bluuebunny/crossref_metadata_2025 huggingface dataset:
Insights into the Gut Microbiota of the Freshwater Crab Sinopotamon planum across Three Seasons and Its Associations with the Surrounding Aquatic Microbiota
Authors: Liu Caixin , Liu Meijun , Wang Yifan , Shi Boyang , Pan Da
Published: April 2023

Gut microbiota is closely related to the health of the host and its adaptation to environmental changes. Sinopotamon planum is a species of freshwater crab that lives in the water for three seasons and plays a key role in freshwater ecosystems as a benthic macroinvertebrate, an important indicator of aquatic ecological health. In this study, we sequenced 60 gut microbial samples of S. planum and nine microbial samples from the surrounding water in spring, summer, and autumn based on the 16S rRNA gene. The results showed that gut microbiota had the highest alpha diversity in summer, which may be related to increased adaptability in summer. Firmicutes, Proteobacteria, and Bacteroidota were the most dominant phyla of gut microbiota across three seasons, with Candidatus Hepatoplasma and Candidatus Bacilloplasma being the main genera. These main phyla and genera may be key to maintaining a stable function of the intestinal environment. Firmicutes was the phylum with the highest relative abundance, which is probably related to the carnivorous behaviour of S. planum. The abundant C. Hepatoplasma may be related to the starvation of S. planum in the wild. In both gut and water microbiota, beta diversity analyses showed significant differences across seasons. Comparative analysis of gut microbes and surrounding water microbes showed significant differences in microbial diversity and composition between gut and surrounding water. In conclusion, the structure of the gut microbial community of S. planum differed significantly between the studied seasons, but the water microbial community around S. planum was less variable and significantly different from the gut microbes. The seasonal differences in gut microbes are more likely the result of self-internal adaptation to changes in water temperature and food resources between seasons.
Error Consistency for Machine Learning Evaluation and Validation with Application to Biomedical Diagnostics
Authors: Levman Jacob , Ewenson Bryan , Apaloo Joe , Berger Derek , Tyrrell Pascal N.
Published: April 2023

Supervised machine learning classification is the most common example of artificial intelligence (AI) in industry and in academic research. These technologies predict whether a series of measurements belong to one of multiple groups of examples on which the machine was previously trained. Prior to real-world deployment, all implementations need to be carefully evaluated with hold-out validation, where the algorithm is tested on different samples than it was provided for training, in order to ensure the generalizability and reliability of AI models. However, established methods for performing hold-out validation do not assess the consistency of the mistakes that the AI model makes during hold-out validation. Here, we show that in addition to standard methods, an enhanced technique for performing hold-out validation—that also assesses the consistency of the sample-wise mistakes made by the learning algorithm—can assist in the evaluation and design of reliable and predictable AI models. The technique can be applied to the validation of any supervised learning classification application, and we demonstrate the use of the technique on a variety of example biomedical diagnostic applications, which help illustrate the importance of producing reliable AI models. The validation software created is made publicly available, assisting anyone developing AI models for any supervised classification application in the creation of more reliable and predictable technologies.
Research of Prostate Cancer Urinary Diagnostic Biomarkers by Proteomics: The Noteworthy Influence of Inflammation
Authors: Bellei Elisa , Caramaschi Stefania , Giannico Giovanna A. , Monari Emanuela , Martorana Eugenio , Reggiani Bonetti Luca , Bergamini Stefania
Published: April 2023

Nowadays, in the case of suspected prostate cancer (PCa), tissue needle biopsy remains the benchmark for diagnosis despite its invasiveness and poor tolerability, as serum prostate-specific antigen (PSA) is limited by low specificity. The aim of this proteomic study was to identify new diagnostic biomarkers in urine, an easily and non-invasively available sample, able to selectively discriminate cancer from benign prostatic hyperplasia (BPH), evaluating whether the presence of inflammation may be a confounding parameter. The analysis was performed by two-dimensional gel electrophoresis (2-DE), mass spectrometry (LC-MS/MS) and Enzyme-Linked Immunosorbent Assay (ELISA) on urine samples from PCa and BPH patients, divided into subgroups based on the presence or absence of inflammation. Significant quantitative and qualitative differences were found in the urinary proteomic profile of PCa and BPH groups. Of the nine differentially expressed proteins, only five can properly be considered potential biomarkers of PCa able to discriminate the two diseases, as they were not affected by the inflammatory process. Therefore, the proteomic research of novel and reliable urinary biomarkers of PCa should be conducted considering the presence of inflammation as a realistic interfering element, as it could hinder the detection of important protein targets.
Simultaneous Super-Resolution and Classification of Lung Disease Scans
Authors: Emara Heba M. , Shoaib Mohamed R. , El-Shafai Walid , Elwekeil Mohamed , Hemdan Ezz El-Din , Fouda Mostafa M. , Taha Taha E. , El-Fishawy Adel S. , El-Rabaie El-Sayed M. , El-Samie Fathi E. Abd
Published: April 2023

Acute lower respiratory infection is a leading cause of death in developing countries. Hence, progress has been made for early detection and treatment. There is still a need for improved diagnostic and therapeutic strategies, particularly in resource-limited settings. Chest X-ray and computed tomography (CT) have the potential to serve as effective screening tools for lower respiratory infections, but the use of artificial intelligence (AI) in these areas is limited. To address this gap, we present a computer-aided diagnostic system for chest X-ray and CT images of several common pulmonary diseases, including COVID-19, viral pneumonia, bacterial pneumonia, tuberculosis, lung opacity, and various types of carcinoma. The proposed system depends on super-resolution (SR) techniques to enhance image details. Deep learning (DL) techniques are used for both SR reconstruction and classification, with the InceptionResNetv2 model used as a feature extractor in conjunction with a multi-class support vector machine (MCSVM) classifier. In this paper, we compare the proposed model performance to those of other classification models, such as Resnet101 and Inceptionv3, and evaluate the effectiveness of using both softmax and MCSVM classifiers. The proposed system was tested on three publicly available datasets of CT and X-ray images and it achieved a classification accuracy of 98.028% using a combination of SR and InceptionResNetv2. Overall, our system has the potential to serve as a valuable screening tool for lower respiratory disorders and assist clinicians in interpreting chest X-ray and CT images. In resource-limited settings, it can also provide a valuable diagnostic support.
Three-Dimensional Cone-Beam Computed Tomography Evaluation of Changes in Naso-Maxillary Complex Associated with Rapid Palatal Expansion
Authors: Georgiadis Thomas , Angelopoulos Christos , Papadopoulos Moschos A. , Kolokitha Olga-Elpis
Published: April 2023

The introduction of Cone-Beam Computed Tomography (CBCT) in orthodontics has added a new tool to diagnosis and treatment planning. The aim of this prospective clinical trial was to investigate the changes in the dimensions of the naso-maxillary complex in growing patients after RPE using CBCT. A total of 16 growing children (8 females, 6 males) with a mean age of 11, 12 ± 1 and 86 years underwent RPE as part of their comprehensive orthodontic treatment. CBCT scans were obtained before RPE (T1), immediately after RPE (T2) and 6 months after RPE (T3). The dimensions of the nasal width, nasal floor and the aperture of the midpalatal suture were calculated in different coronal slices of CBCT. Evaluation of the mean value variance per measurement at the three time intervals were performed using the paired Wilcoxon signed-rank test. Differences between the three time intervals were assessed by performing Multiple Pairwise Comparisons. A statistically significant increase in all measurements was seen immediately after RPE expansion (T2–T1) and six months after expansion (T3–T1). Between the end of expansion and 6 months in retention (T3–T2), a decrease was observed for all measurements. RPE can cause expansion of the nasal cavity in growing patients. The expansion of the midpalatal suture follows a triangular pattern of opening.
Combined CT Coronary Artery Assessment and TAVI Planning
Authors: Renker Matthias , Schoepf U. Joseph , Kim Won Keun
Published: April 2023

Computed tomography angiography (CTA) of the aorta and the iliofemoral arteries is crucial for preprocedural planning of transcatheter aortic valve implantation (TAVI) in patients with severe aortic stenosis (AS), because it provides details on a variety of aspects required for heart team decision-making. In addition to providing relevant diagnostic information on the degree of aortic valve calcification, CTA allows for a customized choice of the transcatheter heart valve system and the TAVI access route. Furthermore, current guidelines recommend the exclusion of relevant coronary artery disease (CAD) prior to TAVI. The feasibility of coronary artery assessment with CTA in patients scheduled for TAVI has been established previously, and accumulating data support its value. In addition, fractional flow reserve determined from CTA (CT–FFR) and machine learning-based CT–FFR were recently shown to improve its diagnostic yield for this purpose. However, the utilization of CTA for coronary artery evaluation remains limited in this specific population of patients due to the relatively high risk of CAD coexistence with severe AS. Therefore, the current diagnostic work-up prior to TAVI routinely includes invasive catheter coronary angiography at most centers. In this article, the authors address technological prerequisites and CT protocol considerations, discuss pitfalls, review the current literature regarding combined CTA coronary artery assessment and preprocedural TAVI evaluation, and provide an overview of unanswered questions and future research goals within the field.
Bacterial Assessment of Stethoscopes Used by Healthcare Workers at a Tertiary Care Government Hospital in Bharatpur, Nepal
Authors: Adhikari Sanjib , Adhikaree Neetu , Paudel Krishna Prasad , Nepal Roshan , Poudel Birendra , Giri Sujan , Khadka Suman , Khawas Saphala , Sapkota Sanjeep , Regmi Ramesh Sharma , Rijal Komal Raj
Published: April 2023

The transmission of healthcare-associated infections (HCAIs) in healthcare settings is a serious challenge in the medical fraternity. Medical devices, such as stethoscopes used by healthcare workers (HCWs), are likely to harbor a considerable number of pathogenic microbes, which may result in the transmission of HCAIs. This study sought to investigate bacterial contamination of stethoscopes used by HCWs at Bharatpur Hospital, Nepal. During the study period of 3 months from December 2019 to February 2020, a total of 87 stethoscopes were examined; bacterial pathogens were isolated and identified by culture and biochemical tests, and their susceptibilities against different antibiotics were determined using standard protocols of the Clinical and Laboratory Standards Institute (CLSI). The disc diffusion method was used primarily to screen for extended-spectrum beta-lactamase (ESBL)- and metallo-beta-lactamase (MBL)-producing isolates, followed by their confirmation using cephalosporin/clavulanate combination discs and the disc potentiation methods, respectively. In addition, molecular detection of blaCTX-M and blaVIM genes was performed using conventional polymerase chain reaction (PCR). Of the 87 stethoscopes examined, more than a quarter (28.7%) were colonized with different pathogenic bacteria. Bacterial contamination of stethoscopes was found to be significantly associated with various factors, such as disinfecting routine, method of disinfection, and department of the hospital (p < 0.05). A higher rate of bacterial contamination was observed on the diaphragm of the stethoscope (12.64%) and among HCWs who overlooked hand hygiene practices (45.45%). The prevalence of methicillin-resistant S. aureus (MRSA) was 44.44%, and approximately half of the Gram-negative isolates (47%) were multidrug resistant (MDR). Imipenem (81.25%) and chloramphenicol (83.33%) were found to be the most effective antibiotics for Gram-negative and Gram-positive bacteria, respectively. Phenotypic screening showed that 43.75% of isolates were ESBL producers, and 18.75% were MBL producers, but blaCTX-M and blaVIM genes were detected in only 31.25% and 6.25% of isolates, respectively. The results of the study call for effective stethoscope disinfection practices along with the judicious use of antibiotics by HCWs in order to minimize cross-contamination, emergence of resistance, and spread of nosocomial infections in clinical settings.
Did Ghana Do Enough? A Scientometric Analysis of COVID-19 Research Output from Ghana within the African Context
Authors: Yalley Akua K. , Ahiatrogah Selasie , Yalley Akuba B. , Yankson Isaac K. , Nii-Trebi Nicholas I. , Yalley Abena Asefuaba
Published: April 2023

The COVID-19 pandemic has generated worldwide research efforts to provide knowledge about the disease. Yet little is known about how Ghana contributed to this critical knowledge production. This scientometric analysis was conducted to ascertain Ghana’s COVID-19 research output within the African context to gain understanding and identify potential future directions. The study retrieved relevant research, spanning 2019 to 2022, from the Scopus database in December 2022. The retrieved data were assessed using various established indices, including collaboration patterns, productive institutions, citation patterns, and major research sponsors, among others. Ghana came seventh in Africa with a total of 1112 publications. For international collaborations, the United States and the United Kingdom were the major partners, while South Africa was the main African collaborator with Ghana. Out of the top 21 most productive authors, 85.7% were males and 14.3% were females, demonstrating a great gender gap in research output in Ghana. Although Ghana has made some contributions to the global COVID-19 research output, there are few intra-continental research collaborations, which limits Africa’s overall research output. Our study demonstrates a critical need for the Ghanaian government to prioritize research and funding and address barriers to women’s research productivity.
Periodontal Disease and Nonsurgical Periodontal Therapy on the OHRQoL of the Patient: A Pilot Study of Case Series
Authors: Moya-Villaescusa María José , Sánchez-Pérez Arturo , Esparza-Marín Jesús , Jornet-García Alfonso , Montoya-Carralero José María
Published: April 2023

The objective of this prospective study was to assess oral health-related quality of life (OHR-QoL) in patients with periodontitis and nonsurgical periodontal treatment. A prospective, longitudinal case series study was carried out at the University Dental Clinic of the Morales Meseguer Hospital in Murcia. Eighty periodontal patients with periodontitis, aged 22 to 72 years, were included in this study. The Spanish version of the Oral Health Impact Profile 14 (OHIP-14sp) questionnaire was used on two occasions: at the first visit and one month after completing the nonsurgical periodontal treatment. Clinical measurements of probing depth, plaque index and bleeding on probing were performed at baseline and after periodontal therapy. Data were analyzed using Student’s t-test and analysis of variance. We found statistically significant differences (p < 0.05) between the pre- and post-treatment in all the dimensions studied except disability. Similarly, statistically significant differences were also found according to the degree or stage of the disease. However, these differences were not observed with respect to the extent of periodontal disease. Periodontal disease has a negative impact on the OHRQoL of periodontal patients, especially in severe stages (III–IV). Basic periodontal treatment can improve the OHRQoL of periodontal patients one month after treatment.
Deep Reinforcement Learning for Vision-Based Navigation of UAVs in Avoiding Stationary and Mobile Obstacles
Authors: Kalidas Amudhini P. , Joshua Christy Jackson , Md Abdul Quadir , Basheer Shakila , Mohan Senthilkumar , Sakri Sapiah
Published: April 2023

Unmanned Aerial Vehicles (UAVs), also known as drones, have advanced greatly in recent years. There are many ways in which drones can be used, including transportation, photography, climate monitoring, and disaster relief. The reason for this is their high level of efficiency and safety in all operations. While the design of drones strives for perfection, it is not yet flawless. When it comes to detecting and preventing collisions, drones still face many challenges. In this context, this paper describes a methodology for developing a drone system that operates autonomously without the need for human intervention. This study applies reinforcement learning algorithms to train a drone to avoid obstacles autonomously in discrete and continuous action spaces based solely on image data. The novelty of this study lies in its comprehensive assessment of the advantages, limitations, and future research directions of obstacle detection and avoidance for drones, using different reinforcement learning techniques. This study compares three different reinforcement learning strategies—namely, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC)—that can assist in avoiding obstacles, both stationary and moving; however, these strategies have been more successful in drones. The experiment has been carried out in a virtual environment made available by AirSim. Using Unreal Engine 4, the various training and testing scenarios were created for understanding and analyzing the behavior of RL algorithms for drones. According to the training results, SAC outperformed the other two algorithms. PPO was the least successful among the algorithms, indicating that on-policy algorithms are ineffective in extensive 3D environments with dynamic actors. DQN and SAC, two off-policy algorithms, produced encouraging outcomes. However, due to its constrained discrete action space, DQN may not be as advantageous as SAC in narrow pathways and twists. Concerning further findings, when it comes to autonomous drones, off-policy algorithms, such as DQN and SAC, perform more effectively than on-policy algorithms, such as PPO. The findings could have practical implications for the development of safer and more efficient drones in the future.
A Sampling-Based Distributed Exploration Method for UAV Cluster in Unknown Environments
Authors: Wang Yue , Li Xinpeng , Zhuang Xing , Li Fanyu , Liang Yutao
Published: April 2023

Rapidly completing the exploration and construction of unknown environments is an important task of a UAV cluster. However, the formulation of an online autonomous exploration strategy based on a real-time detection map is still a problem that needs to be discussed and optimized. In this paper, we propose a distributed unknown environment exploration framework for a UAV cluster that comprehensively considers the path and terminal state gain, which is called the Distributed Next-Best-Path and Terminal (DNBPT) method. This method calculates the gain by comprehensively calculating the new exploration grid brought by the exploration path and the guidance of the terminal state to the unexplored area to guide the UAV’s next decision. We propose a suitable multistep selective sampling method and an improved Discrete Binary Particle Swarm Optimization algorithm for path optimization. The simulation results show that the DNBPT can realize rapid exploration under high coverage conditions in multiple scenes.
On the Different Abilities of Cross-Sample Entropy and K-Nearest-Neighbor Cross-Unpredictability in Assessing Dynamic Cardiorespiratory and Cerebrovascular Interactions
Authors: Porta Alberto , Bari Vlasta , Gelpi Francesca , Cairo Beatrice , De Maria Beatrice , Tonon Davide , Rossato Gianluca , Faes Luca
Published: April 2023

Nonlinear markers of coupling strength are often utilized to typify cardiorespiratory and cerebrovascular regulations. The computation of these indices requires techniques describing nonlinear interactions between respiration (R) and heart period (HP) and between mean arterial pressure (MAP) and mean cerebral blood velocity (MCBv). We compared two model-free methods for the assessment of dynamic HP–R and MCBv–MAP interactions, namely the cross-sample entropy (CSampEn) and k-nearest-neighbor cross-unpredictability (KNNCUP). Comparison was carried out first over simulations generated by linear and nonlinear unidirectional causal, bidirectional linear causal, and lag-zero linear noncausal models, and then over experimental data acquired from 19 subjects at supine rest during spontaneous breathing and controlled respiration at 10, 15, and 20 breaths·minute−1 as well as from 13 subjects at supine rest and during 60° head-up tilt. Linear markers were computed for comparison. We found that: (i) over simulations, CSampEn and KNNCUP exhibit different abilities in evaluating coupling strength; (ii) KNNCUP is more reliable than CSampEn when interactions occur according to a causal structure, while performances are similar in noncausal models; (iii) in healthy subjects, KNNCUP is more powerful in characterizing cardiorespiratory and cerebrovascular variability interactions than CSampEn and linear markers. We recommend KNNCUP for quantifying cardiorespiratory and cerebrovascular coupling.
Generation of Pseudo-Random Quantum States on Actual Quantum Processors
Authors: Cenedese Gabriele , Bondani Maria , Rosa Dario , Benenti Giuliano
Published: April 2023

The generation of a large amount of entanglement is a necessary condition for a quantum computer to achieve quantum advantage. In this paper, we propose a method to efficiently generate pseudo-random quantum states, for which the degree of multipartite entanglement is nearly maximal. We argue that the method is optimal, and use it to benchmark actual superconducting (IBM’s ibm_lagos) and ion trap (IonQ’s Harmony) quantum processors. Despite the fact that ibm_lagos has lower single-qubit and two-qubit error rates, the overall performance of Harmony is better thanks to its low error rate in state preparation and measurement and to the all-to-all connectivity of qubits. Our result highlights the relevance of the qubits network architecture to generate highly entangled states.
Parental Help-Giving Orientations Scale (PHGOs) in Children’s Learning: Construction and Validation
Authors: Grinshtain Yael , Harpaz Gal , Yaffe Yosi
Published: April 2023

Parental involvement in the education and learning processes of children in general has become central in the last few decades. Following this involvement, the home arena is considered highly influential in providing a supportive environment for children’s learning processes. Help-giving orientations of parents to their children in relation to homework and learning assignments can be crucial for the children’s futures. According to the Help Relations theory regarding the two main orientations—dependent versus autonomy help (dependent help-giving rather than autonomy help), prevents opportunities to develop autonomous coping abilities in future. The Parental Help-Giving Orientations scale was designed to measure parental help to their children in learning at home. In Study 1, eleven experts evaluated orientations of parental help-giving that emerged from interviews. In Study 2 (N = 255), exploratory factor analyses (EFA) indicated four reliable factors: autonomic, dependent reminder, dependent partner, and dependent student. The confirmatory factor analysis (CFA) in Study 3 in an independent sample of parents (N = 303) exhibited a good model-fit of the data and demonstrated measurement invariance across parental gender. The scale can be used to measure individual differences in orientations in help-seeking among mothers and fathers.
The Design of Intelligent Building Lighting Control System Based on CNN in Embedded Microprocessor
Authors: Ding Xisheng , Yu Junqi
Published: April 2023

A convolutional neural network (CNN) was designed and built on an embedded building lighting control system to determine whether the application of CNN could increase the accuracy of image recognition and reduce energy consumption. Currently, lighting control systems rely mainly on information technology, with sensors to detect people’s existence or absence in an environment. However, due to the deviation of this perception, the accuracy of image detection is not high. In order to validate the effectiveness of the new system based on CNN, an experiment was designed and operated. The importance of the research lies in the fact that high image detection would bring in less energy consumption. The result of the experiment indicated that, when comparing the actual position with the positioning position, the difference was between 0.01 to 0.20 m, indicating that the image recognition accuracy of the CNN-based embedded control system was very high. Moreover, comparing the luminous flux of the designed system with natural light and the designed system without natural light with the system without intelligent control, the energy savings is about 40%.
Managing Expatriate Employment Contracts with Blockchain
Authors: Alabdulkarim Yazeed , Alameer Abdulmajeed , Almukaynizi Mohammed , Allheeib Nasser , Alkadyan Fahad , Almaslukh Abdulaziz
Published: April 2023

Expatriates, or migrant workers, are employees who work outside their home country and reside in a foreign country for the purpose of work. They are often subject to job fraud, employment contract violations, and poor working conditions. These calamities are mainly due to language barriers, limited legal protection, and feeling inferior in their host countries. Many reports have indicated that minimum working and living standards for expatriates are not as adequately enforced as those for domestic employees. These issues may be elevated with the presence of an employment contract framework, which would enable better enforcement and wider visibility for both workers and employers. Thus, we propose a blockchain-powered framework to represent expatriate employment contracts as digital assets managed by smart contracts. It enables employers to create contracts to which employees agree in a decentralized, tamper-proof, transparent, and traceable manner. This framework facilitates auditability, tracking, and enhanced visibility of expatriate employment contracts and job history verification for both workers and employers. We provide a prototype implementation using the Hyperledger Fabric platform and analyze the framework qualitatively from scalability, efficiency, security, and privacy perspectives.
A Review of the Evaluation System for Curriculum Learning
Authors: Liu Fengchun , Zhang Tong , Zhang Chunying , Liu Lu , Wang Liya , Liu Bin
Published: April 2023

In recent years, deep learning models have been more and more widely used in various fields and have become a research hotspot for various tasks in artificial intelligence, but there are significant limitations in non-convex optimization problems. As a model training strategy for non-convex optimization, curriculum learning advocates that models learn in the order of easier to more difficult data, mimicking the basic idea of gradual human learning as they learn curriculum. This strategy has been widely used in the fields of computer vision, natural language processing, and reinforcement learning; it can effectively solve the non-convex optimization problem and improve the generalization ability and convergence speed of models. This paper first introduces the application of curriculum learning at three major levels: data, task, and model, and summarizes the evaluators designed using curriculum learning methods in various domains, including difficulty evaluators, training schedulers, and loss evaluators, which correspond to the three stages of difficulty evaluation, training schedule, and loss evaluation in the application of curriculum learning to model training. We also discuss how to choose an appropriate evaluation system and the differences between terms used in different types of research. Finally, we summarize five methods similar to curriculum learning in the field of machine learning and provide a summary and outlook of the curriculum learning evaluation system.
Hybrid Mode Reconfigurable Antenna with V-Shaped Extrudes for Cognitive Radio Applications
Authors: Sharma Abha , Rathi Amit , Al-Khafaji Hamza Mohammed Ridha , Siddiqui Mohammad Gulman , Yadav Ajay K. S.
Published: April 2023

As the same device may be used for various types of applications, antennas must have the capacity to perform multiple functions. Reconfiguring the polarization, operational frequency, and radiation pattern can create an outcome of multi-functionality. A hybrid, frequency- and pattern-reconfigurable antenna with two V-shaped extrudes in a patch is proposed. To increase the bandwidth of the proposed antenna, the defected ground structure (DGS) phenomenon is presented, which is significant in the bandwidth of 3200 MHz from 2.7 GHz to 5.9 GHz, providing a gain of 3.5 dB. For this design, an FR-4 material was chosen as the substrate, and CST software was used for simulation. Using two p-i-n diodes, the projected antenna illustrates switching for multi-frequency bands such as 1.5, 2.6, 3.2, 5.1, and 8.1 GHz, with a constant radiation pattern and a main lobe direction of 155 degrees. It has C-band, WLAN, cognitive radio (CR), and Bluetooth applications. The proposed antenna displays radiation pattern switching at 3.5 GHz, showing variation between 7°, 158°, 173°, and 175°, and at 4 GHz, it demonstrates switching between 11°, 15°, 165°, and 344°, which defines the hybrid reconfiguration.
Transformer-Based User Alignment Model across Social Networks
Authors: Lei Tianliang , Ji Lixin , Wang Gengrun , Liu Shuxin , Wu Lan , Pan Fei
Published: April 2023

Cross-social network user identification refers to finding users with the same identity in multiple social networks, which is widely used in the cross-network recommendation, link prediction, personality recommendation, and data mining. At present, the traditional method is to obtain network structure information from neighboring nodes through graph convolution, and embed social networks into the low-dimensional vector space. However, as the network depth increases, the effect of the model will decrease. Therefore, in order to better obtain the network embedding representation, a Transformer-based user alignment model (TUAM) across social networks is proposed. This model converts the node information and network structure information from the graph data form into sequence data through a specific encoding method. Then, it inputs the data to the proposed model to learn the low-dimensional vector representation of the user. Finally, it maps the two social networks to the same feature space for alignment. Experiments on real datasets show that compared with GAT, TUAM improved ACC@10 indicators by 11.61% and 16.53% on Facebook–Twitter and Weibo–Douban datasets, respectively. This illustrates that the proposed model has a better performance compared to other user alignment models.
Assessment of the Impact of Technological Development and Scenario Forecasting of the Sustainable Development of the Fuel and Energy Complex
Authors: Zhukovskiy Yuriy , Koshenkova Anastasia , Vorobeva Valeriya , Rasputin Daniil , Pozdnyakov Roman
Published: April 2023

The beginning of the 21st century for the fuel and energy complexes of most countries is characterized as a period of active restructuring and a fundamental shift in developmental priorities. The basis of these changes is technological development. Industry 4.0 technologies have particular importance in achieving maximum optimization of production processes. In the same way, they are applicable in establishing effective interaction between the energy sector and other sectors of the economy. The authors outline an approach to assessing the country’s fuel and energy balance state through the selected properties: sustainability, accessibility, efficiency, adaptability and reliability. Hence, a model of the fuel and energy complex was created on the example of the Russian Federation, considering the country’s territorial and functional division. The methodology is based on scenario modeling of the influence level of external challenges in conjunction with the accompanying technological development. The mathematical model allowed forecasting changes in the properties of the energy system. The scientific significance of the work lies in the application of a consistent hybrid modeling approach to forecast the state of the fuel and energy balance. The results of the study are useful in compiling scenarios for the regional and entire development of the fuel and energy complex. Further model improvements should include an expansion of the number of counted industries and their relations.
Energy Behaviors of Prosumers in Example of Polish Households
Authors: Gajdzik Bożena , Jaciow Magdalena , Wolniak Radosław , Wolny Robert , Grebski Wieslaw Wes
Published: April 2023

This paper explores ways to save energy in households with energy prosumers who generate energy using photovoltaic panels and heat pumps. On the basis of a literature analysis, we formulated a research gap in the case of the energy behaviors of prosumers. This research is important due to the growing demand for energy and the transitions of countries toward renewable energy sources. The role of prosumers in the economy is growing as they ensure energy independence and cost savings. The main purpose of this research is to understand the energy behaviors of prosumers and to examine the differences in energy behaviors between users of photovoltaic systems and heat pumps. A sample of 326 Polish prosumer households was selected using the CAWI method in order to obtain empirical data. The results suggest that prosumers show advanced ecological behaviors, and more than half of the respondents implement pro-ecological behaviors in their homes. Being a prosumer is associated with energy independence, which leads to economic stability and less dependence on traditional energy sources. The results indicate that prosumers show a general inclination toward pro-ecological behavior. Thus, this study recommends promoting prosumers and encouraging the use of pro-ecological energy as a priority for the economy. This initiative will contribute to a reduction in energy consumption in various areas, thus raising ecological awareness and a sense of responsibility for the environment.
A Review on Recent Developments of RCCI Engines Operated with Alternative Fuels
Authors: Dwarshala Siva Krishna Reddy , Rajakumar Siva Subramaniam , Kummitha Obula Reddy , Venkatesan Elumalai Perumal , Veza Ibham , Samuel Olusegun David
Published: April 2023

Environmental concerns over automotive exhaust emissions and consumer demand for higher fuel efficiency have led to the development of low-temperature combustion concepts. The reactivity-controlled compression ignition (RCCI) engine is one among them and has the potential to reduce NOx and smoke emissions simultaneously. In this concept, a low-reactivity fuel is injected into the intake port and another high-reactivity fuel is injected into the cylinder directly. This results in reactivity stratification and provides more control over the rate of heat release. However, operating parameters such as reactivity of fuels, premixing ratio, injection strategies, exhaust gas recirculation ratio, piston bowl geometry, and compression ratio influence emissions formation. The article reviews recent developments on the effect of the above operating parameters on the performance and emission characteristics of RCCI engines operated with alternative fuels. The combustion strategies used to extend the RCCI mode to higher loads are also reviewed. Applications of computational fluid dynamics (CFDs) to design the combustion chamber for RCCI engines are discussed. The need for further improvements in the CFD models for RCCI engines is explained. After presenting a thorough review of recent literature, directions for future research on RCCI engines are proposed.
Fatigue Life Convergence of Offshore Wind Turbine Support Structure According to Wind Measurement Period
Authors: Lee Gee-Nam , Ngo Duc-Vu , Lee Sang-Il , Kim Dong-Hyawn
Published: April 2023

This paper investigated the fatigue life of offshore wind turbine (OWT) support structures. For this purpose, a 3 MW-capacity typical wind turbine is investigated using time-domain finite element simulations. In numerical simulations, different stochastic wind models corresponding to different accumulation periods are applied. Then, the stress-based fatigue life is estimated following the rain-flow counting algorithm and Palmgren-Miner linearly cumulative damage rule. The study also addresses the joint distribution of loads at the site of interest. Generally, the study emphasizes the significance of the long-term distribution of the applied environment loads and its influence on the fatigue life of OWT’s substructures. The results imply that the wind measurement period is directly linked to the fatigue life of offshore wind turbine support structures. Accordingly, its fatigue life is significantly reduced at the 25-year accumulative period of wind. Therefore, this study recommends that a sufficient number of accumulative periods of wind and other environmental loads should be considered appropriately.
Experimental Research on Integrated Disassembly Equipment of Super Large Offshore Oilfield Facilities
Authors: Li Changjiang , Xiao Wensheng , Cui Junguo , Li Quan , Fan Lianghuan , Tan Liping
Published: April 2023

Based on the key module-lifting arm system, based on the principle of similarity and the hydrodynamic experimental method of a multi-dimension vibration test platform, an experimental platform for dismantling equipment is designed and built. Subsequently, the motion control model of the six-degrees-of-freedom platform is established. The three-ring control model of a servo electric cylinder is established, and the active heave compensation control of a servo electric cylinder is realized by combining position control theory. Based on the co-simulation of ADAMS and Simulink, the co-simulation system of the integrated dismantling equipment experimental platform is designed and built, and the simulation system is tested and verified. Finally, simulation and experimental verification are carried out based on the experimental platform and co-simulation system. The results show that the heave compensation rate reaches 58.3% in third-class sea conditions, 61.2% in fourth-class sea conditions, and 62.4% in fifth-class sea conditions. The integrated dismantling scheme of super large offshore oilfield facilities is feasible but, in order to ensure the safety and reliability of the operation, a heave compensation system needs to be added. The error between the simulation results and the experimental results is about 15%. Based on the analysis of external interference factors in the experiment, the error results are within a reasonable range, which proves that the experimental platform, the co-simulation system of the experimental platform, and the heave compensation strategy are accurate and effective. This study, for the first time in China, provides an effective experimental platform and co-simulation platform for the design and optimization of the integrated dismantling equipment of super large offshore oilfield facilities and lays a good research foundation for the construction and engineering demonstration of subsequent equipment.
Modelling and Validation of Typical PV Mini-Grids in Kenya: Experience from RESILIENT Project
Authors: Hanbashi Khalid , Iqbal Zafar , Mignard Dimitri , Pritchard Colin , Djokic Sasa Z.
Published: April 2023

PV-based mini-grids are identified as a feasible and, often, only economically viable option for the electrification of Kenyan remote and scattered rural areas, where connection to the national grid is challenging, and the related costs are high, if not prohibitive. This paper presents the analysis of typical Kenyan PV mini-grids by using some results of the work in the project “Reliable, Efficient and Sustainable Mini-Grids for Rural Infrastructure Development in Kenya (RESILIENT)”. After presenting average annual and seasonal daily load profiles of residential and small commercial mini-grid customers identified from the measured demands, the paper introduces the main mini-grid components and their models, including a simplified, but reasonably accurate, model of a mini-grid battery storage system based on the manufacturer’s charge–discharge curves. All mini-grid components are assembled in a scalable and easily reconfigurable simulation model of an actual Kenyan PV mini-grid, and they are implemented for the evaluation of PV mini-grid performance and the potential for expansion and connection of additional residential and small commercial customers. During the validation of the developed simulation model using available measurement data, an empirical approach for adjusting the PV system output power is specified for a more accurate match with the measurements. The presented results indicate the importance of the information on the actual control algorithms and control settings of the mini-grid energy management systems, on the thermal dependencies and characteristics of both PV generation system and battery storage system, and on the availability of on-site measurements of temperature and input solar irradiance. The developed PV mini-grid model can be used for further analyses, such as to study the techno-economic performance of different mini-grid configurations, to identify the optimal sizing of mini-grid components, and to specify efficient control and operation schemes based on the locally available resources.
Tidal Turbines
Authors: Guillou Sylvain , Bibeau Eric
Published: April 2023

Tidal turbines generate energy from tidal currents [...]
Accelerated Particle Swarm Optimization Algorithms Coupled with Analysis of Variance for Intelligent Charging of Plug-in Hybrid Electric Vehicles
Authors: Bakht Khush , Kashif Syed Abdul Rahman , Fakhar Muhammad Salman , Khan Irfan Ahmad , Abbas Ghulam
Published: April 2023

Plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) have gained enormous attention for their ability to reduce fuel consumption in transportation and are, thus, helpful in the reduction of the greenhouse effect and pollution. However, they bring up some technical problems that should be resolved. Due to the ever-increasing demand for these PHEVs, the simultaneous connection of large PEVs and PHEVs to the electric grid can cause overloading, which results in disturbance to overall power system stability and quality and can cause a blackout. Such situations can be avoided by adequately manipulating power available from the grid and vehicle power demand. State of charge (SoC) is the leading performance parameter that should be optimized using computational techniques to charge vehicles efficiently. In this research, an efficient metaheuristic algorithm, accelerated particle swarm optimization (APSO), and its five variants were applied to allocate power to vehicles connected to the grid intelligently. For this, the maximization of average SoC is considered a fitness function, and each PHEV can be connected to the grid once a day so that the maximum number of cars can be charged daily. To statistically compare the performance of these six algorithms, one-way ANOVA was used. Simulation and statistical results obtained by maximizing this highly non-linear objective function show that accelerated particle swarm optimization with Variant 5 achieved some improvements in terms of computational time and best fitness value. The APSO-5 solution has a considerable percentage increase compared with the solution of other variants of APSO for the four PHEV datasets considered. Moreover, after 30 trials, APSO 5 gives the highest possible fitness value among all the algorithms.
Super-Twisting Sliding Mode Control to Improve Performances and Robustness of a Switched Reluctance Machine for an Electric Vehicle Drivetrain Application
Authors: Sehab Rabia , Akrad Ahmad , Saadi Yakoub
Published: April 2023

In electric vehicles, performances of electric vehicle drivetrains depend on the electric machine and the control. Switched Reluctance Machines (SRMs) are today an alternative to rare earth magnets machines such as Permanent Magnet Synchronous Machine (PMSM), which is used in the vehicle drivetrain. Because of its high nonlinear behavior, the classical control designed for SRMs is not sufficient to obtain good performances. The objective of this paper is to make performance and robustness comparisons of the designed robust controllers considering the high nonlinear behavior of SRMs. Sliding Mode Control (SMC) and Super-Twisting Sliding Mode Control (STSMC) are developed and validated by simulation for the velocity control loop and the current control loops of the control strategy. However, an evaluation of their performances compared to classical control based on PI controllers is carried out. For a robustness comparison, a variation of SRM parameters is carried out by simulation using the three controllers. Finally, an experimental validation on a developed test bench using the three controllers is conducted to show that Super-Twisting Sliding Mode Control (STSMC) is the best in terms of performances and robustness for an electric vehicle application.
A Review of Eco-Product Value Realization and Eco-Industry with Enlightenment toward the Forest Ecosystem Services in Karst Ecological Restoration
Authors: Yang Biliang , Zhang Yu , Xiong Kangning , Huang Huiqiong , Yang Ying
Published: April 2023

Eco-product value realization and eco-industry are in a rapid development stage, but the eco-product value realization mechanism is still unclear. Strengthening research on eco-industry and eco-product value realization not only helps to coordinate the relationship between regional ecologies and the economy but also contributes to regional sustainable development. This study conducted a systematic literature review based on related articles retrieved from the Web of Science database and China National Knowledge Infrastructure database. The results showed the following: (1) Regarding time series, the average annual number of published works in the literature from 2000 to 2017 is less than 4, while the average annual number of published works in the literature from 2018 to 2022 is more than 97. The overall study on eco-product value realization and eco-industry has shown a year-on-year growth trend. (2) The research content primarily involves five aspects, including the connotation and extension of eco-products, eco-product supply, eco-product value accounting, eco-product value realization, and eco-industry. (3) It is essential to deepen the study on the improvement mechanism of the eco-product supply capacity, eco-products property rights, evaluation system for eco-product value realization, ecological compensation mechanism, driving mechanism for eco-products to eco-industry, and coupled development of ecological restoration and eco-industry. Based on the above research, this paper presents an enlightment of the forest ecosystem services in the karst ecological restoration area from three aspects: enhancing the supply ability of eco-products, improving the compensation mechanism of forest ecological services, and coordinating the development of ecosystem services and eco-industries.