Terminal Conditions Research Articles

An approach for regime-switching stochastic control problems with memory and terminal conditions

In this research article, we focus on a stochastic optimal control problem with two types of terminal constraints. These specific conditions provide real-valued and stochastic Lagrange multipliers. Our model evolves according to a Markov regime-switching jump diffusion model with memory. In this context, the memory is represented by a Stochastic Differential Delay Equation. We present two theorems for each constraint within the general formulation of stochastic optimal control theory in a Lagrangian environment. We approach to this task from a theoretical perspective and provide mild technical assumptions, which make our theorems applicable for a broad class of stochastic control problems as well as for a wide range of disciplines such as engineering, biology, operations research, medicine, computer science and economics. In this work, we apply Stochastic Maximum Principle to demonstrate an optimal dividend policy corresponding to a time-delayed wealth process of a company. Moreover, we determine the real-valued Lagrange multiplier of this control problem explicitly.

Simulation and Optimization of Automated Guided Vehicle Charging Strategy for U-Shaped Automated Container Terminal Based on Improved Proximal Policy Optimization

As the decarbonization strategies of automated container terminals (ACTs) continue to advance, electrically powered Battery-Automated Guided Vehicles (B-AGVs) are being widely adopted in ACTs. The U-shaped ACT, as a novel layout, faces higher AGV energy consumption due to its deep yard characteristics. A key issue is how to adopt charging strategies suited to varying conditions to reduce the operational capacity loss caused by charging. This paper proposes a simulation-based optimization method for AGV charging strategies in U-shaped ACTs based on an improved Proximal Policy Optimization (PPO) algorithm. Firstly, Gated Recurrent Unit (GRU) structures are incorporated into the PPO to capture temporal correlations in state information. To effectively limit policy update magnitudes in the PPO, we improve the clipping function. Secondly, a simulation model is established by mimicking the operational process of the U-shaped ACTs. Lastly, iterative training of the proposed method is conducted based on the simulation model. The experimental results indicate that the proposed method converges faster than standard PPO and Deep Q-network (DQN). When comparing the proposed method-based charging threshold with a fixed charging threshold strategy across six different scenarios with varying charging rates, the proposed charging strategy demonstrates better adaptability to terminal condition variations in two-thirds of the scenarios.

Accelerated-gradient-based generalized Levenberg–Marquardt method with oracle complexity bound and local quadratic convergence

AbstractMinimizing the sum of a convex function and a composite function appears in various fields. The generalized Levenberg–Marquardt (LM) method, also known as the prox-linear method, has been developed for such optimization problems. The method iteratively solves strongly convex subproblems with a damping term. This study proposes a new generalized LM method for solving the problem with a smooth composite function. The method enjoys three theoretical guarantees: iteration complexity bound, oracle complexity bound, and local convergence under a Hölderian growth condition. The local convergence results include local quadratic convergence under the quadratic growth condition; this is the first to extend the classical result for least-squares problems to a general smooth composite function. In addition, this is the first LM method with both an oracle complexity bound and local quadratic convergence under standard assumptions. These results are achieved by carefully controlling the damping parameter and solving the subproblems by the accelerated proximal gradient method equipped with a particular termination condition. Experimental results show that the proposed method performs well in practice for several instances, including classification with a neural network and nonnegative matrix factorization.

The role of digital health interventions in supporting family caregivers for people with dementia to improve quality of life.

Dementia is a chronic progressive terminal condition. Most care is provided by family caregivers (including close friends); their wellbeing is a public health priority. Caregivers manage increasingly complex needs with disease progression, and declining cognitive and physical function. This can impact the well-being of caregivers, and meaningful support is essential. This review article aims to understand what the benefits and challenges of digital health interventions are and provide considerations for future development of digital health interventions for family caregivers for people with dementia, to improve quality of life. Benefits include the valuable source of support from connective platforms; 24/7 accessibility; and opportunity for remote monitoring. However, this needs to balance with challenges, including the privacy of data concerns; and the digital divide driving inequalities in care provision for family caregivers with no access to internet devices. Digital health interventions can positively impact the overall well-being of family caregivers for people with dementia. If challenges are addressed and digital health interventions are designed to meet priorities for family caregivers, this can help improve the quality of life for family caregivers of people with dementia.

Impact of supplemental irrigation and organic manure on growth and yield performance of rice variety BRRI dhan103 under terminal drought condition in Aman season

BRRI dhan103 is a transplanting Aman (T. Aman) rice variety that demands sufficient organic manure and supplemental irrigation to ensure successful production. Seven treatments, viz., T1= rainfed control; T2= one supplemental irrigation at 80 days after transplanting (DAT); T3= two supplemental irrigations at 70 and 80 DAT; T4= additional 5 t ha-1 poultry compost under rainfed condition; T5= additional 10 t ha-1 poultry compost under rainfed condition; T6= additional 2.5 t ha-1 biochar under rainfed condition; and T7= additional 5 t ha-1 biochar under rainfed condition, were evaluated against BRRI dhan103 to assess the impact of supplemental irrigation and manuring under terminal drought conditions. It was revealed that supplemental irrigation and manuring significantly improved all growth parameters and yield attributes compared to the rainfed control. Notably, T5 treatment provided its greatest performance statistically in respect of SPAD value (36.13), root dry weight (12.43 g), number of tillers hill-1 (13.22), panicle length (28.87 cm), panicles hill-1 at harvest (9.13), total spikelet (194.53), spikelet panicle-1 (197.63), filled spikelet panicle-1 (197.63), thousand grain weight (33.78 g), grain weight panicle-1 (28.70 g), grain yield hill-1 (31.97 g), and above-ground biological yield (3043.35 g m-2). The highest tiller hill-1 at harvest (9.53) was achieved when an additional 10 t ha-1 of poultry compost was applied under rainfed conditions. This study suggested that additional 10 t ha-1 poultry compost under rainfed conditions performed best to enhance the growth and yield of BRRI dhan103.

Improved Monte Carlo tree search formulation with multiple root nodes for discrete sizing optimization of truss structures

This article proposes a novel reinforcement learning algorithm using an improved Monte Carlo tree search (IMCTS) formulation for the discrete optimum design of truss structures. IMCTS with multiple root nodes includes the update process, best reward, accelerating technique and terminal condition. The update process means that once a final solution is found, it is used as the initial solution for the next search tree. The best reward is used in the backpropagation step. The accelerating technique is introduced by decreasing the width of the search tree and reducing the maximum number of iterations. The agent is trained to minimize the total structural weight under various constraints until the terminal condition is satisfied. The optimal solution is the minimum value of all solutions found by the search trees. Numerical examples show that the agent can find the optimal solution with low computational cost, stably produces an optimal design, and is suitable for multi-objective structural optimization and large-scale structures.

Free evolution in the Ginzburg-Landau equation and other complex diffusion equations

Abstract New ordinary differential equations (ODEs) for the evolution of spectral components are derived from the complex Ginzburg-Landau equation (CGLe) for one-dimensional spatial domains without boundaries (free evolution) and with one fixed boundary (semi-free evolution). For such evolution, a complex or imaginary diffusion term creates a tendency for waves to lengthen. This requires a novel ansatz and auxiliary condition that treat wavenumbers as time-varying. The ansatz consists of a discrete spatial Fourier transform modified with a time-dependent wavenumber for the peak spectral component. The wavenumbers of the other components are fixed relative to this wavenumber. The new auxiliary condition is the terminal condition for complex diffusion (after wavenumbers evolve to zero, they remain at zero). The derived free and semi-free ODEs are solved along characteristic lines located symmetrically about a fixed spatial point. Waves lengthen with time away from this point in both directions. Laboratory experiments on the formation of channel sandbars, theoretically described by the CGLe, show two regions whose evolutionary behaviour is qualitatively predicted by the free and semi-free evolution equations. This analysis applies to other time-dependent partial differential equations with complex or imaginary diffusion terms. New freely evolving solutions are derived for the complex heat equation and Schrödinger equation (linear and nonlinear).

Evaluation of rotating components by combining individual classification with adaptive updating searching method in aero engine

Abstract In order to evaluate the roundness error of rotating components in aero engine with increased speed and more accurate estimation, this paper presents an enhanced method that integrates individual classification with adaptive updating searching (ICAUS) strategy. The individual classification method simulates the renewal regulations of various candidate points as iterations increase, subsequently generating new solutions for the next iteration. Additionally, an adaptive updating search zone method that varies in accordance with the increment of iterations is incorporated. These newly generated solutions are distributed within the updated search zone to ensure that the global optimal solution is not overlooked and the search zone is constrained to prevent excessive divergence, thereby enhancing computational efficiency. To validate the efficacy and accuracy of the proposed method, we utilized datasets and conducted comparative experiments using experimental methods previously studied by predecessors. The experimental results and comparisons demonstrate that the proposed ICAUS method exhibits superior convergence characteristics compared to previous studies. Across five distinct datasets, the average computation time satisfying the terminal conditions ranged from 0.0013s to 0.0018s, the standard deviation of the roundness was less than 6.2051×10–9 mm and the optimal solutions were obtained within 10 to 20 iterations, achieving the solution accuracy of ε = 10–8 mm.

Method for enhancing fault feature signals of rolling bearings in gas turbine engines

Aiming at the problem that the weak fault signal of rolling bearings in gas turbine engines (GTEs) is affected by environmental noise, which leads to the difficulty of effective information extraction and easy to ignore, the characterization method for cyclic extraction of main bearing fault feature components in GTEs is proposed. The proposed method begins by subjecting raw vibration signals to wavelet packet decomposition and correlating correlation coefficient and kurtosis values for each node component. These values are then normalized and amalgamated into a comprehensive parameter denoted as P. Subsequently, a confidence interval is established to categorize node components into three groups: high signal-to-noise ratio signals, low signal-to-noise ratio signals, and high-noise signals. Then, the high signal-to-noise ratio signals are continuously filtered according to the feature component cyclic extraction criterion until the termination condition is reached. Finally, all high signal-to-noise ratio signals are reconstructed, followed by envelope demodulation to extract subtle bearing fault characteristics. The findings underscore the efficacy of this approach in extracting fault features within the intricate transmission path of rolling bearings, offering a robust solution for the intricate signal processing and diagnosis of complex rolling bearing faults in GTEs.

UAV-Based Phenotyping: A Non-Destructive Approach to Studying Wheat Growth Patterns for Crop Improvement and Breeding Programs

Rising food demands require new techniques to achieve higher genetic gains for crop production, especially in regions where climate can negatively affect agriculture. Wheat is a staple crop that often encounters this challenge, and ideotype breeding with optimized canopy traits for grain yield, such as determinate tillering, synchronized flowering, and stay-green (SG), can potentially improve yield under terminal drought conditions. Among these traits, SG has emerged as a key factor for improving grain quality and yield by prolonging photosynthetic activity during reproductive stages. This study aims to highlight the importance of growth dynamics in a wheat mapping population by using multispectral images obtained from uncrewed aerial vehicles as a high-throughput phenotyping technique to assess the effectiveness of using such images for determining correlations between vegetation indices and grain yield, particularly regarding the SG trait. Results show that the determinate group exhibited a positive correlation between NDVI and grain yield, indicating the effectiveness of these traits in yield improvement. In contrast, the indeterminate group, characterized by excessive vegetative growth, showed no significant NDVI–grain yield relationship, suggesting that NDVI values in this group were influenced by sterile tillers rather than contributing to yield. These findings provide valuable insights for crop breeders by offering a non-destructive approach to enhancing genetic gains through the improved selection of resilient wheat genotypes.

Competing for Pixels: A Self-play Algorithm for Weakly-supervised Semantic Segmentation.

Weakly-supervised semantic segmentation (WSSS) methods, reliant on image-level labels indicating object presence, lack explicit correspondence between labels and regions of interest (ROIs), posing a significant challenge. Despite this, WSSS methods have attracted attention due to their much lower annotation costs compared to fully-supervised segmentation. Leveraging reinforcement learning (RL) self-play, we propose a novel WSSS method that gamifies image segmentation of a ROI. We formulate segmentation as a competition between two agents that compete to select ROI-containing patches until exhaustion of all such patches. The score at each time-step, used to compute the reward for agent training, represents likelihood of object presence within the selection, determined by an object presence detector pre-trained using only image-level binary classification labels of object presence. Additionally, we propose a game termination condition that can be called by either side upon exhaustion of all ROI-containing patches, followed by the selection of a final patch from each. Upon termination, the agent is incentivised if ROI-containing patches are exhausted or disincentivised if a ROI-containing patch is found by the competitor. This competitive setup ensures minimisation of over- or under-segmentation, a common problem with WSSS methods. Extensive experimentation across four datasets demonstrates significant performance improvements over recent state-of-the-art methods. Code: https://github.com/s-sd/spurl/tree/main/wss.

An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination.

Most deep anomaly detection models are based on learning normality from datasets due to the difficulty of defining abnormality by its diverse and inconsistent nature. Therefore, it has been a common practice to learn normality under the assumption that anomalous data are absent in a training dataset, which we call normality assumption. However, in practice, the normality assumption is often violated due to the nature of real data distributions that includes anomalous tails, i.e., a contaminated dataset. Thereby, the gap between the assumption and actual training data affects detrimentally in learning of an anomaly detection model. In this work, we propose a learning framework to reduce this gap and achieve better normality representation. Our key idea is to identify sample-wise normality and utilize it as an importance weight, which is updated iteratively during the training. Our framework is designed to be model-agnostic and hyperparameter insensitive so that it applies to a wide range of existing methods without careful parameter tuning. We apply our framework to three different representative approaches of deep anomaly detection that are classified into one-class classification-, probabilistic model-, and reconstruction-based approaches. In addition, we address the importance of a termination condition for iterative methods and propose a termination criterion inspired by the anomaly detection objective. We validate that our framework improves the robustness of the anomaly detection models under different levels of contamination ratios on five anomaly detection benchmark datasets and two image datasets. On various contaminated datasets, our framework improves the performance of three representative anomaly detection methods, measured by area under the ROC curve.

Investigation of Drought Stress on Chickpea (Cicer arietinum L.) Genotypes Employing Various Physiological Enzymatic and Non-Enzymatic Biochemical Parameters.

Drought stress is a universal crisis in sustaining the growth and production of major legumes, including the chickpea. Drought severely reduces the biomass of chickpea plants, with the effect on leaves appearing the most apparent. The aim of this study was to investigate, using various physiological and biochemical markers throughout the pod filling stage, how 78 desi chickpea genotypes tolerated drought stress. Most of the evaluated characteristics showed significant variations between control and drought treatments. The mean performance of most of the investigated parameters significantly decreased under moisture-stressed conditions. RWC, SWD, MSI, and CTD were investigated under terminal drought-stressed conditions. Except for saturated water deficit (SWD), all remaining characteristics declined with increasing stress. Genotypes SAGL152210, SAGL152252, SAGL152347, SAGL22-115, and JG11 were recognized as drought-tolerant based on physiological characteristics. Biochemical markers viz., protein content, total soluble sugar, lipid peroxidation, and proline content, had an impact on osmotic adjustment. Based on non-enzymatic biochemical traits, genotypes SAGL22-115, ICC4958, ICCV201108, ICCV201107, SAGL152252, and JG11 were identified for their capability to survive under drought-stressed conditions. H2O2 content, CAT, SOD, POD, APX, and DPPH were considered antioxidant agents. Genotypes SAGL152208, SAGL22-105, SAGL22-112, ICC201108, SAGL152278, SAGL152252, SAGL162371, SAGL162390, ICC 4958, and JG315 may be considered drought-tolerant based on antioxidant activities. These genotypes are believed to be better equipped with physio-biochemical mechanisms and antioxidant defense systems at the cellular level and can be used in breeding programs to breed drought-tolerant cultivar(s). They can also be screened in the future, allowing the line(s) that have remained consistent over time to be recognized and registered as drought-tolerant donors.

Exotic Stable Branches with Efficient TOV Sequences

Modern inference schemes for the neutron star (NS) equation of state (EoS) require large numbers of stellar models constructed with different EoS, and these stellar models must capture all the behavior of stable stars. I introduce termination conditions for sequences of stellar models for cold nonrotating NSs that can identify all stable stellar configurations up to arbitrarily large central pressures along with an efficient algorithm to build accurate interpolators for macroscopic properties. I explore the behavior of stars with both high- and low-central pressures. Interestingly, I find that EoSs with monotonically increasing sound-speed can produce multiple stable branches (twin stars) and that large phase transitions at high densities can produce stable branches at nearly any mass scale, including sub-solar masses while still supporting stars with M > 2 M ⊙. I conclude with some speculation about the astrophysical implications of this behavior.

A novel life-cycle analysis approach using pavement performance curves with cost and performance considerations

ABSTRACT The life-cycle duration (TLC) is typically assumed constant in pavement life-cycle analysis. The added scientific value of this paper is that (TLC) is considered a variable parameter to be estimated from the performance curve using a variable terminal condition rating. A variable (TLC) requires using the equivalent annual value (EAV) instead of the net present value for cost estimation. The life-cycle performance (LCP) curve is constructed using two routine maintenance cycles separated by one major rehabilitation action. The time parameters associated with LCP curve are estimated from the inverse performance function based on the initial, present and terminal condition ratings. The equivalent annual cost and performance associated with a particular LCP curve are used to compute cost/performance (C/P) ratio for optimal maintenance and rehabilitation (M&R) plan selection. The EAV represents the costs of two routine maintenance cycles and one major rehabilitation action. Sample results obtained from three case studies have indicated the reliability of the proposed LCA approach in yielding optimal M&R plans. At the project-level, the optimal M&R plan associated with 7 m/km terminal IRI value resulted in (0.949) minimal (C/P) value. However, the optimal M&R plan with 3.25 terminal PSI yielded (1.50) minimal (C/P) value.

Destination and time-series inference of moving objects via conditionally Markov process

This paper presents a destination and time-series inference algorithm for tracking moving targets. The destination of the object is considered the intent, and inference and state estimation are performed in the Bayesian framework. To describe the destination-aware target motion, we construct the state transition model using a conditionally Markov process. We introduce a multiple model to achieve simultaneous intent and time-series inferences. Given finite destination candidates, the maximum a posteriori hypothesis is chosen as the destination. For time-series inference, local estimates obtained from Kalman filters are fused to yield target state estimates. To address unspecified terminal conditions, the proposed algorithm incorporates parameter correction techniques based on relative geometry. Numerical simulations are performed to validate the proposed inference algorithm.

Staying with the Climate Trouble

In teaching about climate, when it comes to the troubling question of “what can we do?” I’ve argued that we try to help students stay with, rather than prematurely and inadequately, resolve that trouble and that we do this by inviting them into our own state of not knowing. How to stay with the pedagogical trouble of doing that, including the trouble of trying to contain the despair that an honest account of the climate crisis may provoke? This essay suggests that one approach to that question in the literature classroom is to teach texts in which characters themselves grapple with the question of “what can we do?” Ultimately, the pedagogical challenge is to help shape these textual encounters into an awareness that, especially as it is shared, despair might be something other than a terminal condition—indeed, might be a necessary passage through to whatever comes next.

The group behavior analysis of the high-frequency traders based on Mean Field Games approach

Abstract We present an approach to describe the group behavior of the high-frequency traders in the stock market based on Mean Field Games and optimal control theory. The problem is formalized as a system of coupled PDEs: Kolmogorov–Fokker–Planck, evolving forward in time with initial condition, and Hamilton–Jacobi–Bellman, evolving backwards in time with terminal condition. Under certain assumptions, the system of PDEs can be reduced to Riccati-type ODEs. We solve the inverse problem to describe the behavior of the high-frequency traders during the Chinese stock market crisis in 2015.

Clinical and socio-demographic profile of children receiving pediatric palliative care in a tertiary hospital of a metropolitan city in India.

Pediatric palliative care is a holistic care of children suffering from life-threatening or life-limiting illnesses and encompasses care of a child's body, mind, and spirit and involves giving support to the family. According to the Global Atlas of Palliative Care, 6% of the global need for palliative care is in children. In order to provide palliative care, one needs to identify and diagnose the conditions requiring palliative care. There has always been a confusion in identifying pediatric conditions requiring palliative care. There is a lot of inconsistency in the diagnosis of such conditions particularly in pre-verbal patients. This study attempts to generate more data about the common palliative care conditions and complaints with which the children present to tertiary care hospitals. To study the socio-demographic details, clinical profile, CCC (complex chronic conditions) designation, and the ACT/ RCPCH (Association for Children with Life-threatening or Terminal Conditions and the Royal College of Pediatrics and Child Health) classification of children suffering from chronic conditions requiring palliative care. The study was conducted as a single-center retrospective observational study of pediatric patients enrolled for palliative care at a tertiary care hospital in a metropolitan city in India from 01.06.2021 to 31.06.2022. The total sample size was 400. The socio-demographic data and the clinical profile were recorded from the case records of all the 400 patients. Classification of the conditions was done as per the CCC as well as the ACT/ RCPCH classification system. The mean age in our study was 5.15years and there was a slighter male (59.5%) preponderance. They presented in OPDs with acute symptoms such as fatigue and fever, and they had other symptoms like tightness of the body, constipation, seizures, and difficulty in swallowing. Majority of the children (55%) were suffering from neurologic and neuromuscular conditions as per CCC followed by hematologic and immunologic conditions (10%). Category 4 (irreversible but non-progressive conditions causing severe disability, leading to susceptibility to health) was reported as the most common category according to the ACT/RCPCH. Conclusion: Children suffering from chronic disease conditions requiring palliative care usually suffer from multiple symptoms which affect their daily life. As most of the patients belong to category 4 according to ACT/RCPCH which is an irreversible but non-progressive life-limiting condition, the course of the disease is prolonged, therefore requiring comprehensive care and services for a long time. It is necessary to establish more pediatric palliative care units to address the needs of such children. What is known: • Pediatric palliative care is a specialized area within palliative care, which focusses on the needs of children with life-limiting illnesses. • Data on pediatric palliative care has largely been limited to oncological conditions. There is a paucity of literature documenting the needs among children suffering from non-cancerous chronic conditions. What is new: • This study provides vital information with respect to palliative care burden among children mainly suffering from non-oncological conditions. • It also provides clinical and socio-demographic profile of the children suffering from chronic life-limiting conditions requiring palliative care in a tertiary hospital setting in a LMIC (low- or middle-income country).

Multi-objective topology optimization of macro structure and microtubule network structure for self-healing material

Abstract Self-healing materials possess the capability to promptly repair minor damages occurring during service, thereby effectively preventing safety accidents. This paper investigates a multi-objective topology optimization method for the macro structure and microtubule network of self-healing materials around pure epoxy resin materials, aiming to enhance the damage healing capability of the microtubule network while meeting the mechanical performance requirements of the macro structure. By introducing the design variables of macro structure and microtubule network, the corresponding topological description functions are established respectively. And study applies logical operations and post-processing techniques to generate an embedded microtubule network structure description. The objective functions include the flexibility of the macro structure, the along-travel head loss, and the total length of the microtubule network, with material volume serving as a constraint. In order to determine the head loss of the three-dimensional microtubule network structure, a Hardy-Cross method based on flow initialization and loop search is proposed. Multi-objective topology optimization is designed based on moving morphable components algorithm, enumeration method and Pareto principle. Develop iterative termination conditions by assessing the disparity between Pareto solution sets in each generation, thereby ensuring algorithm convergence. The numerical example of the Messerschmitt–Bölkow–Blohm (MBB) beamyields a flexibility of 0.059 without a carrier and 0.0728 with a carrier the macrostructural flexibility without a carrier is 81.0% compared to with a carrier, and the macrostructural profiles and the overall flexibility of the MBB beams with/without a carrier are close to each other. This method serves as a reference for optimizing large-scale self-healing structures.