bluuebunny/crossref_metadata_2025_split huggingface dataset:
Groundwater DNAPL contamination source-sink analysis research system based on the simulation-random statistical method for a practical case
Authors: Wang Han , Lu Wenxi 
Published: February 2022

Abstract
Groundwater DNAPL contamination source-sink analysis (GDCSA) has seldom been included in previous research on groundwater DNAPL contamination. In this study, a complete GDCSA research system was first established to include groundwater DNAPL contamination source identification (GDCSI), groundwater DNAPL contamination source-sink response relationship (GDCSRR), and spatial and temporal distribution and quantity (sink) of contamination. The object of most previous GDCSI studies has been a simple hypothetical example in which the contamination source was always generalized as a point. For a complex contamination source that cannot be generalized as a point, identifying its shape depicts the source in actual practical terms and ensures the accuracy of GDCSI. In this case study of a practical dye chemical factory in Hebei Province, China, firstly shape of DNAPL contamination source was identified. Then, GDCSRR was determined based on GDCSI result. Finally, sink of contamination based on GDCSRR was determined. Compared with a single study of GDCSI, this complete GDCSA research system provides a reference for identifying the polluter and also provides a more precise basis for contamination remediation scheme design and contamination risk assessment in practical cases. In this paper, we applied parallel heuristic search iterative process (PHSIP) based on simulation-random statistics method to solve GDCSI. However, the repetitive invocation of numerical simulation model has high computational cost during PHSIP. An effective method is to construct surrogate to emulate the simulation model at low computational cost. However, there is a complex nonlinear mapping relationship between input and output for multiphase flow simulation model with large number of variables and high dimension. The accuracy of a surrogate using shallow learning methods needs to be improved. Therefore, we have introduced deep-belief neural network (DBNN) surrogate to emulate the simulation model. A combination of hypothetical and practical cases was adopted in this study. Test of the proposed approaches for the hypothetical case revealed that the DBNN surrogate approximated the multiphase flow simulation model more closely than shallow learning surrogates, and that the PHSIP obtained accurate identification results. These tested approaches and the GDCSA research system were finally applied to the practical case.
The clinical target volume delineation of para aortic lymph node region for adjuvant radiotherapy based on the regularity of lymph node metastasis in biliary tract cancer
Authors: Long Xin , Wu Han , Peng Jin , Yang Lei , Xu Hui , Dai Jing , Wang Wenbo , Xia Ling , Zhou Fuxiang 
Published: February 2022

Abstract
Background: To determine the high-risk lymphatic drainage area and the precise delineation of para aortic clinical target volume(CTV) for biliary tract cancer(BTC) according to the regularity of lymph node metastasis(LNM). Methods: The imaging records of patients with BTC from 2015 to 2020 were analyzed retrospectively. The metastasis risk for different lymph node regions was assessed. The impact of different recurrence sites on survival was evaluated by Kaplan–Meier method. The diagnosed para aortic lymph nodes(PALNs) were manually mapped to standard axial CT images. Referring to the distance from the volume center of lymph nodes to the edge of aorta, the asymmetric CTV expansion from para aortic were defined. Results: A total of 251 LNMs were identified in study cohort. These lymph nodes mainly concentrated in group 16a2 (13%) and 16b1 (17%). In validation cohort, a total of 93 LNMs were diagnosed and the highest involvement region was 16b1(33%), followed by 16a2. The upper boundary of 16a2 and the lower boundary of 16b1 were defined as the superior border and the inferior border of para aortic CTV，respectively. 18 mm in the front, 12 mm on the left and 24 mm on the right for para aortic CTV were considered as the optimal expansion range. The CTV expansion included 96% (73/76) of PALNs in 16a2 and 16b1 in study cohort and 96% (47/49) of PALNs in validation cohort. Besides, the prognosis in patients with PALN recurrence was significantly better than that in patients with distant organ recurrence(Hazard Ratio(HR)=0.219; 95% confidence interval（CI）0.067-0.711; P=0.006) and was not significantly worse than that of patients without PALN (HR=3.271; 95%CI 0.704-15.185; P=0.111). Conclusions: The lymph metastasis risk of PALN 16a2 and 16b1 was the highest and Based on the distribution of PALNs, a new para aortic CTV was defined to construct a more accurate target volume.
Evaluating the perceptions of ESL students towards collaborative learning on improving their writing skills
Authors: Bonsu Emmanuel Mensah 
Published: February 2022

Abstract
Developing the writing skills of ESL students have been the focus of language education scholars for the past three decades. This relates to the importance writing has accrued among the other language skills. Collaborative learning has been debated as an approach to improve the writing skills of students. The study investigated (a) the perceptions of ESL students towards collaborative learning, and (ii) the perceived effect of collaborative learning on the writing skills of ESL students. Thirty participants from an English language course were randomly sampled. The quantitative analyses and discussion show that students have a positive perception towards collaborative learning which influences their interest to be actively and frequently engaged in it. Peer feedback was essential to improving the writing skills and developing general language skills through collaborative learning. The study reports an overall effect of collaborative learning improving the writing skills of learners. After providing the limitations of the study, implications and recommendations are stated for further research.
Preparation of novel N-CNT nanocomposite as an adsorbent for removal of As+3 toxic ions and DFT calculation
Authors: Meshkat Seyyed Salar , Hoseini Zeinab , Mehrabi Romina , Ghasemy Ebrahim , Esrafili Mehdi 
Published: February 2022

Abstract
This work aimed at developing novel adsorbents to remove arsenic ions in an aqueous solution. To this end, the use of polymeric materials and functionalized nitrogen-doped carbon nanotubes (N-CNT) was considered to adsorb arsenic ions from aqueous solutions. In this regard, the effects of experimental parameters including pH, contact time, initial concentration of metal, and the adsorption temperature were studied. According to experiments, the optimum solution pH for arsenic adsorption at 20 °C was 7. As the contact time increased, the adsorption rate increased and reached equilibrium after 10 minutes. The arsenic adsorption capacity of Polyether Sulfone/N-CNT nanocomposites increased with increasing temperature. The highest adsorption capacity was achieved at 49 °C. The equilibrium data obtained in the initial concentration range of Arsenic and the studied temperature was consistent with the Freundlich isotherm. The adsorption kinetics revealed that the arsenic adsorption followed the Pseudo-First order and Weber Morris equation. The thermodynamic parameters (ΔH, ΔG, and ΔS) indicated that the arsenic adsorption in the temperature range of 332-296 K was spontaneous and endothermic. The adsorption properties of the modified N-doped carbon nanotubes towards arsenic ions were also studied by density functional theory (DFT) calculations.
A Predictive Model for Student Performance in Classrooms Using Student Interactions With an eTextbook
Authors: AbdElrahman Ahmed , Soliman Taysir Hassan A , Taloba Ahmed I. , Farghally Mohammed F. 
Published: February 2022

Abstract
With the rise of online eTextbooks and Massive Open Online Courses (MOOCs), a huge amount of data has been collected related to students’ learning. With the careful analysis of this data, educators can gain useful insights into the performance of their students and their behavior in learning a particular topic. This paper proposes a new model for predicting student performance based on an analysis of how students interact with an interactive online eTextbook. By being able to predict students’ performance early in the course, educators can easily identify students at risk and provide a suitable intervention. We considered two main issues the prediction of good/bad performance and the prediction of the final exam grade. To build the proposed model, we evaluated the most popular classification and regression algorithms on data from a data structures and algorithms course (CS2) offered in a large public research university. Random Forest Regression and Multiple Linear Regression have been applied in Regression. While Logistic Regression, decision tree, Random Forest Classifier, K Nearest Neighbors, and Support Vector Machine have been applied in classification. Based on the findings of the experiments, the algorithm with the best result overall in classification for this data was Random Forest Classifier with an accuracy equal to 91.7% while in the regression it was Random Forest Regression with an R² equal to 0.977.
Influence of tillage tools on agrophysical indicators of soil fertility and rice productivity
Authors: Bekzhanov Serik , Kultasov Bekzatkhan , Tokhetova Laura , Tautenov Ibadulla 
Published: February 2022

Abstract
Under the Kyzylorda region, the zonal technology of the tillage system for rice provides for up to 8–10 passes of various units in the same field. The technologies used are often based on the use of obsolete machines, which leads to many negative developments. According to the scheme of field experiments, a traditional plow PLN-5-35 (control) and a swing plow Lemken Juwel 7 have been studied during fall plowing. It was found that the use of the Lemken Juwel 7 swing plow contributed to base on, obtaining the ridges coefficient of 1.09 due to the same size and shape of the layers and their location at the same distance, while in the check this figure was 1.15. The use of the Lemken Juwel 7 swing plow and the Horsch Terrano 4 FX cultivator in the tillage system of meadow-boggy soils for rice in the conditions of the Kyzylorda region contributes to an increase in yield by an average of 0,71 t/ha.
Unravelling the interplay of ecological processes structuring the bacterial rare biosphere
Authors: Jia Xiu , Dini-Andreote Francisco , Salles Joana Falcão 
Published: February 2022

Abstract
Background: Although biological communities harbor many rare species, i.e., the rare biosphere, relatively little is known about how distinct ecological processes structure their existence. Here, we combined null modelling with spatiotemporal turnover in taxa rarity to model assembly processes of the rare biosphere. Results: We found that an overall influence of homogeneous selection (i.e., spatiotemporally constant variables) in driving the assembly of the rare biosphere whereas the common biosphere is governed by a broader array of processes, from which variable selection (i.e., spatiotemporally fluctuating variables) plays the primary role. By partitioning rarity types, we further found that homogeneous selection explained the prevalence of permanently rare taxa, suggesting their persistent low abundances to be restrained by physiological traits. Variable selection explained the dynamics of conditionally rare taxa, indicating their ability to switch between rarity and commonness by responding to spatiotemporal variation. Conclusion: This study contributes to the understanding of ecological mechanisms that maintain microbial diversity from the rare biosphere perspective. It also provides a new perspective on how to predict dynamic changes in the rare biosphere through space and time.
Estimates of prevalence of anti-SARS-CoV-2 antibodies among blood donors in eight provinces of South Africa in November 2021
Authors: Cable Russel , Coleman Charl , Glatt Tanya , Grebe Eduard , Mhlanga Laurette , Nyano Cynthia , Pieterson Nadia , Swanevelder Ronel , Swarts Avril , Sykes Wendy , Berg Karin van den , Vermeulen Marion , Welte Alex 
Published: February 2022

Abstract
In line with previous instalments of analysis from this ongoing study to monitor ‘Covid Seroprevalence’ among blood donors in South Africa, we report on analysis of 3395 samples obtained from 8 to 12 November 2021 in all provinces of South Africa except the Western Cape. As in our previous analyses, we see no evidence of age and sex dependence of prevalence, but substantial variation by province, and by race within each province, from which we generated provincial total point estimates (EC-74%; FS-75%; GP-68%; ZN-73%; LP-66; MP-73%; NC-63%; NW-81% ) and a ‘South Africa minus Western Cape’ national prevalence estimate of 71% (95%CI 69-74%). We note that sample collection occurred just before the omicron variant driven wave in South Africa, but otherwise present these results without significant interpretation.
CAA-PPI: A Computational Feature Design to Predict Protein-Protein Interaction using different Encoding Strategies
Authors: Mewara Bhawna , Sahni Gunjan , Lalwani Soniya , Kumar Rajesh 
Published: February 2022

Abstract
Protein-protein interactions (PPIs) carry out an extensive variety of biological procedures, containing cell-to-cell interactions, metabolic and developmental control. PPI is becoming one of the most important aims of system biology. PPI act as a fundamental part in predicting the protein function of the target protein and drug ability of molecules. Abundant work has been done to nurture methods to predict PPIs computationally as this supplements laboratory trials and offers cost-effective way of predicting the most likely set of interactions at the entire proteome scale. This article presents an innovative feature representation method (CAA-PPI) to extract features from protein sequence using two different encoding strategies and then an ensemble learning method, the Random Forest method is used as a classifier for PPIs prediction. CAA-PPI considers the role of trigram and bond of given amino acid with its nearby ones. The proposed PPI model achieves more than 98 % prediction accuracies with one encoding scheme and more than 95 % prediction accuracies with another encoding scheme respectively for the two diverse PPI datasets i.e. H. Pylori and Yeast. Further investigations are made to compare the CAA-PPI approach with existing sequence-based methods and reveals the proficiency of the proposed method with both encoding strategies. To further assess the practical prediction competence, a blind test has implemented on five other species’ datasets independent of the training set, and obtained result ascertains the productivity of CAA-PPI with both encoding schemes.
Only Header: A Reliable Encrypted Traffic Classification Framework without Privacy Risk
Authors: Cui Susu , Liu Jian , Dong Cong , Lu Zhigang , DU Dan 
Published: February 2022

Abstract
Encrypted traffic classification plays a critical role in network management, providing appropriate Quality-of-Service and Network Intrusion Detection. Conventional port-based and deep packet inspection (DPI) approaches cannot classify encrypted traffic effectively. Methods based on machine learning can classify encrypted traffic by extracting statistical features of the flow. However, they require manual extraction of features. Recent studies show that the approaches based on deep learning are compelling for the task. They can automatically learn raw traffic features without manual feature extraction. However, these studies still take the payload of encrypted traffic as the model input, which may cause privacy risks. Besides, a massive encrypted payload causes great storage pressure on traffic classification. In this paper, we propose a reliable encrypted traffic classification framework by only using the flow header called Only Header, which avoids privacy risks and achieves lightweight storage. Firstly, we introduce a twice segmentation mechanism to dilute the interference traffic and increase the weight of effective traffic. Then we use capsule neural networks (CapsNet) to learn spatial and byte features of the flow header. The Only Header's effectiveness is compared with other methods using two public datasets, including ISCX VPN-nonVPN and ISCX Tor-nonTor datasets.
Load Balancing Technique Based on Network Segmentation and Adaptive Sleep Scheduling for 5G-IoT Networks
Authors: Rashad Rashad , Sudhir Sudhir 
Published: February 2022

Abstract
In Fifth Generation (5G) network, small cells have been incorporated to meet the growing demands for mobile traffic and ubiquitous service. It also enables Internet of Things (IoT) applications that will be positioned all over with mobile broadband technology. As modern IoT applications have active and varied necessities, several systems arrange mixed sorts of network sources consequently. Prevailing studies recommend methods for sustaining the distribution of network segments to diverse applications proficiently.Balancing the loads amongst the cells turns out to be one of the significant problems in cellular network. In this paper, a load balancing technique based on network segmentation and adaptive sleep scheduling for 5G-IoT networks is proposed. In this technique, for each network segment,sub-segments are formed and grouped to process IoT application shaving different Quality of Service (QoS)requirements. The improved DBSCAN algorithm is used for sub-segmentation and grouping. based on a combined rank of QoS factors. In the next phase, each small cell base station (SBS) executes adaptive dynamic sleep scheduling based on its load level. In the load balancing policy of SBS, when the average load of any SBS increases beyond the load of the Macrocell, the overloaded traffic can be moved to the Macrocell. Simulation results are validated against the analytical results and hence prove that the proposed LBNSASS technique achieves higher success probability and power efficiency with reduced energy consumption and packet drops of SBSs when compared to existing technique.
Experimental investigation of oxygen transfer efficiency in hydraulic jumps, plunging jets, and plunging breaking waves
Authors: Hoque Ashabul , Paul Anip Kumar 
Published: February 2022

Abstract
The scope of the paper is to analyse the different similarities of air entrainment among the hydraulic jumps, plunging jets, and plunging breaking waves and to discuss current practices. The measured data are reexamined and scrutinised to investigate the gas exchange phenomena through an air-water interface. In particular, oxygen transfer efficiency and penetration depth by air bubbles are discussed. The calculated results highlight that the oxygen transfer efficiency is decreased with the increase of energy dissipation rate both in plunging jets and breaking waves. In contrast, it is shifted almost parallel in the case of hydraulic jumps. In addition, the aeration lengths in the hydraulic jumps and penetration depths both in plunging jets and plunging breaking waves were dependent on the jet impact velocity.
Elogio da demora: Bill Viola e a estética da desaceleração
Authors: De Sales Cristiano 
Published: February 2022

Este ensaio apresenta partes de um estudo que tem sido feito acerca das possibilidades de se vivenciar, por meio da arte, instantes de fissura no tempo acelerado da vida contemporânea. O artista norte americano Bill Viola tem se dedicado à videoarte desde a década de 1970 e um dos temas que aparece poetizado em suas instalações, sobretudo a partir do século XXI, é a desaceleração do tempo. O repertório teórico com o qual olhamos para esse fenômeno vem da crítica cultural e filosofia contemporâneas, tais como Rebecca Solnit (2016), Jonathan Crary (2016), Jacques Rancière (2012), entre outros. Trata-se de um estudo especulativo ensaístico.
Communicating Treatment-Related Symptoms Using Passively Collected Data and Satisfaction/Loyalty Ratings: Exploratory Study
Authors: Kudel Ian , Perry Toni 
Published: February 2022

Background
Electronic patient-reported outcomes’ real time communication of treatment-related symptoms is increasingly associated with better outcomes including longer survival and less health care resource use, but the primary method of collecting this information, static questionnaires, has not evolved.

Objective
The aim of this paper is to describe the use of Noona’s three methods of communicating treatment-related symptoms, which are as follows: (1) Noona symptom questionnaires (NSQ), which incorporate branching logic; (2) a diary; and (3) secure messaging, the last two of which have NSQ reporting functionality. It also aims to explore, using multivariable analyses, whether patients find value using these features.

Methods
Noona users (N=1081) who have an active account for more than 30 days, who responded to the satisfaction/loyalty item, and who were undergoing active cancer treatment (systemic or radiotherapy) in the United States were included in this study. All study data were collected via software embedded within Noona code. This includes metadata, patient activities (measured in clicks), and responses to a satisfaction/loyalty question (“How likely are you to recommend Noona to another patient”) displayed on the Noona home page.

Results
Noona users expressed a high degree of satisfaction/loyalty when asked to rate how likely they would recommend Noona to another patient. Multivariable analyses indicate small but significant effects for some of the analyses. Use of NSQs were significantly related to satisfaction/loyalty, users of NSQs had significantly higher satisfaction/loyalty than those who did not use any, and secure communication use was significantly higher for those who rated the app highly compared to those who did not. These relationships will likely be further explicated with the use of satisfaction/loyalty questions that focus specifically on feature use.

Conclusions
Noona is well liked by respondents, and exploratory multivariable analyses demonstrate the potential for using passively and minimally invasive data to demonstrate value.