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Non-toroidal-shaped primary pass-through protection current transformers (CTs) are used to measure high currents. Their design provides them with a big airgap that allow the passing of several cables per phase though them, which is the main advantage versus toroidal types, as the number of CTs required to measure the whole phase current is drastically reduced. The cables passed through the transformer window can be in several positions. As the isolines of the magnetic field generated by the primary currents are centered in the cables, if these cables are not centered in the transformer window, then the magnetic field will be non-uniform along the transformer core. Consequently, local saturations can appear if the cables are not properly disposed, causing the malfunction of the CT. In this paper, the performance of a non-toroidal-shaped protection CT is studied. This research is focused on the influence of the cable position on possible partial saturations of the CT when it is operating near to its accuracy limit. Depending on the cable position, the ratio of the primary and secondary currents can depart from the assigned ratio. The validation of this phenomenon was carried out via finite element analysis (FEA), showing that partial transformer core saturations appear in areas of the magnetic core close to the cable. By applying FEA, the admissible accuracy region for cable positioning inside the CT is also delimited. Finally, the simulation results are ratified with experimental tests performed in non-toroidal protection CTs, varying the primary cables' positions, which are subjected to currents up to 5 kA, achieving satisfactory results. From this analysis, installation recommendations are given. [ABSTRACT FROM AUTHOR]

As the scale of power systems continue to expand and their structure becomes increasingly complex, it is likely that branch power flow exceedance may occur during the operation of power systems, posing threats to the safe and stable operation of entire systems. This paper addresses the issue of branch flow exceedance in power networks. To enhance the operational efficiency and optimize the adjustment effects, this paper proposes a method for eliminating branch power flow exceedance by improving the particle swarm optimization (PSO) algorithm through the introduction of sensitivity and distribution factors. Firstly, it introduces the basic theory and calculation methods of sensitivity analysis, focusing on deriving the calculation principles of power flow sensitivity and voltage sensitivity, used to predict the responses of power flow at each branch in the power network to power or voltage changes. Subsequently, the paper provides a detailed derivation of the calculation principles for the line outage distribution factor (LODF), which effectively assesses the changes in branch power flow in the power network under specific conditions. Finally, a method for eliminating branch power flow exceedance based on a combination of sensitivity analysis and PSO algorithm is proposed. Through case analysis, it is demonstrated how to use the sensitivity and distribution factor to predict and control the power flow exceedance issues in power systems, verifying the efficiency and practicality of the proposed method for eliminating branch power flow exceedance. The study shows that this method can rapidly and accurately predict and address branch power flow exceedance in power system, thereby enhancing the operational safety of the power system. [ABSTRACT FROM AUTHOR]

With the large-scale integration of wind power, it is essential to establish an electromagnetic transient (EMT) model of a wind turbine system. Focusing on the problem of the difficulty in obtaining the parameters of the direct-driven permanent magnet synchronous generator (PMSG) model, this manuscript proposes a method based on trajectory sensitivity analysis and improved gray wolf optimization (IGWO) for identifying the parameters of the PMSG EMT model. First, a model of a PMSG wind turbine is established on an EMT simulation platform. Then, the key parameters of the model are determined based on the sensitivity analysis. Five control parameters are selected as the key parameters for their higher sensitivity indexes. Finally, the key parameters are accurately identified, using the proposed IGWO algorithm. The final case study demonstrates that the proposed IGWO algorithm has better optimization performance compared with the GWO algorithm and particle swarm optimization (PSO) algorithm. In addition, the simulation waveforms show that the identified parameters are accurate and applicable to other operating conditions. [ABSTRACT FROM AUTHOR]

The High-Frequency (HF) radar network has become an effective method for detecting coastal currents. In this study, we confirmed the effectiveness of the HF radar measurements by comparing with the Acoustic Doppler Current Profiler (ADCP) and explore the possibility of assimilating radar data into a regional coastal ocean model. A regional high-resolution model with resolution of 10 m was first built in the Pearl River Estuary (PRE). However, analysis of the Hovmöller diagrams from the model simulations in this study indicated a significant deficiency in representing Near-Inertial Oscillations (NIOs) in the PRE, particularly in the east–west direction, despite including wind fields in the input data, during the week from 3 to 8 August 2022. To overcome the model deficiency, we conducted a set of assimilation experiments and performed sensitivity analyses. The results of sensitivity experiments indicate that the model exhibits a sufficient capacity to replicate NIOs after assimilation, lasting approximately 5–6 days. To further analyze the reasons for the decay in the magnitude of the NIOs, data from the three ADCP stations were compared with model results by applying the momentum equation. The assimilated vertical diffusion term outperforms the unassimilated model in representing NIOs. These findings highlight the importance of the vertical diffusion term for simulating NIOs and the data assimilation in improving the model's representation of physical processes. [ABSTRACT FROM AUTHOR]

In response to the fairness issue arising from the unequal delay of vehicles in different phases at intersections and considering the actual situation of small and variable delays for vehicles in low-saturation intersection phases, this paper proposes the concept of "sacrificing efficiency for fairness". Firstly, the universality of unfair delay phenomena at intersection phases is explained, especially at low-saturation intersections where the fluctuation in phase delays is 1.87 times higher than at other intersections. Then, a fairness evaluation index is constructed using information entropy, and the feasibility of the proposed approach is demonstrated. Subsequently, a signal optimization model that balances efficiency and fairness is proposed. Finally, the proposed model is validated through case studies, showing that it not only simultaneously considers efficiency and fairness but also has minimal impact on efficiency. Moreover, the changes to timing schemes in the efficiency model are much smaller compared to the model that only considers fairness. Sensitivity analysis reveals that the model performs better under low-saturation intersection conditions. [ABSTRACT FROM AUTHOR]

This study proposes an innovative carbon benefits-based signal control method for connected vehicle (CV) environments, aiming to reduce carbon emissions at urban intersections. By integrating a Carbon Inclusion Mechanism (CIM), the proposed approach offers carbon rewards to vehicles adhering to speed guidance. The method exhibits the following features: (i) higher ceiling of carbon emissions reduction at signal control intersection; (ii) higher compliance rate (CR) of vehicles by taking advantage of carbon economic incentives; (iii) a method for calculating carbon emissions reduction at the intersection. To validate the effectiveness, performance evaluations of emissions, stop frequencies, and delays were conducted through microscopic simulation. Sensitivity analysis encompassed various traffic demands, different CRs of carbon-benefit connected vehicles (CBCVs), and unbalanced traffic demand. The results demonstrated that the proposed method excels in reducing traffic emissions, stop frequencies, and delays. Specifically, carbon emissions were reduced by 5.24% to 17.60%, stop frequencies decreased by 14.8% to 75.4%, and delays were reduced by 22.82% to 52.62%. By utilizing connected vehicle technology and CIM, this study contributes to sustainable urban traffic management, laying a foundation for future research and the practical implementation of emission reduction strategies. [ABSTRACT FROM AUTHOR]

Featured Application: Design Optimization and Sensitivity Analysis of Reactor Systems. The analysis of liquid-fueled molten-salt reactors (LFMSRs) during steady state, operational transients and accident scenarios requires addressing unique reactor multi-physics challenges with coupling between thermal hydraulics, neutronics, inventory control and species distribution phenomena. This work utilizes the General Nuclear Field Operation and Manipulation (GeN-Foam) code to perform coupled thermal-hydraulics and neutronics calculations of an LFMSR design. A framework is proposed as part of this study to perform modeling, design optimization, and uncertainty quantification. The framework aims to establish a protocol for the studies and analyses of LFMSR which can later be expanded to other advanced reactor concepts too. The Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) statistical analysis tool was successfully coupled with GeN-Foam to perform uncertainty quantification studies. The uncertainties were propagated through the input design parameters, and the output uncertainties were characterized using statistical analysis and Spearman rank correlation coefficients. Three analyses are performed (namely, scalar, functional, and three-dimensional analyses) to understand the impact of input uncertainty propagation on temperature and velocity predictions. Preliminary three-dimensional reactor analysis showed that the thermal expansion coefficient, heat transfer coefficient, and specific heat of the fuel salt are the crucial input parameters that influence the temperature and velocity predictions inside the LFMSR system. [ABSTRACT FROM AUTHOR]

Abutment scour is a major cause of bridge failures worldwide, leading to disruptions, economic losses, and loss of life. The present experimental study examines countermeasures against abutment scour using hooked-collar protections on vertical-wall and wing-wall abutments (at 45° and 60°) under different flow conditions. All 60 experiments were performed under sub-critical flow conditions by investigating scour around an abutment 20 cm long, 20 cm wide, and 25 cm tall. Two distinct values of the Froude number, 0.154 and 0.179, and a sediment particle diameter (d50) of 0.88 mm were used throughout the experimental phase. The resulting equilibrium scour around the abutments was compared to those with collar and hooked-collar protections. It was determined that the maximum abutment scour depth reduction was 83.89% when hooked collars were placed on vertical wall abutments beneath the bed surface level, and for wing-wall abutments at 45° and 60°, it was 74.2% and 73.5%, respectively, at the bed surface level. Regression analysis was conducted to assess the non-dimensional scour depth (Ds/Yf) and scour reduction (RDs/Yf), with a high enough coefficient of determination (R2 values of 0.96 and 0.93, respectively), indicating high confidence in the analysis. The sensitivity analysis findings demonstrate that the width of the collar (Wc) and La are the most influencing factors affecting Ds/Yf and RDs/Yf. [ABSTRACT FROM AUTHOR]

Groundwater flow and transport models are essential tools for assessing and quantifying the migration of organic contaminants at polluted sites. Uncertainties in the hydrodynamic and transport parameters of the aquifer have a significant effect on model predictions. Uncertainties can be quantified with advanced sensitivity methods such as Sobol's High Dimensional Model Reduction (HDMR) and Variogram Analysis of Response Surfaces (VARS). Here we present the application of VARS and HDMR to assess the global sensitivities of the outputs of a transient groundwater flow model of the Gállego alluvial aquifer which is located downstream of the Sardas landfill in Huesca (Spain). The aquifer is subject to the tidal effects caused by the daily oscillations of the water level in the Sabiñánigo reservoir. Global sensitivities are analyzed for hydraulic heads, aquifer/reservoir fluxes, groundwater Darcy velocity, and hydraulic head calibration metrics. Input parameters include aquifer hydraulic conductivities and specific storage, aquitard vertical hydraulic conductivities, and boundary inflows and conductances. VARS, HDMR, and graphical methods agree to identify the most influential parameters, which for most of the outputs are the hydraulic conductivities of the zones closest to the landfill, the vertical hydraulic conductivity of the most permeable zones of the aquitard, and the boundary inflow coming from the landfill. The sensitivity of heads and aquifer/reservoir fluxes with respect to specific storage change with time. The aquifer/reservoir flux when the reservoir level is high shows interactions between specific storage and aquitard conductivity. VARS and HDMR parameter rankings are similar for the most influential parameters. However, there are discrepancies for the less relevant parameters. The efficiency of VARS was demonstrated by achieving stable results with a relatively small number of simulations. [ABSTRACT FROM AUTHOR]

Transportation Network Companies (TNCs) play a prominent role in mobility within cities across the globe. However, their activity has impacts on vehicle miles traveled (VMT) and greenhouse gas (GHG) emissions. This study quantifies the change in personal vehicle ownership and total miles driven by TNC drivers in three metropolitan areas: San Francisco, CA; Los Angeles, CA; and Washington, D.C. The data sources for this analysis comprise two surveys, one for TNC passengers (N = 8630) and one for TNC drivers (N = 5034), in addition to data provided by the TNC operators Uber and Lyft. The passenger survey was deployed within the three metropolitan areas in July and August 2016, while the driver survey was deployed from October to November 2016. The TNC operator data corresponded with these time frames and informed the distance driven by vehicles, passenger frequency of use, and fleet level fuel economies. The data from these sources were analyzed to estimate the impact of TNCs on travel behavior, personal vehicle ownership and associated VMT changes, as well as the VMT of TNCs, including app-off driving. These impacts were scaled to the population level and collectively evaluated to determine the net impacts of TNCs on VMT and GHG emissions using fuel economy factors. The results showed that the presence of TNCs led to a net increase of 234 and 242 miles per passenger per year, respectively, in Los Angeles and San Francisco, while yielding a net decrease of 83 miles per passenger per year in Washington, D.C. A sensitivity analysis evaluating net VMT change resulting from vehicle activity and key behavioral impacts revealed the conditions under which TNCs can contribute to transportation sustainability goals. [ABSTRACT FROM AUTHOR]

Water scarcity is a significant global challenge, exacerbated by leakages in water distribution networks. This paper addresses the challenge of detecting leakages in rural and urban–rural water supply systems through hydraulic modelling and a sensitivity analysis. Two distinct real-world network models were studied to assess real and simulated leakage scenarios varying in location and magnitude. A distinct leakage detection approach utilizing outflow measurements from hydrants was tested. Additionally, the effectiveness of various statistical measures—such as correlation, angular closeness, Euclidean distance, Manhattan distance, Chebyshev distance, cosine similarity, and Spearman correlation—were evaluated to determine their efficacy in leakage detection. Different methods for identifying leak candidates were explored and compared, either by selecting a single leak candidate based on similarity measures or by identifying a group of candidates to mark leak hotspots. Density-based spatial clustering of applications with noise was used to assess the number of potential leak candidate groups. The study's findings contribute to the optimization of leak detection strategies in water supply networks, particularly in rural settings, where detection is challenging due to scarce measurement datasets, budget restrictions, and operational constraints. [ABSTRACT FROM AUTHOR]

The regional source–grid–load friendly interaction and precision regulation of adjustable resources with high sensitivity provide important support for the development of a new power system, improving its regulation ability and efficiency and promoting the safe, efficient, and reliable operation. Therefore, a precision regulation strategy for a regional source–grid–load friendly interaction based on a double-layer sensitivity model is proposed in this paper. Firstly, from the two perspectives of power grid-application requirements and power node-adjustable resources, the overall implementation process of the double-layer sensitivity analysis method is described, and the layered logic architecture of the precision regulation platform for regional source–grid–load friendly interaction is constructed in order to realize and apply the proposed double-layer sensitivity model and precision-regulation strategy. Secondly, the first-layer power transmission line-sensitivity analysis model is proposed for the power grid side to obtain the power transmission line-sensitivity matrix. A fine-grained second-layer adjustable resources sensitivity-analysis model is further established for the diversified adjustable resource aggregated under the power node to complete the quantitative ranking of adjustable resource-regulation sensitivity. Then, the application process of double-layer sensitivity analysis results is designed in detail, and the regulation sequence and regulation quantity of source and load are defined. Finally, taking a power system with seven lines, five power nodes, two power sources, and seven types of adjustable resources as an example, the effectiveness and rationality of the proposed model and strategy are verified. The results indicate that the proposed precise regulation strategy can accurately select high-sensitivity power nodes and effectively sort high-sensitivity adjustable resources when solving the multiple scenarios' application needs of power grids. [ABSTRACT FROM AUTHOR]

The aim of this research is to simulate and analyze a combined power cycle (Steam turbine and gas turbine cycles) by studying the effect of changing the natural gas flow rate on the developed power. Therefore, reducing the amount of used natural gas in the combustion chamber of the gas turbine cycle from 9.2 to 4 kg/s showed a significant drop in the power produced by the gas turbine, i.e., from 123.7 to 57.7 MW. Additionally, this change in the combusted natural gas amount affected the heat recovered in both heat recovery steam generators (HRSGs), i.e., from 219.79 to 100.35 MW, respectively. Consequently, the amount of generated steam in the high pressure HRSGs and the power developed in the steam turbine changed from 60.88 to 27.79 kg/s and from 56.39 to 27.13 MW, respectively. A heat exchanger (HFHX) utilizing a heating fluid was used as an external source of energy to compensate the reduction in the generated heat and to increase the amount of generated steam up to 157.32 kg/s, which keeps the power plant capacity at 180 MW. Existing combined local plant data were used in this study and were simulated in Aspen Plus software V11. A sensitivity analysis was made to optimize the cycle operating conditions that use less natural gas and produce the same amount of power. [ABSTRACT FROM AUTHOR]

U-tube steam generators (UTSGs) are crucial in nuclear power plants, serving as the interface between the primary and secondary coolant loops. UTSGs ensure efficient heat exchange, operational stability, and safety, directly impacting the plant's efficiency and reliability. Existing UTSG models have fixed structures, which can only be used when certain parameters are given as model input. Such constraints hinder their ability to accommodate the diverse operating conditions, where input and output parameters can vary significantly. To address this challenge, we propose a machine learning-based method for developing a high-degree-of-freedom UTSG thermal model. The most notable feature of this approach is its capacity to flexibly interchange input and output parameters. By adopting comprehensive parameter sensitivity analysis, the most efficient method for training dataset generation is determined. Leveraging a lightweight machine learning method, the prediction accuracy for all UTSG parameters is improved to within 2.1%. The flexibility of the proposed machine learning approach ensures that the UTSG model can accommodate any type of parameter input without extensive reconfiguration of the model structure, thereby enhancing its applicability and robustness in real-world applications. [ABSTRACT FROM AUTHOR]

This work continues the review and illustrative application to energy systems of the "Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling" (4th-BERRU-PM) methodology. The 4th-BERRU-PM methodology uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses with respect to model parameters. The 4th-BERRU-PM methodology yields the fourth-order MaxEnt posterior distribution of experimentally measured and computed model responses and parameters in the combined phase space of model responses and parameters. The 4th-BERRU-PM methodology encompasses fourth-order sensitivity analysis (SA) and uncertainty quantification (UQ), which were reviewed in the accompanying work (Part 1), as well as fourth-order data assimilation (DA) and model calibration (MC) capabilities, which will be reviewed and illustrated in this work (Part 2). The applicability of the 4th-BERRU-PM methodology to energy systems is illustrated by using the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark, which is modeled using the linear neutron transport Boltzmann equation, involving 21,976 imprecisely known parameters. This benchmark is representative of "large-scale computations" such as those involved in the modeling of energy systems. The result ("response") of interest for the PERP benchmark is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. The impact of the high-order sensitivities of the response with respect to the PERP model parameters is quantified for "high-precision" parameters (2% standard deviations) and "typical-precision" parameters (5% standard deviations). Analyzing the best-estimate results with reduced uncertainties for the 1st—through 4th-order moments (mean values, covariance, skewness, and kurtosis) produced by the 4th-BERRU-PM methodology for the PERP benchmark indicates that, even for systems modeled by linear equations (e.g., the PERP benchmark), retaining only first-order sensitivities is insufficient for reliable predictive modeling (including SA, UQ, DA, and MC). At least second-order sensitivities should be retained in order to obtain reliable predictions. [ABSTRACT FROM AUTHOR]

This work (in two parts) will review the recently developed predictive modeling methodology called "4th-BERRU-PM" and its applicability to nuclear energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the "Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling" methodology, which uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, while yielding best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC), as will be illustrated in this work by means of the above-mentioned OECD/NEA reactor physics benchmark. This benchmark is modeled using the neutron transport Boltzmann equation involving 21,976 imprecisely known parameters, the solution of which is representative of "large-scale computations". The model result ("response") of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties. [ABSTRACT FROM AUTHOR]

Aerospace-grade SRAM-based field-programmable gate arrays (FPGAs) used in space applications are highly susceptible to single event effects, leading to soft errors in FPGAs. Additionally, as FPGAs scale up, the difficulty of correcting soft errors also increases. This paper proposes that performing soft error sensitivity analysis on FPGAs can help target the more sensitive areas for detection and correction, thereby improving the efficiency of soft error repair. Firstly, in accordance with the dual-layer architecture of SRAM-based FPGAs, methods for the soft error sensitivity analysis of FPGA application layer resources and configuration bitstreams are reviewed. Subsequently, based on the analysis results, it also covers corresponding application layer memory scrubbing and configuration scrubbing techniques. A prospective look at emerging soft error mitigation technologies is discussed at the end of this review, supporting the development of highly reliable aerospace-grade SRAM-based FPGAs. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to studying divorce dynamics and elimination strategies using nonlinear differential equations. A mathematical model is formulated to capture the key factors influencing divorce rates. The model undergoes a rigorous theoretical analysis, including parameter estimation, solution existence/uniqueness, positivity, boundedness, and invariant regions. A qualitative analysis explores equilibria, stability conditions, and a sensitivity analysis. Numerical simulations and discussions are presented to validate the model and shed light on divorce dynamics. Finally, conclusions and future research directions are outlined. This work offers valuable insights for understanding and potentially mitigating divorce rates through targeted interventions. [ABSTRACT FROM AUTHOR]

This study revisits the freely vibrating problem of asymmetric L-shaped membranes using a three-segmented domain decomposition (3-SDD) strategy. Motivated by the need for more accurate and flexible methods, the 3-SDD strategy is compared with the previously proposed two-segmented domain decomposition (2-SDD) strategy. The region-matching technique is used to derive an alternative series solution, and the eigenvalues obtained are compared with those in existing research. The convergence behavior and sensitivity analyses reveal that the 3-SDD strategy offers improved accuracy and stability, particularly for higher truncation terms. Detailed comparisons of the first four eigenvalue squares show strong agreement between the 3-SDD and 2-SDD strategies, confirming the reliability of both methods. This research establishes a foundation for the vibration analysis of complex membrane structures, emphasizing the benefits of the 3-SDD approach for upcoming engineering applications and showcasing its potential for broader applicability in practical scenarios. The findings underscore the importance of utilizing multi-segmented decomposition strategies to enhance the accuracy and flexibility of free vibration analysis. [ABSTRACT FROM AUTHOR]

With rapid urbanization growth, considerable amounts of construction waste are generated on an annual basis, posing significant economic and environmental challenges worldwide. Re-cycling construction waste is a sustainable way for waste disposal, leading to the necessity of meticulous planning of recycling centers. A well-designed plan for constructing recycling centers can effectively improve the recycling rate of construction waste while minimizing investment. This paper formulates a two-stage stochastic model for planning recycling centers with the objective of maximizing the recycling rate under different scenarios. This study comprehensively considers various uncertain factors, such as the amount of construction waste generated and the demand for recycled materials. A case study of Guangzhou is used for validation, which demonstrates the effectiveness of the developed model in planning recycling center construction. The comparison between the proposed model and a conventional mean value model shows the importance of accounting for uncertainties. Specifically, the derived results indicate that 7% more construction waste is recycled with the same investment in constructing recycling centers. Additionally, via a sensitivity analysis, valuable managerial insights on investing resources in recycling center construction are provided to decision makers. Ultimately, the research findings are expected to enhance the recycling rate of construction waste, thereby contributing to sustainable industry development. [ABSTRACT FROM AUTHOR]

More than two-thirds of the installed solar power in Brazil comes from distributed mini-generation and microgeneration (DMMG), mostly residential and for local self-consumption of solar PV. DMMG had its legal framework approved in 2022, gradually introduces charges for distribution network use, previously exempt. To understand these implications, this article compiles recent legislative and normative changes, particularly for solar PV DMMG projects. In addition, the present study focuses on the use of DMMG systems in the city of Fortaleza in the Northeast Region. The study includes an analysis of the integration of photovoltaic plants in homes in Fortaleza and their economic viability under the new conditions established after 2020. Two residential photovoltaic systems, with varying power levels common in the residential sector of Fortaleza, were studied. Each plant underwent three analyses: one with vested rights; another with 100% taxation of FIO Bl and a third for the current scenario, factoring in the growing FIO B charge from 15% in 2023 to 100% in 2029. Customers who obtained the system by cash payment or financing were considered. An economic feasibility study factored in a 5.79% yearly inflation rate and an 11.35% annual tariff readjustment across the two plants, including sensitivity analysis for this variable. [ABSTRACT FROM AUTHOR]

Power systems are complex and often subject to faults, requiring accurate mathematical models for a thorough analysis. Traditional time-domain models are employed to evaluate the dynamic response of power system elements during transmission system faults. However, only the positive sequence components are considered for unbalanced faults, so the small-signal stability analysis is no longer accurate when assuming balanced conditions for asymmetrical faults. The dynamic phasor approach extends traditional models by representing synchronous machines with harmonic sequence components, making it suitable for an unbalanced condition analysis and revealing dynamic couplings not evident in conventional methods. By modeling electrical and mechanical equations with harmonic sequence components, the study implements an eigenvalue sensitivity analysis and participation factor analysis to identify the variable with significant participation in the critical modes and consequently in the dynamic response of synchronous machines during asymmetric faults, thereby control strategies can be proposed to improve system stability. The article validates the dynamic phasor model through simulations of a single-phase short circuit, demonstrating its accuracy and effectiveness in representing the transient and dynamic behavior of synchronous machines, and correctly identifies the harmonic sequence component with significant participation in the critical modes identified by the eigenvalue sensitivity to the rotor angular velocity and rotor angle. [ABSTRACT FROM AUTHOR]

Quantitative systems pharmacology (QSP) models are rarely applied prospectively for decision-making in clinical practice. We therefore aimed to operationalize a QSP model for potas-sium homeostasis to predict potassium trajectories based on spironolactone administrations. For this purpose, we proposed a general workflow that was applied to electronic health records (EHR) from patients treated in a German tertiary care hospital. The workflow steps included model exploration, local and global sensitivity analyses (SA), identifiability analysis (IA) of model parameters, and specification of their inter-individual variability (IIV). Patient covariates, selected parameters, and IIV then defined prior information for the Bayesian a posteriori prediction of individual potassium trajectories of the following day. Following these steps, the successfully operationalized QSP model was interactively explored via a Shiny app. SA and IA yielded five influential and estimable parameters (extracellular fluid volume, hyperaldosteronism, mineral corticoid receptor abundance, potassium intake, sodium intake) for Bayesian prediction. The operationalized model was validated in nine pilot patients and showed satisfactory performance based on the (absolute) average fold error. This provides proof-of-principle for a Prescribing Monitoring of potassium concentrations in a hospital system, which could suggest preemptive clinical measures and therefore potentially avoid dangerous hyperkalemia or hypokalemia. [ABSTRACT FROM AUTHOR]

High-performance computing (HPC) plays a crucial role in scientific computing, and the efficient utilization of HPC to accomplish computational tasks remains a focal point of research. This study addresses the issue of parameter tuning for Earth system models by proposing a comprehensive solution based on the concept of scientific workflows. This solution encompasses detailed methods from sensitivity analysis to parameter tuning and incorporates various approaches to enhance result accuracy. We validated the reliability of our methods using five cases in the Single Column Atmosphere Model (SCAM). Specifically, we investigated the influence of fluctuations of 11 typical parameters on 10 output variables. The experimental results show that the magnitude of the impact on the results varies significantly when different parameters are perturbed. These findings will help researchers develop more reasonable parameterization schemes for different regions and seasons. [ABSTRACT FROM AUTHOR]

Porous hierarchical structures are extensively utilized in engineering for their high specific strength, enhanced corrosion resistance, and multifunctionality. Over the past two decades, multiscale topology optimization for these structures has garnered significant attention. This paper introduces a novel hybrid MMCs (Moving Morphable Components)–density topology optimization method for quasi-periodic cellular structures. The term 'quasi-periodic' refers to microstructures whose different macroscopic points exhibit similar topologies with varying parameters. The primary concept involves using the MMC method to describe microstructural topology, while employing variable density to depict macro layouts. This approach leverages the advantage of MMCs in explicitly describing structural topology alongside the variable density of arbitrary microstructures. Sensitivity analyses of the optimization functions concerning design variables are shown, and a gradient optimization solver is employed to solve the optimization model. The examples effectively show the efficacy of the proposed method, illustrating that quasi-periodic cellular structures outperform single-scale solid structures. [ABSTRACT FROM AUTHOR]

This research aims to analyze the behavior of bubble size distribution in the HydroFloat® with seawater and tap water. The study characterized bubble size in a two-phase gas–water system in a fluidized-bed flotation cell. The impact of seawater was compared to tap water using two frothers, MIBC and polyglycol F507. The experimental design was used to investigate the influence of various parameters such as superficial air velocity, superficial liquid velocity, frother concentration, and seawater concentration on bubble size. The results indicate that the critical coalescence concentration followed the order of MIBC > F507. Bubble size decreases with increasing superficial liquid velocity, while the superficial gas velocity and frother/seawater concentration have the opposite effect. ANOVA results reveal that all linear factors are significant, the quadratic terms of the frother and seawater concentrations are significant, and the interaction term for the superficial air velocity–superficial liquid velocity is nonsignificant for bubble size. Global sensitivity analysis demonstrates that the variables significantly affecting bubble size are frother concentration and seawater concentration, followed by superficial water velocity. The superficial gas velocity has minimal impact on bubble size under the conditions studied. [ABSTRACT FROM AUTHOR]

Smart community construction (SCC) is pivotal for urban modernization, yet extant research often neglects the residents' perceptions of these initiatives, focusing predominantly on technological advancements. A hybrid evaluation method integrating the Entropy Weight Method (EWM) and PROMETHEE II was developed to quantify residents' perceptions of SCC. Through the literature review and expert interview, the evaluation indicators for residents' perceptions of the SCC were identified, leading to the development of an evaluation indicator system consisting of 5 dimensions and 25 indicators. The hybrid evaluation method that combines EWM and PROMETHEE II was then applied to quantify the level of residents' perceptions of SCC across five communities in Shenzhen city of China, using data from 410 valid questionnaires. The results indicated that the Baolong community had the highest level of the SCC, while the Nanyuan community exhibited the lowest ranking. Sensitivity analysis confirmed the robustness of these results, thereby validating the evaluation process. Moreover, recommendations were proposed to enhance residents' perceptions of smart community initiatives. This research not only introduces a novel evaluation method that expands the body of knowledge on the SCC and residents' perceptions but also helps the government develop sustainable SCC strategies. [ABSTRACT FROM AUTHOR]

This paper shows the usage of a numerical analysis model that enables the calculation of the life of railway wheels used for low-floor freight wagons as a function of its primary operating factors, which allows for carrying out sensitivity analyses. Low-floor wagons are being increasingly used for combined transport applications, and many types of bogies have been proposed to constitute the wagons. Due to the uniqueness of this type of wagon, the bogie configurations in terms of wheelbase and axle load have hardly been analyzed so far. The numerical analysis model used addresses the primary challenges that arise in the vehicle–track interaction and establishes the relations among them. The main aspects of this model have been described in this paper, which has been later used to calculate the life of an ordinary-diameter wheel for several wheelbase and axle load values. This study has been replicated with reduced-diameter wheels, which are commonly used for low-floor wagons. In this way, it is possible to know the evolution of the life depending on the wheelbase and the axle load. The observed behaviors are not so dissimilar for the different types of wheels, and they point out huge increases in wear as the axle load and the wheelbase rise, especially with axle load. The root causes can be explained by the entire understanding of the rolling phenomenon provided by the full analytical work. [ABSTRACT FROM AUTHOR]

Ball bearings operating at low speeds and under heavy loads are susceptible to wear failure, leading to significant economic losses. The existing reliability-based robust design optimization method of the fourth-moment method has high accuracy and does not need to determine the random distribution of the input variables, but it is not possible to apply it to ball bearing wear due to the complexity of the bearing wear state function that cannot be characterized as an explicit form. To address this issue, this paper proposes a novel design method for ball bearing wear. Firstly, a surrogate model is constructed using the Kriging model method to establish a relationship between the bearing design parameters and the mechanical response. Subsequently, a wear reliability model is developed on the basis of the fourth-moment method, and reliability sensitivity analysis is conducted. Finally, the ball bearing wear reliability-based robust design optimization is accomplished through the use of a genetic algorithm. The results of the case calculations demonstrate that the proposed method effectively calculates the ball bearing wear reliability and analyzes the impact of design parameter randomness on reliability. Furthermore, optimizing the design parameters reduces the sensitivity of wear reliability to parameter randomness. [ABSTRACT FROM AUTHOR]

Arylcyclohexylamine (ACH) compounds represent a predominant faction within new psychoactive substances. Due to their powerful dissociative effects, they are used in recreational contexts but also in situations of drug-facilitated sexual assault, and therefore, they are a constant target of analysis by forensic experts. In recent years, their consumption has been notably high, especially the use of ketamine, presenting daily challenges for laboratories in the determination of this and other ACH analogues. This review comprises the recent strategies that forensic specialists use to identify and quantify ACH compounds in the laboratory with more traditional analytical techniques and technology, and on the point-of-care testing via sensor technology. The study focuses on analogues of phencyclidine (PCP), ketamine, and eticyclidine, highlighting the consistent need for higher sensitivity in the analysis of various samples collected from real cases and simulations of possible matrices. The review also emphasises the ongoing research to develop more sensitive, quicker, and more capable sensors. [ABSTRACT FROM AUTHOR]

Due to the high uncertainty of model predictions, it is often challenging to draw definitive conclusions when evaluating river water quality in the context of management options. The major aim of this study is to present a statistical evaluation of the Hydrologic Simulation Program FORTRAN (HSPF), which is a water quality modeling system, and how this modeling system can be used as a valuable tool to enhance monitoring planning and reduce uncertainty in water quality predictions. The authors' findings regarding the sensitivity analysis of the HSPF model in relation to water quality predictions are presented. The application of the computer model was focused on the Ave River watershed in Portugal. Calibration of the hydrology was performed at two stations over five years, starting from January 1990 and ending in December 1994. Following the calibration, the hydrology model was then validated for another five-year period, from January 1995 to December 1999. A comprehensive evaluation framework is proposed, which includes a two-step statistical evaluation based on commonly used hydrology criteria for model calibration and validation. To thoroughly assess model uncertainty and parameter sensitivity, a Monte Carlo method uncertainty evaluation approach is integrated, along with multi-parametric sensitivity analyses. The Monte Carlo simulation considers the probability distributions of fourteen HSPF water quality parameters, which are used as input factors. The parameters that had the greatest impact on the simulated in-stream fecal coliform concentrations were those that represented the first-order decay rate and the surface runoff mechanism, which effectively removed 90 percent of the fecal coliform from the pervious land surface. These parameters had a more significant influence compared to the accumulation and maximum storage rates. When it comes to the oxygen governing process, the parameters that showed the highest sensitivity were benthal oxygen demand and nitrification/denitrification rate. The insights that can be derived from this study play a critical role in the development of robust water management strategies, and their significance lies in their potential to contribute to the advancement of predictive models in the field of water resources. [ABSTRACT FROM AUTHOR]

Disc cutter wear has emerged as a critical concern impacting the efficiency and cost budgets of TBMs (tunnel boring machines). Through statistical analysis of field data on cutter wear in a TBM tunnel, this study explores the wear rules of different types of disc cutters in granite. Grey sensitivity analysis is employed to investigate the sensitivity between the cutter ring wear rate of face cutters and two types of cutter wear influence parameters. Subsequently, reasonable parameters are selected to develop a new cutter life prediction model for face cutters. The results show that, with increases in the installation radius, the accumulated wear extent shows a linearly increasing trend for both the center and the face cutter, while it first increases and then decreases for gauge cutters, and the accumulated replacement number shows a linear growth trend for face cutters. The accumulated wear extent of the average single cutter position of gauge cutters is about 3 times that of face cutters and 7 times that of center cutters; the number of replaced cutter rings of the average single cutter position for gauge cutters is about 3–4 times that for center cutters and face cutters; and the average utilization rate of gauge cutters is the highest (80.97%). The cutter ring wear rate of face cutters is the most sensitive to three intact rock parameters (uniaxial compressive strength (UCS), Cerchar abrasion index (CAI), and equivalent quartz content (EQC)) and two TBM tunnelling parameters (cutterhead thrust (F) and cutterhead rotational speed (RPM)). Finally, a new cutter life prediction model (R2 = 0.964) for face cutters is developed based on F, UCS, and RPM. The research results can provide a certain theoretical basis for cutter wear and cutter life prediction for the face cutters of TBM projects in similar geological conditions and TBM specifications. [ABSTRACT FROM AUTHOR]

The accurate evaluation of aerodynamic characteristics is a prerequisite and foundation for the design of high-performance aerodynamic shapes, navigation guidance, and strength of projectiles. The nonlinearity of aerodynamic calculations for a projectile is high, and the modeling and simulation are difficult, especially under the high-angle of attack flight conditions. Small variations in flight conditions, and structural parameters, etc., may cause large deviations in aerodynamic responses. Taking a small cylindrical projectile as an example, and to realize its attitude control, it is necessary to conduct aerodynamic characteristics analysis on it and analyze the main influencing factors of its aerodynamic characteristics parameters. In this paper, the finite volume method is used to solve the three-dimensional unsteady N-S equation, combined with the SST k-ω turbulence model, the overlapping grid technology, and the forced pitching vibration method, and the aerodynamic characteristics analysis model of the projectile is established, which realizes the accurate simulation of the surrounding flow field, aerodynamic coefficients, and dynamic derivative of the projectile under different flight conditions. On this basis, the Sobol global sensitivity analysis method based on the augmented radial basis function surrogate model of aerodynamics characteristics and Latin hypercube sampling is used to efficiently analyze and obtain the main influence parameters of cylindrical projectile aerodynamic characteristics. This paper provides a basic theory and fast algorithm for subsequent engineering system design, which has important theoretical and engineering value. [ABSTRACT FROM AUTHOR]

In order to obtain stable and reliable configuration transformation ability, reliability optimization design is regarded as an effective way to reduce the probability of kinematic function failure for the constrained metamorphic mechanism. Based on the structural composition principle of multi-configuration source metamorphic mechanism that can operate in an under-actuated state, the modularized calculation methods are established for the force analysis of augmented Assur groups including metamorphic kinematic joints. According to the equivalent resistance gradient model of metamorphic mechanisms, with considering the uncertainties in the link dimensions, masses, and compliance parameters et al., a probabilistic evaluation method for describing the configuration transformation ability of the constrained metamorphic mechanism is established. Based on reliability evaluation and reliability sensitivity analysis, a reliability optimization design method for improving the configuration transformation ability is proposed, and then the optimization design is carried out for tolerances of random variables focusing on those structural parameters with higher reliability sensitivity, so that the optimized results can satisfy the requirements of both reliability and economic simultaneously. Finally, the feasibility and effectiveness of the proposed method is verified by the illustration of a paper folding metamorphic mechanism. The research provides the foundation of reliability design of metamorphic mechanisms to obtain the high-probability repeated execution ability of configuration transformation, it also has theoretical and practical significance to promote the engineering application of metamorphic mechanisms. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed modeling solution enables realistic multi-body simulations of subgrade layers in the railway infrastructure context, for the purpose of modal analysis. A novel modeling solution (MS) was created that can provide realistically accurate results on the natural frequencies of a railway subgrade layer in the context of a railway infrastructure numerical simulation. This MS was specifically developed for implementation in a multibody simulation (MBS) model, which constitutes a step forward in relation to the state-of-art of this specific topic. The main obstacle was to find a criterion to define the geometry and dimensions of the subgrade elements inside the numerical model, so that the resulting resonance frequencies would be realistically accurate and representative. The proposed MS combines four theoretical models to create a set of equations that enables a deterministic relationship between the height and the diameter of a cylindrically shaped subgrade layer element to be implemented in the MBS model. This MS is dedicated to simulating scenarios involving railway infrastructure and a railway vehicle. However, its background theoretical models are adaptable to simulating other model configurations. This article details the MS and a sensitivity analysis that was performed to assess it. These tests were performed using the MBS software Simpack® (version 2021x). The results obtained from these tests support the selected theoretical equations and the overall validity of the proposed MS. [ABSTRACT FROM AUTHOR]

Mercury and arsenic are two highly toxic pollutants, and many researchers have explored the effects of the two substances on the environment. However, the research content of toxic substances in frozen periods is relatively small. To explore the spatial and vertical distribution of mercury and arsenic in the ice, water, and sediments of Wuliangsuhai Lake under ice conditions, and to assess the harm degree of the two toxic substances to human beings. We collected the ice, water, and sediments of the lake in December 2020, and tested the contents of Hg and As. The single-factor pollution index method, the local cumulative index method, and the ecological risk coding method were used to assess the pollution status in these three environmental media, and the Monte Carlo simulation combined with the quantitative model recommended by USEPA was used to assess the population health risk. The results showed that (1) The average single-factor pollution values of Hg and As in water were 0.367 and 0.114, both pollutants were at clean levels during the frozen period. (2) The mean Igeo values of Hg and As were 0.657 and −0.948. The bioavailability of Hg in the sediments of Wuliangsuhai Lake during the frozen period was high, and its average value was 7.8%, which belonged to the low-risk grade. The bioavailability of As ranged from 0.2% to 3.7%, with an average value of 1.3%. (3) Monte Carlo simulation results indicate acceptable levels of health risks in both water and ice. This study preliminarily investigated the distribution characteristics of toxic substances and their potential effects on human health in lakes in cold and arid regions during the frozen period. It not only clarified the pollution characteristics of lakes in cold and arid regions during the frozen period, but also provided beneficial supplements for the ecological protection of lake basins. This study lays a foundation for further environmental science research in the region in the future. [ABSTRACT FROM AUTHOR]

It is more difficult to accurately control the cycle fuel mass of a gasoline engine as the injection pressure increases, and the difference in the injection mass flow will become intense due to the machining error of GDI injector structural parameters. In order to guarantee the uniform cycle fuel mass of the GDI injector, this paper investigates the effect of the inlet orifice on the injection mass flow and the sensitivity of inner-flow characteristics in the GDI injector nozzle to the inlet orifice radius by the single factor analysis method. The results show that the local resistance of the injector nozzle will decrease and the discharge coefficient will rise due to the increase in the inlet orifice radius; the sensitivity of cycle fuel mass to the inlet orifice radius is most strong at r = 0.02 mm, and its accuracy should be improved when grinding the chamfer of the nozzle inlet; the sensitivity coefficient of the inlet orifice radius will rise up, and the machining accuracy of the inlet orifice radius should be improved as there is an increase in injection pressure. [ABSTRACT FROM AUTHOR]

During the operation of commingled production wells, inter-layer interference is a key factor affecting the recovery rate and flooding extraction efficiency. This study proposes a well deliverability equation that is characterized by a multi-parameter coupled inter-layer interference coefficient, which is applied to quantify the commingled production wells in a multi-layer reservoir. This study used principal component analysis (PCA) to study the combined effects of correlation parameters for inter-layer interference. The results show that the starting pressure gradient and the crude oil viscosity contrast have the most significant impact on inter-layer interference. Changes in these two parameters directly enhance the heterogeneity between different oil layers, thereby intensifying inter-layer interference. Additionally, the positive correlation between permeability and permeability contrast also highlights the contribution of physical property differences in the oil layers to the interference. The sensitivity analysis shows the main influencing factors of inter-layer interference in commingled production wells. providing references for subsequent improvements and optimization of the exploitation schemes and adjustments in reservoir management measures. The results not only enhance the understanding of the mechanisms of inter-layer interference in the exploitation of multi-layer oil reservoirs but also provide scientific evidence and technical support for oilfield development. [ABSTRACT FROM AUTHOR]

Saltwater intrusion is an essential problem in estuaries that can threaten the ecological environment, especially in high-salinity situations. Therefore in this paper, traditional multiple linear regression (MLR) and artificial neural network (ANN) modeling are applied to forecast overall and high salinity in the Lower Scheldt Estuary, Belgium. Mutual information (MI) and conditional mutual information (CMI) are used to select optimal driving forces (DFs), with the daily discharge (Q), daily water temperature (WT), and daily sea level (SL) selected as the main DFs. Next, we analyze whether applying a discrete wavelet transform (DWT) to remove the noise from the original time series improves the results. Here, the DWT is applied in Signal-hybrid (SH) and Within-hybrid (WH) frameworks. Both the MLR and ANN models demonstrate satisfactory performance in daily overall salinity simulation over the Scheldt Estuary. The relatively complex ANN models outperform MLR because of their capabilities of capturing complex interactions. Because the nonlinear relationship between salinity and DFs is variable at different locations, the performance of the MLR models in the midstream region is far inferior to that in the downstream region during spring and winter. The results reveal that the application of DWT enhances simulation of both overall and high salinity in this region, especially for the ANN model with the WH framework. With the effect of Q decline or SL rise, the salinity in the middle Scheldt Estuary increases more significantly, and the ANN models are more sensitive to these perturbations. [ABSTRACT FROM AUTHOR]

The hydrodynamics of the flow around piers affects the motion of ships navigating near these structures, while the motion of the ships, in turn, affects the distribution of the flow field near the piers. This study investigates the forces exerted on a ship in various ship–pier transverse distances using commercial computational fluid dynamics (CFD) software, Fluent 13.0, based on the RNG k-ε model, complemented by experiments with a physical model. The interaction between the ship's motion and the flow field near the piers was considered. The results indicate that during the encounter between the ship and the pier, the boundary of the approaching ship affects the flow field near the pier, thereby affecting the generation and detachment of vortices behind the pier. The yaw moment of the ship demonstrates a marked "positive peak–negative peak–positive peak" pattern. Moreover, as the ship–pier transverse distance increases, the impact of the pier on the ship's motion decreases, and it becomes negligible when the distance reaches 0.9 times the diameter of the pier (D), suggesting that the pier has a minimal impact on ship navigation if the ship–pier transverse distance exceeds this threshold. [ABSTRACT FROM AUTHOR]

The Port of Valencia, a prominent maritime center, is actively working towards minimizing its carbon emissions and aims to become a completely carbon-neutral port soon. This research uses data-driven sensitivity analysis to explore realistic power-generating options for a seaport to reduce its emissions. This approach comprises changing key parameters in power consumption and deploying renewable energies (rather than electricity and infrastructure prices, which are beyond the scope of this study) to assess their impact on the port's overall emissions profile. Through sensitivity analysis, policymakers and managers discover each scenario's efficacy and find the best decarbonization strategies. After thoroughly examining four realistic scenarios, our research findings show that each scenario's emission reduction share and sensitivity are practical and feasible. It becomes clear that gradually replacing traditional fossil fuels for electricity generation with renewables is a reasonable and realistic option for emissions reduction. The results demonstrate that focusing on reasonable targets, such as replacing 30% and 50% of electricity generation with renewables, is more achievable and beneficial in the medium term than ambitious goals, like replacing all electricity with renewable energy. This research contributes to reducing emissions of the Port of Valencia by using data-driven sensitivity analysis to find practical renewable energy strategies. It provides actionable insights for managers and policymakers to implement feasible decarbonization plans, emphasizing gradual adoption of renewables over ambitious goals, thus supporting sustainable maritime operations. [ABSTRACT FROM AUTHOR]

This study seeks to provide guidance on how top-down greenhouse gas emission reduction targets (GHG ERTs), deriving, for example, from corporate decarbonization strategies, can be translated into quantifiable targets for component measures. Furthermore, it shows how these targets need to be adjusted during the development process to account for parameter uncertainties resulting from the lack of data availability and validity in the early design stage. The scope of the analysis focuses on ecological measure magnitude (EMM) targets for mass reduction and the content of recycled material. The study is split into two sections: The first section introduces a method on how to calculate EMMs based on a partial carbon footprint assessment (CFPA). The second and main section elaborates on an analysis of how parameter uncertainties in the CFPA influence initially defined EMM targets by using perturbation analysis. In the presented paper, the method is applied exemplarily to an automotive component in an internal combustion engine vehicle. The study shows that a parameter uncertainty in the environmental impact of the mass-induced use phase or the primary material production (and semi-finished product provision) has a significant influence on the required EMMs. In the authors' opinion, this study can increase the awareness of how CFPA parameter uncertainties can affect the credibility of EMM development targets. The used approach can help designers and engineers to minimize the risk of a non-fulfillment of GHG emission-related development targets. [ABSTRACT FROM AUTHOR]

Recently, with the widespread adoption of quick response (QR) code payments, there have been incidents of unauthorized use of QR codes presented at the time of payment, due to theft or duplication. As a countermeasure, conventional QR code payment systems are designed to update the QR code periodically. However, there is a problem: it is possible to make a payment using an illegally obtained QR code until the update. Therefore, it is necessary to encrypt the QR code itself to prevent its duplication. The objective of this research is to prevent fraudulent use of QR payments by combining image encryption using random numbers and Rivest Cipher 4 (RC4). In this paper, we perform image encryption using random numbers generated from a uniform distribution for QR codes presented at the time of payment and encrypt the seed value, which is the decryption key, using RC4. As a result, the proposed encryption method prevents unauthorized use of the QR code used for payment by stealing the image, and the processing speed and encryption strength are sufficient. Histogram analysis, key sensitivity analysis, and correlation coefficients were used to measure encryption strength. Finally, the proposed method is expected to enable more secure use of QR payments compared to conventional systems. [ABSTRACT FROM AUTHOR]

Considering the voltage stability problem brought by large-scale distributed PV access to the distribution network, this paper proposes a generalized load model that considers the fault ride-through capability of distributed PV. Firstly, the detailed model of the distribution network is established, and the detailed model is calibrated based on the measured data, the simulation errors are below 1%. And then establish a generalized load model considering distributed PV high and low voltage traversal ability. The sensitivity analysis results are used to rank the parameters to be identified, and the parameters with higher sensitivity are identified. The parameters are obtained from the detailed model and measured data, and four sets of parameters are identified and simulated under different PV penetration rates and fault conditions. The calculated fitting errors are less than 1%. The results show that the generalized load gray box model of the distribution network with distributed PV high and low voltage ride-through capability can reflect the dynamic characteristics of the distribution network well. [ABSTRACT FROM AUTHOR]

The InSAR technique is known to be a powerful tool for precise monitoring of wide areas in terms of displacements. It is conceivable to also use this technique to monitor landslide areas, but geometrical distortions due to ground morphology and land cover could make InSAR processing ineffective for such applications. Because of the computational burden of InSAR processing, it is important to have preliminary knowledge about the possible suitability of the technique for the inspected area before acquiring and processing the data. This paper aims to perform a preliminary analysis of the InSAR sensitivity for the specific case of landslide monitoring. A new approach is proposed considering aspects specific to landslide displacements, which are basically tangent to the slope direction. Pre-processed coherence maps were used to account for the impact of land cover. The whole analysis can be carried out without acquiring cumbersome SAR datasets and can be used as a preliminary step. The Italian Emilia-Romagna region has been considered as the study area, with landslide areas accounting for more than 12% of its territory. The outcomes show that the inspected area has favourable morphological conditions, mainly thanks to its mild slopes and the limited number of landslides facing north, but the land cover has a strong negative impact on the InSAR sensitivity. Nevertheless, 7.5% of the landslide areas have promising conditions for monitoring using radar interferometry. [ABSTRACT FROM AUTHOR]

There is uncertainty in the results of any mathematical model due to different reasons. It is important to estimate this uncertainty. Sensitivity analysis is commonly used to estimate how the changes in the input parameters affect the solutions of the model. In this paper, we discuss different ways of performing local and global sensitivity analyses and apply them to two models: an epidemic model and a new myocardial infarction model, both based on ordinary differential equations. The first model is a simple model used to explain the ideas, while the second one shows how to apply them to a model with more state variables and parameters. We find that if the parameters are not accurately known, local sensitivity analysis can be misleading and that global sensitivity methods that sample the whole parameter space, varying all the values of the parameters at the same time, are the most reliable. We also show how the sensitivity analysis results can be used to determine the uncertainty in the results of the model. We present numerical simulations. [ABSTRACT FROM AUTHOR]

Topology optimization has become a popular tool for designing optimal shapes while meeting specific objectives and restrictions. However, the resulting shape from the optimization process may not be easy to manufacture using typical methods like machining and may require interpretation and validation. Additionally, the final shape depends on chosen parameters. In this study, we conduct a sensitivity analysis of the main parameters involved in 3D topology optimization—penalization and filter radius—focusing on the density-based method. We analyze the features and characteristics of the results, concluding that a machinable and low interpretable part is not an attainable result in by-default topology optimization. Therefore, we propose a new method for obtaining more manufacturable and easily interpretable parts. The main goal is to assist designers in choosing appropriate parameters and understanding what to consider when seeking optimized shapes, giving them a new plug-and-play tool for manufacturable designs. We chose the density-based topology optimization method due to its popularity in commercial packages, and the conclusions may directly influence designers' work. Finally, we verify the study results through different cases to ensure the validity of the conclusions. [ABSTRACT FROM AUTHOR]

Power converters are the basic elements of any power electronics system in many areas and applications. Among them, the push–pull converter topology is one of the most widespread due to its high efficiency, versatility, galvanic isolation, reduced number of switching devices and the possibility of implementing high conversion ratios with respect to non-isolated topologies. Optimal design and control requires very accurate models that consider all the non-idealities associated with the actual converter. However, this leads to the use of high-order models, which are impractical for the design of model-based controllers in real-time applications. To obtain a trade-off model that combines the criteria of simplicity and accuracy, it is appropriate to assess whether it is necessary to consider all non-idealities to accurately model the dynamic response of the converter. For this purpose, this paper proposes a methodology based on a sensitivity analysis that allows quantifying the impact of each non-ideality on the converter behaviour response as a function of the converter topology, power and frequency. As a result of the study, practical models that combine the trade-off between precision and simplicity are obtained. The behaviour of the simplified models for each topology was evaluated and validated by simulation against the most complete and accurate non-ideal model found in the literature. The results have been excellent, with an error rate of less than 5% in all cases. [ABSTRACT FROM AUTHOR]

Hydrological modeling plays a vital role in water-resource management and climate-change studies in hyper-arid regions. In the present investigation, surface runoff was estimated by a Soil and Water Assessment Tool (SWAT) model for Wadi Al-Aqul, Saudi Arabia. The Sequential Uncertainty Fitting version 2 (SUFI-2) technique in SWAT-CUP was adopted for the sensitivity analysis, calibration, and validation of the SWAT model's components. The observational runoff data were scarce and only available from 1979 to 1984; such data scarcity is a common problem in hyper-arid regions. The results show good agreement with the observed daily runoff, as indicated by a Pearson Correlation Coefficient (r) of 0.86, a regression (R2) of 0.76, and a Nash–Sutcliffe coefficient (NSE) of 0.61. Error metrics, including the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), were notably low at 0.05 and 0.58, respectively. In the daily validation, the model continued to perform well, with a correlation of 0.76 and regression of 0.58. As a new approach, fitted parameters of daily calibration were incorporated into the monthly simulation, and they demonstrated an even better performance. The correlation coefficient (regression) and Nash–Sutcliffe were found to be extremely high during the calibration period of the monthly simulation, reaching 0.97 (0.95) and 0.73, respectively; meanwhile, they reached 0.99 (0.98) and 0.63 in the validation period, respectively. The sensitivity analysis using the SUFI-2 algorithm highlighted that, in the streamflow estimation, the Curve Number (CN) was found to be the most responsive parameter, followed by Soil Bulk Density (SOL_BD). Notably, the monthly results showed a higher performance than the daily results, indicating the inherent capability of the model in regard to data aggregation and reducing the impact of random fluctuations. These findings highlight the applicability of the SWAT model in predicting runoff and its implication for climate-change studies in hyper-arid regions. [ABSTRACT FROM AUTHOR]

In response to escalating concerns about climate change, there is a growing imperative to prioritize the decarbonization of the telecom sector and effectively reduce its carbon emissions. This study presents a thorough techno-economic optimization framework for implementing renewable-dominated hybrid standalone systems for the base transceiver station (BTS) encapsulation telecom sector in Pakistan. It is noted that from the results obtained from 42 BTS sites overall, 21 BTS sites had a feasible combination of a photovoltaic battery system, having a diesel generator as a backup source with an average LCOE of 0.1246 USD/kWh to 0.2325 USD/kWh. Thus, seven BTS sites had an optimal combination of biomass, with photovoltaic and battery storage systems and with a varied LCOE of 0.1175 USD/kWh to 0.1318 USD/kWh. Moreover, due to the high flow of hydro water in the north region, five BTS sites presented an ideal configuration of a hydro system coupled with a photovoltaic, wind, and battery storage system, with a varied LCOE of 0.04547 USD/kWh to 0.07419 USD/kWh. Wind energy systems are dominant in the southern region; therefore, five BTS sites presented an ideal combination of a wind energy system coupled with a photovoltaic battery storage system, having DGs as backup sources for sustainability and with a varied LCOE of 0.1096 USD/kWh to 0.1294 USD/kWh. In addition, 02 BTSs had an optimal combination of photovoltaic systems coupled with hydro and wind systems, with diesel generators having a varied LCOE of 0.07618 USD/kWh to 0.04575 USD/kWh. The remaining 02 BTS sites had a feasible combination of wind–hydro-battery and diesel generator–photovoltaic–hydro-battery systems, with an LCOE of 0.7035 USD/kWh and 0.1073 USD/kWh, respectively. Finally, an environmental analysis based on carbon emissions, as well as sensitivity analyses based on different uncertainties, i.e., wind speed, solar irradiance, inflation rate, discount rate, and load demand, was performed to evaluate the behavior of the proposed systems. The optimization of these systems and comparative study findings indicate that the hybrid BTS system is the best option, better than conventional diesel-operated BTS systems in terms of cost-effectiveness, environmental friendliness, and sustainability. [ABSTRACT FROM AUTHOR]