bluuebunny/crossref_metadata_2025 huggingface dataset:
Foederis Arca—The Ark of the Covenant, a Biblical Symbol of the Virgin Mary
Authors: Salvador-González José María
Published: December 2024

This article attempts to document why the Virgin Mary is symbolically designated by the biblical figure “Ark of the Covenant” (Foederis Arca), as reflected in one of the invocations of the Litany of Loreto (Litaniae Lauretanae). To justify such a designation, the author refers to the systematic analysis of the patristic, theological, and hymnic sources of the Eastern and Western Churches, in which the Virgin Mary is labeled as the “Ark of the Covenant” for her virginal divine motherhood, her supreme holiness, and her supernatural privileges. The perfect coincidence, with which for more than a millennium the Fathers, theologians, and liturgical hymnographers of the Greek-Eastern and Latin Churches alluded to the Virgin Mary through this biblical symbol, demonstrates the strong coherence of the Mariological theses of the Christian doctrinal tradition on the person and spiritual attributes of the Virgin Mary. These coincident interpretations of the Fathers, theologians, and hymnographers of the Eastern and Western Churches will allow us to justify our iconographic interpretations of 10 European pictorial annunciations of the fourteenth and fifteenth centuries in whose scenes a container appears, almost always with books inside: such circumstance allows us to conjecture that the intellectual authors of these paintings of the Annunciation included in them this container to illustrate, as a visual metaphor, the textual metaphor with which the Fathers, theologians, and hymnographers symbolized the Virgin Mary as the Ark of the Covenant containing the Legislator of the new covenant.
Negotiating Wasatiyyah: Soft Securitization and Civic Activism in Ukraine
Authors: Yarosh Oleg
Published: December 2024

This article addresses religious governance in Ukraine in relation to local Muslim organizations associated with the Council of European Muslims (CEM), formerly known as the Federation of Islamic Organizations in Europe (FIOE). Specifically, it focuses on the Council of Ukrainian Muslims (CUM), formerly known as Alraid, and the Spiritual Administrations of Ukrainian Muslims Ummah (SAUM Ummah). Addressing the policymaking aspect of securitization, the article concerns state policies in Ukraine as ‘soft securitization’, meaning the execution of limited interventions and restrictions on the activities of Muslim organizations in Ukraine, particularly those at the focus of this article and labeled as ‘Islamist’. The FIOE in Europe and Alraid in Ukraine developed a response to these policies, informed by the wasatiyyah (moderation) post-Islamist ideology. The article analyzes how the wasatiyyah ideology was appropriated and negotiated in the discourse of these Ukrainian organizations, and how it informed their civic activism.
On Inhabiting Monastic Heritage: Monastery “Nieuw Sion” in the Netherlands as Material Burden and Spiritual Opportunity
Authors: Kock Wouter , Venbrux Eric , Quartier Thomas
Published: December 2024

For decades, the Catholic monastic tradition has been defined by demographic decline and the closure of monasteries. And yet, new religious groups have emerged that have cultivated their own take on the monastic tradition, while old monasteries have become treasured heritage objects. In recent years, these new religious groups have started to acquire and inhabit old monasteries. It follows that simultaneous re-religionization and heritagization is taking place at these locations. This combination presents a unique opportunity to scrutinize an unexplored entanglement of religion and heritage. Although re-religionization might seem to conflict with the cultural heritage narrative surrounding these monasteries, this article presents Klooster Nieuw Sion—a new Dutch religious initiative—as a case study, indicating that these developments do not only coexist or oppose each other but are also mutually reinforcing. Drawing on our fieldwork, we explore how heritagization can offer new spiritual opportunities for religious actors within these revitalized monastic spaces.
Profitability Drivers in European Banks: Analyzing Internal and External Factors in the Post-2009 Financial Landscape
Authors: Laporšek Suzana , Švagan Barbara , Stubelj Mojca , Stubelj Igor
Published: December 2024

The paper examines the key determinants of European banks’ profitability by analyzing the return on assets (ROA), return on equity (ROE), net interest margin (NIM), and the risk-adjusted measures of profitability, RAROAA and RAROAE, across 34 European countries during the period from 2013 to 2018—a time characterized by economic recovery and significant regulatory reforms, including the implementation of Basel III standards. Using the Generalized Method of Moments (GMM) approach and data of 3076 European banks, the research addresses the complex interplay between internal (bank-specific) factors and external factors, including macroeconomic and industry-specific factors. The results show that profitability is positively associated with a higher capital adequacy, liquidity risk, and income diversification, but not for risk-adjusted profitability ratios. Credit risk, management efficiency, and excessive size have a negative effect on all studied profitability measures. Macroeconomic conditions, in particular, GDP growth and inflation, also have a significant impact on profitability. The findings offer valuable insights for policymakers, regulators, and financial institutions aiming to enhance profitability while maintaining the stability of the European banking sector.
Optimal Benefit Distribution of a Tontine-like Annuity Fund with Age-Structured Models
Authors: Zhang Fan , Chen Ping , Wu Xueyuan
Published: December 2024

This paper introduces a tontine-like annuity fund designed to provide lifelong income to its participants. Initially, each member contributes a lump-sum payment into a trust fund as a joining premium. Participants then receive benefits over time, based on their survival. As members pass away, their share of payouts is redistributed among the survivors, resulting in increased payouts for those remaining. Differing from traditional tontines, which assume a uniform mortality risk, this fund accommodates participants of various ages and allows new members to join during its operation. To accommodate these features, the authors utilize age-structured models (ASMs) to determine fair premiums for new entrants and to analyze the dynamics of benefit distribution. The core objective of this paper is to develop a pension model using ASMs, recognizing its significant potential for adaptation and expansion. The primary mathematical approach employed is the Maximum Principle from optimal control theory, which helps in deriving explicit solutions for the optimal subsidy strategy. Through numerical examples and detailed illustrations, the paper demonstrates that participants who remain in the cohort longer receive greater subsidies. Additionally, the study finds that adverse shocks lead to a smaller population and thus fewer subsidies. Conversely, starting with a larger initial cohort population tends to increase the overall population, resulting in more subsidies. However, higher costs associated with subsidies lead to their reduction. Our analysis reveals the complex interplay of factors influencing the sustainability and effectiveness of the proposed annuity model.
Dividend-Based Labor Remuneration and Tradable Shares in Worker Cooperatives
Authors: Tortia Ermanno C.
Published: December 2024

This paper analyzes the possibility of creating worker cooperatives in which members are paid not through wages but through dividends calculated on the organization’s residual income, as stipulated by the economic theory of the labor-managed firm. It is shown how dividends paid to members can be linked to the value of their financial participation in the capital of the cooperative. In the presence of a financial market, cooperative shares would be issued and allocated to both members and non-member outside investors, thus addressing the problem of the under-capitalization of worker cooperatives. It is hypothesized that the strong financial incentives of this type of capital structure, together with involvement in the democratic governance of the cooperative, peer pressure, and other horizontal monitoring mechanisms, would support members’ intrinsic motivation to work and help overcome the problem of free-riding in the labor process. Flexible economic and financial structure in the absence of fixed wages would promote job stability, as already observed in existing worker cooperatives.
MHRA-MS-3D-ResNet-BiLSTM: A Multi-Head-Residual Attention-Based Multi-Stream Deep Learning Model for Soybean Yield Prediction in the U.S. Using Multi-Source Remote Sensing Data
Authors: Fathi Mahdiyeh , Shah-Hosseini Reza , Moghimi Armin , Arefi Hossein
Published: December 2024

Accurate prediction of soybean yield is important for safeguarding food security and improving agricultural management. Recent advances have highlighted the effectiveness and ability of Machine Learning (ML) models in analyzing Remote Sensing (RS) data for this purpose. However, most of these models do not fully consider multi-source RS data for prediction, as processing these increases complexity and limits their accuracy and generalizability. In this study, we propose the Multi-Residual Attention-Based Multi-Stream 3D-ResNet-BiLSTM (MHRA-MS-3D-ResNet-BiLSTM) model, designed to integrate various RS data types, including Sentinel-1/2 imagery, Daymet climate data, and soil grid information, for improved county-level U.S. soybean yield prediction. Our model employs a multi-stream architecture to process diverse data types concurrently, capturing complex spatio-temporal features effectively. The 3D-ResNet component utilizes 3D convolutions and residual connections for pattern recognition, complemented by Bidirectional Long Short-Term Memory (BiLSTM) for enhanced long-term dependency learning by processing data arrangements in forward and backward directions. An attention mechanism further refines the model’s focus by dynamically weighting the significance of different input features for efficient yield prediction. We trained the MHRA-MS-3D-ResNet-BiLSTM model using multi-source RS datasets from 2019 and 2020 and evaluated its performance with U.S. soybean yield data for 2021 and 2022. The results demonstrated the model’s robustness and adaptability to unseen data, achieving an R2 of 0.82 and a Mean Absolute Percentage Error (MAPE) of 9% in 2021, and an R2 of 0.72 and MAPE of 12% in 2022. This performance surpassed some of the state-of-the-art models like 3D-ResNet-BiLSTM and MS-3D-ResNet-BiLSTM, and other traditional ML methods like Random Forest (RF), XGBoost, and LightGBM. These findings highlight the methodology’s capability to handle multiple RS data types and its role in improving yield predictions, which can be helpful for sustainable agriculture.
Unmanned Aerial Geophysical Remote Sensing: A Systematic Review
Authors: Dadrass Javan Farzaneh , Samadzadegan Farhad , Toosi Ahmad , van der Meijde Mark
Published: December 2024

Geophysical surveys, a means of analyzing the Earth and its environments, have traditionally relied on ground-based methodologies. However, up-to-date approaches encompass remote sensing (RS) techniques, employing both spaceborne and airborne platforms. The emergence of Unmanned Aerial Vehicles (UAVs) has notably catalyzed interest in UAV-borne geophysical RS. The objective of this study is to comprehensively review the state-of-the-art UAV-based geophysical methods, encompassing magnetometry, gravimetry, gamma-ray spectrometry/radiometry, electromagnetic (EM) surveys, ground penetrating radar (GPR), traditional UAV RS methods (i.e., photogrammetry and LiDARgrammetry), and integrated approaches. Each method is scrutinized concerning essential aspects such as sensors, platforms, challenges, applications, etc. Drawing upon an extensive systematic review of over 435 scholarly works, our analysis reveals the versatility of these systems, which ranges from geophysical development to applications over various geoscientific domains. Among the UAV platforms, rotary-wing multirotors were the most used (64%), followed by fixed-wing UAVs (27%). Unmanned helicopters and airships comprise the remaining 9%. In terms of sensors and methods, imaging-based methods and magnetometry were the most prevalent, which accounted for 35% and 27% of the research, respectively. Other methods had a more balanced representation (6–11%). From an application perspective, the primary use of UAVs in geoscience included soil mapping (19.6%), landslide/subsidence mapping (17.2%), and near-surface object detection (13.5%). The reviewed studies consistently highlight the advantages of UAV RS in geophysical surveys. UAV geophysical RS effectively balances the benefits of ground-based and traditional RS methods regarding cost, resolution, accuracy, and other factors. Integrating multiple sensors on a single platform and fusion of multi-source data enhance efficiency in geoscientific analysis. However, implementing geophysical methods on UAVs poses challenges, prompting ongoing research and development efforts worldwide to find optimal solutions from both hardware and software perspectives.
Room Temperature NO2-Sensing Properties of N-Doped ZnO Nanoparticles Activated by UV-Vis Light
Authors: Ferlazzo Angelo , Neri Giovanni , Donato Andrea , Gugliandolo Giovanni , Latino Mariangela
Published: December 2024

Zinc oxide nanoparticles (ZnO NPs) with varying levels of nitrogen (N) doping were synthesized using a straightforward sol–gel approach. The morphology and microstructure of the N-doped ZnO NPs were examined through techniques such as SEM, XRD, photoluminescence, and Raman spectroscopy. The characterization revealed visible changes in the morphology and microstructure resulting from the incorporation of nitrogen into the ZnO lattice. These N-doped ZnO NPs were used in the fabrication of conductometric gas sensors designed to operate at room temperature (RT) for detecting low concentrations of NO2 in the air, under LED UV-Vis irradiation (λ = 400 nm). The influence of nitrogen doping on sensor performance was systematically studied. The findings indicate that N-doping effectively enhances ZnO-based sensors’ ability to detect NO2 at RT, achieving a notable response (S = R/R0) of approximately 18 when exposed to 5 ppm of NO2. These improvements in gas-sensing capabilities are attributed to the reduction in particle size and the narrowing of the optical band gap.
Do PROMs or Sensor-Based Monitoring Detect Improvements in Patients’ Knee Function After Total-Knee Arthroplasty?—A Study Protocol for a Prospective Controlled Study
Authors: Mba Lotanna , Prill Robert , Lettner Jonathan , Ramadanov Nikolai , Krause Robert , Reichmann Jan , Becker Roland
Published: December 2024

Determining whether preoperative performance-based knee function predicts postoperative performance-based knee function and whether patient-reported outcome measures (PROMs) completed by participants can detect these changes could significantly enhance the planning of postoperative rehabilitation for patients following total knee arthroplasty (TKA). This study aims to collect data on performance-based knee function using inertial measurement units (IMUs) worn by participants both preoperatively and postoperatively. PROMs will be completed by the patients before and after surgery to assess their ability to detect the same changes in performance-based knee function measured by the sensors. Additionally, the study will investigate the correlation between the degree of knee alignment correction and postoperative performance-based knee function in participants after TKA.
An Ultrasound Prototype for Remote Hand Movement Sensing: The Finger Tapping Case
Authors: Franceschini Stefano , Autorino Maria Maddalena , Ambrosanio Michele , Pascazio Vito , Baselice Fabio
Published: December 2024

In the context of neurodegenerative diseases, finger tapping is a gold-standard test used by clinicians to evaluate the severity of the condition. The finger tapping test involves repetitive tapping between the index finger and thumb. Subjects affected by neurodegenerative diseases, such as Parkinson’s disease, often exhibit symptoms like bradykinesia, rigidity, and tremor. As a result, when these individuals perform the finger tapping task, instability in both the tap rate and finger displacement can be observed. Currently, clinicians assess bradykinesia by visually observing the patient’s finger tapping movements and qualitatively rating their severity. In this work, we present a novel ultrasound contactless system that provides quantitative measurements of finger tapping, including tap rate and finger displacements. The system functions as an ultrasound sonar capable of measuring the Doppler spectrum of waves reflected by the hand. From this spectrum, various characteristics of the hand movement can be extracted through appropriate processing techniques. Specifically, by performing time–frequency analysis and applying specialized data processing, tapping rates and finger displacements can be estimated. The system has been tested in real-world scenarios involving volunteer finger tapping sessions, demonstrating its potential for accurately measuring both tap rates and displacements.
Accuracy of Rhythm Diagnostic Systems’ MultiSense® in Detection of Arterial Oxygen Saturation and Respiratory Rate During Hypoxia in Humans: Effects of Skin Color and Device Localization
Authors: Evrard Charles , El Attaoui Amina , Pistea Cristina , Enache Irina , Marriott Mark , Mayaud Louis , Charloux Anne , Geny Bernard
Published: December 2024

The continuous monitoring of oxygen saturation (SpO2) and respiratory rates (RRs) are major clinical issues in many cardio-respiratory diseases and have been of tremendous importance during the COVID-19 pandemic. The early detection of hypoxemia was crucial since it precedes significant complications, and SpO2 follow-up allowed early hospital discharge in patients needing oxygen therapy. Nevertheless, fingertip devices showed some practical limitations. In this study, we investigated the reliability of the new Multisense® pulse oximetry system compared to a reference pulse oximeter (Vyntus CPX Pulse Oximeter) during hypoxia. In a population of sixteen healthy male subjects (mean age: 31.5 ± 7.0 years, BMI: 24.9 ± 3.6 kg/m², and 35% with darker skin tones), simultaneous SpO2 and RR measurements were collected over 12.4 h, during which FiO2 was progressively reduced from 21% to 10.5%. The average root mean square error (ARMS) of SpO2 for Multisense® placed on the back and chest was 2.94% and 2.98%, respectively, with permutation testing confirming a significant ARMS below 3.5% for both positions and no statistically significant difference in the ARMS between patch placements. Positive correlations and acceptable accuracy between devices were observed at both locations (r = 0.92, p < 0.001 and r = 0.90, p < 0.001 for back and chest placements, respectively). Bland–Altman analysis further indicated limits of agreement that support consistency across placements, with similar agreement levels noted across skin tones. Similar findings were obtained with the RR measurements. In conclusion, Multisense® demonstrated robust accuracy in measuring SpO2 and RRs during hypoxia in humans comparable to standard hospital-grade equipment. The effectiveness of the findings suggests that this wearable device is a valuable tool for the continuous monitoring of SpO2 and RRs, potentially enhancing patient safety and optimizing hospital resource allocation. Nevertheless, to overcome study limitations and allow generalized use, further work on a larger population sample, including more subjects with a high phototype and desaturation below 80%, would be useful.
Physics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral Sensing
Authors: Wang Po-Tong , Tseng Chiu Wang , Fang Li-Der
Published: December 2024

The proliferation of sophisticated counterfeiting poses critical challenges to global security and commerce, with annual losses exceeding $2.2 trillion. This paper presents a novel physics-constrained deep learning framework for high-precision security ink colorimetry, integrating three key innovations: a physics-informed neural architecture achieving unprecedented color prediction accuracy (CIEDE2000 (ΔE00): 0.70 ± 0.08, p < 0.001), advanced attention mechanisms improving feature extraction efficiency by 58.3%, and a Bayesian optimization framework ensuring robust parameter tuning. Validated across 1500 industrial samples under varying conditions (±2 °C, 30–80% RH), this system demonstrates substantial improvements in production efficiency with a 50% reduction in rejections, a 35% decrease in calibration time, and 96.7% color gamut coverage. These achievements establish new benchmarks for security printing applications and provide scalable solutions for next-generation anti-counterfeiting technologies, offering a promising outlook for the future.
Cross-Shaped Heat Tensor Network for Morphometric Analysis Using Zebrafish Larvae Feature Keypoints
Authors: Chai Xin , Sun Tan , Li Zhaoxin , Zhang Yanqi , Sun Qixin , Zhang Ning , Qiu Jing , Chai Xiujuan
Published: December 2024

Deep learning-based morphometric analysis of zebrafish is widely utilized for non-destructively identifying abnormalities and diagnosing diseases. However, obtaining discriminative and continuous organ category decision boundaries poses a significant challenge by directly observing zebrafish larvae from the outside. To address this issue, this study simplifies the organ areas to polygons and focuses solely on the endpoint positioning. Specifically, we introduce a deep learning-based feature endpoint detection method for quantitatively determining zebrafish larvae’s essential phenotype and organ features. We propose the cross-shaped heat tensor network (CSHT-Net), a feature point detection framework consisting of a novel keypoint training method named cross-shaped heat tensor and a feature extractor called combinatorial convolutional block. Our model alleviates the problem of the heatmap-based method that restricts attention to local regions around key points while enhancing the model’s ability to learn continuous, strip-like features. Moreover, we compiled a dataset of 4389 bright-field micrographs of zebrafish larvae at 120 h post-fertilization for the model training and algorithm evaluation of zebrafish phenotypic traits. The proposed framework achieves an average precision (AP) of 83.2% and an average recall (AR) of 85.8%, outperforming multiple widely adopted keypoint detection techniques. This approach enables robust phenotype extraction and reliable morphometric analysis for zebrafish larvae, fostering efficient hazard identification for chemicals and medical products.
Feasibility of an 8-Week Home-Based Sensory Perception Training Game for People with Fibromyalgia: A Pilot Study
Authors: Demoulin Christophe , Costes Chloé , Sadok Mélanie , Grosdent Stéphanie , Kaux Jean-François , Vanderthommen Marc
Published: December 2024

People with fibromyalgia syndrome (FMS) may have difficulty attending rehabilitation sessions. We investigated the feasibility (adherence and satisfaction) of implementing an 8-week home-based somatosensory, entirely remote, self-training programme using the TrainPain smartphone app in people with FMS. The secondary aim was to evaluate the effect on pain symptoms. The training was performed 15 min/day, 7 days/week for 8 weeks. Participants identified the number of vibrations emitted by vibrotactile pods positioned on the most painful site and the contralateral side of the body. They completed the Brief Pain Inventory before, during (4 weeks), and at the end of the 8-week programme. At 8 weeks, they also rated satisfaction and the overall perceived change. The app recorded session completion. Of the 34 individuals recruited, 29 (mean, age 46 [SD] 9 years; 27 women; median duration of symptoms 7 [5;10] years) completed all assessments. Participants completed 75% of sessions and rated the programme easy-to-use and enjoyable, 94% would recommend the programme, and 38% reported a very strong improvement at 8 weeks. Pain intensity reduced from pre to post (effect size 0.77), as did interference (effect size 0.7 to 1.17). This treatment could be a useful addition to a multidisciplinary, multicomponent approach to FMS.
An Optical Wireless Communication System for Physiological Data Transmission in Small Animals
Authors: Domingues Ana R. , Pereira Diogo , Silva Manuel F. , Pimenta Sara , Correia José H.
Published: December 2024

In biomedical research, telemetry is used to take automated physiological measurements wirelessly from animals, as it reduces their stress and allows recordings for large data collection over long periods. The ability to transmit high-throughput data from an in-body device (e.g., implantable systems, endoscopic capsules) to external devices can also be achieved by radiofrequency (RF), a standard wireless communication procedure. However, wireless in-body RF devices do not exceed a transmission speed of 2 Mbit/s, as signal absorption increases dramatically with tissue thickness and at higher frequencies. This paper presents the design of an optical wireless communication system (OWCS) for neural probes with an optical transmitter, sending out physiological data through an optical signal that is detected by an optical receiver. The optical receiver position is controlled by a tracking system of the small animal position, based on a cage with a piezoelectric floor. To validate the concept, an OWCS based on a wavelength of 850 nm for a data transfer of 5 Mbit/s, with an optical power of 55 mW, was demonstrated for a tissue thickness of approximately 10 mm, measured in an optical tissue phantom.
Empowering the Sports Scientist with Artificial Intelligence in Training, Performance, and Health Management
Authors: Mateus Nuno , Abade Eduardo , Coutinho Diogo , Gómez Miguel-Ángel , Peñas Carlos Lago , Sampaio Jaime
Published: December 2024

Artificial Intelligence (AI) is transforming the field of sports science by providing unprecedented insights and tools that enhance training, performance, and health management. This work examines how AI is advancing the role of sports scientists, particularly in team sports environments, by improving training load management, sports performance, and player well-being. It explores key dimensions such as load optimization, injury prevention and return-to-play, sports performance, talent identification and scouting, off-training behavior, sleep quality, and menstrual cycle management. Practical examples illustrate how AI applications have significantly advanced each area and how they support and enhance the effectiveness of sports scientists. This manuscript also underscores the importance of ensuring that AI technologies are context-specific and communicated transparently. Additionally, it calls for academic institutions to update their curriculums with AI-focused education, preparing future sports professionals to fully harness its potential. Finally, the manuscript addresses future challenges, such as the unpredictable nature of team sports, emphasizing the need for interdisciplinary collaboration, including clear communication and mutual understanding between sports scientists and AI experts, and the critical balance between AI-driven insights and human expertise.
Impact of Particular Stages of the Manufacturing Process on the Reliability of Flexible Printed Circuits
Authors: Kiernich Andrzej , Kalenik Jerzy , Stęplewski Wojciech , Kościelski Marek , Chołaj Aneta
Published: December 2024

The purpose of the experiment was to indicate which element of the production process of flexible printed circuit boards is optimal in terms of the reliability of final products. According to the Taguchi method, in the experiment, five factors with two levels each were chosen for the subsequent analysis. These included the number of conductive layers, the thickness of the laminate layer, the type of the laminate, the diameter of the plated holes, and the current density in the galvanic bath. The reliability of the PCBs in the produced variations was verified using the Interconnect Stress Test environmental test. The qualitatively best variant of the board construction was indicated using the signal-to-noise ratio and the analysis of variance method for each factor. The factors determined to be the most important in terms of reliability were the number of conductive layers and the current density in the galvanic bath. The optimal variant of the board construction was two conductive layers on a polyimide laminate, where the laminate layer was 100 μm thick, the hole diameter was equal to 0.4 mm, and current density was 2 A/dm2 in the galvanic bath. Therefore, the plated experiment indicated the factors needed to obtain a high-quality product with a low failure rate.
Background Light Suppression for Multispectral Imaging in Surgical Settings
Authors: Gerlich Moritz , Schmid Andreas , Greiner Thomas , Kray Stefan
Published: December 2024

Multispectral imaging (MSI) enables non-invasive tissue differentiation based on spectral characteristics and has shown great potential as a tool for surgical guidance. However, adapting MSI to open surgeries is challenging. Systems that rely on light sources present in the operating room experience limitations due to frequent lighting changes, which distort the spectral data and require countermeasures such as disruptive recalibrations. On the other hand, MSI systems that rely on dedicated lighting require external light sources, such as surgical lights, to be turned off during open surgery settings. This disrupts the surgical workflow and extends operation times. To this end, we present an approach that addresses these issues by combining active illumination with smart background suppression. By alternately capturing images with and without a modulated light source at a desired wavelength, we isolate the target signal, enabling artifact-free spectral scanning. We demonstrate the performance of our approach using a smart pixel camera, emphasizing its signal-to-noise ratio (SNR) advantage over a conventional high-speed camera. Our results show that accurate reflectance measurements can be achieved in clinical settings with high background illumination. Medical application is demonstrated through the estimation of blood oxygenation, and its suitability for open surgeries is discussed.
New Torsional Surface Elastic Waves in Cylindrical Metamaterial Waveguides for Sensing Applications
Authors: Kiełczyński Piotr , Wieja Krzysztof , Balcerzak Andrzej
Published: December 2024

In this paper, we demonstrate that torsional surface elastic waves can propagate along the curved surface of a metamaterial elastic rod (cylinder) embedded in a conventional elastic medium. The crucial parameter of the metamaterial rod is its elastic compliance s44(1)ω, which varies as a function of frequency ω analogously to the dielectric function εω in Drude’s model of metals. As a consequence, the elastic compliance s44(1)ω can take negative values s44(1)ω<0 as a function of frequency ω. Negative elastic compliance (s44(1)ω<0) enables the emergence of new surface states, i.e., new types of surface elastic waves. In fact, the proposed torsional elastic surface waves can be considered as an elastic analog of Surface Plasmon Polariton (SPP) electromagnetic (optical) waves propagating along a metallic rod (cylinder) embedded in a dielectric medium. Consequently, we developed the corresponding analytical equations, for the dispersion relation and group velocity of the new torsional elastic surface wave. The newly discovered torsional elastic surface waves exhibit virtually all extraordinary properties of their electromagnetic SPP counterparts, such as strong subwavelength concentration of the wave energy in the vicinity of the cylindrical surface (r=a) of the guiding rod, very low phase and group velocities, etc. Therefore, the new torsional elastic surface waves can be used in: (a) near-field subwavelength acoustic imaging (super-resolution), (b) acoustic wave trapping (zero group and phase velocity), etc. Importantly, the newly discovered torsional elastic surface waves can form a basis for the development of a new generation of ultrasonic sensors (e.g., viscosity sensors), biosensors, and chemosensors with a very high mass sensitivity.
Hongtang Bridge Expansion Joints InSAR Deformation Monitoring with Advanced Phase Unwrapping and Mixed Total Least Squares in Fuzhou China
Authors: Wang Baohang , Zhu Wu , Zhao Chaoying , Yan Bojie , Liu Xiaojie , Li Guangrong , Li Wenhong , Yang Liye
Published: December 2024

Bridge expansion joints are critical components that accommodate the movement of a bridge caused by temperature fluctuations, concrete shrinkage, and vehicular loads. Analyzing the spatiotemporal deformation of these expansion joints is essential for monitoring bridge safety. This study investigates the deformation characteristics of Hongtang Bridge in Fuzhou, China, using synthetic aperture radar interferometry (InSAR). We optimize the network paths to enhance the phase unwrapping process of InSAR. Additionally, to address design matrix bias resulting from inaccurate temperature data, we employ the mixed total least squares method to estimate deformation parameters. Subsequently, we utilize independent component analysis to analyze the spatiotemporal deformation characteristics of the bridge. The average standard deviation of the unwrapped phase and the modeling residuals have been reduced by 87% and 5%, respectively. Our findings indicate that thermal expansion deformation is primarily concentrated in the expansion joints, measuring approximately 0.6 mm/°C. In contrast, the cable-stayed bridge deck exhibits the largest deformation magnitude, exceeding 2.0 mm/°C. This research focuses on bridge structures to identify typical deformation locations and evaluate their deformation characteristics. Such analysis is beneficial for conducting safety assessments of bridges.
Progressive Bounded Error Piecewise Linear Approximation with Resolution Reduction for Time Series Data Compression
Authors: Lin Jeng-Wei , Liao Shih-wei , Tsai Yu-Hung , Huang Ching-Che
Published: December 2024

Today, huge amounts of time series data are sensed continuously by AIoT devices, transmitted to edge nodes, and to data centers. It costs a lot of energy to transmit these data, store them, and process them. Data compression technologies are commonly used to reduce the data size and thus save energy. When a certain level of data accuracy is sacrificed, lossy compression technologies can achieve better compression ratios. However, different applications may have different requirements for data accuracy. Instead of keeping multiple compressed versions of a time series w.r.t. different error bounds, HIRE hierarchically maintains a tree, where the root records a constant function to approximate the whole time series, and each other node records a constant function to approximate a part of the residual function of its parent for a particular time period. To retrieve data w.r.t. a specific error bound, it traverses the tree from the root down to certain levels according to the requested error bound and aggregates the constant functions on the visited nodes to generate a new bounded error compressed version dynamically. However, the number of nodes to be visited is unknown before the tree traversal completes, and thus the data size of the new version. In this paper, a time series is progressively decomposed into multiple piecewise linear functions. The first function is an approximation of the original time series w.r.t. the largest error bound. The second function is an approximation of the residual function between the original time series and the first function w.r.t. the second largest error bound, and so forth. The sum of the first, second, …, and m-th functions is an approximation of the original time series w.r.t. the m-th error bound. For each iteration, Swing-RR is used to generate a Bounded Error Piecewise Linear Approximation (BEPLA). Resolution Reduction (RR) plays an important role. Eight real-world datasets are used to evaluate the proposed method. For each dataset, approximations w.r.t. three typical error bounds, 5%, 1%, and 0.5%, are requested. Three BEPLAs are generated accordingly, which can be summed up to form three approximations w.r.t. the three error bounds. For all datasets, the total data size of the three BEPLAs is almost the same with the size used to store just one version w.r.t. the smallest error bound and significantly smaller than the size used to keep three independent versions. The experiment result shows that the proposed method, referred to as PBEPLA-RR, can achieve very good compression ratios and provide multiple approximations w.r.t. different error bounds.
The Use of Principal Component Analysis for Reduction in Sleep Quality and Quantity Data in Female Professional Soccer
Authors: Barba Eider , Casamichana David , Figueiredo Pedro , Nakamura Fábio Yuzo , Castellano Julen
Published: December 2024

The main aim of the present study was to uncover multivariate relationships between sleep quantity and quality using principal component analysis (PCA) in professional female soccer players. A second aim was to examine the extent to which objective sleep quantity and quality variables can discriminate between perceived sleep. Ten objective sleep variables from the multisensory sleep-tracker were analyzed. PCA was conducted on the sleep variables, and meaningful principal components (PCs) were identified (eigenvalue > 2). Two sleep PCs were identified, representing the ‘quantity of sleep’ (quantity PC: eigenvalue = 4.1 and variance explained = 45.1%) and the ‘quality of sleep’ (quality PC: eigenvalue = 2.4 and variance explained = 24.1%). Cluster analysis grouped the players’ sleep into three types: long and efficient, short and efficient, and long and inefficient; however, no association was found between the perceived sleep and the sleep clusters. In conclusion, a combination of both quantity and quality sleep metrics is recommended for sleep monitoring of professional female soccer players. Players should undergo a training process to improve self-assessment of sleep quality recorded from a subjective questionnaire, contrasting the perceived information with the sleep quality recorded objectively during a defined period in order to optimize the validity of their perceptions. The aim is to optimize the validity of their perceptions of sleep quality.
Effect of Depth Band Replacement on Red, Green and Blue Image for Deep Learning Weed Detection
Authors: Vandrol Jan , Perren Janis , Koller Adrian
Published: December 2024

Automated agricultural robots are becoming more common with the decreased cost of sensor devices and increased computational capabilities of single-board computers. Weeding is one of the mundane and repetitive tasks that robots could be used to perform. The detection of weeds in crops is now common, and commercial solutions are entering the market rapidly. However, less work is carried out on combatting weeds in pastures. Weeds decrease the grazing yield of pastures and spread over time. Mowing the remaining weeds after grazing is not guaranteed to remove entrenched weeds. Periodic but selective cutting of weeds can be a solution to this problem. However, many weeds share similar textures and structures with grazing plants, making their detection difficult using the classic RGB (Red, Green, Blue) approach. Pixel depth estimation is considered a viable source of data for weed detection. However, systems utilizing RGBD (RGB plus Depth) are computationally expensive, making them nonviable for small, lightweight robots. Substituting one of the RGB bands with depth data could be a solution to this problem. In this study, we examined the effect of band substitution on the performance of lightweight YOLOv8 models using precision, recall and mAP50 metrics. Overall, the RDB band combination proved to be the best option for YOLOv8 small and medium detection models, with 0.621 and 0.634 mAP50 (for a mean average precision at 50% intersection over union) scores, respectively. In both instances, the classic RGB approach yielded lower accuracies of 0.574 and 0.613.
Enhancing Manufacturing Precision: Leveraging Motor Currents Data of Computer Numerical Control Machines for Geometrical Accuracy Prediction Through Machine Learning
Authors: Berus Lucijano , Hernavs Jernej , Potocnik David , Sket Kristijan , Ficko Mirko
Published: December 2024

Direct verification of the geometric accuracy of machined parts cannot be performed simultaneously with active machining operations, as it usually requires subsequent inspection with measuring devices such as coordinate measuring machines (CMMs) or optical 3D scanners. This sequential approach increases production time and costs. In this study, we propose a novel indirect measurement method that utilizes motor current data from the controller of a Computer Numerical Control (CNC) machine in combination with machine learning algorithms to predict the geometric accuracy of machined parts in real-time. Different machine learning algorithms, such as Random Forest (RF), k-nearest neighbors (k-NN), and Decision Trees (DT), were used for predictive modeling. Feature extraction was performed using Tsfresh and ROCKET, which allowed us to capture the patterns in the motor current data corresponding to the geometric features of the machined parts. Our predictive models were trained and validated on a dataset that included motor current readings and corresponding geometric measurements of a mounting rail later used in an engine block. The results showed that the proposed approach enabled the prediction of three geometric features of the mounting rail with an accuracy (MAPE) below 0.61% during the learning phase and 0.64% during the testing phase. These results suggest that our method could reduce the need for post-machining inspections and measurements, thereby reducing production time and costs while maintaining required quality standards.
Fibre Optic Method for Detecting Oil Fluorescence in Marine Sediments
Authors: Baszanowska Emilia , Otremba Zbigniew , Kubacka Maria
Published: December 2024

The aim of this study is to verify the possibility of detecting oil in the bottom sediment using a fibre optic system. The presence of oil is assessed on excitation–emission spectra obtained from spectral fluorescence signals of the sediment sample. A factory spectrofluorometer coupled with an experimental fibre optic measurement system was used. During the determination of spectra, the fibre optic system is set at a 45° angle to the sediment surface and placed above its surface. The light exciting the fluorescence and the light emitted by the sediment are transmitted in a combined bundle of fibre optic threads. The analysis of excitation–emission spectra of sediments contaminated with oil shows variability of the shapes of fluorescence spectra depending on the type and degree of oil contamination, which indicates the feasibility of the sensor design for detecting oil in the sediment in situ.
Improving the Orbit Injection Accuracy of a Launch Vehicle by Using a Mode-Switching Strategy for a Dual-Axis Rotational Inertial Navigation System
Authors: Li Jun , Zhang Shifeng , Ma Zhenlin
Published: December 2024

Due to a short flight time, the dual-axis rotational inertial navigation system carried by some launch vehicles or missiles is often only used for self-calibration and self-alignment. It is generally in the strap-down state rather than the rotation modulation state during flight. This wastes the precision potential of the navigation system. Therefore, a mode-switching strategy is proposed in this paper to improve the orbit injection accuracy of a launch vehicle. The rotational navigation system of the launch vehicle is in the near-platform mode or strap-down mode at different periods. It is switched in a timely manner to take advantage of the various modes. Numerical simulation and turntable experiment results show that compared with the traditional strap-down inertial mode, the mode-switching strategy can effectively improve the orbit accuracy of the launch vehicle by about 12% to 13% and hardly increases any costs.
Personalized Federated Learning Scheme for Autonomous Driving Based on Correlated Differential Privacy
Authors: Tian Yuan , Shi Yanfeng , Zhang Yue , Tian Qikun
Published: December 2024

In the era of big data, advanced data processing devices and smart sensors greatly benefit us in many areas. As for each individual user, data sharing can be an essential part of the process of data collection and transmission. However, the issue of constant attacks on data privacy arouses huge concerns among the public. This work proposes a personalized federated learning method associated with correlated differential privacy for autonomous driving. First, instead of transmitting raw data to the server following collection, a device that employs federated learning can perform calculations to obtain the training model at each node. Second, we specifically perform a correlated classification analysis to encrypt data that share high relevance, which can minimize the system cost. Then, correlated differential privacy is utilized to achieve the preservation of data privacy before sharing. In contrast to the traditional differential privacy, the proposed solution guarantees enhanced privacy to meet the demands of customization. The experimental results show that our scheme is more refined in terms of user heterogeneity and the utility of data than others without violating privacy.
In Situ Raman Spectroscopy for Early Corrosion Detection in Coated AA2024-T3
Authors: Delluva Adrienne K. , Cook Ronald L. , Peppel Matt , Diaz Sami , Martin Rhia M. , Nguyen Vinh T. , Elliott Jeannine E. , Biller Joshua R.
Published: December 2024

Here we describe the synthesis and evaluation of a molecular corrosion sensor that can be applied in situ in aerospace coatings, then used to detect corrosion after the coating has been applied. A pH-sensitive molecule, 4-mercaptopyridin (4-MP), is attached to a gold nanoparticle to allow surface-enhanced Raman-scattering (SERS) for signal amplification. These SERS nanoparticles, when combined with an appropriate micron-sized carrier system, are incorporated directly into an MIL-SPEC coating and used to monitor the process onset and progression of corrosion using pH changes occurring at the metal–coating interface. The sensor can track corrosion spatially as it proceeds underneath the coating, due to the mobility of the proton front generated during corrosion and the homogeneous distribution of the sensor in the coating layer. To our knowledge, this report is the first time a 4-MP functionalized gold nanoparticle has been used, along with SERS spectroscopy, to monitor corrosion in an applied commercial coating in a fast, non-contact way.
Machine Condition Monitoring System Based on Edge Computing Technology
Authors: Halenar Igor , Halenarova Lenka , Tanuska Pavol , Vazan Pavel
Published: December 2024

The core of this publication is the design of a system for evaluating the condition of production equipment and machines by monitoring selected parameters of the production process with an additional sensor subsystem. The main positive of the design is the processing of data from the sensor layer using artificial intelligence (AI) and expert systems (ESs) with the use of edge computing (EC). Sensor information is processed directly at the sensor level on the monitored equipment, and the results of the individual subsystems are stored in the form of triggers in a database for use in the predictive maintenance process. The whole solution includes the design of suitable sensors and of the implementation of the sensor layer, the description of data processing algorithms, the design on the communication infrastructure for the whole system, and tests in the form of experimental operation of the device in laboratory conditions. The solution includes the visualisation of the production system status for the operator using an interactive online map.