bluuebunny/crossref_metadata_2025 huggingface dataset:
Thiamethoxam-Induced Intergenerational Sublethal Effects on the Life History and Feeding Behavior of Rhopalosiphum padi
Authors: Gul Hina , Haq Ihsan ul , Güncan Ali , Abbas Arzlan , Khan Shanza , Yaseen Aqsa , Ullah Farman , Desneux Nicolas , Liu Xiaoxia
Published: March 2024

Thiamethoxam, a second-generation neonicotinoid insecticide is widely used for controlling sap-sucking insect pests including Rhopalosiphum padi. The current study aimed to investigate the life-history parameters and feeding behavior of R. padi following treatments with sublethal concentrations of thiamethoxam. The lethal concentration 50 (LC50) value of thiamethoxam against adult R. padi was 11.458 mg L−1 after 48 h exposure. The sublethal concentrations of thiamethoxam (LC5 and LC10) significantly decreased the adult longevity, fecundity, and reproductive days in the directly exposed aphids (F0 generation). In the progeny R. padi (F1), the developmental durations and total prereproductive period (TPRP) were decreased while the adult longevity, fecundity, and reproductive days (RPd) were increased at both thiamethoxam concentrations. The demographic parameters including the net reproductive rate (R0), intrinsic rate of increase (r), and finite rate of increase (λ) were prolonged only at the LC5 of thiamethoxam. The EPG results indicated that the sublethal concentrations of thiamethoxam increases the total duration of non-probing (Np) while reducing the total duration of E2 in directly exposed aphids (F0). Interestingly, the E2 were significantly increased in the progeny generation (F1) descending from previously exposed parental aphids (F0). Overall, this study showed that thiamethoxam exhibited high toxicity against directly exposed individuals (F0), while inducing intergenerational hormetic effects on the progeny generation (F1) of R. padi. These findings provided crucial details about thiamethoxam-induced hormetic effects that might be useful in managing resurgences of this key pest.
Morphological, Anatomical, and Physiological Characteristics of Heteroblastic Acacia melanoxylon Grown under Weak Light
Authors: Bai Xiaogang , Chen Zhaoli , Chen Mengjiao , Zeng Bingshan , Li Xiangyang , Tu Panfeng , Hu Bing
Published: March 2024

Acacia melanoxylon is a fast-growing macrophanerophyte with strong adaptability whose leaf enables heteromorphic development. Light is one of the essential environmental factors that induces the development of the heteroblastic leaf of A. melanoxylon, but its mechanism is unclear. In this study, the seedlings of A. melanoxylon clones were treated with weak light (shading net with 40% of regular light transmittance) and normal light (control) conditions for 90 d and a follow-up observation. The results show that the seedlings’ growth and biomass accumulation were inhibited under weak light. After 60 days of treatment, phyllodes were raised under the control condition while the remaining compound was raised under weak light. The balance of root, stem, and leaf biomass changed to 15:11:74 under weak light, while it was 40:15:45 under control conditions. After comparing the anatomical structures of the compound leaves and phyllode, they were shown to have their own strategies for staying hydrated, while phyllodes were more able to control water loss and adapt to intense light. The compound leaves exhibited elevated levels of K, Cu, Ca, and Mg, increased antioxidant enzyme activity and proline content, and higher concentrations of chlorophyll a, carotenoids, ABA, CTK, and GA. However, they displayed a relatively limited photosynthetic capacity. Phyllodes exhibited higher levels of Fe, cellulose, lignin, IAA content, and high photosynthetic capacity with a higher maximum net photosynthetic rate, light compensation point, dark respiration rate, and water use efficiency. The comparative analysis of compound leaves and phyllodes provides a basis for understanding the diverse survival strategies that heteroblastic plants employ to adapt to environmental changes.
The Effects of Heavy Metal Pollution on Soil Nitrogen Transformation and Rice Volatile Organic Compounds under Different Water Management Practices
Authors: Afzal Muhammad , Muhammad Sajid , Tan Dedong , Kaleem Sidra , Khattak Arif Ali , Wang Xiaolin , Chen Xiaoyuan , Ma Liangfang , Mo Jingzhi , Muhammad Niaz , Jan Mehmood , Tan Zhiyuan
Published: March 2024

One of the most concerning global environmental issues is the pollution of agricultural soils by heavy metals (HMs), especially cadmium, which not only affects human health through Cd-containing foods but also impacts the quality of rice. The soil’s nitrification and denitrification processes, coupled with the release of volatile organic compounds by plants, raise substantial concerns. In this review, we summarize the recent literature related to the deleterious effects of Cd on both soil processes related to the N cycle and rice quality, particularly aroma, in different water management practices. Under both continuous flooding (CF) and alternate wetting and drying (AWD) conditions, cadmium has been observed to reduce both the nitrification and denitrification processes. The adverse effects are more pronounced in alternate wetting and drying (AWD) as compared to continuous flooding (CF). Similarly, the alteration in rice aroma is more significant in AWD than in CF. The precise modulation of volatile organic compounds (VOCs) by Cd remains unclear based on the available literature. Nevertheless, HM accumulation is higher in AWD conditions compared to CF, leading to a detrimental impact on volatile organic compounds (VOCs). The literature concludes that AWD practices should be avoided in Cd-contaminated fields to decrease accumulation and maintain the quality of the rice. In the future, rhizospheric engineering and plant biotechnology can be used to decrease the transport of HMs from the soil to the plant’s edible parts.
Cold Atmospheric Plasma Medicine: Applications, Challenges, and Opportunities for Predictive Control
Authors: Kazemi Ali , Nicol McKayla J. , Bilén Sven G. , Kirimanjeswara Girish S. , Knecht Sean D.
Published: March 2024

Plasma medicine is an emerging field that applies the science and engineering of physical plasma to biomedical applications. Low-temperature plasma, also known as cold plasma, is generated via the ionization of atoms in a gas, generally via exposure to strong electric fields, and consists of ions, free radicals, and molecules at varying energy states. Plasmas generated at low temperatures (approximately room temperature) have been used for applications in dermatology, oncology, and anti-microbial strategies. Despite current and ongoing clinical use, the exact mechanisms of action and the full range of effects of cold plasma treatment on cells are only just beginning to be understood. Direct and indirect effects of plasma on immune cells have the potential to be utilized for various applications such as immunomodulation, anti-infective therapies, and regulating inflammation. In this review, we combine diverse expertise in the fields of plasma chemistry, device design, and immunobiology to cover the history and current state of plasma medicine, basic plasma chemistry and their implications, the effects of cold atmospheric plasma on host cells with their potential immunological consequences, future directions, and the outlook and recommendations for plasma medicine.
The Crystallization Morphology and Conformational Changes of Polypropylene Random Copolymer Induced by a Novel β-Nucleating Agent
Authors: Wu Bo , Zheng Xian , Ren Yanwei , Yu Hailong , Wang Yubo , Jiang Huanfeng
Published: March 2024

The crystal morphology and conformational changes during crystallization of a polypropylene random copolymer (PPR) are the basis for understanding its crystallization process. In this work, novel rare-earth β-nucleating agent WBN-28 was directly added into PPR to induce β-crystallization. The results of differential scanning calorimetry (DSC) showed that it has an excellent β-crystal-induced effect. The β-crystal content could surpass 85%, calculated from wide-angle X-ray diffraction (WAXD) data. The morphology of the β-crystal and α-crystal was intuitively observed via a polarizing optical microscope (POM). The β-crystallites were interconnected to naturally develop plate-like crystalline regions possessing a certain size, and the α-crystallites with sufficient thicknesses possessed a cross-hatched phenomenon. The bundle-like supramolecular structure of the β-crystal induced by WBN-28 was further observed via a scanning electron microscope (SEM). The conformational changes in the crystallization process of PPR were resolved via high-resolution infrared spectroscopy to understand its β-crystallization in depth. The conformational changes during the crystallization of PPR were found to be different from those of the isotactic polypropylene homopolymer (PPH); they had their own characteristics. This will provide guidance for understanding the β-crystallization of PPR in depth.
Progress and Prospect of Ion Imprinting Technology in Targeted Extraction of Lithium
Authors: Zhi Keke , Duan Jinwang , Zhang Jiarui , Huang Lianting , Guo Lianghui , Wang Lulu
Published: March 2024

Ion Imprinting Technology (IIT) is an innovative technique that produces Ion-Imprinted polymers (IIPs) capable of selectively extracting ions. IIPs exhibit strong specificity, excellent stability, and high practicality. Due to their superior characteristics, the application of IIPs for lithium resource extraction has garnered significant attention. This paper discusses the following aspects based on existing conventional processes for lithium extraction and the latest research progress in lithium IIPs: (1) a detailed exposition of existing lithium extraction processes, including comparisons and summaries; (2) classification, comparison, and summarization of the latest lithium IIPs based on different material types and methods; (3) summarization of the applications of various lithium IIPs, along with a brief description of future directions in the development of lithium IIP applications. Finally, the prospects for targeted recovery of lithium resources using lithium IIPs are presented.
Monitoring Aggregation Processes in Multiphase Systems: A Review
Authors: Rasteiro Maria Graça , Koponen Antti
Published: March 2024

Particle aggregation is essential in many industrial processes, spanning the pharmaceutical and food industries, polymer production, and the environment, among others. However, aggregation can also occur, in some processes, as a non-desired side effect. Thus, to be able to monitor aggregation in industrial processes is of high importance to guarantee that the final, required product characteristics are obtained. In this paper, we present an extensive review of the different techniques available for monitoring particle characteristics in industrial processes involving particulate materials, with special emphasis on aggregation processes. These methods include both off-line and on-line techniques, based either on image acquisition techniques or different radiation scattering techniques (light-scattering and ultrasound spectroscopy). The principles behind each technique are addressed, together with their relevant applications, advantages, and disadvantages.
Fundamental Mechanisms in Membrane Receptology: Old Paradigms, New Concepts and Perspectives
Authors: Fantini Jacques
Published: March 2024

Receptology, the science of receptors, is a multidimensional field of research which can be dissected into biosynthesis, membrane sorting, ligand binding and signal transduction. Plasma membrane receptors connect the cells with their environment and transmit signals that are translated into biological information. The historical paradigm of ligand–receptor interactions is the lock-and-key model. This model presupposes that both partners have a precise 3D shape that perfectly fits together to form the ligand–receptor complex. However, this simple model suffers from severe limitations due to several levels of simplifications: (i) water molecules and membrane lipids are not considered; (ii) not all ligands have a stable 3D structure; (iii) the ligand-binding pocket of the receptor is often flexible and conformationally rearranged after the initial binding step (induced fit mechanism) and/or subjected to conformational selection by the ligand; (iv) there are signal transduction mechanisms which can be either purely mechanical (conformational change of the receptor induced after binding of the ligand), lipid-assisted (e.g., by raft lipids such as cholesterol or gangliosides), or in some instances of quantic nature (detection of odorant molecules). The aim of the present review is to challenge the old paradigms and present new concepts of membrane receptology that consider the impact of critical parameters such as water molecules, membrane lipids, electrostatic surface potential and quantum mechanisms.
Goethe’s Platonic Natural Philosophy: How Goethean Science Provides an Alternative Conception of the Cosmos
Authors: Hart Seth P.
Published: March 2024

While popularly known for his works of literature and poetry, Johann Wolfgang von Goethe viewed his lesser-known scientific pieces as his most enduring achievement. I will argue that Goethe’s unique scientific methodology is informed by a metaphysical commitment to a form of Platonism and that Goethe provides an intriguing alternative paradigm that unifies science, philosophy, theology, and ethics. I begin by demonstrating how Goethe’s concept of the Urphänomen offers a Platonic conception of natural beings. I then briefly outline how this alternative scientific approach ultimately derives from his Platonic commitments. Next, I demonstrate the ethical and spiritual implications of Goethean science, establishing that Goethe’s approach bridges the divide between our scientific endeavors and spiritual formation. There is, then, a continued relevance for Goethe in conversations regarding ecological ethics and our perception of nature.
Change Detection in Point Clouds Using 3D Fractal Dimension
Authors: Casas-Rosa Juan C. , Navarro Pablo , Segura-Sánchez Rafael J. , Rueda-Ruiz Antonio J. , López-Ruiz Alfonso , Fuertes José M. , Delrieux Claudio , Ogayar-Anguita Carlos J.
Published: March 2024

The management of large point clouds obtained by LiDAR sensors is an important topic in recent years due to the widespread use of this technology in a wide variety of applications and the increasing volume of data captured. One of the main applications of LIDAR systems is the study of the temporal evolution of the real environment. In open environments, it is important to know the evolution of erosive processes or landscape transformation. In the context of civil engineering and urban environments, it is useful for monitoring urban dynamics and growth, and changes during the construction of buildings or infrastructure facilities. The main problem with change detection (CD) methods is erroneous detection due to precision errors or the use of different capture devices at different times. This work presents a method to compare large point clouds, based on the study of the local fractal dimension of point clouds at multiple scales. Our method is robust in the presence of environmental and sensor factors that produce abnormal results with other methods. Furthermore, it is more stable than others in cases where there is no significant displacement of points but there is a local alteration of the structure of the point cloud. Furthermore, the precision can be adapted to the complexity and density of the point cloud. Finally, our solution is faster than other CD methods such as distance-based methods and can run at O(1) under some conditions, which is important when working with large datasets. All these improvements make the proposed method more suitable than the others to solve complex problems with LiDAR data, such as storage, time series data management, visualization, etc.
Causal Meta-Reinforcement Learning for Multimodal Remote Sensing Data Classification
Authors: Zhang Wei , Wang Xuesong , Wang Haoyu , Cheng Yuhu
Published: March 2024

Multimodal remote sensing data classification can enhance a model’s ability to distinguish land features through multimodal data fusion. In this context, how to help models understand the relationship between multimodal data and target tasks has become the focus of researchers. Inspired by the human feedback learning mechanism, causal reasoning mechanism, and knowledge induction mechanism, this paper integrates causal learning, reinforcement learning, and meta learning into a unified remote sensing data classification framework and proposes causal meta-reinforcement learning (CMRL). First, based on the feedback learning mechanism, we overcame the limitations of traditional implicit optimization of fusion features and customized a reinforcement learning environment for multimodal remote sensing data classification tasks. Through feedback interactive learning between agents and the environment, we helped the agents understand the complex relationships between multimodal data and labels, thereby achieving full mining of multimodal complementary information.Second, based on the causal inference mechanism, we designed causal distribution prediction actions, classification rewards, and causal intervention rewards, capturing pure causal factors in multimodal data and preventing false statistical associations between non-causal factors and class labels. Finally, based on the knowledge induction mechanism, we designed a bi-layer optimization mechanism based on meta-learning. By constructing a meta training task and meta validation task simulation model in the generalization scenario of unseen data, we helped the model induce cross-task shared knowledge, thereby improving its generalization ability for unseen multimodal data. The experimental results on multiple sets of multimodal datasets showed that the proposed method achieved state-of-the-art performance in multimodal remote sensing data classification tasks.
Comparison of Three Approaches for Estimating Understory Biomass in Yanshan Mountains
Authors: Li Yuanqi , Hu Ronghai , Xing Yuzhen , Pang Zhe , Chen Zhi , Niu Haishan
Published: March 2024

Aboveground biomass (AGB) of shrubs and low-statured trees constitutes a substantial portion of the total carbon pool in temperate forest ecosystems, contributing much to local biodiversity, altering tree-regeneration growth rates, and determining above- and belowground food webs. Accurate quantification of AGB at the shrub layer is crucial for ecological modeling and still remains a challenge. Several methods for estimating understory biomass, including inventory and remote sensing-based methods, need to be evaluated against measured datasets. In this study, we acquired 158 individual terrestrial laser scans (TLS) across 45 sites in the Yanshan Mountains and generated metrics including leaf area and stem volume from TLS data using voxel- and non-voxel-based approaches in both leaf-on and leaf-off scenarios. Allometric equations were applied using field-measured parameters as an inventory approach. The results indicated that allometric equations using crown area and height yielded results with higher accuracy than other inventory approach parameters (R2 and RMSE ranging from 0.47 to 0.91 and 12.38 to 38.11 g, respectively). The voxel-based approach using TLS data provided results with R2 and RMSE ranging from 0.86 to 0.96 and 6.43 to 21.03 g. Additionally, the non-voxel-based approach provided similar or slightly better results compared to the voxel-based approach (R2 and RMSE ranging from 0.93 to 0.96 and 4.23 to 11.27 g, respectively) while avoiding the complexity of selecting the optimal voxel size that arises during voxelization.
CroplandCDNet: Cropland Change Detection Network for Multitemporal Remote Sensing Images Based on Multilayer Feature Transmission Fusion of an Adaptive Receptive Field
Authors: Wu Qiang , Huang Liang , Tang Bo-Hui , Cheng Jiapei , Wang Meiqi , Zhang Zixuan
Published: March 2024

Dynamic monitoring of cropland using high spatial resolution remote sensing images is a powerful means to protect cropland resources. However, when a change detection method based on a convolutional neural network employs a large number of convolution and pooling operations to mine the deep features of cropland, the accumulation of irrelevant features and the loss of key features will lead to poor detection results. To effectively solve this problem, a novel cropland change detection network (CroplandCDNet) is proposed in this paper; this network combines an adaptive receptive field and multiscale feature transmission fusion to achieve accurate detection of cropland change information. CroplandCDNet first effectively extracts the multiscale features of cropland from bitemporal remote sensing images through the feature extraction module and subsequently embeds the receptive field adaptive SK attention (SKA) module to emphasize cropland change. Moreover, the SKA module effectively uses spatial context information for the dynamic adjustment of the convolution kernel size of cropland features at different scales. Finally, multiscale features and difference features are transmitted and fused layer by layer to obtain the content of cropland change. In the experiments, the proposed method is compared with six advanced change detection methods using the cropland change detection dataset (CLCD). The experimental results show that CroplandCDNet achieves the best F1 and OA at 76.04% and 94.47%, respectively. Its precision and recall are second best of all models at 76.46% and 75.63%, respectively. Moreover, a generalization experiment was carried out using the Jilin-1 dataset, which effectively verified the reliability of CroplandCDNet in cropland change detection.
Effects of Geomorphic Spatial Differentiation on Vegetation Distribution Based on Remote Sensing and Geomorphic Regionalization
Authors: Xu Hua , Cheng Weiming , Wang Baixue , Song Keyu , Zhang Yichi , Wang Ruibo , Bao Anming
Published: March 2024

As the core area of human activities and economic development in the Xinjiang Autonomous Region, the hilly oasis zone of Xinjiang directly affects the regional sustainable development and stability of the ecosystem. Understanding the effects of different geomorphic types on vegetation distribution is crucial for maintaining vegetation growth and development, especially the improvement in the terrestrial ecological environment in arid areas under the background of climate change. However, there are few studies on the effect of spatial differences in detailed geomorphic types on vegetation distribution patterns. Therefore, this paper divides the Xinjiang hilly oasis zone into six geomorphologic level zones and innovatively investigates the influence of detailed geomorphologic types on the spatial distribution of vegetation and vegetation cover. Further, the area proportion of detailed landform types corresponding to different vegetation coverage in each geomorphic area was quantitatively calculated. Finally, the Geodetector method was used to detect the drivers of interactions between vegetation and the environment. The findings are shown as follows: (1) In the same climate zone, the spatial differentiation of landforms has a great influence on the vegetation distribution, manifesting as the significantly different vegetation distribution in different landform types. Grassland is the main vegetation type in the erosion and denudation of Nakayama; cultivated vegetation and meadows have a larger coverage in the alluvial flood plain and alluvial plain; and the distribution of vegetation in the Tianshan economic zone is characterized by obvious vertical zoning with the geomorphology. (2) The landform type and morphological types are the strongest driving factors for vegetation coverage with q values of 0.433 and 0.295, respectively, which effectually fill the gap caused by only using two terrain indicators, slope and elevation, to study the relationship between landforms and vegetation. (3) In addition, the improved nonlinear interaction resulting from the double factor of landform type and slope is 0.486, which has a stronger control on vegetation coverage than the single factor of landform type. These findings are conducive to enhancing the supply services of vegetation to the ecosystem in arid areas as well as providing important scientific guidance for the construction of ecological civilization and sustainable development in Xinjiang.
The Potential Role of Wearable Inertial Sensors in Laboring Women with Walking Epidural Analgesia
Authors: Dziadzko Mikhail , Péneaud Adrien , Bouvet Lionel , Robert Thomas , Fradet Laetitia , Desseauve David
Published: March 2024

There is a growing interest in wearable inertial sensors to monitor and analyze the movements of pregnant women. The noninvasive and discrete nature of these sensors, integrated into devices accumulating large datasets, offers a unique opportunity to study the dynamic changes in movement patterns during the rapid physical transformations induced by pregnancy. However, the final cut of the third trimester of pregnancy, particularly the first stage of labor up to delivery, remains underexplored. The growing popularity of “walking epidural”, a neuraxial analgesia method allowing motor function preservation, ambulation, and free movement throughout labor and during delivery, opens new opportunities to study the biomechanics of labor using inertial sensors. Critical research gaps exist in parturient fall prediction and detection during walking epidural and understanding pain dynamics during labor, particularly in the presence of pelvic girdle pain. The analysis of fetal descent, upright positions, and their relationship with dynamic pelvic movements facilitated by walking during labor is another area where inertial sensors can play an interesting role. Moreover, as contemporary obstetrics advocate for less restricted or non-restricted movements during labor, the role of inertial sensors in objectively measuring the quantity and quality of women’s movements becomes increasingly important. This includes studying the impact of epidural analgesia on maternal mobility, walking patterns, and associated obstetrical outcomes. In this paper, the potential use of wearable inertial sensors for gait analysis in the first stage of labor is discussed.
Playing Flappy Bird Based on Motion Recognition Using a Transformer Model and LIDAR Sensor
Authors: Dirgová Luptáková Iveta , Kubovčík Martin , Pospíchal Jiří
Published: March 2024

A transformer neural network is employed in the present study to predict Q-values in a simulated environment using reinforcement learning techniques. The goal is to teach an agent to navigate and excel in the Flappy Bird game, which became a popular model for control in machine learning approaches. Unlike most top existing approaches that use the game’s rendered image as input, our main contribution lies in using sensory input from LIDAR, which is represented by the ray casting method. Specifically, we focus on understanding the temporal context of measurements from a ray casting perspective and optimizing potentially risky behavior by considering the degree of the approach to objects identified as obstacles. The agent learned to use the measurements from ray casting to avoid collisions with obstacles. Our model substantially outperforms related approaches. Going forward, we aim to apply this approach in real-world scenarios.
Enhancing Security in Visible Light Communication: A Tabu-Search-Based Method for Transmitter Selection
Authors: Shi Ge , Cheng Wei , Gao Xiang , Wei Fupeng , Zhang Heng , Wang Qingzheng
Published: March 2024

In this paper, we explore the secrecy performance of a visible light communication (VLC) system consisting of distributed light-emitting diodes (LEDs) and multiple users (UEs) randomly positioned within an indoor environment while considering the presence of an eavesdropper. To enhance the confidentiality of the system, we formulate a problem of maximizing the sum secrecy rate for UEs by searching for an optimal LED for each UE. Due to the non-convex and non-continuous nature of this security maximization problem, we propose an LED selection algorithm based on tabu search to avoid getting trapped in local optima and expedite the search process by managing trial vectors from previous iterations. Moreover, we introduce three LED selection strategies with a low computational complexity. The simulation results demonstrate that the proposed algorithm achieves a secrecy performance very close to the global optimal value, with a gap of less than 1%. Additionally, the proposed strategies exhibit a performance gap of 28% compared to the global optimal.
PAR-Net: An Enhanced Dual-Stream CNN–ESN Architecture for Human Physical Activity Recognition
Authors: Khan Imran Ullah , Lee Jong Weon
Published: March 2024

Physical exercise affects many facets of life, including mental health, social interaction, physical fitness, and illness prevention, among many others. Therefore, several AI-driven techniques have been developed in the literature to recognize human physical activities. However, these techniques fail to adequately learn the temporal and spatial features of the data patterns. Additionally, these techniques are unable to fully comprehend complex activity patterns over different periods, emphasizing the need for enhanced architectures to further increase accuracy by learning spatiotemporal dependencies in the data individually. Therefore, in this work, we develop an attention-enhanced dual-stream network (PAR-Net) for physical activity recognition with the ability to extract both spatial and temporal features simultaneously. The PAR-Net integrates convolutional neural networks (CNNs) and echo state networks (ESNs), followed by a self-attention mechanism for optimal feature selection. The dual-stream feature extraction mechanism enables the PAR-Net to learn spatiotemporal dependencies from actual data. Furthermore, the incorporation of a self-attention mechanism makes a substantial contribution by facilitating targeted attention on significant features, hence enhancing the identification of nuanced activity patterns. The PAR-Net was evaluated on two benchmark physical activity recognition datasets and achieved higher performance by surpassing the baselines comparatively. Additionally, a thorough ablation study was conducted to determine the best optimal model for human physical activity recognition.
A Survey of PAPR Techniques Based on Machine Learning
Authors: da Silva Bianca S. de C. , Souto Victoria D. P. , Souza Richard D. , Mendes Luciano L.
Published: March 2024

Orthogonal Frequency Division Multiplexing (OFDM) is the modulation technology used in Fourth Generation (4G) and Fifth Generation (5G) wireless communication systems, and it will likely be essential to Sixth Generation (6G) wireless communication systems. However, OFDM introduces a high Peak to Average Power Ratio (PAPR) in the time domain due to constructive interference among multiple subcarriers, increasing the complexity and cost of the amplifiers and, consequently, the cost and complexity of 6G networks. Therefore, the development of new solutions to reduce the PAPR in OFDM systems is crucial to 6G networks. The application of Machine Learning (ML) has emerged as a promising avenue for tackling PAPR issues. Along this line, this paper presents a comprehensive review of PAPR optimization techniques with a focus on ML approaches. From this survey, it becomes clear that ML solutions offer customized optimization, effective search space navigation, and real-time adaptability. In light of the demands of evolving 6G networks, integration of ML is a necessity to propel advancements and meet increasing prerequisites. This integration not only presents possibilities for PAPR reduction but also calls for continued exploration to harness its potential and ensure efficient and reliable communication within 6G networks.
Ultralow-Noise Chopper Amplifier for Seafloor E-Field Measurement
Authors: Song Sixuan , Chen Kai
Published: March 2024

The seafloor E-field signal is extremely weak and difficult to measured, even with a high signal-to-noise ratio. The preamplifier for electrodes is a key technology for ocean-bottom electromagnetic receivers. In this study, a chopper amplifier was proposed and developed to measure the seafloor E-field signal in the nanovolt to millivolt range at significantly low frequencies. It included a modulator, transformer, AC amplifier, high-impedance (hi-Z) module, demodulator, low-pass filter, and chopper clock generator. The injected charge in complementary metal-oxide semiconductor (CMOS) switches that form the modulator is the main source of 1/f noise. Combined with the principles of peak filtering and dead bands, a hi-Z module was designed to effectively reduce low-frequency noise. The chopper amplifier achieved an ultralow voltage noise of 0.6 nV/rt (Hz) at 1 Hz and 1.2 nV/rt (Hz) at 0.001 Hz. The corner frequency was less than 100 mHz, and there were few 1/f noises in the effective observation frequency band used for detecting electric fields. Each component is described with relevant tradeoffs that realize low noise in the low-frequency range. The amplifier was compact, measuring Ø 68 mm × H 12 mm, and had a low power consumption of approximately 23 mW (two channels). The fixed gain was 1500 with an input voltage range of 2.7 mVPP. The chopper amplifiers demonstrated stable performance in offshore geophysical prospecting applications.
A Novel Bio-Inspired Bat Node Scheduling Algorithm for Dependable Safety-Critical Wireless Sensor Network Systems
Authors: Al-Nader Issam , Lasebae Aboubaker , Raheem Rand , Ekembe Ngondi Gerard
Published: March 2024

The multi-objective optimization (MOO) problem in wireless sensor networks (WSNs) is concerned with optimizing the operation of the WSN across three dimensions: coverage, connectivity, and lifetime. Most works in the literature address only one or two dimensions of this problem at a time, except for the randomized coverage-based scheduling (RCS) algorithm and the clique-based scheduling algorithm. More recently, a Hidden Markov Model (HMM)-based algorithm was proposed that improves on the latter two; however, the question remains open if further improvement is possible as previous algorithms explore solutions in terms of local minima and local maxima, not in terms of the full search space globally. Therefore, the main contribution of this paper is to propose a new scheduling algorithm based on bio-inspired computation (the bat algorithm) to address this limitation. First, the algorithm defines a fitness and objective function over a search space, which returns all possible sleep and wake-up schedules for each node in the WSN. This yields a (scheduling) solution space that is then organized by the Pareto sorting algorithm, whose output coordinates are the distance of each node to the base station and the residual energy of the node. We evaluated our results by comparing the bat and HMM node scheduling algorithms implemented in MATLAB. Our results show that network lifetime has improved by 30%, coverage by 40%, and connectivity by 26.7%. In principle, the obtained solution will be the best scheduling that guarantees the best network lifetime performance as well as the best coverage and connectedness for ensuring the dependability of safety-critical WSNs.
Deep Learning-Based Multi-Class Segmentation of the Paranasal Sinuses of Sinusitis Patients Based on Computed Tomographic Images
Authors: Whangbo Jongwook , Lee Juhui , Kim Young Jae , Kim Seon Tae , Kim Kwang Gi
Published: March 2024

Accurate paranasal sinus segmentation is essential for reducing surgical complications through surgical guidance systems. This study introduces a multiclass Convolutional Neural Network (CNN) segmentation model by comparing four 3D U-Net variations—normal, residual, dense, and residual-dense. Data normalization and training were conducted on a 40-patient test set (20 normal, 20 abnormal) using 5-fold cross-validation. The normal 3D U-Net demonstrated superior performance with an F1 score of 84.29% on the normal test set and 79.32% on the abnormal set, exhibiting higher true positive rates for the sphenoid and maxillary sinus in both sets. Despite effective segmentation in clear sinuses, limitations were observed in mucosal inflammation. Nevertheless, the algorithm’s enhanced segmentation of abnormal sinuses suggests potential clinical applications, with ongoing refinements expected for broader utility.
Online Collaborative Perception of Full Bridge Deck Driving Visual of Far Blind Area on Suspension Bridge during Vortex-Induced Vibration
Authors: Dan Danhui , Zeng Gang , Yu Xuewen
Published: March 2024

During a vertical vortex-induced vibration (VVIV), an undulating bridge deck will affect drivers’ sightlines, causing the phenomenon of drifting and changes in the far blind area, thus presenting a potential threat to driving safety. Consequently, to ensure the safety of driving on a suspension bridge deck under VVIV, it is necessary to perceive the far blind spot caused by the occlusion of the driving sightlines under this condition, and to establish an online perception and evaluation mechanism for driving safety. With a long-span suspension bridge experiencing VVIV as the engineering background, this paper utilizes the acceleration integration algorithm and the sine function fitting method to achieve the online perception of real-time dynamic configurations of the main girder. Then, based on the configurations, the maximum height of the driver’s far blind area and effective sight distance are calculated accordingly, and the impact of different driving conditions on them is discussed. The proposed technical framework for driving safety perception in far blind spots is feasible, as it can achieve real-time estimation of the maximum height and effective distance of the far blind area, thereby providing technical support for bridge–vehicle–human collaborative perception and traffic control during vortex-induced vibration.
Development of Wearable Devices for Collecting Digital Rehabilitation/Fitness Data from Lower Limbs
Authors: Huang Yu-Jung , Chang Chao-Shu , Wu Yu-Chi , Han Chin-Chuan , Cheng Yuan-Yang , Chen Hsian-Min
Published: March 2024

Lower extremity exercises are considered a standard and necessary treatment for rehabilitation and a well-rounded fitness routine, which builds strength, flexibility, and balance. The efficacy of rehabilitation programs hinges on meticulous monitoring of both adherence to home exercise routines and the quality of performance. However, in a home environment, patients often tend to inaccurately report the number of exercises performed and overlook the correctness of their rehabilitation motions, lacking quantifiable and systematic standards, thus impeding the recovery process. To address these challenges, there is a crucial need for a lightweight, unbiased, cost-effective, and objective wearable motion capture (Mocap) system designed for monitoring and evaluating home-based rehabilitation/fitness programs. This paper focuses on the development of such a system to gather exercise data into usable metrics. Five radio frequency (RF) inertial measurement unit (IMU) devices (RF-IMUs) were developed and strategically placed on calves, thighs, and abdomens. A two-layer long short-term memory (LSTM) model was used for fitness activity recognition (FAR) with an average accuracy of 97.4%. An intelligent smartphone algorithm was developed to track motion, recognize activity, and calculate key exercise variables in real time for squat, high knees, and lunge exercises. Additionally, a 3D avatar on the smartphone App allows users to observe and track their progress in real time or by replaying their exercise motions. A dynamic time warping (DTW) algorithm was also integrated into the system for scoring the similarity in two motions. The system’s adaptability shows promise for applications in medical rehabilitation and sports.
From Movements to Metrics: Evaluating Explainable AI Methods in Skeleton-Based Human Activity Recognition
Authors: Pellano Kimji N. , Strümke Inga , Ihlen Espen A. F.
Published: March 2024

The advancement of deep learning in human activity recognition (HAR) using 3D skeleton data is critical for applications in healthcare, security, sports, and human–computer interaction. This paper tackles a well-known gap in the field, which is the lack of testing in the applicability and reliability of XAI evaluation metrics in the skeleton-based HAR domain. We have tested established XAI metrics, namely faithfulness and stability on Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM) to address this problem. This study introduces a perturbation method that produces variations within the error tolerance of motion sensor tracking, ensuring the resultant skeletal data points remain within the plausible output range of human movement as captured by the tracking device. We used the NTU RGB+D 60 dataset and the EfficientGCN architecture for HAR model training and testing. The evaluation involved systematically perturbing the 3D skeleton data by applying controlled displacements at different magnitudes to assess the impact on XAI metric performance across multiple action classes. Our findings reveal that faithfulness may not consistently serve as a reliable metric across all classes for the EfficientGCN model, indicating its limited applicability in certain contexts. In contrast, stability proves to be a more robust metric, showing dependability across different perturbation magnitudes. Additionally, CAM and Grad-CAM yielded almost identical explanations, leading to closely similar metric outcomes. This suggests a need for the exploration of additional metrics and the application of more diverse XAI methods to broaden the understanding and effectiveness of XAI in skeleton-based HAR.
Estimating Compressive and Shear Forces at L5-S1: Exploring the Effects of Load Weight, Asymmetry, and Height Using Optical and Inertial Motion Capture Systems
Authors: Nail-Ulloa Iván , Zabala Michael , Sesek Richard , Chen Howard , Schall Mark C. , Gallagher Sean
Published: March 2024

This study assesses the agreement of compressive and shear force estimates at the L5-S1 joint using inertial motion capture (IMC) within a musculoskeletal simulation model during manual lifting tasks, compared against a top-down optical motion capture (OMC)-based model. Thirty-six participants completed lifting and lowering tasks while wearing a modified Plug-in Gait marker set for the OMC and a full-body IMC set-up consisting of 17 sensors. The study focused on tasks with variable load weights, lifting heights, and trunk rotation angles. It was found that the IMC system consistently underestimated the compressive forces by an average of 34% (975.16 N) and the shear forces by 30% (291.77 N) compared with the OMC system. A critical observation was the discrepancy in joint angle measurements, particularly in trunk flexion, where the IMC-based model underestimated the angles by 10.92–11.19 degrees on average, with the extremes reaching up to 28 degrees. This underestimation was more pronounced in tasks involving greater flexion, notably impacting the force estimates. Additionally, this study highlights significant differences in the distance from the spine to the box during these tasks. On average, the IMC system showed an 8 cm shorter distance on the X axis and a 12–13 cm shorter distance on the Z axis during lifting and lowering, respectively, indicating a consistent underestimation of the segment length compared with the OMC system. These discrepancies in the joint angles and distances suggest potential limitations of the IMC system’s sensor placement and model scaling. The load weight emerged as the most significant factor affecting force estimates, particularly at lower lifting heights, which involved more pronounced flexion movements. This study concludes that while the IMC system offers utility in ergonomic assessments, sensor placement and anthropometric modeling accuracy enhancements are imperative for more reliable force and kinematic estimations in occupational settings.
Stereo Vision for Plant Detection in Dense Scenes
Authors: Ruigrok Thijs , van Henten Eldert J. , Kootstra Gert
Published: March 2024

Automated precision weed control requires visual methods to discriminate between crops and weeds. State-of-the-art plant detection methods fail to reliably detect weeds, especially in dense and occluded scenes. In the past, using hand-crafted detection models, both color (RGB) and depth (D) data were used for plant detection in dense scenes. Remarkably, the combination of color and depth data is not widely used in current deep learning-based vision systems in agriculture. Therefore, we collected an RGB-D dataset using a stereo vision camera. The dataset contains sugar beet crops in multiple growth stages with a varying weed densities. This dataset was made publicly available and was used to evaluate two novel plant detection models, the D-model, using the depth data as the input, and the CD-model, using both the color and depth data as inputs. For ease of use, for existing 2D deep learning architectures, the depth data were transformed into a 2D image using color encoding. As a reference model, the C-model, which uses only color data as the input, was included. The limited availability of suitable training data for depth images demands the use of data augmentation and transfer learning. Using our three detection models, we studied the effectiveness of data augmentation and transfer learning for depth data transformed to 2D images. It was found that geometric data augmentation and transfer learning were equally effective for both the reference model and the novel models using the depth data. This demonstrates that combining color-encoded depth data with geometric data augmentation and transfer learning can improve the RGB-D detection model. However, when testing our detection models on the use case of volunteer potato detection in sugar beet farming, it was found that the addition of depth data did not improve plant detection at high vegetation densities.
Evaluating Technicians’ Workload and Performance in Diagnosis for Corrective Maintenance
Authors: Shin Hyunjong , Rothrock Ling , Prabhu Vittaldas
Published: March 2024

The advancement in digital technology is transforming the world. It enables smart product–service systems that improve productivity by changing tasks, processes, and the ways we work. There are great opportunities in maintenance because many tasks require physical and cognitive work, but are still carried out manually. However, the interaction between a human and a smart system is inevitable, since not all tasks in maintenance can be fully automated. Therefore, we conducted a controlled laboratory experiment to investigate the impact on technicians’ workload and performance due to the introduction of smart technology. Especially, we focused on the effects of different diagnosis support systems on technicians during maintenance activity. We experimented with a model that replicates the key components of a computer numerical control (CNC) machine with a proximity sensor, a component that requires frequent maintenance. Forty-five participants were evenly assigned to three groups: a group that used a Fault-Tree diagnosis support system (FTd-system), a group that used an artificial intelligence diagnosis support system (AId-system), and a group that used neither of the diagnosis support systems. The results show that the group that used the FTd-system completed the task 15% faster than the group that used the AId-system. There was no significant difference in the workload between groups. Further analysis using the NGOMSL model implied that the difference in time to complete was probably due to the difference in system interfaces. In summary, the experimental results and further analysis imply that adopting the new diagnosis support system may improve maintenance productivity by reducing the number of diagnosis attempts without burdening technicians with new workloads. Estimates indicate that the maintenance time and the cognitive load can be reduced by 8.4 s and 15% if only two options are shown in the user interface.