bluuebunny/crossref_metadata_2025 huggingface dataset:
Body Image Concerns in People Who Underwent a Total Laryngectomy
Authors: Guimarães Isabel , Torrejano Gabriela , Aires Raquel , Gonçalves Filomena , Vaz Freitas Susana , Correia Paula , Romeiro Cláudia , Silvestre Inês , Bom Rita , Martins Paulo , Santos Ana R.
Published: February 2024

Background: Body image is a potential psychological burden after total laryngectomy (TL) with devastating effects on patients’ health-related quality of life (HRQOL) and communication. This study focused on TL patients to determine the prevalence of dissatisfied body image and whether they have poorer HRQOL and difficulty adjusting to their new voice than TL patients with satisfied body image. It also aimed to investigate the potential predictors of body image. Methods: A multicenter cross-sectional study was conducted. For TL patients, the Body Image Scale (BIS), the European Organization for Research on Cancer Quality of Life Questionnaire, Core and Neck Module (EORTC QLQ C30 and EORCT H&N35), and the Self-Experiences of Communication after Laryngeal cancer (SECEL) were used. Patients were categorized as dissatisfied with their body image if the BIS score was ≥8. Multiple regression analysis was performed using the BIS as the dependent measure and HRQOL (QLQ C30 and H& N35) and communication (SECEL) as independent variables. Results: Overall, 31.3% of TL patients had dissatisfied body image, significantly worse HRQOL, and difficulty adjusting to their new voice than patients with satisfied body image. The regression model showed that social eating and socializing (H&N35) and adjustment to their new voice (SECEL) were independent predictors of body image. The model explained 52% of the variance. Conclusions: Screening TL patients at risk for body image concerns may help develop effective interventions to optimize HRQOL and patient communication.
In Vitro Assessment of Wound-Healing Efficacy of Stabilized Basic Fibroblast Growth Factor (FGF-2) Solutions
Authors: Benington Leah , Mo Jingxin , Li Mingxin , Rajan Gunesh , Locher Cornelia , Lim Lee Yong
Published: February 2024

Chronic tympanic membrane perforations (TMP) pose a significant clinical challenge, but basic fibroblast growth factor (FGF-2) shows promise for their treatment, despite its instability in aqueous solutions which hampers the sustained delivery crucial for the healing process. Addressing this, our research focused on the development of stabilized FGF-2 formulations, F5 and F6, incorporating dual, generally regarded as safe (GRAS) excipients to enhance stability and therapeutic efficacy. F5 combined FGF-2 (1600 ng/mL) with 0.05% w/v methylcellulose (MC) and 20 mM alanine, while F6 used FGF-2 with 0.05% w/v MC and 1 mg/mL human serum albumin (HSA). Our findings demonstrate that these novel formulations not only significantly improve the cytoproliferation of human dermal fibroblasts but also exhibit the most potent chemoattractant effects, leading to the highest fibroblast monolayer closure rates (92.5% for F5 and 94.1% for F6 within 24 h) compared to other FGF-2 solutions tested. The comparable performance of F5 and F6 underscores their potential as innovative, less invasive, and cost-effective options for developing otic medicinal products aimed at the effective treatment of chronic TMP.
Integrating Nanotechnological Advancements of Disease-Modifying Anti-Rheumatic Drugs into Rheumatoid Arthritis Management
Authors: Singh Sukhbir , Tiwary Neha , Sharma Neelam , Behl Tapan , Antil Anita , Anwer Md. , Ramniwas Seema , Sachdeva Monika , Elossaily Gehan , Gulati Monica , Ohja Shreesh
Published: February 2024

Disease-modifying anti-rheumatic drugs (DMARDs) is a class of anti-rheumatic medicines that are frequently prescribed to patients suffering from rheumatoid arthritis (RA). Methotrexate, sulfasalazine, hydroxychloroquine, and azathioprine are examples of non-biologic DMARDs that are being used for alleviating pain and preventing disease progression. Biologic DMARDs (bDMARDs) like infliximab, rituximab, etanercept, adalimumab, tocilizumab, certolizumab pegol, and abatacept have greater effectiveness with fewer adverse effects in comparison to non-biologic DMARDs. This review article delineates the classification of DMARDs and their characteristic attributes. The poor aqueous solubility or permeability causes the limited oral bioavailability of synthetic DMARDs, while the high molecular weights along with the bulky structures of bDMARDs have posed few obstacles in their drug delivery and need to be addressed through the development of nanoformulations like cubosomes, nanospheres, nanoemulsions, solid lipid nanoparticles, nanomicelles, liposome, niosomes, and nanostructured lipid carrier. The main focus of this review article is to highlight the potential role of nanotechnology in the drug delivery of DMARDs for increasing solubility, dissolution, and bioavailability for the improved management of RA. This article also focusses on the different aspects of nanoparticles like their applications in biologics, biocompatibility, body clearance, scalability, drug loading, and stability issues.
Synergistic Influence of Arbuscular mycorrhizal Fungi Inoculation with Nanoparticle Foliar Application Enhances Chili (Capsicum annuum L.) Antioxidant Enzymes, Anatomical Characteristics, and Productivity under Cold-Stress Conditions
Authors: Sayed Eman G. , Desoukey S. F. , Desouky Abeer F. , Farag Mervat F. , EL-kholy Ragab I. , Azoz Samah N.
Published: February 2024

In this study, we aimed to evaluate the effects of Arbuscular mycorrhiza fungus (AMF) inoculation, foliar application of zinc oxide and selenium nanoparticles (ZnO-NPs and Se-NPs), and their combined interactions on the growth and productivity of chili pepper under cold-stress conditions. Two field experiments were successfully conducted during the winter seasons of 2021 and 2022 in an experimental field at the Faculty of Agriculture, Cairo University, Giza, Egypt. The results showed that, under cold stress, the combination of AMF inoculation and ZnO-NPs + Se-NPs as a foliar spray increased the average fruit weight by 92.4% and 98.7%, and the number of fruits by 34.6% and 54.8 compared to control treatment in the 2021 and 2022 seasons, respectively. Additionally, the combination of AMF and a mixture of nanoparticles (ZnO-NPs + Se-NPs) significantly increased the total marketable yield by 95.8% and 94.7% compared to the control, which recorded values of 2.4 and 1.9 kg m−2 in the 2021 and 2022 seasons, respectively. Furthermore, the combination of AMF and a mixture of nanoparticles (ZnO-NPs + Se-NPs) showed the highest total content of ascorbic acid and capsaicin in chili fruits compared to the other treatments. The combination of AMF and a mixture of nanoparticles (ZnO-NPs + Se-NPs) stimulated the accumulation of peroxidase (POD) and nitrogen glutamate dehydrogenase (GDH) while decreasing hydrogen peroxide (H2O2) and lipid peroxidation (MDA) contents. SDS analysis revealed that the application of ZnO-NPs, Se-NPs, AMF + ZnO-NPs, and AMF + ZnO-NPs + Se-NPs induced the emergence of new protein bands and reconstitution of those damaged by cold stress. Regarding histological structure, the combination of AMF inoculation and ZnO-NPs + Se-NPs as a foliar spray showed an enhancement in the thickness of grana thylakoids and increased the number of chloroplasts. Intriguingly, the findings showed that AMF and a mixture of nanoparticles (ZnO-NPs + Se-NPs) could offer guidance for increasing plant development and productivity under cold-stress conditions.
Tracking Phenological Changes over 183 Years in Endemic Species of a Mediterranean Mountain (Sierra Nevada, SE Spain) Using Herbarium Specimens
Authors: Rondinel-Mendoza Katy V. , Lorite Juan , Marín-Rodulfo Macarena , Cañadas Eva M.
Published: February 2024

Phenological studies have a crucial role in the global change context. The Mediterranean basin constitutes a key study site since strong climate change impacts are expected, particularly in mountain areas such as Sierra Nevada, where we focus. Specifically, we delve into phenological changes in endemic vascular plants over time by analysing data at three scales: entire massif, altitudinal ranges, and particular species, seeking to contribute to stopping biodiversity loss. For this, we analysed 5262 samples of 2129 herbarium sheets from Sierra Nevada, dated from 1837 to 2019, including reproductive structure, complete collection date, and precise location. We found a generalized advancement in phenology at all scales, and particularly in flowering onset and flowering peak. Thus, plants flower on average 11 days earlier now than before the 1970s. Although similar trends have been confirmed for many territories and species, we address plants that have been studied little in the past regarding biotypes and distribution, and which are relevant for conservation. Thus, we analysed phenological changes in endemic plants, mostly threatened, from a crucial hotspot within the Mediterranean hotspot, which is particularly vulnerable to global warming. Our results highlight the urgency of phenological studies by species and of including ecological interactions and effects on their life cycles.
Properties Related to the HLB Value of Hybrid Thermoelectric Nanofluids at Different Temperatures
Authors: Wang Rong-Tsu , Wang Jung-Chang
Published: February 2024

This article aims to explore the impact of HLB (Hydrophile-Lipophile Balance) values on two key properties, namely the thermoelectric conductivities and the stability of the suspension, of a hybrid nanofluid composed of TiO2 and CuO nanoparticles. The present study employed a two-step synthesis method to prepare the polymeric nanofluid, which meant that the nanoparticles were mixed with the base fluid using an ultrasonic oscillator, which was easier and cheaper than the one-step synthesis method. To ensure that the nanoparticles remain evenly dispersed in the base fluid, two distinct polymer-emulsifier combinations with different HLB values were employed as the dispersing agents. The first pair of polymeric emulsifiers consisted of Span#20 and Tween#20, and the second pair was Span#80 and Tween#80 composed to four HLB values of 12, 13, 14, and 15. The experiment measured the properties of the nanofluid, including the particle size, Zeta potential, and thermoelectric conductivities at different temperatures from 20 °C to 50 °C. The experimental outcomes indicated that an HLB value of 13 was the best for the two sets of polymeric emulsifiers tested. This value corresponded to the most reduced particle size, measured at 170 nm, alongside the most elevated Zeta potential, recorded at −30 mV. Additionally, this HLB value was associated with the peak thermoelectric conductivity, which was 1.46 W/m∙K. This suggests that there may be some variation in the best HLB value depending on the type of polymeric emulsifiers and the temperature of the hybrid nanofluid.
Amorphous Elastomeric Ultra-High Molar Mass Polypropylene in High Yield by Half-Titanocene Catalysts
Authors: Losio Simona , Bertini Fabio , Vignali Adriano , Fujioka Taiga , Nomura Kotohiro , Tritto Incoronata
Published: February 2024

Propylene polymerizations with different ketimide-modified half-titanocene catalysts, Cp’TiCl2(N=CtBu2) [Cp’ = C5H5 (1), C5Me5 (2), Me3SiC5H4 (3)], with MAO as a cocatalyst, were investigated. The obtained polymers were studied in detail by determining their microstructure, molar masses, thermal, and mechanical properties. The Cp*-ketimide, (C5Me5)TiCl2(N=CtBu2) (2), exhibited higher catalytic activities than Cp’TiCl2(N=CtBu2) (1,3), yielding higher molar mass polymers, Mw up to 1400 Kg/mol. All the synthesized polypropylenes (PP) are atactic and highly regioregular, with predominant rrrr pentads, especially PP prepared with catalyst 1. Differential scanning calorimetry (DSC) established that the polymers are fully amorphous aPP, and no melting endotherm events are detected. Glass transition temperatures were detected between −2 and 2 °C. These polypropylenes have been established to be high-performance thermoplastic elastomers endowed with remarkably high ductility, and a tensile strain at break higher than 2000%.
Use of Electrospinning for Sustainable Production of Nanofibers: A Comparative Assessment of Smart Textiles-Related Applications
Authors: Stramarkou Marina , Tzegiannakis Ioannis , Christoforidi Erifyli , Krokida Magdalini
Published: February 2024

Textile production is a major component of the global industry, with sales of over USD 450 billion and estimations of an 84% increase in their demand in the next 20 years. In recent decades, protective and smart textiles have played important roles in the social economy and attracted widespread popularity thanks to their wide spectrum of applications with properties, such as antimicrobial, water-repellent, UV, chemical, and thermal protection. Towards the sustainable manufacturing of smart textiles, biodegradable, recycled, and bio-based plastics are used as alternative raw materials for fabric and yarn production using a wide variety of techniques. While conventional techniques present several drawbacks, nanofibers produced through electrospinning have superior structural properties. Electrospinning is an innovative method for fiber production based on the use of electrostatic force to create charged threads of polymer solutions. Electrospinning shows great potential since it provides control of the size, porosity, and mechanical resistance of the fibers. This review summarizes the advances in the rapidly evolving field of the production of nanofibers for application in smart and protective textiles using electrospinning and environmentally friendly polymers as raw materials, and provides research directions for optimized smart fibers in the future.
FlaxPack: Tailored Natural Fiber Reinforced (NFRP) Compliant Folding Corrugation for Reversibly Deployable Bending-Active Curved Structures
Authors: Saslawsky Kevin , Steixner Christian , Tucker Michael , Costalonga Vanessa , Dahy Hanaa
Published: February 2024

As the use of Natural Fiber Reinforced Polymers (NFRPs) become increasingly popular in the built environment, steps in established workflows, including molding and transportation, continue to impose constraints on what is possible in the material’s fabrication process. This research builds on previous studies of moldless fiber composites using tailored fiber placement (TFP) as a fabrication method. By integrating compliant folding mechanisms into the flat preform to give shape to the final desired geometry this research replaces all dependencies on molds and formworks during the resin curing process with programmed formal deformations. The desired geometry is digitally simulated from its two-dimensional state into its resultant three-dimensional state and then subsequently structurally analyzed. The flat pack components are material efficient and can be transported flat to the site for their final assembly into their programmed geometry. This form is locked into its bent active state through the use of a simple drawstring that can later be removed to revert the form back into its flat state. This method is demonstrated through the digital fabrication of a stool where flat-packed elements can be deployed into elegant solutions that embody structure, material, and form simultaneously.
Polyvinyl Alcohol, a Versatile Excipient for Pharmaceutical 3D Printing
Authors: Couți Nadine , Porfire Alina , Iovanov Rareș , Crișan Andrea Gabriela , Iurian Sonia , Casian Tibor , Tomuță Ioan
Published: February 2024

Three-dimensional (3D) printing in the pharmaceutical field allows rapid manufacturing of a diverse range of pharmaceutical dosage forms, including personalized items. The application of this technology in dosage form manufacturing requires the judicious selection of excipients because the selected materials must be appropriate to the working principle of each technique. Most techniques rely on the use of polymers as the main material. Among the pharmaceutically approved polymers, polyvinyl alcohol (PVA) is one of the most used, especially for fused deposition modeling (FDM) technology. This review summarizes the physical and chemical properties of pharmaceutical-grade PVA and its applications in the manufacturing of dosage forms, with a particular focus on those fabricated through FDM. The work provides evidence on the diversity of dosage forms created using this polymer, highlighting how formulation and processing difficulties may be overcome to get the dosage forms with a suitable design and release profile.
Enhanced Mechanical Properties of Polylactic Acid/Poly(Butylene Adipate-co-Terephthalate) Modified with Maleic Anhydride
Authors: Nam Kibeom , Kim Sang Gu , Kim Do Young , Lee Dong Yun
Published: February 2024

An increase in plastic waste pollution and the strengthening of global environmental policies have heightened the need for research on biodegradable plastics. In this regard, polylactic acid (PLA) and poly(butylene adipate-co-terephthalate) (PBAT) are notable examples, serving as alternatives to traditional plastics. In this study, the compatibility and mechanical properties of PLA/PBAT blends were improved by the chemical grafting of maleic anhydride (MAH). In addition, qualitative analyses were conducted, dynamic mechanical properties were investigated, and the structure and mechanical characteristics of the blends were analyzed. With an increase in the MAH concentration, the grafting yield of the blends increased, and significantly improved the compatibility of the PLA/PBAT blend, with an ~2 and 2.9 times increase in the tensile strength and elongation at break, respectively. These findings indicate that the modified PLA/PBAT blend demonstrates potential for applications that require sustainable plastic materials, thereby contributing to the development of environmentally friendly alternatives in the plastics industry.
Lean-and-Green Datacentric Engineering in Laser Cutting: Non-Linear Orthogonal Multivariate Screening Using Gibbs Sampling and Pareto Frontier
Authors: Sembou Georgia , Besseris George
Published: February 2024

Metal processing may benefit from innovative lean-and-green datacentric engineering techniques. Broad process improvement opportunities in the efficient usage of materials and energy are anticipated (United Nations Sustainable Development Goals #9, 12). A CO2 laser cutting method is investigated in this study in terms of product characteristics (surface roughness (SR)) and process characteristics (energy (EC) and gas consumption (GC) as well as cutting time (CT)). The examined laser cutter controlling factors were as follows: (1) the laser power (LP), (2) the cutting speed (CS), (3) the gas pressure (GP) and, (4) the laser focus length (F). The selected 10mm-thick carbon steel (EN10025 St37-2) workpiece was arranged to have various geometric configurations so as to simulate a variety of real industrial milling demands. Non-linear saturated screening/optimization trials were planned using the Taguchi-type L9(34) orthogonal array. The resulting multivariate dataset was treated using a combination of the Gibbs sampler and the Pareto frontier method in order to approximate the strength of the studied effects and to find a solution that comprises the minimization of all the tested process/product characteristics. The Pareto frontier optimal solution was (EC, GC, CT, SR) = (4.67 kWh, 20.35 Nm3, 21 s, 5.992 μm) for the synchronous screening/optimization of the four characteristics. The respective factorial settings were optimally adjusted at the four inputs (LP, CS, GP, F) located at (4 kW, 1.9 mm/min, 0.75 bar, +2.25 mm). The linear regression analysis was aided by the Gibbs sampler and promoted the laser power and the cutting speed on energy consumption to be stronger effects. Similarly, a strong effect was identified of the cutting speed and the gas pressure on gas consumption as well as a reciprocal effect of the cutting speed on the cutting time. Further industrial explorations may involve more intricate workpiece geometries, burr formation phenomena, and process economics.
New Ballistic Neutron Guide for the Time-of-Flight Spectrometer FOCUS at PSI
Authors: Juranyi Fanni , Yamada Masako , Klauser Christine , Holitzner Lothar , Filges Uwe
Published: February 2024

FOCUS is a direct-geometry cold neutron time-of-flight spectrometer at SINQ (PSI, CH). Its neutron guide was exchanged in 2019/2020 within the SINQ Upgrade project, while the rest of the instrument remained unchanged. The new guide provided a significant intensity increase across the whole spectrum, especially at short wavelengths, due to the more efficient transport and extended phase space of the transported neutrons. The practically available energy transfer range (at the neutron energy loss side) was increased to about 40 meV. The main reason for the intensity benefit at short incident wavelengths was the improved guide coating, whereas at long wavelengths it was the new ballistic shape. The interesting part of the guide is the “peanut shape” of the curved part in the horizontal plane. For this, we derived the analytical restriction on the geometry to avoid a direct line of sight from the source. The guide geometry and the supermirror coating were optimized using Mcoptimize, a particle swarm optimization routine employing Mcstas. Future ballistic neutron guides may profit from the presented approaches, optimization strategy, and results.
Revaluing Gender and Religion in the Anthropological Debate of the Anthropocene: A Critique on the Threefold Culture–Nature–Supernature Divide
Authors: Notermans Catrien , Tonnaer Anke
Published: February 2024

This study argues that current anthropological research on human–nature relatedness lacks an explicit focus on gender and religion. It brings to the forefront that most current studies in Anthropocene anthropology that move away from anthropocentrism and towards studying more-than-human relatedness imply a disregard of gender that concerns both the ‘human’ and the ‘non-human’ in their mutual relationships. Presuming that the concept of sociality does not distinguish between human and nonhuman, the authors believe, however, that expressions of gender in more-than-human social relatedness cannot be denied. Simultaneously, they state that Anthropocene scholarship, by conceiving a secular future for humans restoring their relatedness with nature, is inclined to leave the ‘supernature’ out and to ignore experiences and embodied practices of enchantment in the modern world. By reviewing the feminist anthropological literature on the nature–culture divide and exploring the potential of enchantment as a way out of the secular condition of anthropology, the authors aim to restore a focus on gender and religion in anthropological Anthropocene scholarship while also transcending the threefold nature–culture–supernature divide. This review offers the theoretical prelude and introduction to the contributions of the Special Issue “Gender, Nature and Religious Re-enchantment in the Anthropocene”.
An Objective Measure of Distributional Estimability as Applied to the Phase-Type Aging Model
Authors: Nie Cong , Liu Xiaoming , Provost Serge B.
Published: February 2024

The phase-type aging model (PTAM) is a class of Coxian-type Markovian models that can provide a quantitative description of the effects of various aging characteristics. Owing to the unique structure of the PTAM, parametric inference on the model is affected by a significant estimability issue, its profile likelihood functions being flat. While existing methods for assessing distributional non-estimability require the subjective specification of thresholds, this paper objectively quantifies estimability in the context of general statistical models. More specifically, this is achieved via a carefully designed cumulative distribution function sensitivity measure, under which the threshold is tailored to the empirical cumulative distribution function, thus becoming an experiment-based quantity. The proposed definition, which is validated to be innately sound, is then employed to determine and enhance the estimability of the PTAM.
Dynamic Liability-Driven Investment under Sponsor’s Loss Aversion
Authors: Lee Dong-Hwa , Sung Joo-Ho
Published: February 2024

This paper investigates a dynamic liability-driven investment policy for defined-benefit (DB) plans by incorporating the loss aversion of a sponsor, who is assumed to be more sensitive to underfunding than overfunding. Through the lens of prospect theory, we first set up a loss-aversion utility function for a sponsor whose utility depends on the funding ratio in each period, obtained from stochastic processes of pension assets and liabilities. We then construct a multi-horizon dynamic control optimization problem to find the optimal investment strategy that maximizes the expected utility of the plan sponsor. A genetic algorithm is employed to provide a numerical solution for our nonlinear dynamic optimization problem. Our results suggest that the overall paths of the optimal equity allocation decline as the age of a plan participant reaches retirement. We also find that the equity portion of the portfolio increases when a sponsor is less loss-averse or the contribution rate is lower.
Fusion of Single and Integral Multispectral Aerial Images
Authors: Youssef Mohamed , Bimber Oliver
Published: February 2024

An adequate fusion of the most significant salient information from multiple input channels is essential for many aerial imaging tasks. While multispectral recordings reveal features in various spectral ranges, synthetic aperture sensing makes occluded features visible. We present a first and hybrid (model- and learning-based) architecture for fusing the most significant features from conventional aerial images with the ones from integral aerial images that are the result of synthetic aperture sensing for removing occlusion. It combines the environment’s spatial references with features of unoccluded targets that would normally be hidden by dense vegetation. Our method outperforms state-of-the-art two-channel and multi-channel fusion approaches visually and quantitatively in common metrics, such as mutual information, visual information fidelity, and peak signal-to-noise ratio. The proposed model does not require manually tuned parameters, can be extended to an arbitrary number and arbitrary combinations of spectral channels, and is reconfigurable for addressing different use cases. We demonstrate examples for search and rescue, wildfire detection, and wildlife observation.
Prediction of Atmospheric Duct Conditions from a Clutter Power Spectrum Using Deep Learning
Authors: Jin Taekyeong , Cho Jeongmin , Jang Doyoung , Choo Hosung
Published: February 2024

This paper presents a method for predicting atmospheric duct conditions from a clutter power spectrum using deep learning. To accurately predict the duct conditions, deep learning with a binary classification is applied to the proposed refractivity from the clutter (RFC) method. The input data set is the artificial clutter data that are generated via the Advanced Refractive Prediction System (AREPS) simulation software Ver. 3.6 in conjunction with random atmospheric refractive indices. The output of the RFC method is then predicted via binary classification, indicating whether the atmospheric conditions are duct or non-duct. For the cross-validation, the clutter power spectrum data are generated based on real atmospheric refractivity data. The results show that the DNN trained with 5600 pieces of data (validation accuracy of 95.99%) exhibits a binary classification accuracy of 98.36%. The deep neural network (DNN) trained with 28,000 pieces of data (validation accuracy of 98.20%) achieves a binary classification accuracy of 99.06% with an F1-score of 0.9921.
Approximation of a Convective-Event-Monitoring System Using GOES-R Data and Ensemble ML Models
Authors: Dávila-Ortiz Rodrigo , Carbajal-Pérez José Noel , Velázquez-Zapata Juan Alberto , Tuxpan-Vargas José
Published: February 2024

The presence of deep convective clouds is directly related to potential convective hazards, such as lightning strikes, hail, severe storms, flash floods, and tornadoes. On the other hand, Mexico has a limited and heterogeneous network of instruments that allow for efficient and reliable monitoring and forecasting of such events. In this study, a quasi-real-time framework for deep convective cloud identification and modeling based on machine learning (ML) models was developed. Eight different ML models and model assembly approaches were fed with Interest Fields estimated from Advanced Baseline Imager (ABI) sensor data on the Geostationary Operational Environmental Satellite-R Series (GOES-R) for one region in central Mexico and another in northeastern Mexico, both selected for their intense convective activity and high levels of vulnerability to severe weather. The results indicate that a simple approach such as Logistic Regression (LR) or Random Forest (RF) can be a good alternative for the identification and simulation of deep convective clouds in both study areas, with a probability of detection of (POD) ≈ 0.84 for Los Mochis and POD of ≈ 0.72 for Mexico City. Similarly, the false alarm ratio (FAR) ≈ 0.2 and FAR ≈ 0.4 values were obtained for Los Mochis and Mexico City, respectively. Finally, a post-processing filter based on lightning incidence (Lightning Filter) was applied with data from the Geostationary Lightning Mapper (GLM) of the GOES-16 satellite, showed great potential to improve the probability of detection (POD) of the ML models. This work sets a precedent for the implementation of an early-warning system for hazards associated with intense convective activity in Mexico.
Oil Tank Detection and Recognition via Monogenic Signal
Authors: Fan Yunqing , Yin Junjun , Yang Jian
Published: February 2024

With the rapid development of synthetic aperture radar (SAR) techniques, satellite systems’ capabilities to acquire information are continually improving. The PAZ satellite, with its high resolution and wide scanning swath, can provide high-quality data support for SAR applications. Oil tanks serve as energy storage devices, and their identification holds significant value in both military and civilian fields. Challenges in the detection and recognition of oil tanks using classical methods include poor detection, slow computation speed, and multiple windows of correct recognition. This paper centers on the analysis of oil tanks using PAZ data. We employ a sliding-window approach to acquire candidate target windows, process the windows through Weibull distribution modeling and hole filling, and extract target features using the monogenic signal based on regional L2 norm. The results demonstrate that the proposed method effectively improves the accuracy, and the model exhibits strong generalization ability and robustness.
GeoViewMatch: A Multi-Scale Feature-Matching Network for Cross-View Geo-Localization Using Swin-Transformer and Contrastive Learning
Authors: Zhang Wenhui , Zhong Zhinong , Chen Hao , Jing Ning
Published: February 2024

Cross-view geo-localization aims to locate street-view images by matching them with a collection of GPS-tagged remote sensing (RS) images. Due to the significant viewpoint and appearance differences between street-view images and RS images, this task is highly challenging. While deep learning-based methods have shown their dominance in the cross-view geo-localization task, existing models have difficulties in extracting comprehensive meaningful features from both domains of images. This limitation results in not establishing accurate and robust dependencies between street-view images and the corresponding RS images. To address the aforementioned issues, this paper proposes a novel and lightweight neural network for cross-view geo-localization. Firstly, in order to capture more diverse information, we propose a module for extracting multi-scale features from images. Secondly, we introduce contrastive learning and design a contrastive loss to further enhance the robustness in extracting and aligning meaningful multi-scale features. Finally, we conduct comprehensive experiments on two open benchmarks. The experimental results have demonstrated the superiority of the proposed method over the state-of-the-art methods.
Identifying Conservation Introduction Sites for Endangered Birds through the Integration of Lidar-Based Habitat Suitability Models and Population Viability Analyses
Authors: Gallerani Erica Marie , Berio Fortini Lucas , Warren Christopher C. , Paxton Eben H.
Published: February 2024

Similar to other single-island endemic Hawaiian honeycreepers, the critically endangered ‘ākohekohe (Palmeria dolei) is threatened by climate-driven disease spread. To avert the imminent risk of extinction, managers are considering novel measures, including the conservation introduction (CI) of ‘ākohekohe from Maui to higher elevation habitats on the Island of Hawai’i. This study integrated lidar-based habitat suitability models (LHSMs) and population viability analyses (PVAs) to assess five candidate sites currently considered by managers for CI. We first developed an LHSM for the species’ native range on Maui. We then projected habitat suitability across candidate CI sites, using forest structure and topography metrics standardized across sensor types. Given the structural variability observed within the five candidate sites, we identified clusters of contiguous, highly suitable habitat as potential release sites. We then determined how many adult individuals could be supported by each cluster based on adult home range estimates. To determine which clusters could house the minimum number of ‘ākohekohe birds necessary for a stable or increasing future population, we conducted PVAs under multiple scenarios of bird releases. We found that canopy height and relative height 90 had the greatest effects on model performance, possibly reflecting ‘ākohekohe’s preference for taller canopies. We found that a small release of at least nine pairs of equal sex ratios were sufficient for an 80% chance of success and a <1% chance of extirpation in 20 years, resulting in a minimum release area of 4.5 ha in size. We integrated the results of the LHSM and PVA into an interactive web application that allowed managers to consider the caveats and uncertainties associated with both LHSMs and PVAs in their decision-making process. As climate change continues to threaten species worldwide, this research demonstrates the value of lidar remote sensing combined with species-specific models to enable rapid, quantitative assessments that can inform the increasing consideration of time-sensitive conservation introductions.
Power-Type Structural Self-Constrained Inversion Methods of Gravity and Magnetic Data
Authors: Ming Yanbo , Ma Guoqing , Wang Taihan , Ma Bingzhen , Meng Qingfa , Li Zongrui
Published: February 2024

The inversion of gravity and magnetic data can obtain the density and magnetic structure of underground space, which provide important information for resource exploration and geological structure division. The most commonly used inversion method is smooth inversion in which the objective function is built with L2-norm, which has good stability, but it produces non-focused results that make subsequent interpretation difficult. The power-type structural self-constrained inversion (PTSS) method with L2-norm is proposed to improve the resolution of smooth inversion. A self-constraint term based on the power gradient of the results is introduced, which takes advantage of the structural feature that the power gradient can better focus on the model boundary to improve the resolution. For the joint inversion of gravity and magnetic data, the power-type mutual-constrained term between different physical structures and the self-constrained term can be simultaneously used to obtain higher-resolution results. The modeling tests demonstrated that the PTSS method can produce converged high-resolution results with good noise immunity in both the respective inversions and the joint inversion. Then, the PTSS joint inversion was applied to the airborne gravity and magnetic data of the iron ore district in Shandong, revealing the shape and location of the mineralized rock mass, which are crucial information for subsequent detailed exploration.
Risks of Drone Use in Light of Literature Studies
Authors: Tubis Agnieszka A. , Poturaj Honorata , Dereń Klaudia , Żurek Arkadiusz
Published: February 2024

This article aims to present the results of a bibliometric analysis of relevant literature and discuss the main research streams related to the topic of risks in drone applications. The methodology of the conducted research consisted of five procedural steps, including the planning of the research, conducting a systematic review of the literature, proposing a classification framework corresponding to contemporary research trends related to the risk of drone applications, and compiling the characteristics of the publications assigned to each of the highlighted thematic groups. This systematic literature review used the PRISMA method. A total of 257 documents comprising articles and conference proceedings were analysed. On this basis, eight thematic categories related to the use of drones and the risks associated with their operation were distinguished. Due to the high content within two of these categories, a further division into subcategories was proposed to illustrate the research topics better. The conducted investigation made it possible to identify the current research trends related to the risk of drone use and pointed out the existing research gaps, both in the area of risk assessment methodology and in its application areas. The results obtained from the analysis can provide interesting material for both industry and academia.
Normalizing Large Scale Sensor-Based MWD Data: An Automated Method toward A Unified Database
Authors: Abbaszadeh Shahri Abbas , Shan Chunling , Larsson Stefan , Johansson Fredrik
Published: February 2024

In the context of geo-infrastructures and specifically tunneling projects, analyzing the large-scale sensor-based measurement-while-drilling (MWD) data plays a pivotal role in assessing rock engineering conditions. However, handling the big MWD data due to multiform stacking is a time-consuming and challenging task. Extracting valuable insights and improving the accuracy of geoengineering interpretations from MWD data necessitates a combination of domain expertise and data science skills in an iterative process. To address these challenges and efficiently normalize and filter out noisy data, an automated processing approach integrating the stepwise technique, mode, and percentile gate bands for both single and peer group-based holes was developed. Subsequently, the mathematical concept of a novel normalizing index for classifying such big datasets was also presented. The visualized results from different geo-infrastructure datasets in Sweden indicated that outliers and noisy data can more efficiently be eliminated using single hole-based normalizing. Additionally, a relational unified PostgreSQL database was created to store and automatically transfer the processed and raw MWD as well as real time grouting data that offers a cost effective and efficient data extraction tool. The generated database is expected to facilitate in-depth investigations and enable application of the artificial intelligence (AI) techniques to predict rock quality conditions and design appropriate support systems based on MWD data.
Method of 3D Coating Accumulation Modeling Based on Inclined Spraying
Authors: Yu Danyang , Su Chengzhi , Wang Enguo , Bao Haifeng , Qu Fuheng
Published: February 2024

In the process of repairing the surface of products in aviation, aerospace, and other fields by spraying, accurate 3D cumulative-coating modeling is an important research issue in spraying-process simulation. The approach to this issue is a 3D cumulative-coating model based on inclined spraying. Firstly, an oblique spraying layer cumulative model was established, which could quickly collect the coating thickness distribution data of different spray distances. Secondly, 3D cumulative-coating modeling was conducted with the distance between the measuring point and the axis of the spray gun and the spraying distance between the measuring points as the input parameters, and the coating thickness of the measuring point as the output parameter. The experimental results show that the mean relative error of the cumulative model of the oblique spraying layer is less than 4.1% in the case of a 170~290 mm spraying distance and that the model is applicable in the range of −80~80 mm, indicating that the data on the oblique spraying coating proposed in this paper is accurate and fast. The accuracy of the 3D cumulative-coating model proposed in this paper is 1.2% and 21.5% higher than that of the two similar models, respectively. Therefore, the approach of 3D cumulative-coating modeling based on inclined distance spraying is discovered, demonstrating the advantages of fast and accurate modeling and enabling accurate 3D cumulative-coating modeling for spraying process simulation.
Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks
Authors: Kumar Nishant , Krause Lukas , Wondrak Thomas , Eckert Sven , Eckert Kerstin , Gumhold Stefan
Published: February 2024

Electrolysis stands as a pivotal method for environmentally sustainable hydrogen production. However, the formation of gas bubbles during the electrolysis process poses significant challenges by impeding the electrochemical reactions, diminishing cell efficiency, and dramatically increasing energy consumption. Furthermore, the inherent difficulty in detecting these bubbles arises from the non-transparency of the wall of electrolysis cells. Additionally, these gas bubbles induce alterations in the conductivity of the electrolyte, leading to corresponding fluctuations in the magnetic flux density outside of the electrolysis cell, which can be measured by externally placed magnetic sensors. By solving the inverse problem of the Biot–Savart Law, we can estimate the conductivity distribution as well as the void fraction within the cell. In this work, we study different approaches to solve the inverse problem including Invertible Neural Networks (INNs) and Tikhonov regularization. Our experiments demonstrate that INNs are much more robust to solving the inverse problem than Tikhonov regularization when the level of noise in the magnetic flux density measurements is not known or changes over space and time.
Calibration of Dual-Channel Raman Spectrometer via Optical Frequency Comb
Authors: Lv Shengyujie , Lou Xiaoping , Gai Qiaona , Mu Taotao
Published: February 2024

The portable Raman spectrometer boasts portability, rapid analysis, and high flexibility. It stands as a crucial and powerful technical tool for analyzing the chemical composition of samples, whether biological or non-biological, across diverse fields. To improve the resolution of grating spectrometers and ensure a wide spectral range, many spectrometer systems have been designed with double-grating structures. However, the impact of external forces, such as installation deviations and inevitable collisions, may cause differences between the actual state of the internal spectrometer components and their theoretical values. Therefore, spectrometers must be calibrated to establish the relationship between the wavelength and the pixel positions. The characteristic peaks of commonly used calibration substances are primarily distributed in the 200–2000 cm−1 range. The distribution of characteristic peaks in other wavenumber ranges is sparse, especially for spectrometers with double-channel spectral structures and wide spectral ranges. This uneven distribution of spectral peaks generates significant errors in the polynomial fitting results used to calibrate spectrometers. Therefore, to satisfy the calibration requirements of a dual-channel portable Raman spectrometer with a wide spectral range, this study designed a calibration method based on an optical frequency comb, which generates dense and uniform comb-like spectral signals at equal intervals. The method was verified experimentally and compared to the traditional calibration method of using a mercury–argon lamp. The results showed that the error bandwidth of the calibration results of the proposed method was significantly smaller than that of the mercury–argon lamp method, thus demonstrating a substantial improvement in the calibration accuracy.
Persistent Monitoring for Points of Interest with Different Data Update Deadlines
Authors: Guo Qing , Peng Jian
Published: February 2024

In this paper, we study the regular sensory data collection of Points of Interest (PoIs) with multiple Unmanned Aerial Vehicles (UAVs) during an extended monitoring period, where each PoI is visited multiple times before its data update deadline to keep the data fresh. We observe that most existing studies ignored the important differences in the data stored in the PoIs, scheduled a plan that dispatched UAVs to visit all PoIs before the same deadline, and simply repeated the plan during the monitoring period, which undoubtedly increased the service cost of the UAVs. Considering the specific data update deadline of each PoI, we formulate a novel UAV cost minimization problem to collect the data stored in each PoI before its deadline by finding a series of plans for UAVs such that the service cost of the UAVs during the monitoring period is minimized; the service cost of the UAVs is composed of the consumed energy of the UAVs utilized for hovering for data collection and the consumed energy of the UAVs utilized for flying. To deal with the above NP-hard problem, we devise an approximation algorithm by grouping the PoIs and accessing them in batches. Then, we analyze the proposed algorithm and evaluate the performance of the algorithm through experimental simulations. The experimental results show that the proposed algorithm is very promising.
Stability and Reactivity of Guaiacylglycerol-β-Guaiacyl Ether, a Compound Modeling β-O-4 Linkage in Lignin
Authors: Rabiei Zeinab , Simons Andrew , Folkmanova Magdalena , Vesela Tereza , Uhlik Ondrej , Kozliak Evguenii , Kubátová Alena
Published: February 2024

Lignin, a complex and abundant biopolymer, is a major constituent of plant cell walls. Due to its chemical and structural complexity, lignin degradation is a challenging task for both natural and engineered systems. Therefore, investigation of lignin degradation using so called “model compounds” has been the focus of many research efforts in recent years. This study addresses the utility of guaiacylglycerol-β-guaiacyl ether (Gβ2) as a model compound for evaluating the β-O-4 bond cleavage under diverse thermal and aqueous medium conditions. Experimental conditions included varied pH (3–10), microbial biodegradation, subcritical water environment (150–250 °C), and mild pyrolysis (150–250 °C). A high-performance liquid chromatography with high-resolution mass spectrometry was employed for accurate detection and quantification of both Gβ2 and its degradation/modification products in an aqueous environment. Pyrolysis experiments were performed using gas chromatography-mass spectrometry analysis with a pyrolyzer. The results showed that Gβ2 remained stable under exposure to moderate pH and several bacterial strains, which were successfully used previously for biodegradation of other recalcitrant pollutants. We report, for the first time, differing Gβ2 breakdown pathways for subcritical water treatment vs. pyrolysis under an inert atmosphere. The scientific novelty lies in the presentation of differences in the degradation pathways of Gβ2 during subcritical water treatment compared to pyrolysis in an inert atmosphere, with water playing a key role. The observed differences are ascribed to the suppression of homolytic reactions by water as a solvent.