bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tree based Regression Models for Predicting the Compressive Strength of Concrete at High Temperature
Authors: Arora Gourav , Kumar Devender , Singh Balraj 
Published: May 2024

Abstract
Predicting the compressive strength of concrete is a complicated process due to the heterogeneous mixture of concrete and high variable materials. Researchers have predicted the compressive strength of concrete for various mixes using soft computing models. In this research, compressive strength of concrete at high temperature with fly ash, super plasticizers, and fibre is predicted using three regression tree-based soft computing models (Random Forest, Random Tree, and Reduced-Error Pruning Tree (REP Tree)). The data used in this study is collected from the literature, and two-thirds of the total data is used for model training, while the remaining third is reserved for testing the prepared model. The model’s performance is evaluated based on scatter plots, variation plots, box plots, and prediction error rates, i.e., R, RMSE, and MAE. The results highlight the highest performance of the Random Forest model, with R of 0.9142; RMSE of 9.6285 MPa and MAE of 6.7931 MPa, outperforming the other competing models. Furthermore, the most influential parameter is determined using sensitivity analysis. Thus, the Random Forest model is the model that can be used for predicting the compressive strength of concrete at high temperatures.
Recycled Materials as Resource in Concrete for Sustainability
Authors: Shashank R , Prashanth S , Shetru Samanyu , Prasad Bs Guru , Prathima G 
Published: May 2024

Abstract
Though concrete offers several benefits like strength, versatility in shape, and cost-effectiveness yet stands far from the point of Sustainability. The natural ingredients are getting exhausted and hence alternative materials have picked up these places and thereby point towards replacement of natural materials by secondary materials.The present study orients towards the use of recycled aggregates for M40 grade concrete that is obtained from construction and demolition waste both coarse and fine as a complete replacement to natural coarse and fine aggregates. The concrete specimens are tested for 7, 28 days normally. This results in understanding the performance of recycled concrete mix. The same may be compared to conventional mix for finding of compressive strength. The normal mix observed has attained nearly 98% of the target strength while the recycled mix was 86% at 28 days of curing, which indicates the secondary materials can provide a replacement for the natural aggregates. Also, in the concrete cement is replaced by fly ash in regular percentages - 20, 40, and 60% and with recycled materials indicates results of better performance in strength at 20 and 40% in both normal and recycle mix than at 60%. Fly ash is considered a good alternative for an eco-friendly environment.
A pragmatic review paper on the use of recycled aggregates in concrete
Authors: Purohit Brahma Dutta , Samaiya Nitin Kumar , Babu VS 
Published: May 2024

Abstract
The incorporation of recycled aggregate into concrete may have a number of potential benefits, one of which is the maintenance of the natural environment, which also contributes to the expansion of the economy. In the not too distant future, recycled aggregates are destined to take the lion’s share of the market for construction materials. Recycled aggregate is rapidly being utilised as a kind of construction material in a broad variety of building projects throughout a number of nations in Europe, North America, and Asia. These building projects can be found in a variety of countries. These countries are among those that are taken into account: In an effort to increase the amount of recycled aggregate that is employed in building projects, several nations are relaxing some restrictions that regulate infrastructure. This is being done in an effort to improve the quantity of recycled aggregate that is used. Within the scope of this investigation, we will look at the fundamental characteristics of recycled fine aggregate in addition to recycled coarse aggregate. In addition to that, it investigates these properties by considering natural aggregates as a reference point. In this study, the researchers investigated a wide variety of treatment strategies, such as Cement treatment, mineral additives, modified concrete mixers, and mineral admixtures, warm mix asphalt and hydrated lime, CO2 curing technique, silica gel techniques, waste oil and asphalt emulsion, mechanical grinding or ball milling, thermal treatment, chemical and calcium metasilicate slurry, carbonation and wrapping methods, non materials, and super plasticizability. The researchers found that chemical treatment, mechanical The application of a ball mill, another kind of grinding equipment, or a certain kind of chemical method might be used. The particulars of each therapy approach, as well as the outcomes of employing each treatment strategy, are explained in a way that is simple to comprehend and does not delve into excessive detail.
Comparative Study of Static and Dynamic Analysis of Multistoried Building
Authors: Rauniyar Akash Prasad , Juneja Er. Deepak 
Published: May 2024

Abstract
In a moderate seismic activity, relying solely on the static force method to estimate the seismic force assessing the vulnerability and behaviour of RCC buildings under seismic loads is insufficient. The seismic response of the building system is highly influenced by the analysis method adopted. In the past years, static approaches were preferred due to their simplicity, although these methods provided safe design, they may have been overly cautious. Dynamic analysis, on the other hand, is a fundamental aspect of structural mechanics, that describes and predicts the behaviour of structures subjected to dynamic loads. Dynamic load includes wind, imposed load, earthquake, etc. Dynamic analysis techniques, such as modal analysis, time history analysis, and dynamic displacement analysis. RCC structures are mainly designed to resist static load. This process of ignoring dynamic events sometimes becomes the cause of disasters, especially in the case of earthquakes. This research paper provides information on the comparison of various parameters in static and dynamic analysis. Two RCC multistoried residential buildings (g+10 and g+25) acting on seismic zone IV are analyzed using the static analysis method and response spectrum method with ETABS 16. The research investigates the effect on various structural parameters such as story displacement, story drift, and support reactions. The results are compared and evaluated to assess the performance of the response spectrum method over the static analysis method.
Inhibition of hepatocellular carcinoma cells through ferroptosis therapy using Fe3O4/Ag nanocomposites
Authors: Lin Hongfeng , Chen Jianhong , Zhang Zhongyan , Wang Yuxu 
Published: May 2024

Abstract
In the current study, Fe3O4/Ag nanohybrids demonstrating high efficiency in liver cancer ferroptosis therapy and MR imaging were synthesized using a facile one-pot approach. Subsequently, their morphology, chemical composition, structure, Fenton reaction activity, and magnetic resonance (MR) contrast effect were systemically assessed. Importantly, the in vitro studies demonstrated that the Fe3O4/Ag nanohybrids had a high therapeutic efficiency in treating liver cancer cells and performed exceptionally well during T2-weight MR imaging. Correspondingly, these Fe3O4/Ag nanohybrids demonstrate significant potential for use in the field of biomedicine.
Emerging functions of two-dimensional materials in memristive neurons
Authors: Hong Yuwan , Liu Yanming , Li Ruonan , Tian He 
Published: May 2024

Abstract
Neuromorphic computing (NC), considered as a promising candidate for future computer architecture, can facilitate more biomimetic intelligence while reducing energy consumption. Neuron is one of the critical building blocks of NC systems. Researchers have been engaged in promoting neuron devices with better electrical properties and more biomimetic functions. Two-dimensional (2D) materials, with ultrathin layers, diverse band structures, featuring excellent electronic properties and various sensing abilities, are promised to realize these requirements. Here, the progress of artificial neurons brought by 2D materials is reviewed, from the perspective of electrical performance of neuron devices, from stability, tunability to power consumption and on/off ratio. Rose up to system-level applications, algorithms and hardware implementation of spiking neural network, stochastic neural network and artificial perception system based on 2D materials are reviewed. 2D materials not only facilitate the realization of NC systems but also increase the integration density. Finally, current challenges and perspectives on developing 2D material-based neurons and NC systems are systematically analyzed, from the bottom 2D materials fabrication to novel neural devices, more brain-like computational algorithms and systems.
Highly efficient nonlinear compression of mJ pulses at 2 μm wavelength to 20 fs in a gas-filled multi-pass cell
Authors: Eisenbach Lucas , Wang Ziyao , Schulte Jan , Heuermann Tobias , Russbüldt Peter , Meyer Rudolf , Gierschke Philipp , Lenski Mathias , Sugiura Muneo , Tamura Koichi , Limpert Jens , Häfner Constantin 
Published: May 2024

Abstract
Within this work we demonstrate the highly efficient nonlinear spectral broadening and subsequent temporal compression of 1.49 mJ pulses at 101 kHz repetition rate from an ultrafast thulium-doped fiber laser system employing a gas-filled multi-pass cell (MPC). To achieve spectral broadening, we use a krypton and helium-filled Herriott-type MPC with highly reflective broadband dielectric mirrors. The spectrally broadened pulses are subsequently compressed using fused-silica plates, resulting in a pulse duration of 20 fs and an overall excellent transmission of 96%. Furthermore, the beam quality is preserved up to the maximum output power of 144 W. It provides, to the best of our knowledge, the highest average power with few-cycle pulses at 2 µm wavelength with almost 10 times more pulse energy and 3 times more average power than previous 2 µm MPCs, enabling future secondary source experiments.
Enhancing hyperspectral imaging through macro and multi-modal capabilities
Authors: Ardini Benedetto , Corti Matteo , Ghirardello Marta , Di Benedetto Alessia , Berti Letizia , Cattò Cristina , Goidanich Sara , Sciutto Giorgia , Prati Silvia , Valentini Gianluca , Manzoni Cristian , Comelli Daniela , Candeo Alessia 
Published: May 2024

Abstract
Hyperspectral imaging (HSI) has emerged as an effective tool to obtain spatially resolved spectral information of artworks by combining optical imaging with spectroscopy. This technique has proven its efficacy in providing valuable information both at the large and microscopic scale. Interestingly, the macro scale has yet to be thoroughly investigated using this technology. While standard HSI methods include the use of spatial or spectral filters, alternative methods based on Fourier-transform interferometry have also been utilised. Among these, a hyperspectral camera employing a birefringent common-path interferometer, named TWINS, has been developed, showing a high robustness and versatility. In this paper, we propose the combination of TWINS with a macro imaging system for the study of cultural heritage (CH). We will show how the macro-HSI system was designed, and we will demonstrate its efficient capabilities to collect interferometric images with high visibility and good signal of both reflectance and fluorescence on the same field of view, even on non-flat samples. Our hyperspectral camera for macro studies of both reflectance and fluorescence data is a completely new asset in the CH panorama and beyond. The relevance of the macro technology is demonstrated in two case studies, aiding in the analysis of biofilms on stone samples and of the degradation of dyed textiles.
Brillouin scattering from biomedical samples: the challenge of heterogeneity
Authors: Cardinali M A , Caponi S , Mattarelli M , Fioretto D 
Published: May 2024

Abstract
Brillouin light scattering (BLS), a non-destructive and non-contact technique, offers a powerful tool for probing the micromechanical properties of biological tissues. However, the inherent heterogeneity of biological tissues can pose significant challenges in interpreting BLS spectra. In this study, we introduce a novel method that harnesses the intensity information within a single BLS spectrum to directly estimate the Voigt average of the longitudinal modulus. Additionally, we use a method to determine the ratio of the squared Pockels coefficients for photoelastically heterogeneous samples, based on global analysis of a 2D BLS map. This method is shown to effectively determine the photoelastic ratio of soft and hard components of human bone tissues, enabling the calculation of the average elastic moduli. Furthermore, it has the remarkable ability to generate maps of the filling factor of the scattering volume, shedding valuable light on the intricate structure and topography of rough surfaces under BLS mapping.
Wear debris analysis of Al-Si/MWCNT nanocomposite during dry sliding wear tests
Authors: T Shivaramu H , U Vignesh Nayak , K S Umashankar 
Published: May 2024

Abstract
This study demonstrates the enhancement of wear resistance achieved by incorporating multi-walled carbon nanotubes (MWCNTs) in LM6 aluminum alloy to form nanocomposites. Experiments on wear resistance study were performed at different test parameters for various compositions of MWCNT in LM6 alloy. The size and nature of debris obtained post experiments were significantly dependent on the MWCNT contents. Wear resistance was found to increase with increase in the MWCNT fraction in the nanocomposite. The worn surface and the shape as well as the size of the debris were studied under scanning electron microscopy (SEM). Energy-dispersive spectroscopy (EDS) analysis of the worn surfaces was used to detect and measure the constituents present in the debris. SEM micrographs of the nanocomposites show that the features of the wear debris are completely altered when MWCNT was added. Further the wear mechanism underwent a change from oxidative in LM6 to that of ploughing in LM6 nanocomposite. The recorded surface roughness values also confirm the above findings and show significantly reduced surface roughness (∼82%, 0.75 wt% MWCNT). These results clearly demonstrate the advantage of addition of MWCNT for enhancing resistance to wear in LM6 alloys.
Network intrusion classification for IoT networks using an extreme learning machine
Authors: Akuthota Uday Chandra , Bhargava Lava 
Published: May 2024

Abstract
The detection of intrusions has a significant impact on providing information security, and it is an essential technology to recognize diverse network threats effectively. This work proposes a machine learning technique to perform intrusion detection and classification using multiple feature extraction and testing using an Extreme learning machine (ELM). The model is evaluated on the two network intrusion datasets (NSL-KDD and UNSW-NB15), which consist of real-time network traffic. The arithmetic, gradient, and statistical features were extracted and evaluated with the proposed model. The method’s efficacy is assessed using accuracy, sensitivity, specificity, precision, and F1-score. The proposed method achieves 94.5%, 97.61%, 96.91%, 96.51%, and 97.05% accuracy, sensitivity, specificity, precision, and F1-score for NSL-KDD and 94.3%, 98,36%, 99.31%, 99.67% and 99.01% of accuracy, sensitivity, specificity, precision and F1-score for the UNSWNB-15 dataset respectively, which is better performance outcomes when compared to other existing works.
Is hepatic GABA transaminase a promising target for obesity and epilepsy treatments?
Authors: Kumrungsee Thanutchaporn 
Published: May 2024

ABSTRACT
γ-Aminobutyric acid (GABA) transaminase (GABA-T) is a GABA-degrading enzyme that plays an essential role in regulating GABA levels and maintaining supplies of GABA. Although GABA in the mammalian brain was discovered 70 years ago, research on GABA and GABA-T has predominantly focused on the brain. Notwithstanding the high activity and expression of GABA-T in the liver, the exact functions of GABA-T in the liver remain unknown. This article reviews the up-to-date information on GABA-T in the liver. It presents recent findings on the role of liver GABA-T in food intake suppression and appetite regulation. Finally, the potential functions of liver GABA-T in other neurological diseases, natural GABA-T inhibitors, and future perspectives in this research area are discussed.
SGCLDGA: unveiling drug–gene associations through simple graph contrastive learning
Authors: Fan Yanhao , Zhang Che , Hu Xiaowen , Huang Zhijian , Xue Jiameng , Deng Lei 
Published: May 2024

Abstract
Drug repurposing offers a viable strategy for discovering new drugs and therapeutic targets through the analysis of drug–gene interactions. However, traditional experimental methods are plagued by their costliness and inefficiency. Despite graph convolutional network (GCN)-based models’ state-of-the-art performance in prediction, their reliance on supervised learning makes them vulnerable to data sparsity, a common challenge in drug discovery, further complicating model development. In this study, we propose SGCLDGA, a novel computational model leveraging graph neural networks and contrastive learning to predict unknown drug–gene associations. SGCLDGA employs GCNs to extract vector representations of drugs and genes from the original bipartite graph. Subsequently, singular value decomposition (SVD) is employed to enhance the graph and generate multiple views. The model performs contrastive learning across these views, optimizing vector representations through a contrastive loss function to better distinguish positive and negative samples. The final step involves utilizing inner product calculations to determine association scores between drugs and genes. Experimental results on the DGIdb4.0 dataset demonstrate SGCLDGA’s superior performance compared with six state-of-the-art methods. Ablation studies and case analyses validate the significance of contrastive learning and SVD, highlighting SGCLDGA’s potential in discovering new drug–gene associations. The code and dataset for SGCLDGA are freely available at https://github.com/one-melon/SGCLDGA.
‘Violence is completely normal’: Managing Violence Through Narrative Normalization
Authors: Storm-Mathisen Frøja 
Published: May 2024

Abstract
This article introduces the concept ‘narrative normalization of violence’ as a theoretical framework for exploring the interplay between crime and marginality in street culture. Drawing from 4 months of ethnographic observations and 24 qualitative interviews with young men involved in a violent street culture in Oslo, Norway, the study identifies three prevalent narratives. The first, ‘Part of the game’, minimizes the danger of violence; the second, ‘It’s all about respect’, internalizes violence as part of a desired subcultural identity; and the third, ‘We come from concrete’, emphasizes the importance of belonging. In distinctive and important ways these narratives shape collective energies that influence beliefs, attitudes and aspirations, which work to narratively render the exceptional nature of violence manageable and mundane.
Data-Driven Patient clusters of Type 2 Diabetes May Help to Identify Patients at Risk and Predict Metabolic Surgery Outcomes on Diabetes Remission and NAFLD Pregression
Authors: Poljo A , Klasen J M , Süsstrunk J , Kraljević M , Peterli R , Müller-Stich B P , Billeter A T 
Published: May 2024

Abstract

Background
Precision medicine and customized medicine have gained enormous attention in recent years, especially in the treatment of type 2 diabetes (T2D). Different subgroups of diabetes have been identified by research employing data-driven cluster analysis, each with a unique diabetes progression and complication risk (Ahlqvist et al., Lancet Diabetes Endocrinol. 2018).

Aims
We aimed to apply the proposed cluster analysis to a patent population post metabolic surgery and investigate the association with T2D remission and presence of NAFLD.

Methods
We retrospectively linked newly defined clusters to metabolic surgery outcomes in 53 T2D patients. Utilizing k-means and hierarchical clustering, three clusters emerged based on glutamate decarboxylase antibodies, age, BMI, HbA1c, and homoeostatic model assessment estimates of β-cell function (HOMA2-B%) and insulin resistance (HOMA2-IR). Intraoperative liver biopsies assessed nonalcoholic fatty liver disease (NAFLD) presence differentiating between simple steatosis (NAFL) and steatohepatitis (NASH). Clinical and biochemical data were collected over two years, focusing on T2D remission and NAFLD improvement.

Results
Cluster 1, characterized by the lowest BMI, highest NASH rate, impaired beta-cell function, and increased insulin resistance, displayed a favorable response to surgery, indicating robust regeneration of beta-cells. Despite increased insulin production, T2D remission was surprisingly low at 44.0% after one year, in contrast to 75.00% in Cluster 2 and 100.00% in Cluster 3. Metabolic surgery notably reduced insulin resistance and promoted NASH remission, evidenced by a significant reduction in a non-invasive NASH detection score and liver enzyme levels across all three clusters.

Conclusion
Our findings suggest that patients in Cluster 1 already show a lack of beta-cell compensation being associated with a higher prevalence of NAFLD and poorer diabetic control and therefore especially might benefit from an earlier intervention. Data-driven classification might help to customize treatment plans and identify patients at higher risk of problems at diagnosis.
Characterization of Colon Anastomosis Healing in Obese Mice
Authors: Delaune V , Lacotte S , Bouroumeau A , Bornand A , Slits F , Gex Q , Buchs N C , Toso C 
Published: May 2024

Abstract

Background
Colon anastomosis leak is the most feared complication in colorectal surgery, with high morbidity and mortality. Despite the identification of correctible risk factors, its incidence has remained stable for the past 50 years. One of the risk factors is obesity, a systemic disease whose prevalence is continuously increasing worldwide. To understand the implicated mechanisms, and to develop therapeutic strategies, characterization of colon anastomosis healing in obese subjects is mandatory.

Aims
We aimed to describe the colon anastomotic healing process in obese mice, with a secondary aim to identify potential targets to decrease post-operative leaks.

Methods
Three groups of mice were fed different diets for 16 weeks: normal diet only (ND), high-fat diet reverted to normal diet on day of surgery (HF-ND), or high-fat diet only (HF-HF). Surgery consisted of left colon hemi-transection on the anti-mesenteric wall, and subsequent anastomosis with separate stitches. N=5 mice from each group were sacrificed at days 3, 5, 7, 10 and 14 after surgery.

Results
HF-HF mice had diarrhea and lost weight up to day 10 after surgery, compared to day 1 for ND mice. Overall, abscess rate was 4% in ND and HF-ND, and 8% in HF-HF mice. Free leak rate was 0% in ND and HF-ND, and 16% in HF-HF mice (p=0.03). Angiogenesis was decreased in HF-HF mice at day 5 compared to ND mice (p=0.02).

Conclusion
Obese mice undergoing continued high-fat diet (HF-HF) globally demonstrated worst outcomes after colon anastomosis with increased leak rates, and decreased angiogenesis. Despite having similarly low angiogenesis, these results seem to be mitigated in high-fat diet fed mice reverting to normal diet on day of surgery (HF-ND), hinting towards an involvement of gut microbiota. Further analysis of gut microbiota and angiogenesis pathways are warranted in order to determine therapeutic strategies.