bluuebunny/crossref_metadata_2025 huggingface dataset:
Acne-like Eruptions: Disease Features and Differential Diagnosis
Authors: Parać Ena , Špiljak Bruno , Lugović-Mihić Liborija , Bukvić Mokos Zrinka
Published: June 2023

The term “acne-like eruptions” encompasses a variety of skin conditions resembling acne vulgaris. While both acne-like dermatoses and true acne are frequently observed in clinical settings, differentiating between the two might be challenging. Similar to acne, the lesions of acne-like eruptions may exhibit papules, pustules, nodules, and cysts. However, contrary to acne, comedones are uncommon but may still be observed in certain types of acne-like eruptions. Moreover, acne-like eruptions can be differentiated from acne based on their sudden onset, the propensity to occur across all age groups, the monomorphic appearance of lesions, and the distributions extending outside the seborrheic regions. The development of acne-like eruptions cannot be attributed to a single underlying mechanism; nevertheless, various factors such as patient age, infections, occupation, habits, cosmetics, and medications may be involved. The observed lesions may not respond to standard acne therapy, necessitating that treatment strategies are tailored in accordance with the identified causative agents. The following review aims to outline distinct entities of acne-like eruptions and present features that set them apart from true acne. Acne-like eruptions in the adult population, infectious diseases resembling acne, and acne-like eruptions resulting from exposure to chemical and physical agents are addressed in this paper. An understanding of the clinical presentation, pathophysiology, and epidemiology of this group of dermatoses is pivotal for a precise diagnosis and provision of appropriate care.
Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review
Authors: Stafie Celina Silvia , Sufaru Irina-Georgeta , Ghiciuc Cristina Mihaela , Stafie Ingrid-Ioana , Sufaru Eduard-Constantin , Solomon Sorina Mihaela , Hancianu Monica
Published: June 2023

Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.
Diabetic Foot Ulcer Identification: A Review
Authors: Das Sujit Kumar , Roy Pinki , Singh Prabhishek , Diwakar Manoj , Singh Vijendra , Maurya Ankur , Kumar Sandeep , Kadry Seifedine , Kim Jungeun
Published: June 2023

Diabetes is a chronic condition caused by an uncontrolled blood sugar levels in the human body. Its early diagnosis may prevent severe complications such as diabetic foot ulcers (DFUs). A DFU is a critical condition that can lead to the amputation of a diabetic patient’s lower limb. The diagnosis of DFU is very complicated for the medical professional as it often goes through several costly and time-consuming clinical procedures. In the age of data deluge, the application of deep learning, machine learning, and computer vision techniques have provided various solutions for assisting clinicians in making more reliable and faster diagnostic decisions. Therefore, the automatic identification of DFU has recently received more attention from the research community. The wound characteristics and visual perceptions with respect to computer vision and deep learning, especially convolutional neural network (CNN) approaches, have provided potential solutions for DFU diagnosis. These approaches have the potential to be quite helpful in current medical practices. Therefore, a detailed comprehensive study of such existing approaches was required. The article aimed to provide researchers with a detailed current status of automatic DFU identification tasks. Multiple observations have been made from existing works, such as the use of traditional ML and advanced DL techniques being necessary to help clinicians make faster and more reliable diagnostic decisions. In traditional ML approaches, image features provide signification information about DFU wounds and help with accurate identification. However, advanced DL approaches have proven to be more promising than ML approaches. The CNN-based solutions proposed by various authors have dominated the problem domain. An interested researcher will successfully be able identify the overall idea in the DFU identification task, and this article will help them finalize the future research goal.
Pathomorphological Manifestations and the Course of the Cervical Cancer Disease Determined by Variations in the TLR4 Gene
Authors: Žilienė Eglė , Inčiūra Arturas , Ugenskienė Rasa , Juozaitytė Elona
Published: June 2023

Cervical cancer (CC) is often associated with human papillomavirus (HPV). Chronic inflammation has been described as one of the triggers of cancer. The immune system fights diseases, including cancer. The genetic polymorphism of pathogen recognition receptors potentially influences the infectious process, development, and disease progression. Many candidate genes SNPs have been contradictory demonstrated to be associated with cervical cancer by association studies, GWAS. TLR4 gene activation can promote antitumor immunity. It can also result in immunosuppression and tumor growth. Our study aimed to investigate eight selected polymorphisms of the TLR4 gene (rs10759932, rs1927906, rs11536898, rs11536865, rs10983755, rs4986790, rs4986791, rs11536897) and to determine the impact of polymorphisms in genotypes and alleles on the pathomorphological characteristics and progression in a group of 172 cervical cancer subjects with stage I–IV. Genotyping was performed by RT-PCR assay. We detected that the CA genotype and A allele of rs11536898 were significantly more frequent in patients with metastases (p = 0.026; p = 0.008). The multivariate logistic regression analysis confirmed this link to be significant. The effect of rs10759932 and rs11536898 on progression-free survival (PFS) and overall survival (OS) has been identified as important. In univariate and multivariate Cox analyses, AA genotype of rs11536898 was a negative prognostic factor for PFS (p = 0.024; p = 0.057, respectively) and OS (p = 0.008; p = 0.042, respectively). Rs11536898 C allele predisposed for longer PFS (univariate and multivariate: p = 0.025; p = 0.048, respectively) and for better OS (univariate and multivariate: p = 0.010; p = 0.043). The worse prognostic factor of rs10759932 in a univariate and multivariate Cox analysis for survival was CC genotype: shorter PFS (p = 0.032) and increased risk of death (p = 0.048; p = 0.015, respectively). The T allele of rs10759932 increased longer PFS (univariate and multivariate: p = 0.048; p = 0.019, respectively) and longer OS (univariate and multivariate: p = 0.037; p = 0.009, respectively). Our study suggests that SNPs rs10759932 and rs11536898 may have the potential to be markers contributing to the assessment of the cervical cancer prognosis. Further studies, preferably with larger groups of different ethnic backgrounds, are needed to confirm the results of the current study.
A Deep Learning Framework for the Detection of Abnormality in Cerebral Blood Flow Velocity Using Transcranial Doppler Ultrasound
Authors: Nisha Naima Nasrin , Podder Kanchon Kanti , Chowdhury Muhammad E. H. , Rabbani Mamun , Wadud Md. Sharjis Ibne , Al-Maadeed Somaya , Mahmud Sakib , Khandakar Amith , Zughaier Susu M.
Published: June 2023

Transcranial doppler (TCD) ultrasound is a non-invasive imaging technique that can be used for continuous monitoring of blood flow in the brain through the major cerebral arteries by calculating the cerebral blood flow velocity (CBFV). Since the brain requires a consistent supply of blood to function properly and meet its metabolic demand, a change in CBVF can be an indication of neurological diseases. Depending on the severity of the disease, the symptoms may appear immediately or may appear weeks later. For the early detection of neurological diseases, a classification model is proposed in this study, with the ability to distinguish healthy subjects from critically ill subjects. The TCD ultrasound database used in this study contains signals from the middle cerebral artery (MCA) of 6 healthy subjects and 12 subjects with known neurocritical diseases. The classification model works based on the maximal blood flow velocity waveforms extracted from the TCD ultrasound. Since the signal quality of the recorded TCD ultrasound is highly dependent on the operator’s skillset, a noisy and corrupted signal can exist and can add biases to the classifier. Therefore, a deep learning classifier, trained on a curated and clean biomedical signal can reliably detect neurological diseases. For signal classification, this study proposes a Self-organized Operational Neural Network (Self-ONN)-based deep learning model Self-ResAttentioNet18, which achieves classification accuracy of 96.05% with precision, recall, f1 score, and specificity of 96.06%, 96.05%, 96.06%, and 96.09%, respectively. With an area under the ROC curve of 0.99, the model proves its feasibility to confidently classify middle cerebral artery (MCA) waveforms in near real-time.
SEM Evaluation of Thermal Effects Produced by a 445 nm Laser on Implant Surfaces
Authors: Pergolini Daniele , Palaia Gaspare , De Angelis Riccardo , Rocchetti Federica , Podda Gian Marco , Tenore Gianluca , Del Vecchio Alessandro , Relucenti Michela , Romeo Umberto
Published: June 2023

The aim of this in vitro study was to evaluate thermal effects on implant surfaces using a 445 nm diode laser (Eltech K-Laser Srl, Treviso, Italy) with different power settings and irradiation modalities. Fifteen new implants (Straumann, Basel, Switzerland) were irradiated to evaluate surface alteration. Each implant was divided into two zones: the anterior and posterior areas. The anterior coronal areas were irradiated with a distance of 1 mm between the optical fiber and the implant; the anterior apical ones were irradiated with the fiber in contact with the implant. Instead, the posterior surfaces of all of the implants were not irradiated and used as control surfaces. The protocol comprised two cycles of laser irradiation, lasting 30 s each, with a one-minute pause between them. Different power settings were tested: a 0.5 W pulsed beam (T-on 25 ms; T-off 25 ms), a 2 W continuous beam and a 3 W continuous beam. Lastly, through a scanning electron microscopy (SEM) analysis, dental implants’ surfaces were evaluated to investigate surface alterations. No surface alterations were detected using a 0.5 W laser beam with a pulsed mode at a distance of 1 mm. Using powers of irradiation of 2 W and 3 W with a continuous mode at 1 mm from the implant caused damage on the titanium surfaces. After the irradiation protocol was changed to using the fiber in contact with the implant, the surface alterations increased highly compared to the non-contact irradiation modality. The SEM results suggest that a power of irradiation of 0.5 W with a pulsed laser light emission mode, using an inactivated optical fiber placed 1 mm away from the implant, could be used in the treatment of peri-implantitis, since no implant surface alterations were detected.
Evaluation of Antibacterial Activity of a Bioactive Restorative Material Versus a Glass-Ionomer Cement on Streptococcus Mutans: In-Vitro Study
Authors: Conti Giulio , Veneri Federica , Amadori Francesca , Garzoni Alba , Majorana Alessandra , Bardellini Elena
Published: June 2023

Background: Dental caries management consists of both preventive and restorative approaches. Pediatric dentists can rely on many techniques and materials to restore decayed teeth, but a high failure rate is still observed, mainly due to secondary caries. New restorative bioactive materials combine the mechanical and aesthetic characteristics of resinous materials with the capability to remineralize and the antimicrobial properties of glass ionomers, thus counteracting the occurrence of secondary caries. The aim of this study was to assess the antimicrobial activity against Streptococcus mutans of a bioactive restorative material (ACTIVA™ BioActive-Restorative™-Pulpdent©) and a glass ionomer cement with silver particles added (Ketac™ Silver—3M©), using agar diffusion assay. Methods: Each material was formed into disks of 4 mm in diameter, and four discs of each material were placed on nine agar plates. The analysis was repeated seven times. Results: Both materials showed statistically significant growth inhibition properties against S. mutans (p < 0.05). The difference in the effectiveness of the two materials was not statistically significant. Conclusion: Both ACTIVA™ and Ketac™ Silver can be recommended since both are similarly effective against S. mutans. However ACTIVA™, given its bioactivity and better aesthetics and mechanical properties compared to GICs, may provide better clinical performance.
Identifying Candidate Gene–Disease Associations via Graph Neural Networks
Authors: Cinaglia Pietro , Cannataro Mario
Published: June 2023

Real-world objects are usually defined in terms of their own relationships or connections. A graph (or network) naturally expresses this model though nodes and edges. In biology, depending on what the nodes and edges represent, we may classify several types of networks, gene–disease associations (GDAs) included. In this paper, we presented a solution based on a graph neural network (GNN) for the identification of candidate GDAs. We trained our model with an initial set of well-known and curated inter- and intra-relationships between genes and diseases. It was based on graph convolutions, making use of multiple convolutional layers and a point-wise non-linearity function following each layer. The embeddings were computed for the input network built on a set of GDAs to map each node into a vector of real numbers in a multidimensional space. Results showed an AUC of 95% for training, validation, and testing, that in the real case translated into a positive response for 93% of the Top-15 (highest dot product) candidate GDAs identified by our solution. The experimentation was conducted on the DisGeNET dataset, while the DiseaseGene Association Miner (DG-AssocMiner) dataset by Stanford’s BioSNAP was also processed for performance evaluation only.
With or without Wikipedia? Integrating Wikipedia into the Teaching Process in Estonian General Education Schools
Authors: Reinsalu Riina , Vija Maigi , Org Andrus , Siiman Ann , Remmik Marvi
Published: June 2023

Today’s education has been shaped by the rapid development of digital technologies and easy accessibility to a large number of electronic sources. This has instigated a genuine need to change current teaching attitudes and practices. Wikipedia, as a multilingual online platform open to all, is a source of information that is used on a daily basis and therefore cannot be ignored in the teaching process. The aim of this study is to explore the possibilities of using the Estonian Wikipedia for teaching purposes in general education schools. The study is based on semi-structured interviews with 49 teachers from 11 general education schools in Estonia. The results of the qualitative content analysis of the interviews indicate that teachers consider the use of Wikipedia to be a suitable for teaching, alongside other information sources and environments. However, teachers acknowledge some uncertainty and caution towards Wikipedia, as they do not consider it a very reliable teaching tool: an attitude largely inherited from the early days of Wikipedia. While teachers themselves are active and frequent Wikipedia users, and allow students to search for information, they do not assign Wikipedia-based text-creation tasks to students. The results of this study provide a basis for discussion about integrating Wikipedia into teaching in a way that is accessible, engaging, and inclusive for students.
Systematic Literature Review on Virtual Electronics Laboratories in Education: Identifying the Need for an Aeronautical Radar Simulator
Authors: Pereira Júnior Enderson Luiz , Moreira Miguel Ângelo Lellis , Portella Anderson Gonçalves , de Azevedo Junior Célio Manso , de Araújo Costa Igor Pinheiro , Fávero Luiz Paulo , Gomes Carlos Francisco Simões , dos Santos Marcos
Published: June 2023

The objective of this work is to propose the development of a virtual electronics laboratory with an aeronautical radar simulator using immersive technologies to help students learn. To verify whether this proposal was viable, the systematic literature review (SLR) methodology was used, whose objective was to verify whether immersive technologies were being used effectively in education and, also, what challenges, opportunities, and benefits they bring to Education 4.0. For this, eight Research Questions (RQs) were formulated to be answered by articles based on the highest SLR scores. The results presented by SLR were as follows: there was an increase in the use of immersive technologies in education, but virtual reality (VR) is still more used in education than AR, despite VR being more expensive than AR; the use of these new technologies brings new challenges, opportunities, and benefits for education; there was an increase in the quality of teaching for complex subjects; and there was an increase in students’ interest in the content presented.
Ensemble Prediction Model for Dust Collection Efficiency of Wet Electrostatic Precipitator
Authors: Choi Sugi , Kim Sunghwan , Jung Haiyoung
Published: June 2023

WESPs (Wet Electrostatic precipitators) are mainly installed in industries and factories where PM (particulate matter) is primarily generated. Such a wet type WESPs exhibits very excellent performance by showing a PM collection efficiency of 97 to 99%, but the PM collection efficiency may decrease rapidly due to a situation in which the dust collector and the discharge electrode is corroded by water. Thus, developing technology to predict efficient PM collection in the design and operation of WESPs is critical. Previous studies have mainly developed machine learning-based models to predict atmospheric PM concentrations using data measured by meteorological agencies. However, the analysis of models for predicting the dust collection efficiency of WESPs installed in factories and industrial facilities is insufficient. In this study, a WESPs was installed, and PM collection experiments were conducted. Nonlinear data such as operating conditions and PM measurements were collected, and ensemble PM collection efficiency prediction models were developed. According to the research results, the random forest model yielded excellent performance, with the best results achieved when the target was PM 7: R2, MAE, and MSE scores of 0.956, 0.747, and 1.748, respectively.
An Enhanced PSO Algorithm for Scheduling Workflow Tasks in Cloud Computing
Authors: Anbarkhan Samar Hussni , Rakrouki Mohamed Ali
Published: June 2023

This paper proposes an enhanced Particle Swarm Optimization (PSO) algorithm in order to deal with the issue that the time and cost of the PSO algorithm is quite high when scheduling workflow tasks in a cloud computing environment. To reduce particle dimensions and ensure initial particle quality, intensive tasks are combined when scheduling workflow tasks. Next, the particle initialization is optimized to ensure better initial particle quality and reduced search space. Then, a suitable self-adaptive function is integrated to determine the best direction of the particles. The experiments show that the proposed enhanced PSO algorithm has better convergence speed and better performance in the execution of workflow tasks.
Multi-Objective Optimization for EE-SE Tradeoff in Space-Air-Ground Internet of Things Networks
Authors: Zhao Jinyi , Mei Yanbin , Gao Xiaozheng , Yang Jian , Shang Jiadong
Published: June 2023

The Internet of Things (IoT) has become increasingly popular, and its communication requirements have grown beyond what traditional ground networks can handle. The space–air–ground (SAG) integrated network has been proposed as a potential solution, where unmanned aerial vehicles (UAVs) collect data from IoT devices and transmit them to satellites. However, the limited energy of UAVs is one of the key factors restricting communication performance, so it is necessary to consider communication energy efficiency (EE). In addition, the improvement of EE will bring about the decline of spectral efficiency (SE). Therefore we consider the tradeoff between EE and SE. Considering a SAG-IoT network, the focus of the paper is to optimize sub-channel selection, power control, and UAV position deployment to maximize EE and SE of the network, which is a multi-objective optimization (MOO) problem. On this premise, we try to improve the data throughput between IoT devices and UAVs. To solve this MOO problem, we use the ϵ-constraint to convert it into a single-objective optimization problem. We then employ various optimization algorithms such as the Dinkelbach algorithm, successive convex approximation algorithm, Lagrange dual algorithm, and block coordinate descent algorithm to solve the mixed integer non-convex problem. Simulation results show that the proposed algorithm converges to at least one sub-optimal solution.
A Blockchain-Based Method for Optimizing the Routing of High-Frequency Carbon-Trading Payment Channels
Authors: Song Yu , Xiong Ao , Qiu Xuesong , Guo Shaoyong , Wang Dong , Li Da , Zhang Xin , Kuang Yue
Published: June 2023

Carbon trading is an effective way to achieve carbon neutrality. It is a market mechanism aimed at reducing global greenhouse gas emissions and carbon dioxide emissions. Blockchain technology can be applied to the carbon-trading scenario using characteristics that guarantee the security, decentralization, data immutability, and data traceability of the carbon-trading process. It would be difficult to implement carbon trading on blockchains for all enterprises and individuals, as the current performance of blockchains does not meet the requirements. There has been extensive research conducted on blockchain performance optimization, and the off-chain payment channel is one of the more mature solutions. This approach involves the transfer of transactions to off-chain transactions, thus avoiding high transaction fees. Existing research has addressed the problem of routing security and efficiency, with less emphasis on factors such as routing transaction costs, node reputation, and routing path considerations. This paper researches the optimization of payment routing in Payment Channel Networks (PCNs) and proposes the Multi-Factor Routing Payment Scheme (MFPS), which integrates factors such as the node reputation, transaction fee cost, and distance to select the route for payment transactions. In order to improve the success ratio of routing transactions, the transaction-splitting algorithm is proposed. To ensure the security and privacy of the transaction process, the Asymmetric Time-Lock Contract (ATLC) protocol is proposed. The results of extensive experimental simulations show that the MFPS proposed in this paper outperforms the ShortestPath, Cheapest, and SplitDistance algorithms. It achieves an approximately 13.8%∼49% improvement in the transaction success ratio and reduces the average transaction processing cost. The security and privacy measures can defend against wormhole and double-flower attacks and exhibit better performance in terms of computational verification and message overhead.
Study on the Systematic Design of a Passive Balancing Algorithm Applying Variable Voltage Deviation
Authors: Song Heewook , Lee Seongjun
Published: June 2023

A balancing circuit in a multi-series battery pack prevents a specific cell from being overcharged by reducing the voltage difference between the cells. Passive cell balancing is widely used for easy implementation and volume and size reduction. For optimal passive cell balancing, the charging/discharging current conditions and the state of charge (voltage condition) of the battery must be determined. In addition, the balancing algorithm must determine an allowable voltage deviation threshold between the cells connected in series to determine whether a specific cell performs a balancing operation. However, previous studies have not dealt with the design of balancing operating conditions in detail. In addition, the balancing time and efficiency improvement effect under specific conditions for arbitrary battery cells used in each previous study were mainly presented. Therefore, this study proposes a variable voltage deviation method in which the threshold for determining the voltage to be balanced is changed by reflecting the battery capacity, rated current specification, open-circuit voltage, and resistance of the balancing circuit. In addition, the voltage management performance and efficiency analysis results of the existing balancing algorithm and the proposed balancing method for the case where there is parameter deviation in the cells of the battery pack are also presented. The proposed method was verified through the simulation and experimental results of a reduced battery module in which three types of battery cells, INR 18650-30Q, INR 18650-29E, and INR 21700-50E, were arranged in 4-series.
Internet of Things (IoT)-Based Wastewater Management in Smart Cities
Authors: Alzahrani Abdullah I. A. , Chauhdary Sajjad Hussain , Alshdadi Abdulrahman A.
Published: June 2023

Wastewater management is a mechanism that is used to extract and refine pollutants from wastewater or drainage that can be recycled to the water supply with minimal environmental effects. New methods and techniques are required to ensure safe and smart wastewater management systems in smart cities because of the present deteriorating environmental state. Wireless sensor networks and the Internet of Things (IoT) represent promising wastewater treatment technologies. The elaborated literature survey formulates a conceptual framework with an Internet of Things (IoT)-based wastewater management system in smart cities (IoT-WMS) using blockchain technology. Blockchain technology is now being used to store information to develop an incentive model for encouraging the reuse of wastewater. Concerning the quality and quantity of recycled wastewater, tokens are issued to households/industries in smart cities. Nevertheless, this often encourages tampering with the information from which these tokens are awarded to include certain rewards. Anomaly detector algorithms are used to identify the possible IoT sensor data which has been tampered with by intruders. The model employs IoT sensors together with quality metrics to measure the amount of wastewater produced and reused. The simulation analysis shows that the proposed method achieves a high wastewater recycling rate of 96.3%, an efficiency ratio of 88.7%, a low moisture content ratio of 32.4%, an increased wastewater reuse of 90.8%, and a prediction ratio of 92.5%.
Optimizing Energy Harvesting: A Gain-Scheduled Braking System for Electric Vehicles with Enhanced State of Charge and Efficiency
Authors: Ghazali Anith Khairunnisa , Hassan Mohd Khair , Radzi Mohd Amran Mohd , As’arry Azizan
Published: June 2023

Recycling braking energy is crucial in increasing the overall energy efficiency of an electric vehicle. Regenerative braking system (RBS) technology makes a significant contribution, but it is quite challenging to design an optimal braking force distribution while ensuring vehicle stability and battery health. In this study, a parallel-distribution braking system that transfers as much energy as possible from the wheel to the battery was investigated. An integrated braking force distribution with gain-scheduling super-twisting sliding mode control (GSTSMC) was proposed to capture the maximum kinetic energy during braking and convert it into electrical energy. Parallel friction and regenerative braking ratios dominate the design of the braking component, which is based on the speed of the vehicle. A GSTSMC was implemented and incorporated into the vehicle dynamics model developed in the ADVISOR environment. Simulation was utilized to rigorously validate the efficacy of the proposed control strategy, ensuring its potential to perform optimally in practical applications. Consideration was given to the vehicle’s slip ratio on dry asphalt to maintain vehicle stability. Simulation results were used to validate the performance of the proposed design in terms of the state of charge (SOC), transmitted energy, motor efficiency, battery temperature, and slip ratio. Based on the results, the proposed control strategy is capable of increasing the SOC value to 54%, overall efficiency to 25.98%, energy transmitted to 14.27%, and energy loss to 87 kJ while considering the vehicle’s speed-tracking ability, battery temperature, and stability.
Evaluation and Improvement of Backup Capacity for Household Electric Vehicle Uninterruptible Power Supply (EV-UPS) Systems
Authors: Zhang Wenping , Wang Yiming , Xu Po , Li Donghui , Liu Baosong
Published: June 2023

The use of electric vehicles (EVs) for household uninterruptible power supplies (UPSs), particularly in rural areas, can greatly improve household power reliability. However, because EVs are mobile, the evaluation of backup capacity for EV-UPS systems is completely different when compared to traditional UPSs. As a result, the focus of this paper is on the evaluation and improvement of backup capacity for EV-UPS systems. The architectures for EV-UPS systems are presented first. The methodology for calculating backup capacity for EV-UPS systems is then presented, followed by four detailed cases based on different grid failure times. Furthermore, the impact of system operating parameters on backup capacity, such as different load power, EV mobility pattern parameters, and grid outage durations, is investigated. The results of backup capacity in cases of different operating parameters are detailed using an EV mobility model. An improved strategy for increasing backup capacity is proposed, in which more backup energy can be released during a power outage. Meanwhile, the next trip requirement is unaffected. The backup capacity with the improved strategy is then calculated and compared to the results with the traditional strategy. Finally, a 5 kW EV-UPS experimental platform is constructed, and the experimental results are presented to validate the proposed method.
Energy, Volume and Cost Analyses of High Temperature Seasonal Thermal Storage for Plus Energy House
Authors: Kasperski Jacek , Oladipo Oluwafunmilola
Published: June 2023

The article analyzes the impact of different filling materials for a seasonal heat storage stack that can be used to heat an innovative plus-energy house in Poland. The storage medium is of the sensible heat type. Twelve filling materials and nine thermal insulation materials readily available in the local market were analyzed. Seven variants comprising a combination of the stack’s filling and thermal insulation materials were selected and then grouped into three classes: advanced, medium, and simple technology. Using a mathematical model, energy analysis of the year-round operation of the HVAC installation in the analyzed building was performed for each of the seven variants. The conducted analyses revealed that for each combination of filling and thermal insulation material, there is an optimal maximum temperature of the stack, at which the volume of the stack is the smallest or its costs are the lowest. The obtained results were evaluated to determine the ideal variant combination, and two solutions were recommended: clinker brick and fireplace wool, for which the stack volume is 23 m3 and the total cost is EUR 12,500; and concrete block and glass wool, for which the stack volume is 27 m3 and the total cost is EUR 1700.
The Energy Situation in the Federal Republic of Germany: Analysis of the Current Situation and Perspectives for a Non-Fossil Energy Supply
Authors: Atzler Frank , Türck Julian , Türck Ralf , Krahl Jürgen
Published: June 2023

Formulating energy policies at national, European, and global levels is extremely challenging. The move away from fossil fuels is associated with a variety of technological, economic, and social implications, each of which is subject to dynamic changes and societal scrutiny and can hardly be predicted with certainty. Therefore, a fact-based assessment for the path to a sustainable green energy future is sought out in this paper, using the road-based mobility sector of the Federal Republic of Germany as an example. The analysis performed in this paper is built on publicly accessible, reputable sources like DESTATIS and EUROSTAT. In addition, some very simple calculations were made, e.g., on the potential for wind and photovoltaic energy within Germany. Such an analysis needs to start with the overall energy consumption of any one country. A basic assumption of the paper is that the energy system of the future will be based to a large extent on electricity and its storage in chemical energy. It is assumed that, in addition to hydrogen, liquid energy sources will play a significant role due to the simplicity of their logistics and the subsequent implications on cost. Examples of green, electricity-based fuels with great potential are methanol, methane, and ammonia. Additionally, biomass plays an important role, either for direct use as a fuel or as a source of non-fossil carbon. Today, biofuels, i.e., biodiesel and bioethanol, deliver the largest contribution to climate protection in the EU transport sector. The main goal—the reduction of greenhouse gas emissions—often collides with geopolitical circumstances or national political necessities. This includes, for example, the current world market situation and its national impacts caused by the Russian attack on Ukraine. The prospect for a green, sustainable, and defossilized energy supply are discussed in this context. The paper concludes that a defossilized world energy supply and trade based on renewable electricity and its derivatives, eHydrogen and refuels, and on biomass, is feasible.
Investigation and Minimization of Power Loss in Radial Distribution Network Using Gray Wolf Optimization
Authors: Alqahtani Mohammed , Marimuthu Ponnusamy , Moorthy Veerasamy , Pangedaiah B. , Reddy Ch. Rami , Kiran Kumar M. , Khalid Muhammad
Published: June 2023

This paper describes a computational procedure to establish the optimal distribution of network reconfiguration by means of a novel gray wolf optimization (GWO) algorithm. The procedure aimed to diminish the system’s power loss and produce a better voltage profile while fulfilling the operating constraints described by different operating conditions. Under practical restrictions, the distribution network reconfiguration (DNR) problem is classified as multimodal and highly nonlinear. Constraint breaches were appropriately handled to produce stable convergence characteristics, and high-quality solutions were obtained in a shorter execution time. The 33-bus and 69-bus systems were used to obtain the optimal reconfiguration by incorporating the method developed in this work. The simulation results obtained were collated and compared with the outcomes of other well-known optimization techniques, confirming the efficacy of the GWO algorithm in solving the DNR problem.
Intentional Controlled Islanding Strategy for Wind Power Plant Integrated Systems
Authors: Babaei Mehdi , Abu-Siada Ahmed
Published: June 2023

The concept of intentional controlled islanding (ICI) is introduced as a proactive measure to safeguard the power system against blackouts in the event of significant disturbances. It involves strategically partitioning the system into self-healing islands, thereby mitigating the impact of such disturbances. This study introduces a new framework for creating stable, controlled islands in power systems with large-scale wind power plants. The proposed islanding strategy takes into account the impact of wind power plants on the coherency grouping of generators as a constraint in the ICI problem. The proposed algorithm employs the Virtual Synchronous Motion Equation (VSME) model of asynchronous generators to replace wind power plants in power systems and groups all generators, including synchronous generators and wind turbine generators, based on their dynamic coupling. Support Vector Clustering is employed in the ICI problem to determine the coherent generator clusters as the cores of the formed islands. The algorithm can identify the optimal number of islands without prior information about the number of clusters. In this study, a Mixed Integer Linear Programming (MILP) model is formulated to address the ICI problem with the objective of minimising the power imbalance in each island after splitting while ensuring the transient stability and dynamic frequency stability of ICI. Simulation results on modified 39-bus and 118-bus test power systems demonstrate that integrating a Virtual Inertia Controller into the wind-integrated power systems results in a high-inertia power system that behaves similarly to a conventional power system with only synchronous generators during the islanding procedure.
Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review
Authors: Suprabhath Koduru Sriranga , Machina Venkata Siva Prasad , Madichetty Sreedhar
Published: June 2023

The importance of and need for cyber security have increased in the last decade. The critical infrastructure of the country, modeled with cyber-physical systems (CPS), is becoming vulnerable because of a lack of efficient safety measures. Attackers are becoming more innovative, and attacks are becoming undetectable, thereby causing huge risks to these systems. In this scenario, intelligent and evolving detection methods should be introduced to replace basic and outworn methods. The ability of artificial intelligence (AI) to analyze data and predict outcomes has created an opportunity for researchers to explore the power of AI in cyber security. This article discusses new-age intelligence and smart techniques such as pattern recognition models, deep neural networks, generative adversarial networks, and reinforcement learning for cyber security in CPS. The differences between the traditional security methods used in information technology and the security methods used in CPS are analyzed, and the need for a transition into intelligent methods is discussed in detail. A deep neural network-based controller that detects and mitigates cyber attacks is designed for microgrid systems. As a case study, a stealthy local covert attack that overcomes the existing microgrid protection is modeled. The ability of the DNN controller to detect and mitigate the SLCA is observed. The experiment is performed in a simulation and also in real-time to analyze the effectiveness of AI in cyber security.
Large Neighborhood Search for Electric Vehicle Fleet Scheduling
Authors: Limmer Steffen , Varga Johannes , Raidl Günther Robert
Published: June 2023

This work considers the problem of planning how a fleet of shared electric vehicles is charged and used for serving a set of reservations. While exact approaches can be used to efficiently solve small to medium-sized instances of this problem, heuristic approaches have been demonstrated to be superior in larger instances. The present work proposes a large neighborhood search approach for solving this problem, which employs a mixed integer linear programming-based repair operator. Three variants of the approach using different destroy operators are evaluated on large instances of the problem. The experimental results show that the proposed approach significantly outperforms earlier state-of-the-art methods on this benchmark set by obtaining solutions with up to 8.5% better objective values.
Modeling and Optimization of Natural Gas CCHP System in the Severe Cold Region
Authors: Song Yidan , Sun Qiaoqun , Zhang Yu , Da Yaodong , Dong Heming , Zhang Hebo , Du Qian , Gao Jianmin
Published: June 2023

A natural gas combined cooling, heating, and power (CCHP) system is a typical integrated energy supply method that optimizes end−use energy. However, how to achieve economically feasible natural gas CCHP in severe cold regions with low−grade heat demand reaching 50% is still a pressing issue. This paper establishes a typical natural gas CCHP system model for severe cold regions and conducts the system. Based on the climate conditions of Harbin, the economic optimization of independent gas turbine systems, internal combustion engines, and gas turbine systems is still a pressing issue. Based on the climate conditions of Harbin, the economic optimization of independent gas turbine systems, internal combustion engine systems, and steam boiler systems under different cooling and heating load ratios was carried out. The combination of “internal combustion engine + steam boiler” has the most optimal cost of RMB 1.766 million (USD 0.255 million), saving 10.7%, 7.8%, and 18.3% compared to the three single equipment subsystems respectively. This provides good theoretical support for the construction of multi−energy heterogeneous energy systems.
Optimal Generation Capacity Allocation and Droop Control Design for Current Sharing in DC Microgrids
Authors: Chapaloglou Spyridon , Abdolmaleki Babak , Tedeschi Elisabetta
Published: June 2023

Considering the increasing amounts of renewable energy generation and energy storage required to meet ambitious environmental goals, it is clear that the next generation of power grids will be dominated by converter-connected devices. In addition, the increasing share of loads connected via power electronics and the general transition to non-synchronous grids with distributed generators make dc microgrids an attractive future alternative. However, achieving optimal utilization of distributed generators in such cases is a complex task, as the performance depends on both the grid and control design. In this paper, we consider such a case where the optimal utilization of distributed generators is achieved by optimal power sharing while taking into account the grid topology, the available generators, and the way they are controlled. For the latter, we consider a droop-based decentralized control scheme whose primary objective is to achieve voltage regulation in the allowable operating range. A novel mixed-integer optimization approach is proposed to identify the optimal converter size and location in the network so that the microgrid can operate safely and with optimal use of the available resources. Time-domain simulations are used to validate the proposed approach and demonstrate its robustness to uncertainty in generator availability.
Analysis of Core Losses in Transformer Working at Static Var Compensator
Authors: Osinski Piotr , Witczak Pawel
Published: June 2023

This article presents the comparison of 3D and 2D finite element models of a power transformer designed for reactive power compensation stations. There is a lack of studies in the literature on internal electromagnetic phenomena in the active part of a transformer operated in these conditions. The results of numerical 2D and 3D calculations of no-load current and losses in the transformer core were obtained by using various methods and models. The impact of considering the hysteresis loop phenomenon on the calculation of core losses was investigated by using the Jiles–Atherton core losses model. The results obtained in the paper show that the model of the core must contain the areas representing the influence of overlappings on the no-load current and also on the flux density field in the core. The capacitive load of the transformer increases the flux density in the core limbs by several percent, so the power losses there must also increase accordingly. As a summary of the research, differences in the values of losses in each core element between the capacitive load and no-load conditions are presented. The results presented in this paper indicate that considering nonlinearity related to the magnetic hysteresis loop has a significant impact on the calculation of the core losses of power transformers.
Correlation between Non-Structural Carbohydrates and C:N:P Stoichiometric Ratio of Haloxylon ammodendron under Different Water–Salt Gradients
Authors: Yang Fang , Lv Guanghui , Qie Yadong
Published: June 2023

Growth is restricted by both water and phosphorus (P), and balancing the relationship between non−structural carbohydrates (NSCs) and carbon: nitrogen: phosphorus (C:N:P) is essential for Haloxylon ammodendron to adapt to arid habitats. The survival and growth strategies of the dominant species H. ammodendron in a desert ecosystem were examined in order to better serve the restoration of degraded ecosystems and desertification control. Three water and salt gradients (high water and high salinity, medium water and salinity, and low water and low salinity) in the Aibi Lake Reserve were selected. We analyzed the accumulation and distribution of NSCs in the assimilation branches and secondary branches of H. ammodendron and the changes in the measurement ratio characteristics of C:N:P, as well as the soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) content. The results showed that: (1) With the decrease of soil water and salt content, the basal diameter, plant height, crown area, and NSC content of H. ammodendron significantly decreased. This morphological adjustment in the aboveground part is similar to a “self thinning” behavior, aimed at reducing transpiration area and balancing carbon distribution in the body. The carbon accumulation in the body helps the H. ammodendron to resist the dual stresses of drought and salt. (2) With the decrease of water and salt content, the C content of assimilating branches increased significantly, while there was no significant change in secondary branches. However, the N and P content of both branches decreased significantly, and the N: P of both branches was greater than 16, indicating that it was mainly limited by P. (3) The NSC of the two branches was significantly negatively correlated with C, and significantly positively correlated with N and P, and there was a strong positive correlation between the assimilation branches NSC: C: P and NSC: C: P. The synthesis and accumulation of NSC of H. ammodendron were influenced by the content and relationship of C, N, and P, and the abundance of P content transported by the root system to the aboveground portion may have an important and decisive role in regulating nutrient balance and non-structural carbon dynamics.