Modeling Methodology Research Articles

Theory of time constant correlation of a porous bed thermal energy storage tank - Experimental and numerical proof of concept

Analytical modeling of energy systems is used to estimate the potential of the system, but several simplifications used lead to progressive deviations from the actual potential. Thermal Energy Storage tanks are most often treated as black boxes, by limiting their characteristics to their energy efficiency and basic capacity only. This paper introduces the theory of a time constant that correlates the basic parameters of a porous bed heat storage tank and allows the charge level of the tank to be determined during the charging stage. The correlation for the time constant has a coefficient that has been fully validated both experimentally and numerically for a wide range of parameters. Coefficient values for different precision test runs have been indicated, and the prediction deviation using the time constant does not exceed a value of 5 % relative to the actual results. The proposed methodology for the analytical model of the tank accurately represents the cyclic operation of the heat storage tank. It has also been shown that the cyclic operation of the heat storage tank fixes the charge level in the range of 0.16–0.79. The methodology presented can be used to modelling and designing energy systems with heat storage.

PID Controller Design for an E. coli Fed-Batch Fermentation Process System Using Chaotic Electromagnetic Field Optimization

This paper presents an optimal tuning of a proportional integral differential (PID) controller used to maintain glucose concentration at a desired set point. The PID controller synthesizes an appropriate feed rate profile for an E. coli fed-batch cultivation process. Mathematical models are developed based on dynamic mass balance equations for biomass, substrate, and product concentration of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation for bacterial phytase extracellular production. For model parameter identification and PID tuning, a hybrid metaheuristic technique—chaotic electromagnetic field optimization (CEFO)—is proposed. In the hybridization, a chaotic map is used for the generation of a new electromagnetic particle instead of the electromagnetic field optimization (EFO) search strategy. The CEFO combines the exploitation capability of the EFO algorithm and the exploration power of ten different chaotic maps. The comparison of the results with classical EFO shows the superior behaviour of the designed CEFO. An improvement of 30% of the objective function is achieved by applying CEFO. Based on the obtained mathematical models, 10 PID controllers are tuned. The simulation experiments show that the designed controllers are robust, resulting in a good control system performance. The closed-loop transient responses for the corresponding controllers are similar to the estimated models. The settling time of the control system based on the third PID controller for all estimated models is approximately 9 min and the overshoot is approximately 15%. The proposed CEFO algorithm can be considered an effective methodology for mathematical modelling and achievement of high quality and better performance of the designed closed-loop system for cultivation processes.

Large-sample detection of reservoir impacts on flow regime alteration through improved paired-catchment approach

The 20th century witnessed a substantial surge in global reservoir construction, representing a significant milestone and resulting in a profound anthropogenic alteration of flow regimes. Despite this surge, a comprehensive large-sample quantification of reservoir impacts on flow regime alteration has been impeded by limitations in statistical and modeling methodologies. Here we present an improved paired-catchment method tailored to discern the impact of reservoirs on flow regime within the context of climate change. Employing this method, we identified and analyzed 177 paired catchments from a selection of over 12,000 global catchments. Our findings highlight the effectiveness of the improved method, demonstrating that reservoirs predominantly alleviate high flows while enhancing low flows. This serves to moderate the annual periodicity of streamflow signals while amplifying their low-frequency components. Furthermore, predetermined water releases play a significant role in diminishing the seasonality of low flows and reducing the sensitivity of runoff to precipitation. Additionally, the extent of flow regime alteration caused by reservoirs is closely linked to their functionalities and regulatory capacity. For example, reservoirs designed for irrigation and water supply show minimal enhancement of low flow values, while the magnitude of reservoir-induced changes rises with higher regulatory capacity. The improved method can be further employed in the future to investigate the impact of reservoirs on extreme hydrological events such as droughts and floods. It emerges as a crucial scientific tool for effective water resource management and the mitigation of water-related disasters in the Anthropocene era.

Kinetic study on the thermal decomposition of iron (II) sulfate using a global optimization approach

The thermal decomposition of sulfates is a major intermediate reaction in oxidizing roasting processes associated with metal sulfides. The optimization of iron (II) sulfate could contribute to lowering processes’ temperatures in complex systems. The present work presents the experimental and modeling results of the thermal decomposition of iron (II) sulfate heptahydrate. The investigation was first based on thermodynamic simulations followed by non-isothermal thermogravimetric analysis. The modeling technique consisted of differential equations formulated to describe each decomposition step individually, with the kinetic parameters estimated using particle swarm optimization (PSO). Using this modeling methodology over a graphical method is advantageous as it can simulate reactions that may occur simultaneously. The modeling succeeded in dehydration and desulfation steps, with an overall R2 value of 0.99. The activation energies and apparent reaction order associated with the dehydration steps were 53.3kJ.mol−1/n=1.65, 88.8kJ.mol−1/n=1.12, and 124kJ.mol−1/n=2.0 for FeSO4.7H2O, FeSO4.4H2O, and FeSO4.H2O, respectively. The desulfation steps were associated with higher activation energy values. In that case, 184kJ.mol−1 and 264kJ.mol−1 for FeSO4 and Fe2(SO4)3, respectively. Regarding the apparent reaction order, both desulfation steps showed to be first-order reactions.

Hidden desires, echoed distress: Dissecting Nigeria’s sexting landscape and its ties to depression

In a broader context of increasing incidences of sexting-related backlashes resulting in victims’ depression and, at times, suicide in Nigeria, this study examines the prevalence, trends, and mental health implications of sexting among 700 Nigerian social media users. With the help of the Patient Health Questionnaire-9 and the Sexting Behaviors and Motives Questionnaire, we found that 58% of respondents engaged in sexting, a high percentage given the cultural conservatism of Nigeria. In addition, more than 41% admitted forwarding or having another forward sexted images or messages without the victims’ consent, increasing the risk of cyberbullying and subsequent mental health problems. In our study, we found a strong positive relationship between sexting and depression; the effects of sexting on depression differed for men and women: Men sexters exhibited higher depression levels than women. Our analysis, which employed descriptive, regression, and Structural Equation Model (SEM) methodologies, suggests that despite regional cultural disparities, sexting behaviors are surprisingly uniform across Nigeria. This study underscores the urgent need for informed strategies addressing digital privacy, security, and mental well-being in the context of sexting in Nigeria.

CFD with LIDAR applied to buildings and vegetation for environmental construction

The influence of urban vegetation on buildings, particularly regarding the alteration of wind patterns, poses a significant challenge for building designers and managers. This paper presents recommendations for a health reference guide that are graphically and accurately depicted. The research applied Building Information Modelling (BIM) methodology and Computational Fluid Dynamics (CFD) to visualize urban comfort conditions. The results were used to evaluate two simulation scenarios aimed at studying the effect of green infrastructure on wind patterns. The first scenario considered the absence of vegetation, while the second included green infrastructure with specimens from two different species. The findings suggest potential improvements in the quality of life for the general population, particularly for individuals with respiratory allergy issues.

Keys to Successful Civil Servant Performance in the Digital Era: Explaining the Role of Visionary-Participative Leadership and Digital Technology Literacy

This study investigates the correlation between visionary-participative leadership style, digital technology literacy, employee involvement, and civil servant performance within the digital transformation context of the Semarang City Regional Revenue Agency. Employing a quantitative methodology, a survey of 105 staff members is conducted. The investigation in this research utilized Structural Equation Modeling methodologies employing the Partial Least Squares (PLS-SEM) framework. This study underscores the significance of adopting a holistic method toward advancing human capital by combining the improvement of digital competencies with tactics to enhance employee involvement. Study findings indicate that digital technology literacy skills substantially and constructively influence employee engagement and performance. Visionary-participative leadership positively impacts employee engagement, yet its direct effect on performance is deemed insignificant. Employee involvement is demonstrated to have a significant and favorable impact on performance. Theoretical and practical implications emphasize the necessity of crafting a more tailored digital transformation framework for the public sector and the importance of revamping leadership development initiatives to address the intricacies of bureaucracy in the digital age.

Optimal caudal needle angulation for lumbar medial branch denervation: A 3D cadaveric and clinical imaging comparison study

BackgroundLumbar medial branch (MB) radiofrequency ablation is a common intervention to treat facetogenic low back pain. The consensus among spine pain interventionalists is that capturing a greater length of the MB correlates with a longer duration of pain relief. Therefore, there has been interest in defining optimal needle angles to achieve parallel cannula placement. Presently, there is inconsistency regarding the optimal caudal needle angles. ObjectivesThe objectives of this study were to: 1) use a dissection-based 3D modelling methodology to quantify optimal caudal needle angles from cadaveric models; and 2) compare optimal cadaver-derived caudal needle angles with real-world patient-derived needle angles. MethodsEighteen formalin embalmed lumbosacral spine specimens were dissected, digitized, and modelled in 3D. Virtual needles were simulated and placed parallel with the L1-L5 MBs. Cadaver-derived caudal needle angles were measured from the high-fidelity 3D models with optimally placed virtual needles. Lateral fluoroscopic images of patients (n = 200) that received lumbar MB denervation were reviewed to measure patient-derived caudal needle angles (L3-L5 MB levels). Descriptive statistics were used to analyze the cadaver (L1-L5 MB levels) and patient-derived (L3-L5 MB levels) caudal needle angles. The cadaver and patient-derived mean caudal needle angles for L3-L5 MB levels were compared. ResultsThere was variability in the cadaver-derived mean caudal needle angles. The lowest mean caudal needle angle was the L1 MB level measured at 41.57 ± 8.56° (range: 27.14° - 53.96°). The highest was the L5 MB level with a mean caudal needle angle of 60.79 ± 8.55° (range: 46.97° - 79.74°). A total of 123 patients were included and 369 caudal needle angles (L3-L5 MB levels) were measured and analyzed. There was variability in the patient-derived mean caudal needle angles. The patient-derived mean caudal needle angles were 29.18 ± 8.77° (range: 11.80° - 61.31°), 33.34 ± 7.23° (range: 16.40° - 54.15°), and 49.08 ± 8.87° (range: 26.45° - 76.95°) for the L3, L4, and L5 MB levels, respectively. There was a significant difference in mean caudal needle angle between cadaver and patient-derived needle angles at the L3, L4, and L5 MB levels. ConclusionsAnalysis of cadaver-derived needle angles versus patient-derived data suggests optimization of lumbar MB denervation requires greater caudal angulation to achieve parallel needle placement. Further research is required to assess the clinical implications.

Environmental variables improve the accuracy of remote sensing estimation of soil organic carbon content

Accurately and quickly estimating the soil organic carbon (SOC) content is crucial in the monitoring of global carbon. Environmental variables play a significant role in improving the accuracy of the SOC content estimation model. This study focuses on modeling methodologies and environmental variables, which significantly influence the SOC content estimation model. The modeling methods used in this research comprise multiple linear regression (MLR), partial least squares regression (PLSR), random forest, and support vector machines (SVM). The analyzed environmental variables include terrain, climate, soil, and vegetation cover factors. The original spectral reflectance (OSR) of Landsat 5 TM images and the spectral reflectivity after the derivative processing were combined with the above environmental variables to estimate SOC content. The results showed that: (1) The SOC content can be efficiently estimated using the OSR of Landsat 5 TM, however, the derived processing method cannot significantly improve the estimation accuracy. (2) Environmental variables can effectively improve the accuracy of SOC content estimation, with climate and soil factors producing the most significant improvements. (3) Machine learning modeling methods provide better estimation accuracy than MLR and PLSR, especially the SVM model which has the highest accuracy. According to our observations, the best estimation model in the study area was the “OSR + SVM” model (R2 = 0.9590, RMSE = 13.9887, MAE = 10.8075), which considered four environmental factors. This study highlights the significance of environmental variables in monitoring SOC content, offering insights for more precise future SOC assessments. It also provides crucial data support for soil health monitoring and sustainable agricultural development in the study area.

Numerical analysis of laminar velocity-forced premixed slit flames using modal decomposition techniques

The relation between Proper Orthogonal Decomposition (POD), Dynamic Mode Decomposition (DMD) and Flame Transfer Functions (FTF) is explored to gain further insight into the dynamics of two-dimensional laminar slit flames externally forced by velocity perturbations. The application of POD to the heat release rate fields suggests that the resultant modes can be split into two groups: the ones that are related to the displacement of the reactive sheet in the normal direction with regards to the flame front, and the ones that contain the local flame front distortions. The latter modes show preferential frequencies associated to the phase values of π, 2π and 3π of the FTFs, which can be related to the maximum gain value depending on the case. Furthermore, the results of the modal analysis seem to support that the flame tip dynamics can be conceptually modelled as a set of standing waves whose joint response can reconstruct a propagation in the normal flame front direction, generating also the temporal fluctuations of the spatially-integrated heat release in the domain. The DMD analysis shows the existence of an interaction between the flame and the flow, and illustrates the fundamental role played by the velocity perturbations at the base for the motion of the reactive sheet. Thus, the analysis shows how data-based decomposition methods can be used to identify complex physical phenomena contained in the FTF graphs with different levels of detail, and extend the modal analysis to the physical space.Novelty and significance statementThis paper proves the potential of modal decomposition techniques like POD and DMD to infer the underlying physics of canonical flames and their relation to the FTF, validating also the hypotheses and methodology of other reduced-order models in the literature. A canonical configuration featuring a two-dimensional slit flame has been selected to analyse the flame dynamics using these methods. Hence, this work aims to set the foundations to elaborate data-driven reduced-order models based on modal decomposition techniques to support the analysis and the study of flame dynamics.

Comparing microstructural and micromechanical deformation of the TMJ disc in two anterior disc displacement models.

Anterior disc displacement (ADD) has been used to establish temporomandibular joint disorder (TMD) models. Based on whether preserve of the retrodiscal attachment, the modelling methodologies include ADD with dissecting the retrodiscal attachment (ADDwd) and ADD without dissecting the retrodiscal attachment (ADDwod). This article aims to determine which model better matches the micromechanical and microstructural progression of TMD. Through meticulous microscopic observations, the microstructure and micromechanical deformation of the TMJ discs in ADDwd and ADDwod rabbit models were compared at 2 and 20 weeks. Scanning electron microscopy and transmission electron microscopy showed that collagen fibres became slenderized and straightened, collagen fibrils lost diameter and arrangement in the ADDwd group at 2 weeks. Meanwhile, nanoindentation and atomic electron microscopy showed that the micro- and nano- mechanical properties decreased dramatically. However, the ADDwod group exhibited no significant microstructure and micromechanical deformations at 2 weeks. Dissection of the retrodiscal attachment contribute in the acceleration of disease progression at the early stage, the devastating discal phenotype remained fundamentally the same within the two models at 20 weeks. ADDwod models, induced stable and persistent disc deformation, therefore, can better match the progression of TMD. While ADDwd models can be considered for experiments which aim to obtain advanced phenotype in a short time.

Diagnosing the MRI brain tumour images through RNN-LSTM

According to the World Health Organization, cancer is the second most common cause of death worldwide (WHO). Death from cancer may sometimes be avoided through early diagnosis, although this is not always practicable. A tumour, unlike cancer, may be benign, pre-cancerous, or malignant. Contrary to malignant tumours, benign tumours may frequently be surgically removed and seldom metastasize to other organs or tissues. The method most often used to differentiate between various types of tumours is magnetic resonance imaging (MRI) (MRI). But because it depends on human subjectivity, it could be challenging for one person to see a lot of data. The radiologist's ability to identify brain tumours early mostly depends on their level of training. Before determining whether the tumour is benign or malignant, the diagnostic process for it could not be finished. A biopsy is often carried out to determine if the tissue is benign or cancerous. The biopsy of a brain tumour is often delayed until the last brain operation, in contrast to tumours discovered elsewhere in the body. An efficient diagnostics tool for tumour segmentation and classification from MRI images is crucial to obtaining accurate diagnoses, avoiding surgery, and eliminating subjectivity. The classification and segmentation of the tumour part of the MRI brain picture is the goal of this study. Prior to feature extraction in the first study, pre-processing is done using the Gray Level Co-occurrence Matrix (GLCM). Based on these features, the data is then separated into normal, glioma, meningioma, and pituitary groups. In prior studies, classification was handled by traditional Deep Neural Networks (DNN), with the best weights selected using optimization methods. Accordingly, a recent study recommends including Long Short-Term Memory based on Recurrent Neural Networks (RNN) (LSTM). The Local Direction Ternary Pattern (LDTP), the Gray Level Co-occurrence Matrix (GLCM), and the Le-Net features are all combined in the proposed Hybrid GLCM-LDTP-Le-Net Feature extraction approach. The hybrid parameters produced by the feature extraction process and the brain MRI data are used by the RNN-LSTM model to categorise the data as benign or malignant. By removing mixed features from the input data, the suggested hybrid feature extraction improves the learnt models' accuracy. The accuracy of 98.99 % of the suggested hybrid feature extraction approach was higher than the current CNN-based model methodologies.

Data-driven aeroelastic analyses of structures in turbulent wind conditions using enhanced Gaussian Processes with aerodynamic priors

Recent advancements in data-driven aeroelasticity have been driven by the wealth of data available in the wind engineering practice, especially in modeling aerodynamic forces. Despite progress, challenges persist in addressing free-stream turbulence and incorporating physics knowledge into data-driven aerodynamic force models. This paper presents a hybrid Gaussian Process (GPs) methodology for non-linear modeling of aerodynamic forces induced by gusts and motion on bluff bodies. Building on a recently developed GP model of the motion-induced forces, we formulate a hybrid GP aerodynamic force model that incorporates both gust- and motion-induced angles of attack as exogenous inputs, alongside a semi-analytical quasi-steady (QS) model as a physics-based prior knowledge. In this manner, the GP model incorporates the absent physics of the QS model, and the non-dimensional hybrid formulation enhances its appeal from an aerodynamic perspective. We devise a training procedure that leverages simultaneous input signals of gust angles, based on random free-stream turbulence, and motion angles, based on random broadband signals. We verify the methodology through analytical linear aerodynamics of a flat plate and non-linear aerodynamics of a bridge deck using Computational Fluid Dynamics (CFD). The standout feature of the presented methodology is its applicability for aeroelastic buffeting analyses, showcasing robustness when handling broadband excitation. Importantly, the non-linear hybrid model preserves its capability to capture higher-order harmonics in the motion-induced forces and remains applicable for flutter analysis, while incorporating both motion and gust angles as input. Applications of the methodology are anticipated in the aeroelastic analysis and monitoring of slender line-like structures.

Impact of oxygen and glucose availability on the viability and connectivity of islet cells: A computational study of reconstructed avascular human islets.

The experimental study and transplantation of pancreatic islets requires their isolation from the surrounding tissue, and therefore, from the vasculature. Under these conditions, avascular islets rely on the diffusion of peripheral oxygen and nutrients to comply with the requirements of islet cells while responding to changes in body glucose. As a complement to the experimental work, computational models have been widely used to estimate how avascular islets would be affected by the hypoxic conditions found both in culture and transplant sites. However, previous models have been based on simplified representations of pancreatic islets which has limited the reach of the simulations performed. Aiming to contribute with a more realistic model of avascular human islets, in this work we used architectures of human islets reconstructed from experimental data to simulate the availability of oxygen for α, β and δ-cells, emulating culture and transplant conditions at different glucose concentrations. The modeling approach proposed allowed us to quantitatively estimate how the loss of cells due to severe hypoxia would impact interactions between islet cells, ultimately segregating the islet into disconnected subnetworks. According to the simulations performed, islet encapsulation, by reducing the oxygen available within the islets, could severely compromise cell viability. Moreover, our model suggests that even without encapsulation, only microislets composed of less than 100 cells would remain viable in oxygenation conditions found in transplant sites. Overall, in this article we delineate a novel modeling methodology to simulate detailed avascular islets in experimental and transplant conditions with potential applications in the field of islet encapsulation.

Reliability-based Design Optimization (RBDO) Study Applied to the Thermal Management of a Multi-Led Chip Package

Solid-state lighting based on LEDs is used in various applications, including display, communications, etc. However, the high junction temperature is still challenging due to the LED chip’s reduced light output and lifetime. To face this challenge, a thermal study was done to determine junction temperature TJ of a multi-led chip package. Then, a sensitivity analysis of different materials was performed using the Sobol method to identify the parameters that most influence the junction temperature. To calculate the probability of failure of the model, the FORM-SORM and Monte Carlo methodologies were employed in this study. It was obtained that the probability of failure of the LED package is roughly 20%. An optimum design was created using reliability-based design optimization (RBDO) to lower this percentage. After application of this method, the junction temperature was lowered by 11% and the reliability level increased from 80% to 91%.

Vulnerability of Physical Infrastructure Network Components to Damage from the 2015 Illapel Tsunami, Coquimbo, Chile

AbstractThis study assesses physical infrastructure vulnerability for infrastructure network components exposed during the 2015 Illapel tsunami in Coquimbo, Chile. We analyse road and utility pole vulnerability to damage, based on interpolated and simulated tsunami hazard intensity (flow depth, flow velocity, hydrodynamic force and momentum flux) and network component characteristics. A Random Forest Model and Spearman’s Rank correlation test are applied to analyse variable importance and monotonic relationships, with respect to damage, between tsunami hazards and network component attributes. These models and tests reveal that flow depth correlates higher with damage, relative to flow velocity, hydrodynamic force and momentum flux. Scour (for roads and utility poles) and debris strikes (for utility poles) are strongly correlated with damage. A cumulative link model methodology is used to fit fragility curves. These fragility curves reveal that, in response to flow depth, Coquimbo roads have higher vulnerability than those analysed in previous tsunami event studies, while utility poles demonstrate lower vulnerability than with previous studies. Although we identify tsunami flow depth as the most important hydrodynamic hazard intensity metric, for causing road and utility pole damage, multiple characteristics correlate with damage and should also be considered when classifying infrastructure damage levels.

2248 Clinical relevance of Donanemab treatment

Abstract Objective To assess in Alzheimer’s disease (ad), the treatment impact of donanemab, an amyloid plaque-reducing monoclonal antibody, on readily interpretable item-measures and constructs that matter to patients, care-partners, and clinicians. Background Positive outcomes were reported from TRAILBLAZER-ALZ2, a randomised, double-blind, placebo-controlled, 18-month, phase 3 study evaluating donanemab as an investigational treatment for mild cognitive impairment (MCI) or mild dementia due to ad. In 1736 participants, donanemab significantly slowed the rate of clinical decline (by 22–36%) as measured by the integrated ad Rating Scale (iADRS) and the Clinical Dementia Rating Scale—Sum-of-Boxes (CDR-SB); both measures of cognition and function as indications of global clinical severity. In these subsequent post-hoc exploratory analyses, the impact of donanemab treatment on individual iADRS cognition and function items, CDR domains, and risks of advancing to greater disease severity were assessed. Methods Mixed model repeated measures and Cox proportional hazard modelling methodology assessed treatment effects on iADRS items and CDR domains. Results Donanemab treatment was associated with significant beneficial effects on: 1) iADRS cognitive items related to episodic memory and executive function, and instrumental activities of daily living items related to communication and others (e.g. being left alone, making a meal, using household appliances); 2) all CDR-SB cognitive and functional domains (i.e. memory, orientation, judgement/problem solving, community affairs, home/hobbies, and personal care); and 3) lowering risk of progression to a more advanced clinical stage of disease. Conclusions These analyses explored the impact of donanemab treatment on constructs that matter to and are considered more readily interpretable by patients, care-partners, and clinicians. These results provide further support that treating those with MCI or mild dementia due to ad with donanemab can meaningfully reduce risk of progression to more severe clinical stages (e.g. moderate stage dementia), and potentially allow greater independence for a longer period of time.

Exploring family supportive supervisor behaviours from a police perspective to ensure life satisfaction: an empirical investigation using importance-performance analysis

PurposeThe primary aim of this research is to analyse the impact of family-supportive supervisor behaviours (FSSB) on the level of life satisfaction among individuals working in the police force. FSSB is informal support by supervisors, which is an emerging topic. Further, this study examines the intermediary role of career calling and job embeddedness in the association between supportive family behaviours of supervisors and life satisfaction. However, only limited studies have been conducted on how informal support by the supervisor performs for police personnel. This research aims to fill this gap.Design/methodology/approachFor the purpose of data collection from police personnel, we used a structured questionnaire. Variance-based partial least squares structural equational modelling (PLS-SEM) methodology was applied using SmartPLS software to investigate the conceptual model of the study. Additionally, using importance-performance map analysis (IMPA), we evaluate the importance and performance of each construct.FindingsThe data analysis findings indicate that job embeddedness and career calling function as complete mediating factors in the association between FSSB and life satisfaction. Additionally, the study revealed that career calling is the most performing construct for promoting life satisfaction but is not perceived as important by the police department.Originality/valueAccording to the authors’ knowledge, there has been no research in the Indian context that examines the effects of supervisor family supportive behaviours, especially among police. The incorporation of career calling and job embeddedness as mediators provides a novel perspective in the examination of the correlation between FSSB and life satisfaction.

Formal Modeling and Verification of Lycklama and Hadzilacos’s Mutual Exclusion Algorithm

This study describes our thorough experience of formal modeling and exhaustive verification of concurrent systems, particularly mutual exclusion algorithms. The experience focuses on Lycklama and Hadzilacos’s (LH) mutual exclusion algorithm. LH rests on the reduced size of the shared state, contains a mechanism that tries to enforce an FCFS order to processes entering their critical section, and embodies Burns and Lamport’s (BL) mutual exclusion algorithm. The modeling methodology is based on timed automata and the model checker of the popular Uppaal toolbox. The effectiveness of the modeling and analysis approach is first demonstrated by studying the BL’s solution and retrieving all its properties, including, in general, its unbounded overtaking, which is the non-limited number of by-passes a process can suffer before accessing its critical section. Then, the LH algorithm is investigated in depth by showing it fulfills all the mutual exclusion properties when it operates with atomic memory. However, as this study demonstrates, LH is not free of deadlocks when used with non-atomic memory. Finally, a state-of-the-art mutual exclusion solution is proposed, which relies on a stripped-down LH version for processes, which is used as the arbitration unit in a tournament tree (TT) organization. This study documents that LH’s TT-based algorithm satisfies all the mutual exclusion properties, with a linear overtaking, both using atomic and non-atomic memory.

Hydrodynamic modeling of dam breach floods for predicting downstream inundation scenarios using integrated approach of satellite data, unmanned aerial vehicles (UAVs), and Google Earth Engine (GEE)

Dam breach floods pose significant threats to downstream areas, necessitating accurate prediction of inundation scenarios to mitigate potential damage. This paper presents a novel methodology for hydrodynamic modeling of dam breach floods, leveraging a comprehensive approach that integrates satellite imagery, unmanned aerial vehicles (UAVs), and Google Earth Engine (GEE) to forecast downstream inundation scenarios. Specifically, UAVs were utilized to generate high-resolution Digital Elevation Models (DEMs) of the flood-affected areas, ensuring precise representation of topography in the model. The approach incorporates Cartosat DEM data for catchment modeling, while NASA's Global Precipitation Measurement mission data, integrated with GEE, facilitated accurate estimation of rainfall in ungagged catchment areas. Furthermore, the Hydrological Engineering Center-Hydrological Modeling System was employed for rainfall-runoff simulation and flood hydrograph derivation, followed by application of the HEC River Analysis System (RAS) for hydrodynamic modeling under dam breach conditions. This integrated modeling approach was applied as a case study of Banaskantha district, Gujarat, India. The outcome was the generation of scenario maps based on HEC-RAS results, which include flood extent, water depth, and flow velocity, highlighting downstream areas affected by flooding. Validation of the hydrodynamic dam breach model performance was conducted using actual field measurements and simulated results, employing statistical analysis methods including Support Vector Regression (SVR) and linear regression to determine coefficient of determination (R2), Root-Mean-Square Error, and Mean Absolute Error of observed and simulated data. The coefficient of determination (R2) values for measured and simulated flow (0.91) and water level (0.86) calculated using SVR demonstrate strong correlation between observed and simulated values. This integrated study of hydrodynamic modeling in data-scarce areas aids in accurate estimation of future probable flooding in downstream areas in the event of a dam break, underscoring the potential of advanced surveying and modeling techniques in flood assessment and management. Ultimately, this integration of technologies aims to enhance community resilience and mitigate socioeconomic costs associated with dam breach floods.