bluuebunny/crossref_metadata_2025 huggingface dataset:
Discovery of Non-Peptide GLP-1 Positive Allosteric Modulators from Natural Products: Virtual Screening, Molecular Dynamics, ADMET Profiling, Repurposing, and Chemical Scaffolds Identification
Authors: Gomaa Mohamed S. , Alturki Mansour S. , Tawfeeq Nada , Hussein Dania A. , Pottoo Faheem H. , Al Khzem Abdulaziz H. , Sarafroz Mohammad , Abubshait Samar
Published: December 2024

Background/Objectives: Glucagon-like peptide-1 (GLP-1) receptor is currently one of the most explored targets exploited for the management of diabetes and obesity, with many aspects of its mechanisms behind cardiovascular protection yet to be fully elucidated. Research dedicated towards the development of oral GLP-1 therapy and non-peptide ligands with broader clinical applications is crucial towards unveiling the full therapeutic capacity of this potent class of medicines. Methods: This study describes the virtual screening of a natural product database consisting of 695,133 compounds for positive GLP-1 allosteric modulation. The database, obtained from the Coconut website, was filtered according to a set of physicochemical descriptors, then was shape screened against the crystal ligand conformation. This filtered database consisting of 26,325 compounds was used for virtual screening against the GLP-1 allosteric site. Results: The results identified ten best hits with the XP score ranging from −9.6 to −7.6 and MM-GBSA scores ranging from −50.8 to −32.4 and another 58 hits from docked pose filter and a second round of XP docking and MM-GBSA calculation followed by molecular dynamics. The analysis of results identified hits from various natural products (NPs) classes, to whom attributed antidiabetic and anti-obesity effects have been previously reported. The results also pointed to β-lactam antibiotics that may be evaluated in drug repurposing studies for off-target effects. The calculated ADMET properties for those hits revealed suitable profiles for further development in terms of bioavailability and toxicity. Conclusions: The current study identified several NPs as potential GLP-1 positive allosteric modulators and revealed common structural scaffolds including peptidomimetics, lactams, coumarins, and sulfonamides with peptidomimetics being the most prominent especially in indole and coumarin cores.
Impact of Citric Acid on the Structure, Barrier, and Tensile Properties of Esterified/Cross-Linked Potato Peel-Based Films and Coatings
Authors: Miller Katharina , Reichert Corina L. , Schmid Markus , Loeffler Myriam
Published: December 2024

The valorization of potato peel side streams for food packaging applications, especially for the substitution of current petrochemical-based oxygen barrier solutions such as EVOH, is becoming increasingly important. Therefore, potato peel-based films and coatings (on PLA) were developed containing 10–50% (w/w potato peel) citric acid (CA). To determine the impact of CA concentration on the structure and physicochemical properties of cast films and coatings, ATR-FTIR spectroscopy, moisture adsorption isotherms, tensile properties, light transmittance, oxygen permeability, carbon dioxide transmission rate, and water vapor transmission rate measurements were performed. The results indicate that an increase in CA concentration from 10% to 30% increased esterification/cross-linking and resulted in minimal values for the oxygen permeability (0.08 cm3 m−2 d−1 bar−1) at 50% RH and water vapor transmission rate (1.6 g m−2 d−1) at 50% → 0% RH, whereas an increase from 30% to 50% increased free CA concentration and resulted in increased flexibility, indicating that CA functioned as a plasticizer within the film/coating at higher concentrations. Overall, potato peel-based coatings containing CA showed comparable barrier properties to EVOH. We assume that an extensive industrial purification or fractionation of potato peel, which was not carried out in this study, could lead to even lower transmission rates.
Study on the Characterization of Physical, Mechanical, and Creep Properties of Masson Pine and Chinese Fir Wood Flour-Reinforced High-Density Polyethylene Composites
Authors: Xu Hailong , Hua Xueshan , Cao Yan , Li Lifen , Liu Baoyu , Yang Xiaohui , Gao Hua
Published: December 2024

Improving the physical, mechanical, and creep properties of wood fiber-reinforced polymer composites is crucial for broadening their application prospect. In this research, seven types of high-density polyethylene (HDPE) composites reinforced with different mass ratios of Masson pine (Pinus massoniana Lamb.) and Chinese fir [Cunninghamia lanceolata (Lamb.) Hook.] were prepared by a two-step extrusion molding method. The mass ratios of the two fibers were 60:0, 50:10, 40:20, 30:30, 20:40, 10:50, and 0:60, respectively. The surface color, density, dimension stability, bending, tensile, impact properties, dynamic mechanical properties, and 24 h creep properties at a 10% stress level of the seven composites were investigated. Additionally, the Rule of Mixtures (ROM), the Inverse Rule of Mixtures (IROM), the Hirsch models, and the improved model were employed to simulate the mechanical properties, while the Findley index model, the two-parameter index model, and the modified ExpAssoc model were employed to simulate the creep performance of the composites. This study revealed that as the proportion of Chinese fir wood flour increased, the mechanical properties of the composites gradually improved, the storage modulus showed an increasing trend, while the loss modulus decreased, and the overall creep strain of the composites increased. Among the various models, the modified model simulated the mechanical properties of the composites the best, while the modified ExpAssoc model simulated the creep behavior most effectively.
Synergistic Effect of CNT and N-Doped Graphene Foam on Improving the Corrosion Resistance of Zn Reinforced Epoxy Composite Coatings
Authors: Mao Yana , Liu Shufu , Liu Shizhong , Wu Guodong , Liu Qi , Du Xusheng
Published: December 2024

The synergistic effect of CNT and three-dimensional N-doped graphene foam (3DNG) on improving corrosion resistance of zinc-reinforced epoxy (ZRE) composite coatings was studied in this work. Although CNT itself was demonstrated to be effective to promote the anti-corrosion performance of the ZRE coating, the incorporation of additional 3DNG leads to further enhancement of its corrosion resistance under the synergistic effect of the hybrid carbon nanofillers with different dimensions. Both the content of the carbonaceous fillers and the ratio between them affected the performance of the coating. The optimal content of hybrid filler in the coating was determined to be only 0.1% with 3DNG:CNT = 1:3. With the modification of hybrid fillers, the corrosion current of the coating could be reduced by more than six orders of magnitude. Additionally, the immersion test of the pre-scratched coating directly demonstrated the evident contribution of the hybrid fillers to the sacrificial anode-based surface protection mechanism of the coating. These results confirmed the synergistic effect of the hybrid 1D and 3D carbonaceous fillers on promoting the corrosion inhibition of their coating, which could be promising for application in other functional composites.
Reactivation of Abandoned Oilfields for Cleaner Energy Generation: Three-Dimensional Modelling of Reservoir Heterogeneity and Geometry
Authors: Storey Benjamin Michael , Worden Richard H. , McNamara David D. , Wheeler John , Parker Julian , Kristen Andre
Published: December 2024

With the changing picture of global energy supplies and the shift toward the energy transition, it has never been more important to look for alternative sources of energy. Globally there are tens of thousands of abandoned oil fields with considerable reserves left behind. These have the potential to be reactivated to become an energy supply that is cleaner than conventional oil and gas. This can be achieved by the use of in situ combustion and the subsequent exploitation of the inherent increase in temperature and pressure to produce geothermal energy, allied to sequestration of the mixture of produced fluids. In situ combustion (ISC) has conventionally been used as an enhanced oil recovery technique, with a high failure rate that has been recently attributed to poor reservoir selection and project design. We suggest that the failure of many earlier ISC projects is due to insufficient appreciation of how the subsurface geology affects the process. With the use of computer numerical modelling, we aim to ascertain how the geometry and heterogeneity of the reservoir control the success of the process. Here we employ simple three-dimensional sector models to assess a variety of different petrophysical heterogeneities, within a set of different reservoir geometries, on the temperature, velocity, propagation stability and enthalpy rate. These models illustrate that the biggest impact on success of the ISC process for geothermal energy generation, as a function of temperature and enthalpy, is the location of the wells relative to the heterogeneities and the scale of heterogeneities. Metre-scale heterogeneities do not have a significant effect on this. Instead, the biggest contributor to the propagation stability and direction of the fire front is the presence of a large-scale (10 s to 100 s of metres) heterogeneities, such as channels, or the geometry of a tilted fault block; both have a strong control over the direction of the propagation, and therefore are important factors with regards to well placement.
Deep Reinforcement Learning-Based Process Control in Biodiesel Production
Authors: Shi Hongyan , Zhang Le , Pan Duotao , Wang Guogang
Published: December 2024

The control of complex industrial processes has been a forefront research topic. Biodiesel production, as a typical complex industrial reaction process, exhibits multivariable coupling, nonlinearity, and high latency, making it challenging for traditional control methods to achieve real-time control under varying operating conditions. To address this issue, this paper proposes a control method based on the twin delayed deep deterministic policy gradient (TD3) algorithm, which dynamically adjusts process parameters to achieve the adaptive optimization control of complex processes. A simplified biodiesel production process model was established to simulate the actual production process. Subsequently, a controller based on the TD3 algorithm was designed and implemented for this model. To evaluate the performance of the proposed controller, comparative experiments were conducted with traditional controllers and deep deterministic policy gradient (DDPG) controllers. The effects of different control strategies were analyzed through simulation experiments. The results demonstrate that the proposed controller achieves the objectives while exhibiting a lower overshoot and shorter settling time and fully validates the effectiveness of the proposed control strategy in terms of both the dynamic and steady-state performance in the production process.
Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology
Authors: Nunes Adonay S. , Patterson Matthew R. , Gerstel Dawid , Khan Sheraz , Guo Christine C. , Neishabouri Ali
Published: December 2024

Wearable accelerometers are widely used as an ecologically valid and scalable solution for long-term at-home sleep monitoring in both clinical research and care. In this study, we applied a deep learning domain adversarial convolutional neural network (DACNN) model to this task and demonstrated that this new model outperformed existing sleep algorithms in classifying sleep–wake and estimating sleep outcomes based on wrist-worn accelerometry. This model generalized well to another dataset based on different wearable devices and activity counts, achieving an accuracy of 80.1% (sensitivity 84% and specificity 58%). Compared to commonly used sleep algorithms, this model resulted in the smallest error in wake after sleep onset (MAE of 48.7, Cole–Kripke of 86.2, Sadeh of 108.2, z-angle of 57.5) and sleep efficiency (MAE of 11.8, Cole–Kripke of 18.4, Sadeh of 23.3, z-angle of 9.3) outcomes. Despite being around for many years, accelerometer-alone devices continue to be useful due to their low cost, long battery life, and ease of use. Improving the accuracy and generalizability of sleep algorithms for accelerometer wrist devices is of utmost importance. We here demonstrated that domain adversarial convolutional neural networks can improve the overall accuracy, especially the specificity, of sleep–wake classification using wrist-worn accelerometer data, substantiating its use as a scalable and valid approach for sleep outcome assessment in real life.
Detection and Quantification of DNA by Fluorophore-Induced Plasmonic Current: A Novel Sensing Approach
Authors: Pierce Daniel R. , Nichols Zach , Cunningham Clifton , Villaver Sean Avryl , Bajwah Abdullah , Oluwarotimi Samuel , Halaa Herbert , Geddes Chris D.
Published: December 2024

We report on the detection and quantification of aqueous DNA by a fluorophore-induced plasmonic current (FIPC) sensing method. FIPC is a mechanism described by our group in the literature where a fluorophore in close proximity to a plasmonically active metal nanoparticle film (MNF) is able to couple with it, when in an excited state. This coupling produces enhanced fluorescent intensity from the fluorophore–MNF complex, and if conditions are met, a current is generated in the film that is intrinsically linked to the properties of the fluorophore in the complex. The magnitude of this induced current is related to the spectral properties of the film, the overlap between these film properties and those of the fluorophore, the spacing between the nanoparticles in the film, the excitation wavelength, and the polarization of the excitation source. Recent literature has shown that the FIPC system is ideal for aqueous ion sensing using turn-on fluorescent probes, and in this paper, we subsequently examine if it is possible to detect aqueous DNA also via a turn-on fluorescent probe, as well as other commercially available DNA detection strategies. We report the effects of DNA concentration, probe concentration, and probe characteristics on the development of an FIPC assay for the detection of non-specific DNA in aqueous solutions.
A Miniaturized and Ultra-Low-Power Wireless Multi-Parameter Monitoring System with Self-Powered Ability for Aircraft Smart Skin
Authors: Wang Chongqi , Wang Yu , Pu Wei , Qiu Lei
Published: December 2024

The aircraft smart skin (ASS) with structural health monitoring capabilities is a promising technology. It enables the real-time acquisition of the aircraft’s structural health status and service environment, thereby improving the performance of the aircraft and ensuring the safety of its operation, which in turn reduces maintenance costs. In this paper, a miniaturized and ultra-low-power wireless multi-parameter monitoring system (WMPMS) for ASS is developed, which is capable of monitoring multiple parameters of an aircraft, including random impact events, vibration, temperature, humidity, and air pressure. The system adopts an all-digital monitoring method and a low-power operating mechanism, and it is integrated into a low-power hardware design. In addition, considering the airborne resources limitations, an energy self-supply module based on a thermoelectric generator (TEG) is developed to continuously power the system during flight. Based on the above design, the system has a size of only 45 mm × 50 mm × 30 mm and an average power consumption of just 7.59 mW. Through experimental validation, the system has excellent performance in multi-parameter monitoring and operating power consumption, and it can realize the self-supply of energy.
Research on the Stress Characteristics of Reuse of Semi-Rigid Base
Authors: Yu Liting , Tang Dong , Kang Haoyi , He Haiqi , Hu Donliang , Li Rui , Pei Jianzhong , Cheng Shihui
Published: December 2024

Semi-rigid bases are widely used in road construction due to their excellent properties, high rigidity, and frost resistance, and they have been in service for many years. However, as the service life increases, the maintenance demands also grow, with traditional maintenance methods still being the primary approach. Based on a typical case using ground-penetrating radar (GPR) technology, this study explores the issue of cracks in semi-rigid bases and their impact on overlay layers. The findings indicate that the overlay layer at semi-rigid base cracks struggles to withstand significant tensile and shear stresses, leading to reflective cracking and reducing pavement durability. To address this problem, this paper investigates the application potential of crushing technology in maintaining semi-rigid bases. Crushing technology has been widely employed in the maintenance of cement concrete panels, effectively eliminating reflective cracks and extending the service life of overlays. Given that semi-rigid bases share similar high-strength characteristics with cement concrete panels, crushing technology shows considerable applicability in semi-rigid base maintenance. This study employs a finite element analysis method to establish a semi-rigid base model under the impact load of multi-hammer equipment. It examines its dynamic mechanical response and evaluates the feasibility and effectiveness of crushing technology for semi-rigid base maintenance. Additionally, this study investigates the influence of the crushed layer’s modulus and thickness on key mechanical design indicators of the overlay and proposes recommendations for optimal design parameters. The research results provide valuable references for the design of the thickness and modulus in maintaining and repairing semi-rigid bases, contributing to improving pavement performance and durability.
Denoising Diffusion Implicit Model Combined with TransNet for Rolling Bearing Fault Diagnosis Under Imbalanced Data
Authors: Wang Chaobing , Huang Cong , Zhang Long , Xiang Zhibin , Xiao Yiwen , Qian Tongshuai , Liu Jiayang
Published: December 2024

Data imbalances present a serious problem for intelligent fault diagnosis. They can lead to reduced diagnostic precision, which can jeopardize equipment reliability and safety. Based on that, this paper proposes a novel fault diagnosis method combining the denoising diffusion implicit model (DDIM) with a new convolutional neural network framework. First, the Gramian angular difference field (GADF) is used to generate 2D images, which are then augmented using DDIM. Next, by utilizing the weight-sharing properties of a convolutional neural network and the self-attention mechanism along with the global data processing capabilities of Transformers, a TransNet model is constructed. The augmented data are input into the model for training to establish a fault diagnosis framework. Finally, the method is validated and analyzed using the CWRU bearing dataset and the Nanchang Railway Bureau dataset. The results show that the proposed method achieves over 99% recognition accuracy on the two datasets. Meanwhile, the proposed model provides better generalization performance and recognition accuracy than existing fault diagnosis methods.
Design and Evaluation of Ecological Interface of Driving Warning System Based on AR-HUD
Authors: Ma Jun , Li Yuhui , Zuo Yuanyang
Published: December 2024

As the global traffic environment becomes increasingly complex, driving safety issues have become more prominent, making manual-response driving warning systems (DWSs) essential. Augmented reality head-up display (AR-HUD) technology can project information directly, enhancing driver attention; however, improper design may increase cognitive load and affect safety. Thus, the design of AR-HUD driving warning interfaces must focus on improving attention and reducing cognitive load. Currently, systematic research on AR-HUD DWS interfaces is relatively scarce. This paper proposes an ecological interface cognitive balance design strategy for AR-HUD DWS based on cognitive load theory and environmental interface design theory. The research includes developing design models, an integrative framework, and experimental validation suitable for warning scenarios. Research results indicate that the proposed design effectively reduces cognitive load and significantly decreases driver response and comprehension times, outperforming existing interfaces. This design strategy and framework possess promotional value, providing theoretical references and methodological guidance for AR-HUD warning interface research.
Hardware-Assisted Low-Latency NPU Virtualization Method for Multi-Sensor AI Systems
Authors: Jean Jong-Hwan , Kim Dong-Sun
Published: December 2024

Recently, AI systems such as autonomous driving and smart homes have become integral to daily life. Intelligent multi-sensors, once limited to single data types, now process complex text and image data, demanding faster and more accurate processing. While integrating NPUs and sensors has improved processing speed and accuracy, challenges like low resource utilization and long memory latency remain. This study proposes a method to reduce processing time and improve resource utilization by virtualizing NPUs to simultaneously handle multiple deep-learning models, leveraging a hardware scheduler and data prefetching techniques. Experiments with 30,000 SA resources showed that the hardware scheduler reduced memory cycles by over 10% across all models, with reductions of 30% for NCF and 70% for DLRM. The hardware scheduler effectively minimized memory latency and idle NPU resources in resource-constrained environments with frequent context switching. This approach is particularly valuable for real-time applications like autonomous driving, enabling smooth transitions between tasks such as object detection and route planning. It also enhances multitasking in smart homes by reducing latency when managing diverse data streams. The proposed system is well suited for resource-constrained environments that demand efficient multitasking and low-latency processing.
MIRA-CAP: Memory-Integrated Retrieval-Augmented Captioning for State-of-the-Art Image and Video Captioning
Authors: Umirzakova Sabina , Muksimova Shakhnoza , Mardieva Sevara , Sultanov Baxtiyarovich Murodjon , Cho Young-Im
Published: December 2024

Generating accurate and contextually rich captions for images and videos is essential for various applications, from assistive technology to content recommendation. However, challenges such as maintaining temporal coherence in videos, reducing noise in large-scale datasets, and enabling real-time captioning remain significant. We introduce MIRA-CAP (Memory-Integrated Retrieval-Augmented Captioning), a novel framework designed to address these issues through three core innovations: a cross-modal memory bank, adaptive dataset pruning, and a streaming decoder. The cross-modal memory bank retrieves relevant context from prior frames, enhancing temporal consistency and narrative flow. The adaptive pruning mechanism filters noisy data, which improves alignment and generalization. The streaming decoder allows for real-time captioning by generating captions incrementally, without requiring access to the full video sequence. Evaluated across standard datasets like MS COCO, YouCook2, ActivityNet, and Flickr30k, MIRA-CAP achieves state-of-the-art results, with high scores on CIDEr, SPICE, and Polos metrics, underscoring its alignment with human judgment and its effectiveness in handling complex visual and temporal structures. This work demonstrates that MIRA-CAP offers a robust, scalable solution for both static and dynamic captioning tasks, advancing the capabilities of vision–language models in real-world applications.
Multi-Agent Reinforcement Learning-Based Computation Offloading for Unmanned Aerial Vehicle Post-Disaster Rescue
Authors: Wang Lixing , Jiao Huirong
Published: December 2024

Natural disasters cause significant losses. Unmanned aerial vehicles (UAVs) are valuable in rescue missions but need to offload tasks to edge servers due to their limited computing power and battery life. This study proposes a task offloading decision algorithm called the multi-agent deep deterministic policy gradient with cooperation and experience replay (CER-MADDPG), which is based on multi-agent reinforcement learning for UAV computation offloading. CER-MADDPG emphasizes collaboration between UAVs and uses historical UAV experiences to classify and obtain optimal strategies. It enables collaboration among edge devices through the design of the ’critic’ network. Additionally, by defining good and bad experiences for UAVs, experiences are classified into two separate buffers, allowing UAVs to learn from them, seek benefits, avoid harm, and reduce system overhead. The performance of CER-MADDPG was verified through simulations in two aspects. First, the influence of key hyperparameters on performance was examined, and the optimal values were determined. Second, CER-MADDPG was compared with other baseline algorithms. The results show that compared with MADDPG and stochastic game-based resource allocation with prioritized experience replay, CER-MADDPG achieves the lowest system overhead and superior stability and scalability.
Estimating a 3D Human Skeleton from a Single RGB Image by Fusing Predicted Depths from Multiple Virtual Viewpoints
Authors: Lie Wen-Nung , Vann Veasna
Published: December 2024

In computer vision, accurately estimating a 3D human skeleton from a single RGB image remains a challenging task. Inspired by the advantages of multi-view approaches, we propose a method of predicting enhanced 2D skeletons (specifically, predicting the joints’ relative depths) from multiple virtual viewpoints based on a single real-view image. By fusing these virtual-viewpoint skeletons, we can then estimate the final 3D human skeleton more accurately. Our network consists of two stages. The first stage is composed of a two-stream network: the Real-Net stream predicts 2D image coordinates and the relative depth for each joint from the real viewpoint, while the Virtual-Net stream estimates the relative depths in virtual viewpoints for the same joints. Our network’s second stage consists of a depth-denoising module, a cropped-to-original coordinate transform (COCT) module, and a fusion module. The goal of the fusion module is to fuse skeleton information from the real and virtual viewpoints so that it can undergo feature embedding, 2D-to-3D lifting, and regression to an accurate 3D skeleton. The experimental results demonstrate that our single-view method can achieve a performance of 45.7 mm on average per-joint position error, which is superior to that achieved in several other prior studies of the same kind and is comparable to that of other sequence-based methods that accept tens of consecutive frames as the input.
Coffee Leaf Rust Disease Detection and Implementation of an Edge Device for Pruning Infected Leaves via Deep Learning Algorithms
Authors: Araaf Raka Thoriq , Minn Arkar , Ahamed Tofael
Published: December 2024

Global warming and extreme climate conditions caused by unsuitable temperature and humidity lead to coffee leaf rust (Hemileia vastatrix) diseases in coffee plantations. Coffee leaf rust is a severe problem that reduces productivity. Currently, pesticide spraying is considered the most effective solution for mitigating coffee leaf rust. However, the application of pesticide spray is still not efficient for most farmers worldwide. In these cases, pruning the most infected leaves with leaf rust at coffee plantations is important to help pesticide spraying to be more efficient by creating a more targeted, accessible treatment. Therefore, detecting coffee leaf rust is important to support the decision on pruning infected leaves. The dataset was acquired from a coffee farm in Majalengka Regency, Indonesia. Only images with clearly visible spots of coffee leaf rust were selected. Data collection was performed via two devices, a digital mirrorless camera and a phone camera, to diversify the dataset and test it with different datasets. The dataset, comprising a total of 2024 images, was divided into three sets with a ratio of 70% for training (1417 images), 20% for validation (405 images), and 10% for testing (202 images). Images with leaves infected by coffee leaf rust were labeled via LabelImg® with the label “CLR”. All labeled images were used to train the YOLOv5 and YOLOv8 algorithms through the convolutional neural network (CNN). The trained model was tested with a test dataset, a digital mirrorless camera image dataset (100 images), a phone camera dataset (100 images), and real-time detection with a coffee leaf rust image dataset. After the model was trained, coffee leaf rust was detected in each frame. The mean average precision (mAP) and recall for the trained YOLOv5 model were 69% and 63.4%, respectively. For YOLOv8, the mAP and recall were approximately 70.2% and 65.9%, respectively. To evaluate the performance of the two trained models in detecting coffee leaf rust on trees, 202 original images were used for testing with the best-trained weight from each model. Compared to YOLOv5, YOLOv8 demonstrated superior accuracy in detecting coffee leaf rust. With a mAP of 73.2%, YOLOv8 outperformed YOLOv5, which achieved a mAP of 70.5%. An edge device was utilized to deploy real-time detection of CLR with the best-trained model. The detection was successfully executed with high confidence in detecting CLR. The system was further integrated into pruning solutions for Arabica coffee farms. A pruning device was designed using Autodesk Fusion 360® and fabricated for testing on a coffee plantation in Indonesia.
A Hybrid Harmonic Curve Model for Multi-Streamer Hydrophone Positioning in Seismic Exploration
Authors: Sang Kaiwei , Kuang Cuilin , Lv Lingsheng , Liu Heng , Zhang Haonan , Yang Yijun , Yang Baocai
Published: December 2024

Towed streamer positioning is a vital and essential stage in marine seismic exploration, and accurate hydrophone coordinates exert a direct and significant influence on the quality and reliability of seismic imaging. Current methods predominantly rely on analytical polynomial models for towed streamer positioning; however, these models often produce significant errors when fitting to streamers with high curvature, particularly during turning scenarios. To address this limitation, this study introduces a novel multi-streamer analytical positioning method that uses a hybrid harmonic function to model the three-dimensional coordinates of streamers. This approach mitigates the substantial modeling errors associated with polynomial models in high-curvature conditions and better captures the dynamic characteristics of streamer fluctuations. Firstly, the mathematical model for the hybrid harmonic function is constructed. Then, the algorithmic implementation of the model is detailed, along with the derivation of the error equation and the multi-sensor fusion solution process. Finally, the validity of the model is verified using both simulated and field data. The results demonstrate that, in the turning scenario without added error, the proposed harmonic model improves simulation accuracy by 35.5% compared to the analytical polynomial model, and by 27.2% when error is introduced. For field data, accuracy improves by 18.1%, underscoring the model’s effectiveness in significantly reducing errors associated with polynomial models in turning scenarios. The performance of the harmonic function model is generally comparable to that of the polynomial model in straight scenarios.
XAI GNSS—A Comprehensive Study on Signal Quality Assessment of GNSS Disruptions Using Explainable AI Technique
Authors: Elango Arul , Landry Rene Jr.
Published: December 2024

The hindering of Global Navigation Satellite Systems (GNSS) signal reception by jamming and spoofing attacks degrades the signal quality. Careful attention needs to be paid when post-processing the signal under these circumstances before feeding the signal into the GNSS receiver’s post-processing stage. The identification of the time domain statistical attributes and the spectral domain characteristics play a vital role in analyzing the behaviour of the signal characteristics under various kinds of jamming attacks, spoofing attacks, and multipath scenarios. In this paper, the signal records of five disruptions (pure, continuous wave interference (CWI), multi-tone continuous wave interference (MCWI), multipath (MP), spoofing, pulse, and chirp) are examined, and the most influential features in both the time and frequency domains are identified with the help of explainable AI (XAI) models. Different Machine learning (ML) techniques were employed to assess the importance of the features to the model’s prediction. From the statistical analysis, it has been observed that the usage of the SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) models in GNSS signals to test the types of disruption in unknown GNSS signals, using only the best-correlated and most important features in the training phase, provided a better classification accuracy in signal prediction compared to traditional feature selection methods. This XAI model reveals the black-box ML model’s output prediction and provides a clear explanation of the specific signal occurrences based on the individual feature contributions. By using this black-box revealer, we can easily analyze the behaviour of the GNSS ground-station signals and employ fault detection and resilience diagnosis in GNSS post-processing.
Remote Sensing Techniques for Water Quality Monitoring: A Review
Authors: Jaywant Swapna A. , Arif Khalid Mahmood
Published: December 2024

Freshwater resources are facing increasing challenges to water quality, due to factors such as population growth, human activities, climate change, and various human-made pressures. While on-site methods, as specified in the USGS water quality sampling handbook, are usually precise, they require more time, are costly, and provide data at specific points, which lacks the essential comprehensive geographic and temporal detail for water body assessment and management. Hence, conventional on-site monitoring methods are unable to provide a complete representation of freshwater systems. To address concerns regarding geographic and time-based coverage, remote sensing has developed into an effective solution, taking advantage of recent advancements in sensor technology and methodologies. The combination of GPS and GIS technologies, along with remotely sensed data, provides an efficient resource for continual monitoring and evaluation of water bodies. The use of remotely sensed data helps to establish a reliable geospatial database, serving as a standard for subsequent evaluations. The review emphasizes the contribution of remote sensing to water monitoring. It starts by exploring various space-borne and airborne sensors utilized for this purpose. Subsequently, the review explores remote sensing applications for water quality. Lastly, the review discusses the overall benefits and challenges related to remote sensing in water monitoring.
Performance Analysis and Design Principles of Wireless Mutual Broadcast Using Heterogeneous Transmit Power for Proximity-Aware Services
Authors: Kwon Taesoo , Lee HyeonWoo
Published: December 2024

As proximity-aware services among devices such as sensors, IoT devices, and user equipment are expected to facilitate a wide range of new applications in the beyond 5G and 6G era, managing heterogeneous environments with diverse node capabilities becomes essential. This paper analytically models and characterizes the performance of heterogeneous random access-based wireless mutual broadcast (RA-WMB) with distinct transmit (Tx) power levels, leveraging a marked Poisson point process to account for nodes’ various Tx power. In particular, this study enables the performance of RA-WMB with heterogeneous Tx power to be represented in terms of the performance of RA-WMB with a common Tx power by deriving an equivalent Tx power based on the probability distribution of heterogeneous Tx power and the path loss exponent. This approach allows for an analytical and quantitative comparison of heterogeneous RA-WMB performance with the common Tx power configuration. Further, the study derives performance ratios among node groups with distinct Tx power levels and formulates an optimization problem to design a heterogeneous Tx power configuration that balances individual node group performance improvements with overall network performance, yielding the optimal Tx power configuration. A closed-form suboptimal transmission probability (TxPr) is also proposed to improve heterogeneous RA-WMB performance, providing an efficient alternative to iterative methods for the optimal TxPr. Numerical results demonstrate the accuracy of performance analysis and highlight the effectiveness of the proposed designs.
Design and Uncertainty Evaluation of a Calibration Setup for Turbine Blades Vibration Measurement
Authors: Capponi Lorenzo , Tribbiani Giulio , Medici Vittoria , Fabri Sara , Prato Andrea , Castellini Paolo , Schiavi Alessandro , Paone Nicola , Rossi Gianluca
Published: December 2024

Turbomachinery engines face significant failure risks due to the combination of thermal loads and high-amplitude vibrations in turbine and compressor blades. Accurate stress distribution measurements are critical for enhancing the performance and safety of these systems. Blade tip timing (BTT) has emerged as an advanced alternative to traditional measurement methods, capturing blade dynamics by detecting deviations in blade tip arrival times through sensors mounted on the stator casing. This research focuses on developing an analytical model to quantify the uncertainty budget involved in designing a calibration setup for BTT systems, ensuring targeted performance levels. Unlike existing approaches, the proposed model integrates both operational variability and sensor performance characteristics, providing a comprehensive framework for uncertainty quantification. The model incorporates various operating and measurement scenarios to create an accurate and reliable calibration tool for BTT systems. In the broader context, this advancement supports the use of BTT for qualification processes, ultimately extending the lifespan of turbomachinery through condition-based maintenance. This approach enhances performance validation and monitoring in power plants and aircraft engines, contributing to safer and more efficient operations.
Unsupervised Learning for Machinery Adaptive Fault Detection Using Wide-Deep Convolutional Autoencoder with Kernelized Attention Mechanism
Authors: Yan Hao , Si Xiangfeng , Liang Jianqiang , Duan Jian , Shi Tielin
Published: December 2024

Applying deep learning to unsupervised bearing fault diagnosis in complex industrial environments is challenging. Traditional fault detection methods rely on labeled data, which is costly and labor-intensive to obtain. This paper proposes a novel unsupervised approach, WDCAE-LKA, combining a wide kernel convolutional autoencoder (WDCAE) with a large kernel attention (LKA) mechanism to improve fault detection under unlabeled conditions, and the adaptive threshold module based on a multi-layer perceptron (MLP) dynamically adjusts thresholds, boosting model robustness in imbalanced scenarios. Experimental validation on two datasets (CWRU and a customized ball screw dataset) demonstrates that the proposed model outperforms both traditional and state-of-the-art methods. Notably, WDCAE-LKA achieved an average diagnostic accuracy of 90.29% in varying fault scenarios on the CWRU dataset and 72.89% in the customized ball screw dataset and showed remarkable robustness under imbalanced conditions; compared with advanced models, it shortens training time by 10–26% and improves average fault diagnosis accuracy by 5–10%. The results underscore the potential of the WDCAE-LKA model as a robust and effective solution for intelligent fault diagnosis in industrial applications.
The Ongoing Multi-Dimensional Impacts of COVID-19 on Wellbeing: Evidence from a Quasi-Longitudinal Survey in Israel
Authors: Israel Fabian , Zaychik Danielle , Rein-Sapir Yonat , Feitelson Eran , Shmueli Deborah , Altshuler Alex , Plaut Pnina , Salzberger Eli
Published: December 2024

During the COVID-19 pandemic, many countries implemented widespread restrictions in an effort to mitigate the spread of the virus. These measures had various impacts on the wellbeing of residents. This study explores the effects of the pandemic on wellbeing across different segments of the population in Israel, a country that rapidly administered vaccines and eased restrictions. Two online surveys were conducted on a representative sample of the population, one during the peak of the crisis and another 15 months later during the tail end. The analysis, conducted using multivariate statistical methods, revealed that the negative effects on wellbeing observed during the height of the crisis had largely diminished over time. The significant lifestyle changes prompted by the pandemic and governmental responses had mostly short-lived consequences on wellbeing. Yet, social connections continued to exhibit the strongest association with mitigating subjective wellbeing impacts. Additionally, this study found that the gap in wellbeing between disadvantaged and privileged groups widened as the crisis subsided, suggesting that some disasters may have an initial equalizing effect that wears off with time. The results emphasize the importance of considering the wellbeing impacts when implementing public health policies and providing ongoing support, particularly for disadvantaged populations, throughout the recovery period.
Integrating Temporal Dimensions in Circularity of the Built Environment Analysis of Two Flemish Industrial Parks
Authors: Timmers Charlotte , Verbiest Ellen , Ottoy Sam , Marin Julie
Published: December 2024

This manuscript explores how incorporating temporal dimensions into built environment research can promote a more circular society, adding societal improvements to efficiency-driven measures closing waste or material cycles. The current circularity approaches in industrial environments mainly focus on short-term innovations reducing resource extraction and waste, overlooking long-term circularity potentials of natural resource management such as living soils as a basis for all life. This study addresses this gap by investigating, analyzing, and drawing interplays between regenerative soil cycles and business development cycles in two Flemish industry parks, Kortrijk-Noord and Haasrode. Using diachronic mapping, a qualitative design and action research tool, the study aims to generate a space–time composite of soil and business cycles, integrating archival research, interviews, and policy document reviews. This method visually captures interplays between geology, land valuation, and economic development, demonstrating that integrating soil and business cycles can suggest new pathways for site-specific circular practices on Flemish industry parks, which can inform site-specific project frameworks for circular built environments. As such, the research advocates a paradigm shift in industry park (re)development, from product and material innovation within a ‘time is money’ framework to an integrated ‘time is life’ approach, where time’s historical and social dimensions are part of circular landscape development.
The Global Challenge of Fluoride Contamination: A Comprehensive Review of Removal Processes and Implications for Human Health and Ecosystems
Authors: Mariappan Santhi Vivek , Periasamy Diwahar , Perumal Muthumari , Sekar Prasanth Morkondan , Varatharajan Varalakshmi , Aravind Dhandapani , Senthilkumar Krishnasamy , Kumaran Sundaresan Thirumalai , Ali Saood , Sankar Subramanipillai , Vijayakumar Nivetha , Boominathan Charubala , Krishnan Ragulasangeerthian Suresh
Published: December 2024

Water resources are vital for humanity, but their quality has degraded in recent years due to increasing industrial activities. One significant issue is fluoride contamination, prevalent worldwide. Fluorides exist in combined states such as calcium fluoride, fluorapatite, and cryolite, originating from industrial processes like aluminum and fertilizer manufacturing. The World Health Organization warns against fluoride levels above 1.5 mg/L in drinking water due to health risks, including dental and skeletal fluorosis. Industrial activities also release fluoride-containing wastes into the environment, endangering ecosystems and human health. Overexposure to fluoride leads to disorders affecting organs including the kidneys, liver, and nervous system. Despite fluoride’s benefits in controlled doses, excessive intake causes health problems, as evidenced by rising dental fluorosis cases in Brazil. Thus, effective and affordable fluoride removal strategies are crucial. Various methods exist, including adsorption, membrane technology, ion exchange process, electrodialysis, and electrocoagulation. Regulation of fluoride levels in drinking water is imperative to safeguard public health from its detrimental long-term effects.
Research on the Stability Mechanism and Control Technology of Surrounding Rock in Filling Working Face with Gob-Side Entry Retaining
Authors: Chen Dingchao , Wang Xiangyu , Bai Jianbiao , Xu Changtao , Chu Yuan , Hou Biao , Niu Zhenpeng , Wang Xian
Published: December 2024

Gob-side entry retaining (GER) in filling working face promotes sustainable mining by preserving roadways for reuse, reducing resource consumption, and minimizing environmental disturbances. This study investigates the deformation mechanism and failure characteristic of the mining roadway during GER in filling working face, using the CT301 headgate at Chahasu Coal Mine as a case study. A UDEC Trigon numerical model was established, and uniaxial compression tests were conducted to calibrate the mechanical parameters of the rock mass and filling material. The deformation, crack distribution, overburden subsidence, and lateral stress were compared under four conditions: caving method and filling rates of 65%, 80%, and 95%. The results showed that compared to the caving method, the filling method can effectively control overburden movement and suppress roadway deformation. As the filling rate increases, the surrounding rock deformation, crack density, subsidence, and lateral stress all decrease. Overall, the 95% filling rate was the most effective, followed by 80% filling rate, 65% filling rate, and then the caving method. After adopting a 95% filling rate at CT301 panel, the maximum deformation of CT301 headgate was only 190 mm, meeting the mine’s production requirements.
Toward Low-Carbon Agriculture: Factor Decomposition and Decoupling Analysis of Agricultural Carbon Emissions in Northeast China
Authors: Lv Donghui , Zhang Yu
Published: December 2024

Chemical fertilizer inputs in China peaked in 2015; however, agricultural carbon emissions continue to rise, and the effect of chemical fertilizer inputs on agricultural carbon emissions remains unclear in this context. This paper aims to offer a useful policy reference for low-carbon agriculture based on agrochemical inputs. Taking northeast China as an example, we incorporated chemical fertilizers as a factor in the generalized Divisia index model (GDIM) and conducted a decoupling analysis using a decoupling effort index (DEI) on data from 2000 to 2020. The factor decomposition results indicate that the chemical fertilizer input scale served as a driving factor with a declining trend, and carbon productivity from chemical fertilizer shifted from an inhibiting effect to a driving effect on agricultural carbon emissions. The results of integrating the GDIM with a DEI indicate that reducing chemical fertilizer inputs and exerting the inhibiting effect of carbon productivity from chemical fertilizer both contribute to effective decoupling.