bluuebunny/crossref_metadata_2025 huggingface dataset:
A new application for fast prediction and protection of electrical drive wheel speed using machine learning methodology
Authors: Khessam Medjdoub , Lousdad Abdelkader , Hazzab Abdeldjebar , Rezkallah Miloud , Chandra Ambrish
Published: June 2022

This paper introduces a non-linear implementation of the speed control technique of permanent magnetic synchronous motors (PMSM) using electronic differential (ED) command. Artificial neural network (ANN) coupled with particles swarm optimization (ANN-PSO) are implemented to control wheel speed and steering angle. The main purpose of the PMSM system and its application is the command of electric vehicles (EV). In the controller design, three-phase currents and rotor speed shall be measurable and eligible for feedback. Our propulsion platform consists of two PMSM in the back. The study with implemented ANN-PSO is performed after collecting the data from the ED to manage the control of speed EV, Left and right of steering angle and steering ahead. Based on this strategy, a new application can be provided in the GPS application to give the information as input (curved path angle) to ANN-PSO. Next, the application of ANN-PSO can estimate the parameters of ED to avoid the slip, as well as improves better performance and dynamic stability of electric vehicle drive systems.
Optimum control for dynamic voltage restorer based on particle swarm optimization algorithm
Authors: Salman Saddam Subhi , Humod Abdulrahim Thiab , Hasan Fadhil A.
Published: June 2022

This article addresses a variety of power quality concerns, including voltage sag and swell, surges, harmonics, and so on, utilizing a dynamic voltage restorer (DVR). The proposed controller for DVR is proportional plus integral (PI) controller. Two methods are used for tuning the parameters of PI controller, trial and error and intelligent optimal method. The utilized optimal method is particle swarm optimization (PSO) method. Results depicted that DVR using PI controller tuned by PSO has improved performance than PI controller tuned by trial and error in term of rise time, maximum overshoot and settling time, as well as total harmonic distortion (THD). These improvements are applicable for voltage sag and swell conditions.
Challenges and recent developments in solar tracking strategies for concentrated solar parabolic dish
Authors: Lorilla Franch Maverick A. , Barroca Renyl
Published: June 2022

This contribution presents an overview of control strategies for parabolic dish concentrated solar power (PD-CSP) sun tracker technologies from the literature on different implementations. This paper also highlights the practical challenges in designing sun trackers for CSP applications. Solar radiation undergoes seasonal changes and transients from the cloud that is exceptionally challenging to manipulate. This paper may provide control engineers with technical information on high-precision solar dynamic tracking and optical accuracy by emphasizing the features, advantages, and limitations of different control techniques and algorithms. Implementation of efficient control strategies addresses concerns in amending dynamics and nonlinearities, thus improving the system responses and extending the operational timeline of CSP plants, which results in a reduced cost per kWh produced.
An arrangement of the number of K-grams in the performance of Rabin Karp algorithm in text adjustment
Authors: Astuti Yuli , Wulandari Irma Rofni
Published: June 2022

Rabin Karp algorithm is frequently used to determine the similarity between texts, using the hash function to compare the string identified and the substring in the text. The choice of the k value in the K-gram is often unrestricted. The number of k values used when cutting some terms will take longer if tried one by one. This research will perform a word cutting test on a script using K-gram 0 to 8. The results will cover the effect of the value of each K used on the similarity percentage produced. This research aims to determine the effect of the number of K-grams on the performance of Rabin Karp in text matching. The test underwent 20 sentences and 10 times using the dice coefficient for text similarity testing. The conclusion of this research should not use the K-gram 0 to 2 due to the K-gram basic principle: character deduction. Subsequently, if the character is 0,1,2, it does not have a meaning yet; thus, it gets a high similarity percentage. Based on trials by taking samples of K-gram 0 to 8 from 10 test data sets; the K-gram 3 is the best among K-grams 0 to 8.
Word recognition and automated epenthesis removal for Indonesian sign system sentence gestures
Authors: Rakun Erdefi , Widhinugraha I Gusti Bagus Hadi , Putra Setyono Noer Fitria
Published: June 2022

This research focuses on building a system to translate continuous Indonesian sign system (SIBI) gestures into text. In a continuous gesture, a signer will add an epenthesis (transitional) gesture, which is hand movement with no meaning but needed to connect the hand movement of one word with the next word in a continuous gesture. Reducing the number of irrelevant inputs to the model through automated epenthesis removal can improve the system's ability to recognize the words in continuous gestures. We implemented threshold conditional random fields (TCRF) to identify epenthesis gestures. The dataset consists of 2,255 videos representing 28 common sentences in SIBI. The translation system consists of MobileNetV2 as a feature extraction technique, removing epenthesis gestures found by the TCRF, and a long short-term memory (LSTM) for the classifier. With the MobileNetV2-TCRF-bidirectional LSTM model, the best word error rate (WER) and sentence accuracy (SAcc) were 33.4% and 16.2%, respectively. Intermediate-stage processing steps consisting of sandwiched majority voting of the TCRF and the removal of word labels whose number of frames is less than two frames, along with LSTM output grouping, were able to reduce WER from 33.4% to 3.4% and increase SAcc from 16.2% to 80.2%.
Challenges of IEEE 802.11a technology and improving its performance using diversity technique
Authors: Mohammed Najat Shyaa Jasim , Awad Buraq Abdulhadi , Abdullah Zainab B. , Shneen Salam W.
Published: June 2022

In this technology, information can be transmitted through the air without the need for cables, wires, or other electronic connectors, using electromagnetic waves such as infrared, radio and satellite frequencies. In this research, the most important challenges and problems faced by wireless communication systems were identified and how to solve and address them. (IEEE 802.11aWLAN) was also simulated and tested using Mat lab program, where the signal generated in (IEEE 802.11aWLAN) was examined in the form of orthogonal frequency division multiplexing (OFDM) modulation by examining the performance of the bit error rate of this system using diversity technology to improve the performance of this system where we adopted the reception diversity technology, In the case of line of sight (LOS) channel and multipath channel using maximal ratio combining (MRC) method. We notice that the use of the MRC receiving diversity technique in the system reduces the bit error rate and increases the efficiency of the system and the performance improve with increase the number of receiving antenna with gain 6 dB.
Data-driven optimal antenna planning for enhanced 4G mobile networks under realistic environment
Authors: Zeleke Seifu Girma , Haile Beneyam Berehanu , Bekele Ephrem Teshale
Published: June 2022

Recently different capacity boosting technologies, including 3D beamforming and expanding operating bandwidth have been investigated and included in the enhanced fourth generation (4G) and fifth-generation standards. For mobile operators to enhance their network performance, applying these advanced technologies is vital. To achieve that, planning and optimization work need to consider the spatiotemporal distribution of users. Although the capacity impact of advanced antenna technologies is investigated well, deployment options to exploit their benefits in a cost-effective manner considering a realistic network environment are seldom reported. This work presents a data driven and multi-objective based optimal vertical beams placement for an enhanced 4G mobile network with the newly introduced C-band. The method is utilized for an area located in Addis Ababa, Ethiopia, taking into account the existing 4G mobile network as a reference configuration. Users are distributed based on the data collected by ethio-telecom network management system. Findings indicate that optimal vertical beams placement achieved for gradual deployment with consideration cell edge and aggregate throughput performances while reducing network cost. While being cost-effective, up to 69.2% and 73.8% cell edge performance gain is achieved at 20 and 30 pareto points relative to an existing macro mobile network.
Highly efficient microstrip patch antenna for wireless gigabit alliance applications
Authors: Barua Soumen , Md. Imran Abu Zafar , Hoque Bhuiyan Md. Emdadul , Barua Subrata , Zahidur Rashid Syed , Gafur Abdul , Ahmed Md. Razu
Published: June 2022

A wireless gigabit alliance (WiGig) is widely known for higher bandwidth which operates at 60GHz unlicensed spectrum for super-fast data speed over short distances for millimeter-wave band application. This study offers an efficient, compact microstrip patch antenna for WiGig with a lesser return loss -50.45 dB (60 GHz). The gain and directivity of the proposed antenna are 13.01 dB and 13.42 dBi. It provides higher efficiency of around 91%. The proposed antenna also covers another mm-wave band that is at 95 GHz. The U.S. military uses electromagnetic radiation of 95 GHz as a non-lethal weapon. So, the anticipated antenna can be used for a miniaturized active denial system. The proposed antenna has a good gain, directivity, and efficiency for the multigigabits/s data rate. The aforesaid properties imply that it can be used for wireless personal area networks (WPAN) and wireless local area network (WLAN) and fifth-generation (5G) applications too.
Joint polar with physical layer network coding and massive MIMO: performance analysis
Authors: Mahmood Alza Abduljabbar , Kadhim Abdulkareem Abdulrahman
Published: June 2022

Large spectral efficiency, reliability, coverage, and energy efficiency are all major requirements to meet the targets of fifth-generation (5G) and beyond communication networks. The transmitted signal is usually susceptible to errors that reduced reliability and throughput of the system. Physical layer network coding (PLNC) is a promising technology to achieve better throughput, low latency, and high transmission rate. This paper considers the combination of PLNC with polar coding using massive multi-input multi-output (MIMO) system to enhance the transmission reliability by reducing the bit error rate (BER) and improve system reliability. This arrangement is investigated in two-way wireless relay transmission over millimetre wave (mmWave) band channel model. The results of the extensive simulation tests demonstrated improvements in throughput and BER achieved using polar code with PLNC and a sufficient number of antenna elements in the mMIMO system. The BER performance of polar-coded PLNC arrangement outperformed PLNC without coding for 128 and 256 receiving antenna elements in the relay node regardless the number of antenna elements in the user equipment side. The tests showed that the combination of the polar code with PLNC and MIMO system is not encouraging at low Signal-to-noise ratio (SNR).
Development and performance evaluation of object and traffic light recognition model by way of deep learning
Authors: Bali Shweta , Kumar Tapas , Tyagi Shyam Sunder
Published: June 2022

Deep learning models have shown incredible achievement in the field of autonomous driving, covering different aspects ranging from recognizing traffic signs and traffic lighs, vehicle detection, license plate detection, pedestrian detection. Most of the algorithms perrform better when the traffic lights are bigger in size, but the performance degrades in case of small-sized traffic lights. In this paper, the main emphasis is on evaluating two most promising deep learning architectures: single shot detector (SSD) and faster region convolutinal network (Faster R-CNN) on “la route automatisée (LaRA) traffic light dataset” which contains small traffic lights as objects. The strengths and weaknesses are evaluated based on different parameters. The performance is compared in terms of mean average Precision (mAP@0.50) and average recall. The impact of data augmentation on the two architectures is also analyzed. ResNet50 V1 as feature extractor for Faster R-CNN achieved 96% mAP (mean average precision) which performed better than Original ResNet50 V1 Faster R-CNN pipeline. Also, different parameters such as batch size, learning rate and optimizer are tuned for detecting and classifying small traffic lights into different categories. 
Traffic sign recognition based color model
Authors: Younus Majid Dherar , Al-Sabawi Emad A.
Published: June 2022

The main problems that encounter the traffic light detection algorithm have become a handicap to the performance of the algorithms. Problems associated with the change of sign color due to bad weather and illumination changes of sunlight make the detection hard task. In the current work, we discuss these problems and propose a new idea of an efficient real time color sign recognition that relies only on color information. The proposed approach is based on building a red-model in hypothetical red, green, blue (RGB) cube using a large database of traffic signs. The segmentation has been implemented on the traffic signs that hold red color only as an example to illustrate the proposed approach. Results showed that the proposed algorithm is accurate as well as the computational cost is reduced.
An approach for metadata extraction and transformation for various data sources using R programming language
Authors: Hasan Forat Falih , Bakar Muhamad Shahbani Abu
Published: June 2022

The metadata system is the key system for sharing and transforming data between various information systems (ISs), and each database system has its own structure for storing and retrieving metadata information. Metadata information must be extracted for data transformation. Furthermore, these procedures were required to communicate with each type of database system and retrieve the stored metadata; these processes required much information and effort. To overcome the challenge of accessing and extracting metadata from any type of data source, a unifomed method must be developed and integrated into any organization's information systems. The semi-structured data extraction method (SeDEM) is a developed method that includes three main operations: logical structure operation, unique key operation, and relationships operation. Finally, the accurate information obtained using the SeDEM addressed data quality issues concerning the integrity and completeness of the data transformation.
Load balancing in cloud computing: an analytical review and proposal
Authors: Maurya Santosh Kumar , Sinha Garima
Published: June 2022

Task scheduling or mapping of requests to machines with specified requirements becomes a very important problem to get optimized results. With the requests of many users, the machine can go under different conditions such as heavy load and minimum load. These various aspects can lead to system failure and loss of data. Hence the load must be balanced to beat the losses of the system. A load balancing concept described how different types of loads could be balanced with each machine. The load can be of memory, computation, and network. The paper mainly focuses on all kinds of load to provide a big view for load balancing in cloud computing. Along with a review of all load balancing techniques, the paper is also presenting a proposal to balance the load with decision theory. The proposed paper introduces the basic techniques and types for load balancing and their implications for innovation.
Toward mobile learning at Jordanian higher education institutions
Authors: Noor Ahmad Shukri Mohd , Atoom Marwan Nasser Yousef , Jalil Masita Abdul
Published: June 2022

Globally, teaching methods and tools in higher education institutions (HEIs) have changed nowadays. Many attempts have been made in Jordanian higher education institutions (JHEIs) in order to improve and continuity of the educational process, especially during coronavirus pandemic. The outbreak of this virus has become a major disruption where all Jordanian universities cancelled classes and moved toward online learning, and mobile learning (ML) has appeared as one of the possible solutions. ML is in its early stages at JHEIs, and it is academically unexplored enough. So, this study explores the ML experience at JHEIs during coronavirus disease 2019 (COVID-19) crisis. The data were collected using a web survey where 272 students in JHEIs participated. The results revealed that the smartphone is the most widely used mobile device for ML ML is easy to use, ML increases the interaction between the instructor and the students and among the students themselves, ML has a positive impact on students’ performance, and also students are willing to use ML in the future. The outcomes of the study support policy makers at JHEIs to make educational decisions relating ML phenomenon.
Diagnosis of hepatitis disease using machine learning techniques
Authors: Ahmed Ibraheem I. , Mohammed Duraid Y. , Zidan Khamis A.
Published: June 2022

Hepatitis is an infection that causes inflammation of liver tissue. Many studies have developed machine learning models for hepatitis disease diagnosis. However, there has been little discussion about the relationship between hepatitis symptoms. The first objective of this study is to provide a brief description of a real-world hepatitis disease symptom dataset. Furthermore, the authors proposed a stand-alone classification platform using random forest, decision tree, and support vector machine into healthy people or hepatitis patients using adaptive wrapper feature selection. It was discovered that there is a strong link between certain characteristics and hepatitis diagnosis. The work presented here may help improve hepatitis diagnosis in the early stages, which may lead to a reduction in the acute effects of hepatitis on human life. It is worth noting that random forest (RF) gave the highest accuracy and stayed slightly consistent through all sets of features in comparison to decision tree (DT) and support vector machines (SVM).
Overlapping clustering with k-median extension algorithm: An effective approach for overlapping clustering
Authors: Danganan Alvincent E. , Arceo Regina P.
Published: June 2022

Most natural world data involves overlapping communities where an object may belong to one or more clusters, referred to as overlapping clustering. However, it is worth mentioning that these algorithms have a significant drawback. Since some of the algorithm uses k-means, it also inherits the characteristics of being noise sensitive due to the arithmetic mean value which noisy data can considerably influence and affects the clustering algorithm by biasing the structure of obtained clusters. This paper proposed a new overlapping clustering algorithm named OCKMEx, which uses k-median to identify overlapping clusters in the presence of outliers. This new method aims to determine the insensitivity of the OCKMEx algorithm in locating data points that overlap even with outliers. An experimental evaluation of the algorithm was conducted wherein synthetic datasets served as a data source, and the F1 measure criterion was applied to assess the OCKMEx algorithm performance. Results indicate that the OCKMEx algorithm implementing the use of k-median performed a higher accuracy rate of 100% in identifying data points that overlap even with outliers compared to the existing k-means algorithm. The algorithm exhibited a promising performance in identifying overlapping clusters and was resistant to outliers.
Artificial neural network modeling for predicting the quality of water in the Sabak Bernam River
Authors: Affandi Faqihah , Rahman Mohamad Faizal Abd , Che Ani Adi Izhar , Sulaiman Mohd Suhaimi
Published: June 2022

Water quality prediction is aided by environmental monitoring, ecological sustainability, and aquaculture. Traditional prediction approaches capture the nonlinearity and non-stationarity of water quality well. Due to their rapid progress, artificial neural networks (ANNs) have become a hotspot in water quality prediction in recent years. ANNs are utilised in this study to predict water quality using soft computing techniques. The feedforward network and the standard back-propagation method of Levenberg-Marquardt and scaled conjugate gradient learning algorithm were employed in this research. One hidden layer has been recommended for the modelling, with the number of hidden neurons set at 3, 24, and 49. For this analysis, six different testing percentages were used, and the output data can be categorised as '0' for clean water and '1' for polluted water. From the results, it can be shown that the most optimised model was from the model of trainlm with a testing percentage of 18% and with 3 number of neurons. This most optimised model obtains an accuracy of 91.7%, the best validation performance of 0.073346 with 24 epochs, and having a receiver operating characteristic (ROC) curve that is closer to the true positive rate compared to other samples.
Implementation of eigenface method and support vector machine for face recognition absence information system
Authors: Annubaha Chakim , Widodo Aris Puji , Adi Kusworo
Published: June 2022

The student attendance system is what is needed in the process of recording attendance in learning and the development of student achievement. Currently several modern educational institutions have implemented a student attendance system using QR codes or fingerprints, but many still use the traditional system by calculating the number of students attending class. Based on these problems, the solution that can be given is to implement a student attendance system through face matching in the Android mobile application with Eigenface algorithm and support vector machine (SVM) algorithm. Eigenface using the principal component analysis (PCA) method can be used to reduce the dimensions of facial images so that they produce fewer variables and are easier to handle. The results obtained are then entered into a pattern classifier to determine the identity of the owner of the face. This study used 100 facial data as test data and training data. The system test results show that the use of Eigenface with SVM as a classifier can provide a fairly high level of accuracy. For facial images that were included in the training, 91% of the identification was correct.
Recommendation method based on learner profile and demonstrated knowledge
Authors: Bourkoukou Outmane , El Bachari Essaid , Lachgar Mohamed
Published: June 2022

The COVID-19 pandemic is increasingly gaining popularity when discussing e-learning in the context of institutional and organizational learning because of its numerous benefits which make it possible for learners to learn regardless of the circumstances and/or the timing. Therefore, the expanding dominion of online learning has caused problem in terms of determining adequate learning activities for the learner in this context, and it relatively becomes a widely used learning technique for learners. Several studies in online learning focused mainly on increasing student achievements based on recommendation systems. An ideal recommender system in e-learning environment should be built with both accurate and pedagogical goals. To address this challenge, we propose a recommendation method based on learner preferences and knowledge level using machine learning technique. The learning approach is designed based on this technology to build a personalized e-learning scenario by selecting the most adequate learning activities for the learner. Moreover, several experiences were conducted in the real environment to evaluate our system. The results show the quality of learning and the learner's satisfaction.
Cancerous brain tumor detection using hybrid deep learning framework
Authors: Kothari Sonali , Chiwhane Shwetambari , Jain Shruti , Baghel Malti
Published: June 2022

Computational models based on deep learning (DL) algorithms have multiple processing layers representing data at multiple levels of abstraction. Deep learning has exploded in popularity in recent years, particularly in medical image processing, medical image analysis, and bioinformatics. As a result, deep learning has effectively modified and strengthened the means of identification, prediction, and diagnosis in several healthcare fields, including pathology, brain tumours, lung cancer, the abdomen, cardiac, and retina. In general, brain tumours are among the most common and aggressive malignant tumour diseases, with a limited life span if diagnosed at a higher grade. After identifying the tumour, brain tumour grading is a crucial step in evaluating a successful treatment strategy. This research aims to propose a cancerous brain tumor detection and classification using deep learning. In this paper, numerous soft computing techniques and a deep learning model to summarise the pathophysiology of brain cancer, imaging modalities for brain cancer, and automated computer-assisted methods for brain cancer characterization is used. In the sense of machine learning and the deep learning model, paper has highlighted the association between brain cancer and other brain disorders such as epilepsy, stroke, Alzheimer's, Parkinson's, and Wilson's disease, leukoaraiosis, and other neurological disorders.
Smart-geofencing for system of reporting inadequate regional infrastructure using crossing and winding number
Authors: Safitri A. Basid Puspa Miladin Nuraida , Nugroho Fresy
Published: June 2022

Infrastructure is a major factor in increasing regional productivity and become the center of economic growth. problems arise when infrastructure improvements are only centered on downtown areas. One of the ways to develop regional infrastructure is with media reporting inadequate infrastructure. The use of smartphone technology and geofencing techniques are considered to be helpful in reporting media. Increasingly sophisticated built-in features in smartphones are undeniable proof of the technology’s highly impressive advancement. Mobile applications utilizing location-based services (LBS) assisted by a global positioning systems (GPS) sensor are rapidly becoming popular. One of its most-favorited features is geofencing. The volunteered-geographic-information (VGI)-based reporting system for regional infrastructure damage uses this feature to obtain the data input directly from the users. In this current research, the author uses two methods in geofencing, including crossing number (CN) and winding number (WN). Several previous studies suggest that the combination of these methods might result in faster computation, compared to the raycasting method. By using winding and crossing number methods, 98% of increased accuracy can be obtained, while a prior method could only obtain 96%. An improvement in the system’s speed is also expected.
Applying reinforcement learning for random early detaction algorithm in adaptive queue management systems
Authors: Yousif Ayman Basheer , Hassan Hassan Jaleel , Muttasher Gaida
Published: June 2022

Recently, the use of internet has been increased all around the hose, the companies, government departments and the video games and so on. Thus, this increased the traffic used in the networks, which generated congestion issues and sent packet drop in the nodes. To solve this problem, certain algorithms are used. The Active queue management is one of the most important algorithms that helps with this issue. For an effective network management, the RL was used, and it will adapt with the parameters of algorithms. Where the suggested algorithm deep Q-networks (DQN) depends on the reinforcement learning (RL) to reduce the drop and delay. Also, the random early detection (RED) (an active queue management (AQM) algorithm) was adopted based on the NS3 situation.
Girth aware normalized min sum decoding algorithm for shorter length low density parity check codes
Authors: Kadi Abdelilah , El Ouakili Hajar , Alami Rachid El , Najah Said
Published: June 2022

Recently, short block codes are in great demand due to the emergent applications requiring the transmission of a short data unit and can guarantee speedy communication, with a minimum of latency and complexity which are among the technical challenges in today’s wireless services and systems. In the context of channel coding using low density parity check (LDPC) codes, the shorter length LDPC block codes are more likely to have short cycles with lengths of 4 and 6. The effect of the cycle with the minimum size is that this one prevents the propagation of the information in the Tanner graph during the iterative process. Therefore, the message decoded by short block code is assumed to be of poor quality due to short cycles. In this work, we present a study of the evolution of the messages on check nodes during the iterative decoding process when using the LDPC decoding algorithm normalized min sum (NMS), to see the destructive effect of short cycles and justify the effectiveness of the girth aware normalized min sum (GA-NMS) decoding LDPC codes algorithm in terms of correction of the errors, particularly for the codes with short cycles 4 and 6. In addition to this, the GA-NMS algorithm is evaluated in terms of bit error rate performance and convergence behavior, using wireless regional area networks (WRAN) LDPC code, which is considered as a short block code.
Knowledge discovery in manufacturing datasets using data mining techniques to improve business performance
Authors: Shaaban Amani Gomaa , Khafagy Mohamed Helmy , Elmasry Mohamed Abbas , El-Beih Heba , Ibrahim Mohamed Hasan
Published: June 2022

Recently due to the explosion in the data field, there is a great interest in the data science areas such as big data, artificial intelligence, data mining, and machine learning. Knowledge gives control and power in numerous manufacturing areas. Companies, factories, and all organizations owners aim to benefit from their huge; recorded data that increases and expands very quickly to improve their business and improve the quality of their products. In this research paper, the knowledge discovery in databases (KDD) technique has been followed, “association rules” algorithms “Apriori algorithm”, and “chi-square automatic interaction detection (CHAID) analysis tree” have been applied on real datasets belonging to (Emisal factory). This factory annually loses tons of production due to the breakdowns that occur daily inside the factory, which leads to a loss of profit. After analyzing and understanding the factory product processes, we found some breakdowns occur a lot of days during the product lifecycle, these breakdowns affect badly on the production lifecycle which led to a decrease in sales. So, we have mined the data and used the mentioned methods above to build a predictive model that will predict the breakdown types and help the factory owner to manage the breakdowns risks by taking accurate actions before the breakdowns happen.
Enhancement in privacy preservation in cloud computing using apriori algorithm
Authors: Mustafa Raniah Ali , Chyad Haitham Salman , Mutar Jinan Redha
Published: June 2022

Cloud computing provides advantages, like flexibly of space, security, cost optimization, accessibility from any remote location. Because of this factor cloud computing is emerging as in primary data storage for individuals as well as organisations. At the same time, privacy preservation is an also a significant aspect of cloud computing. In regrades to privacy preservation, association rule mining was proposed by previous researches to protect the privacy of users. However, the algorithm involves creation of fake transaction and this algorithm also fails to maintain the privacy of data frequency. In this research an apriori algorithm is proposed to enhance the privacy of encrypted data. The proposed algorithm is integrated with elagmal cryptography and it does not require fake transactions. In this way, the proposed algorithm improves the data protection as well as query privacy and it hides data frequency. Result analysis shows that the proposed algorithm improves the privacy as compared to previously proposed association rule mining and the algorithm also shows 3% to 5% improvement in performance when compared to other existing algorithms. This performance analysis with varying number of the data and fake transactions shows that the proposed algorithm doesn’t require fake transactions, like data privacy association rule mining.
Hypertension, Arterial Stiffness, and Diabetes: a Prospective Cohort Study
Authors: Tian Xue , Zuo Yingting , Chen Shuohua , Zhang Yijun , Zhang Xiaoli , Xu Qin , Wu Shouling , Wang Anxin
Published: June 2022

Background:

Whether the combination of different blood pressure and arterial stiffness (AS) status is independently associated with diabetes has not been fully investigated so far. This study aimed at investigating the status of hypertension and AS in determining diabetes.

Methods:

This prospective cohort study included 11 156 participants from the Kailuan study. AS was measured by brachial-ankle pulse wave velocity. We compared the risk of diabetes between individuals with ideal vascular function (defined as normotension with normal AS), normotension with elevated AS, hypertension with normal AS, and hypertension with elevated AS.

Results:

After a median follow-up of 6.16 years, diabetes occurred in 768 participants. Compared with ideal vascular function group, the highest risk of diabetes was observed in hypertension with elevated AS group (hazard ratio, 2.42 [95% CI, 1.93–3.03]), followed by normotension with elevated AS group (hazard ratio, 2.11 [95% CI, 1.68–2.66]), hypertension with normal AS group exhibited the lowest risk of diabetes (hazard ratio, 1.48 [95% CI, 1.08–2.02]). Multiple sensitivity and subgroup analyses yielded similar results. Furthermore, the additional of AS to a conventional model including traditional risk factors had a higher incremental effect on the predictive value for diabetes than the addition of hypertension (the C statistics was 0.707 versus 0.695; the integrated discrimination improvement was 0.65% versus 0.28%; net reclassification improvement was 40.48% versus 34.59%).

Conclusions:

Diabetes is associated with not only hypertension but also AS. Additionally, AS shows a better predictive ability than hypertension in predicting diabetes.
Day to Day Blood Pressure Variability Associated With Cerebral Arterial Dilation and White Matter Hyperintensity
Authors: Zhang Boyu , Huo Yajing , Yang Zidong , Lv Huihui , Wang Yilin , Feng Jianfeng , Han Yan , Wang He
Published: June 2022

Background:

Previous studies suggested blood pressure variability (BPV) might help reveal interactions between blood pressure fluctuation and white matter lesions, and the impact of elevated BPV on white matter hyperintensity (WMH) or cerebral arterial dilation is unclear.

Methods:

This retrospective observational study involved 2634 stroke-free individuals (68.6±11.1 years, 50.3% female), who underwent magnetic resonance imaging and magnetic resonance angiography scans, from a single center in Shanghai, China. Measurements for variability of blood pressure were made based on 7 days blood pressure recordings. WMHs were quantified from T2-FLAIR images and further classified as periventricular WMH or deep WMH. M1 segment of middle cerebral artery dilation was assessed from magnetic resonance angiography images. General linear model was used to examine the associations.

Results:

Both increased systolic and diastolic BPV were associated with increased WMH volume (systolic:
β
=0.02 [95% CI, 0.004–0.03],
P
=0.01; diastolic:
β
=0.05 [95% CI, 0.03–0.08],
P
<0.001). Only periventricular WMH was associated with BPV (systolic:
β
=0.02 [95% CI, 0.005–0.04],
P
=0.01; diastolic:
β
=0.06 [95% CI, 0.04–0.09],
P
<0.001). MCA dilation was found in 125 individuals (4.75%). Systolic BPV was associated with MCA dilation only in the hypertensive individuals (
β
=0.11 [95% CI, 0.06–0.17],
P
<0.001). Increased WMH volume was found associated with dilated MCA (
β
=0.17 [95% CI, 0.11–0.23],
P
<0.001).

Conclusions:

Increased BPV might be one of the pathophysiological phenomena involving in the small vessel disease independent of hypertension. Increased BPV might independently contribute to intracranial arterial dilation. Management of BPV might be a target to preserve cerebrovascular wellness.
Coppinsiella extremiorientalis (Teloschistaceae, lichenized Ascomycota), a new species from the Russian Far East and a new genus to the region
Authors: FROLOV IVAN V. , PROKOPIEV ILYA A. , YAKOVCHENKO LIDIA S. , GALANINA IRINA A. , EZHKIN ALEXANDER K.
Published: June 2022

A new epiphytic species of crustose Teloschistaceae, Coppinsiella extremiorientalis, is described from the floodplain forests of the boreal zone of the Russian Far East. It is characterized by its white to whitish grey, thin to disappearing thallus mostly without anthraquinones, dull orange zeorine pruinose apothecia with an anthraquinones composition corresponding to chemosyndrome A. Phylogeny of the genus Coppinsiella, which was previously unknown in the Far East, is discussed, as well as the morphology, ecology and geographical distribution of Coppinsiella species. A new combination for Caloplaca fiumana, an older name for Coppinsiella orbicularis, is also formally proposed.
Changepoint Detection: An Analysis of the Central England Temperature Series
Authors: Shi Xueheng , Beaulieu Claudie , Killick Rebecca , Lund Robert
Published: June 2022

This paper presents a statistical analysis of structural changes in the Central England temperature series, one of the longest surface temperature records available. A changepoint analysis is performed to detect abrupt changes, which can be regarded as a preliminary step before further analysis is conducted to identify the causes of the changes (e.g., artificial, human-induced, or natural variability). Regression models with structural breaks, including mean and trend shifts, are fitted to the series and compared via two commonly used multiple changepoint penalized likelihood criteria that balance model fit quality (as measured by likelihood) against parsimony considerations. Our changepoint model fits, with independent and short-memory errors, are also compared with a different class of models termed long-memory models that have been previously used by other authors to describe persistence features in temperature series. In the end, the optimal model is judged to be one containing a changepoint in the late 1980s, with a transition to an intensified warming regime. This timing and warming conclusion is consistent across changepoint models compared in this analysis. The variability of the series is not found to be significantly changing, and shift features are judged to be more plausible than either short- or long-memory autocorrelations. The final proposed model is one including trend shifts (both intercept and slope parameters) with independent errors. The analysis serves as a walk-through tutorial of different changepoint techniques, illustrating what can be statistically inferred.
Unintentional Acute Poisoning Related Emergency Department Visits in Children in a Single-Center: A Nine-Year Prospective Survey
Authors: Wiener Amram Hila , Daviko Bat-Hen Annie , Dalal Yotam , Meirson Gila , Brantz Ilona , Tasher Diana , Ovadia Adi , Dalal Ilan
Published: June 2022

Pediatric morbidity due to unintentional poison exposure is a significant burden on public health. We prospectively characterize patterns of unintentional poison exposure in a single pediatric emergency department, using a detailed computerized questionnaire for all unintentional injuries admitted during 2009 to 2017. Out of 71,765 visits due to unintentional injuries, 252 children were admitted due to unintentional poison exposure. Most (198/252, 79%) were between 1 and 3 years of age. The majority of events (209/252, 82.9%) occurred at the patient’s home and 81% (205/255) were classified as exploratory ingestion. In 41/252 (14%) cases, exposure to more than one substance was reported. Most events 231/293 (79%) involved medications and 21% were due to domestic products. Four medications account for 45% of the events (Paracetamol, Salbutamol, Antihypertensive, and Antidepressants). Opioids were responsible for only 1.7%. By, collaboration between government, public health, educational institutions and commercial companies, can the burden of pediatric unintentional poison exposure be reduced.
Implementation of a Clinical Decision Support (CDS) Tool for the Management of Functional Constipation in Pediatric Primary Care
Authors: Webster Jennifer B. , Lourie Eli M. , Lawton L. Gregory , Zsolway Kathleen M. , Huang Elena
Published: June 2022

Constipation is common in general pediatrics and often results in potentially unnecessary referrals to pediatric gastroenterology. We hypothesized that a clinical decision-making tool would support primary care providers to manage pediatric constipation, improve workflow, and prevent unnecessary subspecialty care. In this pilot quality improvement initiative, a multidisciplinary team completed a root cause analysis related to challenges with the care of pediatric constipation. The results informed the development of interventions including a Clinical Decision Support tool and patient educational materials embedded within an existing order-set in the electronic health record, which we implemented in our primary care network. The use of the updated order-set continues to increase monthly, and there is reported improved workflow and increased confidence by providers. These interventions demonstrated that it is feasible to implement tools to support primary care clinicians in their management of pediatric patients with constipation.
Disabled youth participation within activism and social movement bases: An empirical investigation of the UK Disabled People’s Movement
Authors: Griffiths Miro
Published: June 2022

Understanding disabled youth activism is key for improving young disabled people’s participation in politics and social change. Young disabled people require opportunities to situate historical and biographical experiences within broader socio-economic contexts. This will lead to a politicised consciousness surrounding disability, emancipation and social justice. This article presents empirical data from the first study on young disabled people’s contemporary position within the UK Disabled People’s Movement. It critically assesses three areas pertinent to youth activism: activist membership, social movement organisation and future considerations for activism. This allows for an exploration of how young disabled activists navigate collective action, influence activist claims and demands and understand the issues for sustaining a disabled people’s social movement. The article illustrates young disabled activists’ desire to disrupt their current position within the UK Disabled People’s Movement and bring into focus a future where young disabled people’s contributions to activism and social movements are accessible, valued and influential. The article argues that a failure to support young disabled people’s participation within social movements will have an adverse impact on their political identities.
Significant Published Articles in 2021 for Pharmacy Nutrition Support Practice
Authors: Dickerson Roland N. , Bingham Angela L. , Canada Todd W. , Chan Lingtak Neander , Cober M. Petrea , Cogle Sarah V. , Tucker Anne M. , Kumpf Vanessa J.
Published: June 2022

Purpose: The purpose of this article is to assist the pharmacist engaged in nutrition support therapy in staying current with pertinent literature. Methods: Several clinical pharmacists engaged in nutrition support therapy compiled a list of articles published in 2021 considered important to their clinical practice. The citation list was compiled into a single spreadsheet where the author participants were asked to assess whether the article was considered important to nutrition support pharmacy practice. A culled list of publications was then identified whereby at least 5 out of the 8 author participants considered the paper to be important. Guideline and consensus papers from professional organizations, important to practice but not ranked, were also included. Results: A total of 211 articles were identified; 8 from the primary literature were voted by the group to be of high importance. An additional 18 guidelines, position, recommendation, or consensus papers were also identified. The top-ranked articles from the primary literature were summarized and a narrative regarding its implications to pharmacy nutrition support practice were provided. Conclusion: We recommend that pharmacists engaged in nutrition support therapy be familiar with these articles as it pertains to their practice.
Review of Medication Error Sources Associated With Inpatient Subcutaneous Insulin: Recommendations for Safe and Cost-Effective Dispensing Practices
Authors: McKay Connor , Schenkat Dan , Murphy Kristin , Hess Elizabeth
Published: June 2022

Background: The challenge of addressing variation in practice is underlined by variation in institutional operational and financial limitations, which ultimately directs institutional insulin dispensing strategy. Insulin therapy is multi-modal, and inpatient pharmacies may be responsible for simultaneously managing up to 8 formulations. While available as a tool for glycemic management in the inpatient setting, intravenous (IV) insulin and insulin pumps are out of scope for this review. Considerations when designing subcutaneous insulin dispensing processes include medication safety, infection control, pharmacy workflow, nursing workflow, drug cost, hardware cost, and hospital policy. Objective: This review provides an overview of common inpatient subcutaneous insulin dispensing practices, describes the regulations directing current practice, and discusses the benefits and risks of each dispensing practice. Finally, the review provides recommendations for subcutaneous insulin dispensing practices with respect to institutional limitations that minimize patient safety risk and consider the variable costs that practices incur. Methods: Common formulations will be presented along with medication safety considerations and potential medication administration errors to preface strategies for management. Conclusion: Nearing the centennial anniversary of the invention of insulin, the practice of dispensing subcutaneous insulin products has remained a core responsibility of the hospital pharmacy as a primary treatment option for Diabetes Mellitus. Despite the continued presence of this product as a universal component of the hospital formulary, subcutaneous insulin dispensing practices remain varied institution-to-institution.
Polychronous and Existential Mode of Time in Africa: A Critique of Mbiti’s Concept of Time
Authors: Ukwamedua Nelson Udoka
Published: June 2022

The concept of time is coeval with man’s existence and coterminous with his endeavors. This is predicated on the fact that it sets and dictates the pace for man. People appreciate reality differently and this includes their concept of time. This premise informed Mbiti’s idea of time in Africa. Mbiti in his submission posited that Africans have no idea of the “future” in time. A critical response to this position of Mbiti toward a restatement of the proper model of time in Africa is what this paper is concerned with. The paper employed the critical-analytic model to examine Mbiti’s position and it was obvious that Mbiti’s position was parochial and untenable. This paper then argued that time in African is rather polychromous, holistic, and existential. Africans “live in time” and if the future is part of the African life and world, then there is a future as far as time in Africa is concerned.