bluuebunny/crossref_metadata_2025 huggingface dataset:
Lipoprotein(a) Response to Dietary Saturated Fat Reduction: Relationship to Apolipoprotein(a) Size Polymorphism in African Americans
Authors: Law Hayley G. , Myagmarsuren Munkhtuya , Bang Heejung , Zhang Wei , Lefevre Michael , Berglund Lars , Enkhmaa Byambaa
Published: January 2025

Background/Objectives: An elevated lipoprotein(a) [Lp(a)] level, which is a prevalent cardiovascular risk factor, is genetically determined by a size polymorphism of its apolipoprotein(a) [apo(a)] component. Despite its genetic control, Lp(a) level increases in response to dietary saturated fat (SFA) reduction. We tested the roles of apo(a) size and characteristics in modulating Lp(a) response to SFA reduction. Methods: We assessed apo(a) characteristics in 165 African Americans experiencing a 24% Lp(a) increase resulting from SFA reduction [16% at an average American Diet diet (AAD) to 6% at a DASH-type diet]. Apo(a) effects were tested based on the following factors: (1) the presence of a small atherogenic size (≤22 kringles), (2) phenotype (single or two isoforms), (3) isoform dominance, and (4) tertiles of combined kringle sizes. Results: There were no significant differences in Lp(a) increases between carriers vs. non-carriers of a small apo(a), between those with a single vs. two expressed isoforms, or in those with differing isoform dominance patterns (p > 0.05 for all). The extent of Lp(a) increase differed across increasing tertiles of combined kringle sizes (p = 0.006 for trend). In a multivariate model, the AAD Lp(a) level was a significant predictor of Lp(a) changes (p < 0.05). Relative increases in the allele-specific apo(a) level—an Lp(a) level associated with a defined apo(a) size—were similar across the apo(a) size spectrum. Conclusions: Reducing dietary SFA intake results in a 24% increase in Lp(a) level in African Americans across apo(a) sizes. Individuals with smaller apo(a) sizes reached an elevated Lp(a) level post-intervention compared to those with larger sizes, in some cases resulting in cardiovascular risk reclassification.
Single-Cell Analysis Dissects the Effects of Vitamin D on Genetic Senescence Signatures Across Murine Tissues
Authors: Sosa-Díaz Emilio , Reyes-Gopar Helena , de Anda-Jáuregui Guillermo , Hernández-Lemus Enrique
Published: January 2025

Background/Objectives: Vitamin D (VD) plays a crucial role in age-related diseases, and its influence on cellular senescence (CS) could help clarify its function in aging. Considering VD’s pleiotropic effects and the heterogeneity of CS. Methods: we utilized single-cell RNA sequencing (scRNA-seq) to explore these dynamics across multiple tissues. We analyzed three murine tissue datasets (bone, prostate, and skin) obtained from public repositories, enriching for senescence gene signatures. We then inferred gene regulatory networks (GRNs) at the tissue and cell-type levels and performed two cell communication analyses: one for senescent cells and another for interactions between senescent and non-senescent cells. Results: VD supplementation significantly decreased senescence scores in the skin (p = 3.96×10−134) and prostate (p=1.56×10−34). GRN analysis of the prostate revealed an altered macrophage–fibroblast regulatory relationship. In bone, distinct aging-related modules emerged for different bone lineages. In skin, contrary differentiation patterns between suprabasal and basal cells were observed. The main VD-modulated pathways were involved in inflammation, extracellular matrix remodeling, protein metabolism, and translation. VD reduced fibroblast–macrophage interactions in the prostate and skin but increased overall cellular crosstalk in bone. Conclusions: Our findings demonstrate that VD alleviates CS burden across tissues by modulating inflammation and metabolic processes and promoting differentiation. Key aging-related genes modulated by VD were linked to anabolism and cellular differentiation, suggesting VD’s potential for therapeutic interventions targeting age-related diseases.
Voluntary Assisted Dying and Community Palliative Care: A Retrospective Study in Victoria, Australia
Authors: Molenaar Robert , Lee Susan , Lynch Jodi , Rogerson Kelly
Published: January 2025

Background/Objectives: Voluntary Assisted Dying in Australia was first legislated in 2019, with significant concern expressed by palliative care services about the impact on services. We aimed to describe the impact of Voluntary Assisted Dying on community-based palliative care client care. Methods: This study was a retrospective cohort study that compared the characteristics and outcomes of clients who expressed interest in VAD, those who chose voluntary assisted death, and the broader client population of the service. Results: Only 4% of the total client population expressed interest in VAD, and 1% died through VAD. Of the clients who expressed interest in VAD, most had malignancy as their primary diagnosis. The median length of palliative care service for clients who expressed interest in VAD was 101 days, compared to 48 days for all service clients. For 97% of individuals who died from taking the substance, death occurred in their place of choice compared with 71% of all service clients. Of the clients who died through VAD, 88% of these deaths occurred in a community setting compared with 56% of all service clients. Conclusions: Most clients who took the VAD medicine died in their place of choice, which was the community. A review of the length of engagement with the service indicated that a longer length of engagement was illustrated by individuals navigating the VAD process. This study emphasised the value of early referral to community-based palliative care, enabling a focus on quality of life, symptom management, and planning for death.
IchthyNet: An Ensemble Method for the Classification of In Situ Marine Zooplankton Shadowgraph Images
Authors: Slocum Brittney , Penta Bradley
Published: January 2025

This study explores the use of machine learning for the automated classification of the ten most abundant groups of marine organisms (in the size range of 5–12 cm) plus marine snow found in the ecosystem of the U.S. east coast. Images used in this process were collected using a shadowgraph imaging system on a towed, undulating platform capable of collecting continuous imagery over large spatiotemporal scales. As a large quantity (29,818,917) of images was collected, the task of locating and identifying all imaged organisms could not be efficiently achieved by human analysis alone. Several tows of data were collected off the coast of Delaware Bay. The resulting images were then cleaned, segmented into regions of interest (ROIs), and fed through three convolutional neural networks (CNNs): VGG-16, ResNet-50, and a custom model created to find more high-level features in this dataset. These three models were used in a Random Forest Classifier-based ensemble approach to reach the best identification fidelity. The networks were trained on a training set of 187,000 ROIs augmented with random rotations and pixel intensity thresholding to increase data variability and evaluated against two datasets. While the performance of each individual model is examined, the best approach is to use the ensemble, which performed with an F1-score of 98% and an area under the curve (AUC) of 99% on both test datasets while its accuracy, precision, and recall fluctuated between 97% and 98%.
Demystifying the Antidepressant Mechanism of Action of Stinels, a Novel Class of Neuroplastogens: Positive Allosteric Modulators of the NMDA Receptor
Authors: Donello John E. , McIntyre Roger S. , Pickel Donald B. , Stahl Stephen M.
Published: January 2025

Plastogens are a class of therapeutics that function by rapidly promoting changes in neuroplasticity. A notable example, ketamine, is receiving great attention due to its combined rapid and long-term antidepressant effects. Ketamine is an N-methyl-D-aspartate receptor (NMDAR) antagonist, and, in addition to its therapeutic activity, it is associated with psychotomimetic and dissociative side effects. Stinels—rapastinel, apimostinel, and zelquistinel—are also plastogens not only with rapid and long-term antidepressant effects but also with improved safety and tolerability profiles compared to ketamine. Previous descriptions of the mechanism by which stinels modulate NMDAR activity have been inconsistent and, at times, contradictory. The purpose of this review is to clarify the mechanism of action and contextualize stinels within a broader class of NMDAR-targeting therapeutics. In this review, we present the rationale behind targeting NMDARs for treatment-resistant depression and other psychiatric conditions, describe the various mechanisms by which NMDAR activity is regulated by different classes of therapeutics, and present evidence for the stinel mechanism. In contrast with previous descriptions of glycine-like NMDAR partial agonists, we define stinels as positive allosteric modulators of NMDAR activity with a novel regulatory binding site.
High-Speed Target Location Based on Photoelectric Imaging and Laser Ranging with Fast Steering Mirror Deflection
Authors: Shi Kui , Yang Hongtao , Peng Jianwei , Ma Yingjun , Zhang Hongwei
Published: January 2025

There is an increasing number of spacecrafts in orbit, and the collision impact of high-speed moving targets, such as space debris, can cause fatal damage to these spacecrafts. It has become increasingly important to rapidly and accurately locate high-speed moving targets in space. In this study, we designed a visible-light telephoto camera for observing high-speed moving targets and a laser rangefinder for measuring the precise distance of these targets, and we proposed a method of using fast steering mirror deflection to quickly direct the emitted laser towards such targets and measure the distance. Based on the principle of photographic imaging and the precise distance of targets, a collinear equation and a spatial target location model based on the internal and external orientation elements of the camera and the target distance were established, and the principle of target location and the method for calculating target point coordinates were determined. We analyzed the composition of target point location error and derived an equation for calculating such errors. Based on the actual values of various error components and the error synthesis theory, the accuracy of target location was calculated to be 26.5 m when the target distance is 30 km (the relative velocity is 8 km/s and the velocity component perpendicular to the camera’s optical axis is less than 3.75 km/s). This study provides a theoretical basis and a method for solving the practical needs of quickly locating high-speed moving targets in space and proposes specific measures to improve target location accuracy.
Identification of the KNOX Gene Family in Salvia miltiorrhiza Revealing Its Response Characteristics to Salt Stress
Authors: Deng Siqi , Ma Wenjing , Cui Chunxu , Wang Shiqian , Jiang Mei
Published: January 2025

Salvia miltiorrhiza is a herbaceous plant that possesses significant medicinal value. Land salinization affects the growth of S. miltiorrhiza, resulting in a decline in its quality and yield. Knotted1-like homeobox (KNOX) genes are transcription factors that play important roles in plant growth and abiotic stress. The characteristics and functions of KNOX genes in S. miltiorrhiza remain unclear. Here, we identified ten KNOX genes in S. miltiorrhiza, all of which possess the characteristic four domains: KNOX1, KNOX2, ELK, and HD. These SmKNOXs were divided into two groups together with homologous genes. Cis-acting element analysis indicated all SmKNOXs contained elements associated with phytohormone, light, and stress response. The SmKNOXs show tissue-specific expression among roots, stems, leaves, and flowers. We assessed the response of the SmKNOXs to salt stress using quantitative RT-PCR analysis. Notably, SmKNOX4 expression significantly decreased within 24 h of salt exposure, while SmKNOX1, SmKNOX2, SmKNOX3, SmKNOX8, and SmKNOX9 showed significant increases. The expression of SmKNOX1, SmKNOX2, and SmKNOX3 was significantly positively correlated with that of their target genes, GA20ox1 and S11 MYB. These findings suggest that SmKNOXs and their target genes respond to salt stress, providing a foundation for studies of SmKNOXs and the potential genetic improvement of S. miltiorrhiza.
Fumaria officinalis Dust as a Source of Bioactives for Potential Dermal Application: Optimization of Extraction Procedures, Phytochemical Profiling, and Effects Related to Skin Health Benefits
Authors: Ahmoda Rabiea Ashowen , Pirković Andrea , Milutinović Violeta , Milošević Milena , Marinković Aleksandar , Jovanović Aleksandra A.
Published: January 2025

Fumaria officinalis (fumitory), in the form of dust, was employed as a source of bioactive extracts whose chemical profile and biological potential were investigated. According to the results of the optimization of the extraction protocol, the extract with the highest polyphenol yield was prepared using fumitory dust under the optimal conditions determined using the statistical tool, 23 full factorial design: 50% ethanol and a 30:1 mL/g ratio during 120 s of microwave extraction (22.56 mg gallic acid equivalent/g of plant material). LC-MS and spectrophotometric/gravimetric analyses quantified the polyphenol, flavonoid, tannin, alkaloid, and protein contents. Caffeoylmalic acid, quercetin dihexoside, quercetin pentoside hexoside, rutin, and methylquercetin dihexoside were the most dominant compounds. The highest total flavonoid, condensed tannin, alkaloid, and protein yields were determined in the extract prepared using microwaves. In addition to the proven antioxidant potential, in the present study, the anti-inflammatory activity of fumitory extracts is also proven in the keratinocyte model, as well as a significant reduction of H2O2-induced reactive oxygen species production in cells and the absence of keratinocyte cytotoxicity. Thus, detailed chemical profiles and investigated biological effects related to skin health benefits encourage the potential application of fumitory dust extracts in dermo-cosmetic and pharmaceutical preparations for dermatological circumstances.
Efficient Tomato Disease Detection Using MaxMin-Diffusion Mechanism and Lightweight Techniques
Authors: Guo Haoxin , Liu Jiarui , Li Yan , Xu Yifei , Xu Keyi , Fan Anzhuo , Hao Jiarui , Hou Yifei , Lv Chunli
Published: January 2025

This paper proposes a disease detection model based on the maxmin-diffusion mechanism, aimed at improving the accuracy and robustness of disease detection tasks in the agricultural field. With the development of smart agriculture, automated disease detection has become one of the key tasks driving agricultural modernization. Traditional disease detection models often suffer from significant accuracy loss and robustness issues when dealing with complex disease types and dynamically changing time-series data. To address these problems, this paper introduces the maxmin-diffusion mechanism, which dynamically adjusts attention weights to enhance the model’s focus on key disease regions while suppressing interference from irrelevant areas, significantly improving the segmentation accuracy of disease regions. Through a series of experiments, the proposed model demonstrates outstanding performance across various disease detection tasks. For bacterial spot disease detection, the model achieves a precision of 0.98, recall of 0.95, accuracy of 0.96, and mIoU of 0.96, indicating that it can efficiently and accurately identify disease regions even in complex backgrounds. Compared to traditional self-attention and CBAM mechanisms, the maxmin-diffusion mechanism shows significant advantages in fine-grained feature extraction and time-series data processing, particularly in the recognition of dynamically changing disease regions, where it exhibits higher detection accuracy and robustness. Furthermore, the model underwent lightweight optimization, enabling the proposed disease detection model to not only achieve high-precision detection but also run efficiently on resource-constrained mobile devices. This provides strong technical support for the application of smart agriculture.
Regulation of Rice Grain Quality by Exogenous Kinetin During Grain-Filling Period
Authors: Xiao Yunhua , Dong Yating , Zhou Meng , Wang Yingfeng , Liu Xiong , Lu Xuedan , Zhang Guilian , Wang Feng , Tang Wenbang , Deng Huabing
Published: January 2025

Cytokinins (CKs) play important functions in plant growth and development and in response to adversity stress. However, little is known about the role CK plays in rice grain quality. We hypothesized that exogenous cytokinins could improve rice grain quality by regulating physiological traits and genes related to starch synthesis. Therefore, we exogenously applied different concentrations of kinetin (KT), an exogenous CK, during the grain-filling period. Our results show that all the different concentrations of exogenous KT treatments resulted in a significant increase in thousand-grain weight. In particular, chalkiness and chalky grain rate were significantly reduced, and gel consistency (GC) content and alkali spreading value (ASV) were significantly increased in 10−8 M KT treatment. Meanwhile, the exogenous application of 10−8 M KT positively affected the transcription of some starch synthesis-related genes, which was in contrast to the 10−5 M KT treatment. In conclusion, the exogenous application of appropriate concentrations of KT during the grain-filling period can ultimately affect rice grain quality by regulating the changes in the relevant indicators, such as appearance quality (AQ) and eating and cooking qualities (ECQ).
Chemical Profile and Bioactivities of Three Species of Mentha Growing in the Campania Region, Southern Italy
Authors: Francolino Rosaria , Martino Mara , Nazzaro Filomena , Sirignano Carmina , Fratianni Florinda , Coppola Francesca , De Martino Laura , Formisano Carmen , De Feo Vincenzo
Published: January 2025

The genus Mentha (Lamiaceae), comprising aromatic perennial plants widely distributed in temperate regions, holds significant medicinal and commercial value. This study aimed to investigate the chemical profile and bioactivities of hydroalcoholic extracts from Mentha longifolia (L.) L., Mentha pulegium L., and Mentha spicata L. harvested from the Campania region, Southern Italy. Chemical analysis using LC-HRESIMS/MS identified a total of 21 compounds. The extracts, particularly M. pulegium, exhibited notable antioxidant activity, evaluated through DPPH and FRAP assays, probably related to their chemical composition. Both M. pulegium and M. longifolia demonstrated a higher phenolic content, with M. pulegium also containing the highest levels of flavonoids. In addition, the extract’s ability to inhibit biofilm formation was evaluated against several pathogenic strains, including Gram-positive bacteria (Listeria monocytogenes and Staphylococcus aureus) and Gram-negative bacteria (Acinetobacter baumannii, Pseudomonas aeruginosa, and Escherichia coli) using crystal violet and MTT assays. All extracts effectively inhibited biofilm formation in A. baumannii and P. aeruginosa, with M. pulegium also showing moderate activity against the metabolism of L. monocytogenes. The pronounced antibacterial and biofilm-inhibitory properties of M. pulegium highlight its potential for pharmaceutical applications.
Transparent Poly(amide-imide)s with Low Coefficient of Thermal Expansion from Trifluoromethylated Trimellitic Anhydride
Authors: Kim Seong Jong , Jeong SeongUk , Byun Taejoon , Kim Jun Sung , Lee Haeshin , Kim Sang Youl
Published: January 2025

Making transparent aromatic polymers with high Tg and low thermal expansion behavior, like glass, is challenging. We report transparent and soluble poly(amide-imide)s (PAIs) with high dimensional stability synthesized from the new monomer, trifluoromethylated trimellitic anhydride. Insertion of trifluoromethyl (CF3) groups into polymer chains enhanced solubility and the optical properties of polymers without sacrificing high thermal stability. Model reactions were utilized to study how the CF3 group in trimellitic anhydride affects the polymerization reaction with aromatic diamine monomers, and a series of new PAIs were synthesized. All the polymers were soluble in polar organic solvents and can be solution-cast into nearly colorless and flexible freestanding films. The obtained PAI films possessed high thermal stability (Td5: 437–452 °C in N2) and high transparency (84~87% transmittance at 550 nm). Interestingly, PAIs prepared in this study exhibited high thermodimensional stability with low CTE values from 9 to 26 ppm/°C. The transparent poly(amide-imide) film with low CTE value finds its application in display and optical devices that require flexible and transparent form factors.
Study of the Physical–Chemical, Thermal, Structural, and Rheological Properties of Four High Andean Varieties of Germinated Chenopodium quinoa
Authors: Ramos-Pacheco Betsy S. , Ligarda-Samanez Carlos A. , Choque-Quispe David , Choque-Quispe Yudith , Solano-Reynoso Aydeé M. , Choque-Quispe Katia , Palomino-Rincón Henry , Taipe-Pardo Fredy , Peralta-Guevara Diego E. , Moscoso-Moscoso Elibet , Diaz-Barrera Yasmine , Agreda-Cerna Henrry Wilfredo
Published: January 2025

Chenopodium quinoa, a high Andean grain with excellent nutritional value and complex molecular structure, presents significant challenges in the bioavailability of nutrients and the functionality of its components. Germination as a biotechnological strategy generated significant modifications in four varieties of quinoa. The ungerminated and germinated samples’ physical–chemical, thermal, structural, and rheological properties were determined. Results showed increases in protein bioavailability (14.13% in Black Collana Quinoa (BCQ) and 12.79% in Red Pasankalla Quinoa (RPQ)), phenolic compounds (30.81 mg Gallic Acid Equivalent/100 g in RPQ), flavonoids (108.53 mg Quercetin Equivalent/100 g in Yellow Marangani Quinoa (YMQ)), and antioxidant capacity (up to 241.43 μmol Trolox Equivalent/g in BCQ). Thermal analysis showed increases in gelatinization temperature (57.53 °C to 59.45 °C in RPQ) and a reduction in enthalpy (1.38 J/g to 0.67 J/g). Structural analysis showed similar functional groups, but variation in spectra intensity was related to starches and proteins. Rheological properties exhibited pseudoplastic behavior at 80 °C. Principal component analysis showed a clear difference between germinated and non-germinated samples. The germination process significantly modified quinoa, improving its nutritional and functional properties and generating new opportunities for its application in the development of biodegradable materials and functional foods.
Study on Hypervelocity Impact Characteristics of Polypropylene Spheres on Whipple Shields
Authors: Zhang Yuxiang , Wang Shengjie , Chi Runqiang , Liu Qingxu , Li Xiaodan , Kan Chunlei , Xu Sainan , Ma Jun
Published: January 2025

The hypervelocity impact characteristics of 7 mm diameter polypropylene spheres on Whipple shields with different bumper thicknesses at a velocity ranging from 2.257 to 4.012 km/s are studied in this work. The study has found that the hypervelocity impact characteristics of the polypropylene sphere contrast markedly with those of the aluminum sphere. The polypropylene sphere transfers greater energy to the bumper upon impact, leading to a larger perforation. Meanwhile, the microstructure at the perforation site is complex. The polypropylene sphere undergoes a phase change after impacting the bumper, forming a debris cloud primarily composed of ‘liquid filaments’. This debris cloud creates a ‘groove’-shaped damage morphology on the rear wall. The polypropylene sphere exhibits weaker perforation capabilities but generates a more extensive damage area on the rear wall.
A Comparative Study on the Structural, Physicochemical, Release, and Antioxidant Properties of Sodium Casein and Gelatin Films Containing Sea Buckthorn Oil
Authors: Kowalczyk Dariusz , Karaś Monika , Kazimierczak Waldemar , Skrzypek Tomasz , Wiater Adrian , Bartkowiak Artur , Basiura-Cembala Monika
Published: January 2025

The aim of this study was to compare the effect of increasing concentrations (0, 1, 2, 4%) of sea buckthorn oil (SBO) on the structural, physicochemical, release, and antioxidant properties of glycerol-plasticized sodium casein (NaCAS) and gelatin (GEL) films. Ultrasonic treatment ensured effective homogenization of SBO in both types of emulsions, resulting in yellow-tinted semi-opaque films with relatively low micro-roughness. Generally, GEL films demonstrated lower UV barrier properties and solubility but exhibited higher compactness, crystallinity, transparency, surface hydrophobicity, oxygen barrier performance, strength, and antiradical activity compared to their NaCAS-based counterparts. In a concentration-dependent manner, SBO decreased the solubility and water absorption of the gelatin-based film and enhanced its oxygen permeability. Conversely, SBO improved the water vapor barrier properties of both films in a concentration-independent manner. At the highest SBO concentration, the tensile strength of NaCAS- and GEL-based films decreased by 27% and 20%, respectively, while their antiradical activity increased by 9.3× and 4.3× (based on the time required for the half-neutralization of 2,2-diphenyl-1-picrylhydrazyl radicals). Migration studies showed that at the lowest concentration, SBO was released (into 95% ethanol) approximately 2× faster from the GEL-based film than from the NaCAS film, whereas at higher concentrations, the trend reversed.
Short-Term Optimal Scheduling of a Cascade Hydro-Photovoltaic System for Maximizing the Expectation of Consumable Electricity
Authors: Hu Shuzhe , Miao Jinniu , Wu Jingyang , Zhao Liqian , Wang Yue , Meng Fanyan , Wei Chao , Zhang Xiaoqin , Zhu Benrui
Published: January 2025

Fully leveraging the regulatory role of cascade hydropower in river basins and realizing complementary joint power generation between cascade hydropower and photovoltaic (PV) systems is a crucial approach to promoting the consumption of clean energy. Given the uncertainty of PV outputs, this paper introduces a short-term scheduling model for cascade hydropower–PV systems. The model aims to maximize electricity consumption by considering individual units, hydropower plant constraints, unit constraints, and grid constraints. By allocating loads among hydropower plants and periods, it optimizes hydropower’s dual roles, supporting grid power supplies and coordinating with PVs, thus boosting the overall system consumption. In terms of model solution, linearization methods and modeling techniques such as piecewise linear approximation, the introduction of 0–1 integer variables, and the discretization of generation headwater are employed to handle the nonlinear constraints in the original model, transforming it into a mixed-integer linear programming problem. Finally, taking a complementary system constructed by 15 units of 4 hydropower stations and 2 photovoltaic groups in a cascade in a river basin in Southwest China as an example, the results show that through the complementary coordination of cascade hydropower and photovoltaic power, under the same grid constraints, the expected value of the power consumption of the complementary system in the model of this paper increased by 863.2 MW·h, among which the power consumption of photovoltaic group 1 increased by 1035.7 MW·h, and the power consumption of photovoltaic group 2 decreased by 172.5 MW·h.
The Woman Who Took the Initiative: A Look at Mark 5:25–34 from the Perspective of Power Dynamics
Authors: König Judith
Published: January 2025

The paper takes a close look at the ‘hemorrhaging woman’ in Mark 5:25–34. The pericope is analyzed from the perspective of power dynamics. Utilizing tools of narratological analysis, both the woman and Jesus come into focus. By also taking concepts of social psychology into consideration, the question of power dynamics in the pericope is seen from a new perspective. Thus, the article differentiates what kind of power the woman and Jesus hold in Mark 5:25–34. The article argues that while Jesus is known by readers to hold both social and personal power, he does not actively exercise it in the pericope under consideration. The Woman who Took the Initiative on the other hand holds no social but a great deal of personal power which she not only holds but also applies to make sure she will receive the healing she has come for.
Multi-Scale Analysis of Knee Joint Acoustic Signals for Cartilage Degeneration Assessment
Authors: Machrowska Anna , Karpiński Robert , Maciejewski Marcin , Jonak Józef , Krakowski Przemysław , Syta Arkadiusz
Published: January 2025

This study focuses on the diagnostic analysis of cartilage damage in the knee joint based on acoustic signals generated by the joint. The research utilizes a combination of advanced signal processing techniques, specifically empirical mode decomposition (EEMD) and detrended fluctuation analysis (DFA), alongside convolutional neural networks (CNNs) for classification and detection tasks. Acoustic signals, often reflecting the mechanical behavior of the joint during movement, serve as a non-invasive diagnostic tool for assessing the cartilage condition. EEMD is applied to decompose the signals into intrinsic mode functions (IMFs), which are then analyzed using DFA to quantify the scaling properties and detect irregularities indicative of cartilage damage. The separation of individual frequency components allows for multi-scale analysis of the signals, with each of the functions resulting from the analysis reflecting local variations in the amplitude and frequency over time and allowing for effective removal of noise present in the signal. The CNN model is trained on features extracted from these signals to accurately classify different stages of cartilage degeneration. The proposed method demonstrates the potential for early detection of knee joint pathology, providing a valuable tool for preventive healthcare and reducing the need for invasive diagnostic procedures. The results suggest that the combination of EEMD-DFA for feature extraction and CNN for classification offers a promising approach for the non-invasive assessment of cartilage damage.
Cross-Modal Transformer-Based Streaming Dense Video Captioning with Neural ODE Temporal Localization
Authors: Muksimova Shakhnoza , Umirzakova Sabina , Sultanov Murodjon , Cho Young Im
Published: January 2025

Dense video captioning is a critical task in video understanding, requiring precise temporal localization of events and the generation of detailed, contextually rich descriptions. However, the current state-of-the-art (SOTA) models face significant challenges in event boundary detection, contextual understanding, and real-time processing, limiting their applicability to complex, multi-event videos. In this paper, we introduce CMSTR-ODE, a novel Cross-Modal Streaming Transformer with Neural ODE Temporal Localization framework for dense video captioning. Our model incorporates three key innovations: (1) Neural ODE-based Temporal Localization for continuous and efficient event boundary prediction, improving the accuracy of temporal segmentation; (2) cross-modal memory retrieval, which enriches video features with external textual knowledge, enabling more context-aware and descriptive captioning; and (3) a Streaming Multi-Scale Transformer Decoder that generates captions in real time, handling objects and events of varying scales. We evaluate CMSTR-ODE on two benchmark datasets, YouCook2, Flickr30k, and ActivityNet Captions, where it achieves SOTA performance, significantly outperforming existing models in terms of CIDEr, BLEU-4, and ROUGE scores. Our model also demonstrates superior computational efficiency, processing videos at 15 frames per second, making it suitable for real-time applications such as video surveillance and live video captioning. Ablation studies highlight the contributions of each component, confirming the effectiveness of our approach. By addressing the limitations of current methods, CMSTR-ODE sets a new benchmark for dense video captioning, offering a robust and scalable solution for both real-time and long-form video understanding tasks.
Controlling a Mecanum-Wheeled Robot with Multiple Swivel Axes Controlled by Three Commands
Authors: Nakagawa Yuto , Igo Naoki , Hoshino Kiyoshi
Published: January 2025

The Mecanum-wheeled robot has four special wheels. It can control four wheels independently and has seven turning axes. The robot can translate in all directions and travel in curves without changing its direction by means of the control commands for turning ratio, speed, and direction of travel. However, no model has been proposed that can accurately simulate the output of the actual machine for the three types of inputs, even when the characteristics of the motor and motor driver are unknown. In this study, we synthesized and simplified transfer functions and estimated the undetermined coefficients that minimize the sum of squared errors to construct a model of the robot that can output the position and posture equivalent to those of the actual robot for the input commands for turning ratio, speed, and the direction of travel. We modeled a Mecanum-wheeled robot using the proposed modeling method and parameter determination method and compared the outputs of the real robot to the step and ramp inputs. The results showed that the errors between the two outputs were very small and accurate enough to simulate AI learning, such as reinforcement learning, using the model of the robot.
An Active Control Method for a Lower Limb Rehabilitation Robot with Human Motion Intention Recognition
Authors: Song Zhuangqun , Zhao Peng , Wu Xueji , Yang Rong , Gao Xueshan
Published: January 2025

This study presents a method for the active control of a follow-up lower extremity exoskeleton rehabilitation robot (LEERR) based on human motion intention recognition. Initially, to effectively support body weight and compensate for the vertical movement of the human center of mass, a vision-driven follow-and-track control strategy is proposed. Subsequently, an algorithm for recognizing human motion intentions based on machine learning is proposed for human-robot collaboration tasks. A muscle–machine interface is constructed using a bi-directional long short-term memory (BiLSTM) network, which decodes multichannel surface electromyography (sEMG) signals into flexion and extension angles of the hip and knee joints in the sagittal plane. The hyperparameters of the BiLSTM network are optimized using the quantum-behaved particle swarm optimization (QPSO) algorithm, resulting in a QPSO-BiLSTM hybrid model that enables continuous real-time estimation of human motion intentions. Further, to address the uncertain nonlinear dynamics of the wearer-exoskeleton robot system, a dual radial basis function neural network adaptive sliding mode Controller (DRBFNNASMC) is designed to generate control torques, thereby enabling the precise tracking of motion trajectories generated by the muscle–machine interface. Experimental results indicate that the follow-up-assisted frame can accurately track human motion trajectories. The QPSO-BiLSTM network outperforms traditional BiLSTM and PSO-BiLSTM networks in predicting continuous lower limb motion, while the DRBFNNASMC controller demonstrates superior gait tracking performance compared to the fuzzy compensated adaptive sliding mode control (FCASMC) algorithm and the traditional proportional–integral–derivative (PID) control algorithm.
TENet: Attention-Frequency Edge-Enhanced 3D Texture Enhancement Network
Authors: Wang Ying , Fu Tao , Zhou Yu , Kong Qinglei , Yu Wenshuai , Liu Jian , Wang Yi , Chen Bo
Published: January 2025

Oblique photogrammetry imagery often suffers from uneven resolution and blurred details, leading to poor surface texture quality in 3D reconstructions, particularly for building facades. To address these challenges, we propose a novel Attention-Frequency Edge-Enhanced 3D Texture Enhancement Network (TENet) and introduce a comprehensive 3D texture enhancement pipeline. This pipeline applies 2D texture super-resolution techniques to 3D models for fine-grained texture restoration, enhancing the surface texture quality. TENet leverages attention mechanisms and frequency-domain techniques to improve texture sharpness and edge accuracy. Our approach includes a Region-Resolution Adaptive Enhancement Module (RAEM) and a Frequency-Domain Edge Enhancement Mechanism (FDEEM) to enhance local details and restore critical edge features. The experimental results demonstrate that TENet outperforms existing methods, significantly improving texture quality and 3D reconstruction performance. Ablation studies confirmed the effectiveness of each component in enhancing 3D texture reconstruction. The network is validated for real-world applications, showing its ability to significantly reduce edge artifacts and restore clear, accurate textures in real-world 3D surface models.
Incorporating Power-Law Model and ERA-5 Data for InSAR Tropospheric Delay Correction Analysis
Authors: Huang Dongxu , Wang Junyu , Li Menghua , Huang Cheng , Tang Bo-Hui
Published: January 2025

InSAR technology effectively monitors urban subsidence and evaluates the stability of infrastructure across extensive regions. Atmospheric tropospheric delay constitutes a significant source of error that adversely impacts the accuracy of InSAR deformation measurements. The meteorological conditions in the highland basin region are complex, and there is a notable deficiency of sufficient sounding balloon observations. This study replaces the sounding balloon data in the power-law model with ERA-5 data (PLE5) to correct the InSAR atmosphere phase delay. This method was tested in Dali utilizing Sentinel-1 data. By comparing its performance against the GACOS model, traditional linear model, and ERA-5 correction, the PLE5 model exhibited the lowest phase standard deviation. This method provides an alternative approach for applying the power-law model in regions with limited sounding balloon data, enhancing the accuracy of InSAR tropospheric delay correction.
A Novel Orchestrator Architecture for Deploying Virtualized Services in Next-Generation IoT Computing Ecosystems
Authors: Mahedero Biot Francisco , Fornes-Leal Alejandro , Vaño Rafael , Reinosa Simón Raúl , Lacalle Ignacio , Guardiola Carlos , Palau Carlos E.
Published: January 2025

The Next-Generation IoT integrates diverse technological enablers, allowing the creation of advanced systems with increasingly complex requirements and maximizing the use of available IoT–edge–cloud resources. This paper introduces an orchestrator architecture for dynamic IoT scenarios, inspired by ETSI NFV MANO and Cloud Native principles, where distributed computing nodes often have unfixed and changing networking configurations. Unlike traditional approaches, this architecture also focuses on managing services across massively distributed mobile nodes, as demonstrated in the automotive use case presented. Apart from working as MANO framework, the proposed solution efficiently handles service lifecycle management in large fleets of vehicles without relying on public or static IP addresses for connectivity. Its modular, microservices-based approach ensures adaptability to emerging trends like Edge Native, WebAssembly and RISC-V, positioning it as a forward-looking innovation for IoT ecosystems.
Classification of Flying Drones Using Millimeter-Wave Radar: Comparative Analysis of Algorithms Under Noisy Conditions
Authors: Larrat Mauro , Sales Claudomiro
Published: January 2025

This study evaluates different machine learning algorithms in detecting and identifying drones using radar data from a 60 GHz millimeter-wave sensor. These signals were collected from a bionic bird and two drones, namely DJI Mavic and DJI Phantom 3 Pro, which were represented in complex form to preserve amplitude and phase information. The first benchmarks used four algorithms, namely long short-term memory (LSTM), gated recurrent unit (GRU), one-dimensional convolutional neural network (Conv1D), and Transformer, and they were benchmarked for robustness under noisy conditions, including artificial noise types like white noise, Pareto noise, impulsive noise, and multipath interference. As expected, Transformer outperformed other algorithms in terms of accuracy, even on noisy data; however, in certain noise contexts, particularly Pareto noise, it showed weaknesses. For this purpose, we propose Multimodal Transformer, which incorporates more statistical features—skewness and kurtosis—in addition to amplitude and phase data. This resulted in a improvement in detection accuracy, even under difficult noise conditions. Our results demonstrate the importance of noise in processing radar signals and the benefits afforded by a multimodal presentation of data in detecting unmanned aerial vehicle and birds. This study sets up a benchmark for state-of-the-art machine learning methodologies for radar-based detection systems, providing valuable insight into methods of increasing the robustness of algorithms to environmental noise.
Impact of the 1742–1743 Plague Epidemic on Global Excess Deaths and Social Dynamics in the City of Córdoba and Along the Camino Real Between Buenos Aires and Lima
Authors: Villafañe Jorge Hugo
Published: January 2025

The 1742–1743 plague epidemic profoundly impacted populations along the Camino Real, the main colonial trade route connecting Buenos Aires and Lima, with Córdoba, Argentina, as a critical focal point. This research examines the multidimensional repercussions of the epidemic, emphasizing its demographic, social, and economic consequences. Utilizing parish records as primary sources, this study quantifies the excess mortality, revealing a dramatic increase, with death rates in Córdoba peaking at 12 times the pre-epidemic average in May 1743. Marginalized groups, particularly enslaved and Indigenous populations, were disproportionately affected, which exacerbated existing social inequalities. Through a comprehensive analysis of historical data, this study contributes to understanding epidemic responses in colonial Latin America, illuminating how public health crises intersect with demographic and socio-economic structures. The findings provide insights that are relevant to contemporary public health perspectives on crisis management and resilience, highlighting how historical epidemics offer valuable understanding into the social dynamics of health emergencies.
Redistricting and Black Political Mobilization: Implications from the 2018 and 2022 Midterm Elections
Authors: Trautman Linda M. , Smith Michael A.
Published: January 2025

This research is an analysis of redistricting between the 2018 and 2022 midterm elections and its consequences regarding Black political mobilization and participation. An examination of redistricting after the 2020 census and its implications on racial mobilization is a defining objective of the study. Essentially, the primary aim of the study is to assess the nature of redistricting and gerrymandering on Black voter participation by comparing the 2018 and 2022 midterm elections.
Research on Heavy Metals in Soils and Sediments
Authors: Sager Manfred
Published: January 2025

This special edition of the journal Soil Systems contains 14 articles dealing with current research on cationic trace elements and semi-metals [...]
Effective Cohesion Policy? Long-Term Economic and Social Convergence in Poland
Authors: Bogdański Marcin , Janusz Marcin
Published: January 2025

The aim of this article is to determine the relationship between the processes of economic and social convergence in Polish voivodeships. The correlation between these two processes is still unclear and the mechanisms behind them are not fully discovered. The goal of this article requires determining whether the changes in the level of disproportions in the GDP per capita translate into changes in social cohesion and what is the nature of this relationship. Those issues are still relevant and important due to their cognitive and practical value. In view of the above, using various analysis tools (i.e., the correlation coefficient and regression function), the authors determine whether economic convergence (both β and σ) and social convergence (with the use of the TOPSIS method) is observed in Polish regions in the years 2000–2022. The obtained results allow us to state that in the analyzed period, economic and social divergence processes occurred in Poland. The constantly deepening disproportions seem to be relatively permanent and caused by differences in the demographic potential (including urbanization potential) of individual regions that are difficult to eliminate, as well as by different development trajectories during the period of political transformation after 1989.
Research on the Dynamic Mechanisms of the Synergetic Development of Construction Industry Clusters
Authors: Zhao Jinyu , Zhao Lu , Xu Yinghui , Yao Xueqian
Published: January 2025

Exploring pathways for sustainable synergistic development is a challenging issue for the construction industry, which is currently in a critical period of transformation. Current research has confirmed that industrial clusters are an important organizational form for promoting sustainable synergistic development. However, the existing research on construction industry clusters has overly focused on cluster attribute analysis and evaluation. Investigating the core mechanisms of synergistic development within construction industry clusters is particularly crucial. From the perspectives of ecological and synergetic theories, this paper constructs the ecosystem of the construction industry cluster and analyzes its synergistic development process. It identifies the driving factors of the synergistic development of the construction industry cluster and uses the ISM model to determine the structural hierarchical relationships among factors. Taking Shandong Province as the data sample, this study employs the Haken model to conduct an empirical investigation of the level of synergy and its driving mechanisms, establishing a three-stage framework for the driving mechanisms. The results show the following results: (1) Operating efficiency and innovation ability are the top factors to promote the synergistic development of the construction industry. (2) Market demand, as an order parameter, is an important condition for triggering and amplifying fluctuations in the construction industry cluster system, and it acts as a controlling factor to promote the system’s self-organization evolution to a new ordered and stable state. (3) The construction industry cluster in Shandong Province is in the initial stage of low synergy and is gradually evolving to moderate synergy. From a new research perspective, this study helps construction industry planners and policymakers understand the mechanisms of evolution and key driving factors of construction industry clusters.
A Bibliometric Analysis of Geological Hazards Monitoring Technologies
Authors: Liu Zhengyao , Huang Jing , Li Yonghong , Liu Xiaokang , Qiang Fei , He Yiping
Published: January 2025

This study systematically analyzed research trends and hot issues in the field of geological hazard prediction using bibliometric analysis methods. A total of 12,123 related articles published from 1976 to 2023 were retrieved from the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases. Co-occurrence analysis and burst detection were conducted on the literature using the VOSviewer and CiteSpace tools to identify the research trends in geological hazard monitoring technologies. The results reveal that “data fusion”, “landslide identification”, “deep learning”, and “risk early warning” are currently the main research hot spots. Additionally, the combined application of Global Navigation Satellite System (GNSS) and Real-Time Kinematic (RTK) technologies, as well as GNSS and Long Short-Term Memory (LSTM) models, were identified as important directions for future research. The bibliometric perspective offers a systematic theoretical framework and technical guidance for future research, thereby facilitating the sustainable advancement of safety, security, and disaster management.