bluuebunny/crossref_metadata_2025 huggingface dataset:
Supplemental Material: Cenozoic delamination of the southwestern Yangtze craton owing to densification during subduction and collision
Authors: WANG JUN , WANG QIANG , al. et
Published: April 2022

Tables S1–S9, and Figures S1–S6. 
Healthy lifestyle and life expectancy with and without Alzheimer’s dementia: population based cohort study
Authors: Dhana Klodian , Franco Oscar H , Ritz Ethan M , Ford Christopher N , Desai Pankaja , Krueger Kristin R , Holland Thomas M , Dhana Anisa , Liu Xiaoran , Aggarwal Neelum T , Evans Denis A , Rajan Kumar B
Published: April 2022

Objective

To determine the impact of lifestyle factors on life expectancy lived with and without Alzheimer’s dementia.

Design

Prospective cohort study.

Setting

The Chicago Health and Aging Project, a population based cohort study in the United States.

Participants

2449 men and women aged 65 years and older.

Main exposure

A healthy lifestyle score was developed based on five modifiable lifestyle factors: a diet for brain health (Mediterranean-DASH Diet Intervention for Neurodegenerative Delay—MIND diet score in upper 40% of cohort distribution), late life cognitive activities (composite score in upper 40%), moderate or vigorous physical activity (≥150 min/week), no smoking, and light to moderate alcohol consumption (women 1-15 g/day; men 1-30 g/day).

Main outcome

Life expectancy with and without Alzheimer’s dementia in women and men.

Results

Women aged 65 with four or five healthy factors had a life expectancy of 24.2 years (95% confidence interval 22.8 to 25.5) and lived 3.1 years longer than women aged 65 with zero or one healthy factor (life expectancy 21.1 years, 19.5 to 22.4). Of the total life expectancy at age 65, women with four or five healthy factors spent 10.8% (2.6 years, 2.0 to 3.3) of their remaining years with Alzheimer’s dementia, whereas women with zero or one healthy factor spent 19.3% (4.1 years, 3.2 to 5.1) with the disease. Life expectancy for women aged 65 without Alzheimer’s dementia and four or five healthy factors was 21.5 years (20.0 to 22.7), and for those with zero or one healthy factor it was 17.0 years (15.5 to 18.3). Men aged 65 with four or five healthy factors had a total life expectancy of 23.1 years (21.4 to 25.6), which is 5.7 years longer than men aged 65 with zero or one healthy factor (life expectancy 17.4 years, 15.8 to 20.1). Of the total life expectancy at age 65, men with four or five healthy factors spent 6.1% (1.4 years, 0.3 to 2.0) of their remaining years with Alzheimer’s dementia, and those with zero or one healthy factor spent 12.0% (2.1 years, 0.2 to 3.0) with the disease. Life expectancy for men aged 65 without Alzheimer’s dementia and four or five healthy factors was 21.7 years (19.7 to 24.9), and for those with zero or one healthy factor life expectancy was 15.3 years (13.4 to 19.1).

Conclusion

A healthy lifestyle was associated with a longer life expectancy among men and women, and they lived a larger proportion of their remaining years without Alzheimer’s dementia. The life expectancy estimates might help health professionals, policy makers, and stakeholders to plan future healthcare services, costs, and needs.
Discrimination and calibration of a prediction model for mortality is decreased in secondary transferred patients: a validation in the TraumaRegister DGU
Authors: Halvachizadeh Sascha , Störmann P J , Özkurtul Orkun , Berk Till , Teuben Michel , Sprengel Kai , Pape Hans-Christoph , Lefering Rolf , Jensen Kai Oliver
Published: April 2022

Introduction

The Revised Injury Severity Classification II (RISC II) score represents a data-derived score that aims to predict mortality in severely injured patients. The aim of this study was to assess the discrimination and calibration of RISC II in secondary transferred polytrauma patients.

Methods

This study was performed on the multicentre database of the TraumaRegister DGU. Inclusion criteria included Injury Severity Score (ISS)≥9 points and complete demographic data. Exclusion criteria included patients with ‘do not resuscitate’ orders or late transfers (>24 hours after initial trauma). Patients were stratified based on way of admission into patients transferred to a European trauma centre after initial treatment in another hospital (group Tr) and primary admitted patients who were not transferred out (group P). The RISC II score was calculated within each group at admission after secondary transfer (group Tr) and at primary admission (group P) and compared with the observed mortality rate. The calibration and discrimination of prediction were analysed.

Results

Group P included 116 112 (91%) patients and group Tr included 11 604 (9%) patients. The study population was predominantly male (n=86 280, 70.1%), had a mean age of 53.2 years and a mean ISS of 20.7 points. Patients in group Tr were marginally older (54 years vs 52 years) and a had slightly higher ISS (21.5 points vs 20.1 points). Median time from accident site to hospital admission was 60 min in group P and 241 min (4 hours) in group Tr. Observed and predicted mortality based on RISC II were nearly identical in group P (10.9% and 11.0%, respectively) but predicted mortality was worse (13.4%) than observed mortality (11.1%) in group Tr.

Conclusion

The way of admission alters the calibration of prediction models for mortality in polytrauma patients. Mortality prediction in secondary transferred polytrauma patients should be calculated separately from primary admitted polytrauma patients.
Impact of regulatory safety notices on valproate prescribing and pregnancy outcome among women of child-bearing potential in Scotland: a population-based cohort study
Authors: McTaggart Stuart , MacColl Gavin , Gronkowski Karen , Wood Rachael , Leach John Paul , Bennie Marion
Published: April 2022

Objective

To examine the impact of Medicines and Healthcare products Regulatory Agency (MHRA) safety alerts on valproate prescribing among women aged 14–45 years in Scotland and examine trends in pregnancies exposed to valproate.

Design

Population-based cohort study.

Participants

21 983 women of all ages who received valproate between January 2011 and December 2019.

Methods

All valproate prescriptions issued to women in Scotland between January 2011 and December 2019 were identified and prevalence/incidence rates per 10 000 population derived. The impact of regulatory safety alerts on prescribing was analysed using Joinpoint models. Linked pregnancy records for January 2011 to September 2019 were identified and annual rates of pregnancy per 1000 valproate-treated women aged 14–45 years were calculated for each pregnancy outcome: live birth, stillbirth, miscarriage and termination.

Results

Annual prevalent and incident rates of valproate prescribing declined in women aged 14–45 years between 2011 and 2019 from 40.5 to 18.3 per 10 000 population (54.8% reduction) and 7.9 to 1.3 per 10 000 population (83.5% reduction), respectively. Statistically significant changes occurred around the times of the MHRA safety alerts. The number of valproate-exposed pregnancies conceived each year fell from 70 in 2011 to 20 in 2018, a 71.4% reduction, and the number of live births fell from 52 to 14, a 73.0% reduction. Expressed as a rate this was a 46.4% decrease from 15.3 to 8.2 per 1000 valproate-treated women aged 14–45 years in 2011 and 2018, respectively. Live birth was the most common pregnancy outcome.

Conclusion

This study demonstrates, for the first time, the capabilities of national data sets to identify drug exposure and derive pregnancy outcome at scale across Scotland. Building on this as part of an evolving national/UK surveillance capability will continue efforts to minimise in-utero exposure to valproate; enabling ongoing surveillance to understand better long-term outcomes, and to inform better provision of health and wider support services.
Outer independent double Italian domination: Complexity, characterization
Authors: Jalaei R. , Mojdeh D. A.
Published: April 2022

An outer independent double Italian dominating function is a double Italian dominating function f for which the set of vertices assigned 0 under f is independent. The outer independent double Italian domination number [Formula: see text] is the minimum weight taken over all outer independent double Italian dominating functions of G. In this work, we present some contributions to the study of outer independent double Italian domination in graphs. We show that the outer independent double Italian domination number is NP-complete even when restricted to planner graphs with maximum degree at most four. We characterize the families of all connected graphs G with [Formula: see text]. We also investigate the families of all graphs G such that [Formula: see text] and for [Formula: see text], the graphs with this property are characterized. Finally, we characterize the families of all connected graphs G of order [Formula: see text], for which [Formula: see text].
RASCv2: Enabling Remote Access to Side-Channels for Mission Critical and IoT Systems
Authors: Bai Yunkai , Stern Andrew , Park Jungmin , Tehranipoor Mark , Forte Domenic
Published: April 2022

The Internet of Things (IoT) and smart devices are currently being deployed in systems such as autonomous vehicles and medical monitoring devices. The introduction of IoT devices into these systems enables network connectivity for data transfer, cloud support, and more, but can also lead to malware injection. Since many IoT devices operate in remote environments, it is also difficult to protect them from physical tampering. Conventional protection approaches rely on software. However, these can be circumvented by the moving target nature of malware or through hardware attacks. Alternatively, insertion of the internal monitoring circuits into IoT chips requires a design trade-off, balancing the requirements of the monitoring circuit and the main circuit. A very promising approach to detecting anomalous behavior in the IoT and other embedded systems is side-channel analysis. To date, however, this can be performed only before deployment due to the cost and size of side-channel setups (e.g., and oscilloscopes, probes) or by internal performance counters. Here, we introduce an external monitoring printed circuit board (PCB) named RASC to provide
r
emote
a
ccess to
s
ide-
c
hannels. RASC reduces the complete side-channel analysis system into two small PCBs (2

\( \times \)

2 cm), providing the ability to monitor power and electromagnetic (EM) traces of the target device. Additionally, RASC can transmit data and/or alerts of anomalous activities detected to a remote host through Bluetooth. To demonstrate RASCs capabilities, we extract keys from encryption modules such as AES implemented on Arduino and FPGA boards. To illustrate RASC’s defensive capabilities, we also use it to perform malware detection. RASC’s success in power analysis is comparable to an oscilloscope/probe setup but is lightweight and two orders of magnitude cheaper.
CAQoE: A Novel No-Reference Context-aware Speech Quality Prediction Metric
Authors: Jaiswal Rahul Kumar , Dubey Rajesh Kumar
Published: April 2022

The quality of speech degrades while communicating over Voice over Internet Protocol applications, for example, Google Meet, Microsoft Skype, and Apple FaceTime, due to different types of background noise present in the surroundings. It reduces human perceived Quality of Experience (QoE). Along this line, this article proposes a novel speech quality prediction metric that can meet human’s desired QoE level. Our motivation is driven by the lack of evidence showing speech quality metrics that can distinguish different noise degradations before predicting the quality of speech. The quality of speech in noisy environments is improved by speech enhancement algorithms, and for measuring and monitoring the quality of speech, objective speech quality metrics are used. With the integration of these components, a novel no-reference context-aware QoE prediction metric (CAQoE) is proposed in this article, which initially identifies the context or noise type or degradation type of the input noisy speech signal and then predicts context-specific speech quality for that input speech signal. It will have of great importance in deciding the speech enhancement algorithms if the types of degradations causing poor speech quality are known along with the quality metric. Results demonstrate that the proposed CAQoE metric outperforms in different contexts as compared to the metric where contexts are not identified before predicting the quality of speech, even in the presence of limited size speech corpus having different contexts available from the NOIZEUS speech database.
A Blockchain Framework to Secure Personal Health Record (PHR) in IBM Cloud-Based Data Lake
Authors: Panwar Arvind , Bhatnagar Vishal , Khari Manju , Salehi Ahmad Waleed , Gupta Gaurav
Published: April 2022

The health system in today’s real world is significant but difficult and overcrowded. These hurdles can be diminished using improved health record management and blockchain technology. These technologies can handle medical data to provide security by monitoring and maintaining patient records. The processing of medical data and patient records is essential to analyze the earlier prescribed medicines and to understand the severity of diseases. Blockchain technology can improve the security, performance, and transparency of sharing the medical records of the current healthcare system. This paper proposed a novel framework for personal health record (PHR) management using IBM cloud data lake and blockchain platform for an effective healthcare management process. The problem in the blockchain-based healthcare management system can be minimized with the utilization of the proposed technique. Significantly, the traditional blockchain system usually decreases the latency. Therefore, the proposed technique focuses on improving latency and throughput. The result of the proposed system is calculated based on various matrices, such as F1 Score, Recall, and Confusion matrices. Therefore, the proposed work scored high accuracy and provided better results than existing techniques.
An Effective and Lightweight Deep Electrocardiography Arrhythmia Recognition Model Using Novel Special and Native Structural Regularization Techniques on Cardiac Signal
Authors: Ullah Hadaate , Bin Heyat Md Belal , AlSalman Hussain , Khan Haider Mohammed , Akhtar Faijan , Gumaei Abdu , Mehdi Aaman , Muaad Abdullah Y. , Islam Md Sajjatul , Ali Arif , Bu Yuxiang , Khan Dilpazir , Pan Taisong , Gao Min , Lin Yuan , Lai Dakun
Published: April 2022

Recently, cardiac arrhythmia recognition from electrocardiography (ECG) with deep learning approaches is becoming popular in clinical diagnosis systems due to its good prognosis findings, where expert data preprocessing and feature engineering are not usually required. But a lightweight and effective deep model is highly demanded to face the challenges of deploying the model in real-life applications and diagnosis accurately. In this work, two effective and lightweight deep learning models named Deep-SR and Deep-NSR are proposed to recognize ECG beats, which are based on two-dimensional convolution neural networks (2D CNNs) while using different structural regularizations. First, 97720 ECG beats extracted from all records of a benchmark MIT-BIH arrhythmia dataset have been transformed into 2D RGB (red, green, and blue) images that act as the inputs to the proposed 2D CNN models. Then, the optimization of the proposed models is performed through the proper initialization of model layers, on-the-fly augmentation, regularization techniques, Adam optimizer, and weighted random sampler. Finally, the performance of the proposed models is evaluated by a stratified 5-fold cross-validation strategy along with callback features. The obtained overall accuracy of recognizing normal beat and three arrhythmias (V-ventricular ectopic, S-supraventricular ectopic, and F-fusion) based on the Association for the Advancement of Medical Instrumentation (AAMI) is 99.93%, and 99.96% for the proposed Deep-SR model and Deep-NSR model, which demonstrate that the effectiveness of the proposed models has surpassed the state-of-the-art models and also expresses the higher model generalization. The received results with model size suggest that the proposed CNN models especially Deep-NSR could be more useful in wearable devices such as medical vests, bracelets for long-term monitoring of cardiac conditions, and in telemedicine to accurate diagnose the arrhythmia from ECG automatically. As a result, medical costs of patients and work pressure on physicians in medicals and clinics would be reduced effectively.
Optimal Recognition of Volleyball Player’s Arm Movement Track Based on Embedded Microprocessor
Authors: Liu Ming , Wu Jingtao , Tao Jiangang
Published: April 2022

An embedded microprocessor is the core part of the integrated circuit system, and it represents one of the highest levels of a digital integrated circuit design. Therefore, the design of low-power embedded microprocessors has become an important research direction in the integrated circuit design. Volleyball has always been a very important sport in our country, and it is a sport that the masses of people like to see. The basic techniques of volleyball include ready posture and movement, serving, hot ball, passing, smashing, and netting. The research content of this article is a simulation study of volleyball players’ arm trajectory optimization recognition. In response to the above problems, this paper proposes an optimized recognition method based on the volleyball player’s arm motion trajectory. The simulation results show that the method has high recognition accuracy and provides a strong scientific basis for improving the volleyball players’ spike skills. Without interference from the right arm, the controller input and position tracking error will not fluctuate in

1
≤
T
≤
3

, the controller input is stable, and the output error is zero. Based on the above simulation analysis, it can be seen that the control method does not require the robot kinematics and dynamics model to generate any regression matrix when designing the controller. The controller is suitable for the performance tracking of humanoid robot arms.
Fusion of EEG-Based Activation, Spatial, and Connection Patterns for Fear Emotion Recognition
Authors: Pan Jiahui , Yang Fuzhou , Qiu Lina , Huang Haiyun
Published: April 2022

At present, emotion recognition based on electroencephalograms (EEGs) has attracted much more attention. Current studies of affective brain-computer interfaces (BCIs) focus on the recognition of happiness and sadness using brain activation patterns. Fear recognition involving brain activities in different spatial distributions and different brain functional networks has been scarcely investigated. In this study, we propose a multifeature fusion method combining energy activation, spatial distribution, and brain functional connection network (BFCN) features for fear emotion recognition. The affective brain pattern was identified by not only the power activation features of differential entropy (DE) but also the spatial distribution features of the common spatial pattern (CSP) and the EEG phase synchronization features of phase lock value (PLV). A total of 15 healthy subjects took part in the experiment, and the average accuracy rate was 85.00% ± 8.13%. The experimental results showed that the fear emotions of subjects were fully stimulated and effectively identified. The proposed fusion method on fear recognition was thus validated and is of great significance to the development of effective emotional BCI systems.
The Reduced-Order Model for Droplet Drift of Aerial Spraying under Random Lateral Wind
Authors: Li Wencheng , Wang Wenyun , Huang Xiaomao , Li Chenyang
Published: April 2022

The droplet drift during aerial spraying process of oilseed rape, which is induced by complex flow field including random lateral wind, is difficult to predict and suppress. In this study, the high-fidelity computational fluid dynamics (CFD) technique is employed to simulate the two-phase flow of droplets in the rotor flow field, and the influence of main operation parameters on spraying effect is investigated numerically. Furthermore, the mechanism of droplet deposition in various operation conditions is discussed according to the analysis of unsteady flow field characteristics. However, the simulation via CFD technique is time-consuming, and it is not suitable for multidisciplinary work and optimization design. To address such issue, a filter white Gaussian noise signal is used to mimic the random lateral wind, and the droplet drift distance is obtained numerically. Based on the input and output dataset of CFD, the recursive algorithm including nonlinear autoregressive exogenous model and surrogate-based recurrence framework and the deep learning method for time-series prediction called long short-term memory neural network are used to build the efficient reduced-order model, respectively. Numerical simulations show that the droplet drift distance can be predicted by measurable lateral wind speed via the reduced-order model approach, which agreed well with the results obtained via the CFD method. In addition, the reduced-order model could decrease computation cost by 6 orders of magnitude with an acceptable accuracy, which indicates that the proposed method could be used for the design of off-line closed-loop controller of a variable spraying system.
Economic Analysis of Animal Husbandry Based on System Dynamics
Authors: Wang Lei , Tan Hongwei
Published: April 2022

In order to improve the effect of animal husbandry economic analysis, this article studies the animal husbandry economy based on system dynamics and studies how to define total factor productivity and its measurement method. Moreover, this article compares and analyzes the production function method, data envelopment analysis, and index method for measuring total factor productivity, selects decision-making units, and determines and processes input-output data. In addition, this article combines the system dynamics model to explore the causal relationship of the animal husbandry economy and builds an intelligent model to intelligently analyze the animal husbandry economy. Finally, this article analyzes the economy and performance of animal husbandry based on simulation experiments. The simulation test results show that the system dynamics model proposed in this article has a good performance in the economic analysis of animal husbandry.
Seismic Vulnerability Assessment of Thermal Power Plants Based on Beta Function
Authors: Deyuan Tian , Rushan Liu , Xuyi Xia , Zhenhui Wang
Published: April 2022

The study on seismic vulnerability of thermal power plants is of great significance for seismic damage prevention of the electrical power system and risk analysis of seismic economic loss. This paper presented a study method for obtaining the seismic vulnerability matrix and vulnerability curve of thermal power plants based on beta function. Firstly, according to the study results of thermal power plants in American ATC-25 report, the expected values of seismic damage indexes of thermal power plants under different seismic intensities were calculated. It was assumed that the expected values of seismic damage indexes of substation and thermal power plant were the same, and the dispersion of distribution of seismic damage indexes was the same. Then, based on the actual seismic vulnerability matrix of substations obtained from statistics in Wenchuan earthquake, the seismic vulnerability matrix of thermal power plants under different seismic intensities was fitted by using the probability density function of beta distribution. Finally, the seismic vulnerability curve of the thermal power plant based on peak ground acceleration (PGA) was obtained by using the cumulative function curve of standard lognormal distribution. The calculation results show that the seismic damage ratio calculated from the seismic vulnerability matrix of thermal power plants is basically consistent with the seismic damage ratio in ATC-25 report.
COVID-19 in an Adult with Down Syndrome: Impact on Autoimmune Response
Authors: Kobayashi Hiroshi , Akiniwa Mana , Yamaguchi Yoshiko , Hirai Yuji , Aoki Akiko
Published: April 2022

We here report a case of COVID-19 with effusion prior to the development of pneumonia in an adult with Down syndrome. Serositis due to rheumatic disease was initially suspected because of a high titer of serum autoantibodies and leukocytopenia; however, SARS-CoV-2 infection was confirmed by reverse transcription polymerase chain reaction on admission after previous negative tests. Several cases of COVID-19 have been associated with autoimmune responses along with some cases of COVID-19 with autoimmune manifestations. Furthermore, patients with Down syndrome have a higher mortality risk from COVID-19 than the general population, and it is believed that a high sensitivity to the interferon response may contribute to the increased severity of the disease. Thus, careful attention should be paid to autoimmune manifestations due to SARS-CoV-2 infection for ensuring a proper and timely diagnosis, especially in patients with Down syndrome.
A Novel Smart Production Management System for the Enhancement of Industrial Sustainability in Industry 4.0
Authors: Tripathi Varun , Chattopadhyaya Somnath , Mukhopadhyay A. K. , Saraswat Suvandan , Sharma Shubham , Li Changhe , Rajkumar S. , Georgise Fasika Bete
Published: April 2022

In industry 4.0, shop floor management teams are increasingly focused on developing an unprecedented strategy to avoid financial losses and address the challenges and problems encountered in operations management. In the present scenario, the management teams use various process optimization approaches for operational control, including lean manufacturing, smart manufacturing, the internet of things, and artificial intelligence. The process optimization approach is used to maximize productivity within limited constraints on the shop floor. The present research aims to develop a smart production management system and suggest an efficient process optimization approach to enhancing industrial sustainability by identifying problems and challenges encountered in the complex shop-floor conditions in industry 4.0. The developed production management system has been prepared by classifying the challenges and problems found in the previous research work and organizing brainstorming sessions. The developed management system has been validated by a comprehensive investigation of a case study of an earthmoving machinery manufacturing unit. The analysis showed that the developed system could enhance operation excellence and industrial sustainability in industry 4.0 by optimizing the utilization of resources on the shop floor within limited constraints. The authors of the present article strongly believe that the developed production management system will improve operational excellence and would be beneficial for industry personnel and researchers in controlling operations management in shop floor management of heavy machinery manufacturing, including industry 4.0.
Network Intrusion Detection Method Based on FCWGAN and BiLSTM
Authors: Ma Zexuan , Li Jin , Song Yafei , Wu Xuan , Chen Chen
Published: April 2022

Imbalanced datasets greatly affect the analysis capability of intrusion detection models, biasing their classification results toward normal behavior and leading to high false-positive and false-negative rates. To alleviate the impact of class imbalance on the detection accuracy of network intrusion detection models and improve their effectiveness, this paper proposes a method based on a feature selection-conditional Wasserstein generative adversarial network (FCWGAN) and bidirectional long short-term memory network (BiLSTM). The method uses the XGBoost algorithm with Spearman’s correlation coefficient to select the data features, filters out useless and redundant features, and simplifies the data structure. A conditional WGAN (CWGAN) is used to generate a small number of samples in the dataset, add them to the original training set to supplement the dataset samples, and apply BiLSTM to complete the training of the model and realize the classification. In comparative tests based on the NSL-KDD and UNSW-NB15 datasets, the accuracy of the proposed model reached 99.57% and 85.59%, respectively, which is 1.44% and 2.98% higher than that of the same type of CWGAN and deep neural network (CWGAN-DNN) model, respectively.
Autonomous Phase Control Combining EKF and Adaptive Neural Network for Remote Sensing Satellites
Authors: Wang Xin , Wang Guogang , Zheng Hongru
Published: April 2022

In this paper, a novel, effective, and feasible autonomous phase control algorithm based on the extended Kalman filtering (EKF) and neural network is firstly developed for addressing the problems of configuration maintenance of remote sensing satellite constellation. A balanced moment arm optimization method is employed to design the installation structure layout of the chemical propulsion system in the satellite. On that basis, an autonomous orbit control strategy is presented for controlling the phase of satellites, and the EKF algorithm is utilized to determine the orbit used to calculate the satellite phase. A radial basis function (RBF) neural network-based attitude control method is proposed to solve the attitude disturbance problem in the course of the phase control, and the RBF neural network is utilized to approximate the coupling torque of the orbit control. The simulation results demonstrate the feasibility of the designed automatic phase control strategy of the satellite.
An Innovative Machine Learning Approach for Classifying ECG Signals in Healthcare Devices
Authors: B Kishore , Gopal Reddy A. Nanda , Kumar Chillara Anila , Hatamleh Wesam Atef , Haouam Kamel Dine , Verma Rohit , Dhevi B. Lakshmi , Atiglah Henry Kwame
Published: April 2022

An ECG is a diagnostic technique that examines and records the heart’s electrical impulses. It is easy to categorise and prevent computational abstractions in the ECG signal using the conventional method for obtaining ECG features. It is a significant issue, but it is also a difficult and time-consuming chore for cardiologists and medical professionals. The proposed classifier eliminates all of the following limitations. Machine learning in healthcare equipment reduces moral transgressions. This study’s primary purpose is to calculate the R-R interval and analyze the blockage utilising simple algorithms and approaches that give high accuracy. The MIT-BIH dataset may be used to rebuild the data. The acquired data may include both normal and abnormal ECGs. A Gabor filter is employed to generate a noiseless signal, and DCT-DOST is used to calculate the signal’s amplitude. The amplitude is computed to detect any cardiac anomalies. A genetic algorithm derives the main highlights from the R peak and cycle segment length underlying the ECG signal. So, combining data with specific qualities maximises identification. The genetic algorithm aids in hereditary computations, which aids in multitarget improvement. Finally, Radial Basis Function Neural Network (RBFNN) is presented as an example. An efficient feedforward neural network lowers the number of local minima in the signal. It shows progress in identifying both normal and abnormal ECG signals.
College Physical Education Teaching and Content Optimization Based on Computer Information Technology
Authors: Xu Biao
Published: April 2022

Curriculum plays a central role in the cultivation of professional talents in colleges and universities and is the carrier of the goal of talent cultivation. Curriculum reform is the core of physical education teaching reform. In the context of social needs, curriculum reform should aim at cultivating professional talents. The overall reform should be guided by social needs to improve the suitability and effectiveness of curriculum reform. This paper aims to study the analysis and content optimization of physical education teaching based on the background of computer information technology. In order to maximize the professional function of physical education teaching and better provide basic guarantee for physical education, this paper firstly carries out a conceptual analysis of physical education and content optimization. Secondly, this paper expounds the advantages of computer information technology and then deeply studies the network computer server cluster method. Then it optimizes the case design of the teaching content of the special course of physical education. The cluster analysis results of college physical education teachers and experts show that, in terms of teachers, 36% of teachers are satisfied with the physical education textbooks, ranking first.
English Teaching Innovation and Cooperation Platform Assisted by Mobile Computing
Authors: Wang Xiaoyu , Zhou Lei
Published: April 2022

Aiming at the problems existing in English teaching, based on mobile computing technology, this paper establishes energy consumption optimization model, delay optimization model, and user mobility model and discusses the design and application of English innovation and cooperation platform in detail. Then, it analyzes the application of the three models in English teaching in primary schools, middle schools, and universities. The results show that the innovative cooperation platform of English teaching based on mobile computing can greatly improve the efficiency of English teaching, reduce the burden of teachers, and increase the English learning enthusiasm of students in different grades. In general, this paper provides some experimental support for the design and application of mobile computing in English teaching innovation platform.
Parameter Design in Carbonitriding of EN36, 16MnCr5, and AISI 4140 Steels Using Principal Component-Based Grey Incidence (PGI)
Authors: Arumparithy G. L. , Adalarasan R. , Santhanakumar M. , Mulugeta Lijalem
Published: April 2022

Case-hardening steels (EN36, 16MnCr5, and AISI 4140) are used in applications demanding good surface properties such as precision gears, shafts, and cam rollers. This study explores the formation of microcoatings to improve the surface characteristics of these steels using carbonitriding, which combines the merits of carburizing and nitriding to offer surfaces with enhanced hardness and wear resistance. Taguchi’s L18 orthogonal array is used for conducting the carbonitriding trials with replications. The effects of various carbonitriding parameters like carbonitriding time, temperature, and flow rate of ammonia are studied on the treated surface characteristics (Vickers microhardness, diffusion depth, and wear loss). A novel integrated approach of principal component-based grey incidence (PGI) that combines the merits of both principal component analysis and grey incidence theory is effectively used to select the optimal carbonitriding inputs (material substrate AISI 4140, carbonitriding temperature −835°C, carbonitriding time-40 min, and flow rate of ammonia 0.4 lit/min). Microscopic images related to diffusion depths are also analyzed. This study offers the necessary guiding principles for obtaining the desired surface coating on EN36, 16MnCr5, and AISI 4140 steels.
Personalized Hybrid Recommendation for Tourist Users Based on Matrix Cluster Apriori Mining Algorithm
Authors: Zhang Qian
Published: April 2022

With the rapid development of Internet technology and the arrival of the era of big data, the rapid expansion of network information resources has formed massive information. Massive information resources have brought great convenience to people’s lives. However, it becomes more and more difficult to find the content that interests you, which is the phenomenon of “information overload.” In order to solve this problem, a solution based on personalized recommendation technology is proposed. In personalized recommendation technology, collaborative filtering algorithm is the most widely used technology. Clustering technology can effectively divide objects into groups, so that the similarity of attributes between objects in the same group is high, and the similarity of objects in different groups is low. The core step of the filtering recommendation algorithm is to find the similar neighbors of the target user by calculating the similarity. Applying the clustering technology to the recommendation can effectively improve the performance of the recommendation system. Aiming at the real-time problem of collaborative filtering recommendation, this paper introduces a method of firstly clustering users on the user item rating matrix, and finding the nearest neighbors in the clusters with high similarity with the target user, which effectively reduces the query space and improves the recommendation. This paper proposes a method to measure the user’s preference for item attributes, which is used in the above clustering process to improve the recommendation accuracy while retaining the advantage of reducing the query space. Aiming at the problem of poor recommendation accuracy, this paper proposes a fuzzy-improved K-means algorithm to cluster items in the product attribute matrix, and then fuses the similarity of the belongingness of items to clusters in the fuzzy clustering. The similarity calculated on the score matrix shows that this method is better than the traditional hybrid recommendation in accuracy.
A Phase Ib/II Study of Sabizabulin, a Novel Oral Cytoskeleton Disruptor, in Men with Metastatic Castration-resistant Prostate Cancer with Progression on an Androgen Receptor–targeting Agent
Authors: Markowski Mark C. , Tutrone Ronald , Pieczonka Christopher , Barnette K. Gary , Getzenberg Robert H. , Rodriguez Domingo , Steiner Mitchell S. , Saltzstein Daniel R. , Eisenberger Mario A. , Antonarakis Emmanuel S.
Published: April 2022

Purpose:

Sabizabulin, an oral cytoskeleton disruptor, was tested in a phase Ib/II clinical study in men with metastatic castration-resistant prostate cancer (mCRPC).

Patients and Methods:

The phase Ib portion utilized a 3+3 design with escalating daily oral doses of 4.5–81 mg and increasing schedule in 39 patients with mCRPC treated with one or more androgen receptor–targeting agents. Prior taxane chemotherapy was allowed. The phase II portion tested a daily dose of 63 mg in 41 patients with no prior chemotherapy. Efficacy was assessed using PCWG3 and RECIST 1.1 criteria.

Results:

The MTD was not defined in the phase Ib and the recommended phase II dose was set at 63 mg/day. The most common adverse events (>10% frequency) at the 63 mg oral daily dosing (combined phase Ib/II data) were predominantly grade 1–2 events. Grade ≥3 events included diarrhea (7.4%), fatigue (5.6%), and alanine aminotransferase/aspartate aminotransferase elevations (5.6% and 3.7%, respectively). Neurotoxicity and neutropenia were not observed. Preliminary efficacy data in patients treated with ≥1 continuous cycle of 63 mg or higher included objective response rate in 6 of 29 (20.7%) patients with measurable disease (1 complete, 5 partial) and 14 of 48 (29.2%) patients had PSA declines. The Kaplan–Meier median radiographic progression-free survival was estimated to be 11.4 months (n = 55). Durable responses lasting >2.75 years were observed.

Conclusions:

This clinical trial demonstrated that chronic oral daily dosing of sabizabulin has a favorable safety profile with preliminary antitumor activity. These data support the ongoing phase III VERACITY trial of sabizabulin in men with mCRPC.
Next-Generation CAR T-cell Therapies
Authors: Young Regina M. , Engel Nils W. , Uslu Ugur , Wellhausen Nils , June Carl H.
Published: April 2022

CD19- and B-cell maturation antigen (BCMA)–directed chimeric antigen receptor (CAR) T cells have enabled unprecedented responses in a subset of refractory patients with B-cell and plasma cell malignancies, leading to their approval by the FDA for the treatment of leukemia, lymphoma, and myeloma. These “living drugs” can become part of a synthetic immune system, persisting at least a decade in some patients. However, despite this tremendous impact, significant unmet treatment needs remain for patients with hematologic malignancies and solid cancers. In this perspective, we highlight recent innovations that advance the field toward production of a more potent and universal cellular immunotherapy of the future. Next-generation CAR T cells will incorporate advances in gene engineering and synthetic biology to enhance functionality and persistence, and reduce treatment-associated toxicities. The combination of autologous CAR T cells with various allogeneic cell treatment strategies designed to target the immunosuppressive tumor microenvironment will broaden the impact of future CAR T-cell therapies.
A história de todos nós: crimes que podem ser esquecidos: a escravidão: o livro gênesis
Authors: Campos Neto Antonio Augusto Machado de
Published: April 2022

Reportando ao título em epígrafe, o propósito do artigo é a de demonstrar que crimes existem desde a Criação da Humanidade, haja vista narrativas bíblicas anotadas e impressas no Pentateuco da Torá sagrada hebraica. No primeiro Livro intitulado Gênesis, a história dos irmãos Caim e Abel; um deles, Abel, é assassinado pelo outro, descontrolado, por inveja. Com toda orientação de Deus a seu favor, Noé, após o Dilúvio, se descontrola na bebida do vinho, excedendo-se, acarretando se sentir confortável pela nudez. O filho mais novo, Cam, assiste o vexame; porém, em vez de cobri-lo, prefere chamar os irmãos a apreciar a cena desconcertante; na época, vista como ridícula. Ciente do episódio acontecido, Noé amaldiçoa Cam e toda a família do filho, inclusive o neto Canaã. Em fuga da maldição, a família de Cam segue para a África, onde se torna até o fim dos tempos escrava. Dentre outros episódios, a venda de José, como escravo, pelos próprios irmãos dominados pela inveja e preferência de José por seu pai, o lendário Jacó, mais tarde por Ordem de Deus passa a ser chamado de Israel. Moisés, Autor da Lei, a Torá, orientado por Deus, liberta seu povo da escravidão; libertos, seguem em direção à Terra Prometida. Exatamente neste resumo a pauta principal: a escravidão. A continuidade deste crime é verificada com estupefação no período medieval em tristes cenários sucessivos, no qual homens da cor da pele negra são arrancados à força de suas terras africanas na condição de escravos. E longa História acontece; para que nunca mais seja esquecida!
Variedades de análise econômica do direito: da nova economia institucional às arquiteturas de incentivos comportamentais
Authors: Amato Lucas Fucci
Published: April 2022

Este artigo pretende indicar como a visão convencional da análise econômica do direito – aquela que enfatiza a eficiência como valor e critério explicativo das decisões jurídicas – é apenas uma das vertentes abertas pela abordagem econômica neoinstitucionalista ao direito. Além de retomar as bases conceituais da análise econômica do direito na nova economia institucional, o texto procura indicar abordagens contemporâneas que se integram a esse corpo teórico, como o estudo das alternativas institucionais (arenas decisórias alternativas onde se tomam decisões econômicas) e dos incentivos comportamentais modelizáveis a partir de diferentes arquiteturas de escolha. Um amplo espectro de ferramentas teóricas se abre à comparação e ao desenho de instituições na interface entre direito e economia.
Vulnerabilidade: uma análise da garantia da reabilitação do nascituro com fissura labiopalatina
Authors: Dutka Jeniffer de Cássia Rillo , Cezar Thyago
Published: April 2022

Este trabalho teve como objetivo demonstrar a incidência do direito à reabilitação do nascituro com deficiência, incluindo a fissura labiopalatina, conferindo-lhe além das proteções para o seu pleno desenvolvimento intrauterino, a garantia de que tanto durante a gestação, quanto após seu nascimento, o mesmo tenha o efetivo recebimento de todos os procedimentos para sua reabilitação. A metodologia para contemplar este objetivo incluiu: a) identificar os dispositivos legais que determinam a obrigatoriedade da garantia da reabilitação do nascituro com deficiência; e b) apontar a necessidade de obediência imposta pelo ordenamento jurídico.
Il non acquisto dello status di «locali della missione»: note a margine della sentenza della Corte Internazionale di Giustizia nel caso Guinea Equatoriale c. Francia
Authors: Travan Federico
Published: April 2022

Questo lavoro è un commento alla sentenza della CIG, di merito, nel caso Immunités et procédures pénales (Guinée Équatoriale c. France), e, allo stesso tempo, uno studio del problema – inedito nella giurisprudenza internazionale – emergente dalla sentenza, quello del momento in cui e delle modalità attraverso le quali un determinato immobile acquista, nel diritto internazionale, lo status di «premises of the mission». La sentenza della Corte, che ha ritenuto necessario il consenso dello Stato accreditatario, è criticata. Lo studio e l’analisi della prassi degli Stati, della Convenzione di Vienna sulle relazioni diplomatiche, della storia dell’istituto dell’inviolabilità dei locali diplomatici e della (poca) letteratura che ha trattato il problema, dimostrano che esiste una norma del diritto internazionale che determina che l’acquisto dello status di «premises of the mission» avvenga attraverso il (e nel momento del) solo inizio d’uso effettivo dei locali per l’esercizio delle funzioni diplomatiche. La sentenza della CIG «crea» quindi un requisito – il consenso dello Stato accreditatario – che non esiste nel diritto internazionale, e ciò comporta gravi potenziali problemi di contrasto e coesistenza tra situazioni di fatto (reali) e situazioni puramente giuridico-formali.
Universidade pública e Open Science: o potencial inventivo no cenário de demandas urgentes
Authors: Borghi Arthur Telles , Delecródio Victor Marques , Amaral Camila Miranda , Juppa Luiza Barreto
Published: April 2022

No cenário de demandas urgentes provocado pela pandemia de Covid-19, a Universidade de São Paulo desenvolveu em tempo recorde um respirador mecânico de baixo custo que ganhou grande repercussão. Pretende-se demonstrar no presente exame que seu derradeiro sucesso vem visceralmente associado a dois aspectos que integram o escopo da presente pesquisa: a universidade pública como seu locus de criação e seu regime jurídico de licença aberta, remetendo ao instituto da open science. Esta análise utiliza o caso bem-sucedido do ventilador pulmonar para, em um primeiro momento, debruçar-se sobre a função da universidade pública e sua capacidade de produção tecnológica, bem como seus déficits e problemas, para então examinar a aplicação pioneira em território brasileiro de uma regulamentação jurídica singular, a licença creative commons, e seu papel no desenvolvimento de inovações e acesso ao conhecimento. Analisa-se, por fim, quais atributos da universidade e do modelo da open science fomentaram a referida produção tecnológica e qual seria a interconexão possível entre esses elementos. Para tanto, o estudo se estrutura em revisões bibliográficas de produções relevantes e na procura de dados e informações quantitativas pertinentes a seu escopo. A utilização desses métodos permitiu a conclusão de que a open science é uma eficiente alternativa ao sistema tradicional de patenteamento e, em seio da universidade pública, pode promover efeitos profundos de estímulo a inovações e acesso ao conhecimento, sobretudo em contextos emergenciais como o da atual pandemia do coronavírus.