Confessing Unrepresentability: Photography and Panoramic Depiction in Tayama Katai’s Russo-Japanese War Diary

This article examines Tayama Katai’s (1872–1930) war diary Dainigun jūsei nikki (Diary of the Second Army going to war, 1905) to elucidate the relation between photography and the panoramic depiction in his writing on the Russo-Japanese War (1904–1905). Concretely, it focuses on Katai’s effort to depict war through panoramic views informed by photography. I argue that Katai’s experience of failing to deliver an objective and realistic description of war contributed to his notion of realism and a quasi-confessional style in his subsequent works.</p

MIB-Net: Balance the mutual information flow in deep learning network for multi-dimensional segmentation of COVID-19 CT images

Computed tomography (CT) based lung screening followed by computer-vision-based automated segmentation has great potential in early diagnosis of COVID-19, but low sample size and high patient-to-patient variability of infection characteristics are two of the major challenges. We propose a novel Mutual Information Balanced Net (MIB-Net) with three distinctive features. First, it captures mutual information across feature maps, CT scans, and predicted segmentation labels to optimize the contribution of different feature maps for better segmentation accuracy. Second, it incorporates a three-dimensional structural prior of CT scans based on predicted segmentation of neighboring CT slices to improve accuracy. Third, a novel Weight Optimization Filter (WOF) is used to adjust the proportion of 3D structural prior to be incorporated, which further improves the segmentation accuracy. Experimental results show that the proposed approach outperforms state-of-the-art biomedical image segmentation models by 7.87%, 3.85%, and 5.01% in Dice Score, respectively for three popular COVID-19 CT datasets. The better performance of the proposed approach suggests that it may serve as an attractive alternative to Lung CT-based screening for COVID-19.</p

An optimal predictive inspection and maintenance policy for a multi-state system: A belief-based SMDP approach

Inspection and maintenance (IM) are crucial for guaranteeing the functionality of engineered systems. In existing studies, an equidistant decision framework is commonly adopted, assuming periodic inspections and immediate maintenance actions (if needed). This assumption limits the search for globally optimal IM decisions. Moreover, the scenario of self-announcing failures and non-negligible IM durations that lead to non-equidistant decision intervals has not been investigated. In this study, we consider the aforementioned factors and propose a novel predictive IM policy that enables decision-makers to conduct non-periodic inspections and perform postponed maintenance actions after an inspection, thereby maximizing the system's long-run profit rate. First, a belief-based Semi-Markov decision process (SMDP) is formulated to characterize a sequential IM decision-making problem based on the belief about the system state, which is then transformed into an equivalent belief-based MDP. Next, we derive the structural properties of the optimal solution to the transformed MDP, including the existence of the control limits. We further demonstrate that these results remain valid for the hidden failure scenario. Then, we demonstrate that when the minimal decision interval in the proposed sequential IM policy is sufficiently short, the policy is equivalent to a predictive IM policy. For computational efficiency, we develop an improved value iteration algorithm that iteratively reduces the minimum decision interval in the belief-based SMDP until convergence. A case study of an industrial water-filter system demonstrates both the performance superiority of the proposed predictive IM policy and the computational efficiency of the proposed algorithm

A dynamic mode decomposition-based Kalman filter for Bayesian inverse problem of nonlinear dynamical systems

Ensemble Kalman filter (EnKF) method has been widely used in parameter estimation of the dynamic models, which needs to compute the forward model repeatedly. For nonlinear parameterized PDEs, constructing an accurate reduced order model is extremely challenging. To accelerate the posterior exploration efficiently, building surrogates of the forward models is necessary. In this paper, the dynamic mode decomposition (DMD) coupled with the weighted and interpolated nearest-neighbors (wiNN) algorithm is introduced to construct the surrogates for nonlinear dynamical systems. This extends the applicability of DMD to parameterized problems. Moreover, a low rank approximation of Kalman gain is used to EnKF, which can avoid the ensemble degenerate from the singularity of the covariance matrix. Finally, we apply the proposed method to nonlinear parameterized PDEs for the two-dimensional fluid flow and investigate their Bayesian inverse problems. The results are presented to show the applicability and efficiency of the proposed EnKF with DMD-wiNN method by taking account of parameters in nonlinear diffusion functions, nonlinear reaction functions and source functions

A multi-agent reinforcement learning (MARL) framework for designing an optimal state-specific hybrid maintenance policy for a series k-out-of-n load-sharing system

The series k-out-of-n: G load-sharing structure is widely adopted in engineering. During their operations, system components are subject to deterioration that causes system failures and shutdowns. Although maintenance reduces system failure-associated costs, it also requires system shutdown and incurs considerable costs. This calls upon a maintenance policy that minimizes the overall long-term cost rate. When the components have continuous and load-dependent deterioration processes and the maintenance duration is non-negligible, the task becomes especially challenging. In this paper, we propose a Markov decision process (MDP)-based multi-agent reinforcement learning (MARL) framework to obtain an optimal state-specific hybrid maintenance policy that determines the maintenance timing and levels for all components holistically. First, we define the policy that dictates whether each component undergoes imperfect repair or replacement at periodic decision epochs. Second, we establish an MDP-based multi-agent framework to quantify the system's cost rate by defining the state and action spaces, modeling the stochastic transitions of components’ dependent deterioration processes, and formulating a well-calibrated penalty function. Third, we customize a MARL algorithm which leverages neural networks to handle the large state space and integrates the Branching Dueling Network structure to decompose the high-dimensional action space, thereby improving the scalability. A heuristic-enhanced penalty function is designed to avoid suboptimal policies. A power plant case study demonstrates the effectiveness of the proposed policy and underscores the importance of accounting for maintenance duration in policy design

Vortioxetine improves illness severity for cannabis users with anxiety and depressive symptoms in a 6-month randomized controlled study

Introduction: Cannabis use and misuse have mental health implications, particularly affecting mood and anxiety symptoms. Vortioxetine, a potent serotonin partial agonist/antagonist reuptake inhibitor antidepressant, has well-established effects in treating depressive and anxiety disorders and may serve as a potential treatment for individuals with cannabis use disorder and comorbid mood symptoms. In the current study, we aimed to investigate the efficacy of vortioxetine for cannabis users with anxiety and depressive symptoms alongside their cannabis dependence. Methods: This 6-month prospective, randomized controlled interventional pilot study investigated if vortioxetine could improve cannabis dependence, comorbid anxiety and/or depressive symptoms, and cognitive and functional outcomes in individuals using cannabis. Participants were randomized to receive either vortioxetine (N = 11) or standard treatment (N = 19). Results: Participants taking vortioxetine (mean dose 10 mg/day) showed significant improvement on clinician-observed overall mood states over time (p < .05) but not on their self-reported anxiety or depressive symptoms. Cannabis users receiving standard treatment did not exhibit similar improvement. No significant differences were found on cannabis dependence, cognition and functional outcomes between the two groups otherwise. Conclusions: The results suggest that the multimodal antidepressant vortioxetine may benefit cannabis users with depressive and anxiety symptoms in ameliorating their overall mood state.</p

Quantifying the impact of montmorillonite on water demand and polycarboxylate superplasticizer efficiency in cement pastes

The presence of montmorillonite (MMT) as an impurity in aggregates and limestone diminishes the plasticization efficacy of polycarboxylate ether (PCE) superplasticizers in fresh cement pastes. Despite extensive research efforts to elucidate the mechanisms behind the reduced efficacy of PCE in cementitious systems with MMT and to design tailored PCE molecules with enhanced MMT tolerance, quantitative insights into PCE behavior, specifically surface adsorption and intercalation, within cement pastes containing MMT remain ambiguous. In this work, a delayed addition method was employed to investigate how two PCEs with different side-chain lengths (P-1000 and P-3000) influence the flowability of cement-MMT pastes through quantification of their adsorption and intercalation behavior. The results indicate that approximately 1 g of MMT necessitates an additional 3 g of water to achieve the equivalent fluidity as the plain mixture without MMT. The maximum adsorption of PCE on MMT in cement-MMT pastes was approximately 35 mg/g, below the threshold (∼40 mg/g) required for intercalation. This demonstrates that the reduction in fluidity primarily arises from the extensive surface adsorption driven by the high specific surface area of MMT, which decreases the availability of PCE for effective dispersion of cement particles.</p

Meso-level pore structures of Strain-Hardening Cementitious Composites (SHCC): Correlation with matrix flowability and application in micromechanical modeling

The meso-level pore structure of Strain-Hardening/Engineered Cementitious Composites (SHCC/ECC) critically governs cracking strength distribution, and consequently the tensile performance. While pore distributions are typically attributed to matrix flowability, this relationship remains rarely quantified for SHCC. This study addresses this gap by linking material processing parameters, realistic pore structures, and tensile cracking behaviors of SHCC. Using X-ray computed tomography (X-CT), the 3D meso-level pore information (including porosity, size distribution, shape factors, and spatial distribution) of SHCC specimens was analyzed and correlated with matrix flowabilities. Mechanisms governing pore formation during mixing and casting were discussed. A statistically derived correlation between meso-level pore structures and matrix flowability was established and applied to predict cracking strength distributions. This correlation demonstrated improved agreement with experimental results over conventional methods. These findings advance the modeling and optimization of SHCC by providing a quantitative framework to account for matrix flowability effects.</p