bluuebunny/crossref_metadata_2025 huggingface dataset:
A Performance Comparison of 3D Survey Instruments for Their Application in the Cultural Heritage Field
Authors: Lunghi Irene , Vannini Emma , Dal Fovo Alice , Di Sarno Valentina , Rocco Alessandra , Fontana Raffaella
Published: June 2024

Thanks to the recent development of innovative instruments and software with high accuracy and resolution, 3D modelling provides useful insights in several sectors (from industrial metrology to cultural heritage). Moreover, the 3D reconstruction of objects of artistic interest is becoming mandatory, not only because of the risks to which works of art are increasingly exposed (e.g., wars and climatic disasters) but also because of the leading role that the virtual fruition of art is taking. In this work, we compared the performance of four 3D instruments based on different working principles and techniques (laser micro-profilometry, structured-light topography and the phase-shifting method) by measuring four samples of different sizes, dimensions and surface characteristics. We aimed to assess the capabilities and limitations of these instruments to verify their accuracy and the technical specifications given in the suppliers’ data sheets. To this end, we calculated the point densities and extracted several profiles from the models to evaluate both their lateral (XY) and axial (Z) resolution. A comparison between the nominal resolution values and those calculated on samples representative of cultural artefacts was used to predict the performance of the instruments in real case studies. Overall, the purpose of this comparison is to provide a quantitative assessment of the performance of the instruments that allows for their correct application to works of art according to their specific characteristics.
Refinement and Validation of the Minimal Information Data-Modelling (MID) Method for Bridge Management
Authors: O’Higgins Connor , Hester David , McGetrick Patrick , Ao Wai Kei , Cross Elizabeth J.
Published: June 2024

Various approaches have been proposed for bridge structural health monitoring. One of the earliest approaches proposed was tracking a bridge’s natural frequency over time to look for abnormal shifts in frequency that might indicate a change in stiffness. However, bridge frequencies change naturally as the structure’s temperature changes. Data models can be used to overcome this problem by predicting normal changes to a structure’s natural frequency and comparing it to the historical normal behaviour of the bridge and, therefore, identifying abnormal behaviour. Most of the proposed data modelling work has been from long-span bridges where you generally have large datasets to work with. A more limited body of research has been conducted where there is a sparse amount of data, but even this has only been demonstrated on single bridges. Therefore, the novelty of this work is that it expands on previous work using sparse instrumentation across a network of bridges. The data collected from four in-operation bridges were used to validate data models and test the capabilities of the data models across a range of bridge types/sizes. The MID approach was found to be able to detect an average frequency shift of 0.021 Hz across all of the data models. The significance of this demonstration across different bridge types is the practical utility of these data models to be used across entire bridge networks, enabling accurate and informed decision making in bridge maintenance and management.
Experimental Investigation of Low-Frequency Distributed Acoustic Sensor Responses to Two Parallel Propagating Fractures
Authors: Reid Teresa , Li Gongsheng , Zhu Ding , Hill A. Daniel
Published: June 2024

Low-frequency distributed acoustic sensing (LF-DAS) is a diagnostic tool for hydraulic fracture propagation with far-field monitoring using fiber optic sensors. LF-DAS senses strain rate variation caused by stress field change due to fracture propagation. Fiber optic sensors are installed in the monitoring wells in the vicinity of a fractured well. From the strain responses, fracture propagation can be evaluated. To understand subsurface conditions with multiple propagating fractures, a laboratory-scale hydraulic fracture experiment was performed simulating the LF-DAS response to fracture propagation with embedded distributed optical fiber strain sensors under these conditions. The experiment was performed using a transparent cube of epoxy with two parallel radial initial flaws centered in the cube. Fluid was injected into the sample to generate fractures along the initial flaws. The experiment used distributed high-definition fiber optic strain sensors with tight spatial resolutions. The sensors were embedded at two different locations on opposite sides of the initial flaws, serving as observation/monitoring locations. We also employed finite element modeling to numerically solve the linear elastic equations of equilibrium continuity and stress–strain relationships. The measured strains from the experiment were compared to simulation results from the finite element model. The experimentally derived strain and strain-rate waterfall plots from this study show the responses to both fractures propagating, while the fracture at the lower position took most of the fluid during the experiment. Interestingly, a fracture first began propagating from the upper flaw of the two flaws, but once the lower fracture was initiated, it grew much faster than the upper fracture. Both fibers were intercepted by the lower fracture, further verifying the strain signature as a fracture is approaching and intersecting an offset fiber.
A Process Algebraic Approach to Predict and Control Uncertainty in Smart IoT Systems for Smart Cities Based on Permissible Probabilistic Equivalence
Authors: Song Junsup , Karagiannis Dimitris , Lee Moonkun
Published: June 2024

Process algebra is one of the most suitable formal methods to model smart IoT systems for smart cities. Each IoT in the systems can be modeled as a process in algebra. In addition, the nondeterministic behavior of the systems can be predicted by defining probabilities on the choice operations in some algebra, such as PALOMA and PACSR. However, there are no practical mechanisms in algebra either to measure or control uncertainty caused by the nondeterministic behavior in terms of satisfiability of the system requirements. In our previous research, to overcome the limitation, a new process algebra called dTP-Calculus was presented to verify probabilistically the safety and security requirements of smart IoT systems: the nondeterministic behavior of the systems was defined and controlled by the static and dynamic probabilities. However, the approach required a strong assumption to handle the unsatisfied probabilistic requirements: enforcing an optimally arbitrary level of high-performance probability from the continuous range of the probability domain. In the paper, the assumption from the previous research is eliminated by defining the levels of probability from the discrete domain based on the notion of Permissible Process and System Equivalences so that satisfiability is incrementally enforced by both Permissible Process Enhancement in the process level and Permissible System Enhancement in the system level. In this way, the unsatisfied probabilistic requirements can be incrementally enforced with better-performing probabilities in the discrete steps until the final decision for satisfiability can be made. The SAVE tool suite has been developed on the ADOxx meta-modeling platform to demonstrate the effectiveness of the approach with a smart EMS (emergency medical service) system example, which is one of the most practical examples for smart cities. SAVE showed that the approach is very applicable to specify, analyze, verify, and especially, predict and control uncertainty or risks caused by the nondeterministic behavior of smart IoT systems. The approach based on dTP-Calculus and SAVE may be considered one of the most suitable formal methods and tools to model smart IoT systems for smart cities.
Few-Shot Object Detection in Remote Sensing Images via Data Clearing and Stationary Meta-Learning
Authors: Yang Zijiu , Guan Wenbin , Xiao Luyang , Chen Honggang
Published: June 2024

Nowadays, the focus on few-shot object detection (FSOD) is fueled by limited remote sensing data availability. In view of various challenges posed by remote sensing images (RSIs) and FSOD, we propose a meta-learning-based Balanced Few-Shot Object Detector (B-FSDet), built upon YOLOv9 (GELAN-C version). Firstly, addressing the problem of incompletely annotated objects that potentially breaks the balance of the few-shot principle, we propose a straightforward yet efficient data clearing strategy, which ensures balanced input of each category. Additionally, considering the significant variance fluctuations in output feature vectors from the support set that lead to reduced effectiveness in accurately representing object information for each class, we propose a stationary feature extraction module and corresponding stationary and fast prediction method, forming a stationary meta-learning mode. In the end, in consideration of the issue of minimal inter-class differences in RSIs, we propose inter-class discrimination support loss based on the stationary meta-learning mode to ensure the information provided for each class from the support set is balanced and easier to distinguish. Our proposed detector’s performance is evaluated on the DIOR and NWPU VHR-10.v2 datasets, and comparative analysis with state-of-the-art detectors reveals promising performance.
Urban Mobility Pattern Detection: Development of a Classification Algorithm Based on Machine Learning and GPS
Authors: Molina-Campoverde Juan José , Rivera-Campoverde Néstor , Molina Campoverde Paúl Andrés , Bermeo Naula Andrea Karina
Published: June 2024

This study introduces an innovative algorithm for classifying transportation modes. It categorizes modes such as walking, biking, tram, bus, taxi, and private vehicles based on data collected through sensors embedded in smartphones. The data include date, time, latitude, longitude, altitude, and speed, gathered using a mobile application specifically designed for this project. These data were collected through the smartphone’s GPS to enhance the accuracy of the analysis. The stopping times of each transport mode, as well as the distance traveled and average speed, are analyzed to identify patterns and distinctive features. Conducted in Cuenca, Ecuador, the study aims to develop and validate an algorithm to enhance urban planning. It extracts significant features from mobility patterns, including speed, acceleration, and over-acceleration, and applies longitudinal dynamics to train the classification model. The classification algorithm relies on a decision tree model, achieving a high accuracy of 94.6% in validation and 94.9% in testing, demonstrating the effectiveness of the proposed approach. Additionally, the precision metric of 0.8938 signifies the model’s ability to make correct positive predictions, with nearly 90% of positive instances correctly identified. Furthermore, the recall metric at 0.83084 highlights the model’s capability to identify real positive instances within the dataset, capturing over 80% of positive instances. The calculated F1-score of 0.86117 indicates a harmonious balance between precision and recall, showcasing the models robust and well-rounded performance in classifying transport modes effectively. The study discusses the potential applications of this method in urban planning, transport management, public transport route optimization, and urban traffic monitoring. This research represents a preliminary stage in generating an origin–destination (OD) matrix to better understand how people move within the city.
Real-Time Sensor-Based Human Activity Recognition for eFitness and eHealth Platforms
Authors: Czekaj Łukasz , Kowalewski Mateusz , Domaszewicz Jakub , Kitłowski Robert , Szwoch Mariusz , Duch Włodzisław
Published: June 2024

Human Activity Recognition (HAR) plays an important role in the automation of various tasks related to activity tracking in such areas as healthcare and eldercare (telerehabilitation, telemonitoring), security, ergonomics, entertainment (fitness, sports promotion, human–computer interaction, video games), and intelligent environments. This paper tackles the problem of real-time recognition and repetition counting of 12 types of exercises performed during athletic workouts. Our approach is based on the deep neural network model fed by the signal from a 9-axis motion sensor (IMU) placed on the chest. The model can be run on mobile platforms (iOS, Android). We discuss design requirements for the system and their impact on data collection protocols. We present architecture based on an encoder pretrained with contrastive learning. Compared to end-to-end training, the presented approach significantly improves the developed model’s quality in terms of accuracy (F1 score, MAPE) and robustness (false-positive rate) during background activity. We make the AIDLAB-HAR dataset publicly available to encourage further research.
A Semi-Automatic Magnetic Resonance Imaging Annotation Algorithm Based on Semi-Weakly Supervised Learning
Authors: Chen Shaolong , Zhang Zhiyong
Published: June 2024

The annotation of magnetic resonance imaging (MRI) images plays an important role in deep learning-based MRI segmentation tasks. Semi-automatic annotation algorithms are helpful for improving the efficiency and reducing the difficulty of MRI image annotation. However, the existing semi-automatic annotation algorithms based on deep learning have poor pre-annotation performance in the case of insufficient segmentation labels. In this paper, we propose a semi-automatic MRI annotation algorithm based on semi-weakly supervised learning. In order to achieve a better pre-annotation performance in the case of insufficient segmentation labels, semi-supervised and weakly supervised learning were introduced, and a semi-weakly supervised learning segmentation algorithm based on sparse labels was proposed. In addition, in order to improve the contribution rate of a single segmentation label to the performance of the pre-annotation model, an iterative annotation strategy based on active learning was designed. The experimental results on public MRI datasets show that the proposed algorithm achieved an equivalent pre-annotation performance when the number of segmentation labels was much less than that of the fully supervised learning algorithm, which proves the effectiveness of the proposed algorithm.
Autonomous Navigation by Mobile Robot with Sensor Fusion Based on Deep Reinforcement Learning
Authors: Ou Yang , Cai Yiyi , Sun Youming , Qin Tuanfa
Published: June 2024

In the domain of mobile robot navigation, conventional path-planning algorithms typically rely on predefined rules and prior map information, which exhibit significant limitations when confronting unknown, intricate environments. With the rapid evolution of artificial intelligence technology, deep reinforcement learning (DRL) algorithms have demonstrated considerable effectiveness across various application scenarios. In this investigation, we introduce a self-exploration and navigation approach based on a deep reinforcement learning framework, aimed at resolving the navigation challenges of mobile robots in unfamiliar environments. Firstly, we fuse data from the robot’s onboard lidar sensors and camera and integrate odometer readings with target coordinates to establish the instantaneous state of the decision environment. Subsequently, a deep neural network processes these composite inputs to generate motion control strategies, which are then integrated into the local planning component of the robot’s navigation stack. Finally, we employ an innovative heuristic function capable of synthesizing map information and global objectives to select the optimal local navigation points, thereby guiding the robot progressively toward its global target point. In practical experiments, our methodology demonstrates superior performance compared to similar navigation methods in complex, unknown environments devoid of predefined map information.
Exploring the Impact of the NULL Class on In-the-Wild Human Activity Recognition
Authors: Cherian Josh , Ray Samantha , Taele Paul , Koh Jung In , Hammond Tracy
Published: June 2024

Monitoring activities of daily living (ADLs) plays an important role in measuring and responding to a person’s ability to manage their basic physical needs. Effective recognition systems for monitoring ADLs must successfully recognize naturalistic activities that also realistically occur at infrequent intervals. However, existing systems primarily focus on either recognizing more separable, controlled activity types or are trained on balanced datasets where activities occur more frequently. In our work, we investigate the challenges associated with applying machine learning to an imbalanced dataset collected from a fully in-the-wild environment. This analysis shows that the combination of preprocessing techniques to increase recall and postprocessing techniques to increase precision can result in more desirable models for tasks such as ADL monitoring. In a user-independent evaluation using in-the-wild data, these techniques resulted in a model that achieved an event-based F1-score of over 0.9 for brushing teeth, combing hair, walking, and washing hands. This work tackles fundamental challenges in machine learning that will need to be addressed in order for these systems to be deployed and reliably work in the real world.
Research on Autonomous Vehicle Path Planning Algorithm Based on Improved RRT* Algorithm and Artificial Potential Field Method
Authors: Li Xiang , Li Gang , Bian Zijian
Published: June 2024

For the RRT* algorithm, there are problems such as greater randomness, longer time consumption, more redundant nodes, and inability to perform local obstacle avoidance when encountering unknown obstacles in the path planning process of autonomous vehicles. And the artificial potential field method (APF) applied to autonomous vehicles is prone to problems such as local optimality, unreachable targets, and inapplicability to global scenarios. A fusion algorithm combining the improved RRT* algorithm and the improved artificial potential field method is proposed. First of all, for the RRT* algorithm, the concept of the artificial potential field and probability sampling optimization strategy are introduced, and the adaptive step size is designed according to the road curvature. The path post-processing of the planned global path is carried out to reduce the redundant nodes of the generated path, enhance the purpose of sampling, solve the problem where oscillation may occur when expanding near the target point, reduce the randomness of RRT* node sampling, and improve the efficiency of path generation. Secondly, for the artificial potential field method, by designing obstacle avoidance constraints, adding a road boundary repulsion potential field, and optimizing the repulsion function and safety ellipse, the problem of unreachable targets can be solved, unnecessary steering in the path can be reduced, and the safety of the planned path can be improved. In the face of U-shaped obstacles, virtual gravity points are generated to solve the local minimum problem and improve the passing performance of the obstacles. Finally, the fusion algorithm, which combines the improved RRT* algorithm and the improved artificial potential field method, is designed. The former first plans the global path, extracts the path node as the temporary target point of the latter, guides the vehicle to drive, and avoids local obstacles through the improved artificial potential field method when encountered with unknown obstacles, and then smooths the path planned by the fusion algorithm, making the path satisfy the vehicle kinematic constraints. The simulation results in the different road scenes show that the method proposed in this paper can quickly plan a smooth path that is more stable, more accurate, and suitable for vehicle driving.
A Dual-Stream Cross AGFormer-GPT Network for Traffic Flow Prediction Based on Large-Scale Road Sensor Data
Authors: Sun Yu , Shi Yajing , Jia Kaining , Zhang Zhiyuan , Qin Li
Published: June 2024

Traffic flow prediction can provide important reference data for managers to maintain traffic order, and can also be based on personal travel plans for optimal route selection. On account of the development of sensors and data collection technology, large-scale road network historical data can be effectively used, but their high non-linearity makes it meaningful to establish effective prediction models. In this regard, this paper proposes a dual-stream cross AGFormer-GPT network with prompt engineering for traffic flow prediction, which integrates traffic occupancy and speed as two prompts into traffic flow in the form of cross-attention, and uniquely mines spatial correlation and temporal correlation information through the dual-stream cross structure, effectively combining the advantages of the adaptive graph neural network and large language model to improve prediction accuracy. The experimental results on two PeMS road network data sets have verified that the model has improved by about 1.2% in traffic prediction accuracy under different road networks.
Frequency-Reconfigurable Millimeter-Wave Rectangular Dielectric Resonator Antenna
Authors: Soltan Akrem , Asfour Rawad , Khamas Salam K.
Published: June 2024

This paper introduces an innovative and cost-effective approach for developing a millimeter-wave (mmWave) frequency-reconfigurable dielectric resonator antenna (DRA), which has not been reported before. The antenna integrates two rectangular DRA elements, where each DRA is centrally fed via a slot. A strategically positioned PIN diode is employed to exert control over performance by modulating the ON–OFF states of the diode, thereby simplifying the design process and reducing losses. In the OFF state, the first DRA, RDRA-I, exclusively supports the TE311 resonance mode at 24.3 GHz, offering a 2.66% impedance bandwidth and achieving a maximum broadside gain of 9.2 dBi. Conversely, in the ON state, RDRA-I and RDRA-II concurrently operate in the TE513 resonance mode at 29.3 GHz, providing a 2.7% impedance bandwidth and yielding a high gain of up to 11.8 dBi. Experimental results substantiate that the proposed antenna presents an attractive solution for applications necessitating frequency-reconfigurable and high-performance mmWave antennas in 5G and Beyond 5G (B5G) communication systems.
Real-Time Detection of Unauthorized Unmanned Aerial Vehicles Using SEB-YOLOv8s
Authors: Fang Ao , Feng Song , Liang Bo , Jiang Ji
Published: June 2024

Aiming at real-time detection of UAVs, small UAV targets are easily missed and difficult to detect in complex backgrounds. To maintain high detection performance while reducing memory and computational costs, this paper proposes the SEB-YOLOv8s detection method. Firstly, the YOLOv8 network structure is reconstructed using SPD-Conv to reduce the computational burden and accelerate the processing speed while retaining more shallow features of small targets. Secondly, we design the AttC2f module and replace the C2f module in the backbone of YOLOv8s with it, enhancing the model’s ability to obtain accurate information and enriching the extracted relevant information. Finally, Bi-Level Routing Attention is introduced to optimize the Neck part of the network, reducing the model’s attention to interfering information and filtering it out. The experimental results show that the mAP50 of the proposed method reaches 90.5% and the accuracy reaches 95.9%, which are improvements of 2.2% and 1.9%, respectively, compared with the original model. The mAP50-95 is improved by 2.7%, and the model’s occupied memory size only increases by 2.5 MB, effectively achieving high-accuracy real-time detection with low memory consumption.
Experimental Setup for Evaluating Depth Sensors in Augmented Reality Technologies Used in Medical Devices
Authors: Stadnytskyi Valentyn , Ghammraoui Bahaa
Published: June 2024

This paper presents a fully automated experimental setup tailored for evaluating the effectiveness of augmented and virtual reality technologies in healthcare settings for regulatory purposes, with a focus on the characterization of depth sensors. The setup is constructed as a modular benchtop platform that enables quantitative analysis of depth cameras essential for extended reality technologies in a controlled environment. We detail a design concept and considerations for an experimental configuration aimed at simulating realistic scenarios for head-mounted displays. The system includes an observation platform equipped with a three-degree-of-freedom motorized system and a test object stage. To accurately replicate real-world scenarios, we utilized an array of sensors, including commonly available range-sensing cameras and commercial augmented reality headsets, notably the Intel RealSense L515 LiDAR camera, integrated into the motion control system. The paper elaborates on the system architecture and the automated data collection process. We discuss several evaluation studies performed with this setup, examining factors such as spatial resolution, Z-accuracy, and pixel-to-pixel correlation. These studies provide valuable insights into the precision and reliability of these technologies in simulated healthcare environments.
Universal Image Restoration with Text Prompt Diffusion
Authors: Yu Bing , Fan Zhenghui , Xiang Xue , Chen Jiahui , Huang Dongjin
Published: June 2024

Universal image restoration (UIR) aims to accurately restore images with a variety of unknown degradation types and levels. Existing methods, including both learning-based and prior-based approaches, heavily rely on low-quality image features. However, it is challenging to extract degradation information from diverse low-quality images, which limits model performance. Furthermore, UIR necessitates the recovery of images with diverse and complex types of degradation. Inaccurate estimations further decrease restoration performance, resulting in suboptimal recovery outcomes. To enhance UIR performance, a viable approach is to introduce additional priors. The current UIR methods have problems such as poor enhancement effect and low universality. To address this issue, we propose an effective framework based on a diffusion model (DM) for universal image restoration, dubbed ETDiffIR. Inspired by the remarkable performance of text prompts in the field of image generation, we employ text prompts to improve the restoration of degraded images. This framework utilizes a text prompt corresponding to the low-quality image to assist the diffusion model in restoring the image. Specifically, a novel text–image fusion block is proposed by combining the CLIP text encoder and the DA-CLIP image controller, which integrates text prompt encoding and degradation type encoding into time step encoding. Moreover, to reduce the computational cost of the denoising UNet in the diffusion model, we develop an efficient restoration U-shaped network (ERUNet) to achieve favorable noise prediction performance via depthwise convolution and pointwise convolution. We evaluate the proposed method on image dehazing, deraining, and denoising tasks. The experimental results indicate the superiority of our proposed algorithm.
SRE-YOLOv8: An Improved UAV Object Detection Model Utilizing Swin Transformer and RE-FPN
Authors: Li Jun , Zhang Jiajie , Shao Yanhua , Liu Feng
Published: June 2024

To tackle the intricate challenges associated with the low detection accuracy of images taken by unmanned aerial vehicles (UAVs), arising from the diverse sizes and types of objects coupled with limited feature information, we present the SRE-YOLOv8 as an advanced method. Our method enhances the YOLOv8 object detection algorithm by leveraging the Swin Transformer and a lightweight residual feature pyramid network (RE-FPN) structure. Firstly, we introduce an optimized Swin Transformer module into the backbone network to preserve ample global contextual information during feature extraction and to extract a broader spectrum of features using self-attention mechanisms. Subsequently, we integrate a Residual Feature Augmentation (RFA) module and a lightweight attention mechanism named ECA, thereby transforming the original FPN structure to RE-FPN, intensifying the network’s emphasis on critical features. Additionally, an SOD (small object detection) layer is incorporated to enhance the network’s ability to recognize the spatial information of the model, thus augmenting accuracy in detecting small objects. Finally, we employ a Dynamic Head equipped with multiple attention mechanisms in the object detection head to enhance its performance in identifying low-resolution targets amidst complex backgrounds. Experimental evaluation conducted on the VisDrone2021 dataset reveals a significant advancement, showcasing an impressive 9.2% enhancement over the original YOLOv8 algorithm.
Miniaturizable Chemiluminescence System for ATP Detection in Water
Authors: Capuano Giuseppe E. , Corso Domenico , Farina Roberta , Pezzotti Escobar Gianni , Screpis Giuseppe A. , Coniglio Maria Anna , Libertino Sebania
Published: June 2024

We present the design, fabrication, and testing of a low-cost, miniaturized detection system that utilizes chemiluminescence to measure the presence of adenosine triphosphate (ATP), the energy unit in biological systems, in water samples. The ATP–luciferin chemiluminescent solution was faced to a silicon photomultiplier (SiPM) for highly sensitive real-time detection. This system can detect ATP concentrations as low as 0.2 nM, with a sensitivity of 79.5 A/M. Additionally, it offers rapid response times and can measure the characteristic time required for reactant diffusion and mixing within the reaction volume, determined to be 0.3 ± 0.1 s. This corresponds to a diffusion velocity of approximately 44 ± 14 mm2/s.
Identification of Shield Tunnel Segment Joint Opening Based on Annular Seam Pressure Monitoring
Authors: Xu Hongbin , Liu Qucheng , Li Bingtian , Guo Chuanrui
Published: June 2024

Tunnels for subways and railways are a vital part of urban transportation systems, where shield tunneling using assembled segmental linings is the predominant construction approach. With increasing operation time and varying geological conditions, shield tunnels usually develop defects that compromise both structural integrity and operational safety. One common issue is the separation of segment joints that may cause water/mud penetration and corrosion. Existing inspection strategies can only detect openings after their occurrence, which cannot provide early warnings for predictive maintenance. To address this issue, this work proposes a multi-point seam contact pressure monitoring method for joint opening identification. It first derived the theoretical correlation between contact pressure distribution and segment opening; then, a finite element model was established to explore the stress and deformation responses under combined axial and bending loads. Finally, multi-point piezoelectric film sensors were implemented on a scaled segment model to validate the theoretical and numerical analyses. Results indicate that the multi-point monitoring method can effectively identify opening amounts at the segment joints with an average error of 8.8%, confirming the method’s feasibility. These findings support the use of this monitoring technique for early detection and assessment of joint openings in shield tunnels.
A Compact, Low-Cost, and Low-Power Turbidity Sensor for Continuous In Situ Stormwater Monitoring
Authors: Wang Miao , Shi Baiqian , Catsamas Stephen , Kolotelo Peter , McCarthy David
Published: June 2024

Turbidity stands as a crucial indicator for assessing water quality, and while turbidity sensors exist, their high cost prohibits their extensive use. In this paper, we introduce an innovative turbidity sensor, and it is the first low-cost turbidity sensor that is designed specifically for long-term stormwater in-field monitoring. Its low cost (USD 23.50) enables the implementation of high spatial resolution monitoring schemes. The sensor design is available under open hardware and open-source licences, and the 3D-printed sensor housing is free to modify based on different monitoring purposes and ambient conditions. The sensor was tested both in the laboratory and in the field. By testing the sensor in the lab with standard turbidity solutions, the proposed low-cost turbidity sensor demonstrated a strong linear correlation between a low-cost sensor and a commercial hand-held turbidimeter. In the field, the low-cost sensor measurements were statistically significantly correlated to a standard high-cost commercial turbidity sensor. Biofouling and drifting issues were also analysed after the sensors were deployed in the field for more than 6 months, showing that both biofouling and drift occur during monitoring. Nonetheless, in terms of maintenance requirements, the low-cost sensor exhibited similar needs compared to the GreenSpan sensor.
Splitter-Based Sensors Realized via POFs Coupled by a Micro-Trench Filled with a Molecularly Imprinted Polymer
Authors: Tavoletta Ines , Arcadio Francesco , Renzullo Luca Pasquale , Oliva Giuseppe , Del Prete Domenico , Verolla Debora , Marzano Chiara , Alberti Giancarla , Pesavento Maria , Zeni Luigi , Cennamo Nunzio
Published: June 2024

An optical–chemical sensor based on two modified plastic optical fibers (POFs) and a molecularly imprinted polymer (MIP) is realized and tested for the detection of 2-furaldehyde (2-FAL). The 2-FAL measurement is a scientific topic of great interest in different application fields, such as human health and life status monitoring in power transformers. The proposed sensor is realized by using two POFs as segmented waveguides (SW) coupled through a micro-trench milled between the fibers and then filled with a specific MIP for the 2-FAL detection. The experimental results show that the developed intensity-based sensor system is highly selective and sensitive to 2-FAL detection in aqueous solutions, with a limit of detection of about 0.04 mg L−1. The proposed sensing approach is simple and low-cost, and it shows performance comparable to that of plasmonic MIP-based sensors present in the literature for 2-FAL detection.
Design and Implementation of an Intensive Care Unit Command Center for Medical Data Fusion
Authors: Feng Wen-Sheng , Chen Wei-Cheng , Lin Jiun-Yi , Tseng How-Yang , Chen Chieh-Lung , Chou Ching-Yao , Cho Der-Yang , Lin Yi-Bing
Published: June 2024

The rapid advancements in Artificial Intelligence of Things (AIoT) are pivotal for the healthcare sector, especially as the world approaches an aging society which will be reached by 2050. This paper presents an innovative AIoT-enabled data fusion system implemented at the CMUH Respiratory Intensive Care Unit (RICU) to address the high incidence of medical errors in ICUs, which are among the top three causes of mortality in healthcare facilities. ICU patients are particularly vulnerable to medical errors due to the complexity of their conditions and the critical nature of their care. We introduce a four-layer AIoT architecture designed to manage and deliver both real-time and non-real-time medical data within the CMUH-RICU. Our system demonstrates the capability to handle 22 TB of medical data annually with an average delay of 1.72 ms and a bandwidth of 65.66 Mbps. Additionally, we ensure the uninterrupted operation of the CMUH-RICU with a three-node streaming cluster (called Kafka), provided a failed node is repaired within 9 h, assuming a one-year node lifespan. A case study is presented where the AI application of acute respiratory distress syndrome (ARDS), leveraging our AIoT data fusion approach, significantly improved the medical diagnosis rate from 52.2% to 93.3% and reduced mortality from 56.5% to 39.5%. The results underscore the potential of AIoT in enhancing patient outcomes and operational efficiency in the ICU setting.
Enhanced Antioxidant Extraction from Lonicerae japonicae Flos Based on a Novel Optimization Strategy with Tailored Deep Eutectic Solvents
Authors: Deng Wen-Wen , Sun Bo , Yang Han , Hou Xiao-Jie , Zhang Yong-Jian , Gan Tian-Xiang , Cheng Xin-Yi , Yuan Ao , Dong Xiao-Yang , Zhou Cong-Yu , Deng Ying , Xiao Ya-Qian , Ghiladi Reza , Li Hui , Wang Jun
Published: June 2024

Lonicerae japonicae Flos (LJF) is a natural plant containing abundant antioxidant ingredients. In order to extract more antioxidants from LJF, in this study, a novel strategy was proposed for optimizing the extraction factor level by response surface methodology with a tailored deep eutectic solvent (DES) as the extraction solvent and antioxidant ability as the evaluation index. After optimizing the composition of DES and the extraction condition, the extracts obtained by our proposed method yielded better antioxidant ability (229.1–249.1 μmol TE/g DW) and higher antioxidant contents (34.2–36.5 mg GAE/g DW for total phenolics and 119.6–123.0 mg RE/g DW for total flavonoids) from LJF in 5 min without organic solvent consumption that were significantly superior to the Chinese Pharmacopoeia extraction method. The K-T solvation parameter and a scanning electron microscope were adopted to explore the extraction mechanism, and the results showed that the polarity and damage effect on plant cells of DES were crucial for the extraction of antioxidants. In addition, after combining the HPLC fingerprint and partial least squares model, chlorogenic acid, rutin, and 3,5-O-Dicaffeoylquinic acid were screened as the antioxidant Q-markers of LJF. This work demonstrates that an optimization strategy based on antioxidant ability and tailored DES has the potential to extract more antioxidants from natural plants.
The Acute Effects of Cold Water Immersion and Percussive Massage Therapy on Neuromuscular Properties and Muscle Soreness after Exercise in Young Male Soccer Players
Authors: Buoite Stella Alex , Dragonetti Angelo Michele , Fontanot Simone , Sabot Raffaele , Martini Miriam , Galmonte Alessandra , Canton Gianluca , Deodato Manuela , Murena Luigi
Published: June 2024

Cold water immersion (CWI) and percussive massage therapy (PMT) are commonly used recovery techniques in team sports. In particular, despite its wide use, PMT has been scarcely investigated in the literature, especially regarding neuromuscular measures and in comparison with other techniques. This study aimed to evaluate and compare the acute and short-term effects (24 h) of CWI and PMT on muscle strength, contractile properties, and soreness after exercise. A randomized crossover study was performed on sixteen male soccer players (22 years, 20–27) who participated in three experimental sessions involving a fatiguing protocol consisting of a Yo-Yo Intermittent Endurance Test followed by 3 × 10 squat jumps and a wall sit for 30 s, and 12 min of recovery including CWI (10 °C water), bilateral PMT on the anterior and posterior thigh, or passive resting. Outcomes were assessed immediately after the exercise protocol, after the recovery intervention, and at 24 h. Isometric knee extension (IKE) and flexion (IKF) and tensiomyography (TMG) were assessed. Muscle soreness and fatigue were scored from 0 to 10. PMT increased strength after the treatment (p = 0.004) and at 24 h (p = 0.007), whereas no significant differences were found for the other two recovery modalities. At post-recovery, compared to CON, CWI resulted in a longer TMG contraction time (p = 0.027). No significant differences were found at 24 h. Finally, PMT and CWI enhanced muscle soreness recovery compared to passive rest (F4,60 = 3.095, p = 0.022, pη2 = 0.171). Preliminary results from this study suggest that PMT might improve isometric strength after strenuous exercise, and both PMT and CWI reduce muscle soreness perception, while the effects on TMG parameters remain controversial.
Empowering Sustainable Industrial and Service Systems through AI-Enhanced Cloud Resource Optimization
Authors: Seo Cheongjeong , Yoo Dojin , Lee Yongjun
Published: June 2024

This study focuses on examining the shift of an application system from a traditional monolithic architecture to a cloud-native microservice architecture (MSA), with a specific emphasis on the impact of this transition on resource efficiency and cost reduction. In order to evaluate whether artificial intelligence (AI) and application performance management (APM) tools can surpass traditional resource management methods in enhancing cost efficiency and operational performance, these advanced technologies are integrated. The research employs the refactor/rearchitect methodology to transition the system to a cloud-native framework, aiming to validate the enhanced capabilities of AI tools in optimizing cloud resources. The main objective of the study is to demonstrate how AI-driven strategies can facilitate more sustainable and economically efficient cloud computing environments, particularly in terms of managing and scaling resources. Moreover, the study aligns with model-based approaches that are prevalent in sustainable systems engineering by structuring cloud transformation through simulation-supported frameworks. It focuses on the synergy between endogenous AI integration within cloud management processes and the overarching goals of Industry 5.0, which emphasize sustainability and efficiency that not only benefit technological advancements but also enhance stakeholder engagement in a human-centric operational environment. This integration exemplifies how AI and cloud technology can contribute to more resilient and adaptive industrial and service systems, furthering the objectives of AI and sustainability initiatives.
The Role of Public Space in Building the Resilience of Cities: Analysis of Representative Projects from IFLA Europe Exhibitions
Authors: Forczek-Brataniec Urszula , Jamioł Katarzyna
Published: June 2024

Climate change exerts a notable influence on the formation of public spaces, necessitating design solutions that address not only aesthetic and functional aspects but also adaptability to local environmental challenges. Public spaces in the form of streets, squares and parks constitute significant parts of cities, creating an opportunity to adapt to climate change through the proper use of ecosystem services. Through the examination of 114 projects from 29 countries showcased in two IFLA Europe exhibitions, this study assesses the extent to which contemporary public spaces integrate responses to prevailing environmental issues and locally contribute to climate change mitigation efforts. The results reveal a discernible rise in the incorporation of environmentally sustainable strategies within projects, particularly those focused on mitigation, protection, reuse, recovery, and education. Additionally, the identification of projects demonstrating the spectrum of responses to local threats is outlined. This research underscores the pivotal role of public spaces in ameliorating the local impacts of climate change within urban environments, emphasizing the increasing prevalence of such solutions in recent years and advocating for their formal recognition in contemporary principles guiding public space design.
Reflective Appraisal of Transformative Innovation Policy: Development of the Sustainability Transition and Innovation Review (STIR) Approach and Application to Canada
Authors: Kaiser Colleen , Miedzinski Michal , McDowall Will , McCarney Geoffrey R.
Published: June 2024

In the context of governing innovation systems for low-carbon transitions, learning is paramount. In this article, we look specifically at the issue of learning through innovation policy review processes. We begin by reviewing the academic literature on innovation policy reviews as well as the emerging literature on transformative innovation policy (TIP) in the contexts of major challenges such as climate change. Drawing from this review, we argue that traditional policy review frameworks fail to provide the kind of learning required to assess challenge-oriented innovation policies and that new, more reflexive approaches are required. We then propose a novel evaluative framework, the Sustainability Transition and Innovation Review (STIR), which incorporates insights from the TIP literature in order to address this gap. The basis for the proposed STIR framework in the theoretical literature is reviewed, and we then describe the results of a test of the STIR framework to evaluate Canada’s policy mix for driving a socio-technical transition from fossil-powered to electric vehicles. Insights from the test application show that the STIR approach helped uncover key explanatory dynamics around incremental vs. transformative change in Canada’s innovation policy performance, and highlighted the interplay between governance and substantive weaknesses in the policy mix. We conclude by arguing that these findings demonstrate the importance of updating policy review frameworks with the insights of the recent TIP literature.
Elucidating the Gap between Green Attitudes, Intentions, and Behavior through the Prism of Greenwashing Concerns
Authors: Margariti Kostoula , Hatzithomas Leonidas , Boutsouki Christina
Published: June 2024

Recent advancements in research on green marketing indicate that green marketing strategies have a notable impact on consumers’ attitudes, intentions, and behavior. However, despite consumers expressing environmental concerns and holding positive attitudes toward green initiatives, their enthusiasm is not consistently translated into actual purchase intentions and green purchases, revealing a noticeable gap between attitudes, intentions, and behaviors. To date, the drivers contributing to this gap have been narrowly investigated, particularly focusing on consumers’ growing apprehensions about greenwashing. Based on an integrated framework of the theory of planned behavior (TPB) and green purchase behavior (GPB), the present study probes the conditional indirect effect of consumers’ attitudes toward green purchases on green purchase behavior through their purchase intentions that are moderated by greenwashing concerns. An online survey with 364 participants indicates that purchase intentions mediate the relationship between attitudes and green product purchase behavior. Attitude toward green products in high (compared to low) levels of greenwashing concerns seems to have a significantly less positive effect on green purchase intention and in turn on green purchase behavior.
The Detection of Railheads: An Innovative Direct Image Processing Method
Authors: Tverdomed Volodymyr , Dmytro Zhuk , Kokriatska Natalia , Lukoševičius Vaidas
Published: June 2024

This study presents a fully automated railhead detection method based on a direct image processing algorithm for use on a railway track. This method functions at a much faster pace than artificial intelligence algorithms that process rail images on embedded systems or low-power devices, as it does not require the use of significant computing resources. With the use of this method, railheads can be analyzed to identify the presence of cracks and other defects. We converted color images to halftone images, performed histogram equalizations to improve the contrast, applied a Gaussian filter to reduce the presence of noise, utilized convolutional filters to extract any vertical and horizontal lines, applied the Canny method and Sobel filters to refine the boundaries of the extracted lines, applied the Hough transform technique to extract lines belonging to the railhead images, and identified the segments with the highest brightness values to process the images of the railheads under study. The method of railhead separation described in this article will allow for further comprehensive diagnostics of the condition of rail threads to ensure the safe and sustainable operation of railway transport. The implementation of intelligent maintenance systems and effective monitoring of railway track conditions can reduce the negative impact on the environment and contribute to the advancement of rail transport as a sustainable, safe, and more environmentally friendly mode of transportation.