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Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world.
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interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the
most exciting work published in the various research areas of the journal.
by
Jesus A. Arenas-Prado, Angel H. Rangel-Rodriguez, Juan P. Amezquita-Sanchez, David Granados-Lieberman, Guillermo Tapia-Tinoco and Martin Valtierra-Rodriguez
Renewable energy technologies play a key role in mitigating climate change and advancing sustainable development. Among these, photovoltaic (PV) systems have experienced significant growth in recent years. However, shading, one of the most common faults in PV modules, can drastically degrade their performance.
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Renewable energy technologies play a key role in mitigating climate change and advancing sustainable development. Among these, photovoltaic (PV) systems have experienced significant growth in recent years. However, shading, one of the most common faults in PV modules, can drastically degrade their performance. This study investigates the application of convolutional neural networks (CNNs) for the automated detection and classification of shading faults, including multiple severity levels, using current–voltage (I–V) curves. Four scenarios were simulated in Simulink: a healthy module and three levels of shading severity (light, moderate, and severe). The resulting I–V curves were transformed into grayscale images and used to train and evaluate several custom-designed CNN architectures. The goal is to assess the capability of CNN-based models to accurately identify shading faults and discriminate between severity levels. Multiple network configurations were tested, varying image resolution, network depth, and filter parameters, to explore their impact on classification accuracy. Furthermore, robustness was evaluated by introducing Gaussian noise at different levels. The best-performing models achieved classification accuracies of 99.5% under noiseless conditions and 90.1% under a 10 dB noise condition, demonstrating that CNN-based approaches can be both effective and computationally lightweight. These results underscore the potential of this methodology for integration into automated diagnostic tools for PV systems, particularly in applications requiring fast and reliable fault detection.
Full article
To explore the effects of micro-nano aeration and oxygenation irrigation on soil characteristics and cotton growth in cotton fields in arid areas, this study was conducted at the National Soil Quality Aksu Observation and Experiment Station in Baicheng County, Xinjiang. “Xinluzao 78” cotton
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To explore the effects of micro-nano aeration and oxygenation irrigation on soil characteristics and cotton growth in cotton fields in arid areas, this study was conducted at the National Soil Quality Aksu Observation and Experiment Station in Baicheng County, Xinjiang. “Xinluzao 78” cotton was used as the experimental material, and the soil column cultivation method was adopted. Four nitrogen concentration gradients (N0: 0 kg·hm−2, NL: 112.5 kg·hm−2, NM: 225 kg·hm−2, and NH: 337.5 kg·hm−2) and two irrigation methods (micro-nano aeration and oxygenation irrigation Y: DO15 mg/L, conventional irrigation C: DO7.6 mg/L) were set up to systematically analyze the total nitrogen content of the soil, enzyme activity, microbial community structure, and the response characteristics of cotton growth and yield. The results show that aeration treatment significantly increases the total nitrogen content in the soil. The total nitrogen content in the 0–15 cm and 15–30 cm soil layers treated with YNM (aeration + local conventional nitrogen application rate) increased by 9.14% and 8.53%, respectively, compared with CNM. YNM treatment significantly increased the activities of soil urease, sucrase, and β-glucosidase, among which total nitrogen had the strongest correlation with the activity of β-glucosidase. Oxygenation significantly increased the richness of soil microorganisms. The Chao1 index of YNM-treated bacteria was 75.7% higher than that of CNM-treated bacteria. YNM treatment increased cotton yield by 26.73% compared with CNM treatment. Moreover, the number of bells formed per plant and the weight of the bells increased by 44.44% and 29.6%, respectively. In conclusion, micro-nano aeration and oxygenation irrigation effectively increase cotton yield. By optimizing the activities of soil enzymes and microorganisms, micro-nano aeration and oxygenation irrigation enhance the ability of cotton to utilize and transform nitrogen, and alleviate the impact of insufficient nitrogen utilization by cotton in arid areas.
Full article
As an important feed source for ruminants, alfalfa’s rational and efficient utilization is of great significance for the production and economic benefits of pastures. This study focuses on Sanhe dairy cows and includes a control group (CON group, alfalfa in the diet is
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As an important feed source for ruminants, alfalfa’s rational and efficient utilization is of great significance for the production and economic benefits of pastures. This study focuses on Sanhe dairy cows and includes a control group (CON group, alfalfa in the diet is hay) and an experimental group (AS group, alfalfa silage partially replaces alfalfa hay of equal dry weight). The feeding experiment lasted for 60 days. The results revealed that, compared with the CON group, the AS group exhibited increased milk yield, milk protein, and milk fat. There were no significant differences in apparent digestibility, serum biochemical indicators, and volatile fatty acid (VFA) levels between the two groups. However, the microbial composition of the rumen differed significantly between the two groups of cows based on β-diversity. On the genus level, compared with the CON group, the relative abundance of Erysipelatoclostridium, Pseudoflavonifractor, and Candidatus Saccharimonas in the AS group was significantly reduced. In summary, partially replacing alfalfa hay with alfalfa silage feed is beneficial for improving the production performance of cows and changing rumen microbial diversity. These findings provide a basis for the effective utilization of alfalfa.
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Chronic inflammation plays a crucial role in the pathogenesis and progression of neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), where sustained neuroinflammatory responses contribute to neuronal damage and functional decline. Recent advances in nanomedicine offer novel therapeutic strategies aimed
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Chronic inflammation plays a crucial role in the pathogenesis and progression of neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), where sustained neuroinflammatory responses contribute to neuronal damage and functional decline. Recent advances in nanomedicine offer novel therapeutic strategies aimed at modulating inflammation, with a focus on targeting the gut–brain axis, a key mediator in the interplay between systemic inflammation and neurodegeneration. Artificial intelligence (AI) has emerged as a transformative tool in this context, facilitating the integration of large, complex datasets to better understand the intricate relationship between gut microbiota dysbiosis, chronic neuroinflammation, the exposome (cumulative impact of lifelong environmental exposures), and disease manifestation. AI-driven approaches and integrating exposome data with AI enable deeper insights into exposure–microbiome–inflammation interactions, enhance our understanding of the inflammatory pathways involved, support the development of predictive models for disease progression, and optimize the delivery of nanomedicine-based therapeutics. Additionally, AI applications in neuroimaging and personalized therapy planning have shown promise in addressing both motor and non-motor symptoms. This review provides a comprehensive synthesis of current knowledge, highlighting the convergence of AI, nanomedicine, and chronic inflammation in neurodegenerative disease care.
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Background: Fluorescence-guided surgery using 5-aminolevulinic acid (5-ALA) enables the intraoperative visualization of glioma. However, its effectiveness varies based on tumor subtype and molecular profile, posing challenges for achieving complete resection. Our systematic review aims to explore the relationship between IDH mutation status and
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Background: Fluorescence-guided surgery using 5-aminolevulinic acid (5-ALA) enables the intraoperative visualization of glioma. However, its effectiveness varies based on tumor subtype and molecular profile, posing challenges for achieving complete resection. Our systematic review aims to explore the relationship between IDH mutation status and intraoperative fluorescence visualization. Importantly, this is the first study to propose that vorasidenib, an emerging IDH-targeting agent, could enhance 5-ALA-guided surgery, marking a novel direction for translational research. Methods: A systematic literature search was conducted using the PubMed, Cochrane Library, Scopus and Web of Science databases up to May 2025, following PRISMA guidelines. The primary outcomes included fluorescence detection rates across different glioma subtypes and their correlation with IDH mutation status. Secondary outcomes comprised surgical efficacy measures such as gross total resection (GTR), overall survival (OS), and progression-free survival (PFS). Additionally, we analyzed the metabolic consequences of IDH mutations and evaluated the potential role of vorasidenib in enhancing 5-ALA-induced fluorescence. Results: Seven studies including 621 patients included in the final analysis. Fluorescence detection was nearly universal in WHO grade 4 gliomas (94–100%), but lower in grade 3 (43–85%) and rare in grade 2 (7–26%). Several cohorts reported reduced fluorescence in IDH-mutant gliomas, although this was not consistent across all studies. In high-grade gliomas, visible fluorescence correlated with higher GTR rates and, in some series, longer OS. Conversely, in lower-grade IDH-mutant gliomas, fluorescence did not increase GTR and was associated with worse PFS and OS. Conclusions: The effectiveness of 5-ALA-guided fluorescence in glioma surgery is significantly influenced by both tumor grade and IDH mutation status. Vorasidenib may represent a potential avenue for modulating tumor metabolism and enhancing intraoperative fluorescence in IDH-mutant gliomas, a hypothesis that warrants further experimental validation.
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Background: Immune-mediated necrotizing myopathy (IMNM) associated with anti-3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) antibody is a rare but critical complication usually triggered by statin use. However, the comprehensive characterization and long-term outcomes of anti-HMGCR-positive IMNM remain underexplored. This study aimed to examine the clinical
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Background: Immune-mediated necrotizing myopathy (IMNM) associated with anti-3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) antibody is a rare but critical complication usually triggered by statin use. However, the comprehensive characterization and long-term outcomes of anti-HMGCR-positive IMNM remain underexplored. This study aimed to examine the clinical characteristics, diagnostic challenges, treatment responses, and long-term outcomes of patients with anti-HMGCR-positive IMNM. Methods: A retrospective review was conducted at a single institution between 2019 and 2025 to analyze the data of patients diagnosed with anti-HMGCR-positive IMNM. Diagnoses were confirmed by detecting anti-HMGCR antibodies and meeting the criteria for IMNM of the European Neuromuscular Center. The analyzed data included demographics, clinical presentation, laboratory findings, imaging results, muscle biopsy characteristics, treatment regimens, and follow-up outcomes. Results: Ten patients (six women and four men) with a median age of 58 (range, 33–86) years were included. Nine patients had a history of statin use for a median duration of two years. The average diagnostic delay was 233 days after the onset of symptoms. The initial creatine kinase (CK) levels ranged from 1438 to over 13,000 IU/L. Muscle biopsies revealed necrosis and regeneration of muscle fibers. CK levels fluctuated and trended downward over 180 days post-treatment. Treatment included corticosteroids, methotrexate, azathioprine, tacrolimus, mycophenolate, intravenous immunoglobulin, and rituximab. Delayed treatment initiation from symptom onset was correlated with prolonged treatment time until the first remission. Conclusions: The prognosis of anti-HMGCR-positive IMNM is less favorable when treatment is delayed after symptom onset. Further research is warranted to identify poor prognostic markers and develop relevant treatments.
Full article
by
Petru Octavian Drăgoescu, Bianca Liana Grigorescu, Andreea Doriana Stănculescu, Andrei Pănuș, Nicolae Dan Florescu, Monica Cara, Maria Andrei, Mihai Radu, George Mitroi and Alice Nicoleta Drăgoescu
Background and Objectives: The severe systemic response to urinary tract infections known as urosepsis is associated with significant morbidity and mortality rates. The neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) are simple blood tests that could be useful in predicting the outcome
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Background and Objectives: The severe systemic response to urinary tract infections known as urosepsis is associated with significant morbidity and mortality rates. The neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) are simple blood tests that could be useful in predicting the outcome of sepsis. Materials and Methods: A prospective observational study was conducted at a tertiary care hospital, where our team studied 223 patients with urosepsis. The patients underwent Sepsis-3 criteria-based urosepsis and septic shock stratification followed by survivor and non-survivor classification. Clinical scores (Sequential Organ Failure Assessment-SOFA, National Early Warning Score-NEWS), laboratory markers (NLR, PLR, PCT-procalcitonin), and patient outcomes were then analysed. Results: An admission NLR ≥ 13 was a strong predictor of septic shock (adjusted Odds Ratio (OR) 2.10, 95% Confidence Interval (CI) 1.25–3.54) and in-hospital mortality (adjusted OR 2.45, 95% CI 1.40–4.28). While the prognostic value of the PLR remained moderate, the NLR demonstrated superior predictive power. As easily measurable biomarkers, the NLR and PLR provide valuable information to help clinicians identify at-risk patients during the early stages of urosepsis. Conclusions: The NLR is an independent predictor with high predictive value for both septic shock and mortality, performing as well as established clinical scores. The combination of these parameters with clinical assessments could lead to better early decisions and improved outcomes for patients with urosepsis.
Full article
This study analyzed the dynamic behavior of EN C45 structural steel under impulse loading generated by a pressure wave. The experiments were conducted on a special test rig using two load configurations: (I) direct contact of the load with the sample surface and
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This study analyzed the dynamic behavior of EN C45 structural steel under impulse loading generated by a pressure wave. The experiments were conducted on a special test rig using two load configurations: (I) direct contact of the load with the sample surface and (II) detonation at a distance of 30 mm. Depending on the loading conditions, the specimens were fragmented or developed extensive internal cracks and plastic deformations. To complement the experimental program, hybrid numerical simulations were performed using the finite element method (FEM), smoothed particles hydrodynamics (SPH), and coupled Euler–Lagrange (CEL) approach. A modified Johnson–Cook (JC) model was used to account for dynamic damage and cracks. Computed tomography (CT) and metallographic analyses provided detailed information on the formation of cracks in MnS inclusions, brittle cracks near the sample axis, and shear deformation zones away from the axis. These observations allowed direct correlation with the predicted numerical deformation and damage fields. The innovative nature of this work lies in the combination of three complementary computational techniques with computed tomography analysis and microstructure analysis, providing a comprehensive framework for describing and confirming the mechanisms of damage and fragmentation of structural steels under explosive loading.
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Public health—understood as both a science and a practice aimed at preventing disease, prolonging life, and promoting health—has become one of the most critical domains of institutional action, shaped by both nation-states and international organizations [...]
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We present a deep learning-based approach for accurate bone segmentation directly from routine T1-weighted MRI scans, with the goal of enabling MRI-only virtual surgical planning in head and neck oncology. Current workflows rely on CT for bone modeling and MRI for tumor delineation,
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We present a deep learning-based approach for accurate bone segmentation directly from routine T1-weighted MRI scans, with the goal of enabling MRI-only virtual surgical planning in head and neck oncology. Current workflows rely on CT for bone modeling and MRI for tumor delineation, introducing challenges related to image registration, radiation exposure, and resource use. To address this, we trained a deep neural network using CT-based segmentations of the mandible, cranium, and inferior alveolar nerve as ground truth. A dataset of 100 patients with paired CT and MRI scans was collected. MRI scans were resampled to the voxel size of CT, and corresponding CT segmentations were rigidly aligned to MRI. The model was trained on 80 cases and evaluated on 20 cases using Dice similarity coefficient, Intersection over Union (IoU), precision, and recall. The network achieved a mean Dice of 0.86 (SD ± 0.03), IoU of 0.76 (SD ± 0.05), and both precision and recall of 0.86 (SD ± 0.05). Surface deviation analysis between CT- and MRI-derived bone models showed a median deviation of 0.21 mm (IQR 0.05) for the mandible and 0.30 mm (IQR 0.05) for the cranium. These results demonstrate that accurate CT-like bone models can be derived from standard MRI, supporting the feasibility of MRI-only surgical planning.
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The State Hygienic Lab at the University of Iowa (SHL) performs newborn blood spot screening (NBS) for IA, AK, ND, and SD. In October 2022, we halted in-house CFTR DNA testing due to the unexpected nonperformance of our newly expanded variant panel. Samples
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The State Hygienic Lab at the University of Iowa (SHL) performs newborn blood spot screening (NBS) for IA, AK, ND, and SD. In October 2022, we halted in-house CFTR DNA testing due to the unexpected nonperformance of our newly expanded variant panel. Samples were sent to a reference laboratory to ensure uninterrupted testing and by December 2022, SHL had selected an alternative test that enabled CFTR panel expansion as envisioned. However, due to circumstances beyond our control, test implementation was severely delayed, and in-house testing was paused. These events were consequential. Firstly, our prolonged utilization of reference labs and fees was a financial strain on the lab. Secondly, our timeliness decreased significantly, and lastly, these issues were burdensome for staff. The lab overcame these problems using three strategies: effective communication; technical expertise; and staff perseverance. Finally, in Aug 2023, SHL successfully resumed in-house testing. As state labs ponder major CFTR algorithm changes, such as the addition of next generation sequencing, the strategies we utilized can be useful during sudden setbacks. Our experience of replacing our CFTR assay underscores the importance of emergency preparedness and partnership within the NBS community.
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This paper is devoted to the study of the existence and uniqueness of solutions for a class of differential problems previously investigated by Laadjal. Our main contribution lies in deriving sharper estimates for the associated Green’s functions and employing Rus’s fixed-point theorem within
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This paper is devoted to the study of the existence and uniqueness of solutions for a class of differential problems previously investigated by Laadjal. Our main contribution lies in deriving sharper estimates for the associated Green’s functions and employing Rus’s fixed-point theorem within a suitably defined metric framework. Notably, the conditions under which our results hold are less restrictive, thereby encompassing a wider class of problems for which the existence and uniqueness of solutions can be rigorously guaranteed. This theoretical advancement is supported by numerical evidence presented in the final stage of our analysis. A key strength of the proposed approach is that it does not rely on stringent contraction conditions, which enhances its potential applicability to more general classes of fractional differential systems.
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Achieving high yield while maintaining disease resistance is a crucial goal in rice breeding programs. In this research, two cultivated rice varieties, Jia58 and Runxiang3, were selected as parental lines. A new variety, designated as the new variety RXN2, was generated and identified
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Achieving high yield while maintaining disease resistance is a crucial goal in rice breeding programs. In this research, two cultivated rice varieties, Jia58 and Runxiang3, were selected as parental lines. A new variety, designated as the new variety RXN2, was generated and identified through a breeding process that involved hybridization of the parental lines followed by irradiation-induced mutagenesis of the offspring. Compared with its parental lines, RXN2 shows increased plant height, higher yield, and stronger resistance to bacterial blight. Comprehensive transcriptomic and metabolic analyses indicate that pathways associated with growth, such as gibberellin and auxin signaling, are upregulated in RXN2. Meanwhile, defense-related pathways, especially those involving jasmonic acid and peroxidase metabolism, are significantly enhanced. These results provide new insights into the trade-offs between growth and defense and elucidate the genetic and metabolic underpinnings of the simultaneous improvement in grain yield and disease resistance in rice.
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Large language models (LLMs) are powerful, but they can unintentionally memorize and leak sensitive information found in their training or input data. To address this issue, we propose SPADR, a semantic privacy anomaly detection and remediation pipeline designed to detect and remove privacy
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Large language models (LLMs) are powerful, but they can unintentionally memorize and leak sensitive information found in their training or input data. To address this issue, we propose SPADR, a semantic privacy anomaly detection and remediation pipeline designed to detect and remove privacy risks from text. SPADR addresses limitations in existing redaction methods by identifying deeper forms of sensitive content, including implied relationships, contextual clues, and non-standard identifiers that traditional NER systems often overlook. SPADR combines semantic anomaly scoring using a denoising autoencoder with named entity recognition and graph-based analysis to detect both direct and hidden privacy risks. It is flexible enough to work on both training data (to prevent memorization) and user input (to prevent leakage at inference time). We evaluate SPADR on the Enron Email Dataset, where it significantly reduces document-level privacy leakage while maintaining strong semantic utility. The enhanced version, SPADR (S2), reduces the PII leak rate from 100% to 16.06% and achieves a BERTScore F1 of 88.03%. Compared to standard NER-based redaction systems, SPADR offers more accurate and context-aware privacy protection. This work highlights the importance of semantic and structural understanding in building safer, privacy-respecting AI systems.
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Bacterial contamination and the escalating crisis of antibiotic resistance represent pressing global public health threats, with approximately 4.95 million deaths linked to antimicrobial resistance (AMR) in 2019 and projections estimating up to 10 million annual fatalities by 2050. As third-generation antimicrobial materials, metal–organic
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Bacterial contamination and the escalating crisis of antibiotic resistance represent pressing global public health threats, with approximately 4.95 million deaths linked to antimicrobial resistance (AMR) in 2019 and projections estimating up to 10 million annual fatalities by 2050. As third-generation antimicrobial materials, metal–organic frameworks (MOFs) have emerged as promising alternatives to conventional agents, leveraging their unique attributes such as high specific surface areas, tunable porosity, and controlled metal ion release kinetics. This review provides a systematic analysis of the foundational principles and core antibacterial mechanisms of MOFs, which include the sustained release of metal ions (e.g., Ag+, Cu2+, Zn2+), the generation of reactive oxygen species (ROS), and synergistic effects with encapsulated functional molecules. We highlight how these mechanisms underpin their efficacy across a range of applications. Rather than offering an exhaustive list of synthesis methods and metal compositions, this review focuses on clarifying structure–function relationships that enable MOF-based materials to outperform conventional antimicrobials. Their potential is particularly evident in several key areas: wound dressings and medical coatings that enhance tissue regeneration and prevent infections; targeted nanotherapeutics against drug-resistant bacteria; and functional coatings for food preservation and water disinfection. Despite existing challenges, including gaps in clinical translation, limited efficacy in complex multi-species infections, and incomplete mechanistic understanding, MOFs hold significant promise to revolutionize antimicrobial therapy. Through interdisciplinary optimization and advancements in translational research, MOFs are poised to drive a paradigm shift from “passive defense” to “active ecological regulation,” offering a critical solution to mitigate the global AMR crisis.
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Accurate hour-ahead photovoltaic (PV) forecasts are essential for grid balancing, intraday trading, and renewable integration. While Transformer architectures have recently reshaped time series forecasting, their application to short-term PV prediction with calibrated uncertainty remains largely unexplored. This study provides a systematic benchmark of
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Accurate hour-ahead photovoltaic (PV) forecasts are essential for grid balancing, intraday trading, and renewable integration. While Transformer architectures have recently reshaped time series forecasting, their application to short-term PV prediction with calibrated uncertainty remains largely unexplored. This study provides a systematic benchmark of five Transformer variants (Autoformer, Informer, FEDformer, DLinear, and PatchTST) evaluated on a five-year, rooftop PV dataset (5 kW peak) against an unseen 12-month test set. All models are trained within a pipeline using a 48-h rolling input window with cyclical temporal encodings to ensure comparability. Beyond point forecasts, we introduce Adaptive Conformal Inference (ACI), a distribution-free and adaptive framework, to quantify uncertainty in real time. The results demonstrate that PatchTST, through its patch-based temporal tokenization, delivers superior accuracy (MAE = 0.194 kW, RMSE = 0.381 kW), outperforming both classical persistence and other Transformer baselines. When coupled with ACI, PatchTST achieves 86.2% empirical coverage with narrow intervals (0.62 kW mean width) and probabilistic scores (CRPS = 0.54; Winkler = 1.86) that strike a balance between sharpness and reliability. The findings establish that combining patch-based Transformers with adaptive conformal calibration provides a novel and viable route to risk-aware PV forecasting.
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Hangzhou Bay (HZB) has become a hot spot in hydro-morphodynamic research due to human impacts and natural influences, as well as the substantial quantities of water discharge and sediment load of the Yangtze River and Qiantang River. Although many previous studies have analyzed
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Hangzhou Bay (HZB) has become a hot spot in hydro-morphodynamic research due to human impacts and natural influences, as well as the substantial quantities of water discharge and sediment load of the Yangtze River and Qiantang River. Although many previous studies have analyzed the spatial–temporal variations in suspended particulate matter (TSM) from in situ and satellite observations, the long-term changes in suspended sediment dynamics remain unclear. In this study, we quantified the long-term variation in TSM load using MODIS/Aqua data during 2003–2024. The TSM products in the HZB displayed a decreasing trend from 2003 to 2024 (k = −1.90 mg/L/year, p < 0.05), which may be attributed to decreased sediment discharge from the Yangtze River. The spatial variation in TSM provided quantitative results for HZB, with a substantially increasing trend in the southern shallow areas and a decreasing trend in the northern deep troughs and central bay. The interannual variations in TSM in winter displayed a positive correlation with the sediment load from the Yangtze River (R = 0.640 for the data during 2014–2022) and with wind speed (R = 0.676 for the data during 2009–2021). The TSM of HZB was partly affected by the combined impacts of human activities and climate change. A distinct difference in TSM concentrations on both sides of the Hangzhou Bay Bridge was observed, with higher TSM on the western side than on the eastern side for most of the year during 2003–2024. A decline in TSM was observed near Yushan Island from 2003 to 2024, attributed to large-scale land reclamation and associated alterations in tide-dominated areas. This study provides valuable insights into the long-term changes in suspended sediment and water quality in HZB, which is crucial for managing water resources, creating effective water strategies, predicting future needs, and ensuring sustainable water management.
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Aligning urban land development intensity with green land-use efficiency (GLUE) is crucial for fostering high-quality regional growth. This study aims to examine the coupling and coordination between built-up land intensity (BUI) and GLUE by utilizing multi-source heterogeneous data for Hainan Island (2017, 2020).
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Aligning urban land development intensity with green land-use efficiency (GLUE) is crucial for fostering high-quality regional growth. This study aims to examine the coupling and coordination between built-up land intensity (BUI) and GLUE by utilizing multi-source heterogeneous data for Hainan Island (2017, 2020). A coupling coordination degree model and Geographical Detector are applied to quantify BUI, GLUE, and their coupling coordination, while also identifying the underlying driving factors. The results reveal the following: (i) Following the Free Trade Port initiative, BUI increased by 15.8%, while GLUE grew by 4.9%; (ii) The BUI–GLUE system is still in an adjustment phase, with 94% of jurisdictions showing low coordination; (iii) The primary drivers of coupling have shifted from economic fundamentals to policy and institutional guidance, with their interactions demonstrating significant synergies. These findings suggest that policy-induced land expansion may outpace improvements in GLUE, potentially leading to an imbalance in the land system. This paper introduces an innovative Driver–Response–Feedback and Production–Living–Ecological (DRF–PLE) framework and develops a transferable diagnostic tool for evaluating land-use system sustainability in rapidly urbanizing regions.
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Background/Objectives: Recent social neuroscience research has increasingly shifted from individual moral decision-making to the study of how people negotiate moral dilemmas in interpersonal contexts. This multimethod hyperscanning study investigated whether initial differences in moral decision-making orientation within a dyad influence neural and autonomic
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Background/Objectives: Recent social neuroscience research has increasingly shifted from individual moral decision-making to the study of how people negotiate moral dilemmas in interpersonal contexts. This multimethod hyperscanning study investigated whether initial differences in moral decision-making orientation within a dyad influence neural and autonomic synchronization during a joint moral negotiation. Methods: Fourteen dyads were classified as homologous or heterologous based on the similarity or dissimilarity of their individual decision-making orientations. Each dyad was asked to negotiate and reach a shared decision on a moral dilemma involving a realistic health emergency scenario. Electroencephalography (EEG) and autonomic signals were recorded simultaneously. Dissimilarity indices were computed to assess inter-brain and autonomic synchronization. Results: EEG analyses revealed a significant effect only in the delta frequency band: all dyads, regardless of orientation, showed greater dissimilarity in the left frontal region compared to the left temporo-central and right parieto-occipital regions. In addition, autonomic data indicated greater heart rate variability (HRV) dissimilarity in homologous dyads than in heterologous ones. However, these results did not confirm our initial hypotheses, indicating the opposite pattern. Conclusions: Left frontal delta dissimilarity emerged as an exploratory candidate marker of moral negotiation across dyads. Greater HRV dissimilarity in homologous dyads suggests that, in these dyads, successful negotiation may be supported by complementary rather than synchronized autonomic responses. This multimethod hyperscanning approach highlights the complex and partially dissociable contributions of neural and autonomic processes to the regulation of shared moral decision-making.
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Background: Non-restorative low anterior resection (NRLAR) may result in inferior oncological outcomes compared to restorative low anterior resection (RLAR) and abdominoperineal resection (APR). While NRLAR is often performed when poor functional or technical challenges are anticipated, comprehensive data on its oncological outcomes remain
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Background: Non-restorative low anterior resection (NRLAR) may result in inferior oncological outcomes compared to restorative low anterior resection (RLAR) and abdominoperineal resection (APR). While NRLAR is often performed when poor functional or technical challenges are anticipated, comprehensive data on its oncological outcomes remain scarce. This study aimed to retrospectively evaluate the intermediate-term oncological outcomes of patients who underwent RLAR, NRLAR, or APR for primary rectal cancer. Methods: This analysis included all elective NRLAR, RLAR, and APR procedures for primary rectal carcinoma performed across 11 Dutch centers from 2013 to 2020. The primary outcome was 3-year disease-free survival (DFS). Secondary outcomes included 3-year overall survival (OS) and 3-year local recurrence (LR). KaplanMeier survival analysis with log-rank testing and multivariate Cox regression analysis were employed. Results: A total of 253 (12.5%) patients underwent NRLAR, 1109 (55.0%) RLAR, and 656 (32.5%) APR. NRLAR was associated with a lower 3-year DFS (71.4%) versus RLAR (82.0%) and APR (77.4%) (p = 0.003). The 3-year OS was lower for NRLAR (82.9%) versus RLAR (93.5%) and APR (90.2%) (p < 0.001), with a higher 3-year LR rate for NRLAR (8.1%) versus RLAR (3.3%) and APR (4.5%) (p = 0.003). Multivariate Cox regression analyses confirmed NRLAR as an independent predictor for poorer DFS (HR 1.34; 95% CI: 1.01 – 1.80; p = 0.046), OS (HR 1.57; 95% CI: 1.04 – 2.36, p = 0.032), and higher LR risk (HR 2.66; 95% CI: 1.53 – 4.65; p = < 0.001). Conclusions: NRLAR is associated with poorer intermediate-term oncological outcomes. When technically feasible, restorative options should be considered, and prospective studies are required to further investigate causal relationships.
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This study presents an approach to stabilizing suspension particles using novel eco-friendly hyperbranched organosilicon surfactants—poly(methyl ethoxysiloxane) with poly(ethylene glycol) groups (PMEOS-PEG). The surface-active properties of PMEOS-PEG polymers at the methyl methacrylate–water interface were thoroughly investigated. We demonstrate the successful preparation of concentrated, stable
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This study presents an approach to stabilizing suspension particles using novel eco-friendly hyperbranched organosilicon surfactants—poly(methyl ethoxysiloxane) with poly(ethylene glycol) groups (PMEOS-PEG). The surface-active properties of PMEOS-PEG polymers at the methyl methacrylate–water interface were thoroughly investigated. We demonstrate the successful preparation of concentrated, stable aqueous suspensions of poly(methyl methacrylate) with tunable particle sizes ranging from 370 nm to 840 nm.
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Cauliflower landraces (Brassica oleracea var. botrytis) safeguard allelic diversity for adaptation, yet their phenotypic breadth under winter field conditions remains under-documented. We evaluated 69 Spanish landraces and two commercial checks from the COMAV-UPV genebank using 15 quantitative and 21 qualitative descriptors.
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Cauliflower landraces (Brassica oleracea var. botrytis) safeguard allelic diversity for adaptation, yet their phenotypic breadth under winter field conditions remains under-documented. We evaluated 69 Spanish landraces and two commercial checks from the COMAV-UPV genebank using 15 quantitative and 21 qualitative descriptors. Seed viability ranged from 0 to 92%, and mature plants showed wide ranges in stem length (coefficient of variation ≈ 72%), leaf size, and head weight (100–723 g). Six curd-colour classes—including uncommon purple and Romanesco green—were recorded. Most accessions (>88%) required more than 120 days from sowing to harvest, but a distinct subset (12%) matured within 60–120 days. Plant stature tended to be positively associated with head mass, whereas highly branched inflorescences matured earlier. Variation was dominated by curd size and plant architecture. Multivariate analyses—principal component analysis for quantitative traits, multiple correspondence analysis for qualitative traits, factor analysis of mixed data, and clustering of FAMD scores by k-means—resolved three phenotypic clusters spanning a gradient of curd size/architecture and plant stature. The collection includes accessions with compact curds, earliness, or distinctive pigmentation that are immediately useful for breeding and for prioritizing regeneration. These results provide a phenotypic baseline for future genomic association studies and the development of cultivars adapted to winter production.
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This study examines the evolution and impact of Big Data technologies across sectors, emphasizing key algorithms, emerging trends, and organizational challenges in their adoption. Special attention is given to ethical concerns related to data privacy, security, and scalability, underscoring the importance of responsible
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This study examines the evolution and impact of Big Data technologies across sectors, emphasizing key algorithms, emerging trends, and organizational challenges in their adoption. Special attention is given to ethical concerns related to data privacy, security, and scalability, underscoring the importance of responsible governance frameworks. The review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to ensure transparency and methodological rigor. A comprehensive literature search identified 83 peer-reviewed articles from high-indexed journals, and a complementary bibliometric analysis of 1108 Scopus-sourced articles (2015–2024) was conducted using R Biblioshiny. This dual-method approach offers both qualitative depth and quantitative insights into major trends, influential sources, and leading countries in Big Data research. Key findings reveal that real-time data processing and AI integration have significantly enhanced data management capabilities, supporting faster and more informed organizational decision-making. This study concludes by highlighting the importance of ethical governance and recommending future research on sector-specific adoption patterns and strategic frameworks that maximize Big Data’s value while safeguarding privacy and trust.
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Assemgul S. Auyezkhanova, Eldar T. Talgatov, Sandugash N. Akhmetova, Aigul I. Jumekeyeva, Akzhol A. Naizabayev, Aigul T. Zamanbekova and Makpal K. Malgazhdarova
In this study, we investigated the influence of polymer nature and support characteristics on the performance of Pd-based heterogeneous catalysts. Catalysts were prepared via sequential adsorption of poly(4-vinylpyridine) (P4VP) or chitosan (CS) and palladium ions onto MgO and SBA-15 supports under ambient conditions.
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In this study, we investigated the influence of polymer nature and support characteristics on the performance of Pd-based heterogeneous catalysts. Catalysts were prepared via sequential adsorption of poly(4-vinylpyridine) (P4VP) or chitosan (CS) and palladium ions onto MgO and SBA-15 supports under ambient conditions. The resulting hybrid materials were characterized by IR spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray photoelectron spectra (XPS). TEM analysis revealed that Pd nanoparticles with an average size of 2–3 nm were well-dispersed on P4VP/MgO, while larger and less uniformly distributed particles (8–10 nm) were observed on SBA-15-based systems. Catalytic tests in the hydrogenation of 2-propen-1-ol, phenylacetylene, and 2-hexyn-1-ol under mild conditions (40 °C, 1 atm H2, ethanol) demonstrated that both the support and polymer type significantly influence activity and selectivity. P4VP-modified catalysts outperformed CS-containing analogs in all reactions. MgO-based systems showed higher activity and selectivity in 2-propen-1-ol hydrogenation compared to SBA-15-based catalysts. The 1%Pd–P4VP/MgO catalyst exhibited the best performance, with a reaction rate of 5.2 × 10−6 mol/s, 83.4% selectivity to propanol, and stable activity over 30 consecutive runs. In phenylacetylene and 2-hexyn-1-ol hydrogenation, all catalysts showed high selectivity to styrene (93–95%) and cis-2-hexen-1-ol (96–97%), respectively. The incorporation of P4VP polymer into the Pd/MgO catalyst leads to smaller and better-distributed palladium particles, resulting in enhanced catalytic activity and stability during hydrogenation reactions. These results confirm that the choice of polymer modifier and inorganic support must be tailored to the specific reaction, enabling the design of highly active and selective polymer-modified Pd catalysts for selective hydrogenation processes under mild conditions.
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Accumulative Roll Bonding (ARB) is a severe plastic deformation process typically used to produce ultra-fine-grained structures. This study investigates the feasibility of using the ARB process to recycle aluminum chips from an Al-Mg-Si alloy (AA6063). The chips were first compacted under a 200
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Accumulative Roll Bonding (ARB) is a severe plastic deformation process typically used to produce ultra-fine-grained structures. This study investigates the feasibility of using the ARB process to recycle aluminum chips from an Al-Mg-Si alloy (AA6063). The chips were first compacted under a 200 kN hydraulic press and then directly hot-rolled at 550 °C without prior heat treatment to a final sheet thickness of 1.5 mm. Subsequent ARB cycles were then applied to achieve full consolidation. Mechanical properties were evaluated through tensile testing and microhardness measurements, while microstructure was characterized using Optical Microscopy and SEM-EBSD. These analyses revealed significant grain refinement and improved homogeneity with increasing ARB cycles. Mechanical testing showed that the ARB process substantially enhanced both tensile strength and hardness of the recycled AA6063 chips while maintaining good ductility. The best results were obtained after two ARB cycles, yielding an ultimate tensile strength (UTS) of 170 MPa and an elongation at rupture of 15.7%. The study conclusively demonstrates that the ARB process represents a viable and effective method for recycling aluminum chips. This approach not only significantly improves mechanical properties and microstructural characteristics but also offers environmental benefits by eliminating the energy-intensive melting stage.
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Urban blue-green infrastructure (UBGI) has been recognized as an effective nature-based solution (NbS) for mitigating urban overheating through temperature reduction. However, there is a paucity of research examining whether UBGI spatial configurations align with the geographical distribution of the heat exposure risks of
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Urban blue-green infrastructure (UBGI) has been recognized as an effective nature-based solution (NbS) for mitigating urban overheating through temperature reduction. However, there is a paucity of research examining whether UBGI spatial configurations align with the geographical distribution of the heat exposure risks of urban residents. This study focuses on this research gap, employing a population-weighted algorithm to conduct a refined assessment of the blue-green spaces exposure and heat exposure risks of urban residents. Then, the heat exposure risk was conceptualized as the demand for cooling services, with exposure to blue-green spaces serving as the supply. A comprehensive assessment was finally conducted of the supply–demand relationship and coupling coordination level for cooling services in central Wuhan. The following findings were revealed: (1) Both heat exposure risks and blue-green exposure demonstrate distinct “west high–east low” spatial gradients. It is evident that extreme high/high-risk zones, which encompass 17.1% of the study area, house 74.49% of the permanent population; (2) A substantial and pervasive positive correlation exists between UGBI exposure and the heat exposure risk. “High-demand–high-supply” areas (14.90% coverage) concentrate in urban cores, overlapping with 61.25% high-risk populations, while 0.29% of zones show “high-demand–low-supply” mismatches, revealing concentrated but ineffective UGBI distribution; (3) A pervasive supply–demand imbalance is evident, with 90.64% of regions exhibiting an unacceptable coupling type range (0 < D ≤ 0.4) and a mere 1.39% attaining an acceptable range (0.6 < D ≤ 1). These findings underscore the inadequacy of prevailing urban blue-green infrastructure configurations in addressing heat exposure risks. The construction of cities with greater heat resilience necessitates the implementation of multidimensional strategies aimed at risk mitigation.
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