bluuebunny/crossref_metadata_2025 huggingface dataset:
Differentiation of Multi-Parametric Groups of Groundwater Bodies through Discriminant Analysis and Machine Learning
Authors: Mohsine Ismail , Kacimi Ilias , Valles Vincent , Leblanc Marc , El Mahrad Badr , Dassonville Fabrice , Kassou Nadia , Bouramtane Tarik , Abraham Shiny , Touiouine Abdessamad , Jabrane Meryem , Touzani Meryem , Barry Abdoul Azize , Yameogo Suzanne , Barbiero Laurent
Published: December 2023

In order to facilitate the monitoring of groundwater quality in France, the groundwater bodies (GWB) in the Provence-Alpes-Côte d’Azur region have been grouped into 11 homogeneous clusters on the basis of their physico-chemical and bacteriological characteristics. This study aims to test the legitimacy of this grouping by predicting whether water samples belong to a given sampling point, GWB or group of GWBs. To this end, 8673 observations and 18 parameters were extracted from the Size-Eaux database, and this dataset was processed using discriminant analysis and various machine learning algorithms. The results indicate an accuracy of 67% using linear discriminant analysis and 69 to 83% using ML algorithms, while quadratic discriminant analysis underperforms in comparison, yielding a less accurate prediction of 59%. The importance of each parameter in the prediction was assessed using an approach combining recursive feature elimination (RFE) techniques and random forest feature importance (RFFI). Major ions show high spatial range and play the main role in discrimination, while trace elements and bacteriological parameters of high local and/or temporal variability only play a minor role. The disparity of the results according to the characteristics of the GWB groups (geography, altitude, lithology, etc.) is discussed. Validating the grouping of GWBs will enable monitoring and surveillance strategies to be redirected on the basis of fewer, homogeneous hydrogeological units, in order to optimize sustainable management of the resource by the health agencies.
The Impact of Professionalism Theory Constructs on the Applicability of Forensic Accounting Services: Evidence from Jordan
Authors: Almashaqbeh Razan Nayef , Alshurafat Hashem , Al Amosh Hamzeh
Published: December 2023

This study aims to provide an understanding of the impact of professionalism theory constructs on the applicability of forensic accounting services, as well as reviewing the obstacles to implementing the profession of forensic accounting through a literature review. The accounting side of the study explores the profession of forensic accounting and the sociological side of the study will focus on professionalism theory, more specifically Pavalko’s 1988 model. A quantitative method was used in this study; 154 questionnaires from Certified Public Accountants (CPAs) were validated and used in the data analysis. Data analysis was conducted using SmartPLS, which was used for testing the study’s hypotheses. The results demonstrated that the professionalism theory constructs have a significant impact on the applicability of forensic accounting services. There was an indication that six out of eight constructs had a positive impact, which are, code of ethics, commitment, intellectual technique, motivation, training, and relevance to social values, leaving only autonomy and sense of community with no impact shown. This study recommends that forensic accountants and practitioners contribute to developing the profession of forensic accounting and to provide services with high quality. The study’s significant constructs can inform the development of training programs for aspiring forensic accountants in Jordan, emphasizing ethics, commitment, technical expertise, motivation, training, and social values. These findings may also apply to other countries.
A False-Negative Newborn Screen for Tyrosinemia Type 1—Need for Re-Evaluation of Newborn Screening with Succinylacetone
Authors: Dijkstra Allysa M. , Evers-van Vliet Kimber , Heiner-Fokkema M. Rebecca , Bodewes Frank A. J. A. , Bos Dennis K. , Zsiros József , van Aerde Koen J. , Koop Klaas , van Spronsen Francjan J. , Lubout Charlotte M. A.
Published: December 2023

Undiagnosed and untreated tyrosinemia type 1 (TT1) individuals carry a significant risk for developing liver fibrosis, cirrhosis and hepatocellular carcinoma (HCC). Elevated succinylacetone (SA) is pathognomonic for TT1 and therefore often used as marker for TT1 newborn screening (NBS). While SA was long considered to be elevated in every TT1 patient, here we present a recent false-negative SA TT1 screen. A nine-year-old boy presented with HCC in a cirrhotic liver. Additional tests for the underlying cause unexpectedly revealed TT1. Nine years prior, the patient was screened for TT1 via SA NBS with a negative result: SA 1.08 µmol/L, NBS cut-off 1.20 µmol/L. To our knowledge, this report is the first to describe a false-negative result from the TT1 NBS using SA. False-negative TT1 NBS results may be caused by milder TT1 variants with lower SA excretion. Such patients are more likely to be missed in NBS programs and can be asymptomatic for years. Based on our case, we advise TT1 to be considered in patients with otherwise unexplained liver pathology, including fibrosis, cirrhosis and HCC, despite a previous negative TT1 NBS status. Moreover, because the NBS SA concentration of this patient fell below the Dutch cut-off value (1.20 µmol/L at that time), as well as below the range of cut-off values used in other countries (1.29–10 µmol/L), it is likely that false-negative screening results for TT1 may also be occurring internationally. This underscores the need to re-evaluate TT1 SA NBS programs.
Numerical Modelling of the 3D Unsteady Flow of an Inlet Particle Separator for Turboshaft Engines
Authors: Castaldi Marco , Mayo Ignacio , Demolis Jacques , Eulitz Frank
Published: December 2023

Helicopter and turboprop engines are susceptible to the ingestion of debris and other foreign objects, especially during take-off, landing, and hover. To avoid deleterious effects, filters such as Inlet Particle Separators (IPS) can be installed. However, the performance and limitations of these systems have to be investigated before the actual equipment can be installed in the aircraft powerplant. In this paper, we propose different numerical methods with increasing resolution in order to provide an aerodynamic characterization of the IPS, i.e., from a simple semi-empirical model to 3D large eddy simulation. We validate these numerical tools that could aid IPS design using experimental data in terms of global parameters such as separation efficiency and pressure losses. For each of those tools, we underline weaknesses and potential benefits in industry practices. Unsteady flow analysis reveals that detached eddy simulation is the trade-off choice that allows designers to most effectively plan experimental campaigns and mitigate risks.
Ferrocene-Based Electrochemical Sensors for Cations
Authors: Torriero Angel A. J. , Mruthunjaya Ashwin K. V.
Published: December 2023

This study investigates novel ferrocene-based electrochemical sensors for metal cation detection via the design, synthesis and characterisation of ferrocene derivatives. Specifically, this research determines the redox potentials of ferrocene versus decamethylferrocene to provide insight into the redox potential variations. The investigation also examines how electrochemical oxidation of the ferrocene moiety can modulate host affinity for transition metal cations via effects such as electrostatic interactions and changes to coordination chemistry. Metal ion coordination to receptors containing functional groups like imine and quinoline is explored to elucidate selectivity mechanisms. These findings advance the fundamental understanding of ferrocene electrochemistry and host–guest interactions, supporting the development of improved cation sensors with optimised recognition properties, sensitivity and selectivity. Overall, this work lays the necessary groundwork for applications in analytical chemistry and sensor technologies via customised ferrocene-derived materials.
A Systematic Approach to Long-Term Storage of Documents Using Digital Twin Technologies
Authors: Solovyev Alexander , Tarkhanov Ivan
Published: December 2023

The article discusses the modeling of the use of digital twin technologies (a digital twin) for the task of organizing long-term storage of various types of documents. A digital twin in this respect differs from a digital copy, which has no connection with the real original object. A review of the problem was carried out. We argue that, despite the active use of digital twin technology in various areas, its applications in the area of long-term storage of documents remain limited. At the same time, the task of increasing the durability of documents during long-term storage remains important and largely unresolved. The complexity of solving the problem of long-term storage of documents is considered, and a formal statement of the problem of long-term storage using digital twin technologies is carried out. Destructive factors that affect long-term storage documents and significantly reduce their durability have been identified. A system of indicators has been developed to assess the durability (preservation) of documents. The modeling of the use of digital twin technology in the organization of long-term preservation within the framework of Industry 4.0 was carried out. In the course of modeling, the goals and the strategies of long-term storage were established. Primary mathematical models for controlling destructive factors as well as technological solutions for digital twin long-term storage are proposed. It is assumed that the key part of this technology is the Industrial Internet of Things. The effectiveness of the use of digital twin technologies for solving the problem was assessed. The spheres of application and further ways of research are determined.
Hippo Signaling Mediates TGFβ-Dependent Transcriptional Inputs in Cardiac Cushion Mesenchymal Cells to Regulate Extracellular Matrix Remodeling
Authors: Chakrabarti Mrinmay , Chattha Ahad , Nair Abhijith , Jiao Kai , Potts Jay D. , Wang Lianming , Branch Scotty , Harrelson Shea , Khan Saeed , Azhar Mohamad
Published: December 2023

The transforming growth factor beta (TGFβ) and Hippo signaling pathways are evolutionarily conserved pathways that play a critical role in cardiac fibroblasts during embryonic development, tissue repair, and fibrosis. TGFβ signaling and Hippo signaling are also important for cardiac cushion remodeling and septation during embryonic development. Loss of TGFβ2 in mice causes cardiac cushion remodeling defects resulting in congenital heart disease. In this study, we used in vitro molecular and pharmacologic approaches in the cushion mesenchymal cell line (tsA58-AVM) and investigated if the Hippo pathway acts as a mediator of TGFβ2 signaling. Immunofluorescence staining showed that TGFβ2 induced nuclear translocation of activated SMAD3 in the cushion mesenchymal cells. In addition, the results indicate increased nuclear localization of Yes-associated protein 1 (YAP1) following a similar treatment of TGFβ2. In collagen lattice formation assays, the TGFβ2 treatment of cushion cells resulted in an enhanced collagen contraction compared to the untreated cushion cells. Interestingly, verteporfin, a YAP1 inhibitor, significantly blocked the ability of cushion cells to contract collagen gel in the absence or presence of exogenously added TGFβ2. To confirm the molecular mechanisms of the verteporfin-induced inhibition of TGFβ2-dependent extracellular matrix (ECM) reorganization, we performed a gene expression analysis of key mesenchymal genes involved in ECM remodeling in heart development and disease. Our results confirm that verteporfin significantly decreased the expression of α-smooth muscle actin (Acta2), collagen 1a1 (Col1a1), Ccn1 (i.e., Cyr61), and Ccn2 (i.e., Ctgf). Western blot analysis indicated that verteporfin treatment significantly blocked the TGFβ2-induced activation of SMAD2/3 in cushion mesenchymal cells. Collectively, these results indicate that TGFβ2 regulation of cushion mesenchymal cell behavior and ECM remodeling is mediated by YAP1. Thus, the TGFβ2 and Hippo pathway integration represents an important step in understanding the etiology of congenital heart disease.
Monoclonal Gammopathy of Undetermined Cardiovascular Significance; Current Evidence and Novel Insights
Authors: Tentolouris Anastasios , Ntanasis-Stathopoulos Ioannis , Gavriatopoulou Maria , Andreadou Ioanna , Terpos Evangelos
Published: December 2023

Monoclonal gammopathy of undetermined significance (MGUS) is a premalignant condition characterized by the presence of low levels of a monoclonal protein in the serum and a low percentage of clonal plasma cells in the bone marrow. MGUS may progress to multiple myeloma or other plasma cell disorders at a rate of 1% annually. However, MGUS may also have adverse effects on the cardiovascular system independent of its malignant potential. Emerging data have shown that MGUS is associated with cardiovascular disease. The mechanisms underlying this association are not fully understood but may involve genetic abnormalities, vascular calcification, cryoglobulinemia, cold agglutinin disease, autoantibodies and the direct or indirect effects of the monoclonal protein on the vascular endothelium. Herein, we review current evidence in this field and we suggest that patients with MGUS may benefit from regular cardiovascular risk assessment to prevent severe cardiovascular complications, in parallel with close hematological follow-up to monitor potential disease progression.
Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look
Authors: Kumari Vandana , Kumar Naresh , Kumar K Sampath , Kumar Ashish , Skandha Sanagala S. , Saxena Sanjay , Khanna Narendra N. , Laird John R. , Singh Narpinder , Fouda Mostafa M. , Saba Luca , Singh Rajesh , Suri Jasjit S.
Published: December 2023

Background and Motivation: Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. Methods: Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. Findings and Conclusions: UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.
Primary Thyroid Dysfunction Is Prevalent in Hidradenitis Suppurativa and Marked by a Signature of Hypothyroid Graves’ Disease: A Case–Control Study
Authors: Abu Rached Nessr , Dietrich Johannes W. , Ocker Lennart , Quast Daniel R. , Scheel Christina , Gambichler Thilo , Bechara Falk G.
Published: December 2023

Hidradenitis suppurativa (HS) is a chronic skin disease that can have an association with endocrine disorders. There is conflicting information in the literature regarding the role of the thyroid gland in HS. This study aimed to close this knowledge gap and investigate how thyroid disease is involved in patients with HS. We carried out a case–control study with a total of 160 patients, of whom 108 were patients with HS and 52 were controls matched for age and sex. Parametric and non-parametric methods were used to analyze the results. We calculated structural parameters of thyroid homeostasis to detect subclinical thyroid disease, non-thyroid disease syndrome and other forms. The severity of HS was not associated with thyroid hormone levels and antibodies (p > 0.05). HS patients with or without hypothyroidism had decreased FT4 levels and a decreased thyroid secretory capacity (SPINA-GT). Titers of TSH receptor autoantibodies (TRAb) were significantly higher in smoking HS patients compared to non-smokers (median: 1.18 vs. 1.08; p = 0.042). The rate of subclinical hypothyroidism was significantly higher in HS patients; thus, subclinical hypothyroidism is an important comorbidity of HS (p < 0.05). Further studies are needed to investigate whether the chronic inflammation of HS is a cause of increased rates of (subclinical) hypothyroidism.
Autophagy Inhibition in Trophoblasts Induces Aberrant Shift in CXCR4+ Decidual NK Cell Phenotype Leading to Pregnancy Loss
Authors: Liu Nan , Shen Huihui , Wang Zehua , Qin Xueyun , Li Mingqing , Zhang Xinyan
Published: December 2023

Background: Pregnancy, a complex biological phenomenon, relies on intricate maternal–fetal interactions for success. Decidual natural killer (dNK) cells and trophoblasts are pivotal in establishing immune tolerance at the maternal–fetal interface. The chemokine receptor CXCR4 plays a crucial role in NK cell development and immune tolerance during early placental development. Methods: Primary decidual immune cells from 42 women with normal pregnancies and 20 patients experiencing recurrent spontaneous abortions (RSAs) were studied. Gene transcription in NK cells was assessed using real-time polymerase chain reaction. In a co-culture system, we examined the influence of trophoblasts on CXCR4 expression in dNK cells, with subsequent analysis conducted via flow cytometry. The proportion of CXCR4+ NK cells was assessed using flow cytometry after co-culture with trophoblasts pre-treated with 3-MA or a p53 activator. Results: Our study confirmed a diminished presence of decidual CXCR4+ NK cells in RSA patients during early pregnancy. Co-culturing with a trophoblast-derived supernatant increased CXCR4 expression in dNK cells. In addition, trophoblast autophagy plays an educative role in regulating the dNK landscape via the IGF2-TP53-CXCR4 axis. Conclusion: Autophagy inhibition in trophoblasts induces an aberrant shift in the CXCR4+ dNK phenotype, potentially contributing to pregnancy loss. This sheds light on the nuanced behavior of dNK cells during pregnancy, offering promising therapeutic avenues to mitigate pregnancy complications.
The Effect of Dexmedetomidine Sedation on Lower Gastrointestinal Motility in Children—Is It Suitable for Anorectal Manometry?
Authors: Berger Tal David , Lukovits Karina , Cavanaugh David , Nurko Samuel , Mason Keira
Published: December 2023

Anorectal manometry is one of the most frequently performed gastrointestinal motility studies in children. It is an important study in diagnosing Hirschsprung disease (HD). These procedures can be uncomfortable, painful and emotionally distressing. Nitrous oxide or midazolam are the only pharmacologic options available, as clinical experience suggests that they do not alter manometry readings. Our study was designed to determine whether Dexmedetomidine (DEX) could provide adequate sedation without disrupting anal and rectal pressure. The effect of DEX on anorectal function has never been studied in children. This prospective study recorded anorectal manometry (ARM) measurements prior to the administration of DEX and then repeated the measurements at 1 and 5 min after DEX. The main ARM measurements included resting intra-anal sphincter pressure (IASP) and the presence and characteristics of the recto-anal inhibitory reflex (RAIR). DEX was administered as a bolus followed by a continuous infusion. Twenty patients were included (60% female; mean age 10.8 ± 4.6 years). The RAIR became absent in 2/16 (12.5%) patients after DEX administration. DEX may alter physiologic ARM and IASP recordings necessary to diagnose gastrointestinal medical conditions.
Objectivizing Measures of Post-Stroke Hand Rehabilitation through Multi-Disciplinary Scales
Authors: Marek Klaudia , Redlicka Justyna , Miller Elżbieta , Zubrycki Igor
Published: December 2023

There is a wide variety of tools and measures for rehabilitation outcomes in post-stroke patients with impairments in the upper limb and hand, such as paralysis, paresis, flaccidity, and spasticity. However, there is a lack of general recommendations for selecting the most appropriate scales, tests, and instruments to objectively evaluate therapy outcomes. Reviews on upper limb and hand measurements reveal that clinicians’ choices of tools and methods are highly varied. Some clinicians and medical teams continue to employ non-standard and unverified metrics in their research and measurements. This review article aims to identify the key parameters, assessed by outcome measures and instruments, that play a crucial role in upper limb and hand rehabilitation for post-stroke patients, specifically focusing on the recovery of hand function. The review seeks to assist researchers and medical teams in selecting appropriate outcome measures when evaluating post-stroke patients. We analyze the measured factors and skills found in these outcome measures and highlight useful tools that diversify assessments and enhance result objectivity through graphical representation. The paper also describes trends and new possibilities in hand outcome measures. Clinicians frequently use proven devices, such as EMG, goniometers, and hand dynamometers. Still, there is a growing trend towards incorporating technologies, such as pose and position estimation, using artificial intelligence, or custom hand grip measurement devices. Researchers are increasingly adopting scales previously successful in orthopedic and surgical patients, recognizing their potential for objectivizing outcomes in neurological patients with post-stroke hand complications. The review included only adults over the age of 18. Outcome measures were tested for usefulness in the rehabilitation of stroke patients.
Oversizing Consideration of Proximal Stent Graft in Hemodynamically Stable and Unstable Patients Undergoing Emergent Endovascular Aortic Repair
Authors: Qi Yuhan , Weng Chengxin , Yuan Ding , Wang Tiehao , Ma Yukui , Yang Yi , Zhao Jichun , Huang Bin
Published: December 2023

Consideration for oversizing the proximal stent graft is suggested in endovascular aortic repair. However, a special recommendation for the proximal oversizing ratio (OSR) in patients with ruptured abdominal aortic aneurysm (rAAA) is ambiguous. This study aims to evaluate the effect of different degrees of the proximal oversizing ratio (OSR) on risk of type IA endoleak (TIAEL) in hemodynamically stable and unstable patients with rAAA undergoing emergency endovascular aortic repair (EVAR). Our study included 134 rAAA patients undergoing emergent EVAR, and we did not observe a significant association between hemodynamic instability and risk of T1AEL (HR 3.89, 95%CI 0.40–37.75, p = 0.24). All three T1AELs in the hemodynamically unstable subgroup were observed in patients with OSR ≤ 30%, but no significant difference was found regarding T1AEL between patients with OSR > 30% and OSR ≤ 30% (0.00% vs. 11.11%, p = 0.19). As for hemodynamically stable patients, OSR > 20% was associated with a significantly decreased risk of T1AEL (HR 0.03, 95%CI 0.01–0.53, p = 0.016). In conclusion, a proximal OSR > 20% is associated with a decreased risk of T1AEL in hemodynamically stable patients, while an OSR > 30% did not add an additional advantage of lowering the risk of T1AEL in hemodynamically unstable patients.
Glass-Containing Matrices Based on Borosilicate Glasses for the Immobilization of Radioactive Wastes
Authors: Koroleva Olga N. , Nevolina Lyubov A. , Korobatova Nadezhda M.
Published: December 2023

Glass-containing materials are widely considered among the most reliable materials for the immobilization of radioactive waste materials. The present work considers the synthesis of glass–ceramic and glass crystalline composite materials based on borosilicate glasses. The synthesis of glass–ceramic materials was carried out by a gradual temperature decrease, followed by crystallization for several hours. Sintering of crushed samples with crystalline components was carried out as an alternative procedure. Porous glasses were produced from glass melts by quenching. After impregnating the resulting porous materials with aqueous solutions of cesium nitrate, compaction of the glass was carried out to form glass crystalline composites. The thermochemical characteristics of the parent glasses were determined using the differential scanning calorimetry method. The phase composition and structure of the glass-containing materials were determined using X-ray phase analysis, X-ray spectral microanalysis, and Raman spectroscopy.
Model-Based Design to Enhance Neotissue Formation in Additively Manufactured Calcium-Phosphate-Based Scaffolds
Authors: Liang Bingbing , Sadeghian Dehkord Ehsan , Van Hede Dorien , Barzegari Mojtaba , Verlée Bruno , Pirson Justine , Nolens Grégory , Lambert France , Geris Liesbet
Published: December 2023

In biomaterial-based bone tissue engineering, optimizing scaffold structure and composition remains an active field of research. Additive manufacturing has enabled the production of custom designs in a variety of materials. This study aims to improve the design of calcium-phosphate-based additively manufactured scaffolds, the material of choice in oral bone regeneration, by using a combination of in silico and in vitro tools. Computer models are increasingly used to assist in design optimization by providing a rational way of merging different requirements into a single design. The starting point for this study was an in-house developed in silico model describing the in vitro formation of neotissue, i.e., cells and the extracellular matrix they produced. The level set method was applied to simulate the interface between the neotissue and the void space inside the scaffold pores. In order to calibrate the model, a custom disk-shaped scaffold was produced with prismatic canals of different geometries (circle, hexagon, square, triangle) and inner diameters (0.5 mm, 0.7 mm, 1 mm, 2 mm). The disks were produced with three biomaterials (hydroxyapatite, tricalcium phosphate, and a blend of both). After seeding with skeletal progenitor cells and a cell culture for up to 21 days, the extent of neotissue growth in the disks’ canals was analyzed using fluorescence microscopy. The results clearly demonstrated that in the presence of calcium-phosphate-based materials, the curvature-based growth principle was maintained. Bayesian optimization was used to determine the model parameters for the different biomaterials used. Subsequently, the calibrated model was used to predict neotissue growth in a 3D gyroid structure. The predicted results were in line with the experimentally obtained ones, demonstrating the potential of the calibrated model to be used as a tool in the design and optimization of 3D-printed calcium-phosphate-based biomaterials for bone regeneration.
Prediction of the Occurrence Probability of Freak Waves in Unidirectional Sea State Using Deep Learning
Authors: Zhou Binzhen , Wang Jiahao , Ding Kanglixi , Wang Lei , Liu Yingyi
Published: December 2023

Predicting extreme waves can foresee the hydrodynamic environment of marine engineering, critical for avoiding disaster risks. Till now, there are barely any available models that can rapidly and accurately predict the occurrence probability of freak waves in a given state. This paper develops a trained model based on the Back Propagation (BP) neural network, with wave parameters of unidirectional sea state fed into the model, such as significant wave height, wave period, spectral type, and the intermodal distance of the peak frequencies. A rapid and accurate model optimized for predicting the occurrence probability of freak waves in a unidirectional sea state, from unimodal to bimodal configuration, is achieved by iterating to reduce accumulation errors. Compared to the regression and least-squares boosting trees, the optimized model performs much better in accurately predicting the occurrence probability of freak waves. Irrespective of whether in unimodal or bimodal sea state, this optimized model is competitive in calculation accuracy compared to theoretical models such as Rayleigh prediction and MER prediction, improved by at least 41%. The established model based on the BP neural network can quickly predict the threshold of freak waves in a given sea state, guiding practical engineering applications.
Effect of Tip Clearance on Force Characteristics of Helical Axial-Flow Blade Pumps under Cavitation Conditions
Authors: Wen Haigang , Li Luyao , Shi Guangtai , Ma Haijun , Peng Xiaodong
Published: December 2023

A spiral axial-flow blade pump (SABP), as the core piece of equipment in the oil and natural gas closed-gathering and transportation process, can not only transport gas–liquid mixtures with a high gas content, but can also transport gas–liquid–solid mixtures containing small amounts of sand. However, due to the complexity of the distribution of transport media groups and the uncertainty of internal flow processes, large vortices often appear in the passage of the pumps, and the existence of vortices can easily induce the occurrence of pump cavitations. In the present work, a self-developed SABP was taken as the research object, and the cavitation performance of the SABP was numerically calculated. The pressure load variation under different tip clearances and different cavitation stages was analyzed, and the characteristics of the axial and radial forces were also analyzed in detail.
Trichoderma harzianum Cellulase Gene thph2 Affects Trichoderma Root Colonization and Induces Resistance to Southern Leaf Blight in Maize
Authors: Lang Bo , Chen Jie
Published: December 2023

Trichoderma, widely distributed all over the world, is commonly found in soil and root ecosystems. It is a group comprising beneficial fungi that improve plant disease resistance and promote plant growth. Studies have shown that Trichoderma cellulases can also improve plant disease resistance. Based on previous studies, we reported that a C6 zinc finger protein (Thc6) regulates two cellulase genes, thph1 and thph2, to induce ISR responses in plants. Therefore, in this study, we focused on the role of thph2 in the colonization of maize roots by T. harzianum and the induction of systemic resistance against southern leaf blight. The results showed that thph2 had a positive regulatory effect on the Trichoderma colonization of maize roots. After the root was treated with Trichoderma, the leaf defense genes AOS, LOX5, HPL, and OPR1 were expressed to resist the attack of Cochliobolus heterostrophus. The pure Thph2 protein also resulted in a similar induction activity of the AOS, LOX5, HPL, and OPR1 expression in maize roots, further demonstrating that thph2 can induce plant defense responses and that signal transduction occurs mainly through the JA signaling pathway.
Abnormal Heart Sound Classification and Model Interpretability: A Transfer Learning Approach with Deep Learning
Authors: Marocchi Milan , Abbott Leigh , Rong Yue , Nordholm Sven , Dwivedi Girish
Published: December 2023

Physician detection of heart sound abnormality is complicated by the inherent difficulty of detecting critical abnormalities in the presence of noise. Computer-aided heart auscultation provides a promising alternative for more accurate detection, with recent deep learning approaches exceeding expert accuracy. Although combining phonocardiogram (PCG) data with electrocardiogram (ECG) data provides more information to an abnormal heart sound classifier, the scarce presence of labelled datasets with this combination impedes training. This paper explores fine-tuning deep convolutional neural networks such as ResNet, VGG, and inceptionv3, on images of spectrograms, mel-spectrograms, and scalograms. By fine-tuning deep pre-trained models on image representations of ECG and PCG, we achieve 91.25% accuracy on the training-a dataset of the PhysioNet Computing in Cardiology Challenge 2016, compared to a previous result of 81.48%. Interpretation of the model’s learned features is also provided, with the results indicative of clinical significance.
Spatiotemporal Changes of Terrestrial Carbon Storage in Rapidly Urbanizing Areas and Their Influencing Factors: A Case Study of Wuhan, China
Authors: Xing Shuxuan , Yang Shengfu , Sun Haonan , Wang Yi
Published: December 2023

Terrestrial carbon storage plays a vital role in limiting global climate change and achieving regional carbon neutrality. However, intensive human activities and rapid urbanization have led to a rapid decline in carbon storage. Understanding what causes carbon storage to decline and how this happens is important for the scientific regulation of urbanization and safeguarding of urban ecological security. This study takes Wuhan as an example and analyzes the quantity, structure, and spatial patterns of urban land-use changes in the context of human activities and natural conditions, and applies correlation methods to identify general relationships between influencing factors and carbon storage. The results of the study are as follows: over the 30-year period studied, the area devoted to construction land increased by 757 km2 and the carbon storage decreased by 7.68 × 106 t. Outside Wuhan’s Third Ring Road, there was a significant increase in the carbon storage, but in the areas where construction increased, there was a reduction in carbon storage. Carbon storage in the remote suburbs was significantly higher than in the city center, and the distribution pattern was characterized by significant spatial heterogeneity. Our analysis revealed that human economic and social activities have affected Wuhan’s ecosystem carbon storage to a significant extent. Policymakers should focus on industrial optimization, strictly control the red line of ecological protection, and ultimately achieve high-quality urban development.
A Nyström Method for 2D Linear Fredholm Integral Equations on Curvilinear Domains
Authors: Laguardia Anna Lucia , Russo Maria Grazia
Published: December 2023

This paper is devoted to the numerical treatment of two-dimensional Fredholm integral equations, defined on general curvilinear domains of the plane. A Nyström method, based on a suitable Gauss-like cubature formula, recently proposed in the literature is proposed. The convergence, stability and good conditioning of the method are proved in suitable subspaces of continuous functions of Sobolev type. The cubature formula, on which the Nyström method is constructed, has an error that behaves like the best polynomial approximation of the integrand function. Consequently, it is also shown how the Nyström method inherits this property and, hence, the proposed numerical strategy is fast when the involved known functions are smooth. Some numerical examples illustrate the efficiency of the method, also in comparison with other methods known in the literature.
Properties and Estimations of a Multivariate Folded Normal Distribution
Authors: Liu Xi , Jin Yiqiao , Yang Yifan , Pan Xiaoqing
Published: December 2023

A multivariate folded normal distribution is a distribution of the absolute value of a Gaussian random vector. In this paper, we provide the marginal and conditional distributions of the multivariate folded normal distribution, and also prove that independence and non-correlation are equivalent for it. In addition, we provide a numerical approach using the R language to fit a multivariate folded normal distribution. The accuracy of the estimated mean and variance parameters is then examined. Finally, a real data application to body mass index data are presented.
Driver Distraction Detection Based on Cloud Computing Architecture and Lightweight Neural Network
Authors: Huang Xueda , Wang Shaowen , Qi Guanqiu , Zhu Zhiqin , Li Yuanyuan , Shuai Linhong , Wen Bin , Chen Shiyao , Huang Xin
Published: December 2023

Distracted behavior detection is an important task in computer-assisted driving. Although deep learning has made significant progress in this area, it is still difficult to meet the requirements of the real-time analysis and processing of massive data by relying solely on local computing power. To overcome these problems, this paper proposes a driving distraction detection method based on cloud–fog computing architecture, which introduces scalable modules and a model-driven optimization based on greedy pruning. Specifically, the proposed method makes full use of cloud–fog computing to process complex driving scene data, solves the problem of local computing resource limitations, and achieves the goal of detecting distracted driving behavior in real time. In terms of feature extraction, scalable modules are used to adapt to different levels of feature extraction to effectively capture the diversity of driving behaviors. Additionally, in order to improve the performance of the model, a model-driven optimization method based on greedy pruning is introduced to optimize the model structure to obtain a lighter and more efficient model. Through verification experiments on multiple driving scene datasets such as LDDB and Statefarm, the effectiveness of the proposed driving distraction detection method is proved.
Modelling Predator–Prey Interactions: A Trade-Off between Seasonality and Wind Speed
Authors: Barman Dipesh , Upadhyay Ranjit Kumar
Published: December 2023

Predator–prey interactions do not solely depend on biotic factors: rather, they depend on many other abiotic factors also. One such abiotic factor is wind speed, which can crucially change the predation efficiency of the predator population. In this article, the impact of wind speed along with seasonality on various parameters has been investigated. Here, we present two continuous-time models with specialist and generalist type predators incorporating the effect of wind and the seasonality on the model parameters. It has been observed that wind speed plays a significant role in controlling the system dynamics for both systems. It makes the systems stable for both of the seasonally unperturbed systems. However, it controls the chaotic dynamics that occur in case of no wind for the seasonally perturbed system with the predator as a specialist. On the other hand, for the seasonally perturbed system with a generalist predator, it controls period-four oscillations (which occur considering no wind speed) to simple limit-cycle oscillations. Furthermore, the wind parameter has a huge impact on the survival of predator species. The survival of predator species may be achieved by ensuring a suitable range of wind speeds in the ecosystem. Therefore, we observe that seasonality introduces chaos, but wind reduces it. These results may be very useful for adopting necessary management for the conservation of endangered species that are massively affected by wind speed in an ecosystem.
Empiric Solutions to Full Fuzzy Linear Programming Problems Using the Generalized “min” Operator
Authors: Stanojević Bogdana , Nǎdǎban Sorin
Published: December 2023

Solving optimization problems in a fuzzy environment is an area widely addressed in the recent literature. De-fuzzification of data, construction of crisp more or less equivalent problems, unification of multiple objectives, and solving a single crisp optimization problem are the general descriptions of many procedures that approach fuzzy optimization problems. Such procedures are misleading (since relevant information is lost through de-fuzzyfication and aggregation of more objectives into a single one), but they are still dominant in the literature due to their simplicity. In this paper, we address the full fuzzy linear programming problem, and provide solutions in full accordance with the extension principle. The main contribution of this paper is in modeling the conjunction of the fuzzy sets using the “product” operator instead of “min” within the definition of the solution concept. Our theoretical findings show that using a generalized “min” operator within the extension principle assures thinner shapes to the derived fuzzy solutions compared to those available in the literature. Thinner shapes are always desirable, since such solutions provide the decision maker with more significant information.
Smart Lithium-Ion Battery Monitoring in Electric Vehicles: An AI-Empowered Digital Twin Approach
Authors: Pooyandeh Mitra , Sohn Insoo
Published: December 2023

This paper presents a transformative methodology that harnesses the power of digital twin (DT) technology for the advanced condition monitoring of lithium-ion batteries (LIBs) in electric vehicles (EVs). In contrast to conventional solutions, our approach eliminates the need to calibrate sensors or add additional hardware circuits. The digital replica works seamlessly alongside the embedded battery management system (BMS) in an EV, delivering real-time signals for monitoring. Our system is a significant step forward in ensuring the efficiency and sustainability of EVs, which play an essential role in reducing carbon emissions. A core innovation lies in the integration of the digital twin into the battery monitoring process, reshaping the landscape of energy storage and alternative power sources such as lithium-ion batteries. Our comprehensive system leverages a cloud-based IoT network and combines both physical and digital components to provide a holistic solution. The physical side encompasses offline modeling, where a long short-term memory (LSTM) algorithm trained with various learning rates (LRs) and optimized by three types of optimizers ensures precise state-of-charge (SOC) predictions. On the digital side, the digital twin takes center stage, enabling the real-time monitoring and prediction of battery activity. A particularly innovative aspect of our approach is the utilization of a time-series generative adversarial network (TS-GAN) to generate synthetic data that seamlessly complement the monitoring process. This pioneering use of a TS-GAN offers an effective solution to the challenge of limited real-time data availability, thus enhancing the system’s predictive capabilities. By seamlessly integrating these physical and digital elements, our system enables the precise analysis and prediction of battery behavior. This innovation—particularly the application of a TS-GAN for data generation—significantly contributes to optimizing battery performance, enhancing safety, and extending the longevity of lithium-ion batteries in EVs. Furthermore, the model developed in this research serves as a benchmark for future digital energy storage in lithium-ion batteries and comprehensive energy utilization. According to statistical tests, the model has a high level of precision. Its exceptional safety performance and reduced energy consumption offer promising prospects for sustainable and efficient energy solutions. This paper signifies a pivotal step towards realizing a cleaner and more sustainable future through advanced EV battery management.
An Enhanced Hybrid-Level Interface-Reduction Method Combined with an Interface Discrimination Algorithm
Authors: Cheon Seunghee , Lee Jaehun
Published: December 2023

This study proposes an interface localizing scheme to enhance the performance of the previous hybrid-level interface-reduction method. The conventional component mode synthesis (CMS) only focuses on interior reduction, while the interface is fully retained for convenient synthesis. Thus, various interface-reduction methods have been suggested to obtain a satisfactory size for the reduced systems. Although previous hybrid-level interface-reduction approaches have addressed major issues associated with conventional interface-reduction methods—in terms of accuracy and efficiency through considering partial substructure synthesis—this method can be applied to limited modeling conditions where interfaces and substructures are independently defined. To overcome this limitation, an interface localizing algorithm is developed to ensure an enhanced performance in the conventional hybrid-level interface-reduction method. The interfaces are discriminated through considering the Boolean operation of substructures, and the interface reduction basis is computed at the localized interface level, which is constructed by a partially coupled system. As a result, a large amount of computational resources are saved, achieving the possibility of efficient design modifications at the semi-substructural level.
Modeling the Solution of the Pursuit–Evasion Problem Based on the Intelligent–Geometric Control Theory
Authors: Khachumov Mikhail , Khachumov Vyacheslav
Published: December 2023

An important action-planning problem is considered for participants of the pursuit–evasion game with multiple pursuers and a high-speed evader. The objects of study are mobile robotic systems and specifically small unmanned aerial vehicles (UAVs). The problem is complicated by the presence of significant wind loads that affect the trajectory and motion strategies of the players. It is assumed that UAVs have limited computing resources, which involves the use of computationally fast and real-time heuristic approaches. A novel and rapidly developing intelligent–geometric theory is applied to address the discussed problem. To accurately calculate the points of the participant’s rapprochement, we use a geometric approach based on the construction of circles or spheres of Apollonius. Intelligent control methods are applied to synthesize complex motion strategies of participants. A method for quickly predicting the evader’s trajectory is proposed based on a two-layer neural network containing a new activation function of the “s-parabola” type. We consider a special backpropagation training scheme for the model under study. A simulation scheme has been developed and tested, which includes mathematical models of dynamic objects and wind loads. The conducted simulations on pursuit–evasion games in close to real conditions showed the prospects and expediency of the presented approach.
Effect of Acetate on Microbiologically Influenced Corrosion of Internal Pipeline Surfaces
Authors: Ismail Mohamed Riyadh , Ismail Mokhtar Che , Shah Syed Zulfiqar Hussain
Published: December 2023

Microbiologically influenced corrosion (MIC) is a serious threat to the integrity of crude oil pipelines. Sulphate-reducing bacteria (SRB) are the primary microorganisms responsible for MIC, and their aggressiveness is dependent on the energy source available to them. Acetate, a common energy source, has been shown to accelerate the corrosion of carbon steel in the presence of SRB. This study investigated the effect of acetate on the growth of SRB and the corrosion of carbon steel plates in simulated anaerobic conditions. The corrosion kinetics were studied using linear polarization resistance (LPR) and weight loss immersion tests for 42 days. The samples were characterized using Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectroscopy (EDX). The results show that the addition of acetate to cultured media significantly increased the corrosion rate of carbon steel plates in both formation water and Postgate Medium B (PMB). This was due to increased growth of SRB in the presence of acetate, which led to the production of more corrosive hydrogen sulphide (H2S). The findings based on experimental data obtained from this study confirm that acetate can accelerate the corrosion of carbon steel in the presence of SRB.