bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of the Effect of Slow-Varying Errors on Rotary Modulation Systems
Authors: Wang Yabo , Chen Sizhuo , Li Heng , Ben Yueyang
Published: August 2024

Rotation modulation is a technique that relies on the specific rotation of an inertial measurement unit (IMU) to achieve the self-compensation of device errors. Common rotation schemes are classified into two modes: continuous rotation and a rotation–stop combination. Aiming at the problem of the poor modulation of slow-varying errors in the rotation–stop combination mode, a detailed analysis is conducted on the modulation effects of slow-varying errors in three schemes employing different rotation modes. Firstly, a detailed mathematical analysis is performed on the influence of gyro slow-varying drifts on two rotation modes, and the analysis results are validated through simulations. Subsequently, simulation experiments are conducted on three schemes to analyze their modulation effects on the slow-varying errors of inertial devices. The simulation results reveal that the modified dual-axis rotation scheme exhibits superior modulation effects on the slow-varying errors of inertial devices compared to the dual-axis sixteen-position rotation scheme and the multi-axis alternating continuous rotation scheme.
Continuous Temperature Telemonitoring of Patients with COVID-19 and Other Infectious Diseases Treated in Hospital-at-Home: Viture® System Validation
Authors: Sesma-Sánchez Laura , Ruiz-Castellano María , Romero-Roldán Ainara , Álvarez-García Laura , Morrás-Gómez Marta , Tabar-Liberal Idoia , Pulido-Fontes Marta , Salmón-García Belén
Published: August 2024

Body temperature must be monitored in patients receiving Hospital-at-Home (HaH) care for COVID-19 and other infectious diseases. Continuous temperature telemonitoring (CTT) detects fever and patient deterioration early, facilitating decision-making. We performed a validation clinical study assessing the safety, comfort, and impact on healthcare practice of Viture®, a CTT system, compared with a standard digital axillary thermometer in 208 patients with COVID-19 and other infectious diseases treated in HaH at the Navarra University Hospital (HUN). Overall, 3258 pairs of measurements showed a clinical bias of −0.02 °C with limits of agreement of −0.96/+0.92 °C, a 95% acceptance rate, and a mean absolute deviation of 0.36 (SD 0.30) °C. Viture® detected 3 times more febrile episodes and revealed fever in 50% more patients compared with spot measurements. Febrile episodes were detected 7.23 h (mean) earlier and modified the diagnostic and/or therapeutic approach in 43.2% of patients. Viture® was validated for use in a clinical setting and was more effective in detecting febrile episodes than conventional methods.
A Low-Cost Handheld Centrifugal Microfluidic System for Multiplexed Visual Detection Based on Isothermal Amplification
Authors: Wang Nan , Dong Xiaobin , Zhou Yijie , Zhu Rui , Liu Luyao , Zhang Lulu , Qiu Xianbo
Published: August 2024

A low-cost, handheld centrifugal microfluidic system for multiplexed visual detection based on recombinase polymerase amplification (RPA) was developed. A concise centrifugal microfluidic chip featuring four reaction units was developed to run multiplexed RPA amplification in parallel. Additionally, a significantly shrunk-size and cost-effective handheld companion device was developed, incorporating heating, optical, rotation, and sensing modules, to perform multiplexed amplification and visual detection. After one-time sample loading, the metered sample was equally distributed into four separate reactors with high-speed centrifugation. Non-contact heating was adopted for isothermal amplification. A tiny DC motor on top of the chip was used to drive steel beads inside reactors for active mixing. Another small DC motor, which was controlled by an elaborate locking strategy based on magnetic sensing, was adopted for centrifugation and positioning. Visual fluorescence detection was optimized from different sides, including material, surface properties, excitation light, and optical filters. With fluorescence intensity-based visual detection, the detection results could be directly observed through the eyes or with a smartphone. As a proof of concept, the handheld device could detect multiple targets, e.g., different genes of African swine fever virus (ASFV) with the comparable LOD (limit of detection) of 75 copies/test compared to the tube-based RPA.
Towards Automated Model Selection for Wind Speed and Solar Irradiance Forecasting
Authors: Blazakis Konstantinos , Schetakis Nikolaos , Bonfini Paolo , Stavrakakis Konstantinos , Karapidakis Emmanuel , Katsigiannis Yiannis
Published: August 2024

Given the recent increase in demand for electricity, it is necessary for renewable energy sources (RESs) to be widely integrated into power networks, with the two most commonly adopted alternatives being solar and wind power. Nonetheless, there is a significant amount of variation in wind speed and solar irradiance, on both a seasonal and a daily basis, an issue that, in turn, causes a large degree of variation in the amount of solar and wind energy produced. Therefore, RES technology integration into electricity networks is challenging. Accurate forecasting of solar irradiance and wind speed is crucial for the efficient operation of renewable energy power plants, guaranteeing the electricity supply at the most competitive price and preserving the dependability and security of electrical networks. In this research, a variety of different models were evaluated to predict medium-term (24 h ahead) wind speed and solar irradiance based on real-time measurement data relevant to the island of Crete, Greece. Illustrating several preprocessing steps and exploring a collection of “classical” and deep learning algorithms, this analysis highlights their conceptual design and rationale as time series predictors. Concluding the analysis, it discusses the importance of the “features” (intended as “time steps”), showing how it is possible to pinpoint the specific time of the day that most influences the forecast. Aside from producing the most accurate model for the case under examination, the necessity of performing extensive model searches in similar studies is highlighted by the current work.
Sub-THz Characterization of Technical Surfaces for Particle Accelerator Vacuum Chambers
Authors: Passarelli Andrea , Masullo Maria Rosaria , Mazaheri Zahra , Andreone Antonello
Published: August 2024

Coatings play a crucial role in the functionality of vacuum chambers in particle accelerators, serving a dual goal by efficiently facilitating pumping and mitigating electron cloud effects. However, their impact on the surface impedance of the chamber walls raises concerns, potentially affecting the machine performance and imposing limitations on achievable energies and currents. Therefore, an electromagnetic characterization is essential for a comprehensive study of accelerator structures, particularly in the context of the next-generation machines where the demand for extremely short particle bunches accentuates the importance of evaluating material responses in the very-high-frequency region. We present a technique for probing the sub-THz response of coating materials by measuring pulsed signals passing through a specifically designed waveguide, in which is placed a slab with the deposited material under test. The proposed methodology allows for a comprehensive exploration of the electromagnetic properties of the most used technical surfaces (substrate plus coatings) in accelerators under realistic conditions, providing valuable insights into their behavior in the sub-THz frequency range. The experimental data of three different Non-Evaporable Getter coating samples, prepared on a copper substrate at the CERN deposition facilities under different sputtering conditions, are discussed. The findings contribute to a deeper understanding of the complex interactions between coatings and accelerator structures, with the aim of optimizing performance and efficiency in the evolving landscape of particle acceleration technologies. The limitations and advantages of the technique are also reported.
Nonintrusive and Effective Volume Reconstruction Model of Swimming Sturgeon Based on RGB-D Sensor
Authors: Lin Kai , Zhang Shiyu , Hu Junjie , Li Hongsong , Guo Wenzhong , Hu Hongxia
Published: August 2024

The sturgeon is an important commercial aquaculture species in China. The measurement of sturgeon mass plays a remarkable role in aquaculture management. Furthermore, the measurement of sturgeon mass serves as a key phenotype, offering crucial information for enhancing growth traits through genetic improvement. Until now, the measurement of sturgeon mass is usually conducted by manual sampling, which is work intensive and time consuming for farmers and invasive and stressful for the fish. Therefore, a noninvasive volume reconstruction model for estimating the mass of swimming sturgeon based on RGB-D sensor was proposed in this paper. The volume of individual sturgeon was reconstructed by integrating the thickness of the upper surface of the sturgeon, where the difference in depth between the surface and the bottom was used as the thickness measurement. To verify feasibility, three experimental groups were conducted, achieving prediction accuracies of 0.897, 0.861, and 0.883, which indicated that the method can obtain the reliable, accurate mass of the sturgeon. The strategy requires no special hardware or intensive calculation, and it provides a key to uncovering noncontact, high-throughput, and highly sensitive mass evaluation of sturgeon while holding potential for evaluating the mass of other cultured fishes.
Hard Successive Interference Cancellation for M-QAM MIMO Links in the Presence of Rayleigh Deep-Fading
Authors: Elgam Avner , Klemfner Meir , Silon Shachar , Peretz Yossi , Pinhasi Yosef
Published: August 2024

In our paper, we propose a generalized version of the Alternating Projections Digital Hard Successive Interference Cancellation (AP-HSIC) algorithm that is capable of decoding any order of constellation M in an M-Quadrature Amplitude Modulation (QAM) system. Our approach applies to Rayleigh deep-fading Multiple-Input Multiple-Output (MIMO) channels with high-level Additive White Gaussian Noise (AWGN). It can handle various destructive phenomena without restricting the number of antenna arrays in the transmitter/receiver. Importantly, it does not rely on closed-loop MIMO feedback or the need for Channel-State Information Transmission (CSIT). We have demonstrated the effectiveness of our approach and provided a Bit Error Rate (BER) analysis for 16-, 32-, and 64-QAM modulation systems. Real-time simulations showcase the differences and advantages of our proposed algorithm compared to the Multi-Group Space-Time Coding (MGSTC) decoding algorithm and the Lagrange Multipliers Hard Successive Interference Cancellation (LM-HSIC) algorithm, which we have also developed here. Additionally, our paper includes a mathematical analysis of the LM-HSIC algorithm. The AP-HSIC algorithm is not only effective and fast in decoding, including interference cancellation computational feedback, but it can also be integrated with any Linear Processing Complex Orthogonal Design (LPCOD) technique, including Complex Orthogonal Design (COD) schemes such as high-order Orthogonal Space–Time Block Code (OSTBC) with high-order QAM symbols.
Research on a Method for Classifying Bolt Corrosion Based on an Acoustic Emission Sensor System
Authors: Di Shuyi , Wu Yin , Liu Yanyi
Published: August 2024

High-strength bolts play a crucial role in ultra-high-pressure equipment such as bridges and railway tracks. Effective monitoring of bolt conditions is of paramount importance for common fault repair and accident prevention. This paper aims to detect and classify bolt corrosion levels accurately. We design and implement a bolt corrosion classification system based on a Wireless Acoustic Emission Sensor Network (WASN). Initially, WASN nodes collect high-speed acoustic emission (AE) signals from bolts. Then, the ReliefF feature selection algorithm is applied to identify the optimal feature combination. Subsequently, the Extreme Learning Machine (ELM) model is utilized for bolt corrosion classification. Additionally, to achieve high prediction accuracy, an improved goose algorithm (GOOSE) is employed to ensure the most suitable parameter combination for the ELM model. Experimental measurements were conducted on five classes of bolt corrosion levels: 0%, 25%, 50%, 75%, and 100%. The classification accuracy obtained using the proposed method was at least 98.04%. Compared to state-of-the-art classification diagnostic models, our approach exhibits superior AE signal recognition performance and stronger generalization ability to adapt to variations in working conditions.
Deep Ensemble Learning-Based Sensor for Flotation Froth Image Recognition
Authors: Zhou Xiaojun , He Yiping
Published: August 2024

Froth flotation is a widespread and important method for mineral separation, significantly influencing the purity and quality of extracted minerals. Traditionally, workers need to control chemical dosages by observing the visual characteristics of flotation froth, but this requires considerable experience and operational skills. This paper designs a deep ensemble learning-based sensor for flotation froth image recognition to monitor actual flotation froth working conditions, so as to assist operators in facilitating chemical dosage adjustments and achieve the industrial goals of promoting concentrate grade and mineral recovery. In our approach, training and validation data on flotation froth images are partitioned in K-fold cross validation, and deep neural network (DNN) based learners are generated through pre-trained DNN models in image-enhanced training data, in order to improve their generalization and robustness. Then, a membership function utilizing the performance information of the DNN-based learners during the validation is proposed to improve the recognition accuracy of the DNN-based learners. Subsequently, a technique for order preference by similarity to an ideal solution (TOPSIS) based on the F1 score is proposed to select the most probable working condition of flotation froth images through a decision matrix composed of the DNN-based learners’ predictions via a membership function, which is adopted to optimize the combination process of deep ensemble learning. The effectiveness and superiority of the designed sensor are verified in a real industrial gold–antimony froth flotation application.
DFOS Technology in Geoengineering Monitoring in the Past 35 Years: A Bibliometric Analysis
Authors: Wang Jia , Garg Ankit , Satyam Neelima , Zhussupbekov Askar , Sushkova Svetlana
Published: August 2024

DFOS (distributed fiber-optic sensing) technology has shown the potential to increase the accuracy of measurement after years of development and experimenting in geoengineering monitoring. To better understand the development of DFOS technology and its contribution to geoengineering, an objective and data-driven review of the development process of DFOS technology in construction was completed. The review was accomplished by using text mining methods on the Web of Science, covering a wide range of relevant data, including 3970 articles from 1989 to 2023. The results indicate that DFOS technology research demonstrates the typical characteristics of multi-author, multi-country, and multi-institution collaborations, spanning various research fields. Over the past 35 years, the number of published articles has exhibited exponential growth, with China making significant contributions and leading in terms of its total publication growth rate, which has been higher than that of the United States since 2016. In the analysis of author keywords, emerging technologies, such as machine learning and distributed acoustic sensing, have garnered attention. The findings contribute to a comprehensive understanding of the development, impact, and future trends of DFOS technology in geotechnical engineering, offering valuable insights for researchers, scholars, and students in the field and inspiring new approaches for research methods in this domain.
Contrastive-Active Transfer Learning-Based Real-Time Adaptive Assessment Method for Power System Transient Stability
Authors: Zhao Jinman , Han Xiaoqing , Wang Chengmin , Yang Jing , Zhang Gengwu
Published: August 2024

The transient stability assessment based on machine learning faces challenges such as sample data imbalance and poor generalization. To address these problems, this paper proposes an intelligent enhancement method for real-time adaptive assessment of transient stability. In the offline phase, a convolutional neural network (CNN) is used as the base classifier. A model training method based on contrastive learning is introduced, aiming to increase the spatial distance between positive and negative samples in the mapping space. This approach effectively improves the accuracy of the model in recognizing unbalanced samples. In the online phase, when real data with different distribution characteristics from the offline data are encountered, an active transfer strategy is employed to update the model. New system samples are obtained through instance transfer from the original system, and an active sampling strategy considering uncertainty is designed to continuously select high-value samples from the new system for labeling. The model parameters are then updated by fine-tuning. This approach drastically reduces the cost of updating while improving the model’s adaptability. Experiments on the IEEE39-node system verify the effectiveness of the proposed method.
Habituation of Central and Electrodermal Responses to an Auditory Two-Stimulus Oddball Paradigm
Authors: Rho Gianluca , Callara Alejandro Luis , Scilingo Enzo Pasquale , Greco Alberto , Bonfiglio Luca
Published: August 2024

The orienting reaction (OR) towards a new stimulus is subject to habituation, i.e., progressively attenuates with stimulus repetition. The skin conductance responses (SCRs) are known to represent a reliable measure of OR at the peripheral level. Yet, it is still a matter of debate which of the P3 subcomponents is the most likely to represent the central counterpart of the OR. The aim of the present work was to study habituation, recovery, and dishabituation phenomena intrinsic to a two-stimulus auditory oddball paradigm, one of the most-used paradigms both in research and clinic, by simultaneously recording SCRs and P3 in twenty healthy volunteers. Our findings show that the target stimulus was capable of triggering a more marked OR, as indexed by both SCRs and P3, compared to the standard stimulus, that could be due to its affective saliency and relevance for task completion; the application of temporal principal components analysis (PCA) to the P3 complex allowed us to identify several subcomponents including both early and late P3a (eP3a; lP3a), P3b, novelty P3 (nP3), and both a positive and a negative Slow Wave (+SW; −SW). Particularly, lP3a and P3b subcomponents showed a similar behavior to that observed for SCRs , suggesting them as central counterparts of OR. Finally, the P3 evoked by the first standard stimulus after the target showed a significant dishabituation phenomenon which could represent a sign of the local stimulus change. However, it did not reach a sufficient level to trigger an SCR/OR since it did not represent a salient event in the context of the task.
In-Shoe Sensor Measures of Loading Asymmetry during Gait as a Predictor of Frailty Development in Community-Dwelling Older Adults
Authors: Nakanowatari Tatsuya , Hoshi Masayuki , Asao Akihiko , Sone Toshimasa , Kamide Naoto , Sakamoto Miki , Shiba Yoshitaka
Published: August 2024

Clinical walk tests may not predict the development of frailty in healthy older adults. With advancements in wearable technology, it may be possible to predict the development of frailty using loading asymmetry parameters during clinical walk tests. This prospective cohort study aimed to test the hypothesis that increased limb loading asymmetry predicts frailty risk in community-living older adults. Sixty-three independently ambulant community-living adults aged ≥ 65 years were recruited, and forty-seven subjects completed the ten-month follow-up after baseline. Loading asymmetry index of net and regional (forefoot, midfoot, and rearfoot) plantar forces were collected using force sensing insoles during a 10 m walk test with their maximum speed. Development of frailty was defined if the participant progressed from baseline at least one grading group of frailty at the follow-up period using the Kihon Checklist. Fourteen subjects developed frailty during the follow-up period. Increased risk of frailty was associated with each 1% increase in loading asymmetry of net impulse (Odds ratio 1.153, 95%CI 1.001 to 1.329). Net impulse asymmetry significantly correlated with asymmetry of peak force in midfoot force. These results indicate the feasibility of measuring plantar forces of gait during clinical walking tests and underscore the potential of using load asymmetry as a tool to augment frailty risk assessment in community-dwelling older adults.
The Prediction and Evaluation of Surface Quality during the Milling of Blade-Root Grooves Based on a Long Short-Term Memory Network and Signal Fusion
Authors: Ni Jing , Chen Kai , Meng Zhen , Li Zuji , Li Ruizhi , Liu Weiguang
Published: August 2024

The surface quality of milled blade-root grooves in industrial turbine blades significantly influences their mechanical properties. The surface texture reveals the interaction between the tool and the workpiece during the machining process, which plays a key role in determining the surface quality. In addition, there is a significant correlation between acoustic vibration signals and surface texture features. However, current research on surface quality is still relatively limited, and most considers only a single signal. In this paper, 160 sets of industrial field data were collected by multiple sensors to study the surface quality of a blade-root groove. A surface texture feature prediction method based on acoustic vibration signal fusion is proposed to evaluate the surface quality. Fast Fourier transform (FFT) is used to process the signal, and the clean and smooth features are extracted by combining wavelet denoising and multivariate smoothing denoising. At the same time, based on the gray-level co-occurrence matrix, the surface texture image features of different angles of the blade-root groove are extracted to describe the texture features. The fused acoustic vibration signal features are input, and the texture features are output to establish a texture feature prediction model. After predicting the texture features, the surface quality is evaluated by setting a threshold value. The threshold is selected based on all sample data, and the final judgment accuracy is 90%.
Nanosensors Based on Bimetallic Plasmonic Layer and Black Phosphorus: Application to Urine Glucose Detection
Authors: Houari Fatima , El Barghouti Mohamed , Mir Abdellah , Akjouj Abdellatif
Published: August 2024

This paper presents a new biosensor design based on the Kretschmann configuration, for the detection of analytes at different refractive indices. Our studied design consists of a TiO2/SiO2 bi-layer sandwiched between a BK7 prism and a bimetallic layer of Ag/Au plasmonic materials, covered by a layer of black phosphorus placed below the analyte-containing detection medium. The different layers of our structure and analyte detection were optimized using the angular interrogation method. High performance was achieved, with a sensitivity of 240 deg/RIU and a quality factor of 34.7 RIU−1. This biosensor can detect analytes with a wide refractive index range between 1.330 and 1.347, such as glucose detection in urine samples using a refractive index variation of 10−3. This capability offers a wide range of applications for biomedical and biochemical detection and selectivity.
Adaptive Frame Structure Design for Sensing-Assisted Downlink Communication in the Vehicle-to-Infrastructure Scenario
Authors: Yao Junliang , Wang Ze , Zhang Chunli , Hui Hui
Published: August 2024

Vehicle-to-everything (V2X) is considered a key factor in driving the future development of intelligent transport, which requires high-quality communication and fast sensing of vehicle information in high-speed mobile scenarios. However, high-speed mobility makes the wireless channel change rapidly, which requires frequent channel estimation and channel feedback between a vehicle and the roadside unit (RSU), resulting in an increase in communication overhead. At the same time, the high maneuverability of vehicles leads to frequent switching and misalignment of communication beams, so the RSU must have better beam prediction and tracking capabilities. To address this problem, this paper proposes an adaptive frame structure design scheme for sensing-assisted downlink (DL) communication. The basic idea of the scheme involves analyzing the communication model during the vehicle’s movement. This analysis aims to establish a theoretical relationship between the Symbol Error Rate (SER) and the following two key factors: the vehicle’s starting position and the distance it travels across. Subsequently, the scheme leverages the vehicle’s position data, as detected by the RSU, to calculate the real-time SER for DL communication. The SER threshold is set based on the requirements of DL communication. If the real-time SER is below this threshold, channel estimation becomes unnecessary. This decreases the frequency of channel estimation and frees up time and frequency resources that would otherwise be occupied by channel estimation processes within the frame structure. The design of an adaptive frame structure, as detailed in the above scheme, is presented. Furthermore, the performance of the proposed method is analyzed and compared with that of the traditional communication protocol frame structure and the beam prediction-based frame structure. The simulation results indicate that the communication throughput of the proposed method can be improved by up to 6% compared with the traditional communication protocol frame structure while maintaining SER performance.
Machine Learning for IoT Applications and Digital Twins
Authors: Rezazadeh Javad , Ameri Sianaki Omid , Farahbakhsh Reza
Published: August 2024

The Internet of Things (IoT) stands as one of the most transformative technologies of our era, significantly enhancing the living conditions and operational efficiencies across various domains [...]
Can a Novel Light Weight Minimal Support Lifting Exoskeleton Modify Lifting Movement in People without Low Back Pain?
Authors: Burjawi Tamer , Chai Rifai , Arrowsmith Matthew , Pranata Adrian
Published: August 2024

Low back pain (LBP) is a major contributor to lifting-related disabilities. To minimize the risk of back pain, emerging technologies known as lifting exoskeletons were designed to optimize lifting movements. However, it is currently unknown whether a minimally supportive exoskeleton can alter the lifting movement in people without LBP. This study aims to investigate if wearing a novel lightweight exoskeleton that minimally supports the back, hip, and knee can alter the lifting range of motion and movement variations in people without LBP. This study also aims to investigate if wearing this novel exoskeleton can result in a reliable between-day lifting movement. In two separate sessions (each one week apart), fourteen participants lifted a box (that weighed 10% of their body weight) ten times, once while wearing an exoskeleton and once while not wearing an exoskeleton. Wearing the novel exoskeleton during lifting produced moderate-high, test-retest reliability (Trunk: ICC3,1 = 0.89, 95% CI [0.67, 0.96], SEM = 9.34°; Hip: ICC3,1 = 0.63, 95% CI [0.22, 0.88], SEM = 2.57°; Knee: ICC3,1 = 0.61, 95% CI [0.23, 0.87], SEM = 2.50°). Wearing an exoskeleton significantly decreased the range of motion of the knee (F1,4 = 4.83, p = 0.031, ηp2 = 0.06). Additionally, wearing an exoskeleton significantly decreased hip (diff = 8.38, p = 0.045) and knee (diff = −8.57, p = 0.038) movement variability; however, wearing an exoskeleton did not decrease the movement variability of the body’s trunk (diff = 0.60, p = 1.00). Therefore, minimally supported lifting through the use of exoskeletons can modify movement in people without LBP and produce reliable lifting movements. Wearing the novel exoskeleton is also desirable for monitoring lifting movements. Future studies should investigate the use of sensors and IMU to monitor lifting movement at work with the least amount of intrusion on an individual’s movement.
Design and Enhancement of a Fog-Enabled Air Quality Monitoring and Prediction System: An Optimized Lightweight Deep Learning Model for a Smart Fog Environmental Gateway
Authors: Pazhanivel Divya Bharathi , Velu Anantha Narayanan , Palaniappan Bagavathi Sivakumar
Published: August 2024

Effective air quality monitoring and forecasting are essential for safeguarding public health, protecting the environment, and promoting sustainable development in smart cities. Conventional systems are cloud-based, incur high costs, lack accurate Deep Learning (DL)models for multi-step forecasting, and fail to optimize DL models for fog nodes. To address these challenges, this paper proposes a Fog-enabled Air Quality Monitoring and Prediction (FAQMP) system by integrating the Internet of Things (IoT), Fog Computing (FC), Low-Power Wide-Area Networks (LPWANs), and Deep Learning (DL) for improved accuracy and efficiency in monitoring and forecasting air quality levels. The three-layered FAQMP system includes a low-cost Air Quality Monitoring (AQM) node transmitting data via LoRa to the Fog Computing layer and then the cloud layer for complex processing. The Smart Fog Environmental Gateway (SFEG) in the FC layer introduces efficient Fog Intelligence by employing an optimized lightweight DL-based Sequence-to-Sequence (Seq2Seq) Gated Recurrent Unit (GRU) attention model, enabling real-time processing, accurate forecasting, and timely warnings of dangerous AQI levels while optimizing fog resource usage. Initially, the Seq2Seq GRU Attention model, validated for multi-step forecasting, outperformed the state-of-the-art DL methods with an average RMSE of 5.5576, MAE of 3.4975, MAPE of 19.1991%, R2 of 0.6926, and Theil’s U1 of 0.1325. This model is then made lightweight and optimized using post-training quantization (PTQ), specifically dynamic range quantization, which reduced the model size to less than a quarter of the original, improved execution time by 81.53% while maintaining forecast accuracy. This optimization enables efficient deployment on resource-constrained fog nodes like SFEG by balancing performance and computational efficiency, thereby enhancing the effectiveness of the FAQMP system through efficient Fog Intelligence. The FAQMP system, supported by the EnviroWeb application, provides real-time AQI updates, forecasts, and alerts, aiding the government in proactively addressing pollution concerns, maintaining air quality standards, and fostering a healthier and more sustainable environment.
Exploring Weak Magnetic Signal Characteristics of Pipeline Welds: Insights into Stress Non-Uniformity Effects
Authors: Fan Xiangfeng , Yang Lijian
Published: August 2024

Weak magnetic detection technology can detect stress concentration areas in ferromagnetic materials. However, the stress non-uniform characteristics of pipeline welds lead to significant differences in stress distribution range and values between inner wall welds and outer wall welds. This discrepancy makes it crucial to further evaluate the impact of stress non-uniformity on magnetic signals. To study the magnetic signal characteristics under the influence of residual stress in weld seams, a magneto-mechanical analytical model was established based on the magnetic charge theory and the distribution characteristics of residual stress in the weld seam. The magneto-mechanical relationship and magnetic signal distribution characteristics at the inner and outer wall welds of the pipeline are calculated. Furthermore, the effects of different excitation intensities on the amplitude growth characteristics of magnetic signals are analyzed and compared. To verify the analysis model, weld detection experiments with different excitation intensities were designed. The results show that both the peak-to-valley values of the normal component and the peak values of the tangential component of the outer wall weld are lower than those of the inner wall weld. Conversely, the peak-to-valley width of the normal component and the peak width of the tangential component are greater than those of the inner wall weld. Additionally, the rate of increase in weak magnetic signal amplitude decreases in a first-order exponential relationship with increasing excitation intensity. The average decay rates of the normal and tangential component amplitude growth rates for the inner wall weld are 34.03% and 27.9%, respectively, while for the outer wall weld, they are 31.75% and 28.01%, respectively. This study contributes to the identification and quantitative assessment of weak magnetic signals in inner and outer wall welds.
Low-Cost, Low-Power Edge Computing System for Structural Health Monitoring in an IoT Framework
Authors: Hidalgo-Fort Eduardo , Blanco-Carmona Pedro , Muñoz-Chavero Fernando , Torralba Antonio , Castro-Triguero Rafael
Published: August 2024

A complete low-power, low-cost and wireless solution for bridge structural health monitoring is presented. This work includes monitoring nodes with modular hardware design and low power consumption based on a control and resource management board called CoreBoard, and a specific board for sensorization called SensorBoard is presented. The firmware is presented as a design of FreeRTOS parallelised tasks that carry out the management of the hardware resources and implement the Random Decrement Technique to minimize the amount of data to be transmitted over the NB-IoT network in a secure way. The presented solution is validated through the characterization of its energy consumption, which guarantees an autonomy higher than 10 years with a daily 8 min monitoring periodicity, and two deployments in a pilot laboratory structure and the Eduardo Torroja bridge in Posadas (Córdoba, Spain). The results are compared with two different calibrated commercial systems, obtaining an error lower than 1.72% in modal analysis frequencies. The architecture and the results obtained place the presented design as a new solution in the state of the art and, thanks to its autonomy, low cost and the graphical device management interface presented, allow its deployment and integration in the current IoT paradigm.
Gas Chromatography–Sensor System Aids Diagnosis of Inflammatory Bowel Disease, and Separates Crohn’s from Ulcerative Colitis, in Children
Authors: Slater Rachael , Tharmaratnam Kukatharmini , Belnour Salma , Auth Marcus Karl-Heinz , Muhammed Rafeeq , Spray Christine , Wang Duolao , de Lacy Costello Ben , García-Fiñana Marta , Allen Stephen , Probert Chris
Published: August 2024

The diagnosis of inflammatory bowel disease (IBD) in children and the need to distinguish between subtypes (Crohn’s disease (CD) and ulcerative colitis (UC)) requires lengthy investigative and invasive procedures. Non-invasive, rapid, and cost-effective tests to support these diagnoses are needed. Faecal volatile organic compounds (VOCs) are distinctive in IBD. VOC profiles can be rapidly determined using a gas chromatography–sensor device (OdoReader©). In an inception-cohort of children presenting with suspected IBD, we directly compared the diagnostic fidelity of faecal calprotectin (FCP, a non-specific protein marker of intestinal inflammation) with OdoReader© VOC profiles of children subsequently diagnosed with IBD with matched controls diagnosed with other gastrointestinal conditions. The OdoReader© was 82% (95% confidence interval 75–89%) sensitive and 71% (61–80%) specific but did not outperform FCP (sensitivity 93% (77–99%) and specificity 86% (67–96%); 250 µg/g FCP cut off) in the diagnosis of IBD from other gastrointestinal conditions when validated in a separate sample from the same cohort. However, unlike FCP and better than other similar technologies, the OdoReader© could distinguish paediatric CD from UC (up to 88% (82–93%) sensitivity and 80% (71–89%) specificity in the validation set) and justifies further validation in larger studies. A non-invasive test based on VOCs could help streamline and limit invasive investigations in children.
Detecting Transitions from Stability to Instability in Robotic Grasping Based on Tactile Perception
Authors: Zhao Zhou , Zheng Dongyuan , Chen Lu
Published: August 2024

Robots execute diverse load operations, including carrying, lifting, tilting, and moving objects, involving load changes or transfers. This dynamic process can result in the shift of interactive operations from stability to instability. In this paper, we respond to these dynamic changes by utilizing tactile images captured from tactile sensors during interactions, conducting a study on the dynamic stability and instability in operations, and propose a real-time dynamic state sensing network by integrating convolutional neural networks (CNNs) for spatial feature extraction and long short-term memory (LSTM) networks to capture temporal information. We collect a dataset capturing the entire transition from stable to unstable states during interaction. Employing a sliding window, we sample consecutive frames from the collected dataset and feed them into the network for the state change predictions of robots. The network achieves both real-time temporal sequence prediction at 31.84 ms per inference step and an average classification accuracy of 98.90%. Our experiments demonstrate the network’s robustness, maintaining high accuracy even with previously unseen objects.
Solar Self-Consumption and Urban Energy Vulnerability: Case Study in Lisbon
Authors: Abadeço Marisa , Rodrigues Maria , Ferrão Paulo , Luz Guilherme , Freitas Sara , Brito Miguel
Published: August 2024

This paper investigates the potential of rooftop photovoltaic (PV) systems in mitigating energy vulnerability in the urban context. Based on a geospatial data-driven approach, it combines georeferenced assessment of solar potential and high-resolution demand data with energy vulnerability indicators for both heating and cooling needs, to identify priority areas for supporting PV deployment. Results show more than 50% saving potential in the energy bill for the selected priority areas. The mismatch between PV supply and demand supports the development of demand-aggregating collective self-consumption approaches such as solar energy communities, whose challenges and opportunities are discussed.
Long-Term Landscape Changes in the Ojców National Park (Poland) and Its Surroundings: Implications for the Effectiveness of Buffer Zones
Authors: Jakiel Michał , Kaim Dominik , Ostafin Krzysztof
Published: August 2024

Protected areas (PAs) serve as crucial elements in biodiversity conservation but are in danger of becoming isolated islands in human-dominated landscapes. It is related to landscape changes, especially changes in land use and land cover (LULC). Over the past decades, most research on the effectiveness of nature conservation has focused mainly on PAs, while the areas surrounding PAs are of key importance for maintaining ecological connectivity and biodiversity. Therefore, the main objective of this study was to determine the long-term changes in LULC within the selected national park in Poland and its surroundings and to assess the effectiveness of the BZ based on these changes. We hypothesized that, despite restrictions within the buffer zone, land development has intensified and increased, in the nearest surroundings of the analyzed national park. For the analysis, we selected Ojców National Park (southern Poland), one of the oldest national parks in Poland. We analyzed landscape changes before (since the 1930s) and after establishing the park and its BZ. We conducted a comprehensive quantitative analysis of the landscape structure and LULC. We used historical maps and the contemporary national LULC database. Our results showed that almost 40% of the study area consisted of lands with non-persistent LULC. The main changes include a three-fold increase in built-up areas and an increase in forest cover, mainly on abandoned agricultural land. We also found that land development around the national park is at a level similar to the general rate for the area outside the BZ. It suggests the ineffectiveness of the buffer zone in preventing land development. The identified long-term landscape changes the basis for sustainable development land management from the nature conservation perspective.
The Effect of Different Densification Levels on the Mechanical Properties of Southern Yellow Pine
Authors: Pradhan Suman , Lamichhane Aadarsha , Belaidi Dalila , Mohammadabadi Mostafa
Published: August 2024

Plantations, typically involving the cultivation of fast-growing trees like southern yellow pine, offer avenues to enhance sustainability and manage limited resources more effectively. However, fast-growing trees suffer from low mechanical properties due to less dense wood. Densification and the development of engineered wood products represent approaches to developing high-performance products from fast-growing tree species. In this study, the correlation between the densification levels and mechanical properties of a fast-growing species, loblolly pine (Pinus taeda L.), was established to improve resource utilization. Wood specimens were densified at three compression ratios: 16.67%, 33.33%, and 50.00%. The impact of densification levels on bending strength, bending stiffness, shear strength, and hardness was studied. The findings highlighted the positive impact of densification on structural integrity, as bending stiffness consistently improved, eventually reaching a 42% enhancement at a compression ratio of 50.00%. However, bending strength showed an initial increasing trend but reached a plateau at higher densification levels. Densification levels showed minimal changes in shear strength parallel to the grain. Notably, densification significantly enhanced hardness properties, particularly on the tangential surface, where a fourfold increase was observed at a 50% compression ratio. Overall, these findings reveal the relation between the compression ratio and the mechanical properties of lumber and are beneficial for utilizing lower-quality wood species in construction and engineering applications.
Exploring Smartphone User Interface Experience-Sharing Behavior: Design Perception and Motivation-Driven Mechanisms through the SOR Model
Authors: Gao Jie , Jia Wenjing , Yin Jun
Published: August 2024

This study investigates user experience (UX) sharing behaviors in the context of smartphone user interface (UI) design, emphasizing their significance for UI enhancement and effective marketing strategies. Grounded in the Stimulus–Organism–Response (SOR) framework, we examine how design perception attributes—perceived usability, novelty, enjoyment, and brand image—influence UX sharing, with a spotlight on the mediating role of individual motivation. A quantitative analysis (N = 472), Structural Equation Modeling (SEM), and mediation analysis were conducted. Our findings confirm that these components can positively impact UX sharing by bolstering personal expectations and self-efficacy in knowledge sharing, with perceived usability being an exception as it unexpectedly showed a negative association with sharing frequency. Moreover, perceived brand image and individual self-expectancy and self-efficacy enhance sharing outcomes. This research enriches our understanding of the strategic importance of user interface (UI) design in the context of smartphones, furnishing empirical grounding for devising sustainable UI design strategies and productive marketing tactics. Consequently, it bears considerable relevance to both theoretical insights and practical applications.
Molecular Composition of Humic Acids and Soil Organic Matter Stabilization Rate of the First Arctic Carbon Measurement Supersite “Seven Larches”
Authors: Nizamutdinov Timur , Bolshiianova Olga , Morgun Evgeniya , Abakumov Evgeny
Published: August 2024

In the framework of the implementation of the all-Russian climatic project “Carbon measurement test sites”, reference soils of “Seven Larches” carbon supersite, which is a benchmark and the only monitoring site in the Artistic latitudes, were investigated. The morphological structure of representative soils was specified, and it was found that soils are classified as Cryosols of different types (Histic, Stagnic or Gleyic). The basic physico-chemical characteristics of the soils were studied. By means of elemental analysis and 13C NMR spectroscopy, the composition and molecular structure of humic acids from organic and mineral soil horizons were studied. The surface (organic) soil horizons are characterized by high values of H:C molar ratios (1.2–1.3), which indicates a lower degree of the molecular structure aromaticity of humic acids from organic soil horizons. Analysis of the molecular structure of humic acids by 13C NMR spectroscopy showed that humic acids of the studied soils are characterized by the predominance of non-substituted aliphatic (0–47 ppm) and aromatic (108–164 ppm) fragments. Mineral soil horizons are characterized by higher stabilization of organic matter (with lower SOC content—0.5–0.9%) and higher hydrophobicity of humic acid molecules. Comparison of the obtained results with previously published data on the structural and elemental composition of humic acids isolated from soils of similar genesis and geographical location did not reveal any significant differences between the data obtained by us and previously published data. Thus, for “Seven Larches” carbon supersite “reference” parameters of elemental composition of humic acids, their molecular composition and degree of stabilization of soil organic matter were identified.
Sustainable Agriculture and Agri-Food
Authors: Lisciani Silvia
Published: August 2024

Food production has an adverse impact on the environment [...]
Spatiotemporal Evolution and Driving Forces of Production-Living-Ecological Space in Arid Ecological Transition Zone Based on Functional and Structural Perspectives: A Case Study of the Hexi Corridor
Authors: Tang Xianglong , Cai Leshan , Du Pengzhen
Published: August 2024

The rational allocation of land resources is crucial to ensuring human well-being, livelihood, and survival. The study of Production-Living-Ecological Space (PLES) provides new perspectives on land resource allocation. However, few studies have assessed the feasibility of PLES optimization in ecological transition zones. For this study, using the composite functional space classification method, a classification and functional utility scoring system were constructed. Various methods, including dynamic attitude, transfer matrix, and spatial autocorrelation, were employed to characterize the evolution of the quantity and quality of PLES in the Hexi Corridor. Moreover, the mechanisms driving these changes were explored using a geodetector. Our findings revealed that: (1) The distribution of Production-Ecological Space (PES) is higher in the west and south and lower in the east and north. Production-Living Space (PLS) is scattered. Ecological-Production Space (EPS) is mostly distributed in the south or west, whereas Ecological Space (ES) is mainly located in the north and west of the Hexi Corridor. (2) From 1980 to 2020, the area of PES and PLS increased by 2037.84 km2 and 673 km2, respectively; the area of EPS was relatively stable, and the area of ES decreased by 2523.06 km2. (3) The evolution of PLES quality indicated that the high functional utility area of PES and PLS was roughly the same as the expanded functional utility area, whereas the expanded functional utility area of EPS and ES is similar to the median functional utility area. (4) The spatiotemporal evolution of PLES is closely linked to natural, economic, and social factors.