bluuebunny/crossref_metadata_2025 huggingface dataset:
Mitochondrial Transplantation Therapy Ameliorates Muscular Dystrophy in mdx Mouse Model
Authors: Dubinin Mikhail V. , Mikheeva Irina B. , Stepanova Anastasia E. , Igoshkina Anastasia D. , Cherepanova Alena A. , Semenova Alena A. , Sharapov Vyacheslav A. , Kireev Igor I. , Belosludtsev Konstantin N.
Published: March 2024

Duchenne muscular dystrophy is caused by loss of the dystrophin protein. This pathology is accompanied by mitochondrial dysfunction contributing to muscle fiber instability. It is known that mitochondria-targeted in vivo therapy mitigates pathology and improves the quality of life of model animals. In the present work, we applied mitochondrial transplantation therapy (MTT) to correct the pathology in dystrophin-deficient mdx mice. Intramuscular injections of allogeneic mitochondria obtained from healthy animals into the hind limbs of mdx mice alleviated skeletal muscle injury, reduced calcium deposits in muscles and serum creatine kinase levels, and improved the grip strength of the hind limbs and motor activity of recipient mdx mice. We noted normalization of the mitochondrial ultrastructure and sarcoplasmic reticulum/mitochondria interactions in mdx muscles. At the same time, we revealed a decrease in the efficiency of oxidative phosphorylation in the skeletal muscle mitochondria of recipient mdx mice accompanied by a reduction in lipid peroxidation products (MDA products) and reduced calcium overloading. We found no effect of MTT on the expression of mitochondrial signature genes (Drp1, Mfn2, Ppargc1a, Pink1, Parkin) and on the level of mtDNA. Our results show that systemic MTT mitigates the development of destructive processes in the quadriceps muscle of mdx mice.
Leveraging Artificial Intelligence to Predict Health Belief Model and COVID-19 Vaccine Uptake Using Survey Text from US Nurses
Authors: Omranian Samaneh , Khoddam Alireza , Campos-Castillo Celeste , Fouladvand Sajjad , McRoy Susan , Rich-Edwards Janet
Published: March 2024

We investigated how artificial intelligence (AI) reveals factors shaping COVID-19 vaccine hesitancy among healthcare providers by examining their open-text comments. We conducted a longitudinal survey starting in Spring of 2020 with 38,788 current and former female nurses in three national cohorts to assess how the pandemic has affected their livelihood. In January and March–April 2021 surveys, participants were invited to contribute open-text comments and answer specific questions about COVID-19 vaccine uptake. A closed-ended question in the survey identified vaccine-hesitant (VH) participants who either had no intention or were unsure of receiving a COVID-19 vaccine. We collected 1970 comments from VH participants and trained two machine learning (ML) algorithms to identify behavioral factors related to VH. The first predictive model classified each comment into one of three health belief model (HBM) constructs (barriers, severity, and susceptibility) related to adopting disease prevention activities. The second predictive model used the words in January comments to predict the vaccine status of VH in March–April 2021; vaccine status was correctly predicted 89% of the time. Our results showed that 35% of VH participants cited barriers, 17% severity, and 7% susceptibility to receiving a COVID-19 vaccine. Out of the HBM constructs, the VH participants citing a barrier, such as allergic reactions and side effects, had the most associated change in vaccine status from VH to later receiving a vaccine.
Strategies for the Preservation of Historic Areas within Existing Middle Eastern Cities: The Case of Historic Jeddah
Authors: Aloshan Mohammed , Elghonaimy Islam , Mesbah Essam , Gharieb Moustafa , Heba Khaled Mahmoud , Alhumaid Mohammed Humaid
Published: March 2024

During the recent founding of Saudi Arabia in 1932, there was no interest in the urban and architectural heritage that Jeddah possesses. As a result, historic Jeddah was exposed to many problems similar to many historical Middle Eastern cities. For example, the historic city wall and many heritage buildings were demolished. With the economic recovery during the 1970s, the original dwellers abandoned the city altogether. They were soon replaced by a class of ex-patriate workers who would inhabit the area, and the city’s distinct heritage fell into neglect. At the beginning of the 1980s, attempts to preserve that area began with the expert Robert Matthew, who studied the remaining historical buildings and proposed strategies for preservation that were based on three main directions: restoration, replacement, or new construction. The issuance of “Saudi’s 2030 vision” included programs to develop Saudi cities, including Jeddah. This program includes the removal of unplanned and slum areas around historic Jeddah without developing a clear master plan for what will replace them. In addition, it includes the complete expropriation of historic Jeddah, without announcing a specific plan for what the area will be used for. Therefore, this study seeks to review current preservation strategies, with the aim of reaching results related to the challenges facing the area; demonstrating the effectiveness of the preservation strategies used; and deducing what could contribute in the future to the development, preservation, and optimal exploitation of the area, without losing its value as a result of the modernization processes currently taking place.
Where Morphological and Molecular Classifications Meet: The Role of p53 Immunohistochemistry in the Prognosis of Low-Risk Endometrial Carcinoma (GLAMOUR Study)
Authors: Puppo Andrea , Fraternali Orcioni Giulio , Clignon Valentino , Musizzano Yuri , Zavattero Carla Angela , Vocino Trucco Giulia , Benazzo Giacomo Maria , Vizzielli Giuseppe , Restaino Stefano , Mariuzzi Laura , Orsaria Maria , Seracchioli Renato , Raimondo Diego , Bertoldo Linda , Uccella Stefano , Caliò Anna , Vittori Antisari Giulia , Garzon Simone , Capozzi Vito Andrea , Berretta Roberto , Cosentino Francesco , Ercoli Alfredo , Ieni Antonio , Arcieri Martina , Ceccaroni Marcello , Pesci Anna , Mantovani Giulia , Bruni Francesco , Roviglione Giovanni , Zeppa Pio , Raffone Antonio , Camanni Marco , Delpiano Elena Maria , Provenza Claudia , Borghese Martina , Migliaretti Giuseppe
Published: March 2024

No prospective study has validated molecular classification to guide adjuvant treatment in endometrial cancer (EC), and not even retrospective data are present for patients with morphological low-risk EC. We conducted a retrospective, multicenter, observational study including 370 patients with low-risk endometrioid EC to evaluate the incidence and prognostic role of p53 abnormal expression (p53abn) in this specific subgroup. Among 370 patients, 18 had abnormal expressions of p53 (4.9%). In 13 out of 370 patients (3.6%), recurrences were observed and two were p53abn. When adjusting for median follow-up time, the odds ratio (OR) for recurrence among those with p53abn versus p53 wild type (p53wt) was 5.23—CI 95% 0.98–27.95, p = 0.053. The most common site of recurrence was the vaginal cuff (46.2%). One recurrence occurred within the first year of follow-up, and the patient exhibited p53abn. Both 1-year and 2-year DFS rates were 94.4% and 100% in the p53abn and p53wt groups, respectively. One patient died from the disease and comprised p53wt. No difference in OS was registered between the two groups; the median OS was 21.9 months (16.4–30.1). Larger multicenter studies are needed to tailor the treatment of low-risk EC patients with p53abn. Performing molecular classification on all EC patients might be cost-effective, and despite the limits of our relatively small sample, p53abn patients seem to be at greater risk of recurrence, especially locally and after two years since diagnosis.
High-Resolution Microscopic Characterization of Tunneling Nanotubes in Living U87 MG and LN229 Glioblastoma Cells
Authors: Matejka Nicole , Amarlou Asieh , Neubauer Jessica , Rudigkeit Sarah , Reindl Judith
Published: March 2024

Tunneling nanotubes (TNTs) are fine, nanometer-sized membrane connections between distant cells that provide an efficient communication tool for cellular organization. TNTs are thought to play a critical role in cellular behavior, particularly in cancer cells. The treatment of aggressive cancers such as glioblastoma remains challenging due to their high potential for developing therapy resistance, high infiltration rates, uncontrolled cell growth, and other aggressive features. A better understanding of the cellular organization via cellular communication through TNTs could help to find new therapeutic approaches. In this study, we investigate the properties of TNTs in two glioblastoma cell lines, U87 MG and LN229, including measurements of their diameter by high-resolution live-cell stimulated emission depletion (STED) microscopy and an analysis of their length, morphology, lifetime, and formation by live-cell confocal microscopy. In addition, we discuss how these fine compounds can ideally be studied microscopically. In particular, we show which membrane-labeling method is suitable for studying TNTs in glioblastoma cells and demonstrate that live-cell studies should be preferred to explore the role of TNTs in cellular behavior. Our observations on TNT formation in glioblastoma cells suggest that TNTs could be involved in cell migration and serve as guidance.
Role of Surgery in Metastatic Melanoma and Review of Melanoma Molecular Characteristics
Authors: Sukniam Kulkaew , Manaise Harsheen K. , Popp Kyle , Popp Reed , Gabriel Emmanuel
Published: March 2024

We aimed to review the molecular characteristics of metastatic melanoma and the role of surgery in metastasectomy for metastatic melanoma. We performed a systematic literature search on PubMed to identify relevant studies focusing on several mutations, including NRAS, BRAF, NF1, MITF, PTEN, TP53, CDKN2A, TERT, TMB, EGFR, and c-KIT. This was performed in the context of metastatic melanoma and the role of metastasectomy in the metastatic melanoma population. A comprehensive review of these molecular characteristics is presented with a focus on their prognosis and role in surgical metastasectomy.
Intravenous MSC-Treatment Improves Impaired Brain Functions in the R6/2 Mouse Model of Huntington’s Disease via Recovered Hepatic Pathological Changes
Authors: Yu-Taeger Libo , El-Ayoubi Ali , Qi Pengfei , Danielyan Lusine , Nguyen Hoa Huu Phuc
Published: March 2024

Huntington’s disease (HD), a congenital neurodegenerative disorder, extends its pathological damages beyond the nervous system. The systematic manifestation of HD has been extensively described in numerous studies, including dysfunction in peripheral organs and peripheral inflammation. Gut dysbiosis and the gut–liver–brain axis have garnered greater emphasis in neurodegenerative research, and increased plasma levels of pro-inflammatory cytokines have been identified in HD patients and various in vivo models, correlating with disease progression. In the present study, we investigated hepatic pathological markers in the liver of R6/2 mice which convey exon 1 of the human mutant huntingtin gene. Furthermore, we evaluated the impact of intravenously administered Mesenchymal Stromal Cells (MSCs) on the liver enzymes, changes in hepatic inflammatory markers, as well as brain pathology and behavioral deficits in R6/2 mice. Our results revealed altered enzyme expression and increased levels of inflammatory mediators in the liver of R6/2 mice, which were significantly attenuated in the MSC-treated R6/2 mice. Remarkably, neuronal pathology and altered motor activities in the MSC-treated R6/2 mice were significantly ameliorated, despite the absence of MSCs in the postmortem brain. Our data highlight the importance of hepatic pathological changes in HD, providing a potential therapeutic approach. Moreover, the data open new perspectives for the search in blood biomarkers correlating with liver pathology in HD.
An Industrial Control System for Cement Sulfates Content Using a Feedforward and Feedback Mechanism
Authors: Tsamatsoulis Dimitris
Published: March 2024

This study examines the design and long-term implementation of a feedforward and feedback (FF–FB) mechanism in a control system for cement sulfates applied to all types of cement produced in two mills at a production facility. We compared the results with those of a previous controller (SC) that operated in the same unit. The Shewhart charts of the annual SO3 mean values and the nonparametric Mann–Whitney test demonstrate that, for the FF–FB controller, the mean values more effectively approach the SO3 target than the older controller in two out of the three cement types. The s-charts for the annual standard deviation of all cement types and mills indicate that the ratio of the central lines of FF–FB to SC ranges from 0.39 to 0.59, representing a significant improvement. The application of the error propagation technique validates and explains these improvements. The effectiveness of the installed system is due to two main factors. The feedforward (FF) component tracks the set point of SO3 when the mill begins grinding a different type of cement, while the feedback (FB) component effectively attenuates the fluctuations in the sulfates of the raw materials.
Clinical and MRI-Based Assessment of Patients with Temporomandibular Disorders Treated by Controlled Mandibular Repositioning
Authors: Singh Diwakar , Landry Alain , Schmid-Schwap Martina , Piehslinger Eva , Gahleitner André , Chen Jiang , Rausch-Fan Xiaohui
Published: March 2024

Background: Occlusal splints and anterior repositioning splints (ARSs) are widely accepted treatments for temporomandibular disorders (TMDs). However, there is uncertainty with regard to the most suitable amount of mandibular repositioning. The aim of this study is to evaluate the clinical and functional effects of the therapeutic position (ThP) established based on the Controlled Mandibular Repositioning (CMR) method. Methods: In this clinical trial, 20 subjects with 37 joints with disc displacement with reduction were recruited. The initial standard functional diagnostic protocol, MRI, and digital condylography were performed, and ThP was calculated with the CMR method. After a 6-month follow-up, the standard diagnostic protocol was repeated. The change in disc position was evaluated by means of MRI after 6 months of CMR therapy. Results: The MRI findings in the parasagittal plane demonstrated that out of the 37 joints presenting disc displacement, 36 discs were successfully repositioned; thus, the condyle–disc–fossa relationship was re-established. Therefore, the success rate of this pilot study was 97.3%. The mean position of the displaced discs was at 10:30 o’clock of the TMJ joint and at 12:00 o’clock after CMR therapy. Conclusions: The ThP determined using the CMR approach reduced all of the anteriorly displaced discs (except one). The CMR method allowed to define an optimum ThP of the mandible thus supporting patients’ effective adaptation to treatment position.
Novel Strategy in the Detection of Adverse Cutaneous Drug Reactions: A Case Series Study
Authors: Bán Erika-Gyöngyi , Lechsner Patrick , Dho-Nagy Eszter-Anna , Balan Maria-Antonia , Major-Szakács István , Brassai Attila , Simon-Szabó Zsuzsánna , Ureche Corina
Published: March 2024

With multimorbidity on the rise, adverse cutaneous drug reactions are becoming a daily challenge in clinical practice. The objective evaluation of the skin lesion is crucial but hardly realized due to missing technology and guidelines. In this study, the novel Dermus SkinScanner-U, an optically guided high-frequency ultrasound imaging device, was evaluated regarding its comparability with the Dermatology Life Quality Index (DLQI) and the pharmacological analysis of the patients’ drug therapy. A total of 40 adult patients were evaluated, all with chronic medication use and skin lesions that led to non-compliance toward the pharmacotherapy. With the ongoing aim of further improving the methodology, the first results, with two detailed patient cases, are presented here. It was concluded that in the cases evaluated, there was a significant correlation between the characteristics of the lesions observed on the optical and ultrasound image, the DLQI score, and the pharmacological analysis. The next steps include increasing the scale of the study to ultimately develop a quality-assured methodology for the correct diagnosis of skin-related adverse drug reactions and to prepare a database with the most frequently observed events.
Mechanisms for Robust Local Differential Privacy
Authors: Lopuhaä-Zwakenberg Milan , Goseling Jasper
Published: March 2024

We consider privacy mechanisms for releasing data X=(S,U), where S is sensitive and U is non-sensitive. We introduce the robust local differential privacy (RLDP) framework, which provides strong privacy guarantees, while preserving utility. This is achieved by providing robust privacy: our mechanisms do not only provide privacy with respect to a publicly available estimate of the unknown true distribution, but also with respect to similar distributions. Such robustness mitigates the potential privacy leaks that might arise from the difference between the true distribution and the estimated one. At the same time, we mitigate the utility penalties that come with ordinary differential privacy, which involves making worst-case assumptions and dealing with extreme cases. We achieve robustness in privacy by constructing an uncertainty set based on a Rényi divergence. By analyzing the structure of this set and approximating it with a polytope, we can use robust optimization to find mechanisms with high utility. However, this relies on vertex enumeration and becomes computationally inaccessible for large input spaces. Therefore, we also introduce two low-complexity algorithms that build on existing LDP mechanisms. We evaluate the utility and robustness of the mechanisms using numerical experiments and demonstrate that our mechanisms provide robust privacy, while achieving a utility that is close to optimal.
The Dynamic Spatial Structure of Flocks
Authors: Russell Nicholas J. , Pilkiewicz Kevin R. , Mayo Michael L.
Published: March 2024

Studies of collective motion have heretofore been dominated by a thermodynamic perspective in which the emergent “flocked” phases are analyzed in terms of their time-averaged orientational and spatial properties. Studies that attempt to scrutinize the dynamical processes that spontaneously drive the formation of these flocks from initially random configurations are far more rare, perhaps owing to the fact that said processes occur far from the eventual long-time steady state of the system and thus lie outside the scope of traditional statistical mechanics. For systems whose dynamics are simulated numerically, the nonstationary distribution of system configurations can be sampled at different time points, and the time evolution of the average structural properties of the system can be quantified. In this paper, we employ this strategy to characterize the spatial dynamics of the standard Vicsek flocking model using two correlation functions common to condensed matter physics. We demonstrate, for modest system sizes with 800 to 2000 agents, that the self-assembly dynamics can be characterized by three distinct and disparate time scales that we associate with the corresponding physical processes of clustering (compaction), relaxing (expansion), and mixing (rearrangement). We further show that the behavior of these correlation functions can be used to reliably distinguish between phenomenologically similar models with different underlying interactions and, in some cases, even provide a direct measurement of key model parameters.
Challenges and Opportunities in Inclusive Education with ICT: Teachers’ Perspectives in the Canary Islands during the COVID-19 Pandemic
Authors: Pérez-Jorge David , González-Herrera Ana Isabel , Alonso-Rodríguez Isabel , Rodríguez-Jiménez María del Carmen
Published: March 2024

This study intends to explore the challenges and opportunities in inclusive education using Information and Technologies (ICTs) during the COVID-19 pandemic from the perspective of teachers based in the Canary Islands. It focuses on the digital division and the implications involved while examining the impact of health on students with special needs (SEN) and Specific Educational Support Needs (SENS). For this research, a validated questionnaire was used to assess educators’ perceptions and attitudes toward the use of ICT in inclusive education practices. It reveals insights into the need for enhanced ICT training, age and gender disparities perceived in ICT proficiency, and the essential role of continuous professional development for teachers. The study highlights the importance of developing inclusive and comprehensive training strategies, as well as the need for educational institutions to provide effective digital resources and platforms. The findings show that students perceive both their ICT skills and the effectiveness of remote teaching methods as moderate, thus emphasizing the necessity for strategic interventions and collaborative efforts to enhance digital competencies in this educational sector.
Best of Both Worlds? Combining Physical and Mental Self-Management Strategies to Support Learning from Split-Attention Examples
Authors: de Koning Björn B.
Published: March 2024

The self-management principle holds that higher learning performance is obtained when learners actively use instructional strategies to manage the working memory load imposed by a learning task. Self-management studies with spatially separated but mutually referring text and pictures (split-attention examples) demonstrate the learning benefits of physical (e.g., annotation) and mental (imagined drag-and-drop) strategies. We investigated whether combining physical and mental strategies supports learning beyond a single strategy. Eighty-four participants studied a split-attention example with or without using a physical strategy and/or a mental strategy. Participants completed retention, comprehension, and transfer tests, and rated their cognitive load. Results showed that the combined use of physical and mental strategies resulted in lower cognitive load during learning than using the physical strategy and was more instructionally efficient compared to all other conditions. There were no significant differences regarding learning outcomes. Together, this suggests that combining physical and mental strategies is most supportive for studying split-attention examples.
Inclusion or Isolation? Differential Student Experiences of Independent Learning and Wellbeing in Higher Education
Authors: Wilbraham Susan J. , Jones Emma , Brewster Liz , Priestley Michael , Broglia Emma , Hughes Gareth , Spanner Leigh
Published: March 2024

Independent learning is frequently identified as instrumental to student success within higher education. Although there is a significant body of literature demonstrating the importance of independent learning for retention and progression, to date, the relationship between independent learning and student wellbeing within higher education has been under-explored. This article addresses the gap in the literature by interrogating this relationship, with a particular emphasis on the barriers and enablers to independent learning and the implications for the wellbeing of university students as part of a whole university approach. Drawing on data from a large national student survey and nine focus groups with university staff conducted during the formulation of the University Mental Health Charter, the findings demonstrate important intersectionalities between independent learning and student wellbeing. In particular, they highlight that facilitating independent learning has the potential to allow students to flourish in higher education but only when factors relating to accessibility, inclusivity, expectations, and goals are explicitly addressed. Going forward, it is imperative that these links are more widely acknowledged and addressed within higher education research, policy, and practice to ensure that students are supported to develop as learners during both their transition into university and their journey through and beyond their studies.
The Data Compression Method and FPGA Implementation in the Mars Rover Subsurface-Penetrating Radar on the Tianwen-1 Mission
Authors: Shen Shaoxiang , Hua Xiaolei , Zhou Bin
Published: March 2024

Since Mars is far away from Earth, the propagation delay between Mars and Earth is very large. To ensure the effective use of the link transmission bandwidth, China’s first Mars exploration mission has put forward a demand for data compression for all scientific payloads. The on-board mature algorithms for data compression are mainly focused on optical images and microwave imaging radar applications. No articles have been published on data compression methods that are applied to subsurface-penetrating radar. Based on the background of this application, this paper proposes a logarithmic lossy compression algorithm which can meet the mission requirements for high compression ratios of 4:1 and 2.5:1. Its compression error is less than that of the block adaptive quantization (BAQ) algorithm. The algorithm is not only easy to implement on field-programmable gate array (FPGA) platforms, but also offers simple ground decompression and fast imaging. The experimental results show that high compression ratios of 4:1 and 2.5:1 can be realized, even if the data in and between traces do not have a strong correlation. And its relative error is less than 2%, which is a new type of high-efficiency data compression method that can be implemented on-board to meet with the demand of subsurface penetrating radar.
Plant Disease Identification Using Machine Learning Algorithms on Single-Board Computers in IoT Environments
Authors: Routis George , Michailidis Marios , Roussaki Ioanna
Published: March 2024

This paper investigates the usage of machine learning (ML) algorithms on agricultural images with the aim of extracting information regarding the health of plants. More specifically, a custom convolutional neural network is trained on Google Colab using photos of healthy and unhealthy plants. The trained models are evaluated using various single-board computers (SBCs) that demonstrate different essential characteristics. Raspberry Pi 3 and Raspberry Pi 4 are the current mainstream SBCs that use their Central Processing Units (CPUs) for processing and are used for many applications for executing ML algorithms based on popular related libraries such as TensorFlow. NVIDIA Graphic Processing Units (GPUs) have a different rationale and base the execution of ML algorithms on a GPU that uses a different architecture than a CPU. GPUs can also implement high parallelization on the Compute Unified Device Architecture (CUDA) cores. Another current approach involves using a Tensor Processing Unit (TPU) processing unit carried by the Google Coral Dev TPU Board, which is an Application-Specific Integrated Circuit (ASIC) specialized for accelerating ML algorithms such as Convolutional Neural Networks (CNNs) via the usage of TensorFlow Lite. This study experiments with all of the above-mentioned devices and executes custom CNN models with the aim of identifying plant diseases. In this respect, several evaluation metrics are used, including knowledge extraction time, CPU utilization, Random Access Memory (RAM) usage, swap memory, temperature, current milli Amperes (mA), voltage (Volts), and power consumption milli Watts (mW).
A 10.5 ppm/°C Modified Sub-1 V Bandgap in 28 nm CMOS Technology with Only Two Operating Points
Authors: Nagulapalli Rajasekhar , Yassine Nabil , Tammam Amr A. , Barker Steve , Hayatleh Khaled
Published: March 2024

Reference voltage/current generation is essential to the Analog circuit design. There have been several ways to generate quality reference voltage using bandgap reference (BGR) and there are mainly two types: current mode and voltage mode. The current-mode bandgap reference (CBGR) is widely accepted in industry due to having an output voltage which is below 1 V. However, its drawbacks include a lack of proportional to absolute temperature (PTAT) current availability, a large silicon area, multiple operating points, and a large temperature coefficient (TC). In this paper, various operating points are explained in detail with diagrams. Similar to the conventional voltage mode bandgap reference (VBGR) circuits, modifications of the existing circuits with only two operating points have also been proposed. Moreover, the proposed BGR occupies a much smaller area due to eliminating the complimentary to absolute temperature (CTAT) current-generating resistor. A new self-biased opamp was introduced to operate from a 1.05 V supply, reducing systematic offset and TC of the BGR. The proposed solution has been implemented in 28 nm CMOS TSMC technology, and extraction simulations were performed to prove the robustness of the proposed circuit. The targeted mean BGR output is 500 mV, and across the industrial temperature range (−40 to 125 °C), the simulated TC is approximately 10.5 ppm/°C. The integrated output noise within the observable frequency band is 19.6 µV (rms). A 200-point Monte Carlo simulation displays a histogram with a 2.6 mV accuracy of 1.2% (±3-sigma). The proposed BGR circuit consumes 32.8 µW of power from a 1.05 V supply in a fast process and hot (125 °C) corner. It occupies a silicon area of 81 × 42 µm (including capacitors). This design can aim for use in biomedical and sensor applications.
Graphical Representation of UWF-ZeekData22 Using Memgraph
Authors: Bagui Sikha S. , Mink Dustin , Bagui Subhash C. , Sung Dae Hyun , Mahmud Farooq
Published: March 2024

This work uses Memgraph, an open-source graph data platform, to analyze, visualize, and apply graph machine learning techniques to detect cybersecurity attack tactics in a newly created Zeek Conn log dataset, UWF-ZeekData22, generated in The University of West Florida’s cyber simulation environment. The dataset is transformed to a representative graph, and the graph’s properties studied in this paper are PageRank, degree, bridge, weakly connected components, node and edge cardinality, and path length. Node classification is used to predict the connection between IP addresses and ports as a form of attack tactic or non-attack tactic in the MITRE framework, implemented using Memgraph’s graph neural networks. Multi-classification is performed using the attack tactics, and three different graph neural network models are compared. Using only three graph features, in-degree, out-degree, and PageRank, Memgraph’s GATJK model performs the best, with source node classification accuracy of 98.51% and destination node classification accuracy of 97.85%.
Modular Circuit Synthesis Oriented Modelling Approach for Non-Isolated DC-DC Converters in DCM
Authors: Masike Lebogang , Gitau Michael Njoroge
Published: March 2024

The continued and envisioned large-scale integration of renewable energy sources as a reaction to rising global temperatures and climate change will need a readily available DC grid to increase commissioning and operating efficiency. The effective operation of these grids is predicated on the correct control of its main control points. A plethora of DC-DC converters that find use in DC microgrids act as the main control points. DC-DC converters are non-linear and can operate in different modes with completely unique characteristics. To utilise classical control techniques, laborious equivalent linear models are derived for DC-DC converters using averaging modelling schemes. The application and limitations of these modelling techniques are well captured in the available literature. The most common limitation of the available modelling schemes is that more focus is dedicated to converter attributes like order, functionality and operating mode, even when optimal power flow and voltage regulation within the DC network are of more interest. Structure-based modelling techniques like the use of basic building blocks nullify converter attributes in the modelling process which translates to modelling efficiency. In light of the merits seen with the use of basic building blocks when modelling converters in CCM, the current study extends these merits to converters operation in DCM. Similar to modelling converters in CCM, modelling techniques that are available in the literature continue to consider converter attributes in the modelling process for DCM operation. Moreover, the two modes of operation are treated as unique entities and often modelled in a non-unified manner, which compromise modelling efficiency since the same converter can operate in a different state solely based on loading. The aim is to increase modelling efficiency but also nullify operating mode in the modelling process. The same basic building blocks are now modelled as two-port networks for DCM operation and adopted based on the exact configuration of a specified converter to compute its steady-state and dynamic models. All the advantages seen when modelling converters in CCM using basic building blocks are retained and augmented when considering DCM operation. Thus, any converter with well-defined basic building blocks can be easily modelled solely based on the connection of constituent basic building blocks.
Performance Analysis and Optimization of SOFC/GT Hybrid Systems: A Review
Authors: Yuan Qiao , Li Xiongzhuang , Han Shuo , Wang Sijia , Wang Mengting , Chen Rentian , Kudashev Sergei , Wei Tao , Chen Daifen
Published: March 2024

This review provides an overview of the solid oxide fuel cell/gas turbine (SOFC/GT) hybrid system, highlighting its potential as a highly efficient and low-emission power generation technology. The operating principles and components of the SOFC/GT system, as well as the various configurations and integration strategies, are discussed. This review also examines the performance, advantages, and challenges of the SOFC/GT system, and discusses the research and development efforts aimed at improving its efficiency, reliability, and cost-effectiveness. This work provides an overview of the research conducted in the area of SOFC-based hybrid systems, which is expected to be beneficial for researchers who are interested in this area.
Impact of Depth on Underground Hydrogen Storage Operations in Deep Aquifers
Authors: Luboń Katarzyna , Tarkowski Radosław , Uliasz-Misiak Barbara
Published: March 2024

Underground hydrogen storage in geological structures is considered appropriate for storing large amounts of hydrogen. Using the geological Konary structure in the deep saline aquifers, an analysis of the influence of depth on hydrogen storage was carried out. Hydrogen injection and withdrawal modeling was performed using TOUGH2 software, assuming different structure depths. Changes in the relevant parameters for the operation of an underground hydrogen storage facility, including the amount of H2 injected in the initial filling period, cushion gas, working gas, and average amount of extracted water, are presented. The results showed that increasing the depth to approximately 1500 m positively affects hydrogen storage (flow rate of injected hydrogen, total capacity, and working gas). Below this depth, the trend was reversed. The cushion gas-to-working gas ratio did not significantly change with increasing depth. Its magnitude depends on the length of the initial hydrogen filling period. An increase in the depth of hydrogen storage is associated with a greater amount of extracted water. Increasing the duration of the initial hydrogen filling period will reduce the water production but increase the cushion gas volume.
Lithium-Ion Battery Capacity Estimation Based on Incremental Capacity Analysis and Deep Convolutional Neural Network
Authors: Zeng Sibo , Chen Sheng , Alkali Babakalli
Published: March 2024

Accurate estimation of Li-ion battery capacity is critical for a battery management system (BMS). This paper proposes an innovative method which combines a convolutional neural network and incremental capacity analysis (ICA). In the present approach, the voltage and temperature, which significantly affect the ICA curve during the discharging process, are adopted as the inputs for CNN. Rather than extracting feature parameters of an IC curve, as is carried out in the available research, the present method uses the whole ICA curve as the input to avoid complicated feature extraction and correlation analysis. The results show that the maximum error of capacity estimation is less than 4.7%, the rectified mean squared error is less than 1.3% for each battery, and the overall RMSE is below 1.12%.
Optical Analysis of Ignition Sparks and Inflammation Using Background-Oriented Schlieren Technique
Authors: Grüninger Moritz , Toedter Olaf , Koch Thomas
Published: March 2024

To determine the timing of inflammation in gas and gasoline combustion engines, the point of 10% mass fraction conversion of fuel (MFB10) is commonly used. The MFB10 can be determined from the heating curve, which in turn is calculated from the in-cylinder pressure curve. However, the cylinder pressure is an indirect parameter with regard to inflammation, as it is the result of the combustion that follows the inflammation. An attempt is made to derive a new, direct parameter of inflammation based on optical measurements in order to detect inflammation more rapidly and accurately. The background-oriented Schlieren technique (BOS) in combination with high-magnification optics and a high-speed camera is used to detect local density changes coming from the particle wave around the ignition kernel of a hydrogen combustion inside a combustion chamber. Via BOS and regular high-magnification high-speed imaging, the influence of ignition coil dwell time and in-cylinder pressure on the spark phases and the inflammation itself are evaluated. As a potential direct parameter for inflammation, the size of the particle wave resulting from the expanding ignition kernel is evaluated. It was found that a higher coil energy supports a faster propagation of the particle wave at ambient pressure. At higher pressures, general combustion effects override the effect of the influence of the coil energy on the propagation speed of the particle wave. In addition, the presence of successful inflammation was found to influence the spark phases. A directly measurable parameter for ignition could be found at a basic level, which will serve as a starting point for further detailed investigations.
A Unified Data Profile for Microgrid Loads, Power Electronics, and Sustainable Energy Management with IoT
Authors: Milczarek Adam , Możdżyński Kamil
Published: March 2024

Controllable devices in a classical centralized grid work independently, providing desired functionalities for the owner only, making grid stability and efficient energy management challenging. Therefore, the dynamically developing communication infrastructure has been crucial in improving local energy management and stability, introducing the Internet of Things and, finally, creating micro- or smart grids. Communication technology already allows for exchanging data and information with high bandwidth in practical industrial and grid applications. However, considering the increasing number of electrical devices with different purposes, parameters, and possibilities to work as an energy source or storage, the challenge is device profile standardization, especially for power electronics devices. As many devices as possible should be able to exchange information with the grid operator or local area/home energy management device, like industrial agents, energy routers, or smart transformers. The number and types of parameters, outputs, and input signals available in the devices from the communication point of view are significantly different, making it extremely difficult to apply the advanced energy management algorithm. Therefore, the unified data profile for modern loads was developed and discussed. The proposed device model was standardized, including mandatory information about the device’s work and parameters, with the possibility of reading the control commands from the supervisor device. The proposed unified model was studied in simulation research, integrating self-balanced and self-control areas.
Evaluating the Barriers to Blockchain Adoption in the Energy Sector: A Multicriteria Approach Using the Analytical Hierarchy Process for Group Decision Making
Authors: Andreoulaki Ioanna , Papapostolou Aikaterini , Marinakis Vangelis
Published: March 2024

The blockchain has been proposed for use in various applications in the energy field. Although the blockchain has technical strengths, several obstacles affect the application of the technology in energy services. The scope of this study is to highlight and prioritise the most important barriers to such applications. The first step in this direction is specifying the potential areas of the implementation of blockchain technology in the energy sector. Two useful tools for market analysis were used: Political, Economic, Social, Technological, Legal and Environmental, PESTLE Analysis, and Strengths, Weaknesses, Opportunities and Threats, SWOT Analysis, which examine external and internal factors, respectively. Thus, a list of the most important elements hindering the incorporation of the blockchain in the energy sector was extracted. The detected barriers were classified and ranked by energy and IT experts using the multicriteria method, “Analytical Hierarchy Process for Group Decision Making”. The results reveal that legal barriers relating to the complexities of deficiencies of regulations are the most significant, while technological barriers, especially those related to security issues, are also important. Sociopolitical barriers related mainly to lack of trust in blockchain, as well as economic concerns such as high upfront costs, are less influential but should still be considered. The conclusions of the conducted research have the potential to guide market actors in their endeavours to modernise energy systems through the use of the blockchain, assisting them in designing the most appropriate market strategies.
Optimal Design of a Single-Phase Bidirectional Rectifier
Authors: Esteve Vicente , Bellido Juan L. , Jordán José
Published: March 2024

This article outlines the comprehensive design and control approach for a single-phase bidirectional rectifier (SPBR) used in bidirectional charging of electric vehicle batteries. The operational parameters of the inverter are determined through a thorough analysis of all switching sequences to accurately assess power losses, considering the type of switching device chosen in each case, enabling proper component sizing, and understanding converter efficiency. An exclusive electronic control circuit is examined, governing two converter operation modes: boost rectifier with power factor correction (PFC) and sine pulse inverter width modulation (SPWM) with a minimum number of adjustments made automatically. One problem that arises when addressing the design of an SPBR is determining the operating frequency. To address this issue, this study offers to conduct a comparative analysis of losses using various power devices and magnetic circuits to determine the optimal operating frequency for achieving maximum energy efficiency. To validate the design’s feasibility, a prototype with 10 kW output power was constructed, achieving a peak efficiency of approximately 97.5% in both directions, unity power factor (PF), and total harmonic distortion (THD) of less than 7% during full power operation.
Global Methods for Calculating Shading and Blocking Efficiency in Central Receiver Systems
Authors: Ortega Guillermo , Barbero Rubén , Rovira Antonio
Published: March 2024

This paper presents three new methods for calculating the shading and blocking efficiency in Central Receiver Systems (CRSs). All of them are characterized by the calculation of multiple useful and total reflecting areas without the need to resort to parallel calculation in the CPU or GPU, and by low computation times and minimum errors. They are being specially designed for implementation in codes focused on heliostat field design and optimization in CRSs. The proposed methods have been compared against two outstanding “individual” methods (homology and Boolean operations), in addition to a reference case based on the Monte Carlo ray-tracing (MCRT) technique. The results indicate that one of the proposed methods presents reduced error values and high computational speed, even relaxing the restrictions on candidate filtering. At the same error level, the global method is up to 7.80 times faster than the fastest individual method (homology) and up to 194 times faster than the method based on the MCRT technique. The causes of the main errors of each method are also analyzed.
Adaptive Equivalent Factor-Based Energy Management Strategy for Plug-In Hybrid Electric Buses Considering Passenger Load Variations
Authors: Song Pengxiang , Song Wenchuan , Meng Ao , Li Hongxue
Published: March 2024

Energy management strategies (EMSs) are one of the key technologies for the development of plug-in hybrid electric buses (PHEBs). This paper addresses the issue of optimal energy distribution for PHEBs under significant variations in passenger load at different bus stations, which cannot be solved by a single equivalent factor equivalent fuel consumption minimization energy management strategy (ECMS). An adaptive equivalent factor equivalent fuel consumption minimization energy management strategy (A-ECMS) considering passenger load is proposed. First, the powertrain system of the PHEB is modeled, and the accuracy of the model is verified in a simulation environment. Then, the reference SOC descent trajectory of the battery is obtained using a dynamic programming (DP) algorithm, the recursive least squares (RLS) method is employed to identify the passenger load, and the influence of different loads on the state of charge (SOC) trajectory under a single equivalent factor is analyzed. Finally, a genetic algorithm (GA) is used to establish the correspondence between passenger load, bus station, and equivalent factor, enabling the actual SOC to follow the reference SOC descent trajectory, thereby achieving optimal energy distribution. Simulation results demonstrate that the A-ECMS reduces fuel consumption of the PHEB per 100 km by 2.59% and 10.10% compared to the ECMS and rule-based EMS, respectively, validating the effectiveness of the proposed strategy.