bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand
Authors: Javed Muhammad Faisal , Khan Majid , Fawad Muhammad , Alabduljabbar Hisham , Najeh Taoufik , Gamil Yaser 
Published: June 2024

AbstractThe use of waste foundry sand (WFS) in concrete production has gained attention as an eco-friendly approach to waste reduction and enhancing cementitious materials. However, testing the impact of WFS in concrete through experiments is costly and time-consuming. Therefore, this study employs machine learning (ML) models, including support vector regression (SVR), decision tree (DT), and AdaBoost regressor (AR) ensemble model to predict concrete properties accurately. Moreover, SVR was employed in conjunction with three robust optimization algorithms: the firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO), to construct hybrid models. Using 397 experimental data points for compressive strength (CS), 146 for elastic modulus (E), and 242 for split tensile strength (STS), the models were evaluated with statistical metrics and interpreted using the SHapley Additive exPlanation (SHAP) technique. The SVR-GWO hybrid model demonstrated exceptional accuracy in predicting waste foundry sand concrete (WFSC) strength characteristics. The SVR-GWO hybrid model exhibited correlation coefficient values (R) of 0.999 for CS and E, and 0.998 for STS. Age was found to be a significant factor influencing WFSC properties. The ensemble model (AR) also exhibited comparable prediction accuracy to the SVR-GWO model. In addition, SHAP analysis revealed an optimal content of input variables in the concrete mix. Overall, the hybrid and ensemble models showed exceptional prediction accuracy compared to individual models. The application of these sophisticated soft computing prediction techniques holds the potential to stimulate the widespread adoption of WFS in sustainable concrete production, thereby fostering waste reduction and bolstering the adoption of environmentally conscious construction practices.
Effect of 2 weeks rest-pause on oxidative stress and inflammation in female basketball players
Authors: Cichoń-Woźniak Justyna , Ostapiuk-Karolczuk Joanna , Cieślicka Mirosława , Dziewiecka Hanna , Basta Piotr , Maciejewski Dariusz , Skarpańska-Stejnborn Anna 
Published: June 2024

AbstractIntense exercise leads to increased production of free radicals, resulting in an inflammatory response in athletes. For this reason, it was decided to investigate whether a single intensive exercise until exhaustion applied after a 2-week rest period would result in a violation of the pro-oxidant-antioxidant balance. Twenty-seven trained female basketball players (age: 16.55 ± 0.96 years, body mass: 66.40 ± 13.68 kg, height: 173.45 ± 5.14 cm) were enrolled to the study following the application of inclusion and exclusion criteria. Study was conducted at the end of the competitive training phase. Participants underwent incremental treadmill exercise, with blood samples collected before the test, immediately post-exercise, and after a 3-h restitution period. Total antioxidant capacity (TAC) levels increased significantly after exercise and remained unchanged after 3 h. Concentration of interleukin-10 (IL-10) and creatine kinase (CK) significantly increased after exercise and then decreased. Concentration of interleukin-2 (IL-2) was significantly reduced immediately and 3 h after exercise, while interleukin-13 (IL-13), interleukin-1α (IL-1α), and tryptophan (TRP) decreased 3 h after exercise. No significant changes were observed in other biochemical parameters. Obtained results show an increased antioxidant capacity which reduced oxidative stress and inflammation in response to intense exercise indicating that rested athletes have a high adaptation and elevated tolerance to effort.
Caffeic acid phenethyl ester promotes oxaliplatin sensitization in colon cancer by inhibiting autophagy
Authors: Xing Fei , Liu Ning , Wang Can , Wang Xu-Dong 
Published: June 2024

AbstractColon cancer ranks as the third most prevalent form of cancer globally, with chemotherapy remaining the primary treatment modality. To mitigate drug resistance and minimize adverse effects associated with chemotherapy, selection of appropriate adjuvants assumes paramount importance. Caffeic acid phenethyl ester (CAPE), a naturally occurring compound derived from propolis, exhibits a diverse array of biological activities. We observed that the addition of CAPE significantly augmented the drug sensitivity of colon cancer cells to oxaliplatin. In SW480 and HCT116 cells, oxaliplatin combined with 10 µM CAPE reduced the IC50 of oxaliplatin from 14.24 ± 1.03 and 84.16 ± 3.02 µM to 2.11 ± 0.15 and 3.92 ± 0.17 µM, respectively. We then used proteomics to detect differentially expressed proteins in CAPE-treated SW480 cells and found that the main proteins showing changes in expression after CAPE treatment were p62 (SQSTM1) and LC3B (MAP1LC3B). Gene ontology analysis revealed that CAPE exerted antitumor and chemotherapy-sensitization effects through the autophagy pathway. We subsequently verified the differentially expressed proteins using immunoblotting. Simultaneously, the autophagy inhibitor bafilomycin A1 and the mCherry-EGFP-LC3 reporter gene were used as controls to detect the effect of CAPE on autophagy levels. Collectively, the results indicate that CAPE may exert antitumor and chemotherapy-sensitizing effects by inhibiting autophagy, offering novel insights for the development of potential chemosensitizing agents.
Establishing the distribution of cerebrovascular resistance using computational fluid dynamics and 4D flow MRI
Authors: Vikström Axel , Holmlund Petter , Holmgren Madelene , Wåhlin Anders , Zarrinkoob Laleh , Malm Jan , Eklund Anders 
Published: June 2024

AbstractCerebrovascular resistance (CVR) regulates blood flow in the brain, but little is known about the vascular resistances of the individual cerebral territories. We present a method to calculate these resistances and investigate how CVR varies in the hemodynamically disturbed brain. We included 48 patients with stroke/TIA (29 with symptomatic carotid stenosis). By combining flow rate (4D flow MRI) and structural computed tomography angiography (CTA) data with computational fluid dynamics (CFD) we computed the perfusion pressures out from the circle of Willis, with which CVR of the MCA, ACA, and PCA territories was estimated. 56 controls were included for comparison of total CVR (tCVR). CVR were 33.8 ± 10.5, 59.0 ± 30.6, and 77.8 ± 21.3 mmHg s/ml for the MCA, ACA, and PCA territories. We found no differences in tCVR between patients, 9.3 ± 1.9 mmHg s/ml, and controls, 9.3 ± 2.0 mmHg s/ml (p = 0.88), nor in territorial CVR in the carotid stenosis patients between ipsilateral and contralateral hemispheres. Territorial resistance associated inversely to territorial brain volume (p < 0.001). These resistances may work as reference values when modelling blood flow in the circle of Willis, and the method can be used when there is need for subject-specific analysis.
A novel signature constructed by mitochondrial function and cell death-related gene for the prediction of prognosis in bladder cancer
Authors: Yan Zhiwei , Liu Yunxun , Wang Minghui , Wang Lei , Chen Zhiyuan , Liu Xiuheng 
Published: June 2024

AbstractBladder urothelial carcinoma (BLCA) presents a persistent challenge in clinical management. Despite recent advancements demonstrating the BLCA efficacy of immune checkpoint inhibitors (ICI) in BLCA patients, there remains a critical need to identify and expand the subset of individuals who benefit from this treatment. Mitochondria, as pivotal regulators of various cell death pathways in eukaryotic cells, exert significant influence over tumor cell fate and survival. In this study, our objective was to investigate biomarkers centered around mitochondrial function and cell death mechanisms to facilitate prognostic prediction and guide therapeutic decision-making in BLCA. Utilizing ssGSEA and LASSO regression, we developed a prognostic signature termed mitochondrial function and cell death (mtPCD). Subsequently, we evaluated the associations between mtPCD score and diverse clinical outcomes, including prognosis, functional pathway enrichment, immune cell infiltration, immunotherapy response analysis and drug sensitivity, within high- and low-risk subgroups. Additionally, we employed single-cell level functional assays, RT-qPCR, and immunohistochemistry to validate the differential expression of genes comprising the mtPCD signature. The mtPCD signature comprises a panel of 10 highly influential genes, strongly correlated with survival outcomes in BLCA patients and exhibiting robust predictive capabilities. Importantly, individuals classified as high-risk according to mtPCD score displayed a subdued overall immune response, characterized by diminished immunotherapeutic efficacy. In summary, our findings highlight the development of a novel prognostic signature, which not only holds promise as a biomarker for BLCA prognosis but also offers insights into the immune landscape of BLCA. This paradigm may pave the way for personalized treatment strategies in BLCA management.
Towards biologically plausible model-based reinforcement learning in recurrent spiking networks by dreaming new experiences
Authors: Capone Cristiano , Paolucci Pier Stanislao 
Published: June 2024

AbstractHumans and animals can learn new skills after practicing for a few hours, while current reinforcement learning algorithms require a large amount of data to achieve good performances. Recent model-based approaches show promising results by reducing the number of necessary interactions with the environment to learn a desirable policy. However, these methods require biological implausible ingredients, such as the detailed storage of older experiences, and long periods of offline learning. The optimal way to learn and exploit world-models is still an open question. Taking inspiration from biology, we suggest that dreaming might be an efficient expedient to use an inner model. We propose a two-module (agent and model) spiking neural network in which “dreaming” (living new experiences in a model-based simulated environment) significantly boosts learning. Importantly, our model does not require the detailed storage of experiences, and learns online the world-model and the policy. Moreover, we stress that our network is composed of spiking neurons, further increasing the biological plausibility and implementability in neuromorphic hardware.
Machine learning enables electrical resistivity modeling of printed lines in aerosol jet 3D printing
Authors: Li Mingdong , Yin Shuai , Liu Zhixin , Zhang Haining 
Published: June 2024

AbstractAmong various non-contact direct ink writing techniques, aerosol jet printing (AJP) stands out due to its distinct advantages, including a more adaptable working distance (2–5 mm) and higher resolution (~ 10 μm). These characteristics make AJP a promising technology for the precise customization of intricate electrical functional devices. However, complex interactions among the machine, process, and materials result in low controllability over the electrical performance of printed lines. This significantly affects the functionality of printed components, thereby limiting the broad applications of AJP. Therefore, a systematic machine learning approach that integrates experimental design, geometrical features extraction, and non-parametric modeling is proposed to achieve printing quality optimization and electrical resistivity prediction for the printed lines in AJP. Specifically, three classical convolutional neural networks (CNNs) architectures are compared for extracting representative features of printed lines, and an optimal operating window is identified to effectively discriminate better line morphology from inferior printed line patterns within the design space. Subsequently, three representative non-parametric machine learning techniques are employed for resistivity modeling. Following that, the modeling performances of the adopted machine learning methods were systematically compared based on four conventional evaluation metrics. Together, these aspects contribute to optimizing the printed line morphology, while simultaneously identifying the optimal resistivity model for accurate predictions in AJP.
Enhanced osteogenesis of mesenchymal stem cells encapsulated in injectable microporous hydrogel
Authors: Edwards Seth D. , Ganash Mrinal , Guan Ziqiang , Lee Jeil , Kim Young Jo , Jeong Kyung Jae 
Published: June 2024

AbstractDelivery of therapeutic stem cells to treat bone tissue damage is a promising strategy that faces many hurdles to clinical translation. Among them is the design of a delivery vehicle which promotes desired cell behavior for new bone formation. In this work, we describe the use of an injectable microporous hydrogel, made of crosslinked gelatin microgels, for the encapsulation and delivery of human mesenchymal stem cells (MSCs) and compared it to a traditional nonporous injectable hydrogel. MSCs encapsulated in the microporous hydrogel showed rapid cell spreading with direct cell–cell connections whereas the MSCs in the nonporous hydrogel were entrapped by the surrounding polymer mesh and isolated from each other. On a per-cell basis, encapsulation in microporous hydrogel induced a 4 × increase in alkaline phosphatase (ALP) activity and calcium mineral deposition in comparison to nonporous hydrogel, as measured by ALP and calcium assays, which indicates more robust osteogenic differentiation. RNA-seq confirmed the upregulation of the genes and pathways that are associated with cell spreading and cell–cell connections, as well as the osteogenesis in the microporous hydrogel. These results demonstrate that microgel-based injectable hydrogels can be useful tools for therapeutic cell delivery for bone tissue repair.
Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease
Authors: Pastika Libor , Sau Arunashis , Patlatzoglou Konstantinos , Sieliwonczyk Ewa , Ribeiro Antônio H. , McGurk Kathryn A. , Khan Sadia , Mandic Danilo , Scott William R. , Ware James S. , Peters Nicholas S. , Ribeiro Antonio Luiz P. , Kramer Daniel B. , Waks Jonathan W. , Ng Fu Siong 
Published: June 2024

AbstractThe electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R2 of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p < 0.001; UKB: HR 1.58, p < 0.001) and diabetes mellitus (BIDMC: HR 1.25, p < 0.001; UKB: HR 2.28, p < 0.001) after adjusting for covariates including measured BMI. Significant enhancements in model fit, reclassification and improvements in discriminatory power were observed with the inclusion of delta-BMI in both cohorts. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits, including SCN10A, SCN5A, EXOG and RXRG. In summary, our AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.
Topological order from measurements and feed-forward on a trapped ion quantum computer
Authors: Iqbal Mohsin , Tantivasadakarn Nathanan , Gatterman Thomas M. , Gerber Justin A. , Gilmore Kevin , Gresh Dan , Hankin Aaron , Hewitt Nathan , Horst Chandler V. , Matheny Mitchell , Mengle Tanner , Neyenhuis Brian , Vishwanath Ashvin , Foss-Feig Michael , Verresen Ruben , Dreyer Henrik 
Published: June 2024

AbstractQuantum systems evolve in time in one of two ways: through the Schrödinger equation or wavefunction collapse. So far, deterministic control of quantum many-body systems in the lab has focused on the former, due to the probabilistic nature of measurements. This imposes serious limitations: preparing long-range entangled states, for example, requires extensive circuit depth if restricted to unitary dynamics. In this work, we use mid-circuit measurement and feed-forward to implement deterministic non-unitary dynamics on Quantinuum’s H1 programmable ion-trap quantum computer. Enabled by these capabilities, we demonstrate a constant-depth procedure for creating a toric code ground state in real-time. In addition to reaching high stabilizer fidelities, we create a non-Abelian defect whose presence is confirmed by transmuting anyons via braiding. This work clears the way towards creating complex topological orders in the lab and exploring deterministic non-unitary dynamics via measurement and feed-forward.
MEchain—A novel mode to improve blockchain's real‐time and throughput
Authors: Cao Yunwei , Yang Ting , Wang Yu , Mao Gang 
Published: June 2024

AbstractBlockchain trilemma is a considerable obstacle for today's decentralized systems. It is hard to achieve a perfect balance among decentralization, security, and scalability. Many popular blockchain platforms sacrifice scalability to preserve decentralization and security, resulting in low speed, reduced throughput, and poor real‐time performance (the time from transaction initiation to confirmation). Currently, there are several technologies, such as sharding, directed acyclic graph technology, sidechains, off‐chain state channels etc., that aim to improve throughput and real‐time performance. However, most of these solutions compromise the core feature of blockchain, which is decentralization, and introduce new security risks. In this paper, the authors propose a novel method, called MEchain, based on the Proof of Time Series Algorithm. MEchain consists of two models: the multi‐chain model and the elastic‐chain model. The authors’ experimental results show that these two models can enhance real‐time performance and throughput to a higher level in the industry.
Event‐triggered adaptive output feedback control of unmanned aerial vehicles under hybrid attacks
Authors: Li Rui , Zhang Lei , Lu Yufeng , Yi Chenying , Chen Liangyuan , Su Yi 
Published: June 2024

AbstractThis study addresses the adaptive dynamic output feedback (DOF) control for unmanned aerial vehicle systems subjected to hybrid attacks. These attacks are delineated by two Bernoulli variables, signifying denial‐of‐service (DoS) and deception attacks, respectively. In an effort to enhance energy efficiency and reduce communication costs in the presence of network delays, the study introduces an innovative probability event‐triggering mechanism (PETM). Subsequently, it crafts a DOF controller that operates based on the available measurement outputs. By harnessing Lyapunov stability theory, the study identifies sufficient conditions that guarantee the stability of the system's augmented dynamics. Finally, the efficacy and applicability of the proposed method are underscored through a case study on an unmanned aerial vehicle.
Stereo sample generation‐based domain generalization network for stereo matching
Authors: Xu Liying , Zhu Jie , Peng Bo , Liu Bingzheng , Zhang Zhe , Lei Jianjun 
Published: June 2024

AbstractRecently, deep learning‐based stereo matching has achieved great success. However, models trained on the source domain dataset encounter substantial performance degradation when directly tested on an unseen target domain dataset because of neglecting the generalization to out‐of‐distribution (OOD) stereo samples. This paper proposes a stereo sample generation‐based domain generalization network (SGDG‐Net) for stereo matching. Specifically, to expand the distribution span of training samples, OOD stereo samples are generated to assist training. To effectively generate OOD left samples, a style transfer‐based generation mechanism is proposed to transmit perturbations to the source left samples. In addition, to generate the OOD right samples, a disparity‐assisted generation strategy is proposed by using disparity map labels as auxiliary information. Experimental results demonstrate that the proposed SGDG‐Net produces remarkable results on four benchmark datasets.
Probabilistic optimal power flow computation for power grid including correlated wind sources
Authors: Xiao Qing , Tan Zhuangxi , Du Min 
Published: June 2024

AbstractThis paper sets out to develop an efficient probabilistic optimal power flow (POPF) algorithm to assess the influence of wind power on power grid. Given a set of wind data at multiple sites, their marginal distributions are fitted by a newly developed generalized Johnson system, whose parameters are specified by a percentile matching method. The correlation of wind speeds is characterized by a flexible Liouville copula, which allows to model the asymmetric dependence structure. In order to improve the efficiency for solving POPF problem, a lattice sampling method is developed to generate wind samples at multiple sites, and a logistic mixture model is proposed to fit distributions of POPF outputs. Finally, case studies are performed, the generalized Johnson system is compared with Weibull distribution and the original Johnson system for fitting wind samples, Liouville copula is compared against Archimedean copula for modelling correlated wind samples, and lattice sampling method is compared with Sobol sequence and Latin hypercube sampling for solving POPF problem on IEEE 118‐bus system, the results indicate the higher accuracy of the proposed methods for recovering the joint cumulative distribution function of correlated wind samples, as well as the higher efficiency for calculating statistical information of POPF outputs.