A unique challenge of implementing SDMs in aridland stream networks is identifying which streams are perennial and which of those offer suitable habitat for obligate aquatic species. Here, we map perennial streams and habitat suitability of invasive virile crayfish (Faxonius virilis) in the Lower Colorado River Basin (LCRB) in the southwestern USA, and quantify drivers of uncertainty in these projections. First, we surveyed F. virilis at 122 stream sites in May–July of 2021, 2022, and 2023. We then implemented an ensemble of SDMs fit using combinations of randomly drawn sites, categories of environmental covariates measured at different spatial scales, survey years, and statistical algorithms. Next, we used open-source street view images from May–July of 2022 and 2023 to assess dry-wet status at 326 road-stream intersections and the same ensemble framework to map perennial stream reaches. Lastly, we quantified drivers of variation in predictive accuracy and mapped habitat suitability across replicates. Median true skill statistic (TSS) across F. virilis replicates was 0.613 and habitat suitability was highest in mountain ecoregions and lowest in desert ecoregions. Of the 130,847 stream km in the LCRB, we estimate 29,078 km (22.2 %) have wetted channels during the May–June dry season and median 50.3 % of these perennial streams provide suitable habitat for F. virilis. The statistical algorithm was the strongest driver of TSS variation across replicates, whereas the spatial scale at which covariates were measured was the strongest driver of variation in mapped habitat suitability across replicates. We confirm the widespread invasion of F. virilis throughout the LCRB, particularly in perennial mountain streams. The modest predictive accuracy highlights the generalistic niche of F. virilis. Overall, we demonstrate that spatiotemporally comprehensive datasets combined with ensemble modeling can guide management at regional extents.

Predicting the potential habitat of bears under a changing climate in Nepal.

In Nepal, the distributions of three bear species vary: sloth bears (Melursus ursinus) in the lowlands, Asiatic black bears (Ursus thibetanus) in the mid-hills, and brown bears (Ursus arctos) in the high Himalayas. We utilized 179 occurrence points for sloth bears, 199 for Asiatic black bears, and 41 points for brown bears to construct a habitat model incorporating climate and topographic variables. Employing various species distribution modeling algorithms in BIOMOD2, the model predicts suitable habitats spanning 10,971.75 km2 for sloth bears; 29,470.75 km2 for Asiatic black bears; and 6152.97 km2 for brown bears. Within protected areas, the habitat for sloth bears is 4120.56 km2, that for Asiatic black bears is 9688.67 km2, and that for brown bears is 4538.67 km2. Chitwan National Park emerged as the prime sloth bear habitat with a core area of 918.55 km2 and a buffer zone of 726.485 km2. The Annapurna Conservation Area was deemed suitable for Asiatic black bears and brown bears, covering 2802.23 km2 and 2795.91 km2, respectively. The models projected a significant reduction in the habitat of these bear species both inside and outside protected areas. As predicted under the Shared Socioeconomic Pathways (SSP)2-4.5 scenario, sloth bears may experience 54.9% (2050) and 44.7% (2070) losses, respectively, of habitat; Asiatic black bears, 11.2% (2050) and 16.8% (2070); and brown bears, 68.41% (2050) and 82.20% (2070) losses. The overlap between sloth bears and black bears spans 38.7 km2, and that between brown bears and black bears is 26.6 km2. Notably, all three bear species exhibited suitability correlations with the intermediate temperature of the driest quarter. Examining current and projected habitats provides essential information for guiding conservation strategies and ensuring the conservation of these bear species in the face of climate change.

Polyploidy linked with species richness but not diversification rates or niche breadth in Australian Pomaderreae (Rhamnaceae).

Polyploidy is an important evolutionary driver for plants and has been linked with higher species richness and increases in diversification rate. These correlations of ploidy with plant radiations could be the result of polyploid lineages exploiting greater niche space and novel niches due to their enhanced adaptability. Ploidy evolution and how it links with diversification of plants across the Australian continent is not well understood. Here, we focused on the ploidy evolution of the Australasian Rhamnaceae tribe Pomaderreae. We generated a densely-sampled phylogeny (90%, 215/240 spp.) of the tribe and used it to test for the evolution of ploidy. We obtained 30 orthologous nuclear loci per sample and dated the phylogeny using TreePL. Ploidy estimates for each sequenced species was obtained using nQuire, based on phased sequence data. We used MiSSE to obtain tip diversification rates and tested for significant relationships between diversification rates and ploidy. We also assessed for relationships between ploidy level and niche breadth, using distributional records, species distributional modelling, and WorldClim data. Polyploidy is extensive across the tribe, with almost half (45%) of species and majority of genera exhibiting this trait. We found a significant positive relationship between polyploidy and genus size (i.e., species richness), but non-significant positive relationship between polyploidy and diversification rates. Polyploidy did not result in significantly wider niche space occupancy for Pomaderreae, however polyploidy did allow for transitions into novel wetter niches. Spatially, eastern Australia is the diversification hotspot for Pomaderreae in contrast to the species hotspot of southwest Western Australia. The relationship between polyploidy and diversification is complex. Ancient polyploidisation events likely played an important role in the diversification of species rich genera. A lag time effect may explain the uncoupling of tip diversification rates and polyploidy of extant lineages. Further studies on other groups are required to validate these hypotheses.

RUFCO: a deep-learning framework to post-process subseasonal precipitation accumulation forecasts

Abstract Post-processing is a critical step in attaining calibrated and reliable probabilistic forecast output from numerical weather prediction models. A novel deep-learning framework is proposed to post-process 20 years of 7- and 14-day precipitation accumulation reforecasts from the Global Ensemble Forecast System at subseasonal timescales (week 1, week 2, and combined weeks 3-4 forecasts) over the contiguous United States. The network builds upon previous studies and is a combination of three parallel-trained components suitable for subseasonal prediction. The first is a ResUnet architecture which learns non-linear relationships between binned observed precipitation and input images of weather and geographical variables. The second conditions the network to the month-of-year via a Feature-wise Linear Modulation (FiLM) layer. The third helps the network learn when to revert the forecast to that of climatology. The RUFCO (named for its components ResUnet, FiLM, and Climatological-Offramp) forecasts are compared against raw and climatological forecasts as well as those from a state-of-the-art distributional regression post-processing model, “CSGD,” and a simple bias-corrected model. At week 1, every method exhibited a competitive advantage over climatological forecasts. At week 2, RUFCO generated forecasts with statistically significant improvement over climatology at 82-94% of the domain, beating CSGD’s coverage of 76-90% of the grid points. At week 3, RUFCO’s skillful coverage was 65-85% while CSGD’s dropped to only 12-37%. At the longer lead times, RUFCO achieved the highest domain-averaged skill scores across seasons. However, the network tends to “smooth” forecast skill making it less competitive with CSGD in limited areas with strongly spatially-varying biases.

Predicting smoke temperature distribution beneath ceiling for a large subway station fire

Fire accident seriously threatens the stable operation and personnel security in the subway station, and clarifying the smoke temperature distribution is conducive to arranging reasonably fire alarm. In this study, model experiments and numerical simulations are conducted to investigate the ceiling temperature profiles, mainly altering the heat release rate (HRR) and fire locations in a large subway station. The repeatability of the experimental data is confirmed, and the numerical model is validated by the measurement data. During a fire happening in a large-wide space, the smoke movement beneath the ceiling can be divided into radial and one-dimensional flow, whose prediction models are proposed under fire source located at the center. On this basis, it can be found that the radial temperature profile is independent of fire source location. While, the one-dimensional temperature distribution model is modified by introducing the effects of transvers and longitudinal fire locations respectively. This work could provide data support and theoretical reference for clarifying the smoke movement for large subway station fire.

Palawan Peacock Pheasant (Polyplectron Napoleonis) Species Distribution Modeling: A Review of the Impact of Typhoon Rai/Odette on the Diversity and Distribution of an Endemic Bird Species

This study quantified the habitat requirements and, consequently, described the remaining suitable habitats through species distribution modeling of Polyplectron napoleonis within Cleopatra’s Needle Forest Reserve and Puerto Princesa Subterranean River National Park after the onslaught of Typhoon Odette in the Palawan region. This was accomplished using MaxEnt software, a statistical machine-learning algorithm that creates niche models with presence-only (PO) data and surrounding environmental variables (Dudik et al., 2007; Merow et al., 2013). Previous studies have identified precipitation, elevation, and land cover as the top environmental variables influencing tandikan habitat suitability, as revealed by their percent contributions. There were earlier claims of the noticeable movement of habitat-suitable areas to the fringes and beyond Cleopatra's Needle Forest Reserve. The migration seems to be going towards highland forests. Two factors could be behind this migration: the tandikan are responding to climate change or avoiding human disturbance. As this species is known to have a high tolerance for temperature change, the latter could be the more plausible factor. For 2022, post-Odette, MaxEnt analysis revealed a shift of percent contributions with elevation and land cover classification outranking aspect and slope. Environmental layers were limited to four, as these were the only currently available data. This shift in percent contributions could be linked to the direct damage to forests brought by the Typhoon in December of 2021. The land cover map generated for this year has shown large areas of forests that were converted into non-forests. Conservation efforts, therefore, for the tandikan, a forest species, should be recalibrated in response to this current development. Further, measures to mitigate the impact of solid typhoons on forests should be reviewed and evaluated.

A novel generalised extreme value gradient boosting decision tree for the class imbalanced problem in credit scoring

The performance of credit scoring models can be compromised when dealing with imbalanced datasets, where the number of defaulted borrowers is significantly lower than that of non-defaulters. To address this challenge, we propose a gradient boosting decision tree with the generalised extreme value distribution model (GEV-GBDT). Our approach replaces the conventional symmetric logistic sigmoid function with the asymmetric cumulative distribution function of the GEV distribution as the activation function. We derive a novel loss function based on the maximum likelihood estimation of the GEV distribution within the boosting framework. This modification allows the model to focus more on the minority class by emphasising the tail of the response curve, and the shape parameter of the GEV distribution offers flexibility in controlling the model’s emphasis on minority samples. We examine the performance of this approach using four real-life loan datasets. The empirical results show that the GEV-GBDT model achieves superior classification performance compared to other commonly used imbalanced learning methods, including the synthetic minority oversampling technique and the cost-sensitive framework. Furthermore, we conduct performance tests on several datasets with varying imbalance ratios and find that GEV-GBDT performs better on extremely imbalanced datasets.

The 2024 Mw 7.0 Wushi Earthquake in Southern Tianshan Convergent Zone: Finite-Fault Model for the Coseismic Rupture and Aftershock

Abstract On 22 January 2024, an Mw 7.0 earthquake struck the oblique thrust Maidan fault (MDF) at the boundary between the Southern Tianshan and Tarim basin, making it the largest earthquake on the fault in the last 100 yr. Here, we use Interferometric Synthetic Aperture Radar (InSAR) to retrieve coseismic and aftershock deformation caused by the earthquake and then constrain fault geometry and slip distribution. Line-of-sight InSAR observations revealed a coseismic deformation area of 70 km × 60 km with a maximum value of ∼0.8 m and also captured the deformation from the aftershock event seven days after the mainshock. Our preferred two-segment coseismic fault model ruptures the MDF locked segment and deeper regions, exhibiting a homogeneous elliptical rupture with a maximum slip of ∼2.7 m on a rectangular plane with a dip angle of ∼60°, a length of ∼55 km, and a depth between 5 and 20 km. The aftershock formed a rupture plane of 10×8 km2 with the maximum slip of ∼0.3 m, causing slip in the shallow area of the fault where the coseismic ruptures were smaller, and supplementarily released the stress in the shallow part of the fault. The strong earthquake (Mw 7) return period on MDF inferred from the interseismic slip rate is 170–200 yr. Oblique thrust slip revealed by the slip distribution model is the result of long-term absorption of oblique convergence strain in the Southern Tianshan by the MDF and then concentrated release. The 2024 event resulted in a 60 km unruptured segment on the MDF being in a stress-triggering zone, increasing the potential seismic hazard, in contrast to the delayed seismicities on 100 km region of the southern Kalping fault (KPF).

A Global Estimation of Potential Climate Change Effects on Pollinator-Dependent Crops

AbstractClimate change poses a threat to global food security by significantly affecting agricultural products, both directly and indirectly. This study aims to assess the impact of climate change on the climate suitability of 61 pollinator-dependent crops worldwide under different climate change scenarios. Utilizing species distribution models (SDMs), we predicted climate suitability maps for these crops in the present and projected conditions for 2070. Our findings indicate that under the most pessimistic climate change scenario in 2070 (SSP585), 16, 31, 34, 29, 29, and 31 crops in Africa, Asia, Australia, Europe, North America, and South America, respectively, may experience a decrease in their suitable habitats. On average, crops in Africa, Asia, Australia, Europe, North America, and South America are expected to see a decrease of − 14.5%, − 11.2%, − 26.2%, − 4.7%, 5.5%, and − 14.4% in their suitability by 2070. Regional variations in the effects of climate change on crops highlight the vulnerability of certain areas, particularly Australia, where a substantial number of crops face a decline in suitable habitats. Conversely, North America may witness an increase in the distribution of specific products. Urgent action is needed to mitigate and adapt to the impacts of climate change, including the development of more resilient crop varieties and improvements in agricultural practices, especially for highly vulnerable pollinator-dependent crops in Asia and Africa.

Domain embeddings for generating complex descriptions of concepts in Italian language.

In this work, we propose a Distributional Semantic resource enriched with linguistic and lexical information extracted from electronic dictionaries. This resource is designed to bridge the gap between the continuous semantic values represented by distributional vectors and the discrete descriptions provided by general semantics theory. Recently, many researchers have focused on the connection between embeddings and a comprehensive theory of semantics and meaning. This often involves translating the representation of word meanings in Distributional Models into a set of discrete, manually constructed properties, such as semantic primitives or features, using neural decoding techniques. Our approach introduces an alternative strategy based on linguistic data. We have developed a collection of domain-specific co-occurrence matrices derived from two sources: a list of Italian nouns classified into four semantic traits and 20 concrete noun sub-categories and Italian verbs classified by their semantic classes. In these matrices, the co-occurrence values for each word are calculated exclusively with a defined set of words relevant to a particular lexical domain. The resource includes 21 domain-specific matrices, one comprehensive matrix, and a Graphical User Interface. Our model facilitates the generation of reasoned semantic descriptions of concepts by selecting matrices directly associated with concrete conceptual knowledge, such as a matrix based on location nouns and the concept of animal habitats. We assessed the utility of the resource through two experiments, achieving promising outcomes in both the automatic classification of animal nouns and the extraction of animal features.

An exactly solvable model for non-Fickian transport in dynamically heterogeneous media

Abstract Diffusion, observed in various condensed phases, finds its theoretical background in Einstein’s theory of Brownian motion, characterized by the linear time-dependence of mean square displacement (MSD) denoting Fickian behavior and the Gaussian distribution of particle displacement. Nevertheless, diverse systems exhibit either non-linear, non-Fickian time-dependence of the MSD or non-Gaussian displacement distribution. Montroll and Weiss’s continuous-time random walk (CTRW) model and the stochastic diffusivity (SD) model have provided insights into anomalous diffusion phenomena and Fickian-yet-non-Gaussian transport in dynamically heterogeneous environments, respectively. Building upon these approaches, Song et al developed a generalized transport equation with an environment-dependent diffusion kernel, providing a quantitative explanation for non-Fickian MSD and non-Gaussian displacement distribution. Based on the generalized transport equation, this study introduces an exactly solvable model for a non-Gaussian displacement distribution, accommodating arbitrary time profiles in its MSD, including both Fickian and non-Fickian behaviors. Our findings confirm the model’s capability in describing such transport processes. Furthermore, the proposed model unifies the CTRW model under fast environmental fluctuations and the SD model under Fickian time dependencies, making it suitable for understanding tracer particle motion within explicit solvent or complex media.

Statistical analysis of apparent morphology and characteristics of corroded rebars of concrete in the marine environment

The residual cross-sectional areas of corroded rebars and their uniformity are the main indexes to evaluate the safety and durability of concrete structures in the marine environment. To evaluate corrosion characteristics of naturally corroded rebars, nineteen corroded rebars were extracted from the prototype components of a high-pile wharf with 35 years of service. The apparent morphology characteristics of corroded rebars were obtained using 3D scanning. The results indicate that the corrosion is non-uniform along both the longitudinal and cross-sectional directions of the corroded rebars. In the marine environment, the residual cross-sectional areas of corroded rebars along the length adhere to a three-parameter Weibull distribution; however, no specific probability distribution model or clear correlation with the distribution pattern exists for rebars with varying corrosion degrees. The spatial heterogeneity factors of the rebars are characterized by a three-parameter Frechet distribution.

The potential for species distribution models to distinguish source populations from sinks.

While species distribution models (SDM) are frequently used to predict species occurrences to help inform conservation management, there is limited evidence evaluating whether habitat suitability can reliably predict intrinsic growth rates or distinguish source populations from sinks. Filling this knowledge gap is critical for conservation science, as applications of SDMs for management purposes ultimately depend on these typically unobserved population or metapopulation dynamics. Using linear regression, we associated previously published population level estimates of intrinsic growth and abundance derived from a Bayesian analysis of mark-recapture data for 17 bird species found in the contiguous United States with SDM habitat suitability estimates fitted here to opportunistic data for these same species. We then used the area under the ROC curve (AUC) to measure how well SDMs can distinguish populations categorized as sources and sinks. We built SDMs using two different approaches, boosted regression trees (BRT) and generalized linear models (GLM), and compared their source/sink predictive performance. Each SDM was built with presence points obtained from eBird (a web-available database) and 10 environmental variables previously selected to model intrinsic growth rates and abundance for these species. We show that SDMs built with opportunistic data are poor predictors of species demography in general; both BRT and GLM explained very little spatial variation of intrinsic growth rate and population abundance (median R2 across 17 species was close to 0.1 for both SDM methods). SDMs, however, estimated higher suitability for source populations as compared to sinks. Out of 13 species which had both source and sink populations, both BRT and GLM had AUC values greater than 0.7 for 7 species when discriminating between sources and sinks. Habitat suitability have the potential to be a useful measure to indicate a population's ability to sustain itself as a source population; however more research on a diverse set of taxa is essential to fully explore this potential. This interpretation of habitat suitability can be particularly useful for conservation practice, and identification of explicit cases of when and how SDMs fail to match population demography can be informative for advancing ecological theory.

Research on freeze resistance and life prediction of Desert sand–Crushed stone fine aggregate concrete

Despite the global abundance of Desert sand (DS), its application in engineering remains limited. This application can help alleviate challenges related to high unit costs and the scarcity of natural fine aggregate supplies. Freeze resistance experiments were conducted on C30 concrete incorporating varying proportions of DS, including 0 %, 25 %, 50 %, 75 %, and 100 %. DS was mixed with Tuff crushed stone fine aggregate (Manufactured sand, referred to as MS). The freeze durability of the composite fine aggregate concrete was comprehensively evaluated from three perspectives: surface morphology, a macroscopic analysis including mass loss and relative dynamic elastic modulus (RDEM), and microscopic examinations encompassing pore structure observation and scanning electron microscope (SEM) imaging. Utilizing the damage degree index of the dynamic elastic modulus and the Weibull distribution model, the life prediction process was conducted to elucidate the time-varying behavior of DS-MS concrete. According to the test findings, the concrete specimen DS25-MS75 demonstrates the highest freeze resistance. The mass loss rate shows a pattern of initial decrease followed by an increase with higher desert sand admixture, whereas the RDEM initially increases and then decreases. Optimal desert sand admixture levels can prevent and delay concrete degradation, thereby enhancing its freeze resistance. Excessive desert sand content leads to increased water demand during the curing process, resulting in elevated porosity and facilitating the formation of voids. Under the action of freezing and thawing cycles in water, DS25 %-MS75 % concrete has the longest expected service life with an actual life of 101 years, while DS100 %-MS0 % concrete has the shortest service life with an actual life of only 54.5 years.

Modeling of Tensile Stress Distribution Considering Anisotropy of Mechanical Properties of Thin-Walled AlSi10Mg Samples Obtained by Selective Laser Melting

The work that is being presented demonstrates that there is a critical point at which the engineering stress–strain diagram’s elastic–plastic region transitions to yield and fracture stresses. This transition is demonstrated using thin-walled specimens made using selective laser melting technology from high-strength aluminum alloys (AlSi10Mg) that have undergone preliminary heat treatment. It was discovered that the strain-hardening coefficient, which was determined in the section from yield strength to fracture strength, and the critical point have a highly statistically significant association (0.83 by Spearman and 0.93 by Pearson). It was possible to derive a regression equation that connected the strain-hardening coefficient with the crucial transition point. The type of stress distribution in the elastic–plastic region changes (the Weibull distribution changes to a normal distribution) as the plasticity of the thin-walled samples increases. Additionally, the contribution of the probability density of the stress distribution described by the Cauchy distribution increases in a mode near the point at which the probability density of the fracture increases.

Homogenization of Pop Music: How Social Media’s Algorithms Prevents Creativity and Innovation

This paper examines the impact of social media on the homogenization of pop music, arguing that platforms like Spotify, YouTube, and TikTok contribute to narrowing creative possibilities within the genre. By analyzing the role of algorithms, audience behavior, and industry practices, the study highlights how social media’s emphasis on viral content and mass appeal stifles artistic diversity and innovation. Algorithms prioritize music with broad, mainstream appeal, creating a feedback loop that reinforces popular trends while marginalizing experimental or niche genres. Audience engagement further drives this homogenization, as artists increasingly tailor their work to fit algorithmic preferences and audience expectations. Industry practices, particularly the focus on producing “TikTok-friendly” hits, exacerbate this trend by favoring formulaic production over creative risk-taking. The research underscores the implications of these dynamics for global music culture, where the pressure to conform to a globalized pop standard threatens to erode local musical traditions. The paper concludes by advocating for strategies to preserve artistic diversity, including alternative distribution models, support for independent artists, and developing algorithms that promote a broader range of musical expressions.