bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of Pharmacists in Minimizing the Risk Inherent in Unbundled Telehealth Services: A 12-Month Retrospective Case Study
Authors: Talay Louis , Vickers Matt , Cheng Tiffany
Published: November 2024

Pharmacists have often been viewed as the last line of defence against prescription errors in traditional care models. Although a large number of chronic care patients are using telehealth services to increase their access to continuous care, researchers have yet to investigate prescription safety in such settings in Australia. The absence of this literature is particularly concerning in the context of the Australian Government’s admission in a 2024 report that the national health system has not adequately addressed the World Health Organization’s ‘Medication without harm’ objective. One of the report’s key findings was that knowledge on digital direct-to-consumer services is insufficient. A defining feature of some of these services is their unbundling of the pharmacy component, which logically increases the risk for prescription errors. This study analyzed the frequency of which the Cloud pharmacy network intercepted prescription errors in an unbundled digital sexual dysfunction service for men. Investigators found that Cloud pharmacists were responsible for intercepting 22 (5.31%) the 414 prescribing errors observed in the Pilot Australia service in 2023, including 12 (8.05%) of the 149 prescription errors for premature ejaculation (PE) patients and 10 (3.77%) of the 265 errors for erectile dysfunction (ED) patients. Seven of the errors intercepted by Cloud pharmacists were of high or medium severity, including four drug contraindications, two cases of inadequate patient history reviews, and one case of inadequate counselling. This study also appears to be the first to provide digital prescribing error rate data in an Australian sexual healthcare setting, observing an error rate of 0.86% from 30,649 ED prescriptions, 1.13% from the 13,154 PE prescriptions, and a total prescription error rate of 0.95% (414 out of 43,792 prescriptions). These findings demonstrate the vital role of pharmacists in intercepting prescribing errors in unbundled telehealth services. Possible implications of these findings include the allocation of additional resources across the pharmacy sector and the establishment of regulatory safety standards for unbundled telehealth services.
Using Artificial Neural Networks to Evaluate the Capacity and Cost of Multi-Fiber Optical Backbone Networks
Authors: Freitas Alexandre , Pires João
Published: November 2024

A possible solution to address the enormous increase in traffic demands faced by network operators is to rely on multi-fiber optical backbone networks. These networks use multiple optical fibers between adjacent nodes, and, when properly designed, they are capable of handling petabits of data per second (Pbit/s). In this paper, an artificial neural network (ANN) model is investigated to estimate both the capacity and cost of a multi-fiber optical network. Furthermore, a fiber assignment algorithm is also proposed to complement the network design, enabling the generation of datasets for training and testing of the developed ANN model. The model consists of three layers, including one hidden layer with 50 hidden units. The results show that for a large network, such as one with 100 nodes, the model can estimate performance metrics with an average relative error of less than 0.4% for capacity and 4% for cost, while achieving a computation time nearly 800 times faster than the heuristic approach used in network simulation. Additionally, the network capacity is around 5 Pbit/s.
Spectrum Allocation Using Integer Linear Programming and Kerr Optical Frequency Combs
Authors: Muñoz-Tapasco Sergio , Calvo-Salcedo Andrés F. , Jaramillo-Villegas Jose A.
Published: November 2024

The rapid increase in Internet usage has led to a growing demand for bandwidth. Optical microring resonators (MRRs) are emerging as a promising solution to meet this need. MRRs generate optical frequency combs (OFCs) that provide multiple wavelengths with high phase coherence, enabling communication via wavelength division multiplexing (WDM). Spectrum allocation methods, such as the Routing, Modulation Level, and Spectrum Assignment (RMLSA) approach, play a crucial role in executing this strategy efficiently. While current algorithms have improved allocation efficiency, further development is necessary to optimize network performance. This paper presents an integer linear programming (ILP)-based method for network resource allocation, aiming to maximize the number request and the bandwidth assigned to each. The proposed approach offers a flexible cost function that prioritizes system constraints such as transmission distance and bandwidth requirements, resulting in significant improvements to the bandwidth blocking rate (BBR). By integrating multilevel modulation and using wavelengths generated by MRRs, this method efficiently handles up to 1075 requests, achieving a BBR of zero. This dynamic and adaptable allocation strategy ensures optimal resource utilization, enhancing overall network performance.
Bio-Inspired Polymeric Solid Lipid Nanoparticles for siRNA Delivery: Cytotoxicity and Cellular Uptake In Vitro
Authors: Jagaran Keelan , Habib Saffiya , Singh Moganavelli
Published: November 2024

Nanomedicine has introduced strategies that provide precise diagnosis and treatment with fewer side effects than traditional therapies. Treatments for neurodegenerative disorders, including Parkinson’s disease, are palliative, necessitating an innovative delivery system with a curative function. This study investigated a solid lipid nanoparticle (SLNP) system’s ability to bind and safely deliver siRNA in vitro. SLNPS were formulated using sphingomyelin and cholesterol, with Ginkgo biloba leaf extract (GBE) incorporated to enhance biocompatibility and neuroprotection. Poly-L-lysine (PLL) functionalization ensured successful siRNA binding, safe transport, and protection from nuclease degradation. SLNPs were physicochemically characterized, with binding and protection of siRNA assessed using agarose gels. Cytotoxicity, apoptotic induction, and cellular uptake studies were undertaken in the human neuroblastoma (SH-SY5Y) and embryonic kidney (HEK293) cells. The GBE-PLL-SLNPs had an average size of 93.2 nm and demonstrated enhanced binding and protection of the siRNA from enzyme digestion, with minimal cytotoxicity in HEK293 (<10%) and SH-SY5Y cells (<15%). Caspase 3/7 activity was significantly reduced in both cells, while efficient cellular uptake was noted. The present study provided a solid basis as a proof of principle study for future applications of the potential therapeutic in vitro, promising to address the unmet medical needs of patients with neurological disorders.
Macromolecular Architecture in the Synthesis of Micro- and Mesoporous Polymers
Authors: Davletbaeva Ilsiya M. , Sazonov Oleg O.
Published: November 2024

Polymers with micro- and mesoporous structure are promising as materials for gas storage and separation, encapsulating agents for controlled drug release, carriers for catalysts and sensors, precursors of nanostructured carbon materials, carriers for biomolecular immobilization and cellular scaffolds, as materials with a low dielectric constant, filtering/separating membranes, proton exchange membranes, templates for replicating structures, and as electrode materials for energy storage. Sol–gel technologies, track etching, and template synthesis are used for their production, including in micelles of surfactants and microemulsions and sublimation drying. The listed methods make it possible to obtain pores with variable shapes and sizes of 5–50 nm and achieve a narrow pore size distribution. However, all these methods are technologically multi-stage and require the use of consumables. This paper presents a review of the use of macromolecular architecture in the synthesis of micro- and mesoporous polymers with extremely high surface area and hierarchical porous polymers. The synthesis of porous polymer frameworks with individual functional capabilities, the required chemical structure, and pore surface sizes is based on the unique possibilities of developing the architecture of the polymer matrix.
Recent Progress on the Synthesis, Morphological Topography, and Battery Applications of Polypyrrole-Based Nanocomposites
Authors: Rahman Khan Mohammad Mizanur , Rumon Md. Mahamudul Hasan
Published: November 2024

Polypyrrole (PPy)-based nanocomposite materials are of great interest to the scientific community owing to their usefulness in designing state-of-the-art industrial applications, such as fuel cells, catalysts and sensors, energy devices, and especially batteries. However, the commercialization of these materials has not yet reached a satisfactory level of implementation. More research is required for the design and synthesis of PPy-based composite materials for numerous types of battery applications. Due to the rising demand for environmentally friendly, cost-effective, and sustainable energy, battery applications are a significant solution to the energy crisis, utilizing suitable materials like PPy-based composites. Among the conducting polymers, PPy is considered an important class of materials owing to their ease of synthesis, low cost, environmentally friendly nature, and so on. In this context, PPy-based nanocomposites may be very promising due to their nanostructural properties and distinct morphological topography, which are vital concerns for their applications for battery applications. Such features of PPy-based nanocomposites make them particularly promising for next-generation electrode materials. However, the design and fabrication of appropriate PPy-based nanocomposites for battery applications is still a challenging area of research. This review paper describes the current progress on the synthesizing of PPy-based composites for battery applications along with their morphological topography. We discussed here the recent progress on the synthesis of different PPy-based composites, including PPy/S, PPy/MnOx, MWCNT/PPy, V2O5/PPy, Cl-doped PPy/rGO, and Fe/α-MnO2@PPy composites, by a polymerization approach for numerous battery applications. The insights presented in this review aim to provide a comprehensive reference for the future development of PPy-based composites in battery technology.
Silk Fibroin Seed Coatings: Towards Sustainable Seed Protection and Enhanced Growth
Authors: Jin Feng , Guan Zhengrong , Zhang Jiahao , Qu Zhigang , Ling Shengjie , Cao Leitao , Ren Jing , Peng Ruoxuan
Published: November 2024

Seed coating technology is vital in agriculture, enhancing seed protection and growth. However, conventional coatings often include chemical fungicides that pose environmental risks, highlighting the need for sustainable alternatives. This study explores silk fibroin (SF), a natural biopolymer with excellent film-forming properties, as a potential seed coating agent, addressing its antimicrobial limitations by combining it with the commercial agent CRUISER® and the antimicrobial peptide Nisin. Experimental methods included solution stability analysis, Fourier-transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), and growth assessments of wheat seeds. Findings reveal that silk fibroin-CRUISER® (SC) composites form stable β-sheet structures, enhancing the coating’s mechanical strength. SF-based coatings improved seedling emergence rates (up to 1.65-fold), plant height (up to 1.05-fold), and root growth (up to 1.2-fold), especially under cold stress. The addition of Nisin further significantly boosted the antibacterial properties, providing sustained pathogen inhibition (p < 0.01). Identifying the optimal concentration of SF was essential for achieving a balance between protection and breathability, a key factor for industrial application. This research provides valuable insights into the development of eco-friendly seed coatings, presenting a viable and sustainable alternative to traditional chemical-based options in agricultural practices.
Nonlinear Dynamic Mechanical and Impact Performance Assessment of Epoxy and Microcrystalline Cellulose-Reinforced Epoxy
Authors: Tüfekci Mertol
Published: November 2024

This study focusses on imrpoving the mechanical performance of epoxy resin by reinforcing it with microcrystalline cellulose (MCC). Epoxy composites with varying MCC mass fractions (0.5%, 1%, 1.5%, and 2%) are prepared and characterised to assess the influence of MCC on strain-rate-dependent flexural properties, impact resistance, and nonlinear viscoelastic behaviour. Three-point bending tests at different strain rates reveal that MCC notably increases the flexural strength and leads to nonlinear mechanical behaviour. It is shown that stiffness, strength and elongation at break increase with rising MCC content. Charpy impact tests show improved energy absorption and toughness, while Dynamic Mechanical Analysis (DMA) demonstrates that the materials prepared exhibit increased storage modulus and improved damping across a frequency range. These results indicate that MCC serves as an effective bio-based reinforcement, significantly boosting the strength and toughness of epoxy composites. The findings contribute to the development of sustainable, high-performance materials for advanced engineering applications.
Uncertainty Quantification Method for Trend Prediction of Oil Well Time Series Data Based on SDMI Loss Function
Authors: Shen Yancen , Wang Xiang , Xie Yixin , Wang Wei , Zhang Rui
Published: November 2024

IoT sensors in oilfields gather real-time data sequences from oil wells. Accurate trend predictions of these data are crucial for production optimization and failure forecasting. However, oil well time series data exhibit strong nonlinearity, requiring not only precise trend prediction but also the estimation of uncertainty intervals. This paper first proposed a data denoising method based on Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) to reduce the noise present in oil well time series data. Subsequently, an SDMI loss function was introduced, combining the respective advantages of Soft Dynamic Time Warping and Mean Squared Error (MSE). The loss function additionally accepts the upper and lower bounds of the uncertainty prediction interval as input and is optimized with the prediction sequence. By predicting the data of the next 48 data points, the prediction results using the SDMI loss function and the existing three common loss functions are compared on multiple data sets. The prediction results before and after data denoising are compared and the results of predicting the uncertainty interval are shown. The experimental results demonstrate that the average coverage rate of the predicted uncertainty intervals across data from seven wells is 81.4%, and the prediction results accurately reflect the trends in real data.
Analysis of the Effect of Structural Parameters on the Internal Flow Field of Composite Curved Inlet Body Hydrocyclone
Authors: Wang Yanchao , Han Hu , Liang Zhitao , Yang Huanbo , Li Feng , Zhang Wen , Zhao Yanrui
Published: November 2024

To enhance the classification efficiency of hydrocyclones, this study introduces a novel hydrocyclone design featuring a composite curved-inlet-body structure. Through numerical simulations, the internal flow field characteristics of this structure are thoroughly investigated. The results reveal several key findings: when the diameter of the overflow tube is reduced below a critical threshold, the axial velocity exhibits predominantly downward movement within the outer cyclone, accompanied by substantial recirculation, leading to a loss of effective separation. Moreover, both static pressure and tangential velocity are largely independent of the insertion depth of the overflow tube. In contrast, the diameter of the bottom flow opening plays a crucial role in determining flow dynamics within the hydrocyclone. An excessively large or small bottom opening leads to flow instabilities, causing fluctuations that disrupt the uniformity of the flow field. Additionally, a small height-to-diameter ratio exacerbates flow instability, increasing turbulence intensity and resulting in irregular fluctuations in the LZVV. These findings provide important theoretical insights for the design of more efficient hydrocyclone separation structures.
Numerical Simulation of Coal’s Mechanical Properties and Fracture Process Under Uniaxial Compression: Dual Effects of Bedding Angle and Loading Rate
Authors: Ou Jianchun , Niu Jizhan , Wang Bican , Zhang Wei , Zhao Jing , Lyu Benming , Zhan Beihuai , Ma Yue
Published: November 2024

In view of the significant influence of bedding structure on the mechanical characteristics and fracture behavior of coal, uniaxial compression discrete element numerical simulation experiments were carried out on coal samples with bedding angles of 0°, 30°, 60°, and 90°, and loading rates of 10−3/s, 10−2/s, 10−1/s, and 100/s, respectively, using PFC 6.0 software. The dual effects of bedding angle and loading rate on the mechanical properties of coal and its damage behavior were analyzed. The results show that (1) as the loading rate increases, the peak strength of the specimen increases, and the damage intensifies. The counts of the three types of cracks increased exponentially, while the crack growth rate was dramatic. (2) With the increase in loading rate, the density of the compressive stress force chain inside the specimen increases and gathers to the two ends, and the density of the tensile stress force chain is basically unchanged but gathers to the middle. The overall strength of the force chain changes according to the law of decreasing and then increasing. (3) With the increase in the bedding angle, the peak strength decreases and then increases, and the curve is approximately “V” shape. When the bedding angle is 60° and 90°, the peak stress is minimum and maximum, respectively. Shear cracks are dominant in the model, and the crack distribution shows a trend of increasing and then decreasing. (4) With the increase in the bedding angle, the density of the compressive stress force chain gradually decreases, and the density of the tensile stress force chain appears to be aggregated. The overall strength of the force chain changes according to the law of decreasing and then increasing.
“So That They Might Turn”: The Possibility of Repentance in Jesus’ Parables
Authors: Hardin Leslie T.
Published: November 2024

Since the publication of Julicher’s Die Gleichnisreden Jesu, parable research has largely focused on the legitimacy of allegory, classification of form, and matters of meaning and polyvalence. Still underdeveloped is an evaluation of the function of parables. This paper argues that one of the functions of Jesus’ parables was to invite hearers to turn from their previously defined notions of the kingdom of God and join him in his vision of the kingdom. The paper begins with an examination of the Markan Jesus’ citation of Isaiah 6:9–10 as a justification for speaking in parables and concludes that, while Israel was largely obdurate, the offer of repentance remained. Preaching repentance is one component of the work of a prophet, and an examination of Jesus’ vocation indicates that his parables function as prophetic witnesses. The paper concludes with an exploration of how the parables might serve as invitations to turn from self-defined notions of kingdom and join Jesus in his movement.
Configuring Psalm 29 as a Poem: Cognitive Strategies and the Artful Reading Experience
Authors: Grosser Emmylou J.
Published: November 2024

The classic modern framework for biblical Hebrew poetry is based upon intertwined conceptions of parallelism and meter. This framework provides certain assumptions for how biblical lines work, as well as (often implicit) strategies for how biblical poems should be read as poems. These assumptions and strategies are apparent in modern analyses of Psalm 29, especially in the kinds of proposals consistently made for altering the Masoretic Text to make it more “regular”, even in the absence of textual evidence. My recent book on biblical poetry (Unparalleled Poetry: A Cognitive Approach to the Free-Rhythm Verse of the Hebrew Bible, 2023) has challenged these assumptions and strategies, proposing a new framework for biblical poetry that is theoretically grounded in cognitive research and based upon the textual data of the biblical poems. Preferring the term “conformation” to parallelism, I propose that the versification system of biblical poetry is constrained by Gestalt perceptual processing and that the listening or reading strategies demanded by this versification system require part–whole processing of lines into line groupings, line groupings into figures, and figures into the whole poem, as the poem aurally unfolds. In this article, I demonstrate that these part–whole strategies of reading biblical poems make sense of the textual shapes of Psalm 29 and lead to an artful experience of the poem.
Does ESG Predict Business Failure in Brazil? An Application of Machine Learning Techniques
Authors: Kaleem Mehwish , Raza Hassan , Ashraf Sumaira , Almeida António Martins , Machado Luiz Pinto
Published: November 2024

The aim of this study is to explore the influence of environmental, social, and governance (ESG) factors on business failure in Brazil by employing advanced machine learning techniques. We collected data from 235 companies and conducted principal component analysis (PCA) on 40 variables already used in the bankruptcy failure literature, resulting in the formation of seven variables that predict business failure. The results indicate that ESG factors significantly predict business failure in Brazil. This study has implications for investors, policymakers, and business leaders, offering a more precise tool for risk assessment and strategic decision-making.
3D Reconstruction of Building Blocks Based on Extraction of Exterior Wall Lines Using Point Cloud Density Generated from Spherical Camera Images
Authors: Askari Qazale , Arefi Hossein , Maboudi Mehdi
Published: November 2024

The 3D modeling of urban buildings has become a common research area in various disciplines such as photogrammetry and computer vision, with different applications such as intelligent city management, navigation of self-driving cars and architecture, just to name a few. The objective of this study is to produce a 3D model of the external facade of the buildings with the required precision, accuracy and level of detail according to the user’s requirements, while minimizing time and cost. This research focuses on the production of 3D models for blocks of residential buildings in Tehran, Iran. The Insta 360 One X2 spherical camera is selected to capture the data due to its low cost and 360 × 180° field of view. The camera specifications have facilitated more efficient data collection in terms of both time and cost. The proposed modeling method is based on extracting lines of external walls through the utilization of the point cloud density concept. Initially, photogrammetric point clouds are generated in with a reconstruction precision of 0.24 m from spherical camera images. In the next step, the 3D point cloud is projected into a 2D point cloud by setting the height component to zero. The 2D point cloud is then rotated based on the direction angle determined by the Hough transform so that the perpendicular walls are parallel to the axes of the coordinate system. Next, a 2D point cloud density analysis is performed by voxelizing the point cloud and counting the number of points in each voxel in both the horizontal and vertical directions. By determining the peaks in the density plot, the lines of the external vertical and horizontal walls are extracted. To extract the diagonal external walls, the density analysis is performed in the direction of the first principal component. Finally, by determining the height of each wall in the point cloud, a 3D model is created at the level of detail one. The resulting model has a precision of 0.32 m compared to real sizes, and the 2D plan has a precision of 0.31 m compared to the ground truth map. The use of the spherical camera and point cloud density analysis makes this method efficient and cost-effective, making it a promising approach for future urban modeling projects.
Classification of Hyperspectral-LiDAR Dual-View Data Using Hybrid Feature and Trusted Decision Fusion
Authors: Liu Jian , Xue Xinzheng , Zuo Qunyang , Ren Jie
Published: November 2024

As remote sensing technology continues to evolve, the integration of multi-view data, including HSI and LiDAR data, has emerged as a pivotal research area aimed at enhancing the precision of classification. However, most current multimodal data fusion methods follow a process of first extracting features from each modality, then combining these features using a fusion strategy, and finally performing classification. This approach may result in the diminution of original information during the feature fusion process and relies heavily on the performance of the Softmax function during classification, without adequately considering the trustworthiness of the results. To address the above issues, this paper presented a hybrid feature and trusted decision fusion (HFTDF) method for dual-view remote sensing data classification. In terms of the research method, the approach first performs preliminary feature extraction on dual-view data using shallow CNN models, while implementing a shallow fusion strategy to integrate original information from different data sources at an early stage. Next, it leverages the proficiency of CNNs in learning localized characteristics and the potential of the Transformer in terms of its handling of overarching information, conducting hybrid feature learning on data from each view. Additionally, a deep fusion strategy serves to investigate the intricate interrelations among diverse perspectives. Finally, evidence theory is applied to model the uncertainty of classification results, generating trusted vectors, and a trusted decision fusion strategy is employed to merge the trusted information from each modality at the decision level, thereby enhancing the reliability of the results. HFTDF achieves overall classification accuracies of 94.68%, 99.17%, and 82.05% on the Houston 2013, Trento, and MUUFL datasets, respectively, when only 20 samples of each class are used for training. The classification results of the experiments reveal that HFTDF outperforms in the classification of dual-view data.
Sequential Mixed Cost-Based Multi-Sensor and Relative Dynamics Robust Fusion for Spacecraft Relative Navigation
Authors: Li Shoupeng , Liu Weiwei
Published: November 2024

The non-redescending convex functions degrade the filtering robustness, whereas the redescending non-convex functions improve filtering robustness, but they tend to converge towards local minima. This work investigates the properties of convex and non-convex cost functions from robustness and stability perspectives, respectively. To improve filtering robustness and stability to the high level of non-Gaussian noise, a sequential mixed convex and non-convex cost strategy is presented. To avoid the matrix singularity induced by applying the non-convex function, the M-estimation type Kalman filter is transformed into its information filtering form. Further, to address the problem of the estimation consistency in the iterated unscented Kalman filter, the iterated sigma point filtering framework is adopted using the statistical linear regression method. The simulation results show that, under different levels of heavy-tailed non-Gaussian noise, the mixed cost strategy can avoid the non-convex function-based filters falling into the local minimum, and further can improve the robustness of the convex function-based filter. Therefore, the mixed cost strategy provides a comprehensive improvement in the efficiency of the robust iterated filter.
Theoretical Analysis and Experimental Evaluation of Wide-Lane Combination for Single-Epoch Positioning with BeiDou-3 Observations
Authors: Wang Yulu , Liu Xin , Zhang Shubi
Published: November 2024

Multi-frequency signals can enable some wide-lane (WL) observations to achieve instantaneous ambiguity resolution (AR) in complex scenarios, but simply adding WL observations will also place additional pressure on real-time kinematic data transmission. With the official service of the third-generation Beidou Navigation Satellite System, which broadcasts five-frequency signals, this dilemma has become increasingly evident. It is significant to explore multi-frequency observation combination methods that take into account both positioning precision and data transmission burden. In this work, we use the least squares method to derive the theoretical precision of the single-epoch WL combination of 16 schemes with varying frequency numbers (three or more) under the ionosphere-fixed model and the ionosphere-float model. The baseline solutions of 4.3 km and 93.56 km confirm that the positioning results are broadly consistent with the theoretical derivations under both models. In the ionosphere-fixed mode, the five-frequency scheme (B1C, B1I, B3I, B2b, B2a) yields the best positioning performance, improving the 3-dimensional positioning error standard deviation, circle error probable (CEP), and spherical error probable at 75% probability by 7.8%, 11.5%, and 6.7%, respectively, compared with the optimal triple-frequency scheme (B1C, B3I, B2a). Under the ionosphere-float model, the quad-frequency scheme (B1C, B3I, B2b, B2a) provides the best positioning performance, with only the CEP at 75% improving by 1.3% over the triple-frequency scheme. Given that the optimal triple-frequency scheme has a lower data volume, this work recommends it as the preferred scheme.
Assessing the Effect of Water on Submerged and Floating Plastic Detection Using Remote Sensing and K-Means Clustering
Authors: Fronkova Lenka , Brayne Ralph P. , Ribeiro Joseph W. , Cliffen Martin , Beccari Francesco , Arnott James H. W.
Published: November 2024

Marine and freshwater plastic pollution is a worldwide problem affecting ecosystems and human health. Although remote sensing has been used to map large floating plastic rafts, there are research gaps in detecting submerged plastic due to the limited amount of in situ data. This study is the first to collect in situ data on submerged and floating plastics in a freshwater environment and analyse the effect of water submersion on the strength of the plastic signal. A large 10 × 10 m artificial polymer tarpaulin was deployed in a freshwater lake for a two-week period and was captured by a multi-sensor and multi-resolution unmanned aerial vehicle (UAV) and satellite. Spectral analysis was conducted to assess the attenuation of individual wavelengths of the submerged tarpaulin in UAV hyperspectral and Sentinel-2 multispectral data. A K-Means unsupervised clustering algorithm was used to classify the images into two clusters: plastic and water. Additionally, we estimated the optimal number of clusters present in the hyperspectral dataset and found that classifying the image into four classes (water, submerged plastic, near surface plastic and buoys) significantly improved the accuracy of the K-Means predictions. The submerged plastic tarpaulin was detectable to ~0.5 m below the water surface in near infrared (NIR) (~810 nm) and red edge (~730 nm) wavelengths. However, the red spectrum (~669 nm) performed the best with ~84% true plastic positives, classifying plastic pixels correctly even to ~1 m depth. These individual bands outperformed the dedicated Plastic Index (PI) derived from the UAV dataset. Additionally, this study showed that in neither Sentinel-2 bands, nor the derived indices (PI or Floating Debris Index (FDI), it is currently possible to determine if and how much of the tarpaulin was under the water surface, using a plastic tarpaulin object of 10 × 10 m. Overall, this paper showed that spatial resolution was more important than spectral resolution in detecting submerged tarpaulin. These findings directly contributed to Sustainable Development Goal 14.1 on mapping large marine plastic patches of 10 × 10 m and could be used to better define systems for monitoring submerged and floating plastic pollution.
Deriving Accurate Nocturnal Heart Rate, rMSSD and Frequency HRV from the Oura Ring
Authors: Liang Tian , Yilmaz Gizem , Soon Chun-Siong
Published: November 2024

Cardiovascular diseases are a major cause of mortality worldwide. Long-term monitoring of nighttime heart rate (HR) and heart rate variability (HRV) may be useful in identifying latent cardiovascular risk. The Oura Ring has shown excellent correlation only with ECG-derived HR, but not HRV. We thus assessed if stringent data quality filters can improve the accuracy of time-domain and frequency-domain HRV measures. 92 younger (<45 years) and 22 older (≥45 years) participants from two in-lab sleep studies with concurrent overnight Oura and ECG data acquisition were analyzed. For each 5 min segment during time-in-bed, the validity proportion (percentage of interbeat intervals rated as valid) was calculated. We evaluated the accuracy of Oura-derived HR and HRV measures against ECG at different validity proportion thresholds: 80%, 50%, and 30%; and aggregated over different durations: 5 min, 30 min, and Night-level. Strong correlation and agreements were obtained for both age groups across all HR and HRV metrics and window sizes. More stringent validity proportion thresholds and averaging over longer time windows (i.e., 30 min and night) improved accuracy. Higher discrepancies were found for HRV measures, with more than half of older participants exceeding 10% Median Absolute Percentage Error. Accurate HRV measures can be obtained from Oura’s PPG-derived signals with a stringent validity proportion threshold of around 80% for each 5 min segment and aggregating over time windows of at least 30 min.
Respiratory Rate Monitoring via a Fibre Bragg Grating-Embedded Respirator Mask with a Wearable Miniature Interrogator
Authors: Limweshasin Nat , Castro Itzel Avila , Korposh Serhiy , Morgan Stephen P. , Hayes-Gill Barrie R. , Faghy Mark A. , Correia Ricardo
Published: November 2024

A respiration rate (RR) monitoring system was created by integrating a Fibre Bragg Grating (FBG) optical fibre sensor into a respirator mask. The system exploits the sensitivity of an FBG to temperature to identify an individual’s RR by measuring airflow temperature variation near the nostrils and mouth. To monitor the FBG response, a portable, battery-powered, wireless miniature interrogator system was developed to replace a relatively bulky benchtop interrogator used in previous studies. A healthy volunteer study was conducted to evaluate the performance of the developed system (10 healthy volunteers). Volunteers were asked to perform normal breathing whilst simultaneously wearing the system and a reference spirometer for 120 s. Individual breaths are then identified using a peak detection algorithm. The result showed that the number of breaths detected by both devices matched exactly (100%) across all volunteer trials.
Evaluation of Performance and Longevity of Ti-Cu Dry Electrodes: Degradation Analysis Using Anodic Stripping Voltammetry
Authors: Carvalho Daniel , Rodrigues Ana Margarida , Santos João , Geraldo Dulce , Ferreira Armando , Correa Marcio Assolin , Alves Eduardo , Barradas Nuno Pessoa , Lopes Claudia , Vaz Filipe
Published: November 2024

This study aimed to investigate the degradation of dry biopotential electrodes using the anodic stripping voltammetry (ASV) technique. The electrodes were based on Ti-Cu thin films deposited on different polymeric substrates (polyurethane, polylactic acid, and cellulose) by Direct Current (DC) magnetron sputtering. TiCu0.34 thin films (chemical composition of 25.4 at.% Cu and 74.6 at.% Ti) were prepared by sputtering a composite Ti target. For comparison purposes, a Cu-pure thin film was prepared under the same conditions and used as a reference. Both films exhibited dense microstructures with differences in surface topography and crystalline structure. The degradation process involved immersing TiCu0.34 and Cu-pure thin films in artificial sweat (prepared following the ISO standard 3160-2) for different durations (1 h, 4 h, 24 h, 168 h, and 240 h). ASV was the technique selected to quantify the amount of Cu(II) released by the electrodes immersed in the sweat solution. The optimal analysis conditions were set for 120 s and −1.0 V for time deposition and potential deposition, respectively, with a quantification limit of 0.050 ppm and a detection limit of 0.016 ppm. The results showed that TiCu0.34 electrodes on polyurethane substrates were significantly more reliable over time compared to Cu-pure electrodes. After 240 h of immersion, the TiCu0.34 electrodes released a maximum of 0.06 ppm Cu, while Cu-pure electrodes released 16 ppm. The results showed the significant impact of the substrate on the electrode’s longevity, with cellulose bases performing poorly. TiCu0.34 thin films on cellulose released 1.15 µg/cm2 of copper after 240 h, compared to 1.12 mg/cm2 from Cu-pure films deposited on the same substrate. Optical microscopy revealed that electrodes based on polylactic acid substrates were more prone to corrosion over time, whereas TiCu thin-film metallic glass-like structures on PU substrates showed extended lifespan. This study underscored the importance of assessing the degradation of dry biopotential electrodes for e-health applications, contributing to developing more durable and reliable sensing devices. While the study simulated real-world conditions using artificial sweat, it did not involve in vivo measurements.
Detecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning
Authors: Punta-Sánchez Irene , Dymerski Tomasz , Calle José Luis P. , Ruiz-Rodríguez Ana , Ferreiro-González Marta , Palma Miguel
Published: November 2024

This article introduces a novel approach to detecting honey adulteration by combining ultra-fast gas chromatography (UF-GC) with advanced machine learning techniques. Machine learning models, particularly support vector regression (SVR) and least absolute shrinkage and selection operator (LASSO), were applied to predict adulteration in orange blossom (OB) and sunflower (SF) honeys. The SVR model achieved R2 values above 0.90 for combined honey types. Treating OB and SF honeys separately resulted in a significant accuracy improvement, with R2 values exceeding 0.99. LASSO proved especially effective when honey types were treated individually. The integration of UF-GC with machine learning not only provides a reliable method for detecting honey adulteration, but also sets a precedent for future research in the application of this technique to other food products, potentially enhancing food authenticity across the industry.
A Closed-Loop Ear-Worn Wearable EEG System with Real-Time Passive Electrode Skin Impedance Measurement for Early Autism Detection
Authors: Sheeraz Muhammad , Aslam Abdul Rehman , Drakakis Emmanuel Mic , Heidari Hadi , Altaf Muhammad Awais Bin , Saadeh Wala
Published: November 2024

Autism spectrum disorder (ASD) is a chronic neurological disorder with the severity directly linked to the diagnosis age. The severity can be reduced if diagnosis and intervention are early (age < 2 years). This work presents a novel ear-worn wearable EEG system designed to aid in the early detection of ASD. Conventional EEG systems often suffer from bulky, wired electrodes, high power consumption, and a lack of real-time electrode–skin interface (ESI) impedance monitoring. To address these limitations, our system incorporates continuous, long-term EEG recording, on-chip machine learning for real-time ASD prediction, and a passive ESI evaluation system. The passive ESI methodology evaluates impedance using the root mean square voltage of the output signal, considering factors like pressure, electrode surface area, material, gel thickness, and duration. The on-chip machine learning processor, implemented in 180 nm CMOS, occupies a minimal 2.52 mm² of active area while consuming only 0.87 µJ of energy per classification. The performance of this ML processor is validated using the Old Dominion University ASD dataset.
A Temporal Network Based on Characterizing and Extracting Time Series in Copper Smelting for Predicting Matte Grade
Authors: Zhang Junjia , Li Zhuorui , Wang Enzhi , Yu Bin , Li Jiangping , Ma Jun
Published: November 2024

Addressing the issues of low prediction accuracy and poor interpretability in traditional matte grade prediction models, which rely on pre-smelting input and assay data for regression, we incorporate process sensors’ data and propose a temporal network based on Time to Vector (Time2Vec) and temporal convolutional network combined with temporal multi-head attention (TCN-TMHA) to tackle the weak temporal characteristics and uncertain periodic information in the copper smelting process. Firstly, we employed the maximum information coefficient (MIC) criterion to select temporal process sensors’ data strongly correlated with matte grade. Secondly, we used a Time2Vec module to extract periodic information from the copper smelting process variables, incorporates time series processing directly into the prediction model. Finally, we implemented the TCN-TMHA module and used specific weighting mechanisms to assign weights to the input features and prioritize relevant key time step features. Experimental results indicate that the proposed model yields more accurate predictions of copper content, and the coefficient of determination (R2) is improved by 2.13% to 11.95% and reduced compared to the existing matte grade prediction models.
Development of Textile-Based Strain Sensors for Compression Measurements in Sportswear (Sports Bra)
Authors: Imran Aqsa , Zaman Shahood uz , Razzaq Mozzan , Ahmad Ayesha , Tao Xuyuan
Published: November 2024

Women sports wearer’s comfort and health are greatly impacted by the breast movements and resultant sports bra compression to prevent excessive movement. However, as sports bras are only made in universal sizes, they do not offer the right kind of support that is required for a certain activity. To prevent this issue, textile-based strain sensors may be utilized to track compression throughout various activities to create activity-specific designed sports bras. Textile-based strain sensors are prepared in this study using various conductive yarns, including steel, Ag-coated polyamide, and polypropylene/steel-blended threads. Various embroidery designs, including straight, zigzag, and square-wave embroidery patterns, etc., were created on knitted fabric and characterized for strain sensing efficiencies. The experiments concluded that strain sensors prepared from polypropylene/steel thread using a 2-thread square-wave design were best performed in terms of linear conductivity, sensitivity of mechanical impact, and wide working range. This best-performed sample was also tested by integrating it into the sportswear for proposed compression measurements in different body movements.
Phased Array Antenna Calibration Based on Autocorrelation Algorithm
Authors: Nguyen Xuan Luong , Nhan Nguyen Trong , Dang Thi Thanh Thuy , Thanh Tran Van , Nguyen Phung Bao , Trien Nguyen Duc
Published: November 2024

The problem of calibrating phased array antennas in a noisy environment using an autocorrelation algorithm is investigated and a mathematical model of the autocorrelation calibration method is presented. The proposed calibration system is based on far-field scanning of the phased array antenna in an environment with internal noise and external interference. The proposed method is applied to a phased array antenna and compared with traditional rotating-element electric-field vector methods, which involve identifying the maximum and minimum vector–sum points (REVmax and REVmin, respectively). The proposed calibration system is verified for a phased array antenna at 3 GHz. Experimental verification of the mathematical model of the proposed method demonstrates that the autocorrelation method is more accurate than the rotating-element electric-field vector methods in determining the amplitude and phase shifts. The measured peak gain of the combined beam in the E-plane increased from 7.83 to 8.37 dB and 3.57 to 4.36 dB compared to the REVmax and REVmin methods, respectively, and the phase error improved from 47° to 55.48° and 19.43° to 29.16°, respectively. The proposed method can be considered an effective solution for large-scale phase calibration at both in-field and in-factory levels, even in the presence of external interference.
Online Monitoring of Catalytic Processes by Fiber-Enhanced Raman Spectroscopy
Authors: Kelly John T. , Koch Christopher J. , Lascola Robert , Guin Tyler
Published: November 2024

An innovative solution for real-time monitoring of reactions within confined spaces, optimized for Raman spectroscopy applications, is presented. This approach involves the utilization of a hollow-core waveguide configured as a compact flow cell, serving both as a conduit for Raman excitation and scattering and seamlessly integrating into the effluent stream of a cracking catalytic reactor. The analytical technique, encompassing device and optical design, ensures robustness, compactness, and cost-effectiveness for implementation into process facilities. Notably, the modularity of the approach empowers customization for diverse gas monitoring needs, as it readily adapts to the specific requirements of various sensing scenarios. As a proof of concept, the efficacy of a spectroscopic approach is shown by monitoring two catalytic processes: CO2 methanation (CO2 + 4H2 → CH4 + 2H2O) and ammonia cracking (2NH3 → N2 + 3H2). Leveraging chemometric data processing techniques, spectral signatures of the individual components involved in these reactions are effectively disentangled and the results are compared to mass spectrometry data. This robust methodology underscores the versatility and reliability of this monitoring system in complex chemical environments.
A Lightweight and High Yield Complementary Metal-Oxide Semiconductor True Random Number Generator with Lightweight Photon Post-Processing
Authors: Ngo Chi Trung , Ko Hyun Woo , Choi Ji Woo , Nam Jae-Won , Hong Jong-Phil
Published: November 2024

This paper introduces a novel TRNG architecture that employs a wave converter to generate random outputs from the jitter noise in a customized ring oscillator (RO). Using a current-starved inverter, the proposed RO offers the option of operating three different oscillation frequencies from a single oscillator. To assess its performance, the core TRNG proposed in this work was designed with multiple samples, employing various transistor sizes for 28 nm CMOS processes. The measurements show that only a small number of measured TRNG samples passed the randomness NIST SP 800-22 tests, which is a common problem, not only with the proposed TRNG but also with other TRNG structures. To solve this issue, a lightweight post-processing algorithm using the Photon hash function was newly applied to the proposed TRNGs topology. The lightweight Photon hash function-based post-processing was implemented with the proposed TRNG topology in a 28 nm CMOS process. The design occupies 16,498 µm2, with a throughput of 0.0142 Mbps and power consumption of 31.12 mW. Measurements showed significant improvement, with a 50% increase in chips passing the NIST SP 800-22 tests. Compared with the conventional DRBG post-processing method, the proposed lightweight Photon post-processing reduces area occupation by five times and power consumption by 65%.