bluuebunny/crossref_metadata_2025 huggingface dataset:
Crystallization Behavior and Mechanical Property of Biodegradable Poly(butylene succinate-co-2-methyl succinate)/Cellulose Nanocrystals Composites
Authors: Yao Wenxin , Pan Siyu , Qiu Zhaobin
Published: June 2024

Biodegradable poly(butylene succinate-co-2-methyl succinate) (PBSMS)/cellulose nanocrystals (CNC) composites were successfully prepared at low CNC loadings with the aims of improving crystallization and mechanical properties and extending the practical application of PBSMS. CNC is finely dispersed in the PBSMS matrix without obvious aggregations. The low content of CNC obviously promoted the crystallization behavior of PBSMS under different conditions. The spherulitic morphology study revealed that CNC, as an effective heterogeneous nucleating agent, provided more nucleation sites during the melt crystallization process. In addition, the nucleation effect of CNC was quantitatively evaluated by the following two parameters, i.e., nucleation activity and nucleation efficiency. The crystal structure and crystallization mechanism of PBSMS remained unchanged in the composites. In addition, as a reinforcing nanofiller, CNC significantly increased Young’s modulus and the yield strength of PBSMS. The crystallization behavior and mechanical properties of PBSMS were significantly improved by the low content of CNC, which should be interesting and essential from the perspective of biodegradable polymer composites.
Hydrophilization and Functionalization of Fullerene C60 with Maleic Acid Copolymers by Forming a Non-Covalent Complex
Authors: Samoilova Nadezhda A. , Krayukhina Maria A. , Klemenkova Zinaida S. , Naumkin Alexander V. , Buzin Michail I. , Mezhuev Yaroslav O. , Turetsky Evgeniy A. , Andreev Sergey M. , Anuchina Nelya M. , Popov Dmitry A.
Published: June 2024

In this study, we report an easy approach for the production of aqueous dispersions of C60 fullerene with good stability. Maleic acid copolymers, poly(styrene-alt-maleic acid) (SM), poly(N-vinyl-2-pyrrolidone-alt-maleic acid) (VM) and poly(ethylene-alt-maleic acid) (EM) were used to stabilize C60 fullerene molecules in an aqueous environment by forming non-covalent complexes. Polymer conjugates were prepared by mixing a solution of fullerene in N-methylpyrrolidone (NMP) with an aqueous solution of the copolymer, followed by exhaustive dialysis against water. The molar ratios of maleic acid residues in the copolymer and C60 were 5/1 for SM and VM and 10/1 for EM. The volume ratio of NMP and water used was 1:1.2–1.6. Water-soluble complexes (composites) dried lyophilically retained solubility in NMP and water but were practically insoluble in non-polar solvents. The optical and physical properties of the preparations were characterized by UV-Vis spectroscopy, FTIR, DLS, TGA and XPS. The average diameter of the composites in water was 120–200 nm, and the ξ-potential ranged from −16 to −20 mV. The bactericidal properties of the obtained nanostructures were studied. Toxic reagents and time-consuming procedures were not used in the preparation of water-soluble C60 nanocomposites stabilized by the proposed copolymers.
Development and Validation of Ultra-Performance Liquid Chromatography (UPLC) Method for Simultaneous Quantification of Hydrochlorothiazide, Amlodipine Besylate, and Valsartan in Marketed Fixed-Dose Combination Tablet
Authors: Alshora Doaa Hasan , Sherif Abdelrahman Y. , Ibrahim Mohamed Abbas
Published: June 2024

Fixed-dose combination therapy is considered a practical approach in the treatment of various diseases, as it can simultaneously target different mechanisms of action that achieve the required therapeutic efficacy through a synergistic effect. A combination of hydrochlorothiazide (HTZ), amlodipine (AMD), and valsartan (VLS) has been created for the treatment of hypertension. Therefore, the aim of this study was to develop an optimized UPLC method for the simultaneous quantification of this combination. A DoE at a level of 32 was used to investigate the effects of column temperature (20, 30, and 40 °C) and formic acid concentration (0.05, 0.15, and 0.25%) on the retention time of each active pharmaceutical ingredient (API), the peak area, and the peak symmetry, as well as the resolution between HTZ-AMD and AMD-VLS peaks. The optimized analytical method was validated and used to extract the three APIs from the marketed product. The optimized analytical condition with a column temperature of 27.86 °C and a formic acid concentration of 0.172% showed good separation of the three APIs in 1.62 ± 0.006, 3.59 ± 0.002, and 3.94 ± 0.002 min for HTZ, AMD, and VST, respectively. The developed method was linear with the LOQ for a HTC, AMD, and VST of 0.028, 0.038, and 0.101 ppm, respectively. Moreover, the developed assay was sustainable and robust, with an RSD % of less than 2%. The application of this method in the extraction of HTZ, AMD, and VST from the Exforge® marketed product showed good separation with a measurable drug content of 23.5 ± 0.7, 9.68 ± 0.1, and 165.2 ± 5.2 mg compared to the label claims of 25/10/160 for HTZ, AMD, and VST, respectively.
Learning More with Less Data in Manufacturing: The Case of Turning Tool Wear Assessment through Active and Transfer Learning
Authors: Papacharalampopoulos Alexios , Alexopoulos Kosmas , Catti Paolo , Stavropoulos Panagiotis , Chryssolouris George
Published: June 2024

Monitoring tool wear is key for the optimization of manufacturing processes. To achieve this, machine learning (ML) has provided mechanisms that work adequately on setups that measure the cutting force of a tool through the use of force sensors. However, given the increased focus on sustainability, i.e., in the context of reducing complexity, time and energy consumption required to train ML algorithms on large datasets dictate the use of smaller samples for training. Herein, the concepts of active learning (AL) and transfer learning (TL) are simultaneously studied concerning their ability to meet the aforementioned objective. A method is presented which utilizes AL for training ML models with less data and then it utilizes TL to further reduce the need for training data when ML models are transferred from one industrial case to another. The method is tested and verified upon an industrially relevant scenario to estimate the tool wear during the turning process of two manufacturing companies. The results indicated that through the application of the AL and TL methodologies, in both companies, it was possible to achieve high accuracy during the training of the final model (1 and 0.93 for manufacturing companies B and A, respectively). Additionally, reproducibility of the results has been tested to strengthen the outcomes of this study, resulting in a small standard deviation of 0.031 in the performance metrics used to evaluate the models. Thus, the novelty presented in this paper is the presentation of a straightforward approach to apply AL and TL in the context of tool wear classification to reduce the dependency on large amounts of high-quality data. The results show that the synergetic combination of AL with TL can reduce the need for data required for training ML models for tool wear prediction.
Onion Peel: A Promising, Economical, and Eco-Friendly Alternative for the Removal of Divalent Cobalt from Aqueous Solutions
Authors: Lizcano-Delgado Yehudy Yelitza , Martínez-Vázquez Osiris Tais , Cristiani-Urbina Eliseo , Morales-Barrera Liliana
Published: June 2024

There is a growing need for an economical and efficient method capable of removing heavy metals from residual water. The current contribution aimed to evaluate the capacity of onion peel, an abundant agroindustrial waste product, to remove divalent cobalt (Co2+) from aqueous solutions. Onion peel was submitted to proximal chemical analysis, and various operational factors involved in biosorption were tested. The most suitable temperature (30 °C), pH (7.0), and biosorbent particle size (300–800 µm) were found. With an initial Co2+ concentration of 380 mg L−1, the maximum capacity of Co2+ removal was 59.88 mg g−1 in 120 min. The pseudo-second order and Langmuir models provided the best fit to the experimental kinetics and equilibrium of Co2+ biosorption, respectively. The thermodynamic study evidenced an exothermic, non-spontaneous, and favorable reaction (ΔH0 = −5.78 kJ mol−1; ΔS0 = −21.13 J mol−1 K−1), suggesting the formation of stable bonds in the biosorbent-Co2+ complex. The carbonyl and hydroxyl groups apparently play a fundamental role in Co2+ removal, and electrostatic attraction, ion exchange, and chemisorption are the principal mechanisms. Thus, the biosorption of Co2+ by onion peel has potential as an economical, eco-friendly, efficient, and sustainable treatment for wastewater.
Research on the Temperature Field Distribution Characteristics of Bottomhole PDC Bits during the Efficient Development of Unconventional Oil and Gas in Long Horizontal Wells
Authors: Fu Li , Yang Henglin , He Chunlong , Wang Yuan , Zhang Heng , Chen Gang , Du Yukun
Published: June 2024

Unconventional tight oil and gas resources, including shale oil and gas, have become the main focus for increasing reserves and production. The safe and efficient development of unconventional oil and gas is a crucial demand for the energy development strategy. Deep tight oil and gas resource development generally adopts horizontal well drilling methods. During drilling, especially in long horizontal sections, the high temperature frequently causes failures of downhole drilling tools and rotary steering tools. The temperature rises sharply during rock breaking with the drill bit. Existing wellbore heat transfer models do not fully consider the impact of heat generated by the drill bit on the wellbore temperature field. This paper aims to experimentally study the temperature rise law of the cutting tooth of the bottom polycrystalline diamond compact (PDC) bit during rock breaking. A set of evaluation devices was developed to study the temperature field distribution characteristics at the bottom of the PDC bit during rock breaking under different experimental conditions. The results indicate that the flow rate of drilling fluid, bit rotation speed, and weight on bit (WOB) significantly affect the distribution of the temperature field at the well bottom. This experimental research on the temperature field distribution characteristics at the bottom of the PDC bit during rock breaking helps reveal the heat transfer characteristics of the long horizontal section wellbore, guide the optimization of drilling parameters, and develop temperature control methods. It is of great significance for the advancement of efficient development technologies for unconventional resources in long horizontal wells.
Knowledge Capital and Stock Returns during Crises in the Manufacturing Sector: Moderating Role of Market Share, Tobin’s Q, and Cash Holdings
Authors: Lee Chaeho Chase , Atukeren Erdal , Kim Hohyun
Published: June 2024

This study analyzes the impact of knowledge capital (KC), a key element of firms’ innovation and competitiveness, on stock returns during economic crises when sustainable competitiveness becomes particularly important. We analyze the impact of the Global Financial Crisis and COVID-19 as economic crises, focusing on manufacturing industries with a high proportion of investment shifts from physical capital to KC. Our findings indicate that KC is positively associated with stock returns during the Global Financial Crisis and COVID-19. This positive relationship is strengthened by the firm’s ability to leverage KC, as measured by greater product market share, higher Tobin’s Q, and larger cash holdings. This study emphasizes the protective role of KC during the economic crisis when the market pays more attention to corporate sustainability and provides implications to corporate managers and investors.
Enhancing Atmospheric Monitoring Capabilities: A Comparison of Low- and High-Cost GNSS Networks for Tropospheric Estimations
Authors: Dabove Paolo , Bagheri Milad
Published: June 2024

Global Navigation Satellite System (GNSS) signals experience delays when passing through the atmosphere due to the presence of free electrons in the ionosphere and air density in the non-ionized part of the atmosphere, known as the troposphere. The Precise Point Positioning (PPP) technique demonstrates highly accurate positioning along with Zenith Tropospheric Delay (ZTD) estimation. ZTD estimation is valuable for various applications including climate modelling and determining atmospheric water vapor. Current GNSS network resolutions are not completely sufficient for the scale of a few kilometres that regional climate and weather models are increasingly adopting. The Centipede-RTK network is a low-cost option for increasing the spatial resolution of tropospheric monitoring. This study is motivated by the question of whether low-cost GNSS networks can provide a viable alternative without compromising data quality or precision. This study compares the performance of the low-cost Centipede-RTK network in calculating the Zenith Tropospheric Delay (ZTD) to that of the existing EUREF Permanent Network (EPN), using two alternative software packages, RTKLIB demo5 version and CSRS-PPP version 3, to ensure robustness and software independence in the findings. This investigation indicated that the ZTD estimations from both networks are almost identical when processed by the CSRS-PPP software, with the highest mean difference being less than 3.5 cm, confirming that networks such as Centipede-RTK could be a reliable option for dense precise atmospheric monitoring. Furthermore, this study revealed that the Centipede-RTK network, when processed using CSRS-PPP, provides ZTD estimations that are very similar and consistent with the EUREF ZTD product values. These findings suggest that low-cost GNSS networks like Centipede-RTK are viable for enhancing network density, thus improving the spatial resolution of tropospheric monitoring and potentially enriching climate modelling and weather prediction capabilities, paving the way for broader application and research in GNSS meteorology.
Polar Stratospheric Cloud Observations at Concordia Station by Remotely Controlled Lidar Observatory
Authors: Di Liberto Luca , Colao Francesco , Serva Federico , Bracci Alessandro , Cairo Francesco , Snels Marcel
Published: June 2024

Polar stratospheric clouds (PSCs) form in polar regions, typically between 15 and 25 km above mean sea level, when the local temperature is sufficiently low. PSCs play an important role in the ozone chemistry and the dehydration and denitrification of the stratosphere. Lidars with a depolarization channel may be used to detect and classify different classes of PSCs. The main PSC classes are water ice, nitric acid trihydrate (NAT), and supercooled ternary solutions (STSs), the latter being liquid droplets consisting of water, nitric acid, and sulfuric acid. PSCs have been observed at the lidar observatory at Concordia Station from 2014 onward. The harsh environmental conditions at Concordia during winter render successful lidar operation difficult. To facilitate the operation of the observatory, several measures have been put in place to achieve an almost complete remote control of the system. PSC occurrence is strongly correlated with local temperatures and is affected by dynamics, as the PSC coverage during the observation season shows. PSC observations in 2021 are shown as an example of the capability and functionality of the lidar observatory. A comparison of the observations with the satellite-borne CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar has been made to demonstrate the quality of the data and their representativeness for the Antarctic Plateau.
Global Navigation Satellite System/Inertial Measurement Unit/Camera/HD Map Integrated Localization for Autonomous Vehicles in Challenging Urban Tunnel Scenarios
Authors: Tao Lu , Zhang Pan , Gao Kefu , Liu Jingnan
Published: June 2024

Lane-level localization is critical for autonomous vehicles (AVs). However, complex urban scenarios, particularly tunnels, pose significant challenges to AVs’ localization systems. In this paper, we propose a fusion localization method that integrates multiple mass-production sensors, including Global Navigation Satellite Systems (GNSSs), Inertial Measurement Units (IMUs), cameras, and high-definition (HD) maps. Firstly, we use a novel electronic horizon module to assess GNSS integrity and concurrently load the HD map data surrounding the AVs. This map data are then transformed into a visual space to match the corresponding lane lines captured by the on-board camera using an improved BiSeNet. Consequently, the matched HD map data are used to correct our localization algorithm, which is driven by an extended Kalman filter that integrates multiple sources of information, encompassing a GNSS, IMU, speedometer, camera, and HD maps. Our system is designed with redundancy to handle challenging city tunnel scenarios. To evaluate the proposed system, real-world experiments were conducted on a 36-kilometer city route that includes nine consecutive tunnels, totaling near 13 km and accounting for 35% of the entire route. The experimental results reveal that 99% of lateral localization errors are less than 0.29 m, and 90% of longitudinal localization errors are less than 3.25 m, ensuring reliable lane-level localization for AVs in challenging urban tunnel scenarios.
Navigation Resource Allocation Algorithm for LEO Constellations Based on Dynamic Programming
Authors: Wang Sixin , Tang Xiaomei , Li Jingyuan , Huang Xinming , Liu Jiyang , Liu Jian
Published: June 2024

Navigation resource allocation for low-earth-orbit (LEO) constellations refers to the optimal allocation of navigational assets when the number and allocation of satellites in the LEO constellation have been determined. LEO constellations can not only transmit navigation enhancement signals but also enable space-based monitoring (SBM) for real-time assessment of GNSS signal quality. However, proximity in the frequencies of LEO navigation signals and SBM can lead to significant interference, necessitating isolated transmission and reception. This separation requires that SBM and navigation signal transmission be carried out by different satellites within the constellation, thus demanding a strategic allocation of satellite resources. Given the vast number of satellites and their rapid movement, the visibility among LEO, medium-earth-orbit (MEO), and geostationary orbit (GEO) satellites is highly dynamic, presenting substantial challenges in resource allocation due to the computational intensity involved. Therefore, this paper proposes an optimal allocation algorithm for LEO constellation navigation resources based on dynamic programming. In this algorithm, a network model for the allocation of navigation resources in LEO constellations is initially established. Under the constraints of visibility time windows and onboard transmission and reception isolation, the objective is set to minimize the number of LEO satellites used while achieving effective navigation signal transmission and SBM. The constraints of resource allocation and the mathematical expression of the optimization objective are derived. A dynamic programming approach is then employed to determine the optimal resource allocation scheme. Analytical results demonstrate that compared to Greedy and Divide-and-Conquer algorithms, this algorithm achieves the highest resource utilization rate and the lowest computational complexity, making it highly valuable for future resource allocation in LEO constellations.
Using Sentinel-2 Imagery to Measure Spatiotemporal Changes and Recovery across Three Adjacent Grasslands with Different Fire Histories
Authors: Taylor Annalise , Dronova Iryna , Sigona Alexii , Kelly Maggi
Published: June 2024

As a result of the advocacy of Indigenous communities and increasing evidence of the ecological importance of fire, California has invested in the restoration of intentional burning (the practice of deliberately lighting low-severity fires) in an effort to reduce the occurrence and severity of wildfires. Recognizing the growing need to monitor the impacts of these smaller, low-severity fires, we leveraged Sentinel-2 imagery to reveal important inter- and intra-annual variation in grasslands before and after fires. Specifically, we explored three methodological approaches: (1) the complete time series of the normalized burn ratio (NBR), (2) annual summary metrics (mean, fifth percentile, and amplitude of NBR), and (3) maps depicting spatial patterns in these annual NBR metrics before and after fire. We also used a classification of pre-fire vegetation to stratify these analyses by three dominant vegetation cover types (grasses, shrubs, and trees). We applied these methods to a unique study area in which three adjacent grasslands had diverging fire histories and showed how grassland recovery from a low-severity intentional burn and a high-severity wildfire differed both from each other and from a reference site with no recent fire. On the low-severity intentional burn site, our results showed that the annual NBR metrics recovered to pre-fire values within one year, and that regular intentional burning on the site was promoting greater annual growth of both grass and shrub species, even in the third growing season following a burn. In the case of the high-severity wildfire, our metrics indicated that this grassland had not returned to its pre-fire phenological signals in at least three years after the fire, indicating that it may be undergoing a longer recovery or an ecological shift. These proposed methods address a growing need to study the effects of small, intentional burns in low-biomass ecosystems such as grasslands, which are an essential part of mitigating wildfires.
Per-Pixel Forest Attribute Mapping and Error Estimation: The Google Earth Engine and R dataDriven Tool
Authors: Francini Saverio , Marcelli Agnese , Chirici Gherardo , Di Biase Rosa Maria , Fattorini Lorenzo , Corona Piermaria
Published: June 2024

Remote sensing products are typically assessed using a single accuracy estimate for the entire map, despite significant variations in accuracy across different map areas or classes. Estimating per-pixel uncertainty is a major challenge for enhancing the usability and potential of remote sensing products. This paper introduces the dataDriven open access tool, a novel statistical design-based approach that specifically addresses this issue by estimating per-pixel uncertainty through a bootstrap resampling procedure. Leveraging Sentinel-2 remote sensing data as auxiliary information, the capabilities of the Google Earth Engine cloud computing platform, and the R programming language, dataDriven can be applied in any world region and variables of interest. In this study, the dataDriven tool was tested in the Rincine forest estate study area—eastern Tuscany, Italy—focusing on volume density as the variable of interest. The average volume density was 0.042, corresponding to 420 m3 per hectare. The estimated pixel errors ranged between 93 m3 and 979 m3 per hectare and were 285 m3 per hectare on average. The ability to produce error estimates for each pixel in the map is a novel aspect in the context of the current advances in remote sensing and forest monitoring and assessment. It constitutes a significant support in forest management applications and also a powerful communication tool since it informs users about areas where map estimates are unreliable, at the same time highlighting the areas where the information provided via the map is more trustworthy. In light of this, the dataDriven tool aims to support researchers and practitioners in the spatially exhaustive use of remote sensing-derived products and map validation.
UAV-Assisted Mobile Edge Computing: Dynamic Trajectory Design and Resource Allocation
Authors: Wang Zhuwei , Zhao Wenjing , Hu Pengyu , Zhang Xige , Liu Lihan , Fang Chao , Sun Yanhua
Published: June 2024

The recent advancements of mobile edge computing (MEC) technologies and unmanned aerial vehicles (UAVs) have provided resilient and flexible computation services for ground users beyond the coverage of terrestrial service. In this paper, we focus on a UAV-assisted MEC system in which the UAV equipped with MEC servers is used to assist user devices in computing their tasks. To minimize the weighted average energy consumption and delay in the UAV-assisted MEC system, a LQR-Lagrange-based DDPG (LLDDPG) algorithm, which jointly optimizes the user task offloading and the UAV trajectory design, is proposed. To be specific, the LLDDPG algorithm consists of three subproblems. The DDPG algorithm is used to address the issue of UAV desired trajectory planning, and subsequently, the LQR-based algorithm is employed to achieve the real-time tracking control of UAV desired trajectory. Finally, the Lagrange duality method is proposed to solve the optimization problem of computational resource allocation. Simulation results indicate that the proposed LLDDPG algorithm can effectively improve the system resource management and realize the real-time UAV trajectory design.
A Novel Generalized Nested Array MIMO Radar for DOA Estimation with Increased Degrees of Freedom and Low Mutual Coupling
Authors: Yang Zhongtian , Bi Zhengyang , Chen Ye , Hao Honghao
Published: June 2024

In array signal processing, the mutual coupling among physical sensors can inevitably affect the estimation of the direction of arrival (DOA). Despite the fact that multiple-input and multiple-output (MIMO) radar can provide greater degrees of freedom (DOFs), the influence of mutual coupling is largely overlooked in many current MIMO radar designs. To tackle this issue, we propose the utilization of a generalized nested array (GNA) in transmitter array and we introduce an expansion factor into the nested array in the receiver array. Thereby, a novel GNA-MIMO radar is put forward. The proposed MIMO radar offers O(N4) consecutive DOFs with N sensors and avoids the adverse effects of high mutual coupling caused by closely located sensors. Furthermore, we derive the closed-form expressions for the position of physical sensors and the attainable consecutive DOFs of the proposed MIMO radar. Through simulation experiments, we demonstrate the superior accuracy of the proposed MIMO configuration in DOA estimation and angle resolution under the condition of mutual coupling effect.
Failure Mode Classification for Rolling Element Bearings Using Time-Domain Transformer-Based Encoder
Authors: Vu Minh Tri , Hiraga Motoaki , Miura Nanako , Masuda Arata
Published: June 2024

In this paper, we propose a Transformer-based encoder architecture integrated with an unsupervised denoising method to learn meaningful and sparse representations of vibration signals without the need for data transformation or pre-trained data. Existing Transformer models often require transformed data or extensive computational resources, limiting their practical adoption. We propose a simple yet competitive modification of the Transformer model, integrating a trainable noise reduction method specifically tailored for failure mode classification using vibration data directly in the time domain without converting them into other domains or images. Furthermore, we present the key architectural components and algorithms underlying our model, emphasizing interpretability and trustworthiness. Our model is trained and validated using two benchmark datasets: the IMS dataset (four failure modes) and the CWRU dataset (four and ten failure modes). Notably, our model performs competitively, especially when using an unbalanced test set and a lightweight architecture.
Spatial and Spectral Dependencies of Maize Yield Estimation Using Remote Sensing
Authors: Burglewski Nathan , Srinivasagan Subhashree , Ketterings Quirine , van Aardt Jan
Published: June 2024

Corn (Zea mays L.) is the most abundant food/feed crop, making accurate yield estimation a critical data point for monitoring global food production. Sensors with varying spatial/spectral configurations have been used to develop corn yield models from intra-field (0.1 m ground sample distance (GSD)) to regional scales (>250 m GSD). Understanding the spatial and spectral dependencies of these models is imperative to result interpretation, scaling, and deploying models. We leveraged high spatial resolution hyperspectral data collected with an unmanned aerial system mounted sensor (272 spectral bands from 0.4–1 μm at 0.063 m GSD) to estimate silage yield. We subjected our imagery to three band selection algorithms to quantitatively assess spectral reflectance features applicability to yield estimation. We then derived 11 spectral configurations, which were spatially resampled to multiple GSDs, and applied to a support vector regression (SVR) yield estimation model. Results indicate that accuracy degrades above 4 m GSD across all configurations, and a seven-band multispectral sensor which samples the red edge and multiple near-infrared bands resulted in higher accuracy in 90% of regression trials. These results bode well for our quest toward a definitive sensor definition for global corn yield modeling, with only temporal dependencies requiring additional investigation.
Insights into Parkinson’s Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis
Authors: Elbatanouny Hagar , Kleanthous Natasa , Dahrouj Hayssam , Alusi Sundus , Almajali Eqab , Mahmoud Soliman , Hussain Abir
Published: June 2024

Parkinson’s Disease (PD) is a complex neurodegenerative disorder characterized by a spectrum of motor and non-motor symptoms, prominently featuring the freezing of gait (FOG), which significantly impairs patients’ quality of life. Despite extensive research, the precise mechanisms underlying FOG remain elusive, posing challenges for effective management and treatment. This paper presents a comprehensive meta-analysis of FOG prediction and detection methodologies, with a focus on the integration of wearable sensor technology and machine learning (ML) approaches. Through an exhaustive review of the literature, this study identifies key trends, datasets, preprocessing techniques, feature extraction methods, evaluation metrics, and comparative analyses between ML and non-ML approaches. The analysis also explores the utilization of cueing devices. The limited adoption of explainable AI (XAI) approaches in FOG prediction research represents a significant gap. Improving user acceptance and comprehension requires an understanding of the logic underlying algorithm predictions. Current FOG detection and prediction research has a number of limitations, which are identified in the discussion. These include issues with cueing devices, dataset constraints, ethical and privacy concerns, financial and accessibility restrictions, and the requirement for multidisciplinary collaboration. Future research avenues center on refining explainability, expanding and diversifying datasets, adhering to user requirements, and increasing detection and prediction accuracy. The findings contribute to advancing the understanding of FOG and offer valuable guidance for the development of more effective detection and prediction methodologies, ultimately benefiting individuals affected by PD.
Design of a Compact Circularly Polarized Implantable Antenna for Capsule Endoscopy Systems
Authors: Song Zhiwei , Xu Xiaoming , Shi Youwei , Wang Lu
Published: June 2024

This research proposes a miniature circular polarization antenna used in a wireless capsule endoscopy system at 2.45 GHz for industrial, scientific, and medical bands. We propose a method of cutting a chamfer rectangular slot on a circular radiation patch and introducing a curved radiation structure into the centerline position of the chamfer rectangular slot, while a short-circuit probe is added to achieve miniaturization. Therefore, we significantly reduced the size of the antenna and made it exhibit circularly polarized radiation characteristics. A cross-slot is cut in the GND to enable the antenna to better cover the operating band while being able to meet the complex human environment. The effective axis ratio bandwidth is 120 MHz (2.38–2.50 GHz). Its size is π × 0.032λ02 × 0.007λ0 (where λ0 is the free-space wavelength of at 2.4 GHz). In addition, the effect of different organs such as muscle, stomach, small intestine, and big intestine on the antenna when it was embedded into the wireless capsule endoscopy (WCE) system was further discussed, and the results proved that the WCE system has better robustness in different organs. The antenna’s specific absorption rate can follow the IEEE Standard Safety Guidelines (IEEE C95.1-1999). A prototype is fabricated and measured. The experimental results are consistent with the simulation results.
Non-Contact Vision-Based Techniques of Vital Sign Monitoring: Systematic Review
Authors: Saikevičius Linas , Raudonis Vidas , Dervinis Gintaras , Baranauskas Virginijus
Published: June 2024

The development of non-contact techniques for monitoring human vital signs has significant potential to improve patient care in diverse settings. By facilitating easier and more convenient monitoring, these techniques can prevent serious health issues and improve patient outcomes, especially for those unable or unwilling to travel to traditional healthcare environments. This systematic review examines recent advancements in non-contact vital sign monitoring techniques, evaluating publicly available datasets and signal preprocessing methods. Additionally, we identified potential future research directions in this rapidly evolving field.
Temporal and Spatial Distribution Characteristics of Crosstalk Lines Generated by Irradiating Progressive Scan Charge-Coupled Device Camera with Continuous Laser
Authors: Yu Chenghao , Ye Jifei , Chang Hao , Li Nanlei , Guo Wei
Published: June 2024

To study the interference effect of the laser in motion mode on a CCD, the continuous laser with the wavelength of 532 nm at different motion speeds was used to scan the CCD. The experimental results show that the crosstalk phenomenon produced by static and dynamic irradiation is significantly different. When the continuous laser statically radiates the CCD, the vertical crosstalk line is observed in the output image. The gray values of the crosstalk line are divided into two stages, with the increase of the laser fluence: linear increase and saturation, which correspond to different formation mechanisms of the crosstalk lines, respectively. In addition, when the irradiation duration of the static laser is less than the integration time of CCD, the effect of delay time on the spatial distribution of the crosstalk line is identified. In addition, when the laser irradiates the CCD at different scanning speeds, crosstalk lines with certain slopes are observed. The slope of the crosstalk line is determined by the scanning speed of the continuous laser and the integration time of the CCD. The results show that the delay time and the irradiation position have important effects on the spatial distribution of the laser spot and crosstalk lines.
A Novel Method for Rolling Bearing Fault Diagnosis Based on Gramian Angular Field and CNN-ViT
Authors: Zhou Zijun , Ai Qingsong , Lou Ping , Hu Jianmin , Yan Junwei
Published: June 2024

Fault diagnosis is one of the important applications of edge computing in the Industrial Internet of Things (IIoT). To address the issue that traditional fault diagnosis methods often struggle to effectively extract fault features, this paper proposes a novel rolling bearing fault diagnosis method that integrates Gramian Angular Field (GAF), Convolutional Neural Network (CNN), and Vision Transformer (ViT). First, GAF is used to convert one-dimensional vibration signals from sensors into two-dimensional images, effectively retaining the fault features of the vibration signal. Then, the CNN branch is used to extract the local features of the image, which are combined with the global features extracted by the ViT branch to diagnose the bearing fault. The effectiveness of this method is validated with two datasets. Experimental results show that the proposed method achieves average accuracies of 99.79% and 99.63% on the CWRU and XJTU-SY rolling bearing fault datasets, respectively. Compared with several widely used fault diagnosis methods, the proposed method achieves higher accuracy for different fault classifications, providing reliable technical support for performing complex fault diagnosis on edge devices.
Design of an Electromagnetic Micro Mirror Driving System for LiDAR
Authors: Chen Jie , Zhang Haiqiang , Zhang Zhongjin , Yan Wenjie
Published: June 2024

Electromagnetic micro mirrors are in great demand for light detection and ranging (LiDAR) applications due to their light weight and low power consumption. The driven frequency of electromagnetic micro mirrors is very important to their performance and consumption. An electromagnetic micro mirror system is proposed in this paper. The model of the system was composed of a micro mirror, an integrated piezoresistive (PR) sensor, and a driving circuit was developed. The twisting angle of the mirror edge was monitored by an integrated PR sensor, which provides frequency feedback signals, and the PR sensor has good sensitivity and linearity in testing, with a maximum of 24.45 mV/deg. Stable sinusoidal voltage excitation and frequency tracking was realized via a phase-locked loop (PLL) in the driving circuit, with a frequency error within 10 Hz. Compared with other high-cost solutions using PLL circuits, it has greater advantages in power consumption, cost, and occupied area. The mechanical and piezoresistive properties of micro mirrors were performed in ANSYS 19.2 software. The behavior-level models of devices, circuits, and systems were validated by MATLAB R2023a Simulink, which contributes to the research on the large-angle deflection and low-power-consumption drive of the electromagnetic micro mirror. The maximum optical scan angle reached 37.6° at 4 kHz in the behavior-level model of the micro mirror.
Evaluating the Electroencephalographic Signal Quality of an In-Ear Wearable Device
Authors: Pazuelo Jeremy , Juez Jose Yesith , Moumane Hanane , Pyrzowski Jan , Mayor Liliana , Segura-Quijano Fredy Enrique , Valderrama Mario , Le Van Quyen Michel
Published: June 2024

Wearable in-ear electroencephalographic (EEG) devices hold significant promise for advancing brain monitoring technologies into everyday applications. However, despite the current availability of several in-ear EEG devices in the market, there remains a critical need for robust validation against established clinical-grade systems. In this study, we carried out a detailed examination of the signal performance of a mobile in-ear EEG device from Naox Technologies. Our investigation had two main goals: firstly, evaluating the hardware circuit’s reliability through simulated EEG signal experiments and, secondly, conducting a thorough comparison between the in-ear EEG device and gold-standard EEG monitoring equipment. This comparison assesses correlation coefficients with recognized physiological patterns during wakefulness and sleep, including alpha rhythms, eye artifacts, slow waves, spindles, and sleep stages. Our findings support the feasibility of using this in-ear EEG device for brain activity monitoring, particularly in scenarios requiring enhanced comfort and user-friendliness in various clinical and research settings.
Safe Robust Adaptive Motion Control for Underactuated Marine Robots
Authors: Nazmara G. Reza , Aguiar A. Pedro
Published: June 2024

This article presents an innovative approach to the design of a safe adaptive backstepping control system. Tailored specifically for underactuated marine robots, the system utilizes simple sensors for enhanced practicality and efficiency. Given their operation in diverse oceanic environments fraught with various sources of uncertainties, ensuring the system’s safe and robust behavior holds paramount importance in the control literature. To address this concern, this paper introduces a control strategy designed to ensure robustness at both the kinematic and dynamic levels. By emphasizing the compensation for the system uncertainties, the design integrates a straightforward fuzzy system structure. To further ensure the system’s safety, a funnel surface is defined, followed by the design of a suitable nonlinear sliding surface as a function of the funnel and tracking error. Using Lyapunov theory, the study formally establishes the Semi-globally Practically Finite-time Stability of the closed-loop system, validated through simulations conducted on underactuated marine robots.
Introducing a Remote Patient Monitoring Usability Impact Model to Overcome Challenges
Authors: Baumann Steffen , Stone Richard T. , Abdelall Esraa
Published: June 2024

Telehealth and remote patient monitoring (RPM), in particular, have been through a massive surge of adoption since 2020. This initiative has proven potential for the patient and the healthcare provider in areas such as reductions in the cost of care. While home-use medical devices or wearables have been shown to be beneficial, a literature review illustrates challenges with the data generated, driven by limited device usability. This could lead to inaccurate data when an exam is completed without clinical supervision, with the consequence that incorrect data lead to improper treatment. Upon further analysis of the existing literature, the RPM Usability Impact model is introduced. The goal is to guide researchers and device manufacturers to increase the usability of wearable and home-use medical devices in the future. The importance of this model is highlighted when the user-centered design process is integrated, which is needed to develop these types of devices to provide the proper user experience.
Digital Twin Sensors in Cultural Heritage Ontology Applications
Authors: Niccolucci Franco , Felicetti Achille
Published: June 2024

This paper concerns the extension of the Heritage Digital Twin Ontology introduced in previous research to describe the reactivity of digital twins used for cultural heritage documentation by including the semantic description of sensors and activators and all of the process of interacting with the real world. After analysing previous work on the use of digital twins in cultural heritage, a summary description of the Heritage Digital Twin Ontology is provided, and the existing applications of digital twins to cultural heritage are overviewed, with references to reviews summarising the large production of scientific contributions on the topic. Then, a novel ontology named the Reactive Digital Twin Ontology is described, in which sensors, activators, and the decision processes are also semantically described, turning the previous synchronic approach to cultural heritage documentation into a diachronic one. Some case studies exemplify this theory.
Energy-Efficient PPG-Based Respiratory Rate Estimation Using Spiking Neural Networks
Authors: Yang Geunbo , Kang Youngshin , Charlton Peter H. , Kyriacou Panayiotis A. , Kim Ko Keun , Li Ling , Park Cheolsoo
Published: June 2024

Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, and heart rate variability. Various physiological signals, such as photoplethysmogram (PPG) signals, are used to extract respiratory information. RR is also estimated by detecting peak patterns and cycles in the signals through signal processing and deep-learning approaches. In this study, we propose an end-to-end RR estimation approach based on a third-generation artificial neural network model—spiking neural network. The proposed model employs PPG segments as inputs, and directly converts them into sequential spike events. This design aims to reduce information loss during the conversion of the input data into spike events. In addition, we use feedback-based integrate-and-fire neurons as the activation functions, which effectively transmit temporal information. The network is evaluated using the BIDMC respiratory dataset with three different window sizes (16, 32, and 64 s). The proposed model achieves mean absolute errors of 1.37 ± 0.04, 1.23 ± 0.03, and 1.15 ± 0.07 for the 16, 32, and 64 s window sizes, respectively. Furthermore, it demonstrates superior energy efficiency compared with other deep learning models. This study demonstrates the potential of the spiking neural networks for RR monitoring, offering a novel approach for RR estimation from the PPG signal.
Lumped Parameter Thermal Network Modeling and Thermal Optimization Design of an Aerial Camera
Authors: Fan Yue , Feng Wei , Ren Zhenxing , Liu Bingqi , Wang Dazhi
Published: June 2024

The quality of aerial remote sensing imaging is heavily impacted by the thermal distortions in optical cameras caused by temperature fluctuations. This paper introduces a lumped parameter thermal network (LPTN) model for the optical system of aerial cameras, aiming to serve as a guideline for their thermal design. By optimizing the thermal resistances associated with convection and radiation while considering the camera’s unique internal architecture, this model endeavors to improve the accuracy of temperature predictions. Additionally, the proposed LPTN framework enables the establishment of a heat leakage network, which offers a detailed examination of heat leakage paths and rates. This analysis offers valuable insights into the thermal performance of the camera, thereby guiding the refinement of heating zones and the development of effective active control strategies. Operating at a total power consumption of 26 W, the thermal system adheres to the low-power limit. Experimental data from thermal tests indicate that the temperatures within the optical system are maintained consistently between 19 °C and 22 °C throughout the flight, with temperature gradients remaining below 3 °C, satisfying the temperature requirements. The proposed LPTN model exhibits swiftness and efficacy in determining thermal characteristics, significantly facilitating the thermal design process and ensuring optimal power allocation for aerial cameras.
Globally Guided Deep V-Network-Based Motion Planning Algorithm for Fixed-Wing Unmanned Aerial Vehicles
Authors: Du Hang , You Ming , Zhao Xinyi
Published: June 2024

Fixed-wing UAVs have shown great potential in both military and civilian applications. However, achieving safe and collision-free flight in complex obstacle environments is still a challenging problem. This paper proposed a hierarchical two-layer fixed-wing UAV motion planning algorithm based on a global planner and a local reinforcement learning (RL) planner in the presence of static obstacles and other UAVs. Considering the kinematic constraints, a global planner is designed to provide reference guidance for ego-UAV with respect to static obstacles. On this basis, a local RL planner is designed to accomplish kino-dynamic feasible and collision-free motion planning that incorporates dynamic obstacles within the sensing range. Finally, in the simulation training phase, a multi-stage, multi-scenario training strategy is adopted, and the simulation experimental results show that the performance of the proposed algorithm is significantly better than that of the baseline method.