bluuebunny/crossref_metadata_2025 huggingface dataset:
Abiotic and Biotic Factors from the Past as Predictors of Alien Bird Richness and Temporal Beta-Diversity
Authors: Andrikou-Charitidou Aristi , Boutsis Georgios , Karadimou Elpida , Tsianou Mariana A. , Lazarina Maria , Kallimanis Athanasios S.
Published: March 2023

The challenge of predicting the distribution of alien species has long been a focus of invasion ecology. Herein, we assessed biotic and abiotic factors from the 1980s as potential predictors of alien bird species patterns 20 years later in the state of New York. To assess the ability of each factor to predict future alien species patterns, we analysed the influence of biotic (native taxonomic, functional and phylogenetic diversity, and human population density) and abiotic (climate and land use) factors from the 1980s on the observed alien species richness patterns in the 2000s and the temporal change in the composition of the alien communities between the 1980s and the 2000s using both single-predictor and multivariate models. Alien species richness from the 1980s was a reliable predictor of the alien species richness and temporal beta-diversity patterns in the 2000s. Among abiotic factors, maximum temperature and agricultural land-uses constituted sufficient predictors of future alien species richness and better predictors than the native biotic factors. The performance of single-predictor models was generally weaker in predicting temporal alien beta-diversity; however, past alien species richness and maximum temperature again outperformed the other factors. Predictions and management decisions should focus on warm and agricultural areas, as well as areas with an already high number of established alien species.
Applications of Artificial Intelligence in Thrombocytopenia
Authors: Elshoeibi Amgad M. , Ferih Khaled , Elsabagh Ahmed Adel , Elsayed Basel , Elhadary Mohamed , Marashi Mahmoud , Wali Yasser , Al-Rasheed Mona , Al-Khabori Murtadha , Osman Hani , Yassin Mohamed
Published: March 2023

Thrombocytopenia is a medical condition where blood platelet count drops very low. This drop in platelet count can be attributed to many causes including medication, sepsis, viral infections, and autoimmunity. Clinically, the presence of thrombocytopenia might be very dangerous and is associated with poor outcomes of patients due to excessive bleeding if not addressed quickly enough. Hence, early detection and evaluation of thrombocytopenia is essential for rapid and appropriate intervention for these patients. Since artificial intelligence is able to combine and evaluate many linear and nonlinear variables simultaneously, it has shown great potential in its application in the early diagnosis, assessing the prognosis and predicting the distribution of patients with thrombocytopenia. In this review, we conducted a search across four databases and identified a total of 13 original articles that looked at the use of many machine learning algorithms in the diagnosis, prognosis, and distribution of various types of thrombocytopenia. We summarized the methods and findings of each article in this review. The included studies showed that artificial intelligence can potentially enhance the clinical approaches used in the diagnosis, prognosis, and treatment of thrombocytopenia.
Diagnostic Performance of Prototype Handheld Ultrasound According to the Fifth Edition of BI-RADS for Breast Ultrasound Compared with Automated Breast Ultrasound among Females with Positive Lumps
Authors: Ibraheem Shahad A. , Mahmud Rozi , Mohamad Saini Suraini , Abu Hassan Hasyma , Keiteb Aysar Sabah
Published: March 2023

(1) Objective: To evaluate the diagnostic performance of prototype handheld ultrasound compared to automated breast ultrasound, according to the fifth edition of BI-RADS categorization, among females with positive lumps. (2) Methods: A total of 1004 lesions in 162 participants who underwent both prototype handheld ultrasound and automated breast ultrasound were included. Two radiologists and a sonographer independently evaluated the sonographic features of each lesion according to the fifth BI-RADS edition. The kappa coefficient (κ) was calculated for each BI-RADS descriptor and final assessment category. The cross-tabulation was performed to see whether there were differences between the ABUS and prototype HHUS results. Specificity and sensitivity were evaluated and compared using the McNamar test. (3) Results: ABUS and prototype HHUS observers found the same number of breast lesions in the 324 breasts of the 162 respondents. There was no significant difference in the mean lesion size, with a maximum mean length dimension of 0.48 ± 0.33 cm. The assessment of the lesion’s shape, orientation, margin, echo pattern, posterior acoustic features, and calcification was obtained with good to excellent agreements between ABUS and prototype HHUS observers (κ = 0.70–1.0). There was absolutely no significant difference between ABUS and prototype HHUS in assessment of lesion except for lesion orientation p = 0.00. Diagnostic accuracy (99.8% and 97.7–98.9%), sensitivity (99.5% and 98.0–99.0%), specificity (99.8% and 99.6–99.8%), positive predictive value (98.1% and 90.3–96.2%), negative predictive value (90.0% and 84.4–88.7%), and areas under the curve (0.98 and 0.83–0.92; p < 0.05) were not significantly different between ABUS and prototype HHUS observers. (4) Conclusion: According to the fifth BI-RADS edition, automated breast ultrasound is not statistically significantly different from prototype handheld ultrasound with regard to interobserver variability and diagnostic performance.
Predictors of CIN2+ in Patients with PAP III-P (ASC-H): A Cross-Sectional Study
Authors: Kaiser Luana Antonia , Kupec Tomas , Najjari Laila , Stickeler Elmar , Wittenborn Julia
Published: March 2023

Background: This study aims to investigate whether specific characteristics of the patient group with ASC-H (PAP III-p) findings increase the likelihood of clinically significant disease (CIN2+), offering implications for risk-adapted clinical management. Methods: 225 patients with an ASC-H smear presenting to our colposcopy unit between 2014 and 2021 were identified and examined retrospectively. A total of 203 patients were included in the cross-sectional study using multivariate logistic regression. Results: Of the 12 variables that entered the regression model, transformation zone (p = 0.045) and HPV infection (p = 0.005) contributed significantly to predicting CIN2+. A T3 transformation zone had a protective effect, reducing the likelihood of significant pathology. Infection with HPV high risk (non 16/18) or HPV high risk (16/18), on the contrary, increased the likelihood of CIN2+ four times and seven times, respectively, compared to the lower-risk category. Conclusion: A differential approach in the workup of ASC-H cytology is advisable. Younger, premenopausal patients with positive HPV high-risk findings are at a significantly higher risk for CIN2+ lesions than older postmenopausal women with negative high-risk HPV test results. As the risk increases from HPV high-risk non 16/18 to HPV 16/18 infections, using HPV PCR testing is advisable.
Determinants of Clinical Decision Making under Uncertainty in Dentistry: A Scoping Review
Authors: Murdoch Alexander Ivon King , Blum Jordan , Chen Jie , Baziotis-Kalfas Dean , Dao Angelie , Bai Kevin , Bekheet Marina , Atwal Nimret , Cho Sarah Sung Hee , Ganhewa Mahen , Cirillo Nicola
Published: March 2023

Clinical decision-making for diagnosing and treating oral and dental diseases consolidates multiple sources of complex information, yet individual clinical judgements are often made intuitively on limited heuristics to simplify decision making, which may lead to errors harmful to patients. This study aimed at systematically evaluating dental practitioners’ clinical decision-making processes during diagnosis and treatment planning under uncertainty. A scoping review was chosen as the optimal study design due to the heterogeneity and complexity of the topic. Key terms and a search strategy were defined, and the articles published in the repository of the National Library of Medicine (MEDLINE/PubMed) were searched, selected, and analysed in accordance with PRISMA-ScR guidelines. Of the 478 studies returned, 64 relevant articles were included in the qualitative synthesis. Studies that were included were based in 27 countries, with the majority from the UK and USA. Articles were dated from 1991 to 2022, with all being observational studies except four, which were experimental studies. Six major recurring themes were identified: clinical factors, clinical experience, patient preferences and perceptions, heuristics and biases, artificial intelligence and informatics, and existing guidelines. These results suggest that inconsistency in treatment recommendations is a real possibility and despite great advancements in dental science, evidence-based practice is but one of a multitude of complex determinants driving clinical decision making in dentistry. In conclusion, clinical decisions, particularly those made individually by a dental practitioner, are potentially prone to sub-optimal treatment and poorer patient outcomes.
Clinical Aspects and Therapeutic Management of an Aggressive Manifestation of Stage III Grade C Periodontitis in a Female Teenager
Authors: Păunica Stana , Giurgiu Marina-Cristina , Ciongaru Dragoș Nicolae , Pădure Cristiana-Elena , Albu Ștefan Dimitrie , Pițuru Silviu-Mirel , Dumitriu Anca Silvia
Published: March 2023

The main objective of this study was to evaluate the improvement of periodontal health in patients with periodontitis treated with non-surgical periodontal therapy and subgingival-administrated local and systemic antimicrobial agents. A female teenager with periodontitis-associated health issues and a history of dental trauma was selected for this study. Clinical indices were obtained, and radiographic examination was performed at the beginning of the study. The patient was treated with periodontal therapy and administration of antibiotics. After this therapy, visits were scheduled at regular intervals to observe the clinical changes. Non-surgical periodontal therapy and administration of local and systemic antibiotics resulted in a reduction in the patient pocket depth probing, plaque index, and bleeding on probing. Gingival and periodontal health improved in terms of gingival overgrowth, plaque, tartar index, and tooth mobility. Suppuration was eliminated, and no gingival inflammation signs were observed.
Toothpaste Abrasion and Abrasive Particle Content: Correlating High-Resolution Profilometric Analysis with Relative Dentin Abrasivity (RDA)
Authors: Enax Joachim , Meyer Frederic , Schulze zur Wiesche Erik , Fuhrmann Ines Christin , Fabritius Helge-Otto
Published: March 2023

In this in vitro study, the influence of the concentration of abrasive particles on the abrasivity of toothpastes was investigated using laser scan profilometry on polymethyl methacrylate (PMMA) surfaces with the aim of providing an alternative method to developers for screening of new toothpaste formulations. PMMA plates were tested in a toothbrush simulator with distilled water and four model toothpastes with increasing content of hydrated silica (2.5, 5.0, 7.5, 10.0 wt%). The viscosity of the model toothpaste formulations was kept constant by means of varying the content of sodium carboxymethyl cellulose and water. The brushed surfaces were evaluated using laser scan profilometry at micrometer-scale resolutions, and the total volume of the introduced scratches was calculated along with the roughness parameters Ra, Rz and Rv. RDA measurements commissioned for the same toothpaste formulations were used to analyze the correlation between results obtained with the different methods. The same experimental procedure was applied to five commercially available toothpastes, and the results were evaluated against our model system. In addition, we characterize abrasive hydrated silica and discuss their effects on PMMA-sample surfaces. The results show that the abrasiveness of a model toothpaste increases with the weight percentage of hydrated silica. Increasing roughness parameter and volume loss values show good correlation with the likewise increasing corresponding RDA values for all model toothpastes, as well as commercial toothpastes without ingredients that can damage the used substrate PMMA. From our results, we deduce an abrasion classification that corresponds to the RDA classification established for marketed toothpastes.
Remineralization Strategies for Teeth with Molar Incisor Hypomineralization (MIH): A Literature Review
Authors: Enax Joachim , Amaechi Bennett T. , Farah Rayane , Liu Jungyi Alexis , Schulze zur Wiesche Erik , Meyer Frederic
Published: March 2023

Molar incisor hypomineralization (MIH) is a highly prevalent dental developmental disorder with a significant health burden for patients and high treatment needs, yet no comprehensive review article on all remineralization systems as a non-invasive treatment approach for MIH has been published. Typical characteristics of MIH-affected teeth are a lower mineral density and lower hardness compared to healthy teeth leading to sensitivity and loss of function. Thus, the use of formulations with calcium phosphates to remineralize MIH-affected teeth is reasonable. This review presents an up-to-date overview of remineralization studies focusing on active ingredients investigated for remineralization of MIH, i.e., casein phosphopeptide amorphous calcium phosphate (CPP-ACP), casein phosphopeptide amorphous calcium fluoride phosphate (CPP-ACFP), hydroxyapatite, calcium glycerophosphate, self-assembling peptide, and fluoride. Overall, 19 studies (in vitro, in situ, and in vivo) were found. Furthermore, an additional search for studies focusing on using toothpaste/dentifrices for MIH management resulted in six studies, where three studies were on remineralization and three on reduction of sensitivity. Overall, the studies analyzed in this review showed that MIH-affected teeth could be remineralized using calcium phosphate-based approaches. In conclusion, calcium phosphates like CPP-ACP, calcium glycerophosphate, and hydroxyapatite can be used to remineralize MIH-affected teeth. In addition to MIH-remineralization, CPP-ACP and hydroxyapatite also offer relief from MIH-associated tooth sensitivity.
Speed-First: An Aggressive Gradient-Based Local Planner for Quadrotor Faster Flight
Authors: Yu Jiajie , Li Jiaqi , Zhang Tong , Yan Binbin , Li Shaoyi , Meng Zhongjie
Published: March 2023

Autonomous flight for quadrotors is maturing with the development of real-time local trajectory planning. However, the current local planning method is too conservative to waste the agility of the quadrotors. So in this paper, we have focused on aggressive local trajectory planning and proposed a gradient-based planning method to rapidly plan faster executable trajectories while ensuring it is collision-free. A distance gradient information generation strategy is proposed, which finds a collision-free Hybrid-A* path to replace the control points in obstacles for safety and creates the distance gradient used in the back-end optimization. Besides, we present a novel and aggressive time span cost term to tackle unfeasibility and improve the overall trajectory speed. Extensive simulations and real-world experiments are tested to validate our method. The results show that our proposed method generates a more aggressive trajectory with a shorter planning time and a faster flight speed than the classical gradient-based method.
Image Adversarial Example Generation Method Based on Adaptive Parameter Adjustable Differential Evolution
Authors: Lin Zhiyi , Peng Changgen , Tan Weijie , He Xing
Published: March 2023

Adversarial example generation techniques for neural network models have exploded in recent years. In the adversarial attack scheme for image recognition models, it is challenging to achieve a high attack success rate with very few pixel modifications. To address this issue, this paper proposes an adversarial example generation method based on adaptive parameter adjustable differential evolution. The method realizes the dynamic adjustment of the algorithm performance by adjusting the control parameters and operation strategies of the adaptive differential evolution algorithm, while searching for the optimal perturbation. Finally, the method generates adversarial examples with a high success rate, modifying just a very few pixels. The attack effectiveness of the method is confirmed in CIFAR10 and MNIST datasets. The experimental results show that our method has a greater attack success rate than the One Pixel Attack based on the conventional differential evolution. In addition, it requires significantly less perturbation to be successful compared to global or local perturbation attacks, and is more resistant to perception and detection.
A Comparison of Entropic Diversity and Variance in the Study of Population Structure
Authors: Karlin Eric F.
Published: March 2023

AMOVA is a widely used approach that focuses on variance within and among strata to study the hierarchical genetic structure of populations. The recently developed Shannon Informational Diversity Translation Analysis (SIDTA) instead tackles exploration of hierarchical genetic structure using entropic allelic diversity. A mix of artificial and natural population data sets (including allopolyploids) is used to compare the performance of SIDTA (a ‘q = 1’ diversity measure) vs. AMOVA (a ‘q = 2’ measure) under different conditions. An additive allelic differentiation index based on entropic allelic diversity measuring the mean difference among populations (ΩAP) was developed to facilitate the comparison of SIDTA with AMOVA. These analyses show that the genetic population structure seen by AMOVA is notably different in many ways from that provided by SIDTA, and the extent of this difference is greatly affected by the stability of the markers employed. Negative among group values are lacking with SIDTA but occur with AMOVA, especially with allopolyploids. To provide more focus on measuring allelic differentiation among populations, additional measures were also tested including Bray–Curtis Genetic Differentiation (BCGD) and several expected heterozygosity-based indices (e.g., GST, G″ST, Jost’s D, and DEST). Corrections, such as almost unbiased estimators, that were designed to work with heterozygosity-based fixation indices (e.g., FST, GST) are problematic when applied to differentiation indices (eg., DEST, G″ST, G′STH).
One-Dimensional Quadratic Chaotic System and Splicing Model for Image Encryption
Authors: Chen Chen , Zhu Donglin , Wang Xiao , Zeng Lijun
Published: March 2023

Digital image transmission plays a very significant role in information transmission, so it is very important to protect the security of image transmission. Based on the analysis of existing image encryption algorithms, this article proposes a new digital image encryption algorithm based on the splicing model and 1D secondary chaotic system. Step one is the algorithm of this article divides the plain image into four sub-parts by using quaternary coding, and these four sub-parts can be coded separately. Only by acquiring all the sub-parts at one time can the attacker recover the useful plain image. Therefore, the algorithm has high security. Additionally, the image encryption scheme in this article used a 1D quadratic chaotic system, which makes the key space big enough to resist exhaustive attacks. The experimental data show that the image encryption algorithm has high security and a good encryption effect.
CMOS Widely Tunable Second-Order Gm-C Bandpass Filter for Multi-Sine Bioimpedance Analysis
Authors: Corbacho Israel , Carrillo Juan M. , Ausín José L. , Domínguez Miguel Á. , Pérez-Aloe Raquel , Duque-Carrillo J. Francisco
Published: March 2023

A CMOS widely tunable second-order Gm-C bandpass filter (BPF), intended to be used in multi-sine bioimpedance applications, is presented. The filter incorporates a tunable transconductor in which the responses of two linearized voltage-to-current converters are subtracted. As a result, the effective transconductance can be continuously adjusted over nearly three decades, which allows a corresponding programmability of the center frequency of the BPF. The circuit was designed and fabricated in 180 nm CMOS technology to operate with a 1.8 V supply, and the experimental characterization was carried out over eight samples of the silicon prototype. The simulated transconductance of the cell can be tuned from 5.3 nA/V up to 19.60 μA/V. The measured range of the experimental transconductance varied, however, between 1.42 μA/V and 20.57 μA/V. Similarly, the center frequency of the BPF, which in the simulations ranged from 500 Hz to 342 kHz, can be programmed in the silicon prototypes from 22.4 kHz to 290 kHz. Monte Carlo and corner simulations were carried out to ascertain the origin of this deviation. Besides, the extensive simulation and experimental characterization of the standalone transconductor and the complete BPF are provided.
Recent Progress of Non-Cadmium and Organic Quantum Dots for Optoelectronic Applications with a Focus on Photodetector Devices
Authors: Shabbir Hasan , Wojnicki Marek
Published: March 2023

Quantum dots (QDs) are zero-dimensional (0D) nanomaterials with charge confinement in all directions that significantly impact various applications. Metal-free organic quantum dots have fascinating properties such as size-dependent bandgap tunability, good optical absorption coefficient, tunability of absorption and emission wavelength, and low-cost synthesis. Due to the extremely small scale of the materials, these characteristics originated from the quantum confinement of electrons. This review will briefly discuss the use of QDs in solar cells and quantum dots lasers, followed by a more in-depth discussion of QD application in photodetectors. Various types of metallic materials, such as lead sulfide and indium arsenide, as well as nonmetallic materials, such as graphene and carbon nanotubes, will be discussed, along with the detection mechanism.
A Filtering Antenna with Slots and Stacked Patch Based on SISL for 5G Communications
Authors: Yan Ningning , Zhou Hetian , Ma Kaixue
Published: March 2023

A filtering antenna based on the Substrate Integrated Suspended Line (SISL) platform applied for the n78 band of 5G is presented in this paper. The antenna has a segmented feed line, a rectangular driven patch etched with a double I-slot, and a squared stacked patch with grooves at the edges of both sides. The etched slots and the stacked patch introduce two new resonance frequencies increasing the bandwidth. Furthermore, the etched slots excite a deep radiation null in the low-frequency band, and the stacked patch coupled with the driven patch produces two deep radiation nulls in the high-frequency band. Three radiation nulls enable high selectivity of the antenna. The filtering antenna works at 3.2–3.89 GHz, which can be applied to the 5G (n78, 3.3–3.8 GHz) frequency band. The peak gain in the band can reach 9.21 dBi, and the out-of-band suppression levels are higher than 18.47 dB.
Performance Evaluation of Standard and Modified OLSR Protocols for Uncoordinated UAV Ad-Hoc Networks in Search and Rescue Environments
Authors: Wheeb Ali H. , Nordin Rosdiadee , Samah Asma’ Abu , Kanellopoulos Dimitris
Published: March 2023

Widespread usage of unmanned aerial vehicles (UAVs) in new and emerging applications needs dynamic and adaptive networking. The development of routing protocols for UAV ad hoc networks faces numerous issues because of the unique characteristics of UAVs, such as rapid mobility, frequent changes in network topology, and limited energy consumption. The Optimized Link State Routing (OLSR) protocol seems to be a promising solution as it offers improved delay performance. It is expected that OLSR will satisfy the strict demands of real-time UAV applications such as “search and rescue” (SAR) missions as it involves the most recent update of routing information. The classical OLSR routing protocol and its enhanced versions, D-OLSR, ML-OLSR, and P-OLSR, use different techniques to make an appropriate decision for routing packets. These routing techniques consider the quality of a wireless link, type of antenna, load, and mobility-aware mechanism to select the best UAV to send the message to the destination. This study evaluates and examines the performance of the original and modified OLSR routing protocols in UAV ad hoc networks for three SAR scenarios: (1) increasing mobility, (2) increasing scalability, and (3) increasing the allowed space of UAVs. It analyzes and validates the performance of the four OLSR-based routing protocols. It determines the best OSLR routing protocol by taking into account the packet delivery ratio, latency, energy consumption, and throughput. The four routing protocols and the SAR scenarios were simulated using NS-3.32. Based on the simulation results, ML-OLSR outperforms OLSR, D-OLSR, and P-OLSR in the considered measures.
Blockchain-Enabled Internet of Vehicles Applications
Authors: Gao Junting , Peng Chunrong , Yoshinaga Tsutomu , Han Guorong , Guleng Siri , Wu Celimuge
Published: March 2023

Internet of Vehicles (IoV) is a network that connects vehicles and everything. IoV shares traffic data by connecting vehicles with the surrounding environment, which brings huge potential to people’s life. However, a large number of connections and data sharing will seriously consume vehicle resources during the interaction. In addition, how to build a safe and reliable connection to ensure vehicle safety is also an issue to consider. To solve the above problems, researchers introduce blockchains into IoV to build a safe and reliable vehicle network relying on the distributed account structure, immutable, transparent and security features of blockchains. We have investigated the application of blockchains in IoV in recent years, and have summarized and compared these studies according to their purposes. On this basis, we also point out the future trends and opportunities.
Capacity Performance Analysis for Terrestrial THz Channels
Authors: Varotsos George K. , Aidinis Konstantinos , Katsis Athanassios , Nistazakis Hector E.
Published: March 2023

The outdoor terrestrial terahertz (THz) communication links have recently attracted great research and commercial interest in response to the emerging bandwidth-hungry demands for extremely high-speed wireless data transmissions. However, their development is hindered by the random behavior of the atmospheric channel due to the molecular attenuation, adverse weather effects, and atmospheric turbulence (along with free space path loss (FSPL) and pointing errors) due to the stochastic misalignments between the transmitter and the receiver. Thus, in this work, we investigate the joint influence of these detrimental effects on both capacities, i.e., average (ergodic) and outage, of such a typical line of sight (LOS) THz communication link. Specifically, atmospheric turbulence-induced intensity fluctuations can be modeled by using either the suitable gamma or the well-known gamma–gamma distribution for weak and moderate to strong turbulence conditions, respectively. Additionally, weak to strong stochastic misalignment-induced intensity fluctuations, due to generalized pointing errors with non-zero boresight (NZB), are emulated by the appropriate Beckman distribution. Taking into additional consideration the unavoidable presence of FSPL and the different but realistic water vapor concentration values along with the influence of weather conditions, an outage performance analysis has been conducted. Considering the abovementioned significant effects, novel analytical mathematical expressions have been extracted for both average (ergodic) and outage capacity, which are critical metrics that first incorporate the total influence of all of the above significant effects on the THz links’ performance. Through the derived expressions, proper analytical results verified by simulations are presented and demonstrate the validity of our analysis. It is notable that the derived expressions can accommodate realistic parameter values involved in all the above-mentioned major effects and link characteristics. In this context, they provide encouraging quantitative results and outcomes for both capacity metrics under investigation. The latter enables the design and the establishment of modern and future high-speed THz links, which are expected to cover longer propagation distances and thus become even more vulnerable to atmospheric turbulence effect. This is modeled and incorporated in our analysis and expressions contrary to most of the previous works in the open technical literature.
Software Testing Techniques for Improving the Quality of Smart-Home IoT Systems
Authors: Vadan Andrei-Mihai , Miclea Liviu-Cristian
Published: March 2023

Software is present in any modern device and is one of the most important components of a new product. IoT systems for smart homes have become more popular in recent years, and testing these systems using advanced methods is very important because it should improve software quality from the beginning of development, resulting in a faster product development overall and a better user experience for the client. In this paper, we describe methods of how to build fast quality assurance software for automation testing in comparison with current trends. Those methods are applicable to teams that are using custom test automation frameworks and working in big projects. The methods have already been applied with success in testing infotainment systems in the automotive industry and our custom-made smart-home IoT system. We will present the system and testing techniques used for testing web interfaces, mobile applications, cross-platform mobile applications, and backend using a new design pattern, called ‘Locate, Execute, Expect’. We compare this new design pattern against Page Object Model and will guide you on how to integrate it in an existing project or how to use it with Gherkin. In conclusion, we will see the main advantages of using this technique and how much faster it is in a real-life scenario, we will learn how it can replace Gherkin, and we will also see the main disadvantages.
Machine Learning Based Fast QTMTT Partitioning Strategy for VVenC Encoder in Intra Coding
Authors: Taabane Ibrahim , Menard Daniel , Mansouri Anass , Ahaitouf Ali
Published: March 2023

The newest video compression standard, Versatile Video Coding (VVC), was finalized in July 2020 by the Joint Video Experts Team (JVET). Its main goal is to reduce the bitrate by 50% over its predecessor video coding standard, the High Efficiency Video Coding (HEVC). Due to the new advanced tools and features included in VVC, it actually provides high coding performances—for instance, the Quad Tree with nested Multi-Type Tree (QTMTT) involved in the partitioning block. Furthermore, VVC introduces various techniques that allow for superior performance compared to HEVC, but with an increase in the computational complexity. To tackle this complexity, a fast Coding Unit partition algorithm based on machine learning for the intra configuration in VVC is proposed in this work. The proposed algorithm is formed by five binary Light Gradient Boosting Machine (LightGBM) classifiers, which can directly predict the most probable split mode for each coding unit without passing through the exhaustive process known as Rate Distortion Optimization (RDO). These LightGBM classifiers were offline trained on a large dataset; then, they were embedded on the optimized implementation of VVC known as VVenC. The results of our experiment show that our proposed approach has good trade-offs in terms of time-saving and coding efficiency. Depending on the preset chosen, our approach achieves an average time savings of 30.21% to 82.46% compared to the VVenC encoder anchor, and a Bjøntegaard Delta Bitrate (BDBR) increase of 0.67% to 3.01%, respectively.
A Study on the Development of ICT Convergence Smart ESP Using Embedded System
Authors: Cho Joon-Ho
Published: March 2023

In this paper, the electrical submersible pump (ESP) is combined with information and communication technologies (ICT) to diagnose the operation status and soundness of the ESP. The ICT convergence provides users with maintenance and repair information through history management and remote control in case of failure. The proposed system includes a monitoring unit (MU) that senses the operating state of the ESP, a monitoring control unit (MCU) that transmits the sensed information to internal and external databases, and a monitoring system that allows users to check the status information. The server and embedded system can monitor the operation status of the submersible pump by storing sensor values in the database and displaying them on the screen. The embedded system retrieves the sensor values stored in the database and performs individual and complex diagnoses. The diagnosis results are sent to the server and status information to the monitoring control unit. The diagnosis of the submersible pump was divided into 23 individual sensor classifications, and a multilayer perceptron was implemented. Weights were set through learning and testing. The proposed ICT-converged Smart ESP is considered to be competitive as it greatly improves the existing system in terms of status and maintenance.
Predictive Maintenance for Distribution System Operators in Increasing Transformers’ Reliability
Authors: Vita Vasiliki , Fotis Georgios , Chobanov Veselin , Pavlatos Christos , Mladenov Valeri
Published: March 2023

Power transformers’ reliability is of the highest importance for distribution networks. A possible failure of them can interrupt the supply to consumers, which will cause inconvenience to them and loss of revenue for electricity companies. Additionally, depending on the type of damage, the recovery time can vary and intensify the problems of consumers. This paper estimates the maintenance required for distribution transformers using Artificial Intelligence (AI). This way the condition of the equipment that is currently in use is evaluated and the time that maintenance should be performed is known. Because actions are only carried out when necessary, this strategy promises cost reductions over routine or time-based preventative maintenance. The suggested methodology uses a classification predictive model to identify with high accuracy the number of transformers that are vulnerable to failure. This was confirmed by training, testing, and validating it with actual data in Colombia’s Cauca Department. It is clear from this experimental method that Machine Learning (ML) methods for early detection of technical issues can help distribution system operators increase the number of selected transformers for predictive maintenance. Additionally, these methods can also be beneficial for customers’ satisfaction with the performance of distribution transformers, which would enhance the highly reliable performance of such transformers. According to the prediction for 2021, 852 transformers will malfunction, 820 of which will be in rural Cauca, which is consistent with previous failure statistics. The 10 kVA transformers will be the most vulnerable, followed by the 5 kVA and 15 kVA transformers.
Time-Sensitive Networking Mechanism Aided by Multilevel Cyclic Queues in LEO Satellite Networks
Authors: Ma Xiao , Li Shangyi , Guan Zechuan , Li Jianxing , Sun Hao , Wang Yong , Guo Hui
Published: March 2023

With the proliferation of low-Earth-orbit (LEO) satellites, existing satellite networks need to be enhanced to better handle time-sensitive flows (TSFs). However, the migration of time-sensitive network (TSN) technology to satellite networks is challenged by the large space–time range and limited on-board resources, particularly in providing differentiated quality-of-service guarantees for multilevel TSFs. To address this issue, we propose a multilevel queue-based TSN technique that uses latency requirements as an indicator for traffic scheduling. This approach improves the time-sensitive transmission capability of services with different requirements in LEO satellite networks. We conducted a simulation evaluation under a Walker constellation, and the results demonstrate that our proposed method could significantly improve network throughput, and reduce the packet loss ratio by 90% and the time-out ratio by 14.5%. Additionally, our proposed mechanism could accommodate more TSFs with acceptable latency requirements.
Task Scheduling Based on Adaptive Priority Experience Replay on Cloud Platforms
Authors: Li Cuixia , Gao Wenlong , Shi Li , Shang Zhiquan , Zhang Shuyan
Published: March 2023

Task scheduling algorithms based on reinforce learning (RL) have been important methods with which to improve the performance of cloud platforms; however, due to the dynamics and complexity of the cloud environment, the action space has a very high dimension. This not only makes agent training difficult but also affects scheduling performance. In order to guide an agent’s behavior and reduce the number of episodes by using historical records, a task scheduling algorithm based on adaptive priority experience replay (APER) is proposed. APER uses performance metrics as scheduling and sampling optimization objectives with which to improve network accuracy. Combined with prioritized experience replay (PER), an agent can decide how to use experiences. Moreover, this algorithm also considers whether a subtask is executed in a workflow to improve scheduling efficiency. Experimental results on Tpc-h, Alibaba cluster data, and scientific workflows show that a model with APER has significant benefits in terms of convergence and performance.
Numerical and Experimental Investigations of CH4/H2 Mixtures: Ignition Delay Times, Laminar Burning Velocity and Extinction Limits
Authors: Drost Simon , Eckart Sven , Yu Chunkan , Schießl Robert , Krause Hartmut , Maas Ulrich
Published: March 2023

In this work, the influence of H2 addition on the auto-ignition and combustion properties of CH4 is investigated experimentally and numerically. Experimental ignition delay times (IDT) are compared with simulations and laminar burning velocities (LBVs), and extinction limits/extinction strain rates (ESRs) are compared with data from the literature. A wide variety of literature data are collected and reviewed, and experimental data points are extracted for IDT, LBV and ESR. The results are used for the validation of existing reaction mechanisms. The reaction mechanisms and models used are able to reproduce the influence of H2 addition to CH4 (e.g., shortening IDTs, increasing ESRs and increasing LBVs). IDTs are investigated in a range from 6 to 15 bar and temperatures from 929 to 1165 K with H2 addition from 10 to 100 mol%. We show that LBV and ESR are predicted in a wide range by the numerical simulations. Moreover, the numerical simulations using detailed Aramco Mech 3.0 (581 species) are compared with the derived reduced reaction mechanism UCB Chen (49 species). The results show that the reduced chemistry obtained by considering only the IDT is also valid for LBV and ESR.
A System Analysis of a Bio-Hydrogen Production System Using Granulated Mine Residue as a H2S Adsorbent
Authors: Torii Kento , Hamazaki Mayu , Kumon Shoichi , Sato Kimitaka , Kato Shogo , Dowaki Kiyoshi
Published: March 2023

An energy production system that combines biomass and fuel cells produces much energy with minimal environmental impact. However, the hydrogen sulfide (H2S) contained in gasified biomass degrades fuel cell performance, thus negating the advantages of this combination. In this study, the removal of H2S by adsorption after biomass gasification was investigated. Metal oxides with high adsorption performance are common H2S adsorbents. However, they have a significant environmental impact in terms of metal depletion, which is an environmental impact indicator. Therefore, neutralized sediment materials from mine drainage treatments can be used as H2S adsorbents. A previous study found that the adsorption performance of H2S adsorbents is equivalent to that of metal oxides, especially in the high-temperature zone (300 °C), and the environmental impact is considerably lower than that of metal oxides. However, because the neutralized sediment is a powder (Φ 4.5 μm on average), there is a possibility that the gas will not flow due to the pressure drop when it is used in a large adsorption column. Therefore, in this study, we propose the use of granulated neutralized sediments for practical plant operations. No studies have investigated the adsorption performance of granulated neutralized sediment through experiments or quantitatively investigated the effect of using waste material as a H2S adsorbent to reduce the environmental impact of hydrogen production. Based on these data, the sulfur capture capacity of the granulated neutralized sediment was experimentally investigated. The extent to which the environmental impact of the hydrogen production system could be reduced when granulated neutralized sediment was used as the H2S adsorbent was assessed. Note that the granulated neutralized sediment is formed with about a Φ 0.56–1.25 mm diameter. The granulated neutralized sediment exhibited approximately 76.8% of the adsorption performance of zinc oxide (ZnO) on a conventional adsorbent. In terms of the LCA, the global warming potential (GWP) and the abiotic depletion potential (ADP) were improved by approximately 0.89% (GWP) and 55.3% (ADP) in the entire hydrogen production process. This study demonstrated that the use of waste materials can significantly reduce the environmental impact on the entire system.
An SMC-MRAS Speed Estimator for Sensor-Less Control of DFIG Systems in Wind Turbine Applications
Authors: Mbukani Mwana Wa Kalaga , Gitau Michael Njoroge , Naidoo Raj
Published: March 2023

A sliding mode control-based model reference adaptive system (SMC-MRAS) estimator for sensor-less control of doubly fed induction generator (DFIG) systems in wind turbine applications is proposed in this paper. The proposed SMC-MRAS estimator uses the rotor current as a variable of interest. The proposed SMC-MRAS estimator has the advantage of being immune to machine parameter variations. The SMC parameters are designed using the Lyapunov stability criteria. The performance of the proposed SMC-MRAS estimator is validated using simulations in MATLAB/SIMULINK. A comparative study between the proposed SMC-MRAS estimator and the PI-MRAS estimator is also conducted to demonstrate the superiority of the proposed SMC-MRAS estimator.
Application of Augmented Echo State Networks and Genetic Algorithm to Improve Short-Term Wind Speed Forecasting
Authors: Gouveia Hugo T. V. , Souza Murilo A. , Ferreira Aida A. , de Albuquerque Jonata C. , Nóbrega Neto Otoni , da Silva Lira Milde Maria , de Aquino Ronaldo R. B.
Published: March 2023

The large-scale integration into electrical systems of intermittent power-generation sources, such as wind power plants, requires greater efforts and knowledge from operators to keep these systems operating efficiently. These sources require reliable output power forecasts to set up the optimal operating point of the electrical system. In previous research, the authors developed an evolutionary approach algorithm called RCDESIGN to optimize the hyperparameters and topology of Echo State Networks (ESN), and applied the model in different time series forecasting, including wind speed. In this paper, RCDESIGN was modified in some aspects of the genetic algorithm, and now it optimizes an ESN with augmented states (ESN-AS) and has been called RCDESIGN-AS. The evolutionary algorithm allows the search for the best parameters and topology of the recurrent neural network to be performed simultaneously. In addition, RCDESIGN-AS has the important characteristic of requiring little computational effort and processing time since it is not necessary for the eigenvalues of the reservoir weight matrix to be reduced and also due to the fact that the augmented states make it possible to reduce the number of neurons in the reservoir. The method was applied for wind speed forecasting with a 24-h ahead horizon using real data of wind speed from five cities in the Northeast Region of Brazil. All results obtained with the proposed method overcame forecasting performed by the persistence method, obtaining prediction gains ranging from 60% to 80% in relation to this reference method. In some datasets, the proposed method also yielded better results than the traditional ESN, showing that RCDESIGN-AS can be a powerful tool for wind-speed forecasting and possibly for other types of time series.
Optimal Placement and Size of SVC with Cost-Effective Function Using Genetic Algorithm for Voltage Profile Improvement in Renewable Integrated Power Systems
Authors: Dandotia Ashish , Gupta Mukesh Kumar , Banerjee Malay Kumar , Singh Suraj Kumar , Đurin Bojan , Dogančić Dragana , Kranjčić Nikola
Published: March 2023

Given the concern for maintaining voltage stability in power systems integrated with renewable power systems due to a mismatch in generation and demand, there remains a need to invoke flexible alternating current transmission system (FACTS) devices in the distribution network. The present paper deals with identifying the locations of placement of a static var compensator in an experimental IEEE 14-bus system; the voltage drop in different buses in an IEEE 14-bus system is calculated by the standard formula. The total voltage drop in the network (TVDN) is also calculated as a reference. The ranking of buses in order of decreasing voltage drop is made, and the weak buses are identified as those showing the maximum or near-maximum voltage drop for the installation of a Static Var Compensator (SVC). The optimum usable size is calculated using a Genetic Algorithm approach to optimize the installation cost. After size optimization, installing a 2 MW solar generator is considered for the weak and most potential bus, which suffers from voltage drops or power loss. Based on the generator at the weakest bus, the total power loss in the network is calculated and compared with a similar method to assess the efficiency of the proposed model. Thus, the voltage stability enhancement problem is solved by applying a Genetic algorithm (GA) to optimize SVC size and using the Total Voltage Drop in Network (TVDN) method to identify weak buses in the systems. It is found that the performance of the proposed system is comparable with another existing system. It is further observed that a gain in power loss to 6.56% is achievable by adopting the proposed strategy, and the gain is better than the other system.
Methodology for the Development of Virtual Representations within the Process Development Framework of Energy Plants: From Digital Model to Digital Predictive Twin—A Review
Authors: Hammerschmid Martin , Rosenfeld Daniel Cenk , Bartik Alexander , Benedikt Florian , Fuchs Josef , Müller Stefan
Published: March 2023

Digital reflections of physical energy plants can help support and optimize energy technologies within their lifecycle. So far, no framework for the evolution of virtual representations throughout the process development lifecycle exists. Based on various concepts of virtual representations in different industries, this review paper focuses on developing a novel virtual representation framework for the process development environment within the energy sector. The proposed methodology enables the continuous evolution of virtual representations along the process development lifecycle. A novel definition for virtual representations in the process development environment is developed. Additionally, the most important virtual representation challenges, properties, and applications for developing a widely applicable framework are summarized. The essential sustainability indicators for the energy sector are listed to standardize the process evaluation throughout the process development lifecycle. The virtual representation and physical facility development can be synchronized by introducing a novel model readiness level. All these thoughts are covered through the novel virtual representation framework. Finally, the digital twin of a Bio-SNG production route is presented, to show the benefits of the methodology through a use case. This methodology helps to accelerate and monitor energy technology developments through the early implementation of virtual representations.