Results Of Experiments Research Articles

3,5-Dihydroxy-4-methoxybenzyl alcohol, a novel antioxidant isolated from oyster meat, inhibits the hypothalamus–pituitary–adrenal axis to regulate the stress response

BackgroundAntioxidants that can scavenge reactive oxygen in the brain and inhibit hyperactivity of the HPA axis are desirable. AimsWe investigated the cerebral translocation of the antioxidant 3,5-dihydroxy-4-methoxybenzyl alcohol (DHMBA) and the effects of DHMBA administration on the hypothalamus–pituitary–adrenal (HPA) axis in stress-loaded rats. MethodsExperiment 1: Plasma and brain DHMBA concentrations were measured over time after oral DHMBA administration to female B6 mice. Experiment 2: Female Wistar Imamichi rats were used. The normal group was not subjected to stress. The stress, DHMBA, and vitamin E groups were subjected to individual and overcrowding stress. Brain and hippocampal 8-hydroxy-2′-deoxyguanosine levels, hippocampal glucocorticoid receptor-α levels, plasma corticosterone levels and RNA levels of glutathione peroxidase 4, catalase, and glutathione reductase in the hippocampus were measured. ResultsIn Experiment 1, DHMBA was not detected in the plasma or brain before DHMBA administration but was detected in both after administration. In Experiment 2, brain and hippocampal 8-hydroxy-2′-deoxyguanosine levels and plasma corticosterone levels were significantly lower in the DHMBA than in the stress group. Glucocorticoid receptor-α levels were higher in the DHMBA than in the stress group. DHMBA increased RNA levels of antioxidant enzymes in the hippocampus. Conclusion: DHMBA was translocated to the brain after administration. DHMBA administration decreased 8-hydroxy-2′-deoxyguanosine levels in the brain and hippocampus, increased hippocampal glucocorticoid receptor-α levels, and decreased the plasma corticosterone concentration, suggesting that DHMBA inhibits hyperactivity of the HPA axis. Nrf2 pathway activity induced by DHMBA resulted in increased antioxidant enzyme levels in the hippocampus

Closing the Gap Between Theory and Practice During Alternating Optimization for GANs.

Synthesizing high-quality and diverse samples is the main goal of generative models. Despite recent great progress in generative adversarial networks (GANs), mode collapse is still an open problem, and mitigating it will benefit the generator to better capture the target data distribution. This article rethinks alternating optimization in GANs, which is a classic approach to training GANs in practice. We find that the theory presented in the original GANs does not accommodate this practical solution. Under the alternating optimization manner, the vanilla loss function provides an inappropriate objective for the generator. This objective forces the generator to produce the output with the highest discriminative probability of the discriminator, which leads to mode collapse in GANs. To address this problem, we introduce a novel loss function for the generator to adapt to the alternating optimization nature. When updating the generator by the proposed loss function, the reverse Kullback-Leibler divergence between the model distribution and the target distribution is theoretically optimized, which encourages the model to learn the target distribution. The results of extensive experiments demonstrate that our approach can consistently boost model performance on various datasets and network structures.

Self-perceived middle-distance race pace is faster in advanced footwear technology spikes

BackgroundQuantifying the potential benefits of advanced footwear technology (AFT) track shoes (i.e., “spikes”) in middle-distance events is challenging, because repeated maximal effort trials (as in sprinting) or aerobic running economy trials (as in long-distance running) are not feasible. MethodsWe introduce a novel approach to assess the benefits of AFT spikes, consisting of a series of 200-m runs at self-perceived middle-distance race pace with 10 min recovery, and conduct 4 experiments to evaluate its validity, sensitivity, reproducibility, and utility. ResultsIn Experiment 1, participants ran 1.2% slower in spikes with 200 g added mass vs. control spikes, which is exactly equal to the known effects of shoe mass on running performance. In Experiment 2, participants ran significantly faster in AFT prototype spikes vs. traditional spikes. In Experiment 3, we compared 2 other AFT prototype spikes against traditional spikes on 3 separate days. Group-level results were consistent across days, but our data indicates that at least 2 separate sessions are needed to evaluate individual responses. In Experiment 4, participants ran significantly faster in 2 AFT spike models vs. traditional spikes (2.1% and 1.6%). Speed was similar between a third AFT spike model and the traditional spikes. These speed results were mirrored by changes in step length as participants took significantly longer steps in the 2 faster AFT spike models (2.3% and 1.9%), while step length was similar between the other spikes. ConclusionOur novel, interval-based approach is a valid and reliable method for quantifying differences between spikes at middle-distance running intensity.

Output Regularization With Cluster-Based Soft Targets.

While supervised learning of over-parameterized neural networks achieved state-of-the-art performance in image classification, it tends to over-fit the labeled training samples to give inferior generalization ability. Output regularization deals with over-fitting by using soft targets as additional training signals. Although clustering is one of the most fundamental data analysis tools for discovering general-purpose and data-driven structures, it has been ignored in existing output regularization approaches. In this article, we leverage this underlying structural information by proposing Cluster-based soft targets for Output Regularization (CluOReg). This approach provides a unified way for simultaneous clustering in embedding space and neural classifier training with cluster-based soft targets via output regularization. By explicitly calculating a class relationship matrix in the cluster space, we obtain classwise soft targets shared by all samples in each class. Results of image classification experiments under various settings on a number of benchmark datasets are provided. Without resorting to external models or designed data augmentation, we get consistent and significant reductions in classification error compared with other approaches, demonstrating that cluster-based soft targets effectively complement the ground-truth label.

ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation.

Natural Language Generation (NLG) accepts input data in the form of images, videos, or text and generates corresponding natural language text as output. Existing NLG methods mainly adopt a supervised approach and rely heavily on coupled data-to-text pairs. However, for many targeted scenarios and for non-English languages, sufficient quantities of labeled data are often not available. As a result, it is necessary to collect and label data-text pairs for training, which is both costly and time-consuming. To relax the dependency on labeled data of downstream tasks, we propose an intuitive and effective zero-shot learning framework, ZeroNLG, which can deal with multiple NLG tasks, including image-to-text (image captioning), video-to-text (video captioning), and text-to-text (neural machine translation), across English, Chinese, German, and French within a unified framework. ZeroNLG does not require any labeled downstream pairs for training. During training, ZeroNLG (i) projects different domains (across modalities and languages) to corresponding coordinates in a shared common latent space; (ii) bridges different domains by aligning their corresponding coordinates in this space; and (iii) builds an unsupervised multilingual auto-encoder to learn to generate text by reconstructing the input text given its coordinate in shared latent space. Consequently, during inference, based on the data-to-text pipeline, ZeroNLG can generate target sentences across different languages given the coordinate of input data in the common space. Within this unified framework, given visual (imaging or video) data as input, ZeroNLG can perform zero-shot visual captioning; given textual sentences as input, ZeroNLG can perform zero-shot machine translation. We present the results of extensive experiments on twelve NLG tasks, showing that, without using any labeled downstream pairs for training, ZeroNLG generates high-quality and "believable" outputs and significantly outperforms existing zero-shot methods.

Effects of Cohabitation on Neurodevelopmental Outcomes in Rats Discordant for Neonatal Exposure to Sevoflurane

BackgroundHaving a sibling with autism spectrum disorder is a risk factor for autism spectrum disorder. We used a rat model in which the general anesthetic sevoflurane (SEVO) induces autism spectrum disorder–like neurodevelopmental abnormalities to test whether they can be transmitted via cohabitation. MethodsMale rat pups from several litters were mixed and randomized to 3 new litter types: SEVO-exposed (SEVO), SEVO-unexposed (control), and equal numbers of SEVO-exposed and SEVO-unexposed (MIXED). After weaning, rats in experiment 1 were housed with littermates in SEVO, control, and MIXED (MIXED-exposed and MIXED-unexposed) pairs. In experiment 2, MIXED-exposed and MIXED-unexposed rats were paired with an unfamiliar naïve cagemate. Corticosterone levels, gene expression, central inflammatory markers (experiment 1), and behavior and corticosterone levels (experiment 2) were assessed in adulthood. ResultsIn experiment 1, compared with control rats, SEVO rats exhibited abnormalities in the hypothalamic-pituitary-adrenal axis, inflammatory markers, oxytocin, arginine vasopressin, and DNA methylation systems. Almost all these measures in MIXED-exposed and MIXED-unexposed rats were statistically indistinguishable from and similar to those in SEVO or control rats, with most measures in MIXED rats being similar to those in SEVO rats. Experiment 2 showed that pairing with unfamiliar, naïve rats after weaning caused MIXED-unexposed and MIXED-exposed rats’ behavior to be no different from that of control and SEVO rats, respectively; however, the 2 groups of MIXED rats also did not differ from each other. ConclusionsThese findings suggest that neurodevelopmental abnormalities can be transmitted to otherwise healthy individuals through interactions during cohabitation; however, subsequent pairing with unfamiliar, naïve cohabitants may weaken this interaction effect.

A Novel Learning-Based Trajectory Generation Strategy for a Quadrotor.

In this article, a learning-based trajectory generation framework is proposed for quadrotors, which guarantees real-time, efficient, and practice-reliable navigation by online making human-like decisions via reinforcement learning (RL) and imitation learning (IL). Specifically, inspired by human driving behavior and the perception range of sensors, a real-time local planner is designed by combining learning and optimization techniques, where the smooth and flexible trajectories are online planned efficiently in the observable area. In particular, the key problems in the framework, temporal optimality (time allocation), and spatial optimality (trajectory distribution) are solved by designing an RL policy, which provides human-like commands in real-time (e.g., slower or faster) to achieve better navigation, instead of generating traditional low-level motions. In this manner, real-time trajectories are calculated using convex optimization according to the efficient and accurate decisions of the RL policy. In addition, to improve generalization performance and to accelerate the training, an expert policy and IL are employed in the framework. Compared with existing works, the kernel contribution is to design a real-time practice-oriented intelligent trajectory generation framework for quadrotors, where human-like decision-making and model-based optimization are integrated to plan high-quality trajectories. The results of comparative experiments in known and unknown environments illustrate the superior performance of the proposed trajectory generation strategy in terms of efficiency, smoothness, and flexibility.

Measurement and optimisation of the perceptual equivalence of the Dutch consonant-vowel-consonant (CVC) word lists using synthetic speech and list pairs

Objectives (1) to determine whether the standard Dutch word lists for speech audiometry are equally intelligible in normal-hearing listeners (Experiment 1), (2) to investigate whether synthetic speech can be used to create word lists (Experiment 1) and (3) to determine whether the list effect found in Experiment 1 can be reduced by combining two lists into pairs (Experiment 2). Design Participants performed speech tests in quiet with the original (natural) and synthetic word lists (Experiment 1.). In Experiment 2, new participants performed speech tests with list pairs from the original lists constructed from the results of Experiment 1. Study samples Twenty-four and twenty-eight normal-hearing adults. Results There was a significant list effect in the natural speech lists; not in the synthetic speech lists. Variability in intelligibility was significantly higher in the former, with list differences up to 20% at fixed presentation levels. The 95% confidence interval of a list with a score of approximately 70% is around 10%-points wider than of a list pair. Conclusions The original Dutch word lists show large variations in intelligibility. List effects can be reduced by combining two lists per condition. Synthetic speech is a promising alternative to natural speech in speech audiometry in quiet.

Online course as a means of developing master's professional competencies: results of a pedagogical experiment

The article proposes a solution to a scientific problem, which is to substantiate the feasibility of using online courses in the professional training of masters in the pedagogical university, to develop an online course and experimentally test its effectiveness as a means of developing the professional competencies of masters in the speciality 014~Secondary Education (Mathematics). The author used the following methods of scientific research: pedagogical experiment, observation (for systematic perception and analysis of learning activities, motivation and personal growth of students in the context of blended learning), testing (to determine the level of logical thinking and development of students’ professional competences), questionnaire (to monitor the ergonomics and efficiency of the online course and the Mathematics teacher’s e-portfolio), expert evaluation of the developed online course, and methods of mathematical statistics (for proper processing of experimental data and verification of the online course’ effectiveness). The results of the control stage of the study indicate a positive trend in the development of mathematical, information and educational, methodological, psychological and pedagogical, and professional and technological competencies of students in the experimental group. The final test of the online course was chosen as a tool for determining the level of mathematical competencies of the masters in the experimental group. The reliability of the assessment of the masters’ professional competencies was confirmed by testing the hypothesis of the normal distribution of test results according to the Shapiro-Wilk test. In addition, a close correlation between students' academic achievement level in an online course and the level of logical thinking development was substantiated using the Student’s t-test. Prospects for further research include the development of online courses on Mathematics teaching methods with an emphasis on methodical, professional, and technological competencies; a detailed study of the impact of the latest information technologies (artificial intelligence, virtual and augmented reality, web programming, etc.) on the development of students’ information and educational professional competences in distance and blended learning.

Mitochondria-Related Candidate Genes and Diagnostic Model to Predict Late-Onset Alzheimer's Disease and Mild Cognitive Impairment.

Late-onset Alzheimer's disease (LOAD) is the most common type of dementia, but its pathogenesis remains unclear, and there is a lack of simple and convenient early diagnostic markers to predict the occurrence. Our study aimed to identify diagnostic candidate genes to predict LOAD by machine learning methods. Three publicly available datasets from the Gene Expression Omnibus (GEO) database containing peripheral blood gene expression data for LOAD, mild cognitive impairment (MCI), and controls (CN) were downloaded. Differential expression analysis, the least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature elimination (SVM-RFE) were used to identify LOAD diagnostic candidate genes. These candidate genes were then validated in the validation group and clinical samples, and a LOAD prediction model was established. LASSO and SVM-RFE analyses identified 3 mitochondria-related genes (MRGs) as candidate genes, including NDUFA1, NDUFS5, and NDUFB3. In the verification of 3 MRGs, the AUC values showed that NDUFA1, NDUFS5 had better predictability. We also verified the candidate MRGs in MCI groups, the AUC values showed good performance. We then used NDUFA1, NDUFS5 and age to build a LOAD diagnostic model and AUC was 0.723. Results of qRT-PCR experiments with clinical blood samples showed that the three candidate genes were expressed significantly lower in the LOAD and MCI groups when compared to CN. Two mitochondrial-related candidate genes, NDUFA1 and NDUFS5, were identified as diagnostic markers for LOAD and MCI. Combining these two candidate genes with age, a LOAD diagnostic prediction model was successfully constructed.

Saturated Sliding Mode Control Scheme for a New Wearable Back-Support Exoskeleton

Low back pain has been torturing people around the world as a common and chronic disease, the main inducement of which is lumbar disc herniation (LDH). To alleviate patients’ pain noninvasively, this paper proposes a new lumbar spinal rehabilitation exoskeleton. This exoskeleton is composed of two bands connected by four motor-driven piston pushrods, where the range of band can be adjusted to adapt to people with different waistlines. The four pushrods provide support forces to hold the upper body to relieve the burden of lumbar. The joints between pushrods and band are universal pairs and spherical pairs. The whole structure can be regarded as a parallel robot, thus the supporting and waist tracking performance can be determined by motion control effect of pushrods. Considering the motor torque of pushrods is limited, in this paper, a saturated sliding mode control scheme is proposed. Meanwhile, an extended state observer (ESO) is employed to estimate the external disturbance and internal uncertainties, then the estimates of perturbations will be compensated by the feedback scheme. Furthermore, to enhance the disturbance-tracking performance of the ESO an integrated sliding mode observer is proposed to compensate the tracking errors of ESO. The stabilities of controller and observers are proved by Lyapunov stability theory. Finally, a simulation and two experiments are conducted to verify the performance of the proposed controller and the new exoskeleton. The simulation results show that the novel/new controller can drive the new exoskeleton to move along with the desirable trajectory to support the upper body so that alleviating the burden of waist. The results of myoelectricity experiments also show favorable effectiveness of exoskeleton on supporting upper body in different postures. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —this article was inspired by the concerns of low back pain that many people are suffering. Most existing rehabilitation robots tend to focus on supporting lower body and arms, while back-support rehabilitation robots are barely seen. Although we can see a few upper-body supporting robots in sporadic papers, these studies have not proposed a good control scheme yet. Besides, it is difficult to find a rehabilitation robot with simple structure as well as conforming to ergonomics. To resolve the shortcomings of the structure of existing robots and control models, we proposed a completely new rehabilitation robot with novel mechanical structure. Meanwhile, a new saturated sliding mode and novel integrated observer are employed in the position-force control system to enhance the stability and controllability of the whole robot control system. Then, some elaborately designed experiments are conducted to test the proposed robot and the control system. Numerical results demonstrate that the proposed method can significantly increase the trajectory tracking performance and alleviate low back pain.

Decentralized Scheduling for Concurrent Tasks in Mobile Edge Computing via Deep Reinforcement Learning

Mobile Edge Computing (MEC) is a promising solution to enhance the computing capability of resource-limited networks. A fundamental problem in MEC is efficiently offloading tasks from user devices to edge servers. However, there still exists a gap to deploy in real-world environments: 1) traditional centralized approaches needs complete information of edge network, ignoring the communication costs generated by synchronization, 2) previous works do not consider concurrent computation on edge servers, which may cause dynamic changes in the environment, and 3) the scheduling algorithm should deliver individualized decisions for different users independently and with high efficiency To solve this mismatch, we studied a multi-user task offloading problem where user devices make offloading decisions independently. We consider the concurrent execution of tasks and formulate a non-divisible and delay-aware task offloading problem to jointly minimize the dropped task ratio and long-term latency. We propose a decentralized task scheduling algorithm based on DRL that makes offloading decisions without knowing the information of other user devices. We employ Double-DQN, Dueling-DQN, Prioritized Replay Memory, and Recurrent Neural Network (RNN) techniques to improve the algorithm's performance. The results of simulation experiments show that our method can significantly reduce the long-term latency and dropped task ratio compared to the baseline algorithms.

Disturbance Observer-Based Fixed-Time Tracking Control for a Tilt Trirotor Unmanned Aerial Vehicle

This paper proposed a new fixed-time trajectory tracking control strategy based on the disturbance observer (DOB) for a tilt trirotor unmanned aerial vehicle (TTUAV) which is subject to unknown external disturbances. To facilitate the TTUAV's trajectory tracking controller design, the dynamic model of the TTUAV is divided into two subsystems, the position subsystem (outer-loop) and the attitude subsystem (inner-loop). For the dynamics of the TTUAV's outer-loop, the unknown external disturbances consist of the unknown wind disturbances, the unknown aerodynamic frictions forces, the lateral force generated by the rear servo and the states coupling between the outer-loop and the inner-loop. Then, a nonlinear DOB is designed to estimate the unknown external disturbances, and a nonsingular terminal sliding-mode controller (NTSMC) is further developed to drive the TTUAV's position tracking errors to zero in a fixed time. For the dynamics of the TTUAV's inner-loop, the unknown external disturbances are mainly introduced by the aerodynamic frictions torques. Then, a modified sliding mode controller is designed to reject the unknown external disturbances, and to ensure that the attitude tracking errors converge to zero exponentially. In addition, a composite stability analysis based on Lyapunov stability theory is provided to guarantee the fixed-time convergence of the closed-loop dynamics of the TTUAV. Finally, the results of the real-time experiments are presented to verify the effectiveness of the proposed control strategy.

Functional Connectivity Prediction With Deep Learning for Graph Transformation.

Inferring resting-state functional connectivity (FC) from anatomical brain wiring, known as structural connectivity (SC), is of enormous significance in neuroscience for understanding biological neuronal networks and treating mental diseases. Both SC and FC are networks where the nodes are brain regions, and in SC, the edges are the physical fiber nerves among the nodes, while in FC, the edges are the nodes' coactivation relations. Despite the importance of SC and FC, until very recently, the rapidly growing research body on this topic has generally focused on either linear models or computational models that rely heavily on heuristics and simple assumptions regarding the mapping between FC and SC. However, the relationship between FC and SC is actually highly nonlinear and complex and contains considerable randomness; additional factors, such as the subject's age and health, can also significantly impact the SC-FC relationship and hence cannot be ignored. To address these challenges, here, we develop a novel SC-to-FC generative adversarial network (SF-GAN) framework for mapping SC to FC, along with additional metafeatures based on a newly proposed graph neural network-based generative model that is capable of learning the stochasticity. Specifically, a new graph-based conditional generative adversarial nets model is proposed, where edge convolution layers are leveraged to encode the graph patterns in the SC in the form of a graph representation. New edge deconvolution layers are then utilized to decode the representation back to FC. Additional metafeatures of subjects' profile information are integrated into the graph representation with newly designed sparse-regularized layers that can automatically select features that impact FC. Finally, we have also proposed new post hoc explainer of our SF-GAN, which can identify which subgraphs in SC strongly influence which subgraphs in FC by a new multilevel edge-correlation-guided graph clustering problem. The results of experiments conducted to test the new model confirm that it significantly outperforms existing state-of-the-art methods, with additional interpretability for identifying important metafeatures and subgraphs.

Self-Reporting Microsensors Inspired by Noctiluca Scintillans for Small-Defect Positioning and Electrical-Stress Visualization in Polymers.

Small defects induce concentrated electrical stress in dielectric polymers, leading to premature failure of materials. Existing sensing methods fail to effectively visualize these defects owing to the invisible-energy state of the electric field. Thus, it is necessary to establish a nondestructive method for the real-time detection of small defects in dielectric polymers. In this study, a self-reporting microsensor (SRM) inspired by Noctiluca scintillans is designed to endow materials with the ability of self-detection for defects and electrical stress. The SRM leverages the energy of a nearby electric field to emit measurable fluorescence, enabling defect localization and diagnosis as well as electrical-stress visualization. A controllable dielectric microsphere is constructed to achieve an adjustable electroluminescence threshold for the SRM, thereby increasing its detection accuracy while decreasing the electroluminescence threshold. The potential degradation in the polymer performance owing to SRM implantation is addressed by assembling long molecular chains on the SRM surface to spontaneously generate an interpenetrating network. Results of finite element analyses and experiments demonstrate that the SRM can effectively realize nondestructive visualization and positioning of small defects and concentrated electrical stress in polymers, positioning it as a promising sensing method for monitoring the electric field and charge distribution in materials.

Introducing active sites and regulating electron distribution to design Mo and P co-doped Ni for efficient alkaline hydrogen evolution reaction

NiMoP electrocatalysts for hydrogen evolution reaction (HER) are received widespread concern, but the mechanism of Mo/P atoms for improving the HER activity still need to be revealed in details. Herein, we synthesize the Mo/P co-doped Ni (NMP) electrocatalysts, and the alkaline HER performances are tested. The NMP exhibit the excellent activity, durability and stability of material characters. Based on the results of experiments and density functional theory calculations, it is found that the introduced P atoms can regulate the electron redistribution, which can result in enhancing the reaction kinetic, increasing the active sites and charge exchange capacity. Besides, it leads the doped Mo to become the active site in both Volmer and Heyrovsky processes, and Ni bonded to Mo and Ni atoms exhibit the increasing activity. The obtained results display a deeper understanding for the doped metal and non-metallic atoms in enhancing HER activity of Ni, which is of great significant for designing the highly efficient and inexpensive electrocatalysts for producing hydrogen.

Inducing Clusters Deep Kernel Gaussian Process for Longitudinal Data

We consider the problem of predictive modeling from irregularly and sparsely sampled longitudinal data with unknown, complex correlation structures and abrupt discontinuities. To address these challenges, we introduce a novel inducing clusters longitudinal deep kernel Gaussian Process (ICDKGP). ICDKGP approximates the data generating process by a zero-mean GP with a longitudinal deep kernel that models the unknown complex correlation structure in the data and a deterministic non-zero mean function to model the abrupt discontinuities. To improve the scalability and interpretability of ICDKGP, we introduce inducing clusters corresponding to centers of clusters in the training data. We formulate the training of ICDKGP as a constrained optimization problem and derive its evidence lower bound. We introduce a novel relaxation of the resulting problem which under rather mild assumptions yields a solution with error bounded relative to the original problem. We describe the results of extensive experiments demonstrating that ICDKGP substantially outperforms the state-of-the-art longitudinal methods on data with both smoothly and non-smoothly varying outcomes.

An integrated model of supply chain resilience considering supply and demand uncertainties

AbstractThe complexity of the global supply chain has increased dramatically over the past few decades as a result of uncertainty caused by various factors. This paper studies the optimal strategy for supply chain resilience models considering supply disruptions and demand fluctuations. We present two‐stage stochastic programming models based on different scenarios, including a risk‐neutral model that considers the expected total cost, a risk‐averse model that considers the conditional value‐at‐risk measure, and a responsiveness model that considers the service level. We also propose multiobjective mathematical programming that considers all three models simultaneously and suggests the solution approach. Finally, we present the results of computational experiments and demonstrate how to cope with uncertainty through flexibility and redundancy. We offer a set of nondominated solutions from the multiobjective model and derive managerial insights, which suggest a decision‐making strategy between the disruption risk, expected total cost, and service level.

Research of the process of biological wastewater treatment under conditions of uneven load of the treatment system

The main purpose of the article is to develop a multifactorial model for rapid assessment of the efficiency of biological wastewater treatment reactors. A mathematical model of the process of biological wastewater treatment has been developed based on: changes in the concentration of organic contaminants in the bioreactor over time, taking into account the uneven flow of wastewater to the treatment plant, the process of substrate entering the bioreactor (different amounts may enter at different times). The software implementation of the proposed algorithm for solving the corresponding model problem in Python is carried out. The results of computer experiments on the study of the efficiency of wastewater treatment in biological treatment reactors for different operating conditions of facilities are presented. In particular, such processes were considered with taking into account the unevenness of the load, because the maximum cleaning loads are in the morning and in the evening. The task was solved to simulate a real situation and show how cleaning takes place at the maximum load at a certain time of the day. The results obtained will be useful for calculations in the design of biological treatment facilities or in the reconstruction of existing bioreactors for their prospective operation under new operating conditions.

Self‐Distancing Regulates the Effect of Incidental Anger (vs. Fear) on Affective Decision‐Making Under Uncertainty

ABSTRACTEmotions integral to a task are often adaptive, particularly in situations where outcomes and probabilities are not known. However, decisions are also influenced by emotions that arise from situations unrelated to the task. This is especially the case with negative emotions like fear and anger, which also tend to be accompanied by ruminative thinking that might divert decision‐makers' attention from the task at hand. In two preregistered experiments, we show how self‐distancing regulates the influence of incidental anger (vs. fear) on decision‐making under uncertainty. Participants recalled and reflected on a fear‐related or anger‐related event from either a self‐immersed or self‐distanced perspective. Next, they completed a task that is commonly used to measure affective decision‐making under uncertainty, the Iowa Gambling Task. The results in both experiments indicated that self‐immersed angry (vs. fearful) decision‐makers were significantly slower to avoid the risky, disadvantageous decks. These findings demonstrate how the ways in which we process negative emotional events shape their carryover effects in decision‐making under uncertainty and point to self‐distancing as a potential tool to control incidental emotional influences.