bluuebunny/crossref_metadata_2025 huggingface dataset:
Cancer Diagnosis of Microscopic Biopsy Images Using a Social Spider Optimisation-Tuned Neural Network
Authors: Balaji Prasanalakshmi , Chidambaram Kumarappan
Published: December 2021

One of the most dangerous diseases that threaten people is cancer. If diagnosed in earlier stages, cancer, with its life-threatening consequences, has the possibility of eradication. In addition, accuracy in prediction plays a significant role. Hence, developing a reliable model that contributes much towards the medical community in the early diagnosis of biopsy images with perfect accuracy comes to the forefront. This article aims to develop better predictive models using multivariate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimisation (SSO) algorithm-tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the neural network classifier by the SSO algorithm. The performance of the proposed strategy is analysed with performance metrics such as accuracy, sensitivity, specificity, and MCC measures, and the attained results are 95.9181%, 94.2515%, 97.125%, and 97.68%, respectively, which shows the effectiveness of the proposed method for cancer disease diagnosis.
Titin-Related Dilated Cardiomyopathy: The Clinical Trajectory and the Role of Circulating Biomarkers in the Clinical Assessment
Authors: Chmielewski Przemysław , Truszkowska Grażyna , Kowalik Ilona , Rydzanicz Małgorzata , Michalak Ewa , Sobieszczańska-Małek Małgorzata , Franaszczyk Maria , Stawiński Piotr , Stępień-Wojno Małgorzata , Oręziak Artur , Lewandowski Michał , Leszek Przemysław , Bilińska Maria , Zieliński Tomasz , Płoski Rafał , Bilińska Zofia T.
Published: December 2021

Titin truncating variants (TTNtv) are known as the leading cause of inherited dilated cardiomyopathy (DCM). Nevertheless, it is unclear whether circulating cardiac biomarkers are helpful in detection and risk assessment. We sought to assess 1) early indicators of cardiotitinopathy including the serum biomarkers high-sensitivity cardiac troponin T (hs-cTnT) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) in clinically stable patients, and 2) predictors of outcome among TTNtv carriers. Our single-center cohort consisted of 108 TTNtv carriers (including 70 DCM patients) from 43 families. Clinical, laboratory and follow-up data were analyzed. The earliest abnormality was left ventricular dysfunction, present in 8, 26 and 47% of patients in the second, third and fourth decade of life, respectively. It was followed by symptoms of heart failure, linked to NT-proBNP elevation and severe left ventricular systolic dysfunction, and later by arrhythmias. Hs-cTnT serum levels were increased in the late stage of the disease only. During the median follow-up of 5.2 years, both malignant ventricular arrhythmia (MVA) and end-stage heart failure (esHF) occurred in 12% of TTNtv carriers. In multivariable analysis, NT-proBNP level ≥650 pg/mL was the best predictor of both composite endpoints (MVA and esHF) and of MVA alone. In conclusion, echocardiographic abnormalities are the first detectable anomalies in the course of cardiotitinopathies. The assessment of circulating cardiac biomarkers is not useful in the detection of the disease onset but may be helpful in risk assessment.
Discordant Liver Fibrosis Predictors in Virologically Suppressed People Living with HIV without Hepatitis Virus Infection
Authors: Rossetti Barbara , Borgo Valentina , Emiliozzi Arianna , Colaneri Marta , Zanelli Giacomo , d’Alessandro Miriana , Motta Davide , Maiocchi Laura , Montagnani Francesca , Moioli Maria Cristina , Baiguera Chiara , Sambo Margherita , Pieri Teresa Chiara , Valsecchi Pietro , Bruno Raffaele , Puoti Massimo , Fabbiani Massimiliano
Published: December 2021

Severe liver fibrosis (LF) is associated with poor long-term liver-related outcomes in people living with HIV (PLWH). The study aimed to explore the prevalence and predictors of LF and the concordance between different non-invasive methods for the estimation of LF in HIV-infected individuals without hepatitis virus infection. We enrolled PLWH with HIV-1-RNA <50 copies/mL for >12 months, excluding individuals with viral hepatitis. LF was assessed by transient elastography (TE) (significant >6.65 kPa), fibrosis-4 (FIB-4) (significant >2.67), and AST-to-platelet ratio index (APRI) (significant >1.5). We included 234 individuals (67% males, median age 49 years, median time from HIV diagnosis 11 years, 38% treated with integrase strand transfer inhibitors). In terms of the TE, 13% had ≥F2 stage; FIB-4 score was >1.5 in 7%; and APRI > 0.5 in 4%. Higher body mass index, diabetes mellitus, detectable baseline HIV-1 RNA and longer atazanavir exposure were associated with higher liver stiffness as per TE. Predictors of higher APRI score were CDC C stage and longer exposure to tenofovir alafenamide, while HBcAb positivity and longer exposure to tenofovir alafenamide were associated to higher FIB-4 scores. Qualitative agreement was poor between FIB-4/TE and between APRI/TE by non-parametric Spearman correlation and kappa statistic. In our study, in the group of PLWH without viral hepatitis, different non-invasive methods were discordant in predicting liver fibrosis.
Development of the Tactile Map Creator (TMC) Application
Authors: Kaplan Howard , Pyayt Anna
Published: December 2021

There are multiple studies demonstrating that 3D printed maps are important to people with blindness. When designed properly, they help users by improving safety and mobility and allow people with blindness to efficiently learn spatial information from the map and plan their travel prior to navigation. However, tactile maps are still not widely among people with blindness, as they are not readily available to them. Creating maps for each person needing a map of a certain location currently requires help from an individual with advanced technical training. They need to use specialized software for 3D models that personnel from disability services and family members of people with blindness usually have no experience with. The goal of this research is to design and implement software that can be used for the automatic generation of maps and be accessible to the general public. Our hypothesis is that by automating all the challenging steps (generation of the 3D model and drawing of optimally designed symbols), this will lead to increased usability and acceptance. Here, we demonstrated an early prototype and evaluated our hypothesis in a user study. To assess the success of this approach, participants answered questions about the process, usability, and social impacts of the software. Overall, participants liked the application because it was easy to use and allowed them to create custom maps with appropriate tactile-encoding features that provide meaningful information to the end user.
Survey on Unmanned Aerial Vehicle for Mars Exploration: Deployment Use Case
Authors: Sharma Manjula , Gupta Akshita , Gupta Sachin Kumar , Alsamhi Saeed Hamood , Shvetsov Alexey V.
Published: December 2021

In recent years, the area of Unmanned Aerial Vehicles (UAVs) has seen rapid growth. There has been a trend to build and produce UAVs that can carry out planetary exploration throughout the past decade. The technology of UAVs has tremendous potential to support various successful space mission solutions. In general, different techniques for observing space objects are available, such as telescopes, probes, and flying spacecraft, orbiters, landers, and rovers. However, a detailed analysis has been carried out due to the benefits of UAVs relative to other planetary exploration techniques. The deployment of UAVs to other solar bodies has been considered by numerous space agencies worldwide, including NASA. This article contributes to investigating the types of UAVs that have been considered for various planetary explorations. This study further investigates the behaviour of UAV prototypes on Mars’ surface in particular. It has been discovered that a prototype UAV flight on Mars has a higher chance of success. In this research, a prototype UAV has been successfully simulated to fly on Mars’ surface. This article discusses the opportunities, challenges, and future scope of deploying UAVs on Mars.
Kernel Estimation of Cumulative Residual Tsallis Entropy and Its Dynamic Version under ρ-Mixing Dependent Data
Authors: Irshad Muhammed Rasheed , Maya Radhakumari , Buono Francesco , Longobardi Maria
Published: December 2021

Tsallis introduced a non-logarithmic generalization of Shannon entropy, namely Tsallis entropy, which is non-extensive. Sati and Gupta proposed cumulative residual information based on this non-extensive entropy measure, namely cumulative residual Tsallis entropy (CRTE), and its dynamic version, namely dynamic cumulative residual Tsallis entropy (DCRTE). In the present paper, we propose non-parametric kernel type estimators for CRTE and DCRTE where the considered observations exhibit an ρ-mixing dependence condition. Asymptotic properties of the estimators were established under suitable regularity conditions. A numerical evaluation of the proposed estimator is exhibited and a Monte Carlo simulation study was carried out.
Forecasting Facing Economic Shifts, Climate Change and Evolving Pandemics
Authors: Castle Jennifer L. , Doornik Jurgen A. , Hendry David F.
Published: December 2021

By its emissions of greenhouse gases, economic activity is the source of climate change which affects pandemics that in turn can impact badly on economies. Across the three highly interacting disciplines in our title, time-series observations are measured at vastly different data frequencies: very low frequency at 1000-year intervals for paleoclimate, through annual, monthly to intra-daily for current climate; weekly and daily for pandemic data; annual, quarterly and monthly for economic data, and seconds or nano-seconds in finance. Nevertheless, there are important commonalities to economic, climate and pandemic time series. First, time series in all three disciplines are subject to non-stationarities from evolving stochastic trends and sudden distributional shifts, as well as data revisions and changes to data measurement systems. Next, all three have imperfect and incomplete knowledge of their data generating processes from changing human behaviour, so must search for reasonable empirical modeling approximations. Finally, all three need forecasts of likely future outcomes to plan and adapt as events unfold, albeit again over very different horizons. We consider how these features shape the formulation and selection of forecasting models to tackle their common data features yet distinct problems.
Coverage and Spectral Efficiency of Network Assisted Full Duplex in a Millimeter Wave System
Authors: He Yingli , Zhao Dongqin , Fan Zhenqi , Lu An , Xia Xinjiang , Wang Dongming
Published: December 2021

To cope with the growing trend of asymmetric data traffic, we introduce a novel network assisted full duplex (NAFD) for a millimeter wave system. NAFD can dynamically allocate the number of remote radio heads in the uplink mode or in the downlink mode, which can facilitate simultaneous uplink and downlink communications. In this manuscript, we use stochastic geometry to analyze the distribution of the signal-to-interference-plus-noise ratio and the data rate in a NAFD system. The numerical results verify the analysis and show that the NAFD outperforms the dynamic time division duplex system and the traditional flexible duplex system in terms of spectral efficiency.
Stability Analysis of Power Hardware-in-the-Loop Simulations for Grid Applications
Authors: Resch Simon , Friedrich Juliane , Wagner Timo , Mehlmann Gert , Luther Matthias
Published: December 2021

Power Hardware-in-the-Loop (PHiL) simulation is an emerging testing methodology of real hardware equipment within an emulated virtual environment. The closed loop interfacing between the Hardware under Test (HuT) and the Real Time Simulation (RTS) enables a realistic simulation but can also result in an unstable system. In addition to fundamentals in PHiL simulation and interfacing, this paper therefore provides a consistent and comprehensive study of PHiL stability. An analytic analysis is compared with a simulative approach and is supplemented by practical validations of the stability limits in PHiL simulation. Special focus is given on the differences between a switching and a linear amplifier as power interface (PI). Stability limits and the respective factors of influence (e.g., Feedback Current Filtering) are elaborated with a minimal example circuit with voltage-type Ideal Transformer Model (ITM) PHiL interface algorithm (IA). Finally, the findings are transferred to a real low-voltage grid PHiL application with residential load and photovoltaic system.
Challenges of Overcoming Defects in Wide Bandgap Semiconductor Power Electronics
Authors: Setera Brett , Christou Aristos
Published: December 2021

The role of crystal defects in wide bandgap semiconductors and dielectrics under extreme environments (high temperature, high electric and magnetic fields, intense radiation, and mechanical stresses) found in power electronics is reviewed. Understanding defects requires real-time in situ material characterization during material synthesis and when the material is subjected to extreme environmental stress. Wide bandgap semiconductor devices are reviewed from the point of view of the role of defects and their impact on performance. It is shown that the reduction of defects represents a fundamental breakthrough that will enable wide bandgap (WBG) semiconductors to reach full potential. The main emphasis of the present review is to understand defect dynamics in WBG semiconductor bulk and at interfaces during the material synthesis and when subjected to extreme environments. High-brightness X-rays from synchrotron sources and advanced electron microscopy techniques are used for atomic-level material probing to understand and optimize the genesis and movement of crystal defects during material synthesis and extreme environmental stress. Strongly linked multi-scale modeling provides a deeper understanding of defect formation and defect dynamics in extreme environments.
Real-Time LiDAR Point Cloud Semantic Segmentation for Autonomous Driving
Authors: Xie Xing , Bai Lin , Huang Xinming
Published: December 2021

LiDAR has been widely used in autonomous driving systems to provide high-precision 3D geometric information about the vehicle’s surroundings for perception, localization, and path planning. LiDAR-based point cloud semantic segmentation is an important task with a critical real-time requirement. However, most of the existing convolutional neural network (CNN) models for 3D point cloud semantic segmentation are very complex and can hardly be processed at real-time on an embedded platform. In this study, a lightweight CNN structure was proposed for projection-based LiDAR point cloud semantic segmentation with only 1.9 M parameters that gave an 87% reduction comparing to the state-of-the-art networks. When evaluated on a GPU, the processing time was 38.5 ms per frame, and it achieved a 47.9% mIoU score on Semantic-KITTI dataset. In addition, the proposed CNN is targeted on an FPGA using an NVDLA architecture, which results in a 2.74x speedup over the GPU implementation with a 46 times improvement in terms of power efficiency.
Development and Fabrication of an Innovative Smart Tool to Monitor the Impact Carving Process on Brittle Stones and Marble
Authors: Vidakis Nectarios , Petousis Markos , Maniadi Athena , Arapis Emmanuel
Published: December 2021

The art of sculpting is related to the processing of brittle materials, such as granite, marble, and stone, and is implemented using percussive hand tools or rotational roughing tools. The outcome of percussion carving is still directly related to the technique, experience, and capacity of the sculptor. Any attempt to automate the art of sculpturing is exhausted in the subtraction method of brittle materials using a rotating tool. In the process of percussion carving, there is no equivalent expertise. In this work, we present the design, manufacturing (3D printing and CNC machining), and use of a smart, percussion carving tool, either manually by the hand of a sculptor, adjusted in a percussive pneumatic hammer, or guided by a digitally driven machine. The scope is to measure and record the technological variables and sizes that describe and document the carving process through the sensors and electronic devices that the smart tool incorporates, the development and programming of which was implemented for the purposes of this work. The smart carving tool was meticulously tested in various carving stones and stressing scenarios to test the functionality and efficacy of the tool. All the tests were successfully implemented according to the specifications set.
Distributed Economic Control for AC/DC Hybrid Microgrid
Authors: Wang Hongjun , Li Wanfeng , Yue Youjun , Zhao Hui
Published: December 2021

In this paper, a new double-layer droop control mode for island AC/DC microgrids is proposed to realize autonomous and cost-effective operation. The optimal power reference iterative algorithm is used to realize the internal active power distribution in the subnet. On this basis, secondary frequency and voltage adjustments are introduced to realize the economic operation, autonomy and stability of the subnet. At the microgrid level, the local control strategy of cost micro increment deviation is designed to optimize the exchange power between subnets. The cooperation of the two can realize the global economic operation of the microgrid, as well as voltage following and frequency regulation in the subnet. Based on the hybrid AC/DC microgrid simulation model, the effectiveness of the proposed method is verified.
Hardware-Based Activation Function-Core for Neural Network Implementations
Authors: González-Díaz_Conti Griselda , Vázquez-Castillo Javier , Longoria-Gandara Omar , Castillo-Atoche Alejandro , Carrasco-Alvarez Roberto , Espinoza-Ruiz Adolfo , Ruiz-Ibarra Erica
Published: December 2021

Today, embedded systems (ES) tend towards miniaturization and the carrying out of complex tasks in applications such as the Internet of Things, medical systems, telecommunications, among others. Currently, ES structures based on artificial intelligence using hardware neural networks (HNNs) are becoming more common. In the design of HNN, the activation function (AF) requires special attention due to its impact on the HNN performance. Therefore, implementing activation functions (AFs) with good performance, low power consumption, and reduced hardware resources is critical for HNNs. In light of this, this paper presents a hardware-based activation function-core (AFC) to implement an HNN. In addition, this work shows a design framework for the AFC that applies a piecewise polynomial approximation (PPA) technique. The designed AFC has a reconfigurable architecture with a wordlength-efficient decoder, i.e., reduced hardware resources are used to satisfy the desired accuracy. Experimental results show a better performance of the proposed AFC in terms of hardware resources and power consumption when it is compared with state of the art implementations. Finally, two case studies were implemented to corroborate the AFC performance in widely used ANN applications.
Deep Learning Neural Network Algorithm for Computation of SPICE Transient Simulation of Nonlinear Time Dependent Circuits
Authors: Černý David , Dobeš Josef
Published: December 2021

In this paper, a special method based on the neural network is presented, which is conveniently used to precompute the steps of numerical integration. This method approximates the behaviour of the numerical integrator with respect to the local truncation error. In other words, it allows the precomputation of the individual steps in such a way that they do not need to be estimated by an algorithm but can be directly estimated by a neural network. Experimental tests were performed on a series of electrical circuits with different component parameters. The method was tested for two integration methods implemented in the simulation program SPICE (Trapez and Gear). For each type of circuit, a custom network was trained. Experimental simulations showed that for well-defined problems with a sufficiently trained network, the method allows in most cases reducing the total number of iteration steps performed by the algorithm during the simulation computation. Applications of this method, drawbacks, and possible further optimizations are also discussed.
A Frequency and Voltage Coordinated Control Strategy of Island Microgrid including Electric Vehicles
Authors: Fan Peixiao , Ke Song , Kamel Salah , Yang Jun , Li Yonghui , Xiao Jinxing , Xu Bingyan , Rashed Ghamgeen Izat
Published: December 2021

Frequency and voltage deviation are important standards for measuring energy indicators. It is important for microgrids to maintain the stability of voltage and frequency (VF). Aiming at the VF regulation of microgrid caused by wind disturbance and load fluctuation, a comprehensive VF control strategy for an islanded microgrid with electric vehicles (EVs) based on Deep Deterministic Policy Gradient (DDPG) is proposed in this paper. First of all, the SOC constraints of EVs are added to construct a cluster-EV charging model, by considering the randomness of users’ travel demand and charging behavior. In addition, a four-quadrant two-way charger capacity model is introduced to build a microgrid VF control model including load, micro gas turbine (MT), EVs, and their random power increment constraints. Secondly, according to the two control goals of microgrid frequency and voltage, the structure of DDPG controller is designed. Then, the definition of space, the design of global and local reward functions, and the selection of optimal hyperparameters are completed. Finally, different scenarios are set up in an islanded microgrid with EVs, and the simulation results are compared with traditional PI control and R(λ) control. The simulation results show that the proposed DDPG controller can quickly and efficiently suppress the VF fluctuations caused by wind disturbance and load fluctuations at the same time.
Phase Shift Optimization Algorithm for Achievable Rate Maximization in Reconfigurable Intelligent Surface-Assisted THz Communications
Authors: Praia João , Pavia João Pedro , Souto Nuno , Ribeiro Marco
Published: December 2021

Terahertz (THz) band communications are considered a crucial technology to support future applications, such as ultra-high bit rate wireless local area networks, in the next generation of wireless communication systems. In this work, we consider an ultra-massive multiple-input multiple-output (UM-MIMO) THz communication system operating in a typical indoor scenario where the direct link between the transmitter and receiver is obstructed due to surrounding obstacles. To help establish communication, we assume the aid of a nearby reconfigurable intelligent surface (RIS) whose phase shifts can be adjusted. To configure the individual phase shifts of the RIS elements, we formulate the problem as a constrained achievable rate maximization. Due to the typical large dimensions of this optimization problem, we apply the accelerated proximal gradient (APG) method, which results in a low complexity algorithm that copes with the non-convex phase shift constraint through simple element-wise normalization. Our numerical results demonstrate the effectiveness of the proposed algorithm even when considering realistic discrete phase shifts’ quantization and imperfect channel knowledge. Furthermore, comparison against existing alternatives reveals improvements between 30% and 120% in terms of range, for a reference rate of 100 Gbps when using the proposed approach with only 81 RIS elements.
Optimization of Different Deployments and Resolution of Antenna Arrays in SWIPT
Authors: Chiu Chien-Ching , Wu Hung-Yu , Chien Wei , Cheng Yu-Ting , Lim Eng Hock
Published: December 2021

In this paper, three different deployment antenna arrays with circular, triangular and rectangular shapes were used to optimize the simultaneous wireless information and power transfer (SWIPT) system for the Internet of Things (IoT). Ray-tracing was employed to channel the model for a real environment. Self-adaptive dynamic differential evolution (SADDE) was used to optimize the harvesting power ratio with bit error rate constrained by the two different resolutions of feed length (high resolution and low resolution). Numerical results show that those three antenna arrays can achieve the goal for information quality in both resolutions. The harvesting power ratio for the circular array is the best and the harvesting power ratio for the rectangular array is the worst. The harvesting power ratio for the low-resolution case is 25% lower than the high-resolution case. However, the circular antenna array is the best deployment in those three different arrays for both high and low resolutions.
Deep Feature Vectors Concatenation for Eye Disease Detection Using Fundus Image
Authors: Paradisa Radifa Hilya , Bustamam Alhadi , Mangunwardoyo Wibowo , Victor Andi Arus , Yudantha Anggun Rama , Anki Prasnurzaki
Published: December 2021

Fundus image is an image that captures the back of the eye (retina), which plays an important role in the detection of a disease, including diabetic retinopathy (DR). It is the most common complication in diabetics that remains an important cause of visual impairment, especially in the young and economically active age group. In patients with DR, early diagnosis can effectively help prevent the risk of vision loss. DR screening was performed by an ophthalmologist by analysing the lesions on the fundus image. However, the increasing prevalence of DR is not proportional to the availability of ophthalmologists who can read fundus images. It can lead to delayed prevention and management of DR. Therefore, there is a need for an automated diagnostic system as it can help ophthalmologists increase the efficiency of the diagnostic process. This paper provides a deep learning approach with the concatenate model for fundus image classification with three classes: no DR, non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR). The model architecture used is DenseNet121 and Inception-ResNetV2. The feature extraction results from the two models are combined and classified using the multilayer perceptron (MLP) method. The method that we propose gives an improvement compared to a single model with the results of accuracy, and average precision and recall of 91% and 90% for the F1-score, respectively. This experiment demonstrates that our proposed deep-learning approach is effective for the automatic DR classification using fundus photo data.
Image Perturbation-Based Deep Learning for Face Recognition Utilizing Discrete Cosine Transform
Authors: Park Jaehun , Kim Kwangsu
Published: December 2021

Face recognition, including emotion classification and face attribute classification, has seen tremendous progress during the last decade owing to the use of deep learning. Large-scale data collected from numerous users have been the driving force in this growth. However, face images containing the identities of the owner can potentially cause severe privacy leakage if linked to other sensitive biometric information. The novel discrete cosine transform (DCT) coefficient cutting method (DCC) proposed in this study combines DCT and pixelization to protect the privacy of the image. However, privacy is subjective, and it is not guaranteed that the transformed image will preserve privacy. To overcome this, a user study was conducted on whether DCC really preserves privacy. To this end, convolutional neural networks were trained for face recognition and face attribute classification tasks. Our survey and experiments demonstrate that a face recognition deep learning model can be trained with images that most people think preserve privacy at a manageable cost in classification accuracy.
A Novel Hybrid Approach Based on Deep CNN to Detect Glaucoma Using Fundus Imaging
Authors: Mahum Rabbia , Rehman Saeed Ur , Okon Ofonime Dominic , Alabrah Amerah , Meraj Talha , Rauf Hafiz Tayyab
Published: December 2021

Glaucoma is one of the eye diseases stimulated by the fluid pressure that increases in the eyes, damaging the optic nerves and causing partial or complete vision loss. As Glaucoma appears in later stages and it is a slow disease, detailed screening and detection of the retinal images is required to avoid vision forfeiture. This study aims to detect glaucoma at early stages with the help of deep learning-based feature extraction. Retinal fundus images are utilized for the training and testing of our proposed model. In the first step, images are pre-processed, before the region of interest (ROI) is extracted employing segmentation. Then, features of the optic disc (OD) are extracted from the images containing optic cup (OC) utilizing the hybrid features descriptors, i.e., convolutional neural network (CNN), local binary patterns (LBP), histogram of oriented gradients (HOG), and speeded up robust features (SURF). Moreover, low-level features are extracted using HOG, whereas texture features are extracted using the LBP and SURF descriptors. Furthermore, high-level features are computed using CNN. Additionally, we have employed a feature selection and ranking technique, i.e., the MR-MR method, to select the most representative features. In the end, multi-class classifiers, i.e., support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN), are employed for the classification of fundus images as healthy or diseased. To assess the performance of the proposed system, various experiments have been performed using combinations of the aforementioned algorithms that show the proposed model based on the RF algorithm with HOG, CNN, LBP, and SURF feature descriptors, providing ≤99% accuracy on benchmark datasets and 98.8% on k-fold cross-validation for the early detection of glaucoma.
Mapping Arbitrary Logic Functions onto Carry Chains in FPGAs
Authors: Senhadji-Navarro Raouf , Garcia-Vargas Ignacio
Published: December 2021

Current Field Programmable Gate Arrays (FPGAs) provide fast routing links and special logic to perform carry operations; however, these resources can also be used to implement non-arithmetic circuits. In this paper, a new approach for mapping logic functions onto carry chains is presented. Unlike other approaches, the proposed technique can be applied to any logic function. The presented technique includes: (1) an architecture that is composed of blocks that implement AND and OR functions (called CANDs and CORs, respectively) by means of Look-Up-Tables (LUTs) and carry-chain resources; and (2) a mapping algorithm to reduce both the delay of the critical path and the number of used FPGA resources. The algorithm uses a heuristic to interconnect CORs and CANDs in order to reduce the delay. The problem of mapping the maxterms (or minterms) of a function to LUTs has been modelled as a Set Bin Packing (SBP) problem. Since SBP is NP-Hard, a greedy algorithm has been proposed, which is based on the First Fit Decreasing (FFD) heuristic. The results obtained have been compared with the conventional technique using both speed and area optimization. For this purpose, a large synthetic set of test cases has been generated. The proposed technique improves both the speed and area results for the vast majority of functions whose conventional implementation requires more than four logic levels. It is important to highlight that the improvement of one parameter (speed or area) is not achieved at the expense of the other.
The Role of Genetic Algorithm Selection Operators in Extending WSN Stability Period: A Comparative Study
Authors: Jannoud Ismael , Jaradat Yousef , Masoud Mohammad Z. , Manasrah Ahmad , Alia Mohammad
Published: December 2021

A genetic algorithm (GA) contains a number of genetic operators that can be tweaked to improve the performance of specific implementations. Parent selection, crossover, and mutation are examples of these operators. One of the most important operations in GA is selection. The performance of GA in addressing the single-objective wireless sensor network stability period extension problem using various parent selection methods is evaluated and compared. In this paper, six GA selection operators are used: roulette wheel, linear rank, exponential rank, stochastic universal sampling, tournament, and truncation. According to the simulation results, the truncation selection operator is the most efficient operator in terms of extending the network stability period and improving reliability. The truncation operator outperforms other selection operators, most notably the well-known roulette wheel operator, by increasing the stability period by 25.8% and data throughput by 26.86%. Furthermore, the truncation selection operator outperforms other selection operators in terms of the network residual energy after each protocol round.
Optimization of Industry 4.0 Implementation Selection Process towards Enhancement of a Manual Assembly Line
Authors: Aljinović Amanda , Gjeldum Nikola , Bilić Boženko , Mladineo Marko
Published: December 2021

Last year’s developments are characterized by a dramatic drop in customer demand leading to stiff competition and more challenges that each enterprise needs to cope with in a globalized market. Production in low-mix/high-volume batches is replaced with low-volume/high-variety production, which demands excessive information flow throughout production facilities. To cope with the excessive information flow, this production paradigm requires the integration of new advanced technology within production that enables the transformation of production towards smart production, i.e., towards Industry 4.0. The procedure that helps the decision-makers to select the most appropriate I4.0 technology to integrate within the current assembly line considering the expected outcomes of KPIs are not significantly been the subject of the research in the literature. Therefore, this research proposes a conceptual procedure that focus on the current state of the individual assembly line and proposes the technology to implement. The proposed solution is aligned with the expected strategic goals of the company since procedure takes into consideration value from the end-user perspective, current production plans, scheduling, throughput, and other relevant manufacturing metrics. The validation of the method was conducted on a real assembly line. The results of the validation study emphasize the importance of the individual approach for each assembly line since the preferences of the user as well as his diversified needs and possibilities affect the optimal technology selection.
Insight into Relationship between Thermal Dissolution of Low-Rank Coals and Their Subsequent Oxidative Depolymerization
Authors: Wang Yugao , Liu Xiaochen , Wang Zhilei , Dong Chuan , Shen Jun , Fan Xing
Published: December 2021

Oxidative depolymerization of low-rank coals is promising for obtaining benzene carboxylic acids (BCAs). However, it is hindered by the low yield of BCAs along with a large number of alphatic acids. Thermal dissolution could modify the physico-chemical structural features of low-rank coals, which is expected to improve the oxidation of LRCs. In this paper, lignite and subbituminous coal were firstly subjected to thermal dissolution with cyclohexane at 250 °C for 2 h. Then, the raw coal and the corresponding thermal insoluble portion (TIP) were oxidized by NaOCl under the same conditions. The residual yields of TIPs oxidation were both lower than those of raw coals oxidation, indicating that TIPs were more easily oxidized than the raw coals. The yield of BCAs obtained by TIPs oxidation was above 19% higher than that from the oxidation of raw coals. Meanwhile, the selectivity of BCAs was improved in the resulting oxidation products from TIPs compared with that from the raw coals. The relationship between BCAs generation and thermal dissolution of low rank coals was investigated by ultimate analysis, Fourier transform infrared spectroscopy, and nitrogen adsorption-desorption analysis. The results suggested that thermal dissolution could enrich aromatic portion in the remaining TIPs, resulting in an increasing of the yield and selectivity of BCAs. Simultaneously, thermal dissolution raised the specific surface area and expanded the looser space structure of TIPS, which were beneficial for the sufficient collision between aromatic structures and oxidant, facilitating the oxidative depolymerization of TIPs. This investigation would provide a novel route for promoting BCAs production by mild oxidative depolymerization of low-rank coals.
The Effect of Dynamic Cold Storage Packed Bed on Liquid Air Energy Storage in an Experiment Scale
Authors: Bian Yong , Wang Chen , Wang Yajun , Qin Run , Song Shunyi , Qu Wenhao , Xue Lu , Zhang Xiaosong
Published: December 2021

Liquid air energy storage (LAES) is one of the most promising large-scale energy storage technologies for the decarburization of networks. When electricity is needed, the liquid air is utilized to generate electricity through expansion, while the cold energy from liquid air evaporation is stored and recovered in the air liquefaction process. The packed bed filled with rocks/pebbles for cold storage is more suitable for real-world application in the near future compared to the fluids for cold storage. A standalone LAES system with packed bed energy storage is proposed in our previous work. However, the utilization of pressurized air for heat transfer fluid in the cold storage packed bed (CSPB) is confusing, and the effect of the CSPB on the system level should be further discussed. To address these issues, the dynamic performance of the CSPB is analyzed with the physical properties of the selected cold storage materials characterized. The system simulation is conducted in an experiment scale with and without considering the exergy loss of the CSPB for comparison. The simulation results show that the proposed LAES system has an ideal round trip efficiency (RTE) of 39.38–52.91%. With the consideration of exergy destruction of the CSPB, the RTE decreases by 19.91%. Furthermore, increasing the cold storage pressure reasonably is beneficial to the exergy efficiency of the CSPB, whether it is non-supercritical (0.1 MPa–3 MPa) or supercritical (4 MPa–9 MPa) air. These findings will give guidance and prediction to the experiments of the LAES and finally promote the development of the industrial application.
Hydrothermal Unit-Commitment Problem of a Large-Scale System with Representation of Forbidden Zones
Authors: Colonetti Bruno , Finardi Erlon , Borges Picarelli Lucas
Published: December 2021

As we move towards electrical networks with a growing presence of renewable generation, the representation of the electrical components becomes more important. In hydro-dominated power systems, modelling the forbidden zones of hydro plants becomes increasingly challenging as the number of plants increases. Such zones are ranges of generation that either should be avoided or are altogether unreachable. However, because representing the forbidden zones introduces a substantial computational burden, hydrothermal unit-commitment problems (HTUC) for large systems are usually formulated ignoring the forbidden zones. Nonetheless, this simplification may demand adjustments to the solution of the HTUC, because the generation of the hydro stations may fall in forbidden zones. In practice, the adjustments are usually performed based on the experience of system operators and, then, can be far from an optimal correction. In this paper, we study the impact of explicitly representing the hydro-generation forbidden zones in a large-scale system with more than 7000 buses, 10,000 lines, and 700 hydro units. Our findings show that the simplified model that is current used can deviate significantly from the model with forbidden zones, both in terms of the generation of hydro plants, as well as the generation of thermal plants and the system marginal costs.
Erratum: Alhasnawi et al. A New Robust Control Strategy for Parallel Operated Inverters in Green Energy Applications. Energies 2020, 13, 3480
Authors: Alhasnawi Bilal Naji , Jasim Basil H. , Issa Walid , Anvari-Moghaddam Amjad , Blaabjerg Frede
Published: December 2021

The authors wish to make the following corrections to this paper [...]
Design of a Step-Up DC–DC Converter for Standalone Photovoltaic Systems with Battery Energy Storages
Authors: Oh Yun-Gyeong , Choi Woo-Young , Kwon Jung-Min
Published: December 2021

This paper proposes a step-up DC–DC converter for a power electronic circuit for standalone photovoltaic systems with battery energy storages. The proposed DC–DC converter effectively converts low DC battery voltage into high DC-link voltage. It operates with soft-switching characteristics, which can reduce switching power losses. The proposed converter operates without output voltage feedback, which simplifies its control design. The operation principle of the proposed converter was described, along with the overall system configuration. The experimental results were discussed for the 500-W prototype system using a 12-V lead-acid battery.
Renewable Energy Sources as the Future of the Energy Sector and Climate in Poland—Truth or Myth in the Opinion of the Society
Authors: Woźniak Marian , Badora Aleksandra , Kud Krzysztof , Woźniak Leszek
Published: December 2021

The electricity sector in the 21st century should be associated with renewable energy sources (RES), which the majority of society currently equates with solar photovoltaics, wind power, and hydroelectricity, with this energy being used mainly in households. Households consume only 20% of energy, with the remainder used in various sectors of the national economy. In these sectors, the possibilities of using renewable energy sources should be sought. Many experts express the opinion that myths about renewable energy sources exist only “in our minds and opinions, that we formed years ago”, mainly under pressure from the decreasing number of supporters of conventional energy sources. Currently, we observe much greater possibilities of using renewable energy sources globally, and all forecasts suggest that—by 2050—the economy, transport, and industry may become almost emission-free and rely on RES. Of course, we cannot present renewable energy sources only in terms of superlatives, because they also involve many unknowns and myths which we will present in the article. Considering the complexity of factors influencing the involvement of young people in shaping the socio-economic reality, a group of people aged between 18 and 40 was selected for the study. The aim of this study was to identify the attitudes of young inhabitants of south-eastern Poland regarding the issues of climate change and renewable energy, and to identify the level of acceptance for changes in Poland’s energy mix. The conducted research shows that the studied group of people appreciated the importance of climate change and considered the anthropogenic impact on this phenomenon to be of key importance. The perception of renewable energy was positive, and respondents saw the solution to the problems of the energy deficit in increasing the use of renewable energy sources. Respondents also showed significant support for nuclear energy and expected government support programs for activities related to energy conservation. The survey shall be repeated on a random sample at the time when the energy price changes caused by the transformation of the energy mix occur—i.e., around 2025.
Achieving Net Zero Emissions in Italy by 2050: Challenges and Opportunities
Authors: Gaeta Maria , Nsangwe Businge Corine , Gelmini Alberto
Published: December 2021

This paper contributes to the climate policy discussion by focusing on the challenges and opportunities of reaching net zero emissions by 2050 in Italy. To support Italian energy planning, we developed energy roadmaps towards national climate neutrality, consistent with the Paris Agreement objectives and the IPCC goal of limiting the increase in global surface temperature to 1.5 °C. Starting from the Italian framework, these scenarios identify the correlations among the main pillars for the change of the energy paradigm towards net emissions by 2050. The energy scenarios were developed using TIMES-RSE, a partial equilibrium and technology-rich optimization model of the entire Italian energy system. Subsequently, an in-depth analysis was developed with the sMTISIM, a long-term simulator of power system and electricity markets. The results show that, to achieve climate neutrality by 2050, the Italian energy system will have to experience profound transformations on multiple and strongly related dimensions. A predominantly renewable-based energy mix (at least 80–90% by 2050) is essential to decarbonize most of the final energy consumption. However, the strong increase of non-programmable renewable sources requires particular attention to new flexibility resources needed for the power system, such as Power-to-X. The green fuels produced from renewables via Power-to-X will be a vital energy source for those sectors where electrification faces technical and economic barriers. The paper’s findings also confirm that the European “energy efficiency first” principle represents the very first step on the road to climate neutrality.
Operation Characteristics of Adjustable Field IPMSM Utilizing Magnetic Saturation
Authors: Iwama Kiyohiro , Noguchi Toshihiko
Published: December 2021

This paper describes an interior permanent magnet synchronous motor (IPMSM) based on a new adjustable field method. The proposed PM motor achieved magnetic field control utilizing magnetic saturation. In this paper, a back electromotive force (e.m.f.) measurement test and a load test using the prototype motor were conducted to clarify if the proposed motor had a wide operation range. In the back e.m.f. measurement test, it was confirmed that the proposed motor had a wide magnetic field controllable range of 51.7%. In addition, it was revealed, through the load test, that the proposed motor had a wide operating range, including both low-speed high-torque and high-speed low-torque driving conditions. Moreover, based on electromagnetic field analysis, the magnetic field control performance of the proposed adjustable field method was compared with the conventional field weakening control and other adjustable field methods. As a comparison result, it was verified that the proposed motor had less copper loss for the magnetic field control and fewer losses in the high-speed operating range.