bluuebunny/crossref_metadata_2025 huggingface dataset:
In Vitro Bioaccessibility of Edible Seaweed Proteins from the Chilean Coast and Proteins from the Novel Seaweed-Derived Mycoprotein
Authors: Landeta-Salgado Catalina , Munizaga Javiera , González-Troncoso María Paz , Daza-Sanchez Anamaría , Martínez Irene , Lienqueo María Elena
Published: January 2025

Seaweed biomass is globally underutilized as a source of proteins despite its nutritional potential, with much of its use focused on hydrocolloid extraction. This study evaluated the nutritional quality and digestibility of protein and amino acids from two brown seaweeds (Durvillaea spp. and Macrocystis pyrifera), one green seaweed (Ulva spp.), and a novel mycoprotein derived from Durvillaea spp. through fungal fermentation. Using an in vitro gastrointestinal digestion Megazyme assay kit, protein digestibility-corrected amino acid scores (PDCAASs) and digestible indispensable amino acid scores (DIASSs) were determined. Compared with seaweeds, seaweed-derived mycoprotein presented significantly greater protein contents (~33%) and amino acid profiles (2.2 times greater than those of Durvillaea spp. and M. pyrifera), with greater digestibility (~100%) than seaweeds (<60%). The PDCAAS values were 0.37, 0.41, 0.53, and 0.89 for Ulva spp., Macrocystis pyrifera, Durvillaea spp., and mycoproteins, respectively. The DIASSs highlighted the superior nutritional quality of the mycoprotein, particularly for lysine (0.59) and histidine (0.67). SDS-PAGE revealed soluble peptides (<25 kDa) in Durvillaea spp., Macrocystis pyrifera, and mycoproteins, whereas Ulva spp. proteins exhibited limited solubility due to structural aggregation. These findings highlight the need to characterize the nutritional properties of edible seaweeds in Chile further and emphasize the importance of optimized processing techniques, such as fermentation or bioconversion, to improve the nutritional potential of seaweeds and develop high-quality food ingredients for diverse applications.
Development of a Population Pharmacokinetic Model Characterizing the Tissue Distribution of Resveratrol After Administration by Different Routes and Doses in Rats
Authors: Cerqueira Cássia , Santos Valdeene , Araújo Jackeline , Pereira Laiz , Batista Fabiana , Soares Denis , Azeredo Francine , Ferreira Ederlan
Published: January 2025

Background: Studies have demonstrated that resveratrol exerts several pharmacological effects. However, the pharmacokinetic parameters are not completely established. Objectives: This study describes the plasma pharmacokinetics and tissue distribution of resveratrol after administration by different routes and doses in rats. Methods: A reliable, simple, and sensitive HPLC method using UV detection for the quantification of resveratrol in rat plasma and tissues was developed and validated. In addition, a pharmacokinetic analysis using non-compartmental and population modeling was performed. Results: The pharmacokinetic parameters of resveratrol after the administration of 5 mg/kg via i.v. bolus calculated by non-compartmental analysis were a constant of elimination (ke) of 0.09 h−1 ± 0.04, a half-life (t1/2) of 9.5 h ± 3.7, an apparent volume of distribution (Vd) of 5.8 L/kg ± 4.7, a clearance (Cl) of 0.39 L/h/Kg ± 0.26, and an area under the curve (AUC) of 6076 ng/h/mL ± 2959. The results obtained after the administration of 100 mg/kg p.o. were an elimination constant (ke) of 0.12 ± 0.07 h−1, a half-life (t1/2) of 7.9 ± 4.2 h, the apparent volume distribution (Vd) of 13.3 ± 3.3 L/kg, a clearance (Cl) of 1.76 ± 0.49 L/h/Kg ± 0.26, and an area under the curve (AUC) of 6519 ± 1592 ng/h/mL. For the tissue distribution analysis, 10 mg/kg of resveratrol was intravenously administered to rats and the molecule was quantified in the liver, lung, kidney, heart, stomach, spleen, adipose tissue, and brain of the animals. Conclusions: The population pharmacokinetic modeling showed that resveratrol has a two-compartment model in both routes of administration and has a higher volume of distribution when it is given orally. In addition, resveratrol showed a high brain concentration after iv administration, which indicates that this molecule is capable of crossing the blood–brain barrier of animals, a crucial capacity for its neuroprotective activity.
Hepatic Estrogen Receptor Alpha Overexpression Protects Against Hepatic Insulin Resistance and MASLD
Authors: Alves Ester S. , Santos Jessica D. M. , Cruz Alessandra G. , Camargo Felipe N. , Talarico Carlos H. Z. , Santos Anne R. M. , Silva Carlos A. A. , Morgan Henrique J. N. , Matos Sandro L. , Araujo Layanne C. C. , Camporez João Paulo
Published: January 2025

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is associated with cardiometabolic risk. Although studies have shown that estradiol positively contributes to energy metabolism via estrogen receptor alpha (ERα), its role specifically in the liver is not defined. Therefore, this study aimed to evaluate the effects of ERα overexpression, specifically in the liver in mice fed a high-fat diet (HFD). Methods: Male C57BL/6J mice were divided into four groups, vehicle fed with regular chow (RC) (RC-Vehicle); vehicle fed an HFD (HFD-Vehicle); AAV-treated fed with RC (RC-AAV); and AAV-treated fed an HFD (HFD-AAV), for 6 weeks (8–10 mice per group). AAV was administered intravenously to induce ERα overexpression. Results: We demonstrate that overexpression of ERα in RC-fed mice reduces body fat (28%). These mice show increased oxygen consumption in cultured primary hepatocytes, both in basal (19%) and maximal respiration (34%). In HFD-fed mice, we showed a decrease in hepatic TAG content (43%) associated with improved hepatic insulin sensitivity (145%). Conclusions: From this perspective, our results prove that hepatic ERα signaling is responsible for some of the metabolic protective effects of estrogen in mice. Overexpression of ERα improves hepatocyte mitochondrial function, consequently reducing hepatic lipid accumulation and protecting animals from hepatic steatosis and hepatic insulin resistance. Further investigations will be needed to determine the exact molecular mechanism by which ERα improves hepatic metabolic health.
Exploring Dysphagia in Congenital Diaphragmatic Hernia: A Retrospective Analysis
Authors: Gilley Jamie , Whalen Elise , Latimore Audrey , Jung Viviane , Hagan Joseph , King Alice
Published: January 2025

Background: Congenital diaphragmatic hernia (CDH) is a complex congenital disorder often accompanied by long-term feeding difficulties. There is a paucity of published data regarding the impact of swallowing difficulties on long-term patient outcomes. Our study attempts to evaluate this phenomenon. Methods: A retrospective chart review of infants born with CDH between 2021 and 2022 identified 45 patients. The following variables were identified: need for swallow study, stomach location, defect type, need for anti-reflux therapy, need for nasogastric tube (NG) or gastric tube (GT) at time of discharge, poor growth, and frequency of respiratory infections during the first 12 months of life. Results: Thirty-one percent of patients (n = 14) underwent a swallow study, 20% (n = 9) required long-term anti-reflux medications, 18% (n = 8) had a GT and 59% (n = 26) had an NG in place at time of discharge, 44% (n = 17) experienced poor growth as an outpatient, and 35% (n = 16) had respiratory infections in the first 12 months of life requiring hospitalization. Infants with a Type D defect commonly required GT at discharge (40%), experienced respiratory infections in the first 12 months (67%), and had poor growth as an outpatient (67%). Conclusions: Our findings underscore the need for routine dysphagia screening in CDH infants during NICU admission. Differences in outcomes based on defect type suggest that early identification and targeted interventions for feeding and swallowing issues may improve long-term growth and respiratory outcomes for CDH patients. Further studies are warranted to develop standardized dysphagia management guidelines for this population.
Exploring the Anti-Osteoporotic Effects of n-Hexane Fraction from Cotoneaster wilsonii Nakai: Activation of Runx2 and Osteoblast Differentiation In Vivo
Authors: Hong Soyeon , Lee Hee Ju , Jung Da Seul , Erdenebileg Saruul , Hwang Hoseong , Kwon Hak Cheol , Kwon Jaeyoung , Yoo Gyhye
Published: January 2025

Background: Osteoporosis is characterized by the microstructural depletion of bone tissue and decreased bone density, leading to an increased risk of fractures. Cotoneaster wilsonii Nakai, an endemic species of the Korean Peninsula, grows wild in Ulleungdo. In this study, we aimed to investigate the effects of C. wilsonii and its components on osteoporosis. Methods and Results: The alkaline phosphatase (ALP) activity of C. wilsonii extracts and fractions was evaluated in MC3T3-E1 pre-osteoblasts, and the n-hexane fraction (CWH) showed the best properties for ALP activity. The effects of the CWH on bone formation were assessed in MC3T3-E1 cells and ovariectomized mice. Biochemical assays and histological analyses focused on the signaling activation of osteoblast differentiation and osteogenic markers, such as ALP, collagen, and osterix. The CWH significantly activated TGF-β and Wnt signaling, enhancing osteoblast differentiation and bone matrix formation. Notably, CWH treatment improved micro-CT indices, such as femoral bone density, and restored serum osteocalcin levels compared to OVX controls. Conclusions: These results highlight the potential of the C. wilsonii Nakai n-hexane fraction as a promising therapeutic agent for managing osteoporosis.
Structural Characterizations and Biological Evaluation of a Natural Polysaccharide from Branches of Camellia oleifera Abel
Authors: Lu Shengjia , Zhang Yali , Ou Yanghui , Xin Jianghui , Yao Hongliang , Guan Litao
Published: January 2025

Background: Camellia oleifera Abel (C. oleifera) is widely cultivated and serves as an important source of edible oil. Yet, during oil production, pruned branches generate significant waste and contribute to environmental pollution. Objectives: In this work, we obtain a natural polysaccharide from the branches of C. oleifera and optimize its extraction using Box–Behnken design (BBD), which is a statistical method commonly used in response surface methodology. Additionally, we study its properties, such as monosaccharide composition, structural features, antioxidant, and anti-inflammatory abilities. Results: BBD was employed to optimize polysaccharide extraction (solid-liquid ratio = 1:40; 90 °C; 130 min) for a higher yield. After purification, the major monosaccharides of branches of C. oleifera’s polysaccharide (CBP) were disclosed as glucose and galactose. Subsequent structural features of CBP were measured. The antioxidant and anti-inflammatory abilities were measured. The highly scavenging rates of the 2,2-diphenyl-1-picrylhydrazyl and hydroxyl radicals, with the chelating capacity of Fe2+, indicate potent antioxidant activity of CBP. Conclusions: In general, CBP demonstrated significant anti-inflammatory activity with down-regulating the expression of IL-6 and IL-1β in the LPS-induced macrophage RAW264.7 model. This bioactive polysaccharide adds value to waste branches by offering a novel approach to waste recycling and the development of C. oleifera.
Integrated Metabolomics and Transcriptome Analysis of Anthocyanin Biosynthetic Pathway in Prunus serrulata
Authors: Ye Qi , Liu Feng , Feng Kai , Fu Tao , Li Wen , Zhang Cheng , Li Meng , Wang Zhilong
Published: January 2025

Prunus serrulata is an important landscape tree species whose flower color has high ornamental value. However, the molecular mechanisms regulating flower color in P. serrulata remain unclear. By studying the metabolomics and transcriptomics of three different color varieties under the species lineage of P. serrulata, ‘Eigeng’ (EG, white), ‘Albo-rosea’ (AR, pink), and ‘Grandiflora’ (GF, green), the biosynthetic mechanisms of different flower colors in P. serrulata were revealed. The results showed that the different colors of the petals were related to the content of chlorophyll and anthocyanins. Among these, cyanidin-3-O-glucoside and cyanidin-3-O-(6-O-malonyl-β-D-glucoside) were highly expressed in AR. A combined transcriptomic analysis revealed that five flavonoid structural genes, including two DFR genes and three UFGT genes, were specifically expressed. In addition, three key transcription factors, PsMYB77, PsMYB17, and PsMYB105, were identified as regulators of the structural genes DFR and UFGT and participants in the forward synthesis of anthocyanin. This study provides convincing evidence elucidating the regulatory mechanisms of anthocyanin synthesis of P. serrulata and provides a theoretical basis for the breeding and development of new varieties and germplasm resource innovation for cherry blossom.
Metal Biomonitoring Through Arboreal Species in Riparian Ecosystems: Pithecellobium dulce as a Bioindicator Species
Authors: Hernández-Maravilla Sayuri , Castrejón-Godínez María Luisa , Tovar-Sánchez Efraín , Saldarriaga-Noreña Hugo Albeiro , Rodríguez Alexis , Rosas-Ramírez Marcos Eduardo , Mussali-Galante Patricia
Published: January 2025

Water pollution by metals is a global environmental problem. In riparian ecosystems, metal pollution generates adverse effects on organisms and reduces water quality. The Cuautla River is of great ecological relevance and an important water supplier. However, it is polluted by multiple wastewater discharges from different origins, with toxic metals being the main pollutants. Therefore, environmental monitoring strategies based on bioindicator species are necessary to evaluate the ecosystem health of riparian ecosystems. Pithecellobium dulce (Roxb.) Benth is a tree species native to Mexico, widely distributed including in riparian ecosystems, and it is also established in contaminated sites. In this study, Cd, Cu, Pb, and Zn concentrations in water and sediment and in leaf and bark of adult P. dulce trees established in six sampling sites along the Cuautla’s riverbed were determined. Likewise, the genotoxic damage derived from metal exposure to leaves was evaluated. The results evidenced the presence of Cd and Pb in water and Cd, Cu, Pb, and Zn in sediment. P. dulce registered high levels of Cu, Pb, and Zn in both leaf and bark, showing higher concentrations in leaf than in bark. In addition, the greater the concentration of Pb in leaves, the greater the genotoxic damage observed, while the concentration of Cu and Zn did not show a relationship with the genotoxic damage in leaves. Overall, Cu and Pb concentrations in leaves enabled us to detect pollution gradients for these metals in water and sediment from the sampling sites. Due to its wide geographic distribution, establishment in polluted sites, and metal absorption capacity, P. dulce can be considered a bioindicator species for environmental health studies in riparian ecosystems contaminated with metals.
Phytochemical Characterisation of Sorbus Species: Unveiling Flavonoid Profiles Related to Ploidy and Hybrid Origin
Authors: Korić Emina , Milutinović Violeta , Hajrudinović-Bogunić Alma , Bogunić Faruk , Kundaković-Vasović Tatjana , Gušić Irma , Radović Selgrad Jelena , Durić Kemal , Nikšić Haris
Published: January 2025

The genetic, morphological and taxonomic diversity of the genus Sorbus is due to homoploid and polyploid hybridisation, autopolyploidy and apomixis, which also influence the production and diversity of secondary metabolites, especially flavonoids. The aim of this study was to investigate the relationships and variations of flavonoids in terms of hybrid origin and ploidy level between the parental species and their hybrid derivatives. The sampling design included leaf material of the following Sorbus accessions from ten natural localities: parental taxa (di-, tri- and tetraploids of S. aria; diploid S. torminalis and S. aucuparia) and their di-, tri- and tetraploid hybrid derivatives from crosses of S. aria × S. torminalis (subg. Tormaria) as well as the tetraploid S. austriaca and S. bosniaca, which originate from crosses of S. aria × S. aucuparia (subg. Soraria). We analysed the flavonoid profiles from the leaf fractions by LC-MS. A total of 23 flavonoids were identified, including apigenin and luteolin derivatives, which distinguish the hybrid groups from each other. This profiling highlights the distinctiveness of the Tormaria and Soraria accessions and emphasises the potential of the subg. Tormaria for further research on bioactive compounds in biological studies.
Elucidating the Underlying Allelopathy Effects of Euphorbia jolkinii on Arundinella hookeri Using Metabolomics Profiling
Authors: Xiao Xue , Ma Zuyan , Zhou Kai , Niu Qiongmei , Luo Qin , Yang Xin , Chu Xiaohui , Shan Guilian
Published: January 2025

Euphorbia jolkinii dominates the subalpine meadows in Shangri-La (Southwest China) owing to its potent allelopathic effects. However, the effects underlying its allelopathy require further characterization at the physiological and molecular levels. In this study, the physiological, biochemical, and metabolic mechanisms underlying E. jolkinii allelopathy were investigated using Arundinella hookeri as a receptor plant. The treatment of A. hookeri seedlings with E. jolkinii aqueous extract (EJAE) disrupted their growth by inhibiting photosynthesis, disrupting oxidation systems, and increasing soluble sugar accumulation and chlorophyll synthesis. Collectively, this causes severe impairment accompanied by abnormal photosynthesis and reduced biomass accumulation. Moreover, EJAE treatment suppressed gibberellin, indoleacetic acid, zeatin, salicylic acid, and jasmonic acid levels while promoting abscisic acid accumulation. Further metabolomic analyses identified numerous differentially abundant metabolites primarily enriched in the α-linolenic, phenylpropanoid, and flavonoid biosynthesis pathways in EJAE-treated A. hookeri seedlings. This study demonstrated that E. jolkinii exhibits potent and comprehensive allelopathic effects on receptor plants, including a significant disruption of endogenous hormone synthesis, the inhibition of photosynthesis, an impairment of membrane and oxidation systems, and changes in crucial metabolic processes associated with α-linolenic, phenylpropanoid, and flavonoid biosynthesis. Thus, our study provides a solid theoretical foundation for understanding the regulatory mechanisms underlying E. jolkinii allelopathy.
The Effect of Exogenous Melatonin on the Photosynthetic Characteristics of Rhododendron simsii Under Cadmium Stress
Authors: Di Haochen , Liang Ying , Gong Yuting , Jin Songheng , Xu Yanxia
Published: January 2025

Rhododendron simsii (R. simsii), a significant ornamental plant species, is adversely affected by the severe soil heavy metal pollution resulting from rapid industrialization, particularly in terms of its growth environment. Cadmium (Cd), a representative heavy metal pollutant, poses a significant threat to plant growth and photosynthetic physiology. Despite the importance of understanding Cd stress resistance in rhododendrons, research in this area is limited. This study focused on the role of exogenous melatonin (MT) in mitigating Cd-induced stress, emphasizing its impact on photosynthetic physiology. Gas exchange parameters, prompt and delayed fluorescence (DF), 820 nm modulated reflectance (Mr820), and antioxidant enzyme activity, were measured. The findings revealed that under Cd stress, MT-free treatment imposed a more severe limitation on both stomatal and non-stomatal processes in R. simsii leaves, significantly reducing the net photosynthetic rate. In contrast, exogenous MT improved photosynthetic efficiency by increasing the maximum photochemical efficiency of photosystem II, the quantum yield of electron transport, and the photosynthetic performance index. DF and Mr820 analysis demonstrated that MT provided robust protection to both the donor and receptor sides of photosystems I and II. Furthermore, MT significantly decreased malondialdehyde (MDA) content, a marker of oxidative stress, and enhanced the activity of antioxidant enzymes, including superoxide dismutase (SOD) and guaiacol peroxidase (POD). In conclusion, exogenous MT plays a critical role in alleviating Cd-induced stress by enhancing antioxidant defense mechanisms and safeguarding the photosynthetic apparatus, thereby improving the Cd tolerance of R. simsii.
Possibility of Using Alkali-Activated Phosphogypsum from the Production of Orthophosphoric Acid for the Building Materials—A Review
Authors: Liczbińska Aleksandra , Gębicki Jacek
Published: January 2025

This paper focuses on the possibility of using phosphogypsum, which is a residue from the production of orthophosphoric acid as an additional source of calcium and the use of spent caustic as an alkaline activator for production of ceramic materials in construction industry. The use of the above-mentioned waste will allow to increase fraction of calcium, sodium and silicate needed for the geopolymerization process and improve properties of material. This review presents a description of the geopolymerization process and the influence of alkaline activator on the reactions occurring in ceramic materials. Collected information, which confirm the possibility of using post-production waste from chemical industry as components for the production of building materials.
An Innovative NOx Emissions Prediction Model Based on Random Forest Feature Selection and Evolutionary Reformer
Authors: Meng Xianyu , Li Xi , Chen Jialei , Fu Yongyan , Zhang Chu , Nazir Muhammad Shahzad , Peng Tian
Published: January 2025

Developing more precise NOx emission prediction models is pivotal for effectively controlling NOx emissions from gas turbines. In this paper, a Reformer is combined with random forest (RF) feature selection and the chaos game optimization (CGO) algorithm to predict NOx in gas turbines. Firstly, RF evaluates the importance of data features and reduces the dimensionality of multidimensional data to improve the predictive performance of the model. Secondly, the Reformer model extracts the inherent pattern of different data and explores the intrinsic connection between gas turbine variables to establish a more accurate NOx emission prediction model. Thirdly, the CGO algorithm is a parameter-free meta-heuristic optimization algorithm used to find the best parameters for the prediction model. The CGO algorithm was improved using Chebyshev Chaos Mapping to improve the initial population quality of the CGO algorithm. To evaluate the efficiency of the proposed model, a dataset of gas turbines in north-western Turkey is studied, and the results obtained are compared with seven benchmark models. The final results of this paper show that RF can select appropriate input variables, and the Reformer can extract the intrinsic links of the data and build a more accurate NOx prediction model. At the same time, ICGO can optimize the parameters of the Reformer effectively.
Advancements in Research on Alternative Protein Sources and Their Application in Food Products: A Systematic Review
Authors: Puljić Leona , Banožić Marija , Kajić Nikolina , Vasilj Višnja , Habschied Kristina , Mastanjević Krešimir
Published: January 2025

To ensure food security amid dwindling natural resources, alternative proteins (APs) have been suggested as a sustainable solution. Yet, the adoption and consumption of APs remain limited. This review aims to delve into the latest progress (following PRISMA guidelines) concerning the utilization of proteins from alternative sources, particularly focusing on their effective incorporation into food products. Our findings reveal that insect proteins can improve amino acid profiles in bakery products. However, consumer acceptance remains low due to cultural biases, with optimal sensory results being achieved at lower substitution levels (5–10%). Mushroom proteins, when incorporated into meat analogs and bakery items, enhance nutritional value and offer favorable sensory properties, making them viable replacements in meat products. Plant-based proteins, such as pea and soy proteins, increase fiber and antioxidants and improve texture in meat alternatives, although formulation adjustments are necessary to meet consumer expectations for taste and overall experience. Microalgae offer unique benefits for bakery, confectionery, and dairy products by boosting protein, fatty acids, and probiotic growth while maintaining sensory acceptability. In conclusion, this study highlights that the effective incorporation of APs into food products can help in the development of healthier, more sustainable diets. That said, the success of AP acceptance will depend on continued innovations in formulation and consumer education.
Presentation and Analysis of “Three Teachings Syncretism” in Song and Jin Poetry and Its Modern Significance
Authors: Lei Enhai , Hu Xudong
Published: January 2025

The “Three Teachings Syncretism” (sanjiao heyi, 三教合一), i.e., the integration of Confucianism, Buddhism, and Daoism, represents an important religious philosophy in ancient China. This article aims to analyze how this ideology is presented and expressed directly in Song and Jin poetry, along with its modern value and significance. To achieve the research objectives, the paper isolates related poems from the Complete Song Poetry and Complete Liao Jin Poetry as the research objects. First, it organizes and classifies the relevant poems through the ways in which the idea of “Three Teachings Syncretism” is presented. Second, it examines the reasons behind this phenomenon, including the intellectualization of poetry and the Confucian academic background of Buddhist monks and Daoist priests. Thirdly, it explores the value and significance of this thought for modern China, showcasing the inherent diversity, inclusiveness, and harmony in Chinese culture.
Earnings Quality Drivers: Do Firm Attributes and Ownership Structure Matter in Emerging Stock Markets?
Authors: Alrobai Fahad , Alrashed Ahmed A. , Albaz Maged M.
Published: January 2025

This research aims to examine the drivers of earnings quality (EQ) in emerging stock markets. By testing the impact of firm attributes and ownership structures on the level of earnings quality. The research followed a mixed-method approach (qualitative and quantitative) and was conducted based on a sample of 75 listed firms in Egypt as an emerging market from 2015 to 2022. The results of the research found that each firm attribute has a mixed impact on earning quality, such as firm size (positive on persistence and no impact on consistency) and ROA (U-shape on persistence and consistency). In addition, ownership structures uniquely and dynamically impact earnings, following positive, U-shape, and N-shape. This research sheds light on the drivers of the earnings quality (firm attributes and ownership structures) in the Egyptian-listed firms and helps policymakers implement appropriate corporate governance mechanisms. Our findings in Egypt can motivate future research to further investigate the most relevant factors that explain variations in earning persistence and consistency as a dimension of financial reporting quality in other emerging markets.
Empirical Evidence of the Market Price of Risk for Delivery Periods
Authors: Kemper Annika , Schmeck Maren Diane
Published: January 2025

In this paper, we provide empirical evidence of the market price of risk for delivery periods (MPDP) of electricity swap contracts. The MPDP enables an accurate pricing of such contracts in the presence of the delivery period such that the typical approximations can be avoided. In our empirical study, we focus on term-structure effects and identify the resulting MPDP. In presence of the Samuelson effect, we find the most pronounced MPDP close to maturity, while the MPDP disappears proportional to the Samuelson effect far away from maturity. Thus, our theory improves the pricing accuracy close to maturity.
An Effective 3D Instance Map Reconstruction Method Based on RGBD Images for Indoor Scene
Authors: Wu Heng , Liu Yanjie , Wang Chao , Wei Yanlong
Published: January 2025

To enhance the intelligence of robots, constructing accurate object-level instance maps is essential. However, the diversity and clutter of objects in indoor scenes present significant challenges for instance map construction. To tackle this issue, we propose a method for constructing object-level instance maps based on RGBD images. First, we utilize the advanced visual odometer ORB-SLAM3 to estimate the poses of image frames and extract keyframes. Next, we perform semantic and geometric segmentation on the color and depth images of these keyframes, respectively, using semantic segmentation to optimize the geometric segmentation results and address inaccuracies in the target segmentation caused by small depth variations. The segmented depth images are then projected into point cloud segments, which are assigned corresponding semantic information. We integrate these point cloud segments into a global voxel map, updating each voxel’s class using color, distance constraints, and Bayesian methods to create an object-level instance map. Finally, we construct an ellipsoids scene from this map to test the robot’s localization capabilities in indoor environments using semantic information. Our experiments demonstrate that this method accurately and robustly constructs the environment, facilitating precise object-level scene segmentation. Furthermore, compared to manually labeled ellipsoidal maps, generating ellipsoidal maps from extracted objects enables accurate global localization.
Potential of Apple Vision Pro for Accurate Tree Diameter Measurements in Forests
Authors: Ofner-Graff Tobias , Sarkleti Valentin , Svazek Philip , Tockner Andreas , Witzmann Sarah , Moik Lukas , Kraßnitzer Ralf , Gollob Christoph , Ritter Tim , Kühmaier Martin , Stampfer Karl , Nothdurft Arne
Published: January 2025

The determination of diameter at breast height (DBH) is critical in forestry, serving as a key metric for deriving various parameters, including tree volume. Light Detection and Ranging (LiDAR) technology has been increasingly employed in forest inventories, and the development of cost-effective, user-friendly smartphone and tablet applications (apps) has expanded its broader use. Among these are augmented reality (AR) apps, which have already been tested on mobile devices for their accuracy in measuring forest attributes. In February 2024, Apple introduced the Mixed-Reality Interface (MRITF) via the Apple Vision Pro (AVP), offering sensor capabilities for field data collection. In this study, two apps using the AVP were tested for DBH measurement on 182 trees across 22 sample plots in a near-natural forest, against caliper-based reference measurements. Compared with the reference measurements, both apps exhibited a slight underestimation bias of −1.00 cm and −1.07 cm, and the root-mean-square error (RMSE) was 3.14 cm and 2.34 cm, respectively. The coefficient of determination (R2) between the reference data and the measurements obtained by the two apps was 0.959 and 0.978. The AVP demonstrated its potential as a reliable field tool for DBH measurement, performing consistently across varying terrain.
Forest Fire Discrimination Based on Angle Slope Index and Himawari-8
Authors: Liu Pingbo , Zhang Gui
Published: January 2025

In the background of high frequency and intensity forest fires driven by future warming and a drying climate, early detection and effective control of fires are extremely important to reduce losses. Meteorological satellite imagery is commonly used for near-real-time forest fire monitoring, thanks to its high temporal resolution. To address the misjudgments and omissions caused by solely relying on changes in infrared band brightness values and a single image in forest fire early discrimination, this paper constructs the angle slope indexes ANIR, AMIR, AMNIR, ∆ANIR, and ∆AMIR based on the reflectance of the red band and near-infrared band, the brightness temperature of the mid-infrared and far-infrared band, the difference between the AMIR and ANIR, and the index difference between time-series images. These indexes integrate the strong inter-band correlations and the reflectance characteristics of visible and short-wave infrared bands to simultaneously monitor smoke and fuel biomass changes in forest fires. We also used the decomposed three-dimensional OTSU (maximum inter-class variance method) algorithm to calculate the segmentation threshold of the sub-regions constructed from the AMNIR data to address the different discrimination thresholds caused by different time and space backgrounds. In this paper, the Himawari-8 satellite imagery was used to detect forest fires based on the angle slope indices thresholds algorithm (ASITR), and the fusion of the decomposed three-dimensional OTSU and ASITR algorithm (FDOA). Results show that, compared with ASITR, the accuracy of FDOA decreased by 3.41% (0.88 vs. 0.85), the omission error decreased by 52.94% (0.17 vs. 0.08), and the overall evaluation increased by 3.53% (0.85 vs. 0.88). The ASITR has higher accuracy, and the fusion of decomposed three-dimensional OTSU and angle slope indexes can reduce forest fire omission error and improve the overall evaluation.
Quantifying Spatiotemporal Changes in Supraglacial Debris Cover in Eastern Pamir from 1994 to 2024 Based on the Google Earth Engine
Authors: Liu Hehe , Zhang Zhen , Liu Shiyin , Xie Fuming , Ding Jing , Li Guolong , Su Haoran
Published: January 2025

Supraglacial debris cover considerably influences sub-debris ablation patterns and the surface morphology of glaciers by modulating the land–atmosphere energy exchange. Understanding its spatial distribution and temporal variations is crucial for analyzing melting processes and managing downstream disaster mitigation efforts. In recent years, the overall slightly positive mass balance or stable state of eastern Pamir glaciers has been referred to as the “Pamir-Karakoram anomaly”. It is important to note that spatial heterogeneity in glacier change has drawn widespread research attention. However, research on the spatiotemporal changes in the debris cover in this region is completely nonexistent, which has led to an inadequate understanding of debris-covered glacier variations. To address this research gap, this study employed Landsat remote sensing images within the Google Earth Engine platform, leveraging the Random Forest algorithm to classify the supraglacial debris cover. The classification algorithm integrates spectral features from Landsat images and derived indices (NDVI, NDSI, NDWI, and BAND RATIO), supplemented by auxiliary factors such as slope and aspect. By extracting the supraglacial debris cover from 1994 to 2024, this study systematically analyzed the spatiotemporal variations and investigated the underlying drivers of debris cover changes from the perspective of mass conservation. By 2024, the area of supraglacial debris in eastern Pamir reached 258.08 ± 20.65 km2, accounting for 18.5 ± 1.55% of the total glacier area. It was observed that the Kungey Mountain region demonstrated the largest debris cover rate. Between 1994 and 2024, while the total glacier area decreased by −2.57 ± 0.70%, the debris-covered areas expanded upward at a rate of +1.64 ± 0.10% yr−1. The expansion of debris cover is driven by several factors in the context of global warming. The rising temperature resulted in permafrost degradation, slope destabilization, and intensified weathering on supply slopes, thereby augmenting the debris supply. Additionally, the steep supply slope in the study area facilitates the rapid deposition of collapsed debris onto glacier surfaces, with frequent avalanche events accelerating the mobilization of rock fragments.
MonoSeg: An Infrared UAV Perspective Vehicle Instance Segmentation Model with Strong Adaptability and Integrity
Authors: Huang Peng , Yin Yan , Hu Kaifeng , Yang Weidong
Published: January 2025

Despite rapid progress in UAV-based infrared vehicle detection, achieving reliable target recognition remains challenging due to dynamic viewpoint variations and platform instability. The inherent limitations of infrared imaging, particularly low contrast ratios and thermal crossover effects, significantly compromise detection accuracy. Moreover, the computational constraints of edge computing platforms pose a fundamental challenge in balancing real-time processing requirements with detection performance. Here, we present MonoSeg, a novel instance segmentation framework optimized for UAV perspective infrared vehicle detection. Our approach introduces three key innovations: (1) the Ghost Feature Bottle Cross module (GFBC), which enhances backbone feature extraction efficiency while significantly reducing computational over-head; (2) the Scale Feature Recombination module (SFR), which optimizes feature selection in the Neck stage through adaptive multi-scale fusion; and (3) Comprehensive Loss function that enforces precise instance boundary delineation. Extensive experimental evaluation on bench-mark datasets demonstrates that MonoSeg achieves state-of-the-art performance across standard metrics, including Box mAP and Mask mAP, while maintaining substantially lower computational requirements compared to existing methods.
DHCT-GAN: Improving EEG Signal Quality with a Dual-Branch Hybrid CNN–Transformer Network
Authors: Cai Yinan , Meng Zhao , Huang Dian
Published: January 2025

Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity studies, cognitive mechanism research, and the diagnosis and treatment of neurological disorders. However, EEG signals are often influenced by various physiological artifacts, which can significantly affect data analysis and diagnosis. Recently, deep learning-based EEG denoising methods have exhibited unique advantages over traditional methods. Most existing methods mainly focus on identifying the characteristics of clean EEG signals to facilitate artifact removal; however, the potential to integrate cross-disciplinary knowledge, such as insights from artifact research, remains an area that requires further exploration. In this study, we developed DHCT-GAN, a new EEG denoising model, using a dual-branch hybrid network architecture. This model independently learns features from both clean EEG signals and artifact signals, then fuses this information through an adaptive gating network to generate denoised EEG signals that accurately preserve EEG signal features while effectively removing artifacts. We evaluated DHCT-GAN’s performance through waveform analysis, power spectral density (PSD) analysis, and six performance metrics. The results demonstrate that DHCT-GAN significantly outperforms recent state-of-the-art networks in removing various artifacts. Furthermore, ablation experiments revealed that the hybrid model surpasses single-branch models in artifact removal, underscoring the crucial role of artifact knowledge constraints in improving denoising effectiveness.
Application of PN Code in Time Delay Measurement of Telephone Network
Authors: Jin Xiaozhen , Hua Yu
Published: January 2025

Telephone time service is a wired time service that transmits time signals through a telephone network, with the advantages of simple receiving equipment and wide coverage. But the performance of time service is not high, usually several milliseconds. The time delay measurement of the telephone network is an important factor limiting the improvement in timing performance. The existing telephone service systems measure time delay based on sending characters. The time fluctuation of modulating and demodulating characters and the low accuracy of detecting characters affect the uncertainty of time delay measurement. This article proposes a time delay measurement method based on sending PN codes. It utilizes the good autocorrelation properties of PN codes to improve detection accuracy and then improve the uncertainty of delay measurement. This article studies and designs the modulation, demodulation, and capture of PN codes. Simulation has verified that this method has strong anti-noise performance. Time delay tests have proved that this method can improve the uncertainty of delay measurement by an order of magnitude in different telephone network environments.
Deep Learning-Based Pointer Meter Reading Recognition for Advancing Manufacturing Digital Transformation Research
Authors: Li Xiang , Zhao Jun , Zeng Changchang , Yao Yong , Zhang Sen , Yang Suixian
Published: January 2025

With the digital transformation of the manufacturing industry, data monitoring and collecting in the manufacturing process become essential. Pointer meter reading recognition (PMRR) is a key element in data monitoring throughout the manufacturing process. However, existing PMRR methods have low accuracy and insufficient robustness due to issues such as blur, uneven illumination, tilt, and complex backgrounds in meter images. To address these challenges, we propose an end-to-end PMRR method based on a decoupled circle head detection algorithm (YOLOX-DC) and a Unet-like pure Transformer segmentation network (PM-SwinUnet). First, according to the characteristics of the pointer dial, the YOLOX-DC detection algorithm is designed based on the exceeding you only look once detector (YOLOX). The decoupled circle head of YOLOX-DC detects the pointer meter dial more accurately than the commonly used rectangular detection head. Second, the window multi-head attention of the PM-SwinUnet network enhances the feature extraction ability of pointer meter images and solves problems of missed scale detection and incomplete pointer segmentation. Additionally, the scale and pointer fitting module is introduced into the PM-SwinUnet to locate the accurate position of the scale and pointer. Finally, through the angle relationship between the pointer and the first two main scale lines, the pointer meter reading is accurately calculated by the improved angle method. Experimental results demonstrate the effectiveness and superiority of the proposed end-to-end method across three-pointer meter datasets. Furthermore, it provides a rapid and robust approach to the digital transformation of manufacturing systems.
Real-Time Monitoring of Environmental Parameters in Schools to Improve Indoor Resilience Under Extreme Events
Authors: Azoulay Kochavi Salit , Kira Oz , Gal Erez
Published: January 2025

Climatic changes lead to many extreme weather events throughout the globe. These extreme weather events influence our behavior, exposing us to different environmental conditions, such as poor indoor quality. Poor indoor air quality (IAQ) poses a significant concern in the modern era, as people spend up to 90% of their time indoors. Ventilation influences key IAQ elements such as temperature, relative humidity, and particulate matter (PM). Children, considered a vulnerable group, spend approximately 30% of their time in educational settings, often housed in old structures with poorly maintained ventilation systems. Extreme weather events lead young students to stay indoors, usually behind closed doors and windows, which may lead to exposure to elevated levels of air pollutants. In our research, we aim to demonstrate how real-time monitoring of air pollutants and other environmental parameters under extreme weather is important for regulating the indoor environment. A study was conducted in a school building with limited ventilation located in an arid region near the Red Sea, which frequently suffers from high PM concentrations. In this study, we tracked the indoor environmental conditions and air quality during the entire month of May 2022, including an extreme outdoor weather event of sandstorms. During this month, we continuously monitored four classrooms in an elementary school built in 1967 in Eilat. Our findings indicate that PM2.5 was higher indoors (statistically significant) by more than 16% during the extreme event. Temperature was also elevated indoors (statistically significant) by more than 5%. The parameters’ deviation highlights the need for better indoor weather control and ventilation systems, as well as ongoing monitoring in schools to maintain healthy indoor air quality. This also warrants us as we are approaching an era of climatic instability, including higher occurrence of similar extreme events, which urge us to develop real-time responses in urban areas.
AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking
Authors: Gerlich Michael
Published: January 2025

The proliferation of artificial intelligence (AI) tools has transformed numerous aspects of daily life, yet its impact on critical thinking remains underexplored. This study investigates the relationship between AI tool usage and critical thinking skills, focusing on cognitive offloading as a mediating factor. Utilising a mixed-method approach, we conducted surveys and in-depth interviews with 666 participants across diverse age groups and educational backgrounds. Quantitative data were analysed using ANOVA and correlation analysis, while qualitative insights were obtained through thematic analysis of interview transcripts. The findings revealed a significant negative correlation between frequent AI tool usage and critical thinking abilities, mediated by increased cognitive offloading. Younger participants exhibited higher dependence on AI tools and lower critical thinking scores compared to older participants. Furthermore, higher educational attainment was associated with better critical thinking skills, regardless of AI usage. These results highlight the potential cognitive costs of AI tool reliance, emphasising the need for educational strategies that promote critical engagement with AI technologies. This study contributes to the growing discourse on AI’s cognitive implications, offering practical recommendations for mitigating its adverse effects on critical thinking. The findings underscore the importance of fostering critical thinking in an AI-driven world, making this research essential reading for educators, policymakers, and technologists.
Enhancing Circular Practices in Olive Oil Production: The Role of Green Finance
Authors: Intonti Mariantonietta , Mola Deborah , De Leonardis Matteo , Starace Giuseppe
Published: January 2025

The production process of extra virgin olive oil, which is a strategic asset of the Italian agrifood sector, can be innovated in its impact on the environment, according to a dual approach: (a) with a view to a circular economy, acting on the waste it produces, either correctly disposing of it or effectively reusing it, and (b) with a view to emissions, reducing the carbon footprint that it generates, starting from the supply chain. This dual approach, clearly contributing to the circular economy paradigm and the achievement of a zero-emission economy, requires the sector to make significant investments in technological innovation. To this end, this study highlights the importance of the financial resources that can be obtained either through financial intermediaries, in particular banks, now able to provide green loans with a positive environmental impact, or through the financial markets and the use of securities issues, such as green bonds.