Search Results (303)

A better understanding of climate change impact on dry/wet conditions is crucial for agricultural planning and the use of renewable energy, in terms of sustainable development and preservation of natural resources for future generations. The objective of this study was to investigate the impact of climate change on temporal fluctuations of dry/wet conditions in Serbia on multiple temporal scales through multifractal analysis of the standardized precipitation evapotranspiration index (SPEI). We used the well-known method of multifractal detrended fluctuation analysis (MFDFA), which is suitable for the analysis of scaling properties of nonstationary temporal series. The complexity of the underlying stochastic process was evaluated through the parameters of the multifractal spectrum: position of maximum ${\alpha }_0$ (persistence), spectrum width $W$ (degree of multifractality) and skew parameter $r$ dominance of large/small fluctuations). MFDFA was applied on SPEI time series for the accumulation time scale of 1, 3, 6 and 12 months that were calculated using the high-resolution meteorological gridded dataset E-OBS for the period from 1961 to 2020. The impact of climate change was investigated by comparing two standard climatic periods (1961–1990 and 1991–2020). We found that all the SPEI series show multifractal properties with the dominant contribution of small fluctuations. The short and medium dry/wet conditions described by SPEI-1, SPEI-3, and SPEI-6 are persistent ($0.5<{\alpha }_0<1$); stronger persistence is found at higher accumulation time scales, while the SPEI-12 time series is antipersistent ($0<{\alpha }_0-1<0.5$). The degree of multifractality increases from SPEI-1 to SPEI-6 and decreases for SPEI-12. In the second period, the SPEI-1, SPEI-3, and SPEI-6 series become more persistent with weaker multifractality, indicating that short and medium dry/wet conditions (which are related to soil moisture and crop stress) become easier to predict, while SPEI-12 changed toward a more random regime and stronger multifractality in the eastern and central parts of the country, indicating that long-term dry/wet conditions (related to streamflow, reservoir levels, and groundwater levels) become more difficult for modeling and prediction. These results indicate that the complexity of dry/wet conditions, in this case described by the multifractal properties of the SPEI temporal series, is affected by climate change. Full article

Water quality and aquatic ecosystems along lakeshores are vital for ecological balance and human well-being. However, research has primarily focused on plankton, with benthic niches being largely overlooked. To enhance understanding of benthic microbial communities, we utilized 16S and 18S rRNA sequencing alongside multivariate statistical methods to analyze samples from the shoreline of Lake Taihu in Huzhou City, Zhejiang Province. Our results reveal a marked difference in species composition between benthic and planktonic microorganisms, with benthic cyanobacteria predominantly comprising filamentous genera like Tychonema, while 95% of planktonic cyanobacteria were Cyanobium. The β-diversity of benthic microorganisms was notably higher than that of planktonic counterparts. The neutral community model indicated that stochastic processes dominated planktonic microbial assembly, while deterministic processes prevailed in benthic communities. Null models showed that homogeneous selection influenced benthic community assembly, whereas planktonic communities were affected by undominated processes and dispersal limitation. Network analysis indicated that planktonic networks were more stable than benthic networks. Importantly, dominant benthic cyanobacterial genera posed potential toxin risks, highlighting the need for enhanced monitoring and ecological risk assessment. Overall, these findings enhance our understanding of benthic and planktonic microbial communities in lakeshores and offer valuable insights for aquatic assessment and management in eutrophicated environments. Full article

The lever or the lever-type mechanism can achieve an inertia amplification effect by appropriately calibrating its structural configuration, and it is also proven to be one of the most cost-effective solution for the inerter realization compared with other mechanical devices. Benefitting from this property, the present paper adopted a new type of tuned lever inerter-like mass damper (TLIMD) for attenuating stochastic load-induced structure dynamic responses. A set of closed-form formulae for the TLIMD optimal parameters are developed by the use of H₂ norm optimization criterion, wherein the structure’s inherent damping is explicitly accounted for. It is theoretically demonstrated that the TLIMD optimal parameters are mainly dominated by three critical parameters, i.e., the damper mass ratio, the lever length ratio (known as the inertia amplification ratio) and also the host structural damping. The proposed formulae for the TLIMD optimization are validated through the seismic analysis, where two classic inerter-based dampers (i.e., the tuned mass damper inerter (TMDI) and the tuned inerter damper (TID)) optimized by the numerical technique are included in the discussion. It is found that the TLIMD has a superior advantage in reducing the structure responses and also exhibits stronger robustness for the detuning condition than the classic inerter-based dampers. Furthermore, the increase in the damper mass ratio and the lever length ratio can be beneficial for enhancing its performance. Full article

To meet the demands of a growing population, natural wetlands are being converted to arable land, significantly impacting soil biodiversity. This study investigated the effects of land use changes on bacterial communities in wetland, arable land, and forest soils in the Lesser Khingan Mountains using Illumina MiSeq 16S rRNA sequencing. Soil physicochemical properties and enzyme activities were measured using standard methods, while microbial diversity was assessed through sequencing analysis. Our findings revealed that forest soils had significantly higher levels of total potassium (2.62 g·kg⁻¹), electrical conductivity (8.22 mS·cm⁻¹), urease (0.18 mg·g⁻¹·d⁻¹), and nitrate reductase (0.13 mg·g⁻¹·d⁻¹), attributed to rich organic matter and active microbial communities. Conversely, arable soils showed lower total potassium (1.94 g·kg⁻¹), reduced electrical conductivity, and suppressed enzyme activities due to frequent tilling and fertilization. Wetland soils exhibited the lowest values primarily due to water saturation, which limits organic matter decomposition and microbial activity. Land use changes notably reduced microbial diversity, with conversion from forest to arable land leading to habitat loss. Forest soils supported higher abundances of Proteobacteria (37.59%) and Actinobacteriota (34.73%), while arable soils favored nitrogen-fixing bacteria. Wetlands were characterized by chemoheterotrophic and anaerobic bacteria. Overall, these findings underscore the profound influence of land use on soil microbial communities and their functional roles, highlighting the need for sustainable management practices. Full article

In today’s era, it is important to analyze and utilize various signals in industrial or laboratory applications. Measured signals provide critical information about the controlled system, which can be contained precisely within a narrow frequency range. Many methods and algorithms exist to process such signals in both the time and frequency domains. In particular, signal processing in the frequency domain is primary in industrial practice because dominant components within a specific narrow frequency band are sought. The discrete Fourier transformation (DFT) algorithm is the tool used in practice to find these frequency components. The DFT algorithm provides the full frequency spectrum with a higher number of calculation steps, and its spectrum frequency resolution is low. Therefore, research has focused on finding a method to achieve high-frequency spectrum resolution. An important factor in selecting the technique was that such an algorithm should be implementable on a microprocessor-based system under harsh industrial conditions. Research results showed that the DFT ZOOM method meets these requirements. The frequency zoom has many advantages but requires some modification. It is implemented in high-performance analyzers, but a thorough and detailed description of the respective algorithm is lacking in technical articles and literature. This article mathematically and theoretically describes the modified frequency zoom algorithm in detail. The steps of the frequency zoom, from creating an analytical signal through frequency shifting and decimation to the frequency analysis of the signal, are realized. The algorithm allows for the analysis of a signal with high-frequency resolution in a limited frequency band. A significant modification of DFT ZOOM is that of using the Hilbert transform to create an analytic signal. This resolves the aliasing issue caused by the overlap between fundamental and sideband spectra. Results from processing deterministic and stochastic signals using the modified DFT ZOOM are presented. The presented experimental results contribute to a more detailed frequency analysis of the signal. As part of this scientific research, the issues of frequency zoom were thoroughly addressed, solving the partial problems of this algorithm, both in theory and in the context of signal theory. Full article

Recent trends in portfolio management emphasize the importance of reducing carbon footprints and aligning investments with sustainable practices. This paper introduces Sensitivity Value-at-Risk (SensitivityVaR), an advanced distortion risk measure that combines Value-at-Risk (VaR) and Expected Shortfall (ES) with the Cornish–Fisher expansion. SensitivityVaR provides a more robust framework for managing risk, particularly under extreme market conditions. By incorporating first- and second-order distorted stochastic dominance criteria, we enhance portfolio decarbonization strategies, aligning financial objectives with environmental targets such as the Paris Agreement’s goal of a 7% annual reduction in carbon intensity from 2019 to 2050. Our empirical analysis evaluates the impact of integrating carbon intensity data—including Scope 1, Scope 2, and Scope 3 emissions—on portfolio optimization, focusing on key sectors like technology, energy, and consumer goods. The results demonstrate the effectiveness of SensitivityVaR in managing both risk and environmental impact. The methodology led to significant reductions in carbon intensity across different portfolio configurations, while preserving competitive risk-adjusted returns. By optimizing tail risks and limiting exposure to carbon-intensive assets, this approach produced more balanced and efficient portfolios that aligned with both financial and sustainability goals. These findings offer valuable insights for institutional investors and asset managers aiming to integrate climate considerations into their investment strategies without compromising financial performance. Full article

Esters are vital flavor compounds in Chinese Nongxiangxing baijiu and greatly affect the quality of baijiu. Microbial communities inhabiting fermented grains (FGs) have a marked impact on esters. However, the specific microorganisms and their assembly patterns remain unclear. This study utilized high-throughput sequencing and a culture-based method to reveal ester-producing microorganisms. A total of 33 esters were detected, including 19 ethyl esters, 9 linear chain esters, and 2 branched chain esters. A correlation analysis indicated that the bacterial genus Lactobacillus (relative abundance in average: 69.05%) and fungal genera Pichia (2.40%), Aspergillus (11.84%), Wickerhamomyces (0.60%), Thermomyces (3.57%), Saccharomycopsis (7.87%), Issatchenkia (0.96%), and Thermoascus (10.83%) were dominant and associated with esters production and their precursors. The numbers of esters positively correlated with them were 1, 17, 3, 2, 1, 1, 1, and 1, respectively. The modified stochasticity ratio (MST) index and Sloan neutral model revealed that bacteria were predominantly governed by deterministic processes while fungal assemblies were more stochastic. Saturnispora silvae and Zygosaccharomyces bailii were isolated and identified with ester synthesis potential. PICRUSt2 analysis showed that fungi in FG had a high potential for synthesizing ethanol, while 14 enzymes related to esters synthesis were all produced by bacteria, especially enzymes catalyzing the synthesis of acyl-CoA. In addition, ester synthesis was mainly catalyzed by carboxylesterase, acylglycerol lipase and triacylglycerol lipase. These findings may provide insights into ester production mechanism and potential strategies to improve the quality of Nongxiangxing baijiu. Full article

Invasive plants have already been observed in the understory of mountain forests, which are often considered a safe shelter for most native plants. Microorganisms might be drivers of plant invasions. Nevertheless, the mechanisms determining variations in microbial community composition (beta diversity) during invasion along altitudinal gradients remain to be elucidated. Here, the elevational patterns and the driving ecological processes (e.g., environmental filtering, co-occurrence patterns, and community assembly processes) of soil bacterial beta diversity were compared between invasive and native plants on the Qinling Mountains. The species turnover dominated bacterial compositional dissimilarities in both invasive and native communities, and its contribution to total beta diversity decreased during invasion. Total soil bacterial dissimilarities and turnover exhibited significant binominal patterns over an altitudinal gradient, with a tipping point of 1413 m. Further analysis showed that the contributions of assembly processes decreased in parallel with an increase in contributions of co-occurrence patterns during the invasion process, indicating that species interdependence rather than niche partitioning is strongly correlated with the bacterial biogeography of invasive communities. Plant invasion affects the relative contributions of stochastic processes and co-occurrence interactions through the regulation of the physiochemical characteristics of soil, and ultimately determines compositional dissimilarities and the components of the bacterial community along altitudinal gradients. Full article

Fungal communities are critical players in the biogeochemical soil processes of forest ecosystems. However, the factors driving their diversity and community assembly are still unclear. In the present study, five typical vegetation types of soil fungal communities in Liziping Nature Reserve, China, were investigated using fungal ITS sequences. The results show that the topsoil fungal community is mainly dominated by the phyla Ascomycota, Basidiomycota, and Mortierellomycota. Although there was no significant difference in α diversity (Shannon, Simpson, and Pielou evenness indices) among different forest types, there was a significant difference in β diversity (community composition). This study found that soil pH, soil organic carbon, total nitrogen (TN), total phosphorus (TP), and the total nitrogen/total phosphorus (N/P) ratio are the main environmental factors that affect soil fungal communities. Each forest type has a specific co-occurrence network, indicating that these community structures have significant specificities and complexities. Deciduous evergreen broad-leaved forests as well as deciduous broad-leaved and evergreen broad-leaved mixed forests showed high modularity and average path lengths, indicating their highly modular nature without distinct small-scale characteristics. Furthermore, our findings indicate that the structures of topsoil fungal communities are mainly shaped by stochastic processes, with the diffusion limitation mechanism playing a particularly significant role. Full article

Artificial water bodies in Central Asia offer unique environments in which to study plankton diversity influenced by topographic barriers. However, the complexity of these ecosystems and limited comprehensive studies in the region challenge our understanding. In this study, we systematically investigated the water environment parameters and phytoplankton community structure by surveying 14 artificial waters on the southern side of the Altai Mountains and the northern and southern sides of the Tianshan Mountains in the Xinjiang region. The survey covered physical and nutrient indicators, and the results showed noticeable spatial differences between waters in different regions. The temperature, dissolved oxygen, total nitrogen, and total phosphorus of artificial water in the southern Altai Mountains vary greatly. In contrast, the waters in the northern Tianshan Mountains have more consistent physical indicators. The results of phytoplankton identification showed that the phytoplankton communities in different regions are somewhat different, with diatom species being the dominant taxon. The cluster analysis and the non-metric multidimensional scaling (NMDS) results also confirmed the variability of the phytoplankton communities in the areas. The variance partitioning analysis (VPA) results showed that climatic and environmental factors can explain some of the variability of the observed data. Nevertheless, the residual values indicated the presence of other unmeasured factors or the influence of stochasticity. This study provides a scientific basis for regional water resource management and environmental protection. Full article

Ceramic dental restorative materials have growing popularity, albeit their brittle and stochastic nature are acknowledged shortcomings that impact the prosthesis lifespan. The mechanical performance of ceramics is dominated by the constitutional microstructural and fracture toughness mechanisms, as well as externally applied triggers. Thus, there is ongoing expanding research in the sphere of ceramic material engineering and thermal refinement, addressing concerns regarding toughness, machinability, reliability, stainability, and biodegradation. While the current trend in dental ceramic manufacturing has transitioned from micrometric crystalline sizes to submicrometric and nanometric ranges, there is an unclear understanding of the microstructural implications on ceramic behaviour. Therefore, this review covers the comprehensive characterisation approaches commonly employed in the scientific literature to describe the multifaceted performance aspects as well as clinical-related prerequisites of dental ceramics. Moreover, updated standardised testing parameters and performance thresholds pertaining to ceramic mannerisms are described in an attempt to translate their clinical applicability. Full article

Lubrication modeling has long been dominated by the well-established Patir and Cheng flow factors method. The flow factors approach allows for accurate estimates of macroscale parameters (such as friction) in a reasonable amount of time. These methods are stochastic representations of microscale interactions and are not able to predict local scale (pressure, film thickness) phenomena with a suitable degree of accuracy. This contrasts with a deterministic approach, where a numerical grid must be applied that fully defines the microscale surface topography across the contact. The mesh resolution required leads to prohibitively long execution times and lacks scalability to engineering systems, but provides accurate predictions of local scale phenomena. In this paper, heterogeneous multiscale methods (HMM) are expanded to model varying and are, therefore, more representative of surface topography within lubricated contacts. This representative topography is derived from measured data, thereby allowing the accuracy of deterministic methods to be achieved with the speed of a flow factor method. This framework is then applied to compare key performance characteristics (pressure, film thickness, etc.) when idealized, Gaussian, and measured surface topography are modeled. The variations in microscale geometry are defined by measurements from across two tilted-pad bearings, demonstrating the ability of the expanded HMM framework to model representative surface topography. A comparison with a deterministic method is included as validation, and outputs of the HMM are discussed in the context of the lubrication across multiple length scales. Full article

Climate change poses great challenges to the survival of plants. Plant endophytes play important roles in improving plant adaptability. However, our knowledge of the effects of climate change on endophytic community structures is limited. Relying on a field experimental platform simulating climate warming, precipitation increases, and their combination in an alpine grassland, the root endophytic bacterial community structures and assembly processes of three coexisting plant species (Elymus nutans, Kobresia humilis, and Melissilus ruthenicus) were measured. The results indicated that Proteobacteria was the dominant phylum, with a relative abundance ranging from 50% to 80%, followed by Actinobacteria and Bacteroidetes. Bacterial diversity decreased significantly under the combined treatment for all three plant species, with the largest reduction observed in E. nutans. The climate manipulation treatments had a minimal effect on the endophytic bacterial community structures. The relative abundance of Burkholderiaceae increased significantly under the combined treatment for the three plant species. Moreover, the endophytic community assembly processes changed from stochastic dominated under control plots to deterministic dominated under the combined plots for E. nutans, while this shift was reversed for M. ruthenicus. The root endophytic bacterial community was affected by the soil’s available nitrogen and stoichiometric ratio. These results revealed that the sensitivity of endophyte community structures to climate change varies with host plant species, which has implications for plant fitness differences. Full article

The population dynamics of early replicators has revealed numerous puzzles, highlighting the difficulty of transitioning from simple template-directed replicating molecules to complex biological systems. The resolution of these puzzles has set the research agenda on prebiotic evolution since the seminal works of Manfred Eigen in the 1970s. Here, we study the effects of demographic noise on the population dynamics of template-directed (non-enzymatic) and protein-mediated (enzymatic) replicators. We borrow stochastic algorithms from evolutionary game theory to simulate finite populations of two types of replicators. These algorithms recover the replicator equation framework in the infinite population limit. For large but finite populations, we use finite-size scaling to determine the probability of fixation and the mean time to fixation near a threshold that delimits the regions of dominance of each replicator type. Since enzyme-producing replicators cannot evolve in a well-mixed population containing replicators that benefit from the enzyme but do not encode it, we study the evolution of enzyme-producing replicators in a finite population structured in temporarily formed random groups of fixed size n. We argue that this problem is identical to the weak-altruism version of the n-player prisoner’s dilemma, and show that the threshold is given by the condition that the reward for altruistic behavior is equal to its cost. Full article

The nitrogen cycling process in alpine wetlands is profoundly affected by precipitation changes, yet the dynamic response mechanism of denitrifiers to long-term precipitation shifts in the alpine wetland of the Qinghai-Tibet Plateau remains enigmatic. Utilizing high-throughput sequencing analysis of nirS-type functional genes, this study delved into the dynamic response mechanism of nirS-type denitrifiers to precipitation changes in the alpine wetland of Qinghai Lake. The findings revealed that nirS-type denitrifiers in the alpine wetland of Qinghai Lake were primarily Proteobacteria, and Alpha diversity exhibited a negative correlation with the precipitation gradient, with deterministic processes predominating in the community assembly of denitrifying microbes. A 50% increase in rainfall shifted the community assembly process of denitrifiers from deterministic to stochastic. Dominant microflora at the genus level responded significantly to precipitation changes, with aerobic bacteria comprising the majority of differentially abundant taxa (55.56%). As precipitation increased, the complexity of the microbial interaction network decreased, and a 25% reduction in precipitation notably elevated the relative abundance of three key functional groups: chemoheterotrophic, aerobic chemoheterotrophic, and nitrogen fixation. Precipitation notably emerged as the primary regulator of nirS-type denitrifiers in the alpine wetland of Qinghai Lake, accounting for 51% of the variation in community composition. In summary, this study offers a fresh perspective for investigating the ecological processes of nitrogen cycling in alpine ecosystems by examining the diversity and community composition of nirS-type denitrifiers in response to precipitation changes. Full article