bluuebunny/crossref_metadata_2025 huggingface dataset:
Rapid Change in Shape of Unruptured Intracranial Aneurysm with Acute Perforating Infarction
Authors: Tanaka Tatsuya , Goto Hirofumi , Momozaki Nobuaki , Honda Eiichiro
Published: October 2022

Acute ischemic stroke is a rare complication resulting from an unruptured intracranial aneurysm (UIA). Ischemic stroke adjacent to the aneurysms is considered the risk of rupture of aneurysms. However, there is presently no consensus on the optimal strategy for the management of UIAs with ischemic stroke. A 27-year-old woman presented with sudden onset left hemiparesis. Acute infarction of the right basal ganglia and an aneurysm of the right middle cerebral artery were discovered on brain imaging. Antiplatelet therapy was used to treat her. The diagnosis revealed ischemic stroke caused by a thrombosed aneurysm due to the change in the shape of the aneurysm on day 4. The UIA clipping procedure was performed on day 21 due to the risk of subarachnoid hemorrhage (SAH). The findings of the surgery and indocyanine green imaging revealed a partially thrombosed aneurysm and occlusion of a perforating artery. As is well known, enlargement of aneurysm size indicates increasing rupture risk. In the present case, after ischemic events developed, magnetic resonance angiography revealed enlargement of the aneurysm. The findings of the surgery revealed possible pathogenic mechanisms were perforating artery occlusion due to local extension of the luminal thrombus. Clinicians should be aware of the risk of ischemic stroke due to luminal thrombosis of the UIA and SAH and should consider urgent treatment of the UIA even immediately after ischemic stroke.
Resolution of Fulminant Idiopathic Intracranial Hypertension Treated with Acetazolamide
Authors: Srivastava Ojas , Micieli Jonathan A.
Published: October 2022

Idiopathic intracranial hypertension (IIH) is a condition of elevated intracranial pressure commonly seen in obese women of childbearing age. Fulminant IIH is a rare subset of IIH that is characterized by rapidly progressive vision loss in less than 4 weeks, and typically requires surgical intervention for treatment. We describe a 36-year-old man with a 3-week history of acute onset vision loss and fulminant IIH in whom severe bilateral hemorrhagic optic disk edema was identified. There were also associated moderate visual field defects. Given the rapid onset of symptoms and severity of papilledema, surgical management was discussed but the patient had opted for medical management and close follow-up. He began oral acetazolamide, which was escalated to the maximal dose of 4 g and seen regularly with close follow-up. Four months after presentation, he was completely symptom free and the bilateral optic disk edema had resolved. His visual fields had also improved . We emphasize the importance of close follow-up in fulminant IIH and highlight that although most cases often require surgical intervention, some patients may show improvement with medical management only.
Pathological Connections between Nonalcoholic Fatty Liver Disease and Chronic Kidney Disease
Authors: Liu Huixia , Zhang Chun , Xiong Jing
Published: October 2022

Background: Nonalcoholic fatty liver disease and chronic kidney disease are major public health issues worldwide. The clinical burden of nonalcoholic fatty liver disease is not only confined to liver-related morbidity and mortality, but it also includes the burden of chronic extrahepatic complications. It is well known that liver and kidney are strictly interconnected in physiological and pathological conditions. Summary: Mounting evidence indicates a strong association between nonalcoholic fatty liver disease and chronic kidney disease, independent of the identified cardiorenal risk factors. The presence and severity of nonalcoholic fatty liver disease are related to the developmental stage and risk of chronic kidney disease. And chronic kidney disease progression also contributes to nonalcoholic fatty liver disease development. Nonalcoholic fatty liver disease and chronic kidney disease mutually contribute to disease progression through pathological links. Shared pathogenic mechanisms also exist between nonalcoholic fatty liver disease and chronic kidney disease, including pyroptosis and ferroptosis. Additionally, the use of combined liver-kidney transplantation has increased exponentially in recent years. Key Messages: This review focuses on the emerging pathological mechanisms linking nonalcoholic fatty liver disease and chronic kidney disease and shared pathogenic mechanisms to find novel targeted therapies and retard the progression of both disease processes.
Dingo optimization algorithm for designing power system stabilizer
Authors: Aribowo Widi , Suprianto Bambang , Three Kartini Unit , Prapanca Aditya
Published: October 2022

The dingo optimization algorithm (DOA) adopts the social life of dingo dogs. The dingo is a breed of ancient dog originating from Australia. Dingo hunting strategies such as assault with persecution, flocking, and scavenging behavior became the inspiration for DOA. In this paper, DOA is applied to a power system stabilizer (PSS) to dampen low-frequency oscillations (LFO) in a single-machine infinite bus (SMIB). DOA is used to obtain optimal parameters for PSS. The damping controller is designed for optimal lead-lag control. To obtain the performance of the DOA method, the results were compared with the uncontrolled method, conventional PSS, Whale optimization algorithm (WOA), and grasshopper optimization algorithm (GOA). Simulation using MATLAB with three different operating conditions, namely light load (20%), medium load (50%) and high load (100%). From the simulation using MATLAB with SMIB modeling, it was found that the application of the DOA method on PSS has the ability to reduce the average undershoot value by 28.16% and reduce the average undershoot value to 65.57% compared to the conventional PSS method.
Adaptive doubly fed induction generator’s control driven wind turbine using luenberger observer optimized by genetic algorithm
Authors: Elaimani Hind , Essadki Ahmed , Elmouhi Noureddine , Bahja Fadoua
Published: October 2022

The calculation of control parameters for a system control method is based on the model of the system with assumed fixed internal parameters. However, these parameters can vary greatly due to several phenomena. This paper presents an adapted control of a doubly fed induction generator machine robust against the rotor resistance variations of the machine used as a generator in wind energy conversion systems. The adaptation is ensured by a system allowing to identify in real time the value of the resistance, the system used is mainly based on a Luenberger observer. The conversion system is divided into two parts, the first mechanical part containing the turbine and the gearbox, the second electrical one consisting of a double fed induction generator, linked on the stator side directly to the grid, and on the rotor, side linked to the grid through two power electronics converters interposed with a direct current (DC) link. The machine-side converter is used to control the active and reactive powers, and the second on the grid side is used to control the DC link voltage. The converters are controlled by the sliding mode strategy, and the validity of the methods is checked by simulation using MATLAB/Simulink.
Design and realization of a dual-wavelength low level light therapy for acne and face rejuvenation treatment
Authors: Watjanatepin Napat , Kiatsookkanatorn Paiboon , Boonmee Chaiyant , Thongkullaphat Sarayoot , Archevapanich Tuanjai , Sritanauthaikorn Patcharanan , Wannakam Khanittha
Published: October 2022

Low-level light therapy (LLLT) uses the light of wavelength between 400–700 nm to treat acne, reduce inflammation, stimulate collagen production, and rejuvenate the facial skin. This study designed and constructed a dual-wavelength LED LLLT device for the facial treatment. The light spectrum, power density, uniformity, stability, and safety of the device were analyzed. The proposed system consisted of an LED array with 415 and 633 nm wavelengths. Human machine interface with embedded system was used to control light intensity and treatment time. The phototherapy device is designed to be curvaceously sized to suit the face shape of Asian people. The results showed that the LLLT device emitted 633±5 nm red and 415±5 nm blue light with a linear adjustable light power density of 0-18.56 mW/cm2 and 0-3.70 mW/cm2, respectively. The spectrum distribution of the red and blue light was relatively constant over 30 minutes of operation. The uniformity and stability of red spectrum were about 89.9% and 95.08% and blue spectrum were 87.6% and 97.08%, respectively. The experimental face’s temperature was below 31.5 . For the future study, the LED phototherapy device will be applied for clinical research in collaboration with dermatologists.Low-level light therapy (LLLT) uses the light of wavelength between 400–700 nm to treat acne, reduce inflammation, stimulate collagen production, and rejuvenate the facial skin. This study designed and constructed a dual-wavelength LED LLLT device for the facial treatment. The light spectrum, power density, uniformity, stability, and safety of the device were analyzed. The proposed system consisted of an LED array with 415 and 633 nm wavelengths. Human machine interface with embedded system was used to control light intensity and treatment time. The phototherapy device is designed to be curvaceously sized to suit the face shape of Asian people. The results showed that the LLLT device emitted 633±5 nm red and 415±5 nm blue light with a linear adjustable light power density of 0-18.56 mW/cm2 and 0-3.70 mW/cm2, respectively. The spectrum distribution of the red and blue light was relatively constant over 30 minutes of operation. The uniformity and stability of red spectrum were about 89.9% and 95.08% and blue spectrum were 87.6% and 97.08%, respectively. The experimental face’s temperature was below 31.5 . For the future study, the LED phototherapy device will be applied for clinical research in collaboration with dermatologists.
Optimal location and sizing of distributed generation to minimize losses using whale optimization algorithm
Authors: Shu Xuan Leong , Juhana Tengku Hashim Tengku , Najib Kamarudin Muhamad
Published: October 2022

The conventional power plants often bring in power quality concerns for instance high power losses and poor voltage profile to the network which are caused by the locations of power plants that are placed a distance away from loads. With proper planning and systematic allocation, the introduction of distributed generation (DG) into the network will enhance the performance and condition of the power system. This paper utilizes the optimization approach named whale optimization algorithm (WOA) in the search of the most ideal location and size of DG while ensuring the reduction of power losses and the minimization of the voltage deviation. WOA implementation is done in the IEEE 33-bus radial distribution system (RDS) utilizing MATPOWER and MATLAB software for no DG, one DG and two DGs installation. The outcome obtained from using WOA was compared to other well-known optimization methods and WOA has shown its competency after comparison; the optimal location of WOA with other methods showing almost the same result. The best result presented was the system with two DGs installed due to the losses of the system was recorded to be the least compared to one DG or no DG installation.
Near-field microwave focusing evaluation of dielectric lens antenna for human body model
Authors: Abd Rahman Amirah , Kamardin Kamilia , Yamada Yoshihide , Takahashi Masaharu
Published: October 2022

Various small focus spot applicators are being investigated for hyperthermia therapy, which requires microwave concentration to heat tumors in the human body. Dielectric lens antenna is frequency independent and has strong focusing capability to achieve a very small focusing spot. In this paper, lenses with diameters of 30, 50 and 70 cm were designed to evaluate the size of the focal spot in the human body model. The electromagnetic simulator, FEKO was used to generate rays and near-field focusing data of dielectric lenses at a frequency of 2.45 GHz. The simulated focal spot sizes agreed well with the theoretical values. An analytical investigation into the power at the focal spot was conducted using the proposed power relations of the focused lens novel equation. The theoretical propagation loss is used to represent the power density degradation at the focal spot caused by microwave absorption by the human body. The simulation results of the focused lens in the human body indicated that the 30 cm lens achieved a larger focal spot with a greater focusing power, 0.714 mW compared to the 70 cm lens, which achieved a smaller focal spot but a lower focusing power, which was 0.393 mW.
Dipole antenna with biconical and pyramidal horn design in radio frequency identification simulations
Authors: M. Africa Aaron Don , M. Tagabuhin Rica Rizabel , S. D. Tirados Jan Jayson
Published: October 2022

Radio frequency identification (RFID) systems are used in several applications. It is widely used in retail, corporations, and schools for several purposes such as inventory, identification, and cashless payments. The components of an RFID system include a tag and a reader. The RFID reader includes an RF module that transmits and receives signals. While the RFID tag transmits embedded signals, which is typically some form of identification. The tag is a passive component powered by the reader. The two components make use of antennas to communicate the signals with each other. The design of the antenna is an important factor to consider in the production of the RFID. The size of the antenna must be small enough to provide convenience and the gain must be strong enough to effectively transmit and receive signals between the two components. In this paper, an antenna for an RFID tag is designed using MATLAB software. The antenna to be designed must be cost-efficient and be able to radiate an acceptable gain. This research creates a dipole antenna with biconical and pyramidal horn design in RFID simulations.
Nonlinear backstepping control of a partially shaded photovoltaic storage system
Authors: Khadija Sabri , Ouadia El Maguiri , Abdelmajid Farchi
Published: October 2022

Many power converter architectures and control approaches, both traditional and unconventional, have been developed, investigated, and adjusted to handle the challenge of tracking the maximum power points of a partially shaded photovoltaic (PV) system, which fluctuates with meteorological conditions (radiation and temperature). A DC-DC converter was used as the power conditioning unit to determine the system’s maximum efficiency. In this research, we focus on developing a nonlinear controller for a DC-DC converter to track the overall maximum power point in a PV storage system under partial shading situations. This study presents a combination of two MPP search algorithms with a backstepping controller. The particle swarm optimization (PSO) and variable step Perturb and Observe (P&O)with global scan (VSP&O/GS) algorithms supply the PV output reference voltage to the backstepping controller in order to recover the maximum power from photovoltaic (PV) systems. The simulation results of the methods compared to the proposed maximum power point (MPPT) algorithm are simulated and examined in the MATLAB/Simulink environment under non-uniform irradiation conditions. To demonstrate the performance and limits of each approach in tracking the maximum power point.
Heading control of an autonomous underwater robot with sliding mode and backstepping techniques
Authors: Abdellilah Hattab , Kamel Yahiaoui
Published: October 2022

The variation of the parameters of the autonomous underwater robot (inertia, hydrodynamics), external disturbances, the non-linearity of the model to be controlled, and measurement errors due to the sensors are the factors that have a negative influence on the robot's trajectory following. Autonomous submarine. A control law that takes these different factors into account would be an adequate solution to this control problem. This work is a simulation study of some advanced control techniques for controlling the heading of an autonomous underwater robot. We have developed two robust command structures. One of them is based on the sliding-mode control, which is applied to the considered model. The other is based on the control of backstepping. Several simulation tests were done to see how the robot moved after the two control methods were used.
The significance of artificial intelligent technique in classifying various grades of agarwood oil
Authors: Fawwaz Mohd Amidon Aqib , Mariatul Hazwa Mohd Huzir Siti , Mohd Yusoff Zakiah , Ismail Nurlaila , Nasir Taib Mohd
Published: October 2022

Agarwood oil quality is often separated into two or three categories. This makes classifying agarwood oil quality using current methods difficult. Current approaches rely solely on human perception to determine the quality of agarwood, whether in raw material or oil. This technique has other undesirable implications. It can affect the human sensory system, particularly the eyes and nose. Categorization takes time, which is a considerable expense to succeed in this method. As a result, a new classification system should be devised. The chemical components in agarwood oil are used to classify it in this study. In this study, samples with preprocessing data from two to five quality levels were used. The purpose is to categorize this data based on its qualities and analyze whether this new quality group is acceptable. The K-nearest neighbours (KNN) approach was used to classify all samples and their properties for this dataset. All samples may be correctly classified by grade without any errors. This shows the chemical compound-based classification of agarwood oil can be retained. With these findings, future agarwood oil research may focus on building a new classification.
Increasing validation accuracy of a face mask detection by new deep learning model-based classification
Authors: Azeez Joodi Mohanad , Hadi Saleh Muna , Jasim Kadhim Dheyaa
Published: October 2022

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieved lower computational complexity and number of layers, while being more reliable compared with other algorithms applied to recognize face masks. The findings reveal that the model's validation accuracy reaches 97.55% to 98.43% at different learning rates and different values of features vector in the dense layer, which represents a neural network layer that is connected deeply of the CNN proposed model training. Finally, the suggested model enhances recognition performance parameters such as precision, recall, and area under the curve (AUC).
Analysis of the development of fruit trees diseases using modified analytical model of fuzzy c-means method
Authors: Abdulkarem Habib Alrammahi Ali , Abbas Obaid Sari Farah , Abdulwahab Habeeb Shamsuldeen Haidar
Published: October 2022

The use of digital technologies in agriculture has become very important to ensure the protection of trees from disease and limit their development, which leads to increased production, so the paper proposes a modified analytical model to analyze the data and graphical parts of the leaves of fruit trees using priority fuzzy C-means (PFCM). Based on the proposed distance scale to obtain a clustering with a less error rate and fairly close to accuracy for the purpose of monitoring the development of diseases of fruit trees, by classifying the diseases and medications needed for each disease, a database was created containing large samples of data and images, where the results of Analysis of previous studies that analyzes of large amounts of data give accurate results. The proposed method was used in smart gardens with large areas and we got the desired results.
Enhanced accuracy for heart disease prediction using artificial neural network
Authors: Rone Sarra Raniya , Musa Dinar Ahmed , Abed Mohammed Mazin
Published: October 2022

Making an accurate and timely diagnosis of cardiac disease is critical for preventing and treating heart failure. The accuracy of results produced by traditional machine learning (ML) algorithms is satisfactory. On the other hand, deep learning algorithms result in higher prediction accuracy. In this study, we used an artificial neural network (ANN) model to construct a deep learning diagnosis system for heart disease prediction. The developed ANN prediction model achieved 93.44% accuracy, which is 7.5% higher than a traditional ML model support vector machine (SVM). Additionally, using a simpler neural network reduced the time taken for training and classification to less than a minute.
Editorial cloud collaborative service improves authorized industrial server database performance
Authors: Narayanan Eswara , Muthukumar Balasundaram
Published: October 2022

The E-commerce platform for the automotive industry has various obstacles regarding product distribution and sales marketing. Problems such as identifying the product's low defect rate and mapping failure features to the product's existing quality set, both of which occur in the actual world, are included in this category. With the help of an editorial cloud collaborative service, this study will focus primarily on identifying the core causes of product failure. The machine learning confidential (MLC) protocol is used to authenticate the editor's identity when accessing the industrial server's database. A cloud-based collaborative editing service can also be used to extract the root cause of a specific problem from customer complaints. There may be some product flaws that can be remedied with extra features gathered from the end-user to understand real-world practicality better and ensure product accuracy reaches 100% target.
Comparative analysis and feature importance of machine learning and deep learning for heart disease prediction
Authors: Gupta Priyanka , Seth Dambarudhar
Published: October 2022

Cardiovascular disease (CVD) or heart disease is one of the main reasons for early death, even at young age and that too often sudden. If it is detected more accurately, much before it seriously affects the individual, life can be saved through proper medication and changes in lifestyles. In this work different machine learning classifiers and a deep learning algorithm multi-layer perceptron (MLP) were applied on two different datasets, Framingham heart study dataset and UCI heart disease dataset for prediction of heart disease. These algorithms were optimized using hyperparameter tuning and compared for their performance measures and prediction accuracies. For different features, feature importance scores were calculated using machine learning algorithms. The features were ranked according to their scores. Out of various classification algorithms, random forest algorithm has shown the best results with prediction accuracy of 97.13% for Framingham dataset. MLP has shown good performance for both the datasets.
An intelligent oil accident predicting and classifying system using deep learning techniques
Authors: ElWahab Yasmen Samhan Abd , Nasr Mona Mohamed , Sheref Fahad Kamal Al
Published: October 2022

This study discusses the problem of oil and gas faults that lead to spills or explosions that lead to a lot of losses in human life, oil field extraction, and costs. Petrol is an important field in our lives because it controls all aspects of human life and their way of life, so our research focused on petrol and its problems in order to introduce a better way of life. The data used in this research was taken from the 3w database that was prepared by Petrobras, the Brazilian oil holding. The 9 classes classified in that work include the normal state that indicates the factors that will not lead to a problem. Deep learning classification techniques were used in this study. 99% accuracy was obtained in that model, and it refers to a successful prediction and classification of each class. Different results were observed when different hidden layers, optimizers, neurons, epochs, and activation functions were used. 99% was achieved when using Adam's optimizer and Tanh's activation function.
Introduction to Sumba’s traditional typical woven fabric motifs based on augmented reality technology
Authors: Maulana Fairuz Iqbal , Al-Abdillah Bening Insaniyah , Pandango Ruth Rade Awang , Djafar Andini Putri Maharani , Permana Frihandhika
Published: October 2022

Sumba woven cloth is one of the points of pride in East Sumba and West Sumba. The process of making Sumba woven cloth is not easy, making the patterned cloth is really valuable. These various motifs have their own stories behind them. Sumba woven fabric is one of the cultural heritages that must be preserved. One way to preserve this cultural heritage is implementing technological developments, such as augmented reality (AR) technology on smartphones. The combination of culture and technology can play a role in preserving Indonesia's cultural diversity. In applying this technology, we use the analysis, design, development, implementation, and evaluation (ADDIE) method starting from the stages of analysis, design, development, implementation, and evaluation. In an implementation, there will be a woven fabric catalog book that becomes a marker to bring up a 3D model of Sumba woven fabric that has been knitted in the form of clothes. The results revealed that the shortest distance between the marker and the camera at which 3D objects may be displayed is 20 cm, and the marker's reading angle is just approximately 0°-45°. The average duration observed is 0.056 seconds, and the average speed obtained is 8.92 meters per second.
Reclust: an efficient clustering algorithm for mixed data based on reclustering and cluster validation
Authors: Arockiam Amala Jayanthi Maria Soosai , Irudhayaraj Elizabeth Shanthi
Published: October 2022

Clustering is a significant approach in data mining, which seeks to find groups or clusters of data. Both numeric and categorical features are frequently used to define the data in real-world applications. Several different clustering algorithms are proposed for the numerical and categorical datasets. In clustering algorithms, the quality of clustering results is evaluated using cluster validation. This paper proposes an efficient clustering algorithm for mixed numerical and categorical data using re-clustering and cluster validation. Initially, the mixed dataset is clustered with four traditional clustering algorithms like expectation-maximization (EM), hierarchical cluster (HC), k-means (KM), and self-organizing map (SOM). These four algorithms are validated, and the best algorithm is selected for re-clustering. It is an iterative process for improving the quality of cluster results. The incorrectly clustered data is iteratively re-clustered and evaluated based on the cluster validation. The performance of the proposed clustering method is evaluated with a real-time dataset in terms of purity, normalized mutual information, rand index, precision, and recall. The experimental results have shown that the proposed reclust algorithm achieves better performance compared to other clustering algorithms.
The use of Arabic linguistic resources to develop learning applications
Authors: Blanchete Ilham , Mourchid Mohammed
Published: October 2022

This paper aims at explaining how Arabic linguistic resources are generated and exploited to enhance Arabic acquisition. We have adopted the root and pattern approach to generate our resources using the linguistic NooJ platform. This work has been carried out in two phases: generating the linguistic resources and developing the application that exploits the pre-built resources. First, we have generated three different resources: comprehensive verbs and masdar resources linked to each other. A nouns-and-adjectives resource, where nouns and adjectives are linked to their broken plural forms. NooJ calls these resources to apply linguistic analysis to a given corpus and returns detailed annotations, which provides accurate morphological, syntactical, and semantic properties of each analyzed word. We have also used the mixed nature of Arabic masdar to implement transformational rules, which generate nominal sentences from verbal ones and vice versa. Second, we have developed an application that provides valuable learning functionalities, like full/semi verb conjugation, the extraction of broken plural forms of a given singular form, the extraction of masdar forms of a given verb form, and return words that share the same root. The developed application can be used by teachers, students/learners, and computational linguists interested in Arabic acquisition.
Evaluation of the functionality of the virtual platform in the teaching process: analysis based on the usability factor
Authors: Chamorro-Atalaya Omar , Arce-Santillan Dora , Morales-Romero Guillermo , Caycho-Salas Beatriz , Guía-Altamirano Teresa , León-Velarde César , Rengifo-Tello Risley
Published: October 2022

The use of virtual platforms has been increasing exponentially during the context of distance education, however, there are still limitations to innovate in pedagogical proposals. This can hinder the assurance of student learning, either due to the little planning that occurred in its incorporation, the little knowledge of teachers and students in the educational use or the lack of use of the functionalities that they have incorporated for communication. The purpose of the research is to evaluate the operability of the virtual platform in the teaching-learning process through analysis based on the usability factor, the results will allow us to continue improving the tools linked to distance higher education. At the development of the investigation, a reliability value of 0.985 was obtained by means of Cronbach's Alpha. It was found as findings that 73.8% perceive an improvement in communication and in the exchange of information. Regarding the usability factor, 73.9% fully agree with the information available on the virtual platform and its accessibility. From what was determined, it is concluded that 65.98% of students consider that the functionality of the virtual platform with respect to the usability factor positively influences the teaching-learning process.
Unified power quality control based microgrid for power quality enhancement using various controlling techniques
Authors: Madhavan Mythreyee , Anandan Nalini
Published: October 2022

This research analyses the introduction of unified power quality control (UPQC) to mitigate the power quality problems, which is caused by using power electronics devices. In this article, the challenges in power quality, the suggested UPQC control approaches that are made by the distributed generation of dynamic voltage restorer (DVR) and static synchronous compensator (STATCOM) with the DC-link. The DVR is a series active power filter (APF) compensator and the STATCOM is a shunt APF compensator. In the meanwhile, DVR compensates voltage related issuesas well as STATCOM provides the needed reactive power supply by load and reducing the current issues associated with the unity power factor. The main objectives of the UPQC to mitigate voltage imbalance, harmonics, negative-sequence current and reactive power. To overcome the PQ issues the multifunction power conditioner is implemented. In addition, several proposed control approaches were employed and compared with series and shunt APFs. In MATLAB/Simulink software is used to illuminate the performance of the control strategies. Finally, the simulation will be evaluated and PQ problems in UPQC will be reduced by various techniques such as unit vector template generation technique (UVTGT), power balancing theory (BPT), proportional integral (PI) control technique and hysteresis control technique.
Decentralized secondary control for frequency regulation based on fuzzy logic control in islanded microgrid
Authors: Bennia Ilyas , Harrag Abdelghani , Daili Yacine , Bouzid Allal , M. Guerrero Josep
Published: October 2022

This paper presents a fuzzy-based decentralized secondary control for frequency restoration and active power-sharing in an islanded microgrid, this controller uses the local frequency error to generate an extra term for compensating the deviation and maintaining accurate active power-sharing. No communication infrastructures are needed, event detection, time dependent-protocols, and state estimation are not required. Its design and implementation are straightforward, also, it offers quick dynamic frequency recovery with accurate active power-sharing. To verify the validity of the proposed controller, several tests have been carried out: frequency restoration and active power-sharing during load disturbances, synchronization with plug and play (PnP) ability, as well the communication latency impact. Moreover, the effect of data drop-out and interferences were analyzed in real-time simulation using Truetime. The results have shown the high capability and the fast response of frequency restoration while maintaining the active power-sharing, no oscillations and ripples were presented in steady-state response, likewise, a smooth dynamic response during PnP test is observed. The communication latency and interferences have no impact on the proposed controller that showing a significant improvement in settling time 50% without frequency packet losses and 81%, 90% in the presence of 30% and 70% of frequency packet losses respectively.
A wireless bionic soft robotic fish using shape-memory alloy actuators
Authors: Ning Kewei , Sawada Hideyuki
Published: October 2022

In this study, we present the construction of a wireless bionic soft robotic fish that has a silicone tail and uses shape-memory alloys (SMAs) as actuators. Even though there have been a lot of recent advancements in the field of soft robotics, the use of SMAs as actuators for soft robots is still not something that is investigated very often. In the course of this research, we plan to work toward the creation of a realistic bionic fish robot that possesses a high level of mobility in the water, in addition to being light enough, strong enough, and flexible enough. The purpose of this study is to expound on the process of optimizing the morphologies of the fish body, as well as the optimization of the electromechanical behavior of the SMAs, in order to generate swimming motions in the fish. Our attention will be on the optimization of these two aspects. This report also outlines some preliminary but promising physical tests that were conducted to create a robotic fish with the similar shape.
Synthesis of control laws for magnetic levitation systems based on serial invariant manifolds
Authors: Chiem Nguyen Xuan , Xuan Thuy Pham
Published: October 2022

In this paper, a nonlinear controller is designed for a magnetic levitation system (MLS) based on serial invariant manifolds. Synthesized controller based on method of synergetic control theory (SCT) through invariant manifolds, asymptotically stable. In this method, the control law is synthesized to ensure the motion of the closed-loop control object from an arbitrary initial state into the vicinity of the desired invariant manifold. Thereby, the control system not only ensures the necessary control quality, but also ensures the asymptotic stability of the entire system. The quality and efficiency of the control law is proven through simulation results and comparison with the sliding mode controller (SMC).
Longitudinal Prediction of Ventricular Arrhythmic Risk in Patients With Arrhythmogenic Right Ventricular Cardiomyopathy
Authors: Carrick Richard T. , te Riele Anneline S.J.M. , Gasperetti Alessio , Bosman Laurens , Muller Steven A. , Pendleton Catherine , Tichnell Crystal , Murray Brittney , Yap Sing-Chien , van den Berg Maarten P. , Wilde Arthur , Zeppenfeld Katja , Hays Allison , Zimmerman Stefan L. , Tandri Harikrishna , Cadrin-Tourigny Julia , van Tintelen Peter , Calkins Hugh , James Cynthia A. , Wu Katherine C.
Published: October 2022

Background:

The arrhythmogenic right ventricular cardiomyopathy (ARVC) risk calculator stratifies risk for incident sustained ventricular arrhythmias (VA) at the time of ARVC diagnosis. However, included risk factors change over time, and how well the ARVC risk calculator performs at follow-up is unknown.

Methods:

This was a retrospective analysis of patients with definite ARVC and without prior sustained VA. Risk factors for VA including age, nonsustained ventricular tachycardia, premature ventricular complex burden, T-wave inversions on electrocardiogram, cardiac syncope, right ventricular function, therapeutic medication use, and exercise intensity were assessed at the time of 2010 Task Force Criteria based ARVC diagnosis and upon repeat evaluations. Changes in these risk factors were analyzed over 5-year follow-up. The 5-year risk of VA was predicted longitudinally using (1) the baseline ARVC risk calculator prediction, (2) the ARVC risk prediction calculated using updated risk factors, and (3) time-varying Cox regression. Discrimination and calibration were assessed in comparison to observed VA event rates.

Results:

Four hundred eight patients with ARVC experiencing 132 primary VA events were included. Matched comparison of risk factors at baseline versus at 5 years of follow-up revealed decreased burdens of premature ventricular complexes (−1200/day) and nonsustained ventricular tachycardia (−14%). Presence of significant right ventricular dysfunction and number of T-wave inversions on electrocardiogram were unchanged. Observed risk for VA decreased by 13% by 5 years follow-up. The baseline ARVC risk calculator’s ability to predict 5-year VA risk worsened during follow-up (C statistics, 0.83 at diagnosis versus 0.68 at 5 years). Both the updated ARVC risk calculator (C statistics of 0.77) and time-varying Cox regression model (C statistics, 0.77) had strong discrimination. The updated ARVC risk calculator overestimated 5-year VA risk by an average of +6%.

ConclusionS:

Risk factors for VA in ARVC are dynamic, and overall risk for incident sustained VA decreases during follow-up. Up-to-date risk factor assessment improves VA risk stratification.
Childhood Maltreatment and Arterial Stiffness Among Midlife Women
Authors: Carson Mary Y. , Barinas‐Mitchell Emma , Maki Pauline M. , Thurston Rebecca C.
Published: October 2022

Background

Childhood maltreatment has been associated with arterial stiffness. This relationship has not been examined specifically among women at midlife, a time of increased arterial stiffness in women. This study tested whether childhood maltreatment is associated with arterial stiffness among a cohort of midlife women.

Methods and Results

A total of 162 nonsmoking perimenopausal and postmenopausal women free of clinical cardiovascular disease (mean age, 54 years; 72% White race, 23% Black race, and 5% Asian/Pacific Islander or Mixed race) completed the Child Trauma Questionnaire at baseline. At a follow‐up visit 5 years later, blood pressure and carotid‐femoral pulse wave velocity (a measure of arterial stiffness) were assessed. Relationships between childhood maltreatment and carotid‐femoral pulse wave velocity were tested in linear regression models, adjusting for time between visits, age, race and ethnicity, education, body mass index, heart rate, hypertension medication, and diastolic blood pressure. Seventy‐one women (44% of the sample) met criteria for a history of childhood maltreatment. Women with a history of childhood maltreatment had higher carotid‐femoral pulse wave velocity (B [SE]=0.47 [0.21];
P
=0.03) than women without this history, controlling for time between visits, age, race and ethnicity, education, body mass index, heart rate, hypertension medication, and diastolic blood pressure.

Conclusions

Among these midlife women, childhood maltreatment was associated with arterial stiffness, highlighting the potential long‐term cardiovascular implications of childhood maltreatment.
First exact record and phylogenetic position of the gecko Ptyodactylus hasselquistii (Squamata: Phyllodactylidae) from Lebanon
Authors: TAMAR KARIN , MORAVEC JIŘÍ
Published: October 2022

The genus Ptyodactylus comprises 12 species distributed in North Africa and the Middle East. The most widely distributed species of the genus, Ptyodactylus hasselquistii, is known for its great morphological and genetic diversity, and ranges from Sudan and Egypt through Israel and Jordan towards Saudi Arabia and Yemen. In 1905 the first specimen of P. hasselquistii was collected from Lebanon, described later as a distinct subspecies, Ptyodactylus hasselquistii krameri. Here we provide a second record of P. hasselquistii from Lebanon collected in 2010, which represents the first exact record of the species in Lebanon in over 105 years. We use morphological comparisons and genetic analyses of two markers to account for the phylogenetic position of the new specimen within Ptyodactylus and to evaluate the taxonomic status of P. h. krameri. This integrative examination revealed a close association of the Lebanese specimen with nominotypic P. hasselquistii populations from Egypt and Sudan suggesting a relatively recent introduction from that general area. Similar morphological characteristics between the two Lebanese specimens and those from Egypt, and identical genetic sequences, suggest rejecting subspecific status of the Lebanese population and synonymizing the name Ptyodactylus hasselquistii krameri with the name Ptyodactylus hasselquistii.
Anthropogenic Influence on the Diurnal Temperature Range since 1901
Authors: Lu Chunhui , Sun Ying , Zhang Xuebin
Published: October 2022

The diurnal temperature range (DTR) as measured by the difference between daily maximum (Tmax) and minimum (Tmin) temperatures is of great importance to human health, ecology, and agriculture. The link of its long-term change to anthropogenic forcing is still unclear. This study shows evidence of human influence on long-term changes in DTR over the globe, five continents, and China during the past century (1901–2014). Using multiple observational datasets, we find a general decrease in the DTR over most of the global land since 1901, especially after the mid-1950s. Changes in DTR are due to different warming rates of Tmax and Tmin in response to external forcings. The climate models that participated in phase 6 of the Coupled Model Intercomparison Project Phase 6 (CMIP6) generally reproduce most of the changes in DTR, along with those in Tmax and Tmin. The models have underestimated the observed changes in DTR, however. A formal detection and attribution analysis shows that the anthropogenic forcing signal, including both greenhouse gas and aerosol emissions but dominated by the greenhouse gas emissions, is the main driver for these changes. The anthropogenic aerosol signal can be detected in Tmax and Tmin but not in DTR during the period of 1901–2014 over the globe and most continents. These indicate the observed decrease in DTR is not a simple response to anthropogenic aerosol emission. The natural signal is negligible in almost all the cases. Globally, anthropogenic influence is estimated to explain more than 90% of the observed changes in the three variables. In China, human influence is also clearly detected, although model simulated results on the regional scale have larger deviation.

Significance Statement

The diurnal temperature range (DTR) is of great importance in many areas. We compare multiple observational datasets with the simulations by climate models that participated in the latest phase (phase 6) of the Coupled Model Intercomparison Project (CMIP6), finding evidence of human influence on long-term changes in DTR over the past century (1901–2014) and robust evidence for the period since the early 1950s. The decrease in DTR as seen in the observational dataset is caused by different warming rates of daily maximum and daily minimum temperature in response to anthropogenic forcing, including both greenhouse gases and aerosols.
Modulation of Upper Ocean Vertical Temperature Structure and Heat Content by a Fast-Moving Tropical Cyclone
Authors: Zhang Han
Published: October 2022

The ocean temperature response to tropical cyclones (TCs) is important for TC development, local air–sea interactions, and the global air–sea heat budget and transport. The modulation of the upper ocean vertical temperature structure after a fast-moving TC was studied at the observation stations in the northern South China Sea, including TCs Kalmaegi (2014), Rammasun (2014), Sarika (2016), and Haima (2016). The upper ocean temperature and heat response to the TCs mainly depended on the combined effect of mixing and vertical advection. Mixing cooled the sea surface and warmed the subsurface, while upwelling (downwelling) reduced (increased) the subsurface warm anomaly and cooled (warmed) the deeper ocean. An ideal parameterization that depends on only the nondimensional mixing depth (HE), nondimensional transition layer thickness (HT), and nondimensional upwelling depth (HU) was able to roughly reproduce sea surface temperature (SST) and upper ocean heat change. After TCs, the subsurface heat anomalies moved into the deeper ocean. The air–sea surface heat flux contributed little to the upper ocean temperature anomaly during the TC forcing stage and did not recover the surface ocean back to pre-TC conditions more than one and a half months after the TC. This work shows how upper ocean temperature and heat content varies by a TC, indicating that TC-induced mixing modulates the warm surface water into the subsurface, and TC-induced advection further modulates the warm water into the deeper ocean and influences the ocean heat budget.

Significance Statement

Tropical cyclones can cause a strong ocean response that modulates the upper ocean temperature structure and contributes to the local heat budget and transport. This manuscript shows how mixing and vertical advection modulate upper ocean temperature after four fast-moving tropical cyclones, and then gives a parameterization of how sea surface temperature and upper ocean heat change depend on the two mechanisms. The temperature anomalies can propagate into deeper ocean after the tropical cyclones, and sea surface heat flux is not important for upper ocean temperature response during a tropical cyclone. These results show how the upper ocean temperature responses to a tropical cyclone, and influences the local heat budget.