bluuebunny/crossref_metadata_2025 huggingface dataset:
Harnessing Magnetic Properties for Precision Thermal Control of Vortex Domain Walls in Constricted Nanowires
Authors: Al Bahri Mohammed , Al-Kamiyani Salim
Published: February 2025

This study investigates the thermal pinning and depinning behaviors of vortex domain walls (VWs) in constricted magnetic nanowires, focusing on the influence of intrinsic magnetic properties on VW stability under thermal stress. Using micromagnetic simulations, we analyze the roles of saturation magnetization (Ms), uniaxial magnetic anisotropy (Ku), and nanowire geometry in determining VW thermal stability. The modeled nanowire has dimensions of 200 nm (width), 30 nm (thickness), and a 50 nm constriction length, chosen based on the dependence of VW formation on nanowire geometry. Our results show that increasing Ms and Ku enhances VW pinning, while thermal fluctuations at higher temperatures promote VW depinning. We demonstrate that temperature and magnetic parameters significantly impact VW structural stability, offering insights for designing high-reliability nanowire-based memory devices. These findings contribute to optimizing nanowire designs for thermally stable, energy-efficient spintronic memory systems.
Impact of Breastfeeding Practices on Autistic Traits in Chinese Children Aged from 3 to 4 Years: Cross-Sectional Study
Authors: Yang Jianhui , Gao Lu , Strodl Esben , Chen Jieping , Tong Feng , Chen Weiqing
Published: February 2025

Background: Although breastfeeding has been extensively documented to confer health benefits to infants in the early stages of their lives, the sustained influence that it has on autistic traits throughout childhood remains unclear. This study endeavors to explore the correlation between the length of exclusive breastfeeding, the overall duration of breastfeeding, and the manifestation of autistic traits in Chinese children. Methods: A cross-sectional study was conducted among 17,382 three-year-olds residing in Longhua District, Shenzhen, China. The participants’ socio-economic status, breastfeeding patterns, and autistic traits were recorded using questionnaires. Breastfeeding durations were analyzed both as continuous and categorical variables. Results: Of the 17,382 children studied, 666 exhibited autistic traits. Exclusive breastfeeding for ≥2 months was notably associated with a decreased risk of developing autistic traits when it was assessed using continuous measures. Furthermore, children who were breastfed for 13 months or longer exhibited a lower risk of developing autistic traits, as compared to those who were breastfed for 6 months or less, when this was measured using categorical methods. Similarly, when it was assessed using continuous measures, children who were breastfed for at least 8 months also demonstrated a reduced risk of developing autistic traits. Linear relationships were discernible between exclusive breastfeeding duration, overall breastfeeding duration, and autistic traits. Conclusions: Exclusive breastfeeding for a period from 2 to 6 months, coupled with extended breastfeeding lasting for at least 8 months, demonstrated a beneficial effect in alleviating autistic traits among Chinese children. These findings contribute to refining and strengthening the existing recommendations concerning breastfeeding practices.
Sleep Duration Is Associated with Fruit and Vegetable Intake in Lower Income Adults from the San Francisco Bay Area: A Cross-Sectional Analysis
Authors: Zamora Astrid N. , Patel Michele L. , Campero Maria I. , Garcia Dulce M. , Portillo Sofia A. , King Abby C.
Published: February 2025

Abstract: Background: Few studies have examined whether sleep is related to dietary intake in aging adults. To address this gap, this study investigated (1) the associations between sleep duration and sleep quality with fruits and vegetables (FV), fiber, and fat intake in lower-income midlife and older adults and (2) sex differences in these relationships. Methods: Baseline data from 163 ethnically diverse, lower-income midlife and older adults in the NIH-funded Steps for Change trial were analyzed. Dietary intake was assessed using the Block Fruit/Vegetable/Fiber and Fat Intake Screeners, operationalized as weekly servings. Sleep duration (hours per night) and quality were self-reported via the Stanford WELL for Life Scale. Linear regression models assessed the association between sleep duration and FV, fiber, and fat intake, adjusting for potential confounding covariates with separate models for sleep quality. Sex differences were tested using interaction terms, with stratified models also used to explore differences. Results: The sample was 73.2% female, with a mean age of 70.5 (SD = 9.7) years (range: 41–99). The mean sleep duration was 6.8 (1.2) hours per night, with 79.2% reporting fairly or very good sleep quality. Mean weekly servings were 24 for FV, 7 for fiber, and 18 for fat. Sleep duration was positively associated with FV intake (β = 2.2; p = 0.02). The interaction between duration and sex was marginally significant (p = 0.08), with a positive association in males (β = 5.5; p = 0.02) but not in females (β = 0.9; p = 0.41). No significant associations were found between sleep duration and fiber or fat intake or between sleep quality and any dietary intake outcomes. Conclusions: This study found that longer sleep duration was associated with higher FV intake in males but not in females. These findings suggest the possibility of sex differences in the sleep–diet relationship among aging populations that merit further exploration in longitudinal studies.
Side Effects of Immunosuppressant Drugs After Liver Transplant
Authors: Gabrielli Filippo , Bernasconi Elisa , Toscano Arianna , Avossa Alessandra , Cavicchioli Alessia , Andreone Pietro , Gitto Stefano
Published: February 2025

Liver transplantation (LT) is the standard of care for both end-stage liver failure and hepatocellular carcinoma (HCC). Side effects of the main used immunosuppressive drugs have a noteworthy impact on the long-term outcome of LT recipients. Consequently, to achieve a balance between optimal immunosuppression and minimal side effects is a cornerstone of the post-LT period. Today, there are no validated markers for overimmunosuppression and underimmunosuppression, only a few drugs have therapeutic drug monitoring, and immunosuppression regimens vary from center to center and from country to country. Currently, there are many drugs with different efficacy and safety profiles. Using different agents permits a decrease in the dosage and minimizes the toxicities. A small subset of recipients achieves immunotolerance with the chance to stop immunosuppressive therapy. This article focuses on the side effects of immunosuppressive drugs, which significantly impact long-term outcomes for LT recipients. The primary aim is to highlight the balance between achieving effective immunosuppression and minimizing adverse effects, emphasizing the role of personalized therapeutic strategies. Moreover, this review evaluates the mechanisms of action and specific complications associated with immunosuppressive agents. Finally, special attention is given to strategies for reducing immunosuppressive burdens, improving patient quality of life, and identifying immunotolerant individuals.
Phytochemical Composition and Skin-Friendly Activities of the Ethyl Acetate Fraction in Ophioglossum vulgatum Linn., an In Vitro Study
Authors: Feng Sihan , Huang Zhiguang , Cao Yichen , Huang Zixuan , Xu Chen , Zeng Yibo , Xu Yuhang , Zhu Lijian , Ding Bin
Published: February 2025

Background: Ophioglossum vulgatum Linn. is a medical herb widely distributed in Southwest China. It has been used for the treatment of various diseases, including wounds or dermatitis, since ancient times, but little is known about its pharmacological and pharmaceutical chemistry. Methods: The ethyl acetate fraction of O. vulgatum (OpvE) was prepared with the reflex extraction and fractional extraction method. Its components were detected and identified with the UPLC-Q/TOF-MS system and the SCIEX OS database. The related biological activities and the underlying mechanisms were predicted by computational analysis. HaCaT cells were treated with gradient concentrations of OpvE, and a CCK-8 assay was performed to determine the cell viability. The OpvE-pretreated HaCaT cells were exposed to H2O2 or LPS for antioxidative and anti-inflammatory assessment. DPPH, GSH, SOD, and MDA kits were used to evaluate oxidative stress. A serially diluted microbiota assay and a disc diffusion assay were used to evaluate anti-Staphylococcus aureus activities. The transcription of genes was semi-quantitatively studied by reversed real-time PCR. Protein levels were determined with western blotting. Results: The extract ratio of OpvE was 2.00 ± 0.12% (g/g). A total of 21 ingredients were identified. The computational analysis found that the PI3K/Akt signaling pathway might be a crucial target of OpvE. OpvE (7.5~125 μg/mL) stimulated HaCaT cell proliferation and migration by stimulating the over-expressed collagen type I alpha 1 Chain (COL1A1) and fibronectin 1 (FN1) and upregulating PI3K/AKT/GSK3-β signaling pathway. In the antioxidative assay test, 250 μg/mL OpvE scavenged obvious 97.28% DPPH-released free radicals. Pretreatment of OpvE inhibited H2O2-induced oxidative stress and protected against LPS-induced inflammatory injury by respectively regulating the Nrf2/HO-1/COX2 and TLR4/MYD88 signaling pathways. OpvE also showed anti-S. aureus properties with a MIC of 1.2 mg/mL, and with this concentration, OpvE produced an 8.3 ± 0.16 mm inhibition zone on a bacterial plate. Conclusions: This work highlighted the phytochemical character and some bioactivities, as well as the underline mechanism, which would support the further studies and application of O. vulgatum Linn.
Nanosponge-Encapsulated Polyoxometalates: Unveiling the Multi-Faceted Potential Against Cancers and Metastases Through Comprehensive Preparation, Characterization, and Computational Exploration
Authors: Sajjad Muhammad , Malik Muhammad Zubair , Awan Ayesha Bint Umar , Shah Hamid Saeed , Sarfraz Muhammad , Usman Faisal , Chohan Tahir Ali , Wani Tanveer A. , Zargar Seema , Jawad Zobia
Published: February 2025

Background/Objectives: This study examined the fabrication and characterization of nanosponges (NS) laden with polyoxometalates (TiW11Co) with the intention of targeting malignancy. Methods: By employing the emulsion solvent diffusion technique, TiW11Co-NS were generated by combining polyvinyl alcohol (PVA) and ethyl cellulose (EC) in different concentrations. Results: A significant numerical results encompassed a hydrodynamic particle diameter of 109.5 nm, loading efficiencies reaching 85.9%, and zeta potentials varying from −24.91 to −27.08 (mV). Scanning and transmission electron microscopy were employed to validate the TiW11Co-NS porous structure and surface morphology. The results of the stability investigation indicated that TiW11Co-NS exhibited prolonged sturdiness. Investigation examining the inhibition of enzymes revealed that TiW11Co-NS exhibited enhanced effectiveness against TNAP. Pharmacological evaluations of TiW11Co-NS demonstrated improved cytotoxicity and apoptotic effects in comparison to pure TiW11Co, thereby indicating their potential utility in targeted cancer therapy. In vivo investigations involving mice revealed that TiW11Co-NS caused a more substantial reduction in tumor weight and increased survival rates in comparison to pure TiW11Co. The resemblance of TiW11Co for crucial proteins associated with cancer proliferation was featured through molecular docking, thereby supporting its therapeutic potential. Conclusions: The TiW11Co-laden nanosponges demonstrated superior stability, enzyme inhibition, cytotoxicity, and in vivo anticancer efficacy, underscoring their potential for targeted cancer therapy.
Formulation of W/O/W Emulsion-Based Chitosan-Alginate Microcapsules for Encapsulation of Cannabidiol and A. annua L. Extract Containing Luteolin and Apigenin: A Response Surface Optimization Approach
Authors: Nemickaite Emilija , Zlabiene Ugne , Mazurkeviciute Agne , Marksa Mindaugas , Bernatoniene Jurga
Published: February 2025

Background/Objectives: Chitosan–alginate microcapsules were produced to encapsulate bioactive compounds from Artemisia annua L. extract (apigenin, luteolin) and cannabidiol (CBD). The study aimed to optimize emulsion composition and encapsulation parameters for potential applications in food supplements and pharmaceuticals. Methods: A water-in-oil-in-water (W/O/W) emulsion and a modified coacervation extrusion technique were employed. The study was conducted in two phases using response surface methodology. Key metrics included encapsulation efficiency (EE), yield (EY), cumulative release in vitro, and physicochemical and morphological properties, analyzed via scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), high-performance liquid chromatography with a diode array detector (HPLC-DAD), and gas chromatography with flame ionization detection (GC-FID). Results: The optimal conditions were identified as 0.1% Tween 20, 3.8% Span 80, 3.8% CBD, 19.9% A. annua L. extract, 1.5% outer-phase Tween 20, 48.5% sodium alginate, 200 rpm stirring for 30 min, and a 0.05 mL/min flow rate. The EE values were 80.32 ± 4.11% for CBD, 88.13 ± 3.13% for apigenin, and 88.41 ± 4.17% for luteolin, with respective cumulative releases of 77.18 ± 4.4%, 75.12 ± 4.81%, and 75.32 ± 4.53%. Conclusions: The developed microcapsules demonstrated high encapsulation efficiency and controlled release, highlighting their potential for further development in food supplements and pharmaceuticals. Future studies should focus on refining the formulation for improved bioavailability and stability.
Glycine-Group-Functionalized Polymeric Materials Impregnated with Zn(II) Used in the Photocatalytic Degradation of Congo Red Dye
Authors: Cocheci Laura , Visa Aurelia , Maranescu Bianca , Lupa Lavinia , Pop Aniela , Dragan Ecaterina Stela , Popa Adriana
Published: February 2025

Reducing the ecological impact of dyes through wastewater discharge into the environment is a challenge that must be addressed in textile wastewater pollution prevention. Congo red (CR) dye is widely used in experimental studies for textile wastewater treatment due to its high organic loads used in its preparation. The degradation of organic dyes of the CR type was investigated using the photocatalytic activity of functionalized polymers. We have employed photodegradation procedures for both polymer-supported glycine groups (Code: AP2) and polymer-supported glycine-Zn(II) (Code: AP2-Zn(II)). A photocatalysis efficiency of 89.2% was achieved for glycine pendant groups grafted on styrene-6.7% divinylbenzene copolymer (AP2) and 95.4% for the AP2-Zn(II) sample by using an initial concentration of CR of 15 mg/L, a catalyst concentration of 1 g/L, and 240 min of photocatalysis. The findings provided here have shown that the two materials (AP2 and AP2-Zn(II)) may be effectively employed in the heterogeneous photocatalysis method to remove CR from water. From the perspective of the degradation mechanism of CR, the two photocatalysts act similarly.
Investigation of the Mechanical Properties of Calcareous Sand Improved by Polyurethane Foam Adhesive Under Fixed Principal Stress Axes Shearing
Authors: Chang Dan , Xie Yongjun , Zhang Xinghua , Liu Jiankun
Published: February 2025

The mechanical properties and envelope curve predictions of polyurethane-improved calcareous sand are significantly influenced by the magnitude and direction of principal stress. This study conducted a series of directional shearing tests with varying polyurethane contents (c = 2.5%, 5%, and 7.5%), stress Lode angles (θσ  = −19.1°, 0°, 19.1°, and 30°), and major principal stress angles (α = 0°, 30°, 45°, 60°, and 90°) to investigate the strength and non-coaxial characteristics of calcareous sand improved by polyurethane foam adhesive (PFA). Key findings revealed that failure strength varied significantly with the major principal stress axis direction, initially decreasing to a minimum at α = 45° before increasing, with a 30% decrease and 25% increase observed at c = 5%. Non-coaxial characteristics between strain increment and stress directions became more pronounced, with angles varying up to 15°. Increasing polyurethane content from 2.5% to 7.5% enhanced sample strength by 20% at θσ = −19.1° and α = 60°. A generalized linear strength theory in the π-plane accurately described strength envelope variations, while a modified Lade criterion, incorporating polymer content, effectively predicted multiaxial strength characteristics with less than 10% deviation from experimental results. These contributions provide quantitative insights into failure strength and non-coaxial behavior, introduce a robust strength prediction framework, and enhance multiaxial strength prediction accuracy, advancing the understanding of polyurethane-improved calcareous sand for engineering applications.
Improving Eco-Friendly Polymer Adhesive Joints: Innovative Toughening Strategies for Consistent Performance Under Various Loading Conditions
Authors: Jalali Shahin , Carbas Ricardo J. C. , Marques Eduardo A. S. , da Silva Lucas F. M.
Published: February 2025

In modern engineering applications, the use of sustainable materials and eco-friendly methods has become increasingly important. Wood joints, especially those strengthened with bio-adhesive, have attracted considerable attention due to their inherent environmental benefits and desirable mechanical properties. Compared to traditional joining methods, adhesive joints offer unique advantages such as improved load distribution, reduced stress concentration, and enhanced aesthetic appeal. This study aims to enhance delamination resistance in wooden adhesive joints using a novel method involving reinforced high-toughness resin on surfaces. Additionally, a hybrid substrate approach applies a tough layer to outer plies and a densified wood core with greater fiber direction strength. Normal, toughened, and hybrid single-lap joint specimens were analyzed through both experimental and numerical methods under various loading conditions, including quasi-static and intermediate rates. The proposed method involved bio-adhesive penetration into the wood substrate, forming a reinforced surface zone. The experimentally validated results show a significant improvement in joint strength, exhibiting an approximate 2.8-fold increase for the toughened joints compared to the reference joints under intermediate-rate conditions. Furthermore, the absorbed energy of the toughened joints increased by a substantial factor of up to 4.5 times under the same conditions. The fracture surfaces analysis revealed that the toughening method changed the failure mechanism of the joints from delamination to fiber breakage, indicating that the strength of the substrate was lower than that of the joint under impact conditions. The viscoelastic behavior of the bio-adhesive also influenced the response of the joints to the changing displacement rate. The toughening method enhanced the resilience and load-bearing capacity of the wood joints, making them more suitable for dynamic applications.
A New Precise Power Quality Disturbance Identification Framework Based on Two-Dimensional Characterization Feature Enhancement and Deep Learning
Authors: Ge Yichen , Li Zonglin , Zhou Wenbin , Guo Xinyu , Peng Zhi , Dong Fei
Published: February 2025

The increasing integration of renewable energy sources into electrical grids has exacerbated power quality issues, necessitating advanced methods for the rapid detection and precise classification of power quality disturbances (PQDs). This study presents a novel PQD identification approach that integrates two-dimensional feature enhancement with a deep learning framework to address these challenges. The proposed method employs the relative position matrix (RPM) technique to transform PQD signals into visual representations, enhancing 2D feature extraction by capturing temporal dependencies and inter-point relationships through spatial arrangement. Building on this, Spatial Group-wise Enhance (SGE)-MobileViT, an advanced identification and classification technique that autonomously extracts image features, was introduced for accurate PQD detection. The SGE-MobileViT model incorporates an attention mechanism that adaptively adjusts the feature map significance, optimizing feature space scalability and enabling the effective capture of both local features and global contextual relationships. Experimental results demonstrated the model’s superior performance, achieving 99.17% classification accuracy in noiseless environments and maintaining high accuracy (95.13%, 97.00%, and 97.50%) at signal-to-noise ratios of 20 dB, 30 dB, and 50 dB, respectively. The robustness and practical applicability of SGE-MobileViT were further validated through comprehensive simulations and hardware platform implementations including an embedded system demonstration. This study offers a significant advancement in PQD identification, providing a reliable solution for power quality management in modern electrical grids with high renewable energy penetration.
Lithology Identification of Buried Hill Reservoir Based on XGBoost with Optimized Interpretation
Authors: Zhao Bin , Liao Wenlong
Published: February 2025

Buried hill reservoirs are characterized by complex formation conditions and highly heterogeneous rock structures, which result in the poor performance of traditional crossplot methods in stratigraphic lithology classification. Logging curves, as comprehensive reflections of various petrophysical properties, are influenced by complex geological factors, leading to overlapping response values even among different lithologies with similar physical properties. This overlap negatively impacts the accuracy of intelligent lithology identification methods. To address this challenge, this study leverages logging response data, experimental data, and mud logging data to propose an optimized inversion method for mineral content, introducing mineral curves to resolve the curve overlap issue. By analyzing six wells in the study area, models were constructed using the calculated mineral content curves and conventional logging features to mitigate the feature overlap. The XGBoost algorithm was employed to identify lithologies by addressing the nonlinear relationships inherent in complex reservoirs. The experimental results indicate that the optimized mineral curves significantly enhance the model’s discriminative capability, effectively addressing the decline in identification accuracy due to feature overlap. Compared to models such as Random Forest (RF) and Support Vector Machine (SVM), the XGBoost model demonstrated superior accuracy and stability, providing a reliable basis for precise reservoir identification in the study area.
Evaluation of Three Drying Options (Solar Energy, Traditional Ovens, and Electric Muffle) for the Manufacture of Bricks Using Recycled Aggregates
Authors: Rojas-Valencia María Neftalí , Rodas-Joo Alberto , Martínez-Cruz Alfredo , Araiza-Aguilar Juan Antonio
Published: February 2025

The informality in brick production and fossil fuel use are environmental problems of concern. Construction and demolition waste (CDW) is included in brick manufacturing. Solar drying is an alternative to traditional fossil fuel ovens. From eight formulation mixtures, considering load resistance and 24 h water absorption tests (WA24), two manufacturing mixtures were selected (mixture 2: 40% clay, CDW ¼” 60%; mixture 4: 40% clay, CDW 1 4” 50%, 10% limestone). Three drying processes were evaluated: solar, electric muffle and a traditional oven. With the traditional combustion furnace, mixture 2 obtained the highest mechanical resistance to compression (MRC), obtaining bricks for structural use (145.1 kg cm−2) and WA24 19.3%. Muffle drying mixture 2 obtained MRC 62.1 kg cm−2 and WA24 18.4%. Solar drying mixture 2 obtained MRC 48.3 kg cm−2. Although the use of the muffle and solar option resulted in bricks for non-structural use, solar drying has the environmental benefit of completely eliminating the use of fossil fuels. The proposal to include at least 50% CDW in bricks is an interesting option for upgrading these residues. In Mexico, a 10% drying implementation would lead to a reduction of 0.27 million tons of CO2 each year.
Investigation of Fracture Propagation Simulation and Parameter Optimization of Multi-Cluster Temporary Plugging Fracturing
Authors: Lu Yu , Xie Xiaoyu , Yu Menghong , Qian Keming , Liu Hong , Nie Zunhao , Liu Chang , Lai Siyu
Published: February 2025

The dense cutting fracturing mode has great potential in the exploitation of unconventional oil and gas reservoirs, but it faces the problem of severe uneven crack propagation. Ball-sealer temporary plugging fracturing (BTPF) is capable of effectively facilitating the uniform growth of multi-cluster fractures. In this research, a multi-cluster fracture propagation model for BTPF was established. Then, the impact of ball-sealer efficiency, plugging timing, number of ball-sealer combinations, number of diversions, and perforation number on the propagation of hydraulic fractures after temporary plugging were simulated. The results indicate that ball-sealer efficiency has a significant impact on perforation sealing and fracture propagation. The optimization of the BTPF timing and the combination of ball sealers revealed that deploying 56.25% of the total number of ball sealers at 2/3 of the total fracturing time results in a higher degree of uniform crack propagation. The pattern of throwing more temporary balls in the first plugging and fewer temporary balls in the second plugging is superior to other two-step plugging patterns. The combined application of limited entry and temporary plugging in the fracturing process is more conducive to the uniform propagation of multi-cluster fractures. The fracture uniformity after BTPF is consistently higher than that achieved with limited-entry fracturing. This study provides valuable guidance for the reasonable design of ball-seal BTPF schemes.
CO2-Enhanced Gas Recovery (EGR) in Offshore Carbon-Rich Gas Reservoirs—Part 2: EGR Performance and Its Dependency
Authors: Ye Qing , Zha Yuqiang , Xiong Runfu , Zhao Nan , Mo Fengyang , Li Minxuan , Zeng Yuqi , Sun Lei , Liang Bin
Published: February 2025

CO2-enhanced gas recovery (EGR) has emerged as a promising method for improving hydrocarbon production and achieving carbon sequestration in offshore gas reservoirs. This study investigates the performance and influencing factors of CO2-based gas displacement using long core displacement experiments. Consolidated synthetic cores were prepared to replicate reservoir conditions, and experiments were conducted at formation pressure and temperature to evaluate the effects of permeability, injection pressure, CO2 concentration, and core length on gas recovery efficiency. The results reveal that (1) for a homogeneous porous medium, permeability and injection pressure have minimal correlation with recovery efficiency when sufficient gas is injected; (2) direct gas displacement after reservoir depletion outperforms pressure-boosting displacement methods; (3) higher CO2 concentrations delay gas breakthrough, enhance piston-like displacement behavior, and improve recovery efficiency; and (4) core length significantly affects recovery, with longer cores resulting in slower breakthroughs and more stable displacement. Cores of at least 1 m in length are essential for accurately simulating field conditions. For a CO2 injection with a pressure of 7 MPa and a temperature of 81 °C, when 0.87 PV of CO2 is injected, the current recovery can reach 87%, after which the displacement efficiency decreases sharply. The ultimate EGR can be as high as 50%. These findings provide valuable insights into optimizing CO2 injection strategies for enhanced gas recovery in offshore reservoirs, offering guidance for both experimental designs and practical applications in the field.
Carnival, Ritual, and Race-Thinking in the Bolivian Andes
Authors: Cordova Ximena , Mercado Adhemar
Published: February 2025

This paper explores the intersection of race, religion, and colonial legacies through the lens of the Oruro Carnival, examining its role in shaping Bolivian identity. Critical religion scholars argue that the entanglement of race and religion is a product of Western modernity and colonialism, which has influenced both historical and contemporary power relations. This framework is applied to analyse the Carnival, where religious practices and festive performances intersect, reflecting colonial efforts at religious conversion and racial categorisation. By focusing on the ethnography of Oruro’s embodied festive practices, this study investigates how the Carnival contributes to the construction of difference amid Bolivia’s socio-political transformations. This paper also examines how, by the 20th century, colonial religious frameworks intertwined with secular racial categories, particularly through the rise of mestizaje as a nation-building discourse. A historical analysis of Carnival performances reveals how race, religion, and power have continually shaped the celebration, tracing its evolution from a segregated religious practice to a national spectacle, particularly after the 1952 revolution. The mutually configuring relationship between race and religion in Carnival highlights its role in both reinforcing and challenging dominant power structures.
The Priority of Interior Life: Berdyaev and Lonergan in Dialogue on Democracy
Authors: Zaccaron Francesca
Published: February 2025

The purpose of this paper is to demonstrate the priority of interior life for democracy, imagining a dialogue between Nicolai Berdyaev and Bernard Lonergan. My claim is that Berdyaev and Lonergan converge on the same perspective, while affirming that only a subject who considers the spiritual life as the source of her own life and actions and is open to conversion, is able to collaborate with others in building what Lonergan calls a cosmopolis, which represents a core aspect of democracy.
Aligned in Human Dignity? Parallel Anthropological Aspects of EU Tech Regulation and Pope Francis’ Teaching on AI
Authors: Török Bernát , Darabos Ádám
Published: February 2025

The latest technological advancements, above all artificial intelligence, are raising pressing questions regarding the relationship between machines and humans. Not only have previously known challenges arisen in the new context of digital technological achievements, but machines have now penetrated into spheres of human existence where they were not present until now. In addition to other ethical problems, there are therefore fundamental anthropological questions needing answers, which would clarify the role of the latest digital technologies in the context of our individual and social lives, and define the boundaries of the inviolable human spheres. In this study, we compare responses to these challenges from two important sources. First, we will review how the use of artificial intelligence and the anthropological challenges it poses appear in the teaching of Pope Francis, in line with the Catholic Church’s wider teaching on technology. Second, we examine how this problem is treated in the legislation of the European Union, which is a leader in the regulation of digital technologies, including artificial intelligence. We will highlight four concrete challenges posed by artificial intelligence to the human person (profiling, predictive algorithms, human intervention and manipulation), to which the legislation of the European Union has responded partly on anthropological grounds. We will argue that a systematic analysis of the relevant EU regulations on digital technologies (the General Data Protection Regulation, the Digital Services Act, and the Artificial Intelligence Act) can shed light on new aspects of the legal concept of human dignity, which—even without religious justifications—clearly mirror significant elements of Christian anthropology that are present in Pope Francis’ teaching on artificial intelligence. The anthropological approach of the European legislation is remarkable from a comparative perspective, since other globally relevant regulators have not yet taken such a position and seem to prioritize practical advantages, especially effectivity in the context of new technologies.
Board Gender Diversity, Information Asymmetry, and Investment Efficiency: Do Female Voices Make a Difference?
Authors: Almutairi Ngeyan N. , Albaz Maged M. , Hashad Tarek M.
Published: February 2025

This paper investigates how board gender diversity (BGD) impacts information asymmetry (IA) and investment efficiency (IE), in addition to the moderating role of board gender diversity on the nexus between information asymmetry and investment efficiency in Egypt, motivated by the unclear lens and mixed evidence in the previous literature within this area, especially in developing countries. Thus, the paper followed a deductive research approach for 84 listed firms on the Egyptian Exchange from 2014 to 2023. The findings present new evidence that differs from the common findings in the literature, such as a U-shaped non-linear impact that was found regarding the impact of BGD on both IA (20.71% turning point) and IE (20.38% turning point). However, the IA shows a U-shaped non-linear effect on IE, and the incorporation of BGD as a moderator cause shifting from a (U) shape curve to an inverted (U) shape curve. Our findings from Egypt can motivate future academic research to re-examine the role of BGD from other perspectives in other countries and circumstances. In addition, this paper gives professional authorities and regulators insights into the optimal level of BGD and its critical role in corporate governance and performance, not just the benefits of increasing the diversity ratio, and can also help promote gender equality in corporate management, which can have social and economic benefits.
FFLKCDNet: First Fusion Large-Kernel Change Detection Network for High-Resolution Remote Sensing Images
Authors: Chen Bochao , Wang Yapeng , Yang Xu , Yuan Xiaochen , Im Sio Kei
Published: February 2025

Change detection is an important technique that identifies areas of change by comparing images of the same location taken at different times, and it is widely used in urban expansion monitoring, resource exploration, land use detection, and post-disaster monitoring. However, existing change detection methods often struggle with balancing the extraction of fine-grained spatial details and effective semantic information integration, particularly for high-resolution remote sensing imagery. This paper proposes a high-resolution remote sensing image change detection model called FFLKCDNet (First Fusion Large-Kernel Change Detection Network) to solve this issue. FFLKCDNet features a Bi-temporal Feature Fusion Module (BFFM) to fuse remote sensing features from different temporal scales, and an improved ResNet network (RAResNet) that combines large-kernel convolution and multi-attention mechanisms to enhance feature extraction. The model also includes a Contextual Dual-Land-Cover Attention Fusion Module (CD-LKAFM) to integrate multi-scale information during the feature recovery stage, improving the resolution of details and the integration of semantic information. Experimental results showed that FFLKCDNet outperformed existing methods on datasets such as GVLM, SYSU, and LEVIR, achieving superior performance in metrics such as Kappa coefficient, mIoU, MPA, and F1 score. The model achieves high-precision change detection for remote sensing images through multi-scale feature fusion, noise suppression, and fine-grained information capture. These advancements pave the way for more precise and reliable applications in urban planning, environmental monitoring, and disaster management.
CE-RoadNet: A Cascaded Efficient Road Network for Road Extraction from High-Resolution Satellite Images
Authors: Cheng Ke-Nan , Ni Weiping , Zhang Han , Wu Junzheng , Xiao Xiao , Yang Zhigang
Published: February 2025

The reconstruction of road networks from high-resolution satellite images is of significant importance across a range of disciplines, including traffic management, vehicle navigation and urban planning. However, existing models are computationally demanding and memory-intensive due to their high model complexity, rendering them impractical in many real-world applications. In this work, we present Cascaded Efficient Road Network (CE-RoadNet), a novel neural network architecture which emphasizes the elegance and simplicity of its design, while also retaining a noteworthy level of performance in road extraction tasks. First, a simple encoder–decoder architecture (Effi-RoadNet) is proposed, which leverages smoothed dilated convolutions combined with an attention-guided feature fusion module to aggregate features from multiple levels. Subsequently, an extended variant termed CE-RoadNet is designed in a cascaded architecture to enhance the feature representation ability of the model. Benefiting from the concise network design and the prominent representational ability of the stacking mechanism, our network can accomplish better trade-offs between accuracy and efficiency. Extensive experiments on public road datasets demonstrate that our approach achieves state-of-the-art results with lower complexity. All codes and models will be released soon to facilitate reproduction of our results.
An Adaptive Constant Acceleration Model for Maneuvering Target Tracking
Authors: Huang Jieyu , Xie Junwei , Zhai Haolong , Li Zhengjie , Feng Weike
Published: February 2025

An adaptive constant acceleration (ACA) model is proposed for the maneuvering target tracking problem. Based on the Taylor series expansion of acceleration, we establish the relationship between the Jerk and the velocity as well as the acceleration so that the maneuvering acceleration variance is approximated by the components in the state error covariance matrix. Then, the latter one is connected with the process noise, and the adaptive adjustment of the ACA model is realized. Combining with the strong tracking square-root cubature filter (ST-SCKF) in our previous work, an ACA-ST-SCKF is developed. The simulation results show that the proposed filter possesses better adaptability, tracking accuracy and lower computational complexity compared with the adaptive current statistical (ACS) model-based ST-SCKF, the modified CS (MCS) model-based ST-SCKF, and the IMM-based STF-SCKF.
Effects of the Mother’s Day Superstorm (10–11 May 2024) over the Global Ionosphere
Authors: Paul Krishnendu Sekhar , Moses Mefe , Haralambous Haris , Oikonomou Christina
Published: February 2025

The present study examines the global ionospheric response to the “Mother’s Day Superstorm” (10–11 May 2024), one of the most intense geomagnetic storms since 1957, with a minimum SYM-H index of −436 nT. Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) Radio Occultation (RO) data indicated an increase in the F2 layer maximum critical frequency (foF2) over midlatitude dayside regions, which was accompanied by a significant F-region uplift (hmF2 increase) on a global scale, even on the nightside during the main and recovery phases. At the same time, a decrease in foF2 was observed on the nightside. High southeastward and vertical drift velocities were observed in the nightside sector of the northern hemisphere with the dayside sector exhibiting upward and southwestward-to-northwestward drifts during the main and recovery phases of the storm. An intense upward drift (~170 m/s) in the southern hemisphere was registered with the poleward expansion of the Equatorial Ionization Anomaly (EIA) during the main phase. Swarm A data highlighted the EIA expansion from ~45°N to 60°S during the dayside main phase and from ~30°N to 40°S on the nightside during recovery.
Strategic Deployment of a Single Mobile Weather Radar for the Enhancement of Meteorological Observation: A Coverage-Based Location Problem
Authors: Parajuli Bikram , Feng Xin
Published: February 2025

Mobile weather radars have been routinely deployed to acquire high-quality meteorological data for research purposes, particularly for monitoring rapidly evolving weather phenomena at low altitudes. However, identifying an optimal location for mobile weather radar deployment is a complex challenge, as it requires consideration of operational safety, data quality, and environmental constraints. In this study, we introduce a framework using a coverage-based location problem to solve the strategic deployment of a single mobile weather radar. This approach aims to enhance weather observation while accounting for the deployment space’s safety constraints and geospatial characteristics. The proposed location problem is solved optimally using the geometric branch-and-bound algorithm and heuristically using swarm-based optimization algorithms. The implementation relies entirely on open-source Python packages, allowing the work to be verified, replicated, and expanded upon by the broader scientific community. Results demonstrate that exact solution methods are ideal when ample time is available for decision-making and optimal deployment locations are desired. In contrast, heuristic algorithms can efficiently identify multiple near-optimal deployment locations, making them highly suitable for rapid decision-making and evaluating alternative deployment options. Moreover, the findings highlight the potential of quantitative decision-making techniques in improving the effectiveness of mobile radar positioning, thereby contributing to efficient weather observation, forecasting, and better-informed emergency response strategies.
Classification of Large Scale Hyperspectral Remote Sensing Images Based on LS3EU-Net++
Authors: Zhao Hengqian , Lu Zhengpu , Sun Shasha , Wang Pan , Jia Tianyu , Xie Yu , Xu Fei
Published: February 2025

Aimed at the limitation that existing hyperspectral classification methods were mainly oriented to small-scale images, this paper proposed a new large-scale hyperspectral remote sensing image classification method, LS3EU-Net++ (Lightweight Encoder and Integrated Spatial Spectral Squeeze and Excitation U-Net++). The method optimized the U-Net++ architecture by introducing a lightweight encoder and combining the Spatial Spectral Squeeze and Excitation (S3E) Attention Module, which maintained the powerful feature extraction capability while significantly reducing the training cost. In addition, the model employed a composite loss function combining focal loss and Jaccard loss, which could focus more on difficult samples, thus improving pixel-level accuracy and classification results. To solve the sample imbalance problem in hyperspectral images, this paper also proposed a data enhancement strategy based on “copy–paste”, which effectively increased the diversity of the training dataset. Experiments on large-scale satellite hyperspectral remote sensing images from the Zhuhai-1 satellite demonstrated that LS3EU-Net++ exhibited superiority over the U-Net++ benchmark. Specifically, the overall accuracy (OA) was improved by 5.35%, and the mean Intersection over Union (mIoU) by 12.4%. These findings suggested that the proposed method provided a robust solution for large-scale hyperspectral image classification, effectively balancing accuracy and computational efficiency.
Pose Estimation of Coil Workpieces by Automated Overhead Cranes Using an Improved Point Pair Features Algorithm
Authors: Zhuang Yongbo , Man Jianli , Jiang Yuchen , Li Qingdang , Zhang Mingyue
Published: February 2025

To facilitate the automation of crane operations for grabbing coil stacks in port storage areas, thereby streamlining the processes of warehousing, stacking, and transshipment for enhanced operational efficiency, this paper utilizes algorithms related to 3D point clouds for the pose estimation of coil workpieces. To overcome the limitations of the traditional point pair feature (PPF) algorithm, a novel point cloud registration algorithm is introduced. This algorithm harnesses the advantages of the PPF algorithm in describing local features and integrates it with the Generalized Iterative Closest Point (GICP) algorithm to enhance the robustness and applicability of registration. Finally, comparative experiments demonstrate that the proposed algorithm delivers superior performance. The average pose estimation errors for one, two, and three coils are 1.1%, 1.1%, and 1.2% of the coil size, respectively, with total processing times of 3.6 s, 3.4 s, and 4.7 s, meeting the practical application requirements in terms of accuracy and timing.
Filtering and Overlapping Data for Accuracy Enhancement of Doppler-Based Location Method
Authors: Szczepanik Rafał , Kelner Jan M.
Published: February 2025

The localization of radio emitters is a fundamental task in reconnaissance systems, and it has become increasingly important with the evolution of mobile networks. The signal Doppler frequency (SDF) method, developed for dual-use applications, leverages Doppler frequency shifts (DFSs) in received signals to estimate the positions of radio transmitters. This paper proposes enhancements to the SDF method through advanced signal processing techniques, including dedicated filtering and a novel two-level overlapping approach, which significantly improve localization accuracy. The overlapping technique increases the number of DFS estimations per time unit by analyzing overlapping segments at both the signal sample level and within the DFS vector. Simulation studies using various filter types and overlapping parameters were conducted to evaluate the effectiveness of these enhancements in a dynamic scenario involving multiple stationary transmitters and a single moving receiver. The results demonstrate that the proposed approach minimizes localization errors. The application of low-pass filtering at the DFS vector level improves localization accuracy. In the study, three types of filters for different cutoff frequencies are considered. Each of the analyzed filters with an appropriately selected cutoff frequency provides a comparable reduction in localization error at the level of about 30%. The use of overlapping at the signal sample level with a factor of 10% allows for more than a twofold decrease in localization errors, while overlapping at the DFS vector provides an increase in the refresh rate of the position of localized objects. Comparative analysis with direct position determination techniques additionally showed high effectiveness of the SDF method, especially using data filtration and overlapping. The simulation studies carried out are of significant importance for the selection of the operating parameters of real localization sensors in unmanned aerial vehicle (UAV) equipment.
High-Precision, Self-Powered Current Online Monitoring System Based on TMR Sensors Array for Distribution Networks
Authors: An Zhengang , Zhang Lei , Wang Zhi , Fan Yanyun , Zu Zhiwei , Li Zhengzhe , Li Dachao
Published: February 2025

Establishing a maintenance-free current sensing network across the entire power grid to facilitate wide-area online monitoring is crucial for realizing a smart grid. However, distribution networks (DNs) frequently lack effective real-time current monitoring owing to the complexity of load types, extensive line distribution, and numerous branches. In this study, we propose a high-precision, self-powered online current monitoring system that integrates a TMR sensors array module, a main control module, a current transformer (CT) power harvesting module, and current online monitoring software. The TMR sensors array module boasts a measurement range of 0–300 A and a high sensitivity of 25.38 mV/A. To address wire eccentricity errors in array sensors, we develop a neural network-based correction algorithm, which identifies wire positions and applies correction coefficients, achieving high accuracy with an average error of 1.23%. Current data are wirelessly transmitted to software terminals via 4G communication for remote monitoring. Furthermore, the CT power harvesting module converts magnetic energy from the power grid into electrical energy, ensuring that the system is self-powered. Validation through continuous 24-h monitoring of DNs demonstrates the system’s high precision and stability. This work presents an effective solution for high-accuracy online current monitoring in DNs.