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18 pages, 2429 KiB  
Article
Conserved and Specific Root-Associated Microbiome Reveals Close Correlation Between Fungal Community and Growth Traits of Multiple Chinese Fir Genotypes
by Xuan Chen, Zhanling Wang, Wenjun Du, Junhao Zhang, Yuxin Liu, Liang Hong, Qingao Wang, Chuifan Zhou, Pengfei Wu, Xiangqing Ma and Kai Wang
Microorganisms 2025, 13(8), 1741; https://doi.org/10.3390/microorganisms13081741 - 25 Jul 2025
Abstract
Plant microbiomes are vital for the growth and health of their host. Tree-associated microbiomes are shaped by multiple factors, of which the host is one of the key determinants. Whether different host genotypes affect the structure and diversity of the tissue-associated microbiome and [...] Read more.
Plant microbiomes are vital for the growth and health of their host. Tree-associated microbiomes are shaped by multiple factors, of which the host is one of the key determinants. Whether different host genotypes affect the structure and diversity of the tissue-associated microbiome and how specific taxa enriched in different tree tissues are not yet well illustrated. Chinese fir (Cunninghamia lanceolata) is an important tree species for both economy and ecosystem in the subtropical regions of Asia. In this study, we investigated the tissue-specific fungal community structure and diversity of nine different Chinese fir genotypes (39 years) grown in the same field. With non-metric multidimensional scaling (NMDS) analysis, we revealed the divergence of the fungal community from rhizosphere soil (RS), fine roots (FRs), and thick roots (TRs). Through analysis with α-diversity metrics (Chao1, Shannon, Pielou, ACE, Good‘s coverage, PD-tree, Simpson, Sob), we confirmed the significant difference of the fungal community in RS, FR, and TR samples. Yet, the overall fungal community difference was not observed among nine genotypes for the same tissues (RS, FR, TR). The most abundant fungal genera were Russula in RS, Scytinostroma in FR, and Subulicystidium in TR. Functional prediction with FUNGuild analysis suggested that ectomycorrhizal fungi were commonly enriched in rhizosphere soil, while saprotroph–parasite and potentially pathogenic fungi were more abundant in root samples. Specifically, genotype N104 holds less ectomycorrhizal and pathogenic fungi in all tissues (RS, FR, TR) compared to other genotypes. Additionally, significant correlations of several endophytic fungal taxa (Scytinostroma, Neonothopanus, Lachnum) with the growth traits (tree height, diameter, stand volume) were observed. This addresses that the interaction between tree roots and the fungal community is a reflection of tree growth, supporting the “trade-off” hypothesis between growth and defense in forest trees. In summary, we revealed tissue-specific, as well as host genotype-specific and genotype-common characters of the structure and functions of their fungal communities. Full article
(This article belongs to the Special Issue Rhizosphere Microbial Community, 4th Edition)
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14 pages, 3187 KiB  
Article
Characterizations of Electrospun PVDF-Based Mixed Matrix Membranes with Nanomaterial Additives
by Haya Taleb, Venkatesh Gopal, Sofian Kanan, Raed Hashaikeh, Nidal Hilal and Naif Darwish
Nanomaterials 2025, 15(15), 1151; https://doi.org/10.3390/nano15151151 - 25 Jul 2025
Abstract
Water scarcity poses a formidable challenge around the world, especially in arid regions where limited availability of freshwater resources threatens both human well-being and ecosystem sustainability. Membrane-based desalination technologies offer a viable solution to address this issue by providing access to clean water. [...] Read more.
Water scarcity poses a formidable challenge around the world, especially in arid regions where limited availability of freshwater resources threatens both human well-being and ecosystem sustainability. Membrane-based desalination technologies offer a viable solution to address this issue by providing access to clean water. This work ultimately aims to develop a novel permselective polymeric membrane material to be employed in an electrochemical desalination system. This part of the study addresses the optimization, preparation, and characterization of a polyvinylidene difluoride (PVDF) polymeric membrane using the electrospinning technique. The membranes produced in this work were fabricated under specific operational, environmental, and material parameters. Five different additives and nano-additives, i.e., graphene oxide (GO), carbon nanotubes (CNTs), zinc oxide (ZnO), activated carbon (AC), and a zeolitic imidazolate metal–organic framework (ZIF-8), were used to modify the functionality and selectivity of the prepared PVDF membranes. Each membrane was synthesized at two different levels of additive composition, i.e., 0.18 wt.% and 0.45 wt.% of the entire PVDF polymeric solution. The physiochemical properties of the prepared membranes were characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), zeta potential, contact angle, conductivity, porosity, and pore size distribution. Based on findings of this study, PVDF/GO membrane exhibited superior results, with an electrical conductivity of 5.611 mS/cm, an average pore size of 2.086 µm, and a surface charge of −38.33 mV. Full article
(This article belongs to the Section Synthesis, Interfaces and Nanostructures)
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25 pages, 6190 KiB  
Article
CRISPR/Cas9-Driven Engineering of AcMNPV Using Dual gRNA for Optimized Recombinant Protein Production
by Rocco Valente, Joaquín Poodts, Joaquín Manuel Birenbaum, María Sol Rodriguez, Ignacio Smith, Jorge Alejandro Simonin, Franco Uriel Cuccovia Warlet, Aldana Trabucchi, Salvador Herrero, María Victoria Miranda, Mariano Nicolás Belaich and Alexandra Marisa Targovnik
Viruses 2025, 17(8), 1041; https://doi.org/10.3390/v17081041 - 25 Jul 2025
Abstract
The CRISPR/Cas9 system is a powerful genome-editing tool that is applied in baculovirus engineering. In this study, we present the first report of the AcMNPV genome deletions for bioproduction purposes, using a dual single-guide RNA (sgRNA) CRISPR/Cas9 approach. We used this method to [...] Read more.
The CRISPR/Cas9 system is a powerful genome-editing tool that is applied in baculovirus engineering. In this study, we present the first report of the AcMNPV genome deletions for bioproduction purposes, using a dual single-guide RNA (sgRNA) CRISPR/Cas9 approach. We used this method to remove nonessential genes for the budded virus and boost recombinant protein yields when applied as BEVS. We show that the co-delivery of two distinct ribonucleoprotein (RNP) complexes, each assembled with a sgRNA and Cas9, into Sf9 insect cells efficiently generated deletions of fragments containing tandem genes in the genome. To evaluate the potential of this method, we assessed the expression of two model proteins, eGFP and HRPc, in insect cells and larvae. The gene deletions had diverse effects on protein expression: some significantly enhanced it while others reduced production. These results indicate that, although the targeted genes are nonessential, their removal can differentially affect recombinant protein yields depending on the host. Notably, HRPC expression increased up to 3.1-fold in Spodoptera frugiperda larvae. These findings validate an effective strategy for developing minimized baculovirus genomes and demonstrate that dual-guide CRISPR/Cas9 editing is a rapid and precise tool for baculovirus genome engineering. Full article
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13 pages, 879 KiB  
Article
Three-Dimensional Speckle Tracking Echocardiography for Detection of Acute Coronary Occlusions in Non-ST-Elevation Acute Coronary Syndrome Patients
by Thomas M. Stokke, Kristina H. Haugaa, Kristoffer Russell, Thor Edvardsen and Sebastian I. Sarvari
Diagnostics 2025, 15(15), 1864; https://doi.org/10.3390/diagnostics15151864 - 25 Jul 2025
Abstract
Objectives: This study aimed to evaluate the ability of three-dimensional (3D) speckle tracking echocardiography (STE) to detect acute coronary occlusions in patients with non-ST-segment elevation acute coronary syndrome (NSTE-ACS) and its potential diagnostic advantage over two-dimensional (2D) STE. Methods: Fifty-six patients [...] Read more.
Objectives: This study aimed to evaluate the ability of three-dimensional (3D) speckle tracking echocardiography (STE) to detect acute coronary occlusions in patients with non-ST-segment elevation acute coronary syndrome (NSTE-ACS) and its potential diagnostic advantage over two-dimensional (2D) STE. Methods: Fifty-six patients with NSTE-ACS (mean age 64 ± 11 years; 80% male) underwent 2D and 3D transthoracic echocardiography prior to coronary angiography. Global longitudinal strain (GLS), global circumferential strain (GCS), and 3D ejection fraction (EF) were analyzed. Acute coronary occlusion was defined as TIMI flow 0–1 in the presumed culprit artery. Results: Acute coronary occlusion was present in 16 patients (29%). Patients with occlusion had significantly more impaired strain compared to those without: 3D GLS (−12.5 ± 2.7% vs. −15.5 ± 2.1%, p < 0.001), 2D GLS (−12.6 ± 2.8% vs. −15.6 ± 2.0%, p < 0.001), 3D GCS (−24.8 ± 4.4% vs. −27.8 ± 4.3%, p = 0.02), and 2D GCS (−18.1 ± 5.5% vs. −22.9 ± 4.7%, p = 0.002). In contrast, 3D EF did not differ significantly between groups (52.5 ± 4.7% vs. 54.7 ± 5.7%, p = 0.16). Receiver operating characteristic analysis showed that 3D and 2D GLS had the highest diagnostic performance (AUCs 0.81 and 0.78), while 3D EF had the lowest (AUC 0.61). Feasibility was lower for 3D STE (86%) than for 2D longitudinal strain (95%, p = 0.03). Conclusions: Both 3D and 2D GLS showed higher diagnostic accuracy than 3D EF in identifying acute coronary occlusion in NSTE-ACS patients. While 3D STE enables simultaneous assessment of multiple parameters, it did not offer incremental diagnostic value over 2D STE and had lower feasibility. Full article
(This article belongs to the Special Issue Recent Advances in Echocardiography, 2nd Edition)
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18 pages, 516 KiB  
Article
A Nested Named Entity Recognition Model Robust in Few-Shot Learning Environments Using Label Description Information
by Hyunsun Hwang, Youngjun Jung, Changki Lee and Wooyoung Go
Appl. Sci. 2025, 15(15), 8255; https://doi.org/10.3390/app15158255 - 24 Jul 2025
Abstract
Nested named entity recognition (NER) is a task that identifies hierarchically structured entities, where one entity can contain other entities within its span. This study introduces a nested NER model for few-shot learning environments, addressing the difficulty of building extensive datasets for general [...] Read more.
Nested named entity recognition (NER) is a task that identifies hierarchically structured entities, where one entity can contain other entities within its span. This study introduces a nested NER model for few-shot learning environments, addressing the difficulty of building extensive datasets for general named entities. We enhance the Biaffine nested NER model by modifying its output layer to incorporate label semantic information through a novel label description embedding (LDE) approach, improving performance with limited training data. Our method replaces the traditional biaffine classifier with a label attention mechanism that leverages comprehensive natural language descriptions of entity types, encoded using BERT to capture rich semantic relationships between labels and input spans. We conducted comprehensive experiments on four benchmark datasets: GENIA (nested NER), ACE 2004 (nested NER), ACE 2005 (nested NER), and CoNLL 2003 English (flat NER). Performance was evaluated across multiple few-shot scenarios (1-shot, 5-shot, 10-shot, and 20-shot) using F1-measure as the primary metric, with five different random seeds to ensure robust evaluation. We compared our approach against strong baselines including BERT-LSTM-CRF with nested tags, the original Biaffine model, and recent few-shot NER methods (FewNER, FIT, LPNER, SpanNER). Results demonstrate significant improvements across all few-shot scenarios. On GENIA, our LDE model achieves 45.07% F1 in five-shot learning compared to 30.74% for the baseline Biaffine model (46.4% relative improvement). On ACE 2005, we obtain 44.24% vs. 32.38% F1 in five-shot scenarios (36.6% relative improvement). The model shows consistent gains in 10-shot (57.19% vs. 49.50% on ACE 2005) and 20-shot settings (64.50% vs. 58.21% on ACE 2005). Ablation studies confirm that semantic information from label descriptions is the key factor enabling robust few-shot performance. Transfer learning experiments demonstrate the model’s ability to leverage knowledge from related domains. Our findings suggest that incorporating label semantic information can substantially enhance NER models in low-resource settings, opening new possibilities for applying NER in specialized domains or languages with limited annotated data. Full article
(This article belongs to the Special Issue Applications of Natural Language Processing to Data Science)
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16 pages, 654 KiB  
Article
Effect of Pharmacogenetics on Renal Outcomes of Heart Failure Patients with Reduced Ejection Fraction (HFrEF) in Response to Dapagliflozin
by Neven Sarhan, Mona F. Schaalan, Azza A. K. El-Sheikh and Bassem Zarif
Pharmaceutics 2025, 17(8), 959; https://doi.org/10.3390/pharmaceutics17080959 - 24 Jul 2025
Abstract
Background/Objectives: Heart failure with reduced ejection fraction (HFrEF) is associated with significant renal complications, affecting disease progression and patient outcomes. Sodium-glucose co-transporter-2 (SGLT2) inhibitors have emerged as a key therapeutic strategy, offering cardiovascular and renal benefits in these patients. However, interindividual variability [...] Read more.
Background/Objectives: Heart failure with reduced ejection fraction (HFrEF) is associated with significant renal complications, affecting disease progression and patient outcomes. Sodium-glucose co-transporter-2 (SGLT2) inhibitors have emerged as a key therapeutic strategy, offering cardiovascular and renal benefits in these patients. However, interindividual variability in response to dapagliflozin underscores the role of pharmacogenetics in optimizing treatment efficacy. This study investigates the influence of genetic polymorphisms on renal outcomes in HFrEF patients treated with dapagliflozin, focusing on variations in genes such as SLC5A2, UMOD, KCNJ11, and ACE. Methods: This prospective, observational cohort study was conducted at the National Heart Institute, Cairo, Egypt, enrolling 200 patients with HFrEF. Genotyping of selected single nucleotide polymorphisms (SNPs) was performed using TaqMan™ assays. Renal function, including estimated glomerular filtration rate (eGFR), Kidney Injury Molecule-1 (KIM-1), and Neutrophil Gelatinase-Associated Lipocalin (NGAL) levels, was assessed at baseline and after six months of dapagliflozin therapy. Results: Significant associations were found between genetic variants and renal outcomes. Patients with AA genotype of rs3813008 (SLC5A2) exhibited the greatest improvement in eGFR (+7.2 mL ± 6.5, p = 0.004) and reductions in KIM-1 (−0.13 pg/mL ± 0.49, p < 0.0001) and NGAL (−6.1 pg/mL ± 15.4, p < 0.0001). Similarly, rs12917707 (UMOD) TT genotypes showed improved renal function. However, rs5219 (KCNJ11) showed no significant impact on renal outcomes. Conclusions: Pharmacogenetic variations influenced renal response to dapagliflozin in HFrEF patients, particularly in SLC5A2 and UMOD genes. These findings highlighted the potential of personalized medicine in optimizing therapy for HFrEF patients with renal complications. Full article
(This article belongs to the Section Clinical Pharmaceutics)
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23 pages, 3463 KiB  
Article
Research on Adaptive Bidirectional Droop Control Strategy for Hybrid AC-DC Microgrid in Islanding Mode
by Can Ding, Ruihua Zhao, Hongrong Zhang and Wenhui Chen
Appl. Sci. 2025, 15(15), 8248; https://doi.org/10.3390/app15158248 - 24 Jul 2025
Abstract
The interlinking converter, an important device in a hybrid AC-DC microgrid, undertakes the task of power distribution between the AC sub-microgrid and DC sub-microgrid. To address the limitations of traditional bidirectional droop control in islanding mode, particularly the lack of consideration for regulation [...] Read more.
The interlinking converter, an important device in a hybrid AC-DC microgrid, undertakes the task of power distribution between the AC sub-microgrid and DC sub-microgrid. To address the limitations of traditional bidirectional droop control in islanding mode, particularly the lack of consideration for regulation priority between AC frequency and DC voltage, this paper proposes an adaptive bidirectional droop control strategy. By introducing an adaptive weight coefficient based on normalized AC frequency and DC voltage, the strategy prioritizes regulating larger deviations in AC frequency or DC voltage. Interlinking converter action thresholds are set to avoid unnecessary frequent starts and stops. Finally, a hybrid AC-DC microgrid system in islanding mode is established in the Matlab/Simulink R2020a simulation platform to verify the effectiveness of the proposed control strategy. Full article
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31 pages, 4584 KiB  
Article
A Discrete-Event Based Power Management System Framework for AC Microgrids
by Paolo C. Erazo Huera, Thamiris B. de Paula, João M. T. do Amaral, Thiago M. Tuxi, Gustavo S. Viana, Emanuel L. van Emmerik and Robson F. S. Dias
Energies 2025, 18(15), 3964; https://doi.org/10.3390/en18153964 - 24 Jul 2025
Abstract
This paper presents a practical framework for the design and real-time implementation of a Power Management System (PMS) for microgrids based on Supervisory Control Theory (SCT) for discrete-event systems. A detailed step-by-step methodology is provided, which covers the entire process from defining discrete [...] Read more.
This paper presents a practical framework for the design and real-time implementation of a Power Management System (PMS) for microgrids based on Supervisory Control Theory (SCT) for discrete-event systems. A detailed step-by-step methodology is provided, which covers the entire process from defining discrete events, modeling microgrid components, synthesizing supervisory controllers, and realizing them in MATLAB (R2024b) Stateflow. This methodology is applied to a case study, where a decentralized supervisor controller is designed for a microgrid containing a Battery Energy Storage System (BESS), a generator set (Genset), a wind and a solar generation system, critical loads, and noncritical loads. Unlike previous works based on SCT, the proposed PMS addresses the following functionalities: (i) grid-connected and islanded operation; (ii) peak shaving; (iii) voltage support; (iv) load shedding. Finally, a CHIL testing is employed to validate the synthesized SCT-based PMS. Full article
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16 pages, 4134 KiB  
Article
Effect of Oxygen-Containing Functional Groups on the Performance of Palladium/Carbon Catalysts for Electrocatalytic Oxidation of Methanol
by Hanqiao Xu, Hongwei Li, Xin An, Weiping Li, Rong Liu, Xinhong Zhao and Guixian Li
Catalysts 2025, 15(8), 704; https://doi.org/10.3390/catal15080704 - 24 Jul 2025
Abstract
The methanol oxidation reaction (MOR) of direct methanol fuel cells (DMFCs) is limited by the slow kinetic process and high reaction energy barrier, significantly restricting the commercial application of DMFCs. Therefore, developing MOR catalysts with high activity and stability is very important. In [...] Read more.
The methanol oxidation reaction (MOR) of direct methanol fuel cells (DMFCs) is limited by the slow kinetic process and high reaction energy barrier, significantly restricting the commercial application of DMFCs. Therefore, developing MOR catalysts with high activity and stability is very important. In this paper, oxygen-functionalised activated carbon (FAC) with controllable oxygen-containing functional groups was prepared by adjusting the volume ratio of H2SO3/HNO3 mixed acid, and Pd/AC and Pd/FAC catalysts were synthesised via the hydrazine hydrate reduction method. A series of characterisation techniques and electrochemical performance tests were used to study the catalyst. The results showed that when V(H2SO3):V(HNO3) = 2:3, more defects were generated on the surface of the AC, and more oxygen-containing functional groups represented by C=O and C–OH were attached to the surface of the support, which increased the anchor sites of Pd and improved the dispersion of Pd nanoparticles (Pd NPs) on the support. At the same time, the mass–specific activity of Pd/FAC for MOR was 2320 mA·mgPd, which is 1.5 times that of Pd/AC, and the stability was also improved to a certain extent. In situ infrared spectroscopy further confirmed that oxygen functionalisation treatment promoted the formation and transformation of *COOH intermediates, accelerated the transformation of COL into COB, reduced the poisoning of COads species adsorbed to the catalyst, optimised the reaction path and improved the catalytic kinetic performance. Full article
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11 pages, 830 KiB  
Article
Machine Learning-Based Prediction of Shoulder Dystocia in Pregnancies Without Suspected Macrosomia Using Fetal Biometric Ratios
by Can Ozan Ulusoy, Ahmet Kurt, Ayşe Gizem Yıldız, Özgür Volkan Akbulut, Gonca Karataş Baran and Yaprak Engin Üstün
J. Clin. Med. 2025, 14(15), 5240; https://doi.org/10.3390/jcm14155240 - 24 Jul 2025
Abstract
Objective: Shoulder dystocia (ShD) is a rare but serious obstetric emergency associated with significant neonatal morbidity. This study aimed to evaluate the predictive performance of machine learning (ML) models based on fetal biometric ratios and clinical characteristics for the identification of ShD [...] Read more.
Objective: Shoulder dystocia (ShD) is a rare but serious obstetric emergency associated with significant neonatal morbidity. This study aimed to evaluate the predictive performance of machine learning (ML) models based on fetal biometric ratios and clinical characteristics for the identification of ShD in pregnancies without clinical suspicion of macrosomia. Methods: We conducted a retrospective case-control study including 284 women (84 ShD cases and 200 controls) who underwent spontaneous vaginal delivery between 37 and 42 weeks of gestation. All participants had an estimated fetal weight (EFW) below the 90th percentile according to Hadlock reference curves. Univariate and multivariate logistic regression analyses were performed on maternal and neonatal parameters, and statistically significant variables (p < 0.05) were used to construct adjusted odds ratio (aOR) models. Supervised ML models—Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGB)—were trained and tested to assess predictive accuracy. Performance metrics included AUC-ROC, sensitivity, specificity, accuracy, and F1-score. Results: The BPD/AC ratio and AC/FL ratio markedly enhanced the prediction of ShD. When added to other features in RF models, the BPD/AC ratio got an AUC of 0.884 (95% CI: 0.802–0.957), a sensitivity of 68%, and a specificity of 83%. On the other hand, the AC/FL ratio, along with other factors, led to an AUC of 0.896 (95% CI: 0.805–0.972), 68% sensitivity, and 90% specificity. Conclusions: In pregnancies without clinical suspicion of macrosomia, ML models integrating fetal biometric ratios with maternal and labor-related factors significantly improved the prediction of ShD. These models may support clinical decision-making in low-risk deliveries where ShD is often unexpected. Full article
(This article belongs to the Section Obstetrics & Gynecology)
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27 pages, 3280 KiB  
Article
Design and Implementation of a Robust Hierarchical Control for Sustainable Operation of Hybrid Shipboard Microgrid
by Arsalan Rehmat, Farooq Alam, Mohammad Taufiqul Arif and Syed Sajjad Haider Zaidi
Sustainability 2025, 17(15), 6724; https://doi.org/10.3390/su17156724 - 24 Jul 2025
Abstract
The growing demand for low-emission maritime transport and efficient onboard energy management has intensified research into advanced control strategies for hybrid shipboard microgrids. These systems integrate both AC and DC power domains, incorporating renewable energy sources and battery storage to enhance fuel efficiency, [...] Read more.
The growing demand for low-emission maritime transport and efficient onboard energy management has intensified research into advanced control strategies for hybrid shipboard microgrids. These systems integrate both AC and DC power domains, incorporating renewable energy sources and battery storage to enhance fuel efficiency, reduce greenhouse gas emissions, and support operational flexibility. However, integrating renewable energy into shipboard microgrids introduces challenges, such as power fluctuations, varying line impedances, and disturbances caused by AC/DC load transitions, harmonics, and mismatches in demand and supply. These issues impact system stability and the seamless coordination of multiple distributed generators. To address these challenges, we proposed a hierarchical control strategy that supports sustainable operation by improving the voltage and frequency regulation under dynamic conditions, as demonstrated through both MATLAB/Simulink simulations and real-time hardware validation. Simulation results show that the proposed controller reduces the frequency deviation by up to 25.5% and power variation improved by 20.1% compared with conventional PI-based secondary control during load transition scenarios. Hardware implementation on the NVIDIA Jetson Nano confirms real-time feasibility, maintaining power and frequency tracking errors below 5% under dynamic loading. A comparative analysis of the classical PI and sliding mode control-based designs is conducted under various grid conditions, such as cold ironing mode of the shipboard microgrid, and load variations, considering both the AC and DC loads. The system stability and control law formulation are verified through simulations in MATLAB/SIMULINK and practical implementation. The experimental results demonstrate that the proposed secondary control architecture enhances the system robustness and ensures sustainable operation, making it a viable solution for modern shipboard microgrids transitioning towards green energy. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Energy Sustainability)
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35 pages, 1745 KiB  
Article
Balanced Fertilization of Winter Wheat with Potassium and Magnesium—An Effective Way to Manage Fertilizer Nitrogen Sustainably
by Agnieszka Andrzejewska, Katarzyna Przygocka-Cyna and Witold Grzebisz
Sustainability 2025, 17(15), 6705; https://doi.org/10.3390/su17156705 - 23 Jul 2025
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Abstract
In agricultural practice, in addition to determining the nitrogen (Nf) dose, it is necessary to effectively control its effect on currently grown crops. Meeting these conditions requires not only the use of phosphorus (P) and potassium (K), but also nutrients such [...] Read more.
In agricultural practice, in addition to determining the nitrogen (Nf) dose, it is necessary to effectively control its effect on currently grown crops. Meeting these conditions requires not only the use of phosphorus (P) and potassium (K), but also nutrients such as magnesium (Mg) and sulfur (S). This hypothesis was verified in a single-factor field experiment with winter wheat (WW) carried out in the 2015/2016, 2016/2017, and 2017/2018 growing seasons. The experiment consisted of seven variants: absolute control (AC), NP, NPK-MOP (K as Muriate of Potash), NPK-MOP+Ki (Kieserite), NPK-KK (K as Korn–Kali), NPK-KK+Ki, and NPK-KK+Ki+ES (Epsom Salt). The use of K as MOP increased grain yield (GY) by 6.3% compared to NP. In the NPK-KK variant, GY was 13% (+0.84 t ha−1) higher compared to NP. Moreover, GYs in this fertilization variant (FV) were stable over the years (coefficient of variation, CV = 9.4%). In NPK-KK+Ki+ES, the yield increase was the highest and mounted to 17.2% compared to NP, but the variability over the years was also the highest (CV ≈ 20%). The amount of N in grain N (GN) increased progressively from 4% for NPK-MOP to 15% for NPK-KK and 25% for NPK-KK+Ki+ES in comparison to NP. The nitrogen harvest index was highly stable, achieving 72.6 ± 3.1%. All analyzed NUE indices showed a significant response to FVs. The PFP-Nf (partial factor productivity of Nf) indices increased on NPK-MOP by 5.8%, NPK-KK by 12.9%, and NPK-KK+Ki+ES by 17.9% compared to NP. The corresponding Nf recovery of Nf in wheat grain was 47.2%, 55.9%, and 64.4%, but its total recovery by wheat (grain + straw) was 67%, 74.5%, and 87.2%, respectively. In terms of the theoretical and practical value of the tested indexes, two indices, namely, NUP (nitrogen unit productivity) and NUA (nitrogen unit accumulation), proved to be the most useful. From the farmer’s production strategy, FV with K applied in the form of Korn–Kali proved to be the most stable option due to high and stable yield, regardless of weather conditions. The increase in the number of nutritional factors optimizing the action of nitrogen in winter wheat caused the phenomenon known as the “scissors effect”. This phenomenon manifested itself in a progressive increase in nitrogen unit productivity (NUP) combined with a regressive trend in unit nitrogen accumulation (NUA) in the grain versus the balance of soil available Mg (Mgb). The studies clearly showed that obtaining grain that met the milling requirements was recorded only for NUA above 22 kg N t−1 grain. This was possible only with the most intensive Mg treatment (NPK-KK+Ki and NPK-KK+Ki+ES). The study clearly showed that three of the six FVs fully met the three basic conditions for sustainable crop production: (i) stabilization and even an increase in grain yield; (ii) a decrease in the mass of inorganic N in the soil at harvest, potentially susceptible to leaching; and (iii) stabilization of the soil fertility of P, K, and Mg. Full article
(This article belongs to the Special Issue Soil Fertility and Plant Nutrition for Sustainable Cropping Systems)
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25 pages, 5190 KiB  
Article
Comparative Evaluation of the Effectiveness and Efficiency of Computational Methods in the Detection of Asbestos Cement in Hyperspectral Images
by Gabriel Elías Chanchí-Golondrino, Manuel Saba and Manuel Alejandro Ospina-Alarcón
Materials 2025, 18(15), 3456; https://doi.org/10.3390/ma18153456 - 23 Jul 2025
Viewed by 138
Abstract
Among the existing challenges in the field of hyperspectral imaging, the need to optimize memory usage and computational capacity in material detection methods stands out, given the vast amount of data associated with the hundreds of reflectance bands. In line with this, this [...] Read more.
Among the existing challenges in the field of hyperspectral imaging, the need to optimize memory usage and computational capacity in material detection methods stands out, given the vast amount of data associated with the hundreds of reflectance bands. In line with this, this article proposes a comparative study on the effectiveness and efficiency of five computational methods for detecting composite material asbestos cement (AC) in hyperspectral images: correlation, spectral differential similarity (SDS), Fourier phase similarity (FPS), area under the curve (AUC), and decision trees (DT). The novelty lies in the comparison between the first four methods, which represent the spectral proximity method and a machine learning method, such as DT. Furthermore, SDS and FPS are novel methods proposed in the present document. Given the accuracy that detection methods based on supervised learning have demonstrated in material identification, the results obtained from the DT model were compared with the percentage of AC detected in a hyperspectral image of the Manga neighborhood in the city of Cartagena by the other four methods. Similarly, in terms of computational efficiency, a 20 × 20 pixel region with 380 bands was selected for the execution of multiple repetitions of each of the five computational methods considered, in order to obtain the average processing time of each method and the relative efficiency of the methods with respect to the method with the best effectiveness. The decision tree (DT) model achieved the highest classification accuracy at 99.4%, identifying 11.44% of asbestos cement (AC) pixels in the reference image. However, the correlation method, while detecting a lower percentage of AC pixels (9.72%), showed the most accurate visual performance and had no spectral overlap, with a 1.4% separation between AC and non-AC pixels. The SDS method was the most computationally efficient, running 23.85 times faster than the DT model. The proposed methods and results can be applied to other hyperspectral imaging tasks involving material identification in urban environments, especially when balancing accuracy and computational efficiency is essential. Full article
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28 pages, 5780 KiB  
Article
Multiscale Modeling and Dynamic Mutational Profiling of Binding Energetics and Immune Escape for Class I Antibodies with SARS-CoV-2 Spike Protein: Dissecting Mechanisms of High Resistance to Viral Escape Against Emerging Variants
by Mohammed Alshahrani, Vedant Parikh, Brandon Foley and Gennady Verkhivker
Viruses 2025, 17(8), 1029; https://doi.org/10.3390/v17081029 - 23 Jul 2025
Viewed by 52
Abstract
The rapid evolution of SARS-CoV-2 has underscored the need for a detailed understanding of antibody binding mechanisms to combat immune evasion by emerging variants. In this study, we investigated the interactions between Class I neutralizing antibodies—BD55-1205, BD-604, OMI-42, P5S-1H1, and P5S-2B10—and the receptor-binding [...] Read more.
The rapid evolution of SARS-CoV-2 has underscored the need for a detailed understanding of antibody binding mechanisms to combat immune evasion by emerging variants. In this study, we investigated the interactions between Class I neutralizing antibodies—BD55-1205, BD-604, OMI-42, P5S-1H1, and P5S-2B10—and the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein using multiscale modeling, which combined molecular simulations with the ensemble-based mutational scanning of the binding interfaces and binding free energy computations. A central theme emerging from this work is that the unique binding strength and resilience to immune escape of the BD55-1205 antibody are determined by leveraging a broad epitope footprint and distributed hotspot architecture, additionally supported by backbone-mediated specific interactions, which are less sensitive to amino acid substitutions and together enable exceptional tolerance to mutational escape. In contrast, BD-604 and OMI-42 exhibit localized binding modes with strong dependence on side-chain interactions, rendering them particularly vulnerable to escape mutations at K417N, L455M, F456L and A475V. Similarly, P5S-1H1 and P5S-2B10 display intermediate behavior—effective in some contexts but increasingly susceptible to antigenic drift due to narrower epitope coverage and concentrated hotspots. Our computational predictions show strong agreement with experimental deep mutational scanning data, validating the accuracy of the models and reinforcing the value of binding hotspot mapping in predicting antibody vulnerability. This work highlights that neutralization breadth and durability are not solely dictated by epitope location, but also by how binding energy is distributed across the interface. The results provide atomistic insight into mechanisms driving resilience to immune escape for broadly neutralizing antibodies targeting the ACE2 binding interface—which stems from cumulative effects of structural diversity in binding contacts, redundancy in interaction patterns and reduced vulnerability to mutation-prone positions. Full article
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22 pages, 4411 KiB  
Article
Synthesis, Structural Characterization, and In Silico Antiviral Prediction of Novel DyIII-, YIII-, and EuIII-Pyridoxal Helicates
by Francisco Mainardi Martins, Yuri Clemente Andrade Sokolovicz, Morgana Maciél Oliveira, Carlos Serpa, Otávio Augusto Chaves and Davi Fernando Back
Inorganics 2025, 13(8), 252; https://doi.org/10.3390/inorganics13080252 - 23 Jul 2025
Viewed by 65
Abstract
The synthesis and structural characterization of three new triple-stranded helical complexes ([Dy2(L2)3]2Cl∙15H2O (C1), [Y2(L2)3]3(NO3)Cl∙14H2O∙DMSO (C2), and [Eu2(L4) [...] Read more.
The synthesis and structural characterization of three new triple-stranded helical complexes ([Dy2(L2)3]2Cl∙15H2O (C1), [Y2(L2)3]3(NO3)Cl∙14H2O∙DMSO (C2), and [Eu2(L4)3]∙12H2O (C3), where L2 and L4 are ligands derived from pyridoxal hydrochloride and succinic or adipic acid dihydrazides, respectively, were described. The X-ray data, combined with spectroscopic measurements, indicated that L2 and L4 act as bis-tridentate ligands, presenting two tridentate chelating cavities O,N,O to obtain the dinuclear complexes C1C3. Their antiviral profile was predicted via in silico calculations in terms of interaction with the structural severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein in the down- and up-states and complexed with the cellular receptor angiotensin-converting enzyme 2 (ACE2). The best affinity energy values (−9.506, −9.348, and −9.170 kJ/mol for C1, C2, and C3, respectively) were obtained for the inorganic complexes docked in the model spike-ACE2, with C1 being suggested as the most promising candidate for a future in vitro validation. The obtained in silico antiviral trend was supported by the prediction of the electronic and physical–chemical properties of the inorganic complexes via the density functional theory (DFT) approach, representing an original and relevant contribution to the bioinorganic and medicinal chemistry fields. Full article
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