Deterministic Approach Research Articles

Optimizing construction duration estimation and monitoring for public building projects in Addis Ababa through an integrated deterministic and probabilistic conceptual model

ABSTRACT Effective time estimation and monitoring are crucial for the success of public building projects, especially in complex environments like Addis Ababa, where project delivery methods introduce considerable uncertainties. Existing practices in Addis Ababa often lead to unreliable schedules and increased risk. This study addresses these issues by developing an integrated conceptual model that merges deterministic and probabilistic approaches to enhance schedule accuracy and reliability. The model incorporates factors such as project scope, regulatory requirements, site conditions, contractual terms, historical data, resource availability, and economic and political influences. Data was collected from clients, consultants, and project supervisors on construction duration estimation, time monitoring, and delivery system uncertainties. Key influential parameters identified include the number of buildings, regulatory approvals, government shutdowns, project locations, risk management strategies, and site conditions. The model proposes using historical data and expert judgment to establish baseline durations, which are adjusted for factors like regulatory delays and site-specific conditions. It employs probabilistic methods like Monte Carlo simulations and PERT to account for variability. The integrated approach combines these results to provide a comprehensive project duration estimate and includes a roadmap for implementation in R and Python, validated using MAE, RMSE, and R2 metrics.

Comparative life-cycle cost analysis of flexible and rigid pavement: a case study of four Arkansas roadway sections

ABSTRACT Pavement management systems encompass pavement maintenance and rehabilitation (M&R) as critical components to postpone reconstruction by addressing pavement deterioration in the early stages of service life. Life-cycle cost analysis (LCCA) serves as a financial tool within these systems, aiding in determining pavement alternatives. The objective of this study was to determine the life-cycle cost of flexible and rigid pavement alternatives, including initial construction, M&R costs to users and agencies, and salvage value over 50-year life-cycle. The study examines four roadways having alternative flexible and rigid pavement designs using a deterministic approach. The frequency of treatment application varies based on average daily truck traffic. LCCA analysis shows that initial cost of pavements represented 72% to 91% of net present value (NPV), while user costs had negligible impact, comprising less than 6% of NPV for each alternative. The highest NPV observed is $0.90 M and $1.34 M for flexible and rigid pavements, respectively. The percentage difference between flexible and rigid pavement in NPV varies from 39% to 56%. In brief, LCCA indicates that flexible pavement is a more cost-effective alternative than rigid pavement across various traffic conditions and pavement thicknesses. However, actual performance in the field will ultimately determine their effectiveness.

Evaluation of Liquefaction Potential in the Budong-Budong Dam Foundation Plan to Prevent Dam Failure Due to Liquefaction Using a Deterministic Approach

Evaluation of Liquefaction Potential in the Budong-Budong Dam Foundation Plan to Prevent Dam Failure Due to Liquefaction Using a Deterministic Approach

GenHMR: Generative Human Mesh Recovery

Human mesh recovery (HMR) is crucial in many computer vision applications; from health to arts and entertainment. HMR from monocular images has predominantly been addressed by deterministic methods that output a single prediction for a given 2D image. However, HMR from a single image is an ill-posed problem due to depth ambiguity and occlusions. Probabilistic methods have attempted to address this by generating and fusing multiple plausible 3D reconstructions, but their performance has often lagged behind deterministic approaches. In this paper, we introduce GenHMR, a novel generative framework that reformulates monocular HMR as an image-conditioned generative task, explicitly modeling and mitigating uncertainties in the 2D-to-3D mapping process. GenHMR comprises two key components: (1) a pose tokenizer to convert 3D human poses into a sequence of discrete tokens in a latent space, and (2) an image-conditional masked transformer to learn the probabilistic distributions of the pose tokens, conditioned on the input image prompt along with randomly masked token sequence. During inference, the model samples from the learned conditional distribution to iteratively decode high-confidence pose tokens, thereby reducing 3D reconstruction uncertainties. To further refine the reconstruction, a 2D pose-guided refinement technique is proposed to directly fine-tune the decoded pose tokens in the latent space, which forces the projected 3D body mesh to align with the 2D pose clues. Experiments on benchmark datasets demonstrate that GenHMR significantly outperforms state-of-the-art methods.

Robust laser cross detection for non-invasivevolumetric camera calibration

Abstract Recent advances in volumetric camera calibration techniques have
established non-invasive methods for multi-camera setups. Laser-beam methods,
particularly those introduced by Hardege et al. (2022, 2023) and enhanced by
Gunady et al. 2024, show significant promise. A critical component of these
calibration procedures is the precise detection of laser beam intersections in 2D
images. Current methodologies, including Hough-Line-Transformation, Template
Matching, RANSAC, and 2D Gaussian beam fitting, exhibit limitations in both
stability and accuracy, especially when encountering reflections, refractions, and
other image artifacts. This paper introduces a deterministic approach for
identifying straight light beams and accurately determining their intersections
in grayscale 2D images. The proposed method exploits the Gaussian intensity
distribution of laser beams, implements adaptive beam width detection,
and eliminates the need for binarization, probabilistic sampling, or manual
intervention such as template creation or parameter initialization. Extensive
evaluation using both synthetic and real-world images demonstrates superior
accuracy and robustness compared to existing methods. The presented approach
reaches subpixel accuracy in noisy conditions at angles as low as 10◦ which further
increases for higher intersection angles.

Improving Risk Assessment of Land-Applied Biosolids with Probabilistic Approaches.

Land application of municipal and industrial wastewater biosolids for use as a fertilizer or soil conditioner is a common practice in the United States. Regulations are in place to reduce pathogens, minimize disease vectors, and limit concentrations of nutrients and some metals, but extensive assessment of the risk of biosolids-associated chemicals to human health and environmental systems is uncommon. Recently, the United States Environmental Protection Agency developed the Biosolids Tool (BST) to facilitate more comprehensive chemical risk assessment of land-applied biosolids based on a deterministic approach that utilizes conservative model inputs without regard for the variability and uncertainty inherent in environmental exposures. Management decisions based on probabilistic risk assessment (PRA), in which variability and uncertainty are quantified and risk is linked to specific population segments, may provide a more accurate understanding of risk. We examined the sediment risk assessment literature and explored the application of probabilistic model inputs within the BST to better understand how deterministic (DRA) and probabilistic (PRA) risk assessment methods compare for characterizing risk. The BST model results for noncancer and cancer risk outcomes associated with total ingestion of aluminum and benzo(a)pyrene in biosolids applied to pastureland for an adult and child indicated that PRA provides a more nuanced understanding of risk than the traditionally used deterministic approach. Receptor-specific risk patterns, model sensitivity, and risk drivers are discussed. Findings underscore the need for incorporating probabilistic methods into regulatory frameworks to improve the accuracy and reliability of risk assessments for biosolids land application.

Система моделирования пространственной динамики бактериальной популяции при вариации режимов антимикробной обработки

<p>This work is devoted to the development and implementation of a model for the evolution of a bacterial population grown on a nutrient medium under conditions of controlled biomass inhibition by an antimicrobial agent. To formalize the model a continuous deterministic approach is used. The mathematical model is described by an initial boundary value problem for a system of reaction-diffusion equations defining the spatio-temporal distributions of nutrient substrate and biomass, taking into account integration with a pharmacokinetic model for a single antimicrobial treatment. The model was implemented by the finite element method using the finite element analysis system – COMSOL Multiphysics platform. A series of computational experiments were performed to establish numerical patterns of changes in bacterial mass concentration with variation in antimicrobial dose. A discussion of the potential application of this approach to investigate the issue of bacterial resistance to antibiotics is presented.</p>

Vulnerability Assessment of Reinforced Concrete Piers Under Vehicle Collision Considering the Influence of Uncertainty

In recent years, the serious damage and even collapse accidents of bridge under vehicle-to-pier collision occurred frequently and have attracted growing attention world widely. Numerous studies have been conducted to examine the structural resistance of bridge piers against vehicle-with-pier collisions. Nevertheless, most of those studies employed a deterministic approach without incorporating the inherent uncertainty in structural and loading parameters. This study proposes a probabilistic approach to investigate the vulnerability of reinforced concrete (RC) piers under collisions with trucks and tractors. To do this, a dynamic mass-spring numerical model was developed to simulate the pier–vehicle collision process, which was further validated through simulating experimental data. A total of four parameters, including concrete strength, pier diameter, stirrup yield strength, and stirrup spacing, were considered and regarded as uncertainty parameters with their probability distributions determined according to the available studies. The Monte Carlo simulation method was used to generate 1000 samples for each of the uncertainty parameters and these random samples were coupled in the simplified numerical model. Through probabilistic analysis, the collapse vulnerability of RC piers was estimated. The results revealed that a tractor with a higher mass can result in higher failure probabilities than a truck. The uncertainty of the pier diameter and concrete strength have a great impact on the vulnerability of RC piers under different damage states.

A comparative study of deterministic and stochastic computational modeling approaches for analyzing and optimizing COVID-19 control

This paper presents a comparative analysis of deterministic and stochastic computational modeling approaches for the optimal control of COVID-19. We formulate a compartmental epidemic model with perturbation by white noise that incorporates various factors influencing disease transmission. By incorporating stochastic effects, the model accounts for uncertainties inherent in real-world epidemic data. We establish the mathematical properties of the model, such as well-posedness and the existence of stationary distributions, which are crucial for understanding long-term epidemic dynamics. Moreover, the study presents an optimal control strategies to mitigate the epidemic’s impact, both in deterministic and stochastic sceneries. Reported data from Algeria are used to parameterize the model, ensuring its relevance and applicability to practical satiation. Through numerical simulations, the study provides insights into the effectiveness of different control measures in managing COVID-19 outbreaks. This research contributes to advancing our understanding of epidemic dynamics and informs decision-making processes for epidemic controlling interventions.

Mortality forecasting via multi-task neural networks

Abstract In recent decades, analysing the progression of mortality rates has become very important for both public and private pension schemes, as well as for the life insurance branch of insurance companies. Traditionally, the tools used in this field were based on stochastic and deterministic approaches that allow extrapolating mortality rates beyond the last year of observation. More recently, new techniques based on machine learning have been introduced as alternatives to traditional models, giving practitioners new opportunities. Among these, neural networks (NNs) play an important role due to their computation power and flexibility to treat the data without any probabilistic assumption. In this paper, we apply multi-task NNs, whose approach is based on leveraging useful information contained in multiple related tasks to help improve the generalized performance of all the tasks, to forecast mortality rates. Finally, we compare the performance of multi-task NNs to that of existing single-task NNs and traditional stochastic models on mortality data from 17 different countries.

Groundwater quality index and AI model for evaluating land use change impact in northern Iran; non-carcinogenic risk

ABSTRACT This study aimed to evaluate the groundwater quality index (GWQI) to evaluate the impact of land use change in northern Iran and the risk of non-carcinogenicity in Babol drinking water. Changes in the concentration of fluoride (F−) were predicted by the LightGBM algorithm. Using data collected between 2016 and 2021, the research utilized. In total, 228 water samples were collected from 38 wells between 2016 and 2021 analyzed for various chemical parameters to determine the GWQI. The results from the deterministic approach showed that 74.6% of water sources show good quality with the range of GWQI from 49.91 to 107.38. However, the probabilistic approach showed that only 0.01% of the water samples showed excellent quality, while the majority (98.37%) were classified as having good quality. Principal component analysis on parameters of chemicals showed three factors that account for 71.55% of the total variance in the groundwater quality dataset. The land use results showed that in residential areas where the population is dense, a higher concentration of NO3– is observed. The study also found that the noncarcinogenic risk caused by F− and NO3– was below the risk limit (HQ < 1) for all age groups, except for infants in the wet season.

Potential health risk estimation through deterministic and stochastic approach for the air pollutants found inside bottling beverage industries of Himachal Pradesh in India

The rapid advancement of global industrialization has significantly increased air pollution, particularly particulate matter (PM), black carbon (BC), and noise, impacting indoor air quality in urban areas and posing notable health risks. This study monitored total volatile organic compounds (TVOCs), noise, PM (PM10, PM2.5, PM1), and BC inside bottling and beverage industries during the monsoon season in Himachal Pradesh, India. The highest recorded values of parameters were 142 ppm (TVOC), 359 µg/m3 (PM10), 102 µg/m3 (PM2.5), 65 µg/m3 (PM1), 26 µg/m3 (BC), and 87 dB (noise). The health risk assessment revealed noncarcinogenic hazard ratios (HRs) ranging from 0.52 to 0.80, with the highest risk observed for males aged 50–60 and females aged 40–50. Carcinogenic risk attributed to BC was estimated between 5.8E−04 and 6.8E-04. The highest uncertainty (2.02%) was observed in the carcinogenic risk for BC among females aged 21–30. In comparison, the lowest uncertainty (0.36%) of risk was found in noncarcinogenic HR for PM2.5 among males of the same age group. While minimal variation for HR estimation (~ 0.87%) was found between deterministic and probabilistic approaches. Despite limitations inherent to its cross-sectional design, the study underscores significant occupational health risks due to indoor air pollution in industrial settings.Graphical

What can socialist geography contribute to the history of global geography? The interplay between continuity and change in post-war Czechoslovakia

ABSTRACT This study examines the interplay between continuity and change in geographical knowledge production, focusing on the transition from pre-war to post-war regional conceptualizations in Czechoslovakia. By comparing Jaromír Korčák's deterministic approach with Jaroslav Okrouhlý's planning-oriented perspective, the research analyzes a corpus of contemporary Czechoslovak geographical literature. It challenges simplistic narratives of paradigm shifts in geography's history, revealing a complex interplay between discursive practices emphasizing discontinuity and material practices enabling continuity. While post-war discourse emphasized a radical break from pre-war approaches, Korčák's regionalization remained influential through a process of “deconceptualization“. This process stripped away theoretical foundations, leaving only material cartographic visualization, allowing pre-war knowledge to be pragmatically adapted to post-war planning needs. The study contributes to debates on continuity and change in geographical thought, the nature of paradigms, and socialist geography's position within global geography. By examining how seemingly incompatible paradigms merged in practice, it highlights the hybrid nature of geographical knowledge production in peripheral contexts. It argues for a nuanced understanding of socialist geography beyond Cold War discourse, emphasizing both discursive and material dimensions in assessing geographical knowledge production and its (dis)continuities. This approach offers new insights into the global history of geography and knowledge production dynamics in peripheral traditions.

Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi‐Member and Stochastic Parameterizations

AbstractDeep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty quantification to learn subgrid convective and turbulent processes and surface radiative fluxes of a superparameterization embedded in an Earth System Model (ESM). We explore three methods to construct stochastic parameterizations: (a) a single Deep Neural Network (DNN) with Monte Carlo Dropout; (b) a multi‐member parameterization; and (c) a Variational Encoder Decoder with latent space perturbation. We show that the multi‐member parameterization improves the representation of convective processes, especially in the planetary boundary layer, compared to individual DNNs. The respective uncertainty quantification illustrates that methods (b) and (c) are advantageous compared to a dropout‐based DNN parameterization regarding the spread of convective processes. Hybrid simulations with our best‐performing multi‐member parameterizations remained challenging and crash within the first days. Therefore, we develop a pragmatic partial coupling strategy relying on the superparameterization for condensate emulation. Partial coupling reduces the computational efficiency of hybrid Earth‐like simulations but enables model stability over 5 months with our multi‐member parameterizations. However, our hybrid simulations exhibit biases in thermodynamic fields and differences in precipitation patterns. Despite this, the multi‐member parameterizations enable improvements in reproducing tropical extreme precipitation compared to a traditional convection parameterization. Despite these challenges, our results indicate the potential of a new generation of multi‐member machine learning parameterizations leveraging uncertainty quantification to improve the representation of stochasticity of subgrid effects.

Enhancing cold storage efficiency: Continuous deep deterministic policy gradient approach to energy optimization utilizing strategic sensor input data

Enhancing cold storage efficiency: Continuous deep deterministic policy gradient approach to energy optimization utilizing strategic sensor input data

A deep deterministic policy gradient approach for optimizing feeding rates and water quality management in recirculating aquaculture systems

Optimizing feeding rates in recirculating aquaculture systems (RAS) is crucial for ensuring fish growth, health, and system efficiency. This research introduces a novel approach to RAS feeding control using a Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm. The developed system integrates feeding rate optimization with comprehensive water quality management to improve overall performance and stability. The DDPG controller demonstrated superior tracking accuracy, reduced feed consumption, and improved operational stability compared to traditional control methods such as Model Predictive Control (MPC), PID, and Bang-Bang control. The learned policy-maintained feeding rates within optimal ranges while adapting to dynamic system requirements and environmental conditions. The integration of water quality monitoring and control further enhanced system stability, ensuring critical parameters remained within target ranges. Comparative analysis revealed the DDPG controller’s advantages in terms of faster recovery times after environmental perturbations, improved long-term stability, and significant economic benefits through reduced operational costs and increased efficiency. The robustness and adaptability of the system were validated through comprehensive testing under various fault conditions, growth phases, and system scales. The successful development and evaluation of the DDPG-based RAS feeding control system represents a significant advancement in aquaculture management. The demonstrated improvements in efficiency, stability, and economic performance establish the potential for this approach to revolutionize feeding practices in commercial RAS operations. Further research and development efforts can build upon these findings to advance the state-of-the-art in intelligent aquaculture management systems and promote the sustainability and profitability of the industry.

A stochastic programming approach for the operational fleet composition problem

A stochastic programming approach for the operational fleet composition problem

A Deterministic Approach to Analyze COVID-19 Reinfection Using a Susceptible Vaccination Exposed Quarantine Asymptomatic Infectious Treatment and Recovered (SVEQAITR) Model

In this paper, we present a mathematical model that incorporates Vaccination, Quarantine, and Treatment compartments into the Covid-19 reinfection model among human populations. Specifically, the model examines the impact of the disease on various population groups, including Susceptible (S), Vaccination (V), Exposed (E), Quarantine (Q), Asymptomatic infected (A), Symptomatic Infectious (I), Treatment (T), and Recovered (R) individuals. Emphasis is placed on the Positivity and Boundednes of solution of the developed SVEQAITR model. Differential equation method is used to systematically analyze the Covid-19 reinfection SVEQAITR model. Results obtained clearly shows that the dynamics of the population under study remains confined within the designated region, D. The non-negativity or Positivity solution also buttresses that the SVEQAITR model consists solely of positive values which affirms that we are dealing with human population.

A Deterministic Modeling Approach in Identifying the Optimal Screening for Human Immunodeficiency Virus Management

A Deterministic Modeling Approach in Identifying the Optimal Screening for Human Immunodeficiency Virus Management

Estimation of Damage Scenarios in the Mexico City Basin Caused by Local Crustal Earthquakes

ABSTRACT The recent seismic swarms in the Mexico City basin underscore the need to evaluate their potential implications for seismic hazard and risk in the capital city. Despite their small magnitude (Mc<3.3), these earthquakes caused unusually large ground accelerations, surpassing previous destructive earthquakes. This work aims to forecast seismic damage scenarios resulting from earthquakes in the Mexico City basin. It is assumed that earthquakes as large as Mw 5.5 may occur. Three scenarios were investigated where previous events took place. A deterministic approach estimates ground-motion intensity measures like peak ground acceleration and/or spectral pseudoacceleration, utilizing ground-motion models from comparable tectonic regions. Local site effects were based on the geotechnical zonation of the city. To estimate the damage, vulnerability curves were calculated for residential buildings in the cadastral record of Mexico City. In scenario 1, La Magdalena, ∼13% of 1–2-story buildings would be affected. In scenario 2, San Juan de Aragón, up to 15% of similar structures may be impacted. Scenario 3, Milpa Alta, shows about 5% damage for 1–2-story buildings, primarily concentrated to the southeast. In the three scenarios, 1- and 2-story buildings suffer damage, independent of the geotechnical region. For 3–5-story buildings, damage is concentrated in the lake zone of the basin, where soft sediments underlie the city. For buildings 6–10 stories high, damage lies preferentially in the transition zone between the soft sediments and the highlands. No appreciable damage is observed for buildings taller than 11 stories in any scenario. Local crustal earthquakes, as considered here, represent a significant risk to Mexico City. Outlining damage scenarios may help decision-makers and insurance entities implement mitigation measures to reduce potential impacts on residential structures.