Set Of Experiments Research Articles

MoO3 based nanocomposites for the photocatalytic degradation of colourants – A review

BackgroundPhotocatalysis is the most environmentally friendly method for reducing and mineralizing organic contaminants because of its high efficacy, low toxicity, and low cost. Semiconductor photocatalysts are one of the most intriguing catalysts because they are safe and produce no secondary pollution, making them one of the best at removing pollutants from water. MethodsTransition metal oxide molybdenum trioxide (MoO3) is a widely used photocatalyst because of its tunnel structure and one-dimensional behaviour. Because it is common, has strong electrochemical activity, is non-toxic, and kills bacteria, MoO3 also plays a big role in semiconductor photocatalysts. MoO3 outperformed other oxide semiconductors as photocatalysts owing to its superior adsorption properties. MoO3 is commonly found in three polymorphs: orthorhombic, monoclinic, and hexagonal. In addition, the efficacy of various experimental settings on the deterioration of various colourants was investigated. Scientific findingsAs a result, the current study examines the photocatalytic activities of various MoO3-based nanocomposites as well as the results of various experimental conditions, with a particular emphasis on extensive colourants.

Partial Multiview Representation Learning With Cross-View Generation.

Multiview learning has made significant progress in recent years. However, an implicit assumption is that multiview data are complete, which is often contrary to practical applications. Due to human or data acquisition equipment errors, what we actually get is partial multiview data, which existing multiview algorithms are limited to processing. Modeling complex dependencies between views in terms of consistency and complementarity remains challenging, especially in partial multiview data scenarios. To address the above issues, this article proposes a deep Gaussian cross-view generation model (named PMvCG), which aims to model views according to the principles of consistency and complementarity and eventually learn the comprehensive representation of partial multiview data. PMvCG can discover cross-view associations by learning view-sharing and view-specific features of different views in the representation space. The missing views can be reconstructed and are applied in turn to further optimize the model. The estimated uncertainty in the model is also considered and integrated into the representation to improve the performance. We design a variational inference and iterative optimization algorithm to solve PMvCG effectively. We conduct comprehensive experiments on multiple real-world datasets to validate the performance of PMvCG. We compare the PMvCG with various methods by applying the learned representation to clustering and classification. We also provide more insightful analysis to explore the PMvCG, such as convergence analysis, parameter sensitivity analysis, and the effect of uncertainty in the representation. The experimental results indicate that PMvCG obtains promising results and surpasses other comparative methods under different experimental settings.

Bacterial aerobic respiration is a major consumer of oxygen in sputum from patients with acute lower respiratory tract infection.

Bacterial aerobic respiration may determine the outcome of antibiotic treatment in experimental settings, but the clinical relevance of bacterial aerobic respiration for the outcome of antibiotic treatment has not been tested. Therefore, we hypothesized that bacterial aerobic respiration is higher in sputum from patients with acute lower respiratory tract infections (aLRTI), than in sputum from patients with chronic LRTI (cLRTI), where the bacteria persist despite antibiotic treatment. The bacterial aerobic respiration was determined according to the dynamics of the oxygen (O2) concentration in sputum from aLRTI patients (n = 52). This result was evaluated by comparison to previously published data from patients with cLRTI. O2 consumption resulting in anoxic zones was more frequent in sputum with detected bacterial pathogens. The bacterial aerobic respiration in aLRTI sputum approximated 55% of the total O2 consumption, which was significantly higher than previously published for cLRTI. The bacterial aerobic respiration in sputum was higher in aLRTI patients than previously seen in cLRTI patients, indicating the presence of bacteria with a sensitive physiology in aLRTI. These variations in bacterial physiology between aLRTI patients and cLRTI patients may contribute the huge difference in treatment success between the two patient groups.

Engagement Strategies in a Peer-quizzing Game: Investigating Student Interactions and Powergaming

Educational games are a popular means of engaging students in learning activities. However, students in game-based learning environments often engage in powergaming to reap undeserved rewards and spoil the experi-ence of their peers. This may happen when the students pretend to engage in game activities or collude with other students, exploiting the game settings and rules to maximize their points. In this explorative study, we investigate powergaming in three settings of an asynchronous online multiplayer peer-quizzing game in a blended learning setting – a first-year programming class in a Canadian university. We designed three experi-mental settings with three versions of an education game which allowed different levels of powergaming. The between-subject study involved three experimental groups: Group 1 used a game version where they received weekly performance feedback and tips on how to improve their performance, Group 2 used a game version with access to an existing resource bank, allowing them to maximize the number if their activities in the game, and Group 3 served as a control group with no additional interventions. The research aimed to investigate 1) the association between the power gamers' activity, their grades and their preference to work alone or in a group, 2) the types of activities (type of quiz questions) most used by power gamers, and 3) which game setting was the most conducive to power gaming. The results show that better grades are not associated with higher activity levels in the game. Students who engaged in more diverse types of game activities had better learning outcomes The control Group 3 had the highest average grades. As we expected, Group 2 engaged in the high-est number of activities, esp. in creating questions, a form of powergaming. The qualitative results showed that contrary to our assumption the powergamers in Group 2 was not harmful, because it created a rich set of re-sources in the game and fostered student engagement in the game.

Counterfactuals and the logic of causal selection.

Everything that happens has a multitude of causes, but people make causal judgments effortlessly. How do people select one particular cause (e.g., the lightning bolt that set the forest ablaze) out of the set of factors that contributed to the event (the oxygen in the air, the dry weather … )? Cognitive scientists have suggested that people make causal judgments about an event by simulating alternative ways things could have happened. We argue that this counterfactual theory explains many features of human causal intuitions, given two simple assumptions. First, people tend to imagine counterfactual possibilities that are both a priori likely and similar to what actually happened. Second, people judge that a factor C caused effect E if C and E are highly correlated across these counterfactual possibilities. In a reanalysis of existing empirical data, and a set of new experiments, we find that this theory uniquely accounts for people's causal intuitions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Exploiting histopathological imaging for early detection of lung and colon cancer via ensemble deep learning model

Cancer seems to have a vast number of deaths due to its heterogeneity, aggressiveness, and significant propensity for metastasis. The predominant categories of cancer that may affect males and females and occur worldwide are colon and lung cancer. A precise and on-time analysis of this cancer can increase the survival rate and improve the appropriate treatment characteristics. An efficient and effective method for the speedy and accurate recognition of tumours in the colon and lung areas is provided as an alternative to cancer recognition methods. Earlier diagnosis of the disease on the front drastically reduces the chance of death. Machine learning (ML) and deep learning (DL) approaches can accelerate this cancer diagnosis, facilitating researcher workers to study a vast majority of patients in a limited period and at a low cost. This research presents Histopathological Imaging for the Early Detection of Lung and Colon Cancer via Ensemble DL (HIELCC-EDL) model. The HIELCC-EDL technique utilizes histopathological images to identify lung and colon cancer (LCC). To achieve this, the HIELCC-EDL technique uses the Wiener filtering (WF) method for noise elimination. In addition, the HIELCC-EDL model uses the channel attention Residual Network (CA-ResNet50) model for learning complex feature patterns. Moreover, the hyperparameter selection of the CA-ResNet50 model is performed using the tuna swarm optimization (TSO) technique. Finally, the detection of LCC is achieved by using the ensemble of three classifiers such as extreme learning machine (ELM), competitive neural networks (CNNs), and long short-term memory (LSTM). To illustrate the promising performance of the HIELCC-EDL model, a complete set of experimentations was performed on a benchmark dataset. The experimental validation of the HIELCC-EDL model portrayed a superior accuracy value of 99.60% over recent approaches.

Designing Efficient Bit-Level Sparsity-Tolerant Memristive Networks.

With the rapid progress of deep neural network (DNN) applications on memristive platforms, there has been a growing interest in the acceleration and compression of memristive networks. As an emerging model optimization technique for memristive platforms, bit-level sparsity training (with the fixed-point quantization) can significantly reduce the demand for analog-to-digital converters (ADCs) resolution, which is critical for energy and area consumption. However, the bit sparsity and the fixed-point quantization will inevitably lead to a large performance loss. Different from the existing training and optimization techniques, this work attempts to explore more sparsity-tolerant architectures to compensate for performance degradation. We first empirically demonstrate that in a certain search space (e.g., 4-bit quantized DARTS space), network architectures differ in bit-level sparsity tolerance. It is reasonable and necessary to search the architectures for efficient deployment on memristive platforms by the neural architecture search (NAS) technology. We further introduce bit-level sparsity-tolerant NAS (BST-NAS), which encapsulates low-precision quantization and bit-level sparsity training into the differentiable NAS, to explore the optimal bit-level sparsity-tolerant architectures. Experimentally, with the same degree of sparsity and experiment settings, our searched architectures obtain a promising performance, which outperform the normal NAS-based DARTS-series architectures (about 5.8% higher than that of DARTS-V2 and 2.7% higher than that of PC-DARTS) on CIFAR10.

AR3n: A Reinforcement Learning-Based Assist-as-Needed Controller for Robotic Rehabilitation

In this article, we present AR3n (pronounced as Aaron), an assist-as-needed (AAN) controller that utilizes reinforcement learning (RL), to supply adaptive assistance during a robot-assisted handwriting rehabilitation task. Unlike previous AAN controllers, our method does not rely on patient-specific controller parameters or physical models. We propose the use of a virtual patient model to generalize AR3n across multiple subjects. The system modulates robotic assistance in real time based on a subject’s tracking errors while minimizing the amount of robotic assistance. The controller is experimentally validated through a set of simulations and human subject experiments. Finally, a comparative study with a traditional rule-based controller is conducted to analyze differences in the assistance mechanisms of the two controllers.

Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training.

Deploying models on target domain data subject to distribution shift requires adaptation. Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available, and instant inference on the target domain is required. Despite many efforts into TTT, there is a confusion over the experimental settings, thus leading to unfair comparisons. In this work, we first revisit TTT assumptions and categorize TTT protocols by two key factors, i.e., whether testing data is sequentially streamed and whether source model is allowed to be trained with modified loss function. Among the multiple protocols, we adopt a realistic sequential test-time training (sTTT) protocol, under which we develop a test-time anchored clustering (TTAC) approach to enable stronger test-time feature learning. TTAC discovers clusters in both source and target domains and matches the target clusters to the source ones to improve adaptation. When source domain information is strictly absent (i.e., source-free) we further develop an efficient method to infer source domain distributions for anchored clustering. Finally, self-training (ST) has demonstrated great success in learning from unlabeled data and we empirically figure out that applying ST alone to TTT is prone to confirmation bias. Therefore, a more effective TTT approach is introduced by regularizing self-training with anchored clustering, and the improved model is referred to as TTAC++. We demonstrate that, under all TTT protocols, TTAC++ consistently outperforms the state-of-the-art methods on five TTT datasets, including corrupted target domain, selected hard samples, synthetic-to-real adaptation and adversarially attacked target domain. We hope this work will provide a fair benchmarking of TTT methods, and future research should be compared within respective protocols.

On Network Structural and Temporal Encodings: A Space and Time Odyssey.

The dynamic network visualization design space consists of two major dimensions: network structural and temporal representation. As more techniques are developed and published, a clear need for evaluation and experimental comparisons between them emerges. Most studies explore the temporal dimension and diverse interaction techniques supporting the participants, focusing on a single structural representation. Empirical evidence about performance and preference for different visualization approaches is scattered over different studies, experimental settings, and tasks. This paper aims to comprehensively investigate the dynamic network visualization design space in two evaluations. First, a controlled study assessing participants' response times, accuracy, and preferences for different combinations of network structural and temporal representations on typical dynamic network exploration tasks, with and without the support of standard interaction methods. Second, the best-performing combinations from the first study are enhanced based on participants' feedback and evaluated in a heuristic-based qualitative study with visualization experts on a real-world network. Our results highlight node-link with animation and playback controls as the best-performing combination and the most preferred based on ratings. Matrices achieve similar performance to node-link in the first study but have considerably lower scores in our second evaluation. Similarly, juxtaposition exhibits evident scalability issues in more realistic analysis contexts.

Perceptron Theory Can Predict the Accuracy of Neural Networks.

Multilayer neural networks set the current state of the art for many technical classification problems. But, these networks are still, essentially, black boxes in terms of analyzing them and predicting their performance. Here, we develop a statistical theory for the one-layer perceptron and show that it can predict performances of a surprisingly large variety of neural networks with different architectures. A general theory of classification with perceptrons is developed by generalizing an existing theory for analyzing reservoir computing models and connectionist models for symbolic reasoning known as vector symbolic architectures. Our statistical theory offers three formulas leveraging the signal statistics with increasing detail. The formulas are analytically intractable, but can be evaluated numerically. The description level that captures maximum details requires stochastic sampling methods. Depending on the network model, the simpler formulas already yield high prediction accuracy. The quality of the theory predictions is assessed in three experimental settings, a memorization task for echo state networks (ESNs) from reservoir computing literature, a collection of classification datasets for shallow randomly connected networks, and the ImageNet dataset for deep convolutional neural networks. We find that the second description level of the perceptron theory can predict the performance of types of ESNs, which could not be described previously. Furthermore, the theory can predict deep multilayer neural networks by being applied to their output layer. While other methods for prediction of neural networks performance commonly require to train an estimator model, the proposed theory requires only the first two moments of the distribution of the postsynaptic sums in the output neurons. Moreover, the perceptron theory compares favorably to other methods that do not rely on training an estimator model.

Creditors’ Role in Shaping Asymmetric Cost Behavior: Evidence from Debt Covenant Violation

ABSTRACT This study uses covenant violations as a quasi-natural experimental setting to examine creditors’ roles in shaping corporate cost behavior. Using a regression discontinuity design, I find that cost stickiness experiences a sharp decline following debt covenant violations when control rights are transferred to creditors. The cost stickiness effect is more substantial for borrowers with lower credit ratings and when creditors possess greater bargaining power. The effect is also more pronounced during industry downturns when borrowers have fewer alternative sources of finance. Results are consistent when I use alternative measures of cost stickiness and alternative research designs. Overall, my evidence indicates that creditors play a monitoring role in firms’ cost behaviors and identifies a specific channel (loan covenants) through which the misalignment of incentives can impact cost asymmetry. JEL Classifications: D24; G32; M41.

Temporally-aware node embeddings for evolving networks topologies

Static node embedding algorithms applied to snapshots of real-world applications graphs are unable to capture their evolving process. As a result, the absence of information about the dynamics in these node representations can harm the accuracy and increase processing time of machine learning tasks related to these applications. Aiming at fill the gap regarding the inability of static methods to capture evolving processes on dynamic networks, we propose a biased random walk method named Evolving Node Embedding (EVNE). EVNE leverages the sequential relationship of graph snapshots by incorporating historic information when generating embeddings for the next snapshot. It learns node representations through a neural network, but differs from existing methods as it: (i) incorporates previously run walks at each step; (ii) starts the optimization of the current embedding from the parameters obtained in the previous iteration; and (iii) uses two time-varying parameters to regulate the behavior of the biased random walks over the process of graph exploration. Through a wide set of experiments we show that our approach generates better embeddings, outperforming baselines by up to 20% in a downstream node classification task. EVNE’s embeddings achieve better performance than others, based on experiments with four classifiers and five datasets. In addition, we present seven variations of our model to show the impact of each of EVNE’s mechanisms.

TMLNet: Triad Multitask Learning Network for multiobjective based change detection

Change detection is an essential computer vision task in remote sensing applications. It faces challenges of image registration errors, variation in image capturing conditions, clouds, etc. It is observed that an accurate change detection task requires effective feature learning, which needs a noiseless representation. Moreover, change detection improves with error-free image reconstruction as an auxiliary task, which needs joint feature representation by a feature extractor. In this work, we proposed a novel triad (which is a combination of input images and its difference) learning based multiresolution architecture TMLNet for effective change detection. We also proposed a multi-context local self-attention module to efficiently calculate long-range pixel relations with multiple contexts. An enhanced backbone module with top-down connections and multi-scale channel and spatial attention is utilized for change map generation. It provides less noisy features extracted through the backbone. Laplacian pyramid loss is used to preserve small details in feature reconstruction. A set of comprehensive experimentations reveals that the proposed scheme achieved the state-of-the-art result for the F1 Score, intersection over union, and overall accuracy values in seven benchmark datasets. Our model code is available at https://github.com/chouhan-avinash/TMLNet.

Domain-Independent Lifelong Problem Solving Through Distributed ALife Actors.

A domain-independent problem-solving system based on principles of Artificial Life is introduced. In this system, DIAS, the input and output dimensions of the domain are laid out in a spatial medium. A population of actors, each seeing only part of this medium, solves problems collectively in it. The process is independent of the domain and can be implemented through different kinds of actors. Through a set of experiments on various problem domains, DIAS is shown able to solve problems with different dimensionality and complexity, to require no hyperparameter tuning for new problems, and to exhibit lifelong learning, that is, to adapt rapidly to run-time changes in the problem domain, and to do it better than a standard, noncollective approach. DIAS therefore demonstrates a role for ALife in building scalable, general, and adaptive problem-solving systems.

Experimental Validation of a Command and Control Traffic Detection Model

Network intrusion detection systems (NIDS) are commonly used to detect malware communications, including command-and-control (C2) traffic from botnets. NIDS performance assessments have been studied for decades, but mathematical modeling has rarely been used to explore NIDS performance. This paper details a mathematical model that describes a NIDS performing packet inspection and its detection of malware's C2 traffic. The paper further describes an emulation testbed and a set of cyber experiments that used the testbed to validate the model. These experiments included a commonly used NIDS (Snort) and traffic with contents from a pervasive malware (Emotet). Results are presented for two scenarios: a nominal scenario and a “stressed” scenario in which the NIDS cannot process all incoming packets. Model and experiment results match well, with model estimates mostly falling within 95 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> confidence intervals on the experiment means. Model results were produced 70-3000 times faster than the experimental results. Consequently, the model's predictive capability could potentially be used to support decisions about NIDS configuration and effectiveness that require high confidence results, quantification of uncertainty, and exploration of large parameter spaces. Furthermore, the experiments provide an example for how emulation testbeds can be used to validate cyber models that include stochastic variability.

Hypergraph Isomorphism Computation.

The isomorphism problem, crucial in network analysis, involves analyzing both low-order and high-order structural information. Graph isomorphism algorithms focus on structural equivalence to simplify solver space, aiding applications like protein design, chemical pathways, and community detection. However, they fall short in capturing complex high-order relationships, unlike hypergraph isomorphism methods. Traditional hypergraph methods face challenges like high memory use and inaccurate identification, leading to poor performance. To overcome these, we introduce a hypergraph Weisfeiler-Lehman (WL) test algorithm, extending the WL test from graphs to hypergraphs, and develop a hypergraph WL kernel framework with two variants: the Hypergraph WL Subtree Kernel and Hypergraph WL Hyperedge Kernel. The Hypergraph WL Subtree Kernel counts different types of rooted subtrees and generates the final feature vector for a given hypergraph by comparing the number of different types of rooted subtrees. The Subtree Kernel identifies different rooted subtrees, while the Hyperedge Kernel focuses on hyperedges' vertex labels, enhancing feature vector generation. In order to fulfill our research objectives, a comprehensive set of experiments was meticulously designed, including seven graph classification datasets and 12 hypergraph classification datasets. Results on graph classification datasets indicate that the Hypergraph WL Subtree Kernel can achieve the same performance compared with the classical Graph Weisfeiler-Lehman Subtree Kernel. Results on hypergraph classification datasets show significant improvements compared to other typical kernel-based methods, which demonstrates the effectiveness of the proposed methods. In our evaluation, our proposed methods outperform the second-best method in terms of runtime, running over 80 times faster when handling complex hypergraph structures. This significant speed advantage highlights the great potential of our methods in real-world applications.

Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing

People use natural language to express their thoughts and wishes. As robots reside in various human environments, such as homes, offices, and hospitals, the need for human–robot communication is increasing. One of the best ways to achieve this communication is the use of natural languages. Natural language processing (NLP) is the most important approach enabling robots to understand natural languages and improve human–robot interaction. Also, due to this need, the amount of research on NLP has increased considerably in recent years. In this study, commands were given to a multiple-mobile-robot system using the Turkish natural language, and the robots were required to fulfill these orders. Turkish is classified as an agglutinative language. In agglutinative languages, words combine different morphemes, each carrying a specific meaning, to create complex words. Turkish exhibits this characteristic by adding various suffixes to a root or base form to convey grammatical relationships, tense, aspect, mood, and other semantic nuances. Since the Turkish language has an agglutinative structure, it is very difficult to decode its sentence structure in a way that robots can understand. Parsing of a given command, path planning, path tracking, and formation control were carried out. In the path-planning phase, the A* algorithm was used to find the optimal path, and a PID controller was used to follow the generated path with minimum error. A leader–follower approach was used to control multiple robots. A platoon formation was chosen as the multi-robot formation. The proposed method was validated on a known map containing obstacles, demonstrating the system’s ability to navigate the robots to the desired locations while maintaining the specified formation. This study used Turtlebot3 robots within the Gazebo simulation environment, providing a controlled and replicable setting for comprehensive experimentation. The results affirm the feasibility and effectiveness of employing NLP techniques for the formation control of multiple mobile robots, offering a robust and effective method for further research and development on human–robot interaction.

A novel bilateral stream neural network for melt pool monitoring during laser direct energy deposition

Detection and classification methods for the melt pool state in laser direct energy deposition (L-DED) can significantly help predict defects and mechanical properties of L-DED metal parts. Although traditional machine learning algorithms based on physical modeling methods and convolutional neural networks have recently been introduced into melt pool state identification, these methods rely on complex artificially designed features or cannot simultaneously detect defects in multiple dimensions. In this paper, a novel bilateral stream neural network was designed for melt pool identification, which performs defect identification in two label dimensions simultaneously. Two sets of single-channel experiments were designed to collect the dataset captured by a high-speed camera. By cutting the metal parts and marking them with professional equipment operated by professionals, the dataset was labeled according to the bonding condition and dilution rate criteria. Without an additive model structure, the model achieved 95.2% accuracy in identifying defects in the bonding condition and 92.8% in determining deficiencies in the dilution rate. In order to explain the identification mechanism of the model, the CAM method was utilized for the visual display of the model recognition process, which provides a potential application solution for the online monitoring method of the L-DED.

Deformation characteristics and creep behaviour of rigid particulates-EPS beads composites

The compression behaviour of the mixture of glass beads (representing rigid particles) and EPS beads (representing deformable particles) during the loading-unloading process is systematically examined through performing two sets of large-size oedometer experiments, including incremental step-by-step and one-step loading scenarios. At each step during the loading-unloading cycle, the void ratio (e) and the at-rest coefficient of lateral earth pressure (K0) are measured for pure rigid samples and rigid-soft particle mixtures. To consider the creep effect, the overburden pressure at the final loading step is maintained on the sample for 24 h prior to unloading. The results show that at a given overburden pressure, with the addition of soft particles to the pure rigid aggregates, the values of e and K0 decrease. Additionally, for both pure rigid samples and rigid-soft particle mixtures, with increasing the overburden pressure, e decreases whereas K0 augments. Moreover, due to the creep behaviour during the constant loading step, K0 decreases over time for both samples; a phenomenon which is observed to be more pronounced for pure rigid aggregates compared to rigid-soft particle mixtures. Finally, a well-established creep model is used to simulate the creep behaviour of pure rigid samples and rigid-soft particle composites.