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Multi-rotor plant protection drones are extensively utilized in rice field operations. However, the airflow generated by the rotors not only disturbs the rice canopy but also affects the deposition of pesticide droplets. The investigation of the impact of rotor wind fields on the protection of rice crops has the potential to enhance the effectiveness of operations of this nature. The present study firstly acquired aerial imagery of plant protection drone operations via the utilization of aerial photography, and subsequently employed machine vision technology to investigate the disturbance patterns of rotor wind fields on rice canopies. Notably, there is currently no effective experimental method to observe the disturbance of rice canopies caused by the wind field generated by plant protection drone rotors, and machine vision processing is proven to be an effective approach, which is a key innovation of this study. This study innovatively adopts an integrated experimental-numerical approach, a distinct advancement over existing Unmanned Aerial Vehicle (UAV) spraying and airflow research that typically focuses on single experimental or simulation methods. Aerial photography and machine vision were used to explore canopy disturbance patterns, while reverse engineering combined with thrust tests and numerical simulations verified rotor model precision and analyzed droplet deposition. Core findings show that flight speeds of 3-4 m/s enable effective overlap between canopy disturbance and droplet deposition zones, achieving optimal plant protection effects; higher speeds cause droplet drift and zone misalignment, reducing efficacy. In addition, this study explores the spray deposition of plant protection drones based on numerical simulation methods, and assists in judging the effect of plant protection operations through the coincidence of droplet deposition and canopy disturbance zones. This integrated approach and related findings provide a reliable technical basis for optimizing UAV flight parameters, and have certain reference significance for the technical research and development of plant protection drones and the setting of operation parameters.

A compact multi-layer silicon beta-ray spectrometer (SBS) has been developed for beta spectrometry and dosimetry in the beta-gamma mixed fields at Canada Deuterium Uranium (CANDU) nuclear plants. Accurate determination of beta fluence spectra from SBS pulse-height data is challenging due to partial energy deposition within each detector. In this work, we present a simulation-based application of Fully Bayesian Unfolding (FBU) to SBS, leveraging Monte Carlo-derived response matrices under anti-coincidence/coincidence conditions. The FBU approach, implemented in Python using PyMC, provides full posterior distributions and credible intervals for unfolded spectra. Performance was evaluated using Geant4 simulations for beta-only and beta-gamma mixed fields with beta-to-gamma source particle ratios from 1 to 0.01. For ratios ⩾ 0.1, the mean unfolded-to-truth ratio for beta fluence above 0.1 MeV was within 20% of unity. The results demonstrate promising fluence unfolding for the SBS, enabling reliable beta fluence spectrum measurements in the beta-gamma mixed radiation fields while providing valuable insights into the gamma component of the field, even at low count rates and low beta-to-gamma ratios. These results demonstrate the feasibility of FBU for SBS, enabling improved beta-gamma discrimination and uncertainty quantification. Future work will focus on optimization of the algorithm to improve the accuracy and efficiency to achieve real-time unfolding for practical measurements.

This study aimed to measure magnetically induced displacement forces on metallic dental appliances and instruments in a 3 T MRI scanner and to examine its relationship with magnetic flux density. Magnetic flux density was measured along the gantry centreline, and both the spatial field gradient and the product of magnetic flux density and spatial field gradient were calculated. Displacement force measurements were conducted according to ASTM F2052-21. Tests were performed on a representative material along the gantry centreline and on 29 metallic dental materials-including fixed and removable dental appliances, and dental and medical instruments-at two positions outside the gantry entrance. Displacement force increased rapidly near the scanner entrance, reflecting trends in spatial field gradient and the product of magnetic flux density and gradient, with a maximum observed between 0 and 5 cm inside the gantry entrance. At 25 cm outside the gantry, all tested single fixed and removable dental appliances exhibited displacement forces below 0.03 N. In contrast, nearly all stainless steel dental and medical instruments demonstrated deflection angles exceeding 90° at the same position. In the MRI environment, dental appliances composed of stainless steel or iron that are not securely fixed in the patient's oral cavity are subject to magnetically induced displacement forces. Therefore, assessing intraoral fixation is essential before and after MRI. Additionally, most stainless steel dental and medical instruments, if inadvertently brought into the MRI environment, pose significant safety risks and must be strictly excluded.

Rapid urbanization has heightened the need for evidence-based healthy city planning. To resolve the methodological limitations of parallel biometric data stacking, this study developed a synchronized cross-modal framework to evaluate the restorative potential of a 9-typology urban-to-natural landscape continuum. A laboratory experiment was conducted with 42 healthy undergraduate students (21 males, 21 females; mean age = 21.4 ± 1.8 years). Brain activity (EEG), visual attention (eye-tracking), and peripheral autonomic signals (EDA, HRV, respiration) were synchronously recorded alongside the Profile of Mood States (POMS) scale. To integrate these multi-scale data streams, we formulated the Cross-Modal Restorative Index (CMRI). The empirical findings reveal a distinct, non-linear hierarchy of environmental restoration. Pristine natural environments, especially Mountainous and Field landscapes, elicited complete "Integrated Restoration," characterized by significant systemic convergence: central cognitive relaxation via posterior α power activation (Field: 7.35; Water: 7.03), robust parasympathetic upregulation (Field HF: 12518.77), and profound down-regulations in subjective tension (Mountainous: 18.6 → 12.3) and fatigue. Conversely, built landscapes demonstrated "Fragmented Restoration." Notably, Road scenes exhibited a localized dissociation where physiological calming (sharp increase in posterior α wave SD from 15.11 to 20.54) was decoupled from visual and psychological domains, with over 74% of visual dwell time remaining locked on artificial elements and subjective fatigue rising (15.3 → 16.2). These findings provide quantitative, systems-level evidence for integrating ecologically authentic blue-green infrastructure into resilient urban design.

Understanding fluid-driven fracture propagation in anisotropic, layered geological formations is critical for optimizing resource recovery in unconventional reservoirs. In this study, we present a novel experimental and numerical investigation into fluid flow-induced deformation dynamics under biaxial loading using analogue models of shale and reservoir rock. A custom-designed low-pressure biaxial compression system is developed to simulate realistic subsurface conditions while allowing real-time, high-resolution visualization of fracture evolution in low-modulus elastic and viscoelastic materials. Gelatin-based analogues, representing both isotropic and anisotropic formations, are employed to mimic geological heterogeneity and elasticity. Fracturing experiments are conducted using water, SAE 140 oil, and oil-toluene mixtures injected at a constant rate of 1 ml/min under controlled loading conditions (confining load: 1.23 bar; top load: 0.52 bar). The evolving fracture geometries are analysed using ImageJ, focusing on metrics such as aspect ratio and fracture length. Numerical simulations based on a phase field damage model are performed to predict fracture initiation and propagation, showing strong agreement with experimental results and qualitative consistency with the analytical Khristianovic-Geertsma-de Klerk (KGD) model. Our integrated approach provides critical insights into the role of fluid viscosity, heterogeneity and loading conditions on fracture dynamics. The experimental setup also supports studies relevant to CO₂ sequestration and gas hydrate dissociation. This work advances the mechanistic understanding of fracture processes in layered formations, offering insights for field-scale hydraulic fracturing and sustainable subsurface resource management.

This work presents the development and evaluation of a compact underwater radioactivity detection system, named Mini KATERINA, designed to be integrated in platform-agnostic mobile robotic vehicles operating in controlled aquatic environments to identify radioactive sources. It employs a 2″ × 2″ NaI(Tl) scintillation crystal coupled to a silicon photomultiplier (SiPM), achieving reduced size, weight, and power consumption. The system was characterized in laboratory conditions using calibrated reference point sources and dedicated simulants (volume sources), enabling training and validation for the identification of suspicious objects. Initial performance assessment was conducted in a water tank to investigate detection capabilities as well as to perform calibrations in terms of energy, energy resolution and full energy peak efficiency. The calculated efficiency of the system for distances between source and detector surface at 4.5, 8.5 and 12.5 cm was 0.0038, 0.0016 and 0.00051, respectively. The limit of detection (LD) of the system at 662 keV for distances between source and detector surface at 4.5, 8.5 and 12.5 cm was 8, 17 and 54 Bq, respectively. Subsequently, field tests were performed in a marine environment to evaluate the limit of detection of a point source using a known radioactive simulant housed in a dedicated enclosure. The Limit of Detection of a point source (LD) of the system in this field configuration in the marine environment resulted a value of 43 Βq when the system is in a distance of 6 cm from the 137Cs point source. The results demonstrate the system's effectiveness for underwater radiation monitoring and its potential integration into diverse mobile underwater platforms.

In narrow conductors, electron-electron collisions can create a viscous fluid state, allowing conductivity beyond ballistic transport into the superballistic regime. Point contacts made from ultrahigh-mobility two-dimensional electron gas serve as a platform to study this effect. Under microwave irradiation, edge magnetoplasmons in point contacts are excited and strongly influence electron dynamics. This study uses photoconductivity signals - changes from microwave exposure - to investigate superballistic electron flow, confirmed by size-dependent photoconductivity, magnetic field effects, and microwave power analysis. At low magnetic fields, weak microwaves lead to positive photoconductivity due to superballistic flow, while strong radiation causes negative photoconductivity because of enhanced phonon scattering.

Artificial intelligence (AI) is a simulation of human intelligence processes by machines, specifically using computer systems. The current article focuses on the application of AI in the field of pharmaceutical analysis. This article describes the history and evolution of AI and its use in different fields along with pharmaceuticals. Current available AI models in pharmaceutical analysis and two creative models were developed and discussed in detail. Visualized schematic diagrams were drawn, and explained hypothesis test procedures with the logical process. In addition, anticipated challenges, advantages and disadvantages were presented. Overall, the current article provides the influence of AI on pharmaceutical analysis in the coming future.

Patients with end-stage liver disease (ESLD) often experience high symptom burden and frequent hospitalizations. Despite studied benefits of palliative care consultation services (PCCS), use in patients with ESLD is underutilized and typically occurs weeks or days before death. We examined patterns of PCCS utilization, associated outcomes, and healthcare costs in hospitalized patients with ESLD. We retrospectively reviewed 3,983 patients (13,509 encounters) with ESLD from 2015-2023, excluding 188 patients. We compared length of stay (LOS), mortality, disposition, readmissions, emergency department visits, and costs between patients receiving PCCS and those receiving usual care (UC), using descriptive analyses (Chi-square, Fisher's exact, and Student's t-tests) and multivariable logistic regression. Analyses were performed using SAS 9.4. Among 12,821 hospitalizations, 6.3% involved PCCS. Because this denominator included all hospitalizations among patients with ESLD, we performed a diagnosis-based secondary analysis using principal problem, primary diagnosis, and diagnosis present on admission fields. Of classified encounters, 70.7% were non-liver primary admissions, 19.8% were supportive of possible ESLD/decompensation admissions, and 9.5% were definite ESLD/decompensation admissions. PCCS occurred in 9.4% of definite ESLD/decompensation admissions. These patients were more likely to require mechanical ventilation (OR 2.3, 95% CI [1.79-3.03]), paracentesis (1.9[1.16-3.0]), and ICU admission (4.2[3.51-5.13]). They had higher in-hospital and 30-day mortality (OR 1.48 [1.24-1.78] and OR 3.1[2.39-3.89], respectively), decreased likelihood of discharge home (OR 0.58 [0.48-0.70]), longer LOS (16.2 versus 7.4 days, p<0.05), and higher total costs ($59,684 versus $23,750, p<0.05). Among patients who died during hospitalization after receiving PCCS, the median interval from consultation to death was 7 days (IQR 3-13). In this multihospital retrospective cohort, inpatient palliative care consultation in ESLD was uncommon and often occurred late in the hospitalization course. The overall encounter-level PCCS rate was partly influenced by inclusion of non-liver primary admissions, though PCCS utilization remained low, even among definite ESLD/decompensation admissions. Among in-hospital decedents who received PCCS, the short interval from consultation to death suggests late integration of palliative care near the end of life. Further prospective studies are needed to clarify optimal consultation timing and identify strategies to embed palliative care effectively within ESLD.

DNA methylation is a crucial epigenetic regulatory mechanism in eukaryotes, and its dysregulation is closely associated with numerous diseases. Recent advances in long-read sequencing (LRS) have transformed the ability to comprehensively characterize the methylation modifications in DNA, including the technically difficult genomic regions. However, the translation of this technological potential into reliable biological insights relies heavily on accurate computational analysis of raw signal data. Currently, a growing number of bioinformatic tools have emerged, demonstrating superior performance in LRS-based methylation detection. Our review first briefly describes the detection principles and technological improvement of LRS, and then provides a comprehensive overview of the existing LRS-based methylation detection tools and related benchmarking studies. We further explore the applications in biomedical research, the current challenges, and the future perspective of LRS-based methylation detection. By highlighting the research progress and key issues in the field, this review aims to provide researchers with an essential framework to advance the further development and application of LRS-based methylation detection.

Enzymes play a crucial role in metabolism. The rate at which enzyme sequence data is emerging far exceeds the rate at which we can measure the associated catalytic constants, posing a significant problem for the advancement of the fields of metabolic engineering and synthetic biology. Here, we present KcatNeuroCortex, an interpretable and novel deep learning framework for enzyme catalytic efficiency prediction. The proposed architecture combines Bi-directional Gated Recurrent Units (Bi-GRU) with a multi-attention mechanism designed to mirror how enzymes work. It first captures local functional motifs through a segmentation-based strategy, and then integrates these into a global representation of long-range interactions that shape the catalytic landscape, leading to improved interpretability and prediction accuracy. KcatNeuroCortex achieves good results, with R&#xb2; values of 0.74 and RMSE values of 0.77, representing a 57% improvement compared to DLKcat. We demonstrate that our segmentation-aware, attention-enhanced approach achieves competitive performance compared to conventional sequence-based models, especially on diverse and low-similarity sequences. The framework is robust, scalable, and interpretable, making it a valuable tool for enzyme engineering and large-scale kinetic parameter estimation. Also, the work establishes that deep learning can move beyond prediction to provide a biologically meaningful understanding of enzyme catalysis, positioning KcatNeuroCortex as a very valuable tool for the enzyme engineering community.