bluuebunny/crossref_metadata_2025 huggingface dataset:
Association of Polymorphisms in FSHR, ESR1, and BMP15 with Primary Ovarian Insufficiency and Meta-Analysis
Authors: Lee Jeong Yong , Kim Young Ran , Ko Eun Ju , Ryu Chang Soo , Kwack KyuBum , Na Eun Duc , Shin Ji Eun , Kim Ji Hyang , Ahn Eun Hee , Kim Nam Keun
Published: August 2024

Primary ovarian insufficiency (POI) can lead to menstrual disturbance, resulting in ovarian dysfunction before age 40. Prevalence of POI is usually less than 1%; however, ethnicity or population characteristics may affect prevalence. POI is a heterogeneous disease that results from abnormalities in immunological and hormonal factors. Genetic factors can also contribute to POI. Here, we examine FSHR, ESR1, and BMP15 polymorphisms in patients with POI, and controls. We examined a hormonal gene that is important for pregnancy, follicle-stimulating hormone receptor (FSHR), as well as estrogen receptor 1 (ESR1), and associated it with FSHR expression, ovulation rate, and bone morphogenetic protein 15 (BMP15). We examined 139 Korean patients under age 40 with POI, and 350 Korean control participants without POI. Genotyping was performed by a polymerase chain reaction–restriction fragment length polymorphism (PCR–RFLP) and TaqMan assays. Each identified genotype was subjected to statistical analysis to determine the odds ratios (ORs) and 95% confidence intervals (CIs). In combination genotype analyses, FSHR rs6165 A > G combined with ESR1 rs9340799 A > G, AG/GG (OR: 5.693; 95% CI: 1.088–29.792), as well as FSHR rs6166 A > G combined with ESR1 rs9340799 C > T, AG/GG (OR: 5.940; 95% CI: 1.134–31.131), were significantly associated with POI prevalence. Furthermore, an FSHR rs6165 A > G and BMP rs17003221 C > T, AG/CC combination was associated with POI prevalence (OR: 1.874; 95% CI: (1.059–3.316; p-value: 0.031)). In meta-analysis, FSHR rs6165 AA vs. AG + GG is associated with POI (p = 0.0013), and ESR1 rs2234693 AA vs. AG + GG is also associated with POI (p = 0.0101). Here, we compared the genotypes of FSHR, ESR1, and BMP15 in patients with POI, and controls. We found significant differences in genotype combinations between polymorphisms in FSHR and other genes. Through meta-analysis, we found that ESR1 rs9340799 and rs2234693 are associated with POI prevalence, and that BMP15 rs17003221 increases POI risk. These findings help to improve POI diagnosis in Korean women.
Utility of Magnetocardiography and Stress Speckle Tracking in Detection of Coronary Artery Disease
Authors: Saleh Ahmed , Brachmann Johannes
Published: August 2024

Introduction: Coronary artery disease (CAD) is a leading cause of death and disability in developed countries. While exercise testing is recommended for diagnosing stable angina pectoris, its limited sensitivity and specificity have been questioned. Myocardial strain is a promising predictor of significant CAD. Aim: To evaluate the utility of myocardial strain obtained by 2D speckle tracking at rest and under stress combined with stress magnetocardiography for detecting CAD in patients with stable or low-risk unstable angina pectoris. Methods: A total of 108 patients meeting the inclusion criteria underwent coronary angiography within 48 h of admission. Myocardial strain was assessed using 2D speckle tracking at rest and during dobutamine stress alongside stress magnetocardiography. Results: Global longitudinal strain at stress showed a moderate correlation with significant CAD (r = 0.41, p <0.0001) and with coronary occlusion severity (r = 0.62, p <0.0001). Strain at stress had a sensitivity of 74.1% and specificity of 76.7% for detecting CAD at a cut-off value of −19.1. The ST fluctuation rate from magnetocardiography demonstrated the highest sensitivity for CAD detection. Conclusions: Longitudinal strain parameters and stress magnetocardiography are effective non-invasive methods for predicting CAD in patients with stable angina, potentially reducing the need for invasive assessments.
Crime and Nourishment: A Narrative Review Examining Ultra-Processed Foods, Brain, and Behavior
Authors: Prescott Susan L. , Logan Alan C. , LaFata Erica M. , Naik Ashka , Nelson David H. , Robinson Matthew B. , Soble Leslie
Published: August 2024

Recently, there has been increased scientific and clinical interest in the potential harms associated with ultra-processed foods, including poor mental health, aggression, and antisocial behavior. Research spanning epidemiology, mechanistic pre-clinical work, addiction science, microbiome and exposome science, and human intervention trials has underscored that nutrition is of relevance along the criminal justice continuum. As such, the emerging dietetics research is salient to the thousands of international psychologists and allied mental health professionals that are engaged in justice work, including forensics, prevention, and intervention. In addition, relationships between nutrition and behavior relate to “food crime”, an emergent area unifying criminal justice researchers with psychology, public health, and other interdisciplinary sectors. Food crime scrutinizes the vast harms, including non-communicable diseases and adverse behavioral outcomes, as influenced by the distribution of addictive ultra-processed food products. Here, we examine the emergent research, including biophysiological mechanisms, and evidence indicating that dietary patterns/components intersect with psychosocial vulnerabilities linked with risks of antisocial behavior and justice involvement. Viewed through a prevention lens, the study of nutrition and aggressive behavior should be prioritized, especially if the outcomes emerge as externalities of the global consumption of ultra-processed food. In the context of criminal justice and behavior, there is a need for forensic examination of how industry influence and power structures can undermine matters of food justice.
Distribution of Woody Biomass on the Outwash Plain of a Retreating Glacier in Southern Iceland: Role of Microhabitat and Substrate
Authors: Pepper Yriberry Fiona , Tanner Lawrence H.
Published: August 2024

The Skaftafellsjökull is an outlet glacier in southern Iceland that has been retreating since 1890. While multiple studies have examined primary succession on the foreland of this glacier, no study has examined the distribution of woody biomass on the outwash plain. We investigated the distribution of one species, Betula pubescens, that grows on the foreland moraines and outwash plain of this glacier. The topography of the outwash plain is heterogeneous, consisting of broad bars of outwash gravel and boulders that are separated by narrow incised channels and broader swales. Vegetation on the outwash plain is primarily a moss–heath community. Birch are sparse on the outwash bar tops, but are more abundant and larger in the channels and swales between the bars. Although the area of the channels on the outwash plain is much less than that of the bar surfaces, the woody biomass of the outwash plain is dominated by the birch within these channels. Consequently, the mean woody biomass of the outwash plain exceeds that of the moraines. We propose that the microhabitat of the outwash plain channels provides a favorable environment for the growth of birch, primarily by providing a fine-grained substrate that promotes successful seeding and growth.
Response of Herbaceous and Woody Plant Species in Southern Portugal to Cope Oak Decline Associated to Phytophthora cinnamomi
Authors: Moreira Ana Cristina , Rodriguez-Romero Manuela , Neno Joana , Rodrigues Abel , Calha Isabel
Published: August 2024

The decline of oak canopies in Iberian woodlands is strongly influenced by abiotic and biotic stress factors, such as the oomycete Phytophthora cinnamomi which has the capacity to infect a wide range of plant species. Understory plant diversity plays an important role in the epidemiology of P. cinnamomi in these ecosystems. This study aimed to identify a set of woody and herbaceous plants that can impact oak decline. Twenty-two herbaceous plant species from three families, and nineteen woody plants (trees and shrubs) from seven families were assessed for their response to infection by P. cinnamomi. Most of the herbaceous species did not show evident susceptibility, only a few exhibited significant biomass root reduction and just seven were identified as hosts. Yellow lupin was the only herbaceous species showing high susceptibility. Among the woody plant species, only two shrub and two tree species exhibited disease symptoms. The other ones, mostly hosts, ranged between low susceptible and tolerant. These results highlight the possibility that many of these species can maintain the pathogen active in the soil or even increase its population. In this context, the findings of this study can contribute to effective management strategies to mitigate Phytophthora infection in woodland soils.
STEM Rocks Research Collective: Building and Sustaining a Collaborative, Equity-Focused Scholarly Community
Authors: Cook Kristin L. , Ivy Jessica , Maiorca Cat , Tripp Octavia , Burton Megan , Jackson Christa , Bush Sarah B. , Mohr-Schroeder Margaret , Edelen Dan , Schroeder Craig , Roberts Thomas
Published: August 2024

Here, we outline the stories of a collective of innovation-focused STEM education professionals engaged in a STEM collective across thirteen institutions of higher education. Using focus group interviews, faculty share how collaboration began and sustains, the affordances and challenges of collaboration across universities and professional fields, support systems through professional organizations and scholarship, and contributions made to the national landscape of STEM education. Guidance is presented on establishing STEM collaborations that are mutually beneficial to faculty, institutions, and the educational community.
Pre-Service Teachers’ Perceptions of Their Digital Competencies and Ways to Acquire Those through Their Studies and Self-Organized Learning
Authors: Göltl Katrin , Ambros Roland , Dolezal Dominik , Motschnig Renate
Published: August 2024

Rapid digitalization and the fast rise of generative AI tools pose fresh challenges to educational institutions, teachers, and students. Teachers of all subjects are expected to pass on key digital competencies along with subject-related knowledge to facilitate students’ employability and navigation in a society characterized by the impact of digital technology in all areas of life. However, several studies have shown that there is still a lack of integration of digital competencies in teacher training programs. Hence, to add to these previous studies, this paper aims to find and explore strategies and measures to equip secondary-level pre-service teachers with digital competencies and support them in professionally promoting their students’ digital skills. This happens by investigating what advanced pre-service teachers at a large European university miss and suggest changing to become more confident in promoting digital competencies at secondary school levels (K5–K12). In this context, the authors deepened a previously conducted university-wide survey among advanced secondary-level pre-service teachers through two focus groups to sharpen insights into challenges and good practices from the perspective of pre-service teachers. A thematic analysis of these focus groups confirmed the presumption that most pre-service teachers are keen on actively acquiring relevant digital competencies and gave insights into how they wish to have digital skills integrated into their studies. Additionally, it became evident that even though pre-service teachers can acquire several digital skills through self-study, they longed for collaboration and guidance to reach a professional level for “teaching” them to their students.
Multi-Feature Fusion in Graph Convolutional Networks for Data Network Propagation Path Tracing
Authors: Jing Dongsheng , Yang Yu , Gu Zhimin , Feng Renjun , Li Yan , Jiang Haitao
Published: August 2024

With the rapid development of information technology, the complexity of data networks is increasing, especially in electric power systems, where data security and privacy protection are of great importance. Throughout the entire distribution process of the supply chain, it is crucial to closely monitor the propagation paths and dynamics of electrical data to ensure security and quickly initiate comprehensive traceability investigations if any data tampering is detected. This research addresses the challenges of data network complexity and its impact on the security of power systems by proposing an innovative data network propagation path tracing model, which is constructed based on graph convolutional networks (GCNs) and the BERT model. Firstly, propagation trees are constructed based on the propagation structure, and the key attributes of data nodes are extracted and screened. Then, GCNs are utilized to learn the representation of node features with different attribute feature combinations in the propagation path graph, while the Bidirectional Encoder Representations from Transformers (BERT) model is employed to capture the deep semantic features of the original text content. The core of this research is to effectively integrate these two feature representations, namely the structural features obtained by GCNs and the semantic features obtained by the BERT model, in order to enhance the ability of the model to recognize the data propagation path. The experimental results demonstrate that this model performs well in power data propagation and tracing tasks, and the data recognition accuracy reaches 92.5%, which is significantly better than the existing schemes. This achievement not only improves the power system’s ability to cope with data security threats but also provides strong support for protecting data transmission security and privacy.
Noise Resilience in Dermoscopic Image Segmentation: Comparing Deep Learning Architectures for Enhanced Accuracy
Authors: Ergin Fatih , Parlak Ismail Burak , Adel Mouloud , Gül Ömer Melih , Karpouzis Kostas
Published: August 2024

Skin diseases and lesions can be ambiguous to recognize due to the similarity of lesions and enhanced imaging features. In this study, we compared three cutting-edge deep learning frameworks for dermoscopic segmentation: U-Net, SegAN, and MultiResUNet. We used a dermoscopic dataset including detailed lesion annotations with segmentation masks to help train and evaluate models on the precise localization of melanomas. SegAN is a special type of Generative Adversarial Network (GAN) that introduces a new architecture by adding generator and discriminator steps. U-Net has become a common strategy in segmentation to encode and decode image features for limited data. MultiResUNet is a U-Net-based architecture that overcomes the insufficient data problem in medical imaging by extracting contextual details. We trained the three frameworks on colored images after preprocessing. We added incremental Gaussian noise to measure the robustness of segmentation performance. We evaluated the frameworks using the following parameters: accuracy, sensitivity, specificity, Dice and Jaccard coefficients. Our accuracy results show that SegAN (92%) and MultiResUNet (92%) both outperform U-Net (86%), which is a well-known segmentation framework for skin lesion analysis. MultiResUNet sensitivity (96%) outperforms the methods in the challenge leaderboard. These results suggest that SegAN and MultiResUNet are more resistant techniques against noise in dermoscopic segmentation.
Research on Predictive Speed Control Scheme for Surface-Mounted Permanent Magnet Servo Systems
Authors: Song Zhe , Zhou Weihong , Mo Yu
Published: August 2024

In order to improve the dynamic response and disturbance rejection performance of electric machines, a deadbeat predictive speed control (DPSC) scheme for a permanent magnet synchronous motor (PMSM) is proposed. To begin with, a DPSC controller was proposed with the purpose of achieving precise control for the next control cycle, and the control parameters were determined based on the optimal parameter design method. For better application, performance comparisons were made with a conventional PI control, and the mismatch effects of inertia and torque were analyzed. In order to improve the disturbance rejection performance of the system, an extended sliding mode observer (ESMO) was constructed to compensate for disturbances. Experimental verification with a conventional PI control indicates that the proposed DPSC control can reduce the speed response time from 0.675 s to 0.650 s. When the electric machine operates stably and is applied to a torque disturbance of 0.4 Nm, the speed fluctuation and settling time can be reduced from 9 rpm and 1.7 s to 6 rpm and 0.5 s, respectively. This proposed method effectively enhances the speed control performance of PMSM and can be applied to high-performance electric machine applications.
Stochastic Convex Cone Programming for Joint Optimal BESS Operation and Q-Placement in Net-Zero Microgrids
Authors: Mohammadyari Milad , Eskandari Mohsen
Published: August 2024

Microgrids have emerged as a pivotal solution in the quest for efficient, resilient, and sustainable energy systems. Comprising diverse distributed energy resources, microgrids present a compelling opportunity to revolutionize how we generate, store, and distribute electricity, while simultaneously reducing carbon footprints. This paper proposes an optimal battery energy storage system (BESS) management scheme, along with capacitor placement for reactive power (Q)-compensation, and scheduling for the purpose of a renewable-based microgrid’s loss minimization. The proposed model evaluates the impact of BESS management on energy efficiency and analyzes how optimal scheduling of BESS influences system losses. Furthermore, it investigates the coordinated planning and operation of active assets within the microgrid, such as controllable capacitor banks, in enhancing overall efficiency. The model is formulated as a mixed-integer second-order cone programming (MISOCP) problem which is solved for both deterministic and stochastic generation and consumption data. The proposed model is tested on a 21-bus microgrid comprising photovoltaic and hydropower energy resources, and the efficacy of the model is approved by several case studies. The simulation results show that the proposed method can reduce microgrid energy losses by approximately 12 percent using the deterministic approach and around 14 percent with the stochastic approach.
Research on Reactive Power Optimization of Synchronous Condensers in HVDC Transmission Based on Reactive Power Conversion Factor
Authors: Tao Zican , Wang Tao , Cai Defu , Chen Rusi
Published: August 2024

With the rapid development of high-voltage direct current (HVDC) transmission systems, the coupling between AC and DC grids is becoming increasingly close. Voltage disturbances in the grid can easily cause commutation failures in the DC system, threatening its safe and stable operation. The new generation of synchronous condensers (SCs) and modified synchronous condenser units are powerful reactive power support devices widely used in large-capacity DC transmission systems. To maximize the voltage support and commutation failure suppression of SCs, this paper proposes improvements in the initial operating state of SCs, using the Shanxi–Wuhan HVDC receiving end in the Hubei power grid as an example, to better support the HVDC commutation process. Additionally, a reactive power output optimization strategy for SCs is proposed, considering the reactive power equivalent factor of electrical connections between grid nodes. This strategy determines the optimal reactive power output limit of SCs near the converter station to suppress DC commutation failures. Simulation results show that this strategy effectively utilizes the dynamic support capabilities of SCs, prevents DC commutation failures, improves HVDC transmission capacity, and enhances the safety and stability of the receiving end power grid, providing theoretical guidance for reactive power output control.
A Reduced-Order Model of a Nuclear Power Plant with Thermal Power Dispatch
Authors: Lew Roger , Poudel Bikash , Wallace Jaron , Westover Tyler L.
Published: August 2024

This paper presents reduced-order modeling of thermal power dispatch (TPD) from a pressurized water reactor (PWR) for providing heat to nearby heat consuming industrial processes that seek to take advantage of nuclear heat to reduce carbon emissions. The reactor model includes the neutronics of the reactor core, thermal–hydraulics of the primary coolant cycle, and a three-lump model of the steam generator (SG). The secondary coolant cycle is represented with quasi-steady state mass and energy balance equations. The secondary cycle consists of a steam extraction system, high-pressure and low-pressure turbines, moisture separator and reheater, high-pressure and low-pressure feedwater heaters, deaerator, feedwater and condensate pumps, and a condenser. The steam produced by the SG is distributed between the turbines and the extraction steam line (XSL) that delivers steam to nearby industrial processes, such as production of clean hydrogen. The reduced-order simulator is verified by comparing predictions with results from separate validated steady-state and transient full-scope PWR simulators for TPD levels between 0% and 70% of the rated reactor power. All simulators indicate that the flow rate of steam in the main steam line and turbine systems decrease with increasing TPD, which causes a reduction in PWR electric power generation. The results are analyzed to assess the impact of TPD on system efficiency and feedwater flow control. Due to the simplicity of the proposed reduced-order model, it can be scaled to represent a PWR of any size with a few parametric changes. In the future, the proposed reduced-order model will be integrated into a power system model in a digital real-time simulator (DRTS) and physical hardware-in-the-loop simulations.
Machine Learning Approaches for Short-Term Photovoltaic Power Forecasting
Authors: Radhi Shahad Mohammed , Al-Majidi Sadeq D. , Abbod Maysam F. , Al-Raweshidy Hamed S.
Published: August 2024

A photovoltaic (PV) power forecasting prediction is a crucial stage to utilize the stability, quality, and management of a hybrid power grid due to its dependency on weather conditions. In this paper, a short-term PV forecasting prediction model based on actual operational data collected from the PV experimental prototype installed at the engineering college of Misan University in Iraq is designed using various machine learning techniques. The collected data are initially classified into three diverse groups of atmosphere conditions—sunny, cloudy, and rainy meteorological cases—for various seasons. The data are taken for 3 min intervals to monitor the swift variations in PV power generation caused by atmospheric changes such as cloud movement or sudden changes in sunlight intensity. Then, an artificial neural network (ANN) technique is used based on the gray wolf optimization (GWO) and genetic algorithm (GA) as learning methods to enhance the prediction of PV energy by optimizing the number of hidden layers and neurons of the ANN model. The Python approach is used to design the forecasting prediction models based on four fitness functions: R2, MAE, RMSE, and MSE. The results suggest that the ANN model based on the GA algorithm accommodates the most accurate PV generation pattern in three different climatic condition tests, outperforming the conventional ANN and GWO-ANN forecasting models, as evidenced by the highest Pearson correlation coefficient values of 0.9574, 0.9347, and 0.8965 under sunny, cloudy, and rainy conditions, respectively.
Artificial Intelligence-Based Improvement of Empirical Methods for Accurate Global Solar Radiation Forecast: Development and Comparative Analysis
Authors: Ali Mohamed , Elsayed Ashraf , Elkabani Islam , Akrami Mohammad , Youssef M. , Hassan Gasser
Published: August 2024

Artificial intelligence (AI) technology has expanded its potential in environmental and renewable energy applications, particularly in the use of artificial neural networks (ANNs) as the most widely used technique. To address the shortage of solar measurement in various places worldwide, several solar radiation methods have been developed to forecast global solar radiation (GSR). With this consideration, this study aims to develop temperature-based GSR models using a commonly utilized approach in machine learning techniques, ANNs, to predict GSR using just temperature data. It also compares the performance of these models to the commonly used empirical technique. Additionally, it develops precise GSR models for five new sites and the entire region, which currently lacks AI-based models despite the presence of proposed solar energy plants in the area. The study also examines the impact of varying lengths of validation datasets on solar radiation models’ prediction and accuracy, which has received little attention. Furthermore, it investigates different ANN architectures for GSR estimation and introduces a comprehensive comparative study. The findings indicate that the most advanced models of both methods accurately predict GSR, with coefficient of determination, R2, values ranging from 96% to 98%. Moreover, the local and general formulas of the empirical model exhibit comparable performance at non-coastal sites. Conversely, the local and general ANN-based models perform almost identically, with a high ability to forecast GSR in any location, even during the winter months. Additionally, ANN architectures with fewer neurons in their single hidden layer generally outperform those with more. Furthermore, the efficacy and precision of the models, particularly ANN-based ones, are minimally impacted by the size of the validation data sets. This study also reveals that the performance of the empirical models was significantly influenced by weather conditions such as clouds and rain, especially at coastal sites. In contrast, the ANN-based models were less impacted by such weather variations, with a performance that was approximately 7% better than the empirical ones at coastal sites. The best-developed models, particularly the ANN-based models, are thus highly recommended. They enable the precise and rapid forecast of GSR, which is useful in the design and performance evaluation of various solar applications, with the temperature data continuously and easily recorded for various purposes.
Non-Intrusive Load Monitoring Based on Dimensionality Reduction and Adapted Spatial Clustering
Authors: Zhang Xu , Zhou Jun , Lu Chunguang , Song Lei , Meng Fanyu , Wang Xianbo
Published: August 2024

Non-invasive load monitoring (NILM) deduces changes in energy consumption patterns and operational statuses of electrical equipment from power signals in the feed line. With the emergence of fine-grained power load distribution, the importance of utilizing this technology for implementing demand-side energy management in smart grid development has become increasingly prominent. To address the issue of low load identification accuracy stemming from complex and diverse load types, this paper introduces a NILM method based on uniform manifold approximation and projection (UMAP) reduction and enhanced density-based spatial clustering of applications with noise (DBSCAN). Firstly, this paper combines the characteristics of user load under transient and steady-state conditions and selects data with significant differences to construct a load-characteristic database. Additionally, UMAP is employed to reduce the dimensionality of high-dimensional load features and rebuild a load feature database. Subsequently, DBSCAN is utilized to categorize typical user loads, followed by a correlation analysis with the load-characteristic database to determine the types or classes of loads that involve switching actions. Finally, this paper simulates and analyzes the proposed method using the electricity consumption data of industrial users from the CER–Electricity–Data dataset. It identifies the electricity load data commonly utilized by users in a specific area of Zhejiang Province in China. The experimental results indicate that the accuracy of the proposed non-invasive load identification method reaches 95%. Compared to the wavelet transform, decision tree, and backpropagation network methods, the improvement is approximately 5%.
Retrofit of a Marine Engine to Dual-Fuel Methane–Diesel: Experimental Analysis of Performance and Exhaust Emission with Continuous and Phased Methane Injection Systems
Authors: Marchitto Luca , De Simio Luigi , Iannaccone Sabato , Pennino Vincenzo , Altieri Nunzio
Published: August 2024

Shipping is a highly energy-intensive sector, and fleet decarbonization initiatives can significantly reduce greenhouse gas emissions. In the short-to-medium term, internal combustion engines will continue to be used for propulsion or as electricity generators onboard ships. Natural gas is an effective solution which can be used to mitigate greenhouse gas emissions from the marine sector. Considered to be a transitional fuel, it can provide a potential reduction in CO2 emissions of around 20–30%, compared with conventional marine fuels. This work investigated the influence of diesel-injection strategies on the performance and emissions of a single-cylinder prototype compression-ignition engine for marine applications, retrofitted to run as a Low-Pressure Dual-Fuel Engine using natural gas. Two different injection systems were used: a mass flow controller enabling continuous-mode gas feeding, and a Solenoid-Operated Gas Admission Valve for marine applications, the latter allowing phased natural-gas injection. Experimental tests were focused on partial-load conditions, which are critical for dual-fuel engines, with a natural gas/diesel mass ratio of 4:1. Phased injection resulted in reductions in fuel consumption, compared to continuous mode, of up to 11%. Further experiments demonstrated reductions in fuel consumption of up to 20.7% (in equivalent diesel); on the other hand, the unburned hydrocarbon emissions which resulted were an order of magnitude larger than the reference values for full diesel, reducing the benefits in terms of greenhouse gas emissions, with a reduction in Global Warming Potential of only 3% compared to full diesel.
A Comparative Evaluation of Community-Used District and Individual Battery Storage Systems for Photovoltaic Energy Systems
Authors: Quernheim Jonas , Waffenschmidt Eberhard
Published: August 2024

The significant expansion of renewable energies has led to an increased importance of storage systems. Decentralized storage solutions, including Home Battery Energy Storage Systems (HBESSs) and District Battery Energy Storage Systems (DBESSs), play a crucial role in this context. This study compares individual HBESSs with a community-used DBESS regarding the grade of autarky and self-consumption, specifically focusing on a planned residential area consisting of 36 single-family houses. A simulation tool was developed to conduct load flow simulations based on household electricity consumption, wallbox profiles for electric vehicle charging, and photovoltaic generation data across various battery capacities and system boundaries. The results demonstrate that the DBESS, compared to individual HBESSs with equivalent cumulative battery capacities, can achieve a maximum increase in the grade of autarky of up to 11.6%, alongside an 8.0% increase in the grade of self-consumption for the given use case. In terms of capacity, the DBESS allows for a saving of up to 68% compared to HBESS to achieve similar results for the studied neighborhood.
Predicting and Forecasting of Vehicle Charging Station Using ECNN with DHFO Algorithm
Authors: Paul Rosebell , Selvan Mercy Paul
Published: August 2024

The forecast of the optimal placement of a charging station (CS) according to the real-time consumption of electric vehicles is a subject of urgency in this new era. The demand of a charging station in an area based on the trend of consumption can be predicted by means of interpolation and the extrapolation of historical data using a linear function of prediction model. The prediction of the charging station system was performed with distance relevancy methods. An adaptive optimal learning model was proposed to enhance the prediction performance for charging station management and to represent the pattern of vehicles’ travelling directions. The proposed model uses Distributional Homogeneity Feature Optimization (DHFO) using artificial intelligence (AI) to categorize and forecast the charging station from the database. The prediction performance of this model is improved more than the conventional classification model by filtering the apt features from all the electric vehicular and charging station attributes in the database. The Enhanced Cladistic Neural Network (ECNN) is used to improve the pattern learning model and increase learning accuracy. By comparing statistical parameters with other state-of-the-art methodologies, the suggested model’s overall findings were verified.
Enhancing Transient Response in a DC-DC Converter for Electric Vehicle DC Fast Charging Applications Using Fractional-Order PI Control
Authors: Thaliyadath Deepti , Kaliyaperumal Deepa , Kolhe Mohan Lal
Published: August 2024

DC fast charging is critical for the widespread adoption of electric vehicles (EVs) due to its impact on convenience, economics, and environmental sustainability. Due to the critical role of DC fast charging in EV adoption, improving its efficiency and performance is paramount. This paper presents a Fractional-Order PI (FOPI) controller for obtaining a well-regulated output voltage of a Dual Active Bridge (DAB) converter, a widely used topology in EV applications. The proposed FOPI is validated to the conventional PI controller in a simulated DAB model that is relevant to DC fast charging applications. The evaluation is performed, and various time-domain parameters and performance indices to evaluate the dynamic response of the model are considered. The results are expected to demonstrate significant improvement in the converter’s transient and steady-state response using the proposed FOPI controller compared to the conventional PI controller, contributing to a more efficient and robust DC fast charging system. This improvement can translate to faster charging times, better stability, and potentially reduced stress on the EV battery during DC fast charging.
Fuzzy Analytic Hierarchy Process–Technique for Order Preference by Similarity to Ideal Solution: A Hybrid Method for Assessing Vegetation Management Strategies under Electricity Distribution Lines to Prevent Deforestation Based on Ecosystem Service Criteria
Authors: Güngör Ersin
Published: August 2024

This study evaluated vegetation management (VM) strategies under electricity distribution lines (EDLs) through ecosystem service (ES) criteria. Deforestation, worsened by insufficient VM practices, poses a threat to ecosystem stability. Using a hybrid FAHP (Fuzzy Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach, ten VM strategies were assessed based on 15 ES criteria. The FAHP results identified biodiversity, timber resources, and erosion control as the most crucial criteria due to their significant weights. The TOPSIS analysis determined that VM6 (creation and restoration of scrub edges) was the most effective strategy, achieving a value of 0.744 for reducing deforestation and enhancing energy security. VM6 helps preserve forest cover and protect infrastructure by creating a “V”-shaped structures within the EDLs corridor. This study underscores the importance of ES-oriented VM strategies for sustainable vegetation management and deforestation mitigation. It also highlights the need for incorporating scientific, ES-based decision support mechanisms into VM strategy development. Future research should expand stakeholder perspectives and conduct a comprehensive assessment of ESs to ensure that VM strategies align with ecological and socio-economic sustainability. This study provides a framework for improving VM practices and offers directions for future sustainable energy management research. This study focuses exclusively on ecological criteria for evaluating VM strategies, neglecting other dimensions. Future research should use methods like ANP and fuzzy cognitive maps to explore inter-dimension relationships and their strengths. Additionally, employing SWARA, PIPRECIA, ELECTRE, and PROMETHEE for ranking VM strategies is recommended.
Changes in Water Dynamics and Vigor of Recalcitrant Phoebe chekiangensis Seeds during Desiccation by Nuclear Magnetic Resonance and Transmission Electron Microscopy
Authors: He Huangpan , Gao Handong , Gu Wen , Huang Ying
Published: August 2024

The vigor of recalcitrant seeds is closely related to seed moisture. Real-time, non-destructive monitoring of changes in water distribution and status during the seed desiccation, utilizing nuclear magnetic resonance (NMR) technology, is crucial for preserving the high vigor of these seeds. In this study, we investigated the changes in the vigor of Phoebe chekiangensis seeds during natural desiccation, focusing on seed germination, seed size, and ultrastructural changes, while also exploring seed moisture dynamics with NMR. Our results indicated that the moisture content of fresh, undehydrated P. chekiangensis seeds was 37.06%. As the seeds dried to 25.09% moisture content, their germination ability decreased by approximately 88%. Magnetic resonance images (MRIs) revealed that the internal water of fresh P. chekiangensis seeds was primarily concentrated in the embryonic axis and the middle of the cotyledons. During desiccation, water loss occurred from the exterior to the interior of the embryonic axis, and from the periphery to the center of the cotyledons. Low-field NMR results demonstrated that fresh, undehydrated seeds contained the highest proportion of free water at 55.47%, followed by immobile water at 37.88% and bound water at 4.36%. As drying progressed, the proportion of free water decreased significantly, while immobile water initially decreased and then increased markedly, and the proportion of bound water also rose. Combined with transmission electron microscopy results, we observed that when the seeds were dried to 28.11% moisture content or lower, the cells in the cotyledons and embryonic axis began to shrink due to free water loss, resulting in plasmic wall separation and a subsequent loss of seed vigor. Correlation analysis further revealed a highly significant relationship between the decrease in germination and the loss of free water of P. chekiangensis seeds.
Factors Affecting Activeness and Sustainability on Forestry in the Family Forests in Japan—From the Comparison between Aso in Japan and Styria in Austria
Authors: Kuboyama Hirofumi , Tsuzuki Nobuyuki , Eda Seira
Published: August 2024

Forestry in Japan and Austria share many similarities in their natural and social conditions. However, the Family Forest Owners (FFOs) in Japan seem not to be active and sustainable. To understand the factors affecting activeness and sustainability in family-owned forests in Japan, in 2021 and 2022, questionnaire surveys were done with members of Forest Owners’ Cooperatives (FOCs) in Aso, Japan, and Styria, Austria. Survey responses were comparatively analyzed via correlation analysis and binary logistic regression. Timber production was found to be more active in FOC Styria than in FOC Aso. One reason for this was the high-income dependence on forestry in Styria. Higher income was realized by self-harvest and the larger size of forest holdings and forest stands. The younger age of the members in FOC Styria, strongly affected by the pension system, leads to a higher self-harvest ratio. The culture of a sole child inheriting the family forest maintains the general size and scale of owned forests and stands in Styria. High distribution costs in FOC Aso reduced forestry income. As a result, sustainability was reduced in Aso because the availability of successors was low, and elderly forest owners who were once motivated by forestry tended to quit forestry.
AI Services-Oriented Dynamic Computing Resource Scheduling Algorithm Based on Distributed Data Parallelism in Edge Computing Network of Smart Grid
Authors: Zou Jing , Xin Peizhe , Wang Chang , Zhang Heli , Wei Lei , Wang Ying
Published: August 2024

Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize distributed model training based on data parallelism for AI services in smart grid. Due to AI services with diversified types, an edge data center has a changing workload in different time periods. Selfish edge data centers from different edge suppliers are reluctant to share their computing resources without a rule for fair competition. AI services-oriented dynamic computational resource scheduling of edge data centers affects both the economic profit of AI service providers and computational resource utilization. This letter mainly discusses the partition and distribution of AI data based on distributed model training and dynamic computational resource scheduling problems among multiple edge data centers for AI services. To this end, a mixed integer linear programming (MILP) model and a Deep Reinforcement Learning (DRL)-based algorithm are proposed. Simulation results show that the proposed DRL-based algorithm outperforms the benchmark in terms of profit of AI service provider, backlog of distributed model training tasks, running time and multi-objective optimization.
Pressure Transient Solutions for Unbounded and Bounded Reservoirs Produced and/or Injected via Vertical Well Systems with Constant Bottomhole Pressure
Authors: Weijermars Ruud , Afagwu Clement
Published: August 2024

Various analytical solutions for computing production and injection-induced pressure changes in aquifers and oil reservoirs have been derived over the past century. All prior solutions assumed a constant well rate as the boundary condition. However, in many practical situations, the fluid withdrawal from and/or injection into such subsurface reservoirs occurs with the aid of pump devices that maintain a constant bottomhole pressure in the well. Until now, how the well rate will decline over time, based on the pressure difference in the well relative to the initial reservoir pressure, could not be rapidly computed analytically (using the diffusivity as the key governing system parameter), because no concise expression had been derived with the boundary condition of a constant bottomhole pressure. The present study shows how the pressure diffusion equation can be readily solved for wells acting as sinks and sources with a constant bottomhole pressure condition. We consider both fractured and unfractured completions, as well as injection and production modes. The new solutions do not require an elaborate time-stepped pressure-matching procedure as in nodal analysis, the only other physics-based analytical method currently available to compute the well rate decline when a constant bottomhole pressure production system is used, which unlike our new method proposed here is limited to single well systems.
Enzymatic Interesterification of Coconut and Hemp Oil Mixtures to Obtain Modified Structured Lipids
Authors: Bandara Ranahansi Rangadharee , Louis-Gavet Chloé , Bryś Joanna , Mańko-Jurkowska Diana , Górska Agnieszka , Brzezińska Rita , Siol Marta , Makouie Sina , Palani Bharani Kumar , Obranović Marko , Koczoń Piotr
Published: August 2024

The interesterification process allows structured lipids (SLs) to be obtained with a modified triacylglycerol (TAG) structure, in which the unfavorable saturated fatty acids (SFAs) are replaced with nutritionally significant fatty acids (FAs) such as monounsaturated (MUFAs) and polyunsaturated (PUFAs). Oxidative stability is crucial for the quality of SLs. This study aimed to characterize and evaluate the FA profile and oxidative stability of SLs synthesized by the enzymatic interesterification of hemp seed oil (HO) and coconut oil (CO) blends. Blends were prepared in three ratios (75% HO:25% CO, 50% HO:50% CO, and 25% HO:75% CO) and interesterified using sn-1,3 regiospecific lipase for 2 or 6 h. FA composition, the FA distribution of TAGs, acid value (AV), peroxide value (PV), and oxidation time were analyzed and compared to non-interesterified blends. Results showed no significant difference in the SFA:MUFA ratios between interesterified and non-interesterified blends with the same proportions. Lauric acid predominantly occupied the sn-2 position in all blends. Interesterified blends had higher AVs, exceeding codex standards, while PVs remained within the acceptable limits. Blends with 75% HO had lower oxidation times compared to those with 75% CO, with no significant difference between interesterified and non-interesterified blends. In the interesterification process of the studied blends, new TAGs with a modified structure were created, which may affect their physical and nutritional properties. This process also had a significant effect on the AV and PV levels, but not on the oxidation time of the modified blends. Therefore, it is necessary to remove free FAs after the enzymatic process to produce SLs characterized by improved hydrolytic stability. This will lead to better technological properties compared to the original oils. Further research is also necessary to enhance the oxidation stability of SLs obtained from blends of CO and HO to improve their storage stability.
Milk Fat Globule Membrane Is Associated with Lower Blood Lipid Levels in Adults: A Meta-Analysis of Randomized Controlled Trials
Authors: Kanon Alexander P. , Spies Sarah J. , MacGibbon Alastair K. H. , Fuad Maher
Published: August 2024

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, with dyslipidemia being a significant risk factor. This meta-analysis provides a comprehensive evaluation of the impact of bovine dairy-derived milk fat globule membrane (MFGM) supplementation on blood lipid profiles in adults. A systematic search was conducted across various databases up until March 2024, resulting in the inclusion of 6 trials with a total of 464 participants. The findings indicated that MFGM phospholipid supplementation may significantly reduce total cholesterol (TC) and low-density lipoprotein (LDL) cholesterol levels. A combined analysis of the effects on TC, LDL, and triglycerides (TG) revealed a significant overall reduction in these markers. However, no significant increase or reduction was observed on high-density lipoprotein (HDL) and TG levels. Overall, MFGM phospholipid intake may significantly decrease the level of TC and LDL, while no significant changes in TG and HDL were observed. These results suggest that MFGM supplementation could be a promising dietary intervention for improving lipid profiles in adults. Nonetheless, further research is warranted to confirm these results and to better understand the potential variability in the impact of MFGM on blood lipid levels.