bluuebunny/crossref_metadata_2025 huggingface dataset:
Excipient Impact on Fenofibrate Equilibrium Solubility in Fasted and Fed Simulated Intestinal Fluids Assessed Using a Design of Experiment Protocol
Authors: Ainousah Bayan E. , Khadra Ibrahim , Halbert Gavin W.
Published: October 2023

Solubility is a critical parameter controlling drug absorption after oral administration. For poorly soluble drugs, solubility is influenced by the complex composition of intestinal media and the influence of dosage form excipients, which can cause bioavailability and bioequivalence issues. This study has applied a small scale design of experiment (DoE) equilibrium solubility approach in order to investigate the impact of excipients on fenofibrate solubility in simulated fasted and fed intestinal media. Seven media parameters (bile salt (BS), phospholipid (PL), fatty acid, monoglyceride, cholesterol, pH and BS/PL ratio) were assessed in the DoE and in excipient-free media, and only pH and sodium oleate in the fasted state had a significant impact on fenofibrate solubility. The impact of excipients were studied at two concentrations, and for polyvinylpyrrolidone (PVP, K12 and K29/32) and hydroxypropylmethylcellulose (HPMC, E3 and E50), two grades were studied. Mannitol had no solubility impact in any of the DoE media. PVP significantly increased solubility in a media-, grade- and concentration-dependent manner, with the biggest change in fasted media. HPMC and chitosan significantly reduced solubility in both fasted and fed states in a media-, grade- and concentration-dependent manner. The results indicate that the impact of excipients on fenofibrate solubility is a complex interplay of media composition in combination with their physicochemical properties and concentration. The results indicate that in vitro solubility studies combining the drug of interest, proposed excipients along with suitable simulated intestinal media recipes will provide interesting information with the potential to guide formulation development.
Rapid Estimation of Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) Composition Using ATR-FTIR
Authors: Alfano Sara , Pagnanelli Francesca , Martinelli Andrea
Published: October 2023

A great research effort is involved in polyhydroxyalkanoates (PHAs) production and characterization since they are an attractive degradable polyester family that potentially could substitute oil-based polymers. This is due to two main key factors: their production is sustainable, being that they are produced by microorganisms possibly fed by organic waste-derived products, and they are degradable. Moreover, PHAs’ thermal and mechanical properties could be tuned by varying their monomeric composition through the proper selection of microorganism feedstock and bioreactor operative conditions. Hence, a rapid and facile determination of the PHA chemical structure by widely available instrumentation is useful. As an alternative to the standard gas-chromatographic method, a new procedure for the composition determination of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (P3HBV), the most common PHA copolymer, by attenuated total reflection FTIR (ATR-FTIR) is presented. It is based on the linear dependence of selected and normalized absorption band intensity with the molar fraction of repeating units. To break free from the crystallinity variability, which affects the result reproducibility and data scattering, the polymer sample was rapidly quenched from the melt directly on the surface of the ATR internal reflection element and analyzed. The data obtained from 14 samples with a molar fraction of 3-hydroxybutyrate repeating units (X3HB) ranging from 0.15 to 1 were analyzed. According to preliminary analyses, the normalized intensity of two absorption bands was selected to develop a calibration method able to predict X3HB of unknown samples and to evaluate the related uncertainty through prediction intervals of inverse regression. The proposed method proves to be useful for an easy and rapid estimation of P3HBV composition.
Self-Polarized P(VDF-TrFE)/Carbon Black Composite Piezoelectric Thin Film
Authors: Muthusamy Lavanya , Uppalapati Balaadithya , Azad Samee , Bava Manav , Koley Goutam
Published: October 2023

Self-polarized energy harvesting materials have seen increasing research interest in recent years owing to their simple fabrication method and versatile application potential. In this study, we systematically investigated self-polarized P(VDF-TrFE)/carbon black (CB) composite thin films synthesized on flexible substrates, with the CB content varying from 0 to 0.6 wt.% in P(VDF-TrFE). The presence of –OH functional groups on carbon black significantly enhances its crystallinity, dipolar orientation, and piezoelectric performance. Multiple characterization techniques were used to investigate the crystalline quality, chemical structure, and morphology of the composite P(VDF-TrFE)/CB films, which indicated no significant changes in these parameters. However, some increase in surface roughness was observed when the CB content increased. With the application of an external force, the piezoelectrically generated voltage was found to systematically increase with higher CB content, reaching a maximum value at 0.6 wt.%, after which the sample exhibited low resistance. The piezoelectric voltage produced by the unpoled 0.6 wt.% CB composite film significantly exceeded the unpoled pure P(VDF-TrFE) film when subjected to the same applied strain. Furthermore, it exhibited exceptional stability in the piezoelectric voltage over time, exceeding the output voltage of the poled pure P(VDF-TrFE) film. Notably, P(VDF_TrFE)/CB composite-based devices can be used in energy harvesting and piezoelectric strain sensing to monitor human motions, which has the potential to positively impact the field of smart wearable devices.
Modeling of the Aqueous Solubility of N-butyl-N-methyl-1-phenylpyrrolo[1,2-a] pyrazine-3-carboxamide: From Micronization to Creation of Amorphous–Crystalline Composites with a Polymer
Authors: Markeev Vladimir B. , Tishkov Sergey V. , Vorobei Anton M. , Parenago Olga O. , Blynskaya Evgenia V. , Alekseev Konstantin V. , Marakhova Anna I. , Vetcher Alexandre A.
Published: October 2023

N-butyl-N-methyl-1-phenylpyrrole[1,2-a] pyrazine-3-carboxamide (GML-3) is a potential candidate for combination drug therapy due to its anxiolytic and antidepressant activity. The anxiolytic activity of GML-3 is comparable to diazepam. The antidepressant activity of GML-3 is comparable to amitriptyline. GML-3 is an 18 kDa mitochondrial translocator protein (TSPO) ligand and is devoid of most of the side effects of diazepam, which makes the research on the creation of drugs based on it promising. However, its low water solubility and tendency to agglomerate prevented its release. This research aimed to study the effect of dry grinding, the rapid expansion of a supercritical solution (RESS), and the eutectic mixture (composite) of GML-3 with polyvinylpyrrolidone (PVP) on the particle size, dissolution rate, and lattice retention of GML-3. The use of supercritical CO2 in the RESS method was promising in terms of particle size reduction, resulting in a reduction in the particle size of GML-3 to 20–40 nm with a 430-fold increase in dissolution rate. However, in addition to particle size reduction after RESS, GML-3 began to show signs of a polymorphism phenomenon, which was also studied in this article. It was found that coarse grinding reduced particle size by a factor of 2 but did not significantly affect solubility or crystal structure. Co-milling with the polymer made it possible to level the effect of the appearance of a residual electrostatic charge on the particles, as in the case of grinding, and the increased solubility in the resulting mechanical mixtures of GML-3 with the polymer may also indicate the dissolving properties of polymers (an increase in 400–800 times). The best result in terms of GML-3 solubility was demonstrated by the resulting GML-3:PVP composite at a ratio of 1:4, which made it possible to achieve a solubility of about 80% active pharmaceutical ingredient (API) within an hour with an increase in the dissolution rate by 1600 times. Thus, the creation of composites is the most effective method for improving the solubility of GML-3, superior to micronization.
Solvent Swelling-Induced Halogenation of Butyl Rubber Using Polychlorinated N-Alkanes: Structure and Properties
Authors: Sukhareva Ksenia Valeriyevna , Sukharev Nikita Romanovich , Levina Irina Ivanovna , Offor Peter Ogbuna , Popov Anatoly Anatolyevich
Published: October 2023

Traditional butyl rubber halogenation technology involves the halogenation of IIR using molecular chlorine or bromine in a solution. However, this method is technologically complex. This study investigated a novel method for the halogenation of butyl rubber to enhance its stability and resistance to thermal oxidation and aggressive media. The butyl rubber was modified through mechanochemical modification, induced by solvent swelling in a polychlorinated n-alkane solution. During the modification, samples were obtained with chlorine content ranging from 3 to 15%. After extraction, the halogen content was quantitatively determined with the oxygen flask combustion method and X-ray photoelectron spectroscopy. It was shown that for samples with total chlorine content of up to 6%, there was almost no leaching of chlorine from the samples. The chemical structure of the extracted rubbers was ascertained using FT-IR and 1H NMR spectroscopy, and it was demonstrated that all samples showed absorption peaks and signals typical for chlorobutyl rubbers. It was observed that modification with polychlorinated n-alkanes improved the thermal and oxidative stability (the oxygen absorption rate decreased by 40%) and chemical resistance, estimated by the degree of swelling, which decreased with the increase in the chlorine content. This technology allows the production of a chlorinated rubber solution that can be directly used by rubber goods manufacturers and suppliers.
Optimizing Task Completion Time in Disaster-Affected Regions with the WMDDPG-GSA Algorithm for UAV-Assisted MEC Systems
Authors: Ma Tianhao , Yang Yulu , Xu Han , Song Tiecheng
Published: October 2023

In this paper, we investigate a UAV-assisted Mobile Edge Computing (MEC) system that is deployed with multiple UAVs to provide timely data processing services to disaster-stricken areas. In our model, since base stations are unavailable in disaster-affected areas, we solely employ UAVs as MEC servers, as well as enable real-time data transmission during UAV flights by estimating and compensating for the Doppler Frequency Shift (DFS). Subsequently, an optimization problem is formulated to jointly optimize the trajectories and offloading strategies of multiple UAVs to minimize the task completion time. We enhance the performance of the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm by using a weighted strategy algorithm, and we thus propose the Weighted-Strategy-Based Multi-Agent Deep Deterministic Policy Gradient (WMDDPG) algorithm for optimizing UAV trajectories. We employ the Greedy-Based Simulated Annealing (GSA) algorithm to overcome the limitations of the greedy algorithm and to obtain the best offloading strategy. The results demonstrate the effectiveness of the proposed WMDDPG-GSA algorithm, as it outperforms benchmark algorithms.
Democracy, Peace, and Religion in Nigeria: Can Religion Be Used to Consolidate or Undermine Democracy and Peace?
Authors: Jatau Victoria , Maza Kangdim Dingji
Published: October 2023

This study looks at the relationships between religion, democracy, and peace. It is based on this premise that this article examines how religion is used to consolidate democracy and promote peace in societies, using Nigeria as its framework of analysis. Using the qualitative method of analysis where emphasis is placed on primary and secondary documents, which involve articles, reports, and newspaper articles triangulating with key informant interviews (KIIs) and stakeholder analysis, the findings of this article suggest that religion plays a significant role in the consolidation of democracy and peace given its important place in advocating some principles and ideals of democracy, which involve equity, fairness, freedom, pluralism, respect for diversities, and defending minority rights, amongst others. However, challenges associated with the weaponization of religion to pursue selfish political agenda and interests by both religious and political elites in Nigeria has become more of a norm than what is expected between both variables. This has helped in the creation of a hostile and unstable political environment, and in the pervasion of democratic and political institutions and agencies responsible for promoting accountable leadership, good governance, representative governance, political interference, and repressive policies, limiting the rights and freedoms of citizens who continue to impede the enhancement of a strongly consolidated democratic culture and peace in transition democracies such as Nigeria. This article suggests the need for relevant actors to ensure that institutions of the state are strengthened to provide the dividends of democracy. This can be achieved by addressing the challenge of the negative use of religion by the elites to advance unholy political interests and agenda. It is also important to create machinery to address the structural problems that breed poverty; political, economic, and social exclusion; and human rights abuses, limiting political and electoral freedoms. Addressing these challenges also requires relevant stakeholders and actors to understand that the process is multi-pronged, which also requires acknowledging, accommodating, and accepting individual and group diversities and, above all, requires the patience and the political will of these actors to ensure its actualization. Addressing these concerns will significantly strengthen and enhance the consolidation of democracy and peace in fragile and transition societies.
The Method of Multi-Angle Remote Sensing Observation Based on Unmanned Aerial Vehicles and the Validation of BRDF
Authors: Cao Hongtao , You Dongqin , Ji Dabin , Gu Xingfa , Wen Jianguang , Wu Jianjun , Li Yong , Cao Yongqiang , Cui Tiejun , Zhang Hu
Published: October 2023

The measurement of bidirectional reflectivity for ground-based objects is a highly intricate task, with significant limitations in the capabilities of both ground-based and satellite-based observations from multiple viewpoints. In recent years, unmanned aerial vehicles (UAVs) have emerged as a novel remote sensing method, offering convenience and cost-effectiveness while enabling multi-view observations. This study devised a polygonal flight path along the hemisphere to achieve bidirectional reflectance distribution function (BRDF) measurements for large zenith angles and all azimuth angles. By employing photogrammetry’s principle of aerial triangulation, accurate observation angles were restored, and the geometric structure of “sun-object-view” was constructed. Furthermore, three BRDF models (M_Walthall, RPV, RTLSR) were compared and evaluated at the UAV scale in terms of fitting quality, shape structure, and reflectance errors to assess their inversion performance. The results demonstrated that the RPV model exhibited superior inversion performance followed, by M_Walthall; however, RTLST performed comparatively poorly. Notably, the M_Walthall model excelled in capturing smooth terrain object characteristics while RPV proved applicable to various types of rough terrain objects with multi-scale applicability for both UAVs and satellites. These methods and findings are crucial for an extensive exploration into the bidirectional reflectivity properties of ground-based objects, and provide an essential technical procedure for studying various ground-based objects’ in-plane reflection properties.
Retrieval of Water Quality Parameters in Dianshan Lake Based on Sentinel-2 MSI Imagery and Machine Learning: Algorithm Evaluation and Spatiotemporal Change Research
Authors: Dong Lei , Gong Cailan , Huai Hongyan , Wu Enuo , Lu Zhihua , Hu Yong , Li Lan , Yang Zhe
Published: October 2023

According to current research, machine learning algorithms have been proven to be effective in detecting both optical and non-optical parameters of water quality. The use of satellite remote sensing is a valuable method for monitoring long-term changes in the quality of lake water. In this study, Sentinel-2 MSI images and in situ data from the Dianshan Lake area from 2017 to 2023 were used. Four machine learning methods were tested, and optimal detection models were determined for each water quality parameter. It was ultimately determined that these models could be applied to long-term images to analyze the spatiotemporal variations and distribution patterns of water quality in Dianshan Lake. Based on the research findings, integrated learning algorithms, especially CatBoost, have achieved good results in the retrieval of all water quality parameters. Spatiotemporal analysis reveals that the overall distribution of water quality parameters is uneven, with significant spatial variations. Permanganate index (CODMn), Total Nitrogen (TN), and Total Phosphorus (TP) show relatively small interannual differences, generally exhibiting a decreasing trend in concentrations. In contrast, chlorophyll-a (Chl-a), dissolved oxygen (DO), and Secchi Disk Depth (SDD) exhibit significant interannual and inter-year differences. Chl-a reached its peak in 2020, followed by a decrease, while DO and SDD showed the opposite trend. Further analysis indicated that the distribution of water quality parameters is significantly influenced by climatic factors and human activities such as agricultural expansion. Overall, there has been an improvement in the water quality of Dianshan Lake. The study demonstrates the feasibility of accurately monitoring water quality even without measured spectral data, using machine learning methods and satellite reflectance data. The research results presented in this paper can provide new insights into water quality monitoring and water resource management in Dianshan Lake.
Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit Mining
Authors: Meng Xiaoliang , Wang Tianyi , Cheng Dayu , Su Wensong , Yao Peng , Ma Xiaoli , He Meizhen
Published: October 2023

The calculation of volumes for irregular bodies holds significant relevance across various production processes. This spans tasks such as evaluating the growth status of crops and fruits, conducting morphological analyses of spatial objects based on volume parameters, and estimating quantities for earthwork and excavation. While methods like drainage, surface reconstruction, and triangulation suffice for smaller irregular bodies, larger ones introduce heightened complexity. Technological advancements, such as UAV photogrammetry and LiDAR, have introduced efficient point cloud data acquisition methods, bolstering precision and efficiency in calculating volumes for substantial irregular bodies. Notably, open-pit mines, characterized by their dynamic surface alterations, exemplify the challenges posed by large irregular bodies. Ensuring accurate excavation quantity calculations in such mines is pivotal, impacting operational considerations, acceptance, as well as production cost management and project oversight. Thus, this study employs UAV-acquired point cloud data from open-pit mines as a case study. In practice, calculating volumes for substantial irregular bodies often relies on the point cloud slicing method. However, this approach grapples with distinguishing multi-contour boundaries, leading to inaccuracies. To surmount this hurdle, this paper introduces an enhanced point cloud slicing method. The methodology involves segmenting point cloud data at fixed intervals, followed by the segmentation of slice contours using the Euclidean clustering method. Subsequently, the concave hull algorithm extracts the contour polygons of each slice. The final volume calculation involves multiplying the area of each polygon by the spacing and aggregating these products. To validate the efficacy of our approach, we employ model-derived volumes as benchmarks, comparing errors arising from both the traditional slicing method and our proposed technique. Experimental outcomes underscore the superiority of our point cloud volume calculation method, manifesting in an average relative error of 1.17%, outperforming the conventional point cloud slicing method in terms of accuracy.
Hierarchical Area-Based and Path-Based Heuristic Approaches for Multirobot Coverage Path Planning with Performance Analysis in Surveillance Systems
Authors: Gong Junghwan , Lee Seunghwan
Published: October 2023

In this study, we present a systematic exploration of hierarchical designs for multirobot coverage path planning (MCPP) with a special focus on surveillance applications. Unlike conventional studies centered on cleaning tasks, our investigation delves into the realm of surveillance problems, specifically incorporating the sensing range (SR) factor equipped on the robots. Conventional path-based MCPP strategies considering SR, primarily rely on naive approaches, generating nodes (viewpoints) to be visited and a global path based on these nodes. Therefore, our study proposes a general MCPP framework for surveillance by dealing with path-based and area-based structures comprehensively. As the traveling salesman problem (TSP) solvers, our approach incorporates not the naive approach but renowned and powerful algorithms such as genetic algorithms (GAs), and ant colony optimization (ACO) to enhance the planning process. We devise six distinct methods within the proposed MCPP framework. Two methods adopt area-based approaches which segments areas via a hierarchical max-flow routing algorithm based on SR and the number of robots. TSP challenges within each area are tackled using a GA or ACO, and the result paths are allocated to individual robots. The remaining four methods are categorized by the path-based approaches with global–local structures such as GA-GA, GA-ACO, ACO-GA, and ACO-ACO. Unlike conventional methods which requires a global path, we further incorporate ACO- or GA-based local planning. This supplementary step at the local level enhances the quality of the path-planning results, particularly when dealing with a large number of nodes, by preventing any degradation in global path-planning outcomes. An extensive comparative analysis is conducted to evaluate the proposed framework based on execution time, total path length, and idle time. The area-based approaches tend to show a better execution time and overall path length performance compared to the path-based approaches. However, the path-based MCPP methods have the advantage of having a smaller idle time than the area-based MCPP methods. Our study finds that the proposed area-based MCPP method excels in path planning, while the proposed path-based MCPP method demonstrates superior coverage balance performance. By selecting an appropriate MCPP structure based on the specific application requirements, leveraging the strengths of both methodologies, efficient MCPP execution becomes attainable. Looking forward, our future work will focus on tailoring these MCPP structures to diverse real-world conditions, aiming to propose the most suitable approach for specific applications.
N-Accesses: A Blockchain-Based Access Control Framework for Secure IoT Data Management
Authors: Hu Teng , Yang Siqi , Wang Yanping , Li Gongliang , Wang Yulong , Wang Gang , Yin Mingyong
Published: October 2023

With the rapid advancement of network communication and big data technologies, the Internet of Things (IoT) has permeated every facet of our lives. Meanwhile, the interconnected IoT devices have generated a substantial volume of data, which possess both economic and strategic value. However, owing to the inherently open nature of IoT environments and the limited capabilities and the distributed deployment of IoT devices, traditional access control methods fall short in addressing the challenges of secure IoT data management. On the one hand, the single point of failure issue is inevitable for the centralized access control schemes. On the other hand, most decentralized access control schemes still face problems such as token underutilization, the insecure distribution of user permissions, and inefficiency.This paper introduces a blockchain-based access control framework to address these challenges. Specifically, the proposed framework enables data owners to host their data and achieves user-defined lightweight data management. Additionally, through the strategic amalgamation of smart contracts and hash-chains, our access control scheme can limit the number of times (i.e., n-times access) a user can access the IoT data before the deadline. This also means that users can utilize their tokens multiple times (predefined by the data owner) within the deadline, thereby improving token utilization while ensuring strict access control. Furthermore, by leveraging the intrinsic characteristics of blockchain, our framework allows data owners to gain capabilities for auditing the access records of their data and verifying them. To empirically validate the effectiveness of our proposed framework and approach, we conducted extensive simulations, and the experimental results demonstrated the feasibility and efficiency of our solution.
Hydrogen Peroxide Electrochemical Sensor Based on Ag/Cu Bimetallic Nanoparticles Modified on Polypyrrole
Authors: Guan Yanxun , Xu Fen , Sun Lixian , Luo Yumei , Cheng Riguang , Zou Yongjin , Liao Lumin , Cao Zhong
Published: October 2023

Due to the strong oxidizing properties of H2O2, excessive discharge of H2O2 will cause great harm to the environment. Moreover, H2O2 is also an energetic material used as fuel, with specific attention given to its safety. Therefore, it is of great importance to explore and prepare good sensitive materials for the detection of H2O2 with a low detection limit and high selectivity. In this work, a kind of hydrogen peroxide electrochemical sensor has been fabricated. That is, polypyrrole (PPy) has been electropolymerized on the glass carbon electrode (GCE), and then Ag and Cu nanoparticles are modified together on the surface of polypyrrole by electrodeposition. SEM analysis shows that Cu and Ag nanoparticles are uniformly deposited on the surface of PPy. Electrochemical characterization results display that the sensor has a good response to H2O2 with two linear intervals. The first linear range is 0.1–1 mM (R2 = 0.9978, S = 265.06 μA/ (mM × cm2)), and the detection limit is 0.027 μM (S/N = 3). The second linear range is 1–35 mM (R2 = 0.9969, 445.78 μA/ (mM × cm2)), corresponding to 0.063 μM of detection limit (S/N = 3). The sensor reveals good reproducibility (σ = 2.104), repeatability (σ = 2.027), anti-interference, and stability. The recoveries of the electrode are 99.84–103.00% (for 0.1–1 mM of linear range) and 98.65–104.80% (for 1–35 mM linear range). Furthermore, the costs of the hydrogen peroxide electrochemical sensor proposed in this work are reduced largely by using non-precious metals without degradation of the sensing performance of H2O2. This study provides a facile way to develop nanocomposite electrochemical sensors.
Study on the Characteristics of Trackside Acoustic Flow Field of High-Speed Train under the Influence of Crosswind
Authors: Zhao Xing , Zhang Lei , Li Lin , Feng Qiying
Published: October 2023

During the on-track acoustic detection process, a potential flow model and an acoustic finite element mathematical model based on synthetic wind are utilized, taking into account the combined effects of vehicle speed, wind direction angle, and crosswind speed. Simulation and modeling are achieved using Automatic Matching of Acoustic Radiation Boundary Conditions (AML) technology, enabling obtaining a distribution map and sound pressure frequency response curve of the trackside acoustic field under crosswind conditions by setting up field point grids. It is found that sound pressure values at the same location gradually increase as the vehicle speed increases in the frequency range of 10 Hz to 70 Hz, at different vehicle speeds. The sound pressure values and distribution area of the trackside acoustic field are the largest when the crosswind speed is 10 m/s (wind force at level five), allowing for easier location of the sound source when a fault occurs. The study also reveals that under different wind direction angles, the same location’s sound pressure value on the trackside gradually decreases as the wind direction angle increases, to lower than that of the non-crosswind condition, severely hindering the reception and diagnosis of acoustic signals.
A Real-Time Inspection System for Industrial Helical Gears
Authors: Idzik Thomas , Veres Matthew , Tarry Cole , Moussa Medhat
Published: October 2023

Manufacturing is an imperfect process that requires frequent checks and verifications to ensure products are being produced properly. In many cases, such as visual inspection, these checks can be automated to a certain degree. Incorporating advanced inspection techniques (i.e., via deep learning) into real-world inspection pipelines requires different mechanical, machine vision, and process-level considerations. In this work, we present an approach that builds upon prior work at an automotive gear facility located in Guelph, Ontario, which is looking to expand its defect detection capabilities. We outline a set of inspection-cell changes, which has led to full-gear surface scanning and inspection at a rate of every 7.5 s, and which is currently able to detect three common types of surface-level defects.
Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers?
Authors: Lin Wenyi , Karahanoglu Fikret Isik , Psaltos Dimitrios , Adamowicz Lukas , Santamaria Mar , Cai Xuemei , Demanuele Charmaine , Di Junrui
Published: October 2023

Wearable accelerometers allow for continuous monitoring of function and behaviors in the participant’s naturalistic environment. Devices are typically worn in different body locations depending on the concept of interest and endpoint under investigation. The lumbar and wrist are commonly used locations: devices placed at the lumbar region enable the derivation of spatio-temporal characteristics of gait, while wrist-worn devices provide measurements of overall physical activity (PA). Deploying multiple devices in clinical trial settings leads to higher patient burden negatively impacting compliance and data quality and increases the operational complexity of the trial. In this work, we evaluated the joint information shared by features derived from the lumbar and wrist devices to assess whether gait characteristics can be adequately represented by PA measured with wrist-worn devices. Data collected at the Pfizer Innovation Research (PfIRe) Lab were used as a real data example, which had around 7 days of continuous at-home data from wrist- and lumbar-worn devices (GENEActiv) obtained from a group of healthy participants. The relationship between wrist- and lumbar-derived features was estimated using multiple statistical methods, including penalized regression, principal component regression, partial least square regression, and joint and individual variation explained (JIVE). By considering multilevel models, both between- and within-subject effects were taken into account. This work demonstrated that selected gait features, which are typically measured with lumbar-worn devices, can be represented by PA features measured with wrist-worn devices, which provides preliminary evidence to reduce the number of devices needed in clinical trials and to increase patients’ comfort. Moreover, the statistical methods used in this work provided an analytic framework to compare repeated measures collected from multiple data modalities.
Rotating Lorentz Force Magnetic Bearings’ Dynamics Modeling and Adaptive Controller Design
Authors: Chen Feiyu , Wang Weijie , Wang Shengjun
Published: October 2023

To address the issues of our agile satellites’ poor attitude maneuverability, low pointing stability, and pointing inaccuracy, this paper proposes a new type of stabilized platform based on seven-degree-of-freedom Lorentz force magnetic levitation. Furthermore, in this study, we designed an adaptive controller based on the RBF neural network for the rotating magnetic bearing, which can improve the pointing accuracy of satellite loads. To begin, the advanced features of the new platform are described in comparison with the traditional electromechanical platform, and the structural characteristics and working principle of the platform are clarified. The significance of rotating magnetic bearings in improving load pointing accuracy is also clarified, and its rotor dynamics model is established to provide the input and output equations. The adaptive controller based on the RBF neural network is designed for the needs of high accuracy of the load pointing, high stability, and strong robustness of the system, and the current feedback inner loop is added to improve the system stiffness and rapidity. The final simulation results show that, when compared to the PID controller and robust sliding mode controller, the controller’s pointing accuracy and anti-interference ability are greatly improved, and the system robustness is strong, which can effectively improve the pointing accuracy and pointing stability of the satellite/payload, as well as provide a powerful means of solving related problems in the fields of laser communication, high score detection, and so on.
Development of Photonic Multi-Sensing Systems Based on Molecular Gates Biorecognition and Plasmonic Sensors: The PHOTONGATE Project
Authors: Nieves Oscar , Ortiz de Zárate David , Aznar Elena , Caballos Isabel , Garrido Eva , Martínez-Máñez Ramón , Dortu Fabian , Bernier Damien , Mengual-Chuliá Beatriz , López-Labrador F. Xavier , Sloth Jens J. , Loeschner Katrin , Duedahl-Olesen Lene , Prado Natalia , Hervello Martín , Menéndez Armando , Gransee Rainer , Klotzbuecher Thomas , Gonçalves M. Clara , Zare Fahimeh , Fuentes López Ana , Fernández Segovia Isabel , Baviera Jose M. Barat , Salcedo Jaime , Recuero Sara , Simón Santiago , Fernández Blanco Ana , Peransi Sergio , Gómez-Gómez Maribel , Griol Amadeu
Published: October 2023

This paper presents the concept of a novel adaptable sensing solution currently being developed under the EU Commission-founded PHOTONGATE project. This concept will allow for the quantification of multiple analytes of the same or different nature (chemicals, metals, bacteria, etc.) in a single test with levels of sensitivity and selectivity at/or over those offered by current solutions. PHOTONGATE relies on two core technologies: a biochemical technology (molecular gates), which will confer the specificity and, therefore, the capability to be adaptable to the analyte of interest, and which, combined with porous substrates, will increase the sensitivity, and a photonic technology based on localized surface plasmonic resonance (LSPR) structures that serve as transducers for light interaction. Both technologies are in the micron range, facilitating the integration of multiple sensors within a small area (mm2). The concept will be developed for its application in health diagnosis and food safety sectors. It is thought of as an easy-to-use modular concept, which will consist of the sensing module, mainly of a microfluidics cartridge that will house the photonic sensor, and a platform for fluidic handling, optical interrogation, and signal processing. The platform will include a new optical concept, which is fully European Union Made, avoiding optical fibers and expensive optical components.
Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
Authors: Tseng Li-Min , Chen Ping-Feng , Wen Chih-Yu
Published: October 2023

Population health monitoring based on the Internet of Medical Things (IoMT) is becoming an important application trend healthcare improvement. This work aims to develop an autonomous network architecture, collecting sensor data with a cluster topology, forwarding information through relay nodes, and applying edge computing and transmission scheduling for network scalability and operational efficiency. The proposed distributed network architecture incorporates data compression technologies and effective scheduling algorithms for handling the transmission scheduling of various physiological signals. Compared to existing scheduling mechanisms, the experimental results depict the network performance and show that in analyzing the delay and jitter, the proposed WFQ-based algorithms have reduced the delay and jitter ratio by about 40% and 19.47% compared to LLQ with priority queueing scheme, respectively. The experimental results also demonstrate that the proposed network topology is more effective than the direct path transmission approach in terms of energy consumption, which suggests that the proposed network architecture may improve the development of medical applications with body area networks such that the goal of self-organizing population health monitoring can be achieved.
The Microwave Temperature and Humidity Profiler: Description and Preliminary Results
Authors: Munoz-Martin Joan Francesc , Bosch-Lluis Xavier , Pradhan Omkar , Brown Shannon T. , Kangaslahti Pekka P. , Tanner Alan B. , Ogut Mehmet , Misra Sidharth , Lim Boon H.
Published: October 2023

This manuscript presents the Microwave Temperature and Humidity Profiler (MTHP), a dual-band spectroradiometer designed for measuring multi-incidence angle temperature and humidity atmospheric profiles from an aircraft platform. The MTHP bands are at 60 GHz for measuring the oxygen complex lines, therefore at this band, MTHP has a hyperspectral radiometer able to provide 2048 channels over an 8 GHz bandwidth, and 183 GHz for measuring water vapor, which only uses four channels since this absorption band’s spectral richness is simpler. The MTHP builds upon the Microwave Temperature Profiler (MTP) with the inclusion of the hyperspectral radiometer. The instrument’s design, components, and calibration methods are discussed in detail, with a focus on the three-point calibration scheme involving internal calibration loads and static air temperature readings. Preliminary results from the Technological Innovation into Iodine and GV aircraft Environmental Research (TI3GER) campaign are presented, showcasing the instrument’s performance during flights across diverse geographical regions. The manuscript presents successful antenna temperature measurements at 60 GHz and 183 GHz. The hyperspectral measurements are compared with a simulated antenna temperature using the Atmospheric Radiative Transfer Simulator (ARTS) showing an agreement better than R2 > 0.88 for three of the flights analyzed. Additionally, the manuscript draws attention to potential Radio Frequency Interference (RFI) effects observed during a specific flight, underscoring the instrument’s sensitivity to external interference. This is the first-ever airborne demonstration of a broadband and hyperspectral multi-incidence angle 60 GHz measurement. Future work on the MTHP could result in an improved spatial resolution of the atmospheric temperature vertical profile and, hence, help in estimating the Planetary Boundary Layer (PBL) with better accuracy. The MTHP and its hyperspectral multi-incidence angle at 60 GHz have the potential to be a valuable tool for investigating the PBL’s role in atmospheric dynamics, offering insights into its impact on Earth’s energy, water, and carbon cycles.
ARiRTN: A Novel Learning-Based Estimation Model for Regressing Illumination
Authors: Choi Ho-Hyoung , Kim Gi-Seok
Published: October 2023

In computational color constancy, regressing illumination is one of the most common approaches to manifesting the original color appearance of an object in a real-life scene. However, this approach struggles with the challenge of accuracy arising from label vagueness, which is caused by unknown light sources, different reflection characteristics of scene objects, and extrinsic factors such as various types of imaging sensors. This article introduces a novel learning-based estimation model, an aggregate residual-in-residual transformation network (ARiRTN) architecture, by combining the inception model with the residual network and embedding residual networks into a residual network. The proposed model has two parts: the feature-map group and the ARiRTN operator. In the ARiRTN operator, all splits perform transformations simultaneously, and the resulting outputs are concatenated into their respective cardinal groups. Moreover, the proposed architecture is designed to develop multiple homogeneous branches for high cardinality, and an increased size of a set of transformations, which extends the network in width and in length. As a result of experimenting with the four most popular datasets in the field, the proposed architecture makes a compelling case that complexity increases accuracy. In other words, the combination of the two complicated networks, residual and inception networks, helps reduce overfitting, gradient distortion, and vanishing problems, and thereby contributes to improving accuracy. Our experimental results demonstrate this model’s outperformance over its most advanced counterparts in terms of accuracy, as well as the robustness of illuminant invariance and camera invariance.
Spatial Distribution of PM2.5 Mass and Number Concentrations in Paris (France) from the Pollutrack Network of Mobile Sensors during 2018–2022
Authors: Renard Jean-Baptiste , Poincelet Eric , Annesi-Maesano Isabella , Surcin Jérémy
Published: October 2023

The presence of particulate matter smaller than 2.5 µm in diameter (PM2.5) in ambient air has a direct pejorative effect on human health. It is thus necessary to monitor the urban PM2.5 values with high spatial resolution to better evaluate the different exposure levels that the population encounters daily. The Pollutrack network of optical mobile particle counters on the roofs of hundreds of vehicles in Paris was used to produce maps with a 1 km2 resolution (108 squares to cover the Paris surface). The study was conducted during the 2018–2022 period, showing temporal variability due to different weather conditions. When averaging all the data, the highest air pollution was found along the Paris motorway ring. Also, the mean mass concentrations of PM2.5 pollution increased from southwest to northeast, due to the typology of the city, with the presence of canyon streets, and perhaps due to the production of secondary aerosols during the transport of airborne pollutants by the dominant winds. The number of days above the new daily threshold of 15 µg.m−3 recommended by the WHO in September 2021 varies from 3.5 to 7 months per year depending on the location in Paris. Pollutrack sensors also provide the number concentrations for particles greater than 0.5 µm. Using number concentrations of very fine particles instead of mass concentrations corresponding to the dry residue of PM2.5 is more representative of the pollutants citizens actually inhale. Some recommendations for the calibration of the sensors used to provide such number concentrations are given. Finally, the consequences of such pollution on human health are discussed.
Quad Element MIMO Antenna for C, X, Ku, and Ka-Band Applications
Authors: Mistri Raj Kumar , Mahto Santosh Kumar , Singh Ajit Kumar , Sinha Rashmi , Al-Gburi Ahmed Jamal Abdullah , Alghamdi Thamer A. H. , Alathbah Moath
Published: October 2023

This article presents a quad-element MIMO antenna designed for multiband operation. The prototype of the design is fabricated and utilizes a vector network analyzer (VNA-AV3672D) to measure the S-parameters. The proposed antenna is capable of operating across three broad frequency bands: 3–15.5 GHz, encompassing the C band (4–8 GHz), X band (8–12.4 GHz), and a significant portion of the Ku band (12.4–15.5 GHz). Additionally, it covers two mm-wave bands, specifically 26.4–34.3 GHz and 36.1–48.9 GHz, which corresponds to 86% of the Ka-band (27–40 GHz). To enhance its performance, the design incorporates a partial ground plane and a top patch featuring a dual-sided reverse 3-stage stair and a straight stick symmetrically placed at the bottom. The introduction of a defected ground structure (DGS) on the ground plane serves to provide a wideband response. The DGS on the ground plane plays a crucial role in improving the electromagnetic interaction between the grounding surface and the top patch, contributing to the wideband characteristics of the antenna. The dimensions of the proposed MIMO antenna are 31.7 mm × 31.7 mm × 1.6 mm. Furthermore, the article delves into the assessment of various performance metrics related to antenna diversity, such as ECC, DG, TARC, MEG, CCL, and channel capacity, with corresponding values of 0.11, 8.87 dB, −6.6 dB, ±3 dB, 0.32 bits/sec/Hz, and 18.44 bits/sec/Hz, respectively. Additionally, the equivalent circuit analysis of the MIMO system is explored in the article. It’s worth noting that the measured results exhibit a strong level of agreement with the simulated results, indicating the reliability of the proposed design. The MIMO antenna’s ability to exhibit multiband response, good diversity performance, and consistent channel capacity across various frequency bands renders it highly suitable for integration into multi-band wireless devices. The developed MIMO system should be applicable on n77/n78/n79 5G NR (3.3–5 GHz); WLAN (4.9–5.725 GHz); Wi-Fi (5.15–5.85 GHz); LTE5537.5 (5.15–5.925 GHz); WiMAX (5.25–5.85 GHz); WLAN (5.725–5.875 GHz); long-distance radio telecommunication (4–8 GHz; C-band); satellite, radar, space communications and terrestrial broadband (8–12 GHz; X-band); and various satellite communications (27–40 GHz; Ka-band).
LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function
Authors: Park Do-Hyun , Jeon Min-Wook , Shin Da-Min , Kim Hyoung-Nam
Published: October 2023

In electronic warfare systems, detecting low-probability-of-intercept (LPI) radar signals poses a significant challenge due to the signal power being lower than the noise power. Techniques using statistical or deep learning models have been proposed for detecting low-power signals. However, as these methods overlook the inherent characteristics of radar signals, they possess limitations in radar signal detection performance. We introduce a deep learning-based detection model that capitalizes on the periodicity characteristic of radar signals. The periodic autocorrelation function (PACF) is an effective time-series data analysis method to capture the pulse repetition characteristic in the intercepted signal. Our detection model extracts radar signal features from PACF and then detects the signal using a neural network employing long short-term memory to effectively process time-series features. The simulation results show that our detection model outperforms existing deep learning-based models that use conventional autocorrelation function or spectrogram as an input. Furthermore, the robust feature extraction technique allows our proposed model to achieve high performance even with a shallow neural network architecture and provides a lighter model than existing models.