bluuebunny/crossref_metadata_2025 huggingface dataset:
A Smart Integrated Brain-Computer Interaction for Epileptic Seizure Detection
Authors: El-Khamisy A. M. , Abd El-Raoof N. M. , Youssef S. M.
Published: December 2021

Epilepsy is brain resulted activities which are affected by suddenly seizures which have unpredictable changes affects brain electrical functionalities. Epilepsy has a significant impact on society on the healthcare treatment, cost, responds, and patients behavior. The study has main objectives to propose accurate integrated framework for epileptic seizure detection from the pre-ictal phase of the EEG signal. Locate the connected channel lobe in region where epileptic is expected to occur. Provide automated and real-time monitoring and send warning messages to patient and epileptologist to take accurate actions before ictal occur. Enable future contribution for different Seizure features and impact. Also reduce cost, time and effort. Based on the hypothesis of entropy of EEG signals during seizure has low value if (n) of channels are detected to have seizure, then they are considered as connected neighbors in brain domain mapping, which is clear alert that patient will have a seizure ictal. This end to end framework has modules of data acquisition, pre-processing, feature extraction, pattern-matching, supports vector machines (SVM) classifier for extracted feature, in addition to monitoring and notification. The extracted features includes lower threshold, homogeneity, weighted permutation entropy, power and energy. Also identify the physiological field located inside the brain which the seizure will expected to occur. The final output results have 92% for True positive rate in addition to 95% of F1 and 98.9% of accuracy. This system has proved consistency during all its phases of seizure detection with valuable and effective support to the society.
Lung Images Segmentation and Classification Based on Deep Learning: A New Automated CNN Approach
Authors: Salama Wessam M. , Aly Moustafa H. , Elbagoury Azza M.
Published: December 2021

Lung cancer became a significant health problem worldwide over the past decades. This paper introduces a new generalized framework for lung cancer detection where many different strategies are explored for the classification. The ResNet50 model is applied to classify CT lung images into benign or malignant. Also, the U-Net, which is one of the most used architectures in deep learning for image segmentation, is employed to segment CT images before classification to increase system performance. Moreover, Image Size Dependent Normalization Technique (ISDNT) and Wiener filter are utilized as the preprocessing phase to enhance the images and suppress the noise. Our proposed framework which comprises preprocessing, segmentation and classification phases, is applied on two databases: Lung Nodule Analysis 2016 (Luna 16) and National Lung Screening Trial (NLST). Data augmentation technique is applied to solve the problem of lung CT images deficiency, and consequently, the overfitting of deep models will be avoided. The classification results show that the preprocessing for the CT lung image as the input for ResNet50-U-Net hybrid model achieves the best performance. The proposed model achieves 98.98% accuracy (ACC), 98.65% area under the ROC curve (AUC), 98.99% sensitivity (Se), 98.43% precision (Pr), 98.86% F1- score and 1.9876 s computational time.
Skin Cancer Diseases Classification using Deep Convolutional Neural Network with Transfer Learning Model
Authors: Moataz Laila , Salama Gouda I. , Abd Elazeem Mohamed H.
Published: December 2021

Skin cancer is becoming increasingly common. Fortunately, early discovery can greatly improve the odds of a patient being healed. Many Artificial Intelligence based approaches to classify skin lesions have recently been proposed. but these approaches suffer from limited classification accuracy. Deep convolutional neural networks show potential for better classification of cancer lesions. This paper presents a fine-tuning on Xception pretrained model for classification of skin lesions by adding a group of layers after the basic ones of the Xception model and all model weights are set to be trained. The model is fine-tuned over HAM10,000 dataset seven classes by augmentation approach to mitigate the data imbalance effect and conducted a comparative study with the most up to date approaches. In comparison to prior models, the results indicate that the proposed model is both efficient and reliable.
Spinal Muscle Atrophy Disease Modelling as Bayesian Network
Authors: Helal Mohammed Ezzat , Helal Manal Ezzat , Fahmy Professor Sherif Fadel
Published: December 2021

We investigate the molecular gene expressions studies and public databases for disease modelling using Probabilistic Graphical Models and Bayesian Inference. A case study on Spinal Muscle Atrophy Genome-Wide Association Study results is modelled and analyzed. The genes up and down-regulated in two stages of the disease development are linked to prior knowledge published in the public domain and co-expressions network is created and analyzed. The Molecular Pathways triggered by these genes are identified. The Bayesian inference posteriors distributions are estimated using a variational analytical algorithm and a Markov chain Monte Carlo sampling algorithm. Assumptions, limitations and possible future work are concluded.
Neurological Disorder Detection of Brain Abnormal Activities Using a New Enhanced Computer-aided Model
Authors: Shoieb D , Youssef S
Published: December 2021

In the field of neurodevelopmental disorders, Autism Spectrum Disorders (ASD) are recognized as one of the dramatically increased etiologically and clinically heterogeneous diseases. Although, increasing the number of children who have difficulties in communication or suffer from sudden malfunction of the brain, the current diagnostic approaches for those kind of disease are time-consuming and are mainly based on clinical interviews. In this paper, a new enhanced diagnostic model is introduced addressing many of the challenges which threats the analysis of Electroencephalography (EEG) signals. A preprocessing stage is proposed to choose the key segment of EEG channel and remove the artifacts in the EEG signals to enhance their quality. The proposed model uses a set of discriminative features based on discrete wavelet transform (DWT) such as skewness, standard division, shannon entropy and relative wave energy. Also, extracting cross correction between brain regions to detect abnormal connectivity and synchronization. Two EEG datasets are used to verify the accuracy of the proposed model. The fusion of two EEG dataset helps in building a more generalized mode to diagnose epilepsy and ASD. In the fused dataset, the combination of the mentioned features and Random Forest have produced a very promising diagnosis result with minimum diagnostic time, with an average accuracy equals to 96.78%. The proposed model obtained better classification accuracy compared to the existing methods.
Intelligent High Payload Audio Watermarking Algorithm Using Colour Image in DWT-SVD Domain
Authors: Elshazly A R , Nasr Mohamed E. , Fouad M M , Abdel-Samie Fathi E.
Published: December 2021

Copyright protection and ownership verification of digital audio tracks have become increasingly important to be enabled by digital watermarking techniques. A novel high payload intelligent audio watermarking scheme with RGB color watermark image is proposed in this paper. The color watermark image is encrypted using Arnold chaotic map and passed through an adaptive scaling filter to scale the image to match the required payload. The encoding process is performed on the scaled encrypted version of the watermark image. A portion of the audio signal is used to embed a synchronization code and the other one is decomposed into short frames. These frames are processed with a two-level discrete wavelet transform (DWT), followed by a singular value decomposition (SVD) process on the approximation coefficients. The encoded watermark is inserted into the diagonal matrix using quantization index modulation (QIM). The inverse process of SVD and DWT is applied to obtain the marked audio signal. Blind extraction of the hidden information from the marked audio signal is performed in the reverse order of the embedding process. Experiments show that security, high payload, transparency and imperceptibility of the algorithm are satisfied. The robustness against several kinds of audio signal processing attacks is shown. Performance evaluation tests with SNR, BER, and FSIM are conducted.
Development of Smart 3D Object Detection using LiDAR Cloud-Data Collection
Authors: Abd Elhamied Essam M. , Youssef Sherin M.
Published: December 2021

Smart cities are made up of autonomous vehicle and they communicate and interact with their environment and require high precision computer vision to maintain driver and pedestrian safety. This paper presents a cost-efficient, non-intrusive and easy to use method for collecting data traffic counts using LiDAR technology. The proposed method incorporates a LiDAR sensor, a Convolutional Neural Network (CNN) and a Hybrid SVM into a single traffic counting framework. As the technology is economical and readily accessible, LiDAR is adopted. The distance data obtained are translated into the signals. Due to the difficulty of urban scenes, automatic detection of objects from remotely sensed data within urban areas is difficult. While recent advances in computer vision have shown that CNNs are very suitable for this task, the design and training of CNNs of this kind remained demanding and time consuming, given the challenge of collecting a large and well-annotated dataset and the specificity of every task. Hybrid SVM is a supervised data classification and regression machine learning tool. In the methodology the Hybrid SVM is used in detection and non-detection cases of highly complex distance data points obtained from the sensor. In order to examine the performance of the proposed method, the test is carried out in three different locations in Alexandria, Egypt. The results of tests show that the pro-imposed method achieves acceptable results in vehicle collection, which results in a precision of 85–89%. The exactness of the method proposed is determined by the colour of a vehicle’s external surface.
Intelligent Control for Pressurizer System in Nuclear Power Plants
Authors: Sheta A A , Ali E H , Fikry R M , Mahmoud T A , El-Araby S M , Mahmoud M I
Published: December 2021

Fine control of output power for nuclear power plants is the essential goal for safe operation. In this work, a Fuzzy analytical proportional-integral-derivative (FPID) controller with different configurations is designed to adjust and control the pressure of the PZR system. The stability analysis of the FPID controller with variable gains is established, and conditions for bounded-input bounded-output stability conditions (BIBO) are derived using the small gain theory. Two scenarios are applied for evaluating the dynamic response of applied controllers. In addition, performance indices are compared between the PZR model and data measured from the PCtran VVER-1200 simulator. Finally, a simulation platform is developed for MATLAB / Simulink to implement the three-region nonlinear non-equilibrium PZR model and the designed pressure controllers. The analysis and evaluation results showed good durability of the designed controllers and satisfactory performance of the control. These results further show that the nonlinear PZR model is accurate, feasible, and valuable for dynamic simulation and control unit design.
Investigation on Online DGA Monitoring to Determine Transformer Health Index Using Machine Learning
Authors: Solehin Shamsudin M , Yakub Fitri , Ibrahim Shapiai M , Mohmad Azlan , Amirah Abd Hamid N
Published: December 2021

The Dissolve Gas Analysis (DGA) to determine the ageing and degradation of the transformer is standard and routine periodic maintenance. In general, there are two DGA analysis methods which are conventional (lab-based) and online monitoring. DGA monitoring will be able to access to detect incipient fault and transformer failure. Several techniques are available to analyse, interpret and diagnose the DGA result, such as IEEE standard, IEC 60599 standard, Key Gas Method, and Duval methods. There are several Machine Learning (ML) techniques has been explored such as Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Neural Neighbours (KNN), Random Neural Network (RNN), and Fuzzy Logic for determining the transformer condition, including fault diagnostic and fault detection. However, there are unexplored studies to combine the commercial device to determine the Health Index (HI) of Transformer. In this study, an ML method with the available input feature from the commercial device to the network is trained to determine the HI. In general, the benchmark dataset from the existing work is employed to validate the proposed investigation. There are 730 datasets comprising five different classes; 1) Very Good, 2) Good, 3) Fair, 4) Poor, 5) Very Poor in determining the HI of a transformer. Conventional rule to partition the train and testing dataset with a 70:30 ratio is employed in this study. The maximum accuracy results and method for 1) M1 is 66.67% for ANN, 2) M2 is 68.49% for ANN, 3) M3 is 76.71% for KNN, 4) M5 is 76.26% for ANN, 5) M6 is 79.00% for ANN and 6) M7 is 86.30% for ANN. In conclusion, the multi-gas device will have a good accuracy performance and provide a good HI indicator to classify the condition of the transformer, which can be used for preventive maintenance.
Single loop PID controller design based on optimization algorithms for parallelly connected dc-dc converters
Authors: Allam Allam M. , Ibrahim A.S. , Nabil Essam
Published: December 2021

This paper addresses a viable single loop PID controller on the bases of optimization algorithms for parallelly connected DC-DC converters to improve current sharing, improve the systems dynamics and guarantee good steady-state performance simultaneously. Because of inconvenience and lack of accuracy of Ziegler-Nichols rule in tuning PID controller parameters, an optimized controller design strategy with the purpose of enhancing the system performance is introduced in this paper. The PID is tuned by the traditional Ziegler -Nichols technique along with three other different algorithms: Genetic algorithm, whale algorithm and grey wolf algorithm. A comparison has been established between these algorithms based on the objective function value, execution time, overshoot, settling time and current sharing. The simulation results were collected to authenticate effectiveness of the proposed techniques and to evaluate the advantages of these optimization algorithms over the traditional tuning method.
Aeroacoustics behavioral study of Savonius wind turbine
Authors: Malainine Maymouna , Khaled Amany , Shabaan Sameh M
Published: December 2021

Vertical Axis Wind Turbines (VAWTs) are appropriate for use in populated areas. If VAWTs were installed at residential areas, the generated aerodynamic noise can be harmful in a way or another. Therefore, in the present study, the aero-acoustics of the conventional Savonius Wind turbine was investigated using Computational Fluid Dynamics (CFD). Both the Unsteady Reynolds-averaged Navier-Stokes (URANS) equations and impermeable Ffowcs Wiliams and Hawkings (FW-H) equation were simultaneously solved. The effect of speed ratio was also studied. The results indicate that; the pressure is inversely proportional to the speed ratio. Additionally, the velocity has been increased due to the increase of the tip speed ratio. Finally, it has improved that for the majority of receivers, the overall sound level increases with increasing speed ratio.
Automated process control system of Jaw crusher production
Authors: Elgendi Elbadr O , Shawki Khaled
Published: December 2021

Rocks go through a series of crushing stages to produce the final aggregate size which suit customer’s needs. Aggregate crushers suffer from many problems in reality that affect its production processes badly and may lead to its stoppage. These problems could be unchangeable feed rate, hidden steel part between rocks and wastage from storing final aggregate stockpile on the ground. The purpose of this research is to develop an automated control system for Jaw crusher production process as a solution for the real specified problems to get more productive aggregate production. First a 3D model which mimic exactly the real crushing plant was built to test the proposed system. Then a C++ code was programmed to control the proposed system components such as motor, sensors and LCD screens. Thereafter, the aggregate flow simulation program (AggFlow) is utilized to simulate the proposed system results. Cost analysis calculations were performed to explore the impact of proposed system on the production process cost and time. Finally, the results confirmed that the proposed system enhance and increase the crusher production process. As it shows increase in the crusher automatic control, the net profit and the feed rate while decrease in the final product waste.
Galerkin Method for Nonlinear Higher-Order Boundary Value Problems Based on Chebyshev Polynomials
Authors: Abbas W. , Fathy Mohamed , Mostafa M. , Hesham A. M. A
Published: December 2021

In the current paper, we develop an algorithm to approximate the analytic solution for the nonlinear boundary value problems in higher-order based on the Galerkin method. Chebyshev polynomials are introduced as bases of the solution. Meanwhile, some theorems are deducted to simplify the nonlinear algebraic set resulted from applying the Galerkin method, while Newton’s method is used to solve the resulting nonlinear system. Numerous examples are presented to prove the usefulness and effectiveness of this algorithm in comparison with some other methods.
Legendre-Galerkin method for the nonlinear Volterra-Fredholm integro-differential equations
Authors: Fathy Mohamed
Published: December 2021

The study of solving nonlinear integro-differential equations in Volterra-Fredholm type presents in this paper. The proposed method tends to use Legendre polynomials as a basis in the Galekin method to obtain the numerical solution. We use the Newton method to get the numerical solution of the nonlinear equations resulted from applying the Galerkin method. The comparison of the present study with the existing results in the literature shows an excellent agreement. Numerical examples explain the convergence, applicability, and efficiency of algorithm.
How extreme could the near term evolution of the Indian Summer Monsoon rainfall be?
Authors: Jain Shipra , Scaife Adam A
Published: December 2021

We provide a methodology to estimate possible extreme changes in seasonal rainfall for the coming decades. We demonstrate this methodology using Indian summer monsoon rainfall as an example. We use an ensemble of 1669 realizations of Indian summer monsoon rainfall from selected seasonal prediction systems to estimate internal variability and show how it can exacerbate or alleviate forced climate change. Our estimates show that for the next decade there is a ∼60% chance of wetting trends, whereas the chance of drying is ∼40%. Wetting trends are systematically more favoured than drying with the increasing length of the period. However, internal variability can easily negate or overwhelm the wetting trends to give temporary drying trends in rainfall. This provides a quantitative explanation for the varying trends in the past observational record of rainfall over India. We also quantify the likelihood of extreme trends and show that there is at least a 1% chance that monsoon rainfall could increase or decrease by one fifth over the next decade and that more extreme trends, though unlikely, are possible. We find that monsoon rainfall trends are influenced by trends in sea-surface temperatures over the Niño3.4 region and tropical Indian Ocean, and ∼1.5° cooling or warming of these regions can approximately double or negate the influence of climate change on rainfall over the next two decades. We also investigate the time-of-emergence of climate change signals in rainfall trends and find that it is unlikely for a climate change signal to emerge by the year 2050 due to the large internal variability of monsoon rainfall. The estimates of extreme rainfall change provided here could be useful for governments to prepare for worst-case scenarios and therefore aid disaster preparedness and decision-making.
Reproducibility, Validity, and Relative Validity of Self-Report Methods for Assessing Physical Activity in Epidemiologic Studies: Findings From the Women’s Lifestyle Validation Study
Authors: Al-Shaar Laila , Pernar Claire H , Chomistek Andrea K , Rimm Eric B , Rood Jennifer , Stampfer Meir J , Eliassen A Heather , Barnett Junaidah B , Willett Walter C
Published: December 2021

Among 683 participants in the Women’s Lifestyle Validation Study (2010–2012), we evaluated the performance of a self-administered physical activity questionnaire (PAQ) and Web-based 24-hour recalls (Activities Completed Over Time in 24 Hours (ACT24)) using multiple comparison methods. Two PAQs, 4 ACT24s, two 7-day accelerometer measurements, 1 doubly labeled water (DLW) physical activity level (PAL) measure (repeated; n = 90), and 4 resting pulse rate measurements were collected over 15 months. The deattenuated correlation between the PAQ and DLW PAL was 0.41 (95% confidence interval (CI): 0.33, 0.49) for total physical activity (PA) and 0.40 (95% CI: 0.31, 0.48) for moderate-to-vigorous PA (MVPA). These correlations were similar when using accelerometry as the comparison method. Single and averaged ACT24 measurements had lower correlations with DLW and accelerometry as comparison methods. The PAQ showed inverse correlations with DLW body fat percentage and resting pulse rate. Using the method of triads, the estimated correlation of the PAQ with true total PA was 0.54 (95% CI: 0.47, 0.62) and that with true MVPA was 0.60 (95% CI: 0.52, 0.69). For averaged ACT24, the estimated correlations were 0.50 (95% CI: 0.43, 0.59) for total PA and 0.47 (95% CI: 0.39, 0.58) for MVPA, and for averaged accelerometry, these estimated correlations were 0.72 (95% CI: 0.64, 0.81) and 0.62 (95% CI: 0.53, 0.71), respectively. The PAQ provided reasonable validity for total PA and MVPA.
Evaluation of density variations to determine impact on sterile compounding
Authors: Amerine Lindsey B , Pasour Tyler , Johnson Shannon “JJ” , Higgins Jordyn P , Pyle Jacqueline , Gehring Charles
Published: December 2021

Purpose

To determine the density variation between (1) the measured density and manually calculated density, (2) density variation of different lots, and (3) density variation of different drug manufacturers in order to support institutions using gravimetric compounding methods.

Seventeen sterile injectable ingredient (drug) vials frequently used to make compounded sterile products (CSPs) were identified based on the ability to ensure that for each drug there were vials produced by 2 different manufacturers and 2 lots produced by the same manufacturer. Each drug’s density was measured using a density meter and by manual calculation using the institution’s density formula. Density differences were compared between the 2 different methods. Overall, the average drug density difference between the measured versus calculated density was determined to be 0.022. Further analysis revealed the average difference between the different lot numbers of the same manufacturers was 0.005 for the nonhazardous drugs and 0.0001 for the hazardous drugs. The average difference between the different manufacturers of the same drug was determined to be 0.008 for the nonhazardous drugs and 0.001 for hazardous drugs.

Conclusion

No clinically meaningful difference exists when manually calculating a drug’s density compared to measuring a drug’s density using a density meter. In addition, there does not appear to be a sizeable density variation between the same drugs in separate lots or produced by different manufacturers.
A state affiliate’s utilization of ASHP’s Practice Advancement Initiative 2030 to identify current state of practice and a process to prioritize goal achievement
Authors: Vest Tyler A , Doucette Lorna F , Eckel Stephen F
Published: December 2021

Purpose

The American Society of Health-System Pharmacists (ASHP) has been a long-standing supporter of advancing pharmacy practice, specifically in the area of pharmacy practice models. In 2019, ASHP began the planning to launch PAI (Practice Advancement Initiative) 2030. PAI 2030 describes and details a bold vision for patient care, medication use, and pharmacy practice over the next decade. This work represents an ambitious goal to continue to advance the profession of pharmacy for the betterment of our patients. While much has been accomplished with the PAI, there is little literature on PAI 2030 (the authors are unaware of any published examples).

The purpose of the article is to explain a novel state affiliate’s prioritization of ASHP’s PAI 2030 recommendations. In the spring of 2020, the North Carolina Association of Pharmacists (NCAP), the North Carolina state affiliate of ASHP, began discussions around PAI 2030. In the fall of 2020, prior to the NCAP Annual Convention, health system pharmacy leaders within NCAP developed a questionnaire to serve as a PAI 2030 self-assessment. This approach allowed a state affiliate to implement an innovative program to act on the recommendations from PAI 2030. After the prioritization, health system pharmacy leaders engaged in discussion to comment on what recommendations have been identified. The goal of this discussion was to provide NCAP a direction to pursue focused efforts to support recommendations of PAI 2030. Ultimately, NCAP seeks that this statewide approach would help advance pharmacy practice, and improve pharmacy practice across the state of North Carolina in collaboration with NCAP.

Conclusion

This discussion illustrates how a state affiliate has pursued implementing PAI 2030. This approach provides a strategy for state affiliates in addressing the recommendations within PAI 2030. A novel statewide approach can help marshal resources to advance practice when health systems partner with a state affiliate.
Can inpatient pharmacists move the needle on smoking cessation? Evaluating reach and representativeness of a pharmacist-led opt-out smoking cessation intervention protocol for hospital settings
Authors: Creswell Paul D , McCarthy Danielle E , Trapskin Philip , Sheehy Ann , Skora Amy , Adsit Robert T , Zehner Mark E , Baker Timothy B , Fiore Michael C
Published: December 2021

Purpose

Hospitalization affords an opportunity to reduce smoking, but fewer than half of patients who smoke receive evidence-based cessation treatment during inpatient stays. This study evaluated a pharmacist-led, electronic health record (EHR)–facilitated opt-out smoking cessation intervention designed to address this need.

Methods

Analyses of EHR records for adult patients who smoked in the past 30 days admitted to an academic medical center in the upper Midwest were conducted using the Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) framework. The reach of a pharmacist-led, EHR-facilitated protocol for smoking cessation treatment was assessed by comparing patients’ receipt of nicotine replacement therapy (NRT) and tobacco quitline referral before and after implementation. χ2 tests, t tests, and multiple logistic regression models were used to compare reach across patient demographic groups to assess treatment disparities and the representativeness of reach. Adoption of the program by hospital services was also assessed.

Results

Of the 70 hospital services invited to implement the program, 88.6% adopted it and 78.6% had eligible admissions. Treatment reach increased as rates of delivering NRT rose from 43.6% of eligible patients before implementation to 50.4% after implementation (P < 0.0001) and quitline referral rates rose from 0.9% to 11.9% (P < 0.0001). Representativeness of reach by sex and ethnicity improved after implementation, although disparities by race and age persisted after adjustment for demographics, insurance, and primary diagnosis. Pharmacists addressed tobacco use for eligible patients in 62.5% of cases after protocol implementation.

Conclusion

Smoking cessation treatment reach and representativeness of reach improved after implementation of a proactive, pharmacist-led, EHR-facilitated opt-out smoking cessation treatment protocol in adult inpatient services.
A puzzle about the fixity of the past
Authors: Lampert Fabio
Published: December 2021

It is a widely held principle that no one is able to do something that would require the past to have been different from how it actually is. This principle of the fixity of the past has been presented in numerous ways, playing a crucial role in arguments for logical and theological fatalism, and for the incompatibility of causal determinism and the ability to do otherwise. I will argue that, assuming bivalence, this principle is in conflict with standard views about knowledge and the semantics for ‘actually’. I also consider many possible responses to the argument.
S2DV: converting SMILES to a drug vector for predicting the activity of anti-HBV small molecules
Authors: Shao Jinsong , Gong Qineng , Yin Zeyu , Pan Wenjie , Pandiyan Sanjeevi , Wang Li
Published: December 2021

In the past few decades, chronic hepatitis B caused by hepatitis B virus (HBV) has been one of the most serious diseases to human health. The development of innovative systems is essential for preventing the complex pathogenesis of hepatitis B and reducing side effects caused by drugs. HBV inhibitory drugs have been developed through various compounds, and they are often limited by routine experimental screening and delay drug development. More recently, virtual screening of compounds has gradually been used in drug research with strong computational capability and is further applied in anti-HBV drug screening, thus facilitating a reliable drug screening process. However, the lack of structural information in traditional compound analysis is an important hurdle for unsatisfactory efficiency in drug screening. Here, a natural language processing technique was adopted to analyze compound simplified molecular input line entry system strings. By using the targeted optimized word2vec model for pretraining, we can accurately represent the relationship between the compound and its substructure. The machine learning model based on training results can effectively predict the inhibitory effect of compounds on HBV and liver toxicity. The reliability of the model is verified by the results of wet-lab experiments. In addition, a tool has been published to predict potential compounds. Hence, this article provides a new perspective on the prediction of compound properties for anti-HBV drugs that can help improve hepatitis B diagnosis and further develop human health in the future.
Long-term outcome of patients treated with antithyroid drugs, radioactive iodine or surgery for persistent or relapsed Graves’ disease
Authors: Liu Xiaodong , Wong Carlos K. H. , Chan Wendy W. L. , Tang Eric H. M. , Woo Yu Cho , Liu Shirley Y. W. , Lam Cindy L. K. , Lang Brian H. H.
Published: December 2021

Background

The aim of this study was to compare long-term mortality, morbidity, and cumulative healthcare costs between antithyroid drugs, radioactive iodine, and surgical treatment for patients with persistent or relapsed Graves’ disease.

Methods

Data on patients with persistent or relapsed Graves’ disease between 2006 and 2018 were retrieved from the Hong Kong Hospital Authority. Hazard ratios (HRs) estimated by Cox proportional hazards regression models were used to compare the risks of all-cause mortality, cardiovascular disease, atrial fibrillation, psychological disease, Graves’ ophthalmopathy, and cancer across treatment groups. The 10-year healthcare cost and change in co-morbidity status were also estimated.

Results

Over a median follow-up of 79 months (22 636 person-years), a total of 3443 patients (antithyroid drug 2294, radioactive iodine 755, surgery 394) were analysed. Compared with antithyroid drug treatment, surgery was associated with significantly lower risks of all-cause mortality (HR 0.40, 95 per cent c.i. 0.36 to 0.45), cardiovascular disease (HR 0.54, 0.48 to 0.60), atrial fibrillation (HR 0.11, 0.09 to 0.14), psychological disease (HR 0.85, 0.79 to 0.92), Graves’ ophthalmopathy (HR 0.09, 0.08 to 0.10), and cancer (HR 0.56, 0.50 to 0.63). Patients who underwent surgery also had a lower risk of all outcome events than those in the radioactive iodine group. The 10-year direct cumulative healthcare cost was €14 754 for surgery compared with €17 390 for antithyroid drugs, and €17 918 for the radioactive iodine group.

Conclusion

Patients who underwent surgery for persistent or relapsed Graves’ disease had lower risks of all-cause mortality and analysed morbidities. The 10-year cumulative healthcare cost in the surgery group was lowest among the three treatment alternatives.
Low-Data Cryptanalysis On SKINNY Block Cipher
Authors: Hua Jialiang , Liu Tai , Cui Yulong , Qin Lingyue , Dong Xiaoyang , Cui Huiyong
Published: December 2021

At CRYPTO 2021, Dong et al. proposed an automatic method of Meet-in-the-Middle (MITM) key-recovery attacks. In this paper, we further extend it to a new automatic model which can be used to find low-data complexity attacks. With the help of the automatic model, we propose MITM attacks against reduced-round versions of all the six members of the SKINNY family with extremely low-data complexity. More precisely, we present MITM attacks against 19-round SKINNY-$n$-$3n$, 15-round SKINNY-$n$-$2n$, 11-round SKINNY-$n$-$n$ with three, two, one plaintext-ciphertext pairs, separately. In addition, we can attack two more rounds and three more rounds with no more than $2^8$ and $2^{32}$ data complexity, respectively.
Laser Beam Welded Aluminum-Titanium Dissimilar Sheet Metals: Neural Network Based Strength and Hardness Prediction Model
Authors: Chandran Sudhin , Rajesh R , Anand M Dev
Published: December 2021

‘Laser Beam Welding (LBW) is a welding technique used to join pieces of metal or thermoplastics with the aid of laser’. The beam offers a concerted heat source, which enabled higher, deeper welds and narrower welding rates. The procedure is commonly exploited in higher volume appliances using mechanization. It is dependent on penetration or keyhole mode welding. This paper intends to design a novel prediction model on LBW using the Optimized Neural Network (NN) framework. The input to the optimized NN is the welding properties like ‘Laser power, welding speed, offset, shielding gas, flow/pressure, focal distance and frequency (where power, speed and offset gets varied)’ that directly predict the hardness and tensile strength of welds since the NN is already trained with the provided data. In order to make the prediction model more accurate, this paper aims to train the NN using a new improved Trial Integer-based Whale Optimization Algorithm (TI-WOA) via updating the weight. Finally, the betterment of the suggested scheme is validated with respect to error analysis. Accordingly, from the analysis, it is observed that the proposed methods are 50%, 13.33%, 6.67% and 4% better than ANN-BP, RBF, ANN-GA and NN-WOA models, respectively, at 70th learning percentage.
An Educational Data Mining System For Predicting And Enhancing Tertiary Students’ Programming Skill
Authors: Marjan Md Abu , Uddin Md Palash , Ibn Afjal Masud
Published: December 2021

Educational Data Mining (EDM) has become a promising research field for improving the quality of students and the education system. Although EDM dates back to several years, there is still lack of works for measuring and enhancing the computer programming skills of tertiary students. As such, we, in this paper, propose an EDM system for evaluating and improving tertiary students’ programming skills. The proposed EDM system comprises two key modules for (i) classification process and (ii) learning process,. The classification module predicts the current status of a student and the learning process module helps generate respective suggestions and feedback to enhance the student’s quality. In particular, for the classification module, we prepare a real dataset related to this task and evaluate the dataset to investigate six key Machine Learning (ML) algorithms, Support Vector Machine (SVM), decision tree, artificial neural network, Random Forest (RF), k-nearest neighbor and naive Bayes classifier, using accuracy-related performance measure metrics and goodness of the fit. The experimental results manifest that RF and SVM can predict the students more accurately than the other models. In addition, critical factors analysis is accomplished to identify the critical features toward achieving high classification accuracy. At last, we design an improvement mechanism in the learning process module that helps the students enhance their programming skills.
Association between the magnitude of periprocedural myocardial injury and prognosis in patients undergoing elective percutaneous coronary intervention
Authors: Zhou You , Chen Zhangwei , Chen Ao , Ma Jiaqi , Dai Chunfeng , Lu Danbo , Wu Yuan , Li Su , Chen Jinxiang , Liu Muyin , Li ChenGuang , Lu Hao , Qian Juying , Ge Junbo
Published: December 2021

Aims

This study aimed to investigate the prognostic implications of increased post-procedural cardiac troponin levels in patients undergoing elective percutaneous coronary intervention (PCI) and to define the threshold of prognostically relevant periprocedural myocardial injury (PMI).

Methods and results

A total of 3249 patients with normal baseline troponin levels referred for elective PCI were enrolled and followed up for a median period of 20 months. The primary endpoint was major adverse cardiovascular events (MACEs) comprising all-cause death, myocardial injury (MI), and ischaemic stroke. Post-PCI high-sensitivity cardiac troponin T (hs-cTnT) >99% upper reference limit (URL) occurred in 78.3% of the patients and did not increase the risk of MACEs [adjusted hazard ratio (adHR) 1.00, 95% confidence interval (CI) 0.58–1.74, P = 0.990], nor did ‘major PMI’, defined as post-PCI hs-cTnT >5× URL (adHR 1.30, 95% CI 0.76–2.23, P = 0.340). Post-PCI troponin >8× URL, with an incidence of 15.2%, started to show an association with a higher risk of MACEs (adHR 1.89, 95% CI 1.06–3.37, P = 0.032), mainly driven by myocardial infarction (adHR 2.38, 95% CI 1.05–5.38, P = 0.037) and ischaemic stroke (adHR 3.35, 95% CI 1.17–9.64, P = 0.025).

Conclusion

In patients with normal baseline troponin values undergoing elective PCI, PMI defined as hs-cTnT >8× URL after PCI was more appropriate for identifying patients with an increased risk of MACEs, which may help guide clinical practice in this population.
Prognosis and treatment of atrial fibrillation in Asian cities: 1-year review of the Asia-Pacific Heart Rhythm Society Atrial Fibrillation Registry
Authors: Tse Hung-Fat , Teo Wee-Siong , Siu Chung-Wah , Chao Tze-Fan , Park Hyung-Wook , Shimizu Wataru , Wong Yuen-Kwun , Lip Gregory Y H
Published: December 2021

Aims

The aim of this study is to describe the implementation of the current guidance for stroke prevention and treatment option in atrial fibrillation (AF) and to evaluate mortality and morbidity in relation to therapeutic decisions, including persistence with treatment at 1 year in Asia-Pacific regions.

Methods and results

We recruited 4664 patients consecutive in- and outpatients with AF who presented to cardiologists in five countries under the Asia-Pacific Heart Rhythm Society (APHRS) in whom 1-year follow-up was completed for 4003 (65.5% male; mean age 68.5 years). Oral anticoagulant (OAC) use remained high, 77% at follow-up, including 17% prescribed a vitamin K antagonist (VKA) and 60% a non-VKA oral anticoagulant (NOAC). At 1-year follow-up, 93% and 88% remained on a VKA or NOAC, respectively. With good adherence to OAC therapy, 1-year mortality was only 2.7%. Most deaths were non-cardiovascular (72.3%) and the 1-year incidence of stroke/transient ischaemic events (TIA) was low (<1%). Hospital readmissions were common for non-cardiovascular cases and atrial tachyarrhythmias. On multivariate analysis, independent baseline predictors of mortality and/or stroke/TIA/peripheral embolism were age, previous heart failure for >12 months, and malignancy. Independent predictors of mortality were age, chronic obstructive pulmonary disease, malignancy, and diuretic use. AF as a primary presentation was predictive of lower mortality and/or stroke/TIA/peripheral embolism as well as mortality.

Conclusion

In this 1-year analysis of the APHRS-AF registry, overall OAC use and persistence were high and were associated with low 1-year cardiovascular mortality and morbidity, but mortality and morbidity related to non-cardiovascular causes were high in AF patients, particularly from malignancy and pneumonia.
Phenotypic and genetic analyses of two Campylobacter fetus isolates from a patient with relapsed prosthetic valve endocarditis
Authors: Lynch Caoimhe T , Buttimer Colin , Epping Lennard , O'Connor James , Walsh Niamh , McCarthy Conor , O'Brien Deirdre , Vaughan Carl , Semmler Torsten , Bolton Declan , Coffey Aidan , Lucey Brigid
Published: December 2021

Campylobacter fetus can cause intestinal and systemic disease in humans and are well-established veterinary and economic pathogens. We report the complete genomic sequences of two C. fetus subsp. fetus (Cff) isolates recovered in 2017 (CITCf01) and 2018 (CITCf02) from a case of recurrent prosthetic valve endocarditis. Both were capable of growth aerobically. Their genomes were found to be highly conserved and syntenic with 99.97% average nucleotide identity (ANI) while differences in their respective sap loci defined the temporal separation of their genomes. Based on core genome phylogeny and ANI of 83 Cff genomes belonging to the previously described human-associated Cff lineage, CITCf01 and CITCf02 grouped in a clade of 11 sequence type (ST)3 Cff (including the Cff type strain NCTC 10842T). CITCf01 and CITCf02 were marked for their lack of unique genomic features when compared to isolates within the subspecies and the type strain in particular. We identified point mutations in oxidative stress response genes, among others, that may contribute to aerobiosis. We report a case of Cff causing relapsed prosthetic valve endocarditis and we highlight the sap island as a polymorphic site within the genetically stable ST3 lineage, central to pathogenicity.
Warning Notes in a Learner’s Dictionary: A Study of the Effectiveness of Different Formats
Authors: Nural Sukru , Nesi Hilary , Cakar Tuna
Published: December 2021

This study used an online correction task to explore the extent to which different types of warning notes in Longman Dictionary of Contemporary English Online were heeded when users tried to correct errors in the use of L2 target words. The task was completed by 332 participants, yielding 1,819 answers produced after clicking on links to relevant entries. Warning notes were categorised in terms of their formatting features, but there were found to be inconsistencies in the way the dictionary associated different categories with different kinds of learner error. Participants judged warning notes with more visual enhancements to be more useful, but in the correction task the position of the warning notes also seemed to affect the degree to which the warnings were successfully applied. Different types of warning notes in learners’ dictionaries have not been examined previously in any depth, and the results suggest that some adjustments to formatting and placement might make them more effective.
Performance Assessment of a Rapid Molecular Respiratory Syncytial Virus Point-of-Care Test: A Prospective Community Study in Older Adults
Authors: Zuurbier Roy P , Korsten Koos , Verheij Theo J M , Butler Chris , Adriaenssens Niels , Coenen Samuel , Gruselle Olivier , Vantomme Valerie , van Houten Marlies A , Bont Louis J , Wildenbeest Joanne G ,   REspiratory Syncytial Virus Consortium in EUrope (RESCEU) Investigators, Zuurbier Roy , Korsten Koos , Verheij Theo , van Houten Marlies , Bont Louis , Wildenbeest Joanne , Adriaenssens Niels , Coenen Samuel , Butler Christopher , Pollard Andrew , Vantomme Valerie , Gruselle Olivier , Leach Amanda , Nair Harish , Campbell Harry , Beutels Philippe , Openshaw Peter , Martinon-Torres Federico , Heikkinen Terho , Meijer Adam , Fischer Thea Kølsen , van den Berge Maarten , Giaquinto Carlo , Abram Michael , Swanson Kena , Aerssens Jeroen , Demont Clarisse , Gallichan Scott , Rosen Brian , Molero Eva
Published: December 2021

Background

Respiratory syncytial virus (RSV) causes a substantial burden in older adults. Viral load in RSV-infected adults is generally lower compared to young children, which could result in suboptimal sensitivity of RSV diagnostics. Although the Xpert® Xpress Flu/RSV assay has been used in routine clinical care, its sensitivity to diagnose RSV infection in older adults is largely unknown. We aimed to compare the performance of the Xpert® Xpress Flu/RSV assay with real-time reverse-transcription polymerase chain reaction (RT-PCR) in home-dwelling older adults (≥60 years of age).

Methods

Nasopharyngeal swabs were tested with Xpert® Xpress Flu/RSV and compared to RSV RT-PCR in older adults with acute respiratory tract infections with different levels of disease severity.

Results

We studied 758 respiratory samples from 561 older adults from 2 consecutive RSV seasons. Thirty-five (4.6%) samples tested positive for RSV by at least 1 of the assays, of which 2 samples were negative by Xpert® Xpress Flu/RSV and 3 samples by real-time RT-PCR. The positive percentage agreement (PPA) was 90.9% (95% confidence interval [CI], 76.4%–96.8%) and negative percentage agreement was 99.7% (95% CI, 99.0%–99.9%). Viral loads were low (≤103 copies/mL or cycle threshold value ≥34) in all cases with discordant results for the 2 assays.

Conclusions

The PPA of Xpert® Xpress Flu/RSV compared to routine RT-PCR is high for RSV detection in home-dwelling older adults. The assay is fast and easy to use at the point of care.

Clinical Trials Registration

NCT03621930.
In vitro effect of synovial fluid from patients undergoing arthroplasty surgery on MRSA biofilm formation
Authors: Pant Narayan , Wallis Steven C. , Roberts Jason A. , Eisen Damon P.
Published: December 2021

Background

Bacterial biofilm is a key component in the pathogenesis of prosthetic joint infection (PJI). Synovial fluid has been shown to have inhibitory activity against planktonic bacteria. However, the contribution of synovial fluid in prevention of Staphylococcus aureus (including MRSA) planktonic and biofilm forms is unknown.

Objectives

To test the antibacterial and antibiofilm activities of synovial fluid, including that containing cefazolin, against MSSA and MRSA.

Materials and methods

We determined the antiplanktonic and antibiofilm activities of synovial fluid collected from patients given preoperative cefazolin while undergoing elective arthroplasty surgery. MICs of cefazolin were determined for planktonic and biofilm cultures of biofilm-forming strains of MSSA and MRSA.

Results

Synovial fluid inhibited planktonic and biofilm cultures of MSSA and MRSA. Cefazolin-containing synovial fluid had greater antibacterial and antibiofilm activities than the same cefazolin concentration in glucose LB (GLB) broth. MSSA and MRSA MICs of cefazolin suspended in synovial fluid were 0.7 mg/L. The MICs of cefazolin diluted in GLB broth were higher, measuring 1.4 mg/L for MSSA and 23 mg/L for MRSA.

Conclusions

Synovial fluid containing cefazolin inhibited biofilm- and planktonic-state MRSA cultures. This may explain the apparent effect of cefazolin in the prevention of MRSA PJI.