Uncertain Conditions Research Articles

Sliding Mode Controller for Temperature Control Lab Arduino Kit

Sliding Mode Control, a resilient and efficient control technique widely adopted in engineering, addresses the challenges of managing systems operating in uncertain environmental conditions. To quickly evaluate and validate the effectiveness of Sliding Mode Control, researchers can select a simple test bench like an open-source APMonitor Temperature Control Lab kit. This kit comes with MATLAB-Simulink/Python to support real-time simulation and testing. The novelty of the work is to design and implement non-linear controllers like Sliding Mode Control and Super Twisting Control techniques. Closed loop hardware in loop simulation with the designed non-linear controllers and detailed analysis is performed under tracking and disturbance rejection conditions.

UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation.

Organ segmentation has become a preliminary task for computer-aided intervention, diagnosis, radiation therapy, and critical robotic surgery. Automatic organ segmentation from medical images is a challenging task due to the inconsistent shape and size of different organs. Besides this, low contrast at the edges of organs due to similar types of tissue confuses the network's ability to segment the contour of organs properly. In this paper, we propose a novel convolution neural network based uncertainty-driven boundary-refined segmentation network (UDBRNet) that segments the organs from CT images. The CT images are segmented first and produce multiple segmentation masks from multi-line segmentation decoder. Uncertain regions are identified from multiple masks and the boundaries of the organs are refined based on uncertainty data. Our method achieves remarkable performance, boasting dice accuracies of 0.80, 0.95, 0.92, and 0.94 for Esophagus, Heart, Trachea, and Aorta respectively on the SegThor dataset, and 0.71, 0.89, 0.85, 0.97, and 0.97 for Esophagus, Spinal Cord, Heart, Left-Lung, and Right-Lung respectively on the LCTSC dataset. These results demonstrate the superiority of our uncertainty-driven boundary refinement technique over state-of-the-art segmentation networks such as UNet, Attention UNet, FC-denseNet, BASNet, UNet++, R2UNet, TransUNet, and DS-TransUNet. UDBRNet presents a promising network for more precise organ segmentation, particularly in challenging, uncertain conditions. The source code of our proposed method will be available at https://github.com/riadhassan/UDBRNet.

Regional policy options for carbon peaking in the Yangtze River Delta under uncertainty

The Yangtze River Delta (YRD) region plays a crucial role in achieving China's carbon peaking goal. However, due to uncertainties surrounding future economic growth, energy consumption, energy structure, and population, the attainment of carbon peaking in this region remains uncertain. To address this issue, this study utilized the generalized Divisia index method to analyze the driving factors of carbon emissions, including economy, energy, investment, and population. Subsequently, Monte Carlo simulations were combined with scenario analysis to dynamically explore the peak path of regional heterogeneity in the YRD from 2022 to 2035 under uncertain conditions. The findings highlighted that economic uncertainty has the most significant impact on carbon emissions. Furthermore, reducing energy intensity and promoting the transformation of the energy consumption structure contribute to carbon reduction. The study also revealed that the carbon peak in the YRD exhibits regional heterogeneity. According to the baseline scenario, carbon emissions in the YRD will not peak before 2035. However, under the low-carbon development scenario, the carbon emissions of Zhejiang and Shanghai will peak before 2030. Moreover, under the enhanced emission reduction (EE) scenario, carbon emissions in Jiangsu, Zhejiang, and Shanghai will peak before 2025, while Anhui will reach its peak before 2030. Collectively, the entire YRD region is forecasted to attain a carbon emissions peak of 2.29 billion tons by 2025 under the EE scenario. This study provides valuable insights into the carbon emission trajectories of the YRD region under uncertain conditions. The findings can be instrumental in formulating carbon peaking policies that account for regional heterogeneity.

Evaluating the potential for continuous update of enhanced forest inventory attributes using optical satellite data

Abstract Timely and detailed inventories of forest resources are of critical importance to guiding sustainable forest management decisions. As forests occur across large spatial extents, remotely sensed data are often used to augment conventional forest inventory measurements. When combined with field plot measurements, airborne laser scanning (ALS) data can be used to derive detailed enhanced forest inventories (EFIs), which provide spatially explicit and wall-to-wall characterizations of forest attributes. However, these EFIs represent a static point in time, and the dynamic nature of forests, coupled with increasing disturbance and uncertain future conditions, generates a need for the continuous updating of forest inventories. This study used a time series of optical satellite data to update an EFI generated for a large (~690 000 ha) forest management unit in Ontario, Canada, at a two-week interval. The two-phase approach involved first building a relationship between single-year EFI attributes (2018) and spectral variables representing within-year slope, amplitude, and trend of a time series (2000–21) of 14 spectral bands and indices. For each of the 20 strata representing different species groups and site productivity classes, a k-nearest neighbor (kNN) model was developed to impute seven common EFI attributes: aboveground biomass, basal area, stem density, Lorey’s height, quadratic mean diameter, and stem volume. Across all strata, models were generally accurate, with relative root mean square error ranging from 11.47% (canopy cover) to 31.82% (stem volume). In the second phase of the approach, models were applied across the entire study area at two-week intervals in order to assess the capacity of the methodology for characterizing change in EFI attributes over a three-year period. Outputs from this second phase demonstrated the potential of the approach for characterizing changes in EFI values in areas experiencing no change or non-stand replacing disturbances. The methods developed herein can be used for EFI update for any temporal interval, thereby enabling more informed decisions by forest managers to prescribe treatments or understand the current state of forest resources.

Optimization of cut-off grades and depletion rates by means of dynamic programming

The determination of cut-off grades is critical for mine appraisal. Because of the time value of money, an optimum schedule of cut-off grades must be used instead of determination of a single cut-off grade for the life of a mine. In order to achieve the maximum net present value, a decreasing order of cut-off grades schedule must be used because of the opportunity cost. In the optimization of cut-off grades by dynamic programming, the depletion rates to be searched are actually limiting the search area. The optimum can be found from among the alternative depletion rates. Selling prices of mining products are volatile and future values cannot be known. This paper describes the use of dynamic programming in cut-off grades optimization and further extension of the method to price volatility in selling prices of mining products. The author introduces the general background of use of dynamic programming in cut-off grades optimization and the case in uncertain market conditions. Software developed in this subject handles to find the optimum sequence of cut-off grades and depletion rates for mineral deposits under market uncertainty. Dynamic programming method can be used as a means of cut-off grades optimization in this context very robustly.

Robust adaptive non‐linear alignment algorithm for SINS/DVL integrated navigation system based on variational Bayesian

AbstractThe problem of dynamic attitude alignment for strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation has become a research hotspot. Underwater complex environment makes DVL output vulnerable to non‐Gaussian noise pollution, which makes it difficult to converge the SINS misalignment angle to within 1° based on analytical coarse alignment. This is to say, the performance of SINS fine alignment via Kalman filter will be degraded because the model of initial alignment exhibits non‐linearity. The inaccurate and/or unknown prior information of measurement noise statistical characteristics will also degrade the filtering performance. To ensure the SINS/DVL alignment accuracy under non‐linear, non‐Gaussian and uncertain conditions, this paper proposed a robust adaptive unscented Kalman filter (UKF) based on Variational Bayesian (VB) method (VBRAUKF). The proposed VBRAUKF improves the adaptability and robustness of UKF from the following two main aspects. Firstly, an adaptive estimation strategy for the measurement noise covariance is designed based on the VB algorithm, which can restrain the effect of inaccurate observation model and improve the adaptive ability of the filtering method. Secondly, an expansion factor is designed based on Mahalanobis distance algorithm, which can restrain the effect of non‐Gaussian noise and improve the robustness of the filtering method. The experimental results for the problem of the SINS/DVL dynamic alignment under mixed Gaussian noise and/or outlier conditions demonstrate the superiority of the proposed VBRAUKF over the traditional ones.

Analysis of the Potential of Coastal Villages Based on Blue Economy in Realizing Sustainable Development Goals

The potential of Indonesia's coastal villages is very large, especially the potential for marine resources. This potential can be utilized to improve the welfare of village communities and realize the Life Below Water goals of the Sustainable Development Goals (SDGs). This research aims to analyze the potential of Gampong Lampulo in realizing the SDGs Life Below Water goals related to the sustainability of marine ecosystems, as well as identifying obstacles that arise in efforts to manage this potential using the blue economy concept. The method used in this research is a descriptive qualitative approach. Data was collected through observation, interviews and documentation. The research informants used were ten people, including one navy commander and nine fishermen. The research results show that Gampong Lampulo has large marine resource potential. This potential can provide a stable source of income for village residents and improve the welfare of coastal communities. However, managing this potential still faces several obstacles, including uncertain natural conditions and high fuel prices.

Continuous Power Management of Decentralized DC Microgrid Based on Transitional Operation Modes under System Uncertainty and Sensor Failure

Continuous power management for a decentralized DC microgrid (DCMG) is proposed in this study to achieve power balance and voltage regulation even under system uncertainty and voltage sensor failure. The DCMG system achieves continuous power management through only the primary controller to reduce the computational burden of each power agent. To enhance the reliability and resilience of the DCMG system under DC bus voltage (DCV) sensor failure, a DCV sensor fault detection algorithm is suggested. In this algorithm, DCV sensor failure is detected by comparing the measured DCV with the estimated DCV. If power agents identify the failure of the DCV sensor, it changes the operation properly according to the proposed control mode decision algorithm to guarantee the stability of the DCMG system. When uncertain conditions like sudden grid disconnection, DCV sensor failure, electricity price change, power variation in distributed generations, and critical battery status occur, the DCMG system is changed to transitional operation modes. These transitional operation modes are employed to transmit the power agent information to other agents without digital communication links (DCLs) and to accomplish power sharing even under such uncertain conditions. In the transitional operation modes of the DCMG system, the DCV levels are temporarily shifted to an appropriate level, enabling each power agent to detect the uncertainty conditions, and subsequently to determine its operation modes based on the DCV levels. The reliability and effectiveness of the proposed control strategy are confirmed via various simulation and experimental tests under different operating conditions.

Nurses under fire: Insights from testimonies of community nurses and midwives in nonhospital settings in the southern Israel conflict zone.

Nurses-with or without prehospital care training-may find themselves delivering immediate care to injured individuals outside a healthcare facility, sometimes even in situations where their own life is at risk. This study explores the experiences of community nurses and midwives who provided immediate care during the Hamas militant movement's attack in southern Israel. The researchers collected and analyzed eight nurse and midwife survivors' testimonies published in digital media to gain a deeper understanding of their perspectives. Through qualitative content analysis, common themes, patterns, and insights were identified. The study aimed to contribute valuable knowledge in this field and followed the Consolidated Criteria for Reporting Qualitative Research (COREQ)checklist for methodological rigor. Two themes emerged from the testimonies, focusing on the nurses' swift realization of necessary action under fire and resilience and ingenuity in practice. Despite lacking training and resources, the nurses professionally assessed the situation and improvised creative solutions to care for the wounded. Although they had no prehospital emergency care background, community nurses and midwives were able to respond effectively, providing lifesaving care. The findings highlight the nurses' resilience, adaptability, and dedication in unprecedented situations. However, the study also emphasizes the importance of providing all nurses with baseline prehospital care training and structured planning of this care to empower them to deliver optimal patient care in uncertain and dangerous conditions, especially in and around conflict and disaster zones.

Efficiency and benchmarks for photovoltaic power generation amid uncertain conditions

Photovoltaic (PV) power generation systems are highly subject to weather and site conditions, thus, the construction of PV power plant projects must consider these uncertainties. This study analyzes the monthly electricity generation of 249 utility-scale PV power plants in Japan to evaluate their electricity generation efficiency. Applying the generic data envelopment analysis, benchmark values were identified for power generation from PV power plants. Furthermore, we implemented a Monte Carlo experiment to evaluate the impact of variability in solar irradiance and temperature on power generation efficiency. For our analysis, we considered three inputs—solar irradiance, temperature, and installed capacity—and electricity generation as the output. The results showed that inter-regional gap in the efficiency score between the west and north regions is 0.03, and this can be covered by a 0.1 increase in the DC/AC ratio. Additionally, variability in weather conditions affect both the efficiency of a power plant and production possibility frontier, in turn causing the benchmark values for a generic decision-making unit to vary. Increasing the generation capacity of power plants and operating them more efficiently is essential to expanding the use of renewable energy resources.

A Learnheuristic Algorithm Based on Thompson Sampling for the Heterogeneous and Dynamic Team Orienteering Problem

The team orienteering problem (TOP) is a well-studied optimization challenge in the field of Operations Research, where multiple vehicles aim to maximize the total collected rewards within a given time limit by visiting a subset of nodes in a network. With the goal of including dynamic and uncertain conditions inherent in real-world transportation scenarios, we introduce a novel dynamic variant of the TOP that considers real-time changes in environmental conditions affecting reward acquisition at each node. Specifically, we model the dynamic nature of environmental factors—such as traffic congestion, weather conditions, and battery level of each vehicle—to reflect their impact on the probability of obtaining the reward when visiting each type of node in a heterogeneous network. To address this problem, a learnheuristic optimization framework is proposed. It combines a metaheuristic algorithm with Thompson sampling to make informed decisions in dynamic environments. Furthermore, we conduct empirical experiments to assess the impact of varying reward probabilities on resource allocation and route planning within the context of this dynamic TOP, where nodes might offer a different reward behavior depending upon the environmental conditions. Our numerical results indicate that the proposed learnheuristic algorithm outperforms static approaches, achieving up to 25% better performance in highly dynamic scenarios. Our findings highlight the effectiveness of our approach in adapting to dynamic conditions and optimizing decision-making processes in transportation systems.

Spatiotemporal organization of ant foraging from a complex systems perspective

We use complex systems science to explore the emergent behavioral patterns that typify eusocial species, using collective ant foraging as a paradigmatic example. Our particular aim is to provide a methodology to quantify how the collective orchestration of foraging provides functional advantages to ant colonies. For this, we combine (i) a purpose-built experimental arena replicating ant foraging across realistic spatial and temporal scales, and (ii) a set of analytical tools, grounded in information theory and spin-glass approaches, to explore the resulting data. This combined approach yields computational replicas of the colonies; these are high-dimensional models that store the experimental foraging patterns through a training process, and are then able to generate statistically similar patterns, in an analogous way to machine learning tools. These in silico models are then used to explore the colony performance under different resource availability scenarios. Our findings highlight how replicas of the colonies trained under constant and predictable experimental food conditions exhibit heightened foraging efficiencies, manifested in reduced times for food discovery and gathering, and accelerated transmission of information under similar conditions. However, these same replicas demonstrate a lack of resilience when faced with new foraging conditions. Conversely, replicas of colonies trained under fluctuating and uncertain food conditions reveal lower efficiencies at specific environments but increased resilience to shifts in food location.

A novel seasonal grey prediction model with time-lag and interactive effects for forecasting the photovoltaic power generation

Electricity is one of the most important energy sources for the progress of human society. Photovoltaic power generation (PPG) has great potential as a renewable energy source to support sustainable development. However, the unpredictability of photovoltaics because of the uncertain weather conditions, time lags and the complex interactions among related factors may result in instabilities in PPG, which limits its applications in the electric power system. Therefore, accurately identifying the related factors and representing the interactions are the key issues in predicting PPG. For this purpose, a novel seasonal grey prediction model with time-lag and interactive effects, denoted as SGMLI(1,n), is developed to predict the capacity of photovoltaic power generation. More specifically, a time-lag driving term, a periodic driving term, and an interaction driving term are proposed to represent the lag relationship, periodicity, and the interactive effects between two factors, respectively. Utilizing the least square method and the Whale Optimization Algorithm (WOA), the optimal solution to the nonlinear programming problem is estimated. For elaboration and verification, six grey forecasting models, two statistical techniques, and two machine learning models, are employed for comparison with the SGMLI(1,n) model. Experimental results reveal that the SGMLI(1,n) outperforms benchmark models in forecasting PPG.

Load frequency control of interconnected power system using cuckoo search algorithm

This paper presents a new time-domain multi-objective function approach for solving load frequency control issue in an interconnected power system. The performance of interconnected power system in each area is validated for overshoot and settling time values of frequency change and tie-line power exchange. An objective function is created with the goal of enhancing proportional integral derivative (PID) controller settings by reducing overshoot and achieving faster time-domain settling times. The efficiency of the proposed time-domain multi-objective function is evaluated in a two-area thermal power plant using a nature-inspired cuckoo search optimization (CSA) technique. By comparing the time-domain simulation results of the test system with the existing integral error-based objective functions IAE, ISE, ITAE, and ITSE, the proposed objective function is validated. Further, a sensitivity analysis were carried out to analyze the robustness of the proposed multi-objective function under various uncertain conditions.

How the Locus of Uncertainty Shapes the Influence of CEO Long-term Compensation on Information Technology Capital Investments

Firms must allocate resources effectively to cope with uncertainty, which can manifest as a disruption and an opportunity. Although information technology (IT) is a means to cope with uncertainty, chief executive officers (CEOs) may not always support IT investments due to the risky nature of IT, especially when facing uncertain conditions. While prior research suggests that CEO long-term compensation positively incentivizes IT investments, little is known about how different loci of uncertainty impact this relationship. To address this research gap, we study how firm-specific uncertainty and competitive uncertainty shape the influence of CEO long-term compensation on a firm’s IT capital investment. Drawing on agency theory and prospect theory, we develop two hypotheses. First, we hypothesize that firm-specific uncertainty and competitive uncertainty positively moderate the influence of CEO long-term compensation on firm IT capital investment. Second, we hypothesize that competitive uncertainty has a stronger positive moderating effect than firm-specific uncertainty on the influence of CEO long-term compensation on firm IT capital investment. Our analysis of secondary longitudinal data from 2000 to 2007 of 357 public firms in the United States supports our hypotheses. In exploratory analyses, we found that CEO long-term compensation results in a higher risk-oriented dominant logic in the firm, particularly in conditions of firm-specific uncertainty and competitive uncertainty, with competitive uncertainty having a stronger positive moderating effect. These findings uncover risk-oriented dominant logic as a theoretical mechanism that explains how CEO long-term compensation positively influences firm IT capital investment in uncertain conditions. We also conducted exploratory analyses using a different secondary dataset of 286 U.S. public firms from 2004 to 2019 to consider firm investments in transformative IT applications and found support for our theory. This finding triangulates our results across different time periods and different types of IT investments. This study contributes to theory and practice by providing a nuanced understanding of boundary conditions surrounding CEO long-term compensation, and decisions CEOs make vis-à-vis IT capital investments.

Adaptive Takagi–Sugeno (T–S) fuzzy observer based fault tolerant control for DC–DC converters with robustness against uncertainties

AbstractThis paper proposes a Takagi–Sugeno fuzzy system model based fault tolerant control scheme for DC–DC converters, which is robust against parameter uncertainties and achieves the output voltage of an ideal converter. The control involves estimating the duty cycle change in the form of a fault parameter required to track the output voltage, in the presence of several uncertain conditions including converter losses, variation in input voltage, and unknown and changing output load. An adaptive law is designed to estimate the fault parameter that guarantees state and parameter error convergence. The adaptive law is derived using the Lyapunov stability theorem and the required parameters are evaluated by solving a linear matrix inequalities optimization problem. The load resistance is estimated in parallel by using a Kalman filter and fed to the fault parameter estimation scheme. Furthermore, a fast and robust method to detect short and open circuit switch faults is also presented. The proposed technique offers a simple, yet effective method to regulate the output voltage under several faulty and uncertain conditions. The proposed technique is tested on a DC–DC boost converter simulation model and the demonstrated MATLAB/Simulink results show the effectiveness of the proposed algorithm.

Dynamic Power Management of Shipboard Hybrid Energy Storage System Under Uncertain Navigation Conditions

Dynamic Power Management of Shipboard Hybrid Energy Storage System Under Uncertain Navigation Conditions

Evaluating alternatives to improve drinking water supply within a complex and uncertain human-environment system: case of the Port-au-Prince Metropolitan Region

ABSTRACT Improving decision making in urban water supply systems under a wide range of changing human-environmental scenarios represents a challenging issue, especially when there is a high level of uncertainty and the available information is limited or scarce. To address this concern, the present study applied a decision aiding process to evaluate long-term planning solutions for the Port-au Prince Metropolitan Region’s water supply in Haiti, which is recognized for having a low coverage of water services and great lack of monitoring data. The methodological framework assessed pre-specified alternatives to face uncertain future conditions, with the main emphasis of being aware of how these uncertainties can be framed in the water management practices. To discover the most robust management solutions, a tradeoff analysis and multi-objective optimization allowed to explore the performance of alternatives across many future states of the world and multiple criteria. The results highlighted that human factors associated with the increase of per-capita water use and population growth posed a greater risk on water supply coverage than climatic factors. In addition, the decision support can offer a comprehensive representation of potential supply/demand futures, and provides decision-makers with the opportunity to discuss a posteriori the most appropriate water supply solution under an extensive variety of information with severe uncertainty.

A dual‐mode integration framework and application to agile feedback design of launch vehicles

AbstractIn recent decades, the design of complex systems like launch vehicles in the aerospace industry has presented engineers with challenges that go beyond system complexity. Issues such as time‐to‐market pressures and intricate industrial processes have underscored the increasing significance of agile design methodologies. Agile design is derived from the simplification of the design process and enhancing cross‐domain data transmission and feedback. While methods based on model‐based system engineering have improved iteration times in system architecture design, challenges persist in cross‐domain data transmission. Due to the diversity of complex system models and data, a single‐mode integration method is difficult to realize the data link construction of all tools used. To address this challenge, this paper proposes a dual‐mode data integration framework with expansibility, universality, and cost‐efficiency which leverages the benefits of Remote Procedure Call and Intermediate Exchange Module, addressing the challenge of constructing cross‐domain data links under single‐mode integration. In this study, two critical requirements of the first‐ and second‐stage separation systems, namely, weight and minimum separation gap, are selected for data feedback. A Modelica‐based multiphysics simulation model is developed in MWorks; visualization and computation of the minimum gap are carried out in CoppeliaSim. To bridge the gap between domain‐specific tools, Matlab and Functional Mock‐up Unit modules are introduced as middleware, facilitating data feedback linkage. The entire simulation process is orchestrated using activity diagrams in the MagicDraw tool. The study delves into the influence of critical design parameters, such as the initial angular velocity of separation and the thrust of the retro rocket, on the minimum separation gap. It provides an analysis of minimum separation gap variations under uncertain operating conditions and examines design margins. Significantly, the paper highlights the significance of controlling the initial angular velocity during separation and the reliability of the retro rocket, providing essential decision supports and valuable insights to agile the process of system design.

Development of a Predictive Model for Maize Growth in Uncertain Climatic Conditions: Modeling and Implementation in the Kananga Environment.

Development of a Predictive Model for Maize Growth in Uncertain Climatic Conditions: Modeling and Implementation in the Kananga Environment.