Search Results (530)

The Greek territory and the surrounding marine area constitute an excellent laboratory for studying moderate-magnitude earthquakes (4–6 M), as such earthquakes occur very frequently in this region. Ten years ago, it was proposed that there is some kind of relation between earthquakes and unusual Schumann Resonance signals one to twenty days prior to an impending earthquake. During the last five years (2020–2025), a fairly large collection of signals has been gathered that may be considered as precursory seismic signals. Unfortunately, individual case studies overestimate their contribution to the final event and may lead to unjustified ‘extended pictures’ of the phenomenon. In the present article, we systematically attempt to evaluate these signals by examining them as a whole, rather than individually as in case studies. We confirmed that while case studies are a reasonable way to start a research project, they do not guarantee the final result. In our case, while individual studies were very hopeful, the present integrated study led to several unresolved issues that need to be addressed. The results of our work will help to determine whether these signals represent a significant part of the broader LAIC scenario, which is currently the only reliable suggestion for triggering and predicting earthquakes, or whether the origin of these signals should be sought elsewhere. Full article

Background/Objectives: Intraoperative neurophysiological monitoring (IONM) is used to detect and prevent neurological injury during extramedullary spinal cord tumor (EMSCT) resection, but its diagnostic accuracy lacks systematic validation with recent evidence. This meta-analysis evaluates the performance of somatosensory evoked potentials (SSEPs), transcranial motor evoked potentials (TcMEPs), and multimodal (SSEP + TcMEP) IONM in predicting deficits during EMSCT resections. Methods: Following PRISMA-DTA guidelines, we searched MEDLINE, PubMed, and Ovid (inception to April 2025) for studies on IONM in EMSCT surgeries (PROSPERO: CRD420251047345). Pooled sensitivity, specificity, and reversibility metrics were calculated using bivariate models, with quality assessed via QUADAS-2. Z-test and Bayesian meta-analysis were used for comparisons. Results: Across 20 studies (2672 patients), multimodal IONM showed a log DOR of 4.310 (95% CI: 3.581–5.039) and an AUC of 94.2%, TcMEP monitoring showed a log DOR of 4.367 (95% CI: 3.765–5.127) and an AUC of 92%, while SSEP monitoring showed a log DOR of 3.463 (95% CI: 2.702–4.224) and an AUC of 82%. All modalities demonstrated high specificity (>95%), indicating low false-positive rates. Bayesian analysis revealed >90% probability that TcMEP-based approaches were superior to SSEPs. Reversible TcMEP changes were associated with an 11% (95% CI: 4–24%) postoperative deficit rate, compared to 35% (95% CI: 12–67%) for SSEPs. Conclusions: These findings caution against relying solely on SSEPs and support the use of multimodal IONM strategies, which enhance early detection of impending neurological injury, enable timely surgical interventions, and help prevent permanent neurological damage in EMSCT resections. Although TcMEP and multimodal monitoring showed similar diagnostic accuracy, we continue to recommend multimodal approaches as the current standard of care, pending prospective studies to determine if TcMEP alone can reliably replace multimodal monitoring. Full article

Bacterial synthetic microbial communities (SynCom) have exhibited significant effects for enhancing plant growth and delivering ecological benefits. However, persistent challenges, including structural instability, limited environmental adaptability, and transient efficacy, remain critical barriers to their practical application. Herein, we propose Arbuscular Mycorrhizal fungi (AMF) as the keystone component to optimize SynCom’s ecological fitness in sustainable agricultural systems. AMF modulate microbiome assembly through hyphal networks, enhancing community stability via facilitative interactions and augmenting nutrient cycling functionalities. This review systematically evaluates methodologies for AMF-based SynCom design and construction, investigates the dynamics of AMF-microbe interactions, delineates plant growth-promoting mechanisms, identifies candidate microbial taxa, and addresses implementation bottlenecks with corresponding strategies. We posit that AMF-Based SynComs represent a transformative management tool for ensuring global food security amid impending climatic perturbations and declining agricultural productivity. Full article

Duchenne muscular dystrophy (DMD) is a rare neuromuscular disorder that is characterized by skeletal muscle wasting, loss of ambulation, and respiratory failure. In addition to these obvious external signs of disease, heart disease, the leading cause of death in DMD patients, is also progressing. Dystrophic cardiomyopathy is largely clinically silent with cardiac dysfunction masked by concurrent loss of skeletal muscle function. In older DMD patients the prevalence of heart disease is very high, offering the unique potential to predict impending heart disease from a much earlier genetic diagnosis. Randomized clinical trial data and subsequent retrospective studies in DMD demonstrate that early initiation of cardiac directed therapy results in a significant delay in the onset of cardiac dysfunction and prolonged survival. Clinical guidelines reflect this, recommending the initiation of cardiac therapy at an age of 10 years old, even in the absence of documented contractile dysfunction. Despite this data, a recent examination of registry data reveals that most DMD patients are not receiving the treatments recommended by these guidelines. While there is great excitement regarding newly developed therapies for DMD, there are so many signs that deploying the safe and effective therapies we already have can improve clinical outcomes. This review will highlight the basic science behind and clinical importance of using early cardiac directed therapy to extend the duration and quality of life of DMD patients and will offer some suggestions that may aid in achieving this goal. Full article

Heart failure (HF) remains a leading cause of recurrent hospitalisations worldwide, largely driven by acute episodes of decompensation. Early identification of impending decompensation could enable timely intervention and potentially prevent costly admissions. Non-invasive wearable devices have emerged as promising tools for continuously monitoring physiological parameters and detecting early signs of deterioration. This review summarises recent advances in wearable technologies designed to predict HF decompensation and appraises their ability to generate clinically useful alerts. It will examine various modalities designed to monitor different aspects of cardiorespiratory physiology that have the potential to detect abnormalities preceding heart failure decompensation. Broadly, these devices either monitor physical activity capacity and cardiac function or monitor changes in pulmonary fluid congestion. We will also cover evidence exploring whether these devices can generate timely alerts for interventions to improve patient outcomes and reduce hospitalisations. However, despite advances in these technologies, challenges remain regarding their accuracy and usability for remote monitoring, as well as concerns with data storage, processing, patient adherence, and integration into existing healthcare workflows. While current limitations exist, previous results warrant further research into this area, with a focus on larger randomised trials, exploring both single- and multi-sensor systems, using artificial intelligence and cost-effectiveness analysis. Overall, non-invasive wearables represent an opportunity to create a more proactive approach to HF management, with the potential to shift the paradigm from reactive treatment to anticipatory care. Full article

Earthquakes are the result of complex interactions between tectonic plates, the mantle, and the lithosphere. Complex geodynamic conditions contribute to the occurrence of seismic phenomena. Tectonic plates can collide, move apart, or slide past each other. Mantle convection by internal heat drives plate motions that deform the lithosphere. Rocks deform elastically as stress accumulates and pore fluid pressure changes. Rupture occurs when stress exceeds frictional resistance. The connection between variations in gravity and the magnitude of earthquakes remains unclear. This work aims to examine aspects of this correlation. Three sets of earthquakes, one with events from all over the world, one from broader Greece, and one from the Hellenic Trench in Greece, aiming to cover all cases of geodynamics, from very different to very similar, were employed. Time series of gravity measurements at earthquake epicenters were extracted from GRACE satellite data. Time derivatives of the gravity field, as well as magnitude-dependent variations—reflecting changes relative to earthquake strength—were computed. Multiple linear regression (MLR), partial least squares (PLS) regression, and neural networks (NN) were used to model the relationship between gravity or its derivatives and earthquake magnitude. A correlation between the earthquake magnitude and magnitude derivatives was found. By using the global and Greek datasets, the best accuracy was obtained with MLR, reporting a mean squared error (MSE) of 0.069 with an R² of 0.979, and MSE was 0.011 with R² score of 0.997, respectively. By using the Hellenic Trench set, PLS regression derived the best correlation results, reporting an MSE of 0.004 and an R² of 0.977. Experimental results suggest that gravity, and therefore crustal density, is related to the magnitude of the impending earthquake, but not to its timing. Full article

Elastic optical networks (EONs) must allocate resources dynamically to accommodate heterogeneous, high-bandwidth demands. However, the continuous setup and teardown of connections with different bit rates can fragment the spectrum and lead to blocking. The blocking predictors enable proactive defragmentation and resource reallocation within network controllers. In this paper, we propose two novel deep learning models (based on CNN–BiLSTM and CNN–LSTM) to predict blocking in EONs by combining spatial feature extraction from spectrum snapshots using 2D convolutional layers with temporal sequence modeling. This hybrid spatio-temporal design learns how local fragmentation patterns evolve over time, allowing it to detect impending blocking scenarios more accurately than conventional methods. We evaluate our model on the simulated NSFNET topology and compare it against multiple baselines, namely 1D CNN, 2D CNN, k-nearest neighbors (KNN), and support vector machines (SVMs). The results show that the proposed CNN–BiLSTM/LSTM models consistently achieve higher performance. The CNN–BiLSTM model achieved the highest accuracy in blocking prediction, while the CNN–LSTM model shows slightly lower accuracy; however, it has much lower complexity and a faster learning time. Full article

Particulate matter (PM) concentration, especially PM_2.5, is a major culprit of environmental pollution from unreasonable energy system emissions that significantly affects visibility, climate, and public health. The prediction of PM_2.5 concentration holds significant importance in the early warning and management of severe air pollution, since it enables the provision of guidance for scientific decision-making through the estimation of impending PM_2.5 concentration. However, due to diversified human activities, seasonal factors and industrial emissions, the air quality data not only show local anomalous mutability, but also global dynamic change characteristics. This hinders existing PM_2.5 prediction models from fully capturing the aforementioned characteristics, thereby deteriorating the model performance. To address these issues, this study proposes a framework integrating multi-scale temporal convolutional networks (TCNs) and a transformer network (called MSTTNet) for PM_2.5 concentration prediction. Specifically, MSTTNet uses multi-scale TCNs to capture the local correlations of meteorological and pollutant data in a fine-grained manner, while using transformers to capture the global temporal relationships. The proposed MSTTNet’s performance has been validated on various air quality benchmark datasets in the cities of China, including Beijing, Shanghai, Chengdu, and Guangzhou, by comparing to its eight compared models. Comprehensive experiments confirm that the MSTTNet model can improve the prediction performance of 2.42%, 2.17%, 2.87%, and 0.34%, respectively, with respect to four evaluation indicators (i.e., Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, and R-square), relative to the optimal baseline model. These results confirm MSTTNet’s effectiveness in improving the accuracy of PM_2.5 concentration prediction. Full article

Cryptocurrencies such as Bitcoin can be classified as commodities under the Commodity Exchange Act (CEA), giving the Commodity Futures Trading Commission (CFTC) jurisdiction over those cryptocurrencies deemed commodities, particularly in the context of futures trading. This paper presents a method for predicting both long- and short-term trends in selected cryptocurrencies based on the Fractal Market Hypothesis (FMH). The FMH applies the self-affine properties of fractal stochastic fields to model financial time series. After introducing the underlying theory and mathematical framework, a fundamental analysis of Bitcoin and Ethereum exchange rates against the U.S. dollar is conducted. The analysis focuses on changes in the polarity of the ‘Beta-to-Volatility’ and ‘Lyapunov-to-Volatility’ ratios as indicators of impending shifts in Bitcoin/Ethereum price trends. These signals are used to recommend long, short, or hold trading positions, with corresponding algorithms (implemented in Matlab R2023b) developed and back-tested. An optimisation of these algorithms identifies ideal parameter ranges that maximise both accuracy and profitability, thereby ensuring high confidence in the predictions. The resulting trading strategy provides actionable guidance for cryptocurrency investment and quantifies the likelihood of bull or bear market dominance. Under stable market conditions, machine learning (using the ‘TuringBot’ platform) is shown to produce reliable short-horizon estimates of future price movements and fluctuations. This reduces trading delays caused by data filtering and increases returns by identifying optimal positions within rapid ‘micro-trends’ that would otherwise remain undetected—yielding gains of up to approximately 10%. Empirical results confirm that Bitcoin and Ethereum exchanges behave as self-affine (fractal) stochastic fields with Lévy distributions, exhibiting a Hurst exponent of roughly 0.32, a fractal dimension of about 1.68, and a Lévy index near 1.22. These findings demonstrate that the Fractal Market Hypothesis and its associated indices provide a robust market model capable of generating investment returns that consistently outperform standard Buy-and-Hold strategies. Full article

In this work, low-zinc epoxy powder coating primers with anticorrosive properties were developed. For this purpose, single-walled carbon nanotubes (SWCNTs) were introduced into powder coatings. The obtained coatings were evaluated by performing the following tests: adhesion to steel, roughness, gloss, color, water contact angle, salt spray, electrochemical impendance spectroscopy (EIS), and transmission scanning microscopy (TEM). The anticorrosion resistance of the powder coating primers obtained depends on the zinc and SWCNT content, as well as the degree of dispersion in the paint. The mechanism of anticorrosion activity was proposed. Full article

Introduction: Twelve-lead electrocardiography (ECG) remains an essential diagnostic tool for patients presenting with chest pain. Timely recognition of specific electrocardiographic patterns is critical for guiding reperfusion strategies and predicting adverse outcomes. Among these, Wellens’ pattern is a high-risk marker of critical left anterior descending (LAD) artery stenosis and an impending anterior myocardial infarction. Although typically described in clinically stable patients without heart failure, its occurrence in the setting of acute decompensation is rare. Case Report: We report the case of a 66-year-old male with hypertension, obesity, and active smoking who presented with exertional chest pain, dyspnea, and signs of acute heart failure. Initial ECG revealed biphasic T waves in V2–V4, consistent with type A Wellens’ pattern. Laboratory evaluation demonstrated elevated troponin I, while point-of-care ultrasound (POCUS) identified systolic and diastolic dysfunction, lateral wall hypokinesia, pericardial effusion, and cardiogenic pulmonary edema. The patient received acute management with antiplatelet therapy, statins, diuretics, and anticoagulation, followed by referral for coronary angiography. This revealed critical stenosis (>90%) of the proximal LAD, successfully treated with percutaneous coronary intervention and drug-eluting stent implantation. The in-hospital course was uneventful, and guideline-directed medical therapy was optimized at discharge, including dual antiplatelet therapy, beta-blocker, renin–angiotensin system inhibitor, and SGLT2 inhibitor. Conclusions: This case highlights the need for early recognition of Wellens’ pattern, even in atypical contexts such as acute heart failure. Integrating ECG interpretation with bedside POCUS facilitated diagnostic accuracy and guided an early invasive strategy, preventing extensive myocardial infarction. In resource-limited settings, strengthening frontline diagnostic capabilities and referral networks is crucial to improving patient outcomes. Full article

For nearly two decades, the Yellow Sea has experienced recurrent green tides, which are now considered the largest of their kind globally; the mechanism behind these outbreaks remains highly complex and not fully understood. This study investigates the pre-outbreak distribution, abundance, and species composition of suspended macroalgae in the Southern Yellow Sea (SYS) during 2023–2024, along with environmental parameters. The results indicate that suspended macroalgae were predominantly distributed in the nearshore waters, particularly along the shallow beaches of northern Jiangsu. Furthermore, their abundance in the surface water layer significantly exceeded that in the bottom water. A total of 1353 and 493 algal filament samples were collected in 2023 and 2024, respectively. Dominant species included Ulva prolifera, Ulva linza, Ulva flexuosa, and Blidingia sp. Nutrient levels positively correlated with filament abundance. As a primary means of rapid proliferation for U. prolifera, suspended macroalgae contribute significantly to the initial expansion of green tides. Furthermore, their abundance holds promise as a biological indicator for forecasting the scale and extent of impending blooms, thereby providing a critical foundation for elucidating the underlying outbreak mechanisms. Full article

This essay theorizes implications of mounting political efforts to dismantle the Department of Education (DoE) as part of the anti–Diversity, Equity, and Inclusion (DEI) movement, with particular attention to consequences for principal preparation programs. While overlooked, federal policies have played a critical albeit complex role in shaping the principal preparation landscape. This essay challenges prevailing narratives that characterize federal impact on preparation programs as minimal, offering a more nuanced account of how federal policies support the development of high-quality and equity-oriented school leaders. This essay identifies three primary areas of federal influence: prioritizing high-needs schools, encouraging the adoption of innovative and evidence-based practices, and facilitating national understanding of the principal preparation landscape. These dimensions are used to theorize consequences of defunding or dismantling the DoE for educational leadership preparation. In addition to implications for policymakers and lobbyists ahead of the impending congressional vote about the DoE’s future, implications are offered for future research and federal policies to improve and expand the scope of influence over preparation programs. Full article

Isoperla powhatan Szczytko & Kondratieff, 2015 is newly proposed as a junior synonym of I. myersi Szczytko & Kondratieff, 2015 based on an integrative analysis of morphological and molecular phylogenetic data. Revised descriptions of the adult male, adult female, and mature egg of I. myersi are presented, together with the first description of the final instar larva. Comparative morphological observations of I. arcana Beaty, Holland & Lenat, 2017 and I. holochlora Klapálek, 1923 improve the taxonomic concepts of each species and provides support for a new species group containing the three taxa. The new synonymy adds 868 unique locations from nine USA states to the distribution of I. myersi, including 826 from North Carolina Department of Environmental Quality samples. This new synonymy has dramatically changed our understanding of the overall range and frequency of occurrence of I. myersi populations, which will impact any impending conservation status assessment of this species. This study includes color light microscopy images, scanning electron micrographs, a substantial amount of new occurrence data, and distribution maps for each species. Full article

Environmental factors affect plant physiological processes. Understanding these factors can increase productivity, especially in tropical mountain ecosystems, where they vary with altitude. This study aimed to analyze the physiological variations related to water vapor and gas exchange in Prunus persica L. Batsch according to the altitudinal gradient in North Santander. One plant was selected per altitude, and six leaves were selected per plant and per branch across three phenological stages. Conductance (gs), stomatal resistance (SR), and transpiration (E) were determined using a calibrated portable porometer over two cycles. Linear mixed-effects models with repeated measurements over time, phenological effects, altitude, and light conditions were used. At higher altitudes, gs and E decreased and SR increased, possibly due to higher ultraviolet radiation and lower temperatures with increasing altitude. Maximum values were reached at EF6. gs and E exhibited diurnal patterns, decreasing at the end of the day to minimize water loss during periods of lower solar radiation. The cultivar adjusted its stomatal and water regulation mechanisms according to altitude. These findings provide advanced insights into plant acclimatization strategies in mountain ecosystems and inform the sustainable management practices needed in the face of impending global climate variability. Full article