bluuebunny/crossref_metadata_2025 huggingface dataset:
Recent Advances in Porphyrin-Based Covalent Organic Frameworks for Synergistic Photodynamic and Photothermal Therapy
Authors: Qi Cheng , Chen Jiayi , Qu Yijie , Luo Xuanxuan , Wang Weiqi , Zheng Xiaohua
Published: December 2024

Porphyrin’s excellent biocompatibility and modifiability make it a widely studied photoactive material. However, its large π-bond conjugated structure leads to aggregation and precipitation in physiological solutions, limiting the biomedical applications of porphyrin-based photoactive materials. It has been demonstrated through research that fabricating porphyrin molecules into nanoscale covalent organic frameworks (COFs) structures can circumvent issues such as poor dispersibility resulting from hydrophobicity, thereby significantly augmenting the photoactivity of porphyrin materials. Porphyrin-based COF materials can exert combined photodynamic and photothermal effects, circumventing the limitations of photodynamic therapy (PDT) due to hypoxia and issues in photothermal therapy (PTT) from heat shock proteins or the adverse impact of excessive heat on the protein activity of normal tissue. Furthermore, the porous structure of porphyrin COFs facilitates the circulation of oxygen molecules and reactive oxygen species and promotes sufficient contact with the lesion site for therapeutic functions. This review covers recent progress regarding porphyrin-based COFs in treating malignant tumors and venous thrombosis and for antibacterial and anti-inflammatory uses via combined PDT and PTT. By summarizing relevant design strategies, ranging from molecular design to functional application, this review provides a reference basis for the enhanced phototherapy application of porphyrin-based COFs as photoactive materials. This review aims to offer valuable insights for more effective biomedical applications of porphyrin-based COFs through the synthesis of existing experimental data, thereby paving the way for their future preclinical utilization.
Twin Screw Melt Granulation of Simvastatin: Drug Solubility and Dissolution Rate Enhancement Using Polymer Blends
Authors: Elkanayati Rasha M. , Karnik Indrajeet , Uttreja Prateek , Narala Nagarjuna , Vemula Sateesh Kumar , Karry Krizia , Repka Michael A.
Published: December 2024

Background/Objectives: This study evaluates the efficacy of twin screw melt granulation (TSMG), and hot-melt extrusion (HME) techniques in enhancing the solubility and dissolution of simvastatin (SIM), a poorly water-soluble drug with low bioavailability. Additionally, the study explores the impact of binary polymer blends on the drug’s miscibility, solubility, and in vitro release profile. Methods: SIM was processed with various polymeric combinations at a 30% w/w drug load, and a 1:1 ratio of binary polymer blends, including Soluplus® (SOP), Kollidon® K12 (K12), Kollidon® VA64 (KVA), and Kollicoat® IR (KIR). The solid dispersions were characterized using modulated differential scanning calorimetry (M-DSC), powder X-ray diffraction (PXRD), and Fourier-transform infrared spectroscopy (FTIR). Dissolution studies compared the developed formulations against a marketed product. Results: The SIM-SOP/KIR blend showed the highest solubility (34 µg/mL), achieving an approximately 5.5-fold enhancement over the pure drug. Dissolution studies showed that SIM-SOP/KIR formulations had significantly higher release profiles than the physical mixture (PM) and pure drug (p < 0.01). Additionally, their release was similar to a marketed formulation, with 100% drug release within 30 min. In contrast, the SIM-K12/KIR formulation exhibited strong miscibility, but limited solubility and slower release rates, suggesting that high miscibility does not necessarily correlate with improved solubility. Conclusions: This study demonstrates the effectiveness of TSMG, and HME as effective continuous manufacturing technologies for improving the therapeutic efficacy of poorly water-soluble drugs. It also emphasizes the complexity of polymer–drug interactions and the necessity of carefully selecting compatible polymers to optimize the quality and performance of pharmaceutical formulations.
Heterogeneity in Mechanical Properties of Plant Cell Walls
Authors: Zhang He , Xiao Liang , Qin Siying , Kuang Zheng , Wan Miaomiao , Li Zhan , Li Lei
Published: December 2024

The acquisition and utilization of cell walls have fundamentally shaped the plant lifestyle. While the walls provide mechanical strength and enable plants to grow and occupy a three-dimensional space, successful sessile life also requires the walls to undergo dynamic modifications to accommodate size and shape changes accurately. Plant cell walls exhibit substantial mechanical heterogeneity due to the diverse polysaccharide composition and different development stages. Here, we review recent research advances, both methodological and experimental, that shed new light on the architecture of cell walls, with a focus on the mechanical heterogeneity of plant cell walls. Facilitated by advanced techniques and tools, especially atomic force microscopy (AFM), research efforts over the last decade have contributed to impressive progress in our understanding of how mechanical properties are associated with cell growth. In particular, the pivotal importance of pectin, the most complex wall polysaccharide, in wall mechanics is rapidly emerging. Pectin is regarded as an important determinant for establishing anisotropic growth patterns of elongating cells. Altogether, the diversity of plant cell walls can lead to heterogeneity in the mechanical properties, which will help to reveal how mechanical factors regulate plant cell growth and organ morphogenesis.
GA3-Induced SlXTH19 Expression Enhances Cell Wall Remodeling and Plant Height in Tomatoes
Authors: Luo Junfeng , Wang Xi , Pang Wenxing , Jiang Jing
Published: December 2024

Plant height represents a pivotal agronomic trait for the genetic enhancement of crops. The plant cell wall, being a dynamic entity, is crucial in determining plant stature; however, the regulatory mechanisms underlying cell wall remodeling remain inadequately elucidated. This study demonstrates that the application of gibberellin 3 (GA3) enhances both plant height and cell wall remodeling in tomato (Solanum lycopersicum L.) plants. RNA sequencing (RNA-seq) results of GA3 treatment showed that the DEGs were mostly enriched for cell wall-related pathways; specifically, GA3 treatment elicited the expression of the cell wall-associated gene XYLOGLUCAN ENDOTRANSGLUCOSYLASE/HYDROLASE 19 (SlXTH19), whose overexpression resulted in increased plant height. Comparative analyses revealed that SlXTH19-overexpressing lines exhibited larger cell dimensions and increased XTH activity, along with higher contents of lignin, cellulose, and hemicellulose, thereby underscoring the gene’s role in maintaining cell wall integrity. Conversely, treatments with ethephon (ETH) and 1-Naphthaleneacetic acid (NAA) led to suppressed plant height and reduced SlXTH19 expression. Collectively, these findings illuminate a competitive interplay between GA and ethylene/auxin signaling pathways in regulating cell wall remodeling via SlXTH19 activation, ultimately influencing tomato plant height.
A Shoot Phenological Study of Certain Phyllostachys Bamboo Taxa Under Central European Climatic Conditions
Authors: Chan Khin Nyein , Veres Anikó , Liang Zhiwei , Kisvarga Szilvia , Neményi András
Published: December 2024

This study aimed to understand the phenological changes in the shoots of temperate bamboo Phyllostachys spp. grown in Hungary, with a focus on how these changes were influenced by local climatic conditions. Data collected over two years on shoot phenology were analyzed with weather variables, especially air temperature and soil temperature. Shoot emergence date, shoot numbers, and shooting period were recorded within and between Phyllostachys spp. The date of shooting was observed between May and July, and species started shooting in May the most, followed by June. Only one species, P. sulphurea, started shoot emergence in July. Shoot emergence started earlier in 2022 than in 2023 and ranged from 3 days to 27 days. Bamboo shoot phenology was strongly influenced by the air temperature and soil temperature each year. According to our regression analysis, the dates of shoot emergence were influenced by air temperature (r2 = 0.819) in 2022 and 2023 (r2 = 0.781), and soil temperature also influenced shoot emergence (r2 = 0.956) in 2022 and 2023 (r2 = 0.769). Sharp air temperature changes between the shooting season and the period before shooting were considered as the reasons for early shooting. The soil temperature in April before the start of shoot emergence was 8.15 °C, increasing to 14.1 °C during shooting time in May. Abrupt fluctuations of air temperature between the shoot emergence season and the month prior to shooting commencement determined early shooting or late shooting. The soil temperature had to reach 9 °C or above prior to shooting time, as this was found to be a critical temperature for shoot emergence in Phyllostachys bamboo taxa.
Comprehensive Physio-Biochemical Evaluation Reveals Promising Genotypes and Mechanisms for Cadmium Tolerance in Tibetan Hull-Less Barley
Authors: Foysal Md Rafat Al , Qiu Cheng-Wei , Sreesaeng Jakkrit , Elhabashy Saad , Akhter Delara , Zhang Shuo , Shi Shou-Heng , Wu Feibo
Published: December 2024

Cadmium (Cd) toxicity in agricultural soil is increasing globally and significantly impacts crop production and food safety. Tibetan hull-less barley (Hordeum vulgare L. var. nudum), an important staple food and economic crop, exhibits high genetic diversity and is uniquely adapted to the harsh conditions of the Qinghai–Tibet Plateau. This study utilized hydroponic experiments to evaluate the genotypic differences in Cd tolerance among 71 Tibetan hull-less barley genotypes. Physiological assessments revealed significant reductions in various growth parameters under Cd stress compared to normal conditions: soil–plant analysis development (SPAD) value, shoot height, root length, shoot and root fresh weight, shoot and root dry weight, of 11.74%, 39.69%, 48.09%, 52.88%, 58.39%, 40.59%, and 40.52%, respectively. Principal component analysis (PCA) revealed key traits contributing to Cd stress responses, explaining 76.81% and 46.56% of the variance in the preliminary and secondary selection. The genotypes exhibited varying degrees of Cd tolerance, with X178, X192, X215, X140, and X162 showing high tolerance, while X38 was the most sensitive based on the integrated score and PCA results. Validation experiments confirmed X178 as the most tolerant genotype and X38 as the most sensitive, with observed variations in morphological, physiological, and biochemical parameters, as well as mineral nutrient responses to Cd stress. Cd-tolerant genotypes exhibited higher chlorophyll content, net photosynthesis rates, and effective photochemical capacity of photosystem II, along with an increased Cd translocation rate and reduced oxidative stress. This was accompanied by elevated activities of antioxidant enzymes, including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), indicating a robust stress response mechanism. These findings could facilitate the development of high-tolerance cultivars, with X178 as a promising candidate for further research and cultivation in Cd-contaminated soils.
Investigation into the Reinforcement Modification of Natural Plant Fibers and the Sustainable Development of Thermoplastic Natural Plant Fiber Composites
Authors: Liao Zhenhao , Hu Yiyun , Shen Yan , Chen Ke , Qiu Cheng , Yang Jinglei , Yang Lei
Published: December 2024

Natural plant fibers (NPFs) have emerged as a sustainable alternative in the manufacture of composites due to their renewability and low environmental impact. This has led to a significant increase in the use of natural plant fiber-reinforced polymers (NPFRPs) in a variety of industries. The diversity of NPF types brings a wide range of properties and functionalities to NPFRPs, which in turn highlights the urgent need to improve the properties of fiber materials in order to enhance their performance and suitability. This paper provides insight into the processing mechanisms behind NPF fiber treatments, exploring how these treatments affect the mechanical, thermal and environmental properties of NPFRPs. It also offers a critical assessment of the advantages and disadvantages of physical, chemical, biological and nanotechnological treatments. The findings of our analysis provide a basis for the development of future treatments that aim to enhance the material properties of NPFRPs, thereby increasing their competitiveness with conventional synthetic fiber-reinforced polymers. Finally, a novel thermoplastic resin composite system, Elium–NPFRP, is proposed that embodies the principles of green development. The system has been designed with the objective of capitalizing on the environmental benefits of NPFs while simultaneously addressing the challenges associated with the integration of NPFs into polymer matrices. The Elium–NPFRP composite system not only exemplifies the potential of NPFs for sustainable materials science, but is also a practical solution that can be implemented in a diverse range of applications, spanning automotive components to construction materials. This has the potential to reduce carbon footprints and promote a circular economy.
Mechanical Property Characterization of Virgin and Recycled PLA Blends in Single-Screw Filament Extrusion for 3D Printing
Authors: Aly Reem , Olalere Olafisoye , Ryder Aaron , Alyammahi Mozah , Samad Wael A.
Published: December 2024

Additive manufacturing is an attractive technology due to its versatility in producing parts with diverse properties from a single material. However, the process often generates plastic waste, particularly from failed prints, making sustainability a growing concern. Recycling this waste material presents a potential solution for reducing environmental impact while creating new, functional parts. In this study, the feasibility of creating printable filaments from recycled polylactic acid (PLA) waste and virgin PLA pellets was explored. Filaments were manufactured in the lab using a single-screw desktop extruder with four temperature zones, with compositions ranging from 100% virgin PLA to 100% recycled PLA in 10% composition increments. Test samples were 3D printed using a Material Extrusion 3D printer and subjected to tensile testing in conjunction with digital image correlation to evaluate their ultimate tensile strength, yield strength, Young’s modulus, ductility, toughness, and strain distribution. The results indicated that the optimal mechanical properties were observed in specimens made from 100% virgin PLA, 100% recycled PLA, and a 50% virgin/50% recycled PLA blend. Additionally, comparisons with a commercially produced PLA filament revealed that 100% virgin and 100% recycled blends have a 50.33% and 48% higher tensile strength than commercial filament, respectively. However, commercial filaments exhibited higher ductility and toughness than the lab-made extruded filament.
Characterization of Odor-Active 2-Ethyldimethyl-1,3,6-trioxocane Isomers in Polyurethane Materials
Authors: Minig Charlotte , Meißner Alexandra , Steinhaus Martin
Published: December 2024

Polyurethane materials, widely used in indoor environments, occasionally exhibit unpleasant odors. An important source of polyurethane odorants is polyether polyols. Previous studies identified odorous 2-ethyldimethyl-1,3,6-trioxocanes in polyurethane materials and polyols but did not investigate the odor activity of the individual isomers. In the present work, an isomer mixture of the precursor dipropylene glycol was fractionated through preparative high-performance liquid chromatography. After the conversion to the corresponding trioxocanes, gas chromatography-olfactometry analyses revealed that just one positional isomer, namely 2-ethyl-4,7-dimethyl-1,3,6-trioxocane, was odor active. Moreover, we observed clear differences in the odor threshold concentrations among its stereoisomers. Only two out of eight isomers displayed an odor, both with an earthy smell and one being approximately 60 times more potent than the other. These insights contribute to a better understanding of polyurethane odor on a molecular level and provide a basis for effective odor control.
Elucidating Extracellular Vesicle Isolation Kinetics via an Integrated Off-Stoichiometry Thiol-Ene and Cyclic Olefin Copolymer Microfluidic Device
Authors: Cipa Janis , Endzelins Edgars , Abols Arturs , Romanchikova Nadezda , Line Aija , Jenster Guido W. , Mozolevskis Gatis , Rimsa Roberts
Published: December 2024

Extracellular vesicles (EVs) are promising biomarkers for diagnosing complex diseases such as cancer and neurodegenerative disorders. Yet, their clinical application is hindered by challenges in isolating cancer-derived EVs efficiently due to their broad size distribution in biological samples. This study introduces a microfluidic device fabricated using off-stoichiometry thiol-ene and cyclic olefin copolymer, addressing the absorption limitations of polydimethylsiloxane (PDMS). The device streamlines a standard laboratory assay into a semi-automated microfluidic chip, integrating sample mixing and magnetic particle separation. Using the microfluidic device, the binding kinetics between EVs and anti-CD9 nanobodies were measured for the first time. Based on the binding kinetics, already after 10 min the EV capture was saturated and comparable to standard laboratory assays, offering a faster alternative to antibody-based immunomagnetic protocols. Furthermore, this study reveals the binding kinetics of EVs to anti-CD9 nanobodies for the first time. Our findings demonstrate the potential of the microfluidic device to enhance clinical diagnostics by offering speed and reducing manual labor without compromising accuracy.
Synthesis and Characterization of Photocurable Difunctional Monomers for Medical Applications
Authors: Demirci Gokhan , Goszczyńska Agata , Sokołowska Martyna , Żwir Marek , Gorący Krzysztof , El Fray Miroslawa
Published: December 2024

Photocurable materials offer a rapid transition from a liquid to a solid state, and have recently received great interest in the medical field. However, while dental resins are very popular, only a few materials have been developed for soft tissue repair. This study aims to synthesize a difunctional methacrylate monomer using a dibutyltin dilaurate which is suitable for the photocuring of soft materials. These soft materials were compared with PhotoBioCure® (Szczecin, Poland) material with a similar molecular weight, of Mn ~7000 g/mol on average. Infrared spectroscopy was used to monitor the two-step synthesis catalyzed with dibutyltin dilaurate, while spectroscopic and chromatographic methods were used to determine the chemical structure and molecular weight of the monomers. Photopolymerization kinetics under varying light intensities were explored in a nitrogen atmosphere for representative difunctional monomers. The mechanical testing of the resulting elastomeric films confirmed tensile strength and modulus values consistent with soft tissue parameters in the range of 3–4 MPa. The 3D printability of the macromonomers was also assessed. Additionally, cytotoxicity assessments using cultured cells showed a high cell viability (97%) for all new materials. Overall, we demonstrate that difunctional methacrylate monomers converted to flexible solids during photopolymerization show great potential for biomedical applications.
Synthesis of a Bimetallic-Doped Phytate-Melamine Composite as an Efficient Additive for Epoxy Resins with High Fire Safety
Authors: Wang Shunxiang , Huang Jianfeng , Wei An , Chen Yulian , Lu Xulan , Zou Yongjin , Xu Fen , Sun Lixian , Lu Yunhao , Xiang Cuili
Published: December 2024

The issue of hazardous smoke and toxic gases released from epoxy resins (EP), which often causes casualties in real fires, has limited its application. Therefore, we have developed a novel flame retardant based on a bimetallic-doped phytate-melamine (BPM) structure with Zn2+ and Fe2+ ions incorporated into the polymer matrix using a straightforward solution-based synthetic method. The combustion performance of the composite was evaluated using a cone calorimeter test, which showed that the peak heat release, total heat release, and total smoke production were reduced by 50%, 31.7%, and 29.2%, respectively, compared to those of EP. Additionally, the fire growth index was noticeably reduced by 60% owing to the synergistic catalytic effect of the bimetallic ions, and the high nitrogen and phosphorus content of the additives. Overall, this study provides new insights into the application of bimetallic doping for flame retardant polymer composites.
Improving the Impact Resistance and Post-Impact Tensile Fatigue Damage Tolerance of Carbon Fiber Reinforced Epoxy Composites by Embedding the Carbon Nanoparticles in Matrix
Authors: Jen Yi-Ming , Chen Yu-Jen , Yu Tzung-Han
Published: December 2024

The effect of dispersing multiwalled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) in the matrix on the low-velocity impact resistance and post-impact residual tensile strength of the carbon fiber reinforced epoxy composite laminates has been experimentally analyzed in this study. The composite specimens with the matrix reinforced by different nanoparticle types and various nanoparticle concentrations (0.1, 0.3, and 0.5 wt.%) were prepared and impacted. The post-impact tensile quasi-static and fatigue tests were performed on the specimens with different configurations to study the influence of aforementioned factors on the impact resistance and damage tolerance. Experimental results show that adding nanoparticles in the matrix increases the maximum impact force, reduces the damage area, and alleviates the dent depth of the laminates remarkedly. Moreover, the improvement in these impact resistances increases with the applied nanoparticle concentrations. The nano-modified composite laminates present higher post-impact static strength and longer fatigue life than the specimens with a neat epoxy matrix. Furthermore, both the post-impact static tensile strength and fatigue life increase with the applied nanoparticle concentrations. The damage areas measured using infrared thermography were found to increase linearly with the applied fatigue cycles for all the studied specimens with various configurations. The damage area growth rates of nano-modified composite laminates decrease significantly as the applied nanoparticle concentrations increase. The MWCNTs present better performance than GNPs in improving post-impact static strength and extending the residual fatigue life, however the effect of applied nanoparticle type on the fatigue damage growth rate is slight.
Detecting Early Degradation of Wood Ultrastructure with Nonlinear Optical Imaging and Fluorescence Lifetime Analysis
Authors: Dal Fovo Alice , Cicchi Riccardo , Gagliardi Claudia , Baria Enrico , Fioravanti Marco , Fontana Raffaella
Published: December 2024

Understanding the deterioration processes in wooden artefacts is essential for accurately assessing their conservation status and developing effective preservation strategies. Advanced imaging techniques are currently being explored to study the impact of chemical changes on the structural and mechanical properties of wood. Nonlinear optical modalities, including second harmonic generation (SHG) and two-photon excited fluorescence (TPEF), combined with fluorescence lifetime imaging microscopy (FLIM), offer a promising non-destructive diagnostic method for evaluating lignocellulose-based materials. In this study, we employed a nonlinear multimodal approach to examine the effects of artificially induced delignification on samples of Norway spruce (Picea abies) and European beech (Fagus sylvatica) subjected to increasing treatment durations. The integration of SHG/TPEF imaging and multi-component fluorescence lifetime analysis enabled the detection of localized variations in nonlinear signals and τ-phase of key biopolymers within wood cell walls. This methodology provides a powerful tool for early detection of wood deterioration, facilitating proactive conservation efforts of wooden artefacts.
Development of a Modular Sandwich Panel with a Composite Core of Recycled Material for Application in Sustainable Building
Authors: Valenzuela Expósito Juan José , Picazo Camilo Elena , Corpas Iglesias Francisco Antonio
Published: December 2024

In recent years, the construction industry has faced challenges related to rising material costs, labor shortages and environmental sustainability, resulting in an increased interest in modular construction cores composed of recycled materials, such as XPS, PUR, PLW and GFRP, from waste from the truck body industry. Two resins, PUR and polyester, were used to bond these recycled composites. Physical, chemical and mechanical analyses showed that the panels formed with PUR resin had superior workability due to the higher open time of the resin, 11.3% better thermal conductivity than the commercial PLW panel (SP-PLW) and reduced porosity compared to those using polyester resin. The mechanical performance of the panels improved with higher structural reinforcement content (PLW and GFRP). Compared to a commercial panel (SP-PLW), the SP-RCM1 recycled panel showed 4% higher performance, demonstrating its potential for sustainable building applications. Thermal and microscopic characterizations showed good adhesion of the materials in the best performing formulations related to higher thermal stability. Therefore, this research aims to demonstrate the feasibility of using waste from the car industry in the manufacture of sandwich panels for modular construction to address these issues.
In Situ Preparation of Silver Nanoparticles/Organophilic-Clay/Polyethylene Glycol Nanocomposites for the Reduction of Organic Pollutants
Authors: Sardi Amina , Boukoussa Bouhadjar , Benmaati Aouicha , Chinoune Kheira , Mokhtar Adel , Hachemaoui Mohammed , Abdelkrim Soumia , Ismail Issam , Iqbal Jibran , Patole Shashikant P. , Viscusi Gianluca , Abboud Mohamed
Published: December 2024

This work focuses on the preparation and application of silver nanoparticles/organophilic clay/polyethylene glycol for the catalytic reduction of the contaminants methylene blue (MB) and 4-nitrophenol (4-NP) in a simple and binary system. Algerian clay was subjected to a series of treatments including acid treatment, ion exchange with the surfactant hexadecyltrimethylammonium bromide (HTABr), immobilization of polyethylene glycol polymer, and finally dispersion of AgNPs. The molecular weight of polyethylene glycol was varied (100, 200, and 4000) to study its effect on the stabilization of silver nanoparticles (AgNPs) and the catalytic activity of the resulting samples. The results showed that the catalyst with the highest molecular weight of polyethylene glycol had the highest AgNP content. Catalyst mass, NaBH4 concentration, and type of catalyst were shown to have a significant influence on the conversion and rate constant. The material with the highest silver nanoparticle content was identified as the optimal catalyst for the reduction of both pollutants. The measured rate constants for the reduction of methylene blue (MB) and 4-nitrophenol (4-NP) were 164 × 10−4 s−1 and 25 × 10−4 s−1, respectively. The reduction of MB and 4-NP in the binary system showed high selectivity for MB dye, with rate constants of 64 × 10−4 s−1 and 9 × 10−4 s−1 for MB and 4-NP, respectively. The reuse of the best catalyst via MB dye reduction for four cycles showed good results without loss of performance.
Current Positron Studies on the Modifications of the Molecular Packing in Green-Based Polymers Through Changes in the Synthesis Procedures or Environmental Conditions
Authors: Consolati Giovanni , Macchi Carlos , Somoza Alberto
Published: December 2024

The sensitivity of positron annihilation characteristics to changes in the molecular packing in network-forming polymers has been demonstrated since the early 1980s. Positron annihilation lifetime spectroscopy (PALS) is a unique technique that can provide direct information on the free volume in polymers through the experimental parameters of the free volume hole distribution, their mean value, and volume fraction. This knowledge is currently applied for PALS investigations on the main processes that govern the molecular organization in some green polymers when subjected to different synthesis procedures or environmental conditions (humidity, physical aging, temperature). In this article, which includes a wide repertoire of works published in the last two decades, results of PALS studies on eco-sustainable polymer systems based on starch, chitosan, or vegetable oils, are analyzed and discussed. Many examples are taken from the direct experience of the authors.
The Gospel According to Paul: Over a Hundred Years of Interpretation
Authors: Oropeza B. J.
Published: December 2024

Paul’s gospel has been the subject of numerous scholarly discussions since the start of the twentieth century. Beginning with Adolf Harnack and ending with Matthew Bates, this article provides insights into the gospel from biblical scholars who have contributed to and influenced this particular area of study for more than a hundred years. The results of this investigation show that some of the major areas of concern center on the origin of Paul’s gospel, its content, and his use of gospel terminology, especially the noun εὐαγγέλιον. A notable issue focuses on the noun form not being found in a sacred-salvific sense in Jewish Scripture but found instead in the imperial cult at the end of first century BCE. Another major point is whether Paul introduced εὐαγγέλιον to the Jesus-believing communities or whether the term originated from Hellenistic believers or Aramaic speakers prior to Paul. Another important subject is whether Scripture and Deutero-Isaiah influenced Paul and the early Christ-community’s use of the gospel. The study concludes, among other things, that Paul uses the gospel noun and verb almost interchangeably, although he may have emphasized the noun as a result of imperial use. Another conclusion is that he was not the first to use this term among Jesus-believing communities.
Optical Frequency Sweeping Nonlinearity Measurement Based on a Calibration-free MZI
Authors: Sun Pengwei , Zhao Bin , Liu Bo
Published: December 2024

Frequency sweeping linearity is essential for Frequency-Modulated Continuous Wave (FMCW) Light Detection and Ranging (LIDAR), as it impacts the ranging resolution and accuracy of the system. Pre-distortion methods can correct for frequency sweeping nonlinearity; however, residual minor nonlinearities can still degrade the system ranging resolution, especially at far distances. Therefore, the precise measurement of minor nonlinearities is particularly essential for long-range FMCW LIDAR. This paper proposes a calibration-free MZI for measuring optical frequency sweeping nonlinearity, which involves alternately inserting two short polarization-maintaining fibers with different delays into one arm of an MZI, and after two rounds of beat collection, the optical frequency sweep curve of the light source is accurately measured for nonlinearity evaluation. Using the proposed method, the nonlinearity of a frequency-swept laser source is measured to be 0.2113%, and the relative nonlinearity is 5.3560 × 10−5. With the measured frequency sweep curve, we simulate the beat signal and compare it with the collected beat signal in time and frequency domain, to verify the accuracy of the proposed method. A test conducted at 24.1 °C, 30.4 °C, 39.5 °C and 44.0 °C demonstrate the method’s insensitivity to temperature fluctuations. Based on the proposed MZI, a tunable laser is pre-distorted and then used as light source of a FMCW lidar. A wall at 45 m and a building at 1.2 km are ranged by the lidar respectively. Before and after laser pre-distortion, the FWHM of echo beat spectrum are 25.635 kHz and 9.736 kHz for 45 m, 747.880 kHz and 22.012 kHz for 1.2 km.
Integration of Structural Characteristics from GEDI Waveforms for Improved Forest Type Classification
Authors: McClure Mary M. , Tsuyuki Satoshi , Hiroshima Takuya
Published: December 2024

Forest types correspond to differences in structural characteristics and species composition that influence biomass and biodiversity values, which are essential measurements for ecological monitoring and management. However, differentiating forest types in tropical regions remains a challenge. This study aimed to improve forest type extent mapping by combining structural information from discrete full-waveform LiDAR returns with multitemporal images. This study was conducted in a tropical forest region over complex terrain in north-eastern Tanzania. First, structural classes were generated by applying time-series clustering algorithms. The results showed four different structural clusters corresponding to forest types, montane–humid forest, montane–dry forest, submontane forest, and non-forest, when using the Kshape algorithm. Kshape considers the shape of the full-sequence LiDAR waveform, requiring little preprocessing. Despite the overlap amongst the original clusters, the averages of structural characteristics were significantly different across all but five metrics. The labeled clusters were then further refined and used as training data to generate a wall-to-wall forest cover type map by classifying biannual images. The highest-performing model was a KNN model with 13 spectral and 3 terrain features achieving 81.7% accuracy. The patterns in the distributions of forest types provide better information from which to adapt forest management, particularly in forest–non-forest transitional zones.
Spatiotemporal Variation and Driving Factors of Ecological Environment Quality on the Loess Plateau in China from 2000 to 2020
Authors: Kang Shuaizhi , Jia Xia , Zhao Yonghua , Han Lei , Ma Chaoqun , Bai Yu
Published: December 2024

The Loess Plateau (LP) in China is an ecologically fragile region that has long faced challenges such as soil erosion, water shortages, and land degradation. The spatial and temporal variations in ecological environment quality on the LP from 2000 to 2020 were analyzed using the Remote Sensing Ecological Index (RSEI) on the Google Earth Engine (GEE) platform. The Sen, Mann–Kendall, and Hurst exponent analyses were used to examine the spatial variation trends over the past 20 years, while Geodetector identified key factors influencing RSEI changes and their interactions. The results indicate that (1) RSEI effectively represents the ecological and environmental quality of the LP, with 47% of the study area’s annual mean RSEI values over the 20-year period classified as moderate, ranging from 0.017 to 0.815. (2) Ecological quality trends showed improvement in 72% of the area, with a 90% overall increase, but 84% of these trends are not likely to continue. (3) Key factors influencing RSEI changes during abrupt change years included precipitation, land use/land cover, and soil sediment content, with precipitation and topography emerging as primary influences on ecological quality. Although natural factors largely drive ecological changes, human activities also exert both positive and negative effects. This study underscores the importance of sustainable ecological management and provides policy insights for advancing ecological civilization on the LP, contributing to the achievement of the Sustainable Development Goals (SDGs).
Cross-Attention-Based High Spatial-Temporal Resolution Fusion of Sentinel-2 and Sentinel-3 Data for Ocean Water Quality Assessment
Authors: Wen Yanfeng , Chen Peng , Zhang Zhenhua , Li Yunzhou
Published: December 2024

Current marine research that leverages remote sensing data urgently requires gridded data of high spatial and temporal resolution. However, such high-quality data is often lacking due to the inherent physical and technical constraints of sensors. A necessary trade-off therefore exists between spatial, temporal, and spectral resolution in satellite remote sensing technology: increasing spatial resolution often reduces the coverage area, thereby diminishing temporal resolution. This manuscript introduces an innovative remote sensing image fusion algorithm that combines Sentinel-2 (high spatial resolution) and Sentinel-3 (relatively high spectral and temporal resolution) satellite data. The algorithm, based on a cross-attention mechanism and referred to as the Cross-Attention Spatio-Temporal Spectral Fusion (CASTSF) model, accounts for variations in spectral channels, spatial resolution, and temporal phase among different sensor images. The proposed method enables the fusion of atmospherically corrected ocean remote sensing reflectance products (Level 2 OSR), yielding high-resolution spatial data at 10 m resolution with a temporal frequency of 1–2 days. Subsequently, the algorithm generates chlorophyll-a concentration remote sensing products characterized by enhanced spatial and temporal fidelity. A comparative analysis against existing chlorophyll-a concentration products demonstrates the robustness and effectiveness of the proposed approach, highlighting its potential for advancing remote sensing applications.
ResTUnet: A Novel Neural Network Model for Nowcasting Using Radar Echo Sequences by Ground-Based Remote Sensing
Authors: Zhang Lei , Zhang Ruoyang , Wu Yu , Wang Yadong , Zhang Yanfeng , Zheng Lijuan , Xu Chongbin , Zuo Xin , Wang Zeyu
Published: December 2024

Radar echo extrapolation by ground-based remote sensing is essential for weather prediction and flight guiding. Existing radar echo extrapolation methods can hardly capture complex spatiotemporal features, resulting in the low accuracy of predictions, and, therefore, severely restrict their use in extreme weather situations. A deep learning method was recently applied for extrapolating radar echoes; however, its accuracy declines too quickly over a short time. In this study, we introduce a solution: Residual Transformer and Unet (ResTUnet), a novel model that improves prediction accuracy and exhibits good stability with a slow rate of accuracy decline. This presented Rest-Net model is designed to solve the issue of declining prediction accuracy by integrating a 1*1 convolution to diminish the neural network parameters. We constructed an observed dataset by Zhengzhou East Airport radar observation from July 2022 to August 2022 and performed 90 min experiments comprising five aspects, including extrapolation images, the Probability of Detection (POD) index, the Critical Success Index (CSI), the False Alarm Rate (FAR) index, and the Heidke Skill Score (HSS) index. The experimental results show that the ResTUnet model improved the CSI, HSS index, and the POD index by 17.20%, 11.97%, and 11.35%, compared to current models, including Convolutional Long Short-Term Memory (convLSTM), the Convolutional Gated Recurrent Unit (convGRU), the Trajectory Gated Recurrent Unit (TrajGRU), and the improved recurrent network for video predictive learning, the Predictive Recurrent Neural Network++ (predRNN++). In addition, the mean squared error of the ResTUnet model remains stable at 15% between 0 and 60 min and starts to increase after 60–90 min, which is 12% better than the current models. This enhancement in prediction accuracy has practical applications in meteorological services and decision making.
Water Quality Inversion Framework for Taihu Lake Based on Multilayer Denoising Autoencoder and Ensemble Learning
Authors: Sun Zhihao , Guo Liang , Tao Zhe , Li Yana , Zhan Yang , Li Shuling , Zhao Ying
Published: December 2024

In river and lake ecosystem management, comprehensive water quality monitoring is crucial. Traditional in situ water quality monitoring is costly, and it is challenging to cover entire water bodies. Remote sensing imagery offers the possibility of efficient monitoring of water quality over large areas. However, remote sensing data typically contain a large amount of noise and redundant information, making it difficult for models to capture the effective spectral information and the relationships in the water quality in the remote sensing data. Consequently, this hinders the achievement of high-precision water quality inversion performance. Therefore, this study proposes a comprehensive water quality inversion framework based on a multilayer denoising autoencoder that automatically extracts effective spectral features, utilizing a multilayer denoising autoencoder to extract effective features from Sentinel-2 remote sensing data, thereby reducing noise in the subsequent model input data and mitigating the overfitting problem in subsequent models. A bagging ensemble learning model was established to invert the total phosphorus concentration in Taihu Lake. This model reduces the prediction bias generated by a single machine learning model and was compared with decision tree, random forest, and linear regression models. The research results indicate that compared to a single model, the bagging ensemble learning model achieved better water quality retrieval results, with a coefficient of determination of 0.9 and an MAE of 0.014, while the linear regression model performed the worst, with a coefficient of determination of 0.42. Additionally, models trained using spectral effective information extracted by multilayer denoising autoencoders showed improved water quality retrieval accuracy compared to those trained with raw data, with the coefficient of determination for the bagging model increasing from 0.62 to 0.9. This study provides a rapid and accurate method for large-scale watershed water quality monitoring using remote sensing data, offering technical support for applying remote sensing data to watershed environmental management and water resource protection.
A Two-Stage Nearshore Seafloor ICESat-2 Photon Data Filtering Method Considering the Spatial Relationship
Authors: Zuo Longjiao , Wang Xuying , Sun Qianzhe , Shi Jian , Zhang Yunsheng
Published: December 2024

“Ice, Cloud, and Land Elevation Satellite-2” (ICESat-2) produces photon-point clouds that can be used to obtain nearshore bathymetric data through density-based filtering methods. However, most traditional methods simplified the variable spatial density distribution of a photon to a linear relationship with water depth, causing a limited extraction effect. To address this limitation, we propose a two-stage filtering method that considers spatial relationships. Stage one constructs the adaptive photon density threshold by mapping a nonlinear relationship between the water depth and photon density to obtain initial signal photons. Stage two adopts a seed-point expanding method to fill gaps in initial signal photons to obtain continuous signal photons that more fully reflect seabed topography. The proposed method is applied to ICESat-2 data from Oahu Island and compared with three other density-based filtering methods: AVEBM (Adaptive Variable Ellipse filtering Bathymetric Method), Bimodal Gaussian fitting, and Quadtree Isolation. Our method (F-measure, F = 0.803) outperforms other methods (F = 0.745, 0.598, and 0.454, respectively). The accuracy of bathymetric data gained from seabed photons filtered using our method can achieve 0.615 m (Mean Absolute Error) and 0.716 m (Root Mean Squared Error). We demonstrate the effectiveness of incorporating photon spatial relationships to enhance the filtering of seabed signal photons.
The Analysis of Land Use and Climate Change Impacts on Lake Victoria Basin Using Multi-Source Remote Sensing Data and Google Earth Engine (GEE)
Authors: Ali Maram , Ali Tarig , Gawai Rahul , Dronjak Lara , Elaksher Ahmed
Published: December 2024

Over 30 million people rely on Lake Victoria for survival in Northeast African countries, including Ethiopia, Eritrea, Somalia, and Djibout. The lake faces significant challenges due to changes in land use and climate. This study used multi-source remote sensing data in the Google Earth Engine (GEE) platform to create Land Use and Land Cover (LULC), land surface temperature (LST), and Normalized Difference Water Index (NDWI) layers in the period 2000–2023 to understand the impact of LULC and climate change on Lake Victoria Basin. The land use/land cover trends before 2020 indicated an increase in the urban areas from 0.13% in 2000 to 0.16% in 2020. Croplands increased from 6.51% in 2000 to 7.88% in 2020. The water surface area averaged 61,559 square km, which has increased since 2000 with an average rate of 1.3%. The “Permanent Wetland” size change from 2000 to 2020 varied from 1.70% to 1.83%. Cropland/Natural Vegetation Mosaics rose from 12.77% to 15.01%, through 2000 to 2020. However, more than 29,000 residents were displaced in mid-2020 as the water increased by 1.21 m from the fall of 2019 to the middle of 2020. Furthermore, land-surface temperature averaged 23.98 degrees in 2000 and 23.49 in 2024.
Self-Supervised and Zero-Shot Learning in Multi-Modal Raman Light Sheet Microscopy
Authors: Kumari Pooja , Kern Johann , Raedle Matthias
Published: December 2024

Advancements in Raman light sheet microscopy have provided a powerful, non-invasive, marker-free method for imaging complex 3D biological structures, such as cell cultures and spheroids. By combining 3D tomograms made by Rayleigh scattering, Raman scattering, and fluorescence detection, this modality captures complementary spatial and molecular data, critical for biomedical research, histology, and drug discovery. Despite its capabilities, Raman light sheet microscopy faces inherent limitations, including low signal intensity, high noise levels, and restricted spatial resolution, which impede the visualization of fine subcellular structures. Traditional enhancement techniques like Fourier transform filtering and spectral unmixing require extensive preprocessing and often introduce artifacts. More recently, deep learning techniques, which have shown great promise in enhancing image quality, face their own limitations. Specifically, conventional deep learning models require large quantities of high-quality, manually labeled training data for effective denoising and super-resolution tasks, which is challenging to obtain in multi-modal microscopy. In this study, we address these limitations by exploring advanced zero-shot and self-supervised learning approaches, such as ZS-DeconvNet, Noise2Noise, Noise2Void, Deep Image Prior (DIP), and Self2Self, which enhance image quality without the need for labeled and large datasets. This study offers a comparative evaluation of zero-shot and self-supervised learning methods, evaluating their effectiveness in denoising, resolution enhancement, and preserving biological structures in multi-modal Raman light sheet microscopic images. Our results demonstrate significant improvements in image clarity, offering a reliable solution for visualizing complex biological systems. These methods establish the way for future advancements in high-resolution imaging, with broad potential for enhancing biomedical research and discovery.
Reinforcement Learning-Based Control for Collaborative Robotic Brain Retraction
Authors: Inziarte-Hidalgo Ibai , Nieto Estela , Roldan Diego , Sorrosal Gorka , Perez-Llano Jesus , Zulueta Ekaitz
Published: December 2024

In recent years, the application of AI has expanded rapidly across various fields. However, it has faced challenges in establishing a foothold in medicine, particularly in invasive medical procedures. Medical algorithms and devices must meet strict regulatory standards before they can be approved for use on humans. Additionally, medical robots are often custom-built, leading to high costs. This paper introduces a cost-effective brain retraction robot designed to perform brain retraction procedures. The robot is trained, specifically the Deep Deterministic Policy Gradient (DDPG) algorithm, using reinforcement learning techniques with a brain contact model, offering a more affordable solution for such delicate tasks.