Search Results (165)

Accurate representation of short-term reservoir water-level dynamics is essential for operational analysis and scenario-based assessment under prescribed inflow–outflow conditions. In many practical applications, physically based modelling is limited by incomplete process knowledge, unavailable boundary conditions, or insufficient temporal resolution of input data. This study presents a data-driven framework for hourly conditional simulation of reservoir water level based on a hybrid Conv1D–LSTM architecture. The model learns nonlinear relationships among hydraulic forcing, operational control, and system state from historical observations, and is evaluated in a recursive multi-step simulation (rollout) mode to reflect its intended use and capture error accumulation over time. A systematic analysis of input sequence length and activation function is performed to identify a robust model configuration. On the test set, the selected configuration ($L = 24$, GELU) achieved RMSE = 0.1057 m, MAE = 0.0881 m, and R² = 0.972 in rollout evaluation. The proposed framework is designed for scenario-based simulation rather than one-step deterministic forecasting, enabling rapid operational screening of alternative inflow–outflow regimes. Unlike many previous studies that emphasize one-step predictive accuracy, this work explicitly assesses model stability in recursive multi-step simulation, which is more relevant for reservoir scenario analysis. Full article

Accurate sex identification in reptiles with genotypic sex determination is essential for breeding management, veterinary care and evolutionary research, yet commonly used methods are often invasive, stressful or unreliable. This study aimed to evaluate a dosage-based quantitative PCR approach for molecular sex determination in caenophidian snakes, using naturally shed epidermal skin as a non-invasive DNA source. Genomic DNA extracted from shed skin was analysed by qPCR targeting conserved Z-linked genes (ADARB2, ARMC4 and TANC2), together with autosomal and reference genes, to assess sex-specific differences in gene copy number. Sixteen caenophidian snake species were examined, including taxa for which molecular sexing data are currently scarce or unavailable. The autosomal control gene showed dosage ratios close to parity between sexes, supporting DNA quality and reference gene reliability; meanwhile, Z-linked markers generally exhibited reduced dosage in females relative to males, consistent with a ZZ/ZW sex determination system. These results demonstrate that dosage-based qPCR applied to shed epidermal skin provides a promising and non-invasive framework for molecular sex determination in caenophidian snakes, without compromising animal welfare. Full article

Background/Objectives: Impella support is increasingly utilized as a crucial bridge to durable left ventricular assist device (LVAD) in patients with refractory cardiogenic shock. However, the transvalvular path of the Impella catheter raises concerns regarding mechanical trauma, potentially precipitating or accelerating aortic regurgitation (AR). We aimed to characterize the complete longitudinal trajectory of AR following Impella bridge-to-LVAD and to determine its association with clinical and hemodynamic sequelae. Methods: We conducted a single-center retrospective cohort study including all patients bridged from Impella to durable LVAD between 2013 and 2024 (n = 19). At Impella initiation, all patients met the retrospective SCAI shock stage D or worse criteria. At LVAD implantation, all patients were classified as INTERMACS 1–2 (INTERMACS 2, n = 13). The Impella models were 5.0 in 11 (axillary access), 2.5 in 5 (femoral access), and CP in 3 (femoral access); no periprocedural Impella complications were recorded. The implanted LVAD systems were HeartMate II (n = 7), HVAD (n = 3), and HeartMate III (n = 9). Patients undergoing concomitant aortic valve intervention were excluded. Transthoracic/TEE echocardiography was performed at prespecified time points (pre-Impella, pre-LVAD, post-LVAD discharge, 12 months, and 24 months) with standardized aortic regurgitation (AR) grading. Right ventricular (RV) function was assessed qualitatively when quantitative indices (TAPSE) were unavailable. Primary endpoints were new or progressive AR and AR severity at LVAD implantation. Secondary endpoints included survival, renal dysfunction, biomarkers, and rehospitalization. Univariate analyses were used to compare outcomes according to AR severity. Results: Nineteen patients (68% male, median age 57 years, IQR 47–60) underwent Impella support for 13.3 ± 9.9 days before HeartMate 3 (84%) or HVAD (16%) implantation. All patients had competent aortic valves (grade 0 AR) at the time of LVAD implantation. AR ≥ mild developed in 9/18 (50%) at discharge, 12/15 (80%) at 12 months, and 13/15 (87%) at 24 months, and 8/15 (53%) progressed to ≥ moderate AR by 24 months. Patients with moderate-to-severe AR had higher NT-proBNP levels at 12 months (median 6318 vs. 2336 pg/mL, p = 0.137). Thirty-day and 24-month survival rates were 95% and 79%, respectively. Conclusions: Aortic regurgitation frequently develops or progresses from the pre-LVAD period to follow-up in patients bridged from Impella to durable LVAD. Although limited by a small sample size and incomplete quantitative RV metrics, these observations support structured echocardiographic surveillance after Impella use and management strategies—routine valve inspection at LVAD implantation and post-LVAD speed/blood pressure targets that encourage aortic valve opening—to mitigate the risk and clinical impact of aortic regurgitation. Full article

Detecting violent content in audio recordings is crucial for public safety, autonomous surveillance, and content moderation, particularly when visual cues are unreliable or unavailable. A resource-aware two-stage cascade system is proposed for acoustic violence detection that combines a lightweight Least Squares Linear Detector (LSLD) as a first-stage screener with a trimmed version of YAMNet as a second-stage classifier. A percentile-based forwarding rule controls the fraction of segments routed to the deep stage, turning the accuracy–cost trade-off into an explicit operating parameter for always-on deployment. The approach is evaluated on a publicly released dataset of real-world violent audio augmented with background noise and artificial reverberation. The results in the low-false-alarm regime show that the proposed cascade preserves performance close to a Stage 2-only baseline while substantially reducing average deep-inference workload. An ablation study validates the role of the LSLD as an inexpensive pre-filter, and robustness is assessed under clean, reverberant, and 12 dB noise conditions. Finally, an analytic energy consumption model is provided, which links computational workload to daily energy demand and photovoltaic sizing on ultra-low-power hardware, supporting sustainable off-grid deployment. Full article

Geographical exclaves face distinctive development challenges as spatial separation creates cross-border dependencies and institutional vulnerabilities. Musandam Governorate, Oman’s exclave separated from the mainland by United Arab Emirates (UAE) territory, exemplifies how exclave status shapes development trajectories, cross-border interactions, and population resilience. This study examines Musandam’s socio-economic dynamics, development patterns, and cross-border relationships, addressing gaps in understanding how exclave residents navigate spatial discontinuity while maintaining mainland and cross-border connections. Mixed methods combined quantitative assessment using the adapted Vera Carstairs Index (VCI) across seven domains (education, skills, employment, housing, living environment, household facilities, health) with qualitative fieldwork spanning four campaigns (2019–2023). Semi-structured interviews with 47 residents across all four wilayaat (provinces), complemented by citizen science approaches engaging twelve community participants, documented mobility patterns and cross-border transactions. Secondary data from the 2010 Population Census and national statistics provided contextual depth. Findings reveal two of four Musandam wilayaat (Daba and Khasab) ranking in the lower half nationally, with low health scores (ranks 1 and 9) and education institution deficits reflecting structural integration into transnational economic and services systems. COVID-19 border closures amplified pre-existing dependencies, converting eight-month isolation into a humanitarian crisis with food shortages, medicine unavailability, and social fragmentation. Residents maintain stronger functional connections with UAE cities than with mainland Oman despite preserving national identity. Policy implications emphasize six strategic priorities: higher education institutions, transportation infrastructure, marine fisheries development, tourism enhancement, small-medium enterprise facilitation, and residential land provision. Full article

This paper presents a simulation-based methodology for evaluating maintainable asset criticality in production systems modelled as complex repairable flow networks (CRFNs). The proposed Flow-Based Asset Criticality Evaluation Methodology (FACE) adopts a consequence-based perspective, assessing criticality according to network-level economic impact rather than probability-weighted risk. FACE introduces two profitability-oriented metrics, the Minimum Consequence of Failure (MCoF) at the maintainable item (MI) and failure mode (FM) levels, computed using multilayered network simulation integrating topology, capacity, failure behaviour, and profitability-driven flow allocation. By directly linking asset unavailability to system-wide gross profitability, the methodology enables objective, data-driven criticality assessment without reliance on subjective inputs, such as guided scoring processes. The approach supports both strategic and operational maintenance decisions by identifying assets and failure modes most consequential to production throughput and profitability. Full article

The integration of artificial intelligence into criminal sentencing decisions represents one of the most consequential applications of algorithmic systems in contemporary governance. While AI-assisted risk assessment tools promise enhanced consistency and predictive accuracy, their deployment in judicial contexts raises profound concerns regarding transparency, due process, and fundamental rights. This paper proposes a comprehensive framework for AI-assisted sentencing modeling that embeds explainability as a foundational constraint rather than an afterthought. Drawing upon the landmark State v. Loomis decision, empirical analyses of the COMPAS algorithm, and emerging regulatory frameworks including the European Union Artificial Intelligence Act, we examine the tension between predictive performance and interpretive transparency. Our framework integrates a three-layer explanation architecture: inherent interpretability through generalized additive models (GA²Ms) providing transparent global structure, exact local feature attribution derived directly from the additive model decomposition without approximation, and counterfactual reasoning that identifies minimal input changes altering risk classifications. We demonstrate through rigorous experimental validation on the ProPublica COMPAS dataset (n = 6172) that explainability-constrained models achieve comparable predictive validity to opaque alternatives (AUC 0.71 versus 0.70–0.72 for black-box methods) while satisfying constitutional due process requirements and emerging regulatory mandates under the EU Artificial Intelligence Act. The impossibility theorems governing algorithmic fairness are examined in light of their implications for sentencing equity, and we propose that transparent model architectures enable targeted interventions unavailable when decision logic remains concealed. The paper concludes with policy guidance for jurisdictions seeking to implement AI-assisted sentencing systems that balance public safety objectives with procedural fairness and individual rights. Full article

Disputes over delay assessments are costly, persistent, prevalent worldwide, often funded by taxpayers, and negatively impact productivity in the construction sector. The identified academic literature argues that the main causes of the escalation of disagreements over delay assessments from contract claims into disputes (or factors) are objective factors, particularly unavailability and/or inadequacy of relevant project data. However, those findings are not based on comprehensive investigations of all factors involved, employing research methodologies that rely upon real-life project data. This article contributes to the fulfilment of the aforementioned knowledge gap. Published literature and twenty-one case studies were evaluated to identify the factors. The research findings revealed that although data-related issues were often important factors, they were not the main and/or most frequently identified ones. Subjective factors, including manipulation of programme activity completion dates, reliance on biased assumptions when data is unavailable, misinterpretation of material records, and self-serving delay analysis, were the main factors. The findings suggest that the root cause of this issue is the exploitation of systemic flaws, including the unavailability of good/best practice guidance on assessing the impact of delays, deficient contract provisions, inadequate impartiality, divergent priority of interests, unexploited technologies, and the confidential nature of dispute resolution methods. Full article

Battery-electric residential storage systems can increase the self-consumption of photovoltaic (PV) generation; however, economical sizing typically requires a high-resolution time series of PV production and household load behind the meter. In practice, such data are often unavailable. This work therefore presents a simulation model for determining the economically optimal residential storage capacity based exclusively on smart-meter data at the point of common coupling (PCC), i.e., hourly import and export time series. Economic performance is assessed using net present value (NPV) over a multi-year evaluation horizon. In addition, technical constraints (SoC limits, power limits, charging/discharging efficiencies) as well as capacity degradation are considered via a semi-empirical aging model. For validation, a reproducible reference scenario is constructed using PVGIS generation data and the standard load profile H23, enabling a direct comparison between the conventional approach (consumption/generation) and the PCC approach (import/export). The results show that the capacity optimum can be reproduced consistently using PCC data, even under smart-meter-like integer kWh quantization. At the same time, large parts of the investigated parameter space indicate that, under the assumed scenarios, foregoing a storage system is often not economically sensible. Sensitivity analyses further highlight the strong impact of load shifting, in particular due to the charging time of electric vehicles. A case study using real PCC measurement data, together with a two-week-window analysis, demonstrates practical applicability and robustness under limited measurement durations. Full article

Reverse engineering (RE) is essential in the automotive and aerospace industries for reconstructing high-precision components, such as exhaust valves, when design documentation is unavailable. However, different measurement methods introduce varied errors that can affect engine performance and safety. This study presents a comparative analysis of contact and optical measurement systems—specifically the CMM Accura II (ZEISS Group, Oberkochen, Germany), Mahr MarSurf XC 20 (Esslingen am Neckar, Germany), GOM Scan 1 (ZEISS/GOM, Braunschweig/Oberkochen, Germany) and MCA-II with an MMD×100 laser head (Nikon Metrology, Leuven, Belgium)—to assess their accuracy in reconstructing exhaust valve geometry. The research procedure involved measuring global surface deviations and critical functional parameters, including stem diameter, straightness, and seat angle. The results indicate that tactile methods (CMM and Mahr) provide significantly higher accuracy and lower dispersion than optical methods. The Mahr system was the most effective for stem precision, while the CMM was the only system to pass the seat angle tolerance requirement unambiguously. In contrast, the MCA-II laser system failed to meet the required precision–mechanical tolerances. The findings suggest that an optimal industrial strategy should adopt a hybrid methodology: utilizing rapid optical scanning (GOM) for general geometry and high-precision tactile systems (CMM, Mahr) for critical functional features. This approach can reduce total inspection time by 30–40% while ensuring technical safety and preventing catastrophic engine failures. Full article

Background/Objectives: Unequal access to cervical cancer screening and treatment remains a significant contributor to preventable morbidity and mortality for women in the Amazon Basin, compounded by geographic, social and infrastructural barriers. Given the fragmented nature of the existing evidence, this systematic review aims to synthesize available findings on barriers to cervical cancer screening and treatment for this region. The implications of these findings are examined to inform the development of actionable strategies to improve equity in prevention and care. Methods: This systematic review was conducted in accordance with PRISMA 2020. Searches were conducted on 7 November 2025, in PubMed/MEDLINE, Web of Science, and SciELO, utilizing combinations of MeSH terms, keywords, and free-text expressions. Studies were considered eligible if they addressed barriers to cervical cancer screening or treatment among women living in the Amazon Region. Two reviewers independently screened the studies and extracted the relevant data. The risk of bias was assessed using the JBI checklists, the Newcastle–Ottawa Scale, and the MMAT. A narrative synthesis summarized the results. Results: Of 57 studies identified, 11 were included. Organizational and health-system barriers were reported most frequently, including scheduling difficulties, long wait times, a shortage of professionals, and equipment unavailability. Socioeconomic barriers were most often related to younger age, low income, limited schooling, and care related expenses. Cultural factors were frequently linked to fear of the procedure and insufficient knowledge about cervical cancer. Geographic barriers included rural residence and travel difficulties. Conclusions: This systematic review indicates that disparities in cervical cancer screening in the Amazon region are primarily associated with organizational and health-system-related barriers, together with socioeconomic, cultural, and geographic factors. These findings highlight the need for equitable, multisectoral interventions to strengthen service organization, improve health literacy, and expand timely access to screening and treatment for underserved women. Full article

In this scientific article, a quantitative assessment is carried out of the influence of the ERTMS modernisation factor on the practical efficiency of operation and resilience of the Belgian railway lines 50A/51A with the application of methodological triangulation in the MATLAB R2025a Update 1 (25.1.0.2973910) software environment (discrete-event modelling, Petri nets, Markov reliability modelling, and correlation analysis). The modelling reveals that the scenario with an expanded level of automation increases the capacity from 18.3 to 26.0 trains over 2 h (+42.1%) and reduces the average waiting time from 1.53 min (baseline level) to 0.21 min—virtually the theoretical lower bound of zero under favourable conditions. The results of the block-occupancy analysis by means of Petri nets show that a more dynamic distribution of blocks provides higher capacity, and Markov chains reflect the reduction of the impact of control centre unavailability in developing communications and virtualisations. Spearman correlation analysis additionally shows coordinated improvement of the metrics of safety, digital protection, resilience, and performance. Relying on the modelling results, a phased roadmap is proposed, combining technical improvements (development of communication systems, readiness for automation, comparable management of rolling stock movement) with compliance with regulatory requirements and the goals of sustainable development, related to SDGs 9, 11, and 13. Full article

Renewable energy sources account for approximately one-quarter of the total electric power generating capacity in the United States. These sources increase system complexity, with potential negative impacts caused by their inherent variability. A microgrid, a decentralized local grid, offers an excellent solution for integrating these sources into the system’s generation mix in a cost-effective and efficient manner. This paper presents a comprehensive fault tree analysis for the reliability assessment of microgrids, ensuring their safe operation. In this work, fault tree analysis of a microgrid in grid-tied mode with solar, wind, and battery energy storage systems is performed, and the results are reported. The analyses and calculations are performed using the Relyence software suite. The fault tree analysis was performed using various calculation methods, including exact (conventional fault tree analysis), simulation (Monte Carlo simulation), cut-set summation, Esary–Proschan, and cross-product. Once these analyses were completed, the results were compared with the ‘exact’ method as the base case. Critical risk measures, such as unavailability, conditional failure intensity, failure frequency, mean unavailability, number of failures, and minimal cut-sets, were documented and compared. Importance measures, such as marginal or Birnbaum, criticality, diagnostic, risk achievement, and risk reduction worth, were also computed and tabulated. Details of all cut-sets and the probability of failure are presented. The calculated importance measures would help microgrid operators focus on events that yield the greatest system improvements and maintain an acceptable range of risk levels to ensure safe operation and improved system reliability. Full article

In the Arctic region, the navigation and positioning accuracy of shipborne and autonomous underwater vehicle (AUV) integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) solutions is severely degraded due to poor satellite geometry, frequent ionospheric disturbances, non-Gaussian measurement noise, and strong multipath effects, as well as long-term INS-based dead-reckoning for AUVs when GNSS is unavailable underwater. In addition, the sparse ground-based augmentation infrastructure and the lack of reliable reference trajectories and dedicated test ranges in polar waters hinder the validation and performance assessment of existing marine navigation systems, further complicating the achievement of accurate and reliable navigation in this region. To improve the positioning accuracy of the GNSS/INS shipborne navigation system, this paper adopts a tightly coupled GNSS/INS navigation approach. To further enhance the accuracy and robustness of tightly coupled GNSS/INS positioning, this paper proposes an improved Adaptive Robust Extended Kalman Filter (IAREKF) algorithm to effectively suppress the effects of gross errors and non-Gaussian noise, thereby significantly enhancing the system’s robustness and positioning accuracy. First, the residuals and Mahalanobis distance are calculated using the Adaptive Robust Extended Kalman Filter (AREKF), and the chi-square test is used to assess the anomalies of the observations. Subsequently, the observation noise covariance matrix is dynamically adjusted to improve the filter’s anti-interference capability in the complex Arctic environment. However, the state estimation accuracy of AREKF is still affected by GNSS signal degradation, leading to a decrease in navigation and positioning accuracy. To further improve the robustness and positioning accuracy of the filter, this paper introduces a sliding window mechanism, which dynamically adjusts the observation noise covariance matrix using historical residual information, thereby effectively improving the system’s stability in harsh environments. Field experiments conducted on an Arctic survey vessel demonstrate that the proposed improved adaptive robust extended Kalman filter significantly enhances the robustness and accuracy of Arctic integrated navigation. In the Arctic voyages at latitudes 80.3° and 85.7°, compared to the Loosely coupled EKF, the proposed method reduced the horizontal root mean square error by 61.78% and 21.7%, respectively. Full article

Movement artifact direction and magnitude are critical parameters in noise detection and image analysis, especially for single-frame images where temporal information is unavailable. This paper introduces the Movement Artifact Direction Estimation (MADE) algorithm, a signal processing-based approach that performs 3D geometric analysis to estimate both the direction (in degrees) and weighted quantity (in pixels) of movement artifacts. Motivated by computational challenges in medical image quality assessment systems such as LUIAS, this work investigates directional multiplicative noise characterization using controlled experimental conditions with optical camera imaging. The MADE algorithm operates on multi-directional quantification outputs from a preprocessing pipeline—MAPE, ROPE, and MAQ. The methodology is designed for computational efficiency and instantaneous processing, providing interpretable outputs. Experimental results using precision-controlled apparatus demonstrate robust estimation of movement artifact direction and magnitude across a range of image shapes and velocities, with principal outputs aligning closely to ground truth parameters. The proposed MADE algorithm offers a methodological proof of concept for movement artifact analysis in single-frame images, emphasizing both directional accuracy and quantitative assessment under controlled imaging conditions. Full article