Search Results (9,703)

Precision Agriculture (PA) is increasingly applied to nutraceutical cropping systems, where agronomic productivity must be integrated with the stabilization of phytochemical quality and environmental sustainability. This structured narrative review synthesizes scientific evidence (primarily 2010–2025) on the use of Unmanned Aerial Vehicle (UAV)-based multispectral and thermal sensing, LiDAR-derived canopy characterization, Internet of Things (IoT) monitoring, and artificial intelligence (AI)-driven analytics in medicinal, aromatic, and functional crops. The literature indicates that PA enhances high-resolution monitoring of crop–environment interactions, supporting site-specific irrigation, nutrient management, and stress detection. Under validated conditions, these interventions are associated with improved yield stability, resource-use efficiency, and modulation of secondary metabolite accumulation. However, reported outcomes vary substantially across species, agroecological contexts, and experimental scales, and most studies remain plot-scale or pilot-scale, limiting large-scale generalization. Moringa oleifera Lam. is examined as a model species for Mediterranean and semi-arid systems. Evidence suggests that integrated spectral, structural, and environmental monitoring can support optimized irrigation scheduling, canopy uniformity, and phytochemical consistency. Nonetheless, genotype-specific calibration, multi-season validation, standardized metabolomic benchmarking, and cross-regional transferability remain significant research gaps. Overall, PA represents a scientifically promising but still maturing framework for nutraceutical agriculture. Future progress will require rigorous multi-site validation, improved model robustness, standardized sustainability metrics, and comprehensive economic assessments to ensure scalability and long-term impact. Full article

Background: Left ventriculo-arterial coupling (VAC) integrates the interaction between left ventricular contractility and the arterial system, representing a key determinant of cardiovascular efficiency. In rapidly changing hemodynamic states such as septic or cardiogenic shock, conventional indices of pressure or flow alone may be misleading. VAC provides a unified physiological framework to assess global cardiovascular performance and guide therapy. Objective: To review the physiological foundations, bedside assessment, and therapeutic applications of VAC in critically ill patients with rapidly fluctuating circulatory conditions. Methods and Content: The article revisits the underlying principles of VAC, expressed as the ratio between arterial elastance (Ea) and end-systolic elastance (Ees), and discusses their derivation from the pressure–volume relationship. Practical echocardiographic methods for bedside estimation, including the non-invasive single-beat approach, are outlined with illustrative figures. The review further examines how VAC patterns evolve in sepsis, cardiogenic shock, and heart failure and how this integrative index clarifies paradoxical responses to vasoactive and inotropic therapies. Specific therapeutic phenotypes are proposed according to Ea/Ees profiles, providing a structured approach to optimise coupling and restore circulatory efficiency. Summary: VAC offers a physiology-based perspective on cardiovascular performance, enabling clinicians to interpret complex hemodynamic changes beyond traditional measures of ejection fraction or mean arterial pressure. Its dynamic tracking may refine the assessment of therapeutic trajectories and improve bedside decision-making. Conclusions: By integrating ventricular and arterial function into a single measure, VAC bridges cardiovascular physiology and clinical practice. Its incorporation into routine critical care monitoring could enhance individualised hemodynamic management and serve as a foundation for future outcome-driven studies. Methodology: This narrative review was conducted using a structured literature search to ensure comprehensive coverage of contemporary evidence regarding ventriculo-arterial coupling (VAC) in critical care and shock states. A systematic search of PubMed/MEDLINE, Embase, and Scopus databases was performed from database inception through October 2025. The following key search terms were used: “ventriculo-arterial coupling”; “arterial elastance”; “end-systolic elastance”; “Ea/Ees”; “pressure–volume loops”; “septic shock”; “cardiogenic shock”; “critical care echocardiography”; “point-of-care ultrasound”; “mechanical circulatory support”. Reference lists of relevant articles, review papers, and consensus documents were also manually screened to identify additional pertinent studies. Only English-language publications were included. Both seminal foundational studies and recent contemporary investigations were reviewed to provide historical context and up-to-date clinical applicability. Full article

Quantum computing offers potential computational advantages, yet its integration into autonomous decision-making systems remains largely unexplored. This paper addresses the need for a unified framework that systematically combines quantum computation with agent-based artificial intelligence. We examine how quantum technologies can enhance the capabilities of autonomous agents and, conversely, how agentic AI can support the advancement of quantum systems. We analyze both directions of this synergy and present conceptual and technical foundations for future quantum–agentic platforms. Our work introduces a formal definition of quantum agents and outlines architectures that integrate quantum computing with agent-based systems. As concrete proof-of-concept implementations, we develop and evaluate three quantum agent prototypes: (i) a Grover-based decision agent for quantum search-driven action selection, (ii) a variational quantum reinforcement learning agent for adaptive policy learning in a multi-armed bandit setting, and (iii) an adaptive quantum image encryption agent that autonomously selects encryption strategies based on entropy-driven feedback. These prototypes demonstrate practical realizations of quantum agency in decision-making, learning, and security contexts under NISQ-era constraints. Furthermore, we discuss application domains including quantum-enhanced optimization, hybrid quantum–classical orchestration, autonomous quantum workflow management, and secure quantum information processing. By bridging these fields, we introduce a structured theoretical and architectural framework for quantum–agentic systems, providing formal definitions, system models, and early operational prototypes that illustrate the feasibility of quantum-enhanced agency under NISQ constraints. Full article

This study assesses the extent to which an AI-driven circular waste management tool, previously developed by the same authors as a decision-support system for the circular management of healthcare waste in compliance with international guidelines, reflects the operational needs and perceived priorities of healthcare professionals and environmental managers. Within a context characterised by high regulatory complexity and increasing pressure toward more sustainable management models, the research adopts a qualitative approach based on the thematic analysis of 11 semi-structured interviews, followed by a systematic mapping of the emergent themes onto the tool’s thematic areas, indicators, and operational actions. The results demonstrate a high degree of alignment between the tool and operational practice, with 93% of the tool’s actions supported by empirical evidence and the emergence of a shared core cluster focused on hard-to-manage waste streams, mandatory training, and day-to-day operational challenges. The alignment between the priorities expressed by interviewees and the importance scores generated by the computational model is high for actions of greater relevance, while it decreases for less frequent actions that are more context-dependent. Circular economy practices are recognised as relevant but remain predominantly positioned at a strategic rather than an operational level. Overall, the study confirms the conceptual robustness of the tool and identifies its main limitations and the conditions required for its integration into hospital workflows. Full article

The purpose of this study is to examine whether analyst-level credit can be assigned quantitatively in a lightweight human-feedback decision-support pipeline. In intelligence and national security workflows, analysts often provide edits, comments, and evaluative feedback during the production of analytic products, yet these intermediate contributions are usually discarded, leaving no auditable record of how individual feedback shaped the final output. To address this problem, this study proposes a proof-of-concept Analyst-of-Record framework that combines synthetic analyst feedback, a linear ridge reward model, first-order influence functions, and additive Shapley aggregation to estimate both feedback-item and analyst-level contribution scores. The research design uses the Fact Extraction and VERification (FEVER) fact-verification dataset under controlled experimental settings. The pipeline retrieves evidence with Best Matching 25 (BM25), generates a grounded template-based response, derives three synthetic analyst feedback channels from FEVER annotations, trains a reward model on simple claim–answer and analyst-identity features, and aggregates per-feedback influence scores into an Analyst Contribution Index (ACI). The main experiments are conducted on a 500-claim subset across five random seeds, with additional ablation and bootstrap analyses used to assess sensitivity and stability. The findings show that the reward model achieves a mean validation $R^2$ of $0.801\pm 0.037$, indicating that the synthetic feedback signals are learnable under the selected featureization. The analyst-level contribution scores remain stable across random seeds, with approximately half of the total influence magnitude attributed to the explanation-quality channel and the remainder split across the other two channels. Ablation results further show that removing the explanation-quality channel collapses validation fit, while bootstrap resampling demonstrates tight concentration of absolute ACI magnitudes. Theoretically, this study extends attribution research beyond document-only grounding by showing how analyst feedback itself can be modeled as an object of contribution analysis. It also demonstrates that influence functions and Shapley-style aggregation can be adapted into a tractable framework for estimating interpretable analyst-level credit in a reproducible experimental setting. Practically, the proposed framework offers an initial foundation for more traceable and accountable decision-support workflows in which intermediate analyst contributions can be preserved rather than lost. The results also provide a feasible implementation path for future systems that incorporate stronger generators, richer evidence representations, and real analyst annotations. Full article

Background: Congenital scoliosis (CS) associated with multiple vertebral anomalies (MVAs) represents a biologically dynamic deformity in which cumulative segmental asymmetry, residual growth potential, and mechanobiological modulation interact to drive progression. Unlike isolated congenital lesions, MVAs exhibit growth-dependent and configuration-specific behavior, complicating risk stratification and timing of intervention. Despite extensive literature on congenital deformities, an integrated growth-oriented decision framework for this subgroup remains lacking. Methods: This narrative review synthesizes embryological, biomechanical, and clinical evidence related to vertebral growth potential, anomaly configuration, progression patterns, and age-dependent treatment strategies in CS with MVAs. A structured literature search of major databases was performed, and findings were analyzed thematically to propose a biologically grounded growth-based decision framework. Results: Across the literature, three interdependent determinants of progression consistently emerge: anomaly configuration, residual segmental growth capacity, and mechanobiological amplification during growth. High-risk configurations—particularly mixed formation–segmentation defects and fully segmented hemivertebrae with contralateral growth arrest—demonstrate rapid and often non-linear progression. Thoracic involvement further modifies clinical urgency due to its impact on pulmonary development. Integration of developmental biology and mechanobiological principles supports a structured, growth-informed approach to surveillance and intervention timing. Conclusions: MVAs should be conceptualized as dynamic growth systems rather than static structural defects. A shift from angle-driven to growth-informed decision-making may enhance early identification of high-risk patterns while minimizing unnecessary premature fusion in lower-risk cases. Adoption of a structured growth-based framework provides a biologically coherent foundation for individualized management and long-term optimization of spinal and thoracic development. Full article

This paper proposes a SIM-compatible hardware coordination architecture for secure radio-frequency (RF)-triggered activation in mobile devices. The proposed concept functions as a passive coordination layer rather than as an additional wireless transceiver, enabling controlled interaction between external low-frequency RFID or high-frequency NFC fields and wireless subsystems already available in the host device. The architecture assumes a flexible nano-SIM-compatible form factor integrating passive RF detection structures, a trusted decision component, and a trigger-generation interface aligned with standard SIM/UICC electrical and logical interaction models. Upon detection of an external electromagnetic field, the coordination layer evaluates predefined authorization conditions and produces a controlled trigger event intended to propagate through existing telephony and system-service pathways. In contrast to architectures that embed active wireless transmitters, the proposed approach seeks to minimize hardware redundancy and reduce potential attack surfaces by relying on the host device’s native Bluetooth Low Energy (BLE) capabilities. Rather than directly controlling wireless modules, the interface operates as a hardware-originated coordination mechanism that may support low-power and context-aware activation scenarios in mobile and embedded environments. This paper focuses on the architectural model, system assumptions, security rationale, and implementation constraints of such a SIM-compatible interface. Particular attention is given to integration considerations related to smartphone baseband architectures, operating-system mediation, and secure-element isolation. The presented concept establishes a foundation for future prototype implementation and platform-specific validation of SIM-compatible RF-triggered coordination mechanisms. Full article

Background/Objectives: Young adulthood is a transitional period associated with changes in lifestyle and preventive dental attendance, which may influence caries risk. In routine practice, the use of comprehensive caries risk assessment systems is often limited by time and diagnostic requirements, highlighting the need for simplified diagnostic screening tools. This study aimed to develop and clinically validate a simplified, questionnaire-based caries risk assessment tool derived from the CAMBRA framework for use in young adults. Methods: An observational cross-sectional study was conducted among 246 Romanian young adults aged 18–25 years. The instrument was designed to enable rapid caries risk stratification based exclusively on questionnaire data, without radiographic or laboratory investigations. Internal consistency, test–retest reliability, and construct validity were evaluated by comparison with clinically recorded indicators, including DMFT values, early enamel changes, visible dental plaque, and active carious lesions. Results: The questionnaire showed acceptable internal consistency (Cronbach’s alpha = 0.71) and good temporal stability (ICC = 0.82). Higher caries risk categories were consistently associated with unfavorable clinical findings, including increased DMFT values, a higher prevalence of early enamel changes, greater plaque accumulation, and more frequent active caries (p < 0.01). Conclusions: The simplified CAMBRA-based questionnaire demonstrated satisfactory reliability and clinical relevance in young adults. It may serve as a practical diagnostic screening and decision-support tool for risk-based caries prevention in routine and community dental settings. Full article

Preeclampsia is a double-hit vascular disorder centred on the VEGF-HO-1-CSE axis. First, excess placental soluble Flt-1 (sFlt-1) neutralises vascular endothelial growth factor (VEGF) and placental growth factor (PlGF), producing an angiogenic deficit that drives endothelial dysfunction, hypertension, proteinuria and end organ injury. Second, the failure of endogenous vascular brakes, heme oxygenase-1 (HO-1/CO) and cystathionine-γ-lyase (CSE)/hydrogen sulfide (H₂S) removes physiological restraint on anti-angiogenic factor release (sFlt-1; soluble endoglin) and amplifies oxidative–inflammatory stress, lowering the threshold at which VEGF loss precipitates severe disease. We synthesise human, animal and translational data that (i) establish placental sFlt-1 source and release, (ii) demonstrate human mechanistic causality via sFlt-1 removal, (iii) show prospective clinical validation that sFlt-1 rises and free PlGF falls before disease onset, and (iv) identify HO-1 and CSE/H₂S as protective pathways that restrain anti-angiogenic drive. Finally, we summarise preclinical evidence that the orally administered H₂S-donor prodrug MZe786 restores the HO-1/CSE axis, lowers sFlt-1 and soluble endoglin (sEng), and improves maternal haemodynamics and foetal outcomes across complementary pregnancy models, and we outline the role of sFlt-1/PlGF and M-PREG-based triage in clinical decision making. While valuable for short-term triage, current sFlt-1/PlGF-based approaches cannot sub-stratify among positive cases. Framing severe preeclampsia as a double-hit vascular disorder provides a biologically grounded framework that can inform risk stratification strategies like M-PREG^®, a clinical decision support system informed by the double hit framework, and prevention strategies, pairing early risk stratification with mechanism-informed interventions. Full article

Global climate change is increasing extreme rainfall events, and severe floods are becoming more frequent. Flood storage and detention basins (FSDBs) are an important part of the flood control system in China. They play a key role in regional flood emergency response and regulation. Therefore, accurate simulation of flood evolution after the activation of FSDBs is urgently needed. This study proposes a high-accuracy flood evolution simulation method that combines terrain clustering and physical propagation constraints. We first build a 2 m resolution digital elevation model (DEM) using GF-7 stereo imagery and laser altimetry data. We then introduce an improved superpixel segmentation algorithm (TSLIC). This method reduces the number of computational units while preserving key micro-topographic features. It groups high-resolution grids into terrain units with similar elevation characteristics and continuous spatial structure. Based on these terrain units, we develop a flood evolution model called RS-CFPM. The model combines flow velocity estimated from the Manning equation with flood propagation speed derived from radar remote sensing. It uses a water balance framework and includes a propagation time delay constraint. This design helps overcome the limitation of traditional static inundation methods that ignore flood travel time. We apply the proposed method to simulate the flood inundation process during the “23·7” extreme basin-scale flood event in the Haihe River Basin. Comparison with multi-temporal radar observations shows that the errors of simulated water level and inundation extent in the Dongdian FSDB are both within 10%. The computational efficiency is also improved by more than 60% compared with traditional methods. This study provides a new approach for rapid and accurate simulation of flood inundation processes in FSDBs under emergency conditions. The method can support flood emergency operation and decision-making. Full article

This study proposes a real-time data-based Digital Value Stream Mapping (Digital VSM) framework that integrates Artificial Intelligence (AI) feature selection and discrete-event simulation validation to enhance production system performance. Unlike conventional VSM approaches that rely on static, manually aggregated data, the proposed framework uses real-time operational data to dynamically quantify Value Added (VA), Non-Value Added (NVA), and Necessary Non-Value Added (NNVA) activities. To improve decision accuracy, an Artificial Neural Network (ANN) combined with Genetic Algorithm (GA) feature selection is employed to identify dominant production variables influencing lead time and line imbalance. Furthermore, Ranked Positional Weight (RPW) optimization results are validated through Tecnomatix Plant Simulation to ensure robustness before physical implementation. The proposed framework was applied to a discrete manufacturing line, resulting in a reduction of total lead time from 8755 s to 6400 s and an increase in process ratio from 33.64% to 45.91%, with line efficiency reaching 91.7%. The findings demonstrate that integrating Digital VSM with AI-driven feature selection and simulation validation transforms Lean analysis from a descriptive tool into a predictive and validated decision-support system suitable for Industry 4.0 environments. Full article

Purpose: Despite extensive evidence supporting synoptic reporting in adult surgical oncology, the pediatric surgical oncology evidence base remains sparse, institution-dependent, and implementation-limited, resulting in a critical translational gap. This systematic review evaluates the implementation and effectiveness of synoptic operative reports (SR) in improving documentation completeness in pediatric oncology surgery compared with traditional narrative reports (NR). Methods: Prospective and retrospective studies evaluating operative report completeness in pediatric oncology surgery were identified through a comprehensive search of PubMed, Scopus, and Web of Science. Of 1926 screened records, 11 articles underwent full-text review, and 4 studies met inclusion criteria. Results: The four included studies analyzed 341 operative reports (217 NRs and 124 SRs). Documentation completeness was the primary outcome. Across all evaluated intraoperative elements, synoptic reports were associated with approximately tenfold higher odds of complete documentation compared with narrative reports (pooled OR for NR vs. SR, 0.10; 95% CI, 0.07–0.14; p < 0.001). Conclusions: Synoptic reporting consistently improves the completeness of pediatric oncologic operative documentation compared with narrative formats; however, adoption in pediatric surgical oncology remains limited. Multicenter and implementation-focused research is needed to assess scalability, integration within electronic medical record (EMR) systems, and the impact of synoptic reporting on communication and clinical decision-making. Full article

Early risk stratification of COVID-19 severity in oncology patients is critical for improving clinical outcomes and optimizing hospital resource allocation. This study proposes a rule-based clinical decision support system (CDSS) designed for integration into digital triage workflows. In practical terms, the score is intended to be applied at hospital admission or triage, where demographic and comorbidity information is routinely available. The computed score can automatically flag high-risk oncology patients for intensified monitoring or early ICU evaluation, supporting rapid resource allocation while preserving clinician decision-making. Using retrospective clinical data from hospitalized oncological patients with confirmed SARS-CoV-2 infection, we developed a scoring algorithm based on four common comorbidities: age ≥ 70, obesity, diabetes mellitus, and hypertension. Each factor was assigned a weighted contribution to a cumulative score ranging from 0 to 7. Patients were classified into three risk levels (low, moderate, high), correlating with observed rates of ICU admission and mortality. The system is built for low-complexity implementation in electronic health records (EHRs) or web-based triage dashboards and includes a software logic model with pseudocode. Results indicate that the score effectively distinguishes patient risk levels with statistical significance (p < 0.01), and can function as an early triage mechanism. The proposed model does not require laboratory data or imaging, making it particularly suitable for rapid deployment in both hospital and remote settings. This work demonstrates a pragmatic, interpretable, and scalable approach to clinical decision support in pandemic contexts involving vulnerable populations such as cancer patients. Full article

In large-scale dam construction, the efficiency of concrete transport operations is fundamentally governed by the coordination between horizontal hauling and vertical hoisting capacities. Traditional experience-based scheduling approaches often fail to capture the stochastic, cyclic, and resource-coupled nature of these transport systems. This study developed a closed queuing network-based stochastic simulation framework to model dam concrete transportation as a finite-population cyclic service system. The process was abstracted into sequential service stages with stochastic service times, and a structured state-space representation combined with time-step simulation was constructed to describe dynamic resource occupation and task transitions under varying truck and cable crane configurations. Application to a real large-scale dam project revealed a characteristic multi-stage performance evolution pattern governed by capacity matching mechanisms. As the truck fleet size increased, system performance transitioned from a transport-limited regime to a capacity-coordination regime and ultimately to a hoisting-saturated regime in which further fleet expansion yielded diminishing returns. Sensitivity analysis demonstrated that hoisting capacity imposed an upper bound on system throughput, while adaptive fleet reconfiguration could restore operational equilibrium under constrained equipment availability. The results indicated that dam concrete transport should be treated as a dynamic capacity regulation problem rather than a static allocation task. The proposed framework provides an interpretable and quantitative decision-support tool for equipment configuration, bottleneck identification, and adaptive scheduling in large-scale hydraulic infrastructure projects. Full article

This study provides a structured review of statistical arbitrage research in the context of artificial intelligence, with a particular focus on machine learning based methods. The reviewed literature highlights the evolution from linear, rule-based strategies to increasingly complex data-driven models, while also documenting persistent challenges related to tail-risk exposure, regime instability, limited interpretability, and regulatory and governance constraints in practical applications. Building on this literature synthesis, the paper develops a conceptual AI-led, human-in-the-loop statistical arbitrage framework that integrates ML-generated signal modeling with structured human oversight—encompassing risk calibration, discretionary intervention, and interpretability review. This framework resonates with human-AI collaboration systems across other financial domains, collectively supporting the proposition that collaborative systems show potential to enhance resilience compared to purely AI-driven alternatives under specific market stress scenarios. It is positioned as a governance-oriented synthesis that qualitatively extends existing human-in-the-loop concepts by structurally embedding adaptive oversight within the statistical arbitrage decision architecture. Full article