Deterministic Factors Research Articles

Machine Learning and Multi-Omics Integration to Reveal Biomarkers and Microbial Community Assembly Differences in Abnormal Stacking Fermentation of Sauce-Flavor Baijiu

Stacking fermentation is critical in sauce-flavor Baijiu production, but winter production often sees abnormal fermentations, like Waistline and Sub-Temp fermentation, affecting yield and quality. This study used three machine learning models (Logistic Regression, KNN, and Random Forest) combined with multi-omics (metagenomics and flavoromics) to develop a classification model for abnormal fermentation. SHAP analysis identified 13 Sub-Temp Fermentation and 9 Waistline microbial biomarkers, along with 9 Sub-Temp Fermentation and 12 Waistline flavor biomarkers. Komagataeibacter and Gluconacetobacter are key for normal fermentation, while Ligilactobacillus and Lactobacillus are critical in abnormal cases. Excessive acid and ester markers caused unbalanced aromas in abnormal fermentations. Additionally, ecological models reveal the bacterial community assembly in abnormal fermentations was influenced by stochastic factors, while the fungal community assembly was influenced by deterministic factors. RDA analysis shows that moisture significantly drove Sub-Temp fermentation. Differential gene analysis and KEGG pathway enrichment identify metabolic pathways for flavor markers. This study provides a theoretical basis for regulating stacking fermentation and ensuring Baijiu quality.

Методика анализа исполнения бюджета энергосбытовой компании в комбинированных моделях аддитивно-мультипликативного типа

The article presents a methodology developed by the authors for analyzing the budget execution of the power supply company utilizing combined additive-multiplicative models. The relevance of this methodology is underscored by the need for organizations to enhance the effectiveness of management decisions through such analytical frameworks. The novelty of this work lies in the adaptation of classical deterministic analysis models to the specific context of power supply companies, taking into account the peculiarities of their financial and economic activities. A plan-fact analysis of the budget execution of the power supply company was conducted using the chain substitution method, which was implemented across all types of deterministic factor models: additive, multiplicative, multiple, and mixed (combined). This analytical approach enabled the determination of the impact of individual factors on the variation of the performance indicator by gradually substituting the baseline value of each factor indicator within the performance measure with the actual value for the reporting period. The authors conclude that the proposed methodology facilitates the analysis of changes in the performance indicator (marginal profit) attributable to factors of the I, II, III levels. It is applied in practice for the formulation of managerial reporting and the adjustment of the marketing policy of the power supply company operating at the territory of Saint Petersburg.

Optimizing Stochastic Transportation Networks with Mixed Constraints Using Pareto Distribution

We propose a novel solution approach combining stochastic programming techniques with Pareto distribution characteristics. This approach involves reformulating the problem into a tractable optimization model using probabilistic constraints and employing advanced algorithms to solve the resulting mixed-integer programming problem. Numerical experiments illustrate the effectiveness of the proposed method and highlight its practical implications for transportation network design and management under uncertain conditions. The results demonstrate that incorporating Pareto-distributed uncertainties into the transportation problem provides a more realistic and adaptable framework for decision-making. The proposed solution approach offers valuable insights for managing complex transportation systems where both stochastic and deterministic factors play a crucial role.

An efficient uncertainty analysis of performance of hydrogen storage systems

The metal hydride hydrogen storage systems are gaining popularity due to their high volumetric capacity, safety and stability. The designing of these systems is complex due to many reasons, including input uncertainties. The performances of these systems are often evaluated in deterministic framework, ignoring uncertainties. This includes over-conservative safety factors in the design process increasing time and costs involved in designs. The uncertainty analysis could be a better alternative to assess system performance under such scenarios. This study investigates– i) the effect of input uncertainties on uncertainties of multiple and implicit system outputs, i.e., reaction fraction and bed temperature, ii) application of response surface and Borgonovo’s global sensitivity analysis for efficient analysis, and iii) a comparative assessment between different uncertainty methods. The methodology is demonstrated for a space heating system. Initially, a surrogate relationship is constructed between inputs-outputs using moving least square response surface, based on known input-output data estimated using COMSOL for random input realizations. Next, the sensitive inputs were identified using Monte-Carlo simulations based Borgonovo’s analysis. Finally, the effect of uncertainties of sensitive inputs on outputs were estimated using different uncertainty methods. Harr’s and Hong’s (2n) point estimate methods were observed to be highly accurate, mathematically simpler and efficient, as compared to other methods. The uncertainties of outputs were directly dependent on uncertainties of sensitive inputs. The probabilistic safety measure, reliability index, estimated using output statistics was of significant practical utility for industries to avoid deterministic safety factors based over-conservative and costly designs of storage systems.

The COVID-19 Shock Through the Lens of the Spanish Sales Daily Data: Modeling and Seasonal Adjustment Challenges

We explore the impact of the COVID-19 shock on a very high frequency indicator: the Spanish daily sales data compiled by the Tax Agency. Firstly, we present a detailed list of the issues related to its modeling, its decomposition (trend, seasonality, and irregularity) and its final seasonal adjustment, which requires a set of deterministic factors linked mostly to calendar effects. Then, we assess the impact of the COVID-19 shock on these tasks. This assessment provides a timely perspective on the evolution of the shock and the challenges that it posed for modeling and seasonal adjustment, clearly related to its unusual and extreme features. The aim of the paper is eminently empirical and dominated by the need to square the unprecedented shock with the available methods and software in a computing environment centered on the R programming language. The methodology draws heavily on a structural approach and it is currently in use by the Tax Agency to compile and disseminate its daily sales data.

Community successional patterns and inter-kingdom interactions during granular biofilm development.

Aerobic granular sludge is a compact and efficient biofilm process used for wastewater treatment which has received much attention and is currently being implemented worldwide. The microbial associations and their ecological implications occurring during granule development, especially those involving inter-kingdom interactions, are poorly understood. In this work, we monitored the prokaryote and eukaryote community composition and structure during the granulation of activated sludge for 343 days in a sequencing batch reactor (SBR) and investigated the influence of abiotic and biotic factors on the granule development. Sludge granulation was accomplished with low-wash-out dynamics at long settling times, allowing for the microbial communities to adapt to the SBR environmental conditions. The sludge granulation and associated changes in microbial community structure could be divided into three stages: floccular, intermediate, and granular. The eukaryotic and prokaryotic communities showed parallel successional dynamics, with three main sub-communities identified for each kingdom, dominating in each stage of sludge granulation. Although inter-kingdom interactions were shown to affect community succession during the whole experiment, during granule development random factors like the availability of settlement sites or drift acquired increasing importance. The prokaryotic community was more affected by deterministic factors, including reactor conditions, while the eukaryotic community was to a larger extent shaped by biotic interactions (including inter-kingdom interactions) and stochasticity.

Pinewood nematode induced changes in the assembly process of gallery microbiomes benefit its vector beetle's development.

Microbiomes play crucial roles in insect adaptation, especially under stress such as pathogen invasion. Yet, how beneficial microbiomes assemble remains unclear. The wood-boring beetle Monochamus alternatus, a major pest and vector of the pine wilt disease (PWD) nematode, offers a unique model. We conducted controlled experiments using amplicon sequencing (16S rRNA and ITS) within galleries where beetles and microbes interact. PWD significantly altered bacterial and fungal communities, suggesting distinct assembly processes. Deterministic factors like priority effects, host selection, and microbial interactions shaped microbiome composition, distinguishing healthy from PWN-infected galleries. Actinobacteria, Firmicutes, and Ophiostomataceae emerged as potentially beneficial, aiding beetle's development and pathogen resistance. This study unveils how nematode-induced changes in gallery microbiomes influence beetle's development, shedding light on microbiome assembly amid insect-pathogen interactions. Insights gleaned enhance understanding of PWD spread and suggest novel management strategies via microbiome manipulation.IMPORTANCEThis study explores the assembly process of gallery microbiomes associated with a wood-boring beetles, Monochamus alternatus, a vector of the pine wilt disease (PWD). By conducting controlled comparison experiments and employing amplicon approaches, the study reveals significant changes in taxonomic composition and functional adaptation of bacterial and fungal communities induced by PWD. It identifies deterministic processes, including priority effects, host selection, and microbial interactions, as major drivers in microbiome assembly. Additionally, the study highlights the presence of potentially beneficial microbes such as Actinobacteria, Firmicutes, and Ophiostomataceae, which could enhance beetle development and resistance to pathogens. These findings shed light on the intricate interplay among insects, microbiomes, and pathogens, contributing to a deeper understanding of PWD prevalence and suggesting innovative management strategies through microbiome manipulation.

Stability analysis of tunnel heading in clay with nonstationary random fields of undrained shear strength

The stability problem of a tunnel heading in clay remains a significant challenge in geotechnical engineering. Specifically, when considering the spatial variability of the soil, the stability factor may be influenced by geographically random fields. This study investigates the effect of random fields on a probabilistic analysis of a tunnel heading in undrained clay. The study assumes that the undrained shear strength of the clay increases linearly with depth due to a strength gradient factor. The random adaptive finite element limit analysis is employed to calculate the stability numbers for tunnel headings. Nonstationary random fields with varying vertical correlation lengths are simulated using Monte Carlo simulation technique. The stability analysis takes into account geometry parameters (i.e., cover depth ratio) and nonstationary random field of undrained shear strength parameters. (i.e., strength gradient, coefficient of variation, and vertical correlation length). The results of tunnel face stability using random adaptive finite element limit analysis have also been utilised to assess the probability of design failure over a practical range of deterministic factors of safety. In the context of probabilistic failure analysis, the failure mechanism resulting from varying vertical correlation lengths could influence the probability of design failure. The findings of this study can be of significant interest to tunnel engineering practitioners during the design phase of tunnel heading projects.

Validity and Reliability of the Farsi Version of the Heartland Forgiveness Scale in Iranian Cardiovascular Patients

Background and Purpose:A Farsi version of the Heartland Forgiveness Scale (HFS-F) for assessing forgiveness in Iranian cardiovascular patients was not available. This study aimed to determine the validity and reliability of the Farsi version of HFS in this population.Methods:In this methodological cross-sectional research design, 400 Iranian cardiovascular patients completed the HFS-F between October 2019 and January 2020. We assessed the characteristics, content, and both exploratory and confirmatory construct validity of the measures. To evaluate the reliability and construct validity of the HFS-F, we calculated Cronbach’s alpha, McDonald’s omega, and the Intraclass Correlation Coefficient.Results:Three factors were identified following confirmatory and deterministic factor analyses. The extracted factors demonstrated satisfactory reliability, exceeding a threshold of 0.7.Conclusions:The HFS-F exhibited a robust level of validity and reliability, rendering it a suitable instrument for future research endeavors.

Rethinking the pathogenesis of endometriosis: Complex interactions of genomic, epigenetic, and environmental factors.

Endometriosis is a complex, multifactorial disease. Recent advances in molecular biology underscore that somatic mutations within the epithelial component of the normal endometrium, alongside aberrant epigenetic alterations within endometrial stromal cells, may serve as stimulators for the proliferation of endometriotic tissue within the peritoneal cavity. Nevertheless, pivotal inquiries persist: the deterministic factors driving endometriosis development in certain women while sparing others, notwithstanding comparable experiences of retrograde menstruation. Within this review, we endeavor to synopsize the current understanding of diverse pathophysiologic mechanisms underlying the initiation and progression of endometriosis and delineate avenues for future research. A literature search without time restriction was conducted utilizing PubMed and Google Scholar. Given that aberrant clonal expansion stemming from cancer-associated mutations is common in normal endometrial tissue, only endometrial cells harboring mutations imparting proliferative advantages may be selected for survival outside the uterus. Endometriotic cells capable of engendering metabolic plasticity and modulating mitochondrial dynamics, thereby orchestrating responses to hypoxia, oxidative stress, inflammation, hormonal stimuli, and immune surveillance, and adeptly acclimating to their harsh surroundings, stand a chance at viability. The genesis of endometriosis appears to reflect the evolutionary principles of mutation, selection, clonal expansion, and adaptation to the environment.

Bleached coral supports high diversity and heterogeneity of bacterial communities: Following the rule of the ‘Anna Karenina principle’

Coral-associated bacteria are sensitive to the health status of coral and proven biomarker(s) of the coral bleaching. However, whether coral specificity or health status play a key role when coral-associated bacteria responding to coral bleaching is not known. Therefore, the bacterial communities of five species of healthy and bleached corals, Acropora millepora, Favites abdita, Galaxea fascicularis, Dipsastraea speciosa and Pocillopora damicornis, were collected along the coast of Sanya, South China Sea and targeted for associated bacterial studies. The relative abundance of the dominant class Gammaproteobacteria tended to be higher in healthy corals, while Alphaproteobacteria were more abundant in bleached corals. Dominant genus Achromobacter demonstrated higher relative abundance in healthy corals (0.675) than in bleached corals (0.151). Most of the bleached corals had high α diversity, β dispersion, heterogeneity and complexity of the co-occurrence network of bacterial communities, which support the ‘Anna Karenina Principle (AKP)’ of diverse in threatened objects and conserved in healthy ones. The bacterial communities in the bleached corals were mostly involved in the selection process, and communities in the healthy corals were involved in the undominated process, which is obtained based on the null model test of β nearest-taxon-index (βNTI) and Bray–Curtis-based Raup–Crick (RCBray). This evidence further confirmed the AKP and revealed that the bacterial communities in the bleached corals were driven by deterministic factors. These findings provide valuable insights into the connection between bacterial and coral status, and the application of the AKP in the changing patterns of bacterial communities during coral bleaching.

Exposure pattern of triclosan and tetracycline change their impacts on methanogenic digestion microbiomes

Triclosan (TCS) and tetracycline (TC) as common antibacterial agents are frequently detected in the influent of wastewater treatment plants. The TCS and TC exposure patterns may determine their impacts on wastewater treatment microbiomes, on which information remains unknown. In this study, the impacts of sequential exposure of TCS and TC on methanogenic digestion microbiomes in upflow anaerobic sludge blanket (UASB) reactors were analyzed and compared with that of the same microbiomes being simultaneously exposed to TCS and TC. Results indicated that the UASB reactor 2 (MD2) with sequential TCS-TC exposure consistently demonstrated higher chemical oxygen demand (COD) removal efficiency (94.7 %). In contrast, in the MD1 reactor, COD removal efficiency decreased from 94.4 % to 82.7 % upon simultaneous exposure to TCS and TC. Accordingly, a 1.8 times higher enrichment of total antibiotic resistance genes (ARGs) was observed in MD1 relative to MD2. Using a dissimilarity-overlap approach, the MD2 microbiome with sequential exposure was predominantly mediated by deterministic factors in their community assembly (largely contributed by abundant and intermediate biospheres), resulting in microbial interaction networks with higher average clustering coefficients and shorter average path lengths, compared to the MD1 microbiomes. Our results could support sustainable management of TCS and TC contamination in wastewater treatment plants.

Characterization of water microbiota and their relationship with resident oysters during an oyster mortality event.

Microorganisms are vital for the health of marine invertebrates, and their assembly is driven by both deterministic and stochastic factors that regulate residents (innate to the host) and transients (from ambient water). However, the role of water microbiota and the significance of deterministic and stochastic processes in aquatic hosts facing mortality threats are largely unknown. This study examines the shifts in water microbiota during an oyster mortality event using amplicon sequencing and compared with those of resident oysters to disentangle the balance of the deterministic and stochastic factors involved. Water temperature and dissolved oxygen significantly shape the microbial community with a distinct monthly pattern, and Cyanobacteria blooms might exacerbate oyster mortality. The comparative analysis of microbial communities in oysters and water revealed that ≤ 21% of the genera were shared between oysters and water, implying that water microbiota cannot easily transfer into oysters. Furthermore, these shared genera had different functions, with oysters more involved in promoting host digestion and nutrient acquisition and water bacteria enriched more in functions promoting their own growth and survival. These findings illustrate that oysters may possess specific selection or barrier mechanisms that permit a small percentage of transients, controlled by stochastic factors and having a minimal effect on oyster mortality, to enter, whereas the majority of oyster microbiota are residents governed by deterministic factors. Consequently, oysters exhibit some plasticity in their symbiotic microbiota, enabling them to maintain microbial homeostasis and adapt to complex microbial surroundings. This may be a shared mechanism among marine invertebrates for survival in complex marine environments.IMPORTANCEPacific oysters are widely cultured and play vital ecological roles. However, the summer mortality hinders sustainable oyster farming. Untangling causative mechanisms of oyster mortality is a complex task due to the intricate "interactome" involving environmental factors, hosts, and pathogens. Interactions between hosts and microorganisms offer an ideal avenue for investigating the truth. We systematically investigated the microbial community in water and resident oysters during a summer mortality event and proposed that the assembly of oyster microbiota is primarily governed by deterministic processes independent of mortality. Pathogens mainly originate from resident members of the oyster microbiota, with a limited influence from the microbial community in the water. Additionally, environmental degraders, such as Cyanobacteria blooms, cannot be overlooked as a contributing factor of oyster mortality. This study evaluated the weight of deterministic and stochastic factors in microbial assembly during an oyster mortality event and greatly broadened our understanding of the "interactome" through the interaction between oysters and water in microbiota.

Limiting out-of-sample performance of optimal unconstrained portfolios

This paper studies the out-of-sample Sharpe ratio of an unconstrained portfolio that combines the global minimum-variance with a hedge portfolio. Furthermore, we investigate how this ratio behaves as the number of risky assets and observations approaches infinity while maintaining a constant ratio. Under these conditions, it becomes possible to simultaneously account for estimation risk and achieve analytical tractability when optimizing the out-of-sample Sharpe ratio. This analysis also provides valuable insights to enhance out-of-sample performance in the finite case by introducing additional deterministic factors to the portfolio components.

An Innovative Method for Deterministic Multifactor Analysis Based on Chain Substitution Averaging

The aims of this paper are to present the methodology, derived mathematical expressions for determining the individual factor influences and the adaptation for the conditions of dynamic deterministic factor analysis and the results of the application of the developed new method for deterministic factor analysis, called the averaged chain substitution method. After formulating the concept of the considered approach, all mathematical expressions used to create models containing up to four factor variables are presented and summarized. The scientific novelty of the study is in the obtained new equations for determining the individual factor influences by the method of averaged chain substitution and the method of analogy for five-factor additive or difference-multiplicative and for five-factor additive or difference-multifactor models with an additive or different part in the numerator of the factor model. The presented mathematical expressions accurately and unambiguously quantify the impact of individual factor influences for all types of factor models and thus significantly expand the applicability of the averaged chain substitution method in the theory and practice of financial-economic analysis. The proposed formalization and algorithmization of the evaluation process makes the method easy to apply by all economic and financial analysts for the purposes of deterministic factor analysis. The methodology was applied to perform a dynamic deterministic factor analysis of the total liquidity of Monbat AD and ELHIM-ISKRA AD for the period 2017–2021, based on the consolidated annual financial statements of the companies, available on the website of the Bulgarian Stock Exchange.

Homogeneous selection and stochasticity overrule heterogeneous selection across biotic taxa and ecosystems

Deterministic factors including homogeneous and heterogeneous selection and stochastic factors jointly shape ecological communities. However, a quantitative synthesis of the factors underlying the balance among different assembly processes is lacking. Here, we synthesized data from 149 datasets covering major biotic groups and ecosystem types globally. We used a null model approach based on Raup–Crick dissimilarities and Bayesian meta‐regression to analyze the data. We found that communities were more under homogeneous selection than heterogeneous selection across biotic taxa and ecosystems. Environment selected species homogeneously more often at small scales while heterogeneously more often at large scales. Stochasticity also showed scale‐dependence as stochastic community assembly increased with study scale. Homogeneous and heterogeneous selection were strongest at high latitudes while stochastic factors were strongest in tropics. Marine systems had the highest degree of homogeneous selection and the lowest stochasticity. We provide the first analysis of community assembly across taxa and ecosystems which should be important for a better understanding of how communities respond to environmental change.

Child Acrobats as a Family Labor Force: An Exploratory Study on the Involvement of Children in Traditional Economic Activities

Child labor has majorly been analyzed through the socio-economic lens, because the deterministic cultural factors have not received much attention. This study attempts to understand the cultural influences on children’s involvement in traditional economic activities. Applying a cultural sociological approach, an intrinsic case study was conducted on the nomadic Nat tribe in India that generates its livelihood through children’s involvement in acrobatics. Findings suggest that the cultural existence of the tribe significantly determines children’s involvement in socio-economic exchanges. Policy attempts toward child upliftment must consider the cultural aspects essential for the tribe’s survival.

Relief item inventory planning under centralized and decentralized bilateral cooperation and uncertain transshipment quantities

Pre-positioning relief inventory ensures timely delivery of in-kind aid after a catastrophe. Tragic disasters like major earthquakes are rare and unpredictable; therefore, stockpiled items may not be used. To avoid over-stocking and reduce shortage risk, the cooperation of two humanitarian agencies in supporting each other in case of shortages is suggested in the literature. In this study, we utilize newsvendor-based quantitative models to optimize the pre-disaster stocking decisions of agencies under centralized and decentralized cooperation mechanisms. In the former, both agencies jointly determine their inventory levels to maximize their combined benefits of relief operations, whereas, in the latter, each agency establishes its stocking level in isolation via a game theoretic approach. In both systems, the two agencies agree to transship their excessive items to the other party if needed. In this regard, we investigate the situation where only a portion of the transshipped items, denoted as the reliability factor, can be received and effectively utilized at the destination due to the chaotic nature of the disaster. Considering a deterministic reliability factor, we obtain the singular optimal inventory levels in the centralized system and identify the unique Nash Equilibrium in the decentralized system. Subsequently, we formulate a two-stage stochastic program, considering a random reliability factor for both cooperation systems. The study concludes by offering a range of managerial insights. Our analyses quantify the sub-optimality resulting from decentralized decision-making across diverse parameter settings using the concept of the price of anarchy. The findings highlight that centralized cooperation becomes particularly advisable when the average demand within either agency is high, the transshipment process is secure (i.e., the reliability factor is high), and transshipment costs remain low.

Calculation of heat protective parameters of clothing packages by method simulation modeling (Monte Carlo method)

The heat-protective properties of clothing have been studied taking into account the influence of deterministic factors, and the influence of random factors on the heat-protective properties of clothing has not been studied. In this regard, to obtain objective values of indicators of the heat-protective properties of clothing, taking into account the nature of the distribution of factors for a batch of products that are random. Knowing the laws of distribution of random factors effectively, it is possible to carry out calculations of the thermal insulation properties of clothing material packages using the simulation method. The work has developed a method for calculating the thermophysical parameters of clothing packages based on simulations of random parameters distributed according to a normal law. For the calculation, an algorithm and calculation program were compiled in the Python programming language. The thermophysical parameters of the clothing package were calculated, and the influence of wind flow speed on them was analyzed. When calculating the wind flow speed, which have a significant impact on the values of the effective thermal conductivity coefficient of the packages and the values of the total coefficient of the packages, they are taken taking into account the Beaufort table and the recommendations of the Ministry of Emergency Situations of Kazakhstan. The developed program for calculating the thermophysical characteristics of a package of clothing materials allows to change the model parameters during the calculation process: introduce new layers of materials into the package, change the thermophysical and linear characteristics of individual layers of the package, change external conditions.

Dynamic Determinated Factors Analysis of Labour Productivity in the Bulgarian Energy Subsector

This article presents a dynamic determined factor analysis of labour productivity in the Bulgarian energy subsector for the period 2013 – 2022. The purpose of this article is to reveal the quantitative influences of the factors operating revenues and number of employed persons on the development of the indicator labour productivity in the Bulgarian energy subsector through the averaged chain substitution method. The quantitative influences of operating revenues and number of employed persons on the deviation of the labour productivity indicator in the energy subsector are outlined. The results of the deterministic factor analysis make it possible to draw reasonable conclusions and to reveal the trends in the development of the factors operating revenues and number of employed persons, as well as the indicator of labour productivity in the Bulgarian energy subsector.