bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Maternal and Neonatal Outcome of Patients with Preterm Prelabor Rupture of Membranes
Authors: Yan Chunmei , Deng Xiaohui , Hong Fanzhen
Published: March 2022

Background. Preterm prelabor rupture of membranes (PPROM) increases risk of maternal and neonatal diseases. Expectant treatment is one major treatment for PPROM patients, but it raises concerns on infection. Currently, the optimal delivery time for PPROM patients is still unclear, and there are various outcomes for the patients with PPROM. Previous studies conducted to analyze the pregnancy outcome showed inconsistent results. The purpose of this study is to retrospectively analyze the maternal and neonatal outcomes for comparison among different latency periods of patients with PPROM at a university hospital in China. Method. This was a retrospective study. We divided all patients with PPROM into four groups according to gestational weeks, namely, group A (GA 24–27+6), group B (GA 28–31+6), group C (GA 32–33+6), and group D (GA34-36+6). The maternal and neonatal outcomes of each group were observed, respectively. Groups B and C were separately divided into two subgroups according to the median latency period of each group, namely, B1, B2, C1, and C2. Then, the differences of pregnancy outcomes between B1 and B2, C1 and C2, were compared, respectively. A

p

value < 0.05 was considered statistically significant. Result. Group A: the common maternal and neonatal complications were the increased infection index before labour, neonatal hyperbilirubinemia and neonatal respiratory distress syndrome. Groups B, C, and D: the common maternal and neonatal complications were the increased infection index before labour, fetal distress, neonatal pneumonia, neonatal hyperbilirubinemia, and patent foramen ovale. Comparison of pregnancy outcome between group B1 and group B2 showed higher incidence rate of increased infection index before labour, lower incidence rate of respiratory distress syndrome, electrolyte disturbance, and premature brain in group B2 than those in group B1. Comparison of pregnancy outcome between group C1 and group C2 showed the higher incidence of increased infection index before labour, bigger birth weight, and shorter hospital stay in group C2 than those in group C1. Conclusion. Increased infection index before labour was common maternal complication in four groups. Neonatal hyperbilirubinemia and neonatal pneumonia were top neonatal complications in four groups. The prolongation of latency period was beneficial to newborns of patients with gestational week at 28–31+6 weeks, while it did not benefit those with gestational week beyond 32 weeks.
A Patient Presenting with Lower Extremity Paralysis due to Acute Aortic Occlusion
Authors: Strom Theodore , McIntosh Mark
Published: March 2022

Acute aortic occlusion (AAO) is a rare and life-threatening condition that is rarely described in limited case series over the past several decades. The etiology and management are diverse across documented accounts, but prompt recognition facilitated by performing a thorough vascular and neurologic exam is critical to prevent delayed diagnosis and adverse outcomes. We report a patient who presented to the emergency department with the complaint of acute-onset lower extremity paralysis due to acute aortic occlusion. Her condition was rapidly diagnosed with a CT angiogram protocolized for aortic dissection and managed with anticoagulation and thrombectomy with eventual near complete recovery of her lower extremity function.
Web Page Classification Algorithm Based on Deep Learning
Authors: Yu Yuanhui
Published: March 2022

Transmit and process information to establish a learning mechanism and realize the processing of image data and sound data. However, the current research on Web page classification algorithm (WPCA) based on deep learning (DL) is not in-depth. Therefore, the main research of this article is the research of WPCA based on DL. This article first uses the keyword weight calculation method to reduce the impact of a small number of high-frequency words in the web page document on the weight calculation and reduces the value of the low-frequency word weights so that the WPCA is more accurate in the calculation process; second, the use of Chinese web pages: the classification method calculates the similarity between the text to be classified and all the class templates and then determines the category of all texts according to the similarity and certain classification rules; finally, in order to improve the learning rate of DL, consider using adaptive parameters. The optimization algorithm automatically adjusts the size of the learning rate, making the research of WPCA based on DL more efficient. After comparing the DL-based WPCA with the traditional algorithm, the data shows that in terms of time expenditure, the DL WPCA is 354 s, the traditional algorithm is 2436 s; in terms of memory overhead, the DL WPCA is 6.35 s, the traditional algorithm is 186.25 s. The experimental results show that WPCA based on DL are faster and more efficient than traditional algorithms and consume less system memory.
Production of Chicken Patties Supplemented with Cantaloupe By-Products: Impact on the Quality, Storage Stability, and Antioxidant Activity
Authors: Ali Maha IK , Ibrahim Rehab Mohamed , Mostafa Aliaa G. M.
Published: March 2022

This study investigated the effect of supplementation with cantaloupe peel (CP) and seeds (CS) (3, 6, 9, and 12%) powder on the quality and antioxidant activity of raw and cooked chicken patties during storage (-20°C/3 months). The addition of CP and CS powder increased protein, fat, ash, and fiber values of chicken patties compared with control, while carbohydrate, pH, and TBA were decreased at zero time and after 3 months of storage. The WHC, cooking yield, fat retention, and moisture retention were increased by increasing CP and CS powder addition ratios, while cooking loss and shrinkage were decreased. Also, CP and CS powder improved antioxidant activity, microbiological quality, and overall acceptability of chicken patties. The hardness of raw and cooked chicken patties was decreased with increasing CP and CS addition ratios. It is recommended to use CP and CS powder as functional ingredients in the preparation of functional foods.
Circulating Immune Bioenergetic, Metabolic, and Genetic Signatures Predict Melanoma Patients' Response to Anti–PD-1 Immune Checkpoint Blockade
Authors: Triozzi Pierre L. , Stirling Elizabeth R. , Song Qianqian , Westwood Brian , Kooshki Mitra , Forbes M. Elizabeth , Holbrook Beth C. , Cook Katherine L. , Alexander-Miller Martha A. , Miller Lance D. , Zhang Wei , Soto-Pantoja David R.
Published: March 2022

Purpose:

Immunotherapy with checkpoint inhibitors is improving the outcomes of several cancers. However, only a subset of patients respond. Therefore, predictive biomarkers are critically needed to guide treatment decisions and develop approaches to the treatment of therapeutic resistance.

Experimental Design:

We compared bioenergetics of circulating immune cells and metabolomic profiles of plasma obtained at baseline from patients with melanoma treated with anti–PD-1 therapy. We also performed single-cell RNA sequencing (scRNAseq) to correlate transcriptional changes associated with metabolic changes observed in peripheral blood mononuclear cells (PBMC) and patient plasma.

Results:

Pretreatment PBMC from responders had a higher reserve respiratory capacity and higher basal glycolytic activity compared with nonresponders. Metabolomic analysis revealed that responder and nonresponder patient samples cluster differently, suggesting differences in metabolic signatures at baseline. Differential levels of specific lipid, amino acid, and glycolytic pathway metabolites were observed by response. Further, scRNAseq analysis revealed upregulation of T-cell genes regulating glycolysis. Our analysis showed that SLC2A14 (Glut-14; a glucose transporter) was the most significant gene upregulated in responder patients' T-cell population. Flow cytometry analysis confirmed significantly elevated cell surface expression of the Glut-14 in CD3+, CD8+, and CD4+ circulating populations in responder patients. Moreover, LDHC was also upregulated in the responder population.

Conclusions:

Our results suggest a glycolytic signature characterizes checkpoint inhibitor responders; consistently, both ECAR and lactate-to-pyruvate ratio were significantly associated with overall survival. Together, these findings support the use of blood bioenergetics and metabolomics as predictive biomarkers of patient response to immune checkpoint inhibitor therapy.
An Unusual Case of Lorlatinib-Induced Pneumonitis: A Case Report
Authors: Harrison Philippa Kate , Boland Helen E , Aherne Noel J , Palmieri David J
Published: March 2022

The discovery of tyrosine kinase oncogenic driver mutations, including anaplastic lymphoma kinase (ALK), has changed the face of non-small cell lung cancer (NSCLC) treatment. Whilst the development of tyrosine kinase inhibitors has improved survival, with their increasing use, it is important to be aware of the risks of rare yet serious adverse events, such as drug-induced pulmonary toxicity. Whilst little is known in regard to drug-induced pneumonitis in the setting of ALK inhibitors, such reactions carry a high morbidity and mortality rate, impacting greatly upon options for further treatment and management. We describe the case of a 73-year-old female with metastatic ALK-positive NSCLC who developed subacute dyspnoea 3 weeks after commencing lorlatinib. She was diagnosed with drug-induced pneumonitis, from which she recovered clinically following the cessation of her targeted therapy. Pneumonitis related to lorlatinib is a rare pulmonary toxicity, and early recognition and intervention is critical to reduce the associated risks of respiratory failure and death.
Detection of Circulating Peanut Components in Serum after Ingestion
Authors: Baumann Katrine , Skov Per Stahl , Eller Esben , Kristensen Bjarne , Bindslev-Jensen Carsten
Published: March 2022

Introduction: Detection of specific food allergens brought into circulation following ingestion is complicated by the minute amounts present in serum. We therefore aimed to assess the utility of selective removal of IgE against peanut components using ImmunoCAP combined with the basophil histamine release (HR) assay (BHRA) to identify the absorbed allergen components after ingestion of peanut. Methods: Serum from six healthy individuals was drawn before and 1 h after ingestion of 100 g peanut. Serum from two peanut allergic patients was depleted for IgE against single allergen components by incubation with ImmunoCAPs coupled with either Ara h 1; 2; 3 or 6 before donor basophils were passively sensitized with the serum preparations. The sensitized cells were challenged with serum obtained from the six individuals before and after ingestion of peanut, and HR was measured after serum provocation. Results: Ara h 2 and Ara h 6 were detected in serum 1 h after ingestion in 6/6 individuals by negative selection. Depletion of specific IgE against Ara h 2 or Ara h 6 almost completely abolished the response to serum provocation, indicating a sequence homology between the two allergen components in serum. Ara h 1 was demonstrated in 5/6 sera and Ara h 3 in 1/6 sera. Conclusion: This study is a proof of concept showing that passive sensitization of basophils with sera depleted of component-specific IgE can be used to identify food allergen components present in serum after ingestion.
Preoperative Cardiac Index as a Predictor of Maturation and Primary Patency of Radiocephalic Arteriovenous Fistula in Hemodialysis
Authors: Wang Qinglian , Lin Jiangong , Han Hui , Wu Dongfeng , Zhou Yan , Zhao Bing
Published: March 2022

Background: Radiocephalic arteriovenous fistula (RCAVF) is the best access modality to be considered initially when planning arteriovenous fistula (AVF) for maintenance hemodialysis. Considering the higher incidence of RCAVF failed maturation (M), it is important to perform proper preoperative evaluation and identification of high-risk patients. There has been no study on the influence of preoperative cardiac function on the M and patency of AVFs. The purpose of this investigation is to determine whether preoperative cardiac index (CI) is a predictor of M and primary patency of RCAVF. Method: A total of 365 end-stage renal disease patients undergoing RCAVF surgery were consecutively enrolled with a median follow-up time of 20 months in this prospective cohort study. Demographics, vascular diameters measured by duplex ultrasound examination, and CI measured by echocardiography, were analyzed for effect on RCAVF primary functional M and primary patency. Result: Patients in the group achieving primary RCAVF functional M had a significantly larger mean CI than the group with early RCAVF failure (2.93 ± 0.77 vs. 3.57 ± 0.76 L/min/m2, p < 0.001). The receiver operating characteristic curve was plotted and demonstrated that preoperative vein diameter and CI can predict failure of RCAVF M. The AUC of CI was higher (0.745 vs. 0.666). Multivariate regression analysis, adjusted for age, sex, diabetes, preoperative dialysis status and vessel diameters, showed that decreased CI remained associated with increased risk of failure of M (FM) and worse primary unassisted patency. The Kaplan-Meier survival analysis suggested that patients with CI <3 L/min/m2 had a worse primary unassisted patency rate at all time points compared with patients with CI ≥3 L/min/m2. Conclusion: This study demonstrated that preoperative CI was associated with RCAVF M and long-term patency. A decreased CI may be a possible predictor of an increased risk of FM and a shorter primary patency time.
Epstein-Barr Virus Encephalitis and Disseminated Adenovirus Infection after Haploidentical Allogeneic Hematopoietic Stem Cell Transplantation for a Patient with Ph-Like Acute Lymphoblastic Leukemia
Authors: Zhou Xiao , Song Xianmin , Wan Liping
Published: March 2022

Viral reactivation or infections are common complications after allogeneic hematopoietic stem cell transplantation, especially in haploidentical transplantation. Here, we presented a young patient with Ph-like acute lymphoblastic leukemia who suffered Epstein-Barr virus (EBV) encephalitis and disseminated adenovirus (ADV) infection after haploidentical peripheral blood (PB) stem cell transplantation. The patient was a 16-year-old boy and received PB stem cells from his HLA-haploidentical matched father. On day +44 after transplantation, he had viremia with cytomegalovirus, and EBV was diagnosed as EBV encephalitis after 2 weeks. On day +117, he had disseminated ADV infection and fulminant ADV hepatitis. It is very rare that successive EBV encephalitis and fulminant ADV hepatitis are present in the same patient. We summarized risk factors, clinical manifestations, diagnostic criteria, and effective treatments about EBV encephalitis and disseminated ADV infection. We try to enhance our understanding of the prevention, diagnosis, and potential treatment of EBV and ADV disease by reviewing the entire procedure.
A current control method for bidirectional multiphase DC-DC boost-buck converter
Authors: Hasyim Gifari Iswandi , Wijanarko Sulistyo , Furqani Jihad , Rizqiawan Arwindra , Dahono Pekik Argo
Published: March 2022

In the future, more and more electric vehicle (EV) batteries are connected to the direct current (DC) microgrid. Depending on the battery state of charge, the battery voltage can be higher or lower than the DC microgrid voltage. A converter that is aimed to fulfil such function must be capable of working in both charging and discharging regardless the voltage level of the battery and DC microgrid. Battery performance degradation due to ripple current entering the battery is also a concern. In this paper, a converter that can minimize ripple current that entering battery and operate in two power-flow directions regardless of battery and DC microgrid voltage level is presented. A current control method for this kind of converter was proposed. Experiment on a prototype was conducted to prove the proposed converter current control method.
Developing low-cost two wheels balancing scooter using proportional derivative controller
Authors: Sutyasadi Petrus , Parnichkun Manukid
Published: March 2022

This study suggests a strategy for creating a low-cost two-wheeled balancing scooter with a simpler system that can work similarly to the commercial ones on the market. A simple system will help people to understand how it works and how to build it. The mechanical parts were made from simple hollow bar iron. Wheelbarrow wheels were attached to two electric bicycle motors, and the controller was using an 8-bit microcontroller running a proportional derivative (PD) controller. PD controller only is not enough to run the scooter with a passenger smoothly. Some strategies were added to overcome some non-linear problems due to the use of low-cost components. Finally, the system is successfully built and can be ride by a 65 kgs weight of rider. The scooter can turn left or right, and even to make a 360-degree spot-rotation.
Improving the iterative back projection estimation through Lorentzian sharp infinite symmetrical filter
Authors: Abdul Rahim Amir Nazren , Yaakob Shahrul Nizam , Seng Lee Yeng , Nasrudin Mohd Wafi , Ismail Iszaidy
Published: March 2022

This study proposed an enhancement technique for improvising the estimation technique in iterative back projection (IBP) by using the Lorentzian error function with a sharp infinite symmetrical filter (SISEF). The IBP estimation is an iteratively based error correction that can minimize the error reconstruction significantly. However, the IBP has a drawback in that it suffers from jaggy and ringing artifacts as a result of the iterative reconstruction method and the absence of edge guidance. Furthermore, because the IBP estimator tended to oscillate at the same solution frequently, numerous iterations were required. Therefore, this study proposed edge enhancement to enhance the estimator by using the combination of the IBP with Lorentzian SISEF to produce a finer high-resolution output image. As a result, the SISEF is used to improvise the estimator by providing high accuracy of edge detail information for enhancing the edge image. At the same time, the Lorentzian error norm helps to increase the robustness of the IBP algorithm from contamination of additional noise and the ringing artifacts.
A single element of multiband switched beam antenna for 5G applications
Authors: Chaipanya Pichaya , Punchin Marisa , Prasoptunya Nopparat , Phothong-ngam Wongsakorn
Published: March 2022

This work proposes a simple design of switched beam antenna on square split-ring resonator to operate in multiband frequencies. The antenna is designed to support fifth generation (5G) wireless applications. The proposed antenna provides two different of the main beams, 45˚/225˚±5˚ and 135˚/315˚±5˚, by shorted circuit at 4 different edges. The designed antenna can support nine frequency bands, 7.071, 9.006, 9.321, 9.906, 10.428, 10.718, 12.967, 13.057 and 14.469 GHz, which are the high-band of 5G spectrum when shorted circuit to the ground conductor. The antenna provides maximum gain of 6.41 dBi. The dimension of the antenna is 6×6 mm2 which the thickness of 1.73 mm. The proposed design is based on a simple beam switching antenna configuration, compact size and low-cost manufacturing.
Undergraduate engineering students employment prediction using hybrid approach in machine learning
Authors: Krishnaiah Vinutha , Hullukere Kadegowda Yogisha
Published: March 2022

The knowledge discovery from student’s data can be very useful in predicting the employment under different categories. The machine learning is helping in this regard up to the great extent. In this paper, a hybrid model of machine learning has proposed to predict the jobs categories, students may get in their campus placement. The considered groups of students are from undergraduate courses from engineering stream having the semester’s scheme in their academic. The mapping of jobs has predicted based on their previous seven semesters marks as well as their personality index. The proposed hybrid model consists of three different model based on multilayer feed forward architecture, radial basis function neural network and K-means based clustering method. The proposed model provided the relative chances of available each job category with high accuracy and consistency.
A sentiment analysis model of Agritech startup on Facebook comments using naive Bayes classifier
Authors: Kewsuwun Nawapon , Kajornkasirat Siriwan
Published: March 2022

Facebook page is a tool able to generate perceptions and acceptance, and support people and investors in making business decisions. Moreover, Facebook page plays a part in engaging people in the form of a community. People share experiences and opinions toward products, services, and trends in particular periods on the Facebook page community. Regarding sentiment analysis on Facebook pages, most education and other general topics in English have only been analyzed in English. However, sentiment analysis regarding Agritech startups topics in Thai language has not been done yet. This study analyzes opinions and categorizes positive and negative comments by using naive Bayes classifier to examine the sentiments and attitudes of people and investors. The results could possibly reflect the perception rate of Agritech startups in Thailand and could be applied to explain attentiveness and assess people’s engagement opinions. Furthermore, it could be applied in studying consumer behavior, marketing analysis, spread of information, and attitudes. The study's model is generic and could be applied in other contexts to provide insightful suggestions.
Depression prognosis using natural language processing and machine learning from social media status
Authors: Hossain Md. Tazmim , Talukder Md. Arafat Rahman , Jahan Nusrat
Published: March 2022

Depression is an acute problem throughout the world. Due to worst and prolong depression many people dies in every year. The problem is that most of the people are not concern of the fact that they are suffering from depression. In this research, our aim was to find out whether an individual is depressed or not by analyzing social media status. Therefore, we focused on real data. Our dataset consists of 2000 sentences, which was collected from different social media platforms Facebook, Twitter, and Instagram. Then, we have performed five data pre-processing approaches for natural language processing (NLP) such as tokenization, removal of stop words, removing empty string, removing punctuations, stemming and lemmatization. For our selected model, we considered that processed data as an input. Finally, we applied six machine learning (ML) classifiers multinomial Naive Bayes (NB), logistic regression, liner support vector classifier, random forest, K-nearest neighbour, and decision tree to achieve better accuracy over our dataset. Among six algorithms, multinomial NB and logistic regression performed well on our dataset and obtained 98% accuracy.
HapPart: partitioning algorithm for multiple haplotyping from haplotype conflict graph
Authors: Abdullah Abu-Bakar Muhammad , Hossain Md. Monowar , Shill Pintu Chandra
Published: March 2022

Each chromosome in the human genome has two copies. The haplotype assembly challenge entails reconstructing two haplotypes (chromosomes) using aligned fragments genomic sequence. Plants viz. wheat, paddy and banana have more than two chromosomes. Multiple haplotype reconstruction has been a major research topic. For reconstructing multiple haplotypes for a polyploid organism, several approaches have been designed. The researchers are still fascinated to the computational challenge. This article introduces a partitioning algorithm, HapPart for dividing the fragments into k-groups focusing on reducing the computational time. HapPart uses minimum error correction curve to determine the value of k at which the growth of gain measures for two consecutive values of k-multiplied by its diversity is maximum. Haplotype conflict graph is used for constructing all possible number of groups. The dissimilarity between two haplotypes represents the distance between two nodes in graph. For merging two nodes with the minimum distance between them this algorithm ensures minimum error among fragments in same group. Experimental results on real and simulated data show that HapPart can partition fragments efficiently and with less computational time.
Establishing a cyclic schedule for nurse in the health unit
Authors: Natheer Alkallak Isra , Zedan Shaban Ruqaya
Published: March 2022

This research presents the interleaving two approaches. These are intelligence's ideas as well as heuristic ‎technique as 8-puzzle and sudoku grid to solve the nurse rostering. The research proposed algorithm ‎to assign shifts cyclically. It is considered by three shifts in one day to 9 nurses, each nurse has 8 days work with ‎a holiday in the cyclically scheduling. The task appeared the allocation of nursing staff in health unit management ‎theoretically. There are three shifts which cover 24 hours. The shifts are early, late, and night. This algorithm ‎simulated the shifts through the directions of blank’s move in 8-puzzle with the methodology of sudoku grid with ‎hard constraints should be met at all times. In our solution do on two goals first, we create a ‎schedule that meets all the tough constraints and guarantees fairness. The second objective is to try to verify as many ‎of the soft constraints as possible, by shifting and rotating while maintaining the soft constraints. The approach ‎was implemented as a simulation, and a satisfactory result was demonstrated. experimental effects are extremely ‎convenient and versatile to find appropriate nursing rostering schedule, rather than using manual techniques. The code developed to simulate it in MATLAB.‎
The susceptible-infected-recovered-dead model for long-term identification of key epidemiological parameters of COVID-19 in Indonesia
Authors: Nanda Muhammad Achirul , Faricha Anifatul , Ulyah Siti Maghfirotul , Tamimah Ni'matut , Indasyah Enny , Malich Salaz Muhammad Falahudin , Mawadatur Rohmah Qurrotun 'Ayun , Abqari Ulfah
Published: March 2022

The COVID-19 epidemic has spread massively to almost all countries including Indonesia, in just a few months. An important step to overcoming the spread of the COVID-19 is understanding its epidemiology through mathematical modeling intervention. Knowledge of epidemic dynamics patterns is an important part of making timely decisions and preparing hospitals for the outbreak peak. In this study, we developed the susceptible-infected-recovered-dead (SIRD) model, which incorporates the key epidemiological parameters to model and estimate the long-term spread of the COVID-19. The proposed model formulation is data-based analysis using public COVID-19 data from March 2, 2020 to May 15, 2021. Based on numerical analysis, the spread of the pandemic will begin to fade out after November 5, 2021. As a consequence of this virus attack, the cumulative number of infected, recovered, and dead people were estimated at ≈ 3,200,000, ≈ 3,437,000 and ≈ 63,000 people, respectively. Besides, the key epidemiological parameter indicates that the average reproduction number value of COVID-19 in Indonesia is 7.32. The long-term prediction of COVID-19 in Indonesia and its epidemiology can be well described using the SIRD model. The model can be applied in specific regions or cities in understanding the epidemic pattern of COVID-19.
Reasoning in inconsistent prioritized knowledge bases: an argumentative approach
Authors: Ho Loan Thi-Thuy , Arch-int Somjit , Arch-int Ngamnij
Published: March 2022

A study of query answering in prioritized ontological knowledge bases (KBs) has received attention in recent years. While several semantics of query answering have been proposed and their complexity is rather well-understood, the problem of explaining inconsistency-tolerant query answers has paid less attention. Explaining query answers permits users to understand not only what is entailed or not entailed by an inconsistent DL-LiteR KBs in the presence of priority, but also why. We, therefore, concern with the use of argumentation frameworks to allow users to better understand explanation techniques of querying answers over inconsistent DL-LiteR KBs in the presence of priority. More specifically, we propose a new variant of Dung’s argumentation frameworks, which corresponds to a given inconsistent DL-LiteR KB. We clarify a close relation between preferred subtheories adopted in such prioritized DL-LiteR setting and acceptable semantics of the corresponding argumentation framework. The significant result paves the way for applying algorithms and proof theories to establish preferred subtheories inferences in prioritized DL-LiteR KBs.
Integrated tripartite modules for intelligent traffic light system
Authors: Abdul Kareem Emad I. , Hoomod Haider K.
Published: March 2022

The traffic in urban areas is primarily controlled by traffic lights, contributing to the excessive, if not properly installed, long waiting times for vehicles. The condition is compounded by the increasing number of road accidents involving pedestrians in cities across the world. Thus, this work presents an integrated tripartite module for an intelligent traffic light system. This system has enough ingredients for success that can solve the above challenges. The proposed system has three modules: the intelligent visual monitoring module, intelligent traffic light control module, and the intelligent recommendation module for emergency vehicles. The monitor module is a visual module capable of identifying the conditions of traffic in the streets. The intelligent traffic light control module configures many intersections in a city to improve the flow of vehicles. Finally, the intelligent recommendation module for emergency vehicles offers an optimal path for emergency vehicles. The evaluation of the proposed system has been carried out in Al-Sader city/Bagdad/Iraq. The intelligent recommendation module for the emergency vehicles module shows that the optimization rate average for the optimal path was in range 67.13% to 92%, where the intelligent traffic light control module shows that the optimization ratio was in range 86% to 91.8%.
Modern drowsiness detection techniques: a review
Authors: Jasim Sarah Saadoon , Abdul Hassan Alia Karim
Published: March 2022

According to recent statistics, drowsiness, rather than alcohol, is now responsible for one-quarter of all automobile accidents. As a result, many monitoring systems have been created to reduce and prevent such accidents. However, despite the huge amount of state-of-the-art drowsiness detection systems, it is not clear which one is the most appropriate. The following points will be discussed in this paper: Initial consideration should be given to the many sorts of existing supervised detecting techniques that are now in use and grouped into four types of categories (behavioral, physiological, automobile and hybrid), Second, the supervised machine learning classifiers that are used for drowsiness detection will be described, followed by a discussion of the advantages and disadvantages of each technique that has been evaluated, and lastly the recommendation of a new strategy for detecting drowsiness.
Extraction of image resampling using correlation aware convolution neural networks for image tampering detection
Authors: Shivanandappa Manjunatha , Patil Malini M.
Published: March 2022

Detecting hybrid tampering attacks in an image is extremely difficult; especially when copy-clone tampered segments exhibit identical illumination and contrast level about genuine objects. The existing method fails to detect tampering when the image undergoes hybrid transformation such as scaling, rotation, compression, and also fails to detect under small-smooth tampering. The existing resampling feature extraction using the Deep learning techniques fails to obtain a good correlation among neighboring pixels in both horizontal and vertical directions. This work presents correlation aware convolution neural network (CA-CNN) for extracting resampling features for detecting hybrid tampering attacks. Here the image is resized for detecting tampering under a small-smooth region. The CA-CNN is composed of a three-layer horizontal, vertical, and correlated layer. The correlated layer is used for obtaining correlated resampling feature among horizontal sequence and vertical sequence. Then feature is aggregated and the descriptor is built. An experiment is conducted to evaluate the performance of the CA-CNN model over existing tampering detection methodologies considering the various datasets. From the result achieved it can be seen the CA-CNN is efficient considering various distortions and post-processing attacks such joint photographic expert group (JPEG) compression, and scaling. This model achieves much better accuracies, recall, precision, false positive rate (FPR), and F-measure compared existing methodologies.
Radial basis network estimator of oxygen content in the flue gas of debutanizer reboiler
Authors: Sembodo Shafanda Nabil , Effendy Nazrul , Dwiantoro Kenny , Muddin Nidlom
Published: March 2022

The energy efficiency in the debutanizer reboiler combustion can be monitored from the oxygen content of the flue gas of the reboiler. The measurement of the oxygen content can be conducted in situ using an oxygen sensor. However, soot that may appear around the sensor due to the combustion process in the debutanizer reboiler can obstruct the sensor’s function. In-situ redundancy sensors’ unavailability is a significant problem when the sensor is damaged, so measures must be made directly by workers using portable devices. On the other hand, worker safety is a primary concern when working in high-risk work areas. In this paper, we propose a software-based measurement or soft sensor to overcome the problems. The radial basis function network model makes soft sensors adapt to data updates because of their advantage as a universal approximator. The estimation of oxygen content with a soft sensor has been successfully carried out. The soft sensor generates an estimated mean square error of 0.216% with a standard deviation of 0.0242%. Stochastics gradient descent algorithm with momentum acceleration and dimension reduction using principal component analysis successfully improves the soft sensors’ performance.
Enhancing multi-class web video categorization model using machine and deep learning approaches
Authors: Yafooz Wael M. S. , Alsaeedi Abdullah , Alluhaibi Reyadh , Mohamed Emara Abdel-Hamid
Published: March 2022

With today’s digital revolution, many people communicate and collaborate in cyberspace. Users rely on social media platforms, such as Facebook, YouTube and Twitter, all of which exert a considerable impact on human lives. In particular, watching videos has become more preferable than simply browsing the internet because of many reasons. However, difficulties arise when searching for specific videos accurately in the same domains, such as entertainment, politics, education, video and TV shows. This problem can be solved through web video categorization (WVC) approaches that utilize video textual information, visual features, or audio approaches. However, retrieving or obtaining videos with similar content with high accuracy is challenging. Therefore, this paper proposes a novel mode for enhancing WVC that is based on user comments and weighted features from video descriptions. Specifically, this model uses supervised learning, along with machine learning classifiers (MLCs) and deep learning (DL) models. Two experiments are conducted on the proposed balanced dataset on the basis of the two proposed algorithms based on multi-classes, namely, education, politics, health and sports. The model achieves high accuracy rates of 97% and 99% by using MLCs and DL models that are based on artificial neural network (ANN) and long short-term memory (LSTM), respectively.
A signature-based data security and authentication framework for internet of things applications
Authors: Fathima Nasreen , Banu Reshma , Ahammed Guttur Fakruddin Ali
Published: March 2022

Internet of things (IoT) is the next big revolution in modernized network technologies connecting a massive number of heterogeneous smart appliances and physical objects. Owing to these technologies' novelty, various issues are characterized by security concerns are the most prioritized issue. A review of existing security approaches highlights that they are very particular about the solution towards a specific attack and cannot resist any unknown attacker. Therefore, this manuscript presents a novel computational model that introduces a unique authentication process using a simplified encryption strategy. The simulated study outcome shows that the proposed system offers efficient security and efficient data transmission performance in the presence of an unknown adversary. Hence, the study outcome exhibits better effects than frequently used security solutions when implemented in a vulnerable IoT environment.
Algorithm for solving fractional partial differential equations using homotopy analysis method with Pade approximation
Authors: Ibraheem Kais Ismail , Mahmmood Heba Shuker
Published: March 2022

In recent years nonlinear problems have several methods to be solved and utilize a well-known analytic tools such as homotopy analysis method. In general, homotopy analysis method had gain a wide focus and improvement especially in typical nonlinear problem. The aim of this paper is to use homotopy method of analysis to solve partial differential equation in addition to improve method’s efficiency. The method in this paper is to apply approximation to Pade’ approach to obtain sufficient efficiency. As a result, the improvement has been verified by solving two cases beside a mean value comparison of the homotopy analysis method’s squared error with the improved form.
Design for Biological Research: Upjohn, Will Burtin, and the Cell
Authors: Olave Diana Cristóbal
Published: March 2022

This article discusses the role of design in collaborative scientific research, through an analysis of the human-scaled models produced by designer Will Burtin for the pharmaceutical conglomerate Upjohn. It argues that such models triggered an intimate co-dependence between architectural techniques and biological theories, consolidating biology into a form of construction—a form of building. Within the context of corporate-sponsored scientific research of American postwar, Upjohn embraced unorthodox methods of mass media communication to engage the public with scientific narratives. It was through such unconventional techniques that design concerns—form, scale, construction, aesthetics—were placed at the forefront of scientific debates.
Analysis of Pioneering Computable Biomedical Knowledge Repositories and their Emerging Governance Structures
Authors: Amara Philip Sahr , Conte Marisa , Flynn Allen , Platt Jodyn , Trinidad Marie Grace
Published: March 2022

A growing interest in producing and sharing computable biomedical knowledge artifacts (CBKs) is increasing the demand for repositories that validate, catalog, and provide shared access to CBKs. However, there is a lack of evidence on how best to manage and sustain CBK repositories. In this paper, we present the results of interviews with several pioneering CBK repository owners. These interviews were informed by the Trusted Repositories Audit and Certification (TRAC) framework. Insights gained from these interviews suggest that the organizations operating CBK repositories are somewhat new, that their initial approaches to repository governance are informal, and that achieving economic sustainability for their CBK repositories is a major challenge. To enable a learning health system to make better use of its data intelligence, future approaches to CBK repository management will require enhanced governance and closer adherence to best practice frameworks to meet the needs of myriad biomedical science and health communities. More effort is needed to find sustainable funding models for accessible CBK artifact collections.
A Visual Translation of the Pandemic
Authors: Rodighiero Dario , Wandl-Vogt Eveline , Carsenat Elian
Published: March 2022

In 1923, Walter Benjamin published translations of Baudelaire’s poetry with a prefatory essay developing the idea that translation is not only the practice of addressing foreign readerships, but rather a process of authorship in which the original text is amplified with further significance. The authors use the term translation with a meaning that is not only linguistic but also visual. They analyze the coronavirus pandemic by translating scientific literacy through the techniques of natural language processing and data visualization. The Cartography of COVID-19 results from a visual translation that invites readers to explore the current pandemic from a different point of view that extends their perception.
Extent of encounter with an embedded food influences how it is processed by an urbanizing macaque species
Authors: Dhananjaya Tejeshwar , Das Sayantan , Vyas Amal K. , Gahlot Prakhar , Singh Mewa
Published: March 2022

Rapid urbanization exerts novel adaptive pressures on animals at the interface of natural and altered environments. Urban animals often rely on synthetic foods that require skilled extraction and flexible processing. We studied how synthetic treatment of an embedded food, peanut, determined its extraction and processing across groups of bonnet macaques (Macaca radiata) differing in encounter and familiarity with peanut. The possibility of the application of processing methods to similar foods was also tested. We found encounter- and form (native/shelled/skinned)-specific familiarity to peanuts, state (raw/boiled/roasted)-specific distinction in skinning, and encounter- and state-specific differences in methods of skinning. The group with the highest encounter with peanuts exhibited novel and manipulatively complex processing. Novel processing was also extended to peas and chickpeas. Our study establishes a strong relationship between familiarity with the condition of food and the processing methods used and further, demonstrates the probable role of categorization in extension of novel methods.
A new cavernicolous assassin bug from the Canary Islands (Hemiptera: Reduviidae: Emesinae: Collartidini)
Authors: DAVRANOGLOU LEONIDAS-ROMANOS , BAŇAŘ PETR , SUÁREZ DANIEL , MARTÍN SONIA , NARANJO MANUEL
Published: March 2022

The Canary Archipelago is home to two species of obligately cavernicolous assassin bugs of the genus Collartida Villiers, 1949 (Hemiptera: Heteroptera: Reduviidae: Emesinae: Collartidini). These species are highly adapted for cave life, being blind and wingless. In the present study, we describe a new species of the genus, C. phantasma sp. nov. from the Federica mine in Gran Canaria. C. phantasma sp. nov. differs from the other two Collartida species found in the Canary Archipelago in that the male is fully winged, the female is wingless, and both sexes have well-developed eyes. We provide information regarding the new species’ habitat, its taxonomic affinities, and its ability to fly.
Contribution to the knowledge of Chinese Gryllacrididae (Orthoptera) VII: Review the genus Apterolarnaca Gorochov, 2004
Authors: LU XIANGYI , ZHANG QIANWEN , BIAN XUN
Published: March 2022

Based on the morphology of male ninth abdominal tergite and subgenital plate, this paper redefines the genus Bianigryllacris Cadena-Castañeda, 2019 to subgeneric status under Apterolarnaca Gorochov, 2004. Two species are described as new: Apterolarnaca (Bianigryllacris) tenuispinacia sp. nov. (Chinese name: 细刺缺翅Ô­蟋螽) and Apterolarnaca (Bianigryllacris) xinganensis sp. nov. (Chinese name: 兴安缺翅Ô­蟋螽). And a key to species of Apterolarnaca is provided. All examined specimens are deposited in Guangxi Normal University.
A new species of the infaunal alpheid shrimp genus Richalpheus Anker & Jeng, 2006 (Decapoda: Caridea) from Kagoshima, Japan
Authors: KOMAI TOMOYUKI , KOREEDA REO
Published: March 2022

A new species of the alpheid shrimp genus Richalpheus Anker & Jeng, 2006 is described on the basis of 10 specimens collected from three locations in Kagoshima Prefecture, Kyushu, Japan. Richalpheus laeuadigitus n. sp. appears close to R. palmeri Anker & Jeng, 2006 and R. dahabensis Anker & Dworschak, 2007 among the three known congeners, but differs from R. palmeri in the proportionally wider telson and the more proximally located tip of the antennular stylocerite, and from R. dahabensis in the different configuration of the major cheliped fingers, in particular the possession of a well-developed bulge-like ridge on the occlusal margin of the dactylus. Two sequences of the mitochondrial 16S rRNA gene from two of the paratypes are newly obtained, and compared with that of R. palmeri downloaded from the GenBank database. The new species is the fourth representative of Richalpheus. Possible association between the new species and other infaunal invertebrates is suggested, although not specified.
Quantifying the Contribution of Urbanization to Summer Extreme High-Temperature Events in the Beijing–Tianjin–Hebei Urban Agglomeration
Authors: Wang Yujie , Xiang Yang , Song Lianchun , Liang Xin-Zhong
Published: March 2022

Determining the contribution of urbanization to extreme high-temperature events is essential to the coordinated development of Beijing, Tianjin, and Hebei (BTH). Based on the dynamic data of land-use change in every 5 years, this study uses the coupled WRF–Building Effect Parameterization/Building Energy Model (BEP/BEM) at 1-km grid spacing to quantify the contribution of BTH urbanization to the intensity and frequency of hourly extreme high-temperature events in summer. From 1990 to 2015, extreme events over Beijing and its south increased by ∼1.5°–2°C in intensity and by 50–100 h in frequency, both of which were even higher in central Beijing and Shijiazhuang. The increases of multiyear average urbanization contribution ratios to the intensity and frequency reached 3.3% and 51.6% at the 99% confidence level (p < 0.01) from 1990 to 2015, respectively. The corresponding contributions increased 1.8 and 1.2 times more significantly in the megacities (i.e., Beijing, Tianjin, and Shijiazhuang) than small and medium-sized cities. Therefore, the rapid urbanization has substantially enhanced the extreme high-temperature events in BTH. It is necessary to limit the urbanization growth rate and implement effective adaptation and mitigation strategies to sustain BTH development.
The Impact of Neglecting Climate Change and Variability on ERCOT’s Forecasts of Electricity Demand in Texas
Authors: Lee Jangho , Dessler Andrew E.
Published: March 2022

The Electric Reliability Council of Texas (ERCOT) manages the electric power across most of Texas. They make short-term assessments of electricity demand on the basis of historical weather over the last two decades, thereby ignoring the effects of climate change and the possibility of weather variability outside the recent historical range. In this paper, we develop an empirical method to predict the impact of weather on energy demand. We use that with a large ensemble of climate model runs to construct a probability distribution of power demand on the ERCOT grid for summer and winter 2021. We find that the most severe weather events will use 100% of available power—if anything goes wrong, as it did during the 2021 winter, there will not be sufficient available power. More quantitatively, we estimate a 5% chance that maximum power demand would be within 4.3 and 7.9 GW of ERCOT’s estimate of best-case available resources during summer and winter 2021, respectively, and a 20% chance it would be within 7.1 and 17 GW. The shortage of power on the ERCOT grid is partially hidden by the fact that ERCOTs seasonal assessments, which are based entirely on historical weather, are too low. Prior to the 2021 winter blackout, ERCOT forecast an extreme peak load of 67 GW. In reality, we estimate hourly peak demand was 82 GW, 22% above ERCOT’s most extreme forecast and about equal to the best-case available power. Given the high stakes, ERCOT should develop probabilistic estimates using modern scientific tools to predict the range of power demand more accurately.