bluuebunny/crossref_metadata_2025 huggingface dataset:
Co-Inoculation of Phosphate-Solubilizing Bacteria and Rhizobia Increases Phosphorus Availability and Promotes the Development of Forage Legumes
Authors: Ríos-Ruiz Winston Franz , Castro-Tuanama Rony , Valdez-Nuñez Renzo Alfredo , Torres-Bernal Lenin , Jave-Concepción Henry Giovani , Daza-Pérez Aurora Consuelo , Barrera-Lozano Marvin , Archentti-Reátegui Fernando
Published: October 2024

Tropical grassland soils, especially those with alkaline properties, often exhibit limited phosphorus availability due to its precipitation in insoluble forms. Phosphate-solubilizing bacteria (PSB) and rhizobia have demonstrated their potential to enhance the availability of this nutrient and promote the growth of forage legumes. This study, conducted under controlled conditions in a mesh house, evaluated the effect of co-inoculation with PSB, including Micrococcus sp. Sfcm-14-01, Agrobacterium sp. Sfl-043-09, and Enterobacter sp. Sfcm-014-02 and Sfcm-054-06, along with rhizobia (Ensifer terangae R1-012-02 and Bradyrhizobium glycinis Rcm-025-01), under different levels of phosphorus fertilization on the legumes Leucaena leucocephala and Centrosema macrocarpum. The results indicate significant increases in various growth parameters, such as chlorophyll levels (SPAD), biomass (dry weight of roots and aerial parts) (mg), the foliar phosphorus concentration (ppm), and the concentration of available phosphorus in the soil, particularly under low-phosphorus fertilization conditions. The highest level of available phosphorus in the soil was achieved with 75% of the recommended fertilization dose, resulting in concentrations of 13.73 ppm for L. leucocephala and 7.69 ppm for C. macrocarpum, representing increases in phosphorus availability of 170.81% and 240.27%, respectively, compared with no fertilization or inoculation. These findings suggest that the co-inoculation of PSB and native rhizobia is a promising strategy to enhance the biomass productivity and mineral content of forage in tropical grazing systems, especially under phosphorus-limited conditions.
The Comprehensive Effects of Biochar Amendments on Soil Organic Carbon Accumulation, Soil Acidification Amelioration and Heavy Metal Availability in the Soil–Rice System
Authors: Tao Juanhua , Chen De , Wu Shaofu , Zhang Qi , Xiao Wendan , Zhao Shouping , Ye Xuezhu , Chu Tianfen
Published: October 2024

In recent years, biochar (BC) and biochar-based soil amendments (CSAs) have been widely used in agriculture and the environment. In the present study, a two-rice-season field study was conducted to explore the comprehensive effects of applying BC (1%) and CSA (0.5% and 1%) on soil organic carbon accumulation, soil acidification amelioration and heavy metal availability in a soil–rice system. The results show that soil pH was increased by 0.5–1.7 units and 0.3–1.0 units, respectively, in the early rice season and late rice season treated by the amendments compared with CK. Soil organic contents were increased by 18–30% in the early rice season and by 15–25% in the late rice season in the amended treatments. In addition, soil available phosphorus contents were largely increased as a result of BC and CSA addition. Soil CaCl2 extractable heavy metals (Cd, Ni, Cu and Zn) were simultaneously decreased by BC or CSA amendments. In addition, Cd contents in early rice grain and late rice grain were significantly reduced by 25–48% and 52–83% in amended treatments, while Zn contents were generally not affected. The uptake of Cu and Ni was also decreased by BC and CSA. This study demonstrates that biochar application alone or combinates with inorganic amendments (limestone, sepiolite and potassium dihydrogen phosphate) can significantly improve soil properties and nutrient content and decrease the heavy metal (especially for Cd and Ni) uptake and accumulation from soil to rice grain, where the combination application is more effective.
Isolation, Identification, and Application of Endophytic Fungi from Lavandula stoechas L.: Mitigating Salinity Stress in Hydroponic Winter Cereal Fodder
Authors: García-Latorre Carlos , Poblaciones María José
Published: October 2024

Soil and irrigation water salinity is a growing global problem affecting farmland, due to poor agricultural practices and climate change, leading to reduced crop yields. Given the limited amount of arable land and the need to boost production, hydroponic systems offer a viable solution. Additionally, endophytic fungi have been shown to mitigate salinity effects through symbiosis with plants. This study evaluated three endophytic fungi isolated from Lavandula stoechas L. in the grasslands of Extremadura (i.e., Diplodia corticola L11, Leptobacillium leptobactrum L15, and Cladosporium cladosporioides L16) for their ability to improve hydroponic forage production under saline conditions. In vitro experiments were conducted assessing plant growth promotion and fungal growth under salinity, followed by research evaluating the impact of fungal inoculation on hydroponic wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) forages irrigated with NaCl solutions (0, 100, and 200 mM). The results showed improved fungal growth and production of plant growth-promoting substances, which could explain the improved plant germination, shoot and root length, fresh and dry weight, and yield of inoculated plants growing under salinity. Plants inoculated with L15 or L16 showed the best performance overall. L15 demonstrated broader bioactivity in vitro, potentially explaining its superior performance in both wheat and barley growth. Conversely, L16 was more effective in barley, while L11 was beneficial in wheat but detrimental in barley. This study provides a preliminary exploration of the capabilities of these fungi and their optimization for hydroponic forage production.
Efficacy of Commercial Disinfectants Against Black Root Rot (Berkeleyomyces rouxiae) and Verticillium Wilt (Verticillium dahliae) Pathogens of Cotton
Authors: Nguyen Chi P. T. , Vo Brenda , Le Duy P.
Published: October 2024

Black root rot (BRR) and Verticillium wilt of cotton are caused by soilborne Berkeleyomyces rouxiae and Verticillium dahliae, respectively, and can individually cause yield loss of a 10–50% in New South Wales (NSW). Both diseases were first detected in a northern valley of NSW but are now present across the state. ‘Come Clean Go Clean’ is a widely practiced biosecurity measure used to minimize the risk of further introducing the pathogens from one field to another by restricting the movement of soil-contaminated farm equipment and machinery. We rely on cleaning agents to effectively wash down and decontaminate the equipment and machinery. In this study, we examined 12 locally available, commercial disinfectants for their efficacy against B. rouxiae and V. dahliae reproductive structures with and without soil contamination of 10% (w/v). We found a significant interaction between pathogens, disinfectants, and soil amendment (p < 0.01). The germination of B. rouxiae chlamydospores and endoconidia in both the presence and absence of soil contamination was completely suppressed even after a short 10 sec exposure to 70% ethanol and 25% bleach. Both 70% ethanol and bleach were highly lethal at 10 sec exposures in similar assays against V. dahliae microsclerotia and conidia. Some other commercial products were able to reduce the germination rate significantly but did not completely kill microsclerotia and conidia even after 30 min of exposure. The lethal effect against B. rouxiae and V. dahliae warrants further exploration of both 70% ethanol and bleach to improve their field applications. Additionally, the efficacy of most tested commercial disinfectants was time-dependent; however, this is not recommended on the labels for their effectiveness. This study provides an additional integrated disease management option aiming to limit the spread of the pathogens.
Biochar Regulates the Humification of Kitchen Waste and the Effects of the Humic Acid Structure of Products on Black Soil
Authors: Ming Long , Dou Sen , Zhou Jianying , Wang Hong , Yang Dongji
Published: October 2024

Kitchen waste is a misplaced resource that is characterized by a high organic matter content, high water content, and a tendency to rot easily. Biochar is a black solid substance produced under high-temperature, anaerobic conditions using agricultural organic wastes as the raw material. It possesses a large specific surface area, a loose and porous structure, and functional groups, which confer high thermal stability and strong adsorption capabilities. However, little is known about how humic products made from biochar affect the composition and structure of soil humus. To solve the above problems, this study carried out a two-year outdoor field experiment by means of element analysis, infrared spectroscopy, and differential thermal analysis (0.4 kg/m2 (W4), 0.8 kg/m2 (W8), 1.2 kg/m2 (W12), 1.6 kg/m2 (W16), and 2.0 kg/m2 (W20)); CK was the blank control (no application). The samples were collected one year and two years after they returned to the field. The results showed that the application of organic materials facilitated the accumulation of soil organic carbon (SOC) and increased the total nitrogen (TN) content. The highest SOC content in the W20 treatment was 12.39 g/kg and 14.67 g/kg in one and two years, respectively. The maximum relative HA content in the W20 treatment was 22.99% one year after returning to the field. The PQ value (the ratio of HA/(fulvic acid (FA) + HA)) for the W20 treatment was 88.21%. The W20 treatment greatly increased the SOC and humus carbon contents. Compared with the CK treatment, all the organic materials applied for one year improved the structure of the humic acid to varying degrees, increased the degree of oxidation, reduced the degree of condensation and thermal stability of the HA in the soil, and gradually simplified the structure of the humic acid; among all the treatments, the W20 treatment had the greatest effect.
Longitudinal Analysis of Honey Bee Colony Health as a Function of Pesticide Exposure
Authors: Kegley Susan E. , Radford Rosemarie , Brown Timothy J. , Anderson Jeff , Cox Darren , Ellis Steve , Marcy Geoffrey W.
Published: October 2024

Sixty commercial honey bee colonies were monitored over the course of one year with the goal of assessing potential correlations between measured colony strength and environmental stressors, including exposures to pesticides and pathogens. We developed a new method for assessing colony health by determining the fractional change in population of the four colonies on each pallet between peak population on 1 June and the last population assessment prior to winter on 1 October. This fractional change in population was evaluated as a function of pesticide load per pallet for each of the 37 pesticide chemicals detected, grouping pallets by beekeeper. The analysis of individual chemicals showed that both imidacloprid and cyprodinil were negatively correlated with colony health, while tau-fluvalinate and dinotefuran (at very low concentrations) were positively correlated, possibly because of effects on Varroa control. Exposure to groups of chemicals was also evaluated. Normalization of each chemical concentration to the maximum observed for that chemical provided an equal weighting for each chemical, even though their relative occurrence in the environment and their effective toxicities might differ. A total of 24 chemical groups whose members share a structural commonality, a functional commonality, or suspected synergistic actions were considered, demonstrating negative correlations between colony health and exposures to neonicotinoids as a group and neonicotinoids in combination with (1) methoxyfenozide (2) organophosphates, and (3) diflubenzuron. Analysis of several groups of fungicides applied to almonds during pollination also showed negative correlations with colony health.
Mechanism of Exogenous Jasmonic Acid-Induced Resistance to Thrips palmi in Hemerocallis citrina Baroni Revealed by Combined Physiological, Biochemical and Transcriptomic Analyses
Authors: Sun Zhuonan , Ma Ning , Yang Ye , Wang Jun , Su Nan , Liu Hongxia , Li Jie
Published: October 2024

Jasmonic acid (JA) is a regulator of plant resistance to phytophagous insects, and exogenous JA treatment induces plant insect resistance. This study investigated the mechanism of exogenous JA-induced resistance of Hemerocallis citrina Baroni (daylily) to Thrips palmi at the biochemical and molecular levels. Daylily leaves sprayed with JA showed significantly higher levels of secondary metabolites—tannins, flavonoids, and total phenols, and activity of defense enzymes—peroxidase, phenylalanine ammonia lyase, polyphenol oxidase, and protease inhibitor (PI) than control leaves; the most significant effects were observed with 1 mmol L−1 JA. Owing to an improved defense system, significantly fewer T. palmi were present on the JA-treated plants than control plants. The JA-treated leaves had a smoother wax layer and fewer stomata, which was unfavorable for insect egg attachment. The differentially expressed genes (DEGs) were significantly enriched in insect resistance pathways such as lignin and wax biosynthesis, cell wall thickening, antioxidant enzyme synthesis, PI synthesis, secondary metabolite synthesis, and defense hormone signaling. A total of 466 DEGs were predicted to be transcription factors, mainly bHLH and WRKY family members. Weighted gene co-expression network analysis identified 13 key genes; TRINITY_DN16412_c0_g1 and TRINITY_DN6953_c0_g1 are associated with stomatal regulation and lipid barrier polymer synthesis, TRINITY_DN7582_c0_g1 and TRINITY_DN11770_c0_g1 regulate alkaloid synthesis, and TRINITY_DN7597_c1_g3 and TRINITY_DN1899_c0_g1 regulate salicylic acid and ethylene biosynthesis. These results indicate that JA treatment of daylily improved its resistance to T. palmi. These findings provide a scientific basis for the utilization of JA as an antagonist to control T. palmi in daylily.
Cultivation and Potential for Biomass Production for Energy and Seed Purposes of Tall Wheatgrass (Agropyron elongatum (Host) Beauv.) Under Sandy Soil and Temperate Climate Conditions
Authors: Jarnuszewski Grzegorz , Kitczak Teodor , Malinowski Ryszard , Kirkiewicz Anna
Published: October 2024

An experiment was conducted to analyse the potential for obtaining biomass for energy purposes and tall wheatgrass (TWG) seeds grown under conditions of varying pre-sowing fertilisation with compost and mineral fertilisation with nitrogen on sandy soils. Field trials were conducted between 2012 and 2015. The study factors were compost from municipal green areas with I-doses of 0, 10 and 20 Mg∙ha−1 added before sowing and nitrogen II-doses of 0, 40, 80 and 120 kg∙ha−1 added each year in the form of ammonium nitrate. During the experiment conducted on sandy soils, a favourable effect of fertilisation on the morphological parameters of above-ground vegetative and generative parts was found. The experiment resulted in high dry matter yields (DMYs) in the range of 9.08–31.38 Mg∙ha−1 and high seed yields (SYs) (635 kg∙ha−1 to as much as 2397 kg∙ha−1), which depended on the applied fertilisation variant. The applied levels of compost fertilisation had a positive effect on the obtained dry matter yields (DMYs) and SY. Analysing the effect of the applied doses of mineral nitrogen fertilisation (40, 80 and 120 kg∙ha−1) on the dry matter yield of TWG under sandy soil conditions, it should be noted that this factor significantly increased the DMY and SY in all years of the study. At the same time, the response of plants to this factor of the study over the years varied and depended on weather conditions. The high energy yield (192.50 GJ∙ha−1–408.93 GJ∙ha−1) closely related to the high DMY indicates the high suitability of TWG as a new grass species under temperate climate conditions grown for biomass energy production and the possibility to harvest seeds when adequately cultivated.
Identification of AP2/ERF Transcription Factors and Characterization of AP2/ERF Genes Related to Low-Temperature Stress Response and Fruit Development in Luffa
Authors: Liu Jianting , Zhong Haifeng , Cao Chengjuan , Wang Yuqian , Zhang Qianrong , Wen Qingfang , Zhu Haisheng , Li Zuliang
Published: October 2024

Plant-specific APETALA2/Ethylene-Responsive Factor (AP2/ERF) transcription factors are involved in the regulation of genes associated with the growth and developmental processes of numerous plants. Although AP2/ERF proteins from other species have been intensively studied, no studies have been reported on the AP2/ERF family of Luffa cylindrica, an important vegetable of the cucurbit family, and one of the most popular vegetables in the world. In this study, 133 genes (315–6696 bp) encoding LcAP2/ERF proteins with complete AP2/ERF domains were identified according to the luffa P93075 genome. These LcAP2/ERF genes were subsequently classified and analyzed for their gene structures, chromosomal distribution locations, promoter cis-acting elements, conserved structural domains of encoded proteins, and responses to abiotic stresses. The LcAP2/ERF genes were identified and divided into five phylogenetic groups (AP2, DREBs, ERFs, RAV, and soloists). These genes were unevenly distributed across 13 chromosomes. An analysis of gene structures indicated the LcAP2/ERF genes contained 0–11 introns (average of 4.4). Additionally, 16 motifs were identified in the LcAP2/ERF proteins that were conserved across different phylogenetic groups. Moreover, 11 cis-acting elements associated with response to the environment were analyzed in a 2000 bp region upstream of the LcAP2/ERF gene promoters. A transcriptome analysis involving RNA-seq data revealed tissue-specific LcAP2/ERF expression profiles and the diversity in LcAP2/ERF expression. The effects of low-temperature stress on LcAP2/ERF expression were determined. Furthermore, fruit-development-related and low-temperature-induced expressional changes were verified by RT-qPCR analyses of 14 differentially expressed LcAP2/ERF genes in luffa. Our findings will help clarify the evolution of the luffa AP2/ERF family, while also providing valuable insights for future studies on AP2/ERF functions.
A Unique Case of Adoption in Golden Snub-Nosed Monkeys
Authors: Zhao Haitao , Li Jiaxuan , Wang Yan , Li Nianlong , Pan Ruliang , Li Baoguo
Published: October 2024

Adoption among nonhuman primates (hereafter primates) has been widely reported, particularly in chimpanzees, renowned for their higher intelligence and well-developed cognition. In contrast to adoption in other Old World monkeys, this case of adoption in golden snub-nosed monkeys (Rhinopithecus roxellana) involves two infants associated with three units characterized by distinct social structures and reproductive functions. Consequently, this case extends beyond traditional hypotheses on allomaternal care and adoption—such as enhancing the fitness of adoptive mothers, fostering maternal behaviors, and improving fitness through social and individual interactions—to necessitate an association with the complex social structure characterized by hierarchical, multilevel composition, akin to human society, and intense sexual selection that frequently results in infanticide. Specifically, adopting an infant with a genetic link to the dominant male of the adopting female may reduce the risk of infanticide against her offspring that do not share a biological link with the dominant male. This adoption pattern suggests that golden snub-nosed monkeys may possess more sophisticated intelligence and cognition, characteristics supported by more developed brain structure and facial muscles than the other Old World monkeys.
Estimating Ross 308 Broiler Chicken Weight Through Integration of Random Forest Model and Metaheuristic Algorithms
Authors: Küçüktopçu Erdem , Cemek Bilal , Yıldırım Didem
Published: October 2024

For accurate estimation of broiler chicken weight (CW), a novel hybrid method was developed in this study where several benchmark methods, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Differential Evolution (DE), and Gravity Search Algorithm (GSA), were employed to adjust the Random Forest (RF) hyperparameters. The performance of the RF models was compared with that of classic linear regression (LR). With this aim, data (temperature, relative humidity, feed consumption, and CW) were collected from six poultry farms in Samsun, Türkiye, covering both the summer and winter seasons between 2014 and 2021. The results demonstrated that PSO and ACO significantly enhanced the performance of the standard RF model in all periods. Specifically, the RF-PSO model achieved a significant improvement by reducing the Mean Absolute Error (MAE) by 5.081% to 60.707%, highlighting its superior prediction accuracy and efficiency. The RF-ACO model also showed remarkable MAE reductions, ranging from 3.066% to 43.399%, depending on the input combinations used. In addition, the computational time required to train the RF models with PSO and ACO was considerably low, indicating their computational efficiency. These improvements emphasize the effectiveness of the PSO and ACO algorithms in achieving more accurate predictions of CW.
Faecal DNA Metabarcoding for Diet Analysis of Endangered Fish Species, Odontobutis obscurus
Authors: Kim Kanghui , You Kyung-A , Kim Jeong-Hui , Park Sang-Hyeon , Baek Seung-Ho , Jeong Kwang-Seuk , Joo Gea-Jae , Jo Hyunbin
Published: October 2024

Understanding the ecological characteristics of endangered species is essential for their management and conservation. The dark sleeper (Odontobutis obscurus) is a carnivorous freshwater fish that has been designated an endangered species in South Korea because of its highly restricted habitat range and small population size. However, ecological research on prey items for this species remains severely limited globally, posing challenges for establishing conservation measures such as translocation management. In this study, the food sources and feeding strategies of O. obscurus were identified through non-invasive faecal DNA metabarcoding. A total of 24 O. obscurus specimens were collected from Sanyang Stream on Geoje Island, and faecal DNA was amplified using mitochondrial cytochrome c oxidase I (COI) primer. As a result, 13 prey items were identified, comprising fish and benthic invertebrates. Dietary metrics revealed the overall food composition and major food source of O. obscurus, and the Costello method enabled graphical diet analysis, suggesting that O. obscurus is a generalist, feeding on a wide variety of food sources. The food selectivity index suggested that O. obscurus prefers specific prey items, such as genus Misgurnus, Zacco (fish), Radix (gastropod), and Ephemera (mayfly). This molecular method facilitated the diet analysis of an endangered fish species that cannot undergo dissection, providing crucial information for their management, particularly regarding translocation efforts.
Helmet Wearing Detection Algorithm Based on YOLOv5s-FCW
Authors: Liu Jingyi , Zhang Hanquan , Lv Gang , Liu Panpan , Hu Shiming , Xiao Dong
Published: October 2024

An enhanced algorithm, YOLOv5s-FCW, is put forward in this study to tackle the problems that exist in the current helmet detection (HD) methods. These issues include having too many parameters, a complex network, and large computation requirements, making it unsuitable for deployment on embedded and other devices. Additionally, existing algorithms struggle with detecting small targets and do not achieve high enough recognition accuracy. Firstly, the YOLOv5s backbone network is replaced by FasterNet for feature extraction (FE), which reduces the number of parameters and computational effort in the network. Secondly, a convolutional block attention module (CBAM) is added to the YOLOv5 model to improve the detection model’s ability to detect small objects such as helmets by increasing its attention to them. Finally, to enhance model convergence, the WIoU_Loss loss function is adopted instead of the GIoU_Loss loss function. As reported by the experimental results, the YOLOv5s-FCW algorithm proposed in this study has improved accuracy by 4.6% compared to the baseline algorithm. The proposed approach not only enhances detection concerning small and obscured targets but also reduces computation for the YOLOv5s model by 20%, thereby decreasing the hardware cost while maintaining a higher average accuracy regarding detection.
Experimental Performance Study on Axial Compressive Load-Bearing Capacity of Steel Slag Micropowder Ecotype UHPC of Short Columns Steel Pipe
Authors: Guo Shangqi , Tang Xianyuan , Feng Chenzhuo , He Binbing , Yang Bai
Published: October 2024

In order to study the axial compression performance of steel pipe concrete short columns filled with steel slag micronized ultra-high-performance concrete (UHPC), this paper designs 27 steel pipe UHPC short columns for axial compression test and compares and analyzes the axial compression performance of the specimens in terms of the damage mode, the deformation curve, and the coefficient of strength enhancement, which is aimed at investigating the differences in the actual load-bearing performance of steel pipe UHPC short columns through changes in the aspect ratio, concrete type, and steel content rate, and so on. The purpose of this paper is to compare and analyze the axial compressive performance of the specimens in terms of damage mode and strength enhancement factor in order to investigate the difference in the actual bearing capacity performance of steel pipe UHPC short columns through the changes in length-to-diameter ratio, concrete type, and steel content. The test results show that the axial compressive performance of steel slag powder steel pipe UHPC short columns is greatly affected by the L/D ratio and steel content; the specimen bearing capacity increases with the increase in the wall thickness of the steel pipe and decreases slightly with the increase in the L/D ratio, and the steel fibers can effectively improve the deformation of the concrete so as to enhance the composite effect with the steel pipe; the contribution of the core UHPC to improve the value of bearing capacity accounts for a higher percentage when UHPC with 1% steel fiber dosage and 20% coarse aggregate dosage gave the best uplift with no change in the type of steel pipe. In this paper, the axial compression test bearing capacity results of steel slag micro powder steel pipe UHPC short column are compared with the calculated bearing capacity results of domestic and international specifications and analyzed from the perspectives of perimeter compression strength, steel fiber mixing of core concrete, and the relevant parameter design suggestions for high-strength steel pipe concrete specimens are put forward.
The Value of Fractal Analysis in Ultrasound Imaging: Exploring Intricate Patterns
Authors: Pirri Carmelo , Pirri Nina , Macchi Veronica , Guidolin Diego , Porzionato Andrea , De Caro Raffaele , Stecco Carla
Published: October 2024

Fractal analysis is a mathematical approach employed to study and describe complex patterns and structures across various disciplines, including mathematics, physics, computer science, biology and finance. Introduced by mathematician Benoit Mandelbrot in the 1970s, fractals are intricate, self-similar patterns that repeat at different scales, exhibiting consistent structures upon magnification or reduction. This analysis involves generating fractals through iterative processes or recursive equations, resulting in highly detailed and complex formations. Fractal analysis enhances medical images by removing noise while preserving details and improving diagnostic quality in magnetic resonance and computed tomography scans. However, there is a lack of comprehensive studies on its application in ultrasound imaging, prompting this narrative review to investigate its use and methodology in this context. Selected papers on the use of fractal analysis in ultrasound imaging were analyzed. Out of 186 records screened, 60 duplicates were removed and 28 were discarded. The text content of 98 potentially eligible papers was checked, with 65 not meeting inclusion criteria. Finally, 33 studies were included in the review. Fractal analysis enhances ultrasound imaging by providing detailed tissue texture characterization, aiding in the diagnosis of conditions like breast and lung cancer, osteoporosis and hypertensive disorders in pregnancy. It quantifies biological structure complexity and improves diagnostic accuracy and reliability. This technique supports clinicians in making informed decisions by offering critical insights into various medical conditions.
The Influence of Selected Parameters of Recycled Polyvinyl Butyral on the Sustainable Filament Extrusion Process
Authors: Martiček Matúš , Tauberová Rebeka , Kaščak Jakub , Vandžura Radoslav , Sukić Enes , Knapčíková Lucia
Published: October 2024

In recent years, sustainability has permeated nearly every aspect of our lives, and the manufacturing sector is no exception. The terms “sustainable manufacturing” and “zero-waste manufacturing” are now part of our everyday vocabulary. This study, which explores the influence of key parameters on the filament extrusion process using recycled polyvinyl butyral (PVB), which is an amorphous polymer commonly obtained from the glass recycling industry, has significant practical implications. By determining the optimal conditions for the extrusion process, we can enhance the mechanical properties of the produced PVB filament yarns and their printability. As a result of identifying errors, optimizing the process, and eliminating the resulting shortcomings, a fiber made of PVB material with a diameter of 1.75 mm (±0.06 mm) was created that can be used in most FDM devices. The length of the created fiber was approx. 20 m, and in the presentation of the results, it will be used for printing samples, adhesion tests to the printing mat, shrinkage tests, and tensile tests of the fiber. After removing all the shortcomings, the ideal extrusion temperature was at 155 °C. This temperature was verified using microscopic cross-sections, and deformations or changes were observed in their cross-sections. The deviation of the material currently undergoing testing for the adhesion of PVB to various types of print beds, which was found suitable for use in FFF devices, was 1.75 −0.25/+0.25. This, in turn, can significantly expand the application of these materials in additive manufacturing, thereby making a substantial contribution to the advancement of sustainable manufacturing.
An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
Authors: Tipper Sarah , Atlam Hany F. , Lallie Harjinder Singh
Published: October 2024

Video deepfake detection has emerged as a critical field within the broader domain of digital technologies driven by the rapid proliferation of AI-generated media and the increasing threat of its misuse for deception and misinformation. The integration of Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) has proven to be a promising approach for improving video deepfake detection, achieving near-perfect accuracy. CNNs enable the effective extraction of spatial features from video frames, such as facial textures and lighting, while LSTM analyses temporal patterns, detecting inconsistencies over time. This hybrid model enhances the ability to detect deepfakes by combining spatial and temporal analysis. However, the existing research lacks systematic evaluations that comprehensively assess their effectiveness and optimal configurations. Therefore, this paper provides a comprehensive review of video deepfake detection techniques utilising hybrid CNN-LSTM models. It systematically investigates state-of-the-art techniques, highlighting common feature extraction approaches and widely used datasets for training and testing. This paper also evaluates model performance across different datasets, identifies key factors influencing detection accuracy, and explores how CNN-LSTM models can be optimised. It also compares CNN-LSTM models with non-LSTM approaches, addresses implementation challenges, and proposes solutions for them. Lastly, open issues and future research directions of video deepfake detection using CNN-LSTM will be discussed. This paper provides valuable insights for researchers and cyber security professionals by reviewing CNN-LSTM models for video deepfake detection contributing to the advancement of robust and effective deepfake detection systems.
Discrete-Event Simulation Integrates an Improved NEH Algorithm for Practical Flowshop Scheduling Problems in the Satellite Industry
Authors: Li Guangzhen , Zhang Lei
Published: October 2024

The production of multiple types of satellites based on a common manufacturing platform represents a permutation flowshop scheduling problem (PFSP) with complex constraints. This is a highly complex scheduling problem, yet there is still a gap between theoretical research and practical application, particularly in the satellite industry. Therefore, we propose a more practical method that integrates discrete-event simulation modelling and an improved NEH algorithm to solve a more realistic PFSP. The discrete-event simulation-based method includes the following three main components: a flexible PFSP simulation modelling approach, an improved NEH algorithm, and an interaction mechanism between the simulation model and the optimisation algorithm. The proposed method allows automatic and flexible simulation modelling according to the characteristics of the actual satellite manufacturing workshop, which determines the practical nature of the approach proposed in this paper and then achieves excellent scheduling results based on the special interaction mechanism. The computational results demonstrate that this is a 9.18% improvement over the initial NEH algorithm and a 1.40% improvement over the best current improved NEH algorithm.
Automated Machine Learning for Optimized Load Forecasting and Economic Impact in the Greek Wholesale Energy Market
Authors: Koutantos Nikolaos , Fotopoulou Maria , Rakopoulos Dimitrios
Published: October 2024

This study investigates the use of automated machine learning to forecast the demand of electrical loads. A stochastic optimization algorithm minimizes the cost and risk of the traded asset across different markets using a generic framework for trading activities of load portfolios. Assuming an always overbought condition in the Day-Ahead as well as in the Futures Market, the excess energy returns without revenue to the market, and the results are compared with a standard contract in Greece, which stands as the lowest as far as the billing price is concerned. The analysis achieved a mean absolute percentage error (MAPE) of 12.89% as the best fitted model and without using any kind of pre-processing methods.
Combined Effect of Propyl Gallate and Tert-Butyl Hydroquinone on Biodiesel and Biolubricant Based on Waste Cooking Oil
Authors: Nogales-Delgado Sergio , Guiberteau Cabanillas Agustina , Catela Rodríguez Antonio
Published: October 2024

The current energy scenario requires the use of alternatives for petrol-based products. In this context, the role of biodiesel and biolubricants can be promising, offering multiple advantages but also some challenges like their low oxidation stability. The use of antioxidants can offset this disadvantage, improving the general performance of these products during storage or usage. The aim of this work was to assess the combined and separate use of two different antioxidants (propyl gallate, PG, and tert-butylhydroquinone, TBHQ) on biodiesel and biolubricant based on waste cooking oil (WCO), carrying out a thorough characterization of these products and quantification of antioxidants through voltammetry techniques. Thus, the innovation of this work consists in the use of combined antioxidants and its simultaneous quality assessment and quantification. As a result, the combined use of antioxidants did not offer a synergistic effect, and the use of PG at low concentrations (300 ppm) is recommended in the case of WCO biolubricant. Voltammetry was a suitable and fast method to quantify both PG and TBHQ.
An Investigation into the Behavior of Cathode and Anode Spots in a Welding Discharge
Authors: Karlina Antonina I. , Balanovskiy Andrey E. , Kondratiev Viktor V. , Romanova Victoria V. , Batukhtin Andrey G. , Karlina Yulia I.
Published: October 2024

The effective development of modern welding technologies and the improvement of equipment and materials inevitably require deep theoretical knowledge about the physical phenomena occurring in the electric arc column and in the near-electrode region. However, there is still no convincing theoretical description of an arc discharge. This article demonstrates, through the generalization of known experimental facts and studies using a high-speed camera, that the conductive channel of an electric arc has a discrete structure, consisting of a set of thin channels through which the main discharge current passes. The cathode spot of an arc discharge is a highly heated and brightly glowing area on the cathode’s surface. Electron emission occurs from this region, which supports the discharge as well as the removal of the cathode material. We propose a new technique to study the reverse side of the cathode spot, revealing a structure consisting of individual cells or fragments of the cathode spot. For the first time, we present the anode spots captured by a high-speed camera. We carry out an analysis of the spots’ structure. We determine the parameters affecting the mobility of cathode and anode spots. We propose a hypothesis based on the obtained experimental facts about the heterogeneous structure of cathode and anode spots in an arc discharge and the existence of current filaments that affect the mobility of spots during arc combustion.
Optimal Energy Management Systems and Voltage Stabilization of Renewable Energy Networks
Authors: Drid Mohamed-Dhiaeddine , Hamdani Samir , Nait-Seghir Amirouche , Chrifi-Alaoui Larbi , Labdai Sami , Drid Said
Published: October 2024

This paper addresses the challenge of integrating multiple energy sources into a single-domain microgrid, commonly found in urban buildings, while also providing a platform for energy management. A Lyapunov stability analysis of a simple boost converter was used as a basis for designing the dual control loop of the grid. The versatility of the developed control structure allows for the incorporation of an arbitrary number of sources hence achieving scalability. Next, the energy in the microgrid was separated into exogenous energy and actuator energy. This yielded a description of the system that quantified the condition of stability independent of the decision made by a would-be energy management system. This, in turns, liberates the process of designing an optimized energy management system from stability concerns. The acquired theoretical findings were then translated to a simulation model, where multiple components of the grid were simulated under a typical scenario of operation. Once the simulation phase was concluded, a prototype of the designed grid was constructed to emulate the theoretical results. The prototype exhibited promising performance, matching the simulation predictions to a reasonable degree.
NexoNet: Blockchain Online Social Media with User-Centric Multiple Incentive Mechanism and PoAP Consensus Mechanism
Authors: Zuo Junzhe , Guo Wei , Ling Li
Published: October 2024

Online social media (OSM) has revolutionized the manner in which communication unfolds, enabling users to spontaneously generate, disseminate, share, and aggregate multimedia data across the internet. Nevertheless, in this exchange of information, OSM platforms assume a dominant, central role, wielding excessive power. Blockchain online social media (BOSM) seeks to mitigate the drawbacks of traditional centralized OSM by leveraging the decentralized nature of blockchain technology, migrating the functionalities of social media into a decentralized realm, and positioning the users at the core of the OSM ecosystem. However, current BOSM models often rely on tokens for incentives and are hampered by the centralized, inefficient blockchain consensus mechanisms, alongside vulnerabilities such as collusion attacks. This paper introduces a novel blockchain system, NexoNet, tailored for decentralized social media, exploring the application of blockchain technology in the realm of online social media from both technical and economic perspectives. The NexoNet quantifies and evaluates user participation within the system, employing a multiple incentive mechanism to equitably distribute value created by users without the need for tokens. Furthermore, we propose the Proof-of-Active-Participation (PoAP) blockchain consensus mechanism, enabling all users to partake in the maintenance of the blockchain system, thus ensuring its security and efficiency. Theoretical analysis and simulations across various scenarios demonstrate that the NexoNet, with extensive user engagement, achieves equitable value distribution through its multiple incentive mechanism. It successfully safeguards against a spectrum of malicious attacks and attains high transaction processing efficiency. The simulation results show that NexoNet achieves an average transaction throughput of 2000 transactions per second (TPS) and a consensus delay of 2.385 s with 100 maintainers in the network. Furthermore, our tests demonstrated that even collusion with users comprising 75% of the total would only allow an additional 30 chances to propose a block. By deeply integrating user behavior with the underlying mechanisms of the blockchain system, the NexoNet fosters a user-centric blockchain social media ecosystem.
AI-Powered Eye Tracking for Bias Detection in Online Course Reviews: A Udemy Case Study
Authors: Šola Hedda Martina , Qureshi Fayyaz Hussain , Khawaja Sarwar
Published: October 2024

The rapid growth of e-learning increased the use of digital reviews to influence consumer purchases. In a pioneering approach, we employed AI-powered eye tracking to evaluate the accuracy of predictions in forecasting purchasing patterns. This study examined customer perceptions of negative, positive, and neutral reviews by analysing emotional valence, review content, and perceived credibility. We measured ‘Attention’, ‘Engagement’, ‘Clarity’, ‘Cognitive Demand’, ‘Time Spent’, ‘Percentage Seen’, and ‘Focus’, focusing on differences across review categories to understand their effects on customers and the correlation between these metrics and navigation to other screen areas, indicating purchasing intent. Our goal was to assess the predictive power of online reviews on future buying behaviour. We selected Udemy courses, a platform with over 70 million learners. Predict (version 1.0.), developed by Stanford University, was used with the algorithm on the consumer neuroscience database (n = 180,000) from Tobii eye tracking (Tobii X2-30, Tobii Pro AB, Danderyd, Sweden). We utilised R programming, ANOVA, and t-tests for analysis. The study concludes that AI neuromarketing techniques in digital feedback analysis offer valuable insights for educators to tailor strategies based on review susceptibility, thereby sparking interest in the innovative possibilities of using AI technology in neuromarketing.
Fault Diagnosis in Power Generators: A Comparative Analysis of Machine Learning Models
Authors: Amaya-Sanchez Quetzalli , Argumedo Marco Julio del Moral , Aguilar-Lasserre Alberto Alfonso , Reyes Martinez Oscar Alfonso , Arroyo-Figueroa Gustavo
Published: October 2024

Power generators are one of the critical assets of power grids. The early detection of faults in power generators is essential to prevent cutoffs of the electrical supply in the power grid. This work presents a comparative analysis of machine learning (ML) models for the generator fault diagnosis. The objective is to show the ability of simple and ensemble ML models to diagnose faults using as attributes partial discharges and dissipation factor data. For this purpose, a generator fault database was built, gathering information from operational data curated by power generator experts. The hyper-parameters of the ML models were selected using a grid search (GS) and cross-validation (CV) optimization. ML models were evaluated with class imbalance and multi-classification metrics, a correspondence analysis, and model performance by class (fault type). Furthermore, the selected ML model was validated by experts through a diagnosis system prototype. The results show that the gradient boosting model presented the best performance according to the performance metrics among single and ensemble ML models. Likewise, the model showed a good capacity to detect type 3 and 4 faults, which are the most catastrophic failures for the generator and must be detected in a timely manner for prompt correction. This work gives an insight into the need and effort required to implement an online diagnostic system that provides information about the power generator health index to help engineers reduce the time taken to find and repair incipient faults and avoid loss of power generation and catastrophic failures of power generators.
Leveraging Large Language Models for Enhancing Literature-Based Discovery
Authors: Taleb Ikbal , Navaz Alramzana Nujum , Serhani Mohamed Adel
Published: October 2024

The exponential growth of biomedical literature necessitates advanced methods for Literature-Based Discovery (LBD) to uncover hidden, meaningful relationships and generate novel hypotheses. This research integrates Large Language Models (LLMs), particularly transformer-based models, to enhance LBD processes. Leveraging LLMs’ capabilities in natural language understanding, information extraction, and hypothesis generation, we propose a framework that improves the scalability and precision of traditional LBD methods. Our approach integrates LLMs with semantic enhancement tools, continuous learning, domain-specific fine-tuning, and robust data cleansing processes, enabling automated analysis of vast text and identification of subtle patterns. Empirical validations, including scenarios on the effects of garlic on blood pressure and nutritional supplements on health outcomes, demonstrate the effectiveness of our LLM-based LBD framework in generating testable hypotheses. This research advances LBD methodologies, fosters interdisciplinary research, and accelerates discovery in the biomedical domain. Additionally, we discuss the potential of LLMs in drug discovery, highlighting their ability to extract and present key information from the literature. Detailed comparisons with traditional methods, including Swanson’s ABC model, highlight our approach’s advantages. This comprehensive approach opens new avenues for knowledge discovery and has the potential to revolutionize research practices. Future work will refine LLM techniques, explore Retrieval-Augmented Generation (RAG), and expand the framework to other domains, with a focus on dehallucination.
Correction: Musumeci et al. Intranasal Administration of a TRAIL Neutralizing Monoclonal Antibody Adsorbed in PLGA Nanoparticles and NLC Nanosystems: An In Vivo Study on a Mouse Model of Alzheimer’s Disease. Biomedicines 2022, 10, 985
Authors: Musumeci Teresa , Di Benedetto Giulia , Carbone Claudia , Bonaccorso Angela , Amato Giovanni , Lo Faro Maria Josè , Burgaletto Chiara , Puglisi Giovanni , Bernardini Renato , Cantarella Giuseppina
Published: October 2024

Figure 5 in the original publication [...]