Practical Applications Research Articles

Performance and Phase Change Kinetic Investigations on Capric-Myristic Acid Eutectic Mixtures for Energy-Saving Construction

In the present study, capric acid and myrisic acid were first selected to prepare a capric acid-myrisic acid eutectic mixture using a compounding method. Then, the thermal properties, structural stability and weight loss of capric acid-myrisic acid eutectic mixtures were analyzed using differential scanning calorimetry, Fourier transform infrared and thermogravimetry. Finally, the activation energy and reaction order of the capric acid-myrisic acid eutectic mixture during solid-liquid transformation were calculated using the phase change kinetic methods of Kissinger and Ozawa. The results indicate that capric acid-myrisic acid eutectic mixtures have good thermal cycle stability and stable energy storage in practical applications. The activation energies of capric acid-myrisic acid eutectic mixtures were calculated using the phase change kinetic methods of Kissinger and Ozawa, with values of 345.6 KJ/mol and 333.3 KJ/mol, respectively, indicating that both phase change kinetic methods have good accuracy.

Fluorescence-Based Lateral Flow Immunoassay for Quantification of Infliximab: Analytical and Clinical Performance Evaluation.

Therapeutic drug monitoring of infliximab (IFX) can improve treatment outcomes; however, the temporal gap between drug concentration monitoring and subsequent availability restricts its practical application. To address this issue, an automated monitoring method, AFIAS IFX, was developed to rapidly and accurately analyze IFX concentration in blood. The analytical and clinical performances of this method were assessed to establish its clinical utility. The analytical performance of AFIAS IFX was evaluated according to Clinical and Laboratory Standard Institute guidelines. For clinical validation, AFIAS IFX was compared with 3 established enzyme-linked immunosorbent assay kits (LISA TRACKER, RIDASCREEN, and ImmunoGuide) using 100 consecutive samples from 28 patients treated with IFX. Passing-Bablok regression and Bland-Altman analyses were performed to compare the methods. The detection and quantification limits of AFIAS IFX were 0.12 and 0.20 mcg/mL, respectively. Furthermore, AFIAS IFX analyzed samples within 10 minutes for concentrations up to 50 mcg/mL, exhibiting reproducibility (coefficient of variation [CV] ≤7.8%) and accuracy (recovery 98%-101%) with serum, plasma, and whole blood samples. Clinically, it exhibited a good correlation with the 3 established enzyme-linked immunosorbent assay kits. For patients treated with Remicade (IFX), the Passing-Bablok regression slope was 1.001-1.259, with a mean difference of -1.48 to 0.28 mcg/mL. For patients treated with CT-P13, the Passing-Bablok regression slope was 0.974-1.254, with a mean difference of -2.44 to 0.15 mcg/mL. AFIAS IFX, a novel fluorescence-based lateral flow assay, exhibited excellent performance in analyzing IFX trough levels and is a potentially powerful tool for therapeutic drug monitoring in clinical settings, with opportunities for further development.

Supporting transdisciplinary STEAM practices: Integrating architectural modelling into mathematics education through a cross-cultural dynamic lesson plan (DLP) tool

This paper proposes a cross-cultural dynamic lesson plan (DLP) tool that could be adopted by teachers across different cultures to implement STEAM (Science, Technology, Engineering, Arts & Mathematics) education practices. An abundance of well-developed lesson plans is available to teachers on numerous Internet sites, but the cultural diversity of such lesson plans – i.e., difficulty to customize for alignment with local curriculum and lesson plan requirements, make it difficult for teachers to utilize and share these resources – due to the lack of flexible customizations for orientation with local curriculum and lesson plan requirements. This study suggests an approach to overcome such challenges by presenting the DLP tool to assist teachers’ STEAM teaching practices using GeoGebra for learning content elaboration. Our proposed transdisciplinary STEAM practice uses architecture as a real-life example that connects mathematics learning to culture and history through mathematical modelling. This study follows a design-based research approach to develop and implement DLP tool and its related design heuristic. Moreover, the study is examining how the DLP tool can be utilized with teachers cross-culturally and how it could support teachers in professional development workshops to design transdisciplinary STEAM lesson plans. The qualitative analysis of teachers’ artefacts developed during these workshops demonstrated the versatility of DLP tool to address cultural diversity in lesson planning through these STEAM practices applications. The emerging themes from this study show that regardless of participants’ cultures, using the DLP tool to implement transdisciplinary STEAM practices could support teachers in developing operational and shareable lesson plans.

AutoDLAR: A Semi-supervised Cross-modal Contact-free Human Activity Recognition System

WiFi-based human activity recognition (HAR) plays an essential role in various applications such as security surveillance, health monitoring, and smart home. Existing HAR methods, though yielding promising performance in indoor scenarios, highly depend on a massive labeled dataset for training which is extremely difficult to acquire in practical applications. In this paper, we present an automatic data labeling and HAR system, termed AutoDLAR. Taking a semi-supervised cross-modal learning framework with a hybrid loss function as the core, AutoDLAR transfers rich visual information to automatically label WiFi signals for WiFi-based HAR. Specifically, we devise a lightweight and multi-view WiFi sensing model with a parallel feature embedding method to accurately identify activities and accelerate recognition speed. Then, we exploit the video data to fine-tune a well-established visual HAR model, generating effective pseudo-labels for guiding the WiFi model’s training. We also build a synchronized Video-WiFi dataset with seven types of human activities under different scenarios to enable training and validating the semi-supervised HAR system. Extensive experiments on our collected activity dataset and the emotion recognition benchmark demonstrate that AutoDLAR attains an average accuracy of over 95.89% without manual labeling and only spends the inference time of 3.35 ms, outperforming the state-of-the-art (SOTA) methods.

OPTIMIZATION OF CNN + MOBILENETV3 FOR INSECT IDENTIFICATION: TOWARD HIGH ACCURACY

Developments in the field of artificial intelligence and deep learning, particularly Convolutional Neural Networks (CNN) techniques, have expanded the research potential and applications in ecology, including efficient and accurate insect classification. However, there are challenges in achieving high levels of accuracy with similar computational efficiency. In response, the efficient MobileNetV3 architecture was investigated to improve the insect pest classification process. Through an analytical descriptive quantitative approach and insect datasets from Kaggle, this study tested the effectiveness of CNN models optimized with MobileNetV3. The results indicated that the optimized model achieved classification accuracy of up to 90%, with consistent performance between training and validation data and significant loss reduction. With high precision and processing efficiency, this discovery makes a substantial contribution to deep learning applications in the field of intelligent agriculture, promising methodological improvements for other classification problems. Despite offering a promising solution, this study recognizes the limitation in dataset diversity and suggests further exploration with more varied datasets to strengthen the model's application in actual agricultural practices.

Research on fault detection and remote monitoring system of variable speed constant frequency wind turbine based on Internet of Things

In order to study the operating characteristics of variable speed constant frequency wind turbine under different working conditions and the monitoring system of wind turbine. In this paper, the simulation model of each component system of wind turbine is established by MATLAB/Simulink module, and the influence law of different wind speed and ground fault types on the output power of wind turbine is studied. The active power of wind turbines under different short-circuit fault types is compared. At the same time, in order to realize real-time monitoring of wind turbine speed and output power, an online monitoring system for wind turbine operation based on industrial Internet of Things is proposed, and the composition and operation characteristics of this remote monitoring system are given. The practical application shows that the on-line monitoring system can accurately and remotely monitor the running status of the wind turbine and avoid the unstable running of the wind turbine. The research conclusions of this paper can provide reference for the design and construction of wind turbines and the operation of connecting to the power grid.

Underground Diagnosis Based on GPR and Learning in the Model Space.

Ground Penetrating Radar (GPR) has been widely used in pipeline detection and underground diagnosis. In practical applications, the characteristics of the GPR data of the detected area and the likely underground anomalous structures could be rarely acknowledged before fully analyzing the obtained GPR data, causing challenges to identify the underground structures or anomalies automatically. In this article, a GPR B-scan image diagnosis method based on learning in the model space is proposed. The idea of learning in the model space is to use models fitted on parts of data as more stable and parsimonious representations of the data. For the GPR image, 2-Direction Echo State Network (2D-ESN) is proposed to fit the image segments through the next item prediction. By building the connections between the points on the image in both the horizontal and vertical directions, the 2D-ESN regards the GPR image segment as a whole and could effectively capture the dynamic characteristics of the GPR image. And then, semi-supervised and supervised learning methods could be further implemented on the 2D-ESN models for underground diagnosis. Experiments on real-world datasets are conducted, and the results demonstrate the effectiveness of the proposed model.

Adaptability and crack resistance effect of continuous fiber anti-crack composite sealing layer

Traditional anti-crack seals are greatly affected by temperature effects and lack adaptability, making them vulnerable under extreme conditions. Therefore, a novel composite sealing layer material for crack prevention was proposed. This material combines fibers with a fine anti-slip wear layer, which is placed on top of the crushed stone layer. Furthermore, the XFEM was utilized to analyze the adaptability and anti-crack effectiveness of the continuous fiber anti-crack composite sealing layer. The conducted experiments revealed that as the cracks in the base pavement widened from 1.0 mm to 2.0 mm, there was an observed increase of 11.4% in stress intensity factor. A variation in the stress intensity factor of 6.3% was observed within the specified range of crack width when the modulus of the continuous fiber anti-crack composite sealing layer was fixed at 300 kPa. These results are of vital importance in comprehending the performance of this material in terms of crack resistance adaptability and its influence on practical applications.

SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-Hoc Interpretable Fault Diagnosis With Limited Fault Samples.

In real industrial processes, fault diagnosis methods are required to learn from limited fault samples since the procedures are mainly under normal conditions and the faults rarely occur. Although attention mechanisms have become increasingly popular for the task of fault diagnosis, the existing attention-based methods are still unsatisfying for the above practical applications. First, pure attention-based architectures like transformers need a substantial quantity of fault samples to offset the lack of inductive biases thus performing poorly under limited fault samples. Moreover, the poor fault classification dilemma further leads to the failure of the existing attention-based methods to identify the root causes. To develop a solution to the aforementioned problems, we innovatively propose a supervised contrastive convolutional attention mechanism (SCCAM) with ante-hoc interpretability, which solves the root cause analysis problem under limited fault samples for the first time. First, accurate classification results are obtained under limited fault samples. More specifically, we integrate the convolutional neural network (CNN) with attention mechanisms to provide strong intrinsic inductive biases of locality and spatial invariance, thereby strengthening the representational power under limited fault samples. In addition, we ulteriorly enhance the classification capability of the SCCAM method under limited fault samples by employing the supervised contrastive learning (SCL) loss. Second, a novel ante-hoc interpretable attention-based architecture is designed to directly obtain the root causes without expert knowledge. The convolutional block attention module (CBAM) is utilized to directly provide feature contributions behind each prediction thus achieving feature-level explanations. The proposed SCCAM method is testified on a continuous stirred tank heater (CSTH) and the Tennessee Eastman (TE) industrial process benchmark. Three common fault diagnosis scenarios are covered, including a balanced scenario for additional verification and two scenarios with limited fault samples (i.e., imbalanced scenario and long-tail scenario). The effectiveness of the presented SCCAM method is evidenced by the comprehensive results that show our method outperforms the state-of-the-art methods in terms of fault classification and root cause analysis.

Meta-Learning With Distributional Similarity Preference for Few-Shot Fault Diagnosis Under Varying Working Conditions.

Few-shot fault diagnosis is a challenging problem for complex engineering systems due to the shortage of enough annotated failure samples. This problem is increased by varying working conditions that are commonly encountered in real-world systems. Meta-learning is a promising strategy to solve this point, open issues remain unresolved in practical applications, such as domain adaptation, domain generalization, etc. This article attempts to improve domain adaptation and generalization by focusing on the distribution-shift robustness of meta-learning from the task generation perspective. In fact, few-shot fault diagnosis under varying working conditions allows to address the distribution shift problem in a natural way. An unsupervised across-tasks meta-learning strategy with distributional similarity preference is proposed, where the core is the distribution-distance-weighting mechanism. Differently from the naive random meta-train task generation strategy used in existing meta-learning methods, the source instances that present a more similar distribution with respect to the target instances gain larger weightings in the task generation. This strategy leads to a meta-task training set that is enough diverse, and at the same time can be easily learned due to the distribution similarity features of the source tasks. The proposed method introduces the concept of maximum mean discrepancy that is applied to derive the distribution distance of the measurements. Moreover, a model-agnostic meta-learning is applied to realize few-shot fault diagnosis under varying working conditions. The proposed solutions are verified and compared by considering two public datasets used for bearing fault diagnosis. The results show that the proposed strategy outperforms different related few-shot fault diagnosis methods under varying working conditions. Moreover, it is thus proved that, meta-learning with distribution similarity feature represents an effective approach for domain adaptation and generalization.

Hybrid Residual Multiexpert Reinforcement Learning for Spatial Scheduling of High-Density Parking Lots.

Industries, such as manufacturing, are accelerating their embrace of the metaverse to achieve higher productivity, especially in complex industrial scheduling. In view of the growing parking challenges in large cities, high-density vehicle spatial scheduling is one of the potential solutions. Stack-based parking lots utilize parking robots to densely park vehicles in the vertical stacks like container stacking, which greatly reduces the aisle area in the parking lot, but requires complex scheduling algorithms to park and take out the vehicles. The existing high-density parking (HDP) scheduling algorithms are mainly heuristic methods, which only contain simple logic and are difficult to utilize information effectively. We propose a hybrid residual multiexpert (HIRE) reinforcement learning (RL) approach, a method for interactive learning in the digital industrial metaverse, which efficiently solves the HDP batch space scheduling problem. In our proposed framework, each heuristic scheduling method is considered as an expert. The neural network trained by RL assigns the expert strategy according to the current parking lot state. Furthermore, to avoid being limited by heuristic expert performance, the proposed hierarchical network framework also sets up a residual output channel. Experiments show that our proposed algorithm outperforms various advanced heuristic methods and the end-to-end RL method in the number of vehicle maneuvers, and has good robustness to the parking lot size and the estimation accuracy of vehicle exit time. We believe that the proposed HIRE RL method can be effectively and conveniently applied to practical application scenarios, which can be regarded as a key step for RL to enter the practical application stage of the industrial metaverse.

A Unified Approach to Coreset Learning.

Coreset of a given dataset and loss function is usually a small weighed set that approximates this loss for every query from a given set of queries. Coresets have shown to be very useful in many applications. However, coresets' construction is done in a problem-dependent manner and it could take years to design and prove the correctness of a coreset for a specific family of queries. This could limit coresets' use in practical applications. Moreover, small coresets provably do not exist for many problems. To address these limitations, we propose a generic, learning-based algorithm for construction of coresets. Our approach offers a new definition of coreset, which is a natural relaxation of the standard definition and aims at approximating the average loss of the original data over the queries. This allows us to use a learning paradigm to compute a small coreset of a given set of inputs with respect to a given loss function using a training set of queries. We derive formal guarantees for the proposed approach. Experimental evaluation on deep networks and classic machine learning problems show that our learned coresets yield comparable or even better results than the existing algorithms with worst case theoretical guarantees (that may be too pessimistic in practice). Furthermore, our approach applied to deep network pruning provides the first coreset for a full deep network, i.e., compresses all the networks at once, and not layer by layer or similar divide-and-conquer methods.

DATA-DRIVEN ASSESSMENT OF SHEAR STRENGTH OF SINUSOIDAL CORRUGATED STEEL WEBS

The construction industry increasingly recognizes the value of steel beams with corrugated webs for their enhanced resistance to buckling, superior shear capacity, and the elimination of the need for web stiffeners. While a wealth of research focuses on beams with trapezoidal corrugated webs, investigations into sinusoidal corrugated webs (SCWs) remain comparatively sparse. This work investigates the variability in the prediction of the various models and assesses the interactions among different influencing parameters affecting the shear strength of sinusoidal corrugated steel web beams. A comprehensive database of 66 experimental data on SCW beams is assembled and rigorously examined to facilitate this analysis. The collected database is used to test the prediction accuracy of the existing design models provided by codes or proposed models in the literature. The Eurocode3 and DASt-Ri. 015 code models are considered in this study. The results of the evaluated prediction models showed considerable scatters, emphasizing the ongoing need for research to enhance the accuracy of these predictive models for better application in construction practices.

DATA-DRIVEN ASSESSMENT OF SHEAR STRENGTH OF SINUSOIDAL CORRUGATED STEEL WEBS

The construction industry increasingly recognizes the value of steel beams with corrugated webs for their enhanced resistance to buckling, superior shear capacity, and the elimination of the need for web stiffeners. While a wealth of research focuses on beams with trapezoidal corrugated webs, investigations into sinusoidal corrugated webs (SCWs) remain comparatively sparse. This work investigates the variability in the prediction of the various models and assesses the interactions among different influencing parameters affecting the shear strength of sinusoidal corrugated steel web beams. A comprehensive database of 66 experimental data on SCW beams is assembled and rigorously examined to facilitate this analysis. The collected database is used to test the prediction accuracy of the existing design models provided by codes or proposed models in the literature. The Eurocode3 and DASt-Ri. 015 code models are considered in this study. The results of the evaluated prediction models showed considerable scatters, emphasizing the ongoing need for research to enhance the accuracy of these predictive models for better application in construction practices.

Oral administration of Bacillus coagulans TQ-35 alleviates allergic responses in OVA-sensitive BALB/c mice

Bacillus coagulans has been extensively studied so far, but there has been a lack of research on its usage in allergy. In this study, we designed to assess the effect of different concentrations of B. coagulans on food allergy in a BALB/c mouse model of ovalbumin (OVA)-induced food allergy and its effect on gut microbes. The assessment of symptoms, specific immunoglobulin E (IgE), T-cell differentiation, and related gene expression levels in sensitized mice by assay indicated that high doses of oral B. coagulans could alleviate allergic symptoms. Treatment with B. coagulans, in the high-dose group, significantly reduced IgE and IgG1 levels and modulated the balance of T helper type 1 cell (Th1) and Th2 and the expression of relevant genes in the spleen. 16S rRNA analysis showed that probiotics improved the structure of the microbiota, in particular by boosting the percentage of Clostridia, Bacteroides vulgatus and Enterococcus faecium, and by increasing the abundance of microbial species, thereby modulating the immune system. Therefore, this study can provide insights into the practical application of B. coagulans doses to alleviate OVA allergy.

Amplitude-Time Dual-View Fused EEG Temporal Feature Learning for Automatic Sleep Staging.

Electroencephalogram (EEG) plays an important role in studying brain function and human cognitive performance, and the recognition of EEG signals is vital to develop an automatic sleep staging system. However, due to the complex nonstationary characteristics and the individual difference between subjects, how to obtain the effective signal features of the EEG for practical application is still a challenging task. In this article, we investigate the EEG feature learning problem and propose a novel temporal feature learning method based on amplitude-time dual-view fusion for automatic sleep staging. First, we explore the feature extraction ability of convolutional neural networks for the EEG signal from the perspective of interpretability and construct two new representation signals for the raw EEG from the views of amplitude and time. Then, we extract the amplitude-time signal features that reflect the transformation between different sleep stages from the obtained representation signals by using conventional 1-D CNNs. Furthermore, a hybrid dilation convolution module is used to learn the long-term temporal dependency features of EEG signals, which can overcome the shortcoming that the small-scale convolution kernel can only learn the local signal variation information. Finally, we conduct attention-based feature fusion for the learned dual-view signal features to further improve sleep staging performance. To evaluate the performance of the proposed method, we test 30-s-epoch EEG signal samples for healthy subjects and subjects with mild sleep disorders. The experimental results from the most commonly used datasets show that the proposed method has better sleep staging performance and has the potential for the development and application of an EEG-based automatic sleep staging system.

Unraveling the Potential of Vinyl Ether as an Ethylene Surrogate in Heteroarene C─H Functionalization via the Spin-Center Shift.

Despite the simplicity and abundance of ethylene, its practical application presents significant hurdles due to its nature as a highly flammable gas. Herein, a strategic use of easily handled vinyl ether is reported as a latent ethylene surrogate achieved via a spin-center shift (SCS) pathway, enabling the successful three-component reaction that bridges heteroarenes and various coupling partners, including sulfinates, thiols, and phosphine oxides. Through a photoredox catalytic process, α-oxy radicals are generated by combining various radicals with phenyl vinyl ether, which are subsequently added to N-heteroarenes. Subsequently, the radical-mediated SCS pathway serves as the driving force for C─O bond cleavage, effectively engaging the phenoxy group as a leaving group. In addition, by broadening the utility of the method, a valuable synthon is provided for efficient C─H vinylation of N-heteroarenes following sulfonyl group elimination. This approach not only enriches the toolbox of synthetic methodology but also provides a more streamlined alternative, circumventing the challenges associated with direct ethylene gas usage. The versatility of the method, particularly evident in late-stage functionalizations of medicinally relevant molecules and peptides, underscores its capability to produce invaluable three-component compounds and vinylated N-heteroarene derivatives.

Predicting Soccer Players' Fitness Status Through a Machine-Learning Approach.

The study had 3 purposes: (1)to develop an index using machine-learning techniques to predict the fitness status of soccer players, (2)to explore the index's validity and its relationship with a submaximal run test (SMFT), and (3)to analyze the impact of weekly training load on the index and SMFT outcomes. The study involved 50 players from an Italian professional soccer club. External and internal loads were collected during training sessions. Various machine-learning algorithms were assessed for their ability to predict heart-rate responses during the training drills based on external load data. The fitness index, calculated as the difference between actual and predicted heart rates, was correlated with SMFT outcomes. Random forest regression (mean absolute error = 3.8 [0.05]) outperformed the other machine-learning algorithms (extreme gradient boosting and linear regression). Average speed, minutes from the start of the training session, and the work:rest ratio were identified as the most important features. The fitness index displayed a very large correlation (r = .70) with SMFT outcomes, with the highest result observed during possession games and physical conditioning exercises. The study revealed that heart-rate responses from SMFT and the fitness index could diverge throughout the season, suggesting different aspects of fitness. This study introduces an "invisible monitoring" approach to assess soccer player fitness in the training environment. The developed fitness index, in conjunction with traditional fitness tests, provides a comprehensive understanding of player readiness. This research paves the way for practical applications in soccer, enabling personalized training adjustments and injury prevention.

Chattering Free Sliding-Mode Controller Design for Underactuated Tower Cranes With Uncertain Disturbance

Tower cranes are widely used in various industrial sites and require a high level of anti-disturbance performance from the crane controller due to the variability of the application environment. Although sliding mode control is a typical anti-disturbance control method, most sliding mode controllers suffer from output chattering and do not reflect well under severe external disturbances. To address these issues from a practical application perspective, a finite time sliding mode control method using the super-twisting algorithm (STA) is proposed for underactuated tower cranes operating in the presence of external disturbances. The proposed method includes a finite-time chattering-free sliding mode controller that eases the output torque chattering problem and a finite-time sliding mode observer based on STA to observe the uncertain disturbances. The observed values are then applied to the controller through feedforward compensation. Experimental results show that the proposed control method effectively weakens output torque chattering, compensates for disturbance quickly, and accurately completes control tasks.

Detecting Line Segments in Motion-Blurred Images With Events.

Making line segment detectors more reliable under motion blurs is one of the most important challenges for practical applications, such as visual SLAM and 3D line mapping. Existing line segment detection methods face severe performance degradation for accurately detecting and locating line segments when motion blur occurs. While event data shows strong complementary characteristics to images for minimal blur and edge awareness at high-temporal resolution, potentially beneficial for reliable line segment recognition. To robustly detect line segments over motion blurs, we propose to leverage the complementary information of images and events. Specifically, we first design a general frame-event feature fusion network to extract and fuse the detailed image textures and low-latency event edges, which consists of a channel-attention-based shallow fusion module and a self-attention-based dual hourglass module. We then utilize the state-of-the-art wireframe parsing networks to detect line segments on the fused feature map. Moreover, due to the lack of line segment detection datasets with pairwise motion-blurred images and events, we contribute two datasets, i.e., synthetic FE-Wireframe and realistic FE-Blurframe, for network training and evaluation. Extensive analyses on the component configurations demonstrate the design effectiveness of our fusion network. When compared to the state-of-the-arts, the proposed approach achieves the highest detection accuracy while maintaining comparable real-time performance. In addition to being robust to motion blur, our method also exhibits superior performance for line detection under high dynamic range scenes.