Sensitivity Analysis Research Articles

Mixed convection and sensitivity analysis of impinging jet flow of engine oil on rotating heated cylinder with high Prandtl numbers

This work investigates the impinging engine oil jet flow on a rotating heated cylinder with high Prandtl numbers through a combination of convection and sensitivity analysis. The impinging jet has an inlet in the middle of the upper wall and two outlets on its left and right sides. The outlets experience zero gradients, and the inlet is thermally cold. The finite element method (FEM) is used to solve the governing equations. The heated cylinder can rotate clockwise and counter-clockwise direction at ω and −ω, respectively. The impinging jet’s upper and lower walls are kept at a cold temperature (Tc). The Prandtl number (10≤Pr≤1000), Reynolds number (100≤Re≤300), angular rotational velocity (−30≤ω≤30), and Richardson number (Ri=1) are among the important parameters that have been numerically simulated. Streamlines, isotherms, velocity, temperature, local and average Nusselt numbers, and sensitivity analysis are used to display the results. Higher Reynolds and Prandtl numbers correspond to an increase in the local Nusselt number. When the Prandtl number rises from Pr=50 to 100 and Pr=1000 at ω=0, the average Nusselt number, Nu¯, increases by 28.2% and 191.85%. A sensitivity analysis was performed using variance analysis (ANOVA). The sensitivity for Pr, Re, and ω is positive at the highest level of Pr, lowest and medium levels of ω, and increases with higher levels of Re.

The impact of tea consumption on the risk of depression: A Mendelian randomization and Bayesian weighting algorithm study.

The precise impact of tea consumption on the risk of depression remains unclear. This study aimed to explore the relationship between the consumption patterns of tea and the likelihood of depression onset, utilizing a two-sample Mendelian randomization (MR) methodology. We utilized available genome-wide association study (GWAS) datasets on tea intake and depressive disorders. To investigate the causal relationship between tea consumption and depression, we employed a set of two-sample Mendelian Randomization (MR) methods. These included the inverse-variance weighted (IVW) analysis, weighted median approach, and MR-Egger regression. Additionally, we utilized MR-PRESSO and the MR-Egger intercept test for the detection of pleiotropic effects. To ensure the robustness and consistency of our findings, a sensitivity analysis was carried out, applying the 'leave-one-out' strategy. The Bayesian weighted Mendelian randomization (BWMR) was employed to conduct additional testing on the obtained results. The study's outcomes revealed a causal association between increased tea intake and an increased risk of depression (Inverse-Variance Weighted Analysis: Odds Ratio [OR] = 1.029, 95% Confidence Interval [CI]: 1.003-1.055, p = 0.027). This was observed despite variations in instrumental variables and the nonexistence of horizontal pleiotropy. Furthermore, the robustness of our Mendelian Randomization investigation was affirmed through the implementation of the 'leave-one-out' method in our sensitivity analysis. The findings from BWMR were in line with those obtained from IVW (BWMR: OR=1.030, 95% CI: 1.003-1.057, p = 0.029). The results from this study indicate a substantial and positive causal link between the regularity of tea drinking and the risk of depression onset.

Sensitivity and uncertainty analysis of neutron activation source terms based on statistical sampling method

The calculation of neutron activation source terms is an essential task in the radiation of nuclear reactors, and its sensitivity and uncertainty (S&U) analysis can help better identify and control the influencing factors and safety margin of activation source terms. In this paper, we studied the statistical sampling strategies for the S&U analysis of activation source terms, established the calculation process of sensitivity coefficient and uncertainty, and the S&U analysis module based on sampling methods was integrated for the proprietary developed activation computation program ABURN. Two sampling ways, Monte Carlo and Latin hypercube sampling treatments, were employed to figure out the cases of pure decay single chain, pure activation single chain, and complex reaction network. The sensitivity coefficients and uncertainty of nuclide inventory were evaluated by taking the uncertainty of decay constant or reaction cross-section into account. The simulation results indicate that after introducing the LHS sampling method, the activation calculation program ABURN can improve computational efficiency in sensitivity and uncertainty analysis. Subsequent results demonstrated that the sensitivity coefficients and uncertainty solutions based on the sampling methods in the ABURN program were in good agreement with the analytical solutions and the FISPACT program, which verified the accuracy of the approaches and program in this paper.

Association between cellular immune and preeclampsia and preterm birth: a Mendelian randomization study

Emerging evidences have highlighted immune-inflammatory imbalances as a critical driver of the pathogenesis for preeclampsia (PE) and preterm birth (PB), but the impact of specific immune factors on the diseases is largely unknown. Our aim was to determine whether these immune cells are causally associated with the onset of PE or PB. Drawing on publicly available genetic data, we applied Mendelian randomization analysis to probe the causal link of 731 immune traits (7 panels: TBNK panel, Regulatory T cells panel, Maturation stages of T-cell panel, Dendritic cell panel, B-cell panel, Monocyte panel and Myeloid cell pane) with the risk of preeclampsia and preterm birth. The inverse variance weighting method (IVW) acted as the primary analysis to estimate the validity of causality, and sensitivity analyses were conducted to assessment of heterogeneity and pleiotropy. After adjusting for P-values for FDR method, CD27 on CD24+ CD27+ B cell, CD80 on plasmacytoid Dendritic Cell, CD33+ HLA DR+ CD14dim Absolute Count, CD33+ HLA DR+ CD14- Absolute Count, CD33+ HLA DR+ Absolute Count were remarkably causally involved in increased risk of PE, while HLA DR on Dendritic Cell exerted a protective causation against PE (PFDR < 0.05). Moreover, we determined that CD45 on CD33dim HLA DR- in myeloid cells decreased PB risk, whereas CD11b on Granulocytic Myeloid-Derived Suppressor Cells had the opposite effect on PB (FFDR < 0.2). This study provided genetic evidence for causal relationships between immune cell traits and PE and PB, offering potential candidate immunophenotypes for future studies on the etiology of pregnancy complications.

A self-consistent design method of the autothermal dual circulating fluidized bed reactor for in-situ gasification chemical looping combustion of coal

This paper establishes a design method for the autothermal dual circulating fluidized bed reactor (DCFBR) of the coal-fueled in-situ gasification chemical looping combustion (iG-CLC) process. This method, grounded in a self-consistent phenomenological model, comprehensively elucidates the factors of mass and energy conservation, fuel and oxygen carrier (OC) reaction processes, and fluidization characteristics within the reactor. It is particularly suitable for the rapid screening of numerous potential designs, obtaining detailed design parameters, and studying their interrelationships. Utilizing this method, the regulation patterns of dual-bed interactive transport-reaction phenomena within a 5 MWthiG-CLC DCFBR are studied. Firstly, the effects of key design parameters on the OC circulation rate and bed inventory are analyzed. Following this, the impacts of circulation rate on the interactive heat and mass transfer between two beds are examined, delineating a reasonable range for the control of autothermal CLC process (circulation rate of 50–90 kg/m2s, temperature difference of 30–90 °C, OC conversion of 0.2–0.35, and oxygen-fuel ratio of 3–6). Subsequently, this paper investigates the influences of main design parameters on the performance metrics, such as gas/solid fuel conversion, operational costs, and operational benefits. It is found that the carbon stripper significantly enhances the carbon capture efficiency of the device, provided that its separation efficiency is maintained above 70–95 %. Sensitivity analysis is employed to study the response patterns of performance metrics to changes in input parameters. Ultimately, based on these analyses, a design scheme for the 5 MWth reactor is determined, with a detailed examination of the pressure balance state of the reactor under the design conditions, and the balanced material and energy flows at the reactor inlets and outlets.

An iterative method for integrated hump sequencing, train makeup, and classification track assignment in railway shunting yard

In a railway shunting yard, the transformation of inbound trains into properly composed outbound trains is a complex task because it involves decisions of multiple operations processes. This study addresses the integrated optimization of hump sequencing, train makeup, and classification track assignment problem in a railway shunting yard. Several key practical yard operation constraints are considered, including train formulation constraints, hump sequencing constraints, and limitations of the maximum number and capacity of classification tracks. By introducing a new representation of block flow, the integrated problem, which adopts the extended single-stage strategy and the train-to-track policy, is formulated as a unified 0-1 integer linear programming model. The objective of the proposed model is to minimize the weighted-sum of the total dwell time of all railcars and the formulation deviation penalties of all outbound trains. Then, an iterative two-phase decomposition approach is developed to reduce the complexity of solving the integrated problem. The first phase aims to explore all feasible humping sequences using a Branch-and-Bound (B&B) algorithm. Each time a new humping sequence is generated in the first phase, the second phase containing a Branch-and-Price (B&P) algorithm is applied to solve the integrated train makeup and classification track assignment problem with the known humping sequence found in the first phase. In addition, greedy heuristics and lower bounding techniques are designed in both phases to improve computational efficiency. Comprehensive experiments are investigated based on a set of real-life instances. The results show that exact approaches provide optimal solutions, whereas heuristic approaches yield satisfactory solutions within a shorter computation time. Moreover, sensitivity analyses on the number of classification tracks and the effects of different deviation penalties are also performed to gain more managerial insights.

War and warming: The effects of climate change on military conflicts in developing countries (1995–2020)

Numerous studies have explored the links between climate change and military conflicts. This study delves into the types and intensities of conflicts likely caused by climate change in developing countries from 1995 to 2020. Through a country-year panel analysis, it isolates the impact of rising global greenhouse gas emissions on conflict occurrence. Findings consistently show a significant positive association between greenhouse gas emissions and military conflicts, especially low-intensity and internationalized intrastate conflicts. Additional tests confirm these results' robustness, including heterogeneity analysis, substitution of key explanatory variables, sensitivity analysis, and alternative analytical methods. The analysis highlights how environmental scarcity due to climate change drives small-scale conflicts within countries, while geopolitics and environmental scarcity also lead to internationalized intrastate wars. The study recommends that developed countries and the international community support developing countries in building resilience against climate change and advocates for collaborative efforts to mitigate its adverse effects on global security.

A modified adaptive large neighborhood search algorithm for solving the multi-port continuous berth allocation problem with vessel speed optimization

Despite its fast-growing popularity in maritime transportation, container shipping is still fraught with risks and uncertainties with its complex operating environments. This paper studies the multi-port continuous berth allocation problem with speed optimization (MCBAP). In the MCBAP, vessels visit multiple ports sequentially, and the problem aims at minimizing the sum of vessel sailing cost, waiting cost, delay cost and port handling cost, while satisfying various constraints related to vessel sailing and berthing. A mixed integer linear programming (MILP) model for MCBAP is formulated, and a modified adaptive large neighborhood search (MALNS) algorithm is proposed for solving large-scale MCBAPs. In the MALNS, an efficient initial solution generation strategy is developed, and a series of neighborhood solution generation operators are proposed. Finally, the proposed MILP model and MALNS algorithm are tested on a range of MCBAP instances. The numerical results demonstrate that the MILP model can be solved to optimality with CPLEX, and the MALNS can efficiently solve instances at various scales. In addition, sensitivity analyses on fuel prices and vessel design speeds (the planned maximum speeds) are performed, and management insights have been provided.

Network-based resilience assessment of an urban rail transit infrastructure with a multi-dimensional performance metric

With urban and public transportation development, the urban rail transit networks (URTN) become increasingly complex, evolving into multi-layer and intricate systems. The operating environment is complex, and the rising frequency of emergencies significantly impacts the entire network. Consequently, the resilient ability of URTN to deal with risk attacks has become an important research field. In this paper, a bilayer URTN model is constructed, encompassing urban and suburban rail transit, proposing the adjacency matrix and the coupling mechanism of subnetworks. Then, the performance of a URTN jointly considering network efficiency, network structure entropy, and the number of trips per unit time is addressed. By considering passenger travel alternatives under disruptions, we develop an improved Logit stochastic user equilibrium (SUE) passenger assignment model based on generalized travel time costs, introducing the failure degree of the stations, to predict passenger flow paths. A multi-dimensional resilience assessment model is proposed using the resilience change curve for attack scenarios and recovery strategies, and a comprehensive network performance system through the AHP method to determine the weight coefficients of indicators. Finally, we present a case study based on the Shanghai URT network to explore the resilience of URTN in the morning peak and simulate the network resilience under different failure scenarios. The results indicate that the proposed model can assess the resilience of the Shanghai MRTN under random and malicious failure, and the former has less comprehensive performance losses and greater resilience. Additionally, the sensitivity analysis is conducted to explore the impact of the number of failed stations, the failure degree of stations, and recovery ability on network resilience. This study provides theoretical support for urban managers and builders regarding network structure and emergency management, and offers relevant suggestions.

Genetically Predicted Iron Status Is a Causal Risk of Rheumatoid Arthritis: A Mendelian Randomization Study.

Background Current knowledge on iron's role in rheumatoid arthritis (RA) development is very limited, with studies yielding inconsistent findings. We conducted a two-sample Mendelian randomization study to assess the associations of iron status with the risk of RA. Methods This study leveraged genetic data from a large genome-wide association study (GWAS) of 257,953 individuals to identify single nucleotide polymorphisms (SNPs) associated with iron status. We then analyzed these data in conjunction with summary-level data on RA from the IEU open GWAS project, which included 5,427 RA cases and 479,171 controls. An inverse-variance weighted method with random effects was employed, along with sensitivity analyses, to assess the relationship between iron status and RA risk. Results Genetic predisposition to high ferritin and serum iron status was causally associated with lower odds of RA. Ferritin had an odds ratio (OR) of 0.997 (95% confidence interval [CI]: 0.995-0.997; p = 0.010), indicating that a one-unit increase in ferritin is associated with a 0.3% decrease in the odds of RA. Similarly, serum iron had an OR of 0.997 (95% CI: 0.995-0.999; p = 0.014). However, MR analyses found no significant causal associations between total iron-binding capacity (OR = 1.0, 95% CI: 0.999-1.002; p = 0.592) or transferrin saturation percentage (OR = 0.998, 95% CI: 0.996-1.000; p = 0.080) and risk of developing RA. Conclusions This study suggests that individuals with genes linked to higher iron levels may have a lower risk of developing RA. Our findings indicate that the total amount of iron in the body, rather than how it is distributed, might be more important for RA. This raises the intriguing possibility that iron supplementation could be a preventative strategy, but further research is necessary.

Post-combustion CO2 capture retrofit from diesel-powered Arctic mines – Techno-economic and environmental assessment

This study evaluates the economic feasibility and environmental impacts of retrofitting a diesel-based powerhouse in the Canadian Arctic with a post-combustion carbon capture process at an active gold mining site isolated from cheaper or cleaner electrical grids. A techno-economic analysis was conducted to determine the total annualized cost (TAC) of implementing a monoethanolamine (MEA) chemical absorption process to mitigate carbon dioxide emissions. The calculated cost per tonne of CO2 captured of $420 reflects the challenges of operating northern sites reliant on diesel fuel. Electricity generation costs, estimated at 0.44 $/kWh, are found to explain most of the variance in cost per tonne compared to other studies. A profitability model, comparing the additional annual expenditure to the current carbon tax exposure (CTE), suggests that carbon pricing alone is insufficient to incentivize investment in energy-intensive carbon capture technologies such as amine-based absorption processes. The sensitivity analysis, which evaluates profitability relative to variations in key variables, highlights the significant impact of the solvent regeneration heat demand. This major cost driver also contributes substantially to the carbon footprint of 0.55 tonnes emitted per tonne captured, as determined by a complementary life cycle assessment.

Evaluation of phthalate acid esters (PAEs) in kefir samples by using magnetic chitosan coated with polyaniline (Fe3O4@CHI@PANI)/GC–MS method: A health risk assessment study

In this study, by magnetic chitosan coated with polyaniline (Fe3O4@CHI@PANI) −gas chromatography/mass spectrometry (GC/MS) method was applied to analyze six PAE compounds in kefir samples. The results obtained from kefir samples showed that the lowest and highest mean of PAEs were associated to dimethyl phthalate (DMP was not detected or nd) and di-n-octyl phthalate (DNOP, 2.12 µg/kg), respectively. Also according to results the average of DEHP (Bis(2-ethylhexyl)phthalate) and total PAEs in all samples were 1.31 ± 0.72 and 4.58 ± 1.87 µg/kg, respectively. Furthermore, maximum and minimum mean of total PAEs were detected in the packages with a volume of 1000 mL (5.47 ± 2.03 μg/kg) and packages with a volume of 1500 mL (3.69 ± 1.23 μg/kg), respectively, and the maximum and minimum mean total PAE (μg/kg) were related to samples with a date less than the expiration date (5.42 ± 1.46) and samples with a date greater than the production date (3.73 ± 1.91), respectively. The level of these contaminants was lower than the existing standard levels. The assessment of human health risks, comprising risks of non-carcinogenic and carcinogenic were calculated with Monte Carlo simulation (MCS) and sensitivity analysis under certain removal efficiencies from 5 to 95 %. The result of the carcinogenic risk for DEHP showed that adults are not threatened by the consumption of kefir (1.06E-05), while children are exposed to negligible risk the consumption of kefir (1/48E-04). In all samples, the total non-carcinogenic risk of PAE compounds was less than 1 (TTHQ was 2.03E-04 and 4.33E-05 for children and adults, respectively), this values presenting there would be unlikely risks of non-carcinogenic of PAEs in kefir for consumers. In conclusion, it can be stated it doesn’t pose any threat to Iranian consumers (adults and children).

Optimization of grid-connected hybrid renewable energy system for the educational institutes in Pakistan

To foster resilient energy transition systems, the deployment of renewable energy sources is potentially helpful in the acceleration towards sustainable, reliable, and diversified solutions. In this feasibility study, aims to pursuit the best optimal solution for renewable energy resources, integrated into the local university's energy mix, to meet the electrical load demand of 8500 kWhr/day and a peak load of 815 kW. A model of a hybrid grid-connected renewable energy (PV/Biomass/Hydel/Diesel generator/Grid) system was implemented in HOMER software, to obtain the most efficient and cost-effective hybrid energy generation. The outcomes of the simulation seem more viable based on sensitivity analysis, as the cost of energy (COE) for an energy system with a hybrid combination is 0.13.$/kWh, meanwhile energy price with the grid system is 0.18 $/kWh. According to the HOMER analysis, a hybrid grid-connected PV/biomass/hydro system demonstrates both technological feasibility and economic viability, meanwhile it will also save 1575 tons of CO2 per year. Therefore, this proposed hybrid energy system, a step towards sustainable development, can be applied at institutional levels, especially in developing countries.

Comprehensive models to estimate the Isobaric heat capacity of deep eutectic solvents based on Machine learning algorithms

Deep eutectic solvents (DESs) have recently gained significant attention due to their sustainable and environmentally friendly properties. Acquiring exact knowledge regarding the isobaric heat capacity (Cp) of DESs is essential for energy-related processes, in which these green solvents are utilized. Hence, this study deals with the development of comprehensive models for estimating the Cp of DESs. To reach this target, 682 experimental data, encompassing the Cp of 36 different DESs over widespread ranges of pressure and temperature, were assembled from the literature. The foregoing data were employed to establish new models based on four machine learning techniques, including Gaussian process method (GPM), Adaptive neuro-fuzzy inference system (ANFIS), Radial basis function neural network (RBF-NN) and Multilayer perceptron neural network (MLP-NN). The evaluations performed based on the statistical indices and graphical tools demonstrated that although all suggested models present excellent estimations, that designed based on the MLP-NN method yields the highest accuracy with Mean absolute percentage error (MAPE) and coefficient of determination (R2) values being 0.4% and 99.93, respectively, for the validation data. A comparison between the results of the literature correlations and those of novel models confirmed the obvious superiority of the latter. Moreover, the proposed models properly described the Cp variations of different DESs versus pressure and temperature. The order of importance of various factors in controlling the Cp of DESs was also determined based on the sensitivity analysis. Eventually, the intelligent models showed excellent performance in estimating the Cp of unseen DESs.

Identification of Prognostic Markers and Potential Therapeutic Targets using Gene Expression Profiling and Simulation Studies in Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) has a 5-year relative survival rate of less than 10% making it one of the most fatal cancers. A lack of early measures of prognosis, challenges in molecular targeted therapy, ineffective adjuvant chemotherapy, and strong resistance to chemotherapy cumulatively make pancreatic cancer challenging to manage. The present study aims to enhance understanding of the disease mechanism and its progression by identifying prognostic biomarkers, potential drug targets, and candidate drugs that can be used for therapy in pancreatic cancer. Gene expression profiles from the GEO database were analyzed to identify reliable prognostic markers and potential drug targets. The disease's molecular mechanism and biological pathways were studied by investigating gene ontologies, KEGG pathways, and survival analysis to understand the strong prognostic power of key DEGs. FDA-approved anti-cancer drugs were screened through cell line databases, and docking studies were performed to identify drugs with high affinity for ARNTL2 and PIK3C2A. Molecular dynamic simulations of drug targets ARNTL2 and PIK3C2A in their native state and complex with nilotinib were carried out for 100 ns to validate their therapeutic potential in PDAC. Differentially expressed genes that are crucial regulators, including SUN1, PSMG3, PIK3C2A, SCRN1, and TRIAP1, were identified. Nilotinib as a candidate drug was screened using sensitivity analysis on CCLE and GDSC pancreatic cancer cell lines. Molecular dynamics simulations revealed the underlying mechanism of the binding of nilotinib with ARNTL2 and PIK3C2A and the dynamic perturbations. It validated nilotinib as a promising drug for pancreatic cancer. This study accounts for prognostic markers, drug targets, and repurposed anti-cancer drugs to highlight their usefulness for translational research on developing novel therapies. Our results revealed potential and prospective clinical applications in drug targets ARNTL2, EGFR, and PI3KC2A for pancreatic cancer therapy.

Identifying modifiable factors and their joint effect on brain health: an exposome-wide association study.

Considerable uncertainty remains regarding the associations of multiple factors with brain health. We aimed to conduct an exposome-wide association study on neurodegenerative disease and neuropsychiatry disorders using data of participants from the UK Biobank. Multivariable Cox regression models with the least absolute shrinkage and selection operator technique as well as principal component analyses were used to evaluate the exposures in relation to common disorders of central nervous system (CNS). Restricted cubic splines were conducted to explore potential nonlinear correlations. Then, weighted standardized scores were generated based on the coefficients to calculate the joint effects of risk factors. We also estimated the potential impact of eliminating the unfavorable profiles of risk domains on CNS disorders using population attributable fraction (PAF). Finally, sensitivity analyses were performed to reduce the risk of reverse causality. The current study discovered the significantly associated exposures fell into six primary exposome categories. The joint effects of identified risk factors demonstrated higher risks for common disorders of CNS (HR = 1.278 ~ 3.743, p < 2e-16). The PAF varied by exposome categories, with lifestyle and medical history contributing to majority of disease cases. In total, we estimated that up to 3.7 ~ 64.1% of disease cases could be prevented.This study yielded modifiable variables of different categories and assessed their joint effects on common disorders of CNS. Targeting the identified exposures might help formulate effective strategies for maintaining brain health.

Dielectric modulation-based biomolecule detection using III-V vertical source-all-around tunnel FET

We present a dielectric modulation-based label-free biosensor utilizing a GaAsSb/GaSb heterojunction in a source-all-around vertical tunnel FET (TFET) configuration. The TFET, enhanced with a heavily doped source pocket and a metal core, exhibits improved gate control, current ratio, and subthreshold swing (SS). This novel design is evaluated for its sensitivity to biomolecules with various dielectric constants (5.5, 7.5, 10, and 12) through extensive TCAD simulations. Sensitivity analysis is conducted for different nanogaps, fill factors, and biomolecule charges (neutral, positive, and negative). The wide tunneling area provided by this source-all-around structure significantly increases the biomolecule capture area, resulting in superior sensitivity compared to other devices. Thus, the proposed TFET demonstrates excellent potential for diverse biomolecule detection applications.

Synergy-Based Hydrogen Pricing in Hydrogen-Integrated Electric Power System: Sensitivity Analysis

Hydrogen price significantly impacts its potential as a viable alternative in the sustainable energy transition. This study introduces a synergy-based Hydrogen Pricing Mechanism (HPM) within an integrated framework. The HPM leverages synergy between a Renewable-Penetrated Electric Power System (RP-EPS) and a Hydrogen Energy System (HES). Utilizing the Alternating Direction Method of Multipliers (ADMM), it facilitates data exchange, quantifying integration levels and simplifying the complexities. The study assesses the HPM's operational sensitivity across various scenarios of hydrogen generation, transportation, and storage. It also evaluates the benefits of synergy-based versus stand-alone HPMs. Findings indicate that the synergy-based HPM effectively integrates infrastructure and operational improvements from both EPS and HES, leading to optimized hydrogen pricing.

An IGDT-WDRCC based optimal bidding strategy of VPP aggregators in new energy market considering multiple uncertainties

This study addresses the volatility and uncertainty challenges in managing renewable energy within electricity markets, particularly focusing on the role of Virtual Power Plant (VPP) aggregators. Recognizing the risks these uncertainties pose to the revenue and stability of power systems, the paper presents a novel information gap decision theory (IGDT)-Wasserstein metric based distributionally robust chance constraint (WDRCC) approach to devise an optimal bidding strategy for VPP operators. It involves a data-driven distributionally robust optimization framework, leveraging the worst-case scenario from the distributed resource uncertainties, guided by an ambiguity set rooted in the Wasserstein metric. Furthermore, the distributionally robust chance constraint modeling is introduced ensuring that uncertainty constraints of distributed resources meet a predefined risk level. Although this method shows promising out-of-sample performance, it relies on forecasted energy prices, a notable limitation given the price volatility and information inadequacy in the newly-opened market. To address this, the risk-averse bidding strategy, grounded in IGDT, is proposed simulataneously to safeguard the operator’s expected returns against price uncertainties, implementing an advanced piecewise linear approximation technique, ”nf4l,” for linearizing the bi-linear term from IGDT. The effectiveness of this approach is empirically validated through a comprehensive case study and sensitivity analysis.

Effect of technology multiplier: A framework for analysis of innovation perspectives on production segment allocation

In the realm of production systems, determining the optimal segment allocation remains a central concern. While several existing models address this issue, a significant gap remains as many overlook the critical role of innovation and lack a holistic perspective. This paper presents a model that emphasizes innovation capabilities and introduces the concept of a “Technology Multiplier” underscoring the compounding influence of technology and innovation on production segment allocation decisions. Within this work, we focus on preliminary studies to establish the “Technology Multiplier” concept employing an Analytical Hierarchy Process (AHP) with sensitivity analysis. The validity of our approach is demonstrated through four case studies from three industries, illustrating the relevance of our elaborated metrics for the concept of “Technology Multipliers”. In particular, a leading automotive company uses our findings to reach a more appropriate strategic decision aligned with innovation and production growth, compared to its previous decisions. These results not only demonstrate a robust fit with our proposed metrics but also indicate that our framework lays the foundation for further research on the “Technology Multiplier”, enriching the decision-making process for production segment allocation.