Search Results (2,239)

This work presents a revised total synthesis of two pharmacologically relevant benzofurans using newly developed environmentally friendly methodologies. In particular, we focused on establishing improved synthetic routes to stilbene dimers under milder and more sustainable reaction conditions. During our investigations, we optimized an efficient Sonogashira coupling carried out in water, which, followed by a Suzuki-like reaction conducted in dimethyl carbonate (DMC) in the absence of any transition metals, served as the key step for the synthesis of the benzofuran core. Full article

The automatic extraction of seismic reflection events is fundamental to seismic interpretation and structural identification in oil and gas exploration, particularly for large-scale regional surveys and preliminary basin-scale assessments. Although the B-COSFIRE (Bar-Combination of Shifted Filter Responses) method has demonstrated strong capability in detecting ridge-like structures, its application in large-scale seismic processing is limited by high computational cost and complex filter bank configuration. Conventional edge detectors such as the Canny operator are computationally efficient but often produce fragmented and noise-sensitive results in low signal-to-noise ratio (SNR) seismic data because they rely solely on local gradient information and ignore the spatial continuity of geological horizons. To overcome these limitations, this study proposes a lightweight and computationally efficient framework for rapid seismic event extraction. The method simplifies the B-COSFIRE architecture by replacing its configurable filter bank with a Difference-of-Gaussian (DoG) operator, which enhances ridge-like reflection features while suppressing background interference through a center–surround mechanism. Furthermore, a Spatial Consistency Constraint (SCC) module is introduced to enforce lateral continuity using directional morphological closing operations. This strategy reconstructs disrupted reflection segments and converts isolated detection responses into spatially coherent linear structures. Adaptive thresholding and skeletonization are then applied to obtain single-pixel-wide reflection contours suitable for geological interpretation and regional structural analysis. The proposed method was evaluated using both synthetic seismic models (Ricker wavelet convolution with Gaussian noise, σ = 0.15) and real post-stack seismic profiles characterized by low SNR conditions. Experimental results demonstrate that the proposed method achieves a Precision of 0.9527, Recall of 1.0000, and F1-score of 0.9758 on synthetic data, outperforming both the standard Canny detector (F1: 0.8972) and B-COSFIRE (F1: 0.7311). The Continuity Index reaches 261.00 pixels, substantially higher than Canny (223.67 pixels) and B-COSFIRE (66.86 pixels). Notably, B-COSFIRE exhibits a severely imbalanced detection profile (Precision: 0.5762, Recall: 1.000), indicating excessive false positives that undermine its practical utility. The proposed method additionally achieves the lowest runtime (0.024 s per profile), representing a 44× speedup over B-COSFIRE (1.039 s), while requiring no training data. Overall, the proposed framework provides a practical and efficient solution for automated seismic event extraction. With only a small number of geologically interpretable parameters and strong robustness across different datasets, the method is well-suited for large-scale seismic data processing and preliminary structural assessment in underexplored regions, enabling rapid first-pass evaluation of extensive survey areas before detailed interpretation and reservoir characterization. These characteristics make the method particularly suitable for computer-assisted interpretation workflows in industrial oil and gas exploration. Unlike prior approaches that treat seismic event extraction as a generic edge detection problem, the proposed framework explicitly encodes geological prior knowledge—specifically, the lateral continuity of stratigraphic interfaces—as a morphological constraint, bridging the gap between image processing methodology and geophysical interpretation requirements. Full article

Advancing environmental monitoring through coordinated autonomous systems is central to sustainable smart region governance and data-driven territorial management. The article presents an engineering-oriented architecture and deployment methodology for an integrated wildfire monitoring and response system that combines unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and stationary sensor networks (SNs). We formalise hub-and-spoke infrastructure placement as a mixed-integer optimisation problem that accounts for platform types, endurance, travel times and logistical constraints, and propose a practical pre-processing pipeline (confidence scoring, resampling, Kalman/median filtering, strategy fusion) for heterogeneous telemetry and imagery. The system couples multimodal neural network processing (image backbones, clustering and time-series models) with online resource-allocation and mission-planning mechanisms to prioritise UAV/UGV sorties and dynamically select launch sites. The article describes scenario-driven operational modes (early warning, alarm verification, autonomous local extinguishing, post-fire recovery, sensor-gap compensation, and inter-hub reinforcement), defines validation protocols (synthetic experiments, precision/recall/F1, and hardware-in-the-loop testing), and proposes KPIs to assess environmental, social, and economic impacts for smart regions. The contribution is a reproducible, deployment-focused blueprint that bridges conceptual UAV–UGV–SN research and practical implementation, highlighting trade-offs in reliability, communication redundancy, and sustainability, and outlining directions for simulation, field pilots and algorithmic refinement. Full article

Background/Objectives: Depot medroxyprogesterone acetate (dMPA) is a synthetic progestin commonly used for contraception. Recent studies have reported an increased association between dMPA exposure and diagnosis of cerebral meningioma. The current systematic review aims to provide a review of literature on the topic of dMPA and cerebral meningioma as well as conduct a meta-analysis by the duration of dMPA use. Methods: The current study presented a systematic review and meta-analysis of observational studies of dMPA and cerebral meningioma derived from PubMed, Web of Science, and Embase database searches for relevant studies published through February 2026. Odds ratios (ORs) and associated 95% confidence intervals were reported to determine the pooled effect of dMPA on cerebral meningioma diagnosis. Quality of evidence was assessed through the GRADE methodology. Results: Nine case-control studies and one cohort study were selected for review and analysis. The overall pooled OR was 2.78 (95% CI 2.20–3.52). This association was strongest for prolonged (i.e., ≥two-years) dMPA exposure (OR 3.49, 95% CI 2.35–5.18). GRADE analysis suggested a moderate quality of evidence. Conclusions: The results of this meta-analysis indicate that dMPA exposure is associated with an over two-fold increased odds of cerebral meningioma. This effect is consistent across studies and is stronger for prolonged dMPA exposure relative to short-term exposure, suggesting a dose–response effect. Clinicians should consider discussing with patients the cerebral meningioma risks associated with dMPA use when considering long-term birth control options. Full article

Islanded photovoltaic microgrids with limited inertial support can undergo steep frequency excursions after sudden generation loss or abrupt load changes. This paper develops and evaluates a synthetic inertia strategy supported by a supercapacitor energy storage unit for fast frequency containment in this type of system. The proposed approach commands rapid active-power injection or absorption from the measured rate of change of frequency, thereby emulating the immediate inertial contribution usually associated with rotating machines while preserving a simple and physically interpretable control structure. The supercapacitor is represented through a resistance–capacitance model that includes equivalent series resistance and is interfaced through a bidirectional buck–boost power converter subject to practical current, voltage, and power limits. Rather than claiming a fundamentally new storage-support concept, the contribution of this paper lies in providing a transparent and constraint-consistent benchmark that integrates measured operating profiles, explicit supercapacitor limits, hybrid frequency–RoCoF support, and stress-aware comparative assessment under a common set of plant assumptions. The methodology is assessed in time-domain simulations under representative benchmark disturbances, including an approximately ten percent photovoltaic generation loss, a ten percent load increase, and a combined event. Performance is evaluated through the peak rate of change of frequency, frequency nadir, integral error indices, time outside the admissible band, and supercapacitor stress indicators such as current peaks, voltage depletion, and energy throughput. An additional non-ideal assessment is also included to examine the behavior of the RoCoF-based support law under bounded frequency-measurement perturbations and delayed control action. A complementary variability-driven case based on a highly fluctuating measured irradiance window is also used to examine the behavior of the adaptive energy-management mechanism under repeated photovoltaic-power variations. A local small-signal analysis is also included to show that the selected gain region is dynamically plausible in the unsaturated regime. The results show that the proposed adaptive hybrid strategy improves the overall frequency response while maintaining admissible supercapacitor operation, thus providing a stronger methodological basis for rapid frequency support in islanded photovoltaic microgrids. Full article

The contemporary green transformation of the economy is a strategic imperative for businesses, especially small and medium-sized enterprises (SMEs) operating in the energy market, forcing the integration of sustainable practices in decision-making processes, including investment efficiency assessment. Classic financial tools, such as the internal rate of return (IRR) and net present value (NPV), commonly used in the SME sector, do not always adequately account for environmental, regulatory, and social risks associated with green transformation, as—particularly in the case of IRR—they rely on the assumption of stable cash flows and do not incorporate regulatory uncertainty, environmental externalities, or ESG-related risks into discounting parameters. The aim of the study was to determine the impact of nominal and real discount rates, adjusted for a synthetic measure of green transformation, on investment decisions. The research methodology combines advanced multi-criteria decision-making techniques, specifically TOPSIS and CRITIC, with sustainable finance concepts, offering an innovative approach to investment decision-making in the SME sector. The study shows that integrating environmental factors, when treated as a risk component, increases the cost of capital and reduces the net present value, while maintaining the profitability of the analysed projects. Incorporating green components into the discount rate enhances valuation appropriateness and improves investment risk management, particularly under macroeconomic uncertainty. The main contribution of the study lies in linking a synthetic green transformation indicator with dynamic discount rate adjustment within a multicriteria framework, extending existing ESG-adjusted valuation models by enabling a more structured and data-driven incorporation of environmental transition risk. Full article

Transition towards sustainable and environmentally friendly practices within the field of chemistry and materials science has become essential in light of current environmental challenges. This review provides a comprehensive overview of the challenges and opportunities in the various steps involved in producing chitosan derivatives, with particular emphasis on eco-friendly strategies. Key methodologies for chitin isolation from diverse natural sources, chitin deacetylation, and the chemical modification of chitosan are discussed, integrating green chemistry principles and eco-efficient processes. Advances in sustainable technologies that prioritize cost-effectiveness, safety, and performance are highlighted. The importance of interdisciplinary collaboration, innovative isolation and purification strategies, the adoption of continuous-flow processes, and greener synthetic approaches, such as click chemistry, are also explored. Overall, this work supports the adoption of a holistic approach for the development of chitosan derivatives, contributing to more sustainable and environmentally responsible materials and production processes. Full article

The adoption of instrumented wheelsets on diagnostic trains offers the possibility of continuous monitoring of wheel–rail contact forces. The collection of large datasets can be exploited for diagnostic purposes, aiming to localize specific track defects, allowing significant improvements in terms of safety and maintenance costs. Machine learning (ML) techniques can be used to automate anomaly detection. In this work, the authors compare the application of various ML algorithms based on the identification of different frequency or time-based features of analyzed signals. To perform the activity, a significant number and variety of local defects have been included in the recorded data. From a practical point of view, the insertion of real known defects into an existing line is extremely time-consuming, expensive, and not immune to safety issues. On the other hand, the design of anomaly detection algorithms involves the usage of relatively extended datasets with different faulty conditions. The authors propose deliberately adding real contact force profiles of healthy lines to a mix of synthetic signals, which substantially reproduce the behavior and the variability of foreseen faulty conditions. The results of this work, although preliminary and still to be completed, offer a contribution to the scientific community both in terms of obtained results and adopted methodologies. Full article

Synthetic datasets are increasingly used in education research for methodological validation, privacy-preserving data sharing, and reproducible equity analysis; however, most generative approaches prioritize marginal distributional similarity without ensuring preservation of multilevel inferential properties. This limitation is consequential for repeated-measures data analyzed using intersectionality-focused hierarchical models, where conclusions depend on variance partitioning, partial pooling, and stratum-level heterogeneity. We introduce MAI-GAN, a hybrid generative framework that implements a structure–residual decomposition approach combining Bayesian longitudinal MAIHDA with conditional GAN-based residual generation. Inferential fidelity is operationalized with respect to multilevel intersectional models by explicitly targeting the preservation of fixed effects, variance components, and variance partitioning coefficients, while baseline composition is maintained via stratified bootstrap resampling. Applied to a six-semester undergraduate biology dataset (N = 2669 students), MAI-GAN was evaluated across multiple independent random seeds and consistently reproduced baseline-dependent residual structure and key inferential quantities. These results demonstrate that model-aligned generative strategies can produce synthetic longitudinal datasets that remain coherent under intersectionality-focused multilevel analysis, offering a principled foundation for equity-oriented synthetic data generation. Full article

Supergiants are luminous post-main-sequence massive stars whose effective temperatures ($T_{\mathrm{eff}}$) are key inputs for stellar evolution and feedback studies. We present a photometry-based procedure to derive $T_{\mathrm{eff}}$ for a sample of galactic supergiants of spectral types B and A by fitting the spectral energy distributions (SEDs) in the UV-to-mid-IR range to ATLAS9 model spectra converted into synthetic photometry using the corresponding passband transmission profiles while simultaneously solving for the line-of-sight extinction. The SEDs were constructed from published data taken in different photometric systems (Johnson or Kron–Cousins $UBVRI$, Strömgren $uvby$, $JHK$ magnitudes from various sources, and AllWISE) and supplemented with UV TD-1 fluxes for brighter stars. The interstellar extinction law is based on Cardelli, Clayton & Mathis approximation assuming a total-to-selective ratio $R_V=A_V/E(B-V)=3.1$. The best-fitting parameters are obtained by minimizing a covariance-weighted ${\chi }^2$ statistic in logarithmic flux space over a grid of $A_V$ values and a discrete model grid. We test the method on 20 targets and find generally good agreement with published literature temperature estimates. The main limitations are non-simultaneous photometry for possibly variable objects and the residual coupling between temperature and reddening in broadband SED fitting. This study is intended as a methodological demonstration on a pilot sample rather than a definitive parameter catalog. Full article

Urban soundscape management is a central challenge to the livability and sustainability of cities and requires approaches that complement level indicators with frameworks capable of integrating context, use and experience. In this framework, the present work applies a multilayer methodology to the Old Town of Bilbao, understood as a useful case study to explore the applicability of soundscape reading in historic centers with intense coexistence of commercial, hospitality and catering uses, pedestrian, logistical and cultural uses. The methodology is organized into two phases. The first focuses on the recording and documentation of control points and routes through sound fieldwork, perceptual descriptions and homogeneous systematization of information. From this corpus, a qualified sound map and a first visual characterization of the sound identity are elaborated. The second phase presented in this article, consists of the interpretative synthesis of the corpus through five analytical dimensions and the preparation of fragments and sound sequences conceived for future application through reactivated listening. The results are presented at three levels: (1) a traceable documentary corpus of records, files and synthetic representations; (2) a comparative reading by dimensions that identifies spatial contrasts between interior, exterior and perimeter, as well as relationships between urban form, uses, persistence, masking and salience; and (3) a set of operational audio materials prepared for subsequent comparison with inhabitants and users. In a transversal way, type–token reading distinguishes between the diversity of sounds and dominance by repetition. The article does not yet carry out participatory validation of these materials; its contribution consists of proposing and applying a traceable analytical protocol as a basis for future phases of social contrast and applied discussion. Full article

Accurate estimation of forest soil organic carbon (SOC) is considered critical for understanding terrestrial carbon cycling and supporting climate change mitigation strategies. However, the canopy block, intricate vertical structure of forests, and the constraints of single-source remote sensing data have presented considerable obstacles for estimating forest SOC. This study proposes a forest SOC estimation and uncertainty analysis (ForSOC-UA) framework to enhance forest SOC estimation and quantify its uncertainty in the natural secondary forests of northern China by integrating hyperspectral imagery (ZY-1F), synthetic aperture radar data (Sentinel-1), and environmental covariates (such as topography, vegetation, and soil indices). The performance of traditional machine learning models (RF, SVM, and CNN), geographically weighted regression (GWR), and a geographically weighted random forest (GWRF) model was compared across three different soil depths (0–5 cm, 5–10 cm, and 10–30 cm). The results showed that GWRF consistently outperformed all other models across all soil depth layers, with the highest accuracy achieved using multi-source data (R² = 0.58, RMSE = 27.49 g/kg, rRMSE = 0.31). Analysis of feature importance revealed that soil moisture, terrain characteristics, and Sentinel-1 polarization attributes were the primary predictors, while spectral derivatives in the red and near-infrared bands from ZY-1F also played a significant role for forest SOC estimation. The uncertainty analysis indicated a forest SOC estimation uncertainty of 37.2 g/kg in the 0–5 cm soil layer, with a decreasing trend as depth increased. This pattern is associated with the vertical spatial distribution of the measured forest SOC. This integrated approach effectively captures spatial heterogeneity and nonlinear relationships between feature and forest SOC, while also assessing estimation uncertainty, so providing a robust methodology for predicting forest SOC. The ForSOC-UA framework addresses the uncertainty quantification of SOC estimation at different vertical depths based on machine learning, providing methodological enhancements for the assessment of large-scale forest SOC and the monitoring of carbon sinks within forest ecosystems. Full article

This study develops generalised equations to predict arsenic removal efficiency during adsorption using synthetic sand, based on two key factors: adsorbent dose and temperature. Previous experimental investigations demonstrated that iron oxide coated sand (IOCS), aluminium oxide coated sand (AOCS), and their mixtures are highly effective for arsenic removal. Best-fit equations were first derived for IOCS and AOCS at discrete temperatures as functions of dose concentration, and these were subsequently unified into single predictive equations capable of estimating removal efficiency across a wide range of temperatures and doses. The resulting models closely replicate experimental results, with correlation coefficients exceeding 0.99 for both IOCS and AOCS. Using the same methodology, an additional equation was developed for a 50:50 mixture of IOCS and AOCS, yielding a slightly lower but still strong correlation coefficient of 0.97. In contrast, linear proportioning of the individual IOCS and AOCS equations failed to accurately predict the removal efficiency of the mixed adsorbent, indicating that simple linear scaling is inadequate for representing the combined adsorption behaviour. Full article

Major Depressive Disorder (MDD) is a significant global health burden that contributes to disability and reduced quality of life. Its impact extends beyond individuals, placing emotional, social, and economic strain on families and healthcare systems worldwide. Despite its prevalence, MDD remains widely misunderstood, with limited mental health literacy and persistent stigma often preventing individuals from seeking help. This research explored the prediction of MDD utilising social media data via Natural Language Processing (NLP), Machine Learning (ML), and explainable Machine Learning (xML) techniques. The research aimed at identifying depressive indicators on X (formerly Twitter) and developing interpretable models for depression risk detection. The study’s methodology followed the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework to ensure a systematic approach to data analysis. Data was collected via X’s API and processed using regex-based noise removal, normalisation, tokenisation, and lemmatisation. Symptoms were mapped to DSM-5-TR criteria at the post-level, with user-level MDD risk assessed based on symptom persistence over a two-week period. Risk levels were classified as No Risk, Monitor, and High Risk to facilitate early intervention. Six ML models were trained and tested, while the Synthetic Minority Over-sampling Technique (SMOTE) was applied to mitigate class imbalance. The dataset was partitioned into training and testing sets using an 80:20 split. ML models were evaluated, and the Extreme Gradient Boosting model outperformed the others. Extreme Gradient Boosting achieved an accuracy of 0.979, F1-score of 0.970, and ROC-AUC of 0.996, surpassing benchmark results reported in prior studies. Explainability techniques, such as LIME and tree-based feature importance, enhance model transparency and clinical interpretability. Depressed mood consistently emerged as the highest-weighted predictor across different models. The findings highlight the value of aligning ML models with validated diagnostic frameworks to improve trustworthiness and reduce false positives. Future research can expand beyond text-based analysis by incorporating multimodal features to broaden diagnostic depth. Full article

Background: Breast cancer (BC) remains the most prevalent malignancy among women worldwide, with one in eight at risk during their lifetime. Platinum-based chemotherapeutic drugs, despite of their binding to the DNA of cancer cells, are plagued by toxicity and resistance, necessitating the need for safer and more effective alternatives, such as organometallic complexes. Both synthetic organometallic complexes and natural compounds have attracted attention in this regard. Organotin(IV) complexes are promising chemotherapeutics due to their structural versatility and bioactivity, while vitamins such as Vitamin D (VD) and Vitamin E (VE) exhibit antiproliferative, anti-inflammatory, and antioxidant properties, making them valuable candidates for combination therapy. Methodology: In this study, six novel organotin(IV) dithiocarbamate complexes [LMe₃Sn (Complex 1), LBu₃Sn (Complex 2), LPh₃Sn (Complex 3), LMe₂SnCl (Complex 4), LBu₂SnCl (Complex 5), and L₂Me₂Sn (Complex 6), where L = (E)-4-styrylpiperazine-1-carbodithioate], were synthesized and characterized by FT-IR, ¹H-, ¹³C-NMR, and elemental analysis. Results: Structural studies confirmed penta- and hexacoordination geometries. In silico docking against six BC-related proteins identified Complexes 2 and 4 with both vitamins as promising candidates, exhibiting strong binding affinities, with stable interaction profiles. However, integration of pharmacokinetic, antioxidant, and anti-inflammatory analyses highlighted Complex 4 with both vitamins as the most potent candidate owing to its superior ADME characteristics and balanced biological properties. Subsequent in vitro assays confirmed these findings, as Complex 4 demonstrated strong cytotoxic activity against both MCF-7 (>1.16-fold) and MDA-MB-231 (>1.46-fold) cell lines, surpassing the efficacy of cisplatin. Remarkably, co-administration of VD or VE with Complex 4 further enhanced its anticancer potential, with Chou–Talalay combination index values < 1 (0.66–0.91) indicating a synergistic interaction. Conclusions: Collectively, these results identify Complex 4 as a promising lead compound, and its synergistic activity with natural vitamins may promote cell death, likely through apoptosis induction and modulation of oxidative stress, underscoring its potential as an effective and less toxic therapeutic strategy for breast cancer management. Full article