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This study focuses on the stochastic modelling and analysis of a serial production network consisting of two manufacturing stations operating under a make-to-stock inventory policy. The outcome is a single type of product and every manufacturing station includes a machine and an output buffer. Both machines are gradually deteriorating during their operation. Deterioration results in a reduced production rate. Continuous-time Markov chain was used to model all the possible states the network transits over time due to the occurrence of certain events, such as client arrival, deterioration failure, production or repair completion. The structure of the Markov chain was thoroughly studied providing useful information, supporting the effort of numerical solving to determine the steady-state probabilities enabling the calculation of useful performance metrics like equipment availability, down time, idle time, utilisation and average inventory. Through a series of numerical experiments, the behaviour of the serial production network was examined while alternating its parameters. Interesting conclusions emerged regarding the factors affecting the operation of such production systems subjected to gradual deterioration. [ABSTRACT FROM AUTHOR]

Current stochastic finite-fault simulation approaches do not fully consider the influence of slip distribution characteristics on the corner frequency and scaling factor. Using data from the 2013 Lushan earthquake (Mw 6.7), we developed a modified finite-fault model. This model was established by incorporating the corner frequency, the rupture velocity, and a scaling factor based on a nonuniform slip distribution. In this model, the radiated energy of the entire rupture fault is distributed to each subfault based on the weighted contribution of each subfault to the total slip of the rupture fault, thus generating an improved scaling factor. The path duration and kappa coefficient are obtained using Lushan event data, while other parameters, such as the stress drop, quality factor, geometric attenuation factor, and site amplification, are selected from previous studies. To verify the reliability of the improved model, the acceleration records taken during the Lushan earthquake from 17 near-field stations with less than 100 km from the fault were synthetized by random and inverted sources, and the simulated pseudospectral acceleration, Fourier acceleration spectrum, and model bias were obtained. The peak ground accelerations of 1681 virtual points were also predicted using the modified model. It was found that the modified model can effectively improve the modelling accuracy in short periods (T < 1 s). [ABSTRACT FROM AUTHOR]

Studying the impact of advertising on consumer consumption of a product is very important because it enables us to understand the relationship between advertising and consumption in order to adopt advertising strategies to increase consumption or sales of a product and in political fields to increase the number of voters for a party, etc. This article aims to model the relationship between advertising and consumption to enable it to be better understood and used. We will use the basic model of diffusion and adoption to find the relationship between advertising and consumption, where adoption will play the role of a mediator in this relationship. We will apply the model to a real dataset on the consumption of an electronic product ChatGPT-3 over a period of 9 months where the model succeeds in real data tests. We will find that advertising directly affects the consumption of the product, and we will use the model to find out the number of adopters needed for a product to maximize the consumption of the target product. [ABSTRACT FROM AUTHOR]

Probabilistic reliability analysis of a prestressed concrete bridge – Part 2 – Mechanical modelling and probabilistic reassessment of a single‐span 6‐webbed t–beam bridge In the second part of this article the limit state functions in the ultimate limit state for bending with axial force, shear with shear reinforcement and torsion are presented. The input variables of the mechanical resistance models are accompanied by numerous illustrations. The probabilistic verifications are carried out on the basis of the probabilistic models from the first part of this article and the mechanical models from this second part. In the subsequently compiled reliability analysis, reference is made to the semiprobabilistic calculation results. With the parameter studies it is shown which input variable measurable by non‐destructive testing methods have the greatest influence on the calculated reliability. On this basis finally it is shown what opportunities arise for a reassessment from conducting non‐destructive testing in the inspection. [ABSTRACT FROM AUTHOR]

For developing nations to achieve net-zero targets, macroeconomic linkages impacting the decoupling of emissions from economic growth must account for non-linear business cycles and economic shocks. This study aims to delineate decarbonization policy pathways for the Indian electricity sector in the aftermath of COVID-19 by analysing the long-term evolution of the economy–electricity–emissions (3E) nexus during the 2008 financial crisis and during COVID-19, covering the period of 1996Q2 to 2020Q3. Upon testing multiple theoretical 3E systems, it was found that a model internalizing trade, inflation, and stochasticity was able to minimize the reproduction errors from growth to recession phases, as well as predict the rebound effect from an economic crisis. This was revealed to be due to more information within the coefficients in a trade stochastic model. Our results confirm the existence of electricity-associated emission decoupling with capital formation in the long-run, post-crisis, while economic growth and inflation increase CO2 emissions. The main finding highlights the negative feedback loop of inflation->trade->emissions, which shows that GDP and emissions are not directly causal. This long-run macroeconomic dynamic death spiral causes decoupling to be inhibited, where fossil fuel imports should not be subsidized for economic shock rebound, and the risk hedging of energy transition investments should occur in the post-COVID-19 era. [ABSTRACT FROM AUTHOR]

Multi-decadal trends in the wintertime North Atlantic Oscillation (NAO) are under-represented by coupled general circulation models (CGCMs), consistent with a lack of autocorrelation in their NAO index series. This study proposes and tests two simple "reddening" approaches for correcting this problem in simulated indices based on simple one parameter short-term (AR; Auto-Regressive order 1) and long-term (FD; Fractional-Difference) time series filters. Using CGCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6), the FD filter successfully improves the autocorrelation structure of the NAO, and in turn the simulation of extreme trends, while the AR filter is less successful. The 1963–1993 NAO trend is the maximum 31-year trend in the historical period. Raw CGCMs underestimate the likelihood of this trend by a factor of ten but this discrepancy is corrected after reddening. CMIP6 future projections show that long-term (2024–2094) NAO ensemble mean trends systematically increase with the magnitude of radiative forcing: -2.4 to 3.5 hPa/century for low-to-high forcing after reddening (more than double the range using raw output). The related likelihood of future maximum 31year trends comparable to 1963–1993 ranges from 3 to 7% whereas none of these CMIP6 projections simulate this without reddening. Near-term projections of the next 31 years (2024–2054) are less sensitive than long term trends to the future scenario, showing weak-to-no forced trend. However, reddening increases the ensemble range by 74% (to +/-1 standard deviation/decade), which could increase/decrease regional climate change signals in the Northern Hemisphere by magnitudes that are underestimated when using raw CGCM output. [ABSTRACT FROM AUTHOR]

This work presents a stochastic framework for the construction of rainfall intensity–time scale–return period relationships, which was applied in the recent regionalization of design rainfall curves over the Greek territory, described in a companion paper. The methodology outlined herein builds upon a widely-used mathematical framework, which has been recently revisited and upgraded, and incorporates two different versions: (a) a theoretically consistent stochastic model applicable for rainfall intensity over any scale of interest; and (b) a simplified version valid over small scales, which makes parameter estimation easier. Special attention is given to the presentation of the simplified version, which suffices for most engineering tasks. Parameter estimation approaches are presented in detail, including the K-moments framework that allows for reliable high-order moment estimation and handling of bias due to spatiotemporal dependence. [ABSTRACT FROM AUTHOR]

Probabilistic reliability analysis of a prestressed concrete bridge – part 1 – Semi‐probabilistic reassessment and probabilistic modeling of a single‐span 6‐webbed t‐beam bridge Maintaining the existing transportation infrastructure is a major challenge as the structures continue to age, the traffic loads increase and comprehensive replacement is impossible given limited budgets and resources. On existing structures present material characteristics, geometries or loads can be recorded on an object‐specific basis. In Germany the reassessment of existing bridges is carried out in accordance with the reassessment guidelines at the stages 1 and 2 using the semi‐probabilistic partial safety concept. Only in exceptional cases will a measurement‐based assessment or a scientific analysis acc. to levels 3 and 4 be carried out. However the inclusion of additional information from condition assessment offers many advantages for a realistic reassessment. In probabilistic calculation methods, such information can be taken directly into account. The aim of this two‐part article is to present the potentials and advantages of directly including the results of non‐destructive testing methods when assessing the condition of bridge structures. The concept is presented on a prestressed concrete bridge. The first part deals with the possibilities for transferring the input values of the usual semi‐probabilistic calculation into stochastic models. Starting from the semi‐probabilistic reassessment acc. to current regulations, the probabilistic modeling of all relevant basic variables for the ultimate limit states of bending, shear and torsion is carried out. [ABSTRACT FROM AUTHOR]

Survival through the larval phase is predicated on the probabilities of successful feeding, which dictates growth rates, and the probabilities of encountering predators. Here I perform a synthesis of feeding, growth, and mortality rates estimated during several studies from coastal Newfoundland, Canada, to provide a description of the probability distribution that can serve as a foundation of the expected distribution of vital rates. The standardized observations clearly follow that of skewed distributions, appropriately fit to a probability gamma distribution, with feeding demonstrating a stronger degree of skewness than either mortality or growth, possibly because each vital rate integrates prey–predator interactions over different time scales. Commonality in the underlying form of the distribution of vital rates in larval fish, along with clear functional relationships between gamma parameters, represents a probabilistic basis of expectations against which observations from prior or new studies can be contrasted. An example of the use of such expectations demonstrates that they can provide useful contextual information about the contrast among observations and our ability to identify their relationship with environmental drivers. [ABSTRACT FROM AUTHOR]

Physical geodesy applies potential theory to study the Earth’s gravitational field in space outside and up to a few km inside the Earth’s mass. Among various tools offered by this theory, boundary-value problems are particularly popular for the transformation or continuation of gravitational field parameters across space. Traditional problems, formulated and solved as early as in the nineteenth century, have been gradually supplemented with new problems, as new observational methods and data are available. In most cases, the emphasis is on formulating a functional relationship involving two functions in 3-D space; the values of one function are searched but unobservable; the values of the other function are observable but with errors. Such mathematical models (observation equations) are referred to as deterministic. Since observed data burdened with observational errors are used for their solutions, the relevant stochastic models must be formulated to provide uncertainties of the estimated parameters against which their quality can be evaluated. This article discusses the boundary-value problems of potential theory formulated for gravitational data currently or in the foreseeable future used by physical geodesy. Their solutions in the form of integral formulas and integral equations are reviewed, practical estimators applicable to numerical solutions of the deterministic models are formulated, and their related stochastic models are introduced. Deterministic and stochastic models represent a complete solution to problems in physical geodesy providing estimates of unknown parameters and their error variances (mean squared errors). On the other hand, analyses of error covariances can reveal problems related to the observed data and/or the design of the mathematical models. Numerical experiments demonstrate the applicability of stochastic models in practice. [ABSTRACT FROM AUTHOR]

This study analyzes the post-pandemic dynamics and investment potential of diverse clean energy equities, including solar, wind, nuclear, and other renewable assets, highlighting nuanced differences and investment opportunities within this critical sector. The analysis reveals that nuclear energy portfolios (NLR) exhibit notable resilience, sustaining growth amidst significant market volatility. Within the mean-variance portfolio optimization (MVO) framework, this study identifies strategic investments that balance risk and return, underscoring NLR’s role as a stabilizing force and return enhancer, as evidenced by its predominant allocation in both Minimum Variance and Tangency Portfolios. Employing advanced stochastic modeling and simulation techniques, the research uses a uniform distribution to generate random portfolio weights, ensuring comprehensive and unbiased exploration of the feasible solution space, thereby enhancing the robustness of the portfolio optimization process. The findings also illustrate the diversification merits of integrating clean energy equities into broader portfolios comprising traditional stocks and bonds, with nuclear-focused equity significantly enhancing the efficient frontier. Results underscore the superiority of the nuclear energy exchange-traded fund (ETF) both as a standalone investment and as a crucial component of diversified portfolios, highlighting its contribution to investment performance and risk management. This approach offers insights for investors and policymakers navigating the intersection of finance, sustainability, and economic growth post-pandemic.

Bleed–feed is a novel technology that allows biomanufacturers to skip bioreactor setups. However, the time at which bleed–feed is performed is important for its successful implementation. In addition, bleed–feed operations involve unique trade-offs that affect the system's throughput. For example, the biomass replenishment amount during bleed–feed affects the expected yield of the system. In this study, we formalise these operational trade-offs and formulate a stochastic optimisation model using renewal reward theory. Our analytical model captures both the biological dynamics and operational trade-offs of bleed–feed decisions to maximise throughput. We present an industry case study to demonstrate the use of our model. Through several practically relevant scenarios, we assess the potential impact of implementing bleed–feed on current practice and develop insights for practitioners. Although our work is primarily motivated by the biomanufacturing industry, our analytical model and insights are applicable to other industries that involve fermentation. Our results show that bleed–feed brings the most benefit for fast-growing cells or high-risk cultures. Although counter-intuitive, our numerical analysis shows that it is optimal not to adopt bleed–feed when biomass accumulation is too slow. [ABSTRACT FROM AUTHOR]

It is critical to estimate the workforce requirements for the production of blocks in shipbuilding. In this study, the number of workforce (man-day) required for the production of a passenger ship’s double bottom block was estimated. Initially, the production of the block was observed, and the average working performance of the mounting, welding, and grinding workers was recorded. Block drawings were examined and the work required was calculated. The amount of work increased, depending on any revisions required due to incorrect or incomplete designs. The average working performance of an employee is uncertain due to environmental factors, including the weather and working conditions, as well as health (both physical and mental). A two-stage stochastic programming model with recourse was established to estimate man-day required and a Sample Average Approximation (SAA) technique was used to obtain a near-optimum solution. The results of the study were compared with shipyard records and an agreement of approximately 90% was achieved.

We study the synchronisation of neurons in a realistic model under the Hodgkin–Huxley dynamics. To focus on the role of the different locations of the excitatory synapses, we use two identical neurons where the set of input signals is grouped at two different distances from the soma. The system is intended to represent a CA1 hippocampal neuron in which the synapses arriving from the CA3 neurons of the trisynaptic pathway appear to be localised in the apical dendritic region and are, in principle, either proximal or distal to the soma. Synchronisation is studied using a specifically defined spiking correlation function as a function of various parameters such as the distance from the soma of one of the synaptic groups, the inhibition weight and the associated activation delay. We found that the neurons’ spiking activity depends nonmonotonically on the relative dendritic location of the synapses and their inhibition weight, while the synchronisation measure always decreases with inhibition, and strongly depends on its activation time delay. In our model, the synaptic random subthreshold background activity substantially reduces synchronisation in a monotonic way, while highlights the importance of a balanced E/I contribution for neuronal synchronisation.

To understand the mechanisms that coordinate the formation of biological tissues, the use of numerical implementations is necessary. The complexity of such models involves many assumptions and parameter choices that result in unpredictable consequences, obstructing the comparison with experimental data. Here, we focus on vertex models, a family of spatial models used extensively to simulate the dynamics of epithelial tissues. Usually, in the literature, the choice of the friction coefficient is not addressed using quasi-static deformation arguments that generally do not apply to realistic scenarios. In this manuscript, we discuss the role that the choice of friction coefficient has on the relaxation times and consequently in the conditions of cell cycle progression and division. We explore the effects that these changes have on the morphology, growth rate and topological transitions of the tissue dynamics. These results provide a deeper understanding of the role that an accurate mechanical description plays in the use of vertex models as inference tools. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'. [ABSTRACT FROM AUTHOR]

The Atmosphere and Ocean non-tidal De-aliasing Level-1B (AOD1B) product is widely used in precise orbit determination and satellite gravimetry to correct for transient effects of atmosphere–ocean mass variability that would otherwise alias into monthly mean global gravity fields. The most recent release is based on the global ERA5 reanalysis and ECMWF operational data together with simulations from the general ocean circulation model MPIOM consistently forced with fields from the corresponding atmospheric dataset. As background models are inevitably imperfect, residual errors will consequently propagate into the resulting geodetic products. Accounting for uncertainties of the background model data in a statistical sense, however, has been shown before to be a useful approach to mitigate the impact of residual errors leading to temporal aliasing artefacts. In light of the changes made in the new release RL07 of AOD1B, previous uncertainty assessments are deemed too pessimistic and thus need to be revisited. We here present an analysis of the residual errors in AOD1B RL07 based on ensemble statistics derived from different atmospheric reanalyses, including ERA5, MERRA2 and JRA55. For the oceans, we investigate the impact of both the forced and intrinsic variability through differences in MPIOM simulation experiments. The atmospheric and oceanic information is then combined to produce a new time-series of true errors, called AOe07, which is applicable in combination with AOD1B RL07. AOe07 is further complemented by a new spatial error variance–covariance matrix. Results from gravity field recovery simulation experiments for the planned Mass-Change and Geosciences International Constellation (MAGIC) based on GFZ’s EPOS software demonstrate improvements that can be expected from rigorously implementing the newly available stochastic information from AOD1B RL07 into the gravity field estimation process. [ABSTRACT FROM AUTHOR]

An optimisation-based calibration technique, using the area metric, is applied to determine the input parameters of a stochastic earthquake-waveform simulation method. The calibration algorithm updates a model prior, specifically an estimate of a region's seismological (source, path and site) parameters, typically developed using waveform data, or existing models, from a wide range of sources. In the absence of calibration, this can result in overestimates of a target region's ground motion variability, and in some cases, introduce biases. The proposed method simultaneously attains optimum estimates of median, range and distribution (uncertainty) of these seismological parameters, and resultant ground motions, for a specific target region, through calibration of physically constrained parametric models to local ground motion data. We apply the method to Italy, a region of moderate seismicity, using response spectra recorded in the European Strong Motion (ESM) dataset. As a prior, we utilise independent seismological models developed using strong motion data across a wider European context. The calibration obtains values of each seismological parameter considered (such as, but not limited to, quality factor, geometrical spreading and stress drop), to develop a suite of optimal parameters for locally adjusted stochastic ground motion simulation. We consider both the epistemic and aleatory variability associated with the calibration process. We were able to reduce the area metric (misfit) value by up to 88% for the simulations using updated parameters, compared to the initial prior. This framework for the calibration and updating of seismological models can help achieve robust and transparent regionally adjusted estimates of ground motion, and to consider epistemic uncertainty through correlated parameters. [ABSTRACT FROM AUTHOR]

Understanding disease transmission in the workplace is essential for protecting workers. To model disease outbreaks, the small populations in many workplaces require that stochastic effects are considered, which results in higher uncertainty. The aim of this study was to quantify and interpret the uncertainty inherent in such circumstances. We assessed how uncertainty of an outbreak in workplaces depends on i) the infection dynamics in the community, ii) the workforce size, iii) spatial structure in the workplace, iv) heterogeneity in susceptibility of workers, and v) heterogeneity in infectiousness of workers. To address these questions, we developed a multiscale model: A deterministic model to predict community transmission, and a stochastic model to predict workplace transmission. We extended this basic workplace model to allow for spatial structure, and heterogeneity in susceptibility and infectiousness in workers. We found a non-monotonic relationship between the workplace transmission rate and the coefficient of variation (CV), which we use as a measure of uncertainty. Increasing community transmission, workforce size and heterogeneity in susceptibility decreased the CV. Conversely, increasing the level of spatial structure and heterogeneity in infectiousness increased the CV. However, when the model predicts bimodal distributions, for example when community transmission is low and workplace transmission is high, the CV fails to capture this uncertainty. Overall, our work informs modellers and policy makers on how model complexity impacts outbreak uncertainty. In particular: workforce size, community and workplace transmission, spatial structure and individual heterogeneity contribute in a specific and individual manner to the predicted workplace outbreak size distribution. [ABSTRACT FROM AUTHOR]

Rice production is inherently risky and volatile, and farmers in Bangladesh face a wide range of risks, including weather, pest and disease attacks, interruptions to input supply, and market-associated risks. Moreover, poor farm households often perceive risks in adopting new technology, even though it could improve productivity and food security. Such households are thus caught in a “risk-induced trap” that precludes them from realizing the benefits of technological innovation. Extension service is one way to help farmers improve risk management skills and escape risk-induced traps, but there is limited empirical analysis of its impact in Bangladesh. The objective of the study is to measure the nexus between agricultural extension services, technology adoption, and production risks as well as women empowerment in agriculture index. IFPRI utilized stratified random sampling to determine the 5603 households in 2018 (which is nationally called the BIHS-2018 dataset) from rural and pre-urban areas of Bangladesh. Out of these 5603 households, 2663 households were specifically selected for the study related to rice farming to achieve the main objective of the study. Focusing on rice farming, a moment-based Poisson regression model is estimated with 2SLS and identifies risks associated with key technologies and potential productivity and risk-reducing effects. The results revealed that wealthier households are more likely to adopt technology for minimizing production risk and women's empowerment which can positively affect productivity by mitigating risk. The result revealed a positive and significant difference in WEAI between the AES participant and non-participant group. We find that engagement in agricultural extension services was associated with technology adoption and production risk reduction. The agricultural extension services increased, technology adoption by 4.2 % and decreased production risk by 2.4 %. Based on the findings, it is concluded that more comprehensive extension services can enhance rice production and ameliorate farmers' risk in rice production to some extent.

DNA methylation occurs predominantly on cytosine-phosphate-guanine (CpG) dinucleotides in the mammalian genome, and the methylation landscape is maintained over mitotic cell division. It has been posited that coupling of maintenance methylation activity among neighbouring CpGs is critical to stability over cellular generations; however, the mechanism is unclear. We used mathematical models and stochastic simulation to analyse data from experiments that probe genome-wide methylation of nascent DNA post-replication in cells. We find that DNA methylation maintenance rates on individual CpGs are locally correlated, and the degree of this correlation varies by genomic regional context. By using theory of protein diffusion along DNA, we show that exponential decay of methylation rate correlation with genomic distance is consistent with enzyme processivity. Our results provide quantitative evidence of genome-wide methyltransferase processivity in vivo. We further developed a method to disentangle different mechanistic sources of kinetic correlations. From the experimental data, we estimate that an individual methyltransferase methylates neighbour CpGs processively if they are 36 basepairs apart, on average. But other mechanisms of coupling dominate for longer inter-CpG distances. Our study demonstrates that quantitative insights into enzymatic mechanisms can be obtained from replication-associated, cell-based genome-wide measurements, by combining data-driven statistical analyses with hypothesis-driven mathematical modelling.

The Optimal Power Flow (OPF) problem, used to obtain efficient operation conditions with the lowest cost or the minimum power loss in electrical power systems, is a non-polynomial problem that becomes even harder to analyze when considering renewable energy sources (RES) with uncertain behavior. Therefore, establishing a manageable number of RES scenarios in the modeling is essential for optimizing cost-effective solutions, including those with constraints such as the valve-point effect and prohibited operational zones. This work compares three differential evolution algorithm (DEA) variants and four well-known metaheuristics: the Particle Swarm Optimization (PSO), the Bio-geographical Based Optimization (BBO), the Artificial Bee Colony Optimization (ABC), and the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The metaheuristics are compared: 1) to determine the one with the best performance considering RES; 2) to establish an approach to minimize and find the best set of scenarios representing variable RES; 3) to compare the success rate of convergence of the penalized function against the real objective function. Results show that BBO and PSO optimization are the best choices for solving the classic objective function of OPF. On the other hand, the DE/best/1 (DEAB) algorithm demonstrates the best performance when the valve-point effect with prohibited zones is considered. DEAB presents the largest weighted cumulative rating (WCR) and the second-best weighted cumulative successful rate (WCSR) for all the evaluated criteria.

Spin noise based quantum random number generators first appeared in 2008 and have since then garnered little further interest, in part because their bit rate is limited by the transverse relaxation time T 2 which for coated alkali vapour cells is typically in the kbit/s range. Here we present two advances. The first is an improved bit generation protocol that allows generating bits at rates exceeding 1 / T 2 with only a minor increase of serial correlations. The second is a significant reduction of the time T 2 itself by removing the coating, increasing the vapour temperature and introducing a magnetic-field gradient. In this way we managed to increase the bit generation rate to 1.04 Mbit/s. We analyse the quality of the generated random bits using entropy estimation and we discuss the extraction methods to obtain high-entropy bitstreams. We accurately predict the entropy output of the device backed with a stochastic model and numerical simulations. [ABSTRACT FROM AUTHOR]

In the current work, we develop and analyse a prey–predator model as a semi-Kolmogorov population model in which the predator has an indirect effect on the prey. The functional response of the model is investigated as Holling type (II). We construct a stochastic environment because of the parameter's random essence to study the influence of environmental fluctuations on this model and introduce a stochastic version of the prey–predator model. Then we present a dynamical analysis of solutions, including existence, uniqueness, positivity, stochastic boundedness, and stochastic extinction of all prey. Finally, some numerical simulations are carried out to validate our theoretical findings and confirm the efficiency and adaptation of our stochastic model. [ABSTRACT FROM AUTHOR]

The interaction between laser beams and backscattering object surfaces lies at the fundamental working principle of any Terrestrial Laser Scanning (TLS) system. Optical properties of surfaces such as concrete, metals, wood, etc., which are commonly encountered in structural health monitoring of buildings and structures, constitute an important category of systematic and random TLS errors. This paper presents an approach for considering the random errors caused by object surfaces. Two surface properties are considered: roughness and reflectance. The effects on TLS measurements are modeled stepwise in form of a so-called synthetic variance-covariance matrix (SVCM) based on the elementary error theory. A line of work is continued for the TLS stochastic model by introducing a new approach for determining variances and covariances in the SVCM. Real measurements of cast stone façade elements of a tall building are used to validate this approach and show that the quality of the estimation can be improved with the appropriate SVCM. [ABSTRACT FROM AUTHOR]

Establishing accurate palaeo‐hydroclimatic reconstructions from lacustrine and marine archives is a long‐standing challenge in palaeoenvironment studies. Closed‐basin evaporites, and especially halite, record episodes of extremely arid conditions during rapid climate change. However, the complex limnological behaviour of deep hypersaline water bodies and the stochastic nature of the hydroclimatic regime and its variations limit detailed palaeo‐hydroclimatic interpretations from such records. Therefore, a mass‐balance model was developed to explore hydrology–limnology–sedimentology relationships in hypersaline environments under both deterministic and stochastic approaches that generates synthetic halite–mud sequences. Applying the model to the Holocene Dead Sea halites yields novel insights into palaeoenvironmental conditions in the Levant. The deterministic framework indicates that: (i) under a series of similar hydroclimatic cycles, the thickness of each subsequent halite interval decreases, due to the depletion of dissolved‐ions storage in the brine; (ii) halite deposition requires lake levels to drop below the minimal lake level of the preceding cycle; (iii) the time interval between halite deposition and the hydrological minimum is increasingly longer in subsequent cycles. Thus, counter‐intuitively, halite deposition mostly takes place as water discharge increases, providing that the water balance is still negative. The stochastic approach produced random sequences comparable to the observed Dead Sea sedimentary record. It demonstrates that some hydrological minima are not represented by halite deposition at all. Furthermore, the thickness and number of halite beds at each hydrological cycle vary substantially, depending on the specific hydrological conditions realized. Finally, these results imply that the major Dead Sea level drop at the pre‐Holocene deglaciation (ca 14 ka bp), previously assumed to be a record minimum, could not have been as pronounced as suggested, and must have been milder than the subsequent drop at the early Holocene (ca 11–10 ka bp). [ABSTRACT FROM AUTHOR]

The assessment of wave energy converters is a key issue for planning and managing the economic feasibility wave power plants. However, obtaining reliable assessments is a difficult goal due to the strong stochastic component of wave behaviour. This paper proposes a simple and straightforward assessment method based on empirical data to estimate not only the expected values of converted power, but also their confidence limits. The method combines Gaussian mixed models with the Monte Carlo method. The proposed approach was validated by assessing five converters with data obtained from two different buoys. The daily converted power values agree with the measured wave parameter patterns. Furthermore, all the observed values of monthly generated energy in the three years after the evaluation fell within the forecast intervals, supporting the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

Construction noise is an occupational noise that is potentially harmful, and it usually originates from machinery in construction sites. The impact of construction noise on the health and safety of construction workers is one of the main concerns in the industry. Prolonged noise exposure duration will cause physiological, physical, psychological damage, and negatively affect the working performance of the workers as well. Furthermore, the adverse impacts arising from construction noise may jeopardize public welfare, particularly for those who live nearby the construction site. Therefore, this research aims to develop a Stochastic Deep Learning (SDL) noise prediction model by integrating stochastic modelling and deep learning techniques. Stochastic modelling is applied in this study to generate a set of simulated randomized data based on several parameters as the input for the deep learning model. The deep learning model is trained with the input from stochastic modelling to predict the noise levels emitted from the construction site. The predictive performance of the deep learning model will be assessed with several statistical measures such as absolute difference, mean absolute difference and root mean square deviation. Ten case studies are conducted to validate the reliability and accuracy of the SDL noise prediction model. The SDL model showed high accuracy of prediction results with an average absolute difference of less than 1.2 A-weighted decibels (dBA) among the case studies as compared to the measurement. The reliability of the results from the prediction model is high. In conclusion, the SDL model is established and provided a promising outcome with satisfactory predictive performance. Finally, further development of the model would be worthwhile to fully exploit the potential of the SDL noise prediction model in construction industries as a planning, managerial and monitoring tool.

For developing nations to achieve net-zero targets, macroeconomic linkages impacting the decoupling of emissions from economic growth must account for non-linear business cycles and economic shocks. This study aims to delineate decarbonization policy pathways for the Indian electricity sector in the aftermath of COVID-19 by analysing the long-term evolution of the economy–electricity–emissions (3E) nexus during the 2008 financial crisis and during COVID-19, covering the period of 1996Q2 to 2020Q3. Upon testing multiple theoretical 3E systems, it was found that a model internalizing trade, inflation, and stochasticity was able to minimize the reproduction errors from growth to recession phases, as well as predict the rebound effect from an economic crisis. This was revealed to be due to more information within the coefficients in a trade stochastic model. Our results confirm the existence of electricity-associated emission decoupling with capital formation in the long-run, post-crisis, while economic growth and inflation increase CO2 emissions. The main finding highlights the negative feedback loop of inflation->trade->emissions, which shows that GDP and emissions are not directly causal. This long-run macroeconomic dynamic death spiral causes decoupling to be inhibited, where fossil fuel imports should not be subsidized for economic shock rebound, and the risk hedging of energy transition investments should occur in the post-COVID-19 era.

Autoignition risk in initially non-premixed flowing systems, such as premixing ducts, must be assessed to help the development of low-NO x systems and hydrogen combustors. Such situations may involve randomly fluctuating inlet conditions that are challenging to model in conventional mixture-fraction-based approaches. A Computational Fluid Dynamics (CFD)-based surrogate modelling strategy is presented here for fast and accurate predictions of the stochastic autoignition behaviour of a hydrogen flow in a hot air turbulent co-flow. The variability of three input parameters, i.e., inlet fuel and air temperatures and average wall temperature, is first sampled via a space-filling design. For each sampled set of conditions, the CFD modelling of the flame is performed via the Incompletely Stirred Reactor Network (ISRN) approach, which solves the reacting flow governing equations in post-processing on top of a Large Eddy Simulation (LES) of the inert hydrogen plume. An accurate surrogate model, namely a Gaussian Process, is then trained on the ISRN simulations of the burner, and the final quantification of the variability of autoignition locations is achieved by querying the surrogate model via Monte Carlo sampling of the random input quantities. The results are in agreement with the observed statistics of the autoignition locations. The methodology adopted in this work can be used effectively to quantify the impact of fluctuations and assist the design of practical combustion systems. [ABSTRACT FROM AUTHOR]

Patient no-shows are a major source of uncertainty for outpatient clinics. A common approach to hedge against the effect of no-shows is to overbook. The trade-off between patient's waiting costs and provider idling/overtime costs determines the optimal level of overbooking. Existing work on appointment scheduling assumes that appointment times cannot be updated once they have been assigned. However, advances in communication technology and the adoption of online (as opposed to in-person) appointments make it possible for appointments to be flexible. In this paper, we describe an intraday dynamic rescheduling model that adjusts upcoming appointments based on observed no-shows. We formulate the problem as a Markov Decision Process in order to compute the optimal pre-day schedule and the optimal policy to update the schedule for every scenario of no-shows. We also propose an alternative formulation based on the idea of 'atomic' actions that allows us to apply a shortest path algorithm to solve for the optimal policy more efficiently. Based on a numerical study using parameter estimates from existing literature, we find that intraday dynamic rescheduling can reduce expected cost by 15% compared to static scheduling. [ABSTRACT FROM AUTHOR]

This article, titled "Editorial: Insights in plant conservation," discusses the importance of plant conservation and the threats that plants face, such as habitat loss, degradation, fragmentation, over-harvesting, and climate change. The article highlights the need for resource-efficient conservation and restoration efforts, as well as the use of herbarium specimens and citizen science in gathering data and monitoring plant species. It also emphasizes the importance of considering environmental and ecological context in plant conservation, particularly in relation to climate change and the decline of keystone plant species. The article concludes by suggesting potential ways forward to make plant conservation more effective, resource-efficient, and adaptable. [Extracted from the article]

Plasmodium falciparum and P. vivax are the two most common causes of malaria. While the majority of deaths and severe morbidity are due to P. falciparum, P. vivax poses a greater challenge to eliminating malaria outside of Africa due to its ability to form latent liver stage parasites (hypnozoites), which can cause relapsing episodes within an individual patient. In areas where P. falciparum and P. vivax are co-endemic, individuals can carry parasites of both species simultaneously. These mixed infections complicate dynamics in several ways: treatment of mixed infections will simultaneously affect both species, P. falciparum can mask the detection of P. vivax, and it has been hypothesised that clearing P. falciparum may trigger a relapse of dormant P. vivax. When mixed infections are treated for only blood-stage parasites, patients are at risk of relapse infections due to P. vivax hypnozoites.We present a stochastic mathematical model that captures interactions between P. falciparum and P. vivax, and incorporates both standard schizonticidal treatment (which targets blood-stage parasites) and radical cure treatment (which additionally targets liver-stage parasites). We apply this model via a hypothetical simulation study to assess the implications of different treatment coverages of radical cure for mixed and P. vivax infections and a “unified radical cure” treatment strategy where P. falciparum, P. vivax, and mixed infections all receive radical cure after screening glucose-6-phosphate dehydrogenase (G6PD) normal. In addition, we investigated the impact of mass drug administration (MDA) of blood-stage treatment. We find that a unified radical cure strategy leads to a substantially lower incidence of malaria cases and deaths overall. MDA with schizonticidal treatment was found to decrease P. falciparum with little effect on P. vivax. We perform a univariate sensitivity analysis to highlight important model parameters.

Stochastic versions of a classical model for natural ventilation are proposed and investigated to demonstrate the effect of random fluctuations on stability and predictability. In a stochastic context, the well-known deterministic result that ventilation driven by the competing effects of buoyancy and wind admits multiple steady states can be misleading. With fluctuations in the buoyancy exchanged with an external environment modelled as a Wiener process, such systems tend to reside in the vicinity of global minima of their potential, rather than states associated with metastable equilibria. For a heated space with a leeward low-level and windward high-level opening, sustained buoyancy-driven flow opposing the wind direction is unlikely for wind strengths exceeding a statistically critical value, which is slightly larger than the critical value of the wind strength at which bifurcation in the deterministic system occurs. When fluctuations in the applied wind strength are modelled as an Ornstein–Uhlenbeck process, the topology of the system's potential is effectively modified due to the nonlinear role that wind strength has in the equation for buoyancy conservation. Consequently, large fluctuations in the wind of sufficient duration rule out the possibility of sustained ventilation opposing the wind direction at large base wind strengths. [ABSTRACT FROM AUTHOR]

Continued surveillance of antimicrobial resistance is critical as a feedback mechanism for the generation of concerted public health action. A characteristic of importance in evaluating disease surveillance systems is representativeness. Scenario tree modelling offers an approach to quantify system representativeness. This paper utilises the modelling approach to assess the Australian Gonococcal Surveillance Programme's representativeness as a case study. The model was built by identifying the sequence of events necessary for surveillance output generation through expert consultation and literature review. A scenario tree model was developed encompassing 16 dichotomous branches representing individual system sub-components. Key classifications included biological sex, clinical symptom status, and location of healthcare service access. The expected sensitivities for gonococcal detection and antibiotic status ascertainment were 0.624 (95% CI; 0.524, 0.736) and 0.144 (95% CI; 0.106, 0.189), respectively. Detection capacity of the system was observed to be high overall. The stochastic modelling approach has highlighted the need to consider differential risk factors such as sex, health-seeking behaviours, and clinical behaviour in sample generation. Actionable points generated by this study include modification of clinician behaviour and supplementary systems to achieve a greater contextual understanding of the surveillance data generation process. [ABSTRACT FROM AUTHOR]

Institutional investors like hedge funds are particularly resilient in times of volatility, primarily due to their access to a slew of complex trading strategies and extensive research capabilities; both of which are generally outside the reach of common investors. Fortunately, the U.S. Securities and Exchange Commission has mandated that large institutional investors publicly disclose the positions held by them at the end of a quarter, within the following 45 days. We then ask the question "Given our access to snapshots of positions held by large investors, can we extract alpha by approximating their aggregate behaviour?" In this paper, we introduce a stock recommendation model driven by the aggregate behaviour of institutional investors. We interpret stocks as the states of a Markov chain. The corresponding state transition matrix is defined based on the aggregate behaviour of investors. By designing the transition matrix to hold for aperiodicity and irreducibility, the steady state distribution of this chain is treated as a ranked list of stocks. We build a long only, equally weighted portfolio of stocks to trade using these recommendations. We observe that the returns on this portfolio beat existing models in the literature, and standard hedge fund indices both in terms of annualised returns and Sharpe ratio. [ABSTRACT FROM AUTHOR]

Some specialty crops, such as strawberries and table grapes, are harvested by large crews of pickers who spend significant amounts of time carrying empty and full (with the harvested crop) trays. A step toward increasing harvest automation for such crops is to deploy harvest-aid robots that transport the empty and full trays, thus increasing harvest efficiency by reducing pickers’ non-productive walking times. To that end, this work addresses human-robot collaboration modeling in a harvesting context. First, a modeling framework for all-manual and robot-aided harvesting was developed, which can be used for off-line simulation by system designers, but also as a representation model for robot control, during real-time operation. To serve both functions, the framework utilizes hybrid systems to model picker and robot activities. Finite state machines model discrete operating states, and difference equations describe motion and mass transfer within each discrete state. To capture the variability in human behavior and performance during harvesting, the human activity model utilizes stochastic parameters (e.g., picking time, walking speed) that can be estimated by measurements during harvesting. The stochastic model does not require direct yield measurements, which are not available for most specialty crops. Second, a stochastic simulator was developed based on the developed model. For a given field and crew size, the simulator samples all stochastic parameters to generate many instances of the harvest operation, and estimates metrics such as pickers’ non-productive time and harvest operation efficiency. Part II of this work presents the calibration and evaluation of the simulator based on field data, and a case study that evaluates the effect of various robot scheduling algorithms on harvest efficiency.

Some specialty crops, such as strawberries and table grapes, are harvested by large crews of pickers who spend significant amounts of time carrying empty and full (with the harvested crop) trays. A step toward increasing harvest automation for such crops is to deploy harvest-aid robots that transport the empty and full trays, thus increasing harvest efficiency by reducing pickers’ non-productive walking times. To that end, this work addresses human-robot collaboration modeling in a harvesting context. First, a modeling framework for all-manual and robot-aided harvesting was developed, which can be used for off-line simulation by system designers, but also as a representation model for robot control, during real-time operation. To serve both functions, the framework utilizes hybrid systems to model picker and robot activities. Finite state machines model discrete operating states, and difference equations describe motion and mass transfer within each discrete state. To capture the variability in human behavior and performance during harvesting, the human activity model utilizes stochastic parameters (e.g., picking time, walking speed) that can be estimated by measurements during harvesting. The stochastic model does not require direct yield measurements, which are not available for most specialty crops. Second, a stochastic simulator was developed based on the developed model. For a given field and crew size, the simulator samples all stochastic parameters to generate many instances of the harvest operation, and estimates metrics such as pickers’ non-productive time and harvest operation efficiency. Part II of this work presents the calibration and evaluation of the simulator based on field data, and a case study that evaluates the effect of various robot scheduling algorithms on harvest efficiency.

This work predicts the dynamics of the COVID-19 under widespread vaccination to anticipate the virus's current and future waves. We focused on establishing two population-based models for predictions: the fractional-order model and the fractional-order stochastic model. Based on dose efficacy, which is one of the main imposed assumptions in our study, some vaccinated people will probably be exposed to infection by the same viral wave. We validated the generated models by applying them to the current viral wave in Egypt. We assumed that the Egyptian current wave began on 10th September 2021. Using current actual data and varying our models’ fractional orders, we generate different predicted wave scenarios. The numerical solution of our models is obtained using the fractional Euler method and the fractional Euler Maruyama method. At the end, we compared the current predicted wave under a high vaccination rate with the previous viral wave. Through this comparison, the vaccination control effect is quantified.

Induced uncertainties during the filament winding process may cause a significant stochastic variation in the mechanical behaviour of composite shells. This paper aims to develop a novel and deep uncertainty quantification (UQ), sensitivity and reliability analyses of filament wound shells considering manufacturing uncertainties. Firstly, a progressive damage analysis is performed to estimate their deterministic burst pressure. Then, a signal-to-noise (SNR) approach is employed using the Taguchi method for sensitivity analysis and screening uncertainties arising from manufacturing. Initial results reveal that the shells are more sensitive to thickness uncertainties for thinner structures. Then, probabilistic and reliability analyses are carried out using the Boosted Decision Trees Regression (BDTR) approach from machine learning algorithms. Despite the complexity and non-linear relationships in the problem, the developed BDTR-based metamodel shows powerful predictive performance. A comparative study shows that ply thickness uncertainty leads to a significant underestimation of failure probability. For expensive and time-consuming models in that only a few runs can be affordable, a modified approximation method for reliability analysis is proposed. Results indicate a high capability at estimating failure probability with high accuracy. [ABSTRACT FROM AUTHOR]

Stochastic Modelling (SM) was a crucial component of least squares adjustment (LSA), particularly when processing data from geodetic networks. The projected variances which generate using SM execute an important part in defining both the accurateness of the computed parameter vectors and the impact of the adjustment outcomes. As positional precision becomes the primary objective, there is still potential for improvement because there are multiple sources of datasets with varying levels of data quality. Concerning the assertion that the National Digital Cadastral Database (NDCDB) is accurate, its development involved the use of historical datasets that were obtained from a number of different measurement classes, specifically the first, second, and third classes. In this study, researchers evaluated whether or not it is possible to employ stochastic modelling to maintain the position correctness of historical data that encompasses a wide range of data quality classes. In order to accomplish this, an approach known as a Least Squares Variance Component Estimator (LS-VCE) was utilised to generate reliable estimates of variances. Two (2) certified plans (CPs) that is CP93387 and CP33758 was selected as measurements for the first and second classes CP, respectively. The experiment showed that the variance that has been estimated by LS-VCE could produce realistic adjustment results, as shown by an analysis of the corrected results obtained by allocating the variance into different data classes. In light of these findings, the investigations showed and demonstrated conclusively that separate variance is necessary for each data classes with the aim of preserving positional accuracy. In conclusion, it is crucial to incorporate a realistic variance component inside a coordinated cadastral database in order to fulfil the objective of ensuring the accurateness of survey data for future time periods. [ABSTRACT FROM AUTHOR]

This paper deals with karst aquifer characterization using time series and stochastic analysis and modelling as well as groundwater quality analyses. Such a methodological approach was applied on the eastern Suva Planina Mt. karst aquifer by evaluating data for the period of 2015–2018, which included monitoring and analysis of major karst springs Mokra and Divljana and their quantity and quality parameters. Quantitative characterization of these karst springs utilised for water supply of Niš city, has shown high karstification degree and very well retention capacity of both, Mokra and Divljana sub-systems with slightly lower storativity of the Divljana sub-system. In addition, simulation models have proved that precipitation is the main driving force of the hydraulic behaviour of the karst groundwater circulation and discharge in this area. Qualitative characterization has shown very small variation of chemical parameters, with expected karst groundwater fingerprint, and isotopic analysis confirmed that water replenishment is relatively fast. Results also speculate that these two springs actually represent discharging points of one aquifer that is fragmented into two interconnected sub-systems. The study proves the necessity for karst groundwater characterization to manage this important natural resource in a sustainable way. [ABSTRACT FROM AUTHOR]

Trigonal‐prismatic coordinated transition metal dichalcogenides (TMDCs) are formed from stacked (chalcogen)–(transition metal)–(chalcogen) triple layers, where the chemical bond is covalent within the triple layers and van der Waals (vdW) forces are effective between the layers. Bonding is at the origin of the great interest in these compounds, which are used as 2D materials in applications such as catalysis, electronics, photoelectronics, sensors, batteries and thermoelectricity. This paper addresses the issue of modelling the structural disorder in multilayer TMDCs. The structural model takes into account stacking faults, correlated displacement of atoms and average crystallite size/shape, and is assessed by simulation of the X‐ray diffraction pattern and fitting to the experimental data relative to a powdered sample of MoS2 exfoliated and restacked via lithiation. From fitting, an average crystallite size of about 50 Å, nearly spherical crystallites and a definite probability of deviation from the fully eclipsed atomic arrangement present in the ordered structure are determined. The increased interlayer distance and correlated intralayer and interlayer atomic displacement are attributed to the presence of lithium intercalated in the vdW gap between triple layers (Li/Mo molar ratio of about 0.06). The model holds for the whole class of trigonal‐prismatic coordinated TMDCs, and is suitably flexible to take into account different preparation routes. [ABSTRACT FROM AUTHOR]