bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of SSR Markers and Evaluation of Genetic Diversity of Endangered Plant Saussurea involucrata
Authors: Hu Lin , Wang Jiancheng , Wang Xiyong , Zhang Daoyuan , Sun Yanxia , Lu Ting , Shi Wei
Published: August 2024

The conservation biology field underscores the importance of understanding genetic diversity and gene flow within plant populations and the factors that influence them. This study employs Simple Sequence Repeat (SSR) molecular markers to investigate the genetic diversity of the endangered plant species Saussurea involucrata, offering a theoretical foundation for its conservation efforts. Utilizing sequencing results to screen SSR loci, we designed and scrutinized 18 polymorphic microsatellite primers across 112 samples from 11 populations in the Bayinbuluke region. Our findings reveal high genetic diversity (I = 0.837, He = 0.470) and substantial gene flow (Nm = 1.390) among S. involucrata populations (China, Xinjiang), potentially attributed to efficient pollen and seed dispersal mechanisms. Principal Coordinate Analysis (PCoA) indicates a lack of distinct genetic structuring within the Bayinbuluke populations. The cluster analysis using STRUCTURE reflected the genetic structure of S. involucrata to a certain extent compared with PCoA. The results showed that all samples were divided into four groups. To safeguard this species, we advocate for the in situ conservation of all S. involucrata populations in the area. The SSR markers developed in this study provide a valuable resource for future genetic research on S. involucrata.
Unveiling the Impact of BMP9 in Liver Diseases: Insights into Pathogenesis and Therapeutic Potential
Authors: Chen Han , Li Ying-Yi , Nio Kouki , Tang Hong
Published: August 2024

Bone morphogenetic proteins (BMPs) are a group of growth factors belonging to the transforming growth factor β(TGF-β) family. While initially recognized for their role in bone formation, BMPs have emerged as significant players in liver diseases. Among BMPs with various physiological activities, this comprehensive review aims to delve into the involvement of BMP9 specifically in liver diseases and provide insights into the complex BMP signaling pathway. Through an enhanced understanding of BMP9, we anticipate the discovery of new therapeutic options and potential strategies for managing liver diseases.
Sensory Neurons Release Cardioprotective Factors in an In Vitro Ischemia Model
Authors: Hoebart Clara , Kiss Attila , Podesser Bruno K. , Tahir Ammar , Fischer Michael J. M. , Heber Stefan
Published: August 2024

Sensory neurons densely innervate the myocardium. The role of their sensing and response to acute and prolonged ischemia is largely unclear. In a cellular model of ischemia-reperfusion injury, the presence of sensory neurons increases cardiomyocyte survival. Here, after the exclusion of classical neurotransmitter release, and measurement of cytokine release, we modified the experiment from a direct co-culture of primary murine cardiomyocytes and sensory neurons to a transfer of the supernatant. Sensory neurons were exposed to ischemia and the resulting conditioned supernatant was transferred onto cardiomyocytes. This approach largely increased the tolerance of cardiomyocytes to ischemia and reperfusion. Towards the identification of the mechanism, it was demonstrated that after ten-fold dilution, the conditioned solution lost its protective effect. The effect remained after removal of extracellular vesicles by ultracentrifugation, and was not affected by exposure to protease activity, and fractionation pointed towards a hydrophilic agent. Solutions conditioned by HEK293t cells or 3T3 fibroblasts also increase cardiomyocyte survival, but to a lower degree. A metabolomic search identified 64 at least two-fold changed metabolites and lipids. Many of these could be identified and are involved in essential cellular functions. In the presented model for ischemia-reperfusion, sensory neurons secrete one or more cardioprotective substances that can improve cardiomyocyte survival.
Neoadjuvant Statistical Algorithm to Predict Individual Risk of Relapse in Patients with Resected Liver Metastases from Colorectal Cancer
Authors: Atienza Ángel Vizcay , Iriarte Olast Arrizibita , Sarrias Oskitz Ruiz , Lizundia Teresa Zumárraga , Beristain Onintza Sayar , Casajús Ana Ezponda , Gigli Laura Álvarez , Sastre Fernando Rotellar , García Ignacio Matos , Rodríguez Javier Rodríguez
Published: August 2024

(1) Background: Liver metastases (LM) are the leading cause of death in colorectal cancer (CRC) patients. Despite advancements, relapse rates remain high and current prognostic nomograms lack accuracy. Our objective is to develop an interpretable neoadjuvant algorithm based on mathematical models to accurately predict individual risk, ensuring mathematical transparency and auditability. (2) Methods: We retrospectively evaluated 86 CRC patients with LM treated with neoadjuvant systemic therapy followed by complete surgical resection. A comprehensive analysis of 155 individual patient variables was performed. Logistic regression (LR) was utilized to develop the predictive model for relapse risk through significance testing and ANOVA analysis. Due to data limitations, gradient boosting machine (GBM) and synthetic data were also used. (3) Results: The model was based on data from 74 patients (12 were excluded). After a median follow-up of 58 months, 5-year relapse-free survival (RFS) rate was 33% and 5-year overall survival (OS) rate was 60.7%. Fifteen key variables were used to train the GBM model, which showed promising accuracy (0.82), sensitivity (0.59), and specificity (0.96) in predicting relapse. Similar results were obtained when external validation was performed as well. (4) Conclusions: This model offers an alternative for predicting individual relapse risk, aiding in personalized adjuvant therapy and follow-up strategies.
A General Method to Screen Nanobodies for Cytochrome P450 Enzymes from a Yeast Surface Display Library
Authors: Sun Yudong , Martinez-Ramos Cristian , Chen Eugene , Osawa Yoichi , Zhang Haoming
Published: August 2024

The availability of yeast surface display nanobody (Nb) libraries offers a convenient way to acquire antigen-specific nanobodies that may be useful for protein structure–function studies and/or therapeutic applications, complementary to the conventional method of acquiring nanobodies through immunization in camelids. In this study, we developed a general approach to select nanobodies for cytochrome P450 enzymes from a highly diverse yeast display library. We tested our method on three P450 enzymes including CYP102A1, neuronal nitric oxide synthase (nNOS), and the complex of CYP2B4:POR, using a novel streamlined approach where biotinylated P450s were bound to fluorescent-labeled streptavidin for Nb screening. The Nb–antigen binders were selectively enriched using magnetic-activated cell sorting (MACS) and fluorescence-activated cell sorting (FACS). After two rounds of MACS, the population of positive binders was enriched by >5-fold compared to the naïve library. The subsequent FACS selection, with a gating of 0.1%, identified 634, 270, and 215 positive binders for CYP102A1, nNOS, and CYP2B4:POR, respectively. The positive binders for CYP102A1 were further triaged based on EC50 determined at various antigen concentrations. DNA sequencing of the top 30 binders of CYP102A1 resulted in 26 unique clones, 8 of which were selected for over-expression and characterization. They were found to inhibit CYP102A1-catalyzed oxidation of omeprazole with IC50 values in the range of 0.16–2.8 µM. These results validate our approach and may be applied to other protein targets for the effective selection of specific nanobodies.
Experimental and Theoretical Study on Tensile Mechanical Properties of GFRP–Steel Composite Bars
Authors: Chen Wei , Zhen Hao , Liu Feng , Wu Baolong , Li Hongming , Wu Zhichao , Huang Jian , Zhu Huanyu , Li Lijuan , Xiong Zhe
Published: August 2024

Glass-fiber-reinforced polymer (GFRP)–steel composite bar, a novel building material, is a promising longitudinal reinforcement for marine engineering in harsh environments. Previous research has primarily focused on altering individual parameters to assess their influence on the performance of composite bars, lacking a systematic and in-depth exploration. In this paper, the tensile properties of composite bars have been investigated by adequate experimental testing considering the type of inner steel bar and the thickness of the GFRP layer. Results show that although composite bars undergo elasticity, hardening, and failure stages under tensile loading, due to differences in interfacial bonding forces, the ultimate failure mode for composite bars with HPB300 inner steel bars is relative slippage, while for those with HRB400 inner steel bars, it is fracturing. While ensuring that composite bars have good initial elastic modulus and durability, it is preferable for the thickness of the external GFRP layer to be as small as possible. However, the thickness of the external GFRP layer of composite bars should not be less than 2 mm to prevent misalignment of the inner steel bars, which can negatively impact the tangent modulus during the hardening stage and the ultimate tensile strength. Furthermore, a stress–strain constitutive model for this composite bar was developed and validated. This model offers a universal framework for accurately representing the mechanical properties of the material across a wide range of research parameters.
Definitive Chemoradiotherapy versus Trimodality Therapy for Locally Advanced Esophageal Adenocarcinoma: A Multi-Institutional Retrospective Cohort Study
Authors: Xu Yang , Chow Ronald , Murdy Kyle , Mahsin Md , Chandereng Theeva , Sinha Rishi , Lee-Ying Richard , Abedin Tasnima , Cheung Winson Y. , Thanh Nguyen X. , Lee Sangjune Laurence
Published: August 2024

The optimal management of patients with locally advanced esophageal adenocarcinoma is unclear. Neoadjuvant chemoradiotherapy followed by esophagectomy (trimodality therapy) is supported as a standard of care, but definitive chemoradiotherapy is frequently given in practice to patients who may have been surgical candidates. This multi-institutional retrospective cohort study compared the outcomes of consecutive patients diagnosed with stage II to IVA esophageal adenocarcinoma between 2004 and 2018 who planned to undergo trimodality therapy or definitive chemoradiotherapy. A total of 493 patients were included, of whom 435 intended to undergo trimodality therapy and 56 intended to undergo definitive chemoradiotherapy. After a median follow-up of 7.3 years, trimodality therapy was associated with a lower risk of locoregional failure (5-year risk, 30.5% vs. 61.3%; HR, 0.39; 95% CI, 0.24–0.62; p<0.001) but not distant metastases (5-year risk, 58.2% vs. 53.9%; HR, 1.21; 95% CI, 0.77–1.91; p=0.40). There were no differences in overall survival (HR, 0.78; 95% CI, 0.56–1.09; p=0.14) or cancer-specific survival (HR, 0.83; 95% CI, 0.57–1.21; p=0.33). Findings were consistent on propensity score-matched sensitivity analyses. In conclusion, trimodality therapy was associated with a lower risk of locoregional failure, but this did not translate into a significantly lower risk of distant failure or improved survival. Further studies are required to accurately estimate the trade-offs between the two treatment strategies.
Imaging in Autologous Breast Reconstruction
Authors: Coleman-Belin Janet C. , Barnett Joshua , Khavanin Nima , Nelson Jonas A. , Stern Carrie S. , Allen Robert J.
Published: August 2024

The evolution of imaging actively shapes clinical management in the field. Ultrasonography (US), computed tomography angiography (CTA), and magnetic resonance angiography (MRA) stand out as the most extensively researched imaging modalities for ABR. Ongoing advancements include “real-time” angiography and three-dimensional (3D) surface imaging, and future prospects incorporate augmented or virtual reality (AR/VR) and artificial intelligence (AI). These technologies may further enhance perioperative efficiency, reduce donor-site morbidity, and improve surgical outcomes in ABR.
Temozolomide and the PARP Inhibitor Niraparib Enhance Expression of Natural Killer Group 2D Ligand ULBP1 and Gamma-Delta T Cell Cytotoxicity in Glioblastoma
Authors: Jones Amber B. , Tuy Kaysaw , Hawkins Cyntanna C. , Quinn Colin H. , Saad Joelle , Gary Sam E. , Beierle Elizabeth A. , Ding Lei , Rochlin Kate M. , Lamb Lawrence S. , Hjelmeland Anita B.
Published: August 2024

Glioblastoma (GBM) is an immunologically cold tumor, but several immunotherapy-based strategies show promise, including the administration of ex vivo expanded and activated cytotoxic gamma delta T cells. Cytotoxicity is partially mediated through interactions with natural killer group 2D ligands (NKG2DL) on tumor cells. We sought to determine whether the addition of the blood–brain barrier penetrant PARP inhibitor niraparib to the standard of care DNA alkylator temozolomide (TMZ) could upregulate NKG2DL, thereby improving immune cell recognition. Changes in viability were consistent with prior publications as there was a growth inhibitory effect of the combination of TMZ and niraparib. However, decreases in viability did not always correlate with changes in NKG2DL mRNA. ULBP1/Mult-1 mRNA was increased with the combination therapy in comparison to either drug alone in two of the three cell types tested, even though viability was consistently decreased. mRNA expression correlated with protein levels and ULBP1/MULT-1 cell surface protein was significantly increased with TMZ and niraparib treatment in four of the five cell types tested. Gamma delta T cell-mediated cytotoxicity at a 10:1 effector-to-target ratio was significantly increased upon pretreatment of cells derived from a GBM PDX with TMZ and niraparib in comparison to the control or either drug alone. Together, these data demonstrate that the combination of PARP inhibition, DNA alkylation, and gamma delta T cell therapy has the potential for the treatment of GBM.
Evaluating Pressure Variability and Influencing Factors during High-Flow Nasal Cannula Therapy in Premature Infants
Authors: Ho Fong-Cheng , Lin Chia-Ying , Chang Ane-Shu , Yeh Ching-Yi , Chen Hsiu-Lin
Published: August 2024

Background: Heated humidified high-flow nasal cannulas (HHHFNCs) are increasingly used as an alternative strategy for weaning from nasal continuous positive airway pressure (NCPAP) in premature infants. However, the optimal pressure provided by HHHFNCs is unknown. This retrospective study investigated the pressure changes and associated factors during HHHFNC therapy in preterm infants. Methods: Clinically stable preterm neonates born with a birth weight of 2500 g or less and receiving HHHFNC therapy for weaning from NCPAP were enrolled. The flow of the HHHNFCs was adjusted to achieve an initial pressure equivalent to the positive expiratory pressure (PEEP) of NCPAP. Subsequent pressure changes in the HHHFNCs were measured by a GiO digital pressure gauge. Results: Nine premature infants were enrolled. Their gestational age (mean ± SD) was 28.33 ± 2.61 weeks, and the birth weight was 1102.00 ± 327.53 g. Overall, 437 pressure measurements were conducted. The median pressure of the HHHFNCs was 5 cmH2O. The generated pressure had a significant association with the body weight, postmenstrual age (PMA) and flow rate. A multiple regression model revealed that the measured pressure (cmH2O) = −5.769 + 1.021 × flow rate (L/min) − 0.797 × body weight (kg) + 0.035 × PMA (days) (r2 = 0.37, p < 0.001). Conclusions: The pressure provided by HHHFNCs is influenced by body weight, PMA, and flow rate. It is feasible to set the delivered pressure of HHHFNCs to match the applied PEEP of NCPAP initially, facilitating the weaning of preterm infants from NCPAP to HHHFNCs.
miRNA Sequencing Analysis in Maize Roots Treated with Neutral and Alkaline Salts
Authors: Chen Ziqi , Liu Yang , Wang Qi , Fei Jianbo , Liu Xiangguo , Zhang Chuang , Yin Yuejia
Published: August 2024

Soil salinization/alkalization is a complex environmental factor that includes not only neutral salt NaCl but also other components like Na2CO3. miRNAs, as small molecules that regulate gene expression post-transcriptionally, are involved in plant responses to abiotic stress. In this study, maize seedling roots were treated for 5 h with 100 mM NaCl, 50 mM Na2CO3, and H2O, respectively. Sequencing analysis of differentially expressed miRNAs under these conditions revealed that the Na2CO3 treatment group had the most differentially expressed miRNAs. Cluster analysis indicated their main involvement in the regulation of ion transport, binding, metabolism, and phenylpropanoid and flavonoid biosynthesis pathways. The unique differentially expressed miRNAs in the NaCl treatment group were related to the sulfur metabolism pathway. This indicates a significant difference in the response patterns of maize to different treatment groups. This study provides theoretical evidence and genetic resources for further analysis of the molecular mechanisms behind maize’s salt–alkali tolerance.
The Role of Mannitol and Vitamin D in Ovarian Ischemia/Reperfusion Injury in Rats with Acute Abdominal
Authors: Karateke Faruk , Karateke Atilla , Topdagi Basak , Atilgan Merve , Dokuyucu Recep
Published: August 2024

This study was designed to investigate the effects of vitamin D and mannitol in an experimental rat ovarian torsion model. Thirty-two female Wistar albino rats were randomly classified as group 1: (sham), group 2: (detorsion), group 3: (detorsion + mannitol), group 4: (detorsion + vitamin D) and group 5: (detorsion + mannitol + vitamin D) (for each group n = 8). All groups were subjected to bilateral adnexal torsion for 2 h except for group 1. Bilateral adnexal detorsion was performed in all groups except for group 1. Groups 3 and 5 intraperitoneally received the injection of mannitol at a dose of 0.3 mg/kg 30 min before detorsion. Also, the group’s 4 and 5 orally received vitamin D in a dose of 500 IU/kg/day for two weeks before torsion. Total oxidant status (TOS), total antioxidant status (TAS), oxidative stress index (OSI) and proliferating cell nuclear antigen (PCNA) levels were analyzed. According to the histopathological analyses, ovarian tissue damage and follicle counting were evaluated. TOS, OSI and histopathologic score values of ovarian tissue were significantly lower in group 5 than groups 2, 3 and 4 (p < 0.05). The PCNA level was significantly higher in group 5 than in groups 2, 3 and 4 (p < 0.05). A strong negative correlation was found between OSI and PCNA in groups 2, 3, 4 and 5 (r = −0.92, p = 0.01; r = −0.98, p < 0.0001; r = −0.98, p < 0.0001 and r = −0.96, p = 0.0002, respectively). The numbers of primordial follicles in group 5 (p < 0.001) and primary follicles in group 4 (p < 0.001) were significantly higher when compared to group 2. Based on the results of this study, it could be suggested that combination treatment of mannitol with vitamin D is more effective in reversing tissue damage induced by ischemia–reperfusion (I/R) injury in the ovarian torsion model than administration of only an agent.
Using eZIS to Predict Progression from MCI to Dementia in Three Years
Authors: Pai Ya-Tang , Matsuda Hiroshi , Pai Ming-Chyi
Published: August 2024

(1) Background: Mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) progresses to dementia at a higher annual rate, while other MCIs may remain stable or even improve over time. Discriminating progressive from non-progressive cases of MCI is crucial and challenging. (2) Methods: A retrospective study of individuals with MCI was conducted at a university hospital located in southern Taiwan. The researchers collected demographic data, comorbidities, the scores of cognitive tests, three easy Z-score imaging system (eZIS) indicators (severity, extent, and ratio), Fazekas scale scores, mesial temporal atrophy (MTA) scores, clinical outcomes including deterioration of Cognitive Abilities Screening Instrument, Mini-mental State Examination, Clinical Dementia Rating Sum of Box scores, and the conversion from MCI to dementia. Those who converted to dementia in three years and non-converters were compared by the three eZIS indicators to test the predictive utility, and the clinical outcomes were evaluated by regression and ROC curve analysis. (3) Results: The three eZIS indicators were significantly higher in the group of progressive MCI than in stable MCI. eZIS severity is positively correlated with a deterioration in the scores of the Cognitive Abilities Screening Instrument and Clinical Dementia Rating Sum of Box. eZIS severity is also positively correlated with conversion from MCI to dementia. The AUC for severity is 0.719, and the optimal cutoff value of severity for predicting conversion is 1.22. (4) Conclusions: During three years of follow-up, MCI individuals with greater eZIS severity were significantly associated with worse cognitive assessment scores and a higher conversion rate to dementia.
Fact-Aware Generative Text Summarization with Dependency Graphs
Authors: Chen Ruoyu , Li Yan , Jiang Yuru , Sun Bochen , Wang Jingqi , Li Zhen
Published: August 2024

Generative text summaries often suffer from factual inconsistencies, where the summary deviates from the original text. This significantly reduces their usefulness. To address this issue, we propose a novel method for improving the factual accuracy of Chinese summaries by leveraging dependency graphs. Our approach involves analyzing the input text to build a dependency graph. This graph, along with the original text, is then processed by separate models: a Relational Graph Attention Neural Network for the dependency graph and a Transformer model for the text itself. Finally, a Transformer decoder generates the summary. We evaluate the factual consistency of the generated summaries using various methods. Experiments demonstrate that our approach improves about 7.79 points compared to the baseline Transformer model on the Chinese LCSTS dataset using ROUGE-1 metric, and 4.48 points in the factual consistency assessment model StructBERT.
Next-Gen Dynamic Hand Gesture Recognition: MediaPipe, Inception-v3 and LSTM-Based Enhanced Deep Learning Model
Authors: Yaseen  , Kwon Oh-Jin , Kim Jaeho , Jamil Sonain , Lee Jinhee , Ullah Faiz
Published: August 2024

Gesture recognition is crucial in computer vision-based applications, such as drone control, gaming, virtual and augmented reality (VR/AR), and security, especially in human–computer interaction (HCI)-based systems. There are two types of gesture recognition systems, i.e., static and dynamic. However, our focus in this paper is on dynamic gesture recognition. In dynamic hand gesture recognition systems, the sequences of frames, i.e., temporal data, pose significant processing challenges and reduce efficiency compared to static gestures. These data become multi-dimensional compared to static images because spatial and temporal data are being processed, which demands complex deep learning (DL) models with increased computational costs. This article presents a novel triple-layer algorithm that efficiently reduces the 3D feature map into 1D row vectors and enhances the overall performance. First, we process the individual images in a given sequence using the MediaPipe framework and extract the regions of interest (ROI). The processed cropped image is then passed to the Inception-v3 for the 2D feature extractor. Finally, a long short-term memory (LSTM) network is used as a temporal feature extractor and classifier. Our proposed method achieves an average accuracy of more than 89.7%. The experimental results also show that the proposed framework outperforms existing state-of-the-art methods.
Improved YOLOv8 for Dangerous Goods Detection in X-ray Security Images
Authors: Wang Aili , Yuan Pengfei , Wu Haibin , Iwahori Yuji , Liu Yan
Published: August 2024

X-ray security images face significant challenges due to complex backgrounds, item overlap, and multi-scale target detection. Traditional methods often struggle to accurately identify objects, especially under cluttered conditions. This paper presents an advanced detection model, called YOLOv8n-GEMA, which incorporates several enhancements to address these issues. Firstly, the generalized efficient layer aggregation network (GELAN) module is employed to augment the feature fusion capabilities. Secondly, to tackle the problems of overlap and occlusion in X-ray images, the efficient multi-scale attention (EMA) module is utilized, effectively managing the feature capture and interdependencies among overlapping items, thereby boosting the model’s detection capability in such scenarios. Lastly, addressing the diverse sizes of items in X-ray images, the Inner-CIoU loss function uses auxiliary bounding boxes at varying scale ratios for loss calculation, ensuring faster and more effective bounding box predictions. The enhanced YOLOv8 model was tested on the public datasets SIXRay, HiXray, CLCXray, and PIDray, where the improved model’s mean average precision (mAP) reached 94.4%, 82.0%, 88.9%, and 85.9%, respectively, showing improvements of 3.6%, 1.6%, 0.9%, and 3.4% over the original YOLOv8. These results demonstrate the effectiveness and universality of the proposed method. Compared to current mainstream X-ray images of dangerous goods detection models, this model significantly reduces the false detection rate of dangerous goods in X-ray security images and achieves substantial improvements in the detection of overlapping and multi-scale targets, realizing higher accuracy in dangerous goods detection.
Data-Driven Distributionally Robust Optimization for Day-Ahead Operation Planning of a Smart Transformer-Based Meshed Hybrid AC/DC Microgrid Considering the Optimal Reactive Power Dispatch
Authors: Núñez-Rodríguez Rafael A. , Unsihuay-Vila Clodomiro , Posada Johnny , Pinzón-Ardila Omar
Published: August 2024

Smart Transformer (ST)-based Meshed Hybrid AC/DC Microgrids (MHMs) present a promising solution to enhance the efficiency of conventional microgrids (MGs) and facilitate higher integration of Distributed Energy Resources (DERs), simultaneously managing active and reactive power dispatch. However, MHMs face challenges in resource management under uncertainty and control of electronic converters linked to the ST and DERs, complicating the pursuit of optimal system performance. This paper introduces a Data-Driven Distributionally Robust Optimization (DDDRO) approach for day-ahead operation planning in ST-based MHMs, focusing on minimizing network losses, voltage deviations, and operational costs by optimizing the reactive power dispatch of DERs. The approach accounts for uncertainties in photovoltaic generator (PVG) output and demand. The Column-and-Constraint Generation (C&CG) algorithm and the Duality-Free Decomposition (DFD) method are employed. The initial mixed-integer non-linear planning problem is also reformulated into a mixed-integer (MI) Second-Order Cone Programming (SOCP) problem using second-order cone relaxation and a positive octagonal constraint method. Simulation results on a connected MHM system validate the model’s efficacy and performance. The study also highlights the advantages of the meshed MG structure and the positive impact of integrating the ST into MHMs, leveraging the multi-stage converter’s flexibility for optimal energy management under uncertain conditions.
Hybrid Energy Solution to Improve Irrigation Systems: HY4RES vs. HOMER Optimization Models
Authors: Coelho João , Alves Afonso , Morillo Jorge , Coronado-Hernández Oscar , Perez-Sanchez Modesto , Ramos Helena
Published: August 2024

A new methodology for hybrid energy systems (HESs) was developed, namely the HY4RES model, tailored for the water sector, covering hybrid energy objective functions and grid or battery support using optimization algorithms in Solver, MATLAB, and Python, with evolutionary methods. HOMER is used for hybrid microgrids and allows for comparison with HY4RES, the newly developed model. Both models demonstrated flexibility in optimizing hybrid renewable solutions. This study analyzed an irrigation system for 3000 m3/ha (without renewables (Base case) and the Proposed system—with PV solar and pumped-hydropower storage to maximize cash flow over 25 years). Case 1—3000 m3/ha presented benefits due to PV supplying ~87% of energy, reducing grid dependency to ~13%. Pumped-hydropower storage (PHS) charges with excess solar energy, ensuring 24 h irrigation. Sensitivity analyses for Case 2—1000—and Case 3—6000 m3/ha—highlighted the advantages and limitations of water-energy management and system optimization. Case 2 was the most economical due to lower water-energy needs with noteworthy energy sales (~73.4%) and no need for the grid. Case 3 led to increased operating costs relying heavily on grid energy (61%), with PV providing only 39%. PHS significantly lowered operating costs and enhanced system flexibility by selling excess energy to the grid.
The Impact of a Market Maker in an Electricity Market
Authors: Arias Sebastián , Santa-Alvarado Adriana M. , Salazar Harold
Published: August 2024

Electricity retailers in an electricity market use over-the-counter (OTC) contracts, or bilateral, and spot market purchases to meet the energy demands of their users. In some markets, OTC contracts face issues with price discrimination and accessibility. This study reveals some inefficiencies of OTC contracts in Colombia that expose regulated users—approximately 70% of the national demand—to market risk. This risk is aggravated by the current tariff design. To mitigate these inefficiencies, this article proposes the incorporation of a market maker that will improve the liquidity of existing energy futures in the country. These futures are mechanisms that the retailers could implement to hedge their demand and reduce the adverse effects of market risk. The characteristics of the market maker and a quantitative analysis of its impact are developed in this paper. While the characterization of the problem with its solution is developed with Colombian data, the conceptual framework could be extended to other countries that are concerned about how energy users are being affected by increases in tariffs due to high exposure to spot market price volatility.
Recent Advances in Ejector-Enhanced Vapor Compression Heat Pump and Refrigeration Systems—A Review
Authors: Gruber Sven , Rola Klemen , Urbancl Danijela , Goričanec Darko
Published: August 2024

The incorporation of ejectors into heat pump and refrigeration cycles has been the subject of growing interest, largely due to their simple structure, high reliability, and cost-effectiveness. This paper investigates the recent advancements in novel design concepts of ejector-enhanced vapor compression heat pump and refrigeration cycles. An overview of novel single-stage and two-stage compression cycles utilizing a single or multiple ejectors is provided. First, the system setup, operational principles, description, and figures of the existing schemes are provided. Second, the main results, such as the coefficient of performance (COP), volumetric heating capacity and exergy destruction, are discussed. In conclusion, the paper presents a coherent summary of the current developments, future prospects, and the current knowledge gap. A plethora of research is present in developing theoretical systems with high efficiency. However, experimental tests for real-life implementations are limited. This review aims to provide the reader with an overview of recent theoretical and experimental studies.
Profitability Analyses for Residential Battery Investments: A Norwegian Case Study
Authors: Nygård Heidi S. , Ottesen Stig Ødegaard , Skonnord Olav Henrik
Published: August 2024

With the higher penetration of intermittent renewable energy sources in the electric power grid, more flexibility is needed to cope with challenges related to stability and reliability. Consumers can be part of the solution through demand response, for example, by investing in residential batteries that can charge and discharge based on price signals (implicit flexibility) or externally controlled based on grid-related needs (explicit flexibility). In this study, we investigate the feasibility of deploying residential batteries through a case study consisting of 20 households located in south-eastern Norway. The potential annual savings from implicit flexibility are optimized based on the retail electricity price, a power-based tariff, and potential revenues by selling electricity to the grid. Real historical price and consumption data with hourly resolutions from the entire year of 2022 are used as input for the optimization, yielding a theoretical profit potential. Based on this, profitability analyses are performed. The results show that the battery investments will not reach an economic break-even point during their lifetime under today’s electricity price conditions. However, future developments in profit increase from implicit flexibility, substantial investment support, or additional revenues from emerging flexibility markets could make the investment economically attractive for a regular consumer.
A Study on Short-Term Air Consumption Prediction Model for Air-Jet Looms Combining Sliding Time Window and Incremental Learning
Authors: Yu Bo , Fang Liaoliao , Wu Zihao , Shen Chunya , Hu Xudong
Published: August 2024

The energy consumption of air-jet looms mainly comes from air compressors. Predicting the air consumption of air-jet looms for the upcoming period is significant for the variable frequency adjustment of air compressors, thereby aiding in energy saving and reducing fabric costs. This paper proposes an innovative method that combines Sliding Time Windows (STW), feature analysis, and incremental learning to improve the accuracy of short-term air consumption prediction. First, the STW method is employed during the data collection phase to enhance data reliability. Through feature analysis, significant factors affecting the air consumption of air-jet looms, beyond traditional research, are explored and incorporated into the prediction model. The experimental results indicate that the introduction of new features improved the model’s R2 from 0.905 to 0.950 and reduced the MSE from 32.369 to 16.239. The STW method applied to the same random forest model increased the R2 from 0.906 to 0.950 and decreased the MSE from 32.244 to 16.239. The decision tree method, compared to the linear regression model, improved the R2 from 0.928 to 0.950 and reduced the MSE from 23.541 to 16.239, demonstrating significant predictive performance enhancement. After establishing the optimal model, incremental learning is used to continuously improve the reliability and accuracy of short-term predictions. Experiments show that the incremental learning method, compared to static models, offers better resilience and reliability when new data are collected. The proposed method significantly improves the accuracy of air consumption prediction for air-jet looms, providing strong support for the variable frequency adjustment of air compressors, and contributes to the goals of energy saving and cost reduction. The research results indicate that this method not only enhances prediction accuracy but also provides new insights and methods for future energy-saving research.
A Rule-Based Energy Management Technique Considering Altitude Energy for a Mini UAV with a Hybrid Power System Consisting of Battery and Solar Cell
Authors: Engin Selin , Çınar Hasan , Kandemir İlyas
Published: August 2024

Nowadays, due to climate change and disappearance of fossil fuels, hybrid electric UAVs using renewable energy sources are being developed. In addition, although research on UAVs with a large wingspan and high weight is common due to their long endurance, research on mini UAVs has remained limited. This study aims to increase the energy capacity of solar-powered mini UAVs and thus extend their endurance by developing a fixed-wing hybrid UAV that can fly with solar energy as much as possible, especially during the cruise phase. In this study, a solar-powered mini VTOL (vertical take-off and landing) UAV with a wingspan of 1.8 m and weight of 3.3 kg is developed and a model of the system consisting of solar cells, a battery, a super capacitor, and a DC/DC converter is created in MATLAB/Simulink software (R2023b). Additionally, state machine control (SMC), a rule-based (RB) energy management strategy (EMS), has been applied to this model. While the power obtained from the sun is divided among the other energy components, the durability of the UAV is increased, and the excess energy is stored as altitude energy to be used when necessary. As a result, in this study, an energy management algorithm including altitude energy has been successfully applied to a solar-powered UAV, achieving an 11.11% energy saving.
Creating High-Resolution Precipitation and Extreme Precipitation Indices Datasets by Downscaling and Improving on the ERA5 Reanalysis Data over Greece
Authors: Ntagkounakis Giorgos , Nastos Panagiotis , Kapsomenakis Yiannis
Published: August 2024

The aim of this study was to construct a high-resolution (1 km × 1 km) database of precipitation, number of wet days, and number of times precipitation exceeded 10 mm and 20 mm over Greece on a monthly and on an annual basis. In order to achieve this, the ERA5 reanalysis dataset was downscaled using regression kriging with histogram-based gradient boosting regression trees. The independent variables used are spatial parameters derived from a high-resolution digital elevation model and a selection of ERA5 reanalysis data, while as the dependent variable in the training stages, we used 97 precipitation gauges from the Hellenic National Meteorological Service for the period 1980–2010. These stations were also used for validation purposes using a leave-one-out cross-validation methodology. The results of the study showed that the algorithm is able to achieve better R2 and RMSE over the standalone ERA5 dataset over the Greek region. Additionally, the largest improvements were noticed in the wet days and in the precipitation over 10 and 20 mm, where the ERA5 reanalysis dataset overestimates the number of wet days and underestimates precipitation over 10 and 20 mm, while geographically, the ERA5 dataset performs the worst in the island regions of Greece. This indicates that the ERA5 dataset does not simulate the precipitation intensity accurately over the Greek region, and using our methodology, we were able to increase the accuracy and the resolution. Our approach delivers higher-resolution data, which are able to more accurately depict precipitation in the Greek region and are needed for comprehensive climate change hazard identification and analysis.
Disentangling the Response of Vegetation Dynamics to Natural and Anthropogenic Drivers over the Minjiang River Basin Using Dimensionality Reduction and a Structural Equation Model
Authors: Kang Yujie , Wang Ziqin , Xu Binni , Shen Wenjie , Chen Yu , Zhou Xiaohui , Liu Yanguo , Zhang Tingbin , Wang Guoyan , Jia Yuling , Li Jingji
Published: August 2024

Located at an average elevation of approximately 2000 m, the Minjiang River Basin (MJB), a key tributary of the Upper Yangtze River, straddles the Western Sichuan Plateau and the Sichuan Basin. Vegetation here is crucial for human life, providing oxygen and energy. However, the influence of climatic variables, human activities, and rugged terrain on vegetation vitality is still debated. This study mainly leverages data from the Normalized Difference Vegetation Index (NDVI), meteorological stations data, and land use data. Analytical techniques include trend analysis, partial correlation coefficient analysis (PCC), principal component analysis (PCA), and partial least squares structural equation modeling (PLS-SEM). Results indicate a stable upward trend in vegetation growth with minimal fluctuations, with a growth rate of 0.95 × 10−3/a (p < 0.01). PCC analysis shows a positive correlation between NDVI and key climatic elements in over 60% of the area. The areas with significant vegetation growth had the highest average PCC. PCA and PLS-SEM identify temperature and precipitation as primary growth drivers, while elevation and land use intensity hinder growth. The MJB landscape reveals thresholds and tipping points, with specific temperature and precipitation benchmarks varying by elevation, delineating the boundary between flourishing vegetation and growth inhibition.
The Interaction between Climate and Soil Properties Influences Tree Species Richness in Tropical and Subtropical Forests of Southern China
Authors: Shi Wei , Zeng Wenhao , Aritsara Amy Ny Aina , Yi Yin , Zhu Shidan , Cao Kunfang
Published: August 2024

The climate and soil properties are major determinants of plant growth and forest community assembly across diverse biomes. However, the contribution of climate and soil on species diversity in tropical and subtropical forests remains controversial. Therefore, this study aims to evaluate the effects of soil and climate on tree species richness using survey data across 495 tropical and subtropical forest plots in Southern China. The selected predictors were categorized as local plot characteristics, climate, and soil factors, and their relationship with tree species richness was modeled using negative binomial generalized linear models. The results revealed that the considering of the interaction between climate and soil properties considerably improved the goodness−of−fit of these models. The individual effects of climate and soil factors had weak relationships with species richness, accounting for 3.61% and 5.77% of the overall 58.9% explained variance in species richness, respectively. Instead, the interaction between climate and soil properties explained most of the statistical variation in tree species richness (84.34% of the overall 58.9% explained variance). The results highlight the importance of soil and climate interactions on tree diversity in tropical and subtropical mature natural forests and their incorporation into biodiversity assessment models to enhance the prediction of community change and responses to climate change.
Initial Medium Optimization of Nigrospora oryzae JL-4 and Its Biocontrol Potential on Solanum rostratum
Authors: Zheng Wanting , Yan Wenfeng , Ding Ning , Zheng Yanan
Published: August 2024

To assess the biocontrol potential of Nigrospora oryzae against Solanum rostratum, the effects of different medium components and fermentation conditions on the biomass of N. oryzae were investigated to determine the optimal medium composition and fermentation conditions. Subsequently, the pathogenicity of S. rostratum increased after artificial inoculation of S. rostratum with N. oryzae. Additionally, the safety of N. oryzae fermentation on seven crops were evaluated. The results revealed that the optimal shake flask culture ratio for N. oryzae was lactose:glycerol:yeast extract:(NH4)2SO4 = 2:2:1:2. The optimal fermentation conditions were as follows: 15 mL of inoculum, 75 mL of fermentation liquid in a 150 mL shake flask, an initial pH of 5.0, a culture temperature of 20 °C, and 4 days of culture. The disease index of S. rostratum after spraying with N. oryzae fermentation agent was 60.00, indicating strong pathogenicity to S. rostratum. Safety evaluation revealed that N. oryzae exhibited no symptoms on Zea mays, S. melongena, Festuca arundinacea, Bromus inermis, or Medicago sativa but showed moderate susceptibility to Triticum aestivum and S. lycopersicum. This study represents the first exploration of the biocontrol potential of N. oryzae against S. rostratum. These findings suggest the potential of N. oryzae for development as a microbial herbicide targeting S. rostratum.
Evaluating Source Complexity in Blended Milk Cheese: Integrated Strontium Isotope and Multi-Elemental Approach to PDO Graviera Naxos
Authors: Nikezić Majda , Chantzi Paraskevi , Irrgeher Johanna , Zuliani Tea
Published: August 2024

Graviera Naxos, a renowned cheese with Protected Designation of Origin status, is crafted from a blend of cow, goat, and sheep milk. This study focused on assessing the Sr isotopic and multi-elemental composition of both the processed cheese and its ingredients, as well as the environmental context of Naxos Island, including samples of milk, water, soil, and feed. The objective was to delineate the geochemical signature of Graviera Naxos cheese and to explore the utility of Sr isotopes as indicators of geographic origin. The 87Sr/86Sr values for Graviera Naxos samples ranged from 0.70891 to 0.70952, indicating a relatively narrow range. However, the Sr isotopic signature of milk, heavily influenced by the feed, which originates from geologically distinct areas, does not always accurately reflect the local breeding environment. Multi-elemental analysis revealed variations in milk composition based on type and season; yet, no notable differences were found between raw and pasteurized milk. A mixing model evaluating the contributions of milk, sea salt, and rennet to the cheese’s Sr isotopic signature suggested a significant average contribution of 73.1% from sea salt. This study highlights the complexities of linking dairy products with their geographical origins and emphasizes the need for sophisticated geochemical authentication methods.
Revealing the Effects of Zinc Sulphate Treatment on Melatonin Synthesis and Regulatory Gene Expression in Germinating Hull-Less Barley through Transcriptomic Analysis
Authors: Guo Yufeng , Zhang Guoqiang , Li Zhenghong , Liao Xueyi , Sun Wu , Jiang Xinhao
Published: August 2024

This study investigated the transcriptomic mechanisms underlying melatonin accumulation and the enhancement of salt tolerance in hull-less barley seeds subjected to zinc sulphate stress. Following zinc sulphate treatment, hull-less barley seeds demonstrated increased melatonin accumulation and improved salt tolerance. Through transcriptome analysis, the study compared gene expression alterations in seeds (using the first letter of seed, this group is marked as ‘S’), seeds treated with pure water (as the control group, is marked as ‘C’), and germinated seeds exposed to varying concentrations of zinc sulphate (0.2 mM and 0.8 mM, the first letter of zinc sulphate, ‘Z’, is used to mark groups ‘Z1’ and ‘Z2’). The analysis revealed that 8176, 759, and 622 differentially expressed genes (DEGs) were identified in the three comparison groups S.vs.C, C.vs.Z1, and C.vs.Z2, respectively. Most of the DEGs were closely associated with biological processes, including oxidative-stress response, secondary metabolite biosynthesis, and plant hormone signaling. Notably, zinc sulphate stress influenced the expression levels of Tryptophan decarboxylase 1 (TDC1), Acetylserotonin O-methyltransferase 1 (ASMT1), and Serotonin N-acetyltransferase 2 (SNAT2), which are key genes involved in melatonin synthesis. Furthermore, the expression changes of genes such as Probable WRKY transcription factor 75 (WRKY75) and Ethylene-responsive transcription factor ERF13 (EFR13) exhibited a strong correlation with fluctuations in melatonin content. These findings contribute to our understanding of the mechanisms underlying melatonin enrichment in response to zinc sulphate stress.