bluuebunny/crossref_metadata_2025 huggingface dataset:
Accelerating the update of a DL-based IDS for IoT using deep transfer learning
Authors: Idrissi Idriss , Azizi Mostafa , Moussaoui Omar
Published: August 2021

Deep learning (DL) models are nowadays broadly applied and have shown outstanding performance in a variety of fields, including our focus topic of "IoTcybersecurity". Deep learning-based intrusion detection system (DL-IDS) models are more fixated and depended on the trained dataset. This poses a problem for these DL-IDS, especially with the known mutation and behavior changes of attacks, which can render them undetected. As a result, the DL-IDShas become outdated. In this work, we present a solution for updating DL-ID Semploying a transfer learning technique that allows us to retrain and fine-tune pre-trained models on small datasets with new attack behaviors. In our experiments, we built CNN-based IDS on the Bot-IoT dataset and updated it on small data from a new dataset named TON-IoT. We obtained promising results in multiple metrics regarding the detection rate and the training between the initial training for the original model and the updated one, in the matter of detecting new attacks behaviors and improving the detection rate for some classes by overcoming the lack of their labeled data.
Increasing life-time of wireless sensor network using energy-efficient and fault tolerance algorithms
Authors: Sabah Sama Hussam , Croock Muayad Sadik
Published: August 2021

Energy-efficiency ofwireless sensor networks (WSN) becomes an essential issue in the research area. This is because of the energy constraints in WSN that depend on a battery, which is difficult to replace or recharge; therefore, multiple clustering algorithms were proposed to achieve efficiency in using the available energy as much as possible. This paper proposed energy-efficient and fault-tolerance algorithms that enhance thelow energy adaptive clustering hierarchy (LEACH) protocol by three algorithms. The first focuses on selecting the best cluster head and the second focuses on minimizing the required nodes within the same cluster. Simultaneously, the third fault tolerance algorithm uses software engineering techniques like sleep schedules to increase network lifetime as much as possible. The testing results of the proposed algorithms prove the claim of enhancing the lifetime of WSN. In order to check improvement of lifetime of WSN we have compered the results of the proposed algorithms with standered algorthim. The results show prove the claim of enhancing the life-time of WSN, where the total lifetime of WSN rise from about 550 rounds to reach 4100 when utilized self-checking process and rised up to 5200 after enhance minimum distans.
A new hybrid conjugate gradient algorithm for optimization models and its application to regression analysis
Authors: Sulaiman Ibrahim Mohammed , Bakar Norsuhaily Abu , Mamat Mustafa , Hassan Basim A. , Malik Maulana , Ahmed Alomari Mohammad
Published: August 2021

The hybrid conjugate gradient (CG) method is among the efficient variants of CG method for solving optimization problems. This is due to their low memory requirements and nice convergence properties. In this paper, we present an efficient hybrid CG method for solving unconstrained optimization models and show that the method satisfies the sufficient descent condition. The global convergence prove of the proposed method would be established under inexact line search. Application of the proposed method to the famous statistical regression model describing the global outbreak of the novel COVID-19 is presented. The study parameterized the model using the weekly increase/decrease of recorded cases from December 30, 2019 to March 30, 2020. Preliminary numerical results on some unconstrained optimization problems show that the proposed method is efficient and promising. Furthermore, the proposed method produced a good regression equation for COVID-19 confirmed cases globally.
User experience assessment of a COVID-19 tracking mobile application (AMAN) in Jordan
Authors: Saleh Ashraf Mousa , Abuaddous Hayfa Y. , Enaizan Odai , Ghabban Fahad
Published: August 2021

This study assesses the user experience of a COVID-19 tracking application, as employed as a case study of AMAN mobile application based on user experience. This paper proposed an assessment of user experience (UX) for AMAN application (COVID-19 tracking mobile application in Jordan) by implementing a user experience questionnaire tool. The study aims to get feedback and identify UX based on user interaction and usage with the tracking application. The data are taken from 1208 participants who have experience using the application; an online questionnaire was implemented and distributed through social media groups. The research method that was adopted used the instrument from user experience questionnaire (UEQ) of Arabic and English versions. The results from the UX assessment using UEQ showed that there are four scales which are categorized as excellent; they are Attractiveness (Mean=1.9), Efficiency (Mean=2.4), Dependability (Mean=2.1), Stimulation (Mean=1.8), while the two scales on the benchmark of good are Perspicuity (Mean=2.0) and Novelty (Mean=1.6). From the scores, above < 0.8 show a positive evaluation. All scores are above >0,08 indicating that the evaluation of UX has a positive impression. It can be concluded that the AMAN application is very good–satisfying users to track infected cases of COVID 19. 
Privacy preserving association rule hiding using border based approach
Authors: B. Suma , G. Shobha
Published: August 2021

Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy. 
Analysis of inventory management of slow-moving spare parts by using ABC techniques and EOQ model-a case study
Authors: Emar Walid , Al-Omari Zakaria Anas , Alharbi Sami
Published: August 2021

Computer spare parts (CSPs) inventory management (IM) is very important for many companies. Excess inventory leads to high storage costs. On the other hand, the lack of CSP has a strong impact on the quality of service. This study was conducted by Power-One Jordan Computer Hardware-Software company (POJCHSC), Amman, Jordan. The focus area was the IM department and the target sample was employees working in the management department. The results showed that factors influencing the management of slow moving CSPs include production costs, obsolescence and CSPs dependence availability and transportation costs. By forecasting during the study, the results showed that the demand for adapters and chargers would increase by 20%. This demand forecast was performed using the economical order quantity (EOQ) model. The percentage of profits made by this company is 48%, and this requires some intervention to prevent losses. The results of this study are useful to the company, as well as to other similar industries that deal with slow-moving items. These results will help to simplify IM of slow-moving items. When we focused on POJCHSC manufacturers, the disadvantages of using the traditional ABC classification model were identified. Therefore, there is a need to have an ABC classification that is improved and which takes into consideration the criticality of the slow-moving CSP.
Cotton-wool spots, red-lesions and hard-exudates distinction using CNN enhancement and transfer learning
Authors: Tan Tian-Swee , As'ari M. A. , Wan Hitam Wan Hazabbah , Ngoo Qi Zhe , Foh thye Matthias Tiong , Chia hiik Kelvin Ling
Published: August 2021

The automatic retinal disease diagnosis by artificial intelligent is an interesting and challenging topic in the medical field. It requires an appropriate image enhancement technique and a sufficient training dataset for the specific retina conditions. The aim of this study was to design an automatic diagnosis convolutional neural network (CNN) model which does not require a large training dataset to specifically identify diabetic retinopathy symptoms, which are cotton wool, exudates spots and red lesionin colour fundus pictures. A novel framework comprised image enhancement method by using upgraded contrast limited adaptive histogram equalization (UCLAHE) filter and transferred pre-trained networks was developed to classify the retinal diseases regarding to the symptoms. The performance of the proposed framework was evaluated based on accuracy, sensitivity and specificity metrics. The collected results have proven the robustness of the proposed framework in offering good accuracy in retina diseases diagnosis. 
Stabilizing the spatial position of a quadrotor by the backstepping procedure
Authors: Kucherov Dmytro , Kozub Andrei , Sushchenko Olha , Skrynkovskyy Ruslan
Published: August 2021

The paper considers the problem of synthesizing a control law that stabilizes the spatial position of an airborne object. The control object is a quadrotor with nonlinear dynamics. To solve the stabilization problem, a mathematical model of the quadrotor has been developed, taking into account its positionin the Cartesian and Euler coordinate systems. The new control law has been synthesized using the backstepping procedure. This control law is based on the Lyapunov-type stabilization criterion. New results analysis of the quadrotor dynamics, where has been showing the dependence of the control accuracy on the parameter of the stabilization criterion also presented. An algorithm for the directed search of the procedure parameter also has been proposed. It ensures the desired quality of the transient process. Simulation results confirming the results of the oretical research have been presented as well.
Towards developing a pocket therapist: an intelligent adaptive psychological support chatbot against mental health disorders in a pandemic situation
Authors: Salhi Intissar , Guemmat Kamal El , Qbadou Mohammed , Mansouri Khalifa
Published: August 2021

Nowadays with COVID-19 ongoing epidemic outbreak, containment for weeks was one of the most effective measures adopted to deal with the spread of the virus until a vaccine could be efficient. Over that period, increased anxiety, depression, suicide attempts and post-traumatic stress disorder are accumulated. Several studies referred to the need of using chatbots, which recognizes human emotions in such pandemic contexts. More recently, numerous research papers improved the ability of artificial intelligence methods to recognize human emotion. However, they are still limited. The aim of this paper is the development of a chatbot against the disturbing psychic consequences of the pandemic, taking human emotion recognition into account. The object is to help people; especially students; suffering from mental disorders, by progressively understanding the reasonsbehind them. This innovative chatbot was developed by using the natural language processing model of deep learning. An advanced model of deep learning has been elaborated the intention for people and that to help them to regulate their mood and to reduce distortion of negative thoughts, that why a collection of a new database was done. The sequence-to-sequence model encoder and decoder consist of Long short-term memory cells and it is defined with the bi-directional dynamic recurrent neural network packets.
Performance evaluation of new blind OFDM signal recognition based on properties of the second-order statistics using universal software radio peripheral platform
Authors: Firdaoussi Mohamed , Ghennioui Hicham , Kamili Mohamed El , Lamrini Mohamed
Published: August 2021

In the context of cognitive radio (CR) or various military and civilian applications, modulation recognition (MR) is one of the most popular technical processes in the field of communication system recognition, by which the modulation type of the unknown received signal can be identified automatically by estimating one or more parameters of the modulated signal. This paper presents the performance evaluation of the new proposed blind system recognition method using only a particular property of the second-order statistics of the orthogonal frequency division multiplexing (OFDM) modulated signal. The effectiveness of the proposed method is illustrated using the implementation on universal software radio peripheral (USRP) platform. A comparison with computer simulations using MATLAB software is also performed, emphasizing the good performances of the method while the results obtained are close. We show the efficiency and behavior of the proposed method in the context of wireless communication systems based on OFDM modulation (3GPP/LTE, WiMAX, DVBT-2K, IEEE 802.22-1K,IEEE 802.22-2K, IEEE 802.22-4K). The proposed method can detect OF DM signals among other digital signals in a systematic and intelligent way even with low SNR values (when approaching to SNR=-2dB, the decision criteria tends towards 0).
Implementing optimization of PID controller for DC motor speed control
Authors: Rashid Yasir G. , Hussain Ahmed Mohammed Abdul
Published: August 2021

The point of this paper presents an optimization technique which is flexible and quick tuning by using a genetic algorithm (GA) to obtain the optimum proportional-integral-derivative (PID) parameters for speed control of aseparately excited DC motor as a benchmark for performance analysis. The optimization method is used for searching for the proper value of PID parameters. The speed controller of DC motor using PID tuning method sincludes three types: MATALB PID tunner app., modified Ziegler-Nicholsmethod and genetic algorithm (GA). PID controller parameters (Kp, Ki and Kd) will be obtained by GA to produce optimal performance for the DC motor control system. Simulation results indicate that the tuning method of PID by using a genetic algorithm is shown to create the finest result in system performance such as settling time, rise time, percentage of overshoot and steady state error. The MATLAB/Simulink software is used to model and simulate the proposed DC motor controller system.
Active voltage balancing strategy of asymmetric stacked multilevel inverter
Authors: Kasim Mostafa Q. , Hassan Raaed F.
Published: August 2021

Multilevel inverters (MLI) play an important role in AC applications and are undergoing continuous development in topology and control. In higher levels inverters, conventional MLIs have high components count which calls for modification of these topologies to obtain the same number of levels with fewer components to reduce cost and size. Balancing of the capacitors voltages is crucial for the operation of the MLI and it becomes more challenging in higher levels. This paper presents an active voltage balancing strategy for a reduced switch count five-leve topology which is the asymmetric stacked multilevel inverter (ASMLI). The ASMLI uses fewer components than the conventional MLIs when used in their five-levelconfiguration. The proposed active voltage balancing strategy uses simple measurements and logic to assure a balanced capacitors voltages during steady state and transients. The performance was examined and compared based on two modulation techniques with LCL filter and RL load using MATLAB/Simulink. The results show that the active voltage balancing strategy can trace all capacitors voltages to the reference value simultaneously with less than 1% voltage error, fast dynamic response, and an acceptable total harmonic distortion (THD) which allows the proposed setup to be an available option for medium voltage applications.

 
Design an intelligent hybrid position/force control for above knee prosthesis based on adaptive neuro-fuzzy inference system
Authors: Raheema Mithaq N. , Kadhim Dhirgaam A. , Hussein Jabbar S.
Published: August 2021

This paper reviews the position/force control approach for governs an efficient knee joint in an active lower limb prosthesis, and the inter facing current control algorithm with human gate parameter is inserted. Two techniques are used to collect gait cycle data of leg: first, the foot ground force is obtained by the force platform device based on its position (x, y), then data of knee joint angles is recorded by using a video-camera device.The collected information is sent and used in the proposed intelligent controller. This intelligent control system used an adaptive neuro-fuzzy inference system (ANFIS) circuit in addition to the proportional integral derivative (PID) controller. This hybrid ANFIS-PID control system simulates and provides the ground force values. The experimental results show anexcellent response and lower root mean square error (RMSE) compared with each of PID and ANFIS controller that implemented for a similar purpose. In summary, the results showed acceptably stable performance of the proposedposition/force controller based on hybrid ANFIS-PID system. It can be concluded that the finest performance of the controlled force, as quantified by the RMSE criteria, is perceived by the proposed hybrid scheme depending on the controller intelligent decision circuit.
Evaluation of a wireless low-energy mote with fuzzy algorithms and neural networks for remote environmental monitoring
Authors: Yauri Ricardo , Lezama Jinmi , Rios Milton
Published: August 2021

The devices developed for applications in the internet of things have evolved technologically in the improvement of hardware and software components, in the area of optimization of the life time and to increase the capacity to save energy. This paper shows the development of a fuzzy logic algorithm and a power propagation neural network algorithm in a wireless mote (IoT end device). The fuzzy algorithm changes the transmission frequency according to the battery voltage and solar cell voltage. Moreover,the implementation of algorithms based on neural networks, implied a challenge in the evaluation and study of the energy commitment for the implementation of the algorithm, memory space optimization and low energy consumption.
IoT for smart home system
Authors: Siswipraptini Puji Catur , Aziza Rosida Nur , Sangadji Iriansyah , Indrianto Indrianto , A. Siregar Riki Ruli , Sondakh Grace
Published: August 2021

This paper examines the integration of smart home and solar panel system that is controlled and monitored using IoT (internet ofthings). To enable the smart home system to monitor the activity within the house and act according to the current conditions, it is equipped with several sensors, actuators and smart appliances. All of these devices have to be connected to a communication network, so they can communicate and provide services forthe smart home’s in habitants. The smart home system was first introduced to provide comfort and convenience, but later it should also address many other things, e.g. the importance of the efficient use of energy or electricity and hybrid use of energy sources. A solar panel is added to the smart home prototype and its addition is studied. Adaptive linear neural network is implemented in the prototype as an algorithm for predicting decisions based on the current conditions. The construction of the proposed integrated systemis carried out through several procedures, i.e. the implementation of the adaptive linear neural network (ADALINE) as the neural network method, the design of the prototype and the testing process. This prototype integrates functionalities of several household appliances into one application controlled by an Android-based framework.
A smart management system of electric vehicles charging plans on the highway charging stations
Authors: El-Fedany Ibrahim , Kiouach Driss , Alaoui Rachid
Published: August 2021

Electric vehicles (EVs) are seen as one of the principal pillars of smart transportation to relieve the airborne pollution induced by fossil CO2 emissions. However, the battery limit, especially where the journey is with a long-distance road remains the most formidable obstacle to the large-scale use of EVs. To overcome the issue of prolonged waiting charging time due to the large number of EVs may have a charging plan at the same charging station (CS) along the highway, we propose a communication system to manage the EVs charging demands. The architecture system contains a smart scheduling algorithm to minimize trip time including waiting time, previous reservations and energyare needed to reach the destination. Moreover, an automatic mechanism for updating reservation is integrated to adjust the EVs charging plans. The results of the evaluation under the Moroccan highway scenario connecting Rabat and Agadir show the effectiveness of our proposal system.

 
Output voltage control of a PMSG with the DPC-SVM technique and high-order sliding mode
Authors: Moumna Mohammed , Taleb Rachid , Boudjema Zinelaabidine
Published: August 2021

This paper aims to study the control of the output voltage of a wind turbine (WT) which is composed of a permanent magnet synchronous generator (PMSG) connected to an inverter/rectifier. The first tested control on PMSG is based on the classical direct power control (CDPC); this technique uses the active and reactive power as a control variable. Then, to improve the quality of energy and evaluate the performance of the system, we proposed a high-order sliding mode (HOSM) with space vector modulation (SVM) to controlthe output voltage. As a result, the proposed approach presents attractive features such as the chattering-free behavior of the sliding mode. This system was designed for a wind power conversion application in the case of an isolated site. The computer simulations were provided to verify the validity of the proposed control algorithm using the MATLAB/Simulink software.
Automated system for monitoring and control of the liquid wax production process
Authors: Díaz-Choque Martín , Dávila-Ignacio Carlos , Sanchez-Ayte Augusto , Morales-Romero Guillermo , Torres-Quiroz Almintor , Alvarado-Bravo Nestor , Aldana-Trejo Florcita
Published: August 2021

This article describes the design of an automated system for the automatic monitoring and control of the liquid wax production process, in order to quantify its effect on productivity indicators. For which initially the procedure for obtaining the automation will be described; then the results obtained will be presented, the same ones that will be identified through a comparative analysis. During the investigation it was determined that, through the use of a programmable logic controller, it was possible to improve the precision of the dosage of components in the liquid wax production process; By achieving acorrect dosage, it is achieved that the physical-chemical factors that intervene in the quality of the final product, which are the pH and specific density, are within the limits established by the company, this is reflected in the decrease 38.77% of the amount of monthly loss of raw material, thus achieving the optimization of the productivity of the production of liquid wax by 83.69% per month, compared to the non-automated process.

 
Performance evaluation of NB-IoT in-band deployment mode in suburban area
Authors: Turzhanova Karina , Konshin Sergey , Tikhvinskiy Valery , Solochshenko Alexandr
Published: August 2021

There given to discuss the study results about one of three deployment scenarios performance (in-band deployment mode) of the narrow-band internet of things (NB-IoT) technology. The study is carrying out with help of simulation modeling and experimental testing of the main network parameters, namely: radio coverage, network capacity, user experience, and their dependencies on each other. Comparison of the results of a physical experiment and simulation modeling shows their high convergence and confirms the adequacy of the applied testing methodology. As a case scenario provided an example of NB-IoT implementation on a 4G mobile network in the 800 MHz band, in a suburban area for remote metering applications. The article presents the applying testing methodology of NB-IoT that adapted to the local conditions of radio network planning. Based on the obtained data, adducing the main conclusions about the feasibility of using an in-band scenario for deploying NB-IoT on a 4G network in a suburban environment.
Investigation of energy efficiency of two-way relay-assisted multi-band machine-to-machine communications
Authors: Van Hoang Thien , Anh Vo Tien , L. Danh Hong , Thi Kim Chi Duong , Nguyen Hoang-Sy
Published: August 2021

In this paper, we improve the uplink energy efficiency (EE) of the multi-band machine-to-machine (M2M) communications underlaying cellular networks. In particular, based on the theory of stochastic geometry, we derive the closed-form expressions of the outage probability (OP), and the average energy efficiency of cellular and Machine-to-Machine users in two-way cooperative relaying networks with three-time-slot setting. We ensure the quality of service (QoS) by considering the OP and the average energy efficiency of all links. It is concluded that the three-time-slot relay-aided Machine-to-Machine communication can offer considerably high QoS and low transmission power for fairly distant machine-to-machine networks.
Some advantages of non-binary Galois fields for digital signal processing
Authors: Moldakhan Inabat , Matrassulova Dinara K. , Shaltykova Dina B. , Suleimenov Ibragim E.
Published: August 2021

It is shown that the convenient processing facilities of digital signals that varying in a finite range of amplitudes are non-binary Galois fields, the numbers of which elements are equal to prime numbers. Within choosing a sampling interval which corresponding to such a Galois field, it becomes possible to construct a Galois field Fourier transform, a distinctive feature of which is the exact correspondence with the ranges of variation of the amplitudes of the original signal and its digital spectrum. This favorably distinguishes the Galois Field Fourier Transform of the proposed type from the spectra, which calculated using, for example, the Walsh basis. It is also shown, that Galois Field Fourier Transforms of the proposed type have the same properties as the Fourier transform associated with the expansion in terms of the basis of harmonic functions. In particular, an analogue of the classical correlation, which connected the signal spectrum and its derivative, was obtained. On this basis proved, that the using of the proposed type of Galois fields makes it possible to develop a complete analogue of the transfer function apparatus, but only for signals presented in digital form.
Optimal path discovery for two moving sinks with a common junction in a wireless sensor network
Authors: Tunga Satish , Chakrasali Sadashiva V. , Shylashree N. , N. Latha B. , S. Mamatha A.
Published: August 2021

A new algorithm is described for determining the optimal round-trip paths for two moving sinks in a wireless sensor network. The algorithm uses binary integer programming to select two non-overlapping shortest paths except having a common junction node to cover all the sensor nodes. The two paths are balanced as nearly equal as possible. That is the sensor nodes along each path are equal or differ by just one depending on whether the total number of sensor nodes excluding the junction node is even or odd. In this method, both the path lengths are made equal or very nearly equal while the total length is minimized. This integrated approach is a novel and unique solution to solve the dual moving sink path problem in a wireless sensor network.
Improving bit error-rate based on adaptive Bose-Chaudhuri Hocquenghem concatenated with convolutional codes
Authors: Samy Ahmed , Hassan Ashraf Y. , Zakaria Hatem M.
Published: August 2021

Several algorithms have been proposed to avoid the error floor region, such as the concatenation codes that requires high computational demands in addition to high complexity. This paper proposes a technique based on using cascaded BCH and convolutional codes that leads to better error correction performance. Moreover, an adaptive method based on sensing the channel's noise to determine the number of the parity bits that will be added to the used BCH that reduces the consumed bandwidth and the transmitted parity bits is presented. A further enhancement is fulfilled by using parallel processing branches, resulting in reducing the consumed time and speed up the performance. The results show that the proposed code presents a better performance. A high reduction in the number of cycles that will be used in the encoding and decoding compared with the classical method and finally a flexible parity bits method based on the signal-to-noise ratio of the channel that reduced the parity bits which leads to reduce the consumed bandwidth. The MATLAB simulation and the field programmable gate array (FPGA) implementation will be provided in this paper to validate the proposed concept.
Sarcasm detection of tweets without #sarcasm: data science approach
Authors: Bagate Rupali Amit , Suguna R.
Published: August 2021

Identifying sarcasm present in the text could be a challenging work. In sarcasm, a negative word can flip the polarity of a positive sentence. Sentences can be classified as sarcastic or non-sarcastic. It is easier to identify sarcasm using facial expression or tonal weight rather detecting from plain text. Thus, sarcasm detection using natural language processing is major challenge without giving away any specific context or clue such as #sarcasm present in a tweet. Therefore, research tries to solve this classification problem using various optimized models. Proposed model, analyzes whether a given tweet, is sarcastic or not without the presnece of hashtag sarcasm or any kind of specific context present in text. To achieve better results, we used different machine learning classification methodology along with deep learning embedding techniques. Our optimized model uses a stacking technique which combines the result of logistic regression and long short-term memory (LSTM) recurrent neural net feed to light gradient boosting technique which generates better result as compare to existing machine learning and neural network algorithm. The key difference of our research work is sarcasm detection done without #sarcasm which has not been much explored earlier by any researcher. The metrics used for evolutionis F1-score and confusion matrix.
Dire, fare, arrivare: corso di italiano per brasiliani: Vol. 2
Authors:   Universidade de São Paulo. Faculdade de Filosofia, Letras e Ciências Humanas, Baccin Paola , Gazzoni Sandra ,   Universidade de São Paulo. Faculdade de Filosofia, Letras e Ciências Humanas
Published: August 2021

Dizer, fazer, chegar! é a segunda parte do curso Dizer, fazer, partir! O volume é composto de um conjunto de material didático projetado para os estudantes brasileiros de italiano. A estrutura do curso prevê videoaulas, exercícios e explicações suplementares para cada unidade didática, reunidas neste livro, e exercícios online que podem ser acessados no site do curso. O projeto é fruto de um partnership entre os departamentos de Italianística da Faculdade de Filosofia Letras e Ciências Humanas e o Departamento de Mídias Digitais da USP. Nesta obra, o aluno terá a oportunidade de conhecer a Cineteca de Bologna e a Alma Mater Università de Bologna.
Simultaneous Measurement of Lung Diffusing Capacity and Pulmonary Hemodynamics Reveals Exertional Alveolar‐Capillary Dysfunction in Heart Failure With Preserved Ejection Fraction
Authors: Fermoyle Caitlin C. , Stewart Glenn M. , Borlaug Barry A. , Johnson Bruce D.
Published: August 2021

Background

Hemodynamic perturbations in heart failure with preserved ejection fraction (HFpEF) may alter the distribution of blood in the lungs, impair gas transfer from the alveoli into the pulmonary capillaries, and reduce lung diffusing capacity. We hypothesized that impairments in lung diffusing capacity for carbon monoxide (DL
CO
) in HFpEF would be associated with high mean pulmonary capillary wedge pressures during exercise.

Methods and Results

Rebreathe DL
CO
and invasive hemodynamics were measured simultaneously during exercise in patients with exertional dyspnea. Pulmonary pressure waveforms and breath‐by‐breath pulmonary gas exchange were recorded at rest, 20 W, and symptom‐limited maximal exercise. Patients with HFpEF (n=20; 15 women, aged 65±11 years, body mass index 36±8 kg/m
2
) achieved a lower symptom‐limited maximal workload (52±27 W versus 106±42 W) compared with controls with noncardiac dyspnea (n=10; 7 women, aged 55±10 years, body mass index 30±5 kg/m
2
). DL
CO
was lower in patients with HFpEF compared with controls at rest (DL
CO
10.4±2.9 mL/min per mm Hg versus 16.4±6.9 mL/min per mm Hg,
P
<0.01) and symptom‐limited maximal exercise (DL
CO
14.6±4.7 mL/min per mm Hg versus 23.8±10.8 mL/min per mm Hg,
P
<0.01) because of a lower alveolar‐capillary membrane conductance in HFpEF (rest 16.8±6.6 mL/min per mm Hg versus 28.4±11.8 mL/min per mm Hg,
P
<0.01; symptom‐limited maximal exercise 25.0±6.7 mL/min per mm Hg versus 45.5±22.2 mL/min per mm Hg,
P
<0.01). DL
CO
was lower in HFpEF for a given mean pulmonary artery pressure, mean pulmonary capillary wedge pressure, pulmonary arterial compliance, and transpulmonary gradient.

Conclusions

Lung diffusing capacity is lower at rest and during exercise in HFpEF due to impaired gas conductance across the alveolar‐capillary membrane. DL
CO
is impaired for a given pulmonary capillary wedge pressure and pulmonary arterial compliance. These data provide new insight into the complex relationships between hemodynamic perturbations and gas exchange abnormalities in HFpEF.
What Do US Physicians and Patients Think About Lipid‐Lowering Therapy and Goals of Treatment? Results From the GOULD Registry
Authors: Arnold Suzanne V. , Cannon Christopher P. , de Lemos James A. , Rosenson Robert S. , Ballantyne Christie M. , Liu Yuyin , Alam Shushama , Mues Katherine E. , Bhatt Deepak L. , Kosiborod Mikhail ,   the GOULD Investigators
Published: August 2021

Background

Because of an increasing number and complexity of treatment options for lipid‐lowering therapy in patients with atherosclerotic cardiovascular disease, guidelines recommend greater active involvement of patients in shared decision‐making. However, patients' understanding and perceptions of the benefits, risks, and treatment objectives of lipid‐lowering therapy are unknown.

Methods and Results

Structured questionnaires were conducted in 5006 US outpatients with atherosclerotic cardiovascular disease and suboptimal low‐density lipoprotein cholesterol (LDL‐C) control (LDL‐C ≥70 mg/dL) or on a PCSK9 (proprotein convertase subtilisin/kexin type 9) inhibitor and in 113 physician providers as a part of the GOULD (Getting to an Improved Understanding of Low‐Density Lipoprotein Cholesterol and Dyslipidemia Management) Registry. Mean age of the patients was 68±10 years, 60% were men, and 86% were White race. Across all patients, 63% believed heart disease was the leading cause of death in men and 46% the leading cause of death in women. Only 28% of patients thought the primary reason they were taking lipid‐lowering medication was to lower the risk of heart attack or stroke, 68% did not know their approximate LDL‐C level, and 69% did not know their LDL‐C goal. Patients on PCSK9 inhibitors (versus LDL‐C cohort), younger patients (versus age ≥65 years), and men (versus women) were somewhat more knowledgeable about their disease and its management. Most physicians (66%) felt that a lack of understanding of the importance and efficacy of statins was the primary factor contributing to nonadherence, as opposed to costs (9%) or side effects (1%). More education was the most commonly used strategy to address patient‐reported side effects.

Conclusions

A large proportion of patients with atherosclerotic cardiovascular disease remain unaware of their underlying atherosclerotic cardiovascular disease risk, reasons for taking lipid‐lowering medications, current LDL‐C levels, or treatment goals. These data highlight a large education gap which, if addressed, may improve shared decision‐making and treatment adherence.

Registration

URL:
https://www.clinicaltrials.org
; Unique identifier: NCT02993120.
Inflammation Alters Relationship Between High‐Density Lipoprotein Cholesterol and Cardiovascular Risk in Patients With Chronic Kidney Disease: Results From KNOW‐CKD
Authors: Kim Jae Young , Park Jung Tak , Kim Hyung Woo , Chang Tae‐Ik , Kang Ea Wha , Ahn Curie , Oh Kook‐Hwan , Lee Joongyub , Chung Wookyung , Kim Yong‐Soo , Kim Soo Wan , Yoo Tae‐Hyun , Kang Shin‐Wook , Han Seung Hyeok ,   the KNOW‐CKD (Korean Cohort Study for Outcomes in Patients With Chronic Kidney Disease) Investigators
Published: August 2021

Background

The function of high‐density lipoprotein can change from protective to proatherosclerotic under inflammatory conditions. Herein, we studied whether inflammation could modify the relationship between high‐density lipoprotein level and risk of adverse outcomes in patients with chronic kidney disease .

Methods and Results

In total, 1864 patients from the prospective KNOW‐CKD (Korean Cohort Study for Outcome in Patients With Chronic Kidney Disease) were enrolled. The main predictor was high‐density lipoprotein cholesterol (HDL‐C) level. Presence of inflammation was defined by hs‐CRP (high‐sensitivity C‐reactive protein) level of ≥1.0 mg/L. The primary outcome was extended major adverse cardiovascular events. During 9231.2 person‐years of follow‐up, overall incidence of the primary outcome was 15.8 per 1000 person‐years. In multivariable Cox analysis after adjusting for confounders, HDL‐C level was not associated with the primary outcome. There was a significant interaction between the inflammatory status and HDL‐C for risk of extended major adverse cardiovascular events (
P
=0.003). In patients without inflammation, the hazard ratios (HRs) (95% CIs) for HDL‐C levels <40, 50 to 59, and ≥60 mg/dL were 1.10 (0.50–1.82), 0.95 (0.50–1.82), and 0.42 (0.19–0.95), respectively, compared with HDL‐C of 40 to 49 mg/dL. However, the significant association for HDL‐C ≥60 mg/dL was not seen after Bonferroni correction. In patients with inflammation, we observed a trend toward increased risk of extended major adverse cardiovascular events in higher HDL‐C groups (HRs [95% CIs], 0.73 [0.37–1.43], 1.24 [0.59–2.61], and 1.56 [0.71–3.45], respectively), but without statistical significance.

Conclusions

The association between HDL‐C level and adverse cardiovascular outcomes showed reverse trends based on inflammation status in Korean patients with chronic kidney disease.

Registration

URL:
https://www.clinicaltrials.gov
; Unique identifier: NCT01630486.
Ability of Video Telemetry to Predict Unplanned Hospital Admissions for Single Ventricle Infants
Authors: Aly Doaa M. , Erickson Lori A. , Hancock Hayley , Apperson Jonathan W. , Gaddis Monica , Shirali Girish , Goudar Suma
Published: August 2021

Background

Our Cardiac High Acuity Monitoring Program (CHAMP) uses home video telemetry (HVT) as an adjunct to monitor infants with single ventricle during the interstage period. This study describes the development of an objective early warning score using HVT, for identification of infants with single ventricle at risk for clinical deterioration and unplanned hospital admissions (UHA).

Methods and Results

Six candidate scoring parameters were selected to develop a pragmatic score for routine evaluation of HVT during the interstage period. We evaluated the individual and combined ability of these parameters to predict UHA. All infants with single ventricle monitored at home by CHAMP between March 2014 and March 2018 were included. Videos obtained within 48 hours before UHA were compared with videos obtained at baseline. We used binary logistic regression models and receiver operating characteristic curves to evaluate the parameters' performance in discriminating the outcome of interest.

Thirty‐nine subjects with 64 UHA were included. We compared 64 pre‐admission videos to 64 paired baseline videos. Scoring was feasible for a mean of 91.6% (83.6%–98%) of all observations. Three different HVT score models were proposed, and a final model composed of respiratory rate, respiratory effort, color, and behavior exhibited an excellent discriminatory capability with an area under the receiver operating characteristic curve of 93% (89%–98%). HVT score of 5 was associated with specificity of 93.8% and sensitivity of 88.7% in predicting UHA.

Conclusions

We developed a feasible and reproducible HVT score that can serve as a tool to predict UHA in infants with single ventricle. Future directions involve prospective, multicenter validation of this tool.
Low‐Density Lipoprotein Cholesterol and Risk of Recurrent Vascular Events in Chinese Patients With Ischemic Stroke With and Without Significant Atherosclerosis
Authors: Lau Kui‐Kai , Chua Bryan J. , Ng Alexander , Leung Ian Yu‐Hin , Wong Yuen‐Kwun , Chan Anna Ho‐Yin , Chiu Yuen‐Kei , Chu Ariane Xia‐Wei , Leung William C. Y. , Tsang Anderson Chun‐On , Teo Kay‐Cheong , Mak Henry Ka‐Fung
Published: August 2021

Background

Recent trials have shown that low‐density lipoprotein cholesterol (LDL‐C) <1.80 mmol/L (<70 mg/dL) is associated with a reduced risk of major adverse cardiovascular events in White patients with ischemic stroke with atherosclerosis. However, it remains uncertain whether the findings can be generalized to Asian patients, or that similar LDL‐C targets should be adopted in patients with stroke without significant atherosclerosis.

Methods and Results

We performed a prospective cohort study and recruited consecutive Chinese patients with ischemic stroke with magnetic resonance angiography of the intra‐ and cervicocranial arteries performed at the University of Hong Kong between 2008 and 2014. Serial postevent LDL‐C measurements were obtained. Risk of major adverse cardiovascular events in patients with mean postevent LDL‐C <1.80 versus ≥1.80 mmol/L, stratified by presence or absence of significant (≥50%) large‐artery disease (LAD) and by ischemic stroke subtypes, were compared. Nine hundred four patients (mean age, 69±12 years; 60% men) were followed up for a mean 6.5±2.4 years (mean, 9±5 LDL‐C readings per patient). Regardless of LAD status, patients with a mean postevent LDL‐C <1.80 mmol/L were associated with a lower risk of major adverse cardiovascular events (with significant LAD: multivariable‐adjusted subdistribution hazard ratio, 0.65; 95% CI, 0.42–0.99; without significant LAD: subdistribution hazard ratio, 0.53; 95% CI, 0.32–0.88) (both
P
<0.05). Similar findings were noted in patients with ischemic stroke attributable to large‐artery atherosclerosis (subdistribution hazard ratio, 0.48; 95% CI, 0.28–0.84) and in patients with other ischemic stroke subtypes (subdistribution hazard ratio, 0.64; 95% CI, 0.43–0.95) (both
P
<0.05).

Conclusions

A mean LDL‐C <1.80 mmol/L was associated with a lower risk of major adverse cardiovascular events in Chinese patients with ischemic stroke with and without significant LAD. Further randomized trials to determine the optimal LDL‐C cutoff in stroke patients without significant atherosclerosis are warranted.
Development and Validation of a Risk Score Model for Predicting the Cardiovascular Outcomes After Breast Cancer Therapy: The CHEMO‐RADIAT Score
Authors: Kim Do Young , Park Myung‐Soo , Youn Jong‐Chan , Lee Sunki , Choi Jae Hyuk , Jung Mi‐Hyang , Kim Lee Su , Kim Sung Hea , Han Seongwoo , Ryu Kyu‐Hyung
Published: August 2021

Background

Cardiovascular disease is an important cause of mortality among survivors of breast cancer (BC). We developed a prediction model for major adverse cardiovascular events after BC therapy, which is based on conventional and BC treatment‐related cardiovascular risk factors.

Methods and Results

The cohort of the study consisted of 1256 Asian female patients with BC from 4 medical centers in Korea and was randomized in a 1:1 ratio into the derivation and validation cohorts. The outcome measures comprised cardiovascular mortality, myocardial infarction, congestive heart failure, and transient ischemic attack/stroke. To correct overfitting, a penalized Cox proportional hazards regression was performed with a cross‐validation approach. Number of cardiovascular diseases (myocardial infarction, peripheral artery disease, heart failure, and transient ischemic attack/stroke), number of baseline cardiovascular risk factors (hypertension, age ≥60, body mass index ≥30 kg/m
2
, estimated glomerular filtration rate <60 mL/min per 1.73 m
2
, dyslipidemia, and diabetes mellitus), radiation to the left breast, and anthracycline dose per 100 mg/m
2
were included in the risk prediction model. The time‐dependent C‐indices at 3 and 7 years after BC diagnosis were 0.876 and 0.842, respectively, in the validation cohort.

Conclusions

A prediction score model, including BC treatment‐related risk factors and conventional risk factors, was developed and validated to predict major adverse cardiovascular events in patients with BC. The CHEMO‐RADIAT (congestive heart failure, hypertension, elderly, myocardial infarction/peripheral artery occlusive disease, obesity, renal failure, abnormal lipid profile, diabetes mellitus, irradiation of the left breast, anthracycline dose, and transient ischemic attack/stroke) score may provide overall cardiovascular risk stratification in survivors of BC and can assist physicians in multidisciplinary decision‐making regarding the BC treatment.
Development of a Decision Aid for Parents Who Elect Tonsillectomy for Obstructive Sleep Apnea
Authors: Williamson Adrian , Newby Maxwell , Phillips Drew , Carr Michele
Published: August 2021

Objective:

To develop a novel patient decision aid (PtDA) for parents considering tonsillectomy for their children diagnosed with obstructive sleep apnea (OSA) and compare it to validated scales related to decision making in this context. These included scales for decisional conflict (DC) and shared decision making (SDM).

Methods:

A parental survey during 2017 to 2018 in a tertiary care pediatric otolaryngology clinic was conducted comparing a validated Decisional Conflict Scale (DCS) with a new PtDA that included an SDM scale, parental treatment goals, and knowledge about adenotonsillectomy and OSA. DCS scores range from 0 to 100 with values less than 25 considered to be low DC. The DQ was determined by a score on the PtDA. The PtDA was composed of a knowledge score, SDM score and 5 related values scored along a continuum (these were: resolution of symptoms, avoiding anesthesia, avoiding surgery, avoiding pain/bleeding, and resumption of normal behavior). A high score meant that all answers were consistent with choosing tonsillectomy and imply better DQ.

Results:

A total of 89 parents or guardians participated in the study. The mean DC score was 4.32 (95% CI: 2.57-6.07). The mean DQ score was 22.69 (95% CI: 21.86-23.51). Mean values score was 5.35 (95% CI: 5.05-5.65). The mean knowledge score was 9.00 (95% CI: 8.60-9.40). SDM score mean was 8.38 (95% CI: 7.85-8.91). Using Spearman’s rho, DC versus DQ inversely correlated with a coefficient −.209 via a 2-tailed test ( P = .05). Cronbach’s alpha for the DQ score was .78.

Conclusion:

DC scores overall were low for the group. DQ, as measured with the novel PtDA, had an inverse correlation with DC scores, suggesting validity of the proposed PtDA. Our instrument has potential use as a PtDA for parents who are offered tonsillectomy for their children.