Random Scenarios Research Articles

Connectivity-Enhanced 3D Deployment Algorithm for Multiple UAVs in Space–Air–Ground Integrated Network

The space–air–ground integrated network (SAGIN) can provide extensive access, continuous coverage, and reliable transmission for global applications. In scenarios where terrestrial networks are unavailable or compromised, deploying unmanned aerial vehicles (UAVs) within air network offers wireless access to designated regions. Meanwhile, ensuring the connectivity between UAVs as well as between UAVs and ground users (GUs) is critical for enhancing the quality of service (QoS) in SAGIN. In this paper, we consider the 3D deployment problem of multiple UAVs in SAGIN subject to the UAVs’ connection capacity limit and the UAV network’s robustness, maximizing the coverage of UAVs. Firstly, the horizontal positions of the UAVs at a fixed height are initialized using the k-means algorithm. Subsequently, the connections between the UAVs are established based on constraint conditions, and a fairness connection strategy is employed to establish connections between the UAVs and GUs. Following this, an improved genetic algorithm (IGA) with elite selection, adaptive crossover, and mutation capabilities is proposed to update the horizontal positions of the UAVs, thereby updating the connection relationships. Finally, a height optimization algorithm is proposed to adjust the height of each UAV, completing the 3D deployment of multiple UAVs. Extensive simulations indicate that the proposed algorithm achieves faster deployment and higher coverage under both random and clustered distribution scenarios of GUs, while also enhancing the robustness and load balance of the UAV network.

Static security assessment of renewable integrated power systems using ensemble of modified ELM with unsupervised feature learning technique

The integration of renewable energy sources into power systems poses various challenges for static security assessment, including intermittency and variability of renewable generation, uncertainty in forecasting and impact on grid stability. Overcoming these challenges involves utilizing advanced modelling methods, refining forecasting algorithms, enhancing monitoring and control systems for the grid, and developing robust static security assessment approaches specifically designed for power systems integrated with renewable energy generation. A modified Extreme learning machine (ELM) based ensemble approach is proposed in this study, where ELM is combined with Levenberg-Marquardt (LM) backpropagation technique to improve the accuracy and robustness of prediction. Further, computational efficiency is improved through an unsupervised feature learning technique in the form of autoencoder to reduce the curse of dimensionality. The ensemble technique provides a comprehensive solution for evaluating the static security of power systems in the presence of uncertainties introduced by renewable energy sources. The uncertainties are incorporated into the test systems by simulating random solar and wind scenarios using a well-established Monte Carlo (MC) simulation method. The effectiveness of this approach is demonstrated through numerical testing on modified IEEE 14-bus, 30-bus, 118-bus, and an Indian practical 75-bus systems. Results show that the proposed model outperforms base learners in terms of reliability and efficiency.

A multitask SHM algorithm to identify damage with random severity and location in IPE beams using EMI technique

Existing electromechanical impedance (EMI) damage identification algorithms often face challenges in terms of generalizability. This paper presents a robust algorithm that can simultaneously estimate the region and severity of damage with random damage scenarios across a surface and any severity, rather than being limited to specific points and severities. The host structure was an I-beam. Simulated damage was introduced as a subtle added mass to evaluate the algorithm's effectiveness in early-stage damage identification. Initially, various EMI tests with different damage specifications were conducted, and validated through numerical simulation. Damage-sensitive features were extracted and were input into three ML models: support vector machine, random forest, and multilayer perceptron. An ensemble learning approach was employed to combine the individual predictions from these models. The algorithm achieved classification accuracies of 97.3 % and 94.4 % on the validation and test sets, respectively, for identifying damaged regions. The algorithm also quantifies damage severity, achieving R-squared values of 92 % and 88 % on the validation and test sets, respectively.

A nonlinear model predictive control for air suspension of hub motor electric vehicle based on hybrid H2/H∞ state estimation

To alleviate the vibration from the road irregularities and the unbalanced electromagnetic forces (UEFs), deteriorating the dynamic performance of hub motor electric vehicles (HM-EVs), a novel nonlinear model predictive control (NMPC) is proposed based on a hybrid H2/H ∞ filtering algorithm for the HM-EVs with air suspension (HM-AS). The dynamic HM-AS model involves the electric vehicle’s longitudinal, lateral, and vertical motion. The air suspension system between the body and the hub motor enhances ride quality and insulates the oscillation delivered to the vehicle body. Then, the dynamic HM-AS model is validated by the actual vehicle test, and the proposed filter is developed to obtain the vehicle states. Besides, the NMPC controller guarantees the dynamic performance of HM-AS by intervening in vehicle attitude, road-holding stability, motor safety, and ride comfort. Finally, compared with the application of PID and MPC, the efficiency of the proposed method is demonstrated based on several random road scenarios. The results indicate the proposed algorithm can alleviate the dynamic performance of HM-AS and reduce motor vibration.

Impact of various fairness-based distribution models on supply chain networks from the perspective of customer satisfaction

This study investigates the effectiveness of two fairness-based distribution approaches—type I and type II—in enhancing the resilience of supply chain networks (SCNs) during disruptions. The research contributes to the growing body of knowledge on supply chain management by offering insights into how fairness principles can be applied to improve the restoration of disrupted networks. A mixed-integer programming model was developed to simulate these fairness-based distribution strategies, focusing on a water supply chain network of a privet company in Saudi Arabia. The SCN consists of a single supplier and ten demand nodes, each requiring multiple commodities. The model was tested under 100 random disruption scenarios, each reducing the capacity of randomly selected network segments. The performance of each fairness-based distribution approach was evaluated based on how quickly and effectively the SCN returned to its required service levels (SLs) across all demand nodes. Results indicate that Fairness Distribution Type I, which aims to minimize unmet demand across the entire network, generally outperformed Type II in terms of speed and efficiency. Type I was more effective at restoring SLs quickly at most nodes, while Type II showed localized advantages, particularly in restoring SLs for specific commodities at select nodes. The study concludes that while Type I is more suited for overall supply chain recovery, Type II may be beneficial in scenarios requiring focused recovery at specific demand nodes. These findings provide actionable insights for supply chain managers seeking to enhance network resilience through fairness-based distribution strategies, and suggest avenues for future research on hybrid and context-specific approaches.

Computation and validation of the Expected Value of Power of Two Terminal Series–Parallel PV arrays

PV arrays are susceptible to various types of failures such as partial shading that can negatively affect their performance and efficiency. Studying the impact of these circumstances in the performance of the PV array is key for the development of more efficient PV systems. In this study, the definition of the expected value of power (EVP) that a PV array can produce under a random failure scenario is revised and improved. An algorithm to compute EVP that minimizes the number of simulations is developed and tested in three different PV arrays. Results show a 96.6% reduction on average of the number of simulations needed to compute the EVP for PV arrays made up of 9 modules. The model is validated through an experiment that reproduces the random fault scenario and determines the mean power produced by the PV arrays. For all three experiments, the computed EVP fits the experimental data with R2>0.995.

Hybrid retirement strategy in South Africa

Background: Many retirees in South Africa face the challenge of either outliving their retirement savings or living below their means. Studies suggest a ‘safe’ withdrawal rate of between 4% and 5%, which is below the average fund size-weighted drawdown rate of approximately 6.66%.Aim: To provide a scientific basis for the success rate of a ‘hybrid’ retirement strategy, whereby a retiree invests a proportion of their savings in a life annuity and the remaining proportion in a living annuity, to increase the success rate for South African retirees.Setting: Historical asset class returns (equities, bonds and inflation) for South Africa were sourced for the period 1900–2020.Method: Bootstrap sampling of historical asset returns was employed to simulate 10 000 random scenarios to investigate the success rate of various compositions of the ‘hybrid’ retirement strategy.Results: The success rate of all ‘hybrid’ portfolio compositions is significantly greater than the success rate of a pure living annuity when the withdrawal rate is less than 8%.Conclusion: In a South African context, a ‘hybrid’ retirement portfolio increases the probability of success for retirees withdrawing less than 8% from their portfolio – which constitutes approximately 50% of the current annuatised population – and may increase the inheritance of a retiree’s heir.Contribution: Where other studies have focussed solely on the success rate of a living annuity, we have shown that a ‘hybrid’ retirement strategy increases a South African retiree’s likelihood of retiring successfully when the withdrawal rate is less than 8%, which is approximately 50% of the annuatised population.

A choice-based approach to dynamic capacitated multi-item lot sizing with demand uncertainty

With the purpose of planning and implementing pricing decisions on a tactical level as well as production decisions on an operational level, we consider – in an integrated form – the capacitated multi-item lot sizing problem with uncertain item demands and price-dependent discrete choice demand. The model is embedded into an overarching rolling horizon procedure allowing for adaptations to changes in demand and cost parameters. We first formulate the static problem version as a nonlinear mathematical program with underlying multinomial logit demand and subsequently linearize it to make it viable for mathematical programming solvers. Uncertainty of demands is taken into account by Monte Carlo simulation. More specifically, we generate random demand scenarios and utilize them as input data for the sample average approximation problem version. We further endow the problem setting with possibilities to incorporate pricing policy requirements such as restricting the number of price adaptations or defining periods without price adaptations. Overall, the developed approach yields a powerful tool for balancing item demands via pricing in a way favorable for adhering to available production capacities and thereby striking a balance between revenues and costs. Computational results confirm that adapting prices to time-dependent demand and cost parameters is exploited effectively to maintain a deliberately controlled production environment. Moreover, the integrated pricing and production setting allows to study the effect of pricing policy restrictions and demand uncertainties upon attainable profits.

Experimental Validation of Truck Cab Suspension Model and Ride Comfort Improvement under Various Semi-Active Control Strategies

The semi-active cab suspension system for trucks is gaining increasing importance due to its economic advantages, low energy consumption, and significant enhancement of ride comfort. This paper investigates the effects of three control methods on improving ride comfort of semi-active cab suspension systems under random and bump road conditions: Proportional-Integral-Derivative (PID) control, fuzzy PID control, and Model Predictive Control (MPC). Initially, an accurate multi-degree-of-freedom truck cab suspension model was developed and validated through actual road tests. Based on this model, three control strategies were designed and implemented. Finally, the effectiveness of each control strategy was evaluated under various road conditions, including random and bump road scenarios. The results indicate that these control strategies can effectively reduce vibrations and impacts, significantly improving ride comfort. This improvement is crucial for alleviating driver fatigue and enhancing driving safety. Among them, the MPC control showed superior performance, reducing vibrations by at least 31% under both random and bump road conditions, outperforming PID and Fuzzy PID in terms of effectiveness and robustness.

PAINe: An Artificial Intelligence–based Virtual Assistant to Aid in the Differentiation of Pain of Odontogenic versus Temporomandibular Origin

IntroductionPain associated with temporomandibular dysfunction (TMD) is often confused with odontogenic pain, which is a challenge in endodontic diagnosis. Validated screening questionnaires can aid in the identification and differentiation of the source of pain. Therefore, this study aimed to develop a virtual assistant based on artificial intelligence using natural language processing techniques to automate the initial screening of patients with tooth pain. MethodsThe PAINe chatbot was developed in Python (Python Software Foundation, Beaverton, OR) language using the PyCharm (JetBrains, Prague, Czech Republic) environment and the openai library to integrate the ChatGPT 4 API (OpenAI, San Francisco, CA) and the Streamlit library (Snowflake Inc, San Francisco, CA) for interface construction. The validated TMD Pain Screener questionnaire and 1 question regarding the current pain intensity were integrated into the chatbot to perform the differential diagnosis of TMD in patients with tooth pain. The accuracy of the responses was evaluated in 50 random scenarios to compare the chatbot with the validated questionnaire. The kappa coefficient was calculated to assess the agreement level between the chatbot responses and the validated questionnaire. ResultsThe chatbot achieved an accuracy rate of 86% and a substantial level of agreement (κ = 0.70). Most responses were clear and provided adequate information about the diagnosis. ConclusionsThe implementation of a virtual assistant using natural language processing based on large language models for initial differential diagnosis screening of patients with tooth pain demonstrated substantial agreement between validated questionnaires and the chatbot. This approach emerges as a practical and efficient option for screening these patients.

Distributed optimal scheduling for EVs charging and discharging: A penalty-based consensus approach

In preparing for the widespread adoption of electric vehicles (EVs), this paper investigates a two-stage optimization problem with priority for EV charging and discharging scheduling, while taking into account power load stability, economic objectives, and technical constraints of the distribution network. To this end, a distribution power system integrating energy source and EV owners is modeled. Considering the disturbance or even loss of the communication link, a periodical connected topology is adopted for information exchange among EV owners with the deployed smart meters, which is substantially different from the existing works. The main challenges of the problem are that the information flow among EV owners is time-varying and asymmetric, and each EV owner’s demand should be satisfied throughout the whole scheduling horizon. A novelty distributed hierarchical optimal algorithm, combined with iterative water-filling-based method and penalty-based consensus method, is proposed for the two-stage optimization problem with priority. The convergence and optimality of the algorithm are presented mathematically. In addition, the proposed method combined with receding horizon optimization is performed for EVs random arrival and departure scenarios in the system. Finally, we demonstrate advantages of the proposed algorithm in both theory and simulation.

Chronological DC transmission expansion planning considering new energy and load uncertainties

Abstract The integration of renewable energy into the grid and the expansion of new loads have significantly increased the uncertainty of the power system, which has brought many new challenges to power system planning. Hence, it is necessary to establish a transmission expansion planning model that adapts to future development to improve the economic benefits of power grid planning. This paper proposes a chronological DC transmission expansion planning method for new energy sources, such as wind and solar energy, considering the time characteristics of source loads. First, the method establishes a chronological DC transmission expansion planning model by simulating long-term grid operations. Among them, the objective function aims to minimize the sum of investment costs and power generation operating costs related costs. Secondly, to simulate unit operation, a loop optimization modeling method is used to optimize the model structure. Finally, simulation analysis is conducted using the improved Garver’s 6 bus system under random planning scenarios as a basis for comparison. By comparing various scenarios, it is concluded that the proposed chronological DC transmission expansion planning can provide a more economical solution than non-chronological planning. This helps ensure that the system operates cost-effectively under uncertain source and load scenarios while enhancing the integration capabilities of wind and solar power generation.

Quantifying pore spatial uniformity: Application on membranes before and after plasma etching

Membranes play a critical role in diverse applications, including filtration and tissue engineering. The importance of membrane performance optimization highlights the necessity of accurately characterizing the pore structure. Traditional Pore Size Distribution methodologies are widely used to quantify size uniformity. Uniformity though, integrates both size and spatial pore structure aspects, thus necessitating the synergy of complementary techniques to analyze pore structure. This work empowers classic pore metrology with stochastic geometry, specifically the Nearest Neighbour Index (NNI) to assess the spatial uniformity of pores in membrane Scanning Electron Microscopy (SEM) images. Through a comprehensive analysis of Polytetrafluoroethylene (PTFE) before and after plasma etching, along with nanofilament coated Polyethersulfone (PES) membranes, this analysis enhances our understanding of membrane morphology through pore structure and pore spatial arrangement. The findings indicate that increasing magnification leads to a decrease in apparent spatial uniformity, indicative of effects regarding the inclusion in analysis of families of finer pores. In almost all cases, NNI values show higher uniformity compared to a fully random scenario. Additionally, it is found that plasma etching does not have significant effects on spatial uniformity introducing only a slight uniformity in pore centroid arrangement, reflected in a small NNI increase. Furthermore, a pore area shuffling technique reveals the effects of pore density and size on spatial uniformity, highlighting patterns inherent to the materials under study.

Simulating the Entire Clinical Process for an Implant-Supported Fixed Prosthesis: In Vitro Study on the Vertical Implications of Implant-Abutment Connections and Rotational Freedom.

The aim of this in vitro study was to investigate whether and to what extent different scenarios of rotational freedom in different IAC designs affect the vertical dimension of a three-part fixed partial denture (FPD). At the same time, the experimental setup should simulate all clinical and laboratory steps of the implementation of such an FPD as accurately as possible. Twenty identical pairs of jaw models were fabricated from aluminum, each lower-jaw model holding two implants with conical or flat IACs. Three impressions of each model were taken to fabricate stone casts and three-unit FPDs. Three assembly scenarios were compared for the vertical position stability they offered for these FPDs, differing by how the sequential implant components (impression posts > laboratory analogs > abutments 1 > abutments 2) were aligned with the positional index of the IAC. In this way, a total of 60 stone casts and FPDs were fabricated and statistically analyzed for changes in vertical dimension (p < 0.05). Regardless of whether a conical/flat IAC was used (p > 0.05), significantly greater mean changes in vertical dimension were consistently (all comparisons p < 0.0001) found in a "worst-case scenario" of component alignment alternating between the left- and right-limit stop of the positional index (0.286/0.350 mm) than in a "random scenario" of 10 dentists and 10 technicians with varying levels of experience freely selecting the alignment (0.003/0.014 mm) or in a "best-case scenario" of all components being aligned with the right-limit stop (-0.019/0.005 mm). The likelihood of integrating a superstructure correctly in terms of vertical dimension appears to vary considerably more with assembly strategies than with IAC designs. Specifically, our findings warrant a recommendation that all implant components should be aligned with the right-limit stop of the positioning index.

A physics-informed deep learning-based urban building thermal comfort modeling and prediction framework for identifying thermally vulnerable building stock

PurposeDue to the increasing frequency of extreme weather and densifying urban landscapes, residences are susceptible to heat-related discomfort, especially those in a naturally ventilated built environment in tropical climates. Indoor thermal comfort is thus paramount to building sustainability and improving occupants' health and well-being. However, to assess indoor thermal comfort considering the urban context, it is conventional to use questionnaire surveys and monitoring units, which are both case-centric and time-intensive. This study presents a dynamic computational thermal comfort modeling framework that can determine indoor thermal comfort at an urban scale to bridge this gap.Design/methodology/approachThe framework culminates in developing a deep learning model for predicting the accurate hourly indoor temperature of urban building stock by the coupling urban scale capabilities of environment modeling with single-building dynamic thermal simulations.FindingsUsing the framework, a surrogate model is created and verified for Dharavi, India's informal urban settlement. The results indicated that the developed surrogate model could predict the building's indoor temperature in several complex new urban scenarios with different building orientations, layouts, building-to-building distances and surrounding building heights, using five different random urban representative scenarios as the training set. The prediction accuracy was reliable, as evidenced by the mean bias error (MBE) and coefficient of (CV) root mean squared error (MSE) falling between 0 and 5%. The findings also showed that if the urban context is ignored, estimates of annual discomfort hours may be inaccurate by as much as 70%.Social implicationsThe developed computational framework could help regulators and policymakers engage in more informed and quantitative decision-making and direct efforts to enhance the thermal comfort of low-income dwellings and informal settlements.Originality/valueUp to this point, majority of literature that has been presented has concentrated on building a body of knowledge about urban-based modeling from an energy management standpoint. In contrast, this study suggests a dynamic computational thermal comfort modeling framework that takes into account the urban context of the neighborhood while examining the indoor thermal comfort of the residential building stock.

Effective early-warning tool in detecting structural anomalies and preventing progressive collapse.

Addressing the challenges of structural monitoring, this research introduces a cutting-edge method for Structural Health Monitoring (SHM). The study investigates the correlation between deformation work patterns occurring during random damage scenarios. The method focuses on deformation work and related parameters as key indicators of structural health, showing promising results in the early detection of potential issues. Despite evidence indicating the robust nature of structurally complex systems in parallel arrangements, no specific study has been conducted in the case of mixed systems presenting series and parallel elements configurations . In this study, the structural response of a set of systems made of rods in series and parallel arrangements has been analyzed. Damage to the system, occurring randomly on elements, is considered, and an energy-based metric is employed.The purpose of the study is not only to explore the behaviour of rods working in the coupled mechanism, but also to identify relevant trends in the case of certain load patterns or geometrical configurations in order to design an effective targeted monitoring system. The findings highlight the feasibility of this approach in detecting structural anomalies, underscoring its potential as an effective early-warning system for preemptive maintenance and contributing to the improvement of structural integrity and safety.

White dwarf constraints on geological processes at the population level

ABSTRACT White dwarf atmospheres are frequently polluted by material from their own planetary systems. Absorption features from Ca, Mg, Fe, and other elements can provide unique insights into the provenance of this exoplanetary material, with their relative abundances being used to infer accretion of material with core- or mantle-like composition. Across the population of white dwarfs, the distribution of compositions reveals the prevalence of geological and collisional processing across exoplanetary systems. By predicting the distribution of compositions in three evolutionary scenarios, this work assesses whether they can explain current observations. We consider evolution in an asteroid belt analogue, in which collisions between planetary bodies that formed an iron core lead to core- or mantle-rich fragments. We also consider layer-by-layer accretion of individual bodies, such that the apparent composition of atmospheric pollution changes during the accretion of a single body. Finally, we consider that compositional spread is due to random noise. We find that the distribution of Ca, Fe, and Mg in a sample of 202 cool DZs is consistent with the random noise scenario, although 7 individual systems show strong evidence of core-mantle differentiation from additional elements and/or low noise levels. Future surveys that detect multiple elements in each of a few hundred white dwarfs, with well-understood biases, have the potential to confidently distinguish between the three models.

Decision-Making for Autonomous Vehicles in Random Task Scenarios at Unsignalized Intersection Using Deep Reinforcement Learning

Decision-Making for Autonomous Vehicles in Random Task Scenarios at Unsignalized Intersection Using Deep Reinforcement Learning

Fractional accumulative calibration-free odds (f-aCFO) design for delayed toxicity in phase I clinical trials.

The calibration-free odds (CFO) design has been demonstrated to be robust, model-free, and practically useful but faces challenges when dealing with late-onset toxicity. The emergence of the time-to-event (TITE) method and fractional method leads to the development of TITE-CFO and fractional CFO (fCFO) designs to accumulate delayed toxicity. Nevertheless, existing CFO-type designs have untapped potential because they primarily consider dose information from the current position and its two neighboring positions. To incorporate information from all doses, we propose the accumulative CFO (aCFO) design by utilizing data at all dose levels similar to a tug-of-war game where players distant from the center also contribute their strength. This approach enhances full information utilization while still preserving the model-free and calibration-free characteristics. Extensive simulation studies demonstrate performance improvement over the original CFO design, emphasizing the advantages of incorporating information from a broader range of dose levels. Furthermore, we propose to incorporate late-onset outcomes into the TITE-aCFO and f-aCFO designs, with f-aCFO displaying superior performance over existing methods in both fixed and random simulation scenarios. In conclusion, the aCFO and f-aCFO designs can be considered robust, efficient, and user-friendly approaches for conducting phase I trials without or with late-onsite toxicity.

Deep reinforcement learning based energy management of a hybrid electricity-heat-hydrogen energy system with demand response

Hybrid electricity-heat-hydrogen energy system with demand response (DR) is promising in enhancing flexibility and energy efficiency. However, the multi-energy coupling and source-load uncertainties makes it challenging to efficiently schedule energy flows of electricity generation, storage and DR. To this end, this paper proposes a continues deep reinforcement learning algorithm, specifically the deep deterministic policy gradient (DDPG), for the energy management optimization. Different Markov decision processes are firstly employed to analyze and compare two kinds of incentive-based electro-thermal DR contracts, i.e., load curtailment and load shifting. Simulation results exemplify the superiority of the proposed DDPG-based scheduling incorporating electro-thermal DR in terms of economy and sustainability, leading to a 16.02 % reduction in scheduling costs for contract load curtailment and an 8.52 % reduction for contract load shifting when compared to that without DR consideration. Furthermore, the robustness of DDPG-based scheduling is verified under 60 random source-load scenarios compared with different algorithms. Compared to results obtained by DDPG, DDPG-LC and DDPG-LS reduce the mean cost by 22.15 % and 12.84 %, while their error from the theoretical optimum is only around 5 %. The results demonstrate that the approximate optimality and rapid decision-making illustrate DDPG's efficient real-time scheduling capability, thereby enhancing the system's adaptability to uncertain environments.