bluuebunny/crossref_metadata_2025 huggingface dataset:
A Comprehensive Review of Cardiovascular Disease Management: Cardiac Biomarkers, Imaging Modalities, Pharmacotherapy, Surgical Interventions, and Herbal Remedies
Authors: Netala Vasudeva Reddy , Teertam Sireesh Kumar , Li Huizhen , Zhang Zhijun
Published: September 2024

Cardiovascular diseases (CVDs) continue to be a major global health concern, representing a leading cause of morbidity and mortality. This review provides a comprehensive examination of CVDs, encompassing their pathophysiology, diagnostic biomarkers, advanced imaging techniques, pharmacological treatments, surgical interventions, and the emerging role of herbal remedies. The review covers various cardiovascular conditions such as coronary artery disease, atherosclerosis, peripheral artery disease, deep vein thrombosis, pulmonary embolism, cardiomyopathy, rheumatic heart disease, hypertension, ischemic heart disease, heart failure, cerebrovascular diseases, and congenital heart defects. The review presents a wide range of cardiac biomarkers such as troponins, C-reactive protein, CKMB, BNP, NT-proBNP, galectin, adiponectin, IL-6, TNF-α, miRNAs, and oxylipins. Advanced molecular imaging techniques, including chest X-ray, ECG, ultrasound, CT, SPECT, PET, and MRI, have significantly enhanced our ability to visualize myocardial perfusion, plaque characterization, and cardiac function. Various synthetic drugs including statins, ACE inhibitors, ARBs, β-blockers, calcium channel blockers, antihypertensives, anticoagulants, and antiarrhythmics are fundamental in managing CVDs. Nonetheless, their side effects such as hepatic dysfunction, renal impairment, and bleeding risks necessitate careful monitoring and personalized treatment strategies. In addition to conventional therapies, herbal remedies have garnered attention for their potential cardiovascular benefits. Plant extracts and their bioactive compounds, such as flavonoids, phenolic acids, saponins, and alkaloids, offer promising cardioprotective effects and enhanced cardiovascular health. This review underscores the value of combining traditional and modern therapeutic approaches to improve cardiovascular outcomes. This review serves as a vital resource for researchers by integrating a broad spectrum of information on CVDs, diagnostic tools, imaging techniques, pharmacological treatments and their side effects, and the potential of herbal remedies.
Maternal Dietary Deficiency in Choline Reduced Levels of MMP-2 Levels in Blood and Brain Tissue of Male Offspring Mice
Authors: Esfandiarei Mitra , Strash Shawn G. U. , Covaleski Amanda , Ille Sharadyn , Li Weidang , Jadavji Nafisa M.
Published: September 2024

Ischemic stroke is one of the leading causes of disability and death globally, with a rising incidence in younger age groups. It is well known that maternal diet during pregnancy and lactation is vital for the early neurodevelopment of offspring. One-carbon (1C) metabolism, including folic acid and choline, plays a vital role in closure of the neural tube in utero. However, the impact of maternal dietary deficiencies in 1C on offspring neurological function following ischemic stroke later in life remains undefined. The aim of this study was to investigate inflammation in the blood and brain tissue of offspring from mothers deficient in dietary folic acid or choline. Female mice were maintained on either a control or deficient diet prior to and during pregnancy and lactation. When offspring were 3 months of age, ischemic stroke was induced. One and a half months later, blood and brain tissue were collected. We measured levels of matrix metalloproteases (MMP)-2 and 9 in both plasma and brain tissue, and reported reduced levels of MMP-2 in ChDD male offspring in both tissue types. No changes were observed in MMP-9. This observation supports our working hypothesis that maternal dietary deficiencies in folic acid or choline during early neurodevelopment impact the levels of inflammation in offspring after ischemic stroke.
A Novel Colorimetric Biosensor for the Detection of Catalase-Positive Staphylococcus aureus Based on an Onion-like Carbon Nanozyme
Authors: Fan Yining , Gao Guanyue , Zhi Jinfang
Published: September 2024

Staphylococcus aureus is one of the leading causes of skin and soft tissue infections, and it is even life-threatening if it enters the bloodstream, lung or heart. In the present work, we proposed a novel colorimetric biosensor for the detection of S. aureus through hydrogen peroxide consumption. An onion-like carbon nanozyme with high peroxidase-like activity was prepared, which competed with the endogenous catalase of S. aureus in consuming hydrogen peroxide. This reaction was further characterized by the colorimetric reaction of 3,3′,5,5′-tetramethylbenzidine. The results showed that our approach allowed for the simple and rapid determination of S. aureus, with a linear range of 2 × 104 to 2 × 107 CFU/mL. Moreover, our method displayed good selectivity, with Bacillus subtilis and Escherichia coli showing negligible responses at the concentration of 2 × 105 CFU/mL. The application of the as-prepared biosensor to analyze S. aureus in real water samples yielded recovery rates ranging from 95% to 112%, with relative standard deviations less than 7%. The method demonstrated good accuracy and specificity, which offers a novel approach for the simple and selective detection of S. aureus.
An Optimization of the Extraction of Phenolic Compounds from Grape Marc: A Comparison between Conventional and Ultrasound-Assisted Methods
Authors: Liu Ziyao , Wu Hanjing , Holland Brendan , Barrow Colin J. , Suleria Hafiz A. R.
Published: September 2024

The green extraction of total phenolic compounds, flavonoids, anthocyanins, and tannins from grape marc was optimized using response surface methodology. The extracts were characterized and analyzed using LC-ESI-QTOF-MS/MS, and free radical scavenging capacity was evaluated. An efficient green extraction method is crucial for improving the recovery rates of these high-value phytochemicals and for sustainably reusing wine by-products. Our study optimized parameters for both conventional and ultrasound-assisted extraction methods, including solution pH, extraction temperature, liquid-to-solvent ratio, and ultrasonic amplitude. The optimized conditions for conventional extraction were identified as 60% ethanol with a pH of 2, a solvent-to-solid ratio of 50:1, extraction time of 16 h at a temperature of 49.2 °C. For ultrasound-assisted extraction, the optimized conditions were determined as 60% ethanol with a pH of 2, a solvent-to-solid ratio of 50:1, and an amplitude of 100% for 5.05 min at a temperature of 60 °C. We also demonstrated that lowering the temperature to 49.5 °C improves the energy efficiency of the extraction process with a minor reduction in recovery rates. Considering all factors, ultrasound-assisted extraction is more suitable for efficiently recovering bioactive compounds from grape marc.
The Use of Audiovisual Distraction Tools in the Dental Setting for Pediatric Subjects with Special Healthcare Needs: A Review and Proposal of a Multi-Session Model for Behavioral Management
Authors: Pisano Massimo , Bramanti Alessia , De Benedetto Giuseppina , Martin Carreras-Presas Carmen , Di Spirito Federica
Published: September 2024

Background: A Special Health Care Need (SHCN) is characterized by any type of physical, mental, sensorial, cognitive, emotional, or developmental condition that requires medical treatment, specialized services, or healthcare interventions. These conditions can negatively impact oral health as SHCN children can hardly cooperate or communicate and experience higher levels of dental fear/anxiety, which interfere with regular appointments. The present narrative review aims to analyze the use of audiovisual (AV) tools in dental setting for the management of SHCN children during dental treatment and to evaluate their effectiveness in anxiety/behavior control from the child, dentist, and care-giver perspectives. This analysis leads to the proposal of a new multi-session model for the behavioral management of SHCN pediatric subjects. Methods: An electronic search on the MEDLINE/Pubmed, Scopus, and Web of Science databases was carried out and through this analysis, a new model was proposed, the “UNISA-Virtual Stepwise Distraction model”, a multi-session workflow combining traditional behavior management and the progressive introduction of AV media to familiarize the SHCN child with dental setting and manage behavior. Results: AV tools helped in most cases to manage SHCN behavior and decreased stress in both the dentist and child during dental treatments. Care-givers also welcomed AV distractors, reporting positive feedback in using them during future treatments. Conclusions: The present narrative review found increasing evidence of the use of AV media for SHCN pediatric subjects as distraction tools during dental treatment. In the majority of the studies, AV tools proved to be effective for the management of anxiety, dental fear, and behavior in dental setting.
Superhydrophilic Surface Creation and Its Temporal Transition to Hydrophobicity on Copper via Femtosecond Laser Texturing
Authors: Ha Jeonghong
Published: September 2024

We analyzed a process to fabricate a superhydrophilic surface on copper by forming various laser-induced periodic surface structures (LIPSS) using a Ti/sapphire femtosecond laser. For these structured surfaces, the correlation between the surface structure and the wetting characteristics was analyzed by scanning electron microscopy, atomic force microscopy, and contact angle (CA) measurement. X-ray photoelectron spectroscopy (XPS) was also employed to analyze variation of the elemental composition of the surfaces. The laser treatment produced micro/nanostructures composed of ripples whose length and width are in microscale and nanoscale, respectively. At specific conditions, the CA of a water droplet was reduced to less than 1°. The superhydrophilcity is attributed to the effect of nanoholes and nanoclusters, which consist of copper (II) oxide and copper hydroxide, having a hydrophilic effect on LIPSS. However, the pristine superhydrophilic surface spontaneously became hydrophobic after being exposed to air at room temperature for about 10 days. According to XPS analysis, the surface’s transition to hydrophobic is attributed not only to the decomposition of Cu(OH)2 but also to the adsorption of oxygen molecules and/or airborne organic molecules containing carbon, which further influences the wettability.
FPGA-Based Numerical Simulation of the Chaotic Synchronization of Chua Circuits
Authors: Rentería Leonardo , Mayacela Margarita , Torres Klever , Ramírez Wladimir , Donoso Rolando , Acosta Rodrigo
Published: September 2024

The objective of this work was to design and implement a system based on reconfigurable hardware as a study tool for the synchronization of chaotic circuits. Mathematical models were established for one circuit, two synchronized, and multiple synchronized Chua circuits. An ordinary differential equation solver was developed applying Euler’s method using the Verilog hardware description language and synthesized on a Spartan 3E FPGA (Field-Programmable Gate Array) equipped with a 32-bit RISC processor, 64 MB of DDR SDRAM, and 4 Mb of PROM. With a step size of 0.005 and a total of 10,000 iterations, the state equations for one and three Chua circuits were solved at a time of 0.2 ms and a frequency of 50 Mhz. The logical resources used by the system did not exceed 4%. To verify the operation, a numerical simulation was carried out using the Octave V9.1.0 calculation software on an Intel(R) Core i7-9750H CPU 2.59 GHz computer, obtaining the same results but in a time of 493 ms and 3.177 s for one and three circuits, respectively.
Discrepancies between Coronary Artery Calcium Score and Coronary Artery Disease Severity in Computed Tomography Angiography Studies
Authors: Gać Paweł , Jaworski Arkadiusz , Parfianowicz Agnieszka , Karwacki Jakub , Wysocki Andrzej , Poręba Rafał
Published: September 2024

The aim of this paper is to demonstrate the difference in usefulness of the coronary artery calcium score (CACS) and the full assessment of the severity of coronary artery disease in coronary computed tomography angiography (CCTA) studies. The difference between the population risk of coronary artery disease (CAD) assessed by the CACS and the severity of CAD was demonstrated in images from two CCTA studies. The first image is from a patient with a CACS of 0 and significant coronary artery stenosis. In the native phase of CCTA examination, no calcified changes were detected in the topography of the coronary arteries. In the middle section of the left descending artery (LAD), at the level of the second diagonal branch (Dg2), a large non-calcified atherosclerotic plaque was visible. Mid-LAD stenosis was estimated to be approximately 70%. The second image features a patient with a high CACS but no significant coronary artery stenosis. The calcium score of individual coronary arteries calculated using the Agatston method was as follows: left main (LM) 0, LAD 403, left circumflex (LCx) 207.7, right coronary artery (RCA) 12. CACS was 622.7, representing a significant population risk of significant CAD. In the proximal and middle sections of the LAD, numerous calcified and mixed atherosclerotic plaques with positive remodeling were visible, causing stenosis of 25–50%. Similarly, in the proximal and middle sections of the LCx, numerous calcified and mixed atherosclerotic plaques with positive remodeling were visualized, causing stenoses of 25–50%. Calcified atherosclerotic plaques were found in the RCA, causing stenosis <25%. The entire CCTA image met CAD-RADS 2 (coronary artery disease reporting and data system) criteria. In summary, CACS may be applicable in population-based studies to assess the risk of significant CAD. In the evaluation of individual patients, a comprehensive assessment of CAD severity based on the angiographic phase of the CCTA examination should be used.
Drone-Based Visible–Thermal Object Detection with Transformers and Prompt Tuning
Authors: Chen Rui , Li Dongdong , Gao Zhinan , Kuai Yangliu , Wang Chengyuan
Published: September 2024

The use of unmanned aerial vehicles (UAVs) for visible–thermal object detection has emerged as a powerful technique to improve accuracy and resilience in challenging contexts, including dim lighting and severe weather conditions. However, most existing research relies on Convolutional Neural Network (CNN) frameworks, limiting the application of the Transformer’s attention mechanism to mere fusion modules and neglecting its potential for comprehensive global feature modeling. In response to this limitation, this study introduces an innovative dual-modal object detection framework called Visual Prompt multi-modal Detection (VIP-Det) that harnesses the Transformer architecture as the primary feature extractor and integrates vision prompts for refined feature fusion. Our approach begins with the training of a single-modal baseline model to solidify robust model representations, which is then refined through fine-tuning that incorporates additional modal data and prompts. Tests on the DroneVehicle dataset show that our algorithm achieves remarkable accuracy, outperforming comparable Transformer-based methods. These findings indicate that our proposed methodology marks a significant advancement in the realm of UAV-based object detection, holding significant promise for enhancing autonomous surveillance and monitoring capabilities in varied and challenging environments.
An Effective Res-Progressive Growing Generative Adversarial Network-Based Cross-Platform Super-Resolution Reconstruction Method for Drone and Satellite Images
Authors: Han Hao , Du Wen , Feng Ziyi , Guo Zhonghui , Xu Tongyu
Published: September 2024

In recent years, accurate field monitoring has been a research hotspot in the domains of aerial remote sensing and satellite remote sensing. In view of this, this study proposes an innovative cross-platform super-resolution reconstruction method for remote sensing images for the first time, aiming to make medium-resolution satellites capable of field-level detection through a super-resolution reconstruction technique. The progressive growing generative adversarial network (PGGAN) model, which has excellent high-resolution generation and style transfer capabilities, is combined with a deep residual network, forming the Res-PGGAN model for cross-platform super-resolution reconstruction. The Res-PGGAN architecture is similar to that of the PGGAN, but includes a deep residual module. The proposed Res-PGGAN model has two main benefits. First, the residual module facilitates the training of deep networks, as well as the extraction of deep features. Second, the PGGAN structure performs well in cross-platform sensor style transfer, allowing for cross-platform high-magnification super-resolution tasks to be performed well. A large pre-training dataset and real data are used to train the Res-PGGAN to improve the resolution of Sentinel-2’s 10 m resolution satellite images to 0.625 m. Three evaluation metrics, including the structural similarity index metric (SSIM), the peak signal-to-noise ratio (PSNR), and the universal quality index (UQI), are used to evaluate the high-magnification images obtained by the proposed method. The images generated by the proposed method are also compared with those obtained by the traditional bicubic method and two deep learning super-resolution reconstruction methods: the enhanced super-resolution generative adversarial network (ESRGAN) and the PGGAN. The results indicate that the proposed method outperforms all the comparison methods and demonstrates an acceptable performance regarding all three metrics (SSIM/PSNR/UQI: 0.9726/44.7971/0.0417), proving the feasibility of cross-platform super-resolution image recovery.
Can a Transparent Machine Learning Algorithm Predict Better than Its Black Box Counterparts? A Benchmarking Study Using 110 Data Sets
Authors: Peterson Ryan A. , McGrath Max , Cavanaugh Joseph E.
Published: September 2024

We developed a novel machine learning (ML) algorithm with the goal of producing transparent models (i.e., understandable by humans) while also flexibly accounting for nonlinearity and interactions. Our method is based on ranked sparsity, and it allows for flexibility and user control in varying the shade of the opacity of black box machine learning methods. The main tenet of ranked sparsity is that an algorithm should be more skeptical of higher-order polynomials and interactions a priori compared to main effects, and hence, the inclusion of these more complex terms should require a higher level of evidence. In this work, we put our new ranked sparsity algorithm (as implemented in the open source R package, sparseR) to the test in a predictive model “bakeoff” (i.e., a benchmarking study of ML algorithms applied “out of the box”, that is, with no special tuning). Algorithms were trained on a large set of simulated and real-world data sets from the Penn Machine Learning Benchmarks database, addressing both regression and binary classification problems. We evaluated the extent to which our human-centered algorithm can attain predictive accuracy that rivals popular black box approaches such as neural networks, random forests, and support vector machines, while also producing more interpretable models. Using out-of-bag error as a meta-outcome, we describe the properties of data sets in which human-centered approaches can perform as well as or better than black box approaches. We found that interpretable approaches predicted optimally or within 5% of the optimal method in most real-world data sets. We provide a more in-depth comparison of the performances of random forests to interpretable methods for several case studies, including exemplars in which algorithms performed similarly, and several cases when interpretable methods underperformed. This work provides a strong rationale for including human-centered transparent algorithms such as ours in predictive modeling applications.
Systematic Configurator for Complexity Management in Manufacturing Systems
Authors: Herrera-Vidal Germán , Coronado-Hernández Jairo R. , Martínez Paredes Breezy P. , Sánchez Ramos Blas Oscar , Sierra David Martinez
Published: September 2024

Complexity management in manufacturing systems is crucial for the economic growth of countries, as efficient management can significantly improve business performance and ensure competitiveness in globalized markets. This research aims to develop a complexity management configurator that identifies critical effects, proposes solution strategies, and optimizes processes through a Lean Production and Industry 4.0 approach. To this end, its systematic design integrates the key stages of management: planning, organization, management and control. The research was structured as an applied study, implementing three main panels: general information, application of a diagnostic tool at each stage of the administrative process, and results focused on measuring the complexity and implementation of advanced technological solutions. The tool enables manufacturing companies not only to diagnose sources of complexity, but also to optimize their operations by adopting Lean methodologies and Industry 4.0 technologies. The findings show how the integration of these strategies contributes to reducing both static and dynamic complexity, resulting in greater operational efficiency and improved performance in an increasingly competitive industrial environment. In conclusion, the proposed configurator is positioned as a key tool to improve the competitiveness and sustainability of manufacturing companies by offering a comprehensive approach to complexity management that is tailored to the demands of modern industry.
The Joint Solvation Interaction
Authors: Hassanali Ali , Egan Colin K.
Published: September 2024

The solvent-induced interactions (SIIs) between flexible solutes can be separated into two distinct components: the solvation-induced conformational effect and the joint solvation interaction (JSI). The JSI quantifies the thermodynamic effect of the solvent simultaneously accommodating the solutes, generalizing the typical notion of the hydrophobic interaction. We present a formal definition of the JSI within the framework of the mixture expansion, demonstrate that this definition is equivalent to the SII between rigid solutes, and propose a method, partially connected molecular dynamics, which allows one to compute the interaction with existing free energy algorithms. We also compare the JSI to the more natural generalization of the hydrophobic interaction, the indirect solvent-mediated interaction, and argue that JSI is a more useful quantity for studying solute binding thermodynamics. Direct calculation of the JSI may prove useful in developing our understanding of solvent effects in self-assembly, protein aggregation, and protein folding, for which the isolation of the JSI from the conformational component of the SII becomes important due to the intra-species flexibility.
Correction: Morzhin, O.V.; Pechen, A.N. Control of the von Neumann Entropy for an Open Two-Qubit System Using Coherent and Incoherent Drives. Entropy 2024, 26, 36
Authors: Morzhin Oleg V. , Pechen Alexander N.
Published: September 2024

In the published article [...]
Targeted Energy Transfer Dynamics and Chemical Reactions
Authors: Almazova Natalya , Aubry Serge , Tsironis Giorgos P.
Published: September 2024

Ultrafast reaction processes take place when resonant features of nonlinear model systems are taken into account. In the targeted energy or electron transfer dimer model this is accomplished through the implementation of nonlinear oscillators with opposing types of nonlinearities, one attractive while the second repulsive. In the present work, we show that this resonant behavior survives if we take into account the vibrational degrees of freedom as well. After giving a summary of the basic formalism of chemical reactions we show that resonant electron transfer can be assisted by vibrations. We find the condition for this efficient transfer and show that in the case of additional interaction with noise, a distinct non-Arrhenius behavior develops that is markedly different from the usual Kramers-like activated transfer.
Securing the Internet of Health Things: Embedded Federated Learning-Driven Long Short-Term Memory for Cyberattack Detection
Authors: Kumar Manish , Kim Sunggon
Published: September 2024

The proliferation of the Internet of Health Things (IoHT) introduces significant benefits for healthcare through enhanced connectivity and data-driven insights, but it also presents substantial cybersecurity challenges. Protecting sensitive health data from cyberattacks is critical. This paper proposes a novel approach for detecting cyberattacks in IoHT environments using a Federated Learning (FL) framework integrated with Long Short-Term Memory (LSTM) networks. The FL paradigm ensures data privacy by allowing individual IoHT devices to collaboratively train a global model without sharing local data, thereby maintaining patient confidentiality. LSTM networks, known for their effectiveness in handling time-series data, are employed to capture and analyze temporal patterns indicative of cyberthreats. Our proposed system uses an embedded feature selection technique that minimizes the computational complexity of the cyberattack detection model and leverages the decentralized nature of FL to create a robust and scalable cyberattack detection mechanism. We refer to the proposed approach as Embedded Federated Learning-Driven Long Short-Term Memory (EFL-LSTM). Extensive experiments using real-world ECU-IoHT data demonstrate that our proposed model outperforms traditional models regarding accuracy (97.16%) and data privacy. The outcomes highlight the feasibility and advantages of integrating Federated Learning with LSTM networks to enhance the cybersecurity posture of IoHT infrastructures. This research paves the way for future developments in secure and privacy-preserving IoHT systems, ensuring reliable protection against evolving cyberthreats.
Securing IoT: Mitigating Sybil Flood Attacks with Bloom Filters and Hash Chains
Authors: Baird Iain , Ghaleb Baraq , Wadhaj Isam , Russell Gordon , Buchanan William J.
Published: September 2024

In the evolving landscape of the Internet of Things (IoT), ensuring the security and integrity of data transmission remains a paramount challenge. Routing Protocol for Low-Power and Lossy Networks (RPL) is commonly utilized in IoT networks to facilitate efficient data routing. However, RPL networks are susceptible to various security threats, with Sybil and flood attacks being particularly detrimental. Sybil attacks involve malicious nodes generating multiple fake identities to disrupt network operations, while flood attacks overwhelm network resources by inundating them with excessive traffic. This paper proposes a novel mitigation strategy leveraging Bloom filters and hash chains to enhance the security of RPL-based IoT networks against sybil and flood attacks. Extensive simulation and performance analysis demonstrate that this solution significantly reduces the impact of sybil and flood attacks while maintaining a low power consumption profile and low computational overhead.
Correlation Statistics and Parameter Optimization Algorithms for RIS-Assisted Marine Wireless Communication Systems
Authors: Sun Caiyun , Wang Honglin , Sun Dasong , Zhu Cheng , Zhou Jie
Published: September 2024

Reconfigurable intelligent surfaces (RISs), as one of the potential key technologies in sixth generation (6G) mobile communications, feature low costs, low energy consumption, and ease of deployment. In this paper, we introduce the RIS technology into the maritime wireless communication scenario, which can transform the wireless transmission environment from uncontrollable to controllable. In the considered communication model, we derive the complex channel impulse response for the RIS propagation link and non-line-of-sight propagation link, respectively. This can be used to capture the physical properties of a communication model from different perspectives. Furthermore, based on the designed communication model, we investigate the correlation properties of propagation links in the space and time domains; they are the space correlation statistics and time correlation statistics. The provided framework addresses the technical bottleneck of the existing RIS channel modeling methods that fail to balance precision and efficiency, improves the channel model matching efficiency in the design process of RIS-enabled near-field maritime communication systems, and provides technical support for the rapid development of the 6G mobile communication industry.
Weighted-Neighborhood-Information-Network-Enabled Anomaly Detection Method for Electronic Sensors and Sensor Networks
Authors: An Chunyan , Liu Yingyi , Li Qi , Si Pengbo
Published: September 2024

As electronic sensors and sensor networks advance, perception data are increasingly characterized by mixed attributes. Traditional anomaly detection methods predominantly focus on numerical attributes. In this paper, we introduce a weighted neighborhood information network (WNIN)-enabled anomaly detection method tailored for mixed-attribute data from electronic sensors and sensor networks. Firstly, we employ the analytic hierarchy process (AHP) to analyze the security of sensor networks, leveraging a hierarchical electronic sensor network model to construct a hierarchical perception security architecture for anomaly detection. Subsequently, a neighborhood information system is established to ascertain the relationships between data objects with mixed attributes. We then develop the WNIN to encapsulate the relationships, and a state-transferring probability matrix based on data object similarity is derived. Ultimately, a random wandering process within the WNIN is executed, and the importance of data objects is evaluated using the steady-state distribution vector, thereby determining the anomaly data. Simulation outcomes reveal that our proposed method attains superior anomaly detection rates compared with existing methods.
Hybrid Control Strategy for 5G Base Station Virtual Battery-Assisted Power Grid Peak Shaving
Authors: Zhu Siqiao , Ma Rui , Zhou Yang , Zhong Shiyuan
Published: September 2024

With the rapid development of the digital new infrastructure industry, the energy demand for communication base stations in smart grid systems is escalating daily. The country is vigorously promoting the communication energy storage industry. However, the energy storage capacity of base stations is limited and widely distributed, making it difficult to effectively participate in power grid auxiliary services by only implementing the centralized control of base stations. Aiming at this issue, an interactive hybrid control mode between energy storage and the power system under the base station sleep control strategy is delved into in this paper. Grounded in the spatiotemporal traits of chemical energy storage and thermal energy storage, a virtual battery model for base stations is established and the scheduling potential of battery clusters in multiple scenarios is explored. Then, based on the time of use electricity price and user fitness indicators, with the maximum transmission signal and minimum operating cost as objective functions, a decentralized control device is used to locally and quickly regulate the communication system. Furthermore, a multi-objective joint peak shaving model for base stations is established, centrally controlling the energy storage system of the base station through a virtual battery management system. Finally, a simulation analysis was conducted on data from different types of base stations in the region, designing two distinct scheduling schemes for four regional categories. The analysis results demonstrate that the proposed model can effectively reduce the power consumption of base stations while mitigating the fluctuation of the power grid load.
Decision Matrix in an Autonomous Power System for Agro-Industrial Complexes with Renewable Energy Sources
Authors: Moldybayeva Nurgul , Keshuov Seitkazy , Kenzhetaev Kajrat , Saule Demessova , Taldybayeva Aigul , Stoyanov Ivaylo , Iliev Teodor
Published: September 2024

The article focuses on systems analysis in identifying optimal areas for using distributed energy sources in the agro-industrial complex. Applying a systems approach enables a comprehensive assessment of various aspects of solar energy, wind energy, hydropower systems and integrated power plants that provide autonomous energy supplies. The research methodology includes a functional–structural approach that helps to assess the internal characteristics of systems, allowing for a deeper understanding of their structure and functions. This, in turn, leads to the creation of different models reflecting specific elements and relationships in energy systems. The main point is to take into account the level of functional and structural perfection, which helps to more accurately determine the areas where the introduction of renewable energy sources will be most effective, taking into account the specifics and needs of the agro-industrial complex. This approach not only contributes to a more optimal allocation of resources but also improves the sustainability and efficiency of energy systems in the face of a changing climate and the growing demand for environmental technologies.
Parameter Identification of PMSG-Based Wind Turbine Based on Sensitivity Analysis and Improved Gray Wolf Optimization
Authors: Zhai Bingjie , Ou Kaijian , Wang Yuhong , Cao Tian , Dai Huaqing , Zheng Zongsheng
Published: September 2024

With the large-scale integration of wind power, it is essential to establish an electromagnetic transient (EMT) model of a wind turbine system. Focusing on the problem of the difficulty in obtaining the parameters of the direct-driven permanent magnet synchronous generator (PMSG) model, this manuscript proposes a method based on trajectory sensitivity analysis and improved gray wolf optimization (IGWO) for identifying the parameters of the PMSG EMT model. First, a model of a PMSG wind turbine is established on an EMT simulation platform. Then, the key parameters of the model are determined based on the sensitivity analysis. Five control parameters are selected as the key parameters for their higher sensitivity indexes. Finally, the key parameters are accurately identified, using the proposed IGWO algorithm. The final case study demonstrates that the proposed IGWO algorithm has better optimization performance compared with the GWO algorithm and particle swarm optimization (PSO) algorithm. In addition, the simulation waveforms show that the identified parameters are accurate and applicable to other operating conditions.
Regional-Scale Assessment of the Potential for Shallow Geothermal Energy Development Using Vertical Ground Source Heat Pumps
Authors: Yu Peng , Xu Yufeng , Liu Honghua , Liu Xinyu , Fu Jiani , Xu Meijun , Zhou Dankun
Published: September 2024

Shallow geothermal energy (SGE) is a widely prevalent geological resource underground, and its utilization offer significant energy conservation and emission reduction benefits, contributing to the achievement of carbon neutrality goals. Assessing the development potential of regional SGE can ensure sustainable development of these resources and prevent adverse effects induced by overexploitation. Jiangsu Province, a developed region in the eastern coastal area of China, has a strong demand for cooling and heating in urban buildings. The primary form of utilizing SGE in this area is through vertical ground source heat pumps (VGSHP). Based on the analysis of the impact of regional geological conditions on the development of SGE, this study specifically evaluated the suitability of developing SGE through VGSHP. After excluding areas unsuitable for development, the heat exchange capacity, heating or cooling area per unit area, and energy conservation and emission reduction benefits of VGSHP were calculated. The results indicate that the area suitable and moderately suitable for developing SGE through VGSHP in Jiangsu Province amounts to 76,453 km2. The total heat exchange capacity for summer is 1.21 × 109 kW, which can provide cooling for an area of 1.21 × 1010 m2. The total heat exchange capacity for winter is 8.70 × 108 kW, which can provide heating for an area of 1.09 × 1010 m2. The annual available resource amount is 2.68 × 1012 kWh, equivalent to 3.30 × 108 tons of standard coal, and a CO2 reduction of 7.86 × 108 tons.
Integrated Optimal Energy Management of Multi-Microgrid Network Considering Energy Performance Index: Global Chance-Constrained Programming Framework
Authors: Hemmati Mohammad , Bayati Navid , Ebel Thomas
Published: September 2024

Distributed generation (DG) sources play a special role in the operation of active energy networks. The microgrid (MG) is known as a suitable substrate for the development and installation of DGs. However, the future of energy distribution networks will consist of more interconnected and complex MGs, called multi-microgrid (MMG) networks. Therefore, energy management in such an energy system is a major challenge for distribution network operators. This paper presents a new energy management method for the MMG network in the presence of battery storage, renewable sources, and demand response (DR) programs. To show the performance of each connected MG’s inefficient utilization of its available generation capacity, an index called unused power capacity (UPC) is defined, which indicates the availability and individual performance of each MG. The uncertainties associated with load and the power output of wind and solar sources are handled by employing the chance-constrained programming (CCP) optimization framework in the MMG energy management model. The proposed CCP ensures the safe operation of the system at the desired confidence level by involving various uncertainties in the problem while optimizing operating costs under Mixed-Integer Linear Programming (MILP). The proposed energy management model is assessed on a sample network concerning DC power flow limitations. The procured power of each MG and power exchanges at the distribution network level are investigated and discussed.
Overview of Wind and Photovoltaic Data Stream Classification and Data Drift Issues
Authors: Zhu Xinchun , Wu Yang , Zhao Xu , Yang Yunchen , Liu Shuangquan , Shi Luyi , Wu Yelong
Published: September 2024

The development in the fields of clean energy, particularly wind and photovoltaic power, generates a large amount of data streams, and how to mine valuable information from these data to improve the efficiency of power generation has become a hot spot of current research. Traditional classification algorithms cannot cope with dynamically changing data streams, so data stream classification techniques are particularly important. The current data stream classification techniques mainly include decision trees, neural networks, Bayesian networks, and other methods, which have been applied to wind power and photovoltaic power data processing in existing research. However, the data drift problem is gradually highlighted due to the dynamic change in data, which significantly impacts the performance of classification algorithms. This paper reviews the latest research on data stream classification technology in wind power and photovoltaic applications. It provides a detailed introduction to the data drift problem in machine learning, which significantly affects algorithm performance. The discussion covers covariate drift, prior probability drift, and concept drift, analyzing their potential impact on the practical deployment of data stream classification methods in wind and photovoltaic power sectors. Finally, by analyzing examples for addressing data drift in energy-system data stream classification, the article highlights the future prospects of data drift research in this field and suggests areas for improvement. Combined with the systematic knowledge of data stream classification techniques and data drift handling presented, it offers valuable insights for future research.
A Novel Approach for Predicting CO2 Emissions in the Building Industry Using a Hybrid Multi-Strategy Improved Particle Swarm Optimization–Long Short-Term Memory Model
Authors: Hu Yuyi , Wang Bojun , Yang Yanping , Yang Liwei
Published: September 2024

The accurate prediction of carbon dioxide (CO2) emissions in the building industry can provide data support and theoretical insights for sustainable development. This study proposes a hybrid model for predicting CO2 emissions that combines a multi-strategy improved particle swarm optimization (MSPSO) algorithm with a long short-term memory (LSTM) model. Firstly, the particle swarm optimization (PSO) algorithm is enhanced by combining tent chaotic mapping, mutation for the least-fit particles, and a random perturbation strategy. Subsequently, the performance of the MSPSO algorithm is evaluated using a set of 23 internationally recognized test functions. Finally, the predictive performance of the MSPSO-LSTM hybrid model is assessed using data from the building industry in the Yangtze River Delta region as a case study. The results indicate that the coefficient of determination (R2) of the model reaches 0.9677, which is more than 10% higher than that of BP, LSTM, and CNN non-hybrid models and demonstrates significant advantages over PSO-LSTM, GWO-LSTM, and WOA-LSTM hybrid models. Additionally, the mean square error (MSE) of the model is 2445.6866 Mt, and the mean absolute error (MAE) is 4.1010 Mt, both significantly lower than those of the BP, LSTM, and CNN non-hybrid models. Overall, the MSPSO-LSTM hybrid model demonstrates high predictive accuracy for CO2 emissions in the building industry, offering robust support for the sustainable development of the industry.
Study of NO and CO Formation Pathways in Jet Flames with CH4/H2 Fuel Blends
Authors: Lu Lin , Jiang Haoyuan
Published: September 2024

The existing natural gas transportation pipelines can withstand a hydrogen content of 0 to 50%, but further research is still needed on the pathways of NO and CO production under moderate or intense low oxygen dilution (MILD) combustion within this range of hydrogen blending. In this paper, we present a computational fluid dynamics (CFD) simulation of hydrogen-doped jet flame combustion in a jet in a hot coflow (JHC) burner. We conducted an in-depth study of the mechanisms by which NO and CO are produced at different locations within hydrogen-doped flames. Additionally, we established a chemical reaction network (CRN) model specifically for the JHC burner and calculated the detailed influence of hydrogen content on the mechanisms of NO and CO formation. The findings indicate that an increase in hydrogen content leads to an expansion of the main NO production region and a contraction of the main NO consumption region within the jet flame. This phenomenon is accompanied by a decline in the sub-reaction rates associated with both the prompt route and NO-reburning pathway via CHi=0–3 radicals, alongside an increase in N2O and thermal NO production rates. Consequently, this results in an overall enhancement of NO production and a reduction in NO consumption. In the context of MILD combustion, CO production primarily arises from the reduction of CO2 through the reaction CH2(S) + CO2 ⇔ CO + CH2O, the introduction of hydrogen into the system exerts an inhibitory effect on this reduction reaction while simultaneously enhancing the CO oxidation reaction, OH + CO ⇔ H + CO2, this dual influence ultimately results in a reduction of CO production.
Multi-Modal Machine Learning to Predict the Energy Discharge Levels from a Multi-Cell Mechanical Draft Cooling Tower
Authors: Sobecki Christopher , Deschaine Larry , d’Entremont Brian
Published: September 2024

An artificial neural network was developed to augment the accuracy of a physically based computer model in relating heat discharge to visible plume volume of a 12-cell mechanical draft cooling tower. In a previous study, Savannah River National Laboratory developed a 1D model to capture the average power plant discharge levels via analysis of a series of visual images but was unable to accurately predict individual cases, resulting in an overall average error of about 5%, but individual comparisons resulted in an R2 of 0.36. Three optimization algorithms were applied to better fit the entrainment coefficients, and the artificial neural network model was applied to 289 cases of a 12-cell mechanical draft cooling tower power generation facility. Two artificial neural networks configurations consisted of 10 and 47 nodes that used as input readily available plant data, observed cooling tower plume conditions, observed operational conditions, local and regional weather, and the predicted plume volume from the physical model; the individual predictions’ accuracy improved to R2>0.95. This article concludes the sensitivities for the 1D model and additional actions to progress this field of study as well as applications for cooling tower monitoring. This strategy demonstrated an encouraging first step towards using multi-modal artificial neural network machine learning technology for information fusion to estimate power levels from external observations.
Assessment of the Impact of Direct Water Cooling and Cleaning System Operating Scenarios on PV Panel Performance
Authors: Sornek Krzysztof
Published: September 2024

Among the various renewable energy-based technologies, photovoltaic panels are characterized by a high rate of development and application worldwide. Many efforts have been made to study innovative materials to improve the performance of photovoltaic cells. However, the most commonly used crystalline panels also have significant potential to enhance their energy yield by providing cooling and cleaning solutions. This paper discusses the possibility of introducing a dedicated direct-water cooling and cleaning system. As assumed, detailed schedules of the operation of the developed direct water cooling and cleaning system should be fitted to actual weather conditions. In this context, different cooling strategies were proposed and tested, including different intervals of opening and closing water flow. All tests were conducted using a dedicated experimental rig. 70 Wp monocrystalline panels were tested under laboratory conditions and 160 Wp polycrystalline panels were tested under real conditions. The results showed that introducing a scenario with a 1-min cooling and a 5-min break allowed for proving the panel’s surface temperature lower than 40 °C. In comparison, the temperature of the uncooled panel under the same operating conditions was close to 60 °C. Consequently, an increase in power generation was observed. The maximum power increase was observed in July and amounted to 15.3%. On the other hand, considering selected weeks in May, July, and September, the average increase in power generation was 3.63%, 7.48%, and 2.51%, respectively. It was concluded that the division of photovoltaic installation allows reasonable operating conditions for photovoltaic panels with a lower amount of energy consumed to power water pumps.
Short-Circuit Conditions and Thermal Behaviour of Different Cable Formations
Authors: Albrechtowicz Paweł , Smugała Dariusz
Published: September 2024

Modern electrical installations widely use cable formations consisting of a few single cables connected in parallel. In this article, the short-circuit problem is analysed with its influence on cable temperature. In the assumed case study of supply lines, five equivalent cases were considered. Calculations of temperature increase during faults were performed using a simplified method and FEM model. This study shows the differences in calculation methods and points to better solutions in cable arrangements due to safety and operating conditions in cases of faults. Additionally, some problems in the simplified method were identified and pointed out in the range of its application.