Experimental Evaluation Research Articles

Experimental evaluation of stress history effect on compressibility characteristics of lime-stabilized expansive soils

ABSTRACT Lime stabilization has long been identified as an effective method for reducing the swelling potential of expansive soils. Despite numerous studies evaluating the volumetric behaviour of lime-stabilized expansive soils, the influence of stress history variations on deformation characteristics of these soils has not been adequately explored. This study specifically evaluates the impact of stress history conditions on compressibility characteristics of lime-stabilized expansive soils, with special attention to the rebound index at different loading stages. In this regard, one-dimensional consolidation-swell tests were conducted on samples containing different lime percentages (3% to 12%) under various stress history conditions. To include different stress history conditions, overconsolidation ratio (OCR), seating pressure, and loading step were varied during the induction of stress history in the samples. Results showed that OCR and seating pressure significantly influenced both the rebound (increased up to 118%) and compression indices (decreased up to 53%), while the loading step had a negligible impact on the compressibility properties of stabilized soils. Additionally, when comparing the relative importance of investigated parameters, it was revealed that, OCR had the highest influence on the rebound index (up to 118.0% increase), while the compression index was most significantly affected by lime percentage (up to 61.4% decrease).

Experimental Evaluation of Mechanical Properties, Thermal Analysis, Morphology, Printability, and Shape Memory Performance of the Novel 3D Printed PETG‐EVA Blends

Experimental Evaluation of Mechanical Properties, Thermal Analysis, Morphology, Printability, and Shape Memory Performance of the Novel 3D Printed PETG‐EVA Blends

Experimental evaluation on pyrolysis and gasification characteristics of simulated waste cation exchange resin

Experimental evaluation on pyrolysis and gasification characteristics of simulated waste cation exchange resin

Semi-TSGAN: Semi-Supervised Learning for Highlight Removal Based on Teacher-Student Generative Adversarial Network

Despite recent notable advancements in highlight image restoration techniques, the dearth of annotated data and the lightweight deployment of highlight removal networks pose significant impediments to further advancements in the field. In this paper, to the best of our knowledge, we first propose a semi-supervised learning paradigm for highlight removal, merging the fusion version of a teacher–student model and a generative adversarial network, featuring a lightweight network architecture. Initially, we establish a dependable repository to house optimal predictions as pseudo ground truth through empirical analyses guided by the most reliable No-Reference Image Quality Assessment (NR-IQA) method. This method serves to assess rigorously the quality of model predictions. Subsequently, addressing concerns regarding confirmation bias, we integrate contrastive regularization into the framework to curtail the risk of overfitting on inaccurate labels. Finally, we introduce a comprehensive feature aggregation module and an extensive attention mechanism within the generative network, considering a balance between network performance and computational efficiency. Our experimental evaluations encompass comprehensive assessments on both full-reference and non-reference highlight benchmarks. The results demonstrate conclusively the substantive quantitative and qualitative enhancements achieved by our proposed algorithm in comparison to state-of-the-art methodologies.

BPA: A Novel Blockchain-Based Privacy-Preserving Authentication Scheme for the Internet of Vehicles

The Internet of Vehicles (IoV) connects an isolated individual on the road to share information, which can improve traffic efficiency. However, the promotion of information sharing brings the critical security issues of identity authentication, followed by privacy protection issues in the authentication process in the IoV. In this study, we designed a blockchain-based conditional privacy-preserving authentication scheme for the IoV (BPA). Our scheme implements zero-knowledge proof (ZKP) to verify the identities of vehicles, which moves the authentication process down to the Roadside Units (RSUs) and achieves decentralized authentication at the edge nodes. Moreover, blockchain technology is utilized to synchronize a consistent ledger across all RSUs for recording and disseminating vehicle authentication states, which enhances the overall authentication process efficiency. We provide a theoretical analysis asserting that the BPA ensures enhanced security and effectively protects the privacy of all participating vehicles. Experimental evaluations confirm that our scheme outperforms existing solutions in terms of the computational and communication overhead.

Experimental Evaluation of Wearable LED Strip and Side-Emitting Fiber for Optical Camera Communications Systems

Experimental Evaluation of Wearable LED Strip and Side-Emitting Fiber for Optical Camera Communications Systems

Analytical and Experimental Evaluation of Two-Layered Unsaturated Sand Bearing Capacity

Pavement design methods based on principles of unsaturated soil mechanics take into account high soil shear strength due to matric suction resulting in more economical design especially in long roads. In this study, the bearing capacity of two-layer unsaturated sand was investigated using both analytical and experimental methods. At first, using the limit equilibrium method an analytical formula was proposed to determine the bearing capacity of two-layer unsaturated sand in which linear suction profile was considered in soil layers. It should be considered that the constant matric suction distribution assumed by the previous researchers doesn’t show the real profile of matric suction within the soil, sometimes resulting in miscalculated unsaturated bearing capacity. Also, the bearing capacity of two-layer unsaturated poorly graded sand was investigated experimentally in different suctions by a special unsaturated chamber apparatus (UCA) designed for this purpose. The results show more than double increase of unsaturated soil bearing capacity with Sr=25% compared to saturated soil. The formation of failure wedges in all experiments was investigated by image processing. An acceptable agreement was obtained between the theoretical and experimental bearing capacity results.

The impact of the world’s first regulatory, multi-setting intervention on sedentary behaviour among children and adolescents (ENERGISE): a natural experiment evaluation

BackgroundRegulatory actions are increasingly used to tackle issues such as excessive alcohol or sugar intake, but such actions to reduce sedentary behaviour remain scarce. World Health Organization (WHO) guidelines on sedentary behaviour call for system-wide policies. The Chinese government introduced the world’s first nation-wide multi-setting regulation on multiple types of sedentary behaviour in children and adolescents in July 2021. This regulation restricts when (and for how long) online gaming businesses can provide access to pupils; the amount of homework teachers can assign to pupils according to their year groups; and when tutoring businesses can provide lessons to pupils. We evaluated the effect of this regulation on sedentary behaviour safeguarding pupils.MethodsWith a natural experiment evaluation design, we used representative surveillance data from 9- to 18-year-old pupils before and after the introduction of the regulation, for longitudinal (n = 7,054, matched individuals, primary analysis) and repeated cross-sectional (n = 99,947, exploratory analysis) analyses. We analysed pre-post differences for self-reported sedentary behaviour outcomes (total sedentary behaviour time, screen viewing time, electronic device use time, homework time, and out-of-campus learning time) using multilevel models, and explored differences by sex, education stage, residency, and baseline weight status.ResultsLongitudinal analyses indicated that pupils had reduced their mean total daily sedentary behaviour time by 13.8% (95% confidence interval [CI]: -15.9 to -11.7%, approximately 46 min) and were 1.20 times as likely to meet international daily screen time recommendations (95% CI: 1.01 to 1.32) one month after the introduction of the regulation compared to the reference group (before its introduction). They were on average 2.79 times as likely to meet the regulatory requirement on homework time (95% CI: 2.47 to 3.14) than the reference group and reduced their daily total screen-viewing time by 6.4% (95% CI: -9.6 to -3.3%, approximately 10 min). The positive effects were more pronounced among high-risk groups (secondary school and urban pupils who generally spend more time in sedentary behaviour) than in low-risk groups (primary school and rural pupils who generally spend less time in sedentary behaviour). The exploratory analyses showed comparable findings.ConclusionsThis regulatory intervention has been effective in reducing total and specific types of sedentary behaviour among Chinese children and adolescents, with the potential to reduce health inequalities. International researchers and policy makers may explore the feasibility and acceptability of implementing regulatory interventions on sedentary behaviour elsewhere.

Trajectory tracking control of parallel manipulator actuated with shape memory wire

This study describes the design of a parallel spatial manipulator with four degrees of freedom actuated with shape memory alloy (SMA) wire to validate the use of SMA in complicated mechatronics systems. The manipulator has a closed kinematic structure, which includes a fixed base and a moving square platform (end effector). The four arms of the manipulator are SMA wires fastened between the fixed base and the end effector. SMA wire-based actuators replace bulky conventional revolute actuators. This work spotlights the development of an actuator model, dimensional analysis, and design of cascade control strategies of various PID and sliding mode controller in their integral and fractional order configurations. Experimental evaluation of the actuator is performed through trajectory tracking to quantify the different controller configurations. The experimental results indicate that the parallel manipulator associated with SMA wire actuators is the best alternative to conventional motion stages for highly precise micro-positioning and tracking applications in the fields of 3D printing, intricate surgical operations, the medical and pharmaceutical industries, and flight and gaming simulators.

Resilient edge predictive analytics by enhancing local models

Abstract In distributed computing environments, the collaboration of nodes for predictive analytics at the network edge plays a crucial role in supporting real-time services. When a node’s service becomes unavailable for various reasons (e.g., service updates, node maintenance, or even node failure), the rest of the available nodes connot efficiently replace its service due to different data and predictive models (e.g., machine learning [ML] models). To address this, we propose decision-making strategies rooted in the statistical signatures of nodes’ data. Specifically, these signatures refer to the unique patterns and behaviors within each node’s data that can be leveraged to predict the suitability of potential surrogate nodes. Recognizing and acting on these statistical nuances ensures a more targeted and efficient response to node failures. Such strategies aim to identify surrogate nodes capable of substituting for failing nodes’ services by building enhanced predictive models. Our resilient framework helps to guide the task requests from failing nodes to the most appropriate surrogate nodes. In this case, the surrogate nodes can use their enhanced models, which can produce equivalent and satisfactory results for the requested tasks. We provide experimental evaluations and comparative assessments with baseline approaches over real datasets. Our results showcase the capability of our framework to maintain the overall performance of predictive analytics under nodes’ failures in edge computing environments.

Securing Internet-of-Medical-Things networks using cancellable ECG recognition

Reinforcement of the Internet of Medical Things (IoMT) network security has become extremely significant as these networks enable both patients and healthcare providers to communicate with each other by exchanging medical signals, data, and vital reports in a safe way. To ensure the safe transmission of sensitive information, robust and secure access mechanisms are paramount. Vulnerabilities in these networks, particularly at the access points, could expose patients to significant risks. Among the possible security measures, biometric authentication is becoming a more feasible choice, with a focus on leveraging regularly-monitored biomedical signals like Electrocardiogram (ECG) signals due to their unique characteristics. A notable challenge within all biometric authentication systems is the risk of losing original biometric traits, if hackers successfully compromise the biometric template storage space. Current research endorses replacement of the original biometrics used in access control with cancellable templates. These are produced using encryption or non-invertible transformation, which improves security by enabling the biometric templates to be changed in case an unwanted access is detected. This study presents a comprehensive framework for ECG-based recognition with cancellable templates. This framework may be used for accessing IoMT networks. An innovative methodology is introduced through non-invertible modification of ECG signals using blind signal separation and lightweight encryption. The basic idea here depends on the assumption that if the ECG signal and an auxiliary audio signal for the same person are subjected to a separation algorithm, the algorithm will yield two uncorrelated components through the minimization of a correlation cost function. Hence, the obtained outputs from the separation algorithm will be distorted versions of the ECG as well as the audio signals. The distorted versions of the ECG signals can be treated with a lightweight encryption stage and used as cancellable templates. Security enhancement is achieved through the utilization of the lightweight encryption stage based on a user-specific pattern and XOR operation, thereby reducing the processing burden associated with conventional encryption methods. The proposed framework efficacy is demonstrated through its application on the ECG-ID and MIT-BIH datasets, yielding promising results. The experimental evaluation reveals an Equal Error Rate (EER) of 0.134 on the ECG-ID dataset and 0.4 on the MIT-BIH dataset, alongside an exceptionally large Area under the Receiver Operating Characteristic curve (AROC) of 99.96% for both datasets. These results underscore the framework potential in securing IoMT networks through cancellable biometrics, offering a hybrid security model that combines the strengths of non-invertible transformations and lightweight encryption.

A Novel Framework on Real Time Chat Interface

In this extensive research paper, we delve deeply into the intricate process of developing a real-time chat interface website utilizing cutting-edge web technologies such as ReactJS, Chat Engine, sockets, hooks, props, JavaScript, and APIs. The primary aim of this project is to cater to the evolving demands for efficient communication and collaboration platforms in the contemporary digital landscape. Through meticulous analysis, detailed experimentation, and critical evaluation, we explore the functionalities, design principles, social relevance, and future implications of the developed application. This research paper serves as a comprehensive guide, providing in-depth insights into the utilization of modern web technologies for constructing interactive and engaging web applications, while also shedding light on the broader societal impacts and implications for the future of digital communication. The advancement of web technologies has facilitated the development of real-time communication platforms that transcend geographical barriers. This research paper delves into the comprehensive development process and technical intricacies involved in creating a real-time chat interface website using modern web technologies, including ReactJS for the frontend, Chat Engine for the backend, and auxiliary technologies such as sockets, hooks, props, JavaScript, and APIs. The project aims to address the growing need for efficient communication and collaboration platforms in today's digital landscape. Through detailed analysis, experimentation, and critical evaluation, this paper explores the functionalities, design principles, social relevance, and future implications of the developed application

H-mrk-means: Enhanced Heuristic mrk-means for Linear Time Clustering of Big Data Using Hybrid Meta-heuristic Algorithm

Big data is generally derived with a large volume and combined categories of attributes like categorical and numerical. Among them, [Formula: see text]-prototypes have been adopted into MapReduce structure, and thus, it provides a better solution for the huge range of data. However, [Formula: see text]-prototypes need to compute all distances among every data point and cluster centres. Moreover, the computations of distances are redundant as data points are often present in similar clusters after fewer iterations. Nowadays, to cluster huge-scale datasets, one of the efficient solutions is [Formula: see text]-means. However, [Formula: see text]-means is not intrinsically appropriate to execute in MapReduce due to the iterative nature of this technique. Moreover, for every iteration, [Formula: see text]-means should perform an independent MapReduce job but, it leads to higher Input/Output (I/O) overhead at every iteration. This research paper presents a novel enhanced linear time clustering for handling big data called Heuristic mrk-means (H-mrk-means) using optimized [Formula: see text]-means on the MapReduce model. In order to manage big data that is time series in nature, the sampling and MapReduce framework are adopted, which utilize different machines for processing data. Before initiating the main clustering process, a sampling process is adopted to get the noteworthy information. The two main phases of the developed method are the map phase (divide and conquer) and the reduce phase (final clustering). In the map phase, the data are divided into diverse chunks that should be stored in assigned machines. In the reduce phase, data clustering is performed. Here, the cluster centroid of data is tuned with the help of hybrid Tunicate-Deer Hunting Optimization (T-DHO) algorithm by attaining a newly derived objective function. This type of optimal tuning of solution enhances the efficiency of clustering when compared over normal iterative [Formula: see text]-means and mrk-means clustering. The experimental evaluation on varied counts of chunks using the proposed H-mrk-means has attained higher quality of clustering results and faster execution times evaluated with other clustering approaches.

Deep Time Series Forecasting Models: A Comprehensive Survey

Deep learning, a crucial technique for achieving artificial intelligence (AI), has been successfully applied in many fields. The gradual application of the latest architectures of deep learning in the field of time series forecasting (TSF), such as Transformers, has shown excellent performance and results compared to traditional statistical methods. These applications are widely present in academia and in our daily lives, covering many areas including forecasting electricity consumption in power systems, meteorological rainfall, traffic flow, quantitative trading, risk control in finance, sales operations and price predictions for commercial companies, and pandemic prediction in the medical field. Deep learning-based TSF tasks stand out as one of the most valuable AI scenarios for research, playing an important role in explaining complex real-world phenomena. However, deep learning models still face challenges: they need to deal with the challenge of large-scale data in the information age, achieve longer forecasting ranges, reduce excessively high computational complexity, etc. Therefore, novel methods and more effective solutions are essential. In this paper, we review the latest developments in deep learning for TSF. We begin by introducing the recent development trends in the field of TSF and then propose a new taxonomy from the perspective of deep neural network models, comprehensively covering articles published over the past five years. We also organize commonly used experimental evaluation metrics and datasets. Finally, we point out current issues with the existing solutions and suggest promising future directions in the field of deep learning combined with TSF. This paper is the most comprehensive review related to TSF in recent years and will provide a detailed index for researchers in this field and those who are just starting out.

Experimental and simulated evaluation of inverse model for shallow underground thermal storage

This study employs an inverse grey-box (IGB) modeling approach, which combines measured data and the physics of systems to predict the performance of shallow underground thermal energy storage (UTES) with top and side insulation. A simplified IGB model of the shallow UTES was developed using thermal network analysis. Experimental studies were conducted for shallow vertical and horizontal UTES configurations. Detailed models representing the field experiments were developed using the TRNSYS simulation tool and calibrated with the measured data from the experimental setup. The 4 Resistance 2 Capacitance (4R2C) IGB model was trained and tested in MATLAB using field experimental data and the data from the calibrated TRNSYS model. In comparing experimental data with the TRNSYS model for UTES, the prediction of outlet water temperature showed good agreement, with root mean square error (RMSE) and coefficient of variation of RMSE (CVRMSE) values of 0.94 °C and 3.16 % for vertical, and 0.99 °C and 2.97 % for horizontal configurations. The IGB model also aligned well with the experimental data, showing CVRMSE of 7.91 % and 3.17 % for vertical and horizontal systems, respectively. Sensitivity analysis revealed that model performance improves with longer training durations and closer testing times to training periods. However, a convergence point of 20 weeks of training data was achieved for making long-term performance predictions.

Diagnostics System for Plant Leaf Diseases Using Convergence of Computer Vision and Deep Learning

Early and accurate detection of plant diseases is crucial for maximizing agricultural yield and minimizing economic losses.Traditional approaches, however, frequently depend on specialized knowledge.They are ineffective for large-scale applications since they take a lot of time.The suggested method makes use of deep learning models' capabilities.With Densenet-121, it automatically extracts and gains knowledge of pertinent aspects from leaf photos.To identify a bundle of leaves, it employs a multi-object deep learning model.It can decipher and comprehend visual data from the leaves using computer vision.Accuracy is increased by utilizing deep learning and computer vision. The technique of transfer learning allows the system to recognize more veggies than just those in the training dataset. The results of our experimental evaluation show that our method is effective in precisely recognizing vegetables and diagnosing plant leaf diseases. Our system provides a scalable solution for agricultural management and plant health monitoring by expanding its coverage to include a variety of vegetables and automating disease diagnosis. KEYWORDS: CNN,Densenet-121,multi-object model

Robustness of embedded fibre optic sensor mesh configurations for monitoring composite structures

Fibre optic sensors are attractive for monitoring of composite structures but the significant risk of damage to fibres where they enter the structure and within the structure itself can make their use unreliable. Mesh configurations of embedded fibre Bragg grating sensors with improved robustness are considered for monitoring composite structures. The configurations of single ended line, bidirectional line, loop, and a novel 2×2 mesh are analysed using a probabilistic model of the sensor system robustness. Numerical evaluation of these configurations has shown that the 2×2 mesh configuration provides the greatest robustness of all of the configurations across all inlet port failure probabilities. The probability of a sensor becoming isolated in a 2×2 mesh is approximately 3 times lower than for the bidirectional line configuration. Using a new matrix approach, the 2×2 mesh configuration is analysed in terms of disaggregation of individual sensor spectra for both fault free and faulty conditions. Simulation and experimental evaluation have demonstrated that successful recovery of individual grating spectra is possible with small spurious signals.

A morphology-based integrated teaching mode combining pathology and radiology with the aid of teaching media: A randomized controlled trial.

The integration of curriculum is an important approach for enhancing medical education and facilitating interdisciplinary connections among students. This study aimed to develop a new morphological integrated teaching mode for undergraduate stomatology education by combining stomatological pathology and radiology courses with instructional media. In total, 63 undergraduates were included in this study and divided into three groups: traditional (Group T; the control group) and two experimental groups: KoPa WiFi EDU (Group K), and KoPa WiFi EDU-cone beam computed tomography (CBCT) (Group K-C). All participants attended a 2-h lecture on periapical cysts and completed the first theoretical test. Subsequently, they underwent a 4-h experimental training session on the pathology and radiology of periapical cysts using different teaching methods. Following the training, participants completed the second theoretical test and underwent the first image-reading skill evaluation. After a 3-month period, participants completed the third theoretical test and underwent the second image-reading skill evaluation. The effectiveness of the teaching methods was assessed by analyzing the differences in theoretical test and experimental skill evaluation scores. There were no significant differences in the first theoretical outcomes among three groups (p>0.05). However, the second theoretical scores, the first objective evaluation scores, and the first subjective evaluation scores were significantly higher in the integrated teaching mode (3D teaching mode with the KoPa WiFi EDU and CBCT: 89.29±4.55, 81.00±8.15, and 61.57±5.52, respectively; 2D teaching mode with the KoPa WiFi EDU system: 80.43±3.41, 73.00±8.01, and 55.67±5.66, respectively) than in the traditional teaching mode (72.57±3.84, 69.38±4.91, and 48.67±5.54, respectively) (p<0.05). Moreover, the long-term teaching effect of the integrated mode was better than that of the traditional mode (p<0.05). The morphology-based integrated teaching mode combining pathology and radiology aroused student enthusiasm for learning, and resulted in enhanced learning outcomes in dental experimental education.

STF-Net: sparsification transformer coding guided network for subcortical brain structure segmentation.

Subcortical brain structure segmentation plays an important role in the diagnosis of neuroimaging and has become the basis of computer-aided diagnosis. Due to the blurred boundaries and complex shapes of subcortical brain structures, labeling these structures by hand becomes a time-consuming and subjective task, greatly limiting their potential for clinical applications. Thus, this paper proposes the sparsification transformer (STF) module for accurate brain structure segmentation. The self-attention mechanism is used to establish global dependencies to efficiently extract the global information of the feature map with low computational complexity. Also, the shallow network is used to compensate for low-level detail information through the localization of convolutional operations to promote the representation capability of the network. In addition, a hybrid residual dilated convolution (HRDC) module is introduced at the bottom layer of the network to extend the receptive field and extract multi-scale contextual information. Meanwhile, the octave convolution edge feature extraction (OCT) module is applied at the skip connections of the network to pay more attention to the edge features of brain structures. The proposed network is trained with ahybrid loss function. The experimental evaluation on two public datasets: IBSR and MALC, shows outstanding performance in terms of objective and subjective quality.

Sludge-based activated carbon experiment design, char properties, and evaluation of methyl orange adsorption

Sludge-based activated carbon experiment design, char properties, and evaluation of methyl orange adsorption