Terminal Conditions Research Articles

Assessment of Terminal Heat Stress Tolerance in Doubled Haploids Derived from Synthetic Hexaploid Wheat (Triticum aestivum L.) Using Genetic Variability and PCA-based Cluster Analyses

A comprehensive study was conducted on germplasm derived from two synthetic wheats and two hexaploid wheats i.e., SHW14102 x BWL4444, SHW14102 x BWL3531, and SHW3761 x BWL4444 to identify the potential doubled haploids that can withstand high temperatures, particularly at the reproductive stage during crop seasons viz., 2020–2021 and 2021–2022. The doubled haploid lines were selected based on phenotypic characteristics using genetic variability analysis. In the preliminary field trial evaluation, 100 lines were selected based on high phenotypic uniformity for further testing against terminal heat stress conditions and high yield potential. Among these, 17 lines had relatively higher values for days to biomass yield, grain yield, thousand-grain weight, and harvest index and relatively lower values for maturity, and grain filling duration based on principal component and cluster analyses. Principal component analysis (PCA) based cluster analysis exhibited that, clusters II and III had clear separation compared to cluster I. All three clusters were analyzed according to their means and standard deviations. The mean values for GYP (385.07), BMYP (1162.37), and TGW (38.13) were relatively higher in cluster 1 than in other clusters. Cluster 2 exhibited a higher value for DH (89.14), SL with awns (18.21), and GFD (18.19) while cluster 3 showed a higher value for HI (41.02). Thus, it was concluded that cluster I demonstrated superior performance in grain yield per plant, biomass yield per plant, and thousand-grain weight, indicating its overall higher productivity compared to the other clusters under terminal heat stress conditions and suggesting them as potential germplasm for future breeding programs.

Abstract 4141497: Left ventricular diastolic function by echocardiography is related to extracellular volume and is an independent predictor of mortality in patients with aortic regurgitation

Background: Cardiac catheterization along with cardiac biopsy studies have shown the presence of abnormally increased left ventricular (LV) chamber stiffness in patients with severe aortic regurgitation (AR) and its significant correlation with interstitial fibrosis. LV diastolic dysfunction (DD) is associated with increased mortality in several patient populations. However, there is a paucity of data on the association of DD with mortality in patients with AR. Hypotheses: 1 - LV DD is related to extracellular volume (ECV), imaging marker of interstitial fibrosis, by cardiac magnetic resonance imaging (CMR) 2 - LV DD by echocardiography is associated with mortality in patients with AR Methods: We studied patients with moderate or greater AR who underwent echocardiographic and CMR imaging within 12 months. We excluded patients with concomitant valve disease, prior valve intervention, cardiomyopathy deemed unrelated to AR, congenital heart disease and terminal conditions. The primary outcome was all-cause death. CMR was used to obtain LV volumes, EF and mass, AR severity, scar extent by delayed hyperenhancement, and ECV. DD was assessed by comprehensive echocardiography. Results: The study included 323 patients. Mean age was 61.9 ± 16.0 years and 22% were female. Median follow-up was 3.8 years, during which 65 patients died and 172 had aortic valve replacement. Median LV EF was 56 (47-65) %, AR volume on CMR was 40 (30-61) mL, and AR fraction was 40 (33-47) %. ECV progressively increased with higher grades of LV DD (Figure 1). Table 1 shows the univariate associations with mortality in the study. On multivariable analysis based on the Lasso variable selection process, Euroscore, Charlson comorbidity index, DD grade, and RAP were associated with total mortality. Conclusion: ECV by CMR progressively increases with higher grades of DD by echocardiography, and DD is independently associated with mortality in patients with AR.

A novel reinforcement learning-based method for structure optimization

With the rapid development of deep learning technology, Reinforcement Learning (RL) has garnered considerable acclaim within the realm of structural optimization owing to its excellent exploration mechanism. However, the widespread application of RL in this field is limited owing to the excessive number of iterations required to converge and the expensive computational cost it brings. To address these challenges, this article presents a novel RL framework for structural optimization, combining Monte Carlo tree search with the proximal policy optimization method, called LMPOM. The key contributions of LMPOM encompass: (1) an enhanced Monte Carlo tree search strategy for partitioning the hybrid design space; (2) a strategy for adaptively updating surrogate models to reduce simulation costs; and (3) the introduction of a novel termination condition for the RL algorithms. Through tests on three benchmark problems, compared with previous RL algorithms, LMPOM consistently shows fewer iterations and better optimization results.

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.