bluuebunny/crossref_metadata_2025_split huggingface dataset:
The influence of sex-specific factors on biological transformations and health outcomes in aging processes
Authors: Huang Yongyin , Li Hongyu , Liang Runyu , Chen Jia , Tang Qiang 
Published: July 2024

AbstractThe aging process demonstrates notable differences between males and females, which are key factors in disease susceptibility and lifespan. The differences in sex chromosomes are fundamental to the presence of sex bias in organisms. Moreover, sex-specific epigenetic modifications and changes in sex hormone levels impact the development of immunity differently during embryonic development and beyond. Mitochondria, telomeres, homeodynamic space, and intestinal flora are intricately connected to sex differences in aging. These elements can have diverse effects on men and women, resulting in unique biological transformations and health outcomes as they grow older. This review explores how sex interacts with these elements and shapes the aging process.
Towards an integrative model of blended design studios: a multiple case study across architecture, design and planning education
Authors: Süner-Pla-Cerdà Sedef , Öztürk Elif , Ünlü Canan Emine 
Published: July 2024

Abstract
Design education embodies distinctive pedagogical practices rooted in experiential learning, frequent interactions between students and instructors, reflective dialogues through project deliverables, and enculturation through activities and encounters facilitated by a physical studio setting. Contrasting the traditional lecture-based format, the design studio fosters a dynamic, participatory environment, making technology-mediated instructional delivery methods more rare and experimental than widely adopted. However, the COVID-19 pandemic forced a sudden shift to emergency remote teaching, which prompted a critical re-evaluation of both conventional and unconventional teaching methods, thereby leading to a reconsideration of traditional pedagogical approaches. This paper outlines common technology-enhanced approaches in design studios and advocates for blended learning as an effective mode of delivery. Multiple case study methodology was employed to elicit collective experiences, practices and future expectations of studio instructor teams from 12 architecture, design and planning departments across six universities. Findings of the thematic analysis are presented utilising Khan’s octagonal framework. The themes are synthesised in an integrative model to aid instructors in planning and implementing blended design studios, and recommendations are made for delivering blended learning experiences. The distinctive contribution of this study is its effort to develop an inclusive, comprehensive, and overarching model that resonates across various studio-based fields.
Fabrication of Nanocellulose/Chitosan Nanocomposite Based on Loofah Sponge for Efficient Removal of Methylene Blue: Thermodynamic and Kinetic Investigations
Authors: Matter E. A. , Hassan Asaad F. , Elfaramawy Nourhan M. , Esmail Ghada 
Published: July 2024

AbstractWe established three nano-solid adsorbents: nanocellulose based on plant loofah sponge (NC), chitosan (CS), and nanocellulose/chitosan composite (CSC). These substances were employed as solid adsorbents to eliminate methylene blue (MB) dye from wastewater. Various characterization techniques were employed to investigate all the synthesized solid adsorbents, including TGA (thermogravimetric analysis), XRD (X-ray diffraction spectra), (BET) nitrogen gas adsorption-desorption, SEM (scanning electron microscope), TEM (transmission electron microscopy), FTIR (Fourier transform infrared) spectrometer, and zeta potential. According to our results, CSC showed greater thermal stability than LS and NC but lower than CS, mesoporous (2.012 nm), higher total pore volume (0.366 cm3. g− 1), specific surface area (639.3 m2. g− 1), and pHpzc of 7.22. The static adsorption of MB was well described by the Langmuir (R2 > 0.9872), Temkin (R2 > 0.9668), and Dubinin-Radushkevich (R2 > 0.9485) models. The composite of nanocellulose and chitosan exhibited the highest Langmuir adsorption capacity (301.20 mg. g− 1) at 47 °C after a 24 h shaking period at a dosage of 2 g. L− 1 as the adsorbent and pH of 7. The adsorption of MB by the fabricated solid materials fitted well with the linear PSO (R2 > 0.9806) and Elovich (R2 > 0.9574) kinetic model. The enthalpy, entropy, and free energy change for the adsorption of MB onto CSC were determined to be 47.11 kJ. mol− 1, 0.172 kJ. mol− 1. K− 1, and − 3.29 kJ. mol− 1, respectively at 20 °C. Thermodynamic investigation showed that MB adsorption is spontaneous, endothermic, favorable (0 < RL<1, 0.017–0.313), and physisorption (EDR < 8 kJ. mol− 1). Compared to the other eluents, nitric acid produced the highest desorption percentage (98.5%).
Synthesis, Characterization, Cytotoxic Activity Studies of N1-phenylbenzene-1,2-diamine @CuhNFs and 1,2-phenylenediamine@CuhNFs, and Molecular Docking Calculations of Their Ligands
Authors: Somturk-Yilmaz Burcu , Turkmenoglu Burçin , Akkoc Senem 
Published: July 2024

AbstractIn recent years, hybrid nanoflowers (hNFs), the newest class of nanoparticles, have been highly preferred due to their excellent activity and stability. In this study, hybrid nanoflower synthesis was carried out using N1-phenylbenzene-1,2-diamine and 1,2-phenylenediamine as the organic part and copper(II) metal ions as the inorganic part. In the first stage, the characterization of the synthesized hybrid nanoflowers was carried out using various techniques. For the characterization of the synthesized hNFs, structure elucidation was performed using Scanning Electron Microscopy (SEM), Energy Dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectrometry (FT-IR), X-ray diffraction (XRD) spectroscopy and elemental mapping. In the other study stage, the cytotoxic effects of hybrid nanoflowers were evaluated using A549 and MCF7 cell lines. When 1,2-phenylenediamine and N1-phenylbenzene-1,2-diamine were converted into CuhNFs, it was effective in MCF7 and A549 cell lines. Docking studies were performed using the Prime MM-GBSA method to estimate binding affinities and determine the binding mode. ADME analysis was performed using the Schrödinger 2021-2 QikProp wizard. Support was obtained from molecular docking to confirm the potential of N1-phenylbenzene-1,2-diamine and 1,2-phenylenediamine compounds for both breast and lung cancer. Molecular docking studies can provide information about binding interactions between compounds with identified targets, which may explain their inhibitory activity. A better result can be obtained by examining the binding patterns in the active binding region of the compounds through molecular docking.Graphical Abstract
Manometric Determination of the Oxygen Diffusion Coefficients in YBa2Cu3−yFeyO6+x
Authors: Kargin J. , Cornejo H. E. Sanchez , Zhangozin K. , Zhanabergenov T. , Kassymkhanov Zh. , Seo Jiwon , Holmes S. N. , Barnes C. H. W. , Aguiar J. Albino , De Los Santos Valladares L. 
Published: July 2024

AbstractThermodynamic parameters, such as the activation energy (Ea) and the diffusion coefficient (D0), of weakly bounded oxygen in Fe-doped YBa2Cu3O6+x (YBa2Cu3-yFeyO6+x) system were obtained utilizing the Manometric technique. It is found that under isothermal conditions, oxygen desorption in YBa2Cu3O6+x (YBCO) occurs following an exponential decay trend and in two distinct stages. During the first stage, the majority of absorbed oxygen is released, while the second stage exhibits a delayed degassing kinetics. Ea and D0 were determined for both, pressed and powdered, samples of the YBCO and YBa2Cu3-yFeyO6+x systems, from experimental kinetic curves, depicting gas release P(t) under isothermal vacuum degassing conditions. It is demonstrated that doping with Fe prevents oxygen exchange in YBCO, as evidenced by the reduction in the quantity of released oxygen with increasing iron index (y). The experimental results validate the efficacy of the Manometric method for analyzing and determining the thermodynamic and kinetic properties of the YBCO superconductor.
MDGCL: Graph Contrastive Learning Framework with Multiple Graph Diffusion Methods
Authors: Li Yuqiang , Zhang Yi , Liu Chun 
Published: July 2024

AbstractIn recent years, some classical graph contrastive learning(GCL) frameworks have been proposed to address the problem of sparse labeling of graph data in the real world. However, in node classification tasks, there are two obvious problems with existing GCL frameworks: first, the stochastic augmentation methods they adopt lose a lot of semantic information; second, the local–local contrasting mode selected by most frameworks ignores the global semantic information of the original graph, which limits the node classification performance of these frameworks. To address the above problems, this paper proposes a novel graph contrastive learning framework, MDGCL, which introduces two graph diffusion methods, Markov and PPR, and a deterministic–stochastic data augmentation strategy while retaining the local–local contrasting mode. Specifically, before using the two stochastic augmentation methods (FeatureDrop and EdgeDrop), MDGCL first uses two deterministic augmentation methods (Markov diffusion and PPR diffusion) to perform data augmentation on the original graph to increase the semantic information, this step ensures subsequent stochastic augmentation methods do not lose too much semantic information. Meanwhile, the diffusion matrices carried by the augmented views contain global semantic information of the original graph, allowing the framework to utilize the global semantic information while retaining the local-local contrasting mode, which further enhances the node classification performance of the framework. We conduct extensive comparative experiments on multiple benchmark datasets, and the results show that MDGCL outperforms the representative baseline frameworks on node classification tasks. Among them, compared with COSTA, MDGCL’s node classification accuracy has been improved by 1.07% and 0.41% respectively on two representative datasets, Amazon-Photo and Coauthor-CS. In addition, we also conduct ablation experiments on two datasets, Cora and CiteSeer, to verify the effectiveness of each improvement work of our framework.
How can research-based studio experience assist in tackling natural disasters?
Authors: Yildirim Yalcin , Cirak Buse Nur , Akin Anil 
Published: July 2024

AbstractNatural disasters threaten human life in various ways, and a better understanding of their components reduces environmental and societal ramifications. This study seeks a pedagogical approach to contribute to such knowledge at a junior landscape architecture studio by concentrating on research-based experience. Assigning five disaster categories, including flood, drought and food, climate change, earthquake, and disaster-resilient society, the students obtained enriched knowledge on the studio process while they had difficulty in applying theoretical aspects of natural disasters to their projects, mainly gearing from analysis (upper scale) to design (lower scale) solutions. The study results propose that natural disaster education and awareness efforts should be integrated into design- and planning-related disciplines sooner rather than later.
A Version of Predicate Logic with Two Variables That has an Incompleteness Property
Authors: Khaled Mohamed 
Published: July 2024

AbstractIn this paper, we consider predicate logic with two individual variables and general assignment models (where the set of assignments of the variables into a model is allowed to be an arbitrary subset of the usual one). We prove that there is a statement such that no general assignment model in which it is true can be finitely axiomatized. We do this by showing that the free relativized cylindric algebras of dimension two are not atomic.
Secure query processing for smart grid data using searchable symmetric encryption
Authors: Souror Samia , Badawy Mohammed , El-Fishawy Nawal 
Published: July 2024

AbstractIn the last decade, the smart grid has utilized many modern technologies and applications compared with the conventional grid. Cloud computing offers numerous managed services and storage solutions for smart grid data. To balance security, privacy, efficiency, and utility, more efforts should be made to keep up with the rapid evolution of technology. Searchable encryption techniques are widely considered an intelligent solution to ensure data privacy and security while maintaining the functionality to search over encrypted data. In this paper, we propose a more reliable and efficient searchable symmetric encryption scheme for smart grid data. It is a dynamic keyword searchable scheme that uses a symmetric cipher based on a key hashing algorithm (DKS-SCKHA) to generate a secure index. This scheme eliminates the false-positive issue associated with the use of bloom filters and narrows the scope of the retrieved search results. Additionally, it efficiently supports both partial and exact query processing on the encrypted database. Both theoretical and security analyses demonstrate the efficiency and security of the DKS-SCKHA scheme compared to other previous schemes. Comprehensive experiments on a smart grid dataset showed that the DKS-SCKHA scheme is 35–68% more efficient than the schemes compared in this paper. The DKS-SCKHA scheme supports three keyword search scenarios: single, conjunctive, and disjunctive. Furthermore, the DKS-SCKHA scheme is extended to support dynamic fuzzy keyword search on the encrypted database (DEFKS-SCKHA). We evaluated the security and efficiency of the DEFKS-SCKHA scheme through security analysis and experimental evaluation. Theoretical analysis shows that the proposed scheme is secure against known-ciphertext and known-background attacks.
Free strain consolidation of soft ground improved by stone columns under time-dependent loading considering smear effects
Authors: Liu Yang , Wu Peichen , Yin Jian-Hua , Zheng Jun-Jie 
Published: July 2024

AbstractThis paper presents a consolidation model for stone column-reinforced soft ground subjected to time-dependent loading under free strain condition. Smear effects and three types of loadings, namely, constant loading, ramp loading, and sinusoidal loading, are considered in the developed consolidation model, which is solved by a numerical method based on a partial differential equation solver. The applicability of the proposed consolidation model and the reliability of the numerical method are demonstrated and verified by well-predicting the consolidation behaviors of two practical engineering cases and one laboratory experiment. The verified model and the numerical method are then employed to investigate the effects of smear zone and time-dependent loading on consolidation characteristics of stone column-improved soft ground. The results indicate that the excess pore water pressure undergoes a sharp change at the interface between the smear zone and the undisturbed zone due to smear effects. The smaller the range of the smear zone, the faster the settlement of the composite foundation develops. The faster the loading rate, the faster the dissipation of excess pore water pressure and the faster the settlement develops. In addition, for the foundation subjected to sinusoidal loading, the higher loading frequency results in a larger amplitude corresponding to the excess pore water pressure and a smaller amplitude corresponding to the settlement of the soil.
Predicting Potato Crop Yield with Machine Learning and Deep Learning for Sustainable Agriculture
Authors: El-Kenawy El-Sayed M. , Alhussan Amel Ali , Khodadadi Nima , Mirjalili Seyedali , Eid Marwa M. 
Published: July 2024

AbstractPotatoes are an important crop in the world; they are the main source of food for a large number of people globally and also provide an income for many people. The true forecasting of potato yields is a determining factor for the rational use and maximization of agricultural practices, responsible management of the resources, and wider regions’ food security. The latest discoveries in machine learning and deep learning provide new directions to yield prediction models more accurately and sparingly. From the study, we evaluated different types of predictive models, including K-nearest neighbors (KNN), gradient boosting, XGBoost, and multilayer perceptron that use machine learning, as well as graph neural networks (GNNs), gated recurrent units (GRUs), and long short-term memory networks (LSTM), which are popular in deep learning models. These models are evaluated on the basis of some performance measures like mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) to know how much they accurately predict the potato yields. The terminal results show that although gradient boosting and XGBoost algorithms are good at potato yield prediction, GNNs and LSTMs not only have the advantage of high accuracy but also capture the complex spatial and temporal patterns in the data. Gradient boosting resulted in an MSE of 0.03438 and an R2 of 0.49168, while XGBoost had an MSE of 0.03583 and an R2 of 0.35106. Out of all deep learning models, GNNs displayed an MSE of 0.02363 and an R2 of 0.51719, excelling in the overall performance. LSTMs and GRUs were reported to be very promising as well, with LSTMs comprehending an MSE of 0.03177 and GRUs grabbing an MSE of 0.03150. These findings underscore the potential of advanced predictive models to support sustainable agricultural practices and informed decision-making in the context of potato farming.
3D mobile regression vision transformer for collateral imaging in acute ischemic stroke
Authors: Jung Sumin , Yang Hyun , Kim Hyun Jeong , Roh Hong Gee , Kwak Jin Tae 
Published: July 2024

Abstract
Purpose
The accurate and timely assessment of the collateral perfusion status is crucial in the diagnosis and treatment of patients with acute ischemic stroke. Previous works have shown that collateral imaging, derived from CT angiography, MR perfusion, and MR angiography, aids in evaluating the collateral status. However, such methods are time-consuming and/or sub-optimal due to the nature of manual processing and heuristics. Recently, deep learning approaches have shown to be promising for generating collateral imaging. These, however, suffer from the computational complexity and cost.

Methods
In this study, we propose a mobile, lightweight deep regression neural network for collateral imaging in acute ischemic stroke, leveraging dynamic susceptibility contrast MR perfusion (DSC-MRP). Built based upon lightweight convolution and Transformer architectures, the proposed model manages the balance between the model complexity and performance.

Results
We evaluated the performance of the proposed model in generating the five-phase collateral maps, including arterial, capillary, early venous, late venous, and delayed phases, using DSC-MRP from 952 patients. In comparison with various deep learning models, the proposed method was superior to the competitors with similar complexity and was comparable to the competitors of high complexity.

Conclusion
The results suggest that the proposed model is able to facilitate rapid and precise assessment of the collateral status of patients with acute ischemic stroke, leading to improved patient care and outcome.
Strain Hardening in Dilute Binary Al–Cu, Al–Zn, and Al–Mn Alloys: Experiment and Modeling
Authors: Viernstein Bernhard , Solyom Laszlo , Kozeschnik Ernst 
Published: July 2024

AbstractDuring thermo-mechanical processing, dissolved alloying elements have a huge impact on the microstructure evolution by influencing the overall dislocation storage rate. Especially, for non-heat treatable Al alloys, the effects of strain-hardening and solid solution strengthening are of significant practical interest. In the present work, a detailed study of the room temperature work-hardening behavior of binary Al–Cu, Al–Zn, and Al–Mn alloys with varying solute concentrations is carried out. Stress–strain curves at different strain rates are recorded and computationally analyzed by an advanced 3-Internal-Variables-Model (3IVM) approach for the dislocation density evolution. The initial strengthening rate is examined as a function of the solute concentration.
Pipeline Circumferential Cracking in Near-Neutral pH Environment Under the Influence of Residual Stress: Dormancy and Crack Initiation
Authors: Shirazi Hamed , Wang Shidong , Eadie Reg , Chen Weixing 
Published: July 2024

AbstractThe purpose of this study is to identify the integrity challenges encountered by buried pipeline steels, specifically to address Circumferential Near-Neutral pH Corrosion Fatigue (C-NNpH-CF). Damage to the pipeline’s protective coating and corrosion conditions increase the risk of service failures caused by C-NNpH-CF. (Note that this mechanism has previously been termed near-neutral pH stress corrosion cracking.) Unlike axial cracking, circumferential cracking is primarily influenced by residual stress from pipeline bending, geohazards, and girth welds. External corrosion pits often lead to dormant cracks, with growth ceasing around 1 mm depth due to reduced dissolution rates. Investigating the impact of bending residual stress (an appropriate source of axial residual stress) and cyclic loading (simulated pipeline pressure fluctuation), the study employs the digital image correlation (DIC) method for stress distribution analysis. Factors like applied loading, initial notch depth, and bending conditions influence crack initiation and recovery from the dormancy stage by affecting stress distribution, stress cells, and stress concentration. Cross-sectional and fractographic images reveal time/stress-dependent mechanisms governing crack initiation, including dissolution rate and hydrogen-enhanced corrosion fatigue. The study emphasizes the role of various residual stress types and their interactions with axial cyclic loading in determining the threshold conditions for crack initiation.