Search Results (122)

A geometry-aware deep learning framework for the reduction of atmospheric noise in SAR (Synthetic Aperture Radar) interferograms has been proposed and validated in this study. Our model has obvious advantages over existing ones in the following three aspects: (1) our objective is to reconstruct the original SAR imagery using an autoencoder and then eliminate noise by subtracting the reconstructed data from the raw data. However, our network architecture is not symmetric, and we choose to employ HRNet-w32 to preserve the details of the input dataset. (2) A deep supervision module equipped with diverse feature-unleashing mechanisms (including geometric, multispectral, and sematic features) is also developed to enhance the model’s predictive capability and interpretability. (3) We emphasize the significance of fractal geometry and variogram inference in the loss function, given that atmospheric disturbances, specifically humidity, clouds, and fogs, often exhibit statistically fractal characteristics. Compared with existing methods and ablation studies, our framework achieves relatively robust APS suppression performance across multiple quantitative metrics, including the Mean Squared Error (MSE), Nash–Sutcliffe Efficiency (NSE), Mean Absolute Error (MAE), Structural Similarity Index (SSIM), and Coefficient of Correlation (CoC), with improvements of at least 5.0% over the baselines. Full article

Water, a precious but limited resource since prehistoric times, has driven humans to develop systems for collecting and storing it. Evidence dating back to third millennium BC documents shows such systems among the Sumerians in the Fertile Crescent, as well as in Asia, Africa, China, and India. Aqueducts and cisterns, along with impluvium–compluvium systems, allowed the Romans to meet private and public needs; in Venice, wells provided filtered water until 1884. Today, climate change and increasing soil sealing urgently demand intelligent water collection and management, aligned with five of the 2030 Agenda Sustainable Development Goals (6, 11, 12, 13, 15). Buildings and construction account for about 35% of the global freshwater consumption. The construction sector, historically involved in the development of innovative rainwater harvesting and reuse systems, now faces a growing challenge in exploring innovative nature-based solutions for climate-resilient buildings (e.g., fog harvesting, green roofs for rainwater storage). Based on these considerations, we propose a scoping literature review of the last 15 years on innovative rainwater harvesting and storage systems. The analysis aims to provide a comparative mapping of the technological solutions that have emerged, to identify the geographical areas and climatic conditions favourable to each system, and to serve as a knowledge base for the development of integrated construction systems suitable for each specific context. A total of 136 peer-reviewed Open Access articles indexed in Scopus (2010–2024) were analysed following the PRISMA-ScR guidelines. Full article

Background/Objective: Patients with peripheral artery disease (PAD) are known to have poor awareness and understanding of the diagnosis. The role of generative AI chatbots in improving PAD patient education is unknown. Our goal is to compare a generative AI chatbot customized for PAD patient education to publicly available AI chatbots. Methods: This is a cross-sectional comparative evaluation of the responses of four AI chatbots to ten prompts that are commonly asked questions about PAD. The three publicly available AI chatbots were ChatGPT-5, Gemini 2.5 Flash, and Claude Sonnet 4.5. We created a customized, voice AI chatbot for PAD education grounded on curated and prompt-injected guidance called Vascular Education and Resources using Artificial Intelligence, or “VERA.” De-identified chatbot-generated responses to inputs were assessed for readability (Flesch–Kincaid Grade Level, Flesch Reading Ease, Gunning Fog Index, Simple Measure of Gobbledygook Index, and Average Reading Level Consensus Score), accuracy, comprehensiveness, and patient education quality (Patient Education Materials Assessment Tool; PEMAT) using validated instruments and expert scoring rubrics. Nonparametric statistical testing was used to compare chatbot performance across all evaluation domains. Results: VERA generated the most accessible text compared to the other chatbots and produced responses at a median grade level of 6.6, which was lower than responses from the other chatbots. PAD expert-rated accuracy scores were high across all the chatbots without significant differences between them. Comprehensiveness scores were more varied and demonstrated that VERA was less comprehensive than the other chatbots. PEMAT understandability scores were uniformly high. PEMAT actionability scores were low overall but did not differ significantly across chatbots on post hoc analysis. Conclusions: A generative AI chatbot research tool customized for PAD patient education generates textual information about PAD that is more accessible (mean grade level 6.6) than publicly available AI chatbots without loss of accuracy, albeit with modestly reduced comprehensiveness that reflects intentional simplification for patient-centered communication. Future research will assess the acceptability and feasibility of this research tool to be adopted as part of PAD patient education. Full article

Relaxor ferroelectric single crystals, such as Pb(In₁/₂Nb₂/₃)O₃–Pb(Mg₁/₂Nb₂/₃)O₃–PbTiO₃, possess extraordinary electro-optic (EO) coefficients, offering immense potential for next-generation integrated modulators. However, the application of PIN-PMN-PT in fiber-optic gyroscopes (FOGs) is hindered by the challenge of fabricating high-quality optical waveguides with strict mode selectivity, as conventional diffusion typically excites multi-mode propagation. Here, the fabrication of high-quality, mode-selective waveguides is achieved in rhombohedral PIN-PMN-PT via a nickel in-diffusion technique. The resulting graded-index structures exhibit a Gaussian profile with a maximum refractive index change (∆n) of 1.53% while preserving the single crystal structure. Under specific processing conditions, we achieve precise mode selectivity, enabling exclusive transverse electric (TE) mode transmission. This mode selectivity fulfills the requirements for single-mode Y-branch geometries, establishing a robust platform for ultra-compact, low driving voltage modulators and advancing the miniaturization of inertial navigation and integrated photonic systems. Full article

While bus transit plays a critical role in promoting urban transport sustainable development, the phenomenon of bus bunching has brought severe challenges. To alleviate bus bunching, speed control strategies have been widely used to improve the stability of bus headway distribution. However, existing research mainly focuses on developing optimized models with more flexible speed adjustments; a critical yet often ignored fundamental assumption behind these models is that all bus drivers can strictly adhere to the speed instructions issued by the bus dispatch center. To further explore how the compliance of bus drivers affects the implementation of speed adjustment instructions, this study designs a driving simulation experiment under different driving conditions. Modeled after a real bus line in Changsha, China, the designed simulator study incorporates three external variables, weather conditions, road conditions and command types, with behavioral data from 48 professional drivers analyzed via linear mixed-effects models. The results have shown that road conditions and command types emerged as main factors affecting compliance patterns. Specifically, congestion reduced average speeds by 5.1 km/h, especially affecting female drivers who showed 15.9% Command Compliance Index (it has been designed to quantify execution efficiency and will be referred to as CCI hereafter) reduction versus 10.6% for males. Compared to high-speed instructions, the execution efficiency of low-speed instructions increased by 12.3%, with drivers exceeding target speeds during 45.69% of sections to balance speed profiles. It is notable that the fog density had a minimal impact on efficiency, with only about 2% difference in efficiency. Despite standardized operational norms minimizing individual behavioral heterogeneity, significant group-level demographic variations persisted. Male drivers consistently maintained higher compliance with speed adjustment commands across all driving conditions; drivers under 40 and over 50 had a 3.3% higher CCI than middle-aged drivers; and prior bus bunching exposure increased compliance by 3.3%. High-CCI bus drivers strategically balanced headway distribution through controlled overspeeding. These findings provide empirical foundations for optimizing speed control strategies based on road sections. This study explores ways to enhance the attractiveness of public transit and promote sustainable development. Full article

This study examines the first corporate disclosures issued under the IFRS Sustainability Standards, with full alignment to IFRS S2, using natural language processing and text mining techniques, and contributes evidence to an underexplored phase of sustainability reporting research. Focusing on an emerging market setting, the analysis covers the 2024 reports of 18 firms included in the Borsa Istanbul Sustainability 25 Index. The reports are evaluated through readability metrics (Flesch–Kincaid, Gunning Fog, and SMOG), conceptual concentration measures (TF–IDF), semantic proximity analysis (Cosine Similarity), and network-based methods. The findings indicate a strong degree of technical discipline and standard adherence in the first year of implementation, alongside a pronounced barrier to linguistic accessibility. Average Gunning Fog and Flesch–Kincaid scores of 18.94 and 14.90 suggest that meaningful interpretation of these disclosures requires advanced academic proficiency. The observed technical density reflects the detailed and standard-driven structure of IFRS-based sustainability reporting and points to a persistent tension between technical precision and interpretability, consistent with the Managerial Obfuscation perspective (H1). High levels of semantic overlap further indicate that, under conditions of reporting uncertainty, firms rely heavily on established disclosure patterns, reinforcing professional convergence through both coercive (regulatory alignment) and mimetic (uncertainty-driven emulation) isomorphism (H2). In contrast, distinct narrative configurations identified through principal component and network analyses are evaluated as potential credibility-enhancing signals within the framework of Signaling Theory (H3). Overall, IFRS Sustainability Standards reporting functions in emerging markets as a learning-oriented and strategically relevant disclosure mechanism that may potentially mitigate information asymmetry through its linguistic properties. Full article

In order to address the challenges of limited accuracy in identifying non-meteorological clutter and the spatial overlap between meteorological and non-meteorological echoes in cloud radar observations under complex weather conditions, in this study, we propose a quality-control method for cloud-radar data, which integrates machine learning with neighborhood filtering, This quality-control method first uses the Light Gradient Boosting Machine (LightGBM) to initially identify clutter, then employs a customized neighborhood filtering module to optimize and eliminate residual isolated clutter. This two-stage framework combines the strengths of accurate machine-learning-based classification and physically motivated filtering optimization, enabling reliable discrimination between meteorological and non-meteorological echoes. Based on multi-region, long-term and multi-model radar baseline observations, which cover typical complex weather types such as snow, fog, rain, low clouds and dust, the refined manual labeling of meteorological and non-meteorological echoes is carried out, combined with multi-source ground observation data such as surface observations, temperature and humidity. Based on this, a feature training dataset for machine learning is constructed, which contains over 20 million samples. A multi-index evaluation system—including echo classification accuracy and non-meteorological clutter rejection rate—is used to quantitatively assess the quality-control performance of the method in different weather scenarios. The results indicate that the proposed method demonstrates stable performance in typical complex weather scenarios, with comprehensive scores of 90.73 (snow), 94.23 (rain), 96.49 (low clouds), 91.10 (fog) and 95.79 (dust) on a 100-point scale. Through typical case studies and statistical data analysis, the proposed algorithm achieves better quality-control scores in comparison with the Random Forest and single LightGBM algorithms. It provides a new technical approach for cloud-radar data quality control and also offers a theoretical basis for the feature selection of machine-learning-based quality-control models, further enhancing the application value of cloud-radar data in refined meteorological observations. Full article

The informational efficiency of stock prices is conditioned by the level of quality of financial reports, contributing to an accurate assessment of the company’s future performance. By approaching informational quality from two perspectives, we conducted an analysis of the impact of faithful representation and readability of annual reports on the reaction of the Romanian capital market, measured by annual stock returns (SR) and cumulative abnormal returns (CAR). The findings revealed an accentuated concern of investors regarding the faithful representation of the firm’s financial results (both at the time of financial statements’ publication and at the year-end) and a diminished significance of the comprehensibility level of financial information in the investment decision-making process. The annual reports of a sample of firms listed on the BSE between 2017 and 2023 have an increased level of linguistic complexity, which entails processing costs, and are intended for sophisticated users with financial expertise. Along with the specialized language, the extensive length of reports delays the incorporation of all information into the stock price, decreasing the informational efficiency of the market. This empirical study applies several indices to assess the readability and conciseness of financial information (FOG index, Flesch–Kincaid index, Flesch Reading Ease Score, and report length) and contributes to the expanding literature by providing a useful basis for future analysis of the influence of financial report quality on investors’ perceptions. Full article

Rapid urbanization and climate change have increased urban air temperatures and intensified the urban heat island effect through the expansion of impervious surfaces, loss of green areas, and high-density development. This study quantitatively evaluates the heat-mitigation performance and outdoor-thermal-comfort benefits of a high-pressure micro-mist cooling-fog system installed in the Oncheoncheon area of Busan, South Korea. Five environmental sensors were deployed in Gwajeong Park to monitor the near-pedestrian air temperature and relative humidity, and thermal comfort was assessed using the Universal Thermal Climate Index and the Physiological Equivalent Temperature derived from meteorological variables. Both indices indicated improved thermal comfort during fog operation relative to the control condition. The relationship between air temperature and perceived thermal conditions was strong, while the mean radiant temperature exhibited substantial dispersion even under similar air temperatures. Higher global horizontal irradiance (GHI: incoming solar radiation on a horizontal surface) was associated with elevated mean radiant temperature, highlighting the importance of radiative load in pedestrian thermal stress. Overall, the findings provide field-based evidence that high-pressure micro-misting can improve outdoor thermal comfort and function as practical cooling infrastructure for heat-stress mitigation and urban climate resilience. Full article

Marine fog is a key non-rainfall water source that sustains microbial activity and transports dissolved nutrients inland, influencing early soil development in hyperarid ecosystems. However, the mechanisms through which sustained fog inputs drive soil surface modification and biocrust formation remain poorly understood. This study evaluated the effects of long-term fog augmentation on soil surface development, biocrust dynamics, and associated microbial communities in the Atacama Desert. We implemented a four-year fog addition field experiment with three sampling times (T0, T24, T48) to assess changes in soil physicochemical properties, biocrust composition, and the integrated multi-diversity of archaea, bacteria, fungi and protist. Sustained fog input transformed bare soils into biological soil crusts, particularly lichen- and moss-dominated stages. This transition was accompanied by increases in soil nitrogen, variations in organic matter accumulation, a shift from alkaline to near-neutral pH, and improvements in soil stability and water retention. Multi-diversity increased over time and was positively associated with ecosystem variables linked to water availability, structural stabilization, and decomposition. These functions, integrated into an ecosystem multifunctionality index, also increased under prolonged fog input, revealing a positive relationship between multifunctionality and multi-diversity. Overall, the results demonstrate that sustained fog input strongly enhances early soil surface development and biocrust establishment, highlighting the ecological importance of marine fog in shaping biodiversity and ecosystem functioning in hyperarid landscapes. Full article

Ghost imaging (GI) offers a robust framework for remote sensing under degraded visibility conditions. However, atmospheric scattering in phenomena such as fog introduces significant noise and signal attenuation, thereby limiting its efficacy. Inspired by the selective attention mechanisms of biological visual systems, this study introduces a novel deep learning (DL) architecture that embeds a self-attention mechanism to enhance GI reconstruction in foggy environments. The proposed approach mimics neural processes by modeling both local and global dependencies within one-dimensional bucket measurements, enabling superior recovery of image details and structural coherence even at reduced sampling rates. Extensive simulations on the Modified National Institute of Standards and Technology (MNIST) and a custom Human-Horse dataset demonstrate that our bio-inspired model outperforms conventional GI and convolutional neural network-based methods. Specifically, it achieves Peak Signal-to-Noise Ratio (PSNR) values between 24.5–25.5$\mathrm{dB}/\mathrm{m}$ and Structural Similarity Index Measure (SSIM) values of approximately 0.8 under high scattering conditions ($\beta \ge 3.0 \mathrm{dB}/\mathrm{m}$) and moderate sampling ratios ($N \ge 50\%$). A comparative analysis confirms the critical role of the self-attention module, providing high-quality image reconstruction over baseline techniques. The model also maintains computational efficiency, with inference times under $0.12 \mathrm{s}$, supporting real-time applications. This work establishes a new benchmark for bio-inspired computational imaging, with significant potential for environmental monitoring, autonomous navigation and defense systems operating in adverse weather. Full article

Effective communication in critical care units, such as the Cardiovascular Intensive Care Unit (CVICU), is vital for patient safety; however, clinical notes from multiple professionals are often lengthy and complex. This study evaluated the Mistral large language model for summarizing Cardiovascular Intensive Care Unit progress notes using the Illness severity, Patient summary, Action list, Situation awareness and contingency planning, and Synthesis by receiver (I-PASS) framework, a standardized mnemonic for patient handoffs in healthcare. A total of 385 patients were included in the cohort, and all the progress notes associated with each patient were combined into a single document and summarized by the model. The readability was assessed using multiple metrics, including Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning-Fog Index, Simple Measure of Gobbledygook Index (SMOG), Automated Readability Index, and Dale-Chall Score. The readability metrics showed that the summaries generated with the Mistral Large Language Model (LLM) were much more difficult to read than the original notes, requiring a higher reading level. In a small clinician review, junior residents rated the summaries overall more favorably than senior residents, who often identified missing clinical details. Although Mistral condensed the documentation, this reduced readability and some loss of context may limit its usefulness for clinical handoffs. As a preliminary study with a small clinician-reviewed sample, these findings are descriptive and will require validation in larger clinical settings. Full article

Efficient resource allocation is a central challenge in multi-tenant cloud, fog, and edge environments, where heterogeneous tenants compete for shared resources under dynamic and uncertain workloads. Static or purely heuristic methods often fail to capture strategic tenant behavior, whereas many existing game-theoretic approaches overlook stochastic demand variability, fairness, or scalability. This paper proposes a Dynamic and Stochastic Game-Theoretic Allocation (DSGTA) model that jointly models non-cooperative tenant interactions, repeated strategy adaptation, and random workload fluctuations. The framework combines a Nash-like dynamic equilibrium, achieved via a lightweight best-response update rule, with an approximate Shapley-value-based fairness mechanism that remains tractable for large tenant populations. The model is evaluated on synthetic scenarios, with a trace-driven setup built from the Google 2019 Cluster dataset, and a scalability study is conducted with up to $K=500$ heterogeneous tenants. Using a consistent set of core metrics (tenant utility, resource cost, fairness index, and SLA satisfaction rate), DSGTA is compared against a static game-theoretic allocation (SGTA) and a dynamic pricing-based allocation (DPBA). The results, supported by statistical significance tests, show that DSGTA achieves higher utility, lower average cost, improved fairness and competitive utilization across diverse strategy profiles and stochastic conditions, thereby demonstrating its practical relevance for scalable, fair, and economically efficient resource allocation in realistic multi-tenant cloud environments. Full article

Smart farming relies heavily on IoT automation and data-driven decision making, but this growing connectivity also increases exposure to cyberattacks. Flow-based unsupervised intrusion detection is a privacy-preserving alternative to signature and payload inspection, yet it still faces three challenges: loss of subtle anomaly cues during Autoencoder (AE) compression, instability of fixed reconstruction-error thresholds, and performance degradation of clustering in noisy high-dimensional spaces. To address these issues, we propose a fog-aware two-stage hierarchical AE with latent-space gating, followed by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for attack categorization. A shallow AE compresses the input into a compact 21-dimensional latent space, reducing computational demand for fog-node deployment. A deep AE then computes reconstruction-error scores to isolate malicious behavior while denoising latent features. Only high-error latent vectors are forwarded to DBSCAN, which improves cluster separability, reduces noise sensitivity, and avoids predefined cluster counts or labels. The framework is evaluated on two benchmark datasets. On CIC IoT-DIAD 2024, it achieves 98.99% accuracy, 0.9897 F1-score, 0.895 Adjusted Rand Index (ARI), and 0.019 Davies–Bouldin Index (DBI). To examine generalizability beyond smart farming traffic, we also evaluate the framework on the CSE-CIC-IDS2018 benchmark, where it achieves 99.33% accuracy, 0.9928 F1-score, 0.9013 ARI, and 0.0174 DBI. These results confirm that the proposed model can reliably detect and categorize major cyberattack families across distinct IoT threat landscapes while remaining compatible with resource-constrained fog computing environments. Full article

Long COVID, a condition marked by persistent symptoms following COVID-19 infection, poses significant challenges in regard to clinical management. While emerging pharmacological treatments have demonstrated limited benefits in isolated studies, clinical experience and the literature suggest that low-dose naltrexone (LDN) may be a promising therapeutic option. Therefore, in this systematic review, we aim to synthesise findings from the available literature and evaluate the overall safety and efficacy of LDN as a potential treatment for long COVID. A literature search was conducted using a combination of key terms—‘COVID’, ‘COVID-19’, ‘SARS-COV-2’, and ‘Naltrexone’— and the following databases: MEDLINE, Web of Science (Clavirate), Embase, Scopus, Cochrane Central Register of Controlled Trials (CENTRAL), and Cumulative Index in Nursing and Allied Health Literature (CINAHL). The methodology is available on the PROSPERO database (CRD42025630362). Screening identified five eligible articles. Four studies were included, but only two provided comparable data suitable for meta-analysis. Meta-analysis demonstrated statistically significant improvements in fatigue, brain fog, and headaches. Preliminary evidence suggests LDN has potential benefits in the treatment of long COVID, particularly with respect to fatigue, brain fog, and headaches, but more robust studies, such as randomised controlled trials, are urgently needed to confirm LDN’s safety and efficacy. Full article