to identify the treatment planning systems (TPS) used in radiotherapy for head and neck cancer patients and evaluate their impact in reducing toxicities to teeth, salivary glands, and nontarget tissues. A comprehensive search was conducted in 6 databases and gray literature sources. Eligible studies involved patients with head and neck cancer undergoing radiotherapy and evaluated dosimetric strategies or planning software to protect critical structures. TPS impact was categorized as low, moderate, or high based on dose reduction and clinical benefit reported in each study. A total of 26 studies were included, covering 8 different TPS. Of these, 23 studies assessed dose reduction to the salivary glands or nontarget tissues and 3 studies focused on dental structures. Overall, 51% of TPS assessments (n = 19) reported a high impact on dose reduction, 35% showed a moderate impact, and 14% demonstrated a low impact in minimizing radiation exposure to critical structures. Eclipse was the most frequently assessed TPS, showing predominantly high or moderate impact across all structure types. These findings indicate a predominance of evidence supporting Eclipse's dosimetric performance, whereas variability among other systems (Pinnacle, Monaco, KonRad, XiO, and CORVUS) suggests differences in optimization algorithms and clinical implementation. TPS show heterogeneous results regarding the sparing of nontarget tissues. Eclipse demonstrated the most consistent positive outcomes, but dental structures remain underexplored concerning TPS approaches.

Deep learning algorithms for license plate recognition: A review.

OpEffiRes Net: Optimization based EfficientNetB0-ResNet50 and correlation based feature selection for software failure prediction.

Hyperparameter Optimization of Non-linear Machine Learning Models Using Bi-level Data-Driven Optimization.

Electrochemically induced CoNiOOH Nanosheets enabling nitrite detection through a catalytic reduction mechanism and machine learning-based concentration prediction.

Statistical optimization of a Kriging interpolation-based dose distribution estimation algorithm

Dual-frequency ultrasonic holography by binary aperture plates.

Clinical behavior and predictors of survival in major salivary gland secretory carcinoma.

• Digital Twin integrates IoT quantum sensors for adaptive greenhouse lighting. • Real-time DT interactions enables energy-efficient and responsive light control. • RL optimizes controller gains under multi-zone variable PPFD and light conditions. • Energy use and electricity costs reduced versus baseline on/off strategy. • GA and RL compared over 12 adaptive control strategies in real greenhouse data. Greenhouse lighting is vital for plant growth and contributes to nearly 30% of operational costs. However, managing lighting in response to dynamic sunlight conditions and varying photosynthetic photon flux density (PPFD) requirements across crop types remains a major challenge, which results in excessive energy use. This paper presents a digital twin (DT) adaptive control framework for greenhouse lighting, leveraging quantum sensors and reinforcement learning (RL) to enable energy-efficient, multi-zone operation. The proposed system dynamically adjusts multi light-emitting diode (LED) intensities in the extended Photosynthetically Active Radiation spectrum to satisfy uniform PPFD thresholds (single-crop scenario) or differentiated PPFD thresholds (multi-crop scenario). A set of 12 adaptive control strategies was evaluated, employing Genetic Algorithm (GA) and RL optimizers to configure proportional–integral-derivative (PID) controller parameters as well as their PI and P subsets. Real world validation demonstrates that the RL-based PI control with shared coefficients (RL-PI (Eq)) delivers the most robust performance across scenarios, achieving an average mean error of 1.248 μ mol s − 1 m − 2 with a standard deviation of 10.661 μ mol s − 1 m − 2 . Compared to a baseline on–off controller, the proposed strategy reduces electrical energy consumption by 23.6% and energy-related costs by 23.2%, while maintaining precise PPFD regulation. These findings highlight the potential of DT adaptive control systems to advance sustainable, cost-effective, and scalable multi crop greenhouse lighting management.

A discrete battlefield optimization algorithm (DBf) for optimal route recommendation with weighted multi-criteria preferences

Knee osteoarthritis classification using Optimized Granger Causality Inspired Graph Neural Network with Accelerated Model-agnostic Explanations.

This paper addresses the Quadratic Multiple Constraints Variable-Sized Bin Packing Problem (QMC-VSBPP), a challenging combinatorial optimization problem that generalizes the classical bin packing problem by incorporating multiple capacity dimensions, heterogeneous bin types, and quadratic interaction costs between items. We propose two complementary methods that advance the current state-of-the-art. First, a linearized mathematical model is introduced to eliminate quadratic terms, enabling the use of exact solvers such as Gurobi to compute strong lower bounds—reported here for the first time for this problem. Second, we develop RKO-ACO, a continuous-domain Ant Colony Optimization algorithm within the Random-Key Optimizer framework, enhanced with adaptive Q-learning parameter control and efficient local search. Extensive computational experiments on benchmark instances show that the proposed linearized model produces significantly tighter lower bounds than the original quadratic model, while RKO-ACO consistently matches or improves upon all best-known solutions in the literature, establishing new upper bounds for large-scale instances. These results provide new reference values for future studies and demonstrate the effectiveness of evolutionary and random-key approaches for solving complex quadratic packing problems. • Advances the state-of-the-art for the QMC-VSBPP with exact and metaheuristic methods. • Proposes a new linearized model, eliminating complex quadratic terms. • Introduces lower bounds via exact solutions of the linearized model using Gurobi. • Develops the RKO-ACO: an adaptation of continuous ACO for the Random-Key Optimizer.

TrCN-HDC: Enhancing patient security with graphical authentication and cloud-assisted cardiac monitoring.

Center evolving optimization algorithm for fine focused muon beamlines

Time-resolved aortic 3D shape reconstruction from a limited number of cine 2D MRI slices.

An adaptive rank-based coevolutionary learning particle swarm optimization algorithm for server placement in edge computing

Research on an intelligent optimization algorithm for P-wave azimuth determination at single stations in high-speed rail earthquake early warning systems

In current engineering practice, the optimal design of the classical viscous damping wall (VDW) model typically relies on traditional optimization algorithms, which often involve the use of a large number of VDWs. To address this problem, this study proposed a three-dimensional VDW and validated a simplified model that matched its performance under multidirectional earthquakes. Based on the proposed three-dimensional VDW model and the corresponding simplified model, a physics-informed VDW automated design framework named VDW Automated Design-DRL (VAD-DRL) was further proposed, which integrated the finite-element method with deep reinforcement learning (DRL). The framework modeled the process of optimizing VDW placement and types as a DRL process, where the agent continuously interacted with the VDW Automated Design-Env to learn the design approach and generate optimal VDW designs under multidirectional earthquakes. An engineering case was finally applied, where VAD-DRL was used for VDW design under multidirectional earthquakes. The elastic and elastoplastic responses of the structures with the two added VDW designs were validated. The results indicated that the Maxwell model was more in line with the working performance of the three-dimensional VDW under multidirectional earthquakes. The VDW design generated by VAD-DRL significantly reduced the structural responses and enhanced the structure’s seismic performance under multidirectional earthquakes.

Dynamic airspace configuration through genetic algorithm optimization using air traffic complexity

The integration of ambient artificial intelligence (AI) scribes into the OpenNotes environment presents a profound governance crisis in healthcare. While patient access to medical records was designed as a transparency reform, the introduction of machine-generated text introduces novel vulnerabilities regarding record integrity, liability, and patients' trust. This study investigates how clinicians discursively negotiate the systemic risks and accountability challenges of patient-facing, AI-assisted documentation. Employing a netnographically informed qualitative design, the research conducted a reflexive thematic analysis of 484 relevant comments across 120 threads from eight clinician-oriented subreddits spanning October 2020 to February 2026. The analysis revealed five distinct governance challenges. First, an accountability vacuum exists where the mandatory clinician signature functions merely as a legal shock absorber for institutional AI liability. Second, clinicians frame AI hallucinations as a mathematically inevitable epistemic risk rather than a correctable technical bug. Third, a "dual-audience" problem emerges, as algorithmic optimization compromises both the individual clinical voice needed for peer communication and the empathetic clarity required for patient readers. Fourth, existing privacy frameworks are structurally inadequate to manage commercial data extraction during patient encounters. Finally, institutional productivity demands and AI-driven over-documentation severely threaten the fiscal credibility of the medical record through inadvertent upcoding. The prevailing regulatory assumption-that a physician's digital signature combined with passive patient visibility guarantees documentation accountability-is a fragile fiction. To protect clinical truth, health systems must transition from models of passive disclosure toward contingent transparency. This requires establishing authoritative, enforceable mechanisms for provenance tracking, error contestation, and vendor accountability.