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This paper proposes a two-stage hybrid framework for biosignal quality validation that produces beat-level or segment-level labels for real-time filtering and offline dataset curation. The framework is quantitatively validated exclusively on ECG data. Its modular architecture is designed to extend to further non-stationary periodic biomedical time-series signals including photoplethysmography (PPG), impedance cardiography (ICG), phonocardiography (PCG), electromyography (EMG), and electroencephalography (EEG) through modality-specific parameter adaptation; however, this broader applicability currently reflects architectural extensibility rather than experimentally validated performance. A prerequisite is synchronized acquisition of the primary biosignal together with inertial motion sensing (IMU/accelerometer) and electrode impedance or lead-off status, with the IMU positioned near the sensing electrodes. The first stage performs sensor-integrity gating to reject intervals corrupted by motion or poor electrode contact. The second stage applies software signal quality indices to the remaining beats, including physiological plausibility constraints (R to R peaks analysis), DTW-based morphological consistency against adaptive templates, frequency domain SNR estimation, and baseline wander quantification. This study systematically evaluates and compares the classification performance of six complementary sensor-level and software-based signal quality assessment methods. When integrated within the proposed hybrid framework, validation against expert-annotated ECG quality labels from 20 healthy participants demonstrates high methodological classification accuracy (98.1%), achieving approximately a 98% F1-score, 99% sensitivity, and 97% specificity. Prospective validation on patient populations with cardiovascular pathology is identified as a necessary step toward clinical deployment. This modular approach improves the reliability of downstream analysis by preventing corrupted data from entering feature extraction and model training pipelines, enabling more stable physiological monitoring in free-living conditions, reducing false alarms in continuous monitoring applications, and generating higher-quality datasets for AI-based diagnostic systems.

Both the latent variable model and the network model have been widely used to conceptualize mental disorders. However, it has been pointed out that there is no clear dichotomy between the two models, and a combination of these two model could enable a better understanding of psychopathology. The recently proposed latent network model (LNM) has provided a statistical framework to enable this combination. Evidence has shown that posttraumatic stress disorder (PTSD) could be a suitable candidate disorder to study the combined model. In the current study, we initiated the first investigation of the latent network of PTSD symptoms. The latent network of DSM-5 PTSD symptoms was estimated in 1196 adult survivors of China's 2008 Wenchuan earthquake. Validation testing of the latent network was conducted in a replication sample of children and adolescent who experienced various trauma types. PTSD symptoms were measured by the PTSD Checklist for DSM-5 (PCL-5). The latent network was estimated using the seven-factor hybrid model of DSM-5 PTSD symptoms, analysed using the R package lvnet. The latent network model demonstrated good fit in both samples. A strong weighted edge between the intrusion and avoidance dimensions was identified (regularized partial correlation = 0.75). The externalizing behaviour dimension demonstrated the highest centrality in the latent network. This study is the first to investigate the latent network of DSM-5 PTSD symptoms. Results suggest that both latent symptom dimension and associations between the dimensions should be considered in future PTSD studies and clinical practices.

Current irrigation water use (IWU) estimation methods confront uncertainties warranting further attention, primarily stemming from constraints within model structure and data quality. This study proposes a hybrid framework that integrates multiple machine learning (ML) methods with theory‐guided strategies to calculate IWU for three principal cereal crops within the Chinese agricultural landscape. We generated high resolution time series data sets of evapotranspiration and surface soil moisture (SM) using remote sensing resources. ML techniques, along with the Bayesian three‐cornered hat ensemble, were employed to drive multiple remote sensing‐derived data sets in IWU calculation. We applied two theory‐guided mechanisms to quantify irrigation signals: first, converting original SM values into logarithmic terms, and second, extracting process‐based SM residuals. Proposed framework has been validated at 12 field stations across China, yielding coefficient of determination (R2) ranging from 0.54 to 0.70, and root mean square error (RMSE) spanning 278–335 mm/yr. Our framework demonstrates considerable strength in IWU estimation when compared to reported IWU values form 341 cities across China. Specifically, for rice, wheat, and maize, the R2 values range from 0.78 to 0.83, 0.68 to 0.76, and 0.53 to 0.64, respectively, with corresponding RMSE measuring 0.22–0.25, 0.10–0.12, and 0.11–0.13 km3/yr, respectively. These findings highlight the effectiveness of theory‐guided strategies in discerning irrigation‐related information, thereby improving overall model performance. Attention should be directed toward the uncertainties in evapotranspiration and precipitation products on model performance, which remained modest, with a relative change of less than 5%. Key Points Hybrid framework is developed to estimate irrigation water use (IWU) for three staple cereal crops in China Machine learning is employed to drive multiple remote sensing‐derived products for precise IWU estimation Proposed framework accurately estimates IWU and incorporates theory‐guided module to reveal implicit irrigation signal

Streamflow observations, essential for various water resource applications, are often unavailable at critical locations in need. Although different models have been proposed to enhance streamflow predictability at ungauged locations, the challenge extends beyond model fidelity. Differences in meteorologic forcing data sets, precipitation in particular, can significantly affect the accuracy of hydrologic predictions. This challenge intensifies across regions characterized by diverse hydro‐climatological and geographical conditions, such as in the conterminous US (CONUS) where a single precipitation product struggles to consistently replicate observed hydrographs, particularly peak flow dynamics. To enhance streamflow predictions, we utilize a VIC‐RAPID hydrologic modeling framework driven by multiple commonly used meteorological forcing data sets, such as Daymet, PRISM, ST4, AORC, and their hybrids and create multiple sets of 40‐year (1980–2019) hourly, daily, and monthly streamflow reanalysis, Dayflow Version 2, for 2.7 million river reaches across the CONUS. Most forcings lead to skillful streamflow performance, except for ST4 in the mountainous west, where severe radar blockage adversely affects the accuracy. The evaluation using over 6,000 hourly stream gauges shows that hourly AORC and ST4 lead to improved annual peak flow performance over Daymet—driven streamflow (Dayflow V1), particularly in smaller basins, highlighting the value of high temporal resolution forcings in hydrologic predictions. Compared with other benchmark data sets like National Water Model V3.0, AORC‐driven VIC‐RAPID exhibits improved regional streamflow performance, with comparable peak flow representation. We envision that multi‐forcing streamflow reanalysis data can inform regions in need of forcing data enhancement, diagnose hydrologic model performance, and benefit diverse water resource applications. Plain Language Summary Accurate prediction of streamflow is challenging in areas where direct observations are lacking. Though existing models aim to improve predictions at ungauged rivers, streamflow predictability is not dependent on the model alone. The quality of meteorological data sets, mainly related to precipitation, significantly influences hydrologic predictions. For regions like conterminous US with diverse hydro‐climatological and geographical conditions, a single forcing data set might not work well for all water resources applications. To overcome these challenges, we use a large‐scale hydrologic model driven by multiple widely used meteorological data sets to produce a 40‐year (1980–2019) high‐resolution streamflow reanalysis, Dayflow Version 2 (https://doi.org/10.13139/OLCF/2222888), for 2.7 million river reaches across the conterminous US. Most of these reaches demonstrate skillful streamflow performance with some regional patterns. The study shows that multi‐forcing streamflow reanalysis data can be valuable for enhancing forcing data in data‐scarce regions, evaluating hydrologic model performance, and supporting various water resource applications. Key Points CONUS‐wide high‐resolution streamflow reanalysis is presented for 1980–2019 across multiple forcings at 2.7 million river reaches Multiple forcings offer distinct advantages for various water resource applications The AORC forcing captures peak flow dynamics better, especially in smaller basins

The purpose of this study is to develop and validate a System Dynamics (SD) model that illustrates how Artificial Intelligence (AI), including generative AI, alters project lifecycle behavior under a hybrid agile–predictive governance approach. The study method uses SD model to operationalize the PMBOK performance domains as an interconnected system of stocks, flows, and feedback loops. These constructs and their interaction represent delivery progress, stakeholder engagement, team capacity, measurement accuracy, governance alignment, and uncertainty exposure. Planning effectiveness is treated as an emergent performance indicator arising from the interaction of the planning-related feedback structures. The proposed model embeds AI levers for planning, risk, measurement, stakeholder sensing, and team support. A calibrated baseline model representing conventional project dynamics was validated in two ways. First it was validated structurally against PMBOK guidance and the SD literature. Secondly, it was validated behaviorally against stylized project trajectories. The AI-augmented variant was then simulated under identical initial conditions to assess marginal effects. Across multiple scenarios, AI integration reduced peak uncertainty exposure by up to 33%. Also, the AI-augmented system showed reduced planning effort by 15%, and improved monitoring and risk sensing by accelerating feedback and reducing delays by 25%. AI also improved measurement accuracy trajectories and accelerated cumulative delivery while lowering volatility in work completion rates. Governance coherence and development approach alignment improved, while stakeholder engagement and team capacity showed smaller changes. The results demonstrate that AI primarily acts as an enabler that strengthens high-impact feedback loops in planning, monitoring, and risk sensing within a hybrid methodology. AI also delineates boundaries where managerial judgment and cultural change remain critical for effective framework validation.

The growing use of electrochemical batteries in renewable energy systems has intensified the need for storage architectures that can sustain power delivery while limiting transient electrical stress and voltage instability challenges. This study addresses the research gap in experimentally establishing a physically interpretable framework that links battery-centered hybrid storage behavior at the DC bus to AC-side inverter performance under load and source disturbances. A laboratory-scale renewable microgrid integrating photovoltaic and wind generation, programmable load variation, inverter-based AC delivery, and hybrid battery–supercapacitor storage is experimentally implemented and evaluated against a battery-only baseline, supported by a unified analytical framework that quantifies how transient buffering improvements propagate through the power conversion chain. The results show that the hybrid configuration reduces DC-bus voltage droop from about 1.1 V to 0.6 V under heavy-load transitions, and from approximately 0.85 V to 0.44 V during source-side variability (e.g., photovoltaic and wind turbine variations). The hybrid system also improves AC-side behavior, yielding unified stabilization indices of 103.03% for the root-mean-square voltage and 79.51% for the peak-to-peak voltage. These findings demonstrate that the experimentally implemented lab-scale renewable microgrid with hybrid battery–supercapacitor storage provides an effective pathway for improving battery-supported microgrid stability, waveform quality, and transient resilience.

AI‐Cinema is a hybrid neural‐symbolic framework addressing the critical challenge of preserving cultural authenticity in Arabic movie scenario generation. The framework integrates transformer‐based neural language models (AraT5‐base and AraGPT2‐medium) with symbolic reasoning encoded in OWL‐DL ontologies and SWRL rules to ensure linguistic fluency, narrative coherence, and cultural preservation. AI‐Cinema introduces a three‐tier architecture comprising a data layer, cultural embedding layer, and scenario generation layer. Central to its design is an attention‐based cultural embedding mechanism leveraging ArabicVerbNet (12,500 culturally annotated verbs) and ArabicNameNet (3653 names with regional annotations), complemented by a mathematically grounded harmony function that dynamically balances neural generation with symbolic constraints. In experiments on 2740 Arabic narratives, AI‐Cinema achieves a BLEU‐4 score of 32.76 (± 0.6), representing a 5.0% relative improvement over AraBERT‐Gen ( p < 0.01, paired bootstrap test) and a 27.5 percentage point gain in cultural preservation metric (CPM) compared with MARBERT‐Gen (82.3% vs. 54.8%). The framework maintains 92.3% dialectal accuracy across Modern Standard Arabic and six regional dialects, with explicit evaluation of code‐switching scenarios. Expert evaluations by 30 Arabic literature scholars demonstrate strong interannotator agreement (Fleiss’ κ = 0.78, p < 0.001), with 87% of evaluators rating generated narratives as culturally authentic (score ≥ 4 on a 5‐point scale). Current limitations include reduced performance on underrepresented dialects (Yemeni: 85.6% and Sudanese: 86.1%) and complex code‐switching scenarios (76.3% for 3+ dialects). The framework incorporates transparent labeling mechanisms for AI‐generated cultural content to address authenticity concerns. All resources are publicly available at https://github.com/Mossab82/AI-Cinema .

The desire to develop software with more and more functionalities to make human work easier pushes the industry towards developing various programming languages. The existence of the various programming languages in today's scenario raises the need for their evaluation. The motive of this research is the development of a deterministic decision support framework to solve the object-oriented programming (OOP) language's selection problem. In the present study, OOP language's selection problem is modeled as a multi-criteria decision-making, and a novel fuzzy-distance based approach is anticipated to solve the same. To demonstrate the working of developed framework, a case study consisting of the selection of seven programming languages is presented. The results of this study depict that Python is the most preferred language compared to other object-oriented programming languages. Selection of OOP languages helps to select the most appropriate language, which provides better opportunities in the business domain and will result in high success for engineering students.

In recent years there has been an increasing interest in the development of hybrid biofilm reactors, especially in the upgrading of existing WWTP that are no longer able to respect concentration limits. In fact, today's challenge is the achievement of a good aquatic state for the receiving water bodies according to the Water Framework Directive requirements, which indeed limit even more the continuous emissions, i.e. coming from WWTP. This paper presents the setting up of a mathematical model for the simulation of a hybrid MBBR system; the model calibration/validation has been carried out considering a field gathering campaign on an experimental pilot plant. The main goal is to gain insight about MBBR processes attempting to overcome main shortcomings in particular referring to the modelling aspects. The model is made up of two connected sub-models for the simulation of the suspended and attached biomass. The model is mainly based on the concepts of the activated sludge model No. 1 (ASM1) for the description of the biokinetic process both for the suspended and for the attached biomass. The results show a good agreement between predicted and observed values both for the attached and for the suspended biomass moreover they are encouraging for further researches.