Search Results (172)

Chile stands out for its renewable energy resources and its commitment to developing green hydrogen. However, achieving cost parity with gray hydrogen remains an obstacle, mainly due to high capital costs and sensitivity to scale. This study assesses the technical and economic feasibility of green hydrogen production, using five different plants located in the Magallanes region in the south of the country as a reference. The model integrates a detailed framework of wind generation, PEM electrolysis, compression, and high-pressure storage subsystems, as well as a stochastic economic layer that combines the CAPEX, NPV, and LCOH assessments using Monte Carlo simulations. It also incorporates real-world capacity distributions and probabilistic fluctuations in systems. A sensitivity analysis confirms production scale as the main factor affecting profitability, with a break-even threshold of 0.5 MW. The results show that the LCOH decreases from 7.1 USD to 3.4 USD/kgH₂ as capacity increases. The analysis reveals that only 23.88% of small-scale configurations yield positive NPV, underscoring the need for scaling to achieve economic viability. Full article

Objective: Radiation-induced meningiomas (RIMs) are a rare but clinically relevant late complication of cranial irradiation, characterized by long latency and potentially aggressive behavior. This study aimed to systematically analyze the relationships between radiation dose, age at irradiation, latency period, histological grade, tumor multiplicity, and recurrence in RIMs. Methods: A systematic review and pooled case analysis of published cases of RIMs was performed, supplemented by a case series of four institutional patients. Data were extracted on primary tumor type, radiation dose, age at irradiation, latency period, World Health Organization (WHO) grade, tumor multiplicity, and recurrence. Radiation dose was categorized as low (<20 gray (Gy)), intermediate (20–40 Gy), or high (>40 Gy). Statistical analyses included χ² tests, Mann–Whitney U tests, Kruskal–Wallis tests, and Spearman correlation analyses. Results: A total of 1809 patients were included. A higher radiation dose was significantly associated with shorter latency (p < 0.001), a higher WHO grade (p < 0.001), and increased tumor multiplicity (p < 0.001). High-grade RIMs occurred predominantly after high-dose irradiation. Tumor recurrence was significantly more frequent in high-grade than low-grade meningiomas (51.5% vs. 18.3%, p < 0.001), but it was not associated with radiation dose. Older age at irradiation correlated with longer latency (Spearman’s ρ = 0.405, p < 0.001). No association was observed between primary tumor category and WHO grade. Conclusions: RIMs demonstrate dose- and age-dependent biological behavior, with higher radiation doses and younger age at irradiation predisposing to earlier onset and increased aggressiveness. These findings suggest that long-term, dose-adapted radiological surveillance may warrant consideration in irradiated patients. Full article

Background: Glycated hemoglobin (HbA1c) is widely used for the diagnosis and monitoring of diabetes mellitus; however, its interpretation is largely based on fixed diagnostic thresholds. This study moves beyond describing a glycemic continuum by translating the non-linear HbA1c–microvascular relationship into an individualized risk estimation framework. Methods: In this cross-sectional observational study, adult subjects from a real-world clinical cohort were analyzed using HbA1c as a continuous variable. Associations between HbA1c and metabolic parameters were assessed using correlation analysis. Linear regression was applied to evaluate the relationship between HbA1c and cumulative diabetes-related complication burden. Non-linear associations between HbA1c and the risk of presenting at least one complication were explored using restricted cubic spline logistic regression models. Additional risk estimation analyses focused on the HbA1c gray zone (5.5–6.4%). Results: HbA1c showed a strong continuous association with fasting plasma glucose (ρ = 0.73, p < 0.001) and was positively associated with cumulative complication burden (β = 0.016 per 1% increase in HbA1c, p = 0.009). Non-linear modeling revealed a progressive increase in complication risk beginning below the diagnostic threshold for diabetes, with an inflection of the risk curve within the HbA1c gray zone. Individuals within this interval exhibited a higher prevalence and increased odds of presenting at least one complication compared with lower HbA1c values, although some estimates did not reach statistical significance. Conclusions: HbA1c acts as a continuous and non-linear marker of metabolic stress, with potentially biologically meaningful increases in complication risk emerging below traditional diagnostic thresholds. We demonstrate a non-linear acceleration of microvascular risk within the 5.5–6.4% interval, rather than a simple linear gradient. These findings support the concept of a glycemic risk continuum and highlight the clinical relevance of the HbA1c sub-diagnostic interval for early risk stratification and preventive strategies. Full article

Buildings account for a substantial share of global energy use, yet the adoption of advanced optimal control strategies remains limited due to high computational costs and the difficulty of safe deployment. This paper presents a fully Python-based, data-driven deep reinforcement learning (DRL) supervisory control framework that leverages gray box surrogate modeling and Imitation Learning to overcome these barriers. The novelty of this work lies in the integration of an ontology-based Twin4Build surrogate model with Imitation Learning and Deep Reinforcement Learning, enabling efficient training of building control policies in a low-cost environment before transfer to a high-fidelity BOPTEST emulator. Results demonstrate that the trade-off of using a lower-accuracy surrogate accelerates training by a factor of 11 compared to high-fidelity models. Furthermore, the RL agent successfully learned load-shifting and peak-shaving strategies, eliminating start-up power spikes and achieving energy savings of up to 28.9%. Beyond substantial energy reductions, this pipeline yields a calibrated digital twin suitable for ongoing building services like anomaly detection, presenting a scalable path for real-world smart building optimization. Full article

To address the issues of color cast, insufficient contrast, and detail loss in underwater optical images, this paper proposes an underwater image enhancement method based on multi-scale fusion and detail sharpening. The algorithm first applies an improved Gray World White Balance method with color compensation to perform color correction on the original underwater image. Subsequently, two processed images are generated for fusion: the first image is obtained by applying a Particle Swarm Optimization-enhanced Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm to the color-corrected image to enhance contrast; the second image is produced by applying an adaptive gamma correction algorithm to improve uneven illumination regions. These two images are then fused using a multi-scale fusion strategy. Finally, a weighted multi-scale detail sharpening technique is employed to further enhance the texture details of the fused image, yielding the final enhanced result. The performance of the proposed method is evaluated using no-reference underwater image quality metrics: the Underwater Image Quality Measure (UIQM) and the Patch-based Contrast Quality Index (PCQI), and tested on the open-source dataset from Nanyang Technological University. Experimental results demonstrate that the proposed method leads to an improvement in underwater image quality in both qualitative and quantitative assessments. Full article

Background: Artificial Intelligence as a Medical Device (AIaMD) introduces regulatory, methodological, ethical, and clinical challenges that are not fully addressed by traditional device trial frameworks. Given rapidly evolving and jurisdiction-specific guidance, a consolidated mapping of trial design expectations and regulatory requirements is needed. Objective: To map regulatory requirements and clinical trial design approaches for AIaMD across major jurisdictions and to identify key methodological and implementation gaps relevant to adaptive/continuously learning systems. Methods: A scoping review was conducted in accordance with the PRISMA-ScR reporting guideline. Peer-reviewed literature (2017–2025) was searched in PubMed, Embase, Web of Science, and the Cochrane Library. Gray literature was identified from major regulators and policy bodies (FDA, EMA, MHRA, PMDA, WHO, CDSCO). Eligible records addressed AIaMD clinical evaluation, trial design, regulatory pathways, post-market surveillance, or reporting standards. Data were charted using a predefined extraction framework and synthesized descriptively with thematic analysis across regulatory, methodological, ethical, and clinical implementation domains. Results: Included sources demonstrate substantial heterogeneity in evidence expectations and AI-specific pathways across jurisdictions. Recurrent themes include the need for predefined change management, performance monitoring and drift controls, dataset representativeness and bias evaluation, transparency and versioning, cybersecurity, and real-world evidence integration. Reporting frameworks (SPIRIT-AI, CONSORT-AI, MI-CLAIM) are frequently cited as mechanisms to improve reproducibility and regulatory readiness. Conclusions: Evidence and regulatory expectations for AIaMD remain fragmented. Harmonization of terminology, trial design principles, and post-market governance—supported by standardized reporting—would improve clinical validity, safety assurance, and scalability across regions. This review has several limitations. As a scoping synthesis, it prioritizes breadth of coverage rather than quantitative meta-analysis. Included sources vary in methodological rigor and reporting detail, and evolving regulatory guidance may change rapidly over time. Nevertheless, integrating peer-reviewed and regulatory evidence provides a comprehensive overview of current expectations and emerging gaps. In conclusion, effective evaluation of AIaMD requires a shift from static, one-time validation toward continuous lifecycle oversight that integrates adaptive trial designs, transparent reporting standards, bias surveillance, and structured post-market monitoring. Regulatory heterogeneity currently poses significant barriers to multinational development; however, coordinated adoption of standardized evidence frameworks and collaborative governance mechanisms may reduce duplication while preserving patient safety. By translating methodological principles into operational guidance, this review aims to support regulators, sponsors, and clinical investigators in designing trials that are both scientifically rigorous and practically implementable for continuously learning systems. Full article

Reliable energy forecasting is essential for the planning and dispatch of power and fuel systems; however, energy series are often short and exhibit pronounced nonlinearity. To tackle this small sample setting, we propose a gray random vector functional link (GRVFL) framework and further derive a kernelized variant (KGRVFL). In GRVFL, an RVFL network is integrated into gray system modeling, and the parameters are learned via sparsity-regularized regression, enabling stable and reproducible training without backpropagation or evolutionary optimization. Hyperparameters are tuned using Bayesian optimization driven by a Top-k mean absolute percentage error (Top-k MAPE) criterion to improve robustness. To further promote compactness, we introduce a fractional ratio-type Fr-$\ell 1$ penalty and solve the resulting problem efficiently using a fractional coordinate descent (FCD) algorithm. The proposed methods are assessed on six real-world energy datasets using eight evaluation metrics. Comparisons with nine gray model baselines and six machine learning forecasters demonstrate that the sparse KGRVFL (SKGRVFL) achieves higher predictive accuracy and improved training stability under small sample conditions. Full article

Background/Objectives: Ciltacabtagene autoleucel (cilta-cel) for relapsed/refractory multiple myeloma is typically administered inpatient (IP) to monitor for cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS). Because cilta-cel toxicities are typically delayed, outpatient (OP) administration (infusion and early monitoring) is being explored. We synthesized available evidence on OP and IP administration. Methods: MEDLINE, Embase, and Cochrane Library were searched from inception to 5 August 2025, supplemented by conference and gray literature searches. Eligible studies of adults with multiple myeloma receiving cilta-cel reported efficacy, safety, resource use, costs, and/or quality-of-life outcomes; findings were synthesized descriptively due to heterogeneity. Results: Seventy-four records (56 studies) were included; 90 patients received OP cilta-cel. OP clinical evidence (primarily three real-world studies) showed high response rates (ORR: 95%; median follow-up 4.6 months) and reported 1-year PFS and OS of 86% and 96%. In IP studies, median ORR was 91%, with median 1-year PFS 76% and median 1-year OS 85%. Any-grade CRS and ICANS occurred in 79–84% and 17–22% of OP patients (largely low grade); IP cohorts reported a median ICANS incidence of 17% (range 5–23%). Most OP patients were later hospitalized (86–93%), but stays were shorter (median 4–6.5 days) than in an IP cohort (median 19 days). Comparisons were unadjusted and may reflect selection differences. One modeling-based economic analysis estimated savings of ~$19,000 per OP-treated patient. Conclusions: OP cilta-cel appears feasible for selected patients and may reduce costs without compromising outcomes. Findings are descriptive and hypothesis-generating and prospective multicenter studies are needed to define long-term safety, durability, quality of life, and cost-effectiveness. Full article

Underwater images often suffer from color casts, insufficient contrast, and blurred details due to wavelength-dependent absorption and scattering, which limits their use in marine observation and underwater target recognition. Therefore, it remains challenging to jointly improve color fidelity, contrast, and detail visibility under varying water types and illumination. To address these issues, this paper proposes an underwater image enhancement method that integrates RGB Space color-cast correction with Lab Space luminance-chrominance synergistic enhancement. First, channel compensation is performed in the RGB space according to color-cast types, together with a Gray-World white-balance strategy, to suppress severe color bias and recover more natural tones. Subsequently, synergistic enhancement is conducted in the Lab space as follows: an improved Auto-MSR is applied on the L channel for contrast restoration while suppressing noise amplification, and an edge-aware detail enhancement module is incorporated to reinforce structural textures. Finally, chrominance balancing is applied to A and B channels to further improve color consistency and rendition. Experiments on the UIEB and UIQS datasets demonstrate that the proposed method achieves the best performance among the methods considered on AG, EI, and CCF. Specifically, it attains AG = 10.716, EI = 107.197, and CCF = 46.935 on UIEB, as well as AG = 10.136, EI = 103.780, and CCF = 39.433 on UIQS. These results indicate clear advantages in image clarity, edge detail preservation, and color rendition. Future work will focus on extending the proposed method to real-time underwater video enhancement. Full article

Underwater image enhancement remains challenging due to wavelength-dependent absorption, spatially varying scattering, and non-uniform illumination, which jointly cause severe color distortion, contrast degradation, and structural information loss. To address these issues, we propose UCS-Net, a degradation-aware dual-path framework that exploits the complementarity between global and local representations. A spatial color balance module first stabilizes the chromatic distribution of degraded inputs through a learnable gray-world-guided normalization, mitigating wavelength-induced color bias prior to feature extraction. The network then adopts a dual-branch architecture, where a hierarchical Swin Transformer branch models long-range contextual dependencies and global color relationships, while a multi-scale residual convolutional branch focuses on recovering local textures and structural details suppressed by scattering. Furthermore, a multi-scale attention fusion mechanism adaptively integrates features from both branches in a degradation-aware manner, enabling dynamic emphasis on global or local cues according to regional attenuation severity. A hue-preserving reconstruction module is finally employed to suppress color artifacts and ensure faithful color rendition. Extensive experiments on UIEB, EUVP, and UFO benchmarks demonstrate that UCS-Net consistently outperforms state-of-the-art methods in both full-reference and non-reference evaluations. Qualitative results further confirm its effectiveness in restoring fine structural details while maintaining globally consistent and visually realistic colors across diverse underwater scenes. Full article

Hospitals are among the most energy-intensive buildings, yet their heating systems often operate below optimal efficiency due to outdated controls and limited sensing. Existing facilities often provide only a few accessible measurement points, many of which are locked within proprietary master controllers and not integrated into the Building Automation System (BAS). To address these limitations, this study proposes a data-driven feature selection approach that supports the development of gray-box emulators for complex, real-world central heating plants. A year of operational and weather data from a large hospital was used to train multiple machine learning models to predict the heating demand and return water temperature of a hospital heating plant system. The model’s performance was evaluated under reduced-sensor conditions by intentionally removing unpredictable values such as the VFD speed and flow rate. XGBoost achieved the highest accuracy with full sensor data and maintained a strong performance when critical sensors were omitted. An explainability analysis using Shapley Additive Explanations (SHAP) is applied to interpret the models, revealing that outdoor temperature and time of day (as an occupancy proxy) are the dominant predictors of boiler load. The results demonstrate that, even under sparse instrumentation, an AI-driven digital twin of the heating plant can reliably capture system dynamics. Full article

To solve the limitations of single optimization algorithms, such as premature convergence, insufficient global exploration, and high susceptibility to local optima, a Hybrid Optimization Algorithm (HOA) based on multi-subpopulation collaboration and multi-strategy fusion is proposed. The HOA uses Logistic chaotic mapping for population initialization to enhance uniformity and diversity. The population is then divided into four subpopulations; each is optimized independently using different strategies, including the genetic algorithm (GA), Gray Wolf Optimizer (GWO), self-attention mechanism, and k-nearest neighbor graph (kNN). This design leverages the strengths of individual algorithms while mitigating their respective limitations. An elite information exchange mechanism facilitates knowledge transfer by randomly reassigning elite individuals across subpopulations at fixed iteration intervals. Additionally, global optimization strategies including differential evolution (DE), Simulated Annealing (SA), Local Search (LS), and time of arrival (TOA) position adjustment are integrated to balance exploration and exploitation, thereby enhancing convergence accuracy and the ability to escape local optima. Evaluated on the CEC2017 benchmark suite and real-world engineering problems, the HOA demonstrates superior performance in convergence speed, accuracy, and robustness compared to single-algorithm approaches—notably, HOA ranks 1st in 30-dimensional CEC2017 functions. By effectively integrating multiple optimization strategies, the HOA provides an effective and reliable solution to complex optimization challenges. Full article

Background and Objectives: Mobile Eye Units (MEUs) have emerged as practical innovations to overcome geographic, financial, and systemic obstacles to eye care. Although numerous programs operate across the United States and Canada, a narrative review describing their structure, implementation and services, remain limited. This narrative review examines various MEUs models in the United States and Canada, using real-world examples to highlight each model’s structure, services, populations served, and key benefits and limitations. Methods: We performed a narrative review of peer-reviewed and gray literature published from 1990 to August 2025, identifying mobile eye units in the United States and Canada. Programs were grouped into four operational models based on services, equipment, and implementation characteristics. Ophthalmology residency program websites in the United States were also reviewed to assess academic involvement in mobile outreach. Results: We identified four operational MEU models: Fully Equipped Mobile Units (FEMUs), Semi-Mobile Outreach Units (SMOUs), School-Based Vision Mobile Units (SBVMUs), and Hybrid Teleophthalmology Units (HTOUs). FEMUs provide comprehensive on-site diagnostic capabilities but require substantial financial and logistical resources. SMOUs are lower-cost and flexible but offer more limited diagnostics. SBVMUs facilitate early detection in children and reduce school-based access barriers but depend on school coordination. HTOUs expand specialist interpretation through remote imaging, although their success relies on reliable digital infrastructure. Across all models, follow-up and continuity of care remain major implementation challenges. Approximately 21% of U.S. ophthalmology residency programs publicly report involvement in mobile outreach. Conclusions: MEUs play a critical role in reducing geographic and structural barriers to eye care for underserved populations across United States and Canada. However, limited outcome reporting, particularly regarding follow-up rates and continuity of care, hinders broader assessment of their effectiveness. Strengthening the integration of MEUs with patient navigators, integrated electronic health record, insurance support and support of local health networks is essential for improving long-term sustainability and impact. Full article

Background/Objectives: Community paramedicine programs have existed since the early 2000s, and while resource optimization remains a predominant driver, innovation in recent years demonstrates that when community paramedicine is integrated into healthcare, it is well-positioned to support the needs of structurally marginalized communities by focusing services for those facing barriers to accessing equitable care. A recent scoping review described the evolving ways community paramedicine models are addressing health and social needs within communities around the world. We aimed to identify and explore existing community needs assessment tools in Canada to guide the initial development of a needs assessment tool for community paramedicine. Methods: We conducted a document analysis of existing community needs assessment resources to identify current tools or processes used to identify community needs, as well as determine gaps to address and support. Documents were collected for review via a targeted literature search of both published and gray sources, and direct document requests of community paramedicine service providers to review guides informing current service planning in Canada. We presented a draft of the tool to participants at a community paramedicine conference for their review and feedback, and we incorporated this feedback into the final version. Results: We reviewed 38 documents to identify and synthesize key elements within community health and social needs assessment tools and frameworks. Findings informed an interim Community Paramedicine Needs Assessment Tool (CPNAT) that the team presented to 112 community paramedicine experts and partners. We received 33 group responses of detailed feedback that we used to further refine and finalize the tool. Conclusions: The CPNAT can support enhanced health equity by guiding community paramedicine programs to better align services, policies, and funding with the health and social care needs of communities. Full article

In the Republic of Kazakhstan (one of the top 10 oil-producing countries in the world), the remediation of oil pollution found in unproductive soils under the conditions of a sharply continental arid climate is a highly relevant problem. The aims of this work are to study the biodegradation capacity of the gray soil of the ‘Daulet Asia’ landfill, assess the degradative potential of indigenous oil-degrading strains and changes in the composition of the soil microbial community. Analytical chemistry methods, distillation and chromatographic mass spectrometry were used for oil analysis; gravimetry and IR spectroscopy were used to evaluate oil degradation. Standard microbiological techniques were employed to isolate and cultivate microorganisms and metagenomic sequencing was carried out using Oxford Nanopore technology. Raw data processing and subsequent analysis were performed using modern software packages. Three isolated strains of interest were identified based on the analysis of 16S rRNA gene fragment sequences. The studied soil has low biodegradation capacity (oil loss was 6.2% on day 60), possibly due to the low abundance and weak activity of indigenous hydrocarbon-oxidizing microorganisms. The taxonomic composition of the microbiome in the studied soil suggests some potential for oil degradation. Assessment of the effectiveness of oil degradation by the indigenous microbiome indicates that this potential can be realized only marginally in situ. Isolated oil-degrading strains were identified as belonging to the Rhodococcus and Kocuria genera. Effective oil removal from the studied soil requires the introduction of active microorganisms (e.g., as part of biopreparations). Considering the characteristics of the hot arid climate, for bioremediation of contaminated sierozems of Southern Kazakhstan, it is advisable to use halotolerant oil-degrading microorganisms with a wide temperature range that are capable of degrading hydrocarbons under moisture deficiency. Full article