bluuebunny/crossref_metadata_2025_split huggingface dataset:
Preparation of foam material via co-sintering of NaCl and PTFE for oil/water separation
Authors: Han Song , Wang Yanqing , xu yr 
Published: September 2023

Abstract
Bulk polytetrafluoroethylene (PTFE) foam is a great potential oil-water separation material, owing to the high hydrophobicity and lipophilicity property. However, the technique of fabricating PTFE foam with low cost, controllable porosity and high mechanical strength remains an urgent requirement due to its high chemical inertness induced limitation. A kind of bulk PTFE foam material is prepared by cold pressing co-sintering method, employing sodium chloride (NaCl) as pore forming agent. SEM measurements reveal that the size and distribution of pore structure are mainly determined by the particle size and content of NaCl, because the migration and diffusion behaviors of PTFE particles suffer from more inhibition induced by increased NaCl particles during sintering. The PTFE foam with uniformly distributed pore structures can be obtained from composite mixed with 70 wt% NaCl and 30 wt% PTFE, with a low thermal conductivity as low as 0.069 W/(m·K). With the decrease of PTFE content in raw composite, the density and mechanical properties of PTFE foam gradually declines. However, the tensile strength remains as low as 0.2 MPa, with an elongation rate of 21%, which guarantees the required mechanical strength avoiding being broken when use as a bulk oil absorption material. Remarkably, the PTFE foam shows a strong hydrophobicity, with a mean water contact angle of 137°, and separation efficiency more than 90%. The proposed bulk PTFE foam with low manufacturing cost and stable chemical and mechanical properties will provide data and theoretical support for the material applied in oil/water separation and thermal-protective coating.
Improved Integral Sliding Mode Control for Adaptive Synchronization of Delayed Recurrent Neural Networks
Authors: Xu Chun-Wei , Xiong Jing-Jing 
Published: September 2023

Abstract
This paper is devoting to addressing the problem of adaptive synchronization for recurrent neural networks with time-varying delays, in which the unknown parameters are taken into account. A new and simple integral sliding mode control (SMC) strategy is developed motivated by the previous idea of the existing literature. Based on drive-response concept, the synchronization error system is firstly derived such that the corresponding synchronization error signal can be applied to construct a simple integral sliding mode manifold. Then, an appropriate sliding mode controller with adaptive laws is designed such that the reachability of the predefined manifold is ensured and thereafter the synchronization problem between the response system and a drive system with unknown parameters is addressed. Finally, two numerical examples are given to substantiate the effectiveness of the presented control strategy.
Network pharmacology of Potentilla nepalensis extract revealed p53, Nf- kB1, and HSP proteins as potential biomarkers
Authors: Praveen Mallari , Yaseen Muhammad , Buendia Ricardo , Sayed Mian Gul , Bhat Mashooq A , Zeiden Noha I 
Published: September 2023

Abstract
Potentilla nepalensis belongs to the Rosaceae family, and have numerous therapeutic applications as potent plant-based medicine. Forty phytoconstituents (PCs) from the root and stem through n-hexane (NR and NS) and methanolic (MR and MS) extracts were identified in our earlier studies. However, the PCs affecting human genes and their roles in the body are not disclosed till now. In this study, we employed network pharmacology, molecular docking, molecular dynamics simulations (MDS), and MMGBSA methodologies. SMILES format of PCs from the PubChem used as input to DIGEP-Pred, 764 identified as the inducing genes. Their enrichment studies have shown inducing genes gene ontology descriptions, involved pathways, associated diseases, and drugs. PPI networks constructed in String DB and network topological analysing parameters done in Cytoscape v3.10 revealed three biomarkers, TP53 from MS, NR, and NS induced genes; HSPCB and Nf-kB1 from MR induced genes. From 40 PCs, two PCs 1b (MR) and 2a (MS), showed better binding scores (kcal/mol) with p53 protein of -8.6, and − 8.0; three PCs 3a, (NR) 4a and 4c (NS) with HSP protein of -9.6, -8.7, and − 8.2. MDS and MMGBSA revealed these complexes are stable without higher deviations with better free energy values. Biomarkers identified in this study, have a prominent role in numerous cancers. Thus, further investigations such as in-vivo and in-vitro should be done on considering the PCs of P.nepalensis.
A retrospective feasibility study of lymph node metastasis in submucosa gastric cancer based on the evaluation of pre- and intra-operative clinical-pathologic characteristics in two centers
Authors: Yu Suyue , Mei Yu , Huang Jingyi , Gao Jianpeng , Feng Tienan , Yan Min , Zhu Zhenggang , Lu Aiguo , Zhu Zhenglun 
Published: September 2023

Abstract
Background. This paper investigates whether or not there are some preoperative predictors identifying lymph node metastasis (LNM) in patients with T1b gastric cancer and discusses the possibility of replacement by segmental gastrectomy (SG) or local gastrectomy (LG).
Methods. This study included patients with gastric cancer who underwent radical gastrectomy between January 2012 and December 2018, as well as those diagnosed with pT1b. Independent risk factors of lymph node metastasis were determined using univariate and multivariable logistics regression among clinicopathological characteristics. ROC curve was established to predict lymph node metastasis based on external data for validation.
Results. Among the 636 patients with pT1b gastric cancer as the internal training set, 168 (26.4%) had no lymph node metastasis. In addition, 3 cm or less in size (odds ratio [OR] 0.56, P<0.05), middle or upper portion (OR 0.64, P<0.05), differentiation (OR 0.40, P<0.001), without lymphatic-vascular invasion (LVI) (OR 0.39, P<0.001), and the negative status of HER2 (OR 0.46, P<0.005), were protective factors of LNM. A score was generated consisting of a middle portion and four other variables. ROC curve analysis on the external validation set revealed excellent discrimination as the area under the curve (AUC) was 0.815. The frequency of lymph node metastasis was 0% with all five protective factors.
Conclusions. Based on this retrospective study, T1b gastric cancer of the middle part of the stomach which is 3 cm or less in size, differentiated-type, negative HER2 status, and without lymphatic-vascular invasion might indicate pylorus-preserving gastrectomy due to lack of lymph node involvement.
Trial registration. The study is retrospectively registered.
Coupled thermoelastic isotropic laminates
Authors: Vannucci Paolo 
Published: September 2023

Abstract
We consider in this paper the general properties of laminates designed to be isotropic in extension and in bending and with a coupling between the in- and out-of plane responses. In particular, we analyze the mathematical properties of the tensors describing the elastic and thermal behavior and the mechanical consequences of these properties. The differences, from the mathematical and mechanical point of view, between the hybrid laminates, i.e. composed by layers of different materials, and those made of identical plies, are pointed out and analyzed. The polar formalism for planar tensors is used in this study.
MSC Classification: 74A40 , 74B05 , 74E10 , 74E30 , 74K20.
Enhancing Aerial Human Action Recognition through GAN-boosted ResNeXt Architecture with Squeeze-and-Excitation Network
Authors: Kapoor Surbhi , Sharma Akashdeep , Verma Amandeep 
Published: September 2023

Abstract
Recognizing human actions in aerial videos captured by Unmanned Aerial Vehicles (UAVs) presents a significant challenge due to reduced resolution and blurry appearance of humans. To address this, we propose a novel two-module system, GAN-SE, that tackles these limitations and achieves remarkable improvements in human action recognition. The first module employs a super-resolution GAN to enhance the low-resolution images of detected humans. By generating high-resolution images, our system recovers lost information and significantly enhances the visual quality of the detections, thereby improving the accuracy of action recognition. In second module, we introduce a Squeeze and Excitation (SE) network combined with ResNeXt101 model to effectively calibrate the correlation between channel features. The SE network adaptively recalibrates feature responses in a channel-wise manner, enabling our model to focus on the most relevant information in feature maps. This leads to superior feature representation and more accurate predictions. To evaluate the performance of GAN-SE, extensive experiments were conducted on three challenging datasets: Aeriform in-action, UCF-ARG, and Okutama-Action. The results demonstrate the effectiveness of our system, achieving an accuracy of 80.78%, 97.36%, and 77.50% on the respective datasets. These results outperform the state-of-the-art methods, reaffirming the superiority of GAN-SE in aerial human action recognition.
On a "keys-in-memory" approach to storage management and querying in object-oriented databases
Authors: Kozynchenko Alexander , Kozynchenko Sergey 
Published: September 2023

Abstract

The paper concerns the NoSQL object-oriented databases intended for processing the highly structured and interrelated data. These databases are characterized by the navigational access and non-declarative interface. To improve the efficiency of databases of this class it is proposed to have a database object divided into two parts. The “lightweight” part placed in RAM contains both own and referential integer identifiers (IDs), whereas a “heavyweight” part with the bulk of data remains on a disk, each in a separate file with the unique file name including an object ID. Such file names provide the quick initial navigation over the set of persistent objects. The eventual deeper search does not require reading an entire object, thus providing high query rates regardless of the amount of data stored. This approach results in reduced memory usage and increased speed. A case study of a desktop student registration system written in C + + shows the feasibility and effectiveness of the approach.
Prediction Method of Longitudinal Surface Settlement Caused by Double Shield Tunnelling Based on Deep Learning
Authors: Shang Wentao , Li Yan , Wei Huanwei , Qiu Youbao , Chen Chaowei , Gao Xiangrong 
Published: September 2023

Abstract
The accurate prediction of longitudinal surface settlement caused by shield excavation is crucial for ensuring the safety of the surrounding environment. This study optimally selects the slime mould algorithm (SMA) to optimize the hyperparameters of a random forest (RF), and uses SMA-RF to reduce the dimensionality of high-dimensional shield operational parameters. A double-input deep neural network (D-DNN) framework is proposed to predict the surface settlement caused by shield excavation. It includes two branches that obtain data from the left and right double-line tunnels as inputs. The performance of the D-DNN is evaluated by comparing it with that of a single-input deep neural network (S-DNN) framework. The results show that the SMA-RF effectively reduces the dimensionality of high-dimensional shield operational parameters by utilising a set of feature contributions and more than 90% of the parameters, thereby yielding highly precise prediction results; the dimension-reduced shield operational parameters following SMA-RF exhibit a strong nonlinear relationship with the surface settlement；the D-DNN considers double-line shield excavation and expands the database capacity, which is more than 1.5 times greater than that of the S-DNN, thereby enhancing the training effectiveness and prediction accuracy.
Ectoparasites in Small Mammals of Chilalo-Galama Mountains Range: Zoonotic Disease Transmission and One Health Significance
Authors: Kasso Mohammed 
Published: September 2023

Abstract

Ectoparasites play a vital role in the transmission of zoonotic diseases and are an essential component of the ecosystem. However, very few studies have been conducted on the ectoparasites of small mammals in Ethiopia, particularly in the Chilalo-Galama Mountains Range. Therefore, this study aimed to determine the species composition, prevalence, distribution, and small mammal host interaction of ectoparasites in the study area. Different traps were used to capture small mammals from where ectoparasites were recovered. The collected ectoparasites were identified to families, genera, and species levels. The parameters like prevalence (P), Abundance (A), mean abundance (MA), Relative Abundance (RA), mean intensity (MI) and constituent ratio (C) were used for the analysis. The study identified disease-carrying ectoparasites based on presence absence of parasite for public and domestic animal health risks. Out of the 204 small mammals examined, 150 were infected by one or more groups of ectoparasites, with an overall prevalence accounting for 73.53%. Out of these individuals the majority 35 (17.16%) were infested by Myobiidae mites followed by Ambyloma 29 (14.22%) and Polyplax 21 (10.29%). Myobiidae mites infested half of the 28 species of hosts examined, followed by Ambyloma (9) and Polyplax(7) species. A total of 1993 individuals of ectoparasites that belonged to 24 taxa in 5 different groups namely flea, lice, tick, mite and flies were recorded. Flea and tick each had five genera, louse six genera, mite six families and bat flies two families were collected from small mammals examined for ectoparasite. Several ectoparasite taxa which have the potential to pose zoonotic disease risks to humans and domestic animals were recorded. The findings underscore the need for further research and surveillance to understand the impact of ectoparasites on small mammal populations and to develop effective strategies for disease control and prevention by promoting the one health approach.
Oscillatory motions of multiple spikes in three-component reaction-diffusion systems
Authors: Xie Shuangquan , Yang Wen , Zhang Jiaojiao 
Published: September 2023

Abstract

For three specific singular perturbed three-component reaction-diffusion systems that admit $N$-spike solutions in one of the components on a finite domain, we present a detailed analysis for the dynamics of temporal oscillations in the spike positions. The onset of these oscillations is induced by N Hopf bifurcations with respect to the translation modes that are excited nearly simultaneously. To understand the dynamics of N spikes in the vicinity of Hopf bifurcations, we combine the center manifold reduction and the matched asymptotic method to derive a set of ordinary differential equations (ODEs) of dimension 2N describing the spikes' locations and velocities, which can be recognized as normal forms of multiple Hopf bifurcations. The reduced ODE system then is represented in the form of linear oscillators with weakly nonlinear damping. By applying the multiple-time method, the leading order of the oscillation amplitudes is further characterized by an N-dimensional ODE system of the Stuart-Landau type. Although the leading order dynamics of these three systems are different, they have the same form after a suitable transformation. On the basis of the reduced systems for the oscillation amplitudes, we prove that there are at most ⌊ N/2 ⌋ +1 stable equilibria, corresponding to ⌊ N/2 ⌋ +1 types of different oscillations. This resolves an open problem proposed by Xie et al. (Nonlinearity, 34 (2021), pp. 5708-5743) for a three-component Schnakenberg system and generalizes the results to two other classic systems. Numerical simulations are presented to verify the analytic results.
Mathematics Subject Classification: 37L10, 35K57, 35B25, 35B36
The use of tools for automatic proof of mathematical logic theorems for modeling the dynamic balance of minerals in the human body
Authors: Kondratenko Viktoria , Slovianov Leonid 
Published: September 2023

Abstract

Computer scientists build a logical framework for a particular subject or phenomenon. This methodology involves the use of axioms, which are self-evident statements that serve as the basis for a logical system. Axiomatic modelling begins by identifying the relevant concepts and objects that will be studied and then defining these concepts using axioms. These axioms are chosen based on their simplicity, consistency, and logical power, and are used to build a series of logical statements or theorems that describe the behaviour of the system being studied. This method allows researchers to build a clear and logical foundation for their work and allows them to prove the validity of their results through logical deduction. It is often used in mathematics and computer science, but can also be applied to other fields such as economics, physics, social science, biology and medicine. We proposed an extension of the method by incorporating knowledge about physiological processes in the human body by introducing biomedical parameters and logic and developed the novel method of Logical Dialectical Modelling (LDM). This original methodology uses, as tools, the logic of predicates of the 1st order and the Robinson method of automatic theorem proving. It prevents errors and simplifies the process of proving statements. The first time we applied LDM for the problem of providing the human body with the necessary dynamic balance of minerals. We analysed data [1,44,45] on the dependence of disease symptoms on the values of quantitative indicators of the concentration of minerals in the hair of children in the Chornobyl zone,LDM can provide a structured, logical approach to diagnostics that can help identify the root causes of problems and guide more effective treatment planning. LDM can be used together with artificial intelligence (AI) systems to improve the accuracy and efficiency of diagnostic processes. LDM is based on logical statements, they can be tested and refined using a rigorous, mathematical approach, which can help to increase the reliability and accuracy of the models. This can be useful for doctors in evaluating patients and making accurate diagnoses promptly. The purpose of the article is to demonstrate the use of LDM through in medicine too.
Extended Random Histogram Forest for Unsupervised Anomaly Detection
Authors: Ansarirad Pouyan , Hashemi Sattar 
Published: September 2023

Abstract
Anomaly detection is a fundamental task in the field of unsupervised machine learning, aimed at identifying instances that significantly deviate from other input data. This problem has various applications, including identifying defective products in industries, detecting network intrusions, medical diagnostics, and many other cases. Despite extensive research conducted in this field, a solution with satisfactory performance under all conditions and types of data has not yet been achieved. One effective unsupervised method is the Random Histogram Forest (RHF) approach, which utilizes a probabilistic approach. Based on the evaluation metric of average precision of the area under the precision-recall curve (AP), this approach has shown better performance compared to other methods in terms of AP. However, due to its inherent mechanism, this approach also has limitations. In this article, these limitations are examined, and solutions and methods are proposed to address these constraints, and an extension approach called Extended Random Histogram Forest (ERHF) is introduced for unsupervised anomaly detection. ERHF utilizes data projection and dimensionality reduction methods to enhance the compatibility between the data and the internal mechanism of RHF. Instead of randomly selecting splitting points in the process of constructing random trees, the Skewness score is used to choose more targeted splitting points. Additionally, multidimensional hyperplanes with random slopes are used for data partitioning instead of pointwise splits. Furthermore, the mechanism for calculating the anomaly score of instances is modified to enable the use of subsampling. The performance evaluation of the proposed ERHF method using various metrics (AP, AUC, Recall, F1) on ODDS datasets demonstrates that ERHF significantly outperforms the RHF method.
Net benefit of smaller human populations to environmental integrity and individual health and well-being
Authors: Saraswati Chitra Maharani , Judge Melinda A. , Weeda Lewis J. Z. , Prata Ndola , Bassat Quique , Souëf Peter N. Le , Bradshaw Corey J. A. 
Published: September 2023

Abstract
Background
The global human population is still growing such that our collective enterprise is driving environmental catastrophe. Despite a decline in average population growth rate, we are still experiencing the highest annual increase of global human population size in the history of our species — averaging an additional 84 million people per year since 1990.
Objectives
No review to date has accumulated the available evidence describing the associations between increasing population and environmental decline, nor solutions for mitigating the problems arising. We summarise the available evidence of the relationships between human population size and growth and environmental integrity, human prosperity and well-being, and climate change.
Methods
We used PubMed, Google Scholar, and Web of Science to identify all relevant peer-reviewed and grey-literature sources examining the consequences of human population size and growth on the biosphere.
Results
We reviewed papers describing and quantifying the risks associated with population growth, especially relating to climate change. These risks are global in scale, such as greenhouse-gas emissions, climate disruption, pollution, loss of biodiversity, and spread of disease — all potentially catastrophic for human standards of living, health, and general well-being. The trends increasing the risks of global population growth are country development, demographics, maternal education, access to family planning, and child and maternal health.
Discussion
Support for nations still going through a demographic transition is required to ensure progress occurs within planetary boundaries and promotes equity and human rights. Ensuring the well-being for all under this aim itself will lower population growth and further promote environmental sustainability.