Set Of Experimental Data Research Articles

Universality of phenotypic distributions in bacteria

Some phenotypic properties in bacteria exhibit universal statistics, with distributions collapsing under scaling. The extent and origins of such universality are not well understood. Using phenomenological modeling of growth and division, we identify compound “shape factors” that describe the distributions throughout a large set of single-cell data. We find that the emergence of universal distributions is associated with the robustness of shape factors across conditions, explaining the universality of cell size and highly expressed protein content and the nonuniversality of times between consecutive divisions. A wide range of experimental data sets support our theory quantitatively. Published by the American Physical Society 2024

Large-Scale Laboratory Experiments On The Wave Generation Due To The Collapse Of Partially And Fully Submerged Granular Columns

Landslides that occur in coastal environments can drive cascading consequences such as wave forces, flooding, and infrastructure damage to coastal communities. It can be difficult to classify these slides as subaerial or submarine, and the mechanics of wave generation associated with partially submerged failures are not well understood. Limited physical modelling has been conducted that encompasses both the triggering of granular landslides and subsequent waves associated with partially and fully submerged mass movements. To date, laboratory work investigating tsunamis generated by submarine landslides has focused on the wave formed in the direction of the mass movement (seaward direction) for rigid block experiments (eg. Rzadkiewicz, 1997) and deformable slide masses (e.g. Grilli et al., 2017, Takabatake, 2020, Bullard et al., 2023). From these experimental data sets, predictive relationships connecting slide acceleration, mass, and initial submergence depth to the amplitude of the wave formed have been presented for the seaward wave. Such relationships have not been presented for the landward directed wave, which propagates in the opposite direction of the submarine landslide motion. Further, not all landslides are easily classified as either subaerial or submarine. Consider the 2018 Anak Krakatoa landslide in which the sliding surface was estimated to be 100 m below sea level (Pakoksung et al., 2020), resulting in one third of the total collapse being submerged. In comparison to the end-member conditions of subaerial and submarine failures, the mechanics of wave generation associated with partially submerged failures is much less clear. Granular column collapse experiments provide an idealized experimental framework to explore momentum transfer processes and the resulting waves generated in partially submerged and fully submerged conditions. Work by Cabrera et al., (2020) made use of granular collapse experiments of partially to fully submerged columns to derive a continuous momentum-based function to estimate the maximum seaward wave amplitude based on the initial column submergence ratio (Hw/Ho). However, these experiments were conducted at a small-scale (Ho = 0.15 m) with a width of one particle (2.4 mm diameter). To address this research gap, a series of 22 large-scale granular collapse experiments were conducted by releasing columns of river stone (0.75 m and 0.50 m high) into a laboratory flume reservoir with water depths ranging up to 1.10 m to explore the wave generation and runup processes in both seaward and landward directions. The columns were released by a rapid pneumatically actuated vertical rising gate designed to enable the near instantaneous loss of support of the source volumes resulting in granular collapse. The failure mechanics were captured with high-speed cameras (Figure 1a,b) and wave amplitudes were measured using wave capacitance gauges (Figure 1c,d). This work also provides the first experimental data set of the landward propagating wave and runup associated with submerged granular collapse experiments. Overall, the seaward wave amplitudes measured in these highly-instrumented, large-scale physical models agree with empirical relationships developed in a previous study using smaller-scale models.

Exploration of Eye Fatigue Detection Features and Algorithm Based on Eye-Tracking Signal

Eye fatigue has a fatiguing effect on the eye muscles, and eye movement performance is a macroscopic response to the eye fatigue state. To detect and prevent the risk of eye fatigue in advance, this study designed an eye fatigue detection experiment, collected experimental data samples, and constructed experimental data sets. In this study, eye-tracking feature extraction was completed, and the significance difference of eye-tracking features under different fatigue states was discussed by two-way repeated-measures ANOVA (Analysis of Variance). The experimental results demonstrate the feasibility of eye fatigue detection from eye-tracking signals. In addition, this study considers the effects of different feature extraction methods on eye fatigue detection accuracy. This study examines the performance of machine learning algorithms based on manual feature calculation (SVM, DT, RM, ET) and deep learning algorithms based on automatic feature extraction (CNN, auto-encoder, transformer) in eye fatigue detection. Based on the combination of the methods, this study proposes the feature union auto-encoder algorithm, and the accuracy of the algorithm for eye fatigue detection on the experimental dataset is improved from 82.4% to 87.9%.

A Physics-Based Model-Data-Driven Method for Spindle Health Diagnosis—Part III: Model Training and Fault Detection

Abstract The primary goal of the paper is to monitor the health of the spindle in machine tools to ensure optimal performance and reduce costly downtimes. Spindle health monitoring is essential to detect wear and cracks in spindle bearings, which can be challenging due to their gradual development and hidden locations. The proposed approach combines physics-based modeling and data-driven techniques to monitor spindle health effectively. In Part I and Part II of the paper, mathematical models of bearing faults and spindle imbalance are integrated into the digital model of the spindle. This allows for simulating the operation of the spindle both with and without faults. The integration of fault models enables the generation of vibrations at sensor locations along the spindle shaft. The generated vibration data from the physics-based model are used to train a recurrent neural network-based (RNN) fault detection algorithm. The RNN learns from the labeled vibration spectra to identify different fault conditions. Bayesian optimization is used to automatically tune the hyperparameters governing the accuracy and efficiency of the learning models during the training process. The RNN classifiers are further fine-tuned using a small set of experimentally collected data for the generalization of the model on real-world data. Once the RNN classifier is trained, it can distinguish between different types of damage and identify their specific locations on the spindle assembly. The proposed algorithms achieved an accuracy of 98.43% on experimental data sets that were not used in training the network.

Acceleration model considering multi‐stress coupling effect and reliability modeling method based on nonlinear Wiener process

AbstractEstablishing an accurate accelerated degradation model is paramount for ensuring precise reliability evaluation results. Unfortunately, current accelerated degradation tests often lack test groups for investigating multi‐stress coupled phenomena. Consequently, existing multi‐stress accelerated models fail to adequately consider the impact of stress coupling when data with stress coupling information is absent. This limitation leads to the development of inaccurate models, ultimately affecting the precision of reliability assessment. To address this challenge, this paper introduces a new modeling method for multi‐stress accelerated degradation models that takes into account stress coupling effects. The proposed modeling method aims to improve the accuracy of reliability assessment under multi‐stress conditions. In the proposed model, the main effect function of stress is determined based on existing single‐stress accelerated models. The coupling effect is first examined through the Multivariate Analysis of Variance (MANOVA), and then the functional form of the coupling effect function is determined from the given candidate functions through correlation analysis. Next, the coupling effect is incorporated into a Wiener process to establish a multi‐stress accelerated degradation model, and the two‐step estimation method combining Least Squares Method (LSM) and Differential Evolution Algorithm (DEA) is proposed. The accuracy and effectiveness of the coupling effect test method, model establishment, and parameter estimation method were validated using two Monte Carlo simulation experimental data sets. Finally, the superiority of the proposed model is demonstrated through examples, providing feasible ideas and technical support for the research on multi‐stress accelerated degradation modeling considering stress coupling.

Similarity indicator and CG-CGAN prediction model for remaining useful life of rolling bearings

To tackle the challenges of performing early fault warning and improving the prediction accuracy for the remaining useful life (RUL) of rolling bearings, this paper proposes a similarity health indicator and a predictive model of CG-conditional generative adversarial network (CGAN), which relies on a CGAN that combines one-dimensional convolutional neural network (CNN) with a bidirectional gate recurrent unit (Bi-GRU). This framework provides a comprehensive theoretical foundation for RUL prediction of rolling bearings. The similarity health indicator allows for early fault warning of rolling bearings without expert knowledge. Within the CGAN framework, the inclusion of constraints guides the generation of samples in a more targeted manner. Additionally, the proposed CG-CGAN model incorporates Bi-GRU to consider both forward and backward information, thus improving the precision of RUL forecasting. Firstly, the similarity indicator between the vibration signals of the rolling bearing over its full life span and the standard vibration signals (healthy status) is calculated. This indicator helps to determine the early deterioration points of the rolling bearings. Secondly, the feature matrix composed of traditional health indicators and similarity health indicator, is utilized to train and test the proposed CG-CGAN model for RUL prediction. Finally, to corroborate the efficacy of the proposed method, two sets of real experiment data of rolling bearing accelerated life from the Intelligent Maintenance Systems (IMS) are utilized. Experimental findings substantiate that the proposed similarity health indicator offers early fault alerts and precisely delineates the performance diminution of the rolling bearing. Furthermore, the put-forward CG-CGAN model achieves high-precision RUL prediction of rolling bearing.

A seven-step guide to spatial, agent-based modelling of tumour evolution.

Spatial agent-based models are frequently used to investigate the evolution of solid tumours subject to localized cell-cell interactions and microenvironmental heterogeneity. As spatial genomic, transcriptomic and proteomic technologies gain traction, spatial computational models are predicted to become ever more necessary for making sense of complex clinical and experimental data sets, for predicting clinical outcomes, and for optimizing treatment strategies. Here we present a non-technical step by step guide to developing such a model from first principles. Stressing the importance of tailoring the model structure to that of the biological system, we describe methods of increasing complexity, from the basic Eden growth model up to off-lattice simulations with diffusible factors. We examine choices that unavoidably arise in model design, such as implementation, parameterization, visualization and reproducibility. Each topic is illustrated with examples drawn from recent research studies and state of the art modelling platforms. We emphasize the benefits of simpler models that aim to match the complexity of the phenomena of interest, rather than that of the entire biological system. Our guide is aimed at both aspiring modellers and other biologists and oncologists who wish to understand the assumptions and limitations of the models on which major cancer studies now so often depend.

HyperFusion: Combating Phishing Attacks through URL, Hyperlink, and User Behavior Analysis

Abstract: Phishing attacks remain a persistent threat in cyberspace, demanding innovative solutions for timely detection. In this paper, HyperFusion, a pioneering approach that combines machine learning with user behavior analysis, is introduced to enhance the real-time identification of phishing websites. By integrating features derived from URL and hyperlink characteristics with dynamic user interaction patterns, HyperFusion achieves unprecedented accuracy without reliance on thirdparty systems. Traditional anti-phishing methods often falter against zero-hour attacks or novel phishing websites, underscoring the need for novel strategies. The methodology addresses this challenge by leveraging user behavior data alongside client-side information, thereby eliminating external dependencies and fortifying defense mechanisms. Additionally, a tailored dataset for rigorous experimentation is presented, facilitating a comprehensive evaluation of the approach. Experimental results demonstrate HyperFusion's superior efficacy, boasting a remarkable detection accuracy of 99.37% with the XG Boost technique.

An enhanced fatigue damage model based on Weibull strength distribution

AbstractConsidering the inherent characteristics of strength degradation, a residual strength model is improved reasonably through the incorporation of the two‐parameter Weibull distribution. Building upon this foundation, an enhanced nonlinear fatigue damage accumulation model that is closer to the actual situation is proposed in view of the randomness of fatigue damage. The model takes into account the effect of load action on fatigue damage. To ascertain the feasibility of the proposed model, the time‐varying reliability of the model is evaluated by applying the probability density evolution method. The model's precision in predicting residual life is corroborated through five experimental data sets under bilevel and multilevel loading conditions. The results demonstrate that the proposed model exhibits excellent applicability and remarkable precision, thereby confirming its potential value in practical applications.

Video anomaly detection: A systematic review of issues and prospects

The increase in the deployment of surveillance camera in outdoor and indoor settings have resulted in a growing demand for intelligent systems that can accurately detect and recognize human actions as well as other entities of interest within the captured video data. Although, human action recognition is a well-established topic in computer vision, abnormal behaviour detection has recently received increased research attention. Several abnormal behaviour detection systems have been proposed over the years to ensure human safety. However, only a few comprehensive and systematic reviews report on the current state and future direction of video anomaly detection(VAD) research. This present effort aims to contribute a systematic and detailed review of current research and advances in the detection of anomalous actions and entities in videos. The review focuses on studies published between 2003 and 2023. During the literature selection process, 530 scholarly articles were identified and evaluated to showcase prevalent research trends, techniques, datasets, and frameworks within the realm of VAD This review aims to offer a comprehensive understanding of key areas of focus among researchers, provide resources for commonly used public datasets for evaluation and experimentation, and examine advancements and integration of network design to accommodate the needs for handling multimedia information. To sum up, this study highlights various potential opportunities and obstacles about the VAD domain.VAD has gained significant interest in recent years due to its potential applications in various domains such as security, surveillance, traffic monitoring, medical imaging among others. Areas of further research include, Anomaly detection in real-time, multi-camera anomaly detection, privacy preserving in VAD to mention a few. The study revealed some key trends and challenges in VAD that can guide future research direction.

Scholarly publications and data set evidence for the Human Reference Atlas

Abstract Experts from 17 consortia are collaborating on the Human Reference Atlas (HRA) which aims to map the human body at single cell resolution. To bridge across scales—from the meter size human body to the micrometer size single-cell level—organ experts are constructing anatomical structures, cell types plus biomarkers (ASCT+B) tables, and associated spatial reference objects. The 3rd HRA (v1.2) release features 26 organ-specific ASCT+B tables that cite 456 scholarly papers and are linked to 61 spatial reference objects and Organ Mapping Antibody Panels (OMAPs); it is authored by more than 120 experts. This paper presents the first analyses and visualizations showcasing what data and scholarly evidence exist for which organs and how experts relate to the organs covered in the HRA. To identify potential HRA authors and reviewers, we queried the Web of Science database for authors who work on the 33 organs targeted for the next HRA release (v1.3). To provide scientific evidence for the HRA, we identified 620 high-quality, single-cell experimental data sets for 58 organs published in 561 unique papers. The results presented are critical for understanding and communicating the quality of the HRA, planning for future tissue data collection, and inviting leading experts to contribute to the evolving atlas.

A structural description of pedestrian movement behavior in multiple surveillance videos

This study purpose of recording pedestrians' activities in all surveillance videos and then convert these records into structured descriptions to analyze a large number of surveillance videos. This paper proposes a four-stage processing method. The first stage uses convolutional neural network (CNN) to detect objects in the surveillance video and classify them. The second stage finds the correlation of pedestrians in continuous images and describes multiple objects' trajectories in the surveillance video. The third stage calculates the similarity of the pedestrian object sets of the surveillance video at different times as the basis for pedestrian re-identification. In the fourth stage, extract the walking path of the pedestrian in each surveillance video. Each pedestrian's actions in each surveillance video, including whether to stand, walking path, direction, clothing color and walking time. To construct a correlation table of pedestrian motion in multiple surveillance videos.This experiment uses two image datasets for experimentation and analysis to evaluate this method's effectiveness for pedestrian movement in multiple surveillance videos. The image datasets are the Multi-Camera Object Tracking (MCT) Challenge and PETS 2009 Benchmark Data. This experiment includes pedestrian detection, pedestrian identification, and multi-pedestrian tracking of pedestrians in single and multiple surveillance cameras. The results show that in the pedestrian detection experiment, the convolutional neural network can effectively solve the problems caused by static objects, light changes, similar colors, crowded pedestrians, and incomplete objects. In the pedestrian recognition and re-recognition experiments, the accuracy is reduced due to the influence of low-resolution surveillance videos. This detection method of the pedestrian is more accurate than traditional dynamic object detection methods. After the pedestrian tracking and re-identification are completed, the pedestrian relationships in multiple surveillance cameras are integrated, and a pedestrian behavior association table is built for surveillance videos.

Diagnosis of brain disease based on the deep learning algorithms of neural imaging techniques

Neuroimaging technology is considered a non-invasive method research the structure and function of the brain which have been widely used in neuroscience, psychiatry, psychology, and other fields. The development of Deep Learning Neural Network (DLNN), based on the deep learning algorithms of neural imaging techniques in brain disease diagnosis plays a more and more important role. In this paper, a deep neural network imaging technology based on Stack Auto-Encoder (SAE) feature extraction is constructed, and then Support Vector Machine (SVM) was used to solve binary classification problems (Alzheimer’s disease [AD] and Mild Cognitive Impairment [MCI]). Four sets of experimental data were employed to perform the training and testing stages of DLNN. The number of neurons in each of the DLNNs was determined using the grid search technique. Overall, the results of DLNNs performance indicated that the SAE feature extraction was superior over (Accuracy Rate [AR] = 74.9% with structure of 93-171-49-22-93) shallow layer features extraction (AR = 70.8% with structure of 93-22-93) and primary features extraction (AR = 69.2%).

Liquid-liquid equilibria and the interfacial properties of the ternary mixtures water + n-propanol + n-butyl acetate and water + n-butanol + n-butyl acetate

In this work, a modified version of the Density Gradient Theory is used to predict the interfacial properties of two industrially relevant systems consisting of water + n-propanol + n-butyl acetate and water + n-butanol + n-butyl acetate and their demixing binary subsystems water + n-butanol and water + n-butyl acetate. Liquid-liquid equilibrium is calculated by applying the NRTL model. Through the combination of the NRTL model with the modified version of the Density Gradient Theory, the interfacial tension and the interfacial concentration profiles of the considered ternary mixtures are predicted based on the influence parameters of the binary demixed subsystems. It can be deduced that the predictions of the interfacial tension of both investigated ternary systems are trusted and experimental data sets from this work are in excellent agreement with the predictions. Phenomena like the selective enrichment of the solubilizing component in the interface are observed.

Polyester Nanoparticles with Controlled Topography for Peroral Drug Delivery Using Insulin as a Model Protein.

Receptor-mediated polyester drug delivery systems have tremendous potential for improving the clinical performance of existing pharmaceutical drugs. Despite significant progress made in this area, it remains unclear how and to what extent the polyester nanoparticle surface topography would affect the in vitro, ex vivo and in vivo performance of a drug, and if there exists a correlation between in vitro and in vivo, as well as healthy versus pathophysiological states. Herein, we report a systematic investigation of the interactions between ligands and receptors as a function of the linker length, two-carbon (2C) versus four-carbon (4C). The in vitro, ex vivo and in vivo in healthy models validate the hypothesis that 4C has better reach and binding to the receptors. The results indicate that 4C offered better performance over 2C in vivo in improving the oral bioavailability of insulin (INS) by 1.1-fold (3.5-fold compared to unfunctionalized nanoparticles) in a healthy rat model. Similar observations were made in pathophysiological models; however, the effects were less prominent compared to those in healthy models. Throughout, ligand decorated nanoparticles outperformed unfunctionalized nanoparticles. Finally, a semimechanistic pharmacokinetic and pharmacodynamic (PKPD) model was developed using the experimental data sets to quantitatively evaluate the effect of P2Ns-GA on oral bioavailability and efficacy of insulin. The study presents a sophisticated oral delivery system for INS or hydrophilic therapeutic cargo, highlighting the significant impact on bioavailability that minor adjustments to the surface chemistry can have.

Cyber security analysis of connected vehicles

AbstractThe sensor‐enabled in‐vehicle communication and infrastructure‐centric vehicle‐to‐everything (V2X) communications have significantly contributed to the spark in the amount of data exchange in the connected and autonomous vehicles (CAV) environment. The growing vehicular communications pose a potential cyber security risk considering online vehicle hijacking. Therefore, there is a critical need to prioritize the cyber security issues in the CAV research theme. In this context, this paper presents a cyber security analysis of connected vehicle traffic environments (CyACV). Specifically, potential cyber security attacks in CAV are critically investigated and validated via experimental data sets. Trust in V2X communication for connected vehicles is explored in detail focusing on trust computation and trust management approaches and related challenges. A wide range of trust‐based cyber security solutions for CAV have been critically investigated considering their strengths and weaknesses. Open research directions have been highlighted as potential new research themes in CAV cyber security area.

Functional Analysis of English Carriers and Related Resources of Cultural Communication in Internet Media

Abstract In the Internet intelligent teaching platform, students’ demand for English cultural content is increasingly obvious. To help students quickly locate the overall content of resources in online autonomous learning, this study constructs a video annotation model for online teaching. This method classifies text by designing an optimized Bidirectional Encoder Representation from the Transformers model and designs a Text Rank keyword extraction model that integrates external knowledge and semantic feature weights. The extraction of knowledge points contained in audio and video resources can be realized. In the experimental data set, a relatively complete video content summary could be obtained by combining the first three sentences with the last two sentences. The F1 value of the classification model was up to 91.3%. In addition, the BERT-T model proposed in this article had the best effect on the experiment. Compared with the original BERT model, the macro-F1 was 0.8% higher and 0.5% higher than the Ro BERTA model. In the keyword extraction experiment, B-Text Rank was 2.19 and 2.85% higher than the traditional Text Rank in the two datasets. The experiment shows that the BERT-Text Rank network resource annotation model has excellent application performance in English online autonomous teaching and could guide students to learn.

Semi-confined blast loading: experiments and simulations of internal detonations

AbstractFar-field blast loading has been studied extensively for decades. Close-in, confined, and semi-confined detonations less so, partly because it is difficult to obtain good experimental data. The increase in computational power in recent years has made it possible to conduct studies of this kind numerically, but the results of such simulations ultimately depend on experimental validation and verification. This work thus aims at using reliable experiments to validate and verify numerical models developed to represent blast loading in general. Test rigs consisting of massive steel cylinders with pressure sensors were used to measure the pressure profiles of semi-confined detonations with different charge sizes. The experimental data set was then used to assess numerical models appropriate for simulating blast loading. In general, the numerical results were in excellent agreement with the experimental data, in both qualitative and quantitative terms. These results may in turn be used to analyse structures exposed to internal blast loads, which constitutes the next phase of this research project.

Accurate Experimental Structure of 1-Chloronaphthalene.

The structure of isolated 1-chloronaphthalene has been investigated in a supersonic expansion by high-resolution chirped-pulse Fourier transform microwave (CP-FTMW) spectroscopy in the 2-8 GHz frequency range. Accurate values of the rotational, centrifugal distortion, and nuclear quadrupole coupling constants for the only availabe conformer have been determined. The intensity of the spectrum allowed us to observe all the heavy atoms isotopologues in natural abundance, determining their rotational constants. From the extensive experimental dataset we derived accurate structures for 1-chloronaphthalene using different methodologies and compared with related compounds.

Elucidation of Spatial Positioning of Ribosomes around Chromosome in Escherichia coli Cytoplasm via a Data-Informed Polymer-Based Model.

The spatial arrangement of ribosomes and chromosome in Escherichia coli's cytoplasm challenges conventional wisdom. Contrary to the notion of ribosomes acting as inert crowders to the chromosome in the cytoplasm, here we propose a nuanced view by integrating a wide array of experimental data sets into a polymer-based computer model. A set of data-informed computer simulations determines that a delicate balance of attractive and repulsive interactions between ribosomes and the chromosome is required in order to reproduce experimentally obtained linear densities and brings forth the view that ribosomes are not mere inert crowders in the cytoplasm. The model finds that the ribosomes represent themselves as a poor solvent for the chromosome with a 50 nm mesh size, consistent with previous experimental analysis. Our multidimensional analysis of ribosome distribution, both free (30S and 50S) and bound (70S polysome), uncovers a relatively less pronounced segregation pattern than previously thought. Notably, we identify a ribosome-rich central region within the innermost core of the nucleoid. Moreover, our exploration of the chromosome mesh size and the conformation of bound ribosomes suggests that these ribosomes maintain elongated shapes, enabling them to navigate through the chromosome mesh and access the central core. This dynamic localization challenges the static segregation model and underscores the pivotal role of ribosome-chromosome interactions in cellular media.