bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using Explainable Machine Learning to Predict Compressive Strength of Blended Concrete: A Data-Driven Metaheuristic Approach
Authors: Kashifi Mohammad Tamim , Salami Babatunde Abiodun , Rahman Syed Masiur , Alimi Wasiu 
Published: June 2023

Abstract
In this study, we use highly developed machine learning techniques to accurately estimate the compressive strength (CS) of blended concrete, considering its composition, including cement, SCMs (ground granulated blast furnace slag (GGBFS) and fly ash (FA)), water, superplasticizer, fine/coarse aggregate, and curing age. In addition to these, we examine an array of models, including XGBoost, decision trees (DT), deep neural networks (DNN), and linear regression (LR). Among them, XGBoost has the best performance in every category. We use the Bayesian optimization method for hyperparameter fine-tuning to improve forecast accuracy. Our in-depth examination demonstrates the better predictive skills of ensemble models like RF and XGBoost over LR, which is limited in its ability to capture data complexity beyond linear relationships. With an R2 of 0.952, RMSE of 4.88, MAE of 3.24, and MAPE of 9.94%, XGBoost performs noticeably better than its rivals. Using SHAP analysis, we determine that curing age, water content and cement concentration constitute the main factors influencing the predictive capacity of the model, with the contributions of superplasticizer and fly ash being minimal. Curing age and cement content have an interesting positive association with CS, but water content has a negative link with CS. These results highlight the value of machine learning, and more especially the effectiveness of XGBoost, as a potent device for forecasting the CS of mixed concrete. Additionally, the knowledge gained from our research provides designers and researchers in the field of concrete materials with useful direction, highlighting the most important factors for compressive strength. Future studies should work toward additional optimization by attempting to verify these models across a wider variety of concrete compositions and test settings.
Immune enhancement effects of neutral lipids, glycolipids, phospholipids from Halocynthia aurantium tunic on RAW264.7 macrophages
Authors: Jang A-yeong , Rod-in Weerawan , Park Woo Jung 
Published: June 2023

Abstract
The fractionated lipids of Halocynthia aurantium (Pyuridae) have been demonstrated to possess anti-inflammatory properties, but their modulatory properties have not been studied. This study aimed at estimating the immune enhancing effects of fractionated lipids from H. aurantium tunic on macrophage cells. The tunic of H. aurantium was used to isolate total lipids, which were then subsequently separated into neutral lipids, glycolipids, and phospholipids. RAW264.7 cells were stimulated with different concentrations (0.5, 1.0, 2.0, and 4.0%) of each fractionated lipid. The cytotoxicity, the production of NO, the expression of immune-associated genes, and signaling pathways were performed. The neutral lipids and glycolipids significantly stimulated the production of NO and the expression of IL-1β, IL-6, TNF-α and COX-2 in a dose-dependent manner, while phospholipids ineffectively induced NO production and mRNA expression. Furthermore, it was found that both the neutral and glycolipids increased NF-κB p-65, p38, ERK1/2 and JNK phosphorylation, suggesting that these lipids may enhance immunity by activating the NF-κB and MAPK signaling pathways. In addition, H. aurantium lipids-induced TNF-α expression was also decreased by blocking the MAPK or NF-κB signaling pathways. These results suggest that the neutral lipids and glycolipids from H. aurantium tunic have potential as a source of immune-enhancement materials.
Multi-objective optimization of cloud task scheduling based on improved deep Q-learning algorithm
Authors: Li Zongru , Guan Peiqi , Dong Yanqiao , Yang Yongquan 
Published: June 2023

Abstract
With the rapid development of cloud computing technology, although cloud computing can provide flexible resources and pay on demand mode, the problem of high energy consumption is still an urgent problem for cloud service providers to optimize. The cloud task scheduling problem of how to reduce operational costs while improving the quality of cloud services has become a research focus of cloud computing technology. In general, researchers will reduce power consumption, optimize load balancing,improve response speed and other aspects of cloud task scheduling algorithm for multi-objective optimization. At present, there are many optimization algorithms, such as traditional scheduling algorithms and heuristic algorithms, but these algorithms are unable to cope with the dynamic online complex cloud environment, resulting in the failure of multi-objective optimization or the high cost and poor effect of multi-objective optimization. However,deep reinforcement learning shows a better effect on multi-objective optimization of cloud task scheduling. Deep reinforcement learning offers new research ideas for cloud task scheduling. In this paper, based on the D3QN algorithm and noise network, NoisyD3QN algorithm is improved,which increases the exploration ability of reinforcement learning model and increases the upper limit of performance. Meanwhile, the NoisyD3QN algorithm-based cloud task scheduling model is constructed using the power consumption model proposed by FAN for simulation experiments. Comparing the performance of the eight algorithms on different optimization targets, the experiments show that the proposed algorithm effectively reduces the cluster power consumption, improves response speed, reduces user waiting time, and optimizes load balancing, Significantly reduces the average standard deviation of cluster CPU utilization.
Elevated Serum Interleukin-6 Levels Lead to Poorer Treatment Outcomes in Patients with Metastatic Colorectal Cancer, a Pathway That Can Be Interrupted by Bevacizumab
Authors: chen Yu , Cai Hao , Liu Yang , Zhang Qiao , Jia HouJun 
Published: June 2023

Abstract
Purpose: To evaluate the relationship between serum IL-6 concentration and the efficacy of anti-angiogenic therapy in patients with liver metastases from colorectal cancer.
Methods: Serum IL-6 and CEA levels were retrospectively analyzed in 53 patients with colorectal cancer with liver metastases at first diagnosis, and the optimal cut-off values for these data were 4.83 ng/ml and 4.85 ng/ml, respectively, according to the ROC curve; the median follow-up time was in the range of 16 months (6-53 months), and the diameters of liver metastases were recorded at the first diagnosis and two months after the start of the first treatment. All 53 patients were found to have colorectal cancer combined with liver metastases at the first visit, and none of the metastases were treated surgically (including arterial catheter embolization). Twenty-six patients underwent resection of the primary lesion to reduce tumor load, 24 received oxaliplatin-based chemotherapy alone, and 29 received chemotherapy including bevacizumab. Overall survival (OS) and progression-free survival (PFS) were estimated using Kaplan-Meier and multivariate Cox proportional risk regression analysis.
Results: The chi-square test verified that high serum IL-6 levels were significantly associated with lower treatment efficiency in patients treated with chemotherapy alone. However, this correlation can not be observed in patients treated with bevacizumab.
Conclusion: IL-6 can VEGF-dependently promote tumor angiogenesis; and anti-angiogenic therapy can eliminate the adverse effects of high levels of il-6 in patients with advanced colorectal cancer.
Somatostatin receptor 2A expression in Von Hippel-Lindau-related hemangioblastomas
Authors: Ahmad Saya , Muhlebner Angelika , Snijders Tom J. , Leng Wendy W. , Seute Tatjana , Leeuwaarde Rachel Sera 
Published: June 2023

Abstract
Purpose.
Central nervous system hemangioblastomas are the most prevalent manifestation of Von Hippel-Lindau (VHL) disease and remain the main cause of mortality. Surgical resection is the primary treatment strategy, but is not always possible, and should be used as restrictively as possible. There is an unmet need for less invasive treatment strategies, such as targeted therapy. Expression of somatostatin receptor 2A (SSTR2A) in VHL-related hemangioblastomas has been described earlier, but the extent of expression in a larger population has yet to be determined. We hypothesize that a substantial subset of VHL-related hemangioblastomas show SSTR2A expression, which may serve as a potential new treatment target.
Methods.
Patients who were surgically treated for a VHL-related hemangioblastoma from 1990 until 2021 at the UMC Utrecht were included. Clinical data was derived from a clinical database. Tissue samples were histopathologically examined with use of hematoxylin and eosin staining, and immunohistochemical analysis of SSTR2A expression was performed.
Results.
Forty-three tissue samples were obtained from 26 patients. Nine showed strong positivity for SSTR2A expression, while 13 showed moderate and 15 sparse expression. Three samples showed no expression of SSTR2A. The distribution showed right-skewedness favoring a strong expression. SSTR2A expression co-localized with endothelial markers and not with stromal cells. Additionally, within-patient variability for SSTR2A expression was described in 14 patients.
Conclusion.
SSTR2A is expressed in varying degrees in the majority of VHL-related hemangioblastomas. Future treatment with somatostatin analogues or even peptide receptor radionuclide treatment may be considered for SSTR2A-positive cases.
Synthesis of multi-stakeholder run time dynamic digital twins and dynamic digital twins networks
Authors: Delhibabu Radhakrishnan , Vodyaho Alexander , Ignatov Dmitry I , Zhukova Nataly , Chervoncev Michael 
Published: June 2023

Abstract
The article describes an approach to the construction of complex distributed cyber-physical systems with a high level of architectural dynamics built on fog and edge computing platforms. The key idea of the developed approach is to use digital twins as dynamic models of the observed and managed systems, which are kept up-to-date by processing the event flow received in the form of logs. A reference architecture of a dynamic runtime digital twin is proposed. The possible approaches to synthesis of models that form the basis of digital twins are discussed. Examples of using the proposed approach to solve practical problems are given. The described approach may be of interest to specialists engaged in research and development of different kinds of information systems realized on 1 the IoT platforms such as smart cities, smart transport, medical information systems, etc.
Shenhuang Plaster Enhances Intestinal Anastomotic Healing in Rabbits Through Activation of the TGF-β and Hippo/YAP Signaling Pathways
Authors: Xiao Fengchun , Zhu Chenchen , Wei Xing , Chen Guiping , Xu Xiaohong 
Published: June 2023

Abstract

Although many efforts have been made to improve management strategies and diagnostic methods in the past several decades, the prevention of anastomotic complications, such as anastomotic leaks and strictures, remain a major clinical challenge. Therefore, new molecular pathways need to be identified that regulate anastomotic healing and to design new treatments for patients after anastomosis to reduce the occurrence of complications. Rabbits were treated with a MST1/2 inhibitor XMU-XP-1, a Chinese medicine formula Shenhuang Plaster (SHP) or a control vehicle immediately after surgery. The anastomotic burst pressure, collagen deposition and hydroxyproline concentration were evaluated at 3 and 7 days after the surgery, and qRT-PCR and western-blot analyses were used to characterize mRNA and protein expression levels. Both XMU-XP-1 and SHP significantly increased anastomotic burst pressure, collagen deposition and the concentration of hydroxyproline in intestinal anastomotic tissue at postoperative day 7 (POD 7). Importantly, SHP could induce TGF-b1 expression, which activated its downstream target Smad-2 to activate the TGF-b1signaling pathway. Moreover, SHP reduced the phosphorylation level of YAP and increased its active form, and treatment with verteporfin, a YAP-TEAD complex inhibitor, significantly suppressed the effects induced by SHP during anastomotic tissue healing. This study demonstrated that activation of the Hippo-YAP pathway enhances anastomotic healing, and that SHP enhances both the TGF-b/Smad and YAP signaling pathways to promote rabbit anastomotic healing after surgery. These results suggest that SHP could be used to treat patients who underwent anastomosis to prevent the occurrence of anastomotic complications.
Fingerprints as Indicators of Craftworkers’ Age and Sex in a Sample of Clay Ushabtis from TT 209, Luxor, Egypt
Authors: Gutiérrez-Redomero Esperanza , Herrerín Jesús , Polo Miguel Ángel Molinero 
Published: June 2023

Abstract

This article analyses fingerprints identified on a set of backed clay Egyptian funerary figurines, known as ushabtis. The strata in which they were found dates from the late Persian to the early Ptolemaic dynasties (ca. fifth to third centuries BCE), but the objects might have been made some two centuries earlier, during the Twenty-Fifth Dynasty, and then reused for a later burial. The authors propose that fingerprints preserved on ancient objects can be used to understand the roles that age and sex played in the organization of production systems in ancient societies. The Mean Ridge Breadth (MRB) and Mean Ridge Density (MRD) of each discrete fingerprint was measured. The craftworkers´ ages and heights were calculated from the MRB using specific regression equations, and their probable sex was determined by correlating the MRD with that of the reference population (contemporary Sudan). An age/sex identification matrix was then created to combine the results of MRB and MRD determinations for each piece. The results show, with high probability, that around 90% of the ushabtis were made by female adolescents between the ages of 11 and 16, and the remainder by children and some adult women. Between four and eight craftworkers manufactured the ushabtis, and due to their age and the relatively low level of skill required, it is proposed that they are likely to have been in the learning phase of their trade.
Evaluation of Gross Ecosystem Product in Taiwan Island and Analysis of Impact of Urbanization
Authors: Zhang Weijie , Luo Kunyu , Yan Zhenyu , Fan Yu , Song Youtao 
Published: June 2023

Abstract
Gross Ecosystem Product (GEP) refers to the total value of products and services provided by ecosystems to human beings. With the emergence of the concept of gross ecosystem product, many researchers have continuously started calculating and analyzing it around the world. However, few scholars calculated the gross ecosystem product of Taiwan. This paper constructs a framework for GEP accounting in Taiwan based on land use information, rainfall conditions, soil data, statistical yearbooks, and the results of domestic and foreign research teams. The gross ecosystem product of the main island terrestrial ecosystem of Taiwan in 2000, 2010, and 2020 was calculated while we analyzed the relevance between urbanization level and GEP. In addition, we also analyze the Green Gold Index(GGI) and the correlation between ecosystem service functions. The relative results can be listed as follows. The GEP of the terrestrial ecosystem of Taiwan's main island in 2000, 2010, and 2020 were $104.12 billion, $97.66 billion, and $82.28 billion, respectively. And the overall distribution of GEP on Taiwan Island is uneven. Plus, The GGI of majority areas in Taiwan Island is less than 1 and shows a downward trend. In addition, Synergy relationship are the dominant type between ecosystem service functions in Taiwan Island, 18 groups are obviously correlated at the level of 0.05 and 4 groups are obviously positive correlation at the level of 0.01. Finally, the relationship between the three indicators of EPS, ERS, GEP and urbanization shows ༂inverse U༂ shape, and ETS is positively correlated with urbanization. The calculation results of GEP and analysis of correlation between GEP and urbanization can provide advice for sustainable development of Taiwan and reference for GEP accounting of other islands.
Determining Student Demographic Attributes Influencing Performance Using Binary Classification in KDP Model
Authors: Issah Iddrisu , Appiahene Peter , Appiah Obed , Inusah Fuseini 
Published: June 2023

Abstract
Machine learning (ML) is one way that can help decipher the intricate relationship between students' data and their performance. When implemented correctly in learning environments, machine learning will improve knowledge of fundamental processes by simplifying the identification, extraction, and evaluation of underlying factors that affect student learning and levels of achievement. This study employed the experimental research approach using binary classification techniques based on the six-step Knowledge Discovery Process (KDP) model. Five classifiers were used within the Rapid Miner's 9.10.010 educational environment as both experimental and analytical tool. The dataset comprised of 2334 records, 17 attributes with one class variable (students’ semester average score) inclusive. Twenty different tests were conducted. The experiments' results were evaluated using 10-fold cross-validation and ratio split validation with bootstrap sampling. The Random Forest algorithm (RF), Rule Induction methods (RI), Naive Bayes (NB), Logistic Regression (LR) and Deep Learning (DL) algorithms were used in the experiment. The experimental results demonstrated that the RF method outperforms the other four techniques in all six-evaluation metrics that were employed for the selection process with the accuracy being 93.96%. According to the RF classifier model, the mother's and father's education levels of students are two recognized demographic factors per this study that significantly influence pre-tertiary students’ academic achievement. This study has significantly reduced the gap in practical knowledge observed in the literature by introducing an intervention scheme for respective student's requiring intensive or minimal academic interventions in its prediction procedure.
A numerical solution on pore diffusion equation with a volume of limited bath boundary condition using the finite difference method
Authors: Kumar Keshav 
Published: June 2023

Abstract
Numerical approaches have long been used to examine material behaviors that are governed by diffusion. The surface water and groundwater become polluted with regard to time. The pollutants travel from place to place in extended time intervals interacting with the solid soil particles. The diffusion of solute with solid soil particles idealized as spheres are studied in this dissertation. The objective was to numerically simulate diffusion through a sphere. A steady-state finite difference method was used to study the diffusion in a sphere. It was assumed that a bulk liquid solution interacts with solid spheres through a diffusion limited process which is equivalent to a volume of limited bath boundary condition. A sphere of radius 0.07 cm is placed in the beaker containing the solution. The radius of the sphere is divided into 15 nodes each of radius 0.005 cm. Initially, the sphere is free from the solute. As time progresses the diffusion in the sphere takes place. The solute from outside the sphere gets infused in the sphere. By using a numerical technique, the concentration at different nodes at different time intervals is studied. The process is carried out for two different solutions with different concentration values.
Vicatia thibetica de Boiss: Botany, Traditional Uses, Phytochemistry,
Quantitative Analysis, and Pharmacology
Authors: Wang Qin-Yuan , He Neng-Xin , Qiu Yong-Wei , Jiang Wei , Zhong Guo-Yue , Sang Zhi-Pei , Ma Qin-Ge , Wei Rong-Rui 
Published: June 2023

Background:
Vicatia thibetica de Boiss is a common Tibetan medicine used for both
medicine and food, belonging to the family Apiaceae. This plant has the functions of dispelling
wind, removing dampness, dispersing cold, and relieving pain. It has great development potential
and application prospects in food development and medicinal value.

Methods:
The related references on botany, traditional uses, phytochemistry, quantitative analysis,
and pharmacology of V. thibetica de Boiss had been retrieved from both online and offline databases,
including PubMed, ScienceDirect, Web of Science, Elsevier, Willy, SpringLink, SciFinder,
Google Scholar, Baidu Scholar, ACS publications, SciHub, Scopus, and CNKI.

Results:
V. thibetica de Boiss exerts nourishing, appetizing, and digestive effects according to the
theory of Tibetan medicine. Phytochemical reports have revealed that V. thibetica de Boiss contains
flavonoids, coumarins, sterols, and organic acids. Meanwhile, the quantitative analysis of the
chemical constituents of V. thibetica de Boiss has been done by means of UPLC-Q-TOF-MS. It
has also been found that V. thibetica de Boiss possesses multiple pharmacological activities, including
anti-fatigue, anti-oxidant, anti-aging, and non-toxic activities.

Conclusion:
This paper has comprehensively summarized botany, traditional uses, phytochemistry,
quantitative analysis, and pharmacology of V. thibetica de Boiss. It will not only provide an important
clue for further studying V. thibetica de Boiss, but also offer an important theoretical basis
and valuable reference for in-depth research and exploitation of this plant in the future.
Invisible Insecurity
Authors: Adlakha-Hutcheon Gitanjali 
Published: June 2023

On May 18, 2023, Dr. Gitanjali Adlakha-Hutcheon, presented Invisible Insecurity. The presentation was followed by a question-and-answer period with questions from the audience and CASIS Vancouver executives. The key points discussed were the multifocal conceptualization and understanding of security, the current and the not quite so visible disruptors of security, and the ways in which these disruptors can be addressed at the individual and collective levels.
Received: 2023-05-21
Revised: 2023-05-29