bluuebunny/crossref_metadata_2025 huggingface dataset:
Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach
Authors: Choi Yongjin , Nguyen Huyen-Tram , Han Taek Hee , Choi Youngjin , Ahn Jaehun
Published: July 2024

Accurate seismic ground response analysis is crucial for the design and safety of civil infrastructure and establishing effective mitigation measures against seismic risks and hazards. This is a complex process due to the nonlinear soil properties and complicated underground geometries. As a simplified approach, the one-dimensional wave propagation model, which assumes that seismic waves travel vertically through a horizontally layered medium, is widely adopted for its reasonable performance in many practical applications. This study explores the potential of sequence deep learning models, specifically 1D convolutional neural networks (1D-CNNs), long short-term memory (LSTM) networks, and transformers, as an alternative for seismic ground response modeling. Utilizing ground motion data from the Kiban Kyoshin Network (KiK-net), we train these models to predict ground surface acceleration response spectra based on bedrock motions. The performance of the data-driven models is compared with the conventional equivalent-linear analysis model, SHAKE2000. The results demonstrate that the deep learning models outperform the physics-based model across various sites, with the transformer model exhibiting the smallest average prediction error due to its ability to capture long-range dependencies. The 1D-CNN model also shows a promising performance, albeit with occasional higher errors than the other models. All the data-driven models exhibit efficient computation times of less than 0.4 s for estimation. These findings highlight the potential of sequence deep learning approaches for seismic ground response modeling.
A Lightweight CER-YOLOv5s Algorithm for Detection of Construction Vehicles at Power Transmission Lines
Authors: Yu Pingping , Yan Yuting , Tang Xinliang , Shang Yan , Su He
Published: July 2024

In the context of power-line scenarios characterized by complex backgrounds and diverse scales and shapes of targets, and addressing issues such as large model parameter sizes, insufficient feature extraction, and the susceptibility to missing small targets in engineering-vehicle detection tasks, a lightweight detection algorithm termed CER-YOLOv5s is firstly proposed. The C3 module was restructured by embedding a lightweight Ghost bottleneck structure and convolutional attention module, enhancing the model’s ability to extract key features while reducing computational costs. Secondly, an E-BiFPN feature pyramid network is proposed, utilizing channel attention mechanisms to effectively suppress background noise and enhance the model’s focus on important regions. Bidirectional connections were introduced to optimize the feature fusion paths, improving the efficiency of multi-scale feature fusion. At the same time, in the feature fusion part, an ERM (enhanced receptive module) was added to expand the receptive field of shallow feature maps through multiple convolution repetitions, enhancing the global information perception capability in relation to small targets. Lastly, a Soft-DIoU-NMS suppression algorithm is proposed to improve the candidate box selection mechanism, addressing the issue of suboptimal detection of occluded targets. The experimental results indicated that compared with the baseline YOLOv5s algorithm, the improved algorithm reduced parameters and computations by 27.8% and 31.9%, respectively. The mean average precision (mAP) increased by 2.9%, reaching 98.3%. This improvement surpasses recent mainstream algorithms and suggests stronger robustness across various scenarios. The algorithm meets the lightweight requirements for embedded devices in power-line scenarios.
Optimality Conditions for Mathematical Programs with Vanishing Constraints Using Directional Convexificators
Authors: Mohapatra Ram Narayan , Sachan Prachi , Laha Vivek
Published: July 2024

This article deals with mathematical programs with vanishing constraints (MPVCs) involving lower semi-continuous functions. We introduce generalized Abadie constraint qualification (ACQ) and MPVC-ACQ in terms of directional convexificators and derive necessary KKT-type optimality conditions. We also derive sufficient conditions for global optimality for the MPVC under convexity utilizing directional convexificators. Further, we introduce a Wolfe-type dual model in terms of directional convexificators and derive duality results. The results are well illustrated by examples.
Investigating the Role of Flow Plate Surface Roughness in Polymer Electrolyte Membrane Fuel Cells with the Use of Multiphysics Simulations
Authors: Gkionis-Konstantatos Odysseas , Tavares Luciana , Ebel Thomas
Published: July 2024

This study investigates the influence of surface roughness on the performance of polymer electrolyte membrane fuel cells (PEMFCs) through computational simulations using COMSOL Multiphysics. Two distinct gas flow channel (GFC) models of serpentine and parallel GFC structures were analysed, featuring various surface roughness levels to examine their impact on gas pressure and velocity dynamics. Rough surfaces are modeled using trigonometric functions to replicate machining-induced variations. Finite element simulations were conducted, assessing the time-dependent relationship between gas pressure and velocity while considering different electrode phase potentials as a function of surface roughness. Rough surfaces generally enhance mass transport, water management, and current distribution compared to smooth surfaces. The results indicated that a surface roughness of approximately 1 µm optimizes PEMFC performance by balancing pressure and velocity, enhancing electrochemical reactions, and reducing excessive pressure drops within the cell. Notably, the 0.7 V operating voltage was found to be the most efficient, achieving rapid stabilization of pressure and velocity levels swiftly. The findings underscore the importance of precise control over GFC roughness to enhance PEMFC performance gains in commercial applications, especially when multiple cells are stacked to achieve high power outputs.
Assessment of Albumin-Incorporating Scores at Hepatocellular Carcinoma Diagnosis Using Machine Learning Techniques: An Evaluation of Prognostic Relevance
Authors: Suárez Miguel , Martínez-Blanco Pablo , Gil-Rojas Sergio , Torres Ana M. , Torralba-González Miguel , Mateo Jorge
Published: July 2024

Hepatocellular carcinoma (HCC) presents high mortality rates worldwide, with limited evidence on prognostic factors at diagnosis. This study evaluates the utility of common scores incorporating albumin as predictors of mortality at HCC diagnosis using Machine Learning techniques. They are also compared to other scores and variables commonly used. A retrospective cohort study was conducted with 191 patients from Virgen de la Luz Hospital of Cuenca and University Hospital of Guadalajara. Demographic, analytical, and tumor-specific variables were included. Various Machine Learning algorithms were implemented, with eXtreme Gradient Boosting (XGB) as the reference method. In the predictive model developed, the Barcelona Clinic Liver Cancer score was the best predictor of mortality, closely followed by the Platelet-Albumin-Bilirubin and Albumin-Bilirubin scores. Albumin levels alone also showed high relevance. Other scores, such as C-Reactive Protein/albumin and Child-Pugh performed less effectively. XGB proved to be the most accurate method across the metrics analyzed, outperforming other ML algorithms. In conclusion, the Barcelona Clinic Liver Cancer, Platelet-Albumin-Bilirubin and Albumin-Bilirubin scores are highly reliable for assessing survival at HCC diagnosis. The XGB-developed model proved to be the most reliable for this purpose compared to the other proposed methods.
Prognostic Significance of VAV3 Gene Variants and Expression in Renal Cell Carcinoma
Authors: Chang Chi-Fen , Bao Bo-Ying , Hsueh Yu-Mei , Chen Pei-Ling , Chang Li-Hsin , Li Chia-Yang , Geng Jiun-Hung , Lu Te-Ling , Huang Chao-Yuan , Huang Shu-Pin
Published: July 2024

Renal cell carcinoma (RCC) is characterized by high mortality and morbidity rates. Vav guanine nucleotide exchange factors (VAVs), crucial for signal transduction between cell membrane receptors and intracellular mediators, have been implicated in carcinogenesis. However, their potential prognostic value in RCC remains unclear. The impact of 150 common VAV polymorphisms on RCC risk and survival was investigated in a cohort of 630 individuals. Publicly available gene expression datasets were utilized to analyze VAV gene expression in relation to patient outcomes. The VAV3 rs17019888 polymorphism was significantly associated with RCC risk and overall survival after adjusting for false discovery rates. Expression quantitative trait loci analysis revealed that the risk allele of rs17019888 is linked to reduced VAV3 expression. Analysis of 19 kidney cancer gene expression datasets revealed lower VAV3 expression in RCC tissues compared to normal tissues, with higher expression correlating with better prognosis. Gene set enrichment analysis demonstrated that VAV3 negatively regulates the ubiquitin–proteasome system, extracellular matrix and membrane receptors, inflammatory responses, matrix metalloproteinases, and cell cycle pathways. Furthermore, elevated VAV3 expression was associated with increased infiltration of B cells, macrophages, and neutrophils into the RCC tumor microenvironment. Our findings suggest that VAV3 gene variants influence RCC risk and survival, contributing to a favorable prognosis in RCC.
Preparation and Characterization of Supramolecular Bonding Polymers Based on a Pullulan Substrate Grafted with Acrylic Acid/Acrylamide by Microwave Irradiation
Authors: Dhahir Salam Abdulla , Braihi Auda Jabbar , Habeeb Salih Abbas
Published: July 2024

A microwave technique was used to prepare a superabsorbent polymer (SAP) by grafting two hydrophilic monomers onto a polysaccharide substrate. The monomers used were acrylic acid (AA) or acrylamide (AM) and were grafted onto a pullulan (PUL) substrate to form PUL-g-AA (SAP1) and PUL-g-AM (SAP2), respectively. The monomers (AM/AA) were grafted together onto a PUL substrate to form PUL-g-(AM/AA) (SAP3). Grafting parameters such as grafting efficiency with the percentage, the conversion of monomer into polymer, gel content, water retention, water adsorption capacity, and swelling kinetics were determined. Additionally, the effect of environmental pH (2, 4, 7, 9, and 12) and sodium dodecylbenzene sulfonate (SDBS) surfactant was evaluated, where 1, 2, 3, 4, and 5 mM of SDBS was added to form SAP4 to SAP8. The FTIR results show that AM was grafted onto PUL through an aliphatic C-N bond, while AA grafting occurred through a single C-C bond. The grafting efficiency with AM was higher than with AA, as well as showing a superior gel content. Water absorbance capacity and water retention increased with the grafting of AA and AM together for SAP3. The highest absorbent capacity, water retention, gel content, and grafting parameters values were obtained with a 3 mM SDBS content and a pH of 7. The swelling kinetics showed that the increases in the theoretical and experimental swelling equilibriums were 72% and 82%, respectively, for SAP6 compared to the values of these parameters for SAP3. The water absorption capacity of the hydrogel increases upon increasing the pH to 7 and then gradually decreases. XRD demonstrated the improved crystallinity and crystalline size of the hydrogel after grafting polymerization of AM/AA onto PUL, in addition to enhanced thermal stability. On the contrary, FE-SEM demonstrated that SDBS improves the porosity and pore size of the hydrogel surface with SAP6.
Determining Quasi-Static Load Carrying Capacity of Composite Sandwich Rotor Blades for Copter-Type Drones
Authors: Jan Chien Wei , Kam Tai Yan
Published: July 2024

The development of light composite rotor blades with acceptable load carrying capacity is an essential issue to be dealt with in the design of relatively large copter-type drones. In this paper, a method is established to determine the quasi-static blade load carrying capacity which is vital to drone reliability. The proposed method, which provides a systematic procedure to determine blade load carrying capacity, consists of three parts, namely, a procedure to determine the distributed quasi-static blade aerodynamic load via the Blade Element Momentum (BEM) approach, a finite element-based failure analysis method to identify the actual blade failure mode, and an optimization method to determine the actual blade load carrying capacity. The experimental failure characteristics (failure mode, failure thrust, failure location) of two types of composite sandwich rotor blades with different skin lamination arrangements have been used to verify the accuracy of the theoretical results obtained using the proposed load carrying capacity determination method. The skin lamination arrangement for attaining the optimal blade-specific load carrying capacity and the blade incipient rotational speed for safe drone operation has been determined using the proposed method.
Evolution of Secondary Periglacial Environment Induced by Thawing Permafrost near China–Russia Crude Oil Pipeline Based on Airborne LiDAR, Geophysics, and Field Observation
Authors: Gao Kai , Li Guoyu , Wang Fei , Cao Yapeng , Chen Dun , Du Qingsong , Chai Mingtang , Fedorov Alexander , Lin Juncen , Shang Yunhu , Huang Shuai , Wu Xiaochen , Bai Luyao , Zhang Yan , Tang Liyun , Jia Hailiang , Wang Miao , Wang Xu
Published: July 2024

The China–Russia crude oil pipeline (CRCOP) operates at a temperature that continuously thaws the surrounding permafrost, leading to secondary periglacial phenomena along the route. However, the evolution and formation mechanisms of these phenomena are still largely unknown. We used multi-temporal airborne light detection and ranging (LiDAR), geophysical, and field observation data to quantify the scale of ponding and icing, capture their dynamic development process, and reveal their development mechanisms. The results show that the average depth of ponding within 5 m on both sides of the pipeline was about 31 cm. The volumes of three icings (A–C) above the pipeline were 133 m3, 440 m3, and 186 m3, respectively. Icing development can be divided into six stages: pipe trench settlement, water accumulation in the pipe trench, ponding pressure caused by water surface freezing, the formation of ice cracks, water overflow, and icing. This study revealed the advantages of airborne LiDAR in monitoring the evolution of periglacial phenomena and provided a new insight on the development mechanisms of the phenomena by combining LiDAR with geophysics and field observation. The results of our study are of great significance for developing disaster countermeasures and ensuring the safe operation of buried pipelines.
An Algorithm for Initial Localization of Feature Waveforms Based on Differential Analysis Parameter Setting and Its Application in Clinical Electrocardiograms
Authors: Xia Tongnan , Wang Bei , Huang Enruo , Du Yijiang , Zhang Laiwu , Liu Ming , Chang Chin-Chen , Sun Yaojie
Published: July 2024

In a biological signal analysis system, signals of the same type may exhibit significant variations in their feature waveforms. Biological signals are typically weak, which increases the complexity of their analysis. Furthermore, clinical biomedical signals are susceptible to various interferences from the human body itself, including muscle movements, respiration, and heartbeat. These interference factors further escalate the complexity and difficulty of signal analysis. Therefore, precise and targeted preprocessing is often required before analyzing these clinical biomedical signals to enhance the accuracy and reliability of subsequent feature extraction and classification. Here, we have established an effective and practical algorithm model that integrates preprocessing with the initial localization of target feature waveforms, achieving the following four objectives: 1. Determining the periodic positions of target feature waveforms. 2. Preserving the original amplitude and shape of target feature waveforms while eliminating negative interference. 3. Reducing or eliminating interference from other feature waveforms in the input signal. 4. Decreasing noise in the input signal, such as baseline drift, powerline interference, and muscle artifacts commonly found in biological signals. We have validated the algorithm on clinical electrocardiogram (ECG) data and the authoritative MIT-BIH open-source ECG database demonstrating its effectiveness and reliability.
A Context Awareness System for Clinical Environments
Authors: Gómez Jorge Gómez , Riaño Velssy Hernández , Ramirez-Gonzalez Gustavo
Published: July 2024

This study addresses the complex management of patient-related information in hospitals and clinical settings. This information includes treatments, medications, vital signs, patient locations, and data exchange between healthcare professionals. The lack of effective synchronization between these elements often delays timely care. This study proposes an architecture based on a semantic representation model that articulates the various components of a hospital environment. This model supports decision-making in healthcare by facilitating inferences from the environment. The semantic model serves as a basis for executing predefined rules that trigger actions through a reasoner, resulting in notifications, such as administering medications or responding to abnormal vital signs. The model integrates supervised learning to improve the accuracy of alerts. The experiment focused on monitoring vital sign parameters, such as Spo2, body temperature, and heart rate. The combination of semantic representation modeling and machine learning algorithms demonstrates a robust approach for improving the efficiency and accuracy of healthcare alerts in clinical settings.
The Willingness to Assess and Contribute to Pinna-SOS Recovery Actions of Marine Fishers/Farmers and Stakeholders
Authors: Theodorou John A. , Katselis George , Anagnopoulos Orestis , Bourdaniotis Nikos , Michaelidis Basile , Moutopoulos Dimitrios K.
Published: July 2024

The present study aimed to address the issue of pressure on the remaining populations of the critical endangered species, fan mussel, Pinna nobilis, in the Eastern Mediterranean. Marine professional (shell/fish farmers, divers, fishers, administrators, etc.) stakeholders’ knowledge (n = 151) in Greece reports that there was a reduction (81.6%) of the P. nobilis individuals during the last 15 years, especially in the years 2010–2012 and 2017–2018. Species’ abundance decline is significantly (ρ = 0.293, p < 0.05) correlated over the last 5 years with the observed natural ecosystem degradation. Participants also stated that the main cause was pathogens alone (28.8%) or in combination with (illegal) fishing (17.1%) or pollution (14.4%). Additionally, 88% of them supported a total prohibition on the use of fan mussels for commercial purposes in order to restore stock levels.; 72.4% stated claimed that no appropriate control was in place and that they (>59.4%) were unsure if this monitoring control is carried out by the competent authorities. Marine stakeholders consider the importance of the species for biodiversity preservation (56.1%), environmental education (35.1%), and diving parks (29.7%). They declared that it is important to record-observe P. nobilis conservation reserves regions (42.6%), monitor areas of responsibility (39.9%), participate in the information society/use of social networks (38.5%), and participate in informational meetings (37.9%). The lack of interest among shell/fish farmers to contribute to P. nobilis on-growing farming indicates the ignorance to the potential benefits of the valuable ecosystem services provided by aquaculture through biodiversity conservation. Nevertheless, there is a demand for promoting the “conservation aquaculture” concept through its incorporation into marine farming activities supplementary to their core business in “production”.
A Compartmental Approach to Modeling the Measles Disease: A Fractional Order Optimal Control Model
Authors: Chatterjee Amar Nath , Sharma Santosh Kumar , Al Basir Fahad
Published: July 2024

Measles is the most infectious disease with a high basic reproduction number (R0). For measles, it is reported that R0 lies between 12 and 18 in an endemic situation. In this paper, a fractional order mathematical model for measles disease is proposed to identify the dynamics of disease transmission following a declining memory process. In the proposed model, a fractional order differential operator is used to justify the effect and success rate of vaccination. The total population of the model is subdivided into five sub-compartments: susceptible (S), exposed (E), infected (I), vaccinated (V), and recovered (R). Here, we consider the first dose of measles vaccination and convert the model to a controlled system. Finally, we transform the control-induced model to an optimal control model using control theory. Both models are analyzed to find the stability of the system, the basic reproduction number, the optimal control input, and the adjoint equations with the boundary conditions. Also, the numerical simulation of the model is presented along with using the analytical findings. We also verify the effective role of the fractional order parameter alpha on the model dynamics and changes in the dynamical behavior of the model with R0=1.
Measure Attractors of Stochastic Fractional Lattice Systems
Authors: Weng Shudong , Mi Shaoyue , Li Dingshi
Published: July 2024

This paper seeks to establish the measure attractors in stochastic fractional lattice systems. First, the presence of these attractor measures is proven by the uniform estimates of the solution. Subsequently, the study also looks at the upper semicontinuous dependence of the measure attractors on the noise intensity as the latter goes to zero. The given asymptotic compactness for the family of probability measures occurring with the solution probability distributions is exhibited by a uniform prior estimation of the far-field solution values.
Pisa Syndrome Secondary to Drugs: A Scope Review
Authors: Pitton Rissardo Jamir , Murtaza Vora Nilofar , Danaf Naseeb , Ramesh Saivignesh , Shariff Sanobar , Fornari Caprara Ana Letícia
Published: July 2024

Background: Pisa syndrome, also known as pleurothotonus, is a neurological condition characterized by more than ten degrees of constant lateral curvature of the spine when upright. In this way, the present manuscript aims to systematically review Pisa syndrome secondary to drugs. Methods: Two reviewers identified and assessed relevant reports in six databases without language restriction between January 1990 and June 2024. Results: The prevalence of Pisa syndrome varied from 0.037 to 9.3%. We found 109 articles containing 191 cases of drug-induced Pisa syndrome reported in the literature. The mean and median ages were 59.70 (SD = 19.02) and 67 (range = 12–98 years). The most prevalent sex was female, 56.91% (107/188). The most frequent medications associated with Pisa syndrome were acetylcholinesterase inhibitors in 87 individuals. Of 112 individuals in which the onset time from the medication to the movement disorder occurrence was reported, 59 took place within a month. In this way, a return to baseline was observed in 45.50% of the cases, and partial recovery was observed in 14.28%. Conclusion: We proposed new diagnostic criteria for Pisa syndrome based on previous findings in the literature. Moreover, multiple mechanisms are probably involved in balance control and the development of lateral trunk flexions.
Transition, Adjustment, and Healthcare Avoidance: African Immigrant Women’s Experiences and Perceptions of Navigating Primary Healthcare in the USA
Authors: Tefera Gashaye M. , Yu Mansoo , Robinson Erin L. , Winter Virginia Ramseyer , Bloom Tina
Published: July 2024

This study explores the transition and adjustment of African immigrant women, particularly Ethiopian immigrant women (EIW), as they navigate the U.S. healthcare system and their ability to access and utilize healthcare services. A qualitative cross-sectional design with a mix of purposive and snowball sampling techniques was utilized to recruit EIW (N = 21, ≥18 years) who arrived in the U.S. within the last five years. One-on-one in-depth interviews were conducted to collect data. The interviews were audio-recorded, transcribed verbatim, and analyzed thematically using Nvivo12 software. The thematic analysis revealed three major themes: (1) settling into new life in the U.S. delays EIWs’ ability to access primary healthcare; (2) adjusting to the U.S. healthcare system: confusions and mixed perceptions; and (3) avoidance of care: EIW’s reasons for PHC visits changed in the U.S. Participants avoided healthcare, except for life-threatening conditions, general check-ups, and maternal healthcare services. Transitional support for legal, residential, employment, and health information could help tackle the challenges of accessing primary healthcare for EIW. Future research should analyze access to healthcare in relation to the everyday struggles of immigrant women, as well as legal and complex structural issues beyond acculturative issues.
Clinical Signs and Symptoms of Anxiety Due to Adverse Childhood Experiences: A Cross-Sectional Trial in Adolescents
Authors: Marini Aikaterini , Farmakopoulou Ignatia , Dritsas Ioannis , Gkintoni Evgenia
Published: July 2024

Background: Adverse Childhood Experiences (ACEs) are linked to the development of anxiety in adolescence. According to recent studies, the COVID-19 pandemic represents a novel ACE that is associated with anxiety among adolescents. This study investigates the relationship between ACEs, COVID-19, and anxiety in adolescents. Methods: A cross-sectional study was conducted using a community sample of 248 boys and girls ages 12 to 15 years (mean = 13.50 years) from five high schools in Eastern Attica. A total of four questionnaires were used: (1) Demographic Questionnaire, (2) State–Trait Anxiety Inventory for Children—STAIC, (3) Adverse Childhood Experiences Scale, and (4) COVID-19 Impact Scale. Results: The results demonstrated a moderate association between the total number of ACEs and anxiety (trait and state) in adolescence (trait anxiety: ρ = 0.37, p < 0.001; state anxiety: ρ = 0.29, p < 0.001). Girls scored significantly higher than boys on both trait anxiety (U = 4353, p < 0.001; mean difference = 5.5) and state anxiety (U = 5822.5, p = 0.014; mean difference = 2). The number of ACEs was found to be significantly related to the impact of COVID-19 (β = 0.025, p < 0.001). Conclusions: This study highlights the significant link between ACEs and increased anxiety in adolescents, which is further exacerbated by the COVID-19 pandemic. The findings indicate that girls are more affected than boys. These results emphasize the need for targeted mental health interventions to enhance coping mechanisms, reduce stress, and address anxiety in adolescents, particularly during global crises like the pandemic. Developing such programs is essential for supporting the mental well-being of youth facing multiple stressors.
Preserving Nature’s Treasure: A Journey into the In Vitro Conservation and Micropropagation of the Endangered Medicinal Marvel—Podophyllum hexandrum Royle
Authors: Khan Zahoor , Khan Bushra , Shah Syed Tanveer , Iqbal Javaid , Basit Abdul , Khan Muhammad Suleman , Iqbal Waleed , Elsadek Mohamed Farouk , Jamal Aftab , Ali Mohammad Ajmal , Prisa Domenico
Published: July 2024

Podophyllum hexandrum Royle, also known as Podophyllum emodi Wall, holds significant ecological, ornamental, and medicinal values. However, it has become endangered due to overexploitation, prolonged seed dormancy, slow natural regeneration, and climate change. This study developed an efficient in vitro protocol for callogenesis and micropropagation of P. hexandrum to conserve germplasm in in vitro conditions. Callus formation from various plant parts, including the leaf, stem, rhizome, radicle, and cotyledon, was induced using Murashige and Skoog (MS) medium supplemented with different plant growth regulators. The combination of benzyladenine at 1 mg L−1 and 4-dichlorophenoxy acetic acid at 3 mg L−1 was optimal for biomass production, yielding 215.88 ± 0.31 mg, with growth per gram at 8.32 ± 0.32 and a growth rate of 13.62 ± 0.25 mg/day on MS medium. For shoot proliferation, benzyladenine (3.5 mg L−1) and naphthalene acetic acid (0.5 mg L−1) combined with activated charcoal showed the highest shoot induction percentage per explant. For shoot regeneration from calluses, 6-benzylaminopurine (0.5 mg L−1) and thidiazuron (2 mg L−1) were most effective, producing superior shoot length, number of regenerations, and regeneration percentage. Root induction was successful with α-naphthalene acetic acid supplementation (0.5 to 1.5 mg L−1) in MS medium, resulting in the highest number per explant (4.08 ± 0.08), length (5.45 ± 0.15 cm), and rooting rate (87.00 ± 1.66%) of roots in plantlets. Subculturing for callus culture was performed every 28 days for up to four subcultures to prevent nutrient depletion and toxic metabolite accumulation, ensuring tissue health and viability. Continuous subculturing of callus on MS medium maintained healthy P. hexandrum germplasm in vitro. Overall, this micropropagation protocol provides a rapid system for conserving P. hexandrum germplasm.
Multi-Criteria Decision Analysis to Evaluate the Geographic Potential of Alternative Photovoltaic Types
Authors: Hübl Franziska , Welscher Franz , Scholz Johannes
Published: July 2024

This paper contributes to the expansion of green energy production, which is being pursued in order to mitigate climate change and carbon emissions from energy production. It addresses the delineation of areas that are suitable for the application of photovoltaics in the context of agricultural areas, water bodies, and parking spaces. Three specific photovoltaic types are examined in order to find out which criteria influence their geographic potential and whether spatial multi-criteria decision analysis methods are suitable for identifying suitable areas. The proposed approach consists of four steps: (1) collecting factors through expert interviews and questionnaires; (2) mapping the criteria to the spatial datasets; (3) deriving weighted scores for individual criteria through expert interviews; (4) applying the multi-criteria decision analysis method to compute and aggregate the final scores. We test our methodology at selected sites in the state of Styria, Austria. The test sites represent the topographical characteristics of the state and are about 5% of the size of Styria, approximately 820 km2. The key contributions are a weighted set of criteria that are relevant for the geographic potential of alternative photovoltaic types and the developed methodology to determine this potential.
Circulating Tumor Cells: Pathological, Molecular and Functional Characteristics
Authors: Grzybowska Ewa
Published: July 2024

This Special Issue, ‘Circulating Tumor Cells: Pathological, Molecular and Functional Characteristics 1 [...]
Ab Initio Molecular Dynamics Insight to Structural Phase Transition and Thermal Decomposition of InN
Authors: Piechota Jacek , Krukowski Stanislaw , Sadovyi Bohdan , Sadovyi Petro , Porowski Sylwester , Grzegory Izabella
Published: July 2024

Extensive ab initio density functional theory molecular dynamics calculations were used to evaluate stability conditions for relevant phases of InN. In particular, the p-T conditions of the thermal decomposition of InN and pressure-induced wurtzite–rocksalt solid–solid phase transition were established. The comparison of the simulation results with the available experimental data allowed for a critical evaluation of the capabilities and limitations of the proposed simulation method. It is shown that ab initio molecular dynamics can be used as an efficient tool for simulations of phase transformations of InN, including solid–solid structural transition and thermal decomposition with formation of N2 molecules. It is of high interest, because InN is an important component of epitaxial quantum structures, but it has not been obtained as a bulk single crystal. This makes it difficult to determine its basic physical properties to develop new applications.
Role of Interleukin 6 in Acute Pancreatitis: A Possible Marker for Disease Prognosis
Authors: Mititelu Alexandra , Grama Alina , Colceriu Marius-Cosmin , Benţa Gabriel , Popoviciu Mihaela-Simona , Pop Tudor Lucian
Published: July 2024

Acute pancreatitis (AP) is a significant cause of morbidity, even in children, and is frequently associated with systemic manifestations. There are many cytokines involved in the inflammatory response characteristic of this disease. Interleukin 6 (IL-6) is one of the most important cytokines involved in AP, beginning from cellular injury and continuing to the systemic inflammatory response and distant organ involvement. IL-6 is a multifunctional cytokine that regulates acute-phase response and inflammation. It is produced by various cells and exerts its biological role on many cells through its high-affinity complex receptor. IL-6 has been investigated as a predicting maker for severe forms of AP. Many studies have validated the use of IL-6 serum levels in the first 48 h as a reliable marker for severe evolution and multisystemic involvement. Still, it has not been used in daily practice until now. This review discusses the main binding mechanisms by which IL-6 triggers cellular response and the AP pathogenetic mechanisms in which IL-6 is involved. We then emphasize the promising role of IL-6 as a prognostic marker, which could be added as a routine marker at admission in children with AP.
Gastric Carcinogenesis and Potential Role of the Transient Receptor Potential Vanilloid 1 (TRPV1) Receptor: An Observational Histopathological Study
Authors: Groen Sylvester R. , Keszthelyi Daniel , Szallasi Arpad , van Veghel Jara A. , Alleleyn Annick M. E. , Csekő Kata , Helyes Zsuzsanna , Samarska Iryna , Grabsch Heike I. , Masclee Ad A. M. , Weerts Zsa Zsa R. M.
Published: July 2024

The potential role of the transient receptor potential Vanilloid 1 (TRPV1) non-selective cation channel in gastric carcinogenesis remains unclear. The main objective of this study was to evaluate TRPV1 expression in gastric cancer (GC) and precursor lesions compared with controls. Patient inclusion was based on a retrospective review of pathology records. Patients were subdivided into five groups: Helicobacter pylori (H. pylori)-associated gastritis with gastric intestinal metaplasia (GIM) (n = 12), chronic atrophic gastritis (CAG) with GIM (n = 13), H. pylori-associated gastritis without GIM (n = 19), GC (n = 6) and controls (n = 5). TRPV1 expression was determined with immunohistochemistry and was significantly higher in patients with H. pylori-associated gastritis compared with controls (p = 0.002). TRPV1 expression was even higher in the presence of GIM compared with patients without GIM and controls (p < 0.001). There was a complete loss of TRPV1 expression in patients with GC. TRPV1 expression seems to contribute to gastric-mucosal inflammation and precursors of GC, which significantly increases in cancer precursor lesions but is completely lost in GC. These findings suggest TRPV1 expression to be a potential marker for precancerous conditions and a target for individualized treatment. Longitudinal studies are necessary to further address the role of TRPV1 in gastric carcinogenesis.
Anti-Inflammatory Potential of Pygeum africanum Bark Extract: An In Vitro Study of Cytokine Release by Lipopolysaccharide-Stimulated Human Peripheral Blood Mononuclear Cells
Authors: Villar Agustín , Silva-Fuentes Fredy , Mulà Anna , Zangara Andrea
Published: July 2024

Pygeum africanum bark has been shown to inhibit the production of pro-inflammatory prostaglandins in the prostate and reduces the production of leukotrienes and other 5-lipoxygenase (5-LO) metabolites. It has been suggested that inflammation plays an important role in the pathophysiology of benign prostatic hyperplasia (BPH). Data from clinical trials have shown that P. africanum improves the symptoms and objective measures of BPH. This in vitro study aimed to assess the anti-inflammatory potential of a proprietary Pygeum bark standardized extract (Prunera®) on cytokine release from lipopolysaccharide-stimulated human peripheral blood mononuclear cells (PBMCs). PBMCs were obtained from four donors, and a bead-based assay (ProcartaPlex™ panel) was used for the detection and quantitation of cytokines. Pygeum africanum bark standardized extract (PABE) induced a statistically significant decrease (p < 0.05) of IL-6 in three donors. Other effects were as follows: IL-2 was lowered in all donors in the absence of a clear dose–response relationship; IL-4, IL-5, IL-9, and IL-13 levels were decreased in most donors; IL-22 levels seemed to be suppressed only for donor 4 at lower and medium concentrations; and IL-27 and TNF-α levels decreased at all PABE concentrations in all donors. The anti-inflammatory effect of PABE, particularly the reduction in IL-6 as a marker of inflammation, supports the potential use of this natural compound in the management of BPH and other conditions in which pro-inflammatory cytokines are involved in their underlying pathophysiological mechanisms.
The Role of Viruses in the Pathogenesis of Immune-Mediated Gastro-Intestinal Diseases
Authors: Bernardi Francesca , Ungaro Federica , D’Amico Ferdinando , Zilli Alessandra , Parigi Tommaso Lorenzo , Massimino Luca , Allocca Mariangela , Danese Silvio , Furfaro Federica
Published: July 2024

Immune-mediated gastrointestinal (GI) diseases, including achalasia, celiac disease, and inflammatory bowel diseases, pose significant challenges in diagnosis and management due to their complex etiology and diverse clinical manifestations. While genetic predispositions and environmental factors have been extensively studied in the context of these conditions, the role of viral infections and virome dysbiosis remains a subject of growing interest. This review aims to elucidate the involvement of viral infections in the pathogenesis of immune-mediated GI diseases, focusing on achalasia and celiac disease, as well as the virome dysbiosis in IBD. Recent evidence suggests that viral pathogens, ranging from common respiratory viruses to enteroviruses and herpesviruses, may trigger or exacerbate achalasia and celiac disease by disrupting immune homeostasis in the GI tract. Furthermore, alterations in the microbiota and, specifically, in the virome composition and viral–host interactions have been implicated in perpetuating chronic intestinal inflammation in IBD. By synthesizing current knowledge on viral contributions to immune-mediated GI diseases, this review aims to provide insights into the complex interplay between viral infections, host genetics, and virome dysbiosis, shedding light on novel therapeutic strategies aimed at mitigating the burden of these debilitating conditions on patients’ health and quality of life.
Optimized Crop Disease Identification in Bangladesh: A Deep Learning and SVM Hybrid Model for Rice, Potato, and Corn
Authors: Barman Shohag , Farid Fahmid Al , Raihan Jaohar , Khan Niaz Ashraf , Hafiz Md. Ferdous Bin , Bhattacharya Aditi , Mahmud Zaeed , Ridita Sadia Afrin , Sarker Md Tanjil , Karim Hezerul Abdul , Mansor Sarina
Published: July 2024

Agriculture plays a vital role in Bangladesh’s economy. It is essential to ensure the proper growth and health of crops for the development of the agricultural sector. In the context of Bangladesh, crop diseases pose a significant threat to agricultural output and, consequently, food security. This necessitates the timely and precise identification of such diseases to ensure the sustainability of food production. This study focuses on building a hybrid deep learning model for the identification of three specific diseases affecting three major crops: late blight in potatoes, brown spot in rice, and common rust in corn. The proposed model leverages EfficientNetB0′s feature extraction capabilities, known for achieving rapid high learning rates, coupled with the classification proficiency of SVMs, a well-established machine learning algorithm. This unified approach streamlines data processing and feature extraction, potentially improving model generalizability across diverse crops and diseases. It also aims to address the challenges of computational efficiency and accuracy that are often encountered in precision agriculture applications. The proposed hybrid model achieved 97.29% accuracy. A comparative analysis with other models, CNN, VGG16, ResNet50, Xception, Mobilenet V2, Autoencoders, Inception v3, and EfficientNetB0 each achieving an accuracy of 86.57%, 83.29%, 68.79%, 94.07%, 90.71%, 87.90%, 94.14%, and 96.14% respectively, demonstrated the superior performance of our proposed model.
Artificial Intelligence for Computational Remote Sensing: Quantifying Patterns of Land Cover Types around Cheetham Wetlands, Port Phillip Bay, Australia
Authors: Lemenkova Polina
Published: July 2024

This paper evaluates the potential of using artificial intelligence (AI) and machine learning (ML) approaches for classification of Landsat satellite imagery for environmental coastal mapping. The aim is to identify changes in patterns of land cover types in a coastal area around Cheetham Wetlands, Port Phillip Bay, Australia. The scripting approach of the Geographic Resources Analysis Support System (GRASS) geographic information system (GIS) uses AI-based methods of image analysis to accurately discriminate land cover types. Four ML algorithms are applied, tested and compared for supervised classification. Technical approaches are based on using the ‘r.learn.train’ module, which employs the scikit-learn library of Python. The methodology includes the following algorithms: (1) random forest (RF), (2) support vector machine (SVM), (3) an ANN-based approach using a multi-layer perceptron (MLP) classifier, and (4) a decision tree classifier (DTC). The tested methods using AI demonstrated robust results for image classification, with the highest overall accuracy exceeding 98% and reached by the SVM and RF models. The presented scripting approach for GRASS GIS accurately detected changes in land cover types in southern Victoria over the period of 2013–2024. From our findings, the use of AI and ML algorithms offers effective solutions for coastal monitoring by analysis of change detection using multi-temporal RS data. The demonstrated methods have potential applications in coastal and wetland monitoring, environmental analysis and urban planning based on Earth observation data.
Acoustic Rapid Detection Technology and Its Application for Rare Earth Element (REE)-Rich Sediments in the Pigafetta Basin of the Western Pacific
Authors: Xue Hua , Du Min , Zeng Fanxiang , Yang Li , Yang Yong , He Gaowen , Sun Xiaoming
Published: July 2024

This study aims to investigate the stratigraphic features and rare earth element (REE) mechanisms of deep-sea REE-rich sediments in the West Pacific Pigafetta Basin using acoustic rapid detection technology. Through an analysis of sub-bottom profile data and synthesis of existing studies, this study reveals the acoustic properties and thickness distribution of the REE-rich sediments. Acoustic spectral records identify three distinct acoustic facies: opaque (O), transparent (T), and laminated (L). This study maps the thickness and spatial distribution of the REE-rich sediment layer in the research area, ranging from approximately 6 to 36 m in thickness. Regions with REE-rich sediments exceeding 30 m in depth are identified, showing concentrated distribution along the northwest–southeast axis and a contiguous zone in the southwest corner of the study area. The method employed in this study can determine the potential bottom boundary of the REE-rich layer by assessing the thickness range of the sedimentary layer, overcoming limitations of traditional sampling methods. Furthermore, the thickness distribution characteristics of the REE-rich sedimentary layer in the study area provide valuable insights for future research on resource evaluation and estimation.
Magnetic Properties of Gd-Doped Bi7Fe3Ti3O21 Aurivillius-Type Ceramics
Authors: Bartkowska Joanna A. , Szalbot Diana , Makowska Jolanta , Adamczyk-Habrajska Małgorzata , Stokłosa Zbigniew
Published: July 2024

The magnetic properties of Aurivillius-phase Bi7Fe3Ti3O21 (BFT) and Bi7−xGdxFe3Ti3O21, where x = 0.2, 0.4, and 0.6 (BGFT), were investigated. Ceramic material undoped (BGF) and doped with Gd3+ ions were prepared by conventional solid-state reaction. In order to confirm that the obtained materials belong to Aurivillius structures, XRD tests were performed. The XRD results confirmed that both the undoped and the gadolinium-doped materials belong to the Aurivillius phases. The qualitative chemical composition of the obtained materials was confirmed based on EDS tests. The temperature dependences of magnetization and magnetic susceptibility were examined for the ceramic material both undoped and doped with Gd3+ ions. The measurements were taken in the temperature range from T = 10 K to T = 300 K. Using Curie’s law, the value of the Curie constant was determined, and on its basis, the number of iron ions that take part in magnetic processes was calculated. The value of Curie constant C = 0.266 K, while the concentration of iron ions Fe3+, which influence the magnetic properties of the material, is equal 3.7 mol% (for BFT). Hysteresis loop measurements were also performed at temperatures of T = 10 K, T = 77 K, and T = 300 K. The dependence of magnetization on the magnetic field was described by the Brillouin function, and on its basis, the concentration of Fe3+ ions, which are involved in magnetic properties, was also calculated (3.4 mol% for BFT). Tests showed that the material is characterized by magnetic properties at low temperatures. At room temperature (RT), it has paramagnetic properties. It was also found that Gd3+ ions improve the magnetic properties of tested material.
A Method to Obtain Frequency Response Functions of Operating Mechanical Systems Based on Experimental Modal Analysis and Operational Modal Analysis
Authors: Shen Cunrui , Lu Chihua
Published: July 2024

The characteristics of a mechanical structure under operating conditions may differ from those in a static state. It is often more desirable to obtain the frequency response function (FRF) of the operating structure in engineering applications. While operational modal analysis (OMA) can estimate modal parameters during operation, it fails to provide mass-normalized mode shapes for FRF synthesis. This paper presents a new method using experimental modal analysis (EMA) to compensate for the absent information in OMA. It categorizes operational mode shapes into changed ones and those that remain the same compared to the static state, applying different scaling techniques accordingly. This method adapts to changes in dynamic characteristics without altering the operating conditions. Stability is emphasized throughout the process. Two examples are provided to verify the method, considering noise and incompleteness in measurement, and disturbances in dynamic properties. The proposed method is proven to be feasible and reliable to capture the changes in operational FRFs.