bluuebunny/crossref_metadata_2025_split huggingface dataset:
Disturbance Compensation-Based Nonlinear Control for Pneumatic Artificial Muscle Systems With Hardware Experiments
Authors: Liu Gendi , Chen Yiheng , Sun Ning , Yang Tong , Qin Yanding , Fang Yongchun 
Published: March 2022

Abstract
Owing to variable stiffness, natural compliance, and similarities with skeletal muscles, pneumatic artificial muscles (PAMs) have been widely utilized in the fields of bionic robots, medical rehabilitation, and industrial manufacturing. However, in addition to high nonlinearities, time variation, and uncertainties, PAMs are extremely sensitive to external disturbances in practical applications, most of which are unknown, complicated, and constantly changing. Regarding these challenging issues, a disturbance compensation-based robust control method is proposed in this paper, which realizes satisfactory tracking control of PAM systems without a prior knowledge of exact models. Particularly, without assuming that disturbances or their first-order derivatives are invariable, a high-order disturbance observer is proposed to estimate lumped disturbances and their nth-order derivatives. On this basis, a nonlinear feedback controller is designed to simultaneously ensure that the observation errors converge to zero in finite time and the tracking error converges to zero asymptotically. Further, based on Lyapunov techniques, the stability analysis for the equilibrium point of the closed-loop system is provided in detail. Finally, the practicability and the robustness of the proposed method are validated by hardware experiments on a self-built horizontal PAM platform.
Wearable Cable-driven Lower Limb Assistive Device with Demonstration System for Walking Assistance
Authors: Zhao Changchen , Lu Xingzhou , Li Jinwen , Zhao Zheng , Feng Yuanjing 
Published: March 2022

Abstract
Flexible assistive device has become an active research topic in recent years due to their promising application prospect in rehabilitation and medical care. Existing devices suffer from the balance between wearable lightness and control effectiveness. This paper introduces a cable-driven lower limb assistive device with a demonstration system for walking assistance. We rely on plantar pressure sensors to determine the gait phase and employ pose sensors to obtain the trajectory of the hip joint angle. We model the human lower limb based on the Lagrangian method and employ the PID algorithm to control the motor’s drive torque during the swing phase. Simulation results demonstrate the effectiveness of the control algorithm on the model’s trajectory tracking and the robustness to disturbance of the flexible device to provide partial walking assistance. Additionally, a software demonstration system based on Unity 3D is designed to monitor the wear’s motion status in real-time, which provides visual feedback for better rehabilitation training.
Research on U-Net based Underwater Holothurian Recognition Method
Authors: Wang Wei , Xing Bowen , Zhang Lan , Wang Wugui 
Published: March 2022

Abstract
Holothurians can integrate into nearby environment by changing body color which increased the difficulty of version identification. A U-Net network based visual recognition algorithm suitable for underwater organisms was proposed. The backbone feature extraction network adopted VGG16. In additional, upper sampling was used twice for feature fusion. Test results shown that the algorithm has high recognition accuracy and good feature extraction for holothurian with different shapes in natural environment.
Safety Helmet Wearing Detection Based on YOLOv5 of Attention Mechanism
Authors: Xu Z P , Zhang Y , Cheng J , Ge G 
Published: March 2022

Abstract
Aiming at problems of low accuracy and strong detection interference of the existing safety helmet wearing detection algorithms, an object detection algorithm by adding the squeeze-and-excitation block based on the YOLOv5 algorithm is proposed in this paper. The proposed method can not only obtain the weight of picture channel, but also accurately separate the foreground and background of the picture. Keeping all parameters unchanged, the proposed method and the YOLOv5 algorithm are applied to detect the safety helmet data set in the experiment. The result shows that the YOLOv5 algorithm with the squeeze-and-excitation block has an average detection accuracy of 94.5% for safety helmets and an average detection accuracy of 92.7% for human heads. The mAP value detected by the proposed method is 2% ∼2.5% higher than using YOLOv5 algorithm directly.
A Continuous Gesture Segmentation and Recognition Method for Human-Robot Interaction
Authors: Fan Jinlong , Yue Yang , Wang Yu , Wan Bei , Li Xudong , Hua Gengpai 
Published: March 2022

Abstract
The process of human-computer cooperation using gesture recognition can make people get rid of the limitations of traditional input devices such as mouse and keyboard, and control artificial intelligence devices more efficiently and naturally. As a new way of human-robot interaction (HRI), gesture recognition has made some progress. There are many ways to realize gesture recognition combined with visual recognition, motion information acquisition and EMG signal. The research on isolated language gesture recognition has been quite mature, but the expression semantics of isolated gestures is single. In order to improve the interaction efficiency, the application of continuous gesture recognition is essential. This paper studies its application in continuous sign language sentence recognition based on inertial sensor and rule matching recognition algorithm. The recognition rate of nine single HRI gestures is 92.7%, and the HRI of combined gestures is realized.
DM-Net:a Depth-separable convolution and Multi-Scale Network for retinal blood vessel segmentation
Authors: Li Wei , Wu Cong , Cheng YuQing , Yang Zhi 
Published: March 2022

Abstract
Retina segmentation plays an important role in the medical field,In recent years, some proposed networks have some problems, such as single receptive field, huge parameters, and difficulty in training, which affect the segmentation results. In this paper, a U-Net-based DM-Net with deep separable convolution and multi-scale is proposed, a residual multiscale module is designed to reduce the parameters and improve the feature extraction ability. In order to cope with the feature information fusion at different levels and the sudden decrease in the number of feature channels in the decoder, the channel attention mechanism is applied. Experiments on the public data set CHASE_DB 1 show that DM-Net has achieved good results compared with other networks, especially in ACC (0.9748) and SP (0.9882). At the same time, it has few parameters and fast convergence speed.
DOI correction for gamma ray energy reconstruction based on energy segment in 3D position-sensitive CdZnTe detectors
Authors: Li Y. , Gong P. , Tang X. , Hu Z. , Wang P. , Tian F. , Wu S. , Ye M. , Zhou C. , Zhu X. 
Published: March 2022

Abstract
The amplitude of the induced signal in 3D CdZnTe detector depends on the depth of interaction (DOI). Therefore, calibrating the detector by using DOI correction technology plays a crucial role in improving the energy resolution of the detectors to gamma rays. The current DOI correction method focuses on the single energy gamma rays, and its application to multiple energy gamma-rays are not found. In this study, we propose an improved energy correction algorithm with excellent correction results in the multiple energy gamma-ray detection. In the experiment, the DOI correction factors of a CdZnTe detector under different energies are discussed. The energy resolution and peak height of multiple energy peaks in the energy spectrum are significantly improved by using the segment energy correction method. We also extend the DOI correction method to the gamma detectors used in the Compton imaging, and the influence of this method on the Compton imaging quality is also discussed.
For a single 60Co point source, the intrinsic efficiency increases from 6.5‰ to 8.3‰.
Biodegradable iron-silicon implants produced by additive manufacturing
Authors: Bondareva J V , Dubinin O N , Kuzminova Y O , Shpichka A I , Kosheleva N V , Lychagin A V , Shibalova A A , Pozdnyakov A A , Akhatov I S , Timashev P S , Evlashin S A 
Published: March 2022

Abstract
Due to many negative and undesirable side effects from the use of permanent implants, the development of temporary implants based on biocompatible and biodegradable materials is a promising area of modern medicine. In the presented study, we have investigated complex-shaped iron-silicon (Fe–Si) scaffolds that can be used as potential biodegradable framework structures for solid implants for bone grafting. Since iron and silicon are biocompatible materials, and their alloy should also have biocompatibility. It has been demonstrated that cells, mesenchymal stromal cells derived from the human umbilical cord (UC-MSC) and 3T3, were attached to, spread, and proliferated on the Fe–Si scaffolds’ surface. Most of UC-MSC and 3T3 remained viable, only single dead cells were observed. According to the results of biological testing, the scaffolds have shown that deposition of calcium phosphate particles occurs on day one in the scaffold at the defect site that can be used as a primary marker of osteodifferentiation. These results demonstrate that the 3D-printed porous iron-silicon (Fe–Si) alloy scaffolds are promising structures for bone grafting and regeneration.
Multifaceted tannin crosslinked bioinspired dECM decorated nanofibers modulating cell–scaffold biointerface for tympanic membrane perforation bioengineering
Authors: Hussain Zahid , Ding Pi , Zhang Liwei , Zhang Yajie , Ullah Salim , Liu Yuanshan , Ullah Ismat , Wang Zhili , Zheng Penghui , Pei Renjun 
Published: March 2022

Abstract
Tympanic membrane (TM) perforation leads to persistent otitis media, conductive deafness, and affects life quality. Ointment medication may not be sufficient to treat TM perforation (TMP) due to the lack of an underlying tissue matrix and thus requiring a scaffold-based application. The engineering of scaffold biointerface close to the matrix via tissue-specific decellularized extracellular matrix (dECM) is crucial in instructing cell behaviour and regulating cell-material interaction in the bioengineering domain. Herein, polycaprolactone (PCL) and TM-dECM (from Sprague–Dawley rats) were combined in a different ratio in nanofibrous form using an electrospinning process and crosslinked via tannic acid. The histological and biochemical assays demonstrated that chemical and enzymatic decellularization steps removed cellular/immunogenic contents while retaining collagen and glycosaminoglycan. The morphological, physicochemical, thermomechanical, contact angle, and surface chemical studies demonstrated that the tannin crosslinked PCL/dECM nanofibers fine-tune biophysical and biochemical properties. The multifaceted crosslinked nanofibers hold the tunable distribution of dECM moieties, assembled into a spool-shaped membrane, and could easily insert into perforated sites. The dECM decorated fibers provide a preferable biomimetic matrix for L929 fibroblast adhesion, proliferation, matrix adsorption, and f-actin saturation, which could be crucial for bioengineering. Overall, dECM patterning, surface hydrophilicity, interconnected microporosities, and multifaceted nanofibrous biosystem modulate cell–scaffold performance and could open opportunities to reconstruct TMP in a biomimetic fashion.
Biomass stocks in California’s fire-prone forests: mismatch in ecology and policy
Authors: Bernal Alexis A , Stephens Scott L , Collins Brandon M , Battles John J 
Published: March 2022

Abstract
Restoration of fire-prone forests can promote resiliency to disturbances, yet such activities may reduce biomass stocks to levels that conflict with climate mitigation goals. Using a set of large-scale historical inventories across the Sierra Nevada/southern Cascade region, we identified underlying climatic and biophysical drivers of historical forest characteristics and projected how restoration of these characteristics manifest under future climate. Historical forest conditions varied with climate and site moisture availability but were generally characterized by low tree density (∼53 trees ha−1), low live basal area (∼22 m2 ha−1), low biomass (∼34 Mg ha−1), and high pine dominance. Our predictions reflected broad convergence in forest structure, frequent fire is the most likely explanation for this convergence. Under projected climate (2040–2069), hotter sites become more prevalent, nearly ubiquitously favoring low tree densities, low biomass, and high pine dominance. Based on these projections, this region may be unable to support aboveground biomass >40 Mg ha−1 by 2069, a value approximately 25% of current average biomass stocks. Ultimately, restoring resilient forests will require adjusting carbon policy to match limited future aboveground carbon stocks in this region.
Climate-induced fire regime amplification in Alberta, Canada
Authors: Whitman Ellen , Parks Sean A , Holsinger Lisa M , Parisien Marc-André 
Published: March 2022

Abstract
Acting as a top-down control on fire activity, climate strongly affects wildfire in North American ecosystems through fuel moisture and ignitions. Departures from historical fire regimes due to climate change have significant implications for the structure and composition of boreal forests, as well as fire management and operations. In this research, we characterize the relationship between trends in climate and fire regime characteristics, for a study area predominantly in Alberta, Canada. We examined trends of fire and climate in northwestern boreal forests using time series analysis of downscaled historical annual climate, fire history (1970–2019), and fire severity (the impacts of wildfire on plants and organic biomass; 1985–2018). We represented fire severity using the relativized burn ratio (RBR) calculated from multispectral Landsat imagery. The climate of the study area has significantly warmed and dried over the past 50 years. Over the same period the annual number of large wildfires, area burned, and fire sizes in the study area significantly increased. Furthermore, the likelihood, area, and number of extreme short-interval reburns (≤15 years between fires; 1985–2019) also significantly increased. During the study period, the portion of forested unburned islands within fire perimeters significantly declined, and fire severity (RBR) increased in open conifer and mixedwood forests. These fire regime changes are significantly correlated with annual climate variability, and a path analysis supports the hypothesis that annual climate patterns have led to fire regime shifts. The increasing fire activity in this region has implications for forest ecology and habitat availability, as the disruption of the fire regime is likely to alter forest recovery. Managers may face increasing challenges to fire suppression if the observed trends of increasing hotter and drier annual climate in the study area persist, driving extreme fire activity.
A modeler’s guide to studying the resilience of social-technical-environmental systems
Authors: Tamberg Lea A , Heitzig Jobst , Donges Jonathan F 
Published: March 2022

Abstract
The term ‘resilience’ is increasingly being used in Earth system science and other disciplines which study what could be called ‘social-technical-environmental systems’—systems composed of closely interacting social (e.g. economic and political), technical (e.g. energy production infrastructure), and environmental components (e.g. climate and the biosphere). However, the diversity of resilience theories and a certain (intended) openness of proposed definitions can lead to misunderstandings and may impede their application to complex systems modelling. We propose a guideline that aims to ease communication as well as to support systematic development of research questions and models in the context of resilience. It can be applied independently of the modelling framework or underlying theory of choice. At the heart of this guideline is a checklist consisting of four questions to be answered: (1) Resilience of what? (2) Resilience regarding what? (3) Resilience against what? (4) Resilience how? We refer to the answers to these resilience questions as the ‘system’, the ‘sustainant’, the ‘adverse influence’, and the ‘response options’. The term ‘sustainant’ is a neologism describing the feature of the system (state, structure, function, pathway, …) that should be maintained (or restored quickly enough) in order to call the system resilient. The use of this proposed guideline in the field of Earth system resilience is demonstrated for the application example of a potential climate tipping element: the Amazon rainforest. The example illustrates the diversity of possible answers to the checklist’s questions as well as their benefits in structuring the modelling process. The guideline supports the modeler in communicating precisely what is actually meant by ‘resilience’ in a specific context. This combination of freedom and precision could help to advance the resilience discourse by building a bridge between those demanding unambiguous definitions and those stressing the benefits of generality and flexibility of the resilience concept.
Hourly accounting of carbon emissions from electricity consumption
Authors: Miller Gregory J , Novan Kevin , Jenn Alan 
Published: March 2022

Abstract
Carbon accounting is important for quantifying the sources of greenhouse gas (GHG) emissions that are driving climate change, and is increasingly being used to guide policy, investment, business, and regulatory decisions. The current practice for accounting emissions from consumed electricity, guided by standards like the GHG protocol, uses annual-average grid emission factors, although previous studies have shown that grid carbon intensity varies across seasons and hours of the day. Previous case studies have shown that annual-average carbon accounting can bias emission inventories, but none have shown that this bias is substantial or widespread. This study addresses this gap by calculating emission inventories for thousands of residential, commercial, industrial, and agricultural facilities across the US, and explores the magnitude and direction of this bias compared to hourly accounting of emissions. Our results show that annual-average accounting can over- or under-estimate carbon inventories as much as 35% in certain settings but result in effectively no bias in others. Bias will be greater in regions with high variation in carbon intensity, and for end-users with high variation in their electricity consumption across hours and seasons. As variation in carbon intensity continues to grow with growing shares of variable and intermittent renewable generation, these biases will only continue to worsen in the future. In most cases, using monthly-average emission factors does not substantially reduce bias compared to annual averages. Thus, the authors recommend that hourly accounting be adopted as the best practice for emissions inventories of consumed electricity.
Spatial variability of global lake evaporation regulated by vertical vapor pressure difference
Authors: Farooq Umar , Liu Heping , Zhang Qianyu , Ma Yulong , Wang Jingfeng , Shen Lian 
Published: March 2022

Abstract
Evaporation (E) from about 300 million lakes worldwide without plant physiological constraints directly reflects hydrological response to atmospheric forcings. However, it remains inadequately understood about what regulate spatial variability of global lake E across seasons. Here we show that vertical vapor pressure difference (

e

D

) accounts for 66% of the spatial variability of annual E, followed by wind speed (16%). The

e

D

is also the predominant factor modulating diurnal variability in E and causing greater E at night than during the daytime. As a consequence, spatial variability in nighttime E strongly regulates that in global E across seasons. Therefore, the observed widespread, heterogeneous changes in lake surface temperature that imply spatial variability in

e

D

may have contributed to changes in global E variability.
Bayesian inference of scaled versus fractional Brownian motion
Authors: Thapa Samudrajit , Park Seongyu , Kim Yeongjin , Jeon Jae-Hyung , Metzler Ralf , Lomholt Michael A 
Published: March 2022

Abstract
We present a Bayesian inference scheme for scaled Brownian motion, and investigate its performance on synthetic data for parameter estimation and model selection in a combined inference with fractional Brownian motion. We include the possibility of measurement noise in both models. We find that for trajectories of a few hundred time points the procedure is able to resolve well the true model and parameters. Using the prior of the synthetic data generation process also for the inference, the approach is optimal based on decision theory. We include a comparison with inference using a prior different from the data generating one.
3D bioprinted, vascularized neuroblastoma tumor environment in fluidic chip devices for precision medicine drug testing
Authors: Nothdurfter Daniel , Ploner Christian , Coraça-Huber Débora C , Wilflingseder Doris , Müller Thomas , Hermann Martin , Hagenbuchner Judith , Ausserlechner Michael J 
Published: March 2022

Abstract
Neuroblastoma is an extracranial solid tumor which develops in early childhood and still has a poor prognosis. One strategy to increase cure rates is the identification of patient-specific drug responses in tissue models that mimic the interaction between patient cancer cells and tumor environment. We therefore developed a perfused and micro-vascularized tumor-environment model that is directly bioprinted into custom-manufactured fluidic chips. A gelatin-methacrylate/fibrin-based matrix containing multiple cell types mimics the tumor-microenvironment that promotes spontaneous micro-vessel formation by embedded endothelial cells. We demonstrate that both, adipocyte- and iPSC-derived mesenchymal stem cells can guide this process. Bioprinted channels are coated with endothelial cells post printing to form a dense vessel—tissue barrier. The tissue model thereby mimics structure and function of human soft tissue with endothelial cell-coated larger vessels for perfusion and micro-vessel networks within the hydrogel-matrix. Patient-derived neuroblastoma spheroids are added to the matrix during the printing process and grown for more than two weeks. We demonstrate that micro-vessels are attracted by and grow into tumor spheroids and that neuroblastoma cells invade the tumor-environment as soon as the spheroids disrupt. In summary, we describe the first bioprinted, micro-vascularized neuroblastoma—tumor-environment model directly printed into fluidic chips and a novel medium-throughput biofabrication platform suitable for studying tumor angiogenesis and metastasis in precision medicine approaches in future.