bluuebunny/crossref_metadata_2025 huggingface dataset:
Refractive Index of Aluminophosphosilicate Glass in Optical Fibers near AlPO4 Join
Authors: Likhachev Mikhail E. , Zaushitsyna Tatiana S. , Agakhanova Vitaliya A. , Aleshkina Svetlana S. , Bubnov Mikhail M. , Lobanov Alexey S. , Oleinik Denis I. , Lipatov Denis S.
Published: December 2024

The minimum refractive index of the aluminophosphosilicate (APS) core in optical fibers has been determined for a wide range of phosphorous and aluminum concentrations. It was found that the APS core refractive index became higher by ~0.0005–0.0012 as compared to that in optical fiber preform. The analysis of the measured data has shown that at least 0.2 mol.% of Al2O3 and P2O5 remain in their ordinary form near their equimolar concentrations and do not form an AlPO4 join (the effect observed for all concentrations of AlPO4 join from 5 to 25 mol.%).
Efficient, Compact Mid-Infrared Sources: A Systematic Study of Intracavity Difference-Frequency Generation
Authors: Salimian Rizi Saeed , Flannigan Liam G. K. , Chiasson Alex , Kashak Tyler , Poitras Daniel , Xu Chang-Qing
Published: December 2024

This study presents a compact intracavity difference-frequency generation (DFG) source designed for mid-infrared (mid-IR) applications. By optimizing overlap between the pump and signal beams, we obtained a black-box efficiency (ηBB) of 3.4% W⁻1 and a maximum DFG output power of 63 mW at a wavelength of 3437 nm for a 50 mm long periodically poled lithium niobate (PPLN) crystal. The mid-IR laser was systematically studied for various PPLN lengths (29 mm, 40 mm, and 50 mm), demonstrating extensive tunability across a range of mid-IR wavelengths. Stability assessments over short-term (2 min) and long-term (2 h) periods were also investigated, showing stability values of 0.91% and 1.08%, respectively. These advancements position our DFG laser as a promising candidate for potential applications in greenhouse gas detection and satellite communication, addressing the growing demand for reliable and efficient mid-IR sources.
Lignin–Polyethylene Oxide Interlocked Phase Change Materials with Enhanced Thermal Stability and Form Retention for Efficient Heat Management
Authors: Park Junsang , Karua Pranto , Tang Songtao , Nguyen Ngoc A. , Cai Lili
Published: December 2024

The rapid advancement of high-performance technologies, such as electric vehicle (EV) batteries; data centers; and AI systems, has underscored the critical need for effective thermal management solutions. Conventional phase change materials (PCMs) often face challenges, like phase leakage, dimensional instability, and environmental concerns, limiting their effectiveness in high-stress applications. This study introduces a novel PCM composed of polyethylene oxide (PEO) and lignin, developed to overcome the existing limitations while improving overall thermal management performance and promoting material sustainability. By chemically crosslinking lignin with aliphatic polymer chains compatible with PEO during co-reactive melt processing, we created an interlocked structure that combines high heat capacity with exceptional structural stability. This structure allows the PCM to retain its form and resist phase transitions even under elevated temperatures, up to 115 °C, far above the melting point of PEO, effectively mitigating leakage issues common in conventional PCMs. Comprehensive thermal characterization and dynamic performance testing demonstrate that the lignin-modified PEO composites effectively absorb and dissipate heat, maintaining dimensional stability and resilience under repeated thermal cycling. These findings position these composites as sustainable, reworkable, and efficient alternatives for advanced thermal management applications, particularly in battery thermal management systems (BTMSs), where stability, durability, and performance are critical.
Zinc Oxide-Loaded Recycled PET Nanofibers for Applications in Healthcare and Biomedical Devices
Authors: Grămadă (Pintilie) Andreea Mihaela , Stoica (Oprea) Alexandra-Elena , Niculescu Adelina-Gabriela , Bîrcă Alexandra Cătălina , Vasile Bogdan Ștefan , Holban Alina Maria , Mihaiescu Teodora , Șerban Andreea Iren , Ciceu Alina , Balta Cornel , Dumitra Simona , Puticiu Monica , Iordache Florin , Hermenean Anca , Alberts Adina , Grumezescu Alexandru Mihai , Oprea Ovidiu Cristian , Ardelean Simona
Published: December 2024

Polyethylene terephthalate (PET) is a widely utilized synthetic polymer, favored in various applications for its desirable physicochemical characteristics and widespread accessibility. However, its extensive utilization, coupled with improper waste disposal, has led to the alarming pollution of the environment. Thus, recycling PET products is essential for diminishing global pollution and turning waste into meaningful materials. Therefore, this study proposes the fabrication of electrospun membranes made of recycled PET nanofibers as a cost-effective valorization method for PET waste. ZnO nanoparticles were coated onto polymeric materials to enhance the antimicrobial properties of the PET fibers. Morphostructural investigations revealed the formation of fibrillar membranes made of unordered nanofibers (i.e., 40–100 nm in diameter), on the surface of which zinc oxide nanoparticles of 10–20 nm were attached. PET@ZnO membranes demonstrated effective antimicrobial and antibiofilm activity against Gram-positive and Gram-negative bacteria, yeasts, and molds, while imparting no toxicity to amniotic fluid stem cells. In vivo tests confirmed the materials’ biocompatibility, as no side effects were observed in mice following membrane implantation. Altogether, these findings highlight the potential of integrating ZnO nanoparticles into recycled PET to develop multifunctional materials suitable for healthcare facilities (such as antimicrobial textiles) and biomedical devices, including applications such as textiles, meshes, and sutures.
Sustainable Production of Microcrystalline and Nanocrystalline Cellulose from Textile Waste Using HCl and NaOH/Urea Treatment
Authors: Işıtan Arzum , Pasquardini Laura , Bersani Massimo , Gök Cem , Fioravanti Simona , Lunelli Lorenzo , Çağlarer Evren , Koluman Ahmet
Published: December 2024

Bio-nanomaterials are gaining increasing attention due to their renewable and eco-friendly characteristics. Among these, nanocrystalline cellulose (NCC) stands out as one of the most advanced materials for applications in food, healthcare, composite production, and beyond. In this study, NCC was successfully extracted from cotton-based textile waste using a combination of chemical and mechanical methods. The cellulose fibers were first hydrolyzed using a dilute HCl solution, neutralized, and then dried, resulting in microcrystalline cellulose (MCC) with diameters ranging from 7 to 15 µm and lengths up to 300 µm (as observed via optical microscopy and scanning electron microscopy, SEM). To achieve nanoscale dimensions, NaOH/urea solution with mechanical treatment was applied, resulting in the successful extraction of NCC in the supernatant, particularly under room-temperature conditions. Dynamic light scattering (DLS) analysis confirmed the presence of nanostructures (average sizes ranging from 120 nm to 750 nm), and atomic force microscopy (AFM) analysis verified the nanoscale range (diameters between 2 and 4 nm and lengths from 200 nm to 1 µm). Fourier transform infrared (FTIR) spectroscopy revealed the conversion of cellulose I to cellulose II, confirming the successful transformation into NCC. For the first time, NCC was obtained from undyed cotton textile wastes using NaOH/urea treatment after HCl hydrolysis, eliminating the need for pre-treatment and intermediate steps.
Revisiting Avian Metapneumovirus Subtype B in Broiler Chickens and Turkeys in Morocco: First Molecular Characterization
Authors: Mernizi Amine , Ghram Abdeljelil , Fathi Hicham , Carceller Enrique , Criado Juan-Luis , Dardi Martina , Arbani Oumayma , Bouslikhane Mohammed , Mouahid Mohammed , Lupini Caterina , Catelli Elena , Fellahi Siham , Nassik Saadia
Published: December 2024

Avian Metapneumovirus (aMPV) is a significant poultry pathogen with a global presence, primarily causing respiratory issues in turkeys. It also affects chickens, although the severity of its impact is often lessened in this species. In Morocco, aMPV has been detected in broiler flocks, prompting the need to deeply analyze circulating strains to better understand the epidemiology and develop control measures accordingly. This research focuses on the sequencing and molecular characterization of aMPV in these flocks. Additionally, aMPV isolated from turkeys displaying Turkey Rhinotracheitis (TRT) signs was included in the study to compare the findings. RNA extracted from positive swabs was subjected to nested PCR, targeting the attachment protein of the G gene, followed by gel electrophoresis. Amplicons were purified and sequenced using the Sanger method. Bioinformatics tools facilitated sequence analyses, including BLAST for similarity searches and Mega® for phylogenetic analysis using the maximum likelihood method with 1000 bootstrap replicates. The investigation unveiled the existence of two distinct clades of the aMPV/B isolates, which originated from used vaccines, all circulating in broilers and turkeys and indicating potential virus transmission between both poultry species. This article presents the first-ever molecular characterization of aMPV isolated from Moroccan broilers and turkeys, encompassing comprehensive investigations on its presence and subtype, and genetic characterization.
Computational Workflow for the Characterization of Size, Shape, and Composition of Particles and Their Separation Behavior During Processing
Authors: Weber Sabrina , Furat Orkun , Kirstein Tom , Leißner Thomas , Peuker Urs A. , Schmidt Volker
Published: December 2024

Separation functions, so-called Tromp functions, are often used to quantitatively analyze the separation behavior in particle processing with respect to individual particle descriptors. However, since the separation behavior of particles is typically influenced by multiple particle descriptors, multivariate Tromp functions are required. This study focuses on methods that allow for the computation of multivariate parametric Tromp functions by means of statistical image analysis and copula-based modeling. The computations are exemplarily performed for the magnetic separation of Li-bearing minerals, including quartz, topaz, zinnwaldite, and muscovite, based on micro-computed tomography images and scanning electron microscopy with energy-dispersive X-ray spectroscopy analysis. In particular, the volume equivalent diameter, zinnwaldite fraction, flatness, and sphericity are examined as possible influencing particle descriptors. Moreover, to compute the Tromp functions, the probability distributions of these descriptors for concentrate and tailing should be used. In this study, 3D image data depicting particles in feed, concentrate, and tailings is available for the computation of Tromp functions. However, concentrate particles tend to be elongated, plate-like, and densely packed, making segmentation for extracting individual particles from image data extremely difficult. Thus, information on the concentrate could not be obtained from the available database. To remedy this, an indirect optimization approach is used to estimate the distribution of particle descriptors of the concentrate. It turned out that this approach can be successfully applied to analyze the influence of size, shape, and composition of particles on their separation behavior.
Microcalorimetry as an Effective Tool for the Determination of Thermodynamic Characteristics of Fulvic–Drug Interactions
Authors: Klučáková Martina , Krouská Jitka
Published: December 2024

The presence of pharmaceuticals in the environment can result in potentially dangerous situations. In soils and sediments, pharmaceuticals can be partially immobilized by interactions with humic substances. Interactions, thus, can strongly affect their mobility and bioavailability. An investigation of the thermodynamic aspects of the interactions is largely missing. Thermodynamic parameters are usually calculated on the basis of sorption experiments. Our study is focused on the direct measurements of the heat effect of interactions between fulvic acids and chosen drugs. Well-characterized fulvic sample standards provided by the International Humic Substances Society were used. Ibuprofen, diclofenac, and sulphapyridine were chosen as drugs. Isothermal titration calorimetry provided a complete set of thermodynamic characteristics of underlying processes—interaction enthalpy, entropy, and Gibbs energy. All studied interactions were found to be exothermic with heat liberation between −496 and −9938 J/mol. The lowest enthalpies were obtained for sulphapyridine and the highest ones for ibuprofen (on average). Changes in Gibbs energy were very similar for all studied interactions (20–28 kJ/mol). The highest change in entropy was determined for ibuprofen (73 J/mol·K); values obtained for diclofenac and sulphapyridine were comparable (57 and 56 J/mol·K, respectively).
Comparison of Vacuum and Atmospheric Deep-Fat Frying of Osmo-Dehydrated Goldenberries
Authors: Guevara-Betancourth Christiam , Arango Oscar , Suárez-Montenegro Zully J. , Tirado Diego F. , Osorio Oswaldo
Published: December 2024

Colombian goldenberries that do not reach sufficient quality for export are exposed to waste, so the search for processes that provide added value while guaranteeing the conservation of this fruit is paramount. Thus, snacks by vacuum frying from goldenberries (Physalis peruviana L.) with low export quality were made. Goldenberry slices previously subjected to ultrasound-assisted osmotic dehydration were used for this purpose. Response surface methodology with different levels of temperature (110 °C and 130 °C), vacuum pressure (0.3 bar and 0.5 bar), and time (2 min and 6 min) was used to optimize the process. At optimal vacuum frying conditions (i.e., 108 °C, 0.5 bar, and 5.5 min), snacks with lower (p ≤ 0.05) oil content were produced, compared to atmospheric frying chips. The optimized snacks had 9% oil, 7% moisture, ΔE of 13 (with respect to fresh fruit), aw of 0.3, and hardness of 14 N. The kinetics and modeling of moisture loss and oil uptake were performed under optimal conditions, obtaining the best fit with the Page (R2 = 99%) and the first-order (R2 = 96%) models, respectively. There was a clear correlation between oil uptake and moisture loss, as the highest oil retention in the product took place when the product had lost the greatest amount of water; therefore, the low initial moisture in the product due to pretreatment resulted in lower oil uptake in it. The obtained goldenberry snack showed adequate physicochemical properties, and the pretreatment yielded a product with much healthier characteristics (i.e., lower oil content, and therefore, a lower caloric intake); so, the proposed process could represent an alternative to the processing of low-export quality Colombian goldenberries.
The Numerical Simulation Study on the Heat Transfer Mechanism in Heavy Oil Reservoirs During In-Situ Combustion
Authors: Sun Jiuzhi , Wang Bo , Shu Yunjie , Wang Yanchao , Pan Yi , Tian Chao
Published: December 2024

The escalating energy demand has prompted nations to prioritize the development of high-viscosity and challenging-to-extract heavy and extra-heavy oil reserves. Consequently, the technique of in-situ combustion in oil reservoirs by injecting air to ignite heavy oil resources, leveraging the generated heat to enhance recovery rates, is a particularly critical extraction method. However, simulation studies of in-situ combustion techniques are still primarily conducted at a macroscopic level. Therefore, conducting more detailed numerical simulation studies holds significant importance. This paper establishes a mathematical model for heat transfer within reservoirs during in-situ combustion, thoroughly investigating the effects of inlet temperature, injection pressure, injection duration, and porosity on the heat transfer processes inside the reservoir. The research demonstrates that the reservoir’s internal temperature gradually rises as the injection duration increases. Additionally, porosity (an increase from 0.1 to 0.3 enhances the heat propagation rate by 15%) and injection pressure (an increase from 5 MPa to 8 MPa boosts the heat propagation rate by 25%) significantly affect the heat transfer rate.
Quantum Computation of the Cobb–Douglas Utility Function via the 2D Clairaut Differential Equation
Authors: Betancur-Hinestroza Isabel Cristina , Velásquez-Sierra Éver Alberto , Caro-Lopera Francisco J. , Bedoya-Calle Álvaro Hernán
Published: December 2024

This paper introduces the integration of the Cobb–Douglas (CD) utility model with quantum computation using the Clairaut-type differential formula. We propose a novel economic–physical model employing envelope theory to establish a link with quantum entanglement, defining emergent probabilities in the optimal utility function for two goods within a given expenditure limit. The study explores the interaction between the CD model and quantum computation, emphasizing system entropy and Clairaut differential equations in understanding utility’s optimal envelopes. Algorithms using the 2D Clairaut equation are introduced for the quantum formulation of the CD function, showcasing representation in quantum circuits for one and two qubits. Our findings, validated through IBM-Q simulations, align with the predictions, demonstrating the robustness of our approach. This methodology articulates the utility–budget relationship through envelope representation, where normalized intercepts signify probabilities. The precision of our results, especially in modeling quantum entanglement, surpasses that of IBM-Q simulations, which require extensive iterations for similar accuracy.
Reproducing the NIRS-QST Clinical Dose Calculations for Carbon Ion Radiotherapy Using Microdosimetric Probability Density Distributions
Authors: Parisi Alessio , Furutani Keith M. , Hartzell Shannon , Beltran Chris J.
Published: December 2024

Ion radiotherapy requires accurate relative biological effectiveness (RBE) calculations to account for the markedly different biological effects of ions compared to photons. Microdosimetric RBE models rely on descriptions of the energy deposition at the microscopic scale, either through radial dose distributions (RDDs) or microdosimetric probability density distributions. While RDD approaches focus on the theoretical description of the energy deposition around the ion track, microdosimetric distributions offer the advantage of being experimentally measurable, which is crucial for quality assurance programs. As the results of microdosimetric RBE models depend on whether RDD or microdosimetric distributions are used, the model parameters are not interchangeable between these approaches. This study presents and validates a method to reproduce the published reference biological and clinical dose calculations at NIRS-QST for only carbon ion radiotherapy by using the modified microdosimetric kinetic model (MKM) alongside microdosimetric distributions instead of the reference RDD approach. To achieve this, Monte Carlo simulations were performed to estimate the variation of the radiation quality within and outside the field of pristine and spread-out Bragg peaks. By appropriately optimizing the modified MKM parameters for microdosimetric distributions assessed within water spheres, we successfully reproduced the results of calculations using the reference NIRS-QST RDD, generally within 2%.
The Christology of Origen
Authors: Solheid John C.
Published: December 2024

In this essay, I will provide an overview of the Christology of Origen. Because Origen expressed his views in a variety of different literary and polemical contexts, and because the person of Christ pervades the entirety of his thought, I will focus my attention primarily on issues pertaining to his reception at Nicaea. I will highlight the key Origenian texts that shed light upon the theological questions with which he was preoccupied and address the role of the Monarchians in shaping his understanding of Christ. I will also address what I regard as one of Origen’s signature contributions to Christology, a contribution that has not yet received adequate attention, namely his understanding of the mystical body of Christ into which we enter at Baptism.
Perspective on Agapeic Ethic and Creation Care
Authors: Onyezonwu Loveday Chigozie , Josiah Ucheawaji Godfrey
Published: December 2024

Ongoing discussions on creation care and agapeic ethic have paid less attention to the interplay between love, creation, waste management challenges, and mission. This paper, therefore, discusses a missional perspective of agapeic ethic as a ground norm for eco-theology and motivation for eco-care (especially proper waste management). An attempt is made to discuss the concept and dimensions of love and the nexus between love, creation, and missional purpose. This paper adopts a non-participant observation of refuse collection as carried out by refuse collectors, the waste disposal practices of people, and the waste handling and disposal practices of selected churches. This research was conducted across Port Harcourt City, Obio Akpor, Ogba/Egbema/Ndoni, Oyibo, and Eleme municipal areas of Rivers State, Nigeria. The churches observed include Protestant Churches (Seventh-Day Adventist Church, Church of Nigeria that is Anglican Communion, and Church of Jesus Christ of Latter-Day Saints); the Roman Catholic Church; Pentecostal Churches (such as Salvation Ministries Worldwide, Redeemed Christian Church of God, and Deeper Life Bible Church); and African Indigenous Churches (namely, the Christ Apostolic Church, Cherubim and Seraphim, and Celestial Church of Christ). The information gathered was critically analysed and used in measuring stakeholders’ disposition to and understanding of the research focus. Ecological liberation hermeneutics was adopted as an interpretative framework, while the eco-justice principles of interconnectedness and purpose were engaged to foreground the underlying issues in this study. This paper argued that Christians’ involvement in proper waste management, keeping both private and public spaces clean, is a morally and divinely imposed duty and a practical testimonial of their love for God, their fellow human beings, and non-human others. This is a fulfilment of the mission where Christian love (agape) serves as an ethical principle of inflicting ‘no harm’ to humans or non-human others.
Restoration of the Greek Catholic Church in Czechoslovakia During the Prague Spring
Authors: Hromják Ľuboslav
Published: December 2024

The Greek Catholic Church in Czechoslovakia found itself outlawed after its violent liquidation by the brachial communist power in 1950, and the members of this church were forced to convert to the Orthodox faith. This study explains the difficult process of renewal of the Greek Catholic Church and its structures in the context of the Ostpolitik of the Holy See, represented by the Vatican diplomat and later secretary of state Agostino Casaroli, and in the context of the political relaxation in Czechoslovakia in 1968, during the so-called Prague Spring. In addition to the scientific literature, this study is based on historical research carried out in the historical archive of the Secretariat of State in the Vatican within the Agostino Casaroli fund, which has so far been processed little. In the study, the author describes the most significant steps which lead to the official legalization of the Greek Catholic Church on June 13, 1968, as well as the difficult process of negotiating the terms of the restored church with the state-enforced Orthodox Church.
Online Self-Supervised Learning for Accurate Pick Assembly Operation Optimization
Authors: Valdés Sergio , Ojer Marco , Lin Xiao
Published: December 2024

The demand for flexible automation in manufacturing has increased, incorporating vision-guided systems for object grasping. However, a key challenge is in-hand error, where discrepancies between the actual and estimated positions of an object in the robot’s gripper impact not only the grasp but also subsequent assembly stages. Corrective strategies used to compensate for misalignment can increase cycle times or rely on pre-labeled datasets, offline training, and validation processes, delaying deployment and limiting adaptability in dynamic industrial environments. Our main contribution is an online self-supervised learning method that automates data collection, training, and evaluation in real time, eliminating the need for offline processes. Building on this, our system collects real-time data during each assembly cycle, using corrective strategies to adjust the data and autonomously labeling them via a self-supervised approach. It then builds and evaluates multiple regression models through an auto machine learning implementation. The system selects the best-performing model to correct the misalignment and dynamically chooses between corrective strategies and the learned model, optimizing the cycle times and improving the performance during the cycle, without halting the production process. Our experiments show a significant reduction in the cycle time while maintaining the performance.
Segmentation of Any Fire Event (SAFE): A Rapid and High-Precision Approach for Burned Area Extraction Using Sentinel-2 Imagery
Authors: Liu Shuaijun , Xue Yong , Chen Hui , Chen Yang , Zhan Tianyu
Published: December 2024

The timely and accurate monitoring of wildfires and other sudden natural disasters is crucial for safeguarding the safety of residents and their property. Satellite imagery for wildfire monitoring offers a unique opportunity to obtain near-real-time disaster information through rapid, large-scale remote sensing mapping. However, existing wildfire monitoring methods are constrained by the temporal and spatial limitations of remote sensing imagery, preventing comprehensive fulfillment of the need for high temporal and spatial resolution in wildfire monitoring and early warning. To address this gap, we propose a rapid, high-precision wildfire extraction method without the need for training—SAFE. SAFE combines the generalization capabilities of the Segmentation Anything Model (SAM) and the high temporal effectiveness of hotspot product data such as MODIS and VIIRS. SAFE employs a two-step localization strategy to incrementally identify burned areas and pixels in post-wildfire imagery, thereby reducing computational load and providing high-resolution wildfire impact areas. The high-resolution burned area data generated by SAFE can subsequently be used to train lightweight regional wildfire extraction models, establishing high-precision detection and extraction models applicable to various regions, ultimately reducing undetected areas. We validated this method in four test regions representing two typical wildfire scenarios—grassland and forest. The results showed that SAFE’s F1-score was, on average, 9.37% higher than alternative methods. Additionally, the application of SAFE in large-scale disaster scenarios demonstrated its potential capability to detect the fine spatial distribution of wildfire impacts on a global scale.
Social Filter Theory—A Sleeping Beauty of Regional Innovation Theories
Authors: Ren Jianhui , Lai Linlin , Pei Binjie , Zhan Wenyu
Published: December 2024

As a branch of regional innovation theories, social filter theory fundamentally reveals the reasons behind the geography of innovation in different countries and regions. However, compared with other regional innovation theories, social filter theory has remained largely ignored and has not been fully developed. To enrich and develop social filter theory, this article systematically traces its origins and comments on four aspects: the concept of social filters, the construction and measurement of social filter indicators, the mechanisms, and empirical research on how social filters affect regional innovation and the transformation of innovation. Until recently, regional innovation theories primarily focused on superficially describing the phenomenon of innovation, but they paid little attention to the local social filter conditions necessary for innovation generation and transformation, which are prerequisites for constructing a mature innovation system. Therefore, further efforts are needed to address the current knowledge gap based on the recommended directions in this article.
Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control
Authors: Gutiérrez-Moreno Rodrigo , Barea Rafael , López-Guillén Elena , Arango Felipe , Sánchez-García Fabio , Bergasa Luis M.
Published: December 2024

The use of Deep Learning algorithms in the domain of Decision Making for Autonomous Vehicles has garnered significant attention in the literature in recent years, showcasing considerable potential. Nevertheless, most of the solutions proposed by the scientific community encounter difficulties in real-world applications. This paper aims to provide a realistic implementation of a hybrid Decision Making module in an Autonomous Driving stack, integrating the learning capabilities from the experience of Deep Reinforcement Learning algorithms and the reliability of classical methodologies. Our Decision Making system is in charge of generating steering and velocity signals using the HD map information and sensors pre-processed data. This work encompasses the implementation of concatenated scenarios in simulated environments, and the integration of Autonomous Driving modules. Specifically, the authors address the Decision Making problem by employing a Partially Observable Markov Decision Process formulation and offer a solution through the use of Deep Reinforcement Learning algorithms. Furthermore, an additional control module to execute the decisions in a safe and comfortable way through a hybrid architecture is presented. The proposed architecture is validated in the CARLA simulator by navigating through multiple concatenated scenarios, outperforming the CARLA Autopilot in terms of completion time, while ensuring both safety and comfort.
Luminescence Lifetime-Based Water Conductivity Sensing Using a Cationic Dextran-Supported Ru(II) Polypyridyl Complex
Authors: Knöbl Ya Jie , Johnston Lauren M. , Quílez-Alburquerque José , Orellana Guillermo
Published: December 2024

Water conductivity sensing relies universally on electrical measurements, which are subject to corrosion of the electrodes and subsequent signal drift in prolonged in situ uses. Furthermore, they cannot provide contactless sensing or remote readout. To this end, a novel device for water conductivity monitoring has been developed by employing a microenvironment-sensitive ruthenium complex, [Ru(2,2′-bipyridine-4,4′-disulfonato)3]4−, embedded into a quaternary ammonium functionalized cross-linked polymer support. The degree of swelling of the latter, which leads to a change in the emission lifetime, depends on the water conductivity. The sensor displays a reversible response (2 min ≤ t90 ≤ 3 min) and has been shown to be stable for >65 h of continuous monitoring of 0.8–12.8 mS cm−1 KCl solutions. Changes to the cation do not affect the sensor response, while changes to the anion type induce small effects. Variations in the dissolved O2 or temperature require corrections of the response. The sensor can be interrogated alongside dissolved O2 and pH luminescent sensors based on the same family of indicator dyes to exploit the definite advantages of luminescence lifetime-based detection.
Refinement of an Algorithm to Detect and Predict Freezing of Gait in Parkinson Disease Using Wearable Sensors
Authors: Haussler Allison M. , Tueth Lauren E. , May David S. , Earhart Gammon M. , Mazzoni Pietro
Published: December 2024

Freezing of gait (FOG) is a debilitating symptom of Parkinson disease (PD). It is episodic and variable in nature, making assessment difficult. Wearable sensors used in conjunction with specialized algorithms, such as our group’s pFOG algorithm, provide objective data to better understand this phenomenon. While these methods are effective at detecting FOG retrospectively, more work is needed. The purpose of this paper is to explore how the existing pFOG algorithm can be refined to improve the detection and prediction of FOG. To accomplish this goal, previously collected data were utilized to assess the prediction ability of the current algorithm, the potency of each FOG assessment task(s) for eliciting FOG, and the maintenance of detection accuracy when modifying the sampling rate. Results illustrate that the algorithm was able to predict upcoming FOG episodes, but false positive rates were high. The Go Out and Turn-Dual Task was most potent for eliciting FOG, and the 360-Dual Task elicited the longest duration of FOG. The detection accuracy of the pFOG algorithm was maintained at a sampling rate of 60 Hz but significantly worse at 30 Hz. This work is an important step in refining the pFOG algorithm for improved clinical utility.
High-Impact Polystyrene Structured Light Components for Terahertz Imaging Applications
Authors: Stanaitis Kasparas , Čižas Vladislovas , Bielevičiūtė Augustė , Grigelionis Ignas , Minkevičius Linas
Published: December 2024

Terahertz frequency range imaging has become more and more attractive for a wide range of practical applications; however, further component optimization is still required. The presented research introduces 3D-printed high-impact polystyrene (HIPS) beam-shaping components for the terahertz range. Gaussian, Bessel, and Airy beam-shaping structures are fabricated, and different combinations are employed to evaluate imaging system performance. The combination of the Gaussian element as focusing and the Bessel element as collecting is revealed to be similarly efficient and less sensitive to misalignment than the classical Gaussian–Gaussian element setup. The presented research paves the way for introducing cost-effective structured light beam-shaping elements into THz imaging systems.
Fatigue Experiment and Failure Mechanism Analysis of Aircraft Titanium Alloy Wing–Body Connection Joint
Authors: Chen Xianmin , Li Shanshan , Liang Yuanbo , Wang Shuo , Yan Liang , Du Shichang
Published: December 2024

Taking the titanium alloy wing–body connection joint at the rear beam of a certain type of aircraft as the research object, this study analyzed the failure mechanism and verified the structural safety of the wing–body connection joint under actual flight loads. Firstly, this study verified the validity of the loading system and the measuring system in the test system through the pre-test, and the repeatability of the test was analyzed for error to ensure the accuracy of the experimental data. Then, the test piece was subjected to 400,000 random load tests of flight takeoffs and landings, 100,000 Class A load tests, and ground–air–ground load tests, and the test piece fractured under the ground–air–ground load tests. Lastly, the mechanism analysis and structural safety verification of the fatigue fracture of the joints were carried out by using a stereo microscope and scanning electron microscope. The results show that fretting fatigue is the main driving force for crack initiation, and the crack shows significant fatigue damage characteristics in the stable growth stage and follows Paris’ law. Entering the final fracture region, the joint mainly experienced ductile fracture, with typical plastic deformation features such as dimples and tear ridges before fracture. The fatigue crack growth behavior of the joint was quantitatively analyzed using Paris’ law, and the calculated crack growth period life was 207,374 loadings. This result proves that the crack initiation life accounts for 95.19% of the full life cycle, which is much higher than the design requirement of 400,000 landings and takeoffs, indicating that the structural design of this test piece is on the conservative side and meets the requirements of aircraft operational safety. This research is of great significance in improving the safety and reliability of aircraft structures.
AI-Enhanced IoT System for Assessing Bridge Deflection in Drive-By Conditions
Authors: Iacussi Leonardo , Chiariotti Paolo , Cigada Alfredo
Published: December 2024

The increasing traffic on roads poses a significant challenge to the structural integrity of bridges and viaducts. Indirect structural monitoring offers a cost-effective and efficient solution for monitoring multiple infrastructures. The presented work aims to explore new sensing strategies based on digital MEMS sensors integrated into an intelligent IoT infrastructure to predict the bridge deflection behaviour for indirect Bridge Structural Health Monitoring purposes. An experimental setup comprising a bridge model and vehicle equipped with a smart sensing node has been used to generate the dataset. Various models for bridge deflection estimation are deployed on the sensorized vehicle, exploiting edge AI capabilities of smart sensors. This study shows the potential of leveraging data-driven technologies to enhance the performance of low-cost sensors. Additionally, it demonstrates the viability of assessing static deflection shapes of bridges through indirect measurements on board vehicles, underlining the potential of this approach to make SHM more cost-effective and scalable.
A Secure Data Sharing Model Utilizing Attribute-Based Signcryption in Blockchain Technology
Authors: Song Chaoyue , Chen Lifeng , Wu Xuguang , Li Yu
Published: December 2024

With the rapid development of the Internet of Things (IoT), the scope of personal data sharing has significantly increased, enhancing convenience in daily life and optimizing resource management. However, this also poses challenges related to data privacy breaches and holdership threats. Typically, blockchain technology and cloud storage provide effective solutions. Nevertheless, the centralized storage architecture of traditional cloud servers is susceptible to single points of failure, potentially leading to system outages. To achieve secure data sharing, access control, and verification auditing, we propose a data security sharing scheme based on blockchain technology and attribute-based encryption, applied within the InterPlanetary File System (IPFS). This scheme employs multi-agent systems and attribute-based signcryption algorithms to process data, thereby enhancing privacy protection and verifying data holdership. The encrypted data are then stored in the distributed IPFS, with the returned hash values and access control policies uploaded to smart contracts, facilitating automated fine-grained access control services. Finally, blockchain data auditing is performed to ensure data integrity and accuracy. The results indicate that this scheme is practical and effective compared to existing solutions.
Electric Vehicle Charging Route Planning for Shortest Travel Time Based on Improved Ant Colony Optimization
Authors: Tan Aiping , Wang Chang , Wang Yan , Dong Chenglong
Published: December 2024

Electric vehicles (EVs) are gaining significant attention as an environmentally friendly transportation solution. However, limitations in battery technology continue to restrict EV range and charging speed, resulting in range anxiety, which hampers widespread adoption. While there has been increasing research on EV route optimization, personalized path planning that caters to individual user needs remains underexplored. To bridge this gap, we propose the electric vehicle charging route planning based on user requirements (EVCRP-UR) problem, which aims to integrate user preferences and multiple constraints. Our approach utilizes topology optimization to reduce computational complexity and improve path planning efficiency. Furthermore, we introduce an improved ant colony optimization (IACO) algorithm incorporating novel heuristic functions and refined probability distribution models to select optimal paths and charging stations. To further enhance charging strategies, we develop a discrete electricity dynamic programming (DE-DP) algorithm to determine charging times at efficiently chosen stations. By combining these methods, the proposed IACO algorithm leverages the strengths of each approach, overcoming their individual limitations and delivering superior performance in EV routing and charging optimization.
Investigating Injury Outcomes of Horse-and-Buggy Crashes in Rural Michigan by Mining Crash Reports Using NLP and CNN Algorithms
Authors: Qawasmeh Baraah , Oh Jun-Seok , Kwigizile Valerian
Published: December 2024

Horse-and-buggy transportation, vital for many rural communities and the Amish population, has been largely overlooked in safety research. This study examines the characteristics and injury severity of horse-and-buggy roadway crashes in Michigan’s rural areas. Detailed crash data are essential for safety studies, as crash scene descriptions are mainly found in narratives and diagrams. However, extracting and utilizing this information from traffic reports is challenging. This research tackles these challenges using image-processing and text-mining techniques to analyze crash diagrams and narratives. The study employs the AlexNet convolutional neural network (CNN) to identify and extract horse-and-buggy crashes, analyzing (2020–2023) Michigan UD-10 rural crash reports. Natural Language Processing (NLP) techniques also identified primary risk factors from crash narratives, analyzing single-word patterns (“unigrams”) and sequences of three consecutive words (“trigrams”). The findings emphasize the risks involved in horse-and-buggy interactions on rural roadways and highlight various contributing factors to the severity of these crashes, including distracted or careless actions by motorists, nighttime visibility issues, and failure to yield, especially by elderly drivers. This study suggests prioritizing horse-and-buggy riders in road safety and public health programs and recommends comprehensive measures that could significantly reduce crash incidence and severity, improving overall safety in Michigan’s rural areas, including better signage, driver education, and community outreach. Also, the study highlights the potential of advanced image-processing techniques in traffic safety research that could lead to more precise and actionable findings, enhancing road safety for all users.
Scarification with a Low Concentration of Acid Facilitates Water Acquisition and Minimizes Cold Stratification Duration, Improving the Seed Germination of Canadian Buffaloberry (Shepherdia canadensis (L.) Nutt.)
Authors: Inoue Sahari , Sobze Jean-Marie
Published: December 2024

Canadian buffaloberry (Shepherdia canadensis (L.) Nutt.) is a perennial shrub known for its drought tolerance, nitrogen-fixing ability, and suitability for land reclamation and vegetation, particularly on nutrient-poor industrially disturbed soils in Alberta, Canada. Despite its ecological importance, commercial nurseries and greenhouses face challenges due to limited knowledge of optimizing seed germination and maintaining genetic diversity in cultivated seedlings. In this study, we investigated the interactive effect of cold stratification duration (0, 2, 4, 8, 12, and 16 weeks) and scarification time (control (no scarification), 1, and 4 h) on buffaloberry seed germination. The seed germination rate was tested using a factorial experiment with two factors arranged in a completely randomized design with four replications. Our findings indicate that scarification with a low concentration of acid for 1 h significantly enhances germination outcomes and shortens the required stratification period from 16 weeks to 12 weeks, achieving an optimal germination rate of 82%. Our novel approach using low-concentration acid to scarify Canadian buffaloberry seeds will help commercial greenhouses and forest nurseries improve seed germination, which utilizes this species for land reclamation and reforestation. Furthermore, this method can be adapted to improve germination in other native species with similar dormancy challenges, broadening its applications in ecological restoration efforts.
Workplace Discrimination Perceived by Venezuelan Immigrants Settled in Cúcuta, Los Patios and La Parada (Colombia)
Authors: Cuberos María-Antonia , Albornoz-Arias Neida , Ramírez-Martínez Carolina , Morffe Peraza Miguel Ángel
Published: December 2024

A form of discrimination associated with international migration is workplace discrimination. This study focused on identifying discriminatory actions in working conditions, as perceived by Venezuelan immigrants residing in Cúcuta, La Parada and Los Patios (Colombia). The goal was to determine the measures employers could implement to reduce such discriminatory actions. A quantitative, non-probabilistic snowball sampling method was adopted, followed by a survey of 177 immigrants. An exploratory and descriptive analysis of the variables under study was conducted using multivariate analysis techniques of multiple correspondences with optimal scaling. The study also explored discrimination perceived by employed and unemployed immigrants, as well as by those who had been denied work during their job search. The study concluded that to address discrimination by employers, culture should be linked to ethics and corporate social responsibility, enabling organisations to successfully raise awareness among their staff, managers and those in charge of human resource management about non-discrimination policies, equal treatment and opportunities, from design to implementation, along with necessary monitoring by Labour Inspecting Offices.
Multidimensional Urban Waterlogging Risk Assessment Based on a Refined Inundation Model
Authors: Yang Haiyan , Jiang Titong , Wang Zhe , Sun Xiaobo
Published: December 2024

To enhance the scientific and accurate assessment methods for urban waterlogging risk in City B and to promote sustainable urban development, this paper conducts a detailed evaluation of waterlogging risk from three dimensions: pedestrian safety, road traffic, and waterlogging-prone areas. After considering existing monitoring technologies and the constructed waterlogging model, the paper identifies standing water depth, standing water duration, and standing water velocity as the key indicators for waterlogging risk assessment and utilizes scenario simulation methods to evaluate waterlogging risk across these dimensions. Additionally, the paper employs boundary conditions of 2-h short-duration rainfall with a 5-year return period and 24-h long-duration rainfall with a 50-year return period for the assessment. The evaluation results indicate that, for pedestrian safety, under both short and long-duration rainfall conditions, low-risk areas represent the largest proportion of risk areas, reaching 6.36% and 10.83% of the total area, respectively. In the road traffic assessment, the proportions of severely congested roads under short- and long-duration rainfall conditions are 27.06% and 57.15%, respectively. In the evaluation of waterlogging-prone areas, high-risk areas account for the largest proportion of risk areas under both short- and long-duration rainfall conditions, reaching 0.64% and 1.42% of the total area, respectively.