Search Results (950)

Rocket launches constitute a major anthropogenic source of disturbance in the near-Earth space environment, inducing significant ionospheric perturbations through both chemical and dynamic mechanisms. This study presents a systematic analysis of ionospheric disturbances—specifically, electron density depletion and traveling ionospheric disturbances (TIDs)—triggered by four rocket launches from China’s Jiuquan Satellite Launch Center between 2023 and 2025. Using high-rate, multi-constellation GNSS data from 370 ground stations and BeiDou GEO satellites, we extracted total electron content (TEC) signals and applied advanced detection methods, including the Multi-Rolling-Multi-Image-Tracking (MRMIT) algorithm for depletion identification and a parametric integration framework for quantitative comparison. Our results reveal that all launches produced rapid TEC depletions, evolving along the rocket trajectory and peaking within approximately 30 min. Launch mass was the dominant factor controlling depletion intensity, while propellant chemistry (UDMH-based vs. liquid oxygen/methane) and local time/background TEC levels modulated the recovery rate and spatial extent. Additionally, distinct TIDs exhibiting wave-like and V-shaped structures were observed, propagating outward from the trajectory with latitudinal variations in amplitude and waveform. These findings highlight the critical roles of rocket attributes and ambient ionospheric conditions in shaping disturbance characteristics. The study underscores the value of multi-source GNSS networks and novel methodologies in monitoring anthropogenic space weather effects, with implications for GNSS performance and sustainable space operations. Full article

Background and Objectives: Sick-day medication guidance involves patients self-adjusting medications during sick days to prevent adverse events and minimise exacerbation of their disease states. This review aimed to summarise and synthesise all sick-day interventions provided by healthcare professionals (HCPs) for patients with chronic illnesses, including diabetes, cardiovascular disease, chronic kidney disease (CKD), adrenal insufficiency (AI), rheumatoid arthritis, chronic obstructive pulmonary disease (COPD), and asthma. Materials and Methods: A search of Embase, Medline, International Pharmaceutical Abstract, Scopus, Google Scholar, and the grey literature was conducted until July 2025. The review followed the methodological framework according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. Data were extracted using a modified TIDieR checklist, and the findings were summarised descriptively and presented thematically. Results: The search included 6932 documents, and 97 met the inclusion criteria: 57 published guidelines/education resources and 40 pieces of original research. Seventy-four interventions were identified for diabetes (18), asthma (32), AI (8), CKD (6), AKI prevention (4), COPD (4), and mixed conditions (2). The most common type of intervention was written information (action plans and information sheets), with education mostly provided by multidisciplinary teams. Novel interventions included 24h phone support and an educational mobile application. Participants showed interest in sick-day interventions and HCPs viewed these interventions as effective, important, and easy to provide. However, interventions did not always translate to improved clinical outcomes, with studies reporting mixed outcomes regarding healthcare utilisation. Nonetheless, some interventions showed improved patient knowledge and satisfaction with care. Conclusions: Multiple interventions are available for asthma and diabetes, with fewer targeting CKD or acute kidney injury (AKI) prevention. While demand for these interventions from consumers and HCPs is high, implementation challenges and inconsistent benefits remain. Further primary research is needed to clarify which intervention features are most effective in yielding meaningful clinical outcomes. Full article

Artificial intelligence (AI) adoption in strategic sectors such as energy is often framed in optimistic narratives, yet its actual economic contribution remains under-quantified. This study proposes a novel, multi-stage methodology at the intersection of machine learning, statistics, and big data analytics to bridge this gap. First, we construct a media-derived AI Adoption Score using natural language processing (NLP) techniques, including dictionary-based keyword extraction, sentiment analysis, and zero-shot classification, applied to a large corpus of firm-related news and scientific publications. To enhance reliability, we introduce a Misinformation Bias Score (MBS)—developed via zero-shot classification and named entity recognition—to penalise speculative or biased reporting, yielding a credibility-adjusted adoption metric. Using these scores, we classify firms and apply a Fixed Effects Difference-in-Differences (FE DiD) econometric model to estimate the causal effect of AI adoption on turnover. Finally, we scale firm-level results to the macroeconomic level via a Leontief Input–Output model, quantifying direct, indirect, and induced contributions to GDP and employment. Results show that AI adoption in Romania’s energy sector accounts for up to 42.8% of adopter turnover, contributing 3.54% to national GDP in 2023 and yielding a net employment gain of over 65,000 jobs, despite direct labour displacement. By integrating machine learning-based text analytics, statistical causal inference, and big data-driven macroeconomic modelling, this study delivers a replicable framework for measuring credible AI adoption and its economy-wide impacts, offering valuable insights for policymakers and researchers in digital transformation, energy economics, and sustainable development. Full article

Context: Modern software systems have become increasingly complex, making it difficult to interpret raw requirements and effectively utilize traditional tools for software design and analysis. Unified Modeling Language (UML) class diagrams are widely used to visualize and understand system architecture, but analyzing them manually, especially for large-scale systems, poses significant challenges. Objectives: This study aims to automate the analysis of UML class diagrams by assessing their complexity using a machine learning approach. The goal is to support software developers in identifying potential design issues early in the development process and to improve overall software quality. Methodology: To achieve this, this research introduces a Visual Question Answering (VQA)-based framework that integrates both computer vision and natural language processing. Vision Transformers (ViTs) are employed to extract global visual features from UML class diagrams, while the BERT language model processes natural language queries. By combining these two models, the system can accurately respond to questions related to software complexity, such as class coupling and inheritance depth. Results: The proposed method demonstrated strong performance in experimental trials. The ViT model achieved an accuracy of 0.8800, with both the F1 score and recall reaching 0.8985. These metrics highlight the effectiveness of the approach in automatically evaluating UML class diagrams. Conclusions: The findings confirm that advanced machine learning techniques can be successfully applied to automate software design analysis. This approach can help developers detect design flaws early and enhance software maintainability. Future work will explore advanced fusion strategies, novel data augmentation techniques, and lightweight model adaptations suitable for environments with limited computational resources. Full article

This study proposes a feature extraction scheme that fuses accelerometric (ACC) and electromyographic (EMG) data to improve shoulder movement identification in individuals with transhumeral amputation, in whom the clinical need for intuitive control strategies enabling reliable activation of full-arm prostheses is underinvestigated. A novel spatiotemporal warping feature extraction architecture was employed to realize EMG and ACC information fusion at the feature level. EMG and ACC data were collected from six participants with intact limbs and four participants with transhumeral amputation using an NI USB-6009 device at 1000 Hz to support the proposed feature extraction scheme. For each participant, a leave-one-trial-out (LOTO) training and testing approach was used for developing pattern recognition models for both the intact-limb (IL) and amputee (AMP) groups. The analysis revealed that the introduction of ACC information has a positive impact when using windows of length (WLs) lower than 150 ms. A linear discriminant analysis (LDA) classifier was able to exceed the accuracy of 90% in each WL condition and for each group. Similar results were observed for an extreme learning machine (ELM), whereas k-nearest neighbors (kNN) and an autonomous learning multi-model classifier showed a mean accuracy of less than 87% for both IL and AMP groups at different WLs, guaranteeing applicability over a large set of shallow pattern-recognition models that can be used in real scenarios. The present work lays the groundwork for future studies involving real-time validation of the proposed methodology on a larger population, acknowledging the current limitation of offline analysis. Full article

The analysis of high-dimensional count data presents a unique set of challenges, including overdispersion, zero-inflation, and complex nonlinear relationships that traditional generalized linear models and standard machine learning approaches often fail to adequately address. This study introduces and validates a novel Random Forest framework specifically developed for high-dimensional Poisson and Negative Binomial regression, designed to overcome the limitations of existing methods. Through comprehensive simulations and a real-world genomic application to the Norwegian Mother and Child Cohort Study, we demonstrate that the proposed methods achieve superior predictive accuracy, quantified by lower root mean squared error and deviance, and critically produced exceptionally stable and interpretable feature selections. Our theoretical and empirical results show that these distribution-optimized ensembles significantly outperform both penalized-likelihood techniques and naive-transformation-based ensembles in balancing statistical robustness with biological interpretability. The study concludes that the proposed frameworks provide a crucial methodological advancement, offering a powerful and reliable tool for extracting meaningful insights from complex count data in fields ranging from genomics to public health. Full article

Background/Objectives: Brain metastasis from uterine leiomyosarcoma (ULMS) is an exceptionally rare complication of an aggressive malignancy. With fewer than 40 cases previously documented, a significant knowledge gap exists regarding its clinical course, management, and outcomes. This study provides the largest analysis of ULMS brain metastases to date, integrating a systematic literature review with a novel case report illustrating the disease’s uniquely rapid progression. Methods: Following PRISMA guidelines, we systematically reviewed four major databases to identify all reported cases of intracranial metastasis from ULMS. Data on patient demographics, clinico-radiological features, treatments, and survival were extracted and analyzed. Methodological quality was assessed using a modified Joanna Briggs Institute (JBI) tool. Results: We analyzed 34 studies with 39 individual cases. Additionally, this review was supplemented by one new illustrative case from our institution. The median patient age was 51.5 years, and most presented with focal neurological symptoms. Common imaging findings included hyperdense lesions on CT and homogeneously enhancing, dural-based masses on MRI, which mimic other intracranial pathologies. Though surgery was the most frequent intervention (76.9%), median survival after a brain metastasis diagnosis was a grim 5 months, with no significant difference observed between treatment modalities. Our illustrative case was remarkable for an extremely rapid volumetric doubling time averaging just 7.3 days. Conclusions: Brain metastasis from ULMS is a lethal event with an extremely poor prognosis. Nonspecific imaging features create diagnostic challenges, necessitating histopathological confirmation. Current therapies, including surgery and radiotherapy, offer palliative benefit but do not significantly alter survival. The aggressive biological behavior demonstrated here underscores the urgent need for increased clinical awareness and collaborative research to develop more effective management strategies and improve outcomes for this devastating diagnosis. Full article

To extend the time between the overhauls of helicopters, a novel collaborative methodology that takes into account uncertain misalignment errors by considering the shape and performance of the gear is built. Firstly, the digital characteristics of contact patterns, such as the reference point and direction angle, are extracted. Secondly, an optimization model calculates the equivalent misalignment by minimizing deviations in the reference point and direction angle between two contact patterns. This equivalent misalignment accounts for uncertainty misalignment errors introduced by complex gear support deformation. Thirdly, the ease-off is utilized to derive the pinion target surface that can sustain meshing performance under an equivalent misalignment, similar to the original gear in real conditions. This way it integrates with the optimization theory for flank reconstruction to redesign the pinion surface. Simulations reveal that the critical digital characteristics of the contact path on the original gear under the equivalent misalignment mirror those of the original gear in real conditions. Moreover, the surface parameters of the redesigned pinion result in an identical surface under a different equivalent misalignment, maintaining similar contact and dynamic performance. This proposed collaborative design approach, considering the shape and performance while accounting for uncertain misalignment errors through ease-off, greatly improves the gear transmission behavior. Full article

Potato above-ground biomass (AGB) and tuber yield estimation remain challenging due to the subjectivity of farmer-based assessments, the high data requirements of spectral analysis methods, and the sensitivity of traditional Structure from Motion (SfM) techniques to soil elevation variability. To address these challenges, this study proposes a novel UAV-based visible-light remote sensing framework to estimate the AGB and predict the tuber yield of potato crops. First, a new vegetation index, the Green-Red Combination Vegetation Index (GRCVI), was developed to improve the separability between vegetation and non-vegetation pixels. Second, an improved single-period SfM method was designed to mitigate errors in canopy height estimation caused by terrain variations. Fractional vegetation coverage (FVC) and plant height (PH) derived from UAV imagery were then integrated into a feedforward neural network (FNN) to predict AGB. Finally, potato tuber yield was predicted using polynomial regression based on AGB. Results showed that GRCVI combined with the numerical intersection method and SVM classification achieved FVC extraction accuracy exceeding 95%. The improved SfM method yielded canopy height estimates with R² values ranging from 0.8470 to 0.8554 and RMSE values below 2.3 cm. The AGB estimation model achieved an R² of 0.8341 and an RMSE of 19.9 g, while the yield prediction model obtained an R² of 0.7919 and an RMSE of 47.0 g. This study demonstrates the potential of UAV-based visible-light imagery for cost-effective, non-destructive, and scalable monitoring of potato growth and yield, providing methodological support for precision agriculture and high-throughput phenotyping. Full article

Background: Spinal deformity correction surgery, particularly in scoliosis, often necessitates long fusion constructs and complex osteotomies that create significant structural bone defects. These defects threaten the integrity of spinal fusion, potentially compromising surgical outcomes. Bone grafting remains the cornerstone of addressing these defects, traditionally relying on autologous bone. However, limitations such as donor site morbidity and insufficient graft volume have made urgent the development and adoption of biologic substitutes and synthetic alternatives. Additionally, innovations in three-dimensional (3D) printing offer emerging solutions for graft customization and improved osseointegration. Objective: This scoping review maps the evidence of the effectiveness of the use of biologic and synthetic bone grafts in scoliosis surgery. It focusses on the role of novel technologies, particularly osteobiologics in combination with 3D-printed scaffolds, in enhancing graft performance and surgical outcomes. Methods: A comprehensive literature search was conducted using PubMed, Scopus, and the Cochrane Library to identify studies published within the last 15 years. Inclusion criteria focused on clinical and preclinical research involving biologic grafts (e.g., allografts, demineralized bone matrix-DBM, bone morphogenetic proteins-BMPs), synthetic substitutes (e.g., ceramics, polymers), and 3D-printed grafts in the context of scoliosis surgery. Data were extracted on graft type, clinical application, outcome measures, and complications. The review followed PRISMA-ScR guidelines and employed the Arksey and O’Malley methodological framework. Results: The included studies revealed diverse grafting strategies across pediatric and adult populations, with varying degrees of fusion success, incorporation rates, and complication profiles. It also included some anime studies. Emerging 3D technologies demonstrated promising preliminary results but require further validation. Conclusions: Osteobiologic and synthetic bone grafts, including those enhanced with 3D technologies, represent a growing area of interest in scoliosis surgery. Despite promising outcomes, more high-quality comparative clinical studies are needed to guide clinical decision-making and standardize practice. Full article

Iodine is an essential element for the synthesis of thyroid hormones. Iodine deficiency leads to a range of health consequences known as iodine deficiency disorders. To assess the iodine nutritional status of a population, urinary iodine (UI) is typically measured. This work introduces a novel and simple analytical method for determining UI using silver triangular nanoplates (AgTNPs) after interfering substances are removed via solid-phase extraction (SPE). The AgTNPs were synthesized and characterized using Transmission Electron Microscopy, UV–vis spectroscopy, and zeta potential measurements. The limit of detection of iodide of the AgTNPs assessed spectrophotometrically was 35.78 µg I⁻/L. However, urine samples interfered with the colorimetric reaction. Thus, an SPE methodology was developed and optimized to eliminate urine interferents that hinder AgTNP performance. A logistic regression analysis was conducted to validate the combined application of SPE and AgTNPs for the qualitative determination of UI. This work demonstrated that the developed SPE methodology eliminates these interferents and extracts iodide from the sample, allowing the accurate determination of UI using AgTNPs. This reliable sample preparation method was then used on actual human urine samples to accurately identify UI deficiency levels. The proposed methodology offers an effective and environmentally friendly approach for monitoring iodine status, without requiring highly complex equipment. Full article

Over the past few years, naturally sourced bioactive molecules have drawn increased attention for their antioxidant capacity and wide-ranging health effects. At the same time, interest in eco-friendly extraction approaches has risen sharply. Atmospheric Room Temperature Plasma (ARTP), a novel non-thermal pretreatment method, has emerged as a promising green technology due to its minimal environmental impact, cost-effectiveness, and superior extraction efficiency compared to conventional methods. In this study, ARTP pretreatment—optimized across variables such as treatment distance, substrate thickness, power, nitrogen flow, and duration—was combined with ultrasonic-assisted extraction to enhance the recovery of bioactive compounds from Moringa oleifera leaves. Both techniques were optimized using Response Surface Methodology (RSM). Under optimal conditions, the extract yielded a total polyphenol content of approximately 40 mg gallic acid equivalents per gram of dry weight. Antioxidant activity, assessed via ferric-reducing antioxidant power (FRAP) and DPPH radical scavenging assays, reached ~280 and ~113 μmol ascorbic acid equivalents per gram dry weight, respectively, and the ascorbic acid content was ~5.3 mg/g. These findings highlight the potential of ARTP as an effective and sustainable pretreatment method for producing high-value phytochemical extracts, with promising applications in the food, pharmaceutical, and cosmetic industries. Full article

(1) Background: Multimodal tactile cognition is paramount for robotic dexterity, yet its advancement is constrained by the limited realism of existing texture datasets and the difficulty of effectively fusing heterogeneous signals. This study introduces a comprehensive framework to overcome these limitations by integrating a parametrically designed dataset with a novel fusion architecture. (2) Methods: To address the challenge of limited dataset realism, we developed a universal texture dataset that leverages information entropy and Perlin noise to simulate a wide spectrum of surfaces. To tackle the difficulty of signal fusion, we designed the Multimodal Fusion Attention Transformer Network (MFT-Net). This architecture strategically combines a Convolutional Neural Network (CNN) for local feature extraction with a Transformer for capturing global dependencies, and it utilizes a Squeeze-and-Excitation attention module for adaptive cross-modal weighting. (3) Results: Evaluated on our custom-designed dataset, MFT-Net achieved a classification accuracy of 86.66%, surpassing traditional baselines by a significant margin of over 21.99%. Furthermore, an information-theoretic analysis confirmed the dataset’s efficacy by revealing a strong positive correlation between the textures’ physical information content and the model’s recognition performance. (4) Conclusions: Our work establishes a novel design-verification paradigm that directly links physical information with machine perception. This approach provides a quantifiable methodology to enhance the generalization of tactile models, paving the way for improved robotic dexterity in complex, real-world environments. Full article

Background: Deep-learning models for Alzheimer’s disease (AD) diagnosis frequently report revolutionary accuracies exceeding 95% yet consistently fail in clinical translation. This scoping review investigates whether methodological flaws, particularly data leakage, systematically inflates performance metrics, and examines the broader landscape of validation practices that impact clinical readiness. Methods: We conducted a scoping review following PRISMA-ScR guidelines, with protocol pre-registered in the Open Science Framework (OSF osf.io/2s6e9). We searched PubMed, Scopus, and CINAHL databases through May 2025 for studies employing deep learning for AD diagnosis. We developed a novel three-tier risk stratification framework to assess data leakage potential and systematically extracted data on validation practices, interpretability methods, and performance metrics. Results: From 2368 identified records, 44 studies met inclusion criteria, with 90.9% published between 2020–2023. We identified a striking inverse relationship between methodological rigor and reported accuracy. Studies with confirmed subject-wise data splitting reported accuracies of 66–90%, while those with high data leakage risk claimed 95–99% accuracy. Direct comparison within a single study demonstrated a 28-percentage point accuracy drop (from 94% to 66%) when proper validation was implemented. Only 15.9% of studies performed external validation, and 79.5% failed to control for confounders. While interpretability methods like Gradient-weighted Class Activation Mapping (Grad-CAM) were used in 18.2% of studies, clinical validation of these explanations remained largely absent. Encouragingly, high-risk methodologies decreased from 66.7% (2016–2019) to 9.5% (2022–2023). Conclusions: Data leakage and associated methodological flaws create a pervasive illusion of near-perfect performance in AD deep-learning research. True accuracy ranges from 66–90% when properly validated—comparable to existing clinical methods but far from revolutionary. The disconnect between technical implementation of interpretability methods and their clinical validation represents an additional barrier. These findings reveal fundamental challenges that must be addressed through adoption of a “methodological triad”: proper data splitting, external validation, and confounder control. Full article

Rosa damascena and Rosa rugosa, which are the two most commercial species in the Rosa genus, are used to make rose oil, cosmetics, and functional foods. The majority of polysaccharide constituents of both species is structurally diverse and demonstrates promising biological activities, such as moisturizing, immunomodulation, and antioxidant activity. The extraction technique has a significant impact on the yield, purity, and bioactivity of polysaccharides. Traditional extraction methods (hot water, ethanol) are simple and economical, yet they typically produce low yields and degrade sensitive compounds. Novel extraction methods (pressurized liquid extraction, enzyme-assisted extraction, ultrasound-assisted extraction, microwave-assisted extraction, supercritical fluid extraction) offer higher efficiency, selectivity, and sustainability, while better preserving polysaccharide structure and bioactivity. This review serves as a comparative summary of conventional versus novel extraction methodologies of polysaccharides from R. damascena and R. rugosa, with particular consideration towards the yield, polysaccharide structural integrity, sustainability, and industrial conduct of each methodology. In addition, it summarizes the distribution and functional role of selected polysaccharides in the various organs of the plants, while also providing an overview of their antioxidant mechanisms and potential bioactive applications in health. Challenges and critical factors that surround specific species, standards for processes, and extraction methods, and that therefore appeal to time and economic considerations, are identified. In efforts to optimize the extraction methodology, the high economic and functional potential of the Rosa species can be maximized in the interest of healthy, functional consumables for the pharmaceutical, nutraceutical, and cosmetic industries. Full article