bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterization of Cosmos sulphureus Cav. (Asteraceae): Phytochemical Screening, Antioxidant Activity and Chromatography Analysis
Authors: Ortega-Medrano Rubí Julieta , Ceja-Torres Luis Fernando , Vázquez-Sánchez Monserrat , Martínez-Ávila Guillermo Cristian Guadalupe , Medina-Medrano José Roberto
Published: February 2023

Cosmos sulphureus Cav. (Asteraceae), and endemic plant of Mexico is used in herbal medicine. In this study, the phytochemical composition, phenolic content, and antioxidant activity of ethanolic and methanolic extracts from C. sulphureus leaves and flowers were determined. The phytochemical analysis showed the presence of compounds such as terpenoids, phenolic compounds, tannins, and flavonoids and the absence of alkaloids, saponins, glycosides, and anthraquinones. The experimental results showed that the extracts have high contents of phenolic, flavonoid, and condensed tannins contents. The phenolic compounds identified in the C. sulphureus extracts by high-performance thin-layer chromatography (HPTLC) include phenolic acids such as chlorogenic acid and caffeic acid as well flavonoids such as rutin and quercetin. The C. sulphureus extracts showed a relevant free radical scavenging activity, ferric-reducing antioxidant power, lipid peroxidation inhibition ability, and oxygen radical antioxidant capacity. This research highlights the phytochemical profile and antioxidant activity of phenolic compounds-rich extracts from C. sulphureus leaves and flowers.
Microbiome Associated with Olive Cultivation: A Review
Authors: Melloni Rogério , Cardoso Elke J. B. N.
Published: February 2023

International research has devoted much effort to the study of the impacts caused to the soil by different management practices applied to olive cultivation. Such management involves techniques considered conventional, including the control of spontaneous plants with herbicides or machines, inorganic fertilizers, and pesticides to control pests and diseases. Equally, some producers use sustainable techniques, including drastic pruning, the use of cultivars that are tolerant to diseases and adverse climates, the use of organic conditioners in the soil, the maintenance of vegetation cover with spontaneous plants, and the use of inoculants, among others. In both conventional and sustainable/organic management, the effects on soil quality, crop development, and production are accessed through the presence, activity, and/or behavior of microorganisms, microbial groups, and their processes in the soil and/or directly in the crop itself, such as endophytes and epiphytes. Thus, our present review seeks to assemble research information, not only regarding the role of microorganisms on growth and development of the olive tree (Olea europaea L.). We looked mainly for reviews that reveal the impacts of different management practices applied in countries that produce olive oil and olives, which can serve as a basis and inspiration for Brazilian studies on the subject.
Progresses of the Influencing Factors and Detection Methods of Domoic Acid
Authors: Yang Aoao , Zhang Haiguang , Yang Yu , Jiang Zhaoyu
Published: February 2023

Domoic acid (DA) is a neurotoxin mainly produced by Pseudo-nitzschia diatom, which belongs to the genera Rhomboida. It can combine with the receptors of glutamate of neurotransmitters, then affecting the normal nerve signal transmission of the organism and causing nervous system disorders. However, as a natural marine drug, DA can also be used for pest prevention and control. Although the distribution of DA in the world has already been reported in the previous reviews, the time and location of its first discovery and the specific information are not complete. Therefore, the review systematically summarizes the first reported situation of DA in various countries (including species, discovery time, and collection location). Furthermore, we update and analyze the factors affecting DA production, including phytoplankton species, growth stages, bacteria, nutrient availability, trace metals, and so on. These factors may indirectly affect the growth environment or directly affect the physiological activities of the cells, then affect the production of DA. Given that DA is widely distributed in the environment, we summarize the main technical methods for the determination of DA, such as bioassay, high-performance liquid chromatography (HPLC), enzyme-linked immunosorbent assay (ELISA), biosensor, and so on, as well as the advantages and disadvantages of each method used so far, which adds more new knowledge in the literature about DA until now. Finally, the DA research forecast and its industrial applications were prospected to prevent its harm and fully explore its potential value.
Characteristics of Graphene Growth at Different Temperatures from the Benzene Ring Structure in Coal Tar
Authors: Zhao Shuhan , Luo Zhongyang , Fang Mengxiang , Wang Qinhui , Cen Jianmeng
Published: February 2023

A large number of aromatic substances can be found in so-called coal tar (containing >10,000 individual compounds), which is a mixture of heavy liquid fractions (dense viscous black liquor, tended to solidification) obtained after the pyrolysis of coal (solid product—coke, gas products, and light liquid products are also produced during the process). Volatile monocyclic aromatic hydrocarbons, which are naturally occurring in coal tar, can be exploited as premium raw materials for the production of graphene by chemical vapor deposition (CVD). Moreover, aromatic chemicals (compounds with benzene rings) can produce graphene at lower temperatures than other small-molecule gas feedstocks (for graphene growth via methane gas, the temperature must be at least 900 °C). The intermediate reaction mechanism involved in the creation of graphene from various temperature ranges of monocyclic aromatic hydrocarbons in benzene ring structures has long been a fascinating enigma. Accordingly, in this paper, we analyze the graphene growth pattern of benzene at different temperatures from 300 to 900 °C. For graphene synthesis in the lower temperature range (300~600 °C), analytical experiments show that benzene rings (almost) do not crack during the gas phase process. Thus, the structure of the benzene ring is directly coupled into graphene in the above temperature range. When benzene is more thoroughly transformed into tiny molecules that are deposited on the surface of copper foil at higher temperatures (700~900 °C), graphene is formed by a complex mixture of carbon sources, including gaseous small molecules (methane and ethane) and benzene. Based on the process above, we provide an alternative solution for the large-scale industrial preparation of graphene, with low energy consumption, via low-temperature synthesis of graphene by the CVD method using the coal tar carbon source at 500 °C, which is the optimal growth temperature of the benzene ring.
Employment Effect of Structural Change in Strategic Emerging Industries
Authors: Liu Li , Wu Cisheng , Zhu Yiyan
Published: February 2023

Stable development of strategic emerging industries promotes its industrial transformation and upgrading, which has affected the development of not only the society and the economy but also other fields, thereby having a great impact on employment. To measure the impact of structural change of strategic emerging industries on employment in China, this paper constructs a regression equation, in which the employment of strategic emerging industries is the dependent variable, while the change direction of strategic emerging industry structure, the employment elasticity of strategic emerging industries and the change speed of industrial structure are the independent variables. The research results are as follows: (i) The change direction of strategic emerging industries is positively correlated with employment. (ii) The employment elasticity of strategic emerging industries is on the rise, and is positively correlated with employment. (iii) The speed of change of strategic emerging industries is unstable, and is negatively correlated with employment. As a result, the structural change in strategic emerging industries has played a role in promoting employment. The government should recognize the impact of structural changes in strategic emerging industries on China’s employment. By implementing the existing strategic emerging industry policies and improving the external environment for the development of strategic emerging industries, the strategic emerging industries will play the role of “innovation, growth and leadership” in economic and social development.
Upscaling Porous Media Using Neural Networks: A Deep Learning Approach to Homogenization and Averaging
Authors: Pal Mayur , Makauskas Pijus , Malik Shruti
Published: February 2023

In recent years machine learning algorithms have been gaining momentum in resolving subsurface flow issues related to hydrocarbon flows, Carbon capture utilization and storage, hydrogen storage, geothermal flows, and enhanced oil recovery. This paper presents and attempts to solve subsurface flow problem using neural upscaling method. The neural upscaling method, described in the present work, is a machine learning approach to calculate effective properties in each grid block for subsurface flow modeling. This method is intended to be more accurate than traditional analytical upscaling methods (which are only accurate for layered or homogeneous media) and numerical upscaling methods (which are more accurate for heterogeneous media but involve higher computational cost and are dependent on boundary conditions). The neural upscaling method is based on learning from a large number of geological realizations, which allows it to account for uncertainty in geology. It is also computationally fast and accurate. The method is demonstrated through a series of 2D test cases, and its accuracy is compared to that of analytical and numerical upscaling methods.
Modeling and Control Design for Distillation Columns Based on the Equilibrium Theory
Authors: Tan Haiyan , Cong Lin
Published: February 2023

Distillation columns represent the most widely used separation equipment in the petrochemical industry. It is usually difficult to apply the traditional mechanism modeling method to online optimization and control because of its complex structure, and common simplified models produce obvious errors. Therefore, we analyze the mass transfer process of gas-liquid fluid on each column tray based on the theory of gas-liquid equilibrium and establish a nonlinear dynamic model of the distillation process. The proposed model can accurately characterize the nonlinear characteristics of the distillation process, and the model structure is largely simplified compared with the traditional mechanism model. Therefore, the model provides a new approach for model-based methods in distillation columns, especially for cases that require efficient online models. Two case studies of benzene-toluene distillation systems show that the nonlinear model has high concentration observation accuracy. Finally, a generic model control scheme is designed based on this model. Simulation results show that this control strategy performs better than a traditional PID control scheme.
A Combined Surgical Approach for the Treatment of a Sino-Naso-Orbital Glomangiopericytoma
Authors: Labouchère Ania , De Sandre Cécile , Broome Martin , Reinhard Antoine , Louvrier Aurélien
Published: February 2023

Glomangiopericytomas are a rare type of sinonasal tumors with high relapse rates. Endoscopic sinus surgery is the classical technique to remove such tumors. Here we report a case of sinonasal glomangiopericytoma with orbital extension, infrequently described in the literature. A 50 year-old woman was referred to our hospital because of a right paranasal painless swelling, which appeared 9 months ago and was slowly increasing in size since her first visit. A Magnetic Resonance Imaging (MRI) revealed a tumor (18 × 25 × 37 mm) centered on the right ethmoidal cells, with adjacent nasal bone erosion and a right orbital invasion. The tumor was removed with a combined surgical approach (endoscopic and trans-sinusal frontal approach) after pre-surgical embolization. Based on the results of the histopathological examination, the diagnosis of a glomangiopericytoma was confirmed. The patient was discharged after one week, and an uneventful long-term follow-up.
On the Influence of AQM on Serialization of Packet Losses
Authors: Chydzinski Andrzej , Adamczyk Blazej
Published: February 2023

We study the influence of the active queue management mechanism based on the queue size on the serialization of packet losses, i.e., the occurrences of losses in long, consecutive series. We use a traffic model able to mimic precisely the autocorrelation function of traffic, which is known to be far from zero in packet networks. The main contribution is a theorem on the burst ratio parameter, describing the serialization of losses, proven for an arbitrary function assigning drop probabilities to queue sizes. In numerical examples, we show the impact of the autocorrelation strength, drop probability function, and load of the link, on the serialization of losses.
Distributed Detection of Malicious Android Apps While Preserving Privacy Using Federated Learning
Authors: Lee Suchul
Published: February 2023

Recently, deep learning has been widely used to solve existing computing problems through large-scale data mining. Conventional training of the deep learning model is performed on a central (cloud) server that is equipped with high computing power, by integrating data via high computational intensity. However, integrating raw data from multiple clients raises privacy concerns that are increasingly being focused on. In federated learning (FL), clients train deep learning models in a distributed fashion using their local data; instead of sending raw data to a central server, they send parameter values of the trained local model to a central server for integration. Because FL does not transmit raw data to the outside, it is free from privacy issues. In this paper, we perform an experimental study that explores the dynamics of the FL-based Android malicious app detection method under three data distributions across clients, i.e., (i) independent and identically distributed (IID), (ii) non-IID, (iii) non-IID and unbalanced. Our experiments demonstrate that the application of FL is feasible and efficient in detecting malicious Android apps in a distributed manner on cellular networks.
Timepix3: Temperature Influence on Radiation Energy Measurement with Si Sensor
Authors: Urban Martin , Nentvich Ondrej , Marek Lukas , Hudec Rene , Sieger Ladislav
Published: February 2023

The Timepix3 readout ASIC chip is a hybrid pixelated radiation detector, designed at CERN, which contains a 256 px × 256 px matrix. Each of the 65,536 radiation-sensitive pixels can record an incoming particle, its energy deposition or time of arrival and measure them simultaneously. Since the detector is suitable for a wide range of applications from particle physics, national security and medicine to space science, it can be used in a wide range of temperatures. Until now, it has to be calibrated every time to the operating point of the application. This paper studies the possibility of energy measurement with Timepix3 equipped with a 500 m thick silicon sensor and MiniPIX readout interface in the temperatures between 10 ∘C and 70 ∘C with only one calibration. The detector has been irradiated by X-ray fluorescence photons in the energy range from 8 keV to 57 keV, and 31 keV to 81 keV photons from the 133Ba radioactive source. A deviation of 5% in apparent energy value may occur for a 10 ∘C change in temperature from the reference point, but, with the next temperature change, it can reach up to −30%. Moreover, Barium photons with an energy of 81 keV appear as deposited energy of only 55 keV at a detector temperature of 70 ∘C. An original compensation method that reduces the relative measurement error from −30% to less than 1% is presented in this paper.
Event-Triggered Kalman Filter and Its Performance Analysis
Authors: Li Xiaona , Hao Gang
Published: February 2023

In estimation of linear systems, an efficient event-triggered Kalman filter algorithm is proposed. Based on the hypothesis test of Gaussian distribution, the significance of the event-triggered threshold is given. Based on the threshold, the actual trigger frequency of the estimated system can be accurately set. Combining the threshold and the proposed event-triggered mechanism, an event-triggered Kalman filter is proposed and the approximate estimation accuracy can also be calculated. Whether it is a steady system or a time-varying system, the proposed algorithm can reasonably set the threshold according to the required accuracy in advance. The proposed event-triggered estimator not only effectively reduces the communication cost, but also has high accuracy. Finally, simulation examples verify the correctness and effectiveness of the proposed algorithm.
Quantification of Comfort for the Development of Binding Parts in a Standing Rehabilitation Robot
Authors: Nam Yejin , Yang Sumin , Kim Jongman , Koo Bummo , Song Sunghyuk , Kim Youngho
Published: February 2023

Human-machine interfaces (HMI) refer to the physical interaction between a user and rehabilitation robots. A persisting excessive load leads to soft tissue damage, such as pressure ulcers. Therefore, it is necessary to define a comfortable binding part for a rehabilitation robot with the subject in a standing posture. The purpose of this study was to quantify the comfort at the binding parts of the standing rehabilitation robot. In Experiment 1, cuff pressures of 10–40 kPa were applied to the thigh, shank, and knee of standing subjects, and the interface pressure and pain scale were obtained. In Experiment 2, cuff pressures of 10–20 kPa were applied to the thigh, and the tissue oxygen saturation and the skin temperature were measured. Questionnaire responses regarding comfort during compression were obtained from the subjects using the visual analog scale and the Likert scale. The greatest pain was perceived in the thigh. The musculoskeletal configuration affected the pressure distribution. The interface pressure distribution by the binding part showed higher pressure at the intermuscular septum. Tissue oxygen saturation (StO2) increased to 111.9 ± 6.7% when a cuff pressure of 10 kPa was applied and decreased to 92.2 ± 16.9% for a cuff pressure of 20 kPa. A skin temperature variation greater than 0.2 °C occurred in the compressed leg. These findings would help evaluate and improve the comfort of rehabilitation robots.
Comparison of Three Motion Capture-Based Algorithms for Spatiotemporal Gait Characteristics: How Do Algorithms Affect Accuracy and Precision of Clinical Outcomes?
Authors: Caron-Laramée Amélie , Walha Roua , Boissy Patrick , Gaudreault Nathaly , Zelovic Nikola , Lebel Karina
Published: February 2023

Gait assessment is of interest to clinicians and researchers because it provides information about patients’ functional mobility. Optoelectronic camera-based systems with gait event detection algorithms are considered the gold standard for gait assessment. Yet, the choice of the algorithm used to process data and extract the desired parameters from those detected gait events has an impact on the validity and reliability of the gait parameters computed. There are multiple techniques documented in the literature for computing gait events, including the analysis of the minimal position of the heel and toe markers, the computation of the relative distance between sacrum and foot markers, and the assessment of the smallest distance between the heel and toe markers. Validation studies conducted on these algorithms report variations in accuracy. Yet, these studies were conducted in different conditions, at varying gait velocities, and on different populations. The purpose of this study is to compare accuracy, precision, and robustness of three algorithms using motion capture data obtained from 25 healthy persons and 21 psoriatic arthritic patients walking at three distinct speeds on an instrumented treadmill. Errors in gait events recognition (heel strike—HS and toe-off—TO) and their impact on gait metrics (stance phase and stride length) are reported and compared to ground reaction force events measured with force plates. Over the 9114 collected steps across all walking speeds, more than 99% of gait events were recognized by all algorithms. On average, HS events were detected within 1.2 ms of the reference for two algorithms, while the third one detected HS late, with an average detection error of 40.7 ms. Yet, significant variations in accuracy were noted with gait speed; the performance decreased for all algorithms at slow speed. TO events were identified early by all algorithms, with an average error ranging from 16.0 to 100.0 ms. These gait events errors lead to 2–15% inaccuracies in stance phase assessment, while the impact on stride length remains below 0.3 cm. Overall, the algorithm based on the relative distance between the sacral and foot markers stood out for its accuracy, precision, and robustness at all walking speeds.
sSLAM: Speeded-Up Visual SLAM Mixing Artificial Markers and Temporary Keypoints
Authors: Romero-Ramirez Francisco J. , Muñoz-Salinas Rafael , Marín-Jiménez Manuel J. , Cazorla Miguel , Medina-Carnicer Rafael
Published: February 2023

Environment landmarks are generally employed by visual SLAM (vSLAM) methods in the form of keypoints. However, these landmarks are unstable over time because they belong to areas that tend to change, e.g., shadows or moving objects. To solve this, some other authors have proposed the combination of keypoints and artificial markers distributed in the environment so as to facilitate the tracking process in the long run. Artificial markers are special elements (similar to beacons) that can be permanently placed in the environment to facilitate tracking. In any case, these systems keep a set of keypoints that is not likely to be reused, thus unnecessarily increasing the computing time required for tracking. This paper proposes a novel visual SLAM approach that efficiently combines keypoints and artificial markers, allowing for a substantial reduction in the computing time and memory required without noticeably degrading the tracking accuracy. In the first stage, our system creates a map of the environment using both keypoints and artificial markers, but once the map is created, the keypoints are removed and only the markers are kept. Thus, our map stores only long-lasting features of the environment (i.e., the markers). Then, for localization purposes, our algorithm uses the marker information along with temporary keypoints created just in the time of tracking, which are removed after a while. Since our algorithm keeps only a small subset of recent keypoints, it is faster than the state-of-the-art vSLAM approaches. The experimental results show that our proposed sSLAM compares favorably with ORB-SLAM2, ORB-SLAM3, OpenVSLAM and UcoSLAM in terms of speed, without statistically significant differences in accuracy.
Cloud-Native Workload Orchestration at the Edge: A Deployment Review and Future Directions
Authors: Vaño Rafael , Lacalle Ignacio , Sowiński Piotr , S-Julián Raúl , Palau Carlos E.
Published: February 2023

Cloud-native computing principles such as virtualization and orchestration are key to transferring to the promising paradigm of edge computing. Challenges of containerization, operative models and scarce availability of established tools make a thorough review indispensable. Therefore, the authors have described the practical methods and tools found in the literature as well as in current community-led development projects, and have thoroughly exposed the future directions of the field. Container virtualization and its orchestration through Kubernetes have dominated the cloud computing domain, while major efforts have been recently recorded focused on the adaptation of these technologies to the edge. Such initiatives have addressed either the reduction of container engines and the development of specific tailored operating systems or the development of smaller K8s distributions and edge-focused adaptations (such as KubeEdge). Finally, new workload virtualization approaches, such as WebAssembly modules together with the joint orchestration of these heterogeneous workloads, seem to be the topics to pay attention to in the short to medium term.
Features of the Formation of Sensitive Films Based on Mycelium of Higher Fungi for Surface and Plate Acoustic Waves Gas Sensors
Authors: Smirnov Andrey , Anisimkin Vladimir , Krasnopolskaya Larissa , Guliy Olga , Sinev Ilya , Simakov Viacheslav , Golyshkin Alexander , Almyasheva Nailya , Ageykin Nikita , Kuznetsova Iren
Published: February 2023

A comparative analysis of the responses of two types of acoustic waves (surface SAW and plate APW) with close frequencies and the same type of waves (SAW) with different frequencies toward various liquid vapors (water, acetone, ethanol) was carried out in this paper. Two types of films based on mycelium of higher fungus Ganoderma lucidum (Curtis) P. Karst (G. lucidum) prepared by various methods were used as sensitive coatings. These films were based on G. lucidum mycelium ethanolic (48% v/v) homogenizate (MEGl) and extract (EGl). A film deposition procedure compatible with acoustic devices technology was developed. Various piezoelectric substrates (YX-LiNbO3, 128 YX-LiNbO3) were used for appropriate acoustic delay lines production. It was found that additional SAW and APW attenuation associated with the appearance of mycelium films on the surface of the acoustic waveguide is two times greater for MEGL than for EGL films in the frequency range of 20–80 MHz The changes in acoustic wave amplitude and phase due to vapor absorption were measured and compared with each other, taking into account the differences in geometry of the samples. It was found that the phase response of the SAW delay lines with EGL films is three times higher than one with the presence of MEGL films for water and ethanol vapors. The films used are demonstrated good reproducibility and long-term stability for at least 2 months. Based on the results obtained, it was concluded that MEGl film is not appropriate for use in high frequency SAW delay lines as a sensitive coating. However, both types of the films (MEGl and EGl) could be used as sensitive coatings for low frequency SAW and APW sensors based on corresponding delay lines. Additionally, it was found that the films used are not sensitive to acetone vapor. As a result of the work carried out, a technique for creating sensitive films based on the mycelium of higher fungi compatible with the planar technology of acoustoelectronic delay lines was developed. The possibility of using such films for the development of gas SAW and APW sensors was shown.
A Novel Technique to Mitigate the Data Redundancy and to Improvise Network Lifetime Using Fuzzy Criminal Search Ebola Optimization for WMSN
Authors: Matheen M. A. , Sundar S.
Published: February 2023

Wireless Multimedia Sensor Network (WMSN) is a powerful technology that is widely used to gather data and monitor the actual environment for analysis. Furthermore, multimedia applications’ needs and the features, such as constrained latency and high bandwidth consumption, complicate the design of WMSN routing protocols. Despite several methods, the trouble of designing WMSNs routing protocol remains a hurdle. The miniaturization and enhancement of hardware facilitate an extensive range of applications in the military and public sectors. On the contrary, the streaming of multimedia content is captured and generated due to some event-triggered surveillance for a long duration of time. Hence, it is necessary for wireless multimedia sensor network (WMSN) to provide a strong hardware foundation, thereby satisfying Quality of Service (QoS) requirements. Initially, the network is clustered into several clusters and the nodes with rich resources are chosen as cluster heads. The significant intention of this paper is to eliminate data redundancy and to select optimal cluster heads, thereby minimizing the energy consumption. Therefore, this paper proposes a novel Fuzzy Criminal Search Ebola Optimization (FCSEO) algorithm for optimal selection of cluster heads. In addition to this, the data redundancy present in the proposed algorithm is mitigated and thus the network lifetime is enhanced. Finally, extensive experimentation is carried out for various performance measures to determine the efficiency of the proposed approach.
Companion: A Pilot Randomized Clinical Trial to Test an Integrated Two-Way Communication and Near-Real-Time Sensing System for Detecting and Modifying Daily Inactivity among Adults >60 Years—Design and Protocol
Authors: Arguello Diego , Rogers Ethan , Denmark Grant H. , Lena James , Goodro Troy , Anderson-Song Quinn , Cloutier Gregory , Hillman Charles H. , Kramer Arthur F. , Castaneda-Sceppa Carmen , John Dinesh
Published: February 2023

Supervised personal training is most effective in improving the health effects of exercise in older adults. Yet, low frequency (60 min, 1–3 sessions/week) of trainer contact limits influence on behavior change outside sessions. Strategies to extend the effect of trainer contact outside of supervision and that integrate meaningful and intelligent two-way communication to provide complex and interactive problem solving may motivate older adults to “move more and sit less” and sustain positive behaviors to further improve health. This paper describes the experimental protocol of a 16-week pilot RCT (N = 46) that tests the impact of supplementing supervised exercise (i.e., control) with a technology-based behavior-aware text-based virtual “Companion” that integrates a human-in-the-loop approach with wirelessly transmitted sensor-based activity measurement to deliver behavior change strategies using socially engaging, contextually salient, and tailored text message conversations in near-real-time. Primary outcomes are total-daily and patterns of habitual physical behaviors after 16 and 24 weeks. Exploratory analyses aim to understand Companion’s longitudinal behavior effects, its user engagement and relationship to behavior, and changes in cardiometabolic and cognitive outcomes. Our findings may allow the development of a more scalable hybrid AI Companion to impact the ever-growing public health epidemic of sedentariness contributing to poor health outcomes, reduced quality of life, and early death.
Lightweight and Energy-Aware Monocular Depth Estimation Models for IoT Embedded Devices: Challenges and Performances in Terrestrial and Underwater Scenarios
Authors: Papa Lorenzo , Proietti Mattia Gabriele , Russo Paolo , Amerini Irene , Beraldi Roberto
Published: February 2023

The knowledge of environmental depth is essential in multiple robotics and computer vision tasks for both terrestrial and underwater scenarios. Moreover, the hardware on which this technology runs, generally IoT and embedded devices, are limited in terms of power consumption, and therefore, models with a low-energy footprint are required to be designed. Recent works aim at enabling depth perception using single RGB images on deep architectures, such as convolutional neural networks and vision transformers, which are generally unsuitable for real-time inferences on low-power embedded hardware. Moreover, such architectures are trained to estimate depth maps mainly on terrestrial scenarios due to the scarcity of underwater depth data. Purposely, we present two lightweight architectures based on optimized MobileNetV3 encoders and a specifically designed decoder to achieve fast inferences and accurate estimations over embedded devices, a feasibility study to predict depth maps over underwater scenarios, and an energy assessment to understand which is the effective energy consumption during the inference. Precisely, we propose the MobileNetV3S75 configuration to infer on the 32-bit ARM CPU and the MobileNetV3LMin for the 8-bit Edge TPU hardware. In underwater settings, the proposed design achieves comparable estimations with fast inference performances compared to state-of-the-art methods. Moreover, we statistically proved that the architecture of the models has an impact on the energy footprint in terms of Watts required by the device during the inference. Then, the proposed architectures would be considered to be a promising approach for real-time monocular depth estimation by offering the best trade-off between inference performances, estimation error and energy consumption, with the aim of improving the environment perception for underwater drones, lightweight robots and Internet of things.
Persistent Homology Approach for Human Presence Detection from 60 GHz OTFS Transmissions
Authors: Maršálek Roman , Zedka Radim , Zöchmann Erich , Vychodil Josef , Závorka Radek , Ghiaasi Golsa , Blumenstein Jiří
Published: February 2023

Orthogonal Time Frequency Space (OTFS) is a new, promising modulation waveform candidate for the next-generation integrated sensing and communication (ISaC) systems, providing environment-awareness capabilities together with high-speed wireless data communications. This paper presents the original results of OTFS-based person monitoring measurements in the 60 GHz millimeter-wave frequency band under realistic conditions, without the assumption of an integer ratio between the actual delays and Doppler shifts of the reflected components and the corresponding resolution of the OTFS grid. As the main contribution of the paper, we propose the use of the persistent homology technique as a method for processing gathered delay-Doppler responses. We highlight the advantages of the persistent homology approach over the standard constant false alarm rate target detector for selected scenarios.
An LEO Constellation Early Warning System Decision-Making Method Based on Hierarchical Reinforcement Learning
Authors: Cheng Yu , Wei Cheng , Sun Shengxin , You Bindi , Zhao Yang
Published: February 2023

The cooperative positioning problem of hypersonic vehicles regarding LEO constellations is the focus of this research study on space-based early warning systems. A hypersonic vehicle is highly maneuverable, and its trajectory is uncertain. New challenges are posed for the cooperative positioning capability of the constellation. In recent years, breakthroughs in artificial intelligence technology have provided new avenues for collaborative multi-satellite intelligent autonomous decision-making technology. This paper addresses the problem of multi-satellite cooperative geometric positioning for hypersonic glide vehicles (HGVs) by the LEO-constellation-tracking system. To exploit the inherent advantages of hierarchical reinforcement learning in intelligent decision making while satisfying the constraints of cooperative observations, an autonomous intelligent decision-making algorithm for satellites that incorporates a hierarchical proximal policy optimization with random hill climbing (MAPPO-RHC) is designed. On the one hand, hierarchical decision making is used to reduce the solution space; on the other hand, it is used to maximize the global reward and to uniformly distribute satellite resources. The single-satellite local search method improves the capability of the decision-making algorithm to search the solution space based on the decision-making results of the hierarchical proximal policy-optimization algorithm, combining both random hill climbing and heuristic methods. Finally, the MAPPO-RHC algorithm’s coverage and positioning accuracy performance is simulated and analyzed in two different scenarios and compared with four intelligent satellite decision-making algorithms that have been studied in recent years. From the simulation results, the decision-making results of the MAPPO-RHC algorithm can obtain more balanced resource allocations and higher geometric positioning accuracy. Thus, it is concluded that the MAPPO-RHC algorithm provides a feasible solution for the real-time decision-making problem of the LEO constellation early warning system.
A Center-Tapped Transformer Based Multifunctional Single-Phase Converter with Wide DC-Bus Control
Authors: Obi Arinze Stephen , Lee Si-Heon , Jung Hyun-Sam , Jung Jae-Jung
Published: February 2023

Alongside the rapid increase in distributed power generation and load, the demand for highly efficient and reliable power converters is increasing. This has resulted in the rise of grid interfaced renewable energy sources (RES), rapid deployment of battery energy storage systems (BESS) coupled with energy managment systems (EMS), and DC based grid. This paper presents a center-tapped transformer-based single-stage single-phase full-bridge (FB) bidirectional AC-DC converter and its control strategy to improve controllability and reliability in applications such as DC distribution, PV/BESS grid interfacing, vehicle to grid (V2G), and so on. In contrast to conventional galvanically isolated topologies, a single-phase center-tapped transformer is introduced. It links and galvanically isolates the converters and the grid and provides its leakage inductance as the needed inductor required for current control (depending on the design). Furthermore, it reduces the number of conventionally required power conversion stages by employing a wide DC-bus voltage control strategy, resulting in a single converter that undergoes a single power conversion. Additionally, the voltage level can be increased to further enhance the output quality by cascading multiple converters (Multi-Level). The structure, operation, and basic control scheme are discussed in detail. Verification through a 220 Vrms, 1.8 kVA, and 45∼100 VDC simulation and small-scale experimental prototype (60∼100 VDC voltage) for practical validation of the topology is also presented.
Energy-Efficient EEG-Based Scheme for Autism Spectrum Disorder Detection Using Wearable Sensors
Authors: Alhassan Sarah , Soudani Adel , Almusallam Manan
Published: February 2023

The deployment of wearable wireless systems that collect physiological indicators to aid in diagnosing neurological disorders represents a potential solution for the new generation of e-health systems. Electroencephalography (EEG), a recording of the brain’s electrical activity, is a promising physiological test for the diagnosis of autism spectrum disorders. It can identify the abnormalities of the neural system that are associated with autism spectrum disorders. However, streaming EEG samples remotely for classification can reduce the wireless sensor’s lifespan and creates doubt regarding the application’s feasibility. Therefore, decreasing data transmission may conserve sensor energy and extend the lifespan of wireless sensor networks. This paper suggests the development of a sensor-based scheme for early age autism detection. The proposed scheme implements an energy-efficient method for signal transformation allowing relevant feature extraction for accurate classification using machine learning algorithms. The experimental results indicate an accuracy of 96%, a sensitivity of 100%, and around 95% of F1 score for all used machine learning models. The results also show that our scheme energy consumption is 97% lower than streaming the raw EEG samples.
A Privacy-Preserving Desk Sensor for Monitoring Healthy Movement Breaks in Smart Office Environments with the Internet of Things
Authors: Maiti Ananda , Ye Anjia , Schmidt Matthew , Pedersen Scott
Published: February 2023

Smart workplace Internet of Things (IoT) solutions rely on several sensors deployed efficiently in the workplace environment to collect accurate data to meet system goals. A vital issue for these sensor-based IoT solutions is privacy. Ideally, the occupants must be monitored discreetly, and the strategies for maintaining privacy are dependent on the nature of the data required. This paper proposes a new sensor design approach for IoT solutions in the workplace that protects occupants’ privacy. We focus on a novel sensor that autonomously detects and captures human movements in the office to monitor a person’s sedentary behavior. The sensor guides an eHealth solution that uses continuous feedback about desk behaviors to prompt healthy movement breaks for seated workers. The proposed sensor and its privacy-preserving characteristics can enhance the eHealth solution system’s performance. Compared to self-reporting, intrusive, and other data collection techniques, this sensor can collect the information reliably and timely. We also present the data analysis specific to this new sensor that measures two physical distance parameters in real-time and uses their difference to determine human actions. This architecture aims to collect precise data at the sensor design level rather than to protect privacy during the data analysis phase.
Hot-Pressing Furnace Current Monitoring and Predictive Maintenance System in Aerospace Applications
Authors: Chen Hong-Ming , Zhang Jia-Hao , Wang Yu-Chieh , Chang Hsiang-Ching , King Jen-Kai , Yang Chao-Tung
Published: February 2023

This research combines the application of artificial intelligence in the production equipment fault monitoring of aerospace components. It detects three-phase current abnormalities in large hot-pressing furnaces through smart meters and provides early preventive maintenance. Different anomalies are classified, and a suitable monitoring process algorithm is proposed to improve the overall monitoring quality, accuracy, and stability by applying AI. We also designed a system to present the heater’s power consumption and the hot-pressing furnace’s fan and visualize the process. Combining artificial intelligence with the experience and technology of professional technicians and researchers to detect and proactively grasp the health of the hot-pressing furnace equipment improves the shortcomings of previous expert systems, achieves long-term stability, and reduces costs. The complete algorithm introduces a model corresponding to the actual production environment, with the best model result being XGBoost with an accuracy of 0.97.
System Identification Methodology of a Gas Turbine Based on Artificial Recurrent Neural Networks
Authors: Aquize Rubén , Cajahuaringa Armando , Machuca José , Mauricio David , Mauricio Villanueva Juan M.
Published: February 2023

The application of identification techniques using artificial intelligence to the gas turbine (GT), whose nonlinear dynamic behavior is difficult to describe through differential equations and the laws of physics, has begun to gain importance for a little more than a decade. NARX (Nonlinear autoregressive network with exogenous inputs) is one of the models used to identify GT because it provides good results. However, existing studies need to show a systematic method to generate robust NARX models that can identify a GT with satisfactory accuracy. In this sense, a systematic method is proposed to design NARX models for identifying a GT, which consists of nine precise steps that go from identifying GT variables to obtaining the optimized NARX model. To validate the method, it was applied to a case study of a 215 MW SIEMENS TG, model SGT6-5000F, using a set of 2305 real-time series data records, obtaining a NARX model with an MSE of 1.945 × 10−5, RMSE of 0.4411% and a MAPE of 0.0643.
Regularization Solver Guided FISTA for Electrical Impedance Tomography
Authors: Wang Qian , Chen Xiaoyan , Wang Di , Wang Zichen , Zhang Xinyu , Xie Na , Liu Lili
Published: February 2023

Electrical impedance tomography (EIT) is non-destructive monitoring technology that can visualize the conductivity distribution in the observed area. The inverse problem for imaging is characterized by a serious nonlinear and ill-posed nature, which leads to the low spatial resolution of the reconstructions. The iterative algorithm is an effective method to deal with the imaging inverse problem. However, the existing iterative imaging methods have some drawbacks, such as random and subjective initial parameter setting, very time consuming in vast iterations and shape blurring with less high-order information, etc. To solve these problems, this paper proposes a novel fast convergent iteration method for solving the inverse problem and designs an initial guess method based on an adaptive regularization parameter adjustment. This method is named the Regularization Solver Guided Fast Iterative Shrinkage Threshold Algorithm (RS-FISTA). The iterative solution process under the L1-norm regular constraint is derived in the LASSO problem. Meanwhile, the Nesterov accelerator is introduced to accelerate the gradient optimization race in the ISTA method. In order to make the initial guess contain more prior information and be independent of subjective factors such as human experience, a new adaptive regularization weight coefficient selection method is introduced into the initial conjecture of the FISTA iteration as it contains more accurate prior information of the conductivity distribution. The RS-FISTA method is compared with the methods of Landweber, CG, NOSER, Newton-Raphson, ISTA and FISTA, six different distributions with their optimal parameters. The SSIM, RMSE and PSNR of RS-FISTA methods are 0.7253, 3.44 and 37.55, respectively. In the performance test of convergence, the evaluation metrics of this method are relatively stable at 30 iterations. This shows that the proposed method not only has better visualization, but also has fast convergence. It is verified that the RS-FISTA algorithm is the better algorithm for EIT reconstruction from both simulation and physical experiments.
Vision-Based Detection of Low-Emission Sources in Suburban Areas Using Unmanned Aerial Vehicles
Authors: Szczepański Marek
Published: February 2023

The paper discusses the problem of detecting emission sources in a low buildings area using unmanned aerial vehicles. The problem was analyzed, and methods of solving it were presented. Various data acquisition scenarios and their impact on the feasibility of the task were analyzed. A method for detecting smoke objects over buildings using stationary video sequences acquired with a drone in hover with the camera in the nadir position is proposed. The method uses differential frame information from stabilized video sequences and the YOLOv7 classifier. A convolutional network classifier was used to detect the roofs of buildings, using a custom training set adapted to the type of data used. Such a solution, although quite effective, is not very practical for the end user, but it enables the automatic generation of a comprehensive training set for classifiers based on deep neural networks. The effectiveness of such a solution was tested for the latest version of the YOLOv7 classifier. The tests proved the effectiveness of the described method, both for single images and video sequences. In addition, the obtained classifier correctly recognizes objects for sequences that do not meet some of the initial assumptions, such as the angle of the camera capturing the image.
Assessing Handrail-Use Behavior during Stair Ascent or Descent Using Ambient Sensing Technology
Authors: Miyazaki Yusuke , Shoda Kohei , Kitamura Koji , Nishida Yoshifumi
Published: February 2023

The increasing geriatric population across the world has necessitated the early detection of frailty through the analysis of daily-life behavioral patterns. This paper presents a system for ambient, automatic, and the continuous measurement and analysis of ascent and descent motions and long-term handrail-use behaviors of participants in their homes using an RGB-D camera. The system automatically stores information regarding the environment and three-dimensional skeletal coordinates of the participant only when they appear within the camera’s angle of view. Daily stair ascent and descent motions were measured in two houses: one house with two participants in their 20s and two in their 50s, and another with two participants in their 70s. The recorded behaviors were analyzed in terms of the stair ascent/descent speed, handrail grasping points, and frequency determined using the decision tree algorithm. The participants in their 70s exhibited a decreased stair ascent/descent speed compared to other participants; those in their 50s and 70s exhibited increased handrail usage area and frequency. The outcomes of the study indicate the system’s ability to accurately detect a decline in physical function through the continuous measurement of daily stair ascent and descent motions.