bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of a History of Different Other Cancers on the Long‐Term Outcomes of Patients with Intrahepatic Cholangiocarcinoma: A Population‐Based Analysis
Authors: Chai Jiawei , Kong Junjie , Zhu Kunbing
Published: February 2022

Background. The characteristics and outcomes of patients with intrahepatic cholangiocarcinoma (ICC) with prior malignancy are poorly clarified. This study is aimed at exploring the impact of prior malignancy on the long‐term outcomes of ICC patients. Methods. Using the Surveillance, Epidemiology, and End Results (SEER) program, ICC patients diagnosed between 2004 and 2018 were identified. Kaplan‐Meier curves and Cox analysis were used to evaluate the impact of prior malignancy on the prognosis of ICC patients. Results. A total of 9667 ICC patients were identified; among them, 782 (8.09%) had a history of prior cancer. Prostate, breast, colorectal, bladder, and liver/gallbladder/other biliary cancers were the most common types of prior cancer. Patients with prior cancer had better tumor‐related profiles than those without prior cancer, namely, the former patients showed a lower proportion of positive AFP levels and vascular invasion, a lower AJCC stage, a smaller tumor size, and a lower stage of tumor grade. The median survival times after the diagnosis of ICC were 10 and 11.5 months for patients with and without prior cancer, respectively. Multivariate regression analysis suggested that prior cancer did not contribute to inferior overall survival (OS, HR 0.870, 95% CI 0.797‐0.950, and p = 0.002) or cancer‐specific survival (CSS, HR 0.820, 95% CI 0.741‐0.906, and p < 0.001). Conclusions. A history of prior cancer does not lead to worse OS or CSS for ICC patients. The exclusion of patients with prior cancer from clinical trials should be reconsidered.
Multirobot Collaborative Pursuit Target Robot by Improved MADDPG
Authors: Zhou Xiao , Zhou Song , Mou Xingang , He Yi
Published: February 2022

Policy formulation is one of the main problems in multirobot systems, especially in multirobot pursuit-evasion scenarios, where both sparse rewards and random environment changes bring great difficulties to find better strategy. Existing multirobot decision-making methods mostly use environmental rewards to promote robots to complete the target task that cannot achieve good results. This paper proposes a multirobot pursuit method based on improved multiagent deep deterministic policy gradient (MADDPG), which solves the problem of sparse rewards in multirobot pursuit-evasion scenarios by combining the intrinsic reward and the external environment. The state similarity module based on the threshold constraint is as a part of the intrinsic reward signal output by the intrinsic curiosity module, which is used to balance overexploration and insufficient exploration, so that the agent can use the intrinsic reward more effectively to learn better strategies. The simulation experiment results show that the proposed method can improve the reward value of robots and the success rate of the pursuit task significantly. The intuitive change is obviously reflected in the real-time distance between the pursuer and the escapee, the pursuer using the improved algorithm for training can get closer to the escapee more quickly, and the average following distance also decreases.
Predicting and Preventing Crime: A Crime Prediction Model Using San Francisco Crime Data by Classification Techniques
Authors: Khan Muzammil , Ali Azmat , Alharbi Yasser
Published: February 2022

The crime is difficult to predict; it is random and possibly can occur anywhere at any time, which is a challenging issue for any society. The study proposes a crime prediction model by analyzing and comparing three known prediction classification algorithms: Naive Bayes, Random Forest, and Gradient Boosting Decision Tree. The model analyzes the top ten crimes to make predictions about different categories, which account for 97% of the incidents. These two significant crime classes, that is, violent and nonviolent, are created by merging multiple smaller classes of crimes. Exploratory data analysis (EDA) is performed to identify the patterns and understand the trends of crimes using a crime dataset. The accuracies of Naive Bayes, Random Forest, and Gradient Boosting Decision Tree techniques are 65.82%, 63.43%, and 98.5%, respectively, and the proposed model is further evaluated for precision and recall matrices. The results show that the Gradient Boosting Decision Tree prediction model is better than the other two techniques for predicting crime, based on historical data from a city. The analysis and prediction model can help the security agencies utilize the resources efficiently, anticipate the crime at a specific time, and serve society well.
Maze Control Training on Kinesthetic Awareness in Patients with Stroke: A Randomized Controlled Trial
Authors: Battesha Hanan Hosny M. , Wadee Amir N. , Shafeek Marian M. , Tawfick Ahmed M. , Ibrahim Hoda M.
Published: February 2022

Objective. To determine the influence of adding maze control training to the selected conventional physical therapy on kinesthetic awareness in patients with chronic stroke. Methods. Thirty adult patients of both genders with chronic cerebral stroke were assigned to control and experimental groups randomly: the control group (A) received the selected conventional physical therapy rehabilitation program, while the experimental group (B) received the same program as group A in addition to the maze control training. Measurements for sway index, risk of fall, and knee proprioception before and after 8 weeks of treatment (24 sessions; three times per week). Results. There were significant decreases of both sway index and risk of fall in both groups (

p
≤
0.001

in all measures), significant improvements of the knee proprioception in 30° and 75° in the experimental group (

p

value = 0.016 and ≤0.001, respectively). The in-between groups’ comparison showed significant differences corresponding to both the sway index and risk of fall (

p
≤
0.001

), and a significant difference in 75° (

p
≤
0.001

). Conclusion. Adding maze control training to the selected conventional physical therapy improved the kinesthetic awareness in patients with chronic stroke.
Design of National Sports Action Feature Extraction System Based on Convolutional Neural Network
Authors: Pang Yajun
Published: February 2022

Human action recognition is one of the hotspots in computer vision research. Its purpose is to detect and recognize target actions from videos, so that computer systems can understand human actions, and thus it has great research significance. Based on the action features of famous sports, this paper proposes an action recognition scheme based on RGB-D video compression to establish action features and deep learning as a means of recognition. By establishing the connection between the bone data of the three-dimensional data type and the depth image data, the depth sequence is analyzed and expressed as a three-level structure diagram sequence, which is the overall figure sequence, partial figure sequence, and joint point figure sequence, and then passes through the two-way pool. The sorting algorithm extracts the action features in the three picture sequences and compresses and generates three types of structured images of the corresponding picture sequences, and these three types of structured images are used as the feature expression of the video. When constructing a three-level structure diagram sequence, the innovation of this paper is to splice the extracted key unit image blocks to obtain a three-level structured moving image based on the three-key unit splicing, so that the image is not only retained. In addition to time-space information, the structure information of the depth image is also strengthened, and the amount of calculation is reduced at the same time. Finally, the three types of structured images are input into the convolutional neural network, respectively, and the judgment and recognition results obtained are multiplicatively fused to obtain the final recognition rate of the action.
Aplastic Anemia Treated with Eltrombopag during Pregnancy
Authors: Suminaga Yuri , Chigusa Yoshitsugu , Kondo Tadakazu , Okamoto Haruko , Kawamura Yosuke , Taki Mana , Mandai Masaki , Mogami Haruta
Published: February 2022

Aplastic anemia is a rare blood disorder characterized by pancytopenia and hypocellular bone marrow. In patients with aplastic anemia, pancytopenia sometimes worsens during pregnancy, and relapse of aplastic anemia in pregnancy is common. Nevertheless, only supportive care with blood products is the mainstay of treatment of aplastic anemia in pregnancy. Thus, the obstetric management and treatment of aplastic anemia in pregnancy is extremely challenging. We herein report the first case of a pregnant woman complicated with aplastic anemia who was successfully treated with eltrombopag, a thrombopoietin receptor agonist. A 27-year-old primigravida woman who had a history of aplastic anemia refractory to immunosuppressive therapy and was treated with eltrombopag became pregnant. Eltrombopag treatment was continued after weighing the benefits and potential risks. Throughout pregnancy, the woman’s pancytopenia did not progress, and she delivered a 2336 g baby vaginally at 38 weeks of gestation. Her postpartum outcome was uneventful, and the neonate did not develop thrombocytosis. Since the efficacy and safety of eltrombopag in pregnancy has not yet been established, its routine use should be avoided. However, if limited to refractory cases and with adequate maternal and fetal monitoring, including neonatal blood examinations, the use of eltrombopag for patients with aplastic anemia during pregnancy may be acceptable and result in favorable maternal and fetal outcomes.
Design of a Network Security Audit System Based on Log Data Mining
Authors: Xing Yanbo
Published: February 2022

The purpose of network security auditing is to safeguard network and information security through the assessment of network security vulnerabilities. Data mining is mainly used to mine potential information from massive amounts of log data, which can provide both accurate and valuable auxiliary data for network security auditing and relevant information for monitoring and management of terminals in different network environments. Firstly, the theory of data mining technology is explained, secondly, some data mining algorithms applied to network security audit are discussed, and finally, the design difficulties and functions of the network security audit system based on data mining are studied, aiming to provide reference for securing network and identifying hidden risks.
Method for Pythagorean Interval 2‐Tuple Linguistic Multiattribute Group Decision Making and Its Application to the Ship Navigation Environment Safety Assessment
Authors: Yuan Fuchun
Published: February 2022

In this paper, some fundamental theories of the Pythagorean interval 2‐tuple linguistic numbers (PI2TLNs) combined with the EDAS (evaluation based on distance from average solution) method are proposed. Firstly, we introduced the original concepts of Pythagorean interval 2‐tuple linguistic sets (PI2TLSs), and two aggregation operators of PI2TLN were also introduced. Then, the steps of the EDAS method were described briefly. Moreover, the PI2TLN‐EDAS model was provided to address the multiattribute group decision‐making (MAGDM) problem, and calculation steps were deduced in detail. The effectiveness of the method was illustrated by taking the ship navigation environment safety assessment as an example. Finally, we conducted comparative analysis between PI2TLN‐EDAS method and Pythagorean interval 2‐tuple linguistic weighted averaging (PI2TLWA) operator/Pythagorean interval 2‐tuple linguistic weighted geometric (PI2TLWG) operator.
Duolingo: An Arabic Speaking Skills’ Learning Platform for Andragogy Education
Authors: Ritonga Mahyudin , Febriani Suci Ramadhanti , Kustati Martin , Khaef Ehsan , Ritonga Apri Wardana , Yasmar Renti
Published: February 2022

Arabic communication skills for non-Arabs are faced with serious problems; therefore, the use of technology-based platforms is considered following students’ wishes in the era of digital technology. Various technological devices have been widely used by educators for multiple levels of education but have not obtained maximum results. The novelty of this research is the use of Duolingo as a platform for teaching Arabic language skills. This study describes that Duolingo is very relevant to be used as a medium for learning Arabic for beginners because the teaching material begins with the new alphabet and vocabulary equipped with pictures. However, Duolingo still needs to make an essential revolution for learning Arabic speaking skills, especially for andragogy learners who have expert Arabic skills. Based on the findings, some implications are put forward.
Incidence and Influencing Factors of Chronic Postthoracotomy Pain in Lung Tumor Patients
Authors: Peng Jing , Wang Zhonghui , Ma Liang , Ma Weihao , Liu Guo , Zhang Hui , Wang Qiongchuan , Zhu Bobo , Zhao Li
Published: February 2022

Objectives. To estimate the incidence of chronic postthoracotomy pain (CPTP) in lung tumor patients and to explore the influencing factors of the intensity of CPTP. Methods. Lung tumor patients who underwent video-assisted thoracoscopic surgery (VATS) or thoracotomy were consecutively recruited from October 2016 to December 2017 at Yunnan Cancer Hospital. All the eligible patients were interviewed via telephone at the end of the third month after surgeries to identify the presence of CPTP. The potential influencing factors of CPTP, including pre-, intra-, and postoperative variables, were collected from medical records. A cumulative logit regression model was used to identify the independent influencing factors of the intensity of CPTP. Results. Three hundred and forty-three patients completed a telephone interview. The estimated overall incidence of CPTP was 67.6% (95% of confidence interval, 95% CI: 62.4%, 72.6%) in lung tumor patients; 70.8% (95% CI: 63.8%, 77.1%) in benign patients and 63.5% (95% CI: 55.2%, 71.3%) in malignant patients; and 78.1% (95% CI: 66.0%, 87.5%) in open chest and 65.2% (95% CI: 59.3%, 70.8%) in VATS. Cumulative logit regression models (intensity order, NRS, 0 ⟶ 1–3 ⟶ 4-) revealed independent influencing factors of CPTP to be patients with diabetes (OR = 0.32; 95% CI: 0.14, 0.76), usage of VATS (OR = 0.47; 95% CI: 027, 0.82), and the amount of intraoperative blood loss (OR = 1.09; 95% CI: 1.00, 1.19). Conclusions. A high incidence of CPTP is detected in lung tumor patients following the thoracic operation. Patients with diabetes and using VATS are the independent protective factors of the intensity of CPTP, and the increasing amount of intraoperative blood loss is an independent risk factor of the intensity of CPTP.
Analysis of Mesquite (Prosopis juliflora) Protein Concentrate for Possible Use as Supplementary Protein
Authors: Jaimes-Morales José , Marrugo-Ligardo Yesid A. , Acevedo-Correa Diofanor
Published: February 2022

Vulnerable populations in developing countries need new protein sources, such as protein concentrates from accessible sources at low economic costs. The main objective of this study was at evaluating the nutritional quality of the protein concentrate of the legume mesquite (Prosopis juliflora), compared with the protein values of other legumes described in literature. For this purpose, flour and protein concentrates from mesquite were obtained, along with their chemical composition. Amino acid profiling was performed by high-performance liquid chromatography (HPLC). Protein quality index evaluation tests were also performed on preschool children and adults. The protein content of mesquite was found to be 68%. However, mesquite covers the requirements of essential amino acids, surpassing 31% of the protein required in adults, except for cysteine sulfur amino acids and aromatic amino acids. In other age groups such as children, mesquite had a high content of histidine, which is necessary and considered essential during infant development. According to the above, mesquite could be used as an alternative protein to produce food with high nutritional content.
[Retracted] Digital Service Model of Red Educational Resources from the Perspective of Excellent Traditional Culture
Authors: Liu Dan
Published: February 2022

In view of the poor service effect caused by the incomplete index base of the traditional red education resources digital service model, this paper puts forward the red education resources digital service model from the perspective of excellent traditional culture. Determine the influencing factors of red education resources in the integration process, integrate the remote scheduling principle of teaching information into the feature vector extraction of red education resources, extract the number of main factors of the feature vector, decompose the feature vector of red education resources, take the wavelet entropy of red education resources as the fusion weight, and integrate the obtained coefficients through wavelet transformation. Get the integration results of red education resources. Build the architecture of digital service model, grab all web pages on the Internet with indexer, and create forward index and reverse index. Use the controller to search web pages and control all web pages to be searched. Restrict query conditions through user interface. Establish a red education resource data index database, query and process documents, and build a digital service model. The experimental results show that the designed service model has good goodness of fit and high performance.
A DC Fault Location Method of Multiterminal Flexible DC Distribution Network
Authors: Wang Chenqing , Li Peng , Xu Xiao , Gao Hui
Published: February 2022

Multiterminal flexible DC distribution network is a complex system made up of many power-electronic devices. Its diversified and comprehensive fault characteristics are featured by multiperiod and strong transient. This paper starts with investigating the mechanisms of typical DC short circuit faults of the MMC-based system. Then, a typical flexible DC distribution network is built in PSCAD to analyse the characteristics of its pole-to-pole short-circuit fault and pole-to-ground fault. With the aid of the above findings, a fault detection and location method is proposed with its effectiveness evaluated through simulations.
Studying the Effects of Cold Plasma Phosphorus Using Physiological and Digital Image Processing Techniques
Authors: Alazzam Malik Bader , Mohammad Walid Theib , Younis Mohammad Bani , Al Sayeh Ahmad Maher , Hajjej Fahima , AlGhamdi Ahmed S. , Rahman Md Adnan
Published: February 2022

The goal of this study is to see how cold plasma affects rabbit bone tissue infected with osteoporosis. The search is divided into three categories: control, infected, and treated. The rabbits were subjected to cold plasma for five minutes in a room with a microwave plasma voltage of “175 V” and a gas flow of “2.” A histopathological photograph of infected bone cells is obtained to demonstrate the influence of plasma on infected bone cells, as well as the extent of destruction and effect of plasma therapy before and after exposure. The findings of the search show that plasma has a clear impact on Ca and vitamin D levels. In the cold plasma, the levels of osteocalcin and alkali phosphates (ALP) respond as well. Image processing techniques (second-order gray level matrix) with textural elements are employed as an extra proof. The outcome gives good treatment indicators, and the image processing result corresponds to the biological result.
A Sentiment Analysis Method for Teaching Evaluation Texts Using Attention Mechanism Combined with CNN-BLSTM Model
Authors: Peng Hong , Zhang Zhenwei , Liu Hua
Published: February 2022

In view of the problems that most existing emotion analysis models ignore the relationship between emotions and are not suitable for students, an emotion analysis model of teaching evaluation text based on deep learning is proposed. Firstly, combining the advantages of CNN extracting phrase features and BLSTM extracting sequence features, the CNN-BLSTM model is constructed to effectively enhance the extraction ability of text information. Then, the attention mechanism is used to adaptively perceive the context information, extract the text features that affect students’ emotion, and construct the CNN-BLSTM-AT model. Finally, the CNN-BLSTM-AT model is used to analyze the students’ emotion types in the dataset, and the mini-batch gradient descent method is used for model training. The experiment uses the weibo_senti_100k dataset to demonstrate the performance of the proposed model. The results show that adding the attention mechanism can improve the accuracy of the model by about 0.23. Also, its recall rate is not less than 0.57 and the minimum value of F1 is 0.748, which is better than other comparison models, thus demonstrating the effectiveness of the proposed model.
A Framework of Selecting Building Flooring Finishing Materials by Using Building Information Modeling (BIM)
Authors: Al Rahhal Al Orabi Mohamed A. , Al-Gahtani Khalid S.
Published: February 2022

Building materials selection is a critical process affected by many complicated factors. Due to the specific function of elements, alternatives to materials must be appropriately chosen as they differ in quality, performance, and cost. Implementing a Value Engineering (VE) process is challenging as it requires effort and several workshops. Thus, this study proposes a framework to automate the VE process of selecting building flooring material. The framework identified selected agreed criteria of flooring finishing materials measured based on agreed standard tests. The developed framework is distinguished from other current research using a combination of Analytic Hierarchy Process (AHP), Pairwise, and Function Analysis System Technique (FAST) methods to define the criteria ranking weight and include the building function. Furthermore, the framework is integrated with Building Information Modeling (BIM) that has been widely used in the Architecture, Engineering, and Construction industry (AEC). Finally, a case study of a residential house in Saudi Arabia was used to demonstrate the framework. The proposed framework was verified by using two questioners and expert consultations.
How to Approach Submucosal Lesions in the Gastrointestinal Tract: Different Ideas between China and USA
Authors: Gao Rui Ping , Zhang Yue Ping , Li Qiu Mei
Published: February 2022

Between 2019 and 2020, the author Gao pursued advanced endoscopic training at the University of Mississippi Medical Center in the USA. She experienced certain different ideas between the East (China) and the West (USA) in terms of endoscopic approach to the submucosal tumors (SMTs) or lesions in the gastrointestinal (GI) tract. In the West (USA), when SMTs are found on gastroscopy, the main goal of endoscopists is to obtain a tissue diagnosis through endoscopic ultrasound-guided fine-needle aspiration or biopsy (EUS-FNA or FNB) or single incision needle-knife biopsy (SINK); if immunohistochemical tests confirmed the GISTs, the first-line treatment is local surgery, that is, diagnosis before treatment, whereas in China, SMTs will be completely resected with endoscopic technology for those with no lymph node metastasis or extremely low risk of lymph node metastasis. There may not be pathological tissue at first, that is, treatment before diagnosis.
An XML Privacy-Preserving Data Disclosure Decision Scheme
Authors: Guo Lihong , Wu Haitao
Published: February 2022

In order to protect the sensitive data represented as XML documents in a trusted collaborative system where sensitive data are not shared, an XML privacy-preserving data disclosure decision scheme was proposed under the assumption of a trusted server. This scheme is inspired by the idea of separating storage structure and content. Temporary access matrix is used to represent structure authorization and the vector represents the content authorization of leaf node. According to the conversion rules, access matrix not only represents access authorization of all nodes but also keeps the main structure of the XML document. With the combination of the vector and matrix, it can provide different access views for different group users with different purposes. In addition, start-end encoding is used to encode all the nodes for locating nodes and the content; privilege matrix solves the problem of privacy synchronization change for all users. At the same time, authentication polynomials are used to verify different users and improve the security level. The experimental results show that the scheme not only effectively protects XML sensitive data but also reduces the storage pressure on the server side; at the same time, from the response time, we know that it is beneficial for the rapid search and information positioning.
Computing the Normalized Laplacian Spectrum and Spanning Tree of the Strong Prism of Octagonal Network
Authors: Ahamad Yasir , Ali Umar , Siddique Imran , Iampan Aiyared , Afifi Walaa A. , Abd-El-Wahed Khalifa Hamiden
Published: February 2022

Spectrum analysis and computing have expanded in popularity in recent years as a critical tool for studying and describing the structural properties of molecular graphs. Let be the strong prism of an octagonal network On. In this study, using the normalized Laplacian decomposition theorem, we determine the normalized Laplacian spectrum of which consists of the eigenvalues of matrices ℒA and ℒS of order 3n + 1. As applications of the obtained results, the explicit formulae of the degree‐Kirchhoff index and the number of spanning trees for are on the basis of the relationship between the roots and coefficients.
Comparison of Five Triage Tools for Identifying Mortality Risk and Injury Severity of Multiple Trauma Patients Admitted to the Emergency Department in the Daytime and Nighttime: A Retrospective Study
Authors: Ying Youguo , Huang Boli , Zhu Yan , Jiang Xiaobin , Dong Jinxiu , Ding Yanfen , Wang Lei , Yuan Huimin , Jiang Ping
Published: February 2022

Effective triage tools are indispensable for doctors to make a prompt decision for the treatment of multiple trauma patients in emergency departments (EDs). The Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), standardized early warning score (SEWS), Modified Rapid Emergency Medicine Score (mREMS), and Revised Trauma Score (RTS) are five common triage tools proposed for trauma management. However, few studies have compared these tools in a multiple trauma cohort and investigated the influence of nighttime admission on the performance of these tools. This retrospective study was aimed at evaluating and comparing the performance of MEWS, NEWS, SEWS, mREMS, and RTS for identifying the mortality risk and trauma severity of patients with multiple trauma admitted to the ED during the daytime and nighttime. Retrospective data were collected from the medical records of patients with multiple trauma admitted in the daytime or nighttime to calculate scores for each triage tool. Logistic regression analysis was conducted on each triage tool for identifying in-hospital mortality and severe trauma (injury severity

score
>
15

) in the daytime and nighttime. The performance of the tools was evaluated and compared by calculating area under the receiver operating characteristic curve (AUROC) of the retrospective logistic model of each tool. We collected data for 1,818 admissions, including 1,070 daytime and 748 nighttime admissions. A comparison of performance for identifying in-hospital mortality between daytime and nighttime yielded the following results (AUROC): MEWS (0.95 vs. 0.93,

p
=
0.384

), NEWS (0.95 vs. 0.94,

p
=
0.708

), SEWS (0.95 vs. 0.94,

p
=
0.683

), mREMS (0.94 vs. 0.92,

p
=
0.286

), and RTS (0.93 vs. 0.93,

p
=
0.87

). Similarly, a comparison of performance for identifying trauma severity between daytime and nighttime yielded the following results (AUROC): MEWS (0.78 vs. 0.78,

p
=
0.95

), NEWS (0.8 vs. 0.8,

p
=
0.885

), SEWS (0.78 vs. 0.78,

p
=
0.818

), mREMS (0.75 vs. 0.69,

p
=
0.019

), and RTS (0.75 vs. 0.74,

p
=
0.619

). All five scores are excellent triage tools (

AUROC
≥
0.9

) for identifying in-hospital mortality for both daytime and nighttime admissions. However, they have only moderate effectiveness (

AUROC
<
0.9

) at identifying severe trauma. The NEWS is the best triage tool for identifying severe trauma for both daytime and nighttime admissions. The MEWS, NEWS, SEWS, and RTS exhibited no significant differences in performance for identifying in-hospital mortality or severe trauma during the daytime or nighttime. However, the mREMS was better at identifying severe trauma during the daytime.
Exploring Technical Decision-Making Risks in Construction Megaprojects Using Grounded Theory and System Dynamics
Authors: Tang Xiaoying , Wang Mengjun , Wang Qian , Zhang Jingxiao , Li Hujun , Tang Juanjuan
Published: February 2022

Technical decision-makings (TDMs) are a vital part of the decision-makings in construction megaprojects, facing high risks brought by technical complexity, dynamic environment, and subject cognition. Identifying technical decision-making risks (TDMRs) and exploring their interactions are important in megaproject management. Due to the high complexity of TDMs in megaprojects, TDMRs are complex and diverse. However, there is a lack of research on exploring the systematic TDMRs in megaprojects. To address this gap in knowledge, this paper aims to better understand the dynamic complexity of TDMRs in megaprojects by identifying the risks and exploring their interactions from a dynamic and systematic perspective. Grounded theory (GT) and system dynamics (SD) were adopted for this research. First, the GT was used to identify TDMRs in megaprojects and create a conceptual model depicting the relationships among TDMRs. Then, an SD model characterizing the causal structure of the TDMRs system in megaprojects is developed in both qualitative and quantitative manners. The developed model involves interrelationships among environmental risks, decision-making process risks, and decision-making execution process risks. After the validation of the model, a model simulation is conducted to predict the dynamic evolution process of the TDMRs. As a result, a multilayer risk list consisting of 42 index layer risk indicators, 13 field layer risk indicators, and 3 standard layer risk indicators is identified. The SD modeling results show that these multilevel TDMRs interact dynamically and have intricate influences on the total risk level of TDMs in megaprojects. The results of this study could be useful for decision-makers to identify and mitigate TDMRs in megaprojects.
Clonal Evolution in Patients with Hormone Receptor Positive, HER-2 Negative Breast Cancer Treated with Chemotherapy or CDK4/6 Inhibitors
Authors: Decker Thomas , Bichler Matthias , Birtel Andrea , Fischer Gerhard , Geiger Kathrin , Gaenger Stella , Nonnenbroich Christoph , Dechow Tobias , Muendlein Axel
Published: February 2022

Introduction: Somatic evolution of the cancer genome resulting in genetically different subclones is thought to be involved in the development of treatment resistance but might also offer new therapeutic opportunities in metastatic breast cancer. No data are available if clonal evolution differs in patients treated with chemotherapy (chemo) or CDK4/6 inhibitors given with endocrine treatment (CE treatment). Methods: We performed a prospective analysis of circulating tumor DNA (ctDNA) by targeted next-generation sequencing in 46 patients before the beginning of a systemic first-line (n = 37) or second-line (n = 9) treatment. Ct DNA was analyzed again upon disease progression. Results: New mutations in ctDNA of patients with progressive disease were detected in 1/11 patients who started chemo, in 4/9 patients treated with chemo followed by CE maintenance treatment, and in 9/26 patients receiving CE therapy. The number of acquired new mutations did not differ significantly between the three therapy cohorts (all p values >0.05). However, in patients classified as secondary resistant (n = 37), occurrence of new mutations significantly differed between patients who started chemo (0/9) compared to patients treated with chemo followed by CE (4/11; p = 0.041) and patients receiving CE therapy (8/19; p = 0.024), respectively. Conclusion: Clonal evolution might differ significantly between metastatic breast cancer patients with hormone receptor positive and HER-2 negative disease treated with chemo or CDK4/6 inhibitors. These results should be confirmed in larger patient cohorts.
Scalp Cooling for Prevention of Chemotherapy-Induced Alopecia for Women and Men with Various Cancer Entities: A Two-Year Survey of an Outpatient Cancer Center in Germany
Authors: Keim Sabine , Hempel Louisa , Ebner Florian , Retzer-Lidl Michaela , Wohlmuth Katharina , Hempel Dirk , Milani Valeria
Published: February 2022

Introduction: The aim of this survey was to assess the efficacy and the feasibility of scalp cooling (SC) in an outpatient hematological and oncological center in a real-world setting. Methods: We prospectively monitored cancer patients from August 2017 to October 2019 receiving oncological treatments with SC, using the sensor-controlled system “DigniCap.” Effectiveness was defined by a self-estimated hair loss < Grad 2 (<50%) according to the Common terminology Criteria for adverse events V4.0 or not requiring a wig. Withdrawal from SC on patient’s demand was considered as failure. Tolerability and safety were also evaluated. Results: Ninety-four patients with chemotherapy for their primary (52%) or metastatic (48%) disease had a total of 634 SC sessions. SC was well accepted with increasing experience of the nurses (withdrawal for any reason 29/94). Among the female population (N = 85) 54% received a (neo-)adjuvant chemotherapy. Forty-eight percentages received a taxane-based therapy, 35% anthracycline-based, 17% platin compounds, and others. The overall success rate in the female sample was 72%. In the male group (N = 9), the majority had a metastatic disease (6/9) and received a taxane-based therapy (5/9). The rate of withdrawal by discomfort and pain was high, and the success rate was 44%. Conclusion: Our study confirms the satisfaction of patients with SC to prevent chemotherapy-induced alopecia. SC increases acceptance of the recommendation and administration of chemotherapy and decreases the degree of distress of patients and their treating physicians. Reimbursement remains a major issue in the out patient setting.
The harmonic reduction techniques in shunt active power filter when integrated with non-conventional energy sources
Authors: Rao Kambhampati Venkata Govardhan , Kumar Malligunta Kiran
Published: February 2022

The article covers the control techniques of shunt active filters using switching devices using artificial neural network (ANN) Theory. The basic idea is to achieve perfect disturbance minimization in both steady and transient states. The paper talks about a four-legged converter with a voltage source that can adjust for biased currents and harmonic elements caused by non-linear loads. A shunt connected active filter is used to minimise harmonic currents. The new proposed ANN controller for the improvement of percent total harmonic distortion (THD) is in comparison. The entire power filter concept is based on a MATLAB-modeled with ANN controller. The proposed circuit in this research is studied under various operating situations and simulated, demonstrating the system's potential.
Emergency congestion management of power systems by static synchronous series compensator
Authors: Zaidan Majeed Rashid , Toos Saber Izadpanah
Published: February 2022

From a transmission system point of view, any overload on the grid lines during operation in situations such as peak load or emergency conditions include line outage or generator outage, is refers to congestion. Generally, the congestion can be managed by controlling power flow. On the other hand, series compensation has a significant role in control power flow; therefore, series compensation equipment like fixed series capacitor (FSC), thyristor-controlled series capacitor (TCSC), and static synchronous series compensator (SSSC) can be used for congestion management. In this paper, an SSSC is used in a transmission line to manage congestion in emergency conditions, line outage and generator outage. The congestion rent contribution method has been used to determine the location of the SSSC in the IEEE 14-bus test system. This technique finds the transmission line 1-2 (from bus 1 to bus 2) is the best location of the SSSC to reduce congestion. After installing an SSSC in the specified line, simulation results show that the power flow has been controlled, leading to reducing the congestion. In other words, the effectiveness of the SSSC can be seen in reducing the total congestion rent, the total generation cost, and network losses.
The measurement set representation of the body posture based on group theory
Authors: Deng Changjian , Xie Xiaona
Published: February 2022

The foundation of measurement is the representation of measurement set. The paper proposes a body posture measurement set representation method based on concepts of group, fields, and ring. It attempts to explore the intrinsic relationship among numerous different measurement set. The attitude sensor is used to measure the attitude, and the measurement set representation and processing are analyzed based on the lie group theory in the paper. In the paper, the paper maps body posture data into image, and it is easier to identify the error of posture measurement data. Meanwhile, the simulation and test results show that the method can represent body posture data and detect it’s defects easily.
The effectiveness of discrete hermite wavelet filters technique in digital image watermarking
Authors: M. Abduldaim Areej , Abdulrahman Asma Abdulelah , Tahir Fouad Shaker
Published: February 2022

In this work, new discrete wavelets were derived Hermite polynomials for obtained discrete hermite wavelet transformation (DHWT), and their efficiency for use in image processing is demonstrated by proving the realization of important theorems. Moreover, the role of the new and proposed waveforms in their effective effect in placing the watermark with the color image is clarified, and a program was created using MATLAB software by creating a subprogram for constructing the new wavelet and proving its efficiency with an analytic image. The process is repeated using DHWT to analyze the image. The color image has been subjected to various attacks after which the watermark is retrieved from the image after comparing it with the proposed algorithm and it has proven its power faster and better than the previously suggested methods. The final conclusion shows that using new wavelets DHWT better peak signal of noise ratio (PSNR)s can be obtained and that the proposed algorithm fills in better the lack of awareness of the watermark and its strength under different attacks.
Text mining approaches for analyzing an Indonesian tafseer and translation of the holy Quran
Authors: Ayu Media Anugerah , Irawan Edi , Mantoro Teddy
Published: February 2022

The Indonesian tafseer and translation of Holy Quran is an important source of information and knowledge for Indonesian muslims, since not many Indonesian muslims understand Arabic language in the Quran.  However, the tafseer is full of the commentaries and explanation of each surah (chapter) and/or ayah (verse), which form a large document and not so easy to be accessed. Thus, the challenge is how to refer to both tafseer and translation in faster and accurate ways as one needs to always refer to them back and forth. Hence, this study proposes several text mining approaches, i.e.  most frequent words, K-means clustering, and association rules, to analyze an Indonesian tafseer and translation of Quran and provide insights of hidden knowledge and relationships based on statistical information derived from it.   These insights could be useful for muslims in general and for people that doing research in related areas.  This study shows interesting results from combined analysis of the approaches used which can help people accessing information in tafseer more efficiently.  As well, interesting relationships have been drawn from terms in the tafseer which could provide further and deeper knowledge on messages in the Quran.
Efficient data sensing and monitoring model for areca nut precision farming with wireless sensor network
Authors: Chandrashekarappa Niranjan Murthy , Mysore Bhagwan Sanjay Pande , Nagur Kotreshi Shivabasappa
Published: February 2022

Arecanut plays a prominent role in economic life in India; it produces ‘betel nut’ which is primarily used for the masticatory purpose. Nutrient’s cycle and environmental factors impact the forming, these impacts can be minimized through sensing technology i.e., wireless sensor network incorporated with internet of things (IoT). Designing of sensing technologies is considered as primary steps in achieving the arecanut production through precision agriculture; This research focuses on designing and developing an efficient monitoring mechanism named efficient data sensing and monitoring (EDSM), the proposed model will minimize the energy, reduce the false alarm rate, and enhance the detection accuracy. EDSM comprises four-step optimal sensing mechanism; first, formulate the energy consumption, further in this step the sensor device information and all the preliminary details are analyzed. Second step, data are sensed optimally, third step includes monitored and alert is generated the fourth step includes the optimization of packet size. EDSM is evaluated considering the different parameters like energy consumption and alert generation for temperature. Performance comparison is carried out with the existing model considering parameters like fault detection, false alarm detection, event detection, and event false alarm rate. Comparative analysis shows proposed methodology simply outperforms the existing model with significant improvisation.
Optimized scheduling of scientific workflows based on iterated local search
Authors: Jihad Alaa Abdalqahar , Faraj Al-Janabi Sufyan T. , Yassen Esam Taha
Published: February 2022

Recent years have witnessed a great interest in scientific applications with large data and processing-intensive, so cloud computing is used which provides the resources needed to implement and run these applications. One of the challenges in the management of scientific workflow applications is scheduling them to solve many combinatorial optimization problems, including reducing execution time, cost, resource utilization, and energy consumption. Due to the fact that the iterated local search algorithm (ILS) has been successfully applied to solve many combinatorial optimization problems, this paper investigates the performance of ILS in solving the scientific workflow scheduling problem which is a highly constrained problem. The main components that are different from one problem to others are the ILS parameters, local search, and perturbation, which must be carefully designed. The performance of the standard ILS has been examined and compared with the latest technology. The experimental results show that the proposed algorithm (ILS) obtained good results compared to the best-known results in the literature. This is due to the ILS being an adaptable metaheuristic, which can be simply adapted to diﬀerent search situations and instances.
Technological tools for virtual teaching and their effect on the satisfaction of online learning
Authors: Chamorro-Atalaya Omar , Alvarado-Bravo Nestor , Aldana-Trejo Florcita , Huarcaya-Godoy Madison , Anton-De los Santos Marco , Anton-De los Santos Juan , Fierro-Bravo Maritte
Published: February 2022

The objective of the research is to analyze the satisfaction of the online learning of the applied electricity subject, when implementing technological tools for virtual teaching. The development of the research determines a high level of student satisfaction, finding the perception of reliability with 93.05%, that of security with 93.2%, that of answer’s capacity with 90.73% and empathy with 82.87%. Satisfaction with the technological tools of virtual teaching is related to the adequate and accessible use of simulation software during online learning, which allowed compliance with the syllable. In addition to the security and confidence when the teacher is willing to help him in the use of the simulation software, responding to it appropriately and quickly. Satisfaction of online learning of the applied electricity subject using virtual teaching tools is related to the teacher's sample of concern towards students regarding their academic needs and their expressed interests.
Effects of using wordnet and spelling checker on classification methods in sentiment analysis for datasets using Bahasa
Authors: Andika Rizky , Suharjito Suharjito
Published: February 2022

Sentiment analysis was a system for recognizing and extracting opinions in documents. There were two weaknesses in sentiment analysis. The first weakness was preprocessing in sentiment analysis can’t recognize slang words so that important words that should have been recognized became unrecognizable. The Second was the feature extraction process in sentiment analysis only recognized words based on the form of the word but can’t recognize the similar word. In this paper, we proposed spelling checker and wordnet to fix these weaknesses. We also used k-nearest neighbor (KNN), Naïve Bayes, and decision tree as methods for check classify the text. The purpose of this research was first to know the effects of used Wordnet and spelling checkers in sentiment analysis and second was to improve the data processing process in the existing sentiment analysis. The dataset that we used in the research was a list of tweets in Bahasa. The results showed wordnet and spelling checker make a decrease in the valued of false positives, false negatives, and true negatives in the calculation of the confusion matrix. It can increase the accuracy of the K-NN from 43% to 72%, Naïve Bayes from 41% to 71% and decision tree from 47% to 75%.
Smart agriculture monitoring system for outdoor and hydroponic environments
Authors: Edwin Bijolin , Veemaraj Ebenezer , Parthiban Pradeepa , Devarajan Joseph Pushparaj , Mariadhas Vargheese , Nainar Ahila Arumuga , Reddy Maheshwar
Published: February 2022

Agriculture plays an important role in economic aspects in most countries like India. Numerous problems associated with farming are continuously affecting the actions that are happening in the country. A potential resolution for such issues to be eradicated, one should combine the technological advancements with current ongoing agricultural practices. Good agricultural practice will increase crop productivity and reduce unwanted water usage. Many authors have done research on temperature, nutrition, and pH-controlled systems. But no one concentrated on alert messages sent to the mobile phone. The main objective of the proposed system measures various natural aspects that use a global system for mobile communication (GSM) module that is connected to an Arduino to transfer the data that is obtained by the sensors to an internet of things (IoT) application programming interface (API) which is a kind of cloud computing of obtained data, this data can be analyzed if needed, and an alert short message service (SMS) is sent to the cell phone/mobile phone. The alert message can be done through conversational artificial intelligence (CAI). It is the collection of technologies behind triggering the message that will be sent automatically to the mobile as an SMS if any of the sensor values that are generating are not under already specified threshold values.