bluuebunny/crossref_metadata_2025 huggingface dataset:
Sign language detection using convolutional neural network for teaching and learning application
Authors: Wan Bejuri Wan Mohd Yaakob , Zakaria Nur’Ain Najiha , Mohamad Mohd Murtadha , Yassin Warusia Mohamed , Syed Ahmad Sharifah Sakinah , Choon Ngo Hea
Published: August 2022

Teaching lower school mathematic could be easy for everyone. For teaching in the situation that cannot speak, using sign language is the answer especially someone that have infected with vocal cord infection or critical spasmodic dysphonia or maybe disable people. However, the situation could be difficult, when the sign language is not understandable by the audience. Thus, the purpose of this research is to design a sign language detection scheme for teaching and learning activity. In this research, the image of hand gestures from teacher or presenter will be taken by using a web camera for the system to anticipate and display the image's name. This proposed scheme will detects hand movements and convert it be meaningful information. As a result, it show the model can be the most consistent in term of accuracy and loss compared to others method. Furthermore, the proposed algorithm is expected to contribute the body of knowledge and the society.
Fine-tuning approach in metaheuristic algorithm to prolong wireless sensor networks nodes lifetime
Authors: Rahiman Amir Rizaan , Williams Temitope Betty , Zakaria Muhammad D.
Published: August 2022

Wireless sensor networks (WSN) have evolved a vibrant and lively research field. It comprises numerous wise and low-power consumption devices for gathering the contiguous atmosphere's data. However, the energy dissipation matter that distorts network lifetime remains the challenge since the sensor node battery is non-rechargeable and irreplaceable. Clustering and routing protocol has become the furthermost solutions and invariably minimizes depletion and prolongs the sensor node lifetime. Such protocols have adopted metaheuristic algorithms to secure the efficiency of the clustering and routing protocols. However, the cluster head's extensive task favors consuming and draining more energy. This study proposed a fine-tuning solution for the sensor node's population and generation sizes. It benefits from the modified problem-oriented genetic algorithm parameters in securing the sensor node lifetime. Besides, the solution works effectively to balance the load of the cluster head nodes. A set of simulations has been performed using MATLAB R2018b on the proposed solution, namely the energy efficient of genetic (EEG) algorithm and has revealed that the solution outperforms the network lifetime and cluster head load of the existing solution.
Naïve-Bayes family for sentiment analysis during COVID-19 pandemic and classification tweets
Authors: Ressan Murtadha B. , Hassan Rehab F.
Published: August 2022

This paper proposes a system to analyze the sentiments of tweeters. It is to build an accurate model to detect different emotions in a tweet. The analysis takes place through several stages (i.e., pre-processing, feature extraction, and training more than one machine learning (ML)). Naïve Bayes, Multinomial Naïve Bayes and Bernoulli Naïve Bayes were selected as supervised machine learning for sentiment analysis using a dataset of 3,057 tweets with users ranging from fear to happiness, anger, and sadness because this method is suitable for solving a problem of this type. This system was also applied to another dataset of 10,000 Tweets (5,000 positive and 5,000 negatives). This approach, consisting of three Naïve Bayes classification models, was applied to two datasets to analyze the sentiment used in them and classify each category separately. The Multinomial Naïve Bayes model outperformed the other models Where it achieved an accuracy of (91.6%) when applied to the first dataset and accuracy (87.6%) when applied to the second dataset. The researchers aim to continue this research with larger data by using other methods of sentiment analysis to predict users' thoughts about COVID-19 or any other problem and to obtain higher accuracy for the models used.
Improved random early detection congestion control algorithm for internet routers
Authors: Hassan Samuel O. , Rufai Adewole U. , Agbaje Michael O. , Enem Theophilus A. , Ogundele Lukman A. , Usman Suleiman A.
Published: August 2022

In the internet, router plays a strategic role in the transmission of data packets. Active queue management (AQM) aimed at managing congestion by keeping a reduced average buffer occupancy and hence a minimal delay. The novel random early detection (RED) algorithm suffers from large average buffer occupancy and delay shortcomings. This problem is due in part to the existence of a distinctive linear packet drop function it deploys. In this paper, we present a new version of RED, called improved RED (IMRED). An important strategy of IM-RED is to deploy two dropping functions: i) nonlinear (i.e. quadratic) to deal with both light-and moderatenetwork traffic load conditions, and ii) linear to deal with heavy traffic load condition. Simulation experiments conducted using open-source ns-3 software to evaluate and compare the functionality of the proposed IM-RED with other two previous AQM algorithms confirmed that IM-RED reduces the average buffer occupancy and obtained an improved delay performance especially at heavy network traffic load scenario. Very fortunately, since RED algorithm is known to appear as a built-in model in ns-3 and even Linux kernel, its implementation can therefore be leveraged to obtain IMRED while only adjusting the packet dropping probability profile and holding on to its other attributes.
Simulation with system dynamics on university student research
Authors: Andrade-Arenas Laberiano , Llulluy Nuñez David , Delgado Alexi
Published: August 2022

Currently, student research at the university under study is carried out from the first semesters until the student graduates, but it is necessary to make adjustments to its guidelines that include student research within a teaching-learning process. The objective of the research is to propose a model of student research, using causal diagrams and forrester diagram; then carry out a simulation of the projection from 2021 to 2026 with the variables, students, quality teachers in the research, completed work for publication in journal or conference; and thus index in Scopus or Web of Science. Besides, in the methodological part, the systems dynamics method was applied, based on the systemic approach. The research work is predictive, which was carried out with the Vensim software, obtaining as a result of the simulation, an increase in the production of complete Works by the students; where in 2026 around 300 articles will be published by the students. In addition, there will be about 12 full-time quality research professors and approximately 6,000 students in 2026. The conclusion reached in the research is that the model helps the authorities make decisions appropriately according to the forecasts made in the different scenarios made with the Vensim software.
Implementation of a mobile application oriented to the tourism sector
Authors: Muñoz Villacorta Roberto Demmis , Aguero Carlos Oscco , Arenas Laberiano Andrade-
Published: August 2022

Currently, countries around the world are experiencing a health crisis due to the rapid spread of COVID-19. As a result, different sectors of the state and the citizens' economy have been affected. However, in Peru, the most affected sector has been tourism, since it contributes 4% of the gross domestic product and generates most of the jobs. The objective of the research is to develop a mobile application that allows the user to be guided on their trip, and that promotes national tourism, both from foreign and national visitors, carrying out the implementation in six phases under the scrum methodology; which consists of 3 sprints (deliverables) at the level of the entire project, which had a total duration of 6 weeks in which 35 story points were developed. In the same way, the prototypes corresponding to the functionality that the application will fulfill were added, allowing a better understanding for the reader. Finally, the difficulty in the use of the application and the fulfillment of its functionality was obtained as a result, this was carried out with the intervention of 20 users external to the project, in order to achieve its effectiveness.
Sentiment analysis through twitter as a mechanism for assessing university satisfaction
Authors: Chamorro-Atalaya Omar , Arce-Santillan Dora , Morales-Romero Guillermo , León-Velarde César , Ramos-Salaza Primitiva , Auqui-Ramos Elizabeth , Levano-Stella Miguel
Published: August 2022

Currently, the data generated in the university environment related to the perception of satisfaction is generated through surveys with categorical response questions defined on a Likert scale, with factors already defined to be evaluated, applied once per academic semester, which generates very biased information. This leads us to wonder why this survey is applied only once and why it only asks about some factors. The objective of the article is to demonstrate the feasibility of a proposal to determine the degree of perception of student satisfaction through the use of data science and natural language processing (NLP), supported by the social network twitter, as an element of data collection. As a result of the application of this proposal based on data science, it was possible to determine the level of student satisfaction, being 57.27%, through sentiment analysis using the Python library "NLTK"; Thus, it was also possible to extract texts linked to the relevant factors of teaching performance to achieve student satisfaction, through the term frequency and inverse document frequency (TF-IDF) approach, these being those linked to the use of tools of simulation in the virtual learning process.
How to determinate water quality using an artificial intelligent model based on grey clustering?
Authors: Delgado Alexi , López Carlos , Jacinto Noe , Chungas Mario , Andrade-Arenas Laberiano
Published: August 2022

Water quality is an important topic for countries like Peru, where the mining sector is one of the main economic activities, so the study of its impact on water quality is also necessary to have a regular control of benefits and dangers. In this way, to achieve this objective, the chosen methodology was grey clustering, which is based on artificial intelligent theory. Specifically, the central point triangular whitening weight function better known as CTWF, which is an approach from grey clustering, was used. The case study was focused on the Mashcon and Chonta rivers, located in the province of Cajamarca, Peru, these rivers are directly affected by an open pit mine. The study was carried out taking into account thirteen monitoring points taken by National Water Authority (ANA). The results showed that all the points considered were classified as not contaminated, A1 category, this using the parameters of the Peruvian government. With these results, the mining company was able to demonstrate that they are taking the water quality into account and that they are making an effort to keep these rivers as healthy as possible.
Document verification using quick response code with modified secure hash algorithm-1 and modified blowfish algorithm
Authors: Quilala Rogel Ladia , Ginoy Quilala Theda Flare
Published: August 2022

A previous study has been conducted integrating modification on secure hash algorithm 1 (SHA-1) to document integrity verification of printed documents using quick response (QR) codes. However, encryption is warranted as data is transmitted in plaintext directly to the server to prevent hacking and ensuring not only data integrity but data security as well. A more secured document integrity verification using QR code was designed and developed by successfully incorporating a better hashing algorithm– modified SHA-1 and integrating a modern encryption algorithm–modified blowfish algorithm. By integrating both, data integrity and data confidentiality is assured as compared to previous research. The developed software was checked against user requirements to check the acceptability of the software. Error rate and accuracy were also checked to see how the software performed. Based on the testing conducted, it has been found that the document integrity verification software using QR code with the integration of modified SHA-1 hash and Modified Blowfish encryption was acceptable, accurate, and more secure.
Secure authentication and privacy-preserving to improve video streaming vehicle ad-hoc network
Authors: Leaby Akeel Kassim , Khalefa Mustafa , A. Hasson Mushtaq , A. Yassin Ali
Published: August 2022

In vehicular ad hoc networks (VANET), the privacy of vehicle data symbolizes a big challenge towards malicious attacks. On the other side, vehicles in VANET can play a staple role in monitoring the environment by sensing the surrounding environment, compute the sensing information, and transfer the results if needed to the authorized party. Most of the modern VANETs systems encrypt the information to prevent hacking it but mostly neglect the decryption that occurred when data need to re-processed. In this paper, we try to cover this weak point by using fully homomorphic encryption (FHE) because of its specifications. The proposed work focus on twofold: first, create secure authentication and permission management system. While the second is to preserve the privacy of vehicle data that transferred among VANET infrastructure. This scheme also deals with metric security features, such as data privacy, data integrity, and key management. In the experimental results, there is good advance in the fields of interest comparing with the related works.
Comparative analysis on virtual private network in the internet of things gateways
Authors: Zakaria Mohd Idzaney , Natashah Norizan Mohd , Mohamad Isa Muammar , Faizal Jamlos Mohd , Mustapa Muslim
Published: August 2022

A virtual private network (VPN) connects a private network to the internet, primarily the public network, through a secure tunnel. Using a local area network (LAN) segment, users can send and receive data from their colleagues in different locations on the network. The development of VPN allows users to gain access to company applications and databases. Therefore, data can be transmitted through a secure tunnel without the need to configure port forwarding for the internet of things (IoT) gateway, allowing users to access it from any location in the world. A method such as dataplicity and pitunnel was examined to compare with the conventional setting. This research paper examines the current deployment of VPN connections in IoT gateways, discussing their characteristics, benefits, and drawbacks, as well as comparing them. The advantage of this method is that the IoT gateway is always accessible and has internet connectivity, w
Machine learning based smart weather prediction
Authors: Meenal Rajasekaran , Kailash Kiruthic , Michael Prawin Angel , Joseph Jeyaraj Jency , Josh Francis Thomas , Rajasekaran Ekambaram
Published: August 2022

Weather forecasting refers to the prediction of atmospheric conditions depending on a given time and location. Weather prediction is essential and it plays a significant role in many sectors namely energy and utililities, marine transportation, aviation, agriculture and forestry to a greater extent. Accurate weather forecast mechanism help the farmers for suitable planning of farming operations that will prevent crop losses. In this work, the weather parameters namely precipitation, relative humidity, wind speed and solar radiation were predicted for few Indian locations using the conventional temperature based empirical models and machine learning algorithms such as linear regression, support-vector machine (SVM) and decision tree. Forecasting of weather parameters, on which agriculture depends, will increase the overall yield and it helps farmers and agricultural-based businesses to plan better. From the current results, it is observed that machine learning (ML) based methods had a better prediction results than the physics based conventional models for weather forecasting with mean square error of 0.1397 and correlation coefficient of 0.9259. The objective of this work is to arrive at an optimized end result and a better weather prediction using the Machine learning models with lesser computational effort.
Supervised learning using support vector machine applied to sentiment analysis of teacher performance satisfaction
Authors: Chamorro-Atalaya Omar , Arce-Santillan Dora , Arévalo-Tuesta José Antonio , Rodas-Camacho Lilia , Dávila-Laguna Ronald Fernando , Alejos-Ipanaque Rufino , Moreno-Chinchay Lilly Rocío
Published: August 2022

Satisfaction with teaching performance is an important measurement process in higher education institutions, for this reason, applying sentiment analysis to the opinions of university students through the support vector machine (SVM) Fine Gaussian supervised learning algorithm represents an important contribution to the academic literature. This article identifies the best classification algorithm according to performance parameters for predicting student satisfaction with teaching performance through sentiment analysis; the subsequent implementation of the research has the purpose of strengthening teaching practices, in addition to allowing continuous training of teaching for the benefit of student learning. This article has provided a compact predictive model, with literature review based on SVM and sentiment analysis techniques. Through the machine learning classification learner technique, it is identified that the SVM algorithm: Fine Gaussian SVM is the one with the best accuracy equal to 98.3%. Likewise, the performance metrics for the four classes of the model were identified, which have a sensitivity equal to 88.89%, a specificity of 98.04%, a precision of 99.21% and an accuracy of 98.85%.
Text mining and sentiment analysis of teacher performance satisfaction in the virtual learning environment
Authors: Chamorro-Atalaya Omar , Arce-Santillan Dora , Arévalo-Tuesta José Antonio , Rodas-Camacho Lilia , Sandoval-Nizama Genaro , Valle-Chavez Rosa , Rocca-Carvajal Yadit
Published: August 2022

Although it is true that artificial intelligence and data science have become key tools that contribute to the improvement of many processes, identifying patterns and contributing to decision making, however, there are environments in which they are not yet being using it relevantly and effectively. The objective of this study is to identify the relevant factors, based on the opinions expressed by the students through the social network Twitter regarding the perception of satisfaction with the teaching performance during the virtual learning environment. For which sentiment analysis and text mining are used under the Python programming language environment, through JupyterLab. As results, it was determined that a predominance of 57.27% of positive polarity, identifying that the relevant factors of student satisfaction with teaching performance, are related to the development of the teacher in the class sessions that contributes to the learning of the process control subject through the use of simulation tools such as simulink and tools linked to proportional integral derivative (PID) controllers; on the other hand, there is a percentage of negative polarity of 15.45% that belongs to the factors linked to the laboratory sessions in which graphic representation and block diagrams were used to explain the class session.
A new three-term conjugate gradient method for training neural networks with global convergence
Authors: Luqman Ibrahim Alaa , Mohammed Mohammed Guhdar
Published: August 2022

Conjugate gradient methods (CG) constitute excellent neural network training methods that are simplicity, flexibility, numerical efficiency, and low memory requirements. In this paper, we introduce a new three-term conjugate gradient method, for solving optimization problems and it has been tested on artificial neural networks (ANN) for training a feed-forward neural network. The new method satisfied the descent condition and sufficient descent condition. Global convergence of the new (NTTCG) method has been tested. The results of numerical experiences on some wellknown test function shown that our new modified method is very effective, by relying on the number of functions evaluation and number of iterations, also included the numerical results for training feed-forward neural networks with other well-known method in this field.
Implementation of augmented reality on historical monuments in Gorontalo Province
Authors: Arif Kaluku Moh Ramdhan , Pakaya Nikmasari , Leoni Yagri Ms Punu Galang
Published: August 2022

Several historical monument buildings in the city of Gorontalo with important colonial historical features have been designated as cultural reserves. The unavailability of information media that can be accessed by visitors so that visitors do not know in detail about the historical place visited, by implementing augmented reality (AR) technology as access to information media using multimedia development life cycle (MDLC) methods, visitors can access information freely and in real time, by presenting information and also displaying three-dimensional (3D) monument buildings with android devices. Based on research conducted, the design of AR applications is used to create an information media, and also one of the methods of introducing gorontalo historical monuments that can be used for prospective visitors outside the area and within the area. Implementing AR on historical monuments in Gorontalo Provides a new alternative, in utilizing technology by providing an information medium for historical monuments in Gorontalo.
Deep learning application for real-time prediction of COVID-19 outbreak with susceptible-infected-recovered-deceased model
Authors: Nguyen Hoang-Sy , Phan Thi Thu Ngan , Huynh Cong-Danh
Published: August 2022

Due to the complex nature of a pandemic such as COVID-19, forecasting how it would behave is difficult, but it is indeed of utmost necessity. Furthermore, adapting predictive models to different data sets obtained from different countries and areas is necessary, as it can provide a wider view of the global pandemic situation and more information on how models can be improved. Therefore, we combine here the long-short-term memory (LSTM) model and the traditional susceptible-infected-recovered-deceased (SIRD) model for the COVID-19 prediction task in Ho Chi Minh City, Vietnam. In particular, LSTM shows its strength in processing and making accurate numerical predictions on a large set of historical input. Following the SIRD model, the whole population is divided into 4 states (S), (I), (R), and (D), and the changes from one state to another are governed by a parameter set. By assessing the numerical output and the corresponding parameter set, we could reveal more insights about the root causes of the changes. The predictive model updates every 10 days to produce an output that is closest to reality. In general, such a combination delivers transparent, accurate, and up-to-date predictions for human experts, which is important for research on COVID-19.
The effect of electrolyte parameter variation upon the performance of lithium iron phosphate (LiFePO4)
Authors: Ranom Rahifa , Syakinah Bacho Risna , Sakinah Ahmad Jamal Saidatul Nur Aisyahtun
Published: August 2022

This study focuses on the effect of electrolyte parameter variation upon the performance of lithium iron phosphate (LiFePO4) due to high demand of the electronic devices among consumers and industrial sectors that require efficicent LiFePO4 batteries. Hence, it is essential to improve its performance by optimizing the electrolyte parameter of the cell. The mathematical models of lithium-ion batteries based on the drift-diffusion model consists of the electrolyte and lithium transport equations in the electrode particles which incorporated the geometry of cell microstructure to the coefficients in the macroscopic model was solved numerically using the method of lines (MOL) technique. The effect of electrolyte parameter variation on the discharge curve of LiFePO4 using lithium hexa-fluoro-phosphate (LiPF6 ) as the base electrolyte is examined by varying the electrolyte parameters: initial concentration of lithium ions (c0), the effective diffusivity of lithium ions (D) and the effective ionic conductivity (𝜅). The simulations show that higher diffusivity of lithium ions produces large discharge curve and higher ionic conductivity increase the mobility of the ion in the cell. Meanwhile, higher initial concentration of lithium ions produced high battery performance compared to lower initial concentration of lithium ion resulting more discharge time before electrolyte depletion occurs.
Comparative analysis of time series prediction model for forecasting COVID-19 trend
Authors: Wahyuni Sri Ngudi , Sediono Eko , Sembiring Irwan , Nahar Khanom Nazmun
Published: August 2022

The outbreak of the COVID-19 pandemic occurred some time ago, making the world a pandemic. Based on this condition is important to predict early to prevent the COVID-19 disease if someday pandemic occurs. The aim of the study is to compare the analysis result of cumulative cases of COVID-19 using multiple linear regression (MLR), ridge regression (RR), and long short term memory (LSTM) models for cases study Java and Bali islands. We chose both islands as a case study because they have very dense populations. These three models are the most widely used time series-based prediction models and have relatively high accuracy values.  The predictive variables used are the number of cumulative cases, the daily cases, and population density. The research data was taken from Kaggle and processed using google collabs. Data was taken from January 20, 2020, to August 8, 2020, and data training was carried out for 12 days. The results show the accuracy of LSTM is better than other models. it can be seen in the accuracy value (99.8 %) of the model test result. The testing model uses R2, mean square error (MSE), and root mean square error (RMSE).
Inheritance issues’ features extraction using Arabic text analyzer (IFAA)
Authors: K. Al-Mashhadany Abeer , B. Mohammed Marwan , Alrawi Mawlood Alrawi
Published: August 2022

Inheritance issue is part of our life. Daily, many persons may die. Person is gone, while his money stays for others. Islamic law took care of the issue of inheritance. Al-Quran has verses dedicated in inheritance issue. Al-Quran gives every person its rights, so; the share for each heir is determined. Islamic law jurists are asked frequently to solve inheritance issues. This work; inheritance issues’ features extraction using Arabic text analyzer (IFAA) hopes to analyze inheritance issue. It receives the issue as Arabic unstructured characterized text. It applies Arabic analyzer system to extract all features. Many commercial applications are constructed to solve inheritance issue; they receive the features manually, while this work is an attempt to computerize features' extracting. This work needs a good experience in analyzing Arabic text. So, this research attempts developing Arabic analyzer system dedicated in inheritance issues, which has the ability to analyze inheritance issue and extract its features. It will be shown that Arabic analyzer system is useful in converting Arabic text into data that are understandable by programming languages, and those data could be used to perform arithmetic calculations, and achieve high accuracy reaches to 100%.
Non-isolated high voltage gain DC to DC converter based on the diode a capacitor switches
Authors: Billy Ibraheem Jawad , Hussein Jasim Farhood
Published: August 2022

Many researchers have put great endeavor to develop DC converter’s designs, into studying how to increase voltage gain with low switching stress and low ripple current. This paper has proposed a circuit to boost the voltage with a high gain conversion ratio. It is a combined adverse parallel two boost conversion. Two inductors are connected on both sides of the input source to decrease the current-ripple of the input current and output sides utilizing the interleaving technique. The proposed converter integrated with an active-network circuit is based on multiplier cells and two output capacitors. The voltage gain and voltage stresses across power semiconductors have been determined using a steady-state analysis. In addition, the input current- ripple and output voltage-ripple are analysis have been reported. This converter's inductors operate in a continuous conduction mode (CCM). The designed converter is capable of achieving significant voltage gain while maintaining a low duty ratio. Furthermore, the active switches and output diodes are under low voltage stress. As a result, low voltage components can be used to decrease conduction loss and cost. Finally, this converter was simulated in MATLAB/Simulink software to verify the theoretical calculations.
Single board re-spin for testing bridge transducer products
Authors: D. de la Rama Frances , L. Tocama Marwin , N. Ortiz Glenn , Joseph B. Enojas Mark
Published: August 2022

Integrated circuit (IC) testing involves equipment and product interface board setup. Complex interfaces and obsolete components are some of the factors affecting the board functionality which results in manufacturing downtime. The legacy boards must be simplified so that it needs advanced tools for the lay-out and designing of test boards. This study proposes a solution for board functionality issues by re-spinning the legacy two-board interface into a single board interface. It converts the standard use of boards and standard contacting to single board interface (SBI) and plunge-to-board (PTB) contacting. As a result, the setup time is improved, minimizing board and system repair due to mismatch and contact issues. The board endorsement for contact issues were trimmed from 82 to 46 counts. Other board related issues are decreased by 60% based on the analysis of the correlation in the processes. Additionally, mismatches, which are system issues, are lessened which promotes fixtures maintainability.
Implementation of a sensor node for monitoring and classification of physiological signals in an edge computing system
Authors: Yauri Ricardo , Castro Antero , Espino Rafael , Gamarra Segundo
Published: August 2022

We describe the design and development of sensor nodes, based on Edge computing technologies, for the processing and classification of events detected in physiological signals such as the electrocardiographic signal (ECG is the electrical signal of the heart), temperature, heart rate, and human movement. The edge device uses a 32-bit Tensilica microcontroller-based module with the ability to transmit data wirelessly using Wi-Fi. In addition, algorithms for classification and detection of movement patterns were implemented to be implemented in devices with limited resources and not only in high-performance computers. The Internet of Things and its application in smart environments can help non-intrusive monitoring of daily activities by implementing support vector machine (SVM is a machine learning algorithm) for implementation in embedded systems with low hardware resources. This paper shows experimental results obtained during the acquisition, transmission, and processing of physiological signals in a edge computing system and their visualization in a web application.
Characterizing the Accuracy of
International Classification of Diseases, Tenth Revision
Administrative Claims for Aortic Valve Disease
Authors: Strom Jordan B. , Xu Jiaman , Sun Tianyu , Song Yang , Sevilla-Cazes Jonathan , Almarzooq Zaid I. , Markson Lawrence J. , Wadhera Rishi K. , Yeh Robert W.
Published: August 2022

Background:

Administrative claims for aortic stenosis (AS) regurgitation may be useful, but their accuracy and ability to identify individuals at risk for valve-related outcomes have not been well characterized.

Methods:

Using echocardiographic (transthoracic echocardiogram [TTE]) reports linked to US Medicare claims, 2017 to 2018, the performance of candidate
International Classification of Diseases, Tenth Revision
claims to ascertain AS/aortic regurgitation was evaluated. The optimal performing algorithm was tested against outcomes at 1-year after TTE in a separate 100% sample of US Medicare claims, 2017 to 2019.

Results:

Of those included in the derivation (N=5497, mean age 74.4±11.0 years, 49.7% female), any AS or aortic regurgitation was present in 24% and 38.8%, respectively. The sensitivity and specificity of
International Classification of Diseases, Tenth Revision
code I35.0 for identification of any AS was 53.1% and 94.8%, respectively. Among those with an I35.0 code, 40.3% had severe AS. Claims were unable to distinguish disease severity (ie, severe versus nonsevere) or subtype (eg, bicuspid or rheumatic AS), and were insensitive and nonspecific for aortic regurgitation of any severity. Among all beneficiaries who received a TTE (N=4 033 844), adjusting for age, sex, and 27 comorbidities, those with an I35.0 code had a higher adjusted risk of all-cause mortality (adjusted hazard ratio, 1.33 [95% CI, 1.31–1.34]), heart failure hospitalization (adjusted hazard ratio, 1.37 [95% CI, 1.34–1.41]), and aortic valve replacement (adjusted hazard ratio, 34.96 [95% CI, 33.74–36.22]).

Conclusions:

Among US Medicare beneficiaries receiving a TTE,
International Classification of Diseases, Tenth Revision
claims, though identifying a population at significant greater risk of valve-related outcomes, failed to identify nearly half of individuals with AS and were unable to distinguish disease severity or subtype. These results argue against the widespread use of
International Classification of Diseases, Tenth Revision
claims to screen for patients with AS and suggests the need for improved coding algorithms and alternative systems to extract TTE data for quality improvement and hospital benchmarking.
The Twitter Following For the Beijing Winter Olympics and the Russian–Chinese Fans’ Alliance: A Social Network Analysis
Authors: Hayat Tsahi (Zack) , Galily Yair , Samuel-Azran Tal
Published: August 2022

The current study examines the extent to which mega sport events play a role in connecting people from different countries, using the 2022 Beijing Winter Olympics as its case study. The analysis examines social connection (on Twitter) between Chinese and Russians and whether these connections are more likely to occur among the followers of the @Beijing2022 Twitter account, if compared with Twitter users in these countries who do not follow this account. The choice of the Russians and the Chinese also stems from their countries’ united front against those Western countries whose diplomats boycotted the games. The analysis reveals that, in cases where two people follow @Beijing2022, the likelihood of there being a connection between them increases by 8%, as compared with those people who do not follow this account, while controlling for other relevant variables. The findings indicate that mega sport events which take place under a boycott have the ability to be enhancers of international social connections.
Online Football-Related Antisemitism in the Context of the COVID-19 Pandemic: A Multi-Method Analysis of the Dutch Twittersphere
Authors: Seijbel Jasmin , van Sterkenburg Jacco , Spaaij Ramón
Published: August 2022

This paper examines online expressions of rivalry and hate speech in relation to antisemitic discourses in Dutch professional men’s football (soccer), with specific attention devoted to how this has developed within the context of the COVID-19 pandemic. This study analyses football-related antisemitic discourses in the Dutch-speaking Twittersphere between 2018 and 2021. Assuming that during the ongoing COVID-19 pandemic fan activity has moved increasingly toward the online domain, we specifically examine whether and how the past pandemic years have influenced football-related antisemitic discourses on Twitter. Tweets were scraped using the Twitter application programming interface and 4CAT (a capture and analysis Toolkit), producing a dataset of 7,917 unique posts. The authors performed thematic analysis of the Tweets and a selection of the Tweets was analyzed in depth using narrative digital discourse analysis. The findings show how these Tweets, while seemingly targeted exclusively at football opponents, contribute to wider exclusionary discourse in football and society that may have become more aggravated during the COVID-19 pandemic.
Hepatocellular-Cholangiocarcinoma Collision Tumors: An Update of Current Management Practices
Authors: Goodwin Brandon , Lou Johanna , Butchy Margaret , Wilson Traeden , Atabek Umur , Spitz Francis , Hong Young
Published: August 2022

Combined hepatocellular-cholangiocarcinoma (cHCC-CC) is a rare form of primary hepatic collision tumor, with an incidence ranging from 0.4 to 14.2%. Given the diagnostic challenges and lack of randomized trials, standardized treatment has yet to be established. We aim to review the literature to summarize the diagnosis, molecular characteristics, current treatment modalities, and challenges for cHCC-CC. A literature review was performed using PubMed. We included studies investigating and describing cHCC-CC, focusing on surgical, medical, and radiologic treatments. Overall prognosis is poor, with a 5-year survival rate under 30%. Minor or major hepatectomy with R0 resection is the only curative treatment; however, recurrence is likely (as high as 50% within 5 years). The role of liver transplantation is also highly debated given the biliary nature of these tumors, with cHCC-CC as a relative contraindication for liver transplantation. Although gemcitabine-based treatments had higher progression-free survival over sorafenib, there is no standard chemotherapy regimen. Treatment with gemcitabine and platinum demonstrates improved disease control rates compared to gemcitabine in conjunction with 5-fluorouracil (78.4% verse 38.5% respectively). Additionally, platinum-containing chemotherapy regimens exhibit a higher overall response rate than non-platinum regimens (21.4% verse 7.0% respectively). These molecular-directed therapies have prolonged survival for HCC, but further investigation needs to be done to assess their utility in patients with cHCC-CC. cHCC-CC is a rare and complex subset of primary hepatic neoplasms with a dismal prognosis and unstandardized treatment options. Further trials need to be performed to investigate systemic chemotherapy and immunotherapy options for patients with unresectable disease.
Indications and Outcomes of Completion Cholecystectomy: A 5-year Experience From a Rural Tertiary Center
Authors: Alser Osaid , Dissanaike Sharmila , Shrestha Kripa , Alghoul Heba , Onkendi Edwin
Published: August 2022

Background

Completion cholecystectomy (CC) is performed for recurrent or persistent biliary symptoms following subtotal cholecystectomy (STC) or incomplete cholecystectomy (IC). Due to its complexity, cases are often referred to hepato-pancreato-biliary (HBP) surgeons. There is little published literature on indications or outcomes of CC.

Methods

Completion cholecystectomy cases performed between 2016 and 2021 by the sole HPB surgeon covering a rural referral base of >250-mile radius in West Texas were included. Primary variables of interest include indications and outcomes of CC.

Results

Of the eleven patients included, 5 (45.5%) had laparoscopic STC, 3 patients (27.3%) had laparoscopic converted to open STC, and 2 (18.2%) had laparoscopic IC. Most STC cases (6/9, 66.6%) were reconstituting, while 3 STC cases were fenestrating (all had persistent bile leak). For reconstituting STC, indications were symptomatic cholelithiasis in 5 patients (45.5%), and choledocholithiasis in 3 patients (27.3%). The median (IQR) duration between index procedure and subsequent CC was 15 (1.4-92) months. The median (IQR) remnant gallbladder length was 4 (3-4.5) cm. Completion cholecystectomy was performed robotically in 8 cases (72.7%). Post-CC complications occurred in 3 patients (27.3%); these were 1 superficial surgical site infection, 1 hepatic abscess requiring percutaneous drainage, and lastly atrial fibrillation.

Conclusions

All patients requiring CC had residual gallbladder remnant >2.5 cm; this is longer than recommended for STC. Completion cholecystectomy is a complex operation that carries significant morbidity, even when performed using minimally invasive techniques. As bailout procedures become more common in severely inflamed cholecystitis, it is important to collate more data on the outcomes of requiring CC.
Do faecal test-based colorectal cancer screening pilots provide data that are reflected in subsequent programmes? Evidence from interval cancer proportions
Authors: Clark Gavin RC , Steele Robert JC , Fraser Callum G
Published: August 2022

Objective

Guidelines on colorectal cancer (CRC) screening with guaiac faecal occult blood tests (gFOBTs) and faecal immunochemical tests (FITs) include the need for a pilot before a programme is introduced. Interval cancers (ICs), cancers arising after a negative screening test result but before the next scheduled invite, are important indicators of programme quality. Our aim was to compare IC in the gFOBT-based Scottish Bowel Screening Programme (SBoSP), a FIT-based pilot, and the FIT-based SBoSP, to assess if the pilot provided data that was reflected in the subsequent programme.

Design

The IC proportions (ICPs) data ([IC/(IC + screen detected CRC)] x 100) from the penultimate year of the gFOBT-based SBoSP, the 6-month pilot and the first year of the FIT-based SBoSP were compared. To ensure appropriate comparison, these data were only from the two pilot NHS Boards.

Results

For all participants, and females and males, the ICPs were very similar in the gFOBT-based SBoSP and the pilot. The faecal haemoglobin concentration (f-Hb) threshold for the pilot was set at ≥80 μg Hb/g faeces. However, in marked contrast, in the FIT-based SBoSP, at the same threshold, the ICPs were lower. In all three groups, the ICPs were higher in females than in males.

Conclusions

Data on variables in pilots, including ICP, can be informative, but only if variables such as FIT system are held consistent between pilot and programme. Lowering the f-Hb threshold for females to give the same ICP as males might be a strategy to minimise sex inequality.
Why Social Enterprises Resist or Collectively Improve Impact Assessment: The Role of Prior Organizational Experience and “Impact Lock-In”
Authors: Ormiston Jarrod
Published: August 2022

This article examines how organizational experience influences social enterprise responses to impact assessment practices. Limited attention has been paid to why organizations resist or challenge impact assessment practices or how prior experience with impact assessment may shape organizational responses. The study draws on interviews with practitioners involved in social enterprise–impact investor dyads in Australia and the United Kingdom. The findings reveal that social enterprises enact either combative or collaborative responses in their relationships with impact investors based on past experiences with impact assessment. The study shows how more experienced social enterprises reach a state of impact lock-in—where they become committed to particular approaches to understanding, measuring, and reporting impact. The article contributes to the literature on impact assessment and impact investment by showing how organizational experience shapes divergent reactions to the demands imposed by impact investors, creating complementary forces of institutionalization and contestation of impact assessment practice.
Medication Use in the Diagnostic Pediatric Lumbar Puncture
Authors: Naik Vishal , Jin Jing , Tinsay Andrea , Pricco Nicholas , Madhok Manu
Published: August 2022

The lumbar puncture (LP) is a common procedure in the pediatric emergency department. A retrospective review was conducted of patients who had LPs from 2012 to 2016 at 2 children’s hospitals to (1) characterize medication use during the pediatric LP and (2) test the hypothesis that varied medication use influences LP outcome. Outcomes were defined as unsuccessful if the LP was documented as unsuccessful, had a cerebrospinal fluid (CSF) red blood cell (RBC) count >400 cells/µL, or if a second LP was performed within 24 hours. In total, 8463 patients were reviewed and 2806 (33%) were included in the study. We noted significant variation in LP medication use. When adjusted for patient demographics, location, weight, position, and provider experience, our regression model revealed that the use of fentanyl, ketamine, nitrous oxide, and propofol were best associated with LP success. These data suggest the need for a standardized LP medication protocol as provider choice in medication significantly influences LP outcome.
Privileging One’s Own? Voting Patterns and Politicized Spending in India
Authors: Jensenius Francesca R. , Chhibber Pradeep
Published: August 2022

How do politicians allocate public resources? Despite the extensive literature on distributive politics, we have limited micro-level evidence for why and under what circumstances politicians choose various allocation strategies. India’s discretionary constituency development scheme (MPLADS) provides an excellent opportunity to study the spending choices of individual politicians. Drawing on an original dataset linking voting patterns across 227,507 villages in the 2009 general elections to MPLADS allocations 2009–2014, we find that politicians generally channel more projects and resources to villages that vote for them in higher numbers. We then leverage a natural experiment created by the delimitation (redistricting) of electoral boundaries in 2008 to provide causal evidence that spending choices are driven by short-term electoral incentives. Finally, we show that allocation patterns differ by the type of party that has brought a politician to power. Our findings contribute to the growing literature on heterogeneity in politicians’ distributional choices.
Community-Based Organizations and Stakeholders’ Engagements: A Dialectics of Countering Violent Extremism and Humanitarian Service Delivery in North-East Nigeria
Authors: Lenshie Nsemba Edward , Miapyen Buhari Shehu , Ugwueze Michael I. , Ezeibe Christian
Published: August 2022

There is still little discussion on how community-based organizations (CBOs) may help counter violent extremism (CVE) in Nigeria. This research explores the implications of CBOs’ use of external networks for CVE and the distribution of humanitarian aid in North-East Nigeria. It finds that because CBOs depend so heavily on outside funding, they are constantly exposed to the demands and whims of donors. We therefore urge the government to prioritize CBOs in CVE programmes and operations to reduce external influence and to limit the spread of violent extremism in the region.
Comparison of Dynamic Balance Performances of Adult Runners With Intellectual Disability Versus Their Sedentary Peers
Authors: Jouira Ghada , Srihi Selim , Ben Waer Fatma , Rebai Haithem , Sahli Sonia
Published: August 2022

Our aim in this study was to compare the dynamic balance between runners (100 sprint) and non-runners with intellectual disability (ID). Our participants were 36 healthy male adults with ID aged 17–29 years, divided into runners ( n = 18) and sedentary non-runners ( n = 18). We assessed dynamic balance in these participants, using the Y-Balance Test (YBT), the Expanded-Timed Up and Go Test (ETUGT) and the Functional Reach Test (FRT). Runners with ID had significantly better balance scores than their sedentary counterparts on YBT (p < .001), the two walking phases of the ETUGT (p < .05) and the FRT (p <.05). Thus, running training (100 m sprint) was associated with better dynamic balance in this sample.