bluuebunny/crossref_metadata_2025 huggingface dataset:
Hair cortisol concentration in finishing pigs on commercial farms: variability between pigs, batches, and farms
Authors: Levallois Pierre , Leblanc-Maridor Mily , Lehébel Anne , Gavaud Solenn , Lieubeau Blandine , Hervé Julie , Fourichon Christine , Belloc Catherine
Published: January 2024

Hair cortisol is a stress indicator and could be used to assess the pigs’ exposure to stressors in the weeks/months prior to non-invasive hair sampling. The main aim of this study was to describe the hair cortisol concentration (HCC) variability between individuals within a batch, between farms and between batches within a farm. The secondary aim was to determine how the number of sampled pigs influences the characterization of HCC within a batch. Twenty farrow-to-finish pig farms were recruited considering the diversity of their management practices and health status (data collected). Hair was sampled in two separate batches, 8 months apart. The necks of 24 finishing pigs were clipped per batch the week prior to slaughter. To describe the variability in HCC, an analysis of the variance model was run with three explanatory variables (batch, farm and their interaction). To identify farm clusters, a principal component analysis followed by a hierarchical clustering was carried out with four active variables (means and standard deviations of the two batches per farm) and 17 supplementary variables (management practices, herd health data). We determined how the number of sampled pigs influenced the characterization of HCC within a batch by selecting subsamples of the results. HCC ranged from 0.4 to 121.6 pg/mg, with a mean of 25.9 ± 16.2 pg/mg. The variability in HCC was mainly explained by differences between pigs (57%), then between farms (24%), between batches within the same farm (16%) and between batches (3%). Three clusters of farms were identified: low homogeneous concentrations (n = 3 farms), heterogeneous concentrations with either higher (n = 7) or lower (n = 10) HCC in batch 2 than in batch 1. The diversity of management practices and health statuses allowed to discuss hypotheses explaining the HCC variations observed. We highlighted the need to sample more than 24 pigs to characterize HCC in a pig batch. HCC differences between batches on six farms suggest sampling pigs in more than one batch to describe the HCC at the farm level. HCC variations described here confirm the need to study its links with exposure of pigs to stressors.
Enhancing Child Sexual Abuse Prevention Knowledge With an Educational Toolkit -Evaluation of the Chinese Doll Program
Authors: Xu Ketong , Fu Jiuqiang , Yang Jianming
Published: January 2024

Objective: In countries with conservative attitudes towards sex and limited resources to prevent child sexual abuse, culturally adapted CSA prevention programs are essential. This study outlines a randomized controlled trial evaluating the effectiveness of the Doll program for preventing CSA in the specific context of China.

Method: 181 children were pre-tested and post-tested (5 weeks later) for knowledge of sexual abuse prevention. Children were assigned to one of three groups; 1) child only (n = 60); 2) child and parent (n = 60); and 3) control (n = 61).

Results: Children in groups 1 and 2 showed significant increases (p < 0.001) in scores on the Appropriate Touch Scale (ATS) and the Inappropriate Touch Scale (ITS), whereas those in the control group did not show a significant increase in ATS scores, but their scores on ITS significantly increased (p < 0.001). Children in group 2 showed significantly increased ITS scores compared to group 1 (p = 0.016).

Conclusion: Doll program effectively enhances children’s CSA prevention knowledge, with parental engagement demonstrating a positive impact on the program.
Energy Efficiency of Pneumatic Actuating Systems with Pressure-Based Air Supply Cut-Off
Authors: Boyko Vladimir , Weber Jürgen
Published: January 2024

To exploit the energy-saving potential of pneumatic actuator systems, various energy-saving circuits have been developed in recent decades. However, the principle of a pressure-based air supply cut-off has only been considered to a limited extent. This article introduces a possible pneumatic circuit solution for this principle and evaluates it via simulation and measurement of the saving potentials and limits of the developed circuit for typical industrial drive tasks. The conducted investigation shows the suitability of the developed energy-saving circuit, especially for the reduction of the actuator oversizing, achieving energy savings of 71% without performance loss. Conversely, applying this principle to an already well-sized cylinder comes with limitations and requires additional damping. The final economic analysis demonstrates that the application of the circuit could achieve comparatively short amortisation times of approx. 1.9 years for a setup with standard pneumatic components.
Measurement of the Convection Velocities in a Hypersonic Turbulent Boundary Layer Using Two-Point Cylindrical-Focused Laser Differential Interferometer
Authors: Huang Ranran , Xue Tao , Wu Jie
Published: January 2024

A two-point cylindrical-focused laser differential interferometer (2P-CFLDI) system and a conventional Z-type Schlieren were used to measure the hypersonic turbulent boundary layer on a flat plate at Mach number Ma = 6 and Reynolds number Re = 1.08 × 106 m−1. The boundary layer thickness at the measurement location and the noise radiation angle were obtained by post-processing the Schlieren image. The 2P-CFLDI data underwent cross-correlation analysis to calculate the mean convective velocities at different heights and compared with previous experimental and numerical results. The experimentally measured mean convective velocities agree with the trend of available DNS and experimental results. The mean convective velocity near the wall is significantly larger than the local mean velocity and is the main noise source region. Further filtering treatment shows that the convective velocity of the disturbed structure decreases gradually with the increase in the disturbance scale. The differences between convective velocities at different scales are significantly larger outside the boundary layer than inside the boundary layer, which is in agreement with the findings of the previous hot wire experiments. Near the wall, large-scale disturbances mainly determine the localized mean convective velocity, which are the main source of noise radiation for the hypersonic turbulent boundary layer.
The Influence of Applying Foliar Micronutrients at Nodulation and the Physiological Properties of Common Soybean Plants
Authors: Jarecki Wacław , Lachowski Tomasz , Migut Dagmara
Published: January 2024

Legumes, due to their symbiosis with papillary bacteria, can receive nitrogen from the air. The remaining nutrients must be supplied in fertilisers, either soil or foliar. In the pot experiment, we recorded the responses of two soybean cultivars (Annushka, Pompei) to the foliar application of micronutrients (control, Zn, Fe, Cu, Mn, B, or Mo). The physiological properties were expressed as net photosynthetic rate (PN), intercellular CO2 concentration (Ci), transpiration rate (E), stomatal conductance (gs), maximum quantum yield of photosystem II (Fv/Fm), maximum quantum yield of primary photochemistry (Fv/F0), photosynthetic performance index (PI), and the development of soil plant analyses (SPAD), which were analysed. The effects of individual micronutrients on nodulation, plant growth, and condition were also investigated. Micronutrient fertilisation had a positive effect on plant fresh weight and no negative effect on plant condition. It was shown that elements such as B, Fe, and Mo had the most beneficial effect on nodulation compared to the control, regardless of the cultivar analysed. The application of single-component foliar fertilisers improved the physiological parameters of the plants. The relative chlorophyll content was most favourably affected by the application of Mn, B, and Mo in the Annushka cultivar, and Fe, Mn, and Mo in the Pompei cultivar. Similarly, in the case of chlorophyll fluorescence, the most stimulating effect was found for Mn and B, regardless of the cultivar. In the case of gas exchange, the application of Fe, Mo, and B for the Annushka cultivar and Cu for the Pompei cultivar had the most favourable effect on physiological measurements. The results obtained indicate that the foliar application of the evaluated micronutrients is justified in soybean cultivation and does not disturb the nodulation process.
Lightweight Pig Face Feature Learning Evaluation and Application Based on Attention Mechanism and Two-Stage Transfer Learning
Authors: Yin Zhe , Peng Mingkang , Guo Zhaodong , Zhao Yue , Li Yaoyu , Zhang Wuping , Li Fuzhong , Guo Xiaohong
Published: January 2024

With the advancement of machine vision technology, pig face recognition has garnered significant attention as a key component in the establishment of precision breeding models. In order to explore non-contact individual pig recognition, this study proposes a lightweight pig face feature learning method based on attention mechanism and two-stage transfer learning. Using a combined approach of online and offline data augmentation, both the self-collected dataset from Shanxi Agricultural University's grazing station and public datasets underwent enhancements in terms of quantity and quality. YOLOv8 was employed for feature extraction and fusion of pig face images. The Coordinate Attention (CA) module was integrated into the YOLOv8 model to enhance the extraction of critical pig face features. Fine-tuning of the feature network was conducted to establish a pig face feature learning model based on two-stage transfer learning. The YOLOv8 model achieved a mean average precision (mAP) of 97.73% for pig face feature learning, surpassing lightweight models such as EfficientDet, SDD, YOLOv5, YOLOv7-tiny, and swin_transformer by 0.32, 1.23, 1.56, 0.43 and 0.14 percentage points, respectively. The YOLOv8-CA model’s mAP reached 98.03%, a 0.3 percentage point improvement from before its addition. Furthermore, the mAP of the two-stage transfer learning-based pig face feature learning model was 95.73%, exceeding the backbone network and pre-trained weight models by 10.92 and 3.13 percentage points, respectively. The lightweight pig face feature learning method, based on attention mechanism and two-stage transfer learning, effectively captures unique pig features. This approach serves as a valuable reference for achieving non-contact individual pig recognition in precision breeding.
Characteristics and Migration Dynamics of Microplastics in Agricultural Soils
Authors: Deng Yuxin , Zeng Zijie , Feng Weiying , Liu Jing , Yang Fang
Published: January 2024

The risks brought by microplastics (MPs) to agricultural soil structure and crop growth in the agricultural system are the focus of global debate. MPs enter the soil through various routes, such as through the use of agricultural mulch and atmospheric deposition. Here, we review the research on MP pollution in the soil during the last 30 years. This review focuses on (i) the sources, types, and distribution characteristics of MPs in agricultural soils; (ii) the migration and transformation of MPs and their interactions with microorganisms, organic matter, and contaminants in agricultural soils; and (iii) the effects of environmental factors on the composition and structure of MPs in agricultural soils. This review also proposes key directions for the future research and management of MPs in the agricultural soil. We aim to provide a theoretical basis for the fine management of agricultural farmland.
Comparative Analysis of Climate Change Impacts on Climatic Variables and Reference Evapotranspiration in Tunisian Semi-Arid Region
Authors: Latrech Basma , Hermassi Taoufik , Yacoubi Samir , Slatni Adel , Jarray Fathia , Pouget Laurent , Ben Abdallah Mohamed Ali
Published: January 2024

Systematic biases in general circulation models (GCM) and regional climate models (RCM) impede their direct use in climate change impact research. Hence, the bias correction of GCM-RCMs outputs is a primary step in such studies. This study compares the potential of two bias correction methods (the method from the third phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3) and Detrended Quantile Matching (DQM)) applied to the raw outputs of daily data of minimum and maximum air temperatures and precipitation, in the Cap-Bon region, from eight GCM-RCM combinations. The outputs of GCM/RCM combinations were acquired from the European branch of the coordinated regional climate downscaling experiment (EURO-CORDEX) dataset for historical periods and under two representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Furthermore, the best combination of bias correction/GCM-RCM was used to assess the impact of climate change on reference evapotranspiration (ET0). Numerous statistical indicators were considered to evaluate the performance of the bias correction/historical GCM-RCMs compared to the observed data. Trends of the Hargreaves–Samani_ET0 model during the historical and projected periods were determined using the TFPMK method. A comparison of the bias correction methods revealed that, for all the studied model combinations, ISIMIP3 performs better in reducing biases in monthly precipitation. However, for Tmax and Tmin, the biases are greatly removed when the DQM bias correction method is applied. In general, better results were obtained when the HadCCLM model was used. Before applying bias correction, the set of used GCM-RCMs projected reductions in precipitation for most of the months compared to the reference period (1982–2006). However, Tmin and Tmax are expected to increase in all months and for the three studied periods. Hargreaves–Samani ET0 values obtained from the best combination (DQM/ HadCCLM) show that RCP8.5 (2075–2098) will exhibit the highest annual ET0 increase compared to the RCP4.5 scenario and the other periods, with a change rate equal to 11.85% compared to the historical period. Regarding spring and summer seasons, the change rates of ET0 are expected to reach 10.44 and 18.07%, respectively, under RCP8.5 (2075–2098). This study shows that the model can be used to determine long-term trends in ET0 patterns for diverse purposes, such as water resources planning, agricultural crop management and irrigation scheduling in the Cap-Bon region.
Associations between Isolation Source, Clonal Composition, and Antibiotic Resistance Genes in Escherichia coli Collected in Washington State, USA
Authors: Jewell Mary , Fuhrmeister Erica R. , Roberts Marilyn C. , Weissman Scott J. , Rabinowitz Peter M. , Hawes Stephen E.
Published: January 2024

Antimicrobial resistance (AMR) is a global health problem stemming from the use of antibiotics in humans, animals, and the environment. This study used whole-genome sequencing (WGS) of E. coli to explore patterns of AMR across sectors in Washington State, USA (WA). The WGS data from 1449 E. coli isolates were evaluated for isolation source (humans, animals, food, or the environment) and the presence of antibiotic resistance genes (ARGs). We performed sequence typing using PubMLST and used ResFinder to identify ARGs. We categorized isolates as being pan-susceptible, resistant, or multidrug-resistant (MDR), defined as carrying resistance genes for at least three or more antimicrobial drug classes. In total, 60% of isolates were pan-susceptible, while 18% were resistant, and 22% exhibited MDR. The proportion of resistant isolates varied significantly according to the source of the isolates (p < 0.001). The greatest resistance was detected in isolates from humans and then animals, while environmental isolates showed the least resistance. This study demonstrates the feasibility of comparing AMR across various sectors in Washington using WGS and a One Health approach. Such analysis can complement other efforts for AMR surveillance and potentially lead to targeted interventions and monitoring activities to reduce the overall burden of AMR.
Antibiofilm Properties and Demineralization Suppression in Early Enamel Lesions Using Dental Coating Materials
Authors: Kornsombut Niraya , Takenaka Shoji , Sotozono Maki , Nagata Ryoko , Ida Takako , Manuschai Jutharat , Saito Rui , Takahashi Ryouhei , Noiri Yuichiro
Published: January 2024

This study aimed to investigate the effects of dental coating materials on Streptococcus mutans biofilm formation. The test materials were PRG Barrier Coat (PRG), BioCoat Ca (BioC), and FluorDental Jelly (FluorJ). Bovine enamel specimens were demineralized to mimic early enamel lesions. The biofilm was developed on a specimen treated with one of the materials by using a modified Robbins device flow-cell system. Scanning electron and fluorescence confocal laser scanning microscopy, viable and total cell counts, and gene expression assessments of the antibiofilm were performed. Ion incorporation was analyzed using a wavelength-dispersive X-ray spectroscopy electron probe microanalyzer. All materials allowed biofilm formation but reduced its volume. FluorJ was the only material that inhibited biofilm accumulation and had a bactericidal effect, revealing 0.66 log CFU in viable cells and 1.23 log copy reduction in total cells compared with the untreated group after 24 h of incubation. The ions released from PRG varied depending on the element. BioC contributed to enamel remineralization by supplying calcium ions while blocking the acid produced from the biofilm. In summary, the dental coating materials physically prevented acid attacks from the biofilm while providing ions to the enamel to improve its mechanical properties.
Source Attribution of Antibiotic Resistance Genes in Estuarine Aquaculture: A Machine Learning Approach
Authors: Salgueiro Helena Sofia , Ferreira Ana Cristina , Duarte Ana Sofia Ribeiro , Botelho Ana
Published: January 2024

Aquaculture located in urban river estuaries, where other anthropogenic activities may occur, has an impact on and may be affected by the environment where they are inserted, namely by the exchange of antimicrobial resistance genes. The latter may ultimately, through the food chain, represent a source of resistance genes to the human resistome. In an exploratory study of the presence of resistance genes in aquaculture sediments located in urban river estuaries, two machine learning models were applied to predict the source of 34 resistome observations in the aquaculture sediments of oysters and gilt-head sea bream, located in the estuaries of the Sado and Lima Rivers and in the Aveiro Lagoon, as well as in the sediments of the Tejo River estuary, where Japanese clams and mussels are collected. The first model included all 34 resistomes, amounting to 53 different antimicrobial resistance genes used as source predictors. The most important antimicrobial genes for source attribution were tetracycline resistance genes tet(51) and tet(L); aminoglycoside resistance gene aadA6; beta-lactam resistance gene blaBRO-2; and amphenicol resistance gene cmx_1. The second model included only oyster sediment resistomes, amounting to 30 antimicrobial resistance genes as predictors. The most important antimicrobial genes for source attribution were the aminoglycoside resistance gene aadA6, followed by the tetracycline genes tet(L) and tet(33). This exploratory study provides the first information about antimicrobial resistance genes in intensive and semi-intensive aquaculture in Portugal, helping to recognize the importance of environmental control to maintain the integrity and the sustainability of aquaculture farms.
Effects of Alkalinity Exposure on Antioxidant Status, Metabolic Function, and Immune Response in the Hepatopancreas of Macrobrachium nipponense
Authors: Jin Shubo , Xu Mingjia , Gao Xuanbin , Jiang Sufei , Xiong Yiwei , Zhang Wenyi , Qiao Hui , Wu Yan , Fu Hongtuo
Published: January 2024

The oriental river prawn Macrobrachium nipponense is an important freshwater economic species in China, producing huge economic benefits. However, M. nipponense shows lower alkali tolerance than fish species, thus genetic selection is urgently needed in order to improve alkali tolerance in this species. In the present study, the effects of alkalinity exposure on the hepatopancreas of M. nipponense were measured under the alkali concentrations of 0 (control), 4, 8, and 12 mmol/L with the exposure time of 96 h through histological observations, measurement of antioxidant enzymes, metabolic profiling analysis, and transcriptome profiling analysis. The present study identified that the low concentration of alkali treatment (<4 mmol/L) did not result in morphological changes in the hepatopancreas and activity changes in antioxidant enzymes, while high-alkali treatment (>8 mmol/L) damaged the normal structures of the lumen and vacuoles and significantly stimulated the levels of superoxide dismutase, catalase, and total antioxidant capacity, indicating these antioxidant enzymes play essential roles in the protection of the body from the damage caused by the alkali treatment. Metabolic profiling analysis revealed that the main enriched metabolic pathways of differentially expressed metabolites in the present study were consistent with the metabolic pathways caused by environmental stress in plants and other aquatic animals. Transcriptome profiling analysis revealed that the alkali concentration of <8 mmol/L did not lead to significant changes in gene expression. The main enriched metabolic pathways were selected from the comparison between 0 mmol/L vs. 12 mmol/L, and some significantly up-regulated genes were selected from these metabolic pathways, predicting these selected metabolic pathways and genes are involved in the adaptation to alkali treatment in M. nipponense. The expressions of Ras-like GTP-binding protein, Doublesex and mab-3 related transcription factor 1a, and Hypothetical protein JAY84 are sensitive to changes in alkali concentrations, suggesting these three genes participated in the process of alkali adaptation in M. nipponense. The present study identified the effects of alkalinity exposure on the hepatopancreas of M. nipponense, including the changes in antioxidant status and the expressions of metabolites and genes, contributing to further studies of alkali tolerance in this species.
Peiminine Exerts Its Anti-Acne Effects by Regulating the NF-κB Pathway
Authors: Cha So Jin , Kim Seon Sook , Shin Jin Hak , Seo Su Ryeon
Published: January 2024

Peiminine is the main natural alkaloid compound extracted from the Chinese herb Fritillaria. Although peiminine is known for its antioxidant and anti-inflammatory effects in conditions such as mastitis and arthritis, its impact on inflammation induced by Cutibacterisum acnes (C. acnes) has not been explored. The aim of this study was to investigate the effect of peiminine on C. acnes-induced inflammatory responses in the skin and to identify the underlying mechanism involved. We discovered that peiminine inhibits the C. acnes-induced expression of inflammatory mediators such as pro-interleukin-1β (pro-IL-1β), cyclooxygenase-2 (COX-2), tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6) in mouse bone marrow-derived macrophages (BMDMs). Peiminine suppressed the activation of nuclear factor-kappa B (NF-κB) without affecting the activation of mitogen-activated protein kinase (MAPK) pathways such as JNK, ERK, and p38 MAPK. In addition, we found that peiminine suppressed inflammatory cytokine expression and ameliorated histological symptoms in C. acnes-induced mouse skin. Our study is the first to provide evidence that peiminine has an inhibitory effect on acne, and it points toward the potential of incorporating peiminine into cosmetic and pharmaceutical formulations for acne treatment.
The Roles of Neutrophil-Derived Myeloperoxidase (MPO) in Diseases: The New Progress
Authors: Lin Wei , Chen Huili , Chen Xijing , Guo Chaorui
Published: January 2024

Myeloperoxidase (MPO) is a heme-containing peroxidase, mainly expressed in neutrophils and, to a lesser extent, in monocytes. MPO is known to have a broad bactericidal ability via catalyzing the reaction of Cl− with H2O2 to produce a strong oxidant, hypochlorous acid (HOCl). However, the overproduction of MPO-derived oxidants has drawn attention to its detrimental role, especially in diseases characterized by acute or chronic inflammation. Broadly speaking, MPO and its derived oxidants are involved in the pathological processes of diseases mainly through the oxidation of biomolecules, which promotes inflammation and oxidative stress. Meanwhile, some researchers found that MPO deficiency or using MPO inhibitors could attenuate inflammation and tissue injuries. Taken together, MPO might be a promising target for both prognostic and therapeutic interventions. Therefore, understanding the role of MPO in the progress of various diseases is of great value. This review provides a comprehensive analysis of the diverse roles of MPO in the progression of several diseases, including cardiovascular diseases (CVDs), neurodegenerative diseases, cancers, renal diseases, and lung diseases (including COVID-19). This information serves as a valuable reference for subsequent mechanistic research and drug development.
A Critical Appraisal of the Protective Activity of Polyphenolic Antioxidants against Iatrogenic Effects of Anticancer Chemotherapeutics
Authors: Purgatorio Rosa , Boccarelli Angelina , Pisani Leonardo , de Candia Modesto , Catto Marco , Altomare Cosimo
Published: January 2024

Polyphenolic compounds, encompassing flavonoids (e.g., quercetin, rutin, and cyanidin) and non-flavonoids (e.g., gallic acid, resveratrol, and curcumin), show several health-related beneficial effects, which include antioxidant, anti-inflammatory, hepatoprotective, antiviral, and anticarcinogenic properties, as well as the prevention of coronary heart diseases. Polyphenols have also been investigated for their counteraction against the adverse effects of common anticancer chemotherapeutics. This review evaluates the outcomes of clinical studies (and related preclinical data) over the last ten years, with a focus on the use of polyphenols in chemotherapy as auxiliary agents acting against oxidative stress toxicity induced by antitumor drugs. While further clinical studies are needed to establish adequate doses and optimal delivery systems, the improvement in polyphenols’ metabolic stability and bioavailability, through the implementation of nanotechnologies that are currently being investigated, could improve therapeutic applications of their pharmaceutical or nutraceutical preparations in tumor chemotherapy.
Photoprotective Effects of Two New Morin-Schiff Base Derivatives on UVB-Irradiated HaCaT Cells
Authors: García-Gil Sara , Rodríguez-Luna Azahara , Ávila-Román Javier , Rodríguez-García Gabriela , del Río Rosa E. , Motilva Virginia , Gómez-Hurtado Mario A. , Talero Elena
Published: January 2024

Ultraviolet (UV) radiation harms the skin, causing oxidative damage, inflammation, and disruption of the skin’s barrier function. There is considerable interest in identifying new natural ingredients with antioxidant and anti-inflammatory properties to serve as adjuvants in sunscreens. The flavonoid morin (1) can undergo structural modifications to enhance its biological properties. The aim of this study was to synthesize two new morin-Schiff base derivatives, morin oxime (2) and morin semicarbazone (3), comparing their photoprotective effects with that of the parent compound on UVB-exposed HaCaT keratinocytes. The chemical structure of the novel compounds was revealed based on spectroscopic data analysis. Our findings demonstrated that derivatives 2 and 3 enhanced the light absorption capability in the UV–visible (vis) range compared to 1. Tested compounds exhibited a higher scavenger capacity than Trolox. Moreover, pre-treatment with all compounds protected HaCaT cells from UVB-induced cell death. Compound 3 demonstrated the strongest antioxidant effect, reducing reactive oxygen species (ROS) generation and, subsequently, malondialdehyde (MDA) levels. Additionally, compounds 2 and 3 exhibited greater anti-inflammatory effects than compound 1, significantly reducing interleukin (IL)-6 production levels at all tested concentrations. These findings have demonstrated, for the first time, a promising photoprotective activity of two new Schiff base derivatives and suggest their use as natural sunscreen ingredients.
Investigating the Influence of Holes as Crack Arrestors in Simulating Crack Growth Behavior Using Finite Element Method
Authors: Fageehi Yahya Ali , Alshoaibi Abdulnaser M.
Published: January 2024

The primary focus of this paper is to investigate the application of ANSYS Workbench 19.2 software’s advanced feature, known as Separating Morphing and Adaptive Remeshing Technology (SMART), in simulating the growth of cracks within structures that incorporate holes. Holes are strategically utilized as crack arrestors in engineering structures to prevent catastrophic failures. This technique redistributes stress concentrations and alters crack propagation paths, enhancing structural integrity and preventing crack propagation. This paper explores the concept of using holes as crack arrestors, highlighting their significance in increasing structural resilience and mitigating the risks associated with crack propagation. The crack growth path is estimated by applying the maximum circumferential stress criterion, while the calculation of the associated stress intensity factors is performed by applying the interaction integral technique. To analyze the impact of holes on the crack growth path and evaluate their effectiveness as crack arrestors, additional specimens with identical external dimensions but without any internal holes were tested. This comparison was conducted to provide a basis for assessing the role of holes in altering crack propagation behavior and their potential as effective crack arrestors. The results of this study demonstrated that the presence of a hole had a significant influence on the crack growth behavior. The crack was observed to be attracted towards the hole, leading to a deviation in its trajectory either towards the hole or deflecting around it. Conversely, in the absence of a hole, the crack propagated without any alteration in its path. To validate these findings, the computed crack growth paths and associated stress intensity factors were compared with experimental and numerical data available in the open literature. The remarkable consistency between the computational study results for crack growth path, stress intensity factors, and von Mises stress distribution, and the corresponding experimental and numerical data, is a testament to the accuracy and reliability of the computational simulations.
Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends
Authors: Ucar Aysegul , Karakose Mehmet , Kırımça Necim
Published: January 2024

Predictive maintenance (PdM) is a policy applying data and analytics to predict when one of the components in a real system has been destroyed, and some anomalies appear so that maintenance can be performed before a breakdown takes place. Using cutting-edge technologies like data analytics and artificial intelligence (AI) enhances the performance and accuracy of predictive maintenance systems and increases their autonomy and adaptability in complex and dynamic working environments. This paper reviews the recent developments in AI-based PdM, focusing on key components, trustworthiness, and future trends. The state-of-the-art (SOTA) techniques, challenges, and opportunities associated with AI-based PdM are first analyzed. The integration of AI technologies into PdM in real-world applications, the human–robot interaction, the ethical issues emerging from using AI, and the testing and validation abilities of the developed policies are later discussed. This study exhibits the potential working areas for future research, such as digital twin, metaverse, generative AI, collaborative robots (cobots), blockchain technology, trustworthy AI, and Industrial Internet of Things (IIoT), utilizing a comprehensive survey of the current SOTA techniques, opportunities, and challenges allied with AI-based PdM.
Multi-Objective Cutting Parameter Optimization Method for the Energy Consumption and Machining Quality of Computerized Numerical Control Lathes
Authors: Li Jian , He Pengbo , Li Huankun , Li Shifa , Xu Liping , He Kui
Published: January 2024

In order to achieve minimum energy consumption in computerized numerical control (CNC) lathe processing under the premise of ensuring the imposed roughness of the machined surface, a black hole-continuous ant colony optimization algorithm (BH-ACOR) is proposed to optimize the turning parameters. Taking turning specific energy and surface roughness as the optimization objectives, a turning test was designed. Subsequently, a multi-objective mathematical model of the cutting stage was formulated through the application of the least-squares method to fit the test data. The black hole algorithm was introduced to mitigate the shortcomings of the continuous-domain ant colony algorithm, which easily falls into a local optimum, so as to put forward a kind of BH-ACOR that is applicable to multi-objective optimization. The algorithm was applied to the multi-objective mathematical model in the turning stage to determine the optimal cutting parameters. Through simulation and test verification, the validity and practicability of the proposed method are further proved.
The Horizontal Bearing Characteristics and Microscopic Soil Deformation Mechanism of Pile-Bucket Composite Foundation in Sand
Authors: Zhang Xin , Yu Dongmin , Zhu Kaifei , Zhao Aolai , Ren Minghao
Published: January 2024

The pile-bucket composite foundation represents an innovative foundation form that surpasses the horizontal bearing performance of both single bucket-shaped foundations and pile foundations. The intricate interplay between piles and buckets introduces the complexity of the factors influencing the bearing performance of composite foundations under horizontal loads. In this paper, the indoor model tests were conducted to investigate the effects of relative density and pile-to-barrel diameter ratio on the horizontal bearing capacity and surrounding soil pressure of the pile-bucket composite foundation. A sensitivity analysis on the bearing characteristics of the pile-bucket foundation was performed using ABAQUS/CAE 2020 software. The results reveal a consistent variation in load–displacement curves across diverse diameter ratios of piles to buckets. The pile-bucket diameter ratio significantly impacts the horizontal bearing characteristics of the composite foundation. Reducing the pile-bucket diameter ratio improves the horizontal bearing capacity of the composite foundation. When the diameter ratio of piles to buckets diminishes to ≤0.317, the influence of this ratio on bearing performance becomes markedly pronounced. The displacement range of the surface soil decreases with an increase in relative density, while the influence depth of the surrounding soil of the composite foundation significantly decreases as the pile-to-barrel diameter ratio decreases.
The Construction and Application of a Deep Learning-Based Primary Support Deformation Prediction Model for Large Cross-Section Tunnels
Authors: Zhang Junling , Mei Min , Wang Jun , Shang Guangpeng , Hu Xuefeng , Yan Jing , Fang Qian
Published: January 2024

The deformation of tunnel support structures during tunnel construction is influenced by geological factors, geometrical factors, support factors, and construction factors. Accurate prediction of tunnel support structure deformation is crucial for engineering safety and optimizing support parameters. Traditional methods for tunnel deformation prediction have often relied on numerical simulations and model experiments, which may not always meet the time-sensitive requirements. In this study, we propose a fusion deep neural network (FDNN) model that combines multiple algorithms with a complementary tunnel information encoding method. The FDNN model utilizes Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to extract features related to tunnel structural deformation. FDNN model is used to predict deformations in the Capital Ring Expressway, and the predictions align well with monitoring results. To demonstrate the superiority of the proposed model, we use four different performance evaluation metrics to analyze the predictive performance of FDNN, DNN, XGBoost, Decision Tree Regression (DTR), and Random Forest Regression (RFR) methods. The results indicate that FDNN exhibits high precision and robustness. To assess the impact of different data types on the predictive results, we use tunnel geometry data as the base and combine geological, support, and construction data. The analysis reveals that models trained on datasets comprising all four data types perform the best. Geological parameters have the most significant impact on the predictive performance of all models. The findings of this research guide predicting tunnel construction parameters, particularly in the dynamic design of support parameters.
Implementing the Dynamic Feedback-Driven Learning Optimization Framework: A Machine Learning Approach to Personalize Educational Pathways
Authors: Song Chuanxiang , Shin Seong-Yoon , Shin Kwang-Seong
Published: January 2024

This study introduces a novel approach named the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF), aimed at personalizing educational pathways through machine learning technology. Our findings reveal that this framework significantly enhances student engagement and learning effectiveness by providing real-time feedback and personalized instructional content tailored to individual learning needs. This research demonstrates the potential of leveraging advanced technology to create more effective and individualized learning environments, offering educators a new tool to support each student’s learning journey. The study thus contributes to the field by showcasing how personalized education can be optimized using modern technological advancements.
Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers
Authors: Hütten Nils , Alves Gomes Miguel , Hölken Florian , Andricevic Karlo , Meyes Richard , Meisen Tobias
Published: January 2024

Quality assessment in industrial applications is often carried out through visual inspection, usually performed or supported by human domain experts. However, the manual visual inspection of processes and products is error-prone and expensive. It is therefore not surprising that the automation of visual inspection in manufacturing and maintenance is heavily researched and discussed. The use of artificial intelligence as an approach to visual inspection in industrial applications has been considered for decades. Recent successes, driven by advances in deep learning, present a possible paradigm shift and have the potential to facilitate automated visual inspection, even under complex environmental conditions. For this reason, we explore the question of to what extent deep learning is already being used in the field of automated visual inspection and which potential improvements to the state of the art could be realized utilizing concepts from academic research. By conducting an extensive review of the openly accessible literature, we provide an overview of proposed and in-use deep-learning models presented in recent years. Our survey consists of 196 open-access publications, of which 31.7% are manufacturing use cases and 68.3% are maintenance use cases. Furthermore, the survey also shows that the majority of the models currently in use are based on convolutional neural networks, the current de facto standard for image classification, object recognition, or object segmentation tasks. Nevertheless, we see the emergence of vision transformer models that seem to outperform convolutional neural networks but require more resources, which also opens up new research opportunities for the future. Another finding is that in 97% of the publications, the authors use supervised learning techniques to train their models. However, with the median dataset size consisting of 2500 samples, deep-learning models cannot be trained from scratch, so it would be beneficial to use other training paradigms, such as self-supervised learning. In addition, we identified a gap of approximately three years between approaches from deep-learning-based computer vision being published and their introduction in industrial visual inspection applications. Based on our findings, we additionally discuss potential future developments in the area of automated visual inspection.
Partial Derivatives Estimation of Multivariate Variance Function in Heteroscedastic Model via Wavelet Method
Authors: Kou Junke , Zhang Hao
Published: January 2024

For derivative function estimation, conventional research only focuses on the derivative estimation of one-dimensional functions. This paper considers partial derivatives estimation of a multivariate variance function in a heteroscedastic model. A wavelet estimator of partial derivatives of a multivariate variance function is proposed. The convergence rates of a wavelet estimator under different estimation errors are discussed. It turns out that the strong convergence rate of the wavelet estimator is the same as the optimal uniform almost sure convergence rate of nonparametric function problems.
A Note on a Fractional Extension of the Lotka–Volterra Model Using the Rabotnov Exponential Kernel
Authors: Khader Mohamed M. , Macías-Díaz Jorge E. , Román-Loera Alejandro , Saad Khaled M.
Published: January 2024

In this article, we study the fractional form of a well-known dynamical system from mathematical biology, namely, the Lotka–Volterra model. This mathematical model describes the dynamics of a predator and prey, and we consider here the fractional form using the Rabotnov fractional-exponential (RFE) kernel. In this work, we derive an approximate formula of the fractional derivative of a power function ζp in terms of the RFE kernel. Next, by using the spectral collocation method (SCM) based on the shifted Vieta–Lucas polynomials (VLPs), the fractional differential system is reduced to a set of algebraic equations. We provide a theoretical convergence analysis for the numerical approach, and the accuracy is verified by evaluating the residual error function through some concrete examples. The results are then contrasted with those derived using the fourth-order Runge-Kutta (RK4) method.
A Review of Lithium-Ion Battery Recycling: Technologies, Sustainability, and Open Issues
Authors: Zanoletti Alessandra , Carena Eleonora , Ferrara Chiara , Bontempi Elza
Published: January 2024

Lithium-ion batteries (LIBs) are a widely used energy storage technology as they possess high energy density and are characterized by the reversible intercalation/deintercalation of Li ions between electrodes. The rapid development of LIBs has led to increased production efficiency and lower costs for manufacturers, resulting in a growing demand for batteries and their application across various industries, particularly in different types of vehicles. In order to meet the demand for LIBs while minimizing climate-impacting emissions, the reuse, recycling, and repurposing of LIBs is a critical step toward achieving a sustainable battery economy. This paper provides a comprehensive review of lithium-ion battery recycling, covering topics such as current recycling technologies, technological advancements, policy gaps, design strategies, funding for pilot projects, and a comprehensive strategy for battery recycling. Additionally, this paper emphasizes the challenges associated with developing LIB recycling and the opportunities arising from these challenges, such as the potential for innovation and the creation of a more sustainable and circular economy. The environmental implications of LIB recycling are also evaluated with methodologies able to provide a sustainability analysis of the selected technology. This paper aims to enhance the comprehension of these trade-offs and encourage discussion on determining the “best” recycling route when targets are in conflict.
Tuning Microelectrodes’ Impedance to Improve Fast Ripples Recording
Authors: Mousavi Hajar , Dauly Gautier , Dieuset Gabriel , El Merhie Amira , Ismailova Esma , Wendling Fabrice , Al Harrach Mariam
Published: January 2024

Epilepsy is a chronic neurological disorder characterized by recurrent seizures resulting from abnormal neuronal hyperexcitability. In the case of pharmacoresistant epilepsy requiring resection surgery, the identification of the Epileptogenic Zone (EZ) is critical. Fast Ripples (FRs; 200–600 Hz) are one of the promising biomarkers that can aid in EZ delineation. However, recording FRs requires physically small electrodes. These microelectrodes suffer from high impedance, which significantly impacts FRs’ observability and detection. In this study, we investigated the potential of a conductive polymer coating to enhance FR observability. We employed biophysical modeling to compare two types of microelectrodes: Gold (Au) and Au coated with the conductive polymer poly(3,4-ethylenedioxythiophene)-poly(styrene sulfonate) (Au/PEDOT:PSS). These electrodes were then implanted into the CA1 hippocampal neural network of epileptic mice to record FRs during epileptogenesis. The results showed that the polymer-coated electrodes had a two-order lower impedance as well as a higher transfer function amplitude and cut-off frequency. Consequently, FRs recorded with the PEDOT:PSS-coated microelectrode yielded significantly higher signal energy compared to the uncoated one. The PEDOT:PSS coating improved the observability of the recorded FRs and thus their detection. This work paves the way for the development of signal-specific microelectrode designs that allow for better targeting of pathological biomarkers.
Apoptosis Inhibitor 5: A Multifaceted Regulator of Cell Fate
Authors: Abbas Hafsia , Derkaoui Dalia Kheira , Jeammet Louise , Adicéam Emilie , Tiollier Jérôme , Sicard Hélène , Braun Thorsten , Poyet Jean-Luc
Published: January 2024

Apoptosis, or programmed cell death, is a fundamental process that maintains tissue homeostasis, eliminates damaged or infected cells, and plays a crucial role in various biological phenomena. The deregulation of apoptosis is involved in many human diseases, including cancer. One of the emerging players in the intricate regulatory network of apoptosis is apoptosis inhibitor 5 (API5), also called AAC-11 (anti-apoptosis clone 11) or FIF (fibroblast growth factor-2 interacting factor). While it may not have yet the same level of notoriety as some other cancer-associated proteins, API5 has garnered increasing attention in the cancer field in recent years, as elevated API5 levels are often associated with aggressive tumor behavior, resistance to therapy, and poor patient prognosis. This review aims to shed light on the multifaceted functions and regulatory mechanisms of API5 in cell fate decisions as well as its interest as therapeutic target in cancer.