bluuebunny/crossref_metadata_2025_split huggingface dataset:
Changes in temperature-precipitation correlations over Europe: Are climate models reliable?
Authors: Vrac Mathieu , Thao Soulivanh , Yiou Pascal 
Published: October 2021

Abstract
Inter-variable correlations (e.g., between daily temperature and precipitation) are key statistical properties to characterise probabilities of simultaneous climate events and compound events. Their correct simulations from climate models, both in values and in changes over time, is then a prerequisite to investigate their future changes and associated impacts.

Therefore, this study first evaluates the capabilities of one 11-member multi-model ensemble (CMIP6) and one 40-member multi-run single model ensemble (CESM) over Europe to reproduce the characteristics of a reanalysis dataset (ERA5) in terms of temperature-precipitation correlations and their historical changes. Next, the ensembles’ correlations for the end of the 21st century are compared to assess the robustness of the future correlation changes.

Over historical period, both CMIP6 and CESM ensembles have season-dependent and spatially structured biases. Moreover, the inter-variable correlations from both ensembles mostly appear stationary. Thus, although reanalyses display significant correlation changes, none of the ensembles is able to reproduce them. However, future correlations show significant changes over large spatial patterns. Yet, those patterns are rather different for CMIP6 and CESM, reflecting a large uncertainty in changes.

In addition, for historical and future projections, an analysis conditional on atmospheric circulation regimes is performed. The conditional correlations given the regimes are found to be the main contributor to the biases in correlation over the historical period, and to the past and future changes of correlation.

These results highlight the importance of the large-scale circulation regimes and the need to understand their physical relationships with local-scale phenomena associated to specific inter-variable correlations.
The Effects of Macrophages Condition Media on the Vessel-Like Structure Formation of Pre-Vascularized Bone Marrow Mesenchymal Stem Cell Sheets
Authors: Long Siqi , Ren Lina , Liu Xiaoning , Shi Jiamin , Liu Jiaxin , Ma Dongyang , Zhou Ping , Ren Liling 
Published: October 2021

Abstract
Background With the development of tissue engineering and regenerative medicine, engineered tissue constructs have become more prevalent and are now used as substitutions of patients’ damaged organs or tissues. To date, a scaffold-free cell transplantation technique based on cell sheets have been widely used in clinic. While poor vascularization and adverse immune reactions to biomedical devices remain crucial challenges in tissue engineering. The inflammatory response represents one of mainly and important regulators of vascularization and engineered tissue function restoration. This study investigated the effect of macrophage secretion on the formation of microvascular-like structures of human umbilical vein endothelial cells (HUVECs) in the bone marrow mesenchymal stem cell (BMSC) sheets. Methods Researchers differentiated the human monocytic leukemia cell line (THP-1) cells into the resting state of macrophage (M0) by phorbol 12-myristate 13-acetate (PMA) and further activated them into pro-inflammation and pro-healing states with the use of various cytokines. Condition media derived from kinds of macrophages were used to investigate the impact of macrophage on the viability and arrangement of HUVECs on BMSC sheets. Cytokines related to angiogenesis were detected in the culture supernatant of HUVECs, BMSC sheets, and pre-vascularized sheets. Results Results indicated that macrophages may guide the arrangement of endothelial cell through the paracrine pathway. The cell sheets that were cultured in the microenvironment with pro-inflammatory macrophages had fewer cell layers compared to those generated in other media. Furthermore, after experiencing high levels of vascular endothelial growth factor (VEGF) and tumor necrosis factor alpha (TNF-α) in the first 3 days while high level of platelet-derived growth factor (PDGF)-BB but low TNF-α and VEGF concentration in the next 4 days, pre-vascularized sheets had the most significant micro-vessel network. Conclusions The fates of pre-vascularized sheets regulated by macrophages secreted factors in microenvironment and sequential released cytokines could even promote neovascularization and angiogenesis.
Impact of ICT on Environment
Authors: Mahdavi Samaneh , Sojoodi Sakineh 
Published: October 2021

Abstract
The impact of information and communications technologies (ICT) on the environment is a complex highly-debated subject. Indeed, ICT can have both positive and negative impacts on the environment. While ICT tools and devices can be used to improve energy efficiency, which results in reduced CO2 emissions and environmental degradation, the manufacturing and use of ICT devices can become a major source of emission. Also, many ICT devices contain non-renewable and non-recyclable components that can cause significant environmental damage. Therefore, one may question that whether ICT improves environmental quality in countries with different income levels? To answer this question, this study investigated the environmental impacts of ICT in three groups of high, middle, and low-income countries from 2005 to 2019 using the Generalized Method of Moments (GMM). The ICT Development Index (IDI) was used as the measure of ICT development. Empirical results showed that the use of ICT led to reduced total CO2 emissions, CO2 emissions from solid fuel consumption, CO2 emissions damage, particulate emissions damage, and energy consumption in the studied countries. Therefore, ICT was found to have a generally negative (favorable) impact on environmental degradation in these countries. Considering this effect of ICT on environmental degradation and pollution, governments are recommended to pursue their energy consumption and emission objectives by promoting the use of ICT in the environmental sector and the implementation of green ICT projects.
Evaluation of Landslide Susceptibility of the Ya'an-Linzhi Section of the Sichuan-Tibet Railway based on Deep Learning
Authors: Wang Shibao , Zhuang Jianqi , Mu Jiaqi , Zheng Jia , Zhan Jiewei , Wang Jie , Fu Yuting 
Published: October 2021

Abstract
The Qinghai-Tibet Plateau is one area with the most frequent landslide hazards due to its unique geology, topography, and climate conditions, posing severe threats to engineering construction and human settlements. The Sichuan-Tibet Railway that is currently under construction crosses the Qinghai-Tibet Plateau; there are frequent landslide disasters along the line, which seriously threaten the construction of the railway. This paper applied two deep learning (DL) algorithms, the convolutional neural network (CNN) and deep neural network (DNN), to landslide susceptibility mapping of the Ya’an-Linzhi section of the Sichuan-Tibet Railway. A geospatial database was generated based on 587 landslide hazards determined by Interferometric Synthetic Aperture Radar (InSAR) Stacking technology, field geological hazard surveys, and 18 landslide influencing factors were selected. The landslides were randomly divided into training data (70%) and validation data (30%) for the modeling training and testing. The Pearson correlation coefficient and information gain method were used to perform the correlation analysis and feature selection of 18 influencing factors. Both models were evaluated and compared using the receiver operating characteristic (ROC) curve and confusion matrix. The results show that better performance in both the training and testing phases was provided by the CNN algorithm (AUC = 0.88) compared to the DNN algorithm (AUC = 0.84). Slope, elevation, and rainfall are the main factors affecting the occurrence of landslides, and the high and very high landslide susceptibilities were primarily distributed in the Jinsha, Lancang, and Nujiang River Basins along the railway. The research results provide a scientific basis for the construction of the Ya'an-Linzhi section of the Sichuan-Tibet Railway within the region, as well as the disaster prevention and mitigation work during future safe operations.
CFDIL: A Context-aware Feature Deep Interaction Learning for App Recommendation
Authors: Hao Qingbo , Zhu Ke , Wang Chundong , Wang Peng , Mo Xiuliang , Liu Zhen 
Published: October 2021

Abstract
The rapid development of Mobile Internet has spa-wned various mobile applications (apps). A large number of apps make it difficult for users to choose apps conveniently, causing the app overload problem. As the most effective tool to solve the problem of app overload, the app recommendation has attracted extensive attention of researchers. Traditional recommendation methods usually use historical data of apps used by users to explore their preferences, and then make an app recommendation list for users. Although the traditional app recommendation methods have achieved certain results, the performance of app recommendation still needs to be improved due to the following two reasons. On the one hand, it is difficult to construct traditional app recommendation models when facing with the sparse user-app interaction data. On the other hand, contextual information has a large impact on users’ app usage preferences, which is often overlooked by traditional app recommendation methods. To overcome the aforementioned problems, we proposed a Context-aware Feature Deep Interaction Learning (CFDIL) method to explore user preferences, and then perform app recommendation by learning potential user-app relationships in different contexts. The novelty of CFDIL is as follows: (1) CFDIL incorporates contextual features into users' preferences modeling by constructing a novel user and app feature portrait. (2) The problem of data sparsity is effectively solved by the use of dense user and app feature portraits, as well as the tensor operations for label sets. (3) CFDIL trains a new deep network structure, which can make accurate app recommendation using the contextual information and attribute information of users and apps. We applied CFDIL on three real datasets and conducted extensive experiments, which showed that CFDIL outperformed the benchmark method.
Early Diagnosis of Skin and Breast Cancer in Women: Influence of Body Image Perception
Authors: Giacomo Dina , Guerra Federica , Ranieri Jessica , Cilli Eleonora , Ciciarelli Valeria , Ventura Alessandra , Ficorella Corrado , Fargnoli Maria Concetta 
Published: October 2021

Abstract
Background. The diagnosis of melanoma and breast cancer may impact many aspects of life with significant reductions in emotional functioning and quality of life. Aims. Aim of the study was to analyze the emotional traits of female patients with oncological diagnosis (early stage melanoma/breast cancer) investigating prevalence and predictors for psychological stress. Methods. A sample of 84 female patients (age range 30-50 years) with diagnosis of melanoma/breast cancer stage T0-T2 participated in the study. The examined emotional variables were: psychological distress, depression, stress, anxiety, metacognitions and body self-perception. Results. Findings showed higher psychological distress in breast cancer than melanoma patients and, that was related to lower positive self-perception of body image. No significant difference between cancer staging. Conclusions. Prevention and intervention protocols must be adapted taking into account tumor type in the adoption of preventive behaviors and compliance with medical recommendations during survivorship.
Better Network Modeling For Link Prediction In Protein-Protein Interaction Networks
Authors: Yuen Ho Yin , Jansson Jesper 
Published: October 2021

Abstract
Background: Protein-protein interaction (PPI) data is an important type of data used in functional genomics. However, inaccuracies in high-throughput experiments often result in incomplete PPI data. Computational techniques are thus used to infer missing data and to evaluate confidence scores, with link prediction being one such approach that uses the structure of the network of PPIs known so far to find good candidates for missing PPIs. Recently, a new idea called the L3 principle introduced biological motivation into PPI link predictions, yielding predictors that are superior to general-purpose link predictors for complex networks. However, the previously developed L3 principle-based link predictors are only an approximate implementation of the L3 principle. As such, not only is the full potential of the L3 principle not realized, they may even lead to candidate PPIs that otherwise fit the L3 principle being penalized. Result: In this article, we propose a formulation of link predictors without approximation that we call ExactL3 (L3E) by addressing missing elements within L3 predictors in the perspective of network modeling. Through statistical and biological metrics, we show that in general, L3E predictors perform better than the previously proposed methods on seven datasets across two organisms (human and yeast) using a reasonable amount of computation time. In addition to L3E being able to rank the PPIs more accurately, we also found that L3-based predictors, including L3E, predicted a different pool of real PPIs than the general-purpose link predictors. This suggests that different types of PPIs can be predicted based on different topological assumptions and that even better PPI link predictors may be obtained in the future by improved network modeling.
High-speed device-independent quantum key distribution against collective attacks
Authors: Zhang Qiang , Liu Wen-Zhao , Zhang Yu-Zhe , Zhen Yi-Zheng , Li Ming-Han , Liu Yang , Fan Jingyun , Xu Feihu , Pan Jian-Wei 
Published: October 2021

Abstract
The security of quantum key distribution (QKD) usually relies on that the users’s devices are well characterized according to the security models made in the security proofs. In contrast,device-independent QKD an entanglement-based protocol permits the security even without any knowledge of the underlying devices. Despite its beauty in theory, device-independent QKD is elusive to realize with current technology. This is because a faithful realization requires ahigh-quality violation of Bell inequality without the fair-sampling assumption. Particularly, in a photonic realization, a rather high detection efficiency is needed where the threshold values depend on the security proofs; this efficiency is far beyond the current reach. Here, both theoretical and experimental innovations yield the realization of device-independent QKD based on a photonic setup. On the theory side, to relax the threshold efficiency for practical deviceindependent QKD, we exploit the random post-selection combined with adding noise for preprocessing, and compute the entropy with complete nonlocal correlations. On the experiment side, we develop a high-quality polarization-entangled photonic source and achieve state-of-theart (heralded) detection efficiency of 87.49%, which outperforms previous experiments and satisfies the threshold efficiency for the first time. Together, we demonstrate device-independent QKD at a secret key rate of 466 bits/s over 20 m standard fiber in the asymptotic limit against collective attacks. Besides, we show the feasibility of generating secret keys at a fiber length of 220 meters. Importantly, our photonic implementation can generate entangled photons at a high rate and in the telecom wavelength, which is desirable for high-speed key generation over long distances. The results not only prove the feasibility of device-independent QKD with realistic devices, but also push the security of communication to an unprecedented level.
Machine Learning and Sustainable Mobility: The Case of the University of Foggia (Italy)
Authors: Cappelletti Giulio Mario , Grilli Luca , Russo Carlo , Santoro Domenico 
Published: October 2021

Abstract
Thanks to the development of increasingly sophisticated machine-learning techniques, it is possible to improve predictions of a certain phenomenon. In this paper, after having analyzed data relating to the mobility habits of University of Foggia (UniFG) community members and deter- mined their emissions of pollutants, we applied machine-learning techniques to these data to estimate the quantities of pollutants (in a certain time period) produced by new subjects not present in the data sets, using very little information. In this way, we developed a method that the university could apply to inform new students about what their emissions of pollutants could be in the near future, through several easily obtainable features. This method could allow the UniFG Rectorate to improve its sustainable mobility policies by encouraging the use of methods that are as appropriate as possible to the users’ needs. In addition, any public/private organization outside the academic environment can use the method, due to the need for little information.
Porous Cu-MOF Nanostructures with Anticancer Properties Prepared by a Controllable Ultrasound-Assisted Reverse Micelle Method
Authors: Zeraati Malihe , Moghaddam-Manesh Mohammadreza , Hosseinzadegan Sara , Kazemzadeh Parya , Chauhan Narendra Pal Singh , Sargazi Ghasem 
Published: October 2021

Abstract
The ultrasonic assisted reverse micelle method was used to create Cu-MOF from Cu(NO3)2•3H2O and 2,6-pyridine dicarboxylic acid in a 1:1 molar proportion. It has been characterized using FT-IR, XRD, nitrogen adsorption analysis SEM and TEM-EDX. Cu-MOF has anticancer properties against MCF-7 breast cancer cells. Cytotoxicity testing was performed on MCF-7 breast cancer cells using the MTT cell viability assay, and cell proliferation and viability were found to be approximately 24 % higher than the control.
Working out loud: consegue ouvir meu trabalho?
Authors: Tavares Maruza Vieira Barboza , Pinheiro Vanessa Saldanha , Freitas Ana Augusta Ferreira de 
Published: October 2021

O objetivo deste estudo é analisar a prática de Working Out Loud (WOL) na mídia social Instagram, com vistas a compreender a maneira como essa narrativa laboral é apresentada pelos indivíduos. Para tanto, foi conduzida uma coleta de dados manual, quando perfis do Instagram foram utilizados para imersão no contexto da prática do WOL. Com uma abordagem para pesquisa de dados qualitativos nas mídias sociais, o corpus do estudo compreendeu 193 postagens que mostravam o usuário apresentando seu trabalho. Os resultados corroboram a importância do estudo do WOL para o entendimento da forma como o trabalho perpassa a vida pessoal dos indivíduos e revelam o modo como esse fenômeno abre a possibilidade para que indivíduos construam suas identidades enquanto profissionais perante o público das redes sociais, que nem sempre estão inseridos em seus ambientes de trabalho. O gerenciamento de impressões, através de legendas, tags e imagens que promovem exposições, ensinamentos e reflexões, mostra-se como elemento constituinte desse processo.
Dissolved oxygen modelling of the Yamuna River using different ANFIS models
Authors: Arora Sameer , Keshari Ashok K. 
Published: October 2021

Abstract
Dissolved oxygen (DO) is one of the prime parameters for assessing the water quality of any stream. Thus, the accurate estimation of DO is necessary to evolve measures for maintaining the riverine ecosystem and designing appropriate water quality improvement plans. Machine learning techniques are becoming valuable tools for the prediction and simulation of water quality parameters. A study has been performed in the Delhi stretch of the Yamuna River, India, and physiochemical parameters were examined for 5 years to simulate the DO using various machine learning techniques. Simulation and prediction competencies of adaptive neuro fuzzy inference system–grid partitioning (ANFIS–GP) and subtractive clustering (ANFIS–SC) were performed on high dimensional river characteristics. Four different models (M1, M2, M3 and M4) were developed using different combination of input parameters to predict DO. Results obtained from the models were evaluated using root mean square error and coefficient of determination (R2) to identify the appropriate combination of parameters to simulate the DO. Results suggest that both types of ANFIS models work adequately and accurately predict the DO; however, ANFIS–GP outperforms the ANFIS–SC. M4 generated R2 of 0.953 from ANFIS–GP compared to 0.911 from ANFIS–SC.
A review on the application of nanoporous zeolite for sanitary landfill leachate treatment
Authors: Turan Abdullah Zahid , Turan Mustafa 
Published: October 2021

Abstract
This review deals with low-cost nanoporous zeolites for the treatment of sanitary landfill leachate. Organic contaminants and ammoniacal nitrogen are significant parameters in landfill leachate treatment. Adsorption processes are regarded as promising alternative treatment options in this respect. Zeolites are aluminosilicate materials that are widely used in separation, ﬁltration, adsorption and catalysis. Natural zeolite is a low-cost and readily available form of zeolite and is a promising candidate to be used as an ion-exchange material for ammonia and other inorganic pollutant removal from landfill leachate. In this review, adsorption isotherms and kinetic models in batch systems are evaluated and adsorption design parameters of the fixed-bed system are presented. Studies on ammonia removal from landfill leachate via zeolites have been thoroughly investigated. Leachate treatment systems combined with zeolites are presented. Cost of zeolites are also reported in comparison with other adsorbents. The investigated studies demonstrate that activated zeolite can improve the removal of chemical oxygen demand, NH3-N and colour significantly compared to the case where raw zeolite is used. Moreover, the composite of activated carbon and zeolite is also favorable for ammonia removal according to reported findings, where best adsorptive removal is attained on the composite media (24.39 mg/g).
Small Molecules as LIM Kinase Inhibitors
Authors: Bukhari Syed N.A. , Tandiary Michael A. , Al-Sanea Mohammad M. , Abdelgawad Mohamed A. , Chee Chin F. , Hussain Muhammad A. 
Published: October 2021

:
LIMK1 and LIMK2 are involved in the regulation of cellular functions that depend
on the dynamics of actin cytoskeleton. Disregulation of LIM kinases has been associated
with diseases, such as tumor progression and metastasis, viral infection, and ocular
diseases. Motivated by this, numerous studies have been carried out to discover small organic
molecules capable of inhibiting LIM kinase effectively and selectively. In this review,
a comprehensive survey of small organic molecules for LIM kinase inhibitors is reported,
together with SAR study results, and the synthesis of these inhibitors.